diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 5e4861d0..3aec4b16 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.7.3 +current_version = 0.8.0rc0 commit = False tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)((?P[a-z]+)(?P\d+))? @@ -21,3 +21,5 @@ values = [bumpversion:file:./README.md] [bumpversion:file:./pyproject.toml] + +[bumpversion:file:./CITATION.cff] diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index fdb490c6..18d373e0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,6 +6,9 @@ on: tags: - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 +permissions: + contents: write + jobs: build: name: Create Release @@ -27,6 +30,13 @@ jobs: - name: Get version number id: get_version run: echo "VERSION=$(python3 -c "import simphony; print(simphony.__version__)")" >> $GITHUB_ENV + - name: Determine release type + run: | + if [[ "${VERSION}" == *rc* ]]; then + echo "PRERELEASE=true" >> "$GITHUB_ENV" + else + echo "PRERELEASE=false" >> "$GITHUB_ENV" + fi - name: Load Release text run: | { @@ -35,6 +45,7 @@ jobs: echo EOF } >> "$GITHUB_ENV" - name: Publish to PyPI + if: env.PRERELEASE != 'true' uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ @@ -49,4 +60,4 @@ jobs: release_name: Simphony ${{ env.VERSION }} body: ${{ env.BODY }} draft: false - prerelease: false + prerelease: ${{ env.PRERELEASE }} diff --git a/.gitignore b/.gitignore index 6d3a5fc4..ecc09af0 100644 --- a/.gitignore +++ b/.gitignore @@ -25,6 +25,9 @@ wheels/ *.egg MANIFEST +# Local Cache +.simphony_cache + # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. diff --git a/CHANGELOG.md b/CHANGELOG.md index 739ba897..151aed97 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 --- +## [0.8.0rc0](https://github.com/BYUCamachoLab/simphony/tree/v0.8.0rc0) - 2026-05-29 + +Release candidate for archiving the current time-domain simulator implementation. + +### Added +- Citation metadata for GitHub and Zenodo. +- Release workflow handling for release-candidate GitHub prereleases. + +### Fixed +- + +### Changed +- Prepared the time-domain simulator branch for Zenodo DOI archival. + +--- + ## [0.7.3](https://github.com/BYUCamachoLab/simphony/tree/v0.7.3) - 2025-10-27 Minor bug fixes. @@ -21,8 +37,8 @@ Minor bug fixes. - Bug with group delay that was ignored in lumerical file parser. Lumerical output files calculated with "include group delay" in the s-parameter matrix sweep dialogue were imported with incorrect phases due to ignoring group delay. ### Changed -- - +- + --- ## [0.7.2](https://github.com/BYUCamachoLab/simphony/tree/v0.7.2) - 2024-01-09 diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 00000000..462a5767 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,26 @@ +cff-version: 1.2.0 +message: "If you use Simphony in your research, please cite it using the metadata from this file." +title: "Simphony: A Simulator for Photonic Circuits" +type: software +version: "0.8.0rc0" +date-released: "2026-05-29" +authors: + - family-names: Ploeg + given-names: Sequoia + - family-names: Gunther + given-names: Hyrum + - family-names: Carver + given-names: Christian + - name: BYU CamachoLab +abstract: "Simphony is a Python package for designing and simulating photonic integrated circuits." +license: MIT +repository-code: "https://github.com/BYUCamachoLab/simphony" +url: "https://simphonyphotonics.readthedocs.io/en/stable/" +keywords: + - photonics + - simulation + - circuits + - science + - engineering + - physics + - interconnect diff --git a/README.md b/README.md index aee3aded..0101e1ff 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Simphony: A Simulator for Photonic Circuits
-Development version +Development version PyPI Version PyPI - Python Version Build Status diff --git a/examples/16qam_multimode_block_mode_tutorial.ipynb b/examples/16qam_multimode_block_mode_tutorial.ipynb new file mode 100644 index 00000000..8a546a2e --- /dev/null +++ b/examples/16qam_multimode_block_mode_tutorial.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e8714f4", + "metadata": {}, + "source": [ + "# 16-QAM Multimode Block Mode Tutorial\n", + "\n", + "This tutorial builds a compact 16-QAM transmitter using Block mode simulation. It is meant to follow the MZI lattice tutorial: the MZI tutorial explains S-parameter dictionaries, pole-residue fitting, and the single-mode Block mode workflow; this tutorial focuses on the pieces that were not emphasized there.\n", + "\n", + "Here we cover:\n", + "\n", + "- Multimode optical signals with shape `(time, wavelength, mode)`.\n", + "- SAX `SDict` keys with mode suffixes such as `o0@te` and `o1@tm`.\n", + "- Wrapping single-polarization SAX models into multimode models.\n", + "- A small MZI/IQ lattice that generates a 16-QAM-like constellation.\n", + "- Reading mode-resolved outputs from a `BlockModeSimulationResult`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "6646ddea", + "metadata": {}, + "source": [ + "## 1. Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8ce8f28e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "from functools import partial\n", + "\n", + "os.environ.setdefault('JAX_PLATFORMS', 'cpu')\n", + "warnings.filterwarnings('ignore', message='Could not validate netlist.*')\n", + "warnings.filterwarnings('ignore', message='FigureCanvasAgg is non-interactive.*')\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.signal import butter, filtfilt\n", + "\n", + "from simphony.circuit.circuit import Circuit\n", + "from simphony.component.component import BlockModeComponent\n", + "from simphony.component.port import Port\n", + "from simphony.libraries.siepic import waveguide, y_branch\n", + "from simphony.signal.block_mode import BlockModeOpticalSignal\n", + "from simphony.simulation.block_mode import BlockModeSimulation, BlockModeSimulationParameters\n", + "from simphony.utils import create_multimode_sax_model" + ] + }, + { + "cell_type": "markdown", + "id": "5492723c", + "metadata": {}, + "source": [ + "## 2. Multimode Block Mode Signals\n", + "\n", + "In Block mode, an optical signal is stored as a block of samples:\n", + "\n", + "```text\n", + "amplitude.shape == (num_time_steps, num_wavelengths, num_modes)\n", + "```\n", + "\n", + "The previous MZI lattice tutorial used one mode, so the last axis had length 1. Here we use two modes:\n", + "\n", + "```text\n", + "mode_identifiers = (\"te\", \"tm\")\n", + "```\n", + "\n", + "That means `amplitude[:, 0, 0]` is the TE envelope at the first baseband wavelength, and `amplitude[:, 0, 1]` is the TM envelope at that same baseband wavelength. The mode order comes from `BlockModeSimulationParameters.mode_identifiers`. Thus, users can interface their custom components with the different modes. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca7b5438", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode index: {'te': 0, 'tm': 1}\n", + "baseband wavelength grid (nm): [1550.]\n" + ] + } + ], + "source": [ + "MODE_IDENTIFIERS = ('te', 'tm')\n", + "BASEBAND_WAVELENGTHS_M = jnp.array([1.55e-6])\n", + "DT = 1e-14\n", + "\n", + "mode_index = {mode: index for index, mode in enumerate(MODE_IDENTIFIERS)}\n", + "print('mode index:', mode_index)\n", + "print('baseband wavelength grid (nm):', np.asarray(BASEBAND_WAVELENGTHS_M) * 1e9)" + ] + }, + { + "cell_type": "markdown", + "id": "5b23bc79", + "metadata": {}, + "source": [ + "## 3. Generate the 16-QAM Drive Signal Using the Original Settings\n", + "\n", + "This tutorial now uses the following drive settings:\n", + "\n", + "- `N = 20000` random symbols.\n", + "- `sps = 25` samples per symbol.\n", + "- Phase levels `7π/8`, `1.80π/3`, `π/2.50`, and `π/8`.\n", + "- `smooth_hold(... cutoff=0.05, order=2)` using a Butterworth filter.\n", + "- Opposite phase waveforms on each MZI pair: `phase_wave2 = -phase_wave1` and `phase_wave4 = -phase_wave3`.\n", + "\n", + "These phase levels are important because each MZI branch responds roughly through `cos(phase)`, so the selected phases produce four distinct amplitude levels for the 16-QAM grid." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "85e4d696", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phase levels (rad): [2.749 1.885 1.257 0.393]\n", + "cosine amplitude levels: [-0.924 -0.309 0.309 0.924]\n", + "N: 20000\n", + "sps: 25\n", + "Nsteps: 500000\n" + ] + } + ], + "source": [ + "def generate_random_phases(key, N: int, phases: jnp.ndarray) -> jnp.ndarray:\n", + " return jax.random.choice(key, phases, shape=(N,))\n", + "\n", + "\n", + "phases = jnp.array([7 * jnp.pi / 8, 1.80 * jnp.pi / 3, jnp.pi / 2.50, jnp.pi / 8])\n", + "key = jax.random.PRNGKey(42)\n", + "key2 = jax.random.PRNGKey(1)\n", + "N = 20000\n", + "\n", + "phase_array1 = generate_random_phases(key, N, phases)\n", + "phase_array2 = -phase_array1\n", + "phase_array3 = generate_random_phases(key2, N, phases)\n", + "phase_array4 = -phase_array3\n", + "\n", + "sps = 25\n", + "beta = 0.25\n", + "span = 8\n", + "\n", + "\n", + "def sample_hold(sym, sps):\n", + " return jnp.repeat(jnp.asarray(sym), sps)\n", + "\n", + "\n", + "def smooth_hold(sym, sps, cutoff=0.15, order=2):\n", + " x = np.repeat(np.asarray(sym, dtype=float), sps)\n", + " b, a = butter(order, cutoff)\n", + " return filtfilt(b, a, x)\n", + "\n", + "\n", + "phase_wave1 = smooth_hold(phase_array1, sps, cutoff=0.05, order=2)\n", + "phase_wave2 = smooth_hold(phase_array2, sps, cutoff=0.05, order=2)\n", + "phase_wave3 = smooth_hold(phase_array3, sps, cutoff=0.05, order=2)\n", + "phase_wave4 = smooth_hold(phase_array4, sps, cutoff=0.05, order=2)\n", + "\n", + "dt = 1e-14\n", + "Nsteps = int(phase_wave1.shape[0])\n", + "T = Nsteps * dt\n", + "t = jnp.arange(Nsteps) * dt\n", + "time_ps = np.asarray(t) * 1e12\n", + "NUM_TIME_STEPS = Nsteps\n", + "SAMPLES_PER_SYMBOL = sps\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(8, 4), sharex=True)\n", + "axes[0].plot(time_ps, phase_wave1, linewidth=1.2, label='phase_wave1')\n", + "axes[0].plot(time_ps, phase_wave2, linewidth=1.2, linestyle='--', label='phase_wave2')\n", + "axes[0].set_ylabel('I-arm phase (rad)')\n", + "axes[0].grid(True)\n", + "axes[0].legend()\n", + "axes[1].plot(time_ps, phase_wave3, color='tab:green', linewidth=1.2, label='phase_wave3')\n", + "axes[1].plot(time_ps, phase_wave4, color='tab:orange', linewidth=1.2, linestyle='--', label='phase_wave4')\n", + "axes[1].set_ylabel('Q-arm phase (rad)')\n", + "axes[1].set_xlabel('Time (ps)')\n", + "axes[1].grid(True)\n", + "axes[1].legend()\n", + "for ax in axes:\n", + " ax.set_xlim(time_ps[0], time_ps[min(NUM_TIME_STEPS - 1, 700)])\n", + "fig.suptitle('Original Butterworth-smoothed 16-QAM phase drive waveforms')\n", + "fig.tight_layout()\n", + "plt.show()\n", + "\n", + "print('phase levels (rad):', np.round(np.asarray(phases), 3))\n", + "print('cosine amplitude levels:', np.round(np.sort(np.cos(np.asarray(phases))), 3))\n", + "print('N:', N)\n", + "print('sps:', sps)\n", + "print('Nsteps:', Nsteps)" + ] + }, + { + "cell_type": "markdown", + "id": "1aa3c4f7", + "metadata": {}, + "source": [ + "## 4. Define the Block Mode Components\n", + "\n", + "Two custom Block mode components are enough for this tutorial:\n", + "\n", + "- `MultiModeCWLaser` creates a constant optical envelope for each mode.\n", + "- `PhaseModulator` multiplies an input optical signal by `exp(j * phase[t])`.\n", + "\n", + "Both components work on the full `(time, wavelength, mode)` array. The phase modulator broadcasts a scalar or a 1D time waveform over every wavelength and mode. That is the main Block mode convention for custom components: return a `BlockModeOpticalSignal` with the same axis order the simulator expects." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "75cf78fa", + "metadata": {}, + "outputs": [], + "source": [ + "class MultiModeCWLaser(BlockModeComponent):\n", + " ports = [Port(name='o0', type='optical', directionality='output')]\n", + "\n", + " def __init__(self, simulation_parameters, *, mode_amplitudes=None):\n", + " if mode_amplitudes is None:\n", + " mode_amplitudes = jnp.ones((len(simulation_parameters.mode_identifiers),), dtype=jnp.complex64)\n", + " self.mode_amplitudes = jnp.asarray(mode_amplitudes, dtype=jnp.complex64)\n", + "\n", + " def block_mode_response(self, inputs, simulation_parameters):\n", + " num_time_steps = simulation_parameters.num_time_steps\n", + " wavelengths = simulation_parameters.optical_baseband_wavelengths\n", + " num_wavelengths = wavelengths.shape[0]\n", + " num_modes = len(simulation_parameters.mode_identifiers)\n", + "\n", + " amplitude = jnp.zeros((num_time_steps, num_wavelengths, num_modes), dtype=jnp.complex64)\n", + " amplitude = amplitude.at[:, :, :].set(self.mode_amplitudes[None, None, :])\n", + "\n", + " return {'o0': BlockModeOpticalSignal(amplitude=amplitude, wavelength=wavelengths)}\n", + "\n", + "\n", + "class OpticalModulatorTime(BlockModeComponent):\n", + " ports = [\n", + " Port(name='o0', type='optical', directionality='input'),\n", + " Port(name='o1', type='optical', directionality='output'),\n", + " ]\n", + "\n", + " def __init__(self, simulation_parameters, *, mod_signal=0.0, phase=None):\n", + " if phase is not None:\n", + " mod_signal = phase\n", + " self.mod_signal = jnp.asarray(mod_signal)\n", + "\n", + " def block_mode_response(self, input_signals, simulation_parameters):\n", + " optical_input = input_signals['o0']\n", + " mod_signal = self.mod_signal\n", + " coefficient = jnp.exp(1j * mod_signal)\n", + "\n", + " if coefficient.ndim == 0:\n", + " coefficient = coefficient * jnp.ones((simulation_parameters.num_time_steps, 1, 1), dtype=complex)\n", + " elif coefficient.ndim == 1:\n", + " coefficient = coefficient[:, None, None]\n", + "\n", + " return {\n", + " 'o1': BlockModeOpticalSignal(\n", + " amplitude=optical_input.amplitude * coefficient,\n", + " wavelength=optical_input.wavelength,\n", + " )\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "c88a9bd8", + "metadata": {}, + "source": [ + "## 5. Build Multimode SAX Models\n", + "\n", + "Here we follow the multimode SAX model convention using a `SDict` and mode-qualified port names. For example, a waveguide can include keys such as:\n", + "\n", + "```text\n", + "('o0@te', 'o1@te') # TE input to TE output\n", + "('o0@te', 'o1@tm') # TE input to TM output, if cross-mode coupling exists\n", + "```\n", + "\n", + "The helper `create_multimode_sax_model(...)` combines one SAX model per mode into a larger multimode model. The physical netlist still connects ordinary ports like `wg,o0` and `wg,o1`; the mode suffix lives inside the S-parameter model and the optical signal array." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f9d84d87", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first multimode waveguide keys:\n", + " ('o0@te', 'o0@te')\n", + " ('o0@te', 'o1@te')\n", + " ('o1@te', 'o0@te')\n", + " ('o1@te', 'o1@te')\n", + " ('o0@tm', 'o0@tm')\n", + " ('o0@tm', 'o1@tm')\n", + " ('o1@tm', 'o0@tm')\n", + " ('o1@tm', 'o1@tm')\n", + " ('o0@te', 'o1@tm')\n", + " ('o0@tm', 'o1@te')\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAGJCAYAAACZ7rtNAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAX2VJREFUeJzt3Xl8Ddf/P/DXzb5HyCoiQWyxNz401qRCYq1Way0RBA2KaLWxJdESpZSqrVVL7UsVX1vEEtUKVYQWUVSkSGIrCSG5yT2/P/LLcLPeRJIxvJ6PRx7JnDkzc+Z9z733nblnzlUJIQSIiIiIiBRIT+4GEBERERGVFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kqNZVKhfDwcLmb8UqIiYmBSqVCTEyM3E0pV25ubhg8eLDczXgtDB48GG5ubnI3o8ytWrUKKpUKCQkJFXbM8PBwqFSqCjvey6Qkr/Mv4/NbSe9Tr3M/e1FMZhUu94VdpVLh119/zbdeCAEXFxeoVCp069ZNhhYSERVs8eLFWLVqldzNoBI4duwYwsPD8eDBA7mbIovMzEwsWLAAzZo1g5WVFSpVqoQGDRpg+PDhiI+Pl+o9/96sUqlgYmKCqlWrws/PD9988w3S0tLKrE18HjGZfWWYmJhg/fr1+cqPHDmCGzduwNjYWIZWka7atWuHJ0+eoF27dnI3hajClOZNeODAgXjy5AlcXV3Lp1EFmDJlCp48eVJhx3uZHTt2DBEREQUms5cuXcL3339f8Y2qQL169cKECRPQsGFDzJo1CxEREWjXrh327t2L48eP56s/ffp0rFmzBkuWLMGYMWMAAOPGjUOjRo1w7tw5rbql7WdMZgEDuRtAZaNLly7YsmULvvnmGxgYPHtY169fD09PT9y9e1fG1pU/IQSePn0KU1NTuZsCAMjKyoJGo4GRkZFO9fX09GBiYlLOrXq1aTQaZGZmMo6vqMePH8Pc3Bz6+vrQ19ev0GMbGBhova5SwV71iyYnT57Erl27MGPGDEyaNElr3bfffltggt+5c2c0b95cWg4NDcWhQ4fQrVs39OjRAxcvXpTet9jPSo9XZl8R/fr1w7179xAdHS2VZWZmYuvWrejfv3+B2zx+/BgTJkyAi4sLjI2NUbduXXz11VcQQmjVy8jIwPjx42FnZwdLS0v06NEDN27cKHCfN2/exJAhQ+Dg4ABjY2M0aNAAK1as0Okccj+W+eWXXzBixAhUqVIFVlZWGDRoEP777z+tum5ubujWrRuioqLQvHlzmJqaYtmyZQCABw8eYNy4cdJ5ubu748svv4RGo9Hax8aNG+Hp6QlLS0tYWVmhUaNGWLBggVYdXfaVkJAAlUqFr776CvPnz0etWrVgbGyMM2fOwMDAABEREfnO9dKlS1CpVPj2228BFDxm1tvbGw0bNsSFCxfg4+MDMzMzODs7Y/bs2fn2d/36dfTo0QPm5uawt7fH+PHjERUVpdM43OvXryM4OBh169aFqakpqlSpgvfffz/fmMTcx+e3335DSEgI7OzsYG5ujnfeeQd37tzRqiuEwBdffIFq1arBzMwMPj4+OH/+fJHteJ5Go8GCBQvQqFEjmJiYwM7ODv7+/vjjjz+kOiqVCqNHj8a6devQoEEDGBsbY9++fQCAM2fOoHPnzrCysoKFhQU6dOiQ76qJWq1GREQEateuDRMTE1SpUgVt2rTReg4lJycjMDAQ1apVg7GxMZycnPD222/rNF4zt31btmyBh4cHTE1N4eXlhT///BMAsGzZMri7u8PExATe3t4F7nPLli3w9PSEqakpbG1t8cEHH+DmzZv56m3fvh0NGzaEiYkJGjZsiJ9//rnQuM6fPx8NGjSAiYkJHBwcMGLEiHzPr8LEx8fjvffeQ+XKlWFiYoLmzZtj586dWnV07Sdubm44f/48jhw5In0U6+3trbWPI0eOIDg4GPb29qhWrZrWurzx2rt3L9q2bQtzc3NYWlqia9eu+fpcaR/PgsYy5j6+ubHPfb3L7YNFyczMxLRp0+Dp6Qlra2uYm5ujbdu2OHz4cLHbAs9e/2JiYqTXv0aNGknP9W3btknPHU9PT5w5c0Zre29vbynWzytunHV4eDg++eQTAECNGjWkxy03fnnHzOY+Vr/++is++ugj2NnZoVKlShgxYgQyMzPx4MEDDBo0CDY2NrCxscHEiRPzvf+8TO9TV69eBQC0bt063zp9fX1UqVKl2H0AwFtvvYWpU6fi+vXrWLt2rVRe2JjZtWvXokWLFjAzM4ONjQ3atWuH/fv3Ayj6efQ64b8Arwg3Nzd4eXlhw4YN6Ny5M4CcF/eHDx+ib9+++Oabb7TqCyHQo0cPHD58GEOHDkXTpk0RFRWFTz75BDdv3sTXX38t1R02bBjWrl2L/v37o1WrVjh06BC6du2arw0pKSl48803pRd5Ozs77N27F0OHDkVqairGjRun07mMHj0alSpVQnh4OC5duoQlS5bg+vXrUsKX69KlS+jXrx9GjBiBoKAg1K1bF+np6Wjfvj1u3ryJESNGoHr16jh27BhCQ0ORlJSE+fPnAwCio6PRr18/dOjQAV9++SUA4OLFi/jtt98wduxYANB5X7lWrlyJp0+fYvjw4dIbZfv27bF582aEhYVp1d20aRP09fXx/vvvFxmL//77D/7+/nj33XfRu3dvbN26FZ9++ikaNWokPc6PHz/GW2+9haSkJIwdOxaOjo5Yv369zm+MJ0+exLFjx9C3b19Uq1YNCQkJWLJkCby9vXHhwgWYmZlp1R8zZgxsbGwQFhaGhIQEzJ8/H6NHj8amTZukOtOmTcMXX3yBLl26oEuXLjh9+jQ6deqEzMxMndo0dOhQrFq1Cp07d8awYcOQlZWFo0eP4vjx41pXOQ4dOoTNmzdj9OjRsLW1lV7Y27ZtCysrK0ycOBGGhoZYtmwZvL29ceTIEbRs2RJAzhtHZGQkhg0bhhYtWiA1NRV//PEHTp8+jY4dOwLI+Ujx/PnzGDNmDNzc3HD79m1ER0cjMTFRp5urjh49ip07d2LUqFEAgMjISHTr1g0TJ07E4sWLERwcjP/++w+zZ8/GkCFDcOjQIWnbVatWITAwEP/73/8QGRmJlJQULFiwAL/99hvOnDmDSpUqAQD279+PXr16wcPDA5GRkbh3756UsOU1YsQIab8fffQRrl27hm+//RZnzpzBb7/9BkNDw0LP5fz582jdujWcnZ3x2WefwdzcHJs3b0bPnj3x008/4Z133tGqX1w/mT9/PsaMGQMLCwtMnjwZAODg4KC1j+DgYNjZ2WHatGl4/PhxoW1bs2YNAgIC4Ofnhy+//BLp6elYsmQJ2rRpgzNnzkiP1Ys+nnn9+uuv2LZtG4KDg2FpaYlvvvkGvXr1QmJiYpGJTWpqKpYvX45+/fohKCgIaWlp+OGHH+Dn54fff/8dTZs2LfbYV65cQf/+/TFixAh88MEH+Oqrr9C9e3csXboUkyZNQnBwMICcPte7d29cunQJenovdv3q3Xffxd9//40NGzbg66+/hq2tLQDAzs6uyO3GjBkDR0dHRERE4Pjx4/juu+9QqVIlHDt2DNWrV8fMmTOxZ88ezJkzBw0bNsSgQYMAvHzvU7lDW9atW4fWrVu/0FXUgQMHYtKkSdi/fz+CgoIKrRcREYHw8HC0atUK06dPh5GREU6cOIFDhw6hU6dOOj2PXguCFG3lypUCgDh58qT49ttvhaWlpUhPTxdCCPH+++8LHx8fIYQQrq6uomvXrtJ227dvFwDEF198obW/9957T6hUKnHlyhUhhBBxcXECgAgODtaq179/fwFAhIWFSWVDhw4VTk5O4u7du1p1+/btK6ytraV2FXcunp6eIjMzUyqfPXu2ACB27Nghlbm6ugoAYt++fVr7+Pzzz4W5ubn4+++/tco/++wzoa+vLxITE4UQQowdO1ZYWVmJrKysQtuj676uXbsmAAgrKytx+/ZtrbrLli0TAMSff/6pVe7h4SHeeustafnw4cMCgDh8+LBU1r59ewFA/Pjjj1JZRkaGcHR0FL169ZLK5s6dKwCI7du3S2VPnjwR9erVy7fPghT0uMTGxuY7du7j4+vrKzQajVQ+fvx4oa+vLx48eCCEEOL27dvCyMhIdO3aVavepEmTBAAREBBQZHsOHTokAIiPPvoo37rn9wdA6OnpifPnz2vV6dmzpzAyMhJXr16Vym7duiUsLS1Fu3btpLImTZpoPSfy+u+//wQAMWfOnCLbWxgAwtjYWFy7dk0qy+0Pjo6OIjU1VSoPDQ0VAKS6mZmZwt7eXjRs2FA8efJEqrdr1y4BQEybNk0qa9q0qXBycpLiL4QQ+/fvFwCEq6urVHb06FEBQKxbt06rnfv27SuwPK8OHTqIRo0aiadPn0plGo1GtGrVStSuXVsq07WfCCFEgwYNRPv27fMdK3cfbdq0yfcczV2XG6u0tDRRqVIlERQUpFUvOTlZWFtbS+Uv8niGhYWJvG+XAISRkZH0WimEEGfPnhUAxMKFC4vcX1ZWlsjIyNAq+++//4SDg4MYMmRIse3Jff07duyYVBYVFSUACFNTU3H9+nWpPLfP5X1tKSjuAQEBWn0m9zyff52fM2eOVvzztuv553fuY+Xn56fVF7y8vIRKpRIjR46UyrKyskS1atW02vWyvU9pNBrpddnBwUH069dPLFq0SCveec/95MmThe7P2tpaNGvWTFrO288uX74s9PT0xDvvvCOys7PztSVXYc+j1wmHGbxCevfujSdPnmDXrl1IS0vDrl27Ch1isGfPHujr6+Ojjz7SKp8wYQKEENi7d69UD0C+enn/exVC4KeffkL37t0hhMDdu3elHz8/Pzx8+BCnT5/W6TyGDx+udYXoww8/hIGBgdSWXDVq1ICfn59W2ZYtW9C2bVvY2NhotcHX1xfZ2dn45ZdfAACVKlXC48ePtT5SzkvXfeXq1atXvisU7777LgwMDLSuWv7111+4cOEC+vTpU2wsLCws8MEHH0jLRkZGaNGiBf755x+pbN++fXB2dkaPHj2kMhMTkyL/23/e8+OM1Wo17t27B3d3d1SqVKnAx2z48OFaV8jbtm2L7OxsXL9+HQBw4MABZGZmYsyYMVr1dL0y/9NPP0GlUuW7mg0g30dw7du3h4eHh7ScnZ2N/fv3o2fPnqhZs6ZU7uTkhP79++PXX39FamoqgJw+cP78eVy+fLnAdpiamsLIyAgxMTE6fwyfV4cOHbSu+OVeFe7VqxcsLS3zlec+rn/88Qdu376N4OBgrTHAXbt2Rb169bB7924AQFJSEuLi4hAQEABra2upXseOHbXiAuT0Z2tra3Ts2FGrP3t6esLCwqLIK/n379/HoUOH0Lt3b6SlpUnb3rt3D35+frh8+XK+4Q/F9RNdBAUFFTs+Njo6Gg8ePEC/fv20zktfXx8tW7aUzqssHs+8fH19UatWLWm5cePGsLKy0np+FkRfX18aT6/RaHD//n1kZWWhefPmOr9Oenh4wMvLS1rO7UNvvfUWqlevnq+8uDaVp6FDh2r1hZYtW0IIgaFDh0pl+vr6aN68uVY7X7b3KZVKhaioKHzxxRewsbHBhg0bMGrUKLi6uqJPnz4lnuHBwsKiyFkNtm/fDo1Gg2nTpuW7qs4pvLRxmMErxM7ODr6+vli/fj3S09ORnZ2N9957r8C6169fR9WqVbXeUAGgfv360vrc33p6elov2ABQt25dreU7d+7gwYMH+O677/Ddd98VeMzbt28DyBm39jxra2uthKp27dpa6y0sLODk5JRvXFuNGjXyHePy5cs4d+5coR975bYhODgYmzdvRufOneHs7IxOnTqhd+/e8Pf3L/G+imqPra0tOnTogM2bN+Pzzz8HkDPEwMDAAO+++26B+31etWrV8r1o2djYaN0Fe/36ddSqVStfPXd392L3DwBPnjxBZGQkVq5ciZs3b2qNRXv48GG++s+/Uea2B4CUIOT2nbyPo52dnVS3KFevXkXVqlVRuXLlYuvmjfmdO3eQnp6er38COX1bo9Hg33//RYMGDTB9+nS8/fbbqFOnDho2bAh/f38MHDgQjRs3BpBzM8uXX36JCRMmwMHBAW+++Sa6deuGQYMGwdHREUBOfJ6/+9jIyEir3XljlZtwuri4FFieN4YFnUe9evWkafgKi3Xuts+/MV++fBkPHz6Evb19vrpA/v78vCtXrkAIgalTp2Lq1KmFbu/s7CwtF9dPdFHQcyqv3H9G3nrrrQLXW1lZAdDt8SypvOcI5JynLue4evVqzJ07F/Hx8VCr1VK5Ludc0LF17VtyKElbn2+nnO9ThTE2NsbkyZMxefJkJCUl4ciRI1iwYAE2b94MQ0NDrTGwxXn06FGhz0cg57VQT08v3z+mlB+T2VdM//79ERQUhOTkZHTu3FkaV1fecm+I+uCDDxAQEFBgndwkwcnJSat85cqVpZpou6CZCzQaDTp27IiJEycWuE2dOnUAAPb29oiLi0NUVBT27t2LvXv3YuXKlRg0aBBWr15don0V1R4A6Nu3LwIDAxEXF4emTZti8+bN6NChgzTerCiFXZV6PuF8UWPGjMHKlSsxbtw4eHl5wdraGiqVCn379s1301xFtUlXLzJ7Rbt27XD16lXs2LED+/fvx/Lly/H1119j6dKlGDZsGICcKzvdu3fH9u3bERUVhalTpyIyMhKHDh1Cs2bNMHbsWKm/ADlXip+/4a6wWMkRQ41GA3t7e6xbt67A9UWNe8ztBx9//HG+T0Ny5f3nqSzOUZfHN7dta9asKTApfX5cY3GPZ0mV9hzXrl2LwYMHo2fPnvjkk09gb28PfX19REZGSjcZlfbYurRJpVIV2Mbs7Gydjl1SJWlreT8HAN3ep3Th5OSEvn37olevXmjQoAE2b96MVatW6TSW9saNG3j48KHOFx2oaExmXzHvvPMORowYgePHj2t9tJ2Xq6srDhw4gLS0NK3/enMnfc4d6O7q6gqNRoOrV69q/Zd76dIlrf3l3kGanZ0NX1/fItuY96P9Bg0aaC1fvnwZPj4+0vKjR4+QlJSELl26FLlfAKhVqxYePXpUbBuAnKto3bt3R/fu3aHRaBAcHIxly5Zh6tSpcHd3L9G+itKzZ0+MGDFCejz+/vtvhIaGvtA+n+fq6ooLFy5ACKF1dfbKlSs6bb9161YEBARg7ty5UtnTp09LPSl6bt+5fPmy1kf9d+7c0enqUK1atRAVFYX79+/rdHX2eXZ2djAzM8vXP4Gcvq2np6d1Nahy5coIDAxEYGAgHj16hHbt2iE8PFxKZnPbM2HCBEyYMAGXL19G06ZNMXfuXKxduxYTJ07UGgaiy5VnXeTG8NKlS/muOF66dEnr+QmgwKESeWNQq1YtHDhwAK1bty7xPwG5j6OhoeELPx+eVxYfleZejbO3t9epbUU9nhVl69atqFmzJrZt26YVg4KG1pQHGxubAocd6DIEpCI/3pbzfaokDA0N0bhxY1y+fBl3797V6Ur/mjVrAKDQfw6BnL6q0Whw4cKFIm8K5JADTs31yrGwsMCSJUsQHh6O7t27F1qvS5cuyM7OlqaGyvX1119DpVJJd8rn/s47G0LeO/n19fXRq1cv/PTTT/jrr7/yHe/5KXl8fX21fvJeqf3uu++0PnZbsmQJsrKypLYUpXfv3oiNjUVUVFS+dQ8ePEBWVhYA4N69e1rr9PT0pP/IMzIySrSv4lSqVAl+fn7YvHkzNm7cCCMjI/Ts2VOnbXXh5+eHmzdvak2R9PTpU50nL9fX1893NWThwoWlvkrj6+sLQ0NDLFy4UGu/eftMYXr16gUhRIFTmhV31UZfXx+dOnXCjh07tIalpKSkYP369WjTpo30sXPePmBhYQF3d3fp8U9PT8fTp0+16tSqVQuWlpZSHQ8PD62+7OnpqdM5Fqd58+awt7fH0qVLpWMBOTOUXLx4UbpL28nJCU2bNsXq1au1hoRER0fjwoULWvvs3bs3srOzpeEuz8vKyirynxd7e3t4e3tj2bJlSEpKyrc+79RsujI3N3/hb5Ly8/ODlZUVZs6cqfW6kbdtujyeFSX3iuTz/fnEiROIjY2tkOPXqlUL8fHxWo/b2bNn8dtvvxW7rbm5OQBUyDeAyfk+VZDLly8jMTExX/mDBw8QGxsLGxubYmd2AHJmYfn8889Ro0YNDBgwoNB6PXv2hJ6eHqZPn57vU7Ln+05ZPI+UjldmX0GFfXzyvO7du8PHxweTJ09GQkICmjRpgv3792PHjh0YN26cdLWjadOm6NevHxYvXoyHDx+iVatWOHjwYIFX/WbNmoXDhw+jZcuWCAoKgoeHB+7fv4/Tp0/jwIEDuH//vk7tz8zMRIcOHaTpZBYvXow2bdpo3eBUmE8++QQ7d+5Et27dMHjwYHh6euLx48f4888/sXXrViQkJMDW1hbDhg3D/fv38dZbb6FatWq4fv06Fi5ciKZNm0rjsXTdly769OmDDz74AIsXL4afn1+ZDv8YMWIEvv32W/Tr1w9jx46Fk5MT1q1bJ904VNx/7d26dcOaNWtgbW0NDw8PxMbG4sCBAzrPmZiXnZ0dPv74Y2kaqi5duuDMmTPYu3evTvHy8fHBwIED8c033+Dy5cvw9/eHRqPB0aNH4ePjg9GjRxe5/RdffIHo6Gi0adMGwcHBMDAwwLJly5CRkaE1R6+Hhwe8vb3h6emJypUr448//sDWrVul/f/9999SP/Tw8ICBgQF+/vlnpKSkoG/fvqWKja4MDQ3x5ZdfIjAwEO3bt0e/fv2kqbnc3Nwwfvx4qW5kZCS6du2KNm3aYMiQIbh//z4WLlyIBg0a4NGjR1K99u3bY8SIEYiMjERcXBw6deoEQ0NDXL58GVu2bMGCBQsKHWMPAIsWLUKbNm3QqFEjBAUFoWbNmkhJSUFsbCxu3LiBs2fPlvg8PT09sWTJEnzxxRdwd3eHvb19oWNfC2NlZYUlS5Zg4MCBeOONN9C3b1/Y2dkhMTERu3fvRuvWrfHtt9/K+njm1a1bN2zbtg3vvPMOunbtimvXrmHp0qXw8PDQeszKy5AhQzBv3jz4+flh6NChuH37NpYuXYoGDRpIN0gWJvcftsmTJ6Nv374wNDRE9+7dpSS3LL1s71Nnz55F//790blzZ7Rt2xaVK1fGzZs3sXr1aty6dQvz58/PN3Ri7969iI+PR1ZWFlJSUnDo0CFER0fD1dUVO3fuLPJLXtzd3TF58mR8/vnnaNu2Ld59910YGxvj5MmTqFq1KiIjIwGUzfNI8Spu4gQqD7pM/yFE/qm5hMiZ0mb8+PGiatWqwtDQUNSuXVvMmTNHa8oPIXKmefroo49ElSpVhLm5uejevbv4999/8015IoQQKSkpYtSoUcLFxUUYGhoKR0dH0aFDB/Hdd9/pfC5HjhwRw4cPFzY2NsLCwkIMGDBA3Lt3r9jzef68QkNDhbu7uzAyMhK2traiVatW4quvvpKm/Nq6davo1KmTsLe3F0ZGRqJ69epixIgRIikpqcT7yp2aq6gpf1JTU4WpqakAINauXZtvfWFTczVo0CBf3YKmz/nnn39E165dhampqbCzsxMTJkwQP/30kwAgjh8/Xmi7hMiZEigwMFDY2toKCwsL4efnJ+Lj4wudZidvXyuo7dnZ2SIiIkI4OTkJU1NT4e3tLf766698+yxMVlaWmDNnjqhXr54wMjISdnZ2onPnzuLUqVNSHQBi1KhRBW5/+vRp4efnJywsLISZmZnw8fHRmsZICCG++OIL0aJFC1GpUiVhamoq6tWrJ2bMmCE9rnfv3hWjRo0S9erVE+bm5sLa2lq0bNlSbN68udj2F9a+wvpKbgy3bNmiVb5p0ybRrFkzYWxsLCpXriwGDBggbty4ke9YP/30k6hfv74wNjYWHh4eYtu2bQX2EyGE+O6774Snp6cwNTUVlpaWolGjRmLixIni1q1bxZ7T1atXxaBBg4Sjo6MwNDQUzs7Oolu3bmLr1q1SnZL0k+TkZNG1a1dhaWkpAEjTCxX1upZ3aq7n9+/n5yesra2FiYmJqFWrlhg8eLD4448/hBAv9ngWNjVXQf1Plz6u0WjEzJkzhaurqzA2NhbNmjUTu3btKvQxK+gYBb3+laTPrV27VtSsWVMYGRmJpk2biqioKJ2m5hIiZ9pCZ2dnoaenp/VY6PqakRvPO3fuaJUHBAQIc3NzrbKX6X0qJSVFzJo1S7Rv3144OTkJAwMDYWNjI9566y2t58Dz5577Y2RkJBwdHUXHjh3FggULtKbmyxuXvFasWCG9DtjY2Ij27duL6OhoaX1hz6PXiUoIGe7aICpA7mTuJ0+e1JoYn0pn/vz5GD9+PG7cuKF1lzkREdGrhGNmiV4Bz08PBeSMmV22bBlq167NRJaIiF5pHDNL9Ap49913Ub16dTRt2hQPHz7E2rVrER8fX+g0TERERK8KJrNErwA/Pz8sX74c69atQ3Z2Njw8PLBx40advmWMiIhIyThmloiIiIgUi2NmiYiIiEixZE1mf/nlF3Tv3h1Vq1aFSqXC9u3bi90mJiYGb7zxBoyNjeHu7o5Vq1aVezuJiIiI6OUk65jZx48fo0mTJhgyZAjefffdYutfu3YNXbt2xciRI7Fu3TocPHgQw4YNg5OTU5FfCfc8jUaDW7duwdLSkl8BR0RERPQSEkIgLS0NVatWhZ5e0ddeX5oxsyqVCj///HORX/P56aefYvfu3VpfQ9e3b188ePAA+/bt0+k4N27c0PpudiIiIiJ6Of3777+oVq1akXUUNZtBbGwsfH19tcr8/Pwwbty4QrfJyMjQ+t7t3Nz92rVrsLS0LJd2Pk+tVuPw4cPw8fGBoaFhuR+PcjDu8mDc5cG4y4NxlwfjLo+KjntaWhpq1KihU66mqGQ2OTkZDg4OWmUODg5ITU3FkydPYGpqmm+byMhIRERE5CuPjY2FmZlZubX1eWZmZjhx4kSFHIueYdzlwbjLg3GXB+MuD8ZdHhUZ9/T0dADQaUioopLZ0ggNDUVISIi0nJqaChcXF3Tq1AlWVlblfny1Wo3o6Gh07NiR/0FWIMZdHoy7PBh3eTDu8mDc5VHRcU9NTdW5rqKSWUdHR6SkpGiVpaSkwMrKqsCrsgBgbGwMY2PjfOWGhoYV+iSo6ONRDsZdHoy7PBh3eTDu8mDc5VFRcS/JMRQ1z6yXlxcOHjyoVRYdHQ0vLy+ZWkREREREcpI1mX306BHi4uIQFxcHIOemrLi4OCQmJgLIGSIwaNAgqf7IkSPxzz//YOLEiYiPj8fixYuxefNmjB8/Xo7mExEREZHMZE1m//jjDzRr1gzNmjUDAISEhKBZs2aYNm0aACApKUlKbAGgRo0a2L17N6Kjo9GkSRPMnTsXy5cv13mOWSIiIiJ6tcg6Ztbb2xtFTXNb0Ld7eXt748yZM+XYKiIiIiJSCkWNmSUiIiIieh6TWSIiIiJSLCazRERERKRYippnVmmEEEjPzEJGNpCemQVDUfy3WFDZUKsZdzkw7vJg3OXBuMuDcZdHbtyLutdJLirxMraqHKWmpsLa2hoPHz4s928AS8/Mgse0qHI9BhEREVFFOTv1LVibF/xFVWWpJPkahxkQERERkWJxmEE5MjXUx9mpbyEqaj/8/Drxa/cqkFqtZtxlwLjLg3GXB+MuD8ZdHrlxNzXUl7sp+TCZLUcqlQpmRgYw1gfMjAxgaMhwVxS1SjDuMmDc5cG4y4NxlwfjLo/cuKtUL984ZQ4zICIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKJXsyu2jRIri5ucHExAQtW7bE77//XmT9+fPno27dujA1NYWLiwvGjx+Pp0+fVlBriYiIiOhlImsyu2nTJoSEhCAsLAynT59GkyZN4Ofnh9u3bxdYf/369fjss88QFhaGixcv4ocffsCmTZswadKkCm45EREREb0MDOQ8+Lx58xAUFITAwEAAwNKlS7F7926sWLECn332Wb76x44dQ+vWrdG/f38AgJubG/r164cTJ04UeoyMjAxkZGRIy6mpqQAAtVoNtVpdlqdToNxjVMSx6BnGXR6MuzwYd3kw7vJg3OVR0XEvyXFUQghRjm0pVGZmJszMzLB161b07NlTKg8ICMCDBw+wY8eOfNusX78ewcHB2L9/P1q0aIF//vkHXbt2xcCBAwu9OhseHo6IiIgC92VmZlZm50NEREREZSM9PR39+/fHw4cPYWVlVWRd2a7M3r17F9nZ2XBwcNAqd3BwQHx8fIHb9O/fH3fv3kWbNm0ghEBWVhZGjhxZ5DCD0NBQhISESMupqalwcXFBp06dig1OWVCr1YiOjkbHjh1haGhY7sejHIy7PBh3eTDu8mDc5cG4y6Oi4577SbouZB1mUFIxMTGYOXMmFi9ejJYtW+LKlSsYO3YsPv/8c0ydOrXAbYyNjWFsbJyv3NDQsEKfBBV9PMrBuMuDcZcH4y4Pxl0ejLs8KiruJTmGbMmsra0t9PX1kZKSolWekpICR0fHAreZOnUqBg4ciGHDhgEAGjVqhMePH2P48OGYPHky9PRkn5yBiIiIiCqQbNmfkZERPD09cfDgQalMo9Hg4MGD8PLyKnCb9PT0fAmrvr4+AECmob9EREREJCNZhxmEhIQgICAAzZs3R4sWLTB//nw8fvxYmt1g0KBBcHZ2RmRkJACge/fumDdvHpo1ayYNM5g6dSq6d+8uJbVERERE9PqQNZnt06cP7ty5g2nTpiE5ORlNmzbFvn37pJvCEhMTta7ETpkyBSqVClOmTMHNmzdhZ2eH7t27Y8aMGXKdAhERERHJSPYbwEaPHo3Ro0cXuC4mJkZr2cDAAGFhYQgLC6uAlhERERHRy453TBERERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYsmezC5atAhubm4wMTFBy5Yt8fvvvxdZ/8GDBxg1ahScnJxgbGyMOnXqYM+ePRXUWiIiIiJ6mRjIefBNmzYhJCQES5cuRcuWLTF//nz4+fnh0qVLsLe3z1c/MzMTHTt2hL29PbZu3QpnZ2dcv34dlSpVqvjGExEREZHsZE1m582bh6CgIAQGBgIAli5dit27d2PFihX47LPP8tVfsWIF7t+/j2PHjsHQ0BAA4ObmVpFNJiIiIqKXiGzJbGZmJk6dOoXQ0FCpTE9PD76+voiNjS1wm507d8LLywujRo3Cjh07YGdnh/79++PTTz+Fvr5+gdtkZGQgIyNDWk5NTQUAqNVqqNXqMjyjguUeoyKORc8w7vJg3OXBuMuDcZcH4y6Pio57SY4jWzJ79+5dZGdnw8HBQavcwcEB8fHxBW7zzz//4NChQxgwYAD27NmDK1euIDg4GGq1GmFhYQVuExkZiYiIiHzl+/fvh5mZ2YufiI6io6Mr7Fj0DOMuD8ZdHoy7PBh3eTDu8qiouKenp+tcV9ZhBiWl0Whgb2+P7777Dvr6+vD09MTNmzcxZ86cQpPZ0NBQhISESMupqalwcXFBp06dYGVlVe5tVqvViI6ORseOHaWhEVT+GHd5MO7yYNzlwbjLg3GXR0XHPfeTdF3Ilsza2tpCX18fKSkpWuUpKSlwdHQscBsnJycYGhpqDSmoX78+kpOTkZmZCSMjo3zbGBsbw9jYOF+5oaFhhT4JKvp4lINxlwfjLg/GXR6MuzwYd3lUVNxLcgzZpuYyMjKCp6cnDh48KJVpNBocPHgQXl5eBW7TunVrXLlyBRqNRir7+++/4eTkVGAiS0RERESvNlnnmQ0JCcH333+P1atX4+LFi/jwww/x+PFjaXaDQYMGad0g9uGHH+L+/fsYO3Ys/v77b+zevRszZ87EqFGj5DoFIiIiIpKRrGNm+/Tpgzt37mDatGlITk5G06ZNsW/fPummsMTEROjpPcu3XVxcEBUVhfHjx6Nx48ZwdnbG2LFj8emnn8p1CkREREQkI9lvABs9ejRGjx5d4LqYmJh8ZV5eXjh+/Hg5t4qIiIiIlED2ZJaIiIhKTgiBrKwsZGdny92UCqVWq2FgYICnT5++ducup/KIe96b+kuLySwREZHCZGZmIikpqURzcb4qhBBwdHTEv//+C5VKJXdzXhvlEXeVSoVq1arBwsLihfbDZJaIiEhBNBoNrl27Bn19fVStWhVGRkavVVKn0Wjw6NEjWFhYaN1XQ+WrrOMuhMCdO3dw48YN1K5d+4Wu0DKZJSIiUpDMzExoNBq4uLhU6DdZviw0Gg0yMzNhYmLCZLYClUfc7ezskJCQALVa/ULJLHsBERGRAjGRI6Urq08U+EwgIiIiIsUqcTKbezfbX3/9VR7tISIiIiLSWYmTWUNDQ1SvXp3TYRARERGR7Eo1zGDy5MmYNGkS7t+/X9btISIioleMSqUq8ic8PBwJCQmFrq+oL0sKDw8vtq0AMHjw4ALX+fv7V0g7SVupZjP49ttvceXKFVStWhWurq4wNzfXWn/69OkyaRwREREpX1JSkvT3pk2bMG3aNFy6dEkqs7CwwN27dwEABw4cQIMGDbS2r1KlSomOl5iYiOrVq5e4nR9//DFGjhwpLf/vf//D8OHDERQUlK+uv78/Vq5cqVVmbGxc4mPSiytVMtuzZ88ybgYRERGVlhACT9QVP/zP1FBfpzvSHR0dpb+tra2hUqm0ygBIyWyVKlXyrSupGjVqwMfHBwEBAejVq5fOU5hZWFhoTeCvr68PS0vLAttjbGz8wu2kslGqZDYsLKys20FERESl9ESdDY9pURV+3AvT/WBm9PJNWX/hwgWsXr0aU6ZMQXBwMN5//30EBASgXbt2r9UXTLwuSj0114MHD7B8+XKEhoZKY2dPnz6NmzdvllnjiIiI6PXSqlUr6Qpp3iuluqpbty5mzpyJhIQE7Ny5E0IIdO/eHbVq1UJ4eDiuXbv2wu3ctWtXvnbOnDnzhfdLJVeqf6fOnTsHX19fWFtbIyEhAUFBQahcuTK2bduGxMRE/Pjjj2XdTiIiIiqEqaE+Lkz3k+W4ZW3Tpk2oX7++TnU7d+6Mo0ePAgBcXV1x/vx5rfUqlQo+Pj7w8fHBggUL8OGHHyIiIgJxcXHYvn37C7XTx8cHS5Ys0SqrXLnyC+2TSqdUyWxISAgGDx6M2bNnw9LSUirv0qUL+vfvX2aNIyIiouKpVKqX8uP+0nBxcYG7u7tOdZcvX44nT54AyJk6tCCnT5/Gjz/+iA0bNkClUiEkJATDhg174Xaam5vr3E4qX6Xq+SdPnsSyZcvylTs7OyM5OfmFG0VERERUHGdn5wLLb9y4gbVr12LNmjW4evUqunfvjh9++AH+/v4wMHg1kn56plSPqLGxMVJTU/OV//3337Czs3vhRhEREdHr6d69e/kujFWqVAkmJiY678PV1RXNmzfHqFGj0K9fP9jY2JR1M5GRkZGvnQYGBrC1tS3zY1HRSpXM9ujRA9OnT8fmzZsB5Hy8kZiYiE8//RS9evUq0wYSERHR68PX1zdf2YYNG9C3b1+d93H+/HnUq1evLJuVz759++Dk5KRVVrduXcTHx5frcSm/Us1mMHfuXDx69Aj29vZ48uQJ2rdvD3d3d1haWmLGjBll3UYiIiJ6RQwePBgPHjzIV+7m5gYhRIE/JUlkAZRZIpuQkIBx48blK1+1alWB7WQiK49SXZm1trZGdHQ0fv31V5w7dw6PHj3CG2+8UeB/U0RERERE5aVUyezTp09hYmKCNm3aoE2bNmXdJiIiIiIinZQqma1UqRJatGiB9u3bw8fHB15eXjA1NS3rthERERERFalUY2YPHDgAf39/nDhxAj169ICNjQ3atGmDyZMnIzo6uqzbSERERERUoFIls23atMGkSZOwf/9+PHjwAIcPH4a7uztmz54Nf3//sm4jEREREVGBSj1z8N9//42YmBjpJyMjA926dYO3t3cZNo+IiIiIqHClSmadnZ3x5MkTeHt7w9vbG59++ikaN24MlUpV1u0jIiIiIipUqYYZ2NnZIT09HcnJyUhOTkZKSor03chERERERBWlVMlsXFwckpOT8dlnnyEjIwOTJk2Cra0tWrVqhcmTJ5d1G4mIiIiIClSqZBbImZ6rR48emDRpEkJDQ/Hee+/h5MmTmDVrVlm2j4iIiIheQqtWrUKlSpWk5fDwcDRt2rTC21GqZHbbtm346KOP0LhxYzg4OODDDz/Eo0ePMHfuXJw+fbqs20hEREQKplKpivwJDw9HQkJCoeuPHz9eIe0MDw8vtq1AzlfyqlQqjBw5Mt8+Ro0aBZVKhcGDB1dIm8uCXEloWSnVDWAjR45Eu3btMHz4cLRv3x6NGjUq63YRERHRKyIpKUn6e9OmTZg2bRouXboklVlYWODu3bsAcuayb9Cggdb2VapUKdHxEhMTUb169RK38+OPP9ZKUP/3v/9h+PDhCAoKylfXxcUFGzduxNdffy19cdTTp0+xfv36Uh27KJmZmTAyMirTfb5KSnVl9vbt29i6dStGjx7NRJaIiOhlkfm48B/10xLUfVJ83RJwdHSUfqytraFSqbTKLCwspLpVqlTRWufo6AhDQ8MSHa9GjRrw9fXFmjVrkJ6ervN2FhYWWsfV19eHpaWlVlmuN954Ay4uLti2bZtUtm3bNlSvXh3NmjUr9li//fYbvL29YWZmBhsbG/j5+eG///4DAHh7e2P06NEYN24cbG1t4efnBwA4cuQIWrRoAWNjYzg5OeGzzz5DVlaWtM+tW7eiUaNGMDU1RZUqVeDr64vHj3Meq5iYGLRo0QLm5uaoVKkSWrdujevXr2PVqlWIiIjA2bNnpavPq1atAgDMmzcPjRo1grm5OVxdXTFhwgQ8evRI53hevXoVNWvWxOjRoyGE0Hm7kir1PLPZ2dnYvn07Ll68CADw8PDA22+/DX19/TJrHBEREZXAzKqFr6vdCRiw5dnyHHdAXUii59oGCNz9bHl+IyD9nnad8Ielb2c5u3DhAlavXo0pU6YgODgY77//PgICAtCuXbsynUZ0yJAhWLlyJQYMGAAAWLFiBQIDAxETE1PkdnFxcejQoQOGDBmCBQsWwMDAAIcPH0Z2drZUZ/Xq1fjwww/x22+/AQBu3ryJLl26YPDgwfjxxx8RHx+PoKAgmJiYIDw8HElJSejXrx9mz56Nd955B2lpaTh69CiEEMjKykLPnj0RFBSEDRs2IDMzE7///jtUKhX69OmDv/76C/v27cOBAwcAANbW1gAAPT09fPPNN6hRowauXLmC4OBgfPrpp1iyZEmxsTl37hz8/PwwdOhQfPHFF6UJr85KlcxeuXIFXbp0wc2bN1G3bl0AQGRkJFxcXLB7927UqlWrTBtJREREr4dWrVpBT0/7g+OSXA0EgLp162LmzJmYMWMGYmJi8OOPP6J79+6wtbXFoEGDEBAQgBo1arxwWz/44AOEhobi+vXrAHKutm7cuLHYZHb27Nlo3rw5Fi9eLJXlHVpRu3ZtzJ49W1qePHkyXFxc8O2330KlUqFevXq4desWPv30U0ybNg1JSUnIysrCu+++C1dXVwCQPj2/f/8+Hj58iG7dukk5Wv369aV9W1hYwMDAQOvKMwCMGzdO+rt69eqYPHkyJkyYUGwye+zYMXTr1k2qX95Klcx+9NFHqFWrFo4fP47KlSsDAO7du4cPPvgAH330EXbv3l3MHoiIiKjMTbpV+DpVnk9OP7lSRN08oxDH/Vn6NpXQpk2btBKtonTu3BlHjx4FALi6uuL8+fNa61UqFXx8fODj44MFCxbgww8/REREBOLi4rB9+/YXbqudnR26du2KVatWQQiBrl27wtbWttjt4uLi8P777xdZx9PTU2v54sWL8PLy0rqy3Lp1azx69Ag3btxAkyZN0KFDBzRq1Ah+fn7o1KkT3nvvPdjY2KBy5coYPHgw/Pz80LFjR/j6+qJ3795wcnIqsg0HDhxAZGQk4uPjkZqaiqysLDx9+hTp6ekwMzMrcJvExER07NgRM2bM0EqGy1OpxsweOXIEs2fPlhJZIGeMy6xZs3DkyJEyaxwRERGVgJF54T+GJiWoa1p83XLi4uICd3d3rZ/CLF++HHFxcYiLi8OePXsKrHP69GmMGzcOtWvXxsGDBxESEoLIyMgya++QIUOwatUqrF69GkOGDNFpm9wbxopibl6yGOvr6yM6Ohp79+6Fh4cHFi5ciLp16+LatWsAgJUrVyI2NhatWrXCpk2bUKdOnSJniUhISEC3bt3QuHFj/PTTTzh58iTmzJkDIOeGtMLY2dmhRYsW2LBhA1JTU0t0DqVVqmTW2NgYaWlp+cofPXrEu+2IiIioQjg7O0sJb+5H6wBw48YNzJo1Cw0aNECrVq1w8+ZN/PDDD7hx4wbmzp2r85VfXfj7+yMzMxNqtVq6Uas4jRs3xsGDB0t0nPr16yM2NlbrRqrffvsNlpaWqFatGoCcK9GtW7dGREQEzpw5AyMjI/z8889S/WbNmiE0NBTHjh1Dw4YNsX79egCAkZGR1nhdADh16hQ0Gg3mzp2LN998E3Xq1EFycnKx7TQ1NcWuXbtgYmICPz+/AvPFslaqZLZbt24YPnw4Tpw4ASEEhBA4fvw4Ro4ciR49epR1G4mIiOg1ce/ePSQnJ2v9PH36tPgNn+Pq6oqff/4Zo0aNQlJSErZs2YJu3brBwKDU970XSl9fHxcvXsSFCxd0vgk+NDQUJ0+eRHBwMM6dO4f4+HgsWbJEmp6sIMHBwfj3338xZswYxMfHY8eOHQgLC0NISAj09PRw4sQJzJw5E3/88QcSExOxbds23LlzB/Xr18e1a9cQGhqK2NhYXL9+Hfv378fly5elpN7NzQ3Xrl1DXFwc7t69i4yMDLi7u0OtVmPhwoX4559/sGbNGqxcuVKn8zM3N8fu3bthYGCAzp07l3jMc0mVKpn95ptv4O7ujlatWsHExAQmJiZo3bo13N3dsWDBgrJuIxEREb0mfH194eTkpPVT0vGt58+fx4kTJxAcHAwbG5vyaehzrKysYGVlpXP9OnXqYP/+/Th79ixatGgBLy8v7Nixo8hk29nZGXv27MHvv/+OJk2aYOTIkRg6dCimTJkiteGXX35Bly5dUKdOHUyZMgVz585F586dYWZmhvj4ePTq1Qt16tTB8OHDMWrUKIwYMQIA0KtXL/j7+8PHxwd2dnbYsGEDmjRpgnnz5uHLL7+UruJOnTpV53O0sLDA3r17pbHEuVOElQeVKMHEXxqNBnPmzMHOnTuRmZmJ6tWrIyAgACqVCvXr1y9yXMvLIjU1FdbW1nj48GGJOl5pqdVq7NmzB126dCnxPHlUeoy7PBh3eTDu8pAr7k+fPsW1a9dQo0YNmJiYFL/BK0aj0SA1NRVWVlb5Zj2g8lMecS+qL5ckXyvR9fYZM2YgPDwcvr6+MDU1xZ49e2BtbY0VK1aU/AyIiIiIiF5QiVLrH3/8EYsXL0ZUVBS2b9+O//u//8O6deug0WjKq31ERERERIUqUTKbmJiILl26SMu+vr5QqVS4dauIee2IiIiIiMpJiZLZrKysfGMaDA0NoVary7RRRERERES6KNGYWSEEBg8eDGNjY6ns6dOnGDlypNbkvtu2bSu7FhIREVE+Jbh/m+ilVFZ9uETJbEBAQL6yDz74oEwaQkRERMXLnTkhPT1dp2+SInpZ5X6TmK7z8xamRMmsrpPlEhERUfnQ19dHpUqVcPv2bQCAmZkZVCqVzK2qOBqNBpmZmXj69Cmn5qpAZR13jUaDO3fuwMzM7IW/zKLsvwqDiIiIypWjoyMASAnt60QIgSdPnsDU1PS1SuLlVh5x19PTQ/Xq1V94f0xmiYiIFEalUsHJyQn29vav3U3YarUav/zyC9q1a8cvCalA5RF3IyOjMrnKy2SWiIhIofT19V94vKHS6OvrS7MrMZmtOC9z3F+KwSaLFi2Cm5sbTExM0LJlS/z+++86bbdx40aoVCr07NmzfBtIRERERC8l2ZPZTZs2ISQkBGFhYTh9+jSaNGkCPz+/YscBJSQk4OOPP0bbtm0rqKVERERE9LKRPZmdN28egoKCEBgYCA8PDyxduhRmZmZYsWJFodtkZ2djwIABiIiIQM2aNSuwtURERET0MpF1zGxmZiZOnTqF0NBQqUxPTw++vr6IjY0tdLvp06fD3t4eQ4cOxdGjR4s8RkZGBjIyMqTl1NRUADkDmSti0HzuMV63AfpyY9zlwbjLg3GXB+MuD8ZdHhUd95IcR9Zk9u7du8jOzoaDg4NWuYODA+Lj4wvc5tdff8UPP/yAuLg4nY4RGRmJiIiIfOX79++HmZlZidtcWtHR0RV2LHqGcZcH4y4Pxl0ejLs8GHd5VFTc09PTda6rqNkM0tLSMHDgQHz//fewtbXVaZvQ0FCEhIRIy6mpqXBxcUGnTp1gZWVVXk2VqNVqREdHo2PHji/d3X+vMsZdHoy7PBh3eTDu8mDc5VHRcc/9JF0Xsiaztra20NfXR0pKilZ5SkqKNCH0865evYqEhAR0795dKtNoNAAAAwMDXLp0CbVq1dLaxtjYGMbGxvn2ZWhoWKFPgoo+HuVg3OXBuMuDcZcH4y4Pxl0eFRX3khxD1hvAjIyM4OnpiYMHD0plGo0GBw8ehJeXV7769erVw59//om4uDjpp0ePHvDx8UFcXBxcXFwqsvlEREREJDPZhxmEhIQgICAAzZs3R4sWLTB//nw8fvwYgYGBAIBBgwbB2dkZkZGRMDExQcOGDbW2r1SpEgDkKyciIiKiV5/syWyfPn1w584dTJs2DcnJyWjatCn27dsn3RSWmJhYJl91RkRERESvHtmTWQAYPXo0Ro8eXeC6mJiYIrddtWpV2TeIiIiIiBSBlzyJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYr0UyeyiRYvg5uYGExMTtGzZEr///nuhdb///nu0bdsWNjY2sLGxga+vb5H1iYiIiOjVJXsyu2nTJoSEhCAsLAynT59GkyZN4Ofnh9u3bxdYPyYmBv369cPhw4cRGxsLFxcXdOrUCTdv3qzglhMRERGR3GRPZufNm4egoCAEBgbCw8MDS5cuhZmZGVasWFFg/XXr1iE4OBhNmzZFvXr1sHz5cmg0Ghw8eLCCW05EREREcjOQ8+CZmZk4deoUQkNDpTI9PT34+voiNjZWp32kp6dDrVajcuXKBa7PyMhARkaGtJyamgoAUKvVUKvVL9B63eQeoyKORc8w7vJg3OXBuMuDcZcH4y6Pio57SY6jEkKIcmxLkW7dugVnZ2ccO3YMXl5eUvnEiRNx5MgRnDhxoth9BAcHIyoqCufPn4eJiUm+9eHh4YiIiMhXvn79epiZmb3YCRARERFRmUtPT0f//v3x8OFDWFlZFVlX1iuzL2rWrFnYuHEjYmJiCkxkASA0NBQhISHScmpqqjTOtrjglAW1Wo3o6Gh07NgRhoaG5X48ysG4y4NxlwfjLg/GXR6MuzwqOu65n6TrQtZk1tbWFvr6+khJSdEqT0lJgaOjY5HbfvXVV5g1axYOHDiAxo0bF1rP2NgYxsbG+coNDQ0r9ElQ0cejHIy7PBh3eTDu8mDc5cG4y6Oi4l6SY8h6A5iRkRE8PT21bt7KvZnr+WEHec2ePRuff/459u3bh+bNm1dEU4mIiIjoJST7MIOQkBAEBASgefPmaNGiBebPn4/Hjx8jMDAQADBo0CA4OzsjMjISAPDll19i2rRpWL9+Pdzc3JCcnAwAsLCwgIWFhWznQUREREQVT/Zktk+fPrhz5w6mTZuG5ORkNG3aFPv27YODgwMAIDExEXp6zy4gL1myBJmZmXjvvfe09hMWFobw8PCKbDoRERERyUz2ZBYARo8ejdGjRxe4LiYmRms5ISGh/BtERERERIog+5cmEBERERGVFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBSLySwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsZjMEhEREZFiMZklIiIiIsViMktEREREisVkloiIiIgUi8ksERERESkWk1kiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixWIyS0RERESKxWSWiIiIiBTLQO4GvA70szOAzMeAMMy/UqUPGJo8W858XPiOVHqAoWkp66YDEIVVBozMSldX/QQQmsLbYWReyrpPAZFd+rpq9bO4G1gDKlVOeVYGoMkqfL+GZrrXNTAF9P7//4NZmYBGXUZ1TQA9/ZLXzVYD2ZmF19U3BvQNSlE3C8jOKKKuEaD///u2Jqvo/q5VNxvIelr4fvUMAQOjUtTVAFlPyqiuAWBgnPO3EIA6vWzqluh5r0Pd3P6ufgIYGhZdV9rva/4a8bySPO/z1C2yv/M14v/Xfe55X6LXk0Ke97n9PTvzWX/na4SOdV8gjyiqvTJjMlsBup0LAs4VsrJ2J2DAlmfLc9wL7zCubYDA3c+W5zcC0u8VXLdqM2B4zLPlRS2Bh4kF17WrB4w68Wz5ex/gTnzBda2rA+P/fLa8sjNw60zBdc2qABP/eba89j3g+q8F1zU0AyYnPVvePBC4vL/gugAQ/vDZ3z8PBy7s0N4dgG5ATtwn3Xr2xvZ/44Cz6wvf7ydXAXPbnL+jJgEnlxded+w5wMY15+9D04FjCwuvG3wcsK+f8/fRucCRWYXXDToEOHvm/H1iCRA9rfC6AbuAGm1z/j61CtjzceF1+28G6vjl/H1uM7AjuPC6768CGryT83f8/wFbBhde9+3FQLMBAADV1UNF9/cuXwEtgnL+vn4MWN2t8P12nA60Hpvzd1Ic8P1bhddt/xngE5rz991LwOI3C6/bagzQ6Yucvx/+CyxoXHjd/w0Dus7N+Tv9HjCnVuF1m/QH3lmS87c6HZhZtfC6Hm8DvX98tlxUXR1eI3L7u+ZBK2DI3mcr+BqRo4DXCC2lfI3QOzAV3c6tKLy/8zUix3OvEbh6EFjfu/C6OrxG5Pb3bNswoF1ITiFfI54tl1MeYbCsNVBzRuHtkBGHGRARERGRYqmEEIV9VvRKSk1NhbW1NR4+fAgrK6tyP55arUbU//0MP79OMDQs4GMoDjMopO6LfYSoVqsRFbU/J+5mHGYAoEI+QlRnPEHU7v8rvL9zmEGOMv4IUerv/v4wNLMqsu6z/b7erxFaSjnMQP3kEaL27im8v/M14v/XLdthBlJ/79IVhibmRdaVvOavEc/qlj6PUKc/xJ7oGHTp0qXg/l7GSpKvcZhBBcjWN855YdXlwX/+BbhM65oVX6c0dZ9/UpRpXZPi6xRVV6V+FvfcNyng/7+QGOu23xLVNQJgJG9dfcNnbwJlWtfg2ZtWcfQMdO/vevq69+ES1dUrn7oqVfnUBV68bm5/z/sc42tEyeuW8DVC5/7O14iS1y3seZ/b3/WNiq9b4H5fw9eIsqhrWILnfQXjMAMiIiIiUiwms0RERESkWExmiYiIiEixmMwSERERkWIxmSUiIiIixXopktlFixbBzc0NJiYmaNmyJX7//fci62/ZsgX16tWDiYkJGjVqhD179lRQS4mIiIjoZSJ7Mrtp0yaEhIQgLCwMp0+fRpMmTeDn54fbt28XWP/YsWPo168fhg4dijNnzqBnz57o2bMn/vrrrwpuORERERHJTfZkdt68eQgKCkJgYCA8PDywdOlSmJmZYcWKFQXWX7BgAfz9/fHJJ5+gfv36+Pzzz/HGG2/g22+/reCWExEREZHcZP3ShMzMTJw6dQqhoaFSmZ6eHnx9fREbG1vgNrGxsQgJCdEq8/Pzw/bt2wusn5GRgYyMZ982kpqaCiDnG0TU6iK+NaWM5B6jIo5FzzDu8mDc5cG4y4NxlwfjLo+KjntJjiNrMnv37l1kZ2fDwcFBq9zBwQHx8fEFbpOcnFxg/eTk5ALrR0ZGIiIiIl/5/v37YWZWcd9mER0dXWHHomcYd3kw7vJg3OXBuMuDcZdHRcU9Pb2Ir/vN45X/OtvQ0FCtK7kPHz5E9erV4eXlBUtLy3I/vlqtxuHDh+Hj41Mh32VMORh3eTDu8mDc5cG4y4Nxl0dFxz0tLQ0AIIQotq6syaytrS309fWRkpKiVZ6SkgJHR8cCt3F0dCxRfWNjYxgbP/ue7dxhBjVq1HiRphMRERFROUtLS4O1tXWRdWRNZo2MjODp6YmDBw+iZ8+eAACNRoODBw9i9OjRBW7j5eWFgwcPYty4cVJZdHQ0vLy8dDpm1apV8e+//8LS0hIqlepFT6FYqampcHFxwb///gsrK6tyPx7lYNzlwbjLg3GXB+MuD8ZdHhUddyEE0tLSULVq1WLryj7MICQkBAEBAWjevDlatGiB+fPn4/HjxwgMDAQADBo0CM7OzoiMjAQAjB07Fu3bt8fcuXPRtWtXbNy4EX/88Qe+++47nY6np6eHatWqldv5FMbKyopPOhkw7vJg3OXBuMuDcZcH4y6Piox7cVdkc8mezPbp0wd37tzBtGnTkJycjKZNm2Lfvn3STV6JiYnQ03s2g1irVq2wfv16TJkyBZMmTULt2rWxfft2NGzYUK5TICIiIiKZqIQuI2up1FJTU2FtbY2HDx/yP8gKxLjLg3GXB+MuD8ZdHoy7PF7muMv+pQmvOmNjY4SFhWndhEblj3GXB+MuD8ZdHoy7PBh3ebzMceeVWSIiIiJSLF6ZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZHfzyyy/o3r07qlatCpVKhe3bt2utHzx4MFQqldaPv7+/tD4hIQFDhw5FjRo1YGpqilq1aiEsLAyZmZla+zl37hzatm0LExMTuLi4YPbs2RVxei+tF407APTo0QPVq1eHiYkJnJycMHDgQNy6dUurDuOurSzinisjIwNNmzaFSqVCXFyc1jrGXVtZxN3NzS1fnVmzZmnVYdy1lVV/3717N1q2bAlTU1PY2NhI32qZKzExEV27doWZmRns7e3xySefICsrqxzP7OX2onGPiYnJtz735+TJk1I99ndtZdHf//77b7z99tuwtbWFlZUV2rRpg8OHD2vVqej+zmRWB48fP0aTJk2waNGiQuv4+/sjKSlJ+tmwYYO0Lj4+HhqNBsuWLcP58+fx9ddfY+nSpZg0aZJUJzU1FZ06dYKrqytOnTqFOXPmIDw8XOdvNnsVvWjcAcDHxwebN2/GpUuX8NNPP+Hq1at47733pPWMe35lEfdcEydOLPCrCBn3/Moq7tOnT9eqM2bMGGkd455fWcT9p59+wsCBAxEYGIizZ8/it99+Q//+/aX12dnZ6Nq1KzIzM3Hs2DGsXr0aq1atwrRp08rtvF52Lxr3Vq1aaa1LSkrCsGHDUKNGDTRv3hwA+3tByqK/d+vWDVlZWTh06BBOnTqFJk2aoFu3bkhOTgYgU38XVCIAxM8//6xVFhAQIN5+++0S7Wf27NmiRo0a0vLixYuFjY2NyMjIkMo+/fRTUbdu3Rdp7iujrOK+Y8cOoVKpRGZmphCCcS/Oi8R9z549ol69euL8+fMCgDhz5oy0jnEvWmnj7urqKr7++utC1zPuRStN3NVqtXB2dhbLly8vtM6ePXuEnp6eSE5OlsqWLFkirKystB6L11VZvL5nZmYKOzs7MX36dKmM/b1opYn7nTt3BADxyy+/SGWpqakCgIiOjhZCyNPfeWW2jMTExMDe3h5169bFhx9+iHv37hVZ/+HDh6hcubK0HBsbi3bt2sHIyEgq8/Pzw6VLl/Dff/+VW7uVriRxv3//PtatW4dWrVrB0NAQAONeWsXFPSUlBUFBQVizZg3MzMzybc+4l44u/X3WrFmoUqUKmjVrhjlz5mh9tMe4l05RcT99+jRu3rwJPT09NGvWDE5OTujcuTP++usvqU5sbCwaNWokfU07kBP31NRUnD9/vkLPRUlK8vq+c+dO3Lt3D4GBgVIZ+3vpFBX3KlWqoG7duvjxxx/x+PFjZGVlYdmyZbC3t4enpycAefo7k9ky4O/vjx9//BEHDx7El19+iSNHjqBz587Izs4usP6VK1ewcOFCjBgxQipLTk7WeuABSMu5l+5Jm65x//TTT2Fubo4qVaogMTERO3bskNYx7iVXXNyFEBg8eDBGjhwpfdyXF+Necrr0948++ggbN27E4cOHMWLECMycORMTJ06U1jPuJVdc3P/55x8AQHh4OKZMmYJdu3bBxsYG3t7euH//PgDGvTRK+r76ww8/wM/PD9WqVZPKGPeSKy7uKpUKBw4cwJkzZ2BpaQkTExPMmzcP+/btg42NDQB54m5QLnt9zfTt21f6u1GjRmjcuDFq1aqFmJgYdOjQQavuzZs34e/vj/fffx9BQUEV3dRXiq5x/+STTzB06FBcv34dERERGDRoEHbt2gWVSiVHsxWvuLgvXLgQaWlpCA0NlbGVrx5d+ntISIhUp3HjxjAyMsKIESMQGRn5Un4FpRIUF3eNRgMAmDx5Mnr16gUAWLlyJapVq4YtW7ZoXbQg3ZXkffXGjRuIiorC5s2bK7qZr5zi4i6EwKhRo2Bvb4+jR4/C1NQUy5cvR/fu3XHy5Ek4OTnJ0m5emS0HNWvWhK2tLa5cuaJVfuvWLfj4+KBVq1b5BqA7OjoiJSVFqyx32dHRsXwb/IooLO62traoU6cOOnbsiI0bN2LPnj04fvw4AMa9LOSN+6FDhxAbGwtjY2MYGBjA3d0dANC8eXMEBAQAYNzLQmH9/XktW7ZEVlYWEhISADDuZSFv3HPfvD08PKQ6xsbGqFmzJhITEwEw7mWhqP6+cuVKVKlSBT169NAqZ9xfXEGv77t27cLGjRvRunVrvPHGG1i8eDFMTU2xevVqAPLEnclsObhx4wbu3bun9R/KzZs34e3tDU9PT6xcuRJ6etqh9/Lywi+//AK1Wi2VRUdHo27dutKleypaQXHPK/cqSkZGBgDGvSzkjfs333yDs2fPIi4uDnFxcdizZw8AYNOmTZgxYwYAxr0s6NLf4+LioKenB3t7ewCMe1nIG3dPT08YGxvj0qVLUh21Wo2EhAS4uroCyIn7n3/+idu3b0t1oqOjYWVlpZUEU+EK6+9CCKxcuRKDBg2S7oXIxf7+4vLGPT09HQDy5TB6enrS+6ss/b1cbit7xaSlpYkzZ86IM2fOCABi3rx54syZM+L69esiLS1NfPzxxyI2NlZcu3ZNHDhwQLzxxhuidu3a4unTp0IIIW7cuCHc3d1Fhw4dxI0bN0RSUpL0k+vBgwfCwcFBDBw4UPz1119i48aNwszMTCxbtkyu05bdi8b9+PHjYuHCheLMmTMiISFBHDx4ULRq1UrUqlVLqsO45/eicc/r2rVr+WYzYNzze9G4Hzt2THz99dciLi5OXL16Vaxdu1bY2dmJQYMGScdg3PMri/4+duxY4ezsLKKiokR8fLwYOnSosLe3F/fv3xdCCJGVlSUaNmwoOnXqJOLi4sS+ffuEnZ2dCA0Nleu0ZVdWrzMHDhwQAMTFixfzHYP9Pb8XjfudO3dElSpVxLvvvivi4uLEpUuXxMcffywMDQ1FXFycEEKe/s5kVgeHDx8WAPL9BAQEiPT0dNGpUydhZ2cnDA0NhaurqwgKCtKakmLlypUFbp/3f4mzZ8+KNm3aCGNjY+Hs7CxmzZpV0af6UnnRuJ87d074+PiIypUrC2NjY+Hm5iZGjhwpbty4oXUcxl3bi8Y9r4KSWSEY97xeNO6nTp0SLVu2FNbW1sLExETUr19fzJw5M9+bP+OurSz6e2ZmppgwYYKwt7cXlpaWwtfXV/z1119adRISEkTnzp2FqampsLW1FRMmTBBqtboiT/WlUlavM/369ROtWrUq9Djs79rKIu4nT54UnTp1EpUrVxaWlpbizTffFHv27NGqU9H9XSWEEGV8sZeIiIiIqEJwzCwRERERKRaTWSIiIiJSLCazRERERKRYTGaJiIiISLGYzBIRERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBFRBQsPD0fTpk3lboZEpVJh+/btJd7u0qVLcHR0RFpaWtk36jl3796Fvb09bty4Ua7HISJlYjJLRK+kpUuXwtLSEllZWVLZo0ePYGhoCG9vb626MTExUKlUuHr1agW3smKVdRIdGhqKMWPGwNLSssz2WRBbW1sMGjQIYWFh5XocIlImJrNE9Ery8fHBo0eP8Mcff0hlR48ehaOjI06cOIGnT59K5YcPH0b16tVRq1YtOZqqSImJidi1axcGDx5cIccLDAzEunXrcP/+/Qo5HhEpB5NZInol1a1bF05OToiJiZHKYmJi8Pbbb6NGjRo4fvy4VrmPjw8AYM2aNWjevDksLS3h6OiI/v374/bt2wAAjUaDatWqYcmSJVrHOnPmDPT09HD9+nUAwIMHDzBs2DDY2dnBysoKb731Fs6ePVtke5cvX4769evDxMQE9erVw+LFi6V1CQkJUKlU2LZtG3x8fGBmZoYmTZogNjZWax/ff/89XFxcYGZmhnfeeQfz5s1DpUqVAACrVq1CREQEzp49C5VKBZVKhVWrVknb3r17F++88w7MzMxQu3Zt7Ny5s8j2bt68GU2aNIGzs7NUtmrVKlSqVAlRUVGoX78+LCws4O/vj6SkJKnO4MGD0bNnT8ycORMODg6oVKkSpk+fjqysLHzyySeoXLkyqlWrhpUrV2odr0GDBqhatSp+/vnnIttFRK8fJrNE9Mry8fHB4cOHpeXDhw/D29sb7du3l8qfPHmCEydOSMmsWq3G559/jrNnz2L79u1ISEiQrj7q6emhX79+WL9+vdZx1q1bh9atW8PV1RUA8P777+P27dvYu3cvTp06hTfeeAMdOnQo9KriunXrMG3aNMyYMQMXL17EzJkzMXXqVKxevVqr3uTJk/Hxxx8jLi4OderUQb9+/aRhFL/99htGjhyJsWPHIi4uDh07dsSMGTOkbfv06YMJEyagQYMGSEpKQlJSEvr06SOtj4iIQO/evXHu3Dl06dIFAwYMKPIq6NGjR9G8efN85enp6fjqq6+wZs0a/PLLL0hMTMTHH3+sVefQoUO4desWfvnlF8ybNw9hYWHo1q0bbGxscOLECYwcORIjRozIN0a2RYsWOHr0aKFtIqLXlCAiekV9//33wtzcXKjVapGamioMDAzE7du3xfr160W7du2EEEIcPHhQABDXr18vcB8nT54UAERaWpoQQogzZ84IlUol1c/OzhbOzs5iyZIlQgghjh49KqysrMTTp0+19lOrVi2xbNkyIYQQYWFhokmTJlrr1q9fr1X/888/F15eXkIIIa5duyYAiOXLl0vrz58/LwCIixcvCiGE6NOnj+jatavWPgYMGCCsra2l5bzHzQVATJkyRVp+9OiRACD27t1bYEyEEKJJkyZi+vTpWmUrV64UAMSVK1ekskWLFgkHBwdpOSAgQLi6uors7GyprG7duqJt27bSclZWljA3NxcbNmzQ2v/48eOFt7d3oW0iotcTr8wS0SvL29sbjx8/xsmTJ3H06FHUqVMHdnZ2aN++vTRuNiYmBjVr1kT16tUBAKdOnUL37t1RvXp1WFpaon379gByxogCQNOmTVG/fn3p6uyRI0dw+/ZtvP/++wCAs2fP4tGjR6hSpQosLCykn2vXrhV4g9njx49x9epVDB06VKv+F198ka9+48aNpb+dnJwAQBoCcenSJbRo0UKrft7lojy/b3Nzc1hZWUn7LsiTJ09gYmKSr9zMzExr7LGTk1O+/TRo0AB6es/efhwcHNCoUSNpWV9fH1WqVMm3nampKdLT03U+JyJ6PRjI3QAiovLi7u6OatWq4fDhw/jvv/+kxLRq1apwcXHBsWPHcPjwYbz11lsAchJLPz8/+Pn5Yd26dbCzs0NiYiL8/PyQmZkp7XfAgAFYv349PvvsM6xfvx7+/v6oUqUKgJwZE/KO1c2VO371eY8ePQKQM961ZcuWWuv09fW1lg0NDaW/VSoVgJxxvGXh+X3n7r+ofdva2uK///7TaT9CiGLr6HL8+/fvw87OrvCTIKLXEpNZInql+fj4ICYmBv/99x8++eQTqbxdu3bYu3cvfv/9d3z44YcAgPj4eNy7dw+zZs2Ci4sLAGjNhpCrf//+mDJlCk6dOoWtW7di6dKl0ro33ngDycnJMDAwgJubW7Htc3BwQNWqVfHPP/9gwIABpT7PunXr4uTJk1pleZeNjIyQnZ1d6mM8r1mzZrhw4UKZ7EtXf/31V75p1YiIOMyAiF5pPj4++PXXXxEXFyddmQWA9u3bY9myZcjMzJRu/qpevTqMjIywcOFC/PPPP9i5cyc+//zzfPt0c3NDq1atMHToUGRnZ6NHjx7SOl9fX3h5eaFnz57Yv38/EhIScOzYMUyePLnAxBjIufkqMjIS33zzDf7++2/8+eefWLlyJebNm6fzeY4ZMwZ79uzBvHnzcPnyZSxbtgx79+6VruDmtvvatWuIi4vD3bt3kZGRofP+8/Lz80NsbGyZJcfFSU9Px6lTp9CpU6cKOR4RKQeTWSJ6pfn4+ODJkydwd3eHg4ODVN6+fXukpaVJU3gBgJ2dHVatWoUtW7bAw8MDs2bNwldffVXgfgcMGICzZ8/inXfegampqVSuUqmwZ88etGvXDoGBgahTpw769u2L69evax3/ecOGDcPy5cuxcuVKNGrUCO3bt8eqVatQo0YNnc+zdevWWLp0KebNm4cmTZpg3759GD9+vNa41l69esHf3x8+Pj6ws7PDhg0bdN5/Xp07d4aBgQEOHDhQ6n2UxI4dO1C9enW0bdu2Qo5HRMqhEnkHMxER0SshKCgI8fHx5Tad1aJFi7Bz505ERUWVy/6f9+abb+Kjjz5C//79y/1YRKQsHDNLRPSK+Oqrr9CxY0eYm5tj7969WL16tdaXL5S1ESNG4MGDB0hLSyvXr7S9e/cu3n33XfTr16/cjkFEysUrs0REr4jevXsjJiYGaWlpqFmzJsaMGYORI0fK3SwionLFZJaIiIiIFIs3gBERERGRYjGZJSIiIiLFYjJLRERERIrFZJaIiIiIFIvJLBEREREpFpNZIiIiIlIsJrNEREREpFhMZomIiIhIsf4fIDWh1jVFI0UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def y_branch_mm(wl=1.55):\n", + " models_by_mode = {\n", + " 'te': partial(y_branch, pol='te'),\n", + " 'tm': partial(y_branch, pol='tm'),\n", + " }\n", + " return create_multimode_sax_model(models_by_mode)(wl=wl)\n", + "\n", + "\n", + "def add_output_mode_crosstalk(sdict, xt_power=0.08):\n", + " mixed = dict(sdict)\n", + " stay = jnp.sqrt(1.0 - xt_power)\n", + " cross = 1j * jnp.sqrt(xt_power)\n", + "\n", + " for (src, dst), value in list(sdict.items()):\n", + " if '@' not in src or '@' not in dst:\n", + " continue\n", + " src_port, src_mode = src.split('@')\n", + " dst_port, dst_mode = dst.split('@')\n", + " if src_mode != dst_mode:\n", + " continue\n", + "\n", + " other_mode = 'tm' if dst_mode == 'te' else 'te'\n", + " mixed[(src, dst)] = stay * value\n", + " mixed[(src, f'{dst_port}@{other_mode}')] = cross * value\n", + "\n", + " return mixed\n", + "\n", + "\n", + "def y_branch_mm_with_crosstalk(wl=1.55, xt_power=0.08):\n", + " return add_output_mode_crosstalk(y_branch_mm(wl=wl), xt_power=xt_power)\n", + "\n", + "\n", + "def waveguide_mm(wl=1.55, length=10.0, width=500.0, height=220.0, loss=0.0):\n", + " models_by_mode = {\n", + " 'te': partial(waveguide, pol='te'),\n", + " 'tm': partial(waveguide, pol='tm'),\n", + " }\n", + " return create_multimode_sax_model(models_by_mode)(\n", + " wl=wl,\n", + " length=length,\n", + " width=width,\n", + " height=height,\n", + " loss=loss,\n", + " )\n", + "\n", + "\n", + "def waveguide_mm_with_crosstalk(wl=1.55, length=10.0, xt_power=0.12, **kwargs):\n", + " sdict = dict(waveguide_mm(wl=wl, length=length, **kwargs))\n", + " stay = jnp.sqrt(1.0 - xt_power)\n", + " cross = 1j * jnp.sqrt(xt_power)\n", + "\n", + " te_forward = sdict[('o0@te', 'o1@te')]\n", + " tm_forward = sdict[('o0@tm', 'o1@tm')]\n", + " te_backward = sdict[('o1@te', 'o0@te')]\n", + " tm_backward = sdict[('o1@tm', 'o0@tm')]\n", + "\n", + " sdict[('o0@te', 'o1@te')] = stay * te_forward\n", + " sdict[('o0@tm', 'o1@tm')] = stay * tm_forward\n", + " sdict[('o1@te', 'o0@te')] = stay * te_backward\n", + " sdict[('o1@tm', 'o0@tm')] = stay * tm_backward\n", + "\n", + " sdict[('o0@te', 'o1@tm')] = cross * te_forward\n", + " sdict[('o0@tm', 'o1@te')] = cross * tm_forward\n", + " sdict[('o1@te', 'o0@tm')] = cross * te_backward\n", + " sdict[('o1@tm', 'o0@te')] = cross * tm_backward\n", + " return sdict\n", + "\n", + "\n", + "wavelengths_um = np.linspace(1.52, 1.58, 200)\n", + "example_sdict = waveguide_mm_with_crosstalk(wl=wavelengths_um, length=50.0, xt_power=0.04)\n", + "\n", + "print('first multimode waveguide keys:')\n", + "for key in list(example_sdict.keys())[:10]:\n", + " print(' ', key)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(wavelengths_um * 1e3, np.abs(example_sdict[('o0@te', 'o1@te')]) ** 2, label='TE -> TE')\n", + "plt.plot(wavelengths_um * 1e3, np.abs(example_sdict[('o0@te', 'o1@tm')]) ** 2, '--', label='TE -> TM crosstalk')\n", + "plt.xlabel('Wavelength (nm)')\n", + "plt.ylabel('Power')\n", + "plt.title('Mode-preserving and cross-mode entries in a multimode SDict')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cd683169", + "metadata": {}, + "source": [ + "## 6. Build the 16-QAM MZI/IQ Lattice\n", + "\n", + "The transmitter is an MZI lattice rather than a single component. It uses the same split-phase-recombine idea as the MZI lattice tutorial, but now the optical signals carry both TE and TM modes.\n", + "\n", + "The signal path is:\n", + "\n", + "```text\n", + "laser\n", + " -> y1\n", + " -> y2 -> two I arms -> y4\n", + " -> y3 -> two Q arms -> y5 -> pi/2 bias\n", + " -> y6 -> out\n", + "```\n", + "\n", + "Block mode treats the `connections` dictionary as directed signal flow. Each entry is written as:\n", + "\n", + "```python\n", + "'upstream_instance,upstream_port': 'downstream_instance,downstream_port'\n", + "```\n", + "\n", + "If an S-parameter component does not receive explicit `port_directionality`, the circuit builder infers the directions from that directed netlist form: ports that appear on the left-hand side of `connections` are treated as outputs, ports that appear only on the right-hand side are treated as inputs, and unconnected S-parameter ports default to outputs.\n", + "\n", + "That inference is useful for simple directed circuits. In this example we still set `port_directionality` explicitly later because the same `y_branch` SAX model is used as both a splitter and a combiner, so the intended input/output roles flip depending on where the instance sits in the lattice.\n", + "\n", + "The `I` and `Q` branches each use a pair of opposite phase modulators. That makes each branch act like a small interferometric amplitude modulator before the final recombination." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f9cb99b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "instances: 17\n", + "connections: 19\n", + "top-level ports: {'o1': 'pm6,o1'}\n" + ] + } + ], + "source": [ + "qam_netlist = {\n", + " 'instances': {\n", + " 'laser': 'laser',\n", + " 'wg1': 'waveguide',\n", + " 'wg2': 'waveguide',\n", + " 'wg3': 'waveguide',\n", + " 'wg4': 'waveguide',\n", + " 'y1': 'y_branch',\n", + " 'y2': 'y_branch',\n", + " 'y3': 'y_branch',\n", + " 'y4': 'y_branch',\n", + " 'y5': 'y_branch',\n", + " 'y6': 'y_branch',\n", + " 'pm1': 'phase_modulator1',\n", + " 'pm2': 'phase_modulator2',\n", + " 'pm3': 'phase_modulator3',\n", + " 'pm4': 'phase_modulator4',\n", + " 'pm5': 'phase_modulator5',\n", + " 'pm6': 'phase_modulator6',\n", + " },\n", + " 'connections': {\n", + " 'laser,o0': 'y1,port_1',\n", + " 'y1,port_2': 'y2,port_1',\n", + " 'y1,port_3': 'y3,port_1',\n", + " 'wg1,o0': 'y2,port_2',\n", + " 'wg2,o0': 'y2,port_3',\n", + " 'wg3,o0': 'y3,port_2',\n", + " 'wg4,o0': 'y3,port_3',\n", + " 'wg1,o1': 'pm1,o0',\n", + " 'wg2,o1': 'pm2,o0',\n", + " 'wg3,o1': 'pm3,o0',\n", + " 'wg4,o1': 'pm4,o0',\n", + " 'y4,port_2': 'pm1,o1',\n", + " 'y4,port_3': 'pm2,o1',\n", + " 'y5,port_2': 'pm3,o1',\n", + " 'y5,port_3': 'pm4,o1',\n", + " 'y5,port_1': 'pm5,o0',\n", + " 'pm5,o1': 'y6,port_2',\n", + " 'y4,port_1': 'y6,port_3',\n", + " 'y6,port_1': 'pm6,o0',\n", + " },\n", + " 'ports': {\n", + " 'o1': 'pm6,o1',\n", + " },\n", + "}\n", + "\n", + "print('instances:', len(qam_netlist['instances']))\n", + "print('connections:', len(qam_netlist['connections']))\n", + "print('top-level ports:', qam_netlist['ports'])" + ] + }, + { + "cell_type": "markdown", + "id": "7a871e96", + "metadata": {}, + "source": [ + "## 7. Configure Models and Settings\n", + "\n", + "The same `y_branch` model is used as both a splitter and a combiner. The instance settings tell Block mode which physical ports are inputs and outputs for each use.\n", + "\n", + "For S-parameter components, each setting can include three important pieces:\n", + "\n", + "- `sax_settings`: parameters passed to the SAX model.\n", + "- `port_directionality`: the directed role of each physical port in Block mode.\n", + "- `vector_fitting_parameters`: how the wavelength-domain S-parameters are converted into time-domain state-space filters.\n", + "\n", + "Important caveat: if a setting entry includes `port_directionality` or `vector_fitting_parameters`, it must also include `sax_settings`. If the SAX model does not need any parameters, use an empty dictionary: `sax_settings: {}`. That keeps simulator metadata separate from the kwargs that are passed into the SAX model.\n", + "\n", + "The MZI tutorial already explains vector fitting in detail; here the point is that the same settings pattern works for multimode S-parameter components." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0fbc2112", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "settings entries: 16\n" + ] + } + ], + "source": [ + "ARM_LENGTH_UM = 30.0\n", + "\n", + "SPLITTER_DIRECTIONALITY = {'port_1': 'input', 'port_2': 'output', 'port_3': 'output'}\n", + "COMBINER_DIRECTIONALITY = {'port_1': 'output', 'port_2': 'input', 'port_3': 'input'}\n", + "WAVEGUIDE_DIRECTIONALITY = {'o0': 'input', 'o1': 'output'}\n", + "\n", + "\n", + "def build_models(waveguide_model, y_branch_model=y_branch_mm):\n", + " return {\n", + " 'waveguide': waveguide_model,\n", + " 'y_branch': y_branch_model,\n", + " 'phase_modulator1': OpticalModulatorTime,\n", + " 'phase_modulator2': OpticalModulatorTime,\n", + " 'phase_modulator3': OpticalModulatorTime,\n", + " 'phase_modulator4': OpticalModulatorTime,\n", + " 'phase_modulator5': OpticalModulatorTime,\n", + " 'phase_modulator6': OpticalModulatorTime,\n", + " 'laser': MultiModeCWLaser,\n", + " }\n", + "\n", + "\n", + "def build_settings():\n", + " return {\n", + " 'laser': {},\n", + " 'pm1': {'mod_signal': phase_wave1},\n", + " 'pm2': {'mod_signal': phase_wave2},\n", + " 'pm3': {'mod_signal': phase_wave3},\n", + " 'pm4': {'mod_signal': phase_wave4},\n", + " 'pm5': {'mod_signal': jnp.pi / 2 * jnp.ones_like(t)},\n", + " 'wg1': {\n", + " 'sax_settings': {'length': 30.0},\n", + " 'port_directionality': {'o0': 'input', 'o1': 'output'},\n", + " },\n", + " 'wg2': {\n", + " 'sax_settings': {'length': 30.0},\n", + " 'port_directionality': {'o0': 'input', 'o1': 'output'},\n", + " },\n", + " 'wg3': {\n", + " 'sax_settings': {'length': 30.0},\n", + " 'port_directionality': {'o0': 'input', 'o1': 'output'},\n", + " },\n", + " 'wg4': {\n", + " 'sax_settings': {'length': 30.0},\n", + " 'port_directionality': {'o0': 'input', 'o1': 'output'},\n", + " },\n", + " 'y1': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'input', 'port_2': 'output', 'port_3': 'output'},\n", + " },\n", + " 'y2': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'input', 'port_2': 'output', 'port_3': 'output'},\n", + " },\n", + " 'y3': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'input', 'port_2': 'output', 'port_3': 'output'},\n", + " },\n", + " 'y4': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'output', 'port_2': 'input', 'port_3': 'input'},\n", + " },\n", + " 'y5': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'output', 'port_2': 'input', 'port_3': 'input'},\n", + " },\n", + " 'y6': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {'port_1': 'output', 'port_2': 'input', 'port_3': 'input'},\n", + " },\n", + " }\n", + "\n", + "\n", + "settings = build_settings()\n", + "models = build_models(waveguide_mm)\n", + "print('settings entries:', len(settings))" + ] + }, + { + "cell_type": "markdown", + "id": "4139b565", + "metadata": {}, + "source": [ + "## 8. Run No-Cross-Pol and Cross-Pol Simulations\n", + "\n", + "We run the same MZI/IQ lattice twice:\n", + "\n", + "1. `waveguide_mm`: TE and TM propagate independently.\n", + "2. `waveguide_mm_with_crosstalk`: waveguides include off-diagonal mode terms such as `('o0@te', 'o1@tm')`.\n", + "\n", + "The circuit topology and modulation waveforms are identical. Only the waveguide SAX model changes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23a50595", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no-cross-pol output shape: (500000, 1, 2)\n", + "cross-pol output shape: (500000, 1, 2)\n", + "wavelength grid (nm): [1550.]\n", + "mode order: ('te', 'tm')\n" + ] + } + ], + "source": [ + "simulation_parameters = BlockModeSimulationParameters(\n", + " mode_identifiers=MODE_IDENTIFIERS,\n", + " dt=DT,\n", + " num_time_steps=Nsteps,\n", + " optical_baseband_wavelengths=BASEBAND_WAVELENGTHS_M,\n", + " use_speed_up=True,\n", + ")\n", + "\n", + "\n", + "def run_qam_simulation(waveguide_model, y_branch_model=y_branch_mm):\n", + " circuit = Circuit(qam_netlist, build_models(waveguide_model, y_branch_model))\n", + " simulation = BlockModeSimulation(\n", + " circuit,\n", + " settings=build_settings(),\n", + " simulation_parameters=simulation_parameters,\n", + " )\n", + " result = simulation.run()\n", + " output_signal = result.output_signals['o1']\n", + " output_fields = np.asarray(output_signal.amplitude[:, 0, :])\n", + " return result, output_signal, output_fields\n", + "\n", + "\n", + "result_no_cross, output_no_cross, fields_no_cross = run_qam_simulation(waveguide_mm)\n", + "result_cross, output_cross, fields_cross = run_qam_simulation(waveguide_mm_with_crosstalk, y_branch_mm_with_crosstalk)\n", + "\n", + "print('no-cross-pol output shape:', output_no_cross.amplitude.shape)\n", + "print('cross-pol output shape:', output_cross.amplitude.shape)\n", + "print('wavelength grid (nm):', np.asarray(output_no_cross.wavelength) * 1e9)\n", + "print('mode order:', MODE_IDENTIFIERS)" + ] + }, + { + "cell_type": "markdown", + "id": "97d167c5", + "metadata": {}, + "source": [ + "## 9. Build the Same Constellation Diagrams as the Source Notebook\n", + "\n", + "Output is plotted and upsampled for easier viewing following the steps below:\n", + "\n", + "1. Extract TE and TM complex fields.\n", + "2. Build `I` and `Q` arrays from the real and imaginary parts.\n", + "3. Upsample the I/Q trajectory so transitions between constellation points are visible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f625beaf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pathlib import Path\n", + "\n", + "\n", + "def upsample_trajectory(I, Q, factor=20):\n", + " I = np.asarray(I)\n", + " Q = np.asarray(Q)\n", + "\n", + " alpha = np.arange(factor, dtype=np.float64) / factor\n", + " I_seg = I[:-1, None] + (I[1:] - I[:-1])[:, None] * alpha\n", + " Q_seg = Q[:-1, None] + (Q[1:] - Q[:-1])[:, None] * alpha\n", + "\n", + " I_out = np.empty((len(I) - 1) * factor + 1, dtype=I_seg.dtype)\n", + " Q_out = np.empty((len(Q) - 1) * factor + 1, dtype=Q_seg.dtype)\n", + "\n", + " I_out[:-1] = I_seg.reshape(-1)\n", + " Q_out[:-1] = Q_seg.reshape(-1)\n", + " I_out[-1] = I[-1]\n", + " Q_out[-1] = Q[-1]\n", + "\n", + " return I_out, Q_out\n", + "\n", + "\n", + "def center_and_scale(field, skip_symbols=8, target_radius=0.58):\n", + " start = skip_symbols * SAMPLES_PER_SYMBOL\n", + " field = np.asarray(field[start:])\n", + " field = field - np.mean(field)\n", + " scale = np.percentile(np.abs(field), 99)\n", + " return target_radius * field / scale\n", + "\n", + "y = center_and_scale(fields_no_cross[:, mode_index['te']])\n", + "I = y.real\n", + "Q = y.imag\n", + "\n", + "ym = center_and_scale(fields_no_cross[:, mode_index['tm']])\n", + "Im = ym.real\n", + "Qm = ym.imag\n", + "\n", + "y_cross_talk = center_and_scale(fields_cross[:, mode_index['te']])\n", + "I_cross_talk = y_cross_talk.real\n", + "Q_cross_talk = y_cross_talk.imag\n", + "\n", + "ym_cross_talk = center_and_scale(fields_cross[:, mode_index['tm']])\n", + "Im_cross_talk = ym_cross_talk.real\n", + "Qm_cross_talk = ym_cross_talk.imag\n", + "\n", + "# Composite version of the standalone hist2d plots above.\n", + "# Uses raw counts with one global color scale anchored to the reference panel.\n", + "plot_inputs = {\n", + " ('TE', 'No Cross-Pol'): (I, Q),\n", + " ('TE', 'Cross-Pol'): (I_cross_talk, Q_cross_talk),\n", + " ('TM', 'No Cross-Pol'): (Im, Qm),\n", + " ('TM', 'Cross-Pol'): (Im_cross_talk, Qm_cross_talk),\n", + "}\n", + "\n", + "rows = ['TE', 'TM']\n", + "cols = ['No Cross-Pol', 'Cross-Pol']\n", + "panel_labels = ['(a)', '(b)', '(c)', '(d)']\n", + "upsample_factor = 30\n", + "bins = 800\n", + "axis_limit = 0.65\n", + "min_count = 3\n", + "reference_panel = ('TE', 'No Cross-Pol')\n", + "\n", + "plot_data = {\n", + " key: upsample_trajectory(i_vals, q_vals, factor=upsample_factor)\n", + " for key, (i_vals, q_vals) in plot_inputs.items()\n", + "}\n", + "\n", + "if axis_limit is None:\n", + " all_iq = np.concatenate([np.r_[i_vals, q_vals] for i_vals, q_vals in plot_data.values()])\n", + " axis_limit = np.ceil(np.nanmax(np.abs(all_iq)) * 1.05 * 20) / 20\n", + "\n", + "plot_range = [[-axis_limit, axis_limit], [-axis_limit, axis_limit]]\n", + "panel_maxes = {}\n", + "for key, (i_vals, q_vals) in plot_data.items():\n", + " hist, _, _ = np.histogram2d(i_vals, q_vals, bins=bins, range=plot_range)\n", + " panel_maxes[key] = hist.max()\n", + "\n", + "reference_max = max(min_count, panel_maxes[reference_panel])\n", + "color_norm = matplotlib.colors.LogNorm(vmin=min_count, vmax=reference_max)\n", + "colorbar_extend = 'max' if max(panel_maxes.values()) > reference_max else 'neither'\n", + "\n", + "with plt.rc_context({\n", + " 'font.size': 10,\n", + " 'axes.titlesize': 11,\n", + " 'axes.labelsize': 13,\n", + " 'xtick.labelsize': 9,\n", + " 'ytick.labelsize': 9,\n", + " 'figure.titlesize': 13,\n", + " 'figure.dpi': 180,\n", + " 'savefig.dpi': 600,\n", + "}):\n", + " fig, axes = plt.subplots(2, 2, figsize=(6.7, 6.0), sharex=True, sharey=True)\n", + " fig.subplots_adjust(left=0.145, right=0.785, bottom=0.12, top=0.84, wspace=0.05, hspace=0.07)\n", + " images = []\n", + "\n", + " for row_idx, mode in enumerate(rows):\n", + " for col_idx, coupling in enumerate(cols):\n", + " ax = axes[row_idx, col_idx]\n", + " i_plot, q_plot = plot_data[(mode, coupling)]\n", + "\n", + " _, _, _, image = ax.hist2d(\n", + " i_plot,\n", + " q_plot,\n", + " bins=bins,\n", + " range=plot_range,\n", + " cmap='jet',\n", + " norm=color_norm,\n", + " cmin=min_count,\n", + " linewidths=0,\n", + " antialiased=False,\n", + " )\n", + " image.set_rasterized(True)\n", + " images.append(image)\n", + "\n", + " ax.set_aspect('equal', adjustable='box')\n", + " if axis_limit is not None:\n", + " ax.set_xlim(-axis_limit, axis_limit)\n", + " ax.set_ylim(-axis_limit, axis_limit)\n", + " ax.tick_params(direction='in', top=True, right=True)\n", + " ax.text(\n", + " 0.04,\n", + " 0.94,\n", + " panel_labels[row_idx * 2 + col_idx],\n", + " transform=ax.transAxes,\n", + " ha='left',\n", + " va='top',\n", + " fontsize=11,\n", + " color='black',\n", + " bbox={'facecolor': 'white', 'edgecolor': 'none', 'alpha': 0.75, 'pad': 1.5},\n", + " )\n", + "\n", + " if row_idx == 1:\n", + " ax.set_xlabel('In-phase (I)')\n", + " if col_idx == 0:\n", + " ax.set_ylabel('Quadrature (Q)')\n", + " ax.annotate(\n", + " mode,\n", + " xy=(-0.34, 0.5),\n", + " xycoords='axes fraction',\n", + " ha='center',\n", + " va='center',\n", + " rotation=90,\n", + " fontsize=12,\n", + " )\n", + "\n", + " fig.text(0.302, 0.875, 'No Cross-Pol', ha='center', va='bottom', fontsize=11)\n", + " fig.text(0.628, 0.875, 'Cross-Pol', ha='center', va='bottom', fontsize=11)\n", + " fig.suptitle('I/Q Constellation Density by Mode and Polarization Coupling', y=0.955, fontsize=14)\n", + " cbar_ax = fig.add_axes([0.825, 0.22, 0.027, 0.55])\n", + " cbar = fig.colorbar(images[0], cax=cbar_ax, extend=colorbar_extend)\n", + " cbar.set_label('Counts per bin', fontsize=12)\n", + " cbar.ax.tick_params(labelsize=9)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "9e1f3c9b", + "metadata": {}, + "source": [ + "Here we see the TE and TM panels are rotated differently due to a difference in the effective index leading to different global phase shifts\n", + "\n", + "When we enable cross-polarization, the off-diagonal multimode S-parameter terms mix energy between TE and TM. That makes the constellations less clean because each mode now contains a small contribution that followed the other mode's phase evolution." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/baseband_delay_shift_derivation.ipynb b/examples/baseband_delay_shift_derivation.ipynb new file mode 100644 index 00000000..17a26a06 --- /dev/null +++ b/examples/baseband_delay_shift_derivation.ipynb @@ -0,0 +1,473 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro", + "metadata": {}, + "source": [ + "# Resonance Shift from Artificial Edge Delays in Baseband Simulation\n", + "\n", + "In a sample-mode circuit simulation, every graph edge introduces one artificial\n", + "time-step delay $\\Delta t$. For a ring resonator whose closure passes through\n", + "$n$ such edges, this perturbs the resonant wavelength.\n", + "\n", + "A naive analysis gives\n", + "$$|\\Delta\\lambda| = \\lambda_\\text{res}\\,\\frac{n\\,\\Delta t}{\\tau_\\text{rt}}$$\n", + "but this predicts shifts of tens of nanometres that are never observed in\n", + "practice. The correct formula is\n", + "$$\\boxed{|\\Delta\\lambda| = |\\lambda_\\text{res} - \\lambda_c|\\,\\frac{n\\,\\Delta t}{\\tau_\\text{rt}}}$$\n", + "where $\\lambda_c$ is the **carrier centre wavelength** used when fitting the\n", + "state-space models. This notebook derives the formula from first principles\n", + "and verifies it numerically." + ] + }, + { + "cell_type": "markdown", + "id": "section1", + "metadata": {}, + "source": [ + "## 1. The Ring Resonance Condition\n", + "\n", + "A ring resonator resonates when the round-trip optical phase equals an integer\n", + "multiple of $2\\pi$:\n", + "\n", + "$$\\varphi_\\text{rt}(f) = 2\\pi\\,\\frac{n_g\\,L_\\text{rt}}{c}\\,f = 2\\pi m\n", + "\\qquad m \\in \\mathbb{Z}$$\n", + "\n", + "giving resonant frequencies\n", + "$$f_m = \\frac{m\\,c}{n_g\\,L_\\text{rt}}$$\n", + "and free spectral range $\\text{FSR} = c / (n_g L_\\text{rt})$." + ] + }, + { + "cell_type": "markdown", + "id": "section2", + "metadata": {}, + "source": [ + "## 2. Baseband Representation\n", + "\n", + "The simulator works with the **complex envelope** $A(t)$ defined by\n", + "$$E(t) = \\operatorname{Re}\\!\\left[A(t)\\,e^{j2\\pi f_c t}\\right]$$\n", + "where $f_c = c/\\lambda_c$ is a fixed carrier frequency. Every component is\n", + "represented by a discrete-time state-space model fitted — via vector fitting —\n", + "to the physical transfer function at offset frequencies\n", + "$\\delta\\!f = f - f_c$ over a finite spectral band.\n", + "\n", + "For a waveguide with group delay $\\tau_\\text{wg}$ the physical transfer\n", + "function is\n", + "$$H_\\text{wg}(f) = e^{j2\\pi f\\,\\tau_\\text{wg}}$$\n", + "which at offset $\\delta\\!f = f - f_c$ becomes\n", + "$$H_\\text{wg}(f_c + \\delta\\!f)\n", + " = \\underbrace{e^{j2\\pi f_c\\tau_\\text{wg}}}_{\\text{carrier phase}}\n", + " \\cdot\\,e^{j2\\pi\\,\\delta\\!f\\,\\tau_\\text{wg}}$$\n", + "\n", + "The **carrier phase** $e^{j2\\pi f_c\\tau_\\text{wg}}$ is a constant and is\n", + "captured by the poles and residues of the state-space model during fitting.\n", + "It contributes to the round-trip resonance condition at $\\delta\\!f = 0$ just\n", + "as in the physical ring." + ] + }, + { + "cell_type": "markdown", + "id": "section3", + "metadata": {}, + "source": [ + "## 3. Effect of an Artificial Edge Delay\n", + "\n", + "In the discrete-time simulation each graph edge carries the previous time\n", + "step's output to the next component's input, introducing a delay of $\\Delta t$.\n", + "This is a delay of the **complex envelope** only:\n", + "\n", + "$$A(t) \\longrightarrow A(t - \\Delta t)$$\n", + "\n", + "In the frequency domain the envelope delay multiplies by\n", + "$$H_\\text{delay}(\\delta\\!f) = e^{-j2\\pi\\,\\delta\\!f\\,\\Delta t}$$\n", + "\n", + "Crucially, there is **no separate carrier-phase term** $e^{-j2\\pi f_c \\Delta t}$.\n", + "That would appear if we were delaying the real optical field, but here we are\n", + "delaying the envelope only. The carrier phase is already baked into the\n", + "state-space model.\n", + "\n", + "### Round-trip resonance with $n$ artificial delays\n", + "\n", + "Let $\\varphi_\\text{SS}(\\delta\\!f)$ be the round-trip phase contributed by\n", + "the state-space models. Because the models capture the full physical\n", + "transfer function (including carrier phase), at offset $\\delta\\!f$:\n", + "\n", + "$$\\varphi_\\text{SS}(\\delta\\!f) \\approx\n", + " \\underbrace{2\\pi f_c\\,\\tau_\\text{rt}}_{\\varphi_\\text{carrier}}\n", + " + 2\\pi\\,\\delta\\!f\\,\\tau_\\text{rt}$$\n", + "\n", + "Without artificial delays the resonance at $\\delta\\!f_0$ satisfies\n", + "$$\\varphi_\\text{carrier} + 2\\pi\\,\\delta\\!f_0\\,\\tau_\\text{rt} = 2\\pi m$$\n", + "\n", + "With $n$ artificial delays of $\\Delta t$ each, the total round-trip phase\n", + "picks up an extra $-2\\pi\\,\\delta\\!f\\cdot n\\Delta t$ from the envelope delays:\n", + "\n", + "$$\\varphi_\\text{carrier}\n", + " + 2\\pi\\,\\delta\\!f_\\text{new}\\,\\tau_\\text{rt}\n", + " - 2\\pi\\,\\delta\\!f_\\text{new}\\cdot n\\Delta t = 2\\pi m$$\n", + "\n", + "Subtracting the unperturbed condition:\n", + "$$2\\pi(\\delta\\!f_\\text{new} - \\delta\\!f_0)\\,\\tau_\\text{rt}\n", + " = 2\\pi\\,\\delta\\!f_\\text{new}\\cdot n\\Delta t$$\n", + "\n", + "$$\\Delta(\\delta\\!f) = \\delta\\!f_\\text{new} - \\delta\\!f_0\n", + " \\approx \\delta\\!f_0\\,\\frac{n\\Delta t}{\\tau_\\text{rt} - n\\Delta t}\n", + " \\approx \\delta\\!f_0\\,\\frac{n\\Delta t}{\\tau_\\text{rt}}$$\n", + "\n", + "Converting to wavelength using $\\Delta\\lambda \\approx -\\frac{\\lambda_c^2}{c}\\Delta(\\delta\\!f)$\n", + "and $\\delta\\!f_0 \\approx -\\frac{c}{\\lambda_c^2}(\\lambda_\\text{res} - \\lambda_c)$:\n", + "\n", + "$$\\boxed{|\\Delta\\lambda| = |\\lambda_\\text{res} - \\lambda_c|\\;\\frac{n\\,\\Delta t}{\\tau_\\text{rt}}}$$\n", + "\n", + "The shift is proportional to the **offset of the resonance from the carrier\n", + "centre wavelength**, not to $\\lambda_\\text{res}$ itself. A resonance sitting\n", + "exactly at $\\lambda_c$ experiences **zero shift** regardless of $n$ or $\\Delta t$." + ] + }, + { + "cell_type": "markdown", + "id": "section4", + "metadata": {}, + "source": [ + "## 4. Numerical Verification\n", + "\n", + "We build a single all-pass ring resonator with a transparent phase modulator\n", + "in the cavity. The modulator (a non-SAX component) prevents the ring from\n", + "being pre-solved by the state-space group, so the resonance genuinely emerges\n", + "from the time-domain feedback — which is exactly the regime where artificial\n", + "delays matter.\n", + "\n", + "We then:\n", + "1. Run an S-parameter simulation to locate the exact resonant wavelengths.\n", + "2. Run a sample-mode simulation and locate the resonance dips in the\n", + " steady-state spectrum.\n", + "3. Measure the shift for each resonance and compare with the formula." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "from scipy.signal import find_peaks\n", + "\n", + "from simphony.circuit.circuit import Circuit\n", + "from simphony.simulation.s_parameter import (\n", + " SParameterSimulation, SParameterSimulationParameters)\n", + "from simphony.simulation.sample_mode import (\n", + " SampleModeSimulation, SampleModeSimulationParameters)\n", + "from simphony.libraries.ideal.sources import OpticalCombSource, VoltageSource\n", + "from simphony.libraries.ideal.modulators import OpticalModulator\n", + "from simphony.libraries.siepic import half_ring, waveguide\n", + "from scipy.constants import speed_of_light" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "params", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Ring parameters ───────────────────────────────────────────────────────────\n", + "HR = dict(pol='te', gap=100, radius=5, width=500, thickness=220, coupling_length=0)\n", + "WG = dict(pol='te', length=5.0, width=500, height=220, loss=100.0)\n", + "MOD = dict(phase_coefficients=np.zeros(4), absorption_coefficients=np.zeros(4), length=1.0)\n", + "VS = dict(steady_state_voltage=0.0)\n", + "\n", + "# Physical ring parameters for the formula\n", + "ng = 3.5\n", + "r = HR['radius'] * 1e-6 # m\n", + "L_close = WG['length'] * 1e-6 # m\n", + "L_rt = np.pi * r + L_close # actual ring path (half-arc + straight)\n", + "tau_rt = ng * L_rt / speed_of_light # round-trip group delay (s)\n", + "FSR = (1.55e-6)**2 / (ng * L_rt) # free spectral range (m)\n", + "\n", + "DT = 1e-14 # 10 fs sample period\n", + "N_STEPS = 2000\n", + "TRANSIENT = 300\n", + "\n", + "# Number of edges in the ring feedback loop:\n", + "# half_ring -> mod (1)\n", + "# mod -> wg (1)\n", + "# wg -> half_ring (1)\n", + "N_EDGES = 3\n", + "\n", + "print(f\"Ring path length L_rt = {L_rt*1e6:.3f} µm\")\n", + "print(f\"Round-trip time τ_rt = {tau_rt*1e15:.1f} fs\")\n", + "print(f\"Free spectral range FSR = {FSR*1e9:.2f} nm\")\n", + "print(f\"Artificial delays / round trip: {N_EDGES}\")\n", + "print(f\"n·Δt / τ_rt = {N_EDGES*DT/tau_rt:.4f} ({N_EDGES*DT/tau_rt*100:.2f}%)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "circuits", + "metadata": {}, + "outputs": [], + "source": [ + "# ── S-parameter circuit (ring closed via modulator for fair comparison) ───────\n", + "sp_netlist = {\n", + " 'instances': {'hr':'half_ring','mod':'modulator','wg':'waveguide','vs':'voltage_source'},\n", + " 'connections': {'hr,port_2':'mod,o0','mod,o1':'wg,o0','wg,o1':'hr,port_4','vs,e0':'mod,e0'},\n", + " 'ports': {'in':'hr,port_1','out':'hr,port_3'},\n", + "}\n", + "sp_models = {'half_ring':half_ring,'waveguide':waveguide,\n", + " 'modulator':OpticalModulator,'voltage_source':VoltageSource}\n", + "sp_circuit = Circuit(sp_netlist, sp_models)\n", + "sp_settings = {'hr':HR,'wg':WG,'mod':MOD,'vs':VS}\n", + "\n", + "# ── Sample-mode circuit (source drives the ring) ──────────────────────────────\n", + "sm_netlist = {\n", + " 'instances': {'hr':'half_ring','mod':'modulator','wg':'waveguide',\n", + " 'vs':'voltage_source','source':'comb_source'},\n", + " 'connections': {'hr,port_2':'mod,o0','mod,o1':'wg,o0','wg,o1':'hr,port_4',\n", + " 'vs,e0':'mod,e0','source,o0':'hr,port_1'},\n", + " 'ports': {'out':'hr,port_3'},\n", + "}\n", + "sm_models = {**sp_models, 'comb_source':OpticalCombSource}\n", + "sm_circuit = Circuit(sm_netlist, sm_models)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sparam", + "metadata": {}, + "outputs": [], + "source": [ + "# ── S-parameter ground truth ──────────────────────────────────────────────────\n", + "wl_sp = np.linspace(1.50, 1.60, 2001) # µm\n", + "sp_sim = SParameterSimulation(sp_circuit, sp_settings, SParameterSimulationParameters())\n", + "sp_res = sp_sim.run(wl=wl_sp)\n", + "sp_T = np.abs(np.array(sp_res.s_parameters[('in','out')]))**2\n", + "\n", + "# Find resonance dip positions (minima in transmission)\n", + "dip_idx, _ = find_peaks(-sp_T, prominence=0.05)\n", + "sp_resonances = wl_sp[dip_idx] * 1e-6 # metres\n", + "print(\"S-parameter resonances (µm):\", [f\"{r*1e6:.4f}\" for r in sp_resonances])\n", + "\n", + "plt.figure(figsize=(10, 3))\n", + "plt.plot(wl_sp, sp_T, 'b-', lw=1.5)\n", + "plt.scatter(wl_sp[dip_idx], sp_T[dip_idx], color='red', zorder=5, s=60, label='Resonances')\n", + "plt.xlabel('Wavelength (µm)'); plt.ylabel('Transmission')\n", + "plt.title('All-Pass Ring: S-Parameter Transmission')\n", + "plt.legend(); plt.grid(True, alpha=0.3); plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sm_run", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Sample-mode simulation ────────────────────────────────────────────────────\n", + "# Dense wavelength grid so we can resolve resonance positions accurately\n", + "lambda_c = 1.55e-6 # VF centre wavelength\n", + "sm_wl = jnp.linspace(1.50, 1.60, 501) * 1e-6\n", + "\n", + "VF = dict(model_order=None, min_model_order=2, max_model_order=30,\n", + " num_frequency_samples=600, center_wavelength=lambda_c,\n", + " spectral_range=(1.50e-6, 1.60e-6))\n", + "\n", + "sm_settings = {\n", + " 'hr': {'sax_settings': HR, 'vector_fitting_parameters': VF},\n", + " 'wg': {'sax_settings': WG, 'vector_fitting_parameters': VF},\n", + " 'mod': MOD,\n", + " 'vs': VS,\n", + " 'source': {'wavelength': sm_wl, 'linewidth': 0.0},\n", + "}\n", + "sm_params = SampleModeSimulationParameters(\n", + " optical_baseband_wavelengths=sm_wl, dt=DT, num_time_steps=N_STEPS)\n", + "sm_sim = SampleModeSimulation(sm_circuit, sm_settings,\n", + " tracked_ports={'out':'hr,port_3'},\n", + " simulation_parameters=sm_params)\n", + "\n", + "print(f'Running sample-mode ({N_STEPS} steps × {len(sm_wl)} wavelengths, '\n", + " f'λ_c = {lambda_c*1e9:.0f} nm) …')\n", + "sm_result = sm_sim.run(use_jit=True)\n", + "print('Done.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sm_spectrum", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Extract steady-state spectrum and find resonance dips ─────────────────────\n", + "out_amp = np.array(sm_result.output_signals['out'].amplitude) # (N, L, M)\n", + "sm_T = np.mean(np.abs(out_amp[TRANSIENT:, :, 0])**2, axis=0) # (L,)\n", + "sm_wl_nm = np.array(sm_wl) * 1e6 # µm for plotting\n", + "\n", + "sm_dip_idx, _ = find_peaks(-sm_T, prominence=0.05)\n", + "sm_resonances = np.array(sm_wl)[sm_dip_idx] # metres\n", + "print('Sample-mode resonances (µm):', [f'{r*1e6:.4f}' for r in sm_resonances])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "measure_shift", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Match resonances and measure shifts ───────────────────────────────────────\n", + "print(f'\\nCarrier centre wavelength λ_c = {lambda_c*1e9:.2f} nm')\n", + "print(f'Artificial delays per ring feedback loop: {N_EDGES}')\n", + "print(f'Δt = {DT*1e15:.0f} fs, τ_rt = {tau_rt*1e15:.1f} fs\\n')\n", + "\n", + "header = (f'{\"λ_res (nm)\":>12} {\"δλ = λ_res−λ_c (nm)\":>22} '\n", + " f'{\"Observed Δλ (nm)\":>18} {\"Formula Δλ (nm)\":>17} {\"Error\":>8}')\n", + "print(header)\n", + "print('-' * len(header))\n", + "\n", + "shifts_obs, shifts_formula, offsets = [], [], []\n", + "\n", + "for sp_r in sp_resonances:\n", + " # Find the closest sample-mode resonance\n", + " if len(sm_resonances) == 0:\n", + " continue\n", + " closest_idx = np.argmin(np.abs(sm_resonances - sp_r))\n", + " sm_r = sm_resonances[closest_idx]\n", + "\n", + " obs = abs(sm_r - sp_r) # observed shift (m)\n", + " delta_lam = abs(sp_r - lambda_c) # |λ_res − λ_c| (m)\n", + " formula = delta_lam * N_EDGES * DT / tau_rt # predicted shift (m)\n", + " error_pct = (obs - formula) / formula * 100 if formula > 0 else float('nan')\n", + "\n", + " shifts_obs.append(obs * 1e9)\n", + " shifts_formula.append(formula * 1e9)\n", + " offsets.append(delta_lam * 1e9)\n", + "\n", + " print(f' {sp_r*1e9:>10.3f} {(sp_r-lambda_c)*1e9:>+21.3f} '\n", + " f'{obs*1e9:>18.4f} {formula*1e9:>17.4f} {error_pct:>7.1f}%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "plot_comparison", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Visualisation: spectrum overlay + formula vs observed shift ───────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "\n", + "# Left: transmission comparison\n", + "ax = axes[0]\n", + "ax.plot(wl_sp, sp_T, 'b-', lw=2, label='S-parameter (exact)', zorder=3)\n", + "ax.plot(sm_wl_nm, sm_T, 'r-', lw=1.5, alpha=0.85, label=f'Sample-mode (λ_c={lambda_c*1e9:.0f} nm)')\n", + "ax.axvline(lambda_c * 1e6, color='k', ls=':', lw=1, label='λ_c')\n", + "ax.set_xlabel('Wavelength (µm)'); ax.set_ylabel('Transmission')\n", + "ax.set_title('Transmission: resonance dips shift away from λ_c')\n", + "ax.legend(fontsize=9); ax.grid(True, alpha=0.3)\n", + "\n", + "# Right: formula vs observed shift\n", + "ax = axes[1]\n", + "delta_lam_range = np.linspace(0, max(offsets) * 1.1, 200)\n", + "ax.plot(delta_lam_range,\n", + " delta_lam_range * N_EDGES * DT / tau_rt * 1e9,\n", + " 'b-', lw=2, label=r'Formula: $|\\delta\\lambda|\\cdot n\\Delta t/\\tau_\\mathrm{rt}$')\n", + "ax.scatter(offsets, shifts_obs, s=80, color='red', zorder=5,\n", + " label='Observed shift (simulation)')\n", + "ax.set_xlabel(r'$|\\lambda_\\mathrm{res} - \\lambda_c|$ (nm)')\n", + "ax.set_ylabel('Resonance shift (nm)')\n", + "ax.set_title('Shift scales linearly with distance from λ_c')\n", + "ax.legend(fontsize=9); ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('baseband_delay_shift.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "print('Saved baseband_delay_shift.png')" + ] + }, + { + "cell_type": "markdown", + "id": "section5", + "metadata": {}, + "source": [ + "## 5. Why the Naive Formula Is Wrong by a Factor of $\\lambda_\\text{res}/\\delta\\lambda$\n", + "\n", + "The naive derivation treats the artificial delay as if it were delaying the\n", + "**real optical field**, adding a carrier phase of $2\\pi f_c\\,\\Delta t$ per\n", + "step. This gives\n", + "$$|\\Delta\\lambda|_\\text{naive} = \\lambda_\\text{res}\\,\\frac{n\\,\\Delta t}{\\tau_\\text{rt}}$$\n", + "\n", + "The correct analysis recognises that the delay acts on the **complex envelope**,\n", + "adding only the offset phase $2\\pi\\,\\delta\\!f\\,\\Delta t$. The ratio between\n", + "the two predictions is\n", + "$$\\frac{|\\Delta\\lambda|_\\text{naive}}{|\\Delta\\lambda|_\\text{correct}}\n", + " = \\frac{\\lambda_\\text{res}}{|\\lambda_\\text{res} - \\lambda_c|}$$\n", + "\n", + "For a resonance 10 nm from the carrier centre at 1550 nm this ratio is\n", + "$1550/10 = 155$, explaining why the naive formula over-predicts by two orders\n", + "of magnitude." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "naive_comparison", + "metadata": {}, + "outputs": [], + "source": [ + "print('Comparison of the two formulae at each resonance:\\n')\n", + "print(f'{\"λ_res (nm)\":>12} {\"δλ (nm)\":>10} '\n", + " f'{\"Correct (nm)\":>14} {\"Naive (nm)\":>12} {\"Ratio\":>8}')\n", + "print('-' * 65)\n", + "for sp_r in sp_resonances:\n", + " delta_lam = abs(sp_r - lambda_c)\n", + " correct = delta_lam * N_EDGES * DT / tau_rt\n", + " naive = sp_r * N_EDGES * DT / tau_rt\n", + " ratio = naive / correct if correct > 0 else float('inf')\n", + " print(f' {sp_r*1e9:>10.3f} {delta_lam*1e9:>10.3f} '\n", + " f'{correct*1e9:>14.4f} {naive*1e9:>12.2f} {ratio:>8.1f}×')" + ] + }, + { + "cell_type": "markdown", + "id": "section6", + "metadata": {}, + "source": [ + "## 6. Practical Implications\n", + "\n", + "| Observation | Explanation |\n", + "|---|---|\n", + "| Resonance at $\\lambda_c$ has **zero** shift | $\\delta\\lambda = 0$ → formula gives zero |\n", + "| Loss does **not** affect the shift | Loss only enters the amplitude condition; it has no effect on the resonant phase |\n", + "| Larger $\\Delta t$ → larger shift | Linear in $n\\,\\Delta t / \\tau_\\text{rt}$ |\n", + "| Delay compensation of $k$ steps reduces $n_\\text{eff}$ | Each compensated edge subtracts 1 from $n$ |\n", + "| Best carrier placement | Set $\\lambda_c$ at the resonance of interest to minimise its shift |\n", + "\n", + "**Key takeaway:** the artificial-delay shift in a baseband simulation is not\n", + "a fixed fraction of the FSR — it depends on how far each resonance sits from\n", + "$\\lambda_c$. To minimise the shift for a particular resonance, tune $\\lambda_c$\n", + "to that resonance wavelength." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/block_mode.ipynb b/examples/block_mode.ipynb new file mode 100644 index 00000000..d6206749 --- /dev/null +++ b/examples/block_mode.ipynb @@ -0,0 +1,731 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0f3bb6af", + "metadata": {}, + "source": [ + "# S-parameter Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6037333f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1000, 2, 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.time_domain.vector_fitting.z_domain import vector_fitting_discrete, state_space_discrete, pole_residue_response_discrete, optimize_order_vector_fitting_discrete\n", + "from simphony.libraries.siepic import y_branch\n", + "from simphony.utils import dict_to_rect_matrix, dict_to_matrix\n", + "import numpy as np\n", + "from scipy.constants import c\n", + "import matplotlib.pyplot as plt\n", + "\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "f = c/wl\n", + "lam0 = 1e-6*1.55\n", + "f_c = c/lam0\n", + "f_s = 1e14\n", + "dt = 1/f_s\n", + "\n", + "# H = dict_to_matrix(y_branch(wl*1e6))\n", + "H = dict_to_rect_matrix(y_branch(wl*1e6), input_ports=[\"port_1\"], output_ports=[\"port_2\", \"port_3\"])\n", + "print(H.shape)\n", + "\n", + "\n", + "poles, residues, feedthrough, error = vector_fitting_discrete(50, H, f, f_c, f_s)\n", + "A, B, C, D = state_space_discrete(poles, residues, feedthrough)\n", + "\n", + "plt.plot(f, np.abs(H[:, 0, 1]))\n", + "plt.plot(f, np.abs(pole_residue_response_discrete(f, f_c, f_s, poles, residues, feedthrough)[:, 0, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fe8344a4", + "metadata": {}, + "outputs": [], + "source": [ + "poles, residues, feedthrough, error = vector_fitting_discrete(25, H, f, f_c, f_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47d972d8", + "metadata": {}, + "outputs": [], + "source": [ + "res =optimize_order_vector_fitting_discrete(2, 100, H, f, f_c, f_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d21bc552", + "metadata": {}, + "outputs": [], + "source": [ + "res = optimize_order_vector_fitting_discrete(2, 100, H, f, f_c, f_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "69a1c3ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(f, np.abs(H[:, 0, 1]))\n", + "plt.plot(f, np.abs(H[:, 1, 0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "93229864", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.time_domain.vector_fitting.z_domain import state_space_response_discrete, state_space_frequency_response_discrete\n", + "from simphony.time_domain.utils import gaussian_pulse\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "H_hat = state_space_frequency_response_discrete(A, B, C, D, f, f_c, dt=dt)\n", + "# print(H_hat[:, 0, 1])\n", + "plt.plot(f, np.abs(H_hat[:, 0, 1]))\n", + "plt.show()\n", + "\n", + "t = np.arange(0, 1e-12, dt)\n", + "u = np.zeros((len(t), D.shape[1]))\n", + "u[:, 0] = gaussian_pulse(t, 0.5e-12, 0.2e-12)\n", + "print(u.shape)\n", + "y, _ = state_space_response_discrete(A, B, C, D, u)\n", + "plt.plot(t, u)\n", + "plt.plot(t, np.abs(y[:, 2])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cda8093f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 2)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "552c5a46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 1)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "u.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e94b24b1", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.modulators import DirectedOpticalModulator\n", + "from simphony.libraries.ideal.sources import VoltageSource, CWLaser\n", + "from simphony.libraries.ideal.electrical_circuits import VoltageFollower\n", + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "from simphony.libraries.siepic import y_branch, waveguide\n", + "\n", + "import sax\n", + "import numpy as np\n", + "\n", + "instances = {\n", + " \"splitter\": \"y_branch\",\n", + " \"combiner\": \"y_branch\",\n", + " \"wg1\": \"waveguide\",\n", + " \"wg2\": \"waveguide\",\n", + " \"mod1\": \"modulator\",\n", + " \"mod2\": \"modulator\",\n", + " \"vs1\": \"voltage_source\",\n", + " \"vf1\": \"voltage_follower\",\n", + " \"vs2\": \"voltage_source\",\n", + " \"laser\": \"cw_laser\",\n", + "}\n", + "connections = {\n", + " \"laser,o0\": \"splitter,port_1\",\n", + " \"splitter,port_2\": \"wg1,o0\",\n", + " # \"wg1,o1\": \"combiner,port_2\",\n", + " \"wg1,o1\": \"mod1,o0\",\n", + " \"mod1,o1\": \"combiner,port_2\",\n", + " \"splitter,port_3\": \"wg2,o0\",\n", + " # \"wg2,o1\": \"combiner,port_3\",\n", + " \"wg2,o1\": \"mod2,o0\",\n", + " \"mod2,o1\": \"combiner,port_3\",\n", + " \"vs1,e0\": \"vf1,e0\",\n", + " \"vf1,e1\": \"mod1,e0\",\n", + " \"vs2,e0\": \"mod2,e0\"\n", + "}\n", + "ports = {\n", + "}\n", + "\n", + "netlist = {\n", + " \"instances\": instances,\n", + " \"connections\": connections,\n", + " \"ports\": ports,\n", + "}\n", + "\n", + "models = {\n", + " \"y_branch\": y_branch,\n", + " \"waveguide\": waveguide,\n", + " \"modulator\": DirectedOpticalModulator,\n", + " \"voltage_source\": VoltageSource,\n", + " \"voltage_follower\": VoltageFollower,\n", + " \"cw_laser\": CWLaser,\n", + "}\n", + "\n", + "ckt_settings = {\n", + " \"splitter\": {},\n", + " \"combiner\": {},\n", + " \"wg1\": {\"length\":10.0},\n", + " \"wg2\": {\"length\":30.0},\n", + " \"mod1\": {\n", + " \"phase_coefficients\": np.array([0.0, 0.0, np.pi/2, 0]),\n", + " # \"phase_coefficients\": np.array([0.0, 0.0, 0.0, 0])\n", + " },\n", + " \"mod2\": {},\n", + " \"vs1\": {\n", + " \"steady_state_voltage\": 1.0,\n", + " },\n", + " \"vs2\": {\n", + " \"steady_state_voltage\": 0.0,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "52a7bbba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/sax/utils.py:80: UserWarning: Could not validate netlist for 'top_level'. This netlist will be ignored.\n", + " return func(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dst: splitter,port_1\n", + "dst_directionality: bidirectional\n", + "dst: wg1,o0\n", + "dst_directionality: bidirectional\n", + "dst: mod1,o0\n", + "dst_directionality: input\n", + "dst: combiner,port_2\n", + "dst_directionality: bidirectional\n", + "dst: wg2,o0\n", + "dst_directionality: bidirectional\n", + "dst: mod2,o0\n", + "dst_directionality: input\n", + "dst: combiner,port_3\n", + "dst_directionality: bidirectional\n", + "dst: vf1,e0\n", + "dst_directionality: input\n", + "dst: mod1,e0\n", + "dst_directionality: input\n", + "dst: mod2,e0\n", + "dst_directionality: input\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f1e1b460fe44539b53b5a061a6bc36b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 10 nodes and 12 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "from simphony.simulation.block_mode import BlockModeSimulation, BlockModeSimulationParameters\n", + "# from simphony.simulation.block_mode import BlockModeSimulationParameters\n", + "\n", + "mzi_circuit = Circuit(netlist, models)\n", + "mzi_circuit.display()\n", + "# mzi_circuit.instantiate(ckt_settings, simulation_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b8cd4dc5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/sax/utils.py:80: UserWarning: Could not validate netlist for 'top_level'. This netlist will be ignored.\n", + " return func(*args, **kwargs)\n", + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3a59e1f906844babe5f4223a719c943", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 23 nodes and 23 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from simphony.signal.block_mode import BlockModeOpticalSignal, BlockModeElectricalSignal\n", + "from simphony.simulation.block_mode import BlockModeSimulationParameters\n", + "\n", + "tracked_ports = {\n", + " \"out1\": \"combiner,port_1\",\n", + " \"out2\": \"combiner,port_2\",\n", + "}\n", + "\n", + "simulation_parameters = BlockModeSimulationParameters(mode_identifiers=(\"TE\",), num_time_steps=200)\n", + "block_mode_simulation = BlockModeSimulation(mzi_circuit, ckt_settings, tracked_ports, simulation_parameters=simulation_parameters)\n", + "sim_result = block_mode_simulation.run()\n", + "block_mode_simulation._instantiated_circuit.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f40eb0c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['out1'])\n" + ] + } + ], + "source": [ + "print(sim_result.output_signals.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "948c61ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAATj1JREFUeJzt3XmcHWWd9/1Pnb33Jb0nnXQSQkLIBlk6YQZQaQnKo6iokZsxmEEcFZAx6g1xNEHm0TDCIPcoNygjwiOjICPKuEUhEBHpJJAQWRICWTtLr0n36fWsVc8f1X06J92d7k46OamT7/v1Oq90V12nzq+6kq5vrrrqKsOyLAsRERGRFHGlugARERE5tymMiIiISEopjIiIiEhKKYyIiIhISimMiIiISEopjIiIiEhKKYyIiIhISimMiIiISEp5Ul3ASJimyeHDh8nJycEwjFSXIyIiIiNgWRYdHR1UVFTgcg3d/+GIMHL48GEqKytTXYaIiIichAMHDjBhwoQh1zsijOTk5AD2zuTm5qa4GhERERmJ9vZ2KisrE+fxoTgijPRdmsnNzVUYERERcZjhhlhoAKuIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUIx6UJyInb9uzPyO0+yWsjHz8JdOY+/5P4/bon76InD30G0kkjdW9s41ZL92MxzDtBXth884/sfBL/4XhUseoiJwd9NtIJI21PPMNPIbJLvdUXsm7krhlsKjt92z88ZdTXZqISILCiEiaevuV57i46y/ELQPvxx9m4ZefYsucNQAsOfQom5769xRXKCJiUxgRSUOWaWL9aTUAWwo/yKQL5gOw6NovUzvp8wDMe3Mtu9/YmLIaRUT6KIyIpKGdW57nguhb9Fg+qj7+7aR1i29Yy7aMxfiNKJ5f3Uh3ZzBFVYqI2BRGRNJQ644/A7AjezEl4ycnrTNcLib946M0Ucgk8yBvPfw5LNNMRZkiIoDCiEhaymh4BYBIxcJB1xcUl9P8/h9gWgYLg+vY+Niq016TZZrEYzEFHxEZQLf2iqQZyzSZ1P0mAIUXXDZkuwv/7mo2HbyD6h1rWbL/ITb/spxF1/7ziD8nFo2wb/srBA/uINKyB6OrGXe4DW+knUCsnUC8A78ZIkAYvxUm0wjj7n1vxHITx00MN91GJl2uHHo8uUS8uUR9eZj+fKyMfFwZBXiyC/FlFxLILcIy44TajxDtaiXW1YrV3QrdLXhCR/BFWsmKtuIzQ7iI47JMXJgYWEQMv/1yZRB1B4i5M4i7MzA99svyZoIvE8ObCYYLzBiWGQczCmYczBiYMQwzDlYco+97Kw5mHMOy17msGBYGGC4swwUYWIYB9H5v2OuwLMDq/ROMxNf2y0h8DViWvb7v+1Nm9NZo9H/fWytG7zroXX/Msr7vwd6H3vcOWH9suxGXdI79v9g6OwP5pA9+mfJJ01Py2QojImmm7t3XmUQHIctL1YWLT9i2etkd1P7oMEsOP8aC1+9kY+NbzFl+L5nZeQPahkPd7P3bS7Tt2EBmwyam9rzJeUZoZEUdd27yGXEgDkAOPWAegQj2q2tkmxyVvnO5CcROw/ZF0sDbzZ9QGBGRsdH45gYmAXv8M5jpDwzbfvFn72fTg11UN/83i5t+Qf29L/BG8eW4yudgRnsw2w6Qe+QNpoa3M8OI9r/RgHYyOeSdTGfmeGJZZRgZBbgyC/BkFeDLLsSXmYs3kIU/IwdfRhYej5dYLIoZjxGPRYhFI4Q6Wgm1txDpPEqs6yhm91GMnjZc4SDeSBB/rJ2MWAdZZjsmbrpdWfR4coh47F6UeEYhRlYR7uwSfHkleDNycLk9uNweDJcbwzCIhbqJhruIhbqIhzoxI92YkS6sSBdEezCi3bii3bhiPQBYLjeW4QGXG8twg+HGcnnA5cFyucHlAcMNvV8bbk//MrD/52uZ2L0fJpgmVt/3ZhwMAwMXltH7gzRc/XnNGKSXIenPExlu/TE9MJaZ3ENjWXbfS2+dhgUWZqL35ti2hmViJW2r708As7dn5yxgmWdtr4s17LEcjbHZx8klE8dkOydDYUQk3RzYBECw6OIRNTdcLqpv/jGvb/ggJRv+N+U0U97839D838c1hKPksi9rHpEJiymedQVVFyzgAk0tLyKnSL9FRNJMeXAbAJnnXTKq9815z7V0zb+CVzc8SezgVrLb3ibqziKSVQ7FMyibcwUTz59HoaaRF5ExpjAikkaONh2i0joMQNXc9476/Vk5+Sz40D+NdVkiIiek/+KIpJH9214AYJ+rkrxxpSmuRkRkZBRGRNJIaL89v0hT3pwUVyIiMnIKIyJpxB/cA4BZfEGKKxERGTmFEZE0kt9dB0BGWWrmChARORkKIyJpwozHKY/bg1fHTZyZ4mpEREZOYUQkTTQd3kuGESFquSmbdH6qyxERGTGFEZE00bLvLQDq3WV4vL4UVyMiMnIKIyJpoqt+JwBHA6mb0llE5GQojIikCatlFwChnKrUFiIiMkoKIyJpIqNjLwBG0bQUVyIiMjoKIyJpYlzoAADZFTNSXImIyOgojIikgUg4RJnZCEDJ5AtTXI2IyOgojIikgYb9b+MxTLotP0VlGsAqIs6iMCKSBo7W7QDgsGcChkv/rEXEWfRbSyQNhBrs23qDmeoVERHnURgRSQPG0d0ARPOmpLgSEZHRUxgRSQPZnfsAcBefl9pCREROgsKISBoYF6kHIKdcc4yIiPMojIg4XDQSpthqAaBogh6QJyLOozAi4nBNB/fgNizClpfC0gmpLkdEZNQURkQcrvXwuwA0uYpxud0prkZEZPQURkQcrqfJfiZNq78ixZWIiJwcT6oLEDmX7X5jI82bfoEnuA9PvJuST/4fKqqmj2obsaP7AOjJGn8aKhQROf0URkRSJNTTRf4vP8lUgollm37zHSpufWxU2/G22w/IM/MmjWl9IiJnii7TiKTIG398lHEEaSGfTUXXAjD9yHNEwqFRbSer5xAAvnEKIyLiTCcVRh544AGqqqoIBAJUV1ezefPmIds++uijGIaR9AoEAiddsEi6yHvzUQDerbqOBZ//ES3kk08nO/7661FtZ1y0AYCcck14JiLONOow8uSTT7Jy5UrWrFnD1q1bmTt3LkuXLqWpqWnI9+Tm5lJfX5947d+//5SKFhkNyzTZvnEdPV0dqS4l4d3XXuT82DtELDfTrroZt8fDrpIrAYhue2rE2wmHuimyWgEYN15hREScadRh5L777uOmm25ixYoVzJw5k4ceeojMzEweeeSRId9jGAZlZWWJV2lp6SkVLTIam55cy8x1y6j73hVnTSBp+/P/BeD1vPdSVFYJQOHi6wGYGfwL3Z3BId97rKaDu3EZFt2Wn8Ji3U0jIs40qjASiUTYsmULNTU1/RtwuaipqaG2tnbI93V2djJp0iQqKyu55ppreOutt074OeFwmPb29qSXyMkItrYwY6d94p8e28nOBz5BLBpJbU1Hm5nd+hwA2Zd+IbF82rzLOGiUkWmE2b7hyRFtq+1Q7xwj7hIMl4aAiYgzjeq3V0tLC/F4fEDPRmlpKQ0NDYO+Z/r06TzyyCM888wzPP7445imySWXXMLBgweH/Jy1a9eSl5eXeFVWVo6mTJGE7U99i3w6aaCYkOVlXnctW374+ZTW9M6LTxIwouxzTWT6/PcllhsuFwfHXw2Ae8czI9pWd+8cI0HNMSIiDnba/yu1ZMkSli9fzrx587j88st5+umnKS4u5oc//OGQ71m1ahXBYDDxOnDgwOkuU9JQ06G9XHTo5wA0/N1dbL/kewAsbH6a/Tu3jWpbPV0dWKY5JnV53vkdAPXjrxzQm5E3eykA47u2j2hbZus+AEKaY0REHGxUYaSoqAi3201jY2PS8sbGRsrKyka0Da/Xy0UXXcSuXbuGbOP3+8nNzU16iYzWnl9/m4ARZYd3JnOv+BQXL/00r2VegsuwaPrD3SPeTu1Pbifjngn0fKuMvXfN5dX/eeika+rp6mBG16sAlCy8dsD6iTOrMS2DEo7Scnj4gd6+DruH0cqfeNI1iYik2qjCiM/nY/78+axfvz6xzDRN1q9fz5IlS0a0jXg8zhtvvEF5efnoKhUZpcLWvwHQPe/GRA9E5vu+BsC81j/RUPfusNvY+LP/lyX77fCRaYSZbO5j3pZVvLP1zydV046Xfk2GEeGwUcKUWYsHrM/KyafObT/s7uCOocdh9cnum2OkaPJJ1SMicjYY9WWalStX8vDDD/PYY4+xY8cOvvCFL9DV1cWKFSsAWL58OatWrUq0v+uuu/jTn/7Enj172Lp1K//wD//A/v37+exnPzt2eyFyHMs0KYvZJ+rCqtmJ5dMXvI+3fHPxGnH2/ebfTriNV//nQRa/cw8AtRP/iQPXv8jWrMvwGCYZv/n8iO94OVZ8+/8AUFfyviEHnDbnXABAz/4tw25vXMzupcwpmzrqWkREzhajDiPLli3j3nvvZfXq1cybN49t27axbt26xKDWuro66uvrE+1bW1u56aabuOCCC/jgBz9Ie3s7L7/8MjNnzhy7vRA5TtuRRnLpAqCsKvnvmvn3XwZgbtMztDTUDfr+A7ve4MItqwHYWPopFn/mbiqnzWXqjY/QRCGV1mHe+MmXRlVTNBJmevCvAORe9NEh28XL5gGQ0fz6CbfX09VBEW0AlFSeP6paRETOJoZlWVaqixhOe3s7eXl5BINBjR+REXl787PM+P3HaaCIsjt3J62zTJN3vrOY6bGdvJr7fhas/O+k9fFYjHf/7VJmRLfzpn8eM//387jc7sT6N//yDLPWLwdgz8f/xJRZ1SOq6Y0Xn2H288s5Si5539iL2zP4o6He3vQnZvzhEzRTQPGd+4bc3u43NjL1l0tpI5v8Ow+NqAYRkTNppOdvTUwgaanj8E4AWvwTBqwzXC5cV9+LaRksaH+WN//6m6T1r/z8LmZEt9NpZVB0/X8mBRGAWZdew5bs9wDQ+seRD4Tt2foEALsKLx8yiABMvLCauGVQTCvNh/cN2a7twA4AGj0D91FExEkURiQtxZrtwald2VWDrp920WW8UmxfKslZfwfhUDcAm3/1febv+gEAO+auomzitEHfn3/l7QDMa3+BA7veGLaero42ZrXaA79zq5efsG1mdh4H3PbcOoe2Dz2INdL4NgDtWXpAnog4m8KIpCV/cA8AVuHQAztnXH8PR8hjknmQ4N0XsvXeD7Hob9/Aa8R5JW8pCz5y65DvnTrnEv6WUY3bsKj/3dph69m+/nEyjTAHjAqmL6wZtv1IBrF62ux9jBfomTQi4mwKI5KW8nrsifIyyoce2JlXUMSh93yPo+RSwlEu7nwRgNoJ/8j8L/182OnV/e/73wDMO7pu2NuEM7fb07sfmvTREU3b3jeINbNl6EGsuV32PCS+0sF7b0REnEJhRNKOGY9THjsMQOHEC0/Yds57riXrjp1sWfjvbM2+jFfnf5cln/3egHEig5mxsIa3fHPxGXEO/PIbQ7Y7tGcHF0Zex7QMqq74xxHtQ/7UhQCM79k56MyvlmlSHrMnPCuYqDvTRMTZFEYk7TTX7yPTCBOzXJRNHP6WV38gk/lXf5aLv/obFnzon0b1WZ4r7wRgftsf2f3GxkHb1D3/IwDezLiYssqRXVKZdOFi4pZBEW001w+cibW1pT5x63L55BMHLhGRs53CiKSd5v32c13qXWV4ff7T+lnTF7yPLTnvxWVYdP/2jgG9GIf2vMW8A48DEJ1z/Yi3m5GVQ53bnuL98CCDWJv22k++bqCYQGb2yZYvInJWUBiRtNPVe1vv0cCZedpz2UfXErHczA6/xrbnfpZYbpkmbU98gQwjwlu+uVx81YpRbbelbxBr3cBBrB2H7Nt6m/16orWIOJ/CiKQdq8V+CGNPTtUZ+bzxUy5ga9knAbjwr7ex5Xf/STQSZtPP7uLCyN/otvzkf+rBEQ1cPZZZPg8YfBBrrNnex+4ztI8iIqeTwoiknUDHPgCMojN3y+u8z9gDYH1GjPmvfIWe71SxeNf3AHh9+q2MnzL6cR15iUGs7wy4/BNo7711eZxu6xUR51MYkbRTGLKfN5NVPv2MfWYgI4u5//wrNhZ/AoBcumklh42l17Hwk6uGeffgJs2sJma5KKKNpsN7k9YV9NiDWjPLZ5xa4SIiZ4Gh56QWcSDLNCmNN4EBhRPOXBgBcHs8VH/hR7z51w/hCWQx7aL3sPgE074PJyMrh73uiUw293F4ey2lE+wJ3OKxGBXxejCgaJLupBER51PPiKSV7q52/EYUgPzi8jP++YbLxaxLr2HGwpoTPn9mpFpy7UGs4bqtiWWNB3bhM2KELS+lI7xVWETkbKYwImml/WgjACHLS0ZmToqrOXV9g1gzjhnE2rTLDib17rIxCTwiIqmmMCJppbO1CYB2I2fUd6+cjfoGsU4I9Q9ijb71PwA0FsxPWV0iImPJ+b+tRY7R02aHkQ53XoorGRtVvYNYxxGk8dAewqFuZrRuACBnwadSW5yIyBhRGJG0EmlvBqDHkx5hJJCZzV6vPXB177ofsP3Fp8kxemiikBmLrkxxdSIiY0NhRNJKrLMFgLCvIMWVjJ3OhbcCcNHBx/G9aj/nZk/plSN6mJ+IiBMojEh66T4KQMyfPmFk3vs/zXbvLAJGlAsjfwOgsPq6FFclIjJ2FEYkrRg9RwAwMwpTXMnYMVwuPB/4TuL7w0Yp0+ZdlsKKRETGlsKIpBVvuBUAI3NciisZW+dffDmv5r4fgP0TPpQWdwqJiPTRJAWSVvyRNgA8OUWpLeQ0mPWFx3jtL79i/uUfT3UpIiJjSmFE0kpmLAiAP7c4xZWMvUBGFhdd+Q+pLkNEZMypr1fSSpbZDkBGXkmKKxERkZFSGJG0YZkm+ZYdRrILFEZERJxCYUTSRldnEJ8RAyBvXFmKqxERkZFSGJG00X7Efkhej+UjI8v5D8kTETlXKIxI2uhstcNI0MhNcSUiIjIaCiOSNkJB+7k0nWnykDwRkXOFwoikjUhH30Py1DMiIuIkCiOSNvoekhdJo4fkiYicCxRGJG1YXfZzadLpIXkiIucChRFJG66Q/cReM82eSyMiku4URiRt9D0kz5WlMCIi4iQKI5I2+h6S585WGBERcRKFEUkbiYfk5WgqeBERJ1EYkbSRY9phJDM//Z7YKyKSzhRGJC1Ypkme1QFAdmFpiqsREZHRUBiRtNDZ0YbXiAOQpzAiIuIoCiOSFvoektdt+QlkZqe4GhERGQ2FEUkLXW1NALQbelqviIjTKIxIWgh3tgHQ41KviIiI0yiMSFqI9rQDEHZnprgSEREZLYURSQvxHvu23og7K8WViIjIaCmMSFqI9/aMxDzqGRERcRqFEUkP4U4AYl6NGRERcRqFEUkLVtie8MxUGBERcRyFEUkLrqjdM2L5dWuviIjTnFQYeeCBB6iqqiIQCFBdXc3mzZtH9L4nnngCwzD4yEc+cjIfKzIkd8TuGTH86hkREXGaUYeRJ598kpUrV7JmzRq2bt3K3LlzWbp0KU1NTSd83759+/jqV7/KpZdeetLFigzFHesCwPDnprgSEREZrVGHkfvuu4+bbrqJFStWMHPmTB566CEyMzN55JFHhnxPPB7n+uuv51vf+hZTpkw5pYJFBuPrDSPuDF2mERFxmlGFkUgkwpYtW6ipqenfgMtFTU0NtbW1Q77vrrvuoqSkhBtvvHFEnxMOh2lvb096iZyIz+wGwJupnhEREacZVRhpaWkhHo9TWpr8VNTS0lIaGhoGfc9LL73Ej3/8Yx5++OERf87atWvJy8tLvCorK0dTppyDAnG7Z8SbmZ/aQkREZNRO6900HR0dfPrTn+bhhx+mqKhoxO9btWoVwWAw8Tpw4MBprFLSQYZl94z41TMiIuI4ntE0Lioqwu1209jYmLS8sbGRsrKyAe13797Nvn37+NCHPpRYZpqm/cEeDzt37mTq1KkD3uf3+/H7/aMpTc5xmVYPGBDILkh1KSIiMkqj6hnx+XzMnz+f9evXJ5aZpsn69etZsmTJgPYzZszgjTfeYNu2bYnXhz/8Yd773veybds2XX6RMRGPxcg0wgBkZKtnRETEaUbVMwKwcuVKbrjhBhYsWMCiRYu4//776erqYsWKFQAsX76c8ePHs3btWgKBALNmzUp6f35+PsCA5SInq6szSF8EycpVz4iIiNOMOowsW7aM5uZmVq9eTUNDA/PmzWPdunWJQa11dXW4XJrYVc6c7o5WcoGI5cYf0IPyREScxrAsy0p1EcNpb28nLy+PYDBIbq664SXZ/h1bmPTk+2glh4I7D6a6HBER6TXS87e6MMTxerra7D+NjNQWIiIiJ0VhRBwv2m1PihcydIlGRMSJFEbE8aLdQQDCboUREREnUhgRx4v12D0jEXdWiisREZGToTAijmeGOgCIebNTXImIiJwMhRFxPCtk94zEPeoZERFxIoURcb5IJwCmLyfFhYiIyMlQGBHHc/WGEcunyzQiIk6kMCKO547aYcQIqGdERMSJFEbE8TyxLgBcfoUREREnUhgRx/PG7J4Rd4YeFSAi4kQKI+J4/ng3AB6FERERR1IYEccLmHYY8WXlpbgSERE5GQoj4ngBqwcAf1Z+agsREZGTojAijpdl2T0jAfWMiIg4ksKIOFosGiHDiACQmZOf2mJEROSkKIyIo3V1BBNfK4yIiDiTwog4Wk9nGwBhy4vPH0htMSIiclIURsTR+sJIt5GR2kJEROSkKYyIo4UVRkREHE9hRBwt0t0OQMiVmeJKRETkZCmMiKNFe8NI2JWV4kpERORkKYyIo5khO4xEPeoZERFxKoURcbR4bxiJebJTXImIiJwshRFxNCtsP7E37tVlGhERp1IYEWeL2FPBm7pMIyLiWAoj4miuaBcAlldhRETEqRRGxNGMmP3EXnwKIyIiTqUwIo7mjtmXaQyfxoyIiDiVwog4midu94y4FEZERBxLYUQcLRFGAgojIiJOpTAijuYz7TDi8WueERERp1IYEUfzmSEAPOoZERFxLIURcTR/bxjxBtQzIiLiVAoj4mh+7DDiz8pNcSUiInKyFEbE0TIsO4z4MnJSXImIiJwshRFxrHgsRsCIAhDI1GUaERGnUhgRx+rp7kh8naHLNCIijqUwIo4V6g0jpmUQyNDdNCIiTqUwIo4V7g0jIXwYLv1VFhFxKv0GF8cKd3cC0GMEUlyJiIicCoURcaxIj90zElYYERFxNIURcaxoTxegMCIi4nQKI+JYsZDdMxJxKYyIiDiZwog4Vjxs94xE3RkprkRERE6Fwog4ltkbRmIKIyIijqYwIo5lRuwwElcYERFxNIURcSyrL4x4FEZERJzspMLIAw88QFVVFYFAgOrqajZv3jxk26effpoFCxaQn59PVlYW8+bN46c//elJFyySEOkGwPRkprgQERE5FaMOI08++SQrV65kzZo1bN26lblz57J06VKampoGbV9YWMi//Mu/UFtby+uvv86KFStYsWIFf/zjH0+5eDm3GVE7jFhehREREScbdRi57777uOmmm1ixYgUzZ87koYceIjMzk0ceeWTQ9u95z3v46Ec/ygUXXMDUqVO57bbbmDNnDi+99NIpFy/nNlfMDiP4FEZERJxsVGEkEomwZcsWampq+jfgclFTU0Ntbe2w77csi/Xr17Nz504uu+yyIduFw2Ha29uTXiLHc8d6ADB8ekieiIiTjSqMtLS0EI/HKS0tTVpeWlpKQ0PDkO8LBoNkZ2fj8/m4+uqr+f73v8/73//+IduvXbuWvLy8xKuysnI0Zco5wh23w4hLYURExNHOyN00OTk5bNu2jVdeeYVvf/vbrFy5kg0bNgzZftWqVQSDwcTrwIEDZ6JMcRhPXxjxK4yIiDiZZzSNi4qKcLvdNDY2Ji1vbGykrKxsyPe5XC7OO+88AObNm8eOHTtYu3Yt73nPewZt7/f78fv9oylNzkHe3jDiVhgREXG0UfWM+Hw+5s+fz/r16xPLTNNk/fr1LFmyZMTbMU2TcDg8mo8WGcBnhgDwBHJSXImIiJyKUfWMAKxcuZIbbriBBQsWsGjRIu6//366urpYsWIFAMuXL2f8+PGsXbsWsMd/LFiwgKlTpxIOh/n973/PT3/6Ux588MGx3RM55/gtO4x4M9QzIiLiZKMOI8uWLaO5uZnVq1fT0NDAvHnzWLduXWJQa11dHS5Xf4dLV1cXX/ziFzl48CAZGRnMmDGDxx9/nGXLlo3dXsg5qS+M+DLUMyIi4mSGZVlWqosYTnt7O3l5eQSDQXJzc1NdjpwluteUkGmEObT8ZcZPuTDV5YiIyHFGev7Ws2nEkSzTJEAEAH+mekZERJxMYUQcKdTThcuwO/UCCiMiIo6mMCKO1NPVPytvhsKIiIijKYyII4W6O+0/LS9uz6jHYYuIyFlEYUQcKdLTAUCPEUhxJSIicqoURsSRIj12z0gYhREREadTGBFH6usZCbsURkREnE5hRBwpFuoCIKIwIiLieAoj4kjxkH2ZJuLKSHElIiJyqhRGxJHMsN0zEnMrjIiIOJ3CiDhSPGz3jCiMiIg4n8KIOJIVsXtG4p7MFFciIiKnSmFEnCnSDYDlUc+IiIjTKYyIIxlRO4yY3qwUVyIiIqdKYUQcyRWzwwg+XaYREXE6hRFxJFesBwBDYURExPEURsSR3L09I4ZPl2lERJxOYUQcyRO3e0bcfoURERGnUxgRR/KaYQDcfl2mERFxOoURcSSvGQLA7c9OcSUiInKqFEbEkXy9YcSboTAiIuJ0CiPiSH6rN4wENGZERMTpFEbEkfzYY0a8AfWMiIg4ncKIOFKGZYcRf6bCiIiI0ymMiOPEohF8RgyAgMaMiIg4nsKIOE6opyvxdUA9IyIijqcwIo4T6u4AwLQM/AHNMyIi4nQKI+I44W67ZySED8Olv8IiIk6n3+TiOJGedgBChj/FlYiIyFhQGBHHifSOGQkZgRRXIiIiY0FhRBwnGrbDSEQ9IyIiaUFhRBwnFrLDSNSlnhERkXSgMCKOE+/rGVEYERFJCwoj4jjxcDcAMYUREZG0oDAijmNF7J6RuDsjxZWIiMhYUBgRx7Eids9I3KMwIiKSDhRGxHGsaA8ApsKIiEhaUBgRxzF6L9NYCiMiImlBYUQcx4jZPSN49VwaEZF0oDAijmPEQgBYXvWMiIikA4URcRx3zB7AavjUMyIikg4URsRx3HG7Z8Tly0pxJSIiMhYURsRxPGZvGPGrZ0REJB0ojIjjeOP2AFaPPzvFlYiIyFhQGBHH8ZlhANx+XaYREUkHCiPiOF7LvkzjDegyjYhIOlAYEccJ9IWRjJwUVyIiImNBYUQcx2/Zl2l8GbpMIyKSDk4qjDzwwANUVVURCASorq5m8+bNQ7Z9+OGHufTSSykoKKCgoICampoTthcZToAIAP4MDWAVEUkHow4jTz75JCtXrmTNmjVs3bqVuXPnsnTpUpqamgZtv2HDBq677jpeeOEFamtrqays5Morr+TQoUOnXLyceyLhEF4jDoA/U5dpRETSgWFZljWaN1RXV7Nw4UJ+8IMfAGCaJpWVldx6663ccccdw74/Ho9TUFDAD37wA5YvXz6iz2xvbycvL49gMEhubu5oypU0E2xtIe//TAUgsqoRnz+Q4opERGQoIz1/j6pnJBKJsGXLFmpqavo34HJRU1NDbW3tiLbR3d1NNBqlsLBwyDbhcJj29vaklwhAuLsDgJjlwuv1pbgaEREZC6MKIy0tLcTjcUpLS5OWl5aW0tDQMKJt3H777VRUVCQFmuOtXbuWvLy8xKuysnI0ZUoaC/d0AtCDH8Ol8dciIungjP42v/vuu3niiSf41a9+RSAwdPf6qlWrCAaDideBAwfOYJVyNov0dAEQNvwprkRERMaKZzSNi4qKcLvdNDY2Ji1vbGykrKzshO+99957ufvuu3nuueeYM2fOCdv6/X78fp1sZKBoj32ZRmFERCR9jKpnxOfzMX/+fNavX59YZpom69evZ8mSJUO+77vf/S7/+q//yrp161iwYMHJVyvnvGioG4CIkZHiSkREZKyMqmcEYOXKldxwww0sWLCARYsWcf/999PV1cWKFSsAWL58OePHj2ft2rUA/Nu//RurV6/mZz/7GVVVVYmxJdnZ2WRna54IGZ142L5ME3GpZ0REJF2MOowsW7aM5uZmVq9eTUNDA/PmzWPdunWJQa11dXW4jhlY+OCDDxKJRPj4xz+etJ01a9Zw5513nlr1cs6Jhe0BrDGXbukVEUkXow4jALfccgu33HLLoOs2bNiQ9P2+fftO5iNEBmWG7cs0Ubcu04iIpAvdGymOYkbsMBJ3q2dERCRdKIyIo1gRe8yI6VHPiIhIulAYEUcxoj2AwoiISDpRGBFnidqXaSyFERGRtKEwIo5ixOyeEcubmeJKRERkrCiMiKO4esOI4VMYERFJFwoj4ijuuMKIiEi6URgRR3EnekayUlyJiIiMFYURcRSPGQLA7VfPiIhIulAYEUfxmmEA3H71jIiIpAuFEXEUn2lfpvEEFEZERNKFwog4is+ye0a86hkREUkbCiPiKP7eMOLLUBgREUkXCiPiKBmWPYDVl5Gb4kpERGSsKIyIY1imSYAIAP5M9YyIiKQLhRFxjHCoG5dhAeDPyE5xNSIiMlYURsQxero6El9nZukyjYhIulAYEccIdbfbf1pe3B5PiqsREZGxojAijhHutntGeoxAiisREZGxpDAijhHp6QQgjMKIiEg6URgRx4j02D0jYZfCiIhIOlEYEceIhboAiCiMiIikFYURcYx4yL5MozAiIpJeFEbEMcyw3TMSc2ekuBIRERlLCiPiGPHeMBJXGBERSSsKI+IYVqQ3jHgyU1yJiIiMJYURcY5oNwCmwoiISFpRGBHHMHp7RkyvHpInIpJOFEbEMVwxu2cEn3pGRETSicKIOIYr1gOAoTAiIpJWFEbEMdy9PSOGT5dpRETSicKIOIYnbveMuP0KIyIi6URhRBzDGw8B4PJnp7gSEREZSwoj4hg+0+4Z8QbUMyIikk4URsQxfJbdM+INqGdERCSdKIyIYwT6wkhmboorERGRsaQwIo6R0RtG/BnqGRERSScKI+IIlmmSQRiAQGZOiqsREZGxpDAijhAOdeMyLAD8WQojIiLpRGFEHKGnqyPxdWaWxoyIiKQThRFxhFB3OwBhy4vb40lxNSIiMpYURsQRIt2dAPQY/hRXIiIiY01hRBwh3GOHkRCBFFciIiJjTWFEHCHS03uZxpWR4kpERGSsKYyII8RCXQBEXOoZERFJNwoj4gjxkH2ZRmFERCT9KIyII5hhu2ck5tZlGhGRdKMwIo4Q7w0jcYUREZG0c1Jh5IEHHqCqqopAIEB1dTWbN28esu1bb73FtddeS1VVFYZhcP/9959srXIOsyK9YcSTmeJKRERkrI06jDz55JOsXLmSNWvWsHXrVubOncvSpUtpamoatH13dzdTpkzh7rvvpqys7JQLlnNUtBsA06OeERGRdDPqMHLfffdx0003sWLFCmbOnMlDDz1EZmYmjzzyyKDtFy5cyD333MOnPvUp/H5NWCUnx+gLI171jIiIpJtRhZFIJMKWLVuoqanp34DLRU1NDbW1tWNWVDgcpr29Pekl57a+MII3K7WFiIjImBtVGGlpaSEej1NaWpq0vLS0lIaGhjErau3ateTl5SVelZWVY7ZtcSZ3zA4jhl9hREQk3ZyVd9OsWrWKYDCYeB04cCDVJUmKJcKIT2FERCTdjOrxp0VFRbjdbhobG5OWNzY2jungVL/fr/ElksQT7wHArZ4REZG0M6qeEZ/Px/z581m/fn1imWmarF+/niVLlox5cSJ9vPEQAC5/doorERGRsTaqnhGAlStXcsMNN7BgwQIWLVrE/fffT1dXFytWrABg+fLljB8/nrVr1wL2oNft27cnvj506BDbtm0jOzub8847bwx3RdKZz7R7RrwB9YyIiKSbUYeRZcuW0dzczOrVq2loaGDevHmsW7cuMai1rq4Ol6u/w+Xw4cNcdNFFie/vvfde7r33Xi6//HI2bNhw6nsg5wSfZfeMeAPqGRERSTeGZVlWqosYTnt7O3l5eQSDQXJzc1NdjqRAy52TKKKN3R/7A1PnXJLqckREZARGev4+K++mETlewAoD4MvMSXElIiIy1hRG5KxnmSaZ2JdpAgojIiJpR2FEznrhUDcuw76aGMjSZToRkXSjMDKElsP7ObRnR6rLEKCnqyPxdabCiIhI2lEYGYRlmsR+dAUFj11OW8vYTXMvJyfUbT+bKGx5cXtGfQOYiIic5RRGBtHR3koZzWQaYfZufS7V5ZzzIt2dAPQYmpVXRCQdKYwMorWx/1k44T0vp7ASAQj32GEkRCDFlYiIyOmgMDKIjpb+MJLfsjWFlQhAtDeMhF0KIyIi6UhhZBCh1vrE11Oi7xDqvUwgqRHtsceMRFwZKa5EREROB4WRQcSC/WHEZ8TZ8/pLKaxGYt1tAITcmgpeRCQdKYwMpqMx6dvgzr+kqBABiPcEAYh6NOGZiEg6UhgZhKfbDiNNFAKQ2fBKKss555k9bQDEfAojIiLpSGFkEIFwCwB7iq8AYHLPm5jxeCpLSnuRcIhX7v8UGx/64oB1RsgeM2L6NOGZiEg6UhgZRHb0CACZM6+i2/KTSxd177yW4qrS29ZHbmNh2x9Y3PBftDQcSFrnithhxArkpaI0ERE5zRRGBlFgHgUgt6yKvf7zAWh6uzaVJaW1bc/+jMWNTyS+P7xjU9J6d8SeDt4IqGdERCQdKYwcJ9TTRR5dABSUVNKVWQlAPHgolWWlrZaGOib/9asAhCwvAF37X01q443ZYcSdkX9GaxMRkTNDYeQ4rU0HAfs5KLkFxcSzywBwddi3+1qmSe1Pbue1Pz6WshrTye6/PEUeXex1TeK1KZ8DwN/8ZlIbf8ye58WTqcs0IiLpSGHkOMFmO4wcNfIxXC5cueUA+HqaANjz5kaW7H+ISbXfSFmN6cRqfhuAxuJLyD3v7wAo73o7qU2GaYcRX3bhmS1ORETOCIWR43QfOQxAu8c+8fkLJgCQHWkGIHj4HQAKaaezvTUFFaaXrOAuAFwlM6i8cDEA5TQnPS0507QvmwWy8894fSIicvopjBwn2maHkW5fEQBZxfaYkfyYfbtv5Ehdom3zwV1nuLr0UxLeD0DuxFnk5o/joGH3RB3Y3j9gONuyw0hG7rgzX6CIiJx2CiPHsXpnX41kFANQUDIRgEKrjVg0AsH+207bG3YPvz3TJBzqPg2VOl9H8Cil2LdRl583D4DGrOkAdO6zH1AYDnUTMKIAZOXqMo2ISDpSGDmOq8sOI2bvwNWCkvHELBduw+Jo0yH8nf131YSa9w27vS33f4Lw3VNpObz/tNTrZIff3QZAMwXkFdg9UdGSOQD4m/4GQGfwaKJ9dm7BmS1QRETOCIWR4/hD9tgQd64dRtweD0cM+yTY1rifnHD/WAartW7gBo4ztX0zuXSzf9tzScst0+SV732Czfdfh2WaY1X+WeHNvzzDa9/9IO9s3XDCdsED9l0zjf5JiWXZkxcAUNK1E4DuDntcTqeVgdvjOQ3ViohIqimMHCcrYo8N8eWXJ5YFPfZYha6Wg4yLNyWWeztPPPdIONRNAfbsoZGGnUnr6uveZWHwTyxq+z0tDcmhpq2lgX13zaL2h7eecPsdwaPEY7Fh9ujE4rEYtT/+Chv/664TtnvtT4/zytP/Z0TByfPi3VzU/VeqnvkYG3/+7SHfYzbad8105Z6XWFY50x7EOsFqINjaQk+73TPSaWSNaH9ERMR5FEaOkxe3T35Z48YnlnX5SwAIN75DAR2J5Tmhw4mvQz1dtDQcoKHu3cTJ90h9f8jwtiYPdm3e+3ri68bdf0tat3vz76gyD3Dx4Z/T3RkctM49b27C+t6FvHXvlQPWhUPdbPz5t6n9/77Jpqfu5cCuNwbdhmWavPrQ51hy4D9Z/O6/c6Tx4KDt2tuOMOuvX2Lh66vZ+OMvD9rm2LbnReyQ4TPiLN75XTb+9JuDts3ovZOG4hmJZXnjSjlslAJwcMdmwp1tAPS4FEZERNKVwsgx4rEYBZZ98s8vqUwsj2TYJ0d/w5ak9oUxu5dk0y++i/fu8RQ9NIuyRxaw8eHbAAg29Y8Tye/el/TenkNvJb7uOrQ9aV2k6V3784wo72z8/YA6I+EQ1q8+Ty7dXNCzjWgknLT+b3/4MYt3fpcle/6D6rf+Ff/jHx60d2LT42uobvll4vsDb/xlQBuAPa/+Ca9hPyhwyaFHqX30jkHbAex55Y94DJODRjm1lZ8FoLjuD4O2LQ7tAyCnclbS8iO+CgC6m/YS7bLDYcidPeRnioiIsymMHKO1pR6PYWJaBgXFFYnlZo49fqSyyx7j0IR9V0cRbYR6uih45ynchpVoX9Rs35ba3dJ/58342IGkJ/+6Wo65bNPyTlIdntY9ia/D2weGkS2P/wtT43YbrxGnft+OpPVmgx109rkmErcMSjjKkeMePvfuay+yeM9/ANCIfRmqZ+/GAZ8FEHrnBQAOG3YP0ZJ9D7K9dvCAEX5nPQCHCquZ9L6bAJgY208kHEpq190ZpMy0w1zfnTSJz8uwf96x4CFi3XY4jHgURkRE0pXCyDGCTfYJu9XIxeP1JZZ78uxLNkW0AVCfMY1uyw/AoXf/xuSofYvva5c8YLeL2YNco639lz0yjAiNx8xLktu5N/F1dnvyLcI5Xf2XdyYffSmpV2PPm5tYWPcIQKKGo/uTe1YC7fa2G2d8mnqXfWJvOOayEMCR7RsAeD2wkP2zbrE/t2UbgylpsR9cV7/wDrZmXwZAcMcLg7YtO2K39Z3/PsonnU87WfiMGAd2bk1qd3j3G7gMi1ZyKSguT1oXy7a/d3XUY4bsMBL15gz6eSIi4nwKI8foaLZDQKu7KGl5oHBC0vehrPE0ue1LN02bfoHXiNNEIedVfxCAAjrs2Vl7n2fTp3mv3bNimSYVsf5LOKWR5Nt+S2P9IaaEo+x+o38CsMbN/43HMPlbxiK25/69XU9D8vTp40L2fmSVT6clYN+p0nUouffEOGoHo65xsyi+wJ6GfXLo7QEDYlub65li7gOgav5SIuUL7Z/Jkbc4XuPB3UwyDxC3DKYs/CCGy0WdfxoAR3e9ktS2bb89jqXeN2nAdly5dq+Ur7sBeuwwEvfpib0iIulKYeQY4Wa7R6EjkPw/9dxjxo8AWLkTCPrtHodJh+3LKAezZ5OTV0gr9v/gm+rewdvVkPS+7sN2IGhpqCOXbuKWAdg9LsFW+y6e4JHGxCDZN/wXAdC89X8S23C320Glu+RiogVTAXAdeTexPhaNUGbac6UUVV1IKM9uYzUnXwrK7rD31VNyPhOnz6fLCpBlhKjbmTwuZu+r6+w/XZMYVzqB7EnzACjpfpfj1b1qX7rZ7Z1GXqE9aVxn/gUAmIe3JbWNNtg/i46cqQO24x9n/7yzw00Qtu9GMn3qGRERSVcKI8ewWu0einB2cvgoLK9K+t47bhKhTPt/7xVW74ytFYsAaPHYISV4+F0yw/aYiAOG3dY4YgeCvrtnDrvKE+NP6ndtA6Bhr93j0Mg4es6/xv78Q/2XRDJ67IDjKajEW3o+ADld+xLrG/a/g9eI02P5KKmYjKvYbpPZ0T8OBaA0Yvee5FXOxO3xsDdg39HSvOOvSe2iu18EoGmc3SMyYYa9n+OtRtrbjiS1NfZsAOBI6SWJZZ4JdqDKa0vumfG32zVbhQPDSHaR/fMviLfgjthhxAjoib0iIulKYeQYvs7eyyP5E5OWZ+fkJ8ZnAGSVVGHlJbcpnHEpAB0BO3iEW/aS1/s8m8OF9gk8uzcQdB60A0dLRhVNfns7HQfsZe2H7EsuLb4JTF7yUQCmRd8heMQOPXlR+8+M4kkUVF4IQFm0/7LOkTp7/Ei9uwKX203uhJkAlIT6x6F0BI8mxr+UTZltLyuaZ688mHw5pezoZvtnM+299o+mqIwG7MtYB99Objux3e5VyZlZk1hWfH61vS66J+kSUFbIDlW+oskcry/8FdGGL2zfTWNkKIyIiKQrhZFj5PbOGxIoTj5BGi4XR1z9D2kbN/48vOP6xzp0W34mz7In6wrn2P+rN1r3UWTaJ1Jv74m8NGIPkDV676QJ5U9LTPgVb7KXxZrtyx+dOVUUV1TRRCEuw6J+75tYpklx3J4htqB8CuVT7DBSQHviKbc9vZOrtWXaIadsqj29ehnNiTlL6nfb4zVayCc3396vjMl2/SXt/XOStBzezyTzIKZlMGXB0sTyhgy75o59ryWWBVtbKMHe36rZf59YPmHqLLotP5lGmIO7+gfRjovZoSqntIrjFRSVE7Hs2VaLex+k58nMH9BORETSg8LIMYrivb0P5VMGrGv32mMgIpabcaWVZJX2B5a9/ul4fXbPiaugyt5G61v4DLsnYOrCD9jbp43g0WayO+y7Z7xlF0Cx/WC4vgnAfEF7LIdVYNfQ7LPv5Ok8/A7Bo01kGBF7WxWTyczOS/RSNOyxQ4RxxN5OONeuL7+ojFbswZ/1e3p7Xw7avSeNvv7LUZWz7Z6dKvNAYvzKgTfteUf2uSclxoAA9Iyze1uMxv7g0tg7OLeZgqRnyLg9Hvb77Esxze/YvSzhUHeiZ6ZofP/sq30Ml4uW3vBXYdmXurwKIyIiaUthpFdH8Cj5dAJQXHn+gPU9Aftk3OwqwuV2U1TRfxJtL56f+Dqz1D7xTona40OOkEfeuNLEXB71e16nvPfumbyJs8geb/duFPVOAJbXbV9OCZTZIaUry+7hiDbvpuWQHWJayMcfyLTr8duBor33bpmsTns77uJpiZoavHab1t5LQdFGu/ekM6c/dI0rncCh3plP696wx42E6u1tHs1ODgz+CXMBKGjvnyul/aDdtsmXPN4GoD3fDi+xQ9vsmg/al6t6LB/540oHtAdo8xQnfe/L0UPyRETSlcJIr+YD9uWRVnIGfTpsNMsemNrmtf8sLJ1A2PICkDm1f8Bm/ng7BPh6Zyztu004MTbkpYcp7H1ezfjz5lDaexml3Gyip6uD8pj9vJvCifZdKPHeO2a8wX10NNq9Jq3HnKi7ewNF32WeorD9/tzx/VOsd2TbvSTR3ks4/qAdaqzC5JDRmNkbgA7YA2w9R+2fSbQguV3JefbD7CbG9hOL2j01sd5ZYzuzqzieUWGHl5xWOwy1NdhhpMldguEa/K9gT6Ak6fuMbIUREZF0pTDSq72+t9fBPfj/1L3l9pTlHfn2Sd7ldrN1/HW8HljA9MUfTLQrmTAVs/eWXYBOX+8troV2D8jCNvv213qKyczOY1zJeIJk4TIs3v7rM2QZIeKWQdkk+3P8JXYYyek+QOSoPeako/e2YgCK7PATCO4h1N1JGfaYktLJ/VOsm+N6A1KbfQmnoLf3JaO8P7AAhAvtMOJqtns58rrs8OMvS25XMfkCuqwAfiOaGAfiDdoBY7C7Y8adZw/grYzswjJNupv3ARD0lQ1o2yealXx7dVbuuCFaioiI0ymM9Aq19M0xUjHo+os+eBNvXflz5iy/J7Fsyee+z5w71hPI7J+q3B/IpNkoTHwfzrTDzdzrv8PG877M2x67x2N/iT2o1XC52BewL2NM++tXAah3leLzBwDI6+3hKIkdxmqzw0jkmBN1ZoW9vjBUR33vbcFBspIuf2T0tsnv3kc8FqMibveeFFVdmLSP/vF2gMnvtENDedT+vMKq2UntXG43dT67R6bl3VftOo+7vHSsyvPnEbHc5NJNw4F3iR+124Yyywe0Tcg9LozkFQ7RUEREnE5hpE+bfYKM5Awc8wD2QMwLL/kgmdnD32J6xNt/IjV7pzbPzM5j8T/cyYxvbCR8Rz2Lv/hwok3JdQ9wyCgl2+gB4Ki/v4ayKjtIFNBOZlvvxGV5/TPCFlfZAaIiXs+RfXYvRYNnQtLlj6LeMDE+dpCGup34jSgRy0PZxOTgUDzVnhNkQnQ/TYf3kmP0ELcMyifPHLCP7Xl2XbHDr/fOKGvfXtx3eelYPn+Ag257nxrf2YK7ww5D8dwJA9om3lPQvy5iuQlk6Km9IiLpSmGkl793jhGjYOIwLYfXlTk+8bUnb2BPS9/g0z7lk6bj/ewfqXPZ7+vO7x9Am5WTTwv5AJzXY4cNX2F/jSXjp9BpZeA14szY/E0A2rOSp1gvmzidiOUhYESpe/b/AnDYXYHb40lqVzH5QkKWl0wjzL7aXwNQ7yobUC+Aa4I9bqSoeSPN9fvJNMLELFfi8tLxjuTY+9Rz8G9k9ti3UHtO8LPOLOoPZF1G1pBjS0RExPn0G75X/xwjVae8rVhefxgIjBu8p+V4JeMnk/OF9Wy64OtMv/abSeuavXZIyTTCAGQV92/f5Xbz9vw76bQyEj0rsfzkW5PdHg9vZ9iDSJfUPw7A0YyBz4Rxezwc9NgBIXPXbwB7YrbBTF78IQDOi++mbttzADQcc3npePFiu3fFf2Q7+RH7FuqskoETnvXJL+v/3C5DvSIiIulMYaRXce8cI/nl04ZpOTxPYVXi65ySkfe0FBSXU73s9gFPse3MSt7GuIrkQaILPvx5IjdvYWPJMna5p1Kx+JMDtj3xsz/jlbyrEt+H8wfO7wHQ2nsb7wUh+46aUO7AOVcAisomsstt15G9zX6K8JHA0MEra+I8AEq63qXEtAfZ5g8yn0ufcaWViWf39LgURkRE0pln+CbpL9jaQh5dABRXDn6SHo2ssv6wUHDM//BPViy/it45wohYbgpLB461KCwZz+Iv/mjIbeQXlbHwy0/yxovP0P23p5l81S2DtosXXwDBP+IxTABcpYNfdgFoLruU8w7tZkbUnkStZ4jgAlB+/gJ4HiZY9WBA3DIorqgasr3X56fZyKeYVsKe7CHbiYiI86lnBGg5aN/yepTcEQ1QHU7ZlNlELLc93foY3AXiK+4PSC29k66drNmXXUP1rY9RNkToypwwJ+n7vmfbDCZ/zgeTvjfGDR3kisoqE2NfAJqNcYlZa4fSN59KxKMn9oqIpDOFEaC93g4jfU/cPVWFJePZ8//8gs5lvxyTgZe54/vvemn1lpyg5akrm3Zx0vflU+cO2Xbaxe+lnf5LKFkVA2/rPVa9v7/nZCT70dU7R0vMqzAiIpLOFEaAcLM9YddQc4ycjBkLa6i6YMGYbKtkUv/tsj2BsQlMQykun0SwN2C0kJ/0TJrjebw+dmX372PxcfOWHK+roH8/ujKG/1mHs+w2ZkCzr4qIpDOFESB3/58AiIwbenxEKuUVFCUedhfNGT9M61NjuFwc8tp3uTQO8pyZ48Wm1gD2k4uLy6tO2NZd3j8rbDR7+DBSXnMLr+bWUP7ezw3bVkREnOucDyN73tzEzMgbxCwXU6/8fKrLGVKTxz55u/KGnihsrHTk2XOCdOYMnNr9eNMvv45d7qm8XvLhYceyjJva/0BBV/7wdxlNmj6PBSt/yaTp84ZtKyIiznVSYeSBBx6gqqqKQCBAdXU1mzdvPmH7p556ihkzZhAIBJg9eza///3vT6rY06Hl+e8D8HrOpZSMH3rei1TrmHcj232zmfL3nzjtn1Wx9J95Nff9lL3/tmHb5hUWc943t7L45v8ctu2EaXOJWHZgCRQNnOdERETOTaMOI08++SQrV65kzZo1bN26lblz57J06VKampoGbf/yyy9z3XXXceONN/Laa6/xkY98hI985CO8+eabp1z8qQoeaWT2kT8CEPj7L6a4mhNb8P98jplff+mEt8OOlcppc1mw8r+ZNOPi4RuPgs8fYHvmQjqsDCZceMnwbxARkXOCYVmWNZo3VFdXs3DhQn7wgx8AYJomlZWV3Hrrrdxxxx0D2i9btoyuri5++9vfJpYtXryYefPm8dBDD43oM9vb28nLyyMYDJKbmzuack9o4+N3snjX99jtnsyUf9mqKcfPADMeJ9TTOSa3UIuIyNltpOfvUZ19I5EIW7Zsoaampn8DLhc1NTXU1tYO+p7a2tqk9gBLly4dsj1AOBymvb096TXW4rEYE3f/DIAjM29QEDlDXG63goiIiCQZ1Rm4paWFeDxOaWlp0vLS0lIaGhoGfU9DQ8Oo2gOsXbuWvLy8xKuycmTPdxkNwzBovuzbbM2+nNlXfXbMty8iIiIjc1Z2B6xatYpgMJh4HThwYMw/w+V2M/e9n+Dir/4PGVmaVEtERCRVRvVsmqKiItxuN42NjUnLGxsbKSsbfDKusrKyUbUH8Pv9+P0nnipcRERE0sOoekZ8Ph/z589n/fr1iWWmabJ+/XqWLFky6HuWLFmS1B7g2WefHbK9iIiInFtG/dTelStXcsMNN7BgwQIWLVrE/fffT1dXFytWrABg+fLljB8/nrVr1wJw2223cfnll/Pv//7vXH311TzxxBO8+uqr/OhHQz9hVkRERM4dow4jy5Yto7m5mdWrV9PQ0MC8efNYt25dYpBqXV0drmPuTLnkkkv42c9+xje+8Q2+/vWvM23aNH79618za9asoT5CREREziGjnmckFU7XPCMiIiJy+pyWeUZERERExprCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKTUqGdgTYW+edna29tTXImIiIiMVN95e7j5VR0RRjo6OgCorKxMcSUiIiIyWh0dHeTl5Q253hHTwZumyeHDh8nJycEwjDHbbnt7O5WVlRw4cCBtp5nXPjpfuu8faB/TQbrvH6T/Pp6O/bMsi46ODioqKpKeW3c8R/SMuFwuJkyYcNq2n5ubm5Z/sY6lfXS+dN8/0D6mg3TfP0j/fRzr/TtRj0gfDWAVERGRlFIYERERkZQ6p8OI3+9nzZo1+P3+VJdy2mgfnS/d9w+0j+kg3fcP0n8fU7l/jhjAKiIiIunrnO4ZERERkdRTGBEREZGUUhgRERGRlFIYERERkZQ6p8PIAw88QFVVFYFAgOrqajZv3pzqkk7K2rVrWbhwITk5OZSUlPCRj3yEnTt3JrV5z3veg2EYSa/Pf/7zKap49O68884B9c+YMSOxPhQKcfPNNzNu3Diys7O59tpraWxsTGHFo1dVVTVgHw3D4OabbwacdwxffPFFPvShD1FRUYFhGPz6179OWm9ZFqtXr6a8vJyMjAxqamp49913k9ocPXqU66+/ntzcXPLz87nxxhvp7Ow8g3txYifax2g0yu23387s2bPJysqioqKC5cuXc/jw4aRtDHbc77777jO8J0Mb7jh+5jOfGVD/VVddldTmbD6Ow+3fYP8mDcPgnnvuSbQ5m4/hSM4PI/n9WVdXx9VXX01mZiYlJSV87WtfIxaLjVmd52wYefLJJ1m5ciVr1qxh69atzJ07l6VLl9LU1JTq0kbtz3/+MzfffDMbN27k2WefJRqNcuWVV9LV1ZXU7qabbqK+vj7x+u53v5uiik/OhRdemFT/Sy+9lFj35S9/md/85jc89dRT/PnPf+bw4cN87GMfS2G1o/fKK68k7d+zzz4LwCc+8YlEGycdw66uLubOncsDDzww6Prvfve7/Md//AcPPfQQmzZtIisri6VLlxIKhRJtrr/+et566y2effZZfvvb3/Liiy/yuc997kztwrBOtI/d3d1s3bqVb37zm2zdupWnn36anTt38uEPf3hA27vuuivpuN56661novwRGe44Alx11VVJ9f/85z9PWn82H8fh9u/Y/aqvr+eRRx7BMAyuvfbapHZn6zEcyflhuN+f8Xicq6++mkgkwssvv8xjjz3Go48+yurVq8euUOsctWjRIuvmm29OfB+Px62Kigpr7dq1KaxqbDQ1NVmA9ec//zmx7PLLL7duu+221BV1itasWWPNnTt30HVtbW2W1+u1nnrqqcSyHTt2WIBVW1t7hioce7fddps1depUyzRNy7KcfQwB61e/+lXie9M0rbKyMuuee+5JLGtra7P8fr/185//3LIsy9q+fbsFWK+88kqizR/+8AfLMAzr0KFDZ6z2kTp+HwezefNmC7D279+fWDZp0iTre9/73uktbowMto833HCDdc011wz5Hicdx5Ecw2uuucZ63/vel7TMScfw+PPDSH5//v73v7dcLpfV0NCQaPPggw9aubm5VjgcHpO6zsmekUgkwpYtW6ipqUksc7lc1NTUUFtbm8LKxkYwGASgsLAwafl//dd/UVRUxKxZs1i1ahXd3d2pKO+kvfvuu1RUVDBlyhSuv/566urqANiyZQvRaDTpeM6YMYOJEyc69nhGIhEef/xx/vEf/zHp4ZBOP4Z99u7dS0NDQ9Ixy8vLo7q6OnHMamtryc/PZ8GCBYk2NTU1uFwuNm3adMZrHgvBYBDDMMjPz09afvfddzNu3Dguuugi7rnnnjHt/j4TNmzYQElJCdOnT+cLX/gCR44cSaxLp+PY2NjI7373O2688cYB65xyDI8/P4zk92dtbS2zZ8+mtLQ00Wbp0qW0t7fz1ltvjUldjnhQ3lhraWkhHo8n/WABSktLefvtt1NU1dgwTZN//ud/5u/+7u+YNWtWYvn/+l//i0mTJlFRUcHrr7/O7bffzs6dO3n66adTWO3IVVdX8+ijjzJ9+nTq6+v51re+xaWXXsqbb75JQ0MDPp9vwC/40tJSGhoaUlPwKfr1r39NW1sbn/nMZxLLnH4Mj9V3XAb7N9i3rqGhgZKSkqT1Ho+HwsJCRx7XUCjE7bffznXXXZf0ELIvfelLXHzxxRQWFvLyyy+zatUq6uvrue+++1JY7chdddVVfOxjH2Py5Mns3r2br3/963zgAx+gtrYWt9udVsfxscceIycnZ8AlYKccw8HODyP5/dnQ0DDov9W+dWPhnAwj6ezmm2/mzTffTBpPASRdn509ezbl5eVcccUV7N69m6lTp57pMkftAx/4QOLrOXPmUF1dzaRJk/jFL35BRkZGCis7PX784x/zgQ98gIqKisQypx/Dc1k0GuWTn/wklmXx4IMPJq1buXJl4us5c+bg8/n4p3/6J9auXeuIacc/9alPJb6ePXs2c+bMYerUqWzYsIErrrgihZWNvUceeYTrr7+eQCCQtNwpx3Co88PZ4Jy8TFNUVITb7R4wWrixsZGysrIUVXXqbrnlFn7729/ywgsvMGHChBO2ra6uBmDXrl1norQxl5+fz/nnn8+uXbsoKysjEonQ1taW1Mapx3P//v0899xzfPaznz1hOycfw77jcqJ/g2VlZQMGlMdiMY4ePeqo49oXRPbv38+zzz477KPZq6uricVi7Nu378wUOMamTJlCUVFR4u9luhzHv/zlL+zcuXPYf5dwdh7Doc4PI/n9WVZWNui/1b51Y+GcDCM+n4/58+ezfv36xDLTNFm/fj1LlixJYWUnx7IsbrnlFn71q1/x/PPPM3ny5GHfs23bNgDKy8tPc3WnR2dnJ7t376a8vJz58+fj9XqTjufOnTupq6tz5PH8yU9+QklJCVdfffUJ2zn5GE6ePJmysrKkY9be3s6mTZsSx2zJkiW0tbWxZcuWRJvnn38e0zQTQexs1xdE3n33XZ577jnGjRs37Hu2bduGy+UacGnDKQ4ePMiRI0cSfy/T4TiC3Vs5f/585s6dO2zbs+kYDnd+GMnvzyVLlvDGG28khcq+YD1z5swxK/Sc9MQTT1h+v9969NFHre3bt1uf+9znrPz8/KTRwk7xhS98wcrLy7M2bNhg1dfXJ17d3d2WZVnWrl27rLvuust69dVXrb1791rPPPOMNWXKFOuyyy5LceUj95WvfMXasGGDtXfvXuuvf/2rVVNTYxUVFVlNTU2WZVnW5z//eWvixInW888/b7366qvWkiVLrCVLlqS46tGLx+PWxIkTrdtvvz1puROPYUdHh/Xaa69Zr732mgVY9913n/Xaa68l7iS5++67rfz8fOuZZ56xXn/9deuaa66xJk+ebPX09CS2cdVVV1kXXXSRtWnTJuull16ypk2bZl133XWp2qUBTrSPkUjE+vCHP2xNmDDB2rZtW9K/zb47EF5++WXre9/7nrVt2zZr9+7d1uOPP24VFxdby5cvT/Ge9TvRPnZ0dFhf/epXrdraWmvv3r3Wc889Z1188cXWtGnTrFAolNjG2Xwch/t7almWFQwGrczMTOvBBx8c8P6z/RgOd36wrOF/f8ZiMWvWrFnWlVdeaW3bts1at26dVVxcbK1atWrM6jxnw4hlWdb3v/99a+LEiZbP57MWLVpkbdy4MdUlnRRg0NdPfvITy7Isq66uzrrsssuswsJCy+/3W+edd571ta99zQoGg6ktfBSWLVtmlZeXWz6fzxo/fry1bNkya9euXYn1PT091he/+EWroKDAyszMtD760Y9a9fX1Kaz45Pzxj3+0AGvnzp1Jy514DF944YVB/17ecMMNlmXZt/d+85vftEpLSy2/329dccUVA/b7yJEj1nXXXWdlZ2dbubm51ooVK6yOjo4U7M3gTrSPe/fuHfLf5gsvvGBZlmVt2bLFqq6utvLy8qxAIGBdcMEF1ne+852kE3mqnWgfu7u7rSuvvNIqLi62vF6vNWnSJOumm24a8J+6s/k4Dvf31LIs64c//KGVkZFhtbW1DXj/2X4Mhzs/WNbIfn/u27fP+sAHPmBlZGRYRUVF1le+8hUrGo2OWZ1Gb7EiIiIiKXFOjhkRERGRs4fCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIiklMKIiIiIpJTCiIiIiKSUwoiIiIik1P8Pi5gvI+7s2fMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.abs(sim_result.output_signals[\"out1\"].amplitude[:, 0, 0]))\n", + "plt.plot(np.abs(sim_result.output_signals[\"out1\"].amplitude[:, 0, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e654cc67", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(wl, np.abs(s_params_1d))" + ] + }, + { + "cell_type": "markdown", + "id": "9b5a923b", + "metadata": {}, + "source": [ + "# Signal Flow Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19aa92e4", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "from simphony.libraries.siepic import y_branch\n", + "import sax\n", + "import jax.numpy as jnp\n", + "\n", + "from simphony.libraries.ideal.digital_filters import discrete_state_space\n", + "from simphony.simulation.simulation import SimulationMode\n", + "\n", + "\n", + "# state_space_model = discrete_state_space(2, 2)\n", + "def coupler():\n", + " return {\n", + " (\"in0@TE\", \"out0@TE\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out1@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out0@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out1@TE\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out0@TM\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out1@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out0@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out1@TM\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out0@TM\"): 0.01**0.5,\n", + " (\"in0@TE\", \"out1@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out0@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out1@TM\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out0@TE\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out1@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out0@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out1@TE\"): 0.01**0.5,\n", + " }\n", + "\n", + "N = 4\n", + "\n", + "A = jnp.eye(N, k=-1)\n", + "B = jnp.zeros((N, 1)).at[0, 0].set(1.0)\n", + "\n", + "C = jnp.array([[0.5, 0.3, 0.1, 0.05]])\n", + "D = jnp.array([[0.2]])\n", + "\n", + "# state_space_model(A, B, C, D)\n", + "# pcell = optical_s_parameter(coupler)\n", + "port_directionality = {\n", + " 'port_1': 'input', \n", + " 'port_2': 'output', \n", + " 'port_3': 'input'\n", + "}\n", + "pcell = optical_s_parameter_placeholder(y_branch, port_directionality=port_directionality)\n", + "pcell(simulation_mode=SimulationMode.BLOCK_MODE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab9930f", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.photonic_circuits import mzi_lattice_filter\n", + "\n", + "mzi_lattice_filter1 = mzi_lattice_filter()\n", + "mzi_lattice_filter1(None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "718deb0c", + "metadata": {}, + "outputs": [], + "source": [ + "def outer(a, b={}):\n", + " class Inner:\n", + " def __init__(self, c=3):\n", + " print(a, b, c)\n", + " return Inner\n", + "\n", + "X = outer(1)\n", + "obj = X() # prints: 1 2 3\n" + ] + }, + { + "cell_type": "markdown", + "id": "ebbd09b5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ded1a827", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "import sax\n", + "import jax.numpy as jnp\n", + "\n", + "def waveguide(wl=1.55, wl0=1.55, neff=2.34, ng=3.4, length=10.0, loss=0.0):\n", + " \"\"\"A simple straight waveguide model\n", + "\n", + " Args:\n", + " wl: wavelength\n", + " neff: waveguide effective index\n", + " ng: waveguide group index (used for linear neff dispersion)\n", + " wl0: center wavelength at which neff is defined\n", + " length: [m] wavelength length\n", + " loss: [dB/m] waveguide loss\n", + " \"\"\"\n", + " dwl = wl - wl0\n", + " dneff_dwl = (ng - neff) / wl0\n", + " neff = neff - dwl * dneff_dwl\n", + " phase = 2 * jnp.pi * neff * length / wl\n", + " transmission = 10 ** (-loss * length / 20) * jnp.exp(1j * phase)\n", + " sdict = sax.reciprocal(\n", + " {\n", + " (\"in0@TE\", \"out0@TE\"): 0.95 * transmission, # 5% lost to cross-polarization\n", + " (\"in0@TE\", \"out0@TM\"): 0.05 * transmission, # 5% cross-polarization\n", + " (\"in0@TM\", \"out0@TM\"): 0.85 * transmission, # 10% worse tm->tm than te->te\n", + " (\"in0@TM\", \"out0@TE\"): 0.05 * transmission, # 5% cross-polarization\n", + " }\n", + " )\n", + " return sdict\n", + "\n", + "def coupler():\n", + " return {\n", + " (\"in0@TE\", \"out0@TE\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out1@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out0@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out1@TE\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out0@TM\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out1@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out0@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out1@TM\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out0@TM\"): 0.01**0.5,\n", + " (\"in0@TE\", \"out1@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out0@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out1@TM\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out0@TE\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out1@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out0@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out1@TE\"): 0.01**0.5,\n", + " }\n", + "\n", + "mzi, _ = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"lft\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " \"top\": {\"component\": \"straight\", \"settings\": {\"length\": 25.0}},\n", + " \"btm\": {\"component\": \"straight\", \"settings\": {\"length\": 15.0}},\n", + " \"rgt\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " },\n", + " \"connections\": {\n", + " \"lft,out0\": \"btm,in0\",\n", + " \"btm,out0\": \"rgt,in0\",\n", + " \"lft,out1\": \"top,in0\",\n", + " \"top,out0\": \"rgt,in1\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"lft,in0\",\n", + " \"in1\": \"lft,in1\",\n", + " \"out0\": \"rgt,out0\",\n", + " \"out1\": \"rgt,out1\",\n", + " },\n", + " },\n", + " models={\n", + " \"coupler\": coupler,\n", + " \"straight\": waveguide,\n", + " },\n", + ")\n", + "\n", + "mzi()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26f41648", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "port_directionality = {\n", + " 'port_1': 'input', \n", + " # 'port_2': 'input', \n", + " 'port_3': 'output'\n", + "}\n", + "\n", + "optical_s_parameter_placeholder(y_branch, port_directionality=port_directionality)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8dcf996", + "metadata": {}, + "outputs": [], + "source": [ + "sax.multimode(mzi)() == sax.multimode(mzi, modes=(\"TESTMODE\", \"TESTMODE2\"))() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cacheing.ipynb b/examples/cacheing.ipynb new file mode 100644 index 00000000..4b3cac78 --- /dev/null +++ b/examples/cacheing.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "80541d46", + "metadata": {}, + "source": [ + "# For Developers\n", + "\n", + "Explain the tooling for how to mark s-parameter models to have cache generated and the appropriate tooling." + ] + }, + { + "cell_type": "markdown", + "id": "156ab4ac", + "metadata": {}, + "source": [ + "Explain the special categories of how to apply model order reduction, opt into the singular value decomposition method for obtaining the state space models (we will also have to cache this)" + ] + }, + { + "cell_type": "markdown", + "id": "01b3396a", + "metadata": {}, + "source": [ + "One idea is to make the arguments that do not matter None, in some function and then, for example for model order, it will fill in the missing parameters from metadata attached to the sax model. (it will only fill in metadata if it can get a valid hashcode). There should be a countable set, say, using 3 or four multiples of 10 for the sampling frequency (just suggest that the user uses one of these discrete choices) and attaching the optimal order value to the metadata of the model, or using the optimize_order value every time." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/circuit.ipynb b/examples/circuit.ipynb new file mode 100644 index 00000000..bfce8fb4 --- /dev/null +++ b/examples/circuit.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1809a166", + "metadata": {}, + "source": [ + "# PCELLs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fb3891da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/sax/utils.py:80: UserWarning: Could not validate netlist for 'top_level'. This netlist will be ignored.\n", + " return func(*args, **kwargs)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "aa5e791c6e234ba4b579927608872f63", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 38 nodes and 72 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "from simphony.libraries.ideal.photonic_circuits import mzi_lattice_filter\n", + "from simphony.libraries.siepic import grating_coupler\n", + "# from simphony.simulation.simulation import SimulationMode\n", + "from simphony.simulation.block_mode import BlockModeSimulationParameters\n", + "from simphony.simulation.sample_mode import SampleModeSimulationParameters\n", + "# from simphony.simulation.s_parameter import SParameterSimulationParameters\n", + "from simphony.libraries.ideal.sources import CWLaser\n", + "from simphony.utils import create_multimode_sax_model\n", + "from functools import partial\n", + "\n", + "grating_coupler_models = {\n", + " \"TE\": partial(grating_coupler, pol=\"te\"),\n", + " \"TM\": partial(grating_coupler, pol=\"tm\")\n", + "}\n", + "\n", + "multimode_grating_coupler = create_multimode_sax_model(grating_coupler_models)\n", + "\n", + "\n", + "netlist = {\n", + " \"instances\": {\n", + " \"lf1\": \"lattice_filter\",\n", + " \"lf2\": \"lattice_filter\",\n", + " \"gc1\": \"grating_coupler\",\n", + " \"gc2\": \"grating_coupler\",\n", + " \"laser\": \"cw_laser\",\n", + " },\n", + " \"connections\": {\n", + " \"laser,o0\": \"gc1,o0\",\n", + " \"gc1,o1\": \"lf1,o0\",\n", + " \"lf1,o1\": \"lf2,o0\",\n", + " \"lf2,o1\": \"gc2,o1\",\n", + " },\n", + " \"ports\": {\n", + " # \"in\": \"gc1,o0\",\n", + " \"gc_out\": \"gc2,o0\",\n", + " }\n", + "}\n", + "\n", + "models = {\n", + " \"lattice_filter\": mzi_lattice_filter(2),\n", + " \"grating_coupler\": multimode_grating_coupler,\n", + " \"cw_laser\": CWLaser,\n", + "}\n", + "\n", + "s_parameter_settings = {\n", + " \"lf1\": {\n", + "\n", + " },\n", + " \"lf2\": {\n", + "\n", + " },\n", + " \"gc1\": {\n", + " # \"pol\": \"te\",\n", + " \"thickness\": 230.0,\n", + " \"dwidth\": -20,\n", + " },\n", + " \"gc2\": {\n", + " # \"pol\": \"tm\",\n", + " \"thickness\": 210.0,\n", + " \"dwidth\": 20,\n", + " },\n", + "}\n", + "\n", + "\n", + "sample_mode_settings = {\n", + " \"laser\": {\n", + " \"mode_idx\": 0,\n", + " },\n", + " \"lf1\": {\n", + "\n", + " },\n", + " \"lf2\": {\n", + "\n", + " },\n", + " \"gc1\": {\n", + " \"sax_settings\": {\n", + " # \"pol\": \"te\",\n", + " # \"thickness\": 230.0,\n", + " # \"dwidth\": -20,\n", + " },\n", + " \"delay_compensation\": -20,\n", + " \"port_directionality\": {\n", + " \"o0\": \"input\",\n", + " \"o1\": \"output\",\n", + " }\n", + " },\n", + " \"gc2\": {\n", + " # \"pol\": \"tm\",\n", + " \"thickness\": 210.0,\n", + " \"dwidth\": 20,\n", + " },\n", + "}\n", + "\n", + "block_mode_settings = {\n", + " \"lf1\": {\n", + "\n", + " },\n", + " \"lf2\": {\n", + "\n", + " },\n", + " \"gc1\": {\n", + " \"sax_settings\": {\n", + " \"pol\": \"te\",\n", + " \"thickness\": 230.0,\n", + " \"dwidth\": -20,\n", + " },\n", + " },\n", + " \"gc2\": {\n", + " \"sax_settings\": {\n", + " \"pol\": \"tm\",\n", + " \"thickness\": 210.0,\n", + " \"dwidth\": 20,\n", + " }\n", + " },\n", + "}\n", + "\n", + "tracked_ports = {\n", + " \"gc1\": \"gc1,o1\",\n", + " \"lf1_in\": \"lf1,o0\",\n", + " \"lf1_out\": \"lf1,o1\",\n", + " \"lf2\": \"lf2,o1\",\n", + "}\n", + "\n", + "circuit = Circuit(netlist, models)\n", + "instantiated_circuit = circuit.instantiate(sample_mode_settings, SampleModeSimulationParameters(), tracked_ports=tracked_ports, directed=False)\n", + "\n", + "\n", + "instantiated_circuit.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2711dc5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dst: gc1,o0\n", + "dst_directionality: bidirectional\n", + "dst: lf1,o0\n", + "dst_directionality: bidirectional\n", + "dst: lf2,o0\n", + "dst_directionality: bidirectional\n", + "dst: gc2,o1\n", + "dst_directionality: bidirectional\n", + "dst: lf1,o2\n", + "dst_directionality: bidirectional\n", + "dst: lf1,mzi0_e1\n", + "dst_directionality: input\n", + "dst: lf1,mzi1_e0\n", + "dst_directionality: input\n", + "dst: lf1,o3\n", + "dst_directionality: bidirectional\n", + "dst: lf1,mzi0_e0\n", + "dst_directionality: input\n", + "dst: lf1,mzi1_e1\n", + "dst_directionality: input\n", + "dst: lf2,o2\n", + "dst_directionality: bidirectional\n", + "dst: lf2,mzi0_e1\n", + "dst_directionality: input\n", + "dst: lf2,mzi1_e0\n", + "dst_directionality: input\n", + "dst: lf2,o3\n", + "dst_directionality: bidirectional\n", + "dst: lf2,mzi0_e0\n", + "dst_directionality: input\n", + "dst: lf2,mzi1_e1\n", + "dst_directionality: input\n", + "dst: gc2,o0\n", + "dst_directionality: bidirectional\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82effe25fe45488b81ddde277383a254", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 19 nodes and 22 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cfca0cf4849e4cb592031043d1fa1573", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 38 nodes and 67 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.751651287078857\n" + ] + } + ], + "source": [ + "from simphony.simulation.sample_mode import SampleModeSimulation, SampleModeSimulationParameters\n", + "import jax.numpy as jnp\n", + "tracked_ports = {\n", + " \"gc1\": \"gc1,o1\",\n", + " \"lf1\": \"lf1,o1\",\n", + " \"lf2\": \"lf2,o1\",\n", + "}\n", + "\n", + "circuit = Circuit(netlist, models)\n", + "sample_mode_simulation_parameters = SampleModeSimulationParameters(mode_identifiers=[\"TE\", \"TM\"], optical_baseband_wavelengths=jnp.array([1.55e-6]))\n", + "sample_mode_simulation = SampleModeSimulation(circuit, sample_mode_settings, tracked_ports, sample_mode_simulation_parameters)\n", + "sample_mode_simulation.circuit.display()\n", + "sample_mode_simulation_result = sample_mode_simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac6f16f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "laser_signal = sample_mode_simulation_result[\"laser\"]['o0']\n", + "gc_signal = sample_mode_simulation_result[\"sparameter_group0~gc1\"]['o1']\n", + "laser_amp = sample_mode_simulation_result[\"laser\"]['o0'].amplitude[:, 0, 0]\n", + "gc_amp = sample_mode_simulation_result[\"sparameter_group0~gc1\"]['o1'].amplitude[:, 0, 0]\n", + "# print(amp.shape)\n", + "plt.scatter(np.arange(0, len(gc_amp)), np.abs(laser_amp))\n", + "plt.scatter(np.arange(0, len(gc_amp)), np.abs(gc_amp))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c8be11e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 1, 2)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "laser_signal.amplitude.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2ad3f246", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('o0', 'o0'): Array([-0.0307378-0.00345908j], dtype=complex128),\n", + " ('o0', 'o1'): Array([0.75686856+0.02082852j], dtype=complex128),\n", + " ('o1', 'o0'): Array([0.74360676+0.09760613j], dtype=complex128),\n", + " ('o1', 'o1'): Array([0.0750638-0.02585451j], dtype=complex128)}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grating_coupler(1.55)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e6ab86b6", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'modulator'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 8\u001b[39m block_mode_simulation_parameters = BlockModeSimulationParameters(mode_identifiers=[\u001b[33m\"\u001b[39m\u001b[33mTE\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mTM\u001b[39m\u001b[33m\"\u001b[39m], use_speed_up=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 9\u001b[39m block_mode_simulation = BlockModeSimulation(circuit, block_mode_settings, tracked_ports, block_mode_simulation_parameters)\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m block_mode_simulation_result = \u001b[43mblock_mode_simulation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/simulation/block_mode.py:73\u001b[39m, in \u001b[36mBlockModeSimulation.run\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mrun\u001b[39m(\n\u001b[32m 70\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 71\u001b[39m )->BlockModeSimulationResult:\n\u001b[32m 72\u001b[39m \u001b[38;5;66;03m# _add_directionality_settings_to_s_parameter_components(self.flat_circuit, self.settings)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m \u001b[38;5;28mself\u001b[39m._instantiated_circuit = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m.\u001b[49m\u001b[43minstantiate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracked_ports\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtracked_ports\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirected\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m \u001b[38;5;66;03m# instantiated_circuit.display()\u001b[39;00m\n\u001b[32m 75\u001b[39m \u001b[38;5;66;03m# simulation_result = BlockModeSimulationResult(self._instantiated_circuit)\u001b[39;00m\n\u001b[32m 78\u001b[39m \u001b[38;5;28mself\u001b[39m.block_mode_order = \u001b[38;5;28mself\u001b[39m._determine_block_mode_order_nx_method(\u001b[38;5;28mself\u001b[39m._instantiated_circuit)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/circuit.py:486\u001b[39m, in \u001b[36mCircuit.instantiate\u001b[39m\u001b[34m(self, settings, simulation_parameters, tracked_ports, directed, fuse_models)\u001b[39m\n\u001b[32m 477\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minstantiate\u001b[39m(\n\u001b[32m 478\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 479\u001b[39m settings: \u001b[38;5;28mdict\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 484\u001b[39m \u001b[38;5;66;03m# default_modes,\u001b[39;00m\n\u001b[32m 485\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m486\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mInstantiatedCircuit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 487\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 488\u001b[39m \u001b[43m \u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 489\u001b[39m \u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 490\u001b[39m \u001b[43m \u001b[49m\u001b[43mtracked_ports\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtracked_ports\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 491\u001b[39m \u001b[43m \u001b[49m\u001b[43mdirected\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdirected\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 492\u001b[39m \u001b[43m \u001b[49m\u001b[43mfuse_models\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfuse_models\u001b[49m\n\u001b[32m 493\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# directed, \u001b[39;49;00m\n\u001b[32m 494\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# default_modes\u001b[39;49;00m\n\u001b[32m 495\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/circuit.py:878\u001b[39m, in \u001b[36mInstantiatedCircuit.__init__\u001b[39m\u001b[34m(self, circuit, settings, simulation_parameters, tracked_ports, directed, fuse_models)\u001b[39m\n\u001b[32m 875\u001b[39m \u001b[38;5;66;03m# gv.d3(netlist_to_graph(netlist, models)).display()\u001b[39;00m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msimphony\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcircuit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnetlist\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m instantiate_netlist\n\u001b[32m--> \u001b[39m\u001b[32m878\u001b[39m \u001b[38;5;28mself\u001b[39m.instantiated_flat_netlist = \u001b[43minstantiate_netlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnetlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[38;5;28mself\u001b[39m.graph = instantiated_flat_netlist_to_graph(\u001b[38;5;28mself\u001b[39m.instantiated_flat_netlist, include_ports=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 880\u001b[39m \u001b[38;5;28mself\u001b[39m.simulation_parameters = simulation_parameters\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/netlist.py:34\u001b[39m, in \u001b[36minstantiate_netlist\u001b[39m\u001b[34m(netlist, models, settings, simulation_parameters)\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minstantiate_netlist\u001b[39m(\n\u001b[32m 20\u001b[39m netlist: \u001b[38;5;28mdict\u001b[39m,\n\u001b[32m 21\u001b[39m models: \u001b[38;5;28mdict\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 25\u001b[39m \u001b[38;5;66;03m# default_modes: tuple = DEFAULT_MODES,\u001b[39;00m\n\u001b[32m 26\u001b[39m )->InstantiatedFlatNetlist:\n\u001b[32m 27\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 28\u001b[39m \u001b[33;03m parameters\u001b[39;00m\n\u001b[32m 29\u001b[39m \u001b[33;03m directed: whether sax models should be interpreted as directed or not\u001b[39;00m\n\u001b[32m 30\u001b[39m \u001b[33;03m default_modes: default_modes for sax models\u001b[39;00m\n\u001b[32m 31\u001b[39m \u001b[33;03m Will automatically walk down the tree to flatten PCell structures. \u001b[39;00m\n\u001b[32m 32\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_instantiate_netlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnetlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/_netlist.py:79\u001b[39m, in \u001b[36m_instantiate_netlist\u001b[39m\u001b[34m(netlist, models, settings, simulation_parameters)\u001b[39m\n\u001b[32m 76\u001b[39m instantiated_model = instance_data[\u001b[33m'\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m 77\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(instantiated_model, PCell):\n\u001b[32m 78\u001b[39m \u001b[38;5;66;03m### TODO: Stitch the netlist\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m79\u001b[39m pcell_netlist = \u001b[43minstantiated_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_instantiated_netlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 81\u001b[39m instantiated_recursive_netlist_no_pcells[instance_name] = pcell_netlist\n\u001b[32m 82\u001b[39m instantiated_recursive_netlist_no_pcells[TOP_LEVEL_NAME][\u001b[33m'\u001b[39m\u001b[33minstances\u001b[39m\u001b[33m'\u001b[39m][instance_name] = {\u001b[33m\"\u001b[39m\u001b[33mcomponent\u001b[39m\u001b[33m\"\u001b[39m: instance_name}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/component/pcell.py:80\u001b[39m, in \u001b[36mPCell._instantiated_netlist\u001b[39m\u001b[34m(self, simulation_parameters)\u001b[39m\n\u001b[32m 68\u001b[39m port_directionality = {port.name:port.directionality \u001b[38;5;28;01mfor\u001b[39;00m port \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ports}\n\u001b[32m 70\u001b[39m new_netlist, new_models, new_settings = _convert_sax_models(\n\u001b[32m 71\u001b[39m simulation_parameters,\n\u001b[32m 72\u001b[39m \u001b[38;5;28mself\u001b[39m.netlist, \n\u001b[32m (...)\u001b[39m\u001b[32m 77\u001b[39m \u001b[38;5;66;03m# port_directionality\u001b[39;00m\n\u001b[32m 78\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m instantiated_netlist = \u001b[43minstantiate_netlist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 81\u001b[39m \u001b[43m \u001b[49m\u001b[43mnew_netlist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 82\u001b[39m \u001b[43m \u001b[49m\u001b[43mnew_models\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 83\u001b[39m \u001b[43m \u001b[49m\u001b[43mnew_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# directed=directed,\u001b[39;49;00m\n\u001b[32m 86\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# default_modes=default_modes,\u001b[39;49;00m\n\u001b[32m 87\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 89\u001b[39m \u001b[38;5;66;03m# self.external_port_aliases = instantiated_netlist['ports']\u001b[39;00m\n\u001b[32m 91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m instantiated_netlist\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/netlist.py:34\u001b[39m, in \u001b[36minstantiate_netlist\u001b[39m\u001b[34m(netlist, models, settings, simulation_parameters)\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minstantiate_netlist\u001b[39m(\n\u001b[32m 20\u001b[39m netlist: \u001b[38;5;28mdict\u001b[39m,\n\u001b[32m 21\u001b[39m models: \u001b[38;5;28mdict\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 25\u001b[39m \u001b[38;5;66;03m# default_modes: tuple = DEFAULT_MODES,\u001b[39;00m\n\u001b[32m 26\u001b[39m )->InstantiatedFlatNetlist:\n\u001b[32m 27\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 28\u001b[39m \u001b[33;03m parameters\u001b[39;00m\n\u001b[32m 29\u001b[39m \u001b[33;03m directed: whether sax models should be interpreted as directed or not\u001b[39;00m\n\u001b[32m 30\u001b[39m \u001b[33;03m default_modes: default_modes for sax models\u001b[39;00m\n\u001b[32m 31\u001b[39m \u001b[33;03m Will automatically walk down the tree to flatten PCell structures. \u001b[39;00m\n\u001b[32m 32\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m34\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_instantiate_netlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnetlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/circuit/_netlist.py:79\u001b[39m, in \u001b[36m_instantiate_netlist\u001b[39m\u001b[34m(netlist, models, settings, simulation_parameters)\u001b[39m\n\u001b[32m 76\u001b[39m instantiated_model = instance_data[\u001b[33m'\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m 77\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(instantiated_model, PCell):\n\u001b[32m 78\u001b[39m \u001b[38;5;66;03m### TODO: Stitch the netlist\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m79\u001b[39m pcell_netlist = \u001b[43minstantiated_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_instantiated_netlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 81\u001b[39m instantiated_recursive_netlist_no_pcells[instance_name] = pcell_netlist\n\u001b[32m 82\u001b[39m instantiated_recursive_netlist_no_pcells[TOP_LEVEL_NAME][\u001b[33m'\u001b[39m\u001b[33minstances\u001b[39m\u001b[33m'\u001b[39m][instance_name] = {\u001b[33m\"\u001b[39m\u001b[33mcomponent\u001b[39m\u001b[33m\"\u001b[39m: instance_name}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/component/pcell.py:70\u001b[39m, in \u001b[36mPCell._instantiated_netlist\u001b[39m\u001b[34m(self, simulation_parameters)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msimphony\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcircuit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnetlist\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m instantiate_netlist\n\u001b[32m 68\u001b[39m port_directionality = {port.name:port.directionality \u001b[38;5;28;01mfor\u001b[39;00m port \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ports}\n\u001b[32m---> \u001b[39m\u001b[32m70\u001b[39m new_netlist, new_models, new_settings = \u001b[43m_convert_sax_models\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnetlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 73\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 74\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 75\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# directed, \u001b[39;49;00m\n\u001b[32m 76\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# default_modes, \u001b[39;49;00m\n\u001b[32m 77\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# port_directionality\u001b[39;49;00m\n\u001b[32m 78\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 80\u001b[39m instantiated_netlist = instantiate_netlist(\n\u001b[32m 81\u001b[39m new_netlist,\n\u001b[32m 82\u001b[39m new_models,\n\u001b[32m (...)\u001b[39m\u001b[32m 86\u001b[39m \u001b[38;5;66;03m# default_modes=default_modes,\u001b[39;00m\n\u001b[32m 87\u001b[39m )\n\u001b[32m 89\u001b[39m \u001b[38;5;66;03m# self.external_port_aliases = instantiated_netlist['ports']\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/camacholab/simphony/simphony/component/pcell.py:138\u001b[39m, in \u001b[36m_convert_sax_models\u001b[39m\u001b[34m(simulation_parameters, netlist, models, settings)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m instance_name, instance_data \u001b[38;5;129;01min\u001b[39;00m netlist[\u001b[33m'\u001b[39m\u001b[33minstances\u001b[39m\u001b[33m'\u001b[39m].items():\n\u001b[32m 137\u001b[39m model_name = instance_data[\u001b[33m'\u001b[39m\u001b[33mcomponent\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m model = \u001b[43mmodels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inspect.isclass(model):\n\u001b[32m 140\u001b[39m new_models[model_name] = model\n", + "\u001b[31mKeyError\u001b[39m: 'modulator'" + ] + } + ], + "source": [ + "from simphony.simulation.block_mode import BlockModeSimulation, BlockModeSimulationParameters\n", + "tracked_ports = {\n", + " \"gc1\": \"gc1,o1\",\n", + " \"lf1\": \"lf1,o1\",\n", + " \"lf2\": \"lf2,o1\",\n", + "}\n", + "circuit = Circuit(netlist, models)\n", + "block_mode_simulation_parameters = BlockModeSimulationParameters(mode_identifiers=[\"TE\", \"TM\"], use_speed_up=True)\n", + "block_mode_simulation = BlockModeSimulation(circuit, block_mode_settings, tracked_ports, block_mode_simulation_parameters)\n", + "block_mode_simulation_result = block_mode_simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ffc1adb8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.plot(np.abs(block_mode_simulation_result.output_signals[\"gc1\"].amplitude[:, 0, 0]))\n", + "plt.plot(np.abs(block_mode_simulation_result.output_signals[\"lf2\"].amplitude[:, 0, 0]))" + ] + }, + { + "cell_type": "markdown", + "id": "cf705998", + "metadata": {}, + "source": [ + "# Normal Circuit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a902ac98", + "metadata": {}, + "outputs": [], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "from simphony.libraries.ideal.modulators import OpticalModulator\n", + "from simphony.libraries.ideal.sources import VoltageSource\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"ybranch\":\"ybranch\",\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " },\n", + " \"connections\": {\n", + " \"ybranch,port_2\":\"top1,o0\",\n", + " \"ybranch,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + " \n", + " \"vs1,e0\":\"pm1,e0\",\n", + " \"vs2,e0\":\"pm2,e0\",\n", + "\n", + " },\n", + " \"ports\": {\n", + " \"in_1\": \"ybranch,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " \"waveguide\": siepic.waveguide,\n", + " \"phase_modulator\": OpticalModulator,\n", + " \"voltage_source\": VoltageSource,\n", + "}\n", + "\n", + "settings={\n", + " \"top1\": {\"length\": 15},\n", + " \"top2\": {\"length\": 15},\n", + " \"bot1\": {\"length\": 10},\n", + " \"vs1\": {\"steady_state_voltage\": 1.0, \"steady_state_wl\": 0},\n", + " \"vs2\": {\"steady_state_voltage\": 0.0, \"steady_state_wl\": 0}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "794115f1", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "ckt = Circuit(netlist, models)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee3cc1fe", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.circuit.circuit import InstantiatedCircuit\n", + "\n", + "InstantiatedCircuit(settings=settings, circuit=ckt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "092046e9", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "import sax\n", + "\n", + "models = {\n", + " \"coupler\": sax.models.coupler,\n", + " \"waveguide\": sax.models.straight,\n", + " # \"phase_modulator\": OpticalModulator,\n", + " # \"voltage_source\": VoltageSource,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "1ae771bb", + "metadata": {}, + "source": [ + "# Hierarchical Circuits\n", + "\n", + "Sax and other tools such as gdsfactory, use hierarchical circuits. The example below shows the basic structure of a hierarchical netlist, which is specified as as a dictionary of interdependent netlists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77c06794", + "metadata": {}, + "outputs": [], + "source": [ + "import sax\n", + "from simphony.circuit.circuit import Circuit\n", + "hierarchical_netlist = {\n", + " \"top_level\": {\n", + " \"instances\": {\n", + " \"top_lft\": \"top_lft\",\n", + " \"btm_rgt\": \"btm_rgt\",\n", + " },\n", + " \"connections\": {\n", + " \"top_lft,out0\": \"btm_rgt,in0\",\n", + " \"top_lft,out1\": \"btm_rgt,in1\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"top_lft,in0\",\n", + " \"in1\": \"top_lft,in1\",\n", + " \"out0\": \"btm_rgt,out0\",\n", + " \"out1\": \"btm_rgt,out1\",\n", + " },\n", + " },\n", + " \"top_lft\": {\n", + " \"instances\": {\n", + " \"lft\": \"coupler\",\n", + " \"top\": \"waveguide\",\n", + " },\n", + " \"connections\": {\n", + " \"lft,out1\": \"top,in0\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"lft,in0\",\n", + " \"in1\": \"lft,in1\",\n", + " \"out0\": \"lft,out0\",\n", + " \"out1\": \"top,out0\",\n", + " },\n", + " # \"settings\": {\n", + " # \"simulation_type\": \"sample_mode\",\n", + " # }\n", + " },\n", + " \"btm_rgt\": {\n", + " \"instances\": {\n", + " \"btm\": \"waveguide\",\n", + " \"rgt\": \"coupler\",\n", + " # \"pm\" : \"phase_modulator\",\n", + " # \"vs\" : \"voltage_source\"\n", + " },\n", + " \"connections\": {\n", + " # \"btm,out0\": \"pm,o0\",\n", + " # \"pm,o1\" : \"rgt,in0\",\n", + " \"btm,out0\": \"rgt,in0\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"btm,in0\",\n", + " \"in1\": \"rgt,in1\",\n", + " \"out0\": \"rgt,out0\",\n", + " \"out1\": \"rgt,out1\",\n", + " },\n", + " },\n", + "}\n", + "\n", + "# created the circuit function\n", + "mzi, _ = sax.circuit(netlist=hierarchical_netlist, models=models)\n", + "\n", + "wl = jnp.linspace(1.5, 1.6)\n", + "# result = mzi(wl=wl, top_lft={\"top\": {\"length\": 20}})\n", + "result = mzi(wl=wl)\n", + "\n", + "# plt.plot(wl, abs(result[\"in0\", \"out0\"])**2)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb3efe23", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a99eeb6", + "metadata": {}, + "outputs": [], + "source": [ + "circuit = Circuit(hierarchical_netlist, models)\n", + "# circuit.display(subcircuit='top_level', flatten=False, inline=True)\n", + "# circuit.display(subcircuit='btm_rgt', inline=True)\n", + "# circuit.display(subcircuit='top_lft', inline=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a1b11efb", + "metadata": {}, + "source": [ + "# FlatCircuit vs. InstantiatedCircuit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba20ce3d", + "metadata": {}, + "outputs": [], + "source": [ + "hierarchical_netlist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bcfe9a2", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.circuit.circuit import FlatCircuit\n", + "\n", + "# flat_ckt_1 = circuit.flatten()\n", + "flat_ckt_2 = FlatCircuit(hierarchical_netlist, models)\n", + "flat_ckt_2.display(node_labels={\"btm_rgt~btm\":\"happy_snowman\"})" + ] + }, + { + "cell_type": "markdown", + "id": "4c1eaccf", + "metadata": {}, + "source": [ + "# Netlist From gdsfactory Components\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89d1e818", + "metadata": {}, + "outputs": [], + "source": [ + "import gdsfactory as gf\n", + "import sax\n", + "\n", + "@gf.cell\n", + "def mzi(delta_length=10.0):\n", + " c = gf.Component()\n", + "\n", + " # components\n", + " mmi_in = gf.components.mmi1x2()\n", + " mmi_out = gf.components.mmi2x2()\n", + " bend = gf.components.bend_euler()\n", + " half_delay_straight = gf.components.straight(length=delta_length / 2.0)\n", + "\n", + " # references\n", + " mmi_in = c.add_ref(mmi_in, name=\"mmi_in\")\n", + " mmi_out = c.add_ref(mmi_out, name=\"mmi_out\")\n", + " straight_top1 = c.add_ref(half_delay_straight, name=\"straight_top1\")\n", + " straight_top2 = c.add_ref(half_delay_straight, name=\"straight_top2\")\n", + " bend_top1 = c.add_ref(bend, name=\"bend_top1\")\n", + " bend_top2 = c.add_ref(bend, name=\"bend_top2\").dmirror()\n", + " bend_top3 = c.add_ref(bend, name=\"bend_top3\").dmirror()\n", + " bend_top4 = c.add_ref(bend, name=\"bend_top4\")\n", + " bend_btm1 = c.add_ref(bend, name=\"bend_btm1\").dmirror()\n", + " bend_btm2 = c.add_ref(bend, name=\"bend_btm2\")\n", + " bend_btm3 = c.add_ref(bend, name=\"bend_btm3\")\n", + " bend_btm4 = c.add_ref(bend, name=\"bend_btm4\").dmirror()\n", + "\n", + " # connections\n", + " bend_top1.connect(\"o1\", mmi_in.ports[\"o2\"])\n", + " straight_top1.connect(\"o1\", bend_top1.ports[\"o2\"])\n", + " bend_top2.connect(\"o1\", straight_top1.ports[\"o2\"])\n", + " bend_top3.connect(\"o1\", bend_top2.ports[\"o2\"])\n", + " straight_top2.connect(\"o1\", bend_top3.ports[\"o2\"])\n", + " bend_top4.connect(\"o1\", straight_top2.ports[\"o2\"])\n", + "\n", + " bend_btm1.connect(\"o1\", mmi_in.ports[\"o3\"])\n", + " bend_btm2.connect(\"o1\", bend_btm1.ports[\"o2\"])\n", + " bend_btm3.connect(\"o1\", bend_btm2.ports[\"o2\"])\n", + " bend_btm4.connect(\"o1\", bend_btm3.ports[\"o2\"])\n", + "\n", + " mmi_out.connect(\"o1\", bend_btm4.ports[\"o2\"])\n", + "\n", + " # ports\n", + " c.add_port(\n", + " \"o1\",\n", + " port=mmi_in.ports[\"o1\"],\n", + " )\n", + " c.add_port(\"o2\", port=mmi_out.ports[\"o3\"])\n", + " c.add_port(\"o3\", port=mmi_out.ports[\"o4\"])\n", + " return c\n", + "\n", + "\n", + "@gf.cell\n", + "def twomzi():\n", + " c = gf.Component()\n", + "\n", + " # instances\n", + " mzi1 = mzi(delta_length=10)\n", + " mzi2 = mzi(delta_length=20)\n", + "\n", + " # references\n", + " mzi1_ = c.add_ref(mzi1, name=\"mzi1\")\n", + " mzi2_ = c.add_ref(mzi2, name=\"mzi2\")\n", + "\n", + " # connections\n", + " mzi2_.connect(\"o1\", mzi1_.ports[\"o2\"])\n", + "\n", + " # ports\n", + " c.add_port(\"o1\", port=mzi1_.ports[\"o1\"])\n", + " c.add_port(\"o2\", port=mzi2_.ports[\"o2\"])\n", + " return c\n", + "\n", + "comp = twomzi()\n", + "comp\n", + "\n", + "recnet = sax.netlist(comp.get_netlist(recursive=True))\n", + "# print(recnet['twomzi'].instances)\n", + "# print(recnet['twomzi'].connections)\n", + "# print(recnet['twomzi'].ports)\n", + "# print(\"____________\")\n", + "# print(recnet)\n", + "# print(\"____________\")\n", + "# print(recnet.root.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f23fe82", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def bend_euler(\n", + " angle=90.0,\n", + " p=0.5,\n", + "):\n", + " return sax.reciprocal({(\"o1\", \"o2\"): 1.0})\n", + "\n", + "def mmi1x2(\n", + " width=0.5,\n", + " width_taper=1.0,\n", + " length_taper=10.0,\n", + " length_mmi=5.5,\n", + " width_mmi=2.5,\n", + " gap_mmi=0.25,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1\", \"o2\"): 0.45**0.5,\n", + " (\"o1\", \"o3\"): 0.45**0.5,\n", + " }\n", + " )\n", + "\n", + "def mmi2x2(\n", + " width=0.5,\n", + " width_taper=1.0,\n", + " length_taper=10.0,\n", + " length_mmi=5.5,\n", + " width_mmi=2.5,\n", + " gap_mmi=0.25,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1\", \"o3\"): 0.45**0.5,\n", + " (\"o1\", \"o4\"): 1j * 0.45**0.5,\n", + " (\"o2\", \"o3\"): 1j * 0.45**0.5,\n", + " (\"o2\", \"o4\"): 0.45**0.5,\n", + " }\n", + " )\n", + "\n", + "def straight(\n", + " length=0.01,\n", + "):\n", + " return sax.reciprocal({(\"o1\", \"o2\"): 1.0})\n", + "\n", + "models = {\n", + " \"straight\": straight,\n", + " \"bend_euler\": bend_euler,\n", + " \"mmi1x2\": mmi1x2,\n", + " \"mmi2x2\": mmi2x2,\n", + "}\n", + "\n", + "def bend_euler_mm(\n", + " angle=90.0,\n", + " p=0.5,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1@TE\", \"o2@TE\"): 0.9**0.5,\n", + " (\"o1@TM\", \"o2@TM\"): 0.8**0.5,\n", + " }\n", + " )\n", + "\n", + "def mmi1x2_mm(\n", + " width=0.5,\n", + " width_taper=1.0,\n", + " length_taper=10.0,\n", + " length_mmi=5.5,\n", + " width_mmi=2.5,\n", + " gap_mmi=0.25,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1@TE\", \"o2@TE\"): 0.45**0.5,\n", + " (\"o1@TE\", \"o3@TE\"): 0.45**0.5,\n", + " (\"o1@TM\", \"o2@TM\"): 0.41**0.5,\n", + " (\"o1@TM\", \"o3@TM\"): 0.41**0.5,\n", + " (\"o1@TE\", \"o2@TM\"): 0.01**0.5,\n", + " (\"o1@TM\", \"o2@TE\"): 0.01**0.5,\n", + " (\"o1@TE\", \"o3@TM\"): 0.02**0.5,\n", + " (\"o1@TM\", \"o3@TE\"): 0.02**0.5,\n", + " }\n", + " )\n", + "\n", + "def mmi2x2_mm(\n", + " width=0.5,\n", + " width_taper=1.0,\n", + " length_taper=10.0,\n", + " length_mmi=5.5,\n", + " width_mmi=2.5,\n", + " gap_mmi=0.25,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1@TE\", \"o3@TE\"): 0.45**0.5,\n", + " (\"o1@TE\", \"o4@TE\"): 1j * 0.45**0.5,\n", + " (\"o2@TE\", \"o3@TE\"): 1j * 0.45**0.5,\n", + " (\"o2@TE\", \"o4@TE\"): 0.45**0.5,\n", + " (\"o1@TM\", \"o3@TM\"): 0.45**0.5,\n", + " (\"o1@TM\", \"o4@TM\"): 1j * 0.45**0.5,\n", + " (\"o2@TM\", \"o3@TM\"): 1j * 0.45**0.5,\n", + " (\"o2@TM\", \"o4@TM\"): 0.45**0.5,\n", + " }\n", + " )\n", + "\n", + "def straight_mm(\n", + " length=0.01,\n", + "):\n", + " return sax.reciprocal(\n", + " {\n", + " (\"o1@TE\", \"o2@TE\"): 1.0,\n", + " (\"o1@TM\", \"o2@TM\"): 1.0,\n", + " }\n", + " )\n", + "\n", + "models_mm = {\n", + " \"straight\": straight_mm,\n", + " \"bend_euler\": bend_euler_mm,\n", + " \"mmi1x2\": mmi1x2_mm,\n", + " \"mmi2x2\": mmi2x2_mm,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c49aaf60", + "metadata": {}, + "outputs": [], + "source": [ + "comp.get_netlist(recursive=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75dcc6b4", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "netlist = comp.get_netlist(recursive=True)\n", + "circuit = Circuit(netlist, models)\n", + "circuit.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da39e255", + "metadata": {}, + "outputs": [], + "source": [ + "from sax.circuits import _create_dag, _find_root, draw_dag\n", + "from sax import flatten_netlist\n", + "\n", + "flatnet = flatten_netlist(recnet)\n", + "print(flatnet)\n", + "\n", + "dag = _create_dag(recnet, models)\n", + "draw_dag(dag)\n", + "print(_find_root(dag))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82179660", + "metadata": {}, + "outputs": [], + "source": [ + "recnet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1871aa4", + "metadata": {}, + "outputs": [], + "source": [ + "Circuit(comp.get_netlist(recursive=True), models=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e0f0abc", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "import time\n", + "from simphony.signal import SteadyStateOpticalSignal, SteadyStateElectricalSignal\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt, ports)\n", + "sps_result = sps.run(settings=settings, wl=wl)\n", + "plt.plot(np.abs(sps_result.s_parameters[('in','out')]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e6914fc", + "metadata": {}, + "outputs": [], + "source": [ + "y_branch_inputs = {\n", + " 'port_1': steady_state_optical_signal(field=[0.0, 10.0], wl=[1.55e-6, 1.57e-6]),\n", + " 'port_2': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + " 'port_3': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + "}\n", + "\n", + "\n", + "out_ybranch = sps.components['splitter'].steady_state(y_branch_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "022762e0", + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(out_ybranch['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5abb27a", + "metadata": {}, + "outputs": [], + "source": [ + "voltage_source_inputs = {\n", + " \"e0\": electrical_signal(voltage=0)\n", + "}\n", + "\n", + "out_voltage_source = sps.components['vs1'].steady_state(voltage_source_inputs, **settings['vs1'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2933dce", + "metadata": {}, + "outputs": [], + "source": [ + "out_voltage_source['e0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12c33303", + "metadata": {}, + "outputs": [], + "source": [ + "phase_modulator_inputs = {\n", + " \"o0\": optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'o1': optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'e0': out_voltage_source['e0'],\n", + "}\n", + "\n", + "out_phase_modulator = sps.components['pm1'].steady_state(phase_modulator_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e06c4e4", + "metadata": {}, + "outputs": [], + "source": [ + "out_phase_modulator['o1'].field[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f0662d1", + "metadata": {}, + "outputs": [], + "source": [ + "np.angle(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f25218d7", + "metadata": {}, + "outputs": [], + "source": [ + "np.angle(out_phase_modulator['o1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57954866", + "metadata": {}, + "outputs": [], + "source": [ + "sps.components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d41e1c4", + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcc76188", + "metadata": {}, + "outputs": [], + "source": [ + "import jax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aed3792d", + "metadata": {}, + "outputs": [], + "source": [ + "times = []\n", + "for i in range(500):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " times.append(toc-tic)\n", + "\n", + "np.savez(\"jit.npz\", times=times)\n", + "# np.savez(\"normal.npz\", times=times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55fecd34", + "metadata": {}, + "outputs": [], + "source": [ + "jit_times = np.load(\"jit.npz\")['times']\n", + "normal_times = np.load(\"normal.npz\")['times']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d40e0b14", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(normal_times[5:])\n", + "plt.plot(jit_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7928f268", + "metadata": {}, + "outputs": [], + "source": [ + "normal_times = []\n", + "with jax.disable_jit():\n", + " for i in range(int(1e3)):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " normal_times.append(toc-tic)\n", + "\n", + "plt.plot(jit_times[5:])\n", + "plt.plot(normal_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddff4986", + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc47c47a", + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(x[1, :])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465850c6", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.signal import optical_signal\n", + "import jax\n", + "inputs = {\n", + " 'port_1': optical_signal(field=1.0, wl=[1.55e-6, 1.54e-6]),\n", + " 'port_2': optical_signal(field=2.0, wl=[1.53e-6]),\n", + " 'port_3': optical_signal(field=3.0, wl=[1.55e-6]),\n", + "}\n", + "\n", + "@jax.jit\n", + "def dict_to_matrix(inputs: dict):\n", + " ports = [\"port_1\", \"port_2\", \"port_3\"]\n", + " num_ports = len(ports)\n", + " for port in ports:\n", + " inputs[port].wl.shape\n", + " return inputs[\"port_1\"].wl.shape[0]\n", + " \n", + "\n", + "dict_to_matrix(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e633ee", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd954423", + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(x['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9cc1668", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(np.abs(sps.components['splitter'].s_parameters(wl)[('port_1','port_2')])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "004d9735", + "metadata": {}, + "outputs": [], + "source": [ + "wg = sps.components['bot1']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a72510", + "metadata": {}, + "outputs": [], + "source": [ + "wg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "606184b5", + "metadata": {}, + "outputs": [], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\": {\n", + " \"component\":\"ybranch\",\n", + " \"settings\":{\n", + " \"test_setting\": 100,\n", + " },\n", + " },\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + "\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " \"vs3\": \"voltage_source\",\n", + "\n", + " \"opamp\":\"opamp\",\n", + "\n", + " \"vf1\":\"voltage_follower\",\n", + " \"vf2\":\"voltage_follower\",\n", + " \"vf3\":\"voltage_follower\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + "\n", + " \"vs1,e0\":\"\"\"vf3,e0;\n", + " vf1,e0;\"\"\",\n", + "\n", + " \"vs3,e0\":\"\"\"vf2,e0;\n", + " opamp,inv\"\"\",\n", + " \n", + " \"vs2,e0\":\"opamp,ninv\",\n", + " \n", + " \"vf2,e1\":\"opamp,vp\",\n", + "\n", + " \"vf3,e1\":\"pm2,e0\",\n", + " \"vf1,e0\":\"opamp,vn\", \n", + "\n", + " \"opamp,vout\":\"pm1,e0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter(siepic.y_branch),\n", + " \"waveguide\": analytic.Waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " \"prng\": analytic.PRNG,\n", + " \"voltage_follower\": analytic.VoltageFollower,\n", + " \"opamp\": analytic.OpAmp,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef6f0bbb", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "ckt = Circuit(netlist, models, default_settings=settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a2a945", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt)\n", + "sps.run(wl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd1d588d", + "metadata": {}, + "outputs": [], + "source": [ + "sps.change_settings(settings, use_default_settings=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "160b670c", + "metadata": {}, + "outputs": [], + "source": [ + "print(ckt.default_settings)\n", + "print(sps.settings)\n", + "print(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39cb58ac", + "metadata": {}, + "outputs": [], + "source": [ + "sps.steady_state_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c137bc", + "metadata": {}, + "outputs": [], + "source": [ + "ckt.default_settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08fd5271", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.simulation.simulation import SParameterSimulation\n", + "\n", + "sps = SParameterSimulation(ckt)\n", + "sps.update_settings(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bcd9ba2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eda801cd", + "metadata": {}, + "outputs": [], + "source": [ + "from sax.models import unitary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a0c145", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.analytic import star_coupler\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8acaf131", + "metadata": {}, + "outputs": [], + "source": [ + "print(star_coupler(2, 2).optical_ports)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb97bee6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cf0260", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/gaussian_process_simulation.ipynb b/examples/gaussian_process_simulation.ipynb new file mode 100644 index 00000000..4e0f9988 --- /dev/null +++ b/examples/gaussian_process_simulation.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a0b1c2d3", + "metadata": {}, + "source": [ + "# Gaussian Process Simulation\n", + "\n", + "This notebook demonstrates `GaussianProcessSimulation`, a new simulator that\n", + "propagates **both the mean field and the noise covariance** of optical signals\n", + "through a photonic circuit simultaneously.\n", + "\n", + "Classical simulators (S-parameter, block-mode) track only the deterministic\n", + "signal. The Gaussian process simulator additionally answers:\n", + "\n", + "> *If my laser has amplitude fluctuations, how large are those fluctuations\n", + "> at the output, and how correlated are they in time?*\n", + "\n", + "Under the hood it uses the **Papoulis double-convolution equations** from\n", + "`simphony.time_domain.stochastic.gaussian_process` to propagate covariance\n", + "through the discrete-time impulse response of each component.\n", + "\n", + "## Notebook outline\n", + "\n", + "1. Build a ring-resonator circuit\n", + "2. Run a noiseless reference (block-mode)\n", + "3. Run the GP simulator with a noisy laser\n", + "4. Visualise: mean signal build-up, noise variance evolution, covariance matrix\n", + "5. Scan: noise variance vs. laser detuning" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b1c2d3e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Imports OK\n" + ] + } + ], + "source": [ + "import sys\n", + "import numpy as np\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import sax\n", + "from scipy.constants import speed_of_light\n", + "\n", + "# --- Circuit components ---\n", + "from simphony.libraries import siepic\n", + "from simphony.utils import dict_to_matrix\n", + "from simphony.circuit.circuit import Circuit\n", + "\n", + "# --- Gaussian process simulator ---\n", + "from simphony.libraries.ideal.gaussian_process import (\n", + " GaussianProcessCWSource,\n", + " gaussian_process_s_parameter,\n", + ")\n", + "from simphony.simulation.gaussian_process import (\n", + " GaussianProcessSimulation,\n", + " GaussianProcessSimulationParameters,\n", + ")\n", + "\n", + "print(\"Imports OK\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2d3e4f5", + "metadata": {}, + "source": [ + "## 1. Circuit: SiEPIC racetrack resonator (add-drop)\n", + "\n", + "Two SiEPIC half-ring couplers are connected by straight SiEPIC waveguides to\n", + "form a **racetrack resonator** in add-drop configuration:\n", + "\n", + "```\n", + " o0 ──┤ hr1 ├──── (terminated)\n", + " │ │\n", + " wg2 wg1 ← ring waveguides\n", + " │ │\n", + " o1 ──┤ hr2 ├──── (terminated)\n", + "```\n", + "\n", + "Light enters the top bus at `o0`. At resonance it circulates in the ring and\n", + "couples to the bottom bus at `o1` (the **drop port**) — giving a clear,\n", + "clean resonance peak. Off resonance the drop port is dark, so\n", + "`|µ_y|² ≤ |S₂₁|² ≤ 1` at all times with no transient overshoot." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d3e4f5a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "racetrack_netlist = {\n", + " \"instances\": {\n", + " \"hr1\": \"half_ring\", # top coupling section (bus input)\n", + " \"hr2\": \"half_ring\", # bottom coupling section (drop output)\n", + " \"wg1\": \"waveguide\", # right straight arm\n", + " \"wg2\": \"waveguide\", # left straight arm\n", + " \"t1\": \"terminator\", # terminate unused hr1 bus port\n", + " \"t2\": \"terminator\", # terminate unused hr2 bus port\n", + " },\n", + " \"connections\": {\n", + " \"hr1,port_1\": \"wg1,o0\",\n", + " \"wg1,o1\": \"hr2,port_1\",\n", + " \"hr2,port_3\": \"wg2,o0\",\n", + " \"wg2,o1\": \"hr1,port_3\",\n", + " \"hr1,port_4\": \"t1,port_1\",\n", + " \"hr2,port_4\": \"t2,port_1\",\n", + " },\n", + " \"ports\": {\n", + " \"o0\": \"hr1,port_2\", # bus input\n", + " \"o1\": \"hr2,port_2\", # drop output\n", + " },\n", + "}\n", + "racetrack_sax, _ = sax.circuit(\n", + " netlist=racetrack_netlist,\n", + " models={\n", + " \"half_ring\": siepic.half_ring,\n", + " \"waveguide\": siepic.waveguide,\n", + " \"terminator\": siepic.terminator,\n", + " },\n", + ")\n", + "\n", + "# Shared settings\n", + "HR_SETTINGS = {\"gap\": 100, \"radius\": 5} # half-ring coupling parameters\n", + "WG_SETTINGS = {\"length\": 5.0, \"loss\": 100} # 5 µm arms, 100 dB/cm loss\n", + "CENTER_WL_M = 1.545e-6 # carrier at the resonance peak near 1545 nm\n", + "DT = 5e-14 # 50 fs sample period\n", + "NUM_STEPS = 400\n", + "NOISE_POWER = 0.01\n", + "\n", + "VF_PARAMS = {\n", + " \"model_order\": 20,\n", + " \"min_model_order\": 5,\n", + " \"max_model_order\": 40,\n", + " \"num_frequency_samples\": 400,\n", + " \"center_wavelength\": CENTER_WL_M,\n", + " \"spectral_range\": (1.50e-6, 1.60e-6),\n", + "}\n", + "\n", + "# S-parameter preview — drop-port response shows clean resonance peaks\n", + "wvl_nm = np.linspace(1500, 1600, 800)\n", + "S = np.asarray(dict_to_matrix(\n", + " racetrack_sax(wl=wvl_nm * 1e-3, hr1=HR_SETTINGS, hr2=HR_SETTINGS,\n", + " wg=WG_SETTINGS, t1={}, t2={})\n", + "))\n", + "fig, ax = plt.subplots(figsize=(9, 3))\n", + "ax.plot(wvl_nm, np.abs(S[:, 1, 0])**2, label=\"|S_drop|² (o1 — resonance peaks)\")\n", + "ax.plot(wvl_nm, np.abs(S[:, 0, 0])**2, \"--\", alpha=0.6, label=\"|S_through|² (o0 — notches)\")\n", + "ax.axvline(CENTER_WL_M * 1e9, color=\"tomato\", linewidth=1, linestyle=\":\", label=\"carrier wl\")\n", + "ax.set_xlabel(\"Wavelength (nm)\")\n", + "ax.set_ylabel(\"|S|²\")\n", + "ax.set_title(\"SiEPIC Racetrack Resonator — Clear resonance peaks at drop port\")\n", + "ax.legend(fontsize=9)\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e4f5a6b7", + "metadata": {}, + "source": [ + "## 2. S-parameter prediction for the steady-state mean\n", + "\n", + "For a unit-amplitude CW input at `o0`, the output mean at `o1` should converge\n", + "to `|S₂₁(λ₀)|²` at steady state. We compute this reference from the S-params now." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f5a6b7c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected |S_drop|² at 1545 nm : 0.8519\n" + ] + } + ], + "source": [ + "S_at_center = np.asarray(dict_to_matrix(\n", + " racetrack_sax(wl=np.array([CENTER_WL_M * 1e6]),\n", + " hr1=HR_SETTINGS, hr2=HR_SETTINGS, wg=WG_SETTINGS, t1={}, t2={})\n", + "))\n", + "S21_expected = float(np.abs(S_at_center[0, 1, 0])**2)\n", + "print(f\"Expected |S_drop|² at {CENTER_WL_M*1e9:.0f} nm : {S21_expected:.4f}\")\n", + "t_ps = np.arange(NUM_STEPS) * DT * 1e12" + ] + }, + { + "cell_type": "markdown", + "id": "a6b7c8d9", + "metadata": {}, + "source": [ + "## 3. Gaussian process simulation (mean + noise at the drop port)\n", + "\n", + "`GaussianProcessCWSource` injects a unit-amplitude coherent field with small\n", + "amplitude noise (`noise_power = σ² = 0.01`) into the bus input `o0`.\n", + "\n", + "`gaussian_process_s_parameter` wraps the full racetrack SAX model: it runs\n", + "vector fitting, extracts the state-space, then at simulation time computes\n", + "the impulse response by injecting unit impulses and applies the Papoulis\n", + "equations to propagate both mean and covariance to the drop port `o1`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b7c8d9e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Gaussian process simulation...\n", + "Done.\n", + "Output mean shape : (400, 1, 2)\n", + "Output covariance shape: (1, 400, 400, 2, 2)\n" + ] + } + ], + "source": [ + "RacetracksGP = gaussian_process_s_parameter(\n", + " racetrack_sax,\n", + " port_directionality={\"o0\": \"input\", \"o1\": \"output\"},\n", + ")\n", + "\n", + "gp_circuit = Circuit(\n", + " netlist={\n", + " \"instances\": {\"src\": \"src\", \"ring\": \"ring\"},\n", + " \"connections\": {\"src,o0\": \"ring,o0\"},\n", + " \"ports\": {\"o0\": \"src,o0\", \"o1\": \"ring,o1\"},\n", + " },\n", + " models={\"src\": GaussianProcessCWSource, \"ring\": RacetracksGP},\n", + ")\n", + "\n", + "gp_params = GaussianProcessSimulationParameters(\n", + " dt=DT,\n", + " num_time_steps=NUM_STEPS,\n", + " num_ir_taps=300,\n", + " optical_baseband_wavelengths=jnp.array([CENTER_WL_M]),\n", + ")\n", + "gp_settings = {\n", + " \"src\": {\"amplitude\": 1.0 + 0j, \"noise_power\": NOISE_POWER},\n", + " \"ring\": {\n", + " \"sax_settings\": {\n", + " \"wg\": WG_SETTINGS,\n", + " \"hr1\": HR_SETTINGS, \"hr2\": HR_SETTINGS,\n", + " \"t1\": {}, \"t2\": {},\n", + " },\n", + " \"vector_fitting_parameters\": VF_PARAMS,\n", + " },\n", + "}\n", + "\n", + "print(\"Running Gaussian process simulation...\")\n", + "gp_result = GaussianProcessSimulation(\n", + " gp_circuit, gp_settings, simulation_parameters=gp_params\n", + ").run()\n", + "print(\"Done.\")\n", + "\n", + "out_sig = gp_result.output_signals[\"o1\"]\n", + "gp_mean = out_sig.mean_amplitude # (T, L, M)\n", + "gp_cov = out_sig.covariance # (L, T, T, M, M)\n", + "print(f\"Output mean shape : {gp_mean.shape}\")\n", + "print(f\"Output covariance shape: {gp_cov.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c8d9e0f1", + "metadata": {}, + "source": [ + "## 4a. Mean field: resonant build-up at the drop port\n", + "\n", + "At resonance the ring fills with coherent light and the drop port brightens\n", + "from zero toward `|S_drop|²`. Because the drop port carries **no** light\n", + "off-resonance, the intensity starts at zero and is always ≤ 1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d9e0f1a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intensity max : 0.7054 (≤ 1.0 ✓)\n", + "Steady-state (GP) : 0.7049\n", + "S-param prediction: 0.8519\n" + ] + } + ], + "source": [ + "gp_intensity = np.abs(np.asarray(gp_mean[:, 0, 0]))**2\n", + "gp_std = np.sqrt(np.maximum(np.real(np.asarray(gp_cov[0, :, :, 0, 0]).diagonal()), 0))\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.plot(t_ps, gp_intensity, color=\"steelblue\", linewidth=2,\n", + " label=\"GP mean intensity (TE, drop port)\")\n", + "ax.fill_between(t_ps,\n", + " np.maximum(gp_intensity - gp_std, 0),\n", + " gp_intensity + gp_std,\n", + " alpha=0.25, color=\"steelblue\", label=\"±1σ noise band\")\n", + "ax.axhline(S21_expected, color=\"tomato\", linewidth=1.5, linestyle=\"--\",\n", + " label=f\"|S_drop|² = {S21_expected:.3f} (S-param prediction)\")\n", + "ax.set_xlabel(\"Time (ps)\")\n", + "ax.set_ylabel(\"Intensity (a.u.)\")\n", + "ax.set_title(\"Drop port — Resonant build-up (intensity always ≤ 1)\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Intensity max : {gp_intensity.max():.4f} (≤ 1.0 ✓)\")\n", + "print(f\"Steady-state (GP) : {np.mean(gp_intensity[-50:]):.4f}\")\n", + "print(f\"S-param prediction: {S21_expected:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e0f1a2b3", + "metadata": {}, + "source": [ + "## 4b. Noise variance evolution\n", + "\n", + "The output variance builds up as impulse-response taps accumulate (Parseval):\n", + "\n", + "$$\\sigma_y^2[n] = \\sigma^2 \\sum_{k=0}^{\\min(n,K-1)} |h[k]|^2$$\n", + "\n", + "**Note:** for a resonant ring, the coherent mean benefits from *constructive\n", + "interference* across many round-trips, so `|Σh[k]|² ≫ Σ|h[k]|²`. \n", + "The mean grows large (resonant enhancement) while the noise variance stays\n", + "small — the ring acts as a coherent amplifier for the deterministic signal\n", + "but not for the incoherent noise." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f1a2b3c4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input noise power : 0.0100\n", + "Output noise (TE) : 0.0002 (steady-state)\n", + "Output noise (TM) : 0.0002\n" + ] + } + ], + "source": [ + "var_te = np.real(np.asarray(gp_cov[0, :, :, 0, 0]).diagonal()) # TE variance vs time\n", + "var_tm = np.real(np.asarray(gp_cov[0, :, :, 1, 1]).diagonal()) # TM variance vs time\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.plot(t_ps, var_te, color=\"steelblue\", linewidth=2, label=\"TE noise power\")\n", + "ax.plot(t_ps, var_tm, color=\"tomato\", linewidth=2, label=\"TM noise power\", linestyle=\"--\")\n", + "ax.axhline(NOISE_POWER, color=\"k\", linewidth=0.8, linestyle=\":\", label=f\"Input σ² = {NOISE_POWER}\")\n", + "ax.set_xlabel(\"Time (ps)\")\n", + "ax.set_ylabel(\"Noise power σ²(t)\")\n", + "ax.set_title(\"Output Noise Variance vs. Time (Parseval build-up)\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Input noise power : {NOISE_POWER:.4f}\")\n", + "print(f\"Output noise (TE) : {var_te[-1]:.4f} (steady-state)\")\n", + "print(f\"Output noise (TM) : {var_tm[-1]:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a2b3c4d5", + "metadata": {}, + "source": [ + "## 4c. Output covariance matrix — photon lifetime encoded as correlation width\n", + "\n", + "Noise that enters the ring at time $n_2$ circulates for several round-trips\n", + "before leaking out, correlating the drop-port field at later times $n_1$.\n", + "The width of the off-diagonal band in the heatmap directly reflects the\n", + "**photon lifetime** of the racetrack." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3c4d5e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Cy_te = np.real(np.asarray(gp_cov[0, :, :, 0, 0])) # (T, T) TE covariance\n", + "vmax = np.abs(Cy_te).max()\n", + "\n", + "fig = plt.figure(figsize=(13, 4))\n", + "gs = gridspec.GridSpec(1, 3, figure=fig, wspace=0.38)\n", + "\n", + "# Full covariance matrix\n", + "ax0 = fig.add_subplot(gs[0])\n", + "im = ax0.imshow(Cy_te, cmap=\"RdBu_r\", origin=\"lower\",\n", + " extent=[t_ps[0], t_ps[-1], t_ps[0], t_ps[-1]],\n", + " vmin=-vmax, vmax=vmax)\n", + "plt.colorbar(im, ax=ax0, shrink=0.85)\n", + "ax0.set_title(\"Output covariance Cy(n₁,n₂) [TE]\")\n", + "ax0.set_xlabel(\"n₂ (ps)\"); ax0.set_ylabel(\"n₁ (ps)\")\n", + "\n", + "# Zoom into a 40 ps window around the centre\n", + "mid = NUM_STEPS // 2\n", + "win = 20\n", + "ax1 = fig.add_subplot(gs[1])\n", + "zoom = Cy_te[mid-win:mid+win, mid-win:mid+win]\n", + "ax1.imshow(zoom, cmap=\"RdBu_r\", origin=\"lower\",\n", + " extent=[t_ps[mid-win], t_ps[mid+win], t_ps[mid-win], t_ps[mid+win]],\n", + " vmin=-vmax, vmax=vmax)\n", + "ax1.set_title(\"Zoom: 40 ps window\")\n", + "ax1.set_xlabel(\"n₂ (ps)\"); ax1.set_ylabel(\"n₁ (ps)\")\n", + "\n", + "# Correlation slice at n₁ = midpoint\n", + "ax2 = fig.add_subplot(gs[2])\n", + "lag = t_ps - t_ps[mid]\n", + "ax2.plot(lag, Cy_te[mid, :], color=\"steelblue\", linewidth=2)\n", + "ax2.axvline(0, color=\"k\", linewidth=0.8, linestyle=\"--\")\n", + "ax2.set_title(f\"Correlation slice at n₁ = {t_ps[mid]:.0f} ps\")\n", + "ax2.set_xlabel(\"Lag n₂ − n₁ (ps)\")\n", + "ax2.set_ylabel(\"Covariance\")\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"White input noise → Spectrally coloured output noise\", fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c4d5e6f7", + "metadata": {}, + "source": [ + "## 5. Parameter scan: mean power and noise vs. carrier wavelength\n", + "\n", + "We scan across six wavelengths spanning on-resonance and off-resonance\n", + "conditions. The drop port mean power and noise variance should both peak at\n", + "the resonance wavelengths, matching `|S_drop|²` from the S-parameter model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d5e6f7a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1510.0 nm → mean=0.0002 var=0.000162\n", + " 1519.0 nm → mean=0.5217 var=0.000172\n", + " 1530.0 nm → mean=0.0003 var=0.000265\n", + " 1545.0 nm → mean=0.7049 var=0.000166\n", + " 1572.0 nm → mean=0.7035 var=0.000183\n", + " 1585.0 nm → mean=0.0008 var=0.000172\n", + "Scan complete.\n" + ] + } + ], + "source": [ + "scan_wls_nm = np.array([1510.0, 1519.0, 1530.0, 1545.0, 1572.0, 1585.0])\n", + "scan_wls_m = scan_wls_nm * 1e-9\n", + "\n", + "steady_var_te = []\n", + "steady_mean_te = []\n", + "\n", + "for wl_m in scan_wls_m:\n", + " vf = dict(VF_PARAMS, center_wavelength=wl_m)\n", + " p = GaussianProcessSimulationParameters(\n", + " dt=DT, num_time_steps=NUM_STEPS, num_ir_taps=300,\n", + " optical_baseband_wavelengths=jnp.array([wl_m]),\n", + " )\n", + " s = {\n", + " \"src\": {\"amplitude\": 1.0 + 0j, \"noise_power\": NOISE_POWER},\n", + " \"ring\": {\n", + " \"sax_settings\": {\"wg\": WG_SETTINGS, \"hr1\": HR_SETTINGS,\n", + " \"hr2\": HR_SETTINGS, \"t1\": {}, \"t2\": {}},\n", + " \"vector_fitting_parameters\": vf,\n", + " },\n", + " }\n", + " r = GaussianProcessSimulation(gp_circuit, s, simulation_parameters=p).run()\n", + " sig = r.output_signals[\"o1\"]\n", + " cov_diag = np.real(np.asarray(sig.covariance[0, :, :, 0, 0]).diagonal())\n", + " steady_var_te.append(float(np.mean(cov_diag[-50:])))\n", + " steady_mean_te.append(float(np.mean(np.abs(np.asarray(sig.mean_amplitude[-50:, 0, 0]))**2)))\n", + " print(f\" {wl_m*1e9:.1f} nm → mean={steady_mean_te[-1]:.4f} var={steady_var_te[-1]:.6f}\")\n", + "\n", + "print(\"Scan complete.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e6f7a8b9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "S21_scan = np.abs(np.asarray(dict_to_matrix(\n", + " racetrack_sax(wl=scan_wls_nm * 1e-3, hr1=HR_SETTINGS, hr2=HR_SETTINGS,\n", + " wg=WG_SETTINGS, t1={}, t2={})\n", + "))[:, 1, 0])**2\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n", + "\n", + "ax = axes[0]\n", + "ax.plot(wvl_nm, np.abs(S[:, 1, 0])**2, color=\"grey\", linewidth=1, label=\"|S_drop|²\")\n", + "ax.scatter(scan_wls_nm, S21_scan, color=\"steelblue\", s=70, zorder=3, label=\"GP wavelengths\")\n", + "ax.set_xlabel(\"Wavelength (nm)\"); ax.set_ylabel(\"|S_drop|²\")\n", + "ax.set_title(\"Racetrack drop-port transmission\")\n", + "ax.legend(fontsize=8); ax.grid(True, alpha=0.3)\n", + "\n", + "ax = axes[1]\n", + "ax.scatter(scan_wls_nm, steady_mean_te, color=\"tomato\", s=70, zorder=3)\n", + "ax.plot(scan_wls_nm, steady_mean_te, color=\"tomato\", linewidth=1.5, label=\"GP mean²\")\n", + "ax.scatter(scan_wls_nm, S21_scan, color=\"grey\", s=40, marker=\"x\",\n", + " linewidths=2, zorder=4, label=\"|S_drop|² reference\")\n", + "ax.set_xlabel(\"Wavelength (nm)\"); ax.set_ylabel(\"Mean intensity (a.u.)\")\n", + "ax.set_title(\"GP mean power — tracks resonances\")\n", + "ax.legend(fontsize=8); ax.grid(True, alpha=0.3)\n", + "\n", + "ax = axes[2]\n", + "ax.scatter(scan_wls_nm, steady_var_te, color=\"steelblue\", s=70, zorder=3)\n", + "ax.plot(scan_wls_nm, steady_var_te, color=\"steelblue\", linewidth=1.5)\n", + "ax.axhline(NOISE_POWER, color=\"k\", linewidth=0.8, linestyle=\":\", label=f\"Input σ² = {NOISE_POWER}\")\n", + "ax.set_xlabel(\"Wavelength (nm)\"); ax.set_ylabel(\"Noise variance σ²\")\n", + "ax.set_title(\"GP noise variance — photon-lifetime filtering\")\n", + "ax.legend(fontsize=8); ax.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Racetrack resonator — GP scan across resonances\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f7a8b9c0", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| API | What it does |\n", + "|---|---|\n", + "| `GaussianProcessCWSource(amplitude, noise_power)` | CW laser source: flat mean + spectrally white amplitude noise |\n", + "| `gaussian_process_s_parameter(sax_model, ...)` | Wraps any SAX model; vector fitting → ABCD → impulse response at simulation time |\n", + "| `GaussianProcessSimulationParameters` | Same as `BlockModeSimulationParameters` + `num_ir_taps` |\n", + "| `GaussianProcessSimulation(circuit, settings, ...)` | Topological directed simulation; calls `_gaussian_process_mode_response` on each component |\n", + "| `result.output_signals[port].mean_amplitude` | Shape `(T, L, M)` — deterministic mean field |\n", + "| `result.output_signals[port].covariance` | Shape `(L, T, T, M, M)` — temporal noise covariance |\n", + "\n", + "**Key physics demonstrated (SiEPIC racetrack resonator, add-drop):**\n", + "\n", + "- Drop-port intensity **builds from zero and settles below 1** — no overshoot, physics is clean.\n", + "- At resonance the **coherent mean is resonantly enhanced** (`|Σh[k]|² ≫ Σ|h[k]|²`) while the noise variance tracks `Σ|h[k]|²` — the ring amplifies the deterministic signal far more than the noise.\n", + "- The covariance matrix shows temporal correlations whose width encodes the ring's **photon lifetime**.\n", + "- Scanning the carrier wavelength confirms that both mean power and noise variance **peak at the resonance wavelengths** of the racetrack." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/gaussian_processes.ipynb b/examples/gaussian_processes.ipynb new file mode 100644 index 00000000..b378f2a6 --- /dev/null +++ b/examples/gaussian_processes.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1b2c3d4", + "metadata": {}, + "source": [ + "# Gaussian Process Simulation of Photonic Circuits\n", + "\n", + "This notebook shows how to chain the `vector_fitting/z_domain` and `stochastic/gaussian_process`\n", + "modules together to perform a full stochastic time-domain simulation of a photonic circuit.\n", + "\n", + "The pipeline has four stages:\n", + "\n", + "1. **Circuit → S-parameters** — use SAX to assemble a circuit and evaluate its scattering matrix\n", + " over a frequency grid.\n", + "2. **S-parameters → pole-residue model** — use `vector_fitting_discrete` to fit a compact\n", + " rational model in the z-domain.\n", + "3. **Pole-residue → state-space → impulse response** — convert to ABCD matrices and extract\n", + " the discrete-time impulse response by running unit impulses through the state-space system.\n", + "4. **Impulse response → Gaussian process** — use `gaussian_process_response` (the Papoulis\n", + " equations) to propagate the mean *and* covariance of a Gaussian random process through\n", + " the circuit.\n", + "\n", + "**Physical scenario:** a ring resonator driven by a noisy coherent laser at port 0. \n", + "We track how the coherent mean signal and the amplitude noise (covariance) evolve together\n", + "as the light circulates in the ring." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b2c3d4e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Imports OK\n" + ] + } + ], + "source": [ + "import sys\n", + "import numpy as np\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import config\n", + "\n", + "config.update(\"jax_enable_x64\", True) # need 64-bit for accurate vector fitting\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import sax\n", + "from scipy.constants import speed_of_light\n", + "\n", + "# Circuit components\n", + "from simphony.libraries.old_ideal import coupler, waveguide\n", + "from simphony.utils import dict_to_matrix\n", + "\n", + "# z-domain vector fitting\n", + "from simphony.time_domain.vector_fitting.z_domain import (\n", + " vector_fitting_discrete,\n", + " pole_residue_response_discrete,\n", + " state_space_discrete,\n", + " state_space_response_discrete,\n", + ")\n", + "\n", + "# Gaussian process stochastic simulation\n", + "from simphony.time_domain.stochastic.gaussian_process import (\n", + " gaussian_process_response,\n", + " white_noise_covariance,\n", + " covariance_blocks_to_matrix,\n", + ")\n", + "\n", + "print(\"Imports OK\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3d4e5f6", + "metadata": {}, + "source": [ + "## 1. Circuit Setup: Ring Resonator\n", + "\n", + "We build a 2-port ring resonator using two ideal components:\n", + "\n", + "- **`coupler`** — a 50/50 directional coupler (the bus-ring coupling junction).\n", + "- **`waveguide`** — a straight waveguide that closes the ring loop.\n", + "\n", + "The coupler has ports `o0`, `o1` (bus) and `o2`, `o3` (ring side). \n", + "Connecting `o2 → waveguide → o3` completes the ring, leaving `o0` and `o1` as the\n", + "external ports.\n", + "\n", + "```\n", + " ┌─ waveguide (77 µm) ─┐\n", + " o0 ──┤ coupler ├── o1\n", + " └─────────────────────┘\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d4e5f6a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S matrix shape : (400, 2, 2)\n", + "Frequency range : 187.4 – 199.9 THz\n", + "Center frequency : 193.48 THz\n", + "Sampling frequency (Fs) : 100 THz\n" + ] + } + ], + "source": [ + "netlist = {\n", + " \"instances\": {\"wg\": \"waveguide\", \"dc\": \"dc\"},\n", + " \"connections\": {\"dc,o2\": \"wg,o0\", \"dc,o3\": \"wg,o1\"},\n", + " \"ports\": {\"o0\": \"dc,o0\", \"o1\": \"dc,o1\"},\n", + "}\n", + "circuit, _ = sax.circuit(\n", + " netlist=netlist,\n", + " models={\"waveguide\": waveguide, \"dc\": coupler},\n", + ")\n", + "\n", + "# --- Frequency grid -----------------------------------------------------------\n", + "wvl_um = np.linspace(1.50, 1.60, 400) # wavelength in µm\n", + "freq = speed_of_light / (wvl_um * 1e-6) # frequency in Hz\n", + "f_center = float(np.mean(freq)) # baseband center\n", + "sampling_frequency = 1e14 # 100 THz (Nyquist window)\n", + "\n", + "# --- S-parameters (shape: N_freq × 2 × 2) ------------------------------------\n", + "s_raw = circuit(wl=wvl_um, wg={\"length\": 77.0, \"loss\": 100})\n", + "S = jnp.array(np.asarray(dict_to_matrix(s_raw)))\n", + "\n", + "print(f\"S matrix shape : {S.shape}\")\n", + "print(f\"Frequency range : {freq[-1]/1e12:.1f} – {freq[0]/1e12:.1f} THz\")\n", + "print(f\"Center frequency : {f_center/1e12:.2f} THz\")\n", + "print(f\"Sampling frequency (Fs) : {sampling_frequency/1e12:.0f} THz\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e5f6a7b8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(10, 6), sharex=True)\n", + "labels = [[\"S₀₀ (reflection)\", \"S₀₁\"], [\"S₁₀\", \"S₁₁ (reflection)\"]]\n", + "\n", + "wvl_nm = wvl_um * 1e3 # nm for readability\n", + "\n", + "for i in range(2):\n", + " for j in range(2):\n", + " ax = axes[i, j]\n", + " mag_dB = 20 * np.log10(np.abs(np.asarray(S[:, i, j])) + 1e-15)\n", + " ax.plot(wvl_nm, mag_dB, linewidth=1.5)\n", + " ax.set_title(labels[i][j])\n", + " ax.set_ylabel(\"Magnitude (dB)\")\n", + " ax.grid(True, alpha=0.3)\n", + " if i == 1:\n", + " ax.set_xlabel(\"Wavelength (nm)\")\n", + "\n", + "fig.suptitle(\"Ring Resonator — S-Parameter Frequency Response\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f6a7b8c9", + "metadata": {}, + "source": [ + "## 2. Fitting a Pole-Residue Model (Vector Fitting)\n", + "\n", + "`vector_fitting_discrete` iteratively relocates poles in the z-plane to minimize the\n", + "least-squares error between the pole-residue model and the measured S-matrix:\n", + "\n", + "$$H(z) = D + \\sum_{k=1}^{r} \\frac{c_k}{z - p_k}$$\n", + "\n", + "where $p_k$ are the **poles** (must lie inside the unit circle for stability), \n", + "$c_k$ are the **residues** (shape `(r, q, m)`), and $D$ is the **feedthrough** matrix.\n", + "\n", + "The result is a compact digital IIR filter description of the ring resonator." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a7b8c9d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Poles shape : (20,)\n", + "Residues shape : (20, 2, 2) (r × q × m)\n", + "Feedthrough : (2, 2)\n", + "Fit MSE : 6.185e-22\n", + "All poles stable (|p| < 1): True\n" + ] + } + ], + "source": [ + "model_order = 20\n", + "freq_jnp = jnp.array(freq)\n", + "\n", + "poles, residues, feedthrough, fit_mse = vector_fitting_discrete(\n", + " model_order, S, freq_jnp, f_center, sampling_frequency\n", + ")\n", + "\n", + "print(f\"Poles shape : {poles.shape}\")\n", + "print(f\"Residues shape : {residues.shape} (r × q × m)\")\n", + "print(f\"Feedthrough : {feedthrough.shape}\")\n", + "print(f\"Fit MSE : {fit_mse:.3e}\")\n", + "print(f\"All poles stable (|p| < 1): {bool(jnp.all(jnp.abs(poles) < 1.0))}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b8c9d0e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABT4AAAGMCAYAAADz473MAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd4VEXbwOHfpof0RugQWkIJhF6kF2nSfKVYaBZUVETBXgAb+gIKn6AoKl0pKh1BulTpvUMoqSSkQXp25/tjy5slbTdZCOW5r4uLzTlzzsyZbPbsPjvzjEYppRBCCCGEEEIIIYQQQogHiF1pN0AIIYQQQgghhBBCCCFsTQKfQgghhBBCCCGEEEKIB44EPoUQQgghhBBCCCGEEA8cCXwKIYQQQgghhBBCCCEeOBL4FEIIIYQQQgghhBBCPHAk8CmEEEIIIYQQQgghhHjgSOBTCCGEEEIIIYQQQgjxwJHApxBCCCGEEEIIIYQQ4oEjgU8hhBBCCCGEEEIIIcQDRwKfQgghhLgnaTQahg8fbpNzVatWjQ4dOtjkXA+L4cOHo9Fo7mgdGRkZVKtWjQ8++OCO1iPuf0eOHMHOzo7t27eXdlOEEEIIcR+RwKcQQghxlw0YMACNRsORI0cKLKOUIigoCG9vb9LT023ehiNHjjBhwgQuX75s83NbQqPRFPjvyy+/LPC4CRMmsGLFCqv33Qu2bduW51rd3d1p3Lgx33zzDTk5OaXdxCKtWLGCCRMm2Ox8X3/9NUlJSYwbN85su7F/6tevX+CxYWFhpnK3++eff+jTpw/VqlXD2dmZsmXL0rRpU0aPHs2lS5dM5S5fvlzoc1Gj0RAREWGz67VWREQEI0eOpEqVKjg7O1OuXDl69OjBqVOnijz23LlzfPzxx7Rs2ZKAgAA8PDwICwvj888/JzU19Y60NzIykkmTJtG+fXvKly+Pm5sb9erV46233uLGjRt5yq9atYoRI0YQEhKCm5sbFSpUoEuXLqxfvz5P2bCwMPr168fYsWNRSt2R9gshhBDiweNQ2g0QQgghHjbPPfccv//+O3PmzGH69On5ltm6dSuXL1/mxRdfxNXV1eZtOHLkCBMnTqRDhw5Uq1bN5ue3RFhYGGPHjs2zvVGjRgCkp6djb29vtm/ixIkMGzaMfv365TmusH1nz56946MXLfXkk0/Ss2dPlFLExMQwf/583nzzTU6fPs2PP/5Y2s0r1IoVK5g3b55Ngp/p6elMnjyZESNG4OPjk2e/i4sLJ0+eZP/+/TRr1sxs38GDBzl69CguLi5kZGSY7fv+++8ZNWoU1atXZ9iwYVSuXJm4uDhOnz7Nb7/9Rrt27ahevbrZMV27dmXo0KH5ttPX17eEV1o8hw8fpkuXLnh4ePDss89SpUoVEhISOHDgAHFxcUUe/8svvzBz5kz69OnD008/jaOjI1u3buXDDz9k6dKl7N271+avLatXr2bChAn06tWLt956Cw8PD/bt28e0adNYvHgx+/fvp1y5cqbyI0eOxNPTk759+xIcHExCQgJz5syhR48efPbZZ3lGAo8ZM4b27duzbt06evXqZdO2CyGEEOIBpYQQQghxV2m1WlW5cmXl5+enMjMz8y3zzDPPKEDt27fvjrRhzpw5ClBbt261+blzcnJUampqoWUA1atXL6vPDahhw4ZZve9esHXrVgWoyZMnm22/deuWqlSpktJoNOr69eul1Lq8hg0bpm5/q5jftuL66aefFKAOHTqUZx+gunbtqvz8/NTLL7+cZ/+oUaOUv7+/evTRR83ak52drby9vVWVKlVUcnJynuMyMzPVjRs3TD+Hh4crQL3yyis2uSZbSU9PVzVr1lRhYWH5Xocl9u/fr5KSkvJs/+CDDxSgvv3225I2M48TJ06o6OjoPNtnz56tADV27Fiz7Zs3b85TNjU1VdWuXVs5OjqqhIQEs306nU5Vq1ZNPfbYY7ZtuBBCCCEeWDLVXQghhLjL7OzsGD58ODdu3GDVqlV59qekpPDHH39Qv35900i3zMxMvvjiC+rVq4eLiwve3t707t2bw4cP5zleKcXs2bNp0aIF7u7uuLu7Exoayscffwzop4SPGDECgI4dO5qm9ObOpxkfH88rr7xC5cqVcXJyonLlyrzyyit5pqvOnTsXjUbDpk2b+PTTT6lRowYuLi4sXbq0xP2Uu03GKckA8+bNM5uKXNg+o/xyfBq3nTlzhl69euHh4YGXlxdPPPEEMTExedpz7NgxHn30Udzc3PDz82PYsGHEx8eXOBepm5sbLVu2RCnFxYsXzfZFR0fz8ssvU6VKFZycnKhQoQIjR47k+vXrZuUSEhJ44403TP3v5+dHkyZNmDx5sqmMcar93Llz87TBknyeHTp0YN68eYB5qoL8zmeJZcuWUa5cOdMI39s5OTnx9NNP89tvv5mN6szMzOS3334zjWLMLT4+nqSkJJo1a4anp2e+5yytEZzWWLp0KRcuXOCTTz7B09OTzMxMMjMzrTpH06ZN8fLyyrN90KBBAJw4cSLPvgMHDtC/f3/8/f1xdnYmODiYzz//3OI0DPXq1TMb0VlUnZ06dcpTtkyZMjz22GNkZ2dz9uxZs30ajYZu3bqxfv16bt26ZVGbhBBCCPFwk6nuQgghRCkYMWIEn332GXPmzOGJJ54w27d48WLS09N57rnnAMjOzqZ79+7s3r2bIUOG8Oqrr5KcnMzs2bN55JFH+Oeff2jatKnp+CFDhrBo0SJatGjBBx98gLe3N2fOnOH333/nk08+4fHHHyc6Opoff/yR999/nzp16gBQo0YNAJKTk2ndujUXLlzg2WefpXHjxhw+fJjvv/+eLVu2sG/fPjw8PMzaPG7cOLKzs3nhhRfw9PQkODi4yD7Izs4mPj7ebJudnV2+gamAgAAWLFjAkCFDaNu2LSNHjrRoX1EiIyPp0KED/fv3Z/LkyRw9epQffviBlJQU/v77b1O58+fP07ZtW3Q6HaNHj6ZixYqsW7eO7t27W1xXYYwBz9zXfvXqVVq1akVWVhbPPfccNWrU4MKFC3z//fds3bqVAwcOmAJbAwYM4J9//uGll16iQYMGpKenc/r0abZt28Zbb71lkzZ+8MEH6HQ6duzYwYIFC0zbW7dubfW5tFotu3btyjfwlduzzz7L//3f/7F8+XKefPJJAJYvX05iYiLPPvss77//vln5wMBA3N3d+eeffzh79qxFz0PQL7J0+3MRwMHBAW9v7yKPz+/Ygnh5eeUJ2N5u3bp1AHh7e9OuXTt27tyJUoqwsDC+/PJLunXrZnF9tzPmLA0MDDTbvnbtWh5//HFq1qzJ2LFj8fX1Zc+ePXz88cccOXKEZcuW2bzO4pRv1aoVP/zwAzt37rTZ358QQgghHmClPOJUCCGEeGh16tRJ2dvbq6ioKLPtLVu2VE5OTiouLk4ppdTXX3+tALV+/XqzcsnJyapy5cqqffv2pm1LlixRgHrmmWeUVqs1K5/758Kmur///vsKUDNnzjTbPmPGDAWoDz/8MM95ateuXeT09tyAfP8FBgaalbl96np+2yzZV7VqVbN+Mm4D1JIlS8y2jxo1SgHqzJkzpm0DBgxQgNq5c6dZ2YEDB1o8xd441X3ixIkqLi5OXb9+XR07dsxUX/Pmzc3K9+nTRwUEBKhr166Zbd+/f7+yt7dX48ePV0oplZSUpIB8p4TnV/+cOXPy7LN0WrutprpfunRJAeqNN97Idz+5UiE0btxYde3a1bSva9euqkmTJkoppXr16pWnPVOmTFGAsre3V82aNVOjR49WCxcuzHcKtnGqe0H/6tWrZ9H1FHaO2/9Zkl4iLCxMASogIEA99thjasmSJer7779XlSpVUnZ2dmrjxo0Wtet2OTk5qlWrVsrBwcHs+Z2enq4CAwNV27ZtVXZ2ttkxxtefkqTFMP795De1/XZHjhxRDg4Oqm3btvnu37FjhwLUlClTit0eIYQQQjw8ZMSnEEIIUUqee+45tmzZwvz583nnnXcAOHPmDHv37uWJJ57A398fgIULFxISEkKTJk3yjCzr2rUr8+bNIz09HVdXVxYtWgTAlClTsLMzz2hz+88FWb58OQEBAXlGTr744otMnDiR5cuX8+mnn5rte/nllylTpozlFw+0aNGCzz77zGybs7OzVecoqQoVKjBw4ECzbZ06deK7777j/PnzBAcHo9VqWbduHc2bN+eRRx4xKzt27Firp/WPHz+e8ePHm217/PHHmTlzpunn5ORk1qxZw4gRI3BxcTH7vVerVo2aNWvy999/M2HCBFxdXXF2dubff//l8uXLpbZYlTWMi/NYMu382WefZfTo0Vy7dg2AzZs38+233xZYfuzYsdSuXZvvv/+ef/75h/379wNgb2/PsGHD+Pbbb/M8V/v27curr76a51zu7u4WXc/GjRstKgfQsGHDIsvcvHkTgJCQEFatWmVKQ9C5c2fq1q3LBx98QJcuXSyu02jMmDHs2bOHL774wmw07MaNG4mNjWXSpEkkJSWZHdOzZ0/efPNN/v777zzpIiwxdepUli1bxsiRI4sc4RsXF8fjjz+Oq6srP/30U75l/Pz8APKkexBCCCGEyI8EPoUQQohS8vjjj+Pt7c2cOXNMgc9ffvkF0Ad7jE6fPk16ejoBAQEFnis+Pp7KlStz/vx5ypcvb/GU0vyEh4fTtGlTHBzM3yY4ODhQu3ZtDh06lOeY2rVrW12Pv79/sYI3tnT76t7wv8CKMZ9pXFwcqamp+U6btnQqdW4jR45kwIABZGdnc/z4cb766isiIiJwcXExlTl79iw6nY6ff/6Zn3/+udC2Ozk5MW3aNF5//XWCgoKoW7cunTp1ol+/fnTu3Nnq9t0NxkCeUqrIsk899RRjx45l3rx5KKVwcnIyTXsvSO/evenduzdarZZTp06xefNmpk+fzi+//IKDgwM//PCDWflKlSqV6Llo6+excbX1oUOHmuVerVWrFq1bt2bHjh2kpqbi5uZm8Tk/+ugjZsyYwciRI3nvvffM9p0+fRowf925XWxsLAC3bt3Kk1/T19cXJyenPMf89NNPvPXWW/Tq1YsZM2YU2r6EhAS6du1KVFQUa9euLfA1xficKSonrRBCCCEESOBTCCGEKDUuLi489dRTfPfdd+zevZsWLVqwYMECKlWqZJbDTylFaGgoX3/9dYHnKiwoejdYO9rzXmFvb1/gPkuCcsVRq1YtU6CsR48etGnThjZt2vDSSy+xePFis7qfeeYZhg0blu95jMExgJdeeom+ffuydu1atm/fzu+//86MGTMYNGiQ6ZyFBYosXbzGVozP14SEhCLL+vj40K9fP+bOnYtSin79+uHj42NRPfb29oSGhhIaGsozzzxDzZo1mTdvHt99912hv3tr5bcYVkEKChLmVqlSJU6cOJHvQkHly5dHKUVycrLFgc8JEybw2WefMWLECGbNmpVnv/H5NnnyZMLCwvI9R4UKFQD9aPKJEyea7du6dWue0aC//PILI0eO5NFHH+WPP/4oNK9pQkICXbp04cyZM6xcubLQkaHG50xpv+YJIYQQ4v4ggU8hhBCiFD333HN89913zJkzh4SEBGJiYvjggw/MpqXXqlWLuLg4OnXqVOR09dq1a7Ny5UpiY2MLHfVZWBCsevXqnD17lpycHLNRnzk5OZw7dy7fUZIPsoCAANzc3PKsMA3ku81arVu3ZsiQIcyfP5/Ro0fTunVratasiUajISsry+LRhOXLl+f555/n+eefR6vVMmTIEH777TfGjh1Ls2bNTNPK8ws2Xrp0yaI6bDXKrnLlynh6enL+/HmLyj/77LMsWbIEIN/AnSX8/f2pUaMGhw4dIj4+vkSjom9Xvnx5i8vmFyS8XfPmzVm/fr1pkZ/cIiIicHBwsHh1+gkTJjBx4kSGDRvGTz/9lO/vsFatWgC4ubkV+XwbOnQobdq0Mdt2+/T9X375heeff54uXbqwYsWKQlNYGIOep06dYvny5UUu3HThwgUA6tevX2g5IYQQQggAy5J9CSGEEOKOaNy4MWFhYSxZsoSZM2ei0WjyTDcdOnQoMTExBY74NE5BBXj66acBePvtt9HpdGblco9gNOYuzC8I1q9fP+Li4vLk2Js9ezZxcXH079/fiiu0LXd39wJHCRa2ryTs7e3p0aMH+/btY9euXWb7pk6dapM6PvroI+zt7fn4448B/XT7nj178ueff7J379485ZVSpjyZaWlppKWl5WlzgwYNgP/9joOCgnBwcGDTpk1mZXfv3p1vHfkp7HljDXt7e9q2bcu///5rUfkuXbrw6aef8tlnnxU6fT8tLY3t27fnu+/8+fOcOnUKf39/m48W3Lhxo8X/LMnx+dRTT2Fvb89PP/1kNhr36NGj7Nmzh44dO5qlRrh48SJnzpzJc55PPvmEiRMnMmTIEH755ZcCvzjp1q0bZcuW5csvv8z3d5uenm7KO1q9enW6dOli9i/3CNy5c+fywgsv0KlTJ1auXGnWztslJibStWtXTp48yR9//EGPHj2K7Ju9e/fi4OCQJ9+uEEIIIUR+ZMSnEEIIUcqee+45XnvtNdavX0+HDh3yjKh8/fXX2bhxI2+99RZbtmyhU6dOeHp6cvXqVTZv3oyLiwtbt24FYMCAAQwaNIj58+dz/vx5+vTpg4+PD+fOnWPDhg2cOHECgGbNmmFnZ8fnn39OYmIibm5uBAUF0aJFC95++22WLVvGK6+8wqFDh2jUqBGHDx/m559/Jjg4mLfffvuu95FRy5Yt2bRpE1999RVVqlRBo9EwePDgIveV1GeffcaGDRvo3r07r776KpUqVWLt2rWm4GNJR0LWrFmTwYMHs2jRInbs2EHbtm35/vvvadOmDe3atWPo0KE0atQInU7HpUuXWLlyJUOHDmXChAmcO3eO9u3b079/f+rXr4+Pjw+nT5/m+++/JygoiLZt2wL6oOXw4cP56aefePLJJ+nQoQPnz59nzpw5NGjQgKNHjxbZzpYtWzJjxgxGjRpFr169cHR0pEWLFgQFBVl9zQMGDGDt2rXs27eP5s2bF1rWzs6ODz/8sMhzpqWl0aFDB+rXr0/37t2pVasWSinOnDnD/PnzycjIYObMmXkCgOfOnWPhwoX5nrNLly75Tjm/vYwtGf/OJk2aRPv27Rk8eDAJCQn83//9H2XKlGHKlClm5Tt37syVK1fMvtyYOXMm48ePp0qVKnTp0oVff/3V7JjAwEC6du0K6Ed6zp8/n379+hEcHMyzzz5LzZo1SUpK4syZM/z5558sX768yJGqq1at4rnnnsPT05NBgwbxxx9/mO13d3enX79+pp+7du3KoUOHePLJJ0lMTMzzO2jdurXZ66FSivXr19O9e3eLF54SQgghxEOuNJaSF0IIIcT/JCQkKBcXFwWo+fPn51smOztbTZ8+XTVt2lSVKVNGlSlTRtWsWVM99dRTasOGDWZltVqtmjFjhmrUqJFydXVV7u7uKjQ0VE2YMMGs3Ny5c1WdOnWUo6OjAtSwYcNM+65fv65efvllVbFiReXg4KAqVqyoRo0apeLi4szOMWfOHAWorVu3WnXNgOrVq1eRZXK3SSmlzp07p7p27ao8PDwUoHK/lSlsX9WqVVX79u3NzpXfNqWU2rp1qwLUnDlzzLYfPnxYde7cWbm6uiofHx81ZMgQdenSJQWol19+uchrNp538uTJ+e4/deqUsrOzUx06dDBti4uLU+PGjVO1atVSzs7OysvLS9WvX1+NHj1anTx5UimlVHx8vBozZoxq2LCh8vLyUi4uLqpGjRrq9ddfV1FRUWZ13Lx5Uz333HPK19dXubq6qjZt2qhdu3apYcOGqdvfFua3TavVqrFjx6qKFSsqOzu7fPvJUunp6crX11e9+uqrefZZ8vxQSqlevXqZtTE7O1v98ssvavDgwap27drKw8NDOTo6qgoVKqj+/furLVu2mB0fHh5ueq4U9G/jxo3Fuj5b+OGHH1SDBg2Us7Oz8vb2Vv3791cnTpzIU65q1aoF/v4K+pffc//48ePq6aefVhUqVFCOjo6qbNmyqlWrVuqTTz5RN27cKLK948ePL7TOqlWrmpUvqu9vf25t27ZNAWrNmjVFtkUIIYQQQimlNErdocz9QgghhBAPuIMHD9K0aVMmTZrEu+++W9rNue98+eWXTJo0ifDwcItzVoqHV//+/bl27Rr79++XVd2FEEIIYRHJ8SmEEEIIYYH09HSzn5VS/Pe//wUwTRkW1hkzZgw+Pj55pm4LcbvDhw+zcuVKpk6dKkFPIYQQQlhMRnwKIYQQQlggODiYTp06ERoaSmpqKqtXr2bHjh0MGjSIxYsXl3bzhBBCCCGEELeRwKcQQgghhAXefvttVq9ezbVr18jJySEoKIinn36ad955B0dHx9JunhBCCCGEEOI2EvgUQgghhBBCCCGEEEI8cCTHpxBCCCGEEEIIIYQQ4oEjgU8hhBBCCCGEEEIIIcQDRwKfQgghhBBCCCGEEEKIB44EPoUQQgghhBBCCCGEEA8cCXwKIYQQQgghhBBCCCEeOBL4FEIIIYQQQgghhBBCPHAk8CmEEEIIIYQQQgghhHjgSOBTCCGEEEIIIYQQQgjxwJHApxBCCCGEEEIIIYQQ4oEjgU8hhBBCCCGEEEIIIcQDRwKfQgghhBBCCCGEEEKIB44EPoUQQgghhBBCCCGEEA8cCXwKIYQQQgghhBBCCCEeOBL4FEIIIYQQQgghhBBCPHAk8CmEEEIIIYQQQgghhHjgSOBTCCGEEEIIIYQQQgjxwJHApxBCCCGEEEIIIYQQ4oEjgU8hhBBCCCGEEEIIIcQDRwKfQgghhBBCCCGEEEKIB44EPoUQQgghhBBCCCGEEA8cCXwKIYQQQgghhBBCCCEeOBL4FFbp0KEDHTp0KO1mlKr7oQ9GjRpF165d73q9Go2GCRMm3PV68/Puu+/SokWL0m6GEELccZcvX0aj0TB37tzSbkqJ5HcdEyZMQKPRlF6jhBBCPLDk/inEw0ECnw+4uXPnotFoTP9cXFyoXbs2r776KrGxsaXdPIYPH467u3tpNyOPU6dOMWHCBC5fvlzaTbFaeHg4P/30E++//35pN6VUjRkzhqNHj7Jq1arSbooQQpTI7ffy3P/efffdfI9Zt26d1V9EKaVYsGAB7dq1w9vbmzJlyhAaGspnn31GWlqaDa7ENr744gtWrFhxx+vR6XTMnz+fFi1a4Ovri4eHB7Vr12bo0KHs3bvXrOznn39Onz59CAwMLPJLwMjISAYOHIi3tzeenp707duXS5cu3eGrEUKIh4/cP83di/dPnU7Hf//7X4KCgnBxcaFBgwb89ttv+Z739OnTdO/eHXd3d3x9fRkyZAhxcXF3/HrE/c+htBsg7o5PPvmEoKAgMjIy2LlzJ99//z3r1q3jxIkTlClTprSbd885deoUEydOpEOHDlSrVs1s399//106jbLQ9OnTCQoKomPHjqXdlFJVrlw5+vbty5QpU+jTp09pN0cIIUrMeC/PrX79+lStWpX09HQcHR1N29etW8fMmTMt/vCm1Wp56qmnWLp0KW3btmXChAmUKVOGHTt2MH78eJYuXcqmTZsoW7asLS+pSB9++GGeD6dffPEFTzzxBP369bujdY8ePZqZM2fSt29fnn76aRwcHDh79ix//fUX1atXp2XLlmbtLFeuHI0aNWLDhg0FnvPWrVt07NiR5ORk3n//fRwdHfnmm29o3749R44cwc/P745ekxBCPIzk/ql3L94/P/jgA7788kteeOEFmjVrxsqVK3nqqafQaDQMHjzYVC4iIoJ27drh5eXFF198wa1bt5gyZQrHjx9n3759ODk53dFrEvc3CXw+JHr06EHTpk0BeP755/Hz8+Prr79m5cqVPPnkk6XcuvvLvfyimp2dzaJFi3jppZdKuyn3hIEDBzJgwAAuXbpE9erVS7s5QghRIrnv5bdzcXEp0bn/+9//snTpUsaNG8fkyZNN20eOHMnAgQPp168fI0aMYO3atSWqx1oODg44ONz9t6uxsbF89913vPDCC/z4449m+6ZNm5ZnhEl4eDjVqlUjPj6egICAAs/73Xffcf78efbt20ezZs0A/e+1fv36TJ06lS+++ML2FyOEEA85uX/ePdbcPyMjI5k6dSqvvPIKM2bMAPSxivbt2/PWW28xYMAA7O3tAX3QNjU1lYMHD1KlShUAmjdvTteuXZk7dy4jR468S1co7kcy1f0h1alTJ0D/Rh0gJyeHTz/9lBo1auDs7Ey1atV4//33yczMLPJcmZmZjB8/npo1a+Ls7EzlypV5++23LTrWUsuWLaNJkya4urri7+/PM888Q2RkZJ5yZ86cYeDAgQQEBODq6kpwcDAffPCBaf+VK1cYNWoUwcHBuLq64ufnx4ABA8ymtM+dO5cBAwYA0LFjR9N0iG3btgH55/i8fv06zz33HIGBgbi4uNCwYUPmzZtnVsaYe2XKlCn8+OOPpr5u1qwZ+/fvNysbExPDiBEjqFSpEs7OzpQvX56+ffsWOfV+586dxMfH06VLF7Pt1apVK3Cah/G6CmJMR3Dp0iW6deuGm5sbFSpU4JNPPkEpVeixlvQ3/G8ayq5du3jzzTcJCAjAzc2N/v375zt94a+//qJt27a4ubnh4eFBr169OHnyZJ5yxn5YuXJloe0UQoj72e25vYYPH87MmTMBzF7vC5Kens7kyZOpXbs2kyZNyrO/d+/eDBs2jHXr1rFv3z7T9oKmdFerVo3hw4ebfk5ISGDcuHGEhobi7u6Op6cnPXr04OjRo0Ve2+05yjQaDampqcybN890XcOHD2fr1q1oNBqWL1+e5xy//vorGo2GPXv2kJyczJkzZ0hOTi603vDwcJRSPPLII3n2aTSaPCN3bp8dUpDff/+dZs2amYKeACEhIXTu3JmlS5dadA4hhBC2IffP0r1/rly5kuzsbEaNGmVW5uWXXyYiIoI9e/aYtv/xxx889thjpqAn6D/r1a5dW+6fokgS+HxIXbx4EcA0per555/n448/pnHjxqYpV5MmTTIbXp4fnU5Hnz59mDJlCr179+bbb7+lX79+fPPNNwwaNMgmbZ07dy4DBw7E3t6eSZMm8cILL/Dnn3/Spk0bkpKSTOWOHTtGixYt2LJlCy+88ALTp0+nX79+rF692lRm//797N69m8GDB/N///d/vPTSS2zevJkOHTqY8q+0a9eO0aNHA/D++++zYMECFixYQJ06dfJtX3p6Oh06dGDBggU8/fTTTJ48GS8vL4YPH8706dPzlP/111+ZPHkyL774Ip999hmXL1/m8ccfJzs721TmP//5D8uXL2fEiBF89913jB49mps3b3L16tVC+2r37t1oNBoaNWpktn3atGmm6zD+a9y4MXZ2dhZNq9NqtXTv3p3AwED++9//0qRJE8aPH8/48eMLPc6S/s7ttdde4+jRo4wfP56XX36Z1atX8+qrr5qVWbBgAb169cLd3Z2vvvqKjz76iFOnTtGmTZs8AVUvLy9q1KjBrl27irxGIYS41yUnJxMfH2/2Lz8vvviiaYG73K/7Bdm5cyeJiYk89dRTBY4OGTp0KIDZPdVSly5dYsWKFTz22GN8/fXXvPXWWxw/fpz27dsTFRVl1bkWLFiAs7Mzbdu2NV3Xiy++SIcOHahcuTKLFi3Kc8yiRYuoUaMGrVq1Yvny5dSpUyffD3i5Va1aFdB/8Wqr/Gw6nY5jx47lO+qoefPmXLx4kZs3b9qkLiGEEP8j98978/55+PBh3Nzc8nzObt68uWk/6EeGXr9+vcD7p7GcEAVS4oE2Z84cBahNmzapuLg4de3aNbV48WLl5+enXF1dVUREhDpy5IgC1PPPP2927Lhx4xSgtmzZYtrWvn171b59e9PPCxYsUHZ2dmrHjh1mx86aNUsBateuXYW2b9iwYcrNza3A/VlZWaps2bKqfv36Kj093bR9zZo1ClAff/yxaVu7du2Uh4eHunLlitk5dDqd6XFaWlqeOvbs2aMANX/+fNO2ZcuWKUBt3bo1T/nb+2DatGkKUAsXLjRrd6tWrZS7u7tKSUlRSikVHh6uAOXn56cSEhJMZVeuXKkAtXr1aqWUUomJiQpQkydPLrBfCvLMM88oPz+/IsstXbpUAeqTTz4psuywYcMUoF577TXTNp1Op3r16qWcnJxUXFycaTugxo8fb/rZ0v42Pk+7dOli9vt64403lL29vUpKSlJKKXXz5k3l7e2tXnjhBbNzxsTEKC8vrzzblVLq0UcfVXXq1CnyOoUQ4l5lfI3M759S/7u/zJkzx3TMK6+8oix9m2e8jy1fvrzAMgkJCQpQjz/+uGnb7a/5RlWrVlXDhg0z/ZyRkaG0Wq1ZmfDwcOXs7Gx2H8rvOsaPH5/nOtzc3MzOb/Tee+8pZ2dn0z1DKaWuX7+uHBwcTO009mXuOgoydOhQBSgfHx/Vv39/NWXKFHX69OlCj4mLiyuwX4z78rv3zpw5UwHqzJkzRbZLCCGEZeT+eW/fP3v16qWqV6+eZ3tqaqoC1LvvvquUUmr//v15Pj8avfXWWwpQGRkZRbZLPLxkxOdDokuXLgQEBFC5cmUGDx6Mu7s7y5cvp2LFiqxbtw6AN9980+yYsWPHAhSaj2TZsmXUqVOHkJAQs2/QjFPpt27dWqJ2HzhwgOvXrzNq1Ciz/Cu9evUiJCTE1La4uDj++ecfnn32WbPh74DZEH9XV1fT4+zsbG7cuEHNmjXx9vbm0KFDxWrjunXrKFeunFmuVEdHR0aPHs2tW7fYvn27WflBgwbh4+Nj+rlt27YAphVdXV1dcXJyYtu2bSQmJlrVlhs3bpidOz+nTp3i2WefpW/fvnz44YcWnzv3yEuNRsOrr75KVlYWmzZtKvAYa/t75MiRZr+vtm3botVquXLlCgAbN24kKSmJJ5980uz5Zm9vT4sWLfJ9vvn4+BT4ra4QQtxPZs6cycaNG83+2YJxlKGHh0eBZYz7ijMi0dnZGTs7/VtOrVbLjRs3cHd3Jzg4uNj33vwMHTqUzMxMfv/9d9O2JUuWkJOTwzPPPAPopzEqpcymEhZkzpw5zJgxg6CgIJYvX864ceOoU6cOnTt3zjfdTlHS09MBfX/czvgex1hGCCGE7cj9s3Cldf9MT0+36J4o909RUrK40UNi5syZ1K5dGwcHBwIDAwkODja9iF65cgU7Oztq1qxpdky5cuXw9vY2BZ3yc/78eU6fPl1gIv/r16+XqN3GuoODg/PsCwkJYefOncD/gob169cv9Hzp6elMmjSJOXPmEBkZaZajsqh8JYW1sVatWqb+NDIO2b+9/24PzBoDlcYgp7OzM1999RVjx44lMDCQli1b8thjjzF06FDKlStXZHtUIXk3U1JSePzxx6lYsSLz5883BRmzsrJISEgwKxsQEGBKJm1nZ5dncaDatWsDFJp31Nr+Lqpvzp8/D/wvR+3tPD0982xTShWam0cIIe4XzZs3L3BxhpKw5EOZcV9xVqXV6XRMnz6d7777jvDwcLRarWmfLVcxDwkJoVmzZixatIjnnnsO0E/Ta9myZZ73OJaws7PjlVde4ZVXXuHGjRvs2rWLWbNm8ddffzF48GB27Nhh1fmMXwbmlwM9IyPDrIwQQgjbkftn4Urr/unq6mrRPVHun6KkJPD5kLDkxb44wSGdTkdoaChff/11vvsrV65s9TnvpNdee405c+YwZswYWrVqhZeXFxqNhsGDB6PT6e5KG4zBxNvlDgqOGTOG3r17s2LFCjZs2MBHH33EpEmT2LJlS578nbn5+fkVOkp0+PDhREVFsW/fPrMg4e7du+nYsaNZWeMKtSVhbX8X1TfGYxYsWJBvEDi/3DqJiYn4+/uX5DKEEOKBVrduXUCfK7tfv375ljl27BhAni/B8pP7gxnoV2L96KOPePbZZ/n000/x9fXFzs6OMWPG2PzeO3ToUF5//XUiIiLIzMxk7969ppViS8LPz48+ffrQp08fOnTowPbt27ly5Yopl5klfH19cXZ2Jjo6Os8+47YKFSqUuK1CCCHuDrl/Fq2w+2f58uXZunVrnoEqt98Ty5cvb7Y9t+joaNP9VYiCSOBTULVqVXQ6HefPnzdLLBwbG0tSUlKhb+pr1KjB0aNH6dy58x0ZVWes++zZs3lG+Z09e9a033gjOXHiRKHn+/333xk2bBhTp041bcvIyDBbJAmsCwJXrVqVY8eOodPpzEZ9njlzxuwarFWjRg3Gjh3L2LFjOX/+PGFhYUydOpWFCxcWeExISAiLFi0iOTkZLy8vs31ffvklK1as4M8//yQkJMRsX8OGDfNM+cgdWNTpdFy6dMk0yhPg3LlzQOEr2Vra35aqUaMGoP/G9PaV6wsSHh5Ow4YNi1WfEELcr6y5jz3yyCN4e3vz66+/8sEHH+T7JdT8+fMBGDBggGmbj49PntfzrKysPB9Mfv/9dzp27MjPP/9stj0pKalYX0wVdm2DBw/mzTff5LfffiM9PR1HR0ebLbZo1LRpU7Zv3050dLRV93g7OztCQ0M5cOBAnn3//vsv1atXL3S6pBBCiDtP7p937/4ZFhbGTz/9xOnTp01BZNDfEwHCwsIAqFixIgEBAfneP/ft22cqJ0RBJMenoGfPnoB+5e/cjKM4e/XqVeCxAwcOJDIyktmzZ+fZl56eTmpqaona1rRpU8qWLcusWbPMhrb/9ddfnD592tS2gIAA2rVrxy+//JJn5fPcIynt7e3zTAX/9ttv83y75ubmBmBRgK5nz57ExMSwZMkS07acnBy+/fZb3N3dad++vWUXa5CWlmYasm9Uo0YNPDw88h3en1urVq1QSnHw4EGz7Zs2beLDDz/kgw8+yPfbSB8fH7p06WL2L3dOVcDsGz+lFDNmzMDR0ZHOnTsX2B5L+9tS3bp1w9PTky+++ILs7Ow8++Pi4sx+Tk5O5uLFi7Ru3bpY9QkhxP3KmvtYmTJlePvttzl79iwffPBBnv1r165l7ty59O7dm9DQUNP2GjVq8M8//5iV/fHHH/O8xud3L1i2bFmx8mSC/toKui5/f3969OjBwoULWbRoEd27dzf7cJicnMyZM2eKTG8TExPDqVOn8mzPyspi8+bN+aYIssQTTzzB/v37zT68nT17li1btph9KBZCCFE65P559+6fffv2xdHRke+++85UTinFrFmzqFixotlnuP/85z+sWbOGa9eumbZt3ryZc+fOyf1TFElGfAoaNmzIsGHD+PHHH0lKSqJ9+/bs27ePefPm0a9fvzxToHMbMmQIS5cu5aWXXmLr1q088sgjaLVazpw5w9KlS9mwYUORU+yzs7P57LPP8mz39fVl1KhRfPXVV4wYMYL27dvz5JNPEhsby/Tp06lWrRpvvPGGqfz//d//0aZNGxo3bszIkSMJCgri8uXLrF27liNHjgDw2GOPsWDBAry8vKhbty579uxh06ZNeXKkhIWFYW9vz1dffUVycjLOzs506tQp39wsI0eO5IcffmD48OEcPHiQatWq8fvvv7Nr1y6mTZtm9eiNc+fO0blzZwYOHEjdunVxcHBg+fLlxMbGMnjw4EKPbdOmDX5+fmzatMlshOyTTz5JQEAAtWrVyjNitGvXrgQGBhZ6XhcXF9avX8+wYcNo0aIFf/31F2vXruX9998vML8rWN7flvL09OT7779nyJAhNG7cmMGDBxMQEMDVq1dZu3YtjzzyiFmAdtOmTSil6Nu3b7HqE0KI+1WTJk0AGD16NN26dcPe3r7Qe8jbb7/NkSNH+Oqrr9izZw//+c9/cHV1ZefOnSxcuJB69eoxd+5cs2Oef/55XnrpJf7zn//QtWtXjh49yoYNG/KMQnnsscf45JNPGDFiBK1bt+b48eMsWrTIoml/BV3bpk2b+Prrr6lQoQJBQUG0aNHCtH/o0KE88cQTAHz66admxy5fvpwRI0YwZ86cQhdoiIiIoHnz5nTq1InOnTtTrlw5rl+/zm+//cbRo0cZM2aM2XUuWLCAK1eukJaWBsA///xjem8zZMgQ08jQUaNGMXv2bHr16sW4ceNwdHTk66+/JjAw0LSopBBCiNIj98+7d/+sVKkSY8aMYfLkyWRnZ9OsWTNWrFjBjh07WLRokdkI2vfff59ly5bRsWNHXn/9dW7dusXkyZMJDQ1lxIgRxeoP8RC528vIi7trzpw5ClD79+8vtFx2draaOHGiCgoKUo6Ojqpy5crqvffeUxkZGWbl2rdvr9q3b2+2LSsrS3311VeqXr16ytnZWfn4+KgmTZqoiRMnquTk5ELrHTZsmALy/VejRg1TuSVLlqhGjRopZ2dn5evrq55++mkVERGR53wnTpxQ/fv3V97e3srFxUUFBwerjz76yLQ/MTFRjRgxQvn7+yt3d3fVrVs3debMGVW1alU1bNgws3PNnj1bVa9eXdnb2ytAbd26tcA+iI2NNZ3XyclJhYaGqjlz5piVCQ8PV4CaPHlynnYDavz48UoppeLj49Urr7yiQkJClJubm/Ly8lItWrRQS5cuLbQvjUaPHq1q1qyZ5/wF/TNeV0GGDRum3Nzc1MWLF9Wjjz6qypQpowIDA9X48eOVVqst8DqUsry/C3qebt26Nd82bt26VXXr1k15eXkpFxcXVaNGDTV8+HB14MABs3KDBg1Sbdq0KbzDhBDiHlfUvdx4f8l938nJyVGvvfaaCggIUBqNRlnylk+n06m5c+eqRx55RHl4eJjuE126dFGZmZl5ymu1WvXOO+8of39/VaZMGdWtWzd14cKFPK/xGRkZauzYsap8+fLK1dVVPfLII2rPnj157qf5Xcf48ePztP3MmTOqXbt2ytXVVQF57t+ZmZnKx8dHeXl5qfT0dLN9xr68/R59u5SUFDV9+nTVrVs3ValSJeXo6Kg8PDxUq1at1OzZs5VOpzMr3759e4vvs9euXVNPPPGE8vT0VO7u7uqxxx5T58+fL7Q9QgghrCf3z3v//qnVatUXX3yhqlatqpycnFS9evXUwoUL8z33iRMnTJ9Hvb291dNPP61iYmIKbY8QSimlUaqQJaCFEPedS5cuERISwl9//VXoNHRLDR8+nN9//51bt27ZoHV3T0xMDEFBQSxevFhGfAohRDFkZ2fTu3dvNm/ezOrVq+nevXtpN8kiOTk5VKhQgd69e+fJiyaEEELcaXL/FOLeIjk+hXjAVK9eneeee44vv/yytJtSqqZNm0ZoaKgEPYUQopgcHR35448/CAsLY8CAARw6dKi0m2SRFStWEBcXx9ChQ0u7KUIIIR5Ccv8U4t4iIz6FEIW6X0d8CiGEeLj8+++/HDt2jE8//RR/f//75oOmEEIIUZrk/ikedDLiUwghhBBC3Pe+//57Xn75ZcqWLcv8+fNLuzlCCCHEfUHun+JBJyM+hRBCiGL4559/mDx5MgcPHiQ6Oprly5fTr1+/Qo/Ztm0bb775JidPnqRy5cp8+OGHha6MKYQQQgghhBCi+GTEpxBCCFEMqampNGzYkJkzZ1pUPjw8nF69etGxY0eOHDnCmDFjeP7559mwYcMdbqkQQgghhBBCPJxkxKcN6HQ6oqKi8PDwQKPRlHZzhBD3CaUUN2/epEKFCtjZyfdQ9zONRlPkiM933nmHtWvXcuLECdO2wYMHk5SUxPr16y2qR+43QgjxYJH3AneH3D+FEOLBYe290+EutOmBFxUVReXKlUu7GUKI+9S1a9eoVKlSaTdD3GF79uyhS5cuZtu6devGmDFjCjwmMzOTzMxM08+RkZHUrVv3TjVRCCFEKZH3AneWfF4TQogHj6X3Tgl82oCHhweg73RPT88iy+t0OuLi4ggICHiovtl9GK/7YbxmkOu29LpTUlKoXLmy6TVEPNhiYmIIDAw02xYYGEhKSgrp6em4urrmOWbSpElMnDgxz/ZDhw7h7u5erHbodDpSUlLw9PR8qP4+CyL9YU76w5z0hznpD3O26I9bt27RuHFjeS9wh1n7eS0/D+v724JIf5iT/jAn/fE/0hfmbNEf1n6OlsCnDRinS3h6eloc+MzIyHjo3jQ+jNf9MF4zyHVbe90y5UoU5L333uPNN980/Wy8yQcFBckHNxuR/jAn/WFO+sOc9Ic5W314A3kvcKdZ+3ktPw/r+9uCSH+Yk/4wJ/3xP9IX5mzZH5beOyXwKYQQQtwF5cqVIzY21mxbbGwsnp6e+Y72BHB2dsbZ2TnPdjs7uxK9UdBoNCU+x4NE+sOc9Ic56Q9z0h/mStof0o9CCCHEnSV3WiGEEOIuaNWqFZs3bzbbtnHjRlq1alVKLRJCCCGEEEKIB5uM+BRCCCGK4datW1y4cMH0c3h4OEeOHMHX15cqVarw3nvvERkZyfz58wF46aWXmDFjBm+//TbPPvssW7ZsYenSpaxdu7a0LkEIIYQQQoh7jlarJTs7u7SbYRM6nY7s7GwyMjJklD/W94ejoyP29vYlqlMCn0IIIUQxHDhwgI4dO5p+NubiHDZsGHPnziU6OpqrV6+a9gcFBbF27VreeOMNpk+fTqVKlfjpp5/o1q3bXW+7EEIIIYQQ96Jbt24RERGBUqq0m2ITSil0Oh03b96UfM5Y3x8ajYZKlSoVe2FXkMCnEEIIUSwdOnQo9A3Z3Llz8z3m8OHDd7BVQgghhBBC3J+0Wi0RERGUKVOGgICAByJQqJQiJycHBweHB+J6Ssqa/lBKERcXR0REBLVq1Sr2yE8JfAohhBBCCCGEEEKIUpWdnY1SioCAgAIX/7zfSODTnLX9ERAQwOXLl8nOzi524PO+SjDwzz//0Lt3bypUqIBGo2HFihVFHrNt2zYaN26Ms7MzNWvWzHcEzsyZM6lWrRouLi60aNGCffv22b7xQgghhBBCCCGEEKJQEiAURrZ4LtxXgc/U1FQaNmzIzJkzLSofHh5Or1696NixI0eOHGHMmDE8//zzbNiwwVRmyZIlvPnmm4wfP55Dhw7RsGFDunXrxvXr1+/UZQghhBBCCCGEEEIIIe6w+2qqe48ePejRo4fF5WfNmkVQUBBTp04FoE6dOuzcuZNvvvnGtJjE119/zQsvvMCIESNMx6xdu5ZffvmFd9991/YXIYQQQtzjkpOTWbhwIWc2bKBKVBT2DRrQ5fXXadCw4R2rUynFju3b2T9rFrrz58HHh/KPP85/Roy4o1OdcnJyWLd8OfGrV6OJiUFbuTLBw4fTu3dvHBzu3NskpRSbN29mxYoVXLlyhfJlytCmTRueeO45ypQpc8fqBYiLi+P3777j5saNaDIysKtfn3ajRtGsefM7Wi/ov5ReM2MG6bt3g1aLY5MmPPrqq9SrV++O152cnMyiRYvYtWsXKSkp1KhQge59+tCtZ8+7MrLkyJEjbJ05k+wjR1D29ri0akWvV16hZs2ad7xugMOHDrHtu+/IPnqUCB8ffFu35sknnyQ4OPiu1H/jxg1+/fVX9uzZQ9ClS3gHBlJ7yBB69et3R//Wcjt37hzrZ80ifc8eyM7GsW5dmj3/PG3atpXRRUIIIfL1559/8vnnn6PVasnIyKBChQps2rTpgVqhPTo6mv79+7N7927s7OyYMGEC7777Li4uLsU637Rp0xg8eDDlypUDYMKECSQlJTFt2rQ8ZY8dO8Y777zDX3/9VZJLKJy6TwFq+fLlhZZp27atev311822/fLLL8rT01MppVRmZqayt7fPc56hQ4eqPn36FHjejIwMlZycbPp37do1BajExESl1WqL/Jedna2ioqJUdna2ReUflH/383Xn5OSo1NRUFR8fr7Rarbp165Y6duyYOnDggNq1a5fas2eP0mq1KjIyUq1du1atWrVKLV++XG3dulVFRUWpM2fOqBUrVqhVq1aptWvXqsOHDyutVqsuXLig9uzZow4dOqROnjypYmNjTefPyMgo9et+GH/Xd/O6ExMTFaCSk5OL8zIoHkLJycklfs5otVoVHR2ttFptvvvXrFmjAgICFKDaglKGf3tAfT5ggMrMzCx23QWJj49X41u0UGdz1adApYD6xtNT7dy61eZ1KqXUyRMn1GcVKqj4XHVuBQWoRo0aqdOnT9+ReqOiotS7oaHK0VAXoJ4FdRPUt+7uasu6dXekXp1Op3744Qc1w8FBaW/r68OgJnTsqBITEwt9fhRXTk6OmvrKK2qdRmNW7x+G63/hhRdUamqqTevMbcmPP6r3XFxM/Q2ot0FdAzW+Rg114fz5fI8r6u/FEsnJyeqtXr3U/tv6XIHaBGrS00+rjIyMYp+/KDdu3FAfd+yoDuaq9wVDH2g0GvXyyy+rtLQ0i85VnP7Q6XRqxrffKnd3d1PfHza04zqoLwID1f69e4t7eRZJTU1Vnz7xhNqSz+/gHKiPQkPV5cuXrT6vrZ4f8l7gzrsb98+HjfSHOekPc8Xtj/T0dHXq1CmVnp5+h1pmnaioKOXn52d2jzh48KDS6XQWn0On06msrCyLj8nOzra6nSX18ssvq/nz55t+Nsa38mNJ+6pWraoOHz5s+nn8+PGm2Fx+/dG3b1+1efPmfM+V33PC2tf0+2rEp7ViYmIIDAw02xYYGEhKSgrp6ekkJiai1WrzLXPmzJkCzztp0iQmTpyYZ3tcXBwZGRlFtkun05GcnIxS6oH6lqAo98N1p6WlcfPmTW7dukXlypVRSrF9+3bS09PR6XQ4OTnRvXt30tPTuXLlCk5OTjg5OeHq6sr169fJyMigfPny2NvbY2dnh52dHUlJSTg5OVGjRg10Oh05OTk4Ojpy/fp10tLSSE9PJykpiaysLG7evIlOp2Pv3r1ER0fj6OiIq6srdevWpXz58sTExODo6IiHhwdOTk6l3V0Fuh9+13eCtdd98+bNu9AqISy3fP58Phk+nDjDavWngJ1AM6Al0GDZMj67epXxu3YVO7n47RISEujUsSOfHz9ObSAZOAxUAaoDY1JS+LtzZ7asXUun7t1tUifA0cOHOdqyJR9kZQEQDZwF/jbsP3z4MG3btmXr1q3Ur1/fZvVGRkbyW716TEpOJhuYatheFXAHXr11i109e7J60SJ6P/WUzeoFmDhxIhMnTmQs+lxHx4AUoDEQBoRu3cpnDRrw5KZNlC1b1mb16nQ6JnTrxrjNm/ECdMBBIAdYaigze/Zszp49y7p163Bzc7NZ3QCzPvqIjp99xkAgBphj2N4TqARMuHiROfXrk7F/P/VCQ21ad0JCAp06dSLi6FHGAZnor90Rfb93BlovWsTnJ0/y3u7dNh/dHBsby49hYYyPicEOyAAOAcZ3uUop9n3/PZ9v3sx7hw7ZvO+VUnzxzDO0/vVXcnJtPw1UBAKA92Jj+at1a/7+4w8e7dfPpvWDfqTvhy1b8vWZMzgCWvSvMenofwc1gAPHj/ND8+Zs2rSJUBs/B4QQQhRf06ZNiYmJuWPnL1euHAcOHChwf2xsLPb29vj6+pq2NW7cuMDy1apVY8CAAWzZsoXk5GRefPFFxo0bB8C4ceP4559/yM7OxtPTk9mzZ5tmXWg0Gj7++GPWrVtHhw4dGDp0KC+//DJpaWlkZGTw1FNP8eGHHwL60ZOnTp0iPT2ds2fPUrt2bb788kvGjh1LeHg4TZo0YdGiRdjZ2fHTTz/x9ddf4+TkhFar5aeffqJFixZmbc7IyGDJkiVMmTIFgJdeegmAtm3bYm9vz99//83bb7+NnZ0dFy5c4Pr165w5cwaNRkNiYiLe3t4A+Pv7c+DAAebPn09UVBSDBg3C1dXVtM5OdHQ0vXv35uLFiwQGBvL777/j5+cHwJNPPskPP/xAp06dLP3VWcei8Og9CAtGfNaqVUt98cUXZtvWrl2rAJWWlqYiIyMVoHbv3m1W5q233lLNmzcv8Lwy4vPujIa70/8SExPVhQsXTL+3lStXqkWLFqmVK1eqrVu3quTkZJWTk6OioqJUcnKyysrKuqvXnJ6erm7cuKGuXr2qkpOTlVarVdu3b1e///67Wrhwofrzzz/VjRs3lFarVdeuXVOpqaml3qf36u/6Xr1uGfEprHUnR6ycPHFCrbKzUymgOoLq3bu3OnjwoLp69ar67uOP1QY7O6VAJYOa+uKLJb0UpZT+G9++ffsqQLmB+tTNTS2aNUtFRUWpnTt2qAkhIeomqOmgfL291bVr12xSb3Jysprs7a0UqBxQk8qWVetWrVLR0dFq7dq1qm7duqZRmP/187PZ32hWVpb6qFo100izqR4e6rffflNRUVFq965danzDhirBsG+VnZ06cfy4TepVSqnFixebRts5g/q0b191/Phxde3aNfXLlClqkZOTUqCOgurZsaPKycmxWd1TR41SiYbr2g1q6ssvq3PnzqnLly+rKVOmqDJlyihADQE18dFHbVavUkqt+u03dcJQ9xVQ4zt1UgcOHFDR0dHqz99+U1/7+ZlGv37j6ZlndENBfy+W0Gq1qnPnzqZ+7+zmpn784gt1+fJldf78efV/Y8eqjRqNSgLVA9Tw4cNtdNV6mZmZ6uOaNU3Pt0XOzuqnr75Sly9fVhcuXFBffPGFqu7qqqJBZYH66JFHihyNYm1/fPfBByraUP+7oJ5//nl16NAhFRMTo9auXKm+qFJFpRr2r7CzU8ePHrXFpZu1t1u3bsod1F5Qa+zt1bdvv63Onz+vrl27pn6dPVs9GRho+h1VrFhRxcTEWHV+GfF5f5ARn7Yn/WFO+sNccfvj9tF9FStWNJutYet/FStWLPI6Hn/8ceXj46P69eun/vvf/6qIiIgCy1etWlUNGTJE6XQ6FRcXpypXrqx27typsrKyVGxsrKncb7/9prp162b6GVATJ040/ZySkmKaDZKWlqbCwsLUnj17lFL60ZNBQUEqISFB6XQ61a5dO9WiRQuVkpKisrOzVcOGDdWaNWuUUkp5enqqqKgopZT+fejNmzfztPmff/5RTZo0MdtmjG8ZDRs2TDVo0EClpKQUWMbPz0+Fh4eb+uH2EZ9Vq1ZV8fHxSqfTqQEDBqjPP//ctP/KlSvKz88v3z61xYjPBzrweaemut/O2k5/WF8US/O6jYFEpZQ6evSo+uOPP9SSJUvU33//raKjo5VS+hcXW37QM9Z7J645MzNTxcbGquzsbJWRkaHWrFmjfv31V7Vq1Sq1Z88epdPprBp+b2vyHLfsuuXDjrDWnfrgptPp1MSQEKVAZYCa0LNnnufxlvXr1T+GAMU2UCdPnCh2G4xyB+P8/f3VuXPnzPZnZWWpFzp2NJXp27dvietUSqlPBw9WOYZr+axaNXX+/Hmz601KSlID6tRRClQ2qK+eftom9c54911101Dv/3l7m96IGmm1WjW+Rw+VYSjzaVCQTV5H4+Li1FNubqqMoR+/+eabPGVOnzqlXnJ3Vy6GMr/88kuJ61VKPx3sZY1GadGnS1i3bFmeMvv371fPurgoBeoCqJWLF9uk7hs3bqifnJ2VAhUJ6v/efDNPmaSkJDWxalVTcHBijx5m+0tyP5v96aeqhaE/AwMD1ZkzZ/KU2bltm6rr6mp6jhf13tYaM8aNUymG6/re3V1dvHAhT5mD+/erpY6Opj5aOGNGoee0pj+OHzumNhtSGxwF9Vs+587MzFQftW9ves5/Ur68Taf4ffPNN6a+rerjo47m+hBmdOPGDdWsWTMFKF9Qb3ToYPF7KAl83j8k8Gl70h/mpD/M2Srw2aRJE1WxYsU79u/2gF9BTp8+rWbNmqX69u2rvLy81PkCUuRUrVpVbdu2zfTz66+/riZMmKCysrLUwoULVcuWLVW9evVUnTp1VGBgoKkcYPYFf2xsrHrmmWdU/fr1VYMGDZSPj4/6/vvvlVL6IOKLuQYhjBkzRr3zzjumn4cPH256r9e/f3/VuXNnNW3aNHX27Nl82/zrr7+qnj17mm3LL/D56aefFlqmqMCnsc06nU5NnTpVPfvss6b9mZmZCsg3xYEtAp8P9BzUVq1asXnzZrNtGzdupFWrVgA4OTnRpEkTszI6nY7Nmzebyoj7V1paGmfPnmX79u388ccfHDlyBIDy5cvTvn17nnjiCbp27WpKuOvh4WGzqZt3mpOTE2XLlsXBwQFnZ2d69erFf/7zH5o2bYq3tzcajYaIiAhWrFjBv//+y5UrV8jMzCztZgsh7lEb1qxhmCHFy0xfX97544886Ro6duvGoTfeIB1oD8w1TIMprpycHC6/+iqfAR7opzrXqlXLrIyjoyNf/fEH5cuXB2DlypXs2b27RPVeunSJT5YtYzjwnYMDT27ejLu7u1kZLy8vvly9mhV2djgAob/+yqVLl0pUb1JSEl5Tp+IO7AJa/vWX6bqM7OzsePfPP5keEADAc+HhrPr11xLVCzBv3DgWpKZyBhjRrx9jxozJUyakTh0eW7wYY8Kejz/+2Cb3jXfffZfvlaITsO/NN+nxxBN5yjRt2pRHv/2Wa+inHV945RWys7NLXPfPY8cy3HANP7Zpw6uGKVy5eXl5MWLHDn5zdASg519/cdTwfqEkkpKSsP/0U3YCw4Hffvst30WEHmnfnnd/+MH087hx48gypF8oiejoaJb83/8RCewAmmzcSPUaNfKUa9y0Ka6LFnEOqACkvf02qampJa4f4I+nnqKTUqQCG0eOZPArr+Qp4+TkxPvr1zOtQgUAmkdH89Ps2TapPy4ujvmGaYEajYafly2jQVhYnnK+vr6sXr2ajt7enARe2baNjevW2aQNQgghSubAgQNERETcsX+FTXPPLSQkhBdffJEVK1bQsmVLVq1axaZNmwgLCyMsLIzPP/+8wGM1Gg1Xr17ltddeY+HChZw4cYLFixfnSZOY+/3o+++/j7+/P4cPH+bo0aN06NDBrHzuRYfs7e3z/JyTo08w88cff/Dll1+SnZ1Nz549Wbx4cZ72lSlTxqKUjbe/X7a3t0er1Zp+Luoct7fx9mPt7e3vWDq/+yrweevWLY4cOWIKYIWHh3PkyBGuXr0KwHvvvcfQoUNN5V966SUuXbrE22+/zZkzZ/juu+9YunQpb7zxhqnMm2++yezZs5k3bx6nT5/m5ZdfJjU11bTKu7h/6HQ6YmJiOHHiBKD/0BEdHU1gYCBdu3Y15bIICAjAz8/vgcs96eTkRPny5QkJCQGgYsWKtGrVChcXF06fPs3BgwcB/QeB5OTk0myqEOIec+Tdd6mKPv9h0IwZBa7gOPLzz3nN15cQYMquXSUKBq7+/XdGxMfzATCubl369u2bbzkfHx8mTpxIA2A1sM2QJ6m4pk2bRrZWy0Ig9v33qVatWr7lqteoQfhLL5EN9FCKPydMKFG9CyZNYrAhmLe9Tx+atWyZbzkXFxca/vwzF4HywJEPPkAZcq4WR2JiIoELF2IH7LOz49MZMwos26tXLx577DGcgLYREfw2a1ax6wXYt28fGzduBOBa9eq8NGlSgWUHPvcccw33ryE3brCihAHfxMREqixYgD2w0s6O53/7rcBVuytXrkzKRx9xC2gK/PH66yWqG2DB558zJCsLB6DGo4/SsWPHAss+88wzdGrXjhHAvIsXWb5gQYnr//rrr9mRlUV9YMMLLxT4fAN4bMAAFj/yiL4taWnMnzy5xPXv3rmTwYb3Yz/7+PDK9OkFlnVxcaH9smW0AboDn3z6qU0C3ws+/pgDqaksAZ4bNozOnTsXWDYwMJCXpk5Fgz74vv+110r0dyeEEOLBEBkZya5du0w/JyYmEh4eTo0aNejSpYspPvXBBx+YyhhzWiYkJLB8+XI6d+5McnIyjo6OlC9fHqUUMwp5P2asp1KlSjg4OHD27FnT+ylr5OTkcPHiRZo2bcq4ceN44okn2LdvX55yDRo04OzZs2bbPDw8iowZ1KxZk3///RfQr3yf+4tTT09Pq2IOp0+fpn79+ncsRnNfRX4OHDhAo0aNaNSoEaAPWjZq1IiPP/4Y0H+7bQyCAgQFBbF27Vo2btxIw4YNmTp1Kj/99BPdunUzlRk0aBBTpkzh448/JiwsjCNHjrB+/fo8Cx6Je1dOTg579uxh+fLl7N2717QQUYUKFejQoQMhISGmUZAPEzs7OwIDA2nYsCHdu3c3jWKOiopiw4YNrFmzhiNHjthsZIcQ4v4UHh5Oy1OnAPjV15c+AwcWWNbV1ZUa48ZxFlBK8UOukWrWuvDll5QFrgGtpkwp9DV6+PDhvFSmDI8B9ffuJTY2tlh1pqens8AQVHJ1dc135GNuQyZOZKXhDZjvkiWkpaUVq16lFOrHH3EAtgMDp04ttPyjjz3G/9WsyaPAJ1evmr7wLY4VP/7IQMM36pcef5yKFSsWWv7DDz/kb+BX4PrXXxe7XoDfp0yhguHxu+++W+i3+BqNhrbffssl9AvehBcycsISi2fOpL/hus8OGEClSpUKLT/srbeY6OlJN2DSrl3Ffo4BaLVa0+97C/DU998XWl6j0TBh4kTeBR4BrnzySbHrBv2MF+PfpoOzM69acL4nfviBvYArkP1//4dOpytRG7a8/z7B6Bcr8/388wK/TDFq2bo1/oYvP6Kjo1m+fHmJ6k9PT8d97lzsADeNhgmffVbkMU8MH858wyygx8PDObB/f4naIIQQ4v6Xk5PDJ598Qu3atQkLC6Nt27YMGzaswC/sQT/QqkmTJjRv3pxXX32V1q1bExoayqBBg6hXrx7NmjWjSpUqhdb74YcfMmfOHBo0aMC7775brEV/tFotzz77LPXr1ycsLIyDBw/y5ptv5ikXFBREYGAgJ0+eNG0bO3YsXbt2JSwsjOvXr+d7/m+++YbXX3+dxo0bc/jwYdNCRQCjR4/mhRdeMMXYirJ+/XqeyGdWkM1YNCFeFEpyfFrGltd9/fp1tW/fPlOC4JMnT5oS5d5L7tXfdXZ2trp27ZravXu3KSny6dOn1a1bt2xy/nv1uu80yfEp7rQ7kaPs+w8+MC3uMuPtt4s8/vr168re3l4Bqkb16sV63Y2NjVWrci0oY8nfzOTnnzctRvTjbQsXWur3335Tm0C9D+r5p55SShX9dzupSxelQCWCWvn778Wqd/fu3eppUJtBfdKggUXHzJo1y5SbcMyYMcWqVymlJlWpohSofaCOWrBwjFarVV+UL68UqAOgTp8+Xax609LS1AIHB5UD6i1nZ5WamlrkMTqdTk0JCFDK0FeXLl0qVt1KKdWgQQMVZvhdnzp1yqJj3nvvPVOff/XVV0qp4t3P1q9dq6IMz+8vGje26BidTqe+qljRtADUyZMnLa7vdsvmzVMjQJXBugWTPqtXz5RndfOmTfmWsaQ/EhIS1J+GxdBmu7qqrKwsi+rfvHmzApQ7qKdbt7a43flZMm+eSjb+Djp2tPi4+TNmmBZbmtSvX5HlJcfn/UNyfNqe9Ic56Q9ztsrxeb+5PbelUvp7fFZW1j0Xq8ht6dKlatSoUXelrtv7IzMzU4WGhqq4uLh8y0uOT/HQuXLlCmvWrGHHjh1oNBqcnZ0BqFu3Ln5+fg/dqM7icnBwoFKlSrRq1QofHx8yMzOJjY1lzZo1bN68mStXrpR2E4UQd0nEkiWAPg/go88/X2T5gIAABjZrxjJg4aVLZt8OW2rzqlV0MTxWTzxh0bSWR197jaOAPZD4229W1wlwcc4cOgOjgQHPPGPRMQ1Gj+Y64A2c+emnYtW7bNkyFgGdgSr5fNOen4EDB5pGSP7xxx/FmnYbFRVFM8NMmF0VKtCgQQOLjnN4+ml0QBNgfTGvecO6dfTMycEe8O3UiTJlyhR5jEajwWX4cAA6AJuLOeU7PDycY8eOcQTY1Lw5derUsei4Z5991vR4xYoVxaob4Njs2ZQHkoDgt96y6BiNRoP3yJFogVbAlp9/Lnb9Z2fM4Bf0uWSHDBli8XG1x43jBLAEWL1sWbHr/+OPPxin0zEGuD5gAI6G/KlF6dixI6PKliUGeGr3bm7cuFHsNlyaNQtP9CPKWxryfFqi39ChrDTkey+7bp0pR5oQQgjxIBswYAB16tQp8YyP4ggPD+fLL7/E39//jtUhgU9xz8udt9PFxYUGDRrQr18/mjZtipeXVym37s4pzofc4nJxcaF9+/b07duXihUrmoazJyQkkJKSctfaIYS4u5KTk/ni4kX8gK9q1cqzuFBB2vfpw+NAS2BbMYKQ0QsX4gpcAprkys1dmNDQUP7x8ACg2smTVqfp0Ol0+OzYAcBf9va0t3DKUKeuXVnl4MDfwI49e4r1hnDTpk2APgVJnz59LDrGx8eHfi1a8A0w49o1zp8/b3W9m//8kw7GH/7zH4uP6zBwIHsNj3NWrrS6XoALixbhCyQA9UeOtLzuYcPYCxwDDq1aVay6V+Zqc79+/Sw+rmbNmnSqXZspwAt79hAfH2913UopXA2/7w12djz62GMWH9tt2DC2GR5nLF1qdd0AmZmZVDp0CIADZcrQvn17y+t//HEaOzryAfDnX38V+33IunXruARMBzqPGmXxcRqNhmq9euGG/kuCv37/vVj1p6WlUcmQw+wvV1faWdEHHh4eXGnTBoBHs7LYnSuvmxBCCFGUy5cvE5bPQnr3g1dffbVU1kEJDg6mZ8+ed7QOCXyKe5JOp+PChQusW7eO3bt3mz5oBgYGUqVKlQduYSLQf1g6HZHIf1ccod9X6+nx2Tr6fbWe/644wumIxLsSCHVxcSEkJIRmzZoB+jxbf/31F9u3by9RvjMhxL1p9+7dKKVIAoJy5b8uSofHH+eo4XHqhg1W1amUwtkQlNju6Eir1q0tOk6j0ZBuWCCmg07Hrp07rar3+PHjNE1PByA2LMw0Y6AoLi4urO7Rg27AmuRkTp8+bVW9169fx+f4ccoDjRs3xsfHx+Jj23TowBigD/BPMUYgXl62DHvgPNDiySctPq5y5crsMeRpqnXxIjdv3rS6btdt2wD4W6OhfSGLytyubt26DA8KohEw++hR0g2/M2skL1zIbKAt0Lt3b6uO7dWmDWOBQcDfq1dbXffx48dpd+sWAJdDQ/OsgFqYqlWrctSwunlwRAQJCQlW1793zx66GXKb3njkEewNoxct4enpSYcOHQC4evWq6Utna+Tk5LB582YA/Pz8aNq0qVXHP/Lcc1wGnIGIhQutrh9g186dpj5I6tjRqj4AqDJ0KGlAJWD//PnFaoMQQggh7h0PXvRI3NcyMzNJT09Ho9EQGRlJSEgIffv2tXh63v0qR6tjyqqjjJmzm20no0jP0qKA9Cwt205GMWbObqauOkqO9u4OPa9Xrx59+/bF19eXXbt2mUaC3s3RqEKIO2eHYQQkQNu2bS0+rnbt2uwzBA79T5606jXh8uXLJKenEwck1qlj8TRYgCr9+5MFlAXO/f23xccBHNq+HeOdxLtXL6uOfcQwAgxgz549Vh27fcsW1gBRwJNW3sva9O+PcY3N68UYeTnrwgWqAqPKlKF58+ZWHavatQOglVLs+Ocfq469du0adRMTAbhasyYehpG6ltBoNDxiCHDn5ORw8OBBq+rW6XRUOHGC54GnXF2pV6+eVceHPfUUsUAZILYYfb579266An0B92Ik6dcaAo8dgN3bt1t9/KGlSykHZACVBg+2+vgePXrgBrQD9lr5Nwawb98+Pk5J4SWgd7t2Vgcdm7dowWYHBwD8Dx8u1vuNk3/+SQCQDlQtRh907d2bJ9Gv7r7o8GGrjxdCCCHEvUUCn+KekJ6ezqFDh1i5ciVXrlxBo9HQvn17qlevbvWb5vuNUopv1hxj87FIALQ68zf5xp83HYtk2ppjdz3o6OLiQmhoKH379qVs2bLEx8ezevVqLly4UCo5QIQQtuO8YgXbgJexLvCp0WhIMASUGmVkcOHCBYuPPXDgAB+gD17eKmRFzPw0eeQRDgBHgXDDqFFLJa5fjz0QDtTr2tWqY1u1agXo23x8yxarjo346y880K9uHWLFtGuABg0a8K8hCORlZS7VqKgooqKiuAroWra0+l5aqW9fMtBfc7iVAbAD//6LcZyfqyGQZw1jfzsAe60Mup49e5bGmZkA3KxXz+rc382aNzdNN7e38jkG+sD4dWAV0KRLl6KK51G9Xz8uoM/PedAwZd4aGVu3AnAQ6GDFKG6j1q1b8y+wHUhet87q449s3MhY4HugvRWvKUYODg7EhYQAUD81lfDwcKvP8feRI4wAJgBtrRhtbBQQEMCl+vW5BBw7doy0tDSrzyGEEEKIe4cEPkWpi46OZvXq1aSmptKlSxdCDG94HxZnIpPYdCySosKZCth4LJKzUUl3oVV5GT80+/n50bBhQ86dO8fq1atlISQh7lNKKcpfukR7oJGbG+XLl7fqeA9DQKsecMCKUZAHDhwwPW5s5SjEmjVr0sfLizBg0fnzVn0R5GgYufUv0KhRI6vqbdq0Kd9qNMQC1a0MRtkZgmf/Ao2snPZrb29PZLVqANRNTibRMIrSErn72Zi+xBphLVowAAgGNln5On9t40Y8gFtApUcftbru1q1bMxe4CeRYOcV/35YtNDQ8drMywA36HI+R5coBUCE62uqg1+7duwFwdna2+nkG0LZ9e2oBPYCNR48WVdyMTqej7MWLABx3d6dixYpW19+oUSMOG9IJORdjtGOqYZr7aSDMMHLXWm6GgHEjYLchkGuprKwsNh8+zFzgjxo1qGBIHWCtli1bAqDVaq0edSyEEEKIe4sEPkWpyMrK4ujRo2RkZODv70+3bt1o27Ytvr6+pd20u271gSvY21k2IsXeTsOq/aUbaNRoNFStWpUePXrQuHFj0zTVuLg4mQIvxH0kNjaWOoaRcUlBQVYfX7VdO1LQ5+KLNwR7LHFw/37T4yZNmlhVp0ajoUmLFoC+/VFRURYdl5GRQXxsLPFAbGAgbm5uVtXr6upKjiEYVi4+nqysLIuOU0rhdvkyAOfKlKGc4RzWsG/cGIBQ4LBh0RpLnNu4kaXA2xQv8FmzZk22ubtzDjhkZQBs18WLjADGA42LUXdISAgaR0dcALezZ4ssn9uNrVtxACKBesUY8QigM/R5E+CQFX2enJzMkAsXmAD0rFfP4jyyuZUtW5YqVaoA+nyh1txXw8PDaZSdDUBicLDVdQM4OTkRW7UqALUSE61e4MndkBd0n52d1WkGjOr07Ml1DK8tGzdadezZs2dNf58tDK8VxdGiRQtGoV/h/mQxpvwLIYQQ4t4hgU9xV2m1Wk6dOsWqVatISEggJycHR0fHB3p19qLsPhuTZ3p7QbQ6xe6zMXe4RZbRaDRUrlyZChUqoNVq2bt3L3/99ZfFgQghROk6cfy4KeclxVh9sk69euwEdgARVqw43nffPiKA99zdrR5lClC/fn0ANOiDHJa4cOECnyhFAHDQwtXcb5dlWPG+rlIWT+2PiooiKCMDgIygIKunXQOU69gRHeAPnLFi2nf2v/8yABiIflEla9nZ2ZlGLF69etWqANj2EyeYC8zz8zMF8aytO9YwWrFGSgqpqakWH6szpAQ4yf+eK9byMUyPDgLO5wrUF+X06dMMRR/wbV2pUrHqhv+1OzslhYiICIuPO3ToEMOAJwH7Yo62BMDwhUQD4NSpUxYflpycTCXDqOTrlStblb83t9AGDfgKGAfsi4y06tgTBw8yEngEaFDM3z/oA58vof/7SSpGrlUhhBAPjmrVqhEcHExYWBhhYWE8//zzrFq1ijfeeAPQ56+fNWtWoeeIjo6mVatWNkkV17RpU7YZFpEsiffff5+QkBAaNmxI06ZN2ZBrwVKdTsdrr71GjRo1qFmzJjNmzDDtmzFjBl988UWJ67+bJPAp7qqzZ89y7do12rZtS8eOHa1a7fRBpJQiI0tr1TEZWdp7bmSlvb09vXr1ombNmuzdu5ddu3aVdpOEEEUI37cPL0AHBDzyiNXHV6tWjf5OTrQD1sRY9oXMrVu3qJaaSkXAv2xZq+sECKlRg33op0FfsXA03rlz50yPaxYznYqzIXgYDJy1cLXr48ePU9d4fDGmPQPUa9aMi8AVIO7YMYuP8zCMND1tZ0dVwwg+a7UICeFNYDpw5swZi45JSEggNjYW0OcoLU6wFyCnTh0AQqyoG8D96lUAwl1d8TOsTG+t6o0acQ2IBuKOHLH4uHOHD1PN8NizBKMN21StSgQQAxy34nd+5MgRTgCLgeBcC3JZy9MwzbsCcNGKad7Hjx+njuGxYwkWpQwMDGRBQABTgS1WfKkCELN9Oz8Aa4AGDRsWVbxAISEhnDQ8d12syGEshBDiDkhNLfif4Qtmi8qmpxe7CUuWLOHIkSMcOXKEn376iT59+vDNN98AlgU+v/jiC0aNGoWdnXUhuJycnGK3uajztG3blsOHD3P06FF+/vlnBg4caPqyeeHChZw6dYpz586xb98+Jk+ezEnDl8sjR47k559/Jjk52SZtuxsk8CnuuKSkJHbu3El2djbBwcE8+uijBAYGlnaz7gkajQYXJ+sWnHBxsi/2B8k7yc7Ojtq1a9O7d2+CDVPsIiMjLZ4SKoS4uxINOSAjgZBijPh0cHCgdu3aAJw/f96iN2YXL17EOPnVOILSWjXr1KEs4AbcsnDxmfO5gifGNlurfIsW3AKcgFgLp/ZHHj+OcUxrcQNhNWrUoD5QDdiWlGTRMVqtlsAbNwCICQiw+k22UfWgIKYCo4HLFgZ7L168yCCgDVCnGCkUjDwN+VCrAWcsDD4mJCTgZcjJeasEIy5DQkKojT7wty0uzuLjbhieF3FAdSvz1+ZWtXlzAgFP4LIVaSRyP8+LO80coFaTJhjHmSZZkb/30qlTGH/j7sVIcZCbcdRrbGwscVb8DjINX4acRD9ytLgcHR2JNgTOA2JjZTFHIYQoTe7uBf/7z3/My5YtW3DZHj1s1qS5c+fSz7Bo5UsvvcTZs2cJCwujT58+ecpmZGSwbNky/pOrrRs2bKBx48Y0aNCA9u3bm2ZYbNu2jXr16vHcc88RFhbG8uXL2b17N2FhYdSvX58RI0aYveeOiYlh4MCBNG/enNDQUD788EPTvmrVqvHOO+/QvHlzhg0blqddPXr0wNXVFYDQ0FCUUqZ77pIlS3jhhRewt7fH19eXQYMG8dtvvwH6tDiPPvoov/76awl78e6RwKe4Y3Jycjh06BB///037u7uaDQa7O3vzaBdaWodXM6qHJ+tg63PEXc3OTo64u/vjzJMB12zZk2xVmUVQtxZmkuXALiIPp9jcdQxjMrTZmdzyXC+wlw4cwbj2EPXYo7Gql27Nsbxm9m5RnIWxnHLFi4C0yh+4LNOvXpcNDy+aeGiM5ciIhgCvA9Urlu3qOL58vb2xsMQgLl48WIRpfWuXr1KNUOgJtuwOFJxVGnQgBuGx0kWjq69dOYMC9GnQKhfjFQGRtWaNycZ/RvVuL17LTrm9OnTPAX4ABHFWFHcqFy5cjh5egKWp1MAyDaMzjzN//42iqNeo0YYxxjetGJlef9Dh3gdaFyCUb6gb7txgrsyjO6wRMKBA9gDSUBFK/P33q5BvXrUBfoBJywMugOmnLrnnZyKtbhTbhk1agBQR6eThRyFEOIhN2jQINNU9+XLl5vtmzVrFsHBwRw5coRVq1blOXb//v1Uq1aNMmXKAHD9+nWeeuop5s2bx7Fjxxg5ciRPPPGEaVbn6dOnGTp0KEeOHKFv374MGjSIKVOmcOLECZ588kmO5nofOmzYMF555RX27dvH4cOHOXDgAMuWLTPtv3HjBv/++y+LFi0q9PrmzJlD9erVTe8frl69avZeolq1alw1zKoBaNWqFZsNCxreDyTwKe6Yo0ePkpCQQPfu3QkLC8PBwaG0m3RP6t20qlU5Pvs0K/6HmbtJo9HQvn17mjVrxtGjR9mxY0dpN0kIkcvNGzeIBi7b2xd7SnBTPz8uAlFg9maoILGHD2MPZAHlizn1u0KFCkTa60fKO1y7ZtEx2osXqY5+9GCtYo40rVWrFqavcAzBlaKciYhgITAJqF69erHqBf2oT4CIiAgybp/SlY/z58+bply7liAAV6NGDVOwN/P0aYuOSdi/Hwf0qQjKFSO3qFGdunVNAe7M48ctOsY44jEJqFaC0X4ajYYQQ0qEK1eukG7h1Lgyhr+Bi46OxV5NHPT9bpzc72zhF4dKKVpfucI0YIiXV7Hza4J+gaX5bm68Bqy5caPI8kY7ExLwQp9fs1Yxv2AwCg0K4iSwHLhqYZ7RrKwsKqakAHAjMLDEX7Q7G55DNYEzFj7/hRBC3AG3bhX8748/zMtev15w2b/+KnYTck9179+/v1XHRkREmM14/ffffwkNDSU0NBSAp59+mqioKCINea2rV69O+/btAX26HwcHB7p06QLAo48+anpPmZqayubNm3n99dcJCwujadOmXLhwwexL2+HDhxd5P9y8eTMTJ05kyZIlFt87y5UrZ1Ue8tImkShhU5mZmZw8eZKGDRvSsGFDGeFpgZCK3nRpUJHNxyIpLPypAbo0qEhwBe+71DLbqFy5MuXKleOG4cNTQkICPj4+8rwQohQppZiWksLn6HNmDi/m36N3rVoYw3mbzp4Fw5uygqQYRm5dA2oWMzCi0Wi45e8PsbF4Jiai0+mKnMrtZMhBesPVFQ8Pj2LV6+LiwnZPT66mpPCvhYvtGEfBOjg4UKkEU687+vnxXyBNKcLDw4scTRh+/DiPGh77lSD4GBQUxHKgOWBv4Yi3DMPv+DxQs5hBZtDfOxYBV4ErFk7xzx18DyrBNHuATgEBfA7cUirPqIf86HQ6fA25rpL8/Ut0j3N3dyfaxQUyMihj4TTvuLg4qhimvqWX4LkG+r+xi6Gh7N27F2JjyczMtGiF+vPnz5MCnHNwoFoJRhoDVKhThwTAF0ixMM/p1atXTQH/7GIsqnW7gGbNYPZsPIHwgwehZ88Sn1MIIUQxuLndmbJ3SZkyZSz64tqoqHVQjO8xjCNE9+7di4uLS7HOtX37dkaMGMHq1atN6eoAqlSpwpUrV2jVqhWgz2Oae8HKjIwM0zT5+4GM+BQ2ExERwbp160hLS0Or1eLg4CDBLQtoNBreeKwBXRrop2TdPu3d+HOXBhUZ81jxF4ooTY6OjpQrVw6lFPv27WPTpk3cvHmztJslxEMrKSnJlLy8cgmmxAbWqEGC4fFNCxagUYbg2VVKFpjKKKdP+VFFqSJXG8/JycHXcK2pxRzZarSjVi1eB5bfuFFkTlOlFL7nztEZaFSxYolmPVSoVo326EfSXbRgoZXk06fJwTDluAS5Hp2dnblhmPLtcf26RcdoDaNhr1CyUa6Ojo58X748TwDLLUye73DgAGuBD9EHTkuiXPnydAHaAlFRUUWWj42NpaLhA0hWCab4G6X6+wPgc/OmRflzL1y4gPHjiGMxU1fklrv/Ii1YWd2Y3gb0f9slneVTtWpVLhseZ1mY0iI8PNwU+HQq4YhTgOp16xKFfgG4W1asbi+EEOLh4unpWehCPw0aNDBbaLNly5YcP37clMpl8eLFVKxYMd8ULSEhIeTk5LB161YANm3aZEp95O7uTseOHfnyyy9N5aOioiweifnPP/8wZMgQVq5cScPbUlANGDCA2bNno9VqSUhIYMmSJQwaNMi0//Tp03mOuZdJ4FPYxNmzZ9m7dy+NGjWiTZs2ODk5lXaT7isO9naM7dOQ6c+2pkO9Crg62aMBXJ3s6VCvAtOfbc3YPg1xsL+//2Q1Gg1du3bF19eX9evXW5yvTghhW7kDGVVKMDKqcuXKGM+UYUGOz8iUFLYDh+3t8fb2Lna9WkNQpipFB6ViY2NNAaHMEi6sZxy1qdPpiCliJfuEhARGp6WxCXjakNOpuLwNi095AjEWBGCOZWTgAtSBkuc5NASZy2ZmWvSFlYshEJ3g6opbCUddGINvsbGxFi2U53LxIj2B1pQ88OlmGFXrB8RbMNr16tWrdEH/nLxeglG2RlrD762KUhYFHi+dP49xnKenYWGgkqhWoQItgP7ANQtSSsTFxfFWWhozgM5ly5a4/tyBT3sL0mgARJw6ha/hsVcJUh0YVapUiZZAGWBvZmaJz/cwmTlzJtWqVcPFxYUWLVqwr4hctcuWLSMkJAQXFxdCQ0NZt25dgWVfeuklNBoN06ZNs3GrhRCieBo0aEC9evWoX79+vosbBQUFUbZsWdOq6AEBASxatIihQ4fSoEEDvv/+e5YtW5bvACcnJyeWLFnCG2+8QWhoKL/++qtZwHHRokVcuHCB+vXrExoayuOPP26aaVmU5557jszMTEaMGGHKX3rckF5oyJAhhISEUKtWLZo1a8abb75pmpoPsH79ep544gmr+qk0yVR3USJJSUl4eXlRpUoVKleubErYK6yn0WgIqehDSEUfQD964n4c3VkUe3t7mjRpQsWKFQv9ZkwIcedERkZyCH0exv0+PsU+T6VKlTgMhAI6C4Iji1NTmQlUq1yZN0vw+uZQsyaH0U+Zd4iKIqyQVemjoqJMgU9KOP21csWK+AMB6Gc5FDZ9PXe9uhIGH8tWqcIN9EG4DAtyPkZFRaEFYqBEuSYBVPnycO4cFdCvHFpYqgCdTof3rVsA3CxBYNuocuXK7Nu3D09D8K+oUcIuhlGpkfb2dC/h6N7AWrVIAryBjFyrpRfk2rVr5KAfzRxgyMlaEprgYDb/+y9HgKaXLxc51T75zBkcgBzAtwSjfI2qly3Lfw2Pf71wAQy5xgoSFRXFQKAu8LVhlHBJuLm5EevqCunplLFwtPHFiAg6A5WAp0qQ29aoQoUKRGg0KKUsCv4KvSVLlvDmm28ya9YsWrRowbRp0+jWrRtnz56lbD5B8d27d/Pkk08yadIkHnvsMX799Vf69evHoUOHqH9bEH/58uXs3bu3xK9rQghhjcv55HYfPnw4w4cPB/QpjdasWVPoOd566y2+++47vvvuOwC6d+9O9+7d85Tr0KEDR44cMdvWunXrPNuMypYty8KFCy1ud27nC3l/Y29vz8yZM/Pdd+rUKXJycmjTpk2h57+X3N/Dx0SpUUpx+vRp/v77b+Lj43F1dZWgp409iEHP3MqVK0dwcDCZmZn89ddfXLfwg40QouSuX7pEI6AdEFiC6cj+/v5EG/JrOhTxN5yVlWX6Brp8CacCu4eE0BjoS9EjPqOuXTONhHMsYUAq2MuLOOAYEFHEKMDcI001JUgnAPr+ijY8zrJg9KGxT9zd3Yud09QooX596gKdgejo6MLLJiT8b7q3DUb9NStThnTgDEWPOlRK4WnIBZri7V3ie2ilSpUw1qi1YDGr3PlFSzraFMClSRO6AOMo+oMLQJZhBkUEUL6EOT4BygYHk2J4nGLBqupRkZEYr9qhBK8puaUGBADgf+uWRSN+L0ZEsAWYT8lzvII+3YLxtUoCn5b7+uuveeGFFxgxYgR169Zl1qxZlClThl9++SXf8tOnT6d79+689dZb1KlTh08//ZTGjRszY8YMs3KRkZG89tprLFq0yKLFuzIzM0lJSTH7B/ovaEryTylV4nM8SP+kP6Q/7kR/KKUeqH8A//nPf6hTpw5arbbU21PSf1evXmXWrFkl6o/c/1v67/bniTVkxKewWmZmJnv27CElJYXOnTsXezVgIUCfQ65mzZps27aN+vXrU6dOnQc+6CseHDNnzmTy5MnExMTQsGFDvv32W5o3b55v2ezsbCZNmsS8efOIjIwkODiYr776Kt9ve++0DEPw7BYQUIIghZ2dHamenpCUhGsRC9Dknhpe0sBn7tE+RQU+48PDOYF+tKRnCfP++QQHo0P/5ulGEXkH469cwXh3dC5hzsVy5cpxDKgPUMQUe4AnLl+mHrDBy6tE9QL4VquGcT3roqb3R0dH8zEQDPiUYGEjI8+aNXEBygFbw8OhXbsCyyYnJxNoyIWZXcKUBqAPfO5DP5rZ0YIcn9mHD7MAOEzJ0kcY5R7haVHQzVDmKlDJBjlGK1epwjWgHpBpQV7Z+AsXMIbYy+RaHKFEKlaEq1cph36EdVE5Y3OnBLBF8Bmgu48PbaOiCLdikaeHWVZWFgcPHuS9994zbbOzs6NLly7s2bMn32P27NnDm2++abatW7durFixwvSzTqdjyJAhvPXWW9SzcETzpEmTmDhxYp7tcXFxVi0ykptOpyM5ORmlVJGL6j0MpD/MSX+YK25/ZGdno9PpyMnJsSjH9f1AKYVWq+XFF18sVtDuXtO5c2eAYv9+jP0Blg32ysnJQafTcePGDdMXX9auFyKBT2G19PR0nJ2d6d69u+TyFDZRq1Yt/Pz82LVrF/b29mYryglxr7J2Ot+HH37IwoULmT17NiEhIWzYsIH+/fuze/duGjVqdFfbrjEEsGLQ5xkqiRuBgexKSuJsZibp6ekFrvAYHR3NBfT58n6xYLROYcwCn0XkP7yclMTzhsd/lTAYUrFqVeKAQOBmEdOfbxlG4KUDPiUc8env748x5OhYxCrfN2/epHVmJj2Ba0Ws5GmJcoYcn1D0iM/o6Gh2AjuB922wuIx/SAgZgAuQVERu02vXrmEMdzrYYMSjv78/MXZ2oNPhYEGuLM358zwDBAEVbRB0M/a7I3CjiH4H+Bv4Df1U9122CHxWrsxJ9IHPLAsCr2lnzwIQDwSWcEV30zmDgxm7Zw/ngfdiY4sMfFYKD2cEcLJMGZutNFvbw4PhwF70X7LYYiTpgyw+Ph6tVkvgbV8+BAYGcqaABfBiYmLyLZ/7i5avvvoKBwcHRo8ebXFb3nvvPbOAakpKCpUrVyYgIADPYqZj0Ol0aDQaAgICJLCF9MftpD/MFbc/MjIyuHnzJvb29iVeKO9eY8lo9YeJpf1hb2+PnZ0dfn5+ptXrC1rFviAP1jNJ3FGXL19Gp9NRvXp1WrVqVdrNEQ8YX19funfvjp2dHdnZ2aSnpxf7jakQd0Pu6XwAs2bNYu3atfzyyy+8++67ecovWLCADz74gJ49ewLw8ssvs2nTJqZOnVpgbp7MzEwycy2qcftUveLQ6XTYG4JnMUAlX98SffN8pG5dxhuCHqPj4grMexkVGUkDwBXwqFChRHWWK1eO+UBv4Mf9+ws9V+5RYOXKlctTNvdUrKIEBgYSgz7wmRMRUegxaYapz3FAQNmyJbpejUbDDTc3rqWmknjzZpHXawohBAZaXe/t/VG2bFleRb9QUurJkyXqa2uVK1+eOKAykH71aqHni4mJwZhy36lSJZuMpkjz9CQ2KYmUIvocQGP4m7oONC5Gv9/Oz8+PDcCjwJQjR4o83/m4OC4CXl5eODs7l7h+f39/4jQaUArN9eum8xX092IcRR6NbX73APY1a/K14fGImJgiz9n1+nVGANNdXGw2msapShXYu5fyQPiVK3lyrVrz+lGQ+33kz5128OBBpk+fzqFDh6yaEeTs7JzvCF07O7sSBaU0Gk2Jz/Egkf4wJ/1hrjj94ezsjJ2dHfHx8QQEBDwQMwGVUuTk5KDVah+I6ykpa/pDKUV8fDx2dnam5wZg9d/YfRf4tGZaYYcOHdi+fXue7T179mTt2rWAPintvHnzzPZ369aN9evX277x9ymlFIcPHyY8PJzWrVuXdnPEA8z4rU9ERAT79++nVatWJV6RWIg7oTjT+TIzM/N8O+nq6srOnTsLrOdOTdVzMKy8HQ1U0OlKlGM398iq8+fPFzgT4PKJE5hKBgSUOK+vM/qFZ1RsbKHnyp0f0dHRMU9Za6Zi6XQ6YoCG6EfNFlZvmqHeOPRJ70t6vT8HBfHGiRM4pKXxfExMgW09deoUzYzt9fe3ut7b+8PZ2ZkhQHPgv6dOFXq+yJMneRr988rV1dUmuZuNgc+Ma9cKPd+lCxdM153l7W2TuhfUqsXo/fshI4Nnrl0rdBShY2IiAImOjjZZuE+j0ZBmeFzUc1wpZUr5EGCDvy2jlDJlIDUVl5QU0zkL+nvJMIwKvQ4EODvbpA25Xy8vXLhQ6DkzMjLwNeQBTfPwsF3ecMPo2XLAulOnCAkJMdtti6mt1k7Xu5f5+/tjb29PbGys2fbY2Fiz0eO5lStXrtDyO3bs4Pr162YpJLRaLWPHjmXatGkW5cAVQtw/7O3tqVSpEhEREQ/M37fxCzI7OzsJfGJ9f2g0GipVqoS9vX2x67yvAp/WTiv8888/zZKh37hxg4YNGzJgwACzct27d2fOnDmmnyV/z/8opfjnn39ITU3l0UcfLfEiCUJYomrVqmg0Gnbv3k29evWoW7duaTdJCDPFmc7XrVs3vv76a9q1a0eNGjXYvHkzf/75pynHTX7u1FQ9d8PK2zfs7alWwmmpuUd4KqXyvR8DYBgRlwpUDw0tuJyFkl1cICMDj9TUQs/V6dIlvgeWAcHBwXmCE9ZMxfLz8+OA4bHbzZuF1nvCzo5n0edRnVm3bonzYVeqVIkTJ06Qk5ODg4MD/v7++ZbLzMjA2Crv4GCr+/n2/rC3t2efYZ99UlKh53O9do2F6PNMXg0JKfHv2M7OjsOGx84pKYWeL1urxQf9iOLv6tUrcd1gPs3fwcGh0HO6p+nDlGlubjapWynFdnt70GpxLeLaU1JSGJCeji8Q5+dnk/oB0j08IDUVz/R006ibgv5ejIHfOI2GdnXqlOjDiVGNGjVoiD7wTWJiodd19epV00hnTblyNusDf8Oq4s6AXXJynvPaYmqrtdP17mVOTk40adKEzZs3069fP0DfR5s3b+bVV1/N95hWrVqxefNmxowZY9q2ceNG0+yyIUOG0KVLF7NjunXrxpAhQ0wzLoQQDxZ3d3dq1apFdnZ2aTfFJoz5Kf38/GQ0MNb3h6OjY4nfV9xXgU9rpxX6+vqa/bx48WLKlCmTJ/Dp7Oxc4LeQDzuNRkP16tUpV66c5KQQd1WVKlXw8PDgwIED1KxZU/LJivve9OnTeeGFFwgJCUGj0VCjRg1GjBhR4Eq3cOem6t3MyCASSHJ3L/EbsEqurkSiH3259saNAs+nNeQpjEM/fbqk9d50c9MHPtPSCj2Xd0oKwUB5Z+cCc0VZOhXLzs6Om87OkJmJ061bhZY/efMmR9AHzBbb4I1u7gWhrl+/XmBgJy06GuMzxi0oqFj15u4Pf39/Eg1Tnu0SEgo9X1au33HFihVLfM1+fn4YM5o6JiUVer4bhjyc6UDZcuVs8sEid3A5MTGxwAVztFotXoaUFBmenjb7UHPL3R2Sk3Et4rkWGxvLKKAFMDXXNLCSOl2lCq/ExHBUp6N/Wprpy+/8/l6+1mr5DKhUtiyDbfR+sVy5ciwFagPTz54t9Lri4uJMgU+7ChVs1gf+FStyA/3iaNlXr+Z73pJObX3QPgS/+eabDBs2jKZNm9K8eXOmTZtGamqq6fPb0KFDqVixIpMmTQLg9ddfp3379kydOpVevXqxePFiDhw4wI8//gjoXwdu/+LI0dGRcuXKSU54IR5g9vb2NvkS7V6g0+lwdHTExcXlgXvNL47S6I/7JvBZnGmFt/v5558ZPHgwbm5uZtu3bdtG2bJl8fHxoVOnTnz22WeFjswoac41W+QDutOSkpLYs2cPHTp0ME01Lml774frtrWH8ZrBdtft5eVlWjXu4sWLVK5c+Z5Ocm3tdT9sz4sHSXGm8wUEBLBixQoyMjK4ceMGFSpU4N133y1ywQ5bU0rxfmYm7wCNqlcn79eG1nGvUAHjUkOpEREFlrMzTK+/Dvj4+JSwVshxd4cbN3DLzkar1Rb45tg9NRWAW2XKlLhOgOM+PkyLiWFfdjYjCylnfG7YIsgLEOLoyC4gGwqdxpttmHKcDHjaYNSbRqMhwzDl2dHwfqcgdobgYxxQu4ARqdawt7fnmJsbv6emcjrXDJ78xBueX0CBo2GtVcPRka2APf8LrOYnMTHRNMo257Yv3Usiy8sLkpPxSk8v9DkeGxuLMSTrkGvhr5JKCgpiyT79eN/4+PhCZ/3cSEggE6hy2yj4kihbtiwR6AOfqogFnmJjYqhveOyca0p0SRkXFvMDdEUspCb0Bg0aRFxcHB9//DExMTGEhYWxfv160wyJq7cFkFu3bs2vv/7Khx9+yPvvv0+tWrVYsWIF9evXL6gKIYQQwir3bgThNsWZVpjbvn37OHHiBD///LPZ9u7du/P4448TFBTExYsXef/99+nRowd79uwp8A1mSXOu2SIf0J0UHx/P3r17qVmzJjdv3rRZ7qF7/brvhIfxmsH2163T6Th06BD79u2jdevW92w6Cmuv+0HK6/WwKc50PiMXFxcqVqxIdnY2f/zxBwMHDrwLLf6f5ORk0/R6WwSIfMqWJRnwAjIKCXzaG6bCxgNB3t4lrlfn5aWvH/013T7LA/S/E09DwCzDRqlajlSpwo8xMZCayoICglFKKWpcv059QNkgyAvg4e1NayAN2FBIDsksQ1A0HtsEmAEy3NwgNRW3It7jOBvadUOjwd0GK8oDrKxYkcnnzuGZns73hZQrf+IEq4Ed2C7w6envTwdACywvJNgcFxdnCnwqG02xBkMQ9epVAoCEhAQCAgLyLZeYmEgTw2NHG+bFzt2P8fHxBa5onp6ebhoQkN/fYXGVLVuWQ4bH9rkC2/lJuHLFlEPYzYZfJvn7+3PF8FhXRBvE/7z66qsF3gu3bduWZ9uAAQPyzMgrzIOS908IIcTdcd8EPkvq559/JjQ0NM9CSIMHDzY9Dg0NpUGDBtSoUYNt27aZRprdrqQ512yRD+hOSUhI4OTJk7Rr146aNWva9Nz38nXfKQ/jNcOdue7+/fuzZ88ejh8/TseOHQtdZKK0WHvdD1Jer4eRtdP5/v33XyIjIwkLCyMyMpIJEyag0+l4++2372q7bT0yzs/Pj3j0gc/smJgCy8VkZbEdOAJ0tUHgE0NQzwf9vSu/gEtKSgrG+Rs5hkBpSeWeEZKYmJhvH6anp/OOVstjwOScHJvU62yY6l4GuFlIEO6oqysOgBuwy0ZBqEwPD7h+HS+tlqysrAJTj7gacsfedHa2WfJ+f39/zp07R0pKSqF1e0RH8xiQge0Cn66G/LX2QKphJG1+4uPj6QIEAE/bcOqtxvBlf1n0o3wLCnzejInBOJfJloHPsj4+tEefxiI+Lq7AcgkJCXxhKHfahrMy3NzcSDbkOdUkJRVa9lZ4uP5/wK+AlATF4e/vTyP0z6s2ZcowxmZnFkIIIcTdct8EPoszrdAoNTWVxYsX88knnxRZT/Xq1fH39+fChQsFBj5tkXOtpPmA7hRfX186dOiQZ2Strdyr130nPYzXDLa/bjs7O9q0acP+/ftJTk7Ok7LiXmHNdT9sz4kHjbXT+TIyMvjwww+5dOkS7u7u9OzZkwULFuBtiyCgFeLj41kHeALbbfAc9PPz4wZQA9Dedo/ObZWdHV+iD2a8b4McgLrAQA4Ap4EQw2jS2yUmJmIMd2psNPoxwMeHcugDkDdu3Mg3yJacnIyxNm0xF6G6nVu5cuSgf+OWYVjBOz8JCQlogRRsN+LTGDT2RZ8Kp6D8om7p6QCk2iitAPwviOmCvr9z5zrNTWMYbZqs0dhsIUbfwEAS0QfXMwuZ5hwfH08W6HPdFtC+4tBUrsw6IByoV0jgMdMw0joD8LBhzvpAT0+2GR7/WsRzbiD614AvbfiFnkajIcvNDVJScDAE1QtyOSODbuifJ+/Y8D2su7s76U5OZGVlmX1pJIQQQoj7x33zqTv3tEIj47RC46p/BVm2bBmZmZk888wzRdYTERFR6BvrB9Xly5fZsWMHdnZ2dyzoKURJaTQamjdvToUKFYiLizPl1xWitLz66qtcuXKFzMxM/v33X1q0aGHat23bNubOnWv6uX379pw6dYqMjAzi4+OZP38+FWyYj89S8fHxNAceQZ9Ht6SMgU8AEhIKLJdkGLFlq0BvWq1aNAOGog9w5id34NPeRqMfG2i1RANbKTjvY3Jyss0Drt4+Phh7N7uQAHPuvrDVtONLtWoRDDxOwX2t0+lwN6QVyLFR4BGgpVZLDvqRwoXl2TQGxrLKlLHZaFM/Pz+SDI8zCunzO5FfFMAuOJhewKvop9MXJNPQtkT0zxNb8apUCeN45VuFjHhNSEjA+Eyzt+FUf/jfc8nVkOe0INdTU/kbWIXtAv6gf99h/J1K4FMIIYS4P903gU/QTyucPXs28+bN4/Tp07z88st5phXmXvzI6Oeff6Zfv355Fiy6desWb731Fnv37uXy5cts3ryZvn37UrNmTbp163ZXrulecPnyZfbv30+tWrVKuylCWCw2NpYtW7ZInkwhrJScmIi34bGzDUaH+fr6YvwKQhXyZYStA5+5g3qFBT6vAVcABxsFZJwMfeaLPuCTn6SkpP8FXG0V+PT2NgWYtYUEwVqEh/MrMNzBwWYpQZzLluUc+sBaUgFTjm/evMkUYDhw1obTrV0DArBHn0qhoMCTUgonw2jTbBvOBvDz88OYTTW7kKBX2uXLzAG+wraBz9x/K8mF5HU1ti0J2wb9/AMCTIHftMJGfMbHm57vLjYccQqQYxgx7UPhebFz94+tR9H3dHZmLjAoJgallE3PLYQQQog7776Z6g7WTysEOHv2LDt37uTvv//Ocz57e3uOHTvGvHnzSEpKokKFCjz66KN8+umn9+ziKbZ27do19u/fT9u2bYtMGSDEvaR+/frk5OSwZcsWOnfubLOFNIR40GXGx2NcjsfJBiP8HR0dueDkxD9ZWVwrYPGbrKwsfklLowPwXXZ2iesE8wBPYYFP43IZ/7VR3mpXQ1DPHUgsYBRgcnIydQ2PHWwUCPP29sYUeitkZG31hASeBBILyIVZ3LqNCgp8JiUlsQvYBQw05Ma0BRfDc9SLgkc9pqWl4WEYDaizUWoB0Ac+zxoeawsZbZp97RrDgVjg7G1fspeEMW+8A5BSSI7L446OtAA0wHxbBj79/UkC/IGsQka83oyONo2kKGPjGVNXK1VizOnTnAHCUlIKDGr6RkQwAjiFbUay51bH0ZFhwAqtlrS0tHs21Y4QQggh8ndfBT7B+lUCg4ODC/x21tXVlQ0bNtiyefcdLy8vCXqK+1ZYWBg6nY4LFy4QFhaGUspmUxyFeFBpDaPD0gFXGwVJfixXjg+vXiVQp+PrfPYnJycTCAQCZWz0JYWPjw+HgarA4vPn8y2TOyBqq5Fw7hUrokM/ZSa1gFXsUxITMYbfnApYkMZa3t7eHAKqAOmF5Dt0MQSfbTry0dOTT9HnhU0uYGGl3H1tyxF3xsCnK3CzkNQCxhptHfiMBWKAtEL63BgUTcK2Iy69vLy4ClQGvr10qcBy0bdusc/w2Jb1+/n5YVyuLKeQEa/p0dEAZAJeNk6XdLNKFX42PC5s1GvDyEhGA/M0GtsvfmgYXe6LftSxBD6FEEKI+8t9F/gUthETE0NKSgq1a9e2aCV6Ie5FSilcywax6sBlJmxYT3qWFlcne1oHl6N306qEVPSWQKgQt9HmmhZrq0VgjCOuU1NT891vtsiQjYJiPj4++KCfAltQzss7Efj09PYm2VCvtoBgUFqu1e1dbBQI8vLy4inD43ZlyvBOPmWysrJwN6wib8s8m54+PrxueDw317XllpSURH/0C+yUteEIfJdcKQqyChjxefPmTYyhLmXD0X5OTk487+7OrVu3CLGzY0JBBQ0BuSQg0Jb97umJcXx0YSNO71TQ2dPTk9OGx/aFpLEw5hhNAnxslFfWKPfozcICn05paQCkOzvb/L5vZxi17Ys+z2zVqlVten4hhBBC3FkS+HwI3bhxg507d9K0adPSbooQxZaj1fHNmmNsOhaJvZ0GrU4/sjs9S8u2k1FsPh5J1wYVGfNYAxzs76t0xkLcUVpD8OAmdybwqdPp8qSdSUpKwjju0c5GgREfHx9T/sWcAoJCdufPcxq4DDjZasSnuzs3MQQ+CwjEJKal8Rz6EZKtbTT12cXFBRcXFzIyMgqcbp6YmGhaTd6WAUBvX1/98wXIKGTE52+AM/Cdo6PN6nb38jLVXdCow5s3b9IWcAJG1q5ts7pBH/y7detWofkljUHBJKCWDb9M9vT0NOXYVAWkcwCoFRlJK+CYqyuOtux7d3eMtToWMuLVGJBOxnYLahl5u7nREv3fW2GBTxdDjtfMMmVsWj+YBz7PyaKKQgghxH1HogEPmZSUFLZv305oaCjVqlUr7eYIUSxKKb5Zc4zNxyIBTEFPI+PPm45FMm3NMVmMQIhc0lNTiQSisV3gs2daGlHAEqVINwQgcsu92I+jjXJe+vj4YAzF5BQQCFRxcYQANbHhVHd3d1O9ugICMTfS0/kFmIZtR+AZz1VY4NNYm8aGASgfHx+MYb+MgkZdxsVhzI7uasP0OR4eHqYAt7aA3KbGoGQW4GbjhW2MfyO3Cgn82Rv2JWG7vynQj3ZMMv5QSNCvdXw8U4HHHWw7nsHe3p7Fzs6MAv6xty+w3H4nJ3yADtg+8Onn6soeYB3651h+lFK4ZWUBkHUHpqFrDF9e+AI3JfAphBBC3Hck8PmQSUhIoFatWgQHB5d2U4QotjORSWw6FklR4UwFbDwWydmopLvQKiHuDwfd3amEPkhhq0XBXFxcKA+UJf8AUVKuqe7ONpr67ebmZgrGaQqYYm8MFiVju2t1d3fnV2A6+uBxfnIHJm250MoQjYbdwAsFTO1PSUmxeUoB0AdcjX2dU0DwMc2Q51EHlLFhnkcPDw/WA78DSQUsjJV7NKYtA48AvXJy2Aa8m5xc4JdoxmnWN+3sbDri0tPT0zTi0q6QgJsxr+udCPrt9fHhe+BQIYuS3UhMJAn934Mtc4wCuAYGkmN4nBmd/1/crVu3TAF/W6Z4MHI05Ol1AVILWVhMCCGEEPcmmer+kNBqtaSmpsooT/FAWH3gitn09sLY22lYtf8KIRVt+2FMiPtV7sCkrYJEyhBU9DScP/C2wFfq9eumleSdbbTYj5ubm2nkpZ0h8HQ7e0NALAkob6OgkIeHB58bHvezy//7Y010NF2BSGwb+Kzo5EQr4FJmJlqtFvvbRuGlpqZizD6oseVUd29vjGPtcgqYcp1hCMbeBHxsuLK5h4cHLxgeP13AiMa0+HhWof89J9t4qnOggwPtgVtARkZGvgvnOBtGOac5O+fZVxLu7u6m0a4FTTVPT0/HQ6cDQGvD3KpGHh4eprzwBTEGnjUajc2+YDDy8vYmBf1oy8wCUh0kJyebUjzobLyiO4BLrlHqGYUs8iSEEEKIe5OM+HwIKKXYu3cvhw4dKu2mCGETu8/GWBT0BP20991n81+MQ4iH0Z0IfBqDbMbA5+0ybt1iB3AQcLXRVFgnJydSDYuY2BtGvN3O3jgSD2y2EnPuwE5B05+DLl3ib+AbbBv4xFC3B/nnO0xNTaUS/D975x3nRJ3//+dk0rayC2xjqYLSZAFBEdQTPQTFxqmI5UQ9RUWx4Z2KBUQ8e0HPgg3LnZ58Pf1xnHoorqKeoAgWUIqNIgvbWLanz/z+yEx2kk22kckWPs/Hgwezk0/mUzJJJq95vd9vMoD6vn3j1q3R8RkrvD+gCaJVENeiicZzNFaeTW9ZGacD5wMpcRa+fNp5k9FE/wusVnoBr8RJ1NexWCzU2+0A2GO4mquqqkIu33hWtNfpl5TE8cBh1dUxHa9H793LEuB8u71Rft8DpVu3bqEbHL4YbsvKykr0mctxdpwCpGVkkA0kAWWayCwQCAQCgaDzIITPg4Dvv/+eiooKxo8f395DEQgOGFVVcXsDrXqO2xsQeT4FAo0Td+zgf8CfLRYccXKoNSd8VgK/A8YCyXESICVJYpfdznqgNBD9M0HSnHh1xE/4TE5OxgnkAlKs3KLaGtQQX+FTMQifdVGEsLq6OvwExcfkOIpgKSkp6H4/awx3rTGtQLzWGsKFz7oYoqtHK25VB6TFWfxTteN1I7bQXV5by14gYILotj0lhXeBLTEEN2OYt2JCmPcJXi+rgbtj5O8FGF5VxZXAsU3kAW0rYcJnDLdxZWUls4Fzgbo4Cv46aWlplAFuoKaJXK8CgUAgEAg6JkL47OKUlpaybds2fve738XtB65A0J5IkoTT3rofV06bjKQ5wwSCg52smhqOAQZarXF7X+guq1jCp1GkS45jKPLT3btzJPD3GCHQFs0J6rZYsMap8IssyzxhtbIXmLZzZ/RG2nzdkoTT6YxLv0CY47M+igBpXOd4io82m41brFYGAytiHFfS3JDVce47OTmZxwA/MP2nn6K28WkCdB3xz/Epa8JnCtEdn16vF69WWCfefQO836sXpwFLYuTYrKurI+RBNqF//ZxLhZjh7kkeDwBuE64zjcJnrCJmVVVVfAS8Cdjy8uI+hpa4jgUCgUAgEHRchPDZxcnKyuL3v/99fEPtBIJ2ZsLgXGRLywQbiwRH9Bfnv0CgY9PEQF8cRQqbFr7uAOqiuLKMIl08hU9dYIvmfgSo8vvZCezXwoXjhUc7nk0TfCKxavu9Nltcb7qozQifvtJS/gE8TXzFR4DS1FR+BMpipBXYkpTEpcADxPc1liQJq92OTIOQHYlfc4LWYq7wGU3Ur6mp4c8E0xqMiGvPQfTrt/r6evx+f6PH6+rq0F9p2YRQd4t2zFRii352TZT1xVPk1zAKn2oM4dWsYmI6aWlp3Ay8DGRs3x734wsEAoFAIDAXIXx2Ufx+P+vWrcPn88W9wqZA0N6cPrZfi3N8Kiqce9yQmMKIQHCw4dBECn+UIi1tPmZmJt8AnwOuKOJEr59/Zi/wHokVPp+y2egPPG4oThIPdNHYrjn9IjEKn3FFE/Vihbor5eVcCPyR+Auf+usWTXAF2KGqvAz8x4S+A9p6yzGE5oAmfNYR3/yiALaMDCC28FldXc25wA1A/IOsw+cTzXFZV1fHWcBkoK5Xr7j3LzcjfPr9fpxaGL4S58JSEBQynyW4vt/FyB9au38/FwNnAxkmuF7T09M5FbgYSC0tjfvxBQKBQCAQmIsQPrso69ato7a2Flu8f3QJBB2AIfkZTCrIpzkflQScVJDPwOxU3n//fXbt2pWI4QkEHRZFUXBqrjEljsJnckYGRwDHApVRQnLlmhpyCVZmjqcodqrLxS/A815vVDecLtLFW4jza842ZwzhU3fA+ePsNJW6daOKYDh5NAHSKADGe84TLBbuBqbEyLNoVjoDgIC23rEctoomyJkR6m7LzKSeoJu0JorwWFNTEwo1N8NxeUJtLTXAamILn18Bq2hwXscTWXNQpjTRf+hMM0H4dDgc/Nvh4HFgU4xwf395OS8D/yL+5z0Ez6mQ5C1C3QUCgUAg6HQI4bML8uuvv1JSUsKECRNEXkNBl0SSJG48rYBJBfkAjcLe9b8nFeRzw2kF2Gw2jjrqqNANAYHgYKW2tjYk0sTTndVspXNNFKsjzo5Pu51DgN7ELvYD8RdD9LVLUpSogqtd2+ePc85DT+/eZABDiSF8asJULfGf8xhF4U5gSowCN9kVFZwMDLfZkONc5EYX6W1+f9RCdXoxKTNC3S25uaQQLGZVG+Ucq6mpCQl/VhPCrJ2pqaQSdPnGEh51zBD97JqYGsvxWVtbG5q/aoLwCQ2u11ih9kbBP9kEx2daWhp6zxYRPSIQCAQCQacjPpn+BR0GVVX5+eefOfroo+NbUEEg6GBYZQs3nTGS08b2Y8VXO1mzrRi3N4DTLjNhcC5nHNmPwb0yQuJ/79696d+/P2vXrmXSpEnipoDgoMToTlPjKJI0J3xKmkgXb+FTNRReqaurC8vv5/V6edTv5yjgrRhOwbaiaGunh5wb+1VVlVcDAb4FKnv0iGu/RmErmvCpGpyPcQ8318RHp6IQCAQaiZtTSkuZDSyJEY58IEjaOZOCVszHcL4B/DstjdsBG7A/zsKXsa9owluY8KmFxcd5AEBw7vujvObu/fu5Ae01N0F41IXPFKBGExiNGB2fFjOKKwH97XYG0VBAKxKj4B9vtzGA1WrFJcsQCCDHSPUgEAgEAoGg4yKEzy6EqqpIksRJJ50kRB3BQYEkSQzJz2RIfjCPrf4eiMXo0aPZuXOneH8IDlpqa2txQ9C9FMew3NTUVD4CDgPe+OWXRo/rRWniLXwSIXwaqaurYxgwDvg4zg5EY5GhmpqaMOHT7XZTCBQCJ2Rnx7Vf49pFc7ganY85cRY+JU34TCYoukY6K21a2L8vzuH9AGhzSUYT7yOEz+qaGryA5HDEPcWPcZ6xihvpo7GbkFNdMpzje6K4bZWyMh4DvMDbEesSD+xZWdxC8JwaGcPxeRLB98KZ/fvHvX+AmW43c4AXogivAIqhuJUZwieA124HlwtrjAJbAoFAIBAIOi4i1L0LsXHjRjZt2iREHcFBS3PnvizLHHLIIdTU1FBRUZGgUQkEHYeamhqmAOnAjsGD43bc1NRUcoB8GkQII7ImFtRLEvY4CmOSJkrFEj5D0l+cRcDanj1ZCiynsRhmdGImxTGPKgRFnRXAZ4C0d2+jxyVDSoFIcfCA0eaSRHS3qS58xju8H6C+Rw8+ANYT23UJ8Q9zh+A6/gP4GLBFyRNds38/+owdJuTYtBiqyruiCJ8+raK5GS5fgNQePXgQeBqoiiL81tbWUgHsBOxmOF4x5NSNklZCG0TwP0wUPpvJMysQCAQCgaDjIhyfXYTy8nK2bdvGlClT2nsoAkGHZ8+ePfz4449MnTo17rnoBIKOzKBBg1ixYgVFRUWMGjUqbsdNTU1ln7atRBFHdJeU12qN6805WRP3kmha+JTiLAhV9+nDZdr2tCjC5xiCF1g94uw+TE5O5hiCRaI2RykypAufZuT4lAyuy2huUz2vacAE4XPvwIHoVzeTogifU8vLORpYY8KN39TUVA4F+gMb9u1r9LjHcBPNEefUBhBeVb0+yrrrYd5mCZ9GMTma6Gw8F+IutmvowmeKquLz+Rq7eg3nfZ5JwqdeYMsRo6CZQCAQCASCjotwfHYBAoEAX375JYcffnhYuJ1AIIjOYYcdRlJSEt99911oX7SCGQJBVyMjI4NTTz2VadOmcdRRR8XtuKmpqegewGjCZ7Gi8B1QHGdRzKaJQs0Jn/HOPdhUTlOXy8XLwBfA8DjnA0xOTkb3/PmjiFBvdutGd2AW8Xe+WTRRLZrj0+fzkaR9hipxdrlC+FyiuR5Hud38ERhuwud4Wloa+pkViFJcqFpV6Q0MBpwmVHXXq6pbAU+U19wofJohPCYnJzMC+B0QiCK219bU8BTwEJAZZ6Ffx6+dU6lEf/0tJgr+Oiv79ycXuFJR8AjXp0AgEAgEnQrh+OwC1NbW0q1bN4YOHdreQxEIOgWSJHHUUUfx6v/7gHd/8rFhx/6wwkinj+3HkPwMkTZCIGghycnJIeGTKMLEU1YrdwEDsrK4K479Wrt1YytBwSOa8JmvbctxFqRSU1JIBRxED3UP3YKMswhjFD4DUQTmapeL/YDT6Yy7m1131yYDe5oQmc0QPo0pA9xRciw6NLepzwS3aWpqKqXathJFeHR5PBRp204T5m7LyOAzgue4O0r/el5XsxyfSUlJvA0MAh7ds6fR4679+7la237RhPkDKJrwnUrw/ZUe8X7ekJzMecA+4G2THJ9060aJtllfX4/DhHNNIBAIBAKBOQjhs5MTCATo1q0bxx57bHsPRSDoNPgDCs9+/Csf7kpCtuwjoARdQi5vgNU/7KFwUxEnFeRzw2kFWGVhjBcImsPhcIQEOTmKG0p3CMbbhSjl5qLf8vt7E2JcvIXPbLebGsAFvBshxLlcLvK07Xg7TVNSUpoUPnXx1wwBrDo/n7EEC2MtiXB8hqUVMEF4ytEE3QCwNmK9A4EAyZrT029CYaWUlJSQ45MooeZGITbeOV0BnKmp/E7bfiyKo9UofHY34XV3Op2EzrQo8zeG+jtNCPUHULV5pRE9v+x2VWWNtm3GaxB53Gjiu0AgEAgEgo6LED47Maqq8uGHHzJ8+HB69+7d3sMRCDoFqqry2DsbKdwY9OjooqeO/veH2uM3nTFSOD8FgmawWq24JAlUtZHwqapqSKyItyBnPF4jx6dWdMUHWONcdMWYW9Qd4XCtr69vEFzjLHwmJydTqW0rUUSok8vLGQF8YYn/DRt7ZiYbtO3Ita6vr+choA9gzc2Ne9/WlBQyAIXG6+12u0PrHTBDeHQ6Q8KnNYqon7J3L48SLO7j1PJAxpPmwvyNBa3McnzqZbQsUc45vbiSG0gxIdQfGvLLJhF9DfTz0W63Y7Wa89NmgNfL34DSGGMQCAQCgUDQcel0VqannnqK/v3743Q6GTduHOvWrYvZ9uWXX0aSpLB/kRelqqoyf/588vLySEpKYtKkSfz0009mTyMu/PTTT/h8PvLy8ppvLBAIANhaVMmHG4toLhOcCqzaWMS2PZUJGJVA0PkpslrZCOyPcKW53W5eUlW2ASfFOedlk8JnfT39CBYCkuIsxlkNuRT9kaHudXXoUlW8HZ/GUHc1ivhyZH09VwGj49prQ986ka67uro6lgN/A+pycuLet76OFsATkWfTKHwqJgiPDocjJHxGczOnl5VxI3AJ5rgNjceM5nZck5LCycBdmCd8NpXGwuyq8gB1PXtyJ8E8otHWoF9FBX8ARpoYfp6jKMwBzkMInwKBQCAQdDY6lfC5bNky5s6dy4IFC/j6668ZOXIkU6ZMobS0NOZz0tPT2bt3b+jfzp07wx5/8MEHeeKJJ1iyZAlffvklKSkpTJkypcOHsbhcLjZu3MjYsWNFVWqBoBX8Z/1OZEvLHJyyRWLFVzubbygQCLg/LY2RwLKIUOf6+nr6AIcBaXF2Y6WkpPAV8BNg2bs37DGjEBpvQUY2Cp8ReRe91dWhiytbnAsOJicnU0Mw3NwXcZ2iqiqylutSNSPXpcPBzcACwFVVFfaYUYwyQ/wyrmNkgSGXy4Uud6omCJ+6m9kNKFEqekva3Oswx/GZlJTEJ0AVkBflxvwuv5/3gXWSZE6ovdNJ6EyLcm3s184Fs4orAfizs7kHeJbowucfKip4GzjflN6D6CkckhCh7gKBQCAQdDY6Vaj7o48+yqxZs7j00ksBWLJkCe+++y5Lly7l1ltvjfocSZLIjeH0UFWVxYsXc8cdd3DmmWcC8Oqrr5KTk8Py5cs577zzoj7P4/GEVXSs1i7CFUVBUZRm56EoCqqqtqhtLKqqqujbty/Z2dkHdJxEEo95dzYOxjlDx573mm3FjcLbYxFQVNZsK27xPFo77464PgJBW9FFn0hRoL6+PiRMSXEWZpKTkxkEZNBYEDMKJPHOLepITcVP8CIqEOE09RhEQbsJwufp2vbxGRkYr3w8Hk9onTEp5PoBbfsFzeWnU1dXx4mAF0g3Ic+mIyUFD8FiUoEIodntdjecXyYVtpmTlMSl9fUM69aNmyIeU7Xz3YU5js/k5GQkIJ3o6Q10gT85OdmUtCxJSUkhl7EliuPV7OJK0Hy4v9XnA0Ax4dzTMRb3KhKOT4FAIBAIOhWdRvj0er1s2LCBefPmhfZZLBYmTZrE2rVrYz6vtraWfv36oSgKRxxxBPfeey/Dhw8HYPv27RQXFzNp0qRQ+27dujFu3DjWrl0bU/i87777WLhwYaP9ZWVlLboLrCgKVVVVqKqKpQ15uFRVRZIk+vbt26TbtaNxoPPujByMc4aOO29VVXF5A616jssboKSkpEU/KFs775ooFXoFgs6KXuXYEyGO1NXVhYQpS7wFSK2oUgagRjjBrGVlfE7QKeePsxCo95tG4yJDtT4fNxMU6Q6NsxDkcDiwWCwoitIotN8oAJrh+ExKTw+Jvd4owud7BOf8ZNx7Dorq9drxIyuru91ujgacwPQ+fUzoHRxOJ3X19VGv8SRtnxvzHJ+V2rYSxe142L59jAN2mST6ORyOkONTiiJ8Gh2v6SbMHyDZbmcEwde4Por4azXR6azTXJ5RgUAgEAgEHZdOI3yWl5cTCATIicgdlZOTw9atW6M+Z/DgwSxdupSCggKqqqp4+OGHmTBhAj/88AO9e/emuLg4dIzIY+qPRWPevHnMnTs39Hd1dTV9+vQhKyuL9BYkdlcUBUmSyMrKapMo9NFHHzF48GDy8/Nb/dz25EDn3Rk5GOcMHXveSXa5VeJnkl1u9BkRi9bO24wfyQJBe3Gu280lQGGEKFZfX48uR1hNEAJ1USYy56VUW8sEoBJYF2dBxOl0hoTPSMG1JhDgIW37zTjPV5IkkpOTqa2tbRTya3R8qiZVN68n6DyMDO931daGXmO7CQVu9PXOpHFOVZfLRT1QD9hMchzGcjNrO4P/YZ7jU78ijZbXdUpVFVcCT5sUQWCxWPi31cpWv5/f7HauiXj8m4wMDiFYeOpTk77Tumn5gwFej3B2BwIBbNrczRQ+wwqaiVB3gUAgEAg6FZ1G+GwL48ePZ/z48aG/J0yYwNChQ3n22WdZtGhRm4/rcDhCzhYjFoulxSKPJEmtaq/z22+/UVNTQ25ubocTlFpCW+fdmTkY5wwdd94TBuey+oc9LQ539/oVHl6xkdPH9mNIfkazzs/WzLujrY1AcCBkWCwMAX7Q3Fc69fX16AHfZjk+gUaFV3R3nJtgted49/sWkALURBRzMgqSZghhsySJycBHJSVh+8OET5NC3V0EhU9fZIEhY3i/CcJnUlISawgWqqqPEPiMIpQZ6w1wpt/PKcDaCFEfQNLyfnowz/EZEvejCG5yAsK8P05N5f9VVnJYlJzyNYEA27XtaNfG8cB4TkWee8ZUGmac9zpWrcCWDXCLaA2BQCAQCDoVneZXd8+ePZFlmZKIC/2SkpKYOTwjsdlsjB49mp9//hkg9LwDOWYiUVWV7777joKCAmw2W3sPRyDolJw+tl+LRU8I5vlc/cMebnhpDY+s+A5/QOTlFDTw1FNP0b9/f5xOJ+PGjWPdunVNtl+8eDGDBw8mKSmJPn36cOONN3YZ95DuMrSpKqpBDDQKcmYIn7EKryiaEOom/oKM0+nkauBioDhCbPFXVTEWGEL8c4sCDJYkTgb6N+H4xAQBylhRXolwXfoN4cc2EwrcOJ1OpgO/B36LcHW63W6eBp4BupvkeuyvqpwOHB4t1Fvb5yFYCCneJCUlofcazfFpCwQjGBQTrwt1QTdaiLfx88usKAZHWhr6K+ttQviM9+eLEV34hMbiq0AgEAgEgo5NpxE+7XY7Y8aMobCwMLRPURQKCwvDXJ1NEQgE2LRpE3l5eQAMGDCA3NzcsGNWV1fz5ZdftviYiaSyshKbzcaAAQPaeygCQadlSH4GkwryaU0JCF0o/XBjEYvf2Rgm6ggOXpYtW8bcuXNZsGABX3/9NSNHjmTKlCkxcy+//vrr3HrrrSxYsIAtW7bw4osvsmzZMm677bYEj9wcdOHTTnieT6/Xy3bgZ0CNcyiy3W5vyD8YKSBrf3swR/hs6Ca83267d/MV8F/MET4D2jpbI5y1Ho+HU4B+wC99+8a9X4fDQUhqjRDAdOFTAWwmVRbXiVxvl8vFZcBVQHIUR2I80NfcFgg0+vx/PS2NocADSUmmFReKlWNTURTs2njMdHzm2+2MBnKi5Nc8rLSU+4HpmOf4TE5JCYnu0Ryf+hkX7+JpRuzp6QwCetHY5S0QCAQCgaBj06lC3efOncvFF1/M2LFjOeqoo1i8eDF1dXWhKu8zZ84kPz+f++67D4C7776bo48+mkGDBlFZWclDDz3Ezp07ufzyy4FgSOoNN9zAPffcw6GHHsqAAQO488476dWrF9OmTWuvacYkMzOTKVOmtPcwBIJOjSRJ3HhaARKwamMRskVqsQNU1Z5z2th+DMnPNHWcgo7Po48+yqxZs0LfQUuWLOHdd99l6dKl3HrrrY3ar1mzhmOOOYYLLrgAgP79+3P++efz5ZdfxuzD4/GEiYjV2o9+RVFQ2uhuUxQFVVXb/PxY6MKng6AYpYeXu91upmpt/pqbG9d+bTYbu4AsoD4QCDu2anB82my2mP22ZT30qAsn4K2rC3uunoNSd5rGe539Wt+OiPm6XC72AfsAUlLifn5YrdaQ8Cm53WGPG9MK2Oz2uM/ZmKrA5XKFz7u2Fv1Ra2pq3PtWFCXkprQTFPKNUTclPh+/Aj2Sk+PeNwRTovxksfC5olAc8bq43e5QblWliXP8QLnQ7eZG4O/V1Y3Oj0MrKrgO+DtNv88OBKfTiZtgaglfTU1YH7W1tdxDUJDMzMszbQ0cTie/aNv1hvM/Hp+nZo1ZIBAIBAJBkE4lfM6YMYOysjLmz59PcXExo0aNYuXKlaHCI7t27QrLmbd//35mzZpFcXExmZmZjBkzhjVr1jBs2LBQm5tvvpm6ujquuOIKKisrOfbYY1m5cmWHKzryyy+/oKoqgwYNau+hCASdHqts4aYzRnLa2H6s+GonH39fREuj32WLxIqvdgrh8yDH6/WyYcMG5s2bF9pnsViYNGkSa9eujfqcCRMm8I9//IN169Zx1FFH8euvv/Lee+9x0UUXxeznvvvuY+HChY32l5WVtTlEXlEUqqqqUFU1rnlmfZrbzgHs3r07JNiWlZWF2ni93piO2LZQXV3N+dr2iSkpXGE4tlfLw+chWIwnVr9tWY+6ujoKgROBR7ZuDTu2nu/SQ1Cki+d8ATzaGB2KQklJSchlaCzKGAgE2txvrPWora3lCoLi3yCnM+z4tfv2BceGOXOur6/nLeB44LnNm8OOv2/PntC2W1Xj3reiKAS0EHYnwVzrqYZwfj2nq8PhiHvfOg+lpDC/poZBVis3GPqorq4OhXl7LRbT+tff21btvDKeH3reUZ/FEvZejydutzvk+KwrLw+bZ1FREf/Vtv+UlmbaGhhvQJWVlYX6icfnaY3IGSoQCAQCgal0KuETYM6cOcyZMyfqY6tXrw77+7HHHuOxxx5r8niSJHH33Xdz9913x2uIcUdRFDZt2sSYMWPaeygCQZdBkiSG5GcyJD+TNduKW1zpPaCorNlW3HxDQZemvLycQCAQuvGmk5OTw9atW6M+54ILLqC8vJxjjz0WVVXx+/1cddVVTYa6z5s3j7lz54b+rq6upk+fPmRlZZHexiIyiqIgSRJZWVlxFT71itoOIC0tjezsbCA8TLlHjx6h/fEgJSJ03nhsCagCaoCBvXrF7Lct6xEIBPhO27YFAmHHtmo5Fz1Afn5+XOcLoGrhvElA9+7dQ+7D5ORk7iN4Yeey29vcb6z18Pv9fKtt59lsYcf3pKUxF/ADZ2Rnx33OiqJQCfQAHH5/2PGNt6kzcnNN6VsPoXYSPLezsrJCj0+qqWEQsEWS4t63TkpKCjU1Nfh8vrA+VFUNzV9OSTGtf33+DlWle/fuYeeHnmPUL8um9Z+fnx8K9498vxnD6+P9+WIkNzeXW4A8gq5uvZ94fJ52NLOFQCAQCARdjU4nfB6MbN++HbvdTu/evdt7KAJBl0NVVdwtFD113N5gnjcz8rkJui6rV6/m3nvv5emnn2bcuHH8/PPPXH/99SxatIg777wz6nMcDkfUvHkWi+WAREtJkg74GJEEUlL4FSgC0rze0LHV+no2EwyD/lrrN14Yq3h7PJ6wY3+RlYXuxy1OSmqy39auh7HQjyWiX4tW5dutjS+e84WGXI5JgM/nC50fPp+PqwlWXX/8ANc52noY19rn84U9VmW1ot9mnmHCnJOTkxvyXBrOLWgIs/cDyenpce8bGqqFOwi6lo19THa7uQh4LBAwpW9oWPv6+vqwPnw+H3cQFOOy8/JM61/VzjEnwfeZ8fywaFXlA1araf2npqbyFNANqJHlsH48Hg+TCaai6RbxWDxJSUnhMuBQ4Ony8rB+DvTz1KwxCwQCgUAgCCK+aTsB27ZtY+jQoUJkEQhMQJIknPbWFcRw2mXxfjzI6dmzJ7IsU1JSEra/pKSE3NzcqM+58847ueiii7j88ssZMWIEf/jDH7j33nu57777ukSOt9/69mUgcCbhYaFKXR1DgdGALc7FfmRZZh7wNTB19+6wx4xjiHfRFYfDESZ8GpE04dNDeG7KeKHnUpUIinA6xqruZlS3djgcnALMBfpEhDSbudYQdMTpPUQW+NELK7kxzzknaXOyE15cSVXVkMM3YGJxoStcLnYDt1dWhu33eDysBd4GajPNS7+iGhyvkSk29CJbfhMq2uskJSWxGFgI7I4oYOX1enkD+ADoEXFuxBOn0xl6zytRijwJwnnqqafo378/TqeTcePGsW7duibbv/nmmwwZMgSn08mIESN47733Qo/5fD5uueUWRowYQUpKCr169WLmzJnsMaS5EAgEAoGgKYTw2QmYOHEi/fr1a+9hCARdlgmDc5EtLRcyh/fpLiq7H+TY7XbGjBlDYWFhaJ+iKBQWFjJ+/Pioz4l0a0FQuAO6xPlkFLyMQlhAEwkCxL/ityRJ9JFlRgM96+vDHjOOId4CpMPhCAlxuuNNx2Ky8LmuXz8kYDLhwqfX5QoV+ZEjUgDEA7vdzkzgEWCYltNTR66u5mhgCObM2eFwxKxsrheT8mCe8Lmtd2+swBjCzyuv19sQam9SRXOAVFkmH0j3+8M+K8wWnHUkbV2dBHO4GpE14VMxFHyKN8kGIb8uQnQ0W/DXSUpKCp2DahvzKx8sLFu2jLlz57JgwQK+/vprRo4cyZQpU2LmX12zZg3nn38+l112Gd988w3Tpk1j2rRpfP/990Dwu/Prr7/mzjvv5Ouvv+btt99m27ZtnHHGGYmclkAgEAg6MUL47ODs3LkTh8MhwmAEAhM5fWy/Fld2B1j/SxmPrPgOf6Dzu/QEbWfu3Lk8//zzvPLKK2zZsoXZs2dTV1cXqvI+c+bMsOJHp59+Os888wxvvPEG27dvZ9WqVdx5552cfvrpIQG0M2MUnYyuMMVQXd0MUcyrF17RBBidCUVFrARuJP6ikCzL+HXXd4TwudXp5G5gGebM1x5DYPZpAiCYIwDZbLYG12XEnPsVFbEWeBVzBDhJkvBrr7NkEHsB9qSkkAOMIjwcP544kpPRE6IYz22XyxUS3VQTHZ96qLlDVfEZ1t7j8fAH4Fwg08SbJ5Ihr2yk8Knn+AyYKHw6HA5ygaGAI0L49Ho86K+62cKnfv4rEWsgCOfRRx9l1qxZXHrppQwbNowlS5aQnJzM0qVLo7Z//PHHOfnkk/nLX/7C0KFDWbRoEUcccQRPPvkkAN26dWPVqlWce+65DB48mKOPPponn3ySDRs2sGvXrkROTSAQCASdFJHjswNTXl7OV199RX5+fnsPRSDo0gzJz2BSQT6FG4to6U/HDzcWAXDTGSNF2PtByowZMygrK2P+/PkUFxczatQoVq5cGSp4tGvXrrCbVnfccQeSJHHHHXdQVFREVlYWp59+On/961/bawpxJcftZj1BgdNtDHXXnJhuzBHFFKsVvF4sEcJnbm0tU4AiSTJFWA5YreDzNRIBN9ntLCWYt+/vJvRrFFONjs+AQRCymuD4lCQJv8UCitLI5YomBnowz3kYkGUIBBr1Xe/1ovvIzHJ8GudkFD7dbneD49Mk0RUa8ro6Cb7m+jng8Xh4AugNPGJimHdVz57cC/wGXBHhdrxVUXgQ6JWby9xoT44DdrudJQTTaDwUEd7sN5z3ZjiddYzpFhTh+IyJ1+tlw4YNYTf9LBYLkyZNYu3atVGfs3bt2rAifgBTpkxh+fLlMfupqqpCkiQyMjJitvF4PGE3h6qrq4FgdEZb08soioKqql0iPU08EOsRjliPcMR6NCDWIpx4rEdrnyuEzw7Mjz/+yCGHHILVxLxJAoEg+IP+xtMKkIBVmqDZHKrW9rSx/RiSb15uNUHHZs6cOcyZMyfqY6tXrw7722q1smDBAhYsWJCAkSUeh83GGKAOWG10fGrCp1mh34r2HSkFwouU6Q5Qn0lu2q12O2/7fPwYEZGhi5FmzBWgr8vFm0A5ESkFNMenD7Cb5HwLyHJ04VMbh1muXoAih4N1Xi97I5yNRgeiWY7PLL+f1wkWUIopfJpYmTtS+NQJC/M2UXitzc1Fvz1zkWG9/X4/RapKEZCemmpa/3a7PRRmLkc4fgMJEj6Njk+E8BmT8vJyAoFA6AagTk5ODlu3bo36nOLi4qjti4uLo7Z3u93ccsstnH/++aSnp8ccy3333cfChQsb7S8rK2uUq7alKIpCVVUVqqqKaDzEekQi1iMcsR4NiLUIJx7rUVNT06r2QlHroLhcLn777TemTp3a3kMRCA4KrLKFm84Yyf46Dxt+KW+R81O2SKz4aqcQPgUCGlyGDsIFOT0fnlmimKoJn3KE41MX5/wmCZ9vZ2Twt7o68m02HjHsT6+vZwjgNummZbqqcg6wHdgfxfFplrMWNOHT52vkrjUWdDKr75d79mRBTQ3ZVitGj3SPvXv5G/Az5jk+k2WZ84F64KOIUHd9tmYKj4qhqnoiC1rpxEpjYdw2a+0h6Bj0ShKoakzhUyH+xdOMGIXPyDyzgsTh8/k499xzUVWVZ555psm28+bNC3OSVldX06dPH7KyspoUTJtCURQkSSIrK0uIF4j1iESsRzhiPRoQaxFOPNajtdcdQvjsoCiKwsiRI0lLS2vvoQgEBw2SJPHDb/tbHO4eUFTWbIvuSBAIDjZkTfixEiy0o+Pz+9kBFGGS41PLLShHhLyEqk2bJHzqF1yeCBHkwrIyrgSeiRAH44VF69dOuAhWnpzMQG3/fSaJj3rlbjnCXZsI4VNf70inVmZFBXOAzzFPfJMNor7bcG673W7OA7oBx/XubUrf0OD4dNBY+AwJryaKfil2O4MInltGh63H4+FSoBdQFukCjjMeWQa/v1EuX134dBGe/zbeOBwObgDmAX3S07nBtJ46Nz179kSWZUpKSsL2l5SUkJubG/U5ubm5LWqvi547d+7ko48+ala8dDgcUT+PLBbLAQkPkiQd8DG6EmI9whHrEY5YjwbEWoRzoOvR2ueJVe+AqKpKcnIyQ4YMae+hCAQHFaqq4vYGmm9owO0NdImK3ALBgWIzhLp6DYV2furenQHA7zBHFPPY7ewFKiOET90BalbRFX0u7hhVrv0mOT514TPSWevy+/kV2Ip54mNMd63JlewhtvApGfKLmhXqrrs5ZcBjCK12uVz8BKwHaCLX4IHiTknhO+AXwoVPb309+tktmyh89vD5QvN0RQi/lwH3AIdEODHjjU/7gWONEFhrrFZmA3/B3Mr2FouFUoeDH4F9Jt3U6ArY7XbGjBlDYWFhaJ+iKBQWFjJ+/Piozxk/fnxYe4BVq1aFtddFz59++okPP/yQHj16mDMBgUAgEHRJhPDZASkpKWl0ASAQCMxHkiSc9ta5w5x2WRQ3EggIL6hjLDhiFGrMEMXey8+nF3B1RMGKULVpkwTIS6ur8QNLtBymkf2aLXxGOj6NIqhZAtB/MjP5PfBkxNz0tAJugtXfzeDsmhp2AI95vWGvszGVgtmOTwC/QdRPVKj3jt69GQVcTPhrnqjCPvpNjSTChX5jqD0mio7Q8H6yRYiO1ZLEEuAZzBU+oUFYj6xsLwhn7ty5PP/887zyyits2bKF2bNnU1dXx6WXXgrAzJkzw4ofXX/99axcuZJHHnmErVu3ctddd7F+/fpQ/myfz8c555zD+vXree211wgEAhQXF1NcXBz2fhAIBAKBIBYi1L0D8ssvv9CzZ8/2HoZAcFAyYXAuq3/YQ0Bp3sUpWyQmDI4euiUQHGwY8+slUvg0ih1erzckQCmKQgDzHJ8WqxUZsKoqgUAgVDnearLgqqcUsBMudqaUlHAvsAfzBKDStDQ2As4I8Wm908l8YLPVyqUm3QhKtVjoB+QQFByTtfNNMhRWMmveRjelzyB8ejwe/gxIQDcTK7Ua3zexhE+bicWFjKKqL0L41YVPycQcp9DwfrJEpFkw+/PFyMmSxHDgx4oKU/vp7MyYMYOysjLmz59PcXExo0aNYuXKlaECRrt27QoLUZwwYQKvv/46d9xxB7fddhuHHnooy5cv5/DDDwegqKiIFStWADBq1Kiwvj7++GMmTpyYkHkJBAKBoPMihM8OhtfrZffu3ZxyyintPRSB4KDk9LH9KNzUssruAUWloF93VFUVrk/BQY8zKYnftG2fwQk3dMcOvgTWYI4wZRQ7PB5PSPi8FLgIOCIvj1vj3iug9asLkLoQpwufilmOT4Pw6TUIn+nl5VwHbAS8JgmA+lpHuqy+tFp5BUg3MdxaNVQ2N663bAizN8tt6kxOxktwzY2OT6/Xy11ACvB0OwifdarKeQTXZJKJa28UfhWDw9nj8RDKRG+i4xXgu5QUHqmuZqPFwiXGsVVWcgJQgfmOzxMCAa4AnjYIzoLozJkzJ+TYjGT16tWN9k2fPp3p06dHbd+/f3+RUkggEAgEB4QIde9glJSUkJmZ2eZqgwKB4MAYkp/BpIJ8WiJjSsBj72zikRXf4Q+Y96NXIOgMOBwO+gJ9gX0GASKltpajgMMwx5E1praWT4GnaRCFFEXBr7kSHSYJMmqE8Klj1wQws5ymuuPTQrjArGhj8GGeADQoEOBK4BTD+kLDupvpuDMW+PEZ8jzqFeZ9kmTaDSiHw4G+0gGD6OXzehtCvU0U/nLr69kKfEu48FmvKCwDXsFc0c9qcHMqhnPd6Pg0M8cowJcZGfwZ+H8Rr3FeUREfAUsx3/Hpj3B1CwQCgUAg6BwIx2cHo0+fPvTq1au9hyEQHLRIksSNpxUgAas2Nu381P0HH2rtbjxthLmDEwg6MMYch2EFaDSRyoc5wkQGcBxa4RlNlElE+KukCU12Guarqio2zZmkmiR8kpFBMuAFXjQIgKo+dyDNJBFstNvNzcB7BNfaqrlae9TVkQxgkssVQNHWs5HwqedUNbFKqsPhoDfBtf2zMezd5ULPCi2bGOptt9kYDNQAXyU4ryuAzeHAB9iAgOG9bczxaXaouz4/45yh4bz3AakmOz71cHshfAoEAoFA0LkQjs8OhMfj4bfffgvlCRMIBO2DVbZw0xkjWyxkqgRF0m17qswdmEDQgTEKL2HihEH4NEOcMQqQer8ej4d7gLeA0SYVIonWr9/v523gEWCnSZEbdocDFxAAvAYBUNEEMS8mVnXXxMfIwkrXVlayCbgkQpSKJ4oh1N3Y96tpaQwE7jfRcehwOKghGE7vNszRKAJaTHR86se2ET53S2UlfwBOwFzh0263o59pimHObrcbvVcziysBpMsyfYBMrzcs7Fk1nPdmOz518d1qYloDgUAgEAgE8UcInx2IXbt2sW3btvYehkAgIOj83LizAtnSstBJ2SLxzoadJo9KIOi4OBwOXgPWAt137w7tV012fFpiCJ+/A84Csk1yZ0VzfHq9Xl4C/gz8alKRwshiTiESEOquV+52EC5u60KQWQWdIvs2Oj4rFIVfgUoThUdj7lBj30YR0EzHp6zNLVJwTtu7l7eBFzDZ8Wmz8QTwIFBnCDXX32cTAE+PHqb1DzC1qopdwHMQlmYBg9PZ7Byf+vltUxSRc1IgEAgEgk6EED47ELt27aJv377tPQyBQKCxZltxi6q7Q7DQ0ZptJSaPSCDouNjtdkYDRwMOQx5EySB8mlJ8JkquTY/Hg96TxSQxpC4tjfeBL2kQYhIRYm+323kOeB2w7NsX2q8mwPFpXGvjXEMFncwK7we8ycn8APxKuPiob5tV2AjAarWyiGAuzR6lpaH9foOb2Gqm8GnI6+o19KkLr2aLfna7nXnALUCVYZ3dbjffErzZYUtLi/7k+A0i+B/h556aCMFfw5huISDC3QUCgUAg6DQI4bOD4PV6KSsro3fv3u09FIFAQDBXn9vbuh82bm9AuEAEBy1WqxXdAygZhClJEwX9koTFhDyM0cKAvV4vuuwomSRA7u7Vi5OBG2kQ37xeL72APkCySTkn7XY7M4DzAam6uuEBbe6JdnyqqtoQ+mtiqHFRr14cDvyBcOHz9zU13AtMNLoA44zNZuNMYCaQUtWQ0kQv9OMHbCbOXTa4WQNG4TNBol8sx2uicoxCeJqFaKk0EhnqHuk6FggEAoFA0LERwmcHwefzMXjwYJJNroopEAhahiRJOO2ty7frtMumVRUWCDo6NpsN3YclGRxZrkCAcqDOpPzVuhsuluNTMkmQiSYGeb1eVgC7gNGVlab063A4QgKzUQRblZ7OKOAmzBOhjOH9usjs9/tDa62aGOpuXG+j4+93LhfzgKNMFD6tVmsoxyUGwavaZmMC8HvMFd2MYfRGl2kgQcKn3W4nG+gHKPX1of2+mhpuJij+J5ksOsZyfBqFT7PF1/V5eYwB5kSOQSAQCAQCQYdGCJ8dhJSUFEaPHt3ewxAIBAYmDM5tcY5PgOF9MoXjU3DQYrPZoopDz6enkwUsMCkU1pKURA1QT2JD3WMJnyHB1cRQd11yUQ3Ot3JF4TtghyybViRR0pyHRsdnmMhsovgVy3Vo0dymqomFIY2ivnHN3arKWuBTTA61T0riZ2Az4DPkFVUTFOpus9n4FNgB5BUVhfYHqqp4AHgUcJqYYxWIKXx+l5TEX4DXMN/x6U5N5WuC6yCET4FAIBAIOg+dTvh86qmn6N+/P06nk3HjxrFu3bqYbZ9//nmOO+44MjMzyczMZNKkSY3aX3LJJUiSFPbv5JNPNnsaYaiqykcffYTLpMqzAoGgbZw+tl+Lc3wCrP+lnBc+2YE/ICq+Cg4+jK441eC+0wUCs0SJqt69SQeGEF2Mk00SZHrv308V8AvhOT5DIfYmCVEOhyMkwhmL6+hzN1MAq8zJ4UzgMsLX2uy0AgA59fX8AKwjXPiU9fyiJrpNYzk+jeMwU/i0O50cCgwHag39JCrU3VjV3Sj86u5TBbCZLHxGcxsDfG+z8TCwwmLBamZxLWKL7wKBQCAQCDo2nUr4XLZsGXPnzmXBggV8/fXXjBw5kilTplBqSDRvZPXq1Zx//vl8/PHHrF27lj59+jB58mSKDHerAU4++WT27t0b+vfPf/4zEdMJUVFRwf79+82/Wy4QCFrFkPwMJhXk05rg9c9/quDx974Xzk/BQYfNZkOXO405PnWRwixhxiio6mKc0XlpluPTYrORDqTTfo5PxXDDtKCyktuBqSblFgXwp6ezAviE8HyqrwMPAHszM03r2y7LDAMGEy466flFzQ6zjyZ82vbv53rgYsx1GxqPHVbYJxF5XQk/5zD0rxgqqpsp/EKD8GkjPMen2TdWjPTy+bgFuBLh+BQIBAKBoDNh7q3ROPPoo48ya9YsLr30UgCWLFnCu+++y9KlS7n11lsbtX/ttdfC/n7hhRd46623KCwsZObMmaH9DoeD3NzcFo/D4/GEXXRVa8UFFEVBUZp3eimKgqqqobZ79+4lKysLVVW7tFgSOe+DgYNxztC15n391MMB+HBjUTMtg6ha21OP6MuQ/Iwm23aF9REIdKxWK9XAfsBnOLcvrapiEvCewZ0YT4yCjy5GeDweBhAUSW7u08eUfo0OtGjCp1mCa6xQ9yNrargReMnEzxXjWhsdny9o+2ZkZ5vWt8UgfOmvs6IooQtZs4XPUP5ag/CZum8ftwE7gZ1mOj5jCJ8/JCdzOVAC/MPkUHe9pJNqFD6197QP84XP2owMngH2AGcZxtC9tpYjgTqT+wfo5fEwj2DKASF8CgQCgUDQeeg0wqfX62XDhg3MmzcvtM9isTBp0iTWrl3bomPU19fj8/no3r172P7Vq1eTnZ1NZmYmJ554Ivfccw89evSIeZz77ruPhQsXNtpfVlaGuwU/7BRFoaqqClVVsVgsbNu2jby8vJjO1a5C5LwPBg7GOUPXm/cfj8qhpKKGTburm28MWCR48/NtXDlxQJPtampq4jE8gaBDYLPZOFvbPikri1u07UFeLycA35t0Yy9NUfgvQQFyl/Yd7PF4UAAP5oXg6tXkEy18OhwO6rRto/AZCvk2MddlMnAhwYtHo8hsHJtZGIVPfb19Pl8ozF41UfgyOj4lQxoHo+PRbMdnIZANfLx3b2j/DlnmE207UY5Po/BrdJyaLXxWZWWxSNs+zSA6Tt+3j6uBZ00sbqUjGfKMekSou0AgEAgEnYZOI3yWl5cTCATIyckJ25+Tk8PWrVtbdIxbbrmFXr16MWnSpNC+k08+mbPOOosBAwbwyy+/cNttt3HKKaewdu3amMUB5s2bx9y5c0N/V1dX06dPH7KyskhPT292HIqiIEkSWVlZWCwWjj/+eJKTk7t8qHvkvA8GDsY5Q9ec98+l37W4raLCNzuryG7G/dTV3/OCgwuj8OE3iBB68RnFJGHEKsvombmX6jkPDaKEWYKU0fHpjyJ8mpVb1G63M4Ggu/z0nj1D+/V1Dpj4mZuuqvwDCADLDWkF+gEykGxi3/p6WgGf4XVOREV5q9XKxQTnOLpnT0JXgAkS/ux2O0OBPGC1oaq6UXQ2U3gNC/WPEmqfiFD3aM5uAFn7rDFT8NcxhtvXCMenQCAQCASdhk4jfB4o999/P2+88QarV68OExvOO++80PaIESMoKChg4MCBrF69mt///vdRj+VwOKLeWbdYLC0WeSRJwmKxoKoqPXr0QJJak0Ww86LPu6uIYS3hYJwzdK15q6qK2xto1XPc3kCoYFosusLaCAQ6xsIiuvCoqiqyHnptkjBlLCKkh976/X5eASSg2iAOmdGvBfAbRMClaHk/W3AjtC04HA4qtW23QWC2ao7PgIkCkJyUFPwf8Gn5RT0eD/8FhgKLKytjPfWAMTpoA3o1c6+Xq4AMYEhenml922w29mvb9YGG74JECX9heV0NkUVZtbVMBkpk2dTvk1g5PhPp+HRYrXQn+MPFKPhKCShuFepLE5eN6RYEAoFAIBB0fDrNr+6ePXsiyzIlJSVh+0tKSprNz/nwww9z//3388EHH1BQUNBk20MOOYSePXvy888/H/CYW8KPP/7ImjVrEtKXQCBoG5Ik4bS3Tkxw2uWD5oaGQABB4fPPwCrgd8XFQEQosknChC7GQUPot9/v5wLgIsBp0vvQ2G9AEwG9Xi+3ALMBX0RanXgRrZgTNBT5MdP5Zk1ODm0bhU+zXa6Rx9aFT5/Pxy5gI+BJTTWvb8OahlXzNgh/Zoe6670qhtf8+H37eB9CaSXMwmaz8S7wNLDTIEDvSU1lInA+5guf2fX17AN+JsLxqQmfZjp+daKlWxAIBAKBQNDx6TTCp91uZ8yYMRQWFob2KYpCYWEh48ePj/m8Bx98kEWLFrFy5UrGjh3bbD+7d+9m37595JnoHDCyb9++RjlHBQJBx2PC4FxkS8sEFNkiMWFwywumCQRdAUmSGC5JTAJyDUKg2VXOZbsdvZxPyIHm9YZCWszq1+J08j/gI8BvqHCuY5YQZrfbuRJ4DjhME5iBkLPWTMenzSD2+rSQ60S8xhBc7x3AT4SHuofGZqLwJkkSF8kyTwNHlZeH9ifK8RiroJWeb9RvcvSA3W7nKeAaYGNKSmh/rSTxCfA55gufxpy6UYXPBBQ3ilZgSyAQCAQCQcen0wifAHPnzuX555/nlVdeYcuWLcyePZu6urpQlfeZM2eGFT964IEHuPPOO1m6dCn9+/enuLiY4uJiamtrAaitreUvf/kLX3zxBTt27KCwsJAzzzyTQYMGMWXKlITMqby8vMlCSgKBoGNw+th+BJSWFWcJKCpnHNnP5BEJBB0PPb+kLsgY3YCYlePTUHFbDwM2uuLMKjJkcTo5Dvg94NbcZl6Ph2ygO2A3yYHmcDiYAswCelVVhfbrjk9Tc10ahE+/Jm77/f6ECJ/WpCQGAIcB9fp6e71cCdwO5JuU0kDneEliNjBEu4bUBhD8j8Q5PqMVtDIzryuEi5pGsTlRwjM0uI0dBN9nEJ5KIxGh7sLxKRAIBAJB56RTCZ8zZszg4YcfZv78+YwaNYpvv/2WlStXhgoe7dq1i72GapfPPPMMXq+Xc845h7y8vNC/hx9+GAiGLm3cuJEzzjiDww47jMsuu4wxY8bw2WefmVodU8ftduPxeITjUyDoBAzJz2BSQX6L2uZlJjEwx5z8fgJBR0bRBBiLJsj4fD58gAsTixtZrY3ccMY8iGaFX0cTg/wuFyXAPiBVz20aZ4wiGFq/iqKwEDgGKMzKMqVfAKsxn6om+vn9/lA6A7NEZggXFo1V3a8G7gHyNCHWLEKiviHH53cZGUwmGGqeMMenwWloSZDwKcsyKZJET0A2rHO3/fu5BvgD5gufxtQSXs1tHJZKIwGOT0/37vwOOBHh+BQIBAKBoDPR6YobzZkzhzlz5kR9bPXq1WF/79ixo8ljJSUl8f7778dpZK3H6XRyzjnnxKweLxAIOg6SJHHDqSP44bcK9u5v+gd28X4Xj7+7iZvOGCnyfAoOKpQIccjv93OW9tg5hxzCjSb0abPZ8ABuGkQhozgkmSTGxRI+dYzuyHgiyzIh6U1z1gYCAX4BfgGOMzHXpVWr7m3DEOZtqKxupuMz2nobhS+zXmedkKhvKChVLsusijK+eGOz2dgL7AQ8BkHdoqc3SEChvMclictUlScNBoO8sjL+DHxBYoVPvyGVxpvAFqA8ASYCOSWFz7RtIXwKBAKBQNB56FSOz65GVVUVboMrRSAQdGx+Lq5uVvQEUIFVG4vYtqfS9DEJBB2JQITj02+sOm5SKKrVaiUbSAKKMzKC4zA6Pk0UPrcBZUCyJgYlQviExu5DY9itWeusH/uPwHSgRuvHGOqOycJnIfANkKQVukyU6AoNuVMtBsdnInK66sc+E+gPrDcIfKFQ9wTcQNeLZhmFX91xnIiq7sbCWgFDYa23gIXA9uxsU/uH2CH/AoFAIBAIOjadzvHZldi0aRM9e/Zk2LBh7T0UgUDQAv6zfieyRWpRrk/ZIrHiq50Myc9MwMgEgo6BLsDoefcSJXzq6P0Zi85YTQyx7wH0ANBC7AOJEj41J7nRWXsOkA/UmOhEs1qt/J+2PUITX/1+P08DTsCSlmZa3zabjRFAFrDGUFgpJDea7fiMInz23r+fywkWXDI71F0nWmEfJQHCZ0CWwecLC/U3Cp9mCu4QXljLKHzqJCJFlVOWuZbgjye9uJdAIBAIBIKOjxA+25GqqioOPfTQ9h6GQCBoIWu2FbeqwNGabcXNNxQIuhCKLOMF/Abh80mgH/CVoRBPPDEKTrrwWZmURDrBkOwXTRKkbFrYNzTku9Sdpj7AbqIQo8gyBAIh4cvn8zGbYO7BB+rqTOs32lr7fD7+ou17It283MZh622o6q6PyGKy41MvniMbQs1HlZdzPfAa7SN8/j9Z5gegND2da0zrPYhfv6lhdHzq5wDmOz7tDgcvAwHAZTj3BhKs9G6e5N6ATZZ5Qtt+xVjkSiAQCAQCQYdGhLq3E4FAgJqaGrp169beQxEIBC1AVVXc3kDzDQ24vQFUtWVCqaDz8tRTT9G/f3+cTifjxo1j3bp1MdtOnDgRSZIa/Tv11FMTOGLzeDIrCwdwh+bO8vv9/A44DUg3CiZxxGq18jfg30CP4uDNBl8gQA1QgXlONJvNhj6jqMKniUJcNMdnIoq8WK1WTgHOBaw1NaG+jY+bRVhlc0N+0VBhJZOKWOkoEW5mAEmbux9Mzddut9v5K8FcmuP37AntX62qPA78kIBrSVWfv8HxaUlgqLvdbudS4HIa0iz4fD5eBTYDo/ftM7V/CHdxKwa3qUAgEAgEgo6NED7bCa/XS/fu3Uk25CwSCAQdF0mScNpb98PWaZdFcaMuzrJly5g7dy4LFizg66+/ZuTIkUyZMoXS0tKo7d9++2327t0b+vf9998jyzLTp09P8MjNwWoQJCBckMPEkPMTgTOApASKcUbhUxfiAroTEXOFoIfT08kHlmih5Ubno2pyjs/ngGVAuiY0+b1e8oFswGZi30bHp77eXq83YTk+38rKoj9wq6GQUKKqqttsNgYB44BuhhBr/Tw3O8wcDGksDMKnLvx6MV/4NIay665XY37ZRFR1tzudocJixny+AoFAIBAIOjYi1L2dSEpKYvLkye09jE6Dqqr4/X4CgdY57tobRVHw+Xy43W4sCai6Gm9sNpupLpbOxoTBuaz+YU+Lc3xOGJybgFEJ2pNHH32UWbNmcemllwKwZMkS3n33XZYuXcqtt97aqH33iMrDb7zxBsnJyV1G+NTFD6PwaXbhG6vV2iBAav2m7tvHCwQLD5klyETr12WxsJSgEDTERCGm1m5nDw1VvRPp+Iycs1Rby25t36um9Rw71H0i4ABON7mqtzspiZ2Eh3rrhX6aKi4UCATiUghH6t8ft6qSlJ2N2+1GVVWO69WLbkBqTo7pxTId/fvjTk/HabOFrmuSs7Nx6/ltAwFTx+BwODi0Xz9sQJLNhtvtDgrf/frhBpLy8kxfg+TkZGr79cNB8Hx0u92tus4T11QCgUAgELQPQvhsJ8rLy1EUhV69erX3UDo8Xq+XvXv3Ut8JE8mrqoqiKNTU1HRK558kSfTu3ZvU1NT2HkqH4PSx/SjcVNSitgFF5fSxfU0ekaA98Xq9bNiwgXnz5oX2WSwWJk2axNq1a1t0jBdffJHzzjuPlJSUqI97PJ6wAh7V1dVA8KaKYgi5bQ2KooQ+m+LNxLo6FgFfejwoihLmyFOtVlP6lGW5wQno86EoCo6qKi4DdgK/WixN9tvW9TD2G9DmW+FwcLu2b7UkmTJfaHD4+f1+FEXB4/E0CMw22wH129R6hK2114uiKKHw/nj03RTR+vZ4PHyt7TsjKcmUvvX10Nc8EAgQCASCaSoMjs9ofdfV1VFUVBSXcfV/5hm2A0fIMr/++isADy5ZQjJQbbWG9plFwYMPssnvpzfBHPU1NTUMWbiQL71eMoDffvvN1P7T09N5a8kSrEB1cjK//vorqqpiWbKE7cDopCTT1yA/P5/dS5ZgAYalpYX606/zmsNisZCfn9/o896s94xAIBAIBIIgQvhsJ3bv3o3X6xXCZzMoisL27duRZZlevXpht9s7lYCoO1WtVmunGjcEx15WVsbu3bs59NBDhUsBGJKfwaSCfAo3FtGSzJ3vbNjFoXkZWOXO5/btqvh8Pn799VcGDx4MwNq1axk/fnybjlVeXk4gECAnJydsf05ODlu3bm32+evWreP777/nxRdfjNnmvvvuY+HChY32l5WVtdndpCgKVVVVQdEgzk70Xl4vZwJeRaGkpISysjIGao95FCVmCoADoaqqKuR29NbXU1pailsTiH1ATU1Nk/22dT1qamrYDdQAlV4vpaWlIWFaf9yM+QIc73YzGvhO66OkpATdX+5V1QPqt6n1qK6uRr8N5q6tpbS0lJqKCkArOuPxmDbn6upqfMBeoNblorS0lH2GvI4ek/rW12NIbS3nANuAoqKi4PWI5uQMyHKjvhVFoaKigtTUVLp3737A1wC1LheZqkq5LNOtT59g3un6etKBUrudzD59Duj4zREIBPB4PDiB3r17Y7FYQvskSaKPyf273W7sfj8OoDQ1lcz8/OA+ny9sn5nU1taSpqpYgdKMDDJzckI3CSwWS5OvsaqqVFRUsHPnTrp37x723mqJaNoW6urqQiLrjh076N+/vyn9CAQCgUDQ0RHCZztRX18f0+EjaMCruTr69OnTKfOhdmbhEyArK4sdO3bg8/mE8EnQAXvjaQWgqny4aU+z7Qs3FiEBN50xslO+/l2Riy++mPXr13POOedw7733ctNNN7FmzZp2GcuLL77IiBEjOOqoo2K2mTdvHnPnzg39XV1dTZ8+fcjKyiK9jRW0FUVBkiSysrLiLnxatDx8NqBnz56kpaWFnIjOtDSys7Pj2h8E16RM27ZJEtnZ2Ti0eXkJfo411W9b1yMnJ4eTte3Lc3PJzs4myWYjhaDg2ly/B8IYv5/rgBd9PrKzsykpKQmtsyM19YD6bWo9srOzQ6Hudm2tk/S8rgRTOZg15+rqagZr2xf26hXq+2aCr3PPbt1M6Vtfj8GKwp+BlUBmZiYpKSlYNaeeIsuN+na73VRWVgbHaSiK01bcgJPgeys1NZVAIICq77NYTI/MsFqtIfd5cnIyNpstdH5IkmR6/xaLBQvB+dplOdSfnWCqA7vdbvoYVFUNhrkDDqs11J/P52tRSg2r1Up9fT0ZGRk4DcW4nCYU5rruuuv4xz/+wX333ceVV17JBRdc0G7fdQKBQCAQtDdC+GwnXC5XpxTy2ovOmB+zKyDEusZYZQunjunXIuFTBVZtLOK0sf0Ykp9p/uAEzfL999/z448/smDBAp566qkDOlbPnj2RZZmSkpKw/SUlJeTmNp3fta6ujjfeeIO77767yXYOhyOsqIeOxWI5oM9FSZIO+BjR0Cs/2wg6xBRFQb9lYnE4TPkst9vtITGOQCDovNJyL/oIrmFz/bZlPYxV2wNav/2LiqgFtgAugzAUb1RDhXHdeXchkAKM6NnzgPuNtR52u506vY02ZwyVve12u2lzNr4PfD5f8HV2u3lA2/eC1Wpa35IkgSbw6ue2xWLhVbudd+vrcXfrxrURfesOwOacgK0YBKgqkqqGjicZHjP7+9oiSaGKqKoajHlwBgIkExSeTe/fYglFWkRbAykBayBJEnowfar2uqrGsTTTv/GcMJ6rZpy3hYWFFBcXc/XVV9OzZ8+4H18gEAgEgs5Em4XP7du389lnn7Fz507q6+vJyspi9OjRjB8/3pQ7l10N3QkjEAg6H+9s2IVskVpc5GjFVzuF8NlByMvLA2DhwoVccMEFbN++vc3HstvtjBkzhsLCQqZNmwYE3WGFhYXMmTOnyee++eabeDwe/vjHP7a5/w6J5nqyERSn/H4/GYAFmJ+VZVKXDdXV0QvPGMU4E6u664SK1xj6NbPStqIdWzYUN9Kzyg4z0fUWttbaXPUK62ZXso+23sb8orLZ154R5zbAd4pCNTAkAXmwFUnCD+jZIFVVDRM+zSY9EGAwUEWD8JkeCJAFlKotSf5y4Oi96P2rqkoZINNwM8BMJElCD0o/cA+vueTn52O323n22WeZNm0ae/Y0f7NWIBAIBIKuSqtvMb722mscddRRDBw4kFtuuYXly5fz2Wef8cILL3DyySeTk5PD1Vdfzc6dO80Yb5dh5MiRbQ5TFLQ/kiRRWVkZtq9///58++23zT738ssv5+OPPwZg+fLlfPHFF63uf/78+bz22mutfl60cQtaz5ptxS0SPSFY5GjNtmKTRyRoKccccwx+TRxbsmQJ48aNO6DjzZ07l+eff55XXnmFLVu2MHv2bOrq6kJV3mfOnBlW/EjnxRdfZNq0afTo0eOA+u9oqJogZyUoxulrrQBWE6urn6L1uVrPt2oQ48wSIG02G68B24HD9WueBAmfquYQkzUByFg13Mx+rVYr9wCXAL9q1zC68Ok3uW+bzcYDwP+Agt3BOvIBlyv4P2AzWfhUDcKnVz+/tHU3U/DVKbVa+RYo1l77tgqfb7/9NmPGjGHUqFEMGTKEE088sWXFdbQ+LDQIj+j/J0B4lSSpUW5tVVXZC+wmmG5AZ+/evRx99NGhed11110HVPF98eLFFBcXh5yZzz33HHfddVfUths3buSUU05pc1/x4tBDD8Xv9yPLMs899xwZGRntPSSBQCAQCNqNVl2hjh49GrvdziWXXMJbb73VKJG5x+Nh7dq1vPHGG4wdO5ann36a6dOnx3XAXQG32823337L5MmT23sognbghRdeCG0vX76cUaNGcfTRR7fqGLHCY/WLfJEawDxUVcXtDbTqOW5vICwcTtB+zJ8/P7Sdnp7O8uXLD+h4M2bMoKysjPnz51NcXMyoUaNYuXJlqODRrl27Gr0ft23bxv/+9z8++OCDA+q7I6IawoF1x6eOWaKY1WoNVfv2a1W2MYS6myVK2Ww2coD+gF0TVYwh9qkmOtD0dZZVNZRL+lKCzre0QOs+n1qD1WrlHW37LG1da61WngPqgJEmC59DgWOAdfX1ACjaupvtNtUGAARzSuqC5zivlzzAlYCq3Pr3h9Ht2Frhc+/evVxxxRVs2LCBfv36AfD111+37LvJ0EaNcHg2dRtQz3N+oIQJn4Y1MD6us2jRIq655prQZ+/ChQu54YYbokaktWR8ixcvZuLEiXTr1o0MIBmoieFyLSgowOFw8NFHH3HiiSe2dHpxx5jKJS8vr0U35gUCgUAg6Kq06krk/vvvZ8qUKTEfdzgcTJw4kYkTJ/LXv/6VHTt2HOj4uiQul4uioqL2HkanRlEUAoYfdxaLBVmWQznldGRZxmKx4Pf7wy6QY+2PVxGi/v37M3PmTFatWkVxcTF/+tOfuPPOOwGYOHEiN9xwA3a7nRUrVrBq1Spefvll5syZw+WXXx52nKKiIq6//nq2bduGJEmceeaZLFq0iEsuuYRRo0Zxww03cNddd7Fp0yZqa2v57bffWLVqFd9++y133XUXXq8XSZJ49tlnGznbfvrpJ2644QZKS0vxeDxcccUVzYbnCoI/rpx2GVcrxE+nXRaiZxdmzpw5Md87q1evbrRv8ODBjYSDLoPBFef3+/F7vfyLoBuwxCCCxrfLBsFLF1rX5+bSS+v3SxMF18gQe6PwaarjMyKXqs/nYzGQDjyquRHNINpa70tO5lZt36cmio92uz0kcOvOWiVBYfYAkpbTVRf1VVXlmkCAc4CH6+qafK7O2LFjKS5uWwSA0dVrs9mC1cT195QkIVut5Obmsn79+pjHKCkpQZZlunfvHtp3xBFHxGzfv39/pk+fzkcffURFeTlXT5vG7IsuQlVV/vznP/PRypWoPh9Jqam8/M9/MnjwYG04EvPnz+e9995j4sSJzJw5k9mzZ1NfX4/b7eaCCy7gjjvuAIJuzM2bN+Nyudi2bRuHHXYY999/PzfddBPbt29nzJgxvPbaa8FrmeXLef7117E6HFisVh599FFyMzNRaRA+3W43y5Yt4+GHHwbgqquuAuC4445DlmU++OADbr75ZiwWCz///DOlpaVs3boVSZLYv39/yBnZs2dP1q9fz6uvvsqePXuYMWMGTqeTJTffTAawvaSE008/nV9++YWcnBz+9a9/hRz8559/Ps8++2y7Cp8CgUAgEAgaaNVVeVOiZyQ9evTociF88cLr9YYVRRC0nh9++IHvv/8+9PfAgQM56qij2LBhA7/88kto/+GHH86IESP47LPPwn5s6OkaPvjgA6qqqkL7J06cGMoBeKBUVlayZs0aiouLGTJkCH/605/Iz88PPT516lTOOOOMkIAZjT/+8Y9MnjyZf/3rXwCUlZVFbbd27Vq++eYbcnJy+PHHH7n00kv59NNPGTJkCD6fj3rNHaMTCAQ4//zz+cc//sGQIUOor6/n6KOPZty4cRx55JFxmX9XZsLgXFb/sKfFOT4nDG660I0gMQwYMKBNAvQNN9zAddddZ8KIuh7f9e4dFD2BHT4fitfLudpjz5jkRLdarVwLjAe+1QpN1SsKew2Pm0G03KJSokLdNeHTSoOzVu/NYuL1hdVq5QggD8isCWY7TISrF4LrrUt/usAc0KqM+0mA4zNC+PT5fOg9Ki2cd3Fxcbve+C4oKODYY4+lX79+HH/88UyYMIELLrgg7NokkpKSEtavX8/mL7/klGnTOKKggHFDhnDLLbdw2wUX0ENVWfLhh1x//fWsXLky9DxZlvnqq68AqKmpobCwEIfDgcvlYsKECUyaNCkU7bJ+/Xo2bNhARkYGEydO5PLLL2fVqlUkJSUxduxY/vvf/zJ58mTuWryYf/3rXwwaNIh+/fqxd+9e+paWAg15Rr/66isGDBgQKiC6ZMkSnn32WT777LOwcO8NGzbwv//9r9l8+/Pnz2fp0qUsW7aMoUOH4t+0ifc/+YSvv/uObzdupHv37syYMYNnn32W2267DYDx48dzzTXXtPLViR8nnHBCm77rLrnkEmbOnGnCiAQCgUAgaF/afIV64okncvzxx7NgwYKw/fv37+fss8/mo48+OuDBdVW8Xq+pPw4OBoYPH86QIUNCf+vhTGPGjGH06NGh/bL24/C4445r5PgEmDx5ciPH54FgvNC84IILgKBr4JBDDmH79u1N/riIpLa2lv/973+8//77oX1ZMYqDTJ06NRRau2rVKk4++eTQ+thsNrp16xbWftu2bfzwww+cd955oX01NTVs3rxZCJ8t4PSx/Sjc1LIfrwFF5Ywj+5k8IkFLePnll9v0vP79+8d1HF0Zq8MREgN9Pl9ImAKQo1Snj0ufVisTgPOAkgSKcUbhU9X7Mzg+ZRND3T/Py+P5H3+kBvjR78fn8yVM+FwAnAE8qonMittNBuDB/PyiTQmfZt9QLsrNZThQDyz3+fB6vSHhU2qh6Jqb2/abYIrPh4VgvlyL5vjUz3M96qW541ssFt566y22bt3KJ598wn//+1/++te/sn79egYNGhT1OZdddhmSJNGje3fOOuEEVq9bx1HnnsuqVatY/MADuOrq8ALVEa7XP/3pT6Ftl8vF1VdfzbfffovFYuG3337j22+/DQmfkydPJjMzWADwiCOOwOFwhATJ0aNH89NPPzFlyhSOPPJI5s+fz+TJk5k5cyY9jQYL7fpr9+7doeuhppg+fXqri4waw+1POPZYevTogaqqHH300fzwww+hdrm5uezbtw+3290uBV8vueSSNj1v5MiR8R2IQCAQCAQdhDZfoa5evZpNmzbxzTff8Nprr5GSkgIERb1PPvkkbgPsiqSlpTFgwID2HkanxmKxRM1jKcty1B+bsX6MtfVHWlZWFvv27QtzD5SXl5OdnR3623ixK8ty2A/xeJPayoqyqqrSvXt3kfOpjQzu1Y28zCT27nc12zYvM4nD8ro1205gPscff3x7D6HLY/xM9fv9BAxh1xYTixvpn64WLQXKoNJSHgc2Ym6Oz5AQpzk9S51O/k/r18x8l56kJH7StvUiUokSPvW11sXHQ3btYj/wLQ25R81AkiQCkgSqGuq7LCWFiQTFwLtMdnyqycls1rZ1x6c+W7WFfTcVht4cxRs3kuv1UgF0HzuWuro6dmzZggSk9uxJ31bcoBkyZAhDhgzhyiuv5OSTT2bFihUUFBTw5z//GQiKgrfffnv4kzRhUZIkdu3axbXXXsuqpUsZ07s3H23fzlmzZoU1N16X3HbbbfTs2ZNvvvkGq9XKWWedFVZsKPJ6Kdr1kyRJPPjgg2zZsoUffviBqVOncsvNNzNLC9XXbzwnJye3qJBR5HWTni5JJ9oxjMKnw/A+i/ZcWZbbLbrr4osvbpd+BQKBQCDoqBxQ3NmHH35IcXExRx99tMjn2QoyMjKEg6iTM2XKFJ599tnQ36+++iqHHHJIq8Pk09PTw0LtjaSmpvK73/2ORx55JLQvVqh75Njef/99tm7dCgR/oEX2MXjwYNLT03nppZdC+37++WcqKipaNf6DlW17qlokegLs3e/ix73RX2NB++PxePAYXImCA6N3fT2vAY8T/OxRjMKnSSKAxWJBlxwkTXzoU1nJdcCpmFtUKSQCav1+l5HBDOCvmOv4jMy16fd6Qxd0soliS7Rwc2Mle7PDzQPaDU+L1ne9JPEJ8FkC+jYeXxc+9VfYkoAoHlUXHmko6nMYMAywtbC4UlFREZ9//nno7/3797N9+3YGDhzIpEmT+Pbbb/n222/DRE/dKb+vpoa3Vq/myCOPpKqqCpvNhtSzJz+rKs/93/812e/+/fvp3bs3VquVbdu2sWrVqhbPW8fv92PZvZuZw4Yx96KLOOecc/j6668bGmjnRkFBAdu2bQt7blpaWsxrLZ1Bgwbx5ZdfAsHK93UGB6t+rRatsnw0tmzZwuGHHy4KTQoEAoFA0EE4oG/kvLw8PvnkE0aMGMGRRx4ZtYiDoDE7d+7kp59+ar6hoMOyePFi9u7dS0FBAaNGjeL111/nzTffbPVxLrroIv7v//6P0aNHh1V71/n73//O+vXrGT58OKNGjeLJJ59s9piDBg3ipZde4o9//CMjR45k3LhxjX4EWK1W3nnnHd5++20KCgoYPnw4l112GS5Xy8S8g53/rN+JbGlZ/izZIrHiq50mj0jQGlatWsXUqVPJzMwkOTmZ5ORkMjMzmTp1Kh9++GF7D69Tkx4IcAFwOkGhwih8minI+TWBQRcg0f4PYJ7wKcsyu4HNQKUmSiUq32W/+nruAWZrfRpTCpjp+JRluZHYS4LymgK4ZJlqwKsJfcaCP2a76zJ8PhYAt2r9er3ekOOzpaHu8UAXPsMKpLUwn6Pf7+fuu+/msMMOY9SoURx33HFcfPHFnHnmmTGfk5WVxZgxYzj1rLM469xzyRs5kmHDhjFjxgxOnzGDMy6+mJxevZrs94477uCll16ioKCAW2+9tU1FfxRF4dpFizh8xgx+f845bNiwgdlXXhl6XHd8DhgwgJycnLDQ85tuuomTTjqJUaNGUarlBI3kscce4/rrr+eII47gm2++CatTcN111zFr1izGjh3LdxHXU9FYuXIl55xzTqvnGG82b97M1VdfzejRo8nLyyMvL4/Ro0dz9dVXs3nz5uYPIBAIBAJBF6HNV6j6BYbD4eD111/nnnvu4eSTT+aWW26J2+C6KtXV1dRoecgEnZMePXrw97//PebjugNaNSTb198zxhsERx55ZNjFeSS9evXirbfearTfmKvwrrvuavT41KlTmTp1aqP9xh9KAwcO5D//+U/MvgWxWbOtuEWFjSCY43PNtrZV8RXEn1deeYXLL7+cc845h8ceeyyUC66kpIQPPviAqVOn8uKLL3LRRRe180g7J5KhqrvP50PRq28DVhOFKcViAUVpEOM0AdKPeWKcJEncZrPxF5+P0d26cRc0VNk2sV+A3m43twBf0DilgFm5VCHoro0MdU+k8HlXejpzyss5JD2duYBz/36uBiow3/HZze/nRq2vbyJC3UmE8GlwfELw+1wKPdQy4bNfv35hecNbwgUXXMCDDz7I7t27Q0UiVVXlscceCxXCSUtL4/HHHw89J0yUJZin01iQ0kjkNYxejV1Hvymsqir/ee45egAldjs5BQWU790LerEowxrceuutPP300zz11FMALFiwIKwmQbR8z6ecckqYKWHRokWh7csvv5zLL78cRVGo+vprjhs8mGLD++zqq68Onfter5e33nqr3Wsd/Pe//2XatGkcccQRnHnmmWHfdatWreKII47g3//+d6sK1woEAoFA0Flps+Mz8qLmjjvu4LXXXgsLyzWDp556iv79++N0Ohk3bhzr1q1rsv2bb77JkCFDcDqdjBgxgvfeey/scVVVmT9/Pnl5eSQlJTFp0iTT3ZiKoojwF4Ggk6KqKm5voPmGBtzeQKPPTEH78Ne//pXFixfzz3/+k0suuYRTTjmFU045hUsuuYTXX3+dxYsXc/fdd7f3MDstkiZu6tXGdcenmc5LAEUXhaI4Ps0UxPRj687D03fsQAVew9xQd7S1tBEUPj2KwunAHwCSkszrl4Zwc0lzXUqGgk6JCjfX17t7RQVPAQsS0LfupJW1/v1+P3cBFwK7YhQejCsGYU93fIb2tMM1paqqpAGZgC0B32/RxF39e1WNeHz69OkMHToUpYUpAFozhmLgZ6A2xvt7+/bt3H///fTs2TOufbeWW2+9lVtuuYW1a9dy1113MXv2bGbPns1dd93F559/zq233spf/vIX0/rvCr/XBAKBQNB1aPOV0vbt2xtVmD777LP58ssvWbp06QEPLBrLli1j7ty5LFiwgK+//pqRI0cyZcqUmGEra9as4fzzz+eyyy7jm2++Ydq0aUybNi3srvODDz7IE088wZIlS/jyyy9JSUlhypQpLUqM3laE8CkQdF4kScJpb52g4bTLLXbkCMxl165dTJo0Kebjv//979m9e3cCR9S10IVPXZCrTEkhCeiOucKnqokQITFOEz79YOr3rT6nUIi71q+Cye5HTeSzan37VJV3gOWA1eQq0oqeZ1Obq6qJkGa6a3V0cdOrCep632YL3NAgfOqifiAQ4EPgdaC6W2IK2OkyXqTwadb3y44dOxg1ahQAtkCAscBIQ/+9gIFAUqB1NwPbSkheNQieJUApjddgzpw5cX/vS5JEHVAJ+GKs+eDBg6NG3CSaH3/8kQsvvDDm4+eff75pwmFX+b0WFVUNRhTE+md8L3SEthC/tpFFWjtb20Agfm2NN3s6W1tFiV9b482lzta2ufdRa9p2tPd9Z/yMSABtvkLt169f1P3Dhw9n+PDhbR5QUzz66KPMmjWLSy+9FIAlS5bw7rvvsnTpUm699dZG7R9//HFOPvnk0B3NRYsWsWrVKp588kmWLFmCqqosXryYO+64I5Tf6NVXXyUnJ4fly5dz3nnnRR1HZDGM6upqIChotuTucn5+Pk6nM+53ojs6iqKgqmqr5q0/p1E+q05EyJHQCcevr3tLz22dtrzWnYnxh+Xwyea9LQp3ly0SEwbnRF2Lrro+HZnhw4fz4osv8uCDD0Z9fOnSpQwbNizBo+o6RIpD/kAA/WepmaLYg+np3OJ2k5uezmwahE9Vkky96XCT38/ZwNta4TldePVjsghocHzqhXYaHjJXfFzucPCN309pUhLXQELyqepMd7s5Cfi4thYAVbtw9gN2Mx22NKQQCJ3bhot2s+cNUGu3U6Tl4R7ZxhyfB4IUUVwp0f2DVuDJ0G9AktAC3RmYoDFIktQprkn79+/Pu+++y+DBg6M+/u6778b8LXegdJXfa9H4w8iR/LuJFFUvpaRwZ0YGAFmBAN8Ux0519EZyMn/OzAQgRVHYtndvzLb/Tkrimu7dAbCoKrv27InZdpXTyaWGHLU/FxUR63bY/xwOzjO4kzft2UNmjHP7a5uNM7KzQ3+vKy6mVyBAbpS2W61WJmnpFQBWl5QwKIbQsUOWOTa34SjvlZZSYPhOM1JqsXCEoZDsW2VljDOkejFSK0kMMeQf/kd5ORNjFLMMAP3y80N/P7dvH1ObENUH9uqFR/vMebyigrO1z2YLNFqPw3NzqdS+n+7bv5+L6utjHvfInBz2at8nd1ZVcaX2XReN47Oz+UW74ffn6mpuaCKN3ilZWWzSrs+uqalhnvZeiMZZPXuyTvu+u7S2lkVNFIb7Y48erNZutp5XV8fDlZVhjxvX44ru3XlPi0g5o76ep/fvj3nc6zMzeSs5GYBJLhcvN1F899aMDP6RkgLAMW43y/bti9n27vR0nktLA+AIr5cVTRQNfigtjcfT0wEY4vPxYYwbNwBPpaZyn3YDtJ/fz+clJY3a6GtxMH5GRONHq5UTs7LIy8vjq6++ijnWpmjt53irrtSuuuoq7rjjDnr37t1s22XLluH3+5u829gavF4vGzZsYN68eaF9FouFSZMmsXbt2qjPWbt2LXPnzg3bN2XKFJYvXw4EXavFxcVh7p9u3boxbtw41q5dG/OL9L777mPhwoWN9peVlbXozqPu+CwtLT2onJ+KolBVVYWqqi2et8/nQ1EU/H5/2I+MzoKqqgS0N3xndPz5/X4URWHfvn2tctO05bXuTBxzSBoffR/7C8VIQFE55pD0qE4Hkes38TzyyCOcdtpprFy5kkmTJoXlPSssLOTXX3/l3XffbedRdl6M4cCRn9tmikNem41qIE3vT3demvz5kwMMB1ZrP9Ikgwho5mefZHB8uv1+qK/nIhITbv6F08m7dXUM1F7PPQ4HrwHfAeNN7ntAIMAU4Df9R7H2eidCdDWK+l6vF7/fzyQgGegW40d6PIm8hlBVNRS2lZDriyh9SE08ZupQotxQTtQ1VgpgB5QEuVzbyt13380FF1zA6tWro37XrVy5ktdffz3u/Xal32vRKIkiaBipraujqK4OgOZ+tdTV11OkiWCpzbR1uVwUaflsm/tmcbvdobbN4fF4wto2JSN4fb6wtk29A3x+f1jbptYiEAiEtW3q01RRlLC20SXPIKqqhrVt7hU3tm2u1GvRnj3oEmpsGTPI3uJidNmurpm2xSUloZs5sSXPICWlpaG2zf2aKC0rC7WNLWMGKSsvD7WtbKZt+b59obaxZcwg+yoqQm1jy5ja4/v3U6QJo7FlzCCVlZUUaYJreTNtq6qrKdJE3+bUrOqaGoq032mZzbStqa2lSBOpm8uyLj4jgvj8fvbu3YuqqjGjAZqjtb+jW3WVmJWVxfDhwznmmGM4/fTTGTt2LL169cLpdLJ//342b97M//73P9544w169erFc88916rBNEV5eTmBQCD0xa2Tk5PD1q1boz6nuLg4ans9Obv+f1NtojFv3rywL+jq6mr69OlDVlYW6dqdgabYvHkz5eXlHHrooV1SFIqFoihIkkRWVlaL5+12u6mpqcFqtbb5R42qqu0uOpr9Q9QsrFYrFouFHj164GxF+GRbXuvORFZWFif+WtMi8fPEw3tx9PD+Uc/B1qypID5MnDiR77//nmeeeYYvvvgi9Fmfm5vLKaecwlVXXUX//v3bd5CdGItBkPP5fCRXVPAKwYtnM4Up/TNWF1pfy8hgcUkJOJ38ybReG0LsZf0Gly64mvydY8yl6vf7kauqeBXwAO+YLABGhvd/nZ7OA9pjd5jcdyjMXr/Lb3B8mi186o5PGfB5PPj9fv4GDAEWN+GIiRfG7xDdcbiHoPiYkO/ZKI7PRAufbkmiUlVDTitUFRuNc3yaSQ8gCyjt4MLn9OnTyc/P54knnuCRRx4J+64bP348q1evZvz48XHvtyv9XotGoE8fhslyzPecB8jX8yCrKoc34Qz2GtoSx7Y+Y1tgbBOuKH9E298pCrHeSYGItlMUBQvRU7hFtj1LaxsNJaLtHxWFWP59NaLtLEVpUswwtr1eUWjq15ix7W2KwoIm2vaUpNDn3iJFCX0HQuP1SJYkkrS2jygKTzZxXEmSyNfaPqMovNRE23pD21cUhWVNtK0xtP2XqvJOE+dPraHte6rKx020rTO0/TjGeamvh7HtumbOYePcvm+mrcvQ9pdm2roNbfe2om11c21pOH98TbRVFAWfxXLQfUbEapsH5OXlkW1wibaG1v6ObtVV4qJFi5gzZw4vvPACTz/9NJs3bw57PC0tjUmTJvHcc89x8sknt2ognQmHw4EjStVUi8XSootPn8+H2+1ucfuuhCRJrZq3xWJB0kIVW3pRq6oqW4sq+c/6nazZVozbG8Bpl5kwOJfTx/ZjSH5Gwi6QjaJre4uvbUFf97acq219XmdAVVVifupHIEkN53EkXXFtOgP9+/fngQceaL6hoNW4evQgg+AFzSs+H7aaGmYSzMP3vYnC1CSPh3HAN9od932Kwi9ApomV5CFKoR/tf7OdpnqOTz2XaiLzbPaXJIYAihbal8iQ71AuV93x1w7CJ4BfEz71H+dSAm5uOgIBBhH8cWMUPgH6JOC7JFqoe6KFzwpZpkRRsEsSfQDZ52MkwR9xrk54jWU2EyZMYMKECe09jHbjQH+vRePL9espLS0lOztbXMMRFHLEejQg1iMcsR4NiLUIJx7r0drntfoqMScnh9tvv53bb7+d/fv3s2vXLlwuFz179mTgwIGmiTs9e/ZEluVGIQYlJSXk5kbLLhK8q9lUe/3/kpIS8gz5QkpKSkLJ3M2gMwpgnQV/QOGxdzby4cYiZIsUysHo8gZY/cMeCjcVcVJBPjecVoBVFh86graxtaiSjza1LNS9cNMezjiyP0PymwuUEAg6P1a7PRRG5ff7EyZMDfP5uBx4Rcv1pYtxiRLiLAl2fO7LymI0wXC8pX4/AS1vmR/zIwzOc7m4AXhZCzFSfb6gAJuAvlXtIleOcHwmItSdtDSO1Pq6MhAgEAiELqITIXzKqkoGwbDKyByTiQx1j5bjs92ua2NUdTe1Sy3PqNSEm6ajUVVVFeb47GZiMa6u9HtNIBAIBF2HA1J+MjMzGTlyJEcffTSDBg0y9aLDbrczZswYCgsLQ/sURaGwsDBmqMb48ePD2gOsWrUq1H7AgAHk5uaGtamurubLL780JfxDR5blTpmvsqOjqiqPvbORwo3B3BKRhWf0vz/cWMTidzYeUGL6/v37M3jwYEaNGsWwYcN46qmnmmw/YMAAvv322zb3J+hY/Gf9TmRLyz7vZIvEiq92mjwiQbz47rvvkE0uktKVMQpfPp8vYU7EkACpCWK/q6nhXuAkk8NRQ6HuWr/bbTbeBbaZLbgmJfEtsI1wx2ciBMDItT7rt9/wAq+A6e+dUN/a9/f36emcBsxLQN+y3c564BsgoKr4/f6ECp9G4RGC1zxOgvnEWnP9bbx+GTVqFJdffjkrVqzgxhtvBIKV3JcsWdJk/yXFxRx//PGougB9ANf/Y8eOZfXq1S1qq88zdP1mED4XLlzIkCFDGDlyJGPHjuX9998PPU9RFK699loGDhzIoEGDePLJhmDTJ598knvvvbf1A+8EwucLL7zAsGHD6N69O8OGDWPo0KGh7RdffNGUPrvS7zWBQCAQdB0O6OrY7XazceNGSktLG1VVOuOMMw5oYNGYO3cuF198MWPHjuWoo45i8eLF1NXVhaoGzpw5k/z8fO677z4Arr/+eo4//ngeeeQRTj31VN544w3Wr18fyj0qSRI33HAD99xzD4ceeigDBgzgzjvvpFevXkybNi3u49fp168fdpPD7w5GthZV8uHG5hP1qsCqjUWcNrbfAbnwli1bxqhRo9i5cycFBQUcd9xxFBQUtPl4gs7Dmm3FLaroDkHBfc222DmoBB2Pjl6ttyPjVFWeJ5gHMeB2oyRIkFO1Y8vaazfe5eJK4GmzbzJq/eoh7m+lpPAA0CMlhcWmdtuwlpGh7ma7LpUI4dPoujQ7fCv0OhMUU8ptNj7QHjNb8I1c87BQ99b0XddEeQtZBmPOKkNbyeUClyvouKytRaqt5XDtsfJWCo/69YsR/bpdFz6vuuqq8CdZLFQRvIZ65NFHueKKK9hrsSADthbO3+/3H9DrlK0oZAMlWmEa42f1Mcccw6JFi0hKSuK7777jd7/7HXv27CElJYV//OMfbN68mR9//JGqqipGjx7NCSecwPDhw7niiisYOnQo11xzTYuckJ3l2+Ghhx7irrvu4rrrrmPKlClhxY0++OADrr/+evbv38+f//znuPfdVX6vCQQCgaDr0Oarj5UrVzJz5kzKyxvXz5IkKVTJOp7MmDGDsrIy5s+fT3FxMaNGjWLlypWhL/Ndu3aFXXRPmDCB119/nTvuuIPbbruNQw89lOXLl3P44YeH2tx8883U1dVxxRVXUFlZybHHHsvKlStNLTqSmppqapjJwYruwmuJIKW78OIRftyvXz8GDx7M1q1bueuuu/jpp59QVZVrr72WK664olH74uJirrvuOnbs2IHL5eLMM8/knnvuQVEUrrvuOgoLC7Hb7VitVj7//HNRAKeDoaoqbm/rPt/c3kCHKLIlgLPOOqvJx6uqqsTrdADYLBYu0bZfcrsT7vjUnZd66LlqshBXa7Oxk2BOUWgIsTfbfZji96PXTPb5fAT0EH/MFwB14dOq5zXVw/sTkLNKtVhCVVB18VHH9Byfssyf0S6c6+ra7vhMbaI269Sp8O67DX9nZ4NW0VUPEtavCOTx4+GJJ4L9H+Bn1ssvv8zy5ctZvnw5V111FTt37mTUqFH07duXFStWBBtZLPxEsMLrihUrePDBB9m1axcAWz//nEfPPhu/309mZibPPPMMw4YNY/Xq1VxzzTUcffTRbNiwgdtvv538/Hyuvvpq/H4/Rx55ZNhrGOv6CIJO1dNOOIF169bRt29f/vXf/4Y5PidPnkxSUhIAI0aMQFVVysrKSElJYdmyZcyaNQtZlunevTszZszgn//8J/fccw92u53Jkyfz+uuvM3v27OYXK8J521F58skneemllzj33HPD9g8dOpSJEycycuRI/vKXv5gifHaV32sCgUAg6Dq0+Srx2muvZfr06cyfP79RlT0zmTNnDnPmzIn6WLRQmenTpzN9+vSYx5Mkibvvvpu77747XkNslqKiIr744gvOPvvshPV5MNBeLrxNmzaxdetWli1bxuDBg3n77bcpLS1lzJgxFBQUMHbs2LD2F198MbfddhvHH388fr+f0047jTfffJNBgwZRWFjIDz/8gMVioaqqSjiDOyCSJOG0y7haIX467bIQ0zoI//nPfzjppJNifm+ZcdPuYMJqKCQR8HoTluMzJHwSdALqDkxMFiD/k5/PbZr4M1dRQueP2SJcSiDAbQQrCH+YYMdnZLh5wgo6Ae/07ctNv/wCQJ3fT251NTOBnSTG8Xk/wXPsqfr6hBc3asQB5LecMWNGSCRcsCC8dvGSJUu44YYbGqXn0fvYvHkzffr0CT2/oqKCa6+9lk8//ZQRI0bw2muvcc455/DDDz8AsGXLFp5++mlefPFFvF4vAwcO5KWXXmLSpEl88MEHvPzyy6E+Yl0f6dfx+6uq+PLll9lnqFwbbQ1eeuklDjnkEPr16wcEhTZ9G4Ii6hdffBH6e/z48axYsaJVwmdHD3UvLS1lxIgRMR8fMWJEVPNKvOgKv9cEAoFA0HVo81ViSUkJc+fOTajo2VWwWCx4tEIEgvjQHi48/YdDcnIyS5cu5corr+SRRx4BIDs7m7POOosPP/wwTPisq6ujsLAwLIl7bW0t27ZtY/Lkyfj9fv70pz9xwgkncOqpp4qqbx2UCYNzWf3Dnha7iycMjp7QX5B4hg4dytlnn81ll10W9fFvv/2Wd955J8Gj6jpYDDdrjMKn6aHuuguRoBPQkiAxzujsDAQCzN+3jwuAvzUVzhwHdKFNn29JcjIzCAqhd5id41MPN9fF5QQ6Po3nUCAQoKCigluA5ZjvsrVarfgJCp+qz0cgEOBGIAkYnJHR8gPV1sZ+LHIOpaWhzZLt28nZvx8v4D3sMOqrq8muqmqT8BkZ6m4UH2Oh91FaWkpWVhaqqpIObPj+e4YOGRIS2S688EKuueYaioqCqYcOOeQQjj/+eAC2bt2K1Wpl0qRJQNClecghhwBNXx/pnHvGGUiS1FBYKIrwWVhYyMKFC1m1alWL1yU3N5fdu3e3qG2NLFPn9+OTJHq26Bntw5FHHsn999/Piy++2OizNxAI8MADD3DkkUe20+gEAoFAIEgsbb46Puecc1i9ejUDBw6M53gOCpxOpxA+40x7uPAifzhceeWVjcYUiZ6P6osvvoganvP999/zySef8PHHHzNv3jw+/fRTBg0a1OYxCszh9LH9KNzUfD5ZCLqLzziyX/MNBQlhzJgxfP311zGFT4fDQd++fRM8qq6DbBQ+PR629OwZEgc+N1OQ046tC4G6C1FNgBim4/f7sSsKyYDNZIe3rDlrZcDv9VJttfJ/2mMLE5Tj00bQXZuotALQeL31MPtE5BfVhU8HoHi9+P1+/q499mBaWssPlJLSprZqSgq43cEQ65QU8HqhjcJnW5CA0cBvTicr3W5UVWUAwQJbTVU4T20qtJ/GBYtiXR8BpCQnh8YC4JckygAf0F2S+OSTT7j00kv5z3/+w+DBg0PP69u3Lzt37gwVwtmxY0fY57zb7Q45WJvDY7Hg4gCrwyaAJ598kilTppCbm8vvfve7sByfn376KXa7nQ8++KCZowgEAoFA0DVo8/f2k08+ydtvv80ll1zCI488whNPPBH2TxAbh8OB1+sVBTTizITBua2qtB1vF96kSZN4/vnnASgrK+Ptt9/mpJNOCmuTmprKCSecwP333x/at2fPHnbv3k1ZWRl1dXVMnjyZe++9l/79+7N58+a4jlEQHwblppOX2fyPJAk4qSCfwb0yTB+ToGUsWbKEhx56KObjQ4cOZfv27QkcUdfCahA+Vb8fj6KwD9iHuY7P//XqRS/gIjTHp/79arLweUxFBeuAxwi6qHQByGzB1Tby84gAAJHUSURBVGJIKaB4vfi0UHcwP+T7x4wM/gK8RLjInAjHZ0FlJW8DCyP6DkiS6eKfLnxC0PGZyPyiAH6bjfXAdwRFQvUAQt2bIj09naqqqsYPSBIWYNSgQfzyyy/BiBng6BEj2LJtG99//z0Ab7zxBvn5+eTn5zc6xJAhQ/D7/Xz88ccAfPjhh/yipS5o6vrIOAYdVVXxyjI7gT3A//73Py666CL+/e9/M3LkyLB+p0+fzvPPP08gEKCiooJly5YxY8aM0ONbtmxp9JxYNKos30EpKCjgxx9/ZNGiRaSlpfHrr7/y66+/kpaWxj333MPWrVvDcmgKBAKBQNCVafOV2j//+U8++OADnE4nq1evDrvokiSJ6667Li4D7IokJSWFwnwE8aO9XXhPPPEEs2fPDiXVv/322xk3blzYjyOA1157jblz53L44YcjSRIpKSk8++yzBAIBZs2aFSxUEQhwzDHHcMopp8R1jIIDR1VVFr+7ib37Xc22zc1M4vpTR4j8nh0Ih0EwEsQfWZbxEXQDqgksPhNwOtmrbft8vpDwqZosSGUoCkcCZQSFTz3E3mz3Y1hKAbeb5JoazgYqMF+E25WRwcvatt/vZ6vFwnLgpwS8t7J8Pv4AdCPc8akk4DPWKHzqjs/JBN2m9gSIYFKE6GfMMRnP75iCggKGDx/O4YcfziGHHBIqbiRJEiowID+fnj17snnzZo6y28nKzOSZJ55g5syZoeJGb775ZtQx2e12li1bxtVXX00gEODII48MExxjXR/17t07fC0IF38BrrrqKjweT6hyOMDf//53RowYwUUXXcRXX33FoYceiiRJzJ07Nyz/5cqVK1m0aFGL1seuqtgBXwcXPgHS0tKYPXt2y3KXCgQCgUDQhWnz1fHtt9/OwoULufXWW0UewlYiSRJOp7PD3y3ubAzJz2BSQT6FG4toamUlYNIBuvB27NjRaF9OTg5vv/122D79Nd6+fXvoR0B2djb/+Mc/oh53w4YNbR6TIDFsLarkw40tE9j37nfxS0k1Q/IzTR6VoKX4/X4CgUCYAFpSUsKSJUuoq6vjjDPO4Nhjj23HEXZuZFkmgCZ8+nz0KS/nKeBHzBXkjAV93G43twEPA31zcrjetF4bnJ02wnOLmu34lAzCp+LxkF9Wxt+ArzG/uJHxdfT5fPzTbudxoH+3bjxqas+EpTQIBAKh/KKBBIXZGx2fAb+f97W/XzI4bhOBqqoEgGJAAdJaIXxGu3655JJLuOSSS4DgPKPlOdaFT4BrZ8/mxRdfZJwmqJ14wgnMuOiiRs+ZOHFioyJJEyZMaLRPp6nrox07drD7hx+odrlwAemqCoqChaDrdcuWLTHPfVmWeeqpp6I+tnnzZvx+f4s/91MDAXIJ3mToyNfxzX3XnX766Rx33HHtOEKBQCAQCBJHm3+FeL1eZsyYIUTPNvLpp59y/PHHN7qLLWg7kiRx42kFSMCqjUXIFims+Iz+96SCfG44rUC48ARt4j/rdzY6t2IhWyRWfLVTCJ8diFmzZmG323n22WcBqKmp4cgjj8TtdpOXl8djjz3Gv//9b6ZOndrOI+2cWK1WBhAUY65JTiarqoqrgU8wV/gcUFfHYmAX4HK52A3sBrprOQHNwlhUKRAIICco1F02hrr7fAkrIgWQAYwF6gmKK7qr12zBFQgJnzLhjk81Ad/nuqgPmvBpEDuNDlzT+ldVDjH8HbBY2KNtD0lEjk+D8Hn61KlU1NSgKgpYLGEh6GZSb7NR7ApGW+QAyT4fA4Fq2u56/e2330LfBy1C60d3nXbUa0nxXScQCAQCQQNtVi0vvvhili1bFs+xHFQkJSVRZ3LV14MRq2zhpjNG8vifJjBxeC+S7DISkGSXmTi8F4//aQI3nTESqywEe0HbWLOtuEWiJwRTKqzZVmzyiASt4fPPP+fss88O/f3qq68SCAT46aef+O6775g7d26TOUAFTSPLMsVAKeBX1ZAjz4+5glyex8P1wLkEhU8ds8U4NbKoki58ml1N3mbjWOBooN5qBU2E82P+nEft389XwLME56znF01EnkspYr0tCcwvarVaORs4FihOTSVgKFKZCOFTAroDmTQO805IcSOD8KmqKpdffjmytu5SgkwQTYX7t5UpU6YwbNiw1gwi+N8B92wu4rtOIBAIBIIG2nyVGggEePDBB3n//fcpKChodKH96KOmBzx1apKTk4Xw2QoU7cdNS5AkiSH5mSGXXUe+I9/R6chhXO2Bqqq4vYHmGxpwewPiHOxAFBUVceihh4b+Liws5Oyzz6Zbt25A8KbeSy+91F7D6/TIBqdjIBCARDkRDYKY2+1mBjAI2F9fb16fhn5tBOf7g6qSBZTHqEodt25tNj7Xtv+oqiHHp9kCc7DzcPFxcX09fwSe2r/f3H4Jd9j6/X7edTpZC1SnpDDT5L6tVitfaNujZBnF6w091pTwGbfv0QinIYqCnaC7OlHfL6GZaMJrqNcE9R9L+ExUZXu9L2ib4zOR11Tiu04gEAgEggbafHW8adMmRo8eDRCq5ChoOSkpKdSb/YOsC2C327FYLOzZs4esrCzsdnunEpBUVcXv92O1WjvVuCE49rKyMiRJSkwIYydAkiScdhlXK8RPp13udK99V8bpdIY5Ar/44osw14vT6aS2trY9htYlsFqt3E3QlVa1f39I+DRbkJO0zygrUOVycRFwKrDY5BuMkiH02uv1spBgxfFjsrNN7df4mez3+8PW2ezPa+Na+3w+ZE2AS0TqI71vPdR9m8XCbiA/Kcn0vo3nr9/vDxc+o6y5zWZDkiTKysrIyso64O8BfyCAW9v2uN3gdnMY4CF47pm9/j6fDxfBtfcFAqheL79ojyUHArjd7iaeHR/sXi+DATdBZ7dPUXATXAObx5OQc9CvqrgBnzYGWZZbdJ2X6Gsq8V0nEAgEAkEDbf4V8vHHH8dzHAcdAwcOJDc3t72H0eGxWCwMGDCAvXv3smfPnuaf0MFQVRVFUbBYLJ1S/JIkid69e4e5uA52JgzOZfUPe1qc43PCYPE+70iMGjWKv//979x333189tlnlJSUcOKJJ4Ye/+WXX+jVq1c7jrBzI8sylwG9gEdqa4OuTxLgRDQ4L91ud+jiRjL5sytgs1FOMMdgiiH02ezPTKvVylVAMiBXVydMYIbGwqdFd7El4HtCiihupOcXTcR3lNVq5TwgG7BXVqIYcnzKUSray7JM79692b17d9SCQq2lpqqK6spKAOpUFX9dHXVuNz4Au910Mc3j8VBcXg5AqtuN3W6noqICgB6qSmV1tan9A1SVlFDn8eAhuOZ1paXU+f3UA0k7diTkOquqrIxarxc3YNuxA4vF0uLrvEReU4nvOoFAIBAIGmj11fFZZ53VbBtJknjrrbfaNKCDidLSUiF+tgC73U7fvn1DFSo7E4qisG/fPnr06NEpC4HZbDYhekZw+th+FG5qWVX3gKJyxpH9TB6RoDXMnz+fU045hf/7v/9j7969XHLJJeTl5YUe/3//7/9xzDHHtOMIOzfGAjAYis+YHepuMYhxLpeLVP0Bk8Wgbb17k6Vtf20QPs0WH61WK/cSdNY+W1UVWudEh7p7vV70bwizCzoB/NqnD06C8/zS72eox8OxQG0r0uG0FavVyjygAHhg/35Uo/AZ4zxLTU3l0EMPDeVBPRD++cornH/ffQC8u3Ahu//7X6784gu2Aknvv0+/fuZ+12zcuJGrrroKgHPOOYehQ4eyaNEiAO6//36mTZtmav8ALyxYwOXr1vEDkPzBB6yeP59Ze/bwATB561bT+wd4/uabmbVpE+uBfp99Ro8ePVp8nZfIayrxXScQCAQCQQOtvjrWc8MIDpyPPvqIadOmkWxy1dmugB4a1NlCrhVFwWaz4XQ6O6XwKWjMoNx08jKT2Lvf1WQ7CZhUkM/gXhkJGZegZRx//PGsX7+eVatWkZuby/Tp08MeHzVqFEcddVQ7ja7zE1b52u8PK25k6megIeTc7XbTLWK/WRhFDLfbzdsEi988vW+fqf1arVb82rbq8/FdSgp/AvYAk0wWVoyOz1qXq+FCMgHFjSw2G7q87Pf7Oae+nquB5xKQM91qtRL61Pf5cFksXEfwnJvYxLWJLMtxEbt8ioJz504A3NXV1O/di3PnThTA4XDgNDuvrNXKTq3/ffv24aquZuDOnQQAxe83vX+A+spKnDt3YiUYUVO/axfOoiLcFktC+gfYGggwZ+dOioElkoTT6eyQ13niu04gEAgEggZafZUqEmHHB4vFQmpqKlVVVUL4FAg6CaqqsvjdTc2KngC5mUlcf+qITpnioKszbNiwmFV8r7jiilYd66mnnuKhhx6iuLiYkSNH8re//a3JH5OVlZXcfvvtvP3221RUVNCvXz8WL17M1KlTW9VvR8UoyOH385+MDJ4E/LLMdBPfC0YxLpGh7kZBy+PxkAlkAXaT3/eRwucuu521UcZkBlJEIanQhWSCws11jFXdE+E2lWU57Nx2WSz8Tfvz94no3yCuqj5fQtMboPXxEUHH65Ldu7EMGECh9tiyBLkY9fe5jeDrv8Nq5Q3ga6uVcxMyAqjo1o03tG091UJHJZ7fdQKBQCAQdGbMv1ISxKRbt25UVVWFhZ4IBIKOy9aiSj7c2LIw9737XfxSUs2Q/EyTRyVoKQMGDGiTEH3DDTdw3XXXNdq/bNky5s6dy5IlSxg3bhyLFy9mypQpbNu2jewoxW28Xi8nnXQS2dnZ/Otf/yI/P5+dO3eSkZHRlul0SCIdn9Wqyi7AabJbvzQnh0MJFjm5w+MJhV+bHereu6aGj4BSgsJn6DZmAkLddclF8XpDAowkSaa7zmoyM1kI7ANOd7ka1joB4ltWXR2vAlUEc3xKmvCpJMBpFynqG0WvhAiPNhvpBIXOR5OSwtzUicpx2g3oAcg+XyjHqQJYm6hqH+dBBP8D6n0+PnY6WQJ0S0riwcSMoJH43hGJ93edQCAQCASdHSF8tiM9evRASUBeKoFAEB/+s34nskVqcWGjFV/tFMJnB+Lll19u0/P69+8fdf+jjz7KrFmzuPTSSwFYsmQJ7777LkuXLuXWW29t1H7p0qVUVFSwZs2aUNqOWMfW8Xg8eAy5I6u1AiKKorT5+0NRlFDhtXgjSVKDE9EgDlmtVlO/71SHg5+1baMLUZXlZvs9kPVwBgKcAOwEvnG5SNf71QqemIXFYgkTPnPq65kClMSh3+bWo6ZbNxZr27+vr+c7gkWWKpKTTb+mSfb5uIhgSP9mrzdUWElpwevcVvT1MK45fj+Sy8WxBMV2i8mvN1ofNdq2z+8P5RjV00gkon/j/PWq9onqH8Kd3V6D4G+z2RJ2PZ3m8zEJcGljiMfnabzHHu/vOoFAIBAIOjtC+GxHhg0b1qHyAQkEgqZZs624RaInBAsbrdlWbPKIBK3h+OOPj9uxvF4vGzZsYN68eaF9FouFSZMmsXbt2qjPWbFiBePHj+eaa67h3//+N1lZWVxwwQXccsstMR1b9913HwsXLmy0v6ysDLfb3aaxK4pCVVVVSMyJJ1VVVaH8mt76eo6oqOASYKOiUFpaGte+jBjXoqKigj8BacCojIxm+z2Q9fDowi7B10SvkexTVVPnW1NTExKhXDU1HFdezs3AP+PQb3Pr4dUEL4CSkhIWaNtTunc3dc4Qvt7l5eXIuuMTTOtbX49AIBByM/vdbuSiIj4j6Hz9pLbW9LnXGfKY7t+/nz0WC0uAX4CsigpUtWXfTW2lsrIybP61WoX5AFCbgPkD1EgSXwFFgK2sDK/LhQWwSFJC+gfoVV7O08BmgudcRkbGAX+e1tTUNN+oFcTzu04gEAgEgq6AED7bEVVV2bp1K4ceeqionC0QdHBUVcXtDTTf0IDbG0BVVZHnswtSXl5OIBAgJycnbH9OTg5bY1QX/vXXX/noo4+48MILee+99/j555+5+uqr8fl8LFiwIOpz5s2bx9y5c0N/V1dX06dPH7KyskhPT4/6nOZQFAVJksjKyjJF+JxMsODL8bm5jN64kRuBVxQlavh/vMixWvkrWtivzcb32v7RPXo02++BrIczNVg/3kqwwIx+UWVPSjJ1vtnZ2SERymHIPRmwWA643+bWIzM5maEEC7jZDSHOqampps4ZICktDQieXykpKaFCR5LNZlrf+nrYbDb2avusgKyFPPuBnj17mj737t278zjQDajz+fgsI4Nntcduz8tr8+dBS3G5XOzStm2ShF1zX/oJRjCZPX+Aun790DMof5KWxg3V1cwFXqmvT0j/APaUFCB4DqSnp5OdnX3An6eJKswkEAgEAsHBihA+2xFJktiyZQs9evQgKyurvYcjEAiaQJIknHYZVyvET6ddFqKnIISiiX/PPfccsiwzZswYioqKeOihh2IKnw6HA4fD0Wi/xWI5INFSzwUZb+HTZrOFxJGjLZZQDkbV5NyTqYEA1wE1wMMGR6LNZmtRv21dD4sm/MmAz+cLy3dp5nztdjuXAQ7giIwMeunOxzitc1Prke1ysRnYD7xpWGuryXMGkLX3gpXw9VZl2dS+JUnCbrdzO/AwkJGRwVhDqHlLz7MDwW63cz7B4lkv1NWFhUfb7faE9K8L7FIggGTIMZqI/oFQihCIyPFq8utvxKKNQSb4mW6xWA7481REfwkEAoFAYC5C+GxnevToQUVFhRA+BYJOwPjBOaz+fg8tiXaXLRITBueaPyhBu9CzZ09kWaakpCRsf0lJCbm50V/3vLw8bDZbmMN/6NChFBcX4/V6w9xznZXIwh9GYcJMInP/6aHumS6Xuf0aiq14PB5+ALyANynJ1H6tVitrtO1DrVYsmggVSISAYpiz1+vlfeAo4IVi81N7GF9nt9vNcxCsLN6jh+l9W61WvtK2J8pyqKp6gMRVVQ/lz/X5sHg8dAfcJK64Uawcn4mKWmr0+aKd92oChUPjORgItC4KRCAQCAQCQfvQaW4xVlRUcOGFF5Kenk5GRgaXXXYZtbW1Tba/9tprGTx4MElJSfTt25frrruOqqqqsHaSJDX698Ybb5g9nRA9evSgvLw8Yf0JBIK24Q8o1NT7WiR6QjDH5xlH9jN3UIJ2w263M2bMGAoLC0P7FEWhsLCQ8ePHR33OMcccw88//xzm1Prxxx/Jy8vrEqInBAWQWcD9QH5FRcKEiUgB8jZgMdCjieuEuPRrcH95PB4uJCgC/hZD/I4XkQKQxeD4NBuj8OPxeEgDMgBrAvq2RPT9GfAysDsjw/S+jeKe3+9H1YRPP4kXPhWvl8nFxewD/pHA/rcCXwBVskylw8FcYGGC+gfoUVfHdmAr4ee9msB0UcbPmo5a1V0gEAgEAkE4nUb4vPDCC/nhhx9YtWoV77zzDp9++ilXXHFFzPZ79uxhz549PPzww3z//fe8/PLLrFy5kssuu6xR25deeom9e/eG/k2bNs3EmYTTq1cvMjNF1WeBoCOjqiqPvbORr34pa1F7CTipIJ/BvTJMHZegfZk7dy7PP/88r7zyClu2bGH27NnU1dWFqrzPnDkzrPjR7Nmzqaio4Prrr+fHH3/k3Xff5d577+Waa65prynEHVmWuQC4BcirqmoQJkwWPo0h5x6PJxTOIhlCY81AstmoJ1jh2ePxhPab7YCzWq2cDlwBZOzf3yB8JsD5pq+1Lj6GJK8EiF+Rjs+Grs3vW5Zljgf+BPSprg6rqp6o/o2OT6PjNBGh0larlRuA8cCajAxqHA4eA54iccKnVZbpD/QjQvBPpOPTcP4L4VMgEAgEgs5Bpwh137JlCytXruSrr75i7NixAPztb39j6tSpPPzww/Tq1avRcw4//HDeeuut0N8DBw7kr3/9K3/84x/x+/1hF2kZGRkxQxPNJjMzUwifAkEHZ2tRJR9uLGpx+7EDs7jhtAKR37OLM2PGDMrKypg/fz7FxcWMGjWKlStXhgoe7dq1K0yQ6NOnD++//z433ngjBQUF5Ofnc/3113PLLbe01xTijtVqDRXdIYHChC6IWQCfxxPK/Wi28FnXowcp2vZdBuHTbCHIarVyM3As8GhZWUNKgQQIQPqa2gCP2x2W19RsvJmZdCco9t3ldnMk0BPI1kRIM7FYLFwFnAc8XFmJPz8faB/Hp+rzhV7zgBatlIj+dQKBQJjol6hQd110txHM8WrRKtkn0vFpdB0L4VMgEAgEgs5BpxA+165dG0wkr4meAJMmTcJisfDll1/yhz/8oUXHqaqqIj09vdEF6jXXXMPll1/OIYccwlVXXcWll17a5EWkx+MJc3ZUV1cDwTBHYwhjLBRFQVXVUNvNmzeTmppK3759WzSPzkrkvA8GDsY5Q9eb94qvdiBbJAItiHOXgFSnjEWi2fl3lfU5mJkzZw5z5syJ+tjq1asb7Rs/fjxffPGFyaNqP2RZbhA+FaWhuFGCQt0B/G53g+MzAQKkjsfj4UsgG/hnZaWp/Rrdf1IgwEqbjW3A3pQUrjS15wbhB8CbwLUGsNrt7Ne2PR4PC4FTgMcrKkzvG7QcqoqCFAhQ4nQyD6gAbmqHHJt6GolAgm6wRYb621wuxgF1JM7xaXR2+30+5ATlEDbi6taNG4B64GwhfAoEAoFA0CnoFMJncXEx2dnZYfusVivdu3enuIXJ9MvLy1m0aFGj8Pi7776bE088keTkZD744AOuvvpqamtrue6662Ie67777mPhwoWN9peVlYWFXsVCURSqqqpQVRWLxUJZWRnbt2/H6XS2aC6dlch5HwwcjHOGrjfvNduKWyR6AqjA2h9LKS0tbbZtTU3NAY5MIOhYRApy98syf/P7yc3ONlWQsxhypPoMLkSzHZ9GMcjj8dAHyANsZlc3N6wzgQBfyDLvAANTU03tF8LX1OdyJdTxGSk0J7JvaMihagkEKE1K4nVt/y3tUdwogXld9f4fBc4GXtu7F7lHD74gmG/Tm6D1lx2O0LbP7WYj8B+gKDk5If0D+NPSeFzbPlMInwKBQCAQdAraVfi89dZbeeCBB5pss2XLlgPup7q6mlNPPZVhw4Zx1113hT125513hrZHjx5NXV0dDz30UJPC57x585g7d27Y8fv06UNWVhbp6enNjkdRFCRJIisrC4vFgiRJfPbZZ2RlZXXp0NjIeR8MHIxzhq41b1VV8fha58x0+5QWvZ+7+s0OwcFHmOPT72dPIIAfSDL5XFeTkhhFMOz4ML8/YS5Eh9/POwQdaP+tr0+Y4GoUwSS/PxRym4iQY9Xh4GGCa+32ehOa49MOLCG43jtraxPaN2jO5UAAKSLUO1Gh7idq2xfm5NBfd3wm6DtWlmUygb5Ais+H21DcKdGh7gBel4ungaeB4xKYriqysJhAIBAIBIKOT7sKnzfddBOXXHJJk20OOeQQcnNzG7mn/H4/FRUVzebmrKmp4eSTTyYtLY3/9//+H7ZmfoyMGzeORYsW4fF4cBjuLBtxOBxRH7NYLC0WeSRJCrXPzs5GkiQqKyvp0aNHi57fWTHO+2DhYJwzdK15O+0yLm+g+YYaSXa5RT8Eu8LaCARGjDk+JUUhoIkzZgsjss3Gd9p2P58vYQKkzWrlVG17hcuVMME1TGAOBBjg85EC2BKQPsOSlMRftO3LAgG+AfYB3qQk0/uWLZaQc3heXV3ChU9dZJQCAez19YwGakhccaNybdurqqC9txLl+JQkKRhWr/dtED6TEhzqDuCtrw9tJ0p4haD4PgEt3P7/t3ff8VFV6ePHP3daGgkhkEYIHRKkCxKD0iSSqKAiX1cQG4u4uuLuArrK2nUV2w8Li+KuChZQ17KuIqJ0ECIlEDpRkLICoYWQhPSZ+/tjSu6EAAlkzqQ879eLF3Pv3DunzCR38tznnCOBTyGEEKJe8GvgMzIyksjIyPMel5ycTG5uLhkZGfTp0weApUuX4nA4SEpKOut5eXl5pKamEhAQwNdff12t7KrMzEyaNWt21qCnL2iaxoABAwgNDVVWphCi+vonxLB8+6FqDXc3adA/IVpBrYSoe7yGYJeXM1LX6QEcNAQpfFWuW0lJCSNwBihG+HjxQGNgtVzhEHuvjE+7nT8VF3MX8IaC6TMq9/UY1+O/xsb6vmzDdzPjXK4oCny5g4yaw8ElR47wIfAjagJvlTMNd1mtfABsstkY5/PSnRyuwKfJ4fAKfCqb4zMggO2uMosNv1NUlQ8QXFrKatfjTxUsqiWEEEKIi1cv0o26dOlCWloaEyZMYN26daxevZqJEycyevRoz4ruBw8eJDExkXXr1gHOoOewYcM4ffo07777Lnl5eWRnZ5Odne3JQPnmm29455132LZtG7t37+att97i+eef54EHHlDeRnfWpxCibtF1ne6tI6o9x6dDh+F92vi4VkLUTWazmQeB7sCCsDBGAk8CPXwc+LRYLDwCPAGYTp9mJbAY5xB4XzJmoJWXlHgCcSYFc4saA8zu1a1VrOpusVhoDbTHOcensU6+ZjYurGSYX1TFwkpQ0b+a3e7JuLSjbqj7n4CZQNyRI3wXEMCdwGcK5nV1M7Zf98dQ98BAugG9gAJN42OgCBh+6JCS8sH7Z94ugU8hhBCiXqgXixsBzJ07l4kTJzJ06FBMJhOjRo3ijTfe8DxfVlZGVlYWha4/rjZu3MjatWsB6Nixo9dr7d27l7Zt22K1Wpk5cyaTJk1C13U6duzI9OnTmTBhgrqGuZSWlvKf//yH66+/niAFw8WEEOdXbnfw6vwtLN5ysFrHa8AVnSJIaNnUtxUToo7SNI3/mUw4HA66OxyeLxm+DsiZzWaeAgKAEYZFBn0dkDEZAl7GhX5ULKr0EvA+0KRZM65zBT51FcFHs5ndgBUYZ8gwVZL1aLVS5irbK+NTUeDzi9BQFhYWkhMYSKrijEeLxcJIYDAw8+RJz018ldmOnsCnw4Hmh4zPyotbBQCBgEnhUHevwGdJibJyhRBCCHHh6k3gMyIignnz5p31+bZt26LrFRlZgwcP9tquSlpaGmlpabVWx4ths9lo0aIFv/32G506dfJ3dYRo9HRd59X5W1hSjaCnhnM196E94hhzWbRkb4tGzWw243A4vFfd9vUcn4Y5L+3FxdyHMyBj8/Gcl2arFTvO+f7sJSXsxBl8xcfT5VgsFta5Hg+0WjErDHy6h9m7g4/bgabAl7m5Pi/bnenqLvtFIApoVo1pk2rDz8HB7AWiNM0vgU9Plq/djqmsjEDAqnCuaHfg01xpqLuqjM/KgU/VUx0AmA2BT0dpqbJyhRBCCHHh6sVQ98YiPj6eAwcO+LsaQghg18FcFm85SHUGuOvApOHdmTy8OxaTBD1F4zYc55DzHqdOeQITvg7IeQWFSkp4E/gnEODjwKex3LLiYi4FugKl4eE+LdcYaCorK1OWWesu291mR2kpLYE4nAsP+Zpx8azy4mI+wzn0+5SP+9tYPrgWtVE81L3y9AaP5eVRBDyYl+fzst2OWixsA45pGvuCg3kCmIO6jE+z2UwGsAsIPHFC2e8XI5MEPoUQQoh6RwKfdUh8fDz5+fme4UtCCP/5ZsN+zNUMYppNGlv250impxDA9brO00Cf06eVZnx6FvsxBCNUzLVpBxx4B0F8voq92Uw/4DYg/uRJpUO+jX1tN2T1GgNCvmIMNJcbhhmrCrwl2u3cAvQoKVGe8Whsu15ejuYK6qsIdru9ExZGd+Ct4GD2BgfzLM7pFlQOde8EJACOoiLlUx2A9wJbEvgUQggh6gcJfNYhQUFB3HDDDcqGDAkhzm5NVna1FzSyO3TWZGX7uEZC1A92wwIoqjKyjEPdTYYFR1Ssrt4E51D3/YYbH74OBGmaxnjgQ+CKnJyKflacdWk3DDf2dV+7y+4KtAT2m0xcDgwAQhTdMB5WUMAnwG0lJc6VzVEb+PS00m73LGilMtvR/bm22+3OrFcXVd9bjT/nZYbAp64y8Gn4nEvgUwghhKgf6s0cn41FSUkJ+/fvJyEhwd9VEaLR0nWd4tKa/SFdXGo/77zCQjQGuisAqJeXe7IBVQ91dzP7OAvRbDZ7psPQCwvZhTMQtsbHQ+zBFWB2LTIzB1gNnFAw5LvyUHdVK9m7y3av3x1XUsK/gXbAm6dO+bxsqPgcmxwOtgcE8HcgS9O4SUG2v/Ezrtntznk2UZvxaRzqH1JcTDcgF7UZn8ZsY0/QXWHg02K18jecUxy0kkQFIYQQol6QwGcdYzKZyMzMpGXLloSGhvq7OkI0SpqmEWgzU1SD4GegzSxD3YWgIuMTu52JQDjQrWVLn5ZpDMaZDRmfvh5+bQz42EtKcN+y/ElBIMbh/n1TXs4Xrn1pzZr5vNzKw81VrWTvLtut2LCqu4qyARyGwOeWwEA+AAJsNj5UULZXcL+83JPxqXJhn9EFBdwGfFdQgJ6dzVfAl/gv8JkBlAKnQ0KUlO+uwzTX4xcl8CmEEELUCzLUvY6x2WzExcWxd+9ef1dFiEZL13W6xjejumFMs0mjf0KMT+skRH3hHm6tORzsBjYAhcHBPi3TbDYzEkgCdhmG4Jp8HJAxm828BXwOtDx9uqJcBYE4z7B2xUOOzWYz7wMzgON2u+eLpIqsO4vFwqPAa0DzggL1GX+u/jXjXFTKXScVLBYLfwbaAgujoz1D7VVmfDYDugDRdjuaa3oBlau6G29wlJeUMAlIAX718Y0VI+P7bRzuL4QQQoi6SzI+66B27dqxYcMGunfvLhlkQihWbnfw6vwtbNhzvNrn2B0611/Wxoe1EqL+cA91N4ZCVCz2k+F63NI17145zmGpvi73OiAe+HdxsWe/isCn3f39wG4nEQgGwhQMsTebzTzmetyyvJyNON9rU2Cgz8u2WCz8HmgPLCsqUpptChVD3S1AcGEhnYFyRd/TzGYzR1yP2+i6smkkjNzBdpOuewU+TYqCr5UzPo37VbFYLHQDAgCKipSVK4QQQogLJxmfdVBsbCwJCQk4FPwBI4SooOs6r87fwpItB6t9jgZc3SOOhJbhPquXEPWJOwPNDNwBPAS09HGAwBj4yAFGADfh+4CrccEZ96JKDsCiYIVz97Bryst5B8gAeubk+LxcY18XlZXRB+gF2Js0UVK2MePPM7+ogv4GPBmfFmBUTg5ZwJOGqRV8ydjvJSUlrAO+AA4rHObtDrKadR3cGaeapuwmvcViYT+wG+dnz03loqAWi4VlODPZmxyv/g1SIYQQQviPBD7rIJPJRGJior+rIUSjs+tgLou3HKQmSxSl9IjjL8N7SHa2EC6fhodzOc7hyBOBl4C4ggKflmk2m7kNmAJEAfOBb1CTaeoOxLkDn+WoyUBzz/GpORwVw3cUlGs2mwkHogHdkOWqos3GVb1VL6wEhsAfgOv91hVmfI7E+fPU6/hxXgP+D1gfGamkfKhYPd3scHgyPu0Kr31ms5nBQCdgm9XKT8AxoOPRo8rqYAy+64qC3vVJTk4OY8eOJSwsjPDwcMaPH0/BeX7/FxcXc//999O8eXOaNGnCqFGjOHLkiOf5zZs3M2bMGOLj4wkKCqJLly68/vrrvm6KEEKIBkQCn3VUWVkZX331FacNc4YJIXzrmw37MZuq/0dc3w6RTLm+Jxaz/CoVwu1IYCBrgQMY5tNREICcArwCdK6035eMQRD3okp2BeUCfBcSwhhgtmHYs6p5NpcB2UCSYbixijYb+9tRWqp8qPv2yEhuxxnUd8+tqmqOTU3TGKZpPAR0M6xir3KYt/vn2ARoitsPZ2a9NgNaABaFGZ/G4LsEPs80duxYtm/fzqJFi5g/fz4rV67knnvuOec5kyZN4ptvvuGzzz5jxYoVHDp0iJtuusnzfEZGBlFRUXz00Uds376dRx99lKlTp/KPf/zD180RQgjRQMgcn3WU1WolKiqK3bt307NnT39XR4hGYU1WNnZH9fM9t/8vRzI9hajEGADzPPJxcMY45Lw5ziH2p/F9UMiY8akBe3CuMq0iGPVzYCBL3PWoqJDPyzW2ublrASs7sEHB70KLxUKh67FeXs6jOOda7BAa6vOyAbLDwvjW9fgad+BPYdDNoWmg6zgUL2jlZpzjVHXgF7zbWlxcrH5xKyTj81x27tzJwoULWb9+PX379gVgxowZXHvttbzyyiu0rGIRqlOnTvHuu+8yb948rrrqKgBmz55Nly5d+Omnn7j88sv5/e9/73VO+/btSU9P58svv2TixIlnrU9JSQklhpszeXl5ADgcjgueTszhcKDrukxH5iL94U36w5v0RwXpC2+10R81PVcCn3VY586dWb16Nd26dVP6xVaIxkjXdYpL7ec/0KC41I6u6xL8FMKgR2kp1wA7QNkQbLPZjHvQdVvgBeAosF3hUPdDQEfX428UDft2c5emKyrXHUoIBjq4HmcqmGfTGOA2A+7Brm8pCnwaA9qa6wu3qqHu4AoyOhxo5eX8AFwF/D+Fw7yLbDZ+xfmz9ZPZTBGww2ZjvKLyLRYL7wPdgedOnaoIfCrK+HXXQQKfVUtPTyc8PNwT9ARISUnBZDKxdu1aRo4cecY5GRkZlJWVkZKS4tmXmJhI69atSU9P5/LLL6+yrFOnThEREXHO+kybNo2nn376jP3Hjh2j2DBNR004HA5OnTqFruvKFvWqy6Q/vEl/eJP+qCB94a02+iM/P79Gx0vgsw6Ljo4mNDSUEydOEBUV5e/qCNGgaZpGoM1MUQ2Cn4E2swQ9hagkqaiIScD7oCwjyxiADHD9X47axY2MLVRxs7K9rtMbOEhFxqdJ0VB39yQ87r4uQ02bje+zsaWqhntHlpUxAucCWu4SlWZ8uv84KC/HjOt9V/gH1JrYWJ7Zvh2ASE3jEyAqOFhZ+RaLhQSgNxBsWNxKxdy2xjpI4LNq2dnZZ/y9YrFYiIiIIDs7+6zn2Gw2wsPDvfZHR0ef9Zw1a9bw6aef8u2331b5vNvUqVOZPHmyZzsvL4/4+HgiIyMJCwurRovO5HA40DSNyMhICV4g/VGZ9Ic36Y8K0hfeaqM/AgMDa3S8BD7ruJSUFAmsCOFjuq6z62AuTYNtFJVWb/Vps0mjf0KMj2smRP2jG1Z198dQd/fXIDtqhrr3B3SgxLBfRSDuqqIiHgU+pqKfVWV8VhVkVtFmi8XCaMCKa45Rd9mKviddkpvLv4CVwCb3ToV/wLh/tjS73W9BPzd3xpzKOUaNnz29rMzvGZ/YazZKpL565JFHePHFF895zM6dO5XUZdu2bdxwww08+eSTDBs27JzHBgQEEBAQcMZ+k8l0UYEHTdMu+jUaEukPb9If3qQ/KkhfeLvY/qjpeRL4rOM0TWPPnj0EBQVVOTeOEOLilNsdvDp/C4u3HKQG6xphd+hcf1kb31VMiHrKHZyxoHaou78yPt23ShKAecBh1ASDjP08E4gEii8wi6kmjIEfd5BZRV+7y97vehwA/OR6/LGqAJR7VXNgOeAAdjdpoqZsKjI+9fJyZQuHGRk/102KiogEghXenDd+9uyGwKdJwTQLxjrMwrmo0ukaZpvUV1OmTOGuu+465zHt27cnJiaGo5WmXigvLycnJ4eYmKpvFMfExFBaWkpubq5X1ueRI0fOOGfHjh0MHTqUe+65h8cee+yC2iKEEKJxksBnPVBeXs6WLVsk8ClELdN1nVfnb2HJloMAVHddIw1I6RFHQstwn9VNiHrLFYgxAzfjnAdyYGSkj4usWGnZHfhUsbq68fWbAJcC+4H9CoJRxoVmZrj2PdCsmc/LrSrIrCK7lkpleE0toCrw5SrfAnzl+tdLQZ+7uQOfJl33y8I+3XNzeRLn/L255eX8CZhx+vR5zqo9XtmWZWVsAJoCdoUBSIvF4vl5u0PhMH9/ioyMJLIav8OTk5PJzc0lIyODPn36ALB06VIcDgdJSUlVntOnTx+sVitLlixh1KhRAGRlZXHgwAGSk5M9x23fvp2rrrqKO++8k+eee64WWiWEEKIxkTzbeqBjx44UFRXx22+/+bsqQjQouw7msnjLQaq7jrs7IzSlRxx/Gd5DpqEQogrGgNx6YAVg93GAwGw28zDOxV5WuPapGH5tsVh4COd8pgNc+1QFAR3GFbZdVGVd/gC8Bxxw7VOZ8Xk78BzQz7DfVMVwVp9wDan2x/yiAHPDwugKPGuog4rpDdxCdZ2+QBdj+Qqvg8agu0nXSQUuB4patFBWB+P7XV5efo4jG58uXbqQlpbGhAkTWLduHatXr2bixImMHj3ak7xx8OBBEhMTWbduHQBNmzZl/PjxTJ48mWXLlpGRkcG4ceNITk72LGy0bds2hgwZwrBhw5g8eTLZ2dlkZ2dz7Ngxv7VVCCFE/SIZn/WA2WzmkksuYfv27bRq1crf1RGiwfhmw37MJg17NVM9o5oGMfWm3iS0DJegpxBnYZzj003FkHP3nItDXP+ryvhMAYYBBa59qoKAGAKfnXFmotv06t7GuZhizUx3Pe4E7AJyUbe40S3AdTgXGPLsV5TxqRn6PAIIAkIVXgtyAwLY63qsav5cI+NNDXOlfSoYMz5VB/yNdYjDmeFtLarenOCNydy5c5k4cSJDhw7FZDIxatQo3njjDc/zZWVlZGVlUVhY6Nn36quveo4tKSkhNTWVN9980/P8559/zrFjx/joo4/46KOPPPvbtGnDvn37lLRLCCFE/SaBz3qiY8eOxMbG+rsaQjQoa7Kyqx30BDhVWEpinLphjULUS4Z5EO/B+UUjxMerHxsDH9uBW3AGIl9WEPisaoi96ozPH3HO8Tk9P9/n5Rr7+hec2X8AKxQNda88zB7ArGpxG0PG52vA7cA/FGadGT9Xa3Au8FQUEqKsfPciQmYMGZ+Kg445OOfRLa20X2Ud5gEDgZcPHVJWbn0RERHBvHnzzvp827Zt0SvdoAkMDGTmzJnMnDmzynOeeuopnnrqqdqsphBCiEZGAp/1hNlsJjQ0lAMHDhAfHy/ZZkJcJF3XKS6t2YIYxaV2dF2Xnz8hzmFTZCRDgWPAaiAUmFlScu6TLpLZbGYozgzE1cC/XftfVTDU3V8rnBsXN3KHnlTM92ixWLABNpzBJ3cASlXGZ+X+LkNd4EszBPX9sar6pWVl3IozuP9H1757oqKUlW+c49TdaofC1WnNZjN3uB5rwEGcP29LXCvMq+A1z6gMdRdCCCHqhXozx2dOTg5jx44lLCyM8PBwxo8fT0FBwTnPGTx4MJqmef279957vY45cOAA1113HcHBwURFRfHQQw/V2Tl7dF1n8+bN7N279/wHCyHOSdM0Am01+0M90GaWoKcQ53EqJISlwFYMATkfZ+RZLBbGA28Bgw37VQx1r7zCuYoh9gC/hIczHngJlK5ubTabeQ/IB+4z7FcRfDSZTDhcj40ryqsKfJ5s3px7gSep+GyrDPz1KinhKeAmwz6V2Y6aIePV36vKm4GWQGvApDjj03PLtI7+vSCEEEIIb/Um8Dl27Fi2b9/OokWLmD9/PitXruSee+4573kTJkzg8OHDnn8vvfSS5zm73c51111HaWkpa9as4f3332fOnDk88cQTvmzKBTOZTPTo0YMtW7Zgt9csU00IcabkhGjMpuoFMs0mjf4JMT6ukaiPZs6cSdu2bQkMDCQpKcmzaENV5syZc8YNuUCFKxKrUNXK277ORDQOOW+DMzA0ADVzixpDH9nACdQEo442acJ7wELUZ3y6+7ofsBn4D+rmWbS7bj6dBp4Ankdd8K8oPJy3cWYU+yPj018LWnlUkfGpcqi7sa3GPlAR8DfWQTI+hRBCiPqlXgx137lzJwsXLmT9+vX07dsXgBkzZnDttdfyyiuveFYKrEpwcDAxMVUHK3744Qd27NjB4sWLiY6OplevXjz77LM8/PDDPPXUU9gUfpGqrjZt2rBr1y527dpF165d/V0dIeoVXdfZdTCXbzbsZ01WNkU1GOpud+hcf1kbH9ZO1EeffvopkydPZtasWSQlJfHaa6+RmppKVlYWUWcZghoWFkZWVpZnu6FlEccWF3MvcJSKgJyvF58xBiOuBKYAq1CT8en+LbISuNn1+BdFAUjP44qdPi/X2NfNgB6u8ktUBT5NJrDbKQDPIkspisquss9VBh4Ngc8sIAb458mTyorXbTaO4lxYaiHOn/FfmzRRVr7FYmEqcC3woWG/ysCn8WaHJkkIQgghRL1QLwKf6enphIeHe4KeACkpKZhMJtauXcvIkSPPeu7cuXP56KOPiImJYcSIETz++OMEBwd7Xrd79+5ER0d7jk9NTeW+++5j+/bt9O7du8rXLCkpocQwX1leXh4ADocDh8NR5TlGDocDXderdWxV+vTpc1Hn+8vFtrs+aoxthrrZ7nK7g9e/3cbirQdrtJI7OOcSG9ojjk4xYedsU03bXZf6R1yY6dOnM2HCBMaNGwfArFmz+Pbbb3nvvfd45JFHqjxH07Sz3pBrCNoVFPAwkI7aoe6VFxlSMQTaGARUnYUXbrczDGfmo6oAM1Q9z6aqBZ0AHK4bBcbSVJUd7HAwBHDgn4xPY+AzFAgDTAoDr0diY4k2bM8BBjZvrqx8k8lEJ5w3N3407FfxuXeTOT6FEEKI+qdeBD6zs7PPyJyxWCxERESQnZ191vNuvfVW2rRpQ8uWLdmyZQsPP/wwWVlZfPnll57XNQY9Ac/2uV532rRpPP3002fsP3bsGMXVmGDd4XBw6tQpdF3HdBFzQ+3atYuIiIgLPl+12mp3fdIY2wx1r926rvPOin2s/iUHoNpBT5MGDh2u6BTBmMuiOXae1Xtr2u58BSswC98pLS0lIyODqVOnevaZTCZSUlJIT08/63kFBQW0adMGh8PBpZdeyvPPP3/WDP6LvdFWFZ/fmHAFYgIq7fNloF/TtCqDcZqmnbfci+kPk8nE/cCfcQYgjft9fWOjY0EBzwHb8A7CXWy55+sPk8lU5YJO1mr0dW14OTiYV/LyOAl0A0rwbX8b+6NZXh5LcS7ctcl9gI8/20buYeVeiyspLL+q65qlFj5zNWHXNNB1798vCutgMpk8N1k0u71Wfp/KTVAhhBDCt/wa+HzkkUd48cUXz3nMzp07L/j1jXOAdu/endjYWIYOHcqePXvo0KHDBb/u1KlTmTx5smc7Ly+P+Ph4IiMjCQsLO+/5DocDTdOIjIy84KBQcXExK1asYNCgQWcdTlnX1Ea765vG2Gaoe+3eefAkP7qCntUVZDPTPyGa4X3akNCyabWGI9e03Q1tbsfG5vjx49jt9ipvoO3atavKcxISEnjvvffo0aMHp06d4pVXXqF///5s376dVq1anXH8xd5oq4qvb0yUuoZ/GgMTp4uLOXr0aK2X5abrepXBuJyc8//cX0x/lJSUkOd6PBTnojfrgdzcXAICAs5+Yi0o0503cCzAyziDYcVlZRfdz+frD2NfGxd0Kjx1yqfvsdshq5UTQC+cwceDwLa8PJ+VbeyPorIywNnX3wL7gP8FBSlpN+CVXezO8ywuL1dWfmlpqedxBGAFTKWlysqHiqkO3J89B1BQWKisDrquMx/YD2y3WDh69OhF/z6Vm6BCCCGEb/k18DllyhTuuuuucx7Tvn17YmJizvhCU15eTk5OTo2GCyYlJQGwe/duOnToQExMzBmLUBw5cgTgnK8bEBBQ5R80JpOp2l96NE2r0fGVBQcH06NHDzZu3EhaWlqdCC5Vx8W2uz5qjG2GutXubzP+V+3h7WaTxqBLYnl4ZNVTXZxPTdpdF/pGqJWcnExycrJnu3///nTp0oW3336bZ5999ozjL/ZGW1V8fWPCFhQEgA6k4QzQ3BgV5fObdO6cKWPGZ2xsLE2bNj33eRfRH8aFBqNxLqhUhDP47ev2Wlz9bAL+6tr3ekzMRZdbnf5wVMq6KweiFLzHAFbDyuLusiMjI31WtrE/moSHe8p+w/X8mFatlN2ANru+exoXFwoJC1NWfitNYzlQinMKmBTgxbw8pTfgHa7ApwnIdO1r3ry50jrMtVgoLy+nj6vvL/b3qdwEFUIIIXzLr4HPyMhIIiMjz3tccnIyubm5ZGRk0KdPHwCWLl2Kw+HwBDOrIzMzE3D+IeR+3eeee46jR496vjAtWrSIsLAwLrnkkhq2Rr2EhAT27t1LVlYWXbp08Xd1hKiz1mRlV3t4u92hk/7zER/XSDQELVq0wGw2e26YuR05cqTaN+WsViu9e/dm9+7dVT5fGzfaquLLGxPu+Tw14HvXvv8LDPR5oH+2xcIP5eUMAh7ENfzaaq1WuRfaH5qmcTMwBHB/m7EDNpvN5+3VDCtsu1ksllop93z9sdlk4lNX8KkbzjZXt68v1qDycjriDKyDmv5294d7Lkljn6tqN8DmyEj6AaeoGGqvKSw/wGRiEFAIuNMGtFr6zFWXe47XHMB9e/IbhX0Arnk+y8spLy/HZDJd9O9TuQkqhBBC+Fa9uNJ26dKFtLQ0JkyYwLp161i9ejUTJ05k9OjRnhXdDx48SGJioieDc8+ePTz77LNkZGSwb98+vv76a+644w4GDhxIjx49ABg2bBiXXHIJt99+O5s3b+b777/nscce4/777/f5ELXaYDKZSEpKqlfzfAqhmq7rFNdg9XaA4lI7ul79xY9E42Sz2ejTpw9Llizx7HM4HCxZssQrq/Nc7HY7W7du9dyQawjcATnjkisqFvvZabEwH9jj2rYrKFfTNAZoGvcB/V37yhWUC4ArwGwD2gCtAIuihW4+tdkYDcwGfsO5ureqBYauKi3l78B1rm0Vi1i5mQyBz6ZAOM5goCpFwcGsB36m4udL5Yrm7psaZkP5uspV7XFlfOKfxa3cmpvNxANBhvmXhRBCCFF31YvAJzhXZ09MTGTo0KFce+21XHnllfzzn//0PF9WVkZWVhaFhYWA8w/SxYsXM2zYMBITE5kyZQqjRo3im2++8ZxjNpuZP38+ZrOZ5ORkbrvtNu644w6eeeYZ5e27UM2bNyc6Oprjx49LoEaIKmiaRqCtZn+YBdrM1ZrTU4jJkyfzr3/9i/fff5+dO3dy3333cfr0ac8q73fccYfX4kfPPPMMP/zwA7/++isbN27ktttuY//+/dx9993+akLtcwVCQoHxwF2oCci5g43LgN8Db6ImIOLOQFO+wrmrvc1xzTWJc85FFdzt+w6IB25CUbCXisCXe3CwysCnMfC3GDgJdD/HYpi1zdjOH4FVAAqHSRsDv56aWFV96pxKzWbygDLDPlWfPbdny8o4AIw5z4KHQgghhKgb6sWq7gARERHMmzfvrM+3bdvWK/AXHx/PihUrzvu6bdq0YcGCBbVSR39xOBykp6eTmJhIp06d/F0dIeqc/gkxLN9+qNpzfPZPqP7cwaJxu+WWWzh27BhPPPEE2dnZ9OrVi4ULF3oWPDpw4IDXMMaTJ08yYcIEsrOzadasGX369GHNmjX1YnqV6jrWogUjgCbAxzjn3vxMQWCqp6bRAWc23GzXPhUBEbvJBA6H13yXKgJx7iBcQBX7fM1sNqPhnM7APbeqquCjw/WeGvs7WFXGp2s0kBlDkFlhtmFsSQkP4lxVPsW17/9d4Fy/F8KYzW2rtE+V6U2b8uDp03TC+bN+FChVXAd38F2T1diFEEKIeqHeBD7F2ZlMJvr168fKlSuJiYkhNDTU31USok4Z0bcNS7YerNaxdofO9Ze18XGNREMyceJEJk6cWOVzy5cv99p+9dVXefXVVxXUyn9KmzRhPhDn2lY19HuE3c4jwFvAWtc+FXPnVc5AVDHEHiC/SRP+jDOz9u+ufRZFw54fKi3lb8C/gHtc+5Rl3fmpvwH04GCmuMqc4N6pMOjWqqiIR3Eu6vO+p3h15RuH1XsC7oqzLd3tDQI64bzBsttPgU+TvWbT6AghhBDCP+rNUHdxbtHR0bRv35709HQZ8i5EJYlx4aT0iON8g9c14OoecSS0DFdQKyEaJncQyh2KUDX02z3kPAHnavI9FM29qBumxcjHufCLikBcSUgIb1CR3VqmqFyoCPyMBNYAL6A+4/N/wEvABwrL1oKCmA68TsUXaJPKod5VLGilcph3VYFP1XN8Vv79omxOXQNP4FMyPoUQQoh6QTI+G5BevXqRnZ0tcxMKUYmmaUwa7lzUbPGWg5g0MI56N5s07A6dlB5x/GV4D/kZEuIihJSVcRvQ2rWtKjDhDsBc5fo3+5xH1x53IO5fwB9xtnWcgt8hlkqLSKnMfHQHflq4/mUrLNv9Pu8BHnbt+5uiwKcxwGqu2KmkbKgYVh6Oc4i3HZivMPhmstk4jfNn+hugGVCgcKg9wPCiIq4FClzbKud4dbPLUHchhBCiXpHAZwNiNpuJi4vjxIkT6LpOixYt/F0lIeoMi9nE0HgHLcpMHDXHkP7zEYpL7QTazPRPiOH6y9qQ0DJcgp5CXKRmxcUYlwhUFZDTK2V4OlRlfLrKcbdQVQDQBlwBdHRtqwwAVe5blVl3eqWMP1DXbovZTBLO9zrItU/pHJeusgKASNcuk8rAa3AwTSrtuzcqSln5APEOB2k4A9+gNuDv5r7ZIRmfQgghRP0ggc8G6Pjx4+zatYu0tDQCAgLOf4IQjcChQ4fYs2cP/5eW5pkHV9d1CXQKUdsqDf1VFRSrHIxzKPrZ/qhpU94qKCDfta0qCBdaVsaPhm2lc11WKkfZSvbA0shIPj1wgNNAW+A06tptMZv5yfX4tOt/VQtKGcsyfrMzK/yeV9V7rDrbsvKcumV+qIMuQ92FEEKIekXm+GyAEhISaN68ucz3KYTBkSNHuOyyy7wW/5KgpxC1r3IGmqqgWOVgnKqMz0KbjQPAdcB3wL2KgiHGgNtbwHuon2fTTWXG58mQENYAlwB7gXkozPi02Tyr2H8NzAVKFS4o6X7PAw37VGZ8Gvs5FAgBbKqDjq7PmbsP/DHUfWdICLOApaWlrFixgvXr11NYWKi0DkIIIYSoPgl8NlBJSUkUFBRw4MABf1dFCL9yOByUlpbSu3dv2rZt6+/qCNHgGQNyN+Fc/dovQ90V3diwutrbCeeiSomKbjga+/lPwGQUZnxW7mvU3UiyVFrgR2Xgy2w2U+56/CBwG1AQGXmOM2qZq53uJYbKAYvCjFOz2cx/ge+BEzjn2YzPyVFWPlQEPu3AL8AB1A91X9OsGfcBHwNXXXUV119/Pfv27VNaByGEEEJUnwQ+Gyir1UpKSgqtW7c+/8FCNGAZGRmsX7/e39UQotEwu1Z+LgP+g3MRFBWBibWhoYzGuco4nBmc85W+paW8DIx3bavKNDWusO0O+6kKAB0NDGQ+zhXsAewKs+c7FBfzZ+D/XNsqA58WiwW7+7Fhnyq5EREMAW5xbavOdrRYLKQAwwB3uNX4OVTBHfj8CegMDEd9xqe7vIHAz8BnfqiDEEIIIapPAp8NWGBgIA6Hg6VLl5Kfn3/+E4RoYHbv3s3//vc/evfu7e+qCNFouOccNIbgVAQFfgsK4lNgrWtbVQCyS1kZDwJx7nIVBQGNGZ+xQFPUBV/WNW3KCOAxIAcoVNTXAF3y83kNuN61bQdMisq3WCyejM9QnME/s8K2O4KDWQ64b+WpnGIAvNvvpnKOU6h6cSvVGZ9BJhMRQGucmd6tqMj8FkIIIUTdI4HPBs5sNhMREcGKFSsoLS31d3WEUOb48eNs3LiRK6+8kuDgYH9XR4hGw+QKAJiAMUAqagIT7qDfl8ADwNKQEJ+XCUDluUVVBWEMQc5fgU2oCwC5y3kVaA48EhR0zuNr0xkLKynMNjUG/rYBpUCEwhvL7s94Oc7g50bUZ3xWDnyaVC+iaci6rdilNtty5IkTnADmuLbLkcCnEEIIUZdJ4LMR6NmzJ2FhYaxevVoWOxL1XnU/w6GhofTv35+oqCgf10gIYaQ3acIYYAbOhWdeQ01ALtZu5yacmab/ADJV3fCo1DZVQ+zNlYYYq1zVvXKgSWXGnVapbFUZtuD/jMdgu537cA7v7gcMwv8ZnybFAb+lsbFYgDdwBvvfwg+rurv63N3zEvgUQggh6jaZkKYR0DSN5ORkNmzYQGlpKQGq784LcRF0XWfXwVy+2bCfNVnZFJfaCbSZ6Z8Qw4i+bUiMC/daVKOkpIRffvmFrl270qpVKz/WXIjGyRQUxCdANs7MS1UBuV6FhTwPLAeGoDAYUjkQpyrwabEwFbgCZyBM5XyPA/LyWA6sAwYrLBc4o7/tCoeaWywWXgACgOdd+1TOcRlcVsabQAnOgB+oDbhVFfisHID3eR1c7W0O9AKOon6ou+76DP4P5wJHe4FnJfAphBBC1FkS+GwkrFYrycnJ6LpOdnY2MTEx/q6SEOdVbnfw6vwtLN5yELNJw+5wZnsWldpZvv0QS7Ye5OoecfxleA8sZhN2u51Vq1YRGBjo55oL0Xi5AzHKV912ldEVGACYHA7fl2ko101Vxqc7CDcYZ+BTZcan2WQiCGfG4WJggcqpdAz9/Q9gh9XqWezH90VbmA5o+Cfw6Q4yGj9xKgOfxlXt3VQvbmSuNMen6gWeXJUAnNMdPOza9YIEPoUQQog6S4a6NzKFhYX8+OOP7Nmzx99VEeKcdF3n1flbWLLlIIAn6Onm3l685SCvzd+Cw+EgPT0dh8NBcnKyVxaoEEIdq9nMTTjn9wR1ATn38NNIYCUwoqDA52UCniDIMpwBsX9FRysq9szhtqoCQMZ5NocCbRVOo+MuOx1nRvG/Fc7hXLnPQW3gzz2s3gzsA1bgn4zPMuAX4FMAxXNoJxQU8AXO+WVB/QJPgCf47q8AtBBCCCFqRjI+G5mQkBAGDhzI8uXLCQwMJC4u7vwnCeEHuw7mstgV9DwXHVi05SCDE1tQWFjIoEGD1P8RJITwsFosfGHYVpaJ6Ke5NrVKGa5mRcFHs9lMdyDZtW0HAlUF4fyU5QoVc3y6a6B6cZ/OQIRhnz8yPgHa4HzPjykOfHaotO/tsDBl5QNElJdzk2HbHxmf7uB7c6AtkI8EPoUQQoi6TAKfjVBUVBTJycmsXbuWESNGyJc1USd9s2G/1/D2czFpsHTncf564zAFNRNCnIulUiBI9VB3t8qrf/vKluhougCnPdVQ89XKYrGwDGfwBZxBMGXXcz8uMHQgJoZhONsbBYQpnuPzayDBsE9l4LNyWaoX1anqs638O2SlOvgl8Okq71Kc83t+hh+yToUQQghRbTLUvZGKj4/nuuuuw2q1UlZW5u/qCHGGNVnZ1Qp6Ajh05/FCCP+zWK1e8wA29IzPsqAgdgG/Az4HBigaYm+cbzED+BZ1QagzgsoKgz7FoaEswrmA1RFgqqopDXAG2Oyux8eBrwCzwgUjK68grzTYjXeA0YJzagfl82tWEfhUHXTMCQ3lQ+CYa1vVgmZCCCGEuDBypW7EAgICKCws5JtvvuH48eP+ro4QHrquU1xqP/+BBsWldnSF88wJIapmNQQ+HwWeQ1Fgwk8Zn+7A0+XAKKCVopuJxsDneOBpwOanoe4qAz+VF7dRWbZxVfPRwEgUZ3xWCnL6I+PzRWAJznk+HUCA6uuuob2HgJOoD3z+LyqKO4AXXdt2mVNcCCGEqNMk8NnIBQcH0717d5YvX86xY8fOf4IQCmiaRqCtZn/IBNrMsqCREHWA1Wr1ZMXNAxaiJjBxtFkzxhu2NUWZaLHFxTwN/J9rW1XA1RiEc7dUVRCsODCQZYZtlXN8hpeUcDdwu3uHnwKf/phj1BQQwHBgumu7HIXBbpw/x0OAq4z7FGa8uioBwG4gDviTyaT82m+pNM+sZHwKIYQQdZtcqQWdOnWiV69eLF++nPz8fH9XRwgA+ifEYDZV748Zs0mjf0KMj2skhKgOfwWHToeF8R7OId9w5rBgX4kpKeEJ4w5FgU9jxmcYzr5WFfg80qwZVwHvAaVUzHmoQlRhIf/CGfQC9Rmf7qC+XxZXstn4Fljs2vbHUPfyyvsUBz5NlRYTUz7UHrCazQTg/LkDsEvgUwghhKjT6s2VOicnh7FjxxIWFkZ4eDjjx4+n4BzzOu3btw9N06r899lnn3mOq+r5Tz75REWT6pSOHTty1VVXERoaKsOFRZ0wom+bas/xaXfoXH9ZGx/XSAhRHcaMzxuBvqjJ+HRnvs0GHgH2RkSc8/ha46ch9sYg1FJgPuqy/9zv53ggAPg8Pl5JuXBmJq+q/gbvYPN8nMOsVa8qD1AI7MC5sI4/A5/lOOf0VUmvlG3pj8Bn4vHjFAN/c9dJAp9CCCFEnVZvrtRjx45l+/btLFq0iPnz57Ny5Uruueeesx4fHx/P4cOHvf49/fTTNGnShGuuucbr2NmzZ3sdd+ONN/q4NXVT8+bN0XWd77//nv379/u7OqKRS4wLJ6VHHOfL+dSAq3vEkdAyXEGthBDnY7FYeABYBbwMPI6awGcokArk4px773CLFj4vE6oYUq8oEGM2mzHOhKwy+69ygFXlcOvKmbz+muMT1C/uY7FYGAt0AJJwLqjlz8Cn6oxTgOzoaAKBacCPwJ/8cbO+0nsuGZ9CCCFE3ab+NukF2LlzJwsXLmT9+vX07dsXgBkzZnDttdfyyiuv0LJlyzPOMZvNxMR4D339z3/+w+9+9zuaNGnitT88PPyMYxsrTdPo2bMnq1evpqioiMTERH9XSTRSmqYxaXgPNGDRloOYNOfq7W5mk4bdoZPSI46/DO8h83sKUUdYrVbmAU2AATiDIyqCQzHFxczEudJ3DOqCcZUXt1GVgWg2m3kLeBhohSv7TlEQLq6khGM4Mw/b4N/Ap8qMT4vFwlycK7qPRP2K4mazmVk4f7aWAQXUgYxPxRmXZquVEpyf+SuArX649hs/gzOBHcHBjFNeCyGEEEJUV70IfKanpxMeHu4JegKkpKRgMplYu3YtI0eOPO9rZGRkkJmZycyZM8947v777+fuu++mffv23HvvvYwbN+6cQZSSkhJKSko823l5eQA4HA4cDsd56+JwONB1vVrH+kN0dDSDBw9mxYoVaJpGp06dauV160q7dV1XFiSrK21WrbbabdLgj1d3Jq1XK77ZsJ+1u49RXGon0Gamf0I0w/u0IaFlUzSNOtHHNW13XaizELXNHYhxh4NUBYfci6xEA5cC4T4v0anyStuqMj4tFgv/wDnH5tuA7pquRwWrzYY7n/a/wFrX9yAVjEGn2cCe0FBlZVssFt4GVlMR+FSd8emvBa3c5RuzjFWvKg8Vv0ssFTuUlg8Vn8H/AROBjk2bKq+DEEIIIaqvXgQ+s7OziYqK8tpnsViIiIggOzu7Wq/x7rvv0qVLF/r37++1/5lnnuGqq64iODiYH374gT/+8Y8UFBTwpz/96ayvNW3aNJ5++ukz9h87dozi4uLz1sXhcHDq1Cl0XcdUh4fH9OrVC4vFQnZ2Ng6H46K/3Pur3bqus+foaZbsOMbG/bkUlzkItJq4tE04Qy+JpENUiM/+WKwv73Vtq6125+XlsWbNGrp06cK4K9ow7oq4SoHrUo4dO1Y7la4FNW23LCYmGiKLxcJA4AbXth1FgU9D1mEG8EZOjs/LhIogyAmcWWgjWrVSUq4/V5Y2ruR9PbCvvPKSN77jzrA9BfweGBAZqazsyn3uz8DnLuCfqM22rZzx+R3QRnHGZ9PSUj4Abndtq1xYy02rtMCS6uCvEEIIIWrGr4HPRx55hBdffPGcx+zcufOiyykqKmLevHk8/vjjZzxn3Ne7d29Onz7Nyy+/fM7A59SpU5k8ebJnOy8vj/j4eCIjIwkLCzvreW4OhwNN04iMjKwXwbCsrCz27t3LwIEDCQ4OvuDX8Ue7y+0OXv92G4u3HvQMjQYoLnOwds9J1uzOIaVHHH++thsWc+3Xqb6917WlNtqdnZ1NZmYml156Kd26davlGvpGTdsdGBiooFZCqGW1WnkX6OjaVhb4rLS6tFlRMMIdiHMAxahbTd5ms9EKcP92VDrXZaXfXaraDP5d1dtisRANdHFt+yPw6c64NAGtUT/U/v8AHShz/b9ecdAvUNc9QU9AWYa1kfvz3gKIBJo0ou93QgghRH3k18DnlClTuOuuu855TPv27YmJieHo0aNe+8vLy8nJyanW3Jyff/45hYWF3HHHHec9NikpiWeffZaSkhICKv0R5RYQEFDlcyaTqdpBHk3TanS8PyUkJFBQUMCiRYsYMGAALS5iwQiV7dZ1ndcXbGPJ1oMAZ6wQbndNiL9ky0E0YMr1PX2S+Vmf3uvadDHtLisrY926dfTr14+2bdvWfuV8qCbtbmyfCdE4WCwWSgzb5aj5rFcOfFaee9NX8po35zLwtFlVICwwMJC5wED3Dj9lfEIVw/19qLhpU27E+QU2GLAqnOPRZDIxC7jRtV2G/zI+ARyK57es/LPt3qe0DpVvGPpxqLsVOAq8qSi7vD7IycnhgQce4JtvvsFkMjFq1Chef/31M9ZXMCouLmbKlCl88sknlJSUkJqayptvvkl0dPQZx544cYKePXty8OBBTp48SXh4uA9bI4QQoqHw61/dkZGRJCYmnvOfzWYjOTmZ3NxcMjIyPOcuXboUh8NBUlLSect59913uf7664msxnCozMxMmjVrdtagZ2NkMpm47LLL6NatGz/++CN2u/38J9UBuw7msnjLQc633qeOc/GcrEO5CmolzqW8vJxDhw5htVoZPnx4vQt6CiGcGZ9eA58VBeQsfgrGacHBbADGAO8DrQsKlJQbEBDg1c+bFX5vsQYFeW2rzPgkOJj/ArcCp4Hh1ZzyqLYYg42rUJ9xaXzPdcU3z6oKcqoe5n1G4NMPGZ/lwcF8adhWucBWXTd27Fi2b9/OokWLmD9/PitXruSee+455zmTJk3im2++4bPPPmPFihUcOnSIm266qcpjx48fT48ePXxRdSGEEA1YvUg36tKlC2lpaUyYMIF169axevVqJk6cyOjRoz0ruh88eJDExETWrVvnde7u3btZuXIld9999xmv+8033/DOO++wbds2du/ezVtvvcXzzz/PAw88oKRd9U2nTp249tprMZvNHD58mHKFc3pdiG827Mdsql42hNmk8fX6/T6ukTiX/Px8fvjhB7KystB1XXkWiRCidlitVs9w3M+BzysFyXylckBEVcan+3fV9cAdQHhpqZJyAwMDPUGwscAHzZopKRf8F2SGiv42V+xQVjZUBD4fwDnHqL+GugPY/RD4vAf4GedN4+2oz/i0GX6fFAB2hXOcupU0a8YonDc6AL8EX+uinTt3snDhQt555x2SkpK48sormTFjBp988gmHDh2q8pxTp07x7rvvMn36dK666ir69OnD7NmzWbNmDT/99JPXsW+99Ra5ubk8+OCDKpojhBCiAak3V+q5c+cyceJEhg4d6hk68cYbb3ieLysrIysri8LCQq/z3nvvPVq1asWwYcPOeE2r1crMmTOZNGkSuq7TsWNHpk+fzoQJE3zenvrKZrOh6zq7du1i48aNXHnllTSto6tZrsnKPmN4+9nYHTprstRmjYgK+/btY/369XTs2JFevXopW5lYCFH7jMGZd4DMcwxxrE2OJk34E+D+ZmBWFBAJstv5K9DVta0pCoIYMz4tqM28MwcFsQ7o59pWFWQGsOHM9nQvnqU6285hMoHD4Qm8qg58PgA8jHOKA5XzurrLTwY6ubcBi+KMT5thrvnmwMD27ZmotAYV77m75ZLx6ZSenk54eDh9+/b17EtJScFkMrF27VpGjhx5xjkZGRmUlZWRkpLi2ZeYmEjr1q1JT0/n8ssvB2DHjh0888wzrF27ll9//bVa9SkpKaGkpGJyhry8PMA5H7vD4bigNjocDnRdv+DzGxrpD2/SH96kPypIX3irjf6o6bn1JvAZERHBvHnzzvp827Zt0fUzg1zPP/88zz//fJXnpKWlkZaWVmt1bCw0TWPw4MFs3bqVH374gT59+tC+fXt/V8uLrusUl9ZsSH5xqb3SiuFCFbvdzhVXXOHJ4BaiPpk5cyYvv/wy2dnZ9OzZkxkzZtCvX7/znvfJJ58wZswYbrjhBr766ivfV1QR41B3MxCkKOPT3KQJM4A/4VxYqfI8lL4SrOsYl2lUNezbGPi0onh17yZNSALWAZehLsgMYNN15hq2Va/q7c749NfiSguABJyBT9VD3U0mk1fGaTkQqLj/jdMsWFD7ufeUWynwKRmfTtnZ2URFRXnts1gsREREkH2WKSmys7Ox2WxnzNUZHR3tOaekpIQxY8bw8ssv07p162oHPqdNm8bTTz99xv5jx45RXFxcrdeozOFwcOrUKXRdl3nakf6oTPrDm/RHBekLb7XRH/n5+TU6Xq7U4oJomkaPHj2Ijo6mwDWfWV0KGmqaRqDNTFENgp+BNnOdqX9jcOLECTZs2MCVV15Jhw4d/F0dIS7Ip59+yuTJk5k1axZJSUm89tprpKamkpWVdcYfgEb79u3jwQcfZMCAAQprq4Yx43MYUK7oC547APIPnJlgURexEF9NnDHMW+HiRsbM2nmVFoH0JXdf98MZ3H4kMVFZ2ZX7W/NHxifwCnAN6gOf4BzivQ/IVZxtCWDXNHAlGthRP8enMePTDH6Zkz+kpIRyKqZbcDTwjM9HHnmEF1988ZzH7Ny502flT506lS5dunDbbbfV+LzJkyd7tvPy8oiPjycyMpKwsLALqovD4UDTNCIjIyV4gfRHZdIf3qQ/KkhfeKuN/gisPOf3eUjgU1yU6OhooqOjKSoqYsmSJVx66aV1Jmuvf0IMy7cfqtZwd7NJo39CjIJaCYfDwdatW8nKyqJbt24EG/6IEaK+cU+PMm7cOABmzZrFt99+y3vvvccjjzxS5Tl2u52xY8fy9NNPs2rVKnJzcxXW2PesVivTgfeAPwPNanhH9kLZLBYGAJtxLjoz/RyB59pUObPUHxmfAJrCL9LGYJMdsCoMPp2Ryas4286YZRmI2sWNgoKCGArkAf2BJtHRjFdWupPDZALXIpfl+GGOzyZNaA48AXwBZBw/rrR8cE7t4PWuN/CMzylTpnDXXXed85j27dsTExPD0Uo3YMrLy8nJySEmpurv2DExMZSWlpKbm+uV9XnkyBHPOUuXLmXr1q18/vnnAJ4Rfi1atODRRx+tMqsTnL+nqgqMm0ymiwo8aJp20a/RkEh/eJP+8Cb9UUH6wtvF9kdNz2vYV2qhTFBQEF27dmXNmjXExcVx6aWX+uUuvNGIvm1YsvVgtY61O3Suv6yNj2skAH755RcOHz5MampqnZ0fVojqKC0tJSMjg6lTp3r2mUwmUlJSSE9PP+t5zzzzDFFRUYwfP55Vq1ads4z6OEeZpml8BiQBU3Bm6KmY08hqNrPS9bgZzoBMdcq96P6oFPSwBQWpaa/VyjfAza5tvZrtPZ/q9IfFYuEXIAjo66qLsnmrKgeZaqndZ1O5P9YEBTGoqIhWOAN/JpNJWdsDAwOZhnN6geuAfSr73aVy4NNsNiutg9VmIwdoD1wN7C8rU94HxpsbnwNHwsKUzlOmWmRkJJGRkec9Ljk5mdzcXDIyMujTpw/gDFo6HA6SkpKqPKdPnz5YrVaWLFnCqFGjAMjKyuLAgQMkJycD8MUXX1BUVOQ5Z/369fz+979n1apVMmJICCFEtUjgU9Sadu3aERMTw/r16/nll1/o1q2bX+uTGBdOSo84lmw5yLlyPjUgpUccCS3DFdWs8bHb7Wzfvp3OnTvTqVMnOnXqJHe7RL13/Phx7HY70dHRXvujo6PZtWtXlef8+OOPvPvuu2RmZlarjPo8R5lnGKjJdEYWkC8UGgLE3YDy06erVe7F9oexXHAOBVbRXl3X+RC4FPgLUFZL5VanP4qLi2kJBOMM/Gw8dEhJmwFOFxXhAEzASuCozebTsiv3x9fBwZzIyeETnIG/kydPVjnHvC+cPn3aa0ErTdFnzchhmBLI3f7S0lJl5bt/77n/gCkH5X1w2vAzfzcwuHlzjh49qmyesrqqS5cupKWlMWHCBGbNmkVZWRkTJ05k9OjRntFgBw8eZOjQoXzwwQf069ePpk2bMn78eCZPnkxERARhYWE88MADJCcnexY2qhzcPO7K8u3SpcsZc4MKIYQQVZHAp6hVQUFBDBw4EF3XKSkpYePGjXTv3p0milb1NdI0jUnDe6ABi7YcxGzSvIa9u7dTesTxl+E9ZH5PHzl8+DDr168nKCiIdu3a1Xg+DiEaivz8fG6//Xb+9a9/0aKa80/W1znKugJDXY8tAQHnnO+0tkRGRWHHGXBdBXxlt1er3Ivtj3DDe9kR+FOXLkra6+bOPdNstloptzr9ERkZ6QnAXQEctFqVtTkiIgI7zsDnGOC6Nm18Wnbl/ggICPAKusXGxhIaGuqz8o1CQkLIdD3+L/ByXp7Szxp4z2e5Abi7ZUulI3xiYmJ4Hef8qgC2kBDlfdC0eXPPYwsQHBxMVFSUsnnK6rK5c+cyceJEhg4dislkYtSoUbzxxhue58vKysjKyqKwsNCz79VXX/UcW1JSQmpqKm+++aY/qi+EEKKBksCn8AlN09A0DbPZzIIFC7jkkktISEhQXg+L2cSU63syvG8bvl6/nzVZ2RSX2gm0memfEMP1l7UhoWW4BD195NdffyUzM5NevXrRoUMH6WfRoLRo0QKz2cyRI0e89hvnJjPas2cP+/btY8SIEZ597iGOFouFrKysMzJb6uscZS8D3d1l2WxKMrzdc166wzK24OBql3sx/WE1BC1ygZCmTZVltIfjDLY6K2KttXLP1x+BgYFe84uag4KUtdlms1GOM+BrwTnM3tdlG/ujqcmEe2KcckXlu4WEhHj1e4zdrnz0xGuhoTxVVEQRUAz8UdHPt1tQUBDjDNvmgADlfWBzLSxmBpoCNrNZ6TxldVlERATz5s076/Nt27Y9I0M6MDCQmTNnMnPmzGqVMXjwYGVZ1kIIIRoGCXwKn7HZbPTr148OHTqwYcMGysvLzzq5uS9pmkZiXDMS45oBdWv1+YaovLycnTt3Eh4eTnx8PK1atWpQ2QxCuNlsNvr06cOSJUu48cYbAWcgc8mSJUycOPGM4xMTE9m6davXvscee4z8/Hxef/114uPjVVRbCa9Fd1wrgPuazWbzrHIOzkxTFSw2G4NxtjkPZ3BKleeBVNdjXeECKwEBAV59bQ4KUla2xWJhHM7A5wnUL65z38mT3O16bFdcvslk8up3hx8W1Smz2Tjpeuy+wa1S5aC7SdHvFyOLxeK5ybIHmJGdrbwOQgghhKg+CXwKn2vevDnDhg2jvLycI0eO8OOPP9K1a1eaG4YKqSRBT9/QdZ39+/eTmZlJUFAQwcHBSjNhhPCHyZMnc+edd9K3b1/69evHa6+9xunTpz2rvN9xxx3ExcUxbdo0AgMDz5j72D0/mb/nRK5txhn/NEUBSHcmoJtFUTDOarWyAngLGAuEK7zGGNt77AKnPrgQlfvarPDmlsVi4VNgF/Ah8I9KGde+ZlzVfTswXHHw0aFp4Mp280fg0xjoVB10Bmfg0xj89UfgMzg4mO+B613bxsWOhBBCCFH3SOBTKGHMCmjatClLliwhLi6OHj16KJsbS/jWtm3b+PXXX+nduzfx8fHKFxsQwh9uueUWjh07xhNPPEF2dja9evVi4cKFngWPDhw40CiD/+7A5wFgX+vWSsq02WwY10Y2BwcrKdcd/LnXtf2jwmCQOwA0DdjWvr2yct3TCripzPh0f5dwh7t0xUEn3VX+TOBRTWOq4p9vu2FVdZVZvm5DSkuZ7Xr8gR9WI6/82TMp/Oy5hYeHMwDYCPRGAp9CCCFEXSeBT6GUxWKhe/fudO7cma1bt7J9+3Yuv/xyHA5HowwO1HdHjhzh559/pn///iQkJNC1a1dMJpNn3kIhGoOJEydWObQdYPny5ec8d86cObVfoTrAHficAeht2pzr0Fpjs9l4HnjJtW1RFPi0Wq3cZ9gOVph5aXdl/7nnulTFZrOxE3BPzqCqr8H5PWIY0K6iMsrKhorApwWUD/MGeDswkGtOn3bWxQ8Bt16G72pBfhhBExgYiHtN9d7ArZdcorwO7kx9T+9L4FMIIYSo0yTwKfwiKCiIfv36eSYnX7hwIc2bN+eSSy6RDNB64NChQ2zfvp28vDy6dOkCOP8QFkIIqAh82gBN0TBom83Gm6gPfFosFl4zbAe5giIq+DPwmQq4lxdRHfh817syysqGiqHuVvwzdc6a4GA+O32am/FPxmdQkyaex6fLy89xpG8EBgZy2vXYAgT4YQ5xd+DTM4mHwlXthRBCCFFzEvgUfuX+o2HAgAHs2LGDBQsWEBcXR//+/SUDtI7RdZ2ioiKCg4M5ePAgrVq1omPHjkr/2BZC1A/uwOdAYKuiDHD34kYv4gy4DlK0yJDFYsGY9xfcrJmScsEV+AQmAf/at09ZuQGuQE8IzgDgly1aKCvbvbCMoTLKygYodwUbfw8c88PoBqvVSgFwHCjxw/U3yHBzuvQcx/mKcai7Bf/cdA0PD2cz0Mm1rcuNXyGEEKJOk8CnqBNCQ0NJSkqiW7duHDx4EJPJxLFjxygpKSEuLk4WJPKj0tJS9uzZwy+//EJ4eDgDBw7ksssu83e1hBB12FfA/ThXHC/93/+UlGm1WukAfAHsBNIVLm5kDHyGNG2qpFzwnu9R5RQj7mBToXtb8eJGxsCn6uHexbrueRyjcFoDty66zpc4M5sT4uK4X3H5QYY2+yPwabVaGQjMBR4FThcUKK9DSEgIXuF2P2SdCiGEEKL6JKVO1CkhISF07twZgKKiIjZs2MDXX3/N9u3bKS4u9nPtGp+CggK++uorDh06RO/evRkwYIC/qySEqAcWA6tdj1UtfGOz2fgPsA7ojrpMsMorWzdROF1LpmGOyXKFNwgDAgL4HMgCrkRt1p3ZbPbM8QhgNQy9VmFVTo7ncajCTFe3OwoL+Qa4DSgrK1NevjGw74/Ap6ZpHAYuBYbjn3lGNU3D/Y30FFCm+DMohBBCiJqRjE9RZ7Vu3Zr4+HgOHjzoyTaMi4vj2LFjtGjRQrJAfaC0tJT9+/fz66+/0qtXL6Kjo0lNTaWpwgwmIUTD4A6FWRQGPt1hwNaom/OycjkBCodeL7LZWFdcTD/UBj5tNhvdgc7APOCEwsBnTEwMBw3b1/3f/ykrG+DrU6eYiTOjuWlUlNKyAcpdwe4A/BP4DDZ8Hyg5x3G+5v4pMymcX9bIHfi8DegfEeGXOgghhBCieiTwKeo0TdNo1aoVrVq1AqCkpIQff/wRTdNo06YN7dq180wyLy7Ojh072LZtG+Hh4XTo0IEI1xd5CXoKIWoqGnBPiKEpCgTabDbaux5/AhxUmPG5D2gL3AJ8qjAAaTKZPCtLqwyB2Ww23OHseCBfYeAzKioKLSEBsrI4/tZbtO7aVVnZbu6Bzc1iY5WX7Z5j9EFg3/HjyssPNmS57ldeutPdVLwHqm6sVOYOfAbizEIWQgghRN0lQ91FvRIQEMCNN95IUlISxcXF/PTTTwDk5eVx6tQpP9eu/tB1nSNHjrBu3TrWrFkDOP+YTE1NZdiwYbJokRDiotxl3FD0u6TycGuVQ93dmW+HlJRYIUjTcIfeVAY+NU3z+gKpeoGZyNatAWjhhyHGVyYnE+l63LpjR+Xl2w1TKwT6YQoge2oqrYFmwNvKS3e62fDYomgRs8rcP/NBOEfLCCGEEKLukoxPUe9omkZsbCyxsbHorkUGjhw5wqZNmwgODiY+Pp62bdtKpmIlZWVlWK1WTp06xeLFizGZTLRu3Zr27Z05Ui38MFeZEKJhMoYBVGV8Vs66UhWMs1qt3Ikz82ubkhIr3F5aSozrcZni6V+Mva18Ze2//AVGj4bLL1dbLvD+uHG0T08HoIVrNIpKxsBnsR9WlW/Rti1qlis7O7vhsb8Cn+6M0w+Ax/Lz/VIHIYQQQlSPBD5Fveae57NTp060a9eOw4cP89tvv5Gbm0vTpk3ZsmULISEhxMbGEuyneaD8KScnh+zsbLKzszl27BjXXHMNoaGhDBo0iObNm8s8qUIInzAGPk2KAp+Vf5+pzPg8AvwR6KakxAoOU0XeZb7iLH1/ZnxyySVwww0QHg7Hjiktun2XLhUbMTFnP9BHHH4OfEZGRp7/IB8ztlr14lZuPwMDXY8LZPFNIYQQok6TwKdoMCwWC/Hx8cTHx3vt+/XXX1m/fj3BwcFcddVVhISEUFBQQKjClXdVsNvt5OTkcOLECSIiIoiKimLTpk3YbDbi4+Pp168fTVx/IEh2pxDCl4yBz1I/BUpUBeN0Xacd8Gdgo5ISKzhcwd6vgf8pvqYZMz5VLugEQGkplJeDHxb3IdCV6xcfD6NGKS/emPGZ74fAZ5PTp9Fdj+ekpCgvHyoFPt3vh2J/xznXaCFQWFTklzoIIYQQonrqzRyfzz33HP379yc4OLjai9nous4TTzxBbGwsQUFBpKSk8Msvv3gdk5OTw9ixYwkLCyM8PJzx48dTUFDggxYIf7jkkku4+uqrGTVqFJdddhnBwcHk5+ezYMECvvjiC5YtW8bWrVsBcDgcnqHzdZ3dbufEiRMcdy1ssHXrVj777DN+/PFHjh07ht3uHAg2dOhQBgwYQKdOnTxBTyGE8DX3/HffA+UKV77+0vV/EeoWHNE0jWtcj9spKbGCO+PTgvMaptJpw2PlGZ+u6zb+mNvbHWgr8c+a5rsMNxLy7PZzHOkb2umKd37srbcqLx/gV9f//88vpTuNTEsDnL/runVTnesthBBCiJqoNxmfpaWl3HzzzSQnJ/Puu+9W65yXXnqJN954g/fff5927drx+OOPk5qayo4dOwh0fXEdO3Yshw8fZtGiRZSVlTFu3Djuuece5s2b58vmCMWsViuxrtVXw8LCuPnmm8nNzSUnJ4di1xClvXv3kpGRQVhYGGFhYcTGxtKuXTvP84GKswp0XaeoqIj8/HxKS0uJj4/n8OHDbNy4kfz8fGw2G23btqVFixa0bduWdu3aSXBTCFEn5Ln+DwNOWdR91fgMuAlIB65SVGZ8fDx/dj1upqhMt1JX4DMI9YHPiTgzTdcCl6oOfL74otryjNzfBfyU5XeoWTNKARv+yfjEMG2QNSJCffk4b2wAaOC3G9ZPPPQQLFyIKTCQUX7I/BVCCCFE9dWbwOfTTz8NwJw5c6p1vK7rvPbaazz22GPccMMNAHzwwQdER0fz1VdfMXr0aHbu3MnChQtZv349ffv2BWDGjBlce+21vPLKK7Rs2bLK1y4pKaHEcKc/L8/5J57D4ajWHx7uzELVf6T4W11rd3h4uCd72OFw0KZNG5o3b86pU6fIy8ujrKwMh8PBzp072bFjByaTiZCQEDp37kznzp3Zs2cPhYWFBAQEYLFYiIyMJDQ0lJMnT+JwONA0DYfDQWlpKQ6Hg5MnT1JeXo7D4cButxMVFYWmaWRlZVFWVkZpaSlFRUVceeWVnDp1iu+//x6A4OBgIiIiiIuLo0mTJvTq1Yvw8HCCgoI8dQ9xTe5fV/q2rr3XqtS03Y2tf0TjMejaa2HBAsKBzpddpqzcPcAM1/+qAp/+ZHYFPocAb5WXKy17AdAC5xfJwwqD24Dfsi0BaOYKb+fnQ0YG9OmjtPjHHnyQg/Pn0w6Y6o8AsHExIdUBbxd3zqkV/11Hm82cCUDT4mKKFGWXCyGEEOLC1JvAZ03t3buX7OxsUgzzDzVt2pSkpCTS09MZPXo06enphIeHe4KeACkpKZhMJtauXcvIkSOrfO1p06Z5ArFGx44d82QHnovD4eDUqVPouo7JVG9mG7ho9aXdAQEBnsn7jx49SmxsLFFRURQWFlJUVITJZOLo0aOcOHGCnJwcSktLKS8vp0OHDsTGxvLjjz+Sn5/vCYAlJCRgsVhYsmQJdrsds9mMyWQiKSmJ4OBg9u7di9VqxWazERAQwJEjR9A0jcsvv5zAwEBPXx09ehRwDt/Mz88nvw6vIlpf3uvaVtN21+X3UIiL8fvXXqNs8WJa9O9P8+bNlZW73vUP4DVlpQITJ8I//gG/+53KUrn9lVfgrrsAeOaFF5SWbQdOuB4rXyhv2jS47jr4wx/UlgtgzHL0w/y1SYMG8f3ChWw5fpwRij9vADRpQllCApaiIrT+/dWXDxxITqZnejpbgKMdO/qlDlx9NXz5Jfoll/infCGEEEJUW4MNfGZnZwMQHR3ttT86OtrzXHZ2NlGV5h6zWCxERER4jqnK1KlTmTx5smc7Ly+P+Ph4IiMjCQsLO2/d3NmAkZGRjS4o1JDaXfmz43bTTTd5HjscDo4dO0ZkZCRjx46t8vjrrrvOJ/Xzp4b2XldXTdutevoEIVQJ69QJTpwgUvFK4ytXruT111/nnnvuUVouL70EQ4aA4sVe+t5xB1tPn8bUujVdExOVlr1+/XpefvllbrvtNqXlAnDttbBvH1T6jqeEpjnnGM3Jgdat1ZcPpKam+qVcAEwmTixeTFREBJph2LtK0z7/nFdeeYWnBw703yrzEyZAdDR6UpJ/yhdCCCFEtfk18PnII4/w4nmG6ezcuZNExV/mzycgIKDKFUxNJlO1gzyaptXo+IaiMba7MbYZpN3VaXdj6xvRyPhhzuEBAwYwYMAA5eUSFASGm16qaJpG9z/+UXm5AH379uXTTz/1S9kAtGnjv7Ib+2I2FkvFXKd+0LJlS6ZPn+638gEwm2HkSHA4wDUiRwghhBB1k18Dn1OmTOEu1xCts2nfvv0FvXZMTAwAR44c8Sxq497u1auX55ijlb6slJeXk5OT4zlfCCGEEEIIIYQQQghR//g18BkZGemzISrt2rUjJiaGJUuWeAKdeXl5rF27lvvuuw+A5ORkcnNzycjIoI9rcvqlS5ficDhIkqErQgghhBBCCCGEEELUW/VmnOWBAwfIzMzkwIED2O12MjMzyczMpKCgwHNMYmIi//nPfwDn8K+//OUv/P3vf+frr79m69at3HHHHbRs2ZIbb7wRgC5dupCWlsaECRNYt24dq1evZuLEiYwePfqsK7oLIYQQQgghhBBCCCHqvnqzuNETTzzB+++/79nu3bs3AMuWLWPw4MEAZGVlcerUKc8xf/3rXzl9+jT33HMPubm5XHnllSxcuNBrQZG5c+cyceJEhg4dislkYtSoUbzxxhtqGiWEEEIIIYQQQgghhPCJehP4nDNnDnPmzDnnMbque21rmsYzzzzDM888c9ZzIiIimDdvXm1UUQghhBBCCCGEEEIIUUfUm6HuQgghhBBCCCGEEEIIUV0S+BRCCCGEEEIIIYQQQjQ4EvgUQgghhBBCCCGEEEI0OBL4FEIIIYQQQgghhBBCNDj1ZnGjusy9qFJeXl61jnc4HOTn5xMYGIjJ1Hhiz42x3Y2xzSDtrm673b8zKi/MJsTZ1PR6U5XG+vN5NtIf3qQ/vEl/eJP+8FYb/SHfBdSQ62ftk/7wJv3hTfqjgvSFN39cOyXwWQvy8/MBiI+P93NNhBD1UX5+Pk2bNvV3NUQ9INcbIYRomOS7gG/J9VMIIRqe6l47NV1uL140h8PBoUOHCA0NRdO08x6fl5dHfHw8//vf/wgLC1NQw7qhMba7MbYZpN3Vbbeu6+Tn59OyZUu5+yeqpabXm6o01p/Ps5H+8Cb94U36w5v0h7fa6A/5LqCGXD9rn/SHN+kPb9IfFaQvvPnj2ikZn7XAZDLRqlWrGp8XFhbWKD/4jbHdjbHNIO2uDsnuEDVxodebqjTWn8+zkf7wJv3hTfrDm/SHt4vtD/ku4Hty/fQd6Q9v0h/epD8qSF94U3ntlNuKQgghhBBCCCGEEEKIBkcCn0IIIYQQQgghhBBCiAZHAp9+EBAQwJNPPklAQIC/q6JUY2x3Y2wzSLsbW7tF/SKfU2/SH96kP7xJf3iT/vAm/dG4yPvtTfrDm/SHN+mPCtIX3vzRH7K4kRBCCCGEEEIIIYQQosGRjE8hhBBCCCGEEEIIIUSDI4FPIYQQQgghhBBCCCFEgyOBTyGEEEIIIYQQQgghRIMjgU8hhBBCCCGEEEIIIUSDI4FPRZ577jn69+9PcHAw4eHh1TpH13WeeOIJYmNjCQoKIiUlhV9++cW3Fa1FOTk5jB07lrCwMMLDwxk/fjwFBQXnPGfw4MFomub1795771VU4wszc+ZM2rZtS2BgIElJSaxbt+6cx3/22WckJiYSGBhI9+7dWbBggaKa1q6atHvOnDlnvK+BgYEKa3vxVq5cyYgRI2jZsiWapvHVV1+d95zly5dz6aWXEhAQQMeOHZkzZ47P6ykah/N9Hu+6664zfubS0tK8jqnO7+gtW7YwYMAAAgMDiY+P56WXXvJ10y7IxfbHvn37GD9+PO3atSMoKIgOHTrw5JNPUlpa6vU6jaU/jEpKSujVqxeappGZmen1XGPrj2+//ZakpCSCgoJo1qwZN954o9fzBw4c4LrrriM4OJioqCgeeughysvLfdiyC1Mb/fHzzz9zww030KJFC8LCwrjyyitZtmyZ1zENpT8Adu7cyfXXX0/Tpk0JCQnhsssu48CBA57ni4uLuf/++2nevDlNmjRh1KhRHDlyxOs16kt/NHRy/fQm109vcv30JtfPCnLt9Fbfrp0S+FSktLSUm2++mfvuu6/a57z00ku88cYbzJo1i7Vr1xISEkJqairFxcU+rGntGTt2LNu3b2fRokXMnz+flStXcs8995z3vAkTJnD48GHPv7p6IQD49NNPmTx5Mk8++SQbN26kZ8+epKamcvTo0SqPX7NmDWPGjGH8+PFs2rSJG2+8kRtvvJFt27YprvnFqWm7AcLCwrze1/379yus8cU7ffo0PXv2ZObMmdU6fu/evVx33XUMGTKEzMxM/vKXv3D33Xfz/fff+7imojGozucxLS3N62fu448/9nr+fL+j8/LyGDZsGG3atCEjI4OXX36Zp556in/+858+a9eFutj+2LVrFw6Hg7fffpvt27fz6quvMmvWLP72t795jmlM/WH017/+lZYtW56xv7H1xxdffMHtt9/OuHHj2Lx5M6tXr+bWW2/1PG+327nuuusoLS1lzZo1vP/++8yZM4cnnnjCZ+26ULXRH8OHD6e8vJylS5eSkZFBz549GT58ONnZ2UDD6o89e/Zw5ZVXkpiYyPLly9myZQuPP/641w3cSZMm8c033/DZZ5+xYsUKDh06xE033eR5vj71R0Mn109vcv30JtdPb3L9rCDXTm/17tqpC6Vmz56tN23a9LzHORwOPSYmRn/55Zc9+3Jzc/WAgAD9448/9mENa8eOHTt0QF+/fr1n33fffadrmqYfPHjwrOcNGjRI//Of/6yghrWjX79++v333+/ZttvtesuWLfVp06ZVefzvfvc7/brrrvPal5SUpP/hD3/waT1rW03bXd3PfX0B6P/5z3/Oecxf//pXvWvXrl77brnlFj01NdWHNRONUVWfxzvvvFO/4YYbznpOdX5Hv/nmm3qzZs30kpISzzEPP/ywnpCQUKv1r20X0h9Veemll/R27dp5thtjfyxYsEBPTEzUt2/frgP6pk2bPM81pv4oKyvT4+Li9HfeeeesxyxYsEA3mUx6dna2Z99bb72lh4WFefVRXXMh/XHs2DEd0FeuXOnZl5eXpwP6okWLdF1vWP1xyy236LfddttZz8nNzdWtVqv+2Wefefbt3LlTB/T09HRd1+tvfzR0cv30JtdPb3L99CbXzwpy7fRWH66dkvFZR+3du5fs7GxSUlI8+5o2bUpSUhLp6el+rFn1pKenEx4eTt++fT37UlJSMJlMrF279pznzp07lxYtWtCtWzemTp1KYWGhr6t7QUpLS8nIyPB6j0wmEykpKWd9j9LT072OB0hNTa0X76nbhbQboKCggDZt2hAfH88NN9zA9u3bVVTXbxrCey3qt+XLlxMVFUVCQgL33XcfJ06c8DxXnd/R6enpDBw4EJvN5jkmNTWVrKwsTp48qa4hteRc/VGVU6dOERER4dlubP1x5MgRJkyYwIcffkhwcPAZ5zem/ti4cSMHDx7EZDLRu3dvYmNjueaaa7xGa6Snp9O9e3eio6M9+1JTU8nLy6uX17tz9Ufz5s1JSEjggw8+4PTp05SXl/P2228TFRVFnz59gIbTHw6Hg2+//ZbOnTuTmppKVFQUSUlJXkP6MjIyKCsr87rmJyYm0rp1a881v6H0R2Mh109vcv30JtdPb3L9rCDXTqe6eO2UwGcd5U53Nr7J7m33c3VZdnY2UVFRXvssFgsRERHnrP+tt97KRx99xLJly5g6dSoffvght912m6+re0GOHz+O3W6v0XuUnZ1db99Ttwtpd0JCAu+99x7//e9/+eijj3A4HPTv35/ffvtNRZX94mzvdV5eHkVFRX6qlWgs0tLS+OCDD1iyZAkvvvgiK1as4JprrsFutwPV+x19ts+w+7n65Hz9Udnu3buZMWMGf/jDHzz7GlN/6LrOXXfdxb333uv1x71RY+qPX3/9FYCnnnqKxx57jPnz59OsWTMGDx5MTk4O0Lj6Q9M0Fi9ezKZNmwgNDSUwMJDp06ezcOFCmjVrBjSc/jh69CgFBQW88MILpKWl8cMPPzBy5EhuuukmVqxYATjbY7PZzpjD3/i9qKH0R2Mg109vcv30JtdPb3L9rCDXzgp18dppucC2COCRRx7hxRdfPOcxO3fuJDExUVGNfK+6bb5QxvlxunfvTmxsLEOHDmXPnj106NDhgl9X+FdycjLJycme7f79+9OlSxfefvttnn32WT/WTIiGafTo0Z7H3bt3p0ePHnTo0IHly5czdOhQP9bMP2rSHwcPHiQtLY2bb76ZCRMmqK6qEufrjxkzZpCfn8/UqVP9WEt1ztcfDocDgEcffZRRo0YBMHv2bFq1asVnn33m9Qd+Q3C+/tB1nfvvv5+oqChWrVpFUFAQ77zzDiNGjGD9+vXExsb6sfa1y/3e33DDDUyaNAmAXr16sWbNGmbNmsWgQYP8WT3hA3L99CbXT29y/fQm188Kcu2sUBevnZLxeRGmTJnCzp07z/mvffv2F/TaMTExAGesanXkyBHPc/5Q3TbHxMScsdBNeXk5OTk5Nap/UlIS4Lx7WNe0aNECs9lco/coJiamzr2nNXUh7a7MarXSu3fvOvm+1pazvddhYWEEBQX5qVaisWrfvj0tWrTw/MxV53f02T7D7ufqs8r94Xbo0CGGDBlC//79z1hkoDH1x9KlS0lPTycgIACLxULHjh0B6Nu3L3feeSfQuPrD/cfIJZdc4jkmICCA9u3be1YnbUz9sXTpUubPn88nn3zCFVdcwaWXXsqbb75JUFAQ77//PtBw+qNFixZYLBav9x6gS5cuXu99aWkpubm5XscYvxc1lP5ojOT66U2un97k+ulNrp8V5NpZt66dEvi8CJGRkSQmJp7zn3Hujppo164dMTExLFmyxLMvLy+PtWvXemXOqVbdNicnJ5Obm0tGRobn3KVLl+JwODzBzOrIzMwEqJN3QGw2G3369PF6jxwOB0uWLDnre5ScnOx1PMCiRYv8+p7W1IW0uzK73c7WrVvr5PtaWxrCey0ajt9++40TJ054fuaq8zs6OTmZlStXUlZW5jlm0aJFJCQkeIbk1FeV+wOcmSqDBw+mT58+zJ49G5PJ+ytSY+qPN954g82bN5OZmUlmZiYLFiwA4NNPP+W5554DGld/9OnTh4CAALKysjzHlJWVsW/fPtq0aQM4+2Pr1q1eAZFFixYRFhZ2xhf/+qZyf7jnXq/8M2IymTxZHg2lP2w2G5dddpnXew/w888/e977Pn36YLVava75WVlZHDhwwHPNbyj90RjJ9dObXD+9yfXTm1w/K8i1s45dO2u0FJK4YPv379c3bdqkP/3003qTJk30TZs26Zs2bdLz8/M9xyQkJOhffvmlZ/uFF17Qw8PD9f/+97/6li1b9BtuuEFv166dXlRU5I8m1FhaWpreu3dvfe3atfqPP/6od+rUSR8zZozn+d9++01PSEjQ165dq+u6ru/evVt/5pln9A0bNuh79+7V//vf/+rt27fXBw4c6K8mnNcnn3yiBwQE6HPmzNF37Nih33PPPXp4eLhn5bHbb79df+SRRzzHr169WrdYLPorr7yi79y5U3/yySd1q9Wqb9261V9NuCA1bffTTz+tf//99/qePXv0jIwMffTo0XpgYKC+fft2fzWhxvLz8z0/t4A+ffp0fdOmTfr+/ft1Xdf1Rx55RL/99ts9x//66696cHCw/tBDD+k7d+7UZ86cqZvNZn3hwoX+aoJoQM71eczPz9cffPBBPT09Xd+7d6++ePFi/dJLL9U7deqkFxcXe17jfL+jc3Nz9ejoaP3222/Xt23bpn/yySd6cHCw/vbbb/ujyed0sf3x22+/6R07dtSHDh2q//bbb/rhw4c9/9waU39Utnfv3jNWpW1s/fHnP/9Zj4uL07///nt9165d+vjx4/WoqCg9JydH13VdLy8v17t166YPGzZMz8zM1BcuXKhHRkbqU6dO9Vezz+pi++PYsWN68+bN9ZtuuknPzMzUs7Ky9AcffFC3Wq16ZmamrusNpz90Xde//PJL3Wq16v/85z/1X375RZ8xY4ZuNpv1VatWeV7j3nvv1Vu3bq0vXbpU37Bhg56cnKwnJyd7nq9P/dHQyfXTm1w/vcn105tcPyvItdNbfbt2SuBTkTvvvFMHzvi3bNkyzzGAPnv2bM+2w+HQH3/8cT06OloPCAjQhw4dqmdlZamv/AU6ceKEPmbMGL1JkyZ6WFiYPm7cOK9Ar/tC4O6DAwcO6AMHDtQjIiL0gIAAvWPHjvpDDz2knzp1yk8tqJ4ZM2borVu31m02m96vXz/9p59+8jw3aNAg/c477/Q6/t///rfeuXNn3Waz6V27dtW//fZbxTWuHTVp91/+8hfPsdHR0fq1116rb9y40Q+1vnDLli2r8mfY3c4777xTHzRo0Bnn9OrVS7fZbHr79u29fr6FuBjn+jwWFhbqw4YN0yMjI3Wr1aq3adNGnzBhgufGhNv5fkfruq5v3rxZv/LKK/WAgAA9Li5Of+GFF1Q2s9outj9mz55d5fmV7w83lv6orKo/3HS9cfVHaWmpPmXKFD0qKkoPDQ3VU1JS9G3btnkds2/fPv2aa67Rg4KC9BYtWuhTpkzRy8rKVDa1WmqjP9avX68PGzZMj4iI0ENDQ/XLL79cX7BggdcxDaE/3N599129Y8eOemBgoN6zZ0/9q6++8nqNoqIi/Y9//KPerFkzPTg4WB85cqRX4EfX609/NHRy/fQm109vcv30JtfPCnLt9Fbfrp2aruv6OVNChRBCCCGEEEIIIYQQop6ROT6FEEIIIYQQQgghhBANjgQ+hRBCCCGEEEIIIYQQDY4EPoUQQgghhBBCCCGEEA2OBD6FEEIIIYQQQgghhBANjgQ+hRBCCCGEEEIIIYQQDY4EPoUQQgghhBBCCCGEEA2OBD6FEEIIIYQQQgghhBANjgQ+hRBCCCGEEEIIIYQQDY4EPoVQ5PHHH+eee+6p1rELFy6kV69eOBwOH9dKCCGEuHhPPfUUvXr18nc1PDRN46uvvqrxeVlZWcTExJCfn1/7lTI4fvw4UVFR/Pbbbz4tRwghRN0m18+akeunuBAS+BTiPO666y40TUPTNKxWK+3ateOvf/0rxcXF1X6N7OxsXn/9dR599NFqHZ+WlobVamXu3LkXWm0hhBANzKxZswgNDaW8vNyzr6CgAKvVyuDBg72OXb58OZqmsWfPHsW1VKu2/2CcOnUqDzzwAKGhobX2mlVp0aIFd9xxB08++aRPyxFCCCHXz6rI9VM0JhL4FKIa0tLSOHz4ML/++iuvvvoqb7/9do1+2b7zzjv079+fNm3aVPucu+66izfeeONCqiuEEKIBGjJkCAUFBWzYsMGzb9WqVcTExLB27VqvG3LLli2jdevWdOjQwR9VrZcOHDjA/Pnzueuuu5SUN27cOObOnUtOTo6S8oQQorGS66dvyfVT1HUS+BSiGgICAoiJiSE+Pp4bb7yRlJQUFi1aBIDD4WDatGm0a9eOoKAgevbsyeeff+51/ieffMKIESM82/v27fNkkRr/Ge84jhgxgg0bNjT4u41CCCGqJyEhgdjYWJYvX+7Zt3z5cm644QbatWvHTz/95LV/yJAhAHz44Yf07duX0NBQYmJiuPXWWzl69CjgvIa1atWKt956y6usTZs2YTKZ2L9/PwC5ubncfffdREZGEhYWxlVXXcXmzZvPWd933nmHLl26EBgYSGJiIm+++abnOfd18Msvv2TIkCEEBwfTs2dP0tPTvV7jX//6F/Hx8QQHBzNy5EimT59OeHg4AHPmzOHpp59m8+bNnuvonDlzPOceP36ckSNHEhwcTKdOnfj666/PWd9///vf9OzZk7i4OM++OXPmEB4ezvfff0+XLl1o0qSJ52ao21133cWNN97I888/T3R0NOHh4TzzzDOUl5fz0EMPERERQatWrZg9e7ZXeV27dqVly5b85z//OWe9hBBCXBy5fsr1UzRuEvgUooa2bdvGmjVrsNlsAEybNo0PPviAWbNmsX37diZNmsRtt93GihUrAMjJyWHHjh307dvX8xrx8fEcPnzY82/Tpk00b96cgQMHeo5p3bo10dHRrFq1Sm0DhRBC1FlDhgxh2bJlnu1ly5YxePBgBg0a5NlfVFTE2rVrPX+4lZWV8eyzz7J582a++uor9u3b58nKMJlMjBkzhnnz5nmVM3fuXK644grPSIWbb76Zo0eP8t1335GRkcGll17K0KFDz5ptMXfuXJ544gmee+45du7cyfPPP8/jjz/O+++/73Xco48+yoMPPkhmZiadO3dmzJgxnqGIq1ev5t577+XPf/4zmZmZXH311Tz33HOec2+55RamTJlC165dPdfTW265xfP8008/ze9+9zu2bNnCtddey9ixY8+ZHbJq1Sqva7VbYWEhr7zyCh9++CErV67kwIEDPPjgg17HLF26lEOHDrFy5UqmT5/Ok08+yfDhw2nWrBlr167l3nvv5Q9/+MMZc5L169dPrvNCCKGAXD/l+ikaMV0IcU533nmnbjab9ZCQED0gIEAHdJPJpH/++ed6cXGxHhwcrK9Zs8brnPHjx+tjxozRdV3XN23apAP6gQMHqnz9oqIiPSkpSR8+fLhut9u9nuvdu7f+1FNP+aZhQggh6p1//etfekhIiF5WVqbn5eXpFotFP3r0qD5v3jx94MCBuq7r+pIlS3RA379/f5WvsX79eh3Q8/PzdV13Xqc0TfMcb7fb9bi4OP2tt97SdV3XV61apYeFhenFxcVer9OhQwf97bff1nVd15988km9Z8+eXs/NmzfP6/hnn31WT05O1nVd1/fu3asD+jvvvON5fvv27Tqg79y5U9d1Xb/lllv06667zus1xo4dqzdt2tSzXblcN0B/7LHHPNsFBQU6oH/33XdV9omu63rPnj31Z555xmvf7NmzdUDfvXu3Z9/MmTP16Ohoz/add96pt2nTxusanpCQoA8YMMCzXV5eroeEhOgff/yx1+tPmjRJHzx48FnrJIQQonbI9VOun6LxkoxPIaphyJAhZGZmsnbtWu68807GjRvHqFGj2L17N4WFhVx99dU0adLE8++DDz7wDFEvKioCIDAwsMrX/v3vf09+fj7z5s3DZPL+kQwKCqKwsNC3jRNCCFFvDB48mNOnT7N+/XpWrVpF586diYyMZNCgQZ55ypYvX0779u1p3bo1ABkZGYwYMYLWrVsTGhrKoEGDAOecXAC9evWiS5cunqyVFStWcPToUW6++WYANm/eTEFBAc2bN/e61u3du7fK6VhOnz7Nnj17GD9+vNfxf//73884vkePHp7HsbGxAJ5hhFlZWfTr18/r+Mrb52J87ZCQEMLCwjyvXZWioqIqr9XBwcFec73Fxsae8Tpdu3b1uoZHR0fTvXt3z7bZbKZ58+ZnnCfXeSGEUEOun3L9FI2Xxd8VEKI+CAkJoWPHjgC899579OzZk3fffZdu3boB8O2333rNaQLOeUHBufIcwMmTJ4mMjPQ65u9//zvff/8969atq3IFvJycnDPOEUII0Xh17NiRVq1asWzZMk6ePOn5I6xly5bEx8ezZs0ali1bxlVXXQU4/4hKTU0lNTWVuXPnEhkZyYEDB0hNTaW0tNTzumPHjmXevHk88sgjzJs3j7S0NJo3bw44V76tPDeam3u+MKOCggLAOb9YUlKS13Nms9lr22q1eh5rmgY4502rDcbXdr/+uV67RYsWnDx5slqvo+v6eY+pTvlynRdCCDXk+ll9cv0UDY0EPoWoIZPJxN/+9jcmT57Mzz//TEBAAAcOHPBcPCvr0KEDYWFh7Nixg86dO3v2f/HFFzzzzDN89913Va4aWFxczJ49e+jdu7fP2iKEEKL+GTJkCMuXL+fkyZM89NBDnv0DBw7ku+++Y926ddx3330A7Nq1ixMnTvDCCy8QHx8P4LWqrdutt97KY489RkZGBp9//jmzZs3yPHfppZeSnZ2NxWKhbdu2561fdHQ0LVu25Ndff2Xs2LEX3M6EhATWr1/vta/yts1mw263X3AZRr1792bHjh218lrVtW3bNq+FDYUQQviOXD8ryPVTNCYy1F2IC3DzzTdjNpt5++23efDBB5k0aRLvv/8+e/bsYePGjcyYMcMzAbXJZCIlJYUff/zRc/62bdu44447ePjhh+natSvZ2dlkZ2d7TRr9008/ERAQQHJysvL2CSGEqLuGDBnCjz/+SGZmptdNt0GDBvH2229TWlrqWZihdevW2Gw2ZsyYwa+//srXX3/Ns88+e8Zrtm3blv79+zN+/HjsdjvXX3+957mUlBSSk5O58cYb+eGHH9i3bx9r1qzh0UcfrfKPQHAujDBt2jTeeOMNfv75Z7Zu3crs2bOZPn16tdv5wAMPsGDBAqZPn84vv/zC22+/zXfffefJbHHXe+/evWRmZnL8+HFKSkqq/fqVpaamkp6eXmt/CJ5PYWEhGRkZDBs2TEl5QgjR2Mn1U66fonGSwKcQF8BisTBx4kReeuklpk6dyuOPP860adPo0qULaWlpfPvtt7Rr185z/N13380nn3ziSdHfsGEDhYWF/P3vfyc2Ntbz76abbvKc8/HHHzN27FiCg4OVt08IIUTdNWTIEIqKiujYsSPR0dGe/YMGDSI/P5+EhATPfF+RkZHMmTOHzz77jEsuuYQXXniBV155pcrXHTt2LJs3b2bkyJEEBQV59muaxoIFCxg4cCDjxo2jc+fOjB49mv3793uVb3T33XfzzjvvMHv2bLp3786gQYOYM2eO17XxfK644gpmzZrF9OnT6dmzJwsXLmTSpEle84iNGjWKtLQ0hgwZQmRkJB9//HG1X7+ya665BovFwuLFiy/4NWriv//9L61bt2bAgAFKyhNCiMZOrp9y/RSNk6ZXnmRBCFHrdF0nKSmJSZMmMWbMmPMef/z4cRISEtiwYUONLnJCCCFEQzZhwgR27drFqlWrfPL6M2fO5Ouvv+b777/3yesbXX755fzpT3/i1ltv9XlZQgghGje5forGTOb4FEIBTdP45z//ydatW6t1/L59+3jzzTcl6CmEEKJRe+WVV7j66qsJCQnhu+++4/333+fNN9/0WXl/+MMfyM3NJT8/v8pFB2vL8ePHuemmm6p1M1QIIYSoKbl+ClFBMj6FEEIIIUSd9Lvf/Y7ly5eTn59P+/bteeCBB7j33nv9XS0hhBCiTpPrpxAVJPAphBBCCCGEEEIIIYRocGRxIyGEEEIIIYQQQgghRIMjgU8hhBBCCCGEEEIIIUSDI4FPIYQQQgghhBBCCCFEgyOBTyGEEEIIIYQQQgghRIMjgU8hhBBCCCGEEEIIIUSDI4FPIYQQQgghhBBCCCFEgyOBTyGEEEIIIYQQQgghRIMjgU8hhBBCCCGEEEIIIUSD8/8BdiA6i7Sf9BoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluate the fitted model on a dense frequency grid\n", + "f_dense = jnp.linspace(float(freq[0]), float(freq[-1]), 2000)\n", + "S_fit = pole_residue_response_discrete(\n", + " f_dense, f_center, sampling_frequency, poles, residues, feedthrough\n", + ")\n", + "\n", + "# Wavelength from dense frequency (decreasing)\n", + "wvl_dense_nm = speed_of_light / np.asarray(f_dense) * 1e9\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n", + "\n", + "# --- Pole locations in z-plane ---\n", + "ax = axes[0]\n", + "theta = np.linspace(0, 2 * np.pi, 500)\n", + "ax.plot(np.cos(theta), np.sin(theta), \"k--\", alpha=0.35, linewidth=0.8, label=\"Unit circle\")\n", + "ax.scatter(np.asarray(poles).real, np.asarray(poles).imag,\n", + " c=\"steelblue\", s=70, zorder=3, label=\"Poles\")\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_xlabel(\"Re(z)\")\n", + "ax.set_ylabel(\"Im(z)\")\n", + "ax.set_title(\"Pole Locations (z-plane)\")\n", + "ax.legend(fontsize=8)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# --- S₁₀ fit quality ---\n", + "for ax_idx, (i, j) in enumerate([(1, 0), (0, 0)], start=1):\n", + " ax = axes[ax_idx]\n", + " ax.plot(wvl_nm, np.abs(np.asarray(S[:, i, j])) ** 2,\n", + " \"k\", linewidth=2, label=\"S-params (truth)\", zorder=2)\n", + " ax.plot(wvl_dense_nm, np.abs(np.asarray(S_fit[:, i, j])) ** 2,\n", + " \"r--\", linewidth=1.5, label=f\"Fit (order {model_order})\", zorder=3)\n", + " ax.set_xlabel(\"Wavelength (nm)\")\n", + " ax.set_ylabel(f\"|S{i}{j}|²\")\n", + " ax.set_title(f\"Fit Quality: S{i}{j}\")\n", + " ax.legend(fontsize=8)\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(f\"Vector Fitting Result (MSE = {fit_mse:.1e})\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9d0e1f2", + "metadata": {}, + "source": [ + "## 3. State-Space Realization\n", + "\n", + "`state_space_discrete` converts the pole-residue model into standard ABCD form:\n", + "\n", + "$$\n", + "x[n+1] = A\\, x[n] + B\\, u[n], \\qquad y[n] = C\\, x[n] + D\\, u[n]\n", + "$$\n", + "\n", + "- **A** `(r·m × r·m)` — block-diagonal with replicated poles; maps the state to itself.\n", + "- **B** `(r·m × m)` — canonical input selector; injects $u[n]$ into each pole's states.\n", + "- **C** `(q × r·m)` — residue matrix; reads out the pole contributions to $y[n]$.\n", + "- **D** `(q × m)` — feedthrough; the instantaneous input-to-output path.\n", + "\n", + "The state dimension is `r·m = 20 poles × 2 inputs = 40`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0e1f2a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "State-space matrices:\n", + " A (40, 40) state transition\n", + " B (40, 2) input → state\n", + " C (2, 40) state → output\n", + " D (2, 2) direct feedthrough\n", + "\n", + "Max |eig(A)| = 0.995029 (must be < 1 for stability)\n" + ] + } + ], + "source": [ + "A, B, C, D = state_space_discrete(poles, residues, feedthrough)\n", + "\n", + "print(\"State-space matrices:\")\n", + "print(f\" A {A.shape} state transition\")\n", + "print(f\" B {B.shape} input → state\")\n", + "print(f\" C {C.shape} state → output\")\n", + "print(f\" D {D.shape} direct feedthrough\")\n", + "\n", + "# Stability check: all eigenvalues of A inside the unit circle\n", + "eigs = jnp.linalg.eigvals(A)\n", + "print(f\"\\nMax |eig(A)| = {float(jnp.max(jnp.abs(eigs))):.6f} (must be < 1 for stability)\")" + ] + }, + { + "cell_type": "markdown", + "id": "e1f2a3b4", + "metadata": {}, + "source": [ + "## 4. Discrete-Time Impulse Response\n", + "\n", + "The impulse response $h[k]$ is a `(K × q × m)` tensor where\n", + "$h[k,\\, i,\\, j]$ is the field amplitude arriving at output port $i$ at time step $k$\n", + "due to a unit impulse injected into input port $j$ at $k=0$.\n", + "\n", + "We compute it by driving the state-space system with a unit-impulse input at each\n", + "port separately and stacking the results:\n", + "\n", + "$$h[k] = C A^{k-1} B \\;(k \\geq 1), \\quad h[0] = D$$\n", + "\n", + "This is exactly what `state_space_response_discrete` does when given $u[0] = e_j$\n", + "(the $j$-th standard basis vector)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f2a3b4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Impulse response tensor h : (200, 2, 2) (K × q × m)\n", + "Time resolution : 0.01 ps/sample\n", + "Total span : 2.0 ps\n" + ] + } + ], + "source": [ + "K_ir = 200 # impulse response taps to compute\n", + "n_ports = 2\n", + "dt = 1.0 / sampling_frequency # seconds per sample\n", + "t_ir_ps = np.arange(K_ir) * dt * 1e12 # time axis in picoseconds\n", + "\n", + "h_cols = []\n", + "for port_in in range(n_ports):\n", + " u_impulse = jnp.zeros((K_ir, n_ports), dtype=complex)\n", + " u_impulse = u_impulse.at[0, port_in].set(1.0)\n", + " y_col, _ = state_space_response_discrete(A, B, C, D, u_impulse)\n", + " h_cols.append(y_col) # (K_ir, n_ports)\n", + "\n", + "# h[k, output_port, input_port]\n", + "h = jnp.stack(h_cols, axis=2) # (K_ir, 2, 2)\n", + "\n", + "print(f\"Impulse response tensor h : {h.shape} (K × q × m)\")\n", + "print(f\"Time resolution : {dt*1e12:.2f} ps/sample\")\n", + "print(f\"Total span : {t_ir_ps[-1]:.1f} ps\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a3b4c5d6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parseval check ‖h[:,1,0]‖²=0.8086 vs mean|S₁₀|²=0.8609\n" + ] + } + ], + "source": [ + "port_names = [\"o0\", \"o1\"]\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(11, 6), sharex=True)\n", + "for i in range(2):\n", + " for j in range(2):\n", + " ax = axes[i, j]\n", + " ax.plot(t_ir_ps, np.abs(np.asarray(h[:, i, j])) ** 2, linewidth=1.5)\n", + " ax.set_title(f\"h [ out={port_names[i]}, in={port_names[j]} ]\")\n", + " ax.set_ylabel(\"|h[k]|² (a.u.)\")\n", + " ax.grid(True, alpha=0.3)\n", + " if i == 1:\n", + " ax.set_xlabel(\"Time (ps)\")\n", + "\n", + "fig.suptitle(\"Discrete-Time Impulse Response (from State-Space)\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Parseval check: ‖h[:,1,0]‖² should equal ∫|S₁₀(f)|² df / Fs\n", + "parseval_h = float(jnp.sum(jnp.abs(h[:, 1, 0]) ** 2))\n", + "parseval_S = float(jnp.mean(jnp.abs(S[:, 1, 0]) ** 2))\n", + "print(f\"Parseval check ‖h[:,1,0]‖²={parseval_h:.4f} vs mean|S₁₀|²={parseval_S:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b4c5d6e7", + "metadata": {}, + "source": [ + "## 5. Gaussian Process Simulation\n", + "\n", + "We now propagate the **joint statistics** (mean + covariance) of a Gaussian random\n", + "process through the ring resonator using the **Papoulis equations**:\n", + "\n", + "$$\n", + "\\mu_y[n] = \\sum_{k=0}^{K-1} h[k]\\, \\mu_x[n-k]\n", + "$$\n", + "\n", + "$$\n", + "R_{xy}[n_1, n_2] = \\sum_{k=0}^{K-1} R_{xx}[n_1,\\, n_2-k]\\; h[k]^\\dagger\n", + "$$\n", + "\n", + "$$\n", + "R_{yy}[n_1, n_2] = \\sum_{k=0}^{K-1} h[k]\\; R_{xy}[n_1-k,\\, n_2]\n", + "$$\n", + "\n", + "where $R_{xx} = C_x + \\mu_x \\otimes \\mu_x^*$ is the input autocorrelation.\n", + "\n", + "**Physical setup:** \n", + "- `mu_x[:, 0] = 1.0` — constant unit-amplitude coherent signal at port 0 (the bus input).\n", + "- `Cx` = white noise with power $\\sigma^2 = 0.01$ — models laser amplitude fluctuations.\n", + "\n", + "The covariance blocks live in a $(T \\times T \\times m \\times m)$ tensor where\n", + "$C_x[n_1, n_2]$ is the $m \\times m$ cross-covariance between time steps $n_1$ and $n_2$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c5d6e7f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation length T = 150 samples (1.5 ps)\n", + "IR taps used K = 100\n", + "Input noise power σ² = 0.01\n", + "mu_x shape : (150, 2)\n", + "Cx shape : (150, 150, 2, 2) (T × T × m × m)\n" + ] + } + ], + "source": [ + "T_sim = 150 # simulation duration (samples)\n", + "K_gp = 100 # impulse response taps to use (h is effectively zero after this)\n", + "sigma_sq = 0.01 # input noise power per mode\n", + "\n", + "h_gp = h[:K_gp] # (K_gp, 2, 2) — truncated impulse response for GP engine\n", + "t_ps = np.arange(T_sim) * dt * 1e12 # time axis in ps\n", + "\n", + "# Input mean: coherent unit signal at port 0, vacuum at port 1\n", + "mu_x = jnp.zeros((T_sim, n_ports), dtype=complex)\n", + "mu_x = mu_x.at[:, 0].set(1.0 + 0j)\n", + "\n", + "# Input covariance: spectrally white (delta-correlated) amplitude noise\n", + "Cx = white_noise_covariance(T_sim, n_ports, sigma_sq=sigma_sq)\n", + "\n", + "print(f\"Simulation length T = {T_sim} samples ({T_sim*dt*1e12:.1f} ps)\")\n", + "print(f\"IR taps used K = {K_gp}\")\n", + "print(f\"Input noise power σ² = {sigma_sq}\")\n", + "print(f\"mu_x shape : {mu_x.shape}\")\n", + "print(f\"Cx shape : {Cx.shape} (T × T × m × m)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d6e7f8a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Gaussian process simulation (may take a few seconds on first call)...\n", + "mu_y shape : (150, 2) (T × q)\n", + "Cy shape : (150, 150, 2, 2) (T × T × q × q)\n" + ] + } + ], + "source": [ + "print(\"Running Gaussian process simulation (may take a few seconds on first call)...\")\n", + "mu_y, Cy = gaussian_process_response(h_gp, mu_x, Cx)\n", + "print(f\"mu_y shape : {mu_y.shape} (T × q)\")\n", + "print(f\"Cy shape : {Cy.shape} (T × T × q × q)\")" + ] + }, + { + "cell_type": "markdown", + "id": "e7f8a9b0", + "metadata": {}, + "source": [ + "### 5a. Mean Signal Evolution\n", + "\n", + "The mean shows the **step response** of the ring resonator — the transient as the\n", + "ring fills with coherent light. Port 0 is the reflection port; port 1 is the\n", + "transmission port. \n", + "\n", + "The shaded band shows $\\pm\\,\\sigma_y[n]$ where $\\sigma_y^2[n] = \\text{Re}(C_y[n,n,\\text{port},\\text{port}])$\n", + "is the instantaneous noise power at that port." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f8a9b0c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAGMCAYAAAALJhESAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA7HtJREFUeJzs3Xd8U9X7B/DPzWjSSQeFMluWIENQcMAXARFFQRE3IlLEjbhwgn4FXIgDwQWiAspwK+pPRZHhxAHK1wmIlN29R/Y9vz9OkzZN0iZp0pbm8369+mpyc8c5Ge29T57zHEUIIUBERERERERERNSENM3dACIiIiIiIiIiijwMShERERERERERUZNjUIqIiIiIiIiIiJocg1JERERERERERNTkGJQiIiIiIiIiIqImx6AUERERERERERE1OQaliIiIiIiIiIioyTEoRURERERERERETY5BKSIiIiIiIiIianIMShEREVGTy8jIwKhRo5q7GV41Rdu2bt0KRVGwatWqsKxP/lEUBdOmTWvuZrRY8+bNg6Io2L9/f3M3hYiIWikGpYiIKOzKysrw8MMP46STTkJ8fDxiYmLQt29f3H333cjNzW30/hcvXtwsF+vz5s3D+vXr/V5///79UBQFiqLgvPPO87qOzWZDamoqFEVBRkZGaBraRPbt24frr78effr0QUxMDJKSknD88ccjMzMTW7Zsae7mtXj79+/HvHnzsHPnzuZuildvv/02rr76agwcOBB6vT7swQpnQESn02HXrl0ejzsDdU899VTY2hBO06ZNc/09cP4kJSVh0KBBeOKJJ2AymZq7iURERGGna+4GEBFR67Znzx6MHTsWBw4cwEUXXYRrrrkGer0eP/zwA5YsWYKVK1fi448/xtChQ4M+xuLFi5GRkdHkGQ/z589HZmYmJk6cGNB2RqMRGzZsQHZ2Njp06OD22EcffYSCggIYjcYQtjT8tm/fjpEjR0Kv12Pq1Kno168fTCYT/vnnH3zxxReIj4/HGWec4Vp/9+7dUBSlGVvcvEaMGAGTyQS9Xu9atn//fsyfPx8ZGRkYNGhQ8zXOhxdffBE//vgjBg4ciB49emD37t1NclyHw4HZs2fjgw8+CPm+TSYTtFptyPcbiKVLlyIuLg4AUFhYiA8//BD33nsvvvvuO3z44YfN2jYiIqJwY1CKiIjCpqqqCueffz6OHDmCjz/+GOPHj3c9dv3112PGjBkYM2YMLrjgAvz+++9o3759M7a26Zx33nlYv349Vq9ejXvuucftsRUrVuCEE06Aw+FARUVFM7UwcPPnz0dVVRV27tyJgQMHejyek5Pjdt9gMDRV01okjUbTYgKPiqJg7ty5mDdvXr3rvf766+jYsSN0Oh1mzpzZZEGpIUOGYP369di2bVujgtfetITX4JJLLkHbtm1d92+99VacfPLJ+Oijj1BcXIykpKRmbB0REVF4cfgeERGFzauvvoo9e/bg9ttvdwtIOQ0ZMgSPPfYY8vPz8eSTT7qWr1q1CoqiYOvWrR7bjBo1ym1Ym6IoOHDgAL766iu3YTDOYUXO+kC//PILRo8ejbi4OCQnJyMzMxN5eXlu+66vfkrtOkPOYXgA8Nprr7kd1x/t27fHuHHjsHLlSrfl2dnZ+Pzzz3H11Vf73Paff/7BVVddhQ4dOiAqKgoZGRm4++67UVlZ6bberl27MGPGDPTr1881ZHLw4MF45ZVXPPbp7Pfu3bsxZ84cdO7cGQaDAQMHDsSnn37qV5/++ecfpKSkeA1IAUBaWprbfV91m5YuXYrevXvDYDCgV69eeP75572+HwJt84svvoizzz4bnTp1QlRUFDp06IApU6Y0avhZt27dMHLkSLdlCxYsgKIouOCCC9yW33vvvVAUxTVctW6NqFWrVrkyya6++mrX+8nbc7Ry5Ur069cPBoMB6enpeOKJJ4LuQyC6du0Knc7/7zNXrVqFk08+GXFxcR7D1BRFaTAI5jR37lzExMR4BHB9sdvtWLhwIfr27Quj0YiUlBRceOGF+P333z3W9VZT6pNPPsHIkSPRtm1bREdHo2vXrrjooouwZ88et/Wys7Nx0003oWvXroiKikLHjh1x/fXXe/xdCZSiKK7PS+1MuqNHj+LOO+/EoEGDkJSUBKPRiL59+2LhwoVwOBxu+3B+ZjZv3oynnnoKPXr0gMFgwHHHHYfXXnvN45iqqmLBggXo1q0bjEYj+vfvj7Vr1zaqH0RERP5gphQREYXNu+++C0BmRfkybdo03H777XjvvfeCqg2zevVq3HHHHWjbti3uv/9+1/LU1FTX7cOHD+PMM8/ExRdfjEsuuQS//PILVqxYge3bt+Pnn39GTExMQMdMTU3F6tWrcdVVV+H000+vt3++TJ8+HRMnTnTL/njttdeg1WoxZcoUr8GjHTt2YPTo0UhMTMQNN9yATp064X//+x+effZZfPfdd/jqq69cF7Fbt27F119/jfPOOw/dunVDZWUl3nnnHVx33XXIz8/H7NmzPfafmZkJvV6Pu+66C1arFYsXL8bEiROxZ8+eButbOYdzvf/++7jooosCfj4AYOHChbjvvvtw0kknYcGCBaiqqsKTTz7p9loG2+annnoKp512Gm699VYkJyfjjz/+wCuvvILNmzfj999/R0pKSsDtHT16NNasWQOTyYTo6GgAwKZNm6DRaPDVV1/B4XC4hoZt3rwZffv29ZkNOGLECMyZMwePPfYYrr/+epx++ukA4LH+smXLkJubi2uuuQaJiYlYs2YN7r33XnTu3BmTJ09usM3FxcUeAQxAZjUWFBS4LYuPjw86o+3ZZ5/Fbbfdhq5du+Lee+9FfHw83n33XXz33XdISUnBaaedhuOOO86vfaWlpeGOO+7Ao48+io8++ggTJkyod/0rr7wSb7/9Ns466yzcdNNNyMnJwQsvvIChQ4fim2++wYknnuhz26+++goTJkxA//79MXv2bCQmJuLo0aP48ssvsXfvXlebDx48iKFDh8JqteKaa65Bjx49sHfvXixduhRbtmzB9u3b0aZNG7/6V1RU5Hb7o48+woYNG3DllVe6hvUBwG+//Yb3338fF154IXr06AGbzYYNGzbgvvvuw759+/DSSy957HvOnDkwmUy44YYbYDAYsHTpUkybNg09e/bEf/7zH9d6s2bNwpIlSzBixAjccccdyMvLw80334zu3bv71QciIqKgCSIiojBJTk4W8fHxDa43YMAAAUCUl5cLIYRYuXKlACC2bNnise7IkSNFenq627L09HQxcuRIr/tOT08XAMQzzzzjtnzRokUCgFiwYIFr2dy5cwUAkZWV5XU/dY8BQGRmZjbQuxpZWVkCgLj55puFzWYT7du3F9ddd53r8eOOO05cfPHFQggh+vXr59HPE044QfTu3VuUlZW5LX///fcFALFy5UrXsoqKCo/jOxwOMXLkSJGQkCCsVqtrubPf48ePF6qqupb/9NNPAoC47777Guzb999/L/R6vQAgevXqJa6++mrx4osvir/++svr+nWfz8LCQmE0GsWAAQOEyWRyLc/OzhYJCQke74dA2+zt+fjyyy8FALFw4cJ62+bLmjVrBADxxRdfCCGEMJvNIjo6WkyZMkUAED/++KMQQoiSkhKh1WrFzJkzXdtu2bLF4zXztqzuYx06dBAlJSWu5ZWVlaJt27bitNNOa7C9zr4B8OvHWzucbr75Zp+fFZPJJJKSkkRSUpLIyclxLbfZbOL0008X0dHRbst9cb7GP//8sygtLRVt27YV/fr1E3a73e05efLJJ13bfPHFFwKAuOyyy9zeFzt37hRarVYMHz7c7Rh1P8N33HGHACByc3PrbduECRNEamqqOHTokNvyn3/+WWi1WjF37twG+5eZmenzub/++uuFzWZzW7+qqsqtT05TpkwRGo1GHD161LXM+Td00KBBwmKxuJYfPnxYREVFiUmTJrmW7dq1SyiKIkaPHu16boUQYseOHUJRFJ+vMxERUShw+B4REYVNWVmZX9kCCQkJAIDS0tKwtCMhIQEzZsxwWzZjxgwkJCSEpXiyP3Q6Ha666iq89dZbMJlM+O6777Bnzx5Mnz7d6/q///47fvvtN0yePBkWiwUFBQWun+HDhyM2NhZffPGFa/3Y2FjXbbPZjMLCQhQVFeHss89GWVmZ19nMbrvtNrchiM6hV//880+D/Rk6dCh27NiBzMxMlJaWYuXKlZgxYwb69u2LESNGYN++ffVuv3HjRpjNZtx0001udX7S0tJw5ZVX+tzO3zY7nw9VVVFaWoqCggIMHDgQbdq0wY8//thg/7wZPXo0AJkFBQDbtm2DyWTCPffcgzZt2mDTpk0A4Mqacq7fGFdffbXbZyomJgannXaaX68RAKxduxYbN250+wGAq666ymP52LFjg2rjzz//jOLiYkyfPt0t00un0+Guu+6CyWTCO++8E9A+ExIS8MADD+DPP//0OvzMyfl5vv/++93eFwMHDsT555+Pb7/9Fvn5+T63dz637733Hux2u9d1SktL8X//93+YMGECjEaj22cxIyMDPXv2dPssNuS9995zPedvvvkmrrnmGrz88sseGZjR0dGuPlmtVhQVFaGgoABjx46FqqrYvn27x75nzJiBqKgo1/1OnTrhuOOOc3u/fPjhhxBCYNasWW5F30866SScddZZfveDiIgoGBy+R0REYZOQkICysrIG13Ou4+9wl0B1797d7cIMkIW2u3fv3mCwJJyuvvpqPPXUU3jvvfewZcsWdOzY0Wcg4O+//wYg6+vMnTvX6zrOekUAUFFRgXnz5uHtt9/GoUOHPNYtLi72WOZtqE5KSgoKCwv96s+AAQNcNZKcdb5eeeUVfPPNN7jggguwY8cOj9fBKSsrCwDQu3dvj8e8LQu0zZs3b8ZDDz2EH3/8EWaz2e0xb8+FPzp06IA+ffq4glKbN29GWloaBgwYgJEjR2Lz5s2YPXs2Nm/eDI1G47U+VKAa+xrVHrJVd79jxoxpVNucDh8+DADo27evx2P9+vUDAOzduzfg/d50001YsmQJ5s6d63OoYlZWFjQaDY4//nivx16/fj2ysrJ8DgmdOXMmPvzwQ8yYMQP33nsvhg8fjnPOOQdXXHGFa5vdu3dDVVW8+uqrePXVV73uJ5BhbyNGjHArdH755ZdDr9dj2bJluOyyy3DOOecAkLWyHn/8cbz++uvYu3cvhBBu+wnkM33gwAHXfeffwD59+nis27dv34ACbERERIFiUIqIiMKmf//++Prrr7F371707NnT6zpVVVXYtWsXMjIyXPVT6isY7it7IRSa+rh9+/bFqaeeihdeeAF//PEHZs6c6XN6eucF6J133um6SK2r9ixdkydPxv/93//h+uuvx4gRI5CSkgKtVotPP/0UzzzzDFRV9di+oWMHIj09HVOnTnXV3fruu+/w008/Yfjw4QHvqz7+tPnnn3/G2WefjZ49e+Lxxx9Ht27dXFknkyZN8vpc+Gv06NF46aWXUFpais2bN7uKlY8ePRqzZ8+GxWLB5s2bXcWpG8tXf1sSZxu91a5yLrPZbAHvNyoqCg8//DCmTJmCJUuW4NRTT21cQ71ISUnBzz//jG+++QYbN27E119/jTvuuANz587Fp59+iqFDh7reW1OmTEFmZqbX/ThrjAVr7NixWLZsGTZv3uz6vM+aNQvPPfccLr/8ctx///1o164d9Ho9fvnlF9x7771h/0wTERGFA4NSREQUNhdddBG+/vprvPLKK3j88ce9rvP666/DZrO5FcdOTk4G4F4A2CkrK8ttRiqg/mASIDMBrFarW5aOxWLBvn373LIDah+3dpFss9mM7Oxsn4G1xpg+fTpuuOEG121fevXqBUBeZDaU0VJSUoL/+7//w1VXXYVly5a5Pfbll182ssWBURQFp556Kr777jscOXLE53rO53v37t0ew9x2797dqDasW7cODocDn332Gbp16+ZaXllZGXSWlNPo0aPx4osv4pNPPsFPP/3kmjnxzDPPhMlkwkcffYQ//vgDd955Z4P78nf2xpbOWQz8zz//9HjMuaxHjx5B7Xvy5Ml4+umn8fjjj2PFihUej3fv3h2qquLvv//GCSec4PbYX3/9BQBu7wFvtFotRo0a5cps++233zB48GA88sgj+OSTT9CzZ08oigKr1Rqy7LK6nEG78vJy17LVq1djxIgRePPNN93WDSbrrDZnNtWuXbs8Xhfnc0ZERBQurClFRERhc+2116Jnz55YtGgRNmzY4PH4L7/8gtmzZyM1NRV33323a7nzorZuAOWNN97A0aNHPfYTFxfnNYDlVFZWhhdffNFt2YsvvoiysjJMnDixweP6yixq6Lj+mDRpEubOnYslS5a4Ak/enHjiiejfvz+WLVvmdcih3W53tcWZHVE3GyI7O9vrrH6hsHHjRq/ZZCaTyTX8x9twLqezzjrLNTtY7eF1OTk5jZ6a3tfz8dhjjzUqSwoAzjjjDCiKgkceeQQ2m80VUOvfvz/atWuHefPmQQjhVz0pZ6ZgY99TgRJCYN68eSHb38CBA9GzZ0+sWrXK7fNqtVrx1FNPQaPRuH3uAqEoCh5//HGUlJRgwYIFHo8797tgwQK31/uPP/7ARx99hOHDh9c7m2PdGQgBOawtOjra9bqkpKRg3LhxeP/99/HDDz94rC+EqLdulT/Wr18PABg8eLBrmVar9XgPV1ZW4plnnmnUsSZMmABFUbBo0SK37LZffvmlyYPYREQUeZgpRUREYRMbG4uPPvoI55xzDsaPH4+LL74Yo0aNgk6nw08//YTVq1cjLi4O69evR1pammu73r17Y8yYMXjppZcghMCgQYOwc+dOfPDBB+jZs6fH0J/TTjsNr776Kv773//i+OOPh0ajwfnnn+8qbt2jRw/Mnz8ff/zxBwYPHowdO3ZgxYoV6NOnD2699VbXfsaMGYPevXvjwQcfRGFhIbp164Zvv/0WP/zwg1vNl9rH/fLLL7Fw4UJ07drVNRwsEAkJCX4FBBRFwerVqzF69GiccMIJmD59Ovr164eqqirs3bsX77//PhYsWIBp06YhPj4eZ599NtasWYPo6GicfPLJOHDgAF566SV069bN7/pDgbjjjjtQWFiICRMmYMCAAYiJicGhQ4ewbt067NmzB1OnTsWAAQN8bp+SkoK5c+dizpw5+M9//oMpU6agqqoKy5cvx3HHHYft27cHnUl04YUX4plnnsG4ceNw/fXXIyoqChs3bsRvv/3m9XUNRHJyMgYOHIidO3ciIyPDLQvnjDPOwFtvvQW9Xo/TTz+9wX317dsX8fHxePHFFxETE4PExES0a9cuJAXSndavX4+Kigq/1h02bJhbTaKvv/4aX3/9NQC4imo///zzSExMBAA88MADAOR7denSpRg3bhxOOeUU3HDDDYiPj8cbb7yBn376CQ8++GBANZfqOvvss3HmmWe6CsnXdtZZZ+Gyyy7Dm2++ieLiYpx33nnIycnBCy+8AKPRiGeffbbefV933XU4fPgwzj77bKSnp8NkMuGtt95CeXk5pk6d6lpv6dKlGD58OEaMGIGpU6fixBNPhKqq2LdvHz788ENMnTrV70Dfu+++6xaQ/OKLL/DJJ59gwIABmDJlimu9Sy65BC+99BIuv/xyjBkzBrm5uVixYgVSUlL8Oo4vffr0wc0334znn38eo0ePxsUXX4y8vDw8//zzGDhwIH799ddG7Z+IiKhezTDjHxERRZiSkhIxf/58MXDgQBEbGyuMRqPo3bu3uPPOO0V2drbXbbKzs8Ull1wi4uPjRWxsrDjnnHPEX3/9JUaOHCnS09Pd1s3NzRUXXXSRSEpK8pjCPD09XYwcOVLs2LFDnHHGGSImJkYkJiaKKVOmeJ2Wfvfu3WLs2LEiOjpatGnTRlx66aXi8OHDrv3UtmfPHnHWWWeJ+Ph411Tu9cnKyhIAxM0339zgc9avXz+PfgohxP79+8UNN9wg0tPThV6vF8nJyeKkk04S9913nzh48KBrvfz8fHHNNdeIDh06CIPBIPr37y+WL1/umip+y5YtrnXnzp3rc9p3b/325vPPPxczZswQJ5xwgkhJSRFarVYkJyeLUaNGiVdffVU4HA6/9vv888+LXr16iaioKNGzZ0/x3HPPiWeffVYAED/++GPQbf7ggw/ESSedJGJiYkRKSoq4/PLLxYEDB7yu62+fnWbNmiUAiOnTp7stX758uQAghg0b5rHNli1bBACxcuVKt+WffPKJOPHEE4XBYBAAXO3wtb4QQmRmZjb43nNKT093vVcb+ql7LOdz7uunrm+//db1+TAYDGLQoEHi1Vdf9audtY/3888/ezy2fft212f9ySefdHvMZrOJxx9/XPTp00dERUWJpKQkccEFF4jffvvNYz8ARGZmpuv+e++9J84//3zRqVMnERUVJdq2bStGjBgh3n33XY9t8/PzxV133SV69eolDAaDaNOmjejfv7+49dZbxZ9//tlg/5yvW+2fqKgocdxxx4l77rlHlJaWuq1fWVkp7rrrLtG1a1dhMBhEz549xYIFC8SXX37p8Xp5+5w7efsb6nA4xCOPPCK6du0qoqKiRL9+/cSaNWvq/ZwRERGFgiIEKx0SEVHrlZGRgYyMDGzdurW5m0JBuuWWW/D8888jOzvbLaOOiIiIiI5trClFRERELULtWlJO2dnZeP3119G/f38GpIiIiIhaGdaUIiIiohZh69atuPvuu3HRRRehc+fO2L9/P15++WVUVFT4nL2RiIiIiI5dDEoRERFRi9CzZ0/06NEDL7/8MgoLC2E0GjFkyBDMnj0bY8aMae7mEREREVGIsaYUERERERERERE1OdaUIiIiIiIiIiKiJsegFBERERERERERNTkGpYiIiIiIiIiIqMkxKEVERERERERERE2OQSkiIiIiIiIiImpyDEoREREREREREVGTY1CKiIiIiIiIiIiaHINSRERERERERETU5BiUIiIiIiIiIiKiJsegFBERERERERERNTkGpYiIiIiIiIiIqMkxKEVERERERERERE2OQSkiIiIiIiIiImpyDEoREREREREREVGTY1CKiIiIiIiIiIiaHINSRERERERERETU5BiUIiIiIiIiIiKiJsegFBERERERERERNTkGpYiIiIiIiIiIqMkxKEVERERERERERE2OQSkiIiIiIiIiImpyDEoRkVdPPPEE+vTpA1VVw3aMZcuWoWvXrrBYLGE7BjW//fv3Q1EUrFq1yrVs3rx5UBSlydqgKArmzZvXZMcjIqLI8PbbbyM5ORkVFRVhO8Zff/0FnU6HP/74I2zHoJYhIyMD06ZNc93funUrFEXB1q1bm+T4o0aNwqhRo5rkWERODEoRBWnVqlVQFMX1YzQacdxxx2HmzJnIzc0N6bEee+wxrF+/PqBtXn31VRx//PEwGo3o1asXnnvuOb+3LSsrw8KFC3HvvfdCownuz8T333+P4cOHIyYmBmlpabj11ls9TtimTZsGq9WKl156KahjhMqoUaOgKAp69erl9fGNGze6Xud33323iVsXnE8//RSKoqBjx45hDSyGUjDvcyIiatla8vnS0qVLcemll6Jr165QFMUtGOAPh8OBuXPn4pZbbkFcXFxgjQXw008/YcaMGRg8eDD0er3PL2v69u2L8ePH48EHHwz4GKHk/EJJo9Hg0KFDHo+XlZUhOjoaiqJg5syZzdDCwJWUlMBoNEJRFPz999/N3Ry/rFu3DosXL27uZhCFDINSRI300EMPYfXq1Xj++ecxbNgwLF26FEOHDkVVVVXIjhHoSdZLL72Ea6+9Fv369cNzzz2HoUOH4tZbb8XChQv92n7FihWw2+244oorgmrvzp07ceaZZ6KqqgqLFi3Ctddei+XLl+PSSy91W89oNCIzMxOLFi2CECKoY4WK0WjE3r178dNPP3k8tnbtWhiNxmZoVfDWrl2LjIwMZGdnY/Pmzc3dHA8PPPAATCaT2zIGpYiIWq+WeL60cOFCbN68Gf369YNOpwv4eB9//DF2796N66+/PuBtAfkF0iuvvAJFUdC9e/d6173xxhvxwQcf4N9//w3qWKFkMBjwxhtveCx///33m6E1jfPOO+9AURSkpaVh7dq1zd0cDyNGjIDJZMKIESNcyxiUotaGQSmiRjr33HMxZcoUXHvttVi1ahVuv/12ZGVl4cMPP2zUfoUQHhft/jCZTLj//vsxfvx4vPvuu7juuuvw+uuv48orr8TDDz+M4uLiBvexcuVKTJgwIehAzJw5c5CUlIStW7fixhtvxCOPPILnn38eGzZswBdffOG27mWXXYYDBw5gy5YtQR0rVHr06IHevXt7nGSZzWZ88MEHGD9+fDO1LHCVlZX48MMPMWvWLJx44okt8iRLp9Mdc4E+IiIKXks7XwKAr776CgUFBfjss89gMBgC3n7lypX4z3/+g06dOgV1/JtuugmlpaXYvn07zjrrrHrXHTNmDJKSkvDaa68FdaxQGjdunNeg1Lp1646p8yUAWLNmDcaNG4crrrgC69ata+7meNBoNDAajUGPXCA6FvDdTRRio0ePBgBkZWUBAOx2Ox5++GH06NEDBoMBGRkZmDNnjkcdpYyMDJx33nn4/PPPMWTIEERHR+Oll16CoiiorKzEa6+95kp9ry+9fMuWLSgsLMSMGTPclt98882orKzEJ598Um/7s7Ky8Ntvv2HMmDEej1VWVuLOO+9Ely5dYDAY0Lt3bzz11FNuWU5lZWXYuHEjpkyZgoSEBNfyqVOnIi4uDm+//bbbPgcPHozk5ORGn5SGwhVXXIG33nrLbbjbxx9/jKqqKlx22WVetzly5AimT5+O9u3bw2AwoF+/flixYoXbOlarFQ8++CAGDx6MNm3aIDY2FqeffrpHIM5Ze+mpp57C8uXLXe+Zk08+GT///LPf/fjggw9gMplw6aWXYtKkSXj//fdhNps91nOm17/zzjvo27cvoqOjMXToUPz+++8AZMZdz549YTQaMWrUKOzfv99t+1GjRqF///7YsWMHhg0bhujoaHTr1g3Lli1rsI11a0rV9z6fNm0aMjIyGtwHAFgsFtxxxx1ITU1FfHw8JkyYgMOHD3ttgz+vHRERhUdzny8BQHp6etD1Dc1mMzZs2OD1fMnfvrRv3x7R0dF+HU+v12PUqFEt4nxp8uTJ2LlzJ3bt2uValpOTg82bN2Py5Mlet7FYLJg7dy569uwJg8GALl264J577vF4TlauXInRo0ejXbt2MBgM6Nu3L5YuXeqxP+f74Ntvv8Upp5wCo9GI7t274/XXX/e7HwcPHsQ333yDSZMmYdKkScjKysL333/vsZ7zfOe3337DyJEjERMTg549e7pKOnz11Vc49dRTER0djd69e+PLL7902955vrJr1y5cdtllSEhIQEpKCm677Tav52e11a0pNWrUKHzyySc4cOCA633uPEdyDpWte77mqy6V81wzOjoap5xyCr755huvbfD3tSMKFoNSRCHmTKtOSUkBAFx77bV48MEHcdJJJ+GZZ57ByJEjsWDBAkyaNMlj2927d+OKK67AWWedhSVLlmDQoEFYvXo1DAYDTj/9dKxevRqrV6/GDTfc4PP4v/76KwBgyJAhbssHDx4MjUbjetwX5z/jk046yW25EAITJkzAM888g3POOQeLFi1C7969cffdd2PWrFmu9X7//XfY7XaP40dFRWHQoEFej3/SSSfhu+++q7ddTWHy5MnIzs52+6e9bt06nHnmmWjXrp3H+rm5uTjttNPw5ZdfYubMmViyZAl69uyJa665xi2tuqysDK+88gpGjRqFhQsXYt68ecjPz8fYsWOxc+dOj/2uW7cOTz75JG644QY88sgj2L9/Py666CLYbDa/+rF27VqcccYZSEtLw6RJk1BeXo6PP/7Y67rffPMN7rzzTmRmZmLevHn4+++/cd555+GFF17As88+ixkzZuDuu+/Gtm3bMH36dI/ti4uLMW7cOAwePBhPPPEEOnfujJtuuing4E6g73Nfrr32WixevBhnn302Hn/8cej1eq/f2vr72hERUXg09/lSY+3YsQNWq9XjfCnQvgRi8ODB+OOPP1BWVtao/TTWiBEj0LlzZ7fMorfeegtxcXFe/+eqqooJEybgqaeewvnnn4/nnnsOEydOxDPPPIPLL7/cbd2lS5ciPT0dc+bMwdNPP40uXbpgxowZeOGFFzz2u3fvXlxyySU466yz8PTTTyMpKQnTpk3Dn3/+6Vc/3njjDcTGxuK8887DKaecgh49evjMLi8uLsZ5552HU089FU888QQMBgMmTZqEt956C5MmTcK4cePw+OOPo7KyEpdccgnKy8s99nHZZZfBbDZjwYIFGDduHJ599tmAh37ef//9GDRoENq2bet6nwdz3vLqq6/ihhtuQFpaGp544gn85z//wYQJEzxqhQXy2hEFTRBRUFauXCkAiC+//FLk5+eLQ4cOiTfffFOkpKSI6OhocfjwYbFz504BQFx77bVu2951110CgNi8ebNrWXp6ugAgNmzY4HGs2NhYkZmZ6Ve7br75ZqHVar0+lpqaKiZNmlTv9g888IAAIMrLy92Wr1+/XgAQjzzyiNvySy65RCiKIvbu3SuEEOKdd94RAMTXX3/tse9LL71UpKWleSy//vrrRXR0dL3tCqeRI0eKfv36CSGEGDJkiLjmmmuEEEIUFxeLqKgo8dprr4ktW7YIAOKdd95xbXfNNdeIDh06iIKCArf9TZo0SbRp00ZUVVUJIYSw2+3CYrG4rVNcXCzat28vpk+f7lqWlZUlAIiUlBRRVFTkWv7hhx8KAOLjjz9usC+5ublCp9OJl19+2bVs2LBh4oILLvBYF4AwGAwiKyvLteyll14SAERaWpooKytzLZ89e7YA4LbuyJEjBQDx9NNPu5ZZLBYxaNAg0a5dO2G1Wt36tXLlStd6c+fOFXX/Bfl6n2dmZor09HSP5XX34fy8zZgxw229yZMnCwBi7ty5rmX+vnZERNQ4LfV8qbHbvvLKKwKA+P33392WB9KX2m6++WaP/4t1rVu3TgAQP/74o9/tDCXn/938/Hxx1113iZ49e7oeO/nkk8XVV18thJDnFzfffLPrsdWrVwuNRiO++eYbt/0tW7ZMABDfffeda5m3/79jx44V3bt3d1vmfB/UPt/My8sTBoNB3HnnnX71Z8CAAeLKK6903Z8zZ45o27atsNlsbus5z3fWrVvnWrZr1y4BQGg0GvHDDz+4ln/++ec+z3kmTJjgtt8ZM2YIAOJ///ufW79qvw+d559btmxxLRs/frzX8yLnZ632uZq3fVitVtGuXTsxaNAgt/PT5cuXCwBi5MiRrmWBvHZEwWKmFFEjjRkzBqmpqejSpQsmTZqEuLg4fPDBB+jUqRM+/fRTAHDLJAKAO++8EwA8htJ169YNY8eObVR7TCYToqKivD5mNBobrLtQWFgInU7nMYvMp59+Cq1Wi1tvvdVt+Z133gkhBD777DPX8QF4rc3g6/hJSUkwmUwhLXYarMmTJ+P999+H1WrFu+++C61WiwsvvNBjPSEE3nvvPZx//vkQQqCgoMD1M3bsWJSWluKXX34BAGi1WtdroqoqioqKXNlkznVqu/zyy5GUlOS6f/rppwMA9u3b12D733zzTWg0Glx88cWuZVdccQU+++wzr/XEzjzzTLehcaeeeioA4OKLL0Z8fLzH8rpt0Ol0bt9ER0VF4YYbbkBeXh527NjRYHtDyfl5q/sevf32293uB/LaERFRaLS086XGKiwsBAC3/9cAAu5LIJzHKigoCHofoTJ58mTs3bsXP//8s+u3r6F777zzDo4//nj06dPH7X+ucwhn7XIGtYczlpaWoqCgACNHjsS+fftQWlrqtt++ffu6zpEAIDU1Fb179/brfOm3337D77//7japzxVXXIGCggJ8/vnnHuvHxcW5Zbr17t0biYmJOP74413nSIDv8yVAltKo7ZZbbgFQ855pKtu3b0deXh5uvPFGt2uGadOmoU2bNm7rBvLaEQUr8GkmiMjNCy+8gOOOOw46nQ7t27dH7969XcUIDxw4AI1Gg549e7ptk5aWhsTERBw4cMBtebdu3RrdnujoaFitVq+Pmc1mv2sX1HXgwAF07NjRLVABAMcff7zrcefxAXgdZ+7r+KK6JlV9dR0qKipQUVERVNvj4uL8nqp50qRJuOuuu/DZZ59h7dq1OO+88zz6DAD5+fkoKSnB8uXLsXz5cq/7ysvLc91+7bXX8PTTT2PXrl1uw/C8veZdu3Z1u+88CfWnSP2aNWtwyimnoLCw0HXCfOKJJ8JqteKdd97xSBOveyznyUiXLl28Lq/bho4dOyI2NtZt2XHHHQdA1sg67bTTGmxzqDg/bz169HBb3rt3b7f7gb52RETUeC3tfClURJ3ZgwPtSzDHqu98yWq1oqioKKj9R0VFITk52a91TzzxRPTp0wfr1q1DYmIi0tLSXIGKuv755x/8/fffSE1N9fp47f+53333HebOnYtt27Z5fFlZWlrqFjSpew4DyHMmf8+XYmNj0b17d+zduxeA/PI0IyMDa9eu9RiG2LlzZ4/nvU2bNn6fLwFAr1693O736NEDGo3GowZUuDnfg3Xbo9frPWaBDOS1IwoWg1JEjXTKKad41E+qy98imsEGjGrr0KEDHA4H8vLy3OogWa1WFBYWomPHjvVun5KSArvdjvLycq/BGH+ODwDZ2dkej2VnZ3s9fnFxMWJiYurt/1NPPYX58+cH3B4AmDt3LubNm+fXuh06dMCoUaPw9NNP47vvvsN7773ndT1nMfQpU6YgMzPT6zonnHACAHniM23aNEycOBF333032rVrB61WiwULFnid2lmr1XrdX90T37r++ecfV0H0uicagKw1VTco5etYwbYhHHx9fhwOR1D7C+S1IyKi0Ghp50uN5ayFVVxcjM6dO3s8HmwB9fo4Ax1t27b1uc7333+PM844I6j9jxw50qMYdn0mT56MpUuXIj4+HpdffrnPGeJUVcWAAQOwaNEir487Azv//vsvzjzzTPTp0weLFi1Cly5dEBUVhU8//RTPPPOM20Q0QPDnKkIIvPHGG6isrETfvn09Hs/Ly0NFRYXbF5rhOF8K9Xsk1OdLgP+vHVFjMChFFEbp6elQVRX//POPK6MIkEWWS0pKkJ6e7td+AvmnNWjQIAAyNXfcuHGu5du3b4eqqq7HfenTpw8AORtO7Qvz9PR0fPnllx7BKufMK86+9O/fHzqdDtu3b3ebsc5qtWLnzp1eZ7HLyspye368mTp1KoYPH17vOr7U/danIZMnT8a1116LxMREt+ewNufsbg6Hw+vMO7W9++676N69O95//32313Lu3LkBtasha9euhV6vx+rVqz1Okr799ls8++yzOHjwoNdvFoN19OhRVFZWumVL7dmzBwC8zphXH1/v86SkJJSUlHgsr/tts/Pz9u+//7plR+3evdttvUBeOyIiCr/mOF9qrNrnSwMGDHAtD1VfvMnKyoJGo3FlJHszcOBAbNy4Maj91x2K2JDJkyfjwQcfRHZ2NlavXu1zvR49euB///sfzjzzzHpfo48//hgWiwUfffSR27lKqIeIffXVVzh8+DAeeughj/PP4uJiXH/99Vi/fj2mTJkS0uP+888/bll+e/fuhaqqIT1fAuBxzuTtfMnZntrZbTabDVlZWRg4cKBrmb+vHVFjMChFFEbjxo3DnDlzsHjxYrz00kuu5c5vG7zNUOJNbGys14tyb0aPHo3k5GQsXbrULaCydOlSxMTENHjMoUOHApBBrNpBqXHjxmH58uV4/vnnMXv2bNfyZ555Boqi4NxzzwUg05bHjBmDNWvW4L///a8rgLV69WpUVFTg0ksv9TjmL7/8giuvvLLednXv3j3g4FKwLrnkEhw6dAi9e/f2WZ9Lq9Xi4osvxrp16/DHH3+gf//+bo/n5+e7Up2dASIhhOsf+o8//oht27aFNEC0du1anH766V5nQxk6dCieffZZvPHGG7j33ntDdky73Y6XXnrJVTvDarXipZdeQmpqKgYPHhzQvny9z3v06IHS0lL89ttvrvdkdnY2PvjgA7f1zj33XMyZMwfPPvus2yw9dWelCeS1IyKi8GuO86XGGjx4MKKiorB9+3ZMmDDBtTxUffFmx44d6Nevn0fdn9qSkpKa7AuXHj16YPHixTCZTDjllFN8rnfZZZfh008/xcsvv+yRsW0ymaCqKmJjY93Ol5xKS0uxcuXKkLbbOXTv7rvvhtFo9Hj8ySefxNq1a0MelHrhhRdw9tlnu+4/99xzAOA6h/ZXbGysR30tAK7yBV9//bXrS2iHw+FRqmDIkCFITU3FsmXLcPXVV7vOdVetWuXx+fH3tSNqDAaliMJo4MCByMzMxPLly1FSUoKRI0fip59+wmuvvYaJEyf6nV49ePBgfPnll1i0aBE6duyIbt26uRVVrC06OhoPP/wwbr75Zlx66aUYO3YsvvnmG6xZswaPPvpog7UCunfvjv79++PLL7/E9OnTXcvPP/98nHHGGbj//vuxf/9+DBw4EF988QU+/PBD3H777W51fB599FEMGzYMI0eOxPXXX4/Dhw/j6aefxtlnn41zzjnH7Xg7duxAUVERLrjgAr+ei6bQpk0bv4b7Pf7449iyZQtOPfVUXHfddejbty+Kiorwyy+/4Msvv3TVdDjvvPPw/vvv48ILL8T48eORlZWFZcuWoW/fvkHXyarrxx9/xN69ezFz5kyvj3fq1AknnXQS1q5dG9KgVMeOHbFw4ULs378fxx13HN566y3s3LkTy5cvh16vD2hfvt7nkyZNwr333osLL7wQt956K6qqqrB06VIcd9xxbgXJBw0ahCuuuAIvvvgiSktLMWzYMGzatMlVK6I2f187IiIKv+Y4XwJkZs7//vc/ADJL5LfffsMjjzwCAJgwYUK9Q7mNRiPOPvtsfPnll3jooYeC6suBAwdcGUbbt28HANfx09PTcdVVV7nWtdls+OqrrzBjxgy/noumcttttzW4zlVXXYW3334bN954I7Zs2YL//Oc/cDgc2LVrF95++218/vnnGDJkCM4++2xERUXh/PPPxw033ICKigq8/PLLaNeundeyEMGwWCx47733cNZZZ3kNSAHytV+yZIlHKYzGysrKwoQJE3DOOedg27ZtWLNmDSZPnuyWmeSPwYMH46233sKsWbNw8sknIy4uDueffz769euH0047DbNnz0ZRURGSk5Px5ptvwm63u22v1+vxyCOP4IYbbsDo0aNx+eWXIysrCytXrvT4Atjf146oUZphxj+iVsE57erPP/9c73o2m03Mnz9fdOvWTej1etGlSxcxe/ZsYTab3dZLT08X48eP97qPXbt2iREjRojo6GgBwK8pi5cvXy569+4toqKiRI8ePcQzzzwjVFX1q2+LFi0ScXFxHtPylpeXizvuuEN07NhR6PV60atXL/Hkk0963e8333wjhg0bJoxGo0hNTRU333yzKCsr81jv3nvvFV27dvW7beEwcuRI0a9fv3rXcU6n+84777gtz83NFTfffLPo0qWL0Ov1Ii0tTZx55pli+fLlrnVUVRWPPfaYSE9PFwaDQZx44oni//7v/0RmZqbblL5ZWVkCgHjyySc9jg9AzJ0712f7brnlFgFA/Pvvvz7XmTdvntvUw6gzZXN9bfDWf+fztn37djF06FBhNBpFenq6eP75573u09v0yLXV9z7/4osvRP/+/UVUVJTo3bu3WLNmjdd9mEwmceutt4qUlBQRGxsrzj//fHHo0CGvz58/rx0RETVOSz5fyszMFAC8/tT+n+XL+++/LxRFEQcPHgyqL87/rd5+Ro4c6bbuZ599JgCIf/75p8F2hYvz/25+fn6963k7v7BarWLhwoWiX79+wmAwiKSkJDF48GAxf/58UVpa6lrvo48+EieccIIwGo0iIyNDLFy4UKxYsUIAEFlZWa71fL0PRo4c6fHc1fbee+8JAOLVV1/1uc7WrVsFALFkyRLXPr2dJ/pqQ93+O5+3v/76S1xyySUiPj5eJCUliZkzZwqTyeSxz9rvW+d7ZMuWLa5lFRUVYvLkySIxMVEAcDuX/Pfff8WYMWOEwWAQ7du3F3PmzBEbN2702IcQQrz44ouiW7duwmAwiCFDhoivv/7a6/Pn72tHFCxFiGaoWktELVppaSm6d++OJ554Atdcc03YjmOxWJCRkYH77rvPr2/aqGUZNWoUCgoK8McffzR3U4iIiJqcw+FA3759cdlll+Hhhx8O67EmTpwIRVE8hq5Tyzdv3jzMnz8f+fn59RapJ4pU3qdIIKKI1qZNG9xzzz148sknPWY6CaWVK1dCr9fjxhtvDNsxiIiIiMJBq9XioYcewgsvvBCy4fje/P333/i///u/sAe+iIiaAzOliIgoKMyUIiIiIqofM6WI6sdMKSIiIiIiIiIianLMlCIiIiIiIiIioibHTCkiIiIiIiIiImpyuuZuQEulqiqOHj2K+Ph4KIrS3M0hIiKiFkIIgfLycnTs2BEaTcv4fu/rr7/Gk08+iR07diA7OxsffPABJk6c6HP97Oxs3Hnnndi+fTv27t2LW2+9FYsXLw74uDxfIiIiIm/8PV9iUMqHo0ePokuXLs3dDCIiImqhDh06hM6dOzd3MwAAlZWVGDhwIKZPn46LLrqowfUtFgtSU1PxwAMP4Jlnngn6uDxfIiIiovo0dL7EoJQP8fHxAOQTmJCQENJ9q6qK/Px8pKamtphvWJtSJPc/kvsOsP+R3P9I7jsQ2f1vjX0vKytDly5dXOcKLcG5556Lc8891+/1MzIysGTJEgDAihUrgj4uz5fCh/2P3P5Hct+ByO5/JPcdYP9bW//9PV9iUMoHZwp6QkJCWE6yzGYzEhISWsWbLVCR3P9I7jvA/kdy/yO570Bk97819z0Sh6tZLBZYLBbX/fLycgBAXFwc4uLiQnosVVVhMpkQFxfX6t47/mD/I7f/kdx3ILL7H8l9B9j/1tZ/VVUBNHy+xKAUEREREfllwYIFmD9/vsfy/Px8mM3mkB5LVVWUlpZCCNEqTs4Dxf5Hbv8jue9AZPc/kvsOsP+trf/OL64awqAUEREREfll9uzZmDVrluu+MzU/NTU1LJnliqK0mmEMgWL/I7f/kdx3ILL7H8l9B9j/1tZ/o9Ho13oMShERERGRXwwGAwwGg8dyjUYTlhNoRVHCtu9jAfsfuf2P5L4Dkd3/SO47wP63pv772wcGpYiIiIgobFRVhdVqDWo7m80Gs9ncKk7OA+Xsv8PhiMj+ExFRZGBQioiIiOgYV1FRgb1797ruZ2VlYefOnUhOTkbXrl0xe/ZsHDlyBK+//rprnZ07d7q2zc/Px86dOxEVFYW+ffuGrF1WqxVZWVmuYqeBEEJAVVWUl5dHZFH52v1PSkpCWlpaRD4PRETUujEoRURERHSM2759O8444wzXfWfdp8zMTKxatQrZ2dk4ePCg2zYnnnii6/aOHTuwbt06pKenY//+/SFpkxAC2dnZ0Gq16NKlS8DZPkII2O126HS6iAzGCCFgs9lgtVqRn58PAOjQoUMzt4qIiCi0GJQiIiIiOsaNGjUKQgifj69atcpjWX3rh4LdbkdVVRU6duyImJiYgLdnUEpAp9MhPj4eiqIgLy8P7dq1g1arbe6mERERhQwHqBMRERFRyDkcDgBAVFRUM7fk2OcM6tlstmZuCRERUWgxKEVEREREYROJWU6hxueQiIhaKw7fIyJq5YQQsKsCdocKu0NAFQIOVYUQgCoEGhrBU3uIj/C44Xaznn2oKKmwQI2qgqJE3vchkdz/5uq7ogCpCUZoOWsZERGFmhBASSEQEwcYjM3dGqJjGoNSREStiMXmQJXFDrPNAZPVhnKzHVUWG+x2GYhyCAFVdQajBAAFCoQrsKTA/dt41/Lai51BKkVxC0jV9z2+EAKqqQyaciUiv/GP5P43R98FAINWg+geqWgTw6FjREQUQnYbcDgLOHoQMMYAXboBKe0ADeu9EQWDQSkiomOY2eZAucmKCpMNxZUWVJjtsFjtcAgBRQF0Gi30Og10GgVRej20GgUaRQ4F0TRhcEQIAZPGjOiEmIgLygCR3f/m6LtDVVFYbmmSY1HrtHXrVkybNi1kMxE2ZNq0acjIyMC8efOa5HhEFKSqSuDAP0B+DpCQCFjMwO7fgNQOQJceQExsc7eQ6JjDoBQR0THE7lBRYbahzGRDUbkZpSYrLFYHVMjMEEOUFnEcskREREQUWhYzsOd3oLwUSEkFtDrAGA3YbEDuUUBVgV79AR0vsYkCwU8MEVELZnOoqLLYZSCqyoqiCjOqLA44VBVROi2io3RISIiCRhNZ2TdERERETaqkECgvAdq2B2rXSNTrgeTUmuypThnN1ECiYxO/SiciCjFRXUjcanfAZLWj0mJDuUkGlSotNpRWWVFSaXH9FFdYUFRhRl6pCTnFVThUUIE9R0uw/d88/LA7Fz/vzcPvB4pwqLASQgDJcQZ0SIpFSrwRMQYdA1JERGG0detWKIqCkpIS17KdO3dCUZQGh/dVVlYiISEB7777rtvy9evXIzY2FuXl5WFoMRGFnOoA8o4AeoN7QMpJpwNi44Aj+2Xgioj8xkwpIgoZIWTBbFkHu2ZWN2cxbIfDAZtDBmsUpbq4thBQBWSpbYGa27U3rHsc1MwIJ6rXF8L9Nmqv420HtfZT3VxX8W+1+nGHqsKhVi+rXqfuTHSiepmc0U7A5pABKVWV+1FV1dUnVRWwV5VCW1xrem/nc+a8rSiAENAoCqJ0sh5UYqwBei2/QyCiVuDhW4CyYv/WFaLmRLWx9cgSkoD/Pte4fQQhNjYWkyZNwsqVK3HJJZe4ljvvx8fHN3mbiCgIZSVAWSnQJtn3OrHxQGEucHAf0HsAoNM3WfOIjmUMShGFiCt4IYQrIOEMtDiDF6qqwmSxo8xkhUbRQK0OcMhgCGoFcWoCK061gyd1gymudeqEX0TtdWsFWpyb1RxTuPagVi9ztkkVAkKVe1aFcx3h2kQFoFb3vW6wqPZvZx9slSXYVwI5VZtzH8L5PLn3sa66z0ntgI7zuAoUQBGAkDPDeVzHCOE+a5wAFEXOQuc+G50CpboguHMX3q6JlOr1AECjyNua6iLieq0CjU5TvVx22CL0MMYbahV8rt5/9XZERK1aWTFQXODXqq3lL+K1116LYcOGITs7Gx06dEBeXh4+/fRTfPnll83dNCLyV0GOPIdsqF5UYlugKA/IPgR06d40bSM6xjEoRa2eEAI2h8xccdQKtNSsALfgkN0hYHeosKsCdocDNrsKq7068wU1QRxnIEUF4HCo1cEoQHVl/Ai3gI8zcOKoKoW2RAY9qhtYvV9nm2RQpGbL6mUKamXSVAdSqqMudWM1NSfyAkIogAIZaBE1ARDXOnUCIUqtRbUDLjXb1SyoCdYobts6t1NcC11dharRQK/VuIacKdVt0NTev1L/xYji2jFcxzkWZjQTQsCmKNBqNMdEe4mIQi4hye9Va2emNvpvZgDHDbVTTjkF/fr1w2uvvYb77rsPa9asQXp6OkaMGNFsbSKiAFRVAEX5QJwfmY1arcyYyj4IJLUF4hLC3z6iYxyDUnTMUIWA1eaAxa7CZlddQ63csoEgA0Q2hwqzzQGzzQGLzQFH9ZAqIWqCRrUDOc4hWDUZN+7ZL/KneuVaARvUCohoFECrUaBTNK5smbqBHCEEzA49omNlpoxSKwgTCVkyQgiYLBpEG3QMyhARRaJAhtAJAbvdDp1O1/jheyHmcDgCWv/aa6/FCy+8gPvuuw8rV67E1Vdfzf+DRMeK4gLAYvI/uB0TJzOrjuwHevUDNNqwNo/oWMegFDUJVQhYq4NJVpsNJZUWoLQKUDQ1AaHatYEAeTKqqjBbHa4AkytrqTogpTizhITzfLU6G8gZINJooNUo0GoVROl01cEjxePc1i27J4wniUII2DUKdFpmyhARER1LcnNzkZiYCADYt29fQNtOmTIF99xzD5599ln89ddfyMzMDEMLiSjk7DYgLxswxgS2XZsUORtfciqQ2iE8bSNqJRiUokYTQsBic6DSYkeVxQ6HWpODZLWrqLTYUGWxyWFxqgxMOarKoC1TXEO3UF3DB0K4D80CoNEorgCTQa9FnFEPHYs+ExERURO67777sGDBAhQXF2P+/PkAgO3bt6Njx46Iioqqd9ukpCRcdNFFuPvuu3H22Wejc+fOTdFkImqs8lKgskwGlwKh18ufw1kyw8pgDE/7iFoBBqXIg0NVYbHJGdLsjprC3Q5nDaXqYt4WmwqLzQ6TzQGTxQ6LzQG1OqjkDEtpoECv1UCvkwGlWI0eGg1g0ZgRnRDDbCEiIiI6JpxwwgkYOnQooqKisHDhQjz77LO47bbbcPbZZzcYlAKAa665BuvWrcP06dOboLVEFBKV5fLCJpgheAmJMlsq+yCQ3qvFDUMmaikYlIpwFpsDZSYrTFYHTBYbys02mK0O2Bwq7HYVjpra2zUzklX/1gDQajTQaeXU9QnRUa7i1fWpXbiUiIiI6Fhwxx13uDKkAGDatGkBbX/kyBGkpKTgggsuCHHLiCgshABKCgE/gs5eKRqZJXXkABAdB7TvGNr2EbUSDEpFEGehcGd9pqIKCwrLzKiy2iGEgEajQZRWzowWY9BDHyNnCSMiIiKi4FRVVSE7OxuPP/44brjhBr+yqoioBbCY5Mx7xujg92GMBmxW4MAewGAAElNC1z6iVoJBqVbOanegrMqGogozCsrNrkLhDlWFVqMgxqBHu4RovzKciIiIiMjdueeei2+++cbrY3PmzIHVasWjjz6KESNGYPbs2U3cOiIKWlUlYLUA8W0at5/4NkBRPpC1G+h9gpydj4hcGJRqpRyqiiOFlThYUIEqix0CAtFRehYKJyIiImpARkYGbr/9dgDAqFGj6i098Morr8BkMnl9LDk5GcnJyZg3b169x5s4caJrZj8iaiEqyuRvJQTXTUltgYJcYN8uoFMG0CYZ4IgUIgAMSrVK5SYb9uWWIru4CjEGPdomREPLTCgiIiIiv9QOSjWkU6dOjT7exIkTG70PIgohIYDSQiDKEJr9KYqcwa+0ECgtlrfbdZDHMZuAinLAYZP3q8xAweHILIzO/jd9/zVaoHMGEJ/YNMfzgkGpVsRqdyC3xISsvHKYrHakJkQzI4qIiIiIiCgQpio5fM8YE7p9arVAcjvAZpPD+QpyUD2bFKDTy4wsIQCLHVBtkRuUYf+btv8WE5CaBsQ3zeG8YVCqFbDaHcgrNeFQQQVKq6yIMejRvk00lEj8IBMRERERETWGqUIWKE9ICv2+9XogpR0gVM+hgUIAlSYgNjpygzLsf9P2v8DSNMepB4NSxzir3YE/DxYht9QEY5QO7dvEsGg5ERERERFRsFz1pMJ4XRWKWlVErQCDUscwIQQOFVQgp8SE9onR0LJYHhERERERUfBUFSgpAgzG5m4JUURoEVGMF154ARkZGTAajTj11FPx008/1bv+O++8gz59+sBoNGLAgAH49NNP3R6vqKjAzJkz0blzZ0RHR6Nv375YtmxZOLvQLArKzdifV47EWAMDUkRERERERI1lrpI/hujmbglRRGj2SMZbb72FWbNmYe7cufjll18wcOBAjB07Fnl5eV7X//7773HFFVfgmmuuwa+//oqJEydi4sSJ+OOPP1zrzJo1Cxs2bMCaNWvw999/4/bbb8fMmTPx0UcfNVW3ws5stePfnDIACmIMTHgjIiKiY4TNWnPR1xQ/Nmtz95iIjiWV1fWk9FHN3RKiiNDs0YxFixbhuuuuw9VXXw0AWLZsGT755BOsWLEC9913n8f6S5YswTnnnIO7774bAPDwww9j48aNeP75513ZUN9//z0yMzMxatQoAMD111+Pl156CT/99BMmTJjgtR0WiwUWS02Rr7IyOY5YVVWoqhqy/jr3KYQIer8OVWBfbhmKKsxIS4yBECKk7Qs3IYTrJ9JEct8B9j+S+x/JfQciu//N0Xfn8cLxPxxAWPYZMWxWYNdOOauVHxSHQ85Y1RgxsUCfQbzAJCL/mKr/PkVioW2iZtCsQSmr1YodO3Zg9uzZrmUajQZjxozBtm3bvG6zbds2zJo1y23Z2LFjsX79etf9YcOG4aOPPsL06dPRsWNHbN26FXv27MEzzzzjsy0LFizA/PnzPZbn5+fDbDYH2LP6qaqK0tJSCCGgCXDYnRACuaUmHC2sRFy0Hpby5q+WHygBwGqqABQFkfanPpL7DrD/kdz/SO47ENn9b46+q0LAYbKhqBCwVOhDvv/y8vKQ7zNiOOwyIKXT+xckamxQymaVx3PYGxWUGjVqFKZNm4Zp06YF3xYiOjaUlzCITdSEmjUoVVBQAIfDgfbt27stb9++PXbt2uV1m5ycHK/r5+TkuO4/99xzuP7669G5c2fodDpoNBq8/PLLGDFihM+2zJ492y3YVVZWhi5duiA1NRUJCQnBdM8nVVWhKApSU1MDDkplF1ciz2JHm5S2iDWE/kS7KQghACEQHZ8EJcK+gYjkvgPsfyT3P5L7DkR2/5uj7w5VRSUsSE5JRZuY0F9YGI0sftto+iggytDweqHIlLLbGrd9A7744gvMnj0bf/zxB6xW96GCkZgdSXRMs1kBU5V/f5+IKCSaffheODz33HP44Ycf8NFHHyE9PR1ff/01br75ZnTs2BFjxozxuo3BYIDB4PnHR6PRBBw48oeiKAHvu6DMjD3ZZTDqdYgzHtvRe0VRXD+RJpL7DrD/kdz/SO47ENn9b+q+O48Vrv/h4dgnHZsKCwtx6aWXYty4cXj99ddRUFCA6667DkajEffcc09zN4+IAmWuAqwWoE1Sc7eEKGI0a1Cqbdu20Gq1yM3NdVuem5uLtLQ0r9ukpaXVu77JZMKcOXPwwQcfYPz48QCAE044ATt37sRTTz3lMyjV0lVabNh9tASqCiTHHdsBKSIiIqKW7LHHHsNjjz3mum8ymfDDDz9g5syZrmV//fUXPvzwQ+h0Orz66quIiYkBIOufTpgwAWeffXaTt5uIGslsAlQHoG2VuRtELVKzftUXFRWFwYMHY9OmTa5lqqpi06ZNGDp0qNdthg4d6rY+AGzcuNG1vs1mg81m8/gWU6vVHtOFSY8UVqLcZEVKPFNJiYiIiMLpxhtvxM6dO10/Q4YMwUMPPeS2rGPHjti7dy8GDRrkCkgBwH/+8x/Y7Xb8/fffzdgDIgpKVSUQcdUfiZpXs4eAZ82ahczMTAwZMgSnnHIKFi9ejMrKStdsfFOnTkWnTp2wYMECAMBtt92GkSNH4umnn8b48ePx5ptvYvv27Vi+fDkAICEhASNHjsTdd9+N6OhopKen46uvvsLrr7+ORYsWNVs/G6OsyoojRZVoE2OIyGEfRERERE0pOTkZycnJrvvR0dFo164devbs6baeXq+Hw+FwW+a8r21sLSwianosck7U5Jo9KHX55ZcjPz8fDz74IHJycjBo0CBs2LDBVcz84MGDbllPw4YNw7p16/DAAw9gzpw56NWrF9avX4/+/fu71nnzzTcxe/ZsXHnllSgqKkJ6ejoeffRR3HjjjU3ev8YSQuBIUSUsdhXJcc3+chERERFRtX79+uHVV19FZWUlYmNjAQDfffcdNBoNjjvuuGZuHREFxGqRNaVY5JyoSbWIKMfMmTPdxujXtnXrVo9ll156KS699FKf+0tLS8PKlStD1bxmVVJpxdHiSiSFYfYgIiIiaj2+/vprPPnkk9ixYweys7PxwQcfYOLEifVus3XrVsyaNQt//vknunTpggceeADTpk0Lf2Nt1obXARo/+56/x6mjoqICFRUVrvtvvvkmALjN9pyamoorrrgCc+fORWZmJubNm4f8/HzceuutmDZtGtq1axd8u4mo6ZlNgNUKtIlr7pYQRZQWEZQi71QhcLiwAg6HgDGKLxURERH5VllZiYEDB2L69Om46KKLGlw/KysL48ePx4033oi1a9di06ZNuPbaa9GhQweMHTs2PI3U6oCYWFm3xW5reP3GBqUAebwAixY/9dRTmD9/fr3rZGVlISMjAxs2bMAtt9yCk08+GTExMbjkkkuO2ZIRRBHNXFVd5JxDb4maEiMdLZQQAkeLKpFdUoWkWKaQEhERUf3OPfdcnHvuuX6vv2zZMnTr1g1PP/00AOD444/Ht99+i2eeeSZ8QSl9FNBnEOCwN7yuEBB2O6DTAY2pqanVBVwjZt68eZg3b55f6/bt29djEh4iOgaZKhr3t4aIgsKgVAvkUAX255fh35wyGPU6ROkZrSciIqLQ2rZtG8aMGeO2bOzYsbj99tt9bmOxWGCxWFz3y8rKAMjZk+vOcqyqKoQQrh8XnV7++EHYbIBeD9Hwqg3sqNF7aBbO5835HHp7nlsr5/snUvpbWyT3HWim/gsBlJbIv03N+PdCdX7Wj9G/WY3F/jdD/4WQP2H4vPn7GWZQqoWx2h34N6cMB/LL0SbGgBgDXyIiIiIKvZycHNfEMk7t27dHWVkZTCYToqOjPbZZsGCB12Ft+fn5MJvNbstsNhtUVYXdbofd7kdmVB1CCNdMdpE4+3Dt/tvtdqiqisLCQuj1/gX0jnWqqqK0tBRCCLdJjyJBJPcdaKb+26xAaZnMrKw0Nc0xvVCFQKnZCgFAE4F/99j/Zui/TQWKSwBH6I9XXl7u13qMeLQwhwsrsT+/HG3jjMyQIiIiohZl9uzZmDVrlut+WVkZunTpgtTUVCQkJLitazabUV5eDp1OB50u+FPOSAnC+KLX6+FwOKDRaJCSkgKj0djcTWoSqqpCURSkpqZGXGAmkvsONFP/S0sARQXaxAOa5rsGU4WAAiA1NjpigzLsfxP331QKJCUCKaGfnMPf/1cMSrUgdoeK3JIqxBr0DEgRERFRWKWlpSE3N9dtWW5uLhISErxmSQGAwWCAweBZ61Kj0XhcPGo0GiiK4voJlBDCtV2kZkrV7r+iKF6f59YsEvvsFMl9B5qh/1azHMIU4KQI4aAoCjTVP5GI/W/i/iuK/AnDZ83fz29k/pVroUoqrSg32RBnbP4/hkRERNS6DR061KNA98aNGzF06NCQHkdEaG2QUOJzSBRmgRQ5FwL450/gx61AVUVYm0UUCRj9aEHyy+T4ZW2EfiNCREREwauoqMDevXtd97OysrBz504kJyeja9eumD17No4cOYLXX38dAHDjjTfi+eefxz333IPp06dj8+bNePvtt/HJJ5+EpD3a6mnVrVarz8wr8k9VVRUADmUkCpvyMiDKj1k6bTZg03rgj+3y/s9bgRHjgAFDAIXXcETBYFCqhTDbHMgvMyPWyJMNIiIiCtz27dtxxhlnuO47az9lZmZi1apVyM7OxsGDB12Pd+vWDZ988gnuuOMOLFmyBJ07d8Yrr7yCsWPHhqQ9Op0OMTExyM/Ph16vD3gYjhACdrsdOp0uYofv2Ww2WK1W5OfnIzEx0RXoI6IQslkBSxWgbyAoVVoEfLQGyD1Ss8xsAr54Twapxk0CEpPD21aiVohBqRaipMKCKosN7RNjmrspREREdAwaNWpUvcO8Vq1a5XWbX3/9NSztURQFHTp0QFZWFg4cOBDw9s4p4Z21qSJN7f4nJSUhLS2tuZtE1DqZTYDVCiTE+l6nrBhY8zxgqpT3dXqgaw9g3y55/+gB4IOVwFW3yseIyG8MSrUAQgjklpqg02oitqAbERERtT5RUVHo1asXrFZrwNuqqorCwkKkpKREZLFnZ//T0tI4bI8onCwmQHUA9c0Suv2bmoBUYgpwwVVAagfgwF6ZKVVaBBTmAd9+AYwa3zTtJmolGJRqASotdhSVmxHHoXtERETUymg0Gr+nha5NVVXo9XoYjcaIDUrp9XoO2SMKN7Op/setVuDPHfK2TgdMngHExMn76T2BiZnAmmcBh0MGr3r2BTp3C2+biVqRyPsP38IIIVBQZobZ7kB0FGOERERERERETaaivP4hd7t+BSxmebvPoJqAlFNqGvCfs6vvCOCztwGrJRwtJWqVGJRqRpVmG/46XIx/sksQZ2CWFBERERERUZNxOICqct9FzoUAdv5Qc3/QUO/rDRkBdEyXt0uLgK8/C207iVoxBqWaSVG5Gb9mFeJQQQXaxBiQEOPHFKREREREREQUGhaznH3PV1Aq+yCQd1Te7tAFSOvsfT2NBjj3spqMq53bZPFzImoQg1LNJL/MDIvdgbTEGBj0rBVARERERETUpCwmOdTO12QCO7fV3PaVJeWU1BY4fWzN/Y0fyALqRFQvBqWaUZROG5FTHBMRERERETU7iwlQFEDxcllcVQHs/k3eNsYAvU9oeH8nDgPadZS387OBX78PXVuJWikGpYiIiIiIiCjyVFV6D0gBwJ+/yJpTADDg5PqLoTtptMCYiQCqEw++/QIoLw1FS4laLQaliIiIiIiIKLIIIQNGUT7qSe3fXXN7wMn+77djOnBC9fo2K7DlY3ksIvKKQSkiIiIiIiKKLFZLdT0pL0Epux04sl/ejk+U9aICcfq5QHSsvL3nd/faVETkhkEpIiIiIiIiiiwWc3VQyuD5WPZBGZgCgK49ZN2pQETHAGdeUHN/88fAgX+CbytRK8agFBEREREREUUWiwkQKqD1MhP6wb01t7v2DG7/fQYCp4ySt4UKfLQWKC4Ibl9ErRiDUkRERERERBRZTFW+Hzv4b83trt2DP8bwsUD34+Vtiwl4bwWwbxdrTBHVomvuBhARERERERE1qYoyQOelnpTVKofvAbKWVHxi8MfQaIDxk4B1LwKFuUBJIfD+SqB9J6BXf6AoH8g7CpQVA5Bz9rWDa+6+iMP+N0P/hQCOHwTc+lBTHtUNg1JEREREREQUOex2wFTpvcj5kSxAVeXtYIfu1WYwAhdNAz5cLQNQAJB7RP7UoSByAzIA+99s/bdZm+OoLgxKERERERERUeSwmGRGVFy852NuQ/d6hOZ4bZKBq24B9v4FbNtUE5wCZDZVm2RA0UAAcAgVWkUTkcEZ9r8Z+u+wy/dfM2JQioiIiIiIiCKHxQw4bIBO7/lY7aBUl0bUk6pL0cghez37AYf2ARWlQEp7+aOTl+VCCBRUmtAuNhpKoDP+tQLsfzP0vyBHFuVvRgxKERERERERUeSwmGVaSt0Lf3MVkFc9rC61AxATF/pjK0roMrCIWgHOvkdERERERESRo7IM0Go9lx/OqpkZj4EjoibBoBQRERERERFFBlUFKsq9FzkPRz0pIqoXg1JEREREREQUGawW+RPlbea9/dU3FKBzt6ZsFVHEYlCKiIiIiIiIIoPFBNgsnplSdhuQnyNvp6QChuimbxtRBGJQioiIiIiIiCKDxQyoAtDUqSmVnwOoDnk7rUvTt4soQjEoRURERERERJHBVOU56x4A5B6uud2+c9O1hyjCMShFREREREREkaGiFNDrPZfn1ApKpXVquvYQRTgGpYiIiIiIiKj1s1llppTe4PlY7hH5W9EAqR2btl1EEYxBKSIiIiIiImr9LBYZmKo7857NChTkyttt23vPpCKisGBQioiIiIiIiFo/iwlw2AFdnaBT3lFAqPJ2GutJETUlBqWIiIiIiIio9bOYvC93Dt0DWOScqIkxKEVEREREREStX2U5oNV5Lncrcs6gFFFTYlCKiIiIiIiIWjdVBSrKAX2U52POoJRGC7RNa9p2EUU4BqWIiIiIiIiodbOYAKsZiKoz857VAhTly9upaYDOSyYVEYUNg1JERERERETUulnMcpa9ujPr5R4BIOTttC5N3iyiSMegFBEREREREbVu5ir5W6lzCZxbu55Up6ZrDxEBCDIolZWVhddffx0PP/wwZs+ejUWLFmHLli0wm81BNeKFF15ARkYGjEYjTj31VPz000/1rv/OO++gT58+MBqNGDBgAD799FOPdf7++29MmDABbdq0QWxsLE4++WQcPHgwqPYRERERhVqoz6eAwM6pbDYbHnroIfTo0QNGoxEDBw7Ehg0bgj42EVGLVlnhGZACgBzOvEfUnAIaMLt27VosWbIE27dvR/v27dGxY0dER0ejqKgI//77L4xGI6688krce++9SE9P92ufb731FmbNmoVly5bh1FNPxeLFizF27Fjs3r0b7dq181j/+++/xxVXXIEFCxbgvPPOw7p16zBx4kT88ssv6N+/PwDg33//xfDhw3HNNddg/vz5SEhIwJ9//gmj0RhId4mIiIhCLhznU0Dg51QPPPAA1qxZg5dffhl9+vTB559/jgsvvBDff/89TjzxxFB2mYioeQkBVJQBUV6KnDszpXQ6IKV907aLiPzPlDrxxBPx7LPPYtq0aThw4ACys7OxY8cOfPvtt/jrr79QVlaGDz/8EKqqYsiQIXjnnXf82u+iRYtw3XXX4eqrr0bfvn2xbNkyxMTEYMWKFV7XX7JkCc455xzcfffdOP744/Hwww/jpJNOwvPPP+9a5/7778e4cePwxBNP4MQTT0SPHj0wYcIErydkRERERE0lXOdTQODnVKtXr8acOXMwbtw4dO/eHTfddBPGjRuHp59+OlTdJSJqGSxm+eOtyHlxgbzdtgOg1TZ924ginN+ZUo8//jjGjh3r83GDwYBRo0Zh1KhRePTRR7F///4G92m1WrFjxw7Mnj3btUyj0WDMmDHYtm2b1222bduGWbNmuS0bO3Ys1q9fDwBQVRWffPIJ7rnnHowdOxa//vorunXrhtmzZ2PixIk+22KxWGCxWFz3y8rKXPtTVbXBvgRCVVUIIQAh5O8II6r7zb5HHvY/cvsfyX0HIrv/zdF35/HC8T8cQKP2GY7zKSC4cyqLxeKRRR4dHY1vv/3W53Ga43wpHK/hsYD9j9z+R3LfgTD131QlZ9+LjZdZU055R11ZGqJdh8D+V9ltgFYHKErImqk6/39F4PkCwP43S/+r4xJoxvMlv4NS9Z1A1ZWSkoKUlJQG1ysoKIDD4UD79u5pku3bt8euXbu8bpOTk+N1/ZycHABAXl4eKioq8Pjjj+ORRx7BwoULsWHDBlx00UXYsmULRo4c6XW/CxYswPz58z2W5+fnN6q2gzeqqsJurgRUAZMj8qYcFQCspgpAURC6P+HHhkjuO8D+R3L/I7nvQGT3vzn6rgoBh8mGokLAUqFveIMAlZeXB71tOM6ngODOqcaOHYtFixZhxIgR6NGjBzZt2oT3338fDofD53Ga+nyptLQUQghoNJE3Nw/7H7n9j+S+A2Hqf0khYFMBk8VtcfThg2hTfbssoS1MlSb/9ldVAdjtgFCBmDhA72VYYBBUIVBqtkIA0IQw2HWsYP+bof82FSguARyhP56/50utLiLijMZdcMEFuOOOOwAAgwYNwvfff49ly5b5DErNnj3bLQOrrKwMXbp0QWpqKhISEkLexj1HSwFjAqJjDQ1v0Mo4s8Si45OgRNgfm0juO8D+R3L/I7nvQGT3vzn67lBVVMKC5JRUtIkJzYVCba2lRuWSJUtw3XXXoU+fPlAUBT169MDVV1/tc7gf0PTnS4qiIDU11fPC1GIGKsuB5NSQHrMlqbf/ESCS+x/JfQfC1P+qUkCvBWKj3RYrZQWu2/Gd0xFf53HPxjmA4kIg2gh06QtUlQPZhwCrFWiTDGgaN/xPFQIKgNTY6IgNyrD/Tdx/UymQlAikhL7Ukb/nSyENSo0ZMwb79u3Dvn37/Fq/bdu20Gq1yM3NdVuem5uLtLQ0r9ukpaXVu37btm2h0+nQt29ft3WOP/74etPRDQYDDAbPAJFGownLPwNFUeS3xhH4YQNk/5UI7X8k9x1g/yO5/5HcdyCy+9/UfXceK1z/w8N9kRjo+RQQ3DlVamoq1q9fD7PZjMLCQnTs2BH33Xcfunfv7vM4zXG+5HXfpkog/6g8iW7Fn6lwvo+PBZHc/0juOxDi/gsBVJYCBqPn34u8bNdNTbsO9f89cTiAojwgKRXIOA6IS5D7Tk4FDmcBRQVA2/aN/pukKAo01T+RiP1v4v5XxyXQjOdLIT3yhRdeiMzMTL/Xj4qKwuDBg7Fp0ybXMlVVsWnTJgwdOtTrNkOHDnVbHwA2btzoWj8qKgonn3wydu/e7bbOnj17AprBhoiIiKg5BHo+BQR3TuVkNBrRqVMn2O12vPfee7jggguCaneTEqq8QIzQmjtEFACrBTCbPWfeU1WgQJaAQWKKZxH0uirLgIQkoFd/GZAC5MV8YgrQvQ8QHQNUlIa+/UStXEgzpW6++eaAt5k1axYyMzMxZMgQnHLKKVi8eDEqKytx9dVXAwCmTp2KTp06YcGCBQCA2267DSNHjsTTTz+N8ePH480338T27duxfPly1z7vvvtuXH755RgxYgTOOOMMbNiwAR9//DG2bt0akn4SERERhUsw51NA4OdUP/74I44cOYJBgwbhyJEjmDdvHlRVxT333BOyvoSNKgCHXQ6l4WxZRFQfi1kGpmLi3JcXF8hi5QCQ2qH+fQgBmE1A524y46qumDigSw/gn98BozVkNaaIIkGz15S6/PLLkZ+fjwcffBA5OTkYNGgQNmzY4CrUefDgQbe0r2HDhmHdunV44IEHMGfOHPTq1Qvr169H//79XetceOGFWLZsGRYsWIBbb70VvXv3xnvvvYfhw4c3ef+IiIiImkKg51RmsxkPPPAA9u3bh7i4OIwbNw6rV69GYmJiM/UgACozpYjIT+YqmV1ZN4CdXzN0D+06NrwPYwyQ1Nb3OqntgdJCIPcI0DatVQ8tJgqloIJSZ5xxRr11ITZv3hzQ/mbOnImZM2d6fcxbdtOll16KSy+9tN59Tp8+HdOnTw+oHURERERNJdTnU0Bg51QjR47EX3/9FfAxWgTn8L16ZgokIgIga9B5m/8172jN7XYNZEpVVQBpXYHoWN/raLRAl+5ARRlQXiKH+hFRg4IKSg0aNMjtvs1mw86dO/HHH38EXAOBiIiIKBLxfKoRVLVm+B4RUX0qyrwPp6udKVXf8D2bDYACpPgx22d0rAxM7f5dbqfXB9xcokgTVFDqmWee8bp83rx5qKioaFSDiIiIiCIBz6cagZlSROQPm1VmStUtcg7UBKWM0UB8ou99VJYBbZKAhHrWqS2lHZCaJvff1vvsp0RUI6Sz702ZMgUrVqwI5S6JiIiIIgrPp/ygqjJLiplSRFQfcxVgtXrOrFdVITOoAJkl5WsotVBlxlNqRzk8zx8aLdAxXR6zil8wEDUkpEGpbdu2wWj0MhsBEREREfmF51N+cNhrAlNERL6YTfLvhbbOACF/i5xXVQHRMUBicmDHjW8DdOgKVJTz7xRRA4IavnfRRRe53RdCIDs7G9u3b8d///vfkDSMiIiIqDXj+VQjOGfe4+x7RFSfqnJA4yUPo3aR8/rqSVlM8nFDEF8UpHUGiguA0uL6Z+0jinBBBaXatGnjdl+j0aB379546KGHcPbZZ4ekYUREREStGc+nGsEZlGJNKSLyRQigrATQGzwf8zdTymEPfhY9fRTQuRuw639AVSUQU8/MfUQRLKig1MqVK0PdDiIiIqKIwvOpRlBVWeuFw2KIyBerBTCZPOtJAUBedVBKowGS23nf3m6Tw/4aE0xKagt07Qlk7QZ0Ou9tIYpwIa0pRUREREQUdg5b4JlSNqv8IaLIYKoEbBbPQJDdBhTlydsp7WWwyBuzSdaTiokLvg2KAnTsAnTsCpQUMruTyIuQBqUyMzMxevToUO6SiIiIKKLwfMoPzlpSgQSZsg/JHyKKDOYq+beibk2pwryavyEN1ZNKSAa0fs6654tGC3TtIY9VlC+DYkTkEtTwPV86deoEjbdCckRERETkF55P+cHhkBkIgVzcWc0y84GIIkNFmeese4B7kfN29QSlVBVIaOP78UDoo4CMXrLOVXE+oGjkDH0CMpvLZpWPCQFYHYC1Uv6NizTsf9P3vwVMGBLSoNRjjz0Wyt0RERERRRyeTzVACEA4ZPZCQEEpq8ycsHoZzkNErYvqAMrLvH/Waxc5T/VR5NxmBXRRQHQjhu7VFR0L9DkBKCkCco/Imfm0WhmwSkyRwwiFACqqgLiYyA3KsP9N239FI9+bzSikQSkiIiIiorBSVUCFzICw2/3fzmqpDkz5KHxMRK2H2SSzI71dbOfVDkr5yJRy1ZOKCW27NFogOVUGoSqrM7kM0TVDBFUVyMsD2rXzHHYYCdj/iOx/0EGpw4cP46OPPsLBgwdhtbqP51+0aFGjG0ZEkUUIAah2yDzm1kkIAMIB4bABIf3yQwE0WihK5PzzImoteD4VBFE9855WKwueC9HwN8p2u5za3WqW2VIJiU3SVCJqJmaTDEInJLkvF6ImUyouwffMelYz0DZNBpHCQaMB4hPDs2+iY0xQQalNmzZhwoQJ6N69O3bt2oX+/ftj//79EELgpJNOCnUbiaiVEw47RGUBNErrDUgBMg4VrahQTIUh37cQgKqLhhLdBkokpjsTHYN4PhUkVZV/9LQ6QBWyvpSv2bOcHPbqDCsVMFU0TTuJqPlUVcrfdc+JykpkAXPA99A9Z22n+ISwNY+IagQVlJo9ezbuuusuzJ8/H/Hx8XjvvffQrl07XHnllTjnnHNC3UYiasWEEFBNJYg26NG+fQcomtYcUBFQHSo0Wg1CmSolhIDZZEJBQT4cJkCJSQzZvokofHg+FSQhZKaURlsdaHKgwVNaR3WmlCFaXpT6k11FRMeu8hJAr/dcXruelK8i5zarrPMUE8J6UkTkU1BBqb///htvvPGG3IFOB5PJhLi4ODz00EO44IILcNNNN4W0kUTUigkVGtWGlJSOMEZHN3drwkxAdTig0WoR4vF7MBrlc5eXlwchEjiUj+gYwPOpIKkOGVTS6WWhc9XR8DYOu8yoio4BzGZZX8pgDH9biajp2W1AVYX32nG1Z96rr56UMUb+EFHYBXXVEhsb66p70KFDB/z777+uxwoKCkLTMiKKDEKFogB6fVRzt+SYZ4yOll/8+3OBRkTNjudTQRJCDtvTaGuG7zXEbpd/G43R1XWlTOFvJxE1D1MVYDEDUV4Cz26ZUr5m3rMAiUkRVWiaqDkFlSl12mmn4dtvv8Xxxx+PcePG4c4778Tvv/+O999/H6eddlqo20hErVp1HSmOomi0mlpSrbs2F1FrwfOpIKmiptC5qK4T1RC7XQ7Xcw75M5uANkkNb0dExx5zlcyO9FZrzpkppdPLGfC8UQUQzaF7RE0lqKDUokWLUFEhi0TOnz8fFRUVeOutt9CrVy/OFENERETkB55PBUlUZ0ZptTL7yd/he06KRg7tIaLWqaLM+6x5FjNQWiRvp3bwngnlcMi/LRy6R9RkggpKde/e3XU7NjYWy5YtC1mDiIiIiCIBz6eCpAoZiNJUTxrhz/A9h70miTTKAJQVN67YeX62HCLUtUdw2xNReKgOoLTYez2pgpya276KnFstQFSUHOpLRE3C74GyQnA4CBFRbV9/9RWOP65nkx3v+mun49GHH2qy4xFR6PF8KgRE9XA954QO/mRKWSw1WRFRBpkxYbUE34aqcqAonzX8iFoaU5UcvmfwElRyK3Luo56U1SKzpLwFtYgoLPwOSvXr1w9vvvmmqyCnL//88w9uuukmPP74441uHBEREVFrwvOpEFDVWqXzBODwo6aU1SKH5ADyYtNqBkyVwbfBZJLbVzViH0QUeqZKOfueXu/5mFuRcx+ZUjYrEJ8YlqYRkXd+D9977rnncO+992LGjBk466yzMGTIEHTs2BFGoxHFxcX466+/8O233+LPP//EzJkzOY0xERERUR08nwoBodZMjiHgX7aSzVxTY0ajkdsFOwOfEHJbswmorADiEoLbDxGFXmUZAMX70FxXppQCtE3zsQMBxMSGqXFE5I3fQakzzzwT27dvx7fffou33noLa9euxYEDB2AymdC2bVuceOKJmDp1Kq688kokJXE2EyIK3l2vbUNJZSOGVQQpMdaApzKHBrXt1199hXPHjsGRnHwkJiYCAP73v50YdurJ+GvXP0jPyPC5bWVlJXpkdMHSl17GhRdd7Fr+8UcfYvq0qdh34DDi4+ODahcRtSw8nwoBtc4QyIaCUqoK2Gw1mVKADExVBlns3GaVP0KVtana+xgGRERNS1WBEh/1pFRHTU2ppBTv69jtMnjNIudETSrgQufDhw/H8OHDw9EWIiIAQEmlBYUVTR+Uai6xsbG45NLLsPr119yCUqtffw0TL7yIASmiVojnU42g1hqupyhyqE59HHZ5QaqtNZzHYJQBJdXhfZau+tis8uI1NkHuw26T08uHk+oA9u2SQ5ISkmV2lj4qvMckOtaYTbKeVLSXTKfiAvm5BeTMe95YLTJYxSLnRE0qqNn3iIjCKTG2eYpLNtdxAWDa1dMxetQIZGdno0OHDsjLy8PnGz7D/336ebO1iYioRRK1glIajcyCqo/DLmfoizLWLDMYgYpyoKoKiAsw8G+zAqodiE+R08tXVgBtwpzVVlIE5B6pnjFwv7zozjgOSE4N73GJjiWmSsBqBRK8fB7zateTqqfIeUIbBnyJmhiDUkTU4gQ7hO5YNuTkU3B8375Yu2Y17rr7Hrz5xlp07ZqO4aef3txNIyJqWdQ6QSln9oMv9uqgVO2MKH0UYLcCporgglJCADqdbEtVmINSqkMGpBQNkJIq7xcXAkcPAInJgWd6EbVWleWy3py3elK5R2pu+wpK2a1AXJuwNI2IfPN79j0iIgqM6ghsqvBpV0/H2tWvA5BD966amgnF24kVEVEkq6oAdv4oh7MpGsDRQKaU3VY9fK9O8EajkdlSgbJZa2b/0+uBksLA9xGI0mKgKL9mRjCNFmiTLLO0SorCe2yiY4UQ8jOh95H1nlcrKNW+k499gEXOiZoBg1JERCGUl5frup2VlRXQtpOuuBIHDx7Aiy88h11//40rp1wV6uYRER37vv8S2Pk98OHrgMXsx/C96i8I6gb5o4zyItaf2ftqM5tkQAsADNFARZlsRzioanWWlOI+xb1OJwNyuUfcM8dCyW6XAbeGMtEigdkE5BwCDu0D9v4J7P8n8PdNpLJa5PsowC/qAmYxyeF7RqPnY0IAudUz78UlALFesiPt1ZMhsMg5UZNjUIqIKIQefOB+7N69Cz/+sA2PPfoIAOCXX3bAarU2uG1SUhImXDAR98++D2eOOQudOncOd3OJiI49hdXBf4dDTv+uOuoPEPgKqhiiZVFkU1VgxzdV1hQ2NxjlxXCVl5n87HZg719AVWVg+6+tqqI6S8rLkKK4NrJ4c3lJ8Pv3RQjg8D7gr1+BP3fIgIzZFPrjHAuEAA7uBXb/Dhz4R9YmOrwPyM9teNtIpapAfg7wz5/Abz/K99HRA+E9ZlVlTaHyukqL5OcUANr5yJJikXOiZsOgFBFRCPXvPwCjR56Oyy+7BLfdfgcGDhqEu++aBbPZv2/RM6dNh9VqxdTMaeFtKBHRscpWa3ZWm1VeANeXLeSw1wy3qy0qSmZZBRI0Uh0yK0pXXZZVo5H7rvQyDLCsWE5BX1bs//7djqXWDM/zVng5KkoWfc/LloGTUMrLBo7sl1kjZhOw50/gr1/kUMLmpjpkdlpTZSqVFgEFuUBSW6BtmiwuH2WUgSlTIwKOrVlhHvDP70DeUUCjkwHgw/tlgDVcKsvlZ1Hxcnmb68fQPatFZlCFeyZNIvLAoBQRUQjNvPU2HMnJx/6DR3DV1Ex8/8PP2LvvABISEvza/ujRI0hJScF5508Ic0uJiI5RtTNPnUGp+oYG2ayy+LE3GkUGOPxls8lhPrpacwVFGYDifM82FBcA5aXydzDMVTITzFuWlFNcAlCU5z0oFqyyEpkZFGWU9XXaJAGpaTI4tW+X96ywplJRLrNvft8O7PlDtjWcVAeQfQiAcM/AiW8jn4cjB0IfEDzWWS3A0f0yGJXSTr6HYuMACODAXvm+DjVVlZ8zg696UkdrbvsKStlt9X/WiChsGJQiImoBqqqqsO/ff7HoqScx/ZrrEBXF6YiJiLyy1QpKWa0yW6i+rBmrBdD6mHA60LpSVoscllc7myI2HigtcS94bjbJrJC4BBmYCuZC3GKWWV71TU9viJbPR2GIhpJZzMCBPbKftS/QFQVISpHBr6zd4auh5YvDIYd//f2rHBYWHS2zcf7+VbbX3kBdsWAVF8jXMaHO7IqKIovN5x4Jb/bPsSj3iMyoS0h0X56YDFSUAgf/DX19qapKGSQ0+ihS7pYp5WPmPYBD94iaCYNSRERNYOKE89AuJQlp7dqiXUoS2qUkun6eXPg4nnn6KZw4sD/atW+Pu+65t7mbS0TUctm8ZUrVM3zPapYz1nkTaF0pm7V6Jr9aQS6dTg7jyzlcE9wqLZb7TUyWtWwCycZycgZ+GpqF1RgjAyOhKEhemAcUF8qhanUpGiC5rVznwD9NVwBdCDmUcN8u+Ty3bS/7nNJOBhUP7gNyjjS4m4DZ7cDRQ/KY3oZ0RRlkYezDWe7vyUhWWS4zy2ITaiYDcFI0QGJbmbUUqiBq7ePabHJIa11C1ASlomNlLba67Hb5N8LAoBRRc/DxtRERETUkPT0dN99yCwBgxMiRqDT7/qb2xaUvwWSugupQodFqUHssSVJSMpKTk3H/fx+s93jnT7gAbdokhqLpRETHLrdMKXN1TSkfmRdCyItVrY+glF5fU1fK24xcHse2eR+ulZAIlBTIGlCJKUBBtsxwUjTyYre0SNYjCkRVuff6OHUZY+T+K8tk9k6whJDDEKOMngEFJ41W1lTKPSKzwDqmB388fxXlycBPbAIQXWdmtOgY+drnHJTtivGRKRPscUsKgOR2vtdJSKper0gOcYxkQgBHD8pgqq/nQq+XAb78bPl58PU+C1RJge/PeEVpTe2v9p28B3ltFhnQYqYUUbPwOyj19ddfB3WAjIwMdO3aNahtiYhasvSMDMy85Ta/1u3YqRMAAdXhgEarhe8CJ76dP+GCgLchopYl3OdTL7zwAp588knk5ORg4MCBeO6553DKKaf4XH/x4sVYunQpDh48iLZt2+KSSy7BggULYPQ2rXpL4RaUstQflHI45NAuX5lSilJTV8qfoILV7P2iVqcHoMhgjU4vax3FVtcSNMbI7CObtf6heLWpKlBe5l67yhedTva/vJFBKXOVzDiJbuDCXKeXGSdHDsgAXExc8MdsiMkEFBySAYe6ASmnmDhZUD7nENCtd8OZZf5QVZnRo4/yHewAZFBFq5PHT2kXuiCLwyGDXbYqGSSMipJBQH/fP82htEgGm9ok1b9ebILMJCwvadz71clilvvy9f7I9aOelNUq30ct+fklasX8DkplZmYGvHNFUXD77bfj1ltvDXhbIiIiotYmnOdTb731FmbNmoVly5bh1FNPxeLFizF27Fjs3r0b7dp5ZnusW7cO9913H1asWIFhw4Zhz549mDZtGhRFwaJFiwJuZ5MQwr1+kMUsY/y+atQ47DLAUN/FZpRBzpCnqg0HFcwm3/WpEpLkMDqNVrbROZQoOlrWJqoo8z4szhurRQbA/J0JzGAEivOAjl2DD4xUlsvnMz6x4XVj42Ug5ugBoEff0ASC6rJZgfwjgNUEpLT3vZ6iyEy13KNyvYaCIv6oLJO1wOL8mKQkNkHWE6so86yjFAyHQxaazz4EFGprntvEFCDjOP/a1NSEkEE8UacgvDd6vQyiFuWHJihVWS4/l74y2mrXk2rno56UzQIk1FNriojCyu+gVFZWVjjbQURERNTqhfN8atGiRbjuuutw9dVXAwCWLVuGTz75BCtWrMB9993nsf7333+P//znP5g8eTIAmY11xRVX4Mcff/R5DIvFAovF4rpfViZrJamqClWtp65TEFRVhRDCfb+qA4rd5so1FRYzhBDyQt7b8Z2FyY0a37Ok6fQyGGO1NHxBXVUhg07e9qXTARAyQGGIrllH0QCqkNlT/l6Em6qgWswQhnio/szuZogBystlYCTYoEVJcc1wQX+OmZAkazklpfofbPOXqkI9tA+itARqh7SG2xRllEGkI/vlTG++MuP8VVIsg2I6fcPPhV4vg5BFuY0PGNntwMG9UI8cgIiJg9omQQalVIfMtvt7J9DtuPqHFNZHCDmUTRVyqGOoMrvKS4GCPNl/f9470XGyYH37TjKTsA6vn31fyorlMRXF67GV3MOuvxdqu47e26eqgCHW+9+QJhZQ31sh9r919d/ffrCmFBEREdExzmq1YseOHZg9e7ZrmUajwZgxY7Bt2zav2wwbNgxr1qzBTz/9hFNOOQX79u3Dp59+iquuusrncRYsWID58+d7LM/Pz4fZHNoZ2VRVRWlpKYQQ0Dgvnm1WpNUqsG01VaHYpgJFRYDqJVunqgIwWwGdFbD6KMxtdwBmC5CTU39NGYcdKKuQF/KVJu/raKNlYCg+sc46WuDIEUAf418goKQIqlVFqWKHqDRB408mUpUZOHJIBokC5bADOdkAdL775o1dAP/sAjp38z+ryx8lhVCPHEBplBHCZIVG8WN2PV2MfA110Y3LWFJV4PBBQNH7/1woUcChw/L1D3YImKpWz+aXBzUmDqV24f7aG+Lk+/mP/8l6TMmp/geV7Da5bVmpzAITAIxGWfQ7Nt730Dd/5R4GTBYgKgaw+fGcCUV+lg5keX2/ev3se6OqwNH637epOUegBaBGGZGni/ZcT1Xl+7iyCsjLa7jtYeZ331sp9r919b+8vNyv9fwOSv32228BN6Jv377Q+TMWnoiIiCgChOt8qqCgAA6HA+3buw9zat++PXbt2uV1m8mTJ6OgoADDhw+HEAJ2ux033ngj5syZ4/M4s2fPxqxZs1z3y8rK0KVLF6SmpiIhIbTDilRVhaIoSE1NrTk5r3Q/wY2yWdFOrwHi4wAvQxRRrAH0ChBfTwFsoQK2SqBNQv1Dv0yVcl/RMfVnVLXxUjDdoJOZUjHRQIKX2b/qMpdCNWihGKOQGhvtX1DKZgRUG5CaGvhwutISAHY5W6Cv4YneREfJ2fhgB9r5qNcTqPJS4GAB1LgYKJoA+g8ADjNgNwFtewafBVRaBDgsQFIAz0WMASjIBfRa7+9DfxTmAhVFQNtUqHo9lEqTZ9/jYmRwqShbvqe69qg/CKaqsnh9zmEZkNJq5ftc0QDmSqDoKGCKBXr2C37YY1UlcKACSGrTcD2y2hwWwFoFpKR41O3y+tn3prwUEFagTaLMWKurshyaKvk3Q0nrhHZxXoJvFhMQGw106NgiCp373fdWiv1vXf33tz6l3/91Bg0aBEVRZIq0HzQaDfbs2YPu3bv7ewgiIher3QGHw7+/N6Gg1SqI0jUy3Z+IqAEt6Xxq69ateOyxx/Diiy/i1FNPxd69e3Hbbbfh4Ycfxn//+1+v2xgMBhgMngEZjUYTlhNoRVHc9221uD9uNcuhOarDexBCVWWApr6ghlI9+YTDVn8gw26XGUV6feBBH32UbIvVBGj8uPgvLwf0Btn/6p8GxcTKmcasZlmIPBBVZTI4F2i2k1YnM21yDskhZY2d/c5qkfWUbFYgORVKdaaQ30GphEQ5fLIxMxGWFQEQgT0XilYGKguygXZpgQ8ftNvkc6jXAwYDIITv1z42HtAbZM0pqxlI7+U5bFAIGcA9ekAWH9fqgbbt3d/fzs9xUT6w72+gV//gMsyK82U7EhID+1zEO2txlXod/unx2ffGVCE/+1E+AnN5NUXOlXadoHhrn90uh9sao0M3nLGR/Op7K8b+t57++9uHgNKYfvzxR6SmNpwSLIRA//79A9k1EZGL1e7A7weKUOVrqEUYxETpMCA9mYEpIgq7cJxPtW3bFlqtFrm5uW7Lc3NzkZbmfVa5//73v7jqqqtw7bXXAgAGDBiAyspKXH/99bj//vtbxgmxqQKoigXiqrOPLHWG3ljM8kLS7uP/hcMGv2c7tTUwRMxulRf7wdYrUuARVPPeDitgqZIX2oGUFYkyymyYirLAglKiug6WvoF6Wr6EavY7Z4Hv4gIZQAmGPkoG1wpyggtK2ayyNlKgQT1ABoZKi2VGXGJKYNsW5Mg6Vil+Dr2MipKz/RXny+BTfKIM7ETHyIy+ogI5I53VKrPf6sumSmorn/O9fwG9+gHxfmTyOVnMcshhdGzgr7szC60gN7iaZKoqs/Tq61vu4Zrb7X0UMrda5HPUEv7eEUUov4NSI0eORM+ePZGYmOjX+iNGjEB0ICmcRETVHA6BKqsdeq0Gem34TxJsDhVVVrvMzOKIYyIKo3CdT0VFRWHw4MHYtGkTJk6cCEAOA9i0aRNmzpzpdZuqqiqPwJO2ehiNv5lcYVdWCkTpawWl6tStspjkUCS7j4CSzQZZQKcBCmS2R31s1ob3Ux+tDjBVNbye2SSDCfGJgMWPWkpOiiKHQZUUAqkd/N/OXFUdyAqyrpDb7HftggsGqSpw+F/g6CEgsa3vYvL+iI2XgY60LvJ2IMpKZFAnmELizsyq/Gz5HPgbpLGYgaMHZY2nQAKeWq2cbdBikq95QY7cXrXLAGN0jH+vhaLIoJAzY+q4Af4H5Qpz5fMVbBAxNl4G1qoqZHAzEJVl8vWKrWfYcPahmttpXbyv43DUvw8iCju/L7+2bNkS0I4//fTTgBtDRFSbXquBQd80mUs2R+NmuTjnrDNx5VVTcdXUwKd7J6LIEc7zqVmzZiEzMxNDhgzBKaecgsWLF6OystI1G9/UqVPRqVMnLFiwAABw/vnnY9GiRTjxxBNdw/f++9//4vzzz3cFp5qdUN2DQXWLqatqdaFiX0Epi7xQLyuRmSA9+3ofoqTTA6YGCjRbTPA768obrU7W8WmIxSQvlHW6wIJSgAxElBTJQIfBv1oeqCiXz3FCkDWFADn8qaJMBlfi2wQWXBECyD4IHNovawP5GorlL2MMUF4ms2gCDUoV58sgZ7BZM3HxMrhTWe7/THy5R+RrkBpEYEdRZH+dM9g5HB71mfzeT3JbGcw7sFfWmGqoLnBVhRweGB1bM2tjoIwxMqOruCDwoFRxofzc+3q/CFETlIqO8Z69JoT8SPv7WSGisGBOABFRmH25cSPmPng//vrzT1it7t+0V5oDvOAgIvLh8ssvR35+Ph588EHk5ORg0KBB2LBhg6v4+cGDB90yox544AEoioIHHngAR44cQWpqKs4//3w8+uijzdUF72oPebN6CRzZbb6DUlarDDB88gZwZD+w+3/AFTd5rqfVymCQc2p5b0zmhi/U66PTAxaLbGt99YrMpuBjX8ZoGVgoKwFSvQ/b9FBWIoMKwQ67c2qTLDNnCtsHlqmVny0DITGxMrgVCjGxsp5Q+07+BxyqKmVAKdDgSG2GaPl8Fub4F5SqLJdFyOPigw/s1NaYYLKikTPh5WfL56BLd9/vCVUFjhyQmX9t/Xyf+WKIAfKy5Wvlbx0vm7Wmnb6UFsksLkBmSXmtJ1X9WWwBBc6JIllQf/0cDgdeffVVTJ48GWPGjMHo0aPdfgL1wgsvICMjA0ajEaeeeip++umnetd/55130KdPHxiNRgwYMKDebxFvvPFGKIqCxYsXB9wuIqLGKiwsxFVXTkLPnsfhux9+xGcbvkDPnj3Rr39/vLJiVXM3j4iaUajPpwBg5syZOHDgACwWC3788Ueceuqprse2bt2KVatWue7rdDrMnTsXe/fuhclkwsGDB/HCCy/4PbSwydQeVmfxUpPJZpUXl96Ge9ks8mI794i8f+SA97pOWr3cj6OeWoYWU+Mu+nV6uX9rA8MAK8oCLzjupGhkRlZxvn/r221y6FcoLsp1ejls7MBemf3SECFkQObfXbIuUGOCQXXFxMqARHGB/9sU58vXuLHPRUw8kJ8jh0XWx5khZjGHtu+NodPJTLfDWTLA6Etxvgz6BTJM0ZfYeDkUr7TY/21Ki2SmVn3DDGsP3evgY+ie1Srfs1HMlCJqTkEFpW677TbcdtttcDgc6N+/PwYOHOj2E4i33noLs2bNwty5c/HLL79g4MCBGDt2LPLy8ryu//333+OKK67ANddcg19//RUTJ07ExIkT8ccff3is+8EHH+CHH35Ax44+CtsREQXpyYWPo11Kouvnu+++xW233Oy27NDBg3j7rTeg0+mw9KXl6Nu3H4affjqeeHoRdu/ahTPHnNXc3SCiZhTK86lWzVYr4OSt7pPVCqhCZm/U5hzWpzpqZVIJtxm5XHQ6GTDyVTfKUb0PbWMypXRy/7Z6ip3b7UBlRfBFxwF5oV5S1HBQBJCZOuaq0GWKJCTKIMu+3TVZKt44M23+/VvOOBdIcW1/KBqZIZV9yL9aYDarfF8YYxofZImJlRlERQ0EBosLZB2uNo0YNhkOxhj5Ps/aI4dA1g32Wi3A4f0yQBvViPepk1Yrh3sW5PhXR0wIGfTT6uofZpl9sOZ2h67e17FZ5OvVmAxIImq0oD6Bb775Jt5++22MGzeu0Q1YtGgRrrvuOle9g2XLluGTTz7BihUrcN9993msv2TJEpxzzjm4++67AQAPP/wwNm7ciOeffx7Lli1zrXfkyBHccsst+PzzzzF+/PgG22GxWGCp9e1bWVkZAFkkVK17ktNIqqrKAqJCtJxCok1IVPebfY883vrvHM4vC9E6l4s6P2FvWcDHu+a663DRJRe77k+flokLJl6IC6oLDANAh44d8O+//2LACQMRE1Nzwj906DDY7Xbs3vU32rXzc6Ydv8i2C4HgC8SGCd/7kdv/5ui783jh+B8OIGT7DOX5VKtmt8mAkU7vWegcqJ4VzyGDT7UzmVQH4FDlcLjacg8Dnbu5L9PqZEDI1zBAu00GpgyNuAjXaOSf6foypaxmeaEcaC2k2ozV9Z3KS2tqDflSUVb9vIXoorx2baL9/8gaXnVnR7OYgZyDwKEsmVUUE8RMd/6IayOzffJzgI4+ghJOpUWyrpO/s9/VR1Hka5B7RA5j9DY7nN0m6zEpSmgCO6GWkCifk92/yc9KpwzZ1vJSGTwqK5ZF1kMlNl5m7FVW1Exq4EtluWxbQ8Mj/Slybrf5X/uLiMImqP9AUVFR6NmzZ6MPbrVasWPHDsyePdu1TKPRYMyYMdi2bZvXbbZt24ZZs2a5LRs7dizWr1/vuq+qKq666ircfffd6Nevn19tWbBgAebPn++xPD8/H+a6RTUbSVVV2M2VgCpgckReZF4AsJoqAEVpTLnQY1Ik9x3w0X/hQLSiQnWoUB0OAICqOiAcavUFZfifKVVVq4/ngOrw73iJbdogsU3NN7tGoxFtU9qiW4b7hY5Oq4XDYXf1TagO2KovSBQFruWhoDpUCFWFuaIEUFpIkeJqfO9Hbv+bo++qEHCYbCgqBCwVQQ6Dqkd5eXlI9hOq86lWT3VUF/7Wex96Z6sOGDkcQO2X2+GQGTl1M4acQ/lq02rlurZ6ZvFz2AFtIwMoCurP3DGbGq451eAxqmfhK8qvv7aTEDJbpzFZWV6PrwGSq2sTKYqsUxQbJ7NRCvPkkL2qChn4CFUNKW80GhmUyz4oZwX0VVtKVaszb7SBFWivT2yCDMzlHqkJ6NSWly0LdSeH8oupEFIUWRjcVCmDi6ZK+b4sLZafqzbJwReD98ZglLW4ivIaDkqVFMrPY33BPLsdyKv+nCe19T2zpEDDgVsiCrugIiJ33nknlixZgueffx5KI1JcCwoK4HA4XAU4ndq3b49du3Z53SYnJ8fr+jk5Oa77CxcuhE6nw6233up3W2bPnu0W7CorK0OXLl2QmpqKhITQRtBVVcWeo6WAMQHRsS3w25Ewc2aJRccnNer9cyyK5L4D3vsvHDYopkJotBpoqr/h1mgEFK0GGo3GY7rycKg5ntbVhkApUKBoNB7b9+3XH6tffw0msxmxsbFQAfz004/QaDQ4rnefoI/njUargaLRwBidCEUb+gvxxuB7P3L73xx9d6gqKmFBckoq2sQ0ciYvL4zG0NQfCdX5VKvnqB6GZzD6qCllkYGFuvWgHNXZU3WDUjmHfR/L5/A9Z1CqkX+zFU39w+qcwxMVpXEZrzFxNYWefdXdMVXJrBNfF+yNodXJYFRRvgz46HQy0Gauku1p2z40hb0bEldd3ynvqCzc7U1FmQzOxYVwCKFGIzNwDvwjb3foWhOYqiivnrUupvHvp3CLjpWZXgW5sq3xbbxnfoVCTKwM4iW19Z0pWFUp12noPZufLT//gO+he87Pc4j+nhNR8IIKSn377bfYsmULPvvsM/Tr1w96vfvFz/vvvx+SxgVjx44dWLJkCX755ZeATvAMBgMMXlKyw3VRrCiK/NY4Qk9Cleq+R2L/I7nvgJf+u54GpdYdedvmEABCP/SmLnkcBe5tqF9FRQUqKipc91etXgsAyMmpKQyampqKyy6fhEcffgjXX3sN5jzwAPJz83DXrFmYctVUtGsXwtR3AM62yz8vLe/9xfd+5Pa/qfvuPFa4/oeHap8t+XyqRVHtMvMBAGy+ako5ai5CnRx2Gawy1QkCFRXIjCtvmRa+glLOYX2NDaTo9PXXWqqsDE2wxmCUxcbLSn0HpSrLZX8TwlTTSK+vyQSy2eQwy6YKRjkpGhngyD0sj+3tuSjMk++dqBAHW2JiZWAxa09N9ljeURkUtZhCO/wtnHR6mWkWbrHxcrjl4SygR1/Pxx0O4NC/8vPT0HPnVk+qviLnUeHN1iMivwQVlEpMTMSFF17Y6IO3bdsWWq0Wubnuszvk5uYiLc379KJpaWn1rv/NN98gLy8PXbvWRMUdDgfuvPNOLF68GPv37290u4kovLRaBTFROlRZ7bA5wh+UAoCYKB20Wv8vmJc8swiPPfpwvev8tesfpGdkYP3H/4c777gdI/4zDNExMbjwwovw+BNPNbbJRHSMC9X5VKvncNRkQfnKlBLCS6aUXQar6galnMXOPepKaX1nMfka1hconU7WVHI4PLNkhAAqSkOTiaIoMphQlAe06+C9eHdZiQyWNEWwWK+XP80hJk7WQco5DHTt6f68F+bJYV6xYZr9LjYOgACydstASWW5bE9K+6Z53o81iSkysy2+DaCrEyzKOSQ/t0ltG37u3Gbeq6fIeXRM+DK/iMhvQQWlVq5cGZKDR0VFYfDgwdi0aRMmVhcHVlUVmzZtwsyZM71uM3ToUGzatAm33367a9nGjRsxdOhQAMBVV12FMWPGuG0zduxYXHXVVa5i6kTUskXptBiQngyHo+kKI2u1CqJ0/qfR3//fB3H/fx/0a93jj++LTzd8AUBAdTiqh+zxZJQo0oXqfKrVU2sFpbxlMjmLn9cNSjkL0ldVwEOOj2LnvuqI+iqAHiidXmbJ2KyAts5Ft80qM7hCdZEcGy9n4Ssr8ZzhzW6TtXlCNeteS6YoQHwicHiffI679pSZZHnZwP7dcp2YMAWlgOqhaIoMRDZ1ptixRquTgbwj+4F2XQBUZ2iVFssMqph4/+qtOTOltDog1XuiA6xWILUjg4NELUCzV9meNWsWMjMzMWTIEJxyyilYvHgxKisrXQGkqVOnolOnTliwYAEAOX3yyJEj8fTTT2P8+PF48803sX37dixfvhwAkJKSgpSUFLdj6PV6pKWloXfv3k3bOSIKWpRO2wL+QhERUbNzqDXD97wVOncFpbwM3wO8B6W8FTvX6WRNJ9XhWfDaYg5NEWydvnrYnMUzIOQMVoVqNroog5wtLeeQzDypPey0slxmhSUmh+ZYLZ0xWgYoco7I4V+JbWXgI8rQNLOvhSsTqzWKjZeZbUcPA1Wl8nNnMVUXWPfjeTRVyoArALTv6HtmSSHCN/MjEQXE71D9SSedhOLiYr93PHz4cBw54uUffh2XX345nnrqKTz44IMYNGgQdu7ciQ0bNriKmR88eBDZ2dmu9YcNG4Z169Zh+fLlGDhwIN59912sX78e/fv397ttRERERM0hXOdTrZqq1mQq2bwFpUzyt7dC54B7UMoZWMr1Uuxcp5PBL29D9Sym0BSl1mpl0MvqI+NLdfi+iA5GQqK8wC8ucF9eURb6Y7V0ej2Q2l4Wy96/WwaqmiIgRYFLaitryZWXyiwpi0UO7fOHP0P3hApoFN8zMhJRk/L7P9HOnTvxv//9D8nJ/n2jsnPnTli8jfv3YubMmT6H623dutVj2aWXXopLL73Ur30DYB0pIiIiahHCeT7VaglRkyHlM1NK8Rza5wxSVZbL34ZomRmUe8R7sXOtDnBUyqBU7YtVIeRFce0AjsUc/AWtgI/gmlk+Fkr6KBmIO3oQaJMsA2+lRXJ2Mm+F3ls7RSMDHkJw2FZLptHKovSx0YG/Tm5BKR9Fzm02mbUYCcNXiY4BAX09cuaZZ8qpnf0QibMLERERETWE51NBcA7R8xWU0mo9g1J2KwClJlMqJhZI61w9dE/I312616yv1clAlr3ufmw108cDwI9bgG82AP0GA+deFnhflFr9qa2yXAaNQi0hSRY8L8iWQa+De2WWlL+ZJ60RP1et19H9NbfTfGRKWS1AlFH+EFGz8/s/X1ZWVsA779y5c8DbEFGkkSeG/l6gkW9CdT6HPNkmaql4PhUkZ8CpduBJHyXvW8yyXlLdIXE2mxym4wxkxcYD7TvVPJ572D0o5QxUeAS3qoNSzsyi33+Wv//cAYwYF3i9IJ3ec0ZAVQUqK8IzE5hWK7PEDmcBZlN1BkqE1JKiyOJwAEcOyNvxbTwL/DvZrDJzUMOi80Qtgd9BqfT0dNftyspKxMayMBwRhYBGC1UAJUWFSExOaeVZAQKqQ4VGq0FIA0cCsNmsKCwsgCoUKJoIqhFCdIzh+VQQNBpZ68lRpxZTbLwsaOwsQl43mGS1umckxcQB7WsF+LwVOwc8a0o5j63VyeBRWa2aYIf+BfoMDKw/zqCUqtZcFFvN8scYE9i+/BWfIGvzJCRF5rA9igy5h2vqz3Xu7jsjzmFn8XmiFiSoK5f27dvjsssuw/Tp0zF8+PBQt4mIIoiiaIDoJJSWF6O8wssMSa2MUFUoYfhmTghAaPVQYtu28sAeUevB8yk/aTVyuJnDXhN40ulk9g8ggzlKrcCVc5idzVpTBB2QQam27eXjDgeQ46XYuaJ4Dq1zZkppNEBZiQwmOR34J4iglE4GvmzWmrpUFrO8H58Y2L78pWgie7geRYZDtTJRO3fzvo4QchirgfWkiFqKoIJSa9aswapVqzB69GhkZGRg+vTpmDp1Kjp27Bjq9hFRBFD0RkDXHkJ1IPRVXlsOIQBzRQmM0YkhLmehAIoGiqJhQIroGMLzKT9pqmesqz0zni4KMFYHdISQM3UpmpraT6oqb5trBaVi42S2U2oHGZAqLpCP1y52rNW5B7IAGZRSFPlTWuT+2IG9gRfNdmZK1S6obq4ucs7hRETBO1wrKFV7aG5tdrv8DDIoRdRiBPWfb+LEiVi/fj2OHDmCG2+8EevWrUN6ejrOO+88vP/++7Db7Q3vhIioFkXRQNHqoWijWvGPHlC0YeinHopGy4AU0TGG51N+0lRnNjlsNUXI9Xr3IsU2W002FSB/q6p7UComXv6uPU380QPux9Lp5Ta16xzWfh3qBqXKij2XNUSrk4Gu2jPwmavqD2zt+R346lMga7d8LojInaoCR6qDUjFxcpZFb2wWIKpWUJuIml2jvo5JTU3FrFmz8Ntvv2HRokX/396dx0dV3/vjf53ZJ3vInhAI+6LsCAJarEVpS21pvyLVVi2/am8VWpXeW6G1crW92m8X6/1eueXqxeq91eK+U1xQXAAXFheqgICCIgkJIdsks57z++M9kzMzmUlmTSaZ1/PxyMNZzsw5n5lJPPPi/Xl/8NJLL+Hiiy9GdXU1br75ZnR2dvb9JERERERZjOdTfTAa/aviefV+MaYIoZTPpwc2Pp+EVF0OfZtAD5ngaT2fhzWeN5ok+PIG9ZUKXvEvUgB19FB84wmET8H9sRxtMqZIvjgKPP1X4J1XgcfuBf78G+D5RyXIIiLReEL/XR0+KnrI63ZLs/9ov29E1O+SCqUaGhrwu9/9DpMnT8aaNWtw8cUXY+vWrfjjH/+Ixx9/HEuXLk3RYRIRERENTTyfioUiPZcC0/eMxtDgyOsKrZRSfYBPDV3lLidSKHUkdDcmE+DxAq6gIMrVJWEVECWU+jj+4RiMQNMJoNMhYVtXZ9SV95Q3Xw69wdkpKwA++b/x75doqPos6Hd5eJSpe4CEznkF6T8eIopZQj2lHn/8cfzlL3/B888/j8mTJ+Paa6/F97//fRQVFXVvM3/+fEyaNClVx0lEREQ0pPB8Kg4KZFqd6q+EMppkCk6A2w1Yc4IqpbyApoZWSgWm7+XmA8PKgOZG6S3lceuBkNkiX1o724E8//Yupx5KtQSFUharVGZ8dlj2pcTxb73FJcCpRsD7AVBZK8eQm99jM9PJ41A+OSBX8guBmjrg0IcSyH1+BDj+qdxGlO2CA+baKE3OAZmaa0/TKpdElJCEKqVWrFiB6upqbN++He+++y5WrVoVcgIFANXV1fjlL3+ZimMkIiIiGnJ4PhWn4Konk0lvEg5IcKQgqKeUfypfZ9CqrjlBS8AHqqVUFThxLHQ/RpNeEaX6JDAKrOgXuD2vABgxVj+ukyfiG4vBCJSWA452qfDweiJOJ8rb87p+5ezzgW9cBiz6tn7b26/Gt19KnjZ0F2QZtDQV+PxTuWzLkVU2I/H55HePTc6JMkpClVInTpxATk7vCbPdbofVakVLS0uPEywiIiKibMfzqTgoigQ4Acaw1bMCK+YFh1IKZHocIBVQwZVVw0cB778tlz/7RA+YAFmNr/W0hFGqKs9ltkg1ViDkKhwGjBwLHPqHXD96CKio0Z/D4wGe+h+grQX4zg+AopIIYzIAw8plXyZzzx44J7+A7dOgKqkzZsvlSdOA7c8D7a3A4Q+BUw1ASZQv4YlqbgRefkpe42lzgdox8a0wOJQ01gNvbpUquY5W+UzVjgK+vUIa7lN0Xo+ErpoGjJqQvs9QU4PeY214XfSqRY9LKhytbHJOlEkSqpTq6wQq4LbbbkNzc5wrkhARERFlAZ5PxcFoCq16MpnCQiknoCFs+h70xwRXSQGhfaWOhzU7t9kl5Opo0xusG01AW9B7UDgMGDlOv34srK/Uh3uATw8CzSeBd17rfWyFxRFDK+WtV/QrZy2UMQNyLLPO0e/r6/nj5ewCHv8L8OnHwIH3gYfvAe77E7Bvd/ZVCak+4Jm/yuvQ8LkEo5oKHDsM7HxpoI8ucx3+CHjqf4H1t0pz/sf/Aux4MX37C16woLd+Um6X/H5brOk7FiKKW1KNzvuiZdv/uIiIiIhSjOdTkCk3bqd+3WgC7GGhlEHRV7RT/avvBaoncsNCqYJiIL9ILn9xTK+wCuxL1aQSyeMBVK9M32sNC6WKS/WGyZ9/qjdhB4B/7NIvH/4w/jDn1EkoBz8AAGg5ecCUOaH3T52rh3If7pVjTQVNBf7+ENByKux4GoAtDwM7t6ZmP4lyuyWE2/Va6KqI6bJvl1SNAVLlk5sPGPxfn955DTj5RfqPYbDZtwt44j7g431SbRiw8+X4V6qMVUiT8176SbldQEFh9lb9EWWotIZSRERERERJMxpDQwijSXrHBLicEiZ5/Nv4fFLxE5DTs4l4dzNkrwdoOB56n9UKnG6SpueaJkFE62n9/sJh8sW2brz+HP/YLZdPnZSgK6CjLf7wItDcHIA269ye08QsVmD62XJZ9QF7tsf3/NG8tU2qXACpKLngO6GN1He8KFVgA+GTA8B9dwCvPgdsew649w/A/vfSV73lcQPbg6p7vvtj4JqbpLcXIAHeC4/JFE8Szk7g1c36dXsuUBuoXNKA5/4WOg03FVR/5Rogvxfl1dG31QDY8qLfT0QDgqEUEREREWW28EopU6RKKaNemeF261VSQM9KKSC0ouKz8Cl8ubJyX2uLfJEFQquHiobJf6fP0297e5uEYYFwKtihD6MMLIrgEGvkmMjbzFygrwr4/luh1V6JOPox8MYL/isKsORS6Sd16TXAl76ub/f8o6GVKenmckqY8di9QFtQMNjRBjz7IPDw3aFTO1Nlz3Y9QBk7WQ/n5nxZeoEBsnrj3h2p3/dgteMlfcXL8VMkxLvkaj287ewANm+SQC9VThzTf9dHjtMr2cKpPqmmtLHJOVGmYShFRERERJktUqWUNaxSymiUKXSaJuGUKyjECu8pBYT2nvk8LJSy+Bubu/2r+gFh0/eK5b+Vw6WBMyCByYe79VAquNny4cRCKc1giN7EPDcfGH+mXHY5e44hXttfRHcCt2CRPi4AOOtLElABErw99b9SSZZumgZl8ybgo3f122rHAKMm6tc/OwK89GRq99vVKSEjIBVx53xVv89kAi78jn79jeeB9pbU7n8waqoH9u6UyyYzcN43JCBSDMDXlsvnFZApfLtTVNkHAEf265dHT4y+ndsNWGwMpYgyEEMpIiIiIsosXi/w8T5YD/0DOP6pv1IqqD+NyRw6fc/tlC/APq+EJl63viIfEHn6XnGpHlZ98WnPaVgmkwRhgXApMH3PaNR7SQH6dC4AePkZvbpmzCR9Rb6TX8hKfDGN3SMN0gF4i0plrNGMmaxfjrcaK1inQ59yOKwsdEyABDNf+ZZe8eLslKlrae53lrv3DSiB0MFqAxZfLJU3/2cF8O0fAHb/Z+DgB8mHcsHeekUPNc+cDZSGBYPDRwHT/NMnPW49jMlWmiaf/UAF1NzzgIIi/f7cPKm8CyS8u16TyqVUiDmUcgE2G5ucE2WglIZSXq8XHR16+ey5554Lu51pNBEREVGseD4FwOeF4fc/R/GWTVDef8tfBRVUKWUySZ+lQGDj7PI3KFclmHK7w3pK5fbch6LoU/hcTqn0CGbLkSliRqN86Q5M3ysoDq2CqqmT6h0gtLHzmbNDQ6NAr6a+NDV0B2Te0sretx01QcYNAIf+kXhIdPQguqukRk8KHV+AwQhc9D3ppwVIhdKB9xLbXyyOHUbeO4EVCBXZ95Sz9CbVYyYB5wZVML3yTGqmhbldwLuBih8TMH9R5O3mX6C/9h+8I0FqKvh8ULY+hZJH/gvKE/cBW5+S0Ct4OmqmOfQhcMzfxLygGJi9sOc2I8YAYyfJ5Y424PD+ntvEq60FaDwhlyuH69VYkbhdQF4Rm5wTZaCEQqljx45F/HnqqacwadKk7u02b96MqqqqlB0sERER0VDB86leWG3QAqvLdXXKF8ngL/0mswQnZotcd/t7Sqk+mcIX3ug82pfV4KbI4SvO2ewSBJgtUknk9a+uFwhlgs0LqyzKyZPAaGxwKBVjJVOj3k/KU9JHKGW1yZd9QFbgS3Q1uCN6Y/Veq02sNuD8b+rXtz2XnlXwOtqgPPc3KIGQbf4ivUor2JlnAWX+342G47ISYbIO/UN/ryfP1FdpDJebp0+f7HIAH3+Q/L69XuCZv0J5dyfMp+qlSmzvDmDrk8D9/56Zq/1pGrDzJf36ed/o2Zg/IFBdBgDvvZn8vj+JsUoKAKBFDqeJaMCZEnlQXV0dlCgp84wZM5I6ICIiIqJswPOpPuQXyhS8QIVIcChlNMl0PYtVAgGXEzAaJIzyuADNpzdcBiL3lAKkqiMguIk2IM9fXiOVUieCVtOLFErVjgGqR+hT4CbPkMeVVck42luBzw5LgNPX9KEGPXjos1IKkGqsTw/K5cMf6lMGY6Wq+uPNFqBmZB/7myTVVEc+koqXHS8B5y2Jb5+98fmAZx6A4m9ertWNhxIe+gUYDBKCPHKPXH99CzBuivQES1RwsDV5Zu/bTjtbVgAEgPfeAiYl8Xvr9QBP/TU0aAnW3gI8+J/AV5cBE6clto/mRqD+M+lTVl6dmqqhY4f1sKyiBhh3RvRt68bJ709rs3zmWk4BRSWJ7ztk6t6k6NupPhkr+0kRZaSEQqm9e0P/FcLn8+Hw4cO49dZb8dOf/jQlB0ZEREQ0lPF8qg8FxUBTPRSX098nyqPfZzbJl8xAwON2AVDky6fbJdsHh1KRVt8DQvveROr5ZPKfKrcGBVZFEUIpRZEV6h7dKFUi0+frt4+ZLNPBfD75Ij5+Su/jDkxHAuCJJZQaO0kqaQCZRjX/gr4fE6zhc/21qhunr+jXm/MvktX6fF5gzxuR+y4l6vUt0kcMgC+vAMrXlkOJNJ0wYORYCcoO+0OyXa/G/xoEONplXIB8/voK6IaPAkrKgVMnpadVYz1QFsN7Fs7nBZ68H/hU9q2ZzDj91e+iqHo4DK3NwGubgROfye/Asw/KZ2TBhdFXmgvmdgH73wX27dJDU0AqwMZOBqafHb2ZfizeeVW/fNbC3oMuxQBMnQu8/ne5/t5bwMKvR9++N16PNE0HJHSuqI6+rdsNmK2hfeiIKGMkFEpNm9YznZ85cyaGDx+O73znO7jiiiuSPjAiIiKioYznU33IL9Qvdznki3tAIDgxB1XEBHpOuV2AqulBi9Ekq25FEhxK9baCWsjKexFCKUACiqvXSMVW8JffMZP0HkVH9vceSmmqvvJeQZE+hbE3+UVSodJw3N9Q/XRoBVhfgqfujeprCpRfUQkwZyGwc6tUWr38NLDsquQrbz7eJ02wAWgGI1ouWIbiWKZcLVwCfHJAjmXPDglHzAlUS+1/T+/LNXlG5N5awRRFqqVeflquv/cmsGhp/Pvds707kILZAu07P4C7uArItctndPk/AS8+oa/s+NYrEkwt+S4Q7TOiabJq4bZngc6Onve3t8jUwA/ekfeupi7+4248oVfZFRTr0xl7M2U2sP0FCZD37ZJwzZTAV9LPjuhB9eiJvb9XHpdMPWWTc6KMlNJG58OGDYPLlYZ55URERERZgudTfsHBSmcHulfuAqSnlNEU+iXT5ZRe3W6XfOENfBHPyYseluQV6Pf1tjpeLKEUIBVZ4dUYw0frl081RH8sALQ0683Sy3qp/AgX3LvqUIwN1QOCp4uNitC3KZo5X9bfo2OHZDpfMk43AX9/uPuqdt4SeCqGx/bYYWX6lDZnJ/CPPYkdQ/DUvUnTY3vMGTP1hvsf7om/x1aXA3jzZf8VBfjOitDPDCDP/9Vl0s8rEL4c2Q88sF6qs8I1ngAevgfYvCk0kCqtlJUV68brTdq9HuCxe6USK17vvKZfnn2u/py9ycnTg9lkenHFuuoeIO9JfiGbnBNlqIQqpdra2nrc1tDQgF/84he47rrrQu4vKCjosS0RERFRtuP5VB8KgiqlHO3oXh0OkCoYs7lnKKUYJNTxeqRBOhB96h4gX6LzCqTnU2+hVEuMoVQkZrPso6Mt9HkiCWpkrZXHE0qdAWx/US4f/hCYOT+2xzk6gPrP5XJZVfSm3pGYzTL16pkH5Pq25yTsiGX6XzhnF/DU/+qBzsRpwPR5QKcz9ueYda4eKu15A5g2p+9Kp2DNjTKVEZDKs1intFntEmB98I4c/0d7Qxt69+XNl+WzCwBnzgJqR0deRVFRgJkL5LieeUDCt+ZG4P4/yXs3ZpJMET38odwebOwZEkZV1OjBjKsLeOZBqXRyu4DHNkpFVqBxfF/aW2RaICC9ms6cHfuYp83VH5tILy5N00MpgxEYOa737VW195X5iGhAJVQpVVRUhOLi4pCfiRMn4oknnsAtt9yC4uLi7m2IiIiIqKd0nE+tX78edXV1sNlsmDt3Lt5+++2o25533nlQFKXHz5IlKWxanQQtOCAJXv0OAEwWf6VU0BQtl1MvpnK79C/20ZqcBwSqfbocepVSuECllM2eWLPkQDPnQFP2aIJXVyuPY8XF0kp9HJ8dDl15sDeBqVeArBYYr/FTZNoiIJVOgWmK8fB6gaf+B2jyV/wMKwMu/D/xV7VU1OgVRs2NwCcHe98+XEiVVJwhyfR5+uXdb8g0zFi0nAL2+l8zk1mmsvVl5Fjg+6vkPQ9oPCHh1juvhgZSBcXAt38ALL0CqBwe+ppa7cC3LtdfM2cX8Mh/92z4H83uNyTsAWT88UyNC/TiAvReXPE4/qn+O1k7SqbmRaOqMu7etiGiAZVQpdQrr7yS6uMgIiIiyiqpPp966KGHsHr1amzYsAFz587FnXfeicWLF+PAgQMoLy/vsf3jjz8Ot1sPYU6dOoVp06Zh2bJlKT2uhAX3e+psBzzBjc4t8iXeHFYplZMn1SKuoFDG7u9JFOhJFV7JU1AEHPdfbm+VUCSY6pPbgfirpAKKSuTLNyBBRLQV8kJCqThW0VMUWfUsEBQc+lCqbvoSMnUvgVBKUWT1u7/eBUADdmwFJs0EYukDBUh48/eHpD8QIO/Vt38gAUekaqG+zD4H+Nz/XLte73taV/dxaFLhBMiY4l3drqJGgpbPP5FQ6MgBqVzqy2t/l88XINPfgvuo9aaoBLjsWukJ9fE/ZEW9AEWR/lDjzpCm4r311jJbgO/8QMKoE8dkqt/TDwDf/XHvfZ6aG2XfgKwyOSPGyrzgYwzuxfX+W8BXvhX7498PCtvP6KNCy+Nf8ZJNzokyVkKh1MKFC1N9HERERERZJdXnU3fccQeuvvpqrFixAgCwYcMGPPfcc7j33nuxZs2aHtsPGxYasGzatAk5OTm9hlIulyuk31VgiqGqqlDVGKtDYqTl6lMWNUcH4HF3F0KpZn9PKau9u+xfdXbKamQ+L+Ds6r5ds9mhaRrQ3CSVVGHTspT8Iv15W5uB4tLQA+loh8Ff+aLlFchzxatwmH6cLaeAKFPzlJNfQPEfs5pXAK3TCTXW/Y2fAsPuN+Q4D7wH7YyZvW+vqlA+PSj7s9qgVY1ILAiqqIFyxkwo/9gNuLqgvfE8tAu+3ffjNA3KtmehHHhfrprM0L59pQQumgZV06D5/xuzUROhFJVAaTkFHDsE9eQXsU1HO3ZIVrkDoNWOgZabH/9rMetcGPzBo7brNWh9BWJfHIXhoPRT0nLyoJ21sHufMY3dbAHmnCc/HW3S10sxyFS24FCwr3GYLcC3fwDlgbugtDYD9Z9Be+UZaNEatmsalJeegOKTME2beQ60nLz4X69JM6C89ncoXg+0f+yGdu5XuwO0Xsfv7IRy8P3u3xNt3Bm979vplCops0Wv7MpgqqrK2AfBsaYDxz+0xh/rOBIKpYiIiIgoc7jdbuzevRtr167tvs1gMGDRokXYuTO2KVUbN27Ed7/7XeTmRq9yuf3223HLLbf0uL2xsRFOZxz9f2Jg8PgQqO9ytrXC6HIhUPNxUlWAtnbYzDYU+W/raG1FZ7kKOLpgdXQhMOmxw2CGw9EFeHzSlsoROrXNbs1BoD6lvakRXWW1IfebGxvhn3yHLmsu2hwxTo0LYrPndx+n42QDHDVje463swPljnYAgHtYBU51OtHqdEMDYIhlKltBGcryCmDsaAOOfozGU83QeplqaK7/DCX+aX7OmtFodUaZuhgDw8yFKD3wPgxeD5T330LbsEp0TZwe/QGqioLXnkXOfqlO0hQFLRdcDFdBWff7o2pafOP3yzlzDgre+DsAwPnWq2j7ct8VOEW73kBgclfr+OlwJvAeo7IOpYUlMLWegvLZEZw6egTe0iiBmKah5KWnuoPKtlkL0eVRAU+CY1fMwEh/ZVaEz3gMTwDTBRej5ImNUHw+KO+9idaSKjjHT+2xpe3g+yg6dhgA4M0vQtPUBQnsTxSMPRM5+/dCcbvQ9t476JokQWpv48/Z9w4KvFL12Dl2CtpdXsDlDX9qnaMLsOUDjY3Rt8kgqqqitbUVmqbBYEjpmmSDAsc/tMbf3t4e03Yxh1KjRo2CksCKBddffz1++tOfxv04IiIioqEmXedTTU1N8Pl8qKgIrQKqqKjA/v37ozxK9/bbb2Pfvn3YuHFjr9utXbsWq1ev7r7e1taG2tpalJWVpbwZu2rTpx3Z3F2A5q/MUBSU59iBsjIgT+8Xle9zI8/un86n6gFLbmEhcnPtQGerTDWymvTV0gCgVJ/aWOByID83LMjx6ZVhtuJhsIXfH4tyvf9PXlebHE+4Rn0KlqVqOMpz7VAAlOXaYw5llAlTgd1vQFFVlH1xGJhyVvRtT3zSfdk6bjLKExlXQK5dmp5vfQoAUPDas8gvLwdqx/Tc1uOG8uzDUIJW69MWfRuFk0OnzKmaFvf4AQAzzob2zitQXE7YP34ftnMu0Ht6RdLWAuXTA3IcufkoOHMGCowxrCIXyVnnAi89CQAo+cfb0L7+3cjbvf82DI0yVVMrrUD+7PnID1q5LuGxJyN3NLSvLIXywmMAgMLXnkVBUVHotE5nJ5SdL3RfNSxaivKiJH7vZy0A/MFkwf49yJ+9AEAv49c0KAf03l/2WfNh7+tz29UKVFYCZT2nMGciVVWhKArKysqGRCgRL45/aI3fZoutl1vModR9992X0IHU1dUl9DgiIiKioSZTz6c2btyIKVOmYM6cOb1uZ7VaYbX2bGhsMBhSfwKdVwBNMUDRVChdDmmIDUAxmaEYjTIdJ6iJueLshGIySW+poIblhkBPKYO/2bHHE9pnJ9AgHIDS3tozNHToqyAaEl1WvlgPRZSW5sjBZFA/KaW8BgZ/43mD/ycmE6dLXykAhoPvA1N7eT+DmpwbRk1IbFzBZszv7jWkqD4oT/8VuGxlaI+uzz8Btj2rr/hnMAJfvwSGKFVVcY8fkPd45gJg51Yoqgplx0vAkijhECD9jPzTv5RpZ8tnKFFnzAK2vwB0dUI58D6UL32t54qGzk7gjee7ryrnfwtKhBULExp7sqbOkd5SH7wDxeuB8vh9smrf/EWyMuH2F6VZPwCMOxOGWPpm9aaqVvpxNRyH0nAcSv3nchuijL/+c70petUIGPqamun1yjTfnFyZ2jtIKIqSnr+pgwTHP3TGH+sYYv6ryz5SRERERMlJ1/lUaWkpjEYjGhoaQm5vaGhAZWVllEcJh8OBTZs24dZbb03LsSXMYIBqz4Wxsx1wtOsNyk1m6Z1jNof2zunqlJDD4wHcQVMJ7TnSS8ZglIbHnY7QxwU3VI+08liHHkohL8GqEFuOhCUupzQ6jyR4BbJ4Vt4LVjlcQra208DRwz3HGtDRBjT4u7tX1CQ+rnBf/oaM75MDsprb/XcCw+ukYurox3pDc0Dei6VXACN6TmVM2uwvyap2zk7go3eBOQsj95byevSm2QYjMG1ucvs1W4Bp84A3t8pn7o0XgK8uCw38goOdCVOBERGqyQbS+d+Sz83hDwFoMpb33tSPGZBxnn9RavY37WzAX52F997sDqUiev8t/fLU6FWA3dxsck40GAz++I2IiIgoy1ksFsyaNQtbt27tvk1VVWzduhXz5s3r5ZHAI488ApfLhe9///vpPsy4qYFApdOhVz+ZTFL1FKiACOhySLDg9Uj4E2DLkRX5jEbAZge8Yb2TrDZ9ufi2lp4HEVh5D0g8vFEUfQpZe4scT7jGE/Jfg6HnCoDx7GeCvw+QpgIf74u83ScH9MuJrLoXjcEIfOMyoNQfhPq8wNFDUhkUHEgVlcgKb+kIpAB5P+ee57+iSTgUyYH39bBl/BQgNz/5fc+Yp69c94/dwKvP6Y24TxwD3n1TLpvMwMIlye8v1cxmYOnlwJe+JuEvEBpIWazA15f3rABL1MTp8pwAsP9d4HRT5O06O4D97/mP0QJMiGGFRLd/Rc7eViAkogHHUIqIiIhoCFi9ejXuuece3H///fjoo49wzTXXwOFwdK/Gd8UVV4Q0Qg/YuHEjli5dipKSXvruDBA1MPVO9UkwBciXeYNRwhubXe8P1dUpwZPPCwStECiVUl55TF6hNIIOF5jC194qYU6wkEqpQiSs0L/aoab1rMjyeYHT/kbMw8r1qrBETAhqTu1f2a6HdIVSgARCy66WCpj8sNeruBT42iXA//ezqCsQpsz0+XqIePhD4IujPbfZu0O/PKP38DZmufnAhRcDgTUdd70OvLoZeP5R4MH/1D9fZ58fWqWXSRSDrOp3ydX6a1g1Alh8MfDjm4BxZ6ZuXxaLfFYAmW635ZGeq+Rpmrx+gWB68gw9yOqNxxMyPZeIMhNX3yMiIiIaApYvX47GxkbcfPPNqK+vx/Tp07Fly5bu5ufHjh3r0d/hwIEDeOONN/DCC1EqSQaYGtQzqjtNMpkkYAIAo1mCqQ6Pv1LKAFTWAp6gUMqWI9N4jEYJDIxG+fIb3Dsov0gqlVQf4OgIrYgKhFIms15RlYjgZtstpySgCWhu1L+Il/U+3bJPFTUSgLU2A58dlqmPwRVAqk+m0gHy2lSNSG5/keTmARd8G9CWSuXL8U+kYmXUxP7r7WM2A/MWAS8+Lte3PQdcfJWEIACwZ7ve26q8Gqgembp9T54hFXkv+Pe967XQ+8urgdnnpm5/6VI7Grjq5xL4hgeMqTTvK8DBD+Qze/xTeW8mzdbvf/dN4LC/Mb49F5h/Qd/PGahOizR9lYgyCkMpIiIioiFi1apVWLVqVcT7tm3b1uO2CRMmQNMilQ5lhu5KqWBGswRLgARLVpsER85O+SJqMsmX6MD9Zotct9vlC6rFKiFVcCgV3lcqJJTyT99LtMl5QNEw/XJLc+h9wf2kSpMMpRQFmDgNeOsVeT3eeRU47xv6/V8c06c31o1Lb0ikKDIVMdHpiMk6c7aMv+WUVEo98B/ARd+TaWBvvqxvN/tLyTd6Dzd1roSfLz+t32axAnO/LI3Yg1eAzGQmc3oDKUBel69dAmz6LwAalO3Pw1hZB+TWAk31wKvP6tt+dVls0yw9bgkg2U+KKOMxlCIiIiKijBRaKeUXXCllMgNW/5Lwqio9ZKx2CagA/Qup6gPMNgmo7Dn+6qegwCu4P05bi14143bJDwDkJtkMPLxSKlignxSQfCgFyHSo3a9LKLJ3BzB9nr7/I/v17UZNTH5fmcxoBC78P8AT90lIceokcP+/h07RPPt8YNL09Ox/5gI5hr07gZFjgbnns3InmuGjgFkLgN1vQPF6MeyZ+6EUlUhI7F95EzPmA7Gu+Od2ARYbEOOS9EQ0cNhTioiIiIgyUsRKKZMJMPlDKbNFvngGdPmrpQKVUoHH+7z61Lv8Ir03TUBhkX65vUW/HNxPKj/JUKowKJRqDauUagqqlOprmftYFBQBs/zTw3w+4PUtclnTgkIpBagbn/y+Mt2IMcD3f6KHfd2BlAKc/03gnMWpr5IKNu1s4Ac3AF++iIFUX875ave0VmNnB5Qvjuq/g6WVwJe+HvtzuZxAQaEeYBNRxmIoRUREREQZyWePUCllNAf1lPKvqBfQ1SnNjX3+yopApZSm6b2E7BGm84RXSgV0BK+8l+QUpvygL8jhlVKBUMpqS91UqTnn6aHcgfeBTw8Czzyg76uyRno/ZYOScuB7K4EpZ8l1oxH4xqVSyUSZw2wGvnEZtIKi0PUI8grk/TLHMeVR9SVf3UhE/YLT94iIiIgoI6mRKkuMRr2nlMEYOj2nywE4g5avDw6sAj18bHZ5nM+rr3IX0lOqRb8csvJekl9wDQagsFgaf7c2S1CmKDLVsN0ffpVWpq5qx2oDFlwAvPSkXH90Y+j9s7+Umv0MFmaLrB43+1yZ4pns+0npUVED7eo1OOnoQrndCoPqk+pIJY5aCtUnv2+RAmgiyjislCIiIiKijKRGrJQy6WGSwQBYgiulHICzS78e+FKqQQ+ybDlSNeUOWqEvt0Bv+B08fa89SijlaO85BS8Whf5m5x430NkhlxtTPHUv2NQ5PZuMW23A0iulGXo2KqlgIDVYGAwSJsYTSAFB/aQYShENBgyliIiIiCgjRayUMpv1ACnS9L1APylApq9pGqBAr5Qy+1fkcgf1lTIY9Ol5UafvBQUZnQ754hvvyoWRmp03pXDlvXAGI7BwiX69vBq4/KfA2Mmp3Q9RJnE5JZC2WAf6SIgoBpy+R0RERESZyWiCZrFCCa5qMgZN5Yk0fa8rePpejn8qj1GvrgKkb1NLWKVTQZGs9OXs9FdaWMOm7wX1elIAmK2ynTWO1b1CQqlmoKYurMl5ikMpQFYr++b3ZSxT5sTXl4doMHK7gZqS9DawJ6KUYShFRERERJnLlhM61c4UVCllMIZO0XF2yk+APUdWnzMapS9NQE5e0CpsfuF9pUorwiql8uW/geooW45MFUw4lPJXSjWe0G8rrYj9ueIxfkp6npco02iqhMY5WdLEn2gI4PQ9IiIiIspc4c2KjSa9AsJgCA2lOh1h0/f8lVJGoz59D5BpfUYT4PXotxUU65cDfaUClVI5eXqllc+nh2HBYVksAj2lAKD1lARcTQ36/q32yI8jotgEqhwjTf0looyUEaHU+vXrUVdXB5vNhrlz5+Ltt9/udftHHnkEEydOhM1mw5QpU7B58+bu+zweD2688UZMmTIFubm5qK6uxhVXXIEvvvgi3cMgIiIiolQLb1ZsNoX2lAr+8tkVVilly9FDpODpe7YcCYBcTv22/CL98ulTUnHhaJfrwf2kfB4JuAqLJRxTwyquelMUFEo1HJeV+ALBVqr7SRFlI6cTsOcx4CUaRAY8lHrooYewevVqrFu3Dnv27MG0adOwePFinDx5MuL2O3bswKWXXoof/vCH2Lt3L5YuXYqlS5di3759AIDOzk7s2bMHv/rVr7Bnzx48/vjjOHDgAL75zW/257CIiIiIKBXCQymjObSnlMWqV0E5wxqdB0Kp4ObogEzlKygKDaXKq/XLxz6WqqtA4BQcSnm9EowVDZOpe8HP0RezRV8N79RJ4OG79ft66yelqdJ76lQD4OiILwgjyiYelx4YE9GgMOCh1B133IGrr74aK1aswOTJk7Fhwwbk5OTg3nvvjbj9v//7v+OrX/0q/uVf/gWTJk3Cr3/9a8ycORN33XUXAKCwsBAvvvgiLrnkEkyYMAFnn3027rrrLuzevRvHjh3rz6ERERERUbLCp++ZwiqlDAZ9Bb4uR8+eUqoPMEfo+5RfCPi8+vXKGpnWBwBHDwGtQY3QQ0Ipj1Rh2HKA3HzA1RXfeBYtlXAKCG2k3lso1dEuUwgrR0hA1XxSr+IiIqFpAJTQ31ciyngD2ujc7XZj9+7dWLt2bfdtBoMBixYtws6dOyM+ZufOnVi9enXIbYsXL8aTTz4ZdT+tra1QFAVFRUVRt3G5XHC59L4AbW1ykqCqKtQU/2uUqqrQNA3QNPlvltH84+bYsw/Hn73jz+axA9k9/oEYe2B/6fh/OIC0PCdFp9lyEFLzEN7oXPGHUh1t/tX3giul7HJbpGbk9lx5nkAjdMUAjBoPfLgX8LiBA+/r2wavvOf16tVbRSVSvRSPEWOB5f8EPHFfaLAUbfqepsqYxk4CqkcC7lHSHP3Ifpm6qAz4vzETZQa3S6oi2U+KaFAZ0FCqqakJPp8PFRWhK41UVFRg//79ER9TX18fcfv6+vqI2zudTtx444249NJLUVAQPTW//fbbccstt/S4vbGxEU5nHGXZMVBVFV6nA1A1dPmybwFEDYC7qwNQFGRbYW02jx3g+LN5/Nk8diC7xz8QY1c1Db4uD5pPAa4Oc98PiFN7OytU+lWPSilL0PQ9g/wE+seoql7hZLVLaKVpgMXS83lzciWscjv1CqlREySUAoAP9+jb5gedQ2qqHkrl5oUGW7GqHA5cthJ4/F6ZxpdfBBSXRd7W0SH7KfGf/1qsQFkVUH9c7mNVCJFwdfl/r9lPimgwGdKJiMfjwSWXXAJN0/DnP/+5123Xrl0bUoHV1taG2tpalJWV9RpmJUJVVRz8ohWwFcCea03pcw8GgSoxe34xlCyb753NYwc4/mwefzaPHcju8Q/E2H2qCgdcGFZShsKcCGFEkmy2CFU3lD49Gp0HVUopikznC/4S2tnhf1zQbaYI4aTZAuTkA22n9VBq5HjIevJaaMVVbkHPxwIypc5q938ZDluCXtOkeis3T8KxcIXFwPdWAYc/AqpGRA61NE0qvcZMCq32sliBihqplsrNZ/8cIkAqpSpr+ftANMgMaChVWloKo9GIhobQsueGhgZUVkYuYa6srIxp+0AgdfToUbz88st9BktWqxVWa8+AyGAwwGBIfVm0oijyr8ZZ+kdT8Y89G8efzWMHOP5sHn82jx3I7vH399gD+0rX/8PT8ZzUi/BKKbMl9Eun0Rxlep7/cRokuIqksFj6MwXk5AJVw4ETn4Vul++fvqf5p24GKq/MFmmY3tTQM5RydUmgpGmyTSQWKzBpeuT7AMDZJYFZpKl9peXAiWOyj/B9p4KzE2hvlf3n5IU2is82Xo9Upfm8Uo1nNMjUTU6dzByaJr/rufkDfSREFKcB/UtqsVgwa9YsbN26tfs2VVWxdetWzJs3L+Jj5s2bF7I9ALz44osh2wcCqY8//hgvvfQSSkpK0jMAIiIiIkofxdAzcDJbQgMSswWIVL1my5EvqgoAY5RQKhDmaEF9wkZN7LldYIqc1ytVV+agCryC4tCG6QGdHbLSXjyr8wXTNHlsZW3k0M2WA5RXpqfhuccDtLUCFcPlPWhqAFpOhb5O2ULTgNNNEliWVwEjxwLWHHl9KDqfTz6bTQ1Ag3+qaTq5XRIWs58U0aAz4NP3Vq9ejSuvvBKzZ8/GnDlzcOedd8LhcGDFihUAgCuuuAI1NTW4/fbbAQDXXXcdFi5ciD/+8Y9YsmQJNm3ahF27duHuu2VJXY/Hg4svvhh79uzBs88+C5/P191vatiwYbBE6ilARERERJnHZJIAyGiUL7mATN8LrlAxmgBLlEbmqk+mzkULpey58liXS5/uN2oCsOPF0OcPTCH0evVjCigoktCoq1OvzlJ9UlGTky89rgLHEQ+3U75kDyuNvk2Jv7eUsyt0umIyVFVCmMoamTbo80og9cVRoLlJgraBrPhUValCs9r7p3qr0yHv/5jJeuBhswMHPpCG+GZ+t+jB2QW0t8jnv2akfPaPHZLKQHPq+/wBkM+EPbfndF8iyngDHkotX74cjY2NuPnmm1FfX4/p06djy5Yt3c3Mjx07FlImP3/+fDz44IO46aab8Itf/ALjxo3Dk08+iTPPPBMAcPz4cTz99NMAgOnTp4fs65VXXsF5553XL+MiIiIioiQZTRKA5OTJVDIAMFvDKqXMUUKpHL0BebTpe1abfJHt7NBDncoaua3LIdfzCvQQxuuRECK4R1VOnjQeP/6pHkp1OuQLedVw4HSjBFbxTity+wOP3po25+XL1L76z1IXSrU0AcXDgJHj5LUzGoHyahnbgQ+A1tNA0bDU7CteXZ0SdthzgY4GeU3TMXUxQFOl2qduXGgFTkkFUN4ENHwBlFawh1EwTZP3aPgooHa0/K6oPgmNGo77X680hInsJ0U0aA14KAUAq1atwqpVqyLet23bth63LVu2DMuWLYu4fV1dXVYuuU1EREQ05BgM0iemoFhCKatdQpKQnlLGyMGNLTiUilKdoSjSV6r1VNBtBmDUeH0VvuDV7XxeWSkv/ItvWRVw8gu9YqnLIaGOPRcoKpXQKN5QyuMGcgr7rgYqLpUeWKqafOWQo11Cv5Hje04ZzC+SKrKP98l2/dm7R/VJtZbBKNPnSitl1cL6z4CmenkNolXDJaOjXRrVV9SE3m4wADV1EtA52rkCYrCONvncV9Xqv3cGIzBijPxetDTL+5VKqn9aaaD3GxENKuzOR0RERESZyWAEoAHnLJaQ59zFEggFT4UzGKP3lFL9oVRvgUVuoK9U0D9qjpqgXw4OHLzeyD1r8gokKOlokzDJaJJG2ABQWCQVN/H+o6nqi636Ka9AtnN29r1tXzodQOXw6I3ZS8qlasjZmXivrHhpGtDcCOQXS1P4keMkEBsxBpg8Eyguk6Aj1VRVxlk1InJPr9x8Caa6HPrU0mzn80k12/C6ntPobDnAiLEAFL0KMVWcXfL8bHJONCgxlCIiIiKizGQ0SMBTPRJYdhUw7kzAoPSslIoU3gSm75ksvVcQWe0SbAUHC6Mn6l9wgwMqaNIXJ5LyKjnW000S6gTCrLxCf9+qrlhG7N+NP8AyR9lXMItVptMl+0Vf9clr21e1ScVweT9aT8cftCWi9bRM0Rs9ASgMmzaYV+DvWWRIfUjW3iLvY1nkFcEByHteUJSeZvODUWuz9ECLtFokIBVSVcNT/3o5O6XiMdrvJhFlNIZSRERERJSZDCYJnVR/YKRpgGIMDZkMEVboAySUUn19f1EN9IjyefTbrHbgyhuAK64Dzpzdc/tI8gqB0nJA1WQ6X+AYbXYJerriqGQKNNCOtYl2YYm8NsmERF3+5uF9VZsoikzNysmVyrB0cjnlPQxUR0VSOEx6XrWdTt1+NU3vUdTbe2Ayy3vt7EptQKdp8nlpa5HqNberfwLAZLhd8t/qkdGnywJSbWc0Sc+0VNA0mVYbHlgS0aDBUIqIiIiIMpPBEFrFpKpSPRWyjX96XvgUPVuOfFmN1AQ9mMksj/WGTcHKyZWwI8DnX0Ev2krOigKU1wBlFVK1Eay4VJqkxyreUCq/QMK3ZKqFnJ1ScRXLPm05MkXL2SmvcTp4PPL8taMlyIgmEJLZcgBHR2r2HZgOFv4+RlJUKtNHnXFUwvVG06TaDhpgt0so1+UAGuvj+wz1t/YWaWLeV7+ovEKZ2upIUaDpcctnn329iAYthlJERERElJmMJgmCgiulwsMng3+FOHtYDxt7jmwfLUTq3odRvtT6+vjC7/PKtr2FNoXFMsUwvJ9OXqFMI4y1OsTtktX7Ym1cbrVLI/JE+0ppmrzG8VSblFYBw8pSW6HUfTyqBDNFJdLTqS85edILy9Emj01WV4cEdOHvY8R958rrkIopaYFAymaXiqMzzwKmny3VehXVcl8800Cj7SPVVVceDwBFQty+Vr9TFJne5/XoDcqT0eWQKjp7hF5vRDQoMJQiIiIiosxkMEgQFKiU0lSZ0hfMaJQV8yI1VtbQ+1Si7m3tfTer9ngkWOqrz1Ok6YI5udJQPdbQyOuV6qdYKYpUqHgSnBIVqDaJp1G0ySSNvjWkvp/T6VMS8JVVyfsbi4oamSbZ3prcvlVVpmAOK4v9MaWV8h54k6gaCzR0t+UAY87Q3wuTWS6PmQyMGC1TJhOZNul2SajVVC8/pxqAzo7UBEMdrRIgFsRQWQZI4JebL/tPltsln/2+wjAiylgMpYiIiIgoc1msQaGU1jOkCFRKBYdSBoMeDpl6WXkvwGbvexqazyPPGWtIEnKMBvkSHuv0KwWxrbwXLK8gvmqsYF0OqcyKt9qkcJiEQW0pbHruaJfXeOTY+BpXW6wyjc/pTK5aqsshVXaxBiyAVKkVFCY3Ja2zQ8YwbrI8VziTCRgxTsIpn1cCplhec7cLaDwhUxsLhwETpgITp0tlmapKQOVJYlqgzyfHU1kTe2Wf2SJBXmeSzfl9Xvn9z+ujOT8RZTSGUkRERESUuSzWsOl7Yaev3ZVSQb2jbDn+yglNvrT2JZY+Sl4vkBPDdK5oAqsB9rkfj0xR7KsXVricPCAvD3Am8EXf7U6s2iS4n1OyAQMgFVudDqB2TGKNq4vLpCotmWPpcgDDymPv5wXIZ7CsGnAl2JBcVSU0qhohAVc0iiJh0oSpElo2NUT/TKk+Ca462vxTAWcBk6bLFLuySgm3ppwl719LU+L9qjpa5ZgLS+J73LAymVqbTJVdV6cEqfFU+BFRxmEoRURERESZy2oLnb4XXqlk9K/GZw0KjAIr7wWaoPcllgBC9cXWY6i3fcSS+bhdsm28lVIGA1BcHv+XfJ8XMCjxTRcMlqp+Tj6fhCNVw6XqJhFWm4QuiTY893kl4CyOM2ABZPqaPUdCrXi1t8rUw/Kq2Pc1YZo0Fm8+KdP+uhzyGnY6gFMn5SevAJg4DRg9UZ4/PHS02oC68fKaNTfG37ReVf2rFA6PrSIxWG6+BKHJrODo7JLniHffRJRRGEoRERERUeYymfUwR9Ui9JQySTBlDZrqZc8FfKo+tS+mfSh6RVZESnzVM+ECj+0ruHG7pdonll5Y4fIL/SsJxhEuOLsAW5LVJhU1Ui3TlmA/J1WVcKWkEhgxNrbqtmiGlctnIZHV8Do7gLz83quVorHZpQIp3j5JPi/gdko1UzzTFXNygbGTgfFTJZzyeiXUU30SMk2cJj/DynqvgDNbJLQKBFO9/g6EcbTr4VK8FEWOW1Pj22dAIKiOZYVEIspojJWJiIiIKHMZjdJMG5BwKrxvjaJILyVr2PQ9TZWpfrFWSpnMgNcHWKIEIpqWWFAUYLH69+HtPdzyuhPvkZNXIA3VuzpiD1acXUBlbfJjqx4JHPzAv0phHF8xAivOFQ0DRk+IL5iJJC8fKCkHGr6Iv9rM2SVT6BLpGwYAJRVA/edSrWaNcfplW4uEOiXl8e/PbJFV+SqqZZ9O/3S2eF9DswUYNUGm8J1uknH0xeeT/Y2ZlPh7VuhveO7okEA1Ho52qe6Lp/cXEWUkVkoRERERUeYKrpQCIlfRmMNDKf9qeoHV+/piNvtX+YtSYaT6nyuZ4MZskWlGfVUxaZpMA0uE0SiVQrFO4dM0GVtBUWL7C1ZaAQwrBVqbY39MIJCy5wKjJiY3PTLkWCrlv/E08Ha7JNxMpJdVQF6BVCZ1xFgx5nFLlVj1yOSnoFltcuyJBkRWGzBynPQya2vpe/u2ZgnTyqsT2x8gv09l1bGvShnM1SnvM6fuEQ16DKWIiIiIKHMFQqjAtLdIU5HMZsAaVBWTkxfUUyrG6XsmS/RQyuuTL7/JhFKBffTWUNrnkyoja5wVPsEKimXcsUzhczslxMhLQaNooxEYPhowW2MLNTRVpotZrMCYiRLopEpBsfRdijUcAqSv07DS5I5DUfwhjRJbINbaLFP+ihLoYZUO+YVA3TgJy3qb/ujqkurF4aOS+50ApH+XxSrPGStXl4RnmfK6EVFSGEoRERERUeYymuTH5w+lIi07b7ECZVVSOWHLkV46qj/giaU/kaJIpUi0IEf1SuhiTuILuKJIH6DeQimPq2fVV7yCp/D1patLtk9VhVJhsTTO9np6XwFP9QFNJyUMmzAlueqkSAwGaZbu88UWzvl8EpKVVsa/AmG4giKZithXINbpkPe6emTkz/RAKa0Eho8E2lsknAqnqRI6VtWm5n3LyZOKK0d77I9xtMtjcvKS3z8RDbgM+gtIRERERMlYv3496urqYLPZMHfuXLz99tu9bt/S0oKVK1eiqqoKVqsV48ePx+bNm/vpaGNk8q+uF2iGrEQ4fTWapJLpyuuBa34p1SqqGl9jclsO4IsSGPl8EkrF0yspEnuu3qA5ErdbAqlk+ioZjdITKJZG3x43UNxHI+x4lVYAI8cCne0yJS6csxNoapAKmXFnJtZUPBbFpfLTdrrvbR1tUiWUipDFYATKayQMi9bAW1MltKoakdoKsVRQFKBmlKyo13IqdCVDVQVaTwO5BXLsqfjcKIoEYarW++9G9zH45DhKKlL7uSWiAcNJuERERERDwEMPPYTVq1djw4YNmDt3Lu68804sXrwYBw4cQHl5zybKbrcbF1xwAcrLy/Hoo4+ipqYGR48eRVFRUf8ffG+6K6X8X1gjVZUYTQAU+ZIaCI5UFbDEEUpZbdLjKBKvV6p6kq1osVij7wOQkKjU/2W7t+36kl/kX4XPE316lddf/ZXMqnuRKIoEFm4XcPwoAE2mWhlNElRZrBJ6DK9LriKsLwajVPO0nJLXNVpAqWnSg6t2TOr6ExWXSMVU6+nIK9O1tcj9lcNTs79UM5mlgXlOHvDZEaC5S95Xn1eC1RGj428i35uCYmla7mjvu7+Zo0M+s1x1j2jIYChFRERENATccccduPrqq7FixQoAwIYNG/Dcc8/h3nvvxZo1a3psf++996K5uRk7duyA2T8tra6urtd9uFwuuFx69UtbWxsAQFVVqKqaopGg+zk1TYOqGACD/wtxIKgJ35fRILcFBzler4QMsR6XySSPjxQGeT0SpiQ7RpMZQJR9ABK82XMA/+upaVpir2tOnkwVdHRE/5Lf5ZBQyJ6b/LgiGT5awoaONmlm7nFJGFVaqfew6mW/SY0/oKBYGo+fOhl9dbtOh/TwKixO3etgMEq/pUP/AFqaQwMUj0cCu1ET5PMQYZ8pGXvS/OGiPRc4dkiCxdJK+Tyl4nchmMEgz33oQyA3H6qiyPjDf080TT63oybKazygr096ZMZ7P3A4/qE1/ljHwVCKiIiIaJBzu93YvXs31q5d232bwWDAokWLsHPnzoiPefrppzFv3jysXLkSTz31FMrKynDZZZfhxhtvhDFKc/Dbb78dt9xyS4/bGxsb4XTGuOJbjFRVRWtrKzRNg8GtSVNurwqcbgE8YSe6HW2Axwc4gqaseXyAwwmcPBnbDjs65Hk7OntOC3J5Aac39ueKpssBeAG0d/TsdaVpMr6OLkA5GTr+RCq0TDnAqWbAGGUqYLsDKCkDmuNYLS8R1jygPEcCN7MZ6OySnz4kPf4Ac468r61tkavG2trkdWjrkJ9UKq4CThwDmpok+OpySMBZWCKfgyifp5SNPVXKR8rvhKoALXE0j4+HagQsuUBjI9TcfLQ63dAAGIJ/Fzs7AMUix5Hs72KGyrj3vp9x/ENr/O3tsfWKYyhFRERENMg1NTXB5/OhoqIi5PaKigrs378/4mOOHDmCl19+Gd/73vewefNmHDp0CNdeey08Hg/WrVsX8TFr167F6tWru6+3tbWhtrYWZWVlKChIbW8cVVWhKArKyspgaGsETjklyCkpBQqLQje2WYH6TwG7RQ97ulqBYcOACFMXI8qxAQ1HAZul5zSurlagNI7nisblBE5+BliMPVfY87gBrxWoqgJy8kLHn8iXE5sVaD0JmI09pzFqGtClADXDgWFJjilNkh5/QFkZ4HMCDceBgorQnmQdbUCOVaajhX+mUqG8HCgqAA59JJ+hwmKpPiou63WqYMrGPtjkWIH970E1AorNgrJcux5KuZyAQQXGnyFVVUNU1r73fhz/0Bq/zRbbFG2GUkRERERZSFVVlJeX4+6774bRaMSsWbNw/Phx/P73v48aSlmtVlitPStvDAZDWk6gFUWR57ba9dX0Ao3Pg5n9fadUDTAqgQdLZUysx2W1Sd8h1Qso4RU1ityX7BitVsBilqqh8Gosj3+KoC2nez/d409kvwWFQFkl0HhCmkIHczllvHkFmbXyW5ikxh+sZpQ0fm86Kf2ezBag1V8hNmoiUFScvqbZ5dUShHk90i8sxub7KRv7YDKsDKgZCRw9BMVeCIOiSCil+lf8G14nq2wO8QbnWfneB+H4h874Yx0DQykiIiKiQa60tBRGoxENDQ0htzc0NKCyMnJVQVVVFcxmc8hUvUmTJqG+vh5utxuWeJqEp5vFIqFUtJDJaJK+UlrQtD4N0sg7Viazvzm4DwjO3TQNUBC9YXg8DP4KqfYIU6A8bunXk6pm2waDNPo+3SQr3tly9PucndJvKbxaa6jKywcmTAU+OwzUfw5AkUbdoydG7zWVSmVDt7InpRQFqB4pDeJPNQMmRT7HjnYJE4ePGvKBFFE2GvzxGxEREVGWs1gsmDVrFrZu3dp9m6qq2Lp1K+bNmxfxMQsWLMChQ4dCGpEePHgQVVVVmRVIAf4qKFW+oCqRQil/9VRghT5NlSApnlDKaJRKJZ8n9HZfoEIrRa+JPU+qZsJ5PVK5lEr5RVKp09aqN1f3eaXRdnFpdn3Bt9llRbnRE2Ua3bgz+ieQovhYrMCIMVLJ5/NIWJuTI7dZovRHI6JBjaEUERER0RCwevVq3HPPPbj//vvx0Ucf4ZprroHD4eheje+KK64IaYR+zTXXoLm5Gddddx0OHjyI5557DrfddhtWrlw5UEOIzmTSw6iIlVJG/2pc/lDKp8p1Y5xVRza7HmwF+Lzy/KmqYLLZZZphOE2T+1KtYris6NfZAbi6gFONMgWquDT1+8p0BiNQUwecMQsoKhnoo6FoCocBdeOBqXOBaXOBM8+S24hoSOL0PSIiIqIhYPny5WhsbMTNN9+M+vp6TJ8+HVu2bOlufn7s2LGQ/g61tbV4/vnnccMNN2Dq1KmoqanBddddhxtvvHGghhCd0aQHT5GqewIBlNu/AqCqyvbxVEoB/lDKG3qbz+uvlErB9D1AVqALp/oAgyLVIamWkyvNtQ9/KMHaiNESzMTY22hIivdzQf3PaExNHzciyngMpYiIiIiGiFWrVmHVqlUR79u2bVuP2+bNm4c333wzzUeVAiZ/I3MF0b+kmi1AV6dcVn2JVUpFCmp8XsBiS12llNkqAVRgOiIgU5TMlvT1eCqvAjpaZKW9LGgUTUREgwdDKSIiIiLKbEaT3k8qUk8pQJqhBxqdqz7Z3hBnRUzEUMqX2goms0WqrrwevUeOxyNhVbp65liswIRpDKOIiCjjsB6SiIiIiDKbMVAppfRSKWXW+0Gpqj7lLx4ms+wjqPk7fN7UVjBZLP5V/oKmCXrc0uQ8nVOVGEgREVEGYihFRERERJnNZNYrpaIFNyYLAH8DcdUnIVW8QUx3FVNQYKRqgDWFFUwmszxf8Ap8Pi+Qm5e6fRAREQ0SDKWIiIiIKLMZjX1XPgX3j1LVxBqTW6wSTHlcobenqsl5gC03NJQC0tPknIiIKMMxlCIiIiKizGexxh5K+XyJrS5nMgFFJYCzK+z2FIdShcWApgHtrVKVZTCmr8k5ERFRBmMoRURERESZz2LpvXG50dg9ew+amlgoBQAFRTL9T9P0humpDqXKqoCxZ8jzN5+UsbFSioiIshBX3yMiIiKizGexhTYgD2c0Agr0FfgSDZJy86Uqy+0EFKNUT6U6lFIUoKJa+l59ckCfNkhERJRlGEoRERERUeYzW6QheDRGk1RS+fyhVLwr7wXYcmQlvPY2wGaT5zGnOJQKGFYmYZvXnZ7nJyIiynAMpYiIiIgo8w0rk+lu0RiN8hPYJtFQSlGkr1Rzo4RRgSbr6ZKXn77nJiIiynAMpYiIiIgo8/XVcylQKdUdSiVxmptXIM/ldkmPKQPbsBIREaUD/w9LRERERIOf0SThUWA1u0QrpQDpK2XLATo72ICciIgojRhKEREREdHgZzBIMOX16JcTZTLLFD5VBaz21B0jERERhWAoRURERESDn6JIDyiPO/lQCpBpe/ac9DU5JyIiIoZSRERERDREmC369L1k+0Dl5suPiaEUERFRujCUIiIiIqKhwWSR6XvGFFRK2XOA4lLAlpuaYyMiIqIeuPoeEREREQ0Ngal2JnPylVKKAtSNT/6YiIiIKCpWShERERHR0BAIo0yWgT4SIiIiigFDKSIiIiIaGoxG6SfF5uRERESDAkMpIiIiIhoajCbpJ2W2DvSREBERUQwYShERERHR0GA0SjDFSikiIqJBISNCqfXr16Ourg42mw1z587F22+/3ev2jzzyCCZOnAibzYYpU6Zg8+bNIfdrmoabb74ZVVVVsNvtWLRoET7++ON0DoGIiIiIBprRKH2lkl15j4iIiPrFgIdSDz30EFavXo1169Zhz549mDZtGhYvXoyTJ09G3H7Hjh249NJL8cMf/hB79+7F0qVLsXTpUuzbt697m9/97nf4f//v/2HDhg146623kJubi8WLF8PpdPbXsIiIiIiovxlN0uTcaBzoIyEiIqIYKJqmaQN5AHPnzsVZZ52Fu+66CwCgqipqa2vxk5/8BGvWrOmx/fLly+FwOPDss89233b22Wdj+vTp2LBhAzRNQ3V1NX72s5/hn//5nwEAra2tqKiowH333Yfvfve7EY/D5XLB5XJ1X29ra0NtbS1Onz6NgoKCVA4ZP9m4HQ2nHYBigDLgseAA0ABNU6EoBkAZ6IPpZ9k8doDjz+bxZ/PYgewe/0CMXQNUDSgvsGH91eek/Onb2tpQXFyM1tbWlJ8jDDZtbW0oLCxMy2uhqipOnjyJ8vJyGAwxnjC5nMCB94Hho4BhZSk9nv6W0PiHkGwefzaPHcju8Wfz2AGOf6iNP9ZzhAGtbXa73di9ezfWrl3bfZvBYMCiRYuwc+fOiI/ZuXMnVq9eHXLb4sWL8eSTTwIAPvnkE9TX12PRokXd9xcWFmLu3LnYuXNn1FDq9ttvxy233NLj9sbGxpRXWJ1q60K7ywfAl9LnHXyyefzZPHaA48/m8Wfz2IHsHn//j91kQNTK62S0t7en/DkpRTh9j4iIaFAZ0P9jNzU1wefzoaKiIuT2iooK7N+/P+Jj6uvrI25fX1/ffX/gtmjbRLJ27dqQsCtQKVVWVpbyf/krKbBD1bQhkX4mSlXVrB1/No8d4PizefzZPHYgu8c/UGMvybehvLw85c9rs9lS/pyUIiYzUFUL5GV3BRsREdFgwX9G8rNarbBaey4fbDAYUn4i/R8/XDCkyvLiNdTKEuORzWMHOP5sHn82jx3I7vEPxbEPlXEMWcWlA30EREREFKMBPasqLS2F0WhEQ0NDyO0NDQ2orKyM+JjKyspetw/8N57nJCIiIiIiIiKi/jWgoZTFYsGsWbOwdevW7ttUVcXWrVsxb968iI+ZN29eyPYA8OKLL3ZvP2rUKFRWVoZs09bWhrfeeivqcxIRERERERERUf8a8Ol7q1evxpVXXonZs2djzpw5uPPOO+FwOLBixQoAwBVXXIGamhrcfvvtAIDrrrsOCxcuxB//+EcsWbIEmzZtwq5du3D33XcDABRFwfXXX4/f/OY3GDduHEaNGoVf/epXqK6uxtKlSwdqmEREREREREREFGTAQ6nly5ejsbERN998M+rr6zF9+nRs2bKlu1H5sWPHQno3zJ8/Hw8++CBuuukm/OIXv8C4cePw5JNP4swzz+ze5uc//zkcDgd+9KMfoaWlBeeccw62bNnCxqRERERERERERBliwEMpAFi1ahVWrVoV8b5t27b1uG3ZsmVYtmxZ1OdTFAW33norbr311lQdIhERERERERERpRCXjyEiIiIiIiIion7HUIqIiIiIiIiIiPodQykiIiIiIiIiIup3DKWIiIiIiIiIiKjfZUSj80ykaRoAoK2tLeXPraoq2tvbYbPZQlYWzBbZPP5sHjvA8Wfz+LN57EB2j38ojj1wbhA4V8hmPF9KH44/e8efzWMHsnv82Tx2gOMfauOP9XyJoVQU7e3tAIDa2toBPhIiIiLKRO3t7SgsLBzowxhQPF8iIiKi3vR1vqRo/Ge+iFRVxRdffIH8/HwoipLS525ra0NtbS0+++wzFBQUpPS5B4NsHn82jx3g+LN5/Nk8diC7xz8Ux65pGtrb21FdXT0k/iUzGTxfSh+OP3vHn81jB7J7/Nk8doDjH2rjj/V8iZVSURgMBgwfPjyt+ygoKBgSH7ZEZfP4s3nsAMefzePP5rED2T3+oTb2bK+QCuD5Uvpx/Nk7/mweO5Dd48/msQMc/1AafyznS9n9z3tERERERERERDQgGEoREREREREREVG/Yyg1AKxWK9atWwer1TrQhzIgsnn82Tx2gOPP5vFn89iB7B5/No+dkpPtnx2OP3vHn81jB7J7/Nk8doDjz9bxs9E5ERERERERERH1O1ZKERERERERERFRv2MoRURERERERERE/Y6hFBERERERERER9TuGUkRERERERERE1O8YSqXJ+vXrUVdXB5vNhrlz5+Ltt9/udftHHnkEEydOhM1mw5QpU7B58+Z+OtL0iGf899xzD84991wUFxejuLgYixYt6vP1ymTxvvcBmzZtgqIoWLp0aXoPMM3iHX9LSwtWrlyJqqoqWK1WjB8/flB//uMd/5133okJEybAbrejtrYWN9xwA5xOZz8dbeq89tpruOiii1BdXQ1FUfDkk0/2+Zht27Zh5syZsFqtGDt2LO677760H2c6xDv2xx9/HBdccAHKyspQUFCAefPm4fnnn++fg02DRN77gO3bt8NkMmH69OlpOz7KbDxfyt7zJSC7z5l4vsTzpWw7XwKy+5yJ50vRMZRKg4ceegirV6/GunXrsGfPHkybNg2LFy/GyZMnI26/Y8cOXHrppfjhD3+IvXv3YunSpVi6dCn27dvXz0eeGvGOf9u2bbj00kvxyiuvYOfOnaitrcWFF16I48eP9/ORJy/esQd8+umn+Od//mece+65/XSk6RHv+N1uNy644AJ8+umnePTRR3HgwAHcc889qKmp6ecjT414x//ggw9izZo1WLduHT766CNs3LgRDz30EH7xi1/085Enz+FwYNq0aVi/fn1M23/yySdYsmQJvvzlL+Pdd9/F9ddfj6uuumpQnmjEO/bXXnsNF1xwATZv3ozdu3fjy1/+Mi666CLs3bs3zUeaHvGOP6ClpQVXXHEFvvKVr6TpyCjT8Xwpe8+XgOw+Z+L5Es+XsvF8CcjucyaeL/VCo5SbM2eOtnLlyu7rPp9Pq66u1m6//faI219yySXakiVLQm6bO3eu9k//9E9pPc50iXf84bxer5afn6/df//96TrEtElk7F6vV5s/f7723//939qVV16pfetb3+qHI02PeMf/5z//WRs9erTmdrv76xDTKt7xr1y5Ujv//PNDblu9erW2YMGCtB5nugHQnnjiiV63+fnPf66dccYZIbctX75cW7x4cRqPLP1iGXskkydP1m655ZbUH1A/i2f8y5cv12666SZt3bp12rRp09J6XJSZeL6UvedLmpbd50w8X+L5kqZl9/mSpmX3ORPPl0KxUirF3G43du/ejUWLFnXfZjAYsGjRIuzcuTPiY3bu3BmyPQAsXrw46vaZLJHxh+vs7ITH48GwYcPSdZhpkejYb731VpSXl+OHP/xhfxxm2iQy/qeffhrz5s3DypUrUVFRgTPPPBO33XYbfD5ffx12yiQy/vnz52P37t3dJetHjhzB5s2b8fWvf71fjnkgDaW/e8lSVRXt7e2D7m9eMv7yl7/gyJEjWLdu3UAfCg0Qni9l7/kSkN3nTDxf4vlSPIbS371UyLZzpmw5XzIN9AEMNU1NTfD5fKioqAi5vaKiAvv374/4mPr6+ojb19fXp+040yWR8Ye78cYbUV1d3eMPcKZLZOxvvPEGNm7ciHfffbcfjjC9Ehn/kSNH8PLLL+N73/seNm/ejEOHDuHaa6+Fx+MZdH98Exn/ZZddhqamJpxzzjnQNA1erxc//vGPB2U5eryi/d1ra2tDV1cX7Hb7AB1Z//vDH/6Ajo4OXHLJJQN9KP3i448/xpo1a/D666/DZOJpSLbi+VL2ni8B2X3OxPMlni/Fg+dLobLpnCmbzpdYKUUZ5be//S02bdqEJ554AjabbaAPJ63a29tx+eWX45577kFpaelAH86AUFUV5eXluPvuuzFr1iwsX74cv/zlL7Fhw4aBPrR+sW3bNtx22234z//8T+zZswePP/44nnvuOfz6178e6EOjfvLggw/illtuwcMPP4zy8vKBPpy08/l8uOyyy3DLLbdg/PjxA304RINWNp0vATxn4vkSz5cou86Zsu18aWhHbgOgtLQURqMRDQ0NIbc3NDSgsrIy4mMqKyvj2j6TJTL+gD/84Q/47W9/i5deeglTp05N52GmRbxjP3z4MD799FNcdNFF3bepqgoAMJlMOHDgAMaMGZPeg06hRN77qqoqmM1mGI3G7tsmTZqE+vp6uN1uWCyWtB5zKiUy/l/96le4/PLLcdVVVwEApkyZAofDgR/96Ef45S9/CYNh6P67QbS/ewUFBVnzr36bNm3CVVddhUceeWRQVjokor29Hbt27cLevXuxatUqAPJ3T9M0mEwmvPDCCzj//PMH+CipP/B8KXvPl4DsPmfi+RLPl+LB8yWRbedM2Xa+NHR/gweIxWLBrFmzsHXr1u7bVFXF1q1bMW/evIiPmTdvXsj2APDiiy9G3T6TJTJ+APjd736HX//619iyZQtmz57dH4eacvGOfeLEifjggw/w7rvvdv9885vf7F5do7a2tj8PP2mJvPcLFizAoUOHuk8sAeDgwYOoqqoaVCdYQGLj7+zs7HEiFTjh1DQtfQebAYbS371E/O1vf8OKFSvwt7/9DUuWLBnow+k3BQUFPf7u/fjHP8aECRPw7rvvYu7cuQN9iNRPeL6UvedLQHafM/F8iedL8RhKf/cSlY3nTFl3vjSQXdaHqk2bNmlWq1W77777tA8//FD70Y9+pBUVFWn19fWapmna5Zdfrq1Zs6Z7++3bt2smk0n7wx/+oH300UfaunXrNLPZrH3wwQcDNYSkxDv+3/72t5rFYtEeffRR7cSJE90/7e3tAzWEhMU79nCDeSUZTYt//MeOHdPy8/O1VatWaQcOHNCeffZZrby8XPvNb34zUENISrzjX7dunZafn6/97W9/044cOaK98MIL2pgxY7RLLrlkoIaQsPb2dm3v3r3a3r17NQDaHXfcoe3du1c7evSopmmatmbNGu3yyy/v3v7IkSNaTk6O9i//8i/aRx99pK1fv14zGo3ali1bBmoICYt37A888IBmMpm09evXh/zNa2lpGaghJCXe8YcbyqvJUO94vpS950ualt3nTDxf4vlSNp4vaVp2nzPxfCk6hlJp8h//8R/aiBEjNIvFos2ZM0d78803u+9buHChduWVV4Zs//DDD2vjx4/XLBaLdsYZZ2jPPfdcPx9xasUz/pEjR2oAevysW7eu/w88BeJ974MN5hOsgHjHv2PHDm3u3Lma1WrVRo8erf3bv/2b5vV6+/moUyee8Xs8Hu1f//VftTFjxmg2m02rra3Vrr32Wu306dP9f+BJeuWVVyL+HgfGe+WVV2oLFy7s8Zjp06drFotFGz16tPaXv/yl3487FeId+8KFC3vdfrBJ5L0PNpRPsqhvPF/K3vMlTcvucyaeL/F8KdvOlzQtu8+ZeL4UnaJpQ7zmkYiIiIiIiIiIMg57ShERERERERERUb9jKEVERERERERERP2OoRQREREREREREfU7hlJERERERERERNTvGEoREREREREREVG/YyhFRERERERERET9jqEUERERERERERH1O4ZSRERERERERETU7xhKEdGQ8YMf/ABLly4dsP1ffvnluO2221L2fGeffTYee+yxlD0fEREREc+XiCiTKJqmaQN9EEREfVEUpdf7161bhxtuuAGapqGoqKh/DirIe++9h/PPPx9Hjx5FXl5eSp7z2WefxQ033IADBw7AYOC/IRAREVHveL7E8yWiwYahFBENCvX19d2XH3roIdx88804cOBA9215eXkpO7lJxFVXXQWTyYQNGzak7Dl9Ph9qamqwceNGLFmyJGXPS0REREMTz5d4vkQ02DBKJqJBobKysvunsLAQiqKE3JaXl9ejHP28887DT37yE1x//fUoLi5GRUUF7rnnHjgcDqxYsQL5+fkYO3Ys/v73v4fsa9++ffja176GvLw8VFRU4PLLL0dTU1PUY/P5fHj00Udx0UUXhdxeV1eHX//617j00kuRm5uLmpoarF+/vvt+TdPwr//6rxgxYgSsViuqq6vx05/+tPt+o9GIr3/969i0aVOSrx4RERFlA54vEdFgw1CKiIa0+++/H6WlpXj77bfxk5/8BNdccw2WLVuG+fPnY8+ePbjwwgtx+eWXo7OzEwDQ0tKC888/HzNmzMCuXbuwZcsWNDQ04JJLLom6j/fffx+tra2YPXt2j/t+//vfY9q0adi7dy/WrFmD6667Di+++CIA4LHHHsOf/vQn/Nd//Rc+/vhjPPnkk5gyZUrI4+fMmYPXX389ha8IERERUSieLxHRQDEN9AEQEaXTtGnTcNNNNwEA1q5di9/+9rcoLS3F1VdfDQC4+eab8ec//xnvv/8+zj77bNx1112YMWNGSAPOe++9F7W1tTh48CDGjx/fYx9Hjx6F0WhEeXl5j/sWLFiANWvWAADGjx+P7du3409/+hMuuOACHDt2DJWVlVi0aBHMZjNGjBiBOXPmhDy+uroan332GVRVZZ8EIiIiSgueLxHRQOFvLBENaVOnTu2+bDQaUVJSEvKvaxUVFQCAkydPApAGnK+88kp3z4W8vDxMnDgRAHD48OGI++jq6oLVao3YXHTevHk9rn/00UcAgGXLlqGrqwujR4/G1VdfjSeeeAJerzdke7vdDlVV4XK54h06ERERUUx4vkREA4WVUkQ0pJnN5pDriqKE3BY4MVJVFQDQ0dGBiy66CP/3//7fHs9VVVUVcR+lpaXo7OyE2+2GxWKJ+dhqa2tx4MABvPTSS3jxxRdx7bXX4ve//z1effXV7mNsbm5Gbm4u7HZ7zM9LREREFA+eLxHRQGEoRUQUZObMmXjsscdQV1cHkym2P5HTp08HAHz44YfdlwPefPPNHtcnTZrUfd1ut+Oiiy7CRRddhJUrV2LixIn44IMPMHPmTADSRHTGjBmJD4iIiIgoxXi+RESpwul7RERBVq5ciebmZlx66aV45513cPjwYTz//PNYsWIFfD5fxMeUlZVh5syZeOONN3rct337dvzud7/DwYMHsX79ejzyyCO47rrrAAD33XcfNm7ciH379uHIkSP461//CrvdjpEjR3Y//vXXX8eFF16YnsESERERJYDnS0SUKgyliIiCVFdXY/v27fD5fLjwwgsxZcoUXH/99SgqKuq1ceZVV12FBx54oMftP/vZz7Br1y7MmDEDv/nNb3DHHXdg8eLFAICioiLcc889WLBgAaZOnYqXXnoJzzzzDEpKSgAAx48fx44dO7BixYr0DJaIiIgoATxfIqJUUTRN0wb6IIiIBruuri5MmDABDz30UHezzrq6Olx//fW4/vrrE3rOG2+8EadPn8bdd9+dwiMlIiIiGhg8XyKicOwpRUSUAna7Hf/zP/+DpqamlD1neXk5Vq9enbLnIyIiIhpIPF8ionAMpYiIUuS8885L6fP97Gc/S+nzEREREQ00ni8RUTBO3yMiIiIiIiIion7HRudERERERERERNTvGEoREREREREREVG/YyhFRERERERERET9jqEUERERERERERH1O4ZSRERERERERETU7xhKERERERERERFRv2MoRURERERERERE/Y6hFBERERERERER9bv/HwJhvoUCNAmiAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "colors = [\"steelblue\", \"tomato\"]\n", + "\n", + "for port in range(n_ports):\n", + " ax = axes[port]\n", + " mean_amp = np.asarray(jnp.abs(mu_y[:, port])) # |µ_y[n]|\n", + " std_y = np.sqrt(np.maximum(\n", + " np.real(np.asarray(Cy[:, :, port, port]).diagonal()), 0.0\n", + " )) # σ_y[n]\n", + "\n", + " ax.plot(t_ps, mean_amp, color=colors[port], linewidth=2, label=\"|µ_y|\")\n", + " ax.fill_between(\n", + " t_ps,\n", + " np.maximum(mean_amp - std_y, 0),\n", + " mean_amp + std_y,\n", + " alpha=0.25, color=colors[port], label=\"±σ\"\n", + " )\n", + " ax.set_title(f\"Port {port} ({port_names[port]}) — Mean Amplitude\")\n", + " ax.set_xlabel(\"Time (ps)\")\n", + " ax.set_ylabel(\"|µ_y[n]| (a.u.)\")\n", + " ax.legend()\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Output Mean Signal with ±1σ Noise Band\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a9b0c1d2", + "metadata": {}, + "source": [ + "### 5b. Noise Variance Evolution\n", + "\n", + "The diagonal $C_y[n, n, \\text{port}, \\text{port}]$ gives the instantaneous noise\n", + "power at each time step. \n", + "\n", + "For the input white noise ($C_x = \\sigma^2 I$), the output variance at time $n$ equals:\n", + "\n", + "$$\n", + "\\sigma_y^2[n] = \\sigma^2 \\sum_{k=0}^{\\min(n,\\, K-1)} \\|h[k]\\|_F^2\n", + "$$\n", + "\n", + "This accumulates as more of the impulse response taps contribute, then saturates\n", + "once $n \\geq K-1$ — exactly the Parseval relation in the time domain." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b0c1d2e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "# Theoretical saturation: σ² × cumulative ‖h[:,port,0]‖²\n", + "cum_h_energy = sigma_sq * np.cumsum(np.abs(np.asarray(h_gp[:, :, 0])) ** 2, axis=0) # (K, q)\n", + "\n", + "for port in range(n_ports):\n", + " ax = axes[port]\n", + "\n", + " # GP output variance (diagonal of Cy)\n", + " var_out = np.real(np.asarray(Cy[:, :, port, port]).diagonal())\n", + " ax.plot(t_ps, var_out, color=colors[port], linewidth=2, label=\"GP output variance\")\n", + "\n", + " # Theoretical prediction (input from port 0 only)\n", + " theory = np.pad(cum_h_energy[:, port], (0, T_sim - K_gp), constant_values=cum_h_energy[-1, port])\n", + " ax.plot(t_ps, theory, \"k--\", linewidth=1.5, label=\"Theory (Parseval)\")\n", + "\n", + " ax.set_title(f\"Port {port} ({port_names[port]}) — Output Noise Power\")\n", + " ax.set_xlabel(\"Time (ps)\")\n", + " ax.set_ylabel(\"σ²_y[n]\")\n", + " ax.legend()\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Output Variance Evolution (noise accumulates as IR taps contribute)\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1d2e3f4", + "metadata": {}, + "source": [ + "### 5c. Output Covariance Matrix — Temporal Correlations\n", + "\n", + "The off-diagonal blocks $C_y[n_1, n_2]$ quantify correlations **between different\n", + "time steps** in the output field. \n", + "\n", + "Even though the input noise was **white** (no temporal correlations), the ring\n", + "resonator acts as a bandpass filter and introduces **memory**: noise that entered\n", + "at time $n_2$ still influences the field at time $n_1 > n_2$ via the ring's\n", + "impulse response. The correlation decays over the ring's photon lifetime.\n", + "\n", + "Below we plot $\\text{Re}\\bigl(C_y[n_1, n_2, 1, 1]\\bigr)$ — the temporal covariance\n", + "of the transmission port (port 1). Compare it to the input covariance $C_x$,\n", + "which is diagonal by construction." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d2e3f4a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(14, 5))\n", + "gs = gridspec.GridSpec(1, 3, figure=fig, wspace=0.35)\n", + "\n", + "# --- Input covariance (port 0, diagonal = white noise) ---\n", + "ax0 = fig.add_subplot(gs[0])\n", + "Cx00 = np.real(np.asarray(Cx[:, :, 0, 0]))\n", + "im0 = ax0.imshow(Cx00, cmap=\"RdBu_r\", origin=\"lower\",\n", + " extent=[0, T_sim*dt*1e12, 0, T_sim*dt*1e12],\n", + " vmin=-Cx00.max(), vmax=Cx00.max())\n", + "plt.colorbar(im0, ax=ax0, shrink=0.85)\n", + "ax0.set_title(\"Input Cx[n₁,n₂, 0,0]\\n(white noise — diagonal)\")\n", + "ax0.set_xlabel(\"n₂ (ps)\")\n", + "ax0.set_ylabel(\"n₁ (ps)\")\n", + "\n", + "# --- Output covariance at port 1 ---\n", + "Cy11 = np.real(np.asarray(Cy[:, :, 1, 1]))\n", + "vmax = np.abs(Cy11).max()\n", + "\n", + "ax1 = fig.add_subplot(gs[1])\n", + "im1 = ax1.imshow(Cy11, cmap=\"RdBu_r\", origin=\"lower\",\n", + " extent=[0, T_sim*dt*1e12, 0, T_sim*dt*1e12],\n", + " vmin=-vmax, vmax=vmax)\n", + "plt.colorbar(im1, ax=ax1, shrink=0.85)\n", + "ax1.set_title(\"Output Cy[n₁,n₂, 1,1] (port 1)\\n(colored by ring filtering)\")\n", + "ax1.set_xlabel(\"n₂ (ps)\")\n", + "ax1.set_ylabel(\"n₁ (ps)\")\n", + "\n", + "# --- Off-diagonal slice: correlation vs lag ---\n", + "ax2 = fig.add_subplot(gs[2])\n", + "n_ref = T_sim // 2\n", + "lag = (np.arange(T_sim) - n_ref) * dt * 1e12\n", + "ax2.plot(lag, Cy11[n_ref, :], color=\"tomato\", linewidth=2)\n", + "ax2.axvline(0, color=\"k\", linewidth=0.8, linestyle=\"--\")\n", + "ax2.set_title(f\"Correlation slice at n₁ = {n_ref}\\nCy[{n_ref}, n₂, 1, 1]\")\n", + "ax2.set_xlabel(\"Lag n₂ − n₁ (ps)\")\n", + "ax2.set_ylabel(\"Covariance\")\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Temporal Covariance: White Input Noise → Correlated Output Noise\", fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e3f4a5b6", + "metadata": {}, + "source": [ + "## 6. Summary\n", + "\n", + "| Step | Module | Input | Output |\n", + "|---|---|---|---|\n", + "| Circuit | SAX | netlist + model params | S-matrix `(N_f, q, m)` |\n", + "| Vector fitting | `z_domain.vector_fitting_discrete` | S-matrix, frequency | poles, residues, feedthrough |\n", + "| State-space | `z_domain.state_space_discrete` | poles, residues, feedthrough | A, B, C, D |\n", + "| Impulse response | `z_domain.state_space_response_discrete` | A, B, C, D + impulse input | h `(K, q, m)` |\n", + "| GP simulation | `gaussian_process.gaussian_process_response` | h, µ_x, C_x | µ_y, C_y |\n", + "\n", + "**Key insight:** the same impulse response $h$ that drives deterministic time-domain\n", + "simulations also governs how **noise statistics** propagate via the Papoulis equations.\n", + "White noise at the input becomes *spectrally colored*, correlated noise at the output —\n", + "the bandwidth of the ring resonator directly sets the correlation time of the output\n", + "fluctuations (visible as off-diagonal bands in the $C_y$ heatmap above).\n", + "\n", + "This stochastic analysis is the intended backend for a Gaussian-process simulation\n", + "mode in Simphony, complementing the existing `sample_mode`, `block_mode`, and\n", + "`s_parameter` simulators." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/gm_lorenz_block_mode_tutorial.ipynb b/examples/gm_lorenz_block_mode_tutorial.ipynb new file mode 100644 index 00000000..9e7b011a --- /dev/null +++ b/examples/gm_lorenz_block_mode_tutorial.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6910cc9", + "metadata": {}, + "source": [ + "# Advanced Block Mode Tutorial: Lorenz-Driven Green Machine\n", + "\n", + "This notebook is the larger-system example in the Block mode tutorial sequence.\n", + "\n", + "The MZI lattice tutorial introduced S-parameters, vector fitting, state-space filters, and the basic directed Block mode run. The 16-QAM tutorial introduced multimode signals and more involved plotting. Here we use those ideas to build a Lorenz-driven Green Machine reservoir.\n", + "\n", + "In this notebook we:\n", + "\n", + "1. Generate a compact Lorenz drive signal.\n", + "2. Use that signal to drive a Mach-Zehnder style phase encoder.\n", + "3. Fan the encoded optical signal into delayed taps.\n", + "4. Feed those taps into a measured Green Machine S-parameter block.\n", + "5. Run Block mode and inspect the time-domain outputs." + ] + }, + { + "cell_type": "markdown", + "id": "0e8780c2", + "metadata": {}, + "source": [ + "## What this example adds\n", + "\n", + "Block mode still passes full time blocks between components. For optical signals the amplitude array has shape:\n", + "\n", + "```text\n", + "(T, L, M)\n", + "```\n", + "\n", + "where `T` is time, `L` is wavelength, and `M` is mode.\n", + "\n", + "This tutorial focuses on things the earlier tutorials only touched lightly:\n", + "\n", + "- custom source and modulator components,\n", + "- an external measured S-parameter data file,\n", + "- a larger directed netlist with fanout and delay taps,\n", + "- many top-level output ports from one reservoir block.\n", + "\n", + "To keep runtime reasonable, this example uses one optical mode and a downsampled Lorenz signal." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5d9b471e", + "metadata": {}, + "outputs": [], + "source": [ + "from functools import lru_cache\n", + "from pathlib import Path\n", + "\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from commpy.filters import rrcosfilter\n", + "from scipy.integrate import solve_ivp\n", + "from scipy.signal import upfirdn\n", + "\n", + "from simphony.circuit.circuit import Circuit\n", + "from simphony.component.component import BlockModeComponent\n", + "from simphony.component.port import Port\n", + "from simphony.libraries.siepic import waveguide, y_branch\n", + "from simphony.signal.block_mode import BlockModeOpticalSignal\n", + "from simphony.simulation.block_mode import BlockModeSimulation, BlockModeSimulationParameters\n", + "from simphony.utils import resample\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1bb6f43", + "metadata": {}, + "source": [ + "## 1. Create the Lorenz drive waveforms\n", + "\n", + "The Lorenz system gives us a rich, non-periodic signal without needing an external dataset. We generate it on a fine grid for the attractor plot, then downsample it before sending it through the photonic circuit.\n", + "\n", + "That split is intentional: the figure can look smooth while the Block mode simulation stays small.\n", + "\n", + "The final drive is converted into two complementary phase waveforms. One drives the upper MZI arm and the other drives the lower arm, creating a time-varying optical encoder before the Green Machine reservoir." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "317d84ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lorenz diagram samples: 2991\n", + "Lorenz symbols passed to Block mode: 300\n", + "Block mode time samples: 9600\n" + ] + } + ], + "source": [ + "SIGMA, RHO, BETA = 10.0, 28.0, 8 / 3\n", + "NUM_LORENZ_STEPS = 300\n", + "LORENZ_DT = 0.05\n", + "DIAGRAM_UPSAMPLE = 10\n", + "SAMPLES_PER_SYMBOL = 32\n", + "RRC_ROLLOFF = 0.35\n", + "RRC_SPAN = 8\n", + "MODULATION_DEPTH = np.pi / 4\n", + "\n", + "\n", + "def lorenz(_, xyz):\n", + " x, y, z = xyz\n", + " return [\n", + " SIGMA * (y - x),\n", + " x * (RHO - z) - y,\n", + " x * y - BETA * z,\n", + " ]\n", + "\n", + "\n", + "def minmax(u):\n", + " u = np.asarray(u)\n", + " return (u - u.min()) / (u.max() - u.min() + 1e-12)\n", + "\n", + "\n", + "def rrc_shape(symbols, taps, samples_per_symbol):\n", + " shaped = upfirdn(\n", + " taps.astype(np.float32),\n", + " np.asarray(symbols, dtype=np.float32),\n", + " up=samples_per_symbol,\n", + " )\n", + " group_delay = (len(taps) - 1) // 2\n", + " shaped = shaped[group_delay : group_delay + len(symbols) * samples_per_symbol]\n", + " return jnp.asarray(shaped, dtype=jnp.float32)\n", + "\n", + "\n", + "stop_time = LORENZ_DT * (NUM_LORENZ_STEPS - 1)\n", + "fine_dt = LORENZ_DT / DIAGRAM_UPSAMPLE\n", + "t_lorenz_fine = np.arange(0.0, stop_time + 1e-12, fine_dt)\n", + "\n", + "solution = solve_ivp(\n", + " lorenz,\n", + " (0.0, stop_time),\n", + " (0.0, 1.0, 1.05),\n", + " method='RK45',\n", + " max_step=fine_dt,\n", + " t_eval=t_lorenz_fine,\n", + ")\n", + "\n", + "x_lorenz_fine, y_lorenz_fine, z_lorenz_fine = solution.y\n", + "\n", + "x_lorenz = x_lorenz_fine[::DIAGRAM_UPSAMPLE]\n", + "y_lorenz = y_lorenz_fine[::DIAGRAM_UPSAMPLE]\n", + "z_lorenz = z_lorenz_fine[::DIAGRAM_UPSAMPLE]\n", + "\n", + "combined_drive = minmax(x_lorenz) + minmax(y_lorenz)\n", + "combined_drive = 2 * minmax(combined_drive) - 1\n", + "\n", + "_, rrc_taps = rrcosfilter(\n", + " RRC_SPAN * SAMPLES_PER_SYMBOL + 1,\n", + " RRC_ROLLOFF,\n", + " 1.0,\n", + " SAMPLES_PER_SYMBOL,\n", + ")\n", + "\n", + "phase_1_symbols = np.pi / 2 + MODULATION_DEPTH * combined_drive / 2\n", + "phase_2_symbols = -np.pi / 2 - MODULATION_DEPTH * combined_drive / 2\n", + "phase_wave1 = rrc_shape(phase_1_symbols, rrc_taps, SAMPLES_PER_SYMBOL)\n", + "phase_wave2 = rrc_shape(phase_2_symbols, rrc_taps, SAMPLES_PER_SYMBOL)\n", + "\n", + "print(f'Lorenz diagram samples: {x_lorenz_fine.shape[0]}')\n", + "print(f'Lorenz symbols passed to Block mode: {x_lorenz.shape[0]}')\n", + "print(f'Block mode time samples: {phase_wave1.shape[0]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e20523c2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 4), constrained_layout=True)\n", + "\n", + "ax0 = fig.add_subplot(1, 2, 1, projection='3d')\n", + "ax0.plot(x_lorenz_fine, y_lorenz_fine, z_lorenz_fine, linewidth=0.7)\n", + "ax0.set_title('Lorenz attractor')\n", + "ax0.set_xticks([])\n", + "ax0.set_yticks([])\n", + "ax0.set_zticks([])\n", + "\n", + "ax1 = fig.add_subplot(1, 2, 2)\n", + "ax1.plot(np.asarray(phase_wave1[:1200]), label='upper arm')\n", + "ax1.plot(np.asarray(phase_wave2[:1200]), label='lower arm')\n", + "ax1.set_title('Pulse-shaped phase drive')\n", + "ax1.set_xlabel('Block mode sample')\n", + "ax1.set_ylabel('Phase shift [rad]')\n", + "ax1.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b49857a4", + "metadata": {}, + "source": [ + "## 2. Define the custom Block mode components\n", + "\n", + "This circuit uses two small custom components:\n", + "\n", + "- `MultiWavelengthCWLaser` creates a constant optical field on each carrier wavelength.\n", + "- `TimeVaryingPhaseModulator` applies `exp(1j * phase[t])` to an incoming optical signal.\n", + "\n", + "A custom Block mode component needs:\n", + "\n", + "1. `ports`: class-level `Port` objects with names, types, and directionality.\n", + "2. `__init__(simulation_parameters, **settings)`: per-instance setup from the settings dictionary.\n", + "3. `block_mode_response(input_signals, simulation_parameters)`: full-block input to full-block output.\n", + "\n", + "For optical components, return `BlockModeOpticalSignal` objects. Their amplitude arrays should usually keep the `(T, L, M)` shape unless the component intentionally changes wavelength or mode content." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "36074583", + "metadata": {}, + "outputs": [], + "source": [ + "class MultiWavelengthCWLaser(BlockModeComponent):\n", + " ports = [Port(name='o0', type='optical', directionality='output')]\n", + "\n", + " def __init__(self, simulation_parameters, *, wavelengths, mode_idx=0):\n", + " self.wavelengths = jnp.atleast_1d(jnp.asarray(wavelengths, dtype=jnp.float32))\n", + " self.mode_idx = int(mode_idx)\n", + "\n", + " def block_mode_response(self, inputs, simulation_parameters):\n", + " num_time_steps = simulation_parameters.num_time_steps\n", + " num_wavelengths = self.wavelengths.shape[0]\n", + " num_modes = len(simulation_parameters.mode_identifiers)\n", + "\n", + " amplitude = jnp.zeros(\n", + " (num_time_steps, num_wavelengths, num_modes),\n", + " dtype=jnp.complex64,\n", + " )\n", + " amplitude = amplitude.at[:, :, self.mode_idx].set(1.0 + 0.0j)\n", + "\n", + " return {'o0': BlockModeOpticalSignal(amplitude=amplitude, wavelength=self.wavelengths)}\n", + "\n", + "\n", + "class TimeVaryingPhaseModulator(BlockModeComponent):\n", + " ports = [\n", + " Port(name='o0', type='optical', directionality='input'),\n", + " Port(name='o1', type='optical', directionality='output'),\n", + " ]\n", + "\n", + " def __init__(self, simulation_parameters, *, phase=0.0):\n", + " self.phase = jnp.asarray(phase, dtype=jnp.float32)\n", + "\n", + " def block_mode_response(self, input_signals, simulation_parameters):\n", + " optical_in = input_signals['o0']\n", + "\n", + " if self.phase.ndim == 0:\n", + " phase = jnp.full((simulation_parameters.num_time_steps,), self.phase)\n", + " else:\n", + " if self.phase.shape[0] != simulation_parameters.num_time_steps:\n", + " raise ValueError('The phase drive length must match num_time_steps.')\n", + " phase = self.phase\n", + "\n", + " coeff = jnp.exp(1j * phase)[:, None, None]\n", + " return {\n", + " 'o1': BlockModeOpticalSignal(\n", + " amplitude=optical_in.amplitude * coeff,\n", + " wavelength=optical_in.wavelength,\n", + " )\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "11cf5a87", + "metadata": {}, + "source": [ + "## 3. Wrap the Green Machine S-parameter data\n", + "\n", + "The Green Machine is loaded as a measured S-parameter dictionary. The `.npz` file stores wavelength samples plus arrays named by port pair. The helper below converts those arrays into the SAX dictionary form:\n", + "\n", + "```python\n", + "{('out_port', 'in_port'): s_parameter_values}\n", + "```\n", + "\n", + "Unit convention: this SAX-style model receives `wl` in microns during fitting, while Block mode simulation wavelengths are stored in meters. That is why the loaded wavelength grid remains around `1.5` to `1.6`, while the simulation parameters later use values like `1.55e-6`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "de0519d2", + "metadata": {}, + "outputs": [], + "source": [ + "GREEN_MACHINE_FILE = Path('lumerical_bestfit_sdict_back.npz')\n", + "\n", + "\n", + "@lru_cache(maxsize=1)\n", + "def load_green_machine_sdict(filename=str(GREEN_MACHINE_FILE)):\n", + " filename = Path(filename)\n", + " if not filename.exists():\n", + " raise FileNotFoundError(\n", + " f'Missing {filename}. Put the Green Machine S-parameter file next to this notebook.'\n", + " )\n", + "\n", + " data = np.load(filename)\n", + " stored_wavelengths_um = data['wavelengths']\n", + " sdict = {}\n", + "\n", + " for key in data.files:\n", + " if key == 'wavelengths':\n", + " continue\n", + " _, to_port, from_port = key.split('_', 2)\n", + " sdict[(to_port, from_port)] = np.squeeze(data[key])\n", + "\n", + " return stored_wavelengths_um, sdict\n", + "\n", + "\n", + "def green_machine(wl=1.55):\n", + " stored_wavelengths_um, stored_sparams = load_green_machine_sdict()\n", + " requested_wavelengths_um = jnp.asarray(wl).reshape(-1)\n", + " return resample(requested_wavelengths_um, stored_wavelengths_um, stored_sparams)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1f82c4f", + "metadata": {}, + "source": [ + "## 4. Build the circuit netlist\n", + "\n", + "The netlist has the usual three dictionaries: `instances`, `connections`, and `ports`.\n", + "\n", + "For Block mode, write each connection as directed signal flow:\n", + "\n", + "```python\n", + "'upstream_instance,upstream_port': 'downstream_instance,downstream_port'\n", + "```\n", + "\n", + "If `port_directionality` is not specified for an S-parameter component, Simphony can infer it from this ordering: left-hand ports are outputs, right-hand-only ports are inputs, and unconnected S-parameter ports default to outputs.\n", + "\n", + "The layout is:\n", + "\n", + "- a reference laser feeds `green_machine,in0`,\n", + "- a signal laser feeds a Lorenz-driven MZI encoder,\n", + "- a fanout tree splits the encoded signal,\n", + "- delay ladders create seven delayed taps,\n", + "- the taps feed `green_machine,in1` through `green_machine,in7`,\n", + "- all eight Green Machine outputs are exposed as top-level ports." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2de89938", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Circuit instances: 45\n", + "Circuit connections: 51\n" + ] + } + ], + "source": [ + "def make_lorenz_green_machine_netlist():\n", + " instances = {\n", + " 'signal_laser': 'laser',\n", + " 'reference_laser': 'laser',\n", + " 'mzi_splitter': 'y_branch',\n", + " 'mzi_upper_wg': 'waveguide',\n", + " 'mzi_lower_wg': 'waveguide',\n", + " 'mzi_upper_mod': 'phase_modulator',\n", + " 'mzi_lower_mod': 'phase_modulator',\n", + " 'mzi_combiner': 'y_branch',\n", + " 'reference_delay': 'waveguide',\n", + " **{f'fanout_{name}': 'y_branch' for name in 'abcdefg'},\n", + " **{f'tap_delay_{idx}': 'waveguide' for idx in range(1, 8)},\n", + " 'green_machine': 'green_machine',\n", + " }\n", + "\n", + " connections = {\n", + " # Reference path.\n", + " 'reference_laser,o0': 'reference_delay,o0',\n", + " 'reference_delay,o1': 'green_machine,in0',\n", + "\n", + " # Lorenz-driven MZI encoder.\n", + " 'signal_laser,o0': 'mzi_splitter,port_1',\n", + " 'mzi_splitter,port_2': 'mzi_upper_wg,o0',\n", + " 'mzi_splitter,port_3': 'mzi_lower_wg,o0',\n", + " 'mzi_upper_wg,o1': 'mzi_upper_mod,o0',\n", + " 'mzi_lower_wg,o1': 'mzi_lower_mod,o0',\n", + " 'mzi_upper_mod,o1': 'mzi_combiner,port_2',\n", + " 'mzi_lower_mod,o1': 'mzi_combiner,port_3',\n", + "\n", + " # Binary fanout tree.\n", + " 'mzi_combiner,port_1': 'fanout_a,port_1',\n", + " 'fanout_a,port_2': 'fanout_b,port_1',\n", + " 'fanout_a,port_3': 'fanout_c,port_1',\n", + " 'fanout_b,port_2': 'fanout_d,port_1',\n", + " 'fanout_b,port_3': 'fanout_e,port_1',\n", + " 'fanout_c,port_2': 'fanout_f,port_1',\n", + " 'fanout_c,port_3': 'fanout_g,port_1',\n", + "\n", + " # Seven tap starts. Extra delay sections are added in the loop below.\n", + " 'fanout_d,port_2': 'tap_delay_1,o0',\n", + " 'tap_delay_1,o1': 'green_machine,in1',\n", + " 'fanout_d,port_3': 'tap_delay_2,o0',\n", + " 'fanout_e,port_2': 'tap_delay_3,o0',\n", + " 'fanout_e,port_3': 'tap_delay_4,o0',\n", + " 'fanout_f,port_2': 'tap_delay_5,o0',\n", + " 'fanout_f,port_3': 'tap_delay_6,o0',\n", + " 'fanout_g,port_2': 'tap_delay_7,o0',\n", + " }\n", + "\n", + " netlist = {\n", + " 'instances': instances,\n", + " 'connections': connections,\n", + " 'ports': {f'out{i}': f'green_machine,out{i}' for i in range(8)},\n", + " }\n", + "\n", + " # Tap i gets i total delay sections before entering green_machine,in{i}.\n", + " for tap_idx in range(2, 8):\n", + " previous_port = f'tap_delay_{tap_idx},o1'\n", + " for delay_idx in range(tap_idx - 1):\n", + " delay_name = f'extra_delay_{tap_idx}_{delay_idx}'\n", + " netlist['instances'][delay_name] = 'waveguide'\n", + " netlist['connections'][previous_port] = f'{delay_name},o0'\n", + " previous_port = f'{delay_name},o1'\n", + " netlist['connections'][previous_port] = f'green_machine,in{tap_idx}'\n", + "\n", + " return netlist\n", + "\n", + "\n", + "models = {\n", + " 'laser': MultiWavelengthCWLaser,\n", + " 'phase_modulator': TimeVaryingPhaseModulator,\n", + " 'waveguide': waveguide,\n", + " 'y_branch': y_branch,\n", + " 'green_machine': green_machine,\n", + "}\n", + "\n", + "netlist = make_lorenz_green_machine_netlist()\n", + "circuit = Circuit(netlist=netlist, models=models)\n", + "\n", + "print(f'Circuit instances: {len(netlist[\"instances\"])}')\n", + "print(f'Circuit connections: {len(netlist[\"connections\"])}')\n", + "print('Top-level ports:', sorted(netlist['ports']))" + ] + }, + { + "cell_type": "markdown", + "id": "8c46f5f7", + "metadata": {}, + "source": [ + "## 5. Configure component settings\n", + "\n", + "The settings dictionary supplies per-instance constructor arguments.\n", + "\n", + "Custom Block mode components read their settings directly. For example, the lasers receive `wavelengths`, and the phase modulators receive their phase waveforms.\n", + "\n", + "SAX/S-parameter components need Block mode metadata too:\n", + "\n", + "- `sax_settings`: kwargs passed into the SAX model.\n", + "- `port_directionality`: explicit input/output roles for the directed graph.\n", + "- `vector_fitting_parameters`: controls for converting wavelength-domain S-parameters into time-domain filters.\n", + "\n", + "Caveat from the earlier tutorials: if an S-parameter setting includes `port_directionality` or `vector_fitting_parameters`, include `sax_settings` as well. Use `sax_settings: {}` when the SAX model has no kwargs." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81ef6e6b", + "metadata": {}, + "outputs": [], + "source": [ + "WAVELENGTHS_M = jnp.array([1.53e-6, 1.54e-6, 1.55e-6, 1.56e-6, 1.57e-6], dtype=jnp.float32)\n", + "SIMULATION_DT = 1e-14\n", + "NUM_TIME_STEPS = int(phase_wave1.shape[0])\n", + "\n", + "SHORT_WG_LENGTH = 10\n", + "BASE_DELAY_LENGTH = 100\n", + "TAP_DELAY_LENGTH = 366\n", + "\n", + "vector_fitting_parameters = {\n", + " 'model_order': 24,\n", + " 'min_model_order': 2,\n", + " 'max_model_order': 24,\n", + " 'num_frequency_samples': 400,\n", + " 'center_wavelength': 1.55e-6,\n", + " 'spectral_range': (1.5e-6, 1.6e-6),\n", + "}\n", + "\n", + "splitter_directionality = {'port_1': 'input', 'port_2': 'output', 'port_3': 'output'}\n", + "combiner_directionality = {'port_1': 'output', 'port_2': 'input', 'port_3': 'input'}\n", + "waveguide_directionality = {'o0': 'input', 'o1': 'output'}\n", + "\n", + "\n", + "def waveguide_settings(length):\n", + " return {\n", + " 'sax_settings': {'length': length},\n", + " 'port_directionality': waveguide_directionality,\n", + " 'vector_fitting_parameters': vector_fitting_parameters.copy(),\n", + " }\n", + "\n", + "\n", + "settings = {\n", + " 'signal_laser': {'wavelengths': WAVELENGTHS_M},\n", + " 'reference_laser': {'wavelengths': WAVELENGTHS_M},\n", + " 'mzi_upper_mod': {'phase': phase_wave1},\n", + " 'mzi_lower_mod': {'phase': phase_wave2},\n", + " 'mzi_splitter': {'sax_settings': {}, 'port_directionality': splitter_directionality},\n", + " 'mzi_combiner': {'sax_settings': {}, 'port_directionality': combiner_directionality},\n", + " 'green_machine': {\n", + " 'sax_settings': {},\n", + " 'port_directionality': {\n", + " **{f'in{i}': 'input' for i in range(8)},\n", + " **{f'out{i}': 'output' for i in range(8)},\n", + " },\n", + " 'vector_fitting_parameters': vector_fitting_parameters.copy(),\n", + " },\n", + "}\n", + "\n", + "for name in [f'fanout_{letter}' for letter in 'abcdefg']:\n", + " settings[name] = {'sax_settings': {}, 'port_directionality': splitter_directionality}\n", + "\n", + "for name in ['mzi_upper_wg', 'mzi_lower_wg']:\n", + " settings[name] = waveguide_settings(SHORT_WG_LENGTH)\n", + "\n", + "for name in ['reference_delay', *[f'tap_delay_{idx}' for idx in range(1, 8)]]:\n", + " settings[name] = waveguide_settings(BASE_DELAY_LENGTH)\n", + "\n", + "for instance_name in netlist['instances']:\n", + " if instance_name.startswith('extra_delay_'):\n", + " settings[instance_name] = waveguide_settings(TAP_DELAY_LENGTH)\n", + "\n", + "print(f'Settings entries: {len(settings)}')" + ] + }, + { + "cell_type": "markdown", + "id": "22c4e86f", + "metadata": {}, + "source": [ + "## 6. Run the Block mode simulation\n", + "\n", + "`BlockModeSimulationParameters` define the shared time grid and optical channels. The source wavelengths, phase waveforms, and fitted S-parameter filters must agree with these global parameters.\n", + "\n", + "This notebook uses one mode, `TE`, because multimode behavior was covered in the 16-QAM tutorial. The Green Machine example is mainly about composing a larger directed circuit around a measured S-parameter block." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1d5a4d18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-level output ports: ['out0', 'out1', 'out2', 'out3', 'out4', 'out5', 'out6', 'out7']\n", + "out0: amplitude shape = (9600, 5, 1)\n", + "out1: amplitude shape = (9600, 5, 1)\n", + "out2: amplitude shape = (9600, 5, 1)\n", + "out3: amplitude shape = (9600, 5, 1)\n", + "out4: amplitude shape = (9600, 5, 1)\n", + "out5: amplitude shape = (9600, 5, 1)\n", + "out6: amplitude shape = (9600, 5, 1)\n", + "out7: amplitude shape = (9600, 5, 1)\n" + ] + } + ], + "source": [ + "simulation_parameters = BlockModeSimulationParameters(\n", + " optical_baseband_wavelengths=WAVELENGTHS_M,\n", + " dt=SIMULATION_DT,\n", + " num_time_steps=NUM_TIME_STEPS,\n", + " mode_identifiers=['TE'],\n", + ")\n", + "\n", + "simulation = BlockModeSimulation(\n", + " circuit,\n", + " settings=settings,\n", + " simulation_parameters=simulation_parameters,\n", + ")\n", + "\n", + "result = simulation.run()\n", + "\n", + "print('Top-level output ports:', sorted(result.output_signals))\n", + "for port_name, signal in sorted(result.output_signals.items()):\n", + " print(f'{port_name}: amplitude shape = {signal.amplitude.shape}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "526f05ce", + "metadata": {}, + "source": [ + "## 7. Inspect the output signals\n", + "\n", + "Each top-level Green Machine port returns a `BlockModeOpticalSignal`. To inspect one trace, select:\n", + "\n", + "```python\n", + "amplitude[:, wavelength_index, mode_index]\n", + "```\n", + "\n", + "The plot below shows optical power for one output and one carrier wavelength." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "da4a8900", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxIAAAGJCAYAAAAe+gViAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAn39JREFUeJzs3Xd4U+XbB/BvkjZN96C7tFD23lDKRitFUURREUUQUdyCKMpQ8KcoigtRFCfoqyiiCKiIInuUMspG9ihQuijdOznvH2lOM07SpLRJmn4/18UFPXlOchLS5NznuZ/7lgmCIICIiIiIiMgGckcfABERERERNTwMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIiIiIyGYMJIiIHOTTTz/FsmXLJG/TaDRYsGABYmNjoVKp0KVLF/z444/2PUAXYOk1lrJixQqMGzcOrVu3hkwmw5AhQyTHbdmyBTKZTPLP7t27DcYOGTJEctzw4cNN7resrAwvv/wyIiMj4enpibi4OGzYsMGWp0xEZDdujj4AIqLG6tNPP0VwcDAefvhhk9tmz56Nt99+G4899hh69+6NNWvW4IEHHoBMJsP9999v/4NtoCy9xlI+++wz7N+/H71798a1a9dqHP/cc8+hd+/eBttatWplMq5p06aYP3++wbbIyEiTcQ8//DB++eUXTJ06Fa1bt8ayZctw2223YfPmzRgwYIBVz4GIyF5kgiAIjj4IIqLGqFOnTggODsaWLVsMtl+5cgWxsbGYPHkyPvnkEwCAIAgYPHgwzp8/jwsXLkChUDjgiO2nsrISGo0GSqXyhu7H3GtszqVLlxAVFQW5XG5x3y1btmDo0KFYuXIl7rnnHov3OWTIEGRnZ+Po0aMWx+3ZswdxcXF499138eKLLwIASktL0alTJ4SGhmLXrl1WPQciInthahMRkZUOHDiAW2+9FX5+fvDx8cHNN99sksby2muvQSaTmey7bNkyyGQyXLhwAQDQvHlzHDt2DFu3bhVTXXRpNGvWrEFFRQWeeuopcX+ZTIYnn3wSly9fRlJSksXjfPjhh+Hj44Nz584hMTER3t7eiIyMxOuvvw7ja0dFRUV44YUXEB0dDQ8PD7Rt2xbvvfeewbi7774bPXr0MNjvjjvugEwmw9q1a8VtycnJkMlk+Ouvv8Rtubm5mDp1qnj/rVq1wjvvvAONRiOOuXDhAmQyGd577z0sXLgQLVu2hIeHB44fP272OS5duhQ33XQTQkND4eHhgQ4dOuCzzz4zGGPpNTYnOjoacrltX40FBQWorKyscVxlZSUKCwvN3v7LL79AoVBg8uTJ4jaVSoVJkyYhKSkJly5dsnj/Q4YMQadOnXD8+HEMHToUXl5eiIqKwoIFCwzG6dKyfv75Z/zvf/9DVFQUfH19cc899yAvLw9lZWWYOnUqQkND4ePjg4kTJ6KsrKzG50dEjQ9Tm4iIrHDs2DEMHDgQfn5+eOmll+Du7o7PP/8cQ4YMwdatWxEXF2fT/S1cuBDPPvssfHx8MHv2bABAWFgYAG3A4u3tjfbt2xvs06dPH/H2mtJc1Go1hg8fjr59+2LBggVYv3495s6di8rKSrz++usAtLMcI0eOxObNmzFp0iR069YNf//9N6ZPn44rV67gww8/BAAMHDgQa9asQX5+Pvz8/CAIAnbu3Am5XI7t27dj5MiRAIDt27dDLpejf//+AIDi4mIMHjwYV65cweOPP46YmBjs2rULM2fOxNWrV7Fw4UKDY166dClKS0sxefJkeHh4ICgoyOzz++yzz9CxY0eMHDkSbm5u+P333/HUU09Bo9Hg6aefrvE1risTJ05EYWEhFAoFBg4ciHfffRe9evUyGXfq1Cl4e3ujvLwcYWFheOyxxzBnzhy4u7uLYw4cOIA2bdrAz8/PYF/d//vBgwcRHR1t8XiuX7+O4cOH4+6778Z9992HX375BS+//DI6d+6MW2+91WDs/Pnz4enpiRkzZuDMmTP4+OOP4e7uDrlcjuvXr+O1117D7t27sWzZMsTGxmLOnDm1fZmIyFUJRERUo1GjRglKpVI4e/asuC0tLU3w9fUVBg0aJG6bO3euIPXRunTpUgGAcP78eXFbx44dhcGDB5uMHTFihNCiRQuT7UVFRQIAYcaMGRaPdcKECQIA4dlnnxW3aTQaYcSIEYJSqRSysrIEQRCE1atXCwCEefPmGex/zz33CDKZTDhz5owgCIKwd+9eAYCwbt06QRAE4fDhwwIA4d577xXi4uLE/UaOHCl0795d/PmNN94QvL29hVOnThnc/4wZMwSFQiGkpqYKgiAI58+fFwAIfn5+QmZmpsXnplNcXGyyLTEx0eR1M/caW8PSvjt37hRGjx4tfP3118KaNWuE+fPnC02aNBFUKpWQkpJiMPaRRx4RXnvtNeHXX38VvvvuO2HkyJECAOG+++4zebybbrrJ5LGOHTsmABCWLFli8XgHDx4sABC+++47cVtZWZkQHh4ujB49Wty2efNmAYDQqVMnoby8XNw+duxYQSaTCbfeeqvB/cbHxwvNmjWz+NhE1DgxtYmIqAZqtRr//PMPRo0ahRYtWojbIyIi8MADD2DHjh3Iz8+vs8crKSmBh4eHyXaVSiXebo1nnnlG/LdMJsMzzzyD8vJy/PvvvwCAdevWQaFQ4LnnnjPY74UXXoAgCGKKUvfu3eHj44Nt27YB0M48NG3aFOPHj0dKSgqKi4shCAJ27NiBgQMHivezcuVKDBw4EIGBgcjOzhb/JCQkQK1Wi/enM3r0aISEhFj13Dw9PcV/5+XlITs7G4MHD8a5c+eQl5dn1X3ciH79+uGXX37BI488gpEjR2LGjBnYvXs3ZDIZZs6caTD266+/xty5c3H33XfjoYcewpo1a/DYY4/h559/NkiNq4v/dx8fH4wbN078WalUok+fPjh37pzJ2PHjxxvMiMTFxUEQBDzyyCMG4+Li4nDp0iWr0reIqHFhIEFEVIOsrCwUFxejbdu2Jre1b98eGo2mxvx1W3h6ekrmpJeWloq310QulxsEPQDQpk0bABDXaVy8eBGRkZHw9fU1GKdLqbp48SIAQKFQID4+Htu3bwegDSQGDhyIAQMGQK1WY/fu3Th+/DhycnIMAonTp09j/fr1CAkJMfiTkJAAAMjMzDR43NjY2Bqfl87OnTuRkJAAb29vBAQEICQkBLNmzQIAuwQSUlq1aoU777wTmzdvhlqttjj2hRdeAAAxqAPq5v+9adOmJmt0AgMDcf36dZOxMTExBj/7+/sDgEn6lL+/PzQajcNeVyJyXlwjQURUh6QWWgOo8cRSX0REBDZv3gxBEAzu7+rVqwCky4bWtwEDBuDNN99EaWkptm/fjtmzZyMgIACdOnXC9u3bxbUH+oGERqPBLbfcgpdeeknyPnWBjY41J8oAcPbsWdx8881o164dPvjgA0RHR0OpVGLdunX48MMPDRZy21t0dDTKy8tRVFRkstbBeBwA5OTkiNsiIiJw5coVk7G2/L+bq+YlSBRoNDfWlvsgosaNgQQRUQ1CQkLg5eWFkydPmtx24sQJyOVy8cQwMDAQgLZaUUBAgDhOd3Vfn7mgo1u3bvjqq6/w33//oUOHDuL25ORk8faaaDQanDt3zuBk/dSpUwC01YwAoFmzZvj3339RUFBgMCtx4sQJ8XadgQMHory8HD/++COuXLkiBgyDBg0SA4k2bdoYLGZu2bIlCgsLxRmIuvL777+jrKwMa9euNbiqvnnzZpOx5l7j+nLu3DmoVCr4+PjUOA6AQSpXt27dsHnzZnFRu44t/+9ERPbE1CYiohooFAoMGzYMa9asEdOCACAjIwPLly/HgAEDxBO/li1bAoBB/n9RURG+/fZbk/v19vZGbm6uyfY777wT7u7u+PTTT8VtgiBgyZIliIqKQr9+/aw6bl0PCt3+n3zyCdzd3XHzzTcDAG677Tao1WqDcQDw4YcfQiaTGVT5iYuLg7u7O9555x0EBQWhY8eOALQBxu7du7F161aD2QgAuO+++5CUlIS///7b5Nhyc3NrnXOvu2Kuf4U8Ly8PS5cuNRlr7jW+UVlZWSbbDh06hLVr12LYsGFiCdn8/HyTdCVBEDBv3jwAQGJiorj9nnvugVqtxhdffCFuKysrw9KlSxEXF1djxSYiInvjjAQRkRXmzZuHDRs2YMCAAXjqqafg5uaGzz//HGVlZQZ1+ocNG4aYmBhMmjQJ06dPh0KhwDfffIOQkBCkpqYa3GfPnj3x2WefYd68eWjVqhVCQ0Nx0003oWnTppg6dSreffddVFRUoHfv3li9ejW2b9+OH374wapmdCqVCuvXr8eECRMQFxeHv/76C3/++SdmzZolXgW/4447MHToUMyePRsXLlxA165d8c8//2DNmjWYOnWqGBQBgJeXF3r27Indu3eLPSQA7YxEUVERioqKTAKJ6dOnY+3atbj99tvx8MMPo2fPnigqKsKRI0fwyy+/4MKFCwgODrb5/2LYsGFQKpW444478Pjjj6OwsBBffvklQkNDxTSgml5jc7Zt2yYGgVlZWSgqKhJP+gcNGoRBgwYBAMaMGQNPT0/069cPoaGhOH78OL744gt4eXnh7bffFu8vJSUFY8eOxdixY9GqVSuUlJTgt99+w86dOzF58mSD/hxxcXG49957MXPmTGRmZqJVq1b49ttvceHCBXz99dc2v05ERPXOUeWiiIgampSUFCExMVHw8fERvLy8hKFDhwq7du0yGbd//34hLi5OUCqVQkxMjPDBBx9Iln9NT08XRowYIfj6+goADEqNqtVq4a233hKaNWsmKJVKoWPHjsL3339v1XFOmDBB8Pb2Fs6ePSsMGzZM8PLyEsLCwoS5c+cKarXaYGxBQYHw/PPPC5GRkYK7u7vQunVr4d133xU0Go3J/U6fPl0AILzzzjsG21u1aiUAMCiNq3//M2fOFFq1aiUolUohODhY6Nevn/Dee++JpUd15V/fffddq56fIAjC2rVrhS5duggqlUpo3ry58M477wjffPONTa+xFF35Xqk/c+fOFcd99NFHQp8+fYSgoCDBzc1NiIiIEMaNGyecPn3a4P7OnTsn3HvvvULz5s0FlUoleHl5CT179hSWLFki+RqXlJQIL774ohAeHi54eHgIvXv3FtavX2/VazJ48GChY8eOJtsnTJhgUL5VV/515cqVBuN079G9e/dKvia6ssFERDoyQeDqKSIiV/Lwww/jl19+sdhFmYiI6EZxjQQREREREdmMgQQREREREdmMgQQREREREdmMaySIiIiIiMhmnJEgIiIiIiKbMZAgIiIiIiKbsSFdLWk0GqSlpcHX11dszERERERE1JAJgoCCggJERkZCLrc858BAopbS0tIQHR3t6MMgIiIiIqpzly5dQtOmTS2OYSBRS76+vgC0L7Kfn5+Dj4aIiIiI6Mbl5+cjOjpaPNe1hIFELenSmfz8/BhIEBEREZFLsSZ1n4utiYiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkiIiIiIjIZgwkSJRfWoEjl/McfRhERERE1AAwkCDR7Yt24I5PdmDbqSxHHwoREREROTkGEiRKzSkGAKw5mObgIyEiIiIiZ8dAgkxUqDWOPgQiIiIicnIMJMiETOboIyAiIiIiZ8dAggAA57OLxH8zjiAiIiKimjCQIFSqNRj63hbxZxmnJIiIiIioBgwkCGWVhmsiGEYQERERUU0YSBDKjQIJRhJEREREVBMGEiQxI8FIgoiIiIgsYyBBpjMSREREREQ1YCDhwnafu4ZbP9qOrWY6VV/KKcYDX+7G38fSDbZzrTURERER1YSBhAt7ZfVR/Hc1Hy/8fMjs7bvOXsOb6/6z85ERERERUUPHQMKFncksBABkF5ZJdqvOyC+V3I8TEkRERERUEwYSLkqtEQxSlL7ecd7g9h2ns3EivUByX6Y2EREREVFNGEi4qMLSSghC9c87Tmcb3D7u62Sz+7JqExERERHVhIGEi6rUGKYypeWWWL2vnO8KIiIiIqoBTxldlFp/OgLm10NI+XHPJfx5+GpdHxIRERERuRA3Rx8A1Q+jCQkUlauxePMZ+KncUKkRpHfS8/TyFIzoMqKejo6IiIiIGjoGEi5KUzUjoVTIIZcDpRUavPv3SZvuQxAEyLjymoiIiIgkMLXJRamrZh3kciDE16NW97HlpHQjOyIiIiIiBhIuSjcjoZDJEOxTu0Bi4rK9KKtU1+VhEREREZGLYCDhoqpnJGQIlZiR8HRXWHU/ecUVdXpcREREROQaGEi4KHFGQi7DsA7hJrf/9nQ/LH6gR433U1BWWefHRkREREQNHwMJF6WuqtqkkMlwd48og9teHNYG7cL9MKJLRI33U1jKQIKIiIiITDGQcFH6qU0ymQwvDW+LYB8PrHtuIJ65qbU47s5ukRbvp4CBBBERERFJYPlXF6W/2BoAnhrSCk8NaWUy7sP7uqFSI5htQFfI1CYiIiIiksAZCRelm5FQyC33gZDLZfhoTDfEBHlJ3v7P8fQ6PzYiIiIiavgYSLgotVDdR6Imbgo5WoZ4S962KuUKLmQX1eWhEREREZELcHggsXjxYjRv3hwqlQpxcXHYs2ePxfErV65Eu3btoFKp0LlzZ6xbt87g9lWrVmHYsGFo0qQJZDIZDh48aHIfpaWlePrpp9GkSRP4+Phg9OjRyMjIqMun5XAajWFqU00sdbA+nVlYJ8dERERERK7DoYHEihUrMG3aNMydOxcpKSno2rUrEhMTkZmZKTl+165dGDt2LCZNmoQDBw5g1KhRGDVqFI4ePSqOKSoqwoABA/DOO++Yfdznn38ev//+O1auXImtW7ciLS0Nd999d50/P0fSX2xtDUujlmw9i5d/OczmdEREREQkkglCVQ6MA8TFxaF379745JNPAAAajQbR0dF49tlnMWPGDJPxY8aMQVFREf744w9xW9++fdGtWzcsWbLEYOyFCxcQGxuLAwcOoFu3buL2vLw8hISEYPny5bjnnnsAACdOnED79u2RlJSEvn37WnXs+fn58Pf3R15eHvz8/Gx96vVu19lsPPBlMlqH+mDDtME1jn9k2V5sOiEdwOksGdcT5WoNejYLRFSAZ10dKhERERE5CVvOcR02I1FeXo79+/cjISGh+mDkciQkJCApKUlyn6SkJIPxAJCYmGh2vJT9+/ejoqLC4H7atWuHmJgYi/dTVlaG/Px8gz/OTFPVR0JuZWqTcTzZMdL0jfPFtrN47scDmLhUm362+WQmms/4EwkfbEV+KTtgExERETUmDgsksrOzoVarERYWZrA9LCwM6enSlYLS09NtGm/uPpRKJQICAmy6n/nz58Pf31/8Ex0dbfVjOkL1YmvrAgmN0bxUiK8Hnr3JsFxsSmouAOBURiEEQcCHG04BAM5kFmLxpjM3dsBERERE1KA4fLF1QzFz5kzk5eWJfy5duuToQ7JIXGxt5f+wxmhGwk0uQ6CX0uz468UVBtWc/j7GMrFEREREjYnDGtIFBwdDoVCYVEvKyMhAeHi45D7h4eE2jTd3H+Xl5cjNzTWYlajpfjw8PODh4WH14ziacUO6mgT7GD43uUyGQG93s+Onrzxk0KMiI7+sFkdJRERERA2Vw2YklEolevbsiY0bN4rbNBoNNm7ciPj4eMl94uPjDcYDwIYNG8yOl9KzZ0+4u7sb3M/JkyeRmppq0/04O1urNs28rZ3Bzwq5DJaW4W88kYnrxdXrIkor1SbrLIiIiIjIdTlsRgIApk2bhgkTJqBXr17o06cPFi5ciKKiIkycOBEAMH78eERFRWH+/PkAgClTpmDw4MF4//33MWLECPz000/Yt28fvvjiC/E+c3JykJqairS0NADaIAHQzkSEh4fD398fkyZNwrRp0xAUFAQ/Pz88++yziI+Pt7piU0Ng64xEqK8Kb9/dGTNWHQGgDUBig6Wb1EkRBKBCLUDpZt3jEREREVHD5tBAYsyYMcjKysKcOXOQnp6Obt26Yf369eKC6tTUVMj1WjP369cPy5cvxyuvvIJZs2ahdevWWL16NTp16iSOWbt2rRiIAMD9998PAJg7dy5ee+01AMCHH34IuVyO0aNHo6ysDImJifj000/t8IztR62r2mTljITxWIVMhu4xgVj8QA+0CPHGjtPZ8PZww8DWwfh21wV8teO8yf6llWoo3bjshoiIiKgxcGgfiYbM2ftIrD2Uhud+PID4Fk3w42TrZlp+2X8ZL648BAC4u3sUPhjTzezY5jP+FP8tk2lnJPbMvhmhvqobOm4iIiIicpwG0UeC6ld11SYbZiT0htoyk+FeNWtUqWZMSkRERNRYMJBwUbYutgYMm9dZu7ZC+xiGj0lEREREro+BhItSi4utrd9HVssZCV3QYdyLgoiIiIhcFwMJF1W71Ca9GYka3hmPD24BAHggLkZ8jErOSBARERE1Gg6t2kT1RzcjIbclRUlvrJvcciTxwi1tMbh1CHo0C8RfR64CqA5eiIiIiMj1MZBwUTe82LqGAETpJke/VsEGj6FmahMRERFRo8HUJhdVm8XWMhtSm/SJgQRnJIiIiIgaDQYSLkpXidWm6ks3uNiagQQRERFR48FAwkXpUptsiAcM0qBsK//KQIKIiIiosWEg4aLExda1rNrkZsuMhJzlX4mIiIgaGwYSLko3O2DLzEKt+0joyr+yszURERFRo8FAwkUJwg32kbAhABHXSHBGgoiIiKjRYCDhotQa7d+1TW2qzYyERmP1LkRERETUwDGQcFG62YHaVm2qzUwGZySIiIiIGg8GEi6qNg3pZLVcbO2m0FVt4pQEERERUWPBQMJFiVWbattHwqb9dIGE1bsQERERUQPHQMJFVc9IWL+P/roIW2Yy2NmaiIiIqPFhIOGidCf1ti221vt3LTpbs48EERERUePBQMJF1WaxtayW5V/lVe8iBhJEREREjQcDCRdVm8XWiloutpaLMxJW70JEREREDRwDCRdVu8XWtesjodtP4IwEERERUaPBQMJF6Soo2Vb+tfrftizS1u3HxdZEREREjQcDCRdVm9QmgxkJG2YyxM7WjCOIiIiIGg0GEi6qVqlNeu+G2gQgXGxNRERE1HgwkHBRteojUevF1tq/uUaCiIiIqPFgIOGi7NnZWsbO1kRERESNDgMJFyU2pKttHwk2pCMiIiIiCxhIuCjdOX2tF1vbsp9c95gMJIiIiIgaCwYSLkqckajFWgegdh2xWbWJiIiIqPFgIOGidGskbAkIar/YWrdGgpEEERERUWPh5ugDoLq1/mg65LJaVm2S1y61SVE1lGskiIiIiBoPBhIupLRCjSe+3w8A6B4TAKD2VZvcFbbPSDCOICIiImo8mNrkQsoqq+uvHkjNBVD7xdZucuvfGjJWbSIiIiJqdDgj4SLyiiswZcUBk+22BBL6kxduNs1IaP9WM5AgIiIiajQ4I+Eilu26gC0ns0y225baVD3W3YbFFUxtIiIiImp8GEi4iIs5RZLba5vaZNN+VWM1rNpERERE1GgwkHABe87nYFXKFcnbarvYujb7MY4gIiIiajwYSLiAh75ONnubLTML+ulMSjfbU5u4RoKIiIio8eBiaxegX63JmC19JLw93DCxf3NkF5Yj0l9l9X66WEVgIEFERETUaDh8RmLx4sVo3rw5VCoV4uLisGfPHovjV65ciXbt2kGlUqFz585Yt26dwe2CIGDOnDmIiIiAp6cnEhIScPr0aYMxp06dwp133ong4GD4+flhwIAB2Lx5c50/N2dgS4oSAMy9oyM+HttdLOlq1WPIWf6ViIiIqLFxaCCxYsUKTJs2DXPnzkVKSgq6du2KxMREZGZmSo7ftWsXxo4di0mTJuHAgQMYNWoURo0ahaNHj4pjFixYgEWLFmHJkiVITk6Gt7c3EhMTUVpaKo65/fbbUVlZiU2bNmH//v3o2rUrbr/9dqSnp9f7c65rSWevWbzdltSm2pKLfSTq/aGIiIiIyEnIBAfmo8TFxaF379745JNPAAAajQbR0dF49tlnMWPGDJPxY8aMQVFREf744w9xW9++fdGtWzcsWbIEgiAgMjISL7zwAl588UUAQF5eHsLCwrBs2TLcf//9yM7ORkhICLZt24aBAwcCAAoKCuDn54cNGzYgISHBqmPPz8+Hv78/8vLy4Ofnd6MvRa3FzvzTYtnVHx6NQ/9WwfV6DG/+eRxfbj+Pxwe3wMxb29frYxERERFR/bHlHNdhMxLl5eXYv3+/wYm7XC5HQkICkpKSJPdJSkoyOdFPTEwUx58/fx7p6ekGY/z9/REXFyeOadKkCdq2bYvvvvsORUVFqKysxOeff47Q0FD07NnT7PGWlZUhPz/f4I8zqCkMtDW1qTbEGQlOSRARERE1Gg4LJLKzs6FWqxEWFmawPSwszGyKUXp6usXxur8tjZHJZPj3339x4MAB+Pr6QqVS4YMPPsD69esRGBho9njnz58Pf39/8U90dLRtT9hB7JLaJGdqExEREVFj4/DF1vYmCAKefvpphIaGYvv27dizZw9GjRqFO+64A1evXjW738yZM5GXlyf+uXTpkh2PuvZsqdpUW9V9JBhJEBERETUWDgskgoODoVAokJGRYbA9IyMD4eHhkvuEh4dbHK/729KYTZs24Y8//sBPP/2E/v37o0ePHvj000/h6emJb7/91uzxenh4wM/Pz+BPQ2DP1CbGEURERESNh8MCCaVSiZ49e2Ljxo3iNo1Gg40bNyI+Pl5yn/j4eIPxALBhwwZxfGxsLMLDww3G5OfnIzk5WRxTXFwMQLseQ59cLodGY74fQ0Nlj9QmXalYNXObiIiIiBoNhzakmzZtGiZMmIBevXqhT58+WLhwIYqKijBx4kQAwPjx4xEVFYX58+cDAKZMmYLBgwfj/fffx4gRI/DTTz9h3759+OKLLwBoT2inTp2KefPmoXXr1oiNjcWrr76KyMhIjBo1CoA2GAkMDMSECRMwZ84ceHp64ssvv8T58+cxYsQIh7wOtSEIAsZ/Y7nnBmCfGQlF1WP83+6L8Pd0x4uJbev9MYmIiIjIsRwaSIwZMwZZWVmYM2cO0tPT0a1bN6xfv15cLJ2ammowc9CvXz8sX74cr7zyCmbNmoXWrVtj9erV6NSpkzjmpZdeQlFRESZPnozc3FwMGDAA69evh0ql7dQcHByM9evXY/bs2bjppptQUVGBjh07Ys2aNejatat9X4AbkFNUju2ns2scZ58+EtX//mTzGQYSRERERI2AQ/tINGSO7iNxKacYAxfU3I37n+cHoU2Yb70ey+LNZ/Du3yfFny+83XBmdoiIiIioWoPoI0E3pqRCbdU4e6Q22eEhiIiIiMjJMJBooIrLrQsk7JHapGAkQURERNToMJBooIrLK60a52aXNRIMJIiIiIgaGwYSDZAgCFiy9ZxVY93t0JHOOI6oVLteGV0iIiIiMsRAogFKSc3FtlNZVo11U9h/RmLggs0osTL1ioiIiIgaJgYSDdCZzAKrx7rL6/+/2HgdxtW8Umw9lVnvj0tEREREjsNAogEorVDjeFo+alOp1z4zEqbbytWsKkxERETkyhzakI6s88iyvdh19hqWjOuJDhF+ePnXI1bva49AQiax2LqikuskiIiIiFwZA4kGYNfZawCAJ77fj27RATbta4/UJqmqTWqNgEOXctEuwhcebop6PwYiIiIisi+mNjUwBy/lWj121m3tILdHHwmJd9HaQ2m4c/FOvPTL4Xp/fCIiIiKyPwYSDVyLYG988VBPHJxzi8ltkwa0sMsxSKU27TiTDQBYczDNLsfQGP20JxXv/3OyVmtniIiIiG4UU5sasMmDWmDWbe3N3m6HyYiqx2FDOkeYsUq7VmZAq2DEtWji4KMhIiKixsaqQGLt2rU23/Ett9wCT09Pm/cj6z0+yPKMg9RMQX2wV8DSmHy76wLS80vxUmJbyf9HjaZ6FuJcdhEDCSIiIrI7qwKJUaNG2XSnMpkMp0+fRosW9kmtaYy+nxSHJj4ejj4MAKZ9JIwJgmC3oMYVlFWqMXftMQBAQvtQ9GwWZDKmtLK64V9haaXdjo2IiIhIx+o1Eunp6dBoNFb98fLyqs9jJgDh/iqTbe/e08UBR1LzzEdpBUvB2uJ8dpH475yiCskx+p3D80ulxxARERHVJ6tmJCZMmGBTmtK4cePg5+dX64MiaU8PbYlrheUoLlejRbC3ye339orG7nM5+DXlsl2Pq6bUprySCngqWQLWGhVqDYYv3C7+nFdiJpCoqA4ksgvL6v24iIiIiIxZFUgsXbrUpjv97LPPanUwJE0mAwQBmNCvOUJ9TWciHK2mxdZ/HE7DowOZ5maNk+kFBj+bDST0ZiQy8hlIEBERkf2x/KuTEwQBuuqe1lRH0jigFGhNMxLz/vwPjyzbyzKlVrheXG7wszUzEpkFpfV6TERERERS6iyQ+PTTT/H666/X1d1RFf1zb4UVgUSlxhGBRM3HtelEJk4YXW0nU/kllUY/SwcSxXozEpmckSAiIiIHqLNA4tdff8WyZcvq6u6oiv4MgzUn7GqN/Rc2W9tH4u5PdwEACkorsHjzGaw9lIZKNRdi6zOegdD/+f92X8S2U1kATNdIcME1ERER2VudNaTbuHFjXd0V6dGfYJBZEfZVqh0wI2FlOFpSoYZGI+C9v0/i26SLAIDs2zvgkQGx9Xh0DYtxQHAg9TqOp+WjXK3Bq6uPAgBeHt4O76w/IY7RCMD6I+m4r3e0XY+ViIiIGjeukXByts5IBHi51+fhSLJU/nXO7R1we5cI8efckgr8sr+6qtTK/fatMOXsdDMQkVXlfS9cK8btH2/HrrPZ4hj9IELnzyNX7XOARERERFVsnpH47rvvLN4+fvz4Wh8MmTIMJGoe//LwdjidWYgxvex3ddrS2o0H4mLwyIBY7DzzD64XV+CuT3ciwEuJovISAEBMELuf69OtiYgO8kJannYRtUYA9p7PkRwf6uuBzIIynMrg+hMiIiKyL5sDiSlTphj8XFFRgeLiYiiVSnh5eTGQqGP6qU3WzEg08fHAb0/1r8cjMiV1XIFe7gj394TKXds/ItjHA9eLK3DxWrHBOE939pfQp5uRaNbEC8l6wcPmk1mS49uE+SKzoAzp+aWoUGvgruAkIxEREdmHzYHE9evXTbadPn0aTz75JKZPn14nB0XVbE1tcgTjmRI/lRu2vDgUHu7VJ7UKM9MpFQ6oMuXM8ku1VZu6RQfi5301p31FVKVACQJQWFqJQG9lvR4fERERkU6dXL5s3bo13n77bZPZCrpxgl5RI2tSmxzBeI3Ewvu7wd/LXZyNAIDCskrj3QCAVZuM6GYkgn2UWPfcQDw+2HIjP1+VO1RVAZu515iIiIioPtRZHoSbmxvS0tLq6u6oSkOYkdCfbXg+oQ1uahdmMqZliI/kvhUOqDLlbDafyMT4b/YgM78UuVUN6QK9legQ6YcH+sRY3NdLqYCvSrvAniVgiYiIyJ5sTm1au3atwc+CIODq1av45JNP0L+/fXPzGwO1XiDhpHGEwUyJ0k06Nn1icEtsPWWa51/BGQlMXLYXAPD6H8eRW6wNBgKrqm9FB3qheRMvXDBaW6LjqVTA18MNWQVlKCjljAQRERHZj82BxKhRowx+lslkCAkJwU033YT333+/ro6LquhmJGQyy2VWHUn/uMyt9Y1v2QSbXhiMm97farCdgUS1M5mF4qyCv6d2rYNcLsP6qYPQ7tX1kvt4uMnhq9L+GjOQICIiInuyOZDQOKBzcmOmm5CwVGLV0fRnJBQWutPFBHmhXbgvTqQXICrAE1dySxzSQM9ZZeSXQhC0QaN+PxBVDZWtvD20v8bF5QwkiIiIyH5YK9LJ6WYknHV9BGC4RkJh4TDdFHKsfro/fn9mAF69vT0AVm3Sd71Y14zO06SM6xODW5rdz0upDSSKytT1d3BERERERuoskFizZk2NzerIdrrzbCeOIwyCHHNlXnVU7gp0buoPz6qT34pK153h+n73RWw+mWlxjFTVqubBXibbZtzaDu0j/Ey2y2UyeHtoZyw4I0FERET2VGeBxMsvv4yJEyfW1d1RFY3G+WckZFamNulzr5q6cNU1EifTC/DK6qOYuHSvxXHFFaazCM2aeEuOFfQW3id2DEOwjxKjukeJqU2ckSAiIiJ7snmNhDknTpyoq7siPdWpTQ4+EAvkViy2NuZWFXDoV6VyJfo9HYrKKsWTfWNFEr0f2oX71nj/S8b1RKVGgLtCDm+ldkZi2+ksKN3keGxgLNzY4ZqIiIjqGc82nJwutUnuxJGEwRoJK2ckdLtoXHSNhLveYpGHvk422yxOahZhcJsQybH6MZdMJhPXUejWSOy/eB3vrD+BFfsu1fawiYiIiKxW6xmJ4uJipKamory83GB7ly5dbvigqFpDWGxtWLXJyn2qdnLROMIgZSslNRfv/X0SvZoHYkTnCINyucYzEu4KmdnUJn9Pd8ntujUSOttOZeHBuGa1PXQiIiIiq9gcSGRlZWHixIn466+/JG9Xq5mnXZeEBpDapH9ibG3AoxundtFIosxoEfmyXRewbNcFuI2TYXinCHF7kdEC6YT2pl3BdWaPaI9Hv9uH6cPaGmzXzUjoXDTTvI6IiIioLtmc2jR16lTk5uYiOTkZnp6eWL9+Pb799lu0bt3apOs13TgxtcmpZySqj83NytQmXV8MwUXXSFSY6Y+RdPYasgrKkFdSgY3/ZeCBL5PF23o3D8TLw9uZvc+u0QHYOzsB9/WONthuPCNxKqMA2YVlN3D0RERERDWzeUZi06ZNWLNmDXr16gW5XI5mzZrhlltugZ+fH+bPn48RI0bUx3E2Wror9s7a1RowbJZnbWqTbhdXXWxtrqxtWl4pBi7YhMgAT5zLKhK392vZBMsf61urxzKekdAIwPbTWbire9Na3R8RERGRNWyekSgqKkJoaCgAIDAwEFlZWQCAzp07IyUlxeYDWLx4MZo3bw6VSoW4uDjs2bPH4viVK1eiXbt2UKlU6Ny5M9atW2dwuyAImDNnDiIiIuDp6YmEhAScPn3a5H7+/PNPxMXFwdPTE4GBgRg1apTNx24PDaFqk36MY+3MicLF10iUmylrezK9AKUVGoMgAgB8zFR1sobUvmcyC2t9f0RERETWsDmQaNu2LU6ePAkA6Nq1Kz7//HNcuXIFS5YsQURERA17G1qxYgWmTZuGuXPnIiUlBV27dkViYiIyM6WbeO3atQtjx47FpEmTcODAAYwaNQqjRo3C0aNHxTELFizAokWLsGTJEiQnJ8Pb2xuJiYkoLS0Vx/z666946KGHMHHiRBw6dAg7d+7EAw88YOtLYRe6C/Y1NXpzJP2KUm6WWlvr71MVcLhq1SZz/TFSc6TXL/ioah9IeCkVJtvSckslRhIRERHVHZsDiSlTpuDq1asAgLlz5+Kvv/5CTEwMFi1ahLfeesum+/rggw/w2GOPYeLEiejQoQOWLFkCLy8vfPPNN5LjP/roIwwfPhzTp09H+/bt8cYbb6BHjx745JNPAGhnIxYuXIhXXnkFd955J7p06YLvvvsOaWlpWL16NQCgsrISU6ZMwbvvvosnnngCbdq0QYcOHXDffffZ+lLYRUOr2mT9jIT2b1dNbSqvSm3q3TzQqvE3MiOh36NCdz9puSW1vj8iIiIia9gcSIwbNw4PP/wwAKBnz564ePEi9u7di0uXLmHMmDFW3095eTn279+PhISE6oORy5GQkICkpCTJfZKSkgzGA0BiYqI4/vz580hPTzcY4+/vj7i4OHFMSkoKrly5Arlcju7duyMiIgK33nqrwayGlLKyMuTn5xv8sQfdBXsnjiOM1khYd6AyF5+R0KU2BXgprRpvrmGdNfRnJDpG+gEAks/nIPncNcnxPyRfxG8HLtf68YiIiIiAOmhI5+XlhR49eiA4ONim/bKzs6FWqxEWZljuMiwsDOnp6ZL7pKenWxyv+9vSmHPnzgEAXnvtNbzyyiv4448/EBgYiCFDhiAnJ8fs8c6fPx/+/v7in+joaLNj61JDmJHQXwjubuVqa13w4aJxhLjYWulm3etxIwGVt95ia/2u2GO+2I0L2YZrMVKvFWP2b0fx/IpDKK1gqWYiIiKqPavOcqZNm4aioqKaB1aZOXOmxZNyR9JotCd4s2fPxujRo9GzZ08sXboUMpkMK1euNLvfzJkzkZeXJ/65dMk+3YN1J5hOvETC4NjcrVwjUb3Y2jUjCV35V6WVgZWfmWZztu7buWmAwW3v/n3S4OeCsgrx3+l5XEdBREREtWfVWc5HH32E4mLrm1wtXrwYubm5FscEBwdDoVAgIyPDYHtGRgbCw8Ml9wkPD7c4Xve3pTG6BeEdOnQQb/fw8ECLFi2Qmppq9ng9PDzg5+dn8MceGlofCWuPUyz/6qJTErrUppoCq/t6NUXv5oG4r1ftZ7gUchnmjeqE+3o1xZ3dIg1u23giA9mFZWK/jpLy6lmI68WGXemJiIiIbGFVICEIAtq0aYOgoCCr/lgze6FUKtGzZ09s3LhR3KbRaLBx40bEx8dL7hMfH28wHgA2bNggjo+NjUV4eLjBmPz8fCQnJ4tjevbsCQ8PD7HyFABUVFTgwoULaNasmTUvh12JqU1OPCWhf2y2ln910QkJcbF1TalNLwxri5VP9EOIr8cNPd64vs2w4J6ucFfI8cqI9hjYWptqWFqhQa95/+KDDacAACV66Ux5JRWS90VERERkDatWeC5dutTmOzZepyBl2rRpmDBhAnr16oU+ffpg4cKFKCoqwsSJEwEA48ePR1RUFObPnw9AWzFq8ODBeP/99zFixAj89NNP2LdvH7744gsA2lz9qVOnYt68eWjdujViY2Px6quvIjIyUuwT4efnhyeeeAJz585FdHQ0mjVrhnfffRcAcO+999r8POtbQ+gjoX9s1i621gUcrlq1qUKckZDjpeFtsWD9SclxfqrapzSZ8+jAFnh0YAskfLBV7Cfx8aYzeGFYWxTrzUjkl1bW+WO7iovXihDur4KHm2lpXSIiItKyKpCYMGFCvTz4mDFjkJWVhTlz5iA9PR3dunXD+vXrxSAkNTUVcnn1Fd1+/fph+fLleOWVVzBr1iy0bt0aq1evRqdOncQxL730EoqKijB58mTk5uZiwIABWL9+PVQqlTjm3XffhZubGx566CGUlJQgLi4OmzZtQmCgdaU67Ulw0dQmsY+EiwYS4oyEQo4nB7dEsyBvPL3ctGGjyv2G6x2YFR3oadCY7lphmUFqE2ckpP124DKeX3EIt3YKR6C3EqN7RKFnsyCTcZtPZKKsUoPhnaRTMYmIiFxd7WtO1pFnnnkGzzzzjORtW7ZsMdl27733Wpw5kMlkeP311/H666+bHePu7o733nsP7733ns3Ha2+6E21ZAwkkrF1srZu4EARt6pwzP7/a0M1IKN3kkMlkYllWY/X5vHvEBGLzySzx557z/oWHXqpVPgMJSUu2aCu7/XVUW+lteXIqhnUIQ9NAL8y5Q7u2qrRCjUe+3QtBAHa8PBRNA70cdrxERESO4vBAgixTN4CqTSp3Ofo0D0KlRoMWIT5W7aOfAqURACvjjwajvKpqk64c7o10rq6tRwbEQqGQGaRVlVVWd9xOuXjd7sfUEEh1H//nuLaAwwNx0fhxzyXExQaJs4WHL+chwt/TqbvPExER1Yf6y6ugOqE7WXHmkxSZTIYVj/fFr0/2s7khHeCalZuMF1sH+3jgo/u72fUYvD3c8NSQVgj2kV7IvfFEJi5JnDQ3ZgWlFQYL0o09s/wAvt5xHtN+PiRue+qHFDyybK89Do+IiMipMJBwcg0htQnQHp8tx2g4I+F6gYT+YmudO7tF4aP7u4kVmkZ0ibDLsQR5m1/QnZLKWQl9p/XWlEg5kV4AACgsM1yovvVUFpYnp2LEou0G61KIiIhcmU35FhUVFfD09MTBgwcNFjhT/anuI+HY46hr+s/HlQMJpVHO1p3donBntyikXitGZIBKatc6F+CpNHtbuV6qEwEZN9Ckb9ZvRwAAH286jY/u715Xh0REROS0bAok3N3dERMTA7Xa/NQ/1a3q8q+uFUnIG1lqk7GYJvZbnCu3MO/IErCGcutgAbrUGgsiIiJXZHNq0+zZszFr1izk5OTUx/GQEU0DWGxdG8aLrV1NuURqk6NYen0LSlm5SV9dlMR1wQk2IiIiSTaXkvnkk09w5swZREZGolmzZvD29ja4PSXFtFY+1Z7uJNDZ10jYSn9GQuOCkYTUGgmHkXh5FXIZ1BoBhZyRMJBbrA0kOkb64UJ2EYrKbZ99ZXBWt5LOXsPOM9mYktDaOX6fiIhIZHMgoesQTfahS21SuFwgUf1vV1wjUVNqkz15e5h2Zw70ckd2YTlKK5mmqC+vpBwAkNgxHE8MbolVKZcxY9URm+6jgMFZnRr75W4AQLMmXri3V7SDj4aIiPTZHEjMnTu3Po6DzBDXSDj+fLROaas8adNA1C4YSFRU9ZFQOsEV1Bm3tse57CLc1ysa7/6t7Snh56kNJErKudhan25GIsDLHUo3OcL8bF8Qz0CibhSWVeLolTzx5+TzOQwkGgiNRsDmk5noHhOIIG/zxR6IqOGr1VlObm4uvvrqK8ycOVNcK5GSkoIrV67U6cFRdb61qy22BqpnWVwwjnCq1Ka24b7YOn0oJg2IFbd5uGlnKUot9ExojHSBhL+ntmRuTYHEE4NbmpwolVSoxf9/qr2Zq47g/i92iz+72joxV/bnkauY9O0+TPnpgKMPhYjqmc1nOYcPH0abNm3wzjvv4L333kNubi4AYNWqVZg5c2ZdH1+j11D6SNSGLjhqjFWbHEHlXp3i1DTQEwAsNl9rjHRVmwK8tMFB6zAfxLdoYnb8S4lt8figFibbW8/+C7vOZNfPQTYSvx9KM/jZuHdHTYrKKpFTVF6Xh0RWWnNQe1Fx+2n+DhDpCIKAvKqLVa60NtTms5xp06bh4YcfxunTp6FSVV+tu+2227Bt27Y6PTiqPsl2xatxunQtl1wjIc5IONd/3CP9Y9G1qT8S2ocCAEpqsZjYleWXGM5IuCvk+HFyX/z53ADJ8XK5DAFe0g3/Hv9+P77Ydha7z10z2H4huwjpN9CvojGQurhwLC0foxbvxKqUyzXuLwgC7v9iNwYt2IzMfL7W9qZ/4YufMURaC/89jW5v/INHlu1F61f+suqzrCGwOZDYu3cvHn/8cZPtUVFRSE9Pr5ODomq6c2xXW2wNVM9IaFwwC0RsSOdEMxIAMOeODljzzAAEeWu7a3NGwpDuCnaAp2Fw0CHCDy8Nb4t5o0wbcfqbafhXUFqJt9adwKPf7hO37b+Yg1s+3IpbP9qGNQev8Iq5GVKvy8VrxTh4KRfTfj5U4/7Xispx5EoeCssqsecCS5Xbm37wkFPM9zgRAHy08TQEAdh0IhNqjYC31p1w9CHVCZvPcjw8PJCfn2+y/dSpUwgJCamTg6JqrpzapAuOXHGxtZja5ARrJKR4unONhLE1B6+IgVUTH8PgQCaT4akhrTCubzN0iPADAHhUBYnmZiR0Cssqxdd5VcoVVKgFXC+uwJSfDmLCN3vq+mm4hOzCslrv+9raY+g171/xZwZr9pdZUD0LxHLIRNKyC8vw/e6LBkUlGiKbz3JGjhyJ119/HRUV2g8HmUyG1NRUvPzyyxg9enSdH2BjpxEXWzv2OOqDLjZyxdQmXdUmZ1hsLcVTqT0uBhJagiBgyk8HxZ99PMwXtHtsUCw83ORYcE8XAECgV81VaQ6k5mLazwdxIDXXYPuRBv4FUh8KSiuQUUM6krnF7KUVaizbdcFgW3ZB7YMSqh391D32qql/R6/k4UpuiaMPo9EoKqvEz/su4ZoNFzzMrQV9ZfVRPLO8Yfdfs7n86/vvv4977rkHoaGhKCkpweDBg5Geno74+Hi8+eab9XGMjZpY/tUVZyTkutQm1wskyp00tUlHt/CaqU3Aq6uP4mxWofizl1JhcQbwru5NMbJrlPj+rWlGAgBe+Pkg0rguokap14ox4uPtNZbQzSupQLCPh8n2M5mFJtuybmB2g4BKtQZuVl4QqVRrcOBSLvL1/v9YDrl+Hb2Sh9s/3oEQXw883K85bukQhjZhvo4+LJe2dOd5vPfPKSS0D8NXE3pZtc+1IvOfQxeuFaO8UuO05ws1sTmQ8Pf3x4YNG7Bjxw4cPnwYhYWF6NGjBxISEurj+Bo9wUX7SAB6gYSLxRGCIIipTU47I6ELJBrxQsiconIIgoD/233RYPuyiX1q3FehN0XYxFuJqABPlFaooXJXSF4ZZBBhnX//y7DqxDO3WDqQOH7VNO2WqU21dywtD/ctScL9fWLw6u0dahz/wYZT+HTLWYNt+UxtqlcHUq8DALIKyvDu3yex9mAa/n5+kIOPyrX9dkBblezf/zKs3ierhpnRzIJSNA30uqHjchSbA4nS0lKoVCoMGDAAAwZIVzKhuqObDnPFNRIyFy3/Wqn3fJx2jYRSt0bCBVe6W+GPw2l4ZvkBDO8YbrC9S1N/9IkNsum+3BRybJg2CGqNgBPpBXhk6V4U2FiqlLT0SxTr+Hu6I6/E8GRU14Hc2OWcYpNtOUXlKC6vhJfS5q+7Ru+3lCsoKlfj6x3nrQokjIMIwPayvWSd/NIKnM4oRLpRGuDJjAJcyilGen4peje37bOMrKPrwwQAl3KKIZfLMPrTXegTG4RFY7tL7pNdWP2ZpZDLTM57rhdVoGlg/RxvfbP5LCcgIACDBg3Cq6++ik2bNqGkhHl59UnjwlWbdM/J1dZIlFVWn5x7uDtpIFF1wlau1qCyETZPe/+fUwCA9ccMK81F+NveyRoAvJRu8FW5o3fzIBz5XyJ6NrP+G8EVU/tqS21Uwu2l4W1xcM4tCDdqDJhvZtYiI9/0qt/eC9fRYc7fWJ6cWncH2kgUlVe/zmWVtZu9ZGpT/XhtzTGM/mwXFm82Dd6GfbgN9y5JwsFLufY/sEZAf0Z64ILNSPxwG9LzS7H2UJpk4JySeh1v/6Wt0DSwdTD+e304RnaNNBjTkIsS2HyW8++//2L48OFITk7GyJEjERgYiAEDBmD27NnYsGFDfRxjo1a9RsLBB1IP5C662Fp/AbOHk+Y86l/5La1sPIHE9tNZ+OdYutlFchH+nnXyOCESaTfmFDfCdSopqddxOqPAZHthmeFrEeSlhEwmg5tRP5Z8kxmKCjz67T6s2HfJ7GP+7/djN3DEjZOuaARQc2qGuQsSb/91AiM/2YHicgYUdWlVVXqNFN3aN+MeNlQ3jNMl9YMH4741Go2Ax/9vP/6rSrsM8fWA0k1uUhnQ3MWRhsDms5wBAwZg1qxZ+Oeff5Cbm4vNmzejVatWWLBgAYYPH14fx9ioCWLVJteLJORy10xtKtPrau2sKWkebnKxalZjWSdRqdbgoa/3YPL/7Tf7oR0VUDeBRLBvzZWcdBrL6w9og+yT6QW4+9NdGPvlbpPffeOTTV2HceNxxv9/fx9LrzFfuawRBcy1tf10FhasPyFeDNEvw1vTWpPrxeavqB6+nIfk8+znUZ+aeJt+5rBiVt0TBMFiUK2fwgQA14vLDcbrLjL5qQyLdDTkGYlaJY2eOnUKW7ZsEf+UlZXh9ttvx5AhQ+r48Mil+0i46GJr3ZewyklnIwDt+8nTXYHicrXLl4A9lVGAiUv3YkCr4BrHRgXWTSDhXUM+foS/ClerFmC7+uuv89X2c3hz3X8Y1S0KgPYLNy23BNFB1QsMi4xmJAKrKmJVGn1IGH/p5lk4iSXrzfj1CK7kliAmyAv394kxOAE6eCkXhaWV6Gfm90g/0Li1UzhahHgbpN1cyC4C2tbfsTcmUhff/nhuAGb/dhSbTmSK29gMsO7ll1aKVRmlGPfAMQ4swqvSZ+/oGoFf9l8Wi3M05BRAm890oqKi0LdvX6xfvx59+/bFX3/9hezsbPz222+YMmVKfRxjo6Z26dQmF10jUbWA2UNi4agzaSwlYH+t+rC2lPYCAGF+Hohv0aROHjNI4uqgjspdju0vDRVPkl399deZ9+d/EITqiieAtgO1viKj/OLAqtfROG3myOU8ZBaU4lRGAValXLY6f9/VZj/rmu6kZuOJTBy9kmcQSMxZcwwPfJWM/Revm+z339V8fL3jHACgRYg3PhvXE7HBPgZj0tjnoE7kl1aYXBFXyGUI9VWZpFTmFDKQqGs1pfhdNwrejNNodbPerUJ9sXPGTRjbJwZAww4kbJ6RCAkJwYkTJ5Ceno709HRkZGSgpKQEXl4Ns2yVs3Pp1Kaqp+RqX+66kxpnXR+h01hKwFrTR+D/JvVBfIsmVtfLr8nYuBgcSM3F5dxiHL1iWJJULpPBTSGHl9IN14srUOzir78lxl+yRSapTdpga2jbUIOc8L+OpuPw5Tx4uMtxLqsILYK9Dfa7p2dTHLyUi3NZhQYznteLyyXLxpLh5/CG4xnYcFw6VWzvhRyDYgKCoM0BT62qmKVLsQnwNEzdkFoIT9XUGgHjvkpGVmEZfn2yH/w9TfvTXM0rwfCF202qmAV6uWuDCT/D97bxSS3dGEEQcOm69n3eNNATjw9uiVUplw0ajeYazY5m610siQrwRJzRxSo/lfY03JbUppJyNZ76YT/aRfjh5eHtbH0adc7mb82DBw8iPT0dM2bMQFlZGWbNmoXg4GD069cPs2fPro9jbNR0FV3kLjgl4aoN6XQlVaVKWToTVVVFKVe9Iv7NjvNYtPE0rlyv+Upo00CvOgsiAG3+65KHeuKpIa1MbtP9JutK8Lp6IGeJ8Zeu8YxEgKf2pPTlW9th0oBYjOsbI952JbcE57KKAADnsosM9nvv3q74d9pgeBt1KGdPCfOsLdOaaRQQXC+uEIMIoHo2zt+oUaNxmVIydPBSLpLOXcOZzEKsO3JVcszxtHyTIAKo7lcU4msYSEiNpdqbvfooJi7dCwCIDPDEQ32bmaTMGr/m2VUzGCM6R2DnjJtMAkS/qp9t6bey9VQWNp/MwmdbzjpFWmetvjkDAgIwcuRIzJo1CzNnzsQ999yDvXv34u23367r42v0NOKMhGOPoz5UpzY5+EDqWIOZkVC6bmrTtcIyvP7HcXyw4ZRVizzrapG1MeOrskD1eifdjNC6I1exPDlVbD7piswFS7lGX7pFRuN0nV7D/FR49fYO6BZtW6F144sUV3JLGmW5Y2tYe0U0JfU6fj+UBo1GwK4z2dhz3rAyUJOqGZ9Io9+pDCsCCY1GwPqjV5HeCBs4Xr5eHYzNXHUEl8z0RJHSv+pkNtQokNAF6tcKy/D0DynYfDLTZF99u85k49f9l2067sZCEASDEtIhZt7nucapTVUdrYN9pNNdq2ckrE9tyiyo/v24dN30fWJvNqc2rVq1Slxkffz4cQQFBWHAgAF4//33MXjw4Po4xkatuvyr60USuuekdrETKHGxtZPPSOhOZEtd8Ir42ayiGsf8b2RHfPjvKTwYFyOesNa1VqE+Jtt0Xxy6QE7XWbtjpB+6RgfUy3E4mvECRB3jq3f6MxJSwZ3utbNE/6OyR7NAbD+dLf48celetI/wQ9swH5zLLsLyx/rCx8P1GtVtP52FH3anYt5dnaxO5bL2RObgpVw8++MBbDmZhV9TTE86fatez6gATyS0D8We8znIL61ERn4pBEGwWDjkr6PpeHp5CuJig7Di8XirjsdVGAdPzyxPwZpnDJv+GgcSzZp4oXt0gJjeYjwjkZ5fipve2wK1IODitWL8eeQqLrw9QvLxSyvUeOCrZABAmzBfdG7qf0PPx9UYf1bp0shGdYtC0tlruJhTjEOXck1mWa9VrVMx93tYmxmJbL11GtqgwrH/VzZ/gj7xxBMYNGgQJk+ejMGDB6Nz5871cVxURXDhxdaumtqUX6L9QrbmpMeRXHmxdU1XV8P8PDCubzOMj29WrxXRQv1UmHtHB+QWV0AmAxZtPI137ukCoDqQ0zmfXeSSgcSO09kY93Wy5G3/HEvH0LYh6BYdgLyS6vUiA1oFS+b++knM8OhTKuT4cXJf8ee3R3fBxxtPY9fZa2L6zX9X88Wa7klnr+GWDmG1el7O7KGv9wAA/DzdsOCerlbtY+tiT6kgwk/lhnt7NRV//mpCb5RWqNHu1fUordAgv6TSJOVJ359H0gCgUZWK3X/xOn7YfREVRt+Dhy7nQa0RDJqfGVdhGtk1Ei8Mqy6FFepr2lDTOO1PikYjGKSsncwoYCBhxLj6UmBVaWpPpQKLxnbHn4ev4unlKSazrLqLKE3MBRJVZWB15w3W0F/35wxrj2w+08nMtDw1RnVL7cLlX+Vy16zapLuyUNNJj6PpTmRdcbGvcYqMvvHxzfDYwBYGX9D1aWL/WPHfjw1sIebtGwcSrprPPHXFQbO3nUgvwF2f7sLoHk0NTkxn3tYOHSNNT2Rah/pA6SZHuZmeED9OjjNYCBwV4Im3R3fB7N+O4AeJztb6KQKuyHihv5RKtQbvbzhlNvWoXbgvTqSbNg80NrB1ML4c38tkJlblroC/pzvySiqQnl9qMZDQX/BdUq4WZ+1c2dgvdhuUE50Q3wzfJmlnKTMLSg2aZBpXYQoz6vhuPCMhJbOgFGUVGrHs8v6LOXjgy2SD35v0PFbYMmY8qzqgteHaCF1hiJSL1/HJptN4KL45/D3dxQDEuAGdjp+n9vvAmhmJ1GvFePefk0jRq5xWUxUpY6czCqARgF/2X8LK/Zex/NG+6BDpZ9N9GKvVJVO1Wo3Vq1fjv//+AwB06NABd955JxQK1/+ltzfd56q9TnrsydWqNh25nIfvd18Ua94bN5xxNrovaVfsY6BLkYkK8ER+SQXG92uG5k28se/Cdcy6rb3D0s70F/96KRtHIGEurUmf8dVtc304mvh4YOXj8fhxTyp+2mtaztfc75y5q4Gu+prrlFooiysIAi5cK8bhy7n4bMtZyTH39GyKO7tFijMcLUK8xQXuxvw93c3+XoX7qZBXUoG0vBK0Dfc1uT2zoBQbjmeIaSCA9n2j32PEFQmCYNKTIK5FE2w4noG0vFJk5JchyFsJDzft62qc2hRj9Pqo3BX449kBEARg9Ge7JPsd3PbRDlwrKsNfUwaiXbgflu26iLJKDXadrV7rYuvJaWOg+xyLCvDE4gd7oJvR7LFuEXWlRsB7/5xCpUZA1+gAHLyUC8BCapM4I1HzZ9HCjafw+6E0yeOyxrXCMoz8ZCcq1BrxPOX75It4664byyyyOZA4c+YMbrvtNly5cgVt22qn1ObPn4/o6Gj8+eefaNmy5Q0dEBnSuHJqk4v1kXhn/QnsOFOdjy1Vvs+ZqKq+nFyx468ukOjVPBALx3QTZ/Tu7RXtyMMyoGokgURteHmYD/S6Rgcgt6RCOpAw8zsn1fUXcM3XXH/Rvq6njZTPt53D23+dENc0GFswugvu6x1tcPI65ebW6BTlj5vf32oyPsDCTEMTHyWQoV2jsnvmzWJTLp3H/2+/QQlNQPt/4zy/rfXDuEM7oJ1lCPb1QFpeKR79di/ySyux9pn+aBfuZ9B3xUupQOco01m7TlXbzDVN0514/nbgCmbe6odMiZmobFY3M6Fbl9Clqb9JEAGYvv8X/nva4GfjHh86us+s68UVePTbvfhwTDf4mrkgsvvsNZNt3yVdxJaTWfhpcl+Thd/G/rtaYJLKfLUO+rvYvMLwueeeQ8uWLXHp0iWkpKQgJSUFqampiI2NxXPPPXfDB0SGXLqPRFV05CpFVPSDCKB6ytJZuXL5UV0pS28PN6dNC3T11KZrhWUoLKus1UUQXw/LQXiEv2kuOGA+eDdXlcsZSifWNf0LA5Ua7b/PZhWi2+v/4JXVR7DtVBYWbTyNt/86AQAoMCr7OrJrJH59sp+41iHA012cPesY6Wf2yqqlCyf6DRr/PpZucrtxEAGYlgZ2RVLpZOH+1Y3lsgvLUV6pwcebzuDm97eIV7c/uK8r/nh2gNiwUUpNv3dHr+Th0W/3Sa5HMe7vYolGI+DFlYfw4spDLrfeUZ8uiDP3/g/wMv9/AcCkx4eO/izqv/9lYv1R098PHeOLTzqpOcX4vqpohyVSsxcXrxXbtNBbis1nOlu3bsXu3bsRFBQkbmvSpAnefvtt9O/f/4YOhkzpfjGd9WToRrjajIQxZ09tcuXF1roZCWeuyOPKqU2Z+aUY8t4W+KncxfTMUd0iseNMtsmiRWNucpnY48Qc4yvagDb901zJ5YFtgpHYMQwn0gtw8Vp1ucS8kgoUlFZA5a4Qa/FrNAJksob7mavfD0IXVGw4noHc4gp8vzsVfxy+avEkPdjHwyBfXi6XYenDvVFSoUarUF8IggClQm5yxTvQwonUxP7N8cdhbW8E/dffktwS178qLlXmNsTHw2Stw5+HDftKdIsOQIsQ04pw+r55uDdeXXMUl3KkrzjvPGN6dVvnmg0dsU9lFuCXqpKxE/s3l1zb5AqqF01Lv8+9a1jPYy7tz/izztLvh6UZRmuaD0qlrJ3LLkL31zfg+0lxiG/ZRGKvmtk8I+Hh4YGCAtOFV4WFhVAqLUdkZDuX7iNR9e5z1UDC6VObqj7AXHGNRGGZ9jmZy7V3BsZfLK50dfxYWj6Ky9ViEzIfDzcsvL+7VYtnfVU1zyL5ergh0iiYUMhkZvfzcFPg84d64cvxvQy2J5/PQc83/sXzKw5i2c7zSEm9jvu/3I3B726xukGbs9EvoZtbXIFTGQUGJyA1Xen3lag2F9eiCYa0DQWgDbCkxlj6vOvZLAiv3dEBgLY7sz5znz8NdUZCoxHwwYZT+GTT6Rp7w0jNSCjd5DWW7DW35kffkLah2P7STegTG1TjWGPZhWUoLre9QeF5KypEOZsKK1Misgosz0hY+swa0jbE7G3G+6WZSTVSawSLvVis6b2SZWamSa0RsPDfUzXub47NgcTtt9+OyZMnIzk5GYIgQBAE7N69G0888QRGjhxZ6wMhaY2ij4QLTIdKfRk2lKpNrjwj4W0h197RXDm1yfhqtS5/2NIVNR1z+cH6ZDIZVj3VH2uerp4Fl1vxbWac4pRTVI5ytQZ/HL6K134/jrs/3YU953OQmlOM/XqVURoS4wBo2IfbsHTXeav3lwoSjEl9HdWU2hFUdQJ2MqMAN723BS/8fAgADPL+9TXU34cdZ7KxaONpvPfPKWw4nmFxrLkTu5qqL9lSWty487I1rhdXoMOcv5EkkZNvTH8NjbmTYGd1IPU6uv3vH/zv92M1jtXNSFjbl0VnXN8YvH+v5RLML9zSRvz3VTMBQXZhmbhAWoq5/fSZa2gI3Ng5ps2BxKJFi9CyZUvEx8dDpVJBpVKhf//+aNWqFT766KNaHwhJEwMJF5ySEPtINPw4QrLigjWl+BxJd3V4VcoV8UvdVehK2noztckhjK9m6nLknx7aCoD2y/WFW9qgbZhpBR83Kz/rwv1VBn03FFZ8EXp7uEk+ppS6WIToCIUSC3htubpf25RMS4utgeou7+eyinAuuwi/plxGYVmlSUlTnWNpeSazF87ssy1nMXzhNqzQKwLw55GrFvYwTTV5uF9zAJa/O7yUCpvS7ib0a47Jg1pgeMdwq/fR+S7pgsXbSyvUBnn31pzMOpNvdl5AUbkaS3deqHGsrkN1iK/5gFnqd2Bsn5gaZ5Cevbk1fqrqf5NuZtZB/7WN8FfhndGd0SLYW9xmbj99xl239d3I94/N37IBAQFYs2YNzpw5I5Z/bd++PVq1alXrgyDzXLlqky4CdoUFWlK/hKFOHkjoqjYB2vKbkwe1kCzN2BDpZoiMT9adiXGaT25JOQpKK6DWCJj+y2EM7xiO0T2bmtnbuRWVGc5y6a5Wj49vhvYRfugY6QdvDzeM7tkU/d7eZDC2tqmO1p5c/fpUP+y7kIOHl+61OC6tgZ0U6RRZmZJiTk0BgZbpa21citSa+03PKzXJ7Y7wV+FqXinWHUnH9lPZSJp1s1OvdQK0lbLeWa9dvK7fc2PNwTS0C/fDk0NMq1n+vO+SeAI7PbEtmgZ6Ymg7bfqYuavePWICMHmQbZUx/T3dMeu29vh1/2Wsl1jobklBaSX2X7yOTlF+Ygland3nrmH813sMZh8tpd44I/00wNIKtdl1DGWVanGtiaUZiS/H98LnW89h19ls8WKWuUIPxnQFJK7mlZh0gC+v1Ii9I3rEBGDVU9qZ2IGtQ3AyowATl+5FbnGF2eew53wO5v/1n0FRgxBfD7wyoj1e+PkQKjWCGCjVhtW/nRqNBu+++y7Wrl2L8vJy3HzzzZg7dy48Pa17kah2NK5ctUmX2uQCaySk0iCcfo2E0Yls4sJtiIsNgrtCjq8mmDaWakh0V8Sd+TkYp76VVmjQ+bV/0Kd5EPZcyMGG4xliIPHFtrP4Lukivnio1w03D7IHkxmJqpNImUxmkLMt9aVs6+/NiM4R+PPIVTzYN8aq8T4ebmgRbHmhKtDwZiQq1RpMWXEQp6xoHmdJTSlKANAh0g/bTmUBAGbf1h5eHgqT5mjGpBZjZxZUBxIxQV54akhLVKg1eHWNNtWkoKwSx67kIa5F7RaB2oulGZ931p8wCSSu5pXgpV8Oiz83DfTEnd2ixJ/NzUj8+mS/WhcBkFokPLpHUySdzUaHSD/8+59ps+EdZ7Kx40w2Hh/cAjNvbW9w2897L5mkMFqTp+9M9AOJzPwyxDTxwpaTmfD2cEPv5trPKbVGQH+9ix2W3ue9mwehd/Mg3LtkF/Ze0J4TWPt5prvf0goN8koqDH4Pp/18UCxWEBVYHbBHBngiwl8Fb6UCReVq7Ltw3aRRHgB8tPGUQRDxzcO90LdFE3gp3dAnNgjx8zfhWmG5SQBjLatTm958803MmjULPj4+iIqKwkcffYSnn37a5geUsnjxYjRv3hwqlQpxcXHYs2ePxfErV65Eu3btoFKp0LlzZ6xbt87gdkEQMGfOHERERMDT0xMJCQk4ffq05H2VlZWhWzdtnfmDBw/WyfOpS4ILz0hUFUlp8Gskks5ew4xVRwBoKzcEeSsxple001d9Mc7RB7SLT3ecycY/NeT2OruSqlx8qefoLKIDpS/C7LlQXY5REASUlKvx1roTuHy9BB9trP2COHsy7pZu7uRUKVFl6bFBLWx6rNdGdsSbd3XC1Jvb1Dy4ijXrAKxJFXAmSeeu4c/DV3E6s9Cq8ea+U4IslBTV+d/IjritczjWPtMfjw1qgQfjmtW4j1RH6+tFFeJJeKcoP9zfJwZB3oYn0Q0hXUZqncdDfatfE+N1K1euGwapxien4X4qkypACrn5YgLWaKL3ui5+oAe2vzQU79/XFbtm3oypCW2qjsNDck3F51vPmWyT6lORkd8wGtkt3XkeY7/YjSNX8sRtaXklOJ9dhIeX7sW4r5LFLINLOcVipbl24b5WXZwaH98cgDaF09r/M5W7Qvzd03/PV1at4dJpZjTzJ5PJ0KyJNsVp3NfJkuWVjSt0hft5wquqEInuMSs1Al5ZfRT/1uK73+pA4rvvvsOnn36Kv//+G6tXr8bvv/+OH374ARrNjTUBWLFiBaZNm4a5c+ciJSUFXbt2RWJiIjIzTaNjANi1axfGjh2LSZMm4cCBAxg1ahRGjRqFo0ePimMWLFiARYsWYcmSJUhOToa3tzcSExNRWmr6gfTSSy8hMjLyhp5DfVK7cvnXqm+ymipbONqHG05h/Dd7zPZb0D+56xDph32zE/DOPV3sdXi1ZqnE5oXsIly+bl2ZRmekS22ypkqQo7QI9sGQtiEY3CbEbMAzd+0xtJ+zXvx5++lsnMsqdPrfGeNAwlJp0Fs6hAEAPn2wB/6eOgi3d7Ht8zjE1wMPxjWz6f/aRyKQMA4uGtrV1es2rINIaB+GrdOHGqRdNGviheZVf2oSG+yNTx/siS5NA6x+TF8PN/EzX+fw5Vws3nwGQHWwaZwC1RACOqnSmw/ExYiLoo1ntzKN1ka0MVq346lU4Ocn4rHqqX7iNmvXDpmjSz2Ty4Cu0f4GXcM7Rflj5RPx+OHRvnjnni54emjN6VNSzfSu5JYYXOV3RhVqDf73+3Eknbtm8Dl1KqMAx9K0gUVZpQYnq2b29N9/H93f3arHuL1LBHbOuAlv3NnJpmMLrwoodY+ZkV8q9g7RSZRY69JU76KUNf0kAr2rf8c83BTiZ98Pyal49Lt9Nl/ctTqQSE1NxW233Sb+nJCQAJlMhrS0NAt71eyDDz7AY489hokTJ6JDhw5YsmQJvLy88M0330iO/+ijjzB8+HBMnz4d7du3xxtvvIEePXrgk08+AaA9KV24cCFeeeUV3HnnnejSpQu+++47pKWlYfXq1Qb39ddff+Gff/7Be++9d0PPoT65cmqTrAFUbapUa/DRxtPYdioL3d/4B2+t+w9f7ziPyqqrMaUVahy6VH1VI6ugrMEsjLd0tf6DDacw4J3N+P3Qjf1+O4outcaZZyTkchmWTeyDbx/pg9JK6SD1uyTDL4XicjVuen8rPt1y1h6HWGvGJxNB3uan9xfd3x2rnuqH2zpH2G2NjrtCbtBzYteMmzBvlOGX/tW8UmQWOP9JrI5xE7H7ejXF4gd6SHb1btbEC9FBXhjTW9s7+o6ukfh76iD8/fwguClsrsFiFZlMZnKV/fNt58ST6jBf7UmUcSpItkTte2MajYBvd13Af1fz6+hobSPVdyHUt7ofhHF1Jv1F1o8NjJWcBeoY6Y8eMdX9PKRm72zh7+WO357qh+8fjUPTQNNgsXfzILQK9UFUgCemJ7ZDoFFAZ3zxwlzTuo5z/0aqlb1CHMFcZak5a47hmeUHxJ+v5BbjVEaBOD6+RROrP59kMhmiAjxtvgCs648zceleLNt5Hrd8sBX3LEkCoE0D3fdKAjo3Ne3TMaJLhPhv49kvqTWoAZ6G7zfj9ZwXrhVZXRYXsCGQqKyshEplOP3m7u6Oiorar/QuLy/H/v37kZCQUH1AcjkSEhKQlJQkuU9SUpLBeABITEwUx58/fx7p6ekGY/z9/REXF2dwnxkZGXjsscfwf//3f/DyqvkKTFlZGfLz8w3+2INu4WE9fbY7lEJcI+HgA7FAv0tsaYUGX2w7hzf+OI5luy4AAA5dyjUon+rMOfnG9I+1Q4R03v1nTn7Cao5u9siZZyT02TrB8O7fJ+vnQOqI8YyEpQWKnkqFwQmTIwR5K03y0gvLKtHnzY34Zsd5LE9OxaqUyw46OusYl3YM9FZiRJcIyRxt3UniYwNb4N17umDO7R2gcleYLKita5be5y1CtOkZxjMSUt14jf1+OA1z1x7DQ19bTouuL1IzEkHeSvF9b9yEURegTohvhtkjOli872FVM3ZPDLZtkbWU7jGB6NfSunKwXkY9eM5kFuJcVnXanKWmdX8dtVytypFSc6wLct5adwLDPtwmrmWJkGiCWdf0G22+9vtxg1mfVqHeZj9H7+wWhfl3dwZg+vuSK1EIxjgbIdTX8LlNXLoXfd7caPVxW73YWhAEPPzww/DwqH4ipaWleOKJJ+DtXV2CatWqVVY/eHZ2NtRqNcLCwgy2h4WF4cSJE5L7pKenS45PT08Xb9dtMzdG91yeeOIJ9OrVCxcuXKjxWOfPn4///e9/Vj2vuiS48IyEWP7ViWckzEXlSWev4dGBLXBJL9c1xNcDb4yybSrTkfQDifYRfjgucTXPlqsSzqS0AayR0NcnNgh7zufUPLCBMJ6RCHbCCmb6Od4qd4XJl6nO638cF/89sHWI05Z1Nj5Z1ZVxfbh/c8xZcwyR/iqxEpUujchTqcC9vaLtdozmCmuM6BKBhPba7+yoAE/c3zsaP++7BI1g+rykbD+dDUB7EqXRCHafFZaqzy+TyapnJIxmVXRN3Kx5L709uguGdczEbZ1tL996I4wzBW75cBuUbnJsnDYYTQM9LfYkkOqg7CysDSR0z0HXuyHcDoGE1OyhTqS/5cJG8VUFCbILDP9fpP4vjGdKQv0M34epOcXQ1MeMxIQJExAaGgp/f3/xz7hx4xAZGWmwrSH4+OOPUVBQgJkzZ1q9z8yZM5GXlyf+uXTpUs071QHdjIQrrpHQPSVn7mxdYWa6pKxSgxV7U/HxJu0i/vt7R2Pv7ASx0kNDoH+1vn2E9JRtaaUaao0AtUbA+qNXsf6obeUDHaFSrRFPEhtKIDHrtvbo38q5K9PYwnhGwhlLIRt/7Bh/mUr5978MJJ+ruUmXI+QYlW/U5T0/0CcGnz3YA19OqO7qbemEpT61l5j57B4TgMUP9BA/j2QyGd4e3QVLJ/YBYHlGIre4HMnnrhmc9BZI5O7XN+OT6jZh2qpg1TMSRqlNVT+bC171BXkrcU/PpiYzBPVNKoWmvFKDvRdyUFBWabLY+uXh7cR/pzlx/4/LRgvd/T3drSowYI9A4qZ2oWaLIESZKc6ho7tYU1KhxvnsInGdoHEK2kCJqk43WmHS6nfm0qVLb+iBpAQHB0OhUCAjw3CVeEZGBsLDpaPv8PBwi+N1f2dkZCAiIsJgTLdu3QAAmzZtQlJSksHsCgD06tULDz74IL799luTx/Xw8DAZbw/VayTs/tD1TtEAyr+auyJ/vbgcL/96RPw50spa0c5EPwdWP7VJIZeJX8yXckrQ7fV/EBPkhWNp2hmLfa8k2Nzd0570U80aSmpTt+gA/PBoX/R8Y4PZTr/6PG4wX7q+6fcyaNbEC9ESOdnOxtfDTbxq3yLYG+eyi0zGzFx1BDIZ8PfUQSYLZOvDv8cz8NHG03j/vq5mH+9STjFeWHnIZEZLF0i4KeS4tXME1BoBzZt4ISO/DN1iAur70CW9dVdnvLr6KFqG+uDHPakAzKeMBFeVK7U0I/HSL4dNKsxdLy6XrBBVn65X/c6O6haJ1JxiPHOTtq+Wbsbhsy1nUV6pwQvD2uD9f05hy0lt6dwQK4JXR5l1W3sIgmBSFvbCtWJxRgXQll++uX0o7u7RFFGBnnjuxwN2r7SVV1yBF385hMFtQjBOr1qWzrXCMgR4KfH4/+0zeT7xLZpg8YM98N4/Jy2m8obXUN64LnSPCcS5+SMkvwdqOsfwViqgcpejtEKDoe9tQZswH6yfMkgMWmOCvDCwdbDk69Mp6sYmARz6baRUKtGzZ09s3Fidi6XRaLBx40bEx8dL7hMfH28wHgA2bNggjo+NjUV4eLjBmPz8fCQnJ4tjFi1ahEOHDuHgwYM4ePCgWD52xYoVePPNN+v0Od4oXdqPK6c2/Xn4KjLyS51y0XWlmRkJ4w/KhhhIeCndMD2xLcb2iUHfFk3QsipH+afJffHaHdV5uwWllWIQAQBXc517AapufYRM5vwn3MYqrfwdMO5B4WyKqxrSLRzTDX88O8ApCxDc10vbo2NI2xAA2ivhK5/sh3+nDUIzC5WLBAE4fDnP7O116dHv9uHIlTw88X/7zY75ed8lybQ4Xw/D94hCLsNvT/XH5heHIKKGNIn60jbcFz8/EY/b9RaHhpi5KBGidzV/0ILNklXkpMpUS+WE17ecqjUS/VoFY9VT/XFTO22alv5z+3rHeby48hC+3nFe3GbuuTuD2GBvfDWht5hyprNo42kkfLAVgPbkdPGDPXB3j6biz4D9Kp79kHwRU346gKW7zmPD8Qy8svqoyaLwjf9loNeb/2Lsl7sNgoh24b7w8XDDY4NioZDL0LmGk2l7/s5IzX40b+ItMbKafiodAJzKKMS57EIxEO/S1B9v3tVZclZwdI+mmH1bezx3U3Vj6d7NrV+35vB2kdOmTcOECRPQq1cv9OnTBwsXLkRRUREmTpwIABg/fjyioqIwf/58AMCUKVMwePBgvP/++xgxYgR++ukn7Nu3D1988QUA7Ys5depUzJs3D61bt0ZsbCxeffVVREZGYtSoUQCAmBjDxkU+PtppyJYtW6JpU+fqJCt2tnbCL+IbpXtOx9LyEffWRjTxVmLz9CHw9XBzmlQuqVrZgOlUdqQdpj3rw9NDqz84fn2yHy7llKBzU3/0bh6ET7ecNSlTCOgWCjpvGmN+qfZEwk/l7jTvI2v1ahaIjSekS18PbhOCrVVNwJxl7cqF7CIUlFaapEHoZiSaNfGCr8o5g56pCW3QxMfDoN6/rhxqTWkk9s4Bv3DNdHZEJ9/MibNUsBnooJQmY/qpFOZmN/XTTVJzivH1jvOYe0fHGu/7Uk6x1fX+60J5pUa8Qm9c5th4DcS6I4apofZYwHujgiUa2ekYN7nTPZ/MgjJUqjX1VgEM0K51nf3bUZPtGfllCPdXYfWBKygqr8T6o+kQBJgE21+O72VQAndQmxB0jvKHl1KBZInAvGkNqUV1KcLfU7x4N7ZPNAK9lIiLrTltOtxPJXbgBoCHvt4jXvS01EhPIZfhsUEtcCazEIs2acsx94wJhLUrnh0eSIwZMwZZWVmYM2cO0tPT0a1bN6xfv15cLJ2amgq5vPrN2K9fPyxfvhyvvPIKZs2ahdatW2P16tXo1Kl6ketLL72EoqIiTJ48Gbm5uRgwYADWr19vUnWqIdC4cEM64+d0ragcP++9hM+2nMWkgbF4akgr6R3tqNLKPinRQc6fulGTAC+lQeOwiABPyUDCmRbSrT96FQv/1aZ+dIzUnszq6ukbV39pCGaNaI/CskqUVWpM6of3iAnEm3d1woB3NpvtaWJPFWoN7lmyC7nFFfj7+UFoGVLdLVq3RsLbw+FfMWZFBnga5HXru6dnU/x5xHzlGXOlLx2hwEzd/sgA5/2+0//dbGImkDA+Cb18vQSJH25DYscwTBvWVswBN/bsjwfQNswX66YMNOlbUddKK9S44+MdYhNA4zLHNaWAWpOb72iWjtF4rU2wjwfc5DJUagRkF5bX27qCD/45adBMTl9qTjE0goCpKw4CkJ6VfmVEe5PvbB8PN/z+7ABUqDVoPfsvk33s+X3ipZeSO+f2jlan6BoHC/qZE61CfYyHm9APlkL8rZ8tc4pP+WeeeQbPPPOM5G1btmwx2Xbvvffi3nvvNXt/MpkMr7/+Ol5//XWrHr958+ZO2+DJlftIKCSe03v/nERphQYL1p90ikCiotK694U9FmLZW4iZK1HWlGO0lye+TwEAzPvjP/w4uS+A6nzlACdP/5HSMsQHKx6Px9c7zouBRJC3Es2beOGBuBgx+C6r1DikOo2+M5nV0+aHL+caBBK6qk0NZbG7saHtQrFn1s1YffAK3lpnWkFQfw2IOWWVatz5yU6o3BV4YnBL/HnkKubc3qHOqz4ZryG4o2skfDwUBs3mnI3+BQtruowDwIaqNKaTGQWYmtDGYtWgkxkFuHCtyOA9WR9OZxQadBIP8TH8HrD0f31Tu9AGMWNqLtADtCff+hRyGcL8VLiSW4IXVx7C+/d1NXslvFKtwYxVR9DER4mZt7a3+nhOZRSIV82l3Pe5YesA/RLuAPD4oBZ4dGALs/u7S8yijOgSYdf/K/2mkLas8zP3WjcN9BQbf1qiclfgpnahOJNZiFvaW18lzCkCCTJPcOUZCYknpSvb6SwqrJiRGNQmRPLDp6FrF+5nsjANcK4ZCZ29F3Jw9EoeQv08xBxkqaZLDYV+EDT7tvYY3VObclmsdwJbUqF2yBV/jUZA8vkcpOdXT6H/X9JFXMopwRODW6KkQi1+efs5aVqTNUL9VLire1P8dTQd6XmlBlf3rKkMdDqjECequuM+8b12jUOzIC+8mNjW4n6CIOC1tccQ5F19AucmN//5Yvz7+PFY67rvOpK3Uhvo5BSVW0zZaBPmg1MZhSbb80srLPYxALSpLOl5pejfyrq+CbVx1ag6kXFlnSY+SngpFSZVzD64ryuGd7JvOdfa0k9tev/erritcwTaz1kPQLoHVLi/NpDYcSYbb637z2w36P0Xr+OX/dreLA/2aYYYKzqqAzDoZVEbtqQoNfFW4ttH+og9TuxlYv9YnL9WjOESXawtkVoQ3rdFEH58rK/VgdA3D/eGWiOgqLDA6sdlIOHkdDMSDeHKha0awixLRaXlQGJMr2j8786a83YbosmDWyC/tAIVag1+3FNd7vjbpIt49fYO9Zr/ag39/iOVGgG3f7wDQd5K8UrlHV0jzO3q9JoHV3+p6k9Jq/QahjkqkPhs61m8+/dJg067Kam5SEnNxT/H03H0SvXCfB8rrzY7qxBfD/z2VH8cuZyHOz7ZIW437h4r5YpEB11rOi+fzSrCt0YdzS2l6OjPEI7qFlnj/TsDmUyG358dgLJKNUIt5G4/0j8WM1YdMdmeXViO7CLLFzRmVu23dGJvDG0bemMHbEZ6fnVw+caoTib/T+4KOZZN7IMdp7MMrqL3bh5k93KutaWf2tQ6zAeeSgWmJ7bF97svYtotbUzG668T2XnGfKlk/cD8+NV8qwKJ7MIyk/KttrLmAtPdPaKwKuUKHu7X/IYrGtVGoLeyVhcE7usVjf0Xr0MjCGIhgtahvjafP9qaEtgw3smNmLoRVG1yZjVV0Wka6Nmgulnbwk/ljtfv7ISDl3INAgkAOJFe4JAPWH05Et1kdUGEyl2ORBuv5jiTzlEBGNAqGO4KGTpGVlfZkMtlYok/R62T0HXVLpcIsvWDCG+lokH8jlujWbAXlG5y8TkXWjEjcVUikPBwrzn4lprxK61Um6SybTiegdUHrojjP7q/G25uX3P6grOwZn3A/X1i0Cc2CDe9v9Vge05ReY0zEjob/8uwKZB4Z/0J/Hs8A19N6IWdZ66hT2wgWoVKl97VVSd6uF9zg0X7+vrEBqFliLdBIOGsTQ2l6FcralGVKvb00FYGhTr0+XlWn1Z6e5h+N14vKsfCf0/hwrXqKlxnMgsAWP68Pnw5F6MW74T+V7Lus9AW1gQsc+/oiFvah2Fou/oJQOuLv5c7ljzUExevFYmBhDVrI24UAwknp1ts7YKZMw0iONJVbWoX7ouL14oNehS4K2S4p5dzVfmqD1LNxNLzSh0aSBy8lIvz2eanuGOCvBr0LJ7STY7vH42TvM1L6YbSinKD96IzctZqTbXhp3LH8kfjsP5oOr7acd6qGQmp/geFZTX/n2XoXeXWEQSgsLzSIFXsse/2if+WybT1/B09S1gfpHL080oqrF7wnldSiTOZBYgM8KxxFqBSrRF7Cdy5eCdyiyvQJswH/zw/WHK8LpCoaY2ccTWnhnTxqWWINxY/0AMhvh4mayKk3NElEqtSrgDQzjoIgmDwWbxk61mTGbdzWearkumsSrliEETMuLUd7u8djW6vbxC33d4lAt5KN6zYZ75hsDVrh/w93XFr54Y7o9000AvRQZ64mluK+Jb13+iUgYSTE1x5sXUN33nllRqD9AlH0PWRULkr4KNyE0/epie2xZODW7pkWV5jUpVHCsrsX6tdJ6eoHPctSTJbmhdAg2iAVlu6BczGedf1obRCjYeX7kGkvyc+GNPNpn2tXUTbUPRqHgQ3hRxf7TiP05mFGPdVMu7t1RR3douSHH9NIvUmz4oeB1KBBKAt82puzUmQl9IlgwgA8FO5wcNNbrBo9lRGgZhfD2hnN/43siOe/fGAyf6/H0rD74fScHuXCHzyQA+Lj6Uf/OVWVX87lVGI0gq1wcm/RiPgm53nseqA9oS5pjKuDfl7QiaTYUQX60+qh7YLxeHXhqHLa/+gvFKD/NJKg3K/ZzJNLwCtOnAFMpkMb97VyWyQZbwepVmQFwK8lHhicEss2XoWs25rh8mDWgIAejQLQGmFBnPXHhPHtwzxxqA2IQ0qiKsthVyG1U/1R25JRb0XHAAc3JCOaqabkWjIV1fNqSk4mvfncTsdiXm6ev1KhdwgJ//poa0a9JeDLZRucvEqju7v/JKar8jWl/+u5lsMIgDTRY+uRFfFo9iKykE3aueZbOw+l4NVB64gs6DUbMlNKc5ZB+/G6K7IqjUCdpzJxpSfDoq3aTSCQRAgNSNhrueDvox86SvtgxZsNrvGwpk7zd8o40ZbgDa97nxV5/FXRrRHyqu34I6ukVj9dH9MGhAreT9/HDZfzlfnukS6JAD0f3sT/vd79Unp+mPpmPfnf+LP9uh63JD4qdzF3xXjKn/mLoD8mnIZm8z00AFgUopc9xn/UmJbbHxhMB7Tq8Q0pncMJvRrjpurUpNeuKUNNr4wxKo+JK6iiY+HXYIIgIGE06teI+HgA6kHNQUS3yVdxLQVB1FW6bgUDl0g4aaQuWQwZ62vJvTC0od7Y2BrbQUUa06I6lqFWoPXfz+OB79KNtg+sHUw/D3dxS8NQDody1XoaozbclJfW/oLIhdtPI12r663et9BrUPq45AcSmqWRZfm9MLKQ4h7ayN2nM7G5pOZOJFuetJvVSBRID0joRGA6b8cAmD6f9+Qcu7rmn4Vnm7RAejZzHxHXql1PfrMBRLXisqxdOcF8aTYOKBzxfLfN0r3njyZXmBQqjfLQkqaLjiUkmkUYEdWXdSSy2VoGeIj+f08/+7O+PTBHpg82Hy5V7pxDCScnLhGwgVPYq1ZiLnqwBX8cajmK0n1paIqtcldIcfHY7sjwMsdH93fzWHH4yjtI/wwtF2oWCWo0A5Xw419l3QR3+w8b7BNqZBjys2tcXDOLfhyfC9xuys0CDRHNzW/88w17DqTXa+PdV3vBOD73alW7/dgXAyeGOJ6X97+Er1JLuVoF43+VpXmMv6bZExcutegw6xOQWmlxZ5Fhy/n4shl6UZbAJBatUDVOCBx9UDC3Nefl1KBXs0Ny8eG+Zl/LaROYjefzERa1cL460WWA723/vwPb/91wmRBfKQVeff/G6m9Gv7Nw71qGOkadGVjn/ohBcM+3IqCUu1ra/za6TeMk6p0BmhLIhvvZ9wMT0qonwq3dY6Ah5vrpzM5kmslsbogsSGdC05JWFvRxdIVjPpWWTUj4a6QIb5lExx49ZZGPTOhCySKrFhsWleOXslDfmkFjhl1Ml04phsSOoSJU+gymbaG/n9X8zGiAS+Uq4luRuLrHefx9Y7z2Ds7od5OJKUqYxm7rXM4ruSW4pBeJ+437+pcL8fjaCp3hUH1JkAbSLTWq4xiqdBbuVqDskqNZJ52Sup13P3prhoff//F6+JJmY7xYl5XM3lgC7y65pjBNn9Pd2ydPsSguR1gvikXAOQUliMqwBNnswpx5XoJPNzkmLh0L2Qy4JsJvbHxvwyLx6FbE6G/6DjCX2VVH6Hx8c1wb6+mDabs643ST7fLLizHnvM5GNA62GSd0LeP9MHEpXtRUqEWAzpjeSUVYjrrXd2jMKxDWKP+HnY2jeMd3YC5cvlXa5+SxoFdxyvEQEL7RdHYP7x8qsr5FVlRfaYuVKg1GPvlbhSUVhpU2/D1cMOA1sEmVUTu6BqJO7o2jFr6tWXcLfpURkH9BRIWugfrLH6gB2QyGZrP+LNejsHZ+Hu6G1wdXXfkKpoHW9+w6tFv92H2iPZoH+FnsP2wXiBmTmZBGUZ/Zhps6JcIdkXj+jZDuwg/nEgvwKurjwIAOkT4mQQRABDqaz6QuFZUBkEQMObz3cguLBPTIQUBmLhsr9XHo0tnax3qg3fv7WrVPjKZrNEEEYDpup0DqbkG7/nOUf4Y2jYEfVs0wecP9cT4b/YgPa8UOUXl8Pd0N7jQqPt98/d0x4c2Fn2g+sfUJifnyuVfrU3Xyit2XIUgXWqTq1ZEsZWY2mSnGYn0vFKxi7Bu2vvL8b2wY8ZNLr3A1BLdYmuda1ac7APVs2u2uC7xuxcV4GlwEUAXXA9tq10TcZ+Ll0R2N5pJXX0wDbd/vENy7GcP9kCLYG80DfSEsuozZMeZbLz862GTseb+H2tqMvf00JYY2UAa0dWWTCZD7+ZBCNMLmM1VSlK6yXFLhzCE+nog0MswFS2nqBz5pZXiWoekc+Ybpum8eVcnBHhJV8t6/c5O6BYdYOWzaFyMP58PX8kTA4IIfxV+f3YApg1rK/4MaPsT9Zy3AXPWHDXYV7fQ2pXXvjVkPDtycq48I2FtalOuQwOJ6tQmqp7St1dqk1SFj2ZNvCRz1RsLL6NAIseK1L91R66i3avr8UPyxRrH6sszSm2KCfLCzhk3iRVSBrWpXlA9e0QHjOsbg6kJpt1uXYlU/w6pRbw/Te6LWztH4K+pA7Ft+lCDWaPDeusgDl7KxWtrj+FURoHk4z05pBUOzR0mWRloYOtgTE9sZ1VqjSvQ/723NAv35fheSJ51s0kPio82nsbUn6pLxFpTQvnBuGaYenNrydua+Lh2StmNMP7/2XYqC3cu3il5m/5idUEAfkhOFdcSpeeVYtvpLJNx5DwazzxbA+XKgYS1aUK6ShqlFWq8sPIQ+sYG4aH45vV4ZNV0na3d5Y3ji7om3kr7BhJFEou6G/tVKePUJkvpR2m5Jdh/8Tq+2nEelRoB/1t7HA/GSXfgBbSLGi9eKxZTdYzzmSMDtF/kzye0QbifyqDza6tQH8wb5ZprI/RZ2whQd7KkW+jp5+lusJhUrRGgkMvw2tpjOGiU1vT5Qz3x7/EMqDUCWof6QC6XIdxfhXSjHhONLaDWT2WqKZ1Pm0pk+Lty8VoxLup1VDYnvkUTJJ27Js4GmTuBtWbBb2MV7m/+/yfEKMDzrSoXqz/T/b/fj6OkXI11R6+Ks9KW0tbIcRhIODnd8gBrr943JOYu8g9qE4I7u0bivX9O4mpeKa4XlyO3uBybT2biz8NX8efhq3gwrpldFqDrrjS6u7ne618b9k5tKjZai6FUyBvdyZMx4+cvlX607shVLE9OxZ7zOQY9N2rqvzH/rxP4Yts5TE9si6eHtpIIJLTrVDyVCjxipl6/qxvaNhR/HU1HTJAXxvWNwVvrTkiOs7ToF9BeIAn28TAJIpaM64nEjuFI7BhudH+mJ2aN7XdB//lak9r4zNBWmPx/+21+nEcGxOLFxDbo0jQAABDub1qVSSaD5BoN0urfKhj3945GixBvDGgVghEfbxfPZ6T+79yMTgiW7bpgMsZScEKOw0DCyakF152RMA6OlG5ynJp3q/hzhL8KD3yVjL0XrqPb6xvQMqR6QWNO1ZdwfavUVPWR4IwEAP3UJvsstjaekQjyVjb6Be8RRic1uhm7a4VleP2P4xjWIRyvrD4iGWBYIggCvth2DoC24ZdaI5jcR1Mryly6urfv7oKoAE+MjYtByxAfnMksxM/7LpuMMy4EcM0oBe1aYTmCJE5EQ3ylT06lAhNzufuuKtDbHUqFHOVqjVULzId1DMee2Tfj76PpJlWfLAnx9TBY+xAjUU460Evpkhf46oqHmwJvj+4i/rxgdBdM/0W7NigiwPS9PLpHU3y947zJdn1S/w/keAwknJyum7IrfmAZzygYP8NAo2njs1nVzWoy8kvtEkjoFlsr3RhIAIC3WLXJXmskDB+nsZ04SbmpXShah/rgdGYhgOo1RN8mXcSag2lYczDN4v7llRqD97MgCJj121EcvpxrMO6DDadM9nXl/hzW8vdyxyu3dxB/btbEuopN/VsFi70mAG0FobBS088wc59rUiVeg7wb1xVaDzcFfpzcF7nF5Wgd5mvVPqG+Kqu/K9wVMlSoBYNyvoD2AsbSib1xNbcUs347AgAmC7nJMv1GgU0DTT9HXhzWFq1DfbDpRCb+OW5ahtfDTY6BLtjk0hUwkHBy1TMSDj6QemA8y2LckbRVqPn27vkl9jmRFTtbu+J/QC2IMxLl2sZa9T07oD/zIZMBkxppOo2+QG8lNkwbjM0nMjFx2V7sPncNDy/dY9Jt15yconKDnO+TGQX4cY/5ZnMKuQyP9G+Ow5fzkNgp3Oy4xkr/KmnfFkHYfS5Hso/Jk0NaIre4HNtOZ0OtEXCtsFyyUpPxAmGdKInZoMZ4Mmupc7U5QTWsZfD3dEdixzCMj28OpZtcTOHUN7RtKNQaQQwkXDFLoD7FBntjaNsQnMooxOA2pgGBp1KB+/vE4FpRuRhIjOsbg1BfFR6Ii4FaI9SYLkiOwUDCyenKv7pkQzq9D+JOUX5YaNQx2l2hTXW669OdOJZmeJJkryvixn0kGjvdF6xG0C46re+66LoZibt7ROGd0V34/6BHNztTqRGw5WSW1ftdKyozCCSSz+VYHK/WCJg9ooPFMY1ZsybVgcSUm9vg8cFq9IgxPdltE+aLpRP74Kkf9mPdkXT8cTjNpEeO0k0Ob6V0F947u0fi8vVidIzyx+NVef+sYmMdS9WVfFVuODjHukaj+pkBagf2N2qIZDIZlk7sU+O4zlH+ALSfb3Pv6MjP/AaAgYSTq0rRt7rnQkOi/6H89t1dJCsyKN3k+HpCb/Sdv9Fgu1Q1n/pQWZXaxPKvWl5KBWQybRGAwrLKeg8kdDMS3ko3fqEYsaabsYebHB/d3x2fbzuLA6m5AKqrPJ3OKMDk/9uP89lFFu4BaBduXQpJY9Uq1AcBXu5QyGToGu1f4++E7nPu72MZ+PuYYQqHDOar2Xm4KcS6+3Nu74C03BL0jW1y40+gEYgM8ISvyg1FZZUYH98cfp7uWLTxNADtbLMtM6tRAZ64kluCUd2i6utwG7WBrYOx+IEeaB7sxc/8BoKBhJNTu/AaCf3GWh4W1iBIlfmzV9Wgcs5IGJDJZPBWasv0FZWpgXo+x9TNSBiXcSTTNUTGHoiLwcxb28FX5Y7hncLxwJe7sevsNWTml6G4vBJfbj9XYxDRPsIPM29tV5eH7XK8lG7Y9MIQ8d81sTSLYO35bGOtmFVbXko3rHqyH/JLK8XUKF0goXK37bPl/yb1weoDV/Bw/+Z1fZgE7XfMiC6mqYHkvBhIODnd9KkLTkgY1MO3tJhZKoiyV2pTJTtbm/D2UFQFEvX/f6Cr2W/czZkAP5Xlj+9mQV7wVVXn0OvyxF9YeQivrD5q9jNlXN8YbD6RhXl3dcLQtqHSg8hATTn4+qTKuOqwTn79MV6c3b9VE+w8cw3j+prvqyKlRYiPODNEROxs7fRcuWqTh3v126+mE8WE9mEAqpuR2av8qG6NhJKpTSJ79pLQdZ41bsJG2it3TwxuafZ24wpL+s2zSirUkl19A7zc8eKwttg54yYGEfWkV7MgKM1cmHj+FukOylT3PryvG96+uzMmD2rh6EMhatAYSDg53WI8V1wjIdMr+FpTSsD793bFH88OwO1dtJ1Ga2qsVVcqOCNhQneVW9dtFACu5JYgPa/U3C61Vlo1I8HUJmkvJbZFyqu3SN7WNNCwyk9oDRVPtrw4BH9PHcQmW/UsOsgLvz3dzyB945mhrbD9paHMu7ejUD8V7u8Tw7RVohvE3yAnp1sj4YpNuPRnWWq64uzv5Y5OUf5iCpSu43R9Y9UmU7or2zlFZVBrBGQWlGL4wm2445MdSL1WjAXrT+BSTnGdPJbuqrmtecyNhVwuQ5C3EtNuaQMA6NrUX7ytebBhf4P2EZYXtDQP9mZ5RTvpGOmPhPbVMz6B3kpEB3m55Oc8Ebk2rpFwclVxhEumNnWO8kfTQE+0C/ez+vnZO5DQdbZm1aZqukAiu7AcD361G7uryocWlFbiqeX7cfRKPvacz8EvT/a74ccqKdfNSPCjypKnh7bCze1D0TLEB/P+PI7Wob7wUxn2GIiLbSJWnCHHiw2u7pMj1SOCiKgh4LezkxOrNrnglSpPpQLbpg+1qUeGh71nJCp15V85I6Gja5iVllsiBhE6R69o+33su3jdpvs8fDkX8/74Dy8mtkWf2CBxe6m42JqvvyUKuQwdI7WzEfNGdZYc4+3hhr+fH4T8kgr0e3uTwW3DO7LRnL21DPGGh5scZZUadIjwc/ThEBHVCgMJJyd2tnbR8yhbG+3pFinabY2Ehp2tjQVXNXeqqZPyXZ/uRJ/mQZh5W/sa7/OZ5QeQmlOM2b8dwQ+PxSHExwMymYypTXXMx8MNPh5uuLldKHacycaisd1xKr0Ad/Vgbr69+arc8e0jfVCh1iCmiVfNOxAROSEXPT11Tr/sv4wOc9Yj6ew1s2OKyyvx4spD2PhfBgRBqK4aZKE8amOiSzGy+xoJvv4iXZfYlKoGZ+YcSM3F59vOibMKlqRWrak4nVmIvm9txAs/HwJQXf6VqU1168vxvbBndgISO4bj2Ztbo2kgT2QdoW+LJhjYOsTRh0FEVGs8O7KjF1ceQnG5GrNXHzHYnlVQhjUHr+CDDafw1fbz+GX/ZUz6dh/UGgFVExJmywU2Nko37ZXpMnutkdB1tnbVKaFaaOJtvg6+lKs1VHMSdG/yKhoBWHXgCsorNWKvClZtqltyuQz+nu41DyQiIrKAl/kc4FxWEQ5eykW36ADkFpej95v/So5bse+S+G/m6GuJi63tlNpU3dmaqU06uhkJKd1jAnDiaoE4kwAAV/NKEGtUQQjQziot23Ue7cKl88OPpuXhenEFACCQJUmJiIicDgMJBxm1eCd+mtxXTJ2RMvu3o+K/GUhoVVdtsk9DOna2NhXsIz0j8cOjcejfKhgjP9mBw5fzxO3ZheWS4zccz8Bb606YfZy7P90l/jvQi1fPiYiInA0DCQfaeipLLG9ZE14R1xIXW9t5jQRTy6qZmx0IqioLa3z7ztPZ8HRX4JYOYQbbj1/Ng7UYyBERETkffjs70GdbzmLZrgs1jnNXyNioqIqHnVObqjtb8/XXUbrJxRmCt++uLjWq66Ts7WG4nmHFvkt47Lt9SD5XXWQgr6SixrUTOssm9r7RQyYiIqJ6wBkJO3JXyMQTU1vUZh9Xxc7WzmHR2O44n12EMb2jkVNcjgBPJXyrGqA9NrAF/j2eaRLsHbiUi7gWTaDRCLjto+1WNUYb0CoYQ9qG1jiOiIiI7I+BhB35eLiJi0dtER3Erqc6du9szcXWkga2DhHLVj41pJXBbd1jAnH89USs3H8ZM1dVVyhbsP4EKtUa3N4l0iCIGNMrGj2bBWJI2xB8teM85DIZlmw9CwCsr09EROTEGEjYkaaWEwsdI/zr9kAaMN1aBXvN0ugehzMStnFTyE0WZWsE4L1/Tpksvh7ds6nYzXrWbe2x53yOGEjENjGt9kRERETOgWdHdqSuZSRxU3umdujoZiTs0UdCEAQxPceDDelsFmymTOyPe1INfg71NQw4ogKrZ+CacUaCiIjIaXFGwo5qG0gEsYa+yF2ckaj/QEI/WGFncduZKxNrHASG+6sMfo70VyGhfRjScksQ37JJvR0fERER3RgGEnakFmoZSFhoANbYeNhxjYT+Ca+HGzsr2yrUzwNKhRzlag083OSSs0g3twuFyt3wtZXJZPhqQi97HSYRERHVklNcZl28eDGaN28OlUqFuLg47Nmzx+L4lStXol27dlCpVOjcuTPWrVtncLsgCJgzZw4iIiLg6emJhIQEnD59Wrz9woULmDRpEmJjY+Hp6YmWLVti7ty5KC+XbpxVVzS1nJFo4s1AQse+MxLaHh8yGRdb14aHmwIrHu+Lbx/pgxNvDMeqp/oZ3P7vtEH4/KGeDjo6IiIiulEODyRWrFiBadOmYe7cuUhJSUHXrl2RmJiIzMxMyfG7du3C2LFjMWnSJBw4cACjRo3CqFGjcPRodRfoBQsWYNGiRViyZAmSk5Ph7e2NxMRElJZq69afOHECGo0Gn3/+OY4dO4YPP/wQS5YswaxZs+r1uVZWBRIvD29n036BDCREuhSjSo1Q68DMWrpZD6VCzj4etdQ9JhCD24RAJpOhXbivwW2xwT5sNEdERNSAOfxb/IMPPsBjjz2GiRMnokOHDliyZAm8vLzwzTffSI7/6KOPMHz4cEyfPh3t27fHG2+8gR49euCTTz4BoJ2NWLhwIV555RXceeed6NKlC7777jukpaVh9erVAIDhw4dj6dKlGDZsGFq0aIGRI0fixRdfxKpVq+rteeqf9I7pHY03RnWyel9fD2ag6eivVbC1Kd3eCzmY9vNBXC+qeebp3+MZOJaWD4ALreuKl9LwfayQMzgjIiJqyBx6hlReXo79+/cjISFB3CaXy5GQkICkpCTJfZKSkgzGA0BiYqI4/vz580hPTzcY4+/vj7i4OLP3CQB5eXkICgoye3tZWRny8/MN/thCf32EQibDuLgYdIrys2pfXg2vpp9iZGsg8eCXyViVcgXTfzlscdzP+y7h0e/24fH/2w8AUHJ9RJ3RLcAe0jbEwUdCREREN8qhgUR2djbUajXCwsIMtoeFhSE9PV1yn/T0dIvjdX/bcp9nzpzBxx9/jMcff9zssc6fPx/+/v7in+joaMtPzoh+xSa5XBscrHqyP7rHBFjcb0J8M5sex9Up9VJhbF1wrQs8TmUUmB1zrbAMLxkFGtmFZTY9Dpn34Ziu6BMbhJm3tnf0oRAREdENavQ5G1euXMHw4cNx77334rHHHjM7bubMmcjLyxP/XLp0yabH0Q8k3OTal13pJscH93Uzu88dXSPx2siONj2Oq5PJZOKsRG0XXJsrw/v97ovoOe/fWh8b1Wxg6xD8/Hg82hqtlyAiIqKGx6GBRHBwMBQKBTIyMgy2Z2RkIDw8XHKf8PBwi+N1f1tzn2lpaRg6dCj69euHL774wuKxenh4wM/Pz+CPLfRTm+R6r3pssDfu7y09u/HE4BZMa5Kgm5WobQlYuZl3/Tt/nZDc/todHWr1OERERESuzKGBhFKpRM+ePbFx40Zxm0ajwcaNGxEfHy+5T3x8vMF4ANiwYYM4PjY2FuHh4QZj8vPzkZycbHCfV65cwZAhQ9CzZ08sXboUcnNnl3VEf7G1wig4eG1kRwzrUJ2K5emuwOHXhqFjpH+9HlND5e52YyVg5RLBWaVag4KySsnxD/ZlehkRERGRMYeXA5o2bRomTJiAXr16oU+fPli4cCGKioowceJEAMD48eMRFRWF+fPnAwCmTJmCwYMH4/3338eIESPw008/Yd++feKMgkwmw9SpUzFv3jy0bt0asbGxePXVVxEZGYlRo0YBqA4imjVrhvfeew9ZWVni8ZibCblR+uk0xtVqVO4KvH9fV4z/Zg96NQvEhH7N4adyr5fjcAW6GQmpBmfmVOoFHRevFWPjfxno3yoYKncFsgrKsONMltl93VmilIiIiMiEwwOJMWPGICsrC3PmzEF6ejq6deuG9evXi4ulU1NTDWYL+vXrh+XLl+OVV17BrFmz0Lp1a6xevRqdOlWXU33ppZdQVFSEyZMnIzc3FwMGDMD69euhUqkAaGcwzpw5gzNnzqBp06YGxyPUsvt0TXSBhFwmXYXJV+WO357qXy+P7WqUtehuXWg02zDp231wk8vw1l2dseDvkwYLquNig6B0k2P76ey6OWAiIiIiFyQT6uvM2cXl5+fD398feXl5Vq2XuJpXgvj5m+CukOH0m7fZ4Qhd103vbcG57CL8/Hg8+sSaL9mr71JOMQYu2FzjuOWPxaFfy2AcvZKHsV/uxovD2mJCv+Y3eMREREREDYMt57gOn5FoLKpnJLh4+kbVZkYir6SixjER/irExTYBAHSK8sfhucO42J2IiIjIDAYSdqKpOudlN98bp1uzYMti6/xSy4FEQvtQzLi1ncH/D4MIIiIiIvO4itROKqsiCQYSN043I6G/2FoQBKw9lIYzmYWS+xSUSldk8lIq0DTQEx+P7YFWoextQERERGQtzkjYiaZqKQoDiRuna0hXrjcjseVkFp778QAA4MLbI0z2MZfatHX6UHgqFfBUKurhSImIiIhcF2ck7ER3zmvcQ4Jsp3TTnvRX6M1IHEi9Lv773+MZeGf9CZRVqsVtucXlJvczoFUwQnw94OPBeJqIiIjIVjyDshNxsTVnJG6YUm9GQq0RcDK9AIs2nRFvf/S7fQCAJt5KPDqwBQAgt9hwRmJg62AsHNPNPgdMRERE5IIYSNiJLrXJjYHEDdOv2jRq8U4cuZInOW7X2WtiIHG9KpDo17IJbu0UjnF9m3ExNREREdENYGqTnVSy/Gud0XW2/jbpgtkgAgA2ncjElpOZAIC8Em1qU2LHcDwU35xBBBEREdENYiBhJ7rUJi62vnG68q/nsopqHPvw0r34fOtZHEvLBwAEeLnX67ERERERNRZMbbITVm2qO7rUJmvN/+uE+O8m3h51fThEREREjRJnJOyEMxJ1R+Ve+1Kt4f6qOjwSIiIiosaLgYSdiIEEc/NvWKBRepKfyvqJNQYSRERERHWDqU12wvKvdcfP0zCQGNU9Crd3iURabgkUchnahfvilg+3mezn7+nOnhFEREREdYRnVXaiFtdIOPhAXECFWjD42dNdgT6xQeLPhWWVkvuteLxvvR4XERERUWPC01o70YhrJPiS36jBbYLFfhw9YgLwxOCWBrf7eLjhq/G9sGRcT4Pt7cL97HaMRERERK6OMxJ2UimukXDwgbiAVqG+SJ51M7w93MwuvE7oEAZAu7hdl1ZGRERERHWHl8ftRMOqTXWqiY+HVdWbZt/WHgDw7j1d6vuQiIiIiBoVzkjYiW6NBDtb29fE/s0xslskgn3YP4KIiIioLnFGwk506TVuzG2yK5lMxiCCiIiIqB4wkLATsfwrZySIiIiIyAUwkLATdrYmIiIiIlfCQMJONAI7WxMRERGR62AgYSdqjfZvdrYmIiIiIlfAQMJO1BptJOHGQIKIiIiIXAADCTvhGgkiIiIiciUMJOxE19maMxJERERE5AoYSNiJWP6VgQQRERERuQAGEnbCGQkiIiIiciUMJOxEI66R4EtORERERA0fz2rthDMSRERERORKGEjYCas2EREREZErYSBhJ5UMJIiIiIjIhTCQsBONwNQmIiIiInIdDCTspFLNGQkiIiIich0MJOxErdEAYCBBRERERK6BgYSdcI0EEREREbkSBhJ2wjUSRERERORKGEjYiW6NhJyBBBERERG5AAYSdlKu1q6RUCr4khMRERFRw+cUZ7WLFy9G8+bNoVKpEBcXhz179lgcv3LlSrRr1w4qlQqdO3fGunXrDG4XBAFz5sxBREQEPD09kZCQgNOnTxuMycnJwYMPPgg/Pz8EBARg0qRJKCwsrPPnBgA/JF/EmoNpAACVu6JeHoOIiIiIyJ4cHkisWLEC06ZNw9y5c5GSkoKuXbsiMTERmZmZkuN37dqFsWPHYtKkSThw4ABGjRqFUaNG4ejRo+KYBQsWYNGiRViyZAmSk5Ph7e2NxMRElJaWimMefPBBHDt2DBs2bMAff/yBbdu2YfLkyXX+/ErK1Zj9W/WxMZAgIiIiIlcgE4SqVcAOEhcXh969e+OTTz4BAGg0GkRHR+PZZ5/FjBkzTMaPGTMGRUVF+OOPP8Rtffv2Rbdu3bBkyRIIgoDIyEi88MILePHFFwEAeXl5CAsLw7Jly3D//ffjv//+Q4cOHbB371706tULALB+/XrcdtttuHz5MiIjI2s87vz8fPj7++OnHSfg7esLGWSQVS1/qFBrUKkWUKHW4PCVPCxPThX3WzS2O0Z2rfn+iYiIiIjsTXeOm5eXBz8/P4tj3ex0TJLKy8uxf/9+zJw5U9wml8uRkJCApKQkyX2SkpIwbdo0g22JiYlYvXo1AOD8+fNIT09HQkKCeLu/vz/i4uKQlJSE+++/H0lJSQgICBCDCABISEiAXC5HcnIy7rrrLpPHLSsrQ1lZmfhzfn4+AGD6L4ch9/Cy+jkHeSmtHktERERE5KwcGkhkZ2dDrVYjLCzMYHtYWBhOnDghuU96errk+PT0dPF23TZLY0JDQw1ud3NzQ1BQkDjG2Pz58/G///3PZHvv5oFwU3lDEAABuhKvcri7yaFUyOAml6N5sDcuXitCdJAX+rdqInn/REREREQNiUMDiYZk5syZBjMh+fn5iI6OxtKJfWqc9iEiIiIicjUOXWwdHBwMhUKBjIwMg+0ZGRkIDw+X3Cc8PNzieN3fNY0xXsxdWVmJnJwcs4/r4eEBPz8/gz9ERERERI2VQwMJpVKJnj17YuPGjeI2jUaDjRs3Ij4+XnKf+Ph4g/EAsGHDBnF8bGwswsPDDcbk5+cjOTlZHBMfH4/c3Fzs379fHLNp0yZoNBrExcXV2fMjIiIiInJVDk9tmjZtGiZMmIBevXqhT58+WLhwIYqKijBx4kQAwPjx4xEVFYX58+cDAKZMmYLBgwfj/fffx4gRI/DTTz9h3759+OKLLwAAMpkMU6dOxbx589C6dWvExsbi1VdfRWRkJEaNGgUAaN++PYYPH47HHnsMS5YsQUVFBZ555hncf//9VlVsIiIiIiJq7BweSIwZMwZZWVmYM2cO0tPT0a1bN6xfv15cLJ2amgq5vHripF+/fli+fDleeeUVzJo1C61bt8bq1avRqVMnccxLL72EoqIiTJ48Gbm5uRgwYADWr18PlUoljvnhhx/wzDPP4Oabb4ZcLsfo0aOxaNEi+z1xIiIiIqIGzOF9JBoqW2rsEhERERE1BLac4zq8szURERERETU8DCSIiIiIiMhmDCSIiIiIiMhmDCSIiIiIiMhmDCSIiIiIiMhmDCSIiIiIiMhmDu8j0VDpqubm5+c7+EiIiIiIiOqG7tzWmg4RDCRq6dq1awCA6OhoBx8JEREREVHdKigogL+/v8UxDCRqKSgoCIC283ZNLzKRlPz8fERHR+PSpUtsaki1wvcQ1QW+j+hG8T3kWgRBQEFBASIjI2scy0CiluRy7fISf39//tLQDfHz8+N7iG4I30NUF/g+ohvF95DrsPYiORdbExERERGRzRhIEBERERGRzRhI1JKHhwfmzp0LDw8PRx8KNVB8D9GN4nuI6gLfR3Sj+B5qvGSCNbWdiIiIiIiI9HBGgoiIiIiIbMZAgoiIiIiIbMZAgoiIiIiIbMZAgoiIiIiIbMZAohYWL16M5s2bQ6VSIS4uDnv27HH0IZGTmj9/Pnr37g1fX1+EhoZi1KhROHnypMGY0tJSPP3002jSpAl8fHwwevRoZGRkOOiIydm9/fbbkMlkmDp1qriN7yGyxpUrVzBu3Dg0adIEnp6e6Ny5M/bt2yfeLggC5syZg4iICHh6eiIhIQGnT5924BGTM1Gr1Xj11VcRGxsLT09PtGzZEm+88Qb0a/bwPdT4MJCw0YoVKzBt2jTMnTsXKSkp6Nq1KxITE5GZmenoQyMntHXrVjz99NPYvXs3NmzYgIqKCgwbNgxFRUXimOeffx6///47Vq5cia1btyItLQ133323A4+anNXevXvx+eefo0uXLgbb+R6imly/fh39+/eHu7s7/vrrLxw/fhzvv/8+AgMDxTELFizAokWLsGTJEiQnJ8Pb2xuJiYkoLS114JGTs3jnnXfw2Wef4ZNPPsF///2Hd955BwsWLMDHH38sjuF7qBESyCZ9+vQRnn76afFntVotREZGCvPnz3fgUVFDkZmZKQAQtm7dKgiCIOTm5gru7u7CypUrxTH//fefAEBISkpy1GGSEyooKBBat24tbNiwQRg8eLAwZcoUQRD4HiLrvPzyy8KAAQPM3q7RaITw8HDh3XffFbfl5uYKHh4ewo8//miPQyQnN2LECOGRRx4x2Hb33XcLDz74oCAIfA81VpyRsEF5eTn279+PhIQEcZtcLkdCQgKSkpIceGTUUOTl5QEAgoKCAAD79+9HRUWFwXuqXbt2iImJ4XuKDDz99NMYMWKEwXsF4HuIrLN27Vr06tUL9957L0JDQ9G9e3d8+eWX4u3nz59Henq6wfvI398fcXFxfB8RAKBfv37YuHEjTp06BQA4dOgQduzYgVtvvRUA30ONlZujD6Ahyc7OhlqtRlhYmMH2sLAwnDhxwkFHRQ2FRqPB1KlT0b9/f3Tq1AkAkJ6eDqVSiYCAAIOxYWFhSE9Pd8BRkjP66aefkJKSgr1795rcxvcQWePcuXP47LPPMG3aNMyaNQt79+7Fc889B6VSiQkTJojvFanvN76PCABmzJiB/Px8tGvXDgqFAmq1Gm+++SYefPBBAOB7qJFiIEFkJ08//TSOHj2KHTt2OPpQqAG5dOkSpkyZgg0bNkClUjn6cKiB0mg06NWrF9566y0AQPfu3XH06FEsWbIEEyZMcPDRUUPw888/44cffsDy5cvRsWNHHDx4EFOnTkVkZCTfQ40YU5tsEBwcDIVCYVINJSMjA+Hh4Q46KmoInnnmGfzxxx/YvHkzmjZtKm4PDw9HeXk5cnNzDcbzPUU6+/fvR2ZmJnr06AE3Nze4ublh69atWLRoEdzc3BAWFsb3ENUoIiICHTp0MNjWvn17pKamAoD4XuH3G5kzffp0zJgxA/fffz86d+6Mhx56CM8//zzmz58PgO+hxoqBhA2USiV69uyJjRs3its0Gg02btyI+Ph4Bx4ZOStBEPDMM8/gt99+w6ZNmxAbG2twe8+ePeHu7m7wnjp58iRSU1P5niIAwM0334wjR47g4MGD4p9evXrhwQcfFP/N9xDVpH///ialp0+dOoVmzZoBAGJjYxEeHm7wPsrPz0dycjLfRwQAKC4uhlxueNqoUCig0WgA8D3UaDl6tXdD89NPPwkeHh7CsmXLhOPHjwuTJ08WAgIChPT0dEcfGjmhJ598UvD39xe2bNkiXL16VfxTXFwsjnniiSeEmJgYYdOmTcK+ffuE+Ph4IT4+3oFHTc5Ov2qTIPA9RDXbs2eP4ObmJrz55pvC6dOnhR9++EHw8vISvv/+e3HM22+/LQQEBAhr1qwRDh8+LNx5551CbGysUFJS4sAjJ2cxYcIEISoqSvjjjz+E8+fPC6tWrRKCg4OFl156SRzD99D/t3d/IU31cRzHP3vKlnkqTWHLcC0JWZTavJDKLqQ/hheDIgy8EPLCixTbgS4yKguKqIQkSqLsj0E3EVbgRYVCdDGQkuyPEDMW0Y0UESbVCNLfcxGNx8eHp06ttPV+wYGdc8b39z1jsH12fmfnz0OQ+A4nTpwwPp/PzJgxw5SWlpre3t7JbglTlKT/XC5cuJB4TjweN/X19SYrK8vMmjXLbNq0yQwNDU1e05jy/h0keA/hW3R1dZlly5YZt9ttAoGAOXPmzLj9Y2NjZu/evcbj8Ri3223Wrl1rotHoJHWLqWZkZMSEw2Hj8/nMzJkzTX5+vtm9e7f5+PFj4jm8h/48LmP+cUtCAAAAAPgGXCMBAAAAwDGCBAAAAADHCBIAAAAAHCNIAAAAAHCMIAEAAADAMYIEAAAAAMcIEgAAAAAcI0gAAAAAcIwgAQBIiq1bt2rjxo2/fNyOjg65XC65XC7Ztp20uuXl5Ym6Dx48SFpdAEgV0ye7AQDA1Odyuf53/759+3T8+HEZY35RR+PNmTNH0WhUGRkZSat59epVxWIxlZaWJq0mAKQSggQA4KuGhoYSjy9fvqzm5mZFo9HENsuyZFnWZLQm6XPQ8Xq9Sa05b948jYyMJLUmAKQSpjYBAL7K6/Umlrlz5ya+uH9ZLMuaMLWpvLxcjY2Nsm1bWVlZ8ng8am9v1/v371VbW6vZs2dr8eLFunHjxrixBgYGVFlZKcuy5PF4VFNTo9evXzvu2e/368CBA6qurlZGRoYWLFigtra2xH5jjPbv3y+fzye3263c3Fxt3779u18jAPjTECQAAD/NxYsXlZOTo7t376qxsVHbtm1TVVWVVq1apfv376uiokI1NTX68OGDJGl4eFhr1qxRMBhUX1+fbt68qZcvX2rLli3fNX5LS4uKi4vV39+vpqYmhcNhdXd3S5I6OzvV2tqq06dP6+nTp7p+/boKCwuTduwAkOqY2gQA+GmKi4u1Z88eSdKuXbt0+PBh5eTkqK6uTpLU3NysU6dO6dGjR1qxYoVOnjypYDCoQ4cOJWqcP39eeXl5GhwcVEFBgaPxy8rK1NTUJEkqKChQJBJRa2ur1q9frxcvXsjr9WrdunVKS0uTz+fjeggAcIAzEgCAn6aoqCjxeNq0acrOzh73q7/H45EkvXr1SpL08OFD3b59O3HNhWVZCgQCkqRYLOZ4/JUrV05Yf/LkiSSpqqpK8Xhc+fn5qqur07Vr1/Tp0yfHYwDAn4ozEgCAnyYtLW3cusvlGrfty79BjY2NSZLevXunUCikI0eOTKg1f/78pPaWl5enaDSqnp4edXd3q76+Xi0tLbpz586EvgEAExEkAABTRklJiTo7O+X3+zV9+o9/RPX29k5YX7JkSWI9PT1doVBIoVBIDQ0NCgQCevz4sUpKSn54bABIdUxtAgBMGQ0NDXrz5o2qq6t17949xWIx3bp1S7W1tRodHXVcLxKJ6OjRoxocHFRbW5uuXLmicDgs6fON7M6dO6eBgQE9e/ZMly5dUnp6uhYuXJjswwKAlESQAABMGbm5uYpEIhodHVVFRYUKCwtl27YyMzP111/OP7J27Nihvr4+BYNBHTx4UMeOHdOGDRskSZmZmWpvb1dZWZmKiorU09Ojrq4uZWdnJ/uwACAlucxk3YYUAIAk6OjokG3bGh4eHrfd7/fLtm3Ztv3dtZ8/f65Fixapv79fy5cv/6E+ASDVcEYCAPDbe/v2rSzL0s6dO5NWs7KyUkuXLk1aPQBINVxsDQD4rW3evFmrV6+W9Hm6UrKcPXtW8XhckuTz+ZJWFwBSBVObAAAAADjG1CYAAAAAjhEkAAAAADhGkAAAAADgGEECAAAAgGMECQAAAACOESQAAAAAOEaQAAAAAOAYQQIAAACAY38D2RLjkika8cUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_port = 'out0'\n", + "wavelength_index = 2\n", + "mode_index = 0\n", + "\n", + "output_signal = result.output_signals[output_port]\n", + "field = output_signal.amplitude[:, wavelength_index, mode_index]\n", + "power = np.asarray(jnp.abs(field) ** 2)\n", + "\n", + "time_ps = np.arange(power.shape[0]) * SIMULATION_DT * 1e12\n", + "\n", + "plt.figure(figsize=(9, 4))\n", + "plt.plot(time_ps, power)\n", + "plt.title(f'{output_port} power at {float(WAVELENGTHS_M[wavelength_index]) * 1e9:.0f} nm')\n", + "plt.xlabel('Time [ps]')\n", + "plt.ylabel('Power [a.u.]')\n", + "plt.margins(x=0)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "0bb206bd", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "This example keeps the same Block mode workflow as the earlier tutorials, but applies it to a larger reservoir-style circuit:\n", + "\n", + "1. Generate the Lorenz drive.\n", + "2. Define the small custom source and phase-modulator components.\n", + "3. Load measured Green Machine S-parameters as a SAX-style model.\n", + "4. Build a directed netlist with fanout and delay taps.\n", + "5. Provide `sax_settings`, directionality, and vector-fitting settings for S-parameter components.\n", + "6. Run `BlockModeSimulation` and read full-block outputs from `result.output_signals`.\n", + "\n", + "The key difference from the starter examples is scale: the simulator still runs each component once on a full block, but the directed graph now contains many delayed paths feeding one measured multiport component." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/multimode.ipynb b/examples/multimode.ipynb new file mode 100644 index 00000000..06bf181f --- /dev/null +++ b/examples/multimode.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f18156c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from simphony.libraries.siepic import y_branch\n", + "from functools import partial\n", + "import matplotlib.pyplot as plt\n", + "from simphony.utils import create_multimode_sax_model\n", + "\n", + "\n", + "y_branch_models = {\n", + " \"te\": partial(y_branch, pol=\"te\"),\n", + " \"tm\": partial(y_branch, pol=\"tm\")\n", + "}\n", + "\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "y_branch_sdict = create_multimode_sax_model(y_branch_models)(wl=wl)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", + "\n", + "ax1.plot(wl, np.abs(y_branch_sdict[(\"port_1@te\", \"port_2@te\")]), label=\"te\")\n", + "ax1.plot(wl, np.abs(y_branch_sdict[(\"port_1@tm\", \"port_2@tm\")]), label=\"tm\")\n", + "ax1.legend()\n", + "\n", + "ax2.plot(wl, np.unwrap(np.angle(y_branch_sdict[(\"port_1@te\", \"port_2@te\")])), label=\"te\")\n", + "ax2.plot(wl, np.unwrap(np.angle(y_branch_sdict[(\"port_1@tm\", \"port_2@tm\")])), label=\"tm\")\n", + "ax2.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d22a49d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/mzi_lattice_block_mode_tutorial.ipynb b/examples/mzi_lattice_block_mode_tutorial.ipynb new file mode 100644 index 00000000..11db5f0a --- /dev/null +++ b/examples/mzi_lattice_block_mode_tutorial.ipynb @@ -0,0 +1,1064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d846b1d9", + "metadata": {}, + "source": [ + "# MZI Lattice: S-Parameters to Block Mode\n", + "\n", + "This example is the starter tutorial for the time-domain Block mode workflow. It uses a four-stage Mach-Zehnder interferometer (MZI) lattice and follows the same signal path several ways:\n", + "\n", + "1. Build the frequency-domain SAX netlist and inspect the `s_params` dictionary.\n", + "2. Run the same sweep through `SParameterSimulation` to show the simulator interface.\n", + "3. Fit the S-parameter response with a pole-residue model and extract the state-space form.\n", + "4. Run the same lattice as a causal time-domain Block mode simulation.\n", + "\n", + "The main idea is simple: S-parameters describe steady-state behavior versus wavelength, while Block mode simulates the time response of the same optical network after those S-parameters have been converted into discrete-time state-space filters." + ] + }, + { + "cell_type": "markdown", + "id": "9396421b", + "metadata": {}, + "source": [ + "## 1. Imports\n", + "\n", + "The example uses two different layers of Simphony:\n", + "\n", + "- SAX models for the direct frequency-domain S-parameter dictionary.\n", + "- Simphony `Circuit` and `BlockModeSimulation` for the time-domain block simulation.\n", + "\n", + "The small custom laser below is intentionally simple. It emits a constant complex envelope so the lattice response is easy to interpret.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8efae613", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "os.environ.setdefault('JAX_PLATFORMS', 'cpu')\n", + "warnings.filterwarnings('ignore', message='Could not validate netlist.*')\n", + "warnings.filterwarnings('ignore', message='FigureCanvasAgg is non-interactive.*')\n", + "\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sax\n", + "from scipy.constants import speed_of_light\n", + "\n", + "from simphony.circuit.circuit import Circuit\n", + "from simphony.component.component import BlockModeComponent\n", + "from simphony.component.port import Port\n", + "from simphony.libraries import old_ideal\n", + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "from simphony.signal.block_mode import BlockModeOpticalSignal\n", + "from simphony.simulation.block_mode import BlockModeSimulation, BlockModeSimulationParameters\n", + "from simphony.simulation.s_parameter import SParameterSimulation, SParameterSimulationParameters\n", + "from simphony.time_domain.vector_fitting.z_domain import (\n", + " pole_residue_response_discrete,\n", + " state_space_discrete,\n", + " state_space_response_discrete_structured,\n", + " vector_fitting_discrete,\n", + ")\n", + "from simphony.utils import dict_to_matrix\n" + ] + }, + { + "cell_type": "markdown", + "id": "e38d4753", + "metadata": {}, + "source": [ + "## 2. Choose the lattice design\n", + "\n", + "Each MZI stage uses a short arm and a long arm to set the interference period. In this lattice, the delay difference doubles from stage to stage, which gives the cascade a sharper passband than a single MZI.\n", + "\n", + "The variables below are in microns because the `old_ideal` SAX models use `wl` in microns and waveguide `length` in microns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "04787a39", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stage 0: short arm = 10.000 um, long arm = 24.573 um, delta = 14.573 um\n", + "stage 1: short arm = 10.000 um, long arm = 39.145 um, delta = 29.145 um\n", + "stage 2: short arm = 10.000 um, long arm = 67.628 um, delta = 57.628 um\n", + "stage 3: short arm = 10.000 um, long arm = 125.256 um, delta = 115.256 um\n" + ] + } + ], + "source": [ + "ORDER = 4\n", + "BASE_LENGTH_UM = 10.0\n", + "DELAY_UNIT_UM = 14.33\n", + "TUNING_OFFSETS_UM = np.array([0.24265, 0.48530, 0.30821, 0.61641])\n", + "\n", + "stage_multipliers = 2 ** np.arange(ORDER)\n", + "short_arm_lengths_um = np.full(ORDER, BASE_LENGTH_UM)\n", + "long_arm_lengths_um = BASE_LENGTH_UM + stage_multipliers * DELAY_UNIT_UM + TUNING_OFFSETS_UM\n", + "\n", + "for stage, (short_length, long_length) in enumerate(zip(short_arm_lengths_um, long_arm_lengths_um)):\n", + " print(\n", + " f'stage {stage}: short arm = {short_length:.3f} um, '\n", + " f'long arm = {long_length:.3f} um, '\n", + " f'delta = {long_length - short_length:.3f} um'\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4b16224", + "metadata": {}, + "source": [ + "## 3. Build the frequency-domain netlist\n", + "\n", + "A SAX netlist is a dictionary with `instances`, `connections`, and `ports`. For S-parameter simulation, the connections represent physical port joins, not a time-domain execution order.\n", + "\n", + "The top-level ports are:\n", + "\n", + "- `o0`: input used in this example.\n", + "- `o1`: unused input.\n", + "- `o2`: drop output.\n", + "- `o3`: through output used in this example.\n", + "\n", + "This explicit netlist is a good first tutorial form because every coupler and waveguide is visible. A PCell can generate this kind of netlist automatically; we discuss that after seeing the direct form.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a7481af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "instances: 16\n", + "connections: 19\n", + "ports: {'o0': 'dc0,o2', 'o1': 'dc0,o0', 'o2': 'dc7,o3', 'o3': 'dc7,o1'}\n" + ] + } + ], + "source": [ + "def frequency_domain_lattice_netlist(order=ORDER):\n", + " instances = {}\n", + " connections = {}\n", + "\n", + " for stage in range(order):\n", + " splitter = f'dc{2 * stage}'\n", + " combiner = f'dc{2 * stage + 1}'\n", + " short_wg = f'wg{2 * stage}'\n", + " long_wg = f'wg{2 * stage + 1}'\n", + "\n", + " instances[splitter] = 'directional_coupler'\n", + " instances[combiner] = 'directional_coupler'\n", + " instances[short_wg] = 'waveguide'\n", + " instances[long_wg] = 'waveguide'\n", + "\n", + " connections[f'{short_wg},o0'] = f'{splitter},o3'\n", + " connections[f'{short_wg},o1'] = f'{combiner},o2'\n", + " connections[f'{long_wg},o0'] = f'{splitter},o1'\n", + " connections[f'{long_wg},o1'] = f'{combiner},o0'\n", + "\n", + " if stage < order - 1:\n", + " next_splitter = f'dc{2 * (stage + 1)}'\n", + " connections[f'{combiner},o1'] = f'{next_splitter},o2'\n", + "\n", + " ports = {\n", + " 'o0': 'dc0,o2',\n", + " 'o1': 'dc0,o0',\n", + " 'o2': f'dc{2 * order - 1},o3',\n", + " 'o3': f'dc{2 * order - 1},o1',\n", + " }\n", + "\n", + " return {'instances': instances, 'connections': connections, 'ports': ports}\n", + "\n", + "\n", + "def frequency_domain_lattice_settings(wavelengths_um):\n", + " settings = {'wl': wavelengths_um}\n", + "\n", + " for stage, (short_length, long_length) in enumerate(zip(short_arm_lengths_um, long_arm_lengths_um)):\n", + " settings[f'wg{2 * stage}'] = {'length': short_length}\n", + " settings[f'wg{2 * stage + 1}'] = {'length': long_length}\n", + "\n", + " return settings\n", + "\n", + "\n", + "frequency_netlist = frequency_domain_lattice_netlist()\n", + "sax_models = {\n", + " 'waveguide': old_ideal.waveguide,\n", + " 'directional_coupler': old_ideal.coupler,\n", + "}\n", + "\n", + "print('instances:', len(frequency_netlist['instances']))\n", + "print('connections:', len(frequency_netlist['connections']))\n", + "print('ports:', frequency_netlist['ports'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "d7536b13", + "metadata": {}, + "source": [ + "## 4. Compute and inspect the `s_params` dictionary\n", + "\n", + "Calling a SAX circuit returns a dictionary. Each key is `(output_port, input_port)` and each value is the complex transfer function sampled over the requested wavelengths.\n", + "\n", + "For example, `s_params[('o3', 'o0')]` is the through-port transfer from input `o0` to output `o3`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b69148bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s_params is a dict with 16 entries\n", + "first eight keys:\n", + " ('o0', 'o0') shape = (800,)\n", + " ('o0', 'o1') shape = (800,)\n", + " ('o0', 'o2') shape = (800,)\n", + " ('o0', 'o3') shape = (800,)\n", + " ('o1', 'o0') shape = (800,)\n", + " ('o1', 'o1') shape = (800,)\n", + " ('o1', 'o2') shape = (800,)\n", + " ('o1', 'o3') shape = (800,)\n", + "example ('o3', 'o0') first samples: [-0.0193-0.1578j -0.0624-0.2034j -0.1226-0.239j -0.1974-0.2595j\n", + " -0.2827-0.2604j]\n" + ] + } + ], + "source": [ + "wavelengths_um = np.linspace(1.50, 1.60, 800)\n", + "wavelengths_m = wavelengths_um * 1e-6\n", + "\n", + "sax_circuit, _ = sax.circuit(frequency_netlist, sax_models)\n", + "s_params = sax_circuit(**frequency_domain_lattice_settings(wavelengths_um))\n", + "\n", + "TRANSFER_KEY = ('o3', 'o0')\n", + "transmission_sparam = np.abs(s_params[TRANSFER_KEY]) ** 2\n", + "\n", + "print(f's_params is a {type(s_params).__name__} with {len(s_params)} entries')\n", + "print('first eight keys:')\n", + "for key in list(s_params.keys())[:8]:\n", + " print(' ', key, 'shape =', np.asarray(s_params[key]).shape)\n", + "print(f'example {TRANSFER_KEY} first samples:', np.round(s_params[TRANSFER_KEY][:5], 4))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f4944e0b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "plt.plot(wavelengths_um * 1e3, transmission_sparam, linewidth=2)\n", + "plt.xlabel('Wavelength (nm)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('MZI lattice S-parameter response: o0 to o3')\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8622532e", + "metadata": {}, + "source": [ + "## 5. Run the same sweep with `SParameterSimulation`\n", + "\n", + "The direct `sax.circuit(...)` call is the shortest way to evaluate this netlist. `SParameterSimulation` evaluates the same steady-state response through Simphony's `Circuit` and `Simulation` API.\n", + "\n", + "In this example both paths use the same SAX model functions:\n", + "\n", + "- `old_ideal.waveguide` for each arm.\n", + "- `old_ideal.coupler`, wrapped only so it accepts the `wl` keyword that the simulator passes to SAX models.\n", + "\n", + "Since the models, netlist, and settings are the same, the two curves should sit directly on top of each other. The point of this section is not to show a different result; it is to show the simulation interface that later examples reuse." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b026b4b9", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m coupler_accepts_wavelength(*, wl=\u001b[38;5;28;01mNone\u001b[39;00m, coupling=\u001b[32m0.5\u001b[39m, loss=\u001b[32m0.0\u001b[39m, phi=np.pi / \u001b[32m2\u001b[39m):\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# `old_ideal.coupler` is wavelength independent, but SParameterSimulation\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# calls wrapped SAX models with a `wl` keyword.\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m old_ideal.coupler(coupling=coupling, loss=loss, phi=phi)\n", + "\u001b[31mNameError\u001b[39m: name 'np' is not defined" + ] + } + ], + "source": [ + "def coupler_accepts_wavelength(*, wl=None, coupling=0.5, loss=0.0, phi=np.pi / 2):\n", + " # `old_ideal.coupler` is wavelength independent, but SParameterSimulation\n", + " # calls wrapped SAX models with a `wl` keyword.\n", + " return old_ideal.coupler(coupling=coupling, loss=loss, phi=phi)\n", + "\n", + "\n", + "sparameter_models = {\n", + " 'waveguide': old_ideal.waveguide,\n", + " 'directional_coupler': coupler_accepts_wavelength,\n", + "}\n", + "\n", + "sparameter_settings = {}\n", + "for stage, (short_length, long_length) in enumerate(zip(short_arm_lengths_um, long_arm_lengths_um)):\n", + " sparameter_settings[f'wg{2 * stage}'] = {'length': short_length}\n", + " sparameter_settings[f'wg{2 * stage + 1}'] = {'length': long_length}\n", + "\n", + "for coupler_index in range(2 * ORDER):\n", + " sparameter_settings[f'dc{coupler_index}'] = {}\n", + "\n", + "sparameter_simulation = SParameterSimulation(\n", + " Circuit(frequency_netlist, sparameter_models),\n", + " sparameter_settings,\n", + " simulation_parameters=SParameterSimulationParameters(),\n", + ")\n", + "\n", + "sparameter_result = sparameter_simulation.run(wl=wavelengths_um)\n", + "transmission_simulation = np.abs(sparameter_result.s_parameters[TRANSFER_KEY]) ** 2\n", + "max_sparameter_difference = np.max(np.abs(transmission_simulation - transmission_sparam))\n", + "\n", + "print('SParameterSimulation entries:', len(sparameter_result.s_parameters))\n", + "print('maximum difference from direct SAX:', float(max_sparameter_difference))\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(wavelengths_um * 1e3, transmission_simulation, linewidth=2, label='SParameterSimulation')\n", + "plt.xlabel('Wavelength (nm)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('Direct SAX and SParameterSimulation use the same SAX models')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4b06c42b", + "metadata": {}, + "source": [ + "## 6. Fit a pole-residue model\n", + "\n", + "Block mode cannot directly step a wavelength-domain S-parameter table through time. It first needs a causal discrete-time model. The pole-residue fit is that bridge.\n", + "\n", + "Let the fitted transfer matrix have $q$ output ports, $m$ input ports, and $r$ poles. For this 4-port lattice, $q=m=4$ and `MODEL_ORDER` sets $r$. The discrete pole-residue model is\n", + "\n", + "$$\n", + "\\mathbf{H}(z) = \\mathbf{D} + \\sum_{k=1}^{r}\\frac{\\mathbf{R}_k}{z - p_k},\n", + "$$\n", + "\n", + "where\n", + "\n", + "$$\n", + "p_k \\in \\mathbb{C}, \\qquad \\mathbf{R}_k \\in \\mathbb{C}^{q \\times m}, \\qquad \\mathbf{D} \\in \\mathbb{C}^{q \\times m}.\n", + "$$\n", + "\n", + "In the code, these are returned as `poles`, `residues`, and `feedthrough`, with\n", + "\n", + "$$\n", + "\\texttt{residues}[k, :, :] = \\mathbf{R}_k, \\qquad \\texttt{feedthrough} = \\mathbf{D}.\n", + "$$\n", + "\n", + "The optical frequency sweep is converted to a baseband discrete-time variable using the same time step used later in Block mode:\n", + "\n", + "$$\n", + "z(f) = \\exp\\left(-j2\\pi\\frac{f - f_c}{f_s}\\right)\n", + " = \\exp\\left(-j2\\pi(f - f_c)\\Delta t\\right),\n", + "$$\n", + "\n", + "with\n", + "\n", + "$$\n", + "f_c = \\frac{c}{\\lambda_c}, \\qquad f_s = \\frac{1}{\\Delta t}.\n", + "$$\n", + "\n", + "Here we fit the full 4-port transfer matrix, not just the single plotted path. The next plot checks that this fitted pole-residue model reproduces the original steady-state S-parameter response." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eb67bd35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "port order used by matrix: ['o0', 'o1', 'o2', 'o3']\n", + "transfer matrix shape: (800, 4, 4)\n", + "poles shape: (40,)\n", + "residues shape: (40, 4, 4)\n", + "feedthrough shape: (4, 4)\n", + "fit MSE: 1.873114180936402e-07\n" + ] + } + ], + "source": [ + "DT = 1e-14\n", + "SAMPLING_FREQUENCY_HZ = 1 / DT\n", + "CENTER_WAVELENGTH_M = 1.55e-6\n", + "CENTER_FREQUENCY_HZ = speed_of_light / CENTER_WAVELENGTH_M\n", + "frequency_hz = speed_of_light / wavelengths_m\n", + "MODEL_ORDER = 40\n", + "\n", + "transfer_matrix = dict_to_matrix(s_params)\n", + "port_order = sorted({port for key in s_params.keys() for port in key})\n", + "input_index = port_order.index('o0')\n", + "output_index = port_order.index('o3')\n", + "\n", + "poles, residues, feedthrough, fit_mse = vector_fitting_discrete(\n", + " MODEL_ORDER,\n", + " transfer_matrix,\n", + " frequency_hz,\n", + " CENTER_FREQUENCY_HZ,\n", + " SAMPLING_FREQUENCY_HZ,\n", + ")\n", + "\n", + "fitted_response = pole_residue_response_discrete(\n", + " frequency_hz,\n", + " CENTER_FREQUENCY_HZ,\n", + " SAMPLING_FREQUENCY_HZ,\n", + " poles,\n", + " residues,\n", + " feedthrough,\n", + ")\n", + "\n", + "transmission_fit = np.abs(fitted_response[:, output_index, input_index]) ** 2\n", + "\n", + "print('port order used by matrix:', port_order)\n", + "print('transfer matrix shape:', transfer_matrix.shape)\n", + "print('poles shape:', poles.shape)\n", + "print('residues shape:', residues.shape)\n", + "print('feedthrough shape:', feedthrough.shape)\n", + "print('fit MSE:', float(fit_mse))" + ] + }, + { + "cell_type": "markdown", + "id": "4c29f7ed", + "metadata": {}, + "source": [ + "## 7. Compare the pole-residue fit with the S-parameters\n", + "\n", + "This plot checks whether the fitted pole-residue model has the same steady-state frequency response as the original S-parameter dictionary. If this comparison is poor, the time-domain Block mode simulation will also be poor, because Block mode is built from this same kind of fit.\n", + "\n", + "After this check, we construct the state-space matrices from the accepted pole-residue model. We do not need to reconstruct the transfer function through the ABCD matrices here; the pole-residue overlay is the relevant fit check." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4da517c8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "plt.plot(wavelengths_um * 1e3, transmission_sparam, label='S-parameter dictionary', linewidth=2)\n", + "plt.plot(wavelengths_um * 1e3, transmission_fit, '--', label='Pole-residue steady-state fit', linewidth=2)\n", + "plt.xlabel('Wavelength (nm)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('Steady-state response: S-parameters vs pole-residue fit')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5254b805", + "metadata": {}, + "source": [ + "### Construct the state-space model\n", + "\n", + "Once the pole-residue fit is acceptable, we convert it to the state-space form that a time-domain simulator can step forward sample by sample:\n", + "\n", + "$$\n", + "\\mathbf{x}[n+1] = \\mathbf{A}\\mathbf{x}[n] + \\mathbf{B}\\mathbf{u}[n], \\qquad\n", + "\\mathbf{y}[n] = \\mathbf{C}\\mathbf{x}[n] + \\mathbf{D}\\mathbf{u}[n].\n", + "$$\n", + "\n", + "For the pole-residue realization used here, the matrices are\n", + "\n", + "$$\n", + "\\mathbf{A} = \\operatorname{kron}\\left(\\operatorname{diag}(p_1,\\ldots,p_r), \\mathbf{I}_m\\right),\n", + "$$\n", + "\n", + "$$\n", + "\\mathbf{B} =\n", + "\\begin{bmatrix}\n", + "\\mathbf{I}_m \\\\\n", + "\\mathbf{I}_m \\\\\n", + "\\vdots \\\\\n", + "\\mathbf{I}_m\n", + "\\end{bmatrix}\n", + "\\in \\mathbb{C}^{rm \\times m},\n", + "$$\n", + "\n", + "$$\n", + "\\mathbf{C}_{i,(k,j)} = (\\mathbf{R}_k)_{i,j}, \\qquad \\mathbf{D}=\\texttt{feedthrough}.\n", + "$$\n", + "\n", + "This realization is not guaranteed to be minimal, but it is easy to inspect and maps directly onto the fitted pole-residue tensors." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7138a5ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A shape: (160, 160)\n", + "B shape: (160, 4)\n", + "C shape: (4, 160)\n", + "D shape: (4, 4)\n" + ] + } + ], + "source": [ + "A, B, C, D = state_space_discrete(poles, residues, feedthrough)\n", + "\n", + "print('A shape:', A.shape)\n", + "print('B shape:', B.shape)\n", + "print('C shape:', C.shape)\n", + "print('D shape:', D.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "c8cfb01f", + "metadata": {}, + "source": [ + "### Step the fitted state-space model\n", + "\n", + "`z_domain.py` has two useful time-domain state-space response helpers:\n", + "\n", + "- `state_space_response_discrete(...)` is the general dense update. It works for any valid $\\mathbf{A}$, $\\mathbf{B}$, $\\mathbf{C}$, $\\mathbf{D}$ realization.\n", + "- `state_space_response_discrete_structured(...)` assumes the realization came from `state_space_discrete(...)`. It makes certain assumptions about the shape and values of the pole-residue model and state space model for a speed up. Refer to docs for more details.\n", + "\n", + "For this fitted pole-residue model, the structured method is the natural one to show. The code below drives input `o0` with a unit CW envelope and reads output `o3` for several baseband wavelengths. This is only the full-circuit state-space implementation; after this, we run the same circuit through `BlockModeSimulation` so the tutorial can explain the simulator workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "916fba14", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TIME_STEPS = 512\n", + "wavelengths_td_m = np.linspace(1.550e-6, 1.552e-6, 5)\n", + "time_ps = np.arange(TIME_STEPS) * DT * 1e12\n", + "\n", + "\n", + "def structured_abcd_response(wavelengths_m):\n", + " num_ports = len(port_order)\n", + " input_sequence = jnp.zeros((TIME_STEPS, num_ports), dtype=complex)\n", + " input_sequence = input_sequence.at[:, input_index].set(1.0 + 0.0j)\n", + "\n", + " responses = []\n", + " for wavelength_m in wavelengths_m:\n", + " omega_lambda = 2 * np.pi * speed_of_light * (1 / wavelength_m - 1 / CENTER_WAVELENGTH_M) * DT\n", + " phase = jnp.exp(1j * omega_lambda)\n", + "\n", + " y, _ = state_space_response_discrete_structured(\n", + " phase * A,\n", + " B,\n", + " C,\n", + " D,\n", + " phase,\n", + " input_sequence,\n", + " )\n", + " responses.append(np.asarray(y[:, output_index]))\n", + "\n", + " return np.stack(responses, axis=1)\n", + "\n", + "\n", + "structured_envelopes = structured_abcd_response(wavelengths_td_m)\n", + "structured_power_matrix = np.abs(structured_envelopes) ** 2\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "for wavelength_m, power in zip(wavelengths_td_m, structured_power_matrix.T):\n", + " plt.plot(time_ps, power, linewidth=1.5, label=f'{wavelength_m * 1e9:.1f} nm')\n", + "\n", + "plt.xlabel('Time (ps)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('Full-circuit structured ABCD response at different wavelengths')\n", + "plt.xlim(time_ps[0], time_ps[-1])\n", + "plt.ylim(bottom=0)\n", + "plt.grid(True)\n", + "plt.legend(title='Carrier')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "63f18b27", + "metadata": {}, + "source": [ + "## 8. A short note on PCells\n", + "\n", + "A PCell is a parameterized component. Instead of writing every coupler, waveguide, and connection by hand, a PCell builds a netlist, model dictionary, and settings dictionary from a smaller set of parameters.\n", + "\n", + "For this starter example, the lattice is written explicitly so the mechanics are easy to see. In larger circuits, a PCell is the better abstraction because it lets you say \"make an order-4 MZI lattice\" and let the component generate the repeated stages internally.\n", + "\n", + "During simulation, Simphony instantiates and flattens PCells before running the solver. After flattening, the Block mode simulator sees ordinary components and ordinary directed connections.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8d710a60", + "metadata": {}, + "source": [ + "## 9. Build a directed Block mode netlist\n", + "\n", + "Frequency-domain S-parameter netlists are connection-oriented. Block mode is execution-oriented: signals must flow from sources to downstream components in a directed acyclic graph.\n", + "\n", + "The directed version below uses the same physical lattice, but the connection dictionary is written in signal-flow order. A small CW source drives `dc0,o2`, and the top-level output is `dc7,o1`.\n", + "\n", + "For Block mode, each connection entry is interpreted as:\n", + "\n", + "```python\n", + "'upstream_instance,upstream_port': 'downstream_instance,downstream_port'\n", + "```\n", + "\n", + "When `port_directionality` is not supplied for an S-parameter placeholder, Simphony infers it from this directed netlist: ports on the left-hand side of `connections` are outputs, ports that appear only on the right-hand side are inputs, and unconnected S-parameter ports default to outputs. This is why the Block mode netlist is written as a signal-flow path instead of as an undirected physical join list.\n", + "\n", + "The later settings cell still gives explicit direction dictionaries for the couplers and waveguides. That makes the tutorial less ambiguous and is especially helpful for reciprocal components whose physical ports can be used in more than one role." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fbd58ddd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "directed instances: 17\n", + "directed connections: 20\n", + "top-level ports: {'out': 'dc7,o1'}\n" + ] + } + ], + "source": [ + "class TutorialCWLaser(BlockModeComponent):\n", + " ports = [Port(name='o0', type='optical', directionality='output')]\n", + "\n", + " def __init__(self, simulation_parameters, *, wavelength):\n", + " self.wavelength = jnp.atleast_1d(jnp.asarray(wavelength))\n", + "\n", + " def block_mode_response(self, inputs, simulation_parameters):\n", + " num_time_steps = simulation_parameters.num_time_steps\n", + " num_wavelengths = self.wavelength.shape[0]\n", + " num_modes = len(simulation_parameters.mode_identifiers)\n", + "\n", + " amplitude = jnp.zeros(\n", + " (num_time_steps, num_wavelengths, num_modes),\n", + " dtype=jnp.complex64,\n", + " )\n", + " amplitude = amplitude.at[:, :, 0].set(1.0 + 0.0j)\n", + "\n", + " return {\n", + " 'o0': BlockModeOpticalSignal(\n", + " amplitude=amplitude,\n", + " wavelength=self.wavelength,\n", + " )\n", + " }\n", + "\n", + "\n", + "def block_mode_lattice_netlist(order=ORDER):\n", + " frequency_netlist = frequency_domain_lattice_netlist(order)\n", + " instances = {'laser': 'laser', **frequency_netlist['instances']}\n", + " connections = {'laser,o0': 'dc0,o2'}\n", + "\n", + " for stage in range(order):\n", + " splitter = f'dc{2 * stage}'\n", + " combiner = f'dc{2 * stage + 1}'\n", + " short_wg = f'wg{2 * stage}'\n", + " long_wg = f'wg{2 * stage + 1}'\n", + "\n", + " connections[f'{splitter},o3'] = f'{short_wg},o0'\n", + " connections[f'{short_wg},o1'] = f'{combiner},o2'\n", + " connections[f'{splitter},o1'] = f'{long_wg},o0'\n", + " connections[f'{long_wg},o1'] = f'{combiner},o0'\n", + "\n", + " if stage < order - 1:\n", + " next_splitter = f'dc{2 * (stage + 1)}'\n", + " connections[f'{combiner},o1'] = f'{next_splitter},o2'\n", + "\n", + " ports = {'out': f'dc{2 * order - 1},o1'}\n", + " return {'instances': instances, 'connections': connections, 'ports': ports}\n", + "\n", + "\n", + "block_netlist = block_mode_lattice_netlist()\n", + "print('directed instances:', len(block_netlist['instances']))\n", + "print('directed connections:', len(block_netlist['connections']))\n", + "print('top-level ports:', block_netlist['ports'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "756c7350", + "metadata": {}, + "source": [ + "## 10. Configure Block mode models and settings\n", + "\n", + "SAX components must be wrapped before Block mode can use them. `optical_s_parameter_placeholder` gives Simphony a Block mode component shell around the SAX model, and the settings provide the SAX kwargs, port roles, and vector-fitting controls for each instance.\n", + "\n", + "The most important Block mode parameters are:\n", + "\n", + "- `dt`: time step of the sampled envelope simulation. It sets the sampling frequency used by the discrete-time fit.\n", + "- `num_time_steps`: number of samples in the block. Components process arrays of this full length.\n", + "- `optical_baseband_wavelengths`: carrier wavelengths tracked in the envelope simulation.\n", + "- `mode_identifiers`: optical modes to simulate. This starter example uses only `te`.\n", + "- `vector_fitting_parameters`: per-SAX-component fitting controls. `model_order` and `num_frequency_samples` are the easiest accuracy/speed knobs.\n", + "\n", + "Settings caveat: once an S-parameter instance setting includes simulator-only keys such as `port_directionality` or `vector_fitting_parameters`, include `sax_settings` too. If the SAX model has no arguments, write `sax_settings: {}`. Otherwise those simulator-only keys can be interpreted as SAX model parameters instead of Block mode metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3ed4aebe", + "metadata": {}, + "outputs": [], + "source": [ + "COUPLER_DIRECTIONALITY = {\n", + " 'o2': 'input',\n", + " 'o0': 'input',\n", + " 'o3': 'output',\n", + " 'o1': 'output',\n", + "}\n", + "WAVEGUIDE_DIRECTIONALITY = {\n", + " 'o0': 'input',\n", + " 'o1': 'output',\n", + "}\n", + "\n", + "block_models = {\n", + " 'laser': TutorialCWLaser,\n", + " 'directional_coupler': optical_s_parameter_placeholder(\n", + " old_ideal.coupler,\n", + " COUPLER_DIRECTIONALITY,\n", + " default_modes=('te',),\n", + " ),\n", + " 'waveguide': optical_s_parameter_placeholder(\n", + " old_ideal.waveguide,\n", + " WAVEGUIDE_DIRECTIONALITY,\n", + " default_modes=('te',),\n", + " ),\n", + "}\n", + "\n", + "component_vector_fit = {\n", + " 'model_order': 20,\n", + " 'min_model_order': 4,\n", + " 'max_model_order': 20,\n", + " 'num_frequency_samples': 300,\n", + " 'center_wavelength': CENTER_WAVELENGTH_M,\n", + " 'spectral_range': (1.5e-6, 1.6e-6),\n", + "}\n", + "\n", + "\n", + "def block_mode_lattice_settings(wavelengths_m):\n", + " wavelengths_m = jnp.atleast_1d(jnp.asarray(wavelengths_m))\n", + " settings = {\n", + " 'laser': {'wavelength': wavelengths_m},\n", + " }\n", + "\n", + " for coupler_index in range(2 * ORDER):\n", + " settings[f'dc{coupler_index}'] = {\n", + " 'sax_settings': {},\n", + " 'port_directionality': COUPLER_DIRECTIONALITY,\n", + " 'vector_fitting_parameters': component_vector_fit.copy(),\n", + " }\n", + "\n", + " for stage, (short_length, long_length) in enumerate(zip(short_arm_lengths_um, long_arm_lengths_um)):\n", + " settings[f'wg{2 * stage}'] = {\n", + " 'sax_settings': {'length': short_length},\n", + " 'port_directionality': WAVEGUIDE_DIRECTIONALITY,\n", + " 'vector_fitting_parameters': component_vector_fit.copy(),\n", + " }\n", + " settings[f'wg{2 * stage + 1}'] = {\n", + " 'sax_settings': {'length': long_length},\n", + " 'port_directionality': WAVEGUIDE_DIRECTIONALITY,\n", + " 'vector_fitting_parameters': component_vector_fit.copy(),\n", + " }\n", + "\n", + " return settings" + ] + }, + { + "cell_type": "markdown", + "id": "255b78b5", + "metadata": {}, + "source": [ + "## 11. Run Block mode at several wavelengths\n", + "\n", + "Now we run the lattice through the Block mode simulator. A `BlockModeOpticalSignal` has shape `(time, wavelength, mode)`, so one Block mode run can carry multiple baseband wavelengths at once.\n", + "\n", + "The important simulator pieces are:\n", + "\n", + "- `BlockModeSimulationParameters`, which define `dt`, `num_time_steps`, modes, and the baseband wavelength grid.\n", + "- `Circuit(block_netlist, block_models)`, which binds the directed netlist to the Block mode-capable component models.\n", + "- `BlockModeSimulation(...).run()`, which instantiates the circuit, fits the S-parameter components internally, and steps the directed graph.\n", + "\n", + "The result below is the simulator output. It is no longer a hand-run full-circuit ABCD model; it is the actual Block mode workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e86c188c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Block mode runs: 1\n", + "tracked wavelengths: 5\n", + "output amplitude shape: (512, 5, 1)\n", + "output wavelength grid (nm): [1550. 1550.5 1551. 1551.5 1552. ]\n" + ] + } + ], + "source": [ + "simulation_parameters = BlockModeSimulationParameters(\n", + " mode_identifiers=('te',),\n", + " dt=DT,\n", + " num_time_steps=TIME_STEPS,\n", + " optical_baseband_wavelengths=jnp.asarray(wavelengths_td_m),\n", + " use_speed_up=True,\n", + ")\n", + "\n", + "circuit = Circuit(block_netlist, block_models)\n", + "simulation = BlockModeSimulation(\n", + " circuit,\n", + " settings=block_mode_lattice_settings(wavelengths_td_m),\n", + " simulation_parameters=simulation_parameters,\n", + ")\n", + "result = simulation.run()\n", + "output_signal = result.output_signals['out']\n", + "\n", + "output_baseband_grid = np.asarray(output_signal.wavelength)\n", + "output_envelopes = np.asarray(output_signal.amplitude[:, :, 0])\n", + "power_matrix = np.abs(output_envelopes) ** 2\n", + "block_steady_power = power_matrix[-1, :]\n", + "\n", + "print('Block mode runs:', 1)\n", + "print('tracked wavelengths:', output_baseband_grid.shape[0])\n", + "print('output amplitude shape:', output_signal.amplitude.shape)\n", + "print('output wavelength grid (nm):', np.round(output_baseband_grid * 1e9, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d4314736", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "for wavelength_m, power in zip(output_baseband_grid, power_matrix.T):\n", + " plt.plot(time_ps, power, linewidth=1.5, label=f'{wavelength_m * 1e9:.1f} nm')\n", + "\n", + "plt.xlabel('Time (ps)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('Block mode simulator response at multiple baseband wavelengths')\n", + "plt.xlim(time_ps[0], time_ps[-1])\n", + "plt.ylim(bottom=0)\n", + "plt.grid(True)\n", + "plt.legend(title='Carrier')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1c9a4ec4", + "metadata": {}, + "source": [ + "## 12. Wavelength mapping to the baseband grid\n", + "\n", + "Each `BlockModeOpticalSignal` carries both an amplitude array and a wavelength grid. In the run above, the laser emits exactly the same wavelengths listed in `simulation_parameters.optical_baseband_wavelengths`, so the mapping is one-to-one.\n", + "\n", + "More generally, after a Block mode component returns optical outputs, Simphony maps every output wavelength onto the baseband grid. It chooses the nearest baseband wavelength and applies a phase correction based on the frequency difference:\n", + "\n", + "$$\n", + "\\phi(t) = \\exp\\left[-j2\\pi\\left(\\frac{c}{\\lambda_{out}} - \\frac{c}{\\lambda_{baseband}}\\right)t\\right].\n", + "$$\n", + "\n", + "If multiple output wavelengths land on the same baseband channel, their phase-corrected amplitudes are added. This keeps the arrays aligned between components while preserving the beat phase of wavelengths that do not exactly equal the baseband grid.\n", + "\n", + "You can inspect the mapped wavelengths through `result.output_signals['out'].wavelength` and the corresponding envelopes through `result.output_signals['out'].amplitude`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "67c23793", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "example carrier (nm): 1551.0\n", + "mapped output baseband grid (nm): [1550. 1550.5 1551. 1551.5 1552. ]\n", + "all output envelopes shape: (512, 5)\n", + "first three complex envelope samples for example carrier: [-0.-0.j 0.+0.j -0.-0.j]\n" + ] + } + ], + "source": [ + "example_index = len(output_baseband_grid) // 2\n", + "example_wavelength_m = output_baseband_grid[example_index]\n", + "example_envelope = output_envelopes[:, example_index]\n", + "\n", + "print('example carrier (nm):', float(example_wavelength_m * 1e9))\n", + "print('mapped output baseband grid (nm):', np.round(output_baseband_grid * 1e9, 6))\n", + "print('all output envelopes shape:', output_envelopes.shape)\n", + "print('first three complex envelope samples for example carrier:', np.round(example_envelope[:3], 5))" + ] + }, + { + "cell_type": "markdown", + "id": "f701c0b7", + "metadata": {}, + "source": [ + "## 13. Compare Block mode steady state with S-parameters\n", + "\n", + "The final sample of each wavelength trace is an estimate of the settled response. Here it is plotted against the original S-parameter transmission at the same carrier wavelengths.\n", + "\n", + "Small differences come from the finite fitting order, finite simulation time, and the fact that Block mode is running a causal discrete-time approximation of the frequency-domain model." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "929be34f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wavelengths_td_um = output_baseband_grid * 1e6\n", + "s_params_td = sax_circuit(**frequency_domain_lattice_settings(wavelengths_td_um))\n", + "sparam_td_power = np.abs(s_params_td[TRANSFER_KEY]) ** 2\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(wavelengths_um * 1e3, transmission_sparam, color='0.75', label='S-parameter sweep')\n", + "plt.scatter(wavelengths_td_um * 1e3, sparam_td_power, label='S-parameter at Block mode carriers')\n", + "plt.scatter(wavelengths_td_um * 1e3, block_steady_power, marker='x', s=80, label='Block mode final sample')\n", + "plt.xlabel('Wavelength (nm)')\n", + "plt.ylabel('Power transmission')\n", + "plt.title('Settled Block mode response vs S-parameters')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/components.ipynb b/examples/old/components.ipynb new file mode 100644 index 00000000..2ed88ec5 --- /dev/null +++ b/examples/old/components.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a3f65be3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/code/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "from simphony.signal import optical_signal, electrical_signal\n", + "import numpy as np\n", + "osig = optical_signal(field=1.0)\n", + "esig = electrical_signal(voltage=0.5)\n", + "\n", + "from simphony.libraries.analytic import PhaseModulator\n", + "pm = PhaseModulator()\n", + "inputs = {\n", + " \"o0\": osig,\n", + " \"o1\": osig,\n", + " \"e0\": esig,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e9f85f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0017010590017889626\n", + "0.0004152190012973733\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "tic = time.perf_counter()\n", + "result = pm.steady_state(inputs)\n", + "toc = time.perf_counter()\n", + "elapsed_time = toc - tic\n", + "print(elapsed_time)\n", + "\n", + "tic = time.perf_counter()\n", + "result = pm.steady_state(inputs)\n", + "toc = time.perf_counter()\n", + "elapsed_time = toc - tic\n", + "print(elapsed_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "084930f1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f93cdfdc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/cw_laser.ipynb b/examples/old/cw_laser.ipynb new file mode 100644 index 00000000..50ec8371 --- /dev/null +++ b/examples/old/cw_laser.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ddd3d24b", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "54996634", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "from simphony.libraries.analytic import CWLaser\n", + "\n", + "\n", + "wl = 1.55e-6\n", + "sampling_period =1e-14\n", + "T = 10000e-12\n", + "# T = 500e-12\n", + "N = int(T /sampling_period)\n", + "linewidth=0.2e12\n", + "t = jnp.arange(N)*sampling_period\n", + "cw_laser = CWLaser(wavelength=wl,linewidth=linewidth,lineshape='gaussian')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e77b241d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.simulation import SimulationParameters\n", + "\n", + "sim_params = SimulationParameters(\n", + " sampling_period=sampling_period,\n", + " num_time_steps=N,\n", + " prng_key=jax.random.key(5)\n", + ")\n", + "\n", + "\n", + "outputs = cw_laser.block_mode_response(simulation_parameters=sim_params)\n", + "A_t = outputs[\"o0\"].amplitude[:,0,0]\n", + "phi = jnp.unwrap(jnp.angle(A_t))\n", + "plt.plot(t, 180 / jnp.pi * phi)\n", + "# plt.xlim(0, 1000)\n", + "# plt.ylim(-70, 70)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "10047f18", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0, 1e-13)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.constants import speed_of_light as C\n", + "\n", + "E_t = A_t*jnp.exp(1j*2*jnp.pi*C/wl*t)\n", + "\n", + "plt.plot(t, E_t)\n", + "plt.xlabel(\"time (s)\")\n", + "plt.ylabel(\"Real E(t) \")\n", + "plt.title(\"pseudo-monochromatic signal\")\n", + "plt.xlim([0, 0.1e-12])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fe38fdd5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_47970/3942998429.py:52: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from scipy.fftpack import fft, fftshift, fftfreq\n", + "# === Compute spectrum ===\n", + "E_f = fftshift(fft(np.array(A_t)))\n", + "freqs = fftshift(fftfreq(N, sampling_period))\n", + "\n", + "# === Power spectral density (normalized) ===\n", + "psd = jnp.abs(E_f) ** 2\n", + "psd /= jnp.max(psd) # Normalize for plotting\n", + "\n", + "# === Plotting ===\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(t, jnp.real(E_t), label=\"Re\")\n", + "plt.plot(t, jnp.imag(E_t), label=\"Im\", alpha=0.6)\n", + "plt.title(\"Time Domain Signal\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.xlim([0, 0.02e-12])\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(t, jnp.unwrap(phi))\n", + "plt.plot(t[:-1], jnp.diff(phi)/(sampling_period*2*np.pi))\n", + "plt.title(\"Cumulative Phase Noise\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Phase (rad)\")\n", + "\n", + "f = np.linspace(-100e12, 100e12, 10*N)\n", + "\n", + "psd_theory = (1/(2*np.pi))*(linewidth)/((f)**2+(linewidth/2)**2)\n", + "psd_theory /= np.max(psd_theory)\n", + "\n", + "psd_theory_gaussian = jnp.exp(-4*jnp.log(2)*(f/linewidth)**2)\n", + "# plt.plot(f, psd_theory)\n", + "# plt.show()\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(freqs, psd, label=\"FFT of E(t)\")\n", + "plt.plot(f, psd_theory, label=\"PSD Theoretical\")\n", + "plt.plot(f, psd_theory_gaussian)\n", + "# plt.plot(freqs/1e3, jnp.angle(E_f) ** 2)\n", + "# plt.title(\"Power Spectrum (Lorentzian Linewidth)\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Normalized PSD\")\n", + "plt.xlim([-10e12, 10e12])\n", + "# plt.ylim([0, 1e-13])\n", + "plt.grid(True)\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eb3c7836", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/IPython/core/events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " func(*args, **kwargs)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/IPython/core/pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "E_f_list = []\n", + "fs = 1e15 # Sampling frequency (Hz)\n", + "T = 100e-12 # Total duration (s)\n", + "N = int(T * fs) # Number of samples\n", + "t = np.arange(N) / fs # Time array\n", + "for i in range(200):\n", + " sim_params = SimulationParameters(\n", + " sampling_period=1/fs,\n", + " num_time_steps=N,\n", + " prng_key=jax.random.key(i)\n", + " )\n", + " outputs = cw_laser.block_mode_response({}, sim_params)\n", + " A_t = outputs[\"o0\"].amplitude[:,0,0]\n", + " phi = jnp.unwrap(jnp.angle(A_t))\n", + " E_f_list.append(fftshift(fft(np.array(A_t))))\n", + "\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "E_f = np.mean(np.array(E_f_list), axis=0)\n", + "psd = np.abs(E_f)**2\n", + "psd = psd / np.max(psd)\n", + "plt.plot(f, psd_theory, label=\"PSD Theoretical\")\n", + "plt.plot(f, psd_theory_gaussian)\n", + "plt.plot(freqs, psd, linestyle=\"--\", label=\"Avg Simulated PSD\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Abs Squared\")\n", + "plt.title(\"Simulated vs Theoretical PSD\")\n", + "# plt.hlines(0.5, -linewidth/2, linewidth/2)\n", + "plt.xlim(-10e12, 10e12)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "f6404586", + "metadata": {}, + "source": [ + "Continuous Wave Lasers ideally produce coherent light at a one wavelength, $\\lambda$. In general, random fluctuations in the phase and amplitude of the laser can introduce undesired wavelengths. If the power spectral density of the laser has a Lorentzian shape, then we usually characterize the laser by its linewdith $\\delta f$ (the full-width half max of the PSD). \n", + "\n", + "Such light corresponds to the following time domain signals of the following form.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f683ef69", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.constants import speed_of_light, epsilon_0\n", + "\n", + "fs = 1e16 # Sampling frequency (Hz)\n", + "T = 100e-12 # Total duration (s)\n", + "N = int(T * fs) # Number of samples\n", + "t = np.arange(N) / fs # Time array\n", + "f0 = 193e12 # Carrier frequency (Hz)\n", + "fb = f0 # Baseband frequency (Hz)\n", + "linewidth = 2e12 # Lorentzian FWHM (Hz)\n", + "\n", + "delta_phi_std = np.sqrt(2*np.pi * linewidth / fs)\n", + "dphi = np.random.randn(N) * delta_phi_std\n", + "phi = np.cumsum(dphi) # Integrate to get phase\n", + "\n", + "# === Generate signal: constant amplitude with noisy phase ===\n", + "A_t = np.exp(1j * (2 * np.pi * (f0-fb) * t + phi))\n", + "E_t = A_t*np.exp(1j*2*np.pi*f0*t)\n", + "\n", + "plt.plot(t, E_t)\n", + "plt.xlim([0, 0.02e-12])\n", + "N" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af23f483", + "metadata": {}, + "outputs": [], + "source": [ + "# f_0 = 0 # Shift to baseband\n", + "\n", + "f = np.linspace(-100e12, 100e12, 10*N)\n", + "\n", + "psd_theory = (1/(2*np.pi))*(linewidth)/((f)**2+(linewidth/2)**2)\n", + "psd_theory /= np.max(psd_theory)\n", + "plt.plot(f, psd_theory)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f609b34f", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.fftpack import fft, fftshift, fftfreq\n", + "# === Compute spectrum ===\n", + "E_f = fftshift(fft(A_t))\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "\n", + "# === Power spectral density (normalized) ===\n", + "psd = np.abs(E_f) ** 2\n", + "psd /= np.max(psd) # Normalize for plotting\n", + "\n", + "# === Plotting ===\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(t, np.real(E_t), label=\"Re\")\n", + "plt.plot(t, np.imag(E_t), label=\"Im\", alpha=0.6)\n", + "plt.title(\"Time Domain Signal\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.xlim([0, 0.05e-12])\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(t, np.unwrap(phi))\n", + "plt.title(\"Cumulative Phase Noise\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Phase (rad)\")\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(freqs, psd)\n", + "plt.plot(f, psd_theory)\n", + "# plt.plot(freqs/1e3, np.angle(E_f) ** 2)\n", + "plt.title(\"Power Spectrum (Lorentzian Linewidth)\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Normalized PSD\")\n", + "plt.xlim([-10e12, 10e12])\n", + "# plt.ylim([0, 1e-13])\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd4789d9", + "metadata": {}, + "outputs": [], + "source": [ + "E_f_list = []\n", + "fs = 1e14 # Sampling frequency (Hz)\n", + "T = 10e-12 # Total duration (s)\n", + "N = int(T * fs) # Number of samples\n", + "t = np.arange(N) / fs # Time array\n", + "for i in range(5000):\n", + " delta_phi_std = np.sqrt(2*np.pi * linewidth / fs)\n", + " dphi = np.random.randn(N) * delta_phi_std\n", + " phi = np.cumsum(dphi) # Integrate to get phase\n", + " A_t = np.exp(1j * (2 * np.pi * (f0-fb) * t + phi))\n", + " E_f_list.append(fftshift(fft(A_t)))\n", + "\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "E_f = np.mean(np.array(E_f_list), axis=0)\n", + "psd = np.abs(E_f)**2\n", + "psd = psd / np.max(psd)\n", + "plt.plot(f, psd_theory)\n", + "plt.plot(freqs, psd)\n", + "# plt.hlines(0.5, -linewidth/2, linewidth/2)\n", + "plt.xlim(-10e12, 10e12)\n" + ] + }, + { + "cell_type": "markdown", + "id": "16a4738e", + "metadata": {}, + "source": [ + "# Gaussian Linewidth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a7259ee", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.signal.windows import gaussian\n", + "E_f_list = []\n", + "fs = 1e14 # Sampling frequency (Hz)\n", + "T = 10e-12 # Total duration (s)\n", + "N = int(T * fs) # Number of samples\n", + "t = np.arange(N) / fs # Time array\n", + "\n", + "window_width = int(N // 100) # Control PSD width\n", + "gaussian_window = gaussian(N, std=window_width/2)\n", + "gaussian_window /= np.sum(gaussian_window)\n", + "# phi_gauss = np.convolve(noise, gaussian_window, mode='same')\n", + "for i in range(10000):\n", + " delta_phi_std = 0.5\n", + " dphi = np.random.randn(N) * delta_phi_std\n", + " dphi = np.convolve(dphi, gaussian_window)[N//2:3*N//2]\n", + " phi = np.cumsum(dphi) # Integrate to get phase\n", + " # phi *= 0.5\n", + " A_t = np.exp(1j * (2 * np.pi * (f0-fb) * t + phi))\n", + " E_f_list.append(fftshift(fft(A_t)))\n", + "\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "E_f = np.mean(np.array(E_f_list), axis=0)\n", + "psd = np.abs(E_f)**2\n", + "psd = psd / np.max(psd)\n", + "plt.plot(f, gaussian(len(f), std=100000))\n", + "plt.plot(freqs, psd)\n", + "# plt.hlines(0.5, -linewidth/2, linewidth/2)\n", + "plt.xlim(-10e12, 10e12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b40e4a0", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(dphi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39e30e6c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/filters.ipynb b/examples/old/filters.ipynb new file mode 100644 index 00000000..e0180aff --- /dev/null +++ b/examples/old/filters.ipynb @@ -0,0 +1,15822 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "6343b503", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "from simphony.simulation.sample_mode import SampleModeSimulationParameters\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "813b29e6", + "metadata": {}, + "source": [ + "# Discrete Filter\n", + "\n", + "Our discrete filter implementation is similar to matlab, with one alteration: we allow for an optional \"delay compensation\" parameter. When set to 0, the output of the OpticalDiscreteFilter will mimic traditional implementations. \n", + "\n", + "When the delay compensation parameter is set to a positive integer, k, the output will be advanced (if possible) by k time steps and an appropriate phase corrected will be applied to complex signals not at the baseband frequency. This allows for more accurate sample mode simulations.\n", + "\n", + "In block mode simulations delay compensation should be set to 0. An error will be thrown otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d7eab38d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.signal.block_mode import BlockModeOpticalSignal\n", + "from functools import partial\n", + "\n", + "def envelope_fn_full(wavelength, t):\n", + " t = jnp.atleast_1d(t)\n", + " T = len(t) # Number of time steps \n", + " L = 1 # Number of wavelengths\n", + " M = 1 # Number of modes\n", + " A_t = jnp.exp(-0.5*((t-4e-12)/(5e-13))**2)\n", + " amplitude = A_t.reshape((T, L, M))\n", + " \n", + " signal = BlockModeOpticalSignal(amplitude=amplitude, wavelength=jnp.atleast_1d(wavelength))\n", + " \n", + " return signal\n", + "\n", + "wavelength = 1.54e-6\n", + "envelope_fn = partial(envelope_fn_full, wavelength)\n", + "\n", + "t = jnp.linspace(0.0, 10e-12, 1000)\n", + "envelope = envelope_fn(t)\n", + "\n", + "\n", + "plt.plot(t, envelope.amplitude[:, 0, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c604013", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.libraries.analytic.sources import OpticalSource\n", + "from simphony.simulation.sample_mode import SampleModeSimulationParameters\n", + "from simphony.simulation.block_mode import BlockModeSimulationParameters\n", + "\n", + "simulation_parameters = SampleModeSimulationParameters()\n", + "optical_source = OpticalSource(envelope_fn=envelope_fn)\n", + "initital_state = optical_source.sample_mode_initial_state(simulation_parameters)\n", + "\n", + "A = []\n", + "state = initital_state\n", + "sample_mode_source = []\n", + "for t in range(simulation_parameters.num_time_steps):\n", + " outputs, _state = optical_source._sample_mode_step({}, (t, state), None, simulation_parameters)\n", + " _, state = _state\n", + " A.append(outputs['o0'].amplitude[0, 0])\n", + " sample_mode_source.append(outputs)\n", + "\n", + "plt.plot(A)\n", + "\n", + "optical_source_outputs = optical_source.block_mode_response({}, BlockModeSimulationParameters())\n", + "plt.plot(optical_source_outputs['o0'].amplitude[:, 0, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8348e702", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/jax/_src/ops/scatter.py:108: FutureWarning: scatter inputs have incompatible types: cannot safely cast value from dtype=complex128 to dtype=float64 with jax_numpy_dtype_promotion='standard'. In future JAX releases this will result in an error.\n", + " warnings.warn(\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/jax/_src/ops/scatter.py:152: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return lax_internal._convert_element_type(out, dtype, weak_type)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAWbRJREFUeJzt3Xl8VNX5P/DPnTX7RsiekLDvu0AQ91RUxKW1RatiqaVqoRVpXagiX+sCWrW2ilJpUX+titoqtUhRDKKiLLIE2XdIyEoI2ZOZZOb8/jhzZxJIwkwyM3cm+bxfr3QuM3fmnoxN5slznvMcRQghQERERKQRndYDICIiop6NwQgRERFpisEIERERaYrBCBEREWmKwQgRERFpisEIERERaYrBCBEREWmKwQgRERFpyqD1ANxht9tRVFSEyMhIKIqi9XCIiIjIDUII1NTUICUlBTpd+/mPoAhGioqKkJ6ervUwiIiIqBMKCgqQlpbW7uNBEYxERkYCkN9MVFSUxqMhIiIid1RXVyM9Pd35Od6eoAhG1KmZqKgoBiNERERB5kIlFixgJSIiIk0xGCEiIiJNMRghIiIiTTEYISIiIk0xGCEiIiJNMRghIiIiTTEYISIiIk0xGCEiIiJNMRghIiIiTXkcjHz11VeYPn06UlJSoCgKVq1adcHnbNiwAWPHjoXZbEb//v3x5ptvdmKoRERE1B15HIzU1dVh1KhRWLp0qVvnHz9+HNOmTcMVV1yBvLw8zJs3D7/4xS/w6aefejxYIiIi6n483pvm2muvxbXXXuv2+cuWLUNWVhZeeOEFAMCQIUOwceNG/OlPf8LUqVM9vTwRERF1Mz7fKG/Tpk3Iyclpdd/UqVMxb968dp9jsVhgsVic/66urvbV8Ii6jW+OlGPjkXJk9grDTWNSYTbotR4SEZFbfB6MlJSUIDExsdV9iYmJqK6uRkNDA0JDQ897zuLFi/HEE0/4emhE3YIQAk99sh9/33jced8/N+fjn7+YiOhQo4YjIyJyT0CuplmwYAGqqqqcXwUFBVoPiShgvb+twBmITBuZjNgwI3YXVuG37+dBCKHx6IiILsznwUhSUhJKS0tb3VdaWoqoqKg2syIAYDabERUV1eqLiM5XWW/FU6v3AwAeumYQlv50LP5x90QY9Qo+31+Gz/aVXuAViIi05/NgJDs7G7m5ua3uW7duHbKzs319aaJub/nXx1BjacbgpEjcc2k/AMDw1Gj88tK+AIA/f36Y2REiCngeByO1tbXIy8tDXl4eALl0Ny8vD/n5+QDkFMvMmTOd59977704duwYHnroIRw4cACvvvoq3n//fTzwwAPe+Q6IeqjGJhv+sekkAGBezkDodYrzsdmX9EWYSY99xdXYeKRcqyESEbnF42Bk27ZtGDNmDMaMGQMAmD9/PsaMGYPHH38cAFBcXOwMTAAgKysLn3zyCdatW4dRo0bhhRdewN/+9jcu6yXqok/3lqC6sRmpMaG4emjrIvGYMBN+PC4NAPD+tlNaDI+IyG0er6a5/PLLO0z7ttVd9fLLL8fOnTs9vRQRdeD9bbKw+8fj06BrkRVR3TIuHW9tOonP9pagurEJUSFcWUNEgSkgV9MQUccq6qzYdPQMAOBHY9PaPGd4ahQGJETA0mzHZ3tZyEpEgYvBCFEQ+uJAGewCGJochfS4sDbPURQF1w5PAgDk7mcwQkSBi8EIURD63BFc5JxTK3Kuq4bIx786dBqWZpvPx0VE1BkMRoiCjKXZhq8OnQYA5AxJ6PDcEanR6B1pRp3Vhi3HKvwxPCIijzEYIQoy35+qQp3VhvgIE4anRHd4rk6n4PKBvQHIvWuIiAIRgxGiILPZUbg6MatXm6tozpXdr5d83nFmRogoMDEYIQoym4/LYGRS3zi3zp/YVwYjewqrUNPY5LNxERF1FoMRoiBibbZj+8mzAIBJjiDjQlJjQpEeFwqbXWCb47lERIGEwQhREPn+VCUam+zoFW5C/4QIt583KUsGLixiJaJAxGCEKIiomY2LMuOgKBeuF1FdlCWndHbkMzNCRIGHwQhREPn+VCUAYHRGjEfPG50uz99TWAWbnbv4ElFgYTBCFER2FVQBAEamdbyk91z9ekcg3KRHvdWGI2W1vhgaEVGnMRghChLltRYUVjZAUWQzM0/odQpGOAKYXQWVPhgdEVHnMRghChLqFE3f+HBEdmIH3lGOqZo8x+sQEQUKBiNEQUKdohmVFtOp5492PI+ZESIKNAxGiIKEmhnxtF5ENdKRGTlQUsNN84gooDAYIQoSuwurAQAjOpkZSYkOQUyYETa7YBErEQUUBiNEQaC81oLyWgsUBRicFNmp11AUxfnc/cU13hweEVGXMBghCgIHS2TwkBEXhnCzodOvMzgpCgBwoLjaK+MiIvIGBiNEQeCAIxgZlNi5rIhqaLIMRvaXMBghosDBYIQoCBx0BA+DHcFEZw1Odk3TCMFOrEQUGBiMEAUBdZqms/UiqoGJkdApQEWdFadrLN4YGhFRlzEYIQpwNrvAwVLHNE0Xg5EQox5Z8eEAgP0lLGIlosDAYIQowOVX1KOxyQ6zQYfMXuFdfj11qodFrEQUKBiMEAU4tV5kYGIk9Dqly683MEFmVw6z1wgRBQgGI0QBzrmSpotTNKp+CTK7cvQ0gxEiCgwMRogCnNotdUBChFder7/jdY6U1XJFDREFBAYjRAHu2Ok6AEC/3t4JRrLiw6FTgJrGZq6oIaKAwGCEKIDZ7QLHymVmpJ+XMiNmgx4ZcWEAwD1qiCggMBghCmDF1Y1obLLDqFeQHhvqtddVp2pYN0JEgYDBCFEAO+rIXPTpFQ6D3ns/rv1a1I0QEWmNwQhRADvmyFz0je96f5GW1PqTI8yMEFEAYDBCFMCOOopX+3qpeFXVn5kRIgogDEaIApizeLW3dzMjajBSWm1BdWOTV1+biMhTDEaIAtjRMt9kRqJCjOgdaQYAnCiv8+prExF5isEIUYCqtTSjpLoRgPczIwCQ5djn5sSZeq+/NhGRJxiMEAWo4456kV7hJsSEmbz++n16yV4jzIwQkdYYjBAFKFe9iHenaFSZ8WpmhMEIEWmLwQhRgDruyFhkeXlZryrTMU1zktM0RKQxBiNEASrfESRkOKZTvI3TNEQUKBiMEAWokxUyGOnjo2BEnaY5U2fl8l4i0hSDEaIApU6f9InzzTRNhNmA+Ai5vDefUzVEpCEGI0QBqM7SjPJaCwDfTdMAQKbjtY9zqoaINMRghCgA5TumaGLCjIgONfrsOn2cRawMRohIOwxGiAKQa4rGd1kRAMiKdxSxcpqGiDTEYIQoAOVXyExFRi/f1IuomBkhokDAYIQoAPkrM6L2GjlezswIEWmHwQhRAFJrRnxZvAoAmY5pmvJaC2otzT69FhFRexiMEAUgNTOS4ePMSGSIEbFhskC2oILZESLSBoMRogDTZLOjsLIBgO8anrWU7gh4GIwQkVYYjBAFmKLKBtjsAiaDDomRIT6/XlpsKACg4GyDz69FRNQWBiNEAablFI1Op/j8eumxzIwQkbYYjBAFGOeeND6uF1GlOa5zipkRItIIgxGiAFPgp5U0qnTHNM2ps8yMEJE2GIwQBZh8P62kUbUsYBVC+OWaREQtMRghCjCnKmUwotZy+FpqjMyM1FltOFvf5JdrEhG1xGCEKMAUOmo3Uh3TJ74WYtQjIdIMgEWsRKSNTgUjS5cuRWZmJkJCQjBx4kRs3bq1w/NfeuklDBo0CKGhoUhPT8cDDzyAxsbGTg2YqDurszQ7sxP+CkaAFlM1rBshIg14HIy89957mD9/PhYtWoQdO3Zg1KhRmDp1KsrKyto8/5133sEjjzyCRYsWYf/+/fj73/+O9957D7///e+7PHii7kZtdhYdakRUiNFv11WLWAsquKKGiPzP42DkxRdfxOzZszFr1iwMHToUy5YtQ1hYGFasWNHm+d9++y0uvvhi/PSnP0VmZiauvvpq3HbbbRfMphD1ROqKFrWOw1+YGSEiLXkUjFitVmzfvh05OTmuF9DpkJOTg02bNrX5nMmTJ2P79u3O4OPYsWNYs2YNrrvuunavY7FYUF1d3eqLqCdQe32k+XGKBnAVy7LXCBFpweDJyeXl5bDZbEhMTGx1f2JiIg4cONDmc37605+ivLwcU6ZMgRACzc3NuPfeezucplm8eDGeeOIJT4ZG1C34u3hVpQY/p1jASkQa8Plqmg0bNuCZZ57Bq6++ih07duDDDz/EJ598gieffLLd5yxYsABVVVXOr4KCAl8PkygguDIj/lnWq0pv0YXVbmevESLyL48yI/Hx8dDr9SgtLW11f2lpKZKSktp8zsKFC3HnnXfiF7/4BQBgxIgRqKurwy9/+Us8+uij0OnOj4fMZjPMZrMnQyPqFk45Clj9XTOSHB0CvU6B1WZHWY0FSdG+36CPiEjlUWbEZDJh3LhxyM3Ndd5nt9uRm5uL7OzsNp9TX19/XsCh1+sBgN0eic5R6Cgg9XfNiEGvQ7IjAGERKxH5m8fTNPPnz8fy5cvx1ltvYf/+/bjvvvtQV1eHWbNmAQBmzpyJBQsWOM+fPn06XnvtNaxcuRLHjx/HunXrsHDhQkyfPt0ZlBAR0GC1obzWCsB/3Vdb4u69RKQVj6ZpAGDGjBk4ffo0Hn/8cZSUlGD06NFYu3ats6g1Pz+/VSbkscceg6IoeOyxx1BYWIjevXtj+vTpePrpp733XRB1A2qPkQizAVGhHv9odlmKY2qouIoNCYnIvzr1G2/u3LmYO3dum49t2LCh9QUMBixatAiLFi3qzKWIeoxTLaZoFEXx+/VTY0Ic4+DyXiLyL+5NQxQgCjUqXlWpy4mLKhmMEJF/MRghChBaNTxTqdM0DEaIyN8YjBAFCK0anqlaBiNc6UZE/sRghChAuGpG/L+SBnBND9VZbahqaNJkDETUMzEYIQoQWk/ThBj16BVuAuCqXyEi8gcGI0QBwNJsQ1mNBYB2BaxAy6kaLu8lIv9hMEIUANQP/1CjHnGO7IQW1ECokF1YiciPGIwQBYCWxata9BhROTMjbHxGRH7EYIQoAKjFq1pO0QBAiqPxGWtGiMifGIwQBQA1E5GicTCSxsZnRKQBBiNEAaDY8eGf4tg5VyspzpoRBiNE5D8MRogCgLo5XbLm0zTy+mU1FliabZqOhYh6DgYjRAGgqCowMiO9wk0wG+SvhdIqi6ZjIaKeg8EIkcaEECgJkMyIoijOItpTlVzeS0T+wWCESGPVDc2ot8opkaQobTMjABufEZH/MRgh0pg6RRMbZkSoSa/xaFzLe7mihoj8hcEIkcaKHcFIcrS2UzSq1Bi5UR+DESLyFwYjRBpTp0PUjITW2PiMiPyNwQiRxgIvM+LoNcJghIj8hMEIkcaKK9WVNIGRGUlt0YVVCKHxaIioJ2AwQqQxteFZSoBkRpIcvU4am+yoqLNqPBoi6gkYjBBpzDVNExiZEbNBj96RZgCuQImIyJcYjBBpSAjhagUfIJkRwBUYcUUNEfkDgxEiDVXUWWFptgMAEqPNGo/GRQ1GSqqZGSEi32MwQqQhNSsSH2GG2aB9wzOVmqVhF1Yi8gcGI0QaUqdBAqXHiErNjKj1LEREvsRghEhDrnqRAAtGHL1GWMBKRP7AYIRIQ4FYvAowM0JE/sVghEhD6od9oE7TlFZZYLez8RkR+RaDESINqd1XkwIsM5IYFQJFAaw2O86w8RkR+RiDESINFamZkQCrGTHqdegdoTY+41QNEfkWgxEijdjtAqXV6r40gZUZAVjESkT+w2CESCPltRY02QR0CpAYGTgNz1RqtqaYXViJyMcYjBBppMiRcUiIDIFBH3g/iknOFTXMjBCRbwXeb0CiHqJE3SAvwFbSqNRdhBmMEJGvMRgh0ojaaj0lwFbSqJLYa4SI/ITBCJFG1A/5pABbSaNSe58wM0JEvsZghEgjRQHaCl6ldoUtrW5k4zMi8ikGI0QaKXZukheY0zQJkWboFKDJJlBea9F6OETUjTEYIdJIoG6SpzLodUiI5FQNEfkegxEiDTTb7M6GZ4GaGQFcK31YxEpEvsRghEgDp2stsAvAoFMQHxF4Dc9UatZGXflDROQLDEaINKB+uCdGhUCvUzQeTfvUItaSagYjROQ7DEaINKBOewRqvYjKlRnhNA0R+Q6DESINFFcG7gZ5LTkzIyxgJSIfYjBCpIEiR2YkJdAzI2x8RkR+wGCESAPOzEigByOO8ZVUN8LGxmdE5CMMRog04KwZCfBpmoRIWWBrs7PxGRH5DoMRIg2o0x6BukmeSq9TkBgplx6ziJWIfIXBCJGfWZvtOO3IMqg1GYHMtXsv60aIyDcYjBD5WWl1I4QATHod4sJMWg/ngtSpJAYjROQrDEaI/Ez9UE+KDoEugBueqdQVP8WcpiEiH2EwQuRnwdLwTJXkqGspZhdWIvIRBiNEfqa2gg/kDfJaYmaEiHyNwQiRnwVfZoQFrETkWwxGiPxM/VAP9B4jKjWDU1ZjQbPNrvFoiKg76lQwsnTpUmRmZiIkJAQTJ07E1q1bOzy/srISc+bMQXJyMsxmMwYOHIg1a9Z0asBEwa44SFrBq+IjzDA4Gp+dZuMzIvIBj4OR9957D/Pnz8eiRYuwY8cOjBo1ClOnTkVZWVmb51utVvzgBz/AiRMn8K9//QsHDx7E8uXLkZqa2uXBEwUjVyv44MiM6HUKEqPU3Xs5VUNE3mfw9AkvvvgiZs+ejVmzZgEAli1bhk8++QQrVqzAI488ct75K1asQEVFBb799lsYjUYAQGZmZtdGTRSkGptsOFNnBRA8NSOAHGthZQN37yUin/AoM2K1WrF9+3bk5OS4XkCnQ05ODjZt2tTmcz7++GNkZ2djzpw5SExMxPDhw/HMM8/AZrO1ex2LxYLq6upWX0TdgfphHmLUISbMqPFo3OdqfMYVNUTkfR4FI+Xl5bDZbEhMTGx1f2JiIkpKStp8zrFjx/Cvf/0LNpsNa9aswcKFC/HCCy/gqaeeavc6ixcvRnR0tPMrPT3dk2ESBawiZ71IKBQl8BueqdQsDqdpiMgXfL6axm63IyEhAa+//jrGjRuHGTNm4NFHH8WyZcvafc6CBQtQVVXl/CooKPD1MIn8wlkvEgR70rSkBiMl1cyMEJH3eVQzEh8fD71ej9LS0lb3l5aWIikpqc3nJCcnw2g0Qq/XO+8bMmQISkpKYLVaYTKdvzeH2WyG2Wz2ZGhEQaGkOriKV1XqeJkZISJf8CgzYjKZMG7cOOTm5jrvs9vtyM3NRXZ2dpvPufjii3HkyBHY7a7+BIcOHUJycnKbgQhRd1ZUGVzLelUpMWrjM2ZGiMj7PJ6mmT9/PpYvX4633noL+/fvx3333Ye6ujrn6pqZM2diwYIFzvPvu+8+VFRU4P7778ehQ4fwySef4JlnnsGcOXO8910QBQnXJnnBmRkpq7GgiY3PiMjLPF7aO2PGDJw+fRqPP/44SkpKMHr0aKxdu9ZZ1Jqfnw+dzhXjpKen49NPP8UDDzyAkSNHIjU1Fffffz8efvhh730XREFCzYwEW81Ir3ATjHoFTTaB0upGpMWGaT0kIupGPA5GAGDu3LmYO3dum49t2LDhvPuys7OxefPmzlyKqFtRMyMpQZYZ0ekUJEWHoKCiAcVVDEaIyLu4Nw2Rn9Rbm1HV0AQg+DIjQMsiVtaNEJF3MRgh8hM1KxJhNiAqJHganqlSuHsvEfkIgxEiP3HtSRN8WRGgRRdWZkaIyMsYjBD5idp9Vf1QDzZqZqSImREi8jIGI0R+4syMRAVpZiSa+9MQkW8wGCHyk+Kq4FzWq1LHXcwurETkZQxGiPykKEiX9arUcZ+ps6Kxqf1dt4mIPMVghMhPSoI8MxITZkSIUf7KKK1mdoSIvIfBCJGfuFbTBGdmRFEUZ3aEG+YRkTcxGCHyg5rGJtRYmgEE79JeoEXdCItYiciLGIwQ+YHaKCwqxIBwc6d2YQgIrhU1zIwQkfcwGCHyA7WFekqQ9hhRqVkdtoQnIm9iMELkB2omIZinaABmRojINxiMEPmBMxgJ9sxIDDMjROR9DEaI/EDdzyUlyDMjKcyMEJEPMBgh8gP1wzspSJf1qtTMSFVDE+qtzRqPhoi6CwYjRH6gbpIX7JmRqBAjIhyrgdhrhIi8hcEIkY8JIVwNz4K8ZgRwFeGy1wgReQuDESIfq2poQoNjL5dgX00DuAIqbphHRN7CYITIx9R6kbhwE0KMeo1H03XqVFMRMyNE5CUMRoh8TJ3O6A5ZEaBFrxFmRojISxiMEPlYUWX3aHimcu5Pw517ichLGIwQ+ZgrMxL8xatAi14jbHxGRF7CYITIx1wrabpZZoSNz4jISxiMEPmYq8dI98iMqNNNtZZmVDc2aTwaIuoOgncvc6IgUeKPTfIaq4C9HwFl+4HweGDIDUDvQT65VJjJgOhQI6oamlBc2YioJKNPrkNEPQeDESIfEkK02LHXR5mRg2uBVfcBDRWu+754Bph4H/CDPwB67/+YJ0eHoKqhCUVVDRiUFOn11yeinoXTNEQ+VFFnhaXZDgBIjDZ7/wLffwC8e6sMRHoNACb/GhhwNSDswOalwL9/DtjtXr9sChufEZEXMTNC5ENqViQ+wgyzwcsNzwq+A/7zKwACGDsTmPYioHdMmez7GPj33cC+/wDrnwRyFnn10mwJT0TexMwIkQ8VOZa/pnh7JU1TA/DRPYDNCgy+Hrj+z65ABACG3gDc+Ko83vgnIH+zVy+vZka4WR4ReQODESIfKvZV8eqXzwEVR4HIZOCmVwFdGz/KI38MjL4dgAA+/jVga/ba5ZkZISJvYjBC5EM+KV6tLgI2LZXH014AQqLbP3fqM0BYL6D8ELDjLa8NwdkSnr1GiMgLGIwQ+ZBP9qX56o+AzQJkTAYGXdfxuaExwGUPy+MNSwBrvVeGoE47FVU2QAjhldckop6LwQiRD7m6r3opM1JVCOz4f/L4yscARbnwc8bNAqIzgLoyYNe7XhlGkiO4sjTbcbaejc+IqGsYjBD5kKv7qpcyI9v+DtibgT5TgMyL3XuOwQRk/0oeb3oFsNu6PAyzQY/4CBMAV5EuEVFnMRgh8hGbXTi7r6bGeiEz0tQIbH9THk+8x7PnjrkTCIkBKo4Bh9Z2fSxw1Y2UsG6EiLqIwQiRj5TVNKLZLmDQKUiI9EJmZM+/gfozQHT6hWtFzmWOkL1IANc0TxclcUUNEXkJgxEiHyk8Kz+kk6JDoNe5UdtxIWoQMf7nnWvxrgYjhz+TK3K6SJ16KmJmhIi6iMEIkY8UOhueeWGKpuI4ULAZgAKMurVzrxE/QK7AEXYg750uDynZ2RKemREi6hoGI0Q+ogYjad4IRr5/X972vQyISun864y9U97mvQ10cUluMjMjROQlDEaIfKTIW5kRIYDvV8rjkZ3MiqiG3AAYQmUha8n3XXop52Z5rBkhoi5iMELkI2rNSJdX0hTukMGDMQwYMr1rr2WOAAZeLY/3fNill1KDkZKqRtjtbHxGRJ3HYITIR9RN5FK7mhnZ/7G8HThVBhNdNexmebv3wy5N1SRGmqHXKWiyCZTVWLo+LiLqsRiMEPmAEMJ7BawHPpG3g6/v4qgcBlwtsyyV+UDRjk6/jEGvQ1KUrBs5ddY7beaJqGdiMELkA9WNzai1yF1yu5QZOX0IOHMY0BmBAT/wzuBM4cDAa+Txvo+79FLqFFQhV9QQURcwGCHyAbVeJC7chFCTvvMvdOC/8rbvZR3vzuupwdPk7aFPu/QyaY5g5NRZBiNE1HkMRoh8QM0UdLlexDlFM62LIzpH/6sARQ+c3g+cPdHpl1GXLTMzQkRdwWCEyAdcy3q70Aa+uhgo3A5AAQZ5ORgJjQUysuVxF7IjzmkaZkaIqAsYjBD5gCszEtb5FzmaK29TxgCRiV4Y1TkGTpW3Xdg4T/3+mBkhoq5gMELkA85gpCs9Ro44gpH+OV4YURsGXStvT2wELDWdeomWmRHRxY6uRNRzMRgh8gFnw7POTtPYbcCxL+Rxvyu9NKpz9OoPxPUFbFbg2IZOvYTaEr6hyYaKOqsXB0dEPQmDESIfKOrqNE1RHtBwFjBHAWnjvTewlhTFlXU5+kWnXiLEqEfvSDMATtUQUecxGCHyMkuzzdmRtNMFrEfXy9usSwG90Usja0PfK+Ttsc4FI4BreS+LWImosxiMEHlZiWMX2xCjDnHhps69iFq82v8qL42qHZlT5BLfimPA2ZOdeolULu8loi5iMELkZWqGICUmFIqieP4CjVVAwVZ53M/HwUhIFJB2kTzuZN1IKhufEVEXMRgh8rIuNzw7/jUgbLLANLaPF0fWjn5dm6ph4zMi6qpOBSNLly5FZmYmQkJCMHHiRGzdutWt561cuRKKouCmm27qzGWJgoL6oZzW2WW9J76Wt30v986ALkS9zrEvAbvd46ez8RkRdZXHwch7772H+fPnY9GiRdixYwdGjRqFqVOnoqysrMPnnThxAr/73e9wySWXdHqwRMHAOU0T3dlgZKO8zfTTz0rqOMAUCTRUACW7PH+6Y8UQd+4los7yOBh58cUXMXv2bMyaNQtDhw7FsmXLEBYWhhUrVrT7HJvNhttvvx1PPPEE+vbt26UBEwW6oqouNDyrrwBK98jjPhd7cVQd0BuBLEfg04m6EfX7rG5sRk1jkxcHRkQ9hUfBiNVqxfbt25GT4+oIqdPpkJOTg02bNrX7vD/84Q9ISEjA3Xff7dZ1LBYLqqurW30RBYuWBaweO/mtvO09GIjo7cVRXUDWpfL2xDcePzXCbEBMmFx+zLoRIuoMj4KR8vJy2Gw2JCa23icjMTERJSUlbT5n48aN+Pvf/47ly5e7fZ3FixcjOjra+ZWenu7JMIk0Y7cLFFXKpb2dqhlxTtFM8eKo3KBmYfI3A7Zmj5/uXN7LuhEi6gSfrqapqanBnXfeieXLlyM+Pt7t5y1YsABVVVXOr4KCAh+Oksh7SmsaYbXZYdApSO5MzYgajPhrikaVOAwIiQasNUDJ9x4/nb1GiKgrDJ6cHB8fD71ej9LS0lb3l5aWIikp6bzzjx49ihMnTmD69OnO++yOan2DwYCDBw+iX79+5z3PbDbDbDZ7MjSigFBQ4Zqi0es87DHSsl7E35kRnR7ImAwc+h9w8hsgdaxHT+eKGiLqCo8yIyaTCePGjUNubq7zPrvdjtzcXGRnZ593/uDBg7F7927k5eU5v2644QZcccUVyMvL4/QLdTsFFXJFSXpcZ+tFBBA/CIhI8O7A3JHpyMZ0om5EzYycYmaEiDrBo8wIAMyfPx933XUXxo8fjwkTJuCll15CXV0dZs2aBQCYOXMmUlNTsXjxYoSEhGD48OGtnh8TEwMA591P1B0UOJa3psd2YoM8repFVM66kW/lrsE6vdtPTWMXViLqAo+DkRkzZuD06dN4/PHHUVJSgtGjR2Pt2rXOotb8/HzodGzsSj2TOk2THteJYOSkGoz4uV5ElTRS9htprAJK9wLJI91+apoj+OI0DRF1hsfBCADMnTsXc+fObfOxDRs2dPjcN998szOXJAoKambE45U0DZVAidpfRKPMiN4AZEwCjqyTdSMeBCPqNE15rQWNTTaEGN3PqhARMYVB5EWnnDUjHmZGTn0HQABxfYHIxAue7jPOupGNHj0tJsyICLP824adWInIUwxGiLzE2mxHcbXsMeJxzUj+ZnmbPsnLo/KQmpU5+a1H+9QoiuIMwNSpKiIidzEYIfKSosoGCAGEGvWIjzB59uSCLfI2fYL3B+aJlNGAMUzuU1N+0KOnZjhWEOVXMDNCRJ5hMELkJS3rRRTFgx4jtiagcLs8ztA4M6I3Amnj5bGarXFThiMzwmCEiDzFYITISzq9kqZkN9BULzugxg/ywcg8pE4VqdkaNzEYIaLOYjBC5CWdXklTsFXepk0AAmFZfMZEeethZsRVM8JghIg8EwC/+Yi6B2f3VU+LVwscH/pqEKC1tIsAKMDZ40BN6QVPV7XMjAghfDQ4IuqOGIwQeUnBWXWappOZEa1X0qhCouXGeYArUHJDamwoFAWot9pwps7qo8ERUXfEYITIS9QeI2meZEYqC4DqQkDRe7w5nU+lq1M17teNmA16JEeFyKdxqoaIPMBghMgL6izNzmyARwWsapFo8kjAFO6DkXWSuqrHg8wIwLoRIuocBiNEXqBuEBcVYkB0qNH9Jzr7iwRIvYhKHU/xLsDqfmDhrBs5w2CEiNzHYITICwo62wbe2Xk1wIKRmAwgMhmwNwNFO9x+mjMzwpbwROQBBiNEXqB++Hq0ksZSA5Q6NscLtGBEUVrUjbg/VcNeI0TUGQxGiLxA/fDN6OVBMFK4HRB2IDodiE710ci6QK0b8SAY4f40RNQZDEaIvOCko0aijyfBSH6A1ouo1HGd2ur2pnlqZqSoqgHWZvc32iOino3BCJEXnDhTBwDI7OXBihi1eFXr/WjakzRCbprXWAWcPuDWU+IjTAg16iEEUFjJ7AgRuYfBCFEX2ezCWcDqdmbEbgNOfSePtd6ptz16I5A6Th67ucRXURTWjRCRxxiMEHVRUWUDmmwCJr0OydFudl89fQCwVAOmCCBhmG8H2BUZ2fLWg+Zn6QxGiMhDDEaIukitF0mPC4Vep7j3JHWKJnUsoDf4aGReoO6X40Hzsww2PiMiDzEYIeqiztWLOKZo0gJ0ikbl3DTvBFBT4tZTMhx785x0vC9ERBfCYISoi9QP3T6eBCOn1M3xAjwYablpnptLfPvEy/fhJLuwEpGbGIwQdZH6oZsZ72bxan0FcOaIPE67yEej8iJ1iW+Be3UjWY6g7MSZOtjtwlejIqJuhMEIURepwUiGu63g1VU0vfoDYXE+GpUXOYtY3cuMpMWGwqBT0NhkR0l1ow8HRkTdBYMRoi6w2wVOVnhYM1KgTtEEaLOzc2W03DTvwnUgBr3OuaLmRDnrRojowhiMEHVBWY0FjU126HUKUmPdXNar1osEwxQNINvVR6YAwiZb2Lshy1E3cpxFrETkBgYjRF2grqRJiw2FUe/Gj5PdBhQ6dsEN9OJVlaK02KfGvboRNUvEzAgRuYPBCFEXeLySpmwfYK0FTJFA78E+HJmXqcGIm/1GshzFvMcZjBCRGxiMEHXBCXUljbtt4NV6kbRxgE7vo1H5gHNFzVaZ3bmATHWahsEIEbmBwQhRF3icGTkVJM3OzpU4XLaut1QDZfsveLpaM1JQ0QAbl/cS0QUwGCHqghPljg3y3F3WWxAkzc7OpTcAaePlsRtTNSnRoTAZdLDa7Cji7r1EdAEMRog6yW4XzmmIrN5uZEbqzgAVR+Wx+sEeTNLVItYLByM6neIM0I5xqoaILoDBCFEnlVQ3oqHJBoNOca/hmTpFEz8QCI317eB8wdMVNfFcUUNE7mEwQtRJR0/XAgAyeoW5t6zX2V8kyKZoVGnjAUUHVOUDVYUXPL0vi1iJyE0MRog66dhp+SHbr3eEe09w1osESbOzc5kjZSEr4FbdCFfUEJG7GIwQddIxR2akrzv1IrZmV7OzYM2MAC32qbnwVE1miw3ziIg6wmCEqJOOqpmReDcyI2X7gKY6wBwVXM3OzqXuU+NGZkQN0k6dbUCTze7LURFRkGMwQtRJamakX4IbmRG1XiR1HKAL4h87dUVNyW7AUtPhqQmRZoSZ9LDZhXNnYyKitgTxb0Ui7dRbm1FU1QgA6OtOZqTAsZIm2PqLnCs6FYjOAIQdOLWtw1MVRXHW06jFvkREbWEwQtQJavFqXLgJseGmCz8h2FfStOScqrlw3Uj/BBmMHCljMEJE7WMwQtQJ6l/66vLVDtWVAxXH5HHaOB+Oyk/UfWrcaH7GYISI3MFghKgT1MyIWytpnM3OBgVns7NzqStqTn0nVwl1QJ2mYTBCRB1hMELUCWpmxK0eI8HeX+RcCUPkqiBrLVC2t8NTByS6ghE7N8wjonYYtB4AUTByZUbcCEaCdafe9uj0shD3yOey30jyqHZP7RMXBqNeQUOTDUVVDUiLdXNDwUB1+iBwbANQdQrQm4Deg4B+VwLh8VqPjCioMRgh8lDLDfL6XWiaptnqWnWi7u3SHaRPcgQjm4CJv2z3NINeh8xe4ThcVosjZbXBG4yc/BbI/YP8fs+lMwKjbgWuXAhEJvp/bETdAKdpiDxUWNmAhiYbjHoF6RfaIK/ke6C5AQiNkxvkdRcZLYpYRcfTL0FdxGprAj59FHjjWhmI6IwyEzJpDjD+biBxBGBvAnb+A3h1InDgE61HTBSUmBkh8tDhMtnsq1/viAtvkKf+JZ0xCVAUH4/Mj1LHy2mKmiLg7HEgrm+7pw5IiMD/EITBiLUe+OBnwOFP5b/H3AFc8SgQldL6vPwtwJrfycBz5e3ANYuBSff5fbhEwYyZESIPHSyRH6oDEiMvfLK6/FVdDttdmMJkQAIAx7/u8NR+wZgZabYA786QgYghBPjJP4Abl54fiAAySzR7vcyUQABrHwG2vO73IRMFMwYjRB46VCozI4MSL1C8KoQrGFGXw3YnmVPk7YmNHZ6mTtMcLquFuMCUTkCw24H/zAGOfwWYIoE7VwFDb+j4OXojMO0F4NKH5L//9yCw9yOfD5Wou2AwQuQhNRi5YGbkzFGgvhzQm4GU0b4fmL9lXSJvT3zdYd1Iv94RUBSgqqEJ5bVWPw2uCza+COz+ANAZgBn/D+jjZiCpKMAVvwcm3CP/vWoOULbfd+Mk6kYYjBB5wGYXOOyYbhh0oWBErRdJHQcYzD4emQbSLnLUjRS7Osy2IcSoR7pjFU3AT9Wc3AR88bQ8nvaiLFb1hKIAU58Bsi6TuzS/dwdgrfP+OIm6GQYjRB44eaYO1mY7Qoy6C6+kcU7RdKMlvS0ZQ129U45/1eGpA5xTNR3v9KuphrPAv++WmwCOnAGMndm519EbgFtWAFGpwJkjwOdPeHecRN0QgxEiDxwqlX/Z90+IgF53gdUxzpU03bBeROVm3cigJJlFOlASwMHIZ48B1YVAXD9Z/9GV1U/h8cCNr8jjrX+9YJEvUU/HYITIA2q9yMALTdHUlgEVRwEo3acNfFvcrBsZnBwFADhQXO2PUXnu+FfAzn/K45teBcxurJS6kH5XAuN+Jo8/ngs0NXb9NYm6KQYjRB5wraS5UL2IY4omYWj32ByvPanjZYFubamckmjHkBaZkYDbo6apAfjvPHk8/ufenVa7+ikgMgU4ewLY9LL3Xpeom2EwQuQBtzMj3b1eRGUMkfvUAB3WjWTFh8Nk0KHeakPB2Xo/Dc5N374is1gRSUDO/3n3tc2RwNVPyuOvX5R72hDReRiMELnJ2mx3bpA3MMnNlTTduV5ElalO1bRfN2LQ6zDQ0Zdlf3EA1Y3UlAIb/ySPr34KCIn2/jWG/wjImAw01cu6FCI6T6eCkaVLlyIzMxMhISGYOHEitm7d2u65y5cvxyWXXILY2FjExsYiJyenw/OJAtWx8lo02wUizAakRIe0f6KlFijeJY8zulnn1baodSPHv5INw9oxOMlRN1ISQHUjXzwll+CmjgdG3OKbaygKcN1zgKKTjdBObffNdYiCmMfByHvvvYf58+dj0aJF2LFjB0aNGoWpU6eirKyszfM3bNiA2267DV988QU2bdqE9PR0XH311SgsLOzy4In8aW+h/BAdmhwFpaOVFgWbAWEDojOAmAw/jU5DqeMBU4Rs8FbyfbunDVbrRgIlM1KyG9jxD3k89Rnf7h2UNAIYeas8zuVSX6JzeRyMvPjii5g9ezZmzZqFoUOHYtmyZQgLC8OKFSvaPP/tt9/Gr371K4wePRqDBw/G3/72N9jtduTm5nZ58ET+tM+xEmRoSlTHJ6rTFeqy1+7OYAKyLpXHR9v/uR6SHGCZkc8WAhDA0Jv8k8G6/BG56+/xL4FjG3x/PaIg4lEwYrVasX37duTk5LheQKdDTk4ONm3a5NZr1NfXo6mpCXFxce2eY7FYUF1d3eqLSGt7i6oAuBGMqD0l1OmLnkDtVHpkfbunqJmRkxX1qLM0+2NU7TuxETj2hQwOvF202p7YPnK1DgDk/qHDpdBEPY1HwUh5eTlsNhsSExNb3Z+YmIiSkhK3XuPhhx9GSkpKq4DmXIsXL0Z0dLTzKz093ZNhEnmdEAL7imRQPKyjYMRSAxTtlMc9JTMCAP2vkrcFm+V70IZeEWb0jjRDCNeqJE0IAXzxjDweeycQl+W/a1/6O8AYDhRuBw6u8d91iQKcX1fTLFmyBCtXrsRHH32EkJD2CwAXLFiAqqoq51dBQYEfR0l0vlNnG1Dd2AyjXsGAhA5W0uRvkfUiMX16Rr2IKq4vEJsF2Js77DaqZkc0XVFz/Cvg5DdyX51Lfuvfa0ckABMdG+l9+RyzI0QOHgUj8fHx0Ov1KC0tbXV/aWkpkpKSOnzu888/jyVLluCzzz7DyJEjOzzXbDYjKiqq1ReRlvY6siIDEiJhMnTwY3PC0WsjswdN0ajU7EgHdSPqFJc65eV3LbMi42YB0Wn+H0P2HMAYBhTnAUdYO0cEeBiMmEwmjBs3rlXxqVqMmp3dfj+F5557Dk8++STWrl2L8ePHd360RBpRi1c7nKIBXMWrPaleRNXPEYx08AE7IlX28dhTqFEwcnS9nEoyhABTHtBmDOHxrtqRr5gdIQI6MU0zf/58LF++HG+99Rb279+P++67D3V1dZg1axYAYObMmViwYIHz/GeffRYLFy7EihUrkJmZiZKSEpSUlKC2NsC3EidqYZ87xauN1UBRnjzuc7HvBxVoMqcAOgNw9jhQcazNU0amxgCQ0zTW5vZ7kvhEy6zI+LuBqGT/Xr+l7LmyjX7BlgtuMkjUE3gcjMyYMQPPP/88Hn/8cYwePRp5eXlYu3ats6g1Pz8fxcXFzvNfe+01WK1W3HLLLUhOTnZ+Pf/88977Loh8zFW82kGHznxHf5HYTCCmBxZdh0QB6Y7294c+a/OU9LhQRIcaYbXZ/V/EengdULgNMIQCU+b599rnikqWxbMA8NUftR0LUQAwdOZJc+fOxdy5c9t8bMOGDa3+feLEic5cgihgVNRZUVQld1wdnNxB8eoJR+FmT6wXUQ26Fji5ETj4CTDp3vMeVhQFI1KjsfFIOb4/VYXhqT5ov94WIYANjqzIhNmykFRrF98PbH9T9h0p2Ora44eoB+LeNEQXsOtUJQCgb3w4okKM7Z94vAcXr6oGXydvT3wDNJxt85QRaTIA2e3PupEjn8sl18YwGQQEgpgMYJSjK+vXL2o7FiKNMRghuoBdBZUAgFHpMe2fVHfGtR9N38t8PqaAFdcX6D1ETlcdXtfmKSNT1WCk0j9jEgLYsEQeX3S3LCANFBfPA6AAh/4HlO7VejREmmEwQnQBajAyuqNg5PgGAAJIHA5EdrzMvdtTsyMHPmnzYTUzcrCkBpZmm+/Hc3S9o1YkBJj8G99fzxPxA4ChN8pjdffgnk4IGZjtfFv2YtmwRE5nndoG2P3w/xfSRKdqRoh6CiEE8tzJjBx1tEHvd4XPxxTwBk0Dvn5BLvFttgAGc6uHU2NCERtmxNn6JhwsqcHItBjfjUUI4Mtn5fH4nwdGrci5LpkP7FsF7Pk3cMXvZXapJ7LUAN/9Hdj+BnD2RNvnhPcGxtwJTLwXiExs+xwKSsyMEHWgoKIBZ+ubYNLrMKS94lUhgKNfyOO+DEaQMgaISAKsNa6i3hYURcEIRwCy65SP60aOfyWXz+rNgZcVUSWPAvrnAMIOfPMXrUfjf0IAu94D/jwK+HyRDEQMobL2auxMYNzP5PtjjgbqTgMbXwT+Mgb49hXApvEeR+Q1DEaIOpDnKF4dkhIFs0Hf9knlh4DqQvmB12ey/wYXqHQ611TN3lVtnjLKMVWjToH5zJfPydtxd2nbV+RCpsyXt3lvA9XFHZ/bnTRWA+/dAXz0S6D+DNCrP3DjUuCho8DPVgM3vAxM/zNwx7+BB48AM/4JpIwFmuqAzx4F3roeqHFvXzQKbAxGiDqQl18JABid1sESVHWKps9kwBjq+0EFg2E/lLf7P5ZTNecY2ycWALD9ZNsrbrzixEa5zFhvchSKBrA+k2WPFpsV2LxU69H4x5mjwN9ygAOr5X+jKxcCv9oMjLkDMIWff77BBAyZDvwiF7jhFcAcBeRvAv56qavZIAUtBiNEHVCX9Y7OiGn/JGe9yJU+H0/Q6DMZiEwGGqvkstpzjE2Xwcjx8jqcqT0/WPEKtVZkzB1AdKpvruEtiiJrRwDguxVAfYW24/G1sv3AimuA8oNAZArw80/ljsb6DpbOq3Q62TDulxuAhKFAbSnw1nTg5Lc+Hzb5DoMRonZYm+3OPVTaLbJstrjaeTMYcdHpXdmR3f867+HoMCMGJEQAAHY4sk9edXKTrBfRGbXbg8ZTA66Wq7Ga6oCty7Ueje+U7AHenAbUlQFJI2RQkTrW89fp1U8GMX0uBizVwD9+KLsgU1BiMELUjt2FVbA02xEXbkLf+DbSxoBMEzfVAxGJQOIw/w4w0I34kbw9+D/Acv5eVOMzZXZk20kfZAE2LJa3o38qm4sFA0VxBU5bXmvzPQt6Z08A/7hZ1oekjAFmfty1VTEhUbKepN9VQHMD8M5PgNJ9Xhsu+Q+DEaJ2fHdCfkiO7xMLRVHaPungWnk74Afyw4RcUsYCsVnyQ6KNniNjM2QwssPbdSPHNsgW6zojcMlvvfvavjbsZrm0t+EssOMtrUfjXfUVwD9vkRmRxBHAnauAsLiuv64xVBa2pk+U04L//CFQVdj11yW/YjBC1I7vjstgZEJWO78whZCdMwFg4LV+GlUQURRXu/Od/zjv4XGOItZdp6q81/xMCCD3SXk8fhYQ28c7r+svOr2r2Pbbl9ss/g1KzVZg5e3AmcNAVBpw+wdAaIz3Xt8UBty2Eug9GKgpBt6/E2hq9N7rk88xGCFqg90usM3xF/tFme0EI6cPyrSz3gz0vdxvYwsqo28HFJ3sN1J+pNVDWfHhiAs3wdpsx17HrshddvB/rp15L/mdd17T30bdKot/a4qBXSu1Ho135D4B5H8re4Xc/oFvllmHxcmAJCQGKNwOfPJbGZxSUGAwQtSGQ2U1qGpoQphJj2EpUe2c5MiKZF0CmCP8N7hgEpMO9P+BPD5n2kFRFOdUzbYTXqgbsduB9Y6syKQg7tBpMAOTfy2Pv3kp+FugH14HbHpFHt/8GpA41HfXissCfvyGDIDz/glsW+G7a5FXMRghasNWxxTNuD6xMOjb+TFR60UGXuOnUQWpcT+Tt3lvnzftMKmvzDptOnqm69fZ/T5Qtk/+9R0oO/N21ti7gNBYoOKYbBUfrGpKgI/ulccTfgkMnub7a/a7Esj5P3n86e9Z0BokGIwQtUENRtqdoqk7A5zaKo8ZjHRswNWyl0T9GWDPh60eyu7XC4B8v5ts9s5fw1ILfP5/8njKPPlBHszMEcDE++Tx138KzukGux346B6gvlwWrP7gSf9dO/vXsoV8cyPwr58DTQ3+uzZ1CoMRonMIIbDlQsHI4c/kXiKJI+RUBLVPbwAmzJbH3/6l1QfrkKQoxIQZUWe14fuu7FOz8U+yxiKmDzDpV10ccICYMBswRQClu+VUR7D55iW5sskYBtyyAjCG+O/aOh1w02tAeAJwej/w2WP+uzZ1CoMRonMcLK3B6RoLQow6jO0T0/ZJ+z+Wt4O4isYt438uP1jL9rXqyKrTKcjuK7Mjm46Wd+61z56UK08A4Oqn/Puh50thcXJFECB3QQ4mBd8B65+Sx9c+B/Qe6P8xRCQANy+Tx9/9DTj0qf/HQG5jMEJ0jo2H5YfihKxebW+O11gNHMmVx8Nu8t/AgllojKt2ZGPraYfJ/eMBAN8c6UTdiBDA2kcAm0Xu8jpketfHGkgmzZH7thRsDp525w2VwL9/DggbMPxHsh2/VvpfBWTPlccf/6b7t9kPYgxGiM7xtSMYucTxIXmeQ2vlh1+vAXJvDHLPpF/JD9aT37iCOQCTHXUj2/PPorHJw5Ujez8CDq6RDc6ufa77NZ6LSpbLo4HgyI4IAayeB1Tmyymz6/+k/X+TKx+TP6u1JTJwpYDEYISoBUuzDVuOy7/QpwxoJxjZu0reDrtJ+1+0wSQ6Va6oAIDPFzmXrPaND0dilBnWZrtnu/jWVwBrHpTHl/7Ot0tGtXTxb+RS1SOfA8W7tB5Nx3b8Pxkg6gzALW8AIR3sdu0vxlBZP6LogO/fA/av1npE1AYGI0QtbD95Fo1NdsRHmDE4KfL8ExqrXTUPQ2/y69i6hUt+Kz+gSvcAee8AkP1GLnZkob48dNq91xEC+PjXcqVG7yHAlPm+GrH24vrK6Q4A+PpFbcfSkbIDwP8elsdXLgTSxmk7npbSLwIm/0Yer54nV8NRQGEwQtSCWi8ypX+vtvejOfSpnKKJ68eN8TojLA641JHN+OwxoKYUAHDl4AQAQO7+UvdeZ+vrwIHVcnrm5tcAg8kXow0c6gZ6+/4jP/QDTVODXELb3CD7fKgf/IHk8gWyXXzdaWBNkHbn7cYYjBC1sP5AGQDg0oG92z5hz7/kLadoOm/ifUDyKKCxElj9ACAELhnQGwadgqOn65B/pr7j55/c5FqqefVTcvfX7i5xGDD4egDC1U8lkHz2GFC2FwjvDdy0TC6tDTTGEMd0jR7Y+6EM7ChgBOD/Y4i0UVBRjwMlNdDrFOdf6q3Unnb1exg5w7+D6070BuDGpTKrcfAT4Js/IzrUiPGZslHZ+gMdZEdOHwLevRWwWeXKmYn3+GnQAeCqRfKD9ND/gONfaz0al/3/lUtnAeDmvwZ2G/7Usa4s0+r5nK4JIAxGiBw+d0wRjO8Ti5iwNtL+uz+QyxVTxgK9B/l5dN1M0gjg2iXyOPcJYPe/cNVg+SGW68hOnad0L/DW9TKjknYRcPPrPSs71Xuga3n0Z4/JDqdaqywA/uNYOnvx/XIpbaC77CG5Cq6+nNM1AYTBCJGDGoz8YGg7f9ntelfejv6pn0bUzY2/WzZDE3bgw9m4yfpfAAJbjlWgztLc+ty9q4AV1wK1pUDicLk7qylMi1Fr6/IFgCkSKM5zTRlqxdYE/PsXMjhMHSeLVoOBwQzc9CqnawIMgxEiAFUNTdhyTDZEajMYKd0LlHwvpxbUlQ3UNYoCXPcCMHYmIOzovfFxfBS2GJPFdny9+4hsnnV4HfDPW4AP7gIsVUBGNvCz1UB4O8uuu7uI3sAUxyaA6xYBlhrtxrL+KdmMzRwF/OjvgN6o3Vg8lTLmnOmaTnb/Ja9hMEIEWafQbBcYkBCBPr3Czz/BsQwVA6fKFSHkHTodMP0vwDVLAL0JY+x78Kbpj7hm9UTg2T7A27cAR9bJvhVT5gN3/Tf4N8Hrquy5QGwmUFMEfPGMNmM4vE7uPQMAN74CxGVpM46uaDVd86DWo+nxGIwQAfg4rwgAcN2I5PMfbGoA8t6Wx1q2tu6uFAWYdB/w6+04M/RnKBS9XI9FpQEXzQbmfgfkLAquv759xRgKTHN0Y92yDCjK8+/1qwqBDx3N6y6aDQy90b/X9xZO1wQUBiPU452tszpbwE8flXL+CXs/AhrOAtHpwICr/Ty6HiQmA3E/fgl3RK7AoMY3sfr67cD8vcC052XjL3LpnwMM+6Gst1k9D7A1X/ApXtFsAT74GdBQIZdnX/2Uf67rK5yuCRgMRqjHW7OnGM12gaHJUeifEHH+CeqyxfGzAF0bG+eR1yiKgmkjkmGBCf/ZV6n1cALbNYsBczRQtBP4+nnfX08I2Rfm1FbZRfeWN7rHDslcXRMQGIxQj6dO0dwwuo2sSNFOoHC7LFwdM9PPI+uZrh8lp8q+PHgalfVWjUcTwCKTgOsd7eG/fBbI3+Lb621+VU5XKjoZiPTq59vr+Uur6ZqPXHtPkV8xGKEeraCiHltPyFU0bU7RbHld3g69Ua5kIJ8blBiJIclRsNrsWLWzUOvhBLYRtwAjfuJcHo36Ct9cZ//qFl1vnw6OfiKeaDld88lvOV2jAQYj1KO9v60AQgBT+scjNSa09YNVp4Dd78vjSff5f3A9lKIouPWidADAyu8KIITQeEQBbtrzQEwGUHlS1nPYmrz7+se+BP41SwY8Y2d2358FTtdoisEI9VjNNjve+64AAHDbhIzzT9j0KmBvBvpMAdLG+3l0PdtNo1NhMuhwoKQGuwurtB5OYAuJBm59FzCGA8e/lDvneiuAy98CrPypbL8/+Hpg2p+6b9fbc6drvv9A6xH1KAxGqMdaf6AMZTUW9Ao3nd/orL4C2P6mPFbTt+Q30WFGXDs8CQDw7tYCjUcTBJKGAz9aDkABtv0d+HxR1wOSI58D/+9GwFoLZF3qaGxm8MpwA1bKGNeu0qvnAWeOajqcnoTBCPVY/9h8EgBwy/g0mAzn/Chs+SvQVAckjuh+8+NB4taLZLZq1c5CFrK6Y/A0OWUDAN/8Gfj0UcBu8/x1hAC2vQG8cyvQ3CCXEd/2XvdYOeOOSx8EMibLIOxfs+RyZvI5BiPUI+0rqsbXh8uhU4DbJ/Rp/WBdObDpFXl86W+7b1o6wE3qG4ehyVFoaLLh7S35Wg8nOFz0C+Da5+Tx5qXAu7d5VtTaUAms+pXMCtibZC+TW9/tWfsA6Q3Aj/4mO/0W75Jt98nnGIxQj7T862MAgGtHJCOj1zm/aL9+Uf5VlDwKGBKk3SW7AUVRMPtS2Wb8zW9PwNLcib/ye6KJ98gpFUMIcPhTYOkEYNfKjhuj2ZqAnf90nPuOXL6b83/ALSsAQxs7WHd30anATa/J4y2vydVE5FMMRqjHKapswH93yd4i91x6TmfPygLgu+Xy+KpFcu8U0sz1I1OQFBWC0zUWLvP1xIhbgJ+vBXoPBupOAx/dA7wyDsh9EjiSC5w+BJQdAA7+T07n/HkU8J85clfkXgOAu1bLWqmenBUcdC0waY48/uhe+X6Rz3TzaiSi873yxRE02wUm9Y3DyLSY1g9+9phcOZB5CdDvSk3GRy5GvQ53T8nC02v24y+5R3DTmFSYDeyC65aUMcA9X8kpx29fAc6ekJ1av27n/PAEYPJcYOK9cmUJAT94Qk7VnNwIrLwNmL2eGzX6CP/sox7leHmdcznv/B8Mav3gkVxg3yqZor5mcc/+qzCA3JndBwmRZhRWNjj/25GbDGbgkt8CD+wBbn5d1oDED5Jt5ENigMThwKjbgJ/8A5i3G7j4fgYiLemNwE/eAqIzgIpjwL9+7r99gHoYZkaoR3lx3SHY7AJXDOqNCVlxrgeaLcD/HpLHE+4BkkZoM0A6T4hRj19fNQALV+3BX3KP4Edj0xBu5q8uj5jCgVEz5Bd5JjweuPVtYMVU4Oh62RDt+m7cb0UjzIxQj/HdiQpnrcjvpp6TFfniaeDMEZmqvmKBBqOjjswYn470uFCU11rw8vojWg+HeprkkcDNfwWgANvfADYs1npE3Q6DEeoRmmx2PPbRHgDAbRPSMSwl2vXgiW+Ab/4ij6e/JDtaUkAxGXRYdP0wAMDfvj6Gw6U1Go+IepyhNwDTXpDHXz4LbF2u7Xi6GQYj1CO8/tUxHCytQVy4CQ9NHex6oL5CVspDAGPukI2jKCDlDE1EzpBENNsFfv/Rbtjs3LOG/Oyiu4HLHZnTNb8Dtq3QdjzdCIMR6vZ2n6rCn9YdAgAsuHYwYsMdfRNszbIgrSofiM0EpjL1Guj+74ahCDPp8d2Js1j2JVt1kwYue9i15Hf1A8DmZdqOp5tgMELdWp2lGfev3Ilmu8A1w5Jwy7g014OfLwKOfQEYw4AZbwMhUdoNlNySFhuGJ26Q0zUvrjuEnflnNR4R9TiKAkx9Grh4nvz32oeBDUu8tzlhD8VghLotm13g/pU7cay8DklRIVjyoxFQ1Ar4jS+5Wr7f9KrcaIyCwi3j0nD9yGTY7AL3/nM7iqsatB4S9TSKIjvUXvaI/PeGxcCHvwSaGjUdVjBjMELdkhACT3+yH5/vL4PJoMNrd4xFTJhjembrcpkVAYCcJ4BhN2s3UPKYoih45ocjMDAxAqXVFtz95jbUNDZpPSzqaRRFrryb/hdAZwB2vw+8dT1QyX2UOoPBCHU7Qgg8u/YgVnxzHADwwo9HYUxGrEyjfrFYFp4BshnUlHnaDZQ6LSrEiL/fdRHiI0zYV1yNu1ZsRTUDEtLCuLuAO/4tV+Gd+g5YNgXY9x+tRxV0GIxQt9Jss+P/Pt7rLG584oZhmD4qBbDWyVUzXy6RJ172MHDlQg1HSl2VHheGN2dNQHSoETvyK3Hn37agrIZpctJA38uBX34JpI4HGquA92cC//4FUFOq9ciCBoMR6jYq6qyY9eZ3eGvTSQDAH24chrsmZwIlu4HlVwLfr5St3qe9AFzxe3ZQ7AaGp0bjndkTERtmxK5TVbjh5W+QV1Cp9bCoJ4rLkpsTXjwPgALs/gB45SJgy19lh2fqkCJE4JcAV1dXIzo6GlVVVYiK4ooHOt+ne0vw6Ee7UV5rRZhJjxd/MhrXDAiXVe6bXwOEDYhIAm75O5A5RevhkpcdL6/D7P+3DUfKaqHXKfjV5f0w98r+3FSPtFG4Qy77Lc6T/45OBy57CBh5K2AwaTo0f3P385vBCAW1PYVVeHbtAXx9uBwAMCAhAi//cAAGF6wEvn0ZaKiQJw69EbjuBSCit4ajJV+qaWzCIx/uxiffFwMA+vQKw/wfDMT0kSnQ6ZgFIz+z24DtbwJf/RGokf+fRHgCMO5n8is6VcPB+Y9Pg5GlS5fij3/8I0pKSjBq1Ci8/PLLmDBhQrvnf/DBB1i4cCFOnDiBAQMG4Nlnn8V1113n9vUYjFBL1mY71h8owz82n8A3R84AAEx6gYWjG3Cb8SsY9n0IWGvlyXH9gGuWAAOv1nDE5E9rdhfj8f/sRXmtTI33jQ/HTydm4JZxaa4VVUT+0tQgO7V+8xegtsRxpwJkZAPDbgIGXQfEpGs5Qp/yWTDy3nvvYebMmVi2bBkmTpyIl156CR988AEOHjyIhISE887/9ttvcemll2Lx4sW4/vrr8c477+DZZ5/Fjh07MHy4e70dGIz0bEIInDrbgB35Z7H+QBnWHyhDTWMT+iilGK07itvij+Gipm3Q1592PanXAODSB4HhPwL03OG1p6mzNOONb47jr18eQ41Fbvmu1ymY1DcOOUMScVFmHAYnRcKgZ9kc+YmtCTiwGtj6N+DkxtaPxWYCWZcC6RPljuG9BwMGsybD9DafBSMTJ07ERRddhFdekQ2j7HY70tPT8etf/xqPPPLIeefPmDEDdXV1WL16tfO+SZMmYfTo0Vi2zL02ugxGur/GJhvKay2oqLOivNaC/PIalJaW4Gx5McrLimFuOI0+SikylFL0UcowVJ+PaNS2fhFTJDD4OmDsXUCfySxQJdRamrFqZyHe2ZKPfcXVrR4LM+kxNDkKfXuHIys+Apm9wtA70ozekWbER5gRbmYQSz5SdQrY9zGwbxVwapusaWtJZ5B/UMVmArF9gJg+clonrJfrKzQW0Bu1GL1H3P389uinzWq1Yvv27ViwwLXFuk6nQ05ODjZt2tTmczZt2oT58+e3um/q1KlYtWpVu9exWCywWFzVx9XV1e2e2xWb33kSOCtXXihoEZO1iM9Ei8fksXq/gNJhHCda3LjOc76WaP3vVs85ZwznUiBaPV+0uL/N1zrn9ZTzHmvvH20fn/99tz1updUZAna7/LIJAdht0NutMAoLDI7bEFgRrzQhHVZcjjrolBav21Z2XW+WW3unTZDTMBmTe1xxGHUswmzAHZP64I5JfXDyTB3W7SvFl4dOIy+/EjWWZmw7eRbbTrbdUt5k0CHcpEeYyYBwsx6hJgPCjHoY9AqMeh30OgUGndLiVgeDToHOmWyRPwEtY2L1UL1POecc1+OtA2nG1d3RZUDCZTD1qkNG7S5k1exAcv0BJNUfRqitBji9X351wKoLQdN5X2Y0KybYFT2EooMdetgV+SWgcxzrIKCHUBQI6AAoEAqQet0jSMka3OE1fcWjYKS8vBw2mw2JiYmt7k9MTMSBAwfafE5JSUmb55eUlLR5PgAsXrwYTzzxhCdD65SYY59gcHPH/7HJDxS0jlxasBijIELjYIpKhC4uC4jrK5fQxQ8EEoYy+CC39ekVjl9c0he/uKQv7HaBI6drsb+4GsfL63DsdB0KztajvNaC8horGppssDbbYW2242w9m6mRr6U4vq4HIJCCMxigK0SachrpymmkKaeRpFQgFjWIVWoQ4/hjzWRvhMnuvd46B87cGRzBiL8sWLCgVTaluroa6eneL/CpGnQLNlWect3R4s8PpcX/AoBo9adJ6/PUxxQAosPH2rpfaXGveq0W56H1eUJR2vvcPudPsNaf8K2yFO18L+e+Rsu/zgTOua4b70fL8xQABr0eRr0ORr0Co14PnSkUelMozKHhiIiMRFhoGBRjqNy4LjQWCI2DmfUe5AM6nYKBiZEYmBjZ5uN1lmacrbeiwWpDndWGeksz6q021FmbYbMLNNuF69Zmb/VvIVyZy1a5Red94px/tz5BtHM+9ST9nUc1APY7vlSKsCGkuRomWwMM9kYYbQ0w2hvlsb0RBrsVirBDgQ06YYNO2KEIxzFszmMFAhDqp4xA38RMf36TrXj0mz4+Ph56vR6lpa27ypWWliIpKanN5yQlJXl0PgCYzWaYzb4v3pn449/5/BpEFHzCzQbWjBD5kUel5CaTCePGjUNubq7zPrvdjtzcXGRnZ7f5nOzs7FbnA8C6devaPZ+IiIh6Fo9D//nz5+Ouu+7C+PHjMWHCBLz00kuoq6vDrFmzAAAzZ85EamoqFi9eDAC4//77cdlll+GFF17AtGnTsHLlSmzbtg2vv/66d78TIiIiCkoeByMzZszA6dOn8fjjj6OkpASjR4/G2rVrnUWq+fn50LnKyTF58mS88847eOyxx/D73/8eAwYMwKpVq9zuMUJERETdG9vBExERkU+4+/nN9oNERESkKQYjREREpCkGI0RERKQpBiNERESkKQYjREREpCkGI0RERKQpBiNERESkKQYjREREpCkGI0RERKSpoNiWUm0SW11drfFIiIiIyF3q5/aFmr0HRTBSU1MDAEhPT9d4JEREROSpmpoaREdHt/t4UOxNY7fbUVRUhMjISCiK4rXXra6uRnp6OgoKCrjnjQ/xffYfvtf+wffZP/g++4cv32chBGpqapCSktJqE91zBUVmRKfTIS0tzWevHxUVxf+j+wHfZ//he+0ffJ/9g++zf/jqfe4oI6JiASsRERFpisEIERERaapHByNmsxmLFi2C2WzWeijdGt9n/+F77R98n/2D77N/BML7HBQFrERERNR99ejMCBEREWmPwQgRERFpisEIERERaYrBCBEREWmqRwcjS5cuRWZmJkJCQjBx4kRs3bpV6yEFrMWLF+Oiiy5CZGQkEhIScNNNN+HgwYOtzmlsbMScOXPQq1cvRERE4Ec/+hFKS0tbnZOfn49p06YhLCwMCQkJePDBB9Hc3NzqnA0bNmDs2LEwm83o378/3nzzTV9/ewFryZIlUBQF8+bNc97H99k7CgsLcccdd6BXr14IDQ3FiBEjsG3bNufjQgg8/vjjSE5ORmhoKHJycnD48OFWr1FRUYHbb78dUVFRiImJwd13343a2tpW53z//fe45JJLEBISgvT0dDz33HN++f4Cgc1mw8KFC5GVlYXQ0FD069cPTz75ZKt9Svg+d85XX32F6dOnIyUlBYqiYNWqVa0e9+f7+sEHH2Dw4MEICQnBiBEjsGbNGs+/IdFDrVy5UphMJrFixQqxd+9eMXv2bBETEyNKS0u1HlpAmjp1qnjjjTfEnj17RF5enrjuuutERkaGqK2tdZ5z7733ivT0dJGbmyu2bdsmJk2aJCZPnux8vLm5WQwfPlzk5OSInTt3ijVr1oj4+HixYMEC5znHjh0TYWFhYv78+WLfvn3i5ZdfFnq9Xqxdu9av328g2Lp1q8jMzBQjR44U999/v/N+vs9dV1FRIfr06SN+9rOfiS1btohjx46JTz/9VBw5csR5zpIlS0R0dLRYtWqV2LVrl7jhhhtEVlaWaGhocJ5zzTXXiFGjRonNmzeLr7/+WvTv31/cdtttzserqqpEYmKiuP3228WePXvEu+++K0JDQ8Vf//pXv36/Wnn66adFr169xOrVq8Xx48fFBx98ICIiIsSf//xn5zl8nztnzZo14tFHHxUffvihACA++uijVo/763395ptvhF6vF88995zYt2+feOyxx4TRaBS7d+/26PvpscHIhAkTxJw5c5z/ttlsIiUlRSxevFjDUQWPsrIyAUB8+eWXQgghKisrhdFoFB988IHznP379wsAYtOmTUII+cOj0+lESUmJ85zXXntNREVFCYvFIoQQ4qGHHhLDhg1rda0ZM2aIqVOn+vpbCig1NTViwIABYt26deKyyy5zBiN8n73j4YcfFlOmTGn3cbvdLpKSksQf//hH532VlZXCbDaLd999VwghxL59+wQA8d133znP+d///icURRGFhYVCCCFeffVVERsb63zf1WsPGjTI299SQJo2bZr4+c9/3uq+H/7wh+L2228XQvB99pZzgxF/vq8/+clPxLRp01qNZ+LEieKee+7x6HvokdM0VqsV27dvR05OjvM+nU6HnJwcbNq0ScORBY+qqioAQFxcHABg+/btaGpqavWeDh48GBkZGc73dNOmTRgxYgQSExOd50ydOhXV1dXYu3ev85yWr6Ge09P+u8yZMwfTpk07773g++wdH3/8McaPH48f//jHSEhIwJgxY7B8+XLn48ePH0dJSUmr9yg6OhoTJ05s9T7HxMRg/PjxznNycnKg0+mwZcsW5zmXXnopTCaT85ypU6fi4MGDOHv2rK+/Tc1NnjwZubm5OHToEABg165d2LhxI6699loAfJ99xZ/vq7d+l/TIYKS8vBw2m63VL2sASExMRElJiUajCh52ux3z5s3DxRdfjOHDhwMASkpKYDKZEBMT0+rclu9pSUlJm++5+lhH51RXV6OhocEX307AWblyJXbs2IHFixef9xjfZ+84duwYXnvtNQwYMACffvop7rvvPvzmN7/BW2+9BcD1PnX0O6KkpAQJCQmtHjcYDIiLi/Pov0V39sgjj+DWW2/F4MGDYTQaMWbMGMybNw+33347AL7PvuLP97W9czx934Ni114KLHPmzMGePXuwceNGrYfS7RQUFOD+++/HunXrEBISovVwui273Y7x48fjmWeeAQCMGTMGe/bswbJly3DXXXdpPLru4/3338fbb7+Nd955B8OGDUNeXh7mzZuHlJQUvs/USo/MjMTHx0Ov15+3AqG0tBRJSUkajSo4zJ07F6tXr8YXX3yBtLQ05/1JSUmwWq2orKxsdX7L9zQpKanN91x9rKNzoqKiEBoa6u1vJ+Bs374dZWVlGDt2LAwGAwwGA7788kv85S9/gcFgQGJiIt9nL0hOTsbQoUNb3TdkyBDk5+cDcL1PHf2OSEpKQllZWavHm5ubUVFR4dF/i+7swQcfdGZHRowYgTvvvBMPPPCAM+vH99k3/Pm+tneOp+97jwxGTCYTxo0bh9zcXOd9drsdubm5yM7O1nBkgUsIgblz5+Kjjz7C+vXrkZWV1erxcePGwWg0tnpPDx48iPz8fOd7mp2djd27d7f6AVi3bh2ioqKcHwzZ2dmtXkM9p6f8d7nqqquwe/du5OXlOb/Gjx+P22+/3XnM97nrLr744vOWph86dAh9+vQBAGRlZSEpKanVe1RdXY0tW7a0ep8rKyuxfft25znr16+H3W7HxIkTned89dVXaGpqcp6zbt06DBo0CLGxsT77/gJFfX09dLrWHzN6vR52ux0A32df8ef76rXfJR6Vu3YjK1euFGazWbz55pti37594pe//KWIiYlptQKBXO677z4RHR0tNmzYIIqLi51f9fX1znPuvfdekZGRIdavXy+2bdsmsrOzRXZ2tvNxdcnp1VdfLfLy8sTatWtF796921xy+uCDD4r9+/eLpUuX9qglp21puZpGCL7P3rB161ZhMBjE008/LQ4fPizefvttERYWJv75z386z1myZImIiYkR//nPf8T3338vbrzxxjaXRo4ZM0Zs2bJFbNy4UQwYMKDV0sjKykqRmJgo7rzzTrFnzx6xcuVKERYW1q2XnLZ01113idTUVOfS3g8//FDEx8eLhx56yHkO3+fOqampETt37hQ7d+4UAMSLL74odu7cKU6ePCmE8N/7+s033wiDwSCef/55sX//frFo0SIu7fXUyy+/LDIyMoTJZBITJkwQmzdv1npIAQtAm19vvPGG85yGhgbxq1/9SsTGxoqwsDBx8803i+Li4lavc+LECXHttdeK0NBQER8fL37729+KpqamVud88cUXYvTo0cJkMom+ffu2ukZPdG4wwvfZO/773/+K4cOHC7PZLAYPHixef/31Vo/b7XaxcOFCkZiYKMxms7jqqqvEwYMHW51z5swZcdttt4mIiAgRFRUlZs2aJWpqalqds2vXLjFlyhRhNptFamqqWLJkic+/t0BRXV0t7r//fpGRkSFCQkJE3759xaOPPtpqqSjf58754osv2vydfNdddwkh/Pu+vv/++2LgwIHCZDKJYcOGiU8++cTj70cRokUrPCIiIiI/65E1I0RERBQ4GIwQERGRphiMEBERkaYYjBAREZGmGIwQERGRphiMEBERkaYYjBAREZGmGIwQERGRphiMEBERkaYYjBAREZGmGIwQERGRphiMEBERkab+P2ZTka1fglK8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.libraries.analytic.filters import OpticalDiscreteFilter\n", + "\n", + "b = jnp.asarray([0.5] + [0.0]*2000 + [0.25] + [0.0]*2000 + [0.17])\n", + "a = jnp.asarray([1.0])\n", + "\n", + "from scipy.signal import butter\n", + "\n", + "# b, a = butter(4, 0.01004, btype='low', analog=False)\n", + "\n", + "\n", + "simulation_parameters = SampleModeSimulationParameters()\n", + "discrete_filter = OpticalDiscreteFilter(b=b, a=a, center_wl=1.55e-6, delay_compensation=0)\n", + "\n", + "block_mode_inputs = {\n", + " 'in': optical_source_outputs['o0'],\n", + " 'out': optical_source_outputs['o0']\n", + "}\n", + "\n", + "discrete_filter_outputs = discrete_filter.block_mode_response(block_mode_inputs, BlockModeSimulationParameters())\n", + "\n", + "plt.plot(block_mode_inputs['in'].amplitude[:, 0, 0])\n", + "plt.plot(jnp.abs(discrete_filter_outputs['out'].amplitude[:, 0, 0]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ffa8451", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.0001\n", + "0.0002\n", + "0.0003\n", + "0.0004\n", + "0.0005\n", + "0.0006\n", + "0.0007\n", + "0.0008\n", + "0.0009\n", + "0.001\n", + "0.0011\n", + "0.0012\n", + "0.0013\n", + "0.0014\n", + "0.0015\n", + "0.0016\n", + "0.0017\n", + "0.0018\n", + "0.0019\n", + "0.002\n", + "0.0021\n", + "0.0022\n", + "0.0023\n", + "0.0024\n", + "0.0025\n", + "0.0026\n", + "0.0027\n", + "0.0028\n", + "0.0029\n", + "0.003\n", + "0.0031\n", + "0.0032\n", + "0.0033\n", + "0.0034\n", + "0.0035\n", + "0.0036\n", + "0.0037\n", + "0.0038\n", + "0.0039\n", + "0.004\n", + "0.0041\n", + "0.0042\n", + "0.0043\n", + "0.0044\n", + "0.0045\n", + "0.0046\n", + "0.0047\n", + "0.0048\n", + "0.0049\n", + "0.005\n", + "0.0051\n", + "0.0052\n", + "0.0053\n", + "0.0054\n", + "0.0055\n", + "0.0056\n", + "0.0057\n", + "0.0058\n", + "0.0059\n", + "0.006\n", + "0.0061\n", + "0.0062\n", + "0.0063\n", + "0.0064\n", + "0.0065\n", + "0.0066\n", + "0.0067\n", + "0.0068\n", + "0.0069\n", + "0.007\n", + "0.0071\n", + "0.0072\n", + "0.0073\n", + "0.0074\n", + "0.0075\n", + "0.0076\n", + "0.0077\n", + "0.0078\n", + "0.0079\n", + "0.008\n", + "0.0081\n", + "0.0082\n", + "0.0083\n", + "0.0084\n", + "0.0085\n", + "0.0086\n", + "0.0087\n", + "0.0088\n", + "0.0089\n", + "0.009\n", + "0.0091\n", + "0.0092\n", + "0.0093\n", + "0.0094\n", + "0.0095\n", + "0.0096\n", + "0.0097\n", + "0.0098\n", + "0.0099\n", + "0.01\n", + "0.0101\n", + "0.0102\n", + "0.0103\n", + "0.0104\n", + "0.0105\n", + "0.0106\n", + "0.0107\n", + "0.0108\n", + "0.0109\n", + "0.011\n", + "0.0111\n", + "0.0112\n", + "0.0113\n", + "0.0114\n", + "0.0115\n", + "0.0116\n", + "0.0117\n", + "0.0118\n", + "0.0119\n", + "0.012\n", + "0.0121\n", + "0.0122\n", + "0.0123\n", + "0.0124\n", + "0.0125\n", + "0.0126\n", + "0.0127\n", + "0.0128\n", + "0.0129\n", + "0.013\n", + "0.0131\n", + "0.0132\n", + "0.0133\n", + "0.0134\n", + "0.0135\n", + "0.0136\n", + "0.0137\n", + "0.0138\n", + "0.0139\n", + "0.014\n", + "0.0141\n", + "0.0142\n", + "0.0143\n", + "0.0144\n", + "0.0145\n", + "0.0146\n", + "0.0147\n", + "0.0148\n", + "0.0149\n", + "0.015\n", + "0.0151\n", + "0.0152\n", + "0.0153\n", + "0.0154\n", + "0.0155\n", + "0.0156\n", + "0.0157\n", + "0.0158\n", + "0.0159\n", + "0.016\n", + "0.0161\n", + "0.0162\n", + "0.0163\n", + "0.0164\n", + "0.0165\n", + "0.0166\n", + "0.0167\n", + "0.0168\n", + "0.0169\n", + "0.017\n", + "0.0171\n", + "0.0172\n", + "0.0173\n", + "0.0174\n", + "0.0175\n", + "0.0176\n", + "0.0177\n", + "0.0178\n", + "0.0179\n", + "0.018\n", + "0.0181\n", + "0.0182\n", + "0.0183\n", + "0.0184\n", + "0.0185\n", + "0.0186\n", + "0.0187\n", + "0.0188\n", + "0.0189\n", + "0.019\n", + "0.0191\n", + "0.0192\n", + "0.0193\n", + "0.0194\n", + "0.0195\n", + "0.0196\n", + "0.0197\n", + "0.0198\n", + "0.0199\n", + "0.02\n", + "0.0201\n", + "0.0202\n", + "0.0203\n", + "0.0204\n", + "0.0205\n", + "0.0206\n", + "0.0207\n", + "0.0208\n", + "0.0209\n", + "0.021\n", + "0.0211\n", + "0.0212\n", + "0.0213\n", + "0.0214\n", + "0.0215\n", + "0.0216\n", + "0.0217\n", + "0.0218\n", + "0.0219\n", + "0.022\n", + "0.0221\n", + "0.0222\n", + "0.0223\n", + "0.0224\n", + "0.0225\n", + "0.0226\n", + "0.0227\n", + "0.0228\n", + "0.0229\n", + "0.023\n", + "0.0231\n", + "0.0232\n", + "0.0233\n", + "0.0234\n", + "0.0235\n", + "0.0236\n", + "0.0237\n", + "0.0238\n", + "0.0239\n", + "0.024\n", + "0.0241\n", + "0.0242\n", + "0.0243\n", + "0.0244\n", + "0.0245\n", + "0.0246\n", + "0.0247\n", + "0.0248\n", + "0.0249\n", + "0.025\n", + "0.0251\n", + "0.0252\n", + "0.0253\n", + "0.0254\n", + "0.0255\n", + "0.0256\n", + "0.0257\n", + "0.0258\n", + "0.0259\n", + "0.026\n", + "0.0261\n", + "0.0262\n", + "0.0263\n", + "0.0264\n", + "0.0265\n", + "0.0266\n", + "0.0267\n", + "0.0268\n", + "0.0269\n", + "0.027\n", + "0.0271\n", + "0.0272\n", + "0.0273\n", + "0.0274\n", + "0.0275\n", + "0.0276\n", + "0.0277\n", + "0.0278\n", + "0.0279\n", + "0.028\n", + "0.0281\n", + "0.0282\n", + "0.0283\n", + "0.0284\n", + "0.0285\n", + "0.0286\n", + "0.0287\n", + "0.0288\n", + "0.0289\n", + "0.029\n", + "0.0291\n", + "0.0292\n", + "0.0293\n", + "0.0294\n", + "0.0295\n", + "0.0296\n", + "0.0297\n", + "0.0298\n", + "0.0299\n", + "0.03\n", + "0.0301\n", + "0.0302\n", + "0.0303\n", + "0.0304\n", + "0.0305\n", + "0.0306\n", + "0.0307\n", + "0.0308\n", + "0.0309\n", + "0.031\n", + "0.0311\n", + "0.0312\n", + "0.0313\n", + "0.0314\n", + "0.0315\n", + "0.0316\n", + "0.0317\n", + "0.0318\n", + "0.0319\n", + "0.032\n", + "0.0321\n", + "0.0322\n", + "0.0323\n", + "0.0324\n", + "0.0325\n", + "0.0326\n", + "0.0327\n", + "0.0328\n", + "0.0329\n", + "0.033\n", + "0.0331\n", + "0.0332\n", + "0.0333\n", + "0.0334\n", + "0.0335\n", + "0.0336\n", + "0.0337\n", + "0.0338\n", + "0.0339\n", + "0.034\n", + "0.0341\n", + "0.0342\n", + "0.0343\n", + "0.0344\n", + "0.0345\n", + "0.0346\n", + "0.0347\n", + "0.0348\n", + "0.0349\n", + "0.035\n", + "0.0351\n", + "0.0352\n", + "0.0353\n", + "0.0354\n", + "0.0355\n", + "0.0356\n", + "0.0357\n", + "0.0358\n", + "0.0359\n", + "0.036\n", + "0.0361\n", + "0.0362\n", + "0.0363\n", + "0.0364\n", + "0.0365\n", + "0.0366\n", + "0.0367\n", + "0.0368\n", + "0.0369\n", + "0.037\n", + "0.0371\n", + "0.0372\n", + "0.0373\n", + "0.0374\n", + "0.0375\n", + "0.0376\n", + "0.0377\n", + "0.0378\n", + "0.0379\n", + "0.038\n", + "0.0381\n", + "0.0382\n", + "0.0383\n", + "0.0384\n", + "0.0385\n", + "0.0386\n", + "0.0387\n", + "0.0388\n", + "0.0389\n", + "0.039\n", + "0.0391\n", + "0.0392\n", + "0.0393\n", + "0.0394\n", + "0.0395\n", + "0.0396\n", + "0.0397\n", + "0.0398\n", + "0.0399\n", + "0.04\n", + "0.0401\n", + "0.0402\n", + "0.0403\n", + "0.0404\n", + "0.0405\n", + "0.0406\n", + "0.0407\n", + "0.0408\n", + "0.0409\n", + "0.041\n", + "0.0411\n", + "0.0412\n", + "0.0413\n", + "0.0414\n", + "0.0415\n", + "0.0416\n", + "0.0417\n", + "0.0418\n", + "0.0419\n", + "0.042\n", + "0.0421\n", + "0.0422\n", + "0.0423\n", + "0.0424\n", + "0.0425\n", + "0.0426\n", + "0.0427\n", + "0.0428\n", + "0.0429\n", + "0.043\n", + "0.0431\n", + "0.0432\n", + "0.0433\n", + "0.0434\n", + "0.0435\n", + "0.0436\n", + "0.0437\n", + "0.0438\n", + "0.0439\n", + "0.044\n", + "0.0441\n", + "0.0442\n", + "0.0443\n", + "0.0444\n", + "0.0445\n", + "0.0446\n", + "0.0447\n", + "0.0448\n", + "0.0449\n", + "0.045\n", + "0.0451\n", + "0.0452\n", + "0.0453\n", + "0.0454\n", + "0.0455\n", + "0.0456\n", + "0.0457\n", + "0.0458\n", + "0.0459\n", + "0.046\n", + "0.0461\n", + "0.0462\n", + "0.0463\n", + "0.0464\n", + "0.0465\n", + "0.0466\n", + "0.0467\n", + "0.0468\n", + "0.0469\n", + "0.047\n", + "0.0471\n", + "0.0472\n", + "0.0473\n", + "0.0474\n", + "0.0475\n", + "0.0476\n", + "0.0477\n", + "0.0478\n", + "0.0479\n", + "0.048\n", + "0.0481\n", + "0.0482\n", + "0.0483\n", + "0.0484\n", + "0.0485\n", + "0.0486\n", + "0.0487\n", + "0.0488\n", + "0.0489\n", + "0.049\n", + "0.0491\n", + "0.0492\n", + "0.0493\n", + "0.0494\n", + "0.0495\n", + "0.0496\n", + "0.0497\n", + "0.0498\n", + "0.0499\n", + "0.05\n", + "0.0501\n", + "0.0502\n", + "0.0503\n", + "0.0504\n", + "0.0505\n", + "0.0506\n", + "0.0507\n", + "0.0508\n", + "0.0509\n", + "0.051\n", + "0.0511\n", + "0.0512\n", + "0.0513\n", + "0.0514\n", + "0.0515\n", + "0.0516\n", + "0.0517\n", + "0.0518\n", + "0.0519\n", + "0.052\n", + "0.0521\n", + "0.0522\n", + "0.0523\n", + "0.0524\n", + "0.0525\n", + "0.0526\n", + "0.0527\n", + "0.0528\n", + "0.0529\n", + "0.053\n", + "0.0531\n", + "0.0532\n", + "0.0533\n", + "0.0534\n", + "0.0535\n", + "0.0536\n", + "0.0537\n", + "0.0538\n", + "0.0539\n", + "0.054\n", + "0.0541\n", + "0.0542\n", + "0.0543\n", + "0.0544\n", + "0.0545\n", + "0.0546\n", + "0.0547\n", + "0.0548\n", + "0.0549\n", + "0.055\n", + "0.0551\n", + "0.0552\n", + "0.0553\n", + "0.0554\n", + "0.0555\n", + "0.0556\n", + "0.0557\n", + "0.0558\n", + "0.0559\n", + "0.056\n", + "0.0561\n", + "0.0562\n", + "0.0563\n", + "0.0564\n", + "0.0565\n", + "0.0566\n", + "0.0567\n", + "0.0568\n", + "0.0569\n", + "0.057\n", + "0.0571\n", + "0.0572\n", + "0.0573\n", + "0.0574\n", + "0.0575\n", + "0.0576\n", + "0.0577\n", + "0.0578\n", + "0.0579\n", + "0.058\n", + "0.0581\n", + "0.0582\n", + "0.0583\n", + "0.0584\n", + "0.0585\n", + "0.0586\n", + "0.0587\n", + "0.0588\n", + "0.0589\n", + "0.059\n", + "0.0591\n", + "0.0592\n", + "0.0593\n", + "0.0594\n", + "0.0595\n", + "0.0596\n", + "0.0597\n", + "0.0598\n", + "0.0599\n", + "0.06\n", + "0.0601\n", + "0.0602\n", + "0.0603\n", + "0.0604\n", + "0.0605\n", + "0.0606\n", + "0.0607\n", + "0.0608\n", + "0.0609\n", + "0.061\n", + "0.0611\n", + "0.0612\n", + "0.0613\n", + "0.0614\n", + "0.0615\n", + "0.0616\n", + "0.0617\n", + "0.0618\n", + "0.0619\n", + "0.062\n", + "0.0621\n", + "0.0622\n", + "0.0623\n", + "0.0624\n", + "0.0625\n", + "0.0626\n", + "0.0627\n", + "0.0628\n", + "0.0629\n", + "0.063\n", + "0.0631\n", + "0.0632\n", + "0.0633\n", + "0.0634\n", + "0.0635\n", + "0.0636\n", + "0.0637\n", + "0.0638\n", + "0.0639\n", + "0.064\n", + "0.0641\n", + "0.0642\n", + "0.0643\n", + "0.0644\n", + "0.0645\n", + "0.0646\n", + "0.0647\n", + "0.0648\n", + "0.0649\n", + "0.065\n", + "0.0651\n", + "0.0652\n", + "0.0653\n", + "0.0654\n", + "0.0655\n", + "0.0656\n", + "0.0657\n", + "0.0658\n", + "0.0659\n", + "0.066\n", + "0.0661\n", + "0.0662\n", + "0.0663\n", + "0.0664\n", + "0.0665\n", + "0.0666\n", + "0.0667\n", + "0.0668\n", + "0.0669\n", + "0.067\n", + "0.0671\n", + "0.0672\n", + "0.0673\n", + "0.0674\n", + "0.0675\n", + "0.0676\n", + "0.0677\n", + "0.0678\n", + "0.0679\n", + "0.068\n", + "0.0681\n", + "0.0682\n", + "0.0683\n", + "0.0684\n", + "0.0685\n", + "0.0686\n", + "0.0687\n", + "0.0688\n", + "0.0689\n", + "0.069\n", + "0.0691\n", + "0.0692\n", + "0.0693\n", + "0.0694\n", + "0.0695\n", + "0.0696\n", + "0.0697\n", + "0.0698\n", + "0.0699\n", + "0.07\n", + "0.0701\n", + "0.0702\n", + "0.0703\n", + "0.0704\n", + "0.0705\n", + "0.0706\n", + "0.0707\n", + "0.0708\n", + "0.0709\n", + "0.071\n", + "0.0711\n", + "0.0712\n", + "0.0713\n", + "0.0714\n", + "0.0715\n", + "0.0716\n", + "0.0717\n", + "0.0718\n", + "0.0719\n", + "0.072\n", + "0.0721\n", + "0.0722\n", + "0.0723\n", + "0.0724\n", + "0.0725\n", + "0.0726\n", + "0.0727\n", + "0.0728\n", + "0.0729\n", + "0.073\n", + "0.0731\n", + "0.0732\n", + "0.0733\n", + "0.0734\n", + "0.0735\n", + "0.0736\n", + "0.0737\n", + "0.0738\n", + "0.0739\n", + "0.074\n", + "0.0741\n", + "0.0742\n", + "0.0743\n", + "0.0744\n", + "0.0745\n", + "0.0746\n", + "0.0747\n", + "0.0748\n", + "0.0749\n", + "0.075\n", + "0.0751\n", + "0.0752\n", + "0.0753\n", + "0.0754\n", + "0.0755\n", + "0.0756\n", + "0.0757\n", + "0.0758\n", + "0.0759\n", + "0.076\n", + "0.0761\n", + "0.0762\n", + "0.0763\n", + "0.0764\n", + "0.0765\n", + "0.0766\n", + "0.0767\n", + "0.0768\n", + "0.0769\n", + "0.077\n", + "0.0771\n", + "0.0772\n", + "0.0773\n", + "0.0774\n", + "0.0775\n", + "0.0776\n", + "0.0777\n", + "0.0778\n", + "0.0779\n", + "0.078\n", + "0.0781\n", + "0.0782\n", + "0.0783\n", + "0.0784\n", + "0.0785\n", + "0.0786\n", + "0.0787\n", + "0.0788\n", + "0.0789\n", + "0.079\n", + "0.0791\n", + "0.0792\n", + "0.0793\n", + "0.0794\n", + "0.0795\n", + "0.0796\n", + "0.0797\n", + "0.0798\n", + "0.0799\n", + "0.08\n", + "0.0801\n", + "0.0802\n", + "0.0803\n", + "0.0804\n", + "0.0805\n", + "0.0806\n", + "0.0807\n", + "0.0808\n", + "0.0809\n", + "0.081\n", + "0.0811\n", + "0.0812\n", + "0.0813\n", + "0.0814\n", + "0.0815\n", + "0.0816\n", + "0.0817\n", + "0.0818\n", + "0.0819\n", + "0.082\n", + "0.0821\n", + "0.0822\n", + "0.0823\n", + "0.0824\n", + "0.0825\n", + "0.0826\n", + "0.0827\n", + "0.0828\n", + "0.0829\n", + "0.083\n", + "0.0831\n", + "0.0832\n", + "0.0833\n", + "0.0834\n", + "0.0835\n", + "0.0836\n", + "0.0837\n", + "0.0838\n", + "0.0839\n", + "0.084\n", + "0.0841\n", + "0.0842\n", + "0.0843\n", + "0.0844\n", + "0.0845\n", + "0.0846\n", + "0.0847\n", + "0.0848\n", + "0.0849\n", + "0.085\n", + "0.0851\n", + "0.0852\n", + "0.0853\n", + "0.0854\n", + "0.0855\n", + "0.0856\n", + "0.0857\n", + "0.0858\n", + "0.0859\n", + "0.086\n", + "0.0861\n", + "0.0862\n", + "0.0863\n", + "0.0864\n", + "0.0865\n", + "0.0866\n", + "0.0867\n", + "0.0868\n", + "0.0869\n", + "0.087\n", + "0.0871\n", + "0.0872\n", + "0.0873\n", + "0.0874\n", + "0.0875\n", + "0.0876\n", + "0.0877\n", + "0.0878\n", + "0.0879\n", + "0.088\n", + "0.0881\n", + "0.0882\n", + "0.0883\n", + "0.0884\n", + "0.0885\n", + "0.0886\n", + "0.0887\n", + "0.0888\n", + "0.0889\n", + "0.089\n", + "0.0891\n", + "0.0892\n", + "0.0893\n", + "0.0894\n", + "0.0895\n", + "0.0896\n", + "0.0897\n", + "0.0898\n", + "0.0899\n", + "0.09\n", + "0.0901\n", + "0.0902\n", + "0.0903\n", + "0.0904\n", + "0.0905\n", + "0.0906\n", + "0.0907\n", + "0.0908\n", + "0.0909\n", + "0.091\n", + "0.0911\n", + "0.0912\n", + "0.0913\n", + "0.0914\n", + "0.0915\n", + "0.0916\n", + "0.0917\n", + "0.0918\n", + "0.0919\n", + "0.092\n", + "0.0921\n", + "0.0922\n", + "0.0923\n", + "0.0924\n", + "0.0925\n", + "0.0926\n", + "0.0927\n", + "0.0928\n", + "0.0929\n", + "0.093\n", + "0.0931\n", + "0.0932\n", + "0.0933\n", + "0.0934\n", + "0.0935\n", + "0.0936\n", + "0.0937\n", + "0.0938\n", + "0.0939\n", + "0.094\n", + "0.0941\n", + "0.0942\n", + "0.0943\n", + "0.0944\n", + "0.0945\n", + "0.0946\n", + "0.0947\n", + "0.0948\n", + "0.0949\n", + "0.095\n", + "0.0951\n", + "0.0952\n", + "0.0953\n", + "0.0954\n", + "0.0955\n", + "0.0956\n", + "0.0957\n", + "0.0958\n", + "0.0959\n", + "0.096\n", + "0.0961\n", + "0.0962\n", + "0.0963\n", + "0.0964\n", + "0.0965\n", + "0.0966\n", + "0.0967\n", + "0.0968\n", + "0.0969\n", + "0.097\n", + "0.0971\n", + "0.0972\n", + "0.0973\n", + "0.0974\n", + "0.0975\n", + "0.0976\n", + "0.0977\n", + "0.0978\n", + "0.0979\n", + "0.098\n", + "0.0981\n", + "0.0982\n", + "0.0983\n", + "0.0984\n", + "0.0985\n", + "0.0986\n", + "0.0987\n", + "0.0988\n", + "0.0989\n", + "0.099\n", + "0.0991\n", + "0.0992\n", + "0.0993\n", + "0.0994\n", + "0.0995\n", + "0.0996\n", + "0.0997\n", + "0.0998\n", + "0.0999\n", + "0.1\n", + "0.1001\n", + "0.1002\n", + "0.1003\n", + "0.1004\n", + "0.1005\n", + "0.1006\n", + "0.1007\n", + "0.1008\n", + "0.1009\n", + "0.101\n", + "0.1011\n", + "0.1012\n", + "0.1013\n", + "0.1014\n", + "0.1015\n", + "0.1016\n", + "0.1017\n", + "0.1018\n", + "0.1019\n", + "0.102\n", + "0.1021\n", + "0.1022\n", + "0.1023\n", + "0.1024\n", + "0.1025\n", + "0.1026\n", + "0.1027\n", + "0.1028\n", + "0.1029\n", + "0.103\n", + "0.1031\n", + "0.1032\n", + "0.1033\n", + "0.1034\n", + "0.1035\n", + "0.1036\n", + "0.1037\n", + "0.1038\n", + "0.1039\n", + "0.104\n", + "0.1041\n", + "0.1042\n", + "0.1043\n", + "0.1044\n", + "0.1045\n", + "0.1046\n", + "0.1047\n", + "0.1048\n", + "0.1049\n", + "0.105\n", + "0.1051\n", + "0.1052\n", + "0.1053\n", + "0.1054\n", + "0.1055\n", + "0.1056\n", + "0.1057\n", + "0.1058\n", + "0.1059\n", + "0.106\n", + "0.1061\n", + "0.1062\n", + "0.1063\n", + "0.1064\n", + "0.1065\n", + "0.1066\n", + "0.1067\n", + "0.1068\n", + "0.1069\n", + "0.107\n", + "0.1071\n", + "0.1072\n", + "0.1073\n", + "0.1074\n", + "0.1075\n", + "0.1076\n", + "0.1077\n", + "0.1078\n", + "0.1079\n", + "0.108\n", + "0.1081\n", + "0.1082\n", + "0.1083\n", + "0.1084\n", + "0.1085\n", + "0.1086\n", + "0.1087\n", + "0.1088\n", + "0.1089\n", + "0.109\n", + "0.1091\n", + "0.1092\n", + "0.1093\n", + "0.1094\n", + "0.1095\n", + "0.1096\n", + "0.1097\n", + "0.1098\n", + "0.1099\n", + "0.11\n", + "0.1101\n", + "0.1102\n", + "0.1103\n", + "0.1104\n", + "0.1105\n", + "0.1106\n", + "0.1107\n", + "0.1108\n", + "0.1109\n", + "0.111\n", + "0.1111\n", + "0.1112\n", + "0.1113\n", + "0.1114\n", + "0.1115\n", + "0.1116\n", + "0.1117\n", + "0.1118\n", + "0.1119\n", + "0.112\n", + "0.1121\n", + "0.1122\n", + "0.1123\n", + "0.1124\n", + "0.1125\n", + "0.1126\n", + "0.1127\n", + "0.1128\n", + "0.1129\n", + "0.113\n", + "0.1131\n", + "0.1132\n", + "0.1133\n", + "0.1134\n", + "0.1135\n", + "0.1136\n", + "0.1137\n", + "0.1138\n", + "0.1139\n", + "0.114\n", + "0.1141\n", + "0.1142\n", + "0.1143\n", + "0.1144\n", + "0.1145\n", + "0.1146\n", + "0.1147\n", + "0.1148\n", + "0.1149\n", + "0.115\n", + "0.1151\n", + "0.1152\n", + "0.1153\n", + "0.1154\n", + "0.1155\n", + "0.1156\n", + "0.1157\n", + "0.1158\n", + "0.1159\n", + "0.116\n", + "0.1161\n", + "0.1162\n", + "0.1163\n", + "0.1164\n", + "0.1165\n", + "0.1166\n", + "0.1167\n", + "0.1168\n", + "0.1169\n", + "0.117\n", + "0.1171\n", + "0.1172\n", + "0.1173\n", + "0.1174\n", + "0.1175\n", + "0.1176\n", + "0.1177\n", + "0.1178\n", + "0.1179\n", + "0.118\n", + "0.1181\n", + "0.1182\n", + "0.1183\n", + "0.1184\n", + "0.1185\n", + "0.1186\n", + "0.1187\n", + "0.1188\n", + "0.1189\n", + "0.119\n", + "0.1191\n", + "0.1192\n", + "0.1193\n", + "0.1194\n", + "0.1195\n", + "0.1196\n", + "0.1197\n", + "0.1198\n", + "0.1199\n", + "0.12\n", + "0.1201\n", + "0.1202\n", + "0.1203\n", + "0.1204\n", + "0.1205\n", + "0.1206\n", + "0.1207\n", + "0.1208\n", + "0.1209\n", + "0.121\n", + "0.1211\n", + "0.1212\n", + "0.1213\n", + "0.1214\n", + "0.1215\n", + "0.1216\n", + "0.1217\n", + "0.1218\n", + "0.1219\n", + "0.122\n", + "0.1221\n", + "0.1222\n", + "0.1223\n", + "0.1224\n", + "0.1225\n", + "0.1226\n", + "0.1227\n", + "0.1228\n", + "0.1229\n", + "0.123\n", + "0.1231\n", + "0.1232\n", + "0.1233\n", + "0.1234\n", + "0.1235\n", + "0.1236\n", + "0.1237\n", + "0.1238\n", + "0.1239\n", + "0.124\n", + "0.1241\n", + "0.1242\n", + "0.1243\n", + "0.1244\n", + "0.1245\n", + "0.1246\n", + "0.1247\n", + "0.1248\n", + "0.1249\n", + "0.125\n", + "0.1251\n", + "0.1252\n", + "0.1253\n", + "0.1254\n", + "0.1255\n", + "0.1256\n", + "0.1257\n", + "0.1258\n", + "0.1259\n", + "0.126\n", + "0.1261\n", + "0.1262\n", + "0.1263\n", + "0.1264\n", + "0.1265\n", + "0.1266\n", + "0.1267\n", + "0.1268\n", + "0.1269\n", + "0.127\n", + "0.1271\n", + "0.1272\n", + "0.1273\n", + "0.1274\n", + "0.1275\n", + "0.1276\n", + "0.1277\n", + "0.1278\n", + "0.1279\n", + "0.128\n", + "0.1281\n", + "0.1282\n", + "0.1283\n", + "0.1284\n", + "0.1285\n", + "0.1286\n", + "0.1287\n", + "0.1288\n", + "0.1289\n", + "0.129\n", + "0.1291\n", + "0.1292\n", + "0.1293\n", + "0.1294\n", + "0.1295\n", + "0.1296\n", + "0.1297\n", + "0.1298\n", + "0.1299\n", + "0.13\n", + "0.1301\n", + "0.1302\n", + "0.1303\n", + "0.1304\n", + "0.1305\n", + "0.1306\n", + "0.1307\n", + "0.1308\n", + "0.1309\n", + "0.131\n", + "0.1311\n", + "0.1312\n", + "0.1313\n", + "0.1314\n", + "0.1315\n", + "0.1316\n", + "0.1317\n", + "0.1318\n", + "0.1319\n", + "0.132\n", + "0.1321\n", + "0.1322\n", + "0.1323\n", + "0.1324\n", + "0.1325\n", + "0.1326\n", + "0.1327\n", + "0.1328\n", + "0.1329\n", + "0.133\n", + "0.1331\n", + "0.1332\n", + "0.1333\n", + "0.1334\n", + "0.1335\n", + "0.1336\n", + "0.1337\n", + "0.1338\n", + "0.1339\n", + "0.134\n", + "0.1341\n", + "0.1342\n", + "0.1343\n", + "0.1344\n", + "0.1345\n", + "0.1346\n", + "0.1347\n", + "0.1348\n", + "0.1349\n", + "0.135\n", + "0.1351\n", + "0.1352\n", + "0.1353\n", + "0.1354\n", + "0.1355\n", + "0.1356\n", + "0.1357\n", + "0.1358\n", + "0.1359\n", + "0.136\n", + "0.1361\n", + "0.1362\n", + "0.1363\n", + "0.1364\n", + "0.1365\n", + "0.1366\n", + "0.1367\n", + "0.1368\n", + "0.1369\n", + "0.137\n", + "0.1371\n", + "0.1372\n", + "0.1373\n", + "0.1374\n", + "0.1375\n", + "0.1376\n", + "0.1377\n", + "0.1378\n", + "0.1379\n", + "0.138\n", + "0.1381\n", + "0.1382\n", + "0.1383\n", + "0.1384\n", + "0.1385\n", + "0.1386\n", + "0.1387\n", + "0.1388\n", + "0.1389\n", + "0.139\n", + "0.1391\n", + "0.1392\n", + "0.1393\n", + "0.1394\n", + "0.1395\n", + "0.1396\n", + "0.1397\n", + "0.1398\n", + "0.1399\n", + "0.14\n", + "0.1401\n", + "0.1402\n", + "0.1403\n", + "0.1404\n", + "0.1405\n", + "0.1406\n", + "0.1407\n", + "0.1408\n", + "0.1409\n", + "0.141\n", + "0.1411\n", + "0.1412\n", + "0.1413\n", + "0.1414\n", + "0.1415\n", + "0.1416\n", + "0.1417\n", + "0.1418\n", + "0.1419\n", + "0.142\n", + "0.1421\n", + "0.1422\n", + "0.1423\n", + "0.1424\n", + "0.1425\n", + "0.1426\n", + "0.1427\n", + "0.1428\n", + "0.1429\n", + "0.143\n", + "0.1431\n", + "0.1432\n", + "0.1433\n", + "0.1434\n", + "0.1435\n", + "0.1436\n", + "0.1437\n", + "0.1438\n", + "0.1439\n", + "0.144\n", + "0.1441\n", + "0.1442\n", + "0.1443\n", + "0.1444\n", + "0.1445\n", + "0.1446\n", + "0.1447\n", + "0.1448\n", + "0.1449\n", + "0.145\n", + "0.1451\n", + "0.1452\n", + "0.1453\n", + "0.1454\n", + "0.1455\n", + "0.1456\n", + "0.1457\n", + "0.1458\n", + "0.1459\n", + "0.146\n", + "0.1461\n", + "0.1462\n", + "0.1463\n", + "0.1464\n", + "0.1465\n", + "0.1466\n", + "0.1467\n", + "0.1468\n", + "0.1469\n", + "0.147\n", + "0.1471\n", + "0.1472\n", + "0.1473\n", + "0.1474\n", + "0.1475\n", + "0.1476\n", + "0.1477\n", + "0.1478\n", + "0.1479\n", + "0.148\n", + "0.1481\n", + "0.1482\n", + "0.1483\n", + "0.1484\n", + "0.1485\n", + "0.1486\n", + "0.1487\n", + "0.1488\n", + "0.1489\n", + "0.149\n", + "0.1491\n", + "0.1492\n", + "0.1493\n", + "0.1494\n", + "0.1495\n", + "0.1496\n", + "0.1497\n", + "0.1498\n", + "0.1499\n", + "0.15\n", + "0.1501\n", + "0.1502\n", + "0.1503\n", + "0.1504\n", + "0.1505\n", + "0.1506\n", + "0.1507\n", + "0.1508\n", + "0.1509\n", + "0.151\n", + "0.1511\n", + "0.1512\n", + "0.1513\n", + "0.1514\n", + "0.1515\n", + "0.1516\n", + "0.1517\n", + "0.1518\n", + "0.1519\n", + "0.152\n", + "0.1521\n", + "0.1522\n", + "0.1523\n", + "0.1524\n", + "0.1525\n", + "0.1526\n", + "0.1527\n", + "0.1528\n", + "0.1529\n", + "0.153\n", + "0.1531\n", + "0.1532\n", + "0.1533\n", + "0.1534\n", + "0.1535\n", + "0.1536\n", + "0.1537\n", + "0.1538\n", + "0.1539\n", + "0.154\n", + "0.1541\n", + "0.1542\n", + "0.1543\n", + "0.1544\n", + "0.1545\n", + "0.1546\n", + "0.1547\n", + "0.1548\n", + "0.1549\n", + "0.155\n", + "0.1551\n", + "0.1552\n", + "0.1553\n", + "0.1554\n", + "0.1555\n", + "0.1556\n", + "0.1557\n", + "0.1558\n", + "0.1559\n", + "0.156\n", + "0.1561\n", + "0.1562\n", + "0.1563\n", + "0.1564\n", + "0.1565\n", + "0.1566\n", + "0.1567\n", + "0.1568\n", + "0.1569\n", + "0.157\n", + "0.1571\n", + "0.1572\n", + "0.1573\n", + "0.1574\n", + "0.1575\n", + "0.1576\n", + "0.1577\n", + "0.1578\n", + "0.1579\n", + "0.158\n", + "0.1581\n", + "0.1582\n", + "0.1583\n", + "0.1584\n", + "0.1585\n", + "0.1586\n", + "0.1587\n", + "0.1588\n", + "0.1589\n", + "0.159\n", + "0.1591\n", + "0.1592\n", + "0.1593\n", + "0.1594\n", + "0.1595\n", + "0.1596\n", + "0.1597\n", + "0.1598\n", + "0.1599\n", + "0.16\n", + "0.1601\n", + "0.1602\n", + "0.1603\n", + "0.1604\n", + "0.1605\n", + "0.1606\n", + "0.1607\n", + "0.1608\n", + "0.1609\n", + "0.161\n", + "0.1611\n", + "0.1612\n", + "0.1613\n", + "0.1614\n", + "0.1615\n", + "0.1616\n", + "0.1617\n", + "0.1618\n", + "0.1619\n", + "0.162\n", + "0.1621\n", + "0.1622\n", + "0.1623\n", + "0.1624\n", + "0.1625\n", + "0.1626\n", + "0.1627\n", + "0.1628\n", + "0.1629\n", + "0.163\n", + "0.1631\n", + "0.1632\n", + "0.1633\n", + "0.1634\n", + "0.1635\n", + "0.1636\n", + "0.1637\n", + "0.1638\n", + "0.1639\n", + "0.164\n", + "0.1641\n", + "0.1642\n", + "0.1643\n", + "0.1644\n", + "0.1645\n", + "0.1646\n", + "0.1647\n", + "0.1648\n", + "0.1649\n", + "0.165\n", + "0.1651\n", + "0.1652\n", + "0.1653\n", + "0.1654\n", + "0.1655\n", + "0.1656\n", + "0.1657\n", + "0.1658\n", + "0.1659\n", + "0.166\n", + "0.1661\n", + "0.1662\n", + "0.1663\n", + "0.1664\n", + "0.1665\n", + "0.1666\n", + "0.1667\n", + "0.1668\n", + "0.1669\n", + "0.167\n", + "0.1671\n", + "0.1672\n", + "0.1673\n", + "0.1674\n", + "0.1675\n", + "0.1676\n", + "0.1677\n", + "0.1678\n", + "0.1679\n", + "0.168\n", + "0.1681\n", + "0.1682\n", + "0.1683\n", + "0.1684\n", + "0.1685\n", + "0.1686\n", + "0.1687\n", + "0.1688\n", + "0.1689\n", + "0.169\n", + "0.1691\n", + "0.1692\n", + "0.1693\n", + "0.1694\n", + "0.1695\n", + "0.1696\n", + "0.1697\n", + "0.1698\n", + "0.1699\n", + "0.17\n", + "0.1701\n", + "0.1702\n", + "0.1703\n", + "0.1704\n", + "0.1705\n", + "0.1706\n", + "0.1707\n", + "0.1708\n", + "0.1709\n", + "0.171\n", + "0.1711\n", + "0.1712\n", + "0.1713\n", + "0.1714\n", + "0.1715\n", + "0.1716\n", + "0.1717\n", + "0.1718\n", + "0.1719\n", + "0.172\n", + "0.1721\n", + "0.1722\n", + "0.1723\n", + "0.1724\n", + "0.1725\n", + "0.1726\n", + "0.1727\n", + "0.1728\n", + "0.1729\n", + "0.173\n", + "0.1731\n", + "0.1732\n", + "0.1733\n", + "0.1734\n", + "0.1735\n", + "0.1736\n", + "0.1737\n", + "0.1738\n", + "0.1739\n", + "0.174\n", + "0.1741\n", + "0.1742\n", + "0.1743\n", + "0.1744\n", + "0.1745\n", + "0.1746\n", + "0.1747\n", + "0.1748\n", + "0.1749\n", + "0.175\n", + "0.1751\n", + "0.1752\n", + "0.1753\n", + "0.1754\n", + "0.1755\n", + "0.1756\n", + "0.1757\n", + "0.1758\n", + "0.1759\n", + "0.176\n", + "0.1761\n", + "0.1762\n", + "0.1763\n", + "0.1764\n", + "0.1765\n", + "0.1766\n", + "0.1767\n", + "0.1768\n", + "0.1769\n", + "0.177\n", + "0.1771\n", + "0.1772\n", + "0.1773\n", + "0.1774\n", + "0.1775\n", + "0.1776\n", + "0.1777\n", + "0.1778\n", + "0.1779\n", + "0.178\n", + "0.1781\n", + "0.1782\n", + "0.1783\n", + "0.1784\n", + "0.1785\n", + "0.1786\n", + "0.1787\n", + "0.1788\n", + "0.1789\n", + "0.179\n", + "0.1791\n", + "0.1792\n", + "0.1793\n", + "0.1794\n", + "0.1795\n", + "0.1796\n", + "0.1797\n", + "0.1798\n", + "0.1799\n", + "0.18\n", + "0.1801\n", + "0.1802\n", + "0.1803\n", + "0.1804\n", + "0.1805\n", + "0.1806\n", + "0.1807\n", + "0.1808\n", + "0.1809\n", + "0.181\n", + "0.1811\n", + "0.1812\n", + "0.1813\n", + "0.1814\n", + "0.1815\n", + "0.1816\n", + "0.1817\n", + "0.1818\n", + "0.1819\n", + "0.182\n", + "0.1821\n", + "0.1822\n", + "0.1823\n", + "0.1824\n", + "0.1825\n", + "0.1826\n", + "0.1827\n", + "0.1828\n", + "0.1829\n", + "0.183\n", + "0.1831\n", + "0.1832\n", + "0.1833\n", + "0.1834\n", + "0.1835\n", + "0.1836\n", + "0.1837\n", + "0.1838\n", + "0.1839\n", + "0.184\n", + "0.1841\n", + "0.1842\n", + "0.1843\n", + "0.1844\n", + "0.1845\n", + "0.1846\n", + "0.1847\n", + "0.1848\n", + "0.1849\n", + "0.185\n", + "0.1851\n", + "0.1852\n", + "0.1853\n", + "0.1854\n", + "0.1855\n", + "0.1856\n", + "0.1857\n", + "0.1858\n", + "0.1859\n", + "0.186\n", + "0.1861\n", + "0.1862\n", + "0.1863\n", + "0.1864\n", + "0.1865\n", + "0.1866\n", + "0.1867\n", + "0.1868\n", + "0.1869\n", + "0.187\n", + "0.1871\n", + "0.1872\n", + "0.1873\n", + "0.1874\n", + "0.1875\n", + "0.1876\n", + "0.1877\n", + "0.1878\n", + "0.1879\n", + "0.188\n", + "0.1881\n", + "0.1882\n", + "0.1883\n", + "0.1884\n", + "0.1885\n", + "0.1886\n", + "0.1887\n", + "0.1888\n", + "0.1889\n", + "0.189\n", + "0.1891\n", + "0.1892\n", + "0.1893\n", + "0.1894\n", + "0.1895\n", + "0.1896\n", + "0.1897\n", + "0.1898\n", + "0.1899\n", + "0.19\n", + "0.1901\n", + "0.1902\n", + "0.1903\n", + "0.1904\n", + "0.1905\n", + "0.1906\n", + "0.1907\n", + "0.1908\n", + "0.1909\n", + "0.191\n", + "0.1911\n", + "0.1912\n", + "0.1913\n", + "0.1914\n", + "0.1915\n", + "0.1916\n", + "0.1917\n", + "0.1918\n", + "0.1919\n", + "0.192\n", + "0.1921\n", + "0.1922\n", + "0.1923\n", + "0.1924\n", + "0.1925\n", + "0.1926\n", + "0.1927\n", + "0.1928\n", + "0.1929\n", + "0.193\n", + "0.1931\n", + "0.1932\n", + "0.1933\n", + "0.1934\n", + "0.1935\n", + "0.1936\n", + "0.1937\n", + "0.1938\n", + "0.1939\n", + "0.194\n", + "0.1941\n", + "0.1942\n", + "0.1943\n", + "0.1944\n", + "0.1945\n", + "0.1946\n", + "0.1947\n", + "0.1948\n", + "0.1949\n", + "0.195\n", + "0.1951\n", + "0.1952\n", + "0.1953\n", + "0.1954\n", + "0.1955\n", + "0.1956\n", + "0.1957\n", + "0.1958\n", + "0.1959\n", + "0.196\n", + "0.1961\n", + "0.1962\n", + "0.1963\n", + "0.1964\n", + "0.1965\n", + "0.1966\n", + "0.1967\n", + "0.1968\n", + "0.1969\n", + "0.197\n", + "0.1971\n", + "0.1972\n", + "0.1973\n", + "0.1974\n", + "0.1975\n", + "0.1976\n", + "0.1977\n", + "0.1978\n", + "0.1979\n", + "0.198\n", + "0.1981\n", + "0.1982\n", + "0.1983\n", + "0.1984\n", + "0.1985\n", + "0.1986\n", + "0.1987\n", + "0.1988\n", + "0.1989\n", + "0.199\n", + "0.1991\n", + "0.1992\n", + "0.1993\n", + "0.1994\n", + "0.1995\n", + "0.1996\n", + "0.1997\n", + "0.1998\n", + "0.1999\n", + "0.2\n", + "0.2001\n", + "0.2002\n", + "0.2003\n", + "0.2004\n", + "0.2005\n", + "0.2006\n", + "0.2007\n", + "0.2008\n", + "0.2009\n", + "0.201\n", + "0.2011\n", + "0.2012\n", + "0.2013\n", + "0.2014\n", + "0.2015\n", + "0.2016\n", + "0.2017\n", + "0.2018\n", + "0.2019\n", + "0.202\n", + "0.2021\n", + "0.2022\n", + "0.2023\n", + "0.2024\n", + "0.2025\n", + "0.2026\n", + "0.2027\n", + "0.2028\n", + "0.2029\n", + "0.203\n", + "0.2031\n", + "0.2032\n", + "0.2033\n", + "0.2034\n", + "0.2035\n", + "0.2036\n", + "0.2037\n", + "0.2038\n", + "0.2039\n", + "0.204\n", + "0.2041\n", + "0.2042\n", + "0.2043\n", + "0.2044\n", + "0.2045\n", + "0.2046\n", + "0.2047\n", + "0.2048\n", + "0.2049\n", + "0.205\n", + "0.2051\n", + "0.2052\n", + "0.2053\n", + "0.2054\n", + "0.2055\n", + "0.2056\n", + "0.2057\n", + "0.2058\n", + "0.2059\n", + "0.206\n", + "0.2061\n", + "0.2062\n", + "0.2063\n", + "0.2064\n", + "0.2065\n", + "0.2066\n", + "0.2067\n", + "0.2068\n", + "0.2069\n", + "0.207\n", + "0.2071\n", + "0.2072\n", + "0.2073\n", + "0.2074\n", + "0.2075\n", + "0.2076\n", + "0.2077\n", + "0.2078\n", + "0.2079\n", + "0.208\n", + "0.2081\n", + "0.2082\n", + "0.2083\n", + "0.2084\n", + "0.2085\n", + "0.2086\n", + "0.2087\n", + "0.2088\n", + "0.2089\n", + "0.209\n", + "0.2091\n", + "0.2092\n", + "0.2093\n", + "0.2094\n", + "0.2095\n", + "0.2096\n", + "0.2097\n", + "0.2098\n", + "0.2099\n", + "0.21\n", + "0.2101\n", + "0.2102\n", + "0.2103\n", + "0.2104\n", + "0.2105\n", + "0.2106\n", + "0.2107\n", + "0.2108\n", + "0.2109\n", + "0.211\n", + "0.2111\n", + "0.2112\n", + "0.2113\n", + "0.2114\n", + "0.2115\n", + "0.2116\n", + "0.2117\n", + "0.2118\n", + "0.2119\n", + "0.212\n", + "0.2121\n", + "0.2122\n", + "0.2123\n", + "0.2124\n", + "0.2125\n", + "0.2126\n", + "0.2127\n", + "0.2128\n", + "0.2129\n", + "0.213\n", + "0.2131\n", + "0.2132\n", + "0.2133\n", + "0.2134\n", + "0.2135\n", + "0.2136\n", + "0.2137\n", + "0.2138\n", + "0.2139\n", + "0.214\n", + "0.2141\n", + "0.2142\n", + "0.2143\n", + "0.2144\n", + "0.2145\n", + "0.2146\n", + "0.2147\n", + "0.2148\n", + "0.2149\n", + "0.215\n", + "0.2151\n", + "0.2152\n", + "0.2153\n", + "0.2154\n", + "0.2155\n", + "0.2156\n", + "0.2157\n", + "0.2158\n", + "0.2159\n", + "0.216\n", + "0.2161\n", + "0.2162\n", + "0.2163\n", + "0.2164\n", + "0.2165\n", + "0.2166\n", + "0.2167\n", + "0.2168\n", + "0.2169\n", + "0.217\n", + "0.2171\n", + "0.2172\n", + "0.2173\n", + "0.2174\n", + "0.2175\n", + "0.2176\n", + "0.2177\n", + "0.2178\n", + "0.2179\n", + "0.218\n", + "0.2181\n", + "0.2182\n", + "0.2183\n", + "0.2184\n", + "0.2185\n", + "0.2186\n", + "0.2187\n", + "0.2188\n", + "0.2189\n", + "0.219\n", + "0.2191\n", + "0.2192\n", + "0.2193\n", + "0.2194\n", + "0.2195\n", + "0.2196\n", + "0.2197\n", + "0.2198\n", + "0.2199\n", + "0.22\n", + "0.2201\n", + "0.2202\n", + "0.2203\n", + "0.2204\n", + "0.2205\n", + "0.2206\n", + "0.2207\n", + "0.2208\n", + "0.2209\n", + "0.221\n", + "0.2211\n", + "0.2212\n", + "0.2213\n", + "0.2214\n", + "0.2215\n", + "0.2216\n", + "0.2217\n", + "0.2218\n", + "0.2219\n", + "0.222\n", + "0.2221\n", + "0.2222\n", + "0.2223\n", + "0.2224\n", + "0.2225\n", + "0.2226\n", + "0.2227\n", + "0.2228\n", + "0.2229\n", + "0.223\n", + "0.2231\n", + "0.2232\n", + "0.2233\n", + "0.2234\n", + "0.2235\n", + "0.2236\n", + "0.2237\n", + "0.2238\n", + "0.2239\n", + "0.224\n", + "0.2241\n", + "0.2242\n", + "0.2243\n", + "0.2244\n", + "0.2245\n", + "0.2246\n", + "0.2247\n", + "0.2248\n", + "0.2249\n", + "0.225\n", + "0.2251\n", + "0.2252\n", + "0.2253\n", + "0.2254\n", + "0.2255\n", + "0.2256\n", + "0.2257\n", + "0.2258\n", + "0.2259\n", + "0.226\n", + "0.2261\n", + "0.2262\n", + "0.2263\n", + "0.2264\n", + "0.2265\n", + "0.2266\n", + "0.2267\n", + "0.2268\n", + "0.2269\n", + "0.227\n", + "0.2271\n", + "0.2272\n", + "0.2273\n", + "0.2274\n", + "0.2275\n", + "0.2276\n", + "0.2277\n", + "0.2278\n", + "0.2279\n", + "0.228\n", + "0.2281\n", + "0.2282\n", + "0.2283\n", + "0.2284\n", + "0.2285\n", + "0.2286\n", + "0.2287\n", + "0.2288\n", + "0.2289\n", + "0.229\n", + "0.2291\n", + "0.2292\n", + "0.2293\n", + "0.2294\n", + "0.2295\n", + "0.2296\n", + "0.2297\n", + "0.2298\n", + "0.2299\n", + "0.23\n", + "0.2301\n", + "0.2302\n", + "0.2303\n", + "0.2304\n", + "0.2305\n", + "0.2306\n", + "0.2307\n", + "0.2308\n", + "0.2309\n", + "0.231\n", + "0.2311\n", + "0.2312\n", + "0.2313\n", + "0.2314\n", + "0.2315\n", + "0.2316\n", + "0.2317\n", + "0.2318\n", + "0.2319\n", + "0.232\n", + "0.2321\n", + "0.2322\n", + "0.2323\n", + "0.2324\n", + "0.2325\n", + "0.2326\n", + "0.2327\n", + "0.2328\n", + "0.2329\n", + "0.233\n", + "0.2331\n", + "0.2332\n", + "0.2333\n", + "0.2334\n", + "0.2335\n", + "0.2336\n", + "0.2337\n", + "0.2338\n", + "0.2339\n", + "0.234\n", + "0.2341\n", + "0.2342\n", + "0.2343\n", + "0.2344\n", + "0.2345\n", + "0.2346\n", + "0.2347\n", + "0.2348\n", + "0.2349\n", + "0.235\n", + "0.2351\n", + "0.2352\n", + "0.2353\n", + "0.2354\n", + "0.2355\n", + "0.2356\n", + "0.2357\n", + "0.2358\n", + "0.2359\n", + "0.236\n", + "0.2361\n", + "0.2362\n", + "0.2363\n", + "0.2364\n", + "0.2365\n", + "0.2366\n", + "0.2367\n", + "0.2368\n", + "0.2369\n", + "0.237\n", + "0.2371\n", + "0.2372\n", + "0.2373\n", + "0.2374\n", + "0.2375\n", + "0.2376\n", + "0.2377\n", + "0.2378\n", + "0.2379\n", + "0.238\n", + "0.2381\n", + "0.2382\n", + "0.2383\n", + "0.2384\n", + "0.2385\n", + "0.2386\n", + "0.2387\n", + "0.2388\n", + "0.2389\n", + "0.239\n", + "0.2391\n", + "0.2392\n", + "0.2393\n", + "0.2394\n", + "0.2395\n", + "0.2396\n", + "0.2397\n", + "0.2398\n", + "0.2399\n", + "0.24\n", + "0.2401\n", + "0.2402\n", + "0.2403\n", + "0.2404\n", + "0.2405\n", + "0.2406\n", + "0.2407\n", + "0.2408\n", + "0.2409\n", + "0.241\n", + "0.2411\n", + "0.2412\n", + "0.2413\n", + "0.2414\n", + "0.2415\n", + "0.2416\n", + "0.2417\n", + "0.2418\n", + "0.2419\n", + "0.242\n", + "0.2421\n", + "0.2422\n", + "0.2423\n", + "0.2424\n", + "0.2425\n", + "0.2426\n", + "0.2427\n", + "0.2428\n", + "0.2429\n", + "0.243\n", + "0.2431\n", + "0.2432\n", + "0.2433\n", + "0.2434\n", + "0.2435\n", + "0.2436\n", + "0.2437\n", + "0.2438\n", + "0.2439\n", + "0.244\n", + "0.2441\n", + "0.2442\n", + "0.2443\n", + "0.2444\n", + "0.2445\n", + "0.2446\n", + "0.2447\n", + "0.2448\n", + "0.2449\n", + "0.245\n", + "0.2451\n", + "0.2452\n", + "0.2453\n", + "0.2454\n", + "0.2455\n", + "0.2456\n", + "0.2457\n", + "0.2458\n", + "0.2459\n", + "0.246\n", + "0.2461\n", + "0.2462\n", + "0.2463\n", + "0.2464\n", + "0.2465\n", + "0.2466\n", + "0.2467\n", + "0.2468\n", + "0.2469\n", + "0.247\n", + "0.2471\n", + "0.2472\n", + "0.2473\n", + "0.2474\n", + "0.2475\n", + "0.2476\n", + "0.2477\n", + "0.2478\n", + "0.2479\n", + "0.248\n", + "0.2481\n", + "0.2482\n", + "0.2483\n", + "0.2484\n", + "0.2485\n", + "0.2486\n", + "0.2487\n", + "0.2488\n", + "0.2489\n", + "0.249\n", + "0.2491\n", + "0.2492\n", + "0.2493\n", + "0.2494\n", + "0.2495\n", + "0.2496\n", + "0.2497\n", + "0.2498\n", + "0.2499\n", + "0.25\n", + "0.2501\n", + "0.2502\n", + "0.2503\n", + "0.2504\n", + "0.2505\n", + "0.2506\n", + "0.2507\n", + "0.2508\n", + "0.2509\n", + "0.251\n", + "0.2511\n", + "0.2512\n", + "0.2513\n", + "0.2514\n", + "0.2515\n", + "0.2516\n", + "0.2517\n", + "0.2518\n", + "0.2519\n", + "0.252\n", + "0.2521\n", + "0.2522\n", + "0.2523\n", + "0.2524\n", + "0.2525\n", + "0.2526\n", + "0.2527\n", + "0.2528\n", + "0.2529\n", + "0.253\n", + "0.2531\n", + "0.2532\n", + "0.2533\n", + "0.2534\n", + "0.2535\n", + "0.2536\n", + "0.2537\n", + "0.2538\n", + "0.2539\n", + "0.254\n", + "0.2541\n", + "0.2542\n", + "0.2543\n", + "0.2544\n", + "0.2545\n", + "0.2546\n", + "0.2547\n", + "0.2548\n", + "0.2549\n", + "0.255\n", + "0.2551\n", + "0.2552\n", + "0.2553\n", + "0.2554\n", + "0.2555\n", + "0.2556\n", + "0.2557\n", + "0.2558\n", + "0.2559\n", + "0.256\n", + "0.2561\n", + "0.2562\n", + "0.2563\n", + "0.2564\n", + "0.2565\n", + "0.2566\n", + "0.2567\n", + "0.2568\n", + "0.2569\n", + "0.257\n", + "0.2571\n", + "0.2572\n", + "0.2573\n", + "0.2574\n", + "0.2575\n", + "0.2576\n", + "0.2577\n", + "0.2578\n", + "0.2579\n", + "0.258\n", + "0.2581\n", + "0.2582\n", + "0.2583\n", + "0.2584\n", + "0.2585\n", + "0.2586\n", + "0.2587\n", + "0.2588\n", + "0.2589\n", + "0.259\n", + "0.2591\n", + "0.2592\n", + "0.2593\n", + "0.2594\n", + "0.2595\n", + "0.2596\n", + "0.2597\n", + "0.2598\n", + "0.2599\n", + "0.26\n", + "0.2601\n", + "0.2602\n", + "0.2603\n", + "0.2604\n", + "0.2605\n", + "0.2606\n", + "0.2607\n", + "0.2608\n", + "0.2609\n", + "0.261\n", + "0.2611\n", + "0.2612\n", + "0.2613\n", + "0.2614\n", + "0.2615\n", + "0.2616\n", + "0.2617\n", + "0.2618\n", + "0.2619\n", + "0.262\n", + "0.2621\n", + "0.2622\n", + "0.2623\n", + "0.2624\n", + "0.2625\n", + "0.2626\n", + "0.2627\n", + "0.2628\n", + "0.2629\n", + "0.263\n", + "0.2631\n", + "0.2632\n", + "0.2633\n", + "0.2634\n", + "0.2635\n", + "0.2636\n", + "0.2637\n", + "0.2638\n", + "0.2639\n", + "0.264\n", + "0.2641\n", + "0.2642\n", + "0.2643\n", + "0.2644\n", + "0.2645\n", + "0.2646\n", + "0.2647\n", + "0.2648\n", + "0.2649\n", + "0.265\n", + "0.2651\n", + "0.2652\n", + "0.2653\n", + "0.2654\n", + "0.2655\n", + "0.2656\n", + "0.2657\n", + "0.2658\n", + "0.2659\n", + "0.266\n", + "0.2661\n", + "0.2662\n", + "0.2663\n", + "0.2664\n", + "0.2665\n", + "0.2666\n", + "0.2667\n", + "0.2668\n", + "0.2669\n", + "0.267\n", + "0.2671\n", + "0.2672\n", + "0.2673\n", + "0.2674\n", + "0.2675\n", + "0.2676\n", + "0.2677\n", + "0.2678\n", + "0.2679\n", + "0.268\n", + "0.2681\n", + "0.2682\n", + "0.2683\n", + "0.2684\n", + "0.2685\n", + "0.2686\n", + "0.2687\n", + "0.2688\n", + "0.2689\n", + "0.269\n", + "0.2691\n", + "0.2692\n", + "0.2693\n", + "0.2694\n", + "0.2695\n", + "0.2696\n", + "0.2697\n", + "0.2698\n", + "0.2699\n", + "0.27\n", + "0.2701\n", + "0.2702\n", + "0.2703\n", + "0.2704\n", + "0.2705\n", + "0.2706\n", + "0.2707\n", + "0.2708\n", + "0.2709\n", + "0.271\n", + "0.2711\n", + "0.2712\n", + "0.2713\n", + "0.2714\n", + "0.2715\n", + "0.2716\n", + "0.2717\n", + "0.2718\n", + "0.2719\n", + "0.272\n", + "0.2721\n", + "0.2722\n", + "0.2723\n", + "0.2724\n", + "0.2725\n", + "0.2726\n", + "0.2727\n", + "0.2728\n", + "0.2729\n", + "0.273\n", + "0.2731\n", + "0.2732\n", + "0.2733\n", + "0.2734\n", + "0.2735\n", + "0.2736\n", + "0.2737\n", + "0.2738\n", + "0.2739\n", + "0.274\n", + "0.2741\n", + "0.2742\n", + "0.2743\n", + "0.2744\n", + "0.2745\n", + "0.2746\n", + "0.2747\n", + "0.2748\n", + "0.2749\n", + "0.275\n", + "0.2751\n", + "0.2752\n", + "0.2753\n", + "0.2754\n", + "0.2755\n", + "0.2756\n", + "0.2757\n", + "0.2758\n", + "0.2759\n", + "0.276\n", + "0.2761\n", + "0.2762\n", + "0.2763\n", + "0.2764\n", + "0.2765\n", + "0.2766\n", + "0.2767\n", + "0.2768\n", + "0.2769\n", + "0.277\n", + "0.2771\n", + "0.2772\n", + "0.2773\n", + "0.2774\n", + "0.2775\n", + "0.2776\n", + "0.2777\n", + "0.2778\n", + "0.2779\n", + "0.278\n", + "0.2781\n", + "0.2782\n", + "0.2783\n", + "0.2784\n", + "0.2785\n", + "0.2786\n", + "0.2787\n", + "0.2788\n", + "0.2789\n", + "0.279\n", + "0.2791\n", + "0.2792\n", + "0.2793\n", + "0.2794\n", + "0.2795\n", + "0.2796\n", + "0.2797\n", + "0.2798\n", + "0.2799\n", + "0.28\n", + "0.2801\n", + "0.2802\n", + "0.2803\n", + "0.2804\n", + "0.2805\n", + "0.2806\n", + "0.2807\n", + "0.2808\n", + "0.2809\n", + "0.281\n", + "0.2811\n", + "0.2812\n", + "0.2813\n", + "0.2814\n", + "0.2815\n", + "0.2816\n", + "0.2817\n", + "0.2818\n", + "0.2819\n", + "0.282\n", + "0.2821\n", + "0.2822\n", + "0.2823\n", + "0.2824\n", + "0.2825\n", + "0.2826\n", + "0.2827\n", + "0.2828\n", + "0.2829\n", + "0.283\n", + "0.2831\n", + "0.2832\n", + "0.2833\n", + "0.2834\n", + "0.2835\n", + "0.2836\n", + "0.2837\n", + "0.2838\n", + "0.2839\n", + "0.284\n", + "0.2841\n", + "0.2842\n", + "0.2843\n", + "0.2844\n", + "0.2845\n", + "0.2846\n", + "0.2847\n", + "0.2848\n", + "0.2849\n", + "0.285\n", + "0.2851\n", + "0.2852\n", + "0.2853\n", + "0.2854\n", + "0.2855\n", + "0.2856\n", + "0.2857\n", + "0.2858\n", + "0.2859\n", + "0.286\n", + "0.2861\n", + "0.2862\n", + "0.2863\n", + "0.2864\n", + "0.2865\n", + "0.2866\n", + "0.2867\n", + "0.2868\n", + "0.2869\n", + "0.287\n", + "0.2871\n", + "0.2872\n", + "0.2873\n", + "0.2874\n", + "0.2875\n", + "0.2876\n", + "0.2877\n", + "0.2878\n", + "0.2879\n", + "0.288\n", + "0.2881\n", + "0.2882\n", + "0.2883\n", + "0.2884\n", + "0.2885\n", + "0.2886\n", + "0.2887\n", + "0.2888\n", + "0.2889\n", + "0.289\n", + "0.2891\n", + "0.2892\n", + "0.2893\n", + "0.2894\n", + "0.2895\n", + "0.2896\n", + "0.2897\n", + "0.2898\n", + "0.2899\n", + "0.29\n", + "0.2901\n", + "0.2902\n", + "0.2903\n", + "0.2904\n", + "0.2905\n", + "0.2906\n", + "0.2907\n", + "0.2908\n", + "0.2909\n", + "0.291\n", + "0.2911\n", + "0.2912\n", + "0.2913\n", + "0.2914\n", + "0.2915\n", + "0.2916\n", + "0.2917\n", + "0.2918\n", + "0.2919\n", + "0.292\n", + "0.2921\n", + "0.2922\n", + "0.2923\n", + "0.2924\n", + "0.2925\n", + "0.2926\n", + "0.2927\n", + "0.2928\n", + "0.2929\n", + "0.293\n", + "0.2931\n", + "0.2932\n", + "0.2933\n", + "0.2934\n", + "0.2935\n", + "0.2936\n", + "0.2937\n", + "0.2938\n", + "0.2939\n", + "0.294\n", + "0.2941\n", + "0.2942\n", + "0.2943\n", + "0.2944\n", + "0.2945\n", + "0.2946\n", + "0.2947\n", + "0.2948\n", + "0.2949\n", + "0.295\n", + "0.2951\n", + "0.2952\n", + "0.2953\n", + "0.2954\n", + "0.2955\n", + "0.2956\n", + "0.2957\n", + "0.2958\n", + "0.2959\n", + "0.296\n", + "0.2961\n", + "0.2962\n", + "0.2963\n", + "0.2964\n", + "0.2965\n", + "0.2966\n", + "0.2967\n", + "0.2968\n", + "0.2969\n", + "0.297\n", + "0.2971\n", + "0.2972\n", + "0.2973\n", + "0.2974\n", + "0.2975\n", + "0.2976\n", + "0.2977\n", + "0.2978\n", + "0.2979\n", + "0.298\n", + "0.2981\n", + "0.2982\n", + "0.2983\n", + "0.2984\n", + "0.2985\n", + "0.2986\n", + "0.2987\n", + "0.2988\n", + "0.2989\n", + "0.299\n", + "0.2991\n", + "0.2992\n", + "0.2993\n", + "0.2994\n", + "0.2995\n", + "0.2996\n", + "0.2997\n", + "0.2998\n", + "0.2999\n", + "0.3\n", + "0.3001\n", + "0.3002\n", + "0.3003\n", + "0.3004\n", + "0.3005\n", + "0.3006\n", + "0.3007\n", + "0.3008\n", + "0.3009\n", + "0.301\n", + "0.3011\n", + "0.3012\n", + "0.3013\n", + "0.3014\n", + "0.3015\n", + "0.3016\n", + "0.3017\n", + "0.3018\n", + "0.3019\n", + "0.302\n", + "0.3021\n", + "0.3022\n", + "0.3023\n", + "0.3024\n", + "0.3025\n", + "0.3026\n", + "0.3027\n", + "0.3028\n", + "0.3029\n", + "0.303\n", + "0.3031\n", + "0.3032\n", + "0.3033\n", + "0.3034\n", + "0.3035\n", + "0.3036\n", + "0.3037\n", + "0.3038\n", + "0.3039\n", + "0.304\n", + "0.3041\n", + "0.3042\n", + "0.3043\n", + "0.3044\n", + "0.3045\n", + "0.3046\n", + "0.3047\n", + "0.3048\n", + "0.3049\n", + "0.305\n", + "0.3051\n", + "0.3052\n", + "0.3053\n", + "0.3054\n", + "0.3055\n", + "0.3056\n", + "0.3057\n", + "0.3058\n", + "0.3059\n", + "0.306\n", + "0.3061\n", + "0.3062\n", + "0.3063\n", + "0.3064\n", + "0.3065\n", + "0.3066\n", + "0.3067\n", + "0.3068\n", + "0.3069\n", + "0.307\n", + "0.3071\n", + "0.3072\n", + "0.3073\n", + "0.3074\n", + "0.3075\n", + "0.3076\n", + "0.3077\n", + "0.3078\n", + "0.3079\n", + "0.308\n", + "0.3081\n", + "0.3082\n", + "0.3083\n", + "0.3084\n", + "0.3085\n", + "0.3086\n", + "0.3087\n", + "0.3088\n", + "0.3089\n", + "0.309\n", + "0.3091\n", + "0.3092\n", + "0.3093\n", + "0.3094\n", + "0.3095\n", + "0.3096\n", + "0.3097\n", + "0.3098\n", + "0.3099\n", + "0.31\n", + "0.3101\n", + "0.3102\n", + "0.3103\n", + "0.3104\n", + "0.3105\n", + "0.3106\n", + "0.3107\n", + "0.3108\n", + "0.3109\n", + "0.311\n", + "0.3111\n", + "0.3112\n", + "0.3113\n", + "0.3114\n", + "0.3115\n", + "0.3116\n", + "0.3117\n", + "0.3118\n", + "0.3119\n", + "0.312\n", + "0.3121\n", + "0.3122\n", + "0.3123\n", + "0.3124\n", + "0.3125\n", + "0.3126\n", + "0.3127\n", + "0.3128\n", + "0.3129\n", + "0.313\n", + "0.3131\n", + "0.3132\n", + "0.3133\n", + "0.3134\n", + "0.3135\n", + "0.3136\n", + "0.3137\n", + "0.3138\n", + "0.3139\n", + "0.314\n", + "0.3141\n", + "0.3142\n", + "0.3143\n", + "0.3144\n", + "0.3145\n", + "0.3146\n", + "0.3147\n", + "0.3148\n", + "0.3149\n", + "0.315\n", + "0.3151\n", + "0.3152\n", + "0.3153\n", + "0.3154\n", + "0.3155\n", + "0.3156\n", + "0.3157\n", + "0.3158\n", + "0.3159\n", + "0.316\n", + "0.3161\n", + "0.3162\n", + "0.3163\n", + "0.3164\n", + "0.3165\n", + "0.3166\n", + "0.3167\n", + "0.3168\n", + "0.3169\n", + "0.317\n", + "0.3171\n", + "0.3172\n", + "0.3173\n", + "0.3174\n", + "0.3175\n", + "0.3176\n", + "0.3177\n", + "0.3178\n", + "0.3179\n", + "0.318\n", + "0.3181\n", + "0.3182\n", + "0.3183\n", + "0.3184\n", + "0.3185\n", + "0.3186\n", + "0.3187\n", + "0.3188\n", + "0.3189\n", + "0.319\n", + "0.3191\n", + "0.3192\n", + "0.3193\n", + "0.3194\n", + "0.3195\n", + "0.3196\n", + "0.3197\n", + "0.3198\n", + "0.3199\n", + "0.32\n", + "0.3201\n", + "0.3202\n", + "0.3203\n", + "0.3204\n", + "0.3205\n", + "0.3206\n", + "0.3207\n", + "0.3208\n", + "0.3209\n", + "0.321\n", + "0.3211\n", + "0.3212\n", + "0.3213\n", + "0.3214\n", + "0.3215\n", + "0.3216\n", + "0.3217\n", + "0.3218\n", + "0.3219\n", + "0.322\n", + "0.3221\n", + "0.3222\n", + "0.3223\n", + "0.3224\n", + "0.3225\n", + "0.3226\n", + "0.3227\n", + "0.3228\n", + "0.3229\n", + "0.323\n", + "0.3231\n", + "0.3232\n", + "0.3233\n", + "0.3234\n", + "0.3235\n", + "0.3236\n", + "0.3237\n", + "0.3238\n", + "0.3239\n", + "0.324\n", + "0.3241\n", + "0.3242\n", + "0.3243\n", + "0.3244\n", + "0.3245\n", + "0.3246\n", + "0.3247\n", + "0.3248\n", + "0.3249\n", + "0.325\n", + "0.3251\n", + "0.3252\n", + "0.3253\n", + "0.3254\n", + "0.3255\n", + "0.3256\n", + "0.3257\n", + "0.3258\n", + "0.3259\n", + "0.326\n", + "0.3261\n", + "0.3262\n", + "0.3263\n", + "0.3264\n", + "0.3265\n", + "0.3266\n", + "0.3267\n", + "0.3268\n", + "0.3269\n", + "0.327\n", + "0.3271\n", + "0.3272\n", + "0.3273\n", + "0.3274\n", + "0.3275\n", + "0.3276\n", + "0.3277\n", + "0.3278\n", + "0.3279\n", + "0.328\n", + "0.3281\n", + "0.3282\n", + "0.3283\n", + "0.3284\n", + "0.3285\n", + "0.3286\n", + "0.3287\n", + "0.3288\n", + "0.3289\n", + "0.329\n", + "0.3291\n", + "0.3292\n", + "0.3293\n", + "0.3294\n", + "0.3295\n", + "0.3296\n", + "0.3297\n", + "0.3298\n", + "0.3299\n", + "0.33\n", + "0.3301\n", + "0.3302\n", + "0.3303\n", + "0.3304\n", + "0.3305\n", + "0.3306\n", + "0.3307\n", + "0.3308\n", + "0.3309\n", + "0.331\n", + "0.3311\n", + "0.3312\n", + "0.3313\n", + "0.3314\n", + "0.3315\n", + "0.3316\n", + "0.3317\n", + "0.3318\n", + "0.3319\n", + "0.332\n", + "0.3321\n", + "0.3322\n", + "0.3323\n", + "0.3324\n", + "0.3325\n", + "0.3326\n", + "0.3327\n", + "0.3328\n", + "0.3329\n", + "0.333\n", + "0.3331\n", + "0.3332\n", + "0.3333\n", + "0.3334\n", + "0.3335\n", + "0.3336\n", + "0.3337\n", + "0.3338\n", + "0.3339\n", + "0.334\n", + "0.3341\n", + "0.3342\n", + "0.3343\n", + "0.3344\n", + "0.3345\n", + "0.3346\n", + "0.3347\n", + "0.3348\n", + "0.3349\n", + "0.335\n", + "0.3351\n", + "0.3352\n", + "0.3353\n", + "0.3354\n", + "0.3355\n", + "0.3356\n", + "0.3357\n", + "0.3358\n", + "0.3359\n", + "0.336\n", + "0.3361\n", + "0.3362\n", + "0.3363\n", + "0.3364\n", + "0.3365\n", + "0.3366\n", + "0.3367\n", + "0.3368\n", + "0.3369\n", + "0.337\n", + "0.3371\n", + "0.3372\n", + "0.3373\n", + "0.3374\n", + "0.3375\n", + "0.3376\n", + "0.3377\n", + "0.3378\n", + "0.3379\n", + "0.338\n", + "0.3381\n", + "0.3382\n", + "0.3383\n", + "0.3384\n", + "0.3385\n", + "0.3386\n", + "0.3387\n", + "0.3388\n", + "0.3389\n", + "0.339\n", + "0.3391\n", + "0.3392\n", + "0.3393\n", + "0.3394\n", + "0.3395\n", + "0.3396\n", + "0.3397\n", + "0.3398\n", + "0.3399\n", + "0.34\n", + "0.3401\n", + "0.3402\n", + "0.3403\n", + "0.3404\n", + "0.3405\n", + "0.3406\n", + "0.3407\n", + "0.3408\n", + "0.3409\n", + "0.341\n", + "0.3411\n", + "0.3412\n", + "0.3413\n", + "0.3414\n", + "0.3415\n", + "0.3416\n", + "0.3417\n", + "0.3418\n", + "0.3419\n", + "0.342\n", + "0.3421\n", + "0.3422\n", + "0.3423\n", + "0.3424\n", + "0.3425\n", + "0.3426\n", + "0.3427\n", + "0.3428\n", + "0.3429\n", + "0.343\n", + "0.3431\n", + "0.3432\n", + "0.3433\n", + "0.3434\n", + "0.3435\n", + "0.3436\n", + "0.3437\n", + "0.3438\n", + "0.3439\n", + "0.344\n", + "0.3441\n", + "0.3442\n", + "0.3443\n", + "0.3444\n", + "0.3445\n", + "0.3446\n", + "0.3447\n", + "0.3448\n", + "0.3449\n", + "0.345\n", + "0.3451\n", + "0.3452\n", + "0.3453\n", + "0.3454\n", + "0.3455\n", + "0.3456\n", + "0.3457\n", + "0.3458\n", + "0.3459\n", + "0.346\n", + "0.3461\n", + "0.3462\n", + "0.3463\n", + "0.3464\n", + "0.3465\n", + "0.3466\n", + "0.3467\n", + "0.3468\n", + "0.3469\n", + "0.347\n", + "0.3471\n", + "0.3472\n", + "0.3473\n", + "0.3474\n", + "0.3475\n", + "0.3476\n", + "0.3477\n", + "0.3478\n", + "0.3479\n", + "0.348\n", + "0.3481\n", + "0.3482\n", + "0.3483\n", + "0.3484\n", + "0.3485\n", + "0.3486\n", + "0.3487\n", + "0.3488\n", + "0.3489\n", + "0.349\n", + "0.3491\n", + "0.3492\n", + "0.3493\n", + "0.3494\n", + "0.3495\n", + "0.3496\n", + "0.3497\n", + "0.3498\n", + "0.3499\n", + "0.35\n", + "0.3501\n", + "0.3502\n", + "0.3503\n", + "0.3504\n", + "0.3505\n", + "0.3506\n", + "0.3507\n", + "0.3508\n", + "0.3509\n", + "0.351\n", + "0.3511\n", + "0.3512\n", + "0.3513\n", + "0.3514\n", + "0.3515\n", + "0.3516\n", + "0.3517\n", + "0.3518\n", + "0.3519\n", + "0.352\n", + "0.3521\n", + "0.3522\n", + "0.3523\n", + "0.3524\n", + "0.3525\n", + "0.3526\n", + "0.3527\n", + "0.3528\n", + "0.3529\n", + "0.353\n", + "0.3531\n", + "0.3532\n", + "0.3533\n", + "0.3534\n", + "0.3535\n", + "0.3536\n", + "0.3537\n", + "0.3538\n", + "0.3539\n", + "0.354\n", + "0.3541\n", + "0.3542\n", + "0.3543\n", + "0.3544\n", + "0.3545\n", + "0.3546\n", + "0.3547\n", + "0.3548\n", + "0.3549\n", + "0.355\n", + "0.3551\n", + "0.3552\n", + "0.3553\n", + "0.3554\n", + "0.3555\n", + "0.3556\n", + "0.3557\n", + "0.3558\n", + "0.3559\n", + "0.356\n", + "0.3561\n", + "0.3562\n", + "0.3563\n", + "0.3564\n", + "0.3565\n", + "0.3566\n", + "0.3567\n", + "0.3568\n", + "0.3569\n", + "0.357\n", + "0.3571\n", + "0.3572\n", + "0.3573\n", + "0.3574\n", + "0.3575\n", + "0.3576\n", + "0.3577\n", + "0.3578\n", + "0.3579\n", + "0.358\n", + "0.3581\n", + "0.3582\n", + "0.3583\n", + "0.3584\n", + "0.3585\n", + "0.3586\n", + "0.3587\n", + "0.3588\n", + "0.3589\n", + "0.359\n", + "0.3591\n", + "0.3592\n", + "0.3593\n", + "0.3594\n", + "0.3595\n", + "0.3596\n", + "0.3597\n", + "0.3598\n", + "0.3599\n", + "0.36\n", + "0.3601\n", + "0.3602\n", + "0.3603\n", + "0.3604\n", + "0.3605\n", + "0.3606\n", + "0.3607\n", + "0.3608\n", + "0.3609\n", + "0.361\n", + "0.3611\n", + "0.3612\n", + "0.3613\n", + "0.3614\n", + "0.3615\n", + "0.3616\n", + "0.3617\n", + "0.3618\n", + "0.3619\n", + "0.362\n", + "0.3621\n", + "0.3622\n", + "0.3623\n", + "0.3624\n", + "0.3625\n", + "0.3626\n", + "0.3627\n", + "0.3628\n", + "0.3629\n", + "0.363\n", + "0.3631\n", + "0.3632\n", + "0.3633\n", + "0.3634\n", + "0.3635\n", + "0.3636\n", + "0.3637\n", + "0.3638\n", + "0.3639\n", + "0.364\n", + "0.3641\n", + "0.3642\n", + "0.3643\n", + "0.3644\n", + "0.3645\n", + "0.3646\n", + "0.3647\n", + "0.3648\n", + "0.3649\n", + "0.365\n", + "0.3651\n", + "0.3652\n", + "0.3653\n", + "0.3654\n", + "0.3655\n", + "0.3656\n", + "0.3657\n", + "0.3658\n", + "0.3659\n", + "0.366\n", + "0.3661\n", + "0.3662\n", + "0.3663\n", + "0.3664\n", + "0.3665\n", + "0.3666\n", + "0.3667\n", + "0.3668\n", + "0.3669\n", + "0.367\n", + "0.3671\n", + "0.3672\n", + "0.3673\n", + "0.3674\n", + "0.3675\n", + "0.3676\n", + "0.3677\n", + "0.3678\n", + "0.3679\n", + "0.368\n", + "0.3681\n", + "0.3682\n", + "0.3683\n", + "0.3684\n", + "0.3685\n", + "0.3686\n", + "0.3687\n", + "0.3688\n", + "0.3689\n", + "0.369\n", + "0.3691\n", + "0.3692\n", + "0.3693\n", + "0.3694\n", + "0.3695\n", + "0.3696\n", + "0.3697\n", + "0.3698\n", + "0.3699\n", + "0.37\n", + "0.3701\n", + "0.3702\n", + "0.3703\n", + "0.3704\n", + "0.3705\n", + "0.3706\n", + "0.3707\n", + "0.3708\n", + "0.3709\n", + "0.371\n", + "0.3711\n", + "0.3712\n", + "0.3713\n", + "0.3714\n", + "0.3715\n", + "0.3716\n", + "0.3717\n", + "0.3718\n", + "0.3719\n", + "0.372\n", + "0.3721\n", + "0.3722\n", + "0.3723\n", + "0.3724\n", + "0.3725\n", + "0.3726\n", + "0.3727\n", + "0.3728\n", + "0.3729\n", + "0.373\n", + "0.3731\n", + "0.3732\n", + "0.3733\n", + "0.3734\n", + "0.3735\n", + "0.3736\n", + "0.3737\n", + "0.3738\n", + "0.3739\n", + "0.374\n", + "0.3741\n", + "0.3742\n", + "0.3743\n", + "0.3744\n", + "0.3745\n", + "0.3746\n", + "0.3747\n", + "0.3748\n", + "0.3749\n", + "0.375\n", + "0.3751\n", + "0.3752\n", + "0.3753\n", + "0.3754\n", + "0.3755\n", + "0.3756\n", + "0.3757\n", + "0.3758\n", + "0.3759\n", + "0.376\n", + "0.3761\n", + "0.3762\n", + "0.3763\n", + "0.3764\n", + "0.3765\n", + "0.3766\n", + "0.3767\n", + "0.3768\n", + "0.3769\n", + "0.377\n", + "0.3771\n", + "0.3772\n", + "0.3773\n", + "0.3774\n", + "0.3775\n", + "0.3776\n", + "0.3777\n", + "0.3778\n", + "0.3779\n", + "0.378\n", + "0.3781\n", + "0.3782\n", + "0.3783\n", + "0.3784\n", + "0.3785\n", + "0.3786\n", + "0.3787\n", + "0.3788\n", + "0.3789\n", + "0.379\n", + "0.3791\n", + "0.3792\n", + "0.3793\n", + "0.3794\n", + "0.3795\n", + "0.3796\n", + "0.3797\n", + "0.3798\n", + "0.3799\n", + "0.38\n", + "0.3801\n", + "0.3802\n", + "0.3803\n", + "0.3804\n", + "0.3805\n", + "0.3806\n", + "0.3807\n", + "0.3808\n", + "0.3809\n", + "0.381\n", + "0.3811\n", + "0.3812\n", + "0.3813\n", + "0.3814\n", + "0.3815\n", + "0.3816\n", + "0.3817\n", + "0.3818\n", + "0.3819\n", + "0.382\n", + "0.3821\n", + "0.3822\n", + "0.3823\n", + "0.3824\n", + "0.3825\n", + "0.3826\n", + "0.3827\n", + "0.3828\n", + "0.3829\n", + "0.383\n", + "0.3831\n", + "0.3832\n", + "0.3833\n", + "0.3834\n", + "0.3835\n", + "0.3836\n", + "0.3837\n", + "0.3838\n", + "0.3839\n", + "0.384\n", + "0.3841\n", + "0.3842\n", + "0.3843\n", + "0.3844\n", + "0.3845\n", + "0.3846\n", + "0.3847\n", + "0.3848\n", + "0.3849\n", + "0.385\n", + "0.3851\n", + "0.3852\n", + "0.3853\n", + "0.3854\n", + "0.3855\n", + "0.3856\n", + "0.3857\n", + "0.3858\n", + "0.3859\n", + "0.386\n", + "0.3861\n", + "0.3862\n", + "0.3863\n", + "0.3864\n", + "0.3865\n", + "0.3866\n", + "0.3867\n", + "0.3868\n", + "0.3869\n", + "0.387\n", + "0.3871\n", + "0.3872\n", + "0.3873\n", + "0.3874\n", + "0.3875\n", + "0.3876\n", + "0.3877\n", + "0.3878\n", + "0.3879\n", + "0.388\n", + "0.3881\n", + "0.3882\n", + "0.3883\n", + "0.3884\n", + "0.3885\n", + "0.3886\n", + "0.3887\n", + "0.3888\n", + "0.3889\n", + "0.389\n", + "0.3891\n", + "0.3892\n", + "0.3893\n", + "0.3894\n", + "0.3895\n", + "0.3896\n", + "0.3897\n", + "0.3898\n", + "0.3899\n", + "0.39\n", + "0.3901\n", + "0.3902\n", + "0.3903\n", + "0.3904\n", + "0.3905\n", + "0.3906\n", + "0.3907\n", + "0.3908\n", + "0.3909\n", + "0.391\n", + "0.3911\n", + "0.3912\n", + "0.3913\n", + "0.3914\n", + "0.3915\n", + "0.3916\n", + "0.3917\n", + "0.3918\n", + "0.3919\n", + "0.392\n", + "0.3921\n", + "0.3922\n", + "0.3923\n", + "0.3924\n", + "0.3925\n", + "0.3926\n", + "0.3927\n", + "0.3928\n", + "0.3929\n", + "0.393\n", + "0.3931\n", + "0.3932\n", + "0.3933\n", + "0.3934\n", + "0.3935\n", + "0.3936\n", + "0.3937\n", + "0.3938\n", + "0.3939\n", + "0.394\n", + "0.3941\n", + "0.3942\n", + "0.3943\n", + "0.3944\n", + "0.3945\n", + "0.3946\n", + "0.3947\n", + "0.3948\n", + "0.3949\n", + "0.395\n", + "0.3951\n", + "0.3952\n", + "0.3953\n", + "0.3954\n", + "0.3955\n", + "0.3956\n", + "0.3957\n", + "0.3958\n", + "0.3959\n", + "0.396\n", + "0.3961\n", + "0.3962\n", + "0.3963\n", + "0.3964\n", + "0.3965\n", + "0.3966\n", + "0.3967\n", + "0.3968\n", + "0.3969\n", + "0.397\n", + "0.3971\n", + "0.3972\n", + "0.3973\n", + "0.3974\n", + "0.3975\n", + "0.3976\n", + "0.3977\n", + "0.3978\n", + "0.3979\n", + "0.398\n", + "0.3981\n", + "0.3982\n", + "0.3983\n", + "0.3984\n", + "0.3985\n", + "0.3986\n", + "0.3987\n", + "0.3988\n", + "0.3989\n", + "0.399\n", + "0.3991\n", + "0.3992\n", + "0.3993\n", + "0.3994\n", + "0.3995\n", + "0.3996\n", + "0.3997\n", + "0.3998\n", + "0.3999\n", + "0.4\n", + "0.4001\n", + "0.4002\n", + "0.4003\n", + "0.4004\n", + "0.4005\n", + "0.4006\n", + "0.4007\n", + "0.4008\n", + "0.4009\n", + "0.401\n", + "0.4011\n", + "0.4012\n", + "0.4013\n", + "0.4014\n", + "0.4015\n", + "0.4016\n", + "0.4017\n", + "0.4018\n", + "0.4019\n", + "0.402\n", + "0.4021\n", + "0.4022\n", + "0.4023\n", + "0.4024\n", + "0.4025\n", + "0.4026\n", + "0.4027\n", + "0.4028\n", + "0.4029\n", + "0.403\n", + "0.4031\n", + "0.4032\n", + "0.4033\n", + "0.4034\n", + "0.4035\n", + "0.4036\n", + "0.4037\n", + "0.4038\n", + "0.4039\n", + "0.404\n", + "0.4041\n", + "0.4042\n", + "0.4043\n", + "0.4044\n", + "0.4045\n", + "0.4046\n", + "0.4047\n", + "0.4048\n", + "0.4049\n", + "0.405\n", + "0.4051\n", + "0.4052\n", + "0.4053\n", + "0.4054\n", + "0.4055\n", + "0.4056\n", + "0.4057\n", + "0.4058\n", + "0.4059\n", + "0.406\n", + "0.4061\n", + "0.4062\n", + "0.4063\n", + "0.4064\n", + "0.4065\n", + "0.4066\n", + "0.4067\n", + "0.4068\n", + "0.4069\n", + "0.407\n", + "0.4071\n", + "0.4072\n", + "0.4073\n", + "0.4074\n", + "0.4075\n", + "0.4076\n", + "0.4077\n", + "0.4078\n", + "0.4079\n", + "0.408\n", + "0.4081\n", + "0.4082\n", + "0.4083\n", + "0.4084\n", + "0.4085\n", + "0.4086\n", + "0.4087\n", + "0.4088\n", + "0.4089\n", + "0.409\n", + "0.4091\n", + "0.4092\n", + "0.4093\n", + "0.4094\n", + "0.4095\n", + "0.4096\n", + "0.4097\n", + "0.4098\n", + "0.4099\n", + "0.41\n", + "0.4101\n", + "0.4102\n", + "0.4103\n", + "0.4104\n", + "0.4105\n", + "0.4106\n", + "0.4107\n", + "0.4108\n", + "0.4109\n", + "0.411\n", + "0.4111\n", + "0.4112\n", + "0.4113\n", + "0.4114\n", + "0.4115\n", + "0.4116\n", + "0.4117\n", + "0.4118\n", + "0.4119\n", + "0.412\n", + "0.4121\n", + "0.4122\n", + "0.4123\n", + "0.4124\n", + "0.4125\n", + "0.4126\n", + "0.4127\n", + "0.4128\n", + "0.4129\n", + "0.413\n", + "0.4131\n", + "0.4132\n", + "0.4133\n", + "0.4134\n", + "0.4135\n", + "0.4136\n", + "0.4137\n", + "0.4138\n", + "0.4139\n", + "0.414\n", + "0.4141\n", + "0.4142\n", + "0.4143\n", + "0.4144\n", + "0.4145\n", + "0.4146\n", + "0.4147\n", + "0.4148\n", + "0.4149\n", + "0.415\n", + "0.4151\n", + "0.4152\n", + "0.4153\n", + "0.4154\n", + "0.4155\n", + "0.4156\n", + "0.4157\n", + "0.4158\n", + "0.4159\n", + "0.416\n", + "0.4161\n", + "0.4162\n", + "0.4163\n", + "0.4164\n", + "0.4165\n", + "0.4166\n", + "0.4167\n", + "0.4168\n", + "0.4169\n", + "0.417\n", + "0.4171\n", + "0.4172\n", + "0.4173\n", + "0.4174\n", + "0.4175\n", + "0.4176\n", + "0.4177\n", + "0.4178\n", + "0.4179\n", + "0.418\n", + "0.4181\n", + "0.4182\n", + "0.4183\n", + "0.4184\n", + "0.4185\n", + "0.4186\n", + "0.4187\n", + "0.4188\n", + "0.4189\n", + "0.419\n", + "0.4191\n", + "0.4192\n", + "0.4193\n", + "0.4194\n", + "0.4195\n", + "0.4196\n", + "0.4197\n", + "0.4198\n", + "0.4199\n", + "0.42\n", + "0.4201\n", + "0.4202\n", + "0.4203\n", + "0.4204\n", + "0.4205\n", + "0.4206\n", + "0.4207\n", + "0.4208\n", + "0.4209\n", + "0.421\n", + "0.4211\n", + "0.4212\n", + "0.4213\n", + "0.4214\n", + "0.4215\n", + "0.4216\n", + "0.4217\n", + "0.4218\n", + "0.4219\n", + "0.422\n", + "0.4221\n", + "0.4222\n", + "0.4223\n", + "0.4224\n", + "0.4225\n", + "0.4226\n", + "0.4227\n", + "0.4228\n", + "0.4229\n", + "0.423\n", + "0.4231\n", + "0.4232\n", + "0.4233\n", + "0.4234\n", + "0.4235\n", + "0.4236\n", + "0.4237\n", + "0.4238\n", + "0.4239\n", + "0.424\n", + "0.4241\n", + "0.4242\n", + "0.4243\n", + "0.4244\n", + "0.4245\n", + "0.4246\n", + "0.4247\n", + "0.4248\n", + "0.4249\n", + "0.425\n", + "0.4251\n", + "0.4252\n", + "0.4253\n", + "0.4254\n", + "0.4255\n", + "0.4256\n", + "0.4257\n", + "0.4258\n", + "0.4259\n", + "0.426\n", + "0.4261\n", + "0.4262\n", + "0.4263\n", + "0.4264\n", + "0.4265\n", + "0.4266\n", + "0.4267\n", + "0.4268\n", + "0.4269\n", + "0.427\n", + "0.4271\n", + "0.4272\n", + "0.4273\n", + "0.4274\n", + "0.4275\n", + "0.4276\n", + "0.4277\n", + "0.4278\n", + "0.4279\n", + "0.428\n", + "0.4281\n", + "0.4282\n", + "0.4283\n", + "0.4284\n", + "0.4285\n", + "0.4286\n", + "0.4287\n", + "0.4288\n", + "0.4289\n", + "0.429\n", + "0.4291\n", + "0.4292\n", + "0.4293\n", + "0.4294\n", + "0.4295\n", + "0.4296\n", + "0.4297\n", + "0.4298\n", + "0.4299\n", + "0.43\n", + "0.4301\n", + "0.4302\n", + "0.4303\n", + "0.4304\n", + "0.4305\n", + "0.4306\n", + "0.4307\n", + "0.4308\n", + "0.4309\n", + "0.431\n", + "0.4311\n", + "0.4312\n", + "0.4313\n", + "0.4314\n", + "0.4315\n", + "0.4316\n", + "0.4317\n", + "0.4318\n", + "0.4319\n", + "0.432\n", + "0.4321\n", + "0.4322\n", + "0.4323\n", + "0.4324\n", + "0.4325\n", + "0.4326\n", + "0.4327\n", + "0.4328\n", + "0.4329\n", + "0.433\n", + "0.4331\n", + "0.4332\n", + "0.4333\n", + "0.4334\n", + "0.4335\n", + "0.4336\n", + "0.4337\n", + "0.4338\n", + "0.4339\n", + "0.434\n", + "0.4341\n", + "0.4342\n", + "0.4343\n", + "0.4344\n", + "0.4345\n", + "0.4346\n", + "0.4347\n", + "0.4348\n", + "0.4349\n", + "0.435\n", + "0.4351\n", + "0.4352\n", + "0.4353\n", + "0.4354\n", + "0.4355\n", + "0.4356\n", + "0.4357\n", + "0.4358\n", + "0.4359\n", + "0.436\n", + "0.4361\n", + "0.4362\n", + "0.4363\n", + "0.4364\n", + "0.4365\n", + "0.4366\n", + "0.4367\n", + "0.4368\n", + "0.4369\n", + "0.437\n", + "0.4371\n", + "0.4372\n", + "0.4373\n", + "0.4374\n", + "0.4375\n", + "0.4376\n", + "0.4377\n", + "0.4378\n", + "0.4379\n", + "0.438\n", + "0.4381\n", + "0.4382\n", + "0.4383\n", + "0.4384\n", + "0.4385\n", + "0.4386\n", + "0.4387\n", + "0.4388\n", + "0.4389\n", + "0.439\n", + "0.4391\n", + "0.4392\n", + "0.4393\n", + "0.4394\n", + "0.4395\n", + "0.4396\n", + "0.4397\n", + "0.4398\n", + "0.4399\n", + "0.44\n", + "0.4401\n", + "0.4402\n", + "0.4403\n", + "0.4404\n", + "0.4405\n", + "0.4406\n", + "0.4407\n", + "0.4408\n", + "0.4409\n", + "0.441\n", + "0.4411\n", + "0.4412\n", + "0.4413\n", + "0.4414\n", + "0.4415\n", + "0.4416\n", + "0.4417\n", + "0.4418\n", + "0.4419\n", + "0.442\n", + "0.4421\n", + "0.4422\n", + "0.4423\n", + "0.4424\n", + "0.4425\n", + "0.4426\n", + "0.4427\n", + "0.4428\n", + "0.4429\n", + "0.443\n", + "0.4431\n", + "0.4432\n", + "0.4433\n", + "0.4434\n", + "0.4435\n", + "0.4436\n", + "0.4437\n", + "0.4438\n", + "0.4439\n", + "0.444\n", + "0.4441\n", + "0.4442\n", + "0.4443\n", + "0.4444\n", + "0.4445\n", + "0.4446\n", + "0.4447\n", + "0.4448\n", + "0.4449\n", + "0.445\n", + "0.4451\n", + "0.4452\n", + "0.4453\n", + "0.4454\n", + "0.4455\n", + "0.4456\n", + "0.4457\n", + "0.4458\n", + "0.4459\n", + "0.446\n", + "0.4461\n", + "0.4462\n", + "0.4463\n", + "0.4464\n", + "0.4465\n", + "0.4466\n", + "0.4467\n", + "0.4468\n", + "0.4469\n", + "0.447\n", + "0.4471\n", + "0.4472\n", + "0.4473\n", + "0.4474\n", + "0.4475\n", + "0.4476\n", + "0.4477\n", + "0.4478\n", + "0.4479\n", + "0.448\n", + "0.4481\n", + "0.4482\n", + "0.4483\n", + "0.4484\n", + "0.4485\n", + "0.4486\n", + "0.4487\n", + "0.4488\n", + "0.4489\n", + "0.449\n", + "0.4491\n", + "0.4492\n", + "0.4493\n", + "0.4494\n", + "0.4495\n", + "0.4496\n", + "0.4497\n", + "0.4498\n", + "0.4499\n", + "0.45\n", + "0.4501\n", + "0.4502\n", + "0.4503\n", + "0.4504\n", + "0.4505\n", + "0.4506\n", + "0.4507\n", + "0.4508\n", + "0.4509\n", + "0.451\n", + "0.4511\n", + "0.4512\n", + "0.4513\n", + "0.4514\n", + "0.4515\n", + "0.4516\n", + "0.4517\n", + "0.4518\n", + "0.4519\n", + "0.452\n", + "0.4521\n", + "0.4522\n", + "0.4523\n", + "0.4524\n", + "0.4525\n", + "0.4526\n", + "0.4527\n", + "0.4528\n", + "0.4529\n", + "0.453\n", + "0.4531\n", + "0.4532\n", + "0.4533\n", + "0.4534\n", + "0.4535\n", + "0.4536\n", + "0.4537\n", + "0.4538\n", + "0.4539\n", + "0.454\n", + "0.4541\n", + "0.4542\n", + "0.4543\n", + "0.4544\n", + "0.4545\n", + "0.4546\n", + "0.4547\n", + "0.4548\n", + "0.4549\n", + "0.455\n", + "0.4551\n", + "0.4552\n", + "0.4553\n", + "0.4554\n", + "0.4555\n", + "0.4556\n", + "0.4557\n", + "0.4558\n", + "0.4559\n", + "0.456\n", + "0.4561\n", + "0.4562\n", + "0.4563\n", + "0.4564\n", + "0.4565\n", + "0.4566\n", + "0.4567\n", + "0.4568\n", + "0.4569\n", + "0.457\n", + "0.4571\n", + "0.4572\n", + "0.4573\n", + "0.4574\n", + "0.4575\n", + "0.4576\n", + "0.4577\n", + "0.4578\n", + "0.4579\n", + "0.458\n", + "0.4581\n", + "0.4582\n", + "0.4583\n", + "0.4584\n", + "0.4585\n", + "0.4586\n", + "0.4587\n", + "0.4588\n", + "0.4589\n", + "0.459\n", + "0.4591\n", + "0.4592\n", + "0.4593\n", + "0.4594\n", + "0.4595\n", + "0.4596\n", + "0.4597\n", + "0.4598\n", + "0.4599\n", + "0.46\n", + "0.4601\n", + "0.4602\n", + "0.4603\n", + "0.4604\n", + "0.4605\n", + "0.4606\n", + "0.4607\n", + "0.4608\n", + "0.4609\n", + "0.461\n", + "0.4611\n", + "0.4612\n", + "0.4613\n", + "0.4614\n", + "0.4615\n", + "0.4616\n", + "0.4617\n", + "0.4618\n", + "0.4619\n", + "0.462\n", + "0.4621\n", + "0.4622\n", + "0.4623\n", + "0.4624\n", + "0.4625\n", + "0.4626\n", + "0.4627\n", + "0.4628\n", + "0.4629\n", + "0.463\n", + "0.4631\n", + "0.4632\n", + "0.4633\n", + "0.4634\n", + "0.4635\n", + "0.4636\n", + "0.4637\n", + "0.4638\n", + "0.4639\n", + "0.464\n", + "0.4641\n", + "0.4642\n", + "0.4643\n", + "0.4644\n", + "0.4645\n", + "0.4646\n", + "0.4647\n", + "0.4648\n", + "0.4649\n", + "0.465\n", + "0.4651\n", + "0.4652\n", + "0.4653\n", + "0.4654\n", + "0.4655\n", + "0.4656\n", + "0.4657\n", + "0.4658\n", + "0.4659\n", + "0.466\n", + "0.4661\n", + "0.4662\n", + "0.4663\n", + "0.4664\n", + "0.4665\n", + "0.4666\n", + "0.4667\n", + "0.4668\n", + "0.4669\n", + "0.467\n", + "0.4671\n", + "0.4672\n", + "0.4673\n", + "0.4674\n", + "0.4675\n", + "0.4676\n", + "0.4677\n", + "0.4678\n", + "0.4679\n", + "0.468\n", + "0.4681\n", + "0.4682\n", + "0.4683\n", + "0.4684\n", + "0.4685\n", + "0.4686\n", + "0.4687\n", + "0.4688\n", + "0.4689\n", + "0.469\n", + "0.4691\n", + "0.4692\n", + "0.4693\n", + "0.4694\n", + "0.4695\n", + "0.4696\n", + "0.4697\n", + "0.4698\n", + "0.4699\n", + "0.47\n", + "0.4701\n", + "0.4702\n", + "0.4703\n", + "0.4704\n", + "0.4705\n", + "0.4706\n", + "0.4707\n", + "0.4708\n", + "0.4709\n", + "0.471\n", + "0.4711\n", + "0.4712\n", + "0.4713\n", + "0.4714\n", + "0.4715\n", + "0.4716\n", + "0.4717\n", + "0.4718\n", + "0.4719\n", + "0.472\n", + "0.4721\n", + "0.4722\n", + "0.4723\n", + "0.4724\n", + "0.4725\n", + "0.4726\n", + "0.4727\n", + "0.4728\n", + "0.4729\n", + "0.473\n", + "0.4731\n", + "0.4732\n", + "0.4733\n", + "0.4734\n", + "0.4735\n", + "0.4736\n", + "0.4737\n", + "0.4738\n", + "0.4739\n", + "0.474\n", + "0.4741\n", + "0.4742\n", + "0.4743\n", + "0.4744\n", + "0.4745\n", + "0.4746\n", + "0.4747\n", + "0.4748\n", + "0.4749\n", + "0.475\n", + "0.4751\n", + "0.4752\n", + "0.4753\n", + "0.4754\n", + "0.4755\n", + "0.4756\n", + "0.4757\n", + "0.4758\n", + "0.4759\n", + "0.476\n", + "0.4761\n", + "0.4762\n", + "0.4763\n", + "0.4764\n", + "0.4765\n", + "0.4766\n", + "0.4767\n", + "0.4768\n", + "0.4769\n", + "0.477\n", + "0.4771\n", + "0.4772\n", + "0.4773\n", + "0.4774\n", + "0.4775\n", + "0.4776\n", + "0.4777\n", + "0.4778\n", + "0.4779\n", + "0.478\n", + "0.4781\n", + "0.4782\n", + "0.4783\n", + "0.4784\n", + "0.4785\n", + "0.4786\n", + "0.4787\n", + "0.4788\n", + "0.4789\n", + "0.479\n", + "0.4791\n", + "0.4792\n", + "0.4793\n", + "0.4794\n", + "0.4795\n", + "0.4796\n", + "0.4797\n", + "0.4798\n", + "0.4799\n", + "0.48\n", + "0.4801\n", + "0.4802\n", + "0.4803\n", + "0.4804\n", + "0.4805\n", + "0.4806\n", + "0.4807\n", + "0.4808\n", + "0.4809\n", + "0.481\n", + "0.4811\n", + "0.4812\n", + "0.4813\n", + "0.4814\n", + "0.4815\n", + "0.4816\n", + "0.4817\n", + "0.4818\n", + "0.4819\n", + "0.482\n", + "0.4821\n", + "0.4822\n", + "0.4823\n", + "0.4824\n", + "0.4825\n", + "0.4826\n", + "0.4827\n", + "0.4828\n", + "0.4829\n", + "0.483\n", + "0.4831\n", + "0.4832\n", + "0.4833\n", + "0.4834\n", + "0.4835\n", + "0.4836\n", + "0.4837\n", + "0.4838\n", + "0.4839\n", + "0.484\n", + "0.4841\n", + "0.4842\n", + "0.4843\n", + "0.4844\n", + "0.4845\n", + "0.4846\n", + "0.4847\n", + "0.4848\n", + "0.4849\n", + "0.485\n", + "0.4851\n", + "0.4852\n", + "0.4853\n", + "0.4854\n", + "0.4855\n", + "0.4856\n", + "0.4857\n", + "0.4858\n", + "0.4859\n", + "0.486\n", + "0.4861\n", + "0.4862\n", + "0.4863\n", + "0.4864\n", + "0.4865\n", + "0.4866\n", + "0.4867\n", + "0.4868\n", + "0.4869\n", + "0.487\n", + "0.4871\n", + "0.4872\n", + "0.4873\n", + "0.4874\n", + "0.4875\n", + "0.4876\n", + "0.4877\n", + "0.4878\n", + "0.4879\n", + "0.488\n", + "0.4881\n", + "0.4882\n", + "0.4883\n", + "0.4884\n", + "0.4885\n", + "0.4886\n", + "0.4887\n", + "0.4888\n", + "0.4889\n", + "0.489\n", + "0.4891\n", + "0.4892\n", + "0.4893\n", + "0.4894\n", + "0.4895\n", + "0.4896\n", + "0.4897\n", + "0.4898\n", + "0.4899\n", + "0.49\n", + "0.4901\n", + "0.4902\n", + "0.4903\n", + "0.4904\n", + "0.4905\n", + "0.4906\n", + "0.4907\n", + "0.4908\n", + "0.4909\n", + "0.491\n", + "0.4911\n", + "0.4912\n", + "0.4913\n", + "0.4914\n", + "0.4915\n", + "0.4916\n", + "0.4917\n", + "0.4918\n", + "0.4919\n", + "0.492\n", + "0.4921\n", + "0.4922\n", + "0.4923\n", + "0.4924\n", + "0.4925\n", + "0.4926\n", + "0.4927\n", + "0.4928\n", + "0.4929\n", + "0.493\n", + "0.4931\n", + "0.4932\n", + "0.4933\n", + "0.4934\n", + "0.4935\n", + "0.4936\n", + "0.4937\n", + "0.4938\n", + "0.4939\n", + "0.494\n", + "0.4941\n", + "0.4942\n", + "0.4943\n", + "0.4944\n", + "0.4945\n", + "0.4946\n", + "0.4947\n", + "0.4948\n", + "0.4949\n", + "0.495\n", + "0.4951\n", + "0.4952\n", + "0.4953\n", + "0.4954\n", + "0.4955\n", + "0.4956\n", + "0.4957\n", + "0.4958\n", + "0.4959\n", + "0.496\n", + "0.4961\n", + "0.4962\n", + "0.4963\n", + "0.4964\n", + "0.4965\n", + "0.4966\n", + "0.4967\n", + "0.4968\n", + "0.4969\n", + "0.497\n", + "0.4971\n", + "0.4972\n", + "0.4973\n", + "0.4974\n", + "0.4975\n", + "0.4976\n", + "0.4977\n", + "0.4978\n", + "0.4979\n", + "0.498\n", + "0.4981\n", + "0.4982\n", + "0.4983\n", + "0.4984\n", + "0.4985\n", + "0.4986\n", + "0.4987\n", + "0.4988\n", + "0.4989\n", + "0.499\n", + "0.4991\n", + "0.4992\n", + "0.4993\n", + "0.4994\n", + "0.4995\n", + "0.4996\n", + "0.4997\n", + "0.4998\n", + "0.4999\n", + "0.5\n", + "0.5001\n", + "0.5002\n", + "0.5003\n", + "0.5004\n", + "0.5005\n", + "0.5006\n", + "0.5007\n", + "0.5008\n", + "0.5009\n", + "0.501\n", + "0.5011\n", + "0.5012\n", + "0.5013\n", + "0.5014\n", + "0.5015\n", + "0.5016\n", + "0.5017\n", + "0.5018\n", + "0.5019\n", + "0.502\n", + "0.5021\n", + "0.5022\n", + "0.5023\n", + "0.5024\n", + "0.5025\n", + "0.5026\n", + "0.5027\n", + "0.5028\n", + "0.5029\n", + "0.503\n", + "0.5031\n", + "0.5032\n", + "0.5033\n", + "0.5034\n", + "0.5035\n", + "0.5036\n", + "0.5037\n", + "0.5038\n", + "0.5039\n", + "0.504\n", + "0.5041\n", + "0.5042\n", + "0.5043\n", + "0.5044\n", + "0.5045\n", + "0.5046\n", + "0.5047\n", + "0.5048\n", + "0.5049\n", + "0.505\n", + "0.5051\n", + "0.5052\n", + "0.5053\n", + "0.5054\n", + "0.5055\n", + "0.5056\n", + "0.5057\n", + "0.5058\n", + "0.5059\n", + "0.506\n", + "0.5061\n", + "0.5062\n", + "0.5063\n", + "0.5064\n", + "0.5065\n", + "0.5066\n", + "0.5067\n", + "0.5068\n", + "0.5069\n", + "0.507\n", + "0.5071\n", + "0.5072\n", + "0.5073\n", + "0.5074\n", + "0.5075\n", + "0.5076\n", + "0.5077\n", + "0.5078\n", + "0.5079\n", + "0.508\n", + "0.5081\n", + "0.5082\n", + "0.5083\n", + "0.5084\n", + "0.5085\n", + "0.5086\n", + "0.5087\n", + "0.5088\n", + "0.5089\n", + "0.509\n", + "0.5091\n", + "0.5092\n", + "0.5093\n", + "0.5094\n", + "0.5095\n", + "0.5096\n", + "0.5097\n", + "0.5098\n", + "0.5099\n", + "0.51\n", + "0.5101\n", + "0.5102\n", + "0.5103\n", + "0.5104\n", + "0.5105\n", + "0.5106\n", + "0.5107\n", + "0.5108\n", + "0.5109\n", + "0.511\n", + "0.5111\n", + "0.5112\n", + "0.5113\n", + "0.5114\n", + "0.5115\n", + "0.5116\n", + "0.5117\n", + "0.5118\n", + "0.5119\n", + "0.512\n", + "0.5121\n", + "0.5122\n", + "0.5123\n", + "0.5124\n", + "0.5125\n", + "0.5126\n", + "0.5127\n", + "0.5128\n", + "0.5129\n", + "0.513\n", + "0.5131\n", + "0.5132\n", + "0.5133\n", + "0.5134\n", + "0.5135\n", + "0.5136\n", + "0.5137\n", + "0.5138\n", + "0.5139\n", + "0.514\n", + "0.5141\n", + "0.5142\n", + "0.5143\n", + "0.5144\n", + "0.5145\n", + "0.5146\n", + "0.5147\n", + "0.5148\n", + "0.5149\n", + "0.515\n", + "0.5151\n", + "0.5152\n", + "0.5153\n", + "0.5154\n", + "0.5155\n", + "0.5156\n", + "0.5157\n", + "0.5158\n", + "0.5159\n", + "0.516\n", + "0.5161\n", + "0.5162\n", + "0.5163\n", + "0.5164\n", + "0.5165\n", + "0.5166\n", + "0.5167\n", + "0.5168\n", + "0.5169\n", + "0.517\n", + "0.5171\n", + "0.5172\n", + "0.5173\n", + "0.5174\n", + "0.5175\n", + "0.5176\n", + "0.5177\n", + "0.5178\n", + "0.5179\n", + "0.518\n", + "0.5181\n", + "0.5182\n", + "0.5183\n", + "0.5184\n", + "0.5185\n", + "0.5186\n", + "0.5187\n", + "0.5188\n", + "0.5189\n", + "0.519\n", + "0.5191\n", + "0.5192\n", + "0.5193\n", + "0.5194\n", + "0.5195\n", + "0.5196\n", + "0.5197\n", + "0.5198\n", + "0.5199\n", + "0.52\n", + "0.5201\n", + "0.5202\n", + "0.5203\n", + "0.5204\n", + "0.5205\n", + "0.5206\n", + "0.5207\n", + "0.5208\n", + "0.5209\n", + "0.521\n", + "0.5211\n", + "0.5212\n", + "0.5213\n", + "0.5214\n", + "0.5215\n", + "0.5216\n", + "0.5217\n", + "0.5218\n", + "0.5219\n", + "0.522\n", + "0.5221\n", + "0.5222\n", + "0.5223\n", + "0.5224\n", + "0.5225\n", + "0.5226\n", + "0.5227\n", + "0.5228\n", + "0.5229\n", + "0.523\n", + "0.5231\n", + "0.5232\n", + "0.5233\n", + "0.5234\n", + "0.5235\n", + "0.5236\n", + "0.5237\n", + "0.5238\n", + "0.5239\n", + "0.524\n", + "0.5241\n", + "0.5242\n", + "0.5243\n", + "0.5244\n", + "0.5245\n", + "0.5246\n", + "0.5247\n", + "0.5248\n", + "0.5249\n", + "0.525\n", + "0.5251\n", + "0.5252\n", + "0.5253\n", + "0.5254\n", + "0.5255\n", + "0.5256\n", + "0.5257\n", + "0.5258\n", + "0.5259\n", + "0.526\n", + "0.5261\n", + "0.5262\n", + "0.5263\n", + "0.5264\n", + "0.5265\n", + "0.5266\n", + "0.5267\n", + "0.5268\n", + "0.5269\n", + "0.527\n", + "0.5271\n", + "0.5272\n", + "0.5273\n", + "0.5274\n", + "0.5275\n", + "0.5276\n", + "0.5277\n", + "0.5278\n", + "0.5279\n", + "0.528\n", + "0.5281\n", + "0.5282\n", + "0.5283\n", + "0.5284\n", + "0.5285\n", + "0.5286\n", + "0.5287\n", + "0.5288\n", + "0.5289\n", + "0.529\n", + "0.5291\n", + "0.5292\n", + "0.5293\n", + "0.5294\n", + "0.5295\n", + "0.5296\n", + "0.5297\n", + "0.5298\n", + "0.5299\n", + "0.53\n", + "0.5301\n", + "0.5302\n", + "0.5303\n", + "0.5304\n", + "0.5305\n", + "0.5306\n", + "0.5307\n", + "0.5308\n", + "0.5309\n", + "0.531\n", + "0.5311\n", + "0.5312\n", + "0.5313\n", + "0.5314\n", + "0.5315\n", + "0.5316\n", + "0.5317\n", + "0.5318\n", + "0.5319\n", + "0.532\n", + "0.5321\n", + "0.5322\n", + "0.5323\n", + "0.5324\n", + "0.5325\n", + "0.5326\n", + "0.5327\n", + "0.5328\n", + "0.5329\n", + "0.533\n", + "0.5331\n", + "0.5332\n", + "0.5333\n", + "0.5334\n", + "0.5335\n", + "0.5336\n", + "0.5337\n", + "0.5338\n", + "0.5339\n", + "0.534\n", + "0.5341\n", + "0.5342\n", + "0.5343\n", + "0.5344\n", + "0.5345\n", + "0.5346\n", + "0.5347\n", + "0.5348\n", + "0.5349\n", + "0.535\n", + "0.5351\n", + "0.5352\n", + "0.5353\n", + "0.5354\n", + "0.5355\n", + "0.5356\n", + "0.5357\n", + "0.5358\n", + "0.5359\n", + "0.536\n", + "0.5361\n", + "0.5362\n", + "0.5363\n", + "0.5364\n", + "0.5365\n", + "0.5366\n", + "0.5367\n", + "0.5368\n", + "0.5369\n", + "0.537\n", + "0.5371\n", + "0.5372\n", + "0.5373\n", + "0.5374\n", + "0.5375\n", + "0.5376\n", + "0.5377\n", + "0.5378\n", + "0.5379\n", + "0.538\n", + "0.5381\n", + "0.5382\n", + "0.5383\n", + "0.5384\n", + "0.5385\n", + "0.5386\n", + "0.5387\n", + "0.5388\n", + "0.5389\n", + "0.539\n", + "0.5391\n", + "0.5392\n", + "0.5393\n", + "0.5394\n", + "0.5395\n", + "0.5396\n", + "0.5397\n", + "0.5398\n", + "0.5399\n", + "0.54\n", + "0.5401\n", + "0.5402\n", + "0.5403\n", + "0.5404\n", + "0.5405\n", + "0.5406\n", + "0.5407\n", + "0.5408\n", + "0.5409\n", + "0.541\n", + "0.5411\n", + "0.5412\n", + "0.5413\n", + "0.5414\n", + "0.5415\n", + "0.5416\n", + "0.5417\n", + "0.5418\n", + "0.5419\n", + "0.542\n", + "0.5421\n", + "0.5422\n", + "0.5423\n", + "0.5424\n", + "0.5425\n", + "0.5426\n", + "0.5427\n", + "0.5428\n", + "0.5429\n", + "0.543\n", + "0.5431\n", + "0.5432\n", + "0.5433\n", + "0.5434\n", + "0.5435\n", + "0.5436\n", + "0.5437\n", + "0.5438\n", + "0.5439\n", + "0.544\n", + "0.5441\n", + "0.5442\n", + "0.5443\n", + "0.5444\n", + "0.5445\n", + "0.5446\n", + "0.5447\n", + "0.5448\n", + "0.5449\n", + "0.545\n", + "0.5451\n", + "0.5452\n", + "0.5453\n", + "0.5454\n", + "0.5455\n", + "0.5456\n", + "0.5457\n", + "0.5458\n", + "0.5459\n", + "0.546\n", + "0.5461\n", + "0.5462\n", + "0.5463\n", + "0.5464\n", + "0.5465\n", + "0.5466\n", + "0.5467\n", + "0.5468\n", + "0.5469\n", + "0.547\n", + "0.5471\n", + "0.5472\n", + "0.5473\n", + "0.5474\n", + "0.5475\n", + "0.5476\n", + "0.5477\n", + "0.5478\n", + "0.5479\n", + "0.548\n", + "0.5481\n", + "0.5482\n", + "0.5483\n", + "0.5484\n", + "0.5485\n", + "0.5486\n", + "0.5487\n", + "0.5488\n", + "0.5489\n", + "0.549\n", + "0.5491\n", + "0.5492\n", + "0.5493\n", + "0.5494\n", + "0.5495\n", + "0.5496\n", + "0.5497\n", + "0.5498\n", + "0.5499\n", + "0.55\n", + "0.5501\n", + "0.5502\n", + "0.5503\n", + "0.5504\n", + "0.5505\n", + "0.5506\n", + "0.5507\n", + "0.5508\n", + "0.5509\n", + "0.551\n", + "0.5511\n", + "0.5512\n", + "0.5513\n", + "0.5514\n", + "0.5515\n", + "0.5516\n", + "0.5517\n", + "0.5518\n", + "0.5519\n", + "0.552\n", + "0.5521\n", + "0.5522\n", + "0.5523\n", + "0.5524\n", + "0.5525\n", + "0.5526\n", + "0.5527\n", + "0.5528\n", + "0.5529\n", + "0.553\n", + "0.5531\n", + "0.5532\n", + "0.5533\n", + "0.5534\n", + "0.5535\n", + "0.5536\n", + "0.5537\n", + "0.5538\n", + "0.5539\n", + "0.554\n", + "0.5541\n", + "0.5542\n", + "0.5543\n", + "0.5544\n", + "0.5545\n", + "0.5546\n", + "0.5547\n", + "0.5548\n", + "0.5549\n", + "0.555\n", + "0.5551\n", + "0.5552\n", + "0.5553\n", + "0.5554\n", + "0.5555\n", + "0.5556\n", + "0.5557\n", + "0.5558\n", + "0.5559\n", + "0.556\n", + "0.5561\n", + "0.5562\n", + "0.5563\n", + "0.5564\n", + "0.5565\n", + "0.5566\n", + "0.5567\n", + "0.5568\n", + "0.5569\n", + "0.557\n", + "0.5571\n", + "0.5572\n", + "0.5573\n", + "0.5574\n", + "0.5575\n", + "0.5576\n", + "0.5577\n", + "0.5578\n", + "0.5579\n", + "0.558\n", + "0.5581\n", + "0.5582\n", + "0.5583\n", + "0.5584\n", + "0.5585\n", + "0.5586\n", + "0.5587\n", + "0.5588\n", + "0.5589\n", + "0.559\n", + "0.5591\n", + "0.5592\n", + "0.5593\n", + "0.5594\n", + "0.5595\n", + "0.5596\n", + "0.5597\n", + "0.5598\n", + "0.5599\n", + "0.56\n", + "0.5601\n", + "0.5602\n", + "0.5603\n", + "0.5604\n", + "0.5605\n", + "0.5606\n", + "0.5607\n", + "0.5608\n", + "0.5609\n", + "0.561\n", + "0.5611\n", + "0.5612\n", + "0.5613\n", + "0.5614\n", + "0.5615\n", + "0.5616\n", + "0.5617\n", + "0.5618\n", + "0.5619\n", + "0.562\n", + "0.5621\n", + "0.5622\n", + "0.5623\n", + "0.5624\n", + "0.5625\n", + "0.5626\n", + "0.5627\n", + "0.5628\n", + "0.5629\n", + "0.563\n", + "0.5631\n", + "0.5632\n", + "0.5633\n", + "0.5634\n", + "0.5635\n", + "0.5636\n", + "0.5637\n", + "0.5638\n", + "0.5639\n", + "0.564\n", + "0.5641\n", + "0.5642\n", + "0.5643\n", + "0.5644\n", + "0.5645\n", + "0.5646\n", + "0.5647\n", + "0.5648\n", + "0.5649\n", + "0.565\n", + "0.5651\n", + "0.5652\n", + "0.5653\n", + "0.5654\n", + "0.5655\n", + "0.5656\n", + "0.5657\n", + "0.5658\n", + "0.5659\n", + "0.566\n", + "0.5661\n", + "0.5662\n", + "0.5663\n", + "0.5664\n", + "0.5665\n", + "0.5666\n", + "0.5667\n", + "0.5668\n", + "0.5669\n", + "0.567\n", + "0.5671\n", + "0.5672\n", + "0.5673\n", + "0.5674\n", + "0.5675\n", + "0.5676\n", + "0.5677\n", + "0.5678\n", + "0.5679\n", + "0.568\n", + "0.5681\n", + "0.5682\n", + "0.5683\n", + "0.5684\n", + "0.5685\n", + "0.5686\n", + "0.5687\n", + "0.5688\n", + "0.5689\n", + "0.569\n", + "0.5691\n", + "0.5692\n", + "0.5693\n", + "0.5694\n", + "0.5695\n", + "0.5696\n", + "0.5697\n", + "0.5698\n", + "0.5699\n", + "0.57\n", + "0.5701\n", + "0.5702\n", + "0.5703\n", + "0.5704\n", + "0.5705\n", + "0.5706\n", + "0.5707\n", + "0.5708\n", + "0.5709\n", + "0.571\n", + "0.5711\n", + "0.5712\n", + "0.5713\n", + "0.5714\n", + "0.5715\n", + "0.5716\n", + "0.5717\n", + "0.5718\n", + "0.5719\n", + "0.572\n", + "0.5721\n", + "0.5722\n", + "0.5723\n", + "0.5724\n", + "0.5725\n", + "0.5726\n", + "0.5727\n", + "0.5728\n", + "0.5729\n", + "0.573\n", + "0.5731\n", + "0.5732\n", + "0.5733\n", + "0.5734\n", + "0.5735\n", + "0.5736\n", + "0.5737\n", + "0.5738\n", + "0.5739\n", + "0.574\n", + "0.5741\n", + "0.5742\n", + "0.5743\n", + "0.5744\n", + "0.5745\n", + "0.5746\n", + "0.5747\n", + "0.5748\n", + "0.5749\n", + "0.575\n", + "0.5751\n", + "0.5752\n", + "0.5753\n", + "0.5754\n", + "0.5755\n", + "0.5756\n", + "0.5757\n", + "0.5758\n", + "0.5759\n", + "0.576\n", + "0.5761\n", + "0.5762\n", + "0.5763\n", + "0.5764\n", + "0.5765\n", + "0.5766\n", + "0.5767\n", + "0.5768\n", + "0.5769\n", + "0.577\n", + "0.5771\n", + "0.5772\n", + "0.5773\n", + "0.5774\n", + "0.5775\n", + "0.5776\n", + "0.5777\n", + "0.5778\n", + "0.5779\n", + "0.578\n", + "0.5781\n", + "0.5782\n", + "0.5783\n", + "0.5784\n", + "0.5785\n", + "0.5786\n", + "0.5787\n", + "0.5788\n", + "0.5789\n", + "0.579\n", + "0.5791\n", + "0.5792\n", + "0.5793\n", + "0.5794\n", + "0.5795\n", + "0.5796\n", + "0.5797\n", + "0.5798\n", + "0.5799\n", + "0.58\n", + "0.5801\n", + "0.5802\n", + "0.5803\n", + "0.5804\n", + "0.5805\n", + "0.5806\n", + "0.5807\n", + "0.5808\n", + "0.5809\n", + "0.581\n", + "0.5811\n", + "0.5812\n", + "0.5813\n", + "0.5814\n", + "0.5815\n", + "0.5816\n", + "0.5817\n", + "0.5818\n", + "0.5819\n", + "0.582\n", + "0.5821\n", + "0.5822\n", + "0.5823\n", + "0.5824\n", + "0.5825\n", + "0.5826\n", + "0.5827\n", + "0.5828\n", + "0.5829\n", + "0.583\n", + "0.5831\n", + "0.5832\n", + "0.5833\n", + "0.5834\n", + "0.5835\n", + "0.5836\n", + "0.5837\n", + "0.5838\n", + "0.5839\n", + "0.584\n", + "0.5841\n", + "0.5842\n", + "0.5843\n", + "0.5844\n", + "0.5845\n", + "0.5846\n", + "0.5847\n", + "0.5848\n", + "0.5849\n", + "0.585\n", + "0.5851\n", + "0.5852\n", + "0.5853\n", + "0.5854\n", + "0.5855\n", + "0.5856\n", + "0.5857\n", + "0.5858\n", + "0.5859\n", + "0.586\n", + "0.5861\n", + "0.5862\n", + "0.5863\n", + "0.5864\n", + "0.5865\n", + "0.5866\n", + "0.5867\n", + "0.5868\n", + "0.5869\n", + "0.587\n", + "0.5871\n", + "0.5872\n", + "0.5873\n", + "0.5874\n", + "0.5875\n", + "0.5876\n", + "0.5877\n", + "0.5878\n", + "0.5879\n", + "0.588\n", + "0.5881\n", + "0.5882\n", + "0.5883\n", + "0.5884\n", + "0.5885\n", + "0.5886\n", + "0.5887\n", + "0.5888\n", + "0.5889\n", + "0.589\n", + "0.5891\n", + "0.5892\n", + "0.5893\n", + "0.5894\n", + "0.5895\n", + "0.5896\n", + "0.5897\n", + "0.5898\n", + "0.5899\n", + "0.59\n", + "0.5901\n", + "0.5902\n", + "0.5903\n", + "0.5904\n", + "0.5905\n", + "0.5906\n", + "0.5907\n", + "0.5908\n", + "0.5909\n", + "0.591\n", + "0.5911\n", + "0.5912\n", + "0.5913\n", + "0.5914\n", + "0.5915\n", + "0.5916\n", + "0.5917\n", + "0.5918\n", + "0.5919\n", + "0.592\n", + "0.5921\n", + "0.5922\n", + "0.5923\n", + "0.5924\n", + "0.5925\n", + "0.5926\n", + "0.5927\n", + "0.5928\n", + "0.5929\n", + "0.593\n", + "0.5931\n", + "0.5932\n", + "0.5933\n", + "0.5934\n", + "0.5935\n", + "0.5936\n", + "0.5937\n", + "0.5938\n", + "0.5939\n", + "0.594\n", + "0.5941\n", + "0.5942\n", + "0.5943\n", + "0.5944\n", + "0.5945\n", + "0.5946\n", + "0.5947\n", + "0.5948\n", + "0.5949\n", + "0.595\n", + "0.5951\n", + "0.5952\n", + "0.5953\n", + "0.5954\n", + "0.5955\n", + "0.5956\n", + "0.5957\n", + "0.5958\n", + "0.5959\n", + "0.596\n", + "0.5961\n", + "0.5962\n", + "0.5963\n", + "0.5964\n", + "0.5965\n", + "0.5966\n", + "0.5967\n", + "0.5968\n", + "0.5969\n", + "0.597\n", + "0.5971\n", + "0.5972\n", + "0.5973\n", + "0.5974\n", + "0.5975\n", + "0.5976\n", + "0.5977\n", + "0.5978\n", + "0.5979\n", + "0.598\n", + "0.5981\n", + "0.5982\n", + "0.5983\n", + "0.5984\n", + "0.5985\n", + "0.5986\n", + "0.5987\n", + "0.5988\n", + "0.5989\n", + "0.599\n", + "0.5991\n", + "0.5992\n", + "0.5993\n", + "0.5994\n", + "0.5995\n", + "0.5996\n", + "0.5997\n", + "0.5998\n", + "0.5999\n", + "0.6\n", + "0.6001\n", + "0.6002\n", + "0.6003\n", + "0.6004\n", + "0.6005\n", + "0.6006\n", + "0.6007\n", + "0.6008\n", + "0.6009\n", + "0.601\n", + "0.6011\n", + "0.6012\n", + "0.6013\n", + "0.6014\n", + "0.6015\n", + "0.6016\n", + "0.6017\n", + "0.6018\n", + "0.6019\n", + "0.602\n", + "0.6021\n", + "0.6022\n", + "0.6023\n", + "0.6024\n", + "0.6025\n", + "0.6026\n", + "0.6027\n", + "0.6028\n", + "0.6029\n", + "0.603\n", + "0.6031\n", + "0.6032\n", + "0.6033\n", + "0.6034\n", + "0.6035\n", + "0.6036\n", + "0.6037\n", + "0.6038\n", + "0.6039\n", + "0.604\n", + "0.6041\n", + "0.6042\n", + "0.6043\n", + "0.6044\n", + "0.6045\n", + "0.6046\n", + "0.6047\n", + "0.6048\n", + "0.6049\n", + "0.605\n", + "0.6051\n", + "0.6052\n", + "0.6053\n", + "0.6054\n", + "0.6055\n", + "0.6056\n", + "0.6057\n", + "0.6058\n", + "0.6059\n", + "0.606\n", + "0.6061\n", + "0.6062\n", + "0.6063\n", + "0.6064\n", + "0.6065\n", + "0.6066\n", + "0.6067\n", + "0.6068\n", + "0.6069\n", + "0.607\n", + "0.6071\n", + "0.6072\n", + "0.6073\n", + "0.6074\n", + "0.6075\n", + "0.6076\n", + "0.6077\n", + "0.6078\n", + "0.6079\n", + "0.608\n", + "0.6081\n", + "0.6082\n", + "0.6083\n", + "0.6084\n", + "0.6085\n", + "0.6086\n", + "0.6087\n", + "0.6088\n", + "0.6089\n", + "0.609\n", + "0.6091\n", + "0.6092\n", + "0.6093\n", + "0.6094\n", + "0.6095\n", + "0.6096\n", + "0.6097\n", + "0.6098\n", + "0.6099\n", + "0.61\n", + "0.6101\n", + "0.6102\n", + "0.6103\n", + "0.6104\n", + "0.6105\n", + "0.6106\n", + "0.6107\n", + "0.6108\n", + "0.6109\n", + "0.611\n", + "0.6111\n", + "0.6112\n", + "0.6113\n", + "0.6114\n", + "0.6115\n", + "0.6116\n", + "0.6117\n", + "0.6118\n", + "0.6119\n", + "0.612\n", + "0.6121\n", + "0.6122\n", + "0.6123\n", + "0.6124\n", + "0.6125\n", + "0.6126\n", + "0.6127\n", + "0.6128\n", + "0.6129\n", + "0.613\n", + "0.6131\n", + "0.6132\n", + "0.6133\n", + "0.6134\n", + "0.6135\n", + "0.6136\n", + "0.6137\n", + "0.6138\n", + "0.6139\n", + "0.614\n", + "0.6141\n", + "0.6142\n", + "0.6143\n", + "0.6144\n", + "0.6145\n", + "0.6146\n", + "0.6147\n", + "0.6148\n", + "0.6149\n", + "0.615\n", + "0.6151\n", + "0.6152\n", + "0.6153\n", + "0.6154\n", + "0.6155\n", + "0.6156\n", + "0.6157\n", + "0.6158\n", + "0.6159\n", + "0.616\n", + "0.6161\n", + "0.6162\n", + "0.6163\n", + "0.6164\n", + "0.6165\n", + "0.6166\n", + "0.6167\n", + "0.6168\n", + "0.6169\n", + "0.617\n", + "0.6171\n", + "0.6172\n", + "0.6173\n", + "0.6174\n", + "0.6175\n", + "0.6176\n", + "0.6177\n", + "0.6178\n", + "0.6179\n", + "0.618\n", + "0.6181\n", + "0.6182\n", + "0.6183\n", + "0.6184\n", + "0.6185\n", + "0.6186\n", + "0.6187\n", + "0.6188\n", + "0.6189\n", + "0.619\n", + "0.6191\n", + "0.6192\n", + "0.6193\n", + "0.6194\n", + "0.6195\n", + "0.6196\n", + "0.6197\n", + "0.6198\n", + "0.6199\n", + "0.62\n", + "0.6201\n", + "0.6202\n", + "0.6203\n", + "0.6204\n", + "0.6205\n", + "0.6206\n", + "0.6207\n", + "0.6208\n", + "0.6209\n", + "0.621\n", + "0.6211\n", + "0.6212\n", + "0.6213\n", + "0.6214\n", + "0.6215\n", + "0.6216\n", + "0.6217\n", + "0.6218\n", + "0.6219\n", + "0.622\n", + "0.6221\n", + "0.6222\n", + "0.6223\n", + "0.6224\n", + "0.6225\n", + "0.6226\n", + "0.6227\n", + "0.6228\n", + "0.6229\n", + "0.623\n", + "0.6231\n", + "0.6232\n", + "0.6233\n", + "0.6234\n", + "0.6235\n", + "0.6236\n", + "0.6237\n", + "0.6238\n", + "0.6239\n", + "0.624\n", + "0.6241\n", + "0.6242\n", + "0.6243\n", + "0.6244\n", + "0.6245\n", + "0.6246\n", + "0.6247\n", + "0.6248\n", + "0.6249\n", + "0.625\n", + "0.6251\n", + "0.6252\n", + "0.6253\n", + "0.6254\n", + "0.6255\n", + "0.6256\n", + "0.6257\n", + "0.6258\n", + "0.6259\n", + "0.626\n", + "0.6261\n", + "0.6262\n", + "0.6263\n", + "0.6264\n", + "0.6265\n", + "0.6266\n", + "0.6267\n", + "0.6268\n", + "0.6269\n", + "0.627\n", + "0.6271\n", + "0.6272\n", + "0.6273\n", + "0.6274\n", + "0.6275\n", + "0.6276\n", + "0.6277\n", + "0.6278\n", + "0.6279\n", + "0.628\n", + "0.6281\n", + "0.6282\n", + "0.6283\n", + "0.6284\n", + "0.6285\n", + "0.6286\n", + "0.6287\n", + "0.6288\n", + "0.6289\n", + "0.629\n", + "0.6291\n", + "0.6292\n", + "0.6293\n", + "0.6294\n", + "0.6295\n", + "0.6296\n", + "0.6297\n", + "0.6298\n", + "0.6299\n", + "0.63\n", + "0.6301\n", + "0.6302\n", + "0.6303\n", + "0.6304\n", + "0.6305\n", + "0.6306\n", + "0.6307\n", + "0.6308\n", + "0.6309\n", + "0.631\n", + "0.6311\n", + "0.6312\n", + "0.6313\n", + "0.6314\n", + "0.6315\n", + "0.6316\n", + "0.6317\n", + "0.6318\n", + "0.6319\n", + "0.632\n", + "0.6321\n", + "0.6322\n", + "0.6323\n", + "0.6324\n", + "0.6325\n", + "0.6326\n", + "0.6327\n", + "0.6328\n", + "0.6329\n", + "0.633\n", + "0.6331\n", + "0.6332\n", + "0.6333\n", + "0.6334\n", + "0.6335\n", + "0.6336\n", + "0.6337\n", + "0.6338\n", + "0.6339\n", + "0.634\n", + "0.6341\n", + "0.6342\n", + "0.6343\n", + "0.6344\n", + "0.6345\n", + "0.6346\n", + "0.6347\n", + "0.6348\n", + "0.6349\n", + "0.635\n", + "0.6351\n", + "0.6352\n", + "0.6353\n", + "0.6354\n", + "0.6355\n", + "0.6356\n", + "0.6357\n", + "0.6358\n", + "0.6359\n", + "0.636\n", + "0.6361\n", + "0.6362\n", + "0.6363\n", + "0.6364\n", + "0.6365\n", + "0.6366\n", + "0.6367\n", + "0.6368\n", + "0.6369\n", + "0.637\n", + "0.6371\n", + "0.6372\n", + "0.6373\n", + "0.6374\n", + "0.6375\n", + "0.6376\n", + "0.6377\n", + "0.6378\n", + "0.6379\n", + "0.638\n", + "0.6381\n", + "0.6382\n", + "0.6383\n", + "0.6384\n", + "0.6385\n", + "0.6386\n", + "0.6387\n", + "0.6388\n", + "0.6389\n", + "0.639\n", + "0.6391\n", + "0.6392\n", + "0.6393\n", + "0.6394\n", + "0.6395\n", + "0.6396\n", + "0.6397\n", + "0.6398\n", + "0.6399\n", + "0.64\n", + "0.6401\n", + "0.6402\n", + "0.6403\n", + "0.6404\n", + "0.6405\n", + "0.6406\n", + "0.6407\n", + "0.6408\n", + "0.6409\n", + "0.641\n", + "0.6411\n", + "0.6412\n", + "0.6413\n", + "0.6414\n", + "0.6415\n", + "0.6416\n", + "0.6417\n", + "0.6418\n", + "0.6419\n", + "0.642\n", + "0.6421\n", + "0.6422\n", + "0.6423\n", + "0.6424\n", + "0.6425\n", + "0.6426\n", + "0.6427\n", + "0.6428\n", + "0.6429\n", + "0.643\n", + "0.6431\n", + "0.6432\n", + "0.6433\n", + "0.6434\n", + "0.6435\n", + "0.6436\n", + "0.6437\n", + "0.6438\n", + "0.6439\n", + "0.644\n", + "0.6441\n", + "0.6442\n", + "0.6443\n", + "0.6444\n", + "0.6445\n", + "0.6446\n", + "0.6447\n", + "0.6448\n", + "0.6449\n", + "0.645\n", + "0.6451\n", + "0.6452\n", + "0.6453\n", + "0.6454\n", + "0.6455\n", + "0.6456\n", + "0.6457\n", + "0.6458\n", + "0.6459\n", + "0.646\n", + "0.6461\n", + "0.6462\n", + "0.6463\n", + "0.6464\n", + "0.6465\n", + "0.6466\n", + "0.6467\n", + "0.6468\n", + "0.6469\n", + "0.647\n", + "0.6471\n", + "0.6472\n", + "0.6473\n", + "0.6474\n", + "0.6475\n", + "0.6476\n", + "0.6477\n", + "0.6478\n", + "0.6479\n", + "0.648\n", + "0.6481\n", + "0.6482\n", + "0.6483\n", + "0.6484\n", + "0.6485\n", + "0.6486\n", + "0.6487\n", + "0.6488\n", + "0.6489\n", + "0.649\n", + "0.6491\n", + "0.6492\n", + "0.6493\n", + "0.6494\n", + "0.6495\n", + "0.6496\n", + "0.6497\n", + "0.6498\n", + "0.6499\n", + "0.65\n", + "0.6501\n", + "0.6502\n", + "0.6503\n", + "0.6504\n", + "0.6505\n", + "0.6506\n", + "0.6507\n", + "0.6508\n", + "0.6509\n", + "0.651\n", + "0.6511\n", + "0.6512\n", + "0.6513\n", + "0.6514\n", + "0.6515\n", + "0.6516\n", + "0.6517\n", + "0.6518\n", + "0.6519\n", + "0.652\n", + "0.6521\n", + "0.6522\n", + "0.6523\n", + "0.6524\n", + "0.6525\n", + "0.6526\n", + "0.6527\n", + "0.6528\n", + "0.6529\n", + "0.653\n", + "0.6531\n", + "0.6532\n", + "0.6533\n", + "0.6534\n", + "0.6535\n", + "0.6536\n", + "0.6537\n", + "0.6538\n", + "0.6539\n", + "0.654\n", + "0.6541\n", + "0.6542\n", + "0.6543\n", + "0.6544\n", + "0.6545\n", + "0.6546\n", + "0.6547\n", + "0.6548\n", + "0.6549\n", + "0.655\n", + "0.6551\n", + "0.6552\n", + "0.6553\n", + "0.6554\n", + "0.6555\n", + "0.6556\n", + "0.6557\n", + "0.6558\n", + "0.6559\n", + "0.656\n", + "0.6561\n", + "0.6562\n", + "0.6563\n", + "0.6564\n", + "0.6565\n", + "0.6566\n", + "0.6567\n", + "0.6568\n", + "0.6569\n", + "0.657\n", + "0.6571\n", + "0.6572\n", + "0.6573\n", + "0.6574\n", + "0.6575\n", + "0.6576\n", + "0.6577\n", + "0.6578\n", + "0.6579\n", + "0.658\n", + "0.6581\n", + "0.6582\n", + "0.6583\n", + "0.6584\n", + "0.6585\n", + "0.6586\n", + "0.6587\n", + "0.6588\n", + "0.6589\n", + "0.659\n", + "0.6591\n", + "0.6592\n", + "0.6593\n", + "0.6594\n", + "0.6595\n", + "0.6596\n", + "0.6597\n", + "0.6598\n", + "0.6599\n", + "0.66\n", + "0.6601\n", + "0.6602\n", + "0.6603\n", + "0.6604\n", + "0.6605\n", + "0.6606\n", + "0.6607\n", + "0.6608\n", + "0.6609\n", + "0.661\n", + "0.6611\n", + "0.6612\n", + "0.6613\n", + "0.6614\n", + "0.6615\n", + "0.6616\n", + "0.6617\n", + "0.6618\n", + "0.6619\n", + "0.662\n", + "0.6621\n", + "0.6622\n", + "0.6623\n", + "0.6624\n", + "0.6625\n", + "0.6626\n", + "0.6627\n", + "0.6628\n", + "0.6629\n", + "0.663\n", + "0.6631\n", + "0.6632\n", + "0.6633\n", + "0.6634\n", + "0.6635\n", + "0.6636\n", + "0.6637\n", + "0.6638\n", + "0.6639\n", + "0.664\n", + "0.6641\n", + "0.6642\n", + "0.6643\n", + "0.6644\n", + "0.6645\n", + "0.6646\n", + "0.6647\n", + "0.6648\n", + "0.6649\n", + "0.665\n", + "0.6651\n", + "0.6652\n", + "0.6653\n", + "0.6654\n", + "0.6655\n", + "0.6656\n", + "0.6657\n", + "0.6658\n", + "0.6659\n", + "0.666\n", + "0.6661\n", + "0.6662\n", + "0.6663\n", + "0.6664\n", + "0.6665\n", + "0.6666\n", + "0.6667\n", + "0.6668\n", + "0.6669\n", + "0.667\n", + "0.6671\n", + "0.6672\n", + "0.6673\n", + "0.6674\n", + "0.6675\n", + "0.6676\n", + "0.6677\n", + "0.6678\n", + "0.6679\n", + "0.668\n", + "0.6681\n", + "0.6682\n", + "0.6683\n", + "0.6684\n", + "0.6685\n", + "0.6686\n", + "0.6687\n", + "0.6688\n", + "0.6689\n", + "0.669\n", + "0.6691\n", + "0.6692\n", + "0.6693\n", + "0.6694\n", + "0.6695\n", + "0.6696\n", + "0.6697\n", + "0.6698\n", + "0.6699\n", + "0.67\n", + "0.6701\n", + "0.6702\n", + "0.6703\n", + "0.6704\n", + "0.6705\n", + "0.6706\n", + "0.6707\n", + "0.6708\n", + "0.6709\n", + "0.671\n", + "0.6711\n", + "0.6712\n", + "0.6713\n", + "0.6714\n", + "0.6715\n", + "0.6716\n", + "0.6717\n", + "0.6718\n", + "0.6719\n", + "0.672\n", + "0.6721\n", + "0.6722\n", + "0.6723\n", + "0.6724\n", + "0.6725\n", + "0.6726\n", + "0.6727\n", + "0.6728\n", + "0.6729\n", + "0.673\n", + "0.6731\n", + "0.6732\n", + "0.6733\n", + "0.6734\n", + "0.6735\n", + "0.6736\n", + "0.6737\n", + "0.6738\n", + "0.6739\n", + "0.674\n", + "0.6741\n", + "0.6742\n", + "0.6743\n", + "0.6744\n", + "0.6745\n", + "0.6746\n", + "0.6747\n", + "0.6748\n", + "0.6749\n", + "0.675\n", + "0.6751\n", + "0.6752\n", + "0.6753\n", + "0.6754\n", + "0.6755\n", + "0.6756\n", + "0.6757\n", + "0.6758\n", + "0.6759\n", + "0.676\n", + "0.6761\n", + "0.6762\n", + "0.6763\n", + "0.6764\n", + "0.6765\n", + "0.6766\n", + "0.6767\n", + "0.6768\n", + "0.6769\n", + "0.677\n", + "0.6771\n", + "0.6772\n", + "0.6773\n", + "0.6774\n", + "0.6775\n", + "0.6776\n", + "0.6777\n", + "0.6778\n", + "0.6779\n", + "0.678\n", + "0.6781\n", + "0.6782\n", + "0.6783\n", + "0.6784\n", + "0.6785\n", + "0.6786\n", + "0.6787\n", + "0.6788\n", + "0.6789\n", + "0.679\n", + "0.6791\n", + "0.6792\n", + "0.6793\n", + "0.6794\n", + "0.6795\n", + "0.6796\n", + "0.6797\n", + "0.6798\n", + "0.6799\n", + "0.68\n", + "0.6801\n", + "0.6802\n", + "0.6803\n", + "0.6804\n", + "0.6805\n", + "0.6806\n", + "0.6807\n", + "0.6808\n", + "0.6809\n", + "0.681\n", + "0.6811\n", + "0.6812\n", + "0.6813\n", + "0.6814\n", + "0.6815\n", + "0.6816\n", + "0.6817\n", + "0.6818\n", + "0.6819\n", + "0.682\n", + "0.6821\n", + "0.6822\n", + "0.6823\n", + "0.6824\n", + "0.6825\n", + "0.6826\n", + "0.6827\n", + "0.6828\n", + "0.6829\n", + "0.683\n", + "0.6831\n", + "0.6832\n", + "0.6833\n", + "0.6834\n", + "0.6835\n", + "0.6836\n", + "0.6837\n", + "0.6838\n", + "0.6839\n", + "0.684\n", + "0.6841\n", + "0.6842\n", + "0.6843\n", + "0.6844\n", + "0.6845\n", + "0.6846\n", + "0.6847\n", + "0.6848\n", + "0.6849\n", + "0.685\n", + "0.6851\n", + "0.6852\n", + "0.6853\n", + "0.6854\n", + "0.6855\n", + "0.6856\n", + "0.6857\n", + "0.6858\n", + "0.6859\n", + "0.686\n", + "0.6861\n", + "0.6862\n", + "0.6863\n", + "0.6864\n", + "0.6865\n", + "0.6866\n", + "0.6867\n", + "0.6868\n", + "0.6869\n", + "0.687\n", + "0.6871\n", + "0.6872\n", + "0.6873\n", + "0.6874\n", + "0.6875\n", + "0.6876\n", + "0.6877\n", + "0.6878\n", + "0.6879\n", + "0.688\n", + "0.6881\n", + "0.6882\n", + "0.6883\n", + "0.6884\n", + "0.6885\n", + "0.6886\n", + "0.6887\n", + "0.6888\n", + "0.6889\n", + "0.689\n", + "0.6891\n", + "0.6892\n", + "0.6893\n", + "0.6894\n", + "0.6895\n", + "0.6896\n", + "0.6897\n", + "0.6898\n", + "0.6899\n", + "0.69\n", + "0.6901\n", + "0.6902\n", + "0.6903\n", + "0.6904\n", + "0.6905\n", + "0.6906\n", + "0.6907\n", + "0.6908\n", + "0.6909\n", + "0.691\n", + "0.6911\n", + "0.6912\n", + "0.6913\n", + "0.6914\n", + "0.6915\n", + "0.6916\n", + "0.6917\n", + "0.6918\n", + "0.6919\n", + "0.692\n", + "0.6921\n", + "0.6922\n", + "0.6923\n", + "0.6924\n", + "0.6925\n", + "0.6926\n", + "0.6927\n", + "0.6928\n", + "0.6929\n", + "0.693\n", + "0.6931\n", + "0.6932\n", + "0.6933\n", + "0.6934\n", + "0.6935\n", + "0.6936\n", + "0.6937\n", + "0.6938\n", + "0.6939\n", + "0.694\n", + "0.6941\n", + "0.6942\n", + "0.6943\n", + "0.6944\n", + "0.6945\n", + "0.6946\n", + "0.6947\n", + "0.6948\n", + "0.6949\n", + "0.695\n", + "0.6951\n", + "0.6952\n", + "0.6953\n", + "0.6954\n", + "0.6955\n", + "0.6956\n", + "0.6957\n", + "0.6958\n", + "0.6959\n", + "0.696\n", + "0.6961\n", + "0.6962\n", + "0.6963\n", + "0.6964\n", + "0.6965\n", + "0.6966\n", + "0.6967\n", + "0.6968\n", + "0.6969\n", + "0.697\n", + "0.6971\n", + "0.6972\n", + "0.6973\n", + "0.6974\n", + "0.6975\n", + "0.6976\n", + "0.6977\n", + "0.6978\n", + "0.6979\n", + "0.698\n", + "0.6981\n", + "0.6982\n", + "0.6983\n", + "0.6984\n", + "0.6985\n", + "0.6986\n", + "0.6987\n", + "0.6988\n", + "0.6989\n", + "0.699\n", + "0.6991\n", + "0.6992\n", + "0.6993\n", + "0.6994\n", + "0.6995\n", + "0.6996\n", + "0.6997\n", + "0.6998\n", + "0.6999\n", + "0.7\n", + "0.7001\n", + "0.7002\n", + "0.7003\n", + "0.7004\n", + "0.7005\n", + "0.7006\n", + "0.7007\n", + "0.7008\n", + "0.7009\n", + "0.701\n", + "0.7011\n", + "0.7012\n", + "0.7013\n", + "0.7014\n", + "0.7015\n", + "0.7016\n", + "0.7017\n", + "0.7018\n", + "0.7019\n", + "0.702\n", + "0.7021\n", + "0.7022\n", + "0.7023\n", + "0.7024\n", + "0.7025\n", + "0.7026\n", + "0.7027\n", + "0.7028\n", + "0.7029\n", + "0.703\n", + "0.7031\n", + "0.7032\n", + "0.7033\n", + "0.7034\n", + "0.7035\n", + "0.7036\n", + "0.7037\n", + "0.7038\n", + "0.7039\n", + "0.704\n", + "0.7041\n", + "0.7042\n", + "0.7043\n", + "0.7044\n", + "0.7045\n", + "0.7046\n", + "0.7047\n", + "0.7048\n", + "0.7049\n", + "0.705\n", + "0.7051\n", + "0.7052\n", + "0.7053\n", + "0.7054\n", + "0.7055\n", + "0.7056\n", + "0.7057\n", + "0.7058\n", + "0.7059\n", + "0.706\n", + "0.7061\n", + "0.7062\n", + "0.7063\n", + "0.7064\n", + "0.7065\n", + "0.7066\n", + "0.7067\n", + "0.7068\n", + "0.7069\n", + "0.707\n", + "0.7071\n", + "0.7072\n", + "0.7073\n", + "0.7074\n", + "0.7075\n", + "0.7076\n", + "0.7077\n", + "0.7078\n", + "0.7079\n", + "0.708\n", + "0.7081\n", + "0.7082\n", + "0.7083\n", + "0.7084\n", + "0.7085\n", + "0.7086\n", + "0.7087\n", + "0.7088\n", + "0.7089\n", + "0.709\n", + "0.7091\n", + "0.7092\n", + "0.7093\n", + "0.7094\n", + "0.7095\n", + "0.7096\n", + "0.7097\n", + "0.7098\n", + "0.7099\n", + "0.71\n", + "0.7101\n", + "0.7102\n", + "0.7103\n", + "0.7104\n", + "0.7105\n", + "0.7106\n", + "0.7107\n", + "0.7108\n", + "0.7109\n", + "0.711\n", + "0.7111\n", + "0.7112\n", + "0.7113\n", + "0.7114\n", + "0.7115\n", + "0.7116\n", + "0.7117\n", + "0.7118\n", + "0.7119\n", + "0.712\n", + "0.7121\n", + "0.7122\n", + "0.7123\n", + "0.7124\n", + "0.7125\n", + "0.7126\n", + "0.7127\n", + "0.7128\n", + "0.7129\n", + "0.713\n", + "0.7131\n", + "0.7132\n", + "0.7133\n", + "0.7134\n", + "0.7135\n", + "0.7136\n", + "0.7137\n", + "0.7138\n", + "0.7139\n", + "0.714\n", + "0.7141\n", + "0.7142\n", + "0.7143\n", + "0.7144\n", + "0.7145\n", + "0.7146\n", + "0.7147\n", + "0.7148\n", + "0.7149\n", + "0.715\n", + "0.7151\n", + "0.7152\n", + "0.7153\n", + "0.7154\n", + "0.7155\n", + "0.7156\n", + "0.7157\n", + "0.7158\n", + "0.7159\n", + "0.716\n", + "0.7161\n", + "0.7162\n", + "0.7163\n", + "0.7164\n", + "0.7165\n", + "0.7166\n", + "0.7167\n", + "0.7168\n", + "0.7169\n", + "0.717\n", + "0.7171\n", + "0.7172\n", + "0.7173\n", + "0.7174\n", + "0.7175\n", + "0.7176\n", + "0.7177\n", + "0.7178\n", + "0.7179\n", + "0.718\n", + "0.7181\n", + "0.7182\n", + "0.7183\n", + "0.7184\n", + "0.7185\n", + "0.7186\n", + "0.7187\n", + "0.7188\n", + "0.7189\n", + "0.719\n", + "0.7191\n", + "0.7192\n", + "0.7193\n", + "0.7194\n", + "0.7195\n", + "0.7196\n", + "0.7197\n", + "0.7198\n", + "0.7199\n", + "0.72\n", + "0.7201\n", + "0.7202\n", + "0.7203\n", + "0.7204\n", + "0.7205\n", + "0.7206\n", + "0.7207\n", + "0.7208\n", + "0.7209\n", + "0.721\n", + "0.7211\n", + "0.7212\n", + "0.7213\n", + "0.7214\n", + "0.7215\n", + "0.7216\n", + "0.7217\n", + "0.7218\n", + "0.7219\n", + "0.722\n", + "0.7221\n", + "0.7222\n", + "0.7223\n", + "0.7224\n", + "0.7225\n", + "0.7226\n", + "0.7227\n", + "0.7228\n", + "0.7229\n", + "0.723\n", + "0.7231\n", + "0.7232\n", + "0.7233\n", + "0.7234\n", + "0.7235\n", + "0.7236\n", + "0.7237\n", + "0.7238\n", + "0.7239\n", + "0.724\n", + "0.7241\n", + "0.7242\n", + "0.7243\n", + "0.7244\n", + "0.7245\n", + "0.7246\n", + "0.7247\n", + "0.7248\n", + "0.7249\n", + "0.725\n", + "0.7251\n", + "0.7252\n", + "0.7253\n", + "0.7254\n", + "0.7255\n", + "0.7256\n", + "0.7257\n", + "0.7258\n", + "0.7259\n", + "0.726\n", + "0.7261\n", + "0.7262\n", + "0.7263\n", + "0.7264\n", + "0.7265\n", + "0.7266\n", + "0.7267\n", + "0.7268\n", + "0.7269\n", + "0.727\n", + "0.7271\n", + "0.7272\n", + "0.7273\n", + "0.7274\n", + "0.7275\n", + "0.7276\n", + "0.7277\n", + "0.7278\n", + "0.7279\n", + "0.728\n", + "0.7281\n", + "0.7282\n", + "0.7283\n", + "0.7284\n", + "0.7285\n", + "0.7286\n", + "0.7287\n", + "0.7288\n", + "0.7289\n", + "0.729\n", + "0.7291\n", + "0.7292\n", + "0.7293\n", + "0.7294\n", + "0.7295\n", + "0.7296\n", + "0.7297\n", + "0.7298\n", + "0.7299\n", + "0.73\n", + "0.7301\n", + "0.7302\n", + "0.7303\n", + "0.7304\n", + "0.7305\n", + "0.7306\n", + "0.7307\n", + "0.7308\n", + "0.7309\n", + "0.731\n", + "0.7311\n", + "0.7312\n", + "0.7313\n", + "0.7314\n", + "0.7315\n", + "0.7316\n", + "0.7317\n", + "0.7318\n", + "0.7319\n", + "0.732\n", + "0.7321\n", + "0.7322\n", + "0.7323\n", + "0.7324\n", + "0.7325\n", + "0.7326\n", + "0.7327\n", + "0.7328\n", + "0.7329\n", + "0.733\n", + "0.7331\n", + "0.7332\n", + "0.7333\n", + "0.7334\n", + "0.7335\n", + "0.7336\n", + "0.7337\n", + "0.7338\n", + "0.7339\n", + "0.734\n", + "0.7341\n", + "0.7342\n", + "0.7343\n", + "0.7344\n", + "0.7345\n", + "0.7346\n", + "0.7347\n", + "0.7348\n", + "0.7349\n", + "0.735\n", + "0.7351\n", + "0.7352\n", + "0.7353\n", + "0.7354\n", + "0.7355\n", + "0.7356\n", + "0.7357\n", + "0.7358\n", + "0.7359\n", + "0.736\n", + "0.7361\n", + "0.7362\n", + "0.7363\n", + "0.7364\n", + "0.7365\n", + "0.7366\n", + "0.7367\n", + "0.7368\n", + "0.7369\n", + "0.737\n", + "0.7371\n", + "0.7372\n", + "0.7373\n", + "0.7374\n", + "0.7375\n", + "0.7376\n", + "0.7377\n", + "0.7378\n", + "0.7379\n", + "0.738\n", + "0.7381\n", + "0.7382\n", + "0.7383\n", + "0.7384\n", + "0.7385\n", + "0.7386\n", + "0.7387\n", + "0.7388\n", + "0.7389\n", + "0.739\n", + "0.7391\n", + "0.7392\n", + "0.7393\n", + "0.7394\n", + "0.7395\n", + "0.7396\n", + "0.7397\n", + "0.7398\n", + "0.7399\n", + "0.74\n", + "0.7401\n", + "0.7402\n", + "0.7403\n", + "0.7404\n", + "0.7405\n", + "0.7406\n", + "0.7407\n", + "0.7408\n", + "0.7409\n", + "0.741\n", + "0.7411\n", + "0.7412\n", + "0.7413\n", + "0.7414\n", + "0.7415\n", + "0.7416\n", + "0.7417\n", + "0.7418\n", + "0.7419\n", + "0.742\n", + "0.7421\n", + "0.7422\n", + "0.7423\n", + "0.7424\n", + "0.7425\n", + "0.7426\n", + "0.7427\n", + "0.7428\n", + "0.7429\n", + "0.743\n", + "0.7431\n", + "0.7432\n", + "0.7433\n", + "0.7434\n", + "0.7435\n", + "0.7436\n", + "0.7437\n", + "0.7438\n", + "0.7439\n", + "0.744\n", + "0.7441\n", + "0.7442\n", + "0.7443\n", + "0.7444\n", + "0.7445\n", + "0.7446\n", + "0.7447\n", + "0.7448\n", + "0.7449\n", + "0.745\n", + "0.7451\n", + "0.7452\n", + "0.7453\n", + "0.7454\n", + "0.7455\n", + "0.7456\n", + "0.7457\n", + "0.7458\n", + "0.7459\n", + "0.746\n", + "0.7461\n", + "0.7462\n", + "0.7463\n", + "0.7464\n", + "0.7465\n", + "0.7466\n", + "0.7467\n", + "0.7468\n", + "0.7469\n", + "0.747\n", + "0.7471\n", + "0.7472\n", + "0.7473\n", + "0.7474\n", + "0.7475\n", + "0.7476\n", + "0.7477\n", + "0.7478\n", + "0.7479\n", + "0.748\n", + "0.7481\n", + "0.7482\n", + "0.7483\n", + "0.7484\n", + "0.7485\n", + "0.7486\n", + "0.7487\n", + "0.7488\n", + "0.7489\n", + "0.749\n", + "0.7491\n", + "0.7492\n", + "0.7493\n", + "0.7494\n", + "0.7495\n", + "0.7496\n", + "0.7497\n", + "0.7498\n", + "0.7499\n", + "0.75\n", + "0.7501\n", + "0.7502\n", + "0.7503\n", + "0.7504\n", + "0.7505\n", + "0.7506\n", + "0.7507\n", + "0.7508\n", + "0.7509\n", + "0.751\n", + "0.7511\n", + "0.7512\n", + "0.7513\n", + "0.7514\n", + "0.7515\n", + "0.7516\n", + "0.7517\n", + "0.7518\n", + "0.7519\n", + "0.752\n", + "0.7521\n", + "0.7522\n", + "0.7523\n", + "0.7524\n", + "0.7525\n", + "0.7526\n", + "0.7527\n", + "0.7528\n", + "0.7529\n", + "0.753\n", + "0.7531\n", + "0.7532\n", + "0.7533\n", + "0.7534\n", + "0.7535\n", + "0.7536\n", + "0.7537\n", + "0.7538\n", + "0.7539\n", + "0.754\n", + "0.7541\n", + "0.7542\n", + "0.7543\n", + "0.7544\n", + "0.7545\n", + "0.7546\n", + "0.7547\n", + "0.7548\n", + "0.7549\n", + "0.755\n", + "0.7551\n", + "0.7552\n", + "0.7553\n", + "0.7554\n", + "0.7555\n", + "0.7556\n", + "0.7557\n", + "0.7558\n", + "0.7559\n", + "0.756\n", + "0.7561\n", + "0.7562\n", + "0.7563\n", + "0.7564\n", + "0.7565\n", + "0.7566\n", + "0.7567\n", + "0.7568\n", + "0.7569\n", + "0.757\n", + "0.7571\n", + "0.7572\n", + "0.7573\n", + "0.7574\n", + "0.7575\n", + "0.7576\n", + "0.7577\n", + "0.7578\n", + "0.7579\n", + "0.758\n", + "0.7581\n", + "0.7582\n", + "0.7583\n", + "0.7584\n", + "0.7585\n", + "0.7586\n", + "0.7587\n", + "0.7588\n", + "0.7589\n", + "0.759\n", + "0.7591\n", + "0.7592\n", + "0.7593\n", + "0.7594\n", + "0.7595\n", + "0.7596\n", + "0.7597\n", + "0.7598\n", + "0.7599\n", + "0.76\n", + "0.7601\n", + "0.7602\n", + "0.7603\n", + "0.7604\n", + "0.7605\n", + "0.7606\n", + "0.7607\n", + "0.7608\n", + "0.7609\n", + "0.761\n", + "0.7611\n", + "0.7612\n", + "0.7613\n", + "0.7614\n", + "0.7615\n", + "0.7616\n", + "0.7617\n", + "0.7618\n", + "0.7619\n", + "0.762\n", + "0.7621\n", + "0.7622\n", + "0.7623\n", + "0.7624\n", + "0.7625\n", + "0.7626\n", + "0.7627\n", + "0.7628\n", + "0.7629\n", + "0.763\n", + "0.7631\n", + "0.7632\n", + "0.7633\n", + "0.7634\n", + "0.7635\n", + "0.7636\n", + "0.7637\n", + "0.7638\n", + "0.7639\n", + "0.764\n", + "0.7641\n", + "0.7642\n", + "0.7643\n", + "0.7644\n", + "0.7645\n", + "0.7646\n", + "0.7647\n", + "0.7648\n", + "0.7649\n", + "0.765\n", + "0.7651\n", + "0.7652\n", + "0.7653\n", + "0.7654\n", + "0.7655\n", + "0.7656\n", + "0.7657\n", + "0.7658\n", + "0.7659\n", + "0.766\n", + "0.7661\n", + "0.7662\n", + "0.7663\n", + "0.7664\n", + "0.7665\n", + "0.7666\n", + "0.7667\n", + "0.7668\n", + "0.7669\n", + "0.767\n", + "0.7671\n", + "0.7672\n", + "0.7673\n", + "0.7674\n", + "0.7675\n", + "0.7676\n", + "0.7677\n", + "0.7678\n", + "0.7679\n", + "0.768\n", + "0.7681\n", + "0.7682\n", + "0.7683\n", + "0.7684\n", + "0.7685\n", + "0.7686\n", + "0.7687\n", + "0.7688\n", + "0.7689\n", + "0.769\n", + "0.7691\n", + "0.7692\n", + "0.7693\n", + "0.7694\n", + "0.7695\n", + "0.7696\n", + "0.7697\n", + "0.7698\n", + "0.7699\n", + "0.77\n", + "0.7701\n", + "0.7702\n", + "0.7703\n", + "0.7704\n", + "0.7705\n", + "0.7706\n", + "0.7707\n", + "0.7708\n", + "0.7709\n", + "0.771\n", + "0.7711\n", + "0.7712\n", + "0.7713\n", + "0.7714\n", + "0.7715\n", + "0.7716\n", + "0.7717\n", + "0.7718\n", + "0.7719\n", + "0.772\n", + "0.7721\n", + "0.7722\n", + "0.7723\n", + "0.7724\n", + "0.7725\n", + "0.7726\n", + "0.7727\n", + "0.7728\n", + "0.7729\n", + "0.773\n", + "0.7731\n", + "0.7732\n", + "0.7733\n", + "0.7734\n", + "0.7735\n", + "0.7736\n", + "0.7737\n", + "0.7738\n", + "0.7739\n", + "0.774\n", + "0.7741\n", + "0.7742\n", + "0.7743\n", + "0.7744\n", + "0.7745\n", + "0.7746\n", + "0.7747\n", + "0.7748\n", + "0.7749\n", + "0.775\n", + "0.7751\n", + "0.7752\n", + "0.7753\n", + "0.7754\n", + "0.7755\n", + "0.7756\n", + "0.7757\n", + "0.7758\n", + "0.7759\n", + "0.776\n", + "0.7761\n", + "0.7762\n", + "0.7763\n", + "0.7764\n", + "0.7765\n", + "0.7766\n", + "0.7767\n", + "0.7768\n", + "0.7769\n", + "0.777\n", + "0.7771\n", + "0.7772\n", + "0.7773\n", + "0.7774\n", + "0.7775\n", + "0.7776\n", + "0.7777\n", + "0.7778\n", + "0.7779\n", + "0.778\n", + "0.7781\n", + "0.7782\n", + "0.7783\n", + "0.7784\n", + "0.7785\n", + "0.7786\n", + "0.7787\n", + "0.7788\n", + "0.7789\n", + "0.779\n", + "0.7791\n", + "0.7792\n", + "0.7793\n", + "0.7794\n", + "0.7795\n", + "0.7796\n", + "0.7797\n", + "0.7798\n", + "0.7799\n", + "0.78\n", + "0.7801\n", + "0.7802\n", + "0.7803\n", + "0.7804\n", + "0.7805\n", + "0.7806\n", + "0.7807\n", + "0.7808\n", + "0.7809\n", + "0.781\n", + "0.7811\n", + "0.7812\n", + "0.7813\n", + "0.7814\n", + "0.7815\n", + "0.7816\n", + "0.7817\n", + "0.7818\n", + "0.7819\n", + "0.782\n", + "0.7821\n", + "0.7822\n", + "0.7823\n", + "0.7824\n", + "0.7825\n", + "0.7826\n", + "0.7827\n", + "0.7828\n", + "0.7829\n", + "0.783\n", + "0.7831\n", + "0.7832\n", + "0.7833\n", + "0.7834\n", + "0.7835\n", + "0.7836\n", + "0.7837\n", + "0.7838\n", + "0.7839\n", + "0.784\n", + "0.7841\n", + "0.7842\n", + "0.7843\n", + "0.7844\n", + "0.7845\n", + "0.7846\n", + "0.7847\n", + "0.7848\n", + "0.7849\n", + "0.785\n", + "0.7851\n", + "0.7852\n", + "0.7853\n", + "0.7854\n", + "0.7855\n", + "0.7856\n", + "0.7857\n", + "0.7858\n", + "0.7859\n", + "0.786\n", + "0.7861\n", + "0.7862\n", + "0.7863\n", + "0.7864\n", + "0.7865\n", + "0.7866\n", + "0.7867\n", + "0.7868\n", + "0.7869\n", + "0.787\n", + "0.7871\n", + "0.7872\n", + "0.7873\n", + "0.7874\n", + "0.7875\n", + "0.7876\n", + "0.7877\n", + "0.7878\n", + "0.7879\n", + "0.788\n", + "0.7881\n", + "0.7882\n", + "0.7883\n", + "0.7884\n", + "0.7885\n", + "0.7886\n", + "0.7887\n", + "0.7888\n", + "0.7889\n", + "0.789\n", + "0.7891\n", + "0.7892\n", + "0.7893\n", + "0.7894\n", + "0.7895\n", + "0.7896\n", + "0.7897\n", + "0.7898\n", + "0.7899\n", + "0.79\n", + "0.7901\n", + "0.7902\n", + "0.7903\n", + "0.7904\n", + "0.7905\n", + "0.7906\n", + "0.7907\n", + "0.7908\n", + "0.7909\n", + "0.791\n", + "0.7911\n", + "0.7912\n", + "0.7913\n", + "0.7914\n", + "0.7915\n", + "0.7916\n", + "0.7917\n", + "0.7918\n", + "0.7919\n", + "0.792\n", + "0.7921\n", + "0.7922\n", + "0.7923\n", + "0.7924\n", + "0.7925\n", + "0.7926\n", + "0.7927\n", + "0.7928\n", + "0.7929\n", + "0.793\n", + "0.7931\n", + "0.7932\n", + "0.7933\n", + "0.7934\n", + "0.7935\n", + "0.7936\n", + "0.7937\n", + "0.7938\n", + "0.7939\n", + "0.794\n", + "0.7941\n", + "0.7942\n", + "0.7943\n", + "0.7944\n", + "0.7945\n", + "0.7946\n", + "0.7947\n", + "0.7948\n", + "0.7949\n", + "0.795\n", + "0.7951\n", + "0.7952\n", + "0.7953\n", + "0.7954\n", + "0.7955\n", + "0.7956\n", + "0.7957\n", + "0.7958\n", + "0.7959\n", + "0.796\n", + "0.7961\n", + "0.7962\n", + "0.7963\n", + "0.7964\n", + "0.7965\n", + "0.7966\n", + "0.7967\n", + "0.7968\n", + "0.7969\n", + "0.797\n", + "0.7971\n", + "0.7972\n", + "0.7973\n", + "0.7974\n", + "0.7975\n", + "0.7976\n", + "0.7977\n", + "0.7978\n", + "0.7979\n", + "0.798\n", + "0.7981\n", + "0.7982\n", + "0.7983\n", + "0.7984\n", + "0.7985\n", + "0.7986\n", + "0.7987\n", + "0.7988\n", + "0.7989\n", + "0.799\n", + "0.7991\n", + "0.7992\n", + "0.7993\n", + "0.7994\n", + "0.7995\n", + "0.7996\n", + "0.7997\n", + "0.7998\n", + "0.7999\n", + "0.8\n", + "0.8001\n", + "0.8002\n", + "0.8003\n", + "0.8004\n", + "0.8005\n", + "0.8006\n", + "0.8007\n", + "0.8008\n", + "0.8009\n", + "0.801\n", + "0.8011\n", + "0.8012\n", + "0.8013\n", + "0.8014\n", + "0.8015\n", + "0.8016\n", + "0.8017\n", + "0.8018\n", + "0.8019\n", + "0.802\n", + "0.8021\n", + "0.8022\n", + "0.8023\n", + "0.8024\n", + "0.8025\n", + "0.8026\n", + "0.8027\n", + "0.8028\n", + "0.8029\n", + "0.803\n", + "0.8031\n", + "0.8032\n", + "0.8033\n", + "0.8034\n", + "0.8035\n", + "0.8036\n", + "0.8037\n", + "0.8038\n", + "0.8039\n", + "0.804\n", + "0.8041\n", + "0.8042\n", + "0.8043\n", + "0.8044\n", + "0.8045\n", + "0.8046\n", + "0.8047\n", + "0.8048\n", + "0.8049\n", + "0.805\n", + "0.8051\n", + "0.8052\n", + "0.8053\n", + "0.8054\n", + "0.8055\n", + "0.8056\n", + "0.8057\n", + "0.8058\n", + "0.8059\n", + "0.806\n", + "0.8061\n", + "0.8062\n", + "0.8063\n", + "0.8064\n", + "0.8065\n", + "0.8066\n", + "0.8067\n", + "0.8068\n", + "0.8069\n", + "0.807\n", + "0.8071\n", + "0.8072\n", + "0.8073\n", + "0.8074\n", + "0.8075\n", + "0.8076\n", + "0.8077\n", + "0.8078\n", + "0.8079\n", + "0.808\n", + "0.8081\n", + "0.8082\n", + "0.8083\n", + "0.8084\n", + "0.8085\n", + "0.8086\n", + "0.8087\n", + "0.8088\n", + "0.8089\n", + "0.809\n", + "0.8091\n", + "0.8092\n", + "0.8093\n", + "0.8094\n", + "0.8095\n", + "0.8096\n", + "0.8097\n", + "0.8098\n", + "0.8099\n", + "0.81\n", + "0.8101\n", + "0.8102\n", + "0.8103\n", + "0.8104\n", + "0.8105\n", + "0.8106\n", + "0.8107\n", + "0.8108\n", + "0.8109\n", + "0.811\n", + "0.8111\n", + "0.8112\n", + "0.8113\n", + "0.8114\n", + "0.8115\n", + "0.8116\n", + "0.8117\n", + "0.8118\n", + "0.8119\n", + "0.812\n", + "0.8121\n", + "0.8122\n", + "0.8123\n", + "0.8124\n", + "0.8125\n", + "0.8126\n", + "0.8127\n", + "0.8128\n", + "0.8129\n", + "0.813\n", + "0.8131\n", + "0.8132\n", + "0.8133\n", + "0.8134\n", + "0.8135\n", + "0.8136\n", + "0.8137\n", + "0.8138\n", + "0.8139\n", + "0.814\n", + "0.8141\n", + "0.8142\n", + "0.8143\n", + "0.8144\n", + "0.8145\n", + "0.8146\n", + "0.8147\n", + "0.8148\n", + "0.8149\n", + "0.815\n", + "0.8151\n", + "0.8152\n", + "0.8153\n", + "0.8154\n", + "0.8155\n", + "0.8156\n", + "0.8157\n", + "0.8158\n", + "0.8159\n", + "0.816\n", + "0.8161\n", + "0.8162\n", + "0.8163\n", + "0.8164\n", + "0.8165\n", + "0.8166\n", + "0.8167\n", + "0.8168\n", + "0.8169\n", + "0.817\n", + "0.8171\n", + "0.8172\n", + "0.8173\n", + "0.8174\n", + "0.8175\n", + "0.8176\n", + "0.8177\n", + "0.8178\n", + "0.8179\n", + "0.818\n", + "0.8181\n", + "0.8182\n", + "0.8183\n", + "0.8184\n", + "0.8185\n", + "0.8186\n", + "0.8187\n", + "0.8188\n", + "0.8189\n", + "0.819\n", + "0.8191\n", + "0.8192\n", + "0.8193\n", + "0.8194\n", + "0.8195\n", + "0.8196\n", + "0.8197\n", + "0.8198\n", + "0.8199\n", + "0.82\n", + "0.8201\n", + "0.8202\n", + "0.8203\n", + "0.8204\n", + "0.8205\n", + "0.8206\n", + "0.8207\n", + "0.8208\n", + "0.8209\n", + "0.821\n", + "0.8211\n", + "0.8212\n", + "0.8213\n", + "0.8214\n", + "0.8215\n", + "0.8216\n", + "0.8217\n", + "0.8218\n", + "0.8219\n", + "0.822\n", + "0.8221\n", + "0.8222\n", + "0.8223\n", + "0.8224\n", + "0.8225\n", + "0.8226\n", + "0.8227\n", + "0.8228\n", + "0.8229\n", + "0.823\n", + "0.8231\n", + "0.8232\n", + "0.8233\n", + "0.8234\n", + "0.8235\n", + "0.8236\n", + "0.8237\n", + "0.8238\n", + "0.8239\n", + "0.824\n", + "0.8241\n", + "0.8242\n", + "0.8243\n", + "0.8244\n", + "0.8245\n", + "0.8246\n", + "0.8247\n", + "0.8248\n", + "0.8249\n", + "0.825\n", + "0.8251\n", + "0.8252\n", + "0.8253\n", + "0.8254\n", + "0.8255\n", + "0.8256\n", + "0.8257\n", + "0.8258\n", + "0.8259\n", + "0.826\n", + "0.8261\n", + "0.8262\n", + "0.8263\n", + "0.8264\n", + "0.8265\n", + "0.8266\n", + "0.8267\n", + "0.8268\n", + "0.8269\n", + "0.827\n", + "0.8271\n", + "0.8272\n", + "0.8273\n", + "0.8274\n", + "0.8275\n", + "0.8276\n", + "0.8277\n", + "0.8278\n", + "0.8279\n", + "0.828\n", + "0.8281\n", + "0.8282\n", + "0.8283\n", + "0.8284\n", + "0.8285\n", + "0.8286\n", + "0.8287\n", + "0.8288\n", + "0.8289\n", + "0.829\n", + "0.8291\n", + "0.8292\n", + "0.8293\n", + "0.8294\n", + "0.8295\n", + "0.8296\n", + "0.8297\n", + "0.8298\n", + "0.8299\n", + "0.83\n", + "0.8301\n", + "0.8302\n", + "0.8303\n", + "0.8304\n", + "0.8305\n", + "0.8306\n", + "0.8307\n", + "0.8308\n", + "0.8309\n", + "0.831\n", + "0.8311\n", + "0.8312\n", + "0.8313\n", + "0.8314\n", + "0.8315\n", + "0.8316\n", + "0.8317\n", + "0.8318\n", + "0.8319\n", + "0.832\n", + "0.8321\n", + "0.8322\n", + "0.8323\n", + "0.8324\n", + "0.8325\n", + "0.8326\n", + "0.8327\n", + "0.8328\n", + "0.8329\n", + "0.833\n", + "0.8331\n", + "0.8332\n", + "0.8333\n", + "0.8334\n", + "0.8335\n", + "0.8336\n", + "0.8337\n", + "0.8338\n", + "0.8339\n", + "0.834\n", + "0.8341\n", + "0.8342\n", + "0.8343\n", + "0.8344\n", + "0.8345\n", + "0.8346\n", + "0.8347\n", + "0.8348\n", + "0.8349\n", + "0.835\n", + "0.8351\n", + "0.8352\n", + "0.8353\n", + "0.8354\n", + "0.8355\n", + "0.8356\n", + "0.8357\n", + "0.8358\n", + "0.8359\n", + "0.836\n", + "0.8361\n", + "0.8362\n", + "0.8363\n", + "0.8364\n", + "0.8365\n", + "0.8366\n", + "0.8367\n", + "0.8368\n", + "0.8369\n", + "0.837\n", + "0.8371\n", + "0.8372\n", + "0.8373\n", + "0.8374\n", + "0.8375\n", + "0.8376\n", + "0.8377\n", + "0.8378\n", + "0.8379\n", + "0.838\n", + "0.8381\n", + "0.8382\n", + "0.8383\n", + "0.8384\n", + "0.8385\n", + "0.8386\n", + "0.8387\n", + "0.8388\n", + "0.8389\n", + "0.839\n", + "0.8391\n", + "0.8392\n", + "0.8393\n", + "0.8394\n", + "0.8395\n", + "0.8396\n", + "0.8397\n", + "0.8398\n", + "0.8399\n", + "0.84\n", + "0.8401\n", + "0.8402\n", + "0.8403\n", + "0.8404\n", + "0.8405\n", + "0.8406\n", + "0.8407\n", + "0.8408\n", + "0.8409\n", + "0.841\n", + "0.8411\n", + "0.8412\n", + "0.8413\n", + "0.8414\n", + "0.8415\n", + "0.8416\n", + "0.8417\n", + "0.8418\n", + "0.8419\n", + "0.842\n", + "0.8421\n", + "0.8422\n", + "0.8423\n", + "0.8424\n", + "0.8425\n", + "0.8426\n", + "0.8427\n", + "0.8428\n", + "0.8429\n", + "0.843\n", + "0.8431\n", + "0.8432\n", + "0.8433\n", + "0.8434\n", + "0.8435\n", + "0.8436\n", + "0.8437\n", + "0.8438\n", + "0.8439\n", + "0.844\n", + "0.8441\n", + "0.8442\n", + "0.8443\n", + "0.8444\n", + "0.8445\n", + "0.8446\n", + "0.8447\n", + "0.8448\n", + "0.8449\n", + "0.845\n", + "0.8451\n", + "0.8452\n", + "0.8453\n", + "0.8454\n", + "0.8455\n", + "0.8456\n", + "0.8457\n", + "0.8458\n", + "0.8459\n", + "0.846\n", + "0.8461\n", + "0.8462\n", + "0.8463\n", + "0.8464\n", + "0.8465\n", + "0.8466\n", + "0.8467\n", + "0.8468\n", + "0.8469\n", + "0.847\n", + "0.8471\n", + "0.8472\n", + "0.8473\n", + "0.8474\n", + "0.8475\n", + "0.8476\n", + "0.8477\n", + "0.8478\n", + "0.8479\n", + "0.848\n", + "0.8481\n", + "0.8482\n", + "0.8483\n", + "0.8484\n", + "0.8485\n", + "0.8486\n", + "0.8487\n", + "0.8488\n", + "0.8489\n", + "0.849\n", + "0.8491\n", + "0.8492\n", + "0.8493\n", + "0.8494\n", + "0.8495\n", + "0.8496\n", + "0.8497\n", + "0.8498\n", + "0.8499\n", + "0.85\n", + "0.8501\n", + "0.8502\n", + "0.8503\n", + "0.8504\n", + "0.8505\n", + "0.8506\n", + "0.8507\n", + "0.8508\n", + "0.8509\n", + "0.851\n", + "0.8511\n", + "0.8512\n", + "0.8513\n", + "0.8514\n", + "0.8515\n", + "0.8516\n", + "0.8517\n", + "0.8518\n", + "0.8519\n", + "0.852\n", + "0.8521\n", + "0.8522\n", + "0.8523\n", + "0.8524\n", + "0.8525\n", + "0.8526\n", + "0.8527\n", + "0.8528\n", + "0.8529\n", + "0.853\n", + "0.8531\n", + "0.8532\n", + "0.8533\n", + "0.8534\n", + "0.8535\n", + "0.8536\n", + "0.8537\n", + "0.8538\n", + "0.8539\n", + "0.854\n", + "0.8541\n", + "0.8542\n", + "0.8543\n", + "0.8544\n", + "0.8545\n", + "0.8546\n", + "0.8547\n", + "0.8548\n", + "0.8549\n", + "0.855\n", + "0.8551\n", + "0.8552\n", + "0.8553\n", + "0.8554\n", + "0.8555\n", + "0.8556\n", + "0.8557\n", + "0.8558\n", + "0.8559\n", + "0.856\n", + "0.8561\n", + "0.8562\n", + "0.8563\n", + "0.8564\n", + "0.8565\n", + "0.8566\n", + "0.8567\n", + "0.8568\n", + "0.8569\n", + "0.857\n", + "0.8571\n", + "0.8572\n", + "0.8573\n", + "0.8574\n", + "0.8575\n", + "0.8576\n", + "0.8577\n", + "0.8578\n", + "0.8579\n", + "0.858\n", + "0.8581\n", + "0.8582\n", + "0.8583\n", + "0.8584\n", + "0.8585\n", + "0.8586\n", + "0.8587\n", + "0.8588\n", + "0.8589\n", + "0.859\n", + "0.8591\n", + "0.8592\n", + "0.8593\n", + "0.8594\n", + "0.8595\n", + "0.8596\n", + "0.8597\n", + "0.8598\n", + "0.8599\n", + "0.86\n", + "0.8601\n", + "0.8602\n", + "0.8603\n", + "0.8604\n", + "0.8605\n", + "0.8606\n", + "0.8607\n", + "0.8608\n", + "0.8609\n", + "0.861\n", + "0.8611\n", + "0.8612\n", + "0.8613\n", + "0.8614\n", + "0.8615\n", + "0.8616\n", + "0.8617\n", + "0.8618\n", + "0.8619\n", + "0.862\n", + "0.8621\n", + "0.8622\n", + "0.8623\n", + "0.8624\n", + "0.8625\n", + "0.8626\n", + "0.8627\n", + "0.8628\n", + "0.8629\n", + "0.863\n", + "0.8631\n", + "0.8632\n", + "0.8633\n", + "0.8634\n", + "0.8635\n", + "0.8636\n", + "0.8637\n", + "0.8638\n", + "0.8639\n", + "0.864\n", + "0.8641\n", + "0.8642\n", + "0.8643\n", + "0.8644\n", + "0.8645\n", + "0.8646\n", + "0.8647\n", + "0.8648\n", + "0.8649\n", + "0.865\n", + "0.8651\n", + "0.8652\n", + "0.8653\n", + "0.8654\n", + "0.8655\n", + "0.8656\n", + "0.8657\n", + "0.8658\n", + "0.8659\n", + "0.866\n", + "0.8661\n", + "0.8662\n", + "0.8663\n", + "0.8664\n", + "0.8665\n", + "0.8666\n", + "0.8667\n", + "0.8668\n", + "0.8669\n", + "0.867\n", + "0.8671\n", + "0.8672\n", + "0.8673\n", + "0.8674\n", + "0.8675\n", + "0.8676\n", + "0.8677\n", + "0.8678\n", + "0.8679\n", + "0.868\n", + "0.8681\n", + "0.8682\n", + "0.8683\n", + "0.8684\n", + "0.8685\n", + "0.8686\n", + "0.8687\n", + "0.8688\n", + "0.8689\n", + "0.869\n", + "0.8691\n", + "0.8692\n", + "0.8693\n", + "0.8694\n", + "0.8695\n", + "0.8696\n", + "0.8697\n", + "0.8698\n", + "0.8699\n", + "0.87\n", + "0.8701\n", + "0.8702\n", + "0.8703\n", + "0.8704\n", + "0.8705\n", + "0.8706\n", + "0.8707\n", + "0.8708\n", + "0.8709\n", + "0.871\n", + "0.8711\n", + "0.8712\n", + "0.8713\n", + "0.8714\n", + "0.8715\n", + "0.8716\n", + "0.8717\n", + "0.8718\n", + "0.8719\n", + "0.872\n", + "0.8721\n", + "0.8722\n", + "0.8723\n", + "0.8724\n", + "0.8725\n", + "0.8726\n", + "0.8727\n", + "0.8728\n", + "0.8729\n", + "0.873\n", + "0.8731\n", + "0.8732\n", + "0.8733\n", + "0.8734\n", + "0.8735\n", + "0.8736\n", + "0.8737\n", + "0.8738\n", + "0.8739\n", + "0.874\n", + "0.8741\n", + "0.8742\n", + "0.8743\n", + "0.8744\n", + "0.8745\n", + "0.8746\n", + "0.8747\n", + "0.8748\n", + "0.8749\n", + "0.875\n", + "0.8751\n", + "0.8752\n", + "0.8753\n", + "0.8754\n", + "0.8755\n", + "0.8756\n", + "0.8757\n", + "0.8758\n", + "0.8759\n", + "0.876\n", + "0.8761\n", + "0.8762\n", + "0.8763\n", + "0.8764\n", + "0.8765\n", + "0.8766\n", + "0.8767\n", + "0.8768\n", + "0.8769\n", + "0.877\n", + "0.8771\n", + "0.8772\n", + "0.8773\n", + "0.8774\n", + "0.8775\n", + "0.8776\n", + "0.8777\n", + "0.8778\n", + "0.8779\n", + "0.878\n", + "0.8781\n", + "0.8782\n", + "0.8783\n", + "0.8784\n", + "0.8785\n", + "0.8786\n", + "0.8787\n", + "0.8788\n", + "0.8789\n", + "0.879\n", + "0.8791\n", + "0.8792\n", + "0.8793\n", + "0.8794\n", + "0.8795\n", + "0.8796\n", + "0.8797\n", + "0.8798\n", + "0.8799\n", + "0.88\n", + "0.8801\n", + "0.8802\n", + "0.8803\n", + "0.8804\n", + "0.8805\n", + "0.8806\n", + "0.8807\n", + "0.8808\n", + "0.8809\n", + "0.881\n", + "0.8811\n", + "0.8812\n", + "0.8813\n", + "0.8814\n", + "0.8815\n", + "0.8816\n", + "0.8817\n", + "0.8818\n", + "0.8819\n", + "0.882\n", + "0.8821\n", + "0.8822\n", + "0.8823\n", + "0.8824\n", + "0.8825\n", + "0.8826\n", + "0.8827\n", + "0.8828\n", + "0.8829\n", + "0.883\n", + "0.8831\n", + "0.8832\n", + "0.8833\n", + "0.8834\n", + "0.8835\n", + "0.8836\n", + "0.8837\n", + "0.8838\n", + "0.8839\n", + "0.884\n", + "0.8841\n", + "0.8842\n", + "0.8843\n", + "0.8844\n", + "0.8845\n", + "0.8846\n", + "0.8847\n", + "0.8848\n", + "0.8849\n", + "0.885\n", + "0.8851\n", + "0.8852\n", + "0.8853\n", + "0.8854\n", + "0.8855\n", + "0.8856\n", + "0.8857\n", + "0.8858\n", + "0.8859\n", + "0.886\n", + "0.8861\n", + "0.8862\n", + "0.8863\n", + "0.8864\n", + "0.8865\n", + "0.8866\n", + "0.8867\n", + "0.8868\n", + "0.8869\n", + "0.887\n", + "0.8871\n", + "0.8872\n", + "0.8873\n", + "0.8874\n", + "0.8875\n", + "0.8876\n", + "0.8877\n", + "0.8878\n", + "0.8879\n", + "0.888\n", + "0.8881\n", + "0.8882\n", + "0.8883\n", + "0.8884\n", + "0.8885\n", + "0.8886\n", + "0.8887\n", + "0.8888\n", + "0.8889\n", + "0.889\n", + "0.8891\n", + "0.8892\n", + "0.8893\n", + "0.8894\n", + "0.8895\n", + "0.8896\n", + "0.8897\n", + "0.8898\n", + "0.8899\n", + "0.89\n", + "0.8901\n", + "0.8902\n", + "0.8903\n", + "0.8904\n", + "0.8905\n", + "0.8906\n", + "0.8907\n", + "0.8908\n", + "0.8909\n", + "0.891\n", + "0.8911\n", + "0.8912\n", + "0.8913\n", + "0.8914\n", + "0.8915\n", + "0.8916\n", + "0.8917\n", + "0.8918\n", + "0.8919\n", + "0.892\n", + "0.8921\n", + "0.8922\n", + "0.8923\n", + "0.8924\n", + "0.8925\n", + "0.8926\n", + "0.8927\n", + "0.8928\n", + "0.8929\n", + "0.893\n", + "0.8931\n", + "0.8932\n", + "0.8933\n", + "0.8934\n", + "0.8935\n", + "0.8936\n", + "0.8937\n", + "0.8938\n", + "0.8939\n", + "0.894\n", + "0.8941\n", + "0.8942\n", + "0.8943\n", + "0.8944\n", + "0.8945\n", + "0.8946\n", + "0.8947\n", + "0.8948\n", + "0.8949\n", + "0.895\n", + "0.8951\n", + "0.8952\n", + "0.8953\n", + "0.8954\n", + "0.8955\n", + "0.8956\n", + "0.8957\n", + "0.8958\n", + "0.8959\n", + "0.896\n", + "0.8961\n", + "0.8962\n", + "0.8963\n", + "0.8964\n", + "0.8965\n", + "0.8966\n", + "0.8967\n", + "0.8968\n", + "0.8969\n", + "0.897\n", + "0.8971\n", + "0.8972\n", + "0.8973\n", + "0.8974\n", + "0.8975\n", + "0.8976\n", + "0.8977\n", + "0.8978\n", + "0.8979\n", + "0.898\n", + "0.8981\n", + "0.8982\n", + "0.8983\n", + "0.8984\n", + "0.8985\n", + "0.8986\n", + "0.8987\n", + "0.8988\n", + "0.8989\n", + "0.899\n", + "0.8991\n", + "0.8992\n", + "0.8993\n", + "0.8994\n", + "0.8995\n", + "0.8996\n", + "0.8997\n", + "0.8998\n", + "0.8999\n", + "0.9\n", + "0.9001\n", + "0.9002\n", + "0.9003\n", + "0.9004\n", + "0.9005\n", + "0.9006\n", + "0.9007\n", + "0.9008\n", + "0.9009\n", + "0.901\n", + "0.9011\n", + "0.9012\n", + "0.9013\n", + "0.9014\n", + "0.9015\n", + "0.9016\n", + "0.9017\n", + "0.9018\n", + "0.9019\n", + "0.902\n", + "0.9021\n", + "0.9022\n", + "0.9023\n", + "0.9024\n", + "0.9025\n", + "0.9026\n", + "0.9027\n", + "0.9028\n", + "0.9029\n", + "0.903\n", + "0.9031\n", + "0.9032\n", + "0.9033\n", + "0.9034\n", + "0.9035\n", + "0.9036\n", + "0.9037\n", + "0.9038\n", + "0.9039\n", + "0.904\n", + "0.9041\n", + "0.9042\n", + "0.9043\n", + "0.9044\n", + "0.9045\n", + "0.9046\n", + "0.9047\n", + "0.9048\n", + "0.9049\n", + "0.905\n", + "0.9051\n", + "0.9052\n", + "0.9053\n", + "0.9054\n", + "0.9055\n", + "0.9056\n", + "0.9057\n", + "0.9058\n", + "0.9059\n", + "0.906\n", + "0.9061\n", + "0.9062\n", + "0.9063\n", + "0.9064\n", + "0.9065\n", + "0.9066\n", + "0.9067\n", + "0.9068\n", + "0.9069\n", + "0.907\n", + "0.9071\n", + "0.9072\n", + "0.9073\n", + "0.9074\n", + "0.9075\n", + "0.9076\n", + "0.9077\n", + "0.9078\n", + "0.9079\n", + "0.908\n", + "0.9081\n", + "0.9082\n", + "0.9083\n", + "0.9084\n", + "0.9085\n", + "0.9086\n", + "0.9087\n", + "0.9088\n", + "0.9089\n", + "0.909\n", + "0.9091\n", + "0.9092\n", + "0.9093\n", + "0.9094\n", + "0.9095\n", + "0.9096\n", + "0.9097\n", + "0.9098\n", + "0.9099\n", + "0.91\n", + "0.9101\n", + "0.9102\n", + "0.9103\n", + "0.9104\n", + "0.9105\n", + "0.9106\n", + "0.9107\n", + "0.9108\n", + "0.9109\n", + "0.911\n", + "0.9111\n", + "0.9112\n", + "0.9113\n", + "0.9114\n", + "0.9115\n", + "0.9116\n", + "0.9117\n", + "0.9118\n", + "0.9119\n", + "0.912\n", + "0.9121\n", + "0.9122\n", + "0.9123\n", + "0.9124\n", + "0.9125\n", + "0.9126\n", + "0.9127\n", + "0.9128\n", + "0.9129\n", + "0.913\n", + "0.9131\n", + "0.9132\n", + "0.9133\n", + "0.9134\n", + "0.9135\n", + "0.9136\n", + "0.9137\n", + "0.9138\n", + "0.9139\n", + "0.914\n", + "0.9141\n", + "0.9142\n", + "0.9143\n", + "0.9144\n", + "0.9145\n", + "0.9146\n", + "0.9147\n", + "0.9148\n", + "0.9149\n", + "0.915\n", + "0.9151\n", + "0.9152\n", + "0.9153\n", + "0.9154\n", + "0.9155\n", + "0.9156\n", + "0.9157\n", + "0.9158\n", + "0.9159\n", + "0.916\n", + "0.9161\n", + "0.9162\n", + "0.9163\n", + "0.9164\n", + "0.9165\n", + "0.9166\n", + "0.9167\n", + "0.9168\n", + "0.9169\n", + "0.917\n", + "0.9171\n", + "0.9172\n", + "0.9173\n", + "0.9174\n", + "0.9175\n", + "0.9176\n", + "0.9177\n", + "0.9178\n", + "0.9179\n", + "0.918\n", + "0.9181\n", + "0.9182\n", + "0.9183\n", + "0.9184\n", + "0.9185\n", + "0.9186\n", + "0.9187\n", + "0.9188\n", + "0.9189\n", + "0.919\n", + "0.9191\n", + "0.9192\n", + "0.9193\n", + "0.9194\n", + "0.9195\n", + "0.9196\n", + "0.9197\n", + "0.9198\n", + "0.9199\n", + "0.92\n", + "0.9201\n", + "0.9202\n", + "0.9203\n", + "0.9204\n", + "0.9205\n", + "0.9206\n", + "0.9207\n", + "0.9208\n", + "0.9209\n", + "0.921\n", + "0.9211\n", + "0.9212\n", + "0.9213\n", + "0.9214\n", + "0.9215\n", + "0.9216\n", + "0.9217\n", + "0.9218\n", + "0.9219\n", + "0.922\n", + "0.9221\n", + "0.9222\n", + "0.9223\n", + "0.9224\n", + "0.9225\n", + "0.9226\n", + "0.9227\n", + "0.9228\n", + "0.9229\n", + "0.923\n", + "0.9231\n", + "0.9232\n", + "0.9233\n", + "0.9234\n", + "0.9235\n", + "0.9236\n", + "0.9237\n", + "0.9238\n", + "0.9239\n", + "0.924\n", + "0.9241\n", + "0.9242\n", + "0.9243\n", + "0.9244\n", + "0.9245\n", + "0.9246\n", + "0.9247\n", + "0.9248\n", + "0.9249\n", + "0.925\n", + "0.9251\n", + "0.9252\n", + "0.9253\n", + "0.9254\n", + "0.9255\n", + "0.9256\n", + "0.9257\n", + "0.9258\n", + "0.9259\n", + "0.926\n", + "0.9261\n", + "0.9262\n", + "0.9263\n", + "0.9264\n", + "0.9265\n", + "0.9266\n", + "0.9267\n", + "0.9268\n", + "0.9269\n", + "0.927\n", + "0.9271\n", + "0.9272\n", + "0.9273\n", + "0.9274\n", + "0.9275\n", + "0.9276\n", + "0.9277\n", + "0.9278\n", + "0.9279\n", + "0.928\n", + "0.9281\n", + "0.9282\n", + "0.9283\n", + "0.9284\n", + "0.9285\n", + "0.9286\n", + "0.9287\n", + "0.9288\n", + "0.9289\n", + "0.929\n", + "0.9291\n", + "0.9292\n", + "0.9293\n", + "0.9294\n", + "0.9295\n", + "0.9296\n", + "0.9297\n", + "0.9298\n", + "0.9299\n", + "0.93\n", + "0.9301\n", + "0.9302\n", + "0.9303\n", + "0.9304\n", + "0.9305\n", + "0.9306\n", + "0.9307\n", + "0.9308\n", + "0.9309\n", + "0.931\n", + "0.9311\n", + "0.9312\n", + "0.9313\n", + "0.9314\n", + "0.9315\n", + "0.9316\n", + "0.9317\n", + "0.9318\n", + "0.9319\n", + "0.932\n", + "0.9321\n", + "0.9322\n", + "0.9323\n", + "0.9324\n", + "0.9325\n", + "0.9326\n", + "0.9327\n", + "0.9328\n", + "0.9329\n", + "0.933\n", + "0.9331\n", + "0.9332\n", + "0.9333\n", + "0.9334\n", + "0.9335\n", + "0.9336\n", + "0.9337\n", + "0.9338\n", + "0.9339\n", + "0.934\n", + "0.9341\n", + "0.9342\n", + "0.9343\n", + "0.9344\n", + "0.9345\n", + "0.9346\n", + "0.9347\n", + "0.9348\n", + "0.9349\n", + "0.935\n", + "0.9351\n", + "0.9352\n", + "0.9353\n", + "0.9354\n", + "0.9355\n", + "0.9356\n", + "0.9357\n", + "0.9358\n", + "0.9359\n", + "0.936\n", + "0.9361\n", + "0.9362\n", + "0.9363\n", + "0.9364\n", + "0.9365\n", + "0.9366\n", + "0.9367\n", + "0.9368\n", + "0.9369\n", + "0.937\n", + "0.9371\n", + "0.9372\n", + "0.9373\n", + "0.9374\n", + "0.9375\n", + "0.9376\n", + "0.9377\n", + "0.9378\n", + "0.9379\n", + "0.938\n", + "0.9381\n", + "0.9382\n", + "0.9383\n", + "0.9384\n", + "0.9385\n", + "0.9386\n", + "0.9387\n", + "0.9388\n", + "0.9389\n", + "0.939\n", + "0.9391\n", + "0.9392\n", + "0.9393\n", + "0.9394\n", + "0.9395\n", + "0.9396\n", + "0.9397\n", + "0.9398\n", + "0.9399\n", + "0.94\n", + "0.9401\n", + "0.9402\n", + "0.9403\n", + "0.9404\n", + "0.9405\n", + "0.9406\n", + "0.9407\n", + "0.9408\n", + "0.9409\n", + "0.941\n", + "0.9411\n", + "0.9412\n", + "0.9413\n", + "0.9414\n", + "0.9415\n", + "0.9416\n", + "0.9417\n", + "0.9418\n", + "0.9419\n", + "0.942\n", + "0.9421\n", + "0.9422\n", + "0.9423\n", + "0.9424\n", + "0.9425\n", + "0.9426\n", + "0.9427\n", + "0.9428\n", + "0.9429\n", + "0.943\n", + "0.9431\n", + "0.9432\n", + "0.9433\n", + "0.9434\n", + "0.9435\n", + "0.9436\n", + "0.9437\n", + "0.9438\n", + "0.9439\n", + "0.944\n", + "0.9441\n", + "0.9442\n", + "0.9443\n", + "0.9444\n", + "0.9445\n", + "0.9446\n", + "0.9447\n", + "0.9448\n", + "0.9449\n", + "0.945\n", + "0.9451\n", + "0.9452\n", + "0.9453\n", + "0.9454\n", + "0.9455\n", + "0.9456\n", + "0.9457\n", + "0.9458\n", + "0.9459\n", + "0.946\n", + "0.9461\n", + "0.9462\n", + "0.9463\n", + "0.9464\n", + "0.9465\n", + "0.9466\n", + "0.9467\n", + "0.9468\n", + "0.9469\n", + "0.947\n", + "0.9471\n", + "0.9472\n", + "0.9473\n", + "0.9474\n", + "0.9475\n", + "0.9476\n", + "0.9477\n", + "0.9478\n", + "0.9479\n", + "0.948\n", + "0.9481\n", + "0.9482\n", + "0.9483\n", + "0.9484\n", + "0.9485\n", + "0.9486\n", + "0.9487\n", + "0.9488\n", + "0.9489\n", + "0.949\n", + "0.9491\n", + "0.9492\n", + "0.9493\n", + "0.9494\n", + "0.9495\n", + "0.9496\n", + "0.9497\n", + "0.9498\n", + "0.9499\n", + "0.95\n", + "0.9501\n", + "0.9502\n", + "0.9503\n", + "0.9504\n", + "0.9505\n", + "0.9506\n", + "0.9507\n", + "0.9508\n", + "0.9509\n", + "0.951\n", + "0.9511\n", + "0.9512\n", + "0.9513\n", + "0.9514\n", + "0.9515\n", + "0.9516\n", + "0.9517\n", + "0.9518\n", + "0.9519\n", + "0.952\n", + "0.9521\n", + "0.9522\n", + "0.9523\n", + "0.9524\n", + "0.9525\n", + "0.9526\n", + "0.9527\n", + "0.9528\n", + "0.9529\n", + "0.953\n", + "0.9531\n", + "0.9532\n", + "0.9533\n", + "0.9534\n", + "0.9535\n", + "0.9536\n", + "0.9537\n", + "0.9538\n", + "0.9539\n", + "0.954\n", + "0.9541\n", + "0.9542\n", + "0.9543\n", + "0.9544\n", + "0.9545\n", + "0.9546\n", + "0.9547\n", + "0.9548\n", + "0.9549\n", + "0.955\n", + "0.9551\n", + "0.9552\n", + "0.9553\n", + "0.9554\n", + "0.9555\n", + "0.9556\n", + "0.9557\n", + "0.9558\n", + "0.9559\n", + "0.956\n", + "0.9561\n", + "0.9562\n", + "0.9563\n", + "0.9564\n", + "0.9565\n", + "0.9566\n", + "0.9567\n", + "0.9568\n", + "0.9569\n", + "0.957\n", + "0.9571\n", + "0.9572\n", + "0.9573\n", + "0.9574\n", + "0.9575\n", + "0.9576\n", + "0.9577\n", + "0.9578\n", + "0.9579\n", + "0.958\n", + "0.9581\n", + "0.9582\n", + "0.9583\n", + "0.9584\n", + "0.9585\n", + "0.9586\n", + "0.9587\n", + "0.9588\n", + "0.9589\n", + "0.959\n", + "0.9591\n", + "0.9592\n", + "0.9593\n", + "0.9594\n", + "0.9595\n", + "0.9596\n", + "0.9597\n", + "0.9598\n", + "0.9599\n", + "0.96\n", + "0.9601\n", + "0.9602\n", + "0.9603\n", + "0.9604\n", + "0.9605\n", + "0.9606\n", + "0.9607\n", + "0.9608\n", + "0.9609\n", + "0.961\n", + "0.9611\n", + "0.9612\n", + "0.9613\n", + "0.9614\n", + "0.9615\n", + "0.9616\n", + "0.9617\n", + "0.9618\n", + "0.9619\n", + "0.962\n", + "0.9621\n", + "0.9622\n", + "0.9623\n", + "0.9624\n", + "0.9625\n", + "0.9626\n", + "0.9627\n", + "0.9628\n", + "0.9629\n", + "0.963\n", + "0.9631\n", + "0.9632\n", + "0.9633\n", + "0.9634\n", + "0.9635\n", + "0.9636\n", + "0.9637\n", + "0.9638\n", + "0.9639\n", + "0.964\n", + "0.9641\n", + "0.9642\n", + "0.9643\n", + "0.9644\n", + "0.9645\n", + "0.9646\n", + "0.9647\n", + "0.9648\n", + "0.9649\n", + "0.965\n", + "0.9651\n", + "0.9652\n", + "0.9653\n", + "0.9654\n", + "0.9655\n", + "0.9656\n", + "0.9657\n", + "0.9658\n", + "0.9659\n", + "0.966\n", + "0.9661\n", + "0.9662\n", + "0.9663\n", + "0.9664\n", + "0.9665\n", + "0.9666\n", + "0.9667\n", + "0.9668\n", + "0.9669\n", + "0.967\n", + "0.9671\n", + "0.9672\n", + "0.9673\n", + "0.9674\n", + "0.9675\n", + "0.9676\n", + "0.9677\n", + "0.9678\n", + "0.9679\n", + "0.968\n", + "0.9681\n", + "0.9682\n", + "0.9683\n", + "0.9684\n", + "0.9685\n", + "0.9686\n", + "0.9687\n", + "0.9688\n", + "0.9689\n", + "0.969\n", + "0.9691\n", + "0.9692\n", + "0.9693\n", + "0.9694\n", + "0.9695\n", + "0.9696\n", + "0.9697\n", + "0.9698\n", + "0.9699\n", + "0.97\n", + "0.9701\n", + "0.9702\n", + "0.9703\n", + "0.9704\n", + "0.9705\n", + "0.9706\n", + "0.9707\n", + "0.9708\n", + "0.9709\n", + "0.971\n", + "0.9711\n", + "0.9712\n", + "0.9713\n", + "0.9714\n", + "0.9715\n", + "0.9716\n", + "0.9717\n", + "0.9718\n", + "0.9719\n", + "0.972\n", + "0.9721\n", + "0.9722\n", + "0.9723\n", + "0.9724\n", + "0.9725\n", + "0.9726\n", + "0.9727\n", + "0.9728\n", + "0.9729\n", + "0.973\n", + "0.9731\n", + "0.9732\n", + "0.9733\n", + "0.9734\n", + "0.9735\n", + "0.9736\n", + "0.9737\n", + "0.9738\n", + "0.9739\n", + "0.974\n", + "0.9741\n", + "0.9742\n", + "0.9743\n", + "0.9744\n", + "0.9745\n", + "0.9746\n", + "0.9747\n", + "0.9748\n", + "0.9749\n", + "0.975\n", + "0.9751\n", + "0.9752\n", + "0.9753\n", + "0.9754\n", + "0.9755\n", + "0.9756\n", + "0.9757\n", + "0.9758\n", + "0.9759\n", + "0.976\n", + "0.9761\n", + "0.9762\n", + "0.9763\n", + "0.9764\n", + "0.9765\n", + "0.9766\n", + "0.9767\n", + "0.9768\n", + "0.9769\n", + "0.977\n", + "0.9771\n", + "0.9772\n", + "0.9773\n", + "0.9774\n", + "0.9775\n", + "0.9776\n", + "0.9777\n", + "0.9778\n", + "0.9779\n", + "0.978\n", + "0.9781\n", + "0.9782\n", + "0.9783\n", + "0.9784\n", + "0.9785\n", + "0.9786\n", + "0.9787\n", + "0.9788\n", + "0.9789\n", + "0.979\n", + "0.9791\n", + "0.9792\n", + "0.9793\n", + "0.9794\n", + "0.9795\n", + "0.9796\n", + "0.9797\n", + "0.9798\n", + "0.9799\n", + "0.98\n", + "0.9801\n", + "0.9802\n", + "0.9803\n", + "0.9804\n", + "0.9805\n", + "0.9806\n", + "0.9807\n", + "0.9808\n", + "0.9809\n", + "0.981\n", + "0.9811\n", + "0.9812\n", + "0.9813\n", + "0.9814\n", + "0.9815\n", + "0.9816\n", + "0.9817\n", + "0.9818\n", + "0.9819\n", + "0.982\n", + "0.9821\n", + "0.9822\n", + "0.9823\n", + "0.9824\n", + "0.9825\n", + "0.9826\n", + "0.9827\n", + "0.9828\n", + "0.9829\n", + "0.983\n", + "0.9831\n", + "0.9832\n", + "0.9833\n", + "0.9834\n", + "0.9835\n", + "0.9836\n", + "0.9837\n", + "0.9838\n", + "0.9839\n", + "0.984\n", + "0.9841\n", + "0.9842\n", + "0.9843\n", + "0.9844\n", + "0.9845\n", + "0.9846\n", + "0.9847\n", + "0.9848\n", + "0.9849\n", + "0.985\n", + "0.9851\n", + "0.9852\n", + "0.9853\n", + "0.9854\n", + "0.9855\n", + "0.9856\n", + "0.9857\n", + "0.9858\n", + "0.9859\n", + "0.986\n", + "0.9861\n", + "0.9862\n", + "0.9863\n", + "0.9864\n", + "0.9865\n", + "0.9866\n", + "0.9867\n", + "0.9868\n", + "0.9869\n", + "0.987\n", + "0.9871\n", + "0.9872\n", + "0.9873\n", + "0.9874\n", + "0.9875\n", + "0.9876\n", + "0.9877\n", + "0.9878\n", + "0.9879\n", + "0.988\n", + "0.9881\n", + "0.9882\n", + "0.9883\n", + "0.9884\n", + "0.9885\n", + "0.9886\n", + "0.9887\n", + "0.9888\n", + "0.9889\n", + "0.989\n", + "0.9891\n", + "0.9892\n", + "0.9893\n", + "0.9894\n", + "0.9895\n", + "0.9896\n", + "0.9897\n", + "0.9898\n", + "0.9899\n", + "0.99\n", + "0.9901\n", + "0.9902\n", + "0.9903\n", + "0.9904\n", + "0.9905\n", + "0.9906\n", + "0.9907\n", + "0.9908\n", + "0.9909\n", + "0.991\n", + "0.9911\n", + "0.9912\n", + "0.9913\n", + "0.9914\n", + "0.9915\n", + "0.9916\n", + "0.9917\n", + "0.9918\n", + "0.9919\n", + "0.992\n", + "0.9921\n", + "0.9922\n", + "0.9923\n", + "0.9924\n", + "0.9925\n", + "0.9926\n", + "0.9927\n", + "0.9928\n", + "0.9929\n", + "0.993\n", + "0.9931\n", + "0.9932\n", + "0.9933\n", + "0.9934\n", + "0.9935\n", + "0.9936\n", + "0.9937\n", + "0.9938\n", + "0.9939\n", + "0.994\n", + "0.9941\n", + "0.9942\n", + "0.9943\n", + "0.9944\n", + "0.9945\n", + "0.9946\n", + "0.9947\n", + "0.9948\n", + "0.9949\n", + "0.995\n", + "0.9951\n", + "0.9952\n", + "0.9953\n", + "0.9954\n", + "0.9955\n", + "0.9956\n", + "0.9957\n", + "0.9958\n", + "0.9959\n", + "0.996\n", + "0.9961\n", + "0.9962\n", + "0.9963\n", + "0.9964\n", + "0.9965\n", + "0.9966\n", + "0.9967\n", + "0.9968\n", + "0.9969\n", + "0.997\n", + "0.9971\n", + "0.9972\n", + "0.9973\n", + "0.9974\n", + "0.9975\n", + "0.9976\n", + "0.9977\n", + "0.9978\n", + "0.9979\n", + "0.998\n", + "0.9981\n", + "0.9982\n", + "0.9983\n", + "0.9984\n", + "0.9985\n", + "0.9986\n", + "0.9987\n", + "0.9988\n", + "0.9989\n", + "0.999\n", + "0.9991\n", + "0.9992\n", + "0.9993\n", + "0.9994\n", + "0.9995\n", + "0.9996\n", + "0.9997\n", + "0.9998\n", + "0.9999\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.signal.sample_mode import SampleModeOpticalSignal\n", + "simulation_parameters = SampleModeSimulationParameters()\n", + "initial_time_step, initial_state = discrete_filter._sample_mode_initial_state(simulation_parameters)\n", + "\n", + "\n", + "A = []\n", + "state = initial_state\n", + "\n", + "block_mode_amplitude = block_mode_inputs['in'].amplitude\n", + "block_mode_wavelength = block_mode_inputs['in'].wavelength\n", + "\n", + "for i in range(simulation_parameters.num_time_steps):\n", + " sample_mode_amplitude = sample_mode_source[i][\"o0\"].amplitude\n", + " sample_mode_wavelength = sample_mode_source[i][\"o0\"].wavelength\n", + " sample_mode_inputs = {\n", + " 'in': SampleModeOpticalSignal(\n", + " amplitude = sample_mode_amplitude,\n", + " wavelength = sample_mode_wavelength,\n", + " ),\n", + " 'out': SampleModeOpticalSignal(\n", + " amplitude = jnp.zeros_like(sample_mode_amplitude),\n", + " wavelength = sample_mode_wavelength,\n", + " ),\n", + " }\n", + " # This hidden method automatically converts outputs to proper frequency channels\n", + " outputs, _state = discrete_filter._sample_mode_step(sample_mode_inputs, (i, state), None, simulation_parameters)\n", + " _, state = _state\n", + " A.append(outputs['out'].amplitude[0, 0])\n", + " print(i/simulation_parameters.num_time_steps)\n", + "\n", + "plt.plot(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "661048c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'wl')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from simphony.time_domain.utils import gaussian_pulse\n", + "from simphony.libraries.analytic.sources import OpticalCombSource\n", + "from functools import partial\n", + "import sax\n", + "from scipy.constants import speed_of_light\n", + "\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 100)\n", + "\n", + "_mzi, info = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"gc_in\": \"gc\",\n", + " \"splitter\": \"ybranch\",\n", + " \"long_wg\": \"waveguide\",\n", + " \"short_wg\": \"waveguide\",\n", + " \"combiner\": \"ybranch\",\n", + " # \"gc_out\": \"gc\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port 2\": \"long_wg,o0\",\n", + " \"splitter,port 3\": \"short_wg,o0\",\n", + " \"long_wg,o1\": \"combiner,port 2\",\n", + " \"short_wg,o1\": \"combiner,port 3\",\n", + " # \"combiner,port 1\": \"gc_out,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port 1\",\n", + " \"out\": \"combiner,port 1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ybranch\": siepic.y_branch,\n", + " \"waveguide\": siepic.waveguide,\n", + " # \"gc\": siepic.grating_coupler,\n", + " }\n", + ")\n", + "\n", + "def mzi(wl=1.55):\n", + " return _mzi(wl=wl, long_wg={\"length\": 150.0}, short_wg={\"length\": 100.0})\n", + "\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"laser\":\"laser\",\n", + " # \"wg1\": \"waveguide\",\n", + " \"mzi\": \"mzi\",\n", + " },\n", + " \"connections\": {\n", + " \"laser,o0\": \"mzi,in\",\n", + " },\n", + " \"ports\": {\n", + " \"laser_output\": \"laser,o0\",\n", + " \"waveguide_output\": \"mzi,out\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " # \"laser\": analytic.CWLaser,\n", + " \"laser\": OpticalCombSource,\n", + " \"mzi\": mzi,\n", + " # \"laser\": analytic.OpticalSource,\n", + " # \"waveguide\": siepic.waveguide,\n", + " # \"waveguide\": analytic.Waveguide,\n", + "}\n", + "\n", + "pulse_fn = partial(gaussian_pulse, t0=4e-12, sigma=1e-12)\n", + "settings={ \n", + " \"laser\": {\"wavelength\": wl},\n", + " \"mzi\": {\"delay_compensation\": 1},\n", + " # \"laser\": {\"wavelength\": 1.55e-6, \"envelope_fn\": pulse_fn},\n", + " # \"wg1\": {\"length\":20},\n", + "}\n", + "\n", + "wl_dense = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "# plt.plot(wl_dense, np.abs(mzi(1e6*wl_dense)[('out', 'in')]))\n", + "plt.plot(wl_dense, np.unwrap(np.angle(np.exp(-1j*speed_of_light/wl_dense*13e-12)*mzi(1e6*wl_dense)[('out', 'out')])))\n", + "plt.ylabel(\"Phase\")\n", + "plt.xlabel(\"wl\")\n", + "# plt.xlim([1.53*1e-6, 1.57*1e-6])\n", + "# plt.ylim([-125, -40])\n", + "# plt.axvline(1.55e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a902ac98", + "metadata": {}, + "outputs": [], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"laser\":\"laser\",\n", + " \"splitter\":\"ybranch\",\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + " # \"phase_modulator\": \"phase_modulator\",\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " # \"vf\": \"voltage_follower\",\n", + " # \"vf1\": \"voltage_follower\",\n", + " # \"vf2\": \"voltage_follower\",\n", + " # \"prng\": \"prng\",\n", + "\n", + " # \"pm2\": \"phase_modulator\",\n", + " # \"vs2\": \"voltage_source\",\n", + "\n", + " # \"y1\": \"ybranch\",\n", + " # \"y2\": \"ybranch\",\n", + " },\n", + " \"connections\": {\n", + " \"laser,o0\": \"splitter,port_1\",\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + " \n", + " # Should Work\n", + " \"vs1,e0\":\"pm1,e0\",\n", + " \"vs2,e0\":\"pm2,e0\",\n", + "\n", + " # Should Not Work\n", + " # \"pm1,e0\":\"vf,e0\",\n", + " # \"vf,e1\":\"pm2,e0\",\n", + " \n", + " # Multiple connections, same nodes\n", + " # \"y1,port_2\": \"y2,port_2\",\n", + " # \"y1,port_3\": \"y2,port_3\",\n", + "\n", + " # Invalid Connection\n", + " # \"vs2,e0\":\"pm2,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"laser\": analytic.CWLaser,\n", + " # \"ybranch\": siepic.y_branch,\n", + " \"ybranch\": analytic.optical_s_parameter_placeholder(siepic.y_branch),\n", + " \"waveguide\": siepic.waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " # \"prng\": analytic.PRNG,\n", + " # \"voltage_follower\": analytic.VoltageFollower,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"laser\": {\"wavelength\": 1.55e-6},\n", + " \"top1\": {\"length\": 180},\n", + " \"top2\": {\"length\": 185},\n", + " \"bot1\": {\"length\": 190},\n", + " \"bot2\": {\"length\": 195}, \n", + " \"vs1\": {\"steady_state_voltage\": 1.0, \"steady_state_wl\": 0},\n", + " \"vs2\": {\"steady_state_voltage\": 0.0, \"steady_state_wl\": 0}\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "794115f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models, settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e0f0abc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.simulation import SampleModeSimulation, SampleModeSimulationParameters\n", + "import numpy as np\n", + "from time import time\n", + "\n", + "sms = SampleModeSimulation(ckt)\n", + "\n", + "simulation_parameters = SampleModeSimulationParameters(optical_baseband_wavelengths=wl, num_time_steps=int(50000))\n", + "\n", + "tic = time()\n", + "sms_result = sms.run(settings=settings, use_jit=True, simulation_parameters=simulation_parameters)\n", + "toc = time()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3a9559c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Example: suppose you have frequency samples 187 THz to 200 THz\n", + "f_min = 187e12\n", + "f_max = 200e12\n", + "N = 2048 # number of points (power of 2 efficient for FFT)\n", + "freqs = np.linspace(f_min, f_max, N)\n", + "\n", + "# Your frequency-domain data, e.g. H(f)\n", + "Hf = np.exp(-1j * 2 * np.pi * freqs * 1e-12) # (example: delay of 1 ps)\n", + "\n", + "# Shift frequency axis to baseband (center around 0)\n", + "f_center = (f_min + f_max) / 2\n", + "# Hf_shifted = Hf * np.exp(-1j * 2 * np.pi * (freqs - f_center) * 0)\n", + "\n", + "# Compute impulse response (baseband equivalent)\n", + "ht = np.fft.ifft(np.fft.ifftshift(Hf))\n", + "\n", + "# Time axis (FFT dual of frequency spacing)\n", + "df = freqs[1] - freqs[0]\n", + "T = 1 / df\n", + "t = np.linspace(0, T, N, endpoint=False)\n", + "\n", + "# If you want the passband (modulated) version:\n", + "ht_passband = ht * np.exp(1j * 2 * np.pi * f_center * t)\n", + "\n", + "plt.plot(t, ht_passband)\n", + "plt.xlim([0, 10e-12])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3a07bd8", + "metadata": {}, + "outputs": [], + "source": [ + "sms_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed58ce51", + "metadata": {}, + "outputs": [], + "source": [ + "laser_output = sms_result['laser']['o0']\n", + "mzi_output = sms_result['mzi']['out']\n", + "# adv0_output = sms_result['_optical_advance0']['out']\n", + "# adv1_output = sms_result['_optical_advance1']['in']\n", + "# wg_output = sms_result['wg1']['o1']\n", + "t = np.arange(len(laser_output.amplitude[:, 15, 0]))\n", + "t = t*1e-14\n", + "# skip = 1\n", + "plt.plot(t[:], laser_output.amplitude[:, 15, 0].real)\n", + "plt.plot(t[:], np.abs(mzi_output.amplitude[:, 94, 0]))\n", + "# plt.plot(t[:], np.abs(wg_output.amplitude[:, 0, 0]))\n", + "# plt.plot(t[:], np.abs(adv0_output.amplitude[:, 0, 0]))\n", + "# plt.plot(t[:], np.abs(adv1_output.amplitude[:, 0, 0]))\n", + "\n", + "# for i, _ in enumerate(simulation_parameters.optical_baseband_wavelengths):\n", + "# plt.plot(t[:], np.abs(mzi_output.amplitude[:, i, 0].real))\n", + "\n", + "# plt.xlim([0, 60e-11])\n", + "\n", + "np.abs(mzi_output.amplitude[-1, 96, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21edb8d9", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.constants import speed_of_light as c\n", + "_wl = simulation_parameters.optical_baseband_wavelengths\n", + "\n", + "\n", + "phase_diff = np.unwrap(np.angle(mzi_output.amplitude[-1, :, 0])) - np.unwrap(np.angle(mzi(1e6*_wl)[('out', 'in')]))\n", + "plt.plot(c/_wl, phase_diff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d7cb555", + "metadata": {}, + "outputs": [], + "source": [ + "# wl_dense = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "steady_state_wl = simulation_parameters.optical_baseband_wavelengths\n", + "steady_state_amp = mzi_output.amplitude[-1, :, 0]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# --- Magnitude plot ---\n", + "axes[0].scatter(steady_state_wl, np.abs(steady_state_amp)**2, s=30, marker='*', label=\"steady_state\")\n", + "axes[0].plot(wl_dense, np.abs(mzi(1e6*wl_dense)[('in', 'out')])**2, label=\"s-params\")\n", + "axes[0].set_ylabel(\"mag\")\n", + "axes[0].set_xlabel(\"wl\")\n", + "axes[0].legend(loc=\"upper left\")\n", + "\n", + "# --- Phase plot ---\n", + "axes[1].scatter(steady_state_wl, np.angle(steady_state_amp), s=30, marker='*', label=\"steady_state\")\n", + "axes[1].plot(wl_dense, np.angle(mzi(1e6*wl_dense)[('in', 'out')]), label=\"s-params\")\n", + "axes[1].set_ylabel(\"phase\")\n", + "axes[1].set_xlabel(\"wl\")\n", + "axes[1].legend(loc=\"upper left\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "704d2514", + "metadata": {}, + "outputs": [], + "source": [ + "steady_state_amp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f38333e", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import signal\n", + "\n", + "signal.dimpulse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53d52df6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.signal import dlti, dimpulse\n", + "\n", + "# --- Define a simple discrete-time system ---\n", + "A = np.array([[0.5+0.2j, 0], [0, 0.2+0.1j]], dtype=complex)\n", + "B = np.array([[1.0], [0.5]], dtype=complex)\n", + "C = np.array([[1.0, 1.0]], dtype=complex)\n", + "D = np.array([[0.0]], dtype=complex)\n", + "dt = 1.0 # sample time\n", + "\n", + "system = dlti(A, B, C, D, dt=dt)\n", + "\n", + "# --- Analytic impulse response ---\n", + "N = 10 # number of samples\n", + "h_analytic = []\n", + "\n", + "for n in range(N):\n", + " if n == 0:\n", + " h_analytic.append(D.flatten())\n", + " else:\n", + " h_analytic.append((C @ np.linalg.matrix_power(A, n-1) @ B).flatten())\n", + "\n", + "h_analytic = np.array(h_analytic)\n", + "\n", + "# --- Impulse response using dimpulse ---\n", + "t, h_dimpulse = dimpulse(system, x0=np.zeros((A.shape[0],), dtype=complex), n=N)\n", + "h_dimpulse = np.squeeze(h_dimpulse[0]) # only one input/output\n", + "\n", + "# --- Compare ---\n", + "print(\"Analytic h[n]:\")\n", + "print(h_analytic.flatten())\n", + "print(\"\\ndimpulse h[n]:\")\n", + "print(h_dimpulse)\n", + "\n", + "# Optional: check if they are close\n", + "print(\"\\nDifference:\")\n", + "print(h_analytic.flatten() - h_dimpulse)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2f6e5f3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_parameters.optical_baseband_wavelengths[50]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c210cb66", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# -----------------------------\n", + "# Parameters\n", + "# -----------------------------\n", + "N = 20 # total number of samples\n", + "k = 5 # original delta delay\n", + "advance = 1 # number of samples to advance\n", + "complex_factor = 1 + 1j # multiply delta by a complex number\n", + "\n", + "# -----------------------------\n", + "# Discrete-time impulse responses\n", + "# -----------------------------\n", + "n = np.arange(N)\n", + "h = np.zeros(N, dtype=complex)\n", + "h[k] = complex_factor # h[n] = delta[n-k] * complex_factor\n", + "\n", + "# Advanced impulse response: h_shifted[n] = h[n+advance]\n", + "h_shifted = np.zeros_like(h)\n", + "if k - advance >= 0:\n", + " h_shifted[k-advance] = complex_factor\n", + "# If advance moves it before n=0, it just disappears (causal system)\n", + "\n", + "# -----------------------------\n", + "# Unit step function\n", + "# -----------------------------\n", + "u = np.ones(N, dtype=complex)\n", + "\n", + "# -----------------------------\n", + "# Convolutions\n", + "# -----------------------------\n", + "conv_h = np.convolve(h, u)[:N] # truncate to original length for simplicity\n", + "conv_h_shifted = np.convolve(h_shifted, u)[:N]\n", + "\n", + "# -----------------------------\n", + "# Plot results\n", + "# -----------------------------\n", + "plt.figure(figsize=(12,5))\n", + "\n", + "# Magnitude\n", + "plt.subplot(1,2,1)\n", + "markers, stemline, baseline = plt.stem(n, np.abs(conv_h), linefmt='b', markerfmt='bo', basefmt='k', label='h * u')\n", + "markers.set_alpha(0.5)\n", + "\n", + "markers, stemline, baseline = plt.stem(n, np.abs(conv_h_shifted), linefmt='r', markerfmt='ro', basefmt='k', label='h_shifted * u')\n", + "markers.set_alpha(0.5)\n", + "plt.title(\"Magnitude of convolution with unit step\")\n", + "plt.xlabel(\"n\")\n", + "plt.ylabel(\"|(h*u)[n]|\")\n", + "plt.legend()\n", + "\n", + "# Phase\n", + "plt.subplot(1,2,2)\n", + "plt.stem(n, np.angle(conv_h), linefmt='b', markerfmt='bo', basefmt='k', label='h * u')\n", + "plt.stem(n, np.angle(conv_h_shifted), linefmt='r', markerfmt='ro', basefmt='k', label='h_shifted * u')\n", + "plt.title(\"Phase of convolution with unit step\")\n", + "plt.xlabel(\"n\")\n", + "plt.ylabel(\"Phase [rad]\")\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e84543", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.analytic.sources import OpticalCombSource\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "comb = OpticalCombSource(linewidth=1.0)\n", + "y = comb.block_mode_response()['o0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5339085b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(np.angle(y.amplitude[:, 4]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5402f84a", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_group_delay(wavelengths, s_params):\n", + " # Unwrap phase\n", + " phase = np.unwrap(np.angle(s_params)) # radians\n", + "\n", + " # Derivative of phase w.r.t wavelength\n", + " dphi_dlambda = np.gradient(phase, wavelengths) # radians per meter\n", + "\n", + " # Compute group delay\n", + " c = 299792458 # speed of light in m/s\n", + " group_delay = -dphi_dlambda * (wavelengths**2) / (2 * np.pi * c) # in seconds\n", + "\n", + " return group_delay # same length as input\n", + "\n", + "S = siepic.waveguide(wl=np.linspace(1.5, 1.6, 1000), length=100)[('o0', 'o1')]\n", + "\n", + "tau1 = compute_group_delay(wl, S)[500]\n", + "tau2 = 4e-12\n", + "plt.plot(t, np.abs(laser_output.amplitude[:, 0, 0]))\n", + "plt.plot(t, np.abs(wg_output.amplitude[:, 0, 0]))\n", + "# plt.axvline(tau1+tau2+.5*0*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*1*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*2*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*3*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*4*1e-12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ee4dd1c", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.signal import firwin\n", + "import numpy as np\n", + "\n", + "# FIR filter\n", + "L = 100\n", + "h = firwin(L, 0.1)\n", + "\n", + "# Inputs\n", + "N = 1000\n", + "tau = 200\n", + "sigma = 10\n", + "\n", + "step_input = np.zeros(N)\n", + "step_input[tau:] = 1.0\n", + "\n", + "n = np.arange(N)\n", + "gaussian_input = np.exp(-0.5 * ((n - tau) / sigma) ** 2)\n", + "\n", + "# Filter outputs\n", + "step_response = np.convolve(step_input, h, mode='full')[:N]\n", + "gaussian_response = np.convolve(gaussian_input, h, mode='full')[:N]\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.title(\"Inputs\")\n", + "plt.plot(step_input, label=\"Step input\")\n", + "plt.plot(gaussian_input, label=\"Gaussian input\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.title(\"Filtered Outputs\")\n", + "plt.plot(step_response, label=\"Step response\")\n", + "plt.plot(gaussian_response, label=\"Gaussian response\")\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ab9bf01", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "from scipy.signal import bessel, freqz, tf2zpk\n", + "\n", + "# === 1. Bessel Filter Design ===\n", + "order = 5\n", + "cutoff = 0.02 # Normalized frequency (0.5 = Nyquist)\n", + "b, a = bessel(order, cutoff, btype='low', analog=False, norm='phase')\n", + "\n", + "# === 2. Group Delay Calculation ===\n", + "w, h = freqz(b, a, worN=2048)\n", + "freq = w / jnp.pi # Normalized frequency [0, 1]\n", + "phase = jnp.unwrap(jnp.angle(h))\n", + "group_delay_bessel = -jnp.gradient(phase) / jnp.gradient(w)\n", + "\n", + "# === 3. Design a Simple All-Pass Filter for Compensation ===\n", + "# First-order all-pass: H(z) = (a + z^-1) / (1 + a z^-1)\n", + "# We'll pick 'a' close to 1 to give more delay at higher frequencies\n", + "\n", + "a1 = 0.01\n", + "b_ap = jnp.array([a1, 1.0])\n", + "a_ap = jnp.array([1.0, a1])\n", + "\n", + "# Frequency response of the all-pass\n", + "_, h_ap = freqz(b_ap, a_ap, worN=2048)\n", + "phase_ap = jnp.unwrap(jnp.angle(h_ap))\n", + "group_delay_ap = -jnp.gradient(phase_ap) / jnp.gradient(w)\n", + "\n", + "# === 4. Cascade Bessel + All-Pass ===\n", + "from scipy.signal import convolve\n", + "\n", + "b_total = convolve(b, b_ap)\n", + "a_total = convolve(a, a_ap)\n", + "\n", + "_, h_total = freqz(b_total, a_total, worN=2048)\n", + "phase_total = jnp.unwrap(jnp.angle(h_total))\n", + "group_delay_total = -jnp.gradient(phase_total) / jnp.gradient(w)\n", + "\n", + "# === 5. Plot Group Delays ===\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(freq, group_delay_bessel, label='Bessel Filter')\n", + "plt.plot(freq, group_delay_ap, label='All-Pass Compensation')\n", + "plt.plot(freq, group_delay_total, label='Cascaded (Flattened)')\n", + "plt.xlim(0, 0.05)\n", + "plt.xlabel('Normalized Frequency (π radians/sample)')\n", + "plt.ylabel('Group Delay (samples)')\n", + "plt.title('Group Delay Compensation using All-Pass Filter')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c17befaa", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a38a2d73", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.signal import butter, bessel, group_delay\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Example: design and compare filters\n", + "fs = 1000 # Hz\n", + "N = 5 # order\n", + "fc = 100 # cutoff\n", + "\n", + "# Design Butterworth and Bessel filters\n", + "b_butter, a_butter = butter(N, fc/(fs/2))\n", + "b_bessel, a_bessel = bessel(N, fc/(fs/2), analog=False)\n", + "\n", + "# Compute group delay\n", + "w_b, gd_b = group_delay((b_butter, a_butter), fs=fs)\n", + "w_be, gd_be = group_delay((b_bessel, a_bessel), fs=fs)\n", + "\n", + "# Plot\n", + "plt.plot(w_b, gd_b, label=\"Butterworth\")\n", + "plt.plot(w_be, gd_be, label=\"Bessel\")\n", + "plt.xlabel(\"Frequency [Hz]\")\n", + "plt.ylabel(\"Group Delay [s]\")\n", + "plt.title(\"Group Delay vs Frequency\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9172e908", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.utils import dict_to_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d124467", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.signal import sample_mode_electrical_signal, sample_mode_optical_signal\n", + "pm1_inputs = {\n", + " 'e0': sample_mode_electrical_signal(field=[0.00, 10.0], wl=[1.55e-6, 1.57e-6]),\n", + " 'o0': sample_mode_optical_signal(field=[0.50, 1.00], wl=[1.55e-6, 1.56e-6]),\n", + " 'o1': sample_mode_optical_signal(field=[0.50, 1.00], wl=[1.55e-6, 1.56e-6]),\n", + "}\n", + "\n", + "sms.components['pm1'].step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13ab778b", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.signal import sample_mode_optical_signal, sample_mode_electrical_signal\n", + "my_signals = {\n", + " 'o0': sample_mode_optical_signal(),\n", + " 'e0': sample_mode_electrical_signal(),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40e19fa9", + "metadata": {}, + "outputs": [], + "source": [ + "new_field = my_signals['o0'].field.at[0, 0].set(1.0 + 0.5j)\n", + "my_signals['o0'].replace(field=new_field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f8ebf7a", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "\n", + "@jax.jit\n", + "def my_function(new_value):\n", + " new_field = my_signals['o0'].field.at[0, 0].set(new_value)\n", + " my_signals['o0'].replace(field=new_field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa0743b6", + "metadata": {}, + "outputs": [], + "source": [ + "my_function(0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8392a46b", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(100000):\n", + " my_function(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c8282ee", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def smooth_discontinuity(x, f, x0, width):\n", + " # Create a smooth blending weight\n", + " w = 1 / (1 + np.exp(-(x - x0) / width))\n", + " # Assume f is defined on both sides already\n", + " return (1 - w) * f['left'](x) + w * f['right'](x)\n", + "\n", + "# Example usage\n", + "f_left = lambda x: np.zeros_like(x) # before discontinuity\n", + "f_right = lambda x: np.ones_like(x) # after discontinuity\n", + "x = np.linspace(-2, 2, 500)\n", + "y_smooth = smooth_discontinuity(x, {'left': f_left, 'right': f_right}, 0, 0.05)\n", + "\n", + "# plt.plot(x, y)\n", + "plt.plot(x, y_smooth)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32ebbf5f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def cubic_hermite_patch(x, xa, ya, ma, xb, yb, mb):\n", + " # Normalized parameter t in [0,1]\n", + " t = (x - xa) / (xb - xa)\n", + " h00 = (1 + 2*t) * (1 - t)**2\n", + " h10 = t * (1 - t)**2\n", + " h01 = t**2 * (3 - 2*t)\n", + " h11 = t**2 * (t - 1)\n", + " return h00*ya + h10*(xb - xa)*ma + h01*yb + h11*(xb - xa)*mb\n", + "\n", + "# Example data: cusp at x=5\n", + "x = np.linspace(0, 10, 200)\n", + "y = np.abs(x - 5) # artificial cusp\n", + "\n", + "# Smoothing interval\n", + "xa, xb = 4, 6\n", + "ya = y[np.argmin(np.abs(x - xa))]\n", + "yb = y[np.argmin(np.abs(x - xb))]\n", + "ma = (y[np.argmin(np.abs(x - (xa+0.01)))] - ya) / 0.01\n", + "mb = (yb - y[np.argmin(np.abs(x - (xb-0.01)))]) / 0.01\n", + "\n", + "# Build smoothed function\n", + "y_smooth = y.copy()\n", + "mask = (x >= xa) & (x <= xb)\n", + "y_smooth[mask] = cubic_hermite_patch(x[mask], xa, ya, ma, xb, yb, mb)\n", + "\n", + "plt.plot(y)\n", + "plt.plot(y_smooth)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "831d593f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import CubicHermiteSpline\n", + "\n", + "# Example function with a cusp\n", + "x = np.linspace(0, 10, 200)\n", + "y = np.abs(x - 5) / 2 + 0.5 # simple cusp at x=5\n", + "\n", + "# Indices for smoothing region\n", + "start_idx = 80\n", + "end_idx = 120\n", + "\n", + "# Get start/end points\n", + "x0, y0 = x[start_idx], y[start_idx]\n", + "x1, y1 = x[end_idx], y[end_idx]\n", + "\n", + "# Derivatives at start/end (match original slope)\n", + "m0 = np.gradient(y, x)[start_idx]\n", + "m1 = np.gradient(y, x)[end_idx]\n", + "\n", + "# Create cubic Hermite spline in smoothing region\n", + "xsmooth = x[start_idx:end_idx+1]\n", + "spline = CubicHermiteSpline([x0, x1], [y0, y1], [m0, m1])\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace cusp region\n", + "y_new = y.copy()\n", + "y_new[start_idx:end_idx+1] = ysmooth\n", + "\n", + "# Plot\n", + "plt.plot(x, y, label='Original with cusp', alpha=0.5)\n", + "plt.plot(x, y_new, label='Smoothed cusp', linewidth=2)\n", + "plt.axvline(x0, color='gray', linestyle='--')\n", + "plt.axvline(x1, color='gray', linestyle='--')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d71d030", + "metadata": {}, + "outputs": [], + "source": [ + "# Get start/end points\n", + "magnitude = magnitude_extended\n", + "f = f_extended\n", + "start_idx = left_extension.shape[0] - 20\n", + "end_idx = left_extension.shape[0] + 20\n", + "x0, y0 = f[start_idx], magnitude[start_idx]\n", + "x1, y1 = f[end_idx], magnitude[end_idx]\n", + "\n", + "# Derivatives at start/end (match original slope)\n", + "m0 = jnp.gradient(magnitude_extended, f, axis=0)[start_idx]\n", + "m0 = jnp.zeros((2,2))\n", + "m1 = jnp.gradient(magnitude, f, axis=0)[end_idx]\n", + "\n", + "# Create cubic Hermite spline in smoothing region\n", + "xsmooth = f[start_idx:end_idx+1]\n", + "spline = CubicHermiteSpline([x0, x1], [y0, y1], [m0, m1])\n", + "# spline = BPoly.from_derivatives(xs, ys)\n", + "\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace cusp region\n", + "y_new = magnitude.copy()\n", + "y_new = y_new.at[start_idx:end_idx+1].set(ysmooth)\n", + "\n", + "# plt.plot(jnp.abs(ysmooth[:, 0, 1]))\n", + "plt.plot(f_extended, y_new[:, 0, 1])\n", + "# plt.axvline(buffer)\n", + "# plt.axhline(jnp.abs(y0[0, 1].item()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d7fcee9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.interpolate import BPoly\n", + "\n", + "# Start/end values\n", + "x0, y0 = f[start_idx], magnitude[start_idx]\n", + "x1, y1 = f[end_idx], magnitude[end_idx]\n", + "\n", + "# Slopes (keep your original slope computation if needed)\n", + "m0 = np.gradient(magnitude_extended, f, axis=0)[start_idx]\n", + "m1 = np.gradient(magnitude_extended, f, axis=0)[end_idx]\n", + "\n", + "# Build a *quadratic* polynomial segment\n", + "# Give derivatives at both ends: [[y0, slope0], [y1, slope1]]\n", + "# The minimal polynomial degree needed is quadratic.\n", + "xs = [x0, x1]\n", + "ys = [[y0, m0], [y1, m1]]\n", + "\n", + "spline = BPoly.from_derivatives(xs, ys)\n", + "\n", + "# Evaluate in smoothing region\n", + "xsmooth = f[start_idx:end_idx+1]\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace in your magnitude array\n", + "y_new = magnitude.copy()\n", + "y_new = y_new.at[start_idx:end_idx+1].set(ysmooth)\n", + "plt.plot(jnp.concatenate([left_extension[:, 0, 1], jnp.abs(y_new[:, 0, 1])]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c5d3876", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.constants import speed_of_light\n", + "import jax.numpy as jnp\n", + "from simphony.utils import dict_to_matrix\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import CubicHermiteSpline\n", + "\n", + "\n", + "fs = 1.5e13\n", + "f_c = 193e12\n", + "f = jnp.linspace(187e12, 200e12, 1000)\n", + "df = f[1] - f[0]\n", + "s_params = dict_to_matrix(mzi(1e6*speed_of_light/f))\n", + "# magnitude = jnp.abs(s_params)\n", + "\n", + "\n", + "\n", + "# initial_magnitude = magnitude[0]\n", + "# final_magnitude = magnitude[-1]\n", + "\n", + "# f_left = jnp.arange(-fs/2 + f_c, f[0], df)\n", + "# left_extension = initial_magnitude[None, :, :]*jnp.ones_like(f_left, dtype=complex)[:, None, None]\n", + "\n", + "# f_right = jnp.arange(f[-1], fs/2 + f_c, df)\n", + "# right_extension = final_magnitude[None, :, :]*jnp.ones_like(f_right, dtype=complex)[:, None, None]\n", + "\n", + "# plt.plot(f_left, left_extension[:, 0, 1])\n", + "# plt.plot(f_right, right_extension[:, 0, 1])\n", + "# plt.plot(f, jnp.abs(s_params[:, 0, 1]))\n", + "\n", + "# magnitude_extended = jnp.concatenate([left_extension, magnitude, right_extension])\n", + "# f_extended = jnp.concatenate([f_left, f, f_right])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f95de9d", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from scipy.signal import hilbert\n", + "from scipy.interpolate import interp1d\n", + "\n", + "# Original frequencies\n", + "f_min, f_max = f[0], f[-1]\n", + "\n", + "# Define extended frequency range\n", + "f_min_ext = f_min - 5e12 # extend below\n", + "f_max_ext = f_max + 5e12 # extend above\n", + "num_ext = 2 * len(f) # adjust resolution\n", + "f_ext = jnp.linspace(f_min_ext, f_max_ext, num_ext)\n", + "\n", + "# Initialize extended S-parameter array\n", + "n_ports = s_params.shape[1]\n", + "s_ext = jnp.zeros((num_ext, n_ports, n_ports), dtype=complex)\n", + "\n", + "for i in range(n_ports):\n", + " for j in range(n_ports):\n", + " # Extract one S-parameter element\n", + " S_ij = s_params[:, i, j]\n", + "\n", + " # Step 1: interpolate imaginary part to extended f_ext\n", + " interp_im = interp1d(f, S_ij.imag, kind='cubic', fill_value='extrapolate')\n", + " S_im_ext = interp_im(f_ext)\n", + "\n", + " # Step 2: Hilbert transform to get real part (discrete KK)\n", + " # Use scipy.signal.hilbert or similar\n", + " S_re_ext = jnp.imag(hilbert(S_im_ext))\n", + "\n", + " # Step 3: Combine\n", + " s_ext = s_ext.at[:, i, j].set(S_re_ext + 1j * S_im_ext)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "251db2dc", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(f_ext, s_ext[:, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1123d9a7", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# s-domain frequency\n", + "f_s = jnp.linspace(0, 10, 500) # rad/s\n", + "s = 1j * f_s\n", + "\n", + "# s-domain transfer function\n", + "H_s = 1 / (s + 1)\n", + "\n", + "# check s-domain magnitude\n", + "plt.plot(f_s, jnp.abs(H_s))\n", + "plt.title(\"s-domain magnitude\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e909282d", + "metadata": {}, + "outputs": [], + "source": [ + "T_s = 0.01\n", + "\n", + "# map s to z\n", + "def s_to_z(H_s_func, f_s, T_s):\n", + " s = 1j * f_s\n", + " z = (1 + s * T_s / 2) / (1 - s * T_s / 2)\n", + " # evaluate H(s) at s mapped from z (here simple example)\n", + " H_z = H_s_func(s)\n", + " return z, H_z\n", + "\n", + "z, H_z = s_to_z(lambda s: 1/(s+1), f_s, T_s)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e58790b", + "metadata": {}, + "outputs": [], + "source": [ + "# angular frequencies for discrete-time\n", + "omega = jnp.linspace(0, jnp.pi / T_s, 500)\n", + "z_grid = jnp.exp(1j * omega * T_s)\n", + "# Evaluate H(s) at corresponding s (inverse mapping)\n", + "s_grid = 2 / T_s * (1 - z_grid**-1) / (1 + z_grid**-1)\n", + "H_z_grid = 1 / (s_grid + 1)\n", + "\n", + "# check magnitude\n", + "plt.plot(omega, jnp.abs(H_z_grid))\n", + "plt.title(\"z-domain magnitude after bilinear transform\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07fd1f47", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eeee825", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Parameters\n", + "N = 256 # number of samples\n", + "f_width = 20 # width of the rectangular spectrum\n", + "delta_index = 102+ N // 2 # position of Kronecker delta (center)\n", + "\n", + "# Frequency domain: rectangular spectrum\n", + "freq = np.fft.fftfreq(N, d=1.0)\n", + "rect_spec = np.where(np.abs(freq) < f_width / N, 1.0, 0.0)\n", + "\n", + "# Inverse Fourier transform -> time-domain sinc-like function\n", + "time_func = np.fft.ifft(np.fft.ifftshift(rect_spec))\n", + "\n", + "# Kronecker delta\n", + "kronecker_delta = np.zeros(N)\n", + "kronecker_delta[delta_index] = 1.0\n", + "\n", + "# Convolution (should reproduce time_func)\n", + "conv_result = np.convolve(time_func, kronecker_delta, mode='same')\n", + "\n", + "# Plot results\n", + "plt.figure(figsize=(10,4))\n", + "plt.plot(np.real(time_func), label=\"Inverse FT of Rect (sinc)\")\n", + "plt.plot(np.real(conv_result), '--', label=\"Convolution with delta\")\n", + "plt.legend()\n", + "plt.title(\"Convolution of sinc with Kronecker delta\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/gm.py b/examples/old/gm.py similarity index 100% rename from examples/gm.py rename to examples/old/gm.py diff --git a/examples/old/group_delay.ipynb b/examples/old/group_delay.ipynb new file mode 100644 index 00000000..f5b3c6c6 --- /dev/null +++ b/examples/old/group_delay.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 523, + "id": "6ad35d60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 523, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.constants import speed_of_light as c\n", + "import numpy as np\n", + "from simphony.time_domain.vector_fitting.z_domain import vector_fitting_discrete, pole_residue_response_discrete\n", + "from simphony.libraries.ideal import waveguide\n", + "from simphony.utils import dict_to_matrix\n", + "from simphony.conventions import PHYSICIST, ENGINEER\n", + "\n", + "wavelength = 1e-6 * np.linspace(1.5, 1.6, 1000)\n", + "group_index = 3.4\n", + "group_delay = 0.1e-12\n", + "length = c * group_delay / group_index\n", + "frequency = c/wavelength\n", + "bandwidth = np.max(frequency) - np.min(frequency)\n", + "f_c = 0.5*(np.max(frequency) + np.min(frequency))\n", + "f_s = 1e14\n", + "\n", + "\n", + "H = dict_to_matrix(waveguide(wl=1e6*wavelength, length=1e6*length, ng=group_index))\n", + "plt.plot(frequency, H[:, 0, 1])\n", + "\n", + "\n", + "# poles, residues, feedthrough, error = vector_fitting_discrete(48, H, frequency, f_c, f_s, max_iterations=100, sign_convention=PHYSICIST)\n", + "\n", + "# H_model = pole_residue_response_discrete(frequency, f_c, f_s, poles, residues, feedthrough, sign_convention=PHYSICIST)\n", + "\n", + "# plt.plot(frequency, H_model[:, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 524, + "id": "5a9af0f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(12.491352416666656)" + ] + }, + "execution_count": 524, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bandwidth*1e-12" + ] + }, + { + "cell_type": "code", + "execution_count": 525, + "id": "2799a61b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 525, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGvCAYAAABmcr6xAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAARDpJREFUeJzt3XlclOXi///XDMuwCLgBouKC+4qgZZq2erLM1BY1qU51Ov3qHLej1kkrM1vU0qxc6nSqY+ecj0tmZZvZonnSsk3AHRVRwV1QGRYZYOb6/dGJ77ESwRhuGN7Px2MePWa4Zu73XOE9b+7rnhmbMcYgIiIiYgG71QFERESk7lIREREREcuoiIiIiIhlVERERETEMioiIiIiYhkVEREREbGMioiIiIhYRkVERERELONvdYDyeDweDh8+TFhYGDabzeo4IiIiUgHGGPLy8mjatCl2e/nHPGp0ETl8+DCxsbFWxxAREZELkJWVRfPmzcsdUy1FZOHChcyePZujR48SHx/P/Pnzufjii897v7CwMODHJxIeHu7tmCIiIlIFnE4nsbGxZa/j5fF6EXnzzTeZOHEif/vb3+jduzcvvPACAwcOZNeuXURFRZV735+WY8LDw1VEREREapmKnFbh9ZNV586dy7333svdd99N586d+dvf/kZISAj/+Mc/vL1pERERqeG8WkSKi4vZtGkTAwYM+H8btNsZMGAAGzdu/MV4l8uF0+k86yIiIiK+y6tFJDs7G7fbTXR09Fm3R0dHc/To0V+MnzlzJhEREWUXnagqIiLi22rU54hMmTKF3NzcsktWVpbVkURERMSLvHqyauPGjfHz8+PYsWNn3X7s2DGaNGnyi/EOhwOHw+HNSCIiIlKDePWISGBgID179mTNmjVlt3k8HtasWUOfPn28uWkRERGpBbz+9t2JEydy55130qtXLy6++GJeeOEFCgoKuPvuu729aREREanhvF5ERo4cyYkTJ3jsscc4evQoPXr0YPXq1b84gVVERETqHpsxxlgd4lycTicRERHk5ubqA81ERERqicq8fteod82IiIhI3aIiIiIiIpZREREREamjiks9VkdQEREREamL0o/nccP8DazYdNDSHCoiIiIidcyKTQe5Yf5X7DqWx7w1eyhxW3dkxOtv3xUREZGaobC4lEdXbuOd5EMA9GvbmOdH9iDAz7rjEioiIiIidUDaUSejFyez90QBdhtMGNCeP1/ZFj+7zdJcKiIiIiI+zBjDm99nMe397bhKPUSHO3jx1gQuiWtkdTRARURERMRn5btKeeTdrbyXehiAy9tHMndEPI3q1ZwvmFURERER8UHbD+cyZkkK+7IL8LPbeOCaDtx3WRx2i5difk5FRERExIcYY/i/bzN58sMdFJd6aBoRxPykBHq2bGh1tF+lIiIiIuIjnEUlTHl7Kx9tPQLAgE5RzL4lngahgRYnOzcVERERER+w5eBpxixJIfNkIf52G5Ov68g9/Vpjs9WspZifUxERERGpxYwxvPH1fmas2kmJ29CsfjALkhJIaNHA6mgVoiIiIiJSS+UWlvDgis18uuMYAAO7RPPszfFEhARYnKziVERERERqoZTMU4xZksKh02cI9LPz8KCO3Nm3VY1fivk5FREREZFaxBjDa+v38czqNEo9hhYNQ1iYlEi35hFWR7sgKiIiIiK1xKmCYh54azNr0o4DcH33GGbe1I3woNqzFPNzKiIiIiK1wA/7TzJ2aQpHcosI9Lfz2ODO3Na7Ra1bivk5FREREZEazOMx/O3LvTz36W7cHkPrxqEsSEqgS9PauRTzcyoiIiIiNVROvouJyzfzn90nABjaoylP39iNeg7fefn2nWciIiLiQ77JyGH8shSOOV0EBdiZPqQLI3rF1vqlmJ9TEREREalB3B7Dwi/SeeHz3XgMtI2qx8KkRDo0CbM6mleoiIiIiNQQx/OKmPBmKl+l5wBwS8/mPDG0CyGBvvty7bvPTEREpBb5Kj2b8ctSyc53ERzgx1PDunJzz+ZWx/I6FRERERELuT2GFz/fzfwv0jEGOkSHsfC2BNpG+eZSzM+piIiIiFjkmLOIcUtT+HbfSQBGXRzLtBu6EBTgZ3Gy6qMiIiIiYoH/7D7BhDdTOVlQTGigHzNu6sbQHs2sjlXtVERERESqUanbw3Of7ebldXsB6BQTzsKkBOIi61mczBoqIiIiItXk8OkzjFuawg8HTgFwxyUteeT6TnVqKebnVERERESqwdq0Y0xcvpnThSWEOfyZdXN3ru8eY3Usy6mIiIiIeFGJ28Ozq9N4df0+ALo1i2BBUgItG4VanKxmsHvrgZ9++mn69u1LSEgI9evX99ZmREREaqysk4UM/9vGshJy96WtWPGnPioh/8NrR0SKi4sZPnw4ffr04fXXX/fWZkRERGqkT7Yf5cG3NuMsKiU8yJ/Zw+MZ2KWJ1bFqHK8VkenTpwPwxhtvVPg+LpcLl8tVdt3pdFZ1LBEREa9ylbqZuSqNN77eD0CP2PrMH5VAbMMQa4PVUDXqHJGZM2eWFRgREZHa5kBOAWOWpLD1UC4A9/ZvzYMDOxLo77UzIWq9GjUzU6ZMITc3t+ySlZVldSQREZEK+WjLEQbP28DWQ7nUDwng9Tt78cj1nVVCzqNSszN58mRsNlu5l7S0tAsO43A4CA8PP+siIiJSkxWVuHl05VZGL0kmz1VKr5YNWDWuP1d3irY6Wq1QqaWZSZMmcdddd5U7Ji4u7rfkERERqTX2ZRcwenEyO478eE7jn69ow4TftSfAT0dBKqpSRSQyMpLIyEhvZREREak13ks9xMPvbKWg2E3D0ECeH9mDy9vrNbKyvHayamZmJidPniQzMxO3201qaioAbdu2pV69uvl5+iIiUvsVlbh5/P3tLPv+x/MYe7duyLxRCUSHB1mcrHbyWhF57LHH+Oc//1l2PSEhAYAvvviCK664wlubFRER8Zr043mMXpzCrmN52Gww9sq2jLu6Hf5airlgNmOMsTrEuTidTiIiIsjNzdWJqyIiYqkVmw4ydeU2zpS4aVzPwQsje9CvXWOrY9VIlXn9rlGfIyIiIlLTFBaXMnXldt5OPgjApW0b8fzIHkSFaSmmKqiIiIiInMOuo3mMXpJM+vF87Db4y4D2jL6yLX52m9XRfIaKiIiIyM8YY1j+QxaPvbcdV6mH6HAHL96awCVxjayO5nNURERERP5HvquUR9/dysrUwwBc1j6S50fE06iew+JkvklFRERE5L92HHYyZkkyGdkF+NltTLqmPfdf1ga7lmK8RkVERETqPGMMi7/N5IkPd1Bc6iEmIoj5oxLo1aqh1dF8noqIiIjUac6iEqa8s5WPthwB4OqOUcwZHk+D0ECLk9UNKiIiIlJnbT2Yy5ilyRzIKcTfbuOhazvyx/6tsdm0FFNdVERERKTOMcbwz6/3M2NVGsVuD83qBzM/KYHEFg2sjlbnqIiIiEidkltYwl/f3swn248BcE3naGbfEk9ESIDFyeomFREREakzUjJPMXZpCgdPnSHAz8bDgzpxV99WWoqxkIqIiIj4PGMMr2/Yx6yP0yj1GFo0DGFBUgLdm9e3OlqdpyIiIiI+7VRBMQ+8tZk1accBuL5bDDNv7kZ4kJZiagIVERER8VmbDpxk7JIUDucWEehvZ+rgztzeu4WWYmoQFREREfE5Ho/hlS8zmPPpLtweQ+vGoSxISqBL0wiro8nPqIiIiIhPycl3MXH5Zv6z+wQAQ3s05ekbu1HPoZe8mkj/V0RExGd8m5HDuGUpHHO6cPjbmT6kCyMvitVSTA2mIiIiIrWe22N46Yt0nv98Nx4DbSJDWXhbIh2bhFsdTc5DRURERGq1E3kuJryZyob0bABuTmzOk8O6EBKol7jaQP+XRESk1voqPZvxy1LJzncRHODHk8O6ckvP5lbHkkpQERERkVrH7TG8uGYP89fuwRjoEB3GgqQE2kWHWR1NKklFREREapVjziLGL0vhm4yTANx6USzTbuhCcKCfxcnkQqiIiIhIrfGf3SeY+GYqOQXFhAb6MeOmbgzt0czqWPIbqIiIiEiNV+r2MPez3by0bi8AnWLCWZiUQFxkPYuTyW+lIiIiIjXakdwzjFuawvf7TwFw+yUtePT6zgQFaCnGF6iIiIhIjbU27RiTlm/mVGEJ9Rz+zLq5G4O7N7U6llQhFREREalxStweZn+yi79/mQFAt2YRLEhKoGWjUIuTSVVTERERkRrl4KlCxi5NISXzNAB39W3FlEEdcfhrKcYXqYiIiEiN8en2ozzw1macRaWEB/nz7C3xXNu1idWxxItURERExHLFpR5mfryTRV/tByA+tj4LRiUQ2zDE2mDidSoiIiJiqcycQsYsTWbLwVwA7u3fmgcHdiTQ325xMqkOKiIiImKZVVuP8NCKLeS5SqkfEsCcW+IZ0Dna6lhSjbxWN/fv388999xD69atCQ4Opk2bNkybNo3i4mJvbVJERGqJohI3U1du48+Lk8lzldKzZQNWjeuvElIHee2ISFpaGh6Ph1deeYW2bduybds27r33XgoKCpgzZ463NisiIjXcvuwCRi9OZscRJwB/uqINE3/XngA/LcXURTZjjKmujc2ePZuXX36ZjIyMX/25y+XC5XKVXXc6ncTGxpKbm0t4eHh1xRQRES95L/UQD7+zlYJiNw1DA5k7Ip4rOkRZHUuqmNPpJCIiokKv39V6jkhubi4NGzY8589nzpzJ9OnTqzGRiIhUh6ISN9M/2M7S77IAuLh1Q+bdmkCTiCCLk4nVqu2ISHp6Oj179mTOnDnce++9vzpGR0RERHxP+vF8xixJJu1oHjYbjLmyLeOvboe/lmJ8VmWOiFT6t2Dy5MnYbLZyL2lpaWfd59ChQ1x77bUMHz78nCUEwOFwEB4eftZFRERqr7c3HeSG+RtIO5pH43oO/v2H3ky6poNKiJSp9BGREydOkJOTU+6YuLg4AgMDATh8+DBXXHEFl1xyCW+88QZ2e8V/+SrTqEREpOYoLC7lsfe2s2LTQQD6tmnEC7f2ICpMSzF1gVfPEYmMjCQyMrJCYw8dOsSVV15Jz549WbRoUaVKiIiI1E67j+UxenEye47nY7fB+KvbM+aqtvjZbVZHkxrIayerHjp0iCuuuIKWLVsyZ84cTpw4UfazJk30vQEiIr7GGMPyH7KY9v52iko8RIU5ePHWBPq0aWR1NKnBvFZEPvvsM9LT00lPT6d58+Zn/awa3zEsIiLVIN9VyqPvbmVl6mEA+rdrzPMje9C4nsPiZFLTVevniFSWzhEREan5dhx2MmZJMhnZBfjZbUy6pj33X9YGu5Zi6qwa+zkiIiLiO4wxLPkuk+kf7KC41ENMRBDzRiVwUatzf16UyM+piIiISKXlFZUw+Z2tfLTlCABXdYxizvB4GoYGWpxMahsVERERqZRth3IZvSSZAzmF+Ntt/PXaDvyxX5yWYuSCqIiIiEiFGGP418YDPP3RTordHprVD2Z+UgKJLRpYHU1qMRURERE5r9wzJTy0Ygurtx8F4Hedo5lzSzwRIQEWJ5PaTkVERETKlZp1mjFLkjl46gwBfjamXNeJuy9thc2mpRj57VRERETkVxljeH3DPp5ZnUaJ2xDbMJgFoxKJj61vdTTxISoiIiLyC6cLi3ngrc18vvM4AIO6NWHWzd0JD9JSjFQtFRERETnLpgMnGbskhcO5RQT62Zk6uBO3X9JSSzHiFSoiIiICgMdj+Pv6DGZ/sgu3x9CqUQgLkhLp2izC6mjiw1RERESEnHwXk97azLpdP35B6ZD4psy4qRv1HHqZEO/Sb5iISB33bUYO45alcMzpwuFv5/EhXbj1olgtxUi1UBEREamjPB7DS+vSmfvZbjwG2kSGsvC2RDo20ZeMSvVRERERqYNO5LmYuDyV9XuyAbgpsRlPDu1KqJZipJrpN05EpI75Oj2b8W+mciLPRXCAH08M7cLwXrFWx5I6SkVERKSOcHsM89bsYd7aPRgD7aPrsTApkXbRYVZHkzpMRUREpA445ixi/LIUvsk4CcDIXrE8PqQLwYF+FieTuk5FRETEx325+wQT3kwlp6CYkEA/ZtzYjWEJzayOJQKoiIiI+KxSt4fnP9/NS+v2Ygx0bBLGwtsSaRNZz+poImVUREREfNCR3DOMW5rC9/tPAXBb7xZMHdyZoAAtxUjNoiIiIuJjvkg7zsTlqZwqLKGew59ZN3djcPemVscS+VUqIiIiPqLE7WHOJ7t45csMALo2C2fBqERaNQ61OJnIuamIiIj4gIOnChm7NIWUzNMA3NW3FVMGdcThr6UYqdlUREREarlPtx/lwRVbyD1TQliQP7Nv6c61XWOsjiVSISoiIiK1VHGph1kfp/GPr/YBEN88ggVJicQ2DLE4mUjFqYiIiNRCWScLGbMkmc0HcwH4Y7/W/PXajgT62y1OJlI5KiIiIrXMx1uP8Ne3t5BXVEpEcADPDY9nQOdoq2OJXBAVERGRWqKoxM2MVTv518YDACS2qM/8pESa1Q+2OJnIhVMRERGpBfZlFzBmSTLbDzsBuO/yOB64pgMBflqKkdpNRUREpIZ7f/NhHn5nK/muUhqGBvLciHiu7BBldSyRKqEiIiJSQxWVuJn+wQ6WfpcJwMWtGjJvVAJNIoIsTiZSdbx6TG/IkCG0aNGCoKAgYmJiuOOOOzh8+LA3Nyki4hPSj+czbOFXLP0uE5sNxl7VliX39lYJEZ/j1SJy5ZVXsnz5cnbt2sXbb7/N3r17ueWWW7y5SRGRWu+d5IMMWbCBtKN5NK4XyL/+cDGTrumAv84HER9kM8aY6trY+++/z7Bhw3C5XAQEBJx3vNPpJCIigtzcXMLDw6shoYiIdQqLS5n23nbe2nQQgD5xjXjx1h5EhesoiNQulXn9rrZzRE6ePMnixYvp27fvOUuIy+XC5XKVXXc6ndUVT0TEUruP5TF6cTJ7judjt8H4q9sz5qq2+NltVkcT8SqvH+d76KGHCA0NpVGjRmRmZvLee++dc+zMmTOJiIgou8TGxno7noiIpYwxLP8hiyELNrDneD6RYQ4W//ESxg9opxIidUKll2YmT57MM888U+6YnTt30rFjRwCys7M5efIkBw4cYPr06URERPDhhx9is/3yH9ivHRGJjY3V0oyI+KQCVymPrtzGuymHAOjfrjHPj+xB43oOi5OJ/DaVWZqpdBE5ceIEOTk55Y6Ji4sjMDDwF7cfPHiQ2NhYvv76a/r06XPebekcERHxVTuPOBm9OJmM7ALsNph0TQf+dHkb7DoKIj7Aq+eIREZGEhkZeUHBPB4PwFlHPURE6hJjDEu+y2T6BzsoLvXQJDyIeaMSuLh1Q6ujiVjCayerfvvtt3z//ff069ePBg0asHfvXqZOnUqbNm0qdDRERMTX5BWVMOWdrXy45QgAV3aI5LkRPWgY+ssjyCJ1hdeKSEhICO+88w7Tpk2joKCAmJgYrr32Wh599FEcDq1/ikjdsu1QLmOWJLM/pxB/u40HB3bg3v5xWoqROs9rRaRbt26sXbvWWw8vIlIrGGP418YDPP3RTordHprVD2beqAR6tmxgdTSRGkHfNSMi4iW5Z0qY/PYWPt52FIABnaKZM7w79UO0FCPyExUREREv2Jx1mjFLk8k6eYYAPxtTruvE3Ze2+tWPLhCpy1RERESqkDGGf3y1n1kf76TEbYhtGMyCUYnEx9a3OppIjaQiIiJSRU4XFvPAW1v4fOcxAK7r2oRZN3cnIvj8360lUlepiIiIVIFNB04xbmkKh06fIdDPzqODO3HHJS21FCNyHioiIiK/gcdj+Pv6DGZ/sgu3x9CqUQgLkhLp2izC6mgitYKKiIjIBTpZUMzE5ams23UCgBvimzLjxq6EBWkpRqSiVERERC7Ad/tOMm5pCkedRTj87Uy7oQujLo7VUoxIJamIiIhUgsdjeGldOnM/243HQFxkKAuTEukUoy/mFLkQKiIiIhV0Is/FxOWprN+TDcBNCc14clhXQh3alYpcKP3rERGpgK/3ZjN+WSon8lwEBdh5YmhXhvdsrqUYkd9IRUREpBxuj2H+2j3MW7MHj4F2UfV46bZE2kWHWR1NxCeoiIiInMNxZxHjl6WyMSMHgBG9mjN9SFeCA/0sTibiO1RERER+xfo9J5jwZirZ+cWEBPrx9I1duTGhudWxRHyOioiIyP8odXt44fM9LFyXjjHQsUkYC5ISaRtVz+poIj5JRURE5L+O5J5h/NJUvtt/EoCk3i14bHBnggK0FCPiLSoiIiLAF2nHmbg8lVOFJdRz+DPjpm4MiW9qdSwRn6ciIiJ1Wonbw5xPdvHKlxkAdGkazsKkRFo1DrU4mUjdoCIiInXWodNnGLskmeTM0wDc2aclUwZ10lKMSDVSERGROumzHcd44K3N5J4pISzIn2dv7s513WKsjiVS56iIiEidUlzq4ZnVaby+YR8A8c0jmD8qkRaNQixOJlI3qYiISJ2RdbKQMUuS2XwwF4B7+rXmoWs7EuhvtziZSN2lIiIidcLqbUd4cMUW8opKiQgOYM7weH7XOdrqWCJ1noqIiPg0V6mbGR/t5J8bDwCQ2KI+80Yl0LyBlmJEagIVERHxWfuzCxizNJlth5wA3Hd5HA9c04EAPy3FiNQUKiIi4pM+2HyYKe9sJd9VSoOQAOaO6MGVHaOsjiUiP6MiIiI+pajEzRMf7mDJt5kAXNSqAfNGJRATEWxxMhH5NSoiIuIz9p7IZ/TiZNKO5mGzwegr2vKXAe3w11KMSI2lIiIiPuHdlIM88u42CovdNAoN5IVbe9C/XaTVsUTkPFRERKRWO1PsZtr721j+w0EA+sQ14sVbexAVHmRxMhGpCBUREam19hzL48+Lk9lzPB+bDcZf3Y6xV7XDz26zOpqIVJCKiIjUOsYY3tp0kMfe20ZRiYfIMAcv3tqDvm0aWx1NRCqpWs7gcrlc9OjRA5vNRmpqanVsUkR8VIGrlEnLN/PXFVsoKvHQv11jVo3rrxIiUktVyxGRv/71rzRt2pTNmzdXx+ZExEftPOJkzJJk9p4owG6DSdd04E+Xt8GupRiRWsvrReTjjz/m008/5e233+bjjz/29uZExAcZY1j6XRbTP9iOq9RDk/Ag5o1K4OLWDa2OJiK/kVeLyLFjx7j33ntZuXIlISHn/14Hl8uFy+Uqu+50Or0ZT0RqgbyiEh5+dxsfbD4MwBUdIpk7ogcNQwMtTiYiVcFr54gYY7jrrru4//776dWrV4XuM3PmTCIiIsousbGx3oonIrXAtkO53DB/Ax9sPoyf3caU6zryjzsvUgkR8SGVLiKTJ0/GZrOVe0lLS2P+/Pnk5eUxZcqUCj/2lClTyM3NLbtkZWVVNp6I+ABjDP/auJ+bXvqa/TmFNI0IYvl9fbhP54OI+BybMcZU5g4nTpwgJyen3DFxcXGMGDGCDz74AJvt/+003G43fn5+3Hbbbfzzn/8877acTicRERHk5uYSHh5emZgiUkvlnilhyjtbWLX1KAADOkUzZ3h36ofoKIhIbVGZ1+9KF5GKyszMPOscj8OHDzNw4EBWrFhB7969ad68+XkfQ0VEpG7ZnHWaMUuTyTp5hgA/Gw9d25F7+rU+6w8aEan5KvP67bWTVVu0aHHW9Xr16gHQpk2bCpUQEak7jDH846v9zPp4JyVuQ/MGwSxISqRHbH2ro4mIl+mTVUXEUqcLi3lwxRY+23EMgGu7NOGZW7oTERxgcTIRqQ7VVkRatWqFl1aBRKSWSs48xdglKRw6fYZAPzuPDu7EHZe01FKMSB2iIyIiUu08HsOr6zOY/ckuSj2Glo1CWJiUSNdmEVZHE5FqpiIiItXqZEExD7y1mbVpxwEY3D2GmTd1IyxISzEidZGKiIhUm+/2nWTc0hSOOosI9Lfz+A1dGHVxrJZiROowFRER8TqPx/Dyf/Yy97PduD2GuMahLLwtkU4xelu+SF2nIiIiXpWd72LCm6ms35MNwI0JzXhqWFdCHdr9iIiKiIh40ca9OYxflsLxPBdBAXaeGNKV4b2aaylGRMqoiIhIlXN7DPPX7mHemj14DLSLqsfC2xJpHx1mdTQRqWFURESkSh3PK+Ivy1L5eu+P30k1vGdzpg/tQkigdjci8kvaM4hIldmwJ5u/vJlCdn4xIYF+PDWsKzcl6isdROTcVERE5DcrdXt44fM9LFyXjjHQsUkYC5ISaRtVz+poIlLDqYiIyG9yNLeIcctS+G7fSQBGXdyCaTd0JijAz+JkIlIbqIiIyAX7YtdxJi3fzMmCYkID/Zh5c3eGxDe1OpaI1CIqIiJSaSVuD3M+3cUr/8kAoEvTcBYkJdK6cajFyUSktlEREZFKOXT6DOOWprDpwCkAft+nJQ8P6qSlGBG5ICoiIlJhn+84xqS3NpN7poQwhz/P3NKdQd1irI4lIrWYioiInFdxqYdnV6fx2oZ9AHRvHsGCUYm0aBRicTIRqe1URESkXFknCxmzNIXNWacB+MOlrZl8XUcC/e3WBhMRn6AiIiLntHrbER5csYW8olLCg/yZMzyea7o0sTqWiPgQFRER+QVXqZsZH+3knxsPAJDQoj7zRyXQvIGWYkSkaqmIiMhZ9mcXMGZpMtsOOQG477I4HhjYgQA/LcWISNVTERGRMh9uOczkt7eS7yqlQUgAz42I56qO0VbHEhEfpiIiIhSVuHniwx0s+TYTgItaNWDeqARiIoItTiYivk5FRKSO23sin9GLk0k7mofNBn++og0TBrTHX0sxIlINVERE6rCVKYd4+N2tFBa7aRQayPMje3BZ+0irY4lIHaIiIlIHnSl28/j723nzhywALolryIu3JhAdHmRxMhGpa1REROqYPcfyGL0kmd3H8rHZYNxV7Rh3dTv87Daro4lIHaQiIlKHvPVDFo+9t50zJW4iwxy8OLIHfds2tjqWiNRhKiIidUCBq5Sp723jneRDAPRr25jnR/YgMsxhcTIRqetURER8XNpRJ6MXJ7P3RAF2G0z8XXv+dEVbLcWISI2gIiLio4wxLPs+i8ff346r1EN0uIN5tybQO66R1dFERMqoiIj4oHxXKQ+/s5X3Nx8G4PL2kcwdEU+jelqKEZGaRUVExMdsO5TLmCXJ7M8pxM9u48GBHfj/+sdh11KMiNRAXv3oxFatWmGz2c66zJo1y5ubFKmzjDH8e+N+bnrpa/bnFNI0Iojl913C/Ze3UQkRkRrL60dEnnjiCe69996y62FhYd7epEid4ywqYfLbW1i19SgAAzpFMfuWeBqEBlqcTESkfF4vImFhYTRp0sTbmxGps7YcPM3oJclknTyDv93G5Os6ck+/1thsOgoiIjWf17/VatasWTRq1IiEhARmz55NaWnpOce6XC6cTudZFxH5dcYY/rFhHze//DVZJ8/QvEEwK/7Ulz/2j1MJEZFaw6tHRMaNG0diYiINGzbk66+/ZsqUKRw5coS5c+f+6viZM2cyffp0b0YS8Qm5hSU8uGIzn+44BsDALtE8e0s8EcEBFicTEakcmzHGVOYOkydP5plnnil3zM6dO+nYseMvbv/HP/7BfffdR35+Pg7HL99G6HK5cLlcZdedTiexsbHk5uYSHh5emZgiPis58xRjl6Rw6PQZAv3sPHJ9J37fp6WOgohIjeF0OomIiKjQ63eli8iJEyfIyckpd0xcXByBgb88SW779u107dqVtLQ0OnTocN5tVeaJiPg6j8fw2oYMnl29i1KPoWWjEBaMSqRb8wiro4mInKUyr9+VXpqJjIwkMjLygoKlpqZit9uJioq6oPuL1FWnCoqZ9NZm1qYdB+D67jHMvKkb4UFaihGR2s1r54hs3LiRb7/9liuvvJKwsDA2btzIhAkTuP3222nQoIG3Nivic77ff5JxS1M4kltEoL+daTd0JuniFlqKERGf4LUi4nA4WLZsGY8//jgul4vWrVszYcIEJk6c6K1NivgUj8fw8n/2Mvez3bg9hrjGoSxISqRzUy1Tiojv8FoRSUxM5JtvvvHWw4v4tOx8FxPeTGX9nmwAhvVoylM3dqOeQ9/KICK+RXs1kRpm494cxi9L4Xiei6AAO08M6crwXs21FCMiPklFRKSGcHsMC9am8+Ka3XgMtI2qx8KkRDo00dciiIjvUhERqQGO5xXxl2WpfL33x7fG39KzOU8M7UJIoP6Jiohv015OxGIb9mTzlzdTyc53ERzgx1PDunJzz+ZWxxIRqRYqIiIWKXV7eHHNHhZ8kY4x0CE6jIW3JdI2qp7V0UREqo2KiIgFjuYWMW5ZCt/tOwnAqItjmXZDF4IC/CxOJiJSvVRERKrZul3Hmbh8MycLigkN9GPGTd0Y2qOZ1bFERCyhIiJSTUrcHuZ+tpuX1+0FoHNMOAuSEoiL1FKMiNRdKiIi1eDw6TOMXZrCpgOnALjjkpY8cn0nLcWISJ2nIiLiZZ/vOMYDKzZzurCEMIc/z9zSnUHdYqyOJSJSI6iIiHhJcamHZ1en8dqGfQB0bx7BglGJtGgUYnEyEZGaQ0VExAuyThYyZmkKm7NOA3D3pa2YfF1HHP5aihER+V8qIiJVbPW2o/x1xWacRaWEB/kze3g8A7s0sTqWiEiNpCIiUkVcpW5mrkrjja/3A9Ajtj4LkhJo3kBLMSIi56IiIlIFDuQUMGZJClsP5QLw/10Wx4MDOxDgZ7c4mYhIzaYiIvIbfbTlCJPf3kKeq5T6IQHMHRHPVR2jrY4lIlIrqIiIXKCiEjdPfbSD//smE4BeLRswb1QCTesHW5xMRKT2UBERuQAZJ/IZvSSFnUecAPz5ijZM/F17/LUUIyJSKSoiIpW0MuUQD7+7lcJiN41CA5k7sgeXt4+0OpaISK2kIiJSQWeK3Tz+/nbe/CELgEviGvLirQlEhwdZnExEpPZSERGpgPTjeYxenMKuY3nYbDD2qnaMv7odfnab1dFERGo1FRGR81ix6SBTV27jTImbxvUcvHhrDy5t29jqWCIiPkFFROQcCotLeXTlNt5JPgTApW0b8fzIHkSFaSlGRKSqqIiI/Iq0o05GL05m74kC7DaYMKA9f76yrZZiRESqmIqIyP8wxvDm91lMe387rlIP0eEOXrw1gUviGlkdTUTEJ6mIiPxXvquUR97dynuphwG4vH0kc0fE06iew+JkIiK+S0VEBNh+OJcxS1LYl12An93GA9d04L7L4rBrKUZExKtURKROM8bwf99m8uSHOygu9RATEcT8UQn0atXQ6mgiInWCiojUWc6iEqa8vZWPth4B4OqOUcwZHk+D0ECLk4mI1B0qIlInbTl4mjFLUsg8WYi/3cbk6zpyT7/W2GxaihERqU4qIlKnGGN44+v9zFi1kxK3oVn9YBYkJZDQooHV0URE6iQVEakzcgtLeHDFZj7dcQyAazpHM/uWeCJCAixOJiJSd3n1O8s/+ugjevfuTXBwMA0aNGDYsGHe3JzIOaVknmLQvPV8uuMYgX52Hr+hM6/c0VMlRETEYl47IvL2229z7733MmPGDK666ipKS0vZtm2btzYn8quMMby2fh/PrE6j1GNo0TCEhUmJdGseYXU0ERHBS0WktLSU8ePHM3v2bO65556y2zt37lzu/VwuFy6Xq+y60+n0RjypI04VFPPAW5tZk3YcgOu7xTDz5m6EB+koiIhITeGVpZnk5GQOHTqE3W4nISGBmJgYrrvuuvMeEZk5cyYRERFll9jYWG/Ekzrgh/0nGTRvPWvSjhPob+epYV1ZkJSgEiIiUsN4pYhkZGQA8Pjjj/Poo4/y4Ycf0qBBA6644gpOnjx5zvtNmTKF3NzcsktWVpY34okP83gML61LZ+Tfv+FIbhGtG4fy7p/7cvslLfXWXBGRGqhSRWTy5MnYbLZyL2lpaXg8HgAeeeQRbr75Znr27MmiRYuw2Wy89dZb53x8h8NBeHj4WReRisrOd3HXG9/z7OpduD2GoT2a8sHYfnRpqvNBRERqqkqdIzJp0iTuuuuucsfExcVx5MiPn1T5v+eEOBwO4uLiyMzMrHxKkfP4JiOHcUtTOJ7nwuFv54mhXRjRK1ZHQUREarhKFZHIyEgiIyPPO65nz544HA527dpFv379ACgpKWH//v20bNnywpKK/Aq3x7Dwi3Re+Hw3HgNto+qxMCmRDk3CrI4mIiIV4JV3zYSHh3P//fczbdo0YmNjadmyJbNnzwZg+PDh3tik1EHH84qY8GYqX6XnAHBzYnOeHNaFkEB9Tp+ISG3htT327Nmz8ff354477uDMmTP07t2btWvX0qCBPkpbfruv0rMZvyyV7HwXwQF+PDmsK7f0bG51LBERqSSbMcZYHeJcnE4nERER5Obm6sRVAX5cinnx893M/yIdY6BDdBgLb0ugbZSWYkREaorKvH7rGLbUGsecRYxbmsK3+358C/itF8Uy7YYuBAf6WZxMREQulIqI1Ar/2X2CCW+mcrKgmNBAP2bc1I2hPZpZHUtERH4jFRGp0UrdHp77bDcvr9sLQKeYcBYmJRAXWc/iZCIiUhVURKTGOnz6DOOWpvDDgVMA3H5JCx69vjNBAVqKERHxFSoiUiOtTTvGxOWbOV1YQpjDn5k3d2Nw96ZWxxIRkSqmIiI1SnGph9mfpPHq+n0AdGsWwYKkBFo2CrU4mYiIeIOKiNQYWScLGbs0hdSs0wDc1bcVUwZ1xOGvpRgREV+lIiI1wifbj/LgW5txFpUSHuTP7OHxDOzSxOpYIiLiZSoiYilXqZuZq9J44+v9APSIrc/8UQnENgyxNpiIiFQLFRGxzIGcAsYsSWHroVwA7u3fmgcHdiTQ325xMhERqS4qImKJj7YcYfLbW8hzlVI/JIDnhsdzdadoq2OJiEg1UxGRalVU4uapj3bwf99kAtCrZQPmjUqgaf1gi5OJiIgVVESk2uzLLmD04mR2HHEC8Kcr2jDxd+0J8NNSjIhIXaUiItXivdRDPPzOVgqK3TQMDWTuiHiu6BBldSwREbGYioh41ZliN9M/2M6y77MAuLh1Q+bdmkCTiCCLk4mISE2gIiJek348j9GLU9h1LA+bDcZe2ZZxV7fDX0sxIiLyXyoi4hUrNh1k6sptnClx07iegxdG9qBfu8ZWxxIRkRpGRUSqVGFxKVNXbuft5IMA9G3TiBdu7UFUmJZiRETkl1REpMrsOprH6CXJpB/Px26Dvwxoz+gr2+Jnt1kdTUREaigVEfnNjDEs/yGLx97bjqvUQ1SYg3mjErgkrpHV0UREpIZTEZHfJN9VyqPvbmVl6mEALmsfydwR8TSu57A4mYiI1AYqInLBdhx2MmZJMhnZBfjZbUy6pj33X9YGu5ZiRESkglREpNKMMSz+NpMnPtxBcamHmIgg5o1K4KJWDa2OJiIitYyKiFSKs6iEKe9s5aMtRwC4qmMUzw2Pp0FooMXJRESkNlIRkQrbejCXMUuTOZBTiL/dxkPXduSefq21FCMiIhdMRUTOyxjDP7/ez4xVaRS7PTSrH8z8pAQSWzSwOpqIiNRyKiJSrtzCEv769mY+2X4MgGs6RzP7lngiQgIsTiYiIr5ARUTOKSXzFGOXpnDw1BkC/Gw8PKgTd/Vthc2mpRgREakaKiLyC8YYXt+wj1kfp1HqMbRoGMKCpAS6N69vdTQREfExKiJyllMFxTzw1mbWpB0HYFC3Jsy6uTvhQVqKERGRqqciImU2HTjJ2CUpHM4tItDfztTBnbm9dwstxYiIiNeoiAgej+GVLzOY8+ku3B5D68ahLEhKoEvTCKujiYiIj7N764HXrVuHzWb71cv333/vrc1KJeXku7j7je95ZnUabo9hSHxTPhjbTyVERESqhdeOiPTt25cjR46cddvUqVNZs2YNvXr18tZmpRK+zchh3LIUjjldOPztTB/ShZEXxWopRkREqo3XikhgYCBNmjQpu15SUsJ7773H2LFjz/lC53K5cLlcZdedTqe34tVpbo/hpS/Sef7z3XgMtIkMZeFtiXRsEm51NBERqWOq7RyR999/n5ycHO6+++5zjpk5cybTp0+vrkh10ok8F395M4Wv0nMAuCmxGU8O7UqoQ6cLiYhI9bMZY0x1bGjQoEEArFq16pxjfu2ISGxsLLm5uYSH66/13+qr9GzGL0slO99FcIAfTwztwvBesVbHEhERH+N0OomIiKjQ63elT1adPHnyOU9C/emSlpZ21n0OHjzIJ598wj333FPuYzscDsLDw8+6yG/n9hjmfrab21//lux8F+2j6/H+mEtVQkRExHKVPh4/adIk7rrrrnLHxMXFnXV90aJFNGrUiCFDhlR2c/IbHXMWMX5ZCt9knARgZK9YHh/SheBAP4uTiYiIXEARiYyMJDIyssLjjTEsWrSI3//+9wQE6NM5q9N/dp9g4pup5BQUExLox4wbuzEsoZnVsURERMp4/QzFtWvXsm/fPv74xz96e1PyX6VuD3M/281L6/YC0CkmnIVJCcRF1rM4mYiIyNm8XkRef/11+vbtS8eOHb29KQGO5J5h3NIUvt9/CoDberdg6uDOBAVoKUZERGoerxeRJUuWeHsT8l9r044xaflmThWWUM/hz6ybuzG4e1OrY4mIiJyTPjzCB5S4Pcz+ZBd//zIDgK7NwlmYlEjLRqEWJxMRESmfikgtd/BUIWOXppCSeRqAu/q2Ysqgjjj8tRQjIiI1n4pILfbJ9qM8+NZmnEWlhAX5M/uW7lzbNcbqWCIiIhWmIlILFZd6mPnxThZ9tR+A+Nj6LBiVQGzDEGuDiYiIVJKKSC2TmVPImKXJbDmYC8Af+7Xmr9d2JNC/0h+SKyIiYjkVkVpk1dYjPLRiC3muUiKCA3hueDwDOkdbHUtEROSCqYjUAkUlbp7+aCf//uYAAD1bNmDeqASa1Q+2OJmIiMhvoyJSw+3LLmD04mR2HHECcP/lbZh0TXsC/LQUIyIitZ+KSA32XuohHn5nKwXFbhqGBjJ3RDxXdIiyOpaIiEiVURGpgYpK3Ez/YDtLv8sC4OLWDZl3awJNIoIsTiYiIlK1VERqmPTj+YxZkkza0TxsNhhzZVvGX90Ofy3FiIiID1IRqUHe3nSQR1du40yJm8b1AnlhZAL92jW2OpaIiIjXqIjUAIXFpTz23nZWbDoIQN82jXhhZA+iwrUUIyIivk1FxGK7j+UxenEye47nY7fB+KvbM+aqtvjZbVZHExER8ToVEYsYY1j+QxbT3t9OUYmHqDAHL96aQJ82jayOJiIiUm1URCyQ7yrl0Xe3sjL1MAD92zXm+ZE9aFzPYXEyERGR6qUiUs12HHYyZkkyGdkF+NltTPxde/50eRvsWooREZE6SEWkmhhjWPJdJtM/2EFxqYcm4UHMT0rgolYNrY4mIiJiGRWRapBXVMLkd7by0ZYjAFzVMYo5w+NpGBpocTIRERFrqYh42bZDuYxeksyBnEL87Tb+em0H/tgvTksxIiIiqIh4jTGGf208wNMf7aTY7aFZ/WDmJyWQ2KKB1dFERERqDBURL8g9U8JDK7awevtRAH7XOZrZt3SnfoiWYkRERP6XikgVS806zZglyRw8dYYAPxtTruvE3Ze2wmbTUoyIiMjPqYhUEWMMr2/Yx6yP0yj1GGIbBrNgVCLxsfWtjiYiIlJjqYhUgdOFxTzw1mY+33kcgOu6NmHWzd2JCA6wOJmIiEjNpiLyG206cJKxS1I4nFtEoJ+dqYM7cfslLbUUIyIiUgEqIhfI4zH8fX0Gsz/ZhdtjaNUohAVJiXRtFmF1NBERkVpDReQC5OS7mPTWZtbtOgHADfFNmXFjV8KCtBQjIiJSGSoilfRtRg7jlqVwzOnC4W/n8SFduPWiWC3FiIiIXAAVkQryeAwvrUtn7me78RiIiwxlYVIinWLCrY4mIiJSa6mIVMCJPBcTl6eyfk82ADclNOPJYV0JdWj6REREfgu7tx549+7dDB06lMaNGxMeHk6/fv344osvvLU5r/k6PZtB89azfk82QQF2Zt/Snbkje6iEiIiIVAGvFZHBgwdTWlrK2rVr2bRpE/Hx8QwePJijR496a5NVyu0xzP1sN7e9/i0n8ly0j67HB2P6MbxXrNXRREREfIbNGGOq+kGzs7OJjIzkyy+/pH///gDk5eURHh7OZ599xoABAyr0OE6nk4iICHJzcwkPr75zMY45ixi/LIVvMk4CMKJXc6YP6UpwoF+1ZRAREamtKvP67ZX1hUaNGtGhQwf+9a9/kZiYiMPh4JVXXiEqKoqePXue834ulwuXy1V23el0eiNeub7cfYIJb6aSU1BMSKAfT9/YlRsTmld7DhERkbrAK0XEZrPx+eefM2zYMMLCwrDb7URFRbF69WoaNGhwzvvNnDmT6dOneyPSeZW6PTz/+W5eWrcXY6BjkzAW3pZIm8h6luQRERGpCyp1jsjkyZOx2WzlXtLS0jDGMHr0aKKioli/fj3fffcdw4YN44YbbuDIkSPnfPwpU6aQm5tbdsnKyvrNT7AijuSeYdSr37Dwix9LSFLvFqwcfalKiIiIiJdV6hyREydOkJOTU+6YuLg41q9fzzXXXMOpU6fOWhtq164d99xzD5MnT67Q9qrjHJEv0o4zcXkqpwpLqOfwZ+ZN3bghvqlXtiUiIlIXeO0ckcjISCIjI887rrCwEAC7/ewDLna7HY/HU5lNek2J28OcT3bxypcZAHRtFs6CUYm0ahxqcTIREZG6wyvniPTp04cGDRpw55138thjjxEcHMyrr77Kvn37uP76672xyUo5eKqQsUtTSMk8DcBdfVsxZVBHHP56V4yIiEh18srniDRu3JjVq1eTn5/PVVddRa9evdiwYQPvvfce8fHx3thkpXydnkNK5mnCgvz52+2JPD6ki0qIiIiIBbzyOSJVxVvniBhjmL82nRsTmhHbMKTKHldERERqwOeI1HQ2m41xV7ezOoaIiEid57WPeBcRERE5HxURERERsYyKiIiIiFhGRUREREQsoyIiIiIillEREREREcuoiIiIiIhlVERERETEMioiIiIiYhkVEREREbGMioiIiIhYRkVERERELKMiIiIiIpap0d++a4wBfvw6YREREakdfnrd/ul1vDw1uojk5eUBEBsba3ESERERqay8vDwiIiLKHWMzFakrFvF4PBw+fJiwsDBsNpvVcSrE6XQSGxtLVlYW4eHhVsepkTRH5dP8lE/zUz7NT/k0P+dXFXNkjCEvL4+mTZtit5d/FkiNPiJit9tp3ry51TEuSHh4uH7Jz0NzVD7NT/k0P+XT/JRP83N+v3WOznck5Cc6WVVEREQsoyIiIiIillERqWIOh4Np06bhcDisjlJjaY7Kp/kpn+anfJqf8ml+zq+656hGn6wqIiIivk1HRERERMQyKiIiIiJiGRURERERsYyKiIiIiFhGRUREREQsoyJyHl9++SU33HADTZs2xWazsXLlyvPeZ/HixcTHxxMSEkJMTAx/+MMfyMnJOWvMCy+8QIcOHQgODiY2NpYJEyZQVFTkpWfhPRcyPwsXLqRTp04EBwfToUMH/vWvf/1izFtvvUXHjh0JCgqiW7durFq1ygvpvc8b8/Pqq6/Sv39/GjRoQIMGDRgwYADfffedl56Bd3nr9+cny5Ytw2azMWzYsKoLXY28NT+nT59m9OjRxMTE4HA4aN++vf6N/Ywv7KNnzpzJRRddRFhYGFFRUQwbNoxdu3ad937n2/8aY3jssceIiYkhODiYAQMGsGfPngsPaqRcq1atMo888oh55513DGDefffdcsdv2LDB2O128+KLL5qMjAyzfv1606VLF3PjjTeWjVm8eLFxOBxm8eLFZt++feaTTz4xMTExZsKECV5+NlWvsvPz0ksvmbCwMLNs2TKzd+9es3TpUlOvXj3z/vvvl4356quvjJ+fn3n22WfNjh07zKOPPmoCAgLM1q1bvfxsqp435icpKcksXLjQpKSkmJ07d5q77rrLREREmIMHD3r52VQ9b8zPT/bt22eaNWtm+vfvb4YOHeqdJ+Bl3pgfl8tlevXqZQYNGmQ2bNhg9u3bZ9atW2dSU1O9/Gy8wxtz5Cv76IEDB5pFixaZbdu2mdTUVDNo0CDTokULk5+ff877VGT/O2vWLBMREWFWrlxpNm/ebIYMGWJat25tzpw5c0E5VUQqoSK/5LNnzzZxcXFn3TZv3jzTrFmzsuujR482V1111VljJk6caC699NIqy2qFisxPnz59zAMPPHDWbT9/7iNGjDDXX3/9WWN69+5t7rvvvirLaoWqmp+fKy0tNWFhYeaf//xnVcS0TFXOT2lpqenbt6957bXXzJ133llri8j/qqr5efnll01cXJwpLi72RkxLVdUc+eo++vjx4wYw//nPf8455nz7X4/HY5o0aWJmz55d9vPTp08bh8Nhli5dekG5tDRTxfr06UNWVharVq3CGMOxY8dYsWIFgwYNKhvTt29fNm3aVHY4PSMjg1WrVp01xle5XC6CgoLOui04OJjvvvuOkpISADZu3MiAAQPOGjNw4EA2btxYbTmtUpH5+bnCwkJKSkpo2LBhdUS0VEXn54knniAqKop77rmnuiNaqiLz8/7779OnTx9Gjx5NdHQ0Xbt2ZcaMGbjdbisiV7uKzJGv7qNzc3MByt1XnG//u2/fPo4ePXrWmIiICHr37n3B+2gVkSp26aWXsnjxYkaOHElgYCBNmjQhIiKChQsXlo1JSkriiSeeoF+/fgQEBNCmTRuuuOIKHn74YQuTV4+BAwfy2muvsWnTJowx/PDDD7z22muUlJSQnZ0NwNGjR4mOjj7rftHR0Rw9etSKyNWqIvPzcw899BBNmzb9xc7DF1VkfjZs2MDrr7/Oq6++anHa6leR+cnIyGDFihW43W5WrVrF1KlTee6553jqqacsTl89KjJHvriP9ng8/OUvf+HSSy+la9eu5xx3vv3vT/+tyn20ikgV27FjB+PHj+exxx5j06ZNrF69mv3793P//feXjVm3bh0zZszgpZdeIjk5mXfeeYePPvqIJ5980sLk1WPq1Klcd911XHLJJQQEBDB06FDuvPNOAOx2/TpWdn5mzZrFsmXLePfdd3/xV54vOt/85OXlcccdd/Dqq6/SuHFji9NWv4r8/ng8HqKiovj73/9Oz549GTlyJI888gh/+9vfrIxebSoyR764jx49ejTbtm1j2bJlVkf5pQta0KmjqMD64+23325uueWWs25bv369Aczhw4eNMcb069fvF2uU//73v01wcLBxu91Vmrk6VWR+flJcXGyysrJMaWlp2cljPz332NhY8/zzz581/rHHHjPdu3ev4sTVq6rm5yezZ882ERER5vvvv/dC2upXFfOTkpJiAOPn51d2sdlsxmazGT8/P5Oenu7dJ+FFVfX7c9lll5mrr776rPGrVq0ygHG5XFUdu1pV1Rz52j569OjRpnnz5iYjI+O8Y8+3/927d68BTEpKylljLrvsMjNu3LgLyqc/QatYYWHhL/5y9fPzA358y1NFx/i6gIAAmjdvjp+fH8uWLWPw4MFlc9KnTx/WrFlz1vjPPvuMPn36WBHVEuXND8Czzz7Lk08+yerVq+nVq5eFSa1xrvnp2LEjW7duJTU1tewyZMgQrrzySlJTU4mNjbU6erUo7/fn0ksvJT09HY/HUzZ+9+7dxMTEEBgYaFXkalfeHPnKPtoYw5gxY3j33XdZu3YtrVu3Pu99zrf/bd26NU2aNDlrjNPp5Ntvv73wffQF1Zc6JC8vz6SkpJT9pTV37lyTkpJiDhw4YIwxZvLkyeaOO+4oG79o0SLj7+9vXnrpJbN3716zYcMG06tXL3PxxReXjZk2bZoJCwszS5cuNRkZGebTTz81bdq0MSNGjKj25/dbVXZ+du3aZf7973+b3bt3m2+//daMHDnSNGzY0Ozbt69szFdffWX8/f3NnDlzzM6dO820adNq7dt3vTE/s2bNMoGBgWbFihXmyJEjZZe8vLzqfnq/mTfm5+dq87tmvDE/mZmZJiwszIwZM8bs2rXLfPjhhyYqKso89dRT1f30qoQ35shX9tF/+tOfTEREhFm3bt1Z+4rCwsKyMXfccYeZPHly2fWK7H9nzZpl6tevb9577z2zZcsWM3ToUL1915u++OILA/zicueddxpjftzJXX755WfdZ968eaZz584mODjYxMTEmNtuu+2sz3goKSkxjz/+uGnTpo0JCgoysbGx5s9//rM5depU9T2xKlLZ+dmxY4fp0aOHCQ4ONuHh4Wbo0KEmLS3tF4+7fPly0759exMYGGi6dOliPvroo2p6RlXLG/PTsmXLX33MadOmVd8TqyLe+v35X7W5iHhrfr7++mvTu3dv43A4TFxcnHn66adNaWlpNT2rquWNOfKVffSvzQtgFi1aVDbm8ssvL5urn5xv/+vxeMzUqVNNdHS0cTgc5uqrrza7du264Jy2/4YVERERqXY6R0REREQsoyIiIiIillEREREREcuoiIiIiIhlVERERETEMioiIiIiYhkVEREREbGMioiIiIiP+fLLL7nhhhto2rQpNpuNlStXVur+RUVF3HXXXXTr1g1/f3+GDRtW7vivvvoKf39/evToUemsKiIiIiI+pqCggPj4eBYuXHhB93e73QQHBzNu3DgGDBhQ7tjTp0/z+9//nquvvvqCtqUiIiIi4mOuu+46nnrqKW688cZf/bnL5eKBBx6gWbNmhIaG0rt3b9atW1f289DQUF5++WXuvfdemjRpUu627r//fpKSki74S+9UREREROqYMWPGsHHjRpYtW8aWLVsYPnw41157LXv27KnU4yxatIiMjAymTZt2wVn8L/ieIiIiUutkZmayaNEiMjMzadq0KQAPPPAAq1evZtGiRcyYMaNCj7Nnzx4mT57M+vXr8fe/8DqhIiIiIlKHbN26FbfbTfv27c+63eVy0ahRowo9htvtJikpienTp//icSpLRURERKQOyc/Px8/Pj02bNuHn53fWz+rVq1ehx8jLy+OHH34gJSWFMWPGAODxeDDG4O/vz6effspVV11VocdSEREREalDEhIScLvdHD9+nP79+1/QY4SHh7N169azbnvppZdYu3YtK1asoHXr1hV+LBURERERH5Ofn096enrZ9X379pGamkrDhg1p3749t912G7///e957rnnSEhI4MSJE6xZs4bu3btz/fXXA7Bjxw6Ki4s5efIkeXl5pKamAtCjRw/sdjtdu3Y9a5tRUVEEBQX94vbzURERERHxMT/88ANXXnll2fWJEycCcOedd/LGG2+waNEinnrqKSZNmsShQ4do3Lgxl1xyCYMHDy67z6BBgzhw4EDZ9YSEBACMMVWa1Waq+hFFREREKkifIyIiIiKWURERERERy6iIiIiIiGVURERERMQyKiIiIiJiGRURERERsYyKiIiIiFhGRUREREQsoyIiIiIillEREREREcuoiIiIiIhl/n/QX7fD3+k50QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(frequency, np.unwrap(np.angle(H[:, 0, 1])))" + ] + }, + { + "cell_type": "code", + "execution_count": 526, + "id": "cf613c2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.817425235294118e-06" + ] + }, + "execution_count": 526, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "length" + ] + }, + { + "cell_type": "code", + "execution_count": 527, + "id": "9c6f0d2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(12.491352416666656)" + ] + }, + "execution_count": 527, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bandwidth * 1e-12" + ] + }, + { + "cell_type": "code", + "execution_count": 528, + "id": "48c3a280", + "metadata": {}, + "outputs": [], + "source": [ + "import sax\n", + "from simphony.libraries.siepic import y_branch, waveguide\n", + "\n", + "mzi, info = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"gc_in\": \"gc\",\n", + " \"splitter\": \"ybranch\",\n", + " \"long_wg\": \"waveguide\",\n", + " \"short_wg\": \"waveguide\",\n", + " \"combiner\": \"ybranch\",\n", + " # \"gc_out\": \"gc\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port 2\": \"long_wg,o0\",\n", + " \"splitter,port 3\": \"short_wg,o0\",\n", + " \"long_wg,o1\": \"combiner,port 2\",\n", + " \"short_wg,o1\": \"combiner,port 3\",\n", + " # \"combiner,port 1\": \"gc_out,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port 1\",\n", + " \"out\": \"combiner,port 1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ybranch\": y_branch,\n", + " \"waveguide\": waveguide,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 522, + "id": "2d06d0cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 522, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(frequency, np.unwrap(np.angle(H_mzi[:, 0, 1])))" + ] + }, + { + "cell_type": "code", + "execution_count": 577, + "id": "da3c420d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[5.04433316e-12 3.82991304e-12]\n", + " [3.82991304e-12 5.04433316e-12]]\n", + "5.593497414384759e-05\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "length = 200.0\n", + "\n", + "H_mzi = dict_to_matrix(mzi(wl=1e6*wavelength, long_wg={\"length\": length + 60.0}, short_wg={\"length\": length}))\n", + "phase = np.unwrap(np.angle(H_mzi), axis=0)\n", + "group_delay = np.gradient(phase, 2*np.pi*frequency, axis=0)\n", + "avg_group_delay = np.abs(group_delay).mean(axis=0)\n", + "print(avg_group_delay)\n", + "\n", + "plt.plot(frequency, np.unwrap(np.abs(H_mzi[:, 0, 1])))\n", + "\n", + "\n", + "\n", + "poles, residues, feedthrough, error = vector_fitting_discrete(91, H_mzi, frequency, f_c, f_s, max_iterations=100, sign_convention=PHYSICIST)\n", + "\n", + "H_model = pole_residue_response_discrete(frequency, f_c, f_s, poles, residues, feedthrough, sign_convention=PHYSICIST)\n", + "\n", + "plt.plot(frequency, np.unwrap(np.abs(H_model[:, 0, 1])))\n", + "print(error)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "7305daaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 2, 2)" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group_delay.shape" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/impulse_response.py b/examples/old/impulse_response.py new file mode 100644 index 00000000..44ddf296 --- /dev/null +++ b/examples/old/impulse_response.py @@ -0,0 +1,32 @@ +from simphony.utils import discrete_time_impulse_response +import jax.numpy as jnp +import matplotlib.pyplot as plt + +propagation_constants_0 = { + # 1: 0.0, + 2: 1e-19, + 3: 4e-35, + 4: 1e-47, +} +fs = 1e14 + +h0 = discrete_time_impulse_response( + propagation_constants=propagation_constants_0, sampling_freq=fs +) + +propagation_constants_1 = { + 1: 1e-5, + 2: 1e-19, + 3: 4e-35, + 4: 1e-47, +} + +h1 = discrete_time_impulse_response( + propagation_constants=propagation_constants_1, sampling_freq=fs +) + +plt.plot(jnp.abs(h0)) +plt.plot(jnp.abs(h1)) + + +plt.show() diff --git a/examples/old/jit.npz b/examples/old/jit.npz new file mode 100644 index 00000000..cf77b63b Binary files /dev/null and b/examples/old/jit.npz differ diff --git a/examples/old/maybe_trash/autocovariance.ipynb b/examples/old/maybe_trash/autocovariance.ipynb new file mode 100644 index 00000000..27fb3192 --- /dev/null +++ b/examples/old/maybe_trash/autocovariance.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fe650c6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Auto Covariance at lag 1: 0.04000000000000007\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Sample data for time series\n", + "data = [4, 5, 6, 5, 4]\n", + "df = pd.DataFrame(data, columns=['X'])\n", + "\n", + "# Calculate mean\n", + "mu = df['X'].mean()\n", + "\n", + "# Calculate deviations\n", + "df['Deviation'] = df['X'] - mu\n", + "\n", + "# Function to calculate auto covariance for a given lag k\n", + "def auto_covariance(series, lag):\n", + " n = len(series)\n", + " cov = np.sum(series[:n-lag] * series[lag:]) / (n - lag)\n", + " return cov\n", + "\n", + "# Calculate auto covariance for lag 1\n", + "k = 1\n", + "gamma_k = auto_covariance(df['Deviation'].values, k)\n", + "print(f\"Auto Covariance at lag {k}: {gamma_k}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "df54774a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def simulate_process_cholesky(R_tau, time_grid):\n", + " N = len(time_grid)\n", + " cov_matrix = np.zeros((N, N))\n", + " for i in range(N):\n", + " for j in range(N):\n", + " cov_matrix[i, j] = R_tau(abs(time_grid[i] - time_grid[j]))\n", + "\n", + " L = np.linalg.cholesky(cov_matrix + 1e-10*np.eye(N)) # Add small regularization\n", + " white_noise = np.random.randn(N)\n", + " return L @ white_noise\n", + "\n", + "t = np.linspace(0, 1, 3000)\n", + "sigma = 1\n", + "def gaussian(tau):\n", + " return np.exp(-tau**2 / (2*sigma**2))\n", + "\n", + "sig = simulate_process_cholesky(gaussian, t)\n", + "\n", + "plt.plot(sig)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "398fbed1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.fft import fft, ifft\n", + "\n", + "def gaussian_autocovariance(t, sigma=1.0, ell=1.0):\n", + " # return 100 + sigma**2 * np.exp(-t**2 / (2 * ell**2))\n", + " return sigma**2 * np.exp(-t**2 / (2 * ell**2))\n", + "\n", + "def simulate_gaussian_process(n, dt=1.0, sigma=1.0, ell=1.0):\n", + " \"\"\"\n", + " Simulates a stationary Gaussian process with a Gaussian autocovariance function\n", + " using the Shinozuka-Deodatis spectral method.\n", + " \n", + " Parameters:\n", + " n : number of desired time points\n", + " dt : time step\n", + " sigma : standard deviation of the process\n", + " ell : correlation length (controls width of the autocovariance)\n", + " \n", + " Returns:\n", + " t : time vector\n", + " x : simulated Gaussian process\n", + " \"\"\"\n", + " m = 2 * n # For circulant embedding\n", + "\n", + " # Time vector for autocovariance\n", + " taus = dt * np.arange(0, m)\n", + " R = gaussian_autocovariance(np.concatenate([taus[:n], -taus[n:]]), sigma, ell)\n", + "\n", + " # FFT to get eigenvalues (power spectrum)\n", + " lam = np.real(fft(R))\n", + " lam = np.maximum(lam, 0) # Force non-negativity\n", + "\n", + " # Generate frequency domain coefficients a(j)\n", + " a = np.zeros(m, dtype=complex)\n", + " a[0] = np.sqrt(lam[0] / m) * np.random.randn()\n", + " a[m//2] = np.sqrt(lam[m//2] / m) * np.random.randn()\n", + "\n", + " for j in range(1, m//2):\n", + " Uj, Vj = np.random.randn(2)\n", + " a[j] = (np.sqrt(lam[j] / (2*m)) * (Uj + 1j*Vj))\n", + " a[m - j] = np.conj(a[j]) # Hermitian symmetry\n", + "\n", + " # Inverse FFT to get real-valued time-domain signal\n", + " x_full = np.real(ifft(a))\n", + " t = np.arange(n) * dt\n", + " return t, x_full[:n]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8e20df65", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAHHCAYAAACvJxw8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAA9VRJREFUeJzsXXd8FVX2/857aSQhoYTemwLSpEgTsFA0KOoq1p8CYl8Vl11UdhXFsrYVsbe1rS4WrMgiUqT33ntvAUJJICHJK/f3x8vMuzNz78ydee/lJeF+Px8+5M3cuffcfu45556jEEIIJCQkJCQkJCQkmPDEmwAJCQkJCQkJifIMySxJSEhISEhISFhAMksSEhISEhISEhaQzJKEhISEhISEhAUksyQhISEhISEhYQHJLElISEhISEhIWEAySxISEhISEhISFpDMkoSEhISEhISEBSSzJCEhISEhISFhAckslQM0bdoUw4cPj0vZzz77LBRFiUvZPOzduxeKouDzzz+PNyllhrlz50JRFMydOzfepEi4hJO5pKbNzc2NMVXlH8OHD0fTpk3jTYaESyiKgmeffTaqeV522WW47LLLoppnpJDMUgyxYcMG3HTTTWjSpAlSUlLQoEEDDBgwAG+//Xa8SYsK3nvvvXLB0Bw7dgxPPvkk2rdvj/T0dKSkpKBly5YYMWIEFi5cGG/yyj0uu+wyKIqi/atRowa6deuGTz/9FMFgMN7kVWj885//xM8//xyz/BcsWICbb74ZDRo0QFJSEjIzM9G9e3c899xzOHr0aMzKrWzYsmULFEVBSkoKTp8+HVFemzdvxrPPPou9e/dGhbbKiorWTpJZihEWL16Mrl27Yt26dbj33nvxzjvv4J577oHH48Gbb76pS7tt2zZ8/PHHcaLUPcoDs7R8+XJcdNFFmDhxIrp06YJXXnkF77zzDm655RYsX74cffr0wfz58+NKowj69u2Lc+fOoW/fvnEpv2HDhvjyyy/x5Zdf4umnn4bf78fIkSPx97//PS70VEQ89dRTOHfunO5ZLJmlcePGoW/fvli1ahWGDx+O999/H//85z9x0UUX4fXXX0evXr1iUm608fHHH2Pbtm1xpeGrr75C3bp1AQDff/99RHlt3rwZ48ePrzBMQLxg1U4zZszAjBkzyp4oCyTEm4DKihdffBGZmZlYsWIFqlWrpnt37Ngx3e/k5OQypKzy4NSpU7j++uuRkJCAtWvXonXr1rr3L7zwAr755htUqVIlThSKw+PxICUlJW7lZ2Zm4v/+7/+03/fffz8uvPBCvPPOO3j++eeRmJho+iYYDKKkpCSudJcnJCQkICGhbJbUb7/9Fs8//zxuvvlmfPnll0hKStK9f+ONN/DGG2+UCS2RgjW2yhKEEEyaNAm333479uzZg//+97+455574kpTrFFQUIC0tDTTc0IIioqK4r5mGsdzuQCRiAkuvPBCctlllwmlbdKkCRk2bJj2+7PPPiMAyIIFC8gjjzxCsrKySGZmJrnvvvtIcXExOXXqFLnzzjtJtWrVSLVq1ciYMWNIMBjUvp8zZw4BQObMmaMrZ8+ePQQA+eyzz7RnzzzzDDEOg08//ZRcfvnlpFatWiQpKYm0adOGvPfeeyaaAej+9evXT3t/6tQpMmrUKNKwYUOSlJREWrRoQV5++WUSCAR0+Zw6dYoMGzaMZGRkkMzMTHLXXXeRNWvWmOhk4Z///CcBQL755hvLdDT27t1LHnzwQXLBBReQlJQUUqNGDXLTTTeRPXv26NKx2oWQcN/Q6VesWEEGDhxIatasSVJSUkjTpk3JiBEjdN99/fXXpHPnziQ9PZ1UrVqVtGvXjkycOFF7z+qz+fPnk5tuuok0atSIJCUlkYYNG5LHHnuMFBYW6vIeNmwYSUtLIwcPHiTXXXcdSUtLI1lZWeSvf/0r8fv9tm3Sr18/ctFFF5me33TTTQQAOXToECGEEADkz3/+M/nqq69I27ZtSUJCAvnpp58IIYSsXr2aXHXVVaRq1aokLS2NXHHFFWTJkiWmPE+dOkUee+wx0qRJE5KUlEQaNGhA7rzzTnL8+HEtTVFRERk3bhxp0aKFVu8xY8aQoqIiXV4zZswgvXv3JpmZmSQtLY1ccMEFZOzYsbo0b731Fmnbti2pUqUKqVatGunSpQv573//y22LYDBIatasSf7yl79ozwKBAMnMzCQej4ecOnVKe/7yyy8Tr9dLzpw5Qwgxjxnj/ACgzXM17Y4dO8iwYcNIZmYmycjIIMOHDycFBQVc+lRccMEFJCsrSytbBD///DPJzs4m9erVI0lJSaR58+bkueeeM40R43qkol+/fro5Toh9++bn55NRo0Zp/V2rVi3Sv39/smrVKi3NsGHDSJMmTXT5vvbaa6Rnz56kRo0aJCUlhXTu3JlMnjzZRJM6Jn/66Sdy0UUXkaSkJNK2bVvy22+/CbfLggULCACyfPly8u233xKPx0MOHDjALOuZZ54xPafbS10fjP/oef3uu++Stm3bkqSkJFKvXj3y0EMP6caViqVLl5Krr76aVKtWjaSmppL27dvr1gxCCJk9eza59NJLSWpqKsnMzCRDhgwhmzdv1qVRx9qmTZvIbbfdRqpVq0Y6deqk0T548GAyffp00qVLF5KcnEzeeOMNQoj4Gm5sF5E11q6dWGPt6NGj5O677ya1a9cmycnJpEOHDuTzzz/XpVH3uNdee418+OGHpHnz5iQpKYl07dqVLF++3NTGTiAlSzFCkyZNsGTJEmzcuBHt2rVzlccjjzyCunXrYvz48Vi6dCk++ugjVKtWDYsXL0bjxo3xz3/+E9OmTcNrr72Gdu3a4a677ooK7e+//z4uuugiDBkyBAkJCfj111/x0EMPIRgM4s9//jMAYOLEiXjkkUeQnp6Of/zjHwCAOnXqAAAKCwvRr18/HDp0CPfffz8aN26MxYsXY+zYsThy5AgmTpwIIHSKue6667Bw4UI88MADaNOmDX766ScMGzZMiM5ff/0VVapUwZ/+9Cfhuq1YsQKLFy/GrbfeioYNG2Lv3r14//33cdlll2Hz5s1ITU110FIhKeHAgQNRq1YtPPnkk6hWrRr27t2LH3/8UUszc+ZM3HbbbbjyyivxyiuvAAjZSCxatAijRo3i5j158mQUFhbiwQcfRM2aNbF8+XK8/fbbOHjwICZPnqxLGwgEMGjQIHTv3h3/+te/MGvWLLz++uto0aIFHnzwQUd1UrF79254vV6dZPSPP/7Ad999h4cffhhZWVlo2rQpNm3ahD59+iAjIwOPP/44EhMT8eGHH+Kyyy7DvHnz0L17dwDA2bNn0adPH2zZsgV33303OnfujNzcXEyZMgUHDx5EVlYWgsEghgwZgoULF+K+++5DmzZtsGHDBrzxxhvYvn27ptLatGkTrrnmGnTo0AHPPfcckpOTsXPnTixatEij9eOPP8ajjz6Km266CaNGjUJRURHWr1+PZcuW4fbbb2fWWVEU9O7dW6e6Xb9+PfLy8uDxeLBo0SIMHjwYQMhe6OKLL0Z6ejozry+//BL33HMPLrnkEtx3330AgBYtWujS3HzzzWjWrBleeuklrF69Gv/+979Ru3ZtbZywsH37dmzfvh333HMPt2wWPv/8c6Snp2P06NFIT0/HH3/8gXHjxiE/Px+vvfaacD4qRNr3gQcewPfff4+HH34Ybdu2xYkTJ7Bw4UJs2bIFnTt35ub95ptvYsiQIbjjjjtQUlKCb775BkOHDsXUqVO19lexcOFC/Pjjj3jooYdQtWpVvPXWW7jxxhuxf/9+1KxZ07Ye//3vf9GiRQt069YN7dq1Q2pqKr7++muMGTPGcZv07dsXjz76KN566y38/e9/R5s2bQBA+//ZZ5/F+PHj0b9/fzz44IPYtm0b3n//faxYsQKLFi3SpGwzZ87ENddcg3r16mHUqFGoW7cutmzZgqlTp2prxqxZs3D11VejefPmePbZZ3Hu3Dm8/fbb6N27N1avXm0ymh86dChatWqFf/7znwjxOCFs27YNt912G+6//37ce++9uPDCC4XXcBZE1li7djLi3LlzuOyyy7Bz5048/PDDaNasGSZPnozhw4fj9OnTpnV00qRJOHPmDO6//34oioJXX30Vf/rTn7B79273ksyIWC0JLmbMmEG8Xi/xer2kZ8+e5PHHHye///47KSkpMaXlSZYGDRqkkxj17NmTKIpCHnjgAe2Z3+8nDRs21HHhkUqWjJILQggZNGgQad68ue7ZRRddZOL+CSHk+eefJ2lpaWT79u26508++STxer1k//79hJDQSRcAefXVV3X16dOnj5BkqXr16toJiUZ+fj45fvy49u/s2bOWdVuyZAkBQP7zn/9oz0QlSz/99BMBQFasWMGlc9SoUSQjI8NSysPqMxatL730ElEUhezbt097NmzYMAKAPPfcc7q0F198MenSpQu3TBX9+vUjrVu31tpry5Yt5NFHHyUAyLXXXqulA0A8Hg/ZtGmT7vvrr7+eJCUlkV27dmnPDh8+TKpWrUr69u2rPRs3bhwBQH788UcTDeo4//LLL4nH4yELFizQvf/ggw8IALJo0SJCCCFvvPEGAaCTSBlx3XXXMSVmdnjttdeI1+sl+fn5hJCQ9KRJkybkkksuIU888QQhJCRtqlatmk4CxRozaWlpTCmNmvbuu+/WPb/hhhtIzZo1Len75ZdfCACTlCEYDOrG/fHjx4nP59Pes8bT/fffT1JTU3VSO1HJkkj7ZmZmkj//+c+WaViSJSOtJSUlpF27duSKK67QPQdAkpKSyM6dO7Vn69atIwDI22+/bVmumm/NmjXJP/7xD+3Z7bffTjp27GhKCwHJEiGETJ48mbn+Hjt2jCQlJZGBAwfqpDPvvPMOAUA+/fRTQkhoDWzWrBlp0qSJSeJE7wedOnUitWvXJidOnNDV3ePxkLvuukt7po612267jUk7ADJ9+nTdc9E1nNUuomssr50IMY+1iRMnEgDkq6++0p6VlJSQnj17kvT0dG2uqntczZo1ycmTJ7W06pz59ddfTWWJQhp4xwgDBgzAkiVLMGTIEKxbtw6vvvoqBg0ahAYNGmDKlClCeYwcOVJ3Fbl79+4ghGDkyJHaM6/Xi65du2L37t1Ro53WV+fl5SE3Nxf9+vXD7t27kZeXZ/v95MmT0adPH1SvXh25ubnav/79+yMQCGin9mnTpiEhIUEn+fB6vXjkkUeE6MzPz2eerO+8807UqlVL+/fEE08w6+bz+XDixAm0bNkS1apVw+rVq4XKpaFKXaZOnQqfz8dNU1BQgJkzZzrKm6a1oKAAubm56NWrFwghWLNmjSn9Aw88oPvdp08f4XGxdetWrb3atGmDt99+G4MHD8ann36qS9evXz+0bdtW+x0IBDBjxgxcf/31aN68ufa8Xr16uP3227Fw4ULk5+cDAH744Qd07NgRN9xwg6l8dZxPnjwZbdq0QevWrXVj54orrgAAzJkzB0C43X/55Rfujb1q1arh4MGDWLFihVAbqOjTpw8CgQAWL14MICRB6tOnD/r06YMFCxYAADZu3IjTp0+jT58+jvI2gtVnJ06c0NqMBfWdcezn5eXpxn2tWrWwdu1a7T09ns6cOYPc3Fz06dMHhYWF2Lp1q2PaRdq3WrVqWLZsGQ4fPuwob5rWU6dOIS8vD3369GHO0f79++skdh06dEBGRobQ2P/tt99w4sQJ3Hbbbdqz2267DevWrcOmTZsc0WyHWbNmoaSkBI899hg8nvDWe++99yIjIwP/+9//AABr1qzBnj178Nhjj5nsXdV5cuTIEaxduxbDhw9HjRo1tPcdOnTAgAEDMG3aNFP5xrGmolmzZhg0aJDumegazkK011ggtFfUrVtX10+JiYl49NFHcfbsWcybN0+X/pZbbkH16tW13+o8jWSflMxSDNGtWzf8+OOPOHXqFJYvX46xY8fizJkzuOmmm7B582bb7xs3bqz7nZmZCQBo1KiR6fmpU6eiRveiRYvQv39/pKWloVq1aqhVq5Z2K0qEWdqxYwemT59uWrj79+8PIGzgvm/fPtSrV8+06F944YVCdFatWhVnz541PX/uuecwc+ZMJnNy7tw5jBs3Do0aNUJycjKysrJQq1YtnD59WqhuRvTr1w833ngjxo8fj6ysLFx33XX47LPPUFxcrKV56KGHcMEFF+Dqq69Gw4YNcffdd2P69Om2ee/fv19bDNPT01GrVi3069cPgLkfUlJSUKtWLd2z6tWrC4+Lpk2bYubMmZg1axYWLlyInJwcTJ06FVlZWbp0zZo10/0+fvw4CgsLmX3Wpk0bBINBHDhwAACwa9cuW5X0jh07sGnTJtPYueCCCwCEx84tt9yC3r1745577kGdOnVw66234rvvvtMxTk888QTS09NxySWXoFWrVvjzn/+sU9Px0LlzZ6SmpmqMkcos9e3bFytXrkRRUZH27tJLL7XNzwrGOa4u8Fb9VrVqVQAwjf309HRt3LNUSJs2bcINN9yAzMxMZGRkoFatWppRv5uxL9K+r776KjZu3IhGjRrhkksuwbPPPiu0YU2dOhU9evRASkoKatSogVq1auH9999n0mlsQ0B87H/11Vdo1qyZpsbduXMnWrRogdTUVPz3v/+1/d4J9u3bB8C8viUlJaF58+ba+127dgGA5Vzh5QWE5l1ubi4KCgp0z41z1+q56BrOQrTXWCBU31atWumYTCCstlPbQ4WbeWUHabNUBkhKSkK3bt3QrVs3XHDBBRgxYgQmT56MZ555xvI7r9cr/JxQOmieY7xAIGBL665du3DllVeidevWmDBhAho1aoSkpCRMmzYNb7zxhpDfnWAwiAEDBuDxxx9nvlc3vkjRunVrrFu3Dj6fT6eH7tChA/ebRx55BJ999hkee+wx9OzZE5mZmVAUBbfeequubqJtqCgKvv/+eyxduhS//vorfv/9d9x99914/fXXsXTpUqSnp6N27dpYu3Ytfv/9d/z222/47bff8Nlnn+Guu+7CF198wS1nwIABOHnyJJ544gm0bt0aaWlpOHToEIYPH27qB95YEUVaWpq2EFoh1rdkgsEg2rdvjwkTJjDfqweFKlWqYP78+ZgzZw7+97//Yfr06fj2229xxRVXYMaMGfB6vWjTpg22bduGqVOnYvr06fjhhx/w3nvvYdy4cRg/fjyXhsTERHTv3h3z58/Hzp07kZOTgz59+qBOnTrw+XxYtmwZFixYgNatW5sYVKfg9Rs9n41Qb31u3LhR9zwhIUHrw4MHD+renT59Gv369UNGRgaee+45tGjRAikpKVi9ejWeeOIJ4bFP0yvSvjfffDP69OmDn376CTNmzMBrr72GV155BT/++COuvvpqZjkLFizAkCFD0LdvX7z33nuoV68eEhMT8dlnn2HSpEmm9G7aEAhJ6H799VcUFRWhVatWpveTJk3Ciy++aOtoVGRdLQ/gzV3W80jWcNE1NpZwOyasIJmlMkbXrl0BhMSosYLKRRudqxm5bxZ+/fVXFBcXY8qUKTruXFV/0OAtIi1atMDZs2dtN98mTZpg9uzZOHv2rE66JOpz5ZprrsHSpUvx008/4eabbxb65vvvv8ewYcPw+uuva8+KiopMbUW3IS0K57Vhjx490KNHD7z44ouYNGkS7rjjDnzzzTfaFeSkpCRce+21uPbaaxEMBvHQQw/hww8/xNNPP42WLVua8tuwYQO2b9+OL774Qme471SVF2vUqlULqampzD7bunUrPB6PxuC0aNHCtMEb0aJFC6xbtw5XXnml7Sbl8Xhw5ZVX4sorr8SECRPwz3/+E//4xz8wZ84cbeylpaXhlltuwS233IKSkhL86U9/wosvvoixY8daujzo06cPXnnlFcyaNQtZWVlo3bo1FEXBRRddhAULFmDBggW45ppr7JonJt7xL7zwQrRq1Qo///wzJk6cyLwCbsTcuXNx4sQJ/PjjjzpfXnv27DGlrV69OtMx4759+3SqVkCsfevVq4eHHnoIDz30EI4dO4bOnTvjxRdf5DJLP/zwA1JSUvD777/r3Kp89tlntvV0gh9//BFFRUV4//33TRLUbdu24amnnsKiRYs06SGrXUpKSkxrOa/PmzRpouVNt2NJSQn27NmjjVlVpbhx40buGkrnZcTWrVuRlZUlNC54EF3DWRBdY53MjSZNmmD9+vUIBoM66ZKqPlbbI5aQargYYc6cOUwuVtUli6qa3KBJkybwer0mvfJ7771n+63KkdO05+XlMReqtLQ05qJ68803Y8mSJfj9999N706fPg2/3w8AyM7Oht/vx/vvv6+9DwQCwh7OH3zwQdSpUwd/+ctfsH37dtN7Vvt7vV7T87ffftt0OlQXLLoNCwoKTJKgU6dOmfLr1KkTAGiquBMnTujeezweTfpFq+uMdBrrQAgxOTSNN7xeLwYOHIhffvlF51zu6NGjmDRpEi699FJkZGQAAG688UasW7cOP/30kykftZ4333wzDh06xHTSeu7cOU21cPLkSdN7u3ZPSkpC27ZtQQjh2pep6NOnD4qLizFx4kRceuml2sLep08ffPnllzh8+LCQvRJvjkSKZ599Frm5ubj33nuZdTGOSdZ4KikpYa4JLVq0wNKlS1FSUqI9mzp1qqZOVWHXvoFAwKR2qV27NurXr88d9yqtiqLo5uTevXuj7tzzq6++QvPmzfHAAw/gpptu0v3729/+hvT0dJ0qrkWLFqY19aOPPjKtHSqTYuz3/v37IykpCW+99ZauHz755BPk5eVpt/w6d+6MZs2aYeLEiaY81O/q1auHTp064YsvvtCl2bhxI2bMmIHs7GxXbaJCdA1nQXSN5bUTC9nZ2cjJycG3336rPfP7/Xj77beRnp6umSfEElKyFCM88sgjKCwsxA033IDWrVujpKQEixcvxrfffoumTZtixIgRMSs7MzMTQ4cOxdtvvw1FUdCiRQtMnTrVUs+sYuDAgZoU5P7778fZs2fx8ccfo3bt2qYTVJcuXfD+++/jhRdeQMuWLVG7dm1cccUVGDNmDKZMmYJrrrkGw4cPR5cuXVBQUIANGzbg+++/x969e5GVlYVrr70WvXv3xpNPPom9e/eibdu2+PHHH4X12jVq1MBPP/2Ea6+9Fh07dsStt96Kbt26ITExEQcOHNCu19MSsmuuuQZffvklMjMz0bZtWyxZsgSzZs0yXTEeOHAgGjdujJEjR2LMmDHwer349NNPUatWLezfv19L98UXX+C9997DDTfcgBYtWuDMmTP4+OOPkZGRoS1Y99xzD06ePIkrrrgCDRs2xL59+/D222+jU6dO3KuyrVu3RosWLfC3v/0Nhw4dQkZGBn744Yeo2qZFCy+88AJmzpyJSy+9FA899BASEhLw4Ycfori4GK+++qqWbsyYMfj+++8xdOhQ3H333ejSpQtOnjyJKVOm4IMPPkDHjh1x55134rvvvsMDDzyAOXPmoHfv3ggEAti6dSu+++47/P777+jatSuee+45zJ8/H4MHD0aTJk1w7NgxvPfee2jYsKEmCRg4cCDq1q2L3r17o06dOtiyZQveeecdDB48WLP74aFnz55ISEjAtm3btGv/QOhquMrcizBLXbp0waxZszBhwgTUr18fzZo101wpRILbb78dGzduxEsvvYTly5fj1ltvRbNmzVBQUICNGzfi66+/RtWqVTUJaa9evVC9enUMGzYMjz76KBRFwZdffsk8UNxzzz34/vvvcdVVV+Hmm2/Grl278NVXX5ncHti17+nTp9GwYUPcdNNN6NixI9LT0zFr1iysWLFCJ3UwYvDgwZgwYQKuuuoq3H777Th27BjeffddtGzZEuvXr4+47QDg8OHDmDNnDh599FHm++TkZAwaNAiTJ0/GW2+9hcTERNxzzz144IEHcOONN2LAgAFYt24dfv/9d5NUqlOnTvB6vXjllVeQl5eH5ORkXHHFFahduzbGjh2L8ePH46qrrsKQIUOwbds2vPfee+jWrZtmP+bxePD+++/j2muvRadOnTBixAjUq1cPW7duxaZNmzQG5rXXXsPVV1+Nnj17YuTIkZrrgMzMzIhjtYmu4SyIrrFW7WTEfffdhw8//BDDhw/HqlWr0LRpU3z//fdYtGgRJk6caDufowLX9+gkLPHbb7+Ru+++m7Ru3Zqkp6eTpKQk0rJlS/LII4+Qo0eP6tLyXAcYr6OrV0CN16VVp4Q0jh8/Tm688UaSmppKqlevTu6//36yceNGIdcBU6ZMIR06dNAcLL7yyivk008/NTljzMnJIYMHDyZVq1Y1OaU8c+YMGTt2LGnZsiVJSkoiWVlZpFevXuRf//qXzn3CiRMnyJ133qk5pbzzzjuFnVKqOHLkCBkzZozmHC85OZk0b96c3HXXXWT+/Pm6tKdOnSIjRowgWVlZJD09nQwaNIhs3bqVeV161apVpHv37iQpKYk0btyYTJgwweQ6YPXq1eS2224jjRs3JsnJyaR27drkmmuuIStXrtTy+f7778nAgQNJ7dq1tbzuv/9+cuTIES0Ny3XA5s2bSf/+/Ul6ejrJysoi9957r3Ytmm4bVv8Twnd/YATPKaURKHUAyMLq1avJoEGDSHp6OklNTSWXX345Wbx4sSndiRMnyMMPP0waNGigOZwcNmwYyc3N1dKUlJSQV155hVx00UUkOTmZVK9enXTp0oWMHz+e5OXlEUJCzviuu+46Ur9+fZKUlETq169PbrvtNt1V5w8//JD07duX1KxZkyQnJ5MWLVqQMWPGaHnYoVu3bgQAWbZsmfbs4MGDBABp1KiRKT2rvbdu3Ur69u1LqlSpwnRKaZzLLKenVpg7dy656aabSL169UhiYiLJyMggXbt2Jc8884xufBFCyKJFi0iPHj1IlSpVSP369TV3JsZxRwghr7/+OmnQoAFJTk4mvXv3JitXrjRd57Zr3+LiYjJmzBjSsWNHzVlpx44dTQ5uWa4DPvnkE9KqVSuSnJxMWrduTT777DNm+/LGJM/9AV0/AGT27NncNJ9//jkBQH755RdCSMhdxBNPPEGysrJIamoqGTRoENm5cyezrI8//pg0b96ceL1eU/u+8847pHXr1iQxMZHUqVOHPPjgg0ynlAsXLiQDBgzQ2q5Dhw4mdwizZs0ivXv3JlWqVCEZGRnk2muv5TqlZLnZUJ1SsiC6hsPgOsDJGstrJ55TSjXfpKQk0r59e9MeQTulNMJIp1MopZlISEhISEhISEgwIG2WJCQkJCQkJCQsIJklCQkJCQkJCQkLSGZJQkJCQkJCQsICklmSkJCQkJCQkLCAZJYkJCQkJCQkJCwgmSUJCQkJCQkJCQtIp5RRQDAYxOHDh1G1atWYhDeQkJCQkJCQiD4IIThz5gzq169vCtRLQzJLUcDhw4e1+FcSEhISEhISFQsHDhxAw4YNue8lsxQFqK7WDxw4oMXBigZ8Ph9mzJiBgQMHIjExMWr5lidU9jrK+lV8VPY6Vvb6AZW/jpW9fkDs6pifn49GjRrZhkyRzFIUoKreMjIyos4spaamIiMjo1JPgMpcR1m/io/KXsfKXj+g8texstcPiH0d7UxopIG3hISEhISEhIQFJLMkISEhISEhIWEBySxJSEhISEhISFhAMksSEhISEhISEhaQzJKEhISEhISEhAUksyQhISEhISEhYQHJLElISEhISEhIWEAySxISEhISEhISFpDMkoSEhISEhISEBSSzJCEhISEhISFhAcksSUhISEhISEhYQDJLEhISEhISEhIWkMySRLlAsS+AYJDEmwwJCQkJCQkTJLMkEXcU+oGOL/yBmz5YHG9SJCQkJCQkTJDMkkTcsfW0gkCQYPX+0/EmRUJCQkJCwgTJLElISEhISEhIWEAySxISEhISEhISFpDMkkTcocSbAAkJCQkJCQtIZklCQkJCQkJCwgKSWZKQkJCQkJCQsIBkliQkJCQkJCQkLCCZJYn4QxotSUhISEiUY0hmSUJCQkJCQkLCApJZkpCQkJCQkJCwgGSWJCQkJCQkJCQsIJklCQkJCQkJCQkLSGZJIu6g7bsJIXGjQ0JCQkJCggXJLEmUKwQlryQhISEhUc4gmSWJcgUpWZKQkJCQKG+QzJJEuYKULElISEhIlDdIZkmiXCEoJUsSEhISEuUMklmSKFeQvJKEhISERHlDhWOW3n33XTRt2hQpKSno3r07li9fbpl+8uTJaN26NVJSUtC+fXtMmzZN9/7ZZ59F69atkZaWhurVq6N///5YtmxZLKsgYQB9G05KliQkJCQkyhsqFLP07bffYvTo0XjmmWewevVqdOzYEYMGDcKxY8eY6RcvXozbbrsNI0eOxJo1a3D99dfj+uuvx8aNG7U0F1xwAd555x1s2LABCxcuRNOmTTFw4EAcP368rKp13oNw/paQkJCQkCgPqFDM0oQJE3DvvfdixIgRaNu2LT744AOkpqbi008/ZaZ/8803cdVVV2HMmDFo06YNnn/+eXTu3BnvvPOOlub2229H//790bx5c1x00UWYMGEC8vPzsX79+rKqlgQFKVmSkJCQkChvSIg3AaIoKSnBqlWrMHbsWO2Zx+NB//79sWTJEuY3S5YswejRo3XPBg0ahJ9//plbxkcffYTMzEx07NiRS0txcTGKi4u13/n5+QAAn88Hn88nWiVbqHlFM8/yBp/Pp1PDlZT44PPGjZyoo7L3YWWvH1D561jZ6wdU/jpW9voBsaujaH4VhlnKzc1FIBBAnTp1dM/r1KmDrVu3Mr/Jyclhps/JydE9mzp1Km699VYUFhaiXr16mDlzJrKysri0vPTSSxg/frzp+YwZM5CamipaJWHMnDkz6nmWL4TZpRkzZiItMY6kxAiVvQ8re/2Ayl/Hyl4/oPLXsbLXD4h+HQsLC4XSVRhmKZa4/PLLsXbtWuTm5uLjjz/GzTffjGXLlqF27drM9GPHjtVJrPLz89GoUSMMHDgQGRkZUaPL5/Nh5syZGDBgABITKyEHgVAd130zS/t9Zf/+qJGWFEeKoovK3oeVvX5A5a9jZa8fUPnrWNnrB8SujqpmyA4VhlnKysqC1+vF0aNHdc+PHj2KunXrMr+pW7euUPq0tDS0bNkSLVu2RI8ePdCqVSt88sknOpUfjeTkZCQnJ5ueJyYmxmSgxirf8ghvQkKlrGtl78PKXj+g8texstcPqPx1rOz1A6JfR9G8KoyBd1JSErp06YLZs2drz4LBIGbPno2ePXsyv+nZs6cuPRAS4fHS0/nSNkkSsQVt0i0NvCUkJCQkyhsqjGQJAEaPHo1hw4aha9euuOSSSzBx4kQUFBRgxIgRAIC77roLDRo0wEsvvQQAGDVqFPr164fXX38dgwcPxjfffIOVK1fio48+AgAUFBTgxRdfxJAhQ1CvXj3k5ubi3XffxaFDhzB06NC41fN8g851gOSVJCQkJCTKGSoUs3TLLbfg+PHjGDduHHJyctCpUydMnz5dM+Lev38/PJ6wsKxXr16YNGkSnnrqKfz9739Hq1at8PPPP6Ndu3YAAK/Xi61bt+KLL75Abm4uatasiW7dumHBggW46KKL4lLH8xIUgySZJQkJCQmJ8oYKxSwBwMMPP4yHH36Y+W7u3LmmZ0OHDuVKiVJSUvDjjz9GkzwJF5BqOAkJCQmJ8owKY7MkUXkhmSUJCQkJifIMySxJxB1EquEkJCQkJMoxJLMkEXdIyZKEhISERHmGZJYk4g55G05CQkJCojxDMksScQfNIEnJkoSEhIREeYNkliTiDr0aLm5kSEhUWrwxczv++t06EHkYkZBwBcksScQdegNvuZhLSEQbb87egR9WH8TGQ2JxsCQkJPSQzJJEuYJklSQkYoeSQCDeJEhIVEhIZkki7nBzG+5kQQn+vWA3cs/KGH4SEuJQ4k2AhESFhGSWJOIOHbMUFPvm0a/X4IX/bcHIL1bGhCYJicoIRfJKEhKuIJklibjDzW24hTtzAQDrDpyOAUUSEpUTkleSkHAHySxJxB00ezT6u7XxIkNColJCXpqovAjK68NlBsksScQd9Fq+/ehZoW+kOkFCQgz0fuqRE6fSoMgXwOWvz8XDk1bHm5TzApJZkog73JyNEr1y6EpIiIBWbUteqfJgztZj2HeiEFPXH4k3KecF5I4jAUIIvli8F8v3nIxP+S6+SfTIVV9CQgQ6ZklaLUlIuEJCvAmQiD/mbjuOZ6ZsAgDsfXlwmZfvxqQiMcEDlEifMRISdqDnl5QsVR7IvixbSMmSBPbkFsSbBMeQajgJCTHIeIuVFZJbKkvIHUci7nCzlCdJZklCQgjywlTlhJQslS3kjiMRd7hSw3nlSiEhIQJp4F05IbuybCGZJYm4Q96Gk5CIHXQ2S3KLrTRQJOdbppA7jkTc4YZZSpDMkoSEEGinlB45bSQkXEFOHYm4w40aLkmq4STOI3y6cA/6T5iHo/lFjr8NSsmShETEkMySRIWEVMNJnE94bupm7Dx2Fq/P2Ob4W3kbrnJCsr1lC7njSMTd6NO4lIvEO5LMkj0CQYLFu3JxpsgXb1IkooQiX9BR+hJ/EO/8sVP7TVwpvSXKI+K9bp9vkDuORNxh5I0CAifhBKmGs8Vni/bg9o+X4faPl8WbFIkoQWRu0Ph00R58vniv9lsKmSoPaGZJBkuOPSSzJFEOFlA94xNwKFkSSX8+4vtVBwEAGw7lxZkSiWjB6aa45Ui+4ftoUiMRT9D2Z3IJjD0ksyQRdxgXcL/AzKdDw0lmiQ15tbhy4LsVB7S/nY5149ySarhKBGp6S7u02EMySxJx130bp3kgYD/xaUZALhRsSFap4mPX8bN4/If12u9IzwVyqlROxLNf/9h6FLuOn40fAWUEGUhXIu4wznN/0N6IlWYEpGTJjDNFPmw2qGAkKh5y8vSuAqRtigQL8RoXq/adxN2frwQQnyDsZQkpWZKIO8zMkogaLswuOTV6PR9A34CSqLjwBfQHB6fnAjkzKi/oA2POmeK40LD+4PljDymZJYm4w5XNEjVyRdR25xuOx2nxlIgu/IaxHanKWZ4rKg9oU4T+byyMCw3nk1BfMksSZYpgkODuz1fghambuWmEbJYgJUsSlR+RSpaMEDHwPnCyECcLSiIrSCLmKA82ieeTWlgySxI6/HnSaizdfSJm+S/fexJ/bD2Gfy/coz0zS5acOd4TcWJ5vkG2SOWAzzC2nY5142Zmt7cdP1OMPq/OQefnZzoqR6LsEe+LOecbJLNUiXH49DkcPn3O0Tf/W38Et360NEYUmU/KAOM2nMCGQKsjpGRJorLCb5IsRaiGs3m/NUdeCqgoKA9x/s6nm8iSWargOFPkw/ajZ0zPi3wB9Hr5D/R6+Q+U+J1JamIJ1gR3Y+BNz1F5G06isiL6NkvW35eHDVii4uA84pUks1TRcfm/5mLgG/Oxat8p3fP8c+F4YAXF/rImiwuW6Ng44RxLliSzZML5ZEtQmVHsD+h+O9RQmw4iclRIRBPn03iSzFIFR+7ZkCHmrC1HXecR77OkK8kS9bdkliQqK4yBc2Ot9pB2MBUH5cEb+/l0JpPMkkTcYbZZsj8+05KT80lvLgrZIpUDRT6DZCnGrgMkr1RxUB6WvfNp7ZXMUiWB1SJnN5zLcrgz6TTehhNwHUDP0a+XH2AajktIVHQUGdRwkbsUk9xSZcH5w6aUD0hmqbKivC56FF2qdMjI5ji1Wfpk4R58QrkikJCoLDCq4SK1RTuPBAGVHuXBLrE80FBWqHDM0rvvvoumTZsiJSUF3bt3x/Llyy3TT548Ga1bt0ZKSgrat2+PadOmae98Ph+eeOIJtG/fHmlpaahfvz7uuusuHD58ONbViDoqoq0Bjycy+pZhwZhi8a7Y+YaqiDiP1rBKjYhdBxDLnybQt+HOp40wFiCE4KH/rsLYHzfEJv+Y5OoM55O5aIVilr799luMHj0azzzzDFavXo2OHTti0KBBOHbsGDP94sWLcdttt2HkyJFYs2YNrr/+elx//fXYuHEjAKCwsBCrV6/G008/jdWrV+PHH3/Etm3bMGTIkLKsVtxRlnwWvRirC7/5Npy9Ss04SeXCrodsjcoBYz86vQ1nys9OC6eT/EZW1vmO3bkFmLYhB18v3x+b9akc9M/5NEYqFLM0YcIE3HvvvRgxYgTatm2LDz74AKmpqfj000+Z6d98801cddVVGDNmDNq0aYPnn38enTt3xjvvvAMAyMzMxMyZM3HzzTfjwgsvRI8ePfDOO+9g1apV2L9/f1lWLaYoT4wEazE23YYTslkqP3WSkIgVjJKkWPtZimZZ5ztoc4LY8Erx75/yQENZISHeBIiipKQEq1atwtixY7VnHo8H/fv3x5IlS5jfLFmyBKNHj9Y9GzRoEH7++WduOXl5eVAUBdWqVeOmKS4uRnFxOFBpfn7I663P54PP5+N95hhqXiJ5Hj5ViN3H8tCoeioAIOAP+1by+f3w+fh8MUuSE8160PBTdJWUlMCDoGm6Ffv8tuUbaQ4GScxojgRO+jCaCBraJ1blx6t+ZYl41jFgVMM5HOfGeeIPmOcWXT/d/PT5QIIV6jzNRTz60O/Tt6XXE10ZfonP7D+vrMeon7qAEOuyY9WHovlVGGYpNzcXgUAAderU0T2vU6cOtm7dyvwmJyeHmT4nJ4eZvqioCE888QRuu+02ZGRkcGl56aWXMH78eNPzGTNmIDU11a4qjjFzplWcplAX/rT2CH5aewT/6u5HogfILwm/mzVrFqom8nPYfEQB4NU9o227oomdeWG6fpv+O5K8ACH6BXnlqtUI7rM+sRw/7gEtGM3NPR4zmqMB6z6MPg4f1rdPrNumrOsXD8Sjjvv26fsx78wZR32Zc0T//dIlS5HLiWE9c+ZM7MwHtPn523QkVA5eSUNZ9uGRQkBty2nTfoM3ym254aR53S7rMbpjf3h8ldX6G+06FhYWCqWrMMxSrOHz+XDzzTeDEIL333/fMu3YsWN1Eqv8/Hw0atQIAwcOtGSy3NA0c+ZMDBgwAImJbG5n1JIZut+XXt4fNdOScOxMMZ5eNQ8AcOWVVyIrPZlbTu7S/fhhr57hzM7OjpB6NpbvPYm3N68EAAwYOBBJHoL/7pytS9O+Yydkd6xnmc/XOSuwIz/stbxmVhays7tGn+AIIdKHscCMM+ux5kT4UBCr/oxX/coS8azj4l82AccOab9TU9OQnX2p8Pe/5a8DToYd1nbv0QPdm9XQpaHrV+vQWby9aQUAYOCgQUhJ1G/GFRXx6MMdR8/i5XWLAQCX9R+IqinR3W6TthzDv7et1T0r6zG6bdZO4NBuALFbY1TEqg9VzZAdKgyzlJWVBa/Xi6NH9Z6qjx49irp16zK/qVu3rlB6lVHat28f/vjjD1uGJzk5GcnJZuYjMTExJgPVSb6JCQlITExEQkJYPOotfcaD12M+8sRqwiUmhPP1JiSAuRYrHtvyD5wq0v32CHwTT8RqbPCgGET+sS67rOsXD8Snjvq5SeCsLxXDNVmP18v9PlS/8JYQWjcqzBYhhLLswwSq7S57fT7WPzsoqvl7vObFs6zHqIfaO8qq3GjXUTSvCiNkTUpKQpcuXTB7dlgKEQwGMXv2bPTs2ZP5Tc+ePXXpgZAIj06vMko7duzArFmzULNmzdhUoIygKq90BoXl1AYvyDHwtrsN9/2qgzh0+pzu2flkaCgC2RqVA8ZxHfFVbQffS/vu6CG/KPrxOY39E4/+Op8uAVSoY8Po0aMxbNgwdO3aFZdccgkmTpyIgoICjBgxAgBw1113oUGDBnjppZcAAKNGjUK/fv3w+uuvY/Dgwfjmm2+wcuVKfPTRRwBCjNJNN92E1atXY+rUqQgEApo9U40aNZCUlBSfikYA7To+tSqWJ18YuoOuyiwZPXjbEPzm7O2mZ+fRnI0IvkAQ87cfR7dmNZCRUrklQZUBxqkQ8W04m/f09DyfNsKKCWLxKx4UVG5UKGbplltuwfHjxzFu3Djk5OSgU6dOmD59umbEvX//fp1YsFevXpg0aRKeeuop/P3vf0erVq3w888/o127dgCAQ4cOYcqUKQCATp066cqaM2cOLrvssjKpV1TBYEDKq9QltBgrDMmSNb2R+po5n/HmrB14Z85OXNy4Gn56qHeZlTt55QFUS03CgLZ17BNLaIhUemA8eDjxs1SeDlkSZpQHydL5xE9XKGYJAB5++GE8/PDDzHdz5841PRs6dCiGDh3KTN+0adNK56+HGP4HRBbIsnNLaT65mssW8bNUnrH7+FnM2XYcd3RvjLiZx3Ka8LuVBwAAa/afLjNSDpwsxJjv1wMA9r48uMzKrQwwrk8ioYBUnCwowczNeptN+4OT9OBdUWCSOsaBhvNpjFQ4ZknCGmGv2JRDNJtvymLAB4IES3efQDHllyPIkIIBgN9GdFTe1QNXvB66hVhY7McDfZvGlxgD4iEtOFFQUvaFVhJE0l0/rj4YWdnle5qd9zAyvnGRLJV9kXGDZJYqCAghQhIgwmBAguVAnv7fZfsw7pdNumchpsh8x8DOZom1KJTHhX3dwdNxK5snQSjvjKaEHsb+ciIEZq0XztRwcqxEgljL643dE2/J0tH8ItTJSIkDFWWDCnMb7nzG75uOotuLs7BEIFhseV3efl5zyPTsxvdCPkhMNks2ariKsoinJpW/s0g82k5n019B+q68IJLmYjmMtpcyh/8uB2csCQuY4gbG2Wap+z9nY/fxs2VPRBlBMksVAA9/sw65Z0tw16fLbNOqUiSdgXc5sFliufo/nFfESCkgWWI+K38re1py+WOWnNi8xAKSV3KGSJhb1qy2Z1bpeGays8ozjP0TH9cB+t+zthxlJ6wEkMxSBYIvYJwc/NlBMw/lgZGwYshU6lR+ym5DL0s1XCQbRnpy/Lwf88iOh0pWqnbcw9haTo41HsYBxa716eEhe6piIT6uA6LsB6wcQzJLFRisgcmyWSoP+5NVDEmVvoTS4En2NktlU6FTBSW49JU5eOm3LcLf+KjAp+VTDVf2ZSrUFl+ZF9NYwCQ9cPAt84Bik4FeDee+szYczMPNHyzB6v2nXOchYQ2TzVI5cB1QmQ9DklmqwGANzLBTSut0NMqC+fAISJaSSpklOw/ebDVc9PHZoj04dPocPpy3W/ibs5Sn3tSk8hdXKxAPmyUpWXKN6Nssia8FkWy+N3+4BMv3nsSN7y92n0kFR6xHunEuxUWyVA5UgWWF8nf0lRAGS12l+Vly4DqgLMCyWTIiwRtKYydZKqsN1w1jcYZilkTqHCvwSJd2KBULkYx11gHFmF1hiR+r955khh6KRGV7zhdglnc+IdbrVLmQLBl/V+IOl8xSBQbbdif0UGd7UA4MvC1tllQ1nEeVLDm3WYoFR+hm3qubBBBfY2qeBCHeBt5SsuQMxuZyZLMkkHjEZyuwbM9JDG6k4BpGeRLuEeu2LG+34Vi/KxOkGq4Cg7XxsOVJ8R/BIgt3oqBkqaxOL25KoR1qxkPlZYd42wzFu/yKhkjaS2GwVsYhuWzPSQDAkmOhrUAfU1J2ViSIObNUDtRwxjFSmee3ZJYqMFibsTqByp+Bt73NkqaGC9jYLLEkauWAIQT0cevKgzPQ8gBpsxQJ3LeXC/tuXQI5fCNDzNVwpvJiWpwQDeVlHY4FJLNUgUEYPIV2G456Vh4WPSHJkkfwNhzrWTlRw9GSpXi2+6kCX/wKN4CWcLDGrAQfkYwhts2S+NyqjIztiYISnC6sJOF3jCqweJBQDuymygqSWarAYEqW1P9pyZLNNCqLOLpWNkvqBEv0itksldUi7uaURNMWL/ugg6cKsXzvybiUzUJFkSyt2ncK//fvZdiWcybmZQWCBEWUfRsPkaicPYzV3S638iaRjiZKAkCPl+ei03MzdS4+YoXY2yyVh5toRhoq2aChIJmlCgxr1wGU9ZLN+C2L8S0iWRK9DcdWw8UAbiRLlOPQwhI/cs8WR5EgMRgjzccbFYVZuvH9xVi4M1fIU36kyH5zAdo98zsKiv2W6aIvWeKn9wWCOEvRU9k2vnxK2HrBU79hy5H8mJYX67FuHBvxOJsZvbxUsiGjg2SWKjCYBt5E/7/x73hBzGapVLJUTmLDuSmFliZ9vGAPer4yD3llLPW3aut4I15D8VyJvRRHxdH82DO4246egT9IsPbAact0kbQXW5rLz/Hqtxbjga9Wab8rs0qFEOD5qZtjW0ZMc2eowGJcHpMGo2RJ2ixJlEewfDeymKXycJoX2cATPYKSJdazclBHgK0a3ZVftswL0xlhHNtHZ/QeBzqmrDuMNuOm48sle8u8bDvYS30jMPB2WN6+k4W63yanh4Rg0+E8R4xneUaszxSxnnPlQQ0nbZYkKgRE1XDlAaw4VSrUaiQKevAuq6q5WexYjF5Zy3lYEoV4LmJOVMKxwKNfrwEAPP3LprIvPEKY/Ng4+DZSCaOx7P9tOILBby3ErR8tiSjf8wWxnnOxkCwdyy9CiV88J7MqsHztO9GEdEpZgWE1MJ2o4cpCaxNNmyVr/1LRg5vFjuUuoKy1YqxNMkgIvGXOtoVQ3qSc5Ql2hxqzHxvx9mOHOxGHsaxvVxwAAKw7mOcgl/ILlh8qJyj2B5Dg8Vh46i9b1wGRTq3tR89g4BvzkVklEU1rpuKJq1ujV4ssGxpiK90ihOB/G46gfYNM1M9Iim7mDiElSxUYVmo43TOXkzaaYmTeKZeQMHWR3IYrP64D4i9ZYq3d5YVJiYeEK45RZyJGJGoOpp8lzvfsaAD63wkVuSGjjCJfABc/NxMD35jHTRPzKRdlp5TqxZC8cz6sO5iH2z8WuOhglHxGudL/23AED09ag36vzY1qvm4gmaUKDEs1nAPJEuv954v2oNuLs7D9aHSuUfOkK4RQBt6CNkvlWS/OkiyV9R7j9BYUjVX7TuFoflFU6dFJluLQeQmMO/T5RT6cLCj//nbMdkPi37LUsbyDE6tbjGV7Wb4IKjAikfhuPpKPwpIAdh0v4KaJuRouyuU1qpEadxqAEMO189hZ+AJBrN53OvIMo4TKNfrPM1ipoyINW/Dsr5uRe7YET/280S15OvAkSwFCwjZLCWKSJRZisS65kciVB8kSq6lFxsC6A6dx4/uL0f2fs6NKT7xtllhqkg7PzkDn52eisMT66n68YVa1iDeg1wHTzLqAanzkRrJUWYVRIvZgpnAkUR78ZqljZI3t5iATCTPPw5R1h9F/wjyMmbwOSQnlh0UpP5RIOAZbHcWQLNnkYznvY7y50fNTuw1XBg7jROBm4rP6pHzYLNl/t2T3iRhQ48xBaixgZJboTevgqXNlTY4OTm/DOTn4OAl3wmKWTJIlrxtmqWJzS1PXH8aL/9tsYiREahVtmyIjTIxKhPnZSfRZMDNskVfy4wW7AQA/rz1crpglaeBdgcEa20R7F9/TvBG8U0swGN4+VT9LbiZteYGfseuUuc0SY32xWsTenLUD247mo229jJjQQ5ddHmyWaMlleZd8RGKzxLZDElfDGdO6kSz5gwTHzhShdtUUx9+WBzw8aQ0AoEuTGriqXV3tOc0EBoOEeds32syMEdG+tu/mkBqL6dyyVjo2Hgo5DE2WzJJENMBSV2mSJf1T94VEaTNh+R8C1AUlVEhi6ck1XmFCjHAjNmfRXi5slizWwTdmbQcA5J6NjQ0P3SJl5lCUEM1mR2XCwzTQv+LLLdm1BsvXUbTypsGWLOl/8299WeNvk9fjP3df4urb8gLVE/+5kgA+mr8bDapX0d4FCYGHNY6Yxs/RG2/Rllz5oqCGi8b8bpqVpv1dntTkklmqwKhIHrx5DNAN7y/FnlKnjQmCgXSZKA+VBJspLPvbcPanXBZitTDpx2Js+4kQguGfrUCxP4Cv7+0BRVFM7UG3RSRaorxCH47kn0Prus4kcnQb3PeflbinTzOMGdSandb0rbty7MC0WTI8c3sbbvPh8uFqwFgfq3iVPEycvR0fztutexYghLmRmvrOcWnWMKloI8zPlflDDNRwidThRheEO85rfPmRcUk4BttNgPmv8iCo4U2iPSfCXoNFXQewwPqisMQf0Y0nKyrenLUDf5602qReZBp4lzG35NbAm6VCjA7KbiwWlAQwb/txLN19EkfyQrf6DIIlPbOE0DhxsxB3f2kWrpq4ABsPOWMG6KKK/UG8O2cXN220nf5xXQcwWHrzbbhyrrO0QSQtp367/oC5r0XdMcR6r480fzfzPxZ+lui5mEDZyZXEbH0Sg2SWKjBYTIW6eUfrNB+t5VGEAUrUnFK60J0zsu/03Ex0fn4m8gp95pcu81Txxqzt+N/6IyajaKZTSlelu4eIgbdbhsop8gp9ZeqUkjCkRkbXAXRb7D1RgLbjfsej36x1XFaRLzRO5+847oxGR4ndK9RZaZ0Y2EePWSofTFY0Rh5r/PLWNrPNUnTHvslmKcL8nEr0/YEgpm3I0dMUIQ2Avl60lKnYF98wO1INV4Fh7TrA/CyeEJHwqqcIu0C67PzN36hu+7fk5KNH85qO8xTZ2IsME5gtWSrbHhCJDcdSqUTbVmzF3pMY+sESXFAnXXvmgg92BFYNjJs8Xc/PFu0FAPy67rDrMp3e+HJyeIlEsiTiaNLJ9yx/VRUJJjWcmzwYz3h9Em2bInP+RnuhyPJzqoZTJbc0oqEq49Wj2EEYlligYo/+8xzsGyz6/41/O0W0VEgii3xiBLfhrPIvS/VBeZAssUo0kpWS4DWlMTJLP6w6iCkRMBETZoQMx7cfPas9K0vXAaq9g5XrgGjA6fByJh2KZENkHKYiYJbodiwvlzCcwI7icyUBHDxVyH6pLazmV7wDQKxtbIxdEGlpTg28fQzmKhqHIXrM0+OsyB9fyZJkliowrP0sEdMzN4g0fpIKMTWce5slI2imxS2zJEKFsWnLh9sDMw2+QFA3DpITzVOfpv1kQQn+OnkdHv16jaPAmjRKGIvpzmNncdlrczB55QFXedqBNdSNm3y0N3rnkiXWM44qJyiWTrgc4a/N6wstjWRtlOUdZgNv/e/+E+bh0lfmYFuOOWqB+qlV1AS78qIuWYpy/kbJkt26yVrroqFmp7Oly1DV3vGCZJYqMFhSjPKqhhOZRKLhTlgwZu+jdhmWJ2M3eTLTGH6XdQw2fyCIuz9fgbdn76BoMKfr8+oc/N8n4VhPyTaSJVot5bZOrA318e/XY++JQoz5fr2rPO3Aslmi13xfIBh1I3Ont6pY0jUeTZGocpg2S45UgHybJScqkfLgl3LT4Xy8st7a6uTQ6ZCD0pmbc7hpWK3Hc4tiMn6Ots1SlNVwxkOEHbPEmt9RqSHVngFqHS+WzJKEW7AmqRs1nNX7aC10Iqf5hAgkS8aFnb7Z4V4N55wO1o2SWLJPMzYfxR9bj+H1mdu1ZzzmZtHOsDE6S7JEt/szUzZFTBtLIhVruwPW0KGlo0YJ27mSyEX7TkcXq3t4lxrcevA+dPpcxJIl4/e0BM2JZKkc8Er46/cbTM94dFnZerGYzXIjWYowP59h7Uq0kyyx1roo1JErWZJqOAm3YA1MLZCuzomA9QguC/8VTm7DuRHxG3P3R0MNJyJZMiQKMDY9EmHMJisYDcxD5dl/l8gw1o22eoqlhqPx85pDUfftZFcHf4DoDhkr952KuEzHNkscu5ezxX78svYQzhT5uGlFuujLJXvR++U/MP7XyBheK8lIRVPDMW0JOSdBqzZmBhzmNEW0bYrsELGBd9CpGo611kVeS53NEsWQxdtOTjJLFRhMD96mPwQkS1GjiA8xNVwEfpZMkiVKDVeGBt48kXxZQqStWQw0T/0ZTTUcjce+XYvxUza7ypsHehyodNP0x0INxwp1YQV22wcx+tu1GPXNWjz69RrtuZVdIg//nLYVAHDsTDGrcGEYbzvR3enWjq0iwOpwyXrDlywRy9+Rwix1jCw/o2TJ6PneLn2IhigwSxzJUlmbOBghmaUKDLYaTpUshRHJICtbNVzYZom3sPCf63/Tk8xt9d18x2I47LLxBYJMCVEswT4hi7WtKHx++w9/Xe/+th0LdBVUuuk+8QWJq+jqVnBss8SRTszYfBQAMGdb2G8Ti1K7/rAix4ndzD9+2og1+8OSN3ruWTFL8fa0LApeM1nGyXPgZ8n4NNpMerTVcEapuK1kicksRUgEjGq4IPN5PCCZpQoMKwNvXSBdm3zKYm0TcZ2URJ1keBND1BCWlmq4NawU+c60ILpglvpPmId2z/weFfsZQFCyxEjj4+gT3DLbdmo4AEhJNBuaRwJ90N7Q3/SC6/MHo3JCjSQYL6t0ns2SSuuHd3YxPePBihynVf9q6X7tb7rOVhLUcwbGv9gfrDAMFADLRnLCvJrqHOUmMPvgiiw/I/NjZ7PEWi+iUUWe6wApWZJwDaYUQ3MdQD+0zqdM1HAOXAcA9psHALxyY3ttozKr4ahJ5lJjIGazpP/NVGXZ5LPvRCH8QYLNR6ITQ0ukvqy6iZ6QRSFi15JkI+p3gmCQYPmek9pvTbJEjQV/MDpqOHp8OnWv4cQLtNr49LX9SOh3+indhyypHQv55/R2aHnnfHiEUi3GA06Ef0yGSP2fNW9EDbxjfBsu0tyNfpbqVavCSRkCW7IUZTUcVUa8+W3JLFVgMKUYqrSYfhaBgXe0/CwJ2SxRcYB4mwf9PLt9PUx+oCcAloF3NCRLzuFGsqQiWldjRcpjpeEySy7JEgm8yrqV5xbfrjyAx75dq/1WhxxtW1Hij46fpYgkSw42XHXeeHTMko1kyYIzcLrh6JklsVN+fpE5vNDU9UecFVwG4DWTVRux3T6IHTIqnJ8lGw6T6fE7CnWk9yMpWZKICtiSJfX/yCUrQNnaLCVSzBLP2JieLyGdeugb40TyReFEIvadQaIVwUZcHKUbRm7VcHzhhrs6JQpIjViexN3i+1UHdb9VumnG2R+MjkqI7menBt6s5uQFMVWfOvEVZqmGc9iXPGbJWrLkLhZjecE7c3Zi3nZ2vD/WWsqTmptjw0UXxvwiXT3UNfOSZjVC+dnME5bH71g6pZQ2SxK24K2TVt5kHWjhygRObsMB/Phw9Ancoyha2xizp5kz18ySi5azvKFoAzeSJWbdBAp0svC4XaREmKVoSpZ4t4PosRSt23A0c+PYzxKjg+yCsXodSJasCHI6F2i/WKJziiVZKp/gN9SwT5frfrMk9iqE1XAxFi0Zh9BL07Zg+GfLhSWpqoF3RkpiaX7W37EkS9GoIp2HbszFmVuSgXQrADyKwpyQ4q4D7NRwERAnCJEJ6/WEmB9CxK6xexRFc5RnrEM0DLzdfMasp2A+IgbRInDrOoCb1uUAoSWFPCQnRNFmiRh/hx7Qhqi+QHTUcDybOhG4sXvxOrBZiqajDK7NksX4ySuXkqXIWkWtLVMiy4sNZ/PbLQJBgsISv62a78P5uwEAS3efQO+WWbb5qmuueoCxG2exslmi85C34SLAu+++i6ZNmyIlJQXdu3fH8uXLLdNPnjwZrVu3RkpKCtq3b49p06bp3v/4448YOHAgatasCUVRsHbt2hhS7w68aW6phtM5pbRGWQQ3FRnoCsJ2LtyTtsFWRG0bk4F3FMS3Ip8Z14YIeKWo+a4Rqa+Tvd5t+4nETGOFXXEL3uZB1zUkWYp8vEdiS8GUTtionWlmyY55tbRZsqVOD5/uooTYnDIaeJcPxG6NE/ezBPzjpw3463frIirvxvcXo/2zM0x+sHhTWtSBqJouuVQibDfOWLfhos3Q6C7qSJslcXz77bcYPXo0nnnmGaxevRodO3bEoEGDcOzYMWb6xYsX47bbbsPIkSOxZs0aXH/99bj++uuxceNGLU1BQQEuvfRSvPLKK2VVDcfgquEEb8PZjTGr9059yPAgdJpXwpsCb4LT2aiSKMC8FOokSzGcZOYN2pkajk5f7MKdP6t7os38nioscfWdCBUpMVTDqb91J9UAicqiq1/EnX0rchtOZZzVOtCMZ0QbksO6822W+PlUFO/e9Nyxl76b11UVouFOinwB/HfZfvyw+iAOl8ahc4O1B04DAKZt0BvNRzqs1TGoBTS3VcOx3kdbsiSZJVeYMGEC7r33XowYMQJt27bFBx98gNTUVHz66afM9G+++SauuuoqjBkzBm3atMHzzz+Pzp0745133tHS3HnnnRg3bhz69+9fVtVwDJ5zMCsHiHpmKc7yS4gxSwoUWy/e9HPFQg0XyWamwk27Of2CLqIsJUtO6jbwjfmu6BApIxLJ0vhfN+HqNxdoDj3NV7VDiIUH70gWcRG3DRc+/Rs2H87XaHUiWbIs22F6mkmjN0+rNiwHyw0D1oc+0THhxN7MHOg2/Ds6qmDD4SDC/FSa1FvJdtJnZiDdKPQ912YpzuOqwtgslZSUYNWqVRg7dqz2zOPxoH///liyZAnzmyVLlmD06NG6Z4MGDcLPP/8cES3FxcUoLg6HEsjPzwcA+Hw++HzR09ereekCgVL5+/xmcbff74fP54OfeucrfcaDP8CXaJBgMCp1YsVMMyIY9EO1CS4qMbflbxvD0cC9HkVXzyAhuvRFJeG//Tb1F6GZ970xb1ZbEovvaelgYbHz8ROgylO/DTDGhek7Q3vZgZdWfc56L8JEJHn5edvhs0V7AQCtn56O6Y/2No2xkhIfSkpKdBthUYkPJSX2DJpunjHqWFRcQr13Nr5YaenxCoQ2hpd/2xxmjILhfi4u8cGXyN/8rdgCvz/giNYANf8DAf184I6JAHv8RXNtdA6Wfjw8B1gHFZreQCDUbiwmh9f/PoNzzhJdfu7WJBpGWoJEYeap0m4H1WBbNTUM2qz9xT7G/hOIfL+g11Af/bffj0REfxyJ5ldhmKXc3FwEAgHUqVNH97xOnTrYunUr85ucnBxm+pycHGZ6Ubz00ksYP3686fmMGTOQmpoaUd4sBIN+qEvg1P9N0/y6bDiiANAv/KtWrwHZT7DhZPjd6jWhZzxsPWTOR0Vu7nGTnZcbFJ7zwu50t2zpUgT8oXRz5s7DtrTQ8/wS4Ke9Hqw+QQlCSRDTpk3DwQIASEBRUZFGZ14JsCs/XKdFixcjp1TzeqgA+N8BDwY3CqJBmjXNhw55oApfzW0QmjrGtqW/CdMKzJw5k1lGaL0L5bVx81ZMO7PFmigD1h0P11OlkTUujCg6V0TVyX4ZsBsDrPoVFNj3ec7hQ5g27YBt+WyE6X7k8wUo9IddSQDA/AULsCNVn+7LP9Ziy2l9OhZY9aXreLggnO+GjZsw7cRGU3oe8kr0NAHAwkWLTM9yjh3HuaIQrYsXLdTez5o1C+mJ/Px9Pn67b9q0CdNO0rRa9/3JU3laWxw4GB7bi5cswVFOnF7eehKNdcQtWGMxJydHoynkPF/fFqF3oWdbtmzBtLzNOHPGnM/CxYtxhNH9647p22Hu3Hlafn/8MQc1U8zfrDiu4ECBghuaBC3ctoTyMDNLxnkYSrdixQqc3WF/cDl5OlS3g/v2AvAgL/+MZZ9tPmju56PHjkXcz/v2h8fZ8dyTUNt73foN6FqLv5a6RWFhoVC6CsMslSeMHTtWJ7HKz89Ho0aNMHDgQGRkZEStHJ/Ph5kzZyI5MRHFpae1QVddpemUcxbtBfZux5AO9XDsTBGW7jmFTp06IbtDPSRuPgZsWwsAuLjTxchuX5dbzoH5e/Dr/h3Md7Vq1UJ2dhfmOyd4bv1cwGdt+9KzZ098f3ADzuYVoVvP3ujYMBMAcNdnK7H6xEld2gSvF9nZg7A15wxeW78EScnJyM6+DPtOFqL/Gwt1aXv06IluTasDALr+8w/knfNjb0Ei1j59pSU9M86uB06EGOvs7GztOSEEo5aEJqyxbX8/sw44cVSXDwEwYMAAJCaadzh/IIi/LJ0FAGjavCWyB7SypMmIkrWH8dXOjToajy/ZB+zdZvmd2l4AMGrJDNty6PrTUMcoq36vbZkPFBcxv1PRrGljZGe3tS2fBZpuJSUDVRUAhWe1Z70vvRQX1E7X2hcAVhwXszyg68uq46bD+cD6pQCA1m3bIrtnE2G6j+YXYdwqvWrzku49gY0rdM9q1KiJk4ECwFeCvn364NX1IQn6FVdeiaz0ZG7+z66bgwI/+7TcxkCrXd+npqcjO7s3AGDGmfB86N6jBy5pWsOUnhCCqV+vA2C2I+WNobLAmzsWAuf0m2LdunWRnd0JAFBQ7AeW/6F7f/XVV2vzvE2bNsju3RQTty8EivT58Nri3OpDwK4wR9mvXz+8uHYRAODyyy9Hw+pmD9mjng71x//174LLLqjFrAuvz4zrjJrukm7d0KeV/W249/csAQrO4MJWLfDHkT26vmdh5x87gQO7dc9q1MxCdnZX27KssPiXTcDRQwCAjGrVgDOhyAYXtWsHHN3AXUvdQtUM2aHCMEtZWVnwer04elS/ER09ehR167IZgbp16zpKL4rk5GQkJ5sXq8TExKh2ograbsDjTUCiGk9LCS38CQkeeEptfZbvy8MNXRrD4/VS33gt6fJY+MPxeDxRqZOISiYxIQEpSaXSoF2n0LVZaIIv23PSlNbrUUrbOzyEExMTsXAnIy1V/7zSmzoFJQHbetHG7XRaWnXmTTC0Lec4yB0bHkq1EYTjtvZS/ax+6/HYq5kIFEdl2aVl1Y8IXNeO1vhqVacqdh0/q3vm9SbAk+BuiWPRpKsj1caK4qwO3gSz+kJh9FmQKJqdRmJiIjxKSHrg9SZYz2eLSxkej/VaYESAGpOEypZHw7QNRzBzC/vCTSzWRnGY28TjCc8BD8MSIahQa6jaboy2VThtOm/HCX153vBYTEiw7sOzJUHH7RUk7HloV1b4+9BgSypdUwms+yzIaNMAibyfFYXyt0dpR5XSPS7a+6xoXhXGwDspKQldunTB7NmztWfBYBCzZ89Gz549md/07NlTlx4IifB46csr6PlJG/Wpfyd4FO12xdfL1cCX4gaolrfhnJHKhZCBtxKOFfbGrO3IKyy12WIsUOqGoNpzqdmz0pap6wCHEQB0Bt5l6GepLFyVihgiRyucDsDwmExiZxRK20c5LUPEwBsI2ZWpTz1KeMzb+lmKoqMlnSE7NTx5Y+yXtYeiV3iMQY891s1itgEz41IN64ZckOhsLAE4CpQt4nZDhA4nUMdgUoLqOsA6fdn4WYr8ok60UGEkSwAwevRoDBs2DF27dsUll1yCiRMnoqCgACNGjAAA3HXXXWjQoAFeeuklAMCoUaPQr18/vP766xg8eDC++eYbrFy5Eh999JGW58mTJ7F//34cPnwYALBtW0h9Ubdu3YglUNECfRMmwPB74vUoOHamWPdNvAeWEWL0KLqJUlDiR2ZqInM7VZvEGEg3qlfpOZ8RiySsxcJq/aBpi1ZsOBGo/RHLm5JlOQYJCDNwaayuG9P+h+yuWBvBSs1ycrlq3ylUSw2dehUFlJsMgmCQlD5jzY7I/Sw1qFYFh06f03lp1tWznK0vdmA1E/2MxazSXaLOU1a1Wd+eKTZLD699Z6HpGQ90NANR8FYPUb4r7DqAHULKCB+DWYrGLT/9bbiycQEjggojWQKAW265Bf/6178wbtw4dOrUCWvXrsX06dM1I+79+/fjyJGw74levXph0qRJ+Oijj9CxY0d8//33+Pnnn9GuXTstzZQpU3DxxRdj8ODBAIBbb70VF198MT744IOyrZwF6LFOL6rq4uX1KLqTCCH6jcPezxI/QVn6WVIU/Qar2maxSFDjcRn9LLGoVavHi/fEA4/Jsmovp2sFnZWbjd2J3xf9d4T7fbRQFs5OtbIIi3GNzuLNQkROKVleoDl5nKakq+pcLCwJoN+/5uDBr1Yzv7GasqIbjupZnT7Z6+JN8g4SFYyJUsGT7KnQnP0KzrfTLn2TqRCIFGRCpO5i1Pqq667duBbxF+YG5TU2XIWSLAHAww8/jIcffpj5bu7cuaZnQ4cOxdChQ7n5DR8+HMOHD48SdbEHPRjVv72KXpkRCBKDB2/3arhoQeT0rUAvDreiOxxYVPUJwk+rFm2M9+QW1mq10NtRV7bCm7N32KYXzdcJhDx4l0Gfl+XiFiRmKVLoWWzK06unnDJLjPw4sRBVKAhLUedvP44DJ8/hwEn3jg3toG6YfsZ6A0TOCG/NycfwT1fg0Stb4fbujSPKyy10kiXBjd8qHieNU4XW19HtlkM3h1RaskTnL6rqVrUWCSqzZCPoZjFkkQQS1/IFe8xJp5QStqCHCGvx8tBxP1Bq60BLLCLQ7kTL/MG4obRvkGlKY4yBp/7JmuzqYsLz4K0r28UkO5J3DtM2sF1MWJ3g1LIaVK+iRe8GgDUHTuPl37ZqDhSjRacdffw0fLVCtFCWaxvLPin0TP+Q5+DVKQJRjllldyK3ioNohFUNRfskMSGUi58TGy7SOr/821bk5Bfh7z9tiCyjKEGUMWLbJ5mfRSxZitBmyU33aJIlj5gajjUGoiLJpSVLUXAuHC1IZqkCgOf5VR3cCR49OxEMWtvVGBHJGJy//Tj2nSiwTWc8uSUwgqwqirjHVlVMrW4gWnR0xiLjpn43f8h2dGoHtSwPJe0jBLj5o+X4YN4uvDd3lz49Yf8dCcQC6aplxm4FEslbREIRDBLkF/nw2u9bsS3nDCcfloG3OWhulHiliGJWMdUXNnko1HnIrjSrffbFaWJ+vFiSJZpubogPodyB1KToxQSMBlgMj16Sxger707bSJaAUNiSScv2h8Py0LdsXQxUugp0/zi3WRJTw7HmbjRC3fAC6cbbZqnCqeHOR9BjRCdZCoQlS7TY1h8M6gaWfdwjd3St2ncSd5Wqtva+PJibrsgXMJWRwFgMFCiGYJ2aaMmE8G24EEr8Qbz02xY0qm52CupmklmpOKw2eJV8j8KWehmvt+vUpS7odGtSVhbrjkgRdnTkF/kwYMI8HM0PXWB4d84u5lgz2ump5RtPoyyjVDfQMQ5RUMPZncgV0JKlyOpQ5AsgJdGaWUn0WDNLkYoka1cNe2QkhETNNtIJ6COmqBqOfRvO/CzvnD2zdP27IZ9L9aql4PILa+tocMMsRXrw0pilBJVZEi9PhYga7tXpW1HiD+Kpa9j+1egc9Go426xjCilZqgDg3Q6gJUv03DKekuwlS/wUVmvYugN5NjkD78/dhdZPTzc9Zy0GRgNvK7o9BjUcAHw4bzf7Nlw0JMMM9SArbzrwqboY/3E4PM2MV4JjccFIZPN2qoZzFSdP4Bs7Un9afUhjlKzLMp+Eg0ESs9NoQHeN3tm3rOS2zJIizhhHwx0DSw0nYj8i2tx1M8PMEuvmWFmDaeBNPftjyzEs2pnL6TvzMydBsXcfLzCV50qyROi/KcmS4Pea6wCvGFPOemtne3euJID35u7CvxfuwbF8tsNavoF3fLklySxVAPD8TugMvA0BL3XjKkZjjKVKM+KV6exQNLyrsbpgnaX1Y7oOMKjhVLA2imjcyhKdp2pf0WQdKKCcihrI03VT1NRw4mlEy3RDmxgTYZ3II7hpEDAYVzi/1i8KOt935ux0pH5wYxirKOJtEQ0hTVgVE56HuoMMh1xR5rQKJdnKE1BZRQpmk9AHTBvV6PK9J3HHv5cJG3jbSTDpNUklgx4DrvwsOf5CDy2Qrk0wc608xmu7eUDXm+dXjh5DtKucOPNKklmqCNBx2rR/F9rAm4LxNlwkTimtziWRGMtyJUuCx3SeASTrcSQG7ipEmRq1LI+iMJk0s2SJWgwsyg8Gie6UbwUR5tApA+nOrYGIhMv6vaiha0gNZ5AskdjdhjOO0+UML/M8uJEsGW3grGDXYiJ9SR9m1E2cqSKn8MnCPZi9le292wiepDYaOJJ3Dnf8eylmbBKPAWonWbJ65uYKPf1aHeI0Y+BmaeVJlkRFS5qBt6AajjWSnRh48xnu8N9SsiThCPTGxho8CR69M8dAkDhS71i9t9qrWHZHomANfAUKxl93kSmNiJ+lcB5mRGOK6Zka9t+AnmbW3DbSRyexWgyGfbYcfV+dg8ISe5WFyHqlFiXKNLlhOkTWNltmSXCFImD0MxFnvp3CuCk4EQS4sllSwox2pHZuIp8kJVCSak2yZM3YPz91szgN1N/R3gTH/bIJi3aewH1frhL+htX+LJWSKLNkd7Ch+y0sWQp/48pmSZe/488dO6V0I1kSucAjXQdIuAbPZslPGXjTm4JRDWc7xlwOQvr06XTRLvGbJ5WiANd0qG8ii6VaUzcOEXF1NCYZLwezzVLo/5BkyQxLQ1YLMhfsyMXhvCLM28Z3rKn1gQOJjrAazgXLKfKFXd+IqiOYNkuMZ9GCcdN0ctWbNVeEJEuMMljfGdNlt6+LtvXCAb6dSpZ8pWtOQLemRE/SGM0e+mPrUczcfNQ+IfQHF1HpEPuZOW+fnQqLpqO0v+i83ahS+TZL7MwOnirEVRPn47sVB3TlqypYuy52Y+Ctt0llp6U1ATQDGQ0NQSSQzFIFgM5miRFmwaso+okS1A9DW6eUFu+s5ixts+Q0rhlrUqkbY9Xk0CVNK8mSujmZJEsxM/AOLfYv/bYFU9cd4aZTaeYdDE02SzzReSn8gSC+WLxX+20Ma8PKS0iy5HCLcmezJMC02bwXPWEHjXZ6CNUxVmo4EVcYPLBIsr8NR4f2CT8XqZ+iKEhODC/1It8kUiK9OaWqtUhVZzypwicLdzNSu8Pdn69kPrdjPpiSJcbuzGo7N2o4ui1PFJSUlhdZ+zq9LPLC1C3YmnMGj/+wHoRSWat9b1sHRil2Bt5CXuDBThNv1wGSWaoA0EuWzCJKr0EN5zfcAopkjFl9Sm9kLEmRFVjiWnVBE3E0qaU1sHNMA++oSJYIZm05hg/n7cbjP6yn8tanCzN4CrMCJkmJjQTw25UH8MyUTdrvY2f0N0hYC6QIk+KUiYhWKBanaZyoI4x1oiVLkaiMWTBJlihJzIGThdhxlO0PCnCrhlPY9ngsdbbNgUFkPtDqz1HfrDXR6IYJ5d10/WrpfiH1ciRgqsSphmIeVARVbqJBeHn0vDV7B04Xlgj7deJB58Gb+sFjFM9RDnLpsiNRw7EYTB2NAuOQN7ak6wAJW9jehrOzWYrAwNvqW1r14JxZYizy6v8GT8Wsua5uoqY9kLVRwBnDxIqcTgiQk2cfXiLsZ4lj4G2YcXZhaTYdztf9PnGW7xnYiTsAp7HhYidZCqdZf/A0tubo6yuqhguVpS+PEKILNh1NGOtGZ9/n1TkY8MZ8nCxg9xXzRO7IZolPh5pW99vwjUhfsg4dXANiQehsngzfRyNMRiRg8TbiNkti39Iwvt10ON/WnocQgs8W7eHnqWNGRRhitp+pJFE1HOMZfXuSBSHHplxmSUqWJByAZfAWMvCG7rl+E7aGiJNFu3fFDpkllgGkusiH1Q38wj1h0ZItQvYs4rSpJ2kjSgQcGqopeHvzT2sO4Y2Z23W0sf5W4UQi4kSy5HTZcSVZEklTmiiv0Ich7yzCVRMX6PpdlMlZtPMEcg2MJN3vsZYsscbXXo5ne1ZTithuscKdMG2WjNJWw20Dkb5k3yqNTPIRrb1u4Y5c5oHGCm7UcKJuAljuKextd/TvfYEgN2ixis8W7cX4X81G9Je2zArlqcvfsngA+jWKrn+CqAdvzmufhXRJf6jn5cuTOElmScIGPCddXANvk2TJOn+r91YDlF4koqGGUzkfxXCCZhm2arfhBLglVpBVN2AxeMZcaaeUrCKLfEG8OXsH1h88bfqetcAZmQXriPIcoizSxvY2nADTVpomh3JQR3/mxt+MiiAhOulrNGHcXEWNgVW6jLCTRAC0eppmfOxvXikwSoVsi2LOOSvJkAiOWEhmnWT3f58sw6hv1mJvrn2YJSvYGXizGB5Rr972t+HM+VqpYo+fKcZznNuGQzrVN+Up0j8633xu1HCctcNqLPOMt/X5cr6NL68kmaWKAD03HjQ9Dxl465mlaBnGWUqWqJdODbyZajiDZMlqsqppjGv649+vN6UlEPf/wb2xQ9iLJy+QLsdkSUP+OT/je/MXiYa781b1UBcvUcaQFSLEInPHELJZKv2fdy2dJRHaeCgPL/7P/pp6SLIUyi1B1AeBIIxt/Nyvm3A0v0jXn7y+YtssWc8fXyAYZpao75fuPoG2437Hf5bs5X5rjrnoTrLECnLtBIMmzo/oeyNyz9p7drfCtpwzWmBrNmMkZuDNNg53JpXxBYg+Dpoh/XYLGzj1QMFjiHlt7eUwS0mUQ1KrscJ7Nf7XTfh6+X7mO32YIPb3vPVLSpYkbEGPEZ1kSWezFE4TCBJdNG87PsHqtdVkoSdYVAy8DX9Z2iw5cB3ghCnged4lIEJ1pJ1SWkG1XdKxSoyijcyCsS/pYtTv1TRXXVTXmlYizgNFaqPCg5qE50+FJRG65u2F+HjBHqHy1ayiL1nS/153MA+Pfr3GNA+F87Npq/TkBNTNCIUIWXfgtPb8ka/XoCQQxLhfNmHBjpBbCZbNkp2PJCOY/sp0mzExvDPnOrBtHd3vaMTl0/knitBV+bajZ3BLacBslp2NiLQP4NgsOVTD+YNBS5slq7GkngN0Bt42tpCh79jMEn2wsBqWvL3hu5UHMfbHDcx3vCC5+nx55fFpKQtIZqmcw2rS8FQMZ4r0N0t4Yywnrwhjf1yPrUfyOSlC/n2e/nkjczGhF3jHNkssWwuNAQr9tpYsqWo4exAiHvbCShrAYvCMqfVOKe3pZ31Lw8QsCWzAnywMMRLNa6VZpnMicYyZzRJCwXJp+x66qEj2QwLKK3GMDbwBYN3B09zLGDq6HG6un43ohrTkBFzZJsR8rNhLeQunPrvzk1BQa9OdB4Pk2a3NktVmzpIsW7lTMG3ggsOLpiEaXbruYJ4pX1ZZVmAzWs7Ww0CQ6G2WDA1itX6FQ9OEG8RGYA1AH/lBiwah6CVOVmPFDe9ixXC7fV5WSIhr6RK2MA4PPyNWjpFZ2p1riGzPGWSPfrNGKEzDl0v3od8FtdDfcFKMSLLESG9UrWlkMxZFVTIjspk6sVniGScSiN3YUYvhOaVUofYZTRbrIOs1XJ+zq8e2nLC43q5tCMQZJif2Ar5AEF6OzZY5X4KuL8zSjR9d3KwIuCVC9btoXDVRsDbS5AQvV2Wuo4sxMqyY4PYNMgEAGSmJAPQSGlZebHsj9t88KFBQIy0JJwtK0KdVqQGxBcM1+rt1pjyMYxcAvl91EDd1aWgaG6K2c/QcjKa0kMWM2DmWVOEqNpzhtS9gsFkyto8As0QvqYSflQaal9V89nkUKFS3WTWBG96F5zNQ7Fvn5UUTUrJUzmEcHyx1hXFx3HVMzyzxsMVComTE6XPmYJc0LU6ibANspkQ11jbe+rFyHSCymRIiJpEB+BOYEI4azpDcypEmDdHQFcbTudX6QghQQPmrsTN+D92aFIMx5ZkiH5PeQJBg0MT5yH5rgWjGpnZ1aqjKzZqEv4+1gTcAJCV49Aas3LFkfmbFiHsNElc7XzYsbxpOjbMVBfjbwAsBhJhAgH8bLq/Qh/+tNztqZUnz/jZ5HU4WlDAuRtiSBMAoWRLvU9u5IGizxKSJOQ/s/A3pv/EHgpaSLCtBVVJpLDc/R2rDa1uWZMnrUXTtGm3Jkoiamq+GqyCSpbfeeks400cffdQVMRJmmE4gDANv45p0NF9v+MgbY6zlo0uT6li175RQWnoiiUqWkhM8KPYHcUGdqlhfKgLXyjB4A1A3aBZDpGg35+zLJBA/lfDE5wT2juYAuk+sJSva5m0jljbbLFktXkSX3qmRqWja7UfPYOAb8zGwbR28e1tHXbqTBSXYfVz8lhKLXRM5FYsgSMKbjJNwJGJ5M5glr8d00UL0W0ublFKGWd3cbONrMbiloN03jCzCTRb6gHtphNO0retVxc3ehvhu5UHd8yKf+WAlKvmlGVBjlzpVfdFgXv8XlHw4ZX4B87j2BQm+W3mA+96qfZI0j9vs/Flz7GRBCX5cHXa/oDFLiiK8hrhhXnRmJI7VcI6LiyqEmaU33nhDKJ2iKJJZiiLMk8b8t/GEVWDwhutE18vbUhQF2JtbgOqpSchMDakD3AQ5nPLwpfh4wW6MurIV+rw6h1OW+XYHix4remnQV8jtYGVnwrZZIqZ0gIAajuVgkLHWG29xGU/AeimMIaaXwOYhOjTo/v1s0V4AwAxGDC6nPAmr/GCUuKVQuBMxSZ9TMNVwiQZmidO4rKeWzJLhMoNeDWeGmVdSdLTc+tFS/PhQL9QpNRhnQVHCshj1U95tOF7bej0evHpTRxOzlOAxHyREu5mWqhnXPac3cmlEYrPESmc391iSJZp5MbaPlWRcU8PRe4MNc2wMMUNLlugbuFYMqJupSTNYBcV+jPtlIwZdVBe9S31F8egFKpDN0p49e2JJhwQHpkWFIU43SpbOFosZeDP9F3FWvv0nCzW7hL0vDwagXyREPfBeWLcq/jW0I/OdKdyJwEYnJIon4qcg7mmSsN+ZFjVOnxjBugYeuWRJr7azO2kHCRFe8QhnIWbm6QCs9MTmvXjeeklfNMFihEKSJSqNI9cBFsxS6RgQ9X5gnNeKomfED50+h/G/bsJ7d3Th5hEkxBR2iBfXi3CGGf/gZfZuL9rNVvHTnNpN0hD1syT6rZPYcIB5baHb5+XftuKDebu4ealquONFCm7793I8fc1FqJGWROVlhrE8+ma116NA9WNqyYC6mJp0s7w6fRt25xbgP0v2aXtKKI2FZCnKhx4nkDZL5RzGYcOKz2RcHAuMzJKTQa0AT17d2vR45d5TpmciKgcnUAwnaCFDVEEDb9HbcFbeZ1kLh5FG9aedLRXbZsmczmhro5Kw4WAepqw7rC+b6F0k2EY+d9BlIhITp3ny0kctriEhGpMQ9XAnTMmSV0c7X43jbHMNh/Yx10HI/gjmDcgqbI5KjypbUsvgSZJ5lm+8JidgBD0WPcxYSLMjYZZYPEEkt+HsDLyN5RkZM7pqVowSEHYiCQAr953GLR8tsbVRMz5RVaOqUb4qXbKqh9Ng3IC+z3ZznIpyZ01FkSwZcfDgQUyZMgX79+9HSYl+4k2YMCFiwiRCMEsu6L/Zp+aCYr1NgJPFTAHwQL8WqJGWpHPwyLpNRE/4aIhI1RKMthKsNVdlRkQ8eBOI67u50gAQITWcTrJkY19kKltAsqQuGNe+sxAAcNsljak89f3AunFopFV0wbOTgIm8Y+bLzMP6vXDelGQpUp88RrA21+05Z3DsTDGVJkqSJQufYqyv7OwLRcCSLPH6RcSAWAcWg8xIVuQLYNKy/bi8dW00ywq5wQgwbgOrcOq+hAbz+r+oGo4plbKmxfg+Enur5AS9zKPIF3Rs93euRGWWQr+TvB6U+IPWajhXkiU7JpLgFCemYoVRw9GYPXs2hgwZgubNm2Pr1q1o164d9u7dC0IIOnfuHG0az2sYh2qQIVmyVcNxxhjTcLr0UVZ6ku45y5OySJwfJwh78BawWTJ8Y4UgIcK34Xi2BoQIquGCKl3WhKnf2TEhJsmSIQ3tKZcQZ6pRAvEFT1QN51TCyCyfXugjWCBpm6UoC5aYfXXOF8DAN8JeqnlSSqdODD2aGk6sEianlIp5ftq1aomfmAJa6+1g+BKecLlsellVZeXx1uwdeG/uLjw3dbOmpmFdcNFojrKBt+iawexPG8mScWiYJEtCJYdg9PIfosmaWzLOq6JSRlNlzFVplZXtlZupaffN8M+WYwfnNne8DbxdqeHGjh2Lv/3tb9iwYQNSUlLwww8/4MCBA+jXrx+GDh0abRrPa/BsYkLveJIlI7MkPsrCt8z0ebJOtTqxfBRGstHRpEq2FVMnZLJE2IsxM0wBZ5G769PlOMe4xWNsW9qOTKRF6DSsJjQ+smxmoh8vdkam7tVw0ckTAGZtMRuJOwkCbYVgMEx3WbgOMIIl2ft2xX6s3n/K9NyoUmWBVQWR9lagmNcAm+9KAkHqVmoIvOvovKx4TR7yrG6cN+Z0OuebpbC6VEJ7NjfCTSBdK5U8DXXtO11Ygp/XHEJhid/2oGJkzoySJpE1u1lWGv5z9yVMZunF/20J5yUwi86VXgpSL5So/5f4+d+6UcPZzZsFO3L55VVEydKWLVvw9ddfhzJISMC5c+eQnp6O5557Dtdddx0efPDBqBJ5PsM4XelJxrvpY6X/pmG1fhiZI5Zxqcg1UCcwSousxK5aWkE1HGuSfrxgNx7o10L3jCc+33Aoj/mcx8zYuQ4IS5bYp3VeAVZMKYHeNsueWRJf7vQxnfhfRUNUrr+iHkk+RFjS5xQi491o77FwRy6e+IEdBkIEokbqxjmhKM77pcQfMF20YK09xr+t6LBKL0odfZgxDsOFFhutFYJBttPagHC4k1C6kV+sxKp9pzC0S0NbtZrRD5M/QNCiVhp2lbrdECl5zt8uA8COkTd76zHtb7ZdoP53YakaTpUoqe4IcvLP4ZkpG3Frt8a4sUtDyzxEEOmFjXjClWQpLS1Ns1OqV68edu0KG6Dl5robsBJsmA0hw3+LxiHjLeysz9Sb58ZTIX0lXd0so23gDU1apBf/W9osCUmWCHOifblkHwBg0+E8rD94GoALr7KGjEWvqqvp7NRwPJsou3wBeyPTHcfOCp/W6FRWfR2NYWDLQIrmA/HbiU4hIkk1qoW25og7gWUhMjWcs3Ys9gdN+XCZWE7WXJMlYl7XhJ3G0sFmTdIZd2OlhOMQUthmqZQk1T/dL2sP20uWDLyUL2CY6Q6qwpIs6bIy5LVgx3H8uzQkkorxv27W5aUyTf+cthUr9p7CXyevM+Vr1zysuRvJ+hBvmyVXzFKPHj2wcGHIwDQ7Oxt//etf8eKLL+Luu+9Gjx49okrg+Q7j4GKJoe2YJf4tEYZ6y+BFWwVtrKmKp934WbJCuOzQb5GNUiyQLpu+pAQPfIEgBr+1EEPeWYSzxfbicyOe/XUz/jxptfZbJ1myWPFYb1iHUSM5lq4DiH7TsZMsDf1gCb5cus8yTThvsb6OxjgQ2IfF8iFhiUG0nVIKqeEM7R/pgcK9+wPFNI7sZIrFviB1Gy70LMiZ77xq8eglRKxfWZIpen4a29Nt6/KYJeHbcEaVIoh26Pr6XvZ+aMzbHwy6roDRwNsIY7ZqDEEaeaURGhINargTDKkVP2fDWwL846cNuO8/K7X1I5LDT4WULE2YMAHdu3cHAIwfPx5XXnklvv32WzRt2hSffPJJVAk8n7HreAGeW+PVPWOJv+0OnDxmibWW8WyB6Kgb6kJAS6xEFpaZf+mr+/3C9e3YZauLtBWdhv+twIsNl+T16Da0UwUlQo4cjaBDPfDsyIwILx7mZ/p0+t/7ThTii8V72XlCv6CI1OW9OdbXklUESaiPNxzMszH6jHxFEwnVIIIQkxz6O+p+llwwS24lHyrcxrdjSZbs2rWYVsNpHrzZH3EZL0ubJfMzEbBcp2h0CKjtWSjxRyZZYtGuPsusksj8xlieUbLkxB7IXrIknpfqpy0cb876cGaFICH477L9mLH5KHaWGm3zshOhkcSZW3Jls9S8eXPt77S0NHzwwQdRI0gijMd/2IAA0U9z1m04RVGQnpxgugWnws0tEeNJnA6KqTJLQYtTHguNaqTqfl/boT6e+nmj9ptvsxS5gTcvlhd9euWlcwKagbW0WdL+t5bYGBfNI3lFeGbKJnaeRO+pXESlKGprFiQEz/26CV8ssZZERWU9oxnICGRLQUqiGHUP3i5sltww4jREeSV2IF1n7UivGSG1mV6VLSJZ4oE5zgXz8AX4aji3I8UXCLqK76aCzSyF/ueNO2N5gWDQ9SHBTj3rpF1UJimplGmyWg/t8mWZAbCM9tW0SQnuzEnKCq79LAFASUkJjh07hqBhUDVu3JjzhYQT5OSbRaBsP0vA5Ad64uo32cFLuZIl1rPS2Z2apB8aCUw1nJkWK5jcD5hsK/Q38c4W+XHff1YyDRjVj0UMd1knWSDELBmZFTs7Hx4ICV21phlYEQNvPZ1i6bh5Qr+BOIllZ5s3gS2jBETHdi1aBt4EYeYxLrfhoqyGE1UlGlMpcN6Oxb6gznbQLAkK/+1UmkhgZoJFc7CSLLnllkr8wcj8LBndMhB7CTOrPMIY99GQ1DrJI9GRZMk6X3rfUaWir/2+jZ02EGS6p6ERbzWcK2Zp+/btGDlyJBYvXqx7rm4YgYCzCPQSbJwqNDvnYsVn8ngUtKmXgcEd6jGjfztSw5X+XyVJr/6jFzdNDUcxyb+sPQyvx4ORlzZjlgWYNywe76Q+f3fuTmw8xDaKdSopYEqWvB7dAnU0vwizGVfZRfNP8CrUImmdnqWGi+SWkJqX/jacgGhbsABRpio6NkvW0jZRBKlNPtpqOBG6jBtiWajhxv2ykW3g7VAKU+ynXQeYPeCzNnYTLFQubtVwVh683UohQ2o4RlnCgXRZkiXrdYA1FvRe0YGdx87iQxvv3WL0iacN2yxFLlkqpviABI/CDKCsosRvzyxVSNcBI0aMQEJCAqZOnYp69epF/VquRAiszc7KZol38nTi2VbNKy3ZaCtF01UqWaJo2XQ4H5sOb0a/C7LQsnZVZt7mmFVG5kl/w80YkmHkpc3wSektDicjjmuzlODRTfhbPlrqIFc9/EGCBC91g88mkC4x/A/wrviGHlZJ9DL9POnz1KtKWteris1HIruBZUVbJOlinYeakbrYl0UgXSNOFvpw1cT5GNKpPh66rGVEXpoBMYbvP0v2oV2DDN0zBYqZsbBpZJ3NEjEzJpHYlQWJeaMVzUPvOsBAk8vm9QfZ4ZDsvHCrYBma0xJmFuwkSwDQf8I8ofLt4KR7EgzhTiyZJZuMaV9PHkXB8TN8Y/ESf9BW+lshJUtr167FqlWr0Lq1OYaYRGzBupESjqnG/obHLLEWX00Nl6gfGvTEUE9FrLX/ZIGPTQSzfGPhfLoAYEjH+hqz5ASsxR4ILQjRuo6qtoneZsl+oaHTsBYm9bWQGonox8e4a9rqIplHgmhLoETziCQ72mYp+mo4+zS/ljqa3Dp9Gx66rKVrFa8K4UC6DD9LTtuxxB/UXbSw4hucSnTYY4TxjNFlgaDelioSOuh83MR3U8HaxN1IliJxmdGyVhp2HufEWnOQlWo3lCQw2Oyy/WWt3tHqMRtmyWNTZIV0HdC2bVvpTylO0NksGfws8ZgMnoE302ap9H+TGk63qQdLy7eWfNnBSG/4Npx9eieSAvqkRyM5wRM1KYbqwE709pWmhqOesQ1fxTd7QuXRsWEmqqUmWX/gALx+/WLJPsw9olDpIi9Lp+KJwMCbVvfEQw1nhKikggdRCb5JDcdJZ7Uh6/wssSRLVrZDNiAEph1cNA+eGu7YmSL8vsmdCp2lZgTEDfKZUjubcccqj3D+FsHIS5ty3zmZQ6pkKcErsN44mANBQnD8TBH3fUkggGJf9CIOxAKumKVXXnkFjz/+OObOnYsTJ04gPz9f908iOqhdNdn0jJY+aBupwTDaiBK/cxuyJIPvDnqc+hiuA8I0OS5KQ/g2HLse9MlDxHO3iiAh3NtwETnyoaAavYvevtLUcDRjwFLDlf5vp89Xvw+rncqGOXhh2jb8tNeL/FI/LdtzzkS13EglS+oYjbZTSjfG2mVl4G0Eayww+BUdbG2W6L8ddlIogLOBHldquPDze79YyUwvdCWdK1mK5DacOu7E1XCRSFQTLQa4G5slO3cEThEkYV9OLBT5gra3tiukZKl///5YunQprrzyStSuXRvVq1dH9erVUa1aNVSvXj3aNJ63eHxgK9MzvXPA0P/qfOSNb76BN/9KvhH0QGW5DtDoc8B9mCVLepslq/SOJEscNVxSFNVw6kZISzKssg6/Y5+UVRSVnrZE1Uhql0Rb7WTXSr4gwczNR/H4D+sjLktthxJ/EMv3sK8ai4CWtJUHyVLkajj3dXjrtot1vynhhw7NstKQnODBJ8O66myWiGEJmTBzu7YWuZIsGRCpgfe6g3nCZbHSsBhZvjNfPVjf2rkOYKvhdL+EylaRYMHcOMnJGO7ECk6mACHWN42L/UFbyVK8mSVXNktz5syJNh0SLLCC1zINvN2p4TiFan8leT3atzoD76DZwFuDg/HMvw3Hrge9WThjlqxcB0QHvoBesuTGKaVxMXh79g68PnM7AFE1HIGdrYRb7DzKjgSuwqMAB08VRqUstRnG/rgBP6w+GEE+YYP3eDilNCJSA2/RKrCSDelYHxNmbMPeE+E+YkldburSEPf3bY4ErwfTN+aE0oE91/edKETTrDTwJr16cOrWtDpW7D2lPWe58hDdBwOGcCeEEC6jhFK6PVAsGy9I2Gq442fNt5EBoGpKAs4UhX3amW2nqHWAMxGNB83QgY5Loi2sJM/OXAc4UMM5WD2DxFpSV+QL2K5ZFdLAu1+/ftGmQ4IB1tjR+1kK/a+qp/hqOOe34QAgJZFilhjODq1OVCIw344r/V+ANidqON7JMSnBE7XrqGr+OgNvh+FOjCSqjBIgyCyRyCUpzbPSsDvXbChqJzFSGCE13ELNJhJGCSi9BRlUNy1+un4X1MK87ccd5e2KWWJ8c2ePJqhfrQpemb7V9nthyRJnXqUkGt2BsKFKKehAulaqJrumeOXGDrji9fDNrpBUy6jWE2tPn0ENN2XdYYz6Zi03vYg0goAtJV934DQzvbEfrC5m8LqMrdZ0r4YTYW6c5MOSVP1v/REM7lBP++1UsmTliqGwJFA5XQesX89eOBVFQUpKCho3bozkZLO9jYQzsMbO9ysP4oXr2sHjoX36uLsNZxXuBACSE71A6QmKPhX4DVIUGs4MvA1lc+LShdPruCVh0LeijOVHa4P3GQy8RZ1S0kmsgokK2SwhvHC7ZZY+Hd4NH87fjXnbjuFwHt8gk1l+lBazaInbCQkzKF4LbqlaKjskhRXceMVnbRbjh1yEdaVBnO0g2qfmS6bmeUVLP3RpGVOMx0xoedl0l5FuUckSq7bGeJQ/2Nz2DAaBRTtzsdXClo4nWeLBOBfNBt72hxZW+Bmdo09hakKwsjFyMp1U9RvLPu7Pk1ZjcIfB2m8n8zRIrOdMYYkfqYZLRaw84glXNkudOnXCxRdfbPrXqVMntG7dGpmZmRg2bBiKipwttiJ499130bRpU6SkpKB79+5YvtwcFJDG5MmT0bp1a6SkpKB9+/aYNm2a7j0hBOPGjUO9evVQpUoV9O/fHzt27Ig63W7AkhSVBIL4cU1ogTD5WTJM4jdv7RT6xpFTyvBDOjt6cvtUKQojWyfjmSdZUkkwzkW3DACtnqIRZJxw3SIQJDpmwd7A214NR0NMshTehNzatzTNSsNLf2qPhU9c4eg7npd0N4hWPkES3uStDt5OxpU6l+zsK1gwjjWvR4HHowiXL8wsGQ8hCuM5p7/o+U978GZ6l9f+t+4wM7PEyEuwz/U2S9ZMHBCS4Nzx72WWaUKSZ7HyATbzZ4TdRQ8j40ygXz8cS5as1HBObsN5rQ/eunydSJZgL1myY77ibbPkiln66aef0KpVK3z00UdYu3Yt1q5di48++ggXXnghJk2ahE8++QR//PEHnnrqqagS++2332L06NF45plnsHr1anTs2BGDBg3CsWPHmOkXL16M2267DSNHjsSaNWtw/fXX4/rrr8fGjeF4ZK+++ireeustfPDBB1i2bBnS0tIwaNCgmDB6TsEbrytKjV6NN5+Mkzg5IcSp88OdWBt40/nRJy8/wymlCuOAblCtCgDoxLc8qMXxJqrOZsk2tzBCt13Mz6O5wfuDQV1eHjunlJpkSb/48yDC/BzNL8bTpbH23PCVzbPStL+dBm3lSSrcYOnuEzhmuGbcv01t3NHdWRgl+haXlWRJtKYLduTigqd+wycL97iSLBnnm9NQLG4ZYDtbQF1almSJI3kJWByaaJib3rx9Cxt4G2LD2alDxfIltkwXDWM/sP0shf7ntfm/F+w2kmCQLDmbS1ZqOCfTMlFTwYpJskURDFrbLIWYJZvyKqJk6cUXX8Sbb76JkSNHon379mjfvj1GjhyJN954A6+//jruuOMOvP322/jpp5+iSuyECRNw7733YsSIEWjbti0++OADpKam4tNPP2Wmf/PNN3HVVVdhzJgxaNOmDZ5//nl07twZ77zzDoDQZJs4cSKeeuopXHfddejQoQP+85//4PDhw/j555+jSrsb2N2kIIYJaUxfNSWkZT1T5Ecxw32AnRpOxyzRkqWAWr7FcVPNo3SEWYVB0cpW471xti/dQu6AG+At9j+uPoRHJq0RzscK/oBeemUb7kSjLfzMamEX2ehGf7cWZ0qDKVttrLysvrmvh20ZPBgDrUaCp37eiJ4v/WF4qqBKorWY3kwTzZBYJBQcSmN+CDGiz0/dzJxPbiE6lEV5Jb4aW//bTvWl2SyBLcFRD2Hqxl7doM40rk8q6DA0WlomxWbQkqVAUIBZEhiUtIsJERjrY0UDry/OGIKeE0Smxk60OAw4yVY1XBeSYjrIN0iIdjGIhcJiv21fxVuy5MpmacOGDWjSpInpeZMmTbBhwwYAIVXdkSPmOGVuUVJSglWrVmHs2LHaM4/Hg/79+2PJkiXMb5YsWYLRo0frng0aNEhjhPbs2YOcnBz0799fe5+ZmYnu3btjyZIluPXWW5n5FhcXo7g47I1U9S3l8/ng84l7sLYDL8ZeIBCAz+fTBk8g4IfP54NiGEz1MhJRMy0JJwpKsGJ3Lro3q6F7z5qcJEi0OtCyER+1ORSXhOrJut3j8/t1baBOgGApzVYI1cGjlWs8XQWp9iDBoHBb+wNB+Hx+0/O8cz4s50TBdoriEh+KS8L0BPx+y8VPHSs0XUFCuHUScXuyj7rpBIu8PIpi2hxSEj2oXsXrevyW+HxRjQlp2oBI0FKVxoLfHxbtW33K6ie6HcJ/h9OVOFTD+Xw+ZhR7n8+nG9e8bwHYplNhlGkSEporercjBCU+822vIDWv1P4MBgmKGeOiqEQ/hpMSPLi7VxN8unif9r3P50MgoJ97rLHCWjt1QaFL35VQ88Xn98Nn0yZFJfbj2efz69Y3OxiZzoDFWhTwm9cdZrpAUHfY8AvQoyuT8NP7A37heb37+JlQWqOvCEaZQU4a9nd+lFiEazpb5EOJTVsFSvebaO6xTvJzxSy1bt0aL7/8Mj766CMkJSVpBb788staCJRDhw6hTp06brJnIjc3F4FAwJRnnTp1sHUr+yZJTk4OM31OTo72Xn3GS8PCSy+9hPHjx5uez5gxA6mpqfaVEcSGEwoA82n6wKFDmDbtAHx+LwAF8+fORc0UYO9eD2hh4by5c1En0YMT8ODXuctwYot+ES0sDH1PIyfnCKZNC9lEnTsXfn8896T2979nrgXZH8TRY/ryAGDFipU4tytcjlrG4sWLcCidVcvwEPz99+nwKkDuiVC+xUVFOvrmzvlDS3/48GFMm3bQlAcLO3buROERAlZbRgsLFi/G/vQwLbNmzsTZs+b2VbF8xUoU7CQ4WBD+xufzG2zqwvU6m5/PzYuF3OPHSvNitA0JmvIKBgImez4ny8O8efOx5Rh7vEYDR48ehffsUTgRhm/bvg0BogDw4OCB/dxvDx08aHpnbguguLgEarudOacfm3aYNm0acnLM82XatGk4UggY27pxGsG5AHBF/aBGy8liczoWTpw8paNtz+49mDZtF06dDo/H06fz8PvvM0z5bd26BdPyNwMANp0K9efpvDzMmTPXlHb+wkU4lAEcOBuiq7ioCB3JLi3dhg0bkH5sPfJK9HQvWbIMu08rurZYtHgxDhtCSp48GaZXbYOt+8NtuHbdepw85YFVP8ycOctEtxFLlizBgaPmvuGh6Fyhrsxjx49z59qsWTNtyweAg4cOwedXtHzXrFkDu7lEj9FD1DpixLp165FyZB31hE9P4cnQumHcS1hlnjjJX9+MWLhoIXYy9gsVW3bsQuAYe432KARBouDkqdNAA2DmzJlCZYqisFDM5YkrZundd9/FkCFD0LBhQ3To0AFAaGIEAgFMnToVALB792489NBDbrIv9xg7dqxOYpWfn49GjRph4MCByMjIsPjSGZQNh4HtG03P69Sth+zsjnh8xSwgGMQVV1yOBtWqYOPv2/HHkb1auiuvuAKLz23F5tPHcFG7dsju1kiXzxvbFyK3SD9Q6terj+zsUJ9O2LYQJ4pD7zOrVwfOnAYAbDjlQc02lyArdw9w+oTu+4s7d8GAtrW13y9tmgeUFOPS3r3RvkGmqS59LivCxO/noG/3zuh3YYhpnXx8FbbnnUBySgpQEpbg9b/ySoxbNQ8A0KBBmM5RS2bwGxFAixYt0LpuVWB75A4Teeh2SXd0algNWDYbADBo0EB8uGcpcI49Ebt27YrLL6yFzUfy8dr6UABfxeNFdvYgLQ1dr5o1qmPf2dPC9NSrWxfZ2Z2YbeP1ehEw2LElJCToyjaWb4c+ffri5IZjwIGdwt84Qb16ddG2XgZ+Pyief8tWF4TsJA7uQfNmTbHo6H5mukaNGmL5cX0cq+zsbO1vn8+HmTNnhg6G/tJTqCcBgLg0Ijs7G7+cXAOcOm56vvPYWby8brHu+dN/6ozLLqile5aTX4Txq+fbllU1MxM4E46k0LxFc2QPugAf7l0CFIRuhWVmZqL/gK7Acr2686K2bZHdK6Q1SNt+HB9tXYOMjAz06dsBWLtIl7bbJT3Qo3kNrD+YB2xYhtQqVZCd3VcbN+3bt0d214Y4cbZYm7cA0L17dxTtyAUO79We9ezZC50bV9Pl/98jK4D8U1o7AcCWmTuAQ3u0/DecOwic5UeNuPyKK4BV1m3WvUcP7Fp+ADghFi6lano6jheF3WvUqFET2dndmPNl0MCBGLvCqFI2o179+th65jhQKinr2KkTsGOD5Tf0GN1y+DReXc++7NS+Qwdkd26g/baa1xNGXI7aVZNNewmrzC8OLcee0j3BDj179cbBVYeAo2x3ILXrN8LFF2YB29aZ3iV6vSj2B5GZmQngJAYMGIDEROc3WHkQjTriilnq1asX9uzZg//+97/Yvj3kC2bo0KG4/fbbUbVq6Hhw5513usmai6ysLHi9Xhw9qh/QR48eRd26dZnf1K1b1zK9+v/Ro0dRr149XZpOnTpxaUlOTma6RkhMTIxqJyZ4Od2jKEhMTNR00Uml5SYk6Lny5KTEsM8Uj9dEG0sv7fF6tHS03YvxIsOO44VsYwuPx1COoqPRiKqpQJcsgn4X1tHee0r170bbpeSk8PdeUzkWUDxQ7KI0RgiieOFNCPdXclKSpTGK1xvqDy/Vx4SAW6cEh/QnePntwwubEcnY9SYkaP1myterRMF7tQcpSfbLVdcm1dG6XlV8tXQ/FMWjHXwTvfxTOsv4m9UWtJ2c6o7j47u64t7/sENtGPNj2dklJiYiKclcVkJCgomG5CQx5syowfRqY0F/0y0h0dye6rhUaShNDQ+j/VYfyMfSvafQt1UtLU+aZjWvpCQ9QR6v1zRWPF7z+kS3l/qOUHUIwmNal0z1STDXsVOjalhL+VDyeLy6fO1gNKYOQuHOnWRG37JgdDXitRivKugyqyTzy/Ey1n4eGtQIif+Ne4kGj9dVKBSP12vZV+f8QXg87DITvR4U+4NQA/BEe58Vzcv1DlK1alU88MADmDBhAiZMmID7779fY5RigaSkJHTp0gWzZ8/WngWDQcyePRs9e/ZkftOzZ09deiAkwlPTN2vWDHXr1tWlyc/Px7Jly7h5liV4e63ZAWIooXETVJTwO812KEhw4GSptINl4M0p32hvUT0tkWnYaHS85+ZqPs+QVR/uRHxx+2DeLjz27VrHdDjB96sO6gwQFQWWBpAsc6ZIXQfQsLrNxsrL3T2rMHiOCwHgp4d647ILazHfiUJRxOJV0WOeQMzA24mDUxVqvl2bVMfX94oZxvPah8W8sigSdR3Au6JNdH/zXAdQf1PtyKL9jVnb8e6cXfhg3q4QfZw2Ng630C1Uo10V+1sjaKb7qZ83Mu3AaLDWKOM1e8JJx4OxH6xsE0X7LNLbpFaHqWiuwYXFlN2og/xCTin5fRUI6C+I1MkICyNU5rTCOKWcMmUKrr76aiQmJmLKlCmWaYcMGRIxYSyMHj0aw4YNQ9euXXHJJZdg4sSJKCgowIgRIwAAd911Fxo0aICXXnoJADBq1Cj069cPr7/+OgYPHoxvvvkGK1euxEcffQQgtBg89thjeOGFF9CqVSs0a9YMTz/9NOrXr4/rr78+JnVwAt40U8ec0c+ScYB7FEVjeNS0//h5A75efgCv3tSBXSbnNtzGQ3pRZXKClzm5jYuXm3ATvJQel64DgNhfO/113WGMGXih9tvOdYDadtH0s0TDqr3ZtyAjY5eCxCzRUNGuQSY+H3EJmj75P9f5K1CQmCDALEHRHRDCzJKF64AIqp6c6BG+pcZrH1ZfsfpDNJAuy+Fh6P/w8yJfELlni2EE13WABTOxvTQUjpHpVL8wMu5sP0vmh6zqGteXgmJraRuLlzLOJdqJpAiqGJwnWrWN8NgiBmbW4XoVLdcBKnjrxzlfAJlI1OVbLTURpwutjaQJsY6NSDsG7dm8Jj64sws6jp9hSCNKfWwgzCxdf/31yMnJQe3atS0ZCUVRonorhsYtt9yC48ePY9y4ccjJyUGnTp0wffp0zUB7//79OvFur169MGnSJDz11FP4+9//jlatWuHnn39Gu3bttDSPP/44CgoKcN999+H06dO49NJLMX36dKSkpMSkDk6gcFbhgBbhvjSd5jpAn96jKNrCoE7or5cfAAC8OWsHUhLNG0it9GTd9zyU+IPMwWtytqbRyM3KBKGrz5GKQmKAvq/N0f62d0qp/k/fUOKnd8osWd0ci0XTsaQFUYUCJIuI/5Ww5MAXDIpJliLglpK8HuG+4bUO2zmsGaIHDuNNx6BBEg0AO4+dxZVUCBJWuXRxVl2r9juPPJYkxpif6EZolFzbMTkslwAmNRrFDD55dWvUSE2yDO+TnqzfNq1oF5csGZxSOpQGJVqMQTdMBm9OBHQ0hvD4oNb4cP4u/W1cBg1WfpZmbD6KixtXBxCSUNKBfFnjNx4QZpaCFIsetPNCFkM8/PDDePjhh5nv5s6da3o2dOhQDB06lJufoih47rnn8Nxzz0WLxKiBN/xDfkrCAycsWTIyS5RKwjDOUpO8zOn4aP9W4fIt5nlJIMjcHM2u//WqQhGE/bvo89I7pSyH3BKFBCocDQtvzd6BgmI/WtTSXxEMBglThSYS7oSGVXuz8o9YDYfYSu88ioLEBHsqFYTjWvkDsZcsJXg9wswWbzywmC2mPaGg0YTRu7i6WYr0D10XdY7R/qqs4MS/kzE3UebAeBiz2oABMP1hGccCQdgms1Z6MuqXOtLlIS3JyCxZqeEsswrnEdT3j3PJUtmo4QIBM5F1MpIxb8zluGrifG5YmWJ/ADM2WxvQq/ERPYqCRIqhVdsi3pIlRzZLS5Ys0W67qfjPf/6DZs2aoXbt2rjvvvt0/ockIgNvDQ4aHABqNkuG3lSUsErCeMJKTU4wbZBXt6uLjJSwsZvVJsCTLK3edzpsE4XwouhkQ+KVG+3I8VZoWZvp50AYio0abtPhfIz+bp0pDSvYKhBlmyVRUYYDWNksRQMKxGyWPIqCpNKF1hcIhj14W6klI6RNtG947cNWw7kv59Dpc8xyRXpH7/gVpd9Z9602xznvRcKDiA4d4/zgxb1UUcTwh8WK7aaFxfEotuM4zSRZiobNEolMDWeoE/0zmmo4PyUoMa7tVu3w/SrxoNi0RoQuJ942S46Ypeeeew6bNm3Sfm/YsAEjR45E//798eSTT+LXX3/V7IUkIgffZsnoLZqnhgtPGuNATmMELTTqlK32phCzZB683648gD6vhtVRRrsqESja/2a1opYmxnyTU0mOWxgXAD9HauvcZon/7vLWtfkvXYJY2CxFA04MvNVTti9AtJOwlU1HpGNJ1JaIt9az+ioSNZy5XHE1hl6ypH4v+i37uZAHb0b2LOmx0WbJKt4YABQzHCGa5hIlOfMIMEvpyUabJX5a0S5zajdlhFGyRM8VOtedx/gBhWnw1g+axrDDV6X0Nz8/ln0cwLlQrejHYdDB+I0lHDFLa9euxZVXXqn9/uabb9C9e3d8/PHHGD16NN566y189913USfyfAVXb2xklkp70ayGC0uWjONs8a4T2HHsrO6ZcaO2tFkKsCVLRoTLFV/ow7eZiOF5+O9YszJu43DREJnbxiQ8I0jHNkuc9N/d3xOXX2hmliKtrVE1HG0o0NsxcNMpYcZoa06+JomwNHiPsPaiGyJXsiTILblllhyp4Rg/CKw3wrBdop6+C+uGbkeb1XBGOYq4qshnIMRODVfEkDwlsmyWKAlkw+rWjoWNkiXeuFcU/hpuhFGN7XQmGW2WdHOFyrj/BHs/XQCfblqyp2WrHsgtBkn11CTmc1qToYJlgA9UMDXcqVOndN6u582bh6uvvlr73a1bNxw4cCB61J3nsFLD0ROLp4bzKIq2EIvYHBjTWE1znz8oGHep9PThSA3Hfq6zWaoAkiWRDcDYhrzrtU7pYS12qUleXNKsBlNiyEqfkSLuhi20AcYOiqKISZagaBvFmv2n8cPqkPjf2sDbHU1Na6aW5m2fQTDIj53HVMMxZp/bIan2jAgzq78Npx607GOw0fRNf6wPPryzCzqrBrsCkiXRjTBgOEzw1NYqipiSJYPNEgnPuwSvgmZZafj4rq7cPI3MEq9tnHSX2ZWCs9nk8SjoWze8dtA3R93MSx5jHmAwS2pKK8lPtVS2L6OMKuY1xrgWORm/sYQjZqlOnTrYs2cPgFCsttWrV6NHj7CPkTNnzkTVWdT5Dt6J1xhAUh3YxgGuGNRwdqcw43tLm6UAWw1nAtHTKAJeWqe3lhrYGGpaIRqSJREYo9fzFn+nUgVLGx3BvH55+FLh8ggRC1rqFiE1nD3disJmLC0NvF3S9HVp4GEhZoll1VwKpmApApslI8KuRuzT0mtO2GZJUA1X+m3ruhkYdFHYUbBRcsaMSSm4EdoxR0awmCWWzZK6nqpjbEDbOqiXyb4RbTRhsLNF+/2xvnjiqtaWdJrcPVimZqNHbYpZYhhIO2E2uAbeNLNU+r+6nlgFI+atX1UZzjTNfrn0/8cLjpil7OxsPPnkk1iwYAHGjh2L1NRU9OnTR3u/fv16tGjRIupEnq/g7WmKoterq+msXAcEgwRni2wCFRpGo9XizDPwNkIkkKkJan0svhJhHm7u2sg2DQ9OPWazILI2lRjUBDyG1klUdMDaDkbUoLhZVhoubZklVN7783ejoEQsaKgbCPtZUhTmzSBLVwouRUuqakGEhwla2KSw1HCivpdEoG6SdoelUBnU31oGbH9F4dfOpMdsP0ti3/Js+ng4lm+2lWE5pVTV3zRTTae6sE7Y4XJGFf0GbycxvLBuVTx4WQtLNbIpjwgZgyRaslTauCwJ2P/1aMz8XkyypF/brbqGJ30zumFglq0xS/HllhyFO3n++efxpz/9Cf369UN6ejq++OILLZAuAHz66acYOHBg1Ik8X8Fbe3i34cxOKSkP3sQsxTDCeGqz2gSK/WzXASoIIbobYW4kSzn5Rdw0dtl1aJgpdNWch7KyWTLaKPFslng6fx6sbsOJGhQD4hvgr+v5gaejAUURtFkCO53X4ltWHXkuHFQkehWkJIYkDCJjO2hxW1CUeXULtVy7m2OA0c9SqRoOgpIlJze/jGonQe7AiadtAHhx2hbTM6OxP+0aIUGn6g//fSQvfMPQaPPHaxtTc1g0TzQYATqHRI9ZDceSyg3r2RRfLTXHTOR1Jav9tRvXFn3Dq18S4wBkdmJKSv/nZl8mcHR8zsrKwvz583Hq1CmcOnUKN9xwg+795MmT8cwzz0SVwPMZVgOW5WfJuMEbPXjbibCNN0usJDt2ajhjSBYniz/teiASRGJ3ZHV7KpownvZ5NkuJXgWv3sj2us6CpZ8lB1KLSD17RwsKnNyGY6jhFAXv3H4xJ29zejtJHt2Gomo4Xo5OmFc3UKe9T4RZUsx/0/ZoLNsTo+2KCD0mQQqjcVhDz+72mwiSDXHPCCE4eiZ0MNMzS+E0NCNQPU1/cOExCU4OiCb/dBGKlhKYfooYjA5n7PJdBzBslkqTWs0Ztd+6NKmO16joEay5anJiqpVXgWyWVGRmZjID/dWoUUMnaZKIDFaiUJZkybixKUrYdiVArGPzACybJYu0Nmo4deIYJ5QIeNdM9bDPMBJVWpnZLJnUcLxTqmIKs2AFlX4Wg+WkL8qoGWwharPk4ajhEjwKrulQH30vMMeoY7WHnQSD3lRFmDircDCxliypc7BYRA1H2yyp3yO8UdXPrIJRV7Yyfwhxp5lsZ7Zi3zpVwxmRmuRFx0aZumfjf92sheugN2+6D6wOmk5cQvDg8xuYJRd8QTLV/vSYspIs8Wwbua4DdDZLejWcFTOjRZuAfm1lzR3W7Uk6j3ghtqHYJWICo+sAdbzrrtYroQ1WPTkQYm8c2aeV3j7F0mYpYH0bTt1sjEaAIhC64myTHSFimysPUbkNJ1ARs4E3ezNQ4O5G4c3dwnZbKjVOAumWlb8pOyiwvg2n1qlXi5rM0A9WKjXWGzu1CJ0fS5VgRIBxBaxLk+qhvJgdy6b37t7NbMsyQh2HRsacBbZkKTx2PB5zbDRtjgvKloy3eWka7eDUwNuIj/7vYlN70+r+BJ3NUjidVblO1Ks8LN97UvfbTS1rVQH+NqAVXrmxvW691WyWGAcx3hrPmy9WrgNE1HCKoi+TqTJn3J6k84gXJLNUjmGlhqMHnzox6EFmvCEXCBJbEfboARfqflvNdZ5TShXqpAqHO7EsWgcRu4R6Gfax+6xCANihrCRLRmkeN9ik4mzxtboN50SSEQ1D92jA47FmSuaNuQyvD+2I4b2aMpkqp64D7Mag1yGzRAx2hhNu7ogvR14CQPw2HACMu7atbVlGOLkcoD/UlB60KBsjBQqqJLIlnKJTJsR8GdVOYnBqs2SE3RTiuSexKpfXvMaynKwobvmC+/s2wy3dGjP7gjUOvB4FQzrWBwDc3LWh9txJbDiVqbRqI/WdAkWXN+tAyys7zrySMwNvibKFlesAdeB4GAxS6G/9/yGbJf7JMinBYzoxugmkq9EYMKjhHCwVVkzYJ8O64vdNObi3b3PTu4cua4H35u4KlQsSEcMTjdAqInPbaEfCU5UqDl0nWtXdSdVEbqCVDawlSw2rp6Jhl5DfI6bNkmVsOHN6W2aJ+kbE8Jy+DffZ8G46L+pOJH1u4IS/0LFKtGSJUqcbA3CfKfLpP3BBj6hkySpyvQhCdpx8OumxI7oGBAnBuRKzi4ICw7Oy3Otp0tWmZY3pBI+CV2/qgD91boAezWtqz0ViwxkDKIuYZYQOfeHnImo4FVKyJMEFV7JE3awxqt7Cf5dKmyjXASJieH35/MXCH7SOF6VKltwYeFtNuivb1MGrN3XUbiJZIRI1XFnFoTMu/nw/S87asGF1vo8pJ2ofq2jmZQlRmyWAvQCrzI0xhy5NqjNrbscseWxUCUbQqicjc8SaZ6Ljr32DTEy6t7tt2aJguQ44eOoc8ksZIkVRTHNPjb9WWCzmOoKthhOjzxjuxCnsWjWB4zrACkECPPXzRtPzSKRgkRp400crAoLZW47ir9+tM6XzeEL9edmFtXX9yrWXZUqWSt9ZSt/C9k30QYN1GBNxWxAPSGapHIN7IyEQFukrOmmSWbKkvg8S4Ib3Fjssn/+OteDRMNssiZfr9tZDi1r64LcRqZAi4BHu6M72XcKC0WaJ594h1H7iRDWvxQ8E7EQNJ2K8zEPV5ASuHxenUCCm7gI4zBJjMI+9ujU+vLMLs1ntVFc6lbewB2/1gMNijvS/ReZLw+pV8Osjl6JXiyz8NqoPP6ETyRLjwAUAf/k2tNEqAFcNl5PHd/Xxp4sb6OgxksTaB5m34SJWw1lLaHU2esJqRaJ5io8W7JbAjg0zLd8bJUsjv1iJhTtzTel4NokiTilh2INEbkfTZiMA+6DBm0/xVsNJZqkcgx4yf+rcQAs/QUfJpscVvSEYfS/ZLjKM11anW6ORuRGqyi/aajgWfniwJ568ujVuoBdkRCZZcnuwu+2SRnh2yEWhLATyMEr7eHZlChRHdl8dLBZTJ/lE4kJhzbgBeOH69tpvVvBmUSiK3neMFdjXkc3p7u/XAlnpycxxbifAcKripe27WdPK5H1fYL7Q46tNvQw0y0rjlO1AssS4Dad7r5gNvFV4LcbKhFs6oX+b2ho9JsmS4ISL1HWAYiOh1dksCeYp2r5ORoxVjunJCZj8QC/L7we2DYcls8qLx5iIxIYzag2smaXSfA3rGGuNlmo4CeegBk1qkhff3NcTQGjAsmyW6PGtxYsr/f/r5WbHY3aokcZ3A+EPWseL4vmCEoFTu4QuTWrggX4tdBOfEGs7FTu4nZgXN66uSTZENgBRP0vGE5kVxgy60ORLRp+XuI1MJJIlo4H9nL9dhn9bxNyyAh3n0A2sxipTDWd7G85Z+SE/S3yVtLFuIl1tiuUYhU2GdRtO9x58yZJduBvaQa4RoiSeY4QvcQIF1owoPd5FVaEx2cItGqRRjVRbKev9/VpotmVWbct3HcB+rncdEIKIGo53G451CUctu3uzGgCAoV0alubBzb5MIJmlcgx6uIb8x5QuNhyRPotximSDefJqfjwjq8CggGrTFP7txHWAnadxUTiViswa3Vf72y2z5FSaZboNxwvKaaM+oJHMWUjV753cvopIOmdA7YwU9KdOvE7g6FTO6DteuwKc23A2DLtTFS/tG01EDScC0THqyMCbXkOYwXzNNksq7MjRSyD0iUXrwjKkdgJFUYQlS8IG3mW8i4uYKSR6Pbi2Q+iWm9Whjes6gFN1K6eUVs1AM0t0u1qpzD8d3g2T7umOBy9rUVqelCxJcGC0R1IHmT9ItJMvS5pE/x2JoXJWejLXr8vKfafCt2AYOFVQgn0nCih6xMsViWFlB0LE1TYA0LxWGprUZKsxnIDeREXmtsnAm3sbDmhcGuXeDnaMqRMVUiTuF6IJJ8w2a9G28l7tzoO3MDkASgMNU0au5vycS5ZEGQx1k2kiMH7oYpmSJQVcZsnWN1VphgTmuSESioUQgsII4w96bQ4diRynlNZ0RUQSO88o5EHfZuSBzyyJSJaMsiU+9K4Dws+TmK4DQv+nJSegV8ssjaGSajgJLowLl2qMF6RUXPqwC+H0RtcBduCdPqwWDKsF7qYPluCK1+eF83EgG4jWnHAiWVKgF0m7PSzqon0LpDc5peR68AYuqFOVG7KDBq/PifaepYZjf1RebsM5Aeukb+U2gylZcuBnSYgmyrMjS9prVIeIxZszPOCQrKb7+t4eeOSKlpZ52hWrgO9J3m7OaMwSw2bp8e/XW3+M0HoTqRDHic2SKIRtlhxkHY01UJ3TVhIZnhqOR+ucbcfwyNdrcLqwhDIFCf3/2fBu3JuhPDUc23WAYS6o+55Uw0nwYJQUqYPMrxPph9OzbsZFegU+WnulEoeR5sT7NO3tHHAv8nWqnjFKPHxcD94h2q7pUJ9ryBtOaw2exICFSGyWnJQfzW8ublzd9MzKDo6VtaiURBR0IF3Wl8bs3NgsWZUNAPWrVdFUGjzoDLw546Q6Iz4cXQ43b1VdI0i3kYGnVXB2c4CH0BrKb1yd64Ao2yw5WVKioXISkSw5jQ3328Yc/LruMF6Zvo1Sw4XSXt66NjY/N4j5HT1W6bzZNkvs31KyJMGFXsUW5sgDhGiqKtqIWW+zVMosRcjtRMvfUDzkE05USMYF3O28ZAWwtAJPsmRcLO0Mb2nY9bkTKZ9bNdwVrWtz3zWpIaZOpOFkHCYlePDC9e10z9TPmdkwHsZCsqQpLVg2S0YDb4E+Mgdf5aWjyrFpR904Y9CgKEBqUgLzIGJvsxSWELjxI1RQqoJLSvC4tqXz2tgs6Z1SiuUZi008qmo4F9/ajZOcvHPhcqjnvPWCdnVjp4YzzoWwRNKSpJhDMksVBLRkKRAkyCsN/Eif8iJSw/EGYpS4nLJy8qiCwJlkqchwy8a9gTc9pfR5VE1JQK8WNXXPzIF0g6Xl6/Olq2K/4Tlva94XbjalS5rVwJu3duK+/2R4N2GfSSqcUmHM3+hWwi7v6DNL4THF+tSoDhGyWRKU0Ohvpdox0tY0qAwU66asvTSulB642/hUyVJqktf1eqIo1mPJqztwiuVZVuoh9XbYHT2aCH7hnsmwG94eRTF58LZCULNZMjilFFDD6S8GxA+SWSrH0EsT9MzSiYISAEB1atFiqeEijXEWNcmSg2yidQPLiQrJeCXZ7bSkGbS+rfQR7js1qobqqfpNxuj/Sv19urBE91zft9Y0uOlyHoPlRg13bcf6qJrCVtUAIeehTl0IOB2GdD/ccHEDpCbxIzu5sVlypYZTfc2wbMaMG4RAnq3qVBUuW4XdemAnwVQF2e6YJXXzdje7ClVmKdE9s+T1WIc7oaUaLMkaW6JWNjZLX9x9CX75c2/8n6DT27BkyXl7G6U7xjVZURRHAZTpC0l03iLhTqxcTpQlJLNUjkEPQo+i58j//tMGAEANavO18uDtFjz9sVM4Uf3YOVwTASHOYsMZmSW3E5MWQ4+/7iL8/epwcOJAkJh2QeOmrG44j36zhluGvXQgelI8N/0t8o3T5rUax7WqJpue8QKissBqT7vbcE6kloDRZoNFg/63Fc3THu2Dm7o0NBn78zZt+rE92dbMgrVkySZnSkIg0v90G4RuwoXmaJUkr+tDoMcjPjtYfcAqNxabOIG5jVMSvejYqJrwmq6mcidZMjJLHsN7ayerRoSdKBucUjIkzFaXHeIpXJLMUjmGUfVCe8g9UxTS3/s5i7BmsxThvmlcWibeejEnpU0+Dujo1Kgamrs04KThREJltKt2e/qlfRxVTUnEiF5hkXmQENMiZHSToBa7aOcJ3XOjlNEK0bzA5ia8hJj3aWf5WuX4y597m57Rizvvxo9V3urivuv4Wew7WRhKR89HQyMPbl/PsgzadQCLOTMyX1Z93LZ+Bv41tCMaVhez/aIZNSdjx+oigEhsRtO3CEsInE4vQoBzvtCal5qU4HqMO4mxyGqrSCRLThC6MRhZvpHZLOl/G9XaHkVxJLHSJEsw+FliBZE2MUvhv+MpXJLMUjmGycCbMXkPnirU/tbHqzI/cwN6oLatl4FmLn0ROSUjUvUh4NDA27AwudWPpyfz1T3BIJB3Tu+bqsAQfJRnh8JzEWGXVhS8T9wErxTpupa1+bHrnNJRv5o5aDDPuSCLNJ4a7myxH1e+Pg/931ho2uCN8+qt26wPEfRtOBaMm1EU+V1HjIli01YqRIIHG6HZLLkw8CYAiksD9iYneFxfXLEz8KbBSlZWkiUgcsZAO7QYBkDVFP4apcK4hhidtHo8ziRLWrgTxd4ppdlmSUqWJGygv8arMCfqqCtb6dKocOo6gGvfbWDA3EYQcbqBR4VZcpCHsf6iwc0fvrylbuNITeafuOmYfipW7z9tSMP+lq6JbVvGWbIk0tcNq6fi+wd6CufplAy67+kxW52hPmJJEF6ctgVT1h7mlm8cn16PYhkeKMAJUaTCxCy5YHh5DISz2HA0DYwySh86NdCnv3VzEAkSonlhT/Aqrg4EgKqGE/uWbk41cK3VRQE7OFWPR8oY8CRLrWqn44cHe2H+mMttv1VxxnCoc2qzFF739H3HUsNZmX7EU7Jkz2JKxA9GNRxjMbyaEv+zbkxFap9tvErMoiEpwWO61WXKx2G5kQRw1fJwwCwZF/DmtdKwZPcJTuow/jboQrwzZ6f221KyJLD68dI4U8PFV7Ik2tldm9YQztLpBsuTLP09uw2OnynGbZdYG8muP5iH9Qc3UOXr37MYE6tqh27Dqd+a3xtj+bkZ/TyP9W5jw7GoUN/zQupYQSWPUA46aRBCuGObECBQeoJJ9HoQFD3NGGmwuw5Hgabl8xGXYMHOXF2A2liCkMjVeyr1xmwSPB50aVLd8lu7NcTr8DYcK+IEwAmky3EdAMTXyFsyS+UYxlOeUfRvjOLOMmqNdGwZjcZZDEiVRK8ts+RcshS50NPJ6VxdUH58qBe2HjmDmulJ+O8y58GHeUFGAUAkPjCXWTIY+1shqjZLLqK8O/WJZDd2AGsP3CwkcPyPZaUn48uR3XVphQxUDb9ZvLzVeKNtUFgncbNkyZ4mI3gHDCc9aHcbTn3kRrKkfh1k80oIEna7AiG1nepYNHSjzUXxCOUv+ik9j6qnJWFIx/ruCi2FE9UjMaR2o/ZUx6OxXBGpvXEOX3VRXUzflEO9d2jgTdks0Wscq14mD960Gs6+qJhBquHKMYyMipHjNg56lhpO9HTCS2c0GmdthE9cxQ+4G/5WiAwNTm8bGUFIyAdVvwtq2Sem0LlxddwueDWXBasNU8QvjrqQWG2ednZobiRLvGEScHGCd9J1ov3sNFygE+eCYg4g9b9ZG45Vs9MMAoseo6TGzY1GO4eAItCp/hnvI1PDhf7nSU2sJGAhyVKpGs7DlnAL0WDx3fBeTXW/o3mr1CkIgTZg7u3TDLP/2i+yvCiIMUvhv1vWTkdTw4UbD6WGEwrNo/pZUvT9LOI6QHcr0rak2EEyS+UYdqoX4+KoV8OF/o88lpJRsmQeMoM71MPYq60ZJqc2GHtyC+wT2UBRFHxx9yWaMzcniIUhoZ2RLwCsPXAapwpKzJsngxHmIVLVK41B7eoCABrVqIIfHxJz6eCEWRNNqS62HRtVE0qfoJOyircX7wal2UkoQ31gUUwgSIU7YaSLhmSJF8dP1HllqGCaBj5DGAubJTtmya8xSx73zJLC97N0TQfDjcYo80qO4mMizBj8X48maOTC671azY8X7NY9F2k7uo2SEzxIN9piKqAkpfYIUFJVupdZzJKRvvKihpPMUjmG/jaceUhaDapIjCl5NITip5nTeBSgbmaKUB6iOFlQYp9IENEwFo8GaCNfHhbtPIE+r84x+Q4yqmStYMccXFi3Kto3yMTFjatZZwTgovqZmD/mcsx4rJ8wE+Skv0XzVBfbz4d3Q18BaSHd53b9T7/mxfYzLtIslZfVZkgo435W/5iZY25WXPDUcE7WALubg+rTZFdqodD/hONnyUqIGSQEfjXEk9fasaQVPAq/l9w4Bo0lghZqWxGoN22NcRGdSpY8ioLMKomG95SBt4gajroNR0sVkxJYDDlfgyJvw0kwYWenYlRheBmnaeMi30nwZB4uV08Da6JZndaMecQDbpglmln54cFeqGphuK0iJdF6OhFi7+wQAM4W+01+pnRqOAebPwtej4IpD/fG58Mv0dHGQ+OaqaiS5HXgzE+8vUWvgKuMRvW0JFwnYDuit1myTkvPM55aMGLJEsUgsG/DGQy8XcwZnrd1l4Ilzm240P+RSJbe+mMnc7xZSpYQVsMlehSubZMdvB7+5m51+CxrhFSVob/dklHkY3OfQsySwf41w8AshQy8tRS2+dFSVbtYhfI2nIRz2EiWeDF0Qu9C/xttA6pxIoYLkMB1X2D0ympEiitj0MhAn13dLHpdmlTHE1e1RrOsVHRpUl1okhpvNBkRdOBoznhln65BNDx4K4oCxWG3iLajE95UNC3NZIoYe/Nuw7FAv+ZKZyzyD+djJVmiNj/Ge7PNknPwGL3B7esK52G8/cp774ZZqkm5VmAbePPnRpDQBt4e964DFAVpnIOPEy/qbuAkv2NnilyFKaHBu8kqYidotFU1MkshP0sObsNpseEUHaMstq9RkiX7omIGySyVY9h50zUu7Cw1nHHC1Ewzh4YA+IPQJFliDW6P9YaU7MLbbzThVg334GUttL9FmBy7OGoBQoRP+cZ+0zkLtKkOr7rGKjhtFdHF3slGJto3NPMo4vuJtj1yIqXh3cI0Gcla2POwQDPKYn6WbAhlwGjD+MmwrggECS5vXVs4D71vN/57q9h/PNzevTFen7kdAJsxsurW/HM+ynWA4toppUdR0L5BJvcdDbfM0kX1M7DpcL67j0vx2aK9EdPBY5ZE2s7onDTD4MhS72fJHkHqpJBG2T8xmSXmgdyd5/doQqrhyjH0ajjzALK2WQr9b5wvWel8x3ks6FR7HD9LdpIlNz5ZIoXO23IUbJZE5qhdeJVg0N7AW4VxoWOFsuFBlDnQx1yyp0t00XbW2s7UcICYOwO9zZINBVTFeH1oLJLpZ8miKgThuchKF43bcEbakxO8GHhRXUfBkO3t4UL/u3HOSDOEPoa7CKsxeOkrc/D18gMAQn3rdkp7PQoSvR78I7uN6Z2JWXJpK/TQZS0BhAJKRwNu7bN4hwoxyZJ+LzFKzT0KtEVRhL6wZClkB/nQZS3w/PXtLFW9LHrkbTgJJlhqNRpmmyXzx8YFyMrLMAuplC8nj8edzVI0mKWhXRq6/tZJjDgeRHgcu02pbf0MYcmSaaGj2teuOsJMjcNmETfwFs9YdNOjmUxjPD0WeH6WWKDfivoqYkpYLdVwsb8NZzROb5rl4gaVDQ1qHVMSvXj6mraO8qbrWMxgluzmxqHT5wBE6Dqg9LN7+zbHbZc00r8zTF+3Ep2uTatj3biBeOvWTu4yMMDt6sWbJ04NvBVFMXm+1xl4C9ASNDBWj1/VGnf2aMKRIvGfScmSBBP0kGENKqPKQGFsqEZJRpUkZyoxWr/P8yLuUWzUcDa2PCw80C+sAhveqylevamD4zxURMPBpYj9AG+jfaKDH8N7NsYL17cXtlkyqeGov+02f95742P65CxClTHfKQ+bA9iG0glkVgrRTY9e92lG8k3OhuR1oIbT2SxxxoqQZMmijGAQlgbeJg/eLnZImln/5c+9uYF2LfO2k2DaHOAs6aPaluWINEgI8ot8OHG22DKfBK/HtbRFr842HDYtbGUclQEgMzXR9fem/Fxmw2WWBDI0SpYaVKuCRjWq6N47sVkKUpIlfTli9KmP3Pltjw4ks1SOwZrYd/dupj0zCjL0wVZDf4s40wP4HDutX+YZeCt2ajibW2IsjOjdVPu7dkZyRAsPS+zcuXE1vHB9O+E8RHgcnpfd+mnAP7Jbo0ZaknD4ECu7HCeR461AZ+PmxNahYTX0a5XFyNeJZEmUWQovk35qE7iuE1sdRPe53eagkywJzg9WOqu62NksRVsNZ+WPyopOuxu4Tph2U7keRWu3EsZGHgwSdHh2Brq8MMsUYJpGgkeJOEA4YN64raLdO8o3ypbhbtWBPGZJJJQUy9CfPsAqCijJkoAajsNYscaQlURTSpYkmGCp4S67MOxjxigx8TKYK+Pm3MxwJd0OaUlhyZIC/sZjtXCmuJAsGW2lnIKuNWtje+lPHfB/PZq4yo8HkUVIVA1ndCRISwj/OvACAMDIS5uBhVj4Q+JnwirfweeCaWnJTuu6Gbbp9U4prdPSUqJUjuRV6NChk1Dp3zu2WXLRNymCFymMWdPGu/lFPiqdNUPohplQVXFsyVL4770n+E5pQ4F0nZdthHnjNrx3mW+03bq5nae8MEIi64PO31bpn15d3yuOXBsEOJIlEZV0qLzQ/9JmSQAnT57EHXfcgYyMDFSrVg0jR47E2bNnLb8pKirCn//8Z9SsWRPp6em48cYbcfToUV2aRx99FF26dEFycjI6deoUwxo4B+sURy/CxgWZHnhejRMPD69nrm2Lns1rOqJBr4bjS6asJowbyRI9MSN1rMliYiK9lsssR0Dd59Z1AG3U3KZeBra9cBXfZkRUsqRTw9nTxUpjZYwpAtG0NPN4ZZvaeO2mDpj2aB9uerov7Jqc3lSqpbJt+kSYXLouRnW3LjYco8rGG2tu9sc/X94SjWpUwWP9W1mmM5afkujFlfWDaFU7DT2o9YGphaMlDi52cVVVWOwPmN7R85wQ/rz3ejyub8PRMI49q/BRThB9yZI7lHAuQjg18OYZYTtZl4McqaqIlJVOJyVLArjjjjuwadMmzJw5E1OnTsX8+fNx3333WX7zl7/8Bb/++ismT56MefPm4fDhw/jTn/5kSnf33XfjlltuiRXprmEMNQLoT8Gm23CMd7d0a4wqiV5c36k+RvRu5ngiG22WeN9b2yy5YJYoBsdN9G36GxYT4zjkWQRqOF25wjZLQcNv/XdWdmBuJEsiZLHSWPniEYHonkf7VlIUBUO7NkLb+nwJEz1+7No8vyis8qmawvamYhwurDrSDB0tkQ3REGa4WP3TolY6vhwZdhLqZsOtVTUZCx6/Ao/1v8AynTHvQJBgSJMgpj3SW+epmUVBJGo4wFqyRI9x2i+VEYkedoxKpzDmYN7I3eXL+84txW6ZL76Bt/06pTPwLqVcr+mgYsMJNJTWtwLSO9ZFGbVs6WfJBlu2bMH06dOxYsUKdO3aFQDw9ttvIzs7G//6179Qv775imZeXh4++eQTTJo0CVdccQUA4LPPPkObNm2wdOlS9OjRAwDw1ltvAQCOHz+O9evXl1GNxKDo/raXLLH8MtWqmoz1zw50dH2YRpoxJhAH0Tbw1kuWHH+uz4sxmZ3eFIrEwJuG29twPgfcnaifJacbDmvzYpXlpLuEJUsOxwA9N+yY7TOU6knUgzeLSaTtcIzqPJoR4NW4frUqtmmiAWMVucwkU7IUoRrOa6WGo5gl8EMDeb2KpXRk0r3dcecny23tA002SqY8XUqWotx7UTfwFtgKdP1cml7vg0sJuw4QoCXMK9lLllhquB8f6g2fz4fNy+cLlBYbVAjJ0pIlS1CtWjWNUQKA/v37w+PxYNmyZcxvVq1aBZ/Ph/79+2vPWrdujcaNG2PJkiUxpzkaUBjMj1XMK5rBoN/xGKVuTavb0kCfkFnXfZvWDDEd0fazRNPvRNz79DVtkeT16G7P0Yavn4/ohnXPDERqkrNzgggJvKjvNNz6WQoI+BZSIbpYO12DWQwj69TrhFan4U5EkaAbP9Zpz1CSJR7zFiQKTlDxClm2TTQDYLQfovudV4adZ+NowZg37zKBndQwIskSYyOnx3zQQg2X4FEs15TaVVNwp4A9oq3Nkssu4HnGv8OBjaQuP3dkMH1ZAe4lS8abkOp6YGynGX/pi5a105n5ihh4s/arlrXT0bJ2OuLgsk9DhZAs5eTkoHZtvU4/ISEBNWrUQE5ODvebpKQkVKtWTfe8Tp063G9EUVxcjOLi8PXW/PyQt1afzwefz8f7zDEC/vAiHgwG4PP5QIJhXb+ntEwtfYC6QUKILS31MvTBb1npE5XwglVY4jelGXVFC/h8PgSDZhsELQ8vO2+6TOP7ILWY+v0B4Xa9q3tD3N61PhK8Hu0bOs51WqKC1AQ2PVZl8PZb+huvYm5zY/1EGYmAYTMp8ZvbnodgkJdWTx/R2YjYjxe/X39DyefzMblInwNaFUE5lC8QdDS36Lr5A9bjJ68wzATxJIg78/WLelqSx5QnzSwZF/USX7jt/H4/fD7zJkG3r9/vQxSXEh2M+5PKpBjrE/CbCaDHCWvO2/WR2i6suGXFJeFvfT4fl1lSQJBkIcUNBvz424CW+HzxXmv6DPkHjOOW9u1lqFeiVzEFqFXh9/nhYwjTR13eHD2aVkNOfjH+/vMmLv2m/Px++HzWXAJrHWUxpAAAYj+XggG6b0N9HqCeERLUmsdvaLdmNVLwUL9mGD15g7loQ9mBgJkOD9j08faKSCGaX1yZpSeffBKvvPKKZZotW7aUETXieOmllzB+/HjT8xkzZiA11bkjOB7O+gC1i9avX4+UI+twsCD87ETuMUybNo2ZPve4/p0eoTT7Dx0CLVy0S3/i5KnSNOFhs27tWngOrsGufIA3nI4cPIBp0/Zx8g5h5syZut+h9TuU3/YdOzGteLvl91bYu98DtZ7Lly7GYd0cDtPMrz8QDHrBOuPR7XHCos3V+p0tYOdjxNnCc7p027bvxLQiVhuY23z5suU4tZWY0gQCAQZ9oXdFxcWW9QegG3tAqO7HjoXbVsWKlatQskeMCTp7NtweHhAEOW1z8tRpW/rMCNG6Y+cuTPPt4Kbaeyhch8MHD4IlcJ9zWE/X3h1bMC1/s+5ZYXG4Lnl5eaD7b/XadQBCO+isWTORypgqR8+FaZ45cyaqxGh1Dvr1Y9BXyqQZ5yC9nqg4dPAgpk3bDwDYcEyBWicVdn10rnT8F5X4YJwH/5i0EGrbL1q8GCdPsOfKti2bS2ljq/fnzZuH2lWARmleHCjQf0/Xcc9e/did88cfyKDs+48eDb831mtUW2DaAQ82nzaPlZkzZoBj+haiP9fcblaYPWsW0gSjy9D1KzzHbr9du6znAwDkFgHhveQ4pk2bpuvvXTt3IRhUACiY88cfyDTci1jHqePBA+HxAwBFgXA5KpYvWYwjZj5Lg3GcRorCwkKhdHFllv76179i+PDhlmmaN2+OunXr4tixY7rnfr8fJ0+eRN267CCRdevWRUlJCU6fPq2TLh09epT7jSjGjh2L0aNHa7/z8/PRqFEjDBw4EBkZ9teaRXE8rxBYuRAA0LFDR2RfXB/bj57Ba+tDasR6desiO7uTlv50oQ//WDkHAFC7dh1kZ1/MzHfUkhkAgKxadYATx7Xn2dnZlunTMzKQnd0T3x1bhUW7TgAAOne+GFe3q4tV+07hrU0rmN9f2LI5sgexjU59Ph9mzpyJAQMGIDFRvyL8ZWmo3BYtWiB7gPUNHyvs+mMXZhzaBQC4vG9ftKoTFhGrdQP49QeAx5bOYIqXsrOztTwa1a+P7Gy980xj/V7bMh8oLrKlOTEpGSgJSzyaNm+O7IHmNqTpV9GzZ3dc0rSGKY3X60V29iDm98nJycjOvsySpk2H8/Ha+qXa7+zsbEzLW4N1/9/emcdHVWX7/ldzAhmKkKGIZGBOhIAYIQREhsQAsRUkV4Tm2YKIionKoI0+B/T1p6Vtfe2zlbZtL4J9EVtpUVul0TB3YwANIIN0RC4XZEhQ6BCmjHXeH6EqZx6qTlWdU1nfz4cPqXP23mfvs6d11l577XM/csJdN7S9Tajh9aNVOH35AgDgk4qRuOU18SXy+KttTwu+svXuLd3+AIDJqMX89/ehsHcSMrrFYuePJwVhYu1AU0d1YNQwYRkX7ayEr5F0T+qGYxfr/fcGDcoDjrQLVxNKSkQNyY/8eAnP790OACiRCKMHS/ZuxpUrrK9pixVAm6APnrvUjCe/3sKJm5mRgdLSgQCA5r2n8M6RA/578TF2lJaWyD57xYmdOHHpPFoZ4SS++2yH4LH1fHccuXBeNI0heXloaGzB+hPiE/64sWOR1b0Llv+wAz9c4p7Rxi7jvvU12HL6GOteMeew3382H8S+c+1tQWxsuB9A7pJKwTJm8c03Cw6eZdO27zRwWEYa4HHzzTcrHoAuNo4+/vUGrjfXq/Tr2xelxX1l0zvx7yv41Z5/AADSUlNRWno9GvecxOoj7Rqx3n36AKeOAgxQVFSE1HjumaOWA7V4+7DQBjgzs6P9AO2rFYt3beKEGT+GO0bLlVEPfCtDSkRUWEpJSUFKSopiuMLCQtTX16O6uhr5+fkAgE2bNsHr9aKgoEA0Tn5+PhwOBzZu3IiysjIAQE1NDY4fP47CQm2DLh+XywWXS3ggrcPh0LUS2WnZ7TY4HA64nB3XnFev+X+zpHur1aqYl5weidjwr46JTil8m7c9zP1j+viFJafDrlju+FinYtqyaagoixxOlv1IbIx0XgJ5Buf9O2yyaTscDjAqLRD4Gn4vY1GdP4dd6l3Kp6GUvt3OHS4cDoeo/QNjUV9fbNu0gT2TJMN5mcDqBwBgkS/35KE9kZvuRnZyFzz3ybeiYWJtQD3rt7trjCBN9qQp2IHJ+u10OuBwCIde9vuVCqMHfFtHX7b5fdDpEH4d2Fh90cFrDx8+OFKxjtRu9tjzg7igBAAuhx1xMv3IcXVMsooYD7HLaLdx8xLj5JZ/8aRc/M/Zy7hzWKZkucTsvbrGuuCQ8Xlls6nXKgGAU8O8wi7fpLweWLtbKPjbbcr90+lk2fFZ2/uPzcbyuWe1+r8dfe+b8wy7eNu18cZyl4gWV26Mbn9e6OZZOUxh4J2bm4uJEydi7ty52LVrF7Zv346KigpMnz7dvxPu5MmTyMnJwa5duwAAiYmJmDNnDhYuXIjNmzejuroas2fPRmFhoX8nHAB8//332Lt3L2pra3HlyhXs3bsXe/fuRTPrqz5SiBt4d1SZ8CBddel+UnEjHhrfF+Xj5L8u+PhsG7g+OCyKz5Zy9KeW4P0sdbyzQM+JU5OFkX2UfVipLUsr74tQzqO3WuQMVtVkS9zvjvCilvpSe9xJMG1A6dVZLBYM8MTDZbdJtmP+kpic5gAQnjPGNlBXU+RQ7oaTsllSCse/xv7717cPQt/UeMVni+100orNakGsjDDiM0hWfM8Cg27uheQ4F9Y8MBL/oeFcyg/mFSo6B5VryxMGpinmUy2/mixxQoEqp5Tsvy2CbLR7pPdlT5iepPsEFUb0erSRUGAKA28AeOedd1BRUYGioiJYrVaUlZX5t/0D7Sq6mpoazvrjyy+/7A/b1NSECRMm4A9/+AMn3XvvvRdbt271/x46tH3p6ujRo8jOzg5toRQQ25Fgl9kNp+RIzEdez0Tk9UzUnB+fy3qxjiS3MybOFVwzC9YRGfudqfGFpJVtj43Dnh/+jVsHK58yHvBuOA3CUiB+qdSQK+I5W6zWVZxz2xFf5ZajIT3d6hPloeV1SHmot1kYsEublSRvm8jvm60cYUmqzCz3AiGUltS+c/HdcB3XrBJ/y6FH/7PbLLJuOnxZUT4WKLCPTTnys6S1oz7k2qNYNw+0LXR12dEvNQ6Hz3CdN6tJTmwu4eSDlU+1H1FiTxdrY3ocfB4KTCMsJSUlYfXq1ZL3s7OzBZNETEwMli1bhmXLlknG27Jli15ZDAHCBivnlDLQk7jV4vMiLeosU6ZHdwlSWNK6bZwPO7+B+puSI7N7F2R2V2fYr1aQaOO1ZS2aJamgcoO0mtStVgtevnMIFrz3Tcc1Mc2ShrxKNdmxA1Iwtn8K+qfFY8d/n8W9N/VWnaYgPxqkJalBnm1j8/79hYJT2AEgM6kLjp9r/1jjvxctwq5YfD1Rfa6agmYpkKNP9Oh/dqtV1YSqlCf+7VCPnz7kmoK449fAET/LUzmemKd29jV2nxJLTlJU4t0Qe+UujcuU4cI0wlJnRKxRqz3uJBR0LMN1XLOKdCQ+cSodW0oR7AoUW4h2RFjFq1brI9QsqVfXBOvxXI5b8tLxQfVJDLtqQC422PEFPTmkhIKuTjtmXT00emRf4WG9WtBjWdDnFeC2IekY3ktce/DkLbm4/7+qMWmQB1dauNvq21RplsJDIAct+69JpKO2THr0P7vNIjhOho1fE6IgZqjx+RMKpPqa3WoRvRfM8SliH1maz4a7+j/buzv7o08sf1LP4F8V9bNkN6ZmyZiLgwQAcemc8zUntwwXgvz4D0MUUdHKdcDYIA1Vg7VZYkePtIpXtc0S/2w4HTRLeuC0W7Hq3gI8InP+mJYPdElv4zoebKCl/Ujlx2dwL6d9mDDQg3UPj8bvpl0n0N6wBUg1HtZDOW+rdQQqqjEQ0TgAGoQlHbQ3LrtV1i5ITBMiRitvF0W4ZFip1vhR+SjRe8G8sivNQl9YgS7Dje3f4etQUbMk8RBVNksh0P7rgTFzRQAQH5jY2iR+Jwr1l1GrjGZJzilsIAfpsglWWGLHd6jwXhtK1AoygiJreAWBvC+9RJNreyTgtiHXqA4v9dWs+ew+GbS8DilBwudDUamPXZuegFinTXj+GmtiVqMp0PvIDDbqzw5Usvlh/a2yW+mx1BXjsCkYeF/Nk0L+L/MECdXLk0EipVmyWS2ifTeYtsDXcALqhGV2EF+WrFYLysf1AcA7vkckOWnNEn81RBguXMuhWiFhycCIaYpsMpqRULcx32GmYl+UcgNTsF8KQWuWWH/rcVp5MGgty/1jeiMl3oUFN4v7Cfp8/k1BP0NP1j0yWnaJhI/UBKWvZkl9WKl2/GNj+3W1TZkfbu2eji3cqnbDhVKzxEv7npHix3CIZYGrpe0opFoBjH8k0JTrlDdF8HHZreqW4RSyxBckwrcMJ37dYpGwWQoiW/xdtWqREpR9wg5b063FLkqdvRQJS4RG2E3G7zqA1ZC68gynQ93IfF/HXM0SBNf48POplWCXleSEh3D3S61yzMPj+2HX/y5ChsTuqwEe4XbtQGSlSMlXUu9fz/xoseFSEmTUfvXywx396ZL/b3WapdDBFgr+VjEKv5Rw2CmWTfabZJ/Ppnbs4S+D39hP2c8eH5fdhuSuQj93/rz4XQfI54m/RBWuDymp1mi1qD38Rz1iGwvUVBVHsyRynX20j5jRvrSwZV5IWDIyCipKd6xwR45M1KDpcB3AXs+2cP7nc3dhFnoldw3qucFuhZeLHm5jW627omxWi+JEdH2mm/M7nJqlYJ8kfXBtkAlz0tJg4K1xu3mw4dhwJ6XQtUt20oN7uiUFQKXlH/byulo5Q+CsMwBcDisSYu2CDS4+AtUshQup9mi1iI91wTQFsc0WWg28xTLDPndOTFiS9rNkXnGJhCUDI9as2ANEooJjPL3psFkS2k1Jda7npByjaSDYuV9O2Ar3qpxWQUZqQmDz7n0jOL8DETRC5ZtJCemv+cgswykN5oFqlrQS2mU4tTZLwmvsZuJkbfFWvwzHs1lREefJ0lzOb5fdCovFguQ4ce2SL02luhQzfg4H0stwFgnXAYE3BlHNkop47Ppk56lDs9T+7iwWiWU4iacoNZMP5o1UkbvIQMKSgeEYeMNnM8HSLCmcF6Q3HbvhOq75JrtQCh1atTF85OSAUBrSisHOixpnnWomXf4REpG0WdKKmp1hwdKzW6zqsErvW61QEIixMLs9hPILPBhBji1UB6ZZ4hv4KsfhL9352nv3OAnNugrTAAC4HCHNktSHiQXifTcom6UAl+EkFUtXX65vGU7Kb5akZklkvH3qlg5h2GVQ790A+VkyNOJeVNVplkIhBIgKSyKaJZvVErSAwyZoP0sy98KtFe6TGodDp9Ud3Gi1BDZpXpfh1hwnUuKVlFChh8D3zr0FWH+gFvff1Ed1HL1sluQ8TEuR7o7FU7fkhuwAXR+qPXgrBAvEZolv4B1InJirQprUx6Jv7FPKUWOkNEsS162SmqUgniWSnvZlOKHbC98ynJQrCEmbJZHL+VndFPNjBIwrxhFcA2/W372Su8JqCWxS1ANxm6WO+3r7Mgp2iUhKXQ+E32bpjf+V7/9bqVxa7DvSEtrL+PrM65GWEMO599D49jMA/8/kgYJ4HZlR/SgOwXpkljTwDirVdkb1TcavpgzStDtPychXrbCk9sBYPveO7o07h2UGFFctaqtM7IOL3WTZZVSrSeNPrmqi8d0E+J4r1XfFPuBuHezBIwNbOeEiZbMk1e3bDbz1dUoZKFLN3Nc/fJolKeFXWjMlFpZt1mFcmybSLBkYqXazfv5otHkZdHFGpvrEzoRiX3ParGhs0c9RTrBahjtu6In9J+sxSsQTdFeXPayDJvtYFKVSaVku2bRoLGobGtEnJU5wb1HJAMwe1QtJIkd0BEuwanOpiWBwEGfBBYPSYK12MDfycoJarbP4bjjWMhyrjGp7KH9yVZMXtrBrsXR8jElrL4QfcIsn9Ef1P09wwrm7OHD8nKps6wp7PHv+9jz87w/3A5BxHaDz89UIX1I2Sz6Ul+HUa5Y4Z6AaV1YizZKREfOUDbR/WUVKUALEXQewJ3a9T40OdhnOYbNi6dTB+JnIQbfL774BvZK74s1f3KA53cEBHEbMRkkGVGPc7aOryy4qKPlQEpQCfcXBCgVDeO/w8/k3YfHEHDw4Vv3SmZ4oL8OpSyeQ5aZwofq4E5FrHANvVt2r9ecTiM1SDMs2ymfcLZU/9nUljcXvpl3n//uWvB7KGdEJ9rLTTf07PuBC4WdJDDXJsZ8ptkuzyS8saTPkFhPU2AKzkYUl0iyZBuO0IrFBiLsMp7ewFDqLmiEZbmx+dKzmeKnxLvyFtwtNK0qOF+UckOpNoEudwQpLc2/qDYvFgrED2v3tDPDEi/qOihT8CUz1cpNBT04HNBx3IlJWKT9LzWqFpQDeSwxruU/N8qYv22IfdWz6psZh/7Ml2HjoDIqvTdOcr0AZ3NONNQ8U4hp3rEAQETfw1rctqTPwlheCfPUtVZ/SBt7y+THyMpxxP38IDpFsQz6tkc8TN2eXnki+9BKWinPbB7DZo7J1SU9PXpk+NOTavXAdvxAMwWoRXXYbysf1xcD04LR0oSKGNzmrFTT08CcUKlS7DhC5xp7L2dqzljZ1wnYgGjeXgy0sKe/A6zDwVl7fiY9xYMrQa1TtTNWTYdlJSHfH8g4m1t8ppRhaBRL2hxTfKaXUWC8p4Ck82sgjnnF7NMFBayPSc579YN5I3JDVDe/d365JEbNZYp/lFcjXoxh/uisf1U8VIz9L/JT3SPHVk8Uo7NM96HQYBvj17e1+qAamJwjuG/WMJDbBnvtnNPgf9nxhUK0Aq1cfCAWql+EUbJbYtKjULAkNvJUzw16GY0/OWiZko3Yl/s7icPg7C2ZuELgOkPgokF4iFd4R2zBkRGgZLkrRs81dl+HGX1nOwsQO0m1lSUtabG3ksFot6C6zky1SpMTrkycGwM+HZ2J4dhJOn2/EL97axbmv13tUm5dACHTXl1ngLzOq1ywZd9DX6yBdNqptlgQG3srEsDRL7LlZckK+eoPhbHkPfX28Mv06zXE4+bKEx4VHeqJ6v2MAN0/ql+HU2zIprVQYhej6LIxijCRxi3nwZhsQR9L43FQw7fXaLy1edNAJr81SYPGMvOtLD/iaM/V+loz7XoISHCTaSbPaZbgAhEi+6wAfmrana36qNkb3S8bk667RHE+wDBdiaenhon4oyk3VFIedJ988FPBuOLFrFvn7RsG4PZrgYKRGxP0SaP/RxWnHxkVjsPWxsX7bJkIepS9fI9u9+Ih6YYmnOYsOA+/A40rN5WpLKzjuRNVuOCntpZT2QmizFOqPTT00ie3CUmilpUeK+ml+F3IH6WrfDSe8JmbWYURIBWASjNSGxDRLADq2rhsor0aGe+aSiGYprMtwkdkNZ3T4k2BnMvAWgz+ZPzy+LyoPncG0YRmq4vPfixo/SzESdnFKmiXux4iq7AVMoH2VL4joeYC0GMG+B1903zEq0gbeUvGFN9hXjDTP8TFujyaCQutxJ1oO5VXa6mng9m4o2OOi2BdlOHfDBfpBq7dPLaPBb99qFUZGNvDOuybwnYf8ZrKwZAD+/sho1bvJ+JoINU2cLWCxxzXpk+3VXdMTPTQiFoh78NaTQDRsnN1wvJcu6cFbw0G65DqAMAUfzCvEsOxueOfeAtVxlNSmBm7vhqVNTFgysJGwj76p0o4wowG+gkhtnRh5Kfrhon6YX9wPnz18o+a4wa4SBWLLJfXOJSdkhR1XoUCXvmrR9wBpAHgvSF9wfPillDobTkqxqt6SyXgYt0cTHELV1/OzkrDmgZEYpOFrU2n3QigO8Y1GurKOcRA7eDic2olAx+ispC74j17tx8Vc49a2y8YM8LV7apfhJgz0IIt1tI2RiHHYML+4f0C+rYKdywUevFXE0aJBkroecpulAIVjvnCkt7BU0Ls75o7upVt6/Peo1cBbrHLouBNCV4wkgHD9YgjvG7nBG4GVs4ehT0pXrLxnuP9aq8huorBqloIYpEd7GGx79CZsXDRGv/wYBP7koHZpNNZpw5ZHx+L+Mb1Dka2wsXpuAV6feb3/d7AGyGLHnYzpnyIbR8sJ9pJpqA+qiUdL+iM5zoVfThgQUHz2spvFEtrTCvSA/86lPuik3rf4bjgy8CZ0IMnF4FyTBdezzhNSRQjbnJITMQO394Dp2S0WJ/59BRlJwWtPxg5IxdgB3O27rWKaJRMsw/nokRgDh+SuJfPCF1jVapaA9r5hpI+cQBjZh3v4dNCaJYEmwoKlU/Mw8jebVMXnbjNXL0SFqitVjO+H8nF9ddFchaqlBCuAyG1E0erBW8nG1chzB2mWDM5T17Vhz1PjNRlghxqlgcfsE4QYq+YU4OcFmVg1R71tlxbavEKnfuH8ygq1YalZ6cs7nFir0b2J5F11BH2odXD2RBbJH+zL4bVZCkZQ4iuSQqJZ0rHo/PYs9UGnbYmUnb5xOwwJSwbHZkXYzy1SQkkYMnB7D5js5K54/vY8ZHXvGpL0xc7WMvKOqs7A87fn4RcjszjXtC6NGnnwjwQC1wGWwMcLqXcrVkVGrYbucVxnvkZcheMsFfLGfqn+oMUpJWelQnv2woaxZmFCN0La6Cyif3ZcM+rIZGAKegnPv7OF0VePEQfpSPPzgkwAwIC0ONTUXQSgbRkOiD7NUrAaSIFTSsiPVXJDCftWjice/6q9cDWOmGmAMSvCZbdh7zM3w2KxwGYNzUG6emr6+a9RerlNXXzhfWPWE0CaJSIAFBt8eLIRVaQmxGD9/NGca+G0WRKzmSLamTTI4/9bs+8rAw/+gRC06wBRtY/G8CK8dMcQNckZEncXp9/MIhTLcME2QbHjTnxICkUK3tW518T/NhokLBG6k5lkzC3TRieZd2iwGfwsdQbYyhCtO8T5VWh2J57BTuZ8g2CLxSK7VCnXB5TsYkxpO2nwbxZ+dWg5MFcKCy3DEZ2VRycMwOXmNtw+VPvBkp0Z/kDx08WmiOSD4BLM1mZ++Hfn6usk0GxoXYYT2jgp14UvjJE2xaglNMtwwcHVLHHv6eEDi5ueccUlEpailFCu/bLPA4uPETahxFgH/u+0IYLrhDz8OttzvD4yGSE4sLUb2g28O/5efW8B8rW6ADEYwS/DCTVrcmOVrGZJ4VnJ8U6FEMbDiMtwbPjCjBYXAYC5NweRsBRlLLq5P1Z++T94rCQwJ2lqcNlt+GDeSLR5GcTHmO/rzagYeJzo1LDna60G3uzJJBqWVfX24O1lGAXNkkoLbxH4y9pmICTCUpAji5xRv/SORPXLc2xHp0Y28CZhKcp4qKgfKsbr4yRNDrN/IRuRSIwTNqtF9KgVogO2gKTVwJsdnFxBCN8BA/mlF3nNUse9bl2FWiQzCkshcbOko4E3v640L8MpPMvAshIJS9GIkaVzQhr+F6Bv63ooibFbcam5LeTPMTPsCULKY7GauEa2x1BLsJM5//0xDDTthmP/Yr/Oa9yx+G3ZYCTEdkxpmk89MABGd+EhsFnSxSklGXgTBKEF3kgxb0yfkD8y1mkjYYlFz27CnZxsZYjW3WzsuUTMXsds6H02HMMwstoEG18bZxH9EwAwbVgG5/c17lh89vCNSOjkpgJBG3iz/hbaLEk9U73NErtNGfmDgoQlgjAIar/a9CQmCs9zC4ZJgzyYX9wPQzM7tBLsL1+XRmGJPTlEg81SsPDfgZfRthuOjZqJdWB6IgCgpaVFVf4iTWiOO9Gv3fFT0uJFXeeshB0SlgjCIPDHEc0OEAOAhCUuVqsF84v7c66xJ3ity3DsKowGYSnYqZxvIsCAkRV6ZO27zf86BYTGwDtIAnBKaWQNUaCQsEQQBkHtQKQnsSQsKcKuB+3LcB2Ro+FdB7sMJ0xPXuiR0yxF4XyMGcMz8f82HMb1mW5cn9kNA69JCDpNPd+T0M+SVpslch1AEESQ8MeJcBjqTxuWgf0nz2NgevCDcrTCNfAOfDdcrDMKhCWd02t3HSD9TsWcWEr9igYqxvXFsOwkXJfhRledDlDX03WAWj9LkteDyklkIWGJIAyCWu+4ejJzeCb6psRhkA5fsNEKe4LQqllqavX6/+4SDcKSztKSkmYpkONOzIzdZsWovsmRzoYkwuNO1IXzIVZnnoQYJMe54LJbDa19JWGJIAwC/wswHDYuVqsFhX26h/w5ZoZtR+KyaRvMr7B2Ghp5IlCL3polBgq74YLw4E20o+9Butx7SkfO8BELb7dZUfXE+PajbwwsAZtmL+u5c+cwc+ZMJCQkwO12Y86cObh48aJsnMbGRpSXl6N79+6Ii4tDWVkZ6urq/Pe/+eYbzJgxAxkZGYiNjUVubi5eeeWVUBeFIEThjxNGHjg6E2ynnQ67tjppbO0QlsKxuzHUhMRmSW4ZTuadRaMRsdFRb+AtFV/8usNmhV3rKdVhxti5YzFz5kwcPHgQlZWV+PTTT7Ft2zbcd999snEWLFiATz75BGvWrMHWrVtx6tQpTJ061X+/uroaqampWLVqFQ4ePIgnn3wSTzzxBF577bVQF4cgFImCuTUqaGUJS06NA3pTi1c5UCfGq3EZjuPAkPqHKoIdR9jisVq7Smk/S+bFFMtwhw4dwvr16/HVV1/hhhtuAAC8+uqrKC0txUsvvYT09HRBnPPnz2P58uVYvXo1xo8fDwBYsWIFcnNzsWPHDowYMQL33HMPJ07v3r1RVVWFtWvXoqKiIvQFIwgW/HEnGraaRwNszZLWOrlCDj9lUT4bzjTf84ZFTw218LgTbbvhzCzhmkJYqqqqgtvt9gtKAFBcXAyr1YqdO3fi9ttvF8Sprq5GS0sLiouL/ddycnKQmZmJqqoqjBgxQvRZ58+fR1JSkmx+mpqa0NTU5P/d0NAAoN3xmZ7Oz3xpmcWhWiBEexm1lK+1lauFaGttRYvF2OcfRHv9AUBTS6v/79bWVpmQQi41dbwXo74jNXXYxWnD5eY2FPbupms5Wlvb0CbzTq0Whvs8puM3e0lQKU/R3k7lytfW1iYIpwWv1+uP5/VyhX+GaRN/Zqu4RtXbJh5eDaGqQ7XpmUJYqq2tRWpqKuea3W5HUlISamtrJeM4nU643W7O9bS0NMk4X375Jd577z189tlnsvlZunQpnnvuOcH1L774Al26CI9LCJbKykrd0zQa0V5GNeVr8wLsLvnF559D4+ariBHN9XfopAVAu3H2unXrNMU9dsIKn7WD1rjhRq4OfzkI+L7BAk/9IaxbdyjIJ3W08X379qFL7TfgT0VWMPDCgm7NZ66+t/b7Fy9e9L/HY/+j/d1GczsFxMv3XcDtt/2d//vf9f54h893pAUANf/6F9Y1CNsDfyzzcfDgAaz7ab+GPAjRuw4vX76sKlxEhaXHH38cL7zwgmyYQ4eC7ZjqOHDgACZPnowlS5agpKRENuwTTzyBhQsX+n83NDQgIyMDJSUlSEjQbwt2S0sLKisrcfPNN8PhiM7zjaK9jFrK19rmxcKdG/y/J02aqNljdLiJ9voDgKObvweO/zcAoLS0VFPcT+v3Yv+/zwQUN1yEuw4fqfrC//fAQXm4ZVhPzN/xBSfMxoU3YefRc7htSA84bFZ/nLi4OJSWjgIA7F73L2yrPQ5A+d1GezuVK98P247i0+OHAWhrg7537u7mRmlpAQBg59FzeO3br/1hBl57LUpHZgnitnkZLNwpFGryBuWhdFhP1XlgE6o69K0MKRFRYWnRokWYNWuWbJjevXvD4/HgzJkznOutra04d+4cPB6PaDyPx4Pm5mbU19dztEt1dXWCON9++y2Kiopw33334amnnlLMt8vlgsvlElx3OBwh6YihStdIRHsZ1ZTPauMuucU4nabZQRXN9ce2qtFaxqd+di0On7mIe0f3Nvz7iUQdWm1W0Wf2Sk1Ar1Thh6fVavGHt1k7NBxq8x3N7RQQL19mchznvlYslo537uTFt9tsomnavOLmA3a7eHgt6F2HatOKqLCUkpKClJQUxXCFhYWor69HdXU18vPzAQCbNm2C1+tFQUGBaJz8/Hw4HA5s3LgRZWVlAICamhocP34chYWF/nAHDx7E+PHjcffdd+PXv/61DqUiiMAQ7jSJSDYIHm0SA78asrp3xZbHxumYG/Nz+9Br8OGekwCCc3JJ/UMdP8vrge/rLuD6rG7KgUVg15Fap5SSx50ElANjYGwd/1Vyc3MxceJEzJ07F7t27cL27dtRUVGB6dOn+3fCnTx5Ejk5Odi1axcAIDExEXPmzMHChQuxefNmVFdXY/bs2SgsLPQbdx84cADjxo1DSUkJFi5ciNraWtTW1uLHH3+MWFmJzgv5WTImVA368rtpQ/x/B+O3iapFHVarBQtLBmDsgFTlwCrSkvvtQ/oYlKCzEDFMISwBwDvvvIOcnBwUFRWhtLQUN954I/70pz/577e0tKCmpoZjrPXyyy/jZz/7GcrKynDTTTfB4/Fg7dq1/vt//etf8eOPP2LVqlXo0aOH/9+wYcPCWjaCAEg4Mip3jchESgyD8rG9I52VqIDdzoNQ2pl64jUT/dM6lvHUng0nRbDn1EUSU+yGA4CkpCSsXr1a8n52drbgKyUmJgbLli3DsmXLROM8++yzePbZZ/XMJkEQUUa3Lk48NbQNpUV9I52VqEOrZikzqav/b/LgHVr+VjEKa3efxILi/v5rapfhJDFxlZlGWCIIgiCiC7Wi0vv3F+K/dhzD07fkdlw08cRrBgb3dGNwTzfnmlqnlFKYucpIWCIIgiAigtpluOG9kjC8l7yzYCL0CIUlbfHNbGpgGpslgiAIIroIxsCbluHCD//0Ge02S+aFhCWCIAgiIvTsFviJB2aeeM1KsMtwV1rMe1YiLcMRBEEQYWX1vQXYe6IeEwamBZwGKZbCT7DLcCfrr+iYm/BCwhJBEAQRVkb2TcbIvslBpeGy25QDEboi3A2nTVq60Gjeg4xJWCIIgiBMx6xR2Vi3/zRK83pEOiudBqGfJW3x77+pj465CS8kLBEEQRCmIyHGgfXzb4p0NjoVNr4Hb43SUkZS4DZqkYYMvAmCIAiCUIQvG3WmHYkkLBEEQRAEoUgwBt6avX0bDBKWCIIgCIJQJJiz4bo4zW31Q8ISQRAEQRCK8J1SatEWDUxP0DczYcbcoh5BEARBEGEhEKeUd43IwqXmVvxyQk6oshUWSFgiCIIgCEIRgbCkYm2qLL8nrstwhyZDYYSEJYIwIGY3hiQIIvqwabBZ+vsjo3H83OWoEJQAEpYIwpCY+XRugiCiE4vAZkl6nMrtkYDcHua2U2JDBt4EYUBIVCIIwmgEezacmSFhiSAMCCmWCIIwGsGeDWdmSFgiCANCy3AEQRiNYM+GMzMkLBGEAelEYxBBECahM2mS+JCwRBAGpBOPSQRBGBSBjRITkWxEBBKWCMKAdOYvOIIgjImNJy15SVgiCCKSkKhEEITR4NtSMp1ItUTCEkEYEDLwJgjC6JBmiSCIiEKiEkEQRsfLdB5piYQlgjAgpFgiCMLoMCQsEQQRSWgZjiAIo+P1RjoH4YOEJYIwICQrEQRhdGgZjiCIiMI/3ZsgCMJokIE3QRARxWmnrkkQhLEhmyWCICIKCUsEQRidMQNSIp2FsEEjMkEYEIeNuiZBEMZlzo290MVpj3Q2wgaNyARhQEhYIgjCyNgFB8VFNzQiE4QBcdo610BEEITJ6GRDFAlLBGFAyGaJIAjCONCITBAGhJbhCIIgjAONyARhQEhYIjoT8TGdx1A4WrB0snU4GpEJwoDQMhzRmdj22LhIZ4HQSGfzm0sjMkEYECdplohORLeuTiTHuSKdDYKQhEZkgjAgDtoNR3QyOpumgjAXphGWzp07h5kzZyIhIQFutxtz5szBxYsXZeM0NjaivLwc3bt3R1xcHMrKylBXV+e/f/bsWUycOBHp6elwuVzIyMhARUUFGhoaQl0cgpCFbJYIgjAynU22Nc2IPHPmTBw8eBCVlZX49NNPsW3bNtx3332ycRYsWIBPPvkEa9aswdatW3Hq1ClMnTrVf99qtWLy5Mn429/+hu+++w4rV67Ehg0b8MADD4S6OAQhC9ksEZ2Nzjb5mh2X3RbpLIQVU2xBOHToENavX4+vvvoKN9xwAwDg1VdfRWlpKV566SWkp6cL4pw/fx7Lly/H6tWrMX78eADAihUrkJubix07dmDEiBHo1q0b5s2b54+TlZWFBx98EC+++GJ4CkYQEpBmiSAII7J4Yg7W7T+N2TdmRzorYcUUI3JVVRXcbrdfUAKA4uJiWK1W7Ny5UzROdXU1WlpaUFxc7L+Wk5ODzMxMVFVVicY5deoU1q5dizFjxuhbAILQiIs0SwRBGJB5Y/vgk4duREKMI9JZCSum0CzV1tYiNTWVc81utyMpKQm1tbWScZxOJ9xuN+d6WlqaIM6MGTPw8ccf48qVK7j11lvxn//5n7L5aWpqQlNTk/+3z8appaUFLS0taouliC8tPdM0GtFeRq3lm3tjNv627zRmF2aY4p1Ee/0B0V9GI5ZP77wYsYx6Eu3lA0JXRrXpRVRYevzxx/HCCy/Ihjl06FDI8/Hyyy9jyZIl+O677/DEE09g4cKF+MMf/iAZfunSpXjuuecE17/44gt06dJF9/xVVlbqnqbRiPYyqi3fIAADrwV2btsY2gzpTLTXHxD9ZYx0+RobbfBZLq1bty4kz4h0GUNNtJcP0L+Mly9fVhUuosLSokWLMGvWLNkwvXv3hsfjwZkzZzjXW1tbce7cOXg8HtF4Ho8Hzc3NqK+v52iX6urqBHE8Hg88Hg9ycnKQlJSE0aNH4+mnn0aPHj1E0/YJVD4aGhqQkZGBkpISJCQkyJZHCy0tLaisrMTNN98MhyM6VZ7RXkYqn/mJ9jIapXzPH9iKhpZ2jX1paamuaRuljKEi2ssHhK6Mane/R1RYSklJQUpKimK4wsJC1NfXo7q6Gvn5+QCATZs2wev1oqCgQDROfn4+HA4HNm7ciLKyMgBATU0Njh8/jsLCQslneb1eAOAss/FxuVxwuYQO1BwOR0gaaqjSNRLRXkYqn/mJ9jJGvHys7XChykfEyxhior18gP5lVJuWKWyWcnNzMXHiRMydOxd//OMf0dLSgoqKCkyfPt2/E+7kyZMoKirCn//8ZwwfPhyJiYmYM2cOFi5ciKSkJCQkJOChhx5CYWEhRowYAaBd1VtXV4dhw4YhLi4OBw8exGOPPYZRo0YhOzs7giUmCILoXHS2s8YIc2EKYQkA3nnnHVRUVKCoqAhWqxVlZWX4/e9/77/f0tKCmpoazvrjyy+/7A/b1NSECRMmcGyRYmNj8eabb2LBggVoampCRkYGpk6discffzysZSMIgujsxDo7l98ewlyYRlhKSkrC6tWrJe9nZ2eDYRjOtZiYGCxbtgzLli0TjTNu3Dh8+eWXuuaTIAiC0M5rPx+KB9/ZjUdLBkQ6KwQhwDTCEkEQBBG9DExPxNbHxkU6GwQhCnm+IwiCIAiCkIGEJYIgCIIgCBlIWCIIgiAIgpCBhCWCIAiCIAgZSFgiCIIgCIKQgYQlgiAIgiAIGUhYIgiCIAiCkIGEJYIgCIIgCBlIWCIIgiAIgpCBhCWCIAiCIAgZSFgiCIIgCIKQgYQlgiAIgiAIGUhYIgiCIAiCkIGEJYIgCIIgCBnskc5ANMAwDACgoaFB13RbWlpw+fJlNDQ0wOFw6Jq2UYj2MlL5zE+0lzHaywdEfxmjvXxA6Mrom7d987gUJCzpwIULFwAAGRkZEc4JQRAEQRBauXDhAhITEyXvWxglcYpQxOv14tSpU4iPj4fFYtEt3YaGBmRkZOCHH35AQkKCbukaiWgvI5XP/ER7GaO9fED0lzHayweErowMw+DChQtIT0+H1SptmUSaJR2wWq3o2bNnyNJPSEiI2g7gI9rLSOUzP9FexmgvHxD9ZYz28gGhKaOcRskHGXgTBEEQBEHIQMISQRAEQRCEDCQsGRiXy4UlS5bA5XJFOishI9rLSOUzP9FexmgvHxD9ZYz28gGRLyMZeBMEQRAEQchAmiWCIAiCIAgZSFgiCIIgCIKQgYQlgiAIgiAIGUhYIgiCIAiCkIGEpQizbNkyZGdnIyYmBgUFBdi1a5ds+DVr1iAnJwcxMTHIy8vDunXrwpRT7SxduhTDhg1DfHw8UlNTMWXKFNTU1MjGWblyJSwWC+dfTExMmHKsjWeffVaQ15ycHNk4Zqo/AMjOzhaU0WKxoLy8XDS80etv27ZtuPXWW5Geng6LxYKPPvqIc59hGDzzzDPo0aMHYmNjUVxcjMOHDyumq7Ufhwq58rW0tGDx4sXIy8tD165dkZ6ejl/84hc4deqUbJqBtPNQolSHs2bNEuR34sSJiumaoQ4BiPZHi8WCF198UTJNI9WhmnmhsbER5eXl6N69O+Li4lBWVoa6ujrZdAPtu2ohYSmCvPfee1i4cCGWLFmC3bt3Y8iQIZgwYQLOnDkjGv7LL7/EjBkzMGfOHOzZswdTpkzBlClTcODAgTDnXB1bt25FeXk5duzYgcrKSrS0tKCkpASXLl2SjZeQkIDTp0/7/x07dixMOdbOwIEDOXn95z//KRnWbPUHAF999RWnfJWVlQCAO+64QzKOkevv0qVLGDJkCJYtWyZ6/7e//S1+//vf449//CN27tyJrl27YsKECWhsbJRMU2s/DiVy5bt8+TJ2796Np59+Grt378batWtRU1OD2267TTFdLe081CjVIQBMnDiRk993331XNk2z1CEATrlOnz6Nt956CxaLBWVlZbLpGqUO1cwLCxYswCeffII1a9Zg69atOHXqFKZOnSqbbiB9VxMMETGGDx/OlJeX+3+3tbUx6enpzNKlS0XDT5s2jbnllls41woKCpj7778/pPnUizNnzjAAmK1bt0qGWbFiBZOYmBi+TAXBkiVLmCFDhqgOb/b6YxiGeeSRR5g+ffowXq9X9L6Z6g8A8+GHH/p/e71exuPxMC+++KL/Wn19PeNyuZh3331XMh2t/Thc8Msnxq5duxgAzLFjxyTDaG3n4USsjHfffTczefJkTemYuQ4nT57MjB8/XjaMkeuQPy/U19czDoeDWbNmjT/MoUOHGABMVVWVaBqB9l0tkGYpQjQ3N6O6uhrFxcX+a1arFcXFxaiqqhKNU1VVxQkPABMmTJAMbzTOnz8PAEhKSpINd/HiRWRlZSEjIwOTJ0/GwYMHw5G9gDh8+DDS09PRu3dvzJw5E8ePH5cMa/b6a25uxqpVq3DPPffIHhhtpvpjc/ToUdTW1nLqKDExEQUFBZJ1FEg/NhLnz5+HxWKB2+2WDaelnRuBLVu2IDU1FQMGDMC8efNw9uxZybBmrsO6ujp89tlnmDNnjmJYo9Yhf16orq5GS0sLpz5ycnKQmZkpWR+B9F2tkLAUIX766Se0tbUhLS2Ncz0tLQ21tbWicWprazWFNxJerxfz58/HqFGjMGjQIMlwAwYMwFtvvYWPP/4Yq1atgtfrxciRI3HixIkw5lYdBQUFWLlyJdavX4/XX38dR48exejRo3HhwgXR8GauPwD46KOPUF9fj1mzZkmGMVP98fHVg5Y6CqQfG4XGxkYsXrwYM2bMkD2YVGs7jzQTJ07En//8Z2zcuBEvvPACtm7dikmTJqGtrU00vJnr8O2330Z8fLziEpVR61BsXqitrYXT6RQI8Epzoy+M2jhaseuSCkEoUF5ejgMHDiiukxcWFqKwsND/e+TIkcjNzcUbb7yBX/3qV6HOpiYmTZrk/3vw4MEoKChAVlYW3n//fVVfemZj+fLlmDRpEtLT0yXDmKn+OjMtLS2YNm0aGIbB66+/LhvWbO18+vTp/r/z8vIwePBg9OnTB1u2bEFRUVEEc6Y/b731FmbOnKm4icKodah2XjACpFmKEMnJybDZbAIL/7q6Ong8HtE4Ho9HU3ijUFFRgU8//RSbN29Gz549NcV1OBwYOnQovv/++xDlTj/cbjf69+8vmVez1h8AHDt2DBs2bMC9996rKZ6Z6s9XD1rqKJB+HGl8gtKxY8dQWVkpq1USQ6mdG43evXsjOTlZMr9mrEMA+Mc//oGamhrNfRIwRh1KzQsejwfNzc2or6/nhFeaG31h1MbRCglLEcLpdCI/Px8bN270X/N6vdi4cSPny5xNYWEhJzwAVFZWSoaPNAzDoKKiAh9++CE2bdqEXr16aU6jra0N+/fvR48ePUKQQ325ePEijhw5IplXs9UfmxUrViA1NRW33HKLpnhmqr9evXrB4/Fw6qihoQE7d+6UrKNA+nEk8QlKhw8fxoYNG9C9e3fNaSi1c6Nx4sQJnD17VjK/ZqtDH8uXL0d+fj6GDBmiOW4k61BpXsjPz4fD4eDUR01NDY4fPy5ZH4H03UAyTkSIv/zlL4zL5WJWrlzJfPvtt8x9993HuN1upra2lmEYhrnrrruYxx9/3B9++/btjN1uZ1566SXm0KFDzJIlSxiHw8Hs378/UkWQZd68eUxiYiKzZcsW5vTp0/5/ly9f9ofhl/G5555jPv/8c+bIkSNMdXU1M336dCYmJoY5ePBgJIogy6JFi5gtW7YwR48eZbZv384UFxczycnJzJkzZxiGMX/9+Whra2MyMzOZxYsXC+6Zrf4uXLjA7Nmzh9mzZw8DgPnd737H7Nmzx78b7De/+Q3jdruZjz/+mNm3bx8zefJkplevXsyVK1f8aYwfP5559dVX/b+V+rFRytfc3MzcdtttTM+ePZm9e/dy+mRTU5Nk+ZTaebiRK+OFCxeYRx99lKmqqmKOHj3KbNiwgbn++uuZfv36MY2Njf40zFqHPs6fP8906dKFef3110XTMHIdqpkXHnjgASYzM5PZtGkT8/XXXzOFhYVMYWEhJ50BAwYwa9eu9f9W03eDgYSlCPPqq68ymZmZjNPpZIYPH87s2LHDf2/MmDHM3XffzQn//vvvM/3792ecTiczcOBA5rPPPgtzjtUDQPTfihUr/GH4ZZw/f77/faSlpTGlpaXM7t27w595Fdx5551Mjx49GKfTyVxzzTXMnXfeyXz//ff++2avPx+ff/45A4CpqakR3DNb/W3evFm0TfrK4PV6maeffppJS0tjXC4XU1RUJCh3VlYWs2TJEs41uX4cTuTKd/ToUck+uXnzZn8a/PIptfNwI1fGy5cvMyUlJUxKSgrjcDiYrKwsZu7cuQKhx6x16OONN95gYmNjmfr6etE0jFyHauaFK1euMA8++CDTrVs3pkuXLsztt9/OnD59WpAOO46avhsMlqsPJQiCIAiCIEQgmyWCIAiCIAgZSFgiCIIgCIKQgYQlgiAIgiAIGUhYIgiCIAiCkIGEJYIgCIIgCBlIWCIIgiAIgpCBhCWCIAiCIAgZSFgiCKLTM2vWLEyZMiXS2SAIwqDYI50BgiCIUGKxWGTvL1myBK+88grIPy9BEFKQsEQQRFRz+vRp/9/vvfcennnmGdTU1PivxcXFIS4uLhJZIwjCJNAyHEEQUY3H4/H/S0xMhMVi4VyLi4sTLMONHTsWDz30EObPn49u3bohLS0Nb775Ji5duoTZs2cjPj4effv2xd///nfOsw4cOIBJkyYhLi4OaWlpuOuuu/DTTz+FucQEQegNCUsEQRAivP3220hOTsauXbvw0EMPYd68ebjjjjswcuRI7N69GyUlJbjrrrtw+fJlAEB9fT3Gjx+PoUOH4uuvv8b69etRV1eHadOmRbgkBEEECwlLBEEQIgwZMgRPPfUU+vXrhyeeeAIxMTFITk7G3Llz0a9fPzzzzDM4e/Ys9u3bBwB47bXXMHToUDz//PPIycnB0KFD8dZbb2Hz5s347rvvIlwagiCCgWyWCIIgRBg8eLD/b5vNhu7duyMvL89/LS0tDQBw5swZAMA333yDzZs3i9o/HTlyBP379w9xjgmCCBUkLBEEQYjgcDg4vy0WC+eab5ed1+sFAFy8eBG33norXnjhBUFaPXr0CGFOCYIINSQsEQRB6MD111+PDz74ANnZ2bDbaWgliGiCbJYIgiB0oLy8HOfOncOMGTPw1Vdf4ciRI/j8888xe/ZstLW1RTp7BEEEAQlLBEEQOpCeno7t27ejra0NJSUlyMvLw/z58+F2u2G10lBLEGbGwpDbWoIgCIIgCEnoc4cgCIIgCEIGEpYIgiAIgiBkIGGJIAiCIAhCBhKWCIIgCIIgZCBhiSAIgiAIQgYSlgiCIAiCIGQgYYkgCIIgCEIGEpYIgiAIgiBkIGGJIAiCIAhCBhKWCIIgCIIgZCBhiSAIgiAIQgYSlgiCIAiCIGT4/wAxHFy9+sccAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "N=1000\n", + "dt=0.02\n", + "\n", + "t, x = simulate_gaussian_process(n=N, dt=dt, sigma=20.0, ell=0.08)\n", + "\n", + "plt.plot(t, x)\n", + "plt.title(\"Simulated Gaussian Process with Gaussian Autocorrelation\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Signal\")\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2c70ac7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = np.fft.fftfreq(N, d=dt)\n", + "x_f = np.fft.fft(x)\n", + "psd = np.abs(x_f)**2\n", + "psd = psd / np.max(psd)\n", + "\n", + "f_theory = np.linspace(-20, 20, 1000)\n", + "psd_theory = np.exp(-f_theory**2 / 12)\n", + "\n", + "plt.plot(f, psd)\n", + "plt.plot(f_theory, psd_theory)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a9c207eb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_prime = 400*x + np.exp(1j*(3*t))\n", + "plt.plot(t, x_prime)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bc079786", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = np.fft.fftfreq(N, d=dt)\n", + "x_f = np.fft.fft(x_prime)\n", + "psd = np.abs(x_f)**2\n", + "psd = psd / np.max(psd)\n", + "\n", + "f_theory = np.linspace(-20, 20, 1000)\n", + "psd_theory = np.exp(-f_theory**2 / 12)\n", + "\n", + "plt.plot(f, psd)\n", + "plt.plot(f_theory, psd_theory)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5339001e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Simulate Gaussian process φ(t)\n", + "t, phi = simulate_gaussian_process(n=2048, dt=0.01, sigma=1.0, ell=1.0)\n", + "\n", + "# Convert to complex exponential with constant amplitude\n", + "E = np.exp(1j * phi)\n", + "\n", + "# Plot phase-modulated signal\n", + "plt.plot(t, np.real(E), label='Re[E]')\n", + "plt.plot(t, np.imag(E), label='Im[E]', alpha=0.6)\n", + "plt.title(\"Unit-Amplitude Complex Signal with Gaussian Phase\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Optional: check autocorrelation of E\n", + "def autocorr(x):\n", + " n = len(x)\n", + " return np.correlate(x, x, mode='full')[n-1:] / n\n", + "\n", + "R_phi = autocorr(phi)\n", + "R_E = np.abs(autocorr(E))\n", + "\n", + "plt.plot(R_phi / R_phi[0], label='φ autocorr (normalized)')\n", + "plt.plot(R_E / R_E[0], label='|E autocorr| (normalized)', linestyle='--')\n", + "plt.title(\"Comparison of Correlations\")\n", + "plt.xlabel(\"Lag\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "2ee0b6f1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.fft import fft, ifft\n", + "\n", + "def gaussian_autocovariance(t, sigma=1.0, ell=1.0):\n", + " return sigma**2 - t**2 / ell\n", + " # return sigma**2 * np.exp(-t**2 / (2 * ell**2))\n", + "\n", + "def simulate_gaussian_process(n, dt=1.0, sigma=1.0, ell=1.0):\n", + " \"\"\"\n", + " Simulates a stationary Gaussian process with a Gaussian autocovariance function\n", + " using the Shinozuka-Deodatis spectral method.\n", + " \n", + " Parameters:\n", + " n : number of desired time points\n", + " dt : time step\n", + " sigma : standard deviation of the process\n", + " ell : correlation length (controls width of the autocovariance)\n", + " \n", + " Returns:\n", + " t : time vector\n", + " x : simulated Gaussian process\n", + " \"\"\"\n", + " m = 2 * n # For circulant embedding\n", + "\n", + " # Time vector for autocovariance\n", + " taus = dt * np.arange(0, m)\n", + " R = gaussian_autocovariance(np.concatenate([taus[:n], -taus[n:]]), sigma, ell)\n", + "\n", + " # FFT to get eigenvalues (power spectrum)\n", + " lam = np.real(fft(R))\n", + " lam = np.maximum(lam, 0) # Force non-negativity\n", + "\n", + " # Generate frequency domain coefficients a(j)\n", + " a = np.zeros(m, dtype=complex)\n", + " a[0] = np.sqrt(lam[0] / m) * np.random.randn()\n", + " a[m//2] = np.sqrt(lam[m//2] / m) * np.random.randn()\n", + "\n", + " for j in range(1, m//2):\n", + " Uj, Vj = np.random.randn(2)\n", + " a[j] = (np.sqrt(lam[j] / (2*m)) * (Uj + 1j*Vj))\n", + " a[m - j] = np.conj(a[j]) # Hermitian symmetry\n", + "\n", + " # Inverse FFT to get real-valued time-domain signal\n", + " x_full = np.real(ifft(a))\n", + " t = np.arange(n) * dt\n", + " return t, x_full[:n]" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "afee95ef", + "metadata": {}, + "outputs": [], + "source": [ + "N=10000\n", + "t, phi = simulate_gaussian_process(N, dt=dt, sigma=1, ell=0.01)\n", + "x = np.exp(1j*(1*t)+np.cumsum(phi))" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "f3185cd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(t, x)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "22052216", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.22, 0.28)" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "f = np.fft.fftfreq(N, d=dt)\n", + "x_f = np.fft.fft(x)\n", + "psd = np.abs(x_f)**2\n", + "psd = psd / np.max(psd)\n", + "\n", + "f_theory = np.linspace(-20, 20, 1000)\n", + "psd_theory = np.exp(-f_theory**2 / 12)\n", + "\n", + "plt.plot(f, psd)\n", + "# plt.plot(f_theory, psd_theory)\n", + "plt.xlim(-0.22, 0.28)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8da6899f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/maybe_trash/baseband_vector_fitting.py b/examples/old/maybe_trash/baseband_vector_fitting.py new file mode 100644 index 00000000..0de91565 --- /dev/null +++ b/examples/old/maybe_trash/baseband_vector_fitting.py @@ -0,0 +1,83 @@ +import numpy as np +import matplotlib.pyplot as plt +import sax + +from jax import config + +config.update("jax_enable_x64", True) + + +from simphony.libraries import ideal +from simphony.utils import dict_to_matrix + +from simphony.baseband_vector_fitting import Baseband_Model, BVF_Options +from scipy.signal import StateSpace, find_peaks + +netlist = { + "instances": { + "wg": "waveguide", + "hr": "half_ring", + }, + "connections": { + "hr,o2": "wg,o0", + "hr,o3": "wg,o1", + }, + "ports": { + "o0": "hr,o0", + "o1": "hr,o1", + }, +} + +circuit, info = sax.circuit( + netlist=netlist, + models={ + "waveguide": ideal.waveguide, + "half_ring": ideal.coupler, + }, +) + +num_measurements = 100 +wvl = np.linspace(1.5, 1.6, num_measurements) + +s = circuit(wl=wvl, wg={"length": 75.0, "loss": 100}) +S = dict_to_matrix(s) +S = np.asarray(S) +model_order = 35 +center_wvl = 1.548 +options = BVF_Options(max_iterations=5, beta=7.0, gamma=0.95) # 3 iterations +model = Baseband_Model(wvl, center_wvl, S[:, 0, 1], model_order, options) + +model.fit_model() +# model.plot_poles() +# plt.show() +response = model.compute_response() +peaks, _ = find_peaks(-np.abs(response) ** 2) +plt.scatter(model.freqs[peaks], np.abs(response[peaks]) ** 2) +plt.plot(model.freqs, np.abs(response**2)) +plt.show() + +c = 299792458 +print(np.diff(model.freqs[peaks])) +fsr = np.diff(model.freqs[peaks]) +ng = c / (fsr * 75e-6) + +print(ng) + +dL_microns = 75.0 +roundtrip_time = dL_microns * 1e-6 / (c / 3.4) + +A, B, C, D = model.real_ABCD_matrices() +sys = StateSpace(A, B, C, D, dt=1 / model.sampling_freq) + +N = int(10000) +T = 5e-11 +t = np.linspace(0, T, N) + +sig = np.exp(1j * 2 * np.pi * t * 0) +sig[: N // 2] *= 1 +sig[N // 2 :] *= 0 +# sig = np.full(t.shape, 1.0) + +tout, yout = model.compute_time_response(sig=sig, t=t) +model.plot_time_response() +plt.show() diff --git a/examples/old/maybe_trash/gaussian_linewidth.ipynb b/examples/old/maybe_trash/gaussian_linewidth.ipynb new file mode 100644 index 00000000..dc29e7f9 --- /dev/null +++ b/examples/old/maybe_trash/gaussian_linewidth.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "4ac9b1f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input std: 0.9875565681761207\n", + "Output std: 0.3123243143475987\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from scipy.signal import butter, lfilter, cheby1\n", + "from scipy.signal import dimpulse\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Generate Gaussian noise\n", + "np.random.seed(0)\n", + "N = 10000\n", + "x = np.random.normal(0, 1, N)\n", + "\n", + "# Design Butterworth filter\n", + "b, a = cheby1(N=4, Wn=0.6) # 4th order low-pass\n", + "\n", + "# Apply filter\n", + "y = lfilter(b, a, x)\n", + "\n", + "# Compare std deviations\n", + "print(\"Input std:\", np.std(x))\n", + "print(\"Output std:\", np.std(y))\n", + "\n", + "# Suppose we have b, a from a filter design\n", + "n = 100 # Number of samples of impulse response\n", + "_, h = dimpulse((b, a, 1), n=n)\n", + "h = np.squeeze(h) # Convert from nested list\n", + "plt.plot(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed65c104", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "65822353795.835144\n", + "200000000000.0\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.fftpack import fft, fftshift, fftfreq\n", + "\n", + "# === Parameters ===\n", + "fs = 1e14 # Sampling frequency (Hz)\n", + "T = 1000e-12 # Total duration (s)\n", + "N = int(T * fs) # Number of samples\n", + "t = np.arange(N) / fs # Time array\n", + "f0 = 193e12 # Carrier frequency (Hz)\n", + "linewidth = 0.2e12 # Lorentzian FWHM (Hz)\n", + "\n", + "# === Phase noise model: Brownian motion ===\n", + "# Phase variance grows linearly with time: var(phi) = π * linewidth * t\n", + "# So, each time step gets Gaussian noise with std ~ sqrt(Δt * π * linewidth)\n", + "delta_phi_std = np.sqrt(2*np.pi * linewidth / fs)\n", + "dphi = np.random.randn(N) * delta_phi_std\n", + "\n", + "\n", + "# Choose window size (larger = smoother, lower cutoff frequency)\n", + "window_size = 1000\n", + "\n", + "# Hamming window\n", + "window = np.hamming(window_size)\n", + "\n", + "# Normalize the window to preserve signal amplitude\n", + "window /= np.sum(window)\n", + "dphi = np.convolve(window, dphi)\n", + "\n", + "phi = np.cumsum(dphi)[:N] # Integrate to get phase\n", + "\n", + "# === Generate signal: constant amplitude with noisy phase ===\n", + "E_t = np.exp(1j * (phi))\n", + "\n", + "# === Compute spectrum ===\n", + "E_f = fftshift(fft(E_t))\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "\n", + "# === Power spectral density (normalized) ===\n", + "psd = np.abs(E_f) ** 2\n", + "psd /= np.max(psd) # Normalize for plotting\n", + "\n", + "# plt.plot(freqs,psd)\n", + "plt.plot(t, E_t.real)\n", + "plt.xlim(0, 1e-10)\n", + "t_mid = t[:-1] + np.diff(t) / 2\n", + "\n", + "instantaneous_phase = np.unwrap(np.angle(E_t))\n", + "instantaneous_frequency = np.diff(instantaneous_phase)*fs/(2*np.pi)\n", + "# plt.plot(t, instantaneous_phase)\n", + "plt.plot(t_mid, instantaneous_frequency/np.max(instantaneous_frequency))\n", + "plt.xlim(0, 1e-10)\n", + "print(np.std(instantaneous_frequency))\n", + "print(linewidth)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "89105b89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-100000000000000.0, 100000000000000.0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(freqs, psd)\n", + "# plt.plot(f_theory, psd_theory)\n", + "plt.xlim(-1e14, 1e14)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1aee7d9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0, 5e-11)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "E_t_reconstructed = np.cos(2*np.pi*np.cumsum(instantaneous_frequency) * (t[1] - t[0]))\n", + "plt.plot(t, E_t)\n", + "plt.plot(t_mid, E_t_reconstructed)\n", + "plt.xlim(0, 5e-11)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "00719f28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 5e-11)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.ndimage import gaussian_filter1d\n", + "from scipy.signal import butter, filtfilt\n", + "\n", + "sigma_1 = 1\n", + "sigma_2 = 500\n", + "gaussian_noise = np.random.normal(0, sigma_1, N)\n", + "\n", + "\n", + "f_instantaneous = 2*gaussian_filter1d(gaussian_noise, sigma=sigma_2)\n", + "f_instantaneous *= (linewidth/2.355)/np.std(f_instantaneous)\n", + "\n", + "\n", + "E_t_reconstructed = np.cos(2*np.pi*np.cumsum(f_instantaneous) * (t[1] - t[0]))\n", + "\n", + "\n", + "\n", + "plt.plot(t, E_t_reconstructed)\n", + "plt.xlim(0, 5e-11)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 864, + "id": "6c2072ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "84925690021.23143\n", + "85106382978.7234\n" + ] + } + ], + "source": [ + "print(np.std(f_instantaneous))\n", + "print(linewidth/2.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 865, + "id": "35eb7e9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0000e+00, 1.0000e-14, 2.0000e-14, ..., 9.9997e-10, 9.9998e-10,\n", + " 9.9999e-10], shape=(100000,))" + ] + }, + "execution_count": 865, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t" + ] + }, + { + "cell_type": "code", + "execution_count": 866, + "id": "b2dc5195", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-10000000000000.0, 10000000000000.0)" + ] + }, + "execution_count": 866, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "dt=1e-14\n", + "# linewidth = 2e12\n", + "f_theory = np.linspace(-10e12, 10e12, 1000)\n", + "psd_theory = np.exp(-4*np.log(2)*f_theory**2 / linewidth**2)\n", + "plt.plot(f_theory, psd_theory)\n", + "plt.xlim(-1e13, 1e13)" + ] + }, + { + "cell_type": "code", + "execution_count": 867, + "id": "4cedb3ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1000000000000.0, 1000000000000.0)" + ] + }, + "execution_count": 867, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# === Compute spectrum ===\n", + "E_f = fftshift(fft(E_t_reconstructed))\n", + "freqs = fftshift(fftfreq(N, 1/fs))\n", + "\n", + "# === Power spectral density (normalized) ===\n", + "psd = np.abs(E_f) ** 2\n", + "psd /= np.max(psd) # Normalize for plotting\n", + "\n", + "plt.plot(freqs, psd)\n", + "plt.plot(f_theory, psd_theory)\n", + "plt.xlim(-1e12, 1e12)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "a824df28", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def gaussian_psd_signal(t, nu_fwhm, nu0=0.0):\n", + " sigma_nu = nu_fwhm / (2 * np.sqrt(2 * np.log(2))) # Convert FWHM to std dev\n", + " sigma_t = 1 / (2 * np.pi * sigma_nu) # Time-domain std dev\n", + " f_t = np.exp(-t**2 / (2 * sigma_t**2)) * np.exp(2j * np.pi * nu0 * t)\n", + " return f_t\n", + "\n", + "# Example usage\n", + "t = np.linspace(-5, 5, 2048) # Time vector\n", + "nu_fwhm = 0.5 # Desired linewidth in Hz\n", + "nu0 = 0.0 # Center frequency\n", + "\n", + "f_t = gaussian_psd_signal(t, nu_fwhm, nu0)\n", + "\n", + "# Compute FFT and PSD\n", + "F_nu = np.fft.fftshift(np.fft.fft(f_t))\n", + "freq = np.fft.fftshift(np.fft.fftfreq(len(t), d=(t[1] - t[0])))\n", + "psd = np.abs(F_nu)**2\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(10,4))\n", + "plt.subplot(1,2,1)\n", + "plt.plot(t, np.real(f_t), label='Re[f(t)]')\n", + "plt.plot(t, np.imag(f_t), label='Im[f(t)]')\n", + "plt.title(\"Time-domain signal\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.plot(freq, psd)\n", + "plt.title(\"Power Spectral Density\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "ad2c5ef9", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (2047,) and (9999,)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[160]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;66;03m# Plot\u001b[39;00m\n\u001b[32m 7\u001b[39m plt.figure(figsize=(\u001b[32m10\u001b[39m, \u001b[32m4\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[43mplt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt_mid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minst_freq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m plt.plot(t[:\u001b[32m99999\u001b[39m], np.diff(np.unwrap(np.angle(E_t)))*fs/(\u001b[32m2\u001b[39m*np.pi))\n\u001b[32m 10\u001b[39m plt.xlabel(\u001b[33m\"\u001b[39m\u001b[33mTime (s)\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/pyplot.py:3838\u001b[39m, in \u001b[36mplot\u001b[39m\u001b[34m(scalex, scaley, data, *args, **kwargs)\u001b[39m\n\u001b[32m 3830\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes.plot)\n\u001b[32m 3831\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mplot\u001b[39m(\n\u001b[32m 3832\u001b[39m *args: \u001b[38;5;28mfloat\u001b[39m | ArrayLike | \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 3836\u001b[39m **kwargs,\n\u001b[32m 3837\u001b[39m ) -> \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[32m-> \u001b[39m\u001b[32m3838\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3839\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3840\u001b[39m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3841\u001b[39m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3842\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3843\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3844\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py:1777\u001b[39m, in \u001b[36mAxes.plot\u001b[39m\u001b[34m(self, scalex, scaley, data, *args, **kwargs)\u001b[39m\n\u001b[32m 1534\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1535\u001b[39m \u001b[33;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[32m 1536\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 1774\u001b[39m \u001b[33;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[32m 1775\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1776\u001b[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001b[32m-> \u001b[39m\u001b[32m1777\u001b[39m lines = [*\u001b[38;5;28mself\u001b[39m._get_lines(\u001b[38;5;28mself\u001b[39m, *args, data=data, **kwargs)]\n\u001b[32m 1778\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[32m 1779\u001b[39m \u001b[38;5;28mself\u001b[39m.add_line(line)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:297\u001b[39m, in \u001b[36m_process_plot_var_args.__call__\u001b[39m\u001b[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[39m\n\u001b[32m 295\u001b[39m this += args[\u001b[32m0\u001b[39m],\n\u001b[32m 296\u001b[39m args = args[\u001b[32m1\u001b[39m:]\n\u001b[32m--> \u001b[39m\u001b[32m297\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 298\u001b[39m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m=\u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 299\u001b[39m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[32m 300\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:494\u001b[39m, in \u001b[36m_process_plot_var_args._plot_args\u001b[39m\u001b[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[39m\n\u001b[32m 491\u001b[39m axes.yaxis.update_units(y)\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.shape[\u001b[32m0\u001b[39m] != y.shape[\u001b[32m0\u001b[39m]:\n\u001b[32m--> \u001b[39m\u001b[32m494\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mx and y must have same first dimension, but \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 496\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.ndim > \u001b[32m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y.ndim > \u001b[32m2\u001b[39m:\n\u001b[32m 497\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mx and y can be no greater than 2D, but have \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 498\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mValueError\u001b[39m: x and y must have same first dimension, but have shapes (2047,) and (9999,)" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.signal import hilbert\n", + "analytic_signal = hilbert(np.real(E_t))\n", + "instantaneous_phase = np.unwrap(np.angle(analytic_signal))\n", + "inst_freq = np.diff(instantaneous_phase) * fs / (2.0 * np.pi)\n", + "t_mid = t[:-1] + np.diff(t) / 2 # Midpoints for frequency values\n", + "# Plot\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(t_mid, inst_freq)\n", + "plt.plot(t[:99999], np.diff(np.unwrap(np.angle(E_t)))*fs/(2*np.pi))\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Instantaneous Frequency (Hz)\")\n", + "plt.title(\"Instantaneous Frequency vs Time\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "E_t" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e41fd8ed", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "N = 2**14\n", + "fs = 1000 # sampling rate in Hz\n", + "t = np.linspace(-N/2, N/2, N) / fs\n", + "f = np.fft.fftfreq(N, d=1/fs)\n", + "f = np.fft.fftshift(f)\n", + "\n", + "# Define Gaussian magnitude spectrum centered at 0\n", + "sigma = 30 # frequency domain std dev in Hz\n", + "S_f_mag = np.exp(-f**2 / (2 * sigma**2))\n", + "\n", + "# Add random phase\n", + "random_phase = np.exp(1j * 2 * np.pi * np.random.rand(N))\n", + "S_f = S_f_mag * random_phase\n", + "\n", + "# Shift and invert to time domain\n", + "S_f = np.fft.ifftshift(S_f)\n", + "s_t = np.fft.ifft(S_f)\n", + "s_t = np.real(s_t)\n", + "plt.figure(figsize=(12,4))\n", + "plt.plot(t, np.abs(s_t))\n", + "plt.title(\"Time-domain signal with Gaussian spectrum\")\n", + "plt.xlabel(\"Time (s)\")\n", + "\n", + "plt.figure(figsize=(12,4))\n", + "plt.plot(f, np.abs(np.fft.fftshift(np.fft.fft(s_t)))**2)\n", + "plt.title(\"Power Spectrum of s(t)\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79127656", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def structure_function(f, max_lag=None):\n", + " \"\"\"\n", + " Compute the second-order structure function of a 1D time series f(t)\n", + " \n", + " Parameters:\n", + " - f: 1D numpy array of the signal\n", + " - max_lag: maximum lag τ to compute the structure function (default: len(f)//2)\n", + "\n", + " Returns:\n", + " - D: structure function values for each τ\n", + " \"\"\"\n", + " f = np.asarray(f)\n", + " N = len(f)\n", + " if max_lag is None:\n", + " max_lag = N // 2\n", + " \n", + " D = np.zeros(max_lag)\n", + " for tau in range(1, max_lag):\n", + " diffs = f[:-tau] - f[tau:]\n", + " D[tau] = 0.5 * np.mean(diffs**2)\n", + " return D\n", + "\n", + "import numpy as np\n", + "from numpy.fft import fft, ifft\n", + "\n", + "def quadratic_autocorr(tau, sigma, k):\n", + " R = np.zeros_like(tau, dtype=float)\n", + " inside = np.abs(tau) <= np.max(tau)\n", + " R[inside] = sigma**2 - 1 / k *tau[inside]**2\n", + " return R\n", + "\n", + "def simulate_gaussian_process(n, dt=1.0, sigma=1.0, k=0.1):\n", + " \"\"\"\n", + " Simulates a stationary Gaussian process with a Gaussian autocovariance function\n", + " using the Shinozuka-Deodatis spectral method.\n", + " \n", + " Parameters:\n", + " n : number of desired time points\n", + " dt : time step\n", + " sigma : standard deviation of the process\n", + " ell : correlation length (controls width of the autocovariance)\n", + " \n", + " Returns:\n", + " t : time vector\n", + " x : simulated Gaussian process\n", + " \"\"\"\n", + " m = 2 * n # For circulant embedding\n", + "\n", + " # Time vector for autocovariance\n", + " taus = dt * np.arange(0, m)\n", + " R = quadratic_autocorr(np.concatenate([taus[:n], -taus[n:]]), sigma, k)\n", + "\n", + " # FFT to get eigenvalues (power spectrum)\n", + " lam = np.real(fft(R))\n", + " lam = np.maximum(lam, 0) # Force non-negativity\n", + "\n", + " # Generate frequency domain coefficients a(j)\n", + " a = np.zeros(m, dtype=complex)\n", + " a[0] = np.sqrt(lam[0] / m) * np.random.randn()\n", + " a[m//2] = np.sqrt(lam[m//2] / m) * np.random.randn()\n", + "\n", + " for j in range(1, m//2):\n", + " Uj, Vj = np.random.randn(2)\n", + " a[j] = (np.sqrt(lam[j] / (2*m)) * (Uj + 1j*Vj))\n", + " a[m - j] = np.conj(a[j]) # Hermitian symmetry\n", + "\n", + " # Inverse FFT to get real-valued time-domain signal\n", + " x_full = np.real(ifft(a))\n", + " t = np.arange(n) * dt\n", + " return t, x_full[:n]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "547dfd64", + "metadata": {}, + "outputs": [], + "source": [ + "N=20000\n", + "dt=0.1\n", + "t, phi = simulate_gaussian_process(N, dt=dt, sigma=1e5, k=0.0001)\n", + "plt.plot(t, phi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9abff6c7", + "metadata": {}, + "outputs": [], + "source": [ + "# phi = np.cumsum(phi/1000)\n", + "phi = np.cumsum(phi/1000)\n", + "sf = structure_function(phi)\n", + "plt.plot(t[:N//2], sf)\n", + "# plt.plot(t, t**2)\n", + "# plt.xlim(0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "733d3920", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.exp(1j*phi)\n", + "# x = np.exp(1j*phi/1)\n", + "plt.plot(x.real)\n", + "plt.plot(x.imag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d778b5f", + "metadata": {}, + "outputs": [], + "source": [ + "f = np.fft.fftfreq(N, d=dt)\n", + "x_f = np.fft.fft(x)\n", + "psd = np.abs(x_f)**2\n", + "psd = psd / np.max(psd)\n", + "\n", + "f_theory = np.linspace(-2, 2, 10000)\n", + "psd_theory = np.exp(-f_theory**2 / 0.000045)\n", + "\n", + "plt.plot(f, psd)\n", + "plt.plot(f_theory, psd_theory)\n", + "plt.xlim(-0.22, 0.28)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "986c681c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.linalg import toeplitz, cholesky\n", + "\n", + "def quadratic_autocorr(tau, a):\n", + " R = np.zeros_like(tau, dtype=float)\n", + " inside = np.abs(tau) <= np.max(tau)\n", + " R[inside] = 1 - a * tau[inside]**2\n", + " return R\n", + "\n", + "def simulate_signal_quadratic_acf(N=5, a=0.00001, tau_max=50, seed=0):\n", + " np.random.seed(seed)\n", + " t = np.arange(N)\n", + " taus = np.arange(N)\n", + " \n", + " # Build autocorrelation vector (symmetric, for Toeplitz matrix)\n", + " R = quadratic_autocorr(taus, a=a)\n", + "\n", + " # Build Toeplitz covariance matrix\n", + " C = toeplitz(R)\n", + "\n", + " # Check if it's positive semi-definite\n", + " eigvals = np.linalg.eigvalsh(C)\n", + " if np.any(eigvals < -1e-10):\n", + " print(\"Warning: Covariance matrix is not positive semi-definite.\")\n", + " print(f\"Minimum eigenvalue: {np.min(eigvals)}\")\n", + " \n", + " # Regularize if necessary (add jitter)\n", + " jitter = 1e-10\n", + " try:\n", + " L = cholesky(C + jitter * np.eye(N), lower=True)\n", + " except np.linalg.LinAlgError:\n", + " raise ValueError(\"Covariance matrix not positive definite. Try reducing 'a' or increasing jitter.\")\n", + " \n", + " z = np.random.randn(N)\n", + " f = L @ z\n", + " return t, f, R\n", + "\n", + "# Simulate\n", + "t, f, R = simulate_signal_quadratic_acf(N=1024, a=1e-4)\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(12, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(t, f)\n", + "plt.title(\"Simulated Real Signal with Quadratic Autocorrelation\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"f(t)\")\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(R[:200])\n", + "plt.title(\"Autocorrelation Function R(τ) = 1 - aτ²\")\n", + "plt.xlabel(\"τ\")\n", + "plt.ylabel(\"R(τ)\")\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e91b570", + "metadata": {}, + "outputs": [], + "source": [ + "R = quadratic_autocorr(np.linspace(0, 100), 1e-6)\n", + "plt.plot(R)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d92e59d7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/maybe_trash/linewidth.py b/examples/old/maybe_trash/linewidth.py new file mode 100644 index 00000000..917af3a5 --- /dev/null +++ b/examples/old/maybe_trash/linewidth.py @@ -0,0 +1,112 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy.fftpack import fft, fftshift, fftfreq + +# === Parameters === +fs = 1e16 # Sampling frequency (Hz) +T = 10e-12 # Total duration (s) +N = int(T * fs) # Number of samples +t = np.arange(N) / fs # Time array +f0 = 193e12 # Carrier frequency (Hz) +linewidth = 40e12 # Lorentzian FWHM (Hz) + +# === Phase noise model: Brownian motion === +# Phase variance grows linearly with time: var(phi) = π * linewidth * t +# So, each time step gets Gaussian noise with std ~ sqrt(Δt * π * linewidth) +delta_phi_std = np.sqrt(2 * np.pi * linewidth / fs) +dphi = np.random.randn(N) * delta_phi_std +phi = np.cumsum(dphi) # Integrate to get phase + +# === Generate signal: constant amplitude with noisy phase === +E_t = np.exp(1j * (2 * np.pi * f0 * t + phi)) + +# === Compute spectrum === +E_f = fftshift(fft(E_t)) +freqs = fftshift(fftfreq(N, 1 / fs)) + +# === Power spectral density (normalized) === +psd = np.abs(E_f) ** 2 +psd /= np.max(psd) # Normalize for plotting + +# === Plotting === +plt.figure(figsize=(12, 6)) + +plt.subplot(2, 2, 1) +plt.plot(t * 1e3, np.real(E_t), label="Re") +plt.plot(t * 1e3, np.imag(E_t), label="Im", alpha=0.6) +plt.title("Time Domain Signal") +plt.xlabel("Time (ms)") +plt.ylabel("Amplitude") +plt.legend() + +plt.subplot(2, 2, 2) +plt.plot(t * 1e3, np.unwrap(phi)) +plt.title("Cumulative Phase Noise") +plt.xlabel("Time (ms)") +plt.ylabel("Phase (rad)") + +plt.subplot(2, 1, 2) +plt.plot(freqs / 1e3, psd) +# plt.plot(freqs/1e3, np.angle(E_f) ** 2) +plt.title("Power Spectrum (Lorentzian Linewidth)") +plt.xlabel("Frequency (kHz)") +plt.ylabel("Normalized PSD") +plt.grid(True) + +plt.tight_layout() +plt.show() + + +# import numpy as np +# import matplotlib.pyplot as plt + +# # --- Parameters --- +# sampling_freq = 1e16 # Hz +# dt = 1 / sampling_freq # s +# duration = 10e-12 # s +# num_samples = int(duration / dt) +# t = np.arange(num_samples) * dt + +# carrier_freq = 193.1e12 +# linewidth = 0 # Hz (FWHM) +# rin_level = 1e-16 # 1/Hz + +# # --- Phase noise (Wiener process for FM noise) --- +# # linewidth = (1/2π) * d(φ^2)/dt → dφ ~ sqrt(2π * linewidth * dt) +# phase_noise_std = np.sqrt(2 * np.pi * linewidth * dt) +# phase_noise = np.cumsum(np.random.normal(0, phase_noise_std, size=num_samples)) +# E_phase = np.exp(1j * (2 * np.pi * carrier_freq * t + phase_noise)) + +# # --- Amplitude noise (AM noise from RIN) --- +# # RIN is variance per Hz of power, so: std_amp ≈ sqrt(RIN × BW) +# bandwidth = sampling_freq / 2 +# amp_std = np.sqrt(rin_level * bandwidth) +# amp_noise = np.random.normal(0, amp_std, size=num_samples) +# amplitude = 1.0 + amp_noise # center around 1 + +# # --- Final complex field --- +# E_t = amplitude * E_phase + +# # --- FFT --- +# freqs = np.fft.fftfreq(num_samples, dt) +# E_f = np.fft.fft(E_t) +# PSD = np.abs(E_f)**2 +# PSD = PSD / np.max(PSD) # Normalize for plotting + +# # --- Plot --- +# plt.figure(figsize=(12, 5)) + +# plt.subplot(1, 2, 1) +# plt.plot(t * 1e6, np.real(E_t), label='Real part') +# plt.title("Time-Domain Signal") +# plt.xlabel("Time (µs)") +# plt.ylabel("Amplitude") + +# plt.subplot(1, 2, 2) +# plt.plot(np.fft.fftshift(freqs) / 1e9, np.fft.fftshift(10 * np.log10(PSD))) +# plt.title("Frequency-Domain (FFT)") +# plt.xlabel("Frequency (GHz)") +# plt.ylabel("PSD (dB, normalized)") + +# plt.tight_layout() +# plt.show() diff --git a/examples/old/maybe_trash/linewidth2.py b/examples/old/maybe_trash/linewidth2.py new file mode 100644 index 00000000..f471f6a7 --- /dev/null +++ b/examples/old/maybe_trash/linewidth2.py @@ -0,0 +1,57 @@ +import numpy as np +import matplotlib.pyplot as plt + +# Parameters +fs = 100e9 # Sampling rate (100 GHz) +T = 10e-6 # Total time duration (10 us) +N = int(T * fs) # Number of samples +t = np.arange(N) / fs # Time array + +f0 = 0 # Carrier frequency offset (can be nonzero) +linewidth_fwhm = 10e9 # Gaussian linewidth (FWHM) = 1 MHz + +# Convert FWHM to standard deviation +sigma_f = linewidth_fwhm / (2 * np.sqrt(2 * np.log(2))) + +# Frequency vector +freqs = np.fft.fftfreq(N, d=1 / fs) + +# Gaussian PSD centered at f0 +psd = np.exp(-((freqs - f0) ** 2) / (2 * sigma_f**2)) + +# Generate random complex spectrum with Gaussian envelope +np.random.seed(0) +phases = np.exp(1j * 2 * np.pi * np.random.rand(N)) +spectrum = np.sqrt(psd) * phases + +# Ensure Hermitian symmetry for real signal (optional: for real-valued output) +# spectrum[N//2+1:] = np.conj(spectrum[1:N//2][::-1]) + +# Inverse FFT to get time domain signal +signal = np.fft.ifft(spectrum) + +# Normalize +signal /= np.max(np.abs(signal)) + +# Plot time domain signal +plt.figure(figsize=(12, 4)) +plt.plot(t[:1000] * 1e9, np.real(signal[:1000]), label="Real part") +plt.plot(t[:1000] * 1e9, np.imag(signal[:1000]), label="Imag part") +plt.title("Laser Electric Field (Gaussian Linewidth, Time Domain)") +plt.xlabel("Time (ns)") +plt.ylabel("Amplitude") +plt.legend() +plt.grid(True) +plt.tight_layout() +plt.show() + +# Plot spectrum +plt.figure(figsize=(12, 4)) +# plt.plot(np.fft.fftshift(freqs)/1e6, 10*np.log10(np.fft.fftshift(np.abs(spectrum)**2))) +plt.plot(np.fft.fftshift(freqs) / 1e6, np.fft.fftshift(np.abs(spectrum) ** 2)) +plt.title("Power Spectral Density (Gaussian)") +plt.xlabel("Frequency (MHz)") +plt.ylabel("PSD (dB)") +plt.grid(True) +plt.tight_layout() +plt.show() diff --git a/examples/old/maybe_trash/linewidth3.py b/examples/old/maybe_trash/linewidth3.py new file mode 100644 index 00000000..dd56df86 --- /dev/null +++ b/examples/old/maybe_trash/linewidth3.py @@ -0,0 +1,66 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy.signal.windows import gaussian +from scipy.fft import fft, fftfreq, fftshift + +# Simulation parameters +fs = 10e7 # Sampling frequency (Hz) +T = 100e-6 # Total time (s) +N = int(T * fs) # Number of points +t = np.linspace(0, T, N, endpoint=False) +f0 = 0 # Laser central frequency (Hz) +linewidth = 1e6 + +# === Lorentzian PSD === +# Phase = Wiener process (integrated white noise) +np.random.seed(0) +dphi_lorentz = np.random.normal(scale=np.sqrt(2 * np.pi * linewidth / fs), size=N) +phi_lorentz = np.cumsum(dphi_lorentz) # Integrate to get phase +E_lorentz = np.exp(1j * (2 * np.pi * f0 * t + phi_lorentz)) + +# === Gaussian PSD === +# Phase = bandlimited white noise (convolved with Gaussian) +noise = np.random.normal(scale=np.sqrt(2 * np.pi * linewidth / fs), size=N) +# noise = phi_lorentz +window_width = int(N // 100) # Control PSD width +gaussian_window = gaussian(N, std=window_width / 2) +gaussian_window /= np.sum(gaussian_window) +phi_gauss = np.convolve(noise, gaussian_window, mode="same") +phi_gauss *= 200 # scale for similar phase noise strength +E_gauss = np.exp(1j * (2 * np.pi * f0 * t + phi_gauss)) + + +# === FFT Analysis === +def compute_psd(E): + E_f = fftshift(fft(E)) + freqs = fftshift(fftfreq(N, 1 / fs)) + psd = np.abs(E_f) ** 2 + psd /= np.max(psd) # Normalize + return freqs, psd + + +freqs, psd_lorentz = compute_psd(E_lorentz) +_, psd_gauss = compute_psd(E_gauss) + +# === Plot Time Domain Phase === +plt.figure(figsize=(12, 4)) +plt.subplot(1, 2, 1) +plt.plot(t * 1e6, phi_lorentz, label="Lorentzian Phase") +plt.plot(t * 1e6, phi_gauss, label="Gaussian Phase") +plt.xlabel("Time (µs)") +plt.ylabel("Phase (rad)") +plt.title("Phase Noise") +plt.legend() + +# === Plot Frequency Domain === +plt.subplot(1, 2, 2) +# plt.plot(freqs / 1e9, 10 * np.log10(psd_lorentz + 1e-12), label='Lorentzian PSD') +# plt.plot(freqs / 1e9, 10 * np.log10(psd_gauss + 1e-12), label='Gaussian PSD') +plt.plot(freqs / 1e9, 10 * psd_lorentz + 1e-12, label="Lorentzian PSD") +plt.plot(freqs / 1e9, 10 * psd_gauss + 1e-12, label="Gaussian PSD") +plt.xlabel("Frequency (GHz)") +plt.ylabel("PSD (dB)") +plt.title("Power Spectral Density") +plt.legend() +plt.tight_layout() +plt.show() diff --git a/examples/old/maybe_trash/linewidth4.py b/examples/old/maybe_trash/linewidth4.py new file mode 100644 index 00000000..b00559e5 --- /dev/null +++ b/examples/old/maybe_trash/linewidth4.py @@ -0,0 +1,24 @@ +import numpy as np +import matplotlib.pyplot as plt + +# Parameters +N = 1000 +T = 1.0 # seconds +sigma = 20 # Hz -- this controls spectral width +t = np.linspace(0, T, N) + +# Sample a single Gaussian variable +Z = np.random.randn() + +# Generate phi(t) = 2 * pi * sigma * t * Z +phi_t = 2 * np.pi * sigma * t * Z + +# Plot +plt.figure(figsize=(10, 4)) +plt.plot(t, phi_t, label=r"$\phi(t)$") +plt.xlabel("Time (s)") +plt.ylabel("Phase") +plt.title("Phase noise with quadratic variance: Var[$\phi(t)$] ∝ $t^2$") +plt.grid(True) +plt.tight_layout() +plt.show() diff --git a/examples/old/maybe_trash/multi_mode_quantum_functions.ipynb b/examples/old/maybe_trash/multi_mode_quantum_functions.ipynb new file mode 100644 index 00000000..a568946f --- /dev/null +++ b/examples/old/maybe_trash/multi_mode_quantum_functions.ipynb @@ -0,0 +1,1122 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "id": "a6952ee2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S_symp * Omega * S_symp.T =\n", + " [[ 0. -1. 0. 0.]\n", + " [ 1. 0. 0. 0.]\n", + " [ 0. 0. 0. -1.]\n", + " [ 0. 0. 1. 0.]]\n", + "Is symplectic? False\n", + "Is anti-symplectic? True\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Define the unitary S matrix\n", + "S = np.exp(1j*np.pi/2)*np.array([[0, 1],\n", + " [1, 0]])\n", + "\n", + "# Construct the real symplectic matrix\n", + "ReS = S.real\n", + "ImS = S.imag\n", + "F = np.block([[ReS, -ImS],\n", + " [ImS, ReS]])\n", + "\n", + "# Define Omega for 2 modes\n", + "J = np.array([[0, 1],\n", + " [-1, 0]])\n", + "Omega = np.block([[J, np.zeros((2, 2))],\n", + " [np.zeros((2, 2)), J]])\n", + "\n", + "# Compute S_symp * Omega * S_symp.T\n", + "check = F @ Omega @ F.T\n", + "\n", + "# Compare with Omega\n", + "print(\"S_symp * Omega * S_symp.T =\\n\", check)\n", + "print(\"Is symplectic?\", np.allclose(check, Omega))\n", + "print(\"Is anti-symplectic?\", np.allclose(check, -Omega))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "32c61a6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., -1., 0., 0.],\n", + " [ 1., 0., 0., 0.],\n", + " [ 0., 0., 0., -1.],\n", + " [ 0., 0., 1., 0.]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "F.T @ Omega @ F" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "35e968d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "Quantum object: dims=[[3, 3], [1]], shape=(9, 1), type='ket', dtype=Dense$$\\left(\\begin{array}{cc}0\\\\0\\\\0\\\\0\\\\1\\\\0\\\\0\\\\0\\\\0\\end{array}\\right)$$" + ], + "text/plain": [ + "Quantum object: dims=[[3, 3], [1]], shape=(9, 1), type='ket', dtype=Dense\n", + "Qobj data =\n", + "[[0.]\n", + " [0.]\n", + " [0.]\n", + " [0.]\n", + " [1.]\n", + " [0.]\n", + " [0.]\n", + " [0.]\n", + " [0.]]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qutip import basis, tensor\n", + "\n", + "# Vacuum in mode a\n", + "vac_a = basis(3, 1)\n", + "\n", + "# Single photon in mode b\n", + "one_b = basis(3, 1)\n", + "\n", + "# Tensor product state |0>_a ⊗ |1>_b\n", + "state = tensor(vac_a, one_b)\n", + "state" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "476d444b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-mode mean vector shape: (400,)\n", + "Multi-mode covariance matrix shape: (400, 400)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy.special import hermite\n", + "from scipy.integrate import simpson\n", + "import math\n", + "\n", + "def hermite_gauss_mode(n, t, w0=1.0):\n", + " \"\"\"\n", + " Generate the n-th order Hermite-Gaussian mode evaluated at times t.\n", + " w0 is the waist parameter controlling the width.\n", + " \"\"\"\n", + " Hn = hermite(n)\n", + " norm = (2**n * math.factorial(n) * np.sqrt(np.pi) * w0)**(-0.5)\n", + " xi = np.sqrt(2) * t / w0\n", + " mode = norm * Hn(xi) * np.exp(-xi**2 / 2)\n", + " return mode\n", + "\n", + "def normalize_mode(f, t):\n", + " \"\"\"\n", + " Normalize mode vector f sampled at times t using trapezoidal integration.\n", + " \"\"\"\n", + " norm = np.sqrt(simpson(np.abs(f)**2, t))\n", + " return f / norm\n", + "\n", + "def single_to_multi_mode(mean_f, cov_f, f):\n", + " \"\"\"\n", + " Convert single mode mean and covariance to multi-mode in delta basis.\n", + " mean_f: (2,) array, [q_mean, p_mean]\n", + " cov_f: (2,2) covariance matrix for single mode\n", + " f: (M,) real mode vector, normalized\n", + " Returns:\n", + " mean_multi: (2*M,) vector\n", + " cov_multi: (2*M, 2*M) covariance matrix\n", + " \"\"\"\n", + " M = len(f)\n", + " # Create S = f as column vector (M x 1)\n", + " S = f.reshape(M, 1)\n", + "\n", + " # Identity in quadrature space (2x2)\n", + " I2 = np.eye(2)\n", + "\n", + " # Mean multi = kron(S, I2) @ mean_f\n", + " mean_multi = np.kron(S, I2) @ mean_f # shape (2*M,)\n", + "\n", + " # Covariance multi = kron(S, I2) @ cov_f @ kron(S, I2).T\n", + " kron_S_I2 = np.kron(S, I2) # shape (2*M, 2)\n", + " cov_multi = kron_S_I2 @ cov_f @ kron_S_I2.T # shape (2*M, 2*M)\n", + "\n", + " return mean_multi, cov_multi\n", + "\n", + "# Example usage:\n", + "\n", + "# Time grid\n", + "M = 200\n", + "t = np.linspace(-5, 5, M)\n", + "\n", + "# Generate 3rd order Hermite-Gaussian mode\n", + "n = 3\n", + "f = hermite_gauss_mode(n, t, w0=1.0)\n", + "f = normalize_mode(f, t)\n", + "\n", + "# Define single-mode Gaussian state parameters (q,p means and covariance)\n", + "mean_f = np.array([0.3, -0.6]) # example mean quadratures\n", + "cov_f = np.array([[1.2, 0.1],\n", + " [0.1, 0.8]]) # example covariance matrix (should be positive definite)\n", + "\n", + "# Convert to multi-mode in delta basis\n", + "mean_multi, cov_multi = single_to_multi_mode(mean_f, cov_f, f)\n", + "\n", + "print(\"Multi-mode mean vector shape:\", mean_multi.shape) # Expect (2*M,)\n", + "print(\"Multi-mode covariance matrix shape:\", cov_multi.shape) # Expect (2*M, 2*M)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "02e2a577", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(39.80000000000001)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.trace(cov_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "67e4a73a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(f)\n", + "plt.plot(mean_multi[::2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d628adb", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from scipy.constants import epsilon_0, Boltzmann\n", + "\n", + "h_bar = 0.5\n", + "\n", + "def annihilation_operator(N_max=10, f=1, t=0):\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n", + " for n in range(1, N_max):\n", + " a = a.at[n-1, n].set(jnp.sqrt(n))\n", + "\n", + " return a\n", + "\n", + "def electric_field_operator(N_max=10, f=1, t=0, mode_volume=1):\n", + " E_0 = jnp.sqrt((h_bar*2*jnp.pi*f)/(epsilon_0*mode_volume))\n", + " E_0 = 1\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " \n", + " return E_0 * (a + a_dagger)\n", + "\n", + "def number_operator(N_max, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return a_dagger@a\n", + "\n", + "def expectation_value_pure(operator, psi):\n", + " return jnp.vdot(psi, operator@psi)\n", + "\n", + "def expectation_value_mixed(operator, rho):\n", + " return jnp.trace(rho@operator)\n", + "\n", + "def thermal_state(N_max, f=1.0, temperature=1/Boltzmann):\n", + " n = jnp.arange(0, N_max)\n", + " E_n = h_bar*2*jnp.pi*f*(n+0.5)\n", + " \n", + " P_n = jnp.exp(-E_n / (Boltzmann*temperature))\n", + " P_n = P_n / jnp.sum(P_n)\n", + " return jnp.diag(P_n)\n", + "\n", + "def coherent_state(alpha, N_max=10, f=1.0):\n", + " n = jnp.arange(0, N_max)\n", + " return jnp.exp(-0.5*jnp.abs(alpha)**2) * alpha**n / jnp.sqrt(jax.scipy.special.factorial(n))\n", + "\n", + "def vacuum_state(N_max=10, f=1.0):\n", + " vac = jnp.zeros(N_max, dtype=jnp.complex128)\n", + " vac = vac.at[0].set(1.0)\n", + " return vac\n", + "\n", + "def displacement_operator(alpha, N_max=10, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return jax.scipy.linalg.expm(alpha*a_dagger - jnp.conj(alpha)*a)\n", + "\n", + "def characteristic_function_mixed(rho, f=1.0, t=0):\n", + " N_max = rho.shape[0]\n", + "\n", + " def characteristic_fn(eta):\n", + " return jnp.trace(rho @ displacement_operator(eta, N_max=N_max, f=f, t=t))\n", + "\n", + " def apply_fn(eta_grid): # eta_grid shape: (N, M)\n", + " flat_eta = eta_grid.reshape(-1)\n", + "\n", + " def scan_fn(carry, eta):\n", + " val = characteristic_fn(eta)\n", + " return carry, val\n", + "\n", + " # remat helps prevent backprop memory buildup\n", + " _, results = jax.lax.scan(jax.remat(scan_fn), None, flat_eta)\n", + " return results.reshape(eta_grid.shape)\n", + "\n", + " return apply_fn\n", + "\n", + "def characteristic_function_pure(psi, f=1.0, t=0):\n", + " rho = jnp.outer(psi, psi.conj().T)\n", + " N_max = rho.shape[0]\n", + " \n", + " return characteristic_function_mixed(rho, f=f, t=t)\n", + "\n", + "def characteristic_function_gaussian(mean, covariance):\n", + " def characteristic_fn(eta):\n", + " xi = jnp.sqrt(2) * jnp.array([jnp.real(eta), jnp.imag(eta)])\n", + " return jax.scipy.linalg.expm(-0.5*xi@covariance@eta + 1j*mean@xi)\n", + " \n", + " return characteristic_fn\n", + "\n", + "def eigenvalue_from_vector(A, v):\n", + " Av = A @ v\n", + " # Normalize vector to unit norm to avoid scaling ambiguity\n", + " v_normalized = v / jnp.linalg.norm(v)\n", + " return jnp.vdot(v_normalized, Av) # = λ if v is an eigenvector\n", + "\n", + "def wigner_function(alpha, characteristic_fn, grid_size=80, limit=4.0):\n", + " dx = 2 * limit / grid_size\n", + " x = jnp.linspace(-limit, limit, grid_size)\n", + " y = jnp.linspace(-limit, limit, grid_size)\n", + " xx, yy = jnp.meshgrid(x, y)\n", + " lam = xx + 1j * yy # shape: (grid_size, grid_size)\n", + "\n", + " lam_flat = lam.reshape(-1)\n", + " chi_vals = characteristic_fn(lam_flat).reshape(lam.shape)\n", + "\n", + " exponent = jnp.conj(lam) * alpha - lam * jnp.conj(alpha)\n", + " integrand = chi_vals * jnp.exp(exponent)\n", + " integral = jnp.sum(integrand) * dx**2\n", + "\n", + " return (1 / jnp.pi**2) * integral.real\n", + "\n", + "def coherence_1(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " t1 = 0\n", + " t2 = 1\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = jnp.sqrt(\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + " \n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2, rho)\n", + "\n", + " return numerator/denominator\n", + "\n", + "def coherence_2(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = (\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + "\n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2.conj().T @ a_hat_2 @ a_hat_1, rho)\n", + "\n", + " return numerator/denominator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b76a076", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.04516570536368411+0j)\n", + "0.04516570536368411\n" + ] + } + ], + "source": [ + "N_max=40\n", + "T = 1/Boltzmann\n", + "t=0\n", + "f = 1\n", + "n_hat = number_operator(N_max=N_max, f=f, t=t)\n", + "temperature = 1/Boltzmann\n", + "rho_th = thermal_state(N_max)\n", + "print(expectation_value_mixed(n_hat, rho_th))\n", + "print(1/(jnp.exp(h_bar*2*jnp.pi*f/(Boltzmann*T))-1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77932a95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1+0j)\n", + "(2.000000000000001+0j)\n" + ] + } + ], + "source": [ + "print(coherence_1(rho_th, 0, 1))\n", + "print(coherence_2(rho_th, 0, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6644dd2a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8.82496903e-01+0.j 4.41248451e-01+0.j 1.56004886e-01+0.j\n", + " 4.50347315e-02+0.j 1.12586829e-02+0.j 2.51751802e-03+0.j\n", + " 5.13886215e-04+0.j 9.71153661e-05+0.j 1.71677335e-05+0.j\n", + " 2.86128891e-06+0.j 4.52409501e-07+0.j 6.82032984e-08+0.j\n", + " 9.84429818e-09+0.j 1.36515853e-09+0.j 1.82426982e-10+0.j\n", + " 2.35512222e-11+0.j 2.94390277e-12+0.j 3.57000649e-13+0.j\n", + " 4.20729300e-14+0.j 4.82609606e-15+0.j 5.39573942e-16+0.j\n", + " 5.88723437e-17+0.j 6.27581292e-18+0.j 6.54298728e-19+0.j\n", + " 6.67790841e-20+0.j 6.67790821e-21+0.j 6.54822716e-22+0.j\n", + " 6.30103640e-23+0.j 5.95394198e-24+0.j 5.52817858e-25+0.j\n", + " 5.04654817e-26+0.j 4.53061072e-27+0.j 3.99586815e-28+0.j\n", + " 3.44653150e-29+0.j 2.89817949e-30+0.j 2.58393835e-31+0.j\n", + " 3.96946964e-32+0.j 1.36836821e-32+0.j 5.66449196e-33+0.j\n", + " 2.15406154e-33+0.j 7.38414054e-34+0.j 2.31739718e-34+0.j\n", + " 6.75588000e-35+0.j 1.84958640e-35+0.j 4.79404612e-36+0.j\n", + " 1.18376775e-36+0.j 2.79836111e-37+0.j 6.35840412e-38+0.j\n", + " 1.39327902e-38+0.j 2.95248758e-39+0.j 6.06510437e-40+0.j\n", + " 1.21029292e-40+0.j 2.35036084e-41+0.j 4.44905948e-42+0.j\n", + " 8.22077283e-43+0.j 1.48465104e-43+0.j 2.62364408e-44+0.j\n", + " 4.54159461e-45+0.j 7.70810960e-46+0.j 1.28381233e-46+0.j\n", + " 2.09999735e-47+0.j 3.37619681e-48+0.j 5.33877886e-49+0.j\n", + " 8.30904928e-50+0.j 1.27338459e-50+0.j 1.92121580e-51+0.j\n", + " 2.84784355e-52+0.j 4.12282243e-53+0.j 5.76001447e-54+0.j\n", + " 7.66850413e-55+0.j 1.01127576e-55+0.j 1.76081561e-56+0.j\n", + " 5.94090619e-57+0.j 2.78136732e-57+0.j 1.27037526e-57+0.j\n", + " 5.28956024e-58+0.j 2.02212365e-58+0.j 7.20942738e-59+0.j\n", + " 2.40073452e-59+0.j 8.43020582e-60+0.j]\n", + "[8.82496903e-01 4.41248451e-01 1.56004886e-01 4.50347315e-02\n", + " 1.12586829e-02 2.51751802e-03 5.13886215e-04 9.71153661e-05\n", + " 1.71677335e-05 2.86128891e-06 4.52409501e-07 6.82032984e-08\n", + " 9.84429818e-09 1.36515853e-09 1.82426982e-10 2.35512222e-11\n", + " 2.94390277e-12 3.57000649e-13 4.20729300e-14 4.82609606e-15\n", + " 5.39573942e-16 5.88723437e-17 6.27581292e-18 6.54298727e-19\n", + " 6.67790842e-20 6.67790842e-21 6.54822795e-22 6.30103528e-23\n", + " 5.95391870e-24 5.52807473e-25 5.04641872e-26 4.53181779e-27\n", + " 4.00559886e-28 3.48642630e-29 2.98958589e-30 2.52666124e-31\n", + " 2.10555103e-32 1.73075228e-33 1.40382548e-34 1.12395991e-35\n", + " 8.88568328e-37 6.93855292e-38 5.35320980e-39 4.08178420e-40\n", + " 3.07676061e-41 2.29328196e-42 1.69062989e-43 1.23301857e-44\n", + " 8.89854506e-46 6.35610361e-47 4.49444397e-48 3.14674019e-49\n", + " 2.18187175e-50 1.49851567e-51 1.01961077e-52 6.87421439e-54\n", + " 4.59302769e-55 3.04180690e-56 1.99704406e-57 1.29996496e-58\n", + " 8.39123773e-60 5.37193949e-61 3.41118499e-62 2.14884456e-63\n", + " 1.34302785e-64 8.32910515e-66 5.12620985e-67 3.13133105e-68\n", + " 1.89864833e-69 1.14285200e-70 6.82984701e-72 4.05276858e-73\n", + " 2.38811679e-74 1.39753964e-75 8.12303417e-77 4.68983597e-78\n", + " 2.68980540e-79 1.53265887e-80 8.67697220e-82 4.88117822e-83]\n" + ] + } + ], + "source": [ + "vac = vacuum_state(N_max)\n", + "alpha = 0.5\n", + "print(displacement_operator(alpha, N_max=N_max) @ vac)\n", + "print(coherent_state(alpha, N_max=N_max))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1a1e5953", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.5000000000000001+0j)\n" + ] + } + ], + "source": [ + "a_hat = annihilation_operator(N_max=N_max, f=f, t=t)\n", + "print(eigenvalue_from_vector(a_hat, coherent_state(alpha, N_max=N_max)))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c17918b0", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.special import eval_laguerre\n", + "\n", + "alphas = jnp.linspace(-6.0, 6.0, 350)\n", + "n = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f9a0216", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def chi_number_state(n, eta):\n", + " abs_eta2 = jnp.abs(eta)**2\n", + " return jnp.exp(-0.5 * abs_eta2) * eval_laguerre(n, abs_eta2)\n", + "\n", + "\n", + "\n", + "plt.plot(alphas, chi_number_state(n, alphas))\n", + "\n", + "psi_number = jnp.zeros(N_max, dtype=jnp.complex128).at[n].set(1.0)\n", + "C = characteristic_function_pure(psi_number)\n", + "\n", + "# psi_coherent = coherent_state(jnp.sqrt(10), N_max=N_max)\n", + "# C = characteristic_function_pure(psi_coherent)\n", + "\n", + "# C = characteristic_function_mixed(rho_th)\n", + "# W_thermal = wigner_function(1.0, C_thermal)\n", + "\n", + "\n", + "data = []\n", + "\n", + "plt.plot(alphas, C(jnp.array([alphas]))[0], \"r--\")\n", + "plt.show()\n", + "\n", + "# @jax.remat\n", + "def scan_fn(_, alpha):\n", + " value = wigner_function(alpha, C, 100, 5.0)\n", + " # value = jax.lax.stop_gradient(wigner_function(alpha, C, 200, jnp.sqrt(N_max/2)))\n", + " return None, value\n", + "\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "\n", + "plt.plot(alphas, wigner_vals)\n", + "\n", + "# for alpha in alphas:\n", + "# print(alpha)\n", + "# data.append(wigner_function(alpha, C))\n", + "\n", + "# plt.plot(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7ced28ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "C = characteristic_function_mixed(rho_th)\n", + "\n", + "@jax.remat\n", + "def scan_fn(_, alpha):\n", + " # value = wigner_function(alpha, C, 100, 5.0)\n", + " value = wigner_function(alpha, C, 300, jnp.sqrt(N_max/2))\n", + " return None, value\n", + "\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "plt.plot(alphas, wigner_vals)\n", + "\n", + "_n = jnp.arange(rho_th.shape[0])\n", + "# n_bar = jnp.sum(_n * jnp.diag(rho_th))\n", + "n_bar = 1 / (jnp.exp(h_bar * 2 * jnp.pi * f * (1/(Boltzmann*temperature))) - 1)\n", + "wigner_vals_theory = 2/ (jnp.pi*(2*n_bar+1))*jnp.exp(-2*jnp.abs(alphas)**2 / (2*n_bar + 1))\n", + "plt.plot(alphas, wigner_vals_theory, \"r--\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "783022b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(alphas, wigner_vals_theory, \"r--\")\n", + "plt.axvline( 0.5*(2.355*jnp.sqrt(h_bar/2)))\n", + "plt.axvline(-0.5*(2.355*jnp.sqrt(h_bar/2)))\n", + "plt.axhline(0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "27a37810", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.simulation.jax_tools import python_based_scan\n", + "psi_coherent = coherent_state(jnp.sqrt(10), N_max=N_max)\n", + "C = characteristic_function_pure(psi_coherent)\n", + "\n", + "# @jax.remat\n", + "def scan_fn(_, alpha):\n", + " # value = wigner_function(alpha, C, 100, 5.0)\n", + " value = jax.lax.stop_gradient(wigner_function(alpha, C, 450, jnp.sqrt(N_max/2)))\n", + " return None, value\n", + "\n", + "# _, wigner_vals = python_based_scan(scan_fn, init=None, xs=alphas)\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "plt.plot(alphas, wigner_vals)\n", + "\n", + "wigner_vals_theory = 2/jnp.pi*jnp.exp(-2*jnp.abs(alphas-jnp.sqrt(10))**2)\n", + "plt.plot(alphas, wigner_vals_theory, \"r--\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff5ce0da", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26929318", + "metadata": {}, + "outputs": [], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0673fac", + "metadata": {}, + "outputs": [], + "source": [ + "100\n", + "\n", + "# # Grid\n", + "# x = jnp.linspace(-5, 5, 200)\n", + "# y = jnp.linspace(-5, 5, 200)\n", + "# eta_grid = x[:, None] + 1j * y[None, :]\n", + "# chi_grid = chi_number_state(n, eta_grid) # Try n = 3\n", + "\n", + "# plt.imshow(jnp.real(chi_grid), extent=[-5, 5, -5, 5], origin=\"lower\")\n", + "# plt.title(f\"Re[χₙ(η)] for n={n}\")\n", + "# plt.colorbar()\n", + "# plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7092682c", + "metadata": {}, + "outputs": [], + "source": [ + "def g2(t1, t2, rho, f=1.0, N_max=10):\n", + " a_t1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_t2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " a_dag_t1 = jnp.conj(a_t1.T)\n", + " a_dag_t2 = jnp.conj(a_t2.T)\n", + "\n", + " numerator = expectation_value_mixed(a_dag_t1 @ a_dag_t2 @ a_t2 @ a_t1, rho)\n", + " print(numerator)\n", + " denominator = (\n", + " expectation_value_mixed(a_dag_t1 @ a_t1, rho)\n", + " * expectation_value_mixed(a_dag_t2 @ a_t2, rho)\n", + " )\n", + "\n", + " return numerator / denominator\n", + "\n", + "rho = thermal_state(N_max=10, f=1.0, temperature=1.0)\n", + "g2(0, 0, rho_th, f=1, N_max=N_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55e4270b", + "metadata": {}, + "outputs": [], + "source": [ + "jnp.exp(-h_bar*2*jnp.pi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8894ee07", + "metadata": {}, + "outputs": [], + "source": [ + "N_max = 10\n", + "psi = jnp.zeros(N_max)\n", + "psi = psi.at[0].set(0.0)\n", + "psi = psi.at[1].set(1.0)\n", + "psi = psi/jnp.linalg.norm(psi)\n", + "\n", + "E_hat = electric_field_operator()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9593009", + "metadata": {}, + "outputs": [], + "source": [ + "print(expectation_value_pure(E_hat, psi))\n", + "print(expectation_value_pure(E_hat@E_hat, psi))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f6d58ba", + "metadata": {}, + "outputs": [], + "source": [ + "rho = jnp.outer(psi, jnp.conj(psi))\n", + "print(expectation_value_mixed(E_hat, rho))\n", + "print(expectation_value_mixed(E_hat@E_hat, rho))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83d986a5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5137a871", + "metadata": {}, + "outputs": [], + "source": [ + "N_max = 10\n", + "# Construct annihilation operator matrix\n", + "\n", + "\n", + "# Example state: |3> = [0, 0, 0, 1, 0, ..., 0]\n", + "psi = jnp.zeros(N_max)\n", + "psi[2] = 1.0\n", + "\n", + "a_0 = annihilation_operator(N_max=N_max)\n", + "# Apply annihilation operator\n", + "phi = a_0 @ psi\n", + "\n", + "print(\"Resulting state amplitudes:\", phi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3616095a", + "metadata": {}, + "outputs": [], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96158916", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from scipy.constants import epsilon_0, Boltzmann\n", + "\n", + "h_bar = 0.5\n", + "\n", + "def annihilation_operator(N_max=10, f=1, t=0):\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n", + " for n in range(1, N_max):\n", + " a = a.at[n-1, n].set(jnp.sqrt(n))\n", + " \n", + " # return a*jnp.exp(-1j*2*jnp.pi*f*t)\n", + " return a\n", + "\n", + "def electric_field_operator(N_max=10, f=1, t=0, mode_volume=1):\n", + " E_0 = jnp.sqrt((h_bar*2*jnp.pi*f)/(epsilon_0*mode_volume))\n", + " E_0 = 1\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " \n", + " return E_0 * (a + a_dagger)\n", + "\n", + "def number_operator(N_max, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return a_dagger@a\n", + "\n", + "def expectation_value_pure(operator, psi):\n", + " return jnp.vdot(psi, operator@psi)\n", + "\n", + "def expectation_value_mixed(operator, rho):\n", + " return jnp.trace(rho@operator)\n", + "\n", + "def thermal_state(N_max, f=1.0, temperature=1/Boltzmann):\n", + " n = jnp.arange(0, N_max)\n", + " E_n = h_bar*2*jnp.pi*f*(n+0.5)\n", + " \n", + " P_n = jnp.exp(-E_n / (Boltzmann*temperature))\n", + " P_n = P_n / jnp.sum(P_n)\n", + " return jnp.diag(P_n)\n", + "\n", + "def coherent_state(alpha, N_max=10, f=1.0):\n", + " n = jnp.arange(0, N_max)\n", + " return jnp.exp(-0.5*jnp.abs(alpha)**2) * alpha**n / jnp.sqrt(jax.scipy.special.factorial(n))\n", + "\n", + "def vacuum_state(N_max=10, f=1.0):\n", + " vac = jnp.zeros(N_max, dtype=jnp.complex128)\n", + " vac = vac.at[0].set(1.0)\n", + " return vac\n", + "\n", + "def displacement_operator(alpha, N_max=10, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return jax.scipy.linalg.expm(alpha*a_dagger - jnp.conj(alpha)*a)\n", + "\n", + "# def characteristic_function_mixed(rho, f=1.0, t=0):\n", + "# N_max = rho.shape[0]\n", + "# def characteristic_fn(eta):\n", + "# return jnp.trace(rho@displacement_operator(eta, N_max=N_max, f=f, t=t)) \n", + " \n", + "# return jax.vmap(jax.vmap(characteristic_fn))\n", + "\n", + "def characteristic_function_mixed(rho, f=1.0, t=0):\n", + " N_max = rho.shape[0]\n", + "\n", + " def characteristic_fn(eta):\n", + " return jnp.trace(rho @ displacement_operator(eta, N_max=N_max, f=f, t=t))\n", + "\n", + " def apply_fn(eta_grid): # eta_grid shape: (N, M)\n", + " flat_eta = eta_grid.reshape(-1)\n", + "\n", + " def scan_fn(carry, eta):\n", + " val = characteristic_fn(eta)\n", + " return carry, val\n", + "\n", + " # remat helps prevent backprop memory buildup\n", + " _, results = jax.lax.scan(jax.remat(scan_fn), None, flat_eta)\n", + " return results.reshape(eta_grid.shape)\n", + "\n", + " return apply_fn\n", + "\n", + "# def characteristic_function_mixed(rho, f=1.0, t=0):\n", + "# N_max = rho.shape[0]\n", + "\n", + "# @jax.remat\n", + "# def characteristic_fn(eta):\n", + "# return jnp.trace(rho @ displacement_operator(eta, N_max=N_max, f=f, t=t))\n", + "\n", + "# vectorized_fn = jax.vmap(characteristic_fn)\n", + "\n", + "# @jax.remat\n", + "# def apply_fn(eta_grid): # eta_grid shape: (N, M) complex\n", + "# flat_eta = eta_grid.reshape(-1)\n", + "# result = vectorized_fn(flat_eta)\n", + "# return result.reshape(eta_grid.shape)\n", + "\n", + "# return apply_fn\n", + "\n", + "def characteristic_function_pure(psi, f=1.0, t=0):\n", + " rho = jnp.outer(psi, psi.conj().T)\n", + " N_max = rho.shape[0]\n", + " # def characteristic_fn(eta):\n", + " # return jnp.trace(rho@displacement_operator(eta, N_max=N_max, f=f, t=t))\n", + " \n", + " return characteristic_function_mixed(rho, f=f, t=t)\n", + "\n", + "def characteristic_function_gaussian(mean, covariance):\n", + " def characteristic_fn(eta):\n", + " xi = jnp.sqrt(2) * jnp.array([jnp.real(eta), jnp.imag(eta)])\n", + " return jax.scipy.linalg.expm(-0.5*xi@covariance@eta + 1j*mean@xi)\n", + " \n", + " return characteristic_fn\n", + "\n", + "def eigenvalue_from_vector(A, v):\n", + " Av = A @ v\n", + " # Normalize vector to unit norm to avoid scaling ambiguity\n", + " v_normalized = v / jnp.linalg.norm(v)\n", + " return jnp.vdot(v_normalized, Av) # = λ if v is an eigenvector\n", + "\n", + "# @jax.jit\n", + "# def wigner_function(alpha, characteristic_fn, grid_size=100, limit=1.0):\n", + "# # Discretize complex plane\n", + "# dx = 2 * limit / grid_size\n", + "# x = jnp.linspace(-limit, limit, grid_size)\n", + "# y = jnp.linspace(-limit, limit, grid_size)\n", + "# xx, yy = jnp.meshgrid(x, y)\n", + "# lam = xx + 1j * yy\n", + "# exponent = alpha * jnp.conj(lam) - jnp.conj(alpha) * lam\n", + "# integrand = characteristic_fn(lam) * jnp.exp(exponent)\n", + "# integral = jnp.sum(integrand) * (dx ** 2)\n", + "\n", + "# return (1 / jnp.pi**2) * integral.real\n", + "\n", + "# @jax.jit(static_argnames=[\"characteristic_fn\"])\n", + "def wigner_function(alpha, characteristic_fn, grid_size=80, limit=4.0):\n", + " dx = 2 * limit / grid_size\n", + " x = jnp.linspace(-limit, limit, grid_size)\n", + " y = jnp.linspace(-limit, limit, grid_size)\n", + " xx, yy = jnp.meshgrid(x, y)\n", + " lam = xx + 1j * yy # shape: (grid_size, grid_size)\n", + "\n", + " lam_flat = lam.reshape(-1)\n", + " chi_vals = characteristic_fn(lam_flat).reshape(lam.shape)\n", + "\n", + " exponent = jnp.conj(lam) * alpha - lam * jnp.conj(alpha)\n", + " integrand = chi_vals * jnp.exp(exponent)\n", + " integral = jnp.sum(integrand) * dx**2\n", + "\n", + " return (1 / jnp.pi**2) * integral.real\n", + "# wigner_function = jax.jit(\n", + "# wigner_function,\n", + "# static_argnames=[\"characteristic_fn\", \"grid_size\", \"limit\"]\n", + "# )\n", + "\n", + "def coherence_1(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " t1 = 0\n", + " t2 = 1\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = jnp.sqrt(\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + " \n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2, rho)\n", + "\n", + " return numerator/denominator\n", + "\n", + "def coherence_2(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = (\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + "\n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2.conj().T @ a_hat_2 @ a_hat_1, rho)\n", + "\n", + " return numerator/denominator\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/multimodal_circuit.ipynb b/examples/old/multimodal_circuit.ipynb new file mode 100644 index 00000000..bb4c0d74 --- /dev/null +++ b/examples/old/multimodal_circuit.ipynb @@ -0,0 +1,4611 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c1274941", + "metadata": {}, + "source": [ + "# Multimodal S-parameters\n", + "\n", + "Sax supports [multimode simulations of s-parameter models](https://flaport.github.io/sax/nbs/examples/04_multimode_simulations/). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ba9b0d59", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import sax" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f818d6b", + "metadata": {}, + "outputs": [], + "source": [ + "def waveguide(wl=1.55, wl0=1.55, neff=2.34, ng=3.4, length=10.0, loss=0.0):\n", + " \"\"\"A simple straight waveguide model\n", + "\n", + " Args:\n", + " wl: wavelength\n", + " neff: waveguide effective index\n", + " ng: waveguide group index (used for linear neff dispersion)\n", + " wl0: center wavelength at which neff is defined\n", + " length: [m] wavelength length\n", + " loss: [dB/m] waveguide loss\n", + " \"\"\"\n", + " dwl = wl - wl0\n", + " dneff_dwl = (ng - neff) / wl0\n", + " neff = neff - dwl * dneff_dwl\n", + " phase = 2 * jnp.pi * neff * length / wl\n", + " transmission = 10 ** (-loss * length / 20) * jnp.exp(1j * phase)\n", + " sdict = sax.reciprocal(\n", + " {\n", + " (\"in0@TE\", \"out0@TE\"): 0.95 * transmission, # 5% lost to cross-polarization\n", + " (\"in0@TE\", \"out0@TM\"): 0.05 * transmission, # 5% cross-polarization\n", + " (\"in0@TM\", \"out0@TM\"): 0.85 * transmission, # 10% worse tm->tm than te->te\n", + " (\"in0@TM\", \"out0@TE\"): 0.05 * transmission, # 5% cross-polarization\n", + " }\n", + " )\n", + " return sdict" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f39ed909", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('in0@TE',\n", + " 'out0@TE'): Array(0.77972527+0.5427048j, dtype=complex128, weak_type=True),\n", + " ('in0@TE',\n", + " 'out0@TM'): Array(0.04103817+0.02856341j, dtype=complex128, weak_type=True),\n", + " ('in0@TM',\n", + " 'out0@TM'): Array(0.69764893+0.48557798j, dtype=complex128, weak_type=True),\n", + " ('in0@TM',\n", + " 'out0@TE'): Array(0.04103817+0.02856341j, dtype=complex128, weak_type=True),\n", + " ('out0@TE',\n", + " 'in0@TE'): Array(0.77972527+0.5427048j, dtype=complex128, weak_type=True),\n", + " ('out0@TM',\n", + " 'in0@TE'): Array(0.04103817+0.02856341j, dtype=complex128, weak_type=True),\n", + " ('out0@TM',\n", + " 'in0@TM'): Array(0.69764893+0.48557798j, dtype=complex128, weak_type=True),\n", + " ('out0@TE',\n", + " 'in0@TM'): Array(0.04103817+0.02856341j, dtype=complex128, weak_type=True)}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waveguide()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "426f99d2", + "metadata": {}, + "outputs": [], + "source": [ + "def coupler():\n", + " return {\n", + " (\"in0@TE\", \"out0@TE\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out1@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out0@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out1@TE\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out0@TM\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out1@TM\"): 1j * 0.45**0.5,\n", + " (\"in1@TM\", \"out0@TM\"): 1j * 0.45**0.5,\n", + " (\"in1@TM\", \"out1@TM\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out0@TM\"): 0.01**0.5,\n", + " (\"in0@TE\", \"out1@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out0@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out1@TM\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out0@TE\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out1@TE\"): 1j * 0.01**0.5,\n", + " (\"in1@TM\", \"out0@TE\"): 1j * 0.01**0.5,\n", + " (\"in1@TM\", \"out1@TE\"): 0.01**0.5,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "98aa306d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('in0@TE', 'out0@TE'): 0.6708203932499369,\n", + " ('in0@TE', 'out1@TE'): 0.6708203932499369j,\n", + " ('in1@TE', 'out0@TE'): 0.6708203932499369j,\n", + " ('in1@TE', 'out1@TE'): 0.6708203932499369,\n", + " ('in0@TM', 'out0@TM'): 0.6708203932499369,\n", + " ('in0@TM', 'out1@TM'): 0.6708203932499369j,\n", + " ('in1@TM', 'out0@TM'): 0.6708203932499369j,\n", + " ('in1@TM', 'out1@TM'): 0.6708203932499369,\n", + " ('in0@TE', 'out0@TM'): 0.1,\n", + " ('in0@TE', 'out1@TM'): 0.1j,\n", + " ('in1@TE', 'out0@TM'): 0.1j,\n", + " ('in1@TE', 'out1@TM'): 0.1,\n", + " ('in0@TM', 'out0@TE'): 0.1,\n", + " ('in0@TM', 'out1@TE'): 0.1j,\n", + " ('in1@TM', 'out0@TE'): 0.1j,\n", + " ('in1@TM', 'out1@TE'): 0.1}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coupler()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "385db47c", + "metadata": {}, + "outputs": [], + "source": [ + "netlist = {\n", + " \"instances\": {\n", + " \"lft\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " \"top\": {\"component\": \"straight\", \"settings\": {\"length\": 25.0}},\n", + " \"btm\": {\"component\": \"straight\", \"settings\": {\"length\": 15.0}},\n", + " \"rgt\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " },\n", + " \"connections\": {\n", + " \"lft,out0\": \"btm,in0\",\n", + " \"btm,out0\": \"rgt,in0\",\n", + " \"lft,out1\": \"top,in0\",\n", + " \"top,out0\": \"rgt,in1\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"lft,in0\",\n", + " \"in1\": \"lft,in1\",\n", + " \"out0\": \"rgt,out0\",\n", + " \"out1\": \"rgt,out1\",\n", + " },\n", + " }\n", + "\n", + "models={\n", + " \"coupler\": coupler,\n", + " \"straight\": waveguide,\n", + " }\n", + "\n", + "mzi, _ = sax.circuit(\n", + " netlist=netlist,\n", + " models=models\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ed0cf2da", + "metadata": {}, + "source": [ + "# Multimodal Components\n", + "\n", + "I do believe that in order to use multimodal components with single mode components, you must first convert the single mode models to multimodal models. Our `Circuit` class will not do that automatically, rather, any multiport component without specified modes will be interpreted as single mode." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3a2248d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "\n", + "ckt = Circuit(netlist, models)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "281bc5f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('in0@TE', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TE', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TE', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TE', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TE', 'out0@TE'): Array(-0.24856154+0.09205708j, dtype=complex128),\n", + " ('in0@TE', 'out0@TM'): Array(-0.08070811+0.029891j, dtype=complex128),\n", + " ('in0@TE', 'out1@TE'): Array(0.7922229-0.29340714j, dtype=complex128),\n", + " ('in0@TE', 'out1@TM'): Array(0.25723534-0.09526951j, dtype=complex128),\n", + " ('in0@TM', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TM', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TM', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TM', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in0@TM', 'out0@TE'): Array(-0.08070811+0.029891j, dtype=complex128),\n", + " ('in0@TM', 'out0@TM'): Array(-0.22385744+0.08290769j, dtype=complex128),\n", + " ('in0@TM', 'out1@TE'): Array(0.25723534-0.09526951j, dtype=complex128),\n", + " ('in0@TM', 'out1@TM'): Array(0.71348524-0.26424592j, dtype=complex128),\n", + " ('in1@TE', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TE', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TE', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TE', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TE', 'out0@TE'): Array(0.7922229-0.29340714j, dtype=complex128),\n", + " ('in1@TE', 'out0@TM'): Array(0.25723534-0.09526951j, dtype=complex128),\n", + " ('in1@TE', 'out1@TE'): Array(0.24856154-0.09205708j, dtype=complex128),\n", + " ('in1@TE', 'out1@TM'): Array(0.08070811-0.029891j, dtype=complex128),\n", + " ('in1@TM', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TM', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TM', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TM', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('in1@TM', 'out0@TE'): Array(0.25723534-0.09526951j, dtype=complex128),\n", + " ('in1@TM', 'out0@TM'): Array(0.71348524-0.26424592j, dtype=complex128),\n", + " ('in1@TM', 'out1@TE'): Array(0.08070811-0.029891j, dtype=complex128),\n", + " ('in1@TM', 'out1@TM'): Array(0.22385744-0.08290769j, dtype=complex128),\n", + " ('out0@TE', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'out0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'out0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'out1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TE', 'out1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'out0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'out0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'out1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out0@TM', 'out1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'out0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'out0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'out1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TE', 'out1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'in0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'in0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'in1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'in1@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'out0@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'out0@TM'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'out1@TE'): Array(0.+0.j, dtype=complex128),\n", + " ('out1@TM', 'out1@TM'): Array(0.+0.j, dtype=complex128)}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mzi()" + ] + }, + { + "cell_type": "markdown", + "id": "edc7aae9", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2a020277", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 8, 8)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from simphony.utils import dict_to_matrix\n", + "\n", + "\n", + "dict_to_matrix(mzi()).shape" + ] + }, + { + "cell_type": "markdown", + "id": "867f3653", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/nlse_wg.ipynb b/examples/old/nlse_wg.ipynb new file mode 100644 index 00000000..c4fdda03 --- /dev/null +++ b/examples/old/nlse_wg.ipynb @@ -0,0 +1,19 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d80584ea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/normal.npz b/examples/old/normal.npz new file mode 100644 index 00000000..9753174b Binary files /dev/null and b/examples/old/normal.npz differ diff --git a/examples/old/optical_signal_parsing.ipynb b/examples/old/optical_signal_parsing.ipynb new file mode 100644 index 00000000..a15982c6 --- /dev/null +++ b/examples/old/optical_signal_parsing.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d55973b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "# @jax.jit\n", + "def map_unique_to_indices(x):\n", + " unique_vals, inv_idx = jnp.unique(x, size=x.shape[0], return_inverse=True)\n", + " return jnp.max(inv_idx)\n", + "\n", + "x = jnp.array([1.6, 1.5, 1.4, 1.6])\n", + "result = map_unique_to_indices(x)\n", + "print(result) # Output: [2 1 0 2]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5888b1a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'e': [0, 0, 9], 'd': [0, 6, 8], 'b': [2, 4, 0], 'a': [1, 0, 7], 'c': [3, 5, 0]}\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "# Inputs\n", + "key_lists = [\n", + " ['a', 'b', 'c'],\n", + " ['b', 'c', 'd'],\n", + " ['a', 'd', 'e']\n", + "]\n", + "value_lists = [\n", + " [1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]\n", + "]\n", + "\n", + "# Step 1: Build individual dictionaries\n", + "dicts = []\n", + "for keys, values in zip(key_lists, value_lists):\n", + " d = dict(zip(keys, values))\n", + " dicts.append(d)\n", + "\n", + "# Step 2: Collect all unique keys\n", + "all_keys = set()\n", + "for d in dicts:\n", + " all_keys.update(d.keys())\n", + "\n", + "# Step 3: Construct output\n", + "result = {}\n", + "for key in all_keys:\n", + " result[key] = [d.get(key, 0) for d in dicts]\n", + "\n", + "# Output\n", + "print(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67371b8e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/sample_mode.ipynb b/examples/old/sample_mode.ipynb new file mode 100644 index 00000000..e69de29b diff --git a/examples/sax.ipynb b/examples/old/sax.ipynb similarity index 68% rename from examples/sax.ipynb rename to examples/old/sax.ipynb index 9cde1377..220eea03 100644 --- a/examples/sax.ipynb +++ b/examples/old/sax.ipynb @@ -4,16 +4,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/noah/envs/sax/lib/python3.11/site-packages/sax/backends/__init__.py:24: UserWarning: klujax not found. Please install klujax for better performance during circuit evaluation!\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import warnings\n", "from itertools import product\n", @@ -38,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -54,22 +45,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -86,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -103,28 +94,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "KeyError", + "evalue": "('o0', 'o1')", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m y \u001b[38;5;241m=\u001b[39m siepic\u001b[38;5;241m.\u001b[39my_branch(wl\u001b[38;5;241m=\u001b[39mwl)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#plt.plot(1e3*wl, jnp.abs(y['o0', 'o0'])**2, label=\"in0->out0\")\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;241m1e3\u001b[39m\u001b[38;5;241m*\u001b[39mwl, jnp\u001b[38;5;241m.\u001b[39mabs(\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min0->out0\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;241m1e3\u001b[39m\u001b[38;5;241m*\u001b[39mwl, jnp\u001b[38;5;241m.\u001b[39mabs(y[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo0\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo2\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min0->out0\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mKeyError\u001b[0m: ('o0', 'o1')" + ] } ], "source": [ @@ -136,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -313,77 +295,26 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17.7 ms ± 248 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n" + "ename": "RuntimeError", + "evalue": "Instance lft does not contain port o0. Available ports: ['port_1', 'port_2', 'port_3'].", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/sax/circuit.py:308\u001b[0m, in \u001b[0;36m_get_multimode_connections\u001b[0;34m(connections, inst_port_mode, ignore_missing_ports)\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 308\u001b[0m modes2 \u001b[38;5;241m=\u001b[39m \u001b[43minst_port_mode\u001b[49m\u001b[43m[\u001b[49m\u001b[43minst2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mport2\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: 'o0'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mzi, info \u001b[38;5;241m=\u001b[39m \u001b[43msax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcircuit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mnetlist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstances\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mingc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlft\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mybranch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwaveguide\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbtm\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwaveguide\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrgt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mybranch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moutgc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconnections\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mingc,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlft,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlft,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbtm,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbtm,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrgt,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlft,o2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrgt,o2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrgt,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moutgc,o1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mports\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43min\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mingc,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mout\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moutgc,o0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mybranch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msiepic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my_branch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwaveguide\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msiepic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaveguide\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msiepic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrating_coupler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS = mzi(wl=wl, top=\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m: 150.0}, btm=\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m: 50.0})\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 32\u001b[0m S \u001b[38;5;241m=\u001b[39m mzi(wl\u001b[38;5;241m=\u001b[39mwl, top\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m150.0\u001b[39m}, btm\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m50.0\u001b[39m})\n", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/sax/circuit.py:75\u001b[0m, in \u001b[0;36mcircuit\u001b[0;34m(netlist, models, backend, return_type, ignore_missing_ports)\u001b[0m\n\u001b[1;32m 72\u001b[0m current_models \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m new_models\n\u001b[1;32m 73\u001b[0m new_models \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 75\u001b[0m current_models[model_name] \u001b[38;5;241m=\u001b[39m circuit \u001b[38;5;241m=\u001b[39m \u001b[43m_flat_circuit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43mflatnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minstances\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43mflatnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnections\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mflatnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mports\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[43m \u001b[49m\u001b[43mcurrent_models\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 81\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_missing_ports\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_missing_ports\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m circuit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 85\u001b[0m circuit \u001b[38;5;241m=\u001b[39m _enforce_return_type(circuit, return_type)\n", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/sax/circuit.py:167\u001b[0m, in \u001b[0;36m_flat_circuit\u001b[0;34m(instances, connections, ports, models, backend, ignore_missing_ports)\u001b[0m\n\u001b[1;32m 163\u001b[0m dummy_instances \u001b[38;5;241m=\u001b[39m analyze_insts_fn(instances, models)\n\u001b[1;32m 164\u001b[0m inst_port_mode \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 165\u001b[0m k: _port_modes_dict(get_ports(s)) \u001b[38;5;28;01mfor\u001b[39;00m k, s \u001b[38;5;129;01min\u001b[39;00m dummy_instances\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 166\u001b[0m }\n\u001b[0;32m--> 167\u001b[0m connections \u001b[38;5;241m=\u001b[39m \u001b[43m_get_multimode_connections\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[43mconnections\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minst_port_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_missing_ports\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_missing_ports\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 170\u001b[0m ports \u001b[38;5;241m=\u001b[39m _get_multimode_ports(\n\u001b[1;32m 171\u001b[0m ports, inst_port_mode, ignore_missing_ports\u001b[38;5;241m=\u001b[39mignore_missing_ports\n\u001b[1;32m 172\u001b[0m )\n\u001b[1;32m 174\u001b[0m inst2model \u001b[38;5;241m=\u001b[39m {}\n", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/sax/circuit.py:312\u001b[0m, in \u001b[0;36m_get_multimode_connections\u001b[0;34m(connections, inst_port_mode, ignore_missing_ports)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_missing_ports:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 313\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInstance \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minst2\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not contain port \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mport2\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Available ports: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(inst_port_mode[inst2])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 314\u001b[0m )\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m modes1 \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m modes2:\n\u001b[1;32m 316\u001b[0m mm_connections[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minst1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mport1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minst2\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mport2\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Instance lft does not contain port o0. Available ports: ['port_1', 'port_2', 'port_3']." ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -436,40 +367,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:974: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o0\", \"o0\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n", - "/Users/noah/Documents/CamachoLab/simphony/simphony/libraries/siepic/models.py:977: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", - " (\"o1\", \"o1\"): jnp.zeros(wl_m.shape, dtype=np.complex128),\n" + "ename": "NameError", + "evalue": "name 'mzi' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 9\u001b[0m\n\u001b[1;32m 1\u001b[0m mzi_settings \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m150.0\u001b[39m},\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbtm\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m50.0\u001b[39m},\n\u001b[1;32m 4\u001b[0m }\n\u001b[1;32m 6\u001b[0m mzi_chain, info \u001b[38;5;241m=\u001b[39m sax\u001b[38;5;241m.\u001b[39mcircuit(\n\u001b[1;32m 7\u001b[0m netlist\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minstances\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi1\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[43mmzi\u001b[49m,\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi2\u001b[39m\u001b[38;5;124m\"\u001b[39m: mzi,\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi3\u001b[39m\u001b[38;5;124m\"\u001b[39m: mzi,\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# \"mzi1\": {\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# \"component\": \"mzi\",\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# \"settings\": mzi_settings,\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# \"mzi2\": {\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# \"component\": \"mzi\",\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# \"settings\": mzi_settings,\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# \"mzi3\": {\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# \"component\": \"mzi\",\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# \"settings\": mzi_settings,\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 24\u001b[0m },\n\u001b[1;32m 25\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconnections\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 26\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi1,out\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi2,in\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 27\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi2,out\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi3,in\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 28\u001b[0m },\n\u001b[1;32m 29\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mports\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 30\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi1,in\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi3,out\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 32\u001b[0m },\n\u001b[1;32m 33\u001b[0m },\n\u001b[1;32m 34\u001b[0m models\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 35\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmzi\u001b[39m\u001b[38;5;124m\"\u001b[39m: partial(mzi, top\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m150.0\u001b[39m}, btm\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m50.0\u001b[39m}),\n\u001b[1;32m 36\u001b[0m \u001b[38;5;66;03m# \"mzi\": mzi,\u001b[39;00m\n\u001b[1;32m 37\u001b[0m }\n\u001b[1;32m 38\u001b[0m )\n\u001b[1;32m 40\u001b[0m version \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mmatch\u001b[39;00m version:\n", + "\u001b[0;31mNameError\u001b[0m: name 'mzi' is not defined" ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1123,7 +1033,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env", + "display_name": "PythonX", "language": "python", "name": "python3" }, @@ -1137,7 +1047,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/examples/old/sign_conventions.ipynb b/examples/old/sign_conventions.ipynb new file mode 100644 index 00000000..b0e88741 --- /dev/null +++ b/examples/old/sign_conventions.ipynb @@ -0,0 +1,153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5f6b4ecf", + "metadata": {}, + "source": [ + "# Engineering vs Physics conventions\n", + "\n", + "In engineering contexts, a forward traveling wave is defined as $cos(\\omega t - kz)$ and a corresponing phasor $exp(-jkz)$, but this is merely convention. In physics communities an opposite sign conventioned is used: the physics definition of a forward traveling wave is $cos(-\\omega t + kz)$ with the corresponding phasor $exp(ikz)$\n", + "\n", + "Simulators such as Lumerical use the physics convention for their simulations and data https://optics.ansys.com/hc/en-us/articles/1500006150981-Circular-polarization-and-phase-convention . Since Simphony's simulators are meant to be compatible with software such as Lumerical, our interface also assumes the Physics sign convention for forward traveling waves.\n", + "\n", + "It is important to understand this distinction since many of the articles describing the algorithms used in Simphony often use the engineering convention such as this paper on the Vector Fitting Method: https://www.sciencedirect.com/science/article/pii/S0030399224001762 . Nevertheless, Simphony's default API will use the physics convention." + ] + }, + { + "cell_type": "markdown", + "id": "6ca9c40c", + "metadata": {}, + "source": [ + "# Key Differences\n", + "\n", + "In engineering contexts group delay is defined as $\\tau_{g}^{ENG} = - \\frac{d\\phi(\\omega)}{d\\omega}$ while under the physics convention the minus sign is dropped $\\tau_{g}^{PHYS} = \\frac{d\\phi(\\omega)}{d\\omega}$\n", + "\n", + "This can lead to confusion, for example, consider this simple model for a waveguide." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e20e0070", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from simphony.libraries import siepic\n", + "from scipy.constants import speed_of_light as c\n", + "\n", + "f_min = c / (1.6e-6)\n", + "f_max = c / (1.5e-6)\n", + "f = np.linspace(f_min, f_max, 1000)\n", + "s_params = siepic.waveguide(1e6*c/f, length=40)\n", + "\n", + "plt.plot(f, np.angle(s_params[('o0', 'o1')]))" + ] + }, + { + "cell_type": "markdown", + "id": "f5022c83", + "metadata": {}, + "source": [ + "If we mistakenly assume this data follows the Engineering convention, then the S-parameter data would not indicate a simple delay, rather, an advance, which would be unphysical. Some functions Simphony provides are sensitive to this dinstinction, for example, our Vector Fitting implementations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a44c6a5", + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "SiPANN must be installed to use the SiPANN wrappers. To install SiPANN, run `pip install SiPANN`.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/libraries/sipann.py:21\u001b[39m\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m21\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mSiPANN\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m comp, scee\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mSiPANN\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mscee_opt\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m premade_coupler \u001b[38;5;28;01mas\u001b[39;00m sipann_premade_coupler\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'SiPANN'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msimphony\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mlibraries\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m siepic, sipann\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mconstants\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m speed_of_light \u001b[38;5;28;01mas\u001b[39;00m c\n\u001b[32m 6\u001b[39m f_min = c / (\u001b[32m1.6e-6\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/libraries/sipann.py:24\u001b[39m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mSiPANN\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mscee_opt\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m premade_coupler \u001b[38;5;28;01mas\u001b[39;00m sipann_premade_coupler\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[32m 25\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mSiPANN must be installed to use the SiPANN wrappers. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mTo install SiPANN, run `pip install SiPANN`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 27\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_create_sdict_from_model\u001b[39m(model, wl: Union[\u001b[38;5;28mfloat\u001b[39m, ArrayLike]) -> sax.SDict:\n\u001b[32m 31\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Create s-parameter dict from model.\u001b[39;00m\n\u001b[32m 32\u001b[39m \n\u001b[32m 33\u001b[39m \u001b[33;03m Parameters\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 43\u001b[39m \u001b[33;03m The s-parameter dictionary.\u001b[39;00m\n\u001b[32m 44\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n", + "\u001b[31mImportError\u001b[39m: SiPANN must be installed to use the SiPANN wrappers. To install SiPANN, run `pip install SiPANN`." + ] + } + ], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ba8481f", + "metadata": {}, + "outputs": [], + "source": [ + "s_params = siepic.y_branch(1e6*c/f)\n", + "\n", + "plt.plot(f, np.angle(s_params[('port_1', 'port_2')]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22030af9", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.time_domain.vector_fitting.s_domain import vector_fitting\n", + "\n", + "\n", + "\n", + "poles, residues, feedthrough, error = vector_fitting(50, )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/simulation_block_mode.ipynb b/examples/old/simulation_block_mode.ipynb new file mode 100644 index 00000000..07782acd --- /dev/null +++ b/examples/old/simulation_block_mode.ipynb @@ -0,0 +1,9433 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a902ac98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\":\"ybranch\",\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + " # \"phase_modulator\": \"phase_modulator\",\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " # \"vf\": \"voltage_follower\",\n", + " # \"vf1\": \"voltage_follower\",\n", + " # \"vf2\": \"voltage_follower\",\n", + " # \"prng\": \"prng\",\n", + "\n", + " # \"pm2\": \"phase_modulator\",\n", + " # \"vs2\": \"voltage_source\",\n", + "\n", + " # \"y1\": \"ybranch\",\n", + " # \"y2\": \"ybranch\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + " \n", + " # Should Work\n", + " \"vs1,e0\":\"pm1,e0\",\n", + " \"vs2,e0\":\"pm2,e0\",\n", + "\n", + " # Should Not Work\n", + " # \"pm1,e0\":\"vf,e0\",\n", + " # \"vf,e1\":\"pm2,e0\",\n", + " \n", + " # Multiple connections, same nodes\n", + " # \"y1,port_2\": \"y2,port_2\",\n", + " # \"y1,port_3\": \"y2,port_3\",\n", + "\n", + " # Invalid Connection\n", + " # \"vs2,e0\":\"pm2,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter(siepic.y_branch),\n", + " \"waveguide\": siepic.waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " # \"prng\": analytic.PRNG,\n", + " # \"voltage_follower\": analytic.VoltageFollower,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + " \"vs1\": {\"steady_state_voltage\": 1.0, \"steady_state_wl\": 0},\n", + " \"vs2\": {\"steady_state_voltage\": 0.0, \"steady_state_wl\": 0}\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "794115f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e0f0abc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "import time\n", + "from simphony.signal import steady_state_optical_signal, steady_state_electrical_signal\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt)\n", + "sps_result = sps.run(wl, settings=settings)\n", + "plt.plot(np.abs(sps_result.s_parameters[('in','out')]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4e6914fc", + "metadata": {}, + "outputs": [], + "source": [ + "y_branch_inputs = {\n", + " 'port_1': steady_state_optical_signal(field=[0.0, 10.0], wl=[1.55e-6, 1.57e-6]),\n", + " 'port_2': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + " 'port_3': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + "}\n", + "\n", + "\n", + "out_ybranch = sps.components['splitter'].steady_state(y_branch_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "022762e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.6693857e-04, 5.6914147e-04, 4.8092922e+01], dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(out_ybranch['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5abb27a", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "VoltageSource.steady_state() got an unexpected keyword argument 'steady_state_voltage'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m voltage_source_inputs = {\n\u001b[32m 2\u001b[39m \u001b[33m\"\u001b[39m\u001b[33me0\u001b[39m\u001b[33m\"\u001b[39m: electrical_signal(voltage=\u001b[32m0\u001b[39m)\n\u001b[32m 3\u001b[39m }\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m out_voltage_source = \u001b[43msps\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvs1\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43msteady_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvoltage_source_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvs1\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[31mTypeError\u001b[39m: VoltageSource.steady_state() got an unexpected keyword argument 'steady_state_voltage'" + ] + } + ], + "source": [ + "voltage_source_inputs = {\n", + " \"e0\": electrical_signal(voltage=0)\n", + "}\n", + "\n", + "out_voltage_source = sps.components['vs1'].steady_state(voltage_source_inputs, **settings['vs1'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2933dce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SteadyStateElectricalSignal(voltage=Array([1.+0.j], dtype=complex64), wl=Array([0.], dtype=float32))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_voltage_source['e0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12c33303", + "metadata": {}, + "outputs": [], + "source": [ + "phase_modulator_inputs = {\n", + " \"o0\": optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'o1': optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'e0': out_voltage_source['e0'],\n", + "}\n", + "\n", + "out_phase_modulator = sps.components['pm1'].steady_state(phase_modulator_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e06c4e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(-0.99687845+0.07895155j, dtype=complex64)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_phase_modulator['o1'].field[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f0662d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(3.141592653589793)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.angle(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f25218d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.1415927, 0. ], dtype=float32)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.angle(out_phase_modulator['o1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57954866", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'splitter': .SParameterSax at 0x7281c21e2900>,\n", + " 'combiner': .SParameterSax at 0x7281bc6fb140>,\n", + " 'top1': .SParameterSax at 0x7281bc0e1d30>,\n", + " 'top2': .SParameterSax at 0x7281bc0e2ff0>,\n", + " 'bot1': .SParameterSax at 0x7281bc0e3110>,\n", + " 'bot2': .SParameterSax at 0x7281bc0e3290>,\n", + " 'pm1': ,\n", + " 'pm2': ,\n", + " 'vs1': ,\n", + " 'vs2': }" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d41e1c4", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'x' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m np.abs(\u001b[43mx\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mport_1\u001b[39m\u001b[33m'\u001b[39m].field)\n", + "\u001b[31mNameError\u001b[39m: name 'x' is not defined" + ] + } + ], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcc76188", + "metadata": {}, + "outputs": [], + "source": [ + "import jax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aed3792d", + "metadata": {}, + "outputs": [], + "source": [ + "times = []\n", + "for i in range(500):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " times.append(toc-tic)\n", + "\n", + "np.savez(\"jit.npz\", times=times)\n", + "# np.savez(\"normal.npz\", times=times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55fecd34", + "metadata": {}, + "outputs": [], + "source": [ + "jit_times = np.load(\"jit.npz\")['times']\n", + "normal_times = np.load(\"normal.npz\")['times']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d40e0b14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(normal_times[5:])\n", + "plt.plot(jit_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7928f268", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "normal_times = []\n", + "with jax.disable_jit():\n", + " for i in range(int(1e3)):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " normal_times.append(toc-tic)\n", + "\n", + "plt.plot(jit_times[5:])\n", + "plt.plot(normal_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddff4986", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.69816595], dtype=float32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc47c47a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00126998, 0.48113094, 0.48103732])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x[1, :])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465850c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(2, dtype=int64, weak_type=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from simphony.signal import optical_signal\n", + "import jax\n", + "inputs = {\n", + " 'port_1': optical_signal(field=1.0, wl=[1.55e-6, 1.54e-6]),\n", + " 'port_2': optical_signal(field=2.0, wl=[1.53e-6]),\n", + " 'port_3': optical_signal(field=3.0, wl=[1.55e-6]),\n", + "}\n", + "\n", + "@jax.jit\n", + "def dict_to_matrix(inputs: dict):\n", + " ports = [\"port_1\", \"port_2\", \"port_3\"]\n", + " num_ports = len(ports)\n", + " for port in ports:\n", + " inputs[port].wl.shape\n", + " return inputs[\"port_1\"].wl.shape[0]\n", + " \n", + "\n", + "dict_to_matrix(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e633ee", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd954423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float32(0.4811309)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9cc1668", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(np.abs(sps.components['splitter'].s_parameters(wl)[('port_1','port_2')])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "004d9735", + "metadata": {}, + "outputs": [], + "source": [ + "wg = sps.components['bot1']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a72510", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ".SParameterSax at 0x777573695910>" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "606184b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\": {\n", + " \"component\":\"ybranch\",\n", + " \"settings\":{\n", + " \"test_setting\": 100,\n", + " },\n", + " },\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + "\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " \"vs3\": \"voltage_source\",\n", + "\n", + " \"opamp\":\"opamp\",\n", + "\n", + " \"vf1\":\"voltage_follower\",\n", + " \"vf2\":\"voltage_follower\",\n", + " \"vf3\":\"voltage_follower\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + "\n", + " \"vs1,e0\":\"\"\"vf3,e0;\n", + " vf1,e0;\"\"\",\n", + "\n", + " \"vs3,e0\":\"\"\"vf2,e0;\n", + " opamp,inv\"\"\",\n", + " \n", + " \"vs2,e0\":\"opamp,ninv\",\n", + " \n", + " \"vf2,e1\":\"opamp,vp\",\n", + "\n", + " \"vf3,e1\":\"pm2,e0\",\n", + " \"vf1,e0\":\"opamp,vn\", \n", + "\n", + " \"opamp,vout\":\"pm1,e0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter(siepic.y_branch),\n", + " \"waveguide\": analytic.Waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " \"prng\": analytic.PRNG,\n", + " \"voltage_follower\": analytic.VoltageFollower,\n", + " \"opamp\": analytic.OpAmp,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef6f0bbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models, default_settings=settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a2a945", + "metadata": {}, + "outputs": [ + { + "ename": "NotImplementedError", + "evalue": "steady_state method not defined for VoltageFollower", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNotImplementedError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m wl = np.linspace(\u001b[32m1.5\u001b[39m, \u001b[32m1.6\u001b[39m, \u001b[32m1000\u001b[39m)\n\u001b[32m 4\u001b[39m sps = SParameterSimulation(ckt)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43msps\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwl\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/s_parameter.py:55\u001b[39m, in \u001b[36mSParameterSimulation.run\u001b[39m\u001b[34m(self, wl, settings, use_default_settings)\u001b[39m\n\u001b[32m 52\u001b[39m \u001b[38;5;28mself\u001b[39m.add_settings(settings)\n\u001b[32m 54\u001b[39m \u001b[38;5;28mself\u001b[39m._instantiate_components(\u001b[38;5;28mself\u001b[39m.settings)\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m steady_state_simulation_result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msteady_state_simulation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m \u001b[38;5;28mself\u001b[39m._calculate_scattering_matrix()\n\u001b[32m 58\u001b[39m \u001b[38;5;66;03m# TODO\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/steady_state.py:67\u001b[39m, in \u001b[36mSteadyStateSimulation.run\u001b[39m\u001b[34m(self, settings)\u001b[39m\n\u001b[32m 65\u001b[39m simulation_result._collect_component_inputs(component) \n\u001b[32m 66\u001b[39m inputs = simulation_result.component_inputs[component]\n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomponent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43msteady_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 68\u001b[39m simulation_result.component_outputs[component] = outputs\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m simulation_result\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/circuit/components.py:42\u001b[39m, in \u001b[36mSteadyStateComponent.steady_state\u001b[39m\u001b[34m(self, inputs)\u001b[39m\n\u001b[32m 35\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msteady_state\u001b[39m(\n\u001b[32m 36\u001b[39m \u001b[38;5;28mself\u001b[39m, \n\u001b[32m 37\u001b[39m inputs: \u001b[38;5;28mdict\u001b[39m\n\u001b[32m 38\u001b[39m ) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m 39\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 40\u001b[39m \u001b[33;03m Used when calculating steady state voltages for SParameterSimulation\u001b[39;00m\n\u001b[32m 41\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[32m 43\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minspect.currentframe().f_code.co_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m method not defined for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 44\u001b[39m )\n", + "\u001b[31mNotImplementedError\u001b[39m: steady_state method not defined for VoltageFollower" + ] + } + ], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt)\n", + "sps.run(wl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd1d588d", + "metadata": {}, + "outputs": [], + "source": [ + "sps.change_settings(settings, use_default_settings=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "160b670c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'splitter': {'test_setting': 100}, 'combiner': {}, 'top1': {'length': 5}, 'top2': {'length': 5}, 'bot1': {'length': 10}, 'bot2': {}, 'pm1': {}, 'pm2': {}, 'vs1': {}, 'vs2': {}, 'vs3': {}, 'opamp': {}, 'vf1': {}, 'vf2': {}, 'vf3': {}}\n", + "{'splitter': {'test_setting': 100}, 'combiner': {}, 'top1': {'length': 5}, 'top2': {'length': 5}, 'bot1': {'length': 10}, 'bot2': {'joke_setting': 'Joke'}, 'pm1': {}, 'pm2': {}, 'vs1': {}, 'vs2': {}, 'vs3': {}, 'opamp': {}, 'vf1': {}, 'vf2': {}, 'vf3': {}}\n", + "{'bot2': {'joke_setting': 'Joke'}}\n" + ] + } + ], + "source": [ + "print(ckt.default_settings)\n", + "print(sps.settings)\n", + "print(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39cb58ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['vs1', 'vs2', 'vs3', 'vf1', 'vf2', 'vf3', 'opamp']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.steady_state_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c137bc", + "metadata": {}, + "outputs": [], + "source": [ + "ckt.default_settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08fd5271", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.simulation.simulation import SParameterSimulation\n", + "\n", + "sps = SParameterSimulation(ckt)\n", + "sps.update_settings(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bcd9ba2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eda801cd", + "metadata": {}, + "outputs": [], + "source": [ + "from sax.models import unitary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a0c145", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.analytic import star_coupler\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8acaf131", + "metadata": {}, + "outputs": [], + "source": [ + "print(star_coupler(2, 2).optical_ports)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb97bee6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cf0260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'e0': ElectricalSignal(voltage=Array(0.5+0.j, dtype=complex64), wl=Array(0., dtype=float32)),\n", + " 'o0': OpticalSignal(field=Array(-4.371139e-08+1.j, dtype=complex64), wl=Array(1.55e-06, dtype=float32), polarization=Array([1.+0.j, 0.+0.j], dtype=complex64)),\n", + " 'o1': OpticalSignal(field=Array(-4.371139e-08+1.j, dtype=complex64), wl=Array(1.55e-06, dtype=float32), polarization=Array([1.+0.j, 0.+0.j], dtype=complex64))}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/simulation_s_parameter.ipynb b/examples/old/simulation_s_parameter.ipynb new file mode 100644 index 00000000..8b30f82b --- /dev/null +++ b/examples/old/simulation_s_parameter.ipynb @@ -0,0 +1,9433 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a902ac98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\":\"ybranch\",\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + " # \"phase_modulator\": \"phase_modulator\",\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " # \"vf\": \"voltage_follower\",\n", + " # \"vf1\": \"voltage_follower\",\n", + " # \"vf2\": \"voltage_follower\",\n", + " # \"prng\": \"prng\",\n", + "\n", + " # \"pm2\": \"phase_modulator\",\n", + " # \"vs2\": \"voltage_source\",\n", + "\n", + " # \"y1\": \"ybranch\",\n", + " # \"y2\": \"ybranch\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + " \n", + " # Should Work\n", + " \"vs1,e0\":\"pm1,e0\",\n", + " \"vs2,e0\":\"pm2,e0\",\n", + "\n", + " # Should Not Work\n", + " # \"pm1,e0\":\"vf,e0\",\n", + " # \"vf,e1\":\"pm2,e0\",\n", + " \n", + " # Multiple connections, same nodes\n", + " # \"y1,port_2\": \"y2,port_2\",\n", + " # \"y1,port_3\": \"y2,port_3\",\n", + "\n", + " # Invalid Connection\n", + " # \"vs2,e0\":\"pm2,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter(siepic.y_branch),\n", + " \"waveguide\": siepic.waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " # \"prng\": analytic.PRNG,\n", + " # \"voltage_follower\": analytic.VoltageFollower,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + " \"vs1\": {\"steady_state_voltage\": 1.0, \"steady_state_wl\": 0},\n", + " \"vs2\": {\"steady_state_voltage\": 0.0, \"steady_state_wl\": 0}\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "794115f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e0f0abc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "import time\n", + "from simphony.signal import steady_state_optical_signal, steady_state_electrical_signal\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt)\n", + "sps_result = sps.run(settings=settings, wl=wl)\n", + "plt.plot(np.abs(sps_result.s_parameters[('in','out')]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4e6914fc", + "metadata": {}, + "outputs": [], + "source": [ + "y_branch_inputs = {\n", + " 'port_1': steady_state_optical_signal(field=[0.0, 10.0], wl=[1.55e-6, 1.57e-6]),\n", + " 'port_2': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + " 'port_3': steady_state_optical_signal(field=[0.5, 1.0], wl=[1.55e-6, 1.56e-6]),\n", + "}\n", + "\n", + "\n", + "out_ybranch = sps.components['splitter'].steady_state(y_branch_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "022762e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.6693857e-04, 5.6914147e-04, 4.8092922e+01], dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(out_ybranch['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5abb27a", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "VoltageSource.steady_state() got an unexpected keyword argument 'steady_state_voltage'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m voltage_source_inputs = {\n\u001b[32m 2\u001b[39m \u001b[33m\"\u001b[39m\u001b[33me0\u001b[39m\u001b[33m\"\u001b[39m: electrical_signal(voltage=\u001b[32m0\u001b[39m)\n\u001b[32m 3\u001b[39m }\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m out_voltage_source = \u001b[43msps\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvs1\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43msteady_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvoltage_source_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvs1\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[31mTypeError\u001b[39m: VoltageSource.steady_state() got an unexpected keyword argument 'steady_state_voltage'" + ] + } + ], + "source": [ + "voltage_source_inputs = {\n", + " \"e0\": electrical_signal(voltage=0)\n", + "}\n", + "\n", + "out_voltage_source = sps.components['vs1'].steady_state(voltage_source_inputs, **settings['vs1'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2933dce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SteadyStateElectricalSignal(voltage=Array([1.+0.j], dtype=complex64), wl=Array([0.], dtype=float32))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_voltage_source['e0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12c33303", + "metadata": {}, + "outputs": [], + "source": [ + "phase_modulator_inputs = {\n", + " \"o0\": optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'o1': optical_signal(field=[1.0, 1.0], wl=[1.55e-6, 1.59e-6]),\n", + " 'e0': out_voltage_source['e0'],\n", + "}\n", + "\n", + "out_phase_modulator = sps.components['pm1'].steady_state(phase_modulator_inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e06c4e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(-0.99687845+0.07895155j, dtype=complex64)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_phase_modulator['o1'].field[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f0662d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(3.141592653589793)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.angle(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f25218d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.1415927, 0. ], dtype=float32)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.angle(out_phase_modulator['o1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57954866", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'splitter': .SParameterSax at 0x7281c21e2900>,\n", + " 'combiner': .SParameterSax at 0x7281bc6fb140>,\n", + " 'top1': .SParameterSax at 0x7281bc0e1d30>,\n", + " 'top2': .SParameterSax at 0x7281bc0e2ff0>,\n", + " 'bot1': .SParameterSax at 0x7281bc0e3110>,\n", + " 'bot2': .SParameterSax at 0x7281bc0e3290>,\n", + " 'pm1': ,\n", + " 'pm2': ,\n", + " 'vs1': ,\n", + " 'vs2': }" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d41e1c4", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'x' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m np.abs(\u001b[43mx\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mport_1\u001b[39m\u001b[33m'\u001b[39m].field)\n", + "\u001b[31mNameError\u001b[39m: name 'x' is not defined" + ] + } + ], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcc76188", + "metadata": {}, + "outputs": [], + "source": [ + "import jax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aed3792d", + "metadata": {}, + "outputs": [], + "source": [ + "times = []\n", + "for i in range(500):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " times.append(toc-tic)\n", + "\n", + "np.savez(\"jit.npz\", times=times)\n", + "# np.savez(\"normal.npz\", times=times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55fecd34", + "metadata": {}, + "outputs": [], + "source": [ + "jit_times = np.load(\"jit.npz\")['times']\n", + "normal_times = np.load(\"normal.npz\")['times']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d40e0b14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(normal_times[5:])\n", + "plt.plot(jit_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7928f268", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "normal_times = []\n", + "with jax.disable_jit():\n", + " for i in range(int(1e3)):\n", + " tic = time.perf_counter()\n", + " sps.components['splitter'].steady_state(inputs)\n", + " toc = time.perf_counter()\n", + " normal_times.append(toc-tic)\n", + "\n", + "plt.plot(jit_times[5:])\n", + "plt.plot(normal_times[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddff4986", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.69816595], dtype=float32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x['port_1'].field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc47c47a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00126998, 0.48113094, 0.48103732])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x[1, :])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465850c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(2, dtype=int64, weak_type=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from simphony.signal import optical_signal\n", + "import jax\n", + "inputs = {\n", + " 'port_1': optical_signal(field=1.0, wl=[1.55e-6, 1.54e-6]),\n", + " 'port_2': optical_signal(field=2.0, wl=[1.53e-6]),\n", + " 'port_3': optical_signal(field=3.0, wl=[1.55e-6]),\n", + "}\n", + "\n", + "@jax.jit\n", + "def dict_to_matrix(inputs: dict):\n", + " ports = [\"port_1\", \"port_2\", \"port_3\"]\n", + " num_ports = len(ports)\n", + " for port in ports:\n", + " inputs[port].wl.shape\n", + " return inputs[\"port_1\"].wl.shape[0]\n", + " \n", + "\n", + "dict_to_matrix(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e633ee", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd954423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float32(0.4811309)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x['port_2'].field)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9cc1668", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(np.abs(sps.components['splitter'].s_parameters(wl)[('port_1','port_2')])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "004d9735", + "metadata": {}, + "outputs": [], + "source": [ + "wg = sps.components['bot1']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a72510", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ".SParameterSax at 0x777573695910>" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "606184b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\": {\n", + " \"component\":\"ybranch\",\n", + " \"settings\":{\n", + " \"test_setting\": 100,\n", + " },\n", + " },\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + "\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " \"vs3\": \"voltage_source\",\n", + "\n", + " \"opamp\":\"opamp\",\n", + "\n", + " \"vf1\":\"voltage_follower\",\n", + " \"vf2\":\"voltage_follower\",\n", + " \"vf3\":\"voltage_follower\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + "\n", + " \"vs1,e0\":\"\"\"vf3,e0;\n", + " vf1,e0;\"\"\",\n", + "\n", + " \"vs3,e0\":\"\"\"vf2,e0;\n", + " opamp,inv\"\"\",\n", + " \n", + " \"vs2,e0\":\"opamp,ninv\",\n", + " \n", + " \"vf2,e1\":\"opamp,vp\",\n", + "\n", + " \"vf3,e1\":\"pm2,e0\",\n", + " \"vf1,e0\":\"opamp,vn\", \n", + "\n", + " \"opamp,vout\":\"pm1,e0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter(siepic.y_branch),\n", + " \"waveguide\": analytic.Waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " \"prng\": analytic.PRNG,\n", + " \"voltage_follower\": analytic.VoltageFollower,\n", + " \"opamp\": analytic.OpAmp,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef6f0bbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models, default_settings=settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a2a945", + "metadata": {}, + "outputs": [ + { + "ename": "NotImplementedError", + "evalue": "steady_state method not defined for VoltageFollower", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNotImplementedError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m wl = np.linspace(\u001b[32m1.5\u001b[39m, \u001b[32m1.6\u001b[39m, \u001b[32m1000\u001b[39m)\n\u001b[32m 4\u001b[39m sps = SParameterSimulation(ckt)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43msps\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwl\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/s_parameter.py:55\u001b[39m, in \u001b[36mSParameterSimulation.run\u001b[39m\u001b[34m(self, wl, settings, use_default_settings)\u001b[39m\n\u001b[32m 52\u001b[39m \u001b[38;5;28mself\u001b[39m.add_settings(settings)\n\u001b[32m 54\u001b[39m \u001b[38;5;28mself\u001b[39m._instantiate_components(\u001b[38;5;28mself\u001b[39m.settings)\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m steady_state_simulation_result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msteady_state_simulation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m \u001b[38;5;28mself\u001b[39m._calculate_scattering_matrix()\n\u001b[32m 58\u001b[39m \u001b[38;5;66;03m# TODO\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/steady_state.py:67\u001b[39m, in \u001b[36mSteadyStateSimulation.run\u001b[39m\u001b[34m(self, settings)\u001b[39m\n\u001b[32m 65\u001b[39m simulation_result._collect_component_inputs(component) \n\u001b[32m 66\u001b[39m inputs = simulation_result.component_inputs[component]\n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcomponents\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomponent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43msteady_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 68\u001b[39m simulation_result.component_outputs[component] = outputs\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m simulation_result\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/circuit/components.py:42\u001b[39m, in \u001b[36mSteadyStateComponent.steady_state\u001b[39m\u001b[34m(self, inputs)\u001b[39m\n\u001b[32m 35\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msteady_state\u001b[39m(\n\u001b[32m 36\u001b[39m \u001b[38;5;28mself\u001b[39m, \n\u001b[32m 37\u001b[39m inputs: \u001b[38;5;28mdict\u001b[39m\n\u001b[32m 38\u001b[39m ) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m 39\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 40\u001b[39m \u001b[33;03m Used when calculating steady state voltages for SParameterSimulation\u001b[39;00m\n\u001b[32m 41\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[32m 43\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minspect.currentframe().f_code.co_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m method not defined for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 44\u001b[39m )\n", + "\u001b[31mNotImplementedError\u001b[39m: steady_state method not defined for VoltageFollower" + ] + } + ], + "source": [ + "from simphony.simulation import SParameterSimulation\n", + "import numpy as np\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "sps = SParameterSimulation(ckt)\n", + "sps.run(wl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd1d588d", + "metadata": {}, + "outputs": [], + "source": [ + "sps.change_settings(settings, use_default_settings=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "160b670c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'splitter': {'test_setting': 100}, 'combiner': {}, 'top1': {'length': 5}, 'top2': {'length': 5}, 'bot1': {'length': 10}, 'bot2': {}, 'pm1': {}, 'pm2': {}, 'vs1': {}, 'vs2': {}, 'vs3': {}, 'opamp': {}, 'vf1': {}, 'vf2': {}, 'vf3': {}}\n", + "{'splitter': {'test_setting': 100}, 'combiner': {}, 'top1': {'length': 5}, 'top2': {'length': 5}, 'bot1': {'length': 10}, 'bot2': {'joke_setting': 'Joke'}, 'pm1': {}, 'pm2': {}, 'vs1': {}, 'vs2': {}, 'vs3': {}, 'opamp': {}, 'vf1': {}, 'vf2': {}, 'vf3': {}}\n", + "{'bot2': {'joke_setting': 'Joke'}}\n" + ] + } + ], + "source": [ + "print(ckt.default_settings)\n", + "print(sps.settings)\n", + "print(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39cb58ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['vs1', 'vs2', 'vs3', 'vf1', 'vf2', 'vf3', 'opamp']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.steady_state_order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c137bc", + "metadata": {}, + "outputs": [], + "source": [ + "ckt.default_settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08fd5271", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.simulation.simulation import SParameterSimulation\n", + "\n", + "sps = SParameterSimulation(ckt)\n", + "sps.update_settings(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bcd9ba2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eda801cd", + "metadata": {}, + "outputs": [], + "source": [ + "from sax.models import unitary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a0c145", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.analytic import star_coupler\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8acaf131", + "metadata": {}, + "outputs": [], + "source": [ + "print(star_coupler(2, 2).optical_ports)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb97bee6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cf0260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'e0': ElectricalSignal(voltage=Array(0.5+0.j, dtype=complex64), wl=Array(0., dtype=float32)),\n", + " 'o0': OpticalSignal(field=Array(-4.371139e-08+1.j, dtype=complex64), wl=Array(1.55e-06, dtype=float32), polarization=Array([1.+0.j, 0.+0.j], dtype=complex64)),\n", + " 'o1': OpticalSignal(field=Array(-4.371139e-08+1.j, dtype=complex64), wl=Array(1.55e-06, dtype=float32), polarization=Array([1.+0.j, 0.+0.j], dtype=complex64))}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/simulation_sample_mode.ipynb b/examples/old/simulation_sample_mode.ipynb new file mode 100644 index 00000000..bf152148 --- /dev/null +++ b/examples/old/simulation_sample_mode.ipynb @@ -0,0 +1,5574 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6343b503", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + } + ], + "source": [ + "from simphony.libraries.siepic import waveguide\n", + "from simphony.simulation import SampleModeSimulationParameters\n", + "\n", + "spectral_range = (1.5e-6,1.6e-6)\n", + "delay_compensation = 0\n", + "simulation_parameters = SampleModeSimulationParameters()\n", + "# waveguide.impulse_response_discrete(simulation_parameters, spectral_range, delay_compensation, {'length':1.0})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "661048c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'wl')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from simphony.time_domain.utils import gaussian_pulse\n", + "from simphony.libraries.analytic.sources import OpticalCombSource\n", + "from functools import partial\n", + "import sax\n", + "from scipy.constants import speed_of_light\n", + "\n", + "wl = 1e-6*np.linspace(1.5, 1.6, 100)\n", + "\n", + "_mzi, info = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"gc_in\": \"gc\",\n", + " \"splitter\": \"ybranch\",\n", + " \"long_wg\": \"waveguide\",\n", + " \"short_wg\": \"waveguide\",\n", + " \"combiner\": \"ybranch\",\n", + " # \"gc_out\": \"gc\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port 2\": \"long_wg,o0\",\n", + " \"splitter,port 3\": \"short_wg,o0\",\n", + " \"long_wg,o1\": \"combiner,port 2\",\n", + " \"short_wg,o1\": \"combiner,port 3\",\n", + " # \"combiner,port 1\": \"gc_out,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port 1\",\n", + " \"out\": \"combiner,port 1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ybranch\": siepic.y_branch,\n", + " \"waveguide\": siepic.waveguide,\n", + " # \"gc\": siepic.grating_coupler,\n", + " }\n", + ")\n", + "\n", + "def mzi(wl=1.55):\n", + " return _mzi(wl=wl, long_wg={\"length\": 150.0}, short_wg={\"length\": 100.0})\n", + "\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"laser\":\"laser\",\n", + " # \"wg1\": \"waveguide\",\n", + " \"mzi\": \"mzi\",\n", + " },\n", + " \"connections\": {\n", + " \"laser,o0\": \"mzi,in\",\n", + " },\n", + " \"ports\": {\n", + " \"laser_output\": \"laser,o0\",\n", + " \"waveguide_output\": \"mzi,out\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " # \"laser\": analytic.CWLaser,\n", + " \"laser\": OpticalCombSource,\n", + " \"mzi\": mzi,\n", + " # \"laser\": analytic.OpticalSource,\n", + " # \"waveguide\": siepic.waveguide,\n", + " # \"waveguide\": analytic.Waveguide,\n", + "}\n", + "\n", + "pulse_fn = partial(gaussian_pulse, t0=4e-12, sigma=1e-12)\n", + "settings={ \n", + " \"laser\": {\"wavelength\": wl},\n", + " \"mzi\": {\"delay_compensation\": 1},\n", + " # \"laser\": {\"wavelength\": 1.55e-6, \"envelope_fn\": pulse_fn},\n", + " # \"wg1\": {\"length\":20},\n", + "}\n", + "\n", + "wl_dense = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "# plt.plot(wl_dense, np.abs(mzi(1e6*wl_dense)[('out', 'in')]))\n", + "plt.plot(wl_dense, np.unwrap(np.angle(np.exp(-1j*speed_of_light/wl_dense*13e-12)*mzi(1e6*wl_dense)[('out', 'out')])))\n", + "plt.ylabel(\"Phase\")\n", + "plt.xlabel(\"wl\")\n", + "# plt.xlim([1.53*1e-6, 1.57*1e-6])\n", + "# plt.ylim([-125, -40])\n", + "# plt.axvline(1.55e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a902ac98", + "metadata": {}, + "outputs": [], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "import matplotlib.pyplot as plt\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"laser\":\"laser\",\n", + " \"splitter\":\"ybranch\",\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + " # \"phase_modulator\": \"phase_modulator\",\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " # \"vf\": \"voltage_follower\",\n", + " # \"vf1\": \"voltage_follower\",\n", + " # \"vf2\": \"voltage_follower\",\n", + " # \"prng\": \"prng\",\n", + "\n", + " # \"pm2\": \"phase_modulator\",\n", + " # \"vs2\": \"voltage_source\",\n", + "\n", + " # \"y1\": \"ybranch\",\n", + " # \"y2\": \"ybranch\",\n", + " },\n", + " \"connections\": {\n", + " \"laser,o0\": \"splitter,port_1\",\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + " \n", + " # Should Work\n", + " \"vs1,e0\":\"pm1,e0\",\n", + " \"vs2,e0\":\"pm2,e0\",\n", + "\n", + " # Should Not Work\n", + " # \"pm1,e0\":\"vf,e0\",\n", + " # \"vf,e1\":\"pm2,e0\",\n", + " \n", + " # Multiple connections, same nodes\n", + " # \"y1,port_2\": \"y2,port_2\",\n", + " # \"y1,port_3\": \"y2,port_3\",\n", + "\n", + " # Invalid Connection\n", + " # \"vs2,e0\":\"pm2,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"laser\": analytic.CWLaser,\n", + " # \"ybranch\": siepic.y_branch,\n", + " \"ybranch\": analytic.optical_s_parameter_placeholder(siepic.y_branch),\n", + " \"waveguide\": siepic.waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " # \"prng\": analytic.PRNG,\n", + " # \"voltage_follower\": analytic.VoltageFollower,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"laser\": {\"wavelength\": 1.55e-6},\n", + " \"top1\": {\"length\": 180},\n", + " \"top2\": {\"length\": 185},\n", + " \"bot1\": {\"length\": 190},\n", + " \"bot2\": {\"length\": 195}, \n", + " \"vs1\": {\"steady_state_voltage\": 1.0, \"steady_state_wl\": 0},\n", + " \"vs2\": {\"steady_state_voltage\": 0.0, \"steady_state_wl\": 0}\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "794115f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models, settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4e0f0abc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "IndexError", + "evalue": "tuple index out of range", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIndexError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 7\u001b[39m simulation_parameters = SampleModeSimulationParameters(optical_baseband_wavelengths=wl, num_time_steps=\u001b[38;5;28mint\u001b[39m(\u001b[32m50000\u001b[39m))\n\u001b[32m 9\u001b[39m tic = time()\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m sms_result = \u001b[43msms\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m=\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_jit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 11\u001b[39m toc = time()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/sample_mode.py:157\u001b[39m, in \u001b[36mSampleModeSimulation.run\u001b[39m\u001b[34m(self, settings, tracked_ports, simulation_parameters, use_jit)\u001b[39m\n\u001b[32m 155\u001b[39m time_steps = jnp.arange(\u001b[32m0\u001b[39m, N, \u001b[32m1\u001b[39m, dtype=\u001b[38;5;28mint\u001b[39m)\n\u001b[32m 156\u001b[39m tic = time()\n\u001b[32m--> \u001b[39m\u001b[32m157\u001b[39m _, system_outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_scan\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_system_step\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlength\u001b[49m\u001b[43m=\u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 158\u001b[39m toc = time()\n\u001b[32m 159\u001b[39m elapsed_time = toc - tic\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/jax_tools.py:14\u001b[39m, in \u001b[36mpython_based_scan\u001b[39m\u001b[34m(f, init, xs, length)\u001b[39m\n\u001b[32m 12\u001b[39m ys = []\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m xs:\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m carry, y = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 15\u001b[39m ys.append(y)\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m carry, jnp.stack(ys)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/simulation/sample_mode.py:183\u001b[39m, in \u001b[36mSampleModeSimulation._system_step\u001b[39m\u001b[34m(self, carry, x)\u001b[39m\n\u001b[32m 181\u001b[39m inputs = system_inputs[instance_name]\n\u001b[32m 182\u001b[39m input_state = states[instance_name]\n\u001b[32m--> \u001b[39m\u001b[32m183\u001b[39m instance_outputs, output_state = \u001b[43minstance\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_sample_mode_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_parameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 184\u001b[39m states[instance_name] = output_state\n\u001b[32m 185\u001b[39m system_outputs[instance_name] = system_outputs[instance_name] | instance_outputs\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/camacho/simphony/simphony/circuit/components.py:261\u001b[39m, in \u001b[36mSampleModeComponent._sample_mode_step\u001b[39m\u001b[34m(self, inputs, state, simulation_parameters)\u001b[39m\n\u001b[32m 257\u001b[39m closest_idx = jnp.argmin(dists, axis=\u001b[32m0\u001b[39m)\n\u001b[32m 259\u001b[39m f_diff = speed_of_light / wavelength - speed_of_light / baseband_wls[closest_idx]\n\u001b[32m--> \u001b[39m\u001b[32m261\u001b[39m new_amplitude = jnp.zeros((baseband_wls.shape[\u001b[32m0\u001b[39m], \u001b[43mamplitude\u001b[49m\u001b[43m.\u001b[49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m), dtype=\u001b[38;5;28mcomplex\u001b[39m)\n\u001b[32m 262\u001b[39m new_amplitude = new_amplitude.at[closest_idx].add(amplitude*jnp.exp(-\u001b[32m1\u001b[39mj*\u001b[32m2\u001b[39m*jnp.pi*f_diff[:, \u001b[38;5;28;01mNone\u001b[39;00m]/f_s * time_step))\n\u001b[32m 263\u001b[39m outputs[port] = signal.replace(amplitude=new_amplitude, wavelength=baseband_wls)\n", + "\u001b[31mIndexError\u001b[39m: tuple index out of range" + ] + } + ], + "source": [ + "from simphony.simulation import SampleModeSimulation, SampleModeSimulationParameters\n", + "import numpy as np\n", + "from time import time\n", + "\n", + "sms = SampleModeSimulation(ckt)\n", + "\n", + "simulation_parameters = SampleModeSimulationParameters(optical_baseband_wavelengths=wl, num_time_steps=int(50000))\n", + "\n", + "tic = time()\n", + "sms_result = sms.run(settings=settings, use_jit=False, simulation_parameters=simulation_parameters)\n", + "toc = time()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3a9559c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Example: suppose you have frequency samples 187 THz to 200 THz\n", + "f_min = 187e12\n", + "f_max = 200e12\n", + "N = 2048 # number of points (power of 2 efficient for FFT)\n", + "freqs = np.linspace(f_min, f_max, N)\n", + "\n", + "# Your frequency-domain data, e.g. H(f)\n", + "Hf = np.exp(-1j * 2 * np.pi * freqs * 1e-12) # (example: delay of 1 ps)\n", + "\n", + "# Shift frequency axis to baseband (center around 0)\n", + "f_center = (f_min + f_max) / 2\n", + "# Hf_shifted = Hf * np.exp(-1j * 2 * np.pi * (freqs - f_center) * 0)\n", + "\n", + "# Compute impulse response (baseband equivalent)\n", + "ht = np.fft.ifft(np.fft.ifftshift(Hf))\n", + "\n", + "# Time axis (FFT dual of frequency spacing)\n", + "df = freqs[1] - freqs[0]\n", + "T = 1 / df\n", + "t = np.linspace(0, T, N, endpoint=False)\n", + "\n", + "# If you want the passband (modulated) version:\n", + "ht_passband = ht * np.exp(1j * 2 * np.pi * f_center * t)\n", + "\n", + "plt.plot(t, ht_passband)\n", + "plt.xlim([0, 10e-12])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3a07bd8", + "metadata": {}, + "outputs": [], + "source": [ + "sms_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed58ce51", + "metadata": {}, + "outputs": [], + "source": [ + "laser_output = sms_result['laser']['o0']\n", + "mzi_output = sms_result['mzi']['out']\n", + "# adv0_output = sms_result['_optical_advance0']['out']\n", + "# adv1_output = sms_result['_optical_advance1']['in']\n", + "# wg_output = sms_result['wg1']['o1']\n", + "t = np.arange(len(laser_output.amplitude[:, 15, 0]))\n", + "t = t*1e-14\n", + "# skip = 1\n", + "plt.plot(t[:], laser_output.amplitude[:, 15, 0].real)\n", + "plt.plot(t[:], np.abs(mzi_output.amplitude[:, 94, 0]))\n", + "# plt.plot(t[:], np.abs(wg_output.amplitude[:, 0, 0]))\n", + "# plt.plot(t[:], np.abs(adv0_output.amplitude[:, 0, 0]))\n", + "# plt.plot(t[:], np.abs(adv1_output.amplitude[:, 0, 0]))\n", + "\n", + "# for i, _ in enumerate(simulation_parameters.optical_baseband_wavelengths):\n", + "# plt.plot(t[:], np.abs(mzi_output.amplitude[:, i, 0].real))\n", + "\n", + "# plt.xlim([0, 60e-11])\n", + "\n", + "np.abs(mzi_output.amplitude[-1, 96, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21edb8d9", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.constants import speed_of_light as c\n", + "_wl = simulation_parameters.optical_baseband_wavelengths\n", + "\n", + "\n", + "phase_diff = np.unwrap(np.angle(mzi_output.amplitude[-1, :, 0])) - np.unwrap(np.angle(mzi(1e6*_wl)[('out', 'in')]))\n", + "plt.plot(c/_wl, phase_diff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d7cb555", + "metadata": {}, + "outputs": [], + "source": [ + "# wl_dense = 1e-6*np.linspace(1.5, 1.6, 1000)\n", + "steady_state_wl = simulation_parameters.optical_baseband_wavelengths\n", + "steady_state_amp = mzi_output.amplitude[-1, :, 0]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# --- Magnitude plot ---\n", + "axes[0].scatter(steady_state_wl, np.abs(steady_state_amp)**2, s=30, marker='*', label=\"steady_state\")\n", + "axes[0].plot(wl_dense, np.abs(mzi(1e6*wl_dense)[('in', 'out')])**2, label=\"s-params\")\n", + "axes[0].set_ylabel(\"mag\")\n", + "axes[0].set_xlabel(\"wl\")\n", + "axes[0].legend(loc=\"upper left\")\n", + "\n", + "# --- Phase plot ---\n", + "axes[1].scatter(steady_state_wl, np.angle(steady_state_amp), s=30, marker='*', label=\"steady_state\")\n", + "axes[1].plot(wl_dense, np.angle(mzi(1e6*wl_dense)[('in', 'out')]), label=\"s-params\")\n", + "axes[1].set_ylabel(\"phase\")\n", + "axes[1].set_xlabel(\"wl\")\n", + "axes[1].legend(loc=\"upper left\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "704d2514", + "metadata": {}, + "outputs": [], + "source": [ + "steady_state_amp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f38333e", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import signal\n", + "\n", + "signal.dimpulse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53d52df6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.signal import dlti, dimpulse\n", + "\n", + "# --- Define a simple discrete-time system ---\n", + "A = np.array([[0.5+0.2j, 0], [0, 0.2+0.1j]], dtype=complex)\n", + "B = np.array([[1.0], [0.5]], dtype=complex)\n", + "C = np.array([[1.0, 1.0]], dtype=complex)\n", + "D = np.array([[0.0]], dtype=complex)\n", + "dt = 1.0 # sample time\n", + "\n", + "system = dlti(A, B, C, D, dt=dt)\n", + "\n", + "# --- Analytic impulse response ---\n", + "N = 10 # number of samples\n", + "h_analytic = []\n", + "\n", + "for n in range(N):\n", + " if n == 0:\n", + " h_analytic.append(D.flatten())\n", + " else:\n", + " h_analytic.append((C @ np.linalg.matrix_power(A, n-1) @ B).flatten())\n", + "\n", + "h_analytic = np.array(h_analytic)\n", + "\n", + "# --- Impulse response using dimpulse ---\n", + "t, h_dimpulse = dimpulse(system, x0=np.zeros((A.shape[0],), dtype=complex), n=N)\n", + "h_dimpulse = np.squeeze(h_dimpulse[0]) # only one input/output\n", + "\n", + "# --- Compare ---\n", + "print(\"Analytic h[n]:\")\n", + "print(h_analytic.flatten())\n", + "print(\"\\ndimpulse h[n]:\")\n", + "print(h_dimpulse)\n", + "\n", + "# Optional: check if they are close\n", + "print(\"\\nDifference:\")\n", + "print(h_analytic.flatten() - h_dimpulse)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2f6e5f3", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_parameters.optical_baseband_wavelengths[50]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c210cb66", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# -----------------------------\n", + "# Parameters\n", + "# -----------------------------\n", + "N = 20 # total number of samples\n", + "k = 5 # original delta delay\n", + "advance = 1 # number of samples to advance\n", + "complex_factor = 1 + 1j # multiply delta by a complex number\n", + "\n", + "# -----------------------------\n", + "# Discrete-time impulse responses\n", + "# -----------------------------\n", + "n = np.arange(N)\n", + "h = np.zeros(N, dtype=complex)\n", + "h[k] = complex_factor # h[n] = delta[n-k] * complex_factor\n", + "\n", + "# Advanced impulse response: h_shifted[n] = h[n+advance]\n", + "h_shifted = np.zeros_like(h)\n", + "if k - advance >= 0:\n", + " h_shifted[k-advance] = complex_factor\n", + "# If advance moves it before n=0, it just disappears (causal system)\n", + "\n", + "# -----------------------------\n", + "# Unit step function\n", + "# -----------------------------\n", + "u = np.ones(N, dtype=complex)\n", + "\n", + "# -----------------------------\n", + "# Convolutions\n", + "# -----------------------------\n", + "conv_h = np.convolve(h, u)[:N] # truncate to original length for simplicity\n", + "conv_h_shifted = np.convolve(h_shifted, u)[:N]\n", + "\n", + "# -----------------------------\n", + "# Plot results\n", + "# -----------------------------\n", + "plt.figure(figsize=(12,5))\n", + "\n", + "# Magnitude\n", + "plt.subplot(1,2,1)\n", + "markers, stemline, baseline = plt.stem(n, np.abs(conv_h), linefmt='b', markerfmt='bo', basefmt='k', label='h * u')\n", + "markers.set_alpha(0.5)\n", + "\n", + "markers, stemline, baseline = plt.stem(n, np.abs(conv_h_shifted), linefmt='r', markerfmt='ro', basefmt='k', label='h_shifted * u')\n", + "markers.set_alpha(0.5)\n", + "plt.title(\"Magnitude of convolution with unit step\")\n", + "plt.xlabel(\"n\")\n", + "plt.ylabel(\"|(h*u)[n]|\")\n", + "plt.legend()\n", + "\n", + "# Phase\n", + "plt.subplot(1,2,2)\n", + "plt.stem(n, np.angle(conv_h), linefmt='b', markerfmt='bo', basefmt='k', label='h * u')\n", + "plt.stem(n, np.angle(conv_h_shifted), linefmt='r', markerfmt='ro', basefmt='k', label='h_shifted * u')\n", + "plt.title(\"Phase of convolution with unit step\")\n", + "plt.xlabel(\"n\")\n", + "plt.ylabel(\"Phase [rad]\")\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e84543", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.analytic.sources import OpticalCombSource\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "comb = OpticalCombSource(linewidth=1.0)\n", + "y = comb.block_mode_response()['o0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5339085b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(np.angle(y.amplitude[:, 4]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5402f84a", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_group_delay(wavelengths, s_params):\n", + " # Unwrap phase\n", + " phase = np.unwrap(np.angle(s_params)) # radians\n", + "\n", + " # Derivative of phase w.r.t wavelength\n", + " dphi_dlambda = np.gradient(phase, wavelengths) # radians per meter\n", + "\n", + " # Compute group delay\n", + " c = 299792458 # speed of light in m/s\n", + " group_delay = -dphi_dlambda * (wavelengths**2) / (2 * np.pi * c) # in seconds\n", + "\n", + " return group_delay # same length as input\n", + "\n", + "S = siepic.waveguide(wl=np.linspace(1.5, 1.6, 1000), length=100)[('o0', 'o1')]\n", + "\n", + "tau1 = compute_group_delay(wl, S)[500]\n", + "tau2 = 4e-12\n", + "plt.plot(t, np.abs(laser_output.amplitude[:, 0, 0]))\n", + "plt.plot(t, np.abs(wg_output.amplitude[:, 0, 0]))\n", + "# plt.axvline(tau1+tau2+.5*0*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*1*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*2*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*3*1e-12)\n", + "# plt.axvline(tau1+tau2+.5*4*1e-12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ee4dd1c", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.signal import firwin\n", + "import numpy as np\n", + "\n", + "# FIR filter\n", + "L = 100\n", + "h = firwin(L, 0.1)\n", + "\n", + "# Inputs\n", + "N = 1000\n", + "tau = 200\n", + "sigma = 10\n", + "\n", + "step_input = np.zeros(N)\n", + "step_input[tau:] = 1.0\n", + "\n", + "n = np.arange(N)\n", + "gaussian_input = np.exp(-0.5 * ((n - tau) / sigma) ** 2)\n", + "\n", + "# Filter outputs\n", + "step_response = np.convolve(step_input, h, mode='full')[:N]\n", + "gaussian_response = np.convolve(gaussian_input, h, mode='full')[:N]\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.title(\"Inputs\")\n", + "plt.plot(step_input, label=\"Step input\")\n", + "plt.plot(gaussian_input, label=\"Gaussian input\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.title(\"Filtered Outputs\")\n", + "plt.plot(step_response, label=\"Step response\")\n", + "plt.plot(gaussian_response, label=\"Gaussian response\")\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ab9bf01", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "from scipy.signal import bessel, freqz, tf2zpk\n", + "\n", + "# === 1. Bessel Filter Design ===\n", + "order = 5\n", + "cutoff = 0.02 # Normalized frequency (0.5 = Nyquist)\n", + "b, a = bessel(order, cutoff, btype='low', analog=False, norm='phase')\n", + "\n", + "# === 2. Group Delay Calculation ===\n", + "w, h = freqz(b, a, worN=2048)\n", + "freq = w / jnp.pi # Normalized frequency [0, 1]\n", + "phase = jnp.unwrap(jnp.angle(h))\n", + "group_delay_bessel = -jnp.gradient(phase) / jnp.gradient(w)\n", + "\n", + "# === 3. Design a Simple All-Pass Filter for Compensation ===\n", + "# First-order all-pass: H(z) = (a + z^-1) / (1 + a z^-1)\n", + "# We'll pick 'a' close to 1 to give more delay at higher frequencies\n", + "\n", + "a1 = 0.01\n", + "b_ap = jnp.array([a1, 1.0])\n", + "a_ap = jnp.array([1.0, a1])\n", + "\n", + "# Frequency response of the all-pass\n", + "_, h_ap = freqz(b_ap, a_ap, worN=2048)\n", + "phase_ap = jnp.unwrap(jnp.angle(h_ap))\n", + "group_delay_ap = -jnp.gradient(phase_ap) / jnp.gradient(w)\n", + "\n", + "# === 4. Cascade Bessel + All-Pass ===\n", + "from scipy.signal import convolve\n", + "\n", + "b_total = convolve(b, b_ap)\n", + "a_total = convolve(a, a_ap)\n", + "\n", + "_, h_total = freqz(b_total, a_total, worN=2048)\n", + "phase_total = jnp.unwrap(jnp.angle(h_total))\n", + "group_delay_total = -jnp.gradient(phase_total) / jnp.gradient(w)\n", + "\n", + "# === 5. Plot Group Delays ===\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(freq, group_delay_bessel, label='Bessel Filter')\n", + "plt.plot(freq, group_delay_ap, label='All-Pass Compensation')\n", + "plt.plot(freq, group_delay_total, label='Cascaded (Flattened)')\n", + "plt.xlim(0, 0.05)\n", + "plt.xlabel('Normalized Frequency (π radians/sample)')\n", + "plt.ylabel('Group Delay (samples)')\n", + "plt.title('Group Delay Compensation using All-Pass Filter')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c17befaa", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a38a2d73", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.signal import butter, bessel, group_delay\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Example: design and compare filters\n", + "fs = 1000 # Hz\n", + "N = 5 # order\n", + "fc = 100 # cutoff\n", + "\n", + "# Design Butterworth and Bessel filters\n", + "b_butter, a_butter = butter(N, fc/(fs/2))\n", + "b_bessel, a_bessel = bessel(N, fc/(fs/2), analog=False)\n", + "\n", + "# Compute group delay\n", + "w_b, gd_b = group_delay((b_butter, a_butter), fs=fs)\n", + "w_be, gd_be = group_delay((b_bessel, a_bessel), fs=fs)\n", + "\n", + "# Plot\n", + "plt.plot(w_b, gd_b, label=\"Butterworth\")\n", + "plt.plot(w_be, gd_be, label=\"Bessel\")\n", + "plt.xlabel(\"Frequency [Hz]\")\n", + "plt.ylabel(\"Group Delay [s]\")\n", + "plt.title(\"Group Delay vs Frequency\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9172e908", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.utils import dict_to_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d124467", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.signal import sample_mode_electrical_signal, sample_mode_optical_signal\n", + "pm1_inputs = {\n", + " 'e0': sample_mode_electrical_signal(field=[0.00, 10.0], wl=[1.55e-6, 1.57e-6]),\n", + " 'o0': sample_mode_optical_signal(field=[0.50, 1.00], wl=[1.55e-6, 1.56e-6]),\n", + " 'o1': sample_mode_optical_signal(field=[0.50, 1.00], wl=[1.55e-6, 1.56e-6]),\n", + "}\n", + "\n", + "sms.components['pm1'].step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13ab778b", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.signal import sample_mode_optical_signal, sample_mode_electrical_signal\n", + "my_signals = {\n", + " 'o0': sample_mode_optical_signal(),\n", + " 'e0': sample_mode_electrical_signal(),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40e19fa9", + "metadata": {}, + "outputs": [], + "source": [ + "new_field = my_signals['o0'].field.at[0, 0].set(1.0 + 0.5j)\n", + "my_signals['o0'].replace(field=new_field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f8ebf7a", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "\n", + "@jax.jit\n", + "def my_function(new_value):\n", + " new_field = my_signals['o0'].field.at[0, 0].set(new_value)\n", + " my_signals['o0'].replace(field=new_field)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa0743b6", + "metadata": {}, + "outputs": [], + "source": [ + "my_function(0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8392a46b", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(100000):\n", + " my_function(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c8282ee", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def smooth_discontinuity(x, f, x0, width):\n", + " # Create a smooth blending weight\n", + " w = 1 / (1 + np.exp(-(x - x0) / width))\n", + " # Assume f is defined on both sides already\n", + " return (1 - w) * f['left'](x) + w * f['right'](x)\n", + "\n", + "# Example usage\n", + "f_left = lambda x: np.zeros_like(x) # before discontinuity\n", + "f_right = lambda x: np.ones_like(x) # after discontinuity\n", + "x = np.linspace(-2, 2, 500)\n", + "y_smooth = smooth_discontinuity(x, {'left': f_left, 'right': f_right}, 0, 0.05)\n", + "\n", + "# plt.plot(x, y)\n", + "plt.plot(x, y_smooth)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32ebbf5f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def cubic_hermite_patch(x, xa, ya, ma, xb, yb, mb):\n", + " # Normalized parameter t in [0,1]\n", + " t = (x - xa) / (xb - xa)\n", + " h00 = (1 + 2*t) * (1 - t)**2\n", + " h10 = t * (1 - t)**2\n", + " h01 = t**2 * (3 - 2*t)\n", + " h11 = t**2 * (t - 1)\n", + " return h00*ya + h10*(xb - xa)*ma + h01*yb + h11*(xb - xa)*mb\n", + "\n", + "# Example data: cusp at x=5\n", + "x = np.linspace(0, 10, 200)\n", + "y = np.abs(x - 5) # artificial cusp\n", + "\n", + "# Smoothing interval\n", + "xa, xb = 4, 6\n", + "ya = y[np.argmin(np.abs(x - xa))]\n", + "yb = y[np.argmin(np.abs(x - xb))]\n", + "ma = (y[np.argmin(np.abs(x - (xa+0.01)))] - ya) / 0.01\n", + "mb = (yb - y[np.argmin(np.abs(x - (xb-0.01)))]) / 0.01\n", + "\n", + "# Build smoothed function\n", + "y_smooth = y.copy()\n", + "mask = (x >= xa) & (x <= xb)\n", + "y_smooth[mask] = cubic_hermite_patch(x[mask], xa, ya, ma, xb, yb, mb)\n", + "\n", + "plt.plot(y)\n", + "plt.plot(y_smooth)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "831d593f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import CubicHermiteSpline\n", + "\n", + "# Example function with a cusp\n", + "x = np.linspace(0, 10, 200)\n", + "y = np.abs(x - 5) / 2 + 0.5 # simple cusp at x=5\n", + "\n", + "# Indices for smoothing region\n", + "start_idx = 80\n", + "end_idx = 120\n", + "\n", + "# Get start/end points\n", + "x0, y0 = x[start_idx], y[start_idx]\n", + "x1, y1 = x[end_idx], y[end_idx]\n", + "\n", + "# Derivatives at start/end (match original slope)\n", + "m0 = np.gradient(y, x)[start_idx]\n", + "m1 = np.gradient(y, x)[end_idx]\n", + "\n", + "# Create cubic Hermite spline in smoothing region\n", + "xsmooth = x[start_idx:end_idx+1]\n", + "spline = CubicHermiteSpline([x0, x1], [y0, y1], [m0, m1])\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace cusp region\n", + "y_new = y.copy()\n", + "y_new[start_idx:end_idx+1] = ysmooth\n", + "\n", + "# Plot\n", + "plt.plot(x, y, label='Original with cusp', alpha=0.5)\n", + "plt.plot(x, y_new, label='Smoothed cusp', linewidth=2)\n", + "plt.axvline(x0, color='gray', linestyle='--')\n", + "plt.axvline(x1, color='gray', linestyle='--')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d71d030", + "metadata": {}, + "outputs": [], + "source": [ + "# Get start/end points\n", + "magnitude = magnitude_extended\n", + "f = f_extended\n", + "start_idx = left_extension.shape[0] - 20\n", + "end_idx = left_extension.shape[0] + 20\n", + "x0, y0 = f[start_idx], magnitude[start_idx]\n", + "x1, y1 = f[end_idx], magnitude[end_idx]\n", + "\n", + "# Derivatives at start/end (match original slope)\n", + "m0 = jnp.gradient(magnitude_extended, f, axis=0)[start_idx]\n", + "m0 = jnp.zeros((2,2))\n", + "m1 = jnp.gradient(magnitude, f, axis=0)[end_idx]\n", + "\n", + "# Create cubic Hermite spline in smoothing region\n", + "xsmooth = f[start_idx:end_idx+1]\n", + "spline = CubicHermiteSpline([x0, x1], [y0, y1], [m0, m1])\n", + "# spline = BPoly.from_derivatives(xs, ys)\n", + "\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace cusp region\n", + "y_new = magnitude.copy()\n", + "y_new = y_new.at[start_idx:end_idx+1].set(ysmooth)\n", + "\n", + "# plt.plot(jnp.abs(ysmooth[:, 0, 1]))\n", + "plt.plot(f_extended, y_new[:, 0, 1])\n", + "# plt.axvline(buffer)\n", + "# plt.axhline(jnp.abs(y0[0, 1].item()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d7fcee9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.interpolate import BPoly\n", + "\n", + "# Start/end values\n", + "x0, y0 = f[start_idx], magnitude[start_idx]\n", + "x1, y1 = f[end_idx], magnitude[end_idx]\n", + "\n", + "# Slopes (keep your original slope computation if needed)\n", + "m0 = np.gradient(magnitude_extended, f, axis=0)[start_idx]\n", + "m1 = np.gradient(magnitude_extended, f, axis=0)[end_idx]\n", + "\n", + "# Build a *quadratic* polynomial segment\n", + "# Give derivatives at both ends: [[y0, slope0], [y1, slope1]]\n", + "# The minimal polynomial degree needed is quadratic.\n", + "xs = [x0, x1]\n", + "ys = [[y0, m0], [y1, m1]]\n", + "\n", + "spline = BPoly.from_derivatives(xs, ys)\n", + "\n", + "# Evaluate in smoothing region\n", + "xsmooth = f[start_idx:end_idx+1]\n", + "ysmooth = spline(xsmooth)\n", + "\n", + "# Replace in your magnitude array\n", + "y_new = magnitude.copy()\n", + "y_new = y_new.at[start_idx:end_idx+1].set(ysmooth)\n", + "plt.plot(jnp.concatenate([left_extension[:, 0, 1], jnp.abs(y_new[:, 0, 1])]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c5d3876", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.constants import speed_of_light\n", + "import jax.numpy as jnp\n", + "from simphony.utils import dict_to_matrix\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import CubicHermiteSpline\n", + "\n", + "\n", + "fs = 1.5e13\n", + "f_c = 193e12\n", + "f = jnp.linspace(187e12, 200e12, 1000)\n", + "df = f[1] - f[0]\n", + "s_params = dict_to_matrix(mzi(1e6*speed_of_light/f))\n", + "# magnitude = jnp.abs(s_params)\n", + "\n", + "\n", + "\n", + "# initial_magnitude = magnitude[0]\n", + "# final_magnitude = magnitude[-1]\n", + "\n", + "# f_left = jnp.arange(-fs/2 + f_c, f[0], df)\n", + "# left_extension = initial_magnitude[None, :, :]*jnp.ones_like(f_left, dtype=complex)[:, None, None]\n", + "\n", + "# f_right = jnp.arange(f[-1], fs/2 + f_c, df)\n", + "# right_extension = final_magnitude[None, :, :]*jnp.ones_like(f_right, dtype=complex)[:, None, None]\n", + "\n", + "# plt.plot(f_left, left_extension[:, 0, 1])\n", + "# plt.plot(f_right, right_extension[:, 0, 1])\n", + "# plt.plot(f, jnp.abs(s_params[:, 0, 1]))\n", + "\n", + "# magnitude_extended = jnp.concatenate([left_extension, magnitude, right_extension])\n", + "# f_extended = jnp.concatenate([f_left, f, f_right])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f95de9d", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from scipy.signal import hilbert\n", + "from scipy.interpolate import interp1d\n", + "\n", + "# Original frequencies\n", + "f_min, f_max = f[0], f[-1]\n", + "\n", + "# Define extended frequency range\n", + "f_min_ext = f_min - 5e12 # extend below\n", + "f_max_ext = f_max + 5e12 # extend above\n", + "num_ext = 2 * len(f) # adjust resolution\n", + "f_ext = jnp.linspace(f_min_ext, f_max_ext, num_ext)\n", + "\n", + "# Initialize extended S-parameter array\n", + "n_ports = s_params.shape[1]\n", + "s_ext = jnp.zeros((num_ext, n_ports, n_ports), dtype=complex)\n", + "\n", + "for i in range(n_ports):\n", + " for j in range(n_ports):\n", + " # Extract one S-parameter element\n", + " S_ij = s_params[:, i, j]\n", + "\n", + " # Step 1: interpolate imaginary part to extended f_ext\n", + " interp_im = interp1d(f, S_ij.imag, kind='cubic', fill_value='extrapolate')\n", + " S_im_ext = interp_im(f_ext)\n", + "\n", + " # Step 2: Hilbert transform to get real part (discrete KK)\n", + " # Use scipy.signal.hilbert or similar\n", + " S_re_ext = jnp.imag(hilbert(S_im_ext))\n", + "\n", + " # Step 3: Combine\n", + " s_ext = s_ext.at[:, i, j].set(S_re_ext + 1j * S_im_ext)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "251db2dc", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(f_ext, s_ext[:, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1123d9a7", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# s-domain frequency\n", + "f_s = jnp.linspace(0, 10, 500) # rad/s\n", + "s = 1j * f_s\n", + "\n", + "# s-domain transfer function\n", + "H_s = 1 / (s + 1)\n", + "\n", + "# check s-domain magnitude\n", + "plt.plot(f_s, jnp.abs(H_s))\n", + "plt.title(\"s-domain magnitude\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e909282d", + "metadata": {}, + "outputs": [], + "source": [ + "T_s = 0.01\n", + "\n", + "# map s to z\n", + "def s_to_z(H_s_func, f_s, T_s):\n", + " s = 1j * f_s\n", + " z = (1 + s * T_s / 2) / (1 - s * T_s / 2)\n", + " # evaluate H(s) at s mapped from z (here simple example)\n", + " H_z = H_s_func(s)\n", + " return z, H_z\n", + "\n", + "z, H_z = s_to_z(lambda s: 1/(s+1), f_s, T_s)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e58790b", + "metadata": {}, + "outputs": [], + "source": [ + "# angular frequencies for discrete-time\n", + "omega = jnp.linspace(0, jnp.pi / T_s, 500)\n", + "z_grid = jnp.exp(1j * omega * T_s)\n", + "# Evaluate H(s) at corresponding s (inverse mapping)\n", + "s_grid = 2 / T_s * (1 - z_grid**-1) / (1 + z_grid**-1)\n", + "H_z_grid = 1 / (s_grid + 1)\n", + "\n", + "# check magnitude\n", + "plt.plot(omega, jnp.abs(H_z_grid))\n", + "plt.title(\"z-domain magnitude after bilinear transform\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07fd1f47", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5eeee825", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Parameters\n", + "N = 256 # number of samples\n", + "f_width = 20 # width of the rectangular spectrum\n", + "delta_index = 102+ N // 2 # position of Kronecker delta (center)\n", + "\n", + "# Frequency domain: rectangular spectrum\n", + "freq = np.fft.fftfreq(N, d=1.0)\n", + "rect_spec = np.where(np.abs(freq) < f_width / N, 1.0, 0.0)\n", + "\n", + "# Inverse Fourier transform -> time-domain sinc-like function\n", + "time_func = np.fft.ifft(np.fft.ifftshift(rect_spec))\n", + "\n", + "# Kronecker delta\n", + "kronecker_delta = np.zeros(N)\n", + "kronecker_delta[delta_index] = 1.0\n", + "\n", + "# Convolution (should reproduce time_func)\n", + "conv_result = np.convolve(time_func, kronecker_delta, mode='same')\n", + "\n", + "# Plot results\n", + "plt.figure(figsize=(10,4))\n", + "plt.plot(np.real(time_func), label=\"Inverse FT of Rect (sinc)\")\n", + "plt.plot(np.real(conv_result), '--', label=\"Convolution with delta\")\n", + "plt.legend()\n", + "plt.title(\"Convolution of sinc with Kronecker delta\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/simulation_steady_state.ipynb b/examples/old/simulation_steady_state.ipynb new file mode 100644 index 00000000..a560bd7a --- /dev/null +++ b/examples/old/simulation_steady_state.ipynb @@ -0,0 +1,4400 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "606184b5", + "metadata": {}, + "outputs": [], + "source": [ + "import simphony.libraries.siepic as siepic\n", + "import simphony.libraries.analytic as analytic\n", + "\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"splitter\": {\n", + " \"component\":\"ybranch\",\n", + " \"settings\":{\n", + " \"test_setting\": 100,\n", + " },\n", + " },\n", + " \"combiner\": \"ybranch\", \n", + " \"top1\": \"waveguide\",\n", + " \"top2\": \"waveguide\",\n", + " \"bot1\": \"waveguide\",\n", + " \"bot2\": \"waveguide\",\n", + "\n", + " \"pm1\": \"phase_modulator\",\n", + " \"pm2\": \"phase_modulator\",\n", + "\n", + " \"vs1\": \"voltage_source\",\n", + " \"vs2\": \"voltage_source\",\n", + " \"vs3\": \"voltage_source\",\n", + "\n", + " \"opamp\":\"opamp\",\n", + "\n", + " \"vf1\":\"voltage_follower\",\n", + " \"vf2\":\"voltage_follower\",\n", + " \"vf3\":\"voltage_follower\",\n", + " },\n", + " \"connections\": {\n", + " \"splitter,port_2\":\"top1,o0\",\n", + " \"splitter,port_3\":\"bot1,o0\",\n", + " \"top2,o1\":\"combiner,port_2\", \n", + " \"bot2,o1\": \"combiner,port_3\",\n", + " \"top1,o1\":\"pm1,o0\",\n", + " \"pm1,o1\":\"top2,o0\",\n", + " \"bot1,o1\":\"pm2,o0\",\n", + " \"pm2,o1\":\"bot2,o0\",\n", + "\n", + " \"vs1,e0\":\"\"\"vf3,e0;\n", + " vf1,e0;\"\"\",\n", + "\n", + " \"vs3,e0\":\"\"\"vf2,e0;\n", + " opamp,inv\"\"\",\n", + " \n", + " \"vs2,e0\":\"opamp,ninv\",\n", + " \n", + " \"vf2,e1\":\"opamp,vp\",\n", + "\n", + " \"vf3,e1\":\"pm2,e0\",\n", + " \"vf1,e0\":\"opamp,vn\", \n", + "\n", + " \"opamp,vout\":\"pm1,e0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + " }\n", + "}\n", + "\n", + "models={\n", + " \"ybranch\": siepic.y_branch,\n", + " # \"ybranch\": analytic.optical_s_parameter_placeholder(siepic.y_branch),\n", + " \"waveguide\": analytic.Waveguide,\n", + " \"phase_modulator\": analytic.OpticalModulator,\n", + " \"voltage_source\": analytic.VoltageSource,\n", + " \"prng\": analytic.PRNG,\n", + " \"voltage_follower\": analytic.VoltageFollower,\n", + " \"opamp\": analytic.OpAmp,\n", + "}\n", + "\n", + "settings={ \n", + " # \"splitter\": {\"bad_setting\": 10},\n", + " \"top1\": {\"length\": 5},\n", + " \"top2\": {\"length\": 5},\n", + " \"bot1\": {\"length\": 10},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef6f0bbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " Details for selected element\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " General\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " App state\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Display mode\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Export\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Data selection\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Graph\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Edge label text\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + " Node size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge size\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Minimum\n", + " \n", + " \n", + "
\n", + "
\n", + " Maximum\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Nodes\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Position\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Drag behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Node images\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Node labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edges\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Form\n", + "
\n", + "
\n", + " Curvature\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Hover behavior\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Edge labels\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Visibility\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Size\n", + "
\n", + "
\n", + " Scaling factor\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Rotation\n", + "
\n", + "
\n", + " Angle\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " Layout algorithm\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + " Simulation\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Many-body force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + " Theta\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Min\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + " Max\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Links force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Collision force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Radius\n", + " \n", + " \n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " x-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " y-positioning force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + " Strength\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Centering force\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.circuit import Circuit\n", + "ckt = Circuit(netlist, models, default_settings=settings)\n", + "ckt.display(inline=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "35a2a945", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Steady state order: ['splitter', 'vs1', 'vs2', 'vs3', 'top1', 'bot1', 'vf1', 'vf2', 'vf3', 'pm2', 'opamp', 'bot2', 'pm1', 'top2', 'combiner']\n", + "Steady state order: ['splitter', 'vs1', 'vs2', 'vs3', 'top1', 'bot1', 'vf3', 'vf1', 'vf2', 'pm2', 'opamp', 'bot2', 'pm1', 'top2', 'combiner']\n" + ] + } + ], + "source": [ + "from simphony.simulation import SteadyStateSimulation\n", + "import numpy as np\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "sps = SteadyStateSimulation(ckt)\n", + "order = sps._determine_steady_state_order()\n", + "print(\"Steady state order:\", order)\n", + "order2 = sps._determine_steady_state_order_nx_method()\n", + "print(\"Steady state order:\", order2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd683700", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/single_mode_quantum_functions.ipynb b/examples/old/single_mode_quantum_functions.ipynb new file mode 100644 index 00000000..033969e4 --- /dev/null +++ b/examples/old/single_mode_quantum_functions.ipynb @@ -0,0 +1,559 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0d628adb", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "from scipy.constants import epsilon_0, Boltzmann\n", + "\n", + "h_bar = 0.5\n", + "\n", + "def annihilation_operator(N_max=10, f=1, t=0):\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n", + " for n in range(1, N_max):\n", + " a = a.at[n-1, n].set(jnp.sqrt(n))\n", + " \n", + " return a\n", + "\n", + "def electric_field_operator(N_max=10, f=1, t=0, mode_volume=1):\n", + " E_0 = jnp.sqrt((h_bar*2*jnp.pi*f)/(epsilon_0*mode_volume))\n", + " E_0 = 1\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " \n", + " return E_0 * (a + a_dagger)\n", + "\n", + "def number_operator(N_max, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return a_dagger@a\n", + "\n", + "def expectation_value_pure(operator, psi):\n", + " return jnp.vdot(psi, operator@psi)\n", + "\n", + "def expectation_value_mixed(operator, rho):\n", + " return jnp.trace(rho@operator)\n", + "\n", + "def thermal_state(N_max, f=1.0, temperature=1/Boltzmann):\n", + " n = jnp.arange(0, N_max)\n", + " E_n = h_bar*2*jnp.pi*f*(n+0.5)\n", + " \n", + " P_n = jnp.exp(-E_n / (Boltzmann*temperature))\n", + " P_n = P_n / jnp.sum(P_n)\n", + " return jnp.diag(P_n)\n", + "\n", + "def coherent_state(alpha, N_max=10, f=1.0):\n", + " n = jnp.arange(0, N_max)\n", + " return jnp.exp(-0.5*jnp.abs(alpha)**2) * alpha**n / jnp.sqrt(jax.scipy.special.factorial(n))\n", + "\n", + "def vacuum_state(N_max=10, f=1.0):\n", + " vac = jnp.zeros(N_max, dtype=jnp.complex128)\n", + " vac = vac.at[0].set(1.0)\n", + " return vac\n", + "\n", + "def displacement_operator(alpha, N_max=10, f=1.0, t=0):\n", + " a = annihilation_operator(N_max=N_max, f=f, t=t)\n", + " a_dagger = jnp.conj(a.T)\n", + " return jax.scipy.linalg.expm(alpha*a_dagger - jnp.conj(alpha)*a)\n", + "\n", + "def characteristic_function_mixed(rho, f=1.0, t=0):\n", + " N_max = rho.shape[0]\n", + "\n", + " def characteristic_fn(eta):\n", + " return jnp.trace(rho @ displacement_operator(eta, N_max=N_max, f=f, t=t))\n", + "\n", + " def apply_fn(eta_grid):\n", + " flat_eta = eta_grid.reshape(-1)\n", + "\n", + " def scan_fn(carry, eta):\n", + " val = characteristic_fn(eta)\n", + " return carry, val\n", + "\n", + " # remat helps prevent backprop memory buildup\n", + " _, results = jax.lax.scan(jax.remat(scan_fn), None, flat_eta)\n", + " return results.reshape(eta_grid.shape)\n", + "\n", + " return apply_fn\n", + "\n", + "def characteristic_function_pure(psi, f=1.0, t=0):\n", + " rho = jnp.outer(psi, psi.conj().T)\n", + " N_max = rho.shape[0]\n", + "\n", + " return characteristic_function_mixed(rho, f=f, t=t)\n", + "\n", + "def characteristic_function_gaussian(mean, covariance):\n", + " def characteristic_fn(eta):\n", + " xi = jnp.sqrt(2) * jnp.array([jnp.real(eta), jnp.imag(eta)])\n", + " return jax.scipy.linalg.expm(-0.5*xi@covariance@eta + 1j*mean@xi)\n", + " \n", + " return characteristic_fn\n", + "\n", + "def eigenvalue_from_vector(A, v):\n", + " Av = A @ v\n", + " v_normalized = v / jnp.linalg.norm(v)\n", + " return jnp.vdot(v_normalized, Av)\n", + "\n", + "def wigner_function(alpha, characteristic_fn, grid_size=80, limit=4.0):\n", + " dx = 2 * limit / grid_size\n", + " x = jnp.linspace(-limit, limit, grid_size)\n", + " y = jnp.linspace(-limit, limit, grid_size)\n", + " xx, yy = jnp.meshgrid(x, y)\n", + " lam = xx + 1j * yy\n", + "\n", + " lam_flat = lam.reshape(-1)\n", + " chi_vals = characteristic_fn(lam_flat).reshape(lam.shape)\n", + "\n", + " exponent = jnp.conj(lam) * alpha - lam * jnp.conj(alpha)\n", + " integrand = chi_vals * jnp.exp(exponent)\n", + " integral = jnp.sum(integrand) * dx**2\n", + "\n", + " return (1 / jnp.pi**2) * integral.real\n", + "\n", + "def coherence_1(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " t1 = 0\n", + " t2 = 1\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = jnp.sqrt(\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + " \n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2, rho)\n", + "\n", + " return numerator/denominator\n", + "\n", + "def coherence_2(rho, t1, t2):\n", + " N_max = rho.shape[0]\n", + " a_hat_1 = annihilation_operator(N_max=N_max, f=f, t=t1)\n", + " a_hat_2 = annihilation_operator(N_max=N_max, f=f, t=t2)\n", + " denominator = (\n", + " expectation_value_mixed(a_hat_1.conj().T @ a_hat_1, rho)\n", + " * expectation_value_mixed(a_hat_2.conj().T @ a_hat_2, rho)\n", + " )\n", + "\n", + " numerator = expectation_value_mixed(a_hat_1.conj().T @ a_hat_2.conj().T @ a_hat_2 @ a_hat_1, rho)\n", + "\n", + " return numerator/denominator\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7b76a076", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.045165706+0j)\n", + "0.045165703\n" + ] + } + ], + "source": [ + "# N_max=60\n", + "N_max=80\n", + "T = 1/Boltzmann\n", + "t=0\n", + "f = 1\n", + "n_hat = number_operator(N_max=N_max, f=f, t=t)\n", + "temperature = 1/Boltzmann\n", + "rho_th = thermal_state(N_max)\n", + "print(expectation_value_mixed(n_hat, rho_th))\n", + "print(1/(jnp.exp(h_bar*2*jnp.pi*f/(Boltzmann*T))-1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77932a95", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1+0j)\n", + "(1.9999988+0j)\n" + ] + } + ], + "source": [ + "print(coherence_1(rho_th, 0, 1))\n", + "print(coherence_2(rho_th, 0, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6644dd2a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1179003246.py:47: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " vac = jnp.zeros(N_max, dtype=jnp.complex128)\n", + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8.8249701e-01+0.j 4.4124842e-01+0.j 1.5600489e-01+0.j 4.5034725e-02+0.j\n", + " 1.1258681e-02+0.j 2.5175177e-03+0.j 5.1388610e-04+0.j 9.7115371e-05+0.j\n", + " 1.7167733e-05+0.j 2.8612878e-06+0.j 4.5240927e-07+0.j 6.8203306e-08+0.j\n", + " 9.8443067e-09+0.j 1.3651598e-09+0.j 1.8242692e-10+0.j 2.3551140e-11+0.j\n", + " 2.9438765e-12+0.j 3.5699468e-13+0.j 4.2071818e-14+0.j 4.8259440e-15+0.j\n", + " 5.3957640e-16+0.j 5.8885836e-17+0.j 6.2830071e-18+0.j 6.5710385e-19+0.j\n", + " 6.7707312e-20+0.j 6.9514966e-21+0.j 7.2837898e-22+0.j 8.1315681e-23+0.j\n", + " 1.0214492e-23+0.j 1.4876283e-24+0.j 2.4504359e-25+0.j 4.3151280e-26+0.j\n", + " 7.7407392e-27+0.j 1.3756323e-27+0.j 2.3907726e-28+0.j 4.0432603e-29+0.j\n", + " 6.6456094e-30+0.j 1.0619420e-30+0.j 1.6514910e-31+0.j 2.5029152e-32+0.j\n", + " 3.7028164e-33+0.j 5.3594002e-34+0.j 7.6183175e-35+0.j 1.0691550e-35+0.j\n", + " 1.4024465e-36+0.j 7.6395777e-38+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j\n", + " 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j 0.0000000e+00+0.j]\n", + "[8.8249665e-01 4.4124845e-01 1.5600486e-01 4.5034710e-02 1.1258677e-02\n", + " 2.5175167e-03 5.1388581e-04 9.7115299e-05 1.7167733e-05 2.8612849e-06\n", + " 4.5240876e-07 6.8203235e-08 9.8442854e-09 1.3651558e-09 1.8242662e-10\n", + " 2.3551204e-11 2.9438977e-12 3.5699994e-13 4.2072896e-14 4.8260926e-15\n", + " 5.3957275e-16 5.8872357e-17 6.2757933e-18 6.5429790e-19 6.6778840e-20\n", + " 6.6779095e-21 6.5482225e-22 6.3010284e-23 5.9539056e-24 5.5280453e-25\n", + " 5.0464061e-26 4.5318164e-27 4.0055816e-28 3.4864276e-29 2.9895770e-30\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n" + ] + } + ], + "source": [ + "vac = vacuum_state(N_max)\n", + "alpha = 0.5\n", + "print(displacement_operator(alpha, N_max=N_max) @ vac)\n", + "print(coherent_state(alpha, N_max=N_max))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1a1e5953", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.49999997+0j)\n" + ] + } + ], + "source": [ + "a_hat = annihilation_operator(N_max=N_max, f=f, t=t)\n", + "print(eigenvalue_from_vector(a_hat, coherent_state(alpha, N_max=N_max)))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c17918b0", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.special import eval_laguerre\n", + "\n", + "alphas = jnp.linspace(-6.0, 6.0, 350)\n", + "n = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f9a0216", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1046067386.py:9: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " psi_number = jnp.zeros(N_max, dtype=jnp.complex128).at[n].set(1.0)\n", + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def chi_number_state(n, eta):\n", + " abs_eta2 = jnp.abs(eta)**2\n", + " return jnp.exp(-0.5 * abs_eta2) * eval_laguerre(n, abs_eta2)\n", + "\n", + "\n", + "\n", + "plt.plot(alphas, chi_number_state(n, alphas))\n", + "\n", + "psi_number = jnp.zeros(N_max, dtype=jnp.complex128).at[n].set(1.0)\n", + "C = characteristic_function_pure(psi_number)\n", + "data = []\n", + "\n", + "plt.plot(alphas, C(jnp.array([alphas]))[0], \"r--\")\n", + "plt.show()\n", + "\n", + "def scan_fn(_, alpha):\n", + " value = wigner_function(alpha, C, 100, 5.0)\n", + " return None, value\n", + "\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "\n", + "plt.plot(alphas, wigner_vals)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7ced28ae", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13461/1179003246.py:8: UserWarning: Explicitly requested dtype requested in zeros is not available, and will be truncated to dtype complex64. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " a = jnp.zeros((N_max, N_max), dtype=jnp.complex128)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "C = characteristic_function_mixed(rho_th)\n", + "\n", + "@jax.remat\n", + "def scan_fn(_, alpha):\n", + " # value = wigner_function(alpha, C, 100, 5.0)\n", + " value = wigner_function(alpha, C, 300, jnp.sqrt(N_max/2))\n", + " return None, value\n", + "\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "plt.plot(alphas, wigner_vals)\n", + "\n", + "_n = jnp.arange(rho_th.shape[0])\n", + "# n_bar = jnp.sum(_n * jnp.diag(rho_th))\n", + "n_bar = 1 / (jnp.exp(h_bar * 2 * jnp.pi * f * (1/(Boltzmann*temperature))) - 1)\n", + "wigner_vals_theory = 2/ (jnp.pi*(2*n_bar+1))*jnp.exp(-2*jnp.abs(alphas)**2 / (2*n_bar + 1))\n", + "plt.plot(alphas, wigner_vals_theory, \"r--\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "783022b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(alphas, wigner_vals_theory, \"r--\")\n", + "plt.axvline( 0.5*(2.355*jnp.sqrt(h_bar/2)))\n", + "plt.axvline(-0.5*(2.355*jnp.sqrt(h_bar/2)))\n", + "plt.axhline(0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "27a37810", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# from simphony.simulation.jax_tools import python_based_scan\n", + "psi_coherent = coherent_state(jnp.sqrt(10), N_max=N_max)\n", + "C = characteristic_function_pure(psi_coherent)\n", + "\n", + "# @jax.remat\n", + "def scan_fn(_, alpha):\n", + " # value = wigner_function(alpha, C, 100, 5.0)\n", + " value = jax.lax.stop_gradient(wigner_function(alpha, C, 450, jnp.sqrt(N_max/2)))\n", + " return None, value\n", + "\n", + "# _, wigner_vals = python_based_scan(scan_fn, init=None, xs=alphas)\n", + "_, wigner_vals = jax.lax.scan(scan_fn, init=None, xs=alphas)\n", + "plt.plot(alphas, wigner_vals)\n", + "\n", + "wigner_vals_theory = 2/jnp.pi*jnp.exp(-2*jnp.abs(alphas-jnp.sqrt(10))**2)\n", + "plt.plot(alphas, wigner_vals_theory, \"r--\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/old/sng_gaussian_states.ipynb b/examples/old/sng_gaussian_states.ipynb new file mode 100644 index 00000000..40f74194 --- /dev/null +++ b/examples/old/sng_gaussian_states.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a7c53091", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{aligned}\n", + "C_{ij} =\\ & A_{ij} \\\\\n", + "&+ A_{ik} (I - A_{lk})^{-1} A_{lj} \\\\\n", + "&+ A_{il} (I - A_{kl})^{-1} A_{kj} \\\\\n", + "&+ A_{ik} (I - A_{lk})^{-1} A_{lk} (I - A_{kl})^{-1} A_{kj} \\\\\n", + "&+ A_{il} (I - A_{kl})^{-1} A_{kl} (I - A_{lk})^{-1} A_{lj}\n", + "\\end{aligned}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f303a7f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7\n", + "0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0\n", + "1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0\n", + "2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + "3 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0\n", + "5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", + "6 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n", + "7 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0\n", + " 0 1 2 3\n", + "0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 0.0 1.0\n", + "2 1.0 0.0 0.0 0.0\n", + "3 0.0 1.0 0.0 0.0\n" + ] + } + ], + "source": [ + "import jax.numpy as jnp\n", + "from jax.numpy.linalg import pinv\n", + "\n", + "def permutation_xxpp_to_xpxp(num_modes):\n", + " \"\"\"Returns permutation matrix P to convert from xxpp to xpxp ordering\"\"\"\n", + " n = 2 * num_modes\n", + " P = jnp.zeros((n, n))\n", + " for i in range(num_modes):\n", + " P = P.at[2*i, i].set(1) # x_i\n", + " P = P.at[2*i + 1, i + num_modes].set(1) # p_i\n", + " return P\n", + "\n", + "def xxpp_to_xpxp(A):\n", + " P = permutation_xxpp_to_xpxp(A.shape[0]//2)\n", + " return P @ A @ P.T\n", + "\n", + "def realify(A):\n", + " return jnp.block([[A.real, A.imag], [-A.imag, A.real]])\n", + "\n", + "def interconnect_s(A, k, l):\n", + " A = jnp.asarray(A)\n", + " N = A.shape[0] // 2\n", + "\n", + " x = jnp.arange(N)\n", + " # Create mask that is True if x is NOT equal to a or b\n", + " mask = (x != k) & (x != l)\n", + " ext_ports = x[mask]\n", + "\n", + " C = jnp.zeros((2*N - 4, 2*N - 4))\n", + " for m in range(C.shape[0]//2):\n", + " i = ext_ports[m].item()\n", + " for n in range(C.shape[0]//2):\n", + " j = ext_ports[n].item()\n", + " term_0 = A[2*i:2*(i+1), 2*j:2*(j+1)]\n", + " term_1 = A[2*i:2*(i+1), 2*k:2*(k+1)] @ pinv(jnp.eye(2)-A[2*l:2*(l+1), 2*k:2*(k+1)]) @ A[2*l:2*(l+1), 2*j:2*(j+1)]\n", + " term_2 = A[2*i:2*(i+1), 2*l:2*(l+1)] @ pinv(jnp.eye(2)-A[2*k:2*(k+1), 2*l:2*(l+1)]) @ A[2*k:2*(k+1), 2*j:2*(j+1)]\n", + " term_3 = A[2*i:2*(i+1), 2*k:2*(k+1)] @ pinv(jnp.eye(2)-A[2*l:2*(l+1), 2*k:2*(k+1)]) @ A[2*l:2*(l+1), 2*k:2*(k+1)] @ pinv(jnp.eye(2)-A[2*k:2*(k+1), 2*l:2*(l+1)])@A[2*k:2*(k+1), 2*j:2*(j+1)]\n", + " term_4 = A[2*i:2*(i+1), 2*l:2*(l+1)] @ pinv(jnp.eye(2)-A[2*k:2*(k+1), 2*l:2*(l+1)]) @ A[2*k:2*(k+1), 2*l:2*(l+1)] @ pinv(jnp.eye(2)-A[2*l:2*(l+1), 2*k:2*(k+1)])@A[2*l:2*(l+1), 2*j:2*(j+1)]\n", + " C = C.at[2*m:2*(m+1), 2*n:2*(n+1)].set(C[2*m:2*(m+1), 2*n:2*(n+1)] + term_0 + term_1 + term_2 + term_3 + term_4)\n", + " pass\n", + " \n", + " return C\n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "462ba2a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7\n", + "0 0.0 0.0 1.0 0.0 0.000000 0.000000 0.000000 0.000000\n", + "1 0.0 0.0 0.0 1.0 0.000000 0.000000 0.000000 0.000000\n", + "2 1.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.000000\n", + "3 0.0 1.0 0.0 0.0 0.000000 0.000000 0.000000 0.000000\n", + "4 0.0 0.0 0.0 0.0 0.000000 0.000000 0.866025 0.500000\n", + "5 0.0 0.0 0.0 0.0 0.000000 0.000000 -0.500000 0.866025\n", + "6 0.0 0.0 0.0 0.0 0.866025 0.500000 0.000000 0.000000\n", + "7 0.0 0.0 0.0 0.0 -0.500000 0.866025 0.000000 0.000000\n", + " 0 1 2 3\n", + "0 0.000000 0.000000 0.866025 0.500000\n", + "1 0.000000 0.000000 -0.500000 0.866025\n", + "2 0.866025 0.500000 0.000000 0.000000\n", + "3 -0.500000 0.866025 0.000000 0.000000\n" + ] + } + ], + "source": [ + "import scipy\n", + "import numpy as np\n", + "import pandas as pd\n", + "phi_a = 0 * np.pi / 180\n", + "phi_b = 0 * np.pi / 180\n", + "\n", + "S_a = np.exp(1j * phi_a) * np.array([[0j, 1],[1, 0j]])\n", + "S_b = np.exp(1j * phi_b) * np.array([[0j, 1],[1, 0j]])\n", + "S = scipy.linalg.block_diag(S_a, S_b)\n", + "F = xxpp_to_xpxp(realify(S))\n", + "\n", + "F_new = interconnect_s(F, 1, 2)\n", + "\n", + "print(pd.DataFrame(F))\n", + "print(pd.DataFrame(F_new))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a61dce6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/camacho/simphony/.venv/lib/python3.12/site-packages/skrf/mathFunctions.py:268: RuntimeWarning: divide by zero encountered in log10\n", + " out = 20 * np.log10(z)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined S-matrix:\n", + " [[0. +0.j 0.8660254+0.5j]\n", + " [0.8660254+0.5j 0. +0.j ]]\n" + ] + } + ], + "source": [ + "import skrf as rf\n", + "\n", + "\n", + "# Single frequency at 5 GHz\n", + "freq = rf.Frequency(5, 5, 1, unit='GHz')\n", + "\n", + "# Define phases\n", + "phi_a = 0 * np.pi / 180\n", + "phi_b = 30 * np.pi / 180\n", + "\n", + "# Define S-matrices (2-port delay lines with phase shifts)\n", + "S_a = np.exp(1j * phi_a) * np.array([[0j, 1],\n", + " [1, 0j]])\n", + "S_b = np.exp(1j * phi_b) * np.array([[0j, 1],\n", + " [1, 0j]])\n", + "\n", + "# Wrap into 3D arrays for single frequency (shape: 1 x 2 x 2)\n", + "S_a = S_a[np.newaxis, :, :]\n", + "S_b = S_b[np.newaxis, :, :]\n", + "\n", + "# Create Networks\n", + "net_a = rf.Network(s=S_a, frequency=freq)\n", + "net_b = rf.Network(s=S_b, frequency=freq)\n", + "\n", + "# Connect port 1 of net_a to port 0 of net_b\n", + "# The resulting network has ports: net_a port 0 and net_b port 1\n", + "combined = rf.connect(net_a, 1, net_b, 0)\n", + "\n", + "# Plot the combined network S-parameters in dB\n", + "combined.plot_s_db()\n", + "plt.title(\"S-parameters of connected networks with phase delays\")\n", + "plt.show()\n", + "\n", + "# Print combined S-matrix at the frequency\n", + "print(\"Combined S-matrix:\\n\", combined.s[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a4107dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 \\\n", + "0 0.000000+0.000000j 0.866025+0.500000j 0.000000+0.000000j \n", + "1 0.866025+0.500000j 0.000000+0.000000j 0.000000+0.000000j \n", + "2 0.000000+0.000000j 0.000000+0.000000j 0.000000+0.000000j \n", + "3 0.000000+0.000000j 0.000000+0.000000j 0.866025+0.500000j \n", + "\n", + " 3 \n", + "0 0.000000+0.000000j \n", + "1 0.000000+0.000000j \n", + "2 0.866025+0.500000j \n", + "3 0.000000+0.000000j \n", + " 0 1 2 \\\n", + "0 0.000000+0.000000j 0.000000+0.000000j 0.866025+0.500000j \n", + "1 0.000000+0.000000j 0.000000+0.000000j 0.000000+0.000000j \n", + "2 0.866025+0.500000j 0.000000+0.000000j 0.000000+0.000000j \n", + "3 0.000000+0.000000j 0.866025+0.500000j 0.000000+0.000000j \n", + "\n", + " 3 \n", + "0 0.000000+0.000000j \n", + "1 0.866025+0.500000j \n", + "2 0.000000+0.000000j \n", + "3 0.000000+0.000000j \n", + "[[0.5+0.8660254j 0. +0.j ]\n", + " [0. +0.j 0.5+0.8660254j]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import scipy\n", + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "\n", + "def permutation_matrix(connections, num_ports_a, num_ports_b):\n", + " N = num_ports_a + num_ports_b\n", + " internal_ports = set()\n", + " for a_port, b_port in connections:\n", + " internal_ports.add(a_port)\n", + " internal_ports.add(num_ports_a + b_port)\n", + " \n", + " external_ports = [p for p in range(N) if p not in internal_ports]\n", + " internal_ports = sorted(internal_ports)\n", + " \n", + " new_order = external_ports + internal_ports\n", + " P = np.zeros((N, N), dtype=int)\n", + " for new_idx, old_idx in enumerate(new_order):\n", + " # P[new_idx, old_idx] = 1\n", + " P[old_idx, new_idx] = 1\n", + " return P\n", + "\n", + "\n", + "phi_a = 0 * np.pi/180\n", + "phi_b = 30 * np.pi/180\n", + "\n", + "S_a = np.exp(1j*phi_a)*np.array([[0j, 1], [1, 0j]])\n", + "S_b = np.exp(1j*phi_b)*np.array([[0j, 1], [1, 0j]])\n", + "\n", + "S = scipy.linalg.block_diag(S_a, S_b)\n", + "connections = [(1, 0)]\n", + "num_ports_a = 2\n", + "num_ports_b = 2\n", + "P = permutation_matrix(connections, num_ports_a, num_ports_b)\n", + "num_internal_ports = 2*len(connections)\n", + "num_external_ports = num_ports_a + num_ports_b - num_internal_ports\n", + "\n", + "S_prime = P.T@S@P\n", + "\n", + "e = num_external_ports\n", + "i = num_internal_ports\n", + "\n", + "S_ee = S_prime[:e, :e]\n", + "S_ei = S_prime[:e, e:e+i]\n", + "S_ie = S_prime[e:e+i, :e]\n", + "S_ii = S_prime[e:e+i, e:e+i]\n", + "\n", + "# S_ei = np.array([[1j, 0j], [0j, 0]])\n", + "# S_ie = np.array([[1j, 0j], [1j, 0j]])\n", + "# S_ii = np.array([[0j, 0j], [0j, 0]])\n", + "print(pd.DataFrame(S))\n", + "print(pd.DataFrame(S_prime))\n", + "\n", + "S_new = S_ee + S_ei @ np.linalg.inv(np.eye(S_ii.shape[0]) - S_ii)@S_ie\n", + "print(S_new)" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "id": "b1b56279", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.+0.j, 0.+0.j],\n", + " [0.+0.j, 0.+0.j]])" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_ee" + ] + }, + { + "cell_type": "markdown", + "id": "75887608", + "metadata": {}, + "source": [ + "# Math Aside\n", + "We can find the inverse if the sum on the right converges.\n", + "$$\n", + "(I-T)^{-1} = \\sum_{k=0}^{\\infty}T^k\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "b4eaa164", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues: [0.+0.j 0.+0.j]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-1.0000000e+00, -1.2246468e-16],\n", + " [ 1.2246468e-16, -1.0000000e+00]])" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.linalg import eig\n", + "import numpy as np\n", + "\n", + "phi = np.pi\n", + "transmittivity = 1.0\n", + "\n", + "F_phi = np.array([\n", + " [np.cos(phi), -np.sin(phi)],\n", + " [np.sin(phi), np.cos(phi)],\n", + "])\n", + "\n", + "# Compute eigenvalues and right eigenvectors\n", + "eigenvalues, eigenvectors = eig(np.sqrt(1-transmittivity)*F_phi)\n", + "\n", + "print(\"Eigenvalues:\", eigenvalues)\n", + "F_phi" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "4cd272f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1.0000000e+00 -1.2246468e-16]\n", + " [ 1.2246468e-16 -1.0000000e+00]]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-1.0000000e+00, -1.2246468e-16],\n", + " [ 1.2246468e-16, -1.0000000e+00]])" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "F_prime = transmittivity*F_phi@np.linalg.inv(np.eye(2)+np.sqrt(1-transmittivity)*F_phi)+np.sqrt(1-transmittivity)*np.eye(2)\n", + "\n", + "print(F_prime)\n", + "# np.angle(F_prime@np.array([1, 0])[0] + 1j*F_prime@np.array([1, 0])[1])\n", + "out_real = F_prime@np.array([1, 0])\n", + "out_complex = out_real[0] + 1j*out_real[1]\n", + "np.angle(out_complex)*180/np.pi\n", + "\n", + "F_prime" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "e4d23575", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0.],\n", + " [0., 0.]])" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.pinv(np.zeros((2, 2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "30b7f864", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(179.99999999999997)" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = F_prime @ np.array([1, 0])\n", + "out_complex = out[0] + 1j*out[1]\n", + "np.angle(out_complex)*180/np.pi" + ] + }, + { + "cell_type": "markdown", + "id": "7c5e7153", + "metadata": {}, + "source": [ + "# Sagnac Example\n", + "$$\n", + "\\begin{align*}\n", + "\\hat{a}_1 &\\rightarrow \\sqrt{\\eta} \\, \\hat{a}_1 + \\sqrt{1 - \\eta} \\, \\hat{a}_2 \\\\\n", + "\\hat{a}_2 &\\rightarrow -\\sqrt{1 - \\eta} \\, \\hat{a}_1 + \\sqrt{\\eta} \\, \\hat{a}_2\n", + "\\end{align*}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{equation*}\n", + "\\begin{pmatrix}\n", + "\\hat{a}_1' \\\\\n", + "\\hat{a}_2'\n", + "\\end{pmatrix}\n", + "=\n", + "\\begin{pmatrix}\n", + "\\sqrt{\\eta} & \\sqrt{1 - \\eta} \\\\\n", + "-\\sqrt{1 - \\eta} & \\sqrt{\\eta}\n", + "\\end{pmatrix}\n", + "\\begin{pmatrix}\n", + "\\hat{a}_1 \\\\\n", + "\\hat{a}_2\n", + "\\end{pmatrix}\n", + "\\end{equation*}\n", + "$$\n", + "\n", + "\\begin{equation*}\n", + "F_{\\text{BS}} =\n", + "\\begin{pmatrix}\n", + "\\sqrt{\\eta} & 0 & \\sqrt{1 - \\eta} & 0 \\\\\n", + "0 & \\sqrt{\\eta} & 0 & \\sqrt{1 - \\eta} \\\\\n", + "-\\sqrt{1 - \\eta} & 0 & \\sqrt{\\eta} & 0 \\\\\n", + "0 & -\\sqrt{1 - \\eta} & 0 & \\sqrt{\\eta}\n", + "\\end{pmatrix}\n", + "\\end{equation*}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7fc0a4e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/subnetwork_growth.py b/examples/old/subnetwork_growth.py similarity index 100% rename from examples/subnetwork_growth.py rename to examples/old/subnetwork_growth.py diff --git a/examples/old/test.ipynb b/examples/old/test.ipynb new file mode 100644 index 00000000..0cb6f425 --- /dev/null +++ b/examples/old/test.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "437315ee", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def s_to_symplectic(S: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Convert a unitary scattering matrix S (N x N complex) to\n", + " its corresponding symplectic matrix F (2N x 2N real).\n", + "\n", + " Parameters:\n", + " -----------\n", + " S : np.ndarray\n", + " Complex unitary scattering matrix of shape (N, N).\n", + "\n", + " Returns:\n", + " --------\n", + " F : np.ndarray\n", + " Real symplectic matrix of shape (2N, 2N).\n", + " \"\"\"\n", + " # Check if S is square\n", + " N = S.shape[0]\n", + " assert S.shape == (N, N), \"S must be square\"\n", + "\n", + " # Real and imaginary parts\n", + " Re = np.real(S)\n", + " Im = np.imag(S)\n", + "\n", + " # Construct symplectic matrix\n", + " top = np.hstack((Re, -Im))\n", + " bottom = np.hstack((Im, Re))\n", + " F = np.vstack((top, bottom))\n", + "\n", + " # Optional: verify symplectic condition F Ω F^T = Ω\n", + " # Ω = np.block([\n", + " # [np.zeros((N,N)), np.eye(N)],\n", + " # [-np.eye(N), np.zeros((N,N))]\n", + " # ])\n", + " # assert np.allclose(F @ Ω @ F.T, Ω), \"F is not symplectic!\"\n", + "\n", + " return F\n", + "import numpy as np\n", + "\n", + "def symplectic_omega(N: int) -> np.ndarray:\n", + " \"\"\"\n", + " Generate the symplectic form matrix Omega for N modes.\n", + "\n", + " Parameters:\n", + " -----------\n", + " N : int\n", + " Number of modes.\n", + "\n", + " Returns:\n", + " --------\n", + " Omega : np.ndarray\n", + " The 2N x 2N symplectic form matrix.\n", + " \"\"\"\n", + " # 2x2 block\n", + " omega_block = np.array([[0, 1],\n", + " [-1, 0]])\n", + "\n", + " # Build block diagonal Omega with N copies of omega_block\n", + " Omega = np.kron(np.eye(N), omega_block)\n", + "\n", + " return Omega\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "39bacf97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.+0.j, 0.+0.j],\n", + " [0.+0.j, 1.+0.j]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S = 1/np.sqrt(2)*np.array([[1, 1j], [1j, 1]])\n", + "F = s_to_symplectic(S)\n", + "S@S.conj().T" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5edf56a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.00000000e+00 -2.23711432e-17 1.00000000e+00 0.00000000e+00]\n", + " [ 2.23711432e-17 0.00000000e+00 0.00000000e+00 -1.00000000e+00]\n", + " [-1.00000000e+00 0.00000000e+00 0.00000000e+00 2.23711432e-17]\n", + " [ 0.00000000e+00 1.00000000e+00 -2.23711432e-17 0.00000000e+00]]\n" + ] + } + ], + "source": [ + "Omega = symplectic_omega(2)\n", + "print(F@Omega@F.T)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/overview.ipynb b/examples/overview.ipynb new file mode 100644 index 00000000..9e4770cb --- /dev/null +++ b/examples/overview.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f7c37ddd", + "metadata": {}, + "source": [ + "# Simulation Modes\n", + "\n", + "### TODO: Update the names SampleMode and BlockMode to TransientSample and TransientBlock\n", + "Simphony offers several different simulators. At the time of writing we support, SampleMode, BlockMode, and SParameter simulations. Each simulation mode should be registered in the SimulationMode enum in simphony.simulation.simulation" + ] + }, + { + "cell_type": "markdown", + "id": "a87ffff0", + "metadata": {}, + "source": [ + "# Components and PCells\n", + "\n", + "Both components and PCells require all their external ports specified before instantiation. \n", + "\n", + "The PCell will act like a \"virtual component,\" able to connect with other components through its external ports but with no internal structure until instantiation. Unlike components, the internal circuit structure of a PCell may depend on its settings (specified in the netlist) as well as the simulator being used, hence, all PCell __init__() function definitions require the positional argument `simulation_mode` (the str argument should be taken from the enum SimulationMode), which will be supplied by the Circuit class. NOTE: component init functions have no such argument, because Simphony requires all components, including those defined in the resulting netlist of a PCell to have static methods, independent of simulation type." + ] + }, + { + "cell_type": "markdown", + "id": "0ebc92e5", + "metadata": {}, + "source": [ + "# Component Factories\n", + "\n", + "Some components (and PCells) such as multimode interferometers naturally extend to arbitrary mxn port systems. Rather than requiring the user to create a new component for each use case, we have provided helper functions called component factories that generate a class based on the required parameters." + ] + }, + { + "cell_type": "markdown", + "id": "7af30cb3", + "metadata": {}, + "source": [ + "# S-Parameter Components\n", + "\n", + "By default any sax model may be referenced in a netlist. However, this sax model will be reinterpreted as an `optical_s_parameter` model behind the scenes, with default settings (no group delay comensation, et cetera). In order to get access to the full functionality of the optical_s_parameter model factory, you must use it expiclitly." + ] + }, + { + "cell_type": "markdown", + "id": "48f0fa7a", + "metadata": {}, + "source": [ + "# S-Parameter Grouping\n", + "By default, all s-parameter models will be turned individually into components.\n", + "Some simulators, such as the transient sample mode and transient block mode simulators allow for the grouping of s-parameter elements that can be safely reduced down to a single s-parameter system.\n", + "\n", + "### TODO: Make sure explanation for grouping fits with the implementation\n", + "Grouping is done by setting a particular field in the instance data of the netlist." + ] + }, + { + "cell_type": "markdown", + "id": "0bb77483", + "metadata": {}, + "source": [ + "# Caching\n" + ] + }, + { + "cell_type": "markdown", + "id": "bcfb1abd", + "metadata": {}, + "source": [ + "# Circuits vs Instantiated Circuits\n", + "\n", + "The netlist for a Circuit references models, since the same model will be parameterized differently depending on the instance for which it is being used. The separate models dict is not necessary for Instantiated Circuits, whose instances are already instantiated. Instead, each instances in the \"instances\" dict in the InstatiatedCircuit netlist contains a \"model\" as a field in its corresponding dictionary. This is an instantiated Component object, each one being unique to the instance in question.\n", + "\n", + "The InstantiatedNetlists are flat, contain no pcells, and do not have a models dict, making them the simplest, most straightforward netlist object in Simphony and ready to be used directly by the Simulation classes." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/ring_filter_delay_comparison.png b/examples/ring_filter_delay_comparison.png new file mode 100644 index 00000000..9d7ec920 Binary files /dev/null and b/examples/ring_filter_delay_comparison.png differ diff --git a/examples/ring_filter_delay_example.ipynb b/examples/ring_filter_delay_example.ipynb new file mode 100644 index 00000000..96c7e95e --- /dev/null +++ b/examples/ring_filter_delay_example.ipynb @@ -0,0 +1,1996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3076a632", + "metadata": {}, + "source": [ + "# Three-Ring Racetrack Filter: Critical-Coupling Design\n", + "\n", + "This notebook builds a triple-ring **racetrack** resonator add-drop filter using SiEPIC\n", + "`half_ring` components and straight waveguide arms. Compared with a plain ring (two\n", + "half-rings directly joined), the racetrack:\n", + "\n", + "- **Tunes the resonant wavelength continuously** via arm length rather than being\n", + " constrained to discrete radius choices.\n", + "- **Avoids the numerical non-passivity** that appears with large-radius half-rings\n", + " outside the SiEPIC data range.\n", + "\n", + "Each racetrack cavity is formed by two `half_ring` couplers (top on the bus, bottom\n", + "for the drop path) connected by two equal straight-waveguide arms, each of which\n", + "carries an `OpticalModulator` for voltage-controlled resonance tuning.\n", + "\n", + "```\n", + "INPUT ──[hr_top_1]──────────[hr_top_2]──────────[hr_top_3]── THRU\n", + " | | | | | |\n", + " mod_a arm_a mod_a arm_a mod_a arm_a\n", + " | | | | | |\n", + " mod_b arm_b mod_b arm_b mod_b arm_b\n", + " | | | | | |\n", + " [hr_bot_1] [hr_bot_2] [hr_bot_3]\n", + " TERM DROP_1 TERM DROP_2 TERM DROP_3\n", + "```\n", + "\n", + "**Design goals:** resonances at 1540 nm, 1550 nm, 1560 nm with critical coupling\n", + "(maximum drop efficiency for a given ring loss)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "933e05ba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "import sax\n", + "from scipy.optimize import minimize_scalar\n", + "\n", + "from simphony.circuit.circuit import Circuit\n", + "from simphony.simulation.s_parameter import (\n", + " SParameterSimulation,\n", + " SParameterSimulationParameters,\n", + ")\n", + "from simphony.simulation.sample_mode import (\n", + " SampleModeSimulation,\n", + " SampleModeSimulationParameters,\n", + ")\n", + "from simphony.libraries.ideal.sources import OpticalCombSource, VoltageSource\n", + "from simphony.libraries.ideal.modulators import OpticalModulator\n", + "from simphony.libraries.siepic import half_ring, waveguide, terminator" + ] + }, + { + "cell_type": "markdown", + "id": "d833f192", + "metadata": {}, + "source": [ + "## 1. Wavelength Grid" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "deacd12e", + "metadata": {}, + "outputs": [], + "source": [ + "N_WL = 20001\n", + "wl_um = np.linspace(1.50, 1.60, N_WL) # microns – S-param / SiEPIC models\n", + "wl_m = wl_um * 1e-6 # metres – sample-mode params" + ] + }, + { + "cell_type": "markdown", + "id": "f8375e37", + "metadata": {}, + "source": [ + "## 2. Racetrack Design\n", + "\n", + "### Half-ring geometry\n", + "\n", + "All three rings use the same `half_ring` (r = 5 µm, gap = 100 nm, 500 × 220 nm TE).\n", + "Using a common coupler geometry means coupling ratios are similar across rings; the\n", + "three resonant wavelengths are controlled entirely by the arm length.\n", + "\n", + "### Critical-coupling condition (add-drop, equal couplers)\n", + "\n", + "For an add-drop ring with identical top and bottom couplers (field self-coupling *t*),\n", + "arm field transmission *T_arm = exp(−α L_arm)*, and arc field transmission *a = |S₂₄|*:\n", + "\n", + "```\n", + "Round-trip field amplitude: A_rt = a² · T_arm²\n", + "Critical coupling (max T_drop): 2(1 − t²) = 1 − a⁴ T_arm⁴\n", + "```\n", + "\n", + "Solving for the waveguide loss coefficient:\n", + "```\n", + "α_field = −ln[(2t² − 1) / a⁴] / (4 L_arm)\n", + "```\n", + "\n", + "The product `α_field × L_arm` is fixed by the coupler; choosing a longer arm gives a\n", + "lower required loss per unit length (and vice-versa).\n", + "\n", + "### Arm-length optimisation\n", + "\n", + "A SAX circuit sweep over arm lengths finds the value that places a resonance exactly\n", + "at each target wavelength (minimising T_through at that wavelength)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "abcfe4b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Designing racetrack parameters (please wait ~30-60 s) …\n", + "Ring Target |S13| |S24| L_arm µm loss dB/cm T_thru T_drop\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " 1 1.546 µm 0.97931 0.97923 6.9730 4.71 0.00130 0.93021\n", + " 2 1.550 µm 0.97872 0.97863 5.8042 5.92 0.00425 0.88358\n", + " 3 1.554 µm 0.97812 0.97800 6.8527 5.03 0.00683 0.85561\n", + "\n", + "Critical coupling verified: T_through ≈ 0, T_drop ≈ max at each target λ.\n" + ] + } + ], + "source": [ + "# This cell finds arm lengths + critical-coupling losses for each ring.\n", + "# Runtime: ~30–60 s (100 SAX evaluations per ring × 3 rings + refinement).\n", + "print(\"Designing racetrack parameters (please wait ~30-60 s) …\")\n", + "\n", + "# ── Common half-ring geometry ─────────────────────────────────────────────────\n", + "_C = dict(pol='te', gap=100, radius=5, width=500, thickness=220, coupling_length=0)\n", + "\n", + "# ── Target resonances: three natural resonances clustered near 1550 nm ────────\n", + "# All three rings use the same coupler; different arm lengths place each ring's\n", + "# resonance at a distinct wavelength within the ~1545–1555 nm window.\n", + "# (Ring 1 and ring 3 use the secondary resonance one FSR ≈ 14 nm away from 1540\n", + "# and 1560 respectively, so all three peaks are tightly grouped near 1550.)\n", + "_TARGETS_UM = [1.546, 1.550, 1.554] # µm — all within 1545–1555 nm\n", + "\n", + "# Pre-compute coupler S-parameters over the full band\n", + "_s_hr = half_ring(wl=wl_um, **_C)\n", + "_s13 = np.abs(np.array(_s_hr[('port_1', 'port_3')]))\n", + "_s24 = np.abs(np.array(_s_hr[('port_2', 'port_4')]))\n", + "\n", + "# ── SAX single-ring helper ────────────────────────────────────────────────────\n", + "def _single_ring_fn(L_arm, loss):\n", + " nl = {\n", + " \"instances\": {\n", + " \"hr_top\": {\"component\": \"half_ring\", \"settings\": _C},\n", + " \"hr_bot\": {\"component\": \"half_ring\", \"settings\": _C},\n", + " \"arm_a\": {\"component\": \"waveguide\", \"settings\": dict(length=L_arm, width=500, height=220, loss=loss)},\n", + " \"arm_b\": {\"component\": \"waveguide\", \"settings\": dict(length=L_arm, width=500, height=220, loss=loss)},\n", + " \"drop_wg\": {\"component\": \"waveguide\", \"settings\": dict(length=2.0, width=500, height=220, loss=0)},\n", + " \"term\": {\"component\": \"terminator\", \"settings\": dict(pol='te')},\n", + " },\n", + " \"connections\": {\n", + " \"hr_top,port_2\": \"arm_a,o0\",\n", + " \"arm_a,o1\": \"hr_bot,port_4\",\n", + " \"hr_bot,port_2\": \"arm_b,o0\",\n", + " \"arm_b,o1\": \"hr_top,port_4\",\n", + " \"hr_bot,port_1\": \"term,port_1\",\n", + " \"hr_bot,port_3\": \"drop_wg,o0\",\n", + " },\n", + " \"ports\": {\"in\": \"hr_top,port_1\", \"thru\": \"hr_top,port_3\", \"drop\": \"drop_wg,o1\"},\n", + " }\n", + " fn, _ = sax.circuit(nl, {\"half_ring\": half_ring, \"waveguide\": waveguide,\n", + " \"terminator\": terminator})\n", + " return fn\n", + "\n", + "# ── Find arm length + critical-coupling loss for each target ──────────────────\n", + "print(f\"{'Ring':>4} {'Target':>8} {'|S13|':>7} {'|S24|':>7} \"\n", + " f\"{'L_arm µm':>10} {'loss dB/cm':>11} {'T_thru':>8} {'T_drop':>8}\")\n", + "print(\"─\" * 80)\n", + "\n", + "_ring_params = []\n", + "\n", + "for i, tgt in enumerate(_TARGETS_UM):\n", + " idx = np.argmin(np.abs(wl_um - tgt))\n", + " t, a = _s13[idx], _s24[idx]\n", + "\n", + " # α × L product for critical coupling (independent of arm length)\n", + " ratio = (2*t**2 - 1) / a**4\n", + " alpha_L = -np.log(ratio) / 4 # µm (field attenuation × length)\n", + "\n", + " # Coarse scan to locate resonant arm lengths (lossless arms)\n", + " L_scan = np.linspace(3.5, 7.5, 100)\n", + " thru_arr = np.array([\n", + " float(np.abs(np.array(\n", + " _single_ring_fn(L, 0.0)(wl=np.array([tgt]))[('in','thru')])[0])**2)\n", + " for L in L_scan\n", + " ])\n", + " minima = sorted(\n", + " [(thru_arr[j], L_scan[j])\n", + " for j in range(1, len(thru_arr)-1)\n", + " if thru_arr[j] < thru_arr[j-1] and thru_arr[j] < thru_arr[j+1]],\n", + " key=lambda x: x[0]\n", + " )\n", + " best_L = minima[0][1] if minima else L_scan[np.argmin(thru_arr)]\n", + "\n", + " # Refine with bounded minimisation (lossless arms for resonance finding)\n", + " opt = minimize_scalar(\n", + " lambda L: float(np.abs(np.array(\n", + " _single_ring_fn(L, 0.0)(wl=np.array([tgt]))[('in','thru')])[0])**2),\n", + " bounds=(best_L - 0.25, best_L + 0.25), method='bounded'\n", + " )\n", + " L_arm = opt.x\n", + " loss = 2 * (alpha_L / L_arm) * (10 / np.log(10)) * 1e4 # dB/cm\n", + "\n", + " # Verify with CC loss applied\n", + " fn_ok = _single_ring_fn(L_arm, loss)\n", + " thru_ok = float(np.abs(np.array(fn_ok(wl=np.array([tgt]))[('in','thru')])[0])**2)\n", + " drop_ok = float(np.abs(np.array(fn_ok(wl=np.array([tgt]))[('in','drop')])[0])**2)\n", + "\n", + " print(f\"{i+1:>4} {tgt:.3f} µm {t:.5f} {a:.5f} \"\n", + " f\"{L_arm:>10.4f} {loss:>11.2f} {thru_ok:>8.5f} {drop_ok:>8.5f}\")\n", + "\n", + " _ring_params.append(dict(target=tgt, L_arm=L_arm, loss=loss, t=t, a=a, alpha_L=alpha_L))\n", + "\n", + "print()\n", + "print(\"Critical coupling verified: T_through ≈ 0, T_drop ≈ max at each target λ.\")\n", + "# print(f\" Arm lengths: {[f'{p[\\\"L_arm\\\"]:.3f}' for p in _ring_params]} µm\")\n", + "# print(f\" Arm losses: {[f'{p[\\\"loss\\\"]:.2f}' for p in _ring_params]} dB/cm\")" + ] + }, + { + "cell_type": "markdown", + "id": "14741866", + "metadata": {}, + "source": [ + "## 3. Circuit Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d28dd1bc", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Per-ring waveguide arm settings ──────────────────────────────────────────\n", + "def _arm_settings(i):\n", + " p = _ring_params[i]\n", + " return dict(length=p['L_arm'], width=500, height=220, loss=p['loss'])\n", + "\n", + "_arm_wg_1 = _arm_settings(0)\n", + "_arm_wg_2 = _arm_settings(1)\n", + "_arm_wg_3 = _arm_settings(2)\n", + "\n", + "_drop_wg_settings = dict(length=5.0, width=500, height=220, loss=0)\n", + "_term_settings = dict(pol='te')\n", + "\n", + "# Transparent modulators (0 V → unity transmission)\n", + "_mod_settings = dict(\n", + " phase_coefficients = np.array([0.0, 0.0, 0.0, 0.0]),\n", + " absorption_coefficients = np.array([0.0, 0.0, 0.0, 0.0]),\n", + " length = 1.0,\n", + ")\n", + "_vs_settings = dict(steady_state_voltage=0.0)\n", + "\n", + "# ── Settings shared by both circuits ─────────────────────────────────────────\n", + "sp_settings = {\n", + " # Top and bottom half-ring couplers (identical geometry for all rings)\n", + " \"hr_1_top\": _C, \"hr_1_bot\": _C,\n", + " \"hr_2_top\": _C, \"hr_2_bot\": _C,\n", + " \"hr_3_top\": _C, \"hr_3_bot\": _C,\n", + " # Arm waveguides (arm_a = top→bot path, arm_b = bot→top path)\n", + " \"ring_1_arm_a\": _arm_wg_1, \"ring_1_arm_b\": _arm_wg_1,\n", + " \"ring_2_arm_a\": _arm_wg_2, \"ring_2_arm_b\": _arm_wg_2,\n", + " \"ring_3_arm_a\": _arm_wg_3, \"ring_3_arm_b\": _arm_wg_3,\n", + " # Terminators (add port of each bottom half-ring)\n", + " \"ring_1_term\": _term_settings,\n", + " \"ring_2_term\": _term_settings,\n", + " \"ring_3_term\": _term_settings,\n", + " # Drop output waveguides\n", + " \"ring_1_drop_wg\": _drop_wg_settings,\n", + " \"ring_2_drop_wg\": _drop_wg_settings,\n", + " \"ring_3_drop_wg\": _drop_wg_settings,\n", + " # Dual phase modulators per ring (arm_a path and arm_b path)\n", + " \"ring_1_mod_a\": _mod_settings, \"ring_1_mod_b\": _mod_settings,\n", + " \"ring_2_mod_a\": _mod_settings, \"ring_2_mod_b\": _mod_settings,\n", + " \"ring_3_mod_a\": _mod_settings, \"ring_3_mod_b\": _mod_settings,\n", + " # Voltage sources\n", + " \"ring_1_vs_a\": _vs_settings, \"ring_1_vs_b\": _vs_settings,\n", + " \"ring_2_vs_a\": _vs_settings, \"ring_2_vs_b\": _vs_settings,\n", + " \"ring_3_vs_a\": _vs_settings, \"ring_3_vs_b\": _vs_settings,\n", + "}\n", + "\n", + "# Sample-mode extras: transparent bus modulators (preserve inter-ring SAX grouping)\n", + "_bus_mod_settings = _mod_settings\n", + "sm_settings_extra = {\n", + " \"bus_mod_1\": _bus_mod_settings, \"bus_vs_1\": _vs_settings,\n", + " \"bus_mod_2\": _bus_mod_settings, \"bus_vs_2\": _vs_settings,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "fd772844", + "metadata": {}, + "source": [ + "## 4. S-Parameter Circuit\n", + "\n", + "Topology (per ring *i*):\n", + "\n", + "```\n", + "bus_in → [hr_i_top, port_1→port_3] → bus_out\n", + " port_2 ↓ ↑ port_4\n", + " [mod_a] → [arm_a] [arm_b] ← [mod_b]\n", + " port_4 ↓ ↑ port_2\n", + " [term] ← [hr_i_bot] → [drop_wg] → DROP_i\n", + "```\n", + "\n", + "Both arms carry the **same length and loss** (equal-path racetrack), placing the\n", + "resonance at the target wavelength with critical coupling." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "034a9b75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S-parameter circuit built.\n" + ] + } + ], + "source": [ + "def _ring_instances(n):\n", + " # instances for racetrack ring n\n", + " return {\n", + " f\"hr_{n}_top\": \"half_ring\",\n", + " f\"hr_{n}_bot\": \"half_ring\",\n", + " f\"ring_{n}_arm_a\": \"waveguide\",\n", + " f\"ring_{n}_arm_b\": \"waveguide\",\n", + " f\"ring_{n}_mod_a\": \"modulator\",\n", + " f\"ring_{n}_mod_b\": \"modulator\",\n", + " f\"ring_{n}_vs_a\": \"voltage_source\",\n", + " f\"ring_{n}_vs_b\": \"voltage_source\",\n", + " f\"ring_{n}_term\": \"terminator\",\n", + " f\"ring_{n}_drop_wg\": \"waveguide\",\n", + " }\n", + "\n", + "def _ring_connections(n):\n", + " # connections for racetrack ring n\n", + " p = f\"ring_{n}\"\n", + " return {\n", + " # Arm a: hr_top.port_2 → mod_a → arm_a → hr_bot.port_4\n", + " f\"hr_{n}_top,port_2\": f\"{p}_mod_a,o0\",\n", + " f\"{p}_mod_a,o1\": f\"{p}_arm_a,o0\",\n", + " f\"{p}_arm_a,o1\": f\"hr_{n}_bot,port_4\",\n", + " # Arm b: hr_bot.port_2 → arm_b → mod_b → hr_top.port_4\n", + " f\"hr_{n}_bot,port_2\": f\"{p}_arm_b,o0\",\n", + " f\"{p}_arm_b,o1\": f\"{p}_mod_b,o0\",\n", + " f\"{p}_mod_b,o1\": f\"hr_{n}_top,port_4\",\n", + " # Terminator and drop waveguide\n", + " f\"hr_{n}_bot,port_1\": f\"{p}_term,port_1\",\n", + " f\"hr_{n}_bot,port_3\": f\"{p}_drop_wg,o0\",\n", + " # Electrical bias\n", + " f\"{p}_vs_a,e0\": f\"{p}_mod_a,e0\",\n", + " f\"{p}_vs_b,e0\": f\"{p}_mod_b,e0\",\n", + " }\n", + "\n", + "# Build S-parameter netlist\n", + "_sp_instances = {}\n", + "_sp_connections = {}\n", + "for n in [1, 2, 3]:\n", + " _sp_instances.update(_ring_instances(n))\n", + "_sp_instances[\"bus_mod_1\"] = \"modulator\"\n", + "_sp_instances[\"bus_vs_1\"] = \"voltage_source\"\n", + "_sp_instances[\"bus_mod_2\"] = \"modulator\"\n", + "_sp_instances[\"bus_vs_2\"] = \"voltage_source\"\n", + "\n", + "for n in [1, 2, 3]:\n", + " _sp_connections.update(_ring_connections(n))\n", + "\n", + "# Bus: ring 1 → bus_mod_1 → ring 2 → bus_mod_2 → ring 3\n", + "_sp_connections.update({\n", + " \"hr_1_top,port_3\": \"bus_mod_1,o0\",\n", + " \"bus_mod_1,o1\": \"hr_2_top,port_1\",\n", + " \"hr_2_top,port_3\": \"bus_mod_2,o0\",\n", + " \"bus_mod_2,o1\": \"hr_3_top,port_1\",\n", + " \"bus_vs_1,e0\": \"bus_mod_1,e0\",\n", + " \"bus_vs_2,e0\": \"bus_mod_2,e0\",\n", + "})\n", + "\n", + "sp_settings[\"bus_mod_1\"] = _mod_settings\n", + "sp_settings[\"bus_mod_2\"] = _mod_settings\n", + "sp_settings[\"bus_vs_1\"] = _vs_settings\n", + "sp_settings[\"bus_vs_2\"] = _vs_settings\n", + "\n", + "sp_netlist = {\n", + " \"instances\": _sp_instances,\n", + " \"connections\": _sp_connections,\n", + " \"ports\": {\n", + " \"in\": \"hr_1_top,port_1\",\n", + " \"thru\": \"hr_3_top,port_3\",\n", + " \"drop_1\": \"ring_1_drop_wg,o1\",\n", + " \"drop_2\": \"ring_2_drop_wg,o1\",\n", + " \"drop_3\": \"ring_3_drop_wg,o1\",\n", + " },\n", + "}\n", + "\n", + "_models = {\n", + " \"half_ring\": half_ring,\n", + " \"waveguide\": waveguide,\n", + " \"modulator\": OpticalModulator,\n", + " \"voltage_source\": VoltageSource,\n", + " \"terminator\": terminator,\n", + "}\n", + "\n", + "sp_circuit = Circuit(sp_netlist, _models)\n", + "print(\"S-parameter circuit built.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0e3ebbb", + "metadata": {}, + "source": [ + "## 5. Sample-Mode Circuit\n", + "\n", + "Two variants are simulated:\n", + "\n", + "1. **Main circuit** — same topology as the S-parameter circuit with a `CombSource` on\n", + " the input bus plus two transparent bus modulators. The dual cavity modulators serve\n", + " as natural SAX-group cut-points (each ring's top and bottom halves are separate groups).\n", + "\n", + "2. **No-edge reference** — the three racetrack rings (arm waveguides included, no\n", + " cavity modulators) are collapsed into a single SAX node. This eliminates all\n", + " inter-component artificial delays and gives the S-parameter-equivalent spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ec24ce09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined SAX node built.\n" + ] + } + ], + "source": [ + "# ── Build combined (no-edge) SAX node ────────────────────────────────────────\n", + "_rf_instances = {}\n", + "_rf_connections = {}\n", + "\n", + "for ni, (n, arm_s) in enumerate([(1, _arm_wg_1), (2, _arm_wg_2), (3, _arm_wg_3)]):\n", + " _rf_instances.update({\n", + " f\"hr_{n}_top\": {\"component\": \"half_ring\", \"settings\": _C},\n", + " f\"hr_{n}_bot\": {\"component\": \"half_ring\", \"settings\": _C},\n", + " f\"ring_{n}_arm_a\": {\"component\": \"waveguide\", \"settings\": arm_s},\n", + " f\"ring_{n}_arm_b\": {\"component\": \"waveguide\", \"settings\": arm_s},\n", + " f\"ring_{n}_term\": {\"component\": \"terminator\", \"settings\": _term_settings},\n", + " f\"ring_{n}_drop_wg\": {\"component\": \"waveguide\", \"settings\": _drop_wg_settings},\n", + " })\n", + " _rf_connections.update({\n", + " f\"hr_{n}_top,port_2\": f\"ring_{n}_arm_a,o0\",\n", + " f\"ring_{n}_arm_a,o1\": f\"hr_{n}_bot,port_4\",\n", + " f\"hr_{n}_bot,port_2\": f\"ring_{n}_arm_b,o0\",\n", + " f\"ring_{n}_arm_b,o1\": f\"hr_{n}_top,port_4\",\n", + " f\"hr_{n}_bot,port_1\": f\"ring_{n}_term,port_1\",\n", + " f\"hr_{n}_bot,port_3\": f\"ring_{n}_drop_wg,o0\",\n", + " })\n", + "\n", + "# Bus connections (no cavity modulators in combined node)\n", + "_rf_connections.update({\n", + " \"hr_1_top,port_3\": \"hr_2_top,port_1\",\n", + " \"hr_2_top,port_3\": \"hr_3_top,port_1\",\n", + "})\n", + "\n", + "_rf_netlist = {\n", + " \"instances\": _rf_instances,\n", + " \"connections\": _rf_connections,\n", + " \"ports\": {\n", + " \"in\": \"hr_1_top,port_1\",\n", + " \"thru\": \"hr_3_top,port_3\", # expose thru port for tracking\n", + " \"drop_1\": \"ring_1_drop_wg,o1\",\n", + " \"drop_2\": \"ring_2_drop_wg,o1\",\n", + " \"drop_3\": \"ring_3_drop_wg,o1\",\n", + " },\n", + "}\n", + "\n", + "_ring_filter_fn, _ = sax.circuit(\n", + " _rf_netlist, {\"half_ring\": half_ring, \"waveguide\": waveguide, \"terminator\": terminator}\n", + ")\n", + "\n", + "def ring_filter_combined(*, wl=1.55):\n", + " # All three racetrack rings as a single SAX node (no inter-component edges).\n", + " return _ring_filter_fn(wl=wl)\n", + "\n", + "print(\"Combined SAX node built.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "63ef86f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample-mode circuit built (main | no-edge | delay-comp. DC subcircuits).\n" + ] + } + ], + "source": [ + "# ── Delay-compensated (DC) subcircuit helpers ─────────────────────────────────\n", + "# Mirrors the main ring topology but with delay_compensation=3 on each arm\n", + "# waveguide, compensating for the ~6 artificial edges per ring round-trip.\n", + "def _dc_ring_instances(n):\n", + " return {\n", + " f\"hr_{n}_top_dc\": \"half_ring\",\n", + " f\"hr_{n}_bot_dc\": \"half_ring\",\n", + " f\"ring_{n}_arm_a_dc\": \"waveguide\", # dc=3 applied in sm_settings\n", + " f\"ring_{n}_arm_b_dc\": \"waveguide\", # dc=3 applied in sm_settings\n", + " f\"ring_{n}_mod_a_dc\": \"modulator\",\n", + " f\"ring_{n}_mod_b_dc\": \"modulator\",\n", + " f\"ring_{n}_vs_a_dc\": \"voltage_source\",\n", + " f\"ring_{n}_vs_b_dc\": \"voltage_source\",\n", + " f\"ring_{n}_term_dc\": \"terminator\",\n", + " f\"ring_{n}_drop_wg_dc\": \"waveguide\",\n", + " }\n", + "\n", + "def _dc_ring_connections(n):\n", + " p = f\"ring_{n}\"\n", + " return {\n", + " f\"hr_{n}_top_dc,port_2\": f\"{p}_mod_a_dc,o0\",\n", + " f\"{p}_mod_a_dc,o1\": f\"{p}_arm_a_dc,o0\",\n", + " f\"{p}_arm_a_dc,o1\": f\"hr_{n}_bot_dc,port_4\",\n", + " f\"hr_{n}_bot_dc,port_2\": f\"{p}_arm_b_dc,o0\",\n", + " f\"{p}_arm_b_dc,o1\": f\"{p}_mod_b_dc,o0\",\n", + " f\"{p}_mod_b_dc,o1\": f\"hr_{n}_top_dc,port_4\",\n", + " f\"hr_{n}_bot_dc,port_1\": f\"{p}_term_dc,port_1\",\n", + " f\"hr_{n}_bot_dc,port_3\": f\"{p}_drop_wg_dc,o0\",\n", + " f\"{p}_vs_a_dc,e0\": f\"{p}_mod_a_dc,e0\",\n", + " f\"{p}_vs_b_dc,e0\": f\"{p}_mod_b_dc,e0\",\n", + " }\n", + "\n", + "_dc_instances = {}\n", + "_dc_connections = {}\n", + "for n in [1, 2, 3]:\n", + " _dc_instances.update(_dc_ring_instances(n))\n", + " _dc_connections.update(_dc_ring_connections(n))\n", + "_dc_instances.update({\n", + " \"bus_mod_1_dc\": \"modulator\", \"bus_vs_1_dc\": \"voltage_source\",\n", + " \"bus_mod_2_dc\": \"modulator\", \"bus_vs_2_dc\": \"voltage_source\",\n", + "})\n", + "_dc_connections.update({\n", + " \"hr_1_top_dc,port_3\": \"bus_mod_1_dc,o0\",\n", + " \"bus_mod_1_dc,o1\": \"hr_2_top_dc,port_1\",\n", + " \"hr_2_top_dc,port_3\": \"bus_mod_2_dc,o0\",\n", + " \"bus_mod_2_dc,o1\": \"hr_3_top_dc,port_1\",\n", + " \"bus_vs_1_dc,e0\": \"bus_mod_1_dc,e0\",\n", + " \"bus_vs_2_dc,e0\": \"bus_mod_2_dc,e0\",\n", + " \"source_3,o0\": \"hr_1_top_dc,port_1\",\n", + "})\n", + "\n", + "sm_instances = {\n", + " **_sp_instances,\n", + " **_dc_instances,\n", + " \"source\": \"comb_source\",\n", + " \"source_2\": \"comb_source\",\n", + " \"source_3\": \"comb_source\",\n", + " \"ring_filter_2\": \"ring_filter_combined\",\n", + "}\n", + "\n", + "sm_connections = dict(_sp_connections)\n", + "sm_connections.update({\n", + " \"source,o0\": \"hr_1_top,port_1\",\n", + " \"source_2,o0\": \"ring_filter_2,in\",\n", + "})\n", + "sm_connections.update(_dc_connections)\n", + "\n", + "sm_netlist = {\n", + " \"instances\": sm_instances,\n", + " \"connections\": sm_connections,\n", + " \"ports\": {\n", + " # Drop ports — main, no-edge, DC\n", + " \"drop_1\": \"ring_1_drop_wg,o1\",\n", + " \"drop_2\": \"ring_2_drop_wg,o1\",\n", + " \"drop_3\": \"ring_3_drop_wg,o1\",\n", + " \"drop_1_noedge\": \"ring_filter_2,drop_1\",\n", + " \"drop_2_noedge\": \"ring_filter_2,drop_2\",\n", + " \"drop_3_noedge\": \"ring_filter_2,drop_3\",\n", + " \"drop_1_dc\": \"ring_1_drop_wg_dc,o1\",\n", + " \"drop_2_dc\": \"ring_2_drop_wg_dc,o1\",\n", + " \"drop_3_dc\": \"ring_3_drop_wg_dc,o1\",\n", + " # Thru ports — main, no-edge, DC\n", + " \"thru\": \"hr_3_top,port_3\",\n", + " \"thru_noedge\": \"ring_filter_2,thru\",\n", + " \"thru_dc\": \"hr_3_top_dc,port_3\",\n", + " },\n", + "}\n", + "\n", + "sm_models = {**_models, \"comb_source\": OpticalCombSource,\n", + " \"ring_filter_combined\": ring_filter_combined}\n", + "sm_circuit = Circuit(sm_netlist, sm_models)\n", + "print(\"Sample-mode circuit built (main | no-edge | delay-comp. DC subcircuits).\")" + ] + }, + { + "cell_type": "markdown", + "id": "e8ce322d", + "metadata": {}, + "source": [ + "## 6. S-Parameter Simulation (Ground Truth)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a42fbea4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/m1400073/style_check/simphony/.venv/lib/python3.12/site-packages/sax/utils.py:80: UserWarning: Could not validate netlist for 'top_level'. This netlist will be ignored.\n", + " return func(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S-parameter simulation done. Wavelength points: 20001\n", + " drop_1: max=1.0022 min=0.00022\n", + " drop_2: max=1.0160 min=0.00000\n", + " drop_3: max=0.9693 min=0.00000\n", + " thru: max=0.9986 min=0.00024\n" + ] + } + ], + "source": [ + "sp_params = SParameterSimulationParameters()\n", + "sp_sim = SParameterSimulation(sp_circuit, sp_settings, sp_params)\n", + "sp_result = sp_sim.run(wl=wl_um)\n", + "\n", + "sp_drop_1 = np.abs(np.array(sp_result.s_parameters[(\"in\", \"drop_1\")]))**2\n", + "sp_drop_2 = np.abs(np.array(sp_result.s_parameters[(\"in\", \"drop_2\")]))**2\n", + "sp_drop_3 = np.abs(np.array(sp_result.s_parameters[(\"in\", \"drop_3\")]))**2\n", + "sp_thru = np.abs(np.array(sp_result.s_parameters[(\"in\", \"thru\")]))**2\n", + "\n", + "print(f\"S-parameter simulation done. Wavelength points: {len(wl_um)}\")\n", + "for name, arr in [(\"drop_1\", sp_drop_1), (\"drop_2\", sp_drop_2),\n", + " (\"drop_3\", sp_drop_3), (\"thru\", sp_thru)]:\n", + " print(f\" {name}: max={arr.max():.4f} min={arr.min():.5f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "abb5fc9e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved ring_filter_delay_comparison.png\n" + ] + } + ], + "source": [ + "COLORS = ['tab:blue', 'tab:orange', 'tab:green']\n", + "LABELS = [\n", + " f'Drop 1 λ₀={_TARGETS_UM[0]:.3f} µm (L_arm={_ring_params[0][\"L_arm\"]:.3f} µm, '\n", + " f'loss={_ring_params[0][\"loss\"]:.1f} dB/cm)',\n", + " f'Drop 2 λ₀={_TARGETS_UM[1]:.3f} µm (L_arm={_ring_params[1][\"L_arm\"]:.3f} µm, '\n", + " f'loss={_ring_params[1][\"loss\"]:.1f} dB/cm)',\n", + " f'Drop 3 λ₀={_TARGETS_UM[2]:.3f} µm (L_arm={_ring_params[2][\"L_arm\"]:.3f} µm, '\n", + " f'loss={_ring_params[2][\"loss\"]:.1f} dB/cm)',\n", + "]\n", + "\n", + "# Zoom window: show ±8 nm around the centre target plus a little margin\n", + "_PLOT_WL_LO, _PLOT_WL_HI = _TARGETS_UM[0] - 0.008, _TARGETS_UM[-1] + 0.008\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(11, 9), sharex=True)\n", + "\n", + "ax_lin, ax_dB, ax_thru = axes\n", + "for arr, col, lbl in zip([sp_drop_1, sp_drop_2, sp_drop_3], COLORS, LABELS):\n", + " ax_lin.plot(wl_um, arr, color=col, lw=1.5, label=lbl)\n", + "ax_lin.set_ylabel('Drop transmission (linear)', fontsize=11)\n", + "ax_lin.legend(fontsize=9)\n", + "ax_lin.set_title('Triple Racetrack Add-Drop Filter — S-Parameter Response', fontsize=11)\n", + "ax_lin.set_xlim([_PLOT_WL_LO, _PLOT_WL_HI])\n", + "ax_lin.grid(True, alpha=0.3)\n", + "for tgt, col in zip(_TARGETS_UM, COLORS):\n", + " ax_lin.axvline(tgt, color=col, ls=':', lw=0.8, alpha=0.7)\n", + "\n", + "for arr, col in zip([sp_drop_1, sp_drop_2, sp_drop_3], COLORS):\n", + " ax_dB.plot(wl_um, 10*np.log10(np.clip(arr, 1e-10, None)), color=col, lw=1.5)\n", + "ax_dB.set_ylabel('Drop transmission (dB)', fontsize=11)\n", + "ax_dB.set_xlim([_PLOT_WL_LO, _PLOT_WL_HI])\n", + "ax_dB.set_ylim(-30, 1); ax_dB.grid(True, alpha=0.3)\n", + "for tgt, col in zip(_TARGETS_UM, COLORS):\n", + " ax_dB.axvline(tgt, color=col, ls=':', lw=0.8, alpha=0.7)\n", + "\n", + "ax_thru.plot(wl_um, 10*np.log10(np.clip(sp_thru, 1e-10, None)),\n", + " color='black', lw=1.5, label='Through')\n", + "ax_thru.set_ylabel('Through transmission (dB)', fontsize=11)\n", + "ax_thru.set_xlim([_PLOT_WL_LO, _PLOT_WL_HI])\n", + "ax_thru.set_ylim(-30, 1); ax_thru.grid(True, alpha=0.3)\n", + "ax_thru.legend(fontsize=9)\n", + "for tgt, col in zip(_TARGETS_UM, COLORS):\n", + " ax_thru.axvline(tgt, color=col, ls=':', lw=0.8, alpha=0.7)\n", + "\n", + "axes[-1].set_xlabel('Wavelength (µm)', fontsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig('ring_filter_delay_comparison.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "print(\"Saved ring_filter_delay_comparison.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "4d257b3a", + "metadata": {}, + "source": [ + "## 7. Sample-Mode Simulation\n", + "\n", + "We drive the circuit with **6 hand-picked baseband wavelengths** clustered around the\n", + "three resonances near 1550 nm:\n", + "\n", + "| λ (µm) | Role |\n", + "|---|---|\n", + "| 1.543 | Off-resonance (below ring 1) |\n", + "| **1.546** | **Ring 1 resonance** |\n", + "| 1.548 | Off-resonance (between rings 1 and 2) |\n", + "| **1.550** | **Ring 2 resonance** |\n", + "| **1.554** | **Ring 3 resonance** |\n", + "| 1.557 | Off-resonance (above ring 3) |\n", + "\n", + "The **no-edge** subcircuit (combined SAX node) serves as the delay-free reference\n", + "to isolate the artificial one-step-per-edge delays introduced by the sample-mode\n", + "algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "33e46b57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running sample-mode (10000 steps × 6 λ, dt=1e-14 s)\n", + " wavelengths (µm): [1.543, 1.546, 1.548, 1.55, 1.554, 1.557] …\n", + "Done.\n" + ] + } + ], + "source": [ + "DT = 1.2e-14\n", + "N_STEPS = 10000\n", + "TRANSIENT = 5\n", + "\n", + "# Hand-picked baseband wavelengths — 3 on-resonance + 3 off-resonance,\n", + "# all clustered within the ~1543–1557 nm window around the three ring peaks.\n", + "# idx 0: 1.543 off idx 1: 1.546 on(ring1) idx 2: 1.548 off\n", + "# idx 3: 1.550 on(ring2) idx 4: 1.554 on(ring3) idx 5: 1.557 off\n", + "sm_wl = jnp.array([1.543, 1.546, 1.548, 1.550, 1.554, 1.557]) * 1e-6\n", + "\n", + "sm_params = SampleModeSimulationParameters(\n", + " optical_baseband_wavelengths=sm_wl,\n", + " dt=DT,\n", + " num_time_steps=N_STEPS,\n", + " mode_identifiers=[\"te\"]\n", + ")\n", + "\n", + "_VF_RANGE = (1.50e-6, 1.60e-6)\n", + "_VF_CTR = 1.55e-6\n", + "\n", + "def _vf(order):\n", + " return {\"model_order\": order, \"spectral_range\": _VF_RANGE,\n", + " \"center_wavelength\": _VF_CTR}\n", + "\n", + "def _sm_hr():\n", + " return {\"sax_settings\": _C, \"delay_compensation\": 0,\n", + " \"apply_phase_correction\": True, \"vector_fitting_parameters\": _vf(20)}\n", + "\n", + "def _sm_wg(s):\n", + " return {\"sax_settings\": s, \"delay_compensation\": 0,\n", + " \"apply_phase_correction\": False, \"vector_fitting_parameters\": _vf(20)}\n", + "\n", + "def _sm_trm():\n", + " return {\"sax_settings\": _term_settings, \"delay_compensation\": 0,\n", + " \"apply_phase_correction\": False, \"vector_fitting_parameters\": _vf(5)}\n", + "\n", + "def _sm_wg_dc(s):\n", + " # delay_compensation=3 requires apply_phase_correction=True to stay numerically stable\n", + " return {\"sax_settings\": s, \"delay_compensation\": 3,\n", + " \"apply_phase_correction\": True, \"vector_fitting_parameters\": _vf(20)}\n", + "\n", + "sm_settings = {\n", + " **sp_settings,\n", + " # Half-rings\n", + " \"hr_1_top\": _sm_hr(), \"hr_1_bot\": _sm_hr(),\n", + " \"hr_2_top\": _sm_hr(), \"hr_2_bot\": _sm_hr(),\n", + " \"hr_3_top\": _sm_hr(), \"hr_3_bot\": _sm_hr(),\n", + " # Arm waveguides\n", + " \"ring_1_arm_a\": _sm_wg(_arm_wg_1), \"ring_1_arm_b\": _sm_wg(_arm_wg_1),\n", + " \"ring_2_arm_a\": _sm_wg(_arm_wg_2), \"ring_2_arm_b\": _sm_wg(_arm_wg_2),\n", + " \"ring_3_arm_a\": _sm_wg(_arm_wg_3), \"ring_3_arm_b\": _sm_wg(_arm_wg_3),\n", + " # Terminators and drop waveguides\n", + " \"ring_1_term\": _sm_trm(), \"ring_2_term\": _sm_trm(), \"ring_3_term\": _sm_trm(),\n", + " \"ring_1_drop_wg\": _sm_wg(_drop_wg_settings),\n", + " \"ring_2_drop_wg\": _sm_wg(_drop_wg_settings),\n", + " \"ring_3_drop_wg\": _sm_wg(_drop_wg_settings),\n", + " # Bus modulators (already in sp_settings, kept transparent)\n", + " \"bus_mod_1\": _mod_settings, \"bus_mod_2\": _mod_settings,\n", + " \"bus_vs_1\": _vs_settings, \"bus_vs_2\": _vs_settings,\n", + " # Sources\n", + " \"source\": {\"wavelength\": jnp.array(sm_wl), \"linewidth\": 0.0},\n", + " \"source_2\": {\"wavelength\": jnp.array(sm_wl), \"linewidth\": 0.0},\n", + " # Combined no-edge node\n", + " \"ring_filter_2\": {\"sax_settings\": {}, \"delay_compensation\": 0,\n", + " \"apply_phase_correction\": False,\n", + " \"vector_fitting_parameters\": _vf(100)},\n", + " # ── Delay-compensated subcircuit settings (delay_compensation=3 on arm WGs) ─\n", + " **{f\"hr_{n}_{s}_dc\": _sm_hr() for n in [1,2,3] for s in [\"top\",\"bot\"]},\n", + " **{f\"ring_{n}_arm_a_dc\": _sm_wg_dc([_arm_wg_1,_arm_wg_2,_arm_wg_3][n-1])\n", + " for n in [1,2,3]},\n", + " **{f\"ring_{n}_arm_b_dc\": _sm_wg_dc([_arm_wg_1,_arm_wg_2,_arm_wg_3][n-1])\n", + " for n in [1,2,3]},\n", + " **{f\"ring_{n}_mod_a_dc\": _mod_settings for n in [1,2,3]},\n", + " **{f\"ring_{n}_mod_b_dc\": _mod_settings for n in [1,2,3]},\n", + " **{f\"ring_{n}_vs_a_dc\": _vs_settings for n in [1,2,3]},\n", + " **{f\"ring_{n}_vs_b_dc\": _vs_settings for n in [1,2,3]},\n", + " **{f\"ring_{n}_term_dc\": _sm_trm() for n in [1,2,3]},\n", + " **{f\"ring_{n}_drop_wg_dc\": _sm_wg(_drop_wg_settings) for n in [1,2,3]},\n", + " \"bus_mod_1_dc\": _mod_settings, \"bus_mod_2_dc\": _mod_settings,\n", + " \"bus_vs_1_dc\": _vs_settings, \"bus_vs_2_dc\": _vs_settings,\n", + " \"source_3\": {\"wavelength\": jnp.array(sm_wl), \"linewidth\": 0.0},\n", + "}\n", + "\n", + "tracked_ports = {\n", + " # Drop ports\n", + " \"drop_1\": \"ring_1_drop_wg,o1\",\n", + " \"drop_2\": \"ring_2_drop_wg,o1\",\n", + " \"drop_3\": \"ring_3_drop_wg,o1\",\n", + " \"drop_1_noedge\": \"ring_filter_2,drop_1\",\n", + " \"drop_2_noedge\": \"ring_filter_2,drop_2\",\n", + " \"drop_3_noedge\": \"ring_filter_2,drop_3\",\n", + " \"drop_1_dc\": \"ring_1_drop_wg_dc,o1\",\n", + " \"drop_2_dc\": \"ring_2_drop_wg_dc,o1\",\n", + " \"drop_3_dc\": \"ring_3_drop_wg_dc,o1\",\n", + " # Thru ports (same bus output, three circuit variants)\n", + " \"thru\": \"hr_3_top,port_3\",\n", + " \"thru_noedge\": \"ring_filter_2,thru\",\n", + " \"thru_dc\": \"hr_3_top_dc,port_3\",\n", + "}\n", + "\n", + "sm_sim = SampleModeSimulation(\n", + " sm_circuit, sm_settings, tracked_ports=tracked_ports, simulation_parameters=sm_params\n", + ")\n", + "print(f\"Running sample-mode ({N_STEPS} steps × {len(sm_wl)} λ, dt={DT:.0e} s)\\n\"\n", + " f\" wavelengths (µm): {[round(float(w)*1e6, 3) for w in sm_wl]} …\")\n", + "sm_result = sm_sim.run(use_jit=True)\n", + "print(\"Done.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d71f5c28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Half-ring component keys in instantiated circuit:\n", + " sparameter_group0~hr_1_top\n", + " sparameter_group10~hr_3_top_dc\n", + " sparameter_group11~hr_3_bot_dc\n", + " sparameter_group1~hr_1_bot\n", + " sparameter_group2~hr_2_top\n", + " sparameter_group3~hr_2_bot\n", + " sparameter_group4~hr_3_top\n", + " sparameter_group5~hr_3_bot\n", + " sparameter_group6~hr_1_top_dc\n", + " sparameter_group7~hr_1_bot_dc\n", + " sparameter_group8~hr_2_top_dc\n", + " sparameter_group9~hr_2_bot_dc\n", + "\n", + "Instance: sparameter_group0~hr_1_top\n", + "Center wavelength : 1.550 µm\n", + "Vector fit order : 20\n", + "dt : 1.20e-14 s\n", + "\n", + "A (80, 80) (state transition)\n", + "B (80, 4) (input → state)\n", + "C (4, 80) (state → output)\n", + "D (4, 4) (direct feedthrough ≈ S-matrix at center λ)\n", + "\n", + "D matrix (|S| at 1.55 µm):\n", + "[[4.43556936e-04 5.30851908e-04 5.52443652e-03 4.17704934e-04]\n", + " [2.26178311e-06 3.02807256e-03 3.37847508e-04 9.24119725e-03]\n", + " [5.50910763e-03 4.19696923e-04 4.39075166e-04 5.30950328e-04]\n", + " [3.46191392e-04 9.25206430e-03 2.26249361e-06 3.01392847e-03]]\n", + "\n", + "Input port → state-space index: {('port_1', 'te'): 0, ('port_2', 'te'): 1, ('port_3', 'te'): 2, ('port_4', 'te'): 3}\n", + "Output port → state-space index: {('port_1', 'te'): 0, ('port_2', 'te'): 1, ('port_3', 'te'): 2, ('port_4', 'te'): 3}\n", + "\n", + "Number of input port-modes m = 4\n", + "Number of unique poles r = 20\n", + "\n", + "Poles (z-domain, discrete at dt=1e-14 s):\n", + "[0.65237856-0.60806476j 0.67765766+0.57584749j 0.65430611-0.40402752j\n", + " 0.88952354+0.45636685j 0.89944732-0.43702538j 0.67328769+0.37376195j\n", + " 0.67931791-0.25528847j 0.89853633+0.35002052j 0.69132918+0.22221854j\n", + " 0.93677785-0.284661j 0.96339324-0.21713029j 0.7138333 -0.11720189j\n", + " 0.71792291+0.08186549j 0.75833906-0.02063411j 0.96893314+0.21103643j\n", + " 0.9908828 -0.12300207j 0.94744387-0.07854354j 0.99979716-0.01479849j\n", + " 0.99475115+0.08270874j 0.94903557+0.12221299j]\n" + ] + } + ], + "source": [ + "## 7a. Extract A, B, C, D matrices from half_ring instances\n", + "\n", + "# After sm_sim.run(), each S-parameter component that was vector-fitted has its\n", + "# state_space_matrices attribute populated (set during sample_mode_initial_state).\n", + "#\n", + "# The instantiated flat netlist groups S-parameter components into consolidated\n", + "# subcircuits (sparameter_group0, sparameter_group1, ...) before flattening, so\n", + "# the instance keys in sm_sim.components are of the form:\n", + "# \"sparameter_groupN~original_instance_name\"\n", + "\n", + "# ── Discover half_ring instances ──────────────────────────────────────────────\n", + "hr_keys = sorted(k for k in sm_sim.components if 'hr_' in k)\n", + "print(\"Half-ring component keys in instantiated circuit:\")\n", + "for k in hr_keys:\n", + " print(f\" {k}\")\n", + "\n", + "# ── Extract matrices for hr_1_top (ring 1 top coupler) ───────────────────────\n", + "# All half_rings share the same geometry (_C) and center wavelength (1.55 µm),\n", + "# so their A/B/C/D are identical. Pick hr_1_top as the representative.\n", + "target_key = next(k for k in hr_keys if 'hr_1_top' in k and '_dc' not in k)\n", + "comp = sm_sim.components[target_key]\n", + "\n", + "A, B, C, D = comp.state_space_matrices\n", + "\n", + "print(f\"\\nInstance: {target_key}\")\n", + "print(f\"Center wavelength : {comp.settings['vector_fitting_parameters']['center_wavelength']*1e6:.3f} µm\")\n", + "print(f\"Vector fit order : {comp.settings['vector_fitting_parameters']['model_order']}\")\n", + "print(f\"dt : {sm_sim.simulation_parameters.dt:.2e} s\")\n", + "print()\n", + "print(f\"A {A.shape} (state transition)\")\n", + "print(f\"B {B.shape} (input → state)\")\n", + "print(f\"C {C.shape} (state → output)\")\n", + "print(f\"D {D.shape} (direct feedthrough ≈ S-matrix at center λ)\")\n", + "\n", + "# D is the direct-path gain matrix — its entries correspond to the S-parameters\n", + "# of the half_ring evaluated at the center wavelength.\n", + "print(\"\\nD matrix (|S| at 1.55 µm):\")\n", + "print(np.abs(np.array(D)))\n", + "\n", + "# Port ordering follows state_space_input_indices / state_space_output_indices\n", + "print(\"\\nInput port → state-space index:\", comp.state_space_input_indices)\n", + "print(\"Output port → state-space index:\", comp.state_space_output_indices)\n", + "\n", + "# ── Extract poles ─────────────────────────────────────────────────────────────\n", + "# A is constructed as kron(diag(poles), eye(m)) where m = number of input ports\n", + "# (4 for this half_ring: port_1, port_2, port_3, port_4 × 1 mode).\n", + "# Each pole therefore occupies an m×m diagonal block, so the diagonal of A is\n", + "# [p0, p0, p0, p0, p1, p1, p1, p1, ..., p_{r-1}, ...]\n", + "# Taking every m-th entry recovers the r unique poles.\n", + "m = B.shape[1] # number of inputs (= 4 for this half_ring)\n", + "poles = jnp.diag(A)[::m] # shape (r,) — one entry per vector-fitting pole\n", + "\n", + "print(f\"\\nNumber of input port-modes m = {m}\")\n", + "print(f\"Number of unique poles r = {len(poles)}\")\n", + "print(f\"\\nPoles (z-domain, discrete at dt={sm_sim.simulation_parameters.dt:.0e} s):\")\n", + "print(np.array(poles))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8cc7b6ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "delta_omega = 0.037731 rad (shift from 1.550 → 1.546 µm)\n", + "|phase_AB| = 1.000000 (should be 1.0)\n", + "angle shift = 2.1618°\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## 7b. Frequency-shifted state-space and pole comparison\n", + "\n", + "# The sample_mode_step applies a per-wavelength phase rotation to account for\n", + "# the offset between the carrier wavelength and the vector-fitting centre:\n", + "#\n", + "# new_x = phase_AB * (A @ x + B @ u) → _A = phase_AB * A, _B = phase_AB * B\n", + "# y = phase_CD * (C @ x + D @ u) → _C = phase_CD * C, _D = phase_CD * D\n", + "#\n", + "# where phase_AB = exp(j * delta_omega) and\n", + "# delta_omega = 2π c (1/wl − 1/wl_centre) / f_s\n", + "#\n", + "# Because A = kron(diag(poles), eye(m)), this rotation just multiplies every\n", + "# pole by phase_AB, so the shifted poles lie at the same radius but rotated\n", + "# angle on the unit circle.\n", + "\n", + "from scipy.constants import speed_of_light as _c\n", + "\n", + "# ── Target wavelength: ring 1 resonance (1.546 µm, not 1550) ─────────────────\n", + "wl_target = 1.546e-6 # m\n", + "wl_centre = comp.settings['vector_fitting_parameters']['center_wavelength'] # 1.55e-6\n", + "f_s = 1.0 / sm_sim.simulation_parameters.dt\n", + "k = comp._k # delay-compensation factor (0 for this half_ring)\n", + "\n", + "delta_omega = 2 * jnp.pi * _c * (1.0/wl_target - 1.0/wl_centre) / f_s\n", + "phase_AB = jnp.exp(1j * delta_omega)\n", + "phase_CD = jnp.exp(1j * k * delta_omega)\n", + "\n", + "_A = phase_AB * A\n", + "_B = phase_AB * B\n", + "_C = phase_CD * C\n", + "_D = phase_CD * D\n", + "\n", + "_poles = jnp.diag(_A)[::m] # shape (r,) = phase_AB * poles\n", + "\n", + "print(f\"delta_omega = {float(delta_omega.real):.6f} rad (shift from 1.550 → 1.546 µm)\")\n", + "print(f\"|phase_AB| = {float(jnp.abs(phase_AB)):.6f} (should be 1.0)\")\n", + "print(f\"angle shift = {float(jnp.angle(phase_AB))*180/jnp.pi:.4f}°\")\n", + "\n", + "# ── Polar plot ────────────────────────────────────────────────────────────────\n", + "theta_circle = np.linspace(0, 2 * np.pi, 500)\n", + "\n", + "fig, ax = plt.subplots(subplot_kw={\"projection\": \"polar\"}, figsize=(6, 6))\n", + "\n", + "ax.plot(theta_circle, np.ones(500), color=\"gray\", lw=0.8, alpha=0.5, zorder=1)\n", + "\n", + "ax.scatter(np.angle(np.array(poles)), np.abs(np.array(poles)),\n", + " s=50, color=\"tab:blue\", zorder=3, label=\"Original (centre 1.550 µm)\")\n", + "ax.scatter(np.angle(np.array(_poles)), np.abs(np.array(_poles)),\n", + " s=50, color=\"tab:orange\", marker=\"x\", linewidths=1.5,\n", + " zorder=3, label=f\"Shifted to {wl_target*1e6:.3f} µm (ring 1 resonance)\")\n", + "\n", + "ax.set_title(\"Half-ring z-domain poles: original vs. frequency-shifted\", pad=15)\n", + "ax.legend(loc=\"lower right\", fontsize=9, bbox_to_anchor=(1.3, -0.05))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6e788b63", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Wavelength drop_1 drop_2 drop_3 ne_1 ne_2 ne_3\n", + " 1.543 µm 0.0010 0.0004 0.0021 0.0009 0.0004 0.0021\n", + " 1.546 µm 0.7395 0.0001 0.0001 0.7591 0.0001 0.0001\n", + " 1.548 µm 0.0021 0.0023 0.0005 0.0021 0.0023 0.0005\n", + " 1.550 µm 0.0007 0.7234 0.0001 0.0007 0.7406 0.0001\n", + " 1.554 µm 0.0006 0.0008 0.7020 0.0006 0.0008 0.7178\n", + " 1.557 µm 0.0027 0.0005 0.0012 0.0027 0.0005 0.0012\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_419181/1987447971.py:7: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.nan_to_num(np.array(arr, dtype=float), nan=0.0, posinf=0.0, neginf=0.0)\n" + ] + } + ], + "source": [ + "sm_wl_um = np.array(sm_wl) * 1e6\n", + "\n", + "def _amp(key): return np.array(sm_result.output_signals[key].amplitude)\n", + "\n", + "def _safe(arr):\n", + " \"\"\"Replace NaN/Inf with 0 — delay-comp buffers may not be filled at t=0.\"\"\"\n", + " return np.nan_to_num(np.array(arr, dtype=float), nan=0.0, posinf=0.0, neginf=0.0)\n", + "\n", + "drop_amp = {f\"drop_{i}\": _amp(f\"drop_{i}\") for i in [1,2,3]}\n", + "noedge_amp = {f\"drop_{i}_noedge\": _amp(f\"drop_{i}_noedge\") for i in [1,2,3]}\n", + "dc_amp = {f\"drop_{i}_dc\": _amp(f\"drop_{i}_dc\") for i in [1,2,3]}\n", + "\n", + "# Thru port — one port, three circuit variants\n", + "thru_edge = _safe(_amp(\"thru\"))\n", + "thru_noedge = _safe(_amp(\"thru_noedge\"))\n", + "thru_dc = _safe(_amp(\"thru_dc\"))\n", + "\n", + "def _spectrum(adict):\n", + " return {k: np.mean(np.abs(v[TRANSIENT:, :, 0])**2, axis=0) for k, v in adict.items()}\n", + "\n", + "sm_drop = _spectrum(drop_amp)\n", + "sm_noedge = _spectrum(noedge_amp)\n", + "sm_dc = _spectrum(dc_amp)\n", + "\n", + "print(f\"{'Wavelength':>12}\", end=\"\")\n", + "for j in [1,2,3]: print(f\" {'drop_'+str(j):>8}\", end=\"\")\n", + "for j in [1,2,3]: print(f\" {'ne_'+str(j):>8}\", end=\"\")\n", + "print()\n", + "for i, wl in enumerate(sm_wl_um):\n", + " print(f\" {wl:.3f} µm \"\n", + " + \" \".join(f\"{sm_drop[f'drop_{j}'][i]:>8.4f}\" for j in [1,2,3]) + \" \"\n", + " + \" \".join(f\"{sm_noedge[f'drop_{j}_noedge'][i]:>8.4f}\" for j in [1,2,3]))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7220d60b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved ring_filter_delay_comparison.png\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(3, 2, figsize=(14, 10), sharex=True)\n", + "\n", + "for row, (i, col, sp_arr) in enumerate(zip([1,2,3], COLORS,\n", + " [sp_drop_1, sp_drop_2, sp_drop_3])):\n", + " lbl = LABELS[row]\n", + " sm_arr = sm_drop[f'drop_{i}']\n", + " sm_ne = sm_noedge[f'drop_{i}_noedge']\n", + "\n", + " ax = axes[row, 0]\n", + " ax.plot(wl_um, sp_arr, color=col, lw=1.8, label='S-param (exact)', zorder=3)\n", + " ax.plot(sm_wl_um, sm_arr, color=col, lw=1.2, ls='--', marker='o', ms=5,\n", + " label='SM — with edges')\n", + " ax.plot(sm_wl_um, sm_ne, color=col, lw=1.2, ls=':', marker='s', ms=5,\n", + " label='SM — no edges')\n", + " ax.set_ylabel('Transmission', fontsize=9); ax.set_title(lbl, fontsize=9)\n", + " ax.set_xlim([_PLOT_WL_LO, _PLOT_WL_HI])\n", + " ax.legend(fontsize=8); ax.grid(True, alpha=0.3)\n", + " ax.axvline(_TARGETS_UM[row], color=col, ls=':', lw=0.8, alpha=0.6)\n", + "\n", + " ax2 = axes[row, 1]\n", + " ax2.plot(wl_um, 10*np.log10(np.clip(sp_arr, 1e-10, None)), color=col, lw=1.8)\n", + " ax2.plot(sm_wl_um, 10*np.log10(np.clip(sm_arr, 1e-10, None)),\n", + " color=col, lw=1.2, ls='--', marker='o', ms=5)\n", + " ax2.plot(sm_wl_um, 10*np.log10(np.clip(sm_ne, 1e-10, None)),\n", + " color=col, lw=1.2, ls=':', marker='s', ms=5)\n", + " ax2.set_ylabel('Transmission (dB)', fontsize=9)\n", + " ax2.set_xlim([_PLOT_WL_LO, _PLOT_WL_HI])\n", + " ax2.set_ylim(-35, 1); ax2.grid(True, alpha=0.3)\n", + " ax2.axvline(_TARGETS_UM[row], color=col, ls=':', lw=0.8, alpha=0.6)\n", + "\n", + "for ax in axes[-1, :]:\n", + " ax.set_xlabel('Wavelength (µm)', fontsize=11)\n", + "\n", + "fig.suptitle(\n", + " f'Racetrack Add-Drop Filter: S-Param vs Sample-Mode\\n'\n", + " f'dt={DT:.0e} s, {N_STEPS} steps | Dotted lines = target resonances',\n", + " fontsize=11,\n", + ")\n", + "plt.tight_layout()\n", + "plt.savefig('ring_filter_delay_comparison.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "print('Saved ring_filter_delay_comparison.png')" + ] + }, + { + "cell_type": "markdown", + "id": "2a339bb1", + "metadata": {}, + "source": [ + "### 7b. Transient Response: With-Edges vs No-Edges\n", + "\n", + "Each panel shows the **drop-port power** building up from zero as the ring resonator\n", + "charges from the CW input. Solid colour = main circuit (inter-component edges present);\n", + "dashed black = no-edge reference (combined SAX node, delay-free). A lighter trace shows\n", + "a nearby off-resonance wavelength for contrast.\n", + "\n", + "The vertical dotted line marks the transient cut-off after which the spectrum is averaged." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0ab9536f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ── Transient comparison: with-edges | no-edges | delay-compensated ───────────\n", + "# DC_COLORS contrast with COLORS so the DC '--' traces are visually distinct.\n", + "DC_COLORS = [\"orange\", \"orange\", \"orange\"]\n", + "\n", + "time_ps = np.arange(N_STEPS) * DT * 1e12\n", + "\n", + "_sm_wl_um = np.array(sm_wl) * 1e6\n", + "# On-resonance indices: ring1→idx1(1.546), ring2→idx3(1.550), ring3→idx4(1.554)\n", + "_res_idx = [int(np.argmin(np.abs(_sm_wl_um - tgt))) for tgt in _TARGETS_UM]\n", + "# Off-resonance pairs: ring1→idx0(1.543), ring2→idx2(1.548), ring3→idx5(1.557)\n", + "_off_idx = [\n", + " int(np.argmin(np.abs(_sm_wl_um - 1.543))),\n", + " int(np.argmin(np.abs(_sm_wl_um - 1.548))),\n", + " int(np.argmin(np.abs(_sm_wl_um - 1.557))),\n", + "]\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(11, 9), sharex=True)\n", + "\n", + "for row, (i, col, dc_col) in enumerate(zip([1, 2, 3], COLORS, DC_COLORS)):\n", + " ax = axes[row]\n", + " ri = _res_idx[row]\n", + " oi = _off_idx[row]\n", + " wl_on = _sm_wl_um[ri]\n", + " wl_off = _sm_wl_um[oi]\n", + "\n", + " p_edge_on = _safe(np.abs(drop_amp[f\"drop_{i}\"][:, ri, 0])**2)\n", + " p_noedge_on = _safe(np.abs(noedge_amp[f\"drop_{i}_noedge\"][:, ri, 0])**2)\n", + " p_dc_on = _safe(np.abs(dc_amp[f\"drop_{i}_dc\"][:, ri, 0])**2)\n", + " p_edge_off = _safe(np.abs(drop_amp[f\"drop_{i}\"][:, oi, 0])**2)\n", + "\n", + " # Off-resonance: thin, faded (edges only, for reference)\n", + " ax.plot(time_ps, p_edge_off, color=col, lw=0.9, alpha=0.30,\n", + " label=f\"Off-res. {wl_off:.3f} µm (edges)\")\n", + "\n", + " # On-resonance — three circuits\n", + " ax.plot(time_ps, p_noedge_on, color=\"blue\", lw=3.0, ls=\"-\",\n", + " label=\"No edges (S-param ref.)\", alpha=0.8)\n", + " ax.plot(time_ps, p_edge_on, color=col, lw=3.0,\n", + " label=f\"With edges {wl_on:.3f} µm\", alpha=0.8)\n", + " ax.plot(time_ps, p_dc_on, color=dc_col, lw=2.0, ls=\"--\",\n", + " label=f\"Delay comp. dc=3 {wl_on:.3f} µm\", alpha=0.8)\n", + "\n", + " ax.axvline(TRANSIENT * DT * 1e12, color=\"gray\", ls=\":\", lw=1.0, alpha=0.7)\n", + " ax.set_ylabel(\"Drop power (norm.)\", fontsize=10)\n", + " ax.set_ylim(bottom=0)\n", + " ax.set_title(\n", + " f\"Drop {i} | ring {i} resonance at {_TARGETS_UM[row]:.3f} µm\",\n", + " fontsize=10,\n", + " )\n", + " ax.legend(fontsize=8, loc=\"upper right\", ncol=2)\n", + " ax.grid(True, alpha=0.25)\n", + " ax.set_xlim([0, 40])\n", + "\n", + "axes[-1].set_xlabel(\"Time (ps)\", fontsize=12)\n", + "fig.suptitle(\n", + " f\"Transient Buildup — With Edges vs Delay Compensation (dc=3) vs No Edges\\n\"\n", + " f\"dt = {DT:.0e} s, {N_STEPS} steps | Solid = with edges, -- = DC, ··· = no edges\",\n", + " fontsize=11,\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e8290ba3", + "metadata": {}, + "source": [ + "### 7c. Thru-Port Transients — Off-Resonance Pass-Through\n", + "\n", + "Off-resonance wavelengths (1.532, 1.545, 1.571 µm) are not dropped by any ring, so the\n", + "thru port should quickly rise to near-unity steady state. Any difference between the\n", + "three circuits (with-edges, delay-compensated, no-edges) reveals how artificial delays\n", + "distort the settling dynamics even when the signal is nominally \"just passing through.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "402da939", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ── Thru port: off-resonance pass-through transients ─────────────────────────\n", + "OFF_WL_UM = [1.543, 1.548, 1.557] # between / outside the three ring peaks\n", + "OFF_COLORS = [\"mediumpurple\", \"darkcyan\", \"crimson\"]\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(11, 9), sharex=True)\n", + "\n", + "for row, (wl_val, col) in enumerate(zip(OFF_WL_UM, OFF_COLORS)):\n", + " ax = axes[row]\n", + " wi = int(np.argmin(np.abs(_sm_wl_um - wl_val)))\n", + "\n", + " p_edge = _safe(np.abs(thru_edge [:, wi, 0])**2)\n", + " p_noedge = _safe(np.abs(thru_noedge[:, wi, 0])**2)\n", + " p_dc = _safe(np.abs(thru_dc [:, wi, 0])**2)\n", + "\n", + " ax.plot(time_ps, p_noedge, color=\"blue\", lw=3.0, ls=\"-\", alpha=0.8,\n", + " label=\"No edges (S-param ref.)\")\n", + " ax.plot(time_ps, p_edge, color=col, lw=3.0, ls=\"-\", alpha=0.8,\n", + " label=f\"With edges {wl_val:.3f} µm\")\n", + " ax.plot(time_ps, p_dc, color=\"orange\",lw=2.0, ls=\"--\", alpha=0.8,\n", + " label=\"Delay comp. dc=3\")\n", + "\n", + " ax.axvline(TRANSIENT * DT * 1e12, color=\"gray\", ls=\":\", lw=1.0, alpha=0.7)\n", + " ax.set_ylabel(\"Thru power (norm.)\", fontsize=10)\n", + " ax.set_ylim(bottom=0)\n", + " ax.set_title(f\"Thru port | off-resonance λ = {wl_val:.3f} µm\", fontsize=10)\n", + " ax.legend(fontsize=8, loc=\"lower right\", ncol=2)\n", + " ax.grid(True, alpha=0.25)\n", + " ax.set_xlim([0, 40])\n", + "\n", + "axes[-1].set_xlabel(\"Time (ps)\", fontsize=12)\n", + "fig.suptitle(\n", + " f\"Thru-Port Transients — Off-Resonance Wavelengths\\n\"\n", + " f\"dt = {DT:.0e} s, {N_STEPS} steps | Solid = with edges, -- = DC, blue = no edges\",\n", + " fontsize=11,\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "db905553", + "metadata": {}, + "source": [ + "### 7d. Thru-Port Transients — On-Resonance Notches\n", + "\n", + "At each ring's resonant wavelength the thru port power should dip toward zero as the\n", + "ring charges up and diverts power to the drop port. The rate of this dip and its\n", + "steady-state depth differ between the three circuits: artificial delays shift the\n", + "effective resonance, changing how much power each circuit perceives as \"on-resonance.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8f21b51b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ── Thru port: on-resonance notch transients ──────────────────────────────────\n", + "# At each ring's resonance the thru port dips — delay artifacts shift the notch depth.\n", + "fig, axes = plt.subplots(3, 1, figsize=(11, 9), sharex=True)\n", + "\n", + "for row, (tgt, col) in enumerate(zip(_TARGETS_UM, COLORS)):\n", + " ax = axes[row]\n", + " wi = int(np.argmin(np.abs(_sm_wl_um - tgt)))\n", + "\n", + " p_edge = _safe(np.abs(thru_edge [:, wi, 0])**2)\n", + " p_noedge = _safe(np.abs(thru_noedge[:, wi, 0])**2)\n", + " p_dc = _safe(np.abs(thru_dc [:, wi, 0])**2)\n", + "\n", + " ax.plot(time_ps, p_noedge, color=\"blue\", lw=3.0, ls=\"-\", alpha=0.8,\n", + " label=\"No edges (S-param ref.)\")\n", + " ax.plot(time_ps, p_edge, color=col, lw=3.0, ls=\"-\", alpha=0.8,\n", + " label=f\"With edges {tgt:.3f} µm\")\n", + " ax.plot(time_ps, p_dc, color=\"orange\", lw=2.0, ls=\"--\", alpha=0.8,\n", + " label=\"Delay comp. dc=3\")\n", + "\n", + " ax.axvline(TRANSIENT * DT * 1e12, color=\"gray\", ls=\":\", lw=1.0, alpha=0.7)\n", + " ax.set_ylabel(\"Thru power (norm.)\", fontsize=10)\n", + " ax.set_ylim(bottom=0)\n", + " ax.set_title(\n", + " f\"Thru port | ring {row+1} resonance λ = {tgt:.3f} µm \"\n", + " f\"(notch depth → S-param: {float(sp_thru[np.argmin(np.abs(wl_um-tgt))]):.3f})\",\n", + " fontsize=10,\n", + " )\n", + " ax.legend(fontsize=8, loc=\"upper right\", ncol=2)\n", + " ax.grid(True, alpha=0.25)\n", + " ax.set_xlim([0, 40])\n", + "\n", + "axes[-1].set_xlabel(\"Time (ps)\", fontsize=12)\n", + "fig.suptitle(\n", + " f\"Thru-Port Transients — On-Resonance Notches\\n\"\n", + " f\"dt = {DT:.0e} s, {N_STEPS} steps | Solid = with edges, -- = DC, blue = no edges\",\n", + " fontsize=11,\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d2304e1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'hr_1_top': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_1_bot': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_2_top': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_2_bot': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_3_top': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_3_bot': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_arm_a': {'sax_settings': {'length': np.float64(6.9730235697620495),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(4.705758997402334)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_arm_b': {'sax_settings': {'length': np.float64(6.9730235697620495),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(4.705758997402334)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_arm_a': {'sax_settings': {'length': np.float64(5.804189125476053),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.922781470848127)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_arm_b': {'sax_settings': {'length': np.float64(5.804189125476053),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.922781470848127)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_arm_a': {'sax_settings': {'length': np.float64(6.8527472444168716),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.02877911933918)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_arm_b': {'sax_settings': {'length': np.float64(6.8527472444168716),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.02877911933918)},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_term': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_term': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_term': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_drop_wg': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_drop_wg': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_drop_wg': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_mod_a': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_1_mod_b': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_2_mod_a': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_2_mod_b': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_3_mod_a': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_3_mod_b': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_1_vs_a': {'steady_state_voltage': 0.0},\n", + " 'ring_1_vs_b': {'steady_state_voltage': 0.0},\n", + " 'ring_2_vs_a': {'steady_state_voltage': 0.0},\n", + " 'ring_2_vs_b': {'steady_state_voltage': 0.0},\n", + " 'ring_3_vs_a': {'steady_state_voltage': 0.0},\n", + " 'ring_3_vs_b': {'steady_state_voltage': 0.0},\n", + " 'bus_mod_1': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'bus_mod_2': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'bus_vs_1': {'steady_state_voltage': 0.0},\n", + " 'bus_vs_2': {'steady_state_voltage': 0.0},\n", + " 'source': {'wavelength': Array([1.543e-06, 1.546e-06, 1.548e-06, 1.550e-06, 1.554e-06, 1.557e-06], dtype=float64),\n", + " 'linewidth': 0.0},\n", + " 'source_2': {'wavelength': Array([1.543e-06, 1.546e-06, 1.548e-06, 1.550e-06, 1.554e-06, 1.557e-06], dtype=float64),\n", + " 'linewidth': 0.0},\n", + " 'ring_filter_2': {'sax_settings': {},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 100,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_1_top_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_1_bot_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_2_top_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_2_bot_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_3_top_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'hr_3_bot_dc': {'sax_settings': {'pol': 'te',\n", + " 'gap': 100,\n", + " 'radius': 5,\n", + " 'width': 500,\n", + " 'thickness': 220,\n", + " 'coupling_length': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_arm_a_dc': {'sax_settings': {'length': np.float64(6.9730235697620495),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(4.705758997402334)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_arm_a_dc': {'sax_settings': {'length': np.float64(5.804189125476053),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.922781470848127)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_arm_a_dc': {'sax_settings': {'length': np.float64(6.8527472444168716),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.02877911933918)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_arm_b_dc': {'sax_settings': {'length': np.float64(6.9730235697620495),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(4.705758997402334)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_arm_b_dc': {'sax_settings': {'length': np.float64(5.804189125476053),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.922781470848127)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_arm_b_dc': {'sax_settings': {'length': np.float64(6.8527472444168716),\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': np.float64(5.02877911933918)},\n", + " 'delay_compensation': 3,\n", + " 'apply_phase_correction': True,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_mod_a_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_2_mod_a_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_3_mod_a_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_1_mod_b_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_2_mod_b_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_3_mod_b_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'ring_1_vs_a_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_2_vs_a_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_3_vs_a_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_1_vs_b_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_2_vs_b_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_3_vs_b_dc': {'steady_state_voltage': 0.0},\n", + " 'ring_1_term_dc': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_term_dc': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_term_dc': {'sax_settings': {'pol': 'te'},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 5,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_1_drop_wg_dc': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_2_drop_wg_dc': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'ring_3_drop_wg_dc': {'sax_settings': {'length': 5.0,\n", + " 'width': 500,\n", + " 'height': 220,\n", + " 'loss': 0},\n", + " 'delay_compensation': 0,\n", + " 'apply_phase_correction': False,\n", + " 'vector_fitting_parameters': {'model_order': 20,\n", + " 'spectral_range': (1.5e-06, 1.6e-06),\n", + " 'center_wavelength': 1.55e-06}},\n", + " 'bus_mod_1_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'bus_mod_2_dc': {'phase_coefficients': array([0., 0., 0., 0.]),\n", + " 'absorption_coefficients': array([0., 0., 0., 0.]),\n", + " 'length': 1.0},\n", + " 'bus_vs_1_dc': {'steady_state_voltage': 0.0},\n", + " 'bus_vs_2_dc': {'steady_state_voltage': 0.0},\n", + " 'source_3': {'wavelength': Array([1.543e-06, 1.546e-06, 1.548e-06, 1.550e-06, 1.554e-06, 1.557e-06], dtype=float64),\n", + " 'linewidth': 0.0}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sm_settings" + ] + }, + { + "cell_type": "markdown", + "id": "d6eb3af1", + "metadata": {}, + "source": [ + "## 8. Conclusions\n", + "\n", + "### Racetrack advantages over plain rings\n", + "\n", + "| Issue (plain ring) | Racetrack fix |\n", + "|---|---|\n", + "| Resonance locked to discrete radius choices | Arm length tunes λ continuously |\n", + "| Non-passivity for large r (SiEPIC extrapolation) | All data within measured 1.50–1.60 µm band |\n", + "| Single modulator per cavity | Dual modulators — symmetric tuning, larger tuning range |\n", + "\n", + "### Critical-coupling design (r = 5 µm, gap = 100 nm, 500 × 220 nm TE)\n", + "\n", + "| Ring | Target | L_arm | Arm loss | T_drop at CC |\n", + "|---|---|---|---|---|\n", + "| 1 | 1.540 µm | see cell above | ~5.8 dB/cm | ≈ 0.94 |\n", + "| 2 | 1.550 µm | see cell above | ~7.6 dB/cm | ≈ 0.88 |\n", + "| 3 | 1.560 µm | see cell above | ~4.9 dB/cm | ≈ 0.93 |\n", + "\n", + "The waveguide loss term is small (each arm is ~4–7 µm, so total arm loss < 0.01 dB)\n", + "and achieves critical coupling by balancing the slight over-coupling of this coupler\n", + "geometry.\n", + "\n", + "### Sample-mode delay artefact\n", + "\n", + "With `dt = 2×10⁻¹⁴ s`, each inter-component graph edge introduces a one-step delay.\n", + "The dual cavity modulators create **four** artificial delays per ring (two per modulator\n", + "position), shifting resonances and distorting the spectrum. The **no-edge** (combined\n", + "SAX node) variant recovers the S-parameter reference." + ] + }, + { + "cell_type": "markdown", + "id": "a1c85fbc", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "91ea29e3", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "f078a8bb", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "85487c5e", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "60d8f1ed", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/ring_filter_phase_comparison.png b/examples/ring_filter_phase_comparison.png new file mode 100644 index 00000000..313ad000 Binary files /dev/null and b/examples/ring_filter_phase_comparison.png differ diff --git a/examples/s_parameters.ipynb b/examples/s_parameters.ipynb new file mode 100644 index 00000000..d96c1b07 --- /dev/null +++ b/examples/s_parameters.ipynb @@ -0,0 +1,473 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0f3bb6af", + "metadata": {}, + "source": [ + "# S-parameter Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e94b24b1", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.modulators import OpticalModulator\n", + "from simphony.libraries.ideal.sources import VoltageSource\n", + "from simphony.libraries.ideal.electrical_circuits import VoltageFollower\n", + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "from simphony.libraries.siepic import y_branch, waveguide\n", + "\n", + "import sax\n", + "import numpy as np\n", + "\n", + "instances = {\n", + " \"splitter\": \"y_branch\",\n", + " \"combiner\": \"y_branch\",\n", + " \"wg1\": \"waveguide\",\n", + " \"wg2\": \"waveguide\",\n", + " \"mod1\": \"modulator\",\n", + " \"mod2\": \"modulator\",\n", + " \"vs1\": \"voltage_source\",\n", + " \"vf1\": \"voltage_follower\",\n", + " \"vs2\": \"voltage_source\",\n", + "}\n", + "connections = {\n", + " \"splitter,port_2\": \"wg1,o0\",\n", + " \"wg1,o1\": \"mod1,o0\",\n", + " \"mod1,o1\": \"combiner,port_2\",\n", + " \"splitter,port_3\": \"wg2,o0\",\n", + " \"wg2,o1\": \"mod2,o0\",\n", + " \"mod2,o1\": \"combiner,port_3\",\n", + " \"vs1,e0\": \"vf1,e0\",\n", + " \"vf1,e1\": \"mod1,e0\",\n", + " \"vs2,e0\": \"mod2,e0\"\n", + "}\n", + "ports = {\n", + " \"in\": \"splitter,port_1\",\n", + " \"out\": \"combiner,port_1\",\n", + "}\n", + "\n", + "netlist = {\n", + " \"instances\": instances,\n", + " \"connections\": connections,\n", + " \"ports\": ports,\n", + "}\n", + "\n", + "models = {\n", + " \"y_branch\": y_branch,\n", + " \"waveguide\": waveguide,\n", + " \"modulator\": OpticalModulator,\n", + " \"voltage_source\": VoltageSource,\n", + " \"voltage_follower\": VoltageFollower,\n", + "}\n", + "\n", + "ckt_settings = {\n", + " \"splitter\": {},\n", + " \"combiner\": {},\n", + " \"wg1\": {\"length\":10.0},\n", + " \"wg2\": {\"length\":30.0},\n", + " \"mod1\": {\n", + " \"phase_coefficients\": np.array([0.0, 0.0, np.pi/2, 0]),\n", + " # \"phase_coefficients\": np.array([0.0, 0.0, 0.0, 0])\n", + " },\n", + " \"mod2\": {},\n", + " \"vs1\": {\n", + " \"steady_state_voltage\": 3.0,\n", + " },\n", + " \"vs2\": {\n", + " \"steady_state_voltage\": 0.0,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "52a7bbba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dst: wg1,o0\n", + "dst_directionality: bidirectional\n", + "dst: mod1,o0\n", + "dst_directionality: bidirectional\n", + "dst: combiner,port_2\n", + "dst_directionality: bidirectional\n", + "dst: wg2,o0\n", + "dst_directionality: bidirectional\n", + "dst: mod2,o0\n", + "dst_directionality: bidirectional\n", + "dst: combiner,port_3\n", + "dst_directionality: bidirectional\n", + "dst: vf1,e0\n", + "dst_directionality: input\n", + "dst: mod1,e0\n", + "dst_directionality: input\n", + "dst: mod2,e0\n", + "dst_directionality: input\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "71e9853217784eeda1f7f1e82cf24d45", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sigma(nx.MultiDiGraph with 11 nodes and 19 edges)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/gravis/_internal/plotting/template_system.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources as _pkg_resources\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from simphony.circuit.circuit import Circuit\n", + "from simphony.simulation.s_parameter import SParameterSimulationParameters\n", + "# from simphony.simulation.block_mode import BlockModeSimulationParameters\n", + "\n", + "simulation_parameters = SParameterSimulationParameters()\n", + "# simulation_parameters = BlockModeSimulationParameters()\n", + "mzi_circuit = Circuit(netlist, models)\n", + "mzi_circuit.display()\n", + "mzi_circuit.instantiate(ckt_settings, simulation_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8cd4dc5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wyrgly/projects/camacholab/simphony/.venv/lib/python3.12/site-packages/sax/utils.py:80: UserWarning: Could not validate netlist for 'top_level'. This netlist will be ignored.\n", + " return func(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 1.57079633 0. ]\n", + "4.71238898038469\n", + "[0. 0. 3.14159265 0. ]\n", + "0.0\n", + "[0. 0. 1.57079633 0. ]\n", + "4.71238898038469\n", + "[0. 0. 3.14159265 0. ]\n", + "0.0\n" + ] + } + ], + "source": [ + "from simphony.simulation.s_parameter import SParameterSimulation\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# wl = np.linspace(1.5e-6, 1.6e-6, 1000)\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "sparam_simulation = SParameterSimulation(mzi_circuit, ckt_settings, simulation_parameters)\n", + "sim_result = sparam_simulation.run(wl=wl)\n", + "\n", + "s_params_1d = sim_result.s_parameters[('in', 'out')]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e654cc67", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(wl, np.abs(s_params_1d))" + ] + }, + { + "cell_type": "markdown", + "id": "9b5a923b", + "metadata": {}, + "source": [ + "# Signal Flow Simulations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19aa92e4", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "from simphony.libraries.siepic import y_branch\n", + "import sax\n", + "import jax.numpy as jnp\n", + "\n", + "from simphony.libraries.ideal.digital_filters import discrete_state_space\n", + "from simphony.simulation.simulation import SimulationMode\n", + "\n", + "\n", + "# state_space_model = discrete_state_space(2, 2)\n", + "def coupler():\n", + " return {\n", + " (\"in0@TE\", \"out0@TE\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out1@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out0@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out1@TE\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out0@TM\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out1@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out0@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out1@TM\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out0@TM\"): 0.01**0.5,\n", + " (\"in0@TE\", \"out1@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out0@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out1@TM\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out0@TE\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out1@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out0@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out1@TE\"): 0.01**0.5,\n", + " }\n", + "\n", + "N = 4\n", + "\n", + "A = jnp.eye(N, k=-1)\n", + "B = jnp.zeros((N, 1)).at[0, 0].set(1.0)\n", + "\n", + "C = jnp.array([[0.5, 0.3, 0.1, 0.05]])\n", + "D = jnp.array([[0.2]])\n", + "\n", + "# state_space_model(A, B, C, D)\n", + "# pcell = optical_s_parameter(coupler)\n", + "port_directionality = {\n", + " 'port_1': 'input', \n", + " 'port_2': 'output', \n", + " 'port_3': 'input'\n", + "}\n", + "pcell = optical_s_parameter_placeholder(y_branch, port_directionality=port_directionality)\n", + "pcell(simulation_mode=SimulationMode.BLOCK_MODE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab9930f", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.photonic_circuits import mzi_lattice_filter\n", + "\n", + "mzi_lattice_filter1 = mzi_lattice_filter()\n", + "mzi_lattice_filter1(None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "718deb0c", + "metadata": {}, + "outputs": [], + "source": [ + "def outer(a, b={}):\n", + " class Inner:\n", + " def __init__(self, c=3):\n", + " print(a, b, c)\n", + " return Inner\n", + "\n", + "X = outer(1)\n", + "obj = X() # prints: 1 2 3\n" + ] + }, + { + "cell_type": "markdown", + "id": "ebbd09b5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ded1a827", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder\n", + "import sax\n", + "import jax.numpy as jnp\n", + "\n", + "def waveguide(wl=1.55, wl0=1.55, neff=2.34, ng=3.4, length=10.0, loss=0.0):\n", + " \"\"\"A simple straight waveguide model\n", + "\n", + " Args:\n", + " wl: wavelength\n", + " neff: waveguide effective index\n", + " ng: waveguide group index (used for linear neff dispersion)\n", + " wl0: center wavelength at which neff is defined\n", + " length: [m] wavelength length\n", + " loss: [dB/m] waveguide loss\n", + " \"\"\"\n", + " dwl = wl - wl0\n", + " dneff_dwl = (ng - neff) / wl0\n", + " neff = neff - dwl * dneff_dwl\n", + " phase = 2 * jnp.pi * neff * length / wl\n", + " transmission = 10 ** (-loss * length / 20) * jnp.exp(1j * phase)\n", + " sdict = sax.reciprocal(\n", + " {\n", + " (\"in0@TE\", \"out0@TE\"): 0.95 * transmission, # 5% lost to cross-polarization\n", + " (\"in0@TE\", \"out0@TM\"): 0.05 * transmission, # 5% cross-polarization\n", + " (\"in0@TM\", \"out0@TM\"): 0.85 * transmission, # 10% worse tm->tm than te->te\n", + " (\"in0@TM\", \"out0@TE\"): 0.05 * transmission, # 5% cross-polarization\n", + " }\n", + " )\n", + " return sdict\n", + "\n", + "def coupler():\n", + " return {\n", + " (\"in0@TE\", \"out0@TE\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out1@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out0@TE\"): 1j * 0.45**0.5,\n", + " (\"in1@TE\", \"out1@TE\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out0@TM\"): 0.45**0.5,\n", + " (\"in0@TM\", \"out1@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out0@TM\"): 1j * 0.45**0.5,\n", + " # (\"in1@TM\", \"out1@TM\"): 0.45**0.5,\n", + " (\"in0@TE\", \"out0@TM\"): 0.01**0.5,\n", + " (\"in0@TE\", \"out1@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out0@TM\"): 1j * 0.01**0.5,\n", + " (\"in1@TE\", \"out1@TM\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out0@TE\"): 0.01**0.5,\n", + " (\"in0@TM\", \"out1@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out0@TE\"): 1j * 0.01**0.5,\n", + " # (\"in1@TM\", \"out1@TE\"): 0.01**0.5,\n", + " }\n", + "\n", + "mzi, _ = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"lft\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " \"top\": {\"component\": \"straight\", \"settings\": {\"length\": 25.0}},\n", + " \"btm\": {\"component\": \"straight\", \"settings\": {\"length\": 15.0}},\n", + " \"rgt\": \"coupler\", # single mode models will be automatically converted to multimode models without cross polarization.\n", + " },\n", + " \"connections\": {\n", + " \"lft,out0\": \"btm,in0\",\n", + " \"btm,out0\": \"rgt,in0\",\n", + " \"lft,out1\": \"top,in0\",\n", + " \"top,out0\": \"rgt,in1\",\n", + " },\n", + " \"ports\": {\n", + " \"in0\": \"lft,in0\",\n", + " \"in1\": \"lft,in1\",\n", + " \"out0\": \"rgt,out0\",\n", + " \"out1\": \"rgt,out1\",\n", + " },\n", + " },\n", + " models={\n", + " \"coupler\": coupler,\n", + " \"straight\": waveguide,\n", + " },\n", + ")\n", + "\n", + "mzi()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26f41648", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "port_directionality = {\n", + " 'port_1': 'input', \n", + " # 'port_2': 'input', \n", + " 'port_3': 'output'\n", + "}\n", + "\n", + "optical_s_parameter_placeholder(y_branch, port_directionality=port_directionality)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8dcf996", + "metadata": {}, + "outputs": [], + "source": [ + "sax.multimode(mzi)() == sax.multimode(mzi, modes=(\"TESTMODE\", \"TESTMODE2\"))() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/single_ring_sm_test.png b/examples/single_ring_sm_test.png new file mode 100644 index 00000000..71c505c4 Binary files /dev/null and b/examples/single_ring_sm_test.png differ diff --git a/examples/single_ring_sm_test.py b/examples/single_ring_sm_test.py new file mode 100644 index 00000000..0c85501f --- /dev/null +++ b/examples/single_ring_sm_test.py @@ -0,0 +1,238 @@ +"""single_ring_sm_test.py. + +Minimal test: sample-mode simulation of a single all-pass SiEPIC ring resonator +versus the exact S-parameter frequency response. + +Goal: verify that the frequency-shifting code (exp(j*delta_omega)*A, B) drives the +sample-mode steady state to the correct S-parameter transmission at each wavelength. + +Topology +-------- + OpticalCombSource ──► half_ring ──► [output] + │ ▲ + ▼ │ + waveguide (ring cavity) + +Run +--- + cd + python examples/single_ring_sm_test.py +""" +import numpy as np +import jax.numpy as jnp +import matplotlib.pyplot as plt + +from simphony.circuit.circuit import Circuit +from simphony.simulation.s_parameter import ( + SParameterSimulation, + SParameterSimulationParameters, +) +from simphony.simulation.sample_mode import ( + SampleModeSimulation, + SampleModeSimulationParameters, +) +from simphony.libraries.ideal.sources import OpticalCombSource, VoltageSource +from simphony.libraries.ideal.modulators import OpticalModulator +from simphony.libraries.siepic import half_ring, waveguide + +# ── Ring parameters ─────────────────────────────────────────────────────────── +# half_ring: gap (nm), radius (µm), width (nm), thickness (nm) +# waveguide: length (µm), width (nm), height (nm), loss (dB/cm) +# Ring round-trip: π*radius + length ≈ π*5 + 5 ≈ 20.7 µm +# T_rt ≈ ng * L_rt / c ≈ 3.7 * 20.7e-6 / 3e8 ≈ 255 fs → dt must be well below this +HR = dict(pol="te", gap=100, radius=5, width=500, thickness=220, coupling_length=0) +WG = dict(pol="te", length=5.0, width=500, height=220, loss=100.0) + +DT = 1e-14 # 10 fs (dt ≪ T_rt ✓, and waveguide VF fit is near machine precision) +N_STEPS = 2000 +TRANSIENT = 300 # steps to discard when averaging steady state + +# ── Wavelength grids ────────────────────────────────────────────────────────── +wl_sp_um = np.linspace(1.50, 1.60, 1001) # dense S-param grid (µm) +sm_wl_m = jnp.linspace(1.50, 1.60, 81) * 1e-6 # sample-mode probe wavelengths (m) +sm_wl_um = np.array(sm_wl_m) * 1e6 # same, in µm for plotting + +# ── Vector-fitting parameters (passed per-component in pre-wrapped format) ──── +VF = dict( + model_order=None, # auto-select via golden-section search + min_model_order=2, + max_model_order=30, + num_frequency_samples=800, + center_wavelength=1.55e-6, + spectral_range=(1.50e-6, 1.60e-6), +) + +# ── S-parameter simulation (ground truth) ───────────────────────────────────── +ring_netlist = { + "instances": {"hr": "half_ring", "wg": "waveguide"}, + "connections": {"hr,port_2": "wg,o0", "wg,o1": "hr,port_4"}, + "ports": {"in": "hr,port_1", "out": "hr,port_3"}, +} +sp_circuit = Circuit(ring_netlist, {"half_ring": half_ring, "waveguide": waveguide}) +sp_settings = {"hr": HR, "wg": WG} +sp_sim = SParameterSimulation(sp_circuit, sp_settings, SParameterSimulationParameters()) +sp_result = sp_sim.run(wl=wl_sp_um) +sp_T = np.abs(np.array(sp_result.s_parameters[("in", "out")])) ** 2 + +# S-param at exactly the sample-mode probe wavelengths (for direct comparison) +sp_at_sm = np.abs(np.array(sp_sim.run(wl=sm_wl_um).s_parameters[("in", "out")])) ** 2 +print(f"S-parameter done. T range: [{sp_T.min():.4f}, {sp_T.max():.4f}]") + +# ── Sample-mode simulation ──────────────────────────────────────────────────── +# Settings in pre-wrapped format so vector_fitting_parameters is picked up +# correctly alongside sax_settings (avoids the auto-wrap that would bury VF params). +MOD = dict( + phase_coefficients=np.array([0.0, 0.0, 0.0, 0.0]), + absorption_coefficients=np.array([0.0, 0.0, 0.0, 0.0]), + length=1.0, +) +VS = dict(steady_state_voltage=0.0) + +sm_netlist = { + "instances": { + "hr": "half_ring", + "mod": "modulator", + "wg": "waveguide", + "vs": "voltage_source", + "source": "comb_source", + }, + "connections": { + "hr,port_2": "mod,o0", + "mod,o1": "wg,o0", + "wg,o1": "hr,port_4", + "vs,e0": "mod,e0", + "source,o0": "hr,port_1", + }, + "ports": {"out": "hr,port_3"}, +} +sm_models = { + "half_ring": half_ring, + "waveguide": waveguide, + "modulator": OpticalModulator, + "voltage_source": VoltageSource, + "comb_source": OpticalCombSource, +} +sm_circuit = Circuit(sm_netlist, sm_models) + +sm_settings = { + "hr": {"sax_settings": HR, "vector_fitting_parameters": VF}, + "wg": {"sax_settings": WG, "vector_fitting_parameters": VF}, + "mod": MOD, + "vs": VS, + "source": {"wavelength": sm_wl_m, "linewidth": 0.0}, +} +sm_params = SampleModeSimulationParameters( + optical_baseband_wavelengths=sm_wl_m, + dt=DT, + num_time_steps=N_STEPS, +) + +sm_sim = SampleModeSimulation( + sm_circuit, + sm_settings, + tracked_ports={"out": "hr,port_3"}, + simulation_parameters=sm_params, +) + +print( + f"Running sample-mode: {N_STEPS} steps × {len(sm_wl_m)} wavelengths, dt={DT:.0e} s …" +) +sm_result = sm_sim.run(use_jit=True) +print("Done.") + +# ── Inspect tracked port signals ───────────────────────────────────────────── +print("\nTracked output signals:") +for port_name, sig in sm_result.output_signals.items(): + if hasattr(sig, "amplitude"): + amp = np.array(sig.amplitude) + print( + f" {port_name!r:20s} shape={str(amp.shape):15s} max|A|={np.abs(amp).max():.4f}" + ) + +print("\nTracked input signals:") +for port_name, sig in sm_result.input_signals.items(): + if hasattr(sig, "amplitude"): + amp = np.array(sig.amplitude) + print( + f" {port_name!r:20s} shape={str(amp.shape):15s} max|A|={np.abs(amp).max():.4f}" + ) + +# ── Extract the through-port output ────────────────────────────────────────── +out_sig = sm_result.output_signals["out"] +out_amp = np.array(out_sig.amplitude) # (N_STEPS, L, M) +print(f"\nUsing output signal: 'out' shape={out_amp.shape}") + +# Steady-state: time-average of |amplitude|² after the transient +sm_T_steady = np.mean(np.abs(out_amp[TRANSIENT:, :, 0]) ** 2, axis=0) # (L,) + +# ── Comparison table ────────────────────────────────────────────────────────── +n_wl = len(sm_wl_m) +print(f"\n{'Wavelength (µm)':<18} {'S-param T':>10} {'SM steady T':>12} {'ratio':>8}") +print("-" * 52) +for i in range(0, n_wl, max(1, n_wl // 10)): + wl = sm_wl_um[i] + spt = sp_at_sm[i] + smt = sm_T_steady[i] + ratio = smt / spt if spt > 1e-6 else float("nan") + print(f" {wl:.4f} {spt:>10.4f} {smt:>10.4f} {ratio:>8.3f}") + +# ── Plots ───────────────────────────────────────────────────────────────────── +t_ps = np.arange(N_STEPS) * DT * 1e12 +res_i = int(np.argmax(sp_at_sm)) # wavelength index closest to a resonance + +fig, axes = plt.subplots(3, 1, figsize=(10, 11)) + +# 1. Filter spectrum: S-param vs sample-mode steady state +ax = axes[0] +ax.plot(wl_sp_um, sp_T, "b-", lw=1.5, label="S-parameter (dense)") +ax.scatter( + sm_wl_um, + sm_T_steady, + s=60, + color="tomato", + zorder=5, + label=f"Sample-mode steady state (last {N_STEPS-TRANSIENT} steps)", +) +ax.plot(sm_wl_um, sm_T_steady, "r--", lw=1.0, alpha=0.6) +ax.set_ylabel("Through-port transmission") +ax.set_xlabel("Wavelength (µm)") +ax.set_title("All-Pass Ring: S-param vs Sample-Mode Steady State") +ax.legend() +ax.grid(True, alpha=0.3) + +# 2. Transient at the resonant wavelength +ax = axes[1] +power_vs_t = np.abs(out_amp[:, res_i, 0]) ** 2 +ax.plot(t_ps, power_vs_t, lw=1.2, color="steelblue", label="Sample-mode |A(t)|²") +ax.axhline( + sp_at_sm[res_i], + color="tomato", + lw=1.5, + ls="--", + label=f"S-param T = {sp_at_sm[res_i]:.4f}", +) +ax.axvline( + TRANSIENT * DT * 1e12, color="grey", ls=":", lw=1.0, label="Transient cutoff" +) +ax.set_xlabel("Time (ps)") +ax.set_ylabel("Power") +ax.set_title(f"Transient at resonance λ ≈ {sm_wl_um[res_i]:.4f} µm") +ax.legend() +ax.grid(True, alpha=0.3) + +# 3. Scatter: SM steady T vs S-param T for all probe wavelengths +ax = axes[2] +ax.scatter(sp_at_sm, sm_T_steady, s=40, color="steelblue") +lim = max(sp_at_sm.max(), sm_T_steady.max()) * 1.05 +ax.plot([0, lim], [0, lim], "k--", lw=1.0, label="Perfect agreement") +ax.set_xlabel("S-parameter T") +ax.set_ylabel("Sample-mode steady-state T") +ax.set_title("Sample-mode vs S-param agreement across all wavelengths") +ax.legend() +ax.grid(True, alpha=0.3) + +plt.tight_layout() +out_path = "examples/single_ring_sm_test.png" +plt.savefig(out_path, dpi=150, bbox_inches="tight") +plt.show() +print(f"\nSaved {out_path}") diff --git a/examples/single_ring_sm_test_working_passive.png b/examples/single_ring_sm_test_working_passive.png new file mode 100644 index 00000000..9f337afa Binary files /dev/null and b/examples/single_ring_sm_test_working_passive.png differ diff --git a/examples/single_ring_sm_test_working_passive.py b/examples/single_ring_sm_test_working_passive.py new file mode 100644 index 00000000..60dfc7a1 --- /dev/null +++ b/examples/single_ring_sm_test_working_passive.py @@ -0,0 +1,240 @@ +"""single_ring_sm_test.py. + +Minimal test: sample-mode simulation of a single all-pass SiEPIC ring resonator +versus the exact S-parameter frequency response. + +Goal: verify that the frequency-shifting code (exp(j*delta_omega)*A, B) drives the +sample-mode steady state to the correct S-parameter transmission at each wavelength. + +Topology +-------- + OpticalCombSource ──► half_ring ──► [output] + │ ▲ + ▼ │ + waveguide (ring cavity) + +Run +--- + cd + python examples/single_ring_sm_test.py +""" +import numpy as np +import jax.numpy as jnp +import matplotlib.pyplot as plt + +from simphony.circuit.circuit import Circuit +from simphony.simulation.s_parameter import ( + SParameterSimulation, + SParameterSimulationParameters, +) +from simphony.simulation.sample_mode import ( + SampleModeSimulation, + SampleModeSimulationParameters, +) +from simphony.libraries.ideal.sources import OpticalCombSource +from simphony.libraries.siepic import half_ring, waveguide + +# ── Ring parameters ─────────────────────────────────────────────────────────── +# half_ring: gap (nm), radius (µm), width (nm), thickness (nm) +# waveguide: length (µm), width (nm), height (nm), loss (dB/cm) +# Ring round-trip: π*radius + length ≈ π*5 + 5 ≈ 20.7 µm +# T_rt ≈ ng * L_rt / c ≈ 3.7 * 20.7e-6 / 3e8 ≈ 255 fs → dt must be well below this +HR = dict(pol="te", gap=100, radius=5, width=500, thickness=220, coupling_length=0) +WG = dict(pol="te", length=5.0, width=500, height=220, loss=100.0) + +DT = 1e-14 # 10 fs (dt ≪ T_rt ✓, and waveguide VF fit is near machine precision) +N_STEPS = 2000 +TRANSIENT = 300 # steps to discard when averaging steady state + +# ── Wavelength grids ────────────────────────────────────────────────────────── +wl_sp_um = np.linspace(1.50, 1.60, 1001) # dense S-param grid (µm) +sm_wl_m = jnp.linspace(1.50, 1.60, 81) * 1e-6 # sample-mode probe wavelengths (m) +sm_wl_um = np.array(sm_wl_m) * 1e6 # same, in µm for plotting + +# ── Vector-fitting parameters (passed per-component in pre-wrapped format) ──── +VF = dict( + model_order=None, # auto-select via golden-section search + min_model_order=2, + max_model_order=30, + num_frequency_samples=800, + center_wavelength=1.55e-6, + spectral_range=(1.50e-6, 1.60e-6), +) + +# ── S-parameter simulation (ground truth) ───────────────────────────────────── +ring_netlist = { + "instances": {"hr": "half_ring", "wg": "waveguide"}, + "connections": {"hr,port_2": "wg,o0", "wg,o1": "hr,port_4"}, + "ports": {"in": "hr,port_1", "out": "hr,port_3"}, +} +sp_circuit = Circuit(ring_netlist, {"half_ring": half_ring, "waveguide": waveguide}) +sp_settings = {"hr": HR, "wg": WG} +sp_sim = SParameterSimulation(sp_circuit, sp_settings, SParameterSimulationParameters()) +sp_result = sp_sim.run(wl=wl_sp_um) +sp_T = np.abs(np.array(sp_result.s_parameters[("in", "out")])) ** 2 + +# S-param at exactly the sample-mode probe wavelengths (for direct comparison) +sp_at_sm = np.abs(np.array(sp_sim.run(wl=sm_wl_um).s_parameters[("in", "out")])) ** 2 +print(f"S-parameter done. T range: [{sp_T.min():.4f}, {sp_T.max():.4f}]") + +# ── Sample-mode simulation ──────────────────────────────────────────────────── +# Settings in pre-wrapped format so vector_fitting_parameters is picked up +# correctly alongside sax_settings (avoids the auto-wrap that would bury VF params). +sm_netlist = { + "instances": { + "hr": "half_ring", + "wg": "waveguide", + "source": "comb_source", + }, + "connections": { + "hr,port_2": "wg,o0", + "wg,o1": "hr,port_4", + "source,o0": "hr,port_1", + }, + "ports": {"out": "hr,port_3"}, +} +sm_models = { + "half_ring": half_ring, + "waveguide": waveguide, + "comb_source": OpticalCombSource, +} +sm_circuit = Circuit(sm_netlist, sm_models) + +sm_settings = { + "hr": {"sax_settings": HR, "vector_fitting_parameters": VF}, + "wg": {"sax_settings": WG, "vector_fitting_parameters": VF}, + "source": {"wavelength": sm_wl_m, "linewidth": 0.0}, +} +sm_params = SampleModeSimulationParameters( + optical_baseband_wavelengths=sm_wl_m, + dt=DT, + num_time_steps=N_STEPS, +) + +sm_sim = SampleModeSimulation( + sm_circuit, + sm_settings, + tracked_ports={"out": "hr,port_3"}, + simulation_parameters=sm_params, +) + +print( + f"Running sample-mode: {N_STEPS} steps × {len(sm_wl_m)} wavelengths, dt={DT:.0e} s …" +) +sm_result = sm_sim.run(use_jit=True) +print("Done.") + +# ── Inspect all output signals ──────────────────────────────────────────────── +print("\nAll output signals in result:") +for inst, ports in sm_result.items(): + for port, sig in ports.items(): + if hasattr(sig, "amplitude"): + amp = np.array(sig.amplitude) + print( + f" {inst!r:45s} port={port!r:10s} " + f"shape={str(amp.shape):15s} max|A|={np.abs(amp).max():.4f}" + ) + +# ── Find the ring through-port output (port_3) ─────────────────────────────── +out_amp = None +out_label = None +n_wl = len(sm_wl_m) + +for inst, ports in sm_result.items(): + for port, sig in ports.items(): + if hasattr(sig, "amplitude"): + amp = np.array(sig.amplitude) + if amp.ndim == 3 and amp.shape[1] == n_wl and "port_3" in port: + out_amp = amp + out_label = f"{inst} port={port}" + break + if out_amp is not None: + break + +if out_amp is None: + # Fallback: highest-power optical signal with correct wavelength count + best = 0.0 + for inst, ports in sm_result.items(): + for port, sig in ports.items(): + if hasattr(sig, "amplitude"): + amp = np.array(sig.amplitude) + if amp.ndim == 3 and amp.shape[1] == n_wl: + p = float(np.abs(amp[TRANSIENT:]).max()) + if p > best: + best, out_amp, out_label = p, amp, f"{inst} port={port}" + +print(f"\nUsing output signal: {out_label}") + +# Steady-state: time-average of |amplitude|² after the transient +sm_T_steady = np.mean(np.abs(out_amp[TRANSIENT:, :, 0]) ** 2, axis=0) # (L,) + +# ── Comparison table ────────────────────────────────────────────────────────── +print(f"\n{'Wavelength (µm)':<18} {'S-param T':>10} {'SM steady T':>12} {'ratio':>8}") +print("-" * 52) +for i in range(0, n_wl, max(1, n_wl // 10)): + wl = sm_wl_um[i] + spt = sp_at_sm[i] + smt = sm_T_steady[i] + ratio = smt / spt if spt > 1e-6 else float("nan") + print(f" {wl:.4f} {spt:>10.4f} {smt:>10.4f} {ratio:>8.3f}") + +# ── Plots ───────────────────────────────────────────────────────────────────── +t_ps = np.arange(N_STEPS) * DT * 1e12 +res_i = int(np.argmax(sp_at_sm)) # wavelength index closest to a resonance + +fig, axes = plt.subplots(3, 1, figsize=(10, 11)) + +# 1. Filter spectrum: S-param vs sample-mode steady state +ax = axes[0] +ax.plot(wl_sp_um, sp_T, "b-", lw=1.5, label="S-parameter (dense)") +ax.scatter( + sm_wl_um, + sm_T_steady, + s=60, + color="tomato", + zorder=5, + label=f"Sample-mode steady state (last {N_STEPS-TRANSIENT} steps)", +) +ax.plot(sm_wl_um, sm_T_steady, "r--", lw=1.0, alpha=0.6) +ax.set_ylabel("Through-port transmission") +ax.set_xlabel("Wavelength (µm)") +ax.set_title("All-Pass Ring: S-param vs Sample-Mode Steady State") +ax.legend() +ax.grid(True, alpha=0.3) + +# 2. Transient at the resonant wavelength +ax = axes[1] +power_vs_t = np.abs(out_amp[:, res_i, 0]) ** 2 +ax.plot(t_ps, power_vs_t, lw=1.2, color="steelblue", label="Sample-mode |A(t)|²") +ax.axhline( + sp_at_sm[res_i], + color="tomato", + lw=1.5, + ls="--", + label=f"S-param T = {sp_at_sm[res_i]:.4f}", +) +ax.axvline( + TRANSIENT * DT * 1e12, color="grey", ls=":", lw=1.0, label="Transient cutoff" +) +ax.set_xlabel("Time (ps)") +ax.set_ylabel("Power") +ax.set_title(f"Transient at resonance λ ≈ {sm_wl_um[res_i]:.4f} µm") +ax.legend() +ax.grid(True, alpha=0.3) + +# 3. Scatter: SM steady T vs S-param T for all probe wavelengths +ax = axes[2] +ax.scatter(sp_at_sm, sm_T_steady, s=40, color="steelblue") +lim = max(sp_at_sm.max(), sm_T_steady.max()) * 1.05 +ax.plot([0, lim], [0, lim], "k--", lw=1.0, label="Perfect agreement") +ax.set_xlabel("S-parameter T") +ax.set_ylabel("Sample-mode steady-state T") +ax.set_title("Sample-mode vs S-param agreement across all wavelengths") +ax.legend() +ax.grid(True, alpha=0.3) + +plt.tight_layout() +out_path = "examples/single_ring_sm_test_working_passive.png" +plt.savefig(out_path, dpi=150, bbox_inches="tight") +plt.show() +print(f"\nSaved {out_path}") diff --git a/examples/test.py b/examples/test.py new file mode 100644 index 00000000..f6ab6366 --- /dev/null +++ b/examples/test.py @@ -0,0 +1,229 @@ +from simphony.circuit.circuit import Circuit +from simphony.libraries.ideal.photonic_circuits import mzi_lattice_filter +from simphony.libraries.siepic import grating_coupler + +# from simphony.simulation.simulation import SimulationMode +from simphony.simulation.sample_mode import SampleModeSimulationParameters + +# from simphony.simulation.s_parameter import SParameterSimulationParameters +from simphony.libraries.ideal.sources import CWLaser +from simphony.utils import create_multimode_sax_model +from functools import partial + +grating_coupler_models = { + "TE": partial(grating_coupler, pol="te"), + "TM": partial(grating_coupler, pol="tm"), +} + +multimode_grating_coupler = create_multimode_sax_model(grating_coupler_models) + + +netlist = { + "instances": { + "lf1": "lattice_filter", + "lf2": "lattice_filter", + "gc1": "grating_coupler", + "gc2": "grating_coupler", + "laser": "cw_laser", + }, + "connections": { + "laser,o0": "gc1,o0", + "gc1,o1": "lf1,o0", + "lf1,o1": "lf2,o0", + "lf2,o1": "gc2,o1", + }, + "ports": { + # "in": "gc1,o0", + "gc_out": "gc2,o0", + }, +} + +models = { + "lattice_filter": mzi_lattice_filter(2), + "grating_coupler": multimode_grating_coupler, + "cw_laser": CWLaser, +} + +s_parameter_settings = { + "lf1": {}, + "lf2": {}, + "gc1": { + # "pol": "te", + "thickness": 230.0, + "dwidth": -20, + }, + "gc2": { + # "pol": "tm", + "thickness": 210.0, + "dwidth": 20, + }, +} + + +sample_mode_settings = { + "lf1": {}, + "lf2": {}, + "gc1": { + "sax_settings": { + # "pol": "te", + "thickness": 230.0, + "dwidth": -20, + }, + "port_directionality": { + "o0": "input", + "o1": "output", + }, + }, + "gc2": { + # "pol": "tm", + "thickness": 210.0, + "dwidth": 20, + }, +} + +block_mode_settings = { + "lf1": {}, + "lf2": {}, + "gc1": { + "sax_settings": { + "pol": "te", + "thickness": 230.0, + "dwidth": -20, + }, + }, + "gc2": { + "sax_settings": { + "pol": "tm", + "thickness": 210.0, + "dwidth": 20, + } + }, +} + +tracked_ports = { + "gc1": "gc1,o1", + "lf1_in": "lf1,o0", + "lf1_out": "lf1,o1", + "lf2": "lf2,o1", +} + +circuit = Circuit(netlist, models) +instantiated_circuit = circuit.instantiate( + sample_mode_settings, + SampleModeSimulationParameters(), + tracked_ports=tracked_ports, + directed=False, +) + + +instantiated_circuit.display() + +from simphony.simulation.sample_mode import ( + SampleModeSimulation, + SampleModeSimulationParameters, +) +import jax.numpy as jnp + +tracked_ports = { + "gc1": "gc1,o1", + "lf1": "lf1,o1", + "lf2": "lf2,o1", +} + +circuit = Circuit(netlist, models) +sample_mode_simulation_parameters = SampleModeSimulationParameters( + mode_identifiers=["TE", "TM"], + optical_baseband_wavelengths=jnp.array([1.5e-6, 1.55e-6, 1.6e-6]), +) +sample_mode_simulation = SampleModeSimulation( + circuit, sample_mode_settings, tracked_ports, sample_mode_simulation_parameters +) +sample_mode_simulation.circuit.display() +sample_mode_simulation_result = sample_mode_simulation.run() +# from simphony.libraries.ideal.modulators import OpticalModulator +# from simphony.libraries.ideal.sources import VoltageSource +# from simphony.libraries.ideal.electrical_circuits import VoltageFollower +# from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder +# from simphony.libraries.siepic import y_branch, waveguide + +# import sax +# import numpy as np + +# instances = { +# "splitter": "y_branch", +# "combiner": "y_branch", +# "wg1": "waveguide", +# "wg2": "waveguide", +# "mod1": "modulator", +# "mod2": "modulator", +# "vs1": "voltage_source", +# "vf1": "voltage_follower", +# "vs2": "voltage_source", +# } +# connections = { +# "splitter,port_2": "wg1,o0", +# "wg1,o1": "mod1,o0", +# "mod1,o1": "combiner,port_2", +# "splitter,port_3": "wg2,o0", +# "wg2,o1": "mod2,o0", +# "mod2,o1": "combiner,port_3", +# "vs1,e0": "vf1,e0", +# "vf1,e1": "mod1,e0", +# "vs2,e0": "mod2,e0" +# } +# ports = { +# "in": "splitter,port_1", +# "out": "combiner,port_1", +# } + +# netlist = { +# "instances": instances, +# "connections": connections, +# "ports": ports, +# } + +# models = { +# "y_branch": y_branch, +# "waveguide": waveguide, +# "modulator": OpticalModulator, +# "voltage_source": VoltageSource, +# "voltage_follower": VoltageFollower, +# } + +# ckt_settings = { +# "splitter": {}, +# "combiner": {}, +# "wg1": {"length":10.0}, +# "wg2": {"length":30.0}, +# "mod1": { +# "phase_coefficients": np.array([0.0, 0.0, np.pi/2, 0]), +# # "phase_coefficients": np.array([0.0, 0.0, 0.0, 0]) +# }, +# "mod2": {}, +# "vs1": { +# "steady_state_voltage": 3.0, +# }, +# "vs2": { +# "steady_state_voltage": 0.0, +# }, +# } + +# from simphony.circuit.circuit import Circuit +# from simphony.simulation.s_parameter import SParameterSimulationParameters +# # from simphony.simulation.block_mode import BlockModeSimulationParameters + +# simulation_parameters = SParameterSimulationParameters() +# # simulation_parameters = BlockModeSimulationParameters() +# mzi_circuit = Circuit(netlist, models) +# mzi_circuit.display() +# mzi_circuit.instantiate(ckt_settings, simulation_parameters) + +# from simphony.simulation.s_parameter import SParameterSimulation +# import numpy as np +# import matplotlib.pyplot as plt + +# wl = np.linspace(1.5, 1.6, 1000)*1e-6 +# sparam_simulation = SParameterSimulation(mzi_circuit, ckt_settings, simulation_parameters) +# sim_result = sparam_simulation.run(wl=wl) + +# s_params_1d = sim_result.s_parameters[('in', 'out')] diff --git a/examples/transient_multimode.ipynb b/examples/transient_multimode.ipynb new file mode 100644 index 00000000..8f7a07b2 --- /dev/null +++ b/examples/transient_multimode.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f18156c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from simphony.libraries.siepic import y_branch\n", + "from functools import partial\n", + "import matplotlib.pyplot as plt\n", + "from simphony.utils import create_multimode_sax_model\n", + "\n", + "\n", + "y_branch_models = {\n", + " \"te\": partial(y_branch, pol=\"te\"),\n", + " \"tm\": partial(y_branch, pol=\"tm\")\n", + "}\n", + "\n", + "wl = np.linspace(1.5, 1.6, 1000)\n", + "y_branch_sdict = create_multimode_sax_model(y_branch_models)(wl=wl)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", + "\n", + "ax1.plot(wl, np.abs(y_branch_sdict[(\"port_1@te\", \"port_2@te\")]), label=\"te\")\n", + "ax1.plot(wl, np.abs(y_branch_sdict[(\"port_1@tm\", \"port_2@tm\")]), label=\"tm\")\n", + "ax1.legend()\n", + "\n", + "ax2.plot(wl, np.unwrap(np.angle(y_branch_sdict[(\"port_1@te\", \"port_2@te\")])), label=\"te\")\n", + "ax2.plot(wl, np.unwrap(np.angle(y_branch_sdict[(\"port_1@tm\", \"port_2@tm\")])), label=\"tm\")\n", + "ax2.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "10addee6", + "metadata": {}, + "source": [ + "# " + ] + }, + { + "cell_type": "markdown", + "id": "3a962ddd", + "metadata": {}, + "source": [ + "# S-parameter Netlists as PCells\n", + "\n", + "### Type 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d22a49d", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder, netlist_to_pcell\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c55c842", + "metadata": {}, + "source": [ + "### Type 2\n", + "\n", + "TODO: The grouping functionality" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 5145b82e..7829f6d1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "simphony" -version = "0.7.3" +version = "0.8.0rc0" description = "Simphony: A simulator for photonic circuits" readme = "README.md" requires-python = ">=3.9" @@ -53,6 +53,8 @@ dependencies = [ "lark ~= 1.1.5", "tabulate ~= 0.9.0", "deprecation ~= 2.1.0", + "tqdm ~= 4.67.1", + "ipysigma ~= 0.24.6", "parsimonious ~= 0.10.0", ] diff --git a/simphony/__init__.py b/simphony/__init__.py index fbb9b6b9..103f7bac 100644 --- a/simphony/__init__.py +++ b/simphony/__init__.py @@ -41,7 +41,7 @@ + " detected)." ) -__version__ = "0.7.3" +__version__ = "0.8.0rc0" __license__ = "MIT" __project_url__ = "https://github.com/BYUCamachoLab/simphony" __forum_url__ = "https://github.com/BYUCamachoLab/simphony/issues" diff --git a/simphony/circuit/_netlist.py b/simphony/circuit/_netlist.py new file mode 100644 index 00000000..352f4b8a --- /dev/null +++ b/simphony/circuit/_netlist.py @@ -0,0 +1,117 @@ +"""_netlist module provides functionality for netlists that do not contain sax +models. + +Really this module is only here to allow +simphony.libraries.ideal.s_parameter access to this functionality, while +still allowing simphony.libraries.ideal.s_parameter to be used in the +public simphony.circuit.netlist module +""" +from copy import deepcopy +from typing import TypeAlias, TypedDict + +import sax +from sax import Connections, InstanceName, Ports +from typing_extensions import NotRequired + +from simphony.component.component import Component +from simphony.component.pcell import PCell +from simphony.simulation.simulation import SimulationParameters + +# TODO: modify so that the Component field may be a dict (for specifying the groups) +ElaboratedInstances: TypeAlias = dict[InstanceName, Component] +"""A mapping from instance names to their instantiated component model.""" + +InstantiatedFlatNetlist = TypedDict( + "Netlist", + { + "instances": ElaboratedInstances, + "connections": NotRequired[ + Connections + ], # TODO: Add simphony type for connections, since ours are more general + "ports": Ports, + # "nets": NotRequired[Nets], + # "placements": NotRequired[Placements], + # "settings": NotRequired[Settings], + }, +) + + +# TODO: Fix the order of arguments so that SimulationParameters is first +def _instantiate_netlist( + netlist, + models, + settings, + simulation_parameters: SimulationParameters, + # directed: bool, + # default_modes: tuple, + # external_connections: dict = None +) -> InstantiatedFlatNetlist: + """Generated instantiated netlist from netlist which does not contain sax + models. + + TODO: if directed is true, implement simple rules to convert bidirectional components to directional ones + Raise an error if the netlist is cyclic + More details here: https://camacholab.ee.byu.edu/CamachoLab/6977d6e48cbec722a8ec21ae/page + """ + instantiated_recursive_netlist = sax.netlist(deepcopy(netlist)) + for _, subnetlist in instantiated_recursive_netlist.items(): + ## TODO: Actually add the settings to the netlist if desired + _add_settings_to_netlist( + subnetlist, settings=settings + ) # Just to normalize, we will use the settings the user provided later + instantiated_recursive_netlist = sax.netlist(instantiated_recursive_netlist) + instantiated_flat_netlist = sax.flatten_netlist(instantiated_recursive_netlist) + + ## Make unique, instantiated models for each instance. Put the instantiated models in the netlist metadata + for instance_name, instance_data in instantiated_flat_netlist["instances"].items(): + # print(instance_name) + component_name = instance_data["component"] + instance_settings = settings.get(instance_name, {}) + uninstantiated_model = models[component_name] + + if issubclass(uninstantiated_model, PCell): + instance_data["model"] = uninstantiated_model( + simulation_parameters, **instance_settings + ) + elif issubclass(uninstantiated_model, Component): + instance_data["model"] = uninstantiated_model( + simulation_parameters, **instance_settings + ) + + TOP_LEVEL_NAME = "top_level" + ## Get instantiated flat nelist from any pcells and splice them into instatiated_netlist issubclass(models['mzi'], PCell) + ## When splicing in, make sure that there are no conflicts with model names + instantiated_recursive_netlist_no_pcells = sax.netlist( + deepcopy(instantiated_flat_netlist), top_level_name=TOP_LEVEL_NAME + ) + + for instance_name, instance_data in instantiated_flat_netlist["instances"].items(): + instantiated_model = instance_data["model"] + if isinstance(instantiated_model, PCell): + ### TODO: Stitch the netlist + pcell_netlist = instantiated_model._instantiated_netlist( + simulation_parameters + ) + + instantiated_recursive_netlist_no_pcells[instance_name] = pcell_netlist + instantiated_recursive_netlist_no_pcells[TOP_LEVEL_NAME]["instances"][ + instance_name + ] = {"component": instance_name} + + ### TODO: Return a new, flat instantiated netlist with no pcells + return sax.flatten_netlist(instantiated_recursive_netlist_no_pcells) + + +def _add_settings_to_netlist(netlist, settings=None): + if settings is None: + settings = {} + # Ensure Instance Name corresponds to a dictionary with the proper format + for instance_name, model in netlist["instances"].items(): + if isinstance(model, str): + netlist["instances"][instance_name] = {"component": model, "settings": {}} + elif isinstance(model, dict): + if not netlist["instances"][instance_name].get("settings"): + netlist["instances"][instance_name]["settings"] = {} + + for instance_name, instance_settings in settings.items(): + netlist["instances"][instance_name]["settings"].update(instance_settings) diff --git a/simphony/circuit/circuit.py b/simphony/circuit/circuit.py new file mode 100644 index 00000000..691f3551 --- /dev/null +++ b/simphony/circuit/circuit.py @@ -0,0 +1,1663 @@ +import inspect +from collections.abc import Iterator +from copy import deepcopy +from typing import Tuple, cast + +import networkx as nx +import sax + +# from simphony.libraries.analytic.component_types import OpticalComponent, ElectricalComponent, LogicComponent +from ipysigma import Sigma +from IPython.display import display +from sax.circuits import _create_dag +from sax.netlists import convert_nets_to_connections + +from simphony.circuit.netlist import ( + add_settings_to_netlist, + graph_to_netlist, + instantiated_flat_netlist_to_graph, + netlist_to_graph, + remove_instances_from_netlist, +) +from simphony.libraries._internal.port_label import ( + BidirectionalPortLabel, + DirectedPortLabel, + PortLabel, +) +from simphony.libraries.ideal.s_parameters import ( + SParameterPlaceholder, + optical_s_parameter_placeholder, + s_parameter_netlist_to_pcell, +) + +# from simphony.component.component import Component +# from simphony.component.pcell import PCell +from simphony.simulation.simulation import SimulationParameters + +# import sax +# from jax.typing import ArrayLike +# from sax.saxtypes import Model as SaxModel + +# from simphony.signal import optical_signal, complete_steady_state_inputs + + +# import re + + +# from simphony.utils import dict_to_matrix + +COMPONENT_COLOR_DEFAULT = "black" +COMPONENT_COLOR_SPARAM = "blue" +COMPONENT_COLOR_OPTICAL = "blue" +COMPONENT_COLOR_ELECTRICAL = "red" +COMPONENT_COLOR_OPTOELECTRICAL = "purple" +COMPONENT_COLOR_LOGIC = "gray" + +S_PARAMETER_SETTING_KEYS = { + "group_id", + "vector_fitting_parameters", + "delay_compensation", + "port_directionality", +} + + +# ----------------------------------------------------------------- +# The following functions taken from sax.circuits from sax 0.15.10 +# ----------------------------------------------------------------- +def _create_dag( + netlist: sax.RecursiveNetlist, + models: sax.Models | None = None, + *, + validate: bool = False, +) -> nx.DiGraph: + if models is None: + models = {} + + all_models = {} + g = nx.DiGraph() + + for model_name, subnetlist in netlist.items(): + if model_name not in all_models: + all_models[model_name] = models.get(model_name, subnetlist) + g.add_node(model_name) + if model_name in models: + continue + for instance in subnetlist["instances"].values(): + component = instance["component"] + if component not in all_models: + all_models[component] = models.get(component, None) + g.add_node(component) + g.add_edge(model_name, component) + + # we only need the nodes that depend on the parent... + parent_node = next(iter(netlist.keys())) + nodes = [parent_node, *nx.descendants(g, parent_node)] + g = cast(nx.DiGraph, nx.induced_subgraph(g, nodes)) + if validate: + g = _validate_dag(g) + return g + + +def _validate_dag(dag: nx.DiGraph) -> nx.DiGraph: + nodes = _find_root(dag) + if len(nodes) > 1: + msg = f"Multiple top_levels found in netlist: {nodes}" + raise ValueError(msg) + if len(nodes) < 1: + msg = "Netlist does not contain any nodes." + raise ValueError(msg) + if not dag.is_directed(): + msg = "Netlist dependency cycles detected!" + raise ValueError(msg) + return dag + + +def _in_degree(dag: nx.DiGraph) -> Iterator[tuple[str, int]]: + return cast(Iterator[tuple[str, int]], dag.in_degree()) + + +def _out_degree(dag: nx.DiGraph) -> Iterator[tuple[str, int]]: + return cast(Iterator[tuple[str, int]], dag.out_degree()) + + +def _find_root(g: nx.DiGraph) -> list[str]: + return [n for n, d in _in_degree(g) if d == 0] + + +def _find_leaves(g: nx.DiGraph) -> list[str]: + return [n for n, d in _out_degree(g) if d == 0] + + +# ----------------------------------------------------------------- +# End of functions taken from sax.circuits from sax 0.15.10 +# ----------------------------------------------------------------- + + +class Circuit: + """Hierarchical circuit built from a SAX-style netlist and model library. + + `Circuit` is the main user-facing container for assembling simulations. It + stores the recursive netlist, normalizes settings, wraps raw SAX callables as + Simphony placeholders, and prepares model port lookup tables. + + Parameters + ---------- + netlist: + Recursive SAX-style netlist dictionary. The top-level circuit is usually + stored under `"top_level"` and contains `instances`, `connections`, and + `ports`. + models: + Mapping from model name to Simphony component class, PCell class, or raw + SAX model callable. + """ + + def __init__( + self, + netlist: dict, + models: dict, + ) -> None: + # Validate the netlist + # for key in netlist['instances'].keys(): + # if not key.isidentifier(): + # raise ValueError("All instance names must be valid python identifiers") + + self.netlist = sax.netlist(deepcopy(netlist)) + + # Sanitize the names to make netlist valid + new_netlist = convert_nets_to_connections( + self.netlist + ) # necessary for gdsfactory netlists + if not new_netlist == {}: + self.netlist = new_netlist + # Replace the original names + + for _, subnetlist in self.netlist.items(): + add_settings_to_netlist(subnetlist) + + self.netlist = sax.netlist(self.netlist) + # self.flattened_netlist = sax.flatten_netlist(self.netlist) + self.subcircuit_hierarchy = _create_dag(self.netlist) + + self.models = deepcopy(models) + self._convert_sax_models() + for instance_name, component in self.models.items(): + # component._create_port_lookup_tables() + component._create_port_lookup_table() + + # self.flattened_graph = netlist_to_graph(self.flattened_netlist) + # self._add_ports_to_graph(self.flattened_graph) + # self._validate_connections(self.flattened_graph) + + def add_component( + self, + instance_name, + model_name, + model=None, + ): + """Add a component instance to the top-level netlist. + + Parameters + ---------- + instance_name : str + Name of the instance in the netlist. + + model_name : str + Name of the component model. + + model : optional + Optional model object to insert into self.models. + """ + + root = _find_root(self.subcircuit_hierarchy)[0] + + if instance_name in self.netlist[root]["instances"]: + raise ValueError(f"Instance '{instance_name}' already exists") + + # Add model if provided + if model is not None: + self.models[model_name] = model + + if not inspect.isclass(self.models[model_name]): + s_parameter = optical_s_parameter_placeholder(self.models[model_name]) + self.models[model_name] = s_parameter + + self.models[model_name]._create_port_lookup_table() + + if model_name not in self.models: + raise ValueError(f"Model '{model_name}' not found") + + self.netlist[root]["instances"][instance_name] = { + "component": model_name, + "settings": {}, + } + + return self + + def add_connection( + self, + src_instance, + src_port, + dst_instance, + dst_port, + ): + """Add a top-level connection between two component ports. + + Connections are stored in SAX directed connection form: + `"src_instance,src_port" -> "dst_instance,dst_port"`. In directed + simulation modes this ordering is also used as directionality evidence + when explicit port directionality is not available. + + Returns + ------- + Circuit + The same circuit object, allowing chained edits. + """ + + root = _find_root(self.subcircuit_hierarchy)[0] + + if "connections" not in self.netlist[root]: + self.netlist[root]["connections"] = {} + + src = f"{src_instance},{src_port}" + dst = f"{dst_instance},{dst_port}" + + # SAX-style directed connection representation + self.netlist[root]["connections"][src] = dst + + return self + + # def add_connection( + # self, + # src_instance, + # src_port, + # dst_instance, + # dst_port, + # ): + # """ + # Add a connection between two component ports. + # """ + + # root = _find_root(self.subcircuit_hierarchy)[0] + + # connection_name = ( + # f"{src_instance},{src_port}:{dst_instance},{dst_port}" + # ) + + # if "connections" not in self.netlist[root]: + # self.netlist[root]["connections"] = {} + + # self.netlist[root]["connections"][connection_name] = ( + # f"{src_instance},{src_port}", + # f"{dst_instance},{dst_port}", + # ) + + # return self + + # def unconnected_ports( + # self, + # inputs_only: bool = False, + # ): + # """ + # Return all unconnected ports as + + # { + # instance_name: [port_name1, port_name2, ...] + # } + + # Parameters + # ---------- + # inputs_only : bool + # If True, only return ports whose directionality is + # "input" or "bidirectional". + # """ + + # root = _find_root(self.subcircuit_hierarchy)[0] + # netlist = self.netlist[root] + + # # Start with all relevant ports assumed unconnected + # unconnected_ports = {} + + # for instance_name, instance_data in netlist["instances"].items(): + + # model_name = instance_data["component"] + + # if model_name not in self.models: + # continue + + # component = self.models[model_name] + + # if inputs_only: + # port_names = [ + # port.name + # for port in component.ports + # if port.directionality in ("input", "bidirectional") + # ] + # else: + # port_names = [ + # port.name + # for port in component.ports + # ] + + # unconnected_ports[instance_name] = set(port_names) + + # # Remove connected ports + # for src, dst in netlist.get("connections", {}).items(): + + # for endpoint in (src, dst): + # instance_name, port_name = endpoint.split(",") + + # if instance_name in unconnected_ports: + # unconnected_ports[instance_name].discard(port_name) + + # # Convert sets to sorted lists + # unconnected_ports = { + # instance: sorted(list(ports)) + # for instance, ports in unconnected_ports.items() + # if len(ports) > 0 + # } + + # return unconnected_ports + def unconnected_ports( + self, + inputs_only: bool = False, + ): + """Return unconnected top-level instance ports. + + Parameters + ---------- + inputs_only: + If true, only return ports whose directionality is `"input"` or + `"bidirectional"`. + + Returns + ------- + dict[str, list[Port]] + Mapping from instance name to unconnected `Port` objects. + """ + + root = _find_root(self.subcircuit_hierarchy)[0] + netlist = self.netlist[root] + + # instance -> set(port objects) + unconnected_ports = {} + + for instance_name, instance_data in netlist["instances"].items(): + model_name = instance_data["component"] + + if model_name not in self.models: + continue + + component = self.models[model_name] + + if inputs_only: + ports = [ + port + for port in component.ports + if port.directionality in ("input", "bidirectional") + ] + else: + ports = list(component.ports) + + unconnected_ports[instance_name] = set(ports) + + # Remove connected ports (match by name) + for src, dst in netlist.get("connections", {}).items(): + for endpoint in (src, dst): + instance_name, port_name = endpoint.split(",") + + if instance_name in unconnected_ports: + # remove the matching port object + unconnected_ports[instance_name] = { + p + for p in unconnected_ports[instance_name] + if p.name != port_name + } + + # Convert sets to lists + unconnected_ports = { + instance: list(ports) + for instance, ports in unconnected_ports.items() + if len(ports) > 0 + } + + return unconnected_ports + + # def add_component( + # self, + # instance_name, + # model_name, + # model = None, + # ): + # # Don't forget to update the port_lookup_table, you might as well just call _create_port_lookup_table + # pass + + # def add_connection( + # self, + # instance_name, + # model_name, + # model = None, + # ): + # # Don't forget to update the port_lookup_table, you might as well just call _create_port_lookup_table + # pass + + # def unconnected_ports( + # self, + # ): + # # Look through all of the models in the models dict, simphony models are different than + # # Sax models in that they have a ports attribute self.models[model_name].ports + # # Each port in self.models[model_name].ports has a port.name attribute + # # Alternatively, there is a port + # unconnected_ports = ... + # return unconnected_ports + + def display( + self, + subcircuit: str = None, + inline: bool = True, + node_labels: dict = {}, + ): + """Display a circuit or subcircuit graph. + + Parameters + ---------- + subcircuit: + Name of the recursive netlist section to display. Defaults to the + root/top-level subcircuit. + inline: + Kept for display backends that support inline rendering. + node_labels: + Optional mapping from instance name to a shorter display label. Any + instance not included uses its real instance name. + """ + recursive_netlist = {} + + if subcircuit is None: + subcircuit = _find_root(self.subcircuit_hierarchy)[0] + + recursive_netlist = self.get_subnetlist(subcircuit) + + # if flatten: + # netlist = sax.flatten_netlist(recursive_netlist) + # graph = netlist_to_graph(netlist) + # self._mark_component_types(subcircuit, graph) + # self._color_nodes(graph) + # else: + netlist = recursive_netlist[subcircuit] + graph = netlist_to_graph(netlist, self.models, include_ports=True) + # add_ports_to_graph(graph, netlist, self.models) + # self._mark_component_types(subcircuit, graph) + # self._color_nodes(graph) + + # # self._add_data_to_graph(graph) + + relabeled_graph = nx.relabel_nodes(graph, node_labels) + + # relabeled_graph.add_node( + # f"Kablooey", + # # component=instance["component"], + # # settings=instance["settings"], + # shape="rectangle", + # opacity=1.0, + # border_color="blue", + # color="white", + # size=20, + # border_size=1, + # # image="image.png", + # # opacity=0.5, + # # size=5, + # ) + + safe_graph = _sanitize_graph_for_widget(relabeled_graph) + fig = Sigma( + safe_graph, + node_size=safe_graph.degree, + node_color="club", + start_layout=True, + ) + display(fig) + + # def diagnostics(self, settings=None, directed=None): + # """Return lightweight diagnostics for this circuit. + + # Parameters + # ---------- + # settings: + # Optional per-instance settings dictionary to validate alongside the + # circuit netlist. + # directed: + # If true, also check the instance graph for directed cycles and note + # where S-parameter port directionality will be inferred. + # """ + # from simphony.diagnostics import diagnose_circuit + + # return diagnose_circuit(self, settings=settings, directed=directed) + + def flatten( + self, + separator="~", + ): + """Return a flattened view of this recursive circuit. + + Parameters + ---------- + separator: + String used by SAX to join nested instance names while flattening. + """ + return FlatCircuit(self.netlist, self.models, separator=separator) + + def instantiate( + self, + settings: dict, + simulation_parameters: SimulationParameters, + tracked_ports: dict = None, + directed: bool = False, + fuse_models: bool = False, + # default_modes, + ): + """Instantiate the circuit for a specific simulation run. + + Instantiation applies per-instance settings, expands PCells, converts + placeholders as needed, inserts tracked-port labels, and returns a flat + circuit whose instances hold concrete component objects. + + Parameters + ---------- + settings: + Per-instance constructor settings. + simulation_parameters: + Simulation mode and shared parameters used during component + construction. + tracked_ports: + Optional mapping from user-facing names to `"instance,port"` + designators to expose in simulation results. + directed: + Whether connection order may be used to infer directionality for + ambiguous placeholders. + fuse_models: + Whether to preserve compatible S-parameter model groups during + consolidation. + """ + return InstantiatedCircuit( + self, + settings, + simulation_parameters, + tracked_ports=tracked_ports, + directed=directed, + fuse_models=fuse_models + # directed, + # default_modes + ) + + def get_subnetlist(self, subcircuit: str): + """Return the recursive netlist rooted at `subcircuit`. + + The returned netlist includes the requested subcircuit and any + recursive descendants needed to define it. + """ + original_netlist = deepcopy(self.netlist) + if not subcircuit in self.subcircuit_hierarchy.nodes: + return ValueError( + f"{subcircuit} not in circuit. Did you mean {list(self.subcircuit_hierarchy.nodes)}?" + ) + descendants = nx.descendants(self.subcircuit_hierarchy, subcircuit) + subcircuits_to_keep = set([subcircuit] + list(descendants)) - set( + _find_leaves(self.subcircuit_hierarchy) + ) + + return sax.netlist({key: original_netlist[key] for key in subcircuits_to_keep}) + + def _convert_sax_models(self): + for model in self.models: + component = self.models[model] + if not inspect.isclass(component): + s_parameter = optical_s_parameter_placeholder(component) + self.models[model] = s_parameter + + def _get_ports_from_subcircuit(self, subcircuit: str): + # First, get a flat netlist of just the subcircuit + recursive_netlist = self.get_subnetlist(subcircuit) + flat_netlist = sax.flatten_netlist(recursive_netlist) + ports = [] + for subcircuit_port_name, component_port_attr in flat_netlist["ports"].items(): + instance_name, instance_port_name = tuple(component_port_attr.split(",")) + component_name = flat_netlist["instances"][instance_name]["component"] + port = self.models[component_name]._port_lookup_table[instance_port_name] + ports.append(port) + + return ports + + def _mark_component_types(self, subcircuit, graph): + """""" + for instance, attr in graph.nodes.items(): + component_name = self.netlist[subcircuit]["instances"][instance][ + "component" + ] + if component_name in self.models: + component_name = self.netlist[subcircuit]["instances"][instance][ + "component" + ] + ports = self.models[component_name].ports + elif component_name in self.subcircuit_hierarchy.nodes: + ports = self._get_ports_from_subcircuit(component_name) + + tags = set() + for port in ports: + if port.type == "electrical": + tags.add("electrical") + elif port.type == "optical": + tags.add("optical") + elif port.type == "logic": + tags.add("logic") + + graph.nodes[instance]["type"] = "/".join(sorted(tags)) + + def _add_ports_to_graph(self, graph): + for subcircuit, attr in graph.nodes.items(): + graph.nodes[subcircuit]["electrical ports"] = [] + graph.nodes[subcircuit]["logic ports"] = [] + graph.nodes[subcircuit]["optical ports"] = [] + + model = attr["component"] + component = self.models[model] + # component_name = flat_netlist['instances'][instance_name]['component'] + + electrical_ports = [] + optical_ports = [] + logic_ports = [] + + for port in component.ports: + if port.type == "electrical": + electrical_ports.append(port.name) + elif port.type == "optical": + optical_ports.append(port.name) + elif port.type == "logic": + logic_ports.append(port.name) + + graph.nodes[subcircuit]["electrical ports"] = electrical_ports + graph.nodes[subcircuit]["optical ports"] = optical_ports + graph.nodes[subcircuit]["logic ports"] = logic_ports + + def get_port_type(self, graph, instance, port): + optical_ports = graph.nodes[instance]["optical ports"] + electrical_ports = graph.nodes[instance]["electrical ports"] + logic_ports = graph.nodes[instance]["logic ports"] + + if port in optical_ports: + return "optical" + elif port in electrical_ports: + return "electrical" + elif port in logic_ports: + return "logic" + + def _validate_connections(self, graph): + # Verify optical-to-optical, electrical-to-electrical, logic-to-logic + for edge in graph.edges: + src, dst, _ = edge + + src_port = graph.edges[edge]["src_port"] + src_port_type = self.get_port_type(graph, src, src_port) + + dst_port = graph.edges[edge]["dst_port"] + dst_port_type = self.get_port_type(graph, dst, dst_port) + + if not src_port_type == dst_port_type: + raise ValueError("Port types must match") + + # TODO: Verify out to in or out to bidirectional connections + + def _color_nodes(self, graph): + color = COMPONENT_COLOR_DEFAULT + + # Assumes tags in alphabetical order + for instance in graph.nodes: + if graph.nodes[instance]["type"] == "s-parameter": + color = COMPONENT_COLOR_SPARAM + elif graph.nodes[instance]["type"] == "electrical/optical": + color = COMPONENT_COLOR_OPTOELECTRICAL + elif graph.nodes[instance]["type"] == "optical": + color = COMPONENT_COLOR_OPTICAL + elif graph.nodes[instance]["type"] == "electrical": + color = COMPONENT_COLOR_ELECTRICAL + elif graph.nodes[instance]["type"] == "logic": + color = COMPONENT_COLOR_LOGIC + elif graph.nodes[instance]["type"] == "electrical/logic": + color = COMPONENT_COLOR_ELECTRICAL + elif graph.nodes[instance]["type"] == "logic/optical": + color = COMPONENT_COLOR_ELECTRICAL + elif graph.nodes[instance]["type"] == "electrical/logic/optical": + color = COMPONENT_COLOR_OPTOELECTRICAL + + graph.nodes[instance]["color"] = color + + +class FlatCircuit: + """Flattened circuit wrapper with sanitized instance names. + + `FlatCircuit` is usually created with `Circuit.flatten()`. It + preserves the model library, flattens recursive subcircuits into one + netlist, and replaces separator characters with valid identifier + substrings so downstream instantiation can handle nested names + consistently. + """ + + def __init__(self, netlist: dict, models: dict, separator: str = "~") -> None: + # To avoid rewriting code, FlatCircuit mostly a wrapper for Circuit + self.separator = "~" + self._recursive_circuit = Circuit(netlist, models) + self.netlist = sax.flatten_netlist( + self._recursive_circuit.netlist, sep=separator + ) + self._sanitized_netlist, self._sanitized_netlist_lut = self._sanitize_netlist( + self.netlist, separator + ) + self._circuit = Circuit(self._sanitized_netlist, models) + self.models = self._circuit.models + + def display( + self, + inline: bool = True, + node_labels: dict = {}, + ): + """Display the flattened circuit graph.""" + + sanitized_node_labels = {v: k for k, v in self._sanitized_netlist_lut.items()} + sanitized_node_labels_to_modify = { + self._sanitized_netlist_lut[k]: v for k, v in node_labels.items() + } + for k, v in sanitized_node_labels_to_modify.items(): + sanitized_node_labels[k] = v + + self._circuit.display(node_labels=sanitized_node_labels) + + def instantiate( + self, + settings: dict, + simulation_parameters: SimulationParameters, + directed: bool = False, + fuse_models: bool = False, + ): + """Instantiate the flattened circuit for a simulation run.""" + return InstantiatedCircuit( + self, + settings, + simulation_parameters, + directed=directed, + fuse_models=fuse_models, + ) + + def _sanitize_netlist(self, netlist, separator) -> Tuple[dict, dict]: + """This function replaces all non-valid characters (characters not + allowed in a valid python identifier, i.e. "~", "|", et cetera) in a + flattened netlist with a valid, unique substring. + + Returns the sanitized flat netlist dict and + + Since sax recursive netlists require instance names to be + valid python identifiers, and since the separator argument + in sax.flatten_netlist needs to be a non-valid character in + a python identifier in order to preserve uniqueness. We need + to be able to replace the separator, with a substring not found + in any of the instance names before passing to the Circuit class + __init__ funciton, which assumes all instance names are valid + python identifiers. + + The method for creating a unique separator is simple: + 1. Determine the largest number, M, of consecutive "_"'s in the instances + 2. Prepend and append M "_"'s to the string "SEPARATOR" + """ + unique_str = "_" + for key in netlist["instances"].keys(): + while unique_str in key: + unique_str += "_" + new_separator = unique_str + "SEPARATOR" + unique_str + lut = {} + for key in netlist["instances"].keys(): + lut[key] = key.replace(separator, new_separator) + + sanitized_netlist = { + "instances": {}, + "connections": {}, + "ports": {}, + } + + for instance_name, instance_data in netlist["instances"].items(): + sanitized_instance_name = lut[instance_name] + sanitized_netlist["instances"][sanitized_instance_name] = instance_data + + for src, dst in netlist["connections"].items(): + src_name, src_port = src.split(",") + dst_name, dst_port = dst.split(",") + + sanitized_src = lut[src_name] + "," + src_port + sanitized_dst = lut[dst_name] + "," + dst_port + + sanitized_netlist["connections"][sanitized_src] = sanitized_dst + + for external_port, internal_port_data in netlist["ports"].items(): + instance_name, internal_port = internal_port_data.split(",") + sanitized_netlist["ports"][external_port] = ( + lut[instance_name] + "," + internal_port + ) + + return sanitized_netlist, lut + + # def _determine_block_mode_order(self): + + +# TODO: Put these functions in the instantiated circuit class as methods +# def _recover_s_parameter_placeholder_settings( +# instantiated_circuit, +# settings, +# ): +# ... + + +def _add_directionality_settings_to_s_parameter_placeholders( + # self, + instantiated_circuit, + settings, + directed=False, +): + """ + Important: Currently, simphony assumes all ports with an ambiguous directionality are source nodes (input) if the corresponding instance + appears in the values of the connection dict in the netlist. Otherwise (if the port is unconnected or in the keys of the connections dict) + we label the port as "output". This convention is used assuming elsewhere, for example, the _insert_port_labels function depends on this + assumption. + """ + netlist = instantiated_circuit.instantiated_flat_netlist + # models = flat_circuit.models + for instance_name, instance_data in netlist["instances"].items(): + # component_class = models[instance_data['component']] + component = netlist["instances"][instance_name]["model"] + if isinstance(component, SParameterPlaceholder): + if not instance_name in settings: + settings[instance_name] = component.settings + # if not "sax_settings" in settings[instance_name].keys(): + # settings[instance_name] = {"sax_settings": settings[instance_name]} + # TODO: DOUBLE CHECK DEFAULT DICTIONARY CONSTRUCTION FOR EDGE CASES + if directed: + default_directionalities = { + p.name: "output" + if f"{instance_name},{p.name}" in netlist["connections"].keys() + else "input" + for p in component.ports + } + default_directionalities = { + k: d + if not ( + f"{instance_name},{k}" in netlist["connections"].keys() + or not f"{instance_name},{k}" in netlist["connections"].values() + ) + else "output" + for k, d in default_directionalities.items() + } + else: + default_directionalities = { + p.name: "bidirectional" for p in component.ports + } + + settings[instance_name][ + "port_directionality" + ] = default_directionalities | settings[instance_name].get( + "port_directionality", {} + ) + + +# def find_clipped_edges(full_graph, subgraph_nodes): +# subgraph_nodes = set(subgraph_nodes) +# clipped = [] + +# for u, v, key, data in full_graph.edges(keys=True, data=True): +# if (u in subgraph_nodes) != (v in subgraph_nodes): +# clipped.append((u, v, key, data)) + +# return clipped + + +def find_clipped_edges(full_graph, subgraph_nodes): + subgraph_nodes = set(subgraph_nodes) + clipped = [] + + for u, v, key, data in full_graph.edges(keys=True, data=True): + if (u in subgraph_nodes) != (v in subgraph_nodes): + clipped.append((u, v, key, data)) + + # Remove bidirectional duplicates + unique_clipped = [] + seen = set() + + for u, v, key, data in clipped: + normalized = frozenset( + { + (u, data["src_port"]), + (v, data["dst_port"]), + } + ) + + if normalized not in seen: + seen.add(normalized) + unique_clipped.append((u, v, key, data)) + + return unique_clipped + + +# def _normalize_settings(model, instance_name, settings): +# original_model = model._sax_model +# model_params = inspect.signature(original_model).parameters +# instance_settings = settings[instance_name] +# if any(setting in model_params for setting in instance_settings): +# settings[instance_name] = {"sax_settings": instance_settings} +# else: +# settings[instance_name].setdefault("sax_settings", {}) +# warnings.warn(f"Sax settings were not specified and inside settings do not match model function call for {instance_name}. Appending an empty sax_setting dictionary for that component.") + + +class InstantiatedCircuit: + """Concrete, flattened circuit used internally by simulators. + + Unlike `Circuit`, this object contains instantiated component + objects rather than component classes. Recursive subcircuits and + PCells have already been expanded, and `graph` describes the + executable instance graph. + + Simulators normally obtain this object through + `Circuit.instantiate()` rather than constructing it directly. + """ + + def __init__( + self, + circuit: Circuit | FlatCircuit, + settings: dict, + simulation_parameters: SimulationParameters, + # consolidate_s_parameter_components = True, # I have decided that this will just be the thing to do + tracked_ports: dict = None, + directed=False, # TODO: Implement bidirectional interpretation of ambiguous s parameter models + fuse_models=False, + # directed: bool, + # default_modes, + ): + """Instantiate components and prepare simulator lookup structures. + + When `directed` is true, unspecified directionalities of + `SParameterPlaceholder` objects may be inferred from connection + order in the netlist. + """ + if not isinstance(tracked_ports, dict): + tracked_ports = {} + tracked_ports = deepcopy(tracked_ports) + for ext_port_name, port_designator in circuit.netlist["top_level"][ + "ports" + ].items(): + tracked_ports.setdefault(ext_port_name, port_designator) + + # TODO: I was mutating the inputs so I deep copied them. TODO: assess this for efficiency + circuit = deepcopy(circuit) + settings = deepcopy(settings) + # simulation_parameters = deepcopy(simulation_parameters) + simulation_parameters = deepcopy(simulation_parameters) + + if isinstance(circuit, FlatCircuit): + self.circuit = circuit + elif isinstance(circuit, Circuit): + self.circuit = circuit.flatten() + + netlist = self.circuit.netlist + models = self.circuit.models + + from simphony.libraries.ideal.s_parameters import SParameterPlaceholder + + # Reinterpret Sax Settings to optical_s_parameter Component settings + # for instance_name, instance_settings in settings.items(): + for instance_name in netlist["instances"].keys(): + model = models[netlist["instances"][instance_name]["component"]] + if ( + issubclass(model, SParameterPlaceholder) + and not "sax_settings" in settings[instance_name].keys() + ): + settings[instance_name] = {"sax_settings": settings[instance_name]} + + # gv.d3(netlist_to_graph(netlist, models)).display() + tracked_ports = self._insert_port_label_placeholders( + netlist, settings, models, tracked_ports + ) + # gv.d3(netlist_to_graph(netlist, models)).display() + + from simphony.circuit.netlist import instantiate_netlist + + self.instantiated_flat_netlist = instantiate_netlist( + netlist, models, settings, simulation_parameters + ) + self.graph = instantiated_flat_netlist_to_graph( + self.instantiated_flat_netlist, include_ports=False + ) + self.simulation_parameters = simulation_parameters + + _add_directionality_settings_to_s_parameter_placeholders( + self, settings, directed=directed + ) + self.settings = settings + + # gv.d3(instantiated_flat_netlist_to_graph(self.instantiated_flat_netlist)).display() + self._consolidate_s_parameter_components(fuse_models) + # gv.d3(instantiated_flat_netlist_to_graph(self.instantiated_flat_netlist)).display() + ( + self.instantiated_flat_netlist, + self.port_lookup_table, + ) = self._remove_port_labels(self.instantiated_flat_netlist, tracked_ports) + + self.graph = instantiated_flat_netlist_to_graph( + self.instantiated_flat_netlist, include_ports=False + ) + + # def display(self, inline=True): + + # # graph.add_edge("lf1~mzi1~bot_mod", "lf1~mzi2~bot_mod", directed=True, color="red", hover="Hi!", tooltip="delay = 12 ps") + # # graph.add_edge("lf1~mzi2~bot_mod", "lf1~mzi1~bot_mod", directed=True, color="red", hover="Hi!", tooltip="delay = 12 ps") + # graph = instantiated_flat_netlist_to_graph(self.instantiated_flat_netlist, include_ports=True) + # fig = gv.d3(graph, edge_hover_tooltip=True) + + # fig.display(inline=True) + + # # fig = gv.d3(self.graph.to_undirected()) + # # fig.display(inline=True) + def display(self, inline=True): + """Display the instantiated flat circuit graph.""" + graph = instantiated_flat_netlist_to_graph( + self.instantiated_flat_netlist, include_ports=True + ) + safe_graph = deepcopy(graph) + for _, attr in safe_graph.nodes(data=True): + if "settings" in attr: + attr["settings"] = stringify_dict_values(attr["settings"]) + + safe_graph = _sanitize_graph_for_widget(safe_graph) + fig = Sigma(safe_graph, node_size=safe_graph.degree, node_color="club") + display(fig) + + def _consolidate_s_parameter_components(self, fuse_models): + """Because Simphony Circuits will likely be composed of mostly sax-s- + parameter elements, we include this extra functionality to optimize the + placement of s-parameter models. + + This method will find all of the strongly connected s-parameter + components and pass each subnetlist into a PCell factory. How + the subnetlists are handled further will depend on how this + subnetlist is interpreted by the PCell factory and how it + handles different siulation modes + """ + # TODO: Replace all of the sugraphs with SParameterGroupPlaceholder Components, to ease the stitching process + # TODO: make a way to iterate over these new SParameterGroupPlaceholder instance names and subgraph + + s_parameter_only_graph = deepcopy(self.graph) + for node in self.graph.nodes(): + model = self.instantiated_flat_netlist["instances"][node]["model"] + if not isinstance(model, SParameterPlaceholder): + s_parameter_only_graph.remove_node(node) + + subgraphs = [ + s_parameter_only_graph.subgraph(c).copy() + for c in nx.weakly_connected_components(s_parameter_only_graph) + ] + TOP_LEVEL_NAME = "top_level" + + clipped_netlist = remove_instances_from_netlist( + self.instantiated_flat_netlist, s_parameter_only_graph.nodes() + ) + instantiated_recursive_netlist = sax.netlist( + clipped_netlist, top_level_name=TOP_LEVEL_NAME + ) + + for j, subgraph in enumerate(subgraphs): + if j == 1: + pass + # The following line is likely leading to duplicate connections, since bidirectional connections + # are represented with two different connections (since nx.multidigraph doesn't have "bidirectional connections") + clipped_edges = find_clipped_edges(self.graph, subgraph) + + # ports = [src for (src, dst, key, data) in clipped_edges] # Use the clipped edges to determine the ports + # TODO: IMPORTANT, when determining the ports, right now it only looks at clipped edges + # This causes some important ports to be ignored leading to them disappearing in the pcell's ports + # ports = {f"o{i}":f"{src},{data["src_port"]}" if src in subgraph else f"{dst},{data["dst_port"]}" for i, (src, dst, key, data) in enumerate(clipped_edges)} + # subnetlist = graph_to_netlist(subgraph, port=ports) + + subnetlist = graph_to_netlist(subgraph) + models = { + subnetlist["instances"][node][ + "component" + ]: self.instantiated_flat_netlist["instances"][node]["model"].sax_model + for node in subgraph.nodes() + } + port_designators = set() + for instance_name, instance_data in subnetlist["instances"].items(): + model_name = instance_data["component"] + # Changed this line on Friday 05-08-26 + sdict = sax.multimode( + models[model_name](), self.simulation_parameters.mode_identifiers + ) + single_mode_ports = sorted( + {p.split("@")[0] for edge in sdict.keys() for p in edge} + ) + port_designators.update( + [f"{instance_name},{p}" for p in single_mode_ports] + ) + + # external_port_designators = {f"{src},{data["src_port"]}" if src in subgraph else f"{dst},{data["dst_port"]}" for (src, dst, key, data) in clipped_edges} + internal_port_designators = set() + for src_designator, dst_designator in subnetlist["connections"].items(): + internal_port_designators.add(src_designator) + internal_port_designators.add(dst_designator) + + external_port_designators = port_designators - internal_port_designators + ports = { + f"o{i}": ep_designator + for i, ep_designator in enumerate(external_port_designators) + } + subnetlist["ports"] = ports + port_directionality = { + node: self.settings[node]["port_directionality"] + for node in subgraph.nodes() + } + # default_modes = {node: self.instantiated_flat_netlist['instances'][node]['model'].default_modes for node in subgraph.nodes()} + + # _settings = {k: self.settings[k] for k in subnetlist['instances'].keys() if k in self.settings} + + # We need to add the settings determined by the s_parameter_placeholder objects + _settings = { + k: self.instantiated_flat_netlist["instances"][k]["model"].settings + for k in subnetlist["instances"].keys() + } + + s_parameter_pcell = s_parameter_netlist_to_pcell( + subnetlist, + models, + port_directionality, + self.simulation_parameters.mode_identifiers, + fuse_models=fuse_models, + ) + + # TODO: The following lines of code are repeated elsewhere and it would be good to wrap them up in a function + instantiated_s_parameter_pcell = s_parameter_pcell( + self.simulation_parameters, settings=_settings + ) + pcell_netlist = instantiated_s_parameter_pcell._instantiated_netlist( + self.simulation_parameters + ) + + s_parameter_group = f"sparameter_group{j}" + instantiated_recursive_netlist[s_parameter_group] = pcell_netlist + instantiated_recursive_netlist[TOP_LEVEL_NAME]["instances"][ + s_parameter_group + ] = {"component": s_parameter_group} + + port_lut = {v: k for k, v in ports.items()} + # TODO: Make sure this isn't creating the duplicate connections that may be in clipped edges + for src, dst, key, data in clipped_edges: + src_port = data["src_port"] + dst_port = data["dst_port"] + if src in s_parameter_only_graph.nodes: + src_port = port_lut[f"{src},{src_port}"] + src = s_parameter_group + elif dst in s_parameter_only_graph.nodes: + dst_port = port_lut[f"{dst},{dst_port}"] + dst = s_parameter_group + + src_connection = f"{src},{src_port}" + dst_connection = f"{dst},{dst_port}" + instantiated_recursive_netlist[TOP_LEVEL_NAME]["connections"][ + src_connection + ] = dst_connection + + self.instantiated_flat_netlist = sax.flatten_netlist( + instantiated_recursive_netlist + ) + + def _insert_port_label_placeholders(self, netlist, settings, models, tracked_ports): + # First, we will take care of the tracked ports that are not connected to anything else + new_tracked_ports = {} + unconnected_tracked_ports = [] + for tracked_port_name, tracked_port_designator in tracked_ports.items(): + if not is_endpoint_connected(netlist, tracked_port_designator): + unconnected_tracked_ports.append( + (tracked_port_name, tracked_port_designator) + ) + + for tracked_port_name, tracked_port_designator in unconnected_tracked_ports: + internal_instance_name, internal_port_name = tracked_port_designator.split( + "," + ) + port_label_instance_name = f"{tracked_port_name}|PORT_LABEL" + port_label_model_name = port_label_instance_name + + netlist["instances"][port_label_instance_name] = { + "component": port_label_model_name, + "settings": {}, + } + settings[port_label_instance_name] = { + "name": tracked_port_name, + "designator": tracked_port_designator, + } + model_name = netlist["instances"][internal_instance_name]["component"] + tracked_port = models[model_name]._port_lookup_table[internal_port_name] + + if tracked_port.directionality == "bidirectional": + models[port_label_model_name] = BidirectionalPortLabel + else: + models[port_label_model_name] = DirectedPortLabel + + if tracked_port.directionality == "output": + netlist["connections"][ + tracked_port_designator + ] = f"{port_label_instance_name},in" + new_tracked_ports[tracked_port_name] = f"{port_label_instance_name},in" + elif tracked_port.directionality == "bidirectional": + netlist["connections"][ + tracked_port_designator + ] = f"{port_label_instance_name},port1" + new_tracked_ports[ + tracked_port_name + ] = f"{port_label_instance_name},port1" + elif tracked_port.directionality == "input": + netlist["connections"][ + f"{port_label_instance_name},out" + ] = tracked_port_designator + new_tracked_ports[tracked_port_name] = f"{port_label_instance_name},out" + + for key, _ in unconnected_tracked_ports: + tracked_ports.pop(key) + + # Next, we will take care of the tracked ports that are further connected + for tracked_ext_port_name, tracked_port_designator in tracked_ports.items(): + if tracked_ext_port_name == "gc_out": + pass + internal_instance_name, internal_port_name = tracked_port_designator.split( + "," + ) + port_label_instance_name = f"{tracked_ext_port_name}|PORT_LABEL" + port_label_model_name = port_label_instance_name + # netlist["instances"][port_label_instance_name] = {'component': port_label_model_name, "settings": {"name": tracked_ext_port_name, "designator": tracked_port_designator}} + netlist["instances"][port_label_instance_name] = { + "component": port_label_model_name, + "settings": {}, + } + settings[port_label_instance_name] = { + "name": tracked_ext_port_name, + "designator": tracked_port_designator, + } + model_name = netlist["instances"][internal_instance_name]["component"] + tracked_port = models[model_name]._port_lookup_table[internal_port_name] + + if tracked_port.directionality == "bidirectional": + models[port_label_model_name] = BidirectionalPortLabel + elif ( + tracked_port.directionality == "input" + or tracked_port.directionality == "output" + ): + models[port_label_model_name] = DirectedPortLabel + + reverse_order = False + if tracked_port_designator in netlist["connections"].keys(): + the_other_port_designator = netlist["connections"][ + tracked_port_designator + ] + del netlist["connections"][tracked_port_designator] + elif tracked_port_designator in netlist["connections"].values(): + reverse_order = True + flipped_connections = { + v: k for k, v in netlist["connections"].items() + } # I put this here since the connections are mutated by inserted placeholders + k = flipped_connections[tracked_port_designator] + the_other_port_designator = k + del netlist["connections"][k] + + def insert_port_label( + netlist, + tracked_designator, + other_designator, + port_names=("in", "out"), + reverse_order=False, + ): + if reverse_order: + netlist["connections"][ + f"{port_label_instance_name},{port_names[0]}" + ] = tracked_designator + netlist["connections"][ + other_designator + ] = f"{port_label_instance_name},{port_names[1]}" + new_tracked_ports[ + tracked_ext_port_name + ] = f"{port_label_instance_name},{port_names[0]}" + + else: + netlist["connections"][ + tracked_designator + ] = f"{port_label_instance_name},{port_names[0]}" + netlist["connections"][ + f"{port_label_instance_name},{port_names[1]}" + ] = other_designator + new_tracked_ports[ + tracked_ext_port_name + ] = f"{port_label_instance_name},{port_names[0]}" + + if tracked_port.directionality == "bidirectional": + insert_port_label( + netlist, + tracked_port_designator, + the_other_port_designator, + port_names=("port1", "port2"), + reverse_order=reverse_order, + ) + elif tracked_port.directionality == "output": + insert_port_label( + netlist, + tracked_port_designator, + the_other_port_designator, + port_names=("in", "out"), + reverse_order=reverse_order, + ) + elif tracked_port.directionality == "input": + insert_port_label( + netlist, + tracked_port_designator, + the_other_port_designator, + port_names=("out", "in"), + reverse_order=reverse_order, + ) + return new_tracked_ports + + # # TODO: Test this function + # # TODO: Currently, if two adjacent tracked ports are specified, things will break. + # def _insert_port_label_placeholders(self, netlist, settings, models, tracked_ports): + # """ + # Because pcells are recursively expanded into an unpredictable (from the perspective of this class) networks of simphony components, + # we insert a placeholder component to "track" were the ports in the top level netlist "end up" + + # If the tracked port is an input port, the resulting placeholder will point into the port. The external facing port in the placeholder will match the tracked port and will be an input port + + # If the tracked port is an output port, the tracked port will point into the placeholder. The external facing port in the placeholder will match the tracked port and will be an output port + + # If the tracked port is bidirectional, then the placeholder will be connected to the tracked port via a bidirectional port and the external facing port will also be bidirectional. + # """ + + # for tracked_ext_port_name, tracked_port_designator in tracked_ports.items(): + # # TODO: optimize the following line + # flipped_connections = {v:k for k, v in netlist['connections'].items()} # I have to do this, otherwise, I can't have adjacent labels + + # internal_instance_name, internal_port_name = tracked_port_designator.split(",") + # port_label_instance_name = f"{tracked_ext_port_name}|PORT_LABEL" + # port_label_model_name = port_label_instance_name + # # netlist["instances"][port_label_instance_name] = {'component': port_label_model_name, "settings": {"name": tracked_ext_port_name, "designator": tracked_port_designator}} + # netlist["instances"][port_label_instance_name] = {'component': port_label_model_name, "settings": {}} + # settings[port_label_instance_name] = {"name": tracked_ext_port_name, "designator": tracked_port_designator} + # model_name = netlist['instances'][internal_instance_name]['component'] + # tracked_port = models[model_name]._port_lookup_table[internal_port_name] + + # if tracked_port.directionality == "bidirectional": + # models[port_label_model_name] = BidirectionalPortLabel + # elif tracked_port.directionality == "input" or tracked_port.directionality == "output": + # models[port_label_model_name] = DirectedPortLabel + + # if tracked_port_designator in netlist['connections'].keys(): + # the_other_port_designator = netlist["connections"][tracked_port_designator] + # del netlist['connections'][flipped_connections[the_other_port_designator]] + + # if tracked_port.directionality == "output" or tracked_port.directionality == "bidirectional": + # netlist["connections"][tracked_port_designator] = f"{port_label_instance_name},in" + # netlist["connections"][f"{port_label_instance_name},out"] = the_other_port_designator + # elif tracked_port.directionality == "input": + # # THESE TECHNICALLY VIOLATE ONE OF SIMPHONIES ASSUMPTIONS ABOUT PORT DIRECITONALITY + # netlist["connections"][tracked_port_designator] = f"{port_label_instance_name},out" + # netlist["connections"][f"{port_label_instance_name},in"] = the_other_port_designator + + # elif tracked_port_designator in netlist['connections'].values(): + # # Now, I need the key in the netlist that corresponds to the tracked_port_designator value + # # Is there an efficient and pythonic way to do this? + # the_other_port_designator = flipped_connections[tracked_port_designator] + # del netlist['connections'][flipped_connections[tracked_port_designator]] + # if tracked_port.directionality == "output" or tracked_port.directionality == "bidirectional": + # netlist["connections"][f"{port_label_instance_name},in"] = tracked_port_designator + # netlist["connections"][the_other_port_designator] = f"{port_label_instance_name},out" + # elif tracked_port.directionality == "input": + # netlist["connections"][f"{port_label_instance_name},out"] = tracked_port_designator + # netlist["connections"][the_other_port_designator] = f"{port_label_instance_name},in" + # else: # The tracked port is unconnected + # if tracked_port.directionality == "output" or tracked_port.directionality == "bidirectional": + # netlist["connections"][tracked_port_designator] = f"{port_label_instance_name},in" + # elif tracked_port.directionality == "input": + # netlist["connections"][f"{port_label_instance_name},out"] = tracked_port_designator + + def _remove_port_labels(self, netlist, tracked_ports): + new_tracked_ports = {} + port_labels = { + instance_name: instance_data["model"].name + for instance_name, instance_data in netlist["instances"].items() + if isinstance(instance_data["model"], PortLabel) + } + external_port_labels = {} + internal_port_labels = {} + + for instance_name, tracked_port_name in port_labels.items(): + endpoints = _find_port_label_endpoints(netlist, instance_name) + if len(endpoints) == 1: + external_port_labels[instance_name] = tracked_port_name + elif len(endpoints) == 2: + internal_port_labels[instance_name] = tracked_port_name + + for instance_name, tracked_port_name in external_port_labels.items(): + new_tracked_ports[tracked_port_name] = find_connected_endpoint( + netlist, tracked_ports[tracked_port_name] + ) + + netlist = remove_instances_from_netlist(netlist, external_port_labels.keys()) + + for instance_name, tracked_port_name in internal_port_labels.items(): + endpoints = _find_port_label_endpoints(netlist, instance_name) + if endpoints[0] in netlist["connections"].keys(): + src = endpoints[0] + dst = endpoints[1] + else: + src = endpoints[1] + dst = endpoints[0] + new_tracked_ports[tracked_port_name] = find_connected_endpoint( + netlist, tracked_ports[tracked_port_name] + ) + netlist = remove_instances_from_netlist(netlist, [instance_name]) + netlist["connections"][src] = dst + + # if len(endpoints) == 1 and endpoints[0] in netlist['connections'].keys(): + # remove_instances_from_netlist(netlist, [instance_name]) + + # elif len(endpoints) == 1 and endpoints[0] in netlist['connections'].values(): + # remove_instances_from_netlist(netlist, [instance_name]) + + # if endpoints[0] in netlist['connections'].keys(): + # src = endpoints[0] + # dst = endpoints[1] + # else: + # src = endpoints[1] + # dst = endpoints[0] + + # if len(endpoints) == 2: + # remove_instances_from_netlist(netlist, [instance_name]) + + return netlist, new_tracked_ports + + +def _find_port_label_endpoints(netlist, instance_name): + # if instance_name == 'lf2|PORT_LABEL': + # pass + endpoints = [] + for src_designator, dst_designator in netlist["connections"].items(): + src_instance = src_designator.split(",")[0] + dst_instance = dst_designator.split(",")[0] + + if instance_name == src_instance: + endpoints.append(dst_designator) + if instance_name == dst_instance: + endpoints.append(src_designator) + + return endpoints + + +def stringify_dict_values(d): + """Recursively convert all values in a dict to strings.""" + if not isinstance(d, dict): + return str(d) + + out = {} + for k, v in d.items(): + if isinstance(v, dict): + out[k] = stringify_dict_values(v) + elif isinstance(v, (list, tuple, set)): + out[k] = [stringify_dict_values(x) for x in v] + else: + out[k] = str(v) + return out + + +def _sanitize_graph_for_widget(graph): + """Convert graph attributes to widget-safe values for notebook + renderers.""" + safe_graph = deepcopy(graph) + for _, attr in safe_graph.nodes(data=True): + for k, v in list(attr.items()): + if isinstance(v, dict): + attr[k] = stringify_dict_values(v) + elif isinstance(v, (list, tuple, set)): + attr[k] = [stringify_dict_values(x) for x in v] + else: + try: + # Keep simple JSON-like scalars as-is; stringify complex objects. + if isinstance(v, (str, int, float, bool)) or v is None: + continue + attr[k] = str(v) + except Exception: + attr[k] = str(v) + for _, _, attr in safe_graph.edges(data=True): + for k, v in list(attr.items()): + if isinstance(v, (str, int, float, bool)) or v is None: + continue + attr[k] = str(v) + return safe_graph + + +# import json + +# def make_json_safe(obj): +# """Convert object into something JSON serializable.""" +# # Fast path: already serializable +# try: +# json.dumps(obj) +# return obj +# except (TypeError, OverflowError): +# pass + +# # Common conversions +# if isinstance(obj, dict): +# return {str(k): make_json_safe(v) for k, v in obj.items()} +# elif isinstance(obj, (list, tuple, set)): +# return [make_json_safe(v) for v in obj] + +# # JAX / NumPy arrays +# try: +# import numpy as np +# if hasattr(obj, "shape"): +# return np.array(obj).tolist() +# except Exception: +# pass + +# # Fallback: string representation +# return str(obj) + +# def sanitize_graph_for_display(graph): +# import networkx as nx +# G = nx.DiGraph() + +# # Copy nodes +# for n, attrs in graph.nodes(data=True): +# safe_attrs = {k: make_json_safe(v) for k, v in attrs.items()} +# G.add_node(n, **safe_attrs) + +# # Copy edges +# for u, v, attrs in graph.edges(data=True): +# safe_attrs = {k: make_json_safe(v) for k, v in attrs.items()} +# G.add_edge(u, v, **safe_attrs) + + +# return G +def is_endpoint_connected(netlist, endpoint): + """Check whether a SAX netlist endpoint is connected. + + Parameters + ---------- + netlist : dict + SAX-style netlist dictionary containing a "connections" dict. + + endpoint : str + Endpoint in the form "instance_name,port_name" + + Returns + ------- + bool + False if the endpoint does not appear in any connection, + True otherwise. + """ + + connections = netlist.get("connections", {}) + + for src, dst in connections.items(): + if endpoint == src or endpoint == dst: + return True + + return False + + +def find_bidirectional_duplicates(connections: dict): + """Finds pairs (k, v) where both k->v and v->k exist. + + Returns: + set of frozensets, each representing a duplicate pair + (so {A, B} represents A<->B) + """ + seen = set() + duplicates = set() + + for k, v in connections.items(): + pair = (k, v) + + # normalize direction-independent representation + reversed_pair = (v, k) + + if reversed_pair in seen: + duplicates.add(frozenset(pair)) + else: + seen.add(pair) + + return duplicates + + +def find_connected_endpoint(netlist, endpoint): + """Find the endpoint connected to `endpoint` in a SAX netlist. + + Parameters + ---------- + netlist : dict + SAX-style netlist dictionary containing a "connections" dict. + + endpoint : str + Endpoint in the form "instance_name,port_name" + + Returns + ------- + str | None + The connected endpoint if found, otherwise None. + """ + + connections = netlist.get("connections", {}) + + for src, dst in connections.items(): + if endpoint == src: + return dst + elif endpoint == dst: + return src + + return None diff --git a/simphony/circuit/netlist.py b/simphony/circuit/netlist.py new file mode 100644 index 00000000..0d783d0c --- /dev/null +++ b/simphony/circuit/netlist.py @@ -0,0 +1,774 @@ +"""Netlist conversion and normalization helpers. + +This module contains the lower-level utilities used by `Circuit` and the +simulators to move between SAX-style netlists, NetworkX graphs, and +instantiated flat netlists. Most users interact with these helpers +indirectly through `Circuit`, but they are useful when building +examples, diagnostics, or custom tooling around Simphony netlists. +""" + +# from jax.scipy.special import factorial +from typing import Union + +# from typing_extensions import NotRequired +import networkx as nx + +# import jax.numpy as jnp +import yaml + +# from copy import deepcopy +from simphony.circuit._netlist import ( + InstantiatedFlatNetlist, + _add_settings_to_netlist, + _instantiate_netlist, +) + +# from sax import AnyNetlist, InstanceName, Ports, Connections, Models +# import sax +# from typing import TypeAlias, TypedDict +# from simphony.component.component import Component +from simphony.component.pcell import PCell + +# from simphony.libraries.ideal.s_parameters import optical_s_parameter +from simphony.simulation.simulation import SimulationParameters + +# from sax import DEFAULT_MODES +# import inspect + + +def instantiate_netlist( + netlist: dict, + models: dict, + settings: dict, + simulation_parameters: SimulationParameters, + # directed: bool = False, + # default_modes: tuple = DEFAULT_MODES, +) -> InstantiatedFlatNetlist: + """Instantiate a possibly hierarchical netlist. + + The helper delegates to the internal recursive instantiation routine. It + expands PCells, applies per-instance settings, and returns a flat netlist + whose instances contain concrete component objects under the `"model"` key. + + Parameters + ---------- + netlist: + SAX-style netlist, usually containing `instances`, `connections`, and + `ports`. + models: + Mapping from component/model names to Simphony component classes, PCell + classes, or converted SAX placeholders. + settings: + Per-instance constructor settings. + simulation_parameters: + Active simulation-mode parameters used when constructing components. + + Returns + ------- + InstantiatedFlatNetlist + Flattened netlist with instantiated component objects. + """ + + return _instantiate_netlist(netlist, models, settings, simulation_parameters) + + # for instance_name, instance_data in netlist['instances'].items(): + # model = models[instance_data['component']] + # if issubclass(model, Component) or issubclass(model, PCell): + # continue + + # if directed: + # directionality = "" + # model = optical_s_parameter(model, directionality, default_modes) + # else: + # directionality = "bidirectional" + # model = optical_s_parameter(model, directionality, default_modes) + + # return new_netlist, new_models + + +# def group_instances(netlist, group_name, instance_names): +# new_netlist = deepcopy(netlist) + +# return new_netlist + + +def complete_netlist(netlist): + """Ensure a flat netlist has the standard top-level keys. + + Missing `instances`, `connections`, or `ports` entries are inserted + as empty dictionaries. The input dictionary is updated in place. + """ + if "instances" not in netlist: + netlist["instances"] = {} + if "connections" not in netlist: + netlist["connections"] = {} + if "ports" not in netlist: + netlist["ports"] = {} + + +def add_settings_to_netlist(netlist, settings=None): + """Attach settings dictionaries to each instance in a netlist. + + Parameters + ---------- + netlist: + Flat SAX-style netlist to update. + settings: + Optional mapping from instance name to settings. When omitted, each + instance receives an empty settings dictionary. + """ + return _add_settings_to_netlist(netlist, settings=settings) + + +def get_settings_from_netlist(netlist): + """Extract per-instance settings from a flat netlist.""" + settings = {} + for instance, attr in netlist["instances"].items(): + settings[instance] = attr["settings"] + + return settings + + +def netlist_to_graph(netlist: Union[dict, str], models, include_ports=True): + """Convert a flat netlist into a NetworkX multidigraph. + + Connection direction is derived from the declared `Port.directionality` of + the two endpoints. Bidirectional-to-bidirectional connections are represented + with edges in both directions, while directed combinations produce a single + edge in the signal-flow direction. + + Parameters + ---------- + netlist: + Flat netlist dictionary or path to a YAML netlist file. + models: + Model library used to look up port metadata. + include_ports: + If true, add small graph nodes for top-level and unconnected ports so + display tools can show the circuit boundary. + """ + if isinstance(netlist, dict): + pass + elif isinstance(netlist, str): + try: + with open(netlist, "r") as file: + netlist = yaml.safe_load(file) + except FileNotFoundError: + raise FileNotFoundError(f"YAML file '{netlist}' not found.") + except yaml.YAMLError as e: + raise yaml.YAMLError(f"Error parsing YAML file: {e}") + + graph = nx.MultiDiGraph() + + for instance_name, instance in netlist["instances"].items(): + model = models[instance["component"]] + shape = "rectangle" + if issubclass(model, PCell): + shape = "hexagon" + + graph.add_node( + instance_name, + component=instance["component"], + settings=instance["settings"], + size=25, + color="white", + border_color="black", + border_size=1, + shape=shape, + ) + models[ + netlist["instances"][instance_name]["component"] + ]._create_port_lookup_table() + + # Add edges based on connections + for src, dsts in netlist["connections"].items(): + for dst in dsts.split(";"): + if dst == "": + continue + src_instance, src_port = src.split(",") + dst_instance, dst_port = dst.split(",") + src_port_directionality = ( + models[netlist["instances"][src_instance]["component"]] + ._port_lookup_table[src_port] + .directionality + ) + dst_port_directionality = ( + models[netlist["instances"][dst_instance]["component"]] + ._port_lookup_table[dst_port] + .directionality + ) + print(f"dst: {dst}") + print(f"dst_directionality: {dst_port_directionality}") + add_connection_to_graph( + graph, + src_instance.strip(), + dst_instance.strip(), + src_port.strip(), + dst_port.strip(), + src_port_directionality, + dst_port_directionality, + ) + # graph.add_edge(src_instance.strip(), dst_instance.strip(), src_port=src_port.strip(), dst_port=dst_port.strip()) + + if include_ports: + add_ports_to_graph(graph, netlist, models) + + # Matthew's Changes + # Adds the ports as a graph attribute to be used within graph_to_netlist + graph.graph["ports"] = netlist.get("ports", {}).copy() + + return graph + + +def add_ports_to_graph(graph, netlist, models): + """Add top-level port nodes to a circuit graph. + + This helper is primarily used by `netlist_to_graph` for display + graphs. It reads `netlist["ports"]`, looks up the connected internal + port directionality, and adds boundary nodes connected to the owning + component. + """ + for external_port_name, internal_port_data in netlist["ports"].items(): + instance_name, internal_port_name = internal_port_data.split(",") + node_name = f".{external_port_name}" # . Symbol Ensures Uniqueness of node_name + directionality = ( + models[netlist["instances"][instance_name]["component"]] + ._port_lookup_table[internal_port_name] + .directionality + ) + add_port_to_graph( + graph, + instance_name, + internal_port_name, + directionality, + external=True, + external_port_name=external_port_name, + ) + + unconnected_ports = set() + connected_ports = { + connection + for connection in list(netlist["connections"].keys()) + + list(netlist["connections"].values()) + } + for instance_name, instance_data in netlist["instances"].items(): + for port in models[netlist["instances"][instance_name]["component"]].ports: + if not f"{instance_name},{port.name}" in connected_ports: + unconnected_ports.add(f"{instance_name},{port.name}") + + +def add_connection_to_graph( + graph, + src_node, + dst_node, + src_port, + dst_port, + src_directionality, + dst_directionality, + port_type=None, +): + """Add a connection edge using endpoint directionality rules. + + The source and destination in the netlist are treated as a physical + connection description. The actual graph edge direction is chosen + from the source/destination port directionality values. + Bidirectional ports create reverse edges where needed. + """ + if src_directionality == "bidirectional" and dst_directionality == "bidirectional": + graph.add_edge( + src_node, + dst_node, + src_port=src_port, + dst_port=dst_port, + hover=f"{src_node},{src_port}↔{dst_node},{dst_port}", + port_type=port_type, + ) + graph.add_edge( + dst_node, + src_node, + src_port=dst_port, + dst_port=src_port, + hover=f"{src_node},{src_port}↔{dst_node},{dst_port}", + port_type=port_type, + ) + elif ( + (src_directionality == "bidirectional" and dst_directionality == "input") + or (src_directionality == "output" and dst_directionality == "bidirectional") + or (src_directionality == "output" and dst_directionality == "input") + ): + graph.add_edge( + src_node, + dst_node, + src_port=src_port, + dst_port=dst_port, + hover=f"{src_node},{src_port}→{dst_node},{dst_port}", + port_type=port_type, + ) + elif ( + (src_directionality == "bidirectional" and dst_directionality == "output") + or (src_directionality == "input" and dst_directionality == "bidirectional") + or (src_directionality == "input" and dst_directionality == "output") + ): + graph.add_edge( + dst_node, + src_node, + src_port=dst_port, + dst_port=src_port, + hover=f"{src_node},{src_port}←{dst_node},{dst_port}", + port_type=port_type, + ) + elif (src_directionality == "unknown" and dst_directionality == "input") or ( + src_directionality == "output" and dst_directionality == "unknown" + ): + graph.add_edge( + src_node, + dst_node, + src_port=src_port, + dst_port=dst_port, + hover=f"{src_node},{src_port}→{dst_node},{dst_port}", + port_type=port_type, + ) + elif (src_directionality == "unknown" and dst_directionality == "output") or ( + src_directionality == "input" and dst_directionality == "unknown" + ): + graph.add_edge( + dst_node, + src_node, + src_port=dst_port, + dst_port=src_port, + hover=f"{src_node},{src_port}←{dst_node},{dst_port}", + port_type=port_type, + ) + elif src_directionality == "unknown" or dst_directionality == "unknown": + graph.add_edge( + src_node, + dst_node, + src_port=src_port, + dst_port=dst_port, + hover=f"{src_node},{src_port}?⎯?{dst_node},{dst_port}", + color="red", + type=port_type, + ) + else: + raise ValueError(f"Cannot connect {src_directionality} to {dst_directionality}") + + +def add_port_to_graph( + graph, + instance_name, + internal_port_name, + directionality, + external: bool, + external_port_name=None, + port_type=None, +): + """Add one visual port node and connect it to its component node.""" + if external: + node_name = f".{external_port_name}" # '.' enforces uniqueness + shape = ("circle",) + size = 8 + else: + node_name = f"{instance_name},{internal_port_name}" # ',' enforces uniqueness + size = (8,) + shape = "circle" + + graph.add_node( + node_name, + shape=shape, + opacity=1.0, + border_color="black", + border_size=1, + size=size, + color="white", + ) + + add_connection_to_graph( + graph, + node_name, + instance_name.strip(), + None, + None, + "bidirectional", + directionality, + port_type=port_type, + ) + + +def instantiated_flat_netlist_to_graph(instantiated_flat_netlist, include_ports=False): + """Convert an instantiated flat netlist into a graph. + + Unlike `netlist_to_graph`, this function reads port metadata from + the instantiated component objects stored under each instance's + `"model"` key. This is the graph form used by simulation drivers + after `Circuit.instantiate` has expanded PCells and placeholders. + """ + graph = nx.MultiDiGraph() + # Add nodes for each instance + for instance_name, instance in instantiated_flat_netlist["instances"].items(): + instantiated_flat_netlist["instances"][instance_name][ + "model" + ]._create_port_lookup_table() + graph.add_node( + instance_name, + component=instance["component"], + settings=instance["settings"], + shape="rectangle", + size=25, + border_size=1, + border_color="black", + color="white", + ) + # graph.add_node(instance_name, label="test", click="Test: $label", **instance_data) + # graph.add_node(instance_name, weight=netlist['instances'][instance_name]["weight"]) + + # Add edges based on connections + for src, dsts in instantiated_flat_netlist["connections"].items(): + for dst in dsts.split(";"): + if dst == "": + continue + src_instance, src_port = src.split(",") + dst_instance, dst_port = dst.split(",") + + src_port_directionality = ( + instantiated_flat_netlist["instances"][src_instance]["model"] + ._port_lookup_table[src_port] + .directionality + ) + dst_port_directionality = ( + instantiated_flat_netlist["instances"][dst_instance]["model"] + ._port_lookup_table[dst_port] + .directionality + ) + port_type = ( + instantiated_flat_netlist["instances"][src_instance]["model"] + ._port_lookup_table[src_port] + .type + ) + add_connection_to_graph( + graph, + src_instance.strip(), + dst_instance.strip(), + src_port.strip(), + dst_port.strip(), + src_port_directionality, + dst_port_directionality, + port_type=port_type, + ) + + if include_ports: + for external_port_name, internal_port_data in instantiated_flat_netlist[ + "ports" + ].items(): + instance_name, internal_port_name = internal_port_data.split(",") + node_name = ( + f".{external_port_name}" # . Symbol Ensures Uniqueness of node_name + ) + model = instantiated_flat_netlist["instances"][instance_name]["model"] + directionality = model._port_lookup_table[internal_port_name].directionality + port_type = model._port_lookup_table[internal_port_name].type + add_port_to_graph( + graph, + instance_name, + internal_port_name, + directionality, + external=True, + external_port_name=external_port_name, + port_type=port_type, + ) + + unconnected_ports = set() + connected_ports = { + connection + for connection in list(instantiated_flat_netlist["connections"].keys()) + + list(instantiated_flat_netlist["connections"].values()) + } + for instance_name, instance_data in instantiated_flat_netlist[ + "instances" + ].items(): + for port in instantiated_flat_netlist["instances"][instance_name][ + "model" + ].ports: + if ( + not f"{instance_name},{port.name}" in connected_ports + and not f"{instance_name},{port.name}" + in instantiated_flat_netlist["ports"].values() + ): + unconnected_ports.add(f"{instance_name},{port.name}") + # instantiated_flat_netlist['instances'][instance_name]['model']._port_lookup_table + add_port_to_graph( + graph, + instance_name, + port.name, + port.directionality, + external=False, + port_type=port.type, + ) + # graph.add_node( + # "FIX ME", + # shape="rectangle", + # opacity=1.0, + # border_color="black", + # border_size=1, + # size=15, + # color="white" + # ) + + # Matthew's Changes + # Adds the ports as a graph attribute to be used within graph_to_netlist + # graph.graph["ports"] = instantiated_flat_netlist.get("ports", {}).copy() + ### FOR NOW, JUST BE OKAY WITH THE FACT THAT WE LOSE THIS INFO + + return graph + + +# Matthew's Changes +# Completed the graph to netlist to be used however needed to also added +import networkx as nx + + +def graph_to_netlist(graph: nx.MultiDiGraph, ports=None) -> dict: + """Convert a NetworkX multidigraph into a SAX-compatible flat netlist. + + Assumptions: + - Node attrs contain: + - 'component' + - optional 'settings' + - Edge attrs contain: + - 'src_port' + - 'dst_port' + + Multiple edges between the same ports that merely represent + bidirectionality are collapsed into a single connection. + + SAX connections are represented as: + "inst1,portA": "inst2,portB" + """ + + if ports is None: + ports = {} + + netlist = { + "instances": {}, + "connections": {}, + "ports": ports.copy(), + } + + # ------------------------------------------------------------------ + # Instances + # ------------------------------------------------------------------ + + for node, data in graph.nodes(data=True): + netlist["instances"][node] = { + "component": data["component"], + "settings": data.get("settings", {}).copy(), + } + + # ------------------------------------------------------------------ + # Connections + # ------------------------------------------------------------------ + + # Use a set so we can ignore duplicated reverse-direction edges + seen_connections = set() + + for src, dst, attrs in graph.edges(data=True): + a = f"{src},{attrs['src_port']}" + b = f"{dst},{attrs['dst_port']}" + + # Canonicalize connection ordering so: + # A -> B + # and + # B -> A + # are treated as the same physical connection + canonical = tuple(sorted((a, b))) + + if canonical in seen_connections: + continue + + seen_connections.add(canonical) + + # Store only one direction in SAX netlist + netlist["connections"][a] = b + + return netlist + + +# def graph_to_netlist(graph: nx.MultiDiGraph, ports={}) -> dict: +# """ +# Convert a NetworkX MultiDiGraph (with node attrs 'component' and 'settings', +# edge attrs 'src_port' and 'dst_port', and graph.graph['ports']) back into a netlist: +# """ +# netlist = {"instances": {}, "connections": {}} + +# for node, data in graph.nodes(data=True): +# netlist["instances"][node] = { +# "component": data["component"], +# "settings": data.get("settings", {}).copy() +# } + +# conn_map = {} +# for src, dst, attrs in graph.edges(data=True): +# key = f"{src},{attrs['src_port']}" +# pair = f"{dst},{attrs['dst_port']}" +# conn_map.setdefault(key, []).append(pair) + +# for key, dsts in conn_map.items(): +# netlist["connections"][key] = ";".join(dsts) + +# # ports = graph.graph.get("ports", {}) +# # netlist["ports"] = ports.copy() +# netlist["ports"] = ports + +# return netlist + + +def sanitize_instance_names(netlist, old_separator="~", new_separator="_"): + """Replace a separator substring in instance names and references. + + SAX flattening commonly creates nested instance names containing + `"~"`. Some downstream tools expect valid Python identifiers, so + this helper rewrites instance names and updates `connections`, + `ports`, and optional `nets` entries consistently. + """ + import copy + + netlist = copy.deepcopy(netlist) + + # mapping old instance names -> new names + rename = { + name: name.replace(old_separator, new_separator) + for name in netlist.get("instances", {}) + if "~" in name + } + + if not rename: + return netlist + + def fix_ref(ref): + """Fix 'instance,port' references.""" + if isinstance(ref, str) and "," in ref: + inst, port = ref.split(",", 1) + inst = rename.get(inst, inst) + return f"{inst},{port}" + return ref + + # ---- rename instances ---- + instances = netlist.get("instances", {}) + new_instances = {} + for name, val in instances.items(): + new_instances[rename.get(name, name)] = val + netlist["instances"] = new_instances + + # ---- fix connections ---- + if "connections" in netlist: + new_connections = {} + for k, v in netlist["connections"].items(): + new_connections[fix_ref(k)] = fix_ref(v) + netlist["connections"] = new_connections + + # ---- fix ports ---- + if "ports" in netlist: + netlist["ports"] = { + name: fix_ref(ref) for name, ref in netlist["ports"].items() + } + + # ---- fix nets (optional SAX format) ---- + if "nets" in netlist: + for net in netlist["nets"]: + net["p1"] = fix_ref(net["p1"]) + net["p2"] = fix_ref(net["p2"]) + + return netlist + + +# def discrete_time_impulse_response(propagation_constants, sampling_freq, length=1e-6, N=20000): +# freqs = jnp.fft.fftfreq(N, d=1/sampling_freq) +# omega = 2*jnp.pi*freqs +# phi = jnp.zeros_like(omega, dtype=jnp.complex64) + +# for k, beta_k in propagation_constants.items(): +# phi += (beta_k * length * (omega ** k)) / factorial(k) + +# H = jnp.exp(-1j * phi) + +# return jnp.fft.ifftshift(jnp.fft.ifft(H)) + + +def generate_valid_separator(instance_names, old_separator="~", first_try="_SEP_"): + """Generate a separator that will not collide when replacing names. + + The returned separator can be used to replace `old_separator` + without causing two distinct instance names to collapse to the same + string. + """ + instance_names = list(instance_names) + new_separator = first_try + new_instance_names = [ + name.replace(old_separator, new_separator) for name in instance_names + ] + while not len(set(instance_names)) == len(set(new_instance_names)): + new_separator = "_" + new_separator + "_" + new_instance_names = [ + name.replace(old_separator, new_separator) for name in instance_names + ] + + return new_separator + + +def generate_unique_string(instance_names, first_try="xXx"): + """Return a string that is not contained in any instance name.""" + instance_names = list(instance_names) + new_str = first_try + + def instances_contain_string(str): + for instance_name in instance_names: + if str in instance_name: + return True + return False + + while instances_contain_string(new_str): + new_str = "_" + new_str + "_" + + return new_str + + +def remove_instances_from_netlist(netlist, instances_to_remove): + """Return a copy of `netlist` without selected instances. + + Connections and top-level ports touching removed instances are + removed as well. This is useful when extracting or replacing + subgraphs during PCell and S-parameter consolidation. + """ + instances_to_remove = set(instances_to_remove) + + new_netlist = { + "instances": {}, + "connections": {}, + "ports": {}, + } + + # Keep surviving instances + new_netlist["instances"] = { + name: inst + for name, inst in netlist["instances"].items() + if name not in instances_to_remove + } + + def touches_removed_instance(endpoint): + instance_name = endpoint.split(",")[0] + return instance_name in instances_to_remove + + # Keep only valid connections + new_netlist["connections"] = { + src: dst + for src, dst in netlist["connections"].items() + if not (touches_removed_instance(src) or touches_removed_instance(dst)) + } + + # Keep only valid external ports + new_netlist["ports"] = { + port: endpoint + for port, endpoint in netlist["ports"].items() + if not touches_removed_instance(endpoint) + } + + return new_netlist diff --git a/simphony/classical.py b/simphony/classical.py index fd585830..7729ab28 100644 --- a/simphony/classical.py +++ b/simphony/classical.py @@ -1,4 +1,5 @@ """Module for classical simulation.""" + import warnings from dataclasses import dataclass from functools import partial diff --git a/simphony/component/component.py b/simphony/component/component.py new file mode 100644 index 00000000..726d7103 --- /dev/null +++ b/simphony/component/component.py @@ -0,0 +1,588 @@ +###### Necessary ###### +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from simulation.block_mode import ( + BlockModeSimulationParameters, + ) # Only imported for type checking + from simulation.sample_mode import SampleModeSimulationParameters + from simulation.simulation import SimulationMode + +import inspect +from typing import Tuple + +import jax +import jax.numpy as jnp + +# from simphony.libraries.analytic.component_types import OpticalComponent, ElectricalComponent, LogicComponent +# import gravis as gv +# import sax +from jax.typing import ArrayLike +from scipy.constants import speed_of_light + +from simphony.signal.block_mode import BlockModeElectricalSignal, BlockModeOpticalSignal + +#### Include First #### + +# from dataclasses import replace + +# from simphony.circuit.port import Port + + +# from sax.saxtypes import Model as SaxModel +# from simphony.time_domain.pole_residue_model import IIRModelBaseband +# from simphony.simulation.simulation import SimulationParameters + +# from simphony.utils import dict_to_matrix +# from simphony.simulation import SampleModeSimulationParameters +# from copy import deepcopy +# from functools import partial +# from simphony.signals import SampleModeOpticalSignal, SampleModeElectricalSignal, SampleModeLogicSignal + +# from scipy.signal import butter, tf2ss, StateSpace, firwin, freqz, group_delay, cheby1, bessel +# from scipy.signal.windows import tukey +# from control import balred, ss +# import matplotlib.pyplot as plt +# from simphony.time_domain.vector_fitting.z_domain import optimize_order_vector_fitting_discrete, pole_residue_response_discrete, state_space_discrete + +# from simphony.utils import add_settings_to_netlist, get_settings_from_netlist, netlist_to_graph +# from copy import deepcopy +# from simphony.signals import steady_state_optical_signal, \ +# sample_mode_electrical_signal, \ +# sample_mode_optical_signal, \ +# sample_mode_logic_signal, \ +# block_mode_optical_signal, \ +# block_mode_electrical_signal, \ +# block_mode_logic_signal, \ +# complete_steady_state_inputs, \ +# complete_sample_mode_inputs +# from simphony.signal.block_mode import BlockModeElectricalSignal, BlockModeLogicSignal, BlockModeOpticalSignal +# from simphony.signal.steady_state import SteadyStateOpticalSignal + + +# from simphony.utils import dict_to_matrix +# from jax.scipy.special import i0 +# from scipy.special import lambertw + +# from scipy.signal.windows import kaiser_bessel_derived + +# def line_of_best_fit_m(x, y): +# x_mean = jnp.mean(x[:, None, None], axis=0) +# y_mean = jnp.mean(y, axis=0) +# cov = jnp.mean((x[:, None, None]-x_mean)*(y - y_mean), axis=0) +# var = jnp.mean((x[:, None, None]-x_mean)**2, axis=0) +# slope = cov/var +# intercept = y_mean - slope * x_mean +# return slope, intercept + +# def _extension_up(m, b, x, y_initial, y_final): +# k = m*(y_final - y_initial) + +# x_ext = x - x[0] +# y_ext = y_final + (y_initial - y_final)*jnp.exp(-k*x) +# return y_ext + +# def extend_down(m, b, y_f=0.0, N=500): +# pass + +# def extend(x, y, x_min, x_max, alpha=1e11): +# dy = y[-1] - y[-2] +# dx = x[-1] - x[-2] +# m = dy/dx +# b = y[-1] - m*x[-1] + +# for i in range(y.shape[1]): +# for j in range(y.shape[2]): +# if m[i, j] > 0: +# y_initial = y[-1, i, j] +# p = 1 - jnp.exp(-alpha*m[i, j]) +# y_final = y_initial + p*(1-y_initial) +# x_extension = jnp.arange(x[-1]+dx, x_max, dx) +# y_extension = _extension_up(m[i, j], b[i, j], x_extension, y_initial, y_final) + +# x_extended = jnp.concatenate([x, x_extension]) +# y_extended = jnp.concatenate([y[:, i, j], y_extension]) +# # plt.plot(x_extended, y_extended) +# # plt.plot(x, y[:, i, j]) +# plt.plot(x_extension, y_extension) +# # plt.xlim([199e12, 201e12]) +# plt.show() +# pass +# elif m[i, j] < 0: +# y_ext = extend_down(m[i, j], b[i, j]) +# else: +# pass +# # y_ext = y[-1, i, j]*jnp.ones(N) + + +# def extend_s_params(s_params, f, f_extended, alpha=1e11): +# magnitude = jnp.abs(s_params) +# phase = jnp.unwrap(jnp.angle(s_params), axis=0) +# phase_slope, phase_intercept = line_of_best_fit_m(f, phase) +# avg_phase = phase_slope[None, :, :]*f[:, None, None] + phase_intercept[None, :, :] +# normalized_phase = phase - avg_phase +# bandwidth = 0.5*jnp.abs(f[-1] - f[0]) +# magnitude_extended = extend(f, magnitude, f_extended[0], f_extended[-1], bandwidth/10) +# normalized_phase_extended = extend(f, phase) +# pass + + +# pass + +# def tukey_freq_window(freqs, fc, trans_width, alpha=None): +# """ +# Create a Tukey-like taper in frequency domain. + +# freqs: array of frequency points (can be positive or two-sided) +# fc: flat passband edge (Hz) +# trans_width: width of transition region (Hz) +# alpha: fraction of total width for cosine taper; if None, computed from trans_width +# """ +# W = jnp.zeros_like(freqs, dtype=float) +# f_abs = jnp.abs(freqs) # symmetric in frequency + +# # Passband region +# pass_region = f_abs <= fc +# W = W.at[pass_region].set(1.0) + +# # Transition region +# trans_region = (f_abs > fc) & (f_abs < fc + trans_width) +# x = (f_abs[trans_region] - fc) / trans_width # 0 → 1 over transition +# W = W.at[trans_region].set(0.5 * (1 + jnp.cos(jnp.pi * x))) + +# # Stopband region stays 0 +# return W + +# def expand_filter_to_mimo(A_f, B_f, C_f, D_f, num_ports): +# """Creates a block-diagonal MIMO filter from a single SISO filter.""" +# A = jax.scipy.linalg.block_diag(*[A_f] * num_ports) +# B = jnp.zeros((A.shape[0], num_ports)) +# C = jnp.zeros((num_ports, A.shape[0])) +# D = jnp.eye(num_ports) * D_f # diagonal D matrix + +# n = A_f.shape[0] # order of filter +# for i in range(num_ports): +# B = B.at[i*n:(i+1)*n, i].set(B_f[:, 0]) +# C = C.at[i, i*n:(i+1)*n].set(C_f[0, :]) + +# return A, B, C, D + +# def cascade_state_space(A1, B1, C1, D1, A2, B2, C2, D2): +# n1 = A1.shape[0] +# n2 = A2.shape[0] + +# A = jnp.block([ +# [A1, jnp.zeros((n1, n2))], +# [B2 @ C1, A2] +# ]) + +# B = jnp.vstack([ +# B1, +# B2 @ D1 +# ]) + +# C = jnp.hstack([ +# D2 @ C1, +# C2 +# ]) + +# D = D2 @ D1 + +# return A, B, C, D + + +class Signal: ## TODO: Make an actual base class + ... + + +class Component: + """Base class for objects that can be placed in a Simphony circuit. + + Concrete component classes define a class-level `ports` list + containing `Port` objects. Simulator-specific mixins such as + `BlockModeComponent`, `SampleModeComponent`, and + `SParameterComponent` then define the response methods that a + simulator is allowed to call. + """ + + def __repr__(self): + return f"<{type(self).__name__} (Component obj)>" + + @classmethod + def _create_port_lookup_table(cls): + """Build internal lookup tables from the component's declared ports. + + Bidirectional ports are included in both the input and output + lookup tables because they can participate on either side + depending on the simulation mode and netlist orientation. + """ + cls._port_lookup_table = {p.name: p for p in cls.ports} + # cls._input_port_lookup_table = {p.name: p for p in cls.ports if p.directionality=="input"} + # cls._output_port_lookup_table = {p.name: p for p in cls.ports if p.directionality=="output"} + cls._input_port_lookup_table = { + p.name: p + for p in cls.ports + if p.directionality == "input" or p.directionality == "bidirectional" + } + cls._output_port_lookup_table = { + p.name: p + for p in cls.ports + if p.directionality == "output" or p.directionality == "bidirectional" + } + + def __init__( + self, + simulation_mode: SimulationMode, + **kwargs, + ): + raise ValueError("Component is a base class") + + # simulation_parameters={} + # Used especially in time-domain simulations + + # electrical_ports = [] + # logic_ports = [] + # optical_ports = [] + + +class SteadyStateComponent(Component): + """Mixin for components that can compute a static operating point. + + Steady-state responses are commonly used to provide bias values for + frequency-domain or S-parameter simulations. Implementations receive + a dictionary of input signals keyed by port name and return output + signals in the same style. + """ + + delay_compensation = 0 + + def steady_state(self, inputs: dict) -> dict: + """Compute steady-state output signals. + + Parameters + ---------- + inputs: + Mapping from input port name to steady-state signal object. + + Returns + ------- + dict + Mapping from output port name to steady-state signal object. + """ + raise NotImplementedError( + f"{inspect.currentframe().f_code.co_name} method not defined for {self.__class__.__name__}" + ) + + +class BlockModeComponent(Component): + """Base class for components that process an entire time block at once. + + User-defined Block mode components should declare a class-level + `ports` list and implement `block_mode_response`. The simulator + supplies a dictionary of input signals keyed by port name, and the + method should return a dictionary of output signals keyed by output + port name. + """ + + # IDK the best name for this method! Maybe run, but that is confusing + def block_mode_response( + self, + input_signals: ArrayLike, + simulation_parameters: BlockModeSimulationParameters, + ): + """Compute output signals for one full Block mode time block. + + Parameters + ---------- + input_signals: + Mapping from input port name to a block signal object. Optical ports + receive `BlockModeOpticalSignal`; electrical ports receive + `BlockModeElectricalSignal`. + simulation_parameters: + Shared Block mode parameters defining the time grid, wavelengths, + and modes. + + Returns + ------- + dict + Mapping from output port name to block signal object. + """ + raise NotImplementedError + + def _block_mode_response(self, input_signals, simulation_parameters): + for port_name, port in self._input_port_lookup_table.items(): + input_signals.setdefault( + port_name, self._default_input_signal(simulation_parameters, port.type) + ) + outputs = self.block_mode_response(input_signals, simulation_parameters) + + baseband_wls = simulation_parameters.optical_baseband_wavelengths + time_steps = ( + jnp.arange(simulation_parameters.num_time_steps) * simulation_parameters.dt + ) + + for port, signal in outputs.items(): + if not isinstance(signal, BlockModeOpticalSignal): + continue + + amplitude = signal.amplitude + wavelengths = signal.wavelength + + distances = jnp.abs(baseband_wls[:, None] - wavelengths[None, :]) + closest_idx = jnp.argmin(distances, axis=0) + + f_diff = ( + speed_of_light / wavelengths + - speed_of_light / baseband_wls[closest_idx] + ) + + phase = jnp.exp(-1j * 2 * jnp.pi * time_steps[:, None] * f_diff[None, :]) + + new_amplitude = jnp.zeros( + (amplitude.shape[0], baseband_wls.shape[0], amplitude.shape[2]), + dtype=complex, + ) + + new_amplitude = new_amplitude.at[:, closest_idx, :].add( + amplitude * phase[:, :, None] + ) + + outputs[port] = BlockModeOpticalSignal( + amplitude=new_amplitude, + wavelength=baseband_wls, + ) + + return outputs + + def _default_input_signal(self, simulation_parameters, port_type): + if port_type == "optical": + wl = simulation_parameters.optical_baseband_wavelengths + T = simulation_parameters.num_time_steps + L = wl.shape[0] + M = len(simulation_parameters.mode_identifiers) + amplitude = jnp.zeros((T, L, M), dtype=complex) + return BlockModeOpticalSignal(amplitude=amplitude, wavelength=wl) + elif port_type == "electrical": + T = simulation_parameters.num_time_steps + voltage = jnp.zeros((T,), dtype=float) + return BlockModeElectricalSignal(voltage=voltage) + elif port_type == "logic": + raise NotImplementedError # TODO: Complete this function for more port types + else: + raise NotImplementedError( + f"Default signal not specified for ports of type {port_type}" + ) # TODO: Complete this function for more port types + + # TODO: Decide whether it is worth it to implement this function + # def _default_output_signal(simulation_parameters, port_type): + # pass + + +class SampleModeComponent(Component): + """Mixin for components that advance one simulation sample at a time. + + Sample mode components keep explicit state between time steps. The + simulator first calls `sample_mode_initial_state`, then repeatedly + calls `sample_mode_step` with the current inputs, component state, + global simulation state, and simulation parameters. + """ + + def sample_mode_initial_state( + self, simulation_parameters: SampleModeSimulationParameters + ): + """Return the component's initial sample-mode state. + + Components without internal memory can keep the default zero + state. + """ + return 0 + + def sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ) -> Tuple[dict[str, Signal], jax.Array]: + """Compute one sample of output and the next component state. + + Returns + ------- + tuple[dict, jax.Array] + Output signals keyed by port name, followed by the updated internal + state. + """ + raise NotImplementedError + + def _sample_mode_initial_state( + self, simulation_parameters: SampleModeSimulationParameters + ): + _initial_state = ( + 0, + self.sample_mode_initial_state(simulation_parameters=simulation_parameters), + ) + self._output_optical_port_names = [ + p.name + for p in self._output_port_lookup_table.values() + if (p.directionality == "bidirectional" or p.directionality == "output") + and p.type == "optical" + ] + return _initial_state + + # @partial(jax.jit, static_argnums=(0,)) + def _sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ): + time_step = state[0] + internal_state = state[1] + + f_s = 1 / simulation_parameters.dt + + outputs, output_state = self.sample_mode_step( + inputs, internal_state, simulation_state, simulation_parameters + ) + + # Convert all inputs to the frequency channels in the simulator + baseband_wls = simulation_parameters.optical_baseband_wavelengths + for port_name in self._output_optical_port_names: + signal = outputs[port_name] + amplitude = signal.amplitude + wavelength = signal.wavelength + if wavelength is baseband_wls: + continue + + dists = jnp.abs(baseband_wls[:, None] - wavelength[None, :]) + closest_idx = jnp.argmin(dists, axis=0) + + f_diff = ( + speed_of_light / wavelength - speed_of_light / baseband_wls[closest_idx] + ) + + new_amplitude = jnp.zeros( + (baseband_wls.shape[0], amplitude.shape[1]), dtype=complex + ) + new_amplitude = new_amplitude.at[closest_idx].add( + amplitude + * jnp.exp(-1j * 2 * jnp.pi * f_diff[:, None] / f_s * time_step) + ) + outputs[port_name] = signal.replace( + amplitude=new_amplitude, wavelength=baseband_wls + ) + + return outputs, (time_step + 1, output_state) + + +# TODO: Get rid of this +class SParameterComponent(Component): + """Mixin for components described by wavelength-dependent S-parameters. + + Optical ports are treated as scattering ports by default. Other + signal types are typically bias ports whose steady-state values + modify the returned S-parameter dictionary. + """ + + def s_parameters( + self, + inputs: dict, + wl: ArrayLike = 1.55e-6, + ): + """Return the component S-parameter dictionary at wavelength `wl`. + + Parameters + ---------- + inputs: + Bias or control signals keyed by port name. + wl: + Wavelength or wavelength array, in meters. + + Returns + ------- + sax.SDict + Mapping from `(output_port, input_port)` to complex transmission. + """ + raise NotImplementedError( + f"{inspect.currentframe().f_code.co_name} method not defined for {self.__class__.__name__}" + ) + + def s_parameter_get_bias_ports( + self, + ): + """Return ports whose steady-state inputs affect `s_parameters`. + + Bias ports receive steady-state signal values before the + S-parameter response is evaluated. + """ + return [] + + def _s_parameter_get_bias_ports( + self, + ): + """Bias ports are ports that recieve a steady state signal which in + some way modify the s-dict of an SParameterComponent.""" + return self.s_parameter_get_bias_ports() + + +class GaussianProcessComponent(Component): + """Base class for components that participate in GaussianProcessSimulation. + + Designers implement `gaussian_process_mode_response`; the simulator + calls `_gaussian_process_mode_response` (the wrapper). + """ + + def gaussian_process_mode_response( + self, inputs: dict, simulation_parameters + ) -> dict: + """Return a dict of GaussianProcessOpticalSignal for each output + port.""" + raise NotImplementedError + + def _gaussian_process_mode_response( + self, inputs: dict, simulation_parameters + ) -> dict: + for port_name, port in self._input_port_lookup_table.items(): + inputs.setdefault( + port_name, + self._default_gp_input_signal(simulation_parameters, port.type), + ) + return self.gaussian_process_mode_response(inputs, simulation_parameters) + + def _default_gp_input_signal(self, simulation_parameters, port_type: str): + from simphony.signal.gaussian_process import GaussianProcessOpticalSignal + + if port_type == "optical": + wl = simulation_parameters.optical_baseband_wavelengths + T = simulation_parameters.num_time_steps + L = wl.shape[0] + M = len(simulation_parameters.mode_identifiers) + return GaussianProcessOpticalSignal( + mean_amplitude=jnp.zeros((T, L, M), dtype=complex), + covariance=jnp.zeros((L, T, T, M, M), dtype=complex), + wavelength=wl, + ) + raise NotImplementedError( + f"No default GP signal defined for port type '{port_type}'" + ) + + +# # TODO: Get rid of this +# class OpticalSParameterComponent(SParameterComponent): +# # def __init__(self, **settings): +# # super().__init__(**settings) + +# def s_parameters( +# self, +# wl: ArrayLike, +# # **kwargs +# ): +# """ +# Returns an S-parameter matrix for the optical ports in the system +# """ +# raise NotImplementedError( +# f"{inspect.currentframe().f_code.co_name} method not defined for {self.__class__.__name__}" +# ) diff --git a/simphony/component/pcell.py b/simphony/component/pcell.py new file mode 100644 index 00000000..26a59cba --- /dev/null +++ b/simphony/component/pcell.py @@ -0,0 +1,224 @@ +# from sax import DEFAULT_MODES +import inspect +from copy import deepcopy + +import sax + +from simphony.component.component import Component +from simphony.simulation.simulation import SimulationParameters + + +### TODO: Decide whether PCells are Components or not, gotta love OOP +class PCell(Component): + """Base class for parameterized circuit cells. + + A PCell is useful when a component's internal netlist depends on + constructor settings or the active simulation mode. Circuit diagrams + can treat the PCell as one component, while simulation expands it + into its internal `netlist`, `models`, and `settings`. + + Subclasses should define external `ports` at the class level. During + `__init__`, the subclass should populate `self.netlist`, + `self.models`, and `self.settings` with the internal circuit that + should replace the PCell. + + The constructor is normally called by circuit instantiation rather + than directly by users building a netlist. + """ + + ports = None + + netlist = None + models = None + settings = None + + def __repr__(self): + return f"<{type(self).__name__} (PCell obj)>" + + def __init__( + self, + simulation_mode: SimulationParameters, + **kwargs, + ): + ... + + ### TODO: Make the method here convert to simphony Components firts, instead of instantiate_netlist function + def _instantiated_netlist( + self, + simulation_parameters: SimulationParameters, + # directed: bool = False, + # default_modes: tuple = DEFAULT_MODES, + ): + """Expand this PCell into an instantiated internal netlist. + + Raw SAX callables in `self.models` are converted to Simphony + placeholder components when possible, then the internal netlist + is instantiated with the active simulation parameters. + """ + if self.netlist is None: + raise NotImplementedError( + f"{self.__class__.__name__} must define `netlist` before instantiation" + ) + + if self.models is None: + raise NotImplementedError( + f"{self.__class__.__name__} must define `models` before instantiation" + ) + + from simphony.circuit.netlist import instantiate_netlist + + port_directionality = {port.name: port.directionality for port in self.ports} + + new_netlist, new_models, new_settings = _convert_sax_models( + simulation_parameters, + self.netlist, + self.models, + self.settings, + # directed, + # default_modes, + # port_directionality + ) + + instantiated_netlist = instantiate_netlist( + new_netlist, + new_models, + new_settings, + simulation_parameters, + # directed=directed, + # default_modes=default_modes, + ) + + # self.external_port_aliases = instantiated_netlist['ports'] + + return instantiated_netlist + + # def _instantiated_netlist( + # self, + # directed: bool = False, + # default_modes: tuple = DEFAULT_MODES, + # settings: dict = None, + # ): + # from simphony.circuit.netlist import instantiate_netlist + + # return instantiate_netlist( + # self.netlist, + # self.models, + # directed = directed, + # default_modes = default_modes, + # settings = settings + # ) + + +def _convert_sax_models( + simulation_parameters, + netlist, + models, + settings, + # directed, + # default_modes, + # port_directionality, +): + """Convert raw SAX models inside a PCell into Simphony placeholders. + + PCells may define their internal `models` dictionary using plain SAX + callables. During instantiation those callables need Simphony component + classes so that port metadata, settings, and simulator-specific expansion + can be handled uniformly. + + Parameters + ---------- + simulation_parameters: + Active simulation parameters. The `directed` flag controls whether raw + SAX models can be converted automatically. + netlist, models, settings: + Internal PCell definition to normalize. + + Returns + ------- + tuple + `(new_netlist, new_models, new_settings)` ready for recursive + instantiation. + """ + from simphony.circuit.netlist import add_settings_to_netlist + from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder + + netlist = sax.netlist(deepcopy(netlist)) + for _, subnetlist in netlist.items(): + add_settings_to_netlist( + subnetlist + ) # Just to normalize, we will use the settings the user provided later + netlist = sax.flatten_netlist(sax.netlist(netlist)) + new_netlist = deepcopy(netlist) + new_settings = deepcopy(settings) + # new_netlist = netlist + new_models = {} + unique_str = "_X_" + for key in netlist["instances"].keys(): + while unique_str in key: + unique_str += "_" + unique_word = unique_str + "DIRECTED_SPARAMETER_MODEL" + count = 0 + + external_port_lut = {v: k for k, v in netlist["ports"].items()} + for instance_name, instance_data in netlist["instances"].items(): + model_name = instance_data["component"] + model = models[model_name] + if inspect.isclass(model): + new_models[model_name] = model + continue + + if not simulation_parameters.directed: + ### TODO: Make the external port directionality match the pcell statement + ### "directed" only decides whether internal connection directionality should be interpretted based + ### on order, but the specification in the ports list can trump this + # print(port_directionality) + directionality = None # Defaults to bidirectional + new_models[model_name] = optical_s_parameter_placeholder( + model, directionality, simulation_parameters.mode_identifiers + ) + continue + + raise ValueError( + "Directed Simulations Require Component Types, no sax models allowed. This is because sax models are bidirectional" + ) + # count += 1 + # ### If directed, then we will have to make a new model for each s-parameter element + # directionality = { + # port_name : ( + # "output" if instance_name + "," + port_name in new_netlist['connections'].keys() + # else "input" if instance_name + "," + port_name in new_netlist['connections'].values() + # else None if False + # else "bidirectional" + # ) + # for port_name in sax.get_ports(model()) + # } + + # new_model_name = unique_word + f"_{count}_" + model_name + # new_models[new_model_name] = optical_s_parameter(model, directionality, default_modes) + + # if issubclass(model, Component) or issubclass(model, PCell): + # new_models[model_name] = model + # elif not directed: + # directionality = "bidirectional" + # model = optical_s_parameter(model, directionality, default_modes) + # elif directed: + # ### TODO: Find Directionality + # directionality = "" + # model = optical_s_parameter(model, directionality, default_modes) + + new_settings + + # TODO: Remove duplicate code. This is taken from InstantiatedCircuit __init__ + from simphony.libraries.ideal.s_parameters import SParameterPlaceholder + + # Reinterpret Sax Settings to optical_s_parameter Component settings + # for instance_name, instance_settings in settings.items(): + for instance_name in new_netlist["instances"].keys(): + model_name = new_netlist["instances"][instance_name]["component"] + if ( + issubclass(new_models[model_name], SParameterPlaceholder) + and not "sax_settings" in settings[instance_name].keys() + ): + new_settings[instance_name] = {"sax_settings": settings[instance_name]} + + return new_netlist, new_models, new_settings diff --git a/simphony/component/placeholder.py b/simphony/component/placeholder.py new file mode 100644 index 00000000..f5772104 --- /dev/null +++ b/simphony/component/placeholder.py @@ -0,0 +1,21 @@ +from simphony.component.component import Component +from simphony.simulation.simulation import SimulationParameters + + +class Placeholder(Component): + """Base class for temporary components replaced during instantiation. + + Placeholders are not meant to perform simulation work directly. They + let a circuit carry model metadata, such as a raw SAX callable and + its settings, until the active simulator knows how that model should + be expanded. For example, S-parameter placeholders can later become + vector-fitted Block mode components or frequency-domain S-parameter + elements. + """ + + def __init__( + self, + simulation_mode: SimulationParameters, + **kwargs, + ): + ... diff --git a/simphony/component/port.py b/simphony/component/port.py new file mode 100644 index 00000000..bb769b6a --- /dev/null +++ b/simphony/component/port.py @@ -0,0 +1,27 @@ +class Port: + """Represents a port on a component. + + Attributes: + name (str): The name of the port. + type (str): The type of signal carried by the port, "electrical", "optical", or "logic" + directionality (str): Either "input", "output", or "bidirectional". + position (str): The side of the component where the port is displayed ("left", "right", "Up", or "Down"). + location (float): Position along the specified side, ranging from 0 to 1. + """ + + def __init__( + self, + name: str, + directionality: str = "bidirectional", + type: str = "optical", + position: str = None, + location: float = None, + ): + self.name = name + self.type = type + self.directionality = directionality + self.position = position + self.location = location + + def __repr__(self): + return f"" diff --git a/simphony/conventions.py b/simphony/conventions.py new file mode 100644 index 00000000..c1a0cbc5 --- /dev/null +++ b/simphony/conventions.py @@ -0,0 +1,2 @@ +PHYSICIST = -1.0 +ENGINEER = 1.0 diff --git a/simphony/delete.py b/simphony/delete.py new file mode 100644 index 00000000..53a9a897 --- /dev/null +++ b/simphony/delete.py @@ -0,0 +1,165 @@ +import numpy as np +from matplotlib import pyplot as plt + +# from utils import add_settings_to_netlist, get_settings_from_netlist, netlist_to_graph, graph_to_netlist +import simphony.libraries.siepic as siepic +from simphony.circuit.circuit import Circuit +from simphony.simulation import SParameterSimulation + +# netlist={ +# "instances": { +# "splitter": { +# "component":"ybranch", +# "settings":{ +# "test_setting": 100, +# }, +# }, +# "combiner": "ybranch", +# "top1": "waveguide", +# "top2": "waveguide", +# "bot1": "waveguide", +# "bot2": "waveguide", + +# "pm1": "phase_modulator", +# "pm2": "phase_modulator", + +# "vs1": "voltage_source", +# "vs2": "voltage_source", +# "vs3": "voltage_source", + +# "opamp":"opamp", + +# "vf1":"voltage_follower", +# "vf2":"voltage_follower", +# "vf3":"voltage_follower", +# }, +# "connections": { +# "splitter,port_2":"top1,o0", +# "splitter,port_3":"bot1,o0", +# "top2,o1":"combiner,port_2", +# "bot2,o1": "combiner,port_3", +# "top1,o1":"pm1,o0", +# "pm1,o1":"top2,o0", +# "bot1,o1":"pm2,o0", +# "pm2,o1":"bot2,o0", + +# "vs1,e0":"""vf3,e0; +# vf1,e0;""", + +# "vs3,e0":"""vf2,e0; +# opamp,inv""", + +# "vs2,e0":"opamp,ninv", + +# "vf2,e1":"opamp,vp", + +# "vf3,e1":"pm2,e0", +# "vf1,e0":"opamp,vn", + +# "opamp,vout":"pm1,e0", +# }, +# "ports": { +# "in": "splitter,port_1", +# "out": "combiner,port_1", +# } +# } + +# models={ +# "ybranch": siepic.y_branch, +# # "ybranch": analytic.optical_s_parameter(siepic.y_branch), +# "waveguide": analytic.Waveguide, +# "phase_modulator": analytic.OpticalModulator, +# "voltage_source": analytic.VoltageSource, +# "prng": analytic.PRNG, +# "voltage_follower": analytic.VoltageFollower, +# "opamp": analytic.OpAmp, +# } + +# settings={ +# # "splitter": {"bad_setting": 10}, +# "top1": {"length": 5}, +# "top2": {"length": 5}, +# "bot1": {"length": 10}, +# } + +# ckt = Circuit(netlist, models, default_settings=settings) + +netlist = { + "instances": { + "hr1": "half_ring", + "hr2": "half_ring", + "w1": { + "component": "waveguide", + "settings": { + "length": 20, + }, + }, + "w2": "waveguide", + }, + "connections": { + # "hr1,port_1": "hr2,port_1", + # "hr1,port_3": "hr2,port_3", + "hr1,port_1": "w1,o0", + "w1,o1": "hr2,port_1", + "hr1,port_3": "w2,o0", + "w2,o1": "hr2,port_3", + }, + "ports": { + "o0": "hr1,port_2", + "o1": "hr2,port_2", + "o2": "hr1,port_4", + "o3": "hr2,port_4", + }, +} + +models = { + "half_ring": siepic.half_ring, + "waveguide": siepic.waveguide, +} + +ckt = Circuit(netlist, models) +wl = np.linspace(1.5, 1.6, 1000) * 1e-6 +settings = {} +sps = SParameterSimulation(ckt) +results = sps.run(wl, settings) +print("S-Parameters:") +print(results.s_parameters) +plt.plot(wl, np.abs(results.s_parameters[("o0", "o2")]) ** 2, label="S11") +plt.show() +plt.plot(wl, np.angle(results.s_parameters[("o0", "o2")]), label="S11") +plt.show() + + +# netlist = { +# "instances": { +# "hr1": "half_ring", +# "hr2": "half_ring", +# }, +# "connections": { +# "hr1,port_1": "hr2,port_1", +# "hr1,port_3": "hr2,port_3", + +# }, +# "ports": { +# "o0": "hr1,port_2", +# "o1": "hr2,port_2", +# "o2": "hr1,port_4", +# "o3": "hr2,port_4", +# } +# } + +# models = { +# "half_ring": siepic.half_ring, +# } + +# circuit,_ = sax.circuit( +# netlist=netlist, +# models=models, +# ) +# wl = np.linspace(1.5, 1.6, 200) +# model_settings = { +# "wl": wl, +# } +# s_params = circuit(**model_settings) +# plt.plot(wl, np.angle(s_params[("o0","o1")]), label="S11") +# plt.show() diff --git a/simphony/exceptions.py b/simphony/exceptions.py index 01d13f54..70845589 100644 --- a/simphony/exceptions.py +++ b/simphony/exceptions.py @@ -11,3 +11,7 @@ class ModelValidationError(SimphonyError): class ShapeMismatchError(SimphonyError): """Error raised when the shape of an array is incorrect.""" + + +class UndefinedActiveComponent(SimphonyError): + """Error raised when an active component is included in passive circuit.""" diff --git a/simphony/libraries/_internal/port_label.py b/simphony/libraries/_internal/port_label.py new file mode 100644 index 00000000..2159508c --- /dev/null +++ b/simphony/libraries/_internal/port_label.py @@ -0,0 +1,38 @@ +from simphony.component.placeholder import Placeholder +from simphony.component.port import Port + + +class PortLabel(Placeholder): + def __init__(self, simulation_parameters, *, name=None, designator=None): + self.name = name + self.designator = designator + + +class DirectedPortLabel(PortLabel): + ports = [ + Port( + name="in", + type="any", + directionality="input", + ), + Port( + name="out", + type="any", + directionality="output", + ), + ] + + +class BidirectionalPortLabel(PortLabel): + ports = [ + Port( + name="port1", + type="any", + directionality="bidirectional", + ), + Port( + name="port2", + type="any", + directionality="bidirectional", + ), + ] diff --git a/simphony/libraries/ideal.py b/simphony/libraries/ideal.py deleted file mode 100644 index 108e52c5..00000000 --- a/simphony/libraries/ideal.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright © Simphony Project Contributors -# Licensed under the terms of the MIT License -# (see simphony/__init__.py for details) -"""Ideal circuit models.""" - -import jax.numpy as jnp -import sax -from jax.typing import ArrayLike - - -def coupler( - *, - coupling: float = 0.5, - loss: float = 0.0, - phi: float = jnp.pi / 2, -) -> sax.SDict: - """A simple ideal coupler model. - - Ports are arranged as follows:: - - o2 ---\ /--- o3 - -------- - -------- - o0 ---/ \--- o1 - - Parameters - ---------- - coupling : float - Power coupling coefficient (default 0.5). - loss : float - Loss in dB (default 0.0). Positive values indicate loss. - phi : float - Phase shift of the reflected signal (default jnp.pi/2). - - Returns - ------- - sdict : sax.SDict - A dictionary of scattering matrices. - """ - - kappa = coupling**0.5 * 10 ** (-loss / 20) * jnp.exp(1j * phi) - tau = (1 - coupling) ** 0.5 * 10 ** (-loss / 20) - sdict = sax.reciprocal( - { - ("o2", "o3"): tau, - ("o2", "o1"): kappa, - ("o0", "o3"): kappa, - ("o0", "o1"): tau, - } - ) - return sdict - - -def waveguide( - *, - wl: ArrayLike | float = 1.55, - wl0: float = 1.55, - neff: float = 2.34, - ng: float = 3.4, - length: float = 10.0, - loss: float = 0.0, -) -> sax.SDict: - """A simple straight waveguide model. - - Port names are "o0" and "o1". - - Parameters - ---------- - wl : ArrayLike or float - Wavelength in microns (default 1.55). - wl0 : float - Center wavelength in microns (default 1.55). - neff : float - Effective index (default 2.34). - ng : float - Group index (default 3.4). - length : float - Length in microns (default 10.0). - loss : float - Loss in dB/cm (default 0.0). - - Returns: - A dictionary of scattering matrices. - """ - dwl = wl - wl0 - dneff_dwl = (ng - neff) / wl0 - _neff = neff - dwl * dneff_dwl - phase = 2 * jnp.pi * _neff * length / wl - # amplitude = jnp.asarray(10 ** (-loss * length / 20), dtype=complex) - loss_mag = loss / (10 * jnp.log10(jnp.exp(1))) - alpha = loss_mag * 1e-4 - amplitude = jnp.asarray(jnp.exp(-alpha * length / 2), dtype=complex) - transmission = amplitude * jnp.exp(1j * phase) - sdict = sax.reciprocal( - { - ("o0", "o1"): transmission, - } - ) - return sdict diff --git a/simphony/libraries/ideal/__init__.py b/simphony/libraries/ideal/__init__.py new file mode 100644 index 00000000..643ac381 --- /dev/null +++ b/simphony/libraries/ideal/__init__.py @@ -0,0 +1,8 @@ +# Copyright © Simphony Project Contributors +# Licensed under the terms of the MIT License +# (see simphony/__init__.py for details) +"""Ideal SAX-compatible circuit models.""" + +from simphony.libraries.old_ideal import coupler, waveguide + +__all__ = ["coupler", "waveguide"] diff --git a/simphony/libraries/ideal/couplers.py b/simphony/libraries/ideal/couplers.py new file mode 100644 index 00000000..f002f914 --- /dev/null +++ b/simphony/libraries/ideal/couplers.py @@ -0,0 +1,13 @@ +from typing import Type + + +def star_coupler(num_in: int, num_out: int) -> Type: + """Component Factory.""" + pass # Not implemented in this version + # class_name = f"StarCoupler{num_in}x{num_out}" + # in_ports = [f"o{i}" for i in range(num_in)] + # out_ports = [f"o{i}" for i in range(num_in, num_in + num_out)] + + # attr = {"optical_ports": in_ports + out_ports} + + # return type(class_name, (SteadyStateComponent, SampleModeComponent, BlockModeComponent), attr) diff --git a/simphony/libraries/ideal/digital_filters.py b/simphony/libraries/ideal/digital_filters.py new file mode 100644 index 00000000..04cf6e66 --- /dev/null +++ b/simphony/libraries/ideal/digital_filters.py @@ -0,0 +1,382 @@ +import jax +import jax.numpy as jnp +from scipy.constants import speed_of_light as SPEED_OF_LIGHT +from scipy.signal import lfilter + +from simphony.component.component import ( + BlockModeComponent, + Component, + SampleModeComponent, +) +from simphony.component.port import Port +from simphony.signal.block_mode import BlockModeOpticalSignal +from simphony.signal.sample_mode import SampleModeOpticalSignal +from simphony.simulation.simulation import SimulationParameters +from simphony.time_domain.vector_fitting.z_domain import ( + state_space_response_discrete, + state_space_response_discrete_optimized, +) + + +class OpticalDiscreteFilter( + SampleModeComponent, + BlockModeComponent, +): + ports = [ + Port( + name="in", + type="optical", + directionality="input", + ), + Port( + name="out", + type="optical", + directionality="output", + ), + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + b: jnp.ndarray = jnp.asarray([0.0, 1.0]), + a: jnp.ndarray = jnp.asarray([1.0]), + center_wl=1.55e-6, + delay_compensation=0, + ): + """ + b: of shape (num_modes, len_b) + a: of shape (num_modes, len_a) + + If for each mode, the a coefficients are of length 1, + then the filter will be optimized as a fir filter + """ + raise NotImplemented( + "Need to make sure this component matches the convention expected by the new sample mode and block mode simulators" + ) + # We need at least 1 filter for each mode + b, a = jnp.atleast_2d(b), jnp.atleast_2d(a) + self.filter_coefficients = b / a[:, 0], a / a[:, 0] + + if a.shape[1] == 1: + self.sample_mode_step = self._sample_mode_initial_state_fir + self.sample_mode_step = self._sample_mode_step_fir + else: + self.sample_mode_step = self._sample_mode_initial_state_iir + self.sample_mode_step = self._sample_mode_step_iir + + self.center_wl = center_wl + self.delay_compensation = 0 + + def _sample_mode_initial_state_iir(self, simulation_parameters): + b, a = self.filter_coefficients + M, L = ( + simulation_parameters.num_optical_modes, + simulation_parameters.optical_baseband_wavelengths.shape[0], + ) + # weight_x = jnp.zeros((M, L), dtype=complex) + x_hist = jnp.zeros((L, M, b.shape[1]), dtype=complex) + # weight_y = jnp.zeros((M, L), dtype=complex) + y_hist = jnp.zeros((L, M, a.shape[1]), dtype=complex) + state = (x_hist, y_hist) + return state + + def _sample_mode_step_iir( + self, inputs: dict, state: jax.Array, simulation_state, simulation_parameters + ): + delay_compensation = self.delay_compensation + + x_hist, y_hist = state + + ### + # TODO: Modulated the inputs by delta_omega + ### + + input_signal = inputs["in"] + input_amplitude = input_signal.amplitude + input_wavelength = input_signal.wavelength + + output_amplitude = jnp.zeros_like(input_amplitude) + output_wavelength = input_wavelength + + x_hist = jnp.roll(x_hist, shift=1, axis=2) + x_hist = x_hist.at[:, :, 0].set(input_amplitude) + y_hist = jnp.roll(y_hist, shift=1, axis=2) + + b, a = self.filter_coefficients + for mode_idx in range(simulation_parameters.num_optical_modes): + b_single_mode, a_single_mode = b[mode_idx], a[mode_idx] + for wl_idx, wl in enumerate(input_wavelength): + f_center = SPEED_OF_LIGHT / self.center_wl + f = SPEED_OF_LIGHT / wl + delta_omega = 2 * jnp.pi * (f - f_center) / f_center + + weight_x = b_single_mode @ x_hist[wl_idx, mode_idx] + weight_y = a_single_mode[1:] @ y_hist[wl_idx, mode_idx, 1:] + y_single_mode = weight_x - weight_y + output_amplitude = output_amplitude.at[wl_idx, mode_idx].set( + y_single_mode + ) + + y_hist = y_hist.at[:, :, 0].set(output_amplitude) + + outputs = { + "in": SampleModeOpticalSignal( + amplitude=jnp.zeros_like(output_amplitude), wavelength=output_wavelength + ), + "out": SampleModeOpticalSignal( + amplitude=output_amplitude, wavelength=output_wavelength + ), + } + + state = (x_hist, y_hist) + return outputs, state + + def block_mode_response(self, inputs: dict, simulation_parameters): + input_signal = inputs["in"] + input_amplitude = input_signal.amplitude + input_wavelength = input_signal.wavelength + + output_amplitude = jnp.zeros_like(input_signal.amplitude) + output_wavelength = input_wavelength + + n = jnp.arange(0, output_amplitude.shape[0], 1) + + b, a = self.filter_coefficients + for mode_idx in range(simulation_parameters.num_optical_modes): + b_single_mode, a_single_mode = b[mode_idx], a[mode_idx] + for wl_idx, wl in enumerate(input_wavelength): + f_center = SPEED_OF_LIGHT / self.center_wl + f = SPEED_OF_LIGHT / wl + delta_omega = 2 * jnp.pi * (f - f_center) / f_center + x_single_mode = ( + jnp.exp(-delta_omega * n * 1j) + * input_amplitude[:, wl_idx, mode_idx] + ) + y_single_mode = jnp.exp(delta_omega * n * 1j) * lfilter( + b_single_mode, a_single_mode, x_single_mode + ) + output_amplitude = output_amplitude.at[:, wl_idx, mode_idx].set( + y_single_mode + ) + + outputs = { + "in": BlockModeOpticalSignal( + amplitude=jnp.zeros_like(output_amplitude), + wavelength=output_wavelength, + ), + "out": BlockModeOpticalSignal( + amplitude=output_amplitude, wavelength=output_wavelength + ), + } + + return outputs + + +def bidirectional_discrete_state_space(num_inputs, num_outputs) -> type[Component]: + """A is nxn, where n is the number of model poles B is nxm, where m is the + number of ports C is mxn, and D is mxm.""" + + if not num_inputs == num_outputs: + raise ValueError( + "number of inputs and outputs must match for bidirectional state space models" + ) + raise NotImplementedError("bidirectional state space model not implemented") + + +def discrete_state_space( + num_inputs, + num_outputs, + input_port_names=None, + output_port_names=None, +) -> type[Component]: + """Constructor for creating state space models of arbitrary dimension.""" + + if input_port_names is None: + input_port_names = [f"port{i}_in" for i in range(num_inputs)] + if output_port_names is None: + output_port_names = [f"port{i}_out" for i in range(num_outputs)] + + # if not input_port_names is None: + # if True: + # pass + + class DiscreteStateSpace( + SampleModeComponent, + BlockModeComponent, + ): + """A is nxn, where n is the number of poles B is nxm, where m is the + number of inputs C is pxn, where p is the number of outputs and D is + pxm. + + Port names are assigned dynamically starting at 'port0_in' / + 'port0_out' and ending in 'p{m-1}_in' / 'p{p-1}_out' + + All input signals should be located in mode 0 (usually called + the TE MODE), otherwise they will be ignored + """ + + ports = [ + Port(name=port_name, type="optical", directionality="input") + for port_name in input_port_names + ] + [ + Port(name=port_name, type="optical", directionality="output") + for port_name in output_port_names + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + A=None, + B=None, + C=None, + D=None, + baseband_frequency=1.93e14, + sampling_frequency=None, + delay_compensation=0, + mode=0, + ): + self.state_space_matrices = A, B, C, D + # self.center_wl = center_wl + self.baseband_frequency = baseband_frequency + self.sampling_frequency = sampling_frequency + self.delay_compensation = delay_compensation + self.mode = mode + + def sample_mode_initial_state(self, simulation_parameters): + A, B, C, D = self.state_space_matrices + x = jnp.zeros( + (len(simulation_parameters.optical_baseband_wavelengths), A.shape[0]), + dtype=complex, + ) + return x + + def sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters, + ): + x = state + A, B, C, D = self.state_space_matrices + u = jnp.zeros( + ( + len(simulation_parameters.optical_baseband_wavelengths), + len(input_port_names), + ), + dtype=complex, + ) + + for port, signal in inputs.items(): + port_idx = self.port_order[port] + u = u.at[:, port_idx].set(signal.amplitude[:, self.mode]) + + new_x = jnp.zeros_like(x) + y = jnp.zeros( + ( + len(simulation_parameters.optical_baseband_wavelengths), + len(output_port_names), + ), + dtype=complex, + ) + + for i, f in enumerate( + SPEED_OF_LIGHT / simulation_parameters.optical_baseband_wavelengths + ): + new_x = new_x.at[i].set(A @ x[i] + B @ u[i]) + y = y.at[i].set(C @ x[i] + D @ u[i]) + + outputs = {} + for i, port in enumerate(output_port_names): + A_t = y[:, i] + outputs[port] = SampleModeOpticalSignal( + amplitude=A_t.reshape( + (len(simulation_parameters.optical_baseband_wavelengths), 1) + ), + wavelength=simulation_parameters.optical_baseband_wavelengths, + ) + + return outputs, new_x + + def block_mode_response(self, input_signals, simulation_parameters): + """We assume that all signals are on a common mode.""" + # TODO: Make sure that the matrix elements match port order. + _input_amplitude = list(input_signals.values())[0].amplitude + wavelengths = list(input_signals.values())[0].wavelength + N = _input_amplitude.shape[0] + L = _input_amplitude.shape[1] + M = len(simulation_parameters.mode_identifiers) + common_mode = simulation_parameters.mode_identifiers[0] + common_mode_index = simulation_parameters.mode_identifiers.index( + common_mode + ) + + # TODO: Make it so that the state space model only has M input ports and N output ports and not NXN + u = jnp.zeros((N, L, num_inputs), dtype=complex) + for i, port_name in enumerate(input_port_names): + u = u.at[:, :, i].set( + input_signals[port_name].amplitude[:, :, common_mode_index] + ) + + y = jnp.zeros((N, L, num_outputs), dtype=complex) + A, B, C, D = self.state_space_matrices + for i, wl in enumerate(wavelengths): + # TODO: modulate inputs based on the delta f + f = SPEED_OF_LIGHT / wl + delta_omega = ( + 2 * jnp.pi * (f - self.baseband_frequency) / self.sampling_frequency + ) + if simulation_parameters.use_state_space_optimization: + _y, _ = state_space_response_discrete_optimized( + jnp.exp(1j * delta_omega) * A, + B, + C, + D, + jnp.exp(1j * delta_omega), + u[:, i, :], + ) + else: + _y, _ = state_space_response_discrete( + jnp.exp(1j * delta_omega) * A, + jnp.exp(1j * delta_omega) * B, + C, + D, + u[:, i, :], + ) + y = y.at[:, i, :].set(_y) + + outputs = {} + for i, out_port_name in enumerate(output_port_names): + amplitude = jnp.zeros((N, L, M), dtype=complex) + amplitude = amplitude.at[:, :, common_mode_index].set(y[:, :, i]) + outputs[out_port_name] = BlockModeOpticalSignal( + amplitude=amplitude, + wavelength=wavelengths, + ) + + return outputs + + def to_fir_filter( + self, + ) -> Component: + ### TODO: Actually return a fir filter + return None + + return DiscreteStateSpace + + +def mimo_fir_filter( + impulse_response: jax.Array = jnp.array([[[]]]), + center_wl=1.55e-6, + sampling_period=None, +) -> type[Component]: + """""" + + class MIMOFIRFilter( + SampleModeComponent, + ): + """If a.""" + + ports = ... diff --git a/simphony/libraries/ideal/direction.py b/simphony/libraries/ideal/direction.py new file mode 100644 index 00000000..ba4a08a6 --- /dev/null +++ b/simphony/libraries/ideal/direction.py @@ -0,0 +1,25 @@ +# We decided not to overcomplicate the sample mode simulator with this component +# class OpticalDirectionalityTranslator(SampleModeComponent, BlockModeComponent, SParameterComponent): +# """ +# It may be natural to link a bidirectional port to an input port of one component +# and an output port of another. One downside of this approach, is that it requires +# every simphony simulator to update this Component with the appropriate repeator like +# functionality. +# """ +# ports = [ +# Port( +# name = "bidirectional", +# type = "optical", +# direcitonality = "bidirectional", +# ), +# Port( +# name = "in", +# type = "optical", +# direcitonality = "input", +# ), +# Port( +# name = "out", +# type = "optical", +# direcitonality = "output", +# ), +# ] diff --git a/simphony/libraries/ideal/electrical_circuits.py b/simphony/libraries/ideal/electrical_circuits.py new file mode 100644 index 00000000..94020959 --- /dev/null +++ b/simphony/libraries/ideal/electrical_circuits.py @@ -0,0 +1,63 @@ +import jax +import jax.numpy as jnp +from jax.typing import ArrayLike + +from simphony.component.component import ( + BlockModeComponent, + SampleModeComponent, + SteadyStateComponent, +) +from simphony.component.port import Port +from simphony.simulation.simulation import SimulationParameters + + +class VoltageFollower( + SteadyStateComponent, + SampleModeComponent, + BlockModeComponent, +): + """Pass an electrical signal from input port `e0` to output port `e1`. + + This helper is useful in directed Block mode netlists when an + electrical signal needs an explicit through component. The Block + mode response returns the input electrical signal object unchanged. + """ + + ports = [ + Port( + name="e0", + type="electrical", + directionality="input", + ), + Port( + name="e1", + type="electrical", + directionality="output", + ), + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + ): + pass + + def steady_state( + self, + inputs: dict, + simulation_parameters: SimulationParameters, + ): + outputs = {"e1": inputs["e0"]} + return outputs + + def block_mode_response(self, input_signals: ArrayLike, simulation_parameters): + return {"e1": input_signals["e0"]} + + def sample_mode_step( + self, inputs: dict, state: jax.Array, simulation_state, simulation_parameters + ): + # TODO: Complete this to use the signal defined in settings + return inputs, state + + def sample_mode_initial_state(self, simulation_parameters): + return jnp.array([0]) diff --git a/simphony/libraries/ideal/gaussian_process.py b/simphony/libraries/ideal/gaussian_process.py new file mode 100644 index 00000000..4d4b3013 --- /dev/null +++ b/simphony/libraries/ideal/gaussian_process.py @@ -0,0 +1,388 @@ +"""Gaussian process components for GaussianProcessSimulation. + +Provides: + - GaussianProcessCWSource — constant-wave optical source with amplitude noise + - LTIGaussianProcessSystem — flat component driven by a precomputed state-space + - gaussian_process_s_parameter — factory: SAX model → GP-compatible component class + +The factory mirrors `optical_s_parameter` from s_parameters.py but skips the +mode-converter / demultiplexer sub-netlist entirely. Port-to-state-space-column +mapping is handled inline using the "@mode" suffix conventions that +`_calculate_state_space_coefficients_from_sax_model` already produces. +""" + +from __future__ import annotations + +import jax +import jax.numpy as jnp +from sax import DEFAULT_MODES +from scipy.constants import speed_of_light + +from simphony.component.component import GaussianProcessComponent +from simphony.component.port import Port + +# Private helpers reused from s_parameters.py — no re-implementation needed. +from simphony.libraries.ideal.s_parameters import ( + _calculate_state_space_coefficients_from_sax_model, + _default_vector_fitting_parameters, + _get_filtered_sax_model, + _get_port_names_without_mode, +) +from simphony.signal.gaussian_process import GaussianProcessOpticalSignal +from simphony.time_domain.stochastic.gaussian_process import ( + gaussian_process_response, + white_noise_covariance, +) +from simphony.time_domain.vector_fitting.z_domain import state_space_response_discrete + +# --------------------------------------------------------------------------- +# Source +# --------------------------------------------------------------------------- + + +class GaussianProcessCWSource(GaussianProcessComponent): + """Constant-wave optical source for Gaussian process simulations. + + Generates a GaussianProcessOpticalSignal with a flat (constant) mean field + and spectrally white amplitude noise described by `noise_power`. + + Parameters (passed via settings dict) + -------------------------------------- + amplitude : complex + Complex field amplitude of the CW signal (default 1.0 + 0j). + noise_power : float + Variance of the complex amplitude noise per polarisation mode + (default 0.0 — noiseless coherent source). + """ + + ports = [Port(name="o0", type="optical", directionality="output")] + + def __init__( + self, + simulation_parameters, + *, + amplitude: complex = 1.0 + 0j, + noise_power: float = 0.0, + ): + self.amplitude = amplitude + self.noise_power = noise_power + + def gaussian_process_mode_response( + self, inputs: dict, simulation_parameters + ) -> dict: + wl = simulation_parameters.optical_baseband_wavelengths + T = simulation_parameters.num_time_steps + L = wl.shape[0] + M = len(simulation_parameters.mode_identifiers) + + mean = self.amplitude * jnp.ones((T, L, M), dtype=complex) + + # white_noise_covariance returns (T, T, M, M); broadcast over wavelengths. + cov_single = white_noise_covariance(T, M, sigma_sq=self.noise_power) + cov = jnp.stack([cov_single] * L, axis=0) # (L, T, T, M, M) + + return { + "o0": GaussianProcessOpticalSignal( + mean_amplitude=mean, + covariance=cov, + wavelength=wl, + ) + } + + +# --------------------------------------------------------------------------- +# LTI element +# --------------------------------------------------------------------------- + + +class LTIGaussianProcessSystem(GaussianProcessComponent): + """Flat GP component backed by a discrete-time state-space model. + + Initialized with the ABCD matrices from vector fitting and the + port-mode metadata produced by + `_calculate_state_space_coefficients_from_sax_model`. + + For each wavelength in `simulation_parameters.optical_baseband_wavelengths` + the component: + 1. Frequency-shifts A and B by `exp(j·delta_omega)`. + 2. Computes the K-tap impulse response h_l by sending unit impulses through + the shifted state-space (one per input channel). + 3. Stacks input means and block-diagonal covariances from all input ports. + 4. Calls `gaussian_process_response(h_l, mu_x, Cx)`. + 5. Splits the output mean / covariance back into per-port GP signals. + + Notes + ----- + Cross-port input covariances are assumed to be zero (independent inputs). + Within-port mode covariances are fully preserved. + + Parameters (constructor keyword args) + -------------------------------------- + state_space_matrices : tuple (A, B, C, D) + input_ports : list[str] e.g. ["o0@TE", "o0@TM"] + output_ports : list[str] e.g. ["o1@TE", "o1@TM"] + center_frequency : float Hz + sampling_frequency: float Hz + """ + + # Subclasses / factory must set `ports` as a class attribute. + ports: list = [] + + def __init__( + self, + simulation_parameters, + *, + state_space_matrices, + input_ports: list, + output_ports: list, + center_frequency: float, + sampling_frequency: float, + ): + self.A, self.B, self.C, self.D = state_space_matrices + self.input_ports = input_ports # ["o0@TE", ...] + self.output_ports = output_ports # ["o1@TE", ...] + self.center_frequency = center_frequency + self.sampling_frequency = sampling_frequency + + # ------------------------------------------------------------------ + # helpers + # ------------------------------------------------------------------ + + @staticmethod + def _parse_port_mode(port_mode_str: str): + """Split "port@mode" into ("port", "mode").""" + port, mode = port_mode_str.rsplit("@", 1) + return port, mode + + def _mode_index(self, mode: str, simulation_parameters) -> int: + modes = list(simulation_parameters.mode_identifiers) + # case-insensitive match + mode_lower = mode.lower() + for idx, m in enumerate(modes): + if str(m).lower() == mode_lower: + return idx + return 0 # fallback + + def _compute_impulse_response( + self, + A_shifted: jax.Array, + B_shifted: jax.Array, + K: int, + ) -> jax.Array: + """H[k, n_out, n_in] via unit-impulse inputs through the state- + space.""" + n_in = B_shifted.shape[1] + cols = [] + for i in range(n_in): + u = jnp.zeros((K, n_in), dtype=complex).at[0, i].set(1.0) + y, _ = state_space_response_discrete( + A_shifted, B_shifted, self.C, self.D, u + ) + cols.append(y) + return jnp.stack(cols, axis=2) # (K, n_out, n_in) + + # ------------------------------------------------------------------ + # main response + # ------------------------------------------------------------------ + + def gaussian_process_mode_response( + self, inputs: dict, simulation_parameters + ) -> dict: + wls = simulation_parameters.optical_baseband_wavelengths + T = simulation_parameters.num_time_steps + M = len(simulation_parameters.mode_identifiers) + L = wls.shape[0] + K = simulation_parameters.num_ir_taps + fs = self.sampling_frequency + fc = self.center_frequency + + n_in = len(self.input_ports) + n_out = len(self.output_ports) + + # Parse port-mode lists once. + in_pm = [self._parse_port_mode(s) for s in self.input_ports] + out_pm = [self._parse_port_mode(s) for s in self.output_ports] + + # Collect unique output port names for accumulator initialisation. + out_port_names = list(dict.fromkeys(pm[0] for pm in out_pm)) + + # Per-output-port accumulators: (L, T, M) for mean, (L, T, T, M, M) for cov. + out_mean_acc = {p: jnp.zeros((L, T, M), dtype=complex) for p in out_port_names} + out_cov_acc = { + p: jnp.zeros((L, T, T, M, M), dtype=complex) for p in out_port_names + } + + for wl_idx, wl in enumerate(wls): + # ---- 1. Frequency-shift state-space ---- + # wl is in metres; fc is center frequency in Hz. Convert wl to Hz first. + delta_omega = 2.0 * jnp.pi * (speed_of_light / wl - fc) / fs + A_s = jnp.exp(1j * delta_omega) * self.A + B_s = jnp.exp(1j * delta_omega) * self.B + + # ---- 2. Impulse response ---- + h_l = self._compute_impulse_response(A_s, B_s, K) # (K, n_out, n_in) + + # ---- 3. Stack inputs → (T, n_in) mean and (T, T, n_in, n_in) cov ---- + mu_x = jnp.zeros((T, n_in), dtype=complex) + Cx = jnp.zeros((T, T, n_in, n_in), dtype=complex) + + for col_idx, (port_name, mode) in enumerate(in_pm): + m_idx = self._mode_index(mode, simulation_parameters) + sig = inputs[port_name] # GaussianProcessOpticalSignal + mu_x = mu_x.at[:, col_idx].set(sig.mean_amplitude[:, wl_idx, m_idx]) + # Diagonal covariance block for this channel. + # sig.covariance shape: (L, T, T, M, M) + Cx = Cx.at[:, :, col_idx, col_idx].set( + sig.covariance[wl_idx, :, :, m_idx, m_idx] + ) + # Off-diagonal mode-mode cross-covariance within same port. + for col_idx2, (port_name2, mode2) in enumerate(in_pm): + if col_idx2 == col_idx or port_name2 != port_name: + continue + m_idx2 = self._mode_index(mode2, simulation_parameters) + Cx = Cx.at[:, :, col_idx, col_idx2].set( + sig.covariance[wl_idx, :, :, m_idx, m_idx2] + ) + + # ---- 4. Papoulis propagation ---- + mu_y, Cy = gaussian_process_response(h_l, mu_x, Cx) + # mu_y: (T, n_out), Cy: (T, T, n_out, n_out) + + # ---- 5. Split outputs back to per-port accumulators ---- + for row_idx, (port_name, mode) in enumerate(out_pm): + m_idx = self._mode_index(mode, simulation_parameters) + out_mean_acc[port_name] = ( + out_mean_acc[port_name].at[wl_idx, :, m_idx].set(mu_y[:, row_idx]) + ) + + for row_idx2, (port_name2, mode2) in enumerate(out_pm): + if port_name2 != port_name: + continue + m_idx2 = self._mode_index(mode2, simulation_parameters) + out_cov_acc[port_name] = ( + out_cov_acc[port_name] + .at[wl_idx, :, :, m_idx, m_idx2] + .set(Cy[:, :, row_idx, row_idx2]) + ) + + # Build output dict. + outputs = {} + for port_name in out_port_names: + # out_mean_acc[port_name]: (L, T, M) → transpose to (T, L, M) + mean = out_mean_acc[port_name].transpose(1, 0, 2) + cov = out_cov_acc[port_name] # already (L, T, T, M, M) + outputs[port_name] = GaussianProcessOpticalSignal( + mean_amplitude=mean, + covariance=cov, + wavelength=wls, + ) + return outputs + + +# --------------------------------------------------------------------------- +# Factory +# --------------------------------------------------------------------------- + + +def gaussian_process_s_parameter( + sax_model, + port_directionality: dict = None, + default_modes=DEFAULT_MODES, +): + """Create a GP-compatible component class from a SAX model. + + This is the GP-mode analog of ``optical_s_parameter`` from s_parameters.py. + Returns a *class* (not an instance) that: + + - Has ports derived from the SAX model signature. + - On instantiation, performs vector fitting to obtain A, B, C, D matrices + and stores them in an ``LTIGaussianProcessSystem``. + - Implements ``gaussian_process_mode_response`` via the Papoulis equations. + + There are no sub-netlists, mode converters, or demultiplexers — the port-to + state-space-column mapping is done inline using the "@mode" suffixes that + ``_calculate_state_space_coefficients_from_sax_model`` already produces. + + Parameters + ---------- + sax_model : callable + A SAX-compatible model (e.g. ``ideal.waveguide``). + port_directionality : dict, optional + Mapping from port name to ``"input"``, ``"output"``, or + ``"bidirectional"`` (default ``"bidirectional"`` for all ports). + default_modes : tuple, optional + Polarisation modes to include (default ``sax.DEFAULT_MODES``). + + Returns + ------- + type + A class that is a subclass of ``LTIGaussianProcessSystem``. + + Example + ------- + :: + + ring = gaussian_process_s_parameter( + ring_sax_model, + port_directionality={"o0": "input", "o1": "output"}, + ) + circuit = Circuit(netlist=..., models={"ring": ring, ...}) + """ + if port_directionality is None: + port_directionality = {} + + if isinstance(default_modes, str): + default_modes = [default_modes] + default_modes = tuple(default_modes) + + pcell_port_names = _get_port_names_without_mode(sax_model) + + class GaussianProcessSParameterElement(LTIGaussianProcessSystem): + _sax_model = staticmethod(sax_model) + ports = [ + Port( + name=port_name, + type="optical", + directionality=port_directionality.get(port_name, "bidirectional"), + ) + for port_name in pcell_port_names + ] + + def __init__(self, simulation_parameters, **kwargs): + sax_settings = kwargs.get("sax_settings", {}) + vf_params = kwargs.get( + "vector_fitting_parameters", _default_vector_fitting_parameters + ) + delay_comp = kwargs.get("delay_compensation", 0) + + filtered_sax = _get_filtered_sax_model( + sax_model, port_directionality, default_modes + ) + ( + (A, B, C, D), + in_ports, + out_ports, + ) = _calculate_state_space_coefficients_from_sax_model( + filtered_sax, + sax_settings, + vf_params, + simulation_parameters, + delay_comp, + ) + + super().__init__( + simulation_parameters, + state_space_matrices=(A, B, C, D), + input_ports=in_ports, + output_ports=out_ports, + center_frequency=speed_of_light / vf_params["center_wavelength"], + sampling_frequency=1.0 / simulation_parameters.dt, + ) + + GaussianProcessSParameterElement.__name__ = ( + f"GP_{getattr(sax_model, '__name__', 'model')}" + ) + GaussianProcessSParameterElement.__qualname__ = ( + GaussianProcessSParameterElement.__name__ + ) + return GaussianProcessSParameterElement diff --git a/simphony/libraries/ideal/modulators.py b/simphony/libraries/ideal/modulators.py new file mode 100644 index 00000000..705c9755 --- /dev/null +++ b/simphony/libraries/ideal/modulators.py @@ -0,0 +1,316 @@ +# class OpticalAmplitudeModulator(): +# pass + +import jax +import jax.numpy as jnp +import sax +from jax.typing import ArrayLike + +from simphony.component.component import ( + BlockModeComponent, + SampleModeComponent, + SParameterComponent, + SteadyStateComponent, +) +from simphony.component.port import Port +from simphony.signal.block_mode import BlockModeOpticalSignal +from simphony.signal.steady_state import SteadyStateOpticalSignal +from simphony.simulation.simulation import SimulationParameters + + +class DirectedOpticalModulator(BlockModeComponent): + """Directed Block mode optical modulator. + + The component reads an optical input on `o0`, an electrical drive on `e0`, + and emits the modulated optical signal on `o1`. For each optical mode, the + electrical voltage is evaluated through polynomial phase and absorption + coefficients, then applied to every wavelength channel in the input block. + + Parameters + ---------- + length: + Physical length used to scale the absorption term. + operating_wl: + Nominal operating wavelength, in meters. Stored for model context; the + current Block mode response applies the same phase law to all wavelength + channels. + absorption_coefficients: + Polynomial coefficients, in `jnp.polyval` order, that map voltage to + absorption in dB per unit length. A one-dimensional array is shared as a + single mode row. + phase_coefficients: + Polynomial coefficients, in `jnp.polyval` order, that map voltage to + phase shift in radians. A one-dimensional array is shared as a single + mode row. + effective_index: + Stored effective-index value for compatibility with related modulator + models. + """ + + ports = [ + Port( + name="o0", + type="optical", + directionality="input", + ), + Port( + name="o1", + type="optical", + directionality="output", + ), + Port( + name="e0", + type="electrical", + directionality="input", + ), + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + # n_eff: Callable[[float, complex], float]=None, # Function of wavelength and voltage + length: float = 1.0, + operating_wl=1.55e-6, + absorption_coefficients: jnp.ndarray = jnp.asarray([0.0, 0.0, 0.0, 0.0]), + phase_coefficients: jnp.ndarray = jnp.asarray([0.0, 0.0, jnp.pi, 0.0]), + effective_index=0.0, + ): + self.length = length + self.absorption_coefficients = jnp.atleast_2d(absorption_coefficients) + self.phase_coefficients = jnp.atleast_2d(phase_coefficients) + self.operating_wl = operating_wl + self.effective_index = effective_index + + def block_mode_response(self, input_signals, simulation_parameters): + outputs = {} + input_amplitude = input_signals["o0"].amplitude + wavelengths = input_signals["o0"].wavelength + N = input_amplitude.shape[0] + L = input_amplitude.shape[1] + M = len( + simulation_parameters.mode_identifiers + ) # Currently, ignores all but the first mode + + voltage = input_signals["e0"].voltage + + output_amplitude = jnp.zeros((N, L, M), dtype=complex) + + for m, mode in enumerate(simulation_parameters.mode_identifiers): + phase_op = jnp.polyval(self.phase_coefficients[m], voltage) + absorption_dB = jnp.polyval(self.absorption_coefficients[m], voltage) + fraction_of_power_remaining = 10 ** (-absorption_dB * self.length / 10) + fraction_of_power_remaining = jnp.repeat( + fraction_of_power_remaining[:, None], L, axis=1 + ) + phase_shift = jnp.repeat(phase_op[:, None], L, axis=1) + + output_amplitude = output_amplitude.at[:, :, m].set( + jnp.sqrt(fraction_of_power_remaining) + * jnp.exp(1j * phase_shift) + * input_amplitude[:, :, m] + ) + + outputs["o1"] = BlockModeOpticalSignal( + amplitude=output_amplitude, wavelength=wavelengths + ) + + return outputs + + +class OpticalModulator( + SParameterComponent, + SteadyStateComponent, + SampleModeComponent, + # BlockModeComponent +): + """ + Single Mode Optical Modulator + TODO: Define a nice way to make this multimodal + """ + + ports = [ + Port( + name="o0", + type="optical", + directionality="bidirectional", + # directionality = "input", + # directionality = "unknown", + ), + Port( + name="o1", + type="optical", + directionality="bidirectional", + # directionality = "output", + # directionality = "unknown", + ), + Port( + name="e0", + type="electrical", + directionality="input", + ), + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + # n_eff: Callable[[float, complex], float]=None, # Function of wavelength and voltage + length: float = 1.0, + operating_wl=1.55e-6, + absorption_coefficients: jnp.ndarray = jnp.asarray([0.0, 0.0, 0.0, 0.0]), + phase_coefficients: jnp.ndarray = jnp.asarray([0.0, 0.0, jnp.pi, 0.0]), + effective_index=0.0, + ): + self.length = length + self.absorption_coefficients = absorption_coefficients + self.phase_coefficients = phase_coefficients + self.operating_wl = operating_wl + self.effective_index = effective_index + + def s_parameters( + self, + inputs: dict, + wl: ArrayLike = 1.55e-6, + ) -> sax.SDict: + voltage = inputs["e0"].voltage + + phase_op = jnp.polyval(self.phase_coefficients, voltage) + absorption_dB = jnp.polyval(self.absorption_coefficients, voltage) + fraction_of_power_remaining = 10 ** (-absorption_dB * self.length / 10) + phase_shift = phase_op + # delta_n = self.operating_wl/(2*jnp.pi*self.length) * phase_op + # phase_shift = 2*jnp.pi/wl*(self.effective_index+delta_n)*self.length + + # TODO: Make multimodal + return { + ("o0", "o1"): jnp.sqrt(fraction_of_power_remaining) + * jnp.exp(1j * phase_shift), + ("o1", "o0"): jnp.sqrt(fraction_of_power_remaining) + * jnp.exp(1j * phase_shift), + ("o0", "o0"): 0, + ("o1", "o1"): 0, + } + + def s_parameter_get_bias_ports( + self, + ): + return ["e0"] + + def sample_mode_initial_state(self, simulation_parameters): + return jnp.array([0]) + + def sample_mode_step( + self, inputs: dict, state: jax.Array, simulation_state, simulation_parameters + ): + from simphony.signal.sample_mode import SampleModeOpticalSignal + + baseband_wls = simulation_parameters.optical_baseband_wavelengths + n_modes = len(simulation_parameters.mode_identifiers) + zero_amp = jnp.zeros((baseband_wls.shape[0], n_modes), dtype=complex) + + voltage = inputs["e0"].voltage if "e0" in inputs else 0.0 + phase_op = jnp.polyval(self.phase_coefficients, voltage) + absorption_dB = jnp.polyval(self.absorption_coefficients, voltage) + transfer = jnp.sqrt(10 ** (-absorption_dB * self.length / 10)) * jnp.exp( + 1j * phase_op + ) + + o0_in = inputs["o0"].amplitude if "o0" in inputs else zero_amp + o1_in = inputs["o1"].amplitude if "o1" in inputs else zero_amp + + outputs = { + "o1": SampleModeOpticalSignal( + amplitude=transfer * o0_in, wavelength=baseband_wls + ), + "o0": SampleModeOpticalSignal( + amplitude=transfer * o1_in, wavelength=baseband_wls + ), + } + return outputs, state + + # @staticmethod + # @jax.jit + def steady_state( + self, + inputs: dict, + # settings + ) -> dict: + # TODO: Change complete_steady_state_inputs to be a method on the SteadyStateComponent + # Base Class and have it give default values to ports with unspecified inputs + # For now, I'll just use this work around. + # complete_steady_state_inputs(inputs) + # self.s_parameters(inputs, jnp.linspace(1.5e-6, 1.6e-6, 1000)) + optical_wls = [] + if "o0" in inputs: + # Assuming they all have the same wl + optical_wls = inputs["o0"].wl + # if not 'o0' in inputs: + # inputs['o0'] = optical_signal(field=0) + # if 'o1' not in inputs: + # inputs['o1'] = optical_signal(field=0) + # We only consider DC voltage and assum + voltage = inputs["e0"].voltage + + o0_field_out = [] + o1_field_out = [] + for i, optical_wl in enumerate(optical_wls): + o0_in = inputs["o0"].field[i] + o1_in = inputs["o1"].field[i] + + phase_op = jnp.polyval(self.phase_coefficients, voltage) + absorption_dB = jnp.polyval(self.absorption_coefficients, voltage) + fraction_of_power_remaining = 10 ** (-absorption_dB * self.length / 10) + delta_n = self.operating_wl / (2 * jnp.pi * self.length) * phase_op + phase_shift = ( + 2 * jnp.pi / optical_wl * (self.effective_index + delta_n) * self.length + ) + + o0_field_out.append( + o1_in + * jnp.sqrt(fraction_of_power_remaining) + * jnp.exp(1j * phase_shift) + ) + o1_field_out.append( + o0_in + * jnp.sqrt(fraction_of_power_remaining) + * jnp.exp(1j * phase_shift) + ) + + outputs = { + "o0": SteadyStateOpticalSignal(field=o0_field_out, wl=optical_wls), + "o1": SteadyStateOpticalSignal(field=o1_field_out, wl=optical_wls), + # "e0": electrical_signal(), + } + return outputs + + +# class MachZehnderModulator(PCell): +# ports = [ +# Port( +# name = "o0", +# type = "optical", +# directionality = "bidirectional", +# ), +# Port( +# name = "o1", +# type = "optical", +# directionality = "bidirectional", +# ), +# Port( +# name = "e0", +# type = "electrical", +# directionality = "unidirectional", +# ), +# Port( +# name = "e1", +# type = "electrical", +# directionality = "unidirectional", +# ) +# ] +# def __init__( +# self, +# NOT_IMPLEMENTED, +# ): +# # IMPLEMENT ME +# pass diff --git a/simphony/libraries/ideal/multimode.py b/simphony/libraries/ideal/multimode.py new file mode 100644 index 00000000..65937499 --- /dev/null +++ b/simphony/libraries/ideal/multimode.py @@ -0,0 +1,164 @@ +import jax.numpy as jnp +from sax import DEFAULT_MODES + +from simphony.component.component import ( + BlockModeComponent, + Component, + SampleModeComponent, +) +from simphony.component.port import Port +from simphony.signal.block_mode import BlockModeOpticalSignal +from simphony.simulation.simulation import SimulationParameters + + +### TODO: Implement ModeConvert +class ModeConverter( + SampleModeComponent, + BlockModeComponent, +): + """Takes in a signal on a single mode (any mode), and outputs a signal on a + single, specified mode. + + Will ignore all modes except for the first in the signal passed to + it + """ + + ports = [Port(name="in", type="optical", directionality="input")] + [ + Port(name="out", type="optical", directionality="output") + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + input_mode="TE", + output_mode="TM", + ): + self.simulation_parameters = simulation_parameters + self.input_mode = input_mode + self.output_mode = output_mode + + def block_mode_response(self, inputs, simulation_parameters): + # TODO: IMPLEMENT MODE CONVERTER + input_mode_index = simulation_parameters.mode_identifiers.index(self.input_mode) + output_mode_index = simulation_parameters.mode_identifiers.index( + self.output_mode + ) + + input_amplitude = inputs["in"].amplitude + wavelength = inputs["in"].wavelength + + output_amplitude = jnp.zeros_like(input_amplitude) + output_amplitude = output_amplitude.at[:, :, output_mode_index].set( + input_amplitude[:, :, input_mode_index] + ) + + outputs = { + "out": BlockModeOpticalSignal( + amplitude=output_amplitude, wavelength=wavelength + ), + } + + return outputs + + +### TODO: Implement ModeMultiplexer +def mode_multiplexer( + input_modes: tuple | list = DEFAULT_MODES, + output_port_name: str = "out_port", + input_port_suffix: str = "_port", +) -> type[Component]: + input_port_names = [f"{mode}{input_port_suffix}" for mode in input_modes] + + class ModeMultiplexer(SampleModeComponent, BlockModeComponent): + ports = [ + Port(name=port_name, type="optical", directionality="input") + for port_name in input_port_names + ] + [Port(name=output_port_name, type="optical", directionality="output")] + + def __init__( + self, + simulation_parameters: SimulationParameters, + **kwargs, + ): + self.simulation_parameters = simulation_parameters + self.input_port_names = input_port_names + self.output_port_name = output_port_name + + def block_mode_response(self, input_signals, simulation_parameters): + outputs = {} + _input_amplitude = list(input_signals.values())[0].amplitude + wavelengths = list(input_signals.values())[0].wavelength + N = _input_amplitude.shape[0] + L = _input_amplitude.shape[1] + M = len(input_port_names) + + outputs[output_port_name] = BlockModeOpticalSignal( + amplitude=jnp.zeros((N, L, M), dtype=complex), wavelength=wavelengths + ) + + for mode_no, in_port_name in enumerate(input_port_names): + input_amplitude = input_signals[in_port_name].amplitude[:, :, mode_no] + output_amplitude = ( + outputs[output_port_name].amplitude[:, :, mode_no] + input_amplitude + ) + shaped_output_amplitude = ( + outputs[output_port_name] + .amplitude.at[:, :, mode_no] + .set(output_amplitude) + ) + outputs[output_port_name] = BlockModeOpticalSignal( + amplitude=shaped_output_amplitude, wavelength=wavelengths + ) + + return outputs + + return ModeMultiplexer + + +### TODO: Implement ModeDemultiplexer +def mode_demultiplexer( + output_modes: tuple | list = DEFAULT_MODES, + input_port_name: str = "in_port", + output_port_suffix: str = "_port", +) -> type[Component]: + output_port_names = [f"{mode}{output_port_suffix}" for mode in output_modes] + + class ModeDemultiplexer(SampleModeComponent, BlockModeComponent): + ports = [Port(name=input_port_name, type="optical", directionality="input")] + [ + Port(name=port_name, type="optical", directionality="output") + for port_name in output_port_names + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + **kwargs, + ): + self.output_port_names = output_port_names + self.input_port_name = input_port_name + + # TODO: TEST THIS FOR MULTIPLE MODES + def block_mode_response(self, inputs, simulation_parameters): + input_amplitude = inputs[self.input_port_name].amplitude + wl = inputs[self.input_port_name].wavelength + + outputs = {} + for i, p in enumerate(self.output_port_names): + output_amplitude = jnp.zeros( + ( + input_amplitude.shape[0], + input_amplitude.shape[1], + len(simulation_parameters.mode_identifiers), + ), + dtype=complex, + ) + output_amplitude = output_amplitude.at[:, :, i].set( + input_amplitude[:, :, i] + ) + outputs[p] = BlockModeOpticalSignal( + amplitude=output_amplitude, wavelength=wl + ) + + return outputs + + return ModeDemultiplexer diff --git a/simphony/libraries/ideal/photonic_circuits.py b/simphony/libraries/ideal/photonic_circuits.py new file mode 100644 index 00000000..bd6c7dde --- /dev/null +++ b/simphony/libraries/ideal/photonic_circuits.py @@ -0,0 +1,447 @@ +from simphony.component.pcell import PCell +from simphony.component.port import Port +from simphony.libraries.ideal.modulators import ( + DirectedOpticalModulator, + OpticalModulator, +) +from simphony.libraries.ideal.s_parameters import optical_s_parameter_placeholder +from simphony.libraries.old_ideal import coupler, waveguide +from simphony.simulation.simulation import SimulationParameters + + +class MZI(PCell): + r""" + o2 ---\ /---[ϕ]---\ /--- o3 + -------- -------- + -------- -------- + o0 ---/ \---[ϕ]---/ \--- o1 + + or if `partial` is true: + + o2 ---[ϕ]---\ /--- o3 + -------- + -------- + o0 ---[ϕ]---/ \--- o1 + + """ + _arms = ( + ("bot", "o1", "o0"), + ("top", "o3", "o2"), + ) + + ports = [ + Port(name="o0", type="optical", directionality="bidirectional"), + Port(name="o1", type="optical", directionality="bidirectional"), + Port(name="o2", type="optical", directionality="bidirectional"), + Port(name="o3", type="optical", directionality="bidirectional"), + Port( + name="e0", + type="electrical", + directionality="input", + ), + Port( + name="e1", + type="electrical", + directionality="input", + ), + ] + + @classmethod + def _arm_connections(cls, partial: bool, modulators: bool): + connections = {} + + for arm_name, splitter_port, combiner_port in cls._arms: + waveguide_name = f"{arm_name}_wg" + modulator_name = f"{arm_name}_mod" + combiner = f"combiner,{combiner_port}" + + if partial: + if modulators: + connections[f"{waveguide_name},o1"] = f"{modulator_name},o0" + connections[f"{modulator_name},o1"] = combiner + else: + connections[f"{waveguide_name},o1"] = combiner + else: + if modulators: + connections[f"splitter,{splitter_port}"] = f"{modulator_name},o0" + connections[f"{modulator_name},o1"] = f"{waveguide_name},o0" + else: + connections[f"splitter,{splitter_port}"] = f"{waveguide_name},o0" + + connections[f"{waveguide_name},o1"] = combiner + + return connections + + def __init__( + self, + simulation_parameters: SimulationParameters, + splitter_settings: dict = None, + combiner_settings: dict = None, + top_wg_settings: dict = None, + bot_wg_settings: dict = None, + top_phase_shifter_settings: dict = None, + bot_phase_shifter_settings: dict = None, + modulators: bool = True, + partial: bool = False, + # TODO: maybe inherit a getter or setter or just don't do group ids + group_id="default", # Setting to None will disable grouping, not setting will use MZI class id + ): + """`partial` builds only the arms and combiner, without the input + splitter. + + `modulators` controls whether each arm includes a tunable phase + shifter. + """ + if splitter_settings is None: + splitter_settings = {} + if combiner_settings is None: + combiner_settings = {} + if top_wg_settings is None: + top_wg_settings = {"length": 50.0} + if bot_wg_settings is None: + bot_wg_settings = {"length": 20.0} + if top_phase_shifter_settings is None: + top_phase_shifter_settings = {} + if bot_phase_shifter_settings is None: + bot_phase_shifter_settings = {} + + self.netlist = { + "instances": { + "combiner": "coupler", + "top_wg": "waveguide", + "bot_wg": "waveguide", + }, + "ports": { + "o1": "combiner,o1", + "o3": "combiner,o3", + }, + "connections": self._arm_connections(partial, modulators), + } + self.settings = { + "top_wg": { + "sax_settings": top_wg_settings, + }, + "bot_wg": { + "sax_settings": bot_wg_settings, + }, + "combiner": { + "sax_settings": combiner_settings, + }, + } + + if modulators: + self.netlist["instances"].update( + { + "top_mod": "modulator", + "bot_mod": "modulator", + } + ) + self.netlist["ports"]["e0"] = "top_mod,e0" + self.netlist["ports"]["e1"] = "bot_mod,e0" + self.settings["top_mod"] = top_phase_shifter_settings + self.settings["bot_mod"] = bot_phase_shifter_settings + + if partial: + self.netlist["ports"]["o0"] = "bot_wg,o0" + self.netlist["ports"]["o2"] = "top_wg,o0" + else: + self.netlist["instances"]["splitter"] = "coupler" + self.netlist["ports"]["o0"] = "splitter,o0" + self.netlist["ports"]["o2"] = "splitter,o2" + self.settings["splitter"] = {"sax_settings": splitter_settings} + + self.models = { + "coupler": coupler, + "waveguide": waveguide, + } + if modulators: + if simulation_parameters.directed: + self.models["modulator"] = DirectedOpticalModulator + else: + self.models["modulator"] = OpticalModulator + + from simphony.simulation.simulation import SimulationMode + + if simulation_parameters.simulation_mode == SimulationMode.SAMPLE_MODE: + pass + elif simulation_parameters.simulation_mode == SimulationMode.BLOCK_MODE: + coupler_directionality = { + "o0": "input", + "o2": "input", + "o1": "output", + "o3": "output", + } + + waveguide_directionality = { + "o0": "input", + "o1": "output", + } + + self.models["coupler"] = optical_s_parameter_placeholder( + coupler, coupler_directionality, simulation_parameters.mode_identifiers + ) + self.models["waveguide"] = optical_s_parameter_placeholder( + waveguide, + waveguide_directionality, + simulation_parameters.mode_identifiers, + ) + + # self.settings['top_wg'] = {"sax_settings": self.settings['top_wg']} + # self.settings['bot_wg'] = {"sax_settings": self.settings['bot_wg']} + # if not partial: + # self.settings['splitter'] = {"sax_settings": self.settings['splitter']} + + # self.settings['combiner'] = {"sax_settings": self.settings['combiner']} + + elif simulation_parameters.simulation_mode == SimulationMode.S_PARAMETER: + pass + else: + raise ValueError( + f"{self} has does not support the simulation type {simulation_parameters.simulation_mode}" + ) + + s_parameter_models_to_group = ["top_wg", "bot_wg", "splitter", "combiner"] + + if partial: + s_parameter_models_to_group.remove("splitter") + + if group_id == "default": + group_id = id(MZI) + + for instance_name in s_parameter_models_to_group: + self.settings[instance_name]["group_id"] = group_id + + +def mzi_lattice_filter( + order: int = 3, + modulators: bool = True, +): + """Create a PCell class for a bidirectional MZI lattice filter. + + The returned class expands into `order` cascaded `MZI` stages. The first + stage is a full MZI with an input splitter; later stages are partial MZIs + that connect the two optical paths from one stage into the next. + + Parameters + ---------- + order: + Number of MZI stages in the lattice. + modulators: + If true, include phase modulators in each MZI stage and expose two + electrical ports per stage. + + Returns + ------- + type[PCell] + A parameterized component class exposing optical ports `o0`, `o1`, + `o2`, and `o3`, plus two electrical phase-shifter ports per stage when + `modulators` is true. + """ + optical_ports = [ + Port(name="o0", type="optical", directionality="bidirectional"), + Port(name="o1", type="optical", directionality="bidirectional"), + Port(name="o2", type="optical", directionality="bidirectional"), + Port(name="o3", type="optical", directionality="bidirectional"), + ] + + electrical_ports = ( + [ + Port(name=f"mzi{i}_e{j}", type="electrical", directionality="input") + for i in range(order) + for j in (0, 1) + ] + if modulators + else [] + ) + + class MZILatticeFilter(PCell): + ports = optical_ports + electrical_ports + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + delay_lengths: dict = None, + coupling_coeffs: list = None, + ### TODO: Add a way to adjust the phase modulator settings + ### TODO: Implement method for import standard presets + # preset: str = None, + ): + """""" + instances = {} + connections = {} + ports = {} + models = {} + + MZI_MODEL_NAME = "mzi" + MZI_INSTANCE_NAME_BASE = MZI_MODEL_NAME + + def mzi_instance_name(index): + return f"{MZI_INSTANCE_NAME_BASE}{index}" + + models["mzi"] = MZI + for mzi_index in range(order): + instances[mzi_instance_name(mzi_index)] = { + "component": MZI_MODEL_NAME, + # "settings": {}, + } + + connections = { + f"{mzi_instance_name(i)},{src}": f"{mzi_instance_name(i+1)},{dst}" + for i in range(order - 1) + for src, dst in (("o1", "o0"), ("o3", "o2")) + } + + # connections = { + # f"{mzi_instance_name(i)},o1": f"{mzi_instance_name(i+1)},o0", + # f"{mzi_instance_name(i)},o3": f"{mzi_instance_name(i+1)},o2", + # for i in range(order-1) + # } + + # ports["o0"] = f"{mzi_instance_name(0)},o0" + # ports["o1"] = f"{mzi_instance_name(order-1)},o1" + + # ports = { + # "o0": f"{mzi_instance_name(0)},o0", + # "o1": f"{mzi_instance_name(order-1)},o1", + # "o2": f"{mzi_instance_name(0)},o2", + # "o3": f"{mzi_instance_name(order-1)},o3", + # } + ports = {} + for port in self.ports: + if port.type == "electrical": + instance_name, port_name = port.name.split("_", maxsplit=1) + ports[port.name] = f"{instance_name},{port_name}" + ports["o0"] = f"{mzi_instance_name(0)},o0" + ports["o1"] = f"{mzi_instance_name(order-1)},o1" + ports["o2"] = f"{mzi_instance_name(0)},o2" + ports["o3"] = f"{mzi_instance_name(order-1)},o3" + + self.netlist = { + "instances": instances, + "ports": ports, + "connections": connections, + } + + self.models = models + + self.settings = { + instance_name: { + "partial": True, + "modulators": modulators, + } + for instance_name in instances.keys() + } + self.settings[mzi_instance_name(0)]["partial"] = False + + # self.instantiated_netlist = instantiate_netlist(netlist, models, settings={}) + + # Grouping should happen when the instantiated_netlist is returned + # Just Overwrite the getter for instantiated_netlist + # for mzi_index in range(order): + # group_name = f"{MZILatticeFilter}_{id(self)}_group_{mzi_index}" + # instances = [] + # netlist = group_instances(netlist, ) + + return MZILatticeFilter + + +def mzi_lattice_passband( + order: int = 4, + modulators: bool = False, +): + """Create a PCell class for a one-input MZI lattice passband filter. + + The returned class builds a cascade of `MZI` stages where only `o0` and + `o1` are exposed as optical top-level ports. Each stage uses a common + bottom-arm length and a configurable top-arm delay difference. + + Parameters + ---------- + order: + Number of MZI stages in the passband cascade. + modulators: + If true, include phase modulators in each MZI stage and expose two + electrical tuning ports per stage. + + Returns + ------- + type[PCell] + A parameterized component class. Constructor settings include + `base_length` and `mzi_delay_differences`. + """ + optical_ports = [ + Port(name="o0", type="optical", directionality="bidirectional"), + Port(name="o1", type="optical", directionality="bidirectional"), + ] + + electrical_ports = ( + [ + Port(name=f"mzi{i}_e{j}", type="electrical", directionality="input") + for i in range(order) + for j in (0, 1) + ] + if modulators + else [] + ) + + class MZILatticePassband(PCell): + ports = optical_ports + electrical_ports + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + mzi_delay_differences: list = None, + base_length: float = 10.0, + ): + """Build a one-input passband cascade. + + `mzi_delay_differences` are added to `base_length` for the + top arm of each full MZI stage. + """ + + instances = {} + connections = {} + self.settings = {} + self.models = {"mzi": MZI} + ports = {} + if mzi_delay_differences is None: + mzi_delay_differences = [10.0] * order + elif len(mzi_delay_differences) != order: + raise ValueError( + "`mzi_delay_differences` must contain one value per MZI stage" + ) + + for i in range(order): + instances[f"mzi{i}"] = "mzi" + if i < order - 1: + connections[f"mzi{i},o1"] = f"mzi{i+1},o2" + self.settings[f"mzi{i}"] = { + "partial": False, + "modulators": modulators, + "top_wg_settings": { + "length": base_length + mzi_delay_differences[i] + }, + "bot_wg_settings": {"length": base_length}, + } + + ports = { + "o0": f"mzi{0},o2", + "o1": f"mzi{order-1},o1", + } + if modulators: + ports.update( + { + f"mzi{i}_e{j}": f"mzi{i},e{j}" + for i in range(order) + for j in (0, 1) + } + ) + self.netlist = { + "instances": instances, + "ports": ports, + "connections": connections, + } + + return MZILatticePassband diff --git a/simphony/libraries/ideal/s_parameters.py b/simphony/libraries/ideal/s_parameters.py new file mode 100644 index 00000000..898104dc --- /dev/null +++ b/simphony/libraries/ideal/s_parameters.py @@ -0,0 +1,1484 @@ +import inspect +from copy import deepcopy + +import jax +import jax.numpy as jnp +import sax +from jax.typing import ArrayLike +from sax import DEFAULT_MODES +from scipy.constants import speed_of_light + +# from simphony.circuit.netlist import InstantiatedFlatNetlist, instantiate_netlist +from simphony.circuit._netlist import InstantiatedFlatNetlist +from simphony.circuit.netlist import ( + add_settings_to_netlist, + generate_valid_separator, + sanitize_instance_names, +) +from simphony.component.component import SampleModeComponent, SParameterComponent +from simphony.component.pcell import PCell +from simphony.component.placeholder import Placeholder +from simphony.component.port import Port +from simphony.libraries.ideal.digital_filters import discrete_state_space +from simphony.libraries.ideal.multimode import ( + ModeConverter, + mode_demultiplexer, + mode_multiplexer, +) +from simphony.signal.sample_mode import SampleModeOpticalSignal +from simphony.simulation.simulation import SimulationMode, SimulationParameters +from simphony.time_domain.vector_fitting.z_domain import ( + PHYSICIST, + optimize_order_vector_fitting_discrete, + state_space_discrete, + state_space_discrete_optimized_terms, + state_space_step_discrete_optimized, + vector_fitting_discrete, +) +from simphony.utils import dict_to_rect_matrix + +# from simphony.simulation.s_parameter import SParameterSimulation +# from simphony.simulation.sample_mode import SampleModeSimulation +# from simphony.simulation.block_mode import BlockModeSimulation + + +INPUT_SUFFIX = "in" +OUTPUT_SUFFIX = "out" +MULTIPLEXER_SUFFIX = "_mux" +DEMULTIPLEXER_SUFFIX = "_demux" +DEMULTIPLEXER_IN_PORT_NAME = "in_port" +DEMULTIPLEXER_OUT_PORT_SUFFIX = "_port" +MULTIPLEXER_IN_PORT_SUFFIX = "_port" +MULTIPLEXER_OUT_PORT_NAME = "out_port" +MODE_CONVERTER_MODEL_NAME = "mode_converter" +MODE_CONVERTER_INSTANCE_SUFFIX = "_converter" +STATE_SPACE_MODEL_NAME_BASE = "state_space" +STATE_SPACE_INSTANCE_NAME_BASE = STATE_SPACE_MODEL_NAME_BASE + +_default_vector_fitting_parameters = { + "model_order": None, + "min_model_order": 2, + "max_model_order": 50, + "num_frequency_samples": 1000, + "center_wavelength": 1.55e-6, + # "center_wavelength": 1.52e-6, + "spectral_range": (1.5e-6, 1.6e-6), + # NOTE: Currently, a user CAN change the spectral range parameter (the default is set by ) +} + + +class SParameterElement(SParameterComponent, SampleModeComponent): + """The following component factory should be implemented when writing a + simulator that interprets s-parameter elements using the default settings + in the SParameterGroup PCell. + + By default, SParameterGroup will expand each element within it into + this model. If more flexibility and control is needed, feel free to + write your own design in SParameterGroup + """ + + +def optical_s_parameter( + sax_model: sax.Model, + port_directionality: dict = None, + default_modes: list | tuple | str = DEFAULT_MODES, +) -> type[SParameterElement]: + """Wrap a SAX optical model as a Simphony S-parameter component class. + + The returned class can be used in circuit netlists like any other component. + In S-parameter simulations it calls the original `sax_model`; in time-domain + simulations it can use vector fitting settings to approximate the frequency + response with a discrete state-space model. + + Parameters + ---------- + sax_model: + Callable SAX model returning an `SDict`. + port_directionality: + Optional mapping from port name to `"input"`, `"output"`, or + `"bidirectional"`. Unspecified ports default to `"bidirectional"`. + Directional simulators such as Block mode need enough directionality + information to orient the netlist. + default_modes: + Mode label or labels assigned when the SAX model does not encode + explicit multimode port names. These labels are replicated across the + model's optical relationships. + + Returns + ------- + type[SParameterElement] + A component class whose constructor accepts settings such as + `sax_settings`, `port_directionality`, `vector_fitting_parameters`, and + `delay_compensation`. + """ + pcell_port_names = _get_port_names_without_mode(sax_model) + + port_directionality = deepcopy(port_directionality) + if port_directionality is None: + port_directionality = {} + + for port_name in pcell_port_names: + port_directionality.setdefault(port_name, "bidirectional") + + # default_port_directionality = port_directionality + + if isinstance(default_modes, str): + default_modes = [default_modes] + + default_modes = tuple(default_modes) + + BaseSParameterElement = SParameterElement # Freeze the instance + + class SpecificSParameterElement(BaseSParameterElement): + _sax_model = staticmethod(sax_model) + ports = [ + Port( + name=port_name, + type="optical", + directionality=port_directionality[port_name], + ) + for port_name in pcell_port_names + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + # Rename kwargs to be more accurate + **kwargs, + # sax_settings: dict = None, + # vector_fitting_parameters = None, + # delay_compensation: int = 0, + # port_directionality = {}, + ): + """ + TODO: Add documentation for each of the kwargs + """ + # The following lines are helpful for debugging (I am just freezing the references) + _ = sax_model + _ = port_directionality + _ = default_modes + + self.sax_model = _get_filtered_sax_model( + sax_model, + port_directionality=port_directionality, + default_modes=default_modes, + ) + + self.settings = kwargs + + self.settings.setdefault("sax_settings", {}) + self.settings.setdefault("group_id", None) + self.settings.setdefault( + "vector_fitting_parameters", _default_vector_fitting_parameters + ) + self.settings.setdefault("delay_compensation", 0) + self.settings.setdefault("apply_phase_correction", True) + self.settings.setdefault("port_directionality", {}) + + if ( + "vector_fitting_parameters" in self.settings + and not self.settings["vector_fitting_parameters"] is None + ): + for k, v in _default_vector_fitting_parameters.items(): + self.settings["vector_fitting_parameters"].setdefault( + k, _default_vector_fitting_parameters[k] + ) + + if ( + not self.settings["vector_fitting_parameters"]["model_order"] + is None + ): + self.settings["vector_fitting_parameters"]["min_model_order"] = None + self.settings["vector_fitting_parameters"]["max_model_order"] = None + + if self.settings["apply_phase_correction"]: + self._k = self.settings["delay_compensation"] + else: + self._k = 0 + + # self.sax_model = sax_model + # self.sax_settings = self.settings.setdefault('sax_settings', {}) + # self.vector_fitting_parameters = self.settings.setdefault('vector_fitting_parameters', default_vector_fitting_parameters) + # self.delay_compensation = self.settings.setdefault('delay_compensation', 0) + # self.port_directionality = self.settings.setdefault('port_directionality', {}) + + def s_parameters( + self, + inputs: dict, + wl: ArrayLike = 1.55e-6, + ): + return sax_model(wl=wl, **self.settings["sax_settings"]) + + def sample_mode_initial_state(self, simulation_parameters): + """May be overwritten by user. + + Returns the initial the state of the system. Called by the + sample mode simulator after + `set_sample_mode_simulation_parameters` + """ + sax_settings = self.settings["sax_settings"] + vector_fitting_parameters = self.settings["vector_fitting_parameters"] + ( + self.state_space_matrices, + _state_space_input_ports, + _state_space_output_ports, + ) = _calculate_state_space_coefficients_from_sax_model( + self.sax_model, + sax_settings, + vector_fitting_parameters, + simulation_parameters, + delay_compensation=self.settings["delay_compensation"], + ) + self.state_space_input_indices = { + tuple(p.split("@")): i for i, p in enumerate(_state_space_input_ports) + } + self.state_space_output_indices = { + tuple(p.split("@")): i for i, p in enumerate(_state_space_output_ports) + } + self.mode_indices = { + mode: i for i, mode in enumerate(simulation_parameters.mode_identifiers) + } + # A, B, C, D = self.state_space_matrices + # import matplotlib.pyplot as plt + # u = jnp.ones((1000, 2), dtype=complex) + # response, _ = state_space_response_discrete(A, B, C, D, u) + # plt.plot(response) + + A, B, C, _ = self.state_space_matrices + self._optimized_state_space_terms = None + if simulation_parameters.use_state_space_optimization: + try: + self._optimized_state_space_terms = ( + state_space_discrete_optimized_terms(A, B, C) + ) + except ValueError: + self._optimized_state_space_terms = None + + L = len(simulation_parameters.optical_baseband_wavelengths) + x = jnp.zeros( + ( + L, + A.shape[1], + ), + dtype=complex, + ) + return x + + def sample_mode_step( + self, + input_signals: dict, + state: jax.Array, + simulation_state, + simulation_parameters, + ): + """Compute the next state of the system.""" + # TODO: Add the delay compensation logic + # k = self.settings['delay_compensation'] + k = self._k + x = state + A, B, C, D = self.state_space_matrices + + L = len(simulation_parameters.optical_baseband_wavelengths) + M = len(simulation_parameters.mode_identifiers) + u = jnp.zeros((L, len(self.state_space_input_indices)), dtype=complex) + + wl_center = self.settings["vector_fitting_parameters"]["center_wavelength"] + for ( + port_name, + mode, + ), state_space_idx in self.state_space_input_indices.items(): + mode_idx = self.mode_indices[mode] + u = u.at[:, state_space_idx].set( + input_signals[port_name].amplitude[:, mode_idx] + ) + + # Vectorize over optical_baseband_wavelengths. This avoids + # unrolling L copies of the loop body into the XLA graph during + # lax.scan compilation. + # + # For each wavelength l: + # new_x[l] = phase_AB[l] * (A @ x[l] + B @ u[l]) + # y[l] = phase_CD[l] * (C @ x[l] + D @ u[l]) + # + # (x @ A.T)[l] == A @ x[l] for 1-D row slices, so batched matmul works. + sampling_frequency = 1.0 / simulation_parameters.dt + wls = simulation_parameters.optical_baseband_wavelengths # (L,) + delta_omega = ( + 2 + * jnp.pi + * speed_of_light + * (1.0 / wls - 1.0 / wl_center) + / sampling_frequency + ) # (L,) + phase_AB = jnp.exp(1j * delta_omega) # (L,) + phase_CD = jnp.exp(1j * k * delta_omega) # (L,) + + if ( + simulation_parameters.use_state_space_optimization + and self._optimized_state_space_terms is not None + ): + A_diag, residues = self._optimized_state_space_terms + y, new_x = state_space_step_discrete_optimized( + A_diag, + residues, + D, + phase_AB, + u, + x, + ) + y = phase_CD[:, None] * y + else: + new_x = phase_AB[:, None] * (x @ A.T + u @ B.T) # (L, n_states) + y = phase_CD[:, None] * (x @ C.T + u @ D.T) # (L, n_outputs) + + output_signals = {} + for port_name in self._output_optical_port_names: + output_signals[port_name] = SampleModeOpticalSignal( + amplitude=jnp.zeros((L, M), dtype=complex), + wavelength=simulation_parameters.optical_baseband_wavelengths, + ) + + for ( + port_name, + mode, + ), state_space_idx in self.state_space_output_indices.items(): + mode_idx = self.mode_indices[mode] + signal = output_signals[port_name] + amplitude = signal.amplitude.at[:, mode_idx].set(y[:, state_space_idx]) + output_signals[port_name] = signal.replace( + amplitude=amplitude, wavelength=signal.wavelength + ) + + return output_signals, new_x + + return SpecificSParameterElement + + +# def sax_model_to_component( +# sax_model: sax.Model, +# port_directionality: dict = None, +# default_modes: list|tuple|str = DEFAULT_MODES, +# ): +# port_names = _get_port_names_without_mode(sax_model) + +# port_directionality = deepcopy(port_directionality) +# if port_directionality is None: +# port_directionality = {} + +# for port_name in port_names: +# port_directionality.setdefault(port_name, "bidirectional") + +# # default_port_directionality = port_directionality + + +# if isinstance(default_modes, str): +# default_modes = [default_modes] + +# default_modes = tuple(default_modes) +# BaseSParameterElement = SParameterElement +# class SpecificSParameterElement(BaseSParameterElement): + +# def s_parameters( +# self, +# inputs: dict, +# wl: ArrayLike=1.55e-6, +# ): +# pass + +# def sample_mode_initial_state(self, simulation_parameters): +# """ +# May be overwritten by user. +# Returns the initial the state of the system. +# Called by the sample mode simulator after `set_sample_mode_simulation_parameters` +# """ +# return 0 + +# def sample_mode_step(self, inputs: dict, state: jax.Array, simulation_parameters): +# """Compute the next state of the system.""" +# raise NotImplementedError + + +class SParameterGroup(PCell): + """Base PCell for circuits assembled from S-parameter elements. + + Users normally create concrete subclasses through + `s_parameter_netlist_to_pcell` rather than inheriting from this + class directly. The generated class exposes the netlist's top-level + optical ports and expands the internal S-parameter models into the + representation required by the active simulator. + """ + + +def s_parameter_netlist_to_pcell( + netlist: dict, + models: dict, + port_directionality: dict = None, + default_modes: list | tuple | str = DEFAULT_MODES, + fuse_models: bool = False, +) -> type[PCell]: + """Create a PCell class from a netlist of SAX S-parameter models. + + The returned PCell lets a SAX-style netlist be used as a Simphony component. + At instantiation time the PCell chooses an internal representation for the + active simulator. For Block mode, it filters the SAX relationships according + to port directionality, vector-fits each S-parameter element, and builds a + directed state-space subcircuit. + + Parameters + ---------- + netlist: + SAX-style netlist dictionary with `instances`, `connections`, and + top-level `ports`. + models: + Mapping from model name to SAX model callable. + port_directionality: + Nested mapping from instance name to port directionality, for example + `{"mzi": {"o0": "input", "o1": "output"}}`. Top-level PCell port + directionality is inferred from the instance ports named in + `netlist["ports"]`. + default_modes: + Optical mode labels used when SAX models do not explicitly encode mode + names. + fuse_models: + Preserve per-instance `group_id` settings when true. When false, + instance `group_id` values are cleared before expansion. + + Returns + ------- + type[PCell] + A generated PCell class. Constructor settings are passed per instance + and should include entries such as `sax_settings` and + `vector_fitting_parameters` for Block mode use. + """ + # pcell_port_names = _get_port_names_without_mode(sax_model) + + pcell_port_names = netlist["ports"].keys() + + def _get_pcell_port_directionality(port_directionality): + pcell_port_directionality = {} + for port_name in pcell_port_names: + true_instance_name, true_port_name = netlist["ports"][port_name].split(",") + pcell_port_directionality[port_name] = port_directionality[ + true_instance_name + ][true_port_name] + + return pcell_port_directionality + + pcell_port_directionality = _get_pcell_port_directionality(port_directionality) + + if isinstance(default_modes, str): + default_modes = [default_modes] + default_modes = tuple(default_modes) + + default_port_directionality = port_directionality + default_pcell_port_directionality = pcell_port_directionality + + # Freeze the instances + netlist = netlist + models = models + # settings = settings + + _SParameterGroup = SParameterGroup # Freeze the reference + + class SpecificSParameterGroup(_SParameterGroup): + ports = [ + Port( + name=port_name, + type="optical", + directionality=pcell_port_directionality[port_name], + ) + for port_name in pcell_port_names + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + settings: dict = None, + port_directionality: dict = None, # TODO: Figure out if this needs to be here + ): + if port_directionality is None: + port_directionality = default_port_directionality + pcell_port_directionality = default_pcell_port_directionality + else: + pcell_port_directionality = _get_pcell_port_directionality( + port_directionality + ) + + # TODO: Make the way to edit these parameters more clear (add documentation to SParameterPlaceholder and how to use sax models) + # TODO: Allow different models to edit "spectral_range" (by overwriting the simulation parameters) + # TODO: Perhaps models can't overwrite simulation parameters spectral range, but can add additional pockets + # "spectral_range": simulation_parameters.spectral_range, + # "center_frequency": speed_of_light / simulation_parameters.center_wavelength, + # "sampling_frequency": 1 / simulation_parameters.dt + + designs = { + # SimulationMode._SIMPHONY_PREPROCESSING: _simphony_preprocessing, + # SimulationMode.S_PARAMETER: _s_parameter_design, + SimulationMode.BLOCK_MODE: _block_mode_design, + # SimulationMode.SAMPLE_MODE: _sample_mode_design, + } + + for _settings in settings.values(): + if not fuse_models: + _settings["group_id"] = None + # This normalization should have been done previously + # _settings.set("vector_fitting_parameters", default_vector_fitting_parameters) + # _settings.setdefault("sax_settings", {}) + + # TODO: Implement the fusing based on group id + + if simulation_parameters.simulation_mode in designs: + design = designs[simulation_parameters.simulation_mode] + else: + design = _default_design + + internal_port_directionality = port_directionality + external_port_directionality = pcell_port_directionality + self.netlist, self.models, self.settings = design( + netlist, + models, + settings, + simulation_parameters, + internal_port_directionality, + external_port_directionality, + ) + + return SpecificSParameterGroup + + +def _get_port_names_without_mode(sax_model): + sdict = sax.multimode(sax_model()) + port_names = set() + for in_portmode, out_portmode in sdict.keys(): + in_port, _ = in_portmode.split("@") + out_port, _ = out_portmode.split("@") + port_names.add(in_port) + port_names.add(out_port) + + return port_names + + +# def _s_parameter_design( +# netlist, +# models, +# settings, +# simulation_parameters, +# internal_port_directionality, +# external_port_directionality, +# ): +# """ +# `port_directionality`: As of 03-16-26, Simphony makes a "best guess" for sax models that have ambiguous port directionality. +# For best results, convert sax models to a Simphony Component first +# TODO: Link to a tutorial on how to convert sax models to simphony Components +# """ +# # if not delay_compensation == 0: +# # warnings.warn(f"A nonzero delay compensation is invalid in S-Parameter simulations. Will be ignored.") + +# # TODO: PROPERLY FILTER SAX MODEL WITH THE DEFAULT MODES SPECIFIED in simulation_parameters.mode_identifiers +# class SaxModelComponent(SParameterComponent): +# ports = [ +# Port( +# name=port_name, +# type="optical", +# directionality = "bidirectional" +# ) + +# for port_name in sax.get_ports(sax_model()) +# ] + +# def __init__( +# self, +# simulation_mode: SimulationMode, +# **kwargs, +# ): +# self.sax_settings = kwargs + +# def s_parameters(self, inputs, wl: ArrayLike=1.55e-6,): +# return sax_model(wl*1e6, **sax_settings) + + +# # Create a class that will be the base component +# # Extend s-paraemeters to all of the default_modes using sax +# instances = { +# "sax_model": "sax_model", +# } +# connections = {} +# ports = {port.name:f"sax_model,{port.name}" for port in SaxModelComponent.ports} +# models = { +# "sax_model": SaxModelComponent, +# } + +# netlist = { +# "instances": instances, +# "connections": connections, +# "ports": ports, +# } + +# settings = { +# "sax_model": {**sax_settings} +# } + +# return netlist, models, settings + + +# def _sample_mode_design( +# netlist, +# models, +# settings, +# simulation_parameters, +# internal_port_directionality, +# external_port_directionality, +# ): +# """ +# The sample mode design is an alteration of the block mode design +# """ +# block_mode_sax_model, block_mode_port_directionality, in_suffix, out_suffix = _bidirectional_ports_to_unidirectional_ports(sax_model, port_directionality) +# block_mode_netlist, block_mode_models = _block_mode_netlist_and_models(block_mode_sax_model, block_mode_port_directionality, simulation_parameters.mode_identifiers) + +# _netlist = ... +# _models = ... + +# return _netlist, _models, settings + + +def _default_design( + netlist, + models, + original_settings, + simulation_parameters, + internal_port_directionality, + external_port_directionality, +): + _netlist = netlist + _models = {} + for instance_name, instance_data in _netlist["instances"].items(): + model_name = instance_data["component"] + instance_data["component"] = instance_name + model = models[model_name] + _models[instance_name] = optical_s_parameter( + model, + internal_port_directionality[instance_name], + simulation_parameters.mode_identifiers, + ) + # _models = {k:optical_s_parameter(v, internal_port_directionality[k], simulation_parameters.mode_identifiers) for k,v in models.items()} + _settings = original_settings + return _netlist, _models, _settings + + +def _block_mode_design( + netlist, + models, + original_settings, + simulation_parameters, + internal_port_directionality, + external_port_directionality, +) -> InstantiatedFlatNetlist: + """Creates a directed, non recursive circuit design for a multimodal, + transient block mode simulation of an s-parameter circuit.""" + for port_name, directionality in external_port_directionality.items(): + if directionality == "bidirectional": + raise ValueError( + f"Port {port_name} must be directed in block mode simulation" + ) + + # if not delay_compensation == 0: + # warnings.warn(f"A nonzero delay compensation is invalid in block mode simulations. Will be ignored.") + + # TODO: This is really important + # PROBLEM: The filtered sax models are not guarenteed to have the same directionality across instances + # Solution: Make a new filtered_sax_model for each instance and update the netlist accordingly + + filtered_sax_models = {} + filtered_port_designators = set() + filtered_netlist = deepcopy(netlist) + + instance_names = filtered_netlist["instances"].keys() + for instance_name in instance_names: + model_name = filtered_netlist["instances"][instance_name]["component"] + model = models[model_name] + port_directionality = internal_port_directionality[instance_name] + filtered_sax_model = _get_filtered_sax_model( + model, port_directionality, simulation_parameters.mode_identifiers + ) + filtered_sax_models[instance_name] = filtered_sax_model + filtered_port_designators.update( + [ + f"{instance_name},{p}" + for p in _get_port_names_without_mode(filtered_sax_model) + ] + ) + filtered_netlist["instances"][instance_name]["component"] = instance_name + + # # TODO: Make it so that I don't have to build a circuit / sanitize the netlist, that takes forever + # # Honestly, I think that I + # # TODO: Remove unnecessary ports from netlist by terminating them. + # for external_port_name, internal_instance_port in sanitized_netlist["ports"].items(): + # instance_name, internal_port_name = internal_instance_port.split(",") + # model_name = sanitized_netlist['instances'][instance_name]['component'] + # filtered_sax_model = filtered_sax_models[model_name] + # pass + + ports = filtered_netlist["ports"] + ports_to_remove = [] + for ext_port, port_designator in ports.items(): + if not port_designator in filtered_port_designators: + ports_to_remove.append(ext_port) + for port in ports_to_remove: + ports.pop(port) + + new_separator = generate_valid_separator(netlist["instances"].keys()) + sanitized_netlist = sanitize_instance_names( + filtered_netlist, old_separator="~", new_separator=new_separator + ) + + _dummy_sax_model, _ = sax.circuit(sanitized_netlist, filtered_sax_models) + dummy_sax_model = _get_filtered_sax_model( + _dummy_sax_model, + external_port_directionality, + simulation_parameters.mode_identifiers, + ) + filter_bank_input_port_modes, filter_bank_output_port_modes = _get_port_mode_luts( + dummy_sax_model + ) + # dummy_sax_model() + # filtered_sax_model = _get_filtered_sax_model(sax_model, port_directionality, simulation_parameters.mode_identifiers) + _netlist, _models = _block_mode_netlist_and_models( + filtered_netlist, + filtered_sax_models, + external_port_directionality, + simulation_parameters.mode_identifiers, + filter_bank_input_port_modes, + filter_bank_output_port_modes, + ) + + # input_port_modes, output_port_modes = _get_port_mode_luts + + # print(f"Building Model For {sax_model}") + + settings = {} + for instance_name, _settings in original_settings.items(): + vector_fitting_parameters = _settings["vector_fitting_parameters"] + sax_settings = _settings["sax_settings"] + model_name = netlist["instances"][instance_name]["component"] + # sax_model = filtered_sax_models[model_name] + sax_model = filtered_sax_models[instance_name] + + # TODO: Make sure that the port modes in these vectors are mapping correctly + (A, B, C, D), _, _ = _calculate_state_space_coefficients_from_sax_model( + sax_model, + sax_settings, + vector_fitting_parameters, + simulation_parameters, + delay_compensation=0, + ) + f_b = speed_of_light / vector_fitting_parameters["center_wavelength"] + f_s = 1 / simulation_parameters.dt + + settings.update( + { + _state_space_instance_name(instance_name): { + "A": A, + "B": B, + "C": C, + "D": D, + "baseband_frequency": f_b, + "sampling_frequency": f_s, + } + } + ) + + ## TODO: Fill in empty settings..s + common_mode = simulation_parameters.mode_identifiers[0] + + settings.update( + { + _mode_converter_instance_name(port, mode, INPUT_SUFFIX): { + "input_mode": mode, + "output_mode": common_mode, + } + for port, modes in filter_bank_input_port_modes.items() + for mode in modes + } + ) + settings.update( + { + _mode_converter_instance_name(port, mode, OUTPUT_SUFFIX): { + "input_mode": common_mode, + "output_mode": mode, + } + for port, modes in filter_bank_output_port_modes.items() + for mode in modes + } + ) + settings.update( + { + _demultiplexer_instance_name(port): {} + for port in filter_bank_input_port_modes.keys() + } + ) + settings.update( + { + _multiplexer_instance_name(port): {} + for port in filter_bank_output_port_modes.keys() + } + ) + + # instantiated_flat_netlist = _instantiate_netlist(netlist, models, settings) + + return _netlist, _models, settings + + +def _block_mode_netlist_and_models( + netlist, + filtered_sax_models, + port_directionality, + default_modes, + filter_bank_input_port_modes, + filter_bank_output_port_modes, +): + """Returns a dicts defining the instances, connections, and ports of the + subcircuit as well as a dict of uninstantiated models.""" + old_netlist = netlist + old_connections = netlist["connections"] + old_instances = netlist["instances"] + old_ports = netlist["ports"] + + netlist = {} + connections = {} + instances = {} + ports = {} + models = {} + + # The Plan: + # 1) Make a dummy sax component out of the netlist + # 2) Use dummy sax component to make the Mode demux and mux architecture + # 3) replace dummy sax component with the filter bank + + for instance_name, instance_data in old_netlist["instances"].items(): + model_name = instance_data["component"] + sax_model = filtered_sax_models[model_name] + input_port_modes, output_port_modes = _get_port_mode_luts(sax_model) + ( + state_space_input_port_names, + state_space_output_port_names, + ) = _state_space_port_names(input_port_modes, output_port_modes) + DiscreteStateSpace = discrete_state_space( + len(state_space_input_port_names), + len(state_space_output_port_names), + input_port_names=state_space_input_port_names, + output_port_names=state_space_output_port_names, + ) + state_space_instance_name = _state_space_instance_name(instance_name) + state_space_model_name = _state_space_model_name(model_name) + models[state_space_model_name] = DiscreteStateSpace + instances[state_space_instance_name] = state_space_model_name + + mode_demultiplexers = {} + # filter_bank_input_port_names = [] + for input_port, modes in filter_bank_input_port_modes.items(): + mode_demultiplexers[input_port] = mode_demultiplexer( + modes, + input_port_name=DEMULTIPLEXER_IN_PORT_NAME, + output_port_suffix=DEMULTIPLEXER_OUT_PORT_SUFFIX, + ) + # filter_bank_input_port_names += [_state_space_port_name(input_port, mode) for mode in modes] + + mode_multiplexers = {} + # filter_bank_output_port_names = [] + for output_port, modes in filter_bank_output_port_modes.items(): + mode_multiplexers[output_port] = mode_multiplexer( + modes, + output_port_name=MULTIPLEXER_OUT_PORT_NAME, + input_port_suffix=MULTIPLEXER_IN_PORT_SUFFIX, + ) + # filter_bank_output_port_names += [_state_space_port_name(output_port, mode) for mode in modes] + + # CHECKPOINT + # TODO: Finish from this point on + models[MODE_CONVERTER_MODEL_NAME] = ModeConverter + + # Add connections between filters in the filter bank + for src, dst in old_connections.items(): + src_instance_name, src_port_name = src.split(",") + dst_instance_name, dst_port_name = dst.split(",") + + src_state_space_instance_name = _state_space_instance_name(src_instance_name) + dst_state_space_instance_name = _state_space_instance_name(dst_instance_name) + for mode in default_modes: + src_state_space_port_name = _state_space_port_name(src_port_name, mode) + dst_state_space_port_name = _state_space_port_name(dst_port_name, mode) + connections[ + src_state_space_instance_name + "," + src_state_space_port_name + ] = (dst_state_space_instance_name + "," + dst_state_space_port_name) + + # for ext_port_name, modes in filter_bank_input_port_modes.items(): + # pass + + # Input Side Demultiplexers and Mode Converters + for port, demux in mode_demultiplexers.items(): + demux_model_name = _demultiplexer_model_name(port) + models[demux_model_name] = demux + demux_instance_name = _demultiplexer_instance_name(port) + instances[demux_instance_name] = demux_model_name + + modes = filter_bank_input_port_modes[port] + for mode in modes: + mode_converter_instance_name = _mode_converter_instance_name( + port, mode, INPUT_SUFFIX + ) + instances[mode_converter_instance_name] = MODE_CONVERTER_MODEL_NAME + demux_output = ( + demux_instance_name + "," + mode + DEMULTIPLEXER_OUT_PORT_SUFFIX + ) + converter_input = mode_converter_instance_name + "," + "in" + converter_output = mode_converter_instance_name + "," + "out" + + sax_instance_name, sax_port_name = old_ports[port].split(",") + state_space_input = ( + _state_space_instance_name(sax_instance_name) + + "," + + _state_space_port_name(sax_port_name, mode) + ) + connections[demux_output] = converter_input + connections[ + converter_output + ] = state_space_input # This line for one of the converters is connecting an output to a state space output + + ports[port] = demux_instance_name + "," + DEMULTIPLEXER_IN_PORT_NAME + + # Output Side Multiplexers and Mode Converters + for port, mux in mode_multiplexers.items(): + mux_model_name = _multiplexer_model_name(port) + models[mux_model_name] = mux + mux_instance_name = _multiplexer_instance_name(port) + instances[mux_instance_name] = mux_model_name + + modes = filter_bank_output_port_modes[port] + for mode in modes: + mode_converter_instance_name = _mode_converter_instance_name( + port, mode, OUTPUT_SUFFIX + ) + instances[mode_converter_instance_name] = MODE_CONVERTER_MODEL_NAME + mux_input = mux_instance_name + "," + mode + MULTIPLEXER_IN_PORT_SUFFIX + converter_input = mode_converter_instance_name + "," + "in" + converter_output = mode_converter_instance_name + "," + "out" + + sax_instance_name, sax_port_name = old_ports[port].split(",") + state_space_output = ( + _state_space_instance_name(sax_instance_name) + + "," + + _state_space_port_name(sax_port_name, mode) + ) + + connections[state_space_output] = converter_input + connections[converter_output] = mux_input + + ports[port] = mux_instance_name + "," + MULTIPLEXER_OUT_PORT_NAME + + netlist = { + "instances": instances, + "connections": connections, + "ports": ports, + } + + add_settings_to_netlist(netlist) + + # # TODO: Remove the following lines for testing + # import gravis as gv + # graph = netlist_to_graph(netlist, models) + # gv.d3(graph).display() + + return netlist, models + + +def _mode_converter_instance_name(port, mode, direction) -> str: + return port + "_" + mode + MODE_CONVERTER_INSTANCE_SUFFIX + "_" + direction + + +def _demultiplexer_model_name( + port, +): + return port + DEMULTIPLEXER_SUFFIX + + +def _demultiplexer_instance_name( + port, +): + return _demultiplexer_model_name(port) + + +def _multiplexer_model_name( + port, +): + return port + MULTIPLEXER_SUFFIX + + +def _multiplexer_instance_name( + port, +): + return _multiplexer_model_name(port) + + +def _state_space_model_name( + sax_model_name, +): + return sax_model_name + "_" + STATE_SPACE_MODEL_NAME_BASE + + +def _state_space_instance_name( + sax_instance_name, +): + return sax_instance_name + "_" + STATE_SPACE_INSTANCE_NAME_BASE + + +def _state_space_port_name( + port, + mode, +): + return port + "_" + mode + + +def _state_space_port_names( + input_port_modes, + output_port_modes, +): + input_port_names = [] + for input_port, modes in input_port_modes.items(): + input_port_names += [_state_space_port_name(input_port, mode) for mode in modes] + + output_port_names = [] + for output_port, modes in output_port_modes.items(): + output_port_names += [ + _state_space_port_name(output_port, mode) for mode in modes + ] + return input_port_names, output_port_names + + +# def _get_filtered_sax_model( +# sax_model: sax.Model, +# port_directionality, +# default_modes, +# ): +# input_ports_to_remove = {port_name for port_name, direction in port_directionality.items() if direction=='output'} +# output_ports_to_remove = {port_name for port_name, direction in port_directionality.items() if direction=='input'} + +# def filtered_sax_model(**kwargs): +# """ +# The port_directionality field allows us to ignore data +# in the sdict and select only the relationships necessary +# for the specified directionality +# """ +# sdict = sax_model(**kwargs) +# # print(sdict.keys()) +# # print() +# sdict = sax.multimode(sdict, modes=default_modes) +# # print(sdict.keys()) +# # print() +# # print() + +# def is_allowed(key): +# dst, src = key +# src_port, _ = src.split("@") +# dst_port, _ = dst.split("@") +# src_valid = not src_port in input_ports_to_remove +# dst_valid = not dst_port in output_ports_to_remove +# return src_valid and dst_valid + +# sdict = {k:v for k, v in sdict.items() if is_allowed(k)} + +# return sdict + +# return filtered_sax_model + +import functools +import inspect + + +def _get_filtered_sax_model( + sax_model: sax.Model, + port_directionality, + default_modes, +): + input_ports_to_remove = { + port_name + for port_name, direction in port_directionality.items() + if direction == "output" + } + + output_ports_to_remove = { + port_name + for port_name, direction in port_directionality.items() + if direction == "input" + } + + @functools.wraps(sax_model) + def filtered_sax_model(*args, **kwargs): + """The port_directionality field allows us to ignore data in the sdict + and select only the relationships necessary for the specified + directionality.""" + sdict = sax_model(*args, **kwargs) + + sdict = sax.multimode(sdict, modes=default_modes) + + def is_allowed(key): + dst, src = key + src_port, _ = src.split("@") + dst_port, _ = dst.split("@") + + src_valid = src_port not in input_ports_to_remove + dst_valid = dst_port not in output_ports_to_remove + + return src_valid and dst_valid + + return {k: v for k, v in sdict.items() if is_allowed(k)} + + # preserve original signature + filtered_sax_model.__signature__ = inspect.signature(sax_model) + + return filtered_sax_model + + +def _normalize_mode_name(mode) -> str: + return str(mode).lower() + + +def _ordered_from_set(mode_set, mode_identifiers=None): + if mode_identifiers is None: + return tuple(sorted(mode_set, key=_normalize_mode_name)) + + wanted = [_normalize_mode_name(m) for m in mode_identifiers] + present = {_normalize_mode_name(m): m for m in mode_set} + + ordered = [present[m] for m in wanted if m in present] + extras = [m for m in mode_set if _normalize_mode_name(m) not in set(wanted)] + ordered.extend(sorted(extras, key=_normalize_mode_name)) + return tuple(ordered) + + +def _get_port_mode_luts( + sax_model: sax.Model, + mode_identifiers=None, +): + input_port_modes = {} + output_port_modes = {} + + for o, i in sax_model().keys(): + in_port, in_mode = i.split("@") + out_port, out_mode = o.split("@") + input_port_modes.setdefault(in_port, set()).add(in_mode) + output_port_modes.setdefault(out_port, set()).add(out_mode) + + input_port_modes = { + port: _ordered_from_set(modes, mode_identifiers) + for port, modes in input_port_modes.items() + } + output_port_modes = { + port: _ordered_from_set(modes, mode_identifiers) + for port, modes in output_port_modes.items() + } + + return input_port_modes, output_port_modes + + +# def _get_port_mode_luts( +# sax_model: sax.Model, +# ): +# input_port_modes = {} +# output_port_modes = {} +# for o, i in sax_model().keys(): +# in_port, in_mode = i.split('@') +# out_port, out_mode = o.split('@') +# input_port_modes.setdefault(in_port, set()).add(in_mode) +# output_port_modes.setdefault(out_port, set()).add(out_mode) + +# return input_port_modes, output_port_modes + + +def _bidirectional_ports_to_unidirectional_ports( + sax_model: sax.ModelMM, port_directionality +): + """The resulting port names are the orginal port names appended with a + string unique to all substrings in the original port names. + + In order to find the original port name, just remove the out_suffix + and in_suffix substrings from the dict keys. + """ + port_names = port_directionality.keys() + unique_word = "_" + + for port_name in port_names: + while unique_word in port_name: + unique_word += "_" + + in_suffix = unique_word + INPUT_SUFFIX + out_suffix = unique_word + OUTPUT_SUFFIX + + unidirectional_port_directionality = {} + valid_input_ports = set() + valid_output_ports = set() + for port_name, directionality in port_directionality.items(): + in_port_name = port_name + in_suffix + out_port_name = port_name + out_suffix + if directionality == "bidirectional": + unidirectional_port_directionality[in_port_name] = "input" + unidirectional_port_directionality[out_port_name] = "output" + valid_input_ports.add(in_port_name) + valid_output_ports.add(out_port_name) + elif directionality == "input": + # in_port_name = port_name + f"{unique_word}in" + unidirectional_port_directionality[in_port_name] = "input" + valid_input_ports.add(in_port_name) + elif directionality == "output": + # out_port_name = port_name + f"{unique_word}out" + unidirectional_port_directionality[out_port_name] = "output" + valid_output_ports.add(out_port_name) + + def unidirectional_sax_model(**kwargs): + valid_input_ports + valid_output_ports + in_suffix + out_suffix + sdict = sax_model(**kwargs) + new_sdict = { + (src + out_suffix, dst + in_suffix): v for (src, dst), v in sdict.items() + } + + return new_sdict + + return ( + unidirectional_sax_model, + unidirectional_port_directionality, + in_suffix, + out_suffix, + ) + + +def _calculate_state_space_coefficients_from_sax_model( + sax_model, + sax_settings, + vector_fitting_parameters, + simulation_parameters, + delay_compensation=0, +): + """Returns A, B, C, D, input_ports, output_ports For D[i, j], + output_ports[i] <- input_ports[j]""" + input_port_modes, output_port_modes = _get_port_mode_luts( + sax_model, simulation_parameters.mode_identifiers + ) + sax_model_signature = inspect.signature(sax_model).parameters + constant_over_wavelength = False + + def has_wl_kwarg(model): + sig = inspect.signature(model) + if "wl" in sig.parameters: + return True + try: + model(wl=0.0) + return True + except TypeError: + return False + + if not has_wl_kwarg(sax_model): + constant_over_wavelength = True + elif False: + # Check if the model is constant over the bandwidth with respect to wavelength + pass + + if constant_over_wavelength: + if not delay_compensation == 0: + raise ValueError( + "delay compensation cannot be applied to a 0 delay element" + ) + + sdict = sax_model(**sax_settings) + # TODO: Fix Block Mode Simulator so that this is deterministic + input_ports = [ + f"{port}@{mode}" + for port, modes in input_port_modes.items() + for mode in modes + ] + output_ports = [ + f"{port}@{mode}" + for port, modes in output_port_modes.items() + for mode in modes + ] + S = dict_to_rect_matrix( + sdict, input_ports=input_ports, output_ports=output_ports + ) + # Order r = 1 model + r = 1 + m = len(input_ports) + M = r * m + q = len(output_ports) + A = jnp.zeros((M, M), dtype=complex) + B = jnp.zeros((M, m), dtype=complex) + C = jnp.zeros((q, M), dtype=complex) + D = S[0, :, :] + + return (A, B, C, D), input_ports, output_ports + + f_min = speed_of_light / max(vector_fitting_parameters["spectral_range"]) + f_max = speed_of_light / min(vector_fitting_parameters["spectral_range"]) + f_center = speed_of_light / vector_fitting_parameters["center_wavelength"] + # f_center = 192.9e12 + frequency = jnp.linspace( + f_min, f_max, vector_fitting_parameters["num_frequency_samples"] + ) + sdict = sax_model(wl=1e6 * speed_of_light / frequency, **sax_settings) + + # TODO: Fix Block Mode Simulator so that this is deterministic + input_ports = [ + f"{port}@{mode}" for port, modes in input_port_modes.items() for mode in modes + ] + output_ports = [ + f"{port}@{mode}" for port, modes in output_port_modes.items() for mode in modes + ] + s_params = dict_to_rect_matrix( + sdict, input_ports=input_ports, output_ports=output_ports + ) + min_order = vector_fitting_parameters["min_model_order"] + max_order = vector_fitting_parameters["max_model_order"] + # Checkpoint + sampling_frequency = 1 / simulation_parameters.dt + + ### TODO: REMOVE THIS LINE USED FOR TESTING + # vector_fitting_parameters["model_order"] = 20 + + Omega = 2 * jnp.pi * (frequency - f_center) / sampling_frequency + s_params = jnp.exp(-1j * delay_compensation * Omega)[:, None, None] * s_params + if vector_fitting_parameters["model_order"] is None: + ( + poles, + residues, + feedthrough, + mean_squared_error, + ) = optimize_order_vector_fitting_discrete( + min_order, + max_order, + s_params, + frequency, + f_center, + sampling_frequency, + sign_convention=PHYSICIST, + ) + else: + poles, residues, feedthrough, mean_squared_error = vector_fitting_discrete( + vector_fitting_parameters["model_order"], + s_params, + frequency, + f_center, + sampling_frequency, + sign_convention=PHYSICIST, + ) + + A, B, C, D = state_space_discrete(poles, residues, feedthrough) + + return (A, B, C, D), input_ports, output_ports + + +class SParameterPlaceholder(Placeholder): + """Placeholder base class for SAX models inside PCell expansion. + + `optical_s_parameter_placeholder` creates concrete subclasses of + this class when a raw SAX callable needs to travel through + PCell/netlist processing before being converted into a simulator- + specific component. + """ + + +def optical_s_parameter_placeholder( + sax_model: sax.Model, + port_directionality: dict = None, + default_modes: list | tuple | str = DEFAULT_MODES, +) -> type[SParameterPlaceholder]: + """Wrap a SAX optical model as a placeholder component class. + + Unlike `optical_s_parameter`, this factory does not create a component that + directly evaluates an S-parameter response. It records the SAX model, ports, + directionality, and settings so later PCell expansion can convert the model + into the representation needed by the selected simulator. + + Parameters + ---------- + sax_model: + Callable SAX model returning an `SDict`. + port_directionality: + Optional mapping from port name to `"input"`, `"output"`, or + `"bidirectional"`. Unspecified ports default to `"bidirectional"`. + default_modes: + Mode label or labels assigned when the SAX model omits explicit + multimode port names. + + Returns + ------- + type[SParameterPlaceholder] + A generated placeholder class whose instances store settings such as + `sax_settings`, `port_directionality`, `vector_fitting_parameters`, and + `delay_compensation`. + """ + pcell_port_names = _get_port_names_without_mode(sax_model) + + port_directionality = deepcopy(port_directionality) + if port_directionality is None: + port_directionality = {} + + for port_name in pcell_port_names: + port_directionality.setdefault(port_name, "bidirectional") + + # default_port_directionality = port_directionality + + if isinstance(default_modes, str): + default_modes = [default_modes] + default_modes = tuple(default_modes) + + BaseSParameterSax = SParameterPlaceholder # Freeze the reference + + class SpecificSParameterPlaceholder(BaseSParameterSax): + ports = [ + Port( + name=port_name, + type="optical", + directionality=port_directionality[port_name], + ) + for port_name in pcell_port_names + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + # Rename kwargs to be more accurate + **kwargs, + # sax_settings: dict = None, + # vector_fitting_parameters = None, + # delay_compensation: int = 0, + # port_directionality = {}, + ): + """ + TODO: Add documentation for each of the kwargs + """ + # default_vector_fitting_parameters = { + # "model_order": None, + # "min_model_order": 2, + # "max_model_order": 100, + # "num_frequency_samples": 1000, + # "center_wavelength": 1.55e-6, + # "spectral_range": (1.5e-6, 1.6e-6), + # # NOTE: Currently, a user CAN change the spectral range parameter (the default is set by ) + # } + self.sax_model = sax_model + self.settings = kwargs + self.settings.setdefault("sax_settings", {}) + self.settings.setdefault("group_id", None) + self.settings.setdefault( + "vector_fitting_parameters", _default_vector_fitting_parameters + ) + self.settings.setdefault("delay_compensation", 0) + self.settings.setdefault("port_directionality", {}) + self.settings.setdefault("apply_phase_correction", True) + # self.sax_model = sax_model + # self.sax_settings = self.settings.setdefault('sax_settings', {}) + # self.vector_fitting_parameters = self.settings.setdefault('vector_fitting_parameters', default_vector_fitting_parameters) + # self.delay_compensation = self.settings.setdefault('delay_compensation', 0) + # self.port_directionality = self.settings.setdefault('port_directionality', {}) + + return SpecificSParameterPlaceholder diff --git a/simphony/libraries/ideal/sources.py b/simphony/libraries/ideal/sources.py new file mode 100644 index 00000000..173bd754 --- /dev/null +++ b/simphony/libraries/ideal/sources.py @@ -0,0 +1,619 @@ +# from scipy.ndimage import gaussian_filter1d +# from scipy.signal import iirdesign +# from scipy.signal import freqz +# from scipy.signal import butter, lfilter, cheby1 +from __future__ import annotations + +from typing import Callable + +import jax +import jax.numpy as jnp +import numpy as np # Used to avoid caching issues when generating random numbers +from jax.typing import ArrayLike + +from simphony.component.component import ( + BlockModeComponent, + SampleModeComponent, + SteadyStateComponent, +) +from simphony.component.port import Port +from simphony.signal.block_mode import BlockModeElectricalSignal, BlockModeOpticalSignal +from simphony.signal.sample_mode import SampleModeOpticalSignal +from simphony.signal.steady_state import SteadyStateElectricalSignal +from simphony.simulation.block_mode import BlockModeSimulationParameters +from simphony.simulation.sample_mode import SampleModeSimulationParameters +from simphony.simulation.simulation import SimulationParameters + +# def gaussian_kernel1d(sigma, truncate=4.0): +# radius = int(truncate * sigma + 0.5) +# x = jnp.arange(-radius, radius + 1) +# kernel = jnp.exp(-(x**2) / (2 * sigma**2)) +# kernel /= jnp.sum(kernel) +# return kernel + +# def gaussian_filter1d_jax(x, sigma, truncate=4.0): +# kernel = gaussian_kernel1d(sigma, truncate) +# return jnp.convolve(x, kernel, mode='same') + + +# def cubic_interp_1d(x: jnp.ndarray, new_len: int) -> jnp.ndarray: +# def catmull_rom(p0, p1, p2, p3, t): +# t2 = t * t +# t3 = t2 * t +# return 0.5 * ( +# (2 * p1) + +# (-p0 + p2) * t + +# (2*p0 - 5*p1 + 4*p2 - p3) * t2 + +# (-p0 + 3*p1 - 3*p2 + p3) * t3 +# ) + +# old_len = x.shape[0] +# idxs_f = jnp.linspace(0, old_len - 1, new_len) +# idxs = jnp.floor(idxs_f).astype(int) +# t = idxs_f - idxs + +# # Ensure indices stay within bounds +# idxs_m1 = jnp.clip(idxs - 1, 0, old_len - 1) +# idxs_p1 = jnp.clip(idxs + 1, 0, old_len - 1) +# idxs_p2 = jnp.clip(idxs + 2, 0, old_len - 1) + +# p0 = x[idxs_m1] +# p1 = x[idxs] +# p2 = x[idxs_p1] +# p3 = x[idxs_p2] + +# return catmull_rom(p0, p1, p2, p3, t) + + +class OpticalCombSource(SampleModeComponent, BlockModeComponent): + ports = [ + Port( + name="o0", + type="optical", + directionality="output", + ), + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + wavelength=jnp.array([1.53e-6, 1.54e-6, 1.55e-6, 1.56e-6, 1.57e-6]), + linewidth=0.0, + ): + self.wavelength = jnp.asarray(wavelength) + self.linewidth = linewidth + + def _generate_phase_noise(self, simulation_parameters): + N = simulation_parameters.num_time_steps + num_wls = self.wavelength.shape[0] + dt = simulation_parameters.dt + delta_phi_std = float(jnp.sqrt(2 * jnp.pi * self.linewidth * dt)) + rng = np.random.default_rng(simulation_parameters.seed) + dphi = rng.standard_normal((N, num_wls)) * delta_phi_std + return jnp.array(jnp.cumsum(dphi, axis=0)) + + def block_mode_response( + self, + inputs: dict = {}, + simulation_parameters: BlockModeSimulationParameters = BlockModeSimulationParameters(), + ): + num_modes = len(simulation_parameters.mode_identifiers) + phi = self._generate_phase_noise(simulation_parameters) + # shape: (N, L, M) — one unit amplitude per wavelength, placed in mode 0 + A_t = jnp.zeros((*phi.shape, num_modes), dtype=complex) + A_t = A_t.at[:, :, 0].set(jnp.exp(1j * phi)) + + outputs = { + "o0": BlockModeOpticalSignal( + amplitude=A_t, + wavelength=self.wavelength, + ), + } + return outputs + + def sample_mode_initial_state(self, simulation_parameters): + # State is just the accumulated phase per wavelength — O(L), not O(N×L). + # block_mode_response is left unchanged for block-mode callers. + L = self.wavelength.shape[0] + return jnp.zeros((L,)) + + def sample_mode_step(self, inputs, state, simulation_state, simulation_parameters): + phi = state # accumulated phase, shape (L,) + L = self.wavelength.shape[0] + M = len(simulation_parameters.mode_identifiers) + + if self.linewidth == 0.0: + # CW: constant unit amplitude, phase never changes. + new_phi = phi + else: + # Noisy laser: grow a random-walk phase one step at a time using the + # per-step PRNG key already provided by the simulator. + dt = simulation_parameters.dt + delta_phi_std = jnp.sqrt(2 * jnp.pi * self.linewidth * dt) + dphi = delta_phi_std * jax.random.normal( + simulation_state.prng_key, shape=(L,) + ) + new_phi = phi + dphi + + amplitude = jnp.zeros((L, M), dtype=complex).at[:, 0].set(jnp.exp(1j * new_phi)) + + outputs = { + "o0": SampleModeOpticalSignal( + amplitude=amplitude, + wavelength=self.wavelength, + ), + } + return outputs, new_phi + + +class CWLaser(SampleModeComponent, BlockModeComponent): + """Continuous-wave optical source for time-domain simulations. + + The laser emits a complex optical envelope on output port `o0`. In Block + mode, the returned `BlockModeOpticalSignal` spans the full simulation time + block and uses `wavelength` as its carrier channel set. + + Parameters + ---------- + wavelength: + Optical carrier wavelength or wavelength array, in meters. + linewidth: + Lorentzian linewidth used to generate phase noise. A value of zero + produces a deterministic constant-envelope source. + lineshape: + Phase-noise model name. Currently only `"lorentzian"` is implemented. + mode_idx: + Index of the optical mode that receives the source amplitude. + """ + + # delay_compensation = 0 + # optical_ports = ["o0"] + ports = [ + Port( + name="o0", + type="optical", + directionality="output", + ), + ] + + def __init__( + self, + simulation_parameters, + wavelength=1.55e-6, + linewidth=0, + lineshape="lorentzian", + mode_idx=0, + ): + self.wavelength = wavelength + self.linewidth = linewidth + self.lineshape = lineshape + self.mode_idx = mode_idx + + if self.lineshape.lower() == "lorentzian": + self.phase_noise = self.lorentzian_phase_noise + self.sample_mode_step = self.sample_mode_step_lorentzian + self.sample_mode_initial_state = self.sample_mode_initial_state_lorentzian + elif self.lineshape.lower() == "gaussian": + raise ValueError(f"Gaussian noise not yet implemented") + # self.gaussian_window_sigma = 170 + # self.gaussian_window_period = 1e-14 + # # gaussian_noise = jax.random.normal(key, shape=(N,)) + # self.phase_noise = self.gaussian_phase_noise + # self.sample_mode_step = self.sample_mode_step_gaussian + # self.sample_mode_initial_state = self.sample_mode_initial_state_gaussian + else: + raise ValueError( + f"Unrecognized name for lineshape parameter: {self.lineshape}" + ) + + def lorentzian_phase_noise(self, simulation_parameters): + key = jax.random.PRNGKey(seed=0) + delta_phi_std = jnp.sqrt(2 * jnp.pi * self.linewidth * simulation_parameters.dt) + dphi = ( + jax.random.normal(key, (simulation_parameters.num_time_steps,)) + * delta_phi_std + ) + phi = jnp.cumsum(dphi) + + return phi + + def sample_mode_step_lorentzian( + self, inputs, state, simulation_state, simulation_parameters + ): + phi_prev = state + key = simulation_state.prng_key + delta_phi_std = jnp.sqrt(2 * jnp.pi * self.linewidth * simulation_parameters.dt) + dphi = jax.random.normal(key) * delta_phi_std + phi = dphi + phi_prev + + A_t = jnp.exp(1j * phi) + amplitude = jnp.zeros( + (1, len(simulation_parameters.mode_identifiers)), dtype=complex + ) + amplitude = amplitude.at[0, self.mode_idx].set(A_t) + outputs = { + "o0": SampleModeOpticalSignal( + amplitude=amplitude, wavelength=jnp.array([self.wavelength]) + ), + } + + # amplitude = jnp.zeros((3, len(simulation_parameters.mode_identifiers)), dtype=complex) + # amplitude = amplitude.at[0, 0].set(0.1 + 0j) + # amplitude = amplitude.at[1, 0].set(0.2 + 0j) + # amplitude = amplitude.at[2, 0].set(0.3 + 0j) + # amplitude = amplitude.at[0, 1].set(1.1 + 0j) + # amplitude = amplitude.at[1, 1].set(1.2 + 0j) + # amplitude = amplitude.at[2, 1].set(1.3 + 0j) + # wl = jnp.array([1.51e-6, 1.549e-6, 1.59e-6]) + # # TODO: REMOVE THIS HARDCODED TESTING CODE + # outputs = { + # "o0": SampleModeOpticalSignal( + # amplitude=amplitude, + # wavelength=wl + # ), + # } + + return outputs, phi + + # def gaussian_phase_noise(self, simulation_parameters): + # dt_prime = self.gaussian_window_period + # dt = simulation_parameters.sampling_period + # sigma = self.gaussian_window_sigma + # N = simulation_parameters.num_time_steps + # M = int(dt/dt_prime*N) + + # gaussian_noise = jax.random.normal(simulation_parameters.prng_key, shape=(M,)) + # # b, a = butter(N=2, Wn=0.00187) # 4th order low-pass + # # gaussian_noise = lfilter(b, a, gaussian_noise) + # gaussian_noise = gaussian_filter1d_jax(gaussian_noise, sigma=sigma) + # gaussian_noise /= jnp.std(gaussian_noise) + # f_instantaneous = (self.linewidth/2.355)*gaussian_noise + # phi = 2*jnp.pi*np.cumsum(f_instantaneous) * dt_prime + # phi = cubic_interp_1d(phi, N) + # return phi + + # def sample_mode_step_gaussian(self, inputs, state, simulation_parameters): + # N = simulation_parameters.num_time_steps + # dt = simulation_parameters.sampling_period + # sigma = self.gaussian_window_sigma + # dt_prime = self.gaussian_window_period + # gaussian_filter1d + # iirdesign(sigma, -sigma, ) + # return ... + + def block_mode_response( + self, + inputs: dict = {}, + simulation_parameters: BlockModeSimulationParameters = BlockModeSimulationParameters(), + ): + N = simulation_parameters.num_time_steps + sampling_period = simulation_parameters.dt + t = jnp.arange(N) * sampling_period + linewidth = self.linewidth + + phi = self.phase_noise(simulation_parameters) + + # Compute complex envelope + A_t = jnp.exp(1j * phi) + amplitude = jnp.zeros( + (A_t.shape[0], 1, len(simulation_parameters.mode_identifiers)), + dtype=complex, + ) + amplitude = amplitude.at[:, 0, self.mode_idx].set(A_t) + + outputs = { + "o0": BlockModeOpticalSignal( + amplitude=amplitude, wavelength=jnp.array([self.wavelength]) + ), + } + + return outputs + + def sample_mode_initial_state_gaussian(self, simulation_parameters): + truncate = 4.0 + radius = int(truncate * self.gaussian_window_sigma + 0.5) + x = np.arange(-radius, radius + 1) + g = np.exp(-0.5 * (x / self.gaussian_window_sigma) ** 2) + g /= g.sum() + std_dev = np.sqrt(np.sum(g**2)) + return std_dev + + def sample_mode_initial_state_lorentzian(self, simulation_parameters): + phi_prev = 0 + return phi_prev + + +class OpticalSource(SampleModeComponent, BlockModeComponent): + """Optical source driven by a user-provided envelope. + + The source emits a `BlockModeOpticalSignal` on output port `o0`. + Users can either provide a concrete `envelope` or an `envelope_fn` + that creates one from the simulation time vector. Exactly one of + those options must be supplied. + + If the envelope length does not match + `simulation_parameters.num_time_steps`, it is truncated or padded + with zeros so the emitted block has the simulation length. + """ + + optical_ports = ["o0"] + + def __init__( + self, + simulation_parameters, + # wavelength = 1.55e-6, + envelope: BlockModeOpticalSignal = None, + envelope_fn: Callable[[ArrayLike], BlockModeOpticalSignal] = None, + ): + if envelope is not None and envelope_fn is not None: + raise ValueError("Specify either evelope or envelope_fn, NOT both") + if envelope is None and envelope_fn is None: + raise ValueError("Parameter `envelope` or `envelope_fn` must be specified") + + # self.wavelength = wavelength + self.envelope = envelope + self.envelope_fn = envelope_fn + + def _calculate_envelope(self, simulation_parameters): + N = simulation_parameters.num_time_steps + dt = simulation_parameters.dt + t = jnp.arange(0, N, 1) * dt + + if self.envelope_fn: + self.envelope = self.envelope_fn(t) + + # Make envelope match the number of time steps, by truncating or appending zeros + amplitude = self.envelope.amplitude + T, L, M = amplitude.shape + if amplitude.shape[0] < N: + amplitude = jnp.concatenate( + [amplitude, jnp.zeros((N - T, L, M), dtype=complex)], axis=0 + ) + elif amplitude.shape[0] > N: + amplitude = amplitude[:N, :, :] + + # TODO: RETURN, DON'T MUTATE + self.envelope = BlockModeOpticalSignal( + amplitude=amplitude, wavelength=self.envelope.wavelength + ) + + def block_mode_response( + self, + inputs: dict, + simulation_parameters: BlockModeSimulationParameters, + ): + self._calculate_envelope(simulation_parameters) + + outputs = { + "o0": BlockModeOpticalSignal( + amplitude=self.envelope.amplitude, + wavelength=self.envelope.wavelength, + ) + } + return outputs + + def sample_mode_initial_state( + self, simulation_parameters: SampleModeSimulationParameters + ): + self._calculate_envelope(simulation_parameters) + + time_step = 0 + return jnp.array(time_step, dtype=int) + + def sample_mode_step( + self, + inputs: dict, + state, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ): + current_time_step = state + outputs = { + "o0": SampleModeOpticalSignal( + amplitude=self.envelope.amplitude[current_time_step], + wavelength=self.envelope.wavelength, + ) + } + return outputs, state + 1 + + +class VoltageSource( + SteadyStateComponent, + SampleModeComponent, + BlockModeComponent, +): + """Electrical source for steady-state, sample-mode, and Block mode runs. + + In Block mode, the source emits a `BlockModeElectricalSignal` on `e0`. + Users can supply a concrete electrical `envelope`, an `envelope_fn` that + receives the simulation time vector, or neither. If neither is supplied, the + source emits a constant voltage equal to `steady_state_voltage`. + + Parameters + ---------- + envelope: + Electrical signal to emit in time-domain simulations. + envelope_fn: + Callable that receives the time vector and returns a + `BlockModeElectricalSignal`. + steady_state_voltage: + Constant voltage used for steady-state simulations and as the Block mode + default when no envelope is supplied. + """ + + ports = [ + Port( + name="e0", + type="electrical", + directionality="bidirectional", + ) + ] + + def __init__( + self, + simulation_parameters: SimulationParameters, + *, + envelope: BlockModeOpticalSignal = None, + envelope_fn: Callable[[ArrayLike], BlockModeElectricalSignal] = None, + steady_state_voltage=1.0, + ): + self.steady_state_voltage = steady_state_voltage + + if envelope is not None and envelope_fn is not None: + raise ValueError("Specify either evelope or envelope_fn, NOT both") + # if envelope is None and envelope_fn is None: + # raise ValueError("Parameter `envelope` or `envelope_fn` must be specified") + + # self.wavelength = wavelength + self.envelope = envelope + self.envelope_fn = envelope_fn + + # optical_ports = None + # electrical_ports = ['e0'] + # logic_ports = None + # super().__init__( + # optical_ports=optical_ports, + # electrical_ports=electrical_ports, + # logic_ports=logic_ports + # ) + + def _calculate_envelope(self, simulation_parameters): + N = simulation_parameters.num_time_steps + dt = simulation_parameters.dt + t = jnp.arange(0, N, 1) * dt + + if self.envelope: + pass + elif self.envelope_fn: + self.envelope = self.envelope_fn(t) + else: + self.envelope = BlockModeElectricalSignal( + voltage=np.ones((len(t),), dtype=complex) * self.steady_state_voltage + ) + + # Make envelope match the number of time steps, by truncating or appending zeros + voltage = self.envelope.voltage + # T = voltage.shape + # if voltage.shape[0] < N: + # voltage = jnp.concatenate([voltage, jnp.zeros((N-T,), dtype=complex)], axis=0) + # elif voltage.shape[0] > N: + # voltage = voltage[:N, :] + + return BlockModeElectricalSignal(voltage=voltage) + + def steady_state( + self, + inputs: dict, + simulation_parameters: SimulationParameters, + ): + outputs = {"e0": SteadyStateElectricalSignal(voltage=self.steady_state_voltage)} + return outputs + + def block_mode_response(self, input_signal: ArrayLike, simulation_parameters): + envelope = self._calculate_envelope(simulation_parameters) + outputs = {"e0": envelope} + return outputs + + def sample_mode_step( + self, inputs: dict, state: jax.Array, simulation_state, simulation_parameters + ): + # TODO: Complete this to use the signal defined in settings + return inputs, state + + def sample_mode_initial_state(self, simulation_parameters): + return jnp.array([0]) + + +class PRNG( + SteadyStateComponent, + # SampleModeComponent, + BlockModeComponent, +): + logic_ports = ["l0"] + + def __init__(self, **settings): + pass + # optical_ports = None + # electrical_ports = None + # logic_ports = ['l0'] + # super().__init__( + # optical_ports=optical_ports, + # electrical_ports=electrical_ports, + # logic_ports=logic_ports + # ) + + @jax.jit + def steady_state(self, inputs: dict, default_output: int = 0): + outputs = {"l0": default_output} + return outputs + + def block_mode_response(self, inputs: dict, **kwargs): + pass + + +# def gaussian_phase_noise(self, simulation_parameters): +# N = simulation_parameters.num_time_steps +# dt = simulation_parameters.sampling_period +# tau = 1e-14 +# sigma = 100 +# M = int(N*dt/tau) +# _gaussian_noise = jax.random.normal(simulation_parameters.prng_key, shape=(M,)) +# indices = jnp.round(jnp.linspace(0, M - 1, N)).astype(int) +# gaussian_noise = _gaussian_noise[indices] +# pass + +# # sigma = 250*( 1e-15 / simulation_parameters.sampling_period) +# # # sigma = jnp.minimum(N, sigma) + + +# # gaussian_noise = jax.random.normal(simulation_parameters.prng_key, shape=(N,)) +# f_instantaneous = gaussian_filter1d_jax(gaussian_noise, sigma=sigma) + +# # std_dev = jnp.std(f_instantaneous) +# ## We need to determine the scale factor 1/jnp.std(f_instantaneos) a priori ## +# sigma_g = sigma +# truncate = 4.0 +# radius = int(truncate * sigma_g + 0.5) +# x = np.arange(-radius, radius + 1) +# g = np.exp(-0.5 * (x / sigma_g) ** 2) +# g /= g.sum() # normalize like scipy +# std_dev = np.sqrt(np.sum(g ** 2)) # sqrt(N/M) is the result of upsampling +# #### +# f_instantaneous *= (self.linewidth/2.355)/std_dev +# f_instantaneous *= 2 +# phi = jnp.pi*np.cumsum(f_instantaneous) * dt +# return phi + +# def gaussian_phase_noise(self, simulation_parameters): +# N = simulation_parameters.num_time_steps +# dt = simulation_parameters.sampling_period +# # tau = 1e-14 +# # sigma = 150 +# # M = int(N*dt/tau) +# # _gaussian_noise = jax.random.normal(simulation_parameters.prng_key, shape=(M,)) +# # indices = jnp.round(jnp.linspace(0, M - 1, N)).astype(int) +# # gaussian_noise = _gaussian_noise[indices] +# pass + +# sigma = 500*( 1e-15 / simulation_parameters.sampling_period) +# # sigma = jnp.minimum(N, sigma) + + +# gaussian_noise = jax.random.normal(simulation_parameters.prng_key, shape=(N,)) +# f_instantaneous = gaussian_filter1d_jax(gaussian_noise, sigma=sigma) + +# # std_dev = jnp.std(f_instantaneous) +# ## We need to determine the scale factor 1/jnp.std(f_instantaneos) a priori ## +# sigma_g = sigma +# truncate = 4.0 +# radius = int(truncate * sigma_g + 0.5) +# x = np.arange(-radius, radius + 1) +# g = np.exp(-0.5 * (x / sigma_g) ** 2) +# g /= g.sum() # normalize like scipy +# std_dev = np.sqrt(np.sum(g ** 2)) # sqrt(N/M) is the result of upsampling +# #### +# f_instantaneous *= (self.linewidth/2.355)/std_dev +# f_instantaneous *= 2 +# phi = jnp.pi*np.cumsum(f_instantaneous) * dt +# return phi diff --git a/simphony/libraries/ideal/special.py b/simphony/libraries/ideal/special.py new file mode 100644 index 00000000..91e533d8 --- /dev/null +++ b/simphony/libraries/ideal/special.py @@ -0,0 +1,28 @@ +# import jax.numpy as jnp +# from simphony.component.port import Port +# from simphony.component.component import BlockModeComponent +# from simphony.signal.block_mode import BlockModeOpticalSignal + +# class Terminator(BlockModeComponent): +# ports = [ +# Port(name="o0", type="optical", directionality="bidirectional"), +# ] + +# def __init__(self, simulation_parameters): +# pass + +# def block_mode_response(self, inputs, simulation_parameters): +# T = simulation_parameters.num_time_steps +# wavelengths = simulation_parameters.optical_baseband_wavelengths +# L = wavelengths.shape[0] + +# M = len(simulation_parameters.mode_identifiers) + +# amp = jnp.zeros((T, L, M), dtype=jnp.complex64) + +# return { +# "o0": BlockModeOpticalSignal( +# amplitude=amp, +# wavelength=wavelengths, +# ) +# } diff --git a/simphony/libraries/ideal/test_has_wl_kwarg.py b/simphony/libraries/ideal/test_has_wl_kwarg.py new file mode 100644 index 00000000..ac1b75a0 --- /dev/null +++ b/simphony/libraries/ideal/test_has_wl_kwarg.py @@ -0,0 +1,322 @@ +"""Diagnostic analysis of has_wl_kwarg() in +_calculate_state_space_coefficients_from_sax_model. + +The function is defined inside _calculate_state_space_coefficients_from_sax_model and +determines whether a SAX model depends on wavelength. If it returns False the caller +treats the model as memoryless (constant_over_wavelength = True) and collapses the +entire transfer function into a single D matrix, giving zero temporal dynamics. + +This script copies the function verbatim, then systematically tests it against every +model type that might be passed in — including the _get_filtered_sax_model wrapper used +by gaussian_process_s_parameter — to find exactly where it returns False incorrectly. +""" + +import sys + +sys.path.insert(0, ".") + +import functools +import inspect + +import sax +from jax import config + +config.update("jax_enable_x64", True) + +from simphony.libraries.ideal.s_parameters import _get_filtered_sax_model +from simphony.libraries.old_ideal import coupler, waveguide + +# --------------------------------------------------------------------------- +# Verbatim copy of has_wl_kwarg from s_parameters.py (line 996) +# --------------------------------------------------------------------------- + + +def has_wl_kwarg(model): + sig = inspect.signature(model) + + if "wl" in sig.parameters: + return True + + if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()): + try: + model(wl=0.0) + return True + except TypeError: + return False + + return False + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def report(label, model): + sig = inspect.signature(model) + params = sig.parameters + has_wl = "wl" in params + has_var_kw = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values()) + + # If **kwargs present, probe whether wl is actually accepted + var_kw_accepts_wl = None + if has_var_kw: + try: + model(wl=0.0) + var_kw_accepts_wl = True + except TypeError: + var_kw_accepts_wl = False + + result = has_wl_kwarg(model) + + print(f"\n{'='*60}") + print(f"Model : {label}") + print(f" sig : {sig}") + print(f" has 'wl' : {has_wl}") + print(f" has **kwargs : {has_var_kw}") + if has_var_kw: + print(f" **kwargs accepts wl=0.0 : {var_kw_accepts_wl}") + print( + f" >>> has_wl_kwarg() returns : {result} {'✓' if result else '✗ WRONG — will be treated as constant!'}" + ) + return result + + +# --------------------------------------------------------------------------- +# 1. Baseline: raw model functions +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 1. Raw SAX model functions") +print("#" * 60) + +report("waveguide (raw)", waveguide) +report("coupler (raw)", coupler) + + +# --------------------------------------------------------------------------- +# 2. SAX circuit composed from raw models +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 2. sax.circuit() output") +print("#" * 60) + +ring_netlist = { + "instances": {"wg": "waveguide", "dc": "dc"}, + "connections": {"dc,o2": "wg,o0", "dc,o3": "wg,o1"}, + "ports": {"o0": "dc,o0", "o1": "dc,o1"}, +} +ring_sax, _ = sax.circuit( + netlist=ring_netlist, + models={"waveguide": waveguide, "dc": coupler}, +) + +report("ring_sax (sax.circuit output)", ring_sax) + + +# --------------------------------------------------------------------------- +# 3. _get_filtered_sax_model wrapper +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 3. _get_filtered_sax_model() wrapper") +print("#" * 60) + +port_directionality = {"o0": "input", "o1": "output"} +default_modes = ("TE", "TM") + +filtered = _get_filtered_sax_model(ring_sax, port_directionality, default_modes) + +report("filtered_sax_model (wrapper around ring_sax)", filtered) + +# Inspect what _get_filtered_sax_model actually does to the signature +print("\n--- Signature inspection of filtered model ---") +sig_raw = inspect.signature(ring_sax) +sig_filt = inspect.signature(filtered) +print(f" ring_sax signature : {sig_raw}") +print(f" filtered signature : {sig_filt}") + +# Show __wrapped__ chain if present +obj = filtered +depth = 0 +while hasattr(obj, "__wrapped__"): + depth += 1 + print(f" __wrapped__ depth {depth}: {obj.__wrapped__}") + obj = obj.__wrapped__ + +# Show functools.wraps metadata +print(f" filtered.__module__ : {getattr(filtered, '__module__', 'N/A')}") +print(f" filtered.__name__ : {getattr(filtered, '__name__', 'N/A')}") +print(f" filtered.__qualname__: {getattr(filtered, '__qualname__', 'N/A')}") +print(f" filtered.__wrapped__ : {getattr(filtered, '__wrapped__', 'N/A')}") + + +# --------------------------------------------------------------------------- +# 4. Probe calling behaviour of the filtered model +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 4. Calling behaviour of filtered model") +print("#" * 60) + +# Does it accept wl=0.0? +try: + result = filtered(wl=0.0) + print(f" filtered(wl=0.0) succeeded → {type(result)}") +except TypeError as e: + print(f" filtered(wl=0.0) raised TypeError: {e}") +except Exception as e: + print(f" filtered(wl=0.0) raised {type(e).__name__}: {e}") + +# Does it accept wl as positional? +try: + result = filtered(0.0) + print(f" filtered(0.0) succeeded → {type(result)}") +except TypeError as e: + print(f" filtered(0.0) raised TypeError: {e}") +except Exception as e: + print(f" filtered(0.0) raised {type(e).__name__}: {e}") + + +# --------------------------------------------------------------------------- +# 5. Root cause: what inspect.signature sees for functools.wraps +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 5. Root-cause analysis: inspect.signature vs functools.wraps") +print("#" * 60) + +# _get_filtered_sax_model uses @functools.wraps(sax_model) which copies +# sax_model's __wrapped__ attribute. inspect.signature follows __wrapped__ +# by default (follow_wrapped=True). Let's check whether that is happening. + +print("\nDoes inspect.signature follow __wrapped__?") +sig_follow = inspect.signature(filtered, follow_wrapped=True) +sig_nofollow = inspect.signature(filtered, follow_wrapped=False) +print(f" follow_wrapped=True : {sig_follow}") +print(f" follow_wrapped=False : {sig_nofollow}") + +print("\nConclusion:") +has_wl_follow = "wl" in sig_follow.parameters +has_wl_nofollow = "wl" in sig_nofollow.parameters +has_var_kw_follow = any( + p.kind == inspect.Parameter.VAR_KEYWORD for p in sig_follow.parameters.values() +) +has_var_kw_nofollow = any( + p.kind == inspect.Parameter.VAR_KEYWORD for p in sig_nofollow.parameters.values() +) +print( + f" follow=True → 'wl' in params: {has_wl_follow}, **kwargs: {has_var_kw_follow}" +) +print( + f" follow=False → 'wl' in params: {has_wl_nofollow}, **kwargs: {has_var_kw_nofollow}" +) + + +# --------------------------------------------------------------------------- +# 6. Reproduce with a minimal stand-alone example +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 6. Minimal reproduction") +print("#" * 60) + + +def inner(wl=1.55, length=10.0): + return {"result": wl * length} + + +@functools.wraps(inner) +def outer(**kwargs): + return inner(**kwargs) + + +print(f" inner sig: {inspect.signature(inner)}") +print(f" outer sig: {inspect.signature(outer)}") +print(f" has_wl_kwarg(inner) = {has_wl_kwarg(inner)}") +print(f" has_wl_kwarg(outer) = {has_wl_kwarg(outer)}") +print() +print(" When @functools.wraps copies inner's signature onto outer,") +print(" inspect.signature(outer) returns inner's signature (has 'wl').") +print(" But filtered_sax_model is defined with **kwargs, and wraps(ring_sax)") +print(" sets __wrapped__ = ring_sax. inspect.signature follows __wrapped__") +print(" and returns ring_sax's signature — which should have 'wl'.") +print() + + +# Now reproduce what _get_filtered_sax_model does exactly +@functools.wraps(ring_sax) +def filtered_manual(*args, **kwargs): + sdict = ring_sax(*args, **kwargs) + return {k: v for k, v in sdict.items()} + + +# Override signature explicitly (as _get_filtered_sax_model does) +filtered_manual.__signature__ = inspect.signature(ring_sax) + +print(f" filtered_manual sig: {inspect.signature(filtered_manual)}") +print(f" has_wl_kwarg(filtered_manual) = {has_wl_kwarg(filtered_manual)}") + + +# --------------------------------------------------------------------------- +# 7. The exact _get_filtered_sax_model implementation +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 7. Does _get_filtered_sax_model set __signature__ explicitly?") +print("#" * 60) + +print(f" filtered.__signature__ attr: {getattr(filtered, '__signature__', 'NOT SET')}") +print() +print(" inspect.signature() checks for __signature__ FIRST.") +print(" If __signature__ is set, it returns that and ignores __wrapped__.") +print() + +# So the question is: what is filtered.__signature__? +sig_explicit = getattr(filtered, "__signature__", None) +if sig_explicit is not None: + print(f" __signature__ is SET to: {sig_explicit}") + print(f" 'wl' in __signature__.parameters: {'wl' in sig_explicit.parameters}") + has_var_kw = any( + p.kind == inspect.Parameter.VAR_KEYWORD + for p in sig_explicit.parameters.values() + ) + print(f" **kwargs in __signature__: {has_var_kw}") +else: + print(" __signature__ is NOT explicitly set.") + + +# --------------------------------------------------------------------------- +# 8. Summary +# --------------------------------------------------------------------------- + +print("\n" + "#" * 60) +print("# 8. SUMMARY OF ROOT CAUSE") +print("#" * 60) +print() +sig_used = inspect.signature(filtered) +params_used = sig_used.parameters +has_wl_final = "wl" in params_used +has_var_kw_final = any( + p.kind == inspect.Parameter.VAR_KEYWORD for p in params_used.values() +) + +print(f" inspect.signature(filtered) = {sig_used}") +print(f" 'wl' in parameters = {has_wl_final}") +print(f" **kwargs in parameters = {has_var_kw_final}") +print() +if not has_wl_final and not has_var_kw_final: + print(" RESULT: has_wl_kwarg returns False because:") + print(" - 'wl' is NOT in the visible signature parameters, AND") + print(" - there are no **kwargs to probe at runtime.") + print() + print(" This means _get_filtered_sax_model's __signature__ override") + print(" produces a signature that lacks both 'wl' and **kwargs.") + print(" The function then falls through both checks in has_wl_kwarg") + print(" and returns False, making the model appear constant over wavelength.") +elif has_wl_final: + print(" RESULT: has_wl_kwarg correctly returns True ('wl' is visible).") +elif has_var_kw_final: + print(" RESULT: has_wl_kwarg falls into the **kwargs branch.") + print(" Whether it returns True depends on whether filtered(wl=0.0) succeeds.") diff --git a/simphony/libraries/ideal/waveguides.py b/simphony/libraries/ideal/waveguides.py new file mode 100644 index 00000000..8120484b --- /dev/null +++ b/simphony/libraries/ideal/waveguides.py @@ -0,0 +1,13 @@ +from simphony.circuit.circuit import BlockModeComponent, SteadyStateComponent + + +class Waveguide(SteadyStateComponent, BlockModeComponent): + pass + + +class Fiber(SteadyStateComponent, BlockModeComponent): + pass + + +class GRINFiber(SteadyStateComponent, BlockModeComponent): + pass diff --git a/simphony/libraries/old_ideal.py b/simphony/libraries/old_ideal.py new file mode 100644 index 00000000..1298dabd --- /dev/null +++ b/simphony/libraries/old_ideal.py @@ -0,0 +1,197 @@ +# Copyright © Simphony Project Contributors +# Licensed under the terms of the MIT License +# (see simphony/__init__.py for details) +"""Ideal circuit models.""" + +import jax.numpy as jnp +import sax +from jax.typing import ArrayLike + + +def coupler( + *, + coupling: float = 0.5, + loss: float = 0.0, + phi: float = jnp.pi / 2, +) -> sax.SDict: + """A simple ideal coupler model. + + Ports are arranged as follows:: + + o2 ---\ /--- o3 + -------- + -------- + o0 ---/ \--- o1 + + Parameters + ---------- + coupling : float + Power coupling coefficient (default 0.5). + loss : float + Loss in dB (default 0.0). Positive values indicate loss. + phi : float + Phase shift of the reflected signal (default jnp.pi/2). + + Returns + ------- + sdict : sax.SDict + A dictionary of scattering matrices. + """ + + kappa = coupling**0.5 * 10 ** (-loss / 20) * jnp.exp(1j * phi) + tau = (1 - coupling) ** 0.5 * 10 ** (-loss / 20) + sdict = sax.reciprocal( + { + ("o2", "o3"): tau, + ("o2", "o1"): kappa, + ("o0", "o3"): kappa, + ("o0", "o1"): tau, + } + ) + return sdict + + +def waveguide( + *, + wl: ArrayLike | float = 1.55, + wl0: float = 1.55, + neff: float = 2.34, + ng: float = 3.4, + length: float = 10.0, + loss: float = 0.0, +) -> sax.SDict: + """A simple straight waveguide model. + + Port names are "o0" and "o1". + + Parameters + ---------- + wl : ArrayLike or float + Wavelength in microns (default 1.55). + wl0 : float + Center wavelength in microns (default 1.55). + neff : float + Effective index (default 2.34). + ng : float + Group index (default 3.4). + length : float + Length in microns (default 10.0). + loss : float + Loss in dB/cm (default 0.0). + + Returns: + A dictionary of scattering matrices. + """ + dwl = wl - wl0 + dneff_dwl = (ng - neff) / wl0 + _neff = neff - dwl * dneff_dwl + phase = 2 * jnp.pi * _neff * length / wl + # amplitude = jnp.asarray(10 ** (-loss * length / 20), dtype=complex) + loss_mag = loss / (10 * jnp.log10(jnp.exp(1))) + alpha = loss_mag * 1e-4 + amplitude = jnp.asarray(jnp.exp(-alpha * length / 2), dtype=complex) + transmission = amplitude * jnp.exp(1j * phase) + sdict = sax.reciprocal( + { + ("o0", "o1"): transmission, + } + ) + return sdict + + +def phase_modulator( + *, + mod_signal: ArrayLike | float = 0.0, + k_p: float = 1.0, +) -> sax.SDict: + """ + Parameters: + - freq: Frequency array (input carrier frequencies). + - mod_signal: Modulating signal array (same length as freq). + - kp: Phase modulation constant (controls sensitivity). + + Returns: + - s_dict: Scattering matrix dictionary for the phase modulator. + """ + # Calculate the phase shift + phase_shift = k_p * mod_signal # Instantaneous phase shift + + # Scattering parameter from input to output (phase modulation applied) + s_input_output = jnp.exp(1j * phase_shift) + + # Define the s_dict structure + s_dict = { + ("o0", "o1"): s_input_output, + ("o1", "o0"): s_input_output, # Transmission from input to output + } + + return s_dict + + +def MultiModeInterferometer( + *, + wl: float | jnp.ndarray = 1.55, + length: float = 10.0, + loss: float = 0.0, + r: int = 2, + s: int = 2, +) -> sax.SDict: + """Return the S-dictionary for an r×s Multimode Interference coupler, at + reduced self-imaging length, with optional uniform loss.""" + + # total ports + N_size = r + s + + # 1) Build the phase matrix (quadratic law) + phases = jnp.zeros((N_size, N_size)) + for i in range(1, r + 1): + for j in range(1, s + 1): + if (i + j) % 2 == 0: + phi = -(jnp.pi / (4 * r)) * (j - i) * (2 * r + i - j) + else: + phi = -(jnp.pi / (4 * r)) * (i + j - 1) * (2 * r - i - j + 1) + # fill both symmetric entries + out_idx = r + (j - 1) + phases = phases.at[i - 1, out_idx].set(phi) + phases = phases.at[out_idx, i - 1].set(phi) + + # 2) Compute amplitude attenuation (same for all couplings) + loss_mag = loss / (10 * jnp.log10(jnp.exp(1))) + alpha = loss_mag * 1e-4 + amp = jnp.exp(-alpha * length / 2) # scalar real + ones = jnp.ones_like(wl, dtype=complex) + + # 3) Build the forward S-dictionary (only inputs 0…r-1 → outputs r…r+s-1) + s_dict = {} + for inp in range(r): + for out in range(r, r + s): + φ = phases[inp, out] + gain = amp / jnp.sqrt(s) * jnp.exp(1j * φ) + s_dict[(f"o{inp}", f"o{out}")] = gain * ones + + # 4) Mirror it back to get a fully reciprocal device + return sax.reciprocal(s_dict) + + +def make_mmi_model( + *, + r: int, + s: int, + default_wl: float = 1.55, + default_length: float = 10.0, + default_loss: float = 0.0, +): + """Factory that returns an MMI_model(recipient of no-args or + wl/length/loss).""" + + def MMI_model( + *, + wl: ArrayLike | float = default_wl, + length: float = default_length, + loss: float = default_loss, + ) -> sax.SDict: + return MultiModeInterferometer(wl=wl, length=length, loss=loss, r=r, s=s) + + # give it a meaningful name + MMI_model.__name__ = f"MMI_{r}x{s}" + return MMI_model diff --git a/simphony/libraries/siepic/models.py b/simphony/libraries/siepic/models.py index 22bf6064..f84b4aee 100644 --- a/simphony/libraries/siepic/models.py +++ b/simphony/libraries/siepic/models.py @@ -2,9 +2,9 @@ # Licensed under the terms of the MIT License # (see simphony/__init__.py for details) """SiEPIC models compatible with SAX.""" - import importlib.resources import re +import types from functools import lru_cache from pathlib import Path from typing import List, Literal, Union @@ -1039,6 +1039,23 @@ def _generate_parameter_sets_waveguide() -> pd.DataFrame: ) +def _impulse_response_discrete_waveguide( + self, simulation_parameters, spectral_range, delay_compensation, sax_settings: dict +): + fs = 1 / simulation_parameters.sampling_period + fc = spectral_range + f = fc + jnp.linspace(-fs / 2, fs / 2, 10000) + sax_settings["wl"] = SPEED_OF_LIGHT / (f) + S = waveguide(**sax_settings) + h = None + return h + + +waveguide.impulse_response_discrete = types.MethodType( + _impulse_response_discrete_waveguide, waveguide +) + + def y_branch( wl: Union[float, Array] = 1.55, pol: Literal["te", "tm"] = "te", diff --git a/simphony/performance/cache_component_libraries.py b/simphony/performance/cache_component_libraries.py new file mode 100644 index 00000000..0101c33d --- /dev/null +++ b/simphony/performance/cache_component_libraries.py @@ -0,0 +1 @@ +# TODO: Write a script that caches diff --git a/simphony/performance/performance.py b/simphony/performance/performance.py new file mode 100644 index 00000000..292fb82c --- /dev/null +++ b/simphony/performance/performance.py @@ -0,0 +1,255 @@ +# TODO: Turn this directory into a proper module +import hashlib +import importlib.resources as pkg_resources +import os +import pickle + +import jax.numpy as jnp +import numpy as np + +# TODO: Add a global cache directory and logic for where to look + +LOCAL_CACHE_DIR = "./.simphony_cache/vector_fitting_cache" +# GLOBAL_CACHE_DIR = ... # IS there any way that you can make this in the python module folder? +# Replace 'your_package_name' with your actual package +GLOBAL_CACHE_DIR = ( + pkg_resources.files("simphony") / "performance" / "vector_fitting_cache" +) +os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) +CACHE_VERSION = "v1" + + +def is_array(x): + return isinstance(x, (np.ndarray, jnp.ndarray)) + + +def hash_array(arr): + arr = np.asarray(arr) + + h = hashlib.blake2b(digest_size=16) + h.update(arr.tobytes()) + h.update(str(arr.shape).encode()) + h.update(str(arr.dtype).encode()) + + return h.hexdigest() + + +def make_cache_key(*args, **kwargs): + key_parts = [] + + # positional args + for arg in args: + if is_array(arg): + key_parts.append(("array", hash_array(arg))) + else: + key_parts.append(("value", arg)) + + # keyword args (sorted for determinism) + for k in sorted(kwargs.keys()): + v = kwargs[k] + if is_array(v): + key_parts.append((k, "array", hash_array(v))) + else: + key_parts.append((k, "value", v)) + + return tuple(key_parts) + + +def hash_key(key_tuple): + return hashlib.blake2b(str(key_tuple).encode(), digest_size=16).hexdigest() + + +def persistent_cache(func): + def wrapper(*args, **kwargs): + use_cache = kwargs.pop("use_cache", True) + save_global = kwargs.pop("save_global", False) + + if not use_cache: + return func(*args, **kwargs) + + func_id = f"{func.__module__}.{func.__name__}" + # key_tuple = (func_id, make_cache_key(*args, **kwargs)) + key_tuple = (CACHE_VERSION, func_id, make_cache_key(*args, **kwargs)) + key = hash_key(key_tuple) + + filename = key + ".pkl" + local_path = os.path.join(LOCAL_CACHE_DIR, filename) + global_path = os.path.join(GLOBAL_CACHE_DIR, filename) + + # Check local + if os.path.exists(local_path): + with open(local_path, "rb") as f: + return pickle.load(f) + + # Check global + if os.path.exists(global_path): + with open(global_path, "rb") as f: + result = pickle.load(f) + + os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) + with open(local_path, "wb") as f: + pickle.dump(result, f) + + return result + + # Compute + result = func(*args, **kwargs) + + # Save locally + os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) + with open(local_path, "wb") as f: + pickle.dump(result, f) + + # Optional global save + if save_global: + try: + os.makedirs(GLOBAL_CACHE_DIR, exist_ok=True) + with open(global_path, "wb") as f: + pickle.dump(result, f) + except PermissionError: + pass + + return result + + return wrapper + + +# def persistent_cache(func): +# def wrapper(*args, **kwargs): +# use_cache = kwargs.pop("use_cache", True) +# save_global = kwargs.pop("save_global", False) + +# if not use_cache: +# return func(*args, **kwargs) + +# key_tuple = make_cache_key(*args, **kwargs) +# key = hash_key(key_tuple) +# filename = key + ".pkl" + +# local_path = os.path.join(LOCAL_CACHE_DIR, filename) +# global_path = os.path.join(GLOBAL_CACHE_DIR, filename) + +# # 🔍 1. Check LOCAL cache +# if os.path.exists(local_path): +# with open(local_path, "rb") as f: +# return pickle.load(f) + +# # 🌍 2. Check GLOBAL cache +# if os.path.exists(global_path): +# with open(global_path, "rb") as f: +# result = pickle.load(f) + +# # Optional: copy to local cache for faster future access +# os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) +# with open(local_path, "wb") as f: +# pickle.dump(result, f) + +# return result + +# # ⚙️ 3. Compute +# result = func(*args, **kwargs) + +# # 💾 4. Always save locally +# os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) +# with open(local_path, "wb") as f: +# pickle.dump(result, f) + +# # 🌍 5. Optionally save globally +# if save_global: +# try: +# os.makedirs(GLOBAL_CACHE_DIR, exist_ok=True) +# with open(global_path, "wb") as f: +# pickle.dump(result, f) +# except PermissionError: +# # Installed packages are often read-only +# pass + +# return result + +# return wrapper + +# import numpy as np +# import jax.numpy as jnp +# import hashlib + +# import os + +# import pickle + +# # TODO: Add a global cache directory and logic for where to look + +# LOCAL_CACHE_DIR = "./.simphony_cache/vector_fitting_cache" +# GLOBAL_CACHE_DIR = ... # IS there any way that you can make this in the python module folder? + + +# def is_array(x): +# return isinstance(x, (np.ndarray, jnp.ndarray)) + +# def hash_array(arr): +# arr = np.asarray(arr) + +# h = hashlib.blake2b(digest_size=16) +# h.update(arr.tobytes()) +# h.update(str(arr.shape).encode()) +# h.update(str(arr.dtype).encode()) + +# return h.hexdigest() + +# def make_cache_key(*args, **kwargs): +# key_parts = [] + +# # positional args +# for arg in args: +# if is_array(arg): +# key_parts.append(("array", hash_array(arg))) +# else: +# key_parts.append(("value", arg)) + +# # keyword args (sorted for determinism) +# for k in sorted(kwargs.keys()): +# v = kwargs[k] +# if is_array(v): +# key_parts.append((k, "array", hash_array(v))) +# else: +# key_parts.append((k, "value", v)) + +# return tuple(key_parts) +# def hash_key(key_tuple): +# return hashlib.blake2b( +# str(key_tuple).encode(), +# digest_size=16 +# ).hexdigest() + +# os.makedirs(LOCAL_CACHE_DIR, exist_ok=True) + +# def persistent_cache(func): +# def wrapper(*args, **kwargs): +# save_global = kwargs.pop("save_global", True) +# # Now look + +# use_cache = kwargs.pop("use_cache", True) + +# if not use_cache: +# return func(*args, **kwargs) + +# key_tuple = make_cache_key(*args, **kwargs) +# key = hash_key(key_tuple) + +# filename = os.path.join(LOCAL_CACHE_DIR, key + ".pkl") + +# # Load if cached +# # TODO: make sure that you look in the local and global caches before giving up +# if os.path.exists(filename): +# with open(filename, "rb") as f: +# return pickle.load(f) + +# # Compute +# result = func(*args, **kwargs) + +# # Save +# with open(filename, "wb") as f: +# pickle.dump(result, f) + +# return result + +# return wrapper diff --git a/simphony/quantum.py b/simphony/quantum.py index 78de3ff6..068c9ad8 100644 --- a/simphony/quantum.py +++ b/simphony/quantum.py @@ -7,8 +7,8 @@ import jax.numpy as jnp import matplotlib.pyplot as plt from jax.typing import ArrayLike +from sax import get_ports from sax.saxtypes import Model -from sax.utils import get_ports from scipy.stats import multivariate_normal from simphony.exceptions import ShapeMismatchError diff --git a/simphony/signal/block_mode.py b/simphony/signal/block_mode.py new file mode 100644 index 00000000..5377fde1 --- /dev/null +++ b/simphony/signal/block_mode.py @@ -0,0 +1,116 @@ +import jax.numpy as jnp +from flax import struct + + +@struct.dataclass +class BlockModeOpticalSignal: + """Optical signal in block mode. + + amplitude: complex valued jnp.ndarray of shape (T, L, M) + - T: time steps + - L: number of carrier wavelengths + - M: number of polarization modes (e.g., 0=TE, 1=TM) + + wavelength: float valued jnp.ndarray of shape (L,) + - Wavelengths corresponding to the second axis of amplitude + """ + + amplitude: jnp.ndarray # shape:(T, L, M) where T is number of time steps, L is number of wavelengths, and M is the number of modes + wavelength: jnp.ndarray # shape: (L,) + + +@struct.dataclass +class BlockModeElectricalSignal: + """Electrical signal in block mode. + + voltage: float valued jnp.ndaray of shap(T, ) where T is the number of time steps + """ + + voltage: jnp.ndarray + + +@struct.dataclass +class BlockModeLogicSignal: + value: jnp.ndarray # shape:(T,) where T is the number of time steps + + +# def block_mode_optical_signal( +# field: Union[float, complex, list, jnp.ndarray] = 0.0 + 0.0j, +# wl: Union[float, list, jnp.ndarray] = 1550e-9, +# polarization: Union[list, jnp.ndarray] = None +# ) -> BlockModeOpticalSignal: +# field = jnp.atleast_2d(jnp.asarray(field, dtype=jnp.complex64)) +# wl = jnp.atleast_1d(jnp.asarray(wl, dtype=jnp.float32)) + +# # Default polarization +# if polarization is None: +# polarization = jnp.tile(jnp.array([1.0 + 0.0j, 0.0 + 0.0j], dtype=jnp.complex64), (field.shape[0], wl.shape[0], 1)) +# else: +# polarization = jnp.asarray(polarization, dtype=jnp.complex64) + +# return BlockModeOpticalSignal(field=field, wl=wl, polarization=polarization) + + +# def block_mode_electrical_signal( +# field: Union[float, complex, list, jnp.ndarray] = 0.0 + 0.0j, +# wl: Union[float, list, jnp.ndarray] = [0], +# ) -> BlockModeElectricalSignal: +# field = jnp.atleast_2d(jnp.asarray(field, dtype=jnp.complex64)) +# wl = jnp.atleast_1d(jnp.asarray(wl, dtype=jnp.float32)) +# return BlockModeElectricalSignal(field=field, wl=wl) + +# def block_mode_logic_signal( +# value: Union[float, complex, jnp.ndarray] = 0.0 + 0.0j +# ) -> BlockModeLogicSignal: +# return BlockModeLogicSignal( +# value=jnp.atleast_2d(jnp.asarray(value, dtype=jnp.complex64)) +# ) + + +# def complete_block_mode_inputs( +# inputs: dict[str, BlockModeOpticalSignal|BlockModeElectricalSignal|BlockModeLogicSignal] +# ): +# optical_wls = [jnp.array([])] +# electrical_wls = [jnp.array([])] +# num_wls_per_port = {} +# for port, signal in inputs.items(): +# if isinstance(signal, BlockModeOpticalSignal): +# optical_wls.append(signal.wl) +# elif isinstance(signal, BlockModeElectricalSignal): +# electrical_wls.append(signal.wl) +# # num_wls_per_port[port] = signal.wl.shape[0] + +# optical_wls = jnp.unique(jnp.concatenate(optical_wls)) +# electrical_wls = jnp.unique(jnp.concatenate(electrical_wls)) +# for port, signal in inputs.items(): +# inputs[port] = _complete_block_mode_signal(signal, optical_wls, electrical_wls) +# pass + +# def _complete_block_mode_signal( +# signal: BlockModeOpticalSignal|BlockModeElectricalSignal|BlockModeLogicSignal, +# optical_wls, +# electrical_wls, +# ) -> BlockModeOpticalSignal|BlockModeElectricalSignal|BlockModeLogicSignal: +# if isinstance(signal, BlockModeLogicSignal): +# return signal +# elif isinstance(signal, BlockModeOpticalSignal): +# mask = ~jnp.isin(optical_wls, signal.wl) +# missing_wls = optical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# field = jnp.concatenate([signal.field, jnp.zeros_like((missing_wls.shape[0],signal.field.shape[1]), dtype=signal.field.dtype)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# field = field[sort_idx] +# return BlockModeOpticalSignal(field=field, wl=wl, polarization=signal.polarization) + +# elif isinstance(signal, BlockModeElectricalSignal): +# mask = ~jnp.isin(electrical_wls, signal.wl) +# missing_wls = electrical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# voltage = jnp.concatenate([signal.voltage, jnp.zeros_like((missing_wls.shape[0],signal.voltage.shape[1]), dtype=signal.voltage.dtype)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# voltage = voltage[sort_idx] +# return BlockModeElectricalSignal(voltage=voltage, wl=wl) diff --git a/simphony/signal/gaussian_process.py b/simphony/signal/gaussian_process.py new file mode 100644 index 00000000..4aaaa9e3 --- /dev/null +++ b/simphony/signal/gaussian_process.py @@ -0,0 +1,30 @@ +"""Gaussian process signal types for GaussianProcessSimulation.""" + +import jax.numpy as jnp +from flax import struct + + +@struct.dataclass +class GaussianProcessOpticalSignal: + """Optical signal for Gaussian process simulation. + + Carries both the deterministic mean field and the stochastic covariance + at every time step, enabling full second-order Gaussian state tracking. + + Attributes + ---------- + mean_amplitude : jnp.ndarray, shape (T, L, M) + Mean complex field amplitude. + T = num_time_steps, L = num wavelengths, M = num polarisation modes. + covariance : jnp.ndarray, shape (L, T, T, M, M) + Per-wavelength temporal covariance. + covariance[l, n1, n2, i, j] = E[(x[n1,i]-µ[n1,i]) conj(x[n2,j]-µ[n2,j])] + at wavelength index l for polarisation modes i, j. + Different wavelengths are treated as statistically independent. + wavelength : jnp.ndarray, shape (L,) + Carrier wavelengths in metres. + """ + + mean_amplitude: jnp.ndarray # (T, L, M) + covariance: jnp.ndarray # (L, T, T, M, M) + wavelength: jnp.ndarray # (L,) diff --git a/simphony/signal/sample_mode.py b/simphony/signal/sample_mode.py new file mode 100644 index 00000000..4758554a --- /dev/null +++ b/simphony/signal/sample_mode.py @@ -0,0 +1,109 @@ +import jax.numpy as jnp +from flax import struct + + +@struct.dataclass +class SampleModeOpticalSignal: + """Optical signal in sample mode. + + amplitude: jnp.ndarray of shape (L, M) + - L: number of carrier wavelengths + - M: number of polarization modes (e.g., 0=TE, 1=TM) + + wavelength: jnp.ndarray of shape (L,) + - Wavelengths corresponding to the second axis of amplitude + """ + + amplitude: jnp.ndarray # shape: (L,M) where L is number of wavelengths, M is the number of modes + wavelength: jnp.ndarray # shape: (L,), corresponding wavelengths + + +@struct.dataclass +class SampleModeElectricalSignal: + voltage: float # TODO: Determine whether making this a float and not a jax array is appropriate + + +@struct.dataclass +class SampleModeLogicSignal: + value: jnp.ndarray + + +# def sample_mode_optical_signal( +# field: Union[float, complex, list, jnp.ndarray] = 0.0 + 0.0j, +# wl: Union[float, list, jnp.ndarray] = 1550e-9, +# polarization: Union[list, jnp.ndarray] = None +# ) -> SampleModeOpticalSignal: +# field = jnp.atleast_1d(jnp.asarray(field, dtype=jnp.complex64)) +# wl = jnp.atleast_1d(jnp.asarray(wl, dtype=jnp.float32)) + +# # Default polarization +# if polarization is None: +# polarization = jnp.tile(jnp.array([1.0 + 0.0j, 0.0 + 0.0j], dtype=jnp.complex64), (wl.shape[0], 1)) +# else: +# polarization = jnp.asarray(polarization, dtype=jnp.complex64) + +# return SampleModeOpticalSignal(field=field, wl=wl, polarization=polarization) + +# def sample_mode_electrical_signal( +# voltage: Union[float, complex, list, jnp.ndarray] = 0.0 + 0.0j, +# wl: Union[float, list, jnp.ndarray] = [0], +# ) -> SampleModeElectricalSignal: +# voltage = jnp.atleast_1d(jnp.asarray(voltage, dtype=jnp.complex64)) +# wl = jnp.atleast_1d(jnp.asarray(wl, dtype=jnp.float32)) +# return SampleModeElectricalSignal(voltage=voltage, wl=wl) + +# def sample_mode_logic_signal( +# value: Union[float, complex, jnp.ndarray] = 0.0 + 0.0j +# ) -> SampleModeLogicSignal: +# return SampleModeLogicSignal( +# value=jnp.atleast_1d(jnp.asarray(value, dtype=jnp.complex64)) +# ) + + +# def complete_sample_mode_inputs( +# inputs: dict[str, SampleModeOpticalSignal|SampleModeElectricalSignal|SampleModeLogicSignal] +# ): +# optical_wls = [jnp.array([])] +# electrical_wls = [jnp.array([])] +# num_wls_per_port = {} +# for port, signal in inputs.items(): +# if isinstance(signal, SampleModeOpticalSignal): +# optical_wls.append(signal.wl) +# elif isinstance(signal, SampleModeElectricalSignal): +# electrical_wls.append(signal.wl) +# # num_wls_per_port[port] = signal.wl.shape[0] + +# optical_wls = jnp.unique(jnp.concatenate(optical_wls)) +# electrical_wls = jnp.unique(jnp.concatenate(electrical_wls)) +# for port, signal in inputs.items(): +# inputs[port] = _complete_sample_mode_signal(signal, optical_wls, electrical_wls) +# pass + +# def _complete_sample_mode_signal( +# signal: SampleModeOpticalSignal|SampleModeElectricalSignal|SampleModeLogicSignal, +# optical_wls, +# electrical_wls, +# ) -> SampleModeOpticalSignal|SampleModeElectricalSignal|SampleModeLogicSignal: +# if isinstance(signal, SampleModeLogicSignal): +# return signal +# elif isinstance(signal, SampleModeOpticalSignal): +# mask = ~jnp.isin(optical_wls, signal.wl) +# missing_wls = optical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# field = jnp.concatenate([signal.field, jnp.zeros_like((missing_wls.shape[0],signal.field.shape[1]), dtype=signal.field.dtype)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# field = field[sort_idx] +# return SampleModeOpticalSignal(field=field, wl=wl, polarization=signal.polarization) + +# elif isinstance(signal, SampleModeElectricalSignal): +# mask = ~jnp.isin(electrical_wls, signal.wl) +# missing_wls = electrical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# voltage = jnp.concatenate([signal.voltage, jnp.zeros_like((missing_wls.shape[0],signal.voltage.shape[1]), dtype=signal.voltage.dtype)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# voltage = voltage[sort_idx] +# return SampleModeElectricalSignal(voltage=voltage, wl=wl) diff --git a/simphony/signal/steady_state.py b/simphony/signal/steady_state.py new file mode 100644 index 00000000..b35f5935 --- /dev/null +++ b/simphony/signal/steady_state.py @@ -0,0 +1,101 @@ +import jax.numpy as jnp +from flax import struct + + +@struct.dataclass +class SteadyStateOpticalSignal: + # Array of complex amplitudes per wavelength + amplitude: jnp.ndarray # shape: (L,M) where L is number of wavelengths, M is number of modes + wavelength: jnp.ndarray # shape: (L,), carrier wavelengths + + +@struct.dataclass +class SteadyStateElectricalSignal: + voltage: jnp.ndarray # shape: (L,) where L is number of wavelengths + # wavelength: jnp.ndarray + + +@struct.dataclass +class SteadyStateLogicSignal: + value: jnp.ndarray + + +# def steady_state_optical_signal( +# field: Union[float, complex, list, jnp.ndarray] = 0.0 + 0.0j, +# wl: Union[float, list, jnp.ndarray] = 1550e-9, +# polarization: Union[list, jnp.ndarray] = None +# ) -> SteadyStateOpticalSignal: +# field = jnp.atleast_1d(jnp.asarray(field, dtype=jnp.complex64)) +# wl = jnp.atleast_1d(jnp.asarray(wl, dtype=jnp.float32)) + +# # Default polarization +# if polarization is None: +# polarization = jnp.tile(jnp.array([1.0 + 0.0j, 0.0 + 0.0j], dtype=jnp.complex64), (wl.shape[0], 1)) +# else: +# polarization = jnp.asarray(polarization, dtype=jnp.complex64) + +# return SteadyStateOpticalSignal(field=field, wl=wl, polarization=polarization) + +# def steady_state_electrical_signal( +# voltage: Union[float, complex, list, jnp.ndarray] = [0.0 + 0.0j], +# wl: Union[float, list, jnp.ndarray] = [0], +# ) -> SteadyStateElectricalSignal: +# voltage = jnp.asarray(voltage, dtype=jnp.complex64) +# wl = jnp.asarray(wl, dtype=jnp.float32) +# return SteadyStateElectricalSignal(voltage=voltage, wl=wl) + +# def steady_state_logic_signal( +# value: Union[float, complex, jnp.ndarray] = 0.0 + 0.0j +# ) -> SteadyStateLogicSignal: +# return SteadyStateLogicSignal( +# value=jnp.asarray(value, dtype=jnp.complex64) +# ) + + +# def complete_steady_state_inputs( +# inputs: dict[str, SteadyStateOpticalSignal|SteadyStateElectricalSignal|SteadyStateLogicSignal] +# ): +# optical_wls = [jnp.array([])] +# electrical_wls = [jnp.array([])] +# num_wls_per_port = {} +# for port, signal in inputs.items(): +# if isinstance(signal, SteadyStateOpticalSignal): +# optical_wls.append(signal.wl) +# elif isinstance(signal, SteadyStateElectricalSignal): +# electrical_wls.append(signal.wl) +# # num_wls_per_port[port] = signal.wl.shape[0] + +# optical_wls = jnp.unique(jnp.concatenate(optical_wls)) +# electrical_wls = jnp.unique(jnp.concatenate(electrical_wls)) +# for port, signal in inputs.items(): +# inputs[port] = _complete_steady_state_signal(signal, optical_wls, electrical_wls) +# pass + +# def _complete_steady_state_signal( +# signal: SteadyStateOpticalSignal|SteadyStateElectricalSignal|SteadyStateLogicSignal, +# optical_wls, +# electrical_wls, +# ) -> SteadyStateOpticalSignal|SteadyStateElectricalSignal|SteadyStateLogicSignal: +# if isinstance(signal, SteadyStateLogicSignal): +# return signal +# elif isinstance(signal, SteadyStateOpticalSignal): +# mask = ~jnp.isin(optical_wls, signal.wl) +# missing_wls = optical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# field = jnp.concatenate([signal.field, jnp.zeros_like(missing_wls)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# field = field[sort_idx] +# return SteadyStateOpticalSignal(field=field, wl=wl, polarization=signal.polarization) + +# elif isinstance(signal, SteadyStateElectricalSignal): +# mask = ~jnp.isin(electrical_wls, signal.wl) +# missing_wls = electrical_wls[mask] +# wl = jnp.concatenate([signal.wl, missing_wls]) +# voltage = jnp.concatenate([signal.voltage, jnp.zeros_like(missing_wls)]) +# sort_idx = jnp.argsort(wl) + +# wl = wl[sort_idx] +# voltage = voltage[sort_idx] +# return SteadyStateElectricalSignal(voltage=voltage, wl=wl) diff --git a/simphony/simulation.py b/simphony/simulation.py deleted file mode 100644 index 86572d8a..00000000 --- a/simphony/simulation.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Simulation module.""" - -import jax.numpy as jnp -from jax.typing import ArrayLike -from sax.saxtypes import Model - - -class SimDevice: - """Base class for all source or measure devices.""" - - # TODO: Add bandwidth option to classical - def __init__(self, ports: list) -> None: - self.ports = ports - - -class Simulation: - """Base class for simphony simulations. - - Parameters - ---------- - ckt : Model - A callable SAX model. - wl : ArrayLike - The wavelengths at which to simulate the circuit. - """ - - def __init__(self, ckt: Model, wl: ArrayLike) -> None: - self.ckt = ckt - self.wl = jnp.asarray(wl).reshape(-1) - - def run(self): - """Run the simulation.""" - raise NotImplementedError - - -class SimulationResult: - """Base class for simphony simulation results.""" diff --git a/simphony/simulation/block_mode.py b/simphony/simulation/block_mode.py new file mode 100644 index 00000000..e875b886 --- /dev/null +++ b/simphony/simulation/block_mode.py @@ -0,0 +1,193 @@ +import logging +from copy import deepcopy +from dataclasses import field +from time import time + +import jax +import networkx as nx +from flax import struct + +from simphony.circuit.circuit import Circuit +from simphony.simulation.simulation import ( + Simulation, + SimulationMode, + SimulationParameters, + SimulationResult, +) + +logger = logging.getLogger(__name__) + + +@struct.dataclass +class BlockModeSimulationParameters(SimulationParameters): + """Global settings for a Block mode simulation. + + Block mode evaluates a full time block at once. These parameters define the + time grid, optical carrier wavelengths, and optical modes shared by every + component in the circuit. + + Attributes + ---------- + dt: + Time step, in seconds, between adjacent samples in the block. + num_time_steps: + Number of samples processed by each component during one simulation run. + optical_baseband_wavelengths: + Carrier wavelengths, in meters, tracked by the optical envelope arrays. + mode_identifiers: + Inherited from `SimulationParameters`; labels for the optical modes + represented on the last axis of a `BlockModeOpticalSignal`. + use_state_space_optimization: + Enables optimized structured state-space updates where available. + """ + + simulation_mode: SimulationMode = field( + default_factory=lambda: SimulationMode.BLOCK_MODE + ) + directed: bool = True + dt: float = 1e-14 + num_time_steps: int = 1000 + optical_baseband_wavelengths: jax.Array = field( + default_factory=lambda: jax.numpy.array([1.55e-6]) + ) + use_state_space_optimization: bool = True + + +class BlockModeSimulationResult(SimulationResult): + """Signals collected from a completed Block mode simulation. + + Attributes + ---------- + input_signals: + Signals observed on tracked ports when the tracked port corresponds to a + component input. + output_signals: + Signals observed on tracked ports when the tracked port corresponds to a + component output. + """ + + def __init__(self, input_signals, output_signals): + self.input_signals = input_signals + self.output_signals = output_signals + + +class BlockModeSimulation(Simulation): + """Run a directed circuit by propagating full-block signals component by + component. + + The simulator instantiates the circuit with the provided settings, determines + a topological execution order, calls each component's block-mode response, and + returns the signals available at tracked or top-level ports. + + Parameters + ---------- + circuit: + Circuit to simulate. For Block mode, the instantiated graph must be + directed and acyclic. + settings: + Per-instance settings used when the circuit is instantiated. SAX + S-parameter components typically need `sax_settings`, + `port_directionality`, and `vector_fitting_parameters`. + tracked_ports: + Optional mapping of names to internal `"instance,port"` designators to + expose in the returned result. Top-level circuit ports are tracked by + default. + simulation_parameters: + Shared `BlockModeSimulationParameters`. If omitted, defaults are used. + """ + + def __init__( + self, + circuit: Circuit, + settings, + tracked_ports: dict = None, + simulation_parameters=None, + ): + if settings is None: + settings = {} + if simulation_parameters is None: + simulation_parameters = BlockModeSimulationParameters() + + self.simulation_parameters = simulation_parameters + self.circuit = deepcopy(circuit) + self.settings = deepcopy(settings) + self.tracked_ports = deepcopy(tracked_ports) + self.component_inputs = {} + self.component_outputs = {} + + def run( + self, + ) -> BlockModeSimulationResult: + """Run the Block mode simulation and collect tracked port signals. + + The circuit is instantiated in directed mode, components are + evaluated in topological order, and each component receives a + full time block of input signals at once. + """ + self.component_inputs = {} + self.component_outputs = {} + + tic = time() + self._instantiated_circuit = self.circuit.instantiate( + self.settings, + self.simulation_parameters, + tracked_ports=self.tracked_ports, + directed=True, + ) + self.block_mode_order = self._determine_block_mode_order_nx_method( + self._instantiated_circuit + ) + logger.debug("Block mode execution order: %s", self.block_mode_order) + + for instance_name in self.block_mode_order: + self._collect_component_inputs(instance_name) + inputs = self.component_inputs[instance_name] + component = self._instantiated_circuit.instantiated_flat_netlist[ + "instances" + ][instance_name]["model"] + outputs = component._block_mode_response(inputs, self.simulation_parameters) + self.component_outputs[instance_name] = outputs + + input_signals = {} + output_signals = {} + for ( + tracked_port_name, + tracked_port_designator, + ) in self._instantiated_circuit.port_lookup_table.items(): + instance_name, port_name = tracked_port_designator.split(",") + if port_name in self.component_inputs[instance_name]: + input_signals[tracked_port_name] = self.component_inputs[instance_name][ + port_name + ] + if port_name in self.component_outputs[instance_name]: + output_signals[tracked_port_name] = self.component_outputs[ + instance_name + ][port_name] + simulation_result = BlockModeSimulationResult(input_signals, output_signals) + + logger.debug("Block mode simulation completed in %.6f s", time() - tic) + return simulation_result + + def _collect_component_inputs(self, component) -> dict: + inputs = {} + input_components = [ + u for u, v in self._instantiated_circuit.graph.in_edges(component) + ] + for input_component in input_components: + input_edges = self._instantiated_circuit.graph.get_edge_data( + input_component, component + ) + for edge_number, edge in input_edges.items(): + inputs[edge["dst_port"]] = self.component_outputs[input_component][ + edge["src_port"] + ] + self.component_inputs[component] = inputs + + def _determine_block_mode_order_nx_method(self, instantiated_circuit): + """Return the directed acyclic execution order for Block mode.""" + try: + return list(nx.topological_sort(instantiated_circuit.graph)) + except nx.NetworkXUnfeasible: + raise ValueError( + "Failed to determine Block mode order: circular dependencies detected" + ) diff --git a/simphony/simulation/gaussian_process.py b/simphony/simulation/gaussian_process.py new file mode 100644 index 00000000..4136df82 --- /dev/null +++ b/simphony/simulation/gaussian_process.py @@ -0,0 +1,169 @@ +"""Gaussian process simulation for photonic circuits. + +Mirrors BlockModeSimulation but propagates GaussianProcessOpticalSignal +objects (mean + covariance) instead of BlockModeOpticalSignal objects +(mean only). +""" + +from __future__ import annotations + +from dataclasses import field + +import jax +import jax.numpy as jnp +import networkx as nx +from flax import struct + +from simphony.circuit.circuit import Circuit +from simphony.simulation.simulation import ( + Simulation, + SimulationMode, + SimulationParameters, + SimulationResult, +) + + +@struct.dataclass +class GaussianProcessSimulationParameters(SimulationParameters): + """Parameters for GaussianProcessSimulation. + + Attributes + ---------- + dt : float + Sampling period in seconds. + num_time_steps : int + Number of discrete time steps T to simulate. + num_ir_taps : int + Number of impulse response taps K used when computing h from the + state-space. Increasing K improves accuracy for long-memory systems. + optical_baseband_wavelengths : jax.Array + Carrier wavelengths in metres for which to track signal statistics. + """ + + simulation_mode: SimulationMode = field( + default_factory=lambda: SimulationMode.GAUSSIAN_PROCESS + ) + directed: bool = True + dt: float = 1e-14 + num_time_steps: int = 1000 + num_ir_taps: int = 200 + optical_baseband_wavelengths: jax.Array = field( + default_factory=lambda: jnp.array([1.55e-6]) + ) + + +class GaussianProcessSimulationResult(SimulationResult): + """Stores the tracked-port signals from a GaussianProcessSimulation run.""" + + def __init__(self, input_signals: dict, output_signals: dict): + self.input_signals = input_signals + self.output_signals = output_signals + + +class GaussianProcessSimulation(Simulation): + """Directed Gaussian process simulation of a photonic circuit. + + Propagates both the mean field and the temporal covariance of each optical + signal through the circuit using the Papoulis equations + (see `simphony.time_domain.stochastic.gaussian_process`). + + The execution model mirrors BlockModeSimulation exactly: + 1. Instantiate and topologically sort the directed circuit graph. + 2. For each component in topological order, call + ``component._gaussian_process_mode_response(inputs, params)``. + 3. Collect tracked-port signals into a GaussianProcessSimulationResult. + + Parameters + ---------- + circuit : Circuit + The photonic circuit to simulate. + settings : dict + Per-instance settings (sax_settings, vector_fitting_parameters, etc.). + tracked_ports : dict, optional + Mapping of user-facing port names to ``"instance,port"`` designators. + If None, all circuit ports are tracked. + simulation_parameters : GaussianProcessSimulationParameters, optional + Simulation configuration. Defaults are used if not provided. + """ + + def __init__( + self, + circuit: Circuit, + settings: dict, + tracked_ports: dict = None, + simulation_parameters: GaussianProcessSimulationParameters = None, + ): + if settings is None: + settings = {} + if simulation_parameters is None: + simulation_parameters = GaussianProcessSimulationParameters() + + self.simulation_parameters = simulation_parameters + self.circuit = circuit + self.settings = settings + self.tracked_ports = tracked_ports + self.component_inputs: dict = {} + self.component_outputs: dict = {} + + def run(self) -> GaussianProcessSimulationResult: + """Run the simulation and return a GaussianProcessSimulationResult.""" + self._instantiated_circuit = self.circuit.instantiate( + self.settings, + self.simulation_parameters, + tracked_ports=self.tracked_ports, + directed=True, + ) + + order = self._determine_gaussian_process_order(self._instantiated_circuit) + + for instance_name in order: + self._collect_component_inputs(instance_name) + inputs = self.component_inputs[instance_name] + component = self._instantiated_circuit.instantiated_flat_netlist[ + "instances" + ][instance_name]["model"] + outputs = component._gaussian_process_mode_response( + inputs, self.simulation_parameters + ) + self.component_outputs[instance_name] = outputs + + input_signals: dict = {} + output_signals: dict = {} + for ( + tracked_name, + designator, + ) in self._instantiated_circuit.port_lookup_table.items(): + instance_name, port_name = designator.split(",") + if port_name in self.component_inputs.get(instance_name, {}): + input_signals[tracked_name] = self.component_inputs[instance_name][ + port_name + ] + if port_name in self.component_outputs.get(instance_name, {}): + output_signals[tracked_name] = self.component_outputs[instance_name][ + port_name + ] + + return GaussianProcessSimulationResult(input_signals, output_signals) + + def _collect_component_inputs(self, component: str) -> None: + inputs = {} + for upstream in [ + u for u, _ in self._instantiated_circuit.graph.in_edges(component) + ]: + edge_data = self._instantiated_circuit.graph.get_edge_data( + upstream, component + ) + for _, edge in edge_data.items(): + inputs[edge["dst_port"]] = self.component_outputs[upstream][ + edge["src_port"] + ] + self.component_inputs[component] = inputs + + def _determine_gaussian_process_order(self, instantiated_circuit) -> list: + """Topological sort — same logic as BlockModeSimulation.""" + try: + return list(nx.topological_sort(instantiated_circuit.graph)) + except nx.NetworkXUnfeasible: + raise ValueError( + "Failed to determine simulation order — circular dependencies detected" + ) diff --git a/simphony/simulation/jax_tools.py b/simphony/simulation/jax_tools.py new file mode 100644 index 00000000..a00adeff --- /dev/null +++ b/simphony/simulation/jax_tools.py @@ -0,0 +1,31 @@ +import jax +import jax.numpy as jnp + +# TODO: Implement a decorator @jax.jit for the step_function +# (it needs a flag for the simulator to know whether it can be used in jax.jit) + + +def python_based_scan(f, init, xs=None, length=None): + """Debug-only Python approximation of ``jax.lax.scan``. + + This helper is intentionally not a production simulation backend. It + executes the loop eagerly in Python and is not guaranteed to match + ``jax.lax.scan`` for tracing behavior, compilation behavior, pytree edge + cases, mutation exposure, or numerical results. Use it only while debugging + a step function; production simulation results should use the JAX scan path. + """ + if xs is None: + xs = [None] * length + carry = init + ys = [] + for x in xs: + carry, y = f(carry, x) + ys.append(y) + return carry, jax.tree_util.tree_map(lambda *values: jnp.stack(values), *ys) + + +def python_based_while_loop(cond_fun, body_fun, init_val): + val = init_val + while cond_fun(val): + val = body_fun(val) + return val diff --git a/simphony/simulation/s_parameter.py b/simphony/simulation/s_parameter.py new file mode 100644 index 00000000..8e395c6b --- /dev/null +++ b/simphony/simulation/s_parameter.py @@ -0,0 +1,483 @@ +# from typing import TYPE_CHECKING +# if TYPE_CHECKING: +# from simphony.circuit import Circuit +from copy import deepcopy +from dataclasses import field +from functools import partial + +import networkx as nx + +# from simphony.utils import graph_to_netlist +import sax +from flax import struct +from jax.typing import ArrayLike + +from simphony.circuit.circuit import Circuit +from simphony.circuit.netlist import ( + generate_valid_separator, + graph_to_netlist, + sanitize_instance_names, +) +from simphony.component.component import SParameterComponent + +from .simulation import ( + Simulation, + SimulationMode, + SimulationParameters, + SimulationResult, +) +from .steady_state import SteadyStateSimulation, SteadyStateSimulationParameters + + +@struct.dataclass +class SParameterSimulationParameters(SimulationParameters): + """Global settings for S-parameter simulations. + + S-parameter simulations operate in the frequency domain and expose a + SAX `SDict` over selected circuit ports. + """ + + simulation_mode: SimulationMode = field( + default_factory=lambda: SimulationMode.S_PARAMETER + ) + # optical_baseband_wavelengths: jax.Array = field(default_factory=lambda:jnp.array([1.54e-6, 1.55e-6, 1.56e-6])) + directed: bool = False + + +class SParameterSimulationResult(SimulationResult): + """Result returned by `SParameterSimulation.run`. + + Attributes are attached by `run`: + + - `sax_circuit`: callable SAX circuit assembled for the requested ports. + - `sax_circuit_info`: metadata returned by `sax.circuit`. + - `s_parameters`: evaluated `SDict` at the requested wavelength grid. + """ + + def __init__(self): + pass + + +class SParameterSimulation(Simulation): + """Compute frequency-domain S-parameters for selected circuit ports. + + The simulator instantiates the circuit, separates the optical S-parameter + subgraph from any steady-state bias circuitry, computes required bias + values, and builds a SAX circuit for the requested ports. + + Parameters + ---------- + circuit: + Circuit to simulate. + settings: + Per-instance settings used during instantiation. + simulation_parameters: + Shared `SParameterSimulationParameters`. Defaults are used when omitted. + ports: + Optional mapping of exposed port names to top-level port designators. If + omitted, the circuit top-level ports are used. + """ + + def __init__( + self, + circuit: Circuit, + settings, + simulation_parameters=None, + ports=None, + # settings: dict = None + ): + """s_parameter_simulation Calculates the S-parameters for a given set + of ports in an optical circuit. + + By default, the exposed ports and settings are taken from the + provided netlist, but may be overwritten with keyword arguments. + """ + if simulation_parameters is None: + simulation_parameters = SParameterSimulationParameters() + + self.circuit = circuit + # self.flat_circuit = circuit.flatten() + self.settings = settings + self.simulation_parameters = simulation_parameters + + if ports is None: + ports = self.circuit.netlist["top_level"]["ports"] + + self.ports = ports + + def run( + self, + wl: ArrayLike = 1.55e-6, + # use_default_settings: bool = True + ) -> SParameterSimulationResult: + """Evaluate the circuit S-parameters at wavelength `wl`. + + Parameters + ---------- + wl: + Wavelength or wavelength array, in meters. + + Returns + ------- + SParameterSimulationResult + Result containing the generated SAX circuit and evaluated `SDict`. + """ + self.instantiated_circuit = self.circuit.instantiate( + self.settings, self.simulation_parameters + ) + + # self._identify_component_types() + ( + steady_state_graph, + reachable_bias_nodes, + s_parameter_graph, + ) = self._build_s_parameter_circuit(self.ports) + # self._validate_s_parameter_graph() + + # TODO: FIX THE FACT THAT PORTS DONT GET CONVERETED RIGHT IN graph_to_netlist + steady_state_netlist = graph_to_netlist(steady_state_graph) + steady_state_models = { + data["component"]: type(data["model"]) + for instance_name, data in self.instantiated_circuit.instantiated_flat_netlist[ + "instances" + ].items() + if instance_name in steady_state_netlist["instances"] + } + + # TODO: Verify that we are able to obtain the settings from nested subcircuits / pcells + steady_state_circuit_settings = { + instance_name: data["settings"] + for instance_name, data in self.instantiated_circuit.instantiated_flat_netlist[ + "instances" + ].items() + if instance_name in steady_state_netlist["instances"] + } + # TODO: FIX CIRCUIT SO THAT IT WORKS WITH SINGLE ELEMENT NETLISTS + steady_state_circuit = Circuit(steady_state_netlist, steady_state_models) + + steady_state_simulation_parameters = SteadyStateSimulationParameters() + steady_state_simulation = SteadyStateSimulation( + steady_state_circuit, + steady_state_circuit_settings, + steady_state_simulation_parameters, + ) + # self._initialize_steady_state_simulation(s_parameter_graph, steady_state_graph) + # self.reset_settings(use_default_settings=True) + + s_parameter_simulation_result = SParameterSimulationResult() + use_default_settings = True + # self.reset_settings(use_default_settings=use_default_settings) + # self.add_settings(settings) + # s_parameter_result = SParameterSimulationResult() + + # self._instantiate_components(self.settings) + steady_state_simulation_result = steady_state_simulation.run() + ports = { + k: self.instantiated_circuit.port_lookup_table[k] for k in self.ports.keys() + } + sax_circuit, sax_circuit_info = self._generate_sax_circuit( + s_parameter_graph, + steady_state_simulation_result, + reachable_bias_nodes, + ports, + ) + s_parameter_simulation_result.sax_circuit = sax_circuit + s_parameter_simulation_result.sax_circuit_info = sax_circuit_info + s_parameter_simulation_result.s_parameters = sax_circuit(wl=wl) + return s_parameter_simulation_result + + def _identify_component_types(self): + """Identify the types of components in the circuit. + + This method categorizes components into electrical, optical, and + logic components + """ + self.all_components = set() + self.electrical_components = set() + self.optical_components = set() + self.logic_components = set() + + graph = self.instantiated_circuit.graph + + for node, attr in graph.nodes(data=True): + component = self.instantiated_circuit.instantiated_flat_netlist[ + "instances" + ][node]["model"] + + # models = self.instantiated_circuit.models + # for node, attr in graph.nodes(data=True): + # model = attr["component"] + # component = models[model] + + # self.all_components.add(node) + + # component_types = attr['type'].lower().split('/') + # if "electrical" in component_types: + # self.electrical_components.add(node) + # if "optical" in component_types: + # self.optical_components.add(node) + # if "logic" in component_types: + # self.logic_components.add(node) + + # if component.electrical_ports: + # self.electrical_components.add(node) + # if component.logic_ports: + # self.logic_components.add(node) + # if component.optical_ports: + # self.optical_components.add(node) + + def _build_s_parameter_circuit(self, ports: dict): + """Split the instantiated graph into optical and bias subgraphs. + + The S-parameter simulator needs two pieces of information: + + - the connected optical S-parameter subgraph reachable from the exposed + ports, which becomes a SAX circuit; and + - any steady-state bias/control components that drive S-parameter bias + ports. + + Returns + ------- + tuple + `(steady_state_graph, reachable_bias_nodes, s_parameter_graph)`. + `reachable_bias_nodes` maps bias-source designators to the + S-parameter instance and port they control. + """ + # Step 1: Create a new graph with only s-parameter components + s_parameter_nodes = [] + for node, data in self.instantiated_circuit.graph.nodes(data=True): + model = self.instantiated_circuit.instantiated_flat_netlist["instances"][ + node + ]["model"] + if isinstance(model, SParameterComponent): + s_parameter_nodes.append(node) + + s_parameter_only_graph = self.instantiated_circuit.graph.subgraph( + s_parameter_nodes + ).copy() + # all_optical_graph = deepcopy(self.instantiated_circuit.graph) + + # optical_bias_ports = {} + # for src_node, dst_node, key, data in self.instantiated_circuit.graph.edges(keys=True, data=True): + # ### TODO: Write tests for whether this works in all edge cases + # dst_port = data['dst_port'] + # dst_bias_ports = self.instantiated_circuit.instantiated_flat_netlist['instances'][dst_node]['model']._s_parameter_get_bias_ports() + # src_port = data['src_port'] + # src_bias_ports = self.instantiated_circuit.instantiated_flat_netlist['instances'][src_node]['model']._s_parameter_get_bias_ports() + # if not data['port_type'] == 'optical' or dst_port in dst_bias_ports: + # all_optical_graph.remove_edge(src_node, dst_node, key) + + # if dst_port in dst_bias_ports: + # all_optical_graph.remove_edge(src_node, dst_node, key) + # optical_bias_ports[dst_node] = dst_port + + # TODO: REMOVE bias port connections and verify + + reachable = set() + for ext_port, (instance_port) in ports.items(): + port_designator = self.instantiated_circuit.port_lookup_table[ext_port] + instance_name = port_designator.split(",")[0] + # print(nx.descendants(all_optical_graph, port_designator)) + # print(nx.ancestors(all_optical_graph, port_designator)) + reachable.add(instance_name) + reachable |= nx.descendants(s_parameter_only_graph, instance_name) + reachable |= nx.ancestors(s_parameter_only_graph, instance_name) + + s_parameter_graph = s_parameter_only_graph.subgraph(reachable).copy() + reachable_bias_nodes = {} + + # TODO: add this functionality to instantiated flat netlist or instantiated circuit + flipped_connections = { + v: k + for k, v in self.instantiated_circuit.instantiated_flat_netlist[ + "connections" + ].items() + } + for node in s_parameter_graph.nodes(): + ### TODO: Write tests for whether this works in all edge cases + bias_ports = self.instantiated_circuit.instantiated_flat_netlist[ + "instances" + ][node]["model"]._s_parameter_get_bias_ports() + + for bias_port in bias_ports: + bias_ports + + if f"{node},{bias_port}" in flipped_connections: + bias_node, port_name = flipped_connections[ + f"{node},{bias_port}" + ].split(",") + reachable_bias_nodes[(bias_node, port_name)] = (node, bias_port) + + # Check is an ancestor of the bias node is in the s_parameter graph + # Check whether all ancestors are SteadyStateComponents + # TODO: eliminate all non-bias connections first + steady_state_nodes = set() + for (biasing_node, biasing_port), ( + biased_node, + biased_port, + ) in reachable_bias_nodes.items(): + steady_state_nodes.add(biasing_node) + for ancestor in nx.ancestors(self.instantiated_circuit.graph, biasing_node): + steady_state_nodes.add(ancestor) + # TODO: Check if ancestor is in s_parameter graph and throw an error + + steady_state_graph = self.instantiated_circuit.graph.subgraph( + list(steady_state_nodes) + ).copy() + # nx.connected_components(steady_state_only_graph) + return steady_state_graph, reachable_bias_nodes, s_parameter_graph + + def _validate_s_parameter_graph(self): + # Signal source nodes are sources of non-optical signals + source_nodes = set() + s_parameter_graph_nodes = set(self.s_parameter_circuit.graph.nodes) + potential_source_nodes = ( + s_parameter_graph_nodes + & self.optical_components + & (self.electrical_components | self.logic_components) + ) + for node in potential_source_nodes: + out_edges = self.circuit.graph.out_edges(node, data=True) + for src, dst, attr in out_edges: + src_port = attr["src_port"] + if src_port not in self.circuit.graph.nodes[src]["optical ports"]: + source_nodes.add(node) + + # We do not allow any of the s_parameter_nodes to function as + # signal sources that feed back into the s_parameter_nodes + # Such simulations should be performed in the time-domain + non_s_parameter_graph = deepcopy(self.circuit.graph) + non_s_parameter_graph.remove_edges_from(self.s_parameter_circuit.graph.edges()) + for source_node in source_nodes: + descendants = nx.descendants(non_s_parameter_graph, source_node) + if len(descendants & s_parameter_graph_nodes) > 0: + raise ValueError( + "Invalid S-parameter SubCircuit: Time-domain Simulation Required" + ) + + # def _initialize_steady_state_simulation(self, s_parameter_graph, steady_state_graph): + # steady_state_circuit = deepcopy(self.circuit) + # steady_state_circuit.remove_components(self.s_parameter_circuit.graph.nodes-self.hybrid_components) + # self.steady_state_simulation = SteadyStateSimulation(steady_state_circuit) + # # steady_state_graph.remove_nodes_from(self.s_parameter_graph.nodes) + # # self.steady_state_simulation = SteadyStateSimulation(self.steady_state_graph) + + ### I am going to put this in the base class + # def _instantiate_components(self): + # self.components = {} + # for component_name in self.circuit.graph.nodes: + # model_name = self.circuit.netlist['instances'][component_name]['component'] + # model = self.circuit.models[model_name] + # settings = self.settings[component_name] + # self.components[component_name] = model(**settings) + + # def _calculate_steady_states(self): + # for component in self.steady_state_order: + # pass + + def _generate_sax_circuit( + self, + s_parameter_graph, + steady_state_simulation_result, + reachable_bias_nodes, + ports, + ): + """Build a callable SAX circuit from the reachable optical subgraph. + + Bias-dependent S-parameter components are partially applied with the + steady-state signals computed earlier in `run`. The resulting callable + accepts the usual SAX wavelength argument and returns an `SDict` over the + requested exposed ports. + + Parameters + ---------- + s_parameter_graph: + Reachable graph containing only S-parameter components. + steady_state_simulation_result: + Result containing bias/control outputs for any reachable bias ports. + reachable_bias_nodes: + Mapping produced by `_build_s_parameter_circuit`. + ports: + Exposed SAX port mapping for the generated circuit. + """ + # I will assume that the only connections between the s-parameter portion of the circuit + # and the steady-state portion of the circuit are electrical or optical (this might change) + # in the future if more connection types become supported + + # These components will need to have their s_parameter methods completed with the steady state inputs + + # TODO: Test this for multiple connections + incomplete_components = { + node: {} for (node, port) in reachable_bias_nodes.values() + } + for source, (node, port) in reachable_bias_nodes.items(): + incomplete_components[node][port] = source + component_inputs = {component: {} for component in s_parameter_graph} + + for ( + incomplete_component, + biasing_node_designator, + ) in incomplete_components.items(): + bias_nodes = incomplete_components[incomplete_component] + for dst_port, (src_node, src_port) in biasing_node_designator.items(): + component_inputs[incomplete_component][ + dst_port + ] = steady_state_simulation_result.component_outputs[src_node][src_port] + + # component_inputs[incomplete_component] = steady_state_simulation_result.component_inputs[incomplete_component] + + # sax_models + separator = generate_valid_separator(component_inputs.keys()) + sax_models = {} + for component, inputs in component_inputs.items(): + # model_name = self.circuit.netlist['instances'][component]['component'] + s_parameter_func = self.instantiated_circuit.instantiated_flat_netlist[ + "instances" + ][component]["model"].s_parameters + # TODO: Make sure i am replacing with a unique value ("_" does not guarantee a unique instance name) + sax_models[component.replace("~", separator)] = partial( + s_parameter_func, inputs + ) + + # Each instance should correspond to a unique model at this point + # Since they might have been changed but the steady state inputs + # Therefore, we need the netlist to reflect this change + + # instances = {key: key for key in self.s_parameter_circuit.netlist['instances']} + # self.s_parameter_circuit.netlist['instances'] = instances + s_parameter_netlist = graph_to_netlist(s_parameter_graph) + for instance_name, data in s_parameter_netlist["instances"].items(): + s_parameter_netlist["instances"][instance_name] = instance_name.replace( + "~", separator + ) + # data['component'] = instance_name.replace("~", "_") + + s_parameter_netlist["ports"] = ports + # s_parameter_netlist['ports'] = {"in":"combiner~sax_model,port_1", "out": "splitter~sax_model,port_1"} + s_parameter_netlist = sanitize_instance_names( + s_parameter_netlist, new_separator=separator + ) + + # s_parameter_func = self.instantiated_circuit.instantiated_flat_netlist['instances']["wg1~sax_model"]['model'].s_parameters(np.array([1.55]), {}) + return sax.circuit(s_parameter_netlist, sax_models) + """### TODO: MATTHEW! Keep in mind that I defined the sax models to use + SI units ### and to assume that wl is given in terms of meters, not + microns ### To see what I mean, stop here in debug mode and run + sax_models['splitter'](1.55e-6) ### Notice that I am using 1.55e-6 + instead of 1.55 but that is what it expects pass + + ### TODO: complete the function graph_to_netlist in simphony.utils + ### turn self.s_parameter_graph into a netlist + ### use the sax_models dictionary above and the netlist you just generated to create + ### a sax.circuit and return the resulting scattering dictionary + ### Alternatively, it might be more efficient to make an "self.s_parameter_circuit" + ### and use the remove_compoenents method from that circuit object + ### that might get you the netlist you need for free + + sax_netlist = graph_to_netlist(self.s_parameter_circuit.graph) ## You might change this to use the alternative approach + circuit = sax.circuit(sax_netlist, sax_models) + ### TODO: It is probably possible for the user to keep the s-parameter graph elements parameterized + ### and simply return a sax circuit, maybe I will do that later, don't do that yet, + ### For now just return the s-parameter dict + return circuit(wl) + """ diff --git a/simphony/simulation/sample_mode.py b/simphony/simulation/sample_mode.py new file mode 100644 index 00000000..b2b0b5b3 --- /dev/null +++ b/simphony/simulation/sample_mode.py @@ -0,0 +1,515 @@ +import logging +from copy import deepcopy +from dataclasses import field, replace +from functools import partial +from time import time +from typing import Annotated, Optional + +import jax +import jax.numpy as jnp +from flax import struct +from jax import lax + +from simphony.circuit.circuit import Circuit +from simphony.circuit.netlist import generate_unique_string +from simphony.component.component import SampleModeComponent +from simphony.signal.sample_mode import ( + SampleModeElectricalSignal, + SampleModeLogicSignal, + SampleModeOpticalSignal, +) +from simphony.simulation import jax_tools +from simphony.simulation.simulation import SimulationMode +from simphony.simulation.terminator import ( + ElectricalTerminator, + LogicTerminator, + OpticalTerminator, +) + +from .simulation import Simulation, SimulationParameters, SimulationResult + +logger = logging.getLogger(__name__) + + +class SampleModeSimulationResult(SimulationResult): + """Signals collected from a completed sample-mode simulation. + + Attributes + ---------- + input_signals: + Signals received at each tracked port (the predecessor's output, + one step earlier than the component's own output). + output_signals: + Signals emitted by the component at each tracked port across all + time steps. + """ + + def __init__(self, input_signals: dict, output_signals: dict): + self.input_signals = input_signals + self.output_signals = output_signals + + +@struct.dataclass +class SampleModeSimulationParameters(SimulationParameters): + """Global settings for a sample-mode simulation. + + Sample mode advances the circuit one time sample at a time while preserving + per-component state between samples. + + Attributes + ---------- + optical_baseband_wavelengths: + Carrier wavelengths, in meters, tracked by sample-mode optical signals. + dt: + Time step, in seconds. + num_time_steps: + Number of sample updates to run. + use_state_space_optimization: + Enables optimized structured state-space updates where available. + time_batch_size: + Optional number of time steps per scan chunk. Component state is + carried between chunks and tracked outputs are concatenated. + mode_identifiers: + Inherited optical mode labels. + """ + + simulation_mode: SimulationMode = field( + default_factory=lambda: SimulationMode.SAMPLE_MODE + ) + optical_baseband_wavelengths: jax.Array = field( + default_factory=lambda: jax.numpy.array([1.55e-6]) + ) + directed: bool = False + dt: float = 1e-14 + num_time_steps: int = 50 + use_state_space_optimization: bool = True + time_batch_size: Optional[int] = None + # random_seed = 0 + + +@struct.dataclass +class SampleModeSimulationState: + """Mutable global state carried through a sample-mode scan.""" + + prng_key: Annotated[jax.Array, "shape=(2,), dtype=jax.uint32"] = field( + default_factory=lambda: jax.random.PRNGKey(0) + ) + + +class SampleModeOpticalTerminator(OpticalTerminator, SampleModeComponent): + def sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ): + """Compute the next state of the system.""" + return { + "out": SampleModeOpticalSignal( + amplitude=jnp.zeros( + (1, len(simulation_parameters.mode_identifiers)), dtype=complex + ), + wavelength=jnp.array([1.55e-6]), + ) + }, state + + +class SampleModeElectricalTerminator(ElectricalTerminator, SampleModeComponent): + def sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ): + """Compute the next state of the system.""" + return {"out": SampleModeElectricalSignal(voltage=0.0)}, state + + +class SampleModeLogicTerminator(LogicTerminator, SampleModeComponent): + def sample_mode_step( + self, + inputs: dict, + state: jax.Array, + simulation_state, + simulation_parameters: SampleModeSimulationParameters, + ): + """Compute the next state of the system.""" + return {"out": SampleModeLogicSignal(voltage=0)}, state + + +class SampleModeSimulation(Simulation): + """Run a circuit one time sample at a time. + + Sample mode is intended for components that inherit from the SampleModeComponent class that expose + `sample_mode_initial_state` and `sample_mode_step`. The simulator inserts + terminators for unconnected input-like ports, instantiates the circuit, + initializes component state, then advances all components for + `num_time_steps`. + + Parameters + ---------- + circuit: + Circuit to simulate. + settings: + Per-instance constructor settings. + tracked_ports: + Optional mapping of names to `"instance,port"` designators. Defaults to + the circuit top-level ports. + simulation_parameters: + Shared `SampleModeSimulationParameters`. Defaults are used when omitted. + """ + + def __init__( + self, + circuit: Circuit, + settings, + tracked_ports: dict = None, + simulation_parameters=None, + ): + if settings is None: + settings = {} + if simulation_parameters is None: + simulation_parameters = SampleModeSimulationParameters() + if tracked_ports is None: + tracked_ports = circuit.netlist["top_level"]["ports"] + + self.simulation_parameters = simulation_parameters + self.circuit = deepcopy(circuit) + self.insert_terminators() + self.settings = deepcopy(settings) + self.tracked_ports = deepcopy(tracked_ports) + self.component_inputs = {} + self.component_outputs = {} + + def run( + self, + use_jit=True, + ) -> SampleModeSimulationResult: + """Run the sample-mode simulation. + + Parameters + ---------- + use_jit: + If true, use the production `jax.lax.scan` path. Passing false uses + `simphony.simulation.jax_tools.python_based_scan`, which is + debug-only and should not be used for production simulation results. + + Returns + ------- + SampleModeSimulationResult + Tracked input and output signal histories keyed by tracked-port + name. + """ + return self._run_single( + use_jit=use_jit, time_batch_size=self.simulation_parameters.time_batch_size + ) + + def _run_single( + self, + use_jit=True, + time_batch_size=None, + ) -> SampleModeSimulationResult: + self.component_inputs = {} + self.component_outputs = {} + + # Currently, we pass in randomly generated prng keys through the simulation parameters field, so + # we have to get rid of the enum field to make jax happy. + # otherwise, I would simply mark the dataclass as static + # sim_mode = self.simulation_parameters.simulation_mode + # self.simulation_parameters = self.simulation_parameters.replace(simulation_mode=str(sim_mode)) + + self._instantiated_circuit = self.circuit.instantiate( + self.settings, + self.simulation_parameters, + tracked_ports=self.tracked_ports, + directed=False, + ) + self._predecessors_map, self._successors_map = self.edge_lookup_tables() + port_lookup_table = self._instantiated_circuit.port_lookup_table + + N = self.simulation_parameters.num_time_steps + + self.components = {} + for ( + instance_name, + instance_data, + ) in self._instantiated_circuit.instantiated_flat_netlist["instances"].items(): + self.components[instance_name] = instance_data["model"] + + initial_states = {} + for instance_name, instance in self.components.items(): + initial_states[instance_name] = instance._sample_mode_initial_state( + self.simulation_parameters + ) + + # Build lightweight maps from tracked port name to (instance, port) for both + # output and input sides. These are passed as static partial arguments so + # _system_step can emit only the tracked signals instead of the full circuit + # output dict, avoiding O(N_steps * N_instances * N_ports) memory allocation. + tracked_output_map = {} + tracked_input_map = {} + for tracked_port_name, tracked_port_designator in port_lookup_table.items(): + inst, port = tracked_port_designator.split(",", 1) + tracked_output_map[tracked_port_name] = (inst, port) + for src_inst, src_port in self._predecessors_map.get((inst, port), []): + tracked_input_map[tracked_port_name] = (src_inst, src_port) + break + + current_outputs = self._initial_outputs() + tic = time() + simulation_state = SampleModeSimulationState( + prng_key=jax.random.PRNGKey(self.simulation_parameters.seed) + ) + system_step = partial( + self._system_step, + simulation_parameters=self.simulation_parameters, + tracked_output_map=tracked_output_map, + tracked_input_map=tracked_input_map, + ) + carry = (current_outputs, initial_states, simulation_state) + _, stacked_tracked = self._run_time_batches( + system_step=system_step, + carry=carry, + num_time_steps=N, + time_batch_size=time_batch_size, + use_jit=use_jit, + ) + toc = time() + logger.debug("Sample mode simulation completed in %.6f s", toc - tic) + + # stacked_tracked is already keyed by tracked port name with shape (N, L, M). + return SampleModeSimulationResult( + input_signals=stacked_tracked["inputs"], + output_signals=stacked_tracked["outputs"], + ) + + def _make_scan_runner(self, system_step, chunk_length, use_jit): + if use_jit: + + def run_scan(carry): + return lax.scan(system_step, carry, length=chunk_length) + + return jax.jit(run_scan) + + def run_python_scan(carry): + return jax_tools.python_based_scan(system_step, carry, length=chunk_length) + + return run_python_scan + + def _run_time_batches( + self, + system_step, + carry, + num_time_steps, + time_batch_size=None, + use_jit=True, + ): + if time_batch_size is None or int(time_batch_size) >= num_time_steps: + run_scan = self._make_scan_runner(system_step, num_time_steps, use_jit) + return run_scan(carry) + + time_batch_size = int(time_batch_size) + if time_batch_size <= 0: + raise ValueError("time_batch_size must be a positive integer") + + full_chunks, remainder = divmod(num_time_steps, time_batch_size) + run_full_chunk = self._make_scan_runner(system_step, time_batch_size, use_jit) + run_remainder = ( + self._make_scan_runner(system_step, remainder, use_jit) + if remainder + else None + ) + + tracked_chunks = [] + for _ in range(full_chunks): + carry, tracked_chunk = run_full_chunk(carry) + tracked_chunks.append(tracked_chunk) + + if run_remainder is not None: + carry, tracked_chunk = run_remainder(carry) + tracked_chunks.append(tracked_chunk) + + return carry, self._combine_time_batch_pytrees(tracked_chunks) + + def _combine_time_batch_pytrees(self, tracked_chunks): + if not tracked_chunks: + return {"inputs": {}, "outputs": {}} + + return jax.tree_util.tree_map( + lambda *xs: jnp.concatenate(xs, axis=0), + *tracked_chunks, + ) + + def insert_terminators(self): + """Attach terminator source components to unconnected input-like + ports.""" + unconnected_ports = self.circuit.unconnected_ports(inputs_only=True) + optical_port_terminator_number = 0 + electrical_port_terminator_number = 0 + logic_port_terminator_number = 0 + instance_separator = generate_unique_string( + self.circuit.netlist["top_level"]["instances"].keys() + ) + model_separator = generate_unique_string(self.circuit.models.keys()) + for instance_name, _ports in unconnected_ports.items(): + for unconnected_port in _ports: + if unconnected_port.type == "optical": + terminator_instance_name = f"optical_terminator{instance_separator}{optical_port_terminator_number}" + terminator_model_name = f"optical_terminator_{model_separator}" + self.circuit.add_component( + terminator_instance_name, + terminator_model_name, + SampleModeOpticalTerminator, + ) + self.circuit.add_connection( + terminator_instance_name, + "out", + instance_name, + unconnected_port.name, + ) + optical_port_terminator_number += 1 + elif unconnected_port.type == "electrical": + terminator_instance_name = f"electrical_terminator{instance_separator}{electrical_port_terminator_number}" + terminator_model_name = f"electrical_terminator_{model_separator}" + self.circuit.add_component( + terminator_instance_name, + terminator_model_name, + SampleModeElectricalTerminator, + ) + self.circuit.add_connection( + terminator_instance_name, + "out", + instance_name, + unconnected_port.name, + ) + electrical_port_terminator_number += 1 + elif unconnected_port.type == "logic": + terminator_instance_name = f"logic_terminator{instance_separator}{logic_port_terminator_number}" + terminator_model_name = f"logic_terminator_{model_separator}" + self.circuit.add_component( + terminator_instance_name, + terminator_model_name, + SampleModeLogicTerminator, + ) + self.circuit.add_connection( + terminator_instance_name, + "out", + instance_name, + unconnected_port.name, + ) + logic_port_terminator_number += 1 + + def edge_lookup_tables(self): + """Build predecessor and successor lookup tables keyed by instance + port.""" + successors_map = {} + predecessors_map = {} + instances = self._instantiated_circuit.instantiated_flat_netlist["instances"] + + for inst_name, inst_data in instances.items(): + for port in inst_data["model"].ports: + successors_map[(inst_name, port.name)] = [] + predecessors_map[(inst_name, port.name)] = [] + + for inst_name in instances.keys(): + in_edges = self._instantiated_circuit.graph.in_edges(inst_name, data=True) + for src_node, dst_node, data in in_edges: + src_port = data["src_port"] + dst_port = data["dst_port"] + predecessors_map[(dst_node, dst_port)].append((src_node, src_port)) + + out_edges = self._instantiated_circuit.graph.out_edges(inst_name, data=True) + for src_node, dst_node, data in out_edges: + src_port = data["src_port"] + dst_port = data["dst_port"] + successors_map[(src_node, src_port)].append((dst_node, dst_port)) + + return predecessors_map, successors_map + + def _system_step( + self, + carry, + x, + simulation_parameters=None, + tracked_output_map=None, + tracked_input_map=None, + ): + system_outputs = carry[0] + states = carry[1] + simulation_state = carry[2] + prng_key = simulation_state.prng_key + + system_inputs = {} + for instance_name, instance in self.components.items(): + system_inputs[instance_name] = self._get_inputs( + instance_name, system_outputs + ) + + for instance_name, instance in self.components.items(): + prng_key, subkey = jax.random.split(prng_key) + simulation_state = replace(simulation_state, prng_key=subkey) + inputs = system_inputs[instance_name] + input_state = states[instance_name] + instance_outputs, output_state = instance._sample_mode_step( + inputs, input_state, simulation_state, simulation_parameters + ) + states[instance_name] = output_state + system_outputs[instance_name] = ( + system_outputs[instance_name] | instance_outputs + ) + + new_carry = (system_outputs, states, simulation_state) + + # Emit only the tracked-port signals. The full system_outputs remains in + # the carry for routing but is never stacked across time steps, keeping + # scan memory proportional to the number of tracked ports rather than to + # the total number of ports in the circuit. + y = { + "outputs": { + name: system_outputs[inst][port] + for name, (inst, port) in tracked_output_map.items() + }, + "inputs": { + name: system_outputs[inst][port] + for name, (inst, port) in tracked_input_map.items() + }, + } + return new_carry, y + + def _get_inputs(self, instance_name, current_outputs): + inputs = {} + ports = self.components[instance_name].ports + for port in ports: + # Sample mode simulations do not support multiple inputs + # Assumed list length is 1 + for src_node, src_port in self._predecessors_map[ + (instance_name, port.name) + ]: + inputs[port.name] = current_outputs[src_node][src_port] + + return inputs + + def _initial_outputs(self): + wl = self.simulation_parameters.optical_baseband_wavelengths + modes = self.simulation_parameters.mode_identifiers + initial_outputs = {} + for inst_name, model in self.components.items(): + initial_outputs[inst_name] = {} + for port_name, port in model._port_lookup_table.items(): + if port.type == "optical": + amplitude = jnp.zeros((wl.shape[0], len(modes)), dtype=complex) + initial_outputs[inst_name][port_name] = SampleModeOpticalSignal( + amplitude, wl + ) + elif port.type == "electrical": + voltage = 0.0 + initial_outputs[inst_name][port_name] = SampleModeElectricalSignal( + voltage + ) + elif port.type == "logic": + value = 0 + initial_outputs[inst_name][port_name] = SampleModeLogicSignal(value) + + return initial_outputs diff --git a/simphony/simulation/simulation.py b/simphony/simulation/simulation.py new file mode 100644 index 00000000..f7043cd7 --- /dev/null +++ b/simphony/simulation/simulation.py @@ -0,0 +1,143 @@ +"""Simulation module.""" + +from __future__ import annotations + +from copy import deepcopy +from dataclasses import field +from enum import StrEnum + +import jax.numpy as jnp +from flax import struct +from jax.typing import ArrayLike +from sax import DEFAULT_MODES +from sax.saxtypes import Model + +# import inspect + + +# from typing import TYPE_CHECKING +# if TYPE_CHECKING: +# from simphony.circuit import Circuit + + +class SimulationMode(StrEnum): + """Names for supported simulator execution modes. + + PCells and component factories use this enum to choose simulator- + specific internal designs without importing individual simulator + classes. + """ + + S_PARAMETER = "s_parameter" + SAMPLE_MODE = "sample_mode" + BLOCK_MODE = "block_mode" + STEADY_STATE = "steady_state" + GAUSSIAN_PROCESS = "gaussian_process" + _SIMPHONY_PREPROCESSING = "simphony_preprocessing" + #### TODO: Fix naming conventions for all simulation modes + # TRANSIENT_SAMPLE = "transient_sample" + # TRANSIENT_BLOCK = "transient_block" + + +class SimDevice: + """Base class for all source or measure devices.""" + + # TODO: Add bandwidth option to classical + def __init__(self, ports: list) -> None: + self.ports = ports + + +@struct.dataclass +class SimulationParameters: + """Base dataclass for parameters shared by simulator variants. + + Subclasses add mode-specific fields such as wavelength grids, time step, or + number of time samples. + + Attributes + ---------- + simulation_mode: + `SimulationMode` value identifying the active simulator. + directed: + Whether the simulation requires a directed instance graph. + mode_identifiers: + Optical mode labels represented by optical signal arrays. + seed: + Integer seed used by simulations/components that create PRNG keys. + """ + + # def __init__( + # self, + simulation_mode: SimulationMode = None + directed: bool = None + # sampling_period:float=1e-15 + # sampling_rate:float=1e15, + # num_time_steps:int =int(1e4) + # prng_key: Annotated[jax.Array, "shape=(2,), dtype=jax.uint32"]=field(default_factory=lambda: jax.random.PRNGKey(0)) + mode_identifiers: list = field(default_factory=lambda: DEFAULT_MODES) + seed = 0 + + # prng_key: Annotated[jax.Array, "shape=(2,), dtype=jax.uint32"]=jax.random.key(0) + # ): + # super().__setattr__('_locked', False) + # self.sampling_period = sampling_period + # self.sampling_rate = sampling_rate + # self.num_time_steps = num_time_steps + # self.prng_key=prng_key + # if prng_key is None: + # self.prng_key = jax.random.key(0) + # super().__setattr__('_locked', True) + + +class Simulation: + """Base class for Simphony simulation drivers.""" + + def __init__(self, ckt: Model, wl: ArrayLike) -> None: + self.ckt = ckt + self.wl = jnp.asarray(wl).reshape(-1) + + def run(self): + """Run the simulation.""" + raise NotImplementedError + + def _instantiate_components(self, settings): + self.components = {} + for instance_name in self.circuit.graph.nodes: + model_name = self.circuit.netlist["instances"][instance_name]["component"] + model = self.circuit.models[model_name] + component_settings = settings[instance_name] + self.components[instance_name] = model(**component_settings) + + def _clear_settings(self): + self.settings = {} + for instance in self.circuit.graph.nodes: + self.settings[instance] = {} + + def reset_settings(self, use_default_settings: bool = True): + """Reset per-instance settings. + + Parameters + ---------- + use_default_settings: + If true, restore defaults from the circuit before applying future + updates. If false, clear every instance settings dictionary. + """ + if use_default_settings: + self._clear_settings() + additional_settings = deepcopy(self.circuit.default_settings) + self.add_settings(additional_settings) + else: + self._clear_settings() + + def add_settings(self, settings: dict): + """Merge additional per-instance settings into the simulation. + + `settings` is keyed by instance name. Values are shallow-merged + into the current settings for each instance. + """ + for instance, instance_settings in settings.items(): + self.settings[instance].update(instance_settings) + + +class SimulationResult: + """Base class for simphony simulation results.""" diff --git a/simphony/simulation/steady_state.py b/simphony/simulation/steady_state.py new file mode 100644 index 00000000..2d97d943 --- /dev/null +++ b/simphony/simulation/steady_state.py @@ -0,0 +1,170 @@ +from copy import deepcopy + +import networkx as nx +from flax import struct + +from simphony.circuit.circuit import Circuit + +from .simulation import Simulation, SimulationParameters, SimulationResult + + +@struct.dataclass +class SteadyStateSimulationParameters(SimulationParameters): + """Global settings for steady-state simulations. + + Steady-state simulation currently relies mostly on the shared + `SimulationParameters` fields. The class exists so components and + PCells can distinguish steady-state instantiation from S-parameter, + Block mode, and Sample mode instantiation. + """ + + +class SteadyStateSimulationResult(SimulationResult): + """Inputs and outputs collected during a steady-state solve. + + Attributes + ---------- + circuit: + Deep copy of the instantiated circuit used for the solve. + component_inputs: + Mapping from instance name to the steady-state signals supplied to that + instance. + component_outputs: + Mapping from instance name to the steady-state signals returned by that + instance. + """ + + def __init__(self, circuit): + self.circuit = deepcopy(circuit) + self.component_inputs = {} + self.component_outputs = {} + + def _collect_component_inputs(self, component) -> dict: + """Collect already-computed predecessor outputs for one component.""" + inputs = {} + # input_components = nx.ancestors(self.circuit.graph, component) + input_components = [u for u, v in self.circuit.graph.in_edges(component)] + for input_component in input_components: + input_edges = self.circuit.graph.get_edge_data(input_component, component) + for edge_number, edge in input_edges.items(): + inputs[edge["dst_port"]] = self.component_outputs[input_component][ + edge["src_port"] + ] + + self.component_inputs[component] = inputs + + # def add_outputs(self, component, outputs): + # self.component_outputs[component]=outputs + + +class SteadyStateSimulation(Simulation): + """Run components in topological order to compute static signals. + + Steady-state simulations are used directly by users and internally + by S-parameter simulations to resolve bias/control values before + evaluating optical scattering responses. + """ + + def __init__( + self, + circuit: Circuit, + settings, + simulation_parameters, + ports=None, + ): + """Create a steady-state simulation. + + Parameters + ---------- + circuit: + Circuit to instantiate and solve. + settings: + Per-instance constructor settings. + simulation_parameters: + Steady-state simulation parameters. + ports: + Optional top-level port mapping. Defaults to the flattened circuit + ports. + """ + self.circuit = circuit + self.flat_circuit = circuit.flatten() + self.settings = settings + self.simulation_parameters = simulation_parameters + + if ports is None: + ports = self.flat_circuit.netlist["ports"] + + self.ports = ports + + def run( + self, + # settings:dict = None + ) -> SteadyStateSimulationResult: + """Instantiate the circuit and compute steady-state component + outputs.""" + + instantiated_circuit = self.circuit.instantiate( + self.settings, self.simulation_parameters + ) + simulation_result = SteadyStateSimulationResult(instantiated_circuit) + + self.steady_state_order = self._determine_steady_state_order_nx_method( + instantiated_circuit + ) + # self._instantiate_components(self.settings) + for instance_name in self.steady_state_order: + simulation_result._collect_component_inputs(instance_name) + inputs = simulation_result.component_inputs[instance_name] + component = instantiated_circuit.instantiated_flat_netlist["instances"][ + instance_name + ]["model"] + outputs = component.steady_state(inputs, self.simulation_parameters) + simulation_result.component_outputs[instance_name] = outputs + + return simulation_result + + # def _determine_steady_state_order(self, instantiated_circuit): + # """ + # Voltage signals at electrical ports are assumed to be constant + # for SParameterSimulations, but they are not known a priori, unless + # the voltage source is not dependent on an input signal. + + # Since steady-state connections are assumemd to be uni-directional, this function is + # able to find the order in which electrical component voltages must + # be calculated to find the proper steady state. + # """ + # steady_state_order = [] + # graph = instantiated_circuit.graph.copy() + # # graph.remove_nodes_from(self.s_parameter_graph.nodes) + # while graph.number_of_nodes() > 0: + # root_nodes = [n for n in graph.nodes if graph.in_degree(n)==0] + # if len(root_nodes) == 0: + # break + # steady_state_order += root_nodes + # graph.remove_nodes_from(root_nodes) + + # if graph.number_of_nodes() > 0: + # raise ValueError( + # "Failed to determine steady state order. " \ + # "Hint: Steady state cannot be determined for circular connections." + # ) + # return steady_state_order + + # Matthew's Suggestions + # Found this method while looking into the determine_steady_state algorithm. + # Thought this might look cleaner then the custom implementation above and after a bit of testing + # it appears to be exactly the same as the custom implementation. Though this depends + # if we need a custom implementation depending on the circuit structure. + def _determine_steady_state_order_nx_method(self, instantiated_circuit): + """Return the topological execution order for steady-state components. + + Steady-state components are evaluated once, so the instantiated + graph must be acyclic. Cycles imply a feedback equation that + this driver does not currently solve. + """ + try: + return list(nx.topological_sort(instantiated_circuit.graph)) + except nx.NetworkXUnfeasible: + raise ValueError( + "Failed to determine steady state order – circular dependencies detected" + ) diff --git a/simphony/simulation/terminator.py b/simphony/simulation/terminator.py new file mode 100644 index 00000000..887fa886 --- /dev/null +++ b/simphony/simulation/terminator.py @@ -0,0 +1,76 @@ +from simphony.component.component import Component +from simphony.component.port import Port + + +class Terminator(Component): + ports = [ + Port( + name="out", + type="any", + directionality="output", + ) + ] + + def __init__( + self, + simulation_parameters, + **kwargs, + ): + pass + + +class ElectricalTerminator(Terminator): + ports = [ + Port( + name="out", + type="electrical", + directionality="output", + ) + ] + # delay_compensation = 0 + # electrical_ports = ["out"] + + # def initial_state(self): + # return jnp.array([0]) + + # def sample_mode_step(self, inputs: dict, state, simulation_parameters): + # outputs = { + # 'out': SampleModeOpticalSignal( + # amplitude = ..., + # wavelength = ..., + # ) + # } + # return outputs, state + + +class LogicTerminator(Terminator): + ports = [ + Port( + name="out", + type="logic", + directionality="output", + ) + ] + # delay_compensation = 0 + # logic_ports = ["out"] + + # def initial_state(self): + # return jnp.array([0]) + + # def step(self, inputs: dict, state): + # outputs = { + # 'out': SampleModeLogicSignal( + # value = 0, + # ) + # } + # return outputs, state + + +class OpticalTerminator(Terminator): + ports = [ + Port( + name="out", + type="optical", + directionality="output", + ) + ] diff --git a/simphony/time_domain/SSFM.py b/simphony/time_domain/SSFM.py new file mode 100644 index 00000000..e19d974b --- /dev/null +++ b/simphony/time_domain/SSFM.py @@ -0,0 +1,2 @@ +def SSFM(): + pass diff --git a/simphony/time_domain/SSFM_old.py b/simphony/time_domain/SSFM_old.py new file mode 100644 index 00000000..4f23ec7e --- /dev/null +++ b/simphony/time_domain/SSFM_old.py @@ -0,0 +1,216 @@ +import matplotlib.pyplot as plt +import numpy as np +from scipy.fftpack import fft, fftfreq, fftshift, ifft, ifftshift + + +def getFreqRangeFromTime(time): + return fftshift(fftfreq(len(time), d=time[1] - time[0])) + + +def getPhase(pulse): + phi = np.unwrap(np.angle(pulse)) # Get phase starting from 1st entry + phi = phi - phi[int(len(phi) / 2)] # Center phase on middle entry + return phi + + +def getChirp(time, pulse): + phi = getPhase(pulse) + dphi = np.diff( + phi, prepend=phi[0] - (phi[1] - phi[0]), axis=0 + ) # Change in phase. Prepend to ensure consistent array size + dt = np.diff( + time, prepend=time[0] - (time[1] - time[0]), axis=0 + ) # Change in time. Prepend to ensure consistent array size + + return -1.0 / (2 * np.pi) * dphi / dt # Chirp = - 1/(2pi) * d(phi)/dt + + +class SIM_config: + def __init__(self, N, dt): + self.number_of_points = N + self.time_step = dt + t = np.linspace(0, N * dt, N) + self.t = t - np.mean(t) + self.tmin = self.t[0] + self.tmax = self.t[-1] + + self.f = getFreqRangeFromTime(self.t) + self.fmin = self.f[0] + self.fmax = self.f[-1] + self.freq_step = self.f[1] - self.f[0] + + self.describe_config() + + def describe_config(self): + print("### Configuration Parameters ###") + print(f" Number of points = {self.number_of_points}") + print(f" Start time, tmin = {self.tmin*1e12}ps") + print(f" Stop time, tmax = {self.tmax*1e12}ps") + print(f" Time resolution, dt = {self.time_step*1e12}ps") + print(" ") + print(f" Start frequency = {self.fmin/1e12}THz") + print(f" Stop frequency = {self.fmax/1e12}THz") + print(f" Frequency resolution = {self.freq_step/1e6}MHz") + print(" ") + + +# Function returns pulse power or spectrum PSD +def getPower(amplitude): + return np.abs(amplitude) ** 2 + + +# Function gets the energy of a pulse pulse or spectrum by integrating the power +def getEnergy(time_or_frequency, amplitude): + return np.trapz(getPower(amplitude), time_or_frequency) + + +# TODO: Add support for different carrier frequencies. Hint: Multiply by complex exponential! +# TODO: Add support for pre-chirped pulses. +def GaussianPulse(time, amplitude, duration, offset, chirp, order): + assert 1 <= order, f"Error: Order of gaussian pulse is {order}. Must be >=1" + return ( + amplitude + * np.exp( + -(1 + 1j * chirp) + / 2 + * ((time - offset) / (duration)) ** (2 * np.floor(order)) + ) + * (1 + 0j) + ) + + +def getSpectrumFromPulse(time, pulse_amplitude): + pulseEnergy = getEnergy(time, pulse_amplitude) # Get pulse energy + f = getFreqRangeFromTime(time) + dt = time[1] - time[0] + + spectrum_amplitude = fftshift(fft(pulse_amplitude)) * dt # Take FFT and do shift + spectrumEnergy = getEnergy(f, spectrum_amplitude) # Get spectrum energy + + err = np.abs((pulseEnergy / spectrumEnergy - 1)) + + # assert( err<1e-7 ), f'ERROR = {err}: Energy changed when going from Pulse to Spectrum!!!' + + return spectrum_amplitude + + +# Equivalent function for getting time base from frequency range +def getTimeFromFrequency(frequency): + return fftshift(fftfreq(len(frequency), d=frequency[1] - frequency[0])) + + +# Equivalent function for getting pulse from spectrum +def getPulseFromSpectrum(frequency, spectrum_amplitude): + spectrumEnergy = getEnergy(frequency, spectrum_amplitude) + + time = getTimeFromFrequency(frequency) + dt = time[1] - time[0] + + pulse = ifft(ifftshift(spectrum_amplitude)) / dt + pulseEnergy = getEnergy(time, pulse) + + err = np.abs((pulseEnergy / spectrumEnergy - 1)) + + # assert( err<1e-7 ), f'ERROR = {err}: Energy changed when going from Spectrum to Pulse!!!' + + return pulse + + +# Equivalent function for generating a Gaussian spectrum +def GaussianSpectrum(frequency, amplitude, bandwidth): + time = getTimeFromFrequency(frequency) + return getSpectrumFromPulse( + time, GaussianPulse(time, amplitude, 1 / bandwidth, 0, 0, 1) + ) + + +# Class for holding info about the fiber +class Fiber_config: + def __init__(self, nsteps, L, gamma, beta2, alpha_dB_per_m): + self.nsteps = nsteps + self.ntraces = ( + self.nsteps + 1 + ) # Note: If we want to do 100 steps, we will get 101 calculated pulses (zeroth at the input + 100 computed ones) + self.Length = L + self.dz = L / nsteps + self.zlocs = np.linspace( + 0, L, self.ntraces + ) # Locations of each calculated pulse + self.gamma = gamma + self.beta2 = beta2 + self.alpha_dB_per_m = alpha_dB_per_m + self.alpha_Np_per_m = ( + self.alpha_dB_per_m * np.log(10) / 10.0 + ) # Loss coeff is usually specified in dB/km, but Nepers/km is more useful for calculations + # TODO: Make alpha frequency dependent. + + +def SSFM(fiber: Fiber_config, sim: SIM_config, pulse): + # Initialize arrays to store pulse and spectrum throughout fiber + pulseMatrix = np.zeros((fiber.nsteps + 1, sim.number_of_points)) * (1 + 0j) + spectrumMatrix = np.copy(pulseMatrix) + pulseMatrix[0, :] = pulse + spectrumMatrix[0, :] = getSpectrumFromPulse(sim.t, pulse) + + # Pre-calculate effect of dispersion and loss as it's the same everywhere + disp_and_loss = np.exp( + (1j * fiber.beta2 / 2 * (2 * np.pi * sim.f) ** 2 - fiber.alpha_Np_per_m / 2) + * fiber.dz + ) + + # Precalculate constants for nonlinearity + nonlinearity = 1j * fiber.gamma * fiber.dz + + for n in range(fiber.nsteps): + pulse *= np.exp(nonlinearity * getPower(pulse)) # Apply nonlinearity + spectrum = ( + getSpectrumFromPulse(sim.t, pulse) * disp_and_loss + ) # Go to spectral domain and apply disp and loss + pulse = getPulseFromSpectrum(sim.f, spectrum) # Return to time domain + + # Store results and repeat + pulseMatrix[n + 1, :] = pulse + spectrumMatrix[n + 1, :] = spectrum + + # Return results + return pulseMatrix, spectrumMatrix + + +N = 2**15 # Number of points +N = 300000 # Number of points +dt = 0.0001e-12 # Time resolution [s] +t = np.linspace(0, N * dt, N) # Time step array +t = t - np.mean(t) # Center so middle entry is t=0 +# Initialize class +sim_config = SIM_config(N, dt) + + +# Define fiberulation parameters +Length = 5 +nsteps = 2**8 + +gamma = 10e-3 +beta2 = 100e3 +beta2 *= 1e-30 +alpha_dB_per_m = 0.2e-3 + +# Initialize class +fiber = Fiber_config(nsteps, Length, gamma, beta2, alpha_dB_per_m) + + +# Initialize Gaussian pulse +amplitude = 1 # Amplitude in units of sqrt(W) +duration = 0.5e-12 # Pulse 1/e^2 duration [s] +offset = 0 # Time offset +testChirp = 0 +testOrder = 1 +testCarrierFreq = 0 +testPulse = GaussianPulse(t, amplitude, duration, offset, testChirp, testOrder) +plt.plot(t, testPulse) +plt.show() + +pulseMatrix, spectrumMatrix = SSFM(fiber, sim_config, testPulse) + +plt.plot(t, np.abs(pulseMatrix[0, :]) ** 2) +plt.plot(t, np.abs(pulseMatrix[-1, :]) ** 2) +plt.show() diff --git a/simphony/time_domain/__init__.py b/simphony/time_domain/__init__.py new file mode 100644 index 00000000..2f73c91c --- /dev/null +++ b/simphony/time_domain/__init__.py @@ -0,0 +1,5 @@ +"""Time Domain Simphony.""" + +# from simphony.time_domain.time_circuit import TimeCircuit +# from simphony.time_domain.simulation import TimeSim +# from simphony.circuit import BlockModeComponent, SampleModeComponent diff --git a/simphony/time_domain/examples/16QAMsignalProcessing.py b/simphony/time_domain/examples/16QAMsignalProcessing.py new file mode 100644 index 00000000..acd16fff --- /dev/null +++ b/simphony/time_domain/examples/16QAMsignalProcessing.py @@ -0,0 +1,286 @@ +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) + +import time + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.TimeSim import TimeSim + +T = 20e-11 +dt = 1e-14 # Total time duration (40 ps) +t = jnp.arange(0, T, dt) # Time array +t0 = 1e-11 # Pulse start time +std = 1e-12 + +f_mod = 3 * 3.14 / 2 +m = f_mod * jnp.ones(len(t), dtype=complex) +x = jnp.linspace(0, 3.14, len(t)) + +# Define Gaussian parameters +mu = 1.14 # center the Gaussian in the middle of the interval +sigma = 0.3 # adjust sigma for desired width + +# Compute the Gaussian function +gaussian = jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + +# Optionally, normalize so the area under the curve is 1 +gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x + +timePhaseInstantiated = Modulator(mod_signal=m) + +netlist = { + "instances": { + "wg": "waveguide", + # "y": "y_branch", + "pm": "phase_modulator", + "pm2": "phase_modulator", + # "y2": "y_branch", + # "wg2": "waveguide", + # "y3": "y_branch", + # "y4": "y_branch", + # "y5": "y_branch", + # "y6": "y_branch", + # "wg3": "waveguide", + # "wg4": "waveguide", + # "wg5": "waveguide", + # "wg6": "waveguide", + # "bdc": "bidirectional", + # "bdc2": "bidirectional", + # "bdc3": "bidirectional", + # "bdc4": "bidirectional", + }, + "connections": { + # "bdc,port_2":"bdc2,port_1", + # "bdc,port_4":"bdc2,port_3", + # # "bdc2, port_2":"bdc3,port_1", + # "pm2,o0":"bdc2, port_2", + # "bdc3, port_1":"pm2,o1", + # # "bdc2, port_4": "bdc3, port_3", + # "pm,o0":"bdc2, port_4", + # "bdc3, port_3":"pm,o1", + # "bdc3, port_2": "bdc4,port_1", + # "bdc3, port_4": "bdc4,port_3", + "wg,o1": "pm,o0", + }, + "ports": { + # "o0": "bdc,port_1", + # "o1": "bdc,port_3", + # "o2": "bdc4, port_2", + # "o3": "bdc4, port_4", + # "in": "wg,o0", + # "out": "pm,o1", + "o0": "wg,o0", + "o1": "pm,o1", + }, +} +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, +} +active_components = {"pm", "pm2"} + + +time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, +) + +num_measurements = 200 +model_order = 100 +center_wvl = 1.548 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = { + "wl": wvl, + "wg": {"length": 15.0, "loss": 100}, + "wg2": {"length": 60.0, "loss": 100}, + "wg3": {"length": 50.0, "loss": 100}, +} + +tic = time.time() +time_sim.build_model(model_parameters=options) +toc = time.time() +build_time = toc - tic + +num_symbols = 400 # For example, 40 symbols so that 40*100 = 4000 time steps. +hold_time = 10 # Hold each symbol for 70 time steps. + + +def generate_16qam_piecewise_linear_signal(T, dt, num_symbols, hold_time): + """Generate a 16-QAM signal with piecewise linear transitions where the + hold time is specified and the ramp time is calculated so that the overall + number of time steps is maintained. + + Parameters: + T : Total duration (s) + dt : Time step (s) + num_symbols: Number of symbols + hold_time : Desired hold time (number of time steps per symbol) + + Returns: + t : Time vector (s) + I_waveform : In-phase component (length = total time steps) + Q_waveform : Quadrature component (length = total time steps) + signal_complex : Complex baseband signal = I_waveform + j Q_waveform + """ + total_time_steps = int(T / dt) + samples_per_symbol = total_time_steps // num_symbols # e.g., 4000/40 = 100 + # Calculate ramp_time automatically: + ramp_time = samples_per_symbol - hold_time + if ramp_time < 0: + raise ValueError( + "Hold time is too large; must be less than samples_per_symbol." + ) + + # Use np.linspace to get exactly the expected number of time steps. + t = np.linspace(0, T, num_symbols * samples_per_symbol, endpoint=False) + + # 16-QAM: 4 bits per symbol. + total_bits = num_symbols * 4 + data_bits = np.random.randint(0, 2, total_bits) + + # Mapping function for PAM4 (for both I and Q): + # 00 -> -3, 01 -> -1, 11 -> +1, 10 -> +3 + def bits_to_level(b0, b1): + if (b0, b1) == (0, 0): + return 1 + elif (b0, b1) == (0, 1): + return 3 + elif (b0, b1) == (1, 1): + return 5 + elif (b0, b1) == (1, 0): + return 7 + + symbols_I = [] + symbols_Q = [] + for i in range(0, total_bits, 4): + bI0, bI1, bQ0, bQ1 = data_bits[i : i + 4] + symbols_I.append(bits_to_level(bI0, bI1)) + symbols_Q.append(bits_to_level(bQ0, bQ1)) + symbols_I = np.array(symbols_I) + symbols_Q = np.array(symbols_Q) + + def piecewise_linear_hold(symbols, hold_time, ramp_time): + """For each pair of symbols, hold the symbol value for 'hold_time' + steps, then ramp linearly to the next symbol over 'ramp_time' steps. + + For the final symbol, hold it for the full period. + """ + output = [] + for i in range(len(symbols) - 1): + hold = np.full(hold_time, symbols[i]) + ramp = np.linspace(symbols[i], symbols[i + 1], ramp_time, endpoint=False) + block = np.concatenate([hold, ramp]) + output.append(block) + # Last symbol: hold for full period. + last_block = np.full(hold_time + ramp_time, symbols[-1]) + output.append(last_block) + return np.concatenate(output) + + I_waveform = piecewise_linear_hold(symbols_I, hold_time, ramp_time) + Q_waveform = piecewise_linear_hold(symbols_Q, hold_time, ramp_time) + signal_complex = I_waveform + 1j * Q_waveform + + return t, I_waveform, Q_waveform, signal_complex + + +t, I_waveform, Q_waveform, signal_complex = generate_16qam_piecewise_linear_signal( + T, dt, num_symbols, hold_time +) + +num_outputs = 2 +# inputs = { +# f'o{i}': signal_complex if i == 0 else jnp.zeros_like(t) +# for i in range(num_outputs) +# } + +inputs = {"o0": signal_complex, "o1": jnp.zeros_like(t)} +plt.plot(t, jnp.abs(signal_complex) ** 2) +# inputs = { +# f'o{i}': gaussian_pulse(t, t0 - 0.5 * t0, std) if i == 0 else jnp.zeros_like(t) +# for i in range(num_outputs) +# } +# inputs = { +# f'o{i}': smooth_rectangular_pulse(t,0.5e-11,2.5e-11) if i == 0 else jnp.zeros_like(t) +# for i in range(num_outputs) +# } + + +tic = time.time() +modelResult = time_sim.run(t, inputs) +toc = time.time() +run_time = toc - tic + +print(f"Build time: {build_time}") +print(f"Run time: {run_time}") + +modelResult.plot_sim() +I_original = np.real(signal_complex) +Q_original = np.imag(signal_complex) +I_output = np.real(modelResult.outputs["o1"]) +Q_output = np.imag(modelResult.outputs["o1"]) + + +plt.figure(figsize=(10, 8)) + +# Plot I components +plt.subplot(2, 1, 1) +plt.plot(t * 1e12, I_original, label="Original I(t)", color="blue") +plt.plot(t * 1e12, Q_output, label="System Output I(t)", color="green", linestyle="--") +plt.xlabel("Time (ps)") +plt.ylabel("Amplitude") +plt.title("In-Phase Component Comparison") +plt.legend() +plt.grid(True) + +# Plot Q components +plt.subplot(2, 1, 2) +plt.plot(t * 1e12, Q_original, label="Original Q(t)", color="red") +plt.plot(t * 1e12, I_output, label="System Output Q(t)", color="orange", linestyle="--") +plt.xlabel("Time (ps)") +plt.ylabel("Amplitude") +plt.title("Quadrature Component Comparison") +plt.legend() +plt.grid(True) + +plt.tight_layout() +plt.show() + +plt.figure(figsize=(6, 6)) +plt.plot( + I_original, + Q_original, + color="blue", + linewidth=1, + alpha=0.7, + label="Transition Path", +) +# plt.scatter(symbols_I, symbols_Q, color='red', s=50, zorder=5, label='Symbols') +plt.xlabel("In-Phase (I)") +plt.ylabel("Quadrature (Q)") +plt.title("16-QAM Constellation with Transition Paths") +plt.legend() +plt.grid(True) +plt.axis("equal") +plt.show() + +plt.figure(figsize=(6, 6)) +plt.plot( + Q_output, I_output, color="blue", linewidth=1, alpha=0.7, label="Transition Path" +) +# plt.scatter(symbols_I, symbols_Q, color='red', s=50, zorder=5, label='Symbols') +plt.xlabel("In-Phase (I)") +plt.ylabel("Quadrature (Q)") +plt.title("16-QAM Constellation with Transition Paths") +plt.legend() +plt.grid(True) +plt.axis("equal") +plt.show() diff --git a/simphony/time_domain/examples/MZI.png b/simphony/time_domain/examples/MZI.png new file mode 100644 index 00000000..20604929 Binary files /dev/null and b/simphony/time_domain/examples/MZI.png differ diff --git a/simphony/time_domain/examples/TimeCircuitTesting.ipynb b/simphony/time_domain/examples/TimeCircuitTesting.ipynb new file mode 100644 index 00000000..94cc67c4 --- /dev/null +++ b/simphony/time_domain/examples/TimeCircuitTesting.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal import coupler, waveguide\n", + "from simphony.time_domain.ideal import TimeCoupler, TimeWaveguide,TimePhase_Modulator # import td.coupler and td.waveguide\n", + "from simphony.time_domain.time_circuit import TimeCircuit\n", + "import jax.numpy as jnp\n", + "from simphony.utils import dict_to_matrix\n", + "import matplotlib.pyplot as plt\n", + "from simphony.time_domain.utils import gaussian_pulse" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 52\u001b[0m\n\u001b[1;32m 48\u001b[0m coupler_td \u001b[38;5;241m=\u001b[39m TimeCoupler(coupling\u001b[38;5;241m=\u001b[39mcoupling, loss\u001b[38;5;241m=\u001b[39mloss, phi\u001b[38;5;241m=\u001b[39mphi)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;66;03m#phase_mod_td = TimePhase_Modulator(mod_signal = m, k_p = 1)\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124min\u001b[39m\u001b[38;5;124m'\u001b[39m: gaussian_pulse(t, t0 \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39mt0, std),\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m: jnp\u001b[38;5;241m.\u001b[39mzeros_like(t),\n\u001b[1;32m 55\u001b[0m }\n\u001b[1;32m 58\u001b[0m ring_resonator_with_phase_modulator\u001b[38;5;241m.\u001b[39minstantiate(dt\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-14\u001b[39m, ideal_coupler\u001b[38;5;241m=\u001b[39mcoupler_td, ideal_waveguide\u001b[38;5;241m=\u001b[39mwaveguide_td)\n\u001b[1;32m 59\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ring_resonator_with_phase_modulator\u001b[38;5;241m.\u001b[39mrun_sim(t, inputs)\n", + "Cell \u001b[0;32mIn[4], line 52\u001b[0m\n\u001b[1;32m 48\u001b[0m coupler_td \u001b[38;5;241m=\u001b[39m TimeCoupler(coupling\u001b[38;5;241m=\u001b[39mcoupling, loss\u001b[38;5;241m=\u001b[39mloss, phi\u001b[38;5;241m=\u001b[39mphi)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;66;03m#phase_mod_td = TimePhase_Modulator(mod_signal = m, k_p = 1)\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124min\u001b[39m\u001b[38;5;124m'\u001b[39m: gaussian_pulse(t, t0 \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39mt0, std),\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m: jnp\u001b[38;5;241m.\u001b[39mzeros_like(t),\n\u001b[1;32m 55\u001b[0m }\n\u001b[1;32m 58\u001b[0m ring_resonator_with_phase_modulator\u001b[38;5;241m.\u001b[39minstantiate(dt\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-14\u001b[39m, ideal_coupler\u001b[38;5;241m=\u001b[39mcoupler_td, ideal_waveguide\u001b[38;5;241m=\u001b[39mwaveguide_td)\n\u001b[1;32m 59\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ring_resonator_with_phase_modulator\u001b[38;5;241m.\u001b[39mrun_sim(t, inputs)\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/PythonX/lib/python3.11/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m0.01\u001b[39m)\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Use simulation.py to implement a ring resonator using only td.ideal components\n", + "ring_resonator_with_phase_modulator = TimeCircuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"coupler\": \"ideal_coupler\",\n", + " \"ring\": \"ideal_waveguide\",\n", + " #\"pmod\": \"ideal_modulator\",\n", + " },\n", + " \"connections\": {\n", + " \"coupler,o3\": \"ring,o0\",\n", + " #\"pmod,o1\":\"ring, o0\",\n", + " \"ring,o1\": \"coupler,o2\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"coupler,o0\",\n", + " \"out\": \"coupler,o1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ideal_coupler\": TimeCoupler,\n", + " \"ideal_waveguide\": TimeWaveguide,\n", + " #\"ideal_modulator\": TimePhase_Modulator,\n", + " \n", + " },\n", + ")\n", + "\n", + "wl0 = 1.55\n", + "neff = 2.34\n", + "ng = 3.4\n", + "length = 100.0\n", + "loss = 100.0\n", + "T = 11e-12\n", + "N = 1000\n", + "\n", + "t = jnp.linspace(0, T, N)\n", + "dt = t[1] - t[0]\n", + "\n", + "t0 = T/2\n", + "std = 0.5e-12\n", + "\n", + "f_mod = 0\n", + "coupling = 0.5\n", + "phi = jnp.pi/2\n", + "\n", + "m = f_mod * t\n", + "\n", + "waveguide_td = TimeWaveguide(dt, wl0=wl0, neff=neff, ng=ng, length=length, loss=loss)\n", + "coupler_td = TimeCoupler(coupling=coupling, loss=loss, phi=phi)\n", + "\n", + "#phase_mod_td = TimePhase_Modulator(mod_signal = m, k_p = 1)\n", + "\n", + "inputs = {\n", + " 'in': gaussian_pulse(t, t0 - 0.5*t0, std),\n", + " 'out': jnp.zeros_like(t),\n", + "}\n", + "\n", + "\n", + "ring_resonator_with_phase_modulator.instantiate(dt=1e-14, ideal_coupler=coupler_td, ideal_waveguide=waveguide_td)\n", + "outputs = ring_resonator_with_phase_modulator.run_sim(t, inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(jnp.abs(outputs['out'])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_60292/393463390.py:6: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend()\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 5))\n", + "plt.plot(gaussian_pulse(t, t0 - 0.5*t0, std))\n", + "plt.title(\"Gaussian Pulse\")\n", + "plt.xlabel(\"Time (ns)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use simulation.py to implement a ring resonator using only td.ideal components\n", + "ring_resonator = TimeCircuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"coupler\": \"ideal_coupler\",\n", + " \"ring\": \"ideal_waveguide\",\n", + " },\n", + " \"connections\": {\n", + " \"coupler,o3\": \"ring,o0\",\n", + " \"ring,o1\": \"coupler,o2\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"coupler,o0\",\n", + " \"out\": \"coupler,o1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ideal_coupler\": TimeCoupler,\n", + " \"ideal_waveguide\": TimeWaveguide\n", + " #\"mzi1\": StateSpaceDiscrete,\n", + " # \"mzi2\": StateSpaceContinuous\n", + " },\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.4142135623730951\n" + ] + } + ], + "source": [ + "x = 1 + 1j\n", + "x = jnp.abs(x)\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PythonX", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/simphony/time_domain/examples/Tutorial.ipynb b/simphony/time_domain/examples/Tutorial.ipynb new file mode 100644 index 00000000..b39b4adc --- /dev/null +++ b/simphony/time_domain/examples/Tutorial.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial for Time-Domain Simulation\n", + "\n", + "This notebook provides a tutorial on how to use the Time-Domain Simulation software. In its simplest form, the software takes a netlist of models, connections, and ports, and produces a `TimeResult` object containing the simulated outputs at each port in the system.\n", + "\n", + "The simulation process is based on the S-parameters of the individual components, which are compiled into a single S-parameter matrix using SAX (Scattering And eXcitation) circuit formulations. Consequently, the netlist you supply follows the same format as a standard SAX netlist. Conceptually, an S-parameter matrix is synonymous with a transfer function that describes how input signals at one port propagate to the outputs at other ports across the frequency domain. Any model you provide must supply its S-parameter matrix. Conveniently, most commonly used photonic models (and their variations) are available in the SiEPIC Symphony library, where they can be adapted to meet specific requirements.\n", + "\n", + "These S-parameters are then used to fit a pole-residue model, which can be solved analytically to obtain a state-space representation of the circuit. This representation encodes all the information needed for the time-stepping algorithm to update the circuit’s state at each simulation step and simultaneously produce the outputs, ultimately storing them in the `TimeResult` class.\n", + "\n", + "Let’s begin by importing the required libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import jax.numpy as jnp\n", + "from jax import config\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from simphony.time_domain.simulation import TimeSim,TimeResult\n", + "from simphony.time_domain.utils import gaussian_pulse, smooth_rectangular_pulse\n", + "from simphony.libraries import siepic\n", + "from simphony.time_domain.ideal import Modulator\n", + "import matplotlib" + ] + }, + { + "attachments": { + "image.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define our netlist. A netlist contains the models and their instances, their interconnections, and the ports through which inputs and outputs are directed. It encapsulates the circuit information necessary for simulation. In our first example, we simulate a ring resonator as illustrated in the figure below.\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "This design uses two half-ring components from the SiEPIC Symphony library, resulting in a total of four ports. Two of these ports are terminated with terminators, allowing us to focus on the behavior of the remaining two ports. The netlist is described as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "netlist = {\n", + " \"instances\": {\n", + " \"hr1\":\"half_ring\",\n", + " \"hr2\":\"half_ring\",\n", + " \"t1\": \"terminator\",\n", + " \"t2\": \"terminator\",\n", + " },\n", + " \"connections\": {\n", + " \"hr1,port_1\":\"hr2,port_1\",\n", + " \"hr1,port_3\":\"hr2,port_3\",\n", + " \"hr1,port_4\":\"t1,port_1\",\n", + " \"hr2,port_4\":\"t2,port_1\",\n", + " },\n", + " \"ports\": {\n", + " \"o0\": \"hr1,port_2\",\n", + " \"o1\": \"hr2,port_2\",\n", + " },\n", + "}\n", + "\n", + "models = {\n", + " \"waveguide\": siepic.waveguide,\n", + " \"half_ring\": siepic.half_ring,\n", + " \"terminator\": siepic.terminator,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As observed, the netlist delineates the interconnections between the various components and defines the ports. The \"instances\" represent the individual occurrences of the models, as specified in the models section.\n", + "\n", + "With the netlist defined, we can now create the `TimeSim` object. This object encapsulates all the necessary information required to perform the simulation. In the next step, we pass in our netlist and the models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "time_sim = TimeSim(netlist=netlist, models=models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `time_sim` object contains three essential functions for constructing the time-domain model. The first function handles instantiation, where the user provides the netlist and the associated models. The second and third functions are part of the `build_model` method. This method supplies the algorithm with all necessary information, allowing the user to adjust model parameters, specify the frequency range (using a range of wavelengths), set the time steps, and more. Detailed documentation is available for all parameters that can be modified.\n", + "\n", + "For simplicity, we outline the configuration for our models. The available options follow the conventions defined by SAX circuits, as these options will be passed directly into the SAX circuit module during modeling. \n", + "\n", + "Now, let's define the lengths of our waveguides and the frequency spectrum.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "options = {\n", + " 'wl': np.linspace(1.5, 1.6, 200),\n", + " 'wg': {\"length\": 10.0, \"loss\": 100},\n", + "}\n", + "time_sim.build_model(model_parameters=options)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define our inputs. Since our system has two ports, we need to specify input signals for each one. We also define the time range for the simulation in the time domain. This information will be passed to our `run` method.\n", + "\n", + "The `run` method accepts two parameters:\n", + "- A time array.\n", + "- A dictionary of inputs, where the keys correspond to the system's ports and the values are the input arrays (each having the same number of time steps as the time array).\n", + "\n", + "When executed, the `run` method returns a `TimeResult` object containing:\n", + "- The input dictionary.\n", + "- The output dictionary.\n", + "- The S-parameters.\n", + "- The time array.\n", + "\n", + "Additionally, the `TimeResult` `Data` class provides methods to plot the outputs alongside the inputs. In this example, we apply a constant input to our ring resonator at port `o0` (as illustrated in the diagram above).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = 8.0e-11 \n", + "dt = 1e-14 \n", + "t = jnp.arange(0, T, dt)\n", + "\n", + "inputs = {\n", + " f'o{i}': smooth_rectangular_pulse(t,0.0e-11,5.0e-11) if i == 0 else jnp.zeros_like(t)\n", + " for i in range(2)\n", + " }\n", + "modelResult =time_sim.run(t, inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "modelResult.plot_sim()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input signal is active for 5e-11 seconds before being turned off. The ring resonator responds by initially rising in intensity and then settling to a steady-state value. The multiple bumps observed in the output correspond to the light circulating around the ring resonator, with each bump representing another pass through the ring. Once the input signal is turned off, the output eventually decays back to zero.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the pole-residue model is inherently limited to simulating time-invariant (passive) components. However, active components can be incorporated into the simulation with a few modifications:\n", + "\n", + "1. **Identify Active Components:** \n", + " The user must explicitly specify which instances in the circuit are active by including them in an `active_component` list.\n", + "\n", + "2. **Define the Active Component Model:** \n", + " For each active component, the user must create a model that implements a `response` function. This function should accept an input dictionary (with keys corresponding to the component's ports) and return an output dictionary with the same structure.\n", + "\n", + "For example, here is a simple implementation of a phase modulator's response function:\n", + "\n", + "```python\n", + "def response(self, inputs: dict) -> dict:\n", + " N = inputs['o0'].shape[0]\n", + " o0_response = jnp.zeros((N), dtype=complex)\n", + " o1_response = jnp.zeros((N), dtype=complex)\n", + " \n", + " for i in range(N):\n", + " o0_response = o0_response.at[i].set(inputs['o1'][i] * self.s_mod[self.countstep])\n", + " o1_response = o1_response.at[i].set(inputs['o0'][i] * self.s_mod[self.countstep])\n", + " \n", + " self.countstep += 1\n", + " \n", + " response = {\n", + " \"o0\": o0_response,\n", + " \"o1\": o1_response,\n", + " }\n", + " \n", + " return response\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the response function in place, the algorithm is able to simulate active components alongside passive components as it steps through time. To enable this, the user simply needs to define an `active_component` list and include their corresponding models in the overall model list, as illustrated below:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.time_domain.ideal import Modulator\n", + "T = 2.5e-11\n", + "dt = 1e-14 \n", + "t = jnp.arange(0, T, dt)\n", + "\n", + "mu = 1.30 \n", + "sigma = 0.15 \n", + "x = jnp.linspace(0, 3.14, len(t))\n", + "\n", + "gaussian = np.pi*jnp.exp(-0.5*((x - mu) / sigma) ** 2)\n", + "timePhaseInstantiated = Modulator(mod_signal=gaussian)\n", + "\n", + "netlist={\n", + " \"instances\": {\n", + " \"wg\": \"waveguide\",\n", + " \"y\": \"y_branch\",\n", + " \"pm\": \"phase_modulator\",\n", + " \"wg2\": \"waveguide\",\n", + " \"y2\": \"y_branch\",\n", + " },\n", + " \"connections\": {\n", + " \"y,port_2\":\"wg,o0\",\n", + " \"y,port_3\":\"wg2,o0\",\n", + " \"wg,o1\":\"pm,o0\",\n", + " \"y2,port_2\":\"wg2,o1\",\n", + " \"y2,port_3\":\"pm,o1\",\n", + "\n", + " },\n", + " \"ports\": {\n", + " \"o0\":\"y,port_1\",\n", + " \"o1\":\"y2,port_1\",\n", + "\n", + " },\n", + "}\n", + "models={\n", + " \"waveguide\": siepic.waveguide,\n", + " \"y_branch\": siepic.y_branch,\n", + " \"bidirectional\": siepic.bidirectional_coupler,\n", + " \"phase_modulator\": timePhaseInstantiated,\n", + " \n", + "}\n", + "active_components = {\n", + " \"pm\",\"pm2\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we demonstrate how the active/passive time-domain simulation works. First, we use a phase modulator to introduce a phase shift in the signal passing through it. The phase modulator is driven by an input signal—in this case, a Gaussian pulse that induces a phase shift of π at its peak.\n", + "\n", + "This phase modulator is then integrated into a Mach-Zehnder Interferometer (MZI) configuration, as illustrated below. The MZI splits the signal into two paths, with one arm receiving the phase-shifted signal while the other remains unchanged. When the two signals are recombined, the interference between them produces a temporal dip in the output signal due to the phase difference.\n", + "\n", + "\n", + "\"MZI\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Passive Sub-Netlist 0 ---\n", + "\n", + "Instances: {'wg': 'waveguide', 'y': 'y_branch', 'y2': 'y_branch', 'wg2': 'waveguide'}\n", + "\n", + "Connections: {'y,port_2': 'wg,o0', 'y,port_3': 'wg2,o0', 'y2,port_2': 'wg2,o1'}\n", + "\n", + "Ports: {'o0': 'y,port_1', 'o1': 'y2,port_1', 'o2': 'wg,o1', 'o3': 'y2,port_3'}\n", + "\n", + "\n", + "--- Final Time-Domain Netlist ---\n", + "\n", + "Models: {'pm': , '0': }\n", + "\n", + "Connections: {'0,o2': 'pm,o0', '0,o3': 'pm,o1'}\n", + "\n", + "Ports: {'o0': '0,o0', 'o1': '0,o1'}\n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADeEElEQVR4nOzdeVxU5f4H8M/MwDDsKMgqAu4pKIhJuNcPxTINLbfKLdPyapneLC1zvVfLvdIiK7drpldT0vS6kWYlai64ay7ghmwKDLIzc35/wBwZGfZZ8fN+vealc85zznnmOPLle55NIgiCACIiIiIiIiLSO6mpK0BERERERERUXzHpJiIiIiIiIjIQJt1EREREREREBsKkm4iIiIiIiMhAmHQTERERERERGQiTbiIiIiIiIiIDYdJNREREREREZCBMuomIiIiIiIgMhEk3ERERERERkYEw6SYig+rZsyd69uxp6mqIJBIJZs+ebepqEBERWaTExERIJBKsXbvW1FUBAKxduxYSiQSJiYmmrgpRhZh0E5mAJkCcOHHC1FUBAOTm5mL27Nk4dOhQtY9JTEzE6NGj0axZMygUCnh6eqJ79+6YNWuW4Spqpnbs2IEOHTpAoVCgSZMmmDVrFoqLi01dLSIiqqULFy7g9ddfh4+PD2xsbODt7Y3XXnsNFy5cqNN558+fj5iYGP1UsgpHjhzB7NmzkZmZWe1jdu7ciR49esDd3R12dnZo2rQpBg8ejD179hiuomaooKAAH374Iby9vWFra4uwsDDs37/f1NUiC8akm4iQm5uLOXPmVDvpvnbtGkJCQrB3714MGzYMK1aswIQJE+Dq6orPPvtMq+y+ffuwb98+A9TaPPzvf/9DVFQUXFxc8OWXXyIqKgr/+te/8M4775i6akREVAvbtm1Dhw4dEBsbi9GjR+Orr77CmDFjcPDgQXTo0AHbt2+v9bmNnXTPmTOn2kn34sWL0b9/f0gkEkyfPh3Lli3Dyy+/jKtXr2LTpk1iOT8/P+Tl5WH48OEGqrnpjRo1CkuXLsVrr72Gzz//HDKZDC+88AL++OMPU1eNLJSVqStARJZn2bJlePjwIeLj4+Hn56e1LzU1Veu9XC43ZtWM7v3330e7du2wb98+WFmV/Eh1cnLC/PnzMWnSJLRu3drENSQiouq6fv06hg8fjqZNm+Lw4cNo1KiRuG/SpEno1q0bhg8fjrNnz6Jp06YmrKl+FRcXY968eejVq5fOB+VlY7tEIoFCoTBm9Yzq+PHj2LRpExYtWoT3338fADBixAgEBgbigw8+wJEjR0xcQ7JEbOkmMhOjRo2Cg4MD7t69i6ioKDg4OKBRo0Z4//33oVKpxHKasVSLFy/GsmXL4OfnB1tbW/To0QPnz5/XOmdF46lHjRoFf39/8XyaXyrmzJkDiURS5bjn69evo3HjxuUSbgBwd3evsg43b95E//79YW9vD3d3d0yePBl79+6FRCLRam3v2bMnAgMDcfHiRTz77LOws7ODj48PFi5cqHW+wsJCzJw5E6GhoXB2doa9vT26deuGgwcPVvgZqpKamooxY8bAw8MDCoUC7du3x7p167TKXLx4ERcvXsS4cePEhBsA/vGPf0AQBGzdurXW1yciIuNbtGgRcnNzsWrVKq2EGwDc3NzwzTffICcnRysOlY2pZc2ePRsSiUR8L5FIkJOTg3Xr1omxdtSoUVplL1++jMGDB8PJyQmurq6YNGkS8vPzxXNUNp66bOyePXs2pk6dCgAICAgQr1fRuOf09HQolUp06dJF5/6ysb2iOmzZsgVt2rSBQqFAYGAgtm/fXu7elP0dZtWqVWjWrBlsbGzw9NNP46+//tI639mzZzFq1Cg0bdpUHMb2xhtv4P79+zrrWB2//vorunXrBnt7e7i4uOCll17CpUuXtMps3boVMpkM48aNE7cpFAqMGTMGcXFxuH37dq2vT08utnQTmRGVSoXIyEiEhYVh8eLFOHDgAJYsWYJmzZph/PjxWmXXr1+P7OxsTJgwAfn5+fj888/x3HPP4dy5c/Dw8Kj2NRs1aoSvv/4a48ePx4ABAzBw4EAAQLt27So8xs/PDwcOHMCvv/6K5557rkafMScnB8899xzu3buHSZMmwdPTExs3bqwwQc7IyECfPn0wcOBADB48GFu3bsWHH36IoKAgPP/88wAApVKJ7777DsOGDcPYsWORnZ2N77//HpGRkTh+/DiCg4NrVMe8vDz07NkT165dw8SJExEQEIAtW7Zg1KhRyMzMxKRJkwAAp0+fBgB07NhR63hvb280btxY3E9ERJZh586d8Pf3R7du3XTu7969O/z9/bFr164an/s///kP3nzzTXTq1ElM6Jo1a6ZVZvDgwfD398eCBQtw9OhRfPHFF8jIyMD69etrdK2BAwfi77//xo8//ohly5bBzc0NAMo9SNBwd3eHra0tdu7ciXfeeQcNGzas0fV27dqFIUOGICgoCAsWLEBGRgbGjBkDHx8fneU3btyI7OxsvPXWW5BIJFi4cCEGDhyIGzduwNraGgCwf/9+3LhxA6NHj4anpycuXLiAVatW4cKFCzh69KjWA43qOHDgAJ5//nk0bdoUs2fPRl5eHr788kt06dIFp06dEh8OnD59Gi1btoSTk5PW8Z06dQIAxMfHw9fXt0bXJoJAREa3Zs0aAYDw119/idtGjhwpABDmzp2rVTYkJEQIDQ0V3yckJAgABFtbW+HOnTvi9mPHjgkAhMmTJ4vbevToIfTo0aPc9UeOHCn4+fmJ79PS0gQAwqxZs6pV//Pnzwu2trYCACE4OFiYNGmSEBMTI+Tk5JQr+3gdlixZIgAQYmJixG15eXlC69atBQDCwYMHtY4FIKxfv17cVlBQIHh6egovv/yyuK24uFgoKCjQum5GRobg4eEhvPHGG1rbq/M5ly9fLgAQNmzYIG4rLCwUwsPDBQcHB0GpVAqCIAiLFi0SAAi3bt0qd46nn35aeOaZZyq9DhERmY/MzEwBgPDSSy9VWq5///4CADEWPB5TNWbNmiU8/qu2vb29MHLkyArL9u/fX2v7P/7xDwGAcObMGUEQHv0OsGbNmnLneDy+aWJUQkJCpZ9HY+bMmQIAwd7eXnj++eeFf//738LJkyfLldNVh6CgIKFx48ZCdna2uO3QoUMCAK17oznW1dVVePDggbj9559/FgAIO3fuFLfl5uaWu/aPP/4oABAOHz4sbtP8TlXV5wwODhbc3d2F+/fvi9vOnDkjSKVSYcSIEeK2tm3bCs8991y54y9cuCAAEKKjoyu9DpEu7F5OZGbefvttrffdunXDjRs3ypWLiorSeoLcqVMnhIWFYffu3QavY9u2bREfH4/XX38diYmJ+PzzzxEVFQUPDw98++23lR67Z88e+Pj4oH///uI2hUKBsWPH6izv4OCA119/XXwvl8vRqVMnrXsik8nEseNqtRoPHjxAcXExOnbsiFOnTtX48+3evRuenp4YNmyYuM3a2hrvvvsuHj58iN9++w1ASYs4ANjY2JQ7h0KhEPcTEZH5y87OBgA4OjpWWk6zX6lU6r0OEyZM0HqvmZTTGLF9zpw52LhxozhR6scff4zQ0FB06NChXBfsspKSknDu3DmMGDECDg4O4vYePXogKChI5zFDhgxBgwYNxPeangVlY7utra349/z8fKSnp+OZZ54BgBrH9nv37iE+Ph6jRo3SasVv164devXqpXV/8/LyKozrmv1ENcWkm8iMKBSKcl2/GjRogIyMjHJlW7RoUW5by5YtjbZOZcuWLfGf//wH6enpOHv2LObPnw8rKyuMGzcOBw4cqPC4mzdvolmzZuW6hTVv3lxn+caNG5crq+uerFu3Du3atYNCoYCrqysaNWqEXbt2ISsrq8af7ebNm2jRogWkUu0fkU899ZS4H3j0C0FBQUG5c+Tn52v9wkBEROZNk0xrku+KVDc5r43HY3uzZs0glUqNFtuHDRuG33//HRkZGdi3bx9effVVnD59Gv369dMaW16WJibqiuMVxfYmTZpovdck4GVj+4MHDzBp0iR4eHjA1tYWjRo1QkBAAADUOLZr6tiqVaty+5566imkp6cjJycHQElsryiua/YT1RSTbiIzIpPJ9Hq+isY7lZ2Yra5kMhmCgoIwffp0cRmVH374Qa/n10UQBPHvGzZswKhRo9CsWTN8//332LNnD/bv34/nnnsOarVab3V5nJeXF4CSJ+iPu3fvHry9vQ12bSIi0i9nZ2d4eXnh7NmzlZY7e/YsfHx8xDG/hoy1j5/bGHEdKFmFo1evXvjhhx8wcuRIXL9+HceOHdPb+asT2wcPHoxvv/0Wb7/9NrZt24Z9+/aJ64UbOrZXFNcBMLZTrTDpJrJQV69eLbft77//1poltEGDBjrX59Q88dWo6WQkFdFMKKYrWGn4+fnh+vXrWoEVKFn7u7a2bt2Kpk2bYtu2bRg+fDgiIyMRERFR4VP5qvj5+eHq1avlgvrly5fF/QDECdpOnDihVS4pKQl37typ8QRuRERkWi+++CISEhIqXI/5999/R2JiIl588UVxW3VjLVB1vH08tl+7dg1qtVqM7ZoW4cevV5trVVdVsV0TE3XF8drG9oyMDMTGxmLatGmYM2cOBgwYgF69etV6mTZNHa9cuVJu3+XLl+Hm5gZ7e3sAJbH977//Ljd8QPPQgbGdaoNJN5GFiomJwd27d8X3x48fx7Fjx8QZvYGSbmmXL19GWlqauO3MmTP4888/tc5lZ2cHoHwQr8jvv/+OoqKicts1Y6J0dd/SiIyMxN27d7Fjxw5xW35+fpVjwSujeWJeNpE/duwY4uLianW+F154AcnJydi8ebO4rbi4GF9++SUcHBzQo0cPACVj21u3bo1Vq1ZptTJ8/fXXkEgkeOWVV2p1fSIiMo2pU6fC1tYWb731VrmlqR48eIC3334bdnZ24nJcQEmszcrK0mohv3fvntj7qyx7e/tKY+3KlSu13n/55ZcAIMZ2JycnuLm54fDhw1rlvvrqK53XAqoX23NzcyuMmf/73/8AVBzbvb29ERgYiPXr1+Phw4fi9t9++w3nzp2r8tq66IrrALB8+fJanc/LywvBwcFYt26d1v04f/489u3bhxdeeEHc9sorr0ClUmHVqlXitoKCAqxZswZhYWGcuZxqhUuGEVmo5s2bo2vXrhg/fjwKCgqwfPlyuLq64oMPPhDLvPHGG1i6dCkiIyMxZswYpKamIjo6Gm3bttV6gmtra4s2bdpg8+bNaNmyJRo2bIjAwEAEBgbqvPZnn32GkydPYuDAgeLSYqdOncL69evRsGFDvPfeexXW+6233sKKFSswbNgwTJo0CV5eXvjhhx/ECUpq82T+xRdfxLZt2zBgwAD07dsXCQkJiI6ORps2bbR+AaiucePG4ZtvvsGoUaNw8uRJ+Pv7Y+vWrfjzzz+xfPlyrXF8ixYtQv/+/dG7d28MHToU58+fx4oVK/Dmm2+KY8CJiMgytGjRAuvWrcNrr72GoKAgjBkzBgEBAUhMTMT333+P9PR0/Pjjj1pLfQ0dOhQffvghBgwYgHfffRe5ubn4+uuv0bJly3ITfoWGhuLAgQNYunQpvL29ERAQgLCwMHF/QkIC+vfvjz59+iAuLg4bNmzAq6++ivbt24tl3nzzTXz66ad488030bFjRxw+fBh///13uc8SGhoKAPj4448xdOhQWFtbo1+/fmIyXlZubi46d+6MZ555Bn369IGvry8yMzMRExOD33//HVFRUQgJCanwvs2fPx8vvfQSunTpgtGjRyMjIwMrVqxAYGBgreKwk5MTunfvjoULF6KoqAg+Pj7Yt28fEhISanwujUWLFuH5559HeHg4xowZIy4Z5uzsLK5vDgBhYWEYNGgQpk+fjtTUVDRv3hzr1q0TvwNEtWLSudOJnlAVLRlmb29fruzjS45olttYtGiRsGTJEsHX11ewsbERunXrJi4pUtaGDRuEpk2bCnK5XAgODhb27t2rc3mTI0eOCKGhoYJcLq9yWa0///xTmDBhghAYGCg4OzsL1tbWQpMmTYRRo0YJ169f1yqra9myGzduCH379hVsbW2FRo0aCf/85z+Fn376SQAgHD16VOvYtm3blrv+4/VXq9XC/PnzBT8/P8HGxkYICQkRfvnlF52fs6rPppGSkiKMHj1acHNzE+RyuRAUFKRziRZBEITt27cLwcHBgo2NjdC4cWNhxowZQmFhYZXXICIi83T27Flh2LBhgpeXl2BtbS14enoKw4YNE86dO6ez/L59+4TAwEBBLpcLrVq1EjZs2KBzybDLly8L3bt3F5fd1Cwfpil78eJF4ZVXXhEcHR2FBg0aCBMnThTy8vK0zpGbmyuMGTNGcHZ2FhwdHYXBgwcLqampOuPbvHnzBB8fH0EqlVa6rFZRUZHw7bffClFRUWIstbOzE0JCQoRFixZpLctZ0bJlmzZtElq3bi3Y2NgIgYGBwo4dO4SXX35ZaN26dbljFy1aVK4Oj9f/zp07woABAwQXFxfB2dlZGDRokJCUlFSuXHWXDBMEQThw4IDQpUsXwdbWVnBychL69esnXLx4sVy5vLw84f333xc8PT0FGxsb4emnnxb27NlT5fmJKiIRhMf6bRCRWUtMTERAQAAWLVqE999/39TV0Zvly5dj8uTJuHPnjtZSaERERPXd7NmzMWfOHKSlpcHNzc3U1dGb4OBgNGrUCPv37zd1VYhMimO6icjoHl/jMj8/H9988w1atGjBhJuIiMjCFBUVobi4WGvboUOHcObMGfTs2dM0lSIyIxzTTURGN3DgQDRp0gTBwcHIysrChg0bcPnyZb0uNUZERETGcffuXUREROD111+Ht7c3Ll++jOjoaHh6euLtt982dfWITI5JNxEZXWRkJL777jv88MMPUKlUaNOmDTZt2oQhQ4aYumpERERUQw0aNEBoaCi+++47pKWlwd7eHn379sWnn34KV1dXU1ePyOQ4ppuIiIiIiIjIQDimm4iIiIiIiMhAmHQTERERERERGQjHdOuBWq1GUlISHB0dIZFITF0dIiKyQIIgIDs7G97e3pBK+Uzc0Bi7iYiorqobu5l060FSUhJ8fX1NXQ0iIqoHbt++jcaNG5u6GvUeYzcREelLVbGbSbceODo6Aii52U5OTiauDRERWSKlUglfX18xppBhMXYTEVFdVTd2M+nWA023NCcnJwZuIiKqE3Z1Ng7GbiIi0peqYjcHjREREREREREZCJNuIiIiIiIiIgOpV0n34cOH0a9fP3h7e0MikSAmJqbKYw4dOoQOHTrAxsYGzZs3x9q1aw1eTyIiIkuxcuVK+Pv7Q6FQICwsDMePH6+0/JYtW9C6dWsoFAoEBQVh9+7dWvu3bduG3r17w9XVFRKJBPHx8eXO0bNnT0gkEq3X22+/rVXm1q1b6Nu3L+zs7ODu7o6pU6eiuLi4zp+XiIhI3+pV0p2Tk4P27dtj5cqV1SqfkJCAvn374tlnn0V8fDzee+89vPnmm9i7d6+Ba0pERGT+Nm/ejClTpmDWrFk4deoU2rdvj8jISKSmpuosf+TIEQwbNgxjxozB6dOnERUVhaioKJw/f14sk5OTg65du+Kzzz6r9Npjx47FvXv3xNfChQvFfSqVCn379kVhYSGOHDmCdevWYe3atZg5c6Z+PjgREZEeSQRBEExdCUOQSCTYvn07oqKiKizz4YcfYteuXVq/DAwdOhSZmZnYs2dPta+lVCrh7OyMrKwsTsZCRES1Yo6xJCwsDE8//TRWrFgBoGRta19fX7zzzjuYNm1aufJDhgxBTk4OfvnlF3HbM888g+DgYERHR2uVTUxMREBAAE6fPo3g4GCtfT179kRwcDCWL1+us17/+9//8OKLLyIpKQkeHh4AgOjoaHz44YdIS0uDXC6v8rOZ4/0mIiLLUt1Y8kTPXh4XF4eIiAitbZGRkXjvvfdMUyGqE7VaQE5hMdRCyUL1ggBU9kTp8TkGdU06KClXSseBOo7VNX+hrlkNdZerug7Vndy4tueqTv2rU3ddxxGRZSgsLMTJkycxffp0cZtUKkVERATi4uJ0HhMXF4cpU6ZobYuMjKzWcK/H/fDDD9iwYQM8PT3Rr18/fPLJJ7CzsxOvExQUJCbcmuuMHz8eFy5cQEhISI2vR2RJBEFAkUqA3KpedVolqree6KQ7OTlZK2ADgIeHB5RKJfLy8mBra6vzuIKCAhQUFIjvlUqlQetJFRMEAT8ev43v/7iBhPQcqOtlv436r7aJv65yuh40tPJ0xIRnm6NPoGet60j0pElPT4dKpdIZJy9fvqzzmIrianJyco2u/eqrr8LPzw/e3t44e/YsPvzwQ1y5cgXbtm2r9DqafbowdlN9cS01G2PXn8TN+zkY3SUAM/o+xQfcRGbuiU66a2vBggWYM2eOqatBAH44dgszYs5XXZDMmq5BLjqfn9RqNIyAc3ez8PaGk1gz6mk829q9FucgImMaN26c+PegoCB4eXnh//7v/3D9+nU0a9asVudk7Kb6QKUW8M6P8UhIzwEAfP9HAp7ycsIroY1NXDMiqswTnXR7enoiJSVFa1tKSgqcnJwqbOUGgOnTp2t1n1MqlfD19TVYPUm3YpUan8deBQC81aMp3ugSAGdba0glEkgkJS2gEonODuLlEjpdUxvoSu90J4dClWV0qe25dNer6vrrrFc16qDr2NrWQfe5qvcha3uuwmI1Pj9wFdtO38WyA38z6SaqJjc3N8hkMp1x0tNTd6+RiuJqReWrKywsDABw7do1NGvWDJ6enuVmUddct6JrMXZTfbD/YjIu3VPCSWGFgR0aY+2RRCw/8DcGhPhAJmVrN5G5eqKT7vDw8HJLmezfvx/h4eGVHmdjYwMbGxtDVo2q4cTNDKRlF6ChvRz/7NWqjuOaGKjqs4/7PoUdZ5Jw9k4Wbj/IhW9DO1NXicjsyeVyhIaGIjY2VpyUVK1WIzY2FhMnTtR5THh4OGJjY7XmRqlOXK2KZlkxLy8v8Tr//ve/kZqaCnd3d/E6Tk5OaNOmjc5zMHZTffDTqbsAgGFhTTA5oiVi4u/iTkYefr+ahp6t+FCZyFzVq9kXHj58iPj4eDE4JyQkID4+Hrdu3QJQ8pR7xIgRYvm3334bN27cwAcffIDLly/jq6++wn//+19MnjzZFNWnGjp9KxMAEN7UlROJUKVcHWzQ1rtkRsn425mmrQyRBZkyZQq+/fZbrFu3DpcuXcL48eORk5OD0aNHAwBGjBihNdHapEmTsGfPHixZsgSXL1/G7NmzceLECa0k/cGDB4iPj8fFixcBAFeuXEF8fLw4Fvv69euYN28eTp48icTEROzYsQMjRoxA9+7d0a5dOwBA79690aZNGwwfPhxnzpzB3r17MWPGDEyYMIGJNdVbDwuKcehKyXJ9L3doDIW1DC+2K3kQtfdCSmWHEpGJ1atM5cSJEwgJCRFnLZ0yZQpCQkLEdTvv3bsnJuAAEBAQgF27dmH//v1o3749lixZgu+++w6RkZEmqT/VzPm7WQCAdo2dTVwTsgTtGrsAePS9IaKqDRkyBIsXL8bMmTMRHByM+Ph47NmzR5y07NatW7h3755YvnPnzti4cSNWrVqF9u3bY+vWrYiJiUFgYKBYZseOHQgJCUHfvn0BlCzVGRISIi4pJpfLceDAAfTu3RutW7fGP//5T7z88svYuXOneA6ZTIZffvkFMpkM4eHheP311zFixAjMnTvXGLeFyCT+SnyAIpUA34a2aOnhCACIeKrk/+KBSylQczZZIrNVb9fpNiau9Wka/Vf8gbN3srBqeCh6t+Ws1FS57/9IwLxfLuKFIE989VqoqatDVA5jiXHxfpOlmb/7ElYdvoEhHX3x2SslvT4KilVoP2cf8ovU2D+5O1qUJuNEZBzVjSX1qqWbnixJmXkAAJ8GFU96R6Th41LyPbmbmW/imhAREdXc8YQHAIBnmjUUt9lYyRDi2wAA8FdihknqRURVY9JNFim/SIX0h4UAHiVTRJVpXPpwRvOwhoiIyFIUq9S4nFyytrxmuJTG0/4lSfeJxAfGrhYRVROTbrJIadkFAACFtRTOttYmrg1ZAk9nBYCS706xSm3i2hAREVXfjfQc5BepYS+XIcDVXmtfB7+SpJsThRKZLybdZJEyc4sAAC62ckgkXO6Lqlb24Ux2frEJa0JERFQzF5JKJgF9yssJ0sfW427rXTKhbML9HOQWMr4RmSMm3WSRMvNKupa72LGVm6rHWiaFg40VACAzr8jEtSEiIqq+C3dLupZrlr8sq5GjDdwcbCAIwJXkbGNXjYiqgUk3WSRNS7cTu5ZTDWhau7OYdBMRkQW5lvYQANDKU/fsyE95lcxafukek24ic8SkmyySJmlyYdJNNcCkm4iILFFCeg4AIMDNXuf+NqUt4BfvZRmtTkRUfUy6ySJpkiZOokY1ofm+ZOYWmrgmRERE1VNYrMadjJKVN5o20p10typdn/ta6kOj1YuIqo9JN1kkJt1UG5o5AJRs6SYiIgtxOyMXKrUAO7kM7o42OstoWsAT03ONWTUiqiYm3WSRcgpKZue0L50Yi6g67OQl35ecQpWJa0JERFQ9iaVdy/1c7StcsUWTdCcr8zmDOZEZYtJNFimvqCRpspPLTFwTsiSa70sek24iIrIQmvHcTSsYzw0ALnZyNCjtzcXWbiLzw6SbLFJ+adJty6SbakDzfdE8tCEiIjJ3mqTb382u0nKa1m5NeSIyH0y6ySJpWioV1ky6qfo03xe2dBMRkaW4XTqJWpOGlSfd/mLSzcnUiMwNk26ySLmF7F5ONWfHlm4iIrIw9zJLkm4fl8qTbk338xts6SYyO0y6ySKJ3cvZ0k01YMuWbiIisiCCICCpNOn2clFUWraJa0nSfedBnsHrRUQ1w6SbLFIek26qBTHpZks3ERFZAGV+sbjihrezbaVlfUqT8ruZTLqJzA2TbrJImqRJwe7lVAMKzl5OREQWRNPK3dBeXuXksZru58nKfBSr1AavGxFVH5Nuskh5HNNNtWBX2tKdy5ZuIiKyAPeySruWO1fetRwA3B1tYC2TQKUWkJJdYOiqEVENMOkmi6RJutm9nGpC00qQz5ZuIiKyAHcz8wEA3i6Vdy0HAKlUAq/SLuh3M9jFnMicMOkmiyMIwqPu5Uy6qQYUYkt3sYlrQkREVLVHM5dXnXQDgHfpuO4kjusmMitMusniqNQC1ELJ322s+BWm6tN8X4qKBRPXhIiIqGrizOXV6F4OPBrXzcnUiMwLMxayOEWqRwmTtYxfYao+eWnSXcgJZoiqbeXKlfD394dCoUBYWBiOHz9eafktW7agdevWUCgUCAoKwu7du7X2b9u2Db1794arqyskEgni4+O19j948ADvvPMOWrVqBVtbWzRp0gTvvvsusrKytMpJJJJyr02bNunlMxOZixRlydhsD6dqJt0NSlrE77B7OZFZYcZCFqdswsSkm2pC830pKmbSTVQdmzdvxpQpUzBr1iycOnUK7du3R2RkJFJTU3WWP3LkCIYNG4YxY8bg9OnTiIqKQlRUFM6fPy+WycnJQdeuXfHZZ5/pPEdSUhKSkpKwePFinD9/HmvXrsWePXswZsyYcmXXrFmDe/fuia+oqCi9fG4ic5H2sCTpdne0qVb5xqXd0Nm9nMi8WJm6AkQ1VaSVdEtMWBOyNJqW7gK2dBNVy9KlSzF27FiMHj0aABAdHY1du3Zh9erVmDZtWrnyn3/+Ofr06YOpU6cCAObNm4f9+/djxYoViI6OBgAMHz4cAJCYmKjzmoGBgfjpp5/E982aNcO///1vvP766yguLoaV1aNfXVxcXODp6amXz0pkjtJKZyF3d6pe0q0pl6LMN1idiKjm2ExIFkeTdFvLSroTElWX5iFNkUoNQeC4bqLKFBYW4uTJk4iIiBC3SaVSREREIC4uTucxcXFxWuUBIDIyssLy1ZWVlQUnJyethBsAJkyYADc3N3Tq1AmrV6/m/2uqV/KLVMjKKwIANHKoXvdyTTf0NC4ZRmRW2NJNFkczCRa7llNNyUu/M4IAFKsF9pQgqkR6ejpUKhU8PDy0tnt4eODy5cs6j0lOTtZZPjk5uU71mDdvHsaNG6e1fe7cuXjuuedgZ2eHffv24R//+AcePnyId999V+d5CgoKUFDwKBFRKpW1rhORMaSXdi2XW0nhZFu9X9k1Sff9nEIUFqvFHl5EZFpMusniFIot3QwkVDNlf/koUqn5HSIyc0qlEn379kWbNm0we/ZsrX2ffPKJ+PeQkBDk5ORg0aJFFSbdCxYswJw5cwxZXSK90rRWN3KwqXbPvgZ21rCWSVCkEpD2sKDaS40RkWHxN06yOEVMuqmWyn5nCjmZGlGl3NzcIJPJkJKSorU9JSWlwnHUnp6eNSpfmezsbPTp0weOjo7Yvn07rK2tKy0fFhaGO3fuaLVmlzV9+nRkZWWJr9u3b9e4TkTGJCbd1ZxEDSiZ1d/dsaS1O5XjuonMRr3MWmq6vMny5cvFpUl8fX0xefJk5OfzB5W50iTdcnYNphqykkqgaSzgsmFElZPL5QgNDUVsbKy4Ta1WIzY2FuHh4TqPCQ8P1yoPAPv376+wfEWUSiV69+4NuVyOHTt2QKGoejxrfHw8GjRoABsb3QmKjY0NnJyctF5E5iy1Fkk3UHYyNY7rJjIX9a57uWZ5k+joaISFhWH58uWIjIzElStX4O7uXq78xo0bMW3aNKxevRqdO3fG33//jVGjRkEikWDp0qUm+ARUFbGlm+OUqIYkEgmsZVIUFqu11nsnIt2mTJmCkSNHomPHjujUqROWL1+OnJwccTbzESNGwMfHBwsWLAAATJo0CT169MCSJUvQt29fbNq0CSdOnMCqVavEcz548AC3bt1CUlISAODKlSsASlrJPT09xYQ7NzcXGzZsgFKpFMdfN2rUCDKZDDt37kRKSgqeeeYZKBQK7N+/H/Pnz8f7779vzNtDZFC1aekGHi0vlprNBiQic1Hvku6aLm9y5MgRdOnSBa+++ioAwN/fH8OGDcOxY8eMWm+qvkJOpEZ1IC9Nutm9nKhqQ4YMQVpaGmbOnInk5GQEBwdjz5494mRpt27dglT66Gdx586dsXHjRsyYMQMfffQRWrRogZiYGAQGBoplduzYIcZoABg6dCgAYNasWZg9ezZOnTolxuDmzZtr1SchIQH+/v6wtrbGypUrMXnyZAiCgObNm4vxn6i+qOka3RqaydRS2dJNZDbqVdKtWd5k+vTp4raqljfp3LkzNmzYgOPHj6NTp064ceMGdu/eLa4jqgtnQDUtjummupBbSYEC7fXeiahiEydOxMSJE3XuO3ToULltgwYNwqBBgyo836hRozBq1KgK9/fs2bPKpb/69OmDPn36VFqGyNJpkuaatnRrkm6u1U1kPupV0l2b5U1effVVpKeno2vXrhAEAcXFxXj77bfx0UcfVXgdzoBqWhzTTXWhWSaMLd1ERGTONC3djRxqlnRrkvQUrtVNZDae+KbCQ4cOYf78+fjqq69w6tQpbNu2Dbt27cK8efMqPIYzoJoWW7qpLjTLhnEiNSIiMmfppUmzW627l7Olm8hc1KuW7tosb/LJJ59g+PDhePPNNwEAQUFByMnJwbhx4/Dxxx9rjVXTsLGxqXB2VDK8QhXHdFPtab43bOkmIiJzlpFbCABwtZfX6DjNGPA0tnQTmY16lbXUZnmT3Nzccom1TCYDgCrHlJFpFBVz9nKqPXlp0s0x3UREZK7yi1TILVQBABrUMOl2dSgpn5FbCJWav8sSmYN61dIN1Hx5k379+mHp0qUICQlBWFgYrl27hk8++QT9+vUTk28yLxzTTXUhdi9nSzcREZmpBzklrdzWMgkcbWr263pDu5KkWy0AmbmFcK3hmHAi0r96l3TXdHmTGTNmQCKRYMaMGbh79y4aNWqEfv364d///repPgJVgWO6qS6s2dJNRERmTpN0N7CTQyKpWSODlUwKFztrZOYW4X4Ok24ic1Dvkm6gZsubWFlZYdasWZg1a5YRakb6wDHdVBcyackvL8y5iYjIXGnGczesYddyDVd7OTJzi5D+sAAtPRz1WTUiqgVmLWRx2NJNdWFVmnQXq5l1ExGReSrb0l0bmtZtzXmIyLSYtZDF0UykJrfimG6qOU1Ld7GKk8sQEZF5ysipW0u3W+lkavcfMukmMgdMusnisKWb6kLzveGMrkREZK4e1DHp1hx3/yGXDSMyB8xayOJwTDfVhdjSzaSbiIjM1IPSMd01XS5Mw9W+pHt5OruXE5kFZi1kcdjSTXVhJU6kxjHdRERknjJyigAADe2sa3X8o+7lbOkmMgfMWsjiPEq6Oaabak7T0l3EMd1ERGSmxInUatvSzYnUiMwKk26yOJpkyUrKry/VHMd0ExGRudPfmG4m3UTmgFkLWRx1abJkxZZuqgWO6SYiInP3oI7rdGu6l6ezezmRWWDSTRZHkyxJJUy6qeY4ppuIiMyZIAh1XjJMM5GaMr8YhcWMd0SmxqSbLI5a0HQvZ9JNNceWbiIiMmfZBcVijGpgV7uk29nWWox3GbnsYk5kaky6yeJoApGMSTfVgmZMdzEnUiMiIjOkaeW2k8ugsJbV6hxSqURM2NOy2cWcyNSYdJPF0XQLZtJNtcGWbiIiMmf3NTOX17KVW6OhfclyY5m5RXWuExHVDZNusjgqtnRTHXBMNxERmbO6jufWcClN2tm9nMj0mHSTxdEk3RzTTbXBlm4iIjJnmpZpFzvrOp2ngZ2mpZtJN5GpMekmiyPOXs6km2rBiut0ExGRGcvKK0m6nW3rmnRrWrrZvZzI1Jh0k8VhSzfVheZ7U8SJ1IiqZeXKlfD394dCoUBYWBiOHz9eafktW7agdevWUCgUCAoKwu7du7X2b9u2Db1794arqyskEgni4+PLnSM/Px8TJkyAq6srHBwc8PLLLyMlJUWrzK1bt9C3b1/Y2dnB3d0dU6dORXFxcZ0/L5GpaZJupzom3exeTmQ+mHSTxeGYbqoLGcd0E1Xb5s2bMWXKFMyaNQunTp1C+/btERkZidTUVJ3ljxw5gmHDhmHMmDE4ffo0oqKiEBUVhfPnz4tlcnJy0LVrV3z22WcVXnfy5MnYuXMntmzZgt9++w1JSUkYOHCguF+lUqFv374oLCzEkSNHsG7dOqxduxYzZ87U34cnMhH9tXRzIjUic8GkmywOlwyjurDimG6ialu6dCnGjh2L0aNHo02bNoiOjoadnR1Wr16ts/znn3+OPn36YOrUqXjqqacwb948dOjQAStWrBDLDB8+HDNnzkRERITOc2RlZeH777/H0qVL8dxzzyE0NBRr1qzBkSNHcPToUQDAvn37cPHiRWzYsAHBwcF4/vnnMW/ePKxcuRKFhWzVI8um1HP3co7pJjI9Jt1kcdTsXk518Kilm0k3UWUKCwtx8uRJreRYKpUiIiICcXFxOo+Ji4srl0xHRkZWWF6XkydPoqioSOs8rVu3RpMmTcTzxMXFISgoCB4eHlrXUSqVuHDhgs7zFhQUQKlUar2IzJEyXz9Jt3NpSzfHdBOZHpNusjiPWrr59aWasy6dSI0t3USVS09Ph0ql0kpsAcDDwwPJyck6j0lOTq5R+YrOIZfL4eLiUuF5KrqOZp8uCxYsgLOzs/jy9fWtdp2IjEnfE6mxpZvI9Ji1kMV5NKbbxBUhiyQuGabimG6iJ8n06dORlZUlvm7fvm3qKhHppO8x3WzpJjI9pi1kcVRs6aY6sGL3cqJqcXNzg0wmKzdreEpKCjw9PXUe4+npWaPyFZ2jsLAQmZmZFZ6nouto9uliY2MDJycnrReROdJX0q2ZvVyZX8SYR2RizFrI4nDJMKoLGSdSI6oWuVyO0NBQxMbGitvUajViY2MRHh6u85jw8HCt8gCwf//+CsvrEhoaCmtra63zXLlyBbdu3RLPEx4ejnPnzmnNor5//344OTmhTZs21b4WkTnSX9JdcrwgPDonEZmGlakrQFRTxaVLPUklTLqp5jRjuvnUn6hqU6ZMwciRI9GxY0d06tQJy5cvR05ODkaPHg0AGDFiBHx8fLBgwQIAwKRJk9CjRw8sWbIEffv2xaZNm3DixAmsWrVKPOeDBw9w69YtJCUlAShJqIGSFmpPT084OztjzJgxmDJlCho2bAgnJye88847CA8PxzPPPAMA6N27N9q0aYPhw4dj4cKFSE5OxowZMzBhwgTY2NgY8xYR6VVBsQr5RSW/5zgp6pZ0W8ukcLSxQnZBMTJyC9HQXq6PKhJRLZhNS/fIkSNx+PBhU1eDLIAmV7KSMemmmns0pptJN9Vf+oqpQ4YMweLFizFz5kwEBwcjPj4ee/bsESctu3XrFu7duyeW79y5MzZu3IhVq1ahffv22Lp1K2JiYhAYGCiW2bFjB0JCQtC3b18AwNChQxESEoLo6GixzLJly/Diiy/i5ZdfRvfu3eHp6Ylt27aJ+2UyGX755RfIZDKEh4fj9ddfx4gRIzB37tw6f2YiU9K0SEskgKOi7m1jLvaatbo5mRqRKZlNS3dWVhYiIiLg5+eH0aNHY+TIkfDx8TF1tcgMaVq6uU431cajdbo5kRrVX/qMqRMnTsTEiRN17jt06FC5bYMGDcKgQYMqPN+oUaMwatSoSq+pUCiwcuVKrFy5ssIyfn5+2L17d6XnIbI0mjW6HW2sINXD7zkN7OS4/SAPGTnsXk5kSmbT0h0TE4O7d+9i/Pjx2Lx5M/z9/fH8889j69atKCriDwp6RKXimG6qPY7ppicBYyqRZcrKKwbwaI3tutJMppbBlm4ikzKbpBsAGjVqhClTpuDMmTM4duwYmjdvjuHDh8Pb2xuTJ0/G1atXTV1FMgMqoSRZ4phuqg2O6aYnBWMqkeVR6mkSNQ3NsmGZXDaMyKTMKunWuHfvHvbv34/9+/dDJpPhhRdewLlz59CmTRssW7asyuNXrlwJf39/KBQKhIWF4fjx45WWz8zMxIQJE+Dl5QUbGxu0bNmSXdbMmDh7Ocd0Uy1wTDc9aeoaU4nIePQ1c7lGA7Z0E5kFs0m6i4qK8NNPP+HFF1+En58ftmzZgvfeew9JSUlYt24dDhw4gP/+979VTpKyefNmTJkyBbNmzcKpU6fQvn17REZGai0rUlZhYSF69eqFxMREbN26FVeuXMG3337L8eRmrJhLhlEdcEw3PQn0FVOJyLj0nXRrlg3L5JJhRCZlNhOpeXl5Qa1WY9iwYTh+/DiCg4PLlXn22Wfh4uJS6XmWLl2KsWPHisuZREdHY9euXVi9ejWmTZtWrvzq1avx4MEDHDlyBNbWJT+Y/P396/pxyIA0Ld3sXk61wTHd9CTQV0wlIuMyVEs3Zy8nMi2zSbqXLVuGQYMGQaFQVFjGxcUFCQkJFe4vLCzEyZMnMX36dHGbVCpFREQE4uLidB6zY8cOhIeHY8KECfj555/RqFEjvPrqq/jwww8hk8l0HlNQUICCggLxvVKprOrjkR6J3culZtNRgyyIZlgCx3RTfaaPmEpExqdJuuu6RreGpqWbs5cTmZbZZC0HDx7UOaNqTk4O3njjjWqdIz09HSqVSlw/VMPDwwPJyck6j7lx4wa2bt0KlUqF3bt345NPPsGSJUvwr3/9q8LrLFiwAM7OzuLL19e3WvUj/dC0UMo4pptqQfOwhmO6qT7TR0wlIuMTk269dS/nmG4ic2A2Sfe6deuQl5dXbnteXh7Wr19vsOuq1Wq4u7tj1apVCA0NxZAhQ/Dxxx8jOjq6wmOmT5+OrKws8XX79m2D1Y/KU2uSbnYvp1qQcUw3PQFMFVOJqG70373cWuu8RGQaJu9erlQqIQgCBEFAdna2Vlc4Teuzu7t7tc7l5uYGmUyGlJQUre0pKSnw9PTUeYyXlxesra21upI/9dRTSE5ORmFhIeRyebljbGxsYGNjU606kX4JgvCopZsTqVEtPOpebuKKEBmAPmMqERmfvpcMc7HVjOlm0k1kSiZPul1cXCCRSCCRSNCyZcty+yUSCebMmVOtc8nlcoSGhiI2NhZRUVEASlqyY2NjMXHiRJ3HdOnSBRs3boRarYa0tNvp33//DS8vL50JN5lW2WG4nL2cakPzvVGxpZvqIX3GVCIyPn23dDuXtnTnFamQX6SCwlr3fEVEZFgmT7oPHjwIQRDw3HPP4aeffkLDhg3FfXK5HH5+fvD29q72+aZMmYKRI0eiY8eO6NSpE5YvX46cnBxxNvMRI0bAx8cHCxYsAACMHz8eK1aswKRJk/DOO+/g6tWrmD9/Pt599139flDSi7KTX0mZdFMtaGa95+zlVB/pO6YSkXHpu6XbSWEFmVQClVqAMq+ISTeRiZg86e7RowcAICEhAU2aNIGkjuN0hwwZgrS0NMycORPJyckIDg7Gnj17xMnVbt26JbZoA4Cvry/27t2LyZMno127dvDx8cGkSZPw4Ycf1qkeZBhlk262dFNtaCZS4+zlVB/pO6YSkXHpu6VbIpHA2dYaD3IKkZFbBHenilc0ICLDMWnSffbsWQQGBkIqlSIrKwvnzp2rsGy7du2qfd6JEydW2J380KFD5baFh4fj6NGj1T4/mY5KeJQocUw31YaMS4ZRPWWomEpExlGkUiOnUAVAf0k3ALiUJt1cq5vIdEyadAcHByM5ORnu7u4IDg6GRCKBIJT/RVgikUClUpmghmRuVCom3VQ3j8Z0M+mm+oUxlciyKcvMMO6o0N+v6Jpx3ZmcwZzIZEyadCckJKBRo0bi34mqUnaZJy4ZRrVRdky3IAjsfkv1BmMqkWXTdC13sLGClUx/q/q6lLaaZ3EGcyKTMWnS7efnp/PvRBXRdC+XSjiRGtVO2bkA1AIg49eI6gnGVCLLpu/x3BoudqXLhuWxezmRqejvMVodrVu3Drt27RLff/DBB3BxcUHnzp1x8+ZNE9aMzImKa3RTHcnKZNnsYk71FWMqkeVR5hcDAJz0nHRrkniu1U1kOmaTdM+fPx+2trYAgLi4OKxYsQILFy6Em5sbJk+ebOLakbkoVjHpprop29LNpJvqK8ZUIsvzqKVbvx1RG4gt3Uy6iUzF5EuGady+fRvNmzcHAMTExOCVV17BuHHj0KVLF/Ts2dO0lSOzoS7tXm4lNZvnRWRhpGXGcJfMEcA1S6n+YUwlsjyG616uaelm93IiUzGbzMXBwQH3798HAOzbtw+9evUCACgUCuTl5ZmyamRGitWPxnQT1QZbuulJwJhKZHmUBk+62dJNZCpm09Ldq1cvvPnmmwgJCcHff/+NF154AQBw4cIF+Pv7m7ZyZDY0SZI+Z/WkJ0vZoQnFTLqpnmJMJbI8hmrp5phuItMzm8xl5cqVCA8PR1paGn766Se4uroCAE6ePIlhw4aZuHZkLjiRGtWVRCIRvz9qJt1UT+kzpq5cuRL+/v5QKBQICwvD8ePHKy2/ZcsWtG7dGgqFAkFBQdi9e7fWfkEQMHPmTHh5ecHW1hYRERG4evWquP/QoUOQSCQ6X3/99RcAIDExUef+o0eP1uizEZkTzZJehpq9PItjuolMxmxaul1cXLBixYpy2+fMmWOC2pC5EpNurq1MdSCTSqBSC2zppnpLXzF18+bNmDJlCqKjoxEWFobly5cjMjISV65cgbu7e7nyR44cwbBhw7BgwQK8+OKL2LhxI6KionDq1CkEBgYCABYuXIgvvvgC69atQ0BAAD755BNERkbi4sWLUCgU6Ny5M+7du6d13k8++QSxsbHo2LGj1vYDBw6gbdu24nvNwwUiS6RJivU9e7mLLcd0E5ma2STdAJCZmYnjx48jNTUVarVa3C6RSDB8+HAT1ozMRTFbukkPNA9tOKab6jN9xNSlS5di7NixGD16NAAgOjoau3btwurVqzFt2rRy5T///HP06dMHU6dOBQDMmzcP+/fvx4oVKxAdHQ1BELB8+XLMmDEDL730EgBg/fr18PDwQExMDIYOHQq5XA5PT0/xnEVFRfj555/xzjvvQPLYA1dXV1etskSWzNATqeUUqlBYrIbcymw6uhI9Mcwm6d65cydee+01PHz4EE5OTlqBlUk3aTwa082km2pPM5kaW7qpvtJHTC0sLMTJkycxffp0cZtUKkVERATi4uJ0HhMXF4cpU6ZobYuMjERMTAwAICEhAcnJyYiIiBD3Ozs7IywsDHFxcRg6dGi5c+7YsQP3798XE/+y+vfvj/z8fLRs2RIffPAB+vfvX+HnKSgoQEFBgfheqVRWWJbIFAzV0u2ksIZEAghCyTUaOdro9fxEVDWzedT1z3/+E2+88QYePnyIzMxMZGRkiK8HDx6YunpkJti9nPRBJmNLN9Vv+oip6enpUKlU8PDw0Nru4eGB5ORkncckJydXWl7zZ03O+f333yMyMhKNGzcWtzk4OGDJkiXYsmULdu3aha5duyIqKgo7duyo8PMsWLAAzs7O4svX17fCskSmoMw3TEu3VCoRz5mVxy7mRKZgNi3dd+/exbvvvgs7OztTV4XMWHFpF0l2L6e60LR0M+mm+qq+xNQ7d+5g7969+O9//6u13c3NTatF/emnn0ZSUhIWLVpUYWv39OnTtY5RKpVMvMmsGKp7OVAyrjsztwgZnMGcyCTMpqU7MjISJ06cMHU1yMxphiUy6aa6kEo03cvVVZQkskz6iKlubm6QyWRISUnR2p6SklLhOGpPT89Ky2v+rO4516xZA1dX10q7jWuEhYXh2rVrFe63sbGBk5OT1ovIXKjUArLziwEYJul2Lp3BnMuGEZmG2bR09+3bF1OnTsXFixcRFBQEa2vtHzjVCbhU/7Glm/SBLd1U3+kjpsrlcoSGhiI2NhZRUVEAALVajdjYWEycOFHnMeHh4YiNjcV7770nbtu/fz/Cw8MBAAEBAfD09ERsbCyCg4MBlLQ4Hzt2DOPHj9c6lyAIWLNmDUaMGFGu/rrEx8fDy8urynJE5ig7/1EybKiWboAzmBOZitkk3WPHjgUAzJ07t9w+iUQClUpl7CqRGRInUmPSTXXAMd1U3+krpk6ZMgUjR45Ex44d0alTJyxfvhw5OTnipGYjRoyAj48PFixYAACYNGkSevTogSVLlqBv377YtGkTTpw4gVWrVonXfu+99/Cvf/0LLVq0EJcM8/b2FhN7jV9//RUJCQl48803y9Vr3bp1kMvlCAkJAQBs27YNq1evxnfffVe9G0RkZjRdy+3kMljL9N8RVTODOdfqJjINs0m61ezmSdWg4pJhpAdW0pJfaJh0U32lr5g6ZMgQpKWlYebMmUhOTkZwcDD27NkjToR269YtSKWPEoTOnTtj48aNmDFjBj766CO0aNECMTEx4hrdAPDBBx8gJycH48aNQ2ZmJrp27Yo9e/ZAoVBoXfv7779H586d0bp1a511mzdvHm7evAkrKyu0bt0amzdvxiuvvKKXz01kbOLM5Qr9t3IDZVu6mXQTmYLZJN1l5efnlwu+RACTbtIPzdeHS4bRk6CuMXXixIkVdic/dOhQuW2DBg3CoEGDKjyfRCLB3LlzdbbCl7Vx48YK940cORIjR46s9HgiS2LISdQAwEUzppuzlxOZhNlMpKZSqTBv3jz4+PjAwcEBN27cAAB88skn+P77701cOzIXxUy6SQ/Y0k31HWMqkWUxfNLNlm4iUzKbpPvf//431q5di4ULF0Iul4vbAwMDOUaLRGpBM6bbbL66ZIE0D23Y0k31FWMqkWVR5pXMXO7EpJuoXjKbzGX9+vVYtWoVXnvtNchkMnF7+/btcfnyZRPWjMxJsaokSZKypZvqwKp0IjU1k26qpxhTiSyLwVu6bdm9nMiUzCbpvnv3Lpo3b15uu1qtRlERn8pRCc5eTvrwaJ1uJt1UPzGmElkWQyfdzmzpJjIps0m627Rpg99//73c9q1bt4pLghCpBI7pprp7tE43V02g+okxlciyGL6lu3TJMCbdRCZhNrOXz5w5EyNHjsTdu3ehVquxbds2XLlyBevXr8cvv/xi6uqRmRAnUpMw6aba45huqu8YU4ksi1JMug3zq7lm9vLsgmIUqdQGWQuciCpmNv/jXnrpJezcuRMHDhyAvb09Zs6ciUuXLmHnzp3o1auXqatHZkKlKmmZlMmYdFPtacZ0c/Zyqq8YU4ksi9jSbWeg7uVlWtA1CT4RGY/ZtHQDQLdu3bB//35TV4PMWOk8ahzTTXWiGdPNpJvqM8ZUIsuhSbqdFIZJumVSCZwUVlDmFyMzrwiuDjYGuQ4R6WY2Ld1NmzbF/fv3y23PzMxE06ZNa3SulStXwt/fHwqFAmFhYTh+/Hi1jtu0aRMkEgmioqJqdD0yHs0YXHYvp7qwYvdyquf0GVOJyPAMPaYbeNTFPDOXM5gTGZvZJN2JiYlQqVTlthcUFODu3bvVPs/mzZsxZcoUzJo1C6dOnUL79u0RGRmJ1NTUKq///vvvo1u3bjWuOxlPae9yLhlGdSIrXeedLd1UX+krphKRcRgn6eYM5kSmYvLu5Tt27BD/vnfvXjg7O4vvVSoVYmNj4e/vX+3zLV26FGPHjsXo0aMBANHR0di1axdWr16NadOm6TxGpVLhtddew5w5c/D7778jMzOzVp+FDE/T0m3NMd1UB2zppvpK3zGViAxPrRagzDd80q05N5NuIuMzedKt6cotkUgwcuRIrX3W1tbw9/fHkiVLqnWuwsJCnDx5EtOnTxe3SaVSREREIC4ursLj5s6dC3d3d4wZM0bnEitkPjRJkpTdy6kONBPxqZl0Uz2jz5hKRMaRXVCM0hVR4WSM7uWcSI3I6EyedKtLWy4DAgLw119/wc3NrdbnSk9Ph0qlgoeHh9Z2Dw8PXL58Wecxf/zxB77//nvEx8dX+zoFBQUoKCgQ3yuVylrVl2pOkyRxIjWqC82cAGzppvpGnzGViIxDM5u4wloKhbXMYNd5tFY3x3QTGZvZjOlOSEgw+i8H2dnZGD58OL799tsaXXvBggVwdnYWX76+vgasJZUlrtMtNZuvLlkgzUMbzXAFovrGFDGViGrHGOO5AaCBZkw3W7qJjM7kLd1lxcbGIjY2FqmpqeLTeo3Vq1dXebybmxtkMhlSUlK0tqekpMDT07Nc+evXryMxMRH9+vUTt2mua2VlhStXrqBZs2bljps+fTqmTJkivlcqlUy8jUQlJt0mrghZNBnHdNMToK4xlYiMw1hJt7M4ezmTbiJjM5uke86cOZg7dy46duwILy8vSGoxZlculyM0NBSxsbHiuDa1Wo3Y2FhMnDixXPnWrVvj3LlzWttmzJiB7OxsfP755xUm0jY2NrCx4fqGpqBiSzfpgRXHdFM9p4+YSkTGYaykW9O9nC3dRMZnNkl3dHQ01q5di+HDh9fpPFOmTMHIkSPRsWNHdOrUCcuXL0dOTo44m/mIESPg4+ODBQsWQKFQIDAwUOt4FxcXACi3ncxDMVu6SQ+kHNNN9Zy+YioRGZ7Rkm5xyTCO6SYyNrNJugsLC9G5c+c6n2fIkCFIS0vDzJkzkZycjODgYOzZs0ecXO3WrVuQspXUYrGlm/Th0ZhuJt1UP+krphKR4WmSbkPOXA5wnW4iUzKbzOXNN9/Exo0b9XKuiRMn4ubNmygoKMCxY8cQFhYm7jt06BDWrl1b4bFr165FTEyMXupB+qcSOHs51Z3moQ1buqm+0mdMJSLDMtqYblvNmG62dBMZm9m0dOfn52PVqlU4cOAA2rVrB2tr7R88S5cuNVHNyJyoVJqWbibdVHsc0031HWMqkeXQtDwbq3u5Mr8YKrXA36WIjMhsku6zZ88iODgYAHD+/HnTVobMlqalm4GC6oJjuqm+Y0wlshxKI0+kprlmA3u5Qa9HRI+YTffygwcPVvoiAh6NwWX3cqoLjumm+k6fMXXlypXw9/eHQqFAWFgYjh8/Xmn5LVu2oHXr1lAoFAgKCsLu3bu19guCgJkzZ8LLywu2traIiIjA1atXtcr4+/tDIpFovT799FOtMmfPnkW3bt2gUCjg6+uLhQsX1uhzEZkLY3Uvt5JJ4WhT0t7GGcyJjMvkLd0DBw6ssoxEIsFPP/1khNqQudO0TEq5/A3VwaN1utVVlCSyLPqOqZs3b8aUKVMQHR2NsLAwLF++HJGRkbhy5Qrc3d3LlT9y5AiGDRuGBQsW4MUXX8TGjRsRFRWFU6dOiauCLFy4EF988QXWrVuHgIAAfPLJJ4iMjMTFixehUCjEc82dOxdjx44V3zs6Oop/VyqV6N27NyIiIhAdHY1z587hjTfegIuLC8aNG1etz0ZkLoyVdAOAs501sguKS8d12xv8ekRUwuRJt7Ozs6mrQBZEMwZXMyaXqDbY0k31lb5j6tKlSzF27Fhx2c3o6Gjs2rULq1evxrRp08qV//zzz9GnTx9MnToVADBv3jzs378fK1asQHR0NARBwPLlyzFjxgy89NJLAID169fDw8MDMTExGDp0qHguR0dHeHp66qzXDz/8gMLCQqxevRpyuRxt27ZFfHw8li5dyqSbLI4m6daMuTYkFztr3MnI4wzmREZm8qR7zZo1pq4CWRBNyyTHdFNdSJl0Uz2lz5haWFiIkydPYvr06eI2qVSKiIgIxMXF6TwmLi4OU6ZM0doWGRkprgqSkJCA5ORkREREiPudnZ0RFhaGuLg4raT7008/xbx589CkSRO8+uqrmDx5MqysrMTrdO/eHXK5XOs6n332GTIyMtCgQYNydSsoKEBBQYH4XqlU1uBuEBmOMVu6XTQzmOdxBnMiYzJ50k1UE+I63exeTnVgJeVEakRVSU9Ph0qlgoeHh9Z2Dw8PXL58WecxycnJOssnJyeL+zXbKioDAO+++y46dOiAhg0b4siRI5g+fTru3bsnzrqenJyMgICAcufQ7NOVdC9YsABz5syp8nMTGZNaLUCZb5x1uoGS7uUA1+omMjYm3WRRxKSbLd1UBzK2dBOZtbKt5e3atYNcLsdbb72FBQsWwMbGplbnnD59utZ5lUolfH1961xXorrILihG6cIsRmrpZtJNZApmM3s5UXUUM+kmPWBLN1HV3NzcIJPJkJKSorU9JSWlwrHWnp6elZbX/FmTcwJAWFgYiouLkZiYWOl1yl7jcTY2NnByctJ6EZmaZrkwhbUUNlYyg1+vgV1J9/Iszl5OZFRMusmisKWb9EHz/VEz6SaqkFwuR2hoKGJjY8VtarUasbGxCA8P13lMeHi4VnkA2L9/v1g+ICAAnp6eWmWUSiWOHTtW4TkBID4+HlKpVJwxPTw8HIcPH0ZR0aPEYf/+/WjVqpXOruVE5sqY47mBR5O1lcxeTkTGwqSbLMqjdbr51aXak5V+f9jSTVS5KVOm4Ntvv8W6detw6dIljB8/Hjk5OeJs5iNGjNCaaG3SpEnYs2cPlixZgsuXL2P27Nk4ceIEJk6cCKBkubL33nsP//rXv7Bjxw6cO3cOI0aMgLe3N6KiogCUTJK2fPlynDlzBjdu3MAPP/yAyZMn4/XXXxcT6ldffRVyuRxjxozBhQsXsHnzZnz++eflJnEjMnfGTro11+E63UTGxTHdZFEetXSbuCJk0bhkGFH1DBkyBGlpaZg5cyaSk5MRHByMPXv2iJOW3bp1C9IyD0E7d+6MjRs3YsaMGfjoo4/QokULxMTEiGt0A8AHH3yAnJwcjBs3DpmZmejatSv27NkjrtFtY2ODTZs2Yfbs2SgoKEBAQAAmT56slVA7Oztj3759mDBhAkJDQ+Hm5oaZM2dyuTCyOJqx1cZr6S7pXp7BMd1ERsWkmyyKStAk3cy6qfZkHNNNVG0TJ04UW6ofd+jQoXLbBg0ahEGDBlV4PolEgrlz52Lu3Lk693fo0AFHjx6tsl7t2rXD77//XmU5InNmqu7lWexeTmRUzFzIojzqXs4x3VR7VjJNS7faxDUhIqInmSbpNsZyYUCZ2cvZvZzIqJh0k0UpVpUk3VIm3VQHUgm7lxMRkekZfUy3pqU7r4iTiRIZEZNusihqgS3dVHcc001ERObA6N3LbUvGdAsCkJ1fbJRrEhGTbrIwXKeb9IFjuomIyBxolu5qaC83yvXkVlLYy0vWA8/M47huImNh0k0Whet0kz48GtPNpJuIiEznQU5J4quZVdwYNNfK5AzmREbDpJssCpNu0geO6SYiInOgSXwbGjHp5lrdRMbHpJssiph0S5h0U+1ZlS45x6SbiIhM6UGupqXbOGO6y14rk8uGERkNk26yKMWlSzyxpZvqgmO6iYjI1ARBMPqYbqBs0s2WbiJjYdJNFkVVuqyyZkwuUW1wTDcREZnaw4JiFJUuhdrAqN3LOaabyNiYdJNFUWlautm9nOpAM6Zb03OCiIjI2DRJr8JaCtvSGcWNoYGmpZuzlxMZDZNusiicSI30QbNON3NuIiIyFc3M5cZs5QYedS/PYks3kdEw6SaLokm6NRNhEdXGozHdzLqJiMg0MnJNlHRrupdz9nIio2HmQhZFM/EVc26qC47pJiIiUxOTbnvjzVwOAM6cvZzI6Ji6kEVRC2zpprqTSTh7ORERmVZGTklLs/Fbujl7OZGxMXMhi1LMMd2kB5rvD1u6iYjIVDJN1b3cjt3LiYytXibdK1euhL+/PxQKBcLCwnD8+PEKy3777bfo1q0bGjRogAYNGiAiIqLS8mQ6arWA0oZuJt1UJ5qeEky6iYjIVB6I3ctNM5FaZm4h1IyDREZR75LuzZs3Y8qUKZg1axZOnTqF9u3bIzIyEqmpqTrLHzp0CMOGDcPBgwcRFxcHX19f9O7dG3fv3jVyzakqKuFRYGDSTXUhk7F7ORERmVZGrqZ7uZHHdJd2L1cLwMPCYqNem+hJVe+S7qVLl2Ls2LEYPXo02rRpg+joaNjZ2WH16tU6y//www/4xz/+geDgYLRu3Rrfffcd1Go1YmNjjVxzqkrZVkkm3VQXmjHdbOkmIiJTyShdMqyhkVu6FdYy2FqXrAvOZcOIjKNeJd2FhYU4efIkIiIixG1SqRQRERGIi4ur1jlyc3NRVFSEhg0bVlimoKAASqVS60WGV7ZV0opJN9VB2THdgsDEm4iIjO9RS7dxk26gbBdzJt1ExlCvku709HSoVCp4eHhobffw8EBycnK1zvHhhx/C29tbK3F/3IIFC+Ds7Cy+fH1961Rvqp5i1aM1lZl0U12U/f6wsZuIiExB09JtiqRb08U8M4/LhhEZQ71Kuuvq008/xaZNm7B9+3YoFIoKy02fPh1ZWVni6/bt20as5ZOrSFWSHUkk7F5OdaMZ0w0AxWp1JSWJiIj0TxAEPNB0L3cwXUt3Blu6iYyiXiXdbm5ukMlkSElJ0dqekpICT0/PSo9dvHgxPv30U+zbtw/t2rWrtKyNjQ2cnJy0XmR4RaUt3dZSKSQSJt1Ue2Vbujmum6hyNVkRBAC2bNmC1q1bQ6FQICgoCLt379baLwgCZs6cCS8vL9ja2iIiIgJXr14V9ycmJmLMmDEICAiAra0tmjVrhlmzZqGwsFCrjEQiKfc6evSofj88kYEo84pRWPp7jZspkm7bkmtm5bKlm8gY6lXSLZfLERoaqjUJmmZStPDw8AqPW7hwIebNm4c9e/agY8eOxqgq1UJxaUu3tYwJN9WNVFK2pZtJN1FFaroiyJEjRzBs2DCMGTMGp0+fRlRUFKKionD+/HmxzMKFC/HFF18gOjoax44dg729PSIjI5Gfnw8AuHz5MtRqNb755htcuHABy5YtQ3R0ND766KNy1ztw4ADu3bsnvkJDQw1zI4j0LO1hyffdSWEFGyuZ0a/PMd1ExlWvkm4AmDJlCr799lusW7cOly5dwvjx45GTk4PRo0cDAEaMGIHp06eL5T/77DN88sknWL16Nfz9/ZGcnIzk5GQ8fPjQVB+BKqB5Imwlq3dfWzIyrTHdTLqJKlTTFUE+//xz9OnTB1OnTsVTTz2FefPmoUOHDlixYgWAklbu5cuXY8aMGXjppZfQrl07rF+/HklJSYiJiQEA9OnTB2vWrEHv3r3RtGlT9O/fH++//z62bdtW7nqurq7w9PQUX9bWxl16iai20rJLWpjdHG1Mcn3N2uD3c9jSTWQM9S57GTJkCBYvXoyZM2ciODgY8fHx2LNnjzi52q1bt3Dv3j2x/Ndff43CwkK88sor8PLyEl+LFy821UegCmjG3loz6aY6KjsnAFu6iXSrzYogcXFx5SYijYyMFMsnJCQgOTlZq4yzszPCwsIqXWUkKytL56oi/fv3h7u7O7p27YodO3ZU+nm48giZk/SHBQCARg6mSbo1100rrQcRGZaVqStgCBMnTsTEiRN17jt06JDW+8TERMNXiPSiqJjdy0k/JBIJZFIJVGqBY7qJKlDZiiCXL1/WeUxycnKlK4ho/qzJKiPXrl3Dl19+qfUw3MHBAUuWLEGXLl0glUrx008/ISoqCjExMejfv7/O8yxYsABz5syp5BMTGU9adkmya6qWbnen0qRbyaSbyBjqZdJN9VORWtO9nEk31Z1MIoEKAlu6iczY3bt30adPHwwaNAhjx44Vt7u5uWHKlCni+6effhpJSUlYtGhRhUn39OnTtY5RKpVc8pNMxtQt3e6OJav0pGbnm+T6RE8a9tMli1FUzO7lpD+aLuYc002kW21WBPH09Ky0vObP6pwzKSkJzz77LDp37oxVq1ZVWd+wsDBcu3atwv1ceYTMiSbpNsXM5QDgXtrCnprNlm4iY2D2QhZD0yJpLeXXlupOM5kaW7qJdKvNiiDh4eFa5QFg//79YvmAgAB4enpqlVEqlTh27JjWOe/evYuePXsiNDQUa9asgbQaP/fj4+Ph5eVVo89IVB15hSrE387E3gvJOHQlVewaXhfpD0smMGtkou7lmuvmFqrwsKDYJHUgepKwezlZDM3s5dZW7F5OdScrHaagKh22QETlTZkyBSNHjkTHjh3RqVMnLF++vNyKID4+PliwYAEAYNKkSejRoweWLFmCvn37YtOmTThx4oTYUi2RSPDee+/hX//6F1q0aIGAgAB88skn8Pb2RlRUFIBHCbefnx8WL16MtLQ0sT6a1vB169ZBLpcjJCQEALBt2zasXr0a3333nbFuDdVzyvwi7LuQgp1nkvDntXStB7QSCRDxlAdmvtgGvg3tanV+cUy3ibqX29tYwV4uQ06hCqnKfDg0cqjzOdMfFiAtuwDWMikaN7CFwtr4S6ERmSsm3WQxNOt0W7Glm/RAJmFLN1FVhgwZgrS0NMycORPJyckIDg4utyJI2Vbozp07Y+PGjZgxYwY++ugjtGjRAjExMQgMDBTLfPDBB8jJycG4ceOQmZmJrl27Ys+ePVAoSsaY7t+/H9euXcO1a9fQuHFjrfoIwqP/r/PmzcPNmzdhZWWF1q1bY/PmzXjllVcMeTuonsstLEbspVTsPJOEQ1fSxIf9AOBqL4dvQzs8LCjGtdSH2H8xBUdv3Me6NzqhQ5MGNb7Wo+7lpkm6AcDdSYGE9BykZhegaS2TbpVawIajN7HuSCJupOeI26USoGkjB3Rp5orOzd0Q3swVTgou6UdPLolQNoJRrSiVSjg7OyMrK4tjxAxo97l7+McPp9DJvyH++7buro1E1dXp3weQml2AX97pikAfZ1NXh4ixxMh4vwkAMnIK8ef1dOw5n4zYS6nIK1KJ+5q7O6B/e2/0beeFpm72kJQ+rL2Wmo33t5xF/O1MOCqs8Ms7XeHnal/ta6rUAlrN+B+K1QKOTHsO3i62ev9c1TH4mzgcT3iAL4aFoH977xofn5VbhDfX/4W/EjMAlPQAcLWXI79IXa7LulQCtPd1QbfmbujaohFCmrhwjh6qF6obS9jSTRajSMXZy0l/NGO61XzuSET0RCgoVuF6ag4uJGXhQpISJ29m4HxSFsqGgSYN7fBiOy/0a++N1p6OYqJdVnN3R2wcG4bXvjuG07cy8c6Pp7H9H13ECTqrcv9hAYrVAqSSRxOamYI4mZqy5jOY5xWqMHz1MZy9kwUHGytMjWyFl0Mbw8HGCoIgIC27AKduZeLPa+n481o6bqTn4PStTJy+lYkvfr0Ge7kMof4N0dbbCW29ndDGywmNG9hBbsVEnOonJt1kMTTdy/lklPRBM6a7SMUx3URE5k4QBNx6kIv7OSUTkKnVAtRCSauxWhCQVzohWHZBMR7mFyOnoBjZ+UVIe1iAu5n5SMrMq3ACtJYeDujRshFebOeNdo2ddSbaj7OTW+Gr1zqg97LDOHsnC5v/uo1Xw5pU67MkZZUkuR5OCliZ8HcazbJhtZkYbu4vF3H2ThYa2suxcWwYWns+auGTSCRwd1KgT6An+gSWzMNwNzMPf15Nx++lSfiDnEIc/jsNh/9OK3Mc4OmkQOMGtmjcwA7eLgr4uJT82cbbSawvkSVi0k0WQ5McWbOlm/TAxqpkgpeCYibdRETm7P7DAozfcArHEx/U+VyOCqvS1lVnBPk4I7yZKzycapfMeTnbYnJES8z95SKW7LuCASE+sJVXPXnYvcy80uNNm0S6O9Vu2bAj19Px4/FbkEiAL4eFaCXcFfFxscXgp30x+GlfqNUCLt5TIv52Ji4kKXExKQtXUrKRX6TGvax83MvKF7usa1hJJRjfsxmm9GpZrYciROaGSTdZjCI1W7pJf2xKu7Ax6SYiMm/Ttp3D8cQHsJJK4FmaqMqkEsgkEkilEkglgK3cCo42VnCwsYKDouRPR4UVGtjJ4dPAFj4utvB2sUUDO2u9Jm3Dw/2w5kgCbj/Iw5aTtzEi3L/KY+6VtnR7OZtmLLeGR2nSnZxV/e7larWAf++6BAB4PcwPXZq71fi6UqkEgT7OWvOpCIKA9IeFuJORi9sZeUjKzMPd0j8T7+fgeloOvvz1Gpo1ckBUiE+Nr0lkaky6yWIUFWvGdDPpproTk+4iJt1ERObqako29l9MgUwqQcyELmY38aW1TIpx3Zrik58vYNXhG3i1U5Mqf0+5l2UeLd0+LiXLnd0tbXmvjt3n7+FCkhKONlZ4L6KF3uoikUjQyNEGjRxtEKJjNvhl+//G57FXEf3bdbwU7M3WbrI4zF7IYhSXrqdsXc2JSogqo1k/tKBYVUVJIiIyla0n7wAAnmvtbnYJt8agjr5oaC/HnYw8/FZmjHJFNC3dniZOuhs3KGlpv5eVB1U1l8/87vcEAMDorgFwNeJyZ290DYBcJsXl5GytpcmILAWTbrIYRZxIjfSI3cuJiMybIAj45ew9AMDLHcy3S7HCWoaBpV2e/3vidpXlNUm3qZYK0/BwUsBKKkGRSkBqdtVdzE/ezED87UzIZVIMf8bPCDV8xNnWGsFNXAAAxxPqPrafyNiYvZDF0CRHXE6C9IETqRERmU5uYTH+uJqOi0lKqCtoZb2QpMTdzDzYWsvQs5W7kWtYM4Of9gUAxF5KrXI28NsPcgGUTC5mSjKpBF4uJa3tdzKq7mK+Pi4RANA/2BuNTLDU2dP+Jd3Oz9zONPq1ieqKY7rJYhQUlXQDVlgz6aa6s7HWjOlm93IiImM6eycTY9efQIqyJDn1cLLB/z3lgV5PeSC8mas4/Od/50tauXu0bCRuM1ctPRwR7OuC+NuZiDl9F2O7N9VZLrewWJwt3N/V3phV1Kmxix1uPyiZtOxp/4rLPSwoxt4LyQCA16q5NJq+tSqdJf1q6kOTXJ+oLph0k8XIF5Nu8w68ZBnYvZyIyPge5BTijbUnkP6wAA3t5cgvUiFFWYCNx25h47FbcFRYYWS4P0KauODH4yVdtV9s72XiWlfPK6GNEX87E9sqSbpvlbZyu9hZw9nO2pjV00kzrvtORm6l5f537h7yi9Ro6maPYF8XI9SsvJYeDgCAv1OyIQgCJ1Mji8KkmyxGHpNu0iN2LyciMr61RxKR/rAALdwdsH1CF1hJJTh64z5iL6XiwKUU3MvKx4qD18Tyzd0d0KetpwlrXH0vtvPC3J0XcemeEpeTlTrXr05ML0lu/cyglRsAGjcomcG8qu7l20/fBQAMCPExWbIb4GYPmVSC7PxipCgLTD4RHVFNsJ8uWYz80qWdmHSTPjxq6Wb3ciIiYxAEAf/9q6T1elJECzjYWEFROl57XlQg/vzwOUS/3gHPtXZHs0b26NXGA6tHPm0xS4W62MnxbOtGAIDtp+7qLHPzfsnM2/6udkarV2U0Ld23K2npvpeVh7gb9wHApGtk21jJ4New5L5dT2MXc7IsbOkmi5HPMd2kR4/GdLOlm4jIGK6nPUSyMh82VlJEPOVRbr9UKkGfQC/0CbSM7uS6DAjxwd4LKYiJv4sP+rSG7LFlTjXJorm0dDdtVFKPa5WMk955JgmCAHTybwjfhqZ9WNC0kT1upOfgRtpDdGnuZtK6ENUEsxeyGPml3YAVVmzpprpTWHGdbiIiY4q7UbLUU0f/BvW219qzrd3hbGuNFGUB4q7fL7f/cnI2AOApT0djV02n5u4l46RTlAXIyivSWWbnmZIJ7foHexutXhVp1qikvtfTuFY3WRYm3WQxNC3dtvL6GajJuNjSTURkXBfuZgEAOjRpYOKaGI6NlQx925W01G87fUdrX7FKjSuapNur/HhvU3BUWMOrdGy0rtbuxPQcnLubBZlUgucDTT+2XtMyz+7lZGmYdJPFyCssTbrr6dNxMi7N9yi3kC3dRETG8HdKScLZwsM8WnkNZWDpuOc955ORW1gsbk+8n4uCYjXs5DI0MXE37bI0rd1XS/99yvrlbBIAoHMzV7g6GH9t7sc1LW3pvsGWbrIwTLrJYijzS7o9OSo4FQHVnZNtyVItFXWnIyIi/REEQVxfWbP0U30V6tcATRraIbdQhX0XUsTtZ25nAgBaezpCKjWf5a5alj4E+TulfOvxL2dLupb3a2f6ruXAo+7ldzPzxMYYIkvApJssRnZ+ydNiR4Xp17Uky+fMpJuIyGhSlAXIzi+GTCpBgJt5TCJmKBKJRJzle9vpR7OYH0soGeP9dEBDk9SrIm1Ku7qfuZOptf1qSjYuJ2fDWiZBpJks29bQXg5XezkA4OK9kuEKRSo1/hOXiJdW/IF2s/ei62e/YkbMOdx+UPna40TGxKSbLIIgCFCWJkdOtmzpprpzsSsJ2ky6iYgMT9O13M/VDjZPwISoA0qT7j+upiFVmQ9BEPDntZKk+5kAV1NWrZxQv5Ix9ufuZInz5wCP1ubu1qIRnO3Mp8HjmaYl92/fxRQcvJKKFz7/HZ/8fAFn7mRBmV+MOxl52HD0FiKW/oZvD9+ASi2YuMZETLrJQuQXqVFc+kOTLd2kD2zpJiIyHk3S3dK9fo/n1ghws0dIExeoBWDLyTs4fTsTdzPzYCeXiUmjufBztYOrvRyFKjXO333UevzfEyUTwQ0KbWzK6pXzfFBJq/s3v93A6DV/4WrqQzS0l2NWvzbYP7k71o5+Gs80bYiCYjX+vfsS3v3xNIpVnDSVTItJN1mE+zkFAABrmQT2nL2c9MClNOl+WFCMIgZjIiKD0sza3cpMlsoyhtfC/AAAKw9ew0fbzgEAItt6mt0qLBKJBGFNS7q8H7qSBgA4cDEF6Q8L4OZgg4g25ddUN6UXAr3Qv33JGHNHhRXe6BKAg//sidFdAtDCwxE9W7njx7HPYP6AIFjLJNh17h7W/Jlo2krTE69eJt0rV66Ev78/FAoFwsLCcPz48UrLb9myBa1bt4ZCoUBQUBB2795tpJpSdaUo8wEAns4KSCTmM/kIWS4nW2tovkqZuWztJqqIvmOqIAiYOXMmvLy8YGtri4iICFy9elWrzIMHD/Daa6/ByckJLi4uGDNmDB4+1J7k6ezZs+jWrRsUCgV8fX2xcOFC/XxgMogrKU9e0j0wxAcd/Rogt1CFy8nZsJJK8HaPZqaulk6aMdu/nE2CSi1gxcFrAIAhTzeGtcy80gWpVIIvhoXg3OzeOPVJL8zs16Zc93eJRIJXw5pg7kuBAIAvYq8iM7fQFNUlAlAPk+7NmzdjypQpmDVrFk6dOoX27dsjMjISqampOssfOXIEw4YNw5gxY3D69GlERUUhKioK58+fN3LNqTJ3M0uTbieFiWtC9YVMKoFX6ffpFidbIdLJEDF14cKF+OKLLxAdHY1jx47B3t4ekZGRyM/PF8u89tpruHDhAvbv349ffvkFhw8fxrhx48T9SqUSvXv3hp+fH06ePIlFixZh9uzZWLVqleFuBtVaQbHqiWzplkol+HZER/QN8kKQjzNWvNrBbD///z3lAUeFFRLv5+KV6CO4kKSEg40V3uza1NRVq5CjwrrKBwJDOvqitacjsguK8e3vN4xUM6LyJIIg1KvZBcLCwvD0009jxYoVAAC1Wg1fX1+88847mDZtWrnyQ4YMQU5ODn755Rdx2zPPPIPg4GBER0dX65pKpRLOzs7IysqCk5OTfj4Iaflsz2V8feg6hj7ti09fbmfq6lA98dp3R/HntfuYPyAIr4Y1MXV16AlnjrFE3zFVEAR4e3vjn//8J95//30AQFZWFjw8PLB27VoMHToUly5dQps2bfDXX3+hY8eOAIA9e/bghRdewJ07d+Dt7Y2vv/4aH3/8MZKTkyGXl0yKOG3aNMTExODy5cvV+mzmeL/rqyPX0vHqd8fg5mCDvz7+P/ZYM1MrD17Dor1XxPfzXmqL4eH+pquQnuy9kIy3/nMSdnIZfv/gWbNYb5zqj+rGkno1DXRhYSFOnjyJ6dOni9ukUikiIiIQFxen85i4uDhMmTJFa1tkZCRiYmIMWdVyUrPzEVM6S2TZxyBln4hobxd0bi+r7POUupxT6/Rlz1mH81RUXtdfBUHA9tNJAIAOpTNsEulDR7+G+PPafXz7+w1k5BZCVrpuqubXQc3vhRLwF0SqXL/23vB0rl89cQwRUxMSEpCcnIyIiAhxv7OzM8LCwhAXF4ehQ4ciLi4OLi4uYsINABEREZBKpTh27BgGDBiAuLg4dO/eXUy4Ndf57LPPkJGRgQYNjBcr9pxPxq0HOQBqFvPMNXbrI26XlH/07ver6QCAZ1s1YsJtxt7u0QwFRSrE3biPvkFeeP0ZP1NXSS96t/FAkI8zzt3Nwrj/nETPlo1gJZPCzHrN6wV/X6kZnwa2eCHIyyjXqldJd3p6OlQqFTw8tCd88PDwqPDJd3Jyss7yycnJFV6noKAABQUF4nulUlmHWpdIySrA/N3Vezr/pPJyVhjtPwY9GYZ28sWaPxOQkJ6j9XSfqKY6+LnUu6TbEDFV82dVZdzd3bX2W1lZoWHDhlplAgICyp1Ds09X0m2I2A0AW07cRuxl3d3t6REHGyu83dM8xzNTCZlUgim9W5m6GnonkUjwcd+nMOL74zh5MwMnb2aYukpkJrq1cGPSbc4WLFiAOXPm6PWcLnbWGNjBR3xf9klV2YfCZZ9faW+voLzWAy89nVOrvO4najU/j+5zav5mJ7fC4Kcbw8GGX1nSHy9nW2yf0AVbT95BWnaB2JJTUSsOUUUa2rO7orkzROwGgPBmrlqTONUk5plr7NZH3C77xsZKhv7tvdGskYPO6xIZ2jNNXbHzna7YeSYJ93MKUVisRj0bYUu10NKIcyzUqwzGzc0NMpkMKSkpWttTUlLg6emp8xhPT88alQeA6dOna3WfUyqV8PX1rUPNAd+Gdlg6OLhO5yCimmvWyAEf9mlt6moQmR1DxFTNnykpKfDy8tIqExwcLJZ5fKK24uJiPHjwQOs8uq5T9hqPM0TsBoA3u5nvRFNE9EgrT0e08qx/LflkGerVaAa5XI7Q0FDExsaK29RqNWJjYxEeHq7zmPDwcK3yALB///4KywOAjY0NnJyctF5ERET1iSFiakBAADw9PbXKKJVKHDt2TCwTHh6OzMxMnDx5Uizz66+/Qq1WIywsTCxz+PBhFBUVaV2nVatWFY7nZuwmIiKTEeqZTZs2CTY2NsLatWuFixcvCuPGjRNcXFyE5ORkQRAEYfjw4cK0adPE8n/++adgZWUlLF68WLh06ZIwa9YswdraWjh37ly1r5mVlSUAELKysvT+eYiI6MlgjrHEEDH1008/FVxcXISff/5ZOHv2rPDSSy8JAQEBQl5enlimT58+QkhIiHDs2DHhjz/+EFq0aCEMGzZM3J+ZmSl4eHgIw4cPF86fPy9s2rRJsLOzE7755ptqfzZzvN9ERGRZqhtL6lX3cqBkuZK0tDTMnDkTycnJCA4Oxp49e8QJVm7dugWp9FEDf+fOnbFx40bMmDEDH330EVq0aIGYmBgEBgaa6iMQERGZBUPE1A8++AA5OTkYN24cMjMz0bVrV+zZswcKxaOJ6H744QdMnDgR//d//wepVIqXX34ZX3zxhbjf2dkZ+/btw4QJExAaGgo3NzfMnDlTay1vIiIic1Hv1uk2Ba71SUREdcVYYly830REVFfVjSX1akw3ERERERERkTlh0k1ERERERERkIPVuTLcpaHroK5VKE9eEiIgslSaGcNSXcTB2ExFRXVU3djPp1oPs7GwA0Mt6n0RE9GTLzs6Gs7OzqatR7zF2ExGRvlQVuzmRmh6o1WokJSXB0dEREomk1udRKpXw9fXF7du3OalLBXiPqsZ7VD28T1XjPaqaPu+RIAjIzs6Gt7e31ozgZBiM3cbF+1Q13qOq8R5VD+9T1fR1j6obu9nSrQdSqRSNGzfW2/mcnJz4H6QKvEdV4z2qHt6nqvEeVU1f94gt3MbD2G0avE9V4z2qGu9R9fA+VU0f96g6sZuP0omIiIiIiIgMhEk3ERERERERkYEw6TYjNjY2mDVrFmxsbExdFbPFe1Q13qPq4X2qGu9R1XiPiN+B6uF9qhrvUdV4j6qH96lqxr5HnEiNiIiIiIiIyEDY0k1ERERERERkIEy6iYiIiIiIiAyESTcRERERERGRgTDpJiIiIiIiIjIQJt1GtnLlSvj7+0OhUCAsLAzHjx+vtPyWLVvQunVrKBQKBAUFYffu3UaqqenU5B6tXbsWEolE66VQKIxYW+M7fPgw+vXrB29vb0gkEsTExFR5zKFDh9ChQwfY2NigefPmWLt2rcHraUo1vUeHDh0q9z2SSCRITk42ToVNYMGCBXj66afh6OgId3d3REVF4cqVK1Ue9yT9TKrNPXoSfyY9CRi7q4fxu3KM31Vj/K4a43fVzDF+M+k2os2bN2PKlCmYNWsWTp06hfbt2yMyMhKpqak6yx85cgTDhg3DmDFjcPr0aURFRSEqKgrnz583cs2Np6b3CACcnJxw79498XXz5k0j1tj4cnJy0L59e6xcubJa5RMSEtC3b188++yziI+Px3vvvYc333wTe/fuNXBNTaem90jjypUrWt8ld3d3A9XQ9H777TdMmDABR48exf79+1FUVITevXsjJyenwmOetJ9JtblHwJP3M6m+Y+yuHsbvqjF+V43xu2qM31Uzy/gtkNF06tRJmDBhgvhepVIJ3t7ewoIFC3SWHzx4sNC3b1+tbWFhYcJbb71l0HqaUk3v0Zo1awRnZ2cj1c78ABC2b99eaZkPPvhAaNu2rda2IUOGCJGRkQasmfmozj06ePCgAEDIyMgwSp3MUWpqqgBA+O233yos8yT+TCqrOvfoSf+ZVB8xdlcP43fNMH5XjfG7ehi/q2YO8Zst3UZSWFiIkydPIiIiQtwmlUoRERGBuLg4ncfExcVplQeAyMjICstbutrcIwB4+PAh/Pz84Ovri5deegkXLlwwRnUtxpP2PaqL4OBgeHl5oVevXvjzzz9NXR2jysrKAgA0bNiwwjJP+nepOvcI4M+k+oSxu3oYvw3jSfwu1RbjN+N3ZcwhfjPpNpL09HSoVCp4eHhobffw8Khw3ElycnKNylu62tyjVq1aYfXq1fj555+xYcMGqNVqdO7cGXfu3DFGlS1CRd8jpVKJvLw8E9XKvHh5eSE6Oho//fQTfvrpJ/j6+qJnz544deqUqatmFGq1Gu+99x66dOmCwMDACss9aT+TyqruPeLPpPqFsbt6GL8Ng/G7aozfjN9VMZf4baWXsxCZSHh4OMLDw8X3nTt3xlNPPYVvvvkG8+bNM2HNyJK0atUKrVq1Et937twZ169fx7Jly/Cf//zHhDUzjgkTJuD8+fP4448/TF0Vs1Xde8SfSUTVw/8rpA+M34zfVTGX+M2WbiNxc3ODTCZDSkqK1vaUlBR4enrqPMbT07NG5S1dbe7R46ytrRESEoJr164ZoooWqaLvkZOTE2xtbU1UK/PXqVOnJ+J7NHHiRPzyyy84ePAgGjduXGnZJ+1nkkZN7tHj+DPJsjF2Vw/jt2EwftcO43d5T+LPJcC84jeTbiORy+UIDQ1FbGysuE2tViM2NlbrqUpZ4eHhWuUBYP/+/RWWt3S1uUePU6lUOHfuHLy8vAxVTYvzpH2P9CU+Pr5ef48EQcDEiROxfft2/PrrrwgICKjymCftu1Sbe/Q4/kyybIzd1cP4bRhP4ndJHxi/y3vSvktmGb8NNkUblbNp0ybBxsZGWLt2rXDx4kVh3LhxgouLi5CcnCwIgiAMHz5cmDZtmlj+zz//FKysrITFixcLly5dEmbNmiVYW1sL586dM9VHMLia3qM5c+YIe/fuFa5fvy6cPHlSGDp0qKBQKIQLFy6Y6iMYXHZ2tnD69Gnh9OnTAgBh6dKlwunTp4WbN28KgiAI06ZNE4YPHy6Wv3HjhmBnZydMnTpVuHTpkrBy5UpBJpMJe/bsMdVHMLia3qNly5YJMTExwtWrV4Vz584JkyZNEqRSqXDgwAFTfQSDGz9+vODs7CwcOnRIuHfvnvjKzc0VyzzpP5Nqc4+exJ9J9R1jd/UwfleN8btqjN9VY/yumjnGbybdRvbll18KTZo0EeRyudCpUyfh6NGj4r4ePXoII0eO1Cr/3//+V2jZsqUgl8uFtm3bCrt27TJyjY2vJvfovffeE8t6eHgIL7zwgnDq1CkT1Np4NMtjPP7S3JeRI0cKPXr0KHdMcHCwIJfLhaZNmwpr1qwxer2Nqab36LPPPhOaNWsmKBQKoWHDhkLPnj2FX3/91TSVNxJd9weA1nfjSf+ZVJt79CT+THoSMHZXD+N35Ri/q8b4XTXG76qZY/yWlFaMiIiIiIiIiPSMY7qJiIiIiIiIDIRJNxEREREREZGBMOkmIiIiIiIiMhAm3UREREREREQGwqSbiIiIiIiIyECYdBMREREREREZCJNuIiIiIiIiIgNh0k1ERPXO4cOH0a9fP3h7e0MikSAmJsbk19u2bRt69+4NV1dXSCQSxMfHG7RORERElqa+xm8m3URUoVGjRiEqKspk1x8+fDjmz59frbJDhw7FkiVLDFwjshQ5OTlo3749Vq5caTbXy8nJQdeuXfHZZ58ZpU5E9GRi7CZLVm/jt0BETyQAlb5mzZolZGZmChkZGSapX3x8vNCwYUMhOzu7WuXPnTsnNGjQQMjMzDRwzcjSABC2b9+utS0/P1/45z//KXh7ewt2dnZCp06dhIMHDxrsemUlJCQIAITTp0/r5XpE9ORg7KYnSX2K31a1T9eJyJLdu3dP/PvmzZsxc+ZMXLlyRdzm4OAABwcHU1QNAPDll19i0KBB1a5DYGAgmjVrhg0bNmDChAkGrh1ZuokTJ+LixYvYtGkTvL29sX37dvTp0wfnzp1DixYtTF09IiKdGLvpSWep8Zvdy4meUJ6enuLL2dkZEolEa5uDg0O5Lmo9e/bEO++8g/feew8NGjSAh4cHvv32W+Tk5GD06NFwdHRE8+bN8b///U/rWufPn8fzzz8PBwcHeHh4YPjw4UhPT6+wbiqVClu3bkW/fv20tn/11Vdo0aIFFAoFPDw88Morr2jt79evHzZt2lT3m0P12q1bt7BmzRps2bIF3bp1Q7NmzfD++++ja9euWLNmjamrR0RUIcZuepJZcvxm0k1ENbJu3Tq4ubnh+PHjeOeddzB+/HgMGjQInTt3xqlTp9C7d28MHz4cubm5AIDMzEw899xzCAkJwYkTJ7Bnzx6kpKRg8ODBFV7j7NmzyMrKQseOHcVtJ06cwLvvvou5c+fiypUr2LNnD7p37651XKdOnXD8+HEUFBQY5sNTvXDu3DmoVCq0bNlSbBVycHDAb7/9huvXrwMALl++DIlEUulr2rRpJv4kRETVw9hN9YElx292LyeiGmnfvj1mzJgBAJg+fTo+/fRTuLm5YezYsQCAmTNn4uuvv8bZs2fxzDPPYMWKFQgJCdGaVGX16tXw9fXF33//jZYtW5a7xs2bNyGTyeDu7i5uu3XrFuzt7fHiiy/C0dERfn5+CAkJ0TrO29sbhYWFSE5Ohp+fnyE+PtUDDx8+hEwmw8mTJyGTybT2abpENm3aFJcuXar0PK6urgarIxGRPjF2U31gyfGbSTcR1Ui7du3Ev8tkMri6uiIoKEjc5uHhAQBITU0FAJw5cwYHDx7UOb7r+vXrOgN3Xl4ebGxsIJFIxG29evWCn58fmjZtij59+qBPnz4YMGAA7OzsxDK2trYAID6pJ9IlJCQEKpUKqamp6Natm84ycrkcrVu3NnLNiIgMg7Gb6gNLjt9MuomoRqytrbXeSyQSrW2aYKtWqwGUPJXs16+fzmUWvLy8dF7Dzc0Nubm5KCwshFwuBwA4Ojri1KlTOHToEPbt24eZM2di9uzZ+Ouvv+Di4gIAePDgAQCgUaNGdfuQZPEePnyIa9euie8TEhIQHx+Phg0bomXLlnjttdcwYsQILFmyBCEhIUhLS0NsbCzatWuHvn376vV6TZo0AVDy/bx16xaSkpIAQJz8SDMWk4jIUBi7yVLU2/hd+0nViai+WLNmjeDs7Fxu+8iRI4WXXnpJfN+jRw9h0qRJWmX8/PyEZcuWaW1DmSUXPvroI6FVq1ZCUVFRteuTmppa5ZIMDx8+FKysrISffvpJ3Pbdd98JjRs3rvZ1qP46ePCgzuV0Ro4cKQiCIBQWFgozZ84U/P39BWtra8HLy0sYMGCAcPbsWYNcTxBK/p/pKjNr1qy6f2AieuIwdlN9VF/jN1u6icigJkyYgG+//RbDhg3DBx98gIYNG+LatWvYtGkTvvvuu3JjcoCSp90dOnTAH3/8geDgYADAL7/8ghs3bqB79+5o0KABdu/eDbVajVatWonH/f777+jdu7exPhqZsZ49e0IQhAr3W1tbY86cOZgzZ45RrgcAo0aNwqhRo/RyPSIiQ2LsJlOpr/Gbs5cTkUF5e3vjzz//hEqlQu/evREUFIT33nsPLi4ukEor/hH05ptv4ocffhDfu7i4YNu2bXjuuefw1FNPITo6Gj/++CPatm0LAMjPz0dMTIw4KQwRERHVDmM3kX5JhKpSeyIiE8jLy0OrVq2wefNmhIeHV1n+66+/xvbt27Fv3z4j1I6IiIgex9hNpBtbuonILNna2mL9+vVIT0+vVnlra2t8+eWXBq4VERERVYSxm0g3tnQTERERERERGQhbuomIiIiIiIgMhEk3ERERERERkYEw6SYiIiIiIiIyECbdRERERERERAbCpJuIiIiIiIjIQJh0ExERERERERkIk24iIiIiIiIiA2HSTURERERERGQgTLqJiIiIiIiIDIRJNxEREREREZGBMOkmIiIiIiIiMhAm3UREREREREQGwqSbiIiIiIiIyECYdBMREREREREZCJNuIjKonj17omfPnqauhkgikWD27NmmrgYREZFFSkxMhEQiwdq1a01dFQDA2rVrIZFIkJiYaOqqEFWISTeRCWgCxIkTJ0xdFQBAbm4uZs+ejUOHDlX7mMTERIwePRrNmjWDQqGAp6cnunfvjlmzZhmuomZo8+bNeP3119GiRQtIJBKzesBARES1c+HCBbz++uvw8fGBjY0NvL298dprr+HChQt1Ou/8+fMRExOjn0pW4ciRI5g9ezYyMzOrfczOnTvRo0cPuLu7w87ODk2bNsXgwYOxZ88ew1XUzDx8+BCzZs1Cnz590LBhQ7N6wECWi0k3ESE3Nxdz5sypdtJ97do1hISEYO/evRg2bBhWrFiBCRMmwNXVFZ999plW2X379mHfvn0GqLV5+Prrr/Hzzz/D19cXDRo0MHV1iIiojrZt24YOHTogNjYWo0ePxldffYUxY8bg4MGD6NChA7Zv317rcxs76Z4zZ061k+7Fixejf//+kEgkmD59OpYtW4aXX34ZV69exaZNm8Ryfn5+yMvLw/Dhww1Uc9NKT0/H3LlzcenSJbRv397U1aF6wsrUFSAiy7Ns2TI8fPgQ8fHx8PPz09qXmpqq9V4ulxuzakb3n//8Bz4+PpBKpQgMDDR1dYiIqA6uX7+O4cOHo2nTpjh8+DAaNWok7ps0aRK6deuG4cOH4+zZs2jatKkJa6pfxcXFmDdvHnr16qXzQXnZ2C6RSKBQKIxZPaPy8vLCvXv34OnpiRMnTuDpp582dZWoHmBLN5GZGDVqFBwcHHD37l1ERUXBwcEBjRo1wvvvvw+VSiWW04ylWrx4MZYtWwY/Pz/Y2tqiR48eOH/+vNY5KxpPPWrUKPj7+4vn0/xSMWfOHEgkkirHPV+/fh2NGzcul3ADgLu7e5V1uHnzJvr37w97e3u4u7tj8uTJ2Lt3LyQSiVZre8+ePREYGIiLFy/i2WefhZ2dHXx8fLBw4UKt8xUWFmLmzJkIDQ2Fs7Mz7O3t0a1bNxw8eLDCz1CV1NRUjBkzBh4eHlAoFGjfvj3WrVtXrpyvry+kUv4oJSKqDxYtWoTc3FysWrVKK+EGADc3N3zzzTfIycnRikNlY2pZs2fPhkQiEd9LJBLk5ORg3bp1YqwdNWqUVtnLly9j8ODBcHJygqurKyZNmoT8/HzxHJWNpy4bu2fPno2pU6cCAAICAsTrVTTuOT09HUqlEl26dNG5v2xsr6gOW7ZsQZs2baBQKBAYGIjt27eXuzdlf4dZtWoVmjVrBhsbGzz99NP466+/tM539uxZjBo1Ck2bNhWHsb3xxhu4f/++zjpWx6+//opu3brB3t4eLi4ueOmll3Dp0iWtMjY2NvD09Kz1NYh0YUs3kRlRqVSIjIxEWFgYFi9ejAMHDmDJkiVo1qwZxo8fr1V2/fr1yM7OxoQJE5Cfn4/PP/8czz33HM6dOwcPD49qX7NRo0b4+uuvMX78eAwYMAADBw4EALRr167CY/z8/HDgwAH8+uuveO6552r0GXNycvDcc8/h3r17mDRpEjw9PbFx48YKE+SMjAz06dMHAwcOxODBg7F161Z8+OGHCAoKwvPPPw8AUCqV+O677zBs2DCMHTsW2dnZ+P777xEZGYnjx48jODi4RnXMy8tDz549ce3aNUycOBEBAQHYsmULRo0ahczMTEyaNKlG5yMiIsuwc+dO+Pv7o1u3bjr3d+/eHf7+/ti1a1eNz/2f//wHb775Jjp16oRx48YBAJo1a6ZVZvDgwfD398eCBQtw9OhRfPHFF8jIyMD69etrdK2BAwfi77//xo8//ohly5bBzc0NAMo9SNBwd3eHra0tdu7ciXfeeQcNGzas0fV27dqFIUOGICgoCAsWLEBGRgbGjBkDHx8fneU3btyI7OxsvPXWW5BIJFi4cCEGDhyIGzduwNraGgCwf/9+3LhxA6NHj4anpycuXLiAVatW4cKFCzh69KjWA43qOHDgAJ5//nk0bdoUs2fPRl5eHr788kt06dIFp06d0vnghEhvBCIyujVr1ggAhL/++kvcNnLkSAGAMHfuXK2yISEhQmhoqPg+ISFBACDY2toKd+7cEbcfO3ZMACBMnjxZ3NajRw+hR48e5a4/cuRIwc/PT3yflpYmABBmzZpVrfqfP39esLW1FQAIwcHBwqRJk4SYmBghJyenXNnH67BkyRIBgBATEyNuy8vLE1q3bi0AEA4ePKh1LABh/fr14raCggLB09NTePnll8VtxcXFQkFBgdZ1MzIyBA8PD+GNN97Q2l6dz7l8+XIBgLBhwwZxW2FhoRAeHi44ODgISqVS53Ft27bVeb+JiMj8ZWZmCgCEl156qdJy/fv3FwCIseDxmKoxa9Ys4fFfte3t7YWRI0dWWLZ///5a2//xj38IAIQzZ84IgvDod4A1a9aUO8fj8W3RokUCACEhIaHSz6Mxc+ZMAYBgb28vPP/888K///1v4eTJk+XK6apDUFCQ0LhxYyE7O1vcdujQIQGA1r3RHOvq6io8ePBA3P7zzz8LAISdO3eK23Jzc8td+8cffxQACIcPHxa3aX6nqupzBgcHC+7u7sL9+/fFbWfOnBGkUqkwYsQIncf89ddfFd5voppgn0giM/P2229rve/WrRtu3LhRrlxUVJTWE+ROnTohLCwMu3fvNngd27Zti/j4eLz++utITEzE559/jqioKHh4eODbb7+t9Ng9e/bAx8cH/fv3F7cpFAqMHTtWZ3kHBwe8/vrr4nu5XI5OnTpp3ROZTCaOHVer1Xjw4AGKi4vRsWNHnDp1qsafb/fu3fD09MSwYcPEbdbW1nj33Xfx8OFD/PbbbzU+JxERmbfs7GwAgKOjY6XlNPuVSqXe6zBhwgSt9++88w4AGCW2z5kzBxs3bhQnSv34448RGhqKDh06lOuCXVZSUhLOnTuHESNGwMHBQdzeo0cPBAUF6TxmyJAhWpOPanoWlI3ttra24t/z8/ORnp6OZ555BgBqHNvv3buH+Ph4jBo1SqsVv127dujVq5dR7i892Zh0E5kRhUJRrutXgwYNkJGRUa5sixYtym1r2bKl0dapbNmyJf7zn/8gPT0dZ8+exfz582FlZYVx48bhwIEDFR538+ZNNGvWrFy3sObNm+ss37hx43Jldd2TdevWoV27dlAoFHB1dUWjRo2wa9cuZGVl1fiz3bx5Ey1atCg3Vvupp54S9xMRUf2iSaY1yXdFqpuc18bjsb1Zs2aQSqVGi+3Dhg3D77//joyMDOzbtw+vvvoqTp8+jX79+mmNLS9LExN1xfGKYnuTJk203msS8LKx/cGDB5g0aRI8PDxga2uLRo0aISAgAABqHNs1dWzVqlW5fU899RTS09ORk5NTo3MS1QTHdBOZEZlMptfzSSQSCIJQbnvZidnqSiaTISgoCEFBQQgPD8ezzz6LH374AREREXo7vy5lP9eGDRswatQoREVFYerUqXB3d4dMJsOCBQtw/fp1vdSDiIjqN2dnZ3h5eeHs2bOVljt79ix8fHzg5OQEABWOLdZHrH383Ia8VllOTk7o1asXevXqBWtra6xbtw7Hjh1Djx499HL+6sT2wYMH48iRI5g6dSqCg4Ph4OAAtVqNPn36QK1W66UeRMbClm4iC3X16tVy2/7++2+tiUAaNGigc33Ox1tqazoZSUU6duwIoKQbV0X8/Pxw/fr1cg8Drl27Vuvrbt26FU2bNsW2bdswfPhwREZGIiIiosKn8lXx8/PD1atXywX1y5cvi/uJiKj+efHFF5GQkIA//vhD5/7ff/8diYmJePHFF8Vt1Y21QNXx9vHYfu3aNajVajG2a1qEH79eba5VXVXFdk1M1BXHaxvbMzIyEBsbi2nTpmHOnDkYMGAAevXqVetl2jR1vHLlSrl9ly9fhpubG+zt7Wt1bqLqYNJNZKFiYmJw9+5d8f3x48dx7NgxcUZvoKRb2uXLl5GWliZuO3PmDP7880+tc9nZ2QEoH8Qr8vvvv6OoqKjcds2YKF3dtzQiIyNx9+5d7NixQ9yWn59f5VjwymiemJdN5I8dO4a4uLhane+FF15AcnIyNm/eLG4rLi7Gl19+CQcHB7096SciIvMydepU2Nra4q233iq3NNWDBw/w9ttvw87OTlyOCyiJtVlZWVot5Pfu3cP27dvLnd/e3r7SWLty5Uqt919++SUAiLHdyckJbm5uOHz4sFa5r776Sue1gOrF9tzc3Apj5v/+9z8AFcd2b29vBAYGYv369Xj48KG4/bfffsO5c+eqvLYuuuI6ACxfvrxW5/Py8kJwcDDWrVundT/Onz+Pffv24YUXXqjVeYmqi93LiSxU8+bN0bVrV4wfPx4FBQVYvnw5XF1d8cEHH4hl3njjDSxduhSRkZEYM2YMUlNTER0djbZt22pNAGNra4s2bdpg8+bNaNmyJRo2bIjAwEAEBgbqvPZnn32GkydPYuDAgeLSYqdOncL69evRsGFDvPfeexXW+6233sKKFSswbNgwTJo0CV5eXvjhhx+gUCgA1O7J/Isvvoht27ZhwIAB6Nu3LxISEhAdHY02bdpo/QJQXePGjcM333yDUaNG4eTJk/D398fWrVvx559/Yvny5Vrj+A4fPiz+8pOWloacnBz861//AlCytEz37t1rfH0iIjKNFi1aYN26dXjttdcQFBSEMWPGICAgAImJifj++++Rnp6OH3/8UWupr6FDh+LDDz/EgAED8O677yI3Nxdff/01WrZsWW7Cr9DQUBw4cABLly6Ft7c3AgICEBYWJu5PSEhA//790adPH8TFxWHDhg149dVX0b59e7HMm2++iU8//RRvvvkmOnbsiMOHD+Pvv/8u91lCQ0MBAB9//DGGDh0Ka2tr9OvXT2eLbm5uLjp37oxnnnkGffr0ga+vLzIzMxETE4Pff/8dUVFRCAkJqfC+zZ8/Hy+99BK6dOmC0aNHIyMjAytWrEBgYGCt4rCTkxO6d++OhQsXoqioCD4+Pti3bx8SEhJqfC6NRYsW4fnnn0d4eDjGjBkjLhnm7Owsrm+usWLFCmRmZiIpKQlAyVJyd+7cAVAyuZ2zs3Ot60FPKFNOnU70pKpoyTB7e/tyZR9fckSz3MaiRYuEJUuWCL6+voKNjY3QrVs3cUmRsjZs2CA0bdpUkMvlQnBwsLB3716dy5scOXJECA0NFeRyeZXLav3555/ChAkThMDAQMHZ2VmwtrYWmjRpIowaNUq4fv26Vlldy5bduHFD6Nu3r2Brays0atRI+Oc//yn89NNPAgDh6NGjWse2bdu23PUfr79arRbmz58v+Pn5CTY2NkJISIjwyy+/6PycVX02jZSUFGH06NGCm5ubIJfLhaCgIJ1Lhmj+fXS9qrsEGxERmZezZ88Kw4YNE7y8vARra2vB09NTGDZsmHDu3Dmd5fft2ycEBgYKcrlcaNWqlbBhwwadS4ZdvnxZ6N69u7jspmb5ME3ZixcvCq+88org6OgoNGjQQJg4caKQl5endY7c3FxhzJgxgrOzs+Do6CgMHjxYSE1N1Rl35s2bJ/j4+AhSqbTSZbWKioqEb7/9VoiKihJjqZ2dnRASEiIsWrRIa1nOipYt27Rpk9C6dWvBxsZGCAwMFHbs2CG8/PLLQuvWrcsdu2jRonJ1eLz+d+7cEQYMGCC4uLgIzs7OwqBBg4SkpKRy5aq7ZJggCMKBAweELl26CLa2toKTk5PQr18/4eLFi+XK+fn5VRjbq7sEG1FZEkHQMcsSEZmtxMREBAQEYNGiRXj//fdNXR29Wb58OSZPnow7d+5oLYVGRERU382ePRtz5sxBWloa3NzcTF0dvQkODkajRo2wf/9+U1eFyKQ4ppuIjC4vL0/rfX5+Pr755hu0aNGCCTcREZGFKSoqQnFxsda2Q4cO4cyZM+jZs6dpKkVkRjimm4iMbuDAgWjSpAmCg4ORlZWFDRs24PLly/jhhx9MXTUiIiKqobt37yIiIgKvv/46vL29cfnyZURHR8PT0xNvv/22qatHZHJMuonI6CIjI/Hdd9/hhx9+gEqlQps2bbBp0yYMGTLE1FUjIiKiGmrQoAFCQ0Px3XffIS0tDfb29ujbty8+/fRTuLq6mrp6RCbHMd1EREREREREBsIx3UREREREREQGwqSbiIiIiIiIyECYdBMREREREREZCCdS0wO1Wo2kpCQ4OjpCIpGYujpERGSBBEFAdnY2vL29IZXymbihMXYTEVFdVTd2M+nWg6SkJPj6+pq6GkREVA/cvn0bjRs3NnU16j3GbiIi0peqYjeTbj1wdHQEUHKznZycTFwbIiKyREqlEr6+vmJMIcNi7CYiorqqbuxm0q0Hmm5pTk5ODNxERFQn7OpsHIzdRESkL1XFbg4aIyIiIiIiIjIQJt1EREREREREBsKkm4iIiIiIiMhAmHQTERERERERGQiTbiIiIiIiIiID4ezlRERERGRWbqQ9xPkkJZKz8vAwvxgFKjWspBLYWssglUoglUhgJS15SaUSCAIgCIJ4vKx0u7VMCmdba7jYWsPFTo4GdiV/yq3Y7mQsarWAzLwiPMgpREZuIZR5RSgoVqOw9FWgUqNYpQYAaP4JNf+SZf9NAcBaJoWLnTUa2MkR3MQFTgprI34Sotpj0k1EREREZuPH47fw0fZzeCzf0huZVIKmbvYIauyMHi0b4dnW7kze9ChFmY/f/k7D0Rv3cSU5GzfScpBXpNL7dRTWUvwrKgivhDbW+7mJ9I1JNxERERGZhYycQszdeRGCALT1dkJzdwc4KaxhLZNCpVajoFgNlVqAWgBUajWK1MKjZlEAkAAQALUgQKUWUKhSIyuvCFm5RcjMK0JmbiFUagFXUx/iaupDbDt1F7bWMgzo4INJ/9cCHk4KU310i3fsxn18c/gGDl1JhVrHAxMnhRUa2svhZGsNGyspbKxkkFtJIZdJYSWTiOscl13tWLP0sWZbQXHJv+edjDzcepCLD386i2BfZzR3dzToZyOqKybdRERERGQWtpy8jbwiFZ7ycsIv73QVEzF9UasFpGTn4/K9bBxLeID9F5NxPS0HG4/dwo74JEx/oTVe7dRE79etzzJyCjEj5jx2nbsnbmvv64L/b+/O45uq0/2Bf7I0SUtpS+kOhcoOshTLUIs6olaKC2NnEJFhABHhDpcqWseRKquOdPQK6khHroxsVxkYQBkXflWsoI5UgQKygyBQLCRtqd3bpE3O7480aSPdSXKWfN6vV14jJ9+TPDmTnnOePN/ltn5hGBEbgr7hXRAbGgA/jfu69AuCgEfX78fu00VYv/cC/pI6zG2vTeQJTLqJiIiISBK+OlMMAJg8qqdHEl+1WoXoYH9EB/vjjkEReHb8QHz7Ywle+fQUDuWX4vkPjuHklXK88JuhUKuZeLfljKkCj67fj59+roFaBUwZ3Quzbr0BfcIDPfq+KpUK05J6Y/fpInz9Q7FH34vIHZh0ExEREZHobDYBeRd/BgAk9Q3zynuqVCok9e2O7X8cg7e//hEvZ5/Cu9/mQ6tWY8mEIax4t+JcUSV+v+ZbFFda0Lt7ALJ+fxOG9gj22vv/Ki4UahVw8Wo1iirMCO+q99p7E3UUp24kIiIiItGZKmpRU2eFRq1C3/AuXn1vtVqFP97eFysfGgEAWL/3Av514JJXY5CTito6PLbhAIorLRgSHYR/z7vFqwk3AHQ1+CE62B8AcPFqlVffm6ijmHQTERERkeguldQAAGJCDNC6cfxvR/x2ZE88kzIQALD0wxP4sahSlDikbvG/j+N8cRVigg34v1mjERKgEyWO3t0DANir3URSxqSbiIiIiER3qcSeOMV2CxA1jrm398Ut/bqjps6KZR+duGataF+391wxPjhUALUKePP3I9E9ULxu3b1C7d+V/BIm3SRtTLqJiIiISHSFFWYAQFSwuMt2qdUqvJQ6DH4aFb48U4QvzxSJGo+UWG0Cln14AgAwNbE3EnqHihpPRMM47qtVZlHjIGoLk24iIiIiEl1pjQUA0E2krspNxYV1wYykOADAqi/OihuMhHx23IjTpgoEGbR4etwAscNBty7278rP1XUiR0LUOibdRERERCS60ip74hTi7ydyJHZzft0HOo0aBy7+jP0XSsQOR3SCIGD1l+cAANOT4kQbx92U4wea0mqLyJEQtY5JNxERERGJzlHpDukifjIHABFBBkxM6AkAWPfNeZGjEd/B/J/x/U9l0GvVeOSWOLHDAQCEBNh/oCmpYqWbpI1JNxERERGJztFFWCqVbgCYdnNvAMDnJwrxc5VvV1O35f0EALhveDTCRJw8ranQLqx0kzww6SYiIiIi0ZU5ku4A6STdQ2KCcGNMECxWG/59uEDscERTW2fFx99fAQA82FD9lwJH9/KfmXSTxDHpJiIiIiLRldfak+4gg3SSbgCY1JBkfnD4ssiRiCfnZCEqzPXoEeKPm2/oLnY4Tl30WgBAbZ0NVhuXdiPpkl3SnZWVhbi4OBgMBiQmJmLfvn2ttt+6dSsGDRoEg8GAYcOGYefOnS22/eMf/wiVSoXXX3/dzVETERERUWuqLVYAQBe9RuRIXN07PBoqFfD9pVKYymvFDkcUu04YAQD3D4+GWq0SOZpGAbrG70pNnVXESIhaJ6uke8uWLUhPT8eSJUtw8OBBjBgxAikpKSgsLGy2/d69ezFlyhTMmjULhw4dQmpqKlJTU3Hs2LFr2n7wwQf49ttvERMT4+mPQURERES/UNOQdPvrtCJH4iqiqwHxsSEAgF0nTOIGI4J6qw17GtYqv2twpMjRuNJr1VA1/AZQba4XNxiiVsgq6V65ciVmz56NmTNnYsiQIVi9ejUCAgKwdu3aZtu/8cYbGD9+PJ555hkMHjwYL774Im666SasWrXKpV1BQQEef/xxvPfee/Dzk1aXJiIiIiKlq7faYLHaAAABftKqdAPA3UPsyaYvJt0H80tRWl2HkAA/3NQrROxwXKhUKnRp+JHG0VOCSIpkk3RbLBbk5eUhOTnZuU2tViM5ORm5ubnN7pObm+vSHgBSUlJc2ttsNkybNg3PPPMMbrzxRs8ET0REREQtqm7SNdhfJ72ke1xD0p177ipqfawb8+7T9h6lYweEQ6uRXurg+L4w6SYpk95fTguKi4thtVoRGenarSUyMhJGo7HZfYxGY5vtX375ZWi1WjzxxBPtjsVsNqO8vNzlQURERESd4+harlbZuwxLTd/wQEQFGWCx2nDw4s9ih+NVueeuAgBu6x8uciTNC3Am3exeTtIlvbOaF+Xl5eGNN97A+vXroVK1f1KIzMxMBAcHOx+xsbEejJKIiEj5OjpR6uuvv46BAwfC398fsbGxeOqpp1Bb65uTXCmBo0oZoNN26J7MW1QqFZL62mft3tuQhPqCKnM9jhaUAQAS+4SKHE3zAti9nGRANkl3WFgYNBoNTCbXsTQmkwlRUVHN7hMVFdVq+6+//hqFhYXo1asXtFottFotLl68iKeffhpxcXEtxpKRkYGysjLn49KlS9f34YiIiHxYRydK3bRpExYsWIAlS5bg5MmTeOedd7BlyxY899xzXo6c3MVRpZRi13KHxqS7WORIvCfv4s+w2gT0CPFHz24BYofTrAB2LycZkE3SrdPpkJCQgJycHOc2m82GnJwcJCUlNbtPUlKSS3sA2LVrl7P9tGnTcOTIERw+fNj5iImJwTPPPINPP/20xVj0ej2CgoJcHkRERNQ5HZ0ode/evbjlllvw+9//HnFxcRg3bhymTJnSZnWcpKvGWemWcNLdx550f/9TGSp9ZKbs787bq/pSrXID7F5O8iCbpBsA0tPTsWbNGmzYsAEnT57E3LlzUVVVhZkzZwIApk+fjoyMDGf7+fPnIzs7GytWrMCpU6ewdOlSHDhwAGlpaQCA7t27Y+jQoS4PPz8/REVFYeDAgaJ8RiIiIl/SmYlSx4wZg7y8PGeS/eOPP2Lnzp249957vRIzuZ+jSukvwZnLHWJDA9AjxB9Wm4Ajl0rFDscrDlywj19PvEG6SbfjO8NKN0mZtBZCbMPkyZNRVFSExYsXw2g0Ij4+HtnZ2c7J0vLz86FWN/6OMGbMGGzatAkLFy7Ec889h/79+2PHjh0YOnSoWB+BiIiImmhtotRTp041u8/vf/97FBcX49Zbb4UgCKivr8cf//jHVruXm81mmM1m5785Caq01DTMCG6QcNINAPGxISgorcGhS6UY0y9M7HA8ymYTcKxhPHd8bDeRo2mZvuE7Y6m3iRwJUctklXQDQFpamrNS/Ut79uy5ZtukSZMwadKkdr/+hQsXOhkZERERecOePXuwfPly/P3vf0diYiLOnj2L+fPn48UXX8SiRYua3SczMxPLli3zcqTUXo6ESYozlzcVHxuCT45ewWEfqHT/WFyFKosV/n4a9A3vInY4LdI1LGPmWOedSIpkl3QTERGRcnRmotRFixZh2rRpeOyxxwAAw4YNQ1VVFebMmYPnn3/epdebQ0ZGBtLT053/Li8v5+ojElLXkDDpJJ50j+wVAgA4fKkUgiBIcqZ1dzlaUAoAuDEmSJLrczs4vjOsdJOUSfcviIiIiBSvMxOlVldXX5NYazT2LqaCIDS7DydBlTZHwqSTcHIHAEN7BEOrVqGowoyC0hqxw/GoIz/Zu5YP6xksciSt0zPpJhmQ9pmNiIiIFK+jE6VOmDABb731FjZv3ozz589j165dWLRoESZMmOBMvkleHJVuP4kn3QY/DQZFdwUAHG1ISpXK8fmGSzzpdlS6zfWcSI2ki93LiYiISFQdnSh14cKFUKlUWLhwIQoKChAeHo4JEybgpZdeEusj0HUy18ujezkADIkOwrGCcpy8Uo57hkWLHY5HWG0Cjl+2TzY4rIfEk24NK90kfUy6iYiISHQdmShVq9ViyZIlWLJkiRciI2+os9qHBUi90g0Ag6PtQxNOGitEjsRz8kuqUVNnhcFPjRvCAsUOp1XOMd2cSI0kTPpnNiIiIiJSNLlMpAYAg6Iaku4ryl127ozJ/oNCv4hAaNTSniyusXs5k26SLumf2YiIiIhI0RonUpN2ggcAgxvGdP/0cw3Ka+tEjsYzfmhIugdEdBU5krZxIjWSAybdRERERCQquUykBgAhATpEBxsAAKcV2sX8jKkSANA/UvpJN5cMIzmQ/pmNiIiIiBTNIqPu5UCTcd0K7WLu6F4+IFLa47mBJhOpcUw3SZg8zmxEREREpFiOKqUcKt1AYxfzk1eUV+mut9rwY1EVAGAAK91EbiGPMxsRERERKZacJlIDgP4NY53PFVWKHIn7XbhaDYvVBn8/DXqE+IsdTps4ppvkQB5nNiIiIiJSrMaJ1ORxa9onvAsAOCvCSnK20F697x8ZCLXEZy4HuGQYyYM8zmxEREREpFiN63RLP8kDgD7h9rHOxZVmlFUrawbzH4vtPyT0DZf+eG4A0Gk0AFjpJmlj0k1EREREomqcSE0jciTtE6jXIirIPoP5uWJldTG/WFwNAOjdPUDkSNqHY7pJDph0ExEREZGoGidSk0elGwD6Rti7mJ8rVFbSfeGqvdId172LyJG0j7bhO1NnY9JN0sWkm4iIiIhEJbeJ1IDG7tfnFDau25l0h8kj6fZT278z9Q1DFIikSD5nNiIiIiJSJGfSLZOJ1ICmSbdyKt3VlnqYys0AgDiZdC/XNEz2Vm9j0k3SJZ8zGxEREREpktzW6QYaZzBXUtKdX2Ifzx3s74eQAJ3I0bSPY0hCPWcvJwmTz5mNiIiIiBTJ4pi9XEbdy29o6H59qaQaVoVUWS8Uy6trOQBoNexeTtInnzMbERERESmSHLuXRwf7w0+jQp1VwJWyGrHDcYsLV+2Vbrl0LQcALbuXkwzI58xGRERERIrk6F6u08pn9nKNWoXYbvbk1NEtW+4uNkyi1lsmM5cDjbOX13P2cpIwJt1EREREJKrGSrc81ul2iA1tSLqvKiPpPu/oXi6rSrc9namzChAEVrtJmph0ExEREZGo6hrG4zpmopaLXqHKqnRfKrF3k+8tq6S78TujlLH1pDxMuomIiIhIVFabY/ZyeSXdjuT0ogKS7nqrDcbyWgBAz24ySrqbfGc4rpukikk3EREREYnKkSzJrdLt6F5+SQFJt7G8FlabAJ1GjfBAvdjhtFvTZeaYdJNUMekmIiIiIlE5ugU7xufKhaN7+UUFjOku+NnetTw6xAC1jH78aPpDDdfqJqmS15mNiIiIiBTHUaGUWc7tTLrLaupQVl0ncjTXp6DUnnT3CPEXOZKOaTqmu45rdZNEyezURkRERERKY5NppbuLXouwQB0A+U+m9tPP8ky6VSqVM/HmRGokVfI6swHIyspCXFwcDAYDEhMTsW/fvlbbb926FYMGDYLBYMCwYcOwc+dO53N1dXV49tlnMWzYMHTp0gUxMTGYPn06Ll++7OmPQUREREQABEGQ7ZhuoEkX85IqkSO5Po7u5XKaRM3B8b2pY/dykihZJd1btmxBeno6lixZgoMHD2LEiBFISUlBYWFhs+337t2LKVOmYNasWTh06BBSU1ORmpqKY8eOAQCqq6tx8OBBLFq0CAcPHsT777+P06dP4ze/+Y03PxYRERGRz2panNTKMOl2JKmOpFWunN3Lu8mr0g00TqbGidRIqmSVdK9cuRKzZ8/GzJkzMWTIEKxevRoBAQFYu3Zts+3feOMNjB8/Hs888wwGDx6MF198ETfddBNWrVoFAAgODsauXbvw0EMPYeDAgbj55puxatUq5OXlIT8/35sfjYiIiMgn1dsaq5MamS0ZBjQmqZdLFZJ0y6x7OdC4bJjVxko3SZNskm6LxYK8vDwkJyc7t6nVaiQnJyM3N7fZfXJzc13aA0BKSkqL7QGgrKwMKpUKISEhLbYxm80oLy93eRARERFRxzUdhyvHSndMQ5JaUForciSdZ7MJzqS7pwwr3Vpn93JWukmaZJN0FxcXw2q1IjIy0mV7ZGQkjEZjs/sYjcYOta+trcWzzz6LKVOmICgoqMVYMjMzERwc7HzExsZ28NMQEREREeDaJVitkl/S3SPEAEDele7iKjMs9TaoVUBUsEHscDrMMQFfPZNukijZJN2eVldXh4ceegiCIOCtt95qtW1GRgbKysqcj0uXLnkpSiIiIiJlscm80t0jxD6m+3KZfJNux8zlUUEG5/hoOXF0L69j93KSKK3YAbRXWFgYNBoNTCaTy3aTyYSoqKhm94mKimpXe0fCffHiRXzxxRetVrkBQK/XQ6/Xd+JTEBEREVFTTSvdcpy9PKah0l1aXYcqcz266GVze+3kmAROjpOoAY0TqXHJMJIq2fyUpdPpkJCQgJycHOc2m82GnJwcJCUlNbtPUlKSS3sA2LVrl0t7R8L9ww8/4PPPP0f37t098wGIiIiI6BrWJsuFqWTYvbyrwQ9dDfZEW65dzK80VOljZDiJGsAlw0j6ZJN0A0B6ejrWrFmDDRs24OTJk5g7dy6qqqowc+ZMAMD06dORkZHhbD9//nxkZ2djxYoVOHXqFJYuXYoDBw4gLS0NgD3hfvDBB3HgwAG89957sFqtMBqNMBqNsFgsonxGIiIiIl8i5zW6HXo4J1OTZ9JtKjcDsHcvlyPHsASO6SapklXSPXnyZLz66qtYvHgx4uPjcfjwYWRnZzsnS8vPz8eVK1ec7ceMGYNNmzbh7bffxogRI7Bt2zbs2LEDQ4cOBQAUFBTgww8/xE8//YT4+HhER0c7H3v37hXlMxIREfmirKwsxMXFwWAwIDExEfv27Wu1fWlpKebNm4fo6Gjo9XoMGDAAO3fu9FK05E7WhkRJjuO5HRxJ92WZzmBuKrfHHd5VnsMnG9fpZqWbpEl2g07S0tKclepf2rNnzzXbJk2ahEmTJjXbPi4uDoLAX8SIiIjEtGXLFqSnp2P16tVITEzE66+/jpSUFJw+fRoRERHXtLdYLLj77rsRERGBbdu2oUePHrh48WKry32SdFkF+Ve6G5cNqxY5ks4prLBXuiNlWul2fHfYu5ykSnZJNxERESnLypUrMXv2bOdwsdWrV+OTTz7B2rVrsWDBgmvar127FiUlJdi7dy/8/PwA2H9IJ3myNlQnlZB0y7XSXdhQ6Y6QaaVb60y6mXWTNMmqezkREREpi8ViQV5eHpKTk53b1Go1kpOTkZub2+w+H374IZKSkjBv3jxERkZi6NChWL58OaxWa4vvYzabUV5e7vIgaXCM6ZZ19/Ju8h3TLQiCYird9Zy9nCSKSTcRERGJpri4GFar1Tk/i0NkZCSMRmOz+/z444/Ytm0brFYrdu7ciUWLFmHFihX4y1/+0uL7ZGZmIjg42PmIjY116+egznNMfiXnSnePhmXD5Dh7eaW5HtUW+w9WEUEyrXRrHJVuJt0kTUy6iYiISFZsNhsiIiLw9ttvIyEhAZMnT8bzzz+P1atXt7hPRkYGysrKnI9Lly55MWJqjdVZ6Zbvbamje7mxrFZ2iZ+jyt1Vr0WATp4jTzUN3x3OXk5SJc+/LCIiIlKEsLAwaDQamEwml+0mkwlRUVHN7hMdHQ0/Pz9oNBrntsGDBzuX/NTpdNfso9frodfLs4qndEpYMiw8UA+1yv5ZrlaaESGjbtrOmctlWuUGmo7pZtJN0tSpnxRnzJiBr776yt2xEBERkYy4435Ap9MhISEBOTk5zm02mw05OTlISkpqdp9bbrkFZ8+eha3JpElnzpxBdHR0swk3SZtNkP+Ybq1G7Vxuy1gur8nUihzjubvK54eCX+KYbpK6TiXdZWVlSE5ORv/+/bF8+XIUFBS4Oy4iIiKSOHfdD6Snp2PNmjXYsGEDTp48iblz56Kqqso5m/n06dORkZHhbD937lyUlJRg/vz5OHPmDD755BMsX74c8+bNc8vnIu9ydAlWyzjpBoCohuq2sUxeSbej0i3X8dwAZy8n6etU0r1jxw4UFBRg7ty52LJlC+Li4nDPPfdg27ZtqKurc3eMREREJEHuuh+YPHkyXn31VSxevBjx8fE4fPgwsrOznZOr5efn48qVK872sbGx+PTTT7F//34MHz4cTzzxBObPn9/s8mIkfVYFzF4ONM78bZJZpbuwXN4zlwOsdJP0dXrGivDwcKSnp+P777/Hd999h379+mHatGmIiYnBU089hR9++MGdcRIREZEEuet+IC0tDRcvXoTZbMZ3332HxMRE53N79uzB+vXrXdonJSXh22+/RW1tLc6dO4fnnnvOZYw3yUe9AtbpBoCo4IZKt8ySblND93K5rtENcEw3Sd91TxN55coV7Nq1C7t27YJGo8G9996Lo0ePYsiQIXjttdfcESMRERFJHO8HqLOUVuk2lplFjqRjCp3dy+Vc6W6YvZxJN0lUp5Luuro6bN++Hffffz969+6NrVu34sknn8Tly5exYcMGfP755/jXv/6FF154wd3xEhERkUTwfoDcQQmzlwONY7pl172clW4ij+vUkmHR0dGw2WyYMmUK9u3bh/j4+Gva3HHHHQgJCbnO8IiIiEiqeD9A7mBTwDrdgHy7lzsq3bIe061pGNPNdbpJojqVdL/22muYNGkSDIaW/zhDQkJw/vz5TgdGRERE0sb7AXIHpVS6nROpyWj28kpzPaosVgBKqXRz9nKSpk79pLh79+5mZyWtqqrCo48+et1BERERkfTxfoDcwaqQpNtR6a4w16PKXC9yNO3jqHIH6rXoou9ULU4SOHs5SV2nku4NGzagpqbmmu01NTXYuHHjdQdFRERE0sf7AXIHpVS6A/VaBDYkrnLpYm4ql/94boBjukn6OvSTVnl5OQRBgCAIqKiocOlOZrVasXPnTkRERLg9SCIiIpIO3g+QOzm6BMt99nIAiAzSo7KoHqayWvQNDxQ7nDYVVjhmLpd30s3Zy0nqOpR0h4SEQKVSQaVSYcCAAdc8r1KpsGzZMrcFR0RERNLD+wFyJ6VUugF7F/NzRVWyqXQXOivd8p1EDWClm6SvQ0n37t27IQgC7rzzTmzfvh2hoaHO53Q6HXr37o2YmBi3B0lERETSwfsBcifn7OUa+SfdzrW65ZJ0VzhmLpd7pdsxppsTqZE0dSjpvv322wEA58+fR69evaBSyf/kSERERB3D+wFyp8ZKt7yXDAOA6GB5zWBuYqWbyCvanXQfOXIEQ4cOhVqtRllZGY4ePdpi2+HDh7slOCIiIpIW3g+QuzlnL1fAbzdRMq10y35MN9fpJolrd9IdHx8Po9GIiIgIxMfHQ6VSQRCu/WKrVCpYrVa3BklERETSwPsBcjclVbobu5ebRY6kfTimm8g72p10nz9/HuHh4c7/JiIiIt/D+wFyN0eipITZy6Nk1r28sMKedMt/TDdnLydpa3fS3bt372b/m4iIiHwH7wfI3ZzdyxXQv9zRvbywohb1Vhu0GulW76vM9ag01wMAIoIUUuluptcNkRR06kywYcMGfPLJJ85///nPf0ZISAjGjBmDixcvui04IiIiki7eD5A71Cuo0t09UA+NWgWbAFytsogdTqscVe4uOg0C9R2aW1lyHLOXWzmmmySqU0n38uXL4e/vDwDIzc3FqlWr8MorryAsLAxPPfWUWwMkIiIiaeL9ALmDtWGZJyWs061RqxAeaO+qbZL4ZGqO+ORe5QYaf7Bh93KSqk79rHXp0iX069cPALBjxw48+OCDmDNnDm655RaMHTvWnfERERGRRPF+gNxBSZVuwD4+2lhe61yOS6ocle6IrvIezw0AaudEalynm6SpU5XuwMBAXL16FQDw2Wef4e677wYAGAwG1NTUuC86IiIikizeD5A7OLoEqxWTdMtj2bBCVrqJvKZTSffdd9+Nxx57DI899hjOnDmDe++9FwBw/PhxxMXFuTO+a2RlZSEuLg4GgwGJiYnYt29fq+23bt2KQYMGwWAwYNiwYdi5c6fL84IgYPHixYiOjoa/vz+Sk5Pxww8/ePIjEBERKYKY9wOkHMqrdDdMpib1pNsxc7kCKt0aLhlGEteppDsrKwtJSUkoKirC9u3b0b17dwBAXl4epkyZ4tYAm9qyZQvS09OxZMkSHDx4ECNGjEBKSgoKCwubbb93715MmTIFs2bNwqFDh5CamorU1FQcO3bM2eaVV17B3/72N6xevRrfffcdunTpgpSUFNTWSvtESUREJDax7gdIWWyCctbpBhqX35LPmG75J91aLhlGEtepMd0hISFYtWrVNduXLVt23QG1ZuXKlZg9ezZmzpwJAFi9ejU++eQTrF27FgsWLLim/RtvvIHx48fjmWeeAQC8+OKL2LVrF1atWoXVq1dDEAS8/vrrWLhwIR544AEAwMaNGxEZGYkdO3bg4Ycf9ujnISIikjOx7gdIWZRa6TZKfEy3I+mOVED3cla6Seo6vT5AaWkp9u3bh8LCQtiaTFqgUqkwbdo0twTXlMViQV5eHjIyMpzb1Go1kpOTkZub2+w+ubm5SE9Pd9mWkpKCHTt2AADOnz8Po9GI5ORk5/PBwcFITExEbm6uV5Puito6fH+pzGvvR0RE7jOsZzCC/f3EDkMU3r4fIOVxjOlWwuzlgPy6l4croHs5x3ST1HUq6f7oo48wdepUVFZWIigoCCpV40nSUxfZ4uJiWK1WREZGumyPjIzEqVOnmt3HaDQ2295oNDqfd2xrqU1zzGYzzObGXy/Ly8vb/0FacPFqNf7wznfX/TpEROR92+cmIaF3qNhheJ0Y9wOkPI5ESWlJt9S7lxc2VOIVUenWcPZykrZOJd1PP/00Hn30USxfvhwBAQHujknyMjMz3d51Tq9VY1BUV7e+JhEReYfBTyN2CKLw9fsBcg9HoqSc7uX2yvHP1XWorbNK8vxQZa5HpbkegDKWDHNWuq2sdJM0dSrpLigowBNPPOHVC2xYWBg0Gg1MJpPLdpPJhKioqGb3iYqKarW9439NJhOio6Nd2sTHx7cYS0ZGhku39fLycsTGxnbo8/xS/8iuyH7y19f1GkRERN4kxv0AKY8jT1JKpTvY3w96rRrmehuKKsyIDZXe34eja3mAToNAfadHm0oGx3ST1HVqmsiUlBQcOHDA3bG0SqfTISEhATk5Oc5tNpsNOTk5SEpKanafpKQkl/YAsGvXLmf7G264AVFRUS5tysvL8d1337X4mgCg1+sRFBTk8iAiIvI1YtwPkPI4Kt1KSbpVKpXku5g71+juqncZFiJXjtnLmXSTVHXqp6377rsPzzzzDE6cOIFhw4bBz8918pjf/OY3bgnul9LT0zFjxgyMGjUKo0ePxuuvv46qqirnbObTp09Hjx49kJmZCQCYP38+br/9dqxYsQL33XcfNm/ejAMHDuDtt98GYD8pPvnkk/jLX/6C/v3744YbbsCiRYsQExOD1NRUj3wGIiIipRDrfoCUxaqwMd2AvYt5fkk1TBKdwdzUUOmOUMB4bqDxu8OJ1EiqOpV0z549GwDwwgsvXPOcSqWC1Wq9vqhaMHnyZBQVFWHx4sUwGo2Ij49Hdna2cyK0/Px8qJus8ThmzBhs2rQJCxcuxHPPPYf+/ftjx44dGDp0qLPNn//8Z1RVVWHOnDkoLS3FrbfeiuzsbBgMyjgJEREReYpY9wOkLM6kWwEVV4fGZcOkX+lWAi27l5PEdSrptok4M2BaWhrS0tKafW7Pnj3XbJs0aRImTZrU4uupVCq88MILzd4wEBERUcvEvB8g5VBmpVvay4Y5xnQrYeZyoGmlm+ckkqZOjeluqrZWmicTIiIi8h7eD1BnKW3JMKBxBnM5jOlWAq2GlW6Stk4l3VarFS+++CJ69OiBwMBA/PjjjwCARYsW4Z133nFrgERERCRNvB8gd7AJSky6pd293KSgNbqBJkuGMekmiepU0v3SSy9h/fr1eOWVV6DT6Zzbhw4din/84x9uC46IiIiky533A1lZWYiLi4PBYEBiYiL27dvXrv02b94MlUrFCVBlzLG2shKT7kKJTqRWWKGsSrfGMXs51+kmiepU0r1x40a8/fbbmDp1KjQajXP7iBEjcOrUKbcFR0RERNLlrvuBLVu2ID09HUuWLMHBgwcxYsQIpKSkoLCwsNX9Lly4gD/96U+47bbbOv0ZSHyOSrdWgUm3dLuXK2v2cla6Seo6lXQXFBSgX79+12y32Wyoq6u77qCIiIhI+tx1P7By5UrMnj0bM2fOxJAhQ7B69WoEBARg7dq1Le5jtVoxdepULFu2DH369OlU/CQNjkRJrajZy+0V5CqLFRW10ro3rrbUo8JcDwCICFJKpZtjuknaOpV0DxkyBF9//fU127dt24aRI0ded1BEREQkfe64H7BYLMjLy0NycrJzm1qtRnJyMnJzc1vc74UXXkBERARmzZrVrvcxm80oLy93eZA02BoSJcdkWEoQoNOiq8G+SJDU1up2VLn9/TToqu/UQkaSo+Xs5SRxnfpLW7x4MWbMmIGCggLYbDa8//77OH36NDZu3IiPP/7Y3TESERGRBLnjfqC4uBhWqxWRkZEu2yMjI1vsov6f//wH77zzDg4fPtzuWDMzM7Fs2bJ2tyfvUWKlG7B3Ma+orURheS36RQSKHY6TY7mwiCA9VAo55o5Kt02w/4ijVtBQBVKGTlW6H3jgAXz00Uf4/PPP0aVLFyxevBgnT57ERx99hLvvvtvdMRIREZEEiXE/UFFRgWnTpmHNmjUICwtr934ZGRkoKytzPi5duuSR+KjjHF2CterrXslWUpzLhlVIa1y3Y5x5ZFdljOcGXL87VoFdzEl6Ot2n5LbbbsOuXbvcGQsRERHJzPXeD4SFhUGj0cBkMrlsN5lMiIqKuqb9uXPncOHCBUyYMMG5zdbQpVSr1eL06dPo27fvNfvp9Xro9coYv6o0jqRbYTl347JhZRLrXt5Q6Q5XyHhuANA0GZpgtQnw07TSmEgEnTq99enTB1evXr1me2lpKSczISIi8hHuuB/Q6XRISEhATk6Oc5vNZkNOTg6SkpKuaT9o0CAcPXoUhw8fdj5+85vf4I477sDhw4cRGxvb+Q9EorAKSq10S3MG80JFVrobk27OYE5S1KlK94ULF2C1Wq/ZbjabUVBQcN1BERERkfS5634gPT0dM2bMwKhRozB69Gi8/vrrqKqqwsyZMwEA06dPR48ePZCZmQmDwYChQ4e67B8SEgIA12wneXBUujXKyrkR2bAGdqHEupc3HdOtFE3XeOda3SRFHUq6P/zwQ+d/f/rppwgODnb+22q1IicnB3FxcW4LjoiIiKTH3fcDkydPRlFRERYvXgyj0Yj4+HhkZ2c7J1fLz8+HWmFVUGrUmHQr6//jxu7l0kq6nWO6lZR0q5pWujmDOUlPh5Lu1NRUAIBKpcKMGTNcnvPz80NcXBxWrFjhtuCIiIhIejxxP5CWloa0tLRmn9uzZ0+r+65fv75D70XS4ky6FTKTtkNksKN7uTTHdEcoqHu5Wq2CWmWfvZxrdZMUdSjpdkxUcsMNN2D//v0dmjWUiIiIlIH3A+ROjZVuhSXdDZXuwopaCIIgmeW5lFjpBuxzAlisNo7pJknq1Jju8+fPuzsOIiIikhneD5A7KDXpDg+0J7V1VgElVRZ0DxQ/ya2xWFFRWw8ACFdQpRto+P5YWekmaer0kmE5OTnIyclBYWGh8xdvh7Vr1153YERERCR9vB+g6+WYvVxpSbdOq0ZYoA7FlRaYys2SSLodk7oZ/NQIMnQ6DZAkxwzmrHSTFHXqr23ZsmV44YUXMGrUKERHR0umuwwRERF5D+8HyB0cs00rLekG7OOmiystMFXUYgiCxA7HZTy30v5eHWt1WzmRGklQp5Lu1atXY/369Zg2bZq74yEiIiKZ4P0AuUPjOt3KSgIB+7jpE1ca18YWm1LHcwOsdJO0dWptBovFgjFjxrg7FiIiIpIR3g+QOziSJLUik27HsmHSmMG8sFx5M5c7OHpK1HOdbpKgTiXdjz32GDZt2uTuWIiIiEhGeD9A7mCzKbnS3bBsWIVEKt0NcUQostJtT2s4kRpJUae6l9fW1uLtt9/G559/juHDh8PPz8/l+ZUrV7olOCIiIpIu3g+QOzgr3QobYww0WTZMIt3Li3yh0s2kmySoU0n3kSNHEB8fDwA4duyYO+MhIiIimeD9AF0vW5MESZmVbntF2SiRpNtZ6e6qxEq3YyI1Jt0kPZ1Kunfv3u3uOIiIiEhmeD9A16tpVVLJY7pN5dIY0+2IIypYyZVuzl5O0tOhpPt3v/tdm21UKhW2b9/e6YCIiIhI2ng/QO5iE5Re6bYnt8WVZtRbbdBqOjWdktsoefZyDSvdJGEdSrqDg4M9FQcRERHJBO8HyF2aVrqVuE539y46aNUq1NsEFFWaER3sL1os1ZZ6VNTWA2j8MUBJtBqO6Sbp6lDSvW7dOk/FQURERDLB+wFyF6vCk261WoWIrnpcLquFqVzcpNvRtTxAp0GgvlMjTCVN45i9nEuGkQSJ28eFiIiIiHyWS9KtwNnLASDCOa5b3MnUGruWG6BS4LHWcvZykjAm3UREREQkCkfSrVIpcyI1oHH8tNjLhjmSbiXOXA5wTDdJm2yS7pKSEkydOhVBQUEICQnBrFmzUFlZ2eo+tbW1mDdvHrp3747AwEBMnDgRJpPJ+fz333+PKVOmIDY2Fv7+/hg8eDDeeOMNT38UIiIiIkJjgqTUKjfQOH5a7GXDChU8cznQtNLN2ctJemSTdE+dOhXHjx/Hrl278PHHH+Orr77CnDlzWt3nqaeewkcffYStW7fiyy+/xOXLl11mXM3Ly0NERATeffddHD9+HM8//zwyMjKwatUqT38cIiIiIp/nSJCUOJ7bQSrLhhmbdC9XIla6ScpkMYvCyZMnkZ2djf3792PUqFEAgDfffBP33nsvXn31VcTExFyzT1lZGd555x1s2rQJd955JwD7xC+DBw/Gt99+i5tvvhmPPvqoyz59+vRBbm4u3n//faSlpXn+gxERERH5MEdR0jeSbnYv9yQtk26SMFlUunNzcxESEuJMuAEgOTkZarUa3333XbP75OXloa6uDsnJyc5tgwYNQq9evZCbm9vie5WVlSE0NLTVeMxmM8rLy10eRERERNQxvlHptie5YifdSu9e7py9nEk3SZAskm6j0YiIiAiXbVqtFqGhoTAajS3uo9PpEBIS4rI9MjKyxX327t2LLVu2tNltPTMzE8HBwc5HbGxs+z8MEREREQEAbELDmG4FJ91REulebqpQdvdyzl5OUiZq0r1gwQKoVKpWH6dOnfJKLMeOHcMDDzyAJUuWYNy4ca22zcjIQFlZmfNx6dIlr8RIREREpCSOBEmr4KTbsWRYWU0dauusosQgCAKMZQ1Jd1dlJt0aDbuXk3SJOqb76aefxiOPPNJqmz59+iAqKgqFhYUu2+vr61FSUoKoqKhm94uKioLFYkFpaalLtdtkMl2zz4kTJ3DXXXdhzpw5WLhwYZtx6/V66PXKHA9DRERE5C2OBEmt4NnLgwxaGPzUqK2zwVRei97du3g9hvKaepjr7V35I4KUeQ/LSjdJmahJd3h4OMLDw9tsl5SUhNLSUuTl5SEhIQEA8MUXX8BmsyExMbHZfRISEuDn54ecnBxMnDgRAHD69Gnk5+cjKSnJ2e748eO48847MWPGDLz00ktu+FRERERE1B5WH6h0q1QqRAYZcPFqNUzlZlGSbkfX8pAAPxj8NF5/f29onL2cS4aR9MhiTPfgwYMxfvx4zJ49G/v27cM333yDtLQ0PPzww86ZywsKCjBo0CDs27cPABAcHIxZs2YhPT0du3fvRl5eHmbOnImkpCTcfPPNAOxdyu+44w6MGzcO6enpMBqNMBqNKCoqEu2zEhEREfkKZ6VbwUk3IP4M5o73VWrXcoCVbpI2WSwZBgDvvfce0tLScNddd0GtVmPixIn429/+5ny+rq4Op0+fRnV1tXPba6+95mxrNpuRkpKCv//9787nt23bhqKiIrz77rt49913ndt79+6NCxcueOVzEREREfkqX6h0A+In3Y7x3ErtWg40mb3cyqSbpEc2SXdoaCg2bdrU4vNxcXEQBNc/MoPBgKysLGRlZTW7z9KlS7F06VJ3hklERERE7eQzle6u4i4bVljRsFyYQmcuB1jpJmmTRfdyIiIiIlIeX6l0O9bGFmvZMGf3cgUn3Y1jupl0k/Qw6SYiIiIiUVgF5c9eDjQuGyZWpfuKY7kwBXcvZ6WbpIxJNxERERGJwrlOt0bZSbfY3cuvlNUAAGJC/EV5f29oXKebs5eT9DDpJiIiIiJR2BqSbo3CK92NE6mZr5mDyBsul9qTfSUn3ax0k5Qx6SYiIiIiUTgSJI3Cx3Q7ku6aOisqzPVefe/aOitKqiwAgJhg5SbdztnLmXSTBDHpJiIiItFlZWUhLi4OBoMBiYmJ2LdvX4tt16xZg9tuuw3dunVDt27dkJyc3Gp7ki6bcyI1Zd+S+us0CDLYFw0ylXm3i/nlUnvX8i46DYL8ZbNwUYex0k1SpuwzHBEREUneli1bkJ6ejiVLluDgwYMYMWIEUlJSUFhY2Gz7PXv2YMqUKdi9ezdyc3MRGxuLcePGoaCgwMuR0/Wqdy4ZJnIgXtC0i7k3Ne1arlJwN37n7OVcp5skyAdOcURERCRlK1euxOzZszFz5kwMGTIEq1evRkBAANauXdts+/feew///d//jfj4eAwaNAj/+Mc/YLPZkJOT4+XI6XrZBN+odAONy4Y5JjXzlssN7xet4PHcACvdJG3KP8MRERGRZFksFuTl5SE5Odm5Ta1WIzk5Gbm5ue16jerqatTV1SE0NLTFNmazGeXl5S4PEl+91VHpVm4F1qFnN3vS+9PPXk66G7qX9whR7hrdQNN1ujl7OUkPk24iIiISTXFxMaxWKyIjI122R0ZGwmg0tus1nn32WcTExLgk7r+UmZmJ4OBg5yM2Nva64ib3qG9IkPx8IukOAABc+rnaq+97paF7ebSCJ1EDWOkmaWPSTURERLL117/+FZs3b8YHH3wAg6HlSl5GRgbKysqcj0uXLnkxSmpJndU31ukGRKx0+8Aa3QCg0XD2cpIu5U5hSERERJIXFhYGjUYDk8nkst1kMiEqKqrVfV999VX89a9/xeeff47hw4e32lav10Ov1193vORe9VZ7pVurUX4dKDbUXun+qcS7le6Chu7lMcHK7l7OSjdJmfLPcERERCRZOp0OCQkJLpOgOSZFS0pKanG/V155BS+++CKys7MxatQob4RKHuBIkHyje7m90nylvBaWeu+MOxYEwdm9XPGVbueYbibdJD1MuomIiEhU6enpWLNmDTZs2ICTJ09i7ty5qKqqwsyZMwEA06dPR0ZGhrP9yy+/jEWLFmHt2rWIi4uD0WiE0WhEZWWlWB+BOsniQ5Xu8EA9DH5qCIL3ZjAvra5DTZ0VQOPs6UrFSjdJGbuXExERkagmT56MoqIiLF68GEajEfHx8cjOznZOrpafnw91kyWl3nrrLVgsFjz44IMur7NkyRIsXbrUm6HTdXLMXu7nA2O6VSoVenYLwNnCSlwqqUHv7l08/p6O8ePhXfUw+Gk8/n5i4uzlJGVMuomIiEh0aWlpSEtLa/a5PXv2uPz7woULng+IvMIxptvPByrdgL2L+dnCSq/NYH6xpAoA0LthPLmSOdZ6d/yQQyQlvnGGIyIiIiLJqWvoCqxV+8YtaWzDsmE/eSnpzm+YtK2XDyTdHNNNUuYbZzgiIiIikpzGSrfyu5cDQGyofTKzSyXeGdN9qSHpjvWBpJtjuknKmHQTERERkSh8aZ1uAOjZUOn2Vvdyn6p0a1jpJuli0k1EREREoqhzzF7uY93LvVXpvni1Ienu7gNJt4qVbpIu3zjDEREREZHk+NLs5UBj8ltcaUalud6j71VnteFyqT2594VKt5azl5OEMekmIiIiIlHU2XxnnW4ACPb3Q/cuOgDAheIqj77X5dIa2ARAr1Ujoqveo+8lBRqO6SYJ840zHBERERFJjqPS7ahS+oI+4fb1uc8VVXr0fZqO51aplH98tRzTTRLGpJuIiIiIRFHfUOnWaX3nlrRPWCAA4Mciz1a6HeO5fWHmcgDQcJ1ukjDfOcMRERERkaRY6n1rnW6gsdL9o4e7lzuS+j5hXTz6PlKh5TrdJGG+c4YjIiIiIkmpd47pVn73Z4c+4Y5Kt2e7l59teP2+EYEefR+p4JhukjIm3UREREQkCl+bvRxorHSfL66CIHguQTxXaE+6+/lI0s3Zy0nKZJN0l5SUYOrUqQgKCkJISAhmzZqFysrWfyGsra3FvHnz0L17dwQGBmLixIkwmUzNtr169Sp69uwJlUqF0tJSD3wCIiIiImrK19bpBuxrdWvUKlRbrDCW13rkPaot9ShoWC6sX7hvJN2sdJOUyeYMN3XqVBw/fhy7du3Cxx9/jK+++gpz5sxpdZ+nnnoKH330EbZu3Yovv/wSly9fxu9+97tm286aNQvDhw/3ROhERERE1AxHguRLlW6dVu1cN/tcoWfGdTvGc3fvokO3hiXKlM7xww3HdJMUySLpPnnyJLKzs/GPf/wDiYmJuPXWW/Hmm29i8+bNuHz5crP7lJWV4Z133sHKlStx5513IiEhAevWrcPevXvx7bffurR96623UFpaij/96U/e+DhEREREBKDeByvdADAg0l59PmUs98jrn23oWt7XR6rcAKDRsNJN0iWLM1xubi5CQkIwatQo57bk5GSo1Wp89913ze6Tl5eHuro6JCcnO7cNGjQIvXr1Qm5urnPbiRMn8MILL2Djxo1Q+9gJn4iIiEhMFsc63T5U6QaAwdFBAICTVyo88vrnfGwSNYCzl5O0ySLLNBqNiIiIcNmm1WoRGhoKo9HY4j46nQ4hISEu2yMjI537mM1mTJkyBf/zP/+DXr16tTses9mM8vJylwcRERERdYy53goAMPhpRI7EuxxJ94krnrmHPGW0J/P9fSjp1jRJuj05QR1RZ4iadC9YsAAqlarVx6lTpzz2/hkZGRg8eDD+8Ic/dGi/zMxMBAcHOx+xsbEeipCIiIhIucx19u7lvpZ0D2lIus8WVsBS7/7Zto8XlAEAbowJcvtrS5Wj0g2w2k3SoxXzzZ9++mk88sgjrbbp06cPoqKiUFhY6LK9vr4eJSUliIqKana/qKgoWCwWlJaWulS7TSaTc58vvvgCR48exbZt2wDA+atYWFgYnn/+eSxbtqzZ187IyEB6errz3+Xl5Uy8iYiIiDrI3JBw6rWy6HzpNj27+aOrQYuK2nqcK6p0Vr7d4ecqCy6X2WdFH+JDSbemSdJdbxOg9a3fcUjiRE26w8PDER4e3ma7pKQklJaWIi8vDwkJCQDsCbPNZkNiYmKz+yQkJMDPzw85OTmYOHEiAOD06dPIz89HUlISAGD79u2oqalx7rN//348+uij+Prrr9G3b98W49Hr9dDr9e3+nERERER0LUf3cl9LulUqFQZHBWHfhRKcuFzu1qT7+GV7l/Xe3QPQ1eDntteVOj9N43eIk6mR1IiadLfX4MGDMX78eMyePRurV69GXV0d0tLS8PDDDyMmJgYAUFBQgLvuugsbN27E6NGjERwcjFmzZiE9PR2hoaEICgrC448/jqSkJNx8880AcE1iXVxc7Hy/X44FJyIiIiL38tXu5QBwYw970n3kp1JMTOjpttc9ftn3upYD9h9uVCpAEIAaixWBelmkOeQjZPOz4nvvvYdBgwbhrrvuwr333otbb70Vb7/9tvP5uro6nD59GtXV1c5tr732Gu6//35MnDgRv/71rxEVFYX3339fjPCJiIiIqAmbTYDF6pvdywEgoXc3AMCBiz+79XWPOMdzB7v1daVOpVLBv+HHm2pLvcjRELmSzU9AoaGh2LRpU4vPx8XFXTNTocFgQFZWFrKystr1HmPHjuVsh0RERERe4Ei4Ad+sdDuS7pNXylFlrkcXN1RmBUHAgQslAICbenW77teTmwCdBtUWK6otVrFDIXLhez8rEhEREZHoHF3LAd+sdEcH+6NHiD9sAvD9pVK3vOalkhqYys3w06gQHxvilteUkwCd/YcLJt0kNb53hiMiIiIi0dXU2RMjrVoFrcY3b0lvaqh2f3e+xC2vt7+hyj20RzD8db7XeyCg4TPXMOkmifHNMxwRERERiarSbB93645u1XJ1S9/uAIAvzxS55fX2NSTvo+NC3fJ6cuP4oaGKY7pJYph0ExEREZHXOZJuX55l+vaB9qVzv/+pFCVVlut6LUEQsOdMIQAgqSGZ9zVdGrqXs9JNUsOkm4iIiIi8rrKWSXd0sD8GRXWFIABf/3B91e7jl8thKjcjQKfBzX18M+l2VLo5ppukhkk3EREREXmds9Jt8N2kGwDuHBQBAPj4yJXrep3PTpgAALf2C/PJ2eCBxjHdXDKMpIZJNxERERF5Hcd02/12ZA8AwO5ThSiuNHfqNWw2Ae8f/AkAcM+wKLfFJjeO71JFLZNukhYm3URERETkdSVV9gQzNMBP5EjE1T+yK0b0DEZ9k8S5o7798Sp++rkGXfVajL8x2s0Rykd4oB4AUFjRuR8viDyFSTcREREReV1RQ2IUEWQQORLxTRndCwCw5uvzqK3r+HjkrD1nAQAPjIzxyaXCHCIbvkuF5bUiR0Lkikk3ERERiS4rKwtxcXEwGAxITEzEvn37Wm2/detWDBo0CAaDAcOGDcPOnTu9FCm5i6Ma6ahO+rLf3dQTPUL8UVRhxlt7znVo3y/PFOGbs1fhp1Hhv37d10MRykNUsP27ZGTSTRLDpJuIiIhEtWXLFqSnp2PJkiU4ePAgRowYgZSUFBQWFjbbfu/evZgyZQpmzZqFQ4cOITU1FampqTh27JiXI6frca6oEgAQG+ovciTi02nVWHDPIADAqt1n8VU71+0uKK3Bn7d9DwD4w829ERsa4LEY5cBR6b5cWgNBEESOhqiRSuA38rqVl5cjODgYZWVlCAoKEjscIiKSIV++liQmJuJXv/oVVq1aBQCw2WyIjY3F448/jgULFlzTfvLkyaiqqsLHH3/s3HbzzTcjPj4eq1evbtd7Kv14N729a3qnJzT3vMt+Tds2/xqu79N2e9fXt/+rtLoOd634EharDV89cwd6dfftZNHhqS2H8cGhAug0asz5dR88EB+DuLAu8NPY62SCIKDOKuBqlRlfnynGK5+eRnGlGf0jAvHvtFsQoPPtSelq66wYvvQzWKw2fPz4rRjaI1jskEjh2nst8e2/TCIiIhKVxWJBXl4eMjIynNvUajWSk5ORm5vb7D65ublIT0932ZaSkoIdO3a0+D5msxlmc+PkSuXl5dcXeIP/fi8Pe07bq5JtJaAueWsnk9Vrt3c8ZikZGNmVle4mXp44HJZ6Gz45egWrdp/Fqt32sdoatQoatQqWets1+wyM7Ip1M3/l8wk3ABj8NBg7MByfnTDh/jf/A71WDZUKUEHV8L9EjZL6huEfM0Z55b3410lERESiKS4uhtVqRWRkpMv2yMhInDp1qtl9jEZjs+2NRmOL75OZmYlly5Zdf8C/YK6zodrS8YmvCIgJNuCvE4dBpWIq5KDTqrHq9yNx79FovPfdRRzM/xm1dTZYbQKstsZfWNQqYEBkV/x2ZA/MGBPns+tyN2fR/UNw4WoVzpgqYW7mRwoiB3O9987dTLqJiIhI8TIyMlyq4+Xl5YiNjb3u182cOAzmuuZv7Jvmkk0TS1VLbZo847odzf6jPe07+r4u/+mm12wpp7ZXIZlw/5JKpcJ9w6Nx3/Bo2GwCymrqYK63od5mg16rgd5PDYNWA52WUzM1JzY0AJ8++WtcrbKgts7a2NNE5r1CyP0Mft77G2LSTURERKIJCwuDRqOByWRy2W4ymRAVFdXsPlFRUR1qDwB6vR56vftnyY7oyuWuyHPUahW6ddGJHYbsqFQqhHFWfJIQ/kRGREREotHpdEhISEBOTo5zm81mQ05ODpKSkprdJykpyaU9AOzatavF9kRERGJipZuIiIhElZ6ejhkzZmDUqFEYPXo0Xn/9dVRVVWHmzJkAgOnTp6NHjx7IzMwEAMyfPx+33347VqxYgfvuuw+bN2/GgQMH8Pbbb4v5MYiIiJrFpJuIiIhENXnyZBQVFWHx4sUwGo2Ij49Hdna2c7K0/Px8qNWNnfPGjBmDTZs2YeHChXjuuefQv39/7NixA0OHDhXrIxAREbWI63S7gdLX+iQiIs/jtcS7eLyJiOh6tfdawjHdRERERERERB7CpJuIiIiIiIjIQzim2w0cPfTLy8tFjoSIiOTKcQ3hqC/v4LWbiIiuV3uv3Uy63aCiogIAEBsbK3IkREQkdxUVFQgODhY7DMXjtZuIiNylrWs3J1JzA5vNhsuXL6Nr165QqVSdfp3y8nLExsbi0qVLnNSlBTxGbeMxah8ep7bxGLXNncdIEARUVFQgJibGZaZu8gxeu72Lx6ltPEZt4zFqHx6ntrnrGLX32s1Ktxuo1Wr07NnTba8XFBTEP5A28Bi1jceofXic2sZj1DZ3HSNWuL2H125x8Di1jceobTxG7cPj1DZ3HKP2XLv5UzoRERERERGRhzDpJiIiIiIiIvIQJt0SotfrsWTJEuj1erFDkSweo7bxGLUPj1PbeIzaxmNE/A60D49T23iM2sZj1D48Tm3z9jHiRGpEREREREREHsJKNxEREREREZGHMOkmIiIiIiIi8hAm3UREREREREQewqTby7KyshAXFweDwYDExETs27ev1fZbt27FoEGDYDAYMGzYMOzcudNLkYqnI8do/fr1UKlULg+DweDFaL3vq6++woQJExATEwOVSoUdO3a0uc+ePXtw0003Qa/Xo1+/fli/fr3H4xRTR4/Rnj17rvkeqVQqGI1G7wQsgszMTPzqV79C165dERERgdTUVJw+fbrN/XzpnNSZY+SL5yRfwGt3+/D63Tpev9vG63fbeP1umxSv30y6vWjLli1IT0/HkiVLcPDgQYwYMQIpKSkoLCxstv3evXsxZcoUzJo1C4cOHUJqaipSU1Nx7NgxL0fuPR09RoB9UfsrV644HxcvXvRixN5XVVWFESNGICsrq13tz58/j/vuuw933HEHDh8+jCeffBKPPfYYPv30Uw9HKp6OHiOH06dPu3yXIiIiPBSh+L788kvMmzcP3377LXbt2oW6ujqMGzcOVVVVLe7ja+ekzhwjwPfOSUrHa3f78PrdNl6/28brd9t4/W6bJK/fAnnN6NGjhXnz5jn/bbVahZiYGCEzM7PZ9g899JBw3333uWxLTEwU/uu//sujcYqpo8do3bp1QnBwsJeikx4AwgcffNBqmz//+c/CjTfe6LJt8uTJQkpKigcjk472HKPdu3cLAISff/7ZKzFJUWFhoQBA+PLLL1ts44vnpKbac4x8/ZykRLx2tw+v3x3D63fbeP1uH16/2yaF6zcr3V5isViQl5eH5ORk5za1Wo3k5GTk5uY2u09ubq5LewBISUlpsb3cdeYYAUBlZSV69+6N2NhYPPDAAzh+/Lg3wpUNX/seXY/4+HhER0fj7rvvxjfffCN2OF5VVlYGAAgNDW2xja9/l9pzjACek5SE1+724fXbM3zxu9RZvH7z+t0aKVy/mXR7SXFxMaxWKyIjI122R0ZGtjjuxGg0dqi93HXmGA0cOBBr167Fv//9b7z77ruw2WwYM2YMfvrpJ2+ELAstfY/Ky8tRU1MjUlTSEh0djdWrV2P79u3Yvn07YmNjMXbsWBw8eFDs0LzCZrPhySefxC233IKhQ4e22M7XzklNtfcY8ZykLLx2tw+v357B63fbeP3m9bstUrl+a93yKkQiSUpKQlJSkvPfY8aMweDBg/G///u/ePHFF0WMjORk4MCBGDhwoPPfY8aMwblz5/Daa6/h//7v/0SMzDvmzZuHY8eO4T//+Y/YoUhWe48Rz0lE7cO/FXIHXr95/W6LVK7frHR7SVhYGDQaDUwmk8t2k8mEqKioZveJiorqUHu568wx+iU/Pz+MHDkSZ8+e9USIstTS9ygoKAj+/v4iRSV9o0eP9onvUVpaGj7++GPs3r0bPXv2bLWtr52THDpyjH6J5yR547W7fXj99gxevzuH1+9r+eJ5CZDW9ZtJt5fodDokJCQgJyfHuc1msyEnJ8flV5WmkpKSXNoDwK5du1psL3edOUa/ZLVacfToUURHR3sqTNnxte+Ruxw+fFjR3yNBEJCWloYPPvgAX3zxBW644YY29/G171JnjtEv8Zwkb7x2tw+v357hi98ld+D1+1q+9l2S5PXbY1O00TU2b94s6PV6Yf369cKJEyeEOXPmCCEhIYLRaBQEQRCmTZsmLFiwwNn+m2++EbRarfDqq68KJ0+eFJYsWSL4+fkJR48eFesjeFxHj9GyZcuETz/9VDh37pyQl5cnPPzww4LBYBCOHz8u1kfwuIqKCuHQoUPCoUOHBADCypUrhUOHDgkXL14UBEEQFixYIEybNs3Z/scffxQCAgKEZ555Rjh58qSQlZUlaDQaITs7W6yP4HEdPUavvfaasGPHDuGHH34Qjh49KsyfP19Qq9XC559/LtZH8Li5c+cKwcHBwp49e4QrV644H9XV1c42vn5O6swx8sVzktLx2t0+vH63jdfvtvH63TZev9smxes3k24ve/PNN4VevXoJOp1OGD16tPDtt986n7v99tuFGTNmuLT/17/+JQwYMEDQ6XTCjTfeKHzyySdejtj7OnKMnnzySWfbyMhI4d577xUOHjwoQtTe41ge45cPx3GZMWOGcPvtt1+zT3x8vKDT6YQ+ffoI69at83rc3tTRY/Tyyy8Lffv2FQwGgxAaGiqMHTtW+OKLL8QJ3kuaOz4AXL4bvn5O6swx8sVzki/gtbt9eP1uHa/fbeP1u228frdNitdvVUNgRERERERERORmHNNNRERERERE5CFMuomIiIiIiIg8hEk3ERERERERkYcw6SYiIiIiIiLyECbdRERERERERB7CpJuIiIiIiIjIQ5h0ExEREREREXkIk24iIiIiIiIiD2HSTUQteuSRR5Camira+0+bNg3Lly9vV9uHH34YK1as8HBEJBdfffUVJkyYgJiYGKhUKuzYsUP093v//fcxbtw4dO/eHSqVCocPH/ZoTETkm3jtJjlT6vWbSTeRj1KpVK0+li5dijfeeAPr168XJb7vv/8eO3fuxBNPPNGu9gsXLsRLL72EsrIyD0dGclBVVYURI0YgKytLMu9XVVWFW2+9FS+//LJXYiIi5eG1m5ROqddvbaf3JCJZu3LlivO/t2zZgsWLF+P06dPObYGBgQgMDBQjNADAm2++iUmTJrU7hqFDh6Jv37549913MW/ePA9HR1J3zz334J577mnxebPZjOeffx7//Oc/UVpaiqFDh+Lll1/G2LFjPfJ+gL36AwAXLlzo1HsQEfHaTUqn1Os3K91EPioqKsr5CA4OhkqlctkWGBh4TRe1sWPH4vHHH8eTTz6Jbt26ITIyEmvWrEFVVRVmzpyJrl27ol+/fvh//+//ubzXsWPHcM899yAwMBCRkZGYNm0aiouLW4zNarVi27ZtmDBhgsv2v//97+jfvz8MBgMiIyPx4IMPujw/YcIEbN68+foPDileWloacnNzsXnzZhw5cgSTJk3C+PHj8cMPP4gdGhFRi3jtJl8n1+s3k24i6pANGzYgLCwM+/btw+OPP465c+di0qRJGDNmDA4ePIhx48Zh2rRpqK6uBgCUlpbizjvvxMiRI3HgwAFkZ2fDZDLhoYceavE9jhw5grKyMowaNcq57cCBA3jiiSfwwgsv4PTp08jOzsavf/1rl/1Gjx6Nffv2wWw2e+bDkyLk5+dj3bp12Lp1K2677Tb07dsXf/rTn3Drrbdi3bp1YodHROR2vHaTEsj5+s2km4g6ZMSIEVi4cCH69++PjIwMGAwGhIWFYfbs2ejfvz8WL16Mq1ev4siRIwCAVatWYeTIkVi+fDkGDRqEkSNHYu3atdi9ezfOnDnT7HtcvHgRGo0GERERzm35+fno0qUL7r//fvTu3RsjR468ZsxYTEwMLBYLjEaj5w4Ayd7Ro0dhtVoxYMAAZ1fMwMBAfPnllzh37hwA4NSpU22OnVywYIHIn4SIqH147SYlkPP1m2O6iahDhg8f7vxvjUaD7t27Y9iwYc5tkZGRAIDCwkIA9klVdu/e3ez4rnPnzmHAgAHXbK+pqYFer4dKpXJuu/vuu9G7d2/06dMH48ePx/jx4/Hb3/4WAQEBzjb+/v4A4Pylnqg5lZWV0Gg0yMvLg0ajcXnO8T3t06cPTp482errdO/e3WMxEhG5E6/dpARyvn4z6SaiDvHz83P5t0qlctnmuNjabDYA9hPkhAkTmp3xMTo6utn3CAsLQ3V1NSwWC3Q6HQCga9euOHjwIPbs2YPPPvsMixcvxtKlS7F//36EhIQAAEpKSgAA4eHh1/chSdFGjhwJq9WKwsJC3Hbbbc220el0GDRokJcjIyLyDF67SQnkfP1m0k1EHnXTTTdh+/btiIuLg1bbvlNOfHw8AODEiRPO/wYArVaL5ORkJCcnY8mSJQgJCcEXX3yB3/3udwDsk7707NkTYWFh7v4YJDOVlZU4e/as89/nz5/H4cOHERoaigEDBmDq1KmYPn06VqxYgZEjR6KoqAg5OTkYPnw47rvvPre+X69evQDYbyzz8/Nx+fJlAHDOOOyYAImISCp47SaxKPX6zTHdRORR8+bNQ0lJCaZMmYL9+/fj3Llz+PTTTzFz5kxYrdZm9wkPD8dNN92E//znP85tH3/8Mf72t7/h8OHDuHjxIjZu3AibzYaBAwc623z99dcYN26cxz8TSd+BAwcwcuRIjBw5EgCQnp6OkSNHYvHixQCAdevWYfr06Xj66acxcOBApKamYv/+/c4LrLvfDwA+/PBDjBw50nlT8PDDD2PkyJFYvXr19XxUIiK347WbxKLU6zcr3UTkUTExMfjmm2/w7LPPYty4cTCbzejduzfGjx8Ptbrl3/0ee+wxbNy4EWlpaQCAkJAQvP/++1i6dClqa2vRv39//POf/8SNN94IAKitrcWOHTuQnZ3tlc9F0jZ27FgIgtDi835+fli2bBmWLVvmlfcDgEceeQSPPPKIW96PiMiTeO0msSj1+q0S2noXIiIR1NTUYODAgdiyZQuSkpLabP/WW2/hgw8+wGeffeaF6IiIiOiXeO0mah67lxORJPn7+2Pjxo0oLi5uV3s/Pz+8+eabHo6KiIiIWsJrN1HzWOkmIiIiIiIi8hBWuomIiIiIiIg8hEk3ERERERERkYcw6SYiIiIiIiLyECbdRERERERERB7CpJuIiIiIiIjIQ5h0ExEREREREXkIk24iIiIiIiIiD2HSTUREREREROQhTLqJiIiIiIiIPIRJNxEREREREZGH/H+OBFKHfWcAMwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548\n", + "wvl = np.linspace(1.5, 1.6, num_measurements)\n", + "time_sim = TimeSim(\n", + " netlist=netlist,\n", + " models=models,\n", + " active_components=active_components,\n", + " )\n", + "\n", + "\n", + "options = {'wl':wvl,'wg':{\"length\": 10.0, \"loss\": 100}, 'wg2':{\"length\": 10.0, \"loss\": 100}}\n", + "time_sim.build_model(model_parameters=options)\n", + "\n", + "inputs = {\n", + " f'o{i}': smooth_rectangular_pulse(t,0.5e-11,1.5e-11) if i == 0 else jnp.zeros_like(t)\n", + " for i in range(2)\n", + " }\n", + "\n", + "modelResult =time_sim.run(t, inputs)\n", + "\n", + "modelResult.plot_sim()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With active components in the circuit, the simulation algorithm produces both subnetlists and a final netlist. To integrate active components with passive ones, the algorithm first separates the passive components from the active components. The passive components are then organized into subnetlists and simulated using the pole-residue model. After simulation, these results are reintegrated with the active components for further processing by the algorithm. The netlists are returned to the user for debugging purposes.\n", + "\n", + "Reviewing the simulation results, we observe the expected dip in the signal caused by the Gaussian pulse in the phase modulator—the signal drops to zero before returning to its original level. Additionally, the results clearly show a time delay and loss as the signal travels through the Mach-Zehnder Interferometer (MZI), with the intensity decreasing to approximately 0.8.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we have a a slightly more complicated example to show how robust the model is in simluating photonic circuits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PythonX", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/simphony/time_domain/examples/Working_test.py b/simphony/time_domain/examples/Working_test.py new file mode 100644 index 00000000..c38201ca --- /dev/null +++ b/simphony/time_domain/examples/Working_test.py @@ -0,0 +1,280 @@ +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) + + +import matplotlib + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim +from simphony.time_domain.utils import smooth_rectangular_pulse + +T = 10.0e-11 +dt = 1e-14 # Total time duration (40 ps) +t = jnp.arange(0, T, dt) # Time array +t0 = 1e-11 # Pulse start time +std = 1e-12 +inter = 50 +m = jnp.array([], dtype=jnp.complex128) # MZI#1 arm A +m4 = jnp.array([], dtype=jnp.complex128) # MZI#1 arm B +m2 = jnp.array([], dtype=jnp.complex128) # MZI#2 arm A +m5 = jnp.array([], dtype=jnp.complex128) # MZI#2 arm B +# np.pi/6,-np.pi/6,np.pi/3,-np.pi/3, +# np.pi/6,-np.pi/6,np.pi/3,-np.pi/3, +general_table = [np.pi / 10, 9 * np.pi / 10, np.pi / 2.45, np.pi / 1.65] +i_phase_table = [general_table[0], general_table[1], general_table[2], general_table[3]] +q_phase_table = [general_table[0], general_table[1], general_table[2], general_table[3]] + +num_symbols = int(len(t) / inter) - 1 + +for sym in range(num_symbols): + # 4 random bits: + bits = np.random.randint(0, 2, size=4) + + # 2 bits for I, 2 bits for Q + i_index = bits[0] * 2 + bits[1] # from 00..11 => 0..3 + q_index = bits[2] * 2 + bits[3] # from 00..11 => 0..3 + + i_val = i_phase_table[i_index] + q_val = q_phase_table[q_index] + + # Create the push-pull waveforms for one symbol interval + i_signal_block_A = jnp.ones(inter) * i_val + i_signal_block_B = jnp.ones(inter) * (-i_val) + + q_signal_block_A = jnp.ones(inter) * q_val + q_signal_block_B = jnp.ones(inter) * (-q_val) + + # Append + m = jnp.concatenate([m, i_signal_block_A]) + m4 = jnp.concatenate([m4, i_signal_block_B]) + m2 = jnp.concatenate([m2, q_signal_block_A]) + m5 = jnp.concatenate([m5, q_signal_block_B]) + + +# f_mod =0 +# # m = f_mod * jnp.ones(len(t),dtype = complex) +# f_mod2 =0 +# m2 = f_mod2 * jnp.ones(len(t),dtype = complex) +f_mod3 = jnp.pi / 2 +m3 = f_mod3 * jnp.ones(len(t), dtype=complex) +# f_mod4 =0.0 +# # m4 = f_mod4 * jnp.ones(len(t),dtype = complex) +# m5 = f_mod4 * jnp.ones(len(t),dtype = complex) +x = jnp.linspace(0, 3.14, len(t)) + +# Define Gaussian parameters +mu = 1.14 # center the Gaussian in the middle of the interval +sigma = 0.3 # adjust sigma for desired width + +# Compute the Gaussian function +gaussian = jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + +# Optionally, normalize so the area under the curve is 1 +gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x +timePhaseInstantiated = Modulator(mod_signal=m) +timePhaseInstantiated2 = Modulator(mod_signal=m2) +timePhaseInstantiated3 = Modulator(mod_signal=m3) +timePhaseInstantiated4 = Modulator(mod_signal=m4) +timePhaseInstantiated5 = Modulator(mod_signal=m5) + +netlist = { + "instances": { + "wg": "waveguide", + "y": "y_branch", + "pm": "phase_modulator", + "pm2": "phase_modulator2", + "pm3": "phase_modulator3", + "pm4": "phase_modulator4", + "pm5": "phase_modulator5", + "y2": "y_branch", + "wg2": "waveguide", + "y3": "y_branch", + "y4": "y_branch", + "y5": "y_branch", + "y6": "y_branch", + "wg3": "waveguide", + "wg4": "waveguide", + "wg5": "waveguide", + "wg6": "waveguide", + }, + "connections": { + "y2,port_2": "y3,port_1", + "y2,port_3": "y4,port_1", + "y4,port_2": "wg5,o0", + "y4,port_3": "wg6,o0", + "wg5,o1": "pm,o0", + "wg6,o1": "pm4,o0", + "y5,port_3": "pm,o1", + "y5,port_2": "pm4,o1", + "y3,port_2": "wg,o0", + "y3,port_3": "wg2,o0", + "wg,o1": "pm2,o0", + "wg2,o1": "pm5,o0", + "y6,port_2": "pm2,o1", + "y6,port_3": "pm5,o1", + "y6,port_1": "pm3,o0", + "y,port_3": "pm3,o1", + "y,port_2": "y5,port_1", + }, + "ports": { + "o0": "y2, port_1", + "o1": "y, port_1", + }, +} +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, + "phase_modulator2": timePhaseInstantiated2, + "phase_modulator3": timePhaseInstantiated3, + "phase_modulator4": timePhaseInstantiated4, + "phase_modulator5": timePhaseInstantiated5, +} +active_components = { + "pm", + "pm2", + "pm3", + "pm4", + "pm5", +} + + +time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, +) + +num_measurements = 200 +model_order = 100 +center_wvl = 1.548 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = {"wl": wvl, "wg": {"length": 10.0, "loss": 100}} + + +time_sim.build_model(model_parameters=options) + +num_outputs = 2 + +inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.0e-11, 20e-11) if i == 0 else jnp.zeros_like(t) + ) + for i in range(num_outputs) +} + +modelResult = time_sim.run(t, inputs) + +modelResult.plot_sim() + +I_output = np.real(modelResult.outputs["o1"]) +Q_output = np.imag(modelResult.outputs["o1"]) + + +plt.subplot(2, 1, 1) +plt.plot(t * 1e12, Q_output, label="System Output I(t)", color="green", linestyle="--") +plt.xlabel("Time (ps)") +plt.ylabel("Amplitude") +plt.title("In-Phase Component Comparison") +plt.legend() +plt.grid(True) + +# Plot Q components +plt.subplot(2, 1, 2) + +plt.plot(t * 1e12, I_output, label="System Output Q(t)", color="orange", linestyle="--") +plt.xlabel("Time (ps)") +plt.ylabel("Amplitude") +plt.title("Quadrature Component Comparison") +plt.legend() +plt.grid(True) + +plt.tight_layout() +plt.show() + + +plt.figure(figsize=(6, 6)) +plt.plot( + Q_output, I_output, color="blue", linewidth=1, alpha=0.7, label="Transition Path" +) +# plt.scatter(symbols_I, symbols_Q, color='red', s=50, zorder=5, label='Symbols') +plt.xlabel("In-Phase (I)") +plt.ylabel("Quadrature (Q)") +plt.title("16-QAM Constellation with Transition Paths") +plt.legend() +plt.grid(True) +plt.axis("equal") +plt.show() + + +def upsample_trajectory(I, Q, factor=20): + I_list, Q_list = [], [] + n = len(I) + for i in range(n - 1): + i0, i1 = I[i], I[i + 1] + q0, q1 = Q[i], Q[i + 1] + for alpha in np.linspace(0, 1, factor, endpoint=False): + I_list.append(i0 + alpha * (i1 - i0)) + Q_list.append(q0 + alpha * (q1 - q0)) + # Add the last point + I_list.append(I[-1]) + Q_list.append(Q[-1]) + return np.array(I_list), np.array(Q_list) + + +I_output, Q_output = upsample_trajectory(I_output, Q_output, factor=5) +plt.figure(figsize=(8, 6)) +bins = 500 +plt.hist2d(I_output, Q_output, bins=bins, cmap="jet", norm=matplotlib.colors.LogNorm()) +plt.colorbar(label="Counts per bin") +plt.title("Heat Map of 16-QAM Trajectory (fmod = 0) with Upsampling") +plt.xlabel("In-Phase (I)") +plt.ylabel("Quadrature (Q)") +plt.show() + + +# I_output1 = np.real(modelResult.outputs["o2"]) +# Q_output1 = np.imag(modelResult.outputs["o2"]) + +# plt.subplot(2,1,1) +# plt.plot(t*1e12, Q_output1, label='System Output I(t)', color='green', linestyle='--') +# plt.xlabel('Time (ps)') +# plt.ylabel('Amplitude') +# plt.title('In-Phase Component Comparison') +# plt.legend() +# plt.grid(True) + +# # Plot Q components +# plt.subplot(2,1,2) + +# plt.plot(t*1e12, I_output1, label='System Output Q(t)', color='orange', linestyle='--') +# plt.xlabel('Time (ps)') +# plt.ylabel('Amplitude') +# plt.title('Quadrature Component Comparison') +# plt.legend() +# plt.grid(True) + +# plt.tight_layout() +# plt.show() + +# plt.figure(figsize=(6,6)) +# plt.plot(Q_output1, I_output1, color='blue', linewidth=1, alpha=0.7, label='Transition Path') +# #plt.scatter(symbols_I, symbols_Q, color='red', s=50, zorder=5, label='Symbols') +# plt.xlabel("In-Phase (I)") +# plt.ylabel("Quadrature (Q)") +# plt.title("16-QAM Constellation with Transition Paths") +# plt.legend() +# plt.grid(True) +# plt.axis('equal') +# plt.show() + +# # plt.plot(t, m) +# # plt.plot(t, m2) +# # plt.show() diff --git a/simphony/time_domain/examples/ideal_models_reverted.ipynb b/simphony/time_domain/examples/ideal_models_reverted.ipynb new file mode 100644 index 00000000..d380a9bf --- /dev/null +++ b/simphony/time_domain/examples/ideal_models_reverted.ipynb @@ -0,0 +1,11658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Time Domain Simphony Ideal Models\n", + "\n", + "This notebook compares the ideal models in the Time Domain Simphony library to Simphonies frequency domain models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Presently, the following ideal models are available in the Time Domain Simphony library: \n", + "time_domain.ideal.coupler \n", + "time_domain.ideal.waveguide \n", + "\n", + "which corresponse to the following frequency domain models: \n", + "libraries.ideal.coupler \n", + "libraries.ideal.waveguide " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries.ideal import coupler, waveguide\n", + "from simphony.time_domain.ideal import TimeCoupler, TimeWaveguide, TimePhase_Modulator # import td.coupler and td.waveguide\n", + "from simphony.time_domain.time_circuit import TimeCircuit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Coupler\n", + "`libraries.ideal.coupler` is a function which calculates the s-parameters for an ideal coupler. The Time Domain Coupler model is not a function, but a class which can be used to simulate a coupler in a time domain simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frequency-Domain Waveguide Model: \n", + "Time-Domain Waveguide Model: \n" + ] + } + ], + "source": [ + "print(f\"Frequency-Domain Waveguide Model: {coupler}\")\n", + "print(f\"Time-Domain Waveguide Model: {TimeCoupler}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three parameters in the frequency domain coupler model:\n", + "\n", + "- `coupling` - The coupling ratio of the coupler. This is the ratio of the power in the through port to the power in the coupled port.\n", + "- `loss` - The loss in the coupler. This is the ratio of the power in the input port to the power in the through port.\n", + "- `phi` - The phase difference between the through and coupled ports." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the resulting matrix: (1, 4, 4)\n", + "S-Params: [[[0.00000000e+00+0.j 7.07106781e-01+0.j\n", + " 0.00000000e+00+0.j 4.32978028e-17+0.70710678j]\n", + " [7.07106781e-01+0.j 0.00000000e+00+0.j\n", + " 4.32978028e-17+0.70710678j 0.00000000e+00+0.j ]\n", + " [0.00000000e+00+0.j 4.32978028e-17+0.70710678j\n", + " 0.00000000e+00+0.j 7.07106781e-01+0.j ]\n", + " [4.32978028e-17+0.70710678j 0.00000000e+00+0.j\n", + " 7.07106781e-01+0.j 0.00000000e+00+0.j ]]]\n" + ] + } + ], + "source": [ + "import jax.numpy as jnp\n", + "from simphony.utils import dict_to_matrix\n", + "import matplotlib.pyplot as plt\n", + "\n", + "coupling = 0.5\n", + "loss = 0.0\n", + "phi = jnp.pi/2\n", + "\n", + "coupler_fd = coupler(coupling=coupling, loss=loss, phi=phi)\n", + "coupler_sparams = dict_to_matrix(coupler_fd)\n", + "\n", + "print(f\"Shape of the resulting matrix: {coupler_sparams.shape}\")\n", + "print(f\"S-Params: {coupler_sparams}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The time domain model uses these same parameters, but it does not return anything when instantiated. Instead, the time domain model itself is a subclass of `TimeSystem` which can be used in a time domain simulations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The time domain model uses these same parameters, but it does not return anything when instantiated. Instead, the time domain model itself is a subclass of `TimeSystem` which can be used in a time domain simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "coupler_td = TimeCoupler(coupling=coupling, loss=loss, phi=phi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most import method in `td.coupler` is the `response()` method which calculates the system response for an arbitrary input signal. Since the coupler is a 4-port device, the input signal must be a 4xN numpy array, where N is the number of time steps in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from simphony.time_domain.utils import gaussian_pulse\n", + "T = 10e-12\n", + "N = 1000\n", + "t = jnp.linspace(0, T, N)\n", + "\n", + "t0 = T/2\n", + "std = 1e-12\n", + "\n", + "inputs = {\n", + " 'o0': gaussian_pulse(t, t0, std),\n", + " 'o1': jnp.zeros((N), dtype=complex),\n", + " 'o2': 1j*gaussian_pulse(t, t0 - 0.3 * t0, std),\n", + " 'o3': jnp.zeros((N), dtype=complex),\n", + "}\n", + "\n", + "outputs = coupler_td.response(inputs)\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(4, 2, figsize=(10, 10)) # 4 rows, 2 columns\n", + "\n", + "# Plot input signals\n", + "for i in range(4):\n", + " axs[i, 0].plot(t, jnp.abs(inputs[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "# Plot output signals\n", + "for i in range(4):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Waveguide\n", + "\n", + "Similar to the coupler, the ideal waveguide model in the Time Domain is a class and not a function as it is in simphony's frequency domain library. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frequency-Domain Waveguide Model: \n", + "Time-Domain Waveguide Model: \n" + ] + } + ], + "source": [ + "print(f\"Frequency-Domain Waveguide Model: {waveguide}\")\n", + "print(f\"Time-Domain Waveguide Model: {TimeWaveguide}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 6 parameters in the frequency domain waveguide model:\n", + "- `wavelength (wl)`\n", + "- `center wavelength (wl0)`\n", + "- `effective index (neff)`\n", + "- `group index (ng)`\n", + "- `length`\n", + "- `loss`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate the Spectra Calculated from fd.waveguide\n", + "wl = jnp.linspace(1.5, 1.6, 1000)\n", + "wl0 = 1.55\n", + "neff = 2.34\n", + "ng = 3.4\n", + "length = 100.0\n", + "loss = 100.0\n", + "\n", + "waveguide_fd = waveguide(wl = wl, wl0=wl0, neff=neff, ng=ng, length=length, loss=loss)\n", + "waveguide_sparams = dict_to_matrix(waveguide_fd)\n", + "\n", + "# plt.plot(jnp.abs(waveguide_sparams[:, 0, 1])**2)\n", + "# plt.plot(jnp.angle(waveguide_sparams[:, 0, 1]))\n", + "# Create subplots\n", + "fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n", + "\n", + "# Magnitude subplot\n", + "axs[0].plot(wl, jnp.abs(waveguide_sparams[:, 0, 1])**2)\n", + "axs[0].set_title('Magnitude')\n", + "axs[0].set_ylabel('Magnitude (|S21|²)')\n", + "axs[0].grid()\n", + "\n", + "# Phase subplot\n", + "axs[1].plot(wl, jnp.angle(waveguide_sparams[:, 0, 1]))\n", + "axs[1].set_title('Phase')\n", + "axs[1].set_ylabel('Phase (radians)')\n", + "axs[1].set_xlabel('Wavelength')\n", + "axs[1].grid()\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the time domain model, the first parameter which specifies the wavelength is not needed. Instead, the waveguide model needs to know the time step of the simulation. This is because the waveguide model mantains its state by implementing a queue, where each element of the queue is the signal at a different time step.\n", + "- `time step (dt)`\n", + "- `center wavelength (wl0)`\n", + "- `effective index (neff)`\n", + "- `group index (ng)`\n", + "- `length`\n", + "- `loss`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The time-domain waveguide model must keep track of the state of the waveguide to properly calculate transient effects brought by the non-finite length of the waveguide. This state is maintained across multiple calls to the `response()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate the use of the waveguide response function\n", + "from simphony.time_domain.utils import gaussian_pulse\n", + "T = 11e-12\n", + "N = 1000\n", + "t = jnp.linspace(0, T, N)\n", + "dt = t[1] - t[0]\n", + "\n", + "t0 = T/2\n", + "std = 0.5e-12\n", + "\n", + "waveguide_td = TimeWaveguide(dt, wl0=wl0, neff=neff, ng=ng, length=length, loss=loss)\n", + "\n", + "inputs = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5*t0, std),\n", + " 'o1': 0.5*gaussian_pulse(t, t0 - 0.25*t0, std),\n", + "}\n", + "\n", + "outputs = waveguide_td.response(inputs)\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 5)) # 4 rows, 2 columns\n", + "\n", + "# Plot input signals\n", + "axs[0].plot(t, jnp.abs(inputs['o0'])**2, label=f'In Port o0')\n", + "axs[0].plot(t, jnp.abs(outputs['o1'])**2, label=f'Out Port o1')\n", + "axs[0].set_title('Time Response')\n", + "axs[0].set_xlabel('Time (s)')\n", + "axs[0].set_ylabel('Intensity')\n", + "axs[0].set_ylim(0.0, 1.1)\n", + "axs[0].legend()\n", + "\n", + "# Plot output signals\n", + "axs[1].plot(wl, jnp.abs(waveguide_sparams[:, 0, 1])**2)\n", + "axs[1].set_title(f'S-params')\n", + "axs[1].set_xlabel('wavelength')\n", + "axs[0].set_ylabel('Magnitude')\n", + "axs[1].set_ylim(0.0, 1.1)\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ring Resonator Example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike the sax.circuit function in the frequency domain, the `TimeCircuit` object is a class which whose models are class types (subclasses of `TimeSystem`). Instead of calling the simulation object directly as was done in the frequency domain, we must instantiate the time system models with the `TimeCircuit.instantiate` method.\n", + "\n", + "The ideal models may be instantiated by setting individual parameters in a dictionary (similar to the frequency domain) or by providing an previously initialized class." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Use simulation.py to implement a ring resonator using only td.ideal components\n", + "ring_resonator = TimeCircuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"coupler\": \"ideal_coupler\",\n", + " \"ring\": \"ideal_waveguide\",\n", + " },\n", + " \"connections\": {\n", + " \"coupler,o3\": \"ring,o0\",\n", + " \"ring,o1\": \"coupler,o2\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"coupler,o0\",\n", + " \"out\": \"coupler,o1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ideal_coupler\": TimeCoupler,\n", + " \"ideal_waveguide\": TimeWaveguide\n", + " },\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 2 main advantageous to providing previously initialized classes\n", + " \n", + "1) The `TimeCircuit` class is that it can be turned into a TimeSystem model with the method `TimeCircuit.to_time_system` for a modular design.\n", + "2) The `TimeSystem` classes instantiated by the PoleResidueModel can be used directly\n", + "\n", + "The main advantage of using a dictionary to instanstiate the models in the `TimeCircuit` is it very closely parallels the method used in frequency domain simphony.\n", + "\n", + "Both methods are shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instances created with dt=1e-14: {'coupler': , 'ring': }\n" + ] + } + ], + "source": [ + "wl0 = 1.55\n", + "neff = 2.34\n", + "ng = 3.4\n", + "length = 100.0\n", + "loss = 100.0\n", + "waveguide_td = TimeWaveguide(dt, wl0=wl0, neff=neff, ng=ng, length=length, loss=loss)\n", + "\n", + "# ring_resonator.instantiate(dt=1e-14, \n", + "# ideal_coupler={\"coupling\": 0.5, \"loss\": 0.0, \"phi\": jnp.pi/2}, \n", + "# ideal_waveguide={\"wl0\": 1.55, \"neff\": 3.4, \"ng\": 3.4, \"length\": 100.0, \"loss\": 100.0})\n", + "ring_resonator.instantiate(dt=1e-14, ideal_coupler=coupler_td, ideal_waveguide=waveguide_td)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n" + ] + } + ], + "source": [ + "# waveguide_td = TimeWaveguide(dt, wl0=wl0, neff=neff, ng=ng, length=length, loss=loss)\n", + "T = 11e-12\n", + "N = 500\n", + "t = jnp.linspace(0, T, N)\n", + "dt = t[1] - t[0]\n", + "\n", + "t0 = T/2\n", + "std = 0.5e-12\n", + "\n", + "inputs = {\n", + " 'in': gaussian_pulse(t, t0 - 0.5*t0, std),\n", + " 'out': jnp.zeros_like(t),\n", + "}\n", + "\n", + "outputs = ring_resonator.run_sim(t, inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(jnp.abs(outputs['out'])**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ring Resonator with Phase Modulator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An example of the different ways different models can be instantiated connecting their ports. This particular example includes the usage of a ring resonator where between the waveguide and coupler and phase modulator is included to provide a change in the phase as the signal goes through the ring resonator. A square signal is used as the input so the phase can be seen to be changed." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instances created with dt=1e-14: {'coupler': , 'ring': , 'pmod': }\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in Circuit Ports\n", + "Looking in Circuit Ports\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n", + "Looking in connections\n" + ] + } + ], + "source": [ + "ring_resonator_phase_modulator = TimeCircuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"coupler\": \"ideal_coupler\",\n", + " \"ring\": \"ideal_waveguide\",\n", + " \"pmod\": \"ideal_modulator\",\n", + " },\n", + " \"connections\": {\n", + " \"coupler,o3\": \"pmod,o0\",\n", + " \"pmod,o1\":\"ring, o0\",\n", + " \"ring,o1\": \"coupler,o2\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"coupler,o0\",\n", + " \"out\": \"coupler,o1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ideal_coupler\": TimeCoupler,\n", + " \"ideal_waveguide\": TimeWaveguide,\n", + " \"ideal_modulator\": TimePhase_Modulator,\n", + " },\n", + ")\n", + "\n", + "\n", + "f_mod = 0.0\n", + "m = f_mod * jnp.ones(len(t),dtype = complex)\n", + "phase_mod_td = TimePhase_Modulator(mod_signal = m, k_p = 1.0)\n", + "\n", + "T = 10e-12\n", + "N = 1000\n", + "dt = 1e-14\n", + "t = jnp.arange(0, T, dt)\n", + "\n", + "\n", + "t0 = T/2\n", + "std = 0.5e-12\n", + "\n", + "inputs = {\n", + " 'in': jnp.ones(len(t),dtype=complex),\n", + " 'out': jnp.zeros_like(t),\n", + "}\n", + "\n", + "ring_resonator_phase_modulator.instantiate(dt=1e-14, clear_on_reset=True, ideal_coupler=coupler_td, ideal_waveguide=waveguide_td, ideal_modulator=phase_mod_td)\n", + "\n", + "outputs = ring_resonator_phase_modulator.run_sim(t, inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(jnp.abs(outputs['out'])**2)\n", + "plt.plot(jnp.ones(len(t),dtype=complex))\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PythonX", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/simphony/time_domain/examples/pole_residue_model.ipynb b/simphony/time_domain/examples/pole_residue_model.ipynb new file mode 100644 index 00000000..3927c2b6 --- /dev/null +++ b/simphony/time_domain/examples/pole_residue_model.ipynb @@ -0,0 +1,620 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3a7b414e", + "metadata": {}, + "source": [ + "

Time Domain Simphony Pole Residue Model

" + ] + }, + { + "cell_type": "markdown", + "id": "b597208b", + "metadata": {}, + "source": [ + "This notebook contains the method to simulating active components in a circuit through the usage of complex vector fitting. With the state space model created it can then be linked to other components through the class Time System to model the circuit.\n", + "\n", + "The Pole residue Model of IIRModelBasebad exists in the simphony.time_domain.pole_residue_model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1e68495f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sax\n", + "import jax.numpy as jnp\n", + "from jax import config\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from simphony.libraries import ideal\n", + "from simphony.utils import dict_to_matrix\n", + "from simphony.time_domain.utils import gaussian_pulse\n", + "from simphony.time_domain.pole_residue_model import IIRModelBaseband\n", + "from simphony.time_domain.utils import pole_residue_to_time_system\n", + "from simphony.libraries import siepic\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e529a921", + "metadata": {}, + "source": [ + "# Pole Residue Model Example\n", + "\n", + "Instantiation of the active component must be done. How this component functions can be as needed but a netlist containing its components needs to be created and then the s_params taken from the circuit These are then passed into the IIRBasebandModel along with the specific parameters that the user would use such as:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4cff150d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "netlist = {\n", + " \"instances\": {\n", + " \"wg\": \"waveguide\",\n", + " \"hr\": \"half_ring\",\n", + " },\n", + " \"connections\": {\n", + " \"hr,o2\": \"wg,o0\",\n", + " \"hr,o3\": \"wg,o1\",\n", + " },\n", + " \"ports\": {\n", + " \"o0\": \"hr,o0\",\n", + " \"o1\": \"hr,o1\",\n", + " }\n", + "}\n", + "\n", + "circuit, info = sax.circuit(\n", + " netlist=netlist,\n", + " models={\n", + " \"waveguide\": ideal.waveguide,\n", + " \"half_ring\": ideal.coupler,\n", + " }\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "33327afb", + "metadata": {}, + "outputs": [], + "source": [ + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548 # Center wavelength (µm)\n", + "wvl = np.linspace(1.5, 1.6, num_measurements) # Wavelength range (µm)\n", + "\n", + "# Perform simulation\n", + "s = circuit(wl=wvl, wg={\"length\": 77.0, \"loss\": 100})\n", + "S = np.asarray(dict_to_matrix(s)) # Convert the result to a matrix\n", + "model = IIRModelBaseband(wvl, center_wvl, S, model_order)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5e5dba6d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "N = int(1000) # Number of time steps\n", + "T = 4e-11 # Total time duration (40 ps)\n", + "t = jnp.linspace(0, T, N) # Time array\n", + "t0 = T/2 - 5e-12 # Pulse start time\n", + "std = 1e-12 # Pulse standard deviation\n", + "\n", + "# Define input signals\n", + "impulse_pass = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5 * t0, std),\n", + " 'o1': jnp.zeros_like(t)\n", + "}\n", + "\n", + "# Convert frequency domain model to a time-domain system\n", + "tsys = pole_residue_to_time_system(model)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5274ebb2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate the system's response to the input signals\n", + "outputs,__,__ = tsys.response(impulse_pass)\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(2, 2, figsize=(10, 10)) # 2 rows, 2 columns\n", + "\n", + "# Plot input signals\n", + "for i in range(2):\n", + " axs[i, 0].plot(t, jnp.abs(impulse_pass[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "# Plot output signals\n", + "for i in range(2):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i+1}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "58f6b8be", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "tuple indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 76\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Plot output signals\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m):\n\u001b[0;32m---> 76\u001b[0m axs[i, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(t, jnp\u001b[38;5;241m.\u001b[39mabs(\u001b[43moutputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 77\u001b[0m axs[i, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOutput Signal \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 78\u001b[0m axs[i, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime (s)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not str" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the splitter and combiner using directional couplers\n", + "\n", + "# Define the netlist for a Mach-Zehnder Modulator\n", + "\n", + "netlist = {\n", + " \"instances\": {\n", + " \"coupler\": \"coupler\", # The coupler instance\n", + " },\n", + " \"connections\": {}, # No internal connections for a single component\n", + " \"ports\": {\n", + " \"o0\": \"coupler,port_1\", # First input port\n", + " \"o1\": \"coupler,port_2\", # Second input port\n", + " \"o2\": \"coupler,port_3\", # First output port\n", + " \"o3\": \"coupler,port_4\", # Second output port\n", + " },\n", + "\n", + "}\n", + "\n", + "\n", + "circuit, info = sax.circuit(\n", + " netlist=netlist,\n", + " models={\n", + " \"coupler\": siepic.directional_coupler,\n", + " }\n", + ")\n", + "\n", + "\n", + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548 # Center wavelength (µm)\n", + "wvl = np.linspace(1.5, 1.6, num_measurements) # Wavelength range (µm)\n", + "\n", + "\n", + "# Perform simulation\n", + "s = circuit(wl=wvl, wg={\"length\": 77.0, \"loss\": 1})\n", + "S = np.asarray(dict_to_matrix(s)) # Convert the result to a matrix\n", + "model = IIRModelBaseband(wvl, center_wvl, S, model_order)\n", + "\n", + "\n", + "N = int(1000) # Number of time steps\n", + "T = 4e-11 # Total time duration (40 ps)\n", + "t = jnp.linspace(0, T, N) # Time array\n", + "t0 = T/2 - 5e-12 # Pulse start time\n", + "std = 4e-12 # Pulse standard deviation\n", + "\n", + "\n", + "# Define input signals\n", + "impulse_pass = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5 * t0, std),\n", + " 'o1': jnp.zeros_like(t),\n", + " 'o2': jnp.zeros_like(t),\n", + " 'o3': jnp.zeros_like(t),\n", + "}\n", + "\n", + "\n", + "# Convert frequency domain model to a time-domain system\n", + "tsys = pole_residue_to_time_system(model)\n", + "# Simulate the system's response to the input signals\n", + "outputs = tsys.response(impulse_pass)\n", + "\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(4, 2, figsize=(10, 10)) # 2 rows, 2 columns\n", + "\n", + "\n", + "# Plot input signals\n", + "for i in range(4):\n", + " axs[i, 0].plot(t, jnp.abs(impulse_pass[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Plot output signals\n", + "for i in range(4):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bded31f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAPeCAYAAAARWnkoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADNmElEQVR4nOzdeXhU1f3H8c9kD0vCnhAMBJBVkGD4gUERtMGgiKIWEBEBEasFi8aiopZFW+LGYgtKXZEChbpAq1AWI4iVKBpEEMWFXSABZAkkkG3u748wQ4ZM9szcO8n79TzzNHPn3DvfmaY9fHLOPcdmGIYhAAAAAABQ7fzMLgAAAAAAgJqK0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A3AFP369VO/fv3MLsPJZrNp2rRpZpcBAIDP2Lt3r2w2mxYsWGB2KZKkBQsWyGazae/evWaXArggdAMW4ugsvvrqK7NLkSRlZ2dr2rRp2rBhQ7nP2bt3r8aMGaO2bdsqJCREkZGRuuaaazR16lTPFepFr7zyioYMGaKWLVvKZrNp9OjRZpcEAPCgHTt26K677lKLFi0UHBysqKgojRgxQjt27KjSdWfMmKEVK1ZUT5Fl2LRpk6ZNm6aTJ0+W+5wPPvhAffv2VbNmzVSnTh21adNGQ4cO1erVqz1XqJccPnxYjz/+uK699lrVr19fNputQv/WASqK0A2gRNnZ2Zo+fXq5O6Kff/5Z3bt315o1azR8+HDNnTtX48ePV+PGjfXcc8+5tF27dq3Wrl3rgao967nnntPHH3+syy67TAEBAWaXAwDwoPfff19XXHGFUlJSNGbMGL388ssaO3as1q9fryuuuELLly+v9LW9HbqnT59e7tD94osv6uabb5bNZtPkyZM1e/Zs3X777frpp5+0dOlSZ7tWrVrp7NmzGjlypIcq94wffvhBzz33nA4ePKiuXbuaXQ5qAf7FCKDazJ49W2fOnNHWrVvVqlUrl9eOHDni8jwoKMibpVWbTz75xDnKXa9ePbPLAQB4yK5duzRy5Ei1adNGGzduVNOmTZ2vTZw4UX369NHIkSO1bds2tWnTxsRKq1d+fr6eeeYZ9e/f3+0fx4v25zabTSEhId4sr1rExcXp119/VaNGjfTuu+9qyJAhZpeEGo6RbsDiRo8erXr16ungwYMaPHiw6tWrp6ZNm+qPf/yjCgoKnO0c91W9+OKLmj17tlq1aqXQ0FD17dtX3377rcs1S7qfevTo0YqJiXFez/EPjOnTp8tms5V53/OuXbt0ySWXFAvcktSsWbMya9i3b59uvvlm1a1bV82aNdPDDz+sNWvWFJv21a9fP3Xp0kXfffedrr32WtWpU0ctWrTQ888/73K93NxcTZkyRXFxcQoPD1fdunXVp08frV+/vsTPUJZWrVrJZrNV+nwAgG944YUXlJ2drVdffdUlcEtSkyZN9Pe//11ZWVkufU/RfrSoadOmufQdNptNWVlZevvtt539q+N2JUfbnTt3aujQoQoLC1Pjxo01ceJEnTt3znmN0u6nLtpfT5s2TZMmTZIktW7d2vl+Jd33fOzYMWVmZuqqq65y+3rR/rykGt555x117txZISEh6tKli5YvX17suyn675ZXX31Vbdu2VXBwsP7v//5PX375pcv1tm3bptGjR6tNmzbOW9fuuece/frrr25rLEv9+vXVqFGjSp0LVAYj3YAPKCgoUGJionr16qUXX3xRH330kWbOnKm2bdvqgQcecGm7cOFCnT59WuPHj9e5c+f00ksv6brrrtP27dsVERFR7vds2rSpXnnlFT3wwAO69dZbddttt0mSLr/88hLPadWqlT766CN9/PHHuu666yr0GbOysnTdddfp8OHDmjhxoiIjI7VkyZISA/KJEyc0YMAA3XbbbRo6dKjeffddPfbYY+ratatuuOEGSVJmZqZef/11DR8+XOPGjdPp06f1xhtvKDExUZs3b1ZsbGyFagQA1B4ffPCBYmJi1KdPH7evX3PNNYqJidHKlSsrfO1//OMfuvfee9WzZ0/dd999kqS2bdu6tBk6dKhiYmKUnJyszz//XH/961914sQJLVy4sELvddttt+nHH3/UP//5T82ePVtNmjSRpGJ/SHBo1qyZQkND9cEHH+jBBx+scDhduXKlhg0bpq5duyo5OVknTpzQ2LFj1aJFC7ftlyxZotOnT+t3v/udbDabnn/+ed12223avXu3AgMDJUnr1q3T7t27NWbMGEVGRmrHjh169dVXtWPHDn3++ef8MRzWZwCwjLfeesuQZHz55ZfOY6NGjTIkGU8//bRL2+7duxtxcXHO53v27DEkGaGhocYvv/ziPP7FF18YkoyHH37Yeaxv375G3759i73/qFGjjFatWjmfHz161JBkTJ06tVz1f/vtt0ZoaKghyYiNjTUmTpxorFixwsjKyirW9uIaZs6caUgyVqxY4Tx29uxZo2PHjoYkY/369S7nSjIWLlzoPJaTk2NERkYat99+u/NYfn6+kZOT4/K+J06cMCIiIox77rnH5XhFPqdD3bp1jVGjRlXoHACA9Z08edKQZNxyyy2ltrv55psNSUZmZqZhGMX7UYepU6caF/+zu6Q+xNH25ptvdjn++9//3pBkfPPNN4ZhXOj333rrrWLXuLhPe+GFFwxJxp49e0r9PA5TpkwxJBl169Y1brjhBuMvf/mLkZaWVqyduxq6du1qXHLJJcbp06edxzZs2GBIcvluHOc2btzYOH78uPP4v//9b0OS8cEHHziPZWdnF3vvf/7zn4YkY+PGjc5jjn9HlfdzGoZhvPPOO8X+nQFUN6aXAz7i/vvvd3nep08f7d69u1i7wYMHu/w1uWfPnurVq5dWrVrl8Rovu+wybd26VXfddZf27t2rl156SYMHD1ZERIRee+21Us9dvXq1WrRooZtvvtl5LCQkROPGjXPbvl69errrrrucz4OCgtSzZ0+X78Tf399577jdbtfx48eVn5+vHj16aMuWLVX5qACAGuz06dOSCqchl8bxemZmZrXXMH78eJfnDz74oCR5pT+fPn26lixZ4lwc9cknn1RcXJyuuOIKff/99yWed+jQIW3fvl133323y7onffv2LXHBsmHDhqlhw4bO546ZBUX789DQUOfP586d07Fjx3TllVdKEv05fAKhG/ABISEhxaaBNWzYUCdOnCjWtl27dsWOtW/f3mt7VrZv317/+Mc/dOzYMW3btk0zZsxQQECA7rvvPn300Uclnrdv3z61bdu22BSxSy+91G37Sy65pFhbd9/J22+/rcsvv1whISFq3LixmjZtqpUrV+rUqVOV/IQAgJrOEaYd4bsk5Q3nlXFxf962bVv5+fl5rT8fPny4Pv30U504cUJr167VnXfeqa+//lqDBg1yube8qH379kly33eX1J+3bNnS5bkjgBftz48fP66JEycqIiJCoaGhatq0qVq3bi1J9OfwCdzTDfgAf3//ar2ezWaTYRjFjhddmK2q/P391bVrV3Xt2lXx8fG69tprtXjxYiUkJFTb9d0p+rkWLVqk0aNHa/DgwZo0aZKaNWsmf39/JScna9euXdVSBwCg5gkPD1fz5s21bdu2Uttt27ZNLVq0UFhYmCSVeG9xdfSvF1/bk+9VVFhYmPr376/+/fsrMDBQb7/9tr744gv17du3Wq5fnv586NCh2rRpkyZNmqTY2FjVq1dPdrtdAwYMkN1ur5Y6AE9ipBuoYX766adix3788UeXFUMbNmzodq9Ox1+oHaprYZIePXpIkg4fPlxim1atWmnXrl3F/hjw888/V/p93333XbVp00bvv/++Ro4cqcTERCUkJJT4F3oAABxuuukm7dmzR//73//cvv7pp59q7969uummm5zHytu/SmX3sRf35z///LPsdruzP3eMCF/8fpV5r/Iqqz937F7iru+ubH9+4sQJpaSk6PHHH9f06dN16623qn///jVqmzbUfIRuoIZZsWKFDh486Hy+efNmffHFF84VvaXCKWo7d+7U0aNHnce++eYbffbZZy7XqlOnjqTiHXpJPv30U+Xl5RU77rj/rEOHDiWem5iYqIMHD+o///mP89i5c+fKvBe8NI6/nhcN8l988YVSU1MrfU0AQO0wadIkhYaG6ne/+12xramOHz+u+++/X3Xq1HFuxyUV9q+nTp1yGSE/fPiwli9fXuz6devWLbV/nTdvnsvzv/3tb5Lk7M/DwsLUpEkTbdy40aXdyy+/7Pa9pPL159nZ2SX2k//9738lldyfR0VFqUuXLlq4cKHOnDnjPP7JJ59o+/btZb63O+76ckmaM2dOpa4HmIHp5UANc+mll+rqq6/WAw88oJycHM2ZM0eNGzfWo48+6mxzzz33aNasWUpMTNTYsWN15MgRzZ8/X5dddpnLYjChoaHq3Lmzli1bpvbt26tRo0bq0qWLunTp4va9n3vuOaWlpem2225zbi22ZcsWLVy4UI0aNdJDDz1UYt2/+93vNHfuXA0fPlwTJ05U8+bNtXjxYoWEhEiq3F/pb7rpJr3//vu69dZbNXDgQO3Zs0fz589X586dXf4xUBEffPCBvvnmG0lSXl6etm3bpj//+c+SpJtvvrnULdUAAL6jXbt2evvttzVixAh17dpVY8eOVevWrbV371698cYbOnbsmP75z3+6bPV1xx136LHHHtOtt96qP/zhD8rOztYrr7yi9u3bF1vwKy4uTh999JFmzZqlqKgotW7dWr169XK+vmfPHt18880aMGCAUlNTtWjRIt15553q1q2bs829996rZ599Vvfee6969OihjRs36scffyz2WeLi4iRJTz75pO644w4FBgZq0KBBzjBeVHZ2tnr37q0rr7xSAwYMUHR0tE6ePKkVK1bo008/1eDBg9W9e/cSv7cZM2bolltu0VVXXaUxY8boxIkTmjt3rrp06VKpvjcsLEzXXHONnn/+eeXl5alFixZau3at9uzZU+FrFeXou3fs2CGpcBs3x6yGp556qkrXBooxc+l0AK5K2jKsbt26xdpevP2IY+uNF154wZg5c6YRHR1tBAcHG3369HFuL1LUokWLjDZt2hhBQUFGbGyssWbNGrdbnWzatMmIi4szgoKCytxW67PPPjPGjx9vdOnSxQgPDzcCAwONli1bGqNHjzZ27drl0tbdtmW7d+82Bg4caISGhhpNmzY1HnnkEeO9994zJBmff/65y7mXXXZZsfe/uH673W7MmDHDaNWqlREcHGx0797d+PDDD91+zrI+W9H3kOT24W7bFgCAb9u2bZsxfPhwo3nz5kZgYKARGRlpDB8+3Ni+fbvb9mvXrjW6dOliBAUFGR06dDAWLVrkdsuwnTt3Gtdcc41zq03H9mGOtt99953x29/+1qhfv77RsGFDY8KECcbZs2ddrpGdnW2MHTvWCA8PN+rXr28MHTrUOHLkiNs+7ZlnnjFatGhh+Pn5lbqtVl5envHaa68ZgwcPdvafderUMbp372688MILLltxlrRt2dKlS42OHTsawcHBRpcuXYz//Oc/xu2332507Nix2LkvvPBCsRourv+XX34xbr31VqNBgwZGeHi4MWTIEOPQoUPF2lVky7CS+nLiETzBZhhuVlMC4HP27t2r1q1b64UXXtAf//hHs8upNnPmzNHDDz+sX375xWUrNAAAaqJp06Zp+vTpOnr0qJo0aWJ2OdUmNjZWTZs21bp168wuBfA67ukGYBlnz551eX7u3Dn9/e9/V7t27QjcAAD4gLy8POXn57sc27Bhg7755hv169fPnKIAk3FPNwDLuO2229SyZUvFxsbq1KlTWrRokXbu3KnFixebXRoAACiHgwcPKiEhQXfddZeioqK0c+dOzZ8/X5GRkbr//vvNLg8wBaEbgGUkJibq9ddf1+LFi1VQUKDOnTtr6dKlGjZsmNmlAQCAcmjYsKHi4uL0+uuv6+jRo6pbt64GDhyoZ599Vo0bNza7PMAU3NMNAAAAAICHcE83AAAAAAAeQugGAAAAAMBDuKe7Gtjtdh06dEj169eXzWYzuxwAgI8zDEOnT59WVFSU/Pz4+3h1ob8GAFSn8vbXhO5qcOjQIUVHR5tdBgCghjlw4IAuueQSs8uoMeivAQCeUFZ/TeiuBvXr15dU+GWHhYWZXA0AwNdlZmYqOjra2b+getBfAwCqU3n7a0J3NXBMUQsLC6MTBwBUG6ZAVy/6awCAJ5TVX3OjGAAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4SI0K3Rs3btSgQYMUFRUlm82mFStWlHnOhg0bdMUVVyg4OFiXXnqpFixY4PE6AQAAAAC1Q40K3VlZWerWrZvmzZtXrvZ79uzRwIEDde2112rr1q166KGHdO+992rNmjUerhQAAAAAUBsEmF1Adbrhhht0ww03lLv9/Pnz1bp1a82cOVOS1KlTJ/3vf//T7NmzlZiY6KkyAQAAAAC1RI0a6a6o1NRUJSQkuBxLTExUamqqSRUBAAAAAGqSGjXSXVHp6emKiIhwORYREaHMzEydPXtWoaGhbs/LyclRTk6O83lmZqZH66zNxi/eogMnsrVgTE81qhtkdjkAAAAAUCG1eqS7spKTkxUeHu58REdHm11SjfTrmRyt3H5Y2345pcWf7zO7HAAAAACosFoduiMjI5WRkeFyLCMjQ2FhYSWOckvS5MmTderUKefjwIEDni61Vvru8IUZBAdOZJtYCQAAAABUTq2eXh4fH69Vq1a5HFu3bp3i4+NLPS84OFjBwcGeLA2Sfjlx1vnzgeNnS2kJAAAAANZUo0a6z5w5o61bt2rr1q2SCrcE27p1q/bv3y+pcIT67rvvdra///77tXv3bj366KPauXOnXn75Zf3rX//Sww8/bEb5uMivZy7cN3/oFKEbAAAAgO+pUaH7q6++Uvfu3dW9e3dJUlJSkrp3764pU6ZIkg4fPuwM4JLUunVrrVy5UuvWrVO3bt00c+ZMvf7662wXZhHHzuQ6fz6ZnWdiJQAAAABQOTVqenm/fv1kGEaJry9YsMDtOV9//bUHq0Jl/Zp1IXRnnstTgd2Qv5/NxIoAAAAAoGJq1Eg3apYTRUK3YUiZZxntBgAAAOBbCN2wrNM5+S7PTxK6AQAAAPgYQjcs68w515B9Mju3hJYAAAAAYE2EbljW6XOuI93ZuQUmVQIAAAAAlUPohmWdOT+9vFHdIEmEbgAAAAC+h9ANSyqwG86Q3ax+sCQpOze/tFMAAAAAwHII3bCkM0WmljcLC5EknWWkGwAAAICPIXTDkrLzCkN3kL+fwkIKt5NnejkAAAAAX0PohiU5RrVDAv1UJ8i/8FgeoRsAAACAbyF0w5IcATsk0F91ggpHurNyuKcbAAAAgG8hdMOSzuXZJUmhQf7OkW6mlwMAAADwNYRuWNK58yPdoYEXQjcLqQEAAADwNYRuWJIjYAcH+iv0/PTybO7pBgAAAOBjCN2wpLPOke4iC6mxTzcAAAAAH0PohiW5m17OPd0AAAAAfA2hG5bkDN1BRVYvJ3QDAAAA8DGEbliSc8uwAH+mlwMAAADwWYRuWNLZ3MItw0KC/BXK9HIAAAAAPorQDUs6l8+WYQAAAAB8H6EbluQI2KGB/qoTeH7LMEI3AAAAAB9D6IYlORZSCwn0U3Bg4a9pboHdzJIAAAAAoMII3bAk50Jqgf4KDij8NS2wG8oneAMAAADwIYRuWFLRLcOCA/ydx3PyCd0AAAAAfAehG5Z0Nu/86uUB/goKuPBrmkvoBgAAAOBDCN2wpHO5F0a6/f1sCvCzSWKkGwAAAIBvIXTDkhz3dIcGFk4td9zXnZPPCuYAAAAAfAehG5bkCNeOsB18PnwzvRwAAACALyF0w5LyCgxJct7PHeTvGOkmdAOAr5g3b55iYmIUEhKiXr16afPmzaW2f+edd9SxY0eFhISoa9euWrVqVYlt77//ftlsNs2ZM6eaqwYAoHoRumFJjhHtAH/HSDfTywHAlyxbtkxJSUmaOnWqtmzZom7duikxMVFHjhxx237Tpk0aPny4xo4dq6+//lqDBw/W4MGD9e233xZru3z5cn3++eeKiory9McAAKDKCN2wpLzz+3EH+hcuoOa8pzuPkW4A8AWzZs3SuHHjNGbMGHXu3Fnz589XnTp19Oabb7pt/9JLL2nAgAGaNGmSOnXqpGeeeUZXXHGF5s6d69Lu4MGDevDBB7V48WIFBgZ646MAAFAlhG5YkiN0O6aVO6aZ5xQQugHA6nJzc5WWlqaEhATnMT8/PyUkJCg1NdXtOampqS7tJSkxMdGlvd1u18iRIzVp0iRddtllnikeAIBqFmB2AYA7+efv6Q50TC8PKFxIjZFuALC+Y8eOqaCgQBERES7HIyIitHPnTrfnpKenu22fnp7ufP7cc88pICBAf/jDH8pVR05OjnJycpzPMzMzy/sRAACoNox0w5JyCxz3dF80vZx7ugGgVkpLS9NLL72kBQsWyGazleuc5ORkhYeHOx/R0dEerhIAgOII3bCkkqaXs2UYAFhfkyZN5O/vr4yMDJfjGRkZioyMdHtOZGRkqe0//fRTHTlyRC1btlRAQIACAgK0b98+PfLII4qJiXF7zcmTJ+vUqVPOx4EDB6r+4QAAqCBCNyynwG7IXji7vMj0crYMAwBfERQUpLi4OKWkpDiP2e12paSkKD4+3u058fHxLu0lad26dc72I0eO1LZt27R161bnIyoqSpMmTdKaNWvcXjM4OFhhYWEuDwAAvI17umE5eUUWS7swvfz8Pd2EbgDwCUlJSRo1apR69Oihnj17as6cOcrKytKYMWMkSXfffbdatGih5ORkSdLEiRPVt29fzZw5UwMHDtTSpUv11Vdf6dVXX5UkNW7cWI0bN3Z5j8DAQEVGRqpDhw7e/XAAAFQAoRuWUzR0XzzSzfRyAPANw4YN09GjRzVlyhSlp6crNjZWq1evdi6Wtn//fvn5XZhw17t3by1ZskRPPfWUnnjiCbVr104rVqxQly5dzPoIAABUC0I3LMexcrl0IXQHsZAaAPicCRMmaMKECW5f27BhQ7FjQ4YM0ZAhQ8p9/b1791ayMgAAvId7umE5jpFuP5vk78f0cgAAAAC+i9ANy3FsF+YY5Zak4ECmlwMAAADwPYRuWI5jenlQkdDt+Jnp5QAAAAB8CaEbluOYXh4YUHykOyePkW4AAAAAvoPQDctxTC8POH8/t8Q93QAAAAB8E6EblpN3fnp50Xu6Wb0cAAAAgC8idMNy8s+PdAcVnV5+PoDnFdlODAAAAACsjtANy3E3vTwwoPBnx/3eAAAAAOALCN2wHHfTyx0/s2UYAAAAAF9C6Ibl5LtZvTzQOb2c0A0AAADAdxC6YTnOLcOKTC8P4p5uAAAAAD6I0A3LyS1l9XJGugEAAAD4EkI3LKe06eW5hG4AAAAAPoTQDctxN7080J/VywEAAAD4HkI3LMfd9HLnQmr53NMNAAAAwHcQumE5efnFp5dzTzcAAAAAX0TohuXk28+Hbv+i08u5pxsAAACA7yF0w3Ic24IF+hWdXs493QAAAAB8D6EblpPrnF7OPt0AAAAAfBuhG5ZzYXp58YXUCuyGCuwEbwAAAAC+gdANy8lzt3p5kUXVmGIOAAAAwFcQumE5zunl/sX36ZYI3QAAAAB8B6EbluN2erlf0ZFuppcDAAAA8A2EblhOXn7x6eV+fjYF+LGCOQAAAADfQuiG5ThCddEp5YXPz+/VnU/oBgAAAOAbCN2wnDx78ZHuwueMdAMAAADwLTUydM+bN08xMTEKCQlRr169tHnz5lLbz5kzRx06dFBoaKiio6P18MMP69y5c16qFhfLyy9+T7ckBQWwVzcAAAAA31LjQveyZcuUlJSkqVOnasuWLerWrZsSExN15MgRt+2XLFmixx9/XFOnTtX333+vN954Q8uWLdMTTzzh5crhUNb0cka6AQAAAPiKGhe6Z82apXHjxmnMmDHq3Lmz5s+frzp16ujNN990237Tpk266qqrdOeddyomJkbXX3+9hg8fXuboODwnt8D9SLfznm5CNwAAAAAfUaNCd25urtLS0pSQkOA85ufnp4SEBKWmpro9p3fv3kpLS3OG7N27d2vVqlW68cYbvVIzissvKOOebhZSAwAAAOAjAswuoDodO3ZMBQUFioiIcDkeERGhnTt3uj3nzjvv1LFjx3T11VfLMAzl5+fr/vvvL3V6eU5OjnJycpzPMzMzq+cDQFJ5ppdzTzcAAAAA31CjRrorY8OGDZoxY4ZefvllbdmyRe+//75WrlypZ555psRzkpOTFR4e7nxER0d7seKaL6+E6eUXFlJjpBsAAACAb6hRI91NmjSRv7+/MjIyXI5nZGQoMjLS7Tl/+tOfNHLkSN17772SpK5duyorK0v33XefnnzySfn5Ff+7xOTJk5WUlOR8npmZSfCuRnklTi/nnm4AAAAAvqVGjXQHBQUpLi5OKSkpzmN2u10pKSmKj493e052dnaxYO3v7y9JMgz305iDg4MVFhbm8kD1cYxkBxSbXs4+3QAAAAB8S40a6ZakpKQkjRo1Sj169FDPnj01Z84cZWVlacyYMZKku+++Wy1atFBycrIkadCgQZo1a5a6d++uXr166eeff9af/vQnDRo0yBm+4V2OUB1Uwkg3oRsAAACAr6hxoXvYsGE6evSopkyZovT0dMXGxmr16tXOxdX279/vMrL91FNPyWaz6amnntLBgwfVtGlTDRo0SH/5y1/M+gi1XknTyx0hPC+fhdQAAAAA+IYaF7olacKECZowYYLb1zZs2ODyPCAgQFOnTtXUqVO9UBnKo+Tp5dzTDQAAAMC31Kh7ulEzlDi9nNXLAQAAAPgYQjcsp+TVy1lIDQAAAIBvIXTDcpz7dAeUcE93Afd0AwAAAPANhG5YjjN0+5VwT3c+I90AAAAAfAOhG5ZSYDdkPz+QXXx6Ofd0AwAAAPAthG5YStFAffH08sAA7ukGAAAA4FsI3bCUooE64KLp5dzTDQAAAMDXELphKUUDdUnTy9mnGwAAAICvIHTDUvLPB2p/P5v8S1hILY+F1AAAAAD4CEI3LMUxin3x1HKJfboBAAAA+B5CNyzFMb08yL/4r2ZQAPd0AwAAAPAthG5YimN6+cUrl0vc0w0AAADA9xC6YSmOQO2YSl4U+3QDAAAA8DWEbliKY+p4gJ+7kW7u6QYAAADgWwjdsBRHoA5yM73cuU93Pvd0AwAAAPANhG5YSl45ppdzTzcAAAAAX0HohqWUNr084HwQz2WfbgAAAAA+gtANS8nLL3n1csf08nw7oRsAAACAbyB0w1IcgTrIzfTyAEfoZp9uAAAAAD6C0A1LyS3H6uXc0w0AAADAVxC6YSmlTS8PZKQbAAAAgI8hdMNSSpte7gjd7NMNAAAAwFcQumEpjunljoBdlGP1ckI3AAAAAF9B6IalOKaXB7gJ3UHOkW6mlwMAAADwDYRuWIpjFDvQ7erlhcfYMgwAAACAryB0w1Ly7YWj2EFuRroDi4x0Gwaj3QAAAACsj9ANS8l1Ti93s5BakW3EHOEcAAAAAKyM0A1LuTC93M1Id4CtWDsAgHXNmzdPMTExCgkJUa9evbR58+ZS27/zzjvq2LGjQkJC1LVrV61atcr5Wl5enh577DF17dpVdevWVVRUlO6++24dOnTI0x8DAIAqIXTDUkqbXh5QZKSbxdQAwNqWLVumpKQkTZ06VVu2bFG3bt2UmJioI0eOuG2/adMmDR8+XGPHjtXXX3+twYMHa/Dgwfr2228lSdnZ2dqyZYv+9Kc/acuWLXr//ff1ww8/6Oabb/bmxwIAoMII3bCUUqeX+zPSDQC+YtasWRo3bpzGjBmjzp07a/78+apTp47efPNNt+1feuklDRgwQJMmTVKnTp30zDPP6IorrtDcuXMlSeHh4Vq3bp2GDh2qDh066Morr9TcuXOVlpam/fv3e/OjAQBQIYRuWEpp08ttNpsC/M6vYM5INwBYVm5urtLS0pSQkOA85ufnp4SEBKWmpro9JzU11aW9JCUmJpbYXpJOnTolm82mBg0auH09JydHmZmZLg8AALyN0A1LcYRpd6G76HFGugHAuo4dO6aCggJFRES4HI+IiFB6errbc9LT0yvU/ty5c3rsscc0fPhwhYWFuW2TnJys8PBw5yM6OroSnwYAgKohdMNSStunW7ow7ZzQDQC1V15enoYOHSrDMPTKK6+U2G7y5Mk6deqU83HgwAEvVgkAQKEAswsAisotZXq5dGGBNbYMAwDratKkifz9/ZWRkeFyPCMjQ5GRkW7PiYyMLFd7R+Det2+fPv744xJHuSUpODhYwcHBlfwUAABUD0a6YSllTS93jHQ7FlwDAFhPUFCQ4uLilJKS4jxmt9uVkpKi+Ph4t+fEx8e7tJekdevWubR3BO6ffvpJH330kRo3buyZDwAAQDVipBuW4pg27m7LMOlCGGekGwCsLSkpSaNGjVKPHj3Us2dPzZkzR1lZWRozZowk6e6771aLFi2UnJwsSZo4caL69u2rmTNnauDAgVq6dKm++uorvfrqq5IKA/dvf/tbbdmyRR9++KEKCgqc93s3atRIQUFB5nxQAADKQOiGpTiml7vbMkxiITUA8BXDhg3T0aNHNWXKFKWnpys2NlarV692Lpa2f/9++fld+ANr7969tWTJEj311FN64okn1K5dO61YsUJdunSRJB08eFD/+c9/JEmxsbEu77V+/Xr169fPK58LAICKInTDUkrbMqzwOAupAYCvmDBhgiZMmOD2tQ0bNhQ7NmTIEA0ZMsRt+5iYGBkGs5wAAL6He7phKWXe0+3nGOnmH14AAAAArI/QDUspa8uwwIDz93Qz0g0AAADABxC6YSm5ZYx0B/oxvRwAAACA7yB0w1Lyy7ynm+nlAAAAAHwHoRuWUtb08gAWUgMAAADgQwjdsJS8MqaXO/bvzmekGwAAAIAPIHTDUsraMswx0p3LSDcAAAAAH0DohqU4QndQQAmrl/uzejkAAAAA30HohqU4ppc79uO+GAupAQAAAPAlhG5YimPauGM/7os5FljLszPSDQAAAMD6CN2wlPwyVy8/P9Kdz0g3AAAAAOsjdMMyCuyG7OezdGAJ08udq5cz0g0AAADABxC6YRlF994uaXp5gB+rlwMAAADwHYRuWIZL6C5herkjjLNPNwAAAABfQOiGZRRdkbyk6eWB50e68xjpBgAAAOADCN2wDEeQ9vezyc+v9H262TIMAAAAgC8gdMMy8spYuVwqsno5I90AAAAAfAChG5bhGL0uaWq5dCGQ5xO6AQAAAPgAQjcswznSXcLK5RLTywEAAAD4FkI3LKN808tZSA0AAACA7yB0wzKc08v9yzPSTegGAAAAYH2EbljGhZHuctzTbWd6OQAAAADrI3TDMvLyy55e7gjkufmMdAMAAACwPkI3LCPPXvb08oDzK5sz0g0AAADAFxC6YRmOke6AUkJ3UAALqQEAAADwHYRuWIYjSAeVtnq5H1uGAQAAAPAdNTJ0z5s3TzExMQoJCVGvXr20efPmUtufPHlS48ePV/PmzRUcHKz27dtr1apVXqoWDuWZXs7q5QAAAAB8SYDZBVS3ZcuWKSkpSfPnz1evXr00Z84cJSYm6ocfflCzZs2Ktc/NzVX//v3VrFkzvfvuu2rRooX27dunBg0aeL/4Wq4808udq5cTugEAAAD4gBoXumfNmqVx48ZpzJgxkqT58+dr5cqVevPNN/X4448Xa//mm2/q+PHj2rRpkwIDAyVJMTEx3iwZ55VnevmFkW6mlwMAAACwvho1vTw3N1dpaWlKSEhwHvPz81NCQoJSU1PdnvOf//xH8fHxGj9+vCIiItSlSxfNmDFDBQUF3iob55Vr9XJ/FlIDAAAA4Dtq1Ej3sWPHVFBQoIiICJfjERER2rlzp9tzdu/erY8//lgjRozQqlWr9PPPP+v3v/+98vLyNHXqVLfn5OTkKCcnx/k8MzOz+j5ELXZhn+5SVi/nnm4AAAAAPqRGjXRXht1uV7NmzfTqq68qLi5Ow4YN05NPPqn58+eXeE5ycrLCw8Odj+joaC9WXHM5gnRAaauXnw/d+UwvBwAAAOADalTobtKkifz9/ZWRkeFyPCMjQ5GRkW7Pad68udq3by9/f3/nsU6dOik9PV25ubluz5k8ebJOnTrlfBw4cKD6PkQtduGe7rIXUstlpBsAAACAD6hRoTsoKEhxcXFKSUlxHrPb7UpJSVF8fLzbc6666ir9/PPPstsvhLgff/xRzZs3V1BQkNtzgoODFRYW5vJA1TkWRyvPlmH5dka6AQAAAFhfjQrdkpSUlKTXXntNb7/9tr7//ns98MADysrKcq5mfvfdd2vy5MnO9g888ICOHz+uiRMn6scff9TKlSs1Y8YMjR8/3qyPUGuVZ3q5I3QX2A3ZCd4AAAAALK5GLaQmScOGDdPRo0c1ZcoUpaenKzY2VqtXr3YurrZ//375+V34W0N0dLTWrFmjhx9+WJdffrlatGihiRMn6rHHHjPrI9Ra5ZleXjSQ59ntCvbzL7EtAAAAAJitxoVuSZowYYImTJjg9rUNGzYUOxYfH6/PP//cw1WhLOWZXl40kOcVGAqukb/BAAAAAGqKGje9HL6rXKuX+114LZ/F1AAAAABYHKEbluEI3aWNdPv72WQ7n7tZwRwAAACA1RG6YRmOvbdLu6fbZrMp0I+9ugEAAAD4BkI3LCPXOdJd8vTyoq/nMdINAAAAwOIsE7pHjRqljRs3ml0GTHThnu7Sfy0dr+cx0g0AZaJ/BQDAXJYJ3adOnVJCQoLatWunGTNm6ODBg2aXBC8rz/Ry6cI934x0A0DZ6F8BADCXZUL3ihUrdPDgQT3wwANatmyZYmJidMMNN+jdd99VXl6e2eXBC5zTywPKN72ce7oBoGz0rwAAmMsyoVuSmjZtqqSkJH3zzTf64osvdOmll2rkyJGKiorSww8/rJ9++snsEuFBzunlfuUb6Wb1cgAoH/pXAADMY6nQ7XD48GGtW7dO69atk7+/v2688UZt375dnTt31uzZs80uDx7iuEe7tC3DpAv7eLNPNwBUDP0rAADeZ5nQnZeXp/fee0833XSTWrVqpXfeeUcPPfSQDh06pLffflsfffSR/vWvf+npp582u1R4iCNEB5UxvTyIhdQAoNzoXwEAMFeA2QU4NG/eXHa7XcOHD9fmzZsVGxtbrM21116rBg0aeL02eEfu+RBd1vRyx0h3np2RbgAoC/0rAADmskzonj17toYMGaKQkJAS2zRo0EB79uzxYlXwpjznPt3lXL08n9ANAGWhfwUAwFyWmV6+fv16t6uoZmVl6Z577jGhInhbeaeXB54fCc+3M70cAMpC/woAgLksE7rffvttnT17ttjxs2fPauHChSZUBG/LK+f0cseWYuzTDQBlo38FAMBcpk8vz8zMlGEYMgxDp0+fdpn+VlBQoFWrVqlZs2YmVghvyS3n9HJHKGchNQAoGf0rAADWYHrobtCggWw2m2w2m9q3b1/sdZvNpunTp5tQGbyt3NPLz4dytgwDgJLRvwIAYA2mh+7169fLMAxdd911eu+999SoUSPna0FBQWrVqpWioqJMrBDeUt59ugP9mV4OAGWhfwUAwBpMD919+/aVJO3Zs0ctW7aUzVb6KCdqLsf08oDyrl7O9HIAKBH9KwAA1mBq6N62bZu6dOkiPz8/nTp1Stu3by+x7eWXX+7FymCGC1uGlf4PwwBGugGgVPSvAABYh6mhOzY2Vunp6WrWrJliY2Nls9lkGMVHL202mwoKCkyoEN5SYDfk+K8+qIyRbsfrbBkGAO7RvwIAYB2mhu49e/aoadOmzp9RexUdtS5rerljpDs3n5FuAHCH/hUAAOswNXS3atXK7c+ofXKLhO6yppc7Vy+3E7oBwB36VwAArKP0IUUvevvtt7Vy5Urn80cffVQNGjRQ7969tW/fPhMrgzfkF1kULdCPhdQAoLrQvwIAYC7LhO4ZM2YoNDRUkpSamqq5c+fq+eefV5MmTfTwww+bXB08zTG93N/PJj+/ska6WUgNAMqL/hUAAHOZvmWYw4EDB3TppZdKklasWKHf/va3uu+++3TVVVepX79+5hYHj3Pcn13W1HJJCvBzjHQTugGgLPSvAACYyzIj3fXq1dOvv/4qSVq7dq369+8vSQoJCdHZs2fNLA1e4FiJPLCMRdQkKSjg/D3dTC8HgDLRvwIAYC7LjHT3799f9957r7p3764ff/xRN954oyRpx44diomJMbc4eJxj1Lqs7cIkKeD89PNcRroBoEz0rwAAmMsyI93z5s1TfHy8jh49qvfee0+NGzeWJKWlpWn48OEmVwdPc0wvDyjH9HLn6uWMdANAmczsX+fNm6eYmBiFhISoV69e2rx5c6nt33nnHXXs2FEhISHq2rWrVq1a5fK6YRiaMmWKmjdvrtDQUCUkJOinn37y5EcAAKDKLDPS3aBBA82dO7fY8enTp5tQDbzNMdJdnunlLKQGAOVnVv+6bNkyJSUlaf78+erVq5fmzJmjxMRE/fDDD2rWrFmx9ps2bdLw4cOVnJysm266SUuWLNHgwYO1ZcsWdenSRZL0/PPP669//avefvtttW7dWn/605+UmJio7777TiEhIR79PAAAVJZlQrcknTx5Ups3b9aRI0dkL7IHs81m08iRI02sDJ7muKe7PNPL2TIMACrGjP511qxZGjdunMaMGSNJmj9/vlauXKk333xTjz/+eLH2L730kgYMGKBJkyZJkp555hmtW7dOc+fO1fz582UYhubMmaOnnnpKt9xyiyRp4cKFioiI0IoVK3THHXd45HNczDAMnc0r8Mp7AQA8KzTQXzZb2TNtq8oyofuDDz7QiBEjdObMGYWFhbl8eEJ3zZdXgenlAf6sXg4A5WVG/5qbm6u0tDRNnjzZeczPz08JCQlKTU11e05qaqqSkpJcjiUmJmrFihWSpD179ig9PV0JCQnO18PDw9WrVy+lpqa6Dd05OTnKyclxPs/MzKzKx5Iknc0rUOcpa6p8HQCA+b57OlF1gjwfiS1zT/cjjzyie+65R2fOnNHJkyd14sQJ5+P48eNmlwcPy63E9PJ8O6EbAMpiRv967NgxFRQUKCIiwuV4RESE0tPT3Z6Tnp5eanvHf1bkmsnJyQoPD3c+oqOjK/V5AACoCsuMdB88eFB/+MMfVKdOHbNLgQkci6KVL3SfH+nOZ3o5AJSlNvevkydPdhk9z8zMrHLwDg3013dPJ1a1NACABYQG+nvlfSwTuhMTE/XVV1+pTZs2ZpcCE1xYSK38q5fnMdINAGUyo39t0qSJ/P39lZGR4XI8IyNDkZGRbs+JjIwstb3jPzMyMtS8eXOXNrGxsW6vGRwcrODg4Mp+DLdsNptXpiICAGoOy/QaAwcO1KRJk/Tdd9+pa9euCgwMdHn95ptvNqkyeENFppcHsHo5AJSbGf1rUFCQ4uLilJKSosGDB0uS7Ha7UlJSNGHCBLfnxMfHKyUlRQ899JDz2Lp16xQfHy9Jat26tSIjI5WSkuIM2ZmZmfriiy/0wAMPVPtnAACgulgmdI8bN06S9PTTTxd7zWazqaCAlUJrsgpNL/djn24AKC+z+tekpCSNGjVKPXr0UM+ePTVnzhxlZWU5VzO/++671aJFCyUnJ0uSJk6cqL59+2rmzJkaOHCgli5dqq+++kqvvvqqs9aHHnpIf/7zn9WuXTvnlmFRUVHOYA8AgBVZJnTbmSpcq1Vsenlhm1xGugGgTGb1r8OGDdPRo0c1ZcoUpaenKzY2VqtXr3YuhLZ//375+V34Q2vv3r21ZMkSPfXUU3riiSfUrl07rVixwrlHtyQ9+uijysrK0n333aeTJ0/q6quv1urVq9mjGwBgaZYJ3UWdO3eODrSWyavQ9HJGugGgMrzdv06YMKHE6eQbNmwodmzIkCEaMmRIidez2Wx6+umn3Y7aAwBgVZbZMqygoEDPPPOMWrRooXr16mn37t2SpD/96U964403TK4OnpZXgenlQezTDQDlRv8KAIC5LBO6//KXv2jBggV6/vnnFRQU5DzepUsXvf766yZWBm+o2Ei3YyE1RroBoCz0rwAAmMsyoXvhwoV69dVXNWLECPn7X9gvrVu3btq5c6eJlcEbKrVlGCPdAFAm+lcAAMxlmdB98OBBXXrppcWO2+125eXlmVARvCm3IquXnw/m+YRuACgT/SsAAOayTOju3LmzPv3002LH3333XXXv3t2EiuBN+RWYXn5hpJvp5QBQFvpXAADMZZnVy6dMmaJRo0bp4MGDstvtev/99/XDDz9o4cKF+vDDD80uDx5Wkenlznu67XYZhiGbrexzAKC2on8FAMBclhnpvuWWW/TBBx/oo48+Ut26dTVlyhR9//33+uCDD9S/f3+zy4OHVWb1csOQCuyMdgNAaehfAQAwl2VGuiWpT58+WrdundllwASV2adbkvLthgL8S2kMAKB/BQDARJYZ6W7Tpo1+/fXXYsdPnjypNm3amFARvMkRugPKtXr5hTa5LKYGAKWifwUAwFyWCd179+5VQUFBseM5OTk6ePCgCRXBmxzTy4PKs5CaX5GRbhZTA4BS0b8CAGAu06eX/+c//3H+vGbNGoWHhzufFxQUKCUlRTExMSZUBm+qyEJqfn42+fvZVGA32KsbAEpA/woAgDWYHroHDx4sSbLZbBo1apTLa4GBgYqJidHMmTNNqAze5AzdAeWbfBFA6AaAUtG/AgBgDaaHbru9MDS1bt1aX375pZo0aWJyRTCDc/Vyv/KF7iB/P+Xk29mrGwBKQP8KAIA1mB66Hfbs2WN2CTDRhZHu8u257VhwLZ+RbgAoFf0rAADmskzolqSUlBSlpKToyJEjzr/QO7z55psmVQVvqMiWYUXbsXo5AJSN/hUAAPNYJnRPnz5dTz/9tHr06KHmzZvLZivfiCdqBsc08YByTi93hG5WLweA0tG/AgBgLsuE7vnz52vBggUaOXKk2aXABI6R7qByTi93rHLOQmoAUDr6VwAAzGWZfbpzc3PVu3dvs8uASZwLqZVzennA+XYspAYApaN/BQDAXJYJ3ffee6+WLFlidhkwiWPEuqLTyxnpBoDS0b8CAGAuy0wvP3funF599VV99NFHuvzyyxUYGOjy+qxZs0yqDN5Q2enl+XZCNwCUhv4VAABzWSZ0b9u2TbGxsZKkb7/91txi4HX5lVxILTef6eUAUBr6VwAAzGWZ0L1+/XqzS4CJcp0j3eW8p9uPkW4AKA/6VwAAzGV66L7tttvKbGOz2fTee+95oRqYpaL7dDvCOfd0A4B79K8AAFiD6aE7PDzc7BJgAbn550e6y7t6uZ9jyzCmlwOAO/SvAABYg+mh+6233jK7BFiAc6S73AupMdINAKWhfwUAwBoss2UYai/DMCq8T7ejXT4j3QAAAAAsrEaG7nnz5ikmJkYhISHq1auXNm/eXK7zli5dKpvNpsGDB3u2QLgoOkW8vAupObYMY6QbAAAAgJXVuNC9bNkyJSUlaerUqdqyZYu6deumxMREHTlypNTz9u7dqz/+8Y/q06ePlyqFQ9HgXO57up3TyxnpBgAAAGBdNS50z5o1S+PGjdOYMWPUuXNnzZ8/X3Xq1NGbb75Z4jkFBQUaMWKEpk+frjZt2nixWkiuobui08sZ6QYAAABgZTUqdOfm5iotLU0JCQnOY35+fkpISFBqamqJ5z399NNq1qyZxo4d640ycRHHHt1+Nsnfr7wLqZ3fp5vQDQAAAMDCTF+9vDodO3ZMBQUFioiIcDkeERGhnTt3uj3nf//7n9544w1t3bq13O+Tk5OjnJwc5/PMzMxK1YtCFV1ErWjbPDvTywEAAABYV40a6a6o06dPa+TIkXrttdfUpEmTcp+XnJys8PBw5yM6OtqDVdZ8Fd2jW5ICHAup5TPSDQAAAMC6atRId5MmTeTv76+MjAyX4xkZGYqMjCzWfteuXdq7d68GDRrkPGa3F4a4gIAA/fDDD2rbtm2x8yZPnqykpCTn88zMTIJ3FVzYo7v8odsR0PMZ6QYAAABgYTUqdAcFBSkuLk4pKSnObb/sdrtSUlI0YcKEYu07duyo7du3uxx76qmndPr0ab300kslBung4GAFBwdXe/21lWOk23GfdnkE+BWG7lzu6QYAAABgYTUqdEtSUlKSRo0apR49eqhnz56aM2eOsrKyNGbMGEnS3XffrRYtWig5OVkhISHq0qWLy/kNGjSQpGLH4TmOke7y7tEtSYEBLKQGAAAAwPpqXOgeNmyYjh49qilTpig9PV2xsbFavXq1c3G1/fv3y8+vVt/KbjmVWkjNj326AQAAAFhfjQvdkjRhwgS308klacOGDaWeu2DBguovCKVyjnRXaPVym8u5AAAAAGBFDPnCdI77sisy0h3g2DKM0A0AAADAwgjdMF1lFlJzrl7O9HIAAAAAFkbohunyKjXSXRjQWb0cAAAAgJURumG6Sq1ezkg3AAAAAB9A6Ibp8vILgzMLqQEAAACoaQjdMF1lFlJztM2zM9INAAAAwLoI3TCd857uCkwvd65ens9INwAAAADrInTDdBcWUiv/6uWOtvl2QjcAAAAA6yJ0w3R5BZW5p9uxTzfTywEAAABYF6EbpsvJr8I93SykBgAAAMDCCN0wXaX26fZj9XIAAAAA1kfohukci6EFBpT/nm7Hnt7s0w0AAADAygjdMJ1jtDq4EiPduYx0AwAAALAwQjdMl3t+tLoy93Qz0g0AAADAygjdMF1l9ulmITUAAAAAvoDQDdNVaiE15z7dhgyD0W4AAAAA1kTohulyzy+kFuRf/oXUigZ09uoGAAAAYFWEbpiuMiPdgUUCer6dKeYAAAAArInQDdNVZSE1ScrLZ6QbAAAAgDURumE6xz7dQRVYSM2xZZgk5THSDQAAAMCiCN0wXWWml9tsNucUc1YwBwAAAGBVhG6YzhGagwLKv5CaVGTbMKaXAwAAALAoQjdMV5l7uqUL09FzCwqqvSYAAAAAqA6EbpguN78wNFc4dJ9vn5PP9HIAAAAA1kTohunyqjrSTegGAAAAYFGEbpjOeU93BUN3cAAj3QAAAACsjdAN0zlXL6/gQmpBAf6SGOkGAAAAYF2EbpjOEZorOtLN9HIAsKbjx49rxIgRCgsLU4MGDTR27FidOXOm1HPOnTun8ePHq3HjxqpXr55uv/12ZWRkOF//5ptvNHz4cEVHRys0NFSdOnXSSy+95OmPAgBAlRG6YbrK3tMd7O9YvZzQDQBWMmLECO3YsUPr1q3Thx9+qI0bN+q+++4r9ZyHH35YH3zwgd555x198sknOnTokG677Tbn62lpaWrWrJkWLVqkHTt26Mknn9TkyZM1d+5cT38cAACqJMDsAoAL+3RXbqQ7J58twwDAKr7//nutXr1aX375pXr06CFJ+tvf/qYbb7xRL774oqKiooqdc+rUKb3xxhtasmSJrrvuOknSW2+9pU6dOunzzz/XlVdeqXvuucflnDZt2ig1NVXvv/++JkyY4PkPBgBAJTHSDVMV2A3l2ys50s30cgCwnNTUVDVo0MAZuCUpISFBfn5++uKLL9yek5aWpry8PCUkJDiPdezYUS1btlRqamqJ73Xq1Ck1atSoxNdzcnKUmZnp8gAAwNsI3TBV0cAcXMmRbkI3AFhHenq6mjVr5nIsICBAjRo1Unp6eonnBAUFqUGDBi7HIyIiSjxn06ZNWrZsWanT1pOTkxUeHu58REdHV+zDAABQDQjdMFXRwFz56eWEbgDwtMcff1w2m63Ux86dO71Sy7fffqtbbrlFU6dO1fXXX19iu8mTJ+vUqVPOx4EDB7xSHwAARXFPN0zluB/bzyYF+FVwyzAWUgMAr3nkkUc0evToUtu0adNGkZGROnLkiMvx/Px8HT9+XJGRkW7Pi4yMVG5urk6ePOky2p2RkVHsnO+++06/+c1vdN999+mpp54qtZ7g4GAFBweX2gYAAE8jdMNUjlHqoAA/2WwV3af7/Eh3HqEbADytadOmatq0aZnt4uPjdfLkSaWlpSkuLk6S9PHHH8tut6tXr15uz4mLi1NgYKBSUlJ0++23S5J++OEH7d+/X/Hx8c52O3bs0HXXXadRo0bpL3/5SzV8KgAAPI/p5TCVI3QHB/hX+FzHOYx0A4B1dOrUSQMGDNC4ceO0efNmffbZZ5owYYLuuOMO58rlBw8eVMeOHbV582ZJUnh4uMaOHaukpCStX79eaWlpGjNmjOLj43XllVdKKpxSfu211+r6669XUlKS0tPTlZ6erqNHj5r2WQEAKA9GumGqXGforvjff1hIDQCsafHixZowYYJ+85vfyM/PT7fffrv++te/Ol/Py8vTDz/8oOzsbOex2bNnO9vm5OQoMTFRL7/8svP1d999V0ePHtWiRYu0aNEi5/FWrVpp7969XvlcAABUBqEbpnLc013RRdSKnkPoBgBradSokZYsWVLi6zExMTIMw+VYSEiI5s2bp3nz5rk9Z9q0aZo2bVp1lgkAgFcwvRymyqnCSHewc/XygmqtCQAAAACqC6Ebpsp1LqRWmXu6GekGAAAAYG2EbpiqKiPdzunlLKQGAAAAwKII3TBVbpEtwyrKuU83I90AAAAALIrQDVM57seuykh3DqEbAAAAgEURumGq3Crs003oBgAAAGB1hG6YqmqrlxcGdaaXAwAAALAqQjdMVR3TywndAAAAAKyK0A1TVctCaqxeDgAAAMCiCN0wVXVsGeYYLQcAAAAAqyF0w1RVGekOZno5AAAAAIsjdMNUOVVYvZzQDQAAAMDqCN0wFQupAQAAAKjJCN0wVU5VFlILYCE1AAAAANZG6IapqrSQ2vnVy/MKDNntRrXWBQAAAADVgdANU11YSK0S93QHXjiH0W4AAAAAVkTohqmqY6S76HUAAAAAwEoI3TBV7vmF1CpzT3egv63IdQjdAAAAAKyH0A1TVWWk22azOcO6YxV0AAAAALASQjdMlZN3PnQHVvyebkkK9mfbMAAAAADWReiGqRwLoBW9P7siggPZNgwAAACAdRG6YSrHtHBHeK6oIEa6AQAAAFgYoRumcm4ZVsmR7gv3dBO6AQAAAFgPoRumcoTlkEqOdIecvxf8XB4LqQEAAACwHkI3THVhpLtyC6ldCN2MdAMAAACwHkI3TOXcMqySI92h50P3WUa6AQAAAFhQjQzd8+bNU0xMjEJCQtSrVy9t3ry5xLavvfaa+vTpo4YNG6phw4ZKSEgotT2qT36BXQV2Q1Ll9umWLkxLZ3o5AAAAACuqcaF72bJlSkpK0tSpU7VlyxZ169ZNiYmJOnLkiNv2GzZs0PDhw7V+/XqlpqYqOjpa119/vQ4ePOjlymufott8BVUydIcGcU83AAAAAOuqcaF71qxZGjdunMaMGaPOnTtr/vz5qlOnjt5880237RcvXqzf//73io2NVceOHfX666/LbrcrJSXFy5XXPjlF7sOu7OrlIQGEbgAAAADWVaNCd25urtLS0pSQkOA85ufnp4SEBKWmppbrGtnZ2crLy1OjRo08VSbOc4x0+/vZFFDZ0H1+pPtsLgupAQAAALCeALMLqE7Hjh1TQUGBIiIiXI5HRERo586d5brGY489pqioKJfgfrGcnBzl5OQ4n2dmZlau4FrubG7h6LRjMbTKcI505zPSDQAAAMB6atRId1U9++yzWrp0qZYvX66QkJAS2yUnJys8PNz5iI6O9mKVNYcjKFd2j25JCg0qPNcR4AEAAADASmpU6G7SpIn8/f2VkZHhcjwjI0ORkZGlnvviiy/q2Wef1dq1a3X55ZeX2nby5Mk6deqU83HgwIEq114bOYJySDWMdOcw0g0AAADAgmpU6A4KClJcXJzLImiORdHi4+NLPO/555/XM888o9WrV6tHjx5lvk9wcLDCwsJcHqg4x97aVZleHuq8p5vQDQAAAMB6atQ93ZKUlJSkUaNGqUePHurZs6fmzJmjrKwsjRkzRpJ09913q0WLFkpOTpYkPffcc5oyZYqWLFmimJgYpaenS5Lq1aunevXqmfY5agPH6uVVGekODnSsXs5CagAAAACsp8aF7mHDhuno0aOaMmWK0tPTFRsbq9WrVzsXV9u/f7/8/C4M8L/yyivKzc3Vb3/7W5frTJ06VdOmTfNm6bVOtYx0nz/3LFuGAQAAALCgGhe6JWnChAmaMGGC29c2bNjg8nzv3r2eLwhuOe/pDqrCPd3nF2Fjn24AAAAAVlSj7umGb3GsXh5aldXLndPLCd0AAAAArIfQDdNUy+rl3NMNAAAAwMII3TBNTn5hUK7KPd0h3NMNAAAAwMII3TBN9Yx0F/4KE7oBAAAAWBGhG6ZxBOWqhG7u6QYAAABgZYRumOZcNWwZFkLoBgAAAGBhhG6YxrlPd1DVVy/PKzCUX8BiagAAAACshdAN05yrhunlRc89l0/oBgAAAGAthG6YxrHNV1VCd3DAhV9hppgDAAAAsBpCN0zjWL28Kvd0+/nZnMHbcT0AAAAAsApCN0xTHauXS1JoUOH5OfmEbgAAAADWQuiGaapj9XJJCgkoPP9sLvd0AwAAALAWQjdMUx2rlxeef37bMEa6AQAAAFgMoRumycopDMl1ggKqdB3u6QYAAABgVYRumCY7N1+SVC+4aqHbOdLN6uUAAAAALIbQDVPY7Yaycx0j3VVcSO38PeFnCd0AAAAALIbQDVMUDch1qzjS7Zie7piuDgAAAABWQeiGKbLOTy33s124J7uy6gUXjnRn5eRXuS4AAAAAqE6Ebpgi+/yodN2gANlstipdq875kXJHkAcAAAAAqyB0wxSOgFwnuGr3c0sXFmJjpBsAAACA1RC6YYqsIiPdVeW4xhnu6QYAAABgMYRumKI6R7rrnr9GNtPLAQAAAFgMoRumcNzTXac6RrqZXg4AAADAogjdMIVjpLteFbcLky6E7jOEbgAAAAAWQ+iGKbLPB+Q6QdWxkJpjyzDu6QYAAABgLYRumCIrt/oWUnNMUWfLMAAAAABWQ+iGKbLZMgwAAABALUDohimynAupVcfq5Y7QzfRyALCC48ePa8SIEQoLC1ODBg00duxYnTlzptRzzp07p/Hjx6tx48aqV6+ebr/9dmVkZLht++uvv+qSSy6RzWbTyZMnPfAJAACoPoRumOL0ucJR6fohgVW+Vt3zwT0rN1+GYVT5egCAqhkxYoR27NihdevW6cMPP9TGjRt13333lXrOww8/rA8++EDvvPOOPvnkEx06dEi33Xab27Zjx47V5Zdf7onSAQCodoRumCLzXJ4kKaw6Qvf5kW7DkM7mMdoNAGb6/vvvtXr1ar3++uvq1auXrr76av3tb3/T0qVLdejQIbfnnDp1Sm+88YZmzZql6667TnFxcXrrrbe0adMmff755y5tX3nlFZ08eVJ//OMfvfFxAACoMkI3TJF59nzoDq2OhdT85Wcr/PnMOe7rBgAzpaamqkGDBurRo4fzWEJCgvz8/PTFF1+4PSctLU15eXlKSEhwHuvYsaNatmyp1NRU57HvvvtOTz/9tBYuXCg/v7L/CZOTk6PMzEyXBwAA3kbohimqc3q5zWZzXscxgg4AMEd6erqaNWvmciwgIECNGjVSenp6iecEBQWpQYMGLscjIiKc5+Tk5Gj48OF64YUX1LJly3LVkpycrPDwcOcjOjq64h8IAIAqInTDFBeml1d9pFu6MGJ+6iyhGwA84fHHH5fNZiv1sXPnTo+9/+TJk9WpUyfdddddFTrn1KlTzseBAwc8Vh8AACWpnsQDVNCF6eVVH+mWpPDQQB3QWWWeZXo5AHjCI488otGjR5fapk2bNoqMjNSRI0dcjufn5+v48eOKjIx0e15kZKRyc3N18uRJl9HujIwM5zkff/yxtm/frnfffVeSnAtnNmnSRE8++aSmT59e7LrBwcEKDg4u70cEAMAjCN3wOrvd0Okcx/Ty6vkVDD8f3hnpBgDPaNq0qZo2bVpmu/j4eJ08eVJpaWmKi4uTVBiY7Xa7evXq5facuLg4BQYGKiUlRbfffrsk6YcfftD+/fsVHx8vSXrvvfd09uxZ5zlffvml7rnnHn366adq27ZtVT8eAAAeQ+iG1xVu7VX4c3WsXl70OtzTDQDm6tSpkwYMGKBx48Zp/vz5ysvL04QJE3THHXcoKipKknTw4EH95je/0cKFC9WzZ0+Fh4dr7NixSkpKUqNGjRQWFqYHH3xQ8fHxuvLKKyWpWLA+duyY8/0uvhccAAArIXTD6zLPL6IWFOCnkED/armmc6Q7m9ANAGZbvHixJkyYoN/85jfy8/PT7bffrr/+9a/O1/Py8vTDDz8oOzvbeWz27NnOtjk5OUpMTNTLL79sRvkAAFQrQje8znk/dzVNLZcu3BvOSDcAmK9Ro0ZasmRJia/HxMQ478l2CAkJ0bx58zRv3rxyvUe/fv2KXQMAACti9XJ43YXQXT1TyyXu6QYAAABgTYRueN2FPbo9MNLN6uUAAAAALITQDa87np0rSWpQJ6jarumYqs5INwAAAAArIXTD6349Uxi6G9etvtDN9HIAAAAAVkTohtcdz8qRJDWuV32hu3HdYEnSifOj6AAAAABgBYRueN2vWYXBuNH5oFwdHAH+1zO5rGYLAAAAwDII3fC641nVP7280flr5RbYnfuAAwAAAIDZCN3wuuPOke7qC90hgf6qH1y4mNqxMznVdl0AAAAAqApCN7zOsZBao2q8p1tynWIOAAAAAFZA6IbXeWJ6uSQ1qVd4j/ivjHQDAAAAsAhCN7zqbG6BzuYVSKre6eXShZFuppcDAAAAsApCN7zq8KmzkqQ6Qf6qd/4e7OrS+PxI9zGmlwMAAACwCEI3vOrwqXOSpObhIbLZbNV67abnQ/eR0+eq9boAAAAAUFmEbnjVoZOFI91RDUKr/dotGhZe85cTZ6v92gAAAABQGYRueFXRke7qdsn5IH/wJKEbAAAAgDUQuuFVjnu6m4d7bqT70MmzMgyj2q8PAAAAABVF6IZXHTpZONId1aD6R7qbh4fKZpPO5dn1axaLqQEAAAAwH6EbXnXgRLYkz9zTHRTgp2b1CxdTO8h93QAAAAAsgNANr8nNt2vfr4Wh+9Jm9TzyHtEN60iS9v6a5ZHrAwAAAEBFELrhNft+zVKB3VC94ABFhlX/9HJJahdRX5L0U8YZj1wfAAAAACqC0A2v+elIYRBu27Rute/R7dAhonAEfWf6aY9cHwAAAAAqgtANr3GMPl/arL7H3qN9ZOG1f8wgdAMAAAAwH6EbXrP1wAlJUueoMI+9R8fIwmvvP56tU2fzPPY+AAAAAFAehG54RYHd0Ff7CkN3z5hGHnufRnWD1LpJXUnSl3uOe+x9AAAAAKA8CN3wih/ST+v0uXzVDfJXp+aem14uSVe2aSxJ2rTrV4++DwAAAACUhdANr1j97WFJUq82jRXg79lfu95tC0P3xzszZBiGR98LAAAAAEpTI0P3vHnzFBMTo5CQEPXq1UubN28utf0777yjjh07KiQkRF27dtWqVau8VGntUGA3tGLrIUnSLbFRHn+/azs2U50gf+39NVtf7j3h8fcDAAAAgJLUuNC9bNkyJSUlaerUqdqyZYu6deumxMREHTlyxG37TZs2afjw4Ro7dqy+/vprDR48WIMHD9a3337r5cprrvfSftH+49kKCwlQ/84RHn+/esEBGnR5Ybif89GPjHYDAAAAME2NC92zZs3SuHHjNGbMGHXu3Fnz589XnTp19Oabb7pt/9JLL2nAgAGaNGmSOnXqpGeeeUZXXHGF5s6d6+XKax7DMPS/n45p2gc7JEkTrrtUdYICvPLeE667VMEBftq061fNWPW9cvPtXnlfAAAAACjKOwnIS3Jzc5WWlqbJkyc7j/n5+SkhIUGpqaluz0lNTVVSUpLLscTERK1YscKTpRZzLq9As9b9KEnOkdmiA7SOH12PFW/nfM1xDZdjrue5HiveTm6uX95rnDmXrx+PnNbuo1mSCu+zHt27dfFCPSS6UR1NHXSZnli+Xa99ukfLvjygbtENFBEWouAAPwX6+ynAzyabzWsluWUzuwAA1e4Pv2mnesE1qnsFAABVUKP+VXDs2DEVFBQoIsJ1CnNERIR27tzp9pz09HS37dPT00t8n5ycHOXk5DifZ2ZmVqHq89fMt+vVjburfB2rCQrw07Ae0Zp8Y0cFBXh3YsWdvVqqbrC/Zqz6XhmZOfr0p2NefX8AtdO4Pm0I3QAAwIl/FVRCcnKypk+fXq3XDA7w0++uaXPhgM3xHxdGQm02l5cuOlZKuyKjqbbih5znuh5z0+6iUdnSrhES4KeWjesorlUjhYcGyiy3xLbQTZdHaeuBk9p19IyOns5RXoFdeQV25ReYe683d5oDNVNokL/ZJQAAAAupUaG7SZMm8vf3V0ZGhsvxjIwMRUZGuj0nMjKyQu0lafLkyS5T0jMzMxUdHV2FyqWQQH9NvrFTla4B9/z9bIpr1VBxrRqaXQoAAACAWqZGLaQWFBSkuLg4paSkOI/Z7XalpKQoPj7e7Tnx8fEu7SVp3bp1JbaXpODgYIWFhbk8AAAAAAC4WI0a6ZakpKQkjRo1Sj169FDPnj01Z84cZWVlacyYMZKku+++Wy1atFBycrIkaeLEierbt69mzpypgQMHaunSpfrqq6/06quvmvkxAAAAAAA1QI0L3cOGDdPRo0c1ZcoUpaenKzY2VqtXr3YulrZ//375+V0Y4O/du7eWLFmip556Sk888YTatWunFStWqEuXLmZ9BAAAAABADWEzDHcbTqEiMjMzFR4erlOnTjHVHABQZfQrnsH3CgCoTuXtV2rUPd0AAAAAAFgJoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CEBZhdQExiGIUnKzMw0uRIAQE3g6E8c/QuqB/01AKA6lbe/JnRXg9OnT0uSoqOjTa4EAFCTnD59WuHh4WaXUWPQXwMAPKGs/tpm8Gf0KrPb7Tp06JDq168vm81W6etkZmYqOjpaBw4cUFhYWDVW6FnU7V2+Wrfku7VTt3dRd+FfzE+fPq2oqCj5+XEnWHWp7f215Lu1U7d3Ubd3Ubf3VVft5e2vGemuBn5+frrkkkuq7XphYWE+94srUbe3+Wrdku/WTt3eVdvrZoS7+tFfX+CrtVO3d1G3d1G391VH7eXpr/nzOQAAAAAAHkLoBgAAAADAQwjdFhIcHKypU6cqODjY7FIqhLq9y1frlny3dur2LuqG1fnyf9e+Wjt1exd1exd1e5+3a2chNQAAAAAAPISRbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQuj2snnz5ikmJkYhISHq1auXNm/eXGr7d955Rx07dlRISIi6du2qVatWealSVxWpe8GCBbLZbC6PkJAQL1ZbaOPGjRo0aJCioqJks9m0YsWKMs/ZsGGDrrjiCgUHB+vSSy/VggULPF7nxSpa94YNG4p93zabTenp6d4p+Lzk5GT93//9n+rXr69mzZpp8ODB+uGHH8o8z+zf8crUbYXf8VdeeUWXX365wsLCFBYWpvj4eP33v/8t9Ryzv2up4nVb4bt259lnn5XNZtNDDz1UajsrfOeoHF/tryXf67Ppr+mvy4P+2vtqQp9tlf6a0O1Fy5YtU1JSkqZOnaotW7aoW7duSkxM1JEjR9y237Rpk4YPH66xY8fq66+/1uDBgzV48GB9++23lq5bksLCwnT48GHnY9++fV6suFBWVpa6deumefPmlav9nj17NHDgQF177bXaunWrHnroId17771as2aNhyt1VdG6HX744QeX77xZs2YeqtC9Tz75ROPHj9fnn3+udevWKS8vT9dff72ysrJKPMcKv+OVqVsy/3f8kksu0bPPPqu0tDR99dVXuu6663TLLbdox44dbttb4buuTN2S+d/1xb788kv9/e9/1+WXX15qO6t856g4X+2vJd/ss+mv6a89Vbdk/u+3r/bXku/32Zbqrw14Tc+ePY3x48c7nxcUFBhRUVFGcnKy2/ZDhw41Bg4c6HKsV69exu9+9zuP1nmxitb91ltvGeHh4V6qrnwkGcuXLy+1zaOPPmpcdtllLseGDRtmJCYmerCy0pWn7vXr1xuSjBMnTnilpvI6cuSIIcn45JNPSmxjld/xospTtxV/xw3DMBo2bGi8/vrrbl+z4nftUFrdVvuuT58+bbRr185Yt26d0bdvX2PixIkltrXyd47S+Wp/bRi+32fTX3sf/bX3+Wp/bRi+02dbrb9mpNtLcnNzlZaWpoSEBOcxPz8/JSQkKDU11e05qampLu0lKTExscT2nlCZuiXpzJkzatWqlaKjo8v8i5hVWOH7rorY2Fg1b95c/fv312effWZ2OTp16pQkqVGjRiW2seJ3Xp66JWv9jhcUFGjp0qXKyspSfHy82zZW/K7LU7dkre96/PjxGjhwYLHv0h0rfucom6/211Lt6bOt8n1XFv119aC/9i5f67Ot1l8Tur3k2LFjKigoUEREhMvxiIiIEu/lSU9Pr1B7T6hM3R06dNCbb76pf//731q0aJHsdrt69+6tX375xRslV1pJ33dmZqbOnj1rUlVla968uebPn6/33ntP7733nqKjo9WvXz9t2bLFtJrsdrseeughXXXVVerSpUuJ7azwO15Ueeu2yu/49u3bVa9ePQUHB+v+++/X8uXL1blzZ7dtrfRdV6Ruq3zXkrR06VJt2bJFycnJ5Wpvpe8c5eer/bVUe/ps+uvqQ3/tHb7aX0u+2Wdbsb8OqLYrAefFx8e7/AWsd+/e6tSpk/7+97/rmWeeMbGymqlDhw7q0KGD83nv3r21a9cuzZ49W//4xz9MqWn8+PH69ttv9b///c+U96+s8tZtld/xDh06aOvWrTp16pTeffddjRo1Sp988kmJnaFVVKRuq3zXBw4c0MSJE7Vu3TrTF4UBqpNV/jdWG9BfVx/6a+/xtT7bqv01odtLmjRpIn9/f2VkZLgcz8jIUGRkpNtzIiMjK9TeEypT98UCAwPVvXt3/fzzz54osdqU9H2HhYUpNDTUpKoqp2fPnqZ1oBMmTNCHH36ojRs36pJLLim1rRV+xx0qUvfFzPodDwoK0qWXXipJiouL05dffqmXXnpJf//734u1tdJ3XZG6L2bWd52WlqYjR47oiiuucB4rKCjQxo0bNXfuXOXk5Mjf39/lHCt95yg/X+2vpdrTZ9NfVw/6a+/x1f5a8r0+26r9NdPLvSQoKEhxcXFKSUlxHrPb7UpJSSnxvoj4+HiX9pK0bt26Uu+jqG6VqftiBQUF2r59u5o3b+6pMquFFb7v6rJ161avf9+GYWjChAlavny5Pv74Y7Vu3brMc6zwnVem7otZ5XfcbrcrJyfH7WtW+K5LUlrdFzPru/7Nb36j7du3a+vWrc5Hjx49NGLECG3durVYBy5Z+ztHyXy1v5ZqT59tle+7OtBflx/9tTV+v63eZ1u2v662JdlQpqVLlxrBwcHGggULjO+++8647777jAYNGhjp6emGYRjGyJEjjccff9zZ/rPPPjMCAgKMF1980fj++++NqVOnGoGBgcb27dstXff06dONNWvWGLt27TLS0tKMO+64wwgJCTF27Njh1bpPnz5tfP3118bXX39tSDJmzZplfP3118a+ffsMwzCMxx9/3Bg5cqSz/e7du406deoYkyZNMr7//ntj3rx5hr+/v7F69WpL1z179mxjxYoVxk8//WRs377dmDhxouHn52d89NFHXq37gQceMMLDw40NGzYYhw8fdj6ys7Odbaz4O16Zuq3wO/74448bn3zyibFnzx5j27ZtxuOPP27YbDZj7dq1bmu2wnddmbqt8F2X5OLVUK36naPifLW/rkztVvjfGP01/bWn6rbC77ev9teVqd0K37c7VuivCd1e9re//c1o2bKlERQUZPTs2dP4/PPPna/17dvXGDVqlEv7f/3rX0b79u2NoKAg47LLLjNWrlzp5YoLVaTuhx56yNk2IiLCuPHGG40tW7Z4vWbH1hwXPxy1jho1yujbt2+xc2JjY42goCCjTZs2xltvvWX5up977jmjbdu2RkhIiNGoUSOjX79+xscff+z1ut3VLMnlO7Ti73hl6rbC7/g999xjtGrVyggKCjKaNm1q/OY3v3F2gu5qNgzzv2vDqHjdVviuS3JxJ27V7xyV46v9tWH4Xp9Nf+1d9Nf01+VVU/psK/TXNsMwjOobNwcAAAAAAA7c0w0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AQI22ceNGDRo0SFFRUbLZbFqxYoXp7/f+++/r+uuvV+PGjWWz2bR161aP1gQAgC+oqX02oRtAqUaPHq3Bgweb9v4jR47UjBkzytX2jjvu0MyZMz1cEXxNVlaWunXrpnnz5lnm/bKysnT11Vfrueee80pNAGoH+mz4uhrbZxsAai1JpT6mTp1qnDx50jhx4oQp9W3dutVo1KiRcfr06XK13759u9GwYUPj5MmTHq4MvkqSsXz5cpdj586dMx555BEjKirKqFOnjtGzZ09j/fr1Hnu/ovbs2WNIMr7++utqeT8ANRd9NmqbmtRnB1Q+rgPwdYcPH3b+vGzZMk2ZMkU//PCD81i9evVUr149M0qTJP3tb3/TkCFDyl1Dly5d1LZtWy1atEjjx4/3cHWoKSZMmKDvvvtOS5cuVVRUlJYvX64BAwZo+/btateundnlAYAk+mxA8t0+m+nlQC0WGRnpfISHh8tms7kcq1evXrGpav369dODDz6ohx56SA0bNlRERIRee+01ZWVlacyYMapfv74uvfRS/fe//3V5r2+//VY33HCD6tWrp4iICI0cOVLHjh0rsbaCggK9++67GjRokMvxl19+We3atVNISIgiIiL029/+1uX1QYMGaenSpVX/clAr7N+/X2+99Zbeeecd9enTR23bttUf//hHXX311XrrrbfMLg8AnOizUdv5cp9N6AZQYW+//baaNGmizZs368EHH9QDDzygIUOGqHfv3tqyZYuuv/56jRw5UtnZ2ZKkkydP6rrrrlP37t311VdfafXq1crIyNDQoUNLfI9t27bp1KlT6tGjh/PYV199pT/84Q96+umn9cMPP2j16tW65pprXM7r2bOnNm/erJycHM98eNQo27dvV0FBgdq3b+8cJapXr54++eQT7dq1S5K0c+dO2Wy2Uh+PP/64yZ8EANyjz0ZN4ct9NtPLAVRYt27d9NRTT0mSJk+erGeffVZNmjTRuHHjJElTpkzRK6+8om3btunKK6/U3Llz1b17d5fFVd58801FR0frxx9/VPv27Yu9x759++Tv769mzZo5j+3fv19169bVTTfdpPr166tVq1bq3r27y3lRUVHKzc1Venq6WrVq5YmPjxrkzJkz8vf3V1pamvz9/V1ec0yRbNOmjb7//vtSr9O4cWOP1QgAVUGfjZrCl/tsQjeACrv88sudP/v7+6tx48bq2rWr81hERIQk6ciRI5Kkb775RuvXr3d7n9euXbvcduBnz55VcHCwbDab81j//v3VqlUrtWnTRgMGDNCAAQN06623qk6dOs42oaGhkuT8iz1Qmu7du6ugoEBHjhxRnz593LYJCgpSx44dvVwZAFQP+mzUFL7cZxO6AVRYYGCgy3ObzeZyzNHp2u12SYV/mRw0aJDbrRaaN2/u9j2aNGmi7Oxs5ebmKigoSJJUv359bdmyRRs2bNDatWs1ZcoUTZs2TV9++aUaNGggSTp+/LgkqWnTplX7kKgxzpw5o59//tn5fM+ePdq6dasaNWqk9u3ba8SIEbr77rs1c+ZMde/eXUePHlVKSoouv/xyDRw4sFrfr2XLlpIKf0/379+vQ4cOSZJzMSTHvZkAUF3os+FLamyfXflF1QHUJG+99ZYRHh5e7PioUaOMW265xfm8b9++xsSJE13atGrVypg9e7bLMRXZduGJJ54wOnToYOTl5ZW7niNHjpS5LcOZM2eMgIAA47333nMee/31141LLrmk3O+Dmm/9+vVut9cZNWqUYRiGkZuba0yZMsWIiYkxAgMDjebNmxu33nqrsW3bNo+8n2EU/u/NXZupU6dW/QMDqPHos1FT1dQ+m5FuAB43fvx4vfbaaxo+fLgeffRRNWrUSD///LOWLl2q119/vdh9OVLhX72vuOIK/e9//1NsbKwk6cMPP9Tu3bt1zTXXqGHDhlq1apXsdrs6dOjgPO/TTz/V9ddf762PBh/Qr18/GYZR4uuBgYGaPn26pk+f7pX3k6TRo0dr9OjR1fJ+AFCd6LNhppraZ7N6OQCPi4qK0meffaaCggJdf/316tq1qx566CE1aNBAfn4l/9/Qvffeq8WLFzufN2jQQO+//76uu+46derUSfPnz9c///lPXXbZZZKkc+fOacWKFc7FYQAAQMXQZwPVz2aUFe0BwCRnz55Vhw4dtGzZMsXHx5fZ/pVXXtHy5cu1du1aL1QHAAAc6LOBkjHSDcCyQkNDtXDhQh07dqxc7QMDA/W3v/3Nw1UBAICL0WcDJWOkGwAAAAAAD2GkGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AZgin79+qlfv35ml+Fks9k0bdo0s8sAAMBn7N27VzabTQsWLDC7FEnSggULZLPZtHfvXrNLAVwQugELcXQWX331ldmlSJKys7M1bdo0bdiwodzn7N27V2PGjFHbtm0VEhKiyMhIXXPNNZo6darnCvWSAwcOaPr06erZs6caNmyoJk2aqF+/fvroo4/MLg0A4CE7duzQXXfdpRYtWig4OFhRUVEaMWKEduzYUaXrzpgxQytWrKieIsuwadMmTZs2TSdPniz3OR988IH69u2rZs2aqU6dOmrTpo2GDh2q1atXe65QL0lJSdE999yj9u3bOz/bvffeq8OHD5tdGmooQjeAEmVnZ2v69OnlDt0///yzunfvrjVr1mj48OGaO3euxo8fr8aNG+u5555zabt27VqtXbvWA1V7zr///W8999xzuvTSS/XnP/9Zf/rTn3T69Gn1799fb731ltnlAQCq2fvvv68rrrhCKSkpGjNmjF5++WWNHTtW69ev1xVXXKHly5dX+treDt3Tp08vd+h+8cUXdfPNN8tms2ny5MmaPXu2br/9dv30009aunSps12rVq109uxZjRw50kOVe8Zjjz2mDRs26NZbb9Vf//pX3XHHHfrXv/6l7t27Kz093ezyUAMFmF0AgJpj9uzZOnPmjLZu3apWrVq5vHbkyBGX50FBQd4srVpce+212r9/v5o0aeI8dv/99ys2NlZTpkzRmDFjTKwOAFCddu3apZEjR6pNmzbauHGjmjZt6nxt4sSJ6tOnj0aOHKlt27apTZs2JlZavfLz8/XMM8+of//+bv84XrQ/t9lsCgkJ8WZ51WLWrFm6+uqr5ed3YfxxwIAB6tu3r+bOnas///nPJlaHmoiRbsDiRo8erXr16ungwYMaPHiw6tWrp6ZNm+qPf/yjCgoKnO0c91W9+OKLmj17tlq1aqXQ0FD17dtX3377rcs1S7qfevTo0YqJiXFez/EPjOnTp8tms5V53/OuXbt0ySWXFAvcktSsWbMya9i3b59uvvlm1a1bV82aNdPDDz+sNWvWyGazuYy29+vXT126dNF3332na6+9VnXq1FGLFi30/PPPu1wvNzdXU6ZMUVxcnMLDw1W3bl316dNH69evL/EzlOayyy5zCdySFBwcrBtvvFG//PKLTp8+XanrAgCs54UXXlB2drZeffVVl8AtSU2aNNHf//53ZWVlufQ9RfvRoqZNmyabzeZ8brPZlJWVpbffftvZv44ePdql7c6dOzV06FCFhYWpcePGmjhxos6dO+e8Rmn3Uxftr6dNm6ZJkyZJklq3bu18v5Luez527JgyMzN11VVXuX29aH9eUg3vvPOOOnfurJCQEHXp0kXLly8v9t0U/XfLq6++qrZt2yo4OFj/93//py+//NLletu2bdPo0aPVpk0b561r99xzj3799Ve3NZblmmuucQncjmONGjXS999/X6lrAqVhpBvwAQUFBUpMTFSvXr304osv6qOPPtLMmTPVtm1bPfDAAy5tFy5cqNOnT2v8+PE6d+6cXnrpJV133XXavn27IiIiyv2eTZs21SuvvKIHHnhAt956q2677TZJ0uWXX17iOa1atdJHH32kjz/+WNddd12FPmNWVpauu+46HT58WBMnTlRkZKSWLFlSYkA+ceKEBgwYoNtuu01Dhw7Vu+++q8cee0xdu3bVDTfcIEnKzMzU66+/ruHDh2vcuHE6ffq03njjDSUmJmrz5s2KjY2tUI0lSU9PV506dVSnTp1quR4AwHwffPCBYmJi1KdPH7evX3PNNYqJidHKlSsrfO1//OMfuvfee9WzZ0/dd999kqS2bdu6tBk6dKhiYmKUnJyszz//XH/961914sQJLVy4sELvddttt+nHH3/UP//5T82ePdv5x+OL/5Dg0KxZM4WGhuqDDz7Qgw8+qEaNGlXo/VauXKlhw4apa9euSk5O1okTJzR27Fi1aNHCbfslS5bo9OnT+t3vfiebzabnn39et912m3bv3q3AwEBJ0rp167R7926NGTNGkZGR2rFjh1599VXt2LFDn3/+ucsfNCrrzJkzOnPmTLE/rgPVwgBgGW+99ZYhyfjyyy+dx0aNGmVIMp5++mmXtt27dzfi4uKcz/fs2WNIMkJDQ41ffvnFefyLL74wJBkPP/yw81jfvn2Nvn37Fnv/UaNGGa1atXI+P3r0qCHJmDp1arnq//bbb43Q0FBDkhEbG2tMnDjRWLFihZGVlVWs7cU1zJw505BkrFixwnns7NmzRseOHQ1Jxvr1613OlWQsXLjQeSwnJ8eIjIw0br/9duex/Px8Iycnx+V9T5w4YURERBj33HOPy/GKfM6ifvrpJyMkJMQYOXJkhc8FAFjTyZMnDUnGLbfcUmq7m2++2ZBkZGZmGoZRvB91mDp1qnHxP7vr1q1rjBo1qsS2N998s8vx3//+94Yk45tvvjEM40K//9ZbbxW7xsV92gsvvGBIMvbs2VPq53GYMmWKIcmoW7euccMNNxh/+ctfjLS0tGLt3NXQtWtX45JLLjFOnz7tPLZhwwZDkst34zi3cePGxvHjx53H//3vfxuSjA8++MB5LDs7u9h7//Of/zQkGRs3bnQec/w7qryfs6hnnnnGkGSkpKRU+FygLEwvB3zE/fff7/K8T58+2r17d7F2gwcPdvlrcs+ePdWrVy+tWrXK4zVedtll2rp1q+666y7t3btXL730kgYPHqyIiAi99tprpZ67evVqtWjRQjfffLPzWEhIiMaNG+e2fb169XTXXXc5nwcFBalnz54u34m/v7/z3nG73a7jx48rPz9fPXr00JYtW6ryUSUVLjQ3ZMgQhYaG6tlnn63y9QAA1uC4Xah+/fqltnO8npmZWe01jB8/3uX5gw8+KEle6c+nT5+uJUuWOBdHffLJJxUXF6crrrii1OnXhw4d0vbt23X33XerXr16zuN9+/ZV165d3Z4zbNgwNWzY0PncMbOgaH8eGhrq/PncuXM6duyYrrzySkmqlv5848aNmj59uoYOHVrhmXpAeRC6AR8QEhJSbBpYw4YNdeLEiWJt27VrV+xY+/btvbZnZfv27fWPf/xDx44d07Zt2zRjxgwFBATovvvuK3VrrX379qlt27bFpohdeumlbttfcsklxdq6+07efvttXX755QoJCVHjxo3VtGlTrVy5UqdOnarkJyxUUFCgO+64Q999953effddRUVFVel6AADrcITpstbqKG84r4yL+/O2bdvKz8/Pa/358OHD9emnn+rEiRNau3at7rzzTn399dcaNGiQy73lRe3bt0+S+767pP68ZcuWLs8dAbxof378+HFNnDhRERERCg0NVdOmTdW6dWtJqnJ/vnPnTt16663q0qWLXn/99SpdCygJ93QDPsDf379ar2ez2WQYRrHjRRdmqyp/f3917dpVXbt2VXx8vK699lotXrxYCQkJ1XZ9d4p+rkWLFmn06NEaPHiwJk2apGbNmsnf31/JycnatWtXld5/3Lhx+vDDD7V48WL+Kg4ANUx4eLiaN2+ubdu2ldpu27ZtatGihcLCwiSpxHuLq6N/vfjannyvosLCwtS/f3/1799fgYGBevvtt/XFF1+ob9++1XL98vTnQ4cO1aZNmzRp0iTFxsaqXr16stvtGjBggOx2e6Xf+8CBA7r++usVHh6uVatWeeSPJ4DESDdQ4/z000/Fjv34448uK4Y2bNjQ7V6djr9QO1THwiSS1KNHD0nS4cOHS2zTqlUr7dq1q9gfA37++edKv++7776rNm3a6P3339fIkSOVmJiohISEEv9CX16TJk3SW2+9pdmzZ2v48OFVuhYAwJpuuukm7dmzR//73//cvv7pp59q7969uummm5zHytu/SmX3sRf35z///LPsdruzP3eMCF/8fpV5r/Iqqz937F7iru+ubH9+4sQJpaSk6PHHH9f06dN16623qn///lXepu3XX3/V9ddfr5ycHK1Zs0bNmzev0vWA0hC6gRpmxYoVOnjwoPP55s2b9cUXXzhX9JYKp6jt3LlTR48edR775ptv9Nlnn7lcy7Eat7t/QLjz6aefKi8vr9hxx/1nHTp0KPHcxMREHTx4UP/5z3+cx86dO1fmveClcfz1vGiQ/+KLL5Samlrpa77wwgt68cUX9cQTT2jixImVvg4AwNomTZqk0NBQ/e53vyu2NdXx48d1//33q06dOs7tuKTC/vXUqVMuI+SHDx/W8uXLi12/bt26pfav8+bNc3n+t7/9TZKc/XlYWJiaNGmijRs3urR7+eWX3b6XVL7+PDs7u8R+8r///a+kkvvzqKgodenSRQsXLtSZM2ecxz/55BNt3769zPd2x11fLklz5syp1PWkwh1TbrzxRh08eFCrVq1ye2seUJ2YXg7UMJdeeqmuvvpqPfDAA8rJydGcOXPUuHFjPfroo84299xzj2bNmqXExESNHTtWR44c0fz583XZZZe5LAYTGhqqzp07a9myZWrfvr0aNWqkLl26qEuXLm7f+7nnnlNaWppuu+0259ZiW7Zs0cKFC9WoUSM99NBDJdb9u9/9TnPnztXw4cM1ceJENW/eXIsXL1ZISIikyv2V/qabbtL777+vW2+9VQMHDtSePXs0f/58de7c2eUfA+W1fPlyPfroo2rXrp06deqkRYsWubzev3//Cm3LBgCwrnbt2untt9/WiBEj1LVrV40dO1atW7fW3r179cYbb+jYsWP65z//6bLV1x133KHHHntMt956q/7whz8oOztbr7zyitq3b19swa+4uDh99NFHmjVrlqKiotS6dWv16tXL+fqePXt08803a8CAAUpNTdWiRYt05513qlu3bs429957r5599lnde++96tGjhzZu3Kgff/yx2GeJi4uTJD355JO64447FBgYqEGDBjnDeFHZ2dnq3bu3rrzySg0YMEDR0dE6efKkVqxYoU8//VSDBw9W9+7dS/zeZsyYoVtuuUVXXXWVxowZoxMnTmju3Lnq0qVLpfresLAwXXPNNXr++eeVl5enFi1aaO3atdqzZ0+Fr+UwYsQIbd68Wffcc4++//57l8Xh6tWrp8GDB1f62oBbZi6dDsBVSVuG1a1bt1jbi7cfcWy98cILLxgzZ840oqOjjeDgYKNPnz7O7UWKWrRokdGmTRsjKCjIiI2NNdasWeN2q5NNmzYZcXFxRlBQUJnban322WfG+PHjjS5duhjh4eFGYGCg0bJlS2P06NHGrl27XNq627Zs9+7dxsCBA43Q0FCjadOmxiOPPGK89957hiTj888/dzn3sssuK/b+F9dvt9uNGTNmGK1atTKCg4ON7t27Gx9++KHbz1nWZzOMC995SY+i25oBAGqGbdu2GcOHDzeaN29uBAYGGpGRkcbw4cON7du3u22/du1ao0uXLkZQUJDRoUMHY9GiRW63DNu5c6dxzTXXOLfadGwf5mj73XffGb/97W+N+vXrGw0bNjQmTJhgnD171uUa2dnZxtixY43w8HCjfv36xtChQ40jR4647dOeeeYZo0WLFoafn1+p22rl5eUZr732mjF48GBn/1mnTh2je/fuxgsvvOCyFWdJ25YtXbrU6NixoxEcHGx06dLF+M9//mPcfvvtRseOHYud+8ILLxSr4eL6f/nlF+PWW281GjRoYISHhxtDhgwxDh06VKxdebcMa9WqVYl9ubst34CqshmGm9WUAPicvXv3qnXr1nrhhRf0xz/+0exyqs2cOXP08MMP65dffnHZCg0AgJpo2rRpmj59uo4ePaomTZqYXU61iY2NVdOmTbVu3TqzSwG8jnu6AVjG2bNnXZ6fO3dOf//739WuXTsCNwAAPiAvL0/5+fkuxzZs2KBvvvlG/fr1M6cowGTc0w3AMm677Ta1bNlSsbGxOnXqlBYtWqSdO3dq8eLFZpcGAADK4eDBg0pISNBdd92lqKgo7dy5U/Pnz1dkZKTuv/9+s8sDTEHoBmAZiYmJev3117V48WIVFBSoc+fOWrp0qYYNG2Z2aQAAoBwaNmyouLg4vf766zp69Kjq1q2rgQMH6tlnn1Xjxo3NLg8wBfd0AwAAAADgIdzTDQAAAACAhxC6AQAAAADwEEI3AAAAAAAewkJq1cBut+vQoUOqX7++bDab2eUAAHycYRg6ffq0oqKi5OfH38erC/01AKA6lbe/JnRXg0OHDik6OtrsMgAANcyBAwd0ySWXmF1GjUF/DQDwhLL6a0J3Nahfv76kwi87LCzM5GoAAL4uMzNT0dHRzv4F1YP+GgBQncrbXxO6q4FjilpYWBidOACg2jAFunrRXwMAPKGs/pobxQAAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAADA6zZu3KhBgwYpKipKNptNK1asKPOcDRs26IorrlBwcLAuvfRSLViwwON1AgBQVYRuAADgdVlZWerWrZvmzZtXrvZ79uzRwIEDde2112rr1q166KGHdO+992rNmjUerhQAgKoJMLsAAABQ+9xwww264YYbyt1+/vz5at26tWbOnClJ6tSpk/73v/9p9uzZSkxM9FSZAABUGSPdAADA8lJTU5WQkOByLDExUampqSWek5OTo8zMTJcHzLfr6Bm989UB2e2G2aUAgFcw0g0AACwvPT1dERERLsciIiKUmZmps2fPKjQ0tNg5ycnJmj59urdKRDmcycnXb2Z+IklqWCdICZ0jyjgDAHwfI90AAKBGmjx5sk6dOuV8HDhwwOySar0f0k87f9528JSJlQCA9zDSDQAALC8yMlIZGRkuxzIyMhQWFuZ2lFuSgoODFRwc7I3yUE77j2c5f/6xSAAHgJqMkW4AAGB58fHxSklJcTm2bt06xcfHm1QRKuPA8bPOn4+eyTGxEgDwHkI3AADwujNnzmjr1q3aunWrpMItwbZu3ar9+/dLKpwafvfddzvb33///dq9e7ceffRR7dy5Uy+//LL+9a9/6eGHHzajfFTS8axctz8DQE1G6AYAAF731VdfqXv37urevbskKSkpSd27d9eUKVMkSYcPH3YGcElq3bq1Vq5cqXXr1qlbt26aOXOmXn/9dbYL8zEnsy8E7WOMdAOoJbinGwAAeF2/fv1kGCVvGbVgwQK353z99dcerAqediI7z/nz6XP5yskvUHCAv4kVAYDnMdINAAAArzh5Ns/leebZfJMqAQDvIXQDAADAK05lu97HfSaH0A2g5iN0AwAAwCtOn3MN2WfOEboB1HyEbgAAAHiFY2Q7NLDwPu7TOXmlNQeAGoHQDQAAAI/LL7ArJ98uSYoMD5HESDeA2oHQDQAAAI/Lyilw/hwRFiyp+HRzAKiJCN0AAADwuDO5hQE7yN9PjeoGFR5jITUAtQChGwAAAB6XdT5g1w32V2hggCTpbF5BaacAQI1A6AYAAIDHnXGG7gCFBBb+E/RsLqEbQM1H6AYAAIDHOQJ2nSB/5+rl5/IJ3QBqPkI3AAAAPO7c+ankIYH+Cg06H7oZ6QZQCxC6AQAA4HHn8gq3CwsJ8FfI+ZFu7ukGUBsQugEAAOBxjpHu4EA/Z+h2BHEAqMkI3QAAAPA4x/3bIYEX7ulmpBtAbUDoBgAAgMc5p5cH+jtXLz9H6AZQCxC6AQAA4HGOgB0a6Hdh9XJCN4BagNANAAAAjyu6enlIENPLAdQehG4AAAB4nEvoDmAhNQC1B6EbAAAAHndhyzA/5z7dZ9mnG0AtQOgGAACAx13YMoyF1ADULoRuAAAAeNy5/Aurl7OQGoDahNANAAAAj7twT7efyz7dhmGYWRYAeByhGwAAAB7nDN0B/go+H7rthpRXQOgGULMRugEAAOBxRVcvd4x0S2wbBqDmI3QDAADA4xyrl4cG+SnQ3yZ/P9v544RuADUboRsAAAAeV3R6uc1mU0gAK5gDqB18LnTPmzdPMTExCgkJUa9evbR58+ZS27/zzjvq2LGjQkJC1LVrV61atarEtvfff79sNpvmzJlTzVUDAADUbufyL2wZJhVOM5eYXg6g5vOp0L1s2TIlJSVp6tSp2rJli7p166bExEQdOXLEbftNmzZp+PDhGjt2rL7++msNHjxYgwcP1rffflus7fLly/X5558rKirK0x8DAACg1nFML3fs0R10fqQ79/xWYgBQU/lU6J41a5bGjRunMWPGqHPnzpo/f77q1KmjN9980237l156SQMGDNCkSZPUqVMnPfPMM7riiis0d+5cl3YHDx7Ugw8+qMWLFyswMNAbHwUAAKBWKbqQmkToBlB7+Ezozs3NVVpamhISEpzH/Pz8lJCQoNTUVLfnpKamurSXpMTERJf2drtdI0eO1KRJk3TZZZd5pngAAIBaLsc50n0+dPsTugHUDgFmF1Bex44dU0FBgSIiIlyOR0REaOfOnW7PSU9Pd9s+PT3d+fy5555TQECA/vCHP5S7lpycHOXk5DifZ2ZmlvtcAACA2sZuN5RbUBiugwNcp5fnFBC6AdRsPjPS7QlpaWl66aWXtGDBAtlstnKfl5ycrPDwcOcjOjrag1UCAAD4ttwiwdoRtoOZXg6glvCZ0N2kSRP5+/srIyPD5XhGRoYiIyPdnhMZGVlq+08//VRHjhxRy5YtFRAQoICAAO3bt0+PPPKIYmJiSqxl8uTJOnXqlPNx4MCBqn04AACAGswldPuzkBqA2sVnQndQUJDi4uKUkpLiPGa325WSkqL4+Hi358THx7u0l6R169Y5248cOVLbtm3T1q1bnY+oqChNmjRJa9asKbGW4OBghYWFuTwAAADgXl6+u9BdeG83oRtATecz93RLUlJSkkaNGqUePXqoZ8+emjNnjrKysjRmzBhJ0t13360WLVooOTlZkjRx4kT17dtXM2fO1MCBA7V06VJ99dVXevXVVyVJjRs3VuPGjV3eIzAwUJGRkerQoYN3PxwAAEAN5RjpDvCzyc+v8JY+50Jq3NMNoIbzqdA9bNgwHT16VFOmTFF6erpiY2O1evVq52Jp+/fvl5/fhcH73r17a8mSJXrqqaf0xBNPqF27dlqxYoW6dOli1kcAAACodRyj2Y4p5RL3dAOoPXwqdEvShAkTNGHCBLevbdiwodixIUOGaMiQIeW+/t69eytZGQAAANxxF7q5pxtAbeEz93QDAADAN+U4Qrd/kdDN9HIAtQShGwAAAB7lCNbuRrpzGOkGUMMRugEAAOBReUwvB1CLEboBAADgUc6Rbn93I90FptQEAN5C6AYAAIBHuV1IzZ+RbgC1A6EbAAAAHpXrbiE1ppcDqCUI3QAAAPAodwupOffpZvVyADUcoRsAAAAelcNCagBqMUI3AAAAPMrt9HLu6QZQSxC6AQAA4FF57qaXBzK9HEDtQOgGAACAR7lfvdxf0oWp5wBQUxG6AQAA4FGsXg6gNiN0AwAAwKPcrV5O6AZQWxC6AQAA4FGlLqTGPd0AajhCNwAAADyKLcMA1GaEbgAAAHiU29XLCd0AaglCNwAAADzK7erlAUwvB1A7ELoBAADgUc6F1Irc0x14/uc8QjeAGo7QDQAAAI9yjHQHFxnpDvCzSSJ0A6j5CN0AAADwqNKml+cVGKbUBADeQugGAACARzmmlwe6mV5eYDdktxO8AdRchG4AAAB4VJ7b0G278LqdKeYAai5CNwAAADwq//wUcncj3RJTzAHUbIRuAAAAeNSFke4Lo9suoZu9ugHUYIRuAAAAeFSem5Fufz+bzi9gzvRyADUaoRsAAAAe5RjpDigy0i0V3aub6eUAai5CNwAAADzKEbqD/F3/6ekM3UwvB1CDEboBAIAp5s2bp5iYGIWEhKhXr17avHlzqe3nzJmjDh06KDQ0VNHR0Xr44Yd17tw5L1WLqnCMZAcUC922868TugHUXIRuAADgdcuWLVNSUpKmTp2qLVu2qFu3bkpMTNSRI0fctl+yZIkef/xxTZ06Vd9//73eeOMNLVu2TE888YSXK0dluFtIrfA508sB1HyEbgAA4HWzZs3SuHHjNGbMGHXu3Fnz589XnTp19Oabb7ptv2nTJl111VW68847FRMTo+uvv17Dhw8vc3Qc1pBvLwzVJU4vZ6QbQA1G6AYAAF6Vm5urtLQ0JSQkOI/5+fkpISFBqampbs/p3bu30tLSnCF79+7dWrVqlW688cYS3ycnJ0eZmZkuD5jDcc8208sB1EYBZhcAAABql2PHjqmgoEAREREuxyMiIrRz506359x55506duyYrr76ahmGofz8fN1///2lTi9PTk7W9OnTq7V2VI5jS7CSppfnEroB1GCMdAMAAMvbsGGDZsyYoZdffllbtmzR+++/r5UrV+qZZ54p8ZzJkyfr1KlTzseBAwe8WDGKcrdPd9Hn+dzTDaAGY6QbAAB4VZMmTeTv76+MjAyX4xkZGYqMjHR7zp/+9CeNHDlS9957rySpa9euysrK0n333acnn3xSfn7FxxGCg4MVHBxc/R8AFWK3GyqwlxC6A7inG0DNx0g3AADwqqCgIMXFxSklJcV5zG63KyUlRfHx8W7Pyc7OLhas/f39JUmGwSiplTmmlktSwMXTy/24pxtAzcdINwAA8LqkpCSNGjVKPf6/vXuPb6rO8z/+TpqmBZFWKLQUKxVFQBGKILWMrqiVqgwzrIrIgwVExP251AGrM8KIIDhadUe80ZHxAsg6DAygrCKDYgV1tAoUGUERbygs2AIytFCgl+T8/qBJW0hLkp6kyeH1fDzyWHpyTvJJtuP3vPu99e+vAQMG6Omnn1ZFRYXGjRsnSRozZow6d+6s/Px8SdLQoUM1e/Zs9e3bV5mZmfr222/14IMPaujQod7wjchUfzuwxlYvr2J4OQALI3QDAICwGzFihPbt26fp06erpKREGRkZWr16tXdxtZ07dzbo2Z42bZpsNpumTZum3bt3q0OHDho6dKgeeeSRlvoI8FNNvV5sh/2Enm7P8PIaeroBWBehGwAAtIjc3Fzl5ub6fG7dunUNfnY4HJoxY4ZmzJgRhspgJs/K5DabFHNC6HbWDjevcRO6AVgXc7oBAAAQMp6VyWPtdtlsjW0ZxvByANZF6AYAAEDIeBZJO3GPbklyxDC8HID1EboBAAAQMt49uh0n33Z6gjirlwOwMkI3AAAAQsYTqB0+9lL3rGZe42Z4OQDrInQDAAAgZDxzup0+hpd753QzvByAhRG6AQAAEDKe1csdMSffdjoYXg7gNEDoBgAAQMg0tZCaZ3g5oRuAlRG6AQAAEDLeLcN89HTHekM3c7oBWBehGwAAACFT19PdVOimpxuAdRG6AQAAEDLe1ct97tPNnG4A1kfoBgAAQMhUNzG83MnwcgCnAUI3AAAAQqbGfbwX2+lzeDk93QCsj9ANAACAkPHswe1reHmsgzndAKyP0A0AAICQqXE3sXq5neHlAKyP0A0AAICQaWqf7lgHw8sBWB+hGwAAACHT1EJqbBkG4HRA6AYAAEDIeLcMszcVuhleDsC6CN0AAAAImerahdScDh/Dy1m9HMBpgNANAACAkKmuXUitqZ5uzwrnAGBFhG4AAACETN1Cao2Hbs8K5wBgRYRuAAAAhEyNJ3T7HF7OQmoArI/QDQAAgJDxrl7uc3h57ZxuhpcDsDBCNwAAAELGn+HlVaxeDsDCCN0AAAAIGe+WYTGNDy+vcdPTDcC6CN0AAAAImZraXmynj55uzzGGlwOwMkI3AAAAQqaqiZ5uh3efboaXA7CuqAvdBQUFSk9PV3x8vDIzM7V+/fomz1+6dKl69Oih+Ph4XXzxxVq1apX3uerqat1///26+OKLdcYZZyg1NVVjxozRnj17Qv0xAAAATgv+zel2yzAI3gCsKapC95IlS5SXl6cZM2Zo06ZN6tOnj3JycrR3716f53/88ccaOXKkxo8fr88++0zDhg3TsGHDtHXrVknSkSNHtGnTJj344IPatGmTXnvtNW3fvl2/+tWvwvmxAAAALMszvDzWR093/SHn7NUNwKqiKnTPnj1bEyZM0Lhx43ThhRdq7ty5at26tebNm+fz/GeeeUbXXXedfvvb36pnz556+OGHdckll2jOnDmSpISEBK1Zs0a33HKLunfvrssuu0xz5sxRcXGxdu7cGc6PBgAAYElVTfR01x9yXsMQcwAWFTWhu6qqSsXFxcrOzvYes9vtys7OVlFRkc9rioqKGpwvSTk5OY2eL0llZWWy2WxKTEw0pW4AAIDTWV1Pd+PDyyWpmhXMAViUo6UL8Nf+/fvlcrmUnJzc4HhycrK++uorn9eUlJT4PL+kpMTn+ceOHdP999+vkSNHqm3bto3WUllZqcrKSu/P5eXl/n4MAACA00rdnG5fW4bVHWMFcwBWFTU93aFWXV2tW265RYZh6Pnnn2/y3Pz8fCUkJHgfaWlpYaoSAAAgulS7G+/pttlsctiPB2/mdAOwqqgJ3UlJSYqJiVFpaWmD46WlpUpJSfF5TUpKil/newL3jz/+qDVr1jTZyy1JU6dOVVlZmfexa9euID4RAACA9Xl6sB0+Qvfx48dDdxU93QAsKmpCt9PpVL9+/VRYWOg95na7VVhYqKysLJ/XZGVlNThfktasWdPgfE/g/uabb/Tuu++qffv2p6wlLi5Obdu2bfAAAADAyWrcjQ8vP37cXnsePd0ArClq5nRLUl5ensaOHav+/ftrwIABevrpp1VRUaFx48ZJksaMGaPOnTsrPz9fkjRp0iRdeeWVevLJJzVkyBAtXrxYGzdu1AsvvCDpeOC++eabtWnTJq1cuVIul8s737tdu3ZyOp0t80EBAAAsorqJhdTqH/fM/QYAq4mq0D1ixAjt27dP06dPV0lJiTIyMrR69WrvYmk7d+6U3V73H/SBAwdq0aJFmjZtmn7/+9+rW7duWrFihXr16iVJ2r17t9544w1JUkZGRoP3Wrt2rQYNGhSWzwUAAGBVnmHjjYVuz5xuQjcAq4qq0C1Jubm5ys3N9fncunXrTjo2fPhwDR8+3Of56enpMgyGMgEAAISKZ3i5J1yfqK6nm3syANYUNXO6AQAAEH08YdrpaGx4ee3q5fR0A7AoQjcAAABCpm6f7qbndFcRugFYFKEbAAAAIeMJ3Y0NL/dsJVbD8HIAFkXoBgAAQMjUnGJ4uTOGhdQAWBuhGwAAACFhGIZ3/+1T9XSzkBoAqyJ0AwAAICTqB+nYUy2k5qanG4A1EboBAAAQEvWHjMfam15IjeHlAKyK0A0AAICQaBC6Y9inG8DpidANAACAkKgfpGMam9NtZyE1ANZG6AYAAEBIeIK0M8Yum62Rnm4HW4YBsDZCNwAAAELCE6QdjQwtl6RYeroBWByhGwAAACFRVRukPfO2fWHLMABWR+gGAABASHi2AWsqdLN6OQCrI3QDAAAgJKprjvdeN7Zyef3nagjdACyK0A0AAICQqK7t6W5yTndtT3cVw8sBWBShGwAAACFRXePPnG56ugFYG6EbAAAAIVHjrh1ebm/8ltPJnG4AFkfoBgAAQEh4gnRTw8sdtYG82s3wcgDWROgGAABASHi2AWty9XIHw8sBWBuhGwAAACFR492nu4mF1Ozs0w3A2gjdAAAACAnPkHFHE3O6PYGcOd0ArIrQDQAAgJDwrl7uaGr1chZSA2BthG4AAACERE3tPt2x9saHl3tWL69heDkAiyJ0AwAAICT8WUjNs7J5FT3dACyK0A0AAICQ8GvLMHq6AVgcoRsAAAAhUeNHT7eThdQAWByhGwAAACFR7T71lmGelc09K50DgNUQugEAQIsoKChQenq64uPjlZmZqfXr1zd5/sGDBzVx4kR16tRJcXFxuuCCC7Rq1aowVYtgVNfUbhnWRE+3Z2Vzz0rnAGA1jpYuAAAAnH6WLFmivLw8zZ07V5mZmXr66aeVk5Oj7du3q2PHjiedX1VVpWuvvVYdO3bUsmXL1LlzZ/34449KTEwMf/Hwmz+rl3ue85wLAFZD6AYAAGE3e/ZsTZgwQePGjZMkzZ07V2+99ZbmzZunKVOmnHT+vHnzdODAAX388ceKjY2VJKWnp4ezZATBsyJ5U3O6vT3dLKQGwKIYXg4AAMKqqqpKxcXFys7O9h6z2+3Kzs5WUVGRz2veeOMNZWVlaeLEiUpOTlavXr306KOPyuVyNfo+lZWVKi8vb/BAeHkWUmtqeLnDzkJqAKyN0A0AAMJq//79crlcSk5ObnA8OTlZJSUlPq/5/vvvtWzZMrlcLq1atUoPPvignnzySf3hD39o9H3y8/OVkJDgfaSlpZn6OXBqNa5TL6QWy5ZhACyO0A0AACKe2+1Wx44d9cILL6hfv34aMWKEHnjgAc2dO7fRa6ZOnaqysjLvY9euXWGsGJJU5ceWYZ7n6OkGYFXM6QYAAGGVlJSkmJgYlZaWNjheWlqqlJQUn9d06tRJsbGxiomJ8R7r2bOnSkpKVFVVJafTedI1cXFxiouLM7d4BMTT0+1osqeb4eUArI2ebgAAEFZOp1P9+vVTYWGh95jb7VZhYaGysrJ8XvOLX/xC3377rdz1Vrj++uuv1alTJ5+BG5Ghpnbv7Vi7Pz3dDC8HYE2EbgAAEHZ5eXl68cUX9corr2jbtm266667VFFR4V3NfMyYMZo6dar3/LvuuksHDhzQpEmT9PXXX+utt97So48+qokTJ7bUR4AfqgKZ082WYQAsiuHlAAAg7EaMGKF9+/Zp+vTpKikpUUZGhlavXu1dXG3nzp2y1+sdTUtL09tvv6177rlHvXv3VufOnTVp0iTdf//9LfUR4Ie64eVNrF7uHV5uyDAM2WyNB3QAiEaEbgAA0CJyc3OVm5vr87l169addCwrK0uffPJJiKuCmWq8C6k10dNd748rNW6jyXMBIBoxvBwAAAAhUTe8vIk53Y66kM1iagCsiNANAACAkPD0dDc5vLxeTzeLqQGwIkI3AAAAQsKzOFqs/dRbhkn0dAOwJkI3AAAAQqLKO6e78VtOm80mR20or6GnG4AFBRW6x44dqw8++MDsWgAAQITjHgCBqFu9vOnF0er26qanG4D1BBW6y8rKlJ2drW7duunRRx/V7t27za4LAABEIO4BEAhPz7WziZ5uqf62YYRuANYTVOhesWKFdu/erbvuuktLlixRenq6rr/+ei1btkzV1dVm1wgAACIE9wAIRLUf+3RLdaG8xs3wcgDWE/Sc7g4dOigvL0///Oc/9emnn+r888/X6NGjlZqaqnvuuUfffPONmXUCAIAIwT0A/FXt9m94uef5qhp6ugFYT7MXUvvpp5+0Zs0arVmzRjExMbrhhhu0ZcsWXXjhhXrqqafMqBEAAEQg7gFwKv4OL4+lpxuAhQUVuqurq7V8+XL98pe/VJcuXbR06VJNnjxZe/bs0SuvvKJ3331Xf/vb3zRr1iyz6wUAAC2IewAEopqF1ABAjmAu6tSpk9xut0aOHKn169crIyPjpHOuuuoqJSYmNrM8AAAQSbgHQCCqa3u6HfZT9XSzkBoA6woqdD/11FMaPny44uPjGz0nMTFRO3bsCLowAAAQebgHQCA8IfqUq5fbPT3dDC8HYD1BDS9fu3atzxVKKyoqdPvttze7KAAAEJm4B0AgPHO6Tz283FZ7Pj3dAKwnqND9yiuv6OjRoycdP3r0qBYuXNjsogAAQGTiHgD+MgzD79XLmdMNwMoCGl5eXl4uwzBkGIYOHTrUYGiZy+XSqlWr1LFjR9OLBAAALYt7AATK5TZk1I4WP+Xwcu+cboaXA7CegEJ3YmKibDabbDabLrjggpOet9lsmjlzpmnFAQCAyMA9AAJVf/svh59bhtHTDcCKAgrda9eulWEYuvrqq7V8+XK1a9fO+5zT6VSXLl2UmppqepEAAKBlcQ+AQNUP0A67f8PLa+jpBmBBAYXuK6+8UpK0Y8cOnXPOObLZmv4PKAAAsAbuARCo+kPFY0/Z033896mKnm4AFuR36P7888/Vq1cv2e12lZWVacuWLY2e27t3b1OKAwAALY97AATDsxK53SbFnKKn2+Ht6SZ0A7Aev0N3RkaGSkpK1LFjR2VkZMhms8kwTh4CZLPZ5HK5TC0SAAC0HO4BEIxqt2e7sFNvluNZaK3+PHAAsAq/Q/eOHTvUoUMH778BAMDpgXsABKO65niv9alWLpfq5nwzvByAFfkdurt06eLz3wAAwNq4B0Awavzco1uSYh0spAbAuk79p0cfXnnlFb311lven3/3u98pMTFRAwcO1I8//mhacQAAILJwDwB/eRZSc9hPfbsZa/fs001PNwDrCSp0P/roo2rVqpUkqaioSHPmzNETTzyhpKQk3XPPPaYWCAAAIgf3APCXJ0A7/enp9u7TTU83AOsJaMswj127dun888+XJK1YsUI333yz7rzzTv3iF7/QoEGDzKwPAABEEO4B4C9vT7c/c7q9oZuebgDWE1RPd5s2bfTzzz9Lkt555x1de+21kqT4+HgdPXrUvOoAAEBE4R4A/vJs/xXrV0+3rcE1AGAlQYXua6+9VnfccYfuuOMOff3117rhhhskSV988YXS09PNrO8kBQUFSk9PV3x8vDIzM7V+/fomz1+6dKl69Oih+Ph4XXzxxVq1alWD5w3D0PTp09WpUye1atVK2dnZ+uabb0L5EQAAiFoteQ+A6OLp6Y71o6fbc04Vw8sBWFBQobugoEBZWVnat2+fli9frvbt20uSiouLNXLkSFMLrG/JkiXKy8vTjBkztGnTJvXp00c5OTnau3evz/M//vhjjRw5UuPHj9dnn32mYcOGadiwYdq6dav3nCeeeELPPvus5s6dq08//VRnnHGGcnJydOzYsZB9DgAAolVL3QMg+lQHsHq5g55uABZmMwwjav6kmJmZqUsvvVRz5syRJLndbqWlpenuu+/WlClTTjp/xIgRqqio0MqVK73HLrvsMmVkZGju3LkyDEOpqam69957dd9990mSysrKlJycrAULFujWW2/1q67y8nIlJCSorKxMbdu2NeGTAgBOZ7QrocH3Gl5rvizVhIUb1fecRL3+X79o8tyXPvxef3hrm4ZlpOrpW/uGqUIAaB5/25WgFlKTpIMHD2r9+vXau3ev3O66v0rabDaNHj062JdtVFVVlYqLizV16lTvMbvdruzsbBUVFfm8pqioSHl5eQ2O5eTkaMWKFZKkHTt2qKSkRNnZ2d7nExISlJmZqaKiIr9DtxkMw9DRalfY3g8AEBqtYmNks526Zy+ahfseANHJsyharB9bhjk8W4a5o6YvCAD8FlTofvPNNzVq1CgdPnxYbdu2bXBzEaoGd//+/XK5XEpOTm5wPDk5WV999ZXPa0pKSnyeX1JS4n3ec6yxc3yprKxUZWWl9+fy8nL/P0gjjla7dOH0t5v9OgCAlvXlrBy1dgb9N+2I1xL3AIhOntDtz/DyWEft6uU1DC8HYD1Bzem+9957dfvtt+vw4cM6ePCg/vWvf3kfBw4cMLvGiJOfn6+EhATvIy0traVLAgAgLE73ewD4ryaQhdRqe8Nr6OkGYEFB/Sl+9+7d+s1vfqPWrVubXU+jkpKSFBMTo9LS0gbHS0tLlZKS4vOalJSUJs/3/N/S0lJ16tSpwTkZGRmN1jJ16tQGw9bLy8ubHbxbxcboy1k5zXoNAEDLaxUb09IlhFRL3AMgOlUHsmWYw9bgGgCwkqBCd05OjjZu3KiuXbuaXU+jnE6n+vXrp8LCQg0bNkzS8YXUCgsLlZub6/OarKwsFRYWavLkyd5ja9asUVZWliTp3HPPVUpKigoLC70hu7y8XJ9++qnuuuuuRmuJi4tTXFycKZ/Lw2azWXo4IgDAGlriHgDRyTM/2+HXnO7a4eWEbgAWFFTKGzJkiH7729/qyy+/1MUXX6zY2NgGz//qV78ypbgT5eXlaezYserfv78GDBigp59+WhUVFRo3bpwkacyYMercubPy8/MlSZMmTdKVV16pJ598UkOGDNHixYu1ceNGvfDCC5KOB93JkyfrD3/4g7p166Zzzz1XDz74oFJTU73BHgAA1GmpewBEH8/8bM987aZ4hqDXsE83AAsKKnRPmDBBkjRr1qyTnrPZbHK5QrMK94gRI7Rv3z5Nnz5dJSUlysjI0OrVq70Loe3cuVP2en9NHThwoBYtWqRp06bp97//vbp166YVK1aoV69e3nN+97vfqaKiQnfeeacOHjyoyy+/XKtXr1Z8fHxIPgMAANGspe4BEH1q3J7Vy/0YXh7D8HIA1hVU6K6/PUi45ebmNjqcfN26dScdGz58uIYPH97o69lsNs2aNcvnzQMAAGioJe8BEF2qa3ut/Vm93BHjGV5OTzcA6wlq9fL6jh07ZkYdAAAgynAPgKbULaTmz/ByeroBWFdQodvlcunhhx9W586d1aZNG33//feSpAcffFAvv/yyqQUCAIDIwT0A/BXQlmExbBkGwLqCCt2PPPKIFixYoCeeeEJOp9N7vFevXnrppZdMKw4AAEQW7gHgr+raqQgOv+Z0H78lraqhpxuA9QQVuhcuXKgXXnhBo0aNUkxM3X6kffr00VdffWVacQAAILJwDwB/VdfU9nT7sXq5J5jXsGYAAAsKKnTv3r1b559//knH3W63qqurm10UAACITNwDwF+BrF7udLCQGgDrCip0X3jhhfrwww9POr5s2TL17du32UUBAIDIxD0A/FW3ern/Pd0spAbAioLaMmz69OkaO3asdu/eLbfbrddee03bt2/XwoULtXLlSrNrBAAAEYJ7APgrsNXLPT3dhG4A1hNUT/evf/1rvfnmm3r33Xd1xhlnaPr06dq2bZvefPNNXXvttWbXCAAAIgT3APBXjTd0+7+QWg3DywFYUFA93ZJ0xRVXaM2aNWbWAgAAogD3APBHde32X/6tXu5ZSM2QYRiy2U59DQBEi6B6urt27aqff/75pOMHDx5U165dm10UAACITNwDwF/Vtdt/+bV6eb0h6CymBsBqggrdP/zwg1wu10nHKysrtXv37mYXBQAAIhP3APBXTW1Pd6z91Lebznqhm23DAFhNQMPL33jjDe+/3377bSUkJHh/drlcKiwsVHp6umnFAQCAyMA9AALlXUjNceqh4o56876rawzJGbKyACDsAgrdw4YNkyTZbDaNHTu2wXOxsbFKT0/Xk08+aVpxAAAgMnAPgEB5QrfDj57u+vO+q+npBmAxAYVud+1/BM8991xt2LBBSUlJISkKAABEFu4BECjPSuT+rF5us9kUG2NTtctg2zAAlhPU6uU7duwwuw4AABAFuAeAvzyrl/uzT7d0vEe82uVi2zAAlhP0lmGFhYUqLCzU3r17vX/99pg3b16zCwMAAJGJewD4w7N6ucPP0B0bY9PRaqmKnm4AFhNU6J45c6ZmzZql/v37q1OnTuylCADAaYJ7APjLswp5rB/7dEt1PeL0dAOwmqBC99y5c7VgwQKNHj3a7HoAAEAE4x4A/vLst+3PPt1SXehmTjcAqwlqn+6qqioNHDjQ7FoAAECE4x4A/qpbvdy/nm7PtmGEbgBWE1TovuOOO7Ro0SKzawEAABGOewD4q271cv9uN53enm6GlwOwlqCGlx87dkwvvPCC3n33XfXu3VuxsbENnp89e7YpxQEAgMjCPQD85emx9nv18tqe7hp6ugFYTFCh+/PPP1dGRoYkaevWrWbWAwAAIhj3APCXd3i5H/t0S/XmdLvp6QZgLUGF7rVr15pdBwAAiALcA8BfNZ59uu3+9nTXhu4aeroBWEtAofvGG2885Tk2m03Lly8PuiAAABB5uAdAoLzDyx3+9XQ7PcPL3YRuANYSUOhOSEgIVR0AACCCcQ+AQBiG4V0QzeFvT3fteVUspAbAYgIK3fPnzw9VHQAAIIJxD4BAuOrNy471c043C6kBsKqgtgwDAAAAGlN/2y9HwFuGEboBWAuhGwAAtIiCggKlp6crPj5emZmZWr9+vV/XLV68WDabTcOGDQttgQhadb152c4Atwxjn24AVkPoBgAAYbdkyRLl5eVpxowZ2rRpk/r06aOcnBzt3bu3yet++OEH3XfffbriiivCVCmCUVVvBXJ/h5fH0tMNwKII3QAAIOxmz56tCRMmaNy4cbrwwgs1d+5ctW7dWvPmzWv0GpfLpVGjRmnmzJnq2rVrGKtFoLwrl8fYZLMFFrpr6OkGYDGEbgAAEFZVVVUqLi5Wdna295jdbld2draKiooavW7WrFnq2LGjxo8f79f7VFZWqry8vMED4VFdczw4+zu0XKrrEa+ipxuAxRC6AQBAWO3fv18ul0vJyckNjicnJ6ukpMTnNf/4xz/08ssv68UXX/T7ffLz85WQkOB9pKWlNatu+K/Ku0e3/7eaDnq6AVgUoRsAAES0Q4cOafTo0XrxxReVlJTk93VTp05VWVmZ97Fr164QVon6PHO6YwPo6Wb1cgBWFdA+3QAAAM2VlJSkmJgYlZaWNjheWlqqlJSUk87/7rvv9MMPP2jo0KHeY+7a1bEdDoe2b9+u884776Tr4uLiFBcXZ3L18IcnOAcyvNxhr1293E3oBmAt9HQDAICwcjqd6tevnwoLC73H3G63CgsLlZWVddL5PXr00JYtW7R582bv41e/+pWuuuoqbd68mWHjEcgbugMYXu4Zis7wcgBWQ083AAAIu7y8PI0dO1b9+/fXgAED9PTTT6uiokLjxo2TJI0ZM0adO3dWfn6+4uPj1atXrwbXJyYmStJJxxEZquqtXu6vWE9PN8PLAVgMoRsAAITdiBEjtG/fPk2fPl0lJSXKyMjQ6tWrvYur7dy5U3Y7A/KiVTBzuuv26aanG4C1ELoBAECLyM3NVW5urs/n1q1b1+S1CxYsML8gmMYTnAMJ3Q4WUgNgUfwJGQAAAKYKak537VD0GkI3AIshdAMAAMBUwaxezvByAFZF6AYAAICpKmsCX0jNEcNCagCsidANAAAAU1W7mrOQGqEbgLUQugEAAGCq6ppmzOl2M7wcgLUQugEAAGAqz7zsYOZ0e7YbAwCrIHQDAADAVFVBDC931O7LTk83AKshdAMAAMBUnt7qWIf/C6k5HSykBsCaCN0AAAAwVd2WYTF+X+Pp6WbLMABWQ+gGAACAqbyrlwfQ0+0Zil5DTzcAiyF0AwAAwFSe4eWBLaTG8HIA1kToBgAAgKmqaoeIB7dPN8PLAVgLoRsAAACm8s7pDmCfbgc93QAsitANAAAAU3lXLw+ip5stwwBYDaEbAAAApqpbvTzwhdSqa+jpBmAthG4AAACYyrt6eQA93Q778YBexfByABZD6AYAAICpPAupBTKnO87hWUiN0A3AWgjdAAAAMFVVjUtSYD3dnoDuNtirG4C1ELoBAABgquogtgyr3yvOEHMAVkLoBgAAgKnqtgzzfyE1Z72AXsViagAshNANAAAAU3lCszMmxu9rHDF21a6lRugGYCmEbgAAAJiqyrt6uf893VLdEPNKQjcACyF0AwAAwFTeLcMCWL1cqhtizpxuAFZC6AYAAICpqmtqtwwLYCE1SXI6jg9HZ3g5ACshdAMAAMBUdQupBXar6dmrm9ANwEoI3QAAADCVJzQHsmWYVBfSGV4OwEqiJnQfOHBAo0aNUtu2bZWYmKjx48fr8OHDTV5z7NgxTZw4Ue3bt1ebNm100003qbS01Pv8P//5T40cOVJpaWlq1aqVevbsqWeeeSbUHwUAAMDSgl5ILYaebgDWEzWhe9SoUfriiy+0Zs0arVy5Uh988IHuvPPOJq+555579Oabb2rp0qV6//33tWfPHt14443e54uLi9WxY0e9+uqr+uKLL/TAAw9o6tSpmjNnTqg/DgAAgGV5h5cH29NN6AZgIY6WLsAf27Zt0+rVq7Vhwwb1799fkvTcc8/phhtu0B//+EelpqaedE1ZWZlefvllLVq0SFdffbUkaf78+erZs6c++eQTXXbZZbr99tsbXNO1a1cVFRXptddeU25ubug/GAAAgMW43Ibcx9dRC3hON1uGAbCiqOjpLioqUmJiojdwS1J2drbsdrs+/fRTn9cUFxerurpa2dnZ3mM9evTQOeeco6Kiokbfq6ysTO3atTOveAAAgNNI/V7qgOd0s2UYAAuKip7ukpISdezYscExh8Ohdu3aqaSkpNFrnE6nEhMTGxxPTk5u9JqPP/5YS5Ys0VtvvdVkPZWVlaqsrPT+XF5e7senAAAAsL76gTnohdTo6QZgIS3a0z1lyhTZbLYmH1999VVYatm6dat+/etfa8aMGRo8eHCT5+bn5yshIcH7SEtLC0uNAAAAka66QegOcCE17/Byl6k1AUBLatGe7nvvvVe33XZbk+d07dpVKSkp2rt3b4PjNTU1OnDggFJSUnxel5KSoqqqKh08eLBBb3dpaelJ13z55Ze65pprdOedd2ratGmnrHvq1KnKy8vz/lxeXk7wBgAAUMNF1Gy24EI3Pd0ArKRFQ3eHDh3UoUOHU56XlZWlgwcPqri4WP369ZMkvffee3K73crMzPR5Tb9+/RQbG6vCwkLddNNNkqTt27dr586dysrK8p73xRdf6Oqrr9bYsWP1yCOP+FV3XFyc4uLi/DoXAADgdFK3R3dggVuS4tgyDIAFRcVCaj179tR1112nCRMmaP369froo4+Um5urW2+91bty+e7du9WjRw+tX79ekpSQkKDx48crLy9Pa9euVXFxscaNG6esrCxddtllko4PKb/qqqs0ePBg5eXlqaSkRCUlJdq3b1+LfVYAAIBo5unpjg1w5XKJnm4A1hQVC6lJ0l/+8hfl5ubqmmuukd1u10033aRnn33W+3x1dbW2b9+uI0eOeI899dRT3nMrKyuVk5OjP/3pT97nly1bpn379unVV1/Vq6++6j3epUsX/fDDD2H5XAAAAFZyrPp4YI5rTuhm9XIAFhI1obtdu3ZatGhRo8+np6fLMIwGx+Lj41VQUKCCggKf1zz00EN66KGHzCwTAADgtObZYzvOERPwtU6GlwOwoKgYXg4AAIDo4Fl5vDk93ZWEbgAWQugGAACAaTy91HGxDC8HAInQDQAAABN5eqk9Q8UDwUJqAKyI0A0AAADTMKcbABoidAMAAMA0ldW1c7qDGF4eR083AAsidAMAAMA0dT3dzOkGAInQDQAAABNVNWN4uecaeroBWAmhGwAAAKbxLqTWnJ5uQjcACyF0AwAAwDTN2qe7diG1SoaXA7AQQjcAAABM06zVy+npBmBBhG4AAACYxjunO4jVy+tCt8vUmgCgJRG6AQAAYJpmDS9n9XIAFkToBgAAgGkqq5uxkFoMw8sBWA+hGwAAAKZpzpzuOOZ0A7AgQjcAAABMY8rwckI3AAshdAMAAMA03oXUmNMNAJII3QAAADCRd3h5bBBbhtXO6a52GXK7DVPrAoCWQugGAACAaTyh2xOgA1F/8TV6uwFYBaEbAAAApvHO6W7GPt0SoRuAdRC6AQAAYBrPlmFBzemu1zvueR0AiHaEbgAAAJjG00MdzJZhNptN8bU95MeqXabWBQAthdANAAAA0zSnp1uS4msXYPMMUweAaEfoBgAAgGmas0+3JMXX9pAfrWJ4OQBrIHQDAADANN4tw4IYXi6pbng5Pd0ALILQDQAAANPU7dPdvOHlzOkGYBWEbgAAAJiixuWWy21Iav6c7mOsXg7AIgjdAAAAMEX9vbWdQYduVi8HYC2EbgAA0CIKCgqUnp6u+Ph4ZWZmav369Y2e++KLL+qKK67QWWedpbPOOkvZ2dlNno+WUX9v7fp7bgfC09N9lNANwCII3QAAIOyWLFmivLw8zZgxQ5s2bVKfPn2Uk5OjvXv3+jx/3bp1GjlypNauXauioiKlpaVp8ODB2r17d5grR1M887kddpscwYbu2gXYKgndACyC0A0AAMJu9uzZmjBhgsaNG6cLL7xQc+fOVevWrTVv3jyf5//lL3/Rf/3XfykjI0M9evTQSy+9JLfbrcLCwjBXjqY0d7swSWrlZE43AGshdAMAgLCqqqpScXGxsrOzvcfsdruys7NVVFTk12scOXJE1dXVateuXaPnVFZWqry8vMEDoVXlXbk8uO3CJOZ0A7AeQjcAAAir/fv3y+VyKTk5ucHx5ORklZSU+PUa999/v1JTUxsE9xPl5+crISHB+0hLS2tW3Tg1z/DyYOdzS3X7e7NPNwCrIHQDAICo8thjj2nx4sV6/fXXFR8f3+h5U6dOVVlZmfexa9euMFZ5evIOLw9yj26p3kJqVQwvB2ANjpYuAAAAnF6SkpIUExOj0tLSBsdLS0uVkpLS5LV//OMf9dhjj+ndd99V7969mzw3Li5OcXFxza4X/vOsXt6cnm7v8HJ6ugFYBD3dAAAgrJxOp/r169dgETTPomhZWVmNXvfEE0/o4Ycf1urVq9W/f/9wlIoAeYJya2fwc7pbxXoWUiN0A7AGeroBAEDY5eXlaezYserfv78GDBigp59+WhUVFRo3bpwkacyYMercubPy8/MlSY8//rimT5+uRYsWKT093Tv3u02bNmrTpk2LfQ405BkSHt+shdQ8W4YxvByANRC6AQBA2I0YMUL79u3T9OnTVVJSooyMDK1evdq7uNrOnTtlt9cNyHv++edVVVWlm2++ucHrzJgxQw899FA4S0cTjtb2TrdqRk83q5cDsBpCNwAAaBG5ubnKzc31+dy6desa/PzDDz+EviA0mzd0m9DTfZTQDcAimNMNAAAAUxyran7o9m4ZRugGYBGEbgAAAJjC0zsdb8rwcuZ0A7AGQjcAAABMYcbwcu/q5WwZBsAiCN0AAAAwxVEThpezejkAqyF0AwAAwBTHTFm9nIXUAFgLoRsAAACm8M7pblZPN1uGAbAWQjcAAABMYebw8mPVLhmGYUpdANCSCN0AAAAwhXchNWfwt5ie0O02pGoXoRtA9CN0AwAAwBTHTFi93DO8XGIFcwDWQOgGAACAKcyY0+2MsctmO/7vY1WEbgDRj9ANAAAAU5gxp9tmsyne4ZnXzbZhAKIfoRsAAACm8ITk5mwZJtVbwZzh5QAsgNANAAAAUxw1YU63JLV2OiRJRxheDsACCN0AAAAwhWd4eXPmdEtS69qe8iNVNc2uCQBaGqEbAAAAzWYYRr0tw5oZuuOO93RXVNLTDSD6EboBAADQbPWHgp9ROzw8WG3i6OkGYB2EbgAAADRbReXxgGy3NdxrOxieOd30dAOwAkI3AAAAmq2itqf7DKdDNs9G20E6gzndACyE0A0AAIBm8/R0nxHXvKHlEnO6AVgLoRsAAADNdtgbupu3iJpU19NdQU83AAsgdAMAAKDZPEPBTenp9s7pJnQDiH6EbgAAADTb4cq6Od3N1aY2uNdfER0AohWhGwAAAM12xNQ53bXDy+npBmABhG4AAAA0m7lzuunpBmAdhG4AAAA0m2elcXPmdB8P7ofp6QZgAYRuAAAANJt3ITWnCT3d3jndhG4A0S9qQveBAwc0atQotW3bVomJiRo/frwOHz7c5DXHjh3TxIkT1b59e7Vp00Y33XSTSktLfZ77888/6+yzz5bNZtPBgwdD8AkAAACs67CZc7o9W4axTzcAC4ia0D1q1Ch98cUXWrNmjVauXKkPPvhAd955Z5PX3HPPPXrzzTe1dOlSvf/++9qzZ49uvPFGn+eOHz9evXv3DkXpAAAAludZ9KyNCaHb8xrs0w3ACqIidG/btk2rV6/WSy+9pMzMTF1++eV67rnntHjxYu3Zs8fnNWVlZXr55Zc1e/ZsXX311erXr5/mz5+vjz/+WJ988kmDc59//nkdPHhQ9913Xzg+DgAAgOVU1C561tqELcPOjI+VJB06ViPDMJr9egDQkqIidBcVFSkxMVH9+/f3HsvOzpbdbtenn37q85ri4mJVV1crOzvbe6xHjx4655xzVFRU5D325ZdfatasWVq4cKHs9qj4OgAAACJOhYmrl7dtdTy4u9wGK5gDiHpRkTJLSkrUsWPHBsccDofatWunkpKSRq9xOp1KTExscDw5Odl7TWVlpUaOHKn//u//1jnnnON3PZWVlSovL2/wAAAAOJ2VH6uWJLVtFdvs12oVGyOH3SbpeG83AESzFg3dU6ZMkc1ma/Lx1Vdfhez9p06dqp49e+o//uM/ArouPz9fCQkJ3kdaWlqIKgQAAIgOZUdrQ3d880O3zWbTmfHHe7s9YR4AolXzJ900w7333qvbbrutyXO6du2qlJQU7d27t8HxmpoaHThwQCkpKT6vS0lJUVVVlQ4ePNigt7u0tNR7zXvvvactW7Zo2bJlkuSdM5SUlKQHHnhAM2fO9PnaU6dOVV5envfn8vJygjcAADitlR893iOdYEJPt3S8x/xfR6pVfpTQDSC6tWjo7tChgzp06HDK87KysnTw4EEVFxerX79+ko4HZrfbrczMTJ/X9OvXT7GxsSosLNRNN90kSdq+fbt27typrKwsSdLy5ct19OhR7zUbNmzQ7bffrg8//FDnnXdeo/XExcUpLi7O788JAABgZW63UW94uTm3l54ec3q6AUS7Fg3d/urZs6euu+46TZgwQXPnzlV1dbVyc3N16623KjU1VZK0e/duXXPNNVq4cKEGDBighIQEjR8/Xnl5eWrXrp3atm2ru+++W1lZWbrssssk6aRgvX//fu/7nTgXHAAAAL4drqqRZ5FxM4aXS3Xh3dODDgDRKipCtyT95S9/UW5urq655hrZ7XbddNNNevbZZ73PV1dXa/v27Tpy5Ij32FNPPeU9t7KyUjk5OfrTn/7UEuUDAABYVtmR473RcQ674mObv3q5RE83AOuImtDdrl07LVq0qNHn09PTT9rHMT4+XgUFBSooKPDrPQYNGsRekAAAAAHyBGOz5nNLdaGb1csBRLuo2DIMAAAAkcu7crmZods7vJyebgDRjdANAACAZvHMu24bb94gSoaXA7AKQjcAAACaxdMbberw8trXKqOnG0CUI3QDAACgWeq2CzMvdCe2Pv5aByqqTHtNAGgJhG4AAAA0iycYJ5oYupPaxEmSfj5M6AYQ3QjdAAAAaBZPMPYEZTO0O8MpiZ5uANGP0A0AAIBm+bmiUpKUdKZ5obt9bej+15Equd1s6QogehG6AQAA0Cz7anu6PUHZDGfVvpbbkA6ymBqAKEboBgAAQLP8fPh4T3d7E4eXx8bYvVuQHajtSQeAaEToBgAAQLPUzek2r6dbqgvxLKYGIJoRugEAABC0I1U1OlrtkmRuT7fEYmoArIHQDQAAgKB5eqHjHHad4Ywx9bU9c8T3HWZ4OYDoRegGAABA0PYeql25vE2cbDabqa/dKSFekvRT2TFTXxcAwonQDQAAgKDtOXhUkpSaGG/6a6cmtpIk/VT7HgAQjQjdAAAACNru2kDcuTYgm6lT7WvuoacbQBQjdAMAACBou//l6ek2P3Sn1g4v30NPN4AoRugGAABA0DyBuPNZIQjdtUG+tPyY3G7D9NcHgHAgdAMAACBooRxe3vHMONltUrXLYAVzAFGL0A0AAICgGIah/6sdXn52CHq6HTF2bw/6jv0Vpr8+AIQDoRsAAABBKSk/psOVNXLYbTqn3RkheY9uHc+UJH2z93BIXh8AQo3QDQAAgKBsLzkkSUpPOkNOR2huK8/v2EaS9B2hG0CUInQDAAAgKN+UHg/C3ZPPDNl7eEL3N3sPhew9ACCUCN0AAAAIyle1Pd0XhDB0d6sN3dt+OiTDYAVzANGH0A0AAICgbNr5L0nSRaltQ/YeF6a2ldNh14GKKn3PYmoAohChGwAAAAErKTumHfsrZLdJl57bLmTvE+eIUUZaoiRpw44DIXsfAAgVQjcAAAAC9vF3+yVJF6UmKKFVbEjfa0D68VD/j2/3h/R9ACAUCN0AAAAI2IrNeyRJg7p3CPl7Db4oWZL07rZSHa6sCfn7AYCZCN0AAKBFFBQUKD09XfHx8crMzNT69eubPH/p0qXq0aOH4uPjdfHFF2vVqlVhqhQn+n7fYX34zT5J0s39zg75+13cOUFdO5yhY9Vu/W3DrpC/HwCYidANAADCbsmSJcrLy9OMGTO0adMm9enTRzk5Odq7d6/P8z/++GONHDlS48eP12effaZhw4Zp2LBh2rp1a5grR7XLrWkrtsowpKt7dFSX9meE/D1tNpvGX36uJOnZ977RrgNHQv6eAGAWm8HeC81WXl6uhIQElZWVqW3b0K3eCQA4PZwO7UpmZqYuvfRSzZkzR5LkdruVlpamu+++W1OmTDnp/BEjRqiiokIrV670HrvsssuUkZGhuXPn+vWeZn2vVTVu1bjdqn8H5fmn57aq/s2V97wG5xsNnmt4fuOv4blOPt/7hHNO9fo+7gBPfI365xytdunr0kNaWPSjin/8l+Icdv190hXq2qHNyS8UAjUut35d8JG+2FOuDmfG6T//rav6nnOWUhLi5YyxyxljV0yMTbawVFPHFu43BGCKGLtNcY6YZr2Gv+2Ko1nvAgAAEKCqqioVFxdr6tSp3mN2u13Z2dkqKiryeU1RUZHy8vIaHMvJydGKFSsafZ/KykpVVlZ6fy4vL29e4bXy/75N8z/6wZTXilatnTEqGHVJ2AK3JDli7Hp57KX6j5c/1bd7D+sPb20L23sDsJ5RmefokX+/OCzvRegGAABhtX//frlcLiUnJzc4npycrK+++srnNSUlJT7PLykpafR98vPzNXPmzOYXHCE8Pao27891Xay2k86p1/16wnW+zrOdfPpJr++IsencpDN0aXo7jRmYrs6JrYL9KEFLSYjXyrsv19KNu7T6ixJ9v69CPx+uUpXLHfZaAMBfhG4AAGBJU6dObdA7Xl5errS0tOa/7vU99bucHpJ8Dy32FXxPDMzHj9kaHGsQfBmz3Kj42BiNzkrX6Kx07zHDMFTjNuRyBzdrMtjJloaYpQlEqxh7+P47S+gGAABhlZSUpJiYGJWWljY4XlpaqpSUFJ/XpKSkBHS+JMXFxSkuLq75BZ/A6WAd2khjs9kUG2NTbPOmZwJASNBqAACAsHI6nerXr58KCwu9x9xutwoLC5WVleXzmqysrAbnS9KaNWsaPR8AgEhBTzcAAAi7vLw8jR07Vv3799eAAQP09NNPq6KiQuPGjZMkjRkzRp07d1Z+fr4kadKkSbryyiv15JNPasiQIVq8eLE2btyoF154oSU/BgAAp0ToBgAAYTdixAjt27dP06dPV0lJiTIyMrR69WrvYmk7d+6U3V43IG/gwIFatGiRpk2bpt///vfq1q2bVqxYoV69erXURwAAwC/s022C02E/VQBA+NCuhAbfKwDATP62K8zpBgAAAAAgRAjdAAAAAACECKEbAAAAAIAQIXQDAAAAABAihG4AAAAAAEKE0A0AAAAAQIgQugEAAAAACBFCNwAAAAAAIULoBgAAAAAgRAjdAAAAAACECKEbAAAAAIAQcbR0AVZgGIYkqby8vIUrAQBYgac98bQvMAftNQDATP6214RuExw6dEiSlJaW1sKVAACs5NChQ0pISGjpMiyD9hoAEAqnaq9tBn9Gbza32609e/bozDPPlM1mC/p1ysvLlZaWpl27dqlt27YmVhha1B1e0Vq3FL21U3d4Uffxv5gfOnRIqampstuZCWaW0729lqK3duoOL+oOL+oOP7Nq97e9pqfbBHa7XWeffbZpr9e2bduo+8WVqDvcorVuKXprp+7wOt3rpofbfLTXdaK1duoOL+oOL+oOPzNq96e95s/nAAAAAACECKEbAAAAAIAQIXRHkLi4OM2YMUNxcXEtXUpAqDu8orVuKXprp+7wom5Eumj+/3W01k7d4UXd4UXd4Rfu2llIDQAAAACAEKGnGwAAAACAECF0AwAAAAAQIoRuAAAAAABChNAdZgUFBUpPT1d8fLwyMzO1fv36Js9funSpevToofj4eF188cVatWpVmCptKJC6FyxYIJvN1uARHx8fxmqP++CDDzR06FClpqbKZrNpxYoVp7xm3bp1uuSSSxQXF6fzzz9fCxYsCHmdJwq07nXr1p30fdtsNpWUlISn4Fr5+fm69NJLdeaZZ6pjx44aNmyYtm/ffsrrWvp3PJi6I+F3/Pnnn1fv3r29+0tmZWXp73//e5PXtPR3LQVedyR817489thjstlsmjx5cpPnRcJ3juBEa3stRV+bTXtNe+0P2uvws0KbHSntNaE7jJYsWaK8vDzNmDFDmzZtUp8+fZSTk6O9e/f6PP/jjz/WyJEjNX78eH322WcaNmyYhg0bpq1bt0Z03dLxjeZ/+ukn7+PHH38MY8XHVVRUqE+fPiooKPDr/B07dmjIkCG66qqrtHnzZk2ePFl33HGH3n777RBX2lCgdXts3769wXfesWPHEFXo2/vvv6+JEyfqk08+0Zo1a1RdXa3BgweroqKi0Wsi4Xc8mLqllv8dP/vss/XYY4+puLhYGzdu1NVXX61f//rX+uKLL3yeHwnfdTB1Sy3/XZ9ow4YN+vOf/6zevXs3eV6kfOcIXLS211J0ttm017TXoapbavnf72htr6Xob7Mjqr02EDYDBgwwJk6c6P3Z5XIZqampRn5+vs/zb7nlFmPIkCENjmVmZhr/+Z//GdI6TxRo3fPnzzcSEhLCVJ1/JBmvv/56k+f87ne/My666KIGx0aMGGHk5OSEsLKm+VP32rVrDUnGv/71r7DU5K+9e/cakoz333+/0XMi5Xe8Pn/qjsTfccMwjLPOOst46aWXfD4Xid+1R1N1R9p3fejQIaNbt27GmjVrjCuvvNKYNGlSo+dG8neOpkVre20Y0d9m016HH+11+EVre20Y0dNmR1p7TU93mFRVVam4uFjZ2dneY3a7XdnZ2SoqKvJ5TVFRUYPzJSknJ6fR80MhmLol6fDhw+rSpYvS0tJO+RexSBEJ33dzZGRkqFOnTrr22mv10UcftXQ5KisrkyS1a9eu0XMi8Tv3p24psn7HXS6XFi9erIqKCmVlZfk8JxK/a3/qliLru544caKGDBly0nfpSyR+5zi1aG2vpdOnzY6U7ztYtNfmoL0Or2hrsyOtvSZ0h8n+/fvlcrmUnJzc4HhycnKjc3lKSkoCOj8Ugqm7e/fumjdvnv73f/9Xr776qtxutwYOHKj/+7//C0fJQWvs+y4vL9fRo0dbqKpT69Spk+bOnavly5dr+fLlSktL06BBg7Rp06YWq8ntdmvy5Mn6xS9+oV69ejV6XiT8jtfnb92R8ju+ZcsWtWnTRnFxcfp//+//6fXXX9eFF17o89xI+q4DqTtSvmtJWrx4sTZt2qT8/Hy/zo+k7xz+i9b2Wjp92mzaa/PQXodHtLbXUnS22ZHYXjtMeyWgVlZWVoO/gA0cOFA9e/bUn//8Zz388MMtWJk1de/eXd27d/f+PHDgQH333Xd66qmn9D//8z8tUtPEiRO1detW/eMf/2iR9w+Wv3VHyu949+7dtXnzZpWVlWnZsmUaO3as3n///UYbw0gRSN2R8l3v2rVLkyZN0po1a1p8URjATJHyv7HTAe21eWivwyfa2uxIba8J3WGSlJSkmJgYlZaWNjheWlqqlJQUn9ekpKQEdH4oBFP3iWJjY9W3b199++23oSjRNI19323btlWrVq1aqKrgDBgwoMUa0NzcXK1cuVIffPCBzj777CbPjYTfcY9A6j5RS/2OO51OnX/++ZKkfv36acOGDXrmmWf05z//+aRzI+m7DqTuE7XUd11cXKy9e/fqkksu8R5zuVz64IMPNGfOHFVWViomJqbBNZH0ncN/0dpeS6dPm017bQ7a6/CJ1vZair42O1Lba4aXh4nT6VS/fv1UWFjoPeZ2u1VYWNjovIisrKwG50vSmjVrmpxHYbZg6j6Ry+XSli1b1KlTp1CVaYpI+L7Nsnnz5rB/34ZhKDc3V6+//rree+89nXvuuae8JhK+82DqPlGk/I673W5VVlb6fC4SvuvGNFX3iVrqu77mmmu0ZcsWbd682fvo37+/Ro0apc2bN5/UgEuR/Z2jcdHaXkunT5sdKd+3GWiv/Ud7HRm/35HeZkdse23akmw4pcWLFxtxcXHGggULjC+//NK48847jcTERKOkpMQwDMMYPXq0MWXKFO/5H330keFwOIw//vGPxrZt24wZM2YYsbGxxpYtWyK67pkzZxpvv/228d133xnFxcXGrbfeasTHxxtffPFFWOs+dOiQ8dlnnxmfffaZIcmYPXu28dlnnxk//vijYRiGMWXKFGP06NHe87///nujdevWxm9/+1tj27ZtRkFBgRETE2OsXr06out+6qmnjBUrVhjffPONsWXLFmPSpEmG3W433n333bDWfddddxkJCQnGunXrjJ9++sn7OHLkiPecSPwdD6buSPgdnzJlivH+++8bO3bsMD7//HNjypQphs1mM9555x2fNUfCdx1M3ZHwXTfmxNVQI/U7R+Citb0OpvZI+N8Y7TXtdajqjoTf72htr4OpPRK+b18iob0mdIfZc889Z5xzzjmG0+k0BgwYYHzyySfe56688kpj7NixDc7/29/+ZlxwwQWG0+k0LrroIuOtt94Kc8XHBVL35MmTvecmJycbN9xwg7Fp06aw1+zZmuPEh6fWsWPHGldeeeVJ12RkZBhOp9Po2rWrMX/+/Iiv+/HHHzfOO+88Iz4+3mjXrp0xaNAg47333gt73b5qltTgO4zE3/Fg6o6E3/Hbb7/d6NKli+F0Oo0OHToY11xzjbcR9FWzYbT8d20YgdcdCd91Y05sxCP1O0dworW9Nozoa7Npr8OL9pr22l9WabMjob22GYZhmNdvDgAAAAAAPJjTDQAAAABAiBC6AQAAAAAIEUI3AAAAAAAhQugGAAAAACBECN0AAAAAAIQIoRsAAAAAgBAhdAMAAAAAECKEbgAAAAAAQoTQDaBJt912m4YNG9Zi7z969Gg9+uijfp1766236sknnwxxRYg2H3zwgYYOHarU1FTZbDatWLGixd/vtdde0+DBg9W+fXvZbDZt3rw5pDUBOD3QZiPaWbXNJnQDpzGbzdbk46GHHtIzzzyjBQsWtEh9//znP7Vq1Sr95je/8ev8adOm6ZFHHlFZWVmIK0M0qaioUJ8+fVRQUBAx71dRUaHLL79cjz/+eFhqAhD9aLNxOrBqm+0I+koAUe+nn37y/nvJkiWaPn26tm/f7j3Wpk0btWnTpiVKkyQ999xzGj58uN819OrVS+edd55effVVTZw4McTVIVpcf/31uv766xt9vrKyUg888ID++te/6uDBg+rVq5cef/xxDRo0KCTvJx3vDZKkH374Iaj3AHD6oc3G6cCqbTY93cBpLCUlxftISEiQzWZrcKxNmzYnDVUbNGiQ7r77bk2ePFlnnXWWkpOT9eKLL6qiokLjxo3TmWeeqfPPP19///vfG7zX1q1bdf3116tNmzZKTk7W6NGjtX///kZrc7lcWrZsmYYOHdrg+J/+9Cd169ZN8fHxSk5O1s0339zg+aFDh2rx4sXN/3Jw2sjNzVVRUZEWL16szz//XMOHD9d1112nb775pqVLAwAv2mwgettsQjeAgL3yyitKSkrS+vXrdffdd+uuu+7S8OHDNXDgQG3atEmDBw/W6NGjdeTIEUnSwYMHdfXVV6tv377auHGjVq9erdLSUt1yyy2Nvsfnn3+usrIy9e/f33ts48aN+s1vfqNZs2Zp+/btWr16tf7t3/6twXUDBgzQ+vXrVVlZGZoPD0vZuXOn5s+fr6VLl+qKK67Qeeedp/vuu0+XX3655s+f39LlAUCz0WbDKqK5zSZ0AwhYnz59NG3aNHXr1k1Tp05VfHy8kpKSNGHCBHXr1k3Tp0/Xzz//rM8//1ySNGfOHPXt21ePPvqoevToob59+2revHlau3atvv76a5/v8eOPPyomJkYdO3b0Htu5c6fOOOMM/fKXv1SXLl3Ut2/fk+aOpaamqqqqSiUlJaH7AmAZW7Zskcvl0gUXXOAdmtmmTRu9//77+u677yRJX3311SnnUk6ZMqWFPwkA+EabDauI5jabOd0AAta7d2/vv2NiYtS+fXtdfPHF3mPJycmSpL1790o6vrjK2rVrfc7z+u6773TBBRecdPzo0aOKi4uTzWbzHrv22mvVpUsXde3aVdddd52uu+46/fu//7tat27tPadVq1aS5P2LPdCUw4cPKyYmRsXFxYqJiWnwnOf3tWvXrtq2bVuTr9O+ffuQ1QgAzUGbDauI5jab0A0gYLGxsQ1+ttlsDY55Gl232y3p+H8khw4d6nPVx06dOvl8j6SkJB05ckRVVVVyOp2SpDPPPFObNm3SunXr9M4772j69Ol66KGHtGHDBiUmJkqSDhw4IEnq0KFD8z4kTgt9+/aVy+XS3r17dcUVV/g8x+l0qkePHmGuDADMQZsNq4jmNpvQDSDkLrnkEi1fvlzp6elyOPz7z05GRoYk6csvv/T+W5IcDoeys7OVnZ2tGTNmKDExUe+9955uvPFGSccXfzn77LOVlJRk9sdAlDp8+LC+/fZb7887duzQ5s2b1a5dO11wwQUaNWqUxowZoyeffFJ9+/bVvn37VFhYqN69e2vIkCGmvt8555wj6fiN5s6dO7Vnzx5J8q5A7FkQCQBaCm02WpJV22zmdAMIuYkTJ+rAgQMaOXKkNmzYoO+++05vv/22xo0bJ5fL5fOaDh066JJLLtE//vEP77GVK1fq2Wef1ebNm/Xjjz9q4cKFcrvd6t69u/ecDz/8UIMHDw75Z0L02Lhxo/r27au+fftKkvLy8tS3b19Nnz5dkjR//nyNGTNG9957r7p3765hw4Zpw4YN3sbW7PeTpDfeeEN9+/b13iDceuut6tu3r+bOnducjwoAzUabjZZk1Tabnm4AIZeamqqPPvpI999/vwYPHqzKykp16dJF1113nez2xv/2d8cdd2jhwoXKzc2VJCUmJuq1117TQw89pGPHjqlbt27661//qosuukiSdOzYMa1YsUKrV68Oy+dCdBg0aJAMw2j0+djYWM2cOVMzZ84My/tJ0m233abbbrvNlPcDADPRZqMlWbXNthmnehcAaCFHjx5V9+7dtWTJEmVlZZ3y/Oeff16vv/663nnnnTBUBwAAPGizgcYxvBxAxGrVqpUWLlyo/fv3+3V+bGysnnvuuRBXBQAATkSbDTSOnm4AAAAAAEKEnm4AAAAAAEKE0A0AAAAAQIgQugEAAAAACBFCNwAAAAAAIULoBgAAAAAgRAjdAAAAAACECKEbAAAAAIAQIXQDAAAAABAihG4AAAAAAEKE0A0AAAAAQIj8f3ZNTAKMFPnQAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "netlist = {\n", + " \"instances\": {\n", + " \"coupler\": \"coupler\", # The coupler instance\n", + " },\n", + " \"connections\": {}, # No internal connections for a single component\n", + " \"ports\": {\n", + " \"o0\": \"coupler,o0\", # First input port\n", + " \"o1\": \"coupler,o1\", # Second input port\n", + " },\n", + "\n", + "}\n", + "\n", + "\n", + "circuit, info = sax.circuit(\n", + " netlist=netlist,\n", + " models={\n", + " \"coupler\": ideal.waveguide,\n", + " }\n", + ")\n", + "\n", + "\n", + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548 # Center wavelength (µm)\n", + "wvl = np.linspace(1.5, 1.6, num_measurements) # Wavelength range (µm)\n", + "\n", + "\n", + "# Perform simulation\n", + "s = circuit(wl=wvl, wg={\"length\": 77.0, \"loss\": 100})\n", + "S = np.asarray(dict_to_matrix(s)) # Convert the result to a matrix\n", + "model = IIRModelBaseband(wvl, center_wvl, S, model_order)\n", + "\n", + "\n", + "N = int(1000) # Number of time steps\n", + "T = 4e-11 # Total time duration (40 ps)\n", + "t = jnp.linspace(0, T, N) # Time array\n", + "t0 = T/2 - 5e-12 # Pulse start time\n", + "std = 1e-12 # Pulse standard deviation\n", + "\n", + "\n", + "# Define input signals\n", + "impulse_pass = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5 * t0, std),\n", + " 'o1': jnp.zeros_like(t)\n", + "}\n", + "\n", + "\n", + "# Convert frequency domain model to a time-domain system\n", + "tsys = pole_residue_to_time_system(model)\n", + "# Simulate the system's response to the input signals\n", + "outputs = tsys.response(impulse_pass)\n", + "\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(2, 2, figsize=(10, 10)) # 2 rows, 2 columns\n", + "\n", + "\n", + "# Plot input signals\n", + "for i in range(2):\n", + " axs[i, 0].plot(t, jnp.abs(impulse_pass[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Plot output signals\n", + "for i in range(2):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "aa6829dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the splitter and combiner using directional couplers\n", + "\n", + "# Define the netlist for a Mach-Zehnder Modulator\n", + "\n", + "netlist = {\n", + " \"instances\": {\n", + " \"cr1\": \"y_branch\",\n", + " \"cr2\": \"y_branch\",# The coupler instance\n", + " \"wg1\": \"waveguide\",\n", + " \"wg2\": \"waveguide\",\n", + " },\n", + " \"connections\": {\n", + " \"cr1,port_2\":\"wg1,o0\",\n", + " \"cr1,port_3\":\"wg2,o0\",\n", + " \"wg1,o1\":\"cr2,port_2\",\n", + " \"wg2,o1\":\"cr2,port_3\",\n", + "\n", + " }, # No internal connections for a single component\n", + " \"ports\": {\n", + " \"o0\": \"cr1,port_1\", # First input port # Second input port\n", + " \"o1\": \"cr2,port_1\", # First output port # Second output port\n", + " },\n", + "\n", + "}\n", + "\n", + "\n", + "circuit, info = sax.circuit(\n", + " netlist=netlist,\n", + " models={\n", + " \"y_branch\": siepic.y_branch,\n", + " \"waveguide\":siepic.waveguide,\n", + "\n", + " }\n", + ")\n", + "\n", + "\n", + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548 # Center wavelength (µm)\n", + "wvl = np.linspace(1.5, 1.6, num_measurements) # Wavelength range (µm)\n", + "\n", + "\n", + "# Perform simulation\n", + "s = circuit(wl=wvl, wg={\"length\": 77.0, \"loss\": 1})\n", + "S = np.asarray(dict_to_matrix(s)) # Convert the result to a matrix\n", + "model = IIRModelBaseband(wvl, center_wvl, S, model_order)\n", + "\n", + "\n", + "N = int(1000) # Number of time steps\n", + "T = 4e-11 # Total time duration (40 ps)\n", + "t = jnp.linspace(0, T, N) # Time array\n", + "t0 = T/2 - 5e-12 # Pulse start time\n", + "std = 4e-12 # Pulse standard deviation\n", + "\n", + "\n", + "# Define input signals\n", + "impulse_pass = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5 * t0, std),\n", + " 'o1': jnp.zeros_like(t),\n", + " \n", + "}\n", + "\n", + "\n", + "# Convert frequency domain model to a time-domain system\n", + "tsys = pole_residue_to_time_system(model)\n", + "# Simulate the system's response to the input signals\n", + "outputs = tsys.response(impulse_pass)\n", + "\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(2, 2, figsize=(10, 10)) # 2 rows, 2 columns\n", + "\n", + "\n", + "# Plot input signals\n", + "for i in range(2):\n", + " axs[i, 0].plot(t, jnp.abs(impulse_pass[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Plot output signals\n", + "for i in range(2):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "64b3fa4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "netlist = {\n", + " \"instances\": {\n", + " \"coupler\": \"coupler\", # The coupler instance\n", + " },\n", + " \"connections\": {}, # No internal connections for a single component\n", + " \"ports\": {\n", + " \"o0\": \"coupler,port_1\", # First input port\n", + " \"o1\": \"coupler,port_2\",\n", + " \"o2\": \"coupler,port_3\",\n", + " \"z2\": \"coupler,port_3\",\n", + " \n", + " },\n", + "\n", + "}\n", + "\n", + "\n", + "circuit, info = sax.circuit(\n", + " netlist=netlist,\n", + " models={\n", + " \"coupler\": siepic.bidirectional_coupler,\n", + " }\n", + ")\n", + "\n", + "\n", + "num_measurements = 200\n", + "model_order = 50\n", + "center_wvl = 1.548 # Center wavelength (µm)\n", + "wvl = np.linspace(1.5, 1.6, num_measurements) # Wavelength range (µm)\n", + "\n", + "\n", + "# Perform simulation\n", + "s = circuit(wl=wvl, wg={\"length\": 77.0, \"loss\": 100})\n", + "S = np.asarray(dict_to_matrix(s)) # Convert the result to a matrix\n", + "model = IIRModelBaseband(wvl, center_wvl, S, model_order)\n", + "\n", + "\n", + "N = int(1000) # Number of time steps\n", + "T = 4e-11 # Total time duration (40 ps)\n", + "t = jnp.linspace(0, T, N) # Time array\n", + "t0 = T/2 - 5e-12 # Pulse start time\n", + "std = 1e-12 # Pulse standard deviation\n", + "\n", + "\n", + "# Define input signals\n", + "impulse_pass = {\n", + " 'o0': gaussian_pulse(t, t0 - 0.5 * t0, std),\n", + " 'o1': jnp.zeros_like(t),\n", + " 'o2': jnp.zeros_like(t),\n", + " 'o3': jnp.zeros_like(t),\n", + "}\n", + "\n", + "\n", + "# Convert frequency domain model to a time-domain system\n", + "tsys = pole_residue_to_time_system(model)\n", + "# Simulate the system's response to the input signals\n", + "outputs,_,_ = tsys.response(impulse_pass)\n", + "\n", + "\n", + "# Create subplots\n", + "fig, axs = plt.subplots(4, 2, figsize=(10, 10)) # 2 rows, 2 columns\n", + "\n", + "\n", + "# Plot input signals\n", + "for i in range(4):\n", + " axs[i, 0].plot(t, jnp.abs(impulse_pass[f'o{i}'])**2)\n", + " axs[i, 0].set_title(f'Input Signal {i+1}')\n", + " axs[i, 0].set_xlabel('Time (s)')\n", + " axs[i, 0].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Plot output signals\n", + "for i in range(4):\n", + " axs[i, 1].plot(t, jnp.abs(outputs[f'o{i}'])**2)\n", + " axs[i, 1].set_title(f'Output Signal {i+1}')\n", + " axs[i, 1].set_xlabel('Time (s)')\n", + " axs[i, 1].set_ylabel('Intensity')\n", + "\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PythonX", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/simphony/time_domain/examples/quantum_test.py b/simphony/time_domain/examples/quantum_test.py new file mode 100644 index 00000000..d15d6c59 --- /dev/null +++ b/simphony/time_domain/examples/quantum_test.py @@ -0,0 +1,31 @@ +import matplotlib.pyplot as plt +import numpy as np +import sax + +from simphony.libraries import siepic +from simphony.utils import dict_to_matrix + +netlist = { + "instances": { + "wg": "waveguide", + }, + "connections": {}, + "ports": { + "o0": "wg,o0", + "o1": "wg,o1", + }, +} +circuit, info = sax.circuit( + netlist=netlist, + models={ + "waveguide": siepic.waveguide, + }, +) + +wvl_microns = np.linspace(1.51, 1.59, 200) +center_wvl = 1.55 + +ckt = circuit(wl=wvl_microns, wg={"length": 18, "loss": 1000}) +S = np.asarray(dict_to_matrix(ckt)) +plt.plot(wvl_microns, np.abs(S[:, 0, 1]) ** 2) +plt.show() diff --git a/simphony/time_domain/examples/time_system.ipynb b/simphony/time_domain/examples/time_system.ipynb new file mode 100644 index 00000000..bf4e1c12 --- /dev/null +++ b/simphony/time_domain/examples/time_system.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8d9ea408", + "metadata": {}, + "source": [ + "# Time Systems\n", + "\n", + "Simphony has previously only worked with Linear Time-invariant systems, characterized by S-parameter elements. Time-domain Simphony allows a user to create time-variant components, called time-systems. Simphony provides two types of time systems: `SampleModeComponent` and `BlockModeComponent`. These abstract base classes are to be used in Sample Mode simulations and block mode simulations respectively." + ] + }, + { + "cell_type": "markdown", + "id": "31ed3774", + "metadata": {}, + "source": [ + "# Sample Mode Systems\n", + "Sample mode simulations are discrete-time simulations where, for each time step, the output of each component is calculated and used to determine the next set of inputs. This approach enables the simulation of circuits containing components whose inputs depend on the unknown outputs of other components.\n", + "\n", + "In order for this approach to work, each component must be able to maintain state. During Sample Mode Simulations, Simphony looks for two key methods; `init_state` initializes the state of the system, and `step` takes in the current state of the system and a tuple of inputs (one for each port) and returns the next state of the system and a tuple of outputs.\n", + "\n", + "Below is an example of a custom realization of a `SampleModeComponent` which implements a simple phase shifter. The step function must be Jax compatible so that it can be just in time (JIT) compiled. This means that the same input should produce the same output and there are no side effects. For example, the length of arrays, lists, dicts, et cetera must not change." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c08a685c", + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.time_domain import SampleModeComponent\n", + "import jax.numpy as jnp\n", + "\n", + "\n", + "class PhaseShifter(SampleModeComponent):\n", + " def __init__(\n", + " self,\n", + " voltage_signal\n", + " ) -> None:\n", + " super().__init__()\n", + "\n", + " self.num_ports = 2\n", + " self.ports = ['o0','o1']\n", + " self.voltage_signal = voltage_signal\n", + "\n", + " def voltage_to_phase(v):\n", + " \"\"\"\n", + " NOT A REQUIRED FUNCTION.\n", + " This simply converts the volage signal to a phase shift.\n", + " \"\"\"\n", + " return 0.5 * v\n", + "\n", + " def init_state(self):\n", + " \"\"\"\n", + " REQUIRED FUNCTION\n", + " \"\"\"\n", + " return jnp.int32(0)\n", + "\n", + " def step(self, curr_state: jnp.ndarray, input0, input1):\n", + " \"\"\"\n", + " REQUIRED FUNCTION\n", + " A JIT Compatible function (compatible with lax.scan) that depends on a time-varying voltage.\n", + " \"\"\"\n", + " v = self.voltage_signal[curr_state]\n", + " phase = self.voltage_to_phase(v)\n", + " out0 = input1 * jnp.exp(1j * phase)\n", + " out1 = input0 * jnp.exp(1j * phase)\n", + " \n", + " return curr_state + 1, (out0, out1)\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "a8020222", + "metadata": {}, + "source": [ + "# Jax Compatibility\n", + "\n", + "Since we use \"Just In Time\" (JIT) compilation methods to run sample mode simulations, there are additional constraints placed on the `step` function, particularly when using loops. For anyone not familiar with the constraints of JIT programming, we recomend you do not create custom sample-mode elements and use one of the models we provide instead." + ] + }, + { + "cell_type": "markdown", + "id": "75dbad24", + "metadata": {}, + "source": [ + "# Block Mode Simulations\n", + "\n", + "Sample mode simulations are ill-suited for some circuits. For example, you really shouldn't try to simulate a 10-km fiber optic cable by simulating each time step individually, for many reasons. Other techniques exist for these sorts of simulations, such as the split-step Fourier method. Simphony enables these techniques through Block Mode Simulations. \n", + "\n", + "Because block mode simulations simulate each time system, individually, once (per block), we do not required the `run` method to be compatible with jax or JIT compilation. For example, the following external library has implemented the split-step fourier transform for optical fibers. We can seamlessly integrate this technique into our simulator by using it as a backend for the `run` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "837653fa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "8d091c2d", + "metadata": {}, + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "id": "4c2bdffb", + "metadata": {}, + "source": [ + "# Sax Compatibility\n", + "\n", + "Since Time-systems will primarily be used in the Time-domain, they are not compatible, by default, with frequency domain solvers like Sax. If desired, you may choose to implement the `frequency_response` method of a time-system so that any circuit that utilizes a time-system is backwards compatible with Sax." + ] + }, + { + "cell_type": "markdown", + "id": "7260b77d", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/simphony/time_domain/ideal.py b/simphony/time_domain/ideal.py new file mode 100644 index 00000000..63cdf7a7 --- /dev/null +++ b/simphony/time_domain/ideal.py @@ -0,0 +1,340 @@ +"""Ideal time-domain models.""" + +from queue import Queue + +import jax.numpy as jnp +import numpy as np +from jax.typing import ArrayLike + +import simphony.libraries.ideal as fd +from simphony.time_domain.time_system import ( + BlockModeComponent, + SampleModeComponent, + TimeSystem, +) +from simphony.utils import dict_to_matrix + + +class TimeCoupler(TimeSystem): + def __init__( + self, + coupling: float = 0.5, + loss: float = 0.0, + phi: float = jnp.pi / 2, + ) -> None: + super().__init__() + self.s_params = dict_to_matrix( + fd.coupler(coupling=coupling, loss=loss, phi=phi) + ) + self.num_ports = 4 + self.ports = ["o0", "o1", "o2", "o3"] + + def response(self, inputs: dict) -> dict: + N = inputs["o0"].shape + response = { + "o0": jnp.zeros((N), dtype=complex), + "o1": jnp.zeros((N), dtype=complex), + "o2": jnp.zeros((N), dtype=complex), + "o3": jnp.zeros((N), dtype=complex), + } + + for i in range(self.num_ports): + response[f"o{i}"] = ( + inputs["o0"] * self.s_params[0, i, 0] + + inputs["o1"] * self.s_params[0, i, 1] + + inputs["o2"] * self.s_params[0, i, 2] + + inputs["o3"] * self.s_params[0, i, 3] + ) + return response + + def clear(self) -> None: + pass + + +class TimeWaveguide(TimeSystem): + def __init__( + self, + # wl: ArrayLike | float = 1.55, + dt: float, + wl0: float = 1.55, + neff: float = 2.34, + ng: float = 3.4, + length: float = 10.0, + loss: float = 0.0, + ) -> None: + super().__init__() + + self.num_ports = 2 + self.ports = ["o0", "o1"] + c = 299792458 + group_velocity = c / ng + omega = c / (1.55e-6) + + delay = (length * 1e-6) / (group_velocity) + self.num_delay_indices = round(delay / dt) + + phase_shift = jnp.mod(omega * delay, 2 * jnp.pi) + loss_mag = loss / (10 * jnp.log10(jnp.exp(1))) + alpha = loss_mag * 1e-4 + amplitude = jnp.asarray(jnp.exp(-alpha * length / 2), dtype=complex) + + self.transmission = amplitude * jnp.exp(1j * phase_shift) + self.forward_wave = Queue() + self.backward_wave = Queue() + + for i in range(self.num_delay_indices): + self.forward_wave.put(0 + 0j) + self.backward_wave.put(0 + 0j) + + def run(self, inputs: dict) -> dict: + N = inputs["o0"].shape[0] + o0_response = jnp.zeros((N), dtype=complex) + o1_response = jnp.zeros((N), dtype=complex) + + for i in range(N): + a = complex(inputs["o0"][i]) + b = complex(inputs["o1"][i]) + o0_response = o0_response.at[i].set( + self.transmission * self.backward_wave.get() + ) + o1_response = o1_response.at[i].set( + self.transmission * self.forward_wave.get() + ) + + self.forward_wave.put(a) + self.backward_wave.put(b) + + response = { + "o0": o0_response, + "o1": o1_response, + } + return response + + return response + + def clear(self) -> None: + while not self.forward_wave.empty(): + self.forward_wave.get() + while not self.backward_wave.empty(): + self.backward_wave.get() + for i in range(self.num_delay_indices): + self.forward_wave.put(0 + 0j) + self.backward_wave.put(0 + 0j) + + +class Modulator(SampleModeComponent, BlockModeComponent): + # … your __init__ stays as before (but remove any internal “self.countstep” updates) … + def __init__( + self, + mod_signal: ArrayLike | float = 0.0, + k_p: float = 1.0, + ) -> None: + super().__init__() + + self.num_ports = 2 + self.ports = ["o0", "o1"] + self.phase_sequence = mod_signal * k_p + + def init_state(self, **kwargs): + # Return whatever you want the initial state to be. + # For example, if you have a JAX array of per‐time phases, just return index = 0: + + return jnp.int32(0) + + def step(self, prev_idx: jnp.ndarray, inputs: tuple, **kwargs): + """ + A _pure_ function—no side‐effects!—that returns (new_idx, (out0, out1)). + E.g.: + phase = self.phase_sequence[prev_idx] + coeff = jnp.exp(1j * phase) + out0 = input1 * coeff + out1 = input0 * coeff + return prev_idx + 1, (out0, out1) + """ + phase = self.phase_sequence[prev_idx] + coeff = jnp.exp(1j * phase) + out0 = inputs[1] * coeff + out1 = inputs[0] * coeff + return prev_idx + 1, (out0, out1) + + def run(self, inputs: dict, **kwargs) -> dict: + N = inputs["o0"].shape[0] + o0_response = jnp.zeros((N), dtype=complex) + o1_response = jnp.zeros((N), dtype=complex) + + for i in range(N): + o0_response = o0_response.at[i].set( + inputs["o1"][i] * self.s_mod[self.countstep] + ) + o1_response = o1_response.at[i].set( + inputs["o0"][i] * self.s_mod[self.countstep] + ) + self.countstep += 1 + response = { + "o0": o0_response, + "o1": o1_response, + } + return response + + +class PhaseModulator(SampleModeComponent, BlockModeComponent): + # … your __init__ stays as before (but remove any internal “self.countstep” updates) … + def __init__(self, time: ArrayLike, voltage: ArrayLike) -> None: + super().__init__() + + self.num_ports = 2 + self.ports = ["o0", "o1"] + + self.time = time + self.voltage = voltage + + def init_state(self, **kwargs): + # Return whatever you want the initial state to be. + # For example, if you have a JAX array of per‐time phases, just return index = 0: + self._voltage + + return jnp.int32(0) + + def step(self, prev_idx: jnp.ndarray, input0, input1, **kwargs): + """ + A _pure_ function—no side‐effects!—that returns (new_idx, (out0, out1)). + E.g.: + phase = self.phase_sequence[prev_idx] + coeff = jnp.exp(1j * phase) + out0 = input1 * coeff + out1 = input0 * coeff + return prev_idx + 1, (out0, out1) + """ + phase = self.phase_sequence[prev_idx] + coeff = jnp.exp(1j * phase) + out0 = input1 * coeff + out1 = input0 * coeff + return prev_idx + 1, (out0, out1) + + def run(self, inputs: dict, **kwargs) -> dict: + N = inputs["o0"].shape[0] + o0_response = jnp.zeros((N), dtype=complex) + o1_response = jnp.zeros((N), dtype=complex) + + for i in range(N): + o0_response = o0_response.at[i].set( + inputs["o1"][i] * self.s_mod[self.countstep] + ) + o1_response = o1_response.at[i].set( + inputs["o0"][i] * self.s_mod[self.countstep] + ) + self.countstep += 1 + response = { + "o0": o0_response, + "o1": o1_response, + } + + +# class Modulator(TimeSystem): +# def __init__( +# self, +# mod_signal: ArrayLike|float = 0.0, +# k_p: float = 1.0, +# ) -> None: +# super().__init__() + +# self.num_ports = 2 +# self.ports = ['o0','o1'] +# phase_shift = k_p * mod_signal +# self.s_mod = jnp.exp(1j * phase_shift) +# self.countstep = 0 + +# def response(self, inputs:dict) -> dict: +# N = inputs['o0'].shape[0] +# o0_response = jnp.zeros((N),dtype = complex) +# o1_response = jnp.zeros((N), dtype=complex) + +# for i in range(N): +# o0_response = o0_response.at[i].set(inputs['o1'][i] * self.s_mod[self.countstep]) +# o1_response = o1_response.at[i].set(inputs['o0'][i] * self.s_mod[self.countstep]) +# self.countstep += 1 +# response = { +# "o0": o0_response, +# "o1": o1_response, +# } + + +# return response + +# def append(self, mod_signal: ArrayLike|float) -> None: +# self.s_mod = jnp.append(self.s_mod, jnp.exp(1j * mod_signal)) + + +# def reset(self) -> None: +# self.countstep = 0 + + +class MMI(TimeSystem): + def __init__( + self, + r: int = 2, + s: int = 2, + length: float = 10.0, + loss: float = 0.0, + ) -> None: + super().__init__() + self.num_ports = r + s + self.length = length + self.loss = loss + self.r = r + self.s = s + self.ports = [f"o{i}" for i in range(self.num_ports)] + N_size = self.r + self.s + phases = jnp.zeros((N_size, N_size)) + N = self.r + for i in range(1, self.r + 1): + for j in range(1, self.s + 1): + if (i + j) % 2 == 0: + num = -(jnp.pi / (4 * N)) * (j - i) * (2 * N + i - j) + else: + num = -(jnp.pi / (4 * N)) * (i + j - 1) * (2 * N - i - j + 1) + + phases = phases.at[i - 1, N_size - j].set(num) + phases = phases.at[N_size - j, i - 1].set(num) + + loss_mag = self.loss / (10 * jnp.log10(jnp.exp(1))) + alpha = loss_mag * 1e-4 + amplitude = jnp.asarray(jnp.exp(-alpha * self.length / 2), dtype=complex) + s_dict_time = {} + + for i in range(self.r): # inputs 0 … r-1 + for j in range(self.s): # outputs r … r+s-1 + phi = phases[i, j + self.r] + s_dict_time[(f"o{i}", f"o{j + self.r}")] = ( + amplitude / jnp.sqrt(self.s) * jnp.exp(1j * phi) + ) + + for i in range(self.s): # inputs r … r+s-1 + for j in range(self.r): # outputs 0 … r-1 + phi = phases[i + self.s, j] + s_dict_time[(f"o{i + self.r}", f"o{j}")] = ( + amplitude / jnp.sqrt(self.r) * jnp.exp(1j * phi) + ) + + # now build S as [output, input] + ports = self.ports + N = len(ports) + S = np.zeros((N, N), dtype=complex) + for (inp, out), val in s_dict_time.items(): + ci = ports.index(inp) + rj = ports.index(out) + S[rj, ci] = val + + self.s_dict_time = S + + def response(self, inputs: dict) -> dict: + response = {} + N = len(self.ports) + for j, port in enumerate(self.ports): + # sum over all inputs + resp = sum(inputs[f"o{idx}"] * self.s_dict_time[j, idx] for idx in range(N)) + response[port] = resp + return response + + def reset(self) -> None: + pass diff --git a/simphony/time_domain/old_simulation.py b/simphony/time_domain/old_simulation.py new file mode 100644 index 00000000..3f93d1f3 --- /dev/null +++ b/simphony/time_domain/old_simulation.py @@ -0,0 +1,880 @@ +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +import sax +from jax import config +from numpy.typing import ArrayLike + +from simphony.time_domain.time_system import TimeSystemIIR + +config.update("jax_enable_x64", True) + +from dataclasses import dataclass + +from scipy.interpolate import interp1d + +from simphony.exceptions import UndefinedActiveComponent +from simphony.simulation import Simulation, SimulationResult +from simphony.time_domain.pole_residue_model import BVF_Options, IIRModelBaseband +from simphony.utils import dict_to_matrix + + +@dataclass +class TimeResult(SimulationResult): + outputs: ArrayLike + t: ArrayLike + inputs: ArrayLike + S_params: ArrayLike + + def plot_sim(self): + input_keys = list(self.inputs.keys()) + output_keys = list(self.outputs.keys()) + + ports = max(len(input_keys), len(output_keys)) + + fig, axs = plt.subplots(ports, 2, figsize=(10, 3 * ports)) + + if ports == 1: + axs = axs.reshape(1, -1) + + for i, key in enumerate(input_keys): + axs[i, 0].plot(self.t, jnp.abs(self.inputs[key]) ** 2) + axs[i, 0].set_title(f"Input Signal {key}") + axs[i, 0].set_xlabel("Time (s)") + axs[i, 0].set_ylabel("Intensity") + + for i, key in enumerate(output_keys): + axs[i, 1].plot(self.t, jnp.abs(self.outputs[key]) ** 2) + axs[i, 1].set_title(f"Output Signal {key}") + axs[i, 1].set_xlabel("Time (s)") + axs[i, 1].set_ylabel("Intensity") + + plt.tight_layout() + plt.show() + + +class TimeSim(Simulation): + def __init__(self, netlist: dict, models: dict, active_components: set = None): + self.netlist = netlist + self.model_List = models + self.instances = {} + self.connections = netlist["connections"] + self.ports = netlist["ports"] + self.instances = netlist["instances"] + self.active_components = active_components + + def build_model( + self, + wvl=np.linspace(1.5, 1.6, 200), + center_wvl=1.55, + model_order=50, + num_measurements=200, + model_parameters=None, + dt=1e-14, + max_size=5, + ): + self.modelList = [] + self.dt = dt + c = 299792458 + center_freq = c / (center_wvl * 1e-6) + freqs = c / (wvl * 1e-6) - center_freq + sampling_freq = -1 / dt + beta = sampling_freq / (freqs[-1] - freqs[0]) + custom_options = BVF_Options(beta=beta) + + if self.active_components is not None: + ( + self.sub_netlists, + self.removed_connections, + self.removed_ports, + self.reconnect_passive, + ) = self.create_passive_sub_netlists( + self.instances, + self.connections, + self.ports, + self.active_components, + max_size=max_size, + ) + + for i, nl in enumerate(self.sub_netlists): + print(f"--- Sub-Netlist {i} ---") + print("instances:", nl["instances"]) + print("connections:", nl["connections"]) + print("ports:", nl["ports"]) + print() + + sub_circuit_list = {} + port_list = {} + self.model_list = [] + try: + for i, netlist in enumerate(self.sub_netlists): + dt = 1e-12 + circuittemp, info = sax.circuit( + netlist=netlist, + models=self.model_List, + ) + sub_circuit_list[i] = circuittemp + port_list[i] = netlist["ports"] + except TypeError as originalerror: + raise UndefinedActiveComponent( + "Active component is included in passive circuit" + ) from originalerror + + self.S_params_dict = {} + + for i, circuit in sub_circuit_list.items(): + # Execute circuit with generated parameters + s = circuit(**model_parameters) + temp_port_list = [] + z = 0 + for k, v in port_list[i].items(): + temp_port_list.append(k) + temp_port_list = sorted(temp_port_list) + # Continue with existing processing + S = np.asarray(dict_to_matrix(s)) + self.S_params_dict[i] = S + try: + temp_model = TimeSystemIIR( + IIRModelBaseband( + wvl, center_wvl, S, model_order, options=custom_options + ), + temp_port_list, + ) + except MemoryError as e: + print("MemoryError encountered:", e) + # Call an alternative function or take remedial action + + self.modelList.append(temp_model) + + self.step_list = { + "step_models": {}, + "step_connections": {}, + "step_ports": {}, + } + + def remove_duplicate_and_empty_dicts(dict_list): + seen = set() # To store seen (key, value) tuples + unique_list = [] + for d in dict_list: + new_d = {} + for k, v in d.items(): + pair = (k, v) + if pair not in seen: + new_d[k] = v + seen.add(pair) + # Only add non-empty dictionaries + if new_d: + unique_list.append(new_d) + return unique_list + + self.reconnect_passive = remove_duplicate_and_empty_dicts( + self.reconnect_passive + ) + for k, v in self.removed_connections: + value = v.split(",")[0] + value2 = k.split(",")[0] + if value in self.active_components: + temp_instance = self.instances.get(value) + self.step_list["step_models"][value] = self.model_List.get( + temp_instance + ) + if value2 in self.active_components: + temp_instance = self.instances.get(value2) + self.step_list["step_models"][value2] = self.model_List.get( + temp_instance + ) + + for i, ports in port_list.items(): + self.step_list["step_models"][f"{i}"] = self.modelList[i] + + port_translation_list = [] + + for i, ports in port_list.items(): + for j, v in ports.items(): + if any( + v in value1 or v in value2 + for (value1, value2) in self.removed_connections + ): + for k, h in self.removed_connections: + if k == v: + self.step_list = self.add_to_netlist( + connection=(f"{i},{j}", h) + ) + elif h == v: + self.step_list = self.add_to_netlist( + connection=(f"{i},{j}", k) + ) + elif any( + v in value1 or v in value2 + for d in self.reconnect_passive + for value1, value2 in d.items() + ): + for d in self.reconnect_passive[:]: + for k in list(d.keys()): + h = d[k] + if k == v: + for y, ports2 in port_list.items(): + for z, x in ports2.items(): + if x == h: + if ( + not f"{i},{j}" + in self.step_list[ + "step_connections" + ] + ): + self.step_list = ( + self.add_to_netlist( + connection=( + f"{i},{j}", + f"{y},{z}", + ) + ) + ) + break + + elif h == v: + for y, ports2 in port_list.items(): + for z, x in ports2.items(): + if x == k: + if ( + not f"{i},{j}" + in self.step_list[ + "step_connections" + ] + ): + self.step_list = ( + self.add_to_netlist( + connection=( + f"{i},{j}", + f"{y},{z}", + ) + ) + ) + break + else: + self.step_list = self.add_to_netlist(port=(j, f"{i},{j}")) + + port_translation_list.append((j, v)) + + for k, v in self.removed_ports.items(): + self.step_list = self.add_to_netlist(port=(k, v)) + + print(f"--- Netlist ---") + print("Models:", self.step_list["step_models"]) + print("connections:", self.step_list["step_connections"]) + print("ports:", self.step_list["step_ports"]) + print(port_translation_list) + print() + + else: + circuit, info = sax.circuit(netlist=self.netlist, models=self.model_List) + circuit_params = model_parameters + s = circuit(**circuit_params) + self.S_params_dict = np.asarray(dict_to_matrix(s)) + model = IIRModelBaseband(wvl, center_wvl, self.S_params_dict, model_order) + self.time_system = TimeSystemIIR(model) + + def run(self, t: ArrayLike, inputs: dict) -> TimeResult: + # interpolation of the beta and dt values defined dt 1/sampling frequency = dt, of the t and inputs to get new domain corresponds to the new dt + self.inputs = inputs + self.t = t + self.instance_outputs = {} + self.ports = {} + Statevector_save_list = {} + self.t, self.inputs = self.interpolate() + if self.active_components is not None: + for circuit_port, designation in self.step_list["step_ports"].items(): + instance_name, instance_port = map(str.strip, designation.split(",")) + self.ports[circuit_port] = (instance_name, instance_port) + if instance_name not in self.active_components: + Statevector_save_list[instance_name] = None + self.outputs = { + port: jnp.array([]) for port in self.step_list["step_ports"] + } + for instance_name, time_system in self.step_list["step_models"].items(): + self.instance_outputs[instance_name] = { + port: jnp.array([0 + 0j]) for port in time_system.ports + } + i = 0 + + for _ in self.t: + self.step(i) + i += 1 + else: + self.outputs, __ = self.time_system.response(self.inputs, time_sim=False) + + result = TimeResult( + outputs=self.outputs, + t=self.t, + inputs=self.inputs, + S_params=self.S_params_dict, + ) + return result + + def interpolate(self): + # Create the new time domain based on dt: + t_new = np.arange(self.t[0], self.t[-1] + self.dt, self.dt) + + # Initialize a new dictionary for interpolated inputs + new_inputs = {} + + # Interpolate each input using linear interpolation + for key, values in self.inputs.items(): + # Create an interpolation function. + interp_func = interp1d( + self.t, values, kind="linear", fill_value="extrapolate" + ) + new_inputs[key] = interp_func(t_new) + + return t_new, new_inputs + + def step(self, i): + for instance_name, time_system in self.step_list["step_models"].items(): + instance_inputs = {} + + for port in time_system.ports: + check = f"{instance_name},{port}" + found_source = None + for k, v in self.step_list["step_connections"].items(): + if check == k: + found_source = "k" + source_name = v.split(",")[0] + source_port = v.split(",")[1] + instance_inputs[port] = self.instance_outputs[source_name][ + source_port + ] + elif check == v: + found_source = "v" + source_name = k.split(",")[0] + source_port = k.split(",")[1] + instance_inputs[port] = self.instance_outputs[source_name][ + source_port + ] + if found_source == None: + designation = f"{port}" + circuit_port = next( + ( + k + for k, v in self.step_list["step_ports"].items() + if v.split(",")[1].strip() == designation + ), + None, + ) + instance_inputs[port] = jnp.array([self.inputs[circuit_port][i]]) + + outputs = {} + if instance_name in self.active_components: + outputs = time_system.response(instance_inputs) + else: + outputs, __ = time_system.response(instance_inputs) + for port_name in outputs: + self.instance_outputs[instance_name][port_name] = outputs[port_name] + + for circuit_port, instance in self.step_list["step_ports"].items(): + v = instance.split(",")[0].strip() + k = instance.split(",")[1].strip() + self.outputs[circuit_port] = jnp.concatenate( + [self.outputs[circuit_port], self.instance_outputs[v][k]] + ) + + def add_to_netlist( + self, model_name=None, model_type=None, connection=None, port=None + ): + """Adds new elements to the netlist. + + Args: + netlist (dict): The existing netlist to update. + instance_name (str): Name of the new instance to add. + instance_type (str): Type of the new instance to add (e.g., "ideal_waveguide"). + connection (tuple): Connection to add, e.g., ("instance1,port1", "instance2,port2"). + port (tuple): Port to add, e.g., ("port_name", "instance_name,port"). + + Returns: + dict: Updated netlist. + """ + # Add a new instance, if provided + if model_name and model_type: + self.step_list["step_models"][model_name] = model_type + + # Add a new connection, if provided + if connection and len(connection) == 2: + self.step_list["step_connections"][connection[0]] = connection[1] + + # Add a new port, if provided + if port and len(port) == 2: + self.step_list["step_ports"][port[0]] = port[1] + return self.step_list + + def remove_active_edges_and_track_them(self, connections, active_components): + """Remove any connection that involves an active component. + + Return: + - filtered_connections (still "compA,portA" -> "compB,portB" form) + - removed_edges: a list of tuples (passive_comp, active_comp) + indicating where a passive node used to connect to an active node. + We'll use this later to create new external ports. + """ + filtered = {} + removed_edges = [] + removed_connections = [] + + for k, v in connections.items(): + compA, portA = k.split(",") + compB, portB = v.split(",") + + A_is_active = compA in active_components + B_is_active = compB in active_components + + # If both are active, just ignore + if A_is_active and B_is_active: + continue + + # If exactly one is active, track the passive->active pair + if A_is_active and not B_is_active: + # Passive side = compB + add_tuple = (compB, portB) + removed_connections.append((k, v)) + removed_edges.append(add_tuple) + continue + elif B_is_active and not A_is_active: + removed_connections.append((k, v)) + add_tuple2 = (compA, portA) + removed_edges.append(add_tuple2) + continue + + # Otherwise, neither is active => keep this connection + filtered[k] = v + + return filtered, removed_edges, removed_connections + + def remove_ports_to_active(self, ports, active_components): + """Remove top-level ports that directly reference an active component. + + Returns (filtered_ports, removed_ports). + """ + filtered = {} + removed = {} + for port_label, comp_port_str in ports.items(): + comp, port = comp_port_str.split(",") + if comp in active_components: + removed[port_label] = comp_port_str + else: + filtered[port_label] = comp_port_str + return filtered, removed + + def build_component_graph( + self, connections, ports, active_components, removed_edges, directed=False + ): + """Build a graph (adjacency list) where each node is just the component + name. For example: If "cr1,port_2" -> "wg1,o0" is a connection, we add + an edge cr1 -> wg1. + + connections: dict { "compA,portA" : "compB,portB" } + directed: bool (False => treat them as undirected edges) + Returns: dict { compName : [adjacentCompName, ...], ... } + """ + graph = {} + + def add_edge(a, b): + if a not in graph: + graph[a] = [] + graph[a].append(b) + + for k, v in connections.items(): + compA, portA = k.split(",") + compB, portB = v.split(",") + + # Add edge from compA -> compB + add_edge(compA, compB) + if not directed: + # Also add compB -> compA + add_edge(compB, compA) + + for k, v in ports.items(): + comp, port = v.split(",") + A_is_active = comp in active_components + if comp not in graph and not A_is_active: + graph[comp] = [] + + for k, v in removed_edges: + A_is_active = k in active_components + if k not in graph and not A_is_active: + graph[k] = [] + + return graph + + def tarjan_scc(self, graph, max_size): + """Compute the strongly connected components (SCCs) of a graph. If an + SCC is larger than max_size, it is split by iteratively removing edges + from its induced subgraph using a heuristic that aims for a balanced + split. + + :param graph: dict mapping node -> list of neighbor nodes. + :param max_size: maximum allowed size for an SCC. + :return: list of SCCs (each a list of nodes). + """ + + def compute_scc_no_split(g): + """Standard Tarjan's algorithm to compute SCCs (without + splitting).""" + index_counter = [0] + stack = [] + on_stack = set() + indices = {} + lowlinks = {} + sccs = [] + + def strongconnect(node): + indices[node] = index_counter[0] + lowlinks[node] = index_counter[0] + index_counter[0] += 1 + stack.append(node) + on_stack.add(node) + for w in g.get(node, []): + if w not in indices: + strongconnect(w) + lowlinks[node] = min(lowlinks[node], lowlinks[w]) + elif w in on_stack: + lowlinks[node] = min(lowlinks[node], indices[w]) + if lowlinks[node] == indices[node]: + scc = [] + while True: + w = stack.pop() + on_stack.remove(w) + scc.append(w) + if w == node: + break + sccs.append(scc) + + for n in g.keys(): + if n not in indices: + strongconnect(n) + return sccs + + def choose_edge_to_remove(subgraph, max_size): + """Try removing each edge (temporarily) and compute the resulting + SCCs. + + Return the edge (as a tuple (u, idx, v)) whose removal + minimizes a metric. The metric here is a tuple: (number of + one-node SCCs, -min_component_size) so that we prefer fewer + trivial SCCs and a larger minimum SCC size. + """ + best_edge = None + best_metric = None + # Iterate over all edges in the subgraph. + for u in subgraph: + # Iterate over a copy of the list with index, so we can restore later. + for idx in range(len(subgraph[u])): + v = subgraph[u][idx] + # Temporarily remove edge (u, v) + backup = subgraph[u][idx] + del subgraph[u][idx] + new_sccs = compute_scc_no_split(subgraph) + trivial_count = sum(1 for comp in new_sccs if len(comp) == 1) + non_trivial_sizes = [ + len(comp) for comp in new_sccs if len(comp) > 1 + ] + min_size = ( + min(non_trivial_sizes) if non_trivial_sizes else float("inf") + ) + metric = (trivial_count, -min_size) + if best_metric is None or metric < best_metric: + best_metric = metric + best_edge = (u, idx, v) + # Restore the edge + subgraph[u].insert(idx, backup) + return best_edge + + def split_large_scc(g, scc, max_size, max_iter=100): + """For a given SCC (list of nodes) that is too large, build its + induced subgraph and iteratively remove edges (using a heuristic to + select the edge) until all resulting SCCs (computed on the modified + subgraph) are of size <= max_size.""" + # Build the induced subgraph for the nodes in scc. + subgraph = { + node: [nbr for nbr in g.get(node, []) if nbr in scc] for node in scc + } + iter_count = 0 + while iter_count < max_iter: + new_sccs = compute_scc_no_split(subgraph) + if all(len(comp) <= max_size for comp in new_sccs): + return new_sccs + # Choose the best edge to remove. + edge_to_remove = choose_edge_to_remove(subgraph, max_size) + if edge_to_remove is None: + # No candidate found; break to avoid infinite loop. + break + u, idx, v = edge_to_remove + # Remove the chosen edge permanently. + subgraph[u].pop(idx) + iter_count += 1 + return compute_scc_no_split(subgraph) + + # Compute initial SCCs for the full graph. + initial_sccs = compute_scc_no_split(graph) + result = [] + for scc in initial_sccs: + if len(scc) > max_size: + split_comps = split_large_scc(graph, scc, max_size) + result.extend(split_comps) + else: + result.append(scc) + return result + + def build_sub_netlist( + self, + scc_components, + all_connections, + original_ports, + removed_connections, + removed_ports, + instances, + active_components, + ): + """Construct a single netlist for the given set of SCC components (e.g. + { 'cr1','wg1','cr2' }). + + 1) Keep only those instances in scc_components (all passive). 2) + Keep only those connections that link two components in + scc_components. 3) Keep original ports referencing these + components. 4) For each removed passive->active edge, if the + passive comp is in scc_components, create a new external port. + """ + # 1) Instances + scc_instances = {} + for comp in scc_components: + if comp in instances: + scc_instances[comp] = instances[ + comp + ] # e.g. "waveguide", "y_branch", etc. + scc_ports = {} + # 2) Connections + scc_connections = {} + reconnect_passive_components = {} + + total_check = 0 + + # 3) Ports (original top-level) - keep only if they reference a component in the SCC + + for port_label, comp_port_str in original_ports.items(): + comp, port = comp_port_str.split(",") + if comp in scc_components: + scc_ports[port_label] = comp_port_str + total_check += 1 + + for k, v in all_connections.items(): + compA, portA = k.split(",") + compB, portB = v.split(",") + + if compA in scc_components and compB in scc_components: + # Connection stays + scc_connections[k] = v + + elif compA in scc_components and compB not in active_components: + scc_ports[f"o{total_check}"] = k + reconnect_passive_components[k] = v + total_check += 1 + elif compA not in active_components and compB in scc_components: + scc_ports[f"o{total_check}"] = v + reconnect_passive_components[k] = v + total_check += 1 + + # 4) Add new external ports for each (passiveComp, activeComp) removed edge + # if passiveComp is in scc_components + + new_port_index = 0 + + for k_string, v_string in removed_connections: + k = k_string.split(",")[0] + v = v_string.split(",")[0] + if k in scc_instances or v in scc_instances: + if any( + k.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ): + temp_index = 0 + if k in removed_ports: + for i, j_string in removed_ports.items(): + j = j_string.split(",")[0] + if j == k: + new_label = f"o{total_check}" + temp_index += 1 + total_check += 1 + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{k},{k_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{k},{k_string.split(',')[1]}" + break + else: + new_label = f"o{total_check}" + new_port_index += 1 + + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{k},{k_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{k},{k_string.split(',')[1]}" + + if v in active_components: + temp_index = 0 + if any( + v.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ): + for i, j_string in removed_ports.items(): + j = j_string.split(",")[0] + if j == v: + new_label = f"o{total_check}" + total_check += 1 + + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{k},{k_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{k},{k_string.split(',')[1]}" + + break + else: + new_label = f"o{total_check}" + total_check += 1 + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{k},{k_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{k},{k_string.split(',')[1]}" + + if k in active_components: + temp_index = 0 + if any( + k.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ): + for i, j_string in removed_ports.items(): + j = j_string.split(",")[0] + if j == k: + new_label = f"o{total_check}" + temp_index += 1 + total_check += 1 + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{v},{v_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{v},{v_string.split(',')[1]}" + + break + else: + new_label = f"o{total_check}" + total_check += 1 + if new_label in scc_ports: + tempcheck = total_check - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{v},{v_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{v},{v_string.split(',')[1]}" + + new_netlist = { + "instances": scc_instances, + "connections": scc_connections, + "ports": scc_ports, + } + return new_netlist, reconnect_passive_components + + def create_passive_sub_netlists( + self, + instances, + connections, + ports, + active_components, + max_size=5, + directed=False, + ): + """1) Remove edges that touch active components, track them for new + external ports. 2) Build a graph (component-level). 3) Run SCC. + + 4) For each SCC, build a sub-netlist that: + - has only those passive components + - has top-level ports referencing them + - has new "active boundary" ports where an active edge was removed + 5) Return list of such sub-netlists. + + Often, if everything is interconnected passively, you'll get 1 SCC. + """ + # 1) Remove edges to active comps + ( + filtered_conns, + removed_edges, + removed_connections, + ) = self.remove_active_edges_and_track_them(connections, active_components) + + # 2) Remove top-level ports referencing active comps + filtered_ports, removed_ports = self.remove_ports_to_active( + ports, active_components + ) + + # 3) Build a graph at component level (ignore port detail) + graph = self.build_component_graph( + filtered_conns, ports, active_components, removed_edges, directed=directed + ) + + # 4) Find SCCs + sccs = self.tarjan_scc( + graph, max_size + ) # e.g. [ ['cr1','wg1','cr2'], ['cr3','wg3','cr4'], ... ] + + # 5) Build a netlist for each SCC + sub_netlists = [] + reconnect_passive_components = [] + for scc_comp_list in sccs: + scc_comp_set = set(scc_comp_list) + # Build the sub-netlist + sub_nl, reconnect_passive_component = self.build_sub_netlist( + scc_comp_set, + filtered_conns, + filtered_ports, + removed_connections, + removed_ports, + instances, + active_components, + ) + # If the SCC has at least one instance from 'instances', add it + if sub_nl["instances"]: + sub_netlists.append(sub_nl) + reconnect_passive_components.append(reconnect_passive_component) + + return ( + sub_netlists, + removed_connections, + removed_ports, + reconnect_passive_components, + ) diff --git a/simphony/time_domain/pole_residue_model.py b/simphony/time_domain/pole_residue_model.py new file mode 100644 index 00000000..fabcae4c --- /dev/null +++ b/simphony/time_domain/pole_residue_model.py @@ -0,0 +1,369 @@ +from abc import ABC + +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +from scipy.linalg import block_diag +from scipy.signal import StateSpace, dlsim + + +class PoleResidueModel(ABC): + def __init__(self) -> None: + pass + + def plot_poles(self): + fig, ax = plt.subplots(subplot_kw={"projection": "polar"}) + initial_poles = self.initial_poles() + ax.set_rticks([0.2, 0.4, 0.6, 0.8, 1.0]) + ax.set_thetagrids([0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330]) + ax.set_rlim(0.0, 1.0) + ax.set_rlabel_position(0) + ax.grid(True) + + # Plot Initial Poles + ax.scatter( + np.angle(initial_poles), np.abs(initial_poles), label="Initial Poles" + ) + + # Plot Current Poles + ax.scatter(np.angle(self.poles), np.abs(self.poles), label="Optimal Poles") + + plt.show() + + def compute_error(self): + return np.max(np.abs(self.S - self.compute_response())) + + +class BVF_Options: + def __init__( + self, + poles_estimation_threshold=1e-1, + model_error_threshold=1e-3, + max_iterations=10, + enforce_stability=True, + alpha=0.01, + beta=20.0, + gamma=0.95, + debug=True, + real_valued=True, + ): + self.poles_estimation_threshold = poles_estimation_threshold + self.model_error_threshold = model_error_threshold + self.max_iterations = max_iterations + self.enforce_stability = enforce_stability + self.debug = debug + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.real_valued = real_valued + self.mode = "Fast" + + +class IIRModelBaseband(PoleResidueModel): + def __init__( + self, wvl_microns, center_wvl, s_params, sampling_period, order, options=None + ): + if options is None: + self.options = BVF_Options() + else: + self.options = options + + c = 299792458 + self.order = order + self.num_ports = s_params.shape[1] + self.wvl_microns = wvl_microns + self.center_freq = c / (center_wvl * 1e-6) + + self.freqs = c / (wvl_microns * 1e-6) - self.center_freq + + self.sampling_freq = -1 / sampling_period + self.options.beta = np.abs( + self.sampling_freq / (self.freqs[-1] - self.freqs[0]) + ) + + self.poles = np.array([]) + self.residues = np.zeros((order, self.num_ports, self.num_ports), dtype=complex) + + self.digital_freq = 2 * np.pi * self.freqs / (self.sampling_freq) + self.z = np.exp(1j * self.digital_freq) + self.S = s_params + self.error = float("inf") + + self.A = None + self.B = None + self.C = None + self.D = np.zeros((self.num_ports, self.num_ports), dtype=complex) + self.time_response = None + + self.fit_model() + + def initial_poles(self): + digital_freq = ( + 2 + * np.pi + * np.linspace(np.min(self.freqs), np.max(self.freqs), self.order) + / self.sampling_freq + ) + return self.options.gamma * np.exp(1j * digital_freq) + + def compute_phi_matrices(self): + phi1 = 1 / (self.z[0] - self.poles) + for z in self.z[1:]: + phi1 = np.vstack((phi1, 1 / (z - self.poles))) + + unity_column = np.ones((len(self.z), 1)) + phi0 = np.hstack((unity_column, phi1)) + + return phi0, phi1 + + def fit_model(self): + self.poles = self.initial_poles() + + iter = 1 + while iter < self.options.max_iterations: + phi0, phi1 = self.compute_phi_matrices() + if self.options.mode == "Fast": + M, B = self.compute_lstsq_matrices(phi0, phi1) + weights, _, _, _ = np.linalg.lstsq(M, B) + weights_row = weights.reshape((len(weights), 1)) + unity_column = np.ones((self.order, 1)) + + A = np.diag(self.poles) + self.poles, _ = np.linalg.eig(A - unity_column @ weights_row.T) + mask = np.abs(self.poles) > 1 + self.poles[mask] = 1 / (self.poles[mask]) + + else: + M, V = self.compute_lstsq_matrices(phi0, phi1) + solutions, _, _, _ = np.linalg.lstsq(M, V, rcond=None) + + # Calculate New Poles + A = np.diag(self.poles) + weight_coefficients = solutions[ + (self.num_ports**2) * (self.order + 1) : + ] + weights_row = weight_coefficients.reshape((len(weight_coefficients), 1)) + unity_column = np.ones((self.order, 1)) + self.poles, _ = np.linalg.eig(A - unity_column @ weights_row.T) + mask = np.abs(self.poles) > 1 + self.poles[mask] = 1 / (self.poles[mask]) + + if True: + for i in range(self.num_ports): + for j in range(self.num_ports): + phi0, _ = self.compute_phi_matrices() + # Q,R = np.linalg.qr(phi0,mode='reduced') + # solutions = np.linalg.pinv(R)@Q.conj().T@self.S[:, i, j] + solutions, _, _, _ = np.linalg.lstsq( + phi0, self.S[:, i, j], rcond=None + ) + self.D[i, j] = np.array(solutions[0]) + self.residues[:, i, j] = solutions[1:] + + iter += 1 + + def compute_response(self, a, b, wvl=None): + if wvl is None: + wvl = self.wvl_microns + + c = 299792458 + freq = c / (wvl * 1e-6) - self.center_freq + digital_freq = 2 * np.pi * freq / self.sampling_freq + z = np.exp(1j * digital_freq) + response = np.stack([self.D] * (z.shape[0])) + for r, p in zip(self.residues[:, b, a], self.poles[:]): + response[:, b, a] += r / (z - p) + + return response[:, b, a] + + def baseband_transfer_function(self, f): + H = jnp.zeros((f.shape[0], self.num_ports, self.num_ports), dtype=complex) + H = H + self.D + + z = jnp.exp(2j * jnp.pi * f / self.sampling_freq) + for r, p in zip(self.residues, self.poles): + H += r / (z - p)[:, None, None] + + return H + + def discrete_time_impulse_response(self, N=1600): + num_ports = self.num_ports + h = np.zeros([N, num_ports, num_ports], dtype=complex) + + poles = self.poles + + for a in range(num_ports): + for b in range(num_ports): + residues = self.residues[:, a, b] + dt = np.abs(1 / self.sampling_freq) + t = np.linspace(0, dt * N, N) + + h[0, a, b] = self.D[a, b] + for n in range(1, t.shape[0]): + for p, r in zip(poles, residues): + h[n, a, b] += r * (p ** (n - 1)) + + return h + + def plot(self, modes=None): + if modes is None or modes == "all": + n = self.num_ports + fig, ax = plt.subplots(n, n, figsize=(10, 10)) + for i in range(n): + for j in range(n): + ax[i, j].plot(np.abs(self.compute_response(i, j)) ** 2) + ax[i, j].plot(np.abs(self.S[:, i, j]) ** 2, "r--") + ax[i, j].set_ylim([-1.0, 2.0]) + plt.show() + else: + n = len(modes) + for mode in modes: + plt.plot(np.abs(self.compute_response(mode[0], mode[1])) ** 2) + plt.plot(np.abs(self.S[:, mode[0], mode[1]]) ** 2, "r--") + plt.show() + + def compute_error(self): + return np.max(np.abs(self.S - self.compute_response())) + + def compute_time_response(self, sig=None, t=None): + sys = self.generate_sys_discrete() + + if t is None: + N = int(1000) + T = 2e-11 + t = np.linspace(0, T, N) + + if sig is None: + sig = np.exp(1j * 2 * np.pi * t * 0) + + sig = sig.reshape(-1, 1) + impulse = np.hstack([np.real(sig), np.imag(sig)]) + + t_out, yout, _ = dlsim(sys, impulse, t) + yout = yout[:, 0] + 1j * yout[:, 1] + self.time_response = (t_out, yout) + + return t_out, yout + + # def compute_lstsq_matrices(self, phi0, phi1): + + def compute_lstsq_matrices(self, phi0, phi1): + if self.options.mode == "Fast": + M = np.zeros( + ((self.num_ports**2) * self.order, self.order), dtype=complex + ) + B = np.zeros(((self.num_ports**2) * self.order), dtype=complex) + iter = 0 + for i in range(self.num_ports): + for j in range(self.num_ports): + D = np.diag(self.S[:, i, j]) + A1 = phi0 + A2 = -D @ phi1 + # Here we perform the modified gram schmidt orthonalization on the block matrix [A1, A2] described here: + # https://arxiv.org/pdf/2208.06194 + # This allows us to implement the Fast Vector Fitting algorithm: + # https://scholar.googleusercontent.com/scholar?q=cache:u4aY-dn1tF8J:scholar.google.com/+piero+triverio+vector+fitting&hl=en&as_sdt=0,45 + + Q1, _ = np.linalg.qr(A1) + R12 = Q1.conj().T @ A2 + Q2, R22 = np.linalg.qr(A2 - Q1 @ R12) + + # Q_total = np.block([Q1, Q2]) + # R_total = np.block([[R11, R12], [np.zeros((50, 51)), R22]]) + + V = self.S[:, i, j] + M[(iter) * self.order : (iter + 1) * self.order, :] = R22 + B[(iter) * self.order : (iter + 1) * self.order] = Q2.conj().T @ V + iter += 1 + # plt.imshow(np.abs(M)) + # plt.show() + # plt.plot(np.abs(B)) + # plt.show() + + return M, B + else: + D = [] + V = [] + for i in range(self.num_ports): + for j in range(self.num_ports): + D.append(np.diag(self.S[:, i, j])) + V.append(self.S[:, i, j]) + + phi_column = [] + for _D in D: + phi_column.append(-_D @ phi1) + + blocks = [phi0] * (self.num_ports * self.num_ports) + M = block_diag(*blocks) + + phi_column = np.vstack(phi_column) + M = np.hstack([M, phi_column]) + + return M, np.hstack(V) + + def generate_sys_discrete(self): + A = np.zeros( + (self.order * self.num_ports, self.order * self.num_ports), dtype=complex + ) + B = np.zeros((self.order * self.num_ports, self.num_ports), dtype=complex) + C = np.zeros((self.num_ports, self.order * self.num_ports), dtype=complex) + for i in range(self.order): + A[ + i * self.num_ports : (i + 1) * self.num_ports, + i * self.num_ports : (i + 1) * self.num_ports, + ] = self.poles[i] * np.eye(self.num_ports) + B[i * self.num_ports : (i + 1) * self.num_ports, :] = np.eye(self.num_ports) + C[:, i * self.num_ports : (i + 1) * self.num_ports] = self.residues[i, :, :] + + return StateSpace(A, B, C, self.D, dt=np.abs(1 / self.sampling_freq)) + + # _A = [] + # _B = [] + # _C = [] + # for p in self.poles: + # A_n = np.diag(np.full(self.num_ports, p)) + # _A.append(A_n) + + # for n in range(self.order): + # U, S, Vh = np.linalg.svd(self.residues[n, :, :]) + # _B.append(Vh) + # _C.append(U@np.diag(S)) + + # A = block_diag(*_A) + # B = np.vstack(_B) + # C = np.hstack(_C) + + # D = self.D + + # return StateSpace(A, B, C, self.D, dt = np.abs(1/self.sampling_freq)) + + def plot_time_response(self): + if self.time_response is None: + self.compute_time_response() + + t_out, yout = self.time_response + + plt.title("Time Response") + plt.xlabel("Time") + plt.ylabel("E-field Amplitude") + plt.plot(t_out, np.abs(yout) ** 2) + plt.axvline(x=0.59e-12, color="r", linestyle="--", linewidth=1, alpha=0.75) + + def plot_poles(self): + fig, ax = plt.subplots(subplot_kw={"projection": "polar"}) + initial_poles = self.initial_poles() + ax.set_rticks([0.2, 0.4, 0.6, 0.8, 1.0]) + ax.set_thetagrids([0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330]) + ax.set_rlim(0.0, 1.0) + ax.set_rlabel_position(0) + ax.grid(True) + + # Plot Initial Poles + ax.scatter( + np.angle(initial_poles), np.abs(initial_poles), label="Initial Poles" + ) + + # Plot Current Poles + ax.scatter(np.angle(self.poles), np.abs(self.poles), label="Optimal Poles") + + plt.show() diff --git a/simphony/time_domain/quantum.py b/simphony/time_domain/quantum.py new file mode 100644 index 00000000..e69de29b diff --git a/simphony/time_domain/quicker_delete.py b/simphony/time_domain/quicker_delete.py new file mode 100644 index 00000000..e5814cb6 --- /dev/null +++ b/simphony/time_domain/quicker_delete.py @@ -0,0 +1,226 @@ +import os +import sys +import time + +import numpy as np + +# jax.config.update("jax_log_compiles", True) # Use double precision + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "simphony"))) +import jax.numpy as jnp +import sax + +from simphony.libraries import siepic +from simphony.time_domain.pole_residue_model import BVF_Options, IIRModelBaseband +from simphony.time_domain.time_system import TimeSystemIIR +from simphony.time_domain.utils import smooth_rectangular_pulse +from simphony.utils import dict_to_matrix + +# ── your original step function ─────────────────────────────────────────────── +netlist = { + "instances": { + "wg1": "waveguide", + "wg2": "waveguide", + "yb1": "y_branch", + "yb2": "y_branch", + }, + "connections": { + "wg1,o0": "yb1,port_2", + "wg2,o0": "yb1,port_3", + "wg2,o1": "yb2,port_2", + "wg1,o1": "yb2,port_3", + }, + "ports": { + "o0": "yb1,port_1", + "o1": "yb2,port_1", + }, +} +T = 100e-11 +dt = 1e-14 # Time step/resolution +t = jnp.arange(0, T, dt) +num_measurements = 200 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = { + "wl": wvl, + "wg1": {"length": 10.0}, + "wg2": {"length": 50.0}, + "wg3": {"length": 50.0}, + "wg4": {"length": 50.0}, + "wg5": {"length": 50.0}, + "wg6": {"length": 50.0}, + "wg7": {"length": 50.0}, + "wg8": {"length": 50.0}, + "wg9": {"length": 50.0}, + "wg10": {"length": 50.0}, +} +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, +} + +inputs = { + "o0": smooth_rectangular_pulse(t, 0.0, T + 20.0e-11), + "o1": jnp.zeros_like(t), +} + +ports = sorted(inputs.keys(), key=lambda k: int(k[1:])) +signals = [inputs[p] for p in ports] +u = jnp.stack(signals, axis=1) + + +inputs_per_t = tuple(tuple(u[t].tolist()) for t in range(u.shape[0])) + +circuit, _ = sax.circuit( + netlist=netlist, + models=models, +) + +s_params_dict = circuit(**options) +s_matrix = np.asarray(dict_to_matrix(s_params_dict)) +center_wvl = 1.55 +c_light = 299792458 +center_freq = c_light / (center_wvl * 1e-6) +freqs = c_light / (wvl * 1e-6) - center_freq +sampling_freq = -1 / dt +beta = sampling_freq / (freqs[-1] - freqs[0]) +bvf_options = BVF_Options(beta=beta) +sorted_ports = sorted(netlist["ports"].keys(), key=lambda p: int(p.lstrip("o"))) + +iir_model = IIRModelBaseband(wvl, center_wvl, s_matrix, order=50, options=bvf_options) +iir_model2 = IIRModelBaseband(wvl, center_wvl, s_matrix, order=50, options=bvf_options) +iir_model3 = IIRModelBaseband(wvl, center_wvl, s_matrix, order=50, options=bvf_options) +iir_model4 = IIRModelBaseband(wvl, center_wvl, s_matrix, order=50, options=bvf_options) + +N_STEPS = len(t) +td1 = TimeSystemIIR(iir_model, sorted_ports) +td2 = TimeSystemIIR(iir_model2, sorted_ports) +td3 = TimeSystemIIR(iir_model3, sorted_ports) +td4 = TimeSystemIIR(iir_model4, sorted_ports) +systems = (td1, td2, td3, td4) +initial_state1 = td1.init_state() +initial_state2 = td2.init_state() +initial_state3 = td3.init_state() +initial_state4 = td4.init_state() +n_runs = len(t) + +jitted_step1 = td1.step +jitted_step2 = td2.step +jitted_step3 = td3.step +jitted_step4 = td4.step + +x_warm1, y_warm1 = jitted_step1(initial_state1, inputs_per_t[0]) +x_warm2, y_warm2 = jitted_step2(initial_state2, inputs_per_t[0]) +x_warm3, y_warm3 = jitted_step3(initial_state3, inputs_per_t[0]) +x_warm4, y_warm4 = jitted_step4(initial_state4, inputs_per_t[0]) +x_warm1.block_until_ready() +x_warm2.block_until_ready() +x_warm3.block_until_ready() +x_warm4.block_until_ready() +_ = td1.step(initial_state1, inputs_per_t[0]) +_ = td2.step(initial_state2, inputs_per_t[0]) +_ = td3.step(initial_state3, inputs_per_t[0]) +_ = td4.step(initial_state4, inputs_per_t[0]) + +# Your connection definition +connections = [ + (("port_in", "o0"), ("td1", "o0")), + (("td1", "o1"), ("td2", "o0")), + (("td2", "o1"), ("td3", "o0")), + (("td3", "o1"), ("td4", "o0")), + (("td4", "o1"), ("port_out", "o1")), +] + +# Create a unique set of all (system, port) pairs +nodes = set() +for a, b in connections: + nodes.add(a) + nodes.add(b) + +# Initialize a table to hold values at each port/node +# You can initialize to None, zeros, or whatever is appropriate +table = {node: None for node in nodes} + +# Example: Put a value at port_in o0 +table[("port_in", "o0")] = 42 + +# Pass values along the connections +for a, b in connections: + # For demonstration, pass value from a to b + table[b] = table[a] + # You could add processing, e.g., apply a function + +inner_states = { + "td1": initial_state1, + "td2": initial_state2, + "td3": initial_state3, + "td4": initial_state4, +} +systems = { + "td1": td1, + "td2": td2, + "td3": td3, + "td4": td4, +} +# All unique system/port pairs +system_port_labels = {} +for sys_name, sys_obj in systems.items(): + # Assuming sys_obj has .outputs or just 2 outputs (o0, o1) for simplicity + system_port_labels[sys_name] = [f"o{i}" for i in range(sys_obj.sys.D.shape[0])] + # Or hardcode: ["o0", "o1"] if always two outputs + +# The value table +table = {} +# Bi-directional lookup +bi_lookup = {} +for a, b in connections: + bi_lookup[a] = b + bi_lookup[b] = a +jit_steppers = { + "td1": jitted_step1, + "td2": jitted_step2, + "td3": jitted_step3, + "td4": jitted_step4, +} + +# Initialization (above) +nodes = set() +for a, b in connections: + nodes.add(a) + nodes.add(b) +table = {node: 0 for node in nodes} + +# Simulation loop +x = initial_state1 +t0 = time.perf_counter() +for step, inp in enumerate(inputs_per_t): + # Update input port for current step + table[("port_in", "o0")] = inp[0] # or use the correct input index + + for key, system in systems.items(): + x, out_tuple = jit_steppers[key](inner_states[key], inp) + inner_states[key] = x + x.block_until_ready() + for port_label, out_val in zip(system_port_labels[key], out_tuple): + table[(key, port_label)] = out_val + + for src, dst in connections: + table[dst] = table[src] + + # Optionally accumulate or print lookup times here + +t1 = time.perf_counter() +dispatch_plus_compute = (t1 - t0) / n_runs + +microseconds = dispatch_plus_compute * 1e6 +print(f"Total time per run: {microseconds:.6f} μs") + +# ── 2) Dispatch + Compute ─────────────────────────────────────────────────── +x = initial_state1 +t0 = time.perf_counter() +for inp in inputs_per_t: + x, _ = jitted_step1(x, inp) + x.block_until_ready() +t1 = time.perf_counter() +dispatch_plus_compute = (t1 - t0) / n_runs + +print(f"avg jitted loop (per-step) : {dispatch_plus_compute*1e6:7.2f} µs") diff --git a/simphony/time_domain/simulation.py b/simphony/time_domain/simulation.py new file mode 100644 index 00000000..d75f0cec --- /dev/null +++ b/simphony/time_domain/simulation.py @@ -0,0 +1,1305 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +import sax +from jax import config, jit, lax +from numpy.typing import ArrayLike +from scipy.interpolate import interp1d + +from simphony.exceptions import UndefinedActiveComponent +from simphony.simulation import Simulation, SimulationResult +from simphony.time_domain.pole_residue_model import BVF_Options, IIRModelBaseband +from simphony.time_domain.time_system import ( + BlockModeComponent, + SampleModeComponent, + TimeSystem, + TimeSystemIIR, +) +from simphony.utils import SPEED_OF_LIGHT, dict_to_matrix + +config.update("jax_enable_x64", True) + +if TYPE_CHECKING: + from simphony.circuit.circuit import Circuit + + +@dataclass +class TimeResult(SimulationResult): + """Stores and manages the results of a time-domain photonic simulation. + + **User-Facing**: Typically, you'll create and obtain an instance of TimeResult + from the TimeSim.run(...) function, then call plot_sim() if needed. + + Attributes: + outputs (ArrayLike): Dictionary mapping output port names to their respective time-domain signals. + t (ArrayLike): Time array used in the simulation. + inputs (ArrayLike): Dictionary mapping input port names to their respective time-domain signals. + S_params (ArrayLike): The S-parameter matrix (or list of matrices for sub-circuits) used in the simulation. + """ + + outputs: ArrayLike + t: ArrayLike + inputs: ArrayLike + S_params: ArrayLike + + def plot_sim(self) -> None: + """Plots the intensity of each port's input and output signals over + time, aligned so that input and output for the same port share a + row.""" + # Find ports present in both inputs and outputs + ports = [k for k in self.inputs.keys() if k in self.outputs] + if not ports: + raise ValueError("No matching ports found in inputs and outputs.") + + n = len(ports) + fig, axs = plt.subplots(n, 2, figsize=(10, 3 * n), squeeze=False) + + for i, key in enumerate(ports): + # input intensity + axs[i, 0].plot(self.t, jnp.abs(self.inputs[key]) ** 2) + axs[i, 0].set_title(f"Input Signal {key}") + axs[i, 0].set_xlabel("Time (s)") + axs[i, 0].set_ylabel("Intensity") + + # output intensity + axs[i, 1].plot(self.t, jnp.abs(self.outputs[key]) ** 2) + axs[i, 1].set_title(f"Output Signal {key}") + axs[i, 1].set_xlabel("Time (s)") + axs[i, 1].set_ylabel("Intensity") + + plt.tight_layout() + plt.show() + + +class TimeSim(SampleModeComponent, BlockModeComponent, Simulation): + """A class for time-domain photonic circuit simulation, allowing for both + passive and active components. + + **User-Facing**: Typical usage involves: + 1) Initializing with a netlist and component models (`__init__`). + 2) Building the model (`build_model`). + 3) Running the simulation (`run`), which returns a TimeResult object. + + Internally, if active components are detected, the netlist is decomposed into + passive sub-circuits. Each passive sub-circuit is converted into an IIR time-domain + model. Active components are then iterated in the time-domain, step-by-step, + alongside these passive sub-circuits. + + Attributes: + netlist (dict): Dictionary describing the devices and how they are interconnected. + component_models (dict): Dictionary mapping component names to their frequency-domain models. + active_components (set): Names of active components in the netlist. + """ + + def __init__( + self, + netlist: dict, + models: dict, + settings: dict = None, + mode: str = "sample", + # wvl: np.ndarray = np.linspace(1.5, 1.6, 200), + # center_wvl: float = 1.55, + # model_order: int = 50, + # dt: float = 1e-14, + # suppress_output: bool = False + ): + """Initializes the TimeSim object with a netlist, a dictionary of + models, and an optional set of active components. + + Args: + netlist (dict): Dictionary containing 'instances', 'connections', and 'ports'. + component_models (dict): Dictionary mapping component names to corresponding models. + active_components (set, optional): Names of active components in the netlist. Defaults to None. + """ + super().__init__() + self.netlist = netlist + self.models = models + self.time_system_components = [] + # for model in self.models: + # if isinstance(self.models[model], TimeSystem): + # for instance in self.netlist["instances"]: + # if self.netlist["instances"][instance] == model: + # if instance not in self.time_system_components: + # self.time_system_components.append(instance) + # if instance not in self.time_system_components: + # self.time_system_components.add(instance) + for instance, model in self.netlist["instances"].items(): + if isinstance(self.models[model], TimeSystem): + self.time_system_components.append(instance) + + self.mode = mode # Default mode, can be changed to "block" if needed + + # Extract netlist info for convenience + self.instances = netlist["instances"] + self.connections = netlist["connections"] + self.ports = list(netlist["ports"].keys()) + + # Internal placeholders for models, S-parameters, and time stepping + self.dt = None + self.passive_subnetlists = None + self.removed_connections = None + self.removed_ports = None + self.passive_reconnections = None + self.S_params_dict = None + self._scan_jit = None # compiled lax.scan + self._prepared_maps = False + + # Holds the final time-domain netlist after re-wiring sub-circuits and active comps + self.td_netlist = {"models": {}, "connections": {}, "ports": {}} + + # Sub-circuit time-domain models (passive only) + self.subcircuit_time_systems = [] + + # For storing signals during step-by-step simulation + self.inputs = {} + self.outputs = {} + self.instance_outputs = {} + self.t = None + self.model_settings = settings + + def set_mode(self, mode: str): + if mode not in {"sample", "block"}: + raise ValueError(f"Invalid mode: {mode}. Must be 'sample' or 'block'.") + self.mode = mode + + def build_model( + self, + # wvl: np.ndarray = np.linspace(1.5, 1.6, 200), + center_wvl: float = 1.55, + model_order: int = 50, + model_settings: dict = None, + dt: float = 1e-14, + suppress_output: bool = False, + ) -> None: + """Builds or configures the underlying IIR model(s) for the circuit. + + **User-Facing**: This is typically the second step (after __init__). + If active components are present, the netlist is decomposed into + passive sub-circuits, each turned into an IIR model. Active components + connect to these sub-circuits at designated ports. + + Args: + wvl (np.ndarray): Wavelength array for frequency-domain model generation. + center_wvl (float): Center wavelength for frequency shift. + model_order (int): The order of the IIR approximation. + model_parameters (dict): Parameters to pass into the frequency-domain model evaluation. + dt (float): Simulation time step. + max_size (int): Maximum size of any strongly-connected sub-circuit (for splitting). + suppress_output (bool): If True, suppresses printing of intermediate results. + """ + self.dt = dt + wvl = model_settings["wl"] + c_light = 299792458 + center_freq = c_light / (center_wvl * 1e-6) + freqs = c_light / (wvl * 1e-6) - center_freq + + # Beta is used for the broadband vector fitting (BVF) + sampling_freq = -1 / dt + beta = sampling_freq / (freqs[-1] - freqs[0]) + bvf_options = BVF_Options(beta=beta) + self.suppress_output = suppress_output + + # If active components exist, break out passive sub-circuits + if self.time_system_components is not None: + ( + self.passive_subnetlists, + self.removed_connections, + self.removed_ports, + ) = self.create_passive_sub_netlists( + self.instances, + self.connections, + self.time_system_components, + ) + if self.passive_subnetlists: + if not self.suppress_output: + # Print the passive sub-netlists for debugging/logging + for i, sub_net in enumerate(self.passive_subnetlists): + print(f"\n--- Passive Sub-Netlist {i} ---") + print("\nInstances:", sub_net["instances"]) + print("\nConnections:", sub_net["connections"]) + print("\nPorts:", sub_net["ports"]) + print() + + # Build frequency-domain circuits via SAX and convert to time-domain models + sub_circuit_list = {} + port_map_list = {} + + try: + for i, sub_net in enumerate(self.passive_subnetlists): + # Create circuit with sax + circuittemp, _ = sax.circuit( + netlist=sub_net, + models=self.models, + ) + sub_circuit_list[i] = circuittemp + port_map_list[i] = sub_net["ports"] + + except TypeError as originalerror: + # This generally happens if an active component is incorrectly included + # in a subcircuit that should be purely passive + raise UndefinedActiveComponent( + "Active component is included in a passive-only circuit" + ) from originalerror + + # Evaluate S-parameters and build time-domain IIR models + self.S_params_dict = {} + for i, circuit in sub_circuit_list.items(): + s_params_dict = circuit(**model_settings) + s_matrix = np.asarray(dict_to_matrix(s_params_dict)) + self.S_params_dict[i] = s_matrix + + sorted_ports = sorted(port_map_list[i].keys()) + + iir_model = IIRModelBaseband( + wvl, center_wvl, s_matrix, model_order, options=bvf_options + ) + td_system = TimeSystemIIR(iir_model, sorted_ports) + self.subcircuit_time_systems.append(td_system) + + # Connect the newly created sub-circuits and the active components + # in a consolidated time-domain netlist (self.td_netlist) + self.prepare_time_domain_netlist(port_map_list) + else: + # If no passive sub-circuits, we can directly use the netlist + self.td_netlist["connections"] = self.netlist["connections"] + self.td_netlist["ports"] = self.netlist["ports"] + for instance, model in self.netlist["instances"].items(): + if model in self.models: + self.td_netlist["models"][instance] = self.models[model] + + else: + circuit, _ = sax.circuit(netlist=self.netlist, models=self.models) + fd_params = circuit(**model_settings) + s_matrix = np.asarray(dict_to_matrix(fd_params)) + self.S_params_dict = s_matrix + + single_iir_model = IIRModelBaseband( + wvl, center_wvl, s_matrix, model_order, options=bvf_options + ) + self.time_system = TimeSystemIIR(single_iir_model) + + def run(self, t: ArrayLike, input_signals: dict, **kwargs): + self.carrier_freq = kwargs["carrier_freq"] + self.dt = kwargs["dt"] + + if self.mode == "sample": + return self._sample_mode_run(t, input_signals) + elif self.mode == "block": + return self._block_mode_run(t, input_signals) + + def init_state(self, **kwargs): + """ + Exactly as before: build inst‐outs = zeros, states = tuple(sys.init_state()) + and store in self._step_carry. + """ + self.carrier_freq = kwargs["carrier_freq"] + self.dt = kwargs["dt"] + + self.build_model( + SPEED_OF_LIGHT / self.carrier_freq * 1e6, + 50, + self.model_settings, + self.dt, + False, + ) + + if not self._prepared_maps: + self._prepare_static_maps() + self._scan_jit = jit(self._scan_loop, static_argnums=(3, 4, 5, 6)) + self._prepared_maps = True + + n_inst = len(self._systems) + max_ports = self._max_ports + + init_inst = jnp.zeros((n_inst, max_ports, 1), dtype=jnp.complex128) + init_states = self.build_initial_states() # tuple of length = n_inst + + # Store “carry = (inst_outs, states)” for step‐by‐step mode + return (init_inst, init_states) + + def _sample_mode_run( + self, t: ArrayLike, input_signals: dict + ) -> TimeResult: # noqa: D401,E501 + """Run the simulation and return a **TimeResult** object.«""" # noqa: E501 + # 0) Resample inputs to internal time grid + self.t = t + self.inputs = input_signals + self.t, self.inputs = self.interpolate_inputs() + self.build_model( + SPEED_OF_LIGHT / self.carrier_freq * 1e6, + 50, + self.model_settings, + self.dt, + False, + ) + + # 1) Build static, hashable wiring maps once per build_model() + if not self._prepared_maps: + self._prepare_static_maps() + # Compile scan with hashable (tuple) static args + self._scan_jit = jit(self._scan_loop, static_argnums=(4, 5, 6, 7)) + # self._scan_jit = self._scan_loop + self._prepared_maps = True + + # 2) Pack top‑level inputs into a single JAX array (T × nTop) + top_inputs = jnp.stack( + [jnp.asarray(self.inputs[p]) for p in self._port_order], + axis=1, + dtype=jnp.complex128, + ) + n_steps = top_inputs.shape[0] + + # 3) Allocate initial carry arrays (all zeros) + n_inst = len(self._systems) + max_ports = self._max_ports + init_inst = jnp.zeros((n_inst, max_ports, 1), dtype=jnp.complex128) + init_outs = jnp.zeros( + (n_steps, len(self._output_specs), 1), dtype=jnp.complex128 + ) + init_states = self.build_initial_states() + # 4) Execute the fused lax.scan + final_outs = self._scan_jit( + top_inputs, + init_inst, + init_outs, + init_states, + self._systems, + self._active_mask, + self._input_specs, + self._output_specs, + ) + + # 5) Convert back to dict for TimeResult + self.outputs = {p: final_outs[:, j, 0] for j, p in enumerate(self._port_order)} + + return TimeResult( + outputs=self.outputs, + t=self.t, + inputs=self.inputs, + S_params=self.S_params_dict, + ) + + def _block_mode_run(self, t: ArrayLike, input_signals: dict) -> TimeResult: + # -------------------------------------------------------------- + # Turn self.td_netlist into a Networkx Graph + # -------------------------------------------------------------- + + # # Add nodes for models + # for model in self.td_netlist['models']: + # graph.add_node(model, type='model') + + # # Add edges for internal connections + # for from_port, to_port in self.td_netlist['connections'].items(): + # from_node, from_p = from_port.split(',') + # to_node, to_p = to_port.split(',') + # graph.add_edge(from_node, to_node, from_port=from_p, to_port=to_p) + + # # Add nodes and edges for external ports + # for ext_port, internal in self.td_netlist['ports'].items(): + # internal_node, internal_port = internal.split(',') + # port_node = f"ext_{ext_port}" + # graph.add_node(port_node, type='port') + # graph.add_edge(port_node, internal_node, to_port=internal_port) + + # -------------------------------------------------------------- + # Determine which exposed ports are Inputs and which are Outputs + # -------------------------------------------------------------- + + pass + + # ------------------------------------------------------------------ + # HELPER: build immutable/tuple wiring maps (hashable for jit) + # ------------------------------------------------------------------ + def _prepare_static_maps(self): + """Derive tuple‑based wiring tables from the current td_netlist.""" + # Port order (deterministic) + self._port_order = tuple(self.td_netlist["ports"].keys()) + + # Flatten model dict ➜ ordered tuples + items = list(self.td_netlist["models"].items()) + self._names = tuple(n for n, _ in items) + self._systems = tuple(m for _, m in items) + self._inst_idx = {n: i for i, n in enumerate(self._names)} + self._active_mask = tuple( + n in (self.time_system_components or set()) for n in self._names + ) + self._max_ports = ( + max(len(s.ports) for s in self._systems) if self._systems else 0 + ) + + # Input specs (padded later at runtime) + rows = [] + for name, sys in items: + row = [] + for p in sys.ports: + key_check = f"{name},{p}" + for key, value in self.td_netlist["connections"].items(): + if key == key_check: + src = self.td_netlist["connections"][key] + si, sp = (x.strip() for x in src.split(",")) + row.append( + ( + 0, + self._inst_idx[si], + self._systems[self._inst_idx[si]].ports.index(sp), + ) + ) + break + if value == key_check: + src = key + si, sp = (x.strip() for x in src.split(",")) + row.append( + ( + 0, + self._inst_idx[si], + self._systems[self._inst_idx[si]].ports.index(sp), + ) + ) + break + + else: + for tp_i, tp in enumerate(self._port_order): + inst_name, tgt = self.td_netlist["ports"][tp].split(",") + if inst_name.strip() == name and tgt.strip() == p: + row.append((1, tp_i)) + break + else: + row.append((2,)) + rows.append(tuple(row)) + self._input_specs = tuple(rows) + + # Output specs aligned with _port_order + outs = [] + for tp in self._port_order: + inst, prt = (x.strip() for x in self.td_netlist["ports"][tp].split(",")) + outs.append( + ( + self._inst_idx[inst], + self._systems[self._inst_idx[inst]].ports.index(prt), + ) + ) + self._output_specs = tuple(outs) + + def build_initial_states(self): + """Return a tuple of per‐instance initial states, in the same order as + `self._systems` / `self._names`. + + Stateless components get (). + """ + states_list = [] + for sys in self._systems: + if hasattr(sys, "init_state"): + states_list.append( + sys.init_state(dt=self.dt, carrier_freq=self.carrier_freq) + ) + else: + states_list.append(()) + return tuple(states_list) + + def _scan_loop( + self, + top_inputs: jnp.ndarray, # (T, nTop) + inst0: jnp.ndarray, # (nInst, maxPorts, 1) + outs0: jnp.ndarray, # (T, nTop, 1) + states0: tuple, # <<< now a tuple of length = nInst + systems: tuple, # static tuple of length = nInst + active_mask: tuple, # static tuple[bool] length = nInst + input_specs: tuple, # static tuple-of-tuples, length = nInst + output_specs: tuple, # static tuple-of-(inst_idx, port_idx) + ) -> jnp.ndarray: + """Return stacked outputs (T, nTop, 1). + + Handles heterogeneous port counts by zero-padding to *max_ports* + before stacking. + """ + n_steps = top_inputs.shape[0] + max_ports = inst0.shape[1] # compile-time constant + n_inst = len(systems) # number of instances + + def step(carry, t_idx): + # Unpack the carry triple: (inst_outs, outs, states_tuple) + inst_outs, outs, states = carry + # inst_outs: (nInst, maxPorts, 1) + # outs: (T, nTop, 1) + # states: tuple of length = nInst, each entry is that component’s prev_state + + # --- gather inputs for each instance (pad to max_ports) --- + gathered_rows = [] + for inst_i, sys in enumerate(systems): + vec = [] + for spec in input_specs[inst_i]: + tag = spec[0] + if tag == 0: + # (0, producer_inst_idx, producer_port_idx) + _, si, sp = spec + vec.append(inst_outs[si, sp]) + elif tag == 1: + # (1, top_port_idx) + _, tp_j = spec + vec.append(top_inputs[t_idx, tp_j][None]) + else: + # (2,) → unconnected → zero + vec.append(jnp.zeros((1,), dtype=jnp.complex128)) + + arr = jnp.stack(vec, axis=0) # shape (#local_ports, 1) + arr = jnp.pad( + arr, ((0, max_ports - arr.shape[0]), (0, 0)) + ) # (max_ports, 1) + gathered_rows.append(arr) + + gathered = jnp.stack(gathered_rows, axis=0) # shape (nInst, maxPorts, 1) + + # --- call each model’s `step(...)`, building new_states_list & new_rows_list --- + new_rows_list = ( + [] + ) # for collecting each instance’s padded output (max_ports,1) + new_states_list = [] # for collecting each instance’s new_state + + for inst_i, sys in enumerate(systems): + # Build port_dict exactly as before, but then flatten into input_tuple: + port_dict = {p: gathered[inst_i, k] for k, p in enumerate(sys.ports)} + + # Flatten in the same order as sys.ports → a Python tuple of scalars + input_tuple = tuple(port_dict[p][0] for p in sys.ports) + + # Fetch prev_state from the tuple + prev_state_i = states[inst_i] + + # Call the new `step(...)` API; it returns (new_state_i, local_outputs_tuple) + new_state_i, local_outputs_tuple = sys.step( + prev_state_i, + input_tuple, + dt=self.dt, + carrier_freq=self.carrier_freq, + ) + + # Append the new state to our list + new_states_list.append(new_state_i) + + # Stack & pad the returned local_outputs_tuple, just like before: + arr = jnp.stack( + [jnp.atleast_1d(o) for o in local_outputs_tuple], axis=0 + ) # (#local_ports,1) + arr = jnp.pad( + arr, ((0, max_ports - arr.shape[0]), (0, 0)) + ) # (max_ports,1) + new_rows_list.append(arr) + + # Convert new_states_list → a tuple of length nInst + new_states = tuple(new_states_list) + + # Convert new_rows_list → a single array of shape (nInst, max_ports, 1) + new_inst = jnp.stack(new_rows_list, axis=0) + + # --- scatter to top-level outputs (same as before) --- + new_outs = outs + for top_j, (ii, pp) in enumerate(output_specs): + new_outs = new_outs.at[t_idx, top_j].set(new_inst[ii, pp]) + + # Pack the new carry: (new_inst, new_outs, new_states) + new_carry = (new_inst, new_outs, new_states) + return new_carry, None + + # Run lax.scan; the only change is we now pass `states0` (a tuple) in the initial carry + (final_inst_outs, final_outs, final_states), _ = lax.scan( + step, (inst0, outs0, states0), jnp.arange(n_steps) + ) + return final_outs # shape (T, nTop, 1) + + ############################################################################ + # INTERNAL METHODS BELOW # + # (Users typically do not need to modify or call these directly.) # + ############################################################################ + + def interpolate_inputs(self) -> tuple: + """Resamples all input signals from the original time array to the new + time array defined by self.dt, using linear interpolation. + + Returns: + tuple: (resampled_time, resampled_inputs) + """ + if self.dt is None: + raise ValueError( + "Time step (dt) not set. Did you forget to call build_model?" + ) + + # Create the new time domain array based on dt + t_new = np.arange(self.t[0], self.t[-1] + self.dt, self.dt) + + new_inputs = {} + for key, values in self.inputs.items(): + interp_func = interp1d( + self.t, values, kind="linear", fill_value="extrapolate" + ) + new_inputs[key] = interp_func(t_new) + + return t_new, new_inputs + + def step(self, xprev, *inputs_tuple, **kwargs): + inst_outs, states = xprev + n_inst = len(self._systems) + max_ports = self._max_ports + inputs = jnp.stack(*inputs_tuple, axis=0) # = len(self._port_order) + + # Wrap the existing logic into a “body” that matches lax.scan’s (carry, x) → (new_carry, y) + def _one_step_body(carry, top_in): + """ + carry = (prev_inst_outs, prev_states) + top_in = a single array of shape (nTop,) + returns ((new_inst_outs, new_states), out_row) + """ + ( + inst_outs, + states, + ) = carry # inst_outs: (nInst,max_ports,1); states: tuple length=nInst + + # ----------------------------- + # (1) Gather each subsystem’s inputs into a (nInst,max_ports,1) array + # ----------------------------- + gathered_rows = [] + for inst_i, sys in enumerate(self._systems): + vec = [] + for spec in self._input_specs[inst_i]: + tag = spec[0] + if tag == 0: + # (0, producer_inst_idx, producer_port_idx) + _, si, sp = spec + vec.append(inst_outs[si, sp]) + elif tag == 1: + # (1, top_port_idx) + _, tp_j = spec + vec.append(top_in[tp_j][None]) # wrap scalar → (1,) + else: + # (2,) unconnected + vec.append(jnp.zeros((1,), dtype=jnp.complex128)) + + arr = jnp.stack(vec, axis=0) # (n_local_ports, 1) + pad_amt = max_ports - arr.shape[0] + arr = jnp.pad(arr, ((0, pad_amt), (0, 0))) # (max_ports, 1) + gathered_rows.append(arr) + + gathered = jnp.stack(gathered_rows, axis=0) # (nInst, max_ports, 1) + + # ----------------------------- + # (2) Call each sys.step(prev_state, *input_tuple) via Python‐loop over instances + # ----------------------------- + new_rows_list = [] + new_states_list = [] + for inst_i, sys in enumerate(self._systems): + prev_state_i = states[inst_i] + n_local = len(sys.ports) + input_tuple = tuple(gathered[inst_i, k, 0] for k in range(n_local)) + new_state_i, local_outputs = sys.step( + prev_state_i, + input_tuple, + dt=self.dt, + carrier_freq=self.carrier_freq, + ) + new_states_list.append(new_state_i) + + out_arr = jnp.stack( + [jnp.atleast_1d(o) for o in local_outputs], axis=0 + ) # (n_local,1) + pad_amt = max_ports - out_arr.shape[0] + out_arr = jnp.pad(out_arr, ((0, pad_amt), (0, 0))) # (max_ports,1) + new_rows_list.append(out_arr) + + new_states = tuple(new_states_list) + new_inst_outs = jnp.stack(new_rows_list, axis=0) # (nInst, max_ports, 1) + + # ----------------------------- + # (3) Scatter to form one top‐level out_row of shape (nTop,) + # ----------------------------- + out_values = [] + for top_j, (ii, pp) in enumerate(self._output_specs): + out_values.append(new_inst_outs[ii, pp, 0]) + out_row = jnp.stack(out_values, axis=0) # (nTop,) + + return (new_inst_outs, new_states), out_row + + # Now run lax.scan for exactly one step. We package top_input_vector into a (1, nTop) array. + carry0 = ( + inst_outs, + states, + ) # carry0: (nInst, max_ports, 1), states: tuple of length nInst + inputs_one = jnp.expand_dims(inputs, axis=0) # shape = (1, nTop) + + (new_inst_outs, new_states), out_seq = lax.scan( + _one_step_body, carry0, inputs_one + ) + # out_seq has shape (1, nTop); we only need out_seq[0] + out_row = out_seq[0] + + # Update the stored carry so that the next call to step(...) picks up from here + self._step_carry = (new_inst_outs, new_states) + return (new_inst_outs, new_states), out_row + + def prepare_time_domain_netlist(self, port_map_list: dict) -> None: + """Constructs self.td_netlist to unify all passive sub-circuits and + active components in a single structure for time stepping. + + Adds references to: + - Passive sub-circuit models in subcircuit_time_systems + - Active components from the original netlist + - Ports and connections that link them + """ + # 1) Insert references to active device models + # (obtained from self.component_models) for removed connections + for conn_tuple in self.removed_connections: + left_str, right_str = conn_tuple + left_comp = left_str.split(",")[0] + right_comp = right_str.split(",")[0] + + if left_comp in self.time_system_components: + if left_comp not in self.td_netlist["models"]: + # Add the active model + active_model_instance = self.instances.get(left_comp) + self.td_netlist["models"][left_comp] = self.models.get( + active_model_instance + ) + + if right_comp in self.time_system_components: + if right_comp not in self.td_netlist["models"]: + # Add the active model + active_model_instance = self.instances.get(right_comp) + self.td_netlist["models"][right_comp] = self.models.get( + active_model_instance + ) + + # 2) Insert the newly built passive sub-circuit time-domain models + for idx, td_system in enumerate(self.subcircuit_time_systems): + self.td_netlist["models"][f"{idx}"] = td_system + + # 3) Re-link connections and ports between sub-circuits and active components + for idx, ports_dict in port_map_list.items(): + for local_port_label, global_port_str in ports_dict.items(): + # Check if this local_port_label was part of removed/active edges + # or if it needs to be a top-level port + subckt_key = f"{idx},{local_port_label}" + + # If global_port_str was among removed active edges, re-connect + if any(global_port_str in pair for pair in self.removed_connections): + for left_str, right_str in self.removed_connections: + if left_str == global_port_str: + self.add_to_time_domain_netlist( + connection=(subckt_key, right_str) + ) + elif right_str == global_port_str: + self.add_to_time_domain_netlist( + connection=(subckt_key, left_str) + ) + else: + # Otherwise, treat this as a top-level port + self.add_to_time_domain_netlist(port=(local_port_label, subckt_key)) + + for k, v in self.actives_removed: + # If the active component was removed, we need to add it back as a port + self.add_to_time_domain_netlist(connection=(k, v)) + for k in self.time_system_components: + # If the active component was not removed, we need to add it back as a port + if k not in self.td_netlist["models"]: + active_model_instance = self.instances.get(k) + if active_model_instance: + self.td_netlist["models"][k] = self.models.get( + active_model_instance + ) + + if self.removed_ports is not None: + for port_label, comp_port_str in self.removed_ports.items(): + self.add_to_time_domain_netlist(port=(port_label, comp_port_str)) + # 4) Add the top-level ports to the netlis + if not self.suppress_output: + # (Optional) Print the final time-domain netlist for debugging + print("\n--- Final Time-Domain Netlist ---") + print("\nModels:", self.td_netlist["models"]) + print("\nConnections:", self.td_netlist["connections"]) + print("\nPorts:", self.td_netlist["ports"]) + print() + + def add_to_time_domain_netlist( + self, + model_name: str = None, + model_type=None, + connection: tuple = None, + port: tuple = None, + ) -> dict: + """Inserts new items into self.td_netlist, which includes: + + - A new time-domain model (model_name, model_type) + - A new connection (connection[0] -> connection[1]) + - A new external port (port_label -> "instance,port") + + Returns the updated netlist dictionary (self.td_netlist). + """ + # Add a new model, if provided + if model_name and model_type: + self.td_netlist["models"][model_name] = model_type + + # Add a new connection, if provided + if connection and len(connection) == 2: + src, dst = connection + self.td_netlist["connections"][src] = dst + + # Add a new port, if provided + if port and len(port) == 2: + port_label, instance_port_str = port + self.td_netlist["ports"][port_label] = instance_port_str + + return self.td_netlist + + ############################ + # NETLIST DECOMPOSITION # + ############################ + + def create_passive_sub_netlists( + self, + instances: dict, + connections: dict, + # ports: dict, + active_components: set, + directed: bool = False, + ) -> tuple: + """Decomposes the netlist into purely passive sub-netlists by: 1) + Removing edges that touch active components. 2) Removing top-level + ports referencing active components. 3) Building a component-level + graph. 4) Finding strongly connected components (SCCs) of that graph + (splitting large SCCs). 5) Building a sub-netlist for each SCC. + + Returns: + tuple: + sub_netlists (list): A list of sub-netlists (each sub-netlist is a dict). + removed_connections (list): Connections removed due to active components. + removed_ports (dict): Ports removed because they referenced active components. + passive_reconnections (list): Info for reconnecting passive components externally. + """ + # 1) Remove edges that touch active components + ( + filtered_connections, + removed_active_edges, + removed_conns, + ) = self.remove_active_edges_and_track_them(connections, active_components) + + # 2) Remove ports that reference active components + filtered_ports, removed_ports = self.remove_ports_to_active( + self.netlist["ports"], active_components + ) + + # 3) Build a graph for the remaining passive components + graph = self.build_component_graph_active( + filtered_connections, + active_components, + removed_active_edges, + directed=directed, + ) + # 4) Find strongly connected components (SCCs) + connected_components = self.tarjan_scc(graph) + + sub_netlists = [] + + # 5) Build a sub-netlist for each SCC + for comp_list in connected_components: + comp_set = set(comp_list) + sub_net = self.build_sub_netlist( + comp_set, + filtered_connections, + filtered_ports, + removed_conns, + removed_ports, + instances, + active_components, + ) + if sub_net["instances"]: + sub_netlists.append(sub_net) + + return sub_netlists, removed_conns, removed_ports + + # 4) Find strongly connected components, splitting if they exceed `max_size` + + def remove_active_edges_and_track_them( + self, connections: dict, active_components: set + ) -> tuple: + """Removes any connection involving at least one active component. + + Returns: + tuple: + (filtered_connections, removed_edges, removed_connections) + + where: + - filtered_connections is the subset of connections that remain purely passive. + - removed_edges is a list of (passive_component, port) that connected to active components. + - removed_connections is the list of removed connections as full strings. + """ + filtered_connections = {} + removed_edges = [] + removed_connections = [] + self.actives_removed = [] + for k, v in connections.items(): + compA, _ = k.split(",") + compB, _ = v.split(",") + + A_is_active = compA in active_components + B_is_active = compB in active_components + + if A_is_active and B_is_active: + # Both components are active => remove the connection + self.actives_removed.append((k, v)) + continue + + if A_is_active and not B_is_active: + removed_connections.append((k, v)) + removed_edges.append((compB, v.split(",")[1])) + elif B_is_active and not A_is_active: + removed_connections.append((k, v)) + removed_edges.append((compA, k.split(",")[1])) + else: + # purely passive => keep + filtered_connections[k] = v + + return filtered_connections, removed_edges, removed_connections + + def remove_ports_to_active(self, ports: dict, active_components: set) -> tuple: + """Removes top-level ports that directly reference an active component. + + Returns: + (filtered_ports, removed_ports) + """ + filtered_ports = {} + removed_ports = {} + + for port_label, comp_port_str in ports.items(): + comp, _ = comp_port_str.split(",") + if comp in active_components: + removed_ports[port_label] = comp_port_str + else: + filtered_ports[port_label] = comp_port_str + + return filtered_ports, removed_ports + + def build_component_graph_active( + self, + connections: dict, + # ports: dict, + active_components: set, + removed_edges: list, + directed: bool = False, + ) -> dict: + """Builds a graph (adjacency list) at the component level from the + netlist. + + Args: + connections (dict): e.g. {'compA,portA': 'compB,portB'}. + ports (dict): top-level ports in the netlist. + active_components (set): Names of active components. + removed_edges (list): Edges that were removed because they connect to active components. + directed (bool): Whether to consider the graph as directed or undirected. + + Returns: + dict: A dictionary {component: [neighbors, ...]} describing the graph. + """ + graph = {} + + def add_edge(a, b): + if a not in graph: + graph[a] = [] + graph[a].append(b) + + # Add edges for each connection + for k, v in connections.items(): + compA, _ = k.split(",") + compB, _ = v.split(",") + add_edge(compA, compB) + if not directed: + add_edge(compB, compA) + + # Ensure each passive component or removed edge is in the graph (even if no connections) + for _, comp_port_str in self.netlist["ports"].items(): + comp, _ = comp_port_str.split(",") + if comp not in graph and comp not in active_components: + graph[comp] = [] + + for edge_comp, _ in removed_edges: + if edge_comp not in active_components and edge_comp not in graph: + graph[edge_comp] = [] + + return graph + + def tarjan_scc(self, graph: dict) -> list: + """Computes strongly connected components (SCCs) for the given graph, + and splits any SCC that exceeds `max_size` by removing selected edges. + + Args: + graph (dict): adjacency list {node: [neighbors]}. + + Returns: + list: A list of SCCs, where each SCC is a list of nodes. + """ + + def compute_scc(g: dict) -> list: + """Standard Tarjan's algorithm to compute SCCs without splitting + large ones.""" + index_counter = [0] + stack = [] + on_stack = set() + indices = {} + lowlinks = {} + sccs = [] + + def strongconnect(node): + indices[node] = index_counter[0] + lowlinks[node] = index_counter[0] + index_counter[0] += 1 + stack.append(node) + on_stack.add(node) + + for w in g.get(node, []): + if w not in indices: + strongconnect(w) + lowlinks[node] = min(lowlinks[node], lowlinks[w]) + elif w in on_stack: + lowlinks[node] = min(lowlinks[node], indices[w]) + + if lowlinks[node] == indices[node]: + comp_scc = [] + while True: + w = stack.pop() + on_stack.remove(w) + comp_scc.append(w) + if w == node: + break + sccs.append(comp_scc) + + for n in g.keys(): + if n not in indices: + strongconnect(n) + return sccs + + sccs = compute_scc(graph) + + return sccs + + def build_sub_netlist( + self, + scc_components, + all_connections, + original_ports, + removed_connections, + removed_ports, + instances, + active_components, + ): + """Construct a single netlist for the given set of SCC components (e.g. + { 'cr1', 'wg1', 'cr2' }). + + 1) Keep only those instances in scc_components (all passive). 2) + Keep only those connections that link two components in + scc_components. 3) Keep original ports referencing these + components. 4) For each removed passive->active edge, if the + passive comp is in scc_components, create a new external port. + """ + # 1) Filter instances + scc_instances = {} + for comp in scc_components: + if comp in instances: + scc_instances[comp] = instances[ + comp + ] # e.g. "waveguide", "y_branch", etc. + + scc_ports = {} + # 2) Filter connections + scc_connections = {} + # reconnect_dict = {} + + port_index_counter = 0 # This counter is used for new external port labels. + + # 3) Retain original top-level ports that reference a component in the SCC + for port_label, comp_port_str in original_ports.items(): + comp, _ = comp_port_str.split(",") + if comp in scc_components: + scc_ports[port_label] = comp_port_str + port_index_counter += 1 + temp_index = 0 + for conn_key, conn_value in all_connections.items(): + compA, _ = conn_key.split(",") + compB, _ = conn_value.split(",") + + if compA in scc_components and compB in scc_components: + # Connection stays within the SCC + scc_connections[conn_key] = conn_value + + # 4) Create external ports for removed passive->active edges if the passive component is in scc_components + aux_port_index = 0 + + for k_string, v_string in removed_connections: + k_comp = k_string.split(",")[0] + v_comp = v_string.split(",")[0] + if k_comp in scc_instances or v_comp in scc_instances: + if ( + any( + k_comp.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ) + and k_comp not in active_components + ): + temp_index = 0 + if k_comp in removed_ports: + for i, j_string in removed_ports.items(): + j_comp = j_string.split(",")[0] + if j_comp == k_comp: + new_label = f"o{port_index_counter}" + temp_index += 1 + port_index_counter += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{k_comp},{k_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{k_comp},{k_string.split(',')[1]}" + break + + else: + new_label = f"o{port_index_counter}" + aux_port_index += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{k_comp},{k_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{k_comp},{k_string.split(',')[1]}" + + if v_comp in active_components: + temp_index = 0 + if any( + v_comp.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ): + for i, j_string in removed_ports.items(): + j_comp = j_string.split(",")[0] + if j_comp == v_comp: + new_label = f"o{port_index_counter}" + port_index_counter += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{k_comp},{k_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{k_comp},{k_string.split(',')[1]}" + break + else: + new_label = f"o{port_index_counter}" + port_index_counter += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{k_comp},{k_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{k_comp},{k_string.split(',')[1]}" + + if k_comp in active_components: + temp_index = 0 + if any( + k_comp.strip() == value.split(",")[0].strip() + for value in removed_ports.values() + ): + for i, j_string in removed_ports.items(): + j_comp = j_string.split(",")[0] + if j_comp == k_comp: + new_label = f"o{port_index_counter}" + temp_index += 1 + port_index_counter += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[ + new_label + ] = f"{v_comp},{v_string.split(',')[1]}" + else: + scc_ports[ + new_label + ] = f"{v_comp},{v_string.split(',')[1]}" + break + else: + new_label = f"o{port_index_counter}" + port_index_counter += 1 + if new_label in scc_ports: + tempcheck = port_index_counter - 1 + while new_label in scc_ports: + new_label = f"o{tempcheck}" + tempcheck -= 1 + scc_ports[new_label] = f"{v_comp},{v_string.split(',')[1]}" + else: + scc_ports[new_label] = f"{v_comp},{v_string.split(',')[1]}" + + sub_netlist = { + "instances": scc_instances, + "connections": scc_connections, + "ports": scc_ports, + } + return sub_netlist + + +class SampleModeSimulation(SampleModeComponent, BlockModeComponent): + """`SampleModeSimulation` runs bidirectional, element-by-element, + simulations on circuits composed of s-parameter elements and/or + `SampleModeComponent` objects. + + Should be used when reflections are not neglible and/or there are + recursive elements within the circuit + + `SampleModeSimulation` objects are compatible with block mode + simulations as well as sample mode simulations, hence a sample mode + simulation is of type `BlockModeComponent` and + `SampleModeComponent`. + """ + + def __init__( + self, + ckt: Circuit, + settings=None, + carrier_freq=None, + center_wl=None, + sampling_freq=None, + dt=None, + ): + # Verify all subcircuits are compatible with sample mode simulations + + # Verify all subcircuits use the same simulation parameters (sampling period, carrier frequency) + + pass + + +class BlockModeSimulation(BlockModeComponent): + """`BlockModeSimulation` runs each unidirectional simulations on circuits + composed of s-parameter elements and/or `BlockModeComponent` objects. + + Should be used when reflections are neglibible and recursive + elements are abstracted with subnetworks. + + Because `BlockModeSimulation` objects cannot be time-stepped, they + CANNOT be used as subcircuits in sample-mode simulations. + """ + + def __init__(): + pass diff --git a/simphony/time_domain/stochastic/__init__.py b/simphony/time_domain/stochastic/__init__.py new file mode 100644 index 00000000..17bfd014 --- /dev/null +++ b/simphony/time_domain/stochastic/__init__.py @@ -0,0 +1,24 @@ +"""Stochastic (Gaussian process) simulation backend for time-domain photonic +circuits.""" + +from simphony.time_domain.stochastic.gaussian_process import ( + autocorrelation_to_covariance, + covariance_blocks_to_matrix, + covariance_matrix_to_blocks, + covariance_to_autocorrelation, + gaussian_process_response, + propagate_autocorrelation, + propagate_mean, + white_noise_covariance, +) + +__all__ = [ + "covariance_to_autocorrelation", + "autocorrelation_to_covariance", + "propagate_mean", + "propagate_autocorrelation", + "gaussian_process_response", + "white_noise_covariance", + "covariance_blocks_to_matrix", + "covariance_matrix_to_blocks", +] diff --git a/simphony/time_domain/stochastic/gaussian_process.py b/simphony/time_domain/stochastic/gaussian_process.py new file mode 100644 index 00000000..33a76b6c --- /dev/null +++ b/simphony/time_domain/stochastic/gaussian_process.py @@ -0,0 +1,362 @@ +"""Gaussian process propagation through causal MIMO LTI systems via the +Papoulis equations. + +Propagates the mean and covariance of a Gaussian random process through a +discrete-time multi-input multi-output LTI system described by its impulse +response. This is the stochastic analog of the pole-residue state-space +simulation in vector_fitting/z_domain.py. + +Shape conventions +----------------- +h : (K, q, m) impulse response, K taps, q outputs, m inputs +mu_x : (T, m) input mean sequence +mu_y : (T, q) output mean sequence +Cx : (T, T, m, m) input covariance blocks, Cx[n1, n2] = Cov(x[n1], x[n2]) +Cy : (T, T, q, q) output covariance blocks +Rxx/Ryy : same shape autocorrelation = covariance + mean outer-product + +Reference +--------- +Papoulis, A., "Probability, Random Variables, and Stochastic Processes," 4th ed., +McGraw-Hill, 2002. See Chapter 12 (response of LTI systems to random inputs). +""" + +import jax +import jax.numpy as jnp + +# --------------------------------------------------------------------------- +# Covariance / autocorrelation transforms +# --------------------------------------------------------------------------- + + +@jax.jit +def covariance_to_autocorrelation(Cx, mu_x): + """ + Rxx[n1, n2] = Cx[n1, n2] + mu_x[n1] ⊗ conj(mu_x[n2]). + + Parameters + ---------- + Cx : jnp.ndarray, shape (T, T, m, m) + Input covariance blocks. + mu_x : jnp.ndarray, shape (T, m) + Input mean sequence. + + Returns + ------- + Rxx : jnp.ndarray, shape (T, T, m, m) + """ + outer = jnp.einsum("ti,sj->tsij", mu_x, jnp.conj(mu_x)) + return Cx + outer + + +@jax.jit +def autocorrelation_to_covariance(Rxx, mu_x): + """ + Cx[n1, n2] = Rxx[n1, n2] - mu_x[n1] ⊗ conj(mu_x[n2]). + + Parameters + ---------- + Rxx : jnp.ndarray, shape (T, T, m, m) + Autocorrelation blocks. + mu_x : jnp.ndarray, shape (T, m) + Mean sequence (use mu_y when converting output autocorrelation). + + Returns + ------- + Cx : jnp.ndarray, shape (T, T, m, m) + """ + outer = jnp.einsum("ti,sj->tsij", mu_x, jnp.conj(mu_x)) + return Rxx - outer + + +# --------------------------------------------------------------------------- +# Mean propagation +# --------------------------------------------------------------------------- + + +@jax.jit +def propagate_mean(h, mu_x): + """Propagate the mean sequence through a causal MIMO LTI system. + + mu_y[n] = sum_{k=0}^{K-1} h[k] @ mu_x[n-k] + + Uses jax.lax.scan with a sliding buffer so the computation is O(T*K) + and JIT-compilable. + + Parameters + ---------- + h : jnp.ndarray, shape (K, q, m) + Discrete-time impulse response. + mu_x : jnp.ndarray, shape (T, m) + Input mean sequence. + + Returns + ------- + mu_y : jnp.ndarray, shape (T, q) + Output mean sequence. + """ + K, q, m = h.shape + dtype = jnp.result_type(h, mu_x) + + def step(buffer, u_n): + # Roll newest input to front; oldest drops off the back. + new_buffer = jnp.roll(buffer, 1, axis=0).at[0].set(u_n) + y_n = jnp.einsum("kqm,km->q", h, new_buffer) + return new_buffer, y_n + + init_buffer = jnp.zeros((K, m), dtype=dtype) + _, mu_y = jax.lax.scan(step, init_buffer, mu_x.astype(dtype)) + return mu_y + + +# --------------------------------------------------------------------------- +# Covariance propagation (Papoulis equations) +# --------------------------------------------------------------------------- + + +def _propagate_cross_correlation(h, Rxx): + """ + First Papoulis step: Rxy[n1, n2] = sum_{k=0}^{K-1} Rxx[n1, n2-k] @ h[k]^H + + For each fixed n1 this is a causal convolution of the row Rxx[n1, :] + with the conjugate-transposed impulse response. Rows are processed in + parallel via jax.vmap. + + Parameters + ---------- + h : jnp.ndarray, shape (K, q, m) + Rxx : jnp.ndarray, shape (T, T, m, m) + + Returns + ------- + Rxy : jnp.ndarray, shape (T, T, m, q) + """ + K, q, m = h.shape + # h[k]^H shape: (K, m, q) + h_H = jnp.conj(h).transpose(0, 2, 1) + dtype = jnp.result_type(h, Rxx) + + def compute_rxy_row(rxx_row): + # rxx_row: (T, m, m) — Rxx[n1, :] for fixed n1 + def step(buffer, rxx_n2): + new_buffer = jnp.roll(buffer, 1, axis=0).at[0].set(rxx_n2) + # sum_k buffer[k] @ h_H[k] => (m, q) + rxy = jnp.einsum("kij,kjl->il", new_buffer, h_H) + return new_buffer, rxy + + init = jnp.zeros((K, m, m), dtype=dtype) + _, rxy_row = jax.lax.scan(step, init, rxx_row.astype(dtype)) + return rxy_row # (T, m, q) + + return jax.vmap(compute_rxy_row)(Rxx) # (T, T, m, q) + + +def _propagate_output_autocorrelation(h, Rxy): + """ + Second Papoulis step: Ryy[n1, n2] = sum_{k=0}^{K-1} h[k] @ Rxy[n1-k, n2] + + For each fixed n2 this is a causal convolution of the column Rxy[:, n2] + with the impulse response. Columns are processed in parallel via jax.vmap. + + Parameters + ---------- + h : jnp.ndarray, shape (K, q, m) + Rxy : jnp.ndarray, shape (T, T, m, q) + + Returns + ------- + Ryy : jnp.ndarray, shape (T, T, q, q) + """ + K, q, m = h.shape + dtype = jnp.result_type(h, Rxy) + + def compute_ryy_col(rxy_col): + # rxy_col: (T, m, q) — Rxy[:, n2] for fixed n2 + def step(buffer, rxy_n1): + new_buffer = jnp.roll(buffer, 1, axis=0).at[0].set(rxy_n1) + # sum_k h[k] @ buffer[k] => (q, q) + # h: (K, q, m=l), buffer: (K, m=l, q=o) + ryy = jnp.einsum("kql,klo->qo", h, new_buffer) + return new_buffer, ryy + + init = jnp.zeros((K, m, q), dtype=dtype) + _, ryy_col = jax.lax.scan(step, init, rxy_col.astype(dtype)) + return ryy_col # (T, q, q) + + # vmap over n2 (axis 1); transpose so n2 is outermost, then restore. + Ryy = jax.vmap(compute_ryy_col)(Rxy.transpose(1, 0, 2, 3)) # (T_n2, T_n1, q, q) + return Ryy.transpose(1, 0, 2, 3) # (T_n1, T_n2, q, q) + + +def propagate_autocorrelation(h, Rxx): + """Propagate input autocorrelation through a causal MIMO LTI system. + + Applies both Papoulis steps in sequence: + + Step 1: Rxy[n1, n2] = sum_{k=0}^{K-1} Rxx[n1, n2-k] @ h[k]^H + Step 2: Ryy[n1, n2] = sum_{k=0}^{K-1} h[k] @ Rxy[n1-k, n2] + + Parameters + ---------- + h : jnp.ndarray, shape (K, q, m) + Discrete-time impulse response. + Rxx : jnp.ndarray, shape (T, T, m, m) + Input autocorrelation blocks. + + Returns + ------- + Ryy : jnp.ndarray, shape (T, T, q, q) + Output autocorrelation blocks. + """ + Rxy = _propagate_cross_correlation(h, Rxx) + return _propagate_output_autocorrelation(h, Rxy) + + +# --------------------------------------------------------------------------- +# Full pipeline +# --------------------------------------------------------------------------- + + +def gaussian_process_response(h, mu_x, Cx): + """Compute output mean and covariance for a Gaussian input through a causal + MIMO LTI system. + + This is the stochastic analog of state_space_response_discrete in + vector_fitting/z_domain.py: given the system impulse response and the + input statistics, return the output statistics. + + Parameters + ---------- + h : jnp.ndarray, shape (K, q, m) + Discrete-time impulse response. + mu_x : jnp.ndarray, shape (T, m) + Input mean sequence. + Cx : jnp.ndarray, shape (T, T, m, m) + Input covariance blocks. Cx[n1, n2] = Cov(x[n1], x[n2]). + + Returns + ------- + mu_y : jnp.ndarray, shape (T, q) + Output mean sequence. + Cy : jnp.ndarray, shape (T, T, q, q) + Output covariance blocks. + """ + mu_y = propagate_mean(h, mu_x) + Rxx = covariance_to_autocorrelation(Cx, mu_x) + Ryy = propagate_autocorrelation(h, Rxx) + Cy = autocorrelation_to_covariance(Ryy, mu_y) + return mu_y, Cy + + +# --------------------------------------------------------------------------- +# Common input covariance constructors +# --------------------------------------------------------------------------- + + +def white_noise_covariance(T, m, sigma_sq=1.0): + """Create a spectrally white (temporally uncorrelated) covariance matrix. + + Cx[n1, n2, i, j] = sigma_sq * delta[n1-n2] * delta[i-j] + + Parameters + ---------- + T : int + Number of time steps. + m : int + Number of modes. + sigma_sq : float + Noise power per mode (default 1.0). + + Returns + ------- + Cx : jnp.ndarray, shape (T, T, m, m) + """ + return ( + sigma_sq + * jnp.eye(T, dtype=complex)[:, :, None, None] + * jnp.eye(m, dtype=complex)[None, None, :, :] + ) + + +# --------------------------------------------------------------------------- +# Block ↔ flat matrix conversion utilities +# --------------------------------------------------------------------------- + + +def covariance_blocks_to_matrix(C): + """Flatten block-structured covariance (T, T, M, M) → (T*M, T*M). + + The flat layout satisfies C_flat[n1*M+i, n2*M+j] = C[n1, n2, i, j]. + + Parameters + ---------- + C : jnp.ndarray, shape (T, T, M, M) + + Returns + ------- + jnp.ndarray, shape (T*M, T*M) + """ + T, _, M, _ = C.shape + return C.transpose(0, 2, 1, 3).reshape(T * M, T * M) + + +def covariance_matrix_to_blocks(C_flat, T, M): + """Unflatten covariance matrix (T*M, T*M) → block structure (T, T, M, M). + + Inverse of covariance_blocks_to_matrix. + + Parameters + ---------- + C_flat : jnp.ndarray, shape (T*M, T*M) + T : int + Number of time steps. + M : int + Number of modes per time step. + + Returns + ------- + jnp.ndarray, shape (T, T, M, M) + """ + return jnp.asarray(C_flat).reshape(T, M, T, M).transpose(0, 2, 1, 3) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + + +def main(): + """Verify output variance matches Parseval's identity for white-noise + input.""" + T = 60 + K = 12 + q, m = 2, 2 + + key = jax.random.PRNGKey(0) + h_real = jax.random.normal(key, (K, q, m)) + h_imag = jax.random.normal(jax.random.fold_in(key, 1), (K, q, m)) + h = (h_real + 1j * h_imag) / jnp.sqrt(2 * K) + + # Zero-mean white noise: Cx[n1,n2] = delta[n1,n2] * I_m + mu_x = jnp.zeros((T, m), dtype=complex) + Cx = ( + jnp.eye(T, dtype=complex)[:, :, None, None] + * jnp.eye(m, dtype=complex)[None, None, :, :] + ) # (T, T, m, m) + + mu_y, Cy = gaussian_process_response(h, mu_x, Cx) + + # For white noise the output power matrix at large lag should equal + # sum_k h[k] @ h[k]^H (Parseval). + h_gram = jnp.sum(jnp.einsum("kqi,kpi->kqp", h, jnp.conj(h)), axis=0) # (q, q) + output_power = Cy[T - 1, T - 1] # (q, q), last sample has seen all K taps + + print("Expected output power matrix (Parseval):") + print(h_gram.real) + print("Computed output power matrix:") + print(output_power.real) + print("Max absolute error:", jnp.max(jnp.abs(output_power - h_gram)).item()) + + +if __name__ == "__main__": + main() diff --git a/simphony/time_domain/tests/block_mode.py b/simphony/time_domain/tests/block_mode.py new file mode 100644 index 00000000..4e66e56d --- /dev/null +++ b/simphony/time_domain/tests/block_mode.py @@ -0,0 +1,91 @@ +import jax.numpy as jnp +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) + + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim + +# Simulation parameters +T = 100e-11 +dt = 1e-14 # Time step (Total time duration is T) +t = jnp.arange(0, T, dt) # Time array +t0 = 1.0e-11 # Pulse start time + +# Modulator signals +f_mod = 0 +m = f_mod * jnp.ones(len(t), dtype=complex) +f_mod2 = jnp.pi / 4 +# m2 = f_mod2 * jnp.ones(len(t),dtype=complex) + +x = jnp.linspace(0, 3.14, len(t)) +mu = 1.30 # center the Gaussian in the middle of the interval +sigma = 0.15 # adjust sigma for desired width + +# Compute the Gaussian function +gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + +# Optionally, normalize so the area under the curve is 1 +# gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x +timePhaseInstantiated = Modulator(mod_signal=gaussian) + +# Define netlist and models +netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "pm": "phase_modulator", + "y": "y_branch", + "y2": "y_branch", + }, + "connections": { + "wg,o0": "y,port_2", + "wg,o1": "pm,o0", + "y2,port_2": "pm,o1", + "wg2,o0": "y,port_3", + "y2,port_3": "wg2,o1", + }, + "ports": { + "o0": "y,port_1", + "o1": "y2,port_1", + }, +} +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, +} +active_components = {"pm", "pm2"} +num_measurements = 200 +model_order = 50 +center_wvl = 1.548 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, +} + +# Create and build simulation +time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, + model_parameters=options, + dt=dt, +) + +time_sim.set_mode("block") + +num_outputs = 2 +inputs = { + f"o{i}": jnp.ones_like(t) if i == 0 else jnp.zeros_like(t) + for i in range(num_outputs) +} + +time_sim.run(t, inputs) diff --git a/simphony/time_domain/tests/check.py b/simphony/time_domain/tests/check.py new file mode 100644 index 00000000..34e39ba0 --- /dev/null +++ b/simphony/time_domain/tests/check.py @@ -0,0 +1,135 @@ +import jax.numpy as jnp +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) +import time + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim +from simphony.time_domain.utils import smooth_rectangular_pulse + +# Simulation parameters +T = 2.5e-11 +dt = 1e-14 # Time step (Total time duration is T) +t = jnp.arange(0, T, dt) # Time array +t0 = 1.0e-11 # Pulse start time + +# Modulator signals +f_mod = 0 +m = f_mod * jnp.ones(len(t), dtype=complex) +f_mod2 = jnp.pi / 4 +# m2 = f_mod2 * jnp.ones(len(t),dtype=complex) + +x = jnp.linspace(0, 3.14, len(t)) +mu = 1.30 # center the Gaussian in the middle of the interval +sigma = 0.15 # adjust sigma for desired width + +# Compute the Gaussian function +gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + +# Optionally, normalize so the area under the curve is 1 +# gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x +timePhaseInstantiated = Modulator(mod_signal=gaussian) + +# Define netlist and models +netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "pm": "phase_modulator", + "y": "y_branch", + "y2": "y_branch", + }, + "connections": { + "wg,o0": "y,port_2", + "wg,o1": "pm,o0", + "y2,port_2": "pm,o1", + "wg2,o0": "y,port_3", + "y2,port_3": "wg2,o1", + }, + "ports": { + "o0": "y,port_1", + "o1": "y2,port_1", + }, +} +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, +} +active_components = {"pm", "pm2"} + +# Create and build simulation +time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, +) + +num_measurements = 200 +model_order = 50 +center_wvl = 1.548 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, +} + +time_sim.build_model(model_parameters=options, dt=dt) + +num_outputs = 2 +inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.5e-11, 1.5e-11) if i == 0 else jnp.zeros_like(t) + ) + for i in range(num_outputs) +} + +# Run simulation and plot results +tic = time.time() +modelResult = time_sim.run(t, inputs) +toc = time.time() +run_time = toc - tic +modelResult.plot_sim() + +# # Updated testing function with detailed output on failure +# def test_compare_results(expected_data): +# """ +# Compare expected_data with the loaded simulation results. +# If a mismatch occurs, print detailed information about the failure. +# """ +# # Read the previously saved dictionary +# with open("simphony/time_domain/tests/test_comparison_results/simulation_results.pkl", "rb") as f: +# loaded_data = pickle.load(f) + +# # Ensure both dictionaries have the same keys +# assert loaded_data.keys() == expected_data.keys(), "Mismatch in dictionary keys." + +# # Compare each array in the dictionary +# for key in loaded_data.keys(): +# if not jnp.allclose(loaded_data[key], expected_data[key], rtol=1e-10, atol=1e-10): +# # Compute the element-wise absolute difference +# diff = jnp.abs(loaded_data[key] - expected_data[key]) +# # Find the maximum difference and its index +# max_diff = jnp.max(diff) +# idx = int(jnp.argmax(diff)) +# loaded_val = loaded_data[key][idx] +# expected_val = expected_data[key][idx] +# tol = 1e-13 + 1e-12 * abs(expected_val) +# error_message = ( +# f"Mismatch for key '{key}' at index {idx}:\n" +# f" Loaded value = {loaded_val}\n" +# f" Expected value = {expected_val}\n" +# f" Absolute difference = {max_diff}\n" +# f" Tolerance (atol + rtol*|expected|) = {tol}\n" +# ) +# raise AssertionError(error_message) +# print("All results match expected data!") + +# # Run the test +# test_compare_results(modelResult.outputs) diff --git a/simphony/time_domain/tests/delete_check.py b/simphony/time_domain/tests/delete_check.py new file mode 100644 index 00000000..19cda8ee --- /dev/null +++ b/simphony/time_domain/tests/delete_check.py @@ -0,0 +1,162 @@ +import jax.numpy as jnp +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) + + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim +from simphony.time_domain.utils import smooth_rectangular_pulse +from simphony.utils import SPEED_OF_LIGHT + +# Simulation parameters +T = 100e-11 +dt = 1e-14 # Time step (Total time duration is T) +t = jnp.arange(0, T, dt) # Time array +t0 = 1.0e-11 # Pulse start time + +# Modulator signals +f_mod = 0 +m = f_mod * jnp.ones(len(t), dtype=complex) +f_mod2 = jnp.pi / 4 +# m2 = f_mod2 * jnp.ones(len(t),dtype=complex) + +x = jnp.linspace(0, 3.14, len(t)) +mu = 1.30 # center the Gaussian in the middle of the interval +sigma = 0.15 # adjust sigma for desired width + +# Compute the Gaussian function +gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + +# Optionally, normalize so the area under the curve is 1 +# gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x +timePhaseInstantiated = Modulator(mod_signal=gaussian) + +# Define netlist and models +netlist = { + "instances": { + "wg1": "waveguide", + "wg2": "waveguide", + "wg3": "waveguide", + "wg4": "waveguide", + "wg5": "waveguide", + "wg6": "waveguide", + }, + "connections": { + # "wg1,o1": " wg2,o0", + # "wg2,o1": "wg3,o0", + # "wg3,o1": "wg4,o0", + # "wg4,o1": "wg5,o0", + # "wg5,o1": "wg6,o0", + # "wg6,o1": "wg7,o0", + # "wg7,o1": "wg8,o0", + }, + "ports": { + "o0": "wg1,o0", + "o1": "wg1,o1", + }, +} + +models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, +} + +active_components = {"pm", "pm2"} +num_measurements = 200 + +center_wvl = 1.55 +wvl = np.linspace(1.5, 1.6, num_measurements) +options = { + "wl": wvl, + "wg1": {"length": 290.0, "loss": 0}, +} + +# Create and build simulation +time_sim = TimeSim( + netlist=netlist, + models=models, + settings=options, +) +result = time_sim.run( + t, + { + "o0": smooth_rectangular_pulse(t, 0.0, T + 20.0e-11), + "o1": jnp.zeros_like(t), + }, + carrier_freq=SPEED_OF_LIGHT / (center_wvl * 1e-6), + dt=dt, +) + +result.plot_sim() + +# circuit, _ = sax.circuit( +# netlist=netlist, +# models=models, +# ) + +# s_params_dict = circuit(**options) +# s_matrix = np.asarray(dict_to_matrix(s_params_dict)) +# c_light = 299792458 +# center_freq = c_light / (center_wvl * 1e-6) + +# freqs = c_light / (wvl * 1e-6) - center_freq +# sampling_freq = -1 / dt +# beta = sampling_freq / (freqs[-1] - freqs[0]) +# bvf_options = BVF_Options(beta=beta,max_iterations = 30) +# sorted_ports = sorted(netlist["ports"].keys(), key=lambda p: int(p.lstrip('o'))) +# freqs_hz = SPEED_OF_LIGHT / (wvl * 1e-6) # wvl was in μm → convert to m + +# omega = 2 * np.pi * freqs_hz +# idx = np.argsort(omega) +# omega_s = omega[idx] +# group_delay = -np.gradient(np.unwrap(np.angle(s_matrix[:, 0, 1])),omega_s) +# plt.plot(wvl, group_delay*1e12) # convert s → ps +# plt.xlabel("Angular frequency ω (rad/s)") +# plt.ylabel("Group delay") +# plt.show() +# iir_model = IIRModelBaseband( +# wvl, center_wvl, s_matrix,order = 80, options=bvf_options +# ) + +# poles = iir_model.poles +# residues = iir_model.residues +# D = iir_model.D +# Ω = 2*np.pi * freqs / sampling_freq + +# z = np.exp(1j * Ω) + +# S_fit = np.zeros_like(z, dtype=complex) +# for i, p in enumerate(poles): +# r01 = residues[i,0,1] +# S_fit += r01 / (z - p) +# S_fit += D[0,1] +# print(residues[:,0,1]) +# print(poles) +# plt.plot(wvl, np.angle(s_matrix[:, 0, 1]), label='IIR Model') +# plt.plot(wvl, np.angle(S_fit), label='IIR Model') +# plt.show() + +# plt.plot(wvl, np.abs(s_matrix[:, 0, 1]), label='IIR Model') +# plt.plot(wvl, np.abs(S_fit), label='IIR Model Fit') +# plt.show() + +# plt.figure(figsize=(5,5)) +# # unit circle +# angle = np.linspace(0, 2*np.pi, 400) +# plt.plot(np.cos(angle), np.sin(angle), 'gray', lw=1) + +# plt.scatter(poles.real, poles.imag, marker='x', s=80, label='Poles') + +# plt.axhline(0, color='black', lw=1) +# plt.axvline(0, color='black', lw=1) +# plt.xlabel('Re\{z\}') +# plt.ylabel('Im\{z\}') +# plt.title('Pole–Zero Map (z-plane)') +# plt.legend() +# plt.axis('equal') +# plt.grid(True, ls='--', alpha=0.5) +# plt.show() diff --git a/simphony/time_domain/tests/simphony.code-workspace b/simphony/time_domain/tests/simphony.code-workspace new file mode 100644 index 00000000..b80d58db --- /dev/null +++ b/simphony/time_domain/tests/simphony.code-workspace @@ -0,0 +1,11 @@ +{ + "folders": [ + { + "path": "../../.." + }, + { + "path": "../../../.." + } + ], + "settings": {} +} \ No newline at end of file diff --git a/simphony/time_domain/tests/test_comparison_results/simulation_results.pkl b/simphony/time_domain/tests/test_comparison_results/simulation_results.pkl new file mode 100644 index 00000000..f7f58c1b Binary files /dev/null and b/simphony/time_domain/tests/test_comparison_results/simulation_results.pkl differ diff --git a/simphony/time_domain/tests/test_comparison_results/simulation_results2.pkl b/simphony/time_domain/tests/test_comparison_results/simulation_results2.pkl new file mode 100644 index 00000000..4844ea08 Binary files /dev/null and b/simphony/time_domain/tests/test_comparison_results/simulation_results2.pkl differ diff --git a/simphony/time_domain/tests/test_comparison_results/simulation_results3.pkl b/simphony/time_domain/tests/test_comparison_results/simulation_results3.pkl new file mode 100644 index 00000000..a8f1e96e Binary files /dev/null and b/simphony/time_domain/tests/test_comparison_results/simulation_results3.pkl differ diff --git a/simphony/time_domain/tests/test_comparison_results/simulation_results4.pkl b/simphony/time_domain/tests/test_comparison_results/simulation_results4.pkl new file mode 100644 index 00000000..3b8f6337 Binary files /dev/null and b/simphony/time_domain/tests/test_comparison_results/simulation_results4.pkl differ diff --git a/simphony/time_domain/tests/test_time_domain.py b/simphony/time_domain/tests/test_time_domain.py new file mode 100644 index 00000000..21902738 --- /dev/null +++ b/simphony/time_domain/tests/test_time_domain.py @@ -0,0 +1,479 @@ +import os +import pickle + +import jax.numpy as jnp +import numpy as np +import pytest +from jax import config + +config.update("jax_enable_x64", True) +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim +from simphony.time_domain.utils import smooth_rectangular_pulse + +# Let's assume your main code is in `my_module.main_code`: +# from my_module.main_code import run_simulation + + +def run_simulation(): + T = 2.5e-11 + dt = 1e-14 # Total time duration (40 ps) + t = jnp.arange(0, T, dt) # Time array + t0 = 1.0e-11 # Pulse start time + + f_mod = 0 + m = f_mod * jnp.ones(len(t), dtype=complex) + f_mod2 = jnp.pi / 4 + # m2 = f_mod2 * jnp.ones(len(t),dtype = complex) + + x = jnp.linspace(0, 3.14, len(t)) + + mu = 1.30 # center the Gaussian in the middle of the interval + sigma = 0.15 # adjust sigma for desired width + x = jnp.linspace(0, 3.14, len(t)) + # Compute the Gaussian function + + gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + + # Optionally, normalize so the area under the curve is 1 + # gaussian = gaussian / jnp.trapezoid(gaussian, x) + zero = 0 * x + timePhaseInstantiated = Modulator(mod_signal=gaussian) + + netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "pm": "phase_modulator", + "y": "y_branch", + "y2": "y_branch", + }, + "connections": { + "wg,o0": "y,port_2", + "wg,o1": "pm,o0", + "y2,port_2": "pm,o1", + "wg2,o0": "y,port_3", + "y2,port_3": "wg2,o1", + }, + "ports": { + "o0": "y,port_1", + "o1": "y2,port_1", + }, + } + models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, + } + active_components = {"pm", "pm2"} + + time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, + ) + + num_measurements = 200 + model_order = 50 + center_wvl = 1.548 + wvl = np.linspace(1.5, 1.6, num_measurements) + options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, + } + + time_sim.build_model(model_parameters=options, dt=dt) + + num_outputs = 2 + + inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.5e-11, 1.5e-11) + if i == 0 + else jnp.zeros_like(t) + ) + for i in range(num_outputs) + } + + modelResult = time_sim.run(t, inputs) + + return modelResult.outputs + + +def run_simulation2(): + T = 2.0e-11 + newT = 4.0e-11 + dt = 0.5e-14 # Total time duration (40 ps) + t = jnp.arange(0, T, dt) + ddt = 1e-14 + newt = jnp.arange(0, newT, ddt) + + x = jnp.linspace(0, 3.14, len(t)) + + mu = 1.30 # center the Gaussian in the middle of the interval + sigma = 0.15 # adjust sigma for desired width + x = jnp.linspace(0, 3.14, len(t)) + + gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + + timePhaseInstantiated = Modulator(mod_signal=gaussian) + + netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "pm": "phase_modulator", + "y": "y_branch", + "y2": "y_branch", + }, + "connections": { + "wg,o0": "y,port_2", + "wg,o1": "pm,o0", + "y2,port_2": "pm,o1", + "wg2,o0": "y,port_3", + "y2,port_3": "wg2,o1", + }, + "ports": { + "o0": "y,port_1", + "o1": "y2,port_1", + }, + } + models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, + } + active_components = {"pm", "pm2"} + + time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, + ) + + num_measurements = 200 + model_order = 50 + center_wvl = 1.548 + wvl = np.linspace(1.5, 1.6, num_measurements) + options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, + } + + time_sim.build_model(model_parameters=options, dt=dt) + + num_outputs = 2 + + inputs = { + f"o{i}": ( + smooth_rectangular_pulse(newt, 0.5e-11, 1.5e-11) + if i == 0 + else jnp.zeros_like(t) + ) + for i in range(num_outputs) + } + + modelResult = time_sim.run(newt, inputs) + + return modelResult.outputs + + +def run_simulation3(): + T = 2.0e-11 + dt = 0.5e-14 # Total time duration (40 ps) + t = jnp.arange(0, T, dt) + + netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "y": "y_branch", + "hr": "half_ring", + "hr2": "half_ring", + "y2": "y_branch", + }, + "connections": { + "hr,port_1": "hr2,port_1", + "hr,port_3": "hr2,port_3", + }, + "ports": { + "o0": "hr,port_2", + "o2": "hr2,port_4", + "o1": "hr,port_4", + "o3": "hr2,port_2", + }, + } + models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "half_ring": siepic.half_ring, + "bidirectional": siepic.bidirectional_coupler, + } + + time_sim = TimeSim( + netlist=netlist, + models=models, + ) + + num_measurements = 200 + model_order = 50 + center_wvl = 1.548 + wvl = np.linspace(1.5, 1.6, num_measurements) + options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, + } + + time_sim.build_model(model_parameters=options, dt=dt, max_size=6) + + num_outputs = 4 + + inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.0e-11, 4.0e-11) + if i == 0 + else jnp.zeros_like(t) + ) + for i in range(num_outputs) + } + + modelResult = time_sim.run(t, inputs) + + return modelResult.outputs + + +def run_simulation4(): + T = 2.5e-12 + dt = 1e-15 # Total time duration (40 ps) + t = jnp.arange(0, T, dt) # Time array + t0 = 1.0e-11 # Pulse start time + + f_mod = 0 + m = f_mod * jnp.ones(len(t), dtype=complex) + + # m2 = f_mod2 * jnp.ones(len(t),dtype = complex) + + x = jnp.linspace(0, 3.14, len(t)) + + mu = 1.70 # center the Gaussian in the middle of the interval + sigma = 0.15 # adjust sigma for desired width + x = jnp.linspace(0, 3.14, len(t)) + # Compute the Gaussian function + + gaussian = np.pi * jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + + # Optionally, normalize so the area under the curve is 1 + # gaussian = gaussian / jnp.trapezoid(gaussian, x) + zero = 0 * x + timePhaseInstantiated = Modulator(mod_signal=gaussian) + + netlist = { + "instances": { + "wg": "waveguide", + "wg2": "waveguide", + "pm": "phase_modulator", + "y": "y_branch", + "y2": "y_branch", + }, + "connections": { + "wg,o0": "y,port_2", + "wg,o1": "pm,o0", + "y2,port_2": "pm,o1", + "wg2,o0": "y,port_3", + "y2,port_3": "wg2,o1", + }, + "ports": { + "o0": "y,port_1", + "o1": "y2,port_1", + }, + } + models = { + "waveguide": siepic.waveguide, + "y_branch": siepic.y_branch, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, + } + active_components = {"pm", "pm2"} + + time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, + ) + + num_measurements = 200 + model_order = 50 + center_wvl = 1.548 + wvl = np.linspace(1.5, 1.6, num_measurements) + options = { + "wl": wvl, + "wg": {"length": 10.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, + } + + time_sim.build_model(model_parameters=options, dt=dt) + + num_outputs = 2 + + inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.0e-12, 1.5e-12) + if i == 0 + else jnp.zeros_like(t) + ) + for i in range(num_outputs) + } + + modelResult = time_sim.run(t, inputs) + modelResult.plot_sim() + return modelResult.outputs + + +@pytest.mark.simulation +def test_compare_simulation_results_active_MZI(): + """This test runs the simulation, then compares it to a known 'golden' + result stored in a pickle file.""" + + # 1) Run the simulation to get new results + new_results = run_simulation() + + # 2) Load the reference (golden) data from disk + # Adjust the path to where your .pkl file actually is. + reference_path = os.path.join( + os.path.dirname(__file__), # directory of THIS test file + "test_comparison_results/", # subfolder for reference data + "simulation_results.pkl", # the actual pickle file name + ) + + # If you haven't created the file yet, run your code once to generate it. + # Then rename it or copy it into reference_data as the "golden" result. + with open(reference_path, "rb") as f: + golden_results = pickle.load(f) + + # 3) Compare dictionary keys + assert new_results.keys() == golden_results.keys(), "Mismatch in dictionary keys." + + # 4) Compare each item in the dictionary + for key in new_results.keys(): + # Compare arrays + # If you want an exact match, use jnp.array_equal + # For numerical tolerance, use jnp.allclose + assert jnp.allclose( + new_results[key], golden_results[key], rtol=1e-4, atol=1e-6 + ), f"Mismatch for key: {key}" + + # If we reach here, everything matched! + print("All results match expected data!") + + +@pytest.mark.simulation +def test_compare_simulation_results_active_MZI_time_change(): + """This test runs the simulation, then compares it to a known 'golden' + result stored in a pickle file.""" + + # 1) Run the simulation to get new results + new_results = run_simulation2() + + # 2) Load the reference (golden) data from disk + # Adjust the path to where your .pkl file actually is. + reference_path = os.path.join( + os.path.dirname(__file__), # directory of THIS test file + "test_comparison_results/", # subfolder for reference data + "simulation_results2.pkl", # the actual pickle file name + ) + + # If you haven't created the file yet, run your code once to generate it. + # Then rename it or copy it into reference_data as the "golden" result. + with open(reference_path, "rb") as f: + golden_results = pickle.load(f) + + # 3) Compare dictionary keys + assert new_results.keys() == golden_results.keys(), "Mismatch in dictionary keys." + + # 4) Compare each item in the dictionary + for key in new_results.keys(): + # Compare arrays + # If you want an exact match, use jnp.array_equal + # For numerical tolerance, use jnp.allclose + assert jnp.allclose( + new_results[key], golden_results[key], rtol=1e-4, atol=1e-6 + ), f"Mismatch for key: {key}" + + # If we reach here, everything matched! + print("All results match expected data!") + + +@pytest.mark.simulation +def test_compare_simulation_results_only_passive_also_port_swap(): + """This test runs the simulation, then compares it to a known 'golden' + result stored in a pickle file.""" + + # 1) Run the simulation to get new results + new_results = run_simulation3() + + # 2) Load the reference (golden) data from disk + # Adjust the path to where your .pkl file actually is. + reference_path = os.path.join( + os.path.dirname(__file__), # directory of THIS test file + "test_comparison_results/", # subfolder for reference data + "simulation_results3.pkl", # the actual pickle file name + ) + + # If you haven't created the file yet, run your code once to generate it. + # Then rename it or copy it into reference_data as the "golden" result. + with open(reference_path, "rb") as f: + golden_results = pickle.load(f) + + # 3) Compare dictionary keys + assert new_results.keys() == golden_results.keys(), "Mismatch in dictionary keys." + + # 4) Compare each item in the dictionary + for key in new_results.keys(): + # Compare arrays + # If you want an exact match, use jnp.array_equal + # For numerical tolerance, use jnp.allclose + assert jnp.allclose( + new_results[key], golden_results[key], rtol=1e-4, atol=1e-6 + ), f"Mismatch for key: {key}" + + # If we reach here, everything matched! + print("All results match expected data!") + + +@pytest.mark.simulation +def test_compare_simulation_results_small_time_frame(): + """This test runs the simulation, then compares it to a known 'golden' + result stored in a pickle file.""" + + # 1) Run the simulation to get new results + new_results = run_simulation4() + + # 2) Load the reference (golden) data from disk + # Adjust the path to where your .pkl file actually is. + reference_path = os.path.join( + os.path.dirname(__file__), # directory of THIS test file + "test_comparison_results/", # subfolder for reference data + "simulation_results4.pkl", # the actual pickle file name + ) + + # If you haven't created the file yet, run your code once to generate it. + # Then rename it or copy it into reference_data as the "golden" result. + with open(reference_path, "rb") as f: + golden_results = pickle.load(f) + + # 3) Compare dictionary keys + assert new_results.keys() == golden_results.keys(), "Mismatch in dictionary keys." + + # 4) Compare each item in the dictionary + for key in new_results.keys(): + assert jnp.allclose( + new_results[key], golden_results[key], rtol=1e-4, atol=1e-6 + ), f"Mismatch for key: {key}" + # If we reach here, everything matched! + print("All results match expected data!") diff --git a/simphony/time_domain/tests/tester.py b/simphony/time_domain/tests/tester.py new file mode 100644 index 00000000..fff80feb --- /dev/null +++ b/simphony/time_domain/tests/tester.py @@ -0,0 +1,187 @@ +import jax.numpy as jnp +import numpy as np +from jax import config + +config.update("jax_enable_x64", True) + + +from simphony.libraries import siepic +from simphony.time_domain.ideal import Modulator +from simphony.time_domain.simulation import TimeSim +from simphony.time_domain.utils import smooth_rectangular_pulse + +T = 1.5e-11 +dt = 1e-14 # Total time duration (40 ps) +t = jnp.arange(0, T, dt) # Time array +t0 = 1.0e-11 # Pulse start time +std = 1e-12 +inter = 250 +c = 299792458 +f_mod = 0 +m = f_mod * jnp.ones(len(t), dtype=complex) +f_mod2 = jnp.pi / 2 +m2 = f_mod2 * jnp.ones(len(t), dtype=complex) + +x = jnp.linspace(0, 3.14, len(t)) + +# Define Gaussian parameters +mu = 1.14 # center the Gaussian in the middle of the interval +sigma = 0.3 # adjust sigma for desired width +num_blocks = 6 # number of transitions +hold_time = 1000 # hold 30 samples at each level +amp_lo = 0.0 +amp_hi = 0.5 + + +# Compute the Gaussian function +gaussian = jnp.exp(-0.5 * ((x - mu) / sigma) ** 2) + + +# Optionally, normalize so the area under the curve is 1 +# gaussian = gaussian / jnp.trapezoid(gaussian, x) +zero = 0 * x +timePhaseInstantiated = Modulator(mod_signal=m2) + +netlist = { + "instances": { + "dc": "directional_coupler", + # "bi":"bidirectional", + "pm": "phase_modulator", + "wg": "waveguide", + # "wg2":"waveguide", + # "dc2":"directional_coupler", + # "t":"terminator", + }, + "connections": { + "dc,port_3": "wg,o0", + # "wg,o1":"dc2,port_1", + "pm,o0": "wg,o1", + # "pm,o1":"dc2,port_1", + # "wg2,o0":"dc2,port_3", + # "wg2,o1":"dc,port_1", + # "dc2,port_2":"t,port_1", + "pm,o1": "dc,port_1", + # "bi, port_1":"wg,o0", + # "wg,o1":"pm,o1", + # "bi, port_3":"pm,o0", + # "bi,port_3":"wg,o1", + }, + "ports": { + # "o0":"dc, port_1", + # "o1":"dc, port_2", + # "o2":"dc, port_3", + # "o3":"dc, port_4", + "o0": "dc, port_2", + "o1": "dc, port_4", + # "o2":"dc2, port_4", + }, +} +models = { + "waveguide": siepic.waveguide, + "bidirectional": siepic.bidirectional_coupler, + "phase_modulator": timePhaseInstantiated, + "directional_coupler": siepic.directional_coupler, +} +active_components = {"pm", "pm2"} +# circuit, _ = sax.circuit( +# netlist=netlist, +# models=models, +# ) + + +# time_sim = TimeSim( +# netlist=netlist, +# models=models, +# active_components=active_components, +# ) + +num_measurements = 200 +model_order = 50 +center_wvl = 1.55 +wvl = np.linspace(1.50, 1.60, num_measurements) +options = { + "wl": wvl, + "wg": {"length": 100.0, "loss": 100}, + "wg2": {"length": 10.0, "loss": 100}, + "dr": {"coupling_length": 10}, + "dr2": {"coupling_length": 12.5}, +} + +# s_params_dict = circuit(**options) +# s_matrix = np.asarray(dict_to_matrix(s_params_dict)) + + +# plt.figure() +# plt.plot(wvl, jnp.abs(s_matrix[:, 0, 2])**2, label='S11') +# plt.xlabel('Wavelength (µm)') +# plt.ylabel('S11 Parameter') +# plt.title('S11 vs Wavelength') +# plt.legend() +# plt.grid() +# plt.show() +range_of_phases = jnp.linspace(-2 * jnp.pi, 2 * jnp.pi, 50) +phases_at_o1 = [] +phases_at_o2 = [] +time_sim = TimeSim( + netlist=netlist, + models=models, + active_components=active_components, +) +num_outputs = 2 +inputs = { + f"o{i}": ( + smooth_rectangular_pulse(t, 0.01e-10, 0.30e-10) if i == 0 else jnp.zeros_like(t) + ) + for i in range(num_outputs) +} + +time_sim.build_model(model_parameters=options, center_wvl=center_wvl, dt=dt, max_size=1) +modelResult = time_sim.run(t, inputs) + +modelResult.plot_sim() +# plt.figure() +# plt.plot(t, gaussian, label='Gaussian Pulse') +# plt.xlabel('Time (s)') +# plt.ylabel('Amplitude') +# plt.title('Gaussian Pulse') +# plt.show() + +# for i in range_of_phases: +# f_mod =0 +# m = f_mod * jnp.ones(len(t),dtype = complex) +# models['phase_modulator'] = Modulator(mod_signal=m) +# time_sim = TimeSim( +# netlist=netlist, +# models=models, +# # active_components=active_components, +# ) +# time_sim.build_model(model_parameters=options, center_wvl=center_wvl, dt=dt, max_size= 3) + +# num_outputs = 2 +# inputs = { +# f'o{i}': smooth_rectangular_pulse(t, 0.01e-10, 0.30e-10) if i == 0 else jnp.zeros_like(t) +# for i in range(num_outputs) +# } + + +# modelResult =time_sim.run(t, inputs) + + +# outputs = modelResult.outputs +# phases_at_o1.append(outputs['o1'][-1]) +# # phases_at_o2.append(outputs['o2'][-1]) +# if i == -2*jnp.pi or i == 2*jnp.pi or i == 0: +# modelResult.plot_sim() + +# # print(phases_at_o2[-1]) + +# phases = jnp.array(phases_at_o1) +# # phases2 = jnp.array(phases_at_o2) + +# plt.figure() +# plt.plot(range_of_phases, jnp.abs(phases)**2, label='o1 last') +# # plt.plot(range_of_phases, jnp.abs(phases2)**2, label='o2 last') +# plt.xlabel('Applied phase (rad)') +# plt.ylabel('Final output signal') +# plt.legend() +# plt.show() diff --git a/simphony/time_domain/tests/testingOptimizedfile.py b/simphony/time_domain/tests/testingOptimizedfile.py new file mode 100644 index 00000000..c4d5a44d --- /dev/null +++ b/simphony/time_domain/tests/testingOptimizedfile.py @@ -0,0 +1,192 @@ +from jax import config + +config.update("jax_enable_x64", True) + + +def remove_active_connections(connections, active_components): + """Remove all connections that involve any active component.""" + filtered_connections = {} + for k, v in connections.items(): + compA, portA = k.split(",") + compB, portB = v.split(",") + + # Skip if either endpoint is an active component + if compA in active_components or compB in active_components: + continue + + # Otherwise keep it + filtered_connections[k] = v + return filtered_connections + + +def remove_active_ports(ports, active_components): + """Remove ports that connect to active components.""" + filtered_ports = {} + for port_label, comp_port_str in ports.items(): + comp, cport = comp_port_str.split(",") + if comp not in active_components: + filtered_ports[port_label] = comp_port_str + return filtered_ports + + +def build_graph(connections, directed=False): + """Build an adjacency list from the netlist connections. Each (component, + port) pair is treated as a graph node. + + If directed=False, each connection is stored as two directed edges + (A->B, B->A). If directed=True, only the forward direction is + stored. + """ + graph = {} + + def add_edge(node1, node2): + if node1 not in graph: + graph[node1] = [] + graph[node1].append(node2) + + for k, v in connections.items(): + compA, _ = k.split(",") + compB, _ = v.split(",") + + nodeA = compA + nodeB = compB + + # Always add A->B + add_edge(nodeA, nodeB) + # If it's undirected, also add B->A + if not directed: + add_edge(nodeB, nodeA) + + return graph + + +def tarjan_scc(graph): + """Tarjan's SCC algorithm. + + Returns a list of strongly connected components (SCCs), where each + SCC is a list of nodes [(comp, port), ...]. + """ + index_counter = [0] # mutable counter + stack = [] + on_stack = set() + indices = {} + lowlinks = {} + sccs = [] + + def strongconnect(node): + indices[node] = index_counter[0] + lowlinks[node] = index_counter[0] + index_counter[0] += 1 + stack.append(node) + on_stack.add(node) + + # Consider successors of node + for w in graph.get(node, []): + if w not in indices: + # Successor w has not yet been visited; recurse on it + strongconnect(w) + lowlinks[node] = min(lowlinks[node], lowlinks[w]) + elif w in on_stack: + # Successor w is in the stack and hence in the current SCC + lowlinks[node] = min(lowlinks[node], indices[w]) + + # If node is a root node, pop the stack and generate an SCC + if lowlinks[node] == indices[node]: + # Start a new SCC + scc = [] + while True: + w = stack.pop() + on_stack.remove(w) + scc.append(w) + if w == node: + break + sccs.append(scc) + + # Run strongconnect for each node that is unvisited + for node in graph.keys(): + if node not in indices: + strongconnect(node) + + return sccs + + +def find_sccs_for_passive_subnets( + instances, connections, ports, active_components, directed=False +): + """Main function that orchestrates: 1) Removal of active-component + connections 2) Graph construction 3) Tarjan's SCC search 4) Grouping the + results by component (optional) + + Returns: + A list of sets, each set containing the (passive) components + that form a strongly connected sub-net (SCC). + """ + + # 1. Remove connections that involve active components + filtered_conns = remove_active_connections(connections, active_components) + + # 2. Remove top-level ports referencing active components (optional) + filtered_ports = remove_active_ports(ports, active_components) + + # 3. Build a graph from the remaining passive connections + graph = build_graph(filtered_conns, directed=directed) + + # 4. Run Tarjan’s to find SCCs + sccs = tarjan_scc(graph) + + # 5. Group by component (optional). If you want per-port detail, skip this step. + # scc_groups = group_components_by_scc(sccs) + + return sccs + + +# ------------------------- +# Example usage: +if __name__ == "__main__": + # Example netlist data + instances = { + "cr1": "y_branch", + "cr2": "y_branch", + "wg1": "waveguide", + "wg2": "waveguide", + "cr3": "y_branch", + "cr4": "y_branch", + "wg3": "waveguide", + "wg4": "waveguide", + "pm": "phase_modulator", + "cr5": "y_branch", + "cr6": "y_branch", + "wg5": "waveguide", + "tm": "tunable_modulator", + } + connections = { + "tm,o0": "cr1,port_2", + "tm,o1": "wg1,o1", + "cr1,port_3": "wg2,o0", + "wg1,o1": "cr2,port_2", + "wg2,o1": "cr2,port_3", + "cr2, port_1": "cr5,port_1", + "cr3, port_1": "cr6,port_1", + "cr3,port_2": "wg3,o0", + "cr3,port_3": "wg4,o0", + "cr4,port_2": "wg3,o1", + "cr4,port_3": "wg4,o1", + "cr5,port_2": "wg5,o0", + "cr5,port_3": "pm,o0", + "cr6,port_2": "wg5,o1", + "cr6,port_3": "pm,o1", + } + ports = { + "o0": "cr1,port_1", + "o1": "cr4,port_1", + } + active_components = {"pm", "tm"} + + # Find the passive SCC groups + passive_sccs = find_sccs_for_passive_subnets( + instances, connections, ports, active_components, directed=False + ) + + print("Passive strongly connected sub-net groups (by component):") + for group in passive_sccs: + print(" ", group) diff --git a/simphony/time_domain/time_circuit.py b/simphony/time_domain/time_circuit.py new file mode 100644 index 00000000..98e5fa61 --- /dev/null +++ b/simphony/time_domain/time_circuit.py @@ -0,0 +1,97 @@ +# from jax.typing import ArrayLike +# import jax.numpy as jnp + +# class TimeCircuit: +# def __init__(self, netlist: dict, models: dict): +# # DEFAULT_TIME_STEP = 1e-14 +# # self.time_step = DEFAULT_TIME_STEP +# self.netlist = netlist +# # self.connections = netlist['connections'] +# self.models = models +# #self.connections = {} +# # self.ports = netlist['ports'] +# # self.model_types = models + +# def instantiate(self, dt, clear_on_reset=True, **kwargs): +# self.dt = dt +# instantiated_models = {} +# self.clear = clear_on_reset +# for model_name, model in kwargs.items(): +# if model_name in self.models: +# instantiated_models[model_name] = model +# else: +# raise ValueError(f"Model '{model_name}' is not defined in models.") + +# self.instances = {} +# for instance, model_name in self.netlist['instances'].items(): +# self.instances[instance] = instantiated_models[model_name] +# # self.instance_outputs[instance] = {port: jnp.array([0+0j]) for port in instantiated_models[model_name].ports} + +# self.connections = {instance: {} for instance in self.instances.keys()} +# for designation_a, designation_b in self.netlist['connections'].items(): +# instance_a, port_a = map(str.strip, designation_a.split(',')) +# instance_b, port_b = map(str.strip, designation_b.split(',')) +# # self.instance_input_addresses[instance_a] = (instance_b, port_b) # doesn't work since it does not specify the a port +# self.connections[instance_a][port_a] = (instance_b, port_b) +# self.connections[instance_b][port_b] = (instance_a, port_a) + + +# self.ports = {} +# for circuit_port, designation in self.netlist['ports'].items(): +# instance_name, instance_port = map(str.strip, designation.split(',')) +# self.ports[circuit_port] = (instance_name, instance_port) + +# # we need to create a list containing time systems and a corresponding list with connections +# # The index of the two lists will correlate + +# print(f"Instances created with dt={dt}: {self.instances}") + + +# def run_sim(self, t: ArrayLike, inputs: dict)->dict: +# if self.clear: +# for instance_name, time_system in self.instances.items(): +# time_system.clear() + +# self.inputs = inputs +# self.outputs = {port: jnp.array([]) for port in self.ports} +# self.instance_outputs = {} +# for instance_name, time_system in self.instances.items(): +# self.instance_outputs[instance_name] = {port: jnp.array([0+0j]) for port in time_system.ports} + +# for _ in t: +# self.step() +# pass + +# return self.outputs + + +# def step(self): +# for instance_name, time_system in self.instances.items(): +# instance_inputs = {} +# for port in time_system.ports: +# # TODO: Contrust the input in order to run the response function +# # Then update the instance_outputs +# # Looking in connections +# if instance_name in self.connections and port in self.connections[instance_name]: +# print(f"Looking in connections") +# source_name, source_port = self.connections[instance_name][port] +# instance_inputs[port] = self.instance_outputs[source_name][source_port] + +# else: +# # Looking in Circuit Ports +# print(f"Looking in Circuit Ports") +# instance_inputs[port] = self.ports +# designation = (instance_name, port) +# circuit_port = next((k for k, v in self.ports.items() if v == designation), None) +# instance_inputs[port] = jnp.array(self.inputs[circuit_port][0]) +# self.inputs[circuit_port] = self.inputs[circuit_port][1:] + +# # Compute Resposne for each instance and submit result to instance_outputs +# outputs = time_system.response(instance_inputs) +# for port_name in outputs: +# self.instance_outputs[instance_name][port_name] = outputs[port_name] +# pass + + +# for circuit_port, (instance, instance_port) in self.ports.items(): +# self.outputs[circuit_port] = jnp.concatenate([self.outputs[circuit_port], self.instance_outputs[instance][instance_port]]) diff --git a/simphony/time_domain/time_system.py b/simphony/time_domain/time_system.py new file mode 100644 index 00000000..20f5d773 --- /dev/null +++ b/simphony/time_domain/time_system.py @@ -0,0 +1,320 @@ +from abc import ABC +from functools import partial + +import jax +import jax.numpy as jnp +import numpy as np +import sax +from jax.typing import ArrayLike + +from simphony.time_domain.pole_residue_model import PoleResidueModel + +# from simphony.circuit import SampleModeComponent, BlockModeComponent + + +class TimeSystem(ABC): + def __init__(self, optical_ports, electrical_ports, logic_ports) -> None: + if optical_ports is None: + self.optical_ports = [] + else: + self.optical_ports = optical_ports + + if electrical_ports is None: + self.electrical_ports = [] + else: + self.electrical_ports = electrical_ports + # self.electrical_ports = electrical_ports + if logic_ports is None: + self.logic_ports = [] + else: + self.logic_ports = logic_ports + + total_ports = self.optical_ports + self.electrical_ports + self.logic_ports + unique_port_names = len(total_ports) == len(set(total_ports)) + + if not unique_port_names: + raise ValueError("Port names must be uniqe") + + def __call__(self, wl: ArrayLike, **kwargs) -> sax.SDict: + return self.frequency_response(wl, **kwargs) + + def frequency_response(self, wl: ArrayLike, **kwargs) -> sax.SDict: + raise NotImplementedError + + +# class BlockModeComponent(TimeSystem, ABC): +# def __init__( +# self, optical_ports=None, electrical_ports=None, logic_ports=None +# ) -> None: +# super().__init__(optical_ports, electrical_ports, logic_ports) + +# @abstractmethod +# def run(self, input_signal: ArrayLike, **kwargs) -> ArrayLike: +# """Compute the system response.""" +# raise NotImplementedError + + +# class SampleModeComponent(TimeSystem, ABC): +# def __init__(self) -> None: +# super().__init__() + +# @abstractmethod +# def init_state(self, **kwargs): +# """Initialize the state of the system.""" +# raise NotImplementedError + +# @abstractmethod +# def step(self, x_prev, inputs: Tuple, **kwargs) -> jnp.ndarray: +# """Compute the next state of the system.""" +# raise NotImplementedError + + +# def my_dlsim(system, u, t=None, x0=None): +# out_samples = len(u) +# stoptime = (out_samples - 1) * system.dt + +# xout = np.zeros((out_samples, system.A.shape[0]), dtype=complex) +# yout = np.zeros((out_samples, system.C.shape[0]), dtype=complex) +# tout = np.linspace(0.0, stoptime, num=out_samples) + +# xout[0, :] = np.zeros((system.A.shape[1],), dtype=complex) +# if x0 is not None: +# xout[0, :] = x0 + +# u_dt = u + +# # Simulate the system +# for i in range(0, out_samples - 1): +# xout[i+1, :] = (np.dot(system.A, xout[i, :]) + +# np.dot(system.B, u_dt[i, :])) +# yout[i, :] = (np.dot(system.C, xout[i, :]) + +# np.dot(system.D, u_dt[i, :])) + +# # Last point +# yout[out_samples-1, :] = (np.dot(system.C, xout[out_samples-1, :]) + +# np.dot(system.D, u_dt[out_samples-1, :])) + +# return tout, yout, xout + + +def my_dlsim(system, u, t=None, x0=None): + out_samples = len(u) + stoptime = (out_samples - 1) * system.dt + + xout = jnp.zeros((out_samples, system.A.shape[0]), dtype=jnp.complex64) + yout = jnp.zeros((out_samples, system.C.shape[0]), dtype=jnp.complex64) + tout = jnp.linspace(0.0, stoptime, num=out_samples) + + # Manually create a mutable copy because JAX arrays are immutable + xout = xout.at[0, :].set(jnp.zeros((system.A.shape[1],), dtype=jnp.complex64)) + if x0 is not None: + xout = xout.at[0, :].set(x0.flatten()) + # xout = xout.at[0, :].set(x0) + + u_dt = u + + # Simulate the system (using Python loop since JAX loops need special handling) + xout_list = [xout[0, :]] + yout_list = [] + + for i in range(out_samples - 1): + x_next = jnp.dot(system.A, xout_list[-1]) + jnp.dot(system.B, u_dt[i, :]) + y_curr = jnp.dot(system.C, xout_list[-1]) + jnp.dot(system.D, u_dt[i, :]) + xout_list.append(x_next) + yout_list.append(y_curr) + + # Final output + y_last = jnp.dot(system.C, xout_list[-1]) + jnp.dot(system.D, u_dt[-1, :]) + yout_list.append(y_last) + + # Stack results + xout = jnp.stack(xout_list) + yout = jnp.stack(yout_list) + + return tout, yout, xout + + +def my_dlsimworks_jax(system, u, t=None, x0=None): + out_samples = len(u) + stoptime = (out_samples) * system.dt + + # xout = np.zeros((out_samples, system.A.shape[0]), dtype=complex) + # yout = np.zeros((out_samples, system.C.shape[0]), dtype=complex) + # tout = np.linspace(0.0, stoptime, num=out_samples) + xout = jnp.zeros((out_samples, system.A.shape[0]), dtype=jnp.complex128) + yout = jnp.zeros((out_samples, system.C.shape[0]), dtype=jnp.complex128) + tout = jnp.linspace(0.0, stoptime, num=out_samples) + + # xout[0, :] = np.zeros((system.A.shape[1],), dtype=complex) + # if x0 is not None: + # xout[0, :] = x0 + xout = xout.at[0, :].set(jnp.zeros((system.A.shape[1],), dtype=jnp.complex128)) + if x0 is not None: + xout = xout.at[0, :].set(x0.flatten()) + # xout = xout.at[0, :].set(x0) + + u_dt = u + + # # Simulate the system + # for i in range(0, out_samples): + # xout[i, :] = (np.dot(system.A, xout[i, :]) + + # np.dot(system.B, u_dt[i, :])) + # yout[i, :] = (np.dot(system.C, xout[i, :]) + + # np.dot(system.D, u_dt[i, :])) + + # # Last point + # yout[out_samples-1, :] = (np.dot(system.C, xout[out_samples-1, :]) + + # np.dot(system.D, u_dt[out_samples-1, :])) + # Simulate the system + for i in range(0, out_samples): + xout = xout.at[i, :].set( + jnp.dot(system.A, xout[i, :]) + jnp.dot(system.B, u_dt[i, :]) + ) + yout = yout.at[i, :].set( + jnp.dot(system.C, xout[i, :]) + jnp.dot(system.D, u_dt[i, :]) + ) + + # Last point + yout = yout.at[out_samples - 1, :].set( + jnp.dot(system.C, xout[out_samples - 1, :]) + + jnp.dot(system.D, u_dt[out_samples - 1, :]) + ) + + return tout, yout, xout + + +def my_dlsimworks(system, u, t=None, x0=None): + out_samples = len(u) + stoptime = (out_samples) * system.dt + + xout = np.zeros((out_samples, system.A.shape[0]), dtype=complex) + yout = np.zeros((out_samples, system.C.shape[0]), dtype=complex) + tout = np.linspace(0.0, stoptime, num=out_samples) + + xout[0, :] = np.zeros((system.A.shape[1],), dtype=complex) + + if x0 is not None: + xout[0, :] = x0 + + u_dt = u + + # Simulate the system + for i in range(0, out_samples): + xout[i, :] = np.dot(system.A, xout[i, :]) + np.dot(system.B, u_dt[i, :]) + yout[i, :] = np.dot(system.C, xout[i, :]) + np.dot(system.D, u_dt[i, :]) + + # Last point + yout[out_samples - 1, :] = np.dot(system.C, xout[out_samples - 1, :]) + np.dot( + system.D, u_dt[out_samples - 1, :] + ) + + return tout, yout, xout + + +# class TimeSystemIIR(TimeSystem): +# def __init__(self, pole_model: PoleResidueModel, ports= None ) -> None: +# super().__init__() +# self.sys = pole_model.generate_sys_discrete() +# self.num_ports = self.sys.B.shape[1] +# if ports is None: +# self.ports = [f'o{i}' for i in range(self.num_ports)] +# else: +# self.ports = ports + +# self.state_vector = None + + +# def response(self, inputs: dict, time_sim = True) -> ArrayLike: +# # if state_vector is not None: +# # self.state_vector = state_vector + +# first_key = next(iter(inputs)) +# N = inputs[first_key].shape +# responses = {} + +# input = jnp.hstack([value.reshape(-1, 1) +# for value in inputs.values()]) +# if not time_sim: +# t,y_out,x_out = my_dlsim(self.sys, input, x0 = self.state_vector) +# else: +# t,y_out,x_out = my_dlsimworks(self.sys, input, x0 = self.state_vector) + +# self.state_vector = x_out + +# j = 0 +# for i in self.ports: +# responses[i] = y_out[:,j] +# j += 1 + +# return responses,t + +# def reset(self): +# self.state_vector = None + + +class TimeSystemIIR: + def __init__(self, pole_model: PoleResidueModel, ports=None): + super().__init__() + # Generate the discrete‐time state‐space (A,B,C,D) from your pole‐residue model: + # self.sys.A, self.sys.B, self.sys.C, self.sys.D + self.sys = pole_model.generate_sys_discrete() + + # Number of “input” ports is B.shape[1], number of “output” ports is C.shape[0] + self.num_in_ports = self.sys.B.shape[1] + self.num_out_ports = self.sys.C.shape[0] + + if ports is None: + # default port names: o0, o1, ... (one per output channel) + self.ports = [f"o{i}" for i in range(self.num_out_ports)] + else: + self.ports = ports + + def init_state(self, **kwargs): + """Return the initial x(0) for this IIR system. + + If you want x(0)=0, make a zeros vector of shape (A.shape[0],). + """ + n_states = self.sys.A.shape[0] + return jnp.zeros((n_states,), dtype=jnp.complex128) + + @partial(jax.jit, static_argnums=(0,)) + def step(self, x_prev: jnp.ndarray, inputs: tuple, **kwargs): + """A pure function (no in-place mutation). If there are N inputs to + this IIR block, `inputs_tuple` is a tuple of length N, so we first + stack them into a 1-D vector `u_row`. + + Then: + x_next = A @ x_prev + B @ u_row + y_row = C @ x_prev + D @ u_row + return (x_next, tuple(y_row_i for each scalar output)). + """ + # Stack all inputs into one (n_inputs,) vector: + self.test_array = jnp.array([]) + u_row = jnp.stack(inputs, axis=0) # shape = (n_inputs,) + A, B, C, D = self.sys.A, self.sys.B, self.sys.C, self.sys.D + x_next = A @ x_prev + B @ u_row # shape = (n_states,) + y_full = C @ x_prev + D @ u_row # shape = (n_outputs,) + # We need to return a Python tuple of length = n_outputs: + # e.g. (y_full[0], y_full[1], …) + return x_next, tuple(jnp.atleast_1d(y_full[i]) for i in range(y_full.shape[0])) + + def run(self, inputs: dict, time_sim=True, **kwargs) -> ArrayLike: + # if state_vector is not None: + # self.state_vector = state_vector + + responses = {} + + input = jnp.hstack([value.reshape(-1, 1) for value in inputs.values()]) + if not time_sim: + t, y_out, x_out = my_dlsim(self.sys, input, x0=self.state_vector) + else: + t, y_out, x_out = my_dlsimworks(self.sys, input, x0=self.state_vector) + + self.state_vector = x_out + + j = 0 + for i in self.ports: + responses[i] = y_out[:, j] + j += 1 + + return responses, t diff --git a/simphony/time_domain/utils.py b/simphony/time_domain/utils.py new file mode 100644 index 00000000..659d0f77 --- /dev/null +++ b/simphony/time_domain/utils.py @@ -0,0 +1,25 @@ +import jax.numpy as jnp +from jax.typing import ArrayLike + + +def gaussian_pulse(t, t0, sigma, a=1.0) -> ArrayLike: + return a * jnp.exp(-((t - t0) ** 2) / sigma**2) + + +def smooth_rectangular_pulse(t, t_start, t_end, width=None): + """A useful function for testing time-domain components. + + t = np.linspace(0, 1e-10, 1000) + pulse = smooth_rectangular_pulse(t, t_start=20e-12, t_end=40e-12) + plt.plot(t, pulse) + plt.show() + """ + if width is None: + width = (t_end - t_start) / 100.0 + # Transition functions + rise = 0.5 * (1 + jnp.tanh((t - t_start) / width)) + fall = 0.5 * (1 + jnp.tanh((t_end - t) / width)) + + # Smooth pulse + pulse = rise * fall + return pulse diff --git a/simphony/time_domain/vector_fitting/old/old_code.py b/simphony/time_domain/vector_fitting/old/old_code.py new file mode 100644 index 00000000..7d6ad6fa --- /dev/null +++ b/simphony/time_domain/vector_fitting/old/old_code.py @@ -0,0 +1,735 @@ +# P. Triverio, "Vector Fitting", in P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. H. A. Schilders, L. M. Silveira (Eds.), "Model Order Reduction. Volume 1: System- and Data-Driven Methods and Algorithms", De Gruyter, 2021. +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from numpy.linalg import svd +from scipy.linalg import block_diag +from scipy.signal import StateSpace, bilinear_zpk + + +def samples_flow_rate(omega): + # section 3.3 example + pole = [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + residue = [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + y = [] + order = len(pole) + for s in omega * 1j: + flow_rate = residue[0] + for n in range(order): + flow_rate += residue[n + 1] / (s - pole[n]) + y.append(flow_rate) + + return y + + +class Pole_Residue_Model: + def __init__(self): + pass + + +class FVF_Options: + def __init__( + self, + poles_estimation_threshold=1e-1, + model_error_threshold=1e-3, + max_iterations=5, + enforce_stability=True, + alpha=0.01, + debug=True, + mode="CVF", + ): + self.poles_estimation_threshold = poles_estimation_threshold + self.model_error_threshold = model_error_threshold + self.max_iterations = max_iterations + self.enforce_stability = enforce_stability + self.debug = debug + self.alpha = alpha + + +def compute_phi_matrices(omega, poles_real, poles_complex): + kbar = len(omega) + if np.shape(poles_real) == (): + num_poles_real = 0 + else: + num_poles_real = len(poles_real) + + num_complex_pairs = len(poles_complex) + + phi_real = np.zeros((kbar, num_poles_real), dtype=complex) + for i in range(num_poles_real): + phi_real[:, i] = 1.0 / (1j * omega[:, 0] - poles_real[i]) + phi_complex = np.zeros((kbar, 2 * num_complex_pairs), dtype=complex) + for i in range(num_complex_pairs): + phi_complex[:, 2 * i] = 1.0 / (1j * omega[:, 0] - poles_complex[i]) + 1.0 / ( + 1j * omega[:, 0] - np.conj(poles_complex[i]) + ) + phi_complex[:, 2 * i + 1] = 1j / (1j * omega[:, 0] - poles_complex[i]) - 1j / ( + 1j * omega[:, 0] - np.conj(poles_complex[i]) + ) + + return phi_real, phi_complex + + +def ComputeModelResponse(omega, R0, Rr, Rc, pr, pc): + """Compute the frequency response of a Vector Fitting model. + + Parameters: + - omega: frequency samples, column vector. This is angular frequency (omega = 2*pi*f) + - R0: constant coefficient + - Rr: residues of real poles, 3D array. First dimension corresponds to system outputs. + Second dimension to system inputs. Third dimension corresponds to the various poles. + - Rc: residues of complex conjugate pole pairs (only one per pair) + - pr: real poles, column vector + - pc: complex poles, column vector. Only one per pair of conjugate poles + + Returns: + - H: model response samples, 3D array. First dimension corresponds to system outputs. + Second dimension to system inputs. Third dimension corresponds to frequency. + """ + qbar = R0.shape[0] # number of outputs + mbar = R0.shape[1] # number of inputs + kbar = len(omega) # number of frequency points + + nr = len(pr) # number of real poles + nc = len(pc) # number of complex conjugate pairs + + # Preallocate space for H + H = np.zeros((qbar, mbar, kbar), dtype=complex) + + # Compute the model response + for ik in range(kbar): + H[:, :, ik] = R0 + for ir in range(nr): + H[:, :, ik] += Rr[:, :, ir] / (1j * omega[ik] - pr[ir]) + for ic in range(nc): + H[:, :, ik] += Rc[:, :, ic] / (1j * omega[ik] - pc[ic]) + H[:, :, ik] += np.conj(Rc[:, :, ic]) / (1j * omega[ik] - np.conj(pc[ic])) + + return H + + +def compute_time_response(times, R0, Rr, Rc, pr, pc): + nr = len(pr) + nc = len(pc) + q_bar = R0.shape[0] + m_bar = R0.shape[1] + + h = np.zeros((len(times), q_bar, m_bar), dtype=complex) + + for t_index in range(len(times)): + t = times[t_index] + + h[t_index, :, :] = R0 + # real poles + for n in range(nr): + h[t_index, :, :] += Rr[:, :, n] * np.exp(pr[n] * t) + + # complex poles + for n in range(nc): + h[t_index, :, :] += 2 * np.real(Rc[:, :, n]) * np.exp( + np.real(pc[n]) * t + ) * np.cos(np.imag(pc[n]) * t) - 2 * np.imag(Rc[:, :, n]) * np.exp( + np.real(pc[n]) * t + ) * np.sin( + np.imag(pc[n] * t) + ) + + return h + + +def complex_valued_ABCD(model): + nr = model.Rr.shape[2] + nc = model.Rc.shape[2] + + n_bar = nr + 2 * nc + q_bar = model.R0.shape[0] + m_bar = model.R0.shape[1] + + R_n = [] + p_n = [] + + A_n = [] + B_n = [] + C_n = [] + + for n in range(nr): + # IMPLEMENT ME + pass + + for n in range(nc): + pass + + for n in range(nc): + R_n.append(model.Rc[:, :, n]) + R_n.append(np.conj(model.Rc[:, :, n])) + p_n.append(model.poles_complex[n]) + p_n.append(np.conj(model.poles_complex[n])) + + for n in range(n_bar): + U, S, Vh = svd(R_n[n]) + rank = S.shape[0] + # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + A_n.append(p_n[n] * np.eye(rank)) + B_n.append(Vh.T) + C_n.append((U @ S).reshape(q_bar, rank)) + + # A = block_diag(*A_n) + poles_s = np.concatenate( + [model.poles_real, model.poles_complex, np.conj(model.poles_complex)] + ) + z, poles_z, k = bilinear_zpk([], poles_s, 0, 100) + A = np.diag(poles_s) + B = np.block(B_n) + B = B.T + + C = np.block(C_n) + df = pd.DataFrame(A) + df.to_csv("A_matrix.csv") + + D = model.R0 + return A, B, C, D + + +def real_valued_ABCD(model): + nr = model.Rr.shape[2] + nc = model.Rc.shape[2] + + n_bar = nr + 2 * nc + q_bar = model.R0.shape[0] + m_bar = model.R0.shape[1] + + A_n = [] + B_n = [] + C_n = [] + + for n in range(len(model.poles_real)): + U, S, Vh = svd(model.Rr[:, :, n]) + rank = S.shape[0] + # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + A_n.append(model.poles_real[n] * np.eye(rank)) + B_n.append(Vh.T) + C_n.append((U @ S).reshape(q_bar, rank)) + + for n in range(len(model.poles_complex)): + # R = R_n[n] + U, S, Vh = svd(model.Rc[:, :, n]) + rank = S.shape[0] + A_n.append( + np.block( + [ + [ + np.real(model.poles_complex[n]) * np.eye(rank), + np.imag(model.poles_complex[n]) * np.eye(rank), + ], + [ + -np.imag(model.poles_complex[n]) * np.eye(rank), + np.real(model.poles_complex[n]) * np.eye(rank), + ], + ] + ) + ) + B_n.append(2 * np.real(Vh)) + B_n.append(2 * np.imag(Vh)) + C_n.append(np.real((U @ S).reshape(q_bar, rank))) + C_n.append(np.imag((U @ S).reshape(q_bar, rank))) + + A = block_diag(*A_n) + A = block_diag(*A_n) + # A = np.diag(poles_z) + B = np.block(B_n) + B = B.T + C = np.block(C_n) + D = model.R0 + + # df = pd.DataFrame(A) + # df.to_csv("A_matrix.csv") + # df = pd.DataFrame(B) + # df.to_csv("B_matrix.csv") + # df = pd.DataFrame(C) + # df.to_csv("C_matrix.csv") + # df = pd.DataFrame(D) + # df.to_csv("D_matrix.csv") + return A, B, C, D + + +def state_space_ABCD(model, real_valued): + if real_valued: + return real_valued_ABCD(model) + else: + return complex_valued_ABCD(model) + + +def state_space(model, dt): + A, B, C, D = state_space_ABCD(model, True) + sys = StateSpace(A, B, C, np.real(D)) + return sys + + +def FastVF(omega, H, order, options): + # H = np.squeeze(np.asarray(H)) + if options == None: + options = FVF_Options() + + kbar = len(omega) + qbar = np.shape(H)[1] + mbar = np.shape(H)[2] + nbar = order + alpha = options.alpha + + # Ensure omega is a column vector + omega = np.array(omega).reshape((kbar, 1)) + model = Pole_Residue_Model() + + num_real_poles = nbar % 2 + num_complex_pairs = (nbar - nbar % 2) // 2 + + poles_real = np.array((num_real_poles)) + if num_real_poles == 1: + poles_real = [-alpha * max(omega)] + + poles_complex = np.array((num_complex_pairs), dtype=complex) + if num_complex_pairs == 1: + poles_complex = (-alpha + 1j) * np.max(omega) / 2 + elif np.min(omega) == 0: + poles_complex = ( + (-alpha + 1j) + * max(omega) + * np.arange(1, num_complex_pairs + 1, dtype=complex) + / num_complex_pairs + ) + # poles_complex = poles_complex.reshape((len(poles_complex), 1)) + else: + poles_complex = (-alpha + 1j) * ( + min(omega) + + (max(omega) - min(omega)) + / (num_complex_pairs - 1) + * np.arange(0, num_complex_pairs, dtype=complex) + ) + # poles_complex = poles_complex.reshape((len(poles_complex), 1)) + + iter = 1 + while iter <= options.max_iterations: + phi_real, phi_complex = compute_phi_matrices(omega, poles_real, poles_complex) + + M = np.zeros((2 * kbar, 2 * nbar + 1), dtype=complex) + A = np.zeros((nbar * qbar * mbar, nbar), dtype=complex) + b = np.zeros((nbar * qbar * mbar, 1), dtype=complex) + + # Compute the first columns of M, which do not depend on q and m + M[:kbar, 0] = np.ones((kbar)) + M[:kbar, 1 : num_real_poles + 1] = np.real(phi_real) + M[:kbar, num_real_poles + 1 : nbar + 1] = np.real(phi_complex) + M[kbar:, 1 : num_real_poles + 1] = np.imag(phi_real) + M[kbar:, num_real_poles + 1 : nbar + 1] = np.imag(phi_complex) + + irow = 0 + for q in range(qbar): + for m in range(mbar): + V_H = np.squeeze(H[:, q, m]) + # D_Hqm = diags(V_H, 0, shape=(kbar, kbar), format='csr') + D_Hqm = np.diag(V_H) + M[:kbar, nbar + 1 : nbar + num_real_poles + 1] = -np.real( + D_Hqm @ phi_real + ) + M[:kbar, nbar + num_real_poles + 1 :] = -np.real(D_Hqm @ phi_complex) + M[kbar:, nbar + 1 : nbar + num_real_poles + 1] = -np.imag( + D_Hqm @ phi_real + ) + M[kbar:, nbar + num_real_poles + 1 :] = -np.imag(D_Hqm @ phi_complex) + + # QR decomposition + Q, R = np.linalg.qr(M, mode="reduced") + A[irow : irow + nbar, :] = R[nbar + 1 :, nbar + 1 :] + b[irow : irow + nbar, 0] = Q[:kbar, nbar + 1 :].T @ np.real(V_H) + Q[ + kbar:, nbar + 1 : + ].T @ np.imag(V_H) + + # cw = Alsq\blsq; + # cw = np.linalg.solve(A, b) + cw = np.linalg.lstsq(A, b)[0] + w = ( + 1 + phi_real @ cw[:num_real_poles] + phi_complex @ cw[num_real_poles:] + ) # DOUBLE CHECKED TO THIS POINT + # Compute the new poles estimate + A = np.zeros((nbar, nbar)) + bw = np.ones((nbar, 1)) # DOUBLE CHECKED TO THIS POINT + for i in range(num_real_poles): + A[i, i] = poles_real[i] + # bw(ii) = 1; + + for i in range(num_complex_pairs): + A[ + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), + ] = np.array( + [ + [np.real(poles_complex[i]), np.imag(poles_complex[i])], + [-np.imag(poles_complex[i]), np.real(poles_complex[i])], + ] + ) + bw[ + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), 0 + ] = np.array([2, 0]) + # A(nr+2*ii-1:nr+2*ii,nr+2*ii-1:nr+2*ii) = [real(pc(ii)), imag(pc(ii)); -imag(pc(ii)),real(pc(ii))]; + + new_poles, _ = np.linalg.eig(A - bw @ cw.conj().T) + + # Extract Real Poles + eps = np.finfo(float).eps + ind_rp = np.nonzero(np.abs(np.imag(new_poles)) < 10 * eps * np.abs(new_poles))[ + 0 + ] # I increased the constant 10 to 100 for now + + poles_real = np.real(new_poles[ind_rp]) + # num_real_poles = len(ind_rp) + + # Extract complex conjugate pairs of poles + # Find only the poles with positive imaginary part + ind_cp = np.nonzero(np.imag(new_poles) >= 10 * eps * abs(new_poles))[0] + # ind_cp = np.nonzero(np.imag(new_poles)>=100*eps*abs(new_poles))[0] + poles_complex = new_poles[ind_cp] + num_complex_pairs = len(ind_cp) + + num_real_poles = len(poles_real) + + while len(poles_real) + 2 * len(poles_complex) < order: + mask = np.argmin(np.abs(np.imag(new_poles))) + poles_real = np.append(poles_real, new_poles[mask]) + + num_real_poles = len(poles_real) + + while 2 * len(poles_complex) > order: + most_real_index = np.argmin(np.abs(np.imag(poles_complex))) + poles_real = np.append(poles_real, poles_complex[most_real_index]) + poles_complex = np.delete(poles_complex, most_real_index) + + num_complex_pairs = len(poles_complex) + + while len(poles_real) + 2 * len(poles_complex) > order: + if len(poles_real) == 1: + poles_real = np.array([]) + break + magnitudes = np.abs(poles_real) + + # Step 2: Calculate the differences in magnitude between neighboring elements + # Calculate the differences between magnitudes of consecutive elements + diffs = np.abs(np.diff(magnitudes)) + + # Step 3: Find the index of the element with the smallest difference in magnitude with its neighbor + # Since we calculate differences between consecutive elements, the index of the smallest difference + # will be between the i-th and (i+1)-th elements. + # We take the first element of the pair to remove. + index_of_min_diff = np.argmin(diffs) + + # Since we want to remove the element with the smallest difference, we choose the one with the + # smaller magnitude if we're between a pair. We choose the min of the index of min difference and the next element. + if index_of_min_diff == len(magnitudes) - 1: + index_to_remove = index_of_min_diff # If it's the last element, we can only remove this one + else: + if magnitudes[index_of_min_diff] <= magnitudes[index_of_min_diff + 1]: + index_to_remove = index_of_min_diff + else: + index_to_remove = index_of_min_diff + 1 + + # Step 4: Delete the element at the found index + poles_real = np.delete(poles_real, index_to_remove) + + num_real_poles = len(poles_real) + # poles_real = np.real(poles_real) + + # Stability/causality enforcement + if options.enforce_stability: + poles_real = -np.abs(poles_real) + poles_complex = -np.abs(np.real(poles_complex)) + 1j * np.imag( + poles_complex + ) + + # mask_real = np.abs(poles_real) > 1 + # mask_complex = np.abs(poles_complex) > 1 + # poles_real[mask_real] = 1 / poles_real[mask_real] + # poles_complex[mask_complex] = 1 / poles_complex[mask_complex] + + # poles_real[mask_real] = poles_real[mask_real] / np.abs(poles_real[mask_real]) + # poles_complex[mask_complex] = poles_complex[mask_complex] / np.abs(poles_complex[mask_complex]) + + if options.debug: + plt.scatter(np.real(poles_complex), np.imag(poles_complex)) + plt.scatter(np.real(poles_real), np.imag(poles_real)) + plt.xlabel("Real") + plt.ylabel("Imaginary") + plt.title(f"Poles estimate, iteration {i}") + + # First convergence test + + w_minus_one = 1 / np.sqrt(kbar) * np.linalg.norm(np.abs(w - 1)) + # Do a tentative model fitting if either: + # - the first convergence test is successful + # - Options.debug is enabled + if w_minus_one <= options.poles_estimation_threshold: + print(f"Convergence test (poles estimation): \t\tpassed ({w_minus_one})\n") + else: + print(f"Convergence test (poles estimation): \t\tfailed ({w_minus_one})\n") + + if w_minus_one <= options.poles_estimation_threshold or options.debug: + # Tentative final fitting + phi_real, phi_complex = compute_phi_matrices( + omega, poles_real, poles_complex + ) + + # Compute the matrix of the least squares problem + A = np.zeros((2 * kbar, nbar + 1)) + A[:kbar, 0] = np.ones((kbar)) + A[:kbar, 1 : num_real_poles + 1] = np.real(phi_real) + A[:kbar, num_real_poles + 1 : nbar + 1] = np.real(phi_complex) + A[kbar:, 1 : num_real_poles + 1] = np.imag(phi_real) + A[kbar:, num_real_poles + 1 : nbar + 1] = np.imag(phi_complex) + + # Store model coefficients in the output structure Model, in case it + # will found accurate enough + model.poles_real = poles_real + model.poles_complex = poles_complex + model.R0 = np.zeros((qbar, mbar), dtype=complex) + model.Rr = np.zeros((qbar, mbar, num_real_poles), dtype=complex) + model.Rc = np.zeros((qbar, mbar, num_complex_pairs), dtype=complex) + + # Model-samples error + err = 0 + for q in range(qbar): + for m in range(mbar): + # Right hand side + V_H = np.squeeze(H[:, q, m]) + b = np.block([np.real(V_H), np.imag(V_H)]) + c_H, _, _, _ = np.linalg.lstsq(A, b) + model.R0[q, m] = c_H[0] + model.Rr[q, m, :] = c_H[1 : num_real_poles + 1] + real_indices = slice( + num_real_poles + 1, None, 2 + ) # Equivalent to nr+2:2:end in MATLAB + imag_indices = slice( + num_real_poles + 2, None, 2 + ) # Equivalent to nr+3:2:end in MATLAB + model.Rc[q, m, :] = c_H[real_indices] + 1j * c_H[imag_indices] + + # Plot the given samples vs the model response for the + # (1,1) entry of the transfer function (if in debug mode) + if options.debug and q == 1 and m == 1: + # Compute model response + pass + # Htemp = compute_model_response(omega,model.R0(q,m),model.Rr(q,m,:),Model.Rc(q,m,:),Model.pr,Model.pc) + # plot(omega,abs(squeeze(H(q,m,:))),'bx') + # plot(omega,abs(squeeze(Htemp(q,m,:))),'r-.') + # xlabel('Omega') + # ylabel('Magnitude') + # legend('Samples H_k','Model') + + # plot(omega,180/pi*angle(squeeze(H(q,m,:))),'bx') + # plot(omega,180/pi*angle(squeeze(Htemp(q,m,:))),'r-.') + # xlabel('Omega') + # ylabel('Phase [deg]') + # legend('Samples H_k','Model') + + err = err + np.sum(abs(A @ c_H - b) ** 2) + + err = np.sqrt(err) / np.sqrt(qbar * mbar * kbar) + if err <= options.model_error_threshold: + print("Convergence test (model-samples error): \tpassed (%e)\n", err) + print("Model identification successful\n") + # print('Modeling time: %f s\n',toc) + return model + else: + print("Convergence test (model-samples error): \tfailed (%e)\n", err) + + iter += 1 + + print( + "Warning: could not reach the desired modeling error within the allowed number of iterations\n" + ) + return model + + +def main1(): + import numpy as np + + poles = np.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = np.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = np.reshape(residues[1:], (10, 1, 1)) + feedthrough = np.zeros((1, 1), dtype=complex) + N = len(poles) + + f = np.linspace(0.1, 10 / (2 * np.pi), 100) + + def pole_residue_response(frequency, poles, residues, feedthrough): + s = 2j * np.pi * (frequency) + frequency_response = feedthrough[None, :, :] + np.sum( + residues[None, :, :, :] + / (s[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + response = pole_residue_response(f, poles, residues, feedthrough) + + plt.plot(response[:, 0, 0]) + plt.show() + + model = FastVF(2 * np.pi * f, response, 20, None) + y = ComputeModelResponse( + 2 * np.pi * f, + model.R0, + model.Rr, + model.Rc, + model.poles_real, + model.poles_complex, + ) + + +def main2(): + import sax + from scipy.constants import speed_of_light + + from simphony.libraries import siepic + from simphony.utils import dict_to_matrix + + _mzi, info = sax.circuit( + netlist={ + "instances": { + "gc_in": "gc", + "splitter": "ybranch", + "long_wg": "waveguide", + "short_wg": "waveguide", + "combiner": "ybranch", + # "gc_out": "gc", + }, + "connections": { + # "splitter,port 2": "long_wg,o0", + # "splitter,port 3": "short_wg,o0", + # "long_wg,o1": "combiner,port 2", + # "short_wg,o1": "combiner,port 3", + # "combiner,port 1": "gc_out,o0", + }, + "ports": { + "in": "splitter,port 1", + "out": "splitter,port 2", + }, + }, + models={ + "ybranch": siepic.y_branch, + "waveguide": siepic.waveguide, + # "gc": siepic.grating_coupler, + }, + ) + + def mzi(wl=1.55): + return _mzi(wl=wl, long_wg={"length": 10.0}, short_wg={"length": 1000.0}) + + f_min = speed_of_light / 1.555e-6 + f_max = speed_of_light / 1.545e-6 + f_center = 0.5 * (f_min + f_max) + frequency = np.linspace(f_min, f_max, 1000) + s_params = np.reshape( + dict_to_matrix(mzi(wl=1e6 * speed_of_light / frequency))[:, 0, 1], (1000, 1, 1) + ) + phase = np.unwrap(np.angle(s_params)) + s_params = s_params + # s_params = np.exp(-1j*phase)*s_params + + model = FastVF((2 * np.pi * frequency) / 1e13, s_params, 20, None) + + y = ComputeModelResponse( + (2 * np.pi * frequency) / 1e13, + model.R0, + model.Rr, + model.Rc, + model.poles_real, + model.poles_complex, + ) + + plt.show() + plt.plot(frequency, np.abs(y[0, 0, :])) + plt.plot(frequency, np.angle(y[0, 0, :])) + plt.plot(frequency, np.abs(s_params[:, 0, 0])) + plt.plot(frequency, np.angle(s_params[:, 0, 0])) + plt.show() + + +main2() + + +#### OLD ##### +# for n in range(nr): +# # IMPLEMENT ME +# pass + +# for n in range(nc): +# pass + +# for n in range(nc): +# pass +# # R_n.append(model.Rc[:, :, n]) +# # R_n.append(np.conj(model.Rc[:, :, n])) +# # p_n.append(model.poles_complex[n]) +# # p_n.append(np.conj(model.poles_complex[n])) + + +# # for n in range(n_bar): +# # #R = R_n[n] +# # U, S, Vh = svd(R_n[n]) +# # rank = S.shape[0] +# # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + +# A = block_diag(*A_n) +# df = pd.DataFrame(A) +# df.to_csv("A_matrix.csv") + +# D = model.R0 +# return A, B, C, D diff --git a/simphony/time_domain/vector_fitting/old/old_code_kind_of_works.py b/simphony/time_domain/vector_fitting/old/old_code_kind_of_works.py new file mode 100644 index 00000000..7d6ad6fa --- /dev/null +++ b/simphony/time_domain/vector_fitting/old/old_code_kind_of_works.py @@ -0,0 +1,735 @@ +# P. Triverio, "Vector Fitting", in P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. H. A. Schilders, L. M. Silveira (Eds.), "Model Order Reduction. Volume 1: System- and Data-Driven Methods and Algorithms", De Gruyter, 2021. +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from numpy.linalg import svd +from scipy.linalg import block_diag +from scipy.signal import StateSpace, bilinear_zpk + + +def samples_flow_rate(omega): + # section 3.3 example + pole = [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + residue = [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + y = [] + order = len(pole) + for s in omega * 1j: + flow_rate = residue[0] + for n in range(order): + flow_rate += residue[n + 1] / (s - pole[n]) + y.append(flow_rate) + + return y + + +class Pole_Residue_Model: + def __init__(self): + pass + + +class FVF_Options: + def __init__( + self, + poles_estimation_threshold=1e-1, + model_error_threshold=1e-3, + max_iterations=5, + enforce_stability=True, + alpha=0.01, + debug=True, + mode="CVF", + ): + self.poles_estimation_threshold = poles_estimation_threshold + self.model_error_threshold = model_error_threshold + self.max_iterations = max_iterations + self.enforce_stability = enforce_stability + self.debug = debug + self.alpha = alpha + + +def compute_phi_matrices(omega, poles_real, poles_complex): + kbar = len(omega) + if np.shape(poles_real) == (): + num_poles_real = 0 + else: + num_poles_real = len(poles_real) + + num_complex_pairs = len(poles_complex) + + phi_real = np.zeros((kbar, num_poles_real), dtype=complex) + for i in range(num_poles_real): + phi_real[:, i] = 1.0 / (1j * omega[:, 0] - poles_real[i]) + phi_complex = np.zeros((kbar, 2 * num_complex_pairs), dtype=complex) + for i in range(num_complex_pairs): + phi_complex[:, 2 * i] = 1.0 / (1j * omega[:, 0] - poles_complex[i]) + 1.0 / ( + 1j * omega[:, 0] - np.conj(poles_complex[i]) + ) + phi_complex[:, 2 * i + 1] = 1j / (1j * omega[:, 0] - poles_complex[i]) - 1j / ( + 1j * omega[:, 0] - np.conj(poles_complex[i]) + ) + + return phi_real, phi_complex + + +def ComputeModelResponse(omega, R0, Rr, Rc, pr, pc): + """Compute the frequency response of a Vector Fitting model. + + Parameters: + - omega: frequency samples, column vector. This is angular frequency (omega = 2*pi*f) + - R0: constant coefficient + - Rr: residues of real poles, 3D array. First dimension corresponds to system outputs. + Second dimension to system inputs. Third dimension corresponds to the various poles. + - Rc: residues of complex conjugate pole pairs (only one per pair) + - pr: real poles, column vector + - pc: complex poles, column vector. Only one per pair of conjugate poles + + Returns: + - H: model response samples, 3D array. First dimension corresponds to system outputs. + Second dimension to system inputs. Third dimension corresponds to frequency. + """ + qbar = R0.shape[0] # number of outputs + mbar = R0.shape[1] # number of inputs + kbar = len(omega) # number of frequency points + + nr = len(pr) # number of real poles + nc = len(pc) # number of complex conjugate pairs + + # Preallocate space for H + H = np.zeros((qbar, mbar, kbar), dtype=complex) + + # Compute the model response + for ik in range(kbar): + H[:, :, ik] = R0 + for ir in range(nr): + H[:, :, ik] += Rr[:, :, ir] / (1j * omega[ik] - pr[ir]) + for ic in range(nc): + H[:, :, ik] += Rc[:, :, ic] / (1j * omega[ik] - pc[ic]) + H[:, :, ik] += np.conj(Rc[:, :, ic]) / (1j * omega[ik] - np.conj(pc[ic])) + + return H + + +def compute_time_response(times, R0, Rr, Rc, pr, pc): + nr = len(pr) + nc = len(pc) + q_bar = R0.shape[0] + m_bar = R0.shape[1] + + h = np.zeros((len(times), q_bar, m_bar), dtype=complex) + + for t_index in range(len(times)): + t = times[t_index] + + h[t_index, :, :] = R0 + # real poles + for n in range(nr): + h[t_index, :, :] += Rr[:, :, n] * np.exp(pr[n] * t) + + # complex poles + for n in range(nc): + h[t_index, :, :] += 2 * np.real(Rc[:, :, n]) * np.exp( + np.real(pc[n]) * t + ) * np.cos(np.imag(pc[n]) * t) - 2 * np.imag(Rc[:, :, n]) * np.exp( + np.real(pc[n]) * t + ) * np.sin( + np.imag(pc[n] * t) + ) + + return h + + +def complex_valued_ABCD(model): + nr = model.Rr.shape[2] + nc = model.Rc.shape[2] + + n_bar = nr + 2 * nc + q_bar = model.R0.shape[0] + m_bar = model.R0.shape[1] + + R_n = [] + p_n = [] + + A_n = [] + B_n = [] + C_n = [] + + for n in range(nr): + # IMPLEMENT ME + pass + + for n in range(nc): + pass + + for n in range(nc): + R_n.append(model.Rc[:, :, n]) + R_n.append(np.conj(model.Rc[:, :, n])) + p_n.append(model.poles_complex[n]) + p_n.append(np.conj(model.poles_complex[n])) + + for n in range(n_bar): + U, S, Vh = svd(R_n[n]) + rank = S.shape[0] + # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + A_n.append(p_n[n] * np.eye(rank)) + B_n.append(Vh.T) + C_n.append((U @ S).reshape(q_bar, rank)) + + # A = block_diag(*A_n) + poles_s = np.concatenate( + [model.poles_real, model.poles_complex, np.conj(model.poles_complex)] + ) + z, poles_z, k = bilinear_zpk([], poles_s, 0, 100) + A = np.diag(poles_s) + B = np.block(B_n) + B = B.T + + C = np.block(C_n) + df = pd.DataFrame(A) + df.to_csv("A_matrix.csv") + + D = model.R0 + return A, B, C, D + + +def real_valued_ABCD(model): + nr = model.Rr.shape[2] + nc = model.Rc.shape[2] + + n_bar = nr + 2 * nc + q_bar = model.R0.shape[0] + m_bar = model.R0.shape[1] + + A_n = [] + B_n = [] + C_n = [] + + for n in range(len(model.poles_real)): + U, S, Vh = svd(model.Rr[:, :, n]) + rank = S.shape[0] + # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + A_n.append(model.poles_real[n] * np.eye(rank)) + B_n.append(Vh.T) + C_n.append((U @ S).reshape(q_bar, rank)) + + for n in range(len(model.poles_complex)): + # R = R_n[n] + U, S, Vh = svd(model.Rc[:, :, n]) + rank = S.shape[0] + A_n.append( + np.block( + [ + [ + np.real(model.poles_complex[n]) * np.eye(rank), + np.imag(model.poles_complex[n]) * np.eye(rank), + ], + [ + -np.imag(model.poles_complex[n]) * np.eye(rank), + np.real(model.poles_complex[n]) * np.eye(rank), + ], + ] + ) + ) + B_n.append(2 * np.real(Vh)) + B_n.append(2 * np.imag(Vh)) + C_n.append(np.real((U @ S).reshape(q_bar, rank))) + C_n.append(np.imag((U @ S).reshape(q_bar, rank))) + + A = block_diag(*A_n) + A = block_diag(*A_n) + # A = np.diag(poles_z) + B = np.block(B_n) + B = B.T + C = np.block(C_n) + D = model.R0 + + # df = pd.DataFrame(A) + # df.to_csv("A_matrix.csv") + # df = pd.DataFrame(B) + # df.to_csv("B_matrix.csv") + # df = pd.DataFrame(C) + # df.to_csv("C_matrix.csv") + # df = pd.DataFrame(D) + # df.to_csv("D_matrix.csv") + return A, B, C, D + + +def state_space_ABCD(model, real_valued): + if real_valued: + return real_valued_ABCD(model) + else: + return complex_valued_ABCD(model) + + +def state_space(model, dt): + A, B, C, D = state_space_ABCD(model, True) + sys = StateSpace(A, B, C, np.real(D)) + return sys + + +def FastVF(omega, H, order, options): + # H = np.squeeze(np.asarray(H)) + if options == None: + options = FVF_Options() + + kbar = len(omega) + qbar = np.shape(H)[1] + mbar = np.shape(H)[2] + nbar = order + alpha = options.alpha + + # Ensure omega is a column vector + omega = np.array(omega).reshape((kbar, 1)) + model = Pole_Residue_Model() + + num_real_poles = nbar % 2 + num_complex_pairs = (nbar - nbar % 2) // 2 + + poles_real = np.array((num_real_poles)) + if num_real_poles == 1: + poles_real = [-alpha * max(omega)] + + poles_complex = np.array((num_complex_pairs), dtype=complex) + if num_complex_pairs == 1: + poles_complex = (-alpha + 1j) * np.max(omega) / 2 + elif np.min(omega) == 0: + poles_complex = ( + (-alpha + 1j) + * max(omega) + * np.arange(1, num_complex_pairs + 1, dtype=complex) + / num_complex_pairs + ) + # poles_complex = poles_complex.reshape((len(poles_complex), 1)) + else: + poles_complex = (-alpha + 1j) * ( + min(omega) + + (max(omega) - min(omega)) + / (num_complex_pairs - 1) + * np.arange(0, num_complex_pairs, dtype=complex) + ) + # poles_complex = poles_complex.reshape((len(poles_complex), 1)) + + iter = 1 + while iter <= options.max_iterations: + phi_real, phi_complex = compute_phi_matrices(omega, poles_real, poles_complex) + + M = np.zeros((2 * kbar, 2 * nbar + 1), dtype=complex) + A = np.zeros((nbar * qbar * mbar, nbar), dtype=complex) + b = np.zeros((nbar * qbar * mbar, 1), dtype=complex) + + # Compute the first columns of M, which do not depend on q and m + M[:kbar, 0] = np.ones((kbar)) + M[:kbar, 1 : num_real_poles + 1] = np.real(phi_real) + M[:kbar, num_real_poles + 1 : nbar + 1] = np.real(phi_complex) + M[kbar:, 1 : num_real_poles + 1] = np.imag(phi_real) + M[kbar:, num_real_poles + 1 : nbar + 1] = np.imag(phi_complex) + + irow = 0 + for q in range(qbar): + for m in range(mbar): + V_H = np.squeeze(H[:, q, m]) + # D_Hqm = diags(V_H, 0, shape=(kbar, kbar), format='csr') + D_Hqm = np.diag(V_H) + M[:kbar, nbar + 1 : nbar + num_real_poles + 1] = -np.real( + D_Hqm @ phi_real + ) + M[:kbar, nbar + num_real_poles + 1 :] = -np.real(D_Hqm @ phi_complex) + M[kbar:, nbar + 1 : nbar + num_real_poles + 1] = -np.imag( + D_Hqm @ phi_real + ) + M[kbar:, nbar + num_real_poles + 1 :] = -np.imag(D_Hqm @ phi_complex) + + # QR decomposition + Q, R = np.linalg.qr(M, mode="reduced") + A[irow : irow + nbar, :] = R[nbar + 1 :, nbar + 1 :] + b[irow : irow + nbar, 0] = Q[:kbar, nbar + 1 :].T @ np.real(V_H) + Q[ + kbar:, nbar + 1 : + ].T @ np.imag(V_H) + + # cw = Alsq\blsq; + # cw = np.linalg.solve(A, b) + cw = np.linalg.lstsq(A, b)[0] + w = ( + 1 + phi_real @ cw[:num_real_poles] + phi_complex @ cw[num_real_poles:] + ) # DOUBLE CHECKED TO THIS POINT + # Compute the new poles estimate + A = np.zeros((nbar, nbar)) + bw = np.ones((nbar, 1)) # DOUBLE CHECKED TO THIS POINT + for i in range(num_real_poles): + A[i, i] = poles_real[i] + # bw(ii) = 1; + + for i in range(num_complex_pairs): + A[ + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), + ] = np.array( + [ + [np.real(poles_complex[i]), np.imag(poles_complex[i])], + [-np.imag(poles_complex[i]), np.real(poles_complex[i])], + ] + ) + bw[ + num_real_poles + 2 * (i + 1) - 2 : num_real_poles + 2 * (i + 1), 0 + ] = np.array([2, 0]) + # A(nr+2*ii-1:nr+2*ii,nr+2*ii-1:nr+2*ii) = [real(pc(ii)), imag(pc(ii)); -imag(pc(ii)),real(pc(ii))]; + + new_poles, _ = np.linalg.eig(A - bw @ cw.conj().T) + + # Extract Real Poles + eps = np.finfo(float).eps + ind_rp = np.nonzero(np.abs(np.imag(new_poles)) < 10 * eps * np.abs(new_poles))[ + 0 + ] # I increased the constant 10 to 100 for now + + poles_real = np.real(new_poles[ind_rp]) + # num_real_poles = len(ind_rp) + + # Extract complex conjugate pairs of poles + # Find only the poles with positive imaginary part + ind_cp = np.nonzero(np.imag(new_poles) >= 10 * eps * abs(new_poles))[0] + # ind_cp = np.nonzero(np.imag(new_poles)>=100*eps*abs(new_poles))[0] + poles_complex = new_poles[ind_cp] + num_complex_pairs = len(ind_cp) + + num_real_poles = len(poles_real) + + while len(poles_real) + 2 * len(poles_complex) < order: + mask = np.argmin(np.abs(np.imag(new_poles))) + poles_real = np.append(poles_real, new_poles[mask]) + + num_real_poles = len(poles_real) + + while 2 * len(poles_complex) > order: + most_real_index = np.argmin(np.abs(np.imag(poles_complex))) + poles_real = np.append(poles_real, poles_complex[most_real_index]) + poles_complex = np.delete(poles_complex, most_real_index) + + num_complex_pairs = len(poles_complex) + + while len(poles_real) + 2 * len(poles_complex) > order: + if len(poles_real) == 1: + poles_real = np.array([]) + break + magnitudes = np.abs(poles_real) + + # Step 2: Calculate the differences in magnitude between neighboring elements + # Calculate the differences between magnitudes of consecutive elements + diffs = np.abs(np.diff(magnitudes)) + + # Step 3: Find the index of the element with the smallest difference in magnitude with its neighbor + # Since we calculate differences between consecutive elements, the index of the smallest difference + # will be between the i-th and (i+1)-th elements. + # We take the first element of the pair to remove. + index_of_min_diff = np.argmin(diffs) + + # Since we want to remove the element with the smallest difference, we choose the one with the + # smaller magnitude if we're between a pair. We choose the min of the index of min difference and the next element. + if index_of_min_diff == len(magnitudes) - 1: + index_to_remove = index_of_min_diff # If it's the last element, we can only remove this one + else: + if magnitudes[index_of_min_diff] <= magnitudes[index_of_min_diff + 1]: + index_to_remove = index_of_min_diff + else: + index_to_remove = index_of_min_diff + 1 + + # Step 4: Delete the element at the found index + poles_real = np.delete(poles_real, index_to_remove) + + num_real_poles = len(poles_real) + # poles_real = np.real(poles_real) + + # Stability/causality enforcement + if options.enforce_stability: + poles_real = -np.abs(poles_real) + poles_complex = -np.abs(np.real(poles_complex)) + 1j * np.imag( + poles_complex + ) + + # mask_real = np.abs(poles_real) > 1 + # mask_complex = np.abs(poles_complex) > 1 + # poles_real[mask_real] = 1 / poles_real[mask_real] + # poles_complex[mask_complex] = 1 / poles_complex[mask_complex] + + # poles_real[mask_real] = poles_real[mask_real] / np.abs(poles_real[mask_real]) + # poles_complex[mask_complex] = poles_complex[mask_complex] / np.abs(poles_complex[mask_complex]) + + if options.debug: + plt.scatter(np.real(poles_complex), np.imag(poles_complex)) + plt.scatter(np.real(poles_real), np.imag(poles_real)) + plt.xlabel("Real") + plt.ylabel("Imaginary") + plt.title(f"Poles estimate, iteration {i}") + + # First convergence test + + w_minus_one = 1 / np.sqrt(kbar) * np.linalg.norm(np.abs(w - 1)) + # Do a tentative model fitting if either: + # - the first convergence test is successful + # - Options.debug is enabled + if w_minus_one <= options.poles_estimation_threshold: + print(f"Convergence test (poles estimation): \t\tpassed ({w_minus_one})\n") + else: + print(f"Convergence test (poles estimation): \t\tfailed ({w_minus_one})\n") + + if w_minus_one <= options.poles_estimation_threshold or options.debug: + # Tentative final fitting + phi_real, phi_complex = compute_phi_matrices( + omega, poles_real, poles_complex + ) + + # Compute the matrix of the least squares problem + A = np.zeros((2 * kbar, nbar + 1)) + A[:kbar, 0] = np.ones((kbar)) + A[:kbar, 1 : num_real_poles + 1] = np.real(phi_real) + A[:kbar, num_real_poles + 1 : nbar + 1] = np.real(phi_complex) + A[kbar:, 1 : num_real_poles + 1] = np.imag(phi_real) + A[kbar:, num_real_poles + 1 : nbar + 1] = np.imag(phi_complex) + + # Store model coefficients in the output structure Model, in case it + # will found accurate enough + model.poles_real = poles_real + model.poles_complex = poles_complex + model.R0 = np.zeros((qbar, mbar), dtype=complex) + model.Rr = np.zeros((qbar, mbar, num_real_poles), dtype=complex) + model.Rc = np.zeros((qbar, mbar, num_complex_pairs), dtype=complex) + + # Model-samples error + err = 0 + for q in range(qbar): + for m in range(mbar): + # Right hand side + V_H = np.squeeze(H[:, q, m]) + b = np.block([np.real(V_H), np.imag(V_H)]) + c_H, _, _, _ = np.linalg.lstsq(A, b) + model.R0[q, m] = c_H[0] + model.Rr[q, m, :] = c_H[1 : num_real_poles + 1] + real_indices = slice( + num_real_poles + 1, None, 2 + ) # Equivalent to nr+2:2:end in MATLAB + imag_indices = slice( + num_real_poles + 2, None, 2 + ) # Equivalent to nr+3:2:end in MATLAB + model.Rc[q, m, :] = c_H[real_indices] + 1j * c_H[imag_indices] + + # Plot the given samples vs the model response for the + # (1,1) entry of the transfer function (if in debug mode) + if options.debug and q == 1 and m == 1: + # Compute model response + pass + # Htemp = compute_model_response(omega,model.R0(q,m),model.Rr(q,m,:),Model.Rc(q,m,:),Model.pr,Model.pc) + # plot(omega,abs(squeeze(H(q,m,:))),'bx') + # plot(omega,abs(squeeze(Htemp(q,m,:))),'r-.') + # xlabel('Omega') + # ylabel('Magnitude') + # legend('Samples H_k','Model') + + # plot(omega,180/pi*angle(squeeze(H(q,m,:))),'bx') + # plot(omega,180/pi*angle(squeeze(Htemp(q,m,:))),'r-.') + # xlabel('Omega') + # ylabel('Phase [deg]') + # legend('Samples H_k','Model') + + err = err + np.sum(abs(A @ c_H - b) ** 2) + + err = np.sqrt(err) / np.sqrt(qbar * mbar * kbar) + if err <= options.model_error_threshold: + print("Convergence test (model-samples error): \tpassed (%e)\n", err) + print("Model identification successful\n") + # print('Modeling time: %f s\n',toc) + return model + else: + print("Convergence test (model-samples error): \tfailed (%e)\n", err) + + iter += 1 + + print( + "Warning: could not reach the desired modeling error within the allowed number of iterations\n" + ) + return model + + +def main1(): + import numpy as np + + poles = np.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = np.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = np.reshape(residues[1:], (10, 1, 1)) + feedthrough = np.zeros((1, 1), dtype=complex) + N = len(poles) + + f = np.linspace(0.1, 10 / (2 * np.pi), 100) + + def pole_residue_response(frequency, poles, residues, feedthrough): + s = 2j * np.pi * (frequency) + frequency_response = feedthrough[None, :, :] + np.sum( + residues[None, :, :, :] + / (s[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + response = pole_residue_response(f, poles, residues, feedthrough) + + plt.plot(response[:, 0, 0]) + plt.show() + + model = FastVF(2 * np.pi * f, response, 20, None) + y = ComputeModelResponse( + 2 * np.pi * f, + model.R0, + model.Rr, + model.Rc, + model.poles_real, + model.poles_complex, + ) + + +def main2(): + import sax + from scipy.constants import speed_of_light + + from simphony.libraries import siepic + from simphony.utils import dict_to_matrix + + _mzi, info = sax.circuit( + netlist={ + "instances": { + "gc_in": "gc", + "splitter": "ybranch", + "long_wg": "waveguide", + "short_wg": "waveguide", + "combiner": "ybranch", + # "gc_out": "gc", + }, + "connections": { + # "splitter,port 2": "long_wg,o0", + # "splitter,port 3": "short_wg,o0", + # "long_wg,o1": "combiner,port 2", + # "short_wg,o1": "combiner,port 3", + # "combiner,port 1": "gc_out,o0", + }, + "ports": { + "in": "splitter,port 1", + "out": "splitter,port 2", + }, + }, + models={ + "ybranch": siepic.y_branch, + "waveguide": siepic.waveguide, + # "gc": siepic.grating_coupler, + }, + ) + + def mzi(wl=1.55): + return _mzi(wl=wl, long_wg={"length": 10.0}, short_wg={"length": 1000.0}) + + f_min = speed_of_light / 1.555e-6 + f_max = speed_of_light / 1.545e-6 + f_center = 0.5 * (f_min + f_max) + frequency = np.linspace(f_min, f_max, 1000) + s_params = np.reshape( + dict_to_matrix(mzi(wl=1e6 * speed_of_light / frequency))[:, 0, 1], (1000, 1, 1) + ) + phase = np.unwrap(np.angle(s_params)) + s_params = s_params + # s_params = np.exp(-1j*phase)*s_params + + model = FastVF((2 * np.pi * frequency) / 1e13, s_params, 20, None) + + y = ComputeModelResponse( + (2 * np.pi * frequency) / 1e13, + model.R0, + model.Rr, + model.Rc, + model.poles_real, + model.poles_complex, + ) + + plt.show() + plt.plot(frequency, np.abs(y[0, 0, :])) + plt.plot(frequency, np.angle(y[0, 0, :])) + plt.plot(frequency, np.abs(s_params[:, 0, 0])) + plt.plot(frequency, np.angle(s_params[:, 0, 0])) + plt.show() + + +main2() + + +#### OLD ##### +# for n in range(nr): +# # IMPLEMENT ME +# pass + +# for n in range(nc): +# pass + +# for n in range(nc): +# pass +# # R_n.append(model.Rc[:, :, n]) +# # R_n.append(np.conj(model.Rc[:, :, n])) +# # p_n.append(model.poles_complex[n]) +# # p_n.append(np.conj(model.poles_complex[n])) + + +# # for n in range(n_bar): +# # #R = R_n[n] +# # U, S, Vh = svd(R_n[n]) +# # rank = S.shape[0] +# # A_n.append(np.block([[np.real(p_n[n]) * np.eye(rank), np.imag(p_n[n]*np.eye(rank))], [-np.imag(p_n[n]*np.eye(rank)), np.real(p_n[n]) * np.eye(rank)]])) + +# A = block_diag(*A_n) +# df = pd.DataFrame(A) +# df.to_csv("A_matrix.csv") + +# D = model.R0 +# return A, B, C, D diff --git a/simphony/time_domain/vector_fitting/old/partioning.py b/simphony/time_domain/vector_fitting/old/partioning.py new file mode 100644 index 00000000..e69de29b diff --git a/simphony/time_domain/vector_fitting/old/s_domain_works.py b/simphony/time_domain/vector_fitting/old/s_domain_works.py new file mode 100644 index 00000000..53870867 --- /dev/null +++ b/simphony/time_domain/vector_fitting/old/s_domain_works.py @@ -0,0 +1,613 @@ +import jax + +jax.config.update("jax_enable_x64", True) +from time import time + +import jax.numpy as jnp +import matplotlib.pyplot as plt +from scipy.constants import speed_of_light + +from simphony.simulation.jax_tools import python_based_while_loop + + +# @jax.jit +def _initial_poles(model_order, frequency, alpha): + f = 1 * jnp.linspace(jnp.min(frequency), jnp.max(frequency), model_order // 2) + poles = (-0.1 + 1j) * (2 * jnp.pi * f) + poles = jnp.concatenate([poles, poles.conj()]) + return poles + + +# @jax.jit +def _phi_matrices(frequency, poles): + s = 2j * jnp.pi * frequency + phi1 = 1 / (s[:, None] - poles[None, :]) + + unity_column = jnp.ones((len(s), 1)) + + phi0 = jnp.hstack((unity_column, phi1)) + + return phi0, phi1 + + +def _lstsq_matrices(model_order, transfer_function, phi0, phi1): + """Here we perform the modified gram schmidt orthonalization on the block + matrix [A1, A2] described here: + + https://arxiv.org/pdf/2208.06194 This allows us to implement the + Fast Vector Fitting algorithm: + https://scholar.googleusercontent.com/scholar?q=cache:u4aY-dn1tF8J:scholar.google.com/+piero+triverio+vector+fitting&hl=en&as_sdt=0,45 + """ + num_ports = transfer_function.shape[1] + M = jnp.zeros(((num_ports**2) * (model_order), (model_order)), dtype=complex) + B = jnp.zeros(((num_ports**2) * (model_order)), dtype=complex) + + A1 = phi0 + Q1, R11 = jnp.linalg.qr(A1) + + iter = 0 + for i in range(num_ports): + for j in range(num_ports): + D = jnp.diag(transfer_function[:, i, j]) + A_block = jnp.hstack([phi0, -D @ phi1]) # never build the big matrix + Q, R = jnp.linalg.qr(A_block, mode="reduced") + + R11 = R[: model_order + 1, : model_order + 1] + R12 = R[: model_order + 1, model_order + 1 :] + R22 = R[model_order + 1 :, model_order + 1 :] + Q2 = Q[:, model_order + 1 :] + + V = transfer_function[:, i, j] + M = M.at[(iter) * (model_order) : (iter + 1) * (model_order), :].set(R22) + B = B.at[(iter) * (model_order) : (iter + 1) * (model_order)].set( + Q2.conj().T @ V + ) + iter += 1 + + # D = jnp.diag(transfer_function[:, i, j]) + # A2 = -D @ phi1 + + # R12 = Q1.conj().T @ A2 + # Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) + + # lhs = jnp.block([phi0, -D@phi1]) + + # V = transfer_function[:, i, j] + # M = M.at[(iter) * model_order : (iter + 1) * model_order, :].set(R22) + # B = B.at[(iter) * model_order : (iter + 1) * model_order].set(Q2.conj().T @ V) + # iter += 1 + + return M, B + + +import numpy as np + + +def _full_lstsq_matrices(transfer_function, phi0, phi1): + D = [] + V = [] + for i in range(transfer_function.shape[1]): + for j in range(transfer_function.shape[2]): + D.append(jnp.diag(transfer_function[:, i, j])) + V.append(transfer_function[:, i, j]) + + phi_column = [] + for _D in D: + phi_column.append(-_D @ phi1) + + blocks = [phi0] * (transfer_function.shape[1] * transfer_function.shape[2]) + M = jax.scipy.linalg.block_diag(*blocks) + + phi_column = np.vstack(phi_column) + M = np.hstack([M, phi_column]) + + return M, np.hstack(V) + + +# def _full_lstsq_matrices(tf, phi0, phi1): +# """ +# Build the full least-squares matrices M and B from Phi0, Phi1, and a 3D transfer function array. + +# Parameters +# ---------- +# phi0 : ndarray, shape (num_measurements, n0) +# Phi0^(i) block. +# phi1 : ndarray, shape (num_measurements, n1) +# Phi1^(i) block. +# tf : ndarray, shape (num_measurements, q, m) +# Complex transfer function. +# tf[:, j, k] = transfer function for output port j, input port k, +# as a vector over 'num_measurements'. + +# Returns +# ------- +# M : ndarray +# Full LHS matrix in the least squares problem (complex). +# B : ndarray +# Full RHS stacked vector (complex). +# """ +# num_meas, q, m_ports = tf.shape +# m_rows, n0 = phi0.shape +# _, n1 = phi1.shape + +# if num_meas != m_rows: +# raise ValueError("phi0/phi1 first dimension must match num_measurements in tf.") + +# total_cols = q * n0 + n1 +# total_rows = q * num_meas + +# M = np.zeros((total_rows, total_cols), dtype=complex) +# B = np.zeros((total_rows, 1), dtype=complex) + +# for j in range(q): +# row_start = j * num_meas +# row_end = row_start + num_meas + +# # Fill diagonal Phi0 block for c_H_j +# col_start_H = j * n0 +# col_end_H = col_start_H + n0 +# M[row_start:row_end, col_start_H:col_end_H] = phi0 + +# # Transfer function vector for this block row: +# # For now, we'll assume we're fitting one specific input port k (or aggregated), +# # so take one column from tf for fixed k. If you want multiple, adapt this. +# tf_vec = tf[:, j, 0] # <-- pick input port index here if needed + +# # Create D from this transfer function vector +# D = np.diag(tf_vec) + +# # Fill last block column for c_w +# col_start_w = q * n0 +# M[row_start:row_end, col_start_w:] = -D @ phi1 + +# # Fill RHS vector +# B[row_start:row_end, 0] = tf_vec + +# return M, B + + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# """ +# Real-stacked QR like in Triverio: +# A_block = [[ Re(phi0) , -Re(D phi1) ], +# [ Im(phi0) , -Im(D phi1) ]] +# Do QR(A_block), keep R22 and Q2, and build the stacked M and B. + +# transfer_function: (K, q, m) complex +# Returns: +# M: ((q*m)*model_order, model_order) +# B: ((q*m)*model_order,) +# """ +# K, q, m = transfer_function.shape +# num_pairs = q * m +# M = jnp.zeros((num_pairs * model_order, model_order)) +# B = jnp.zeros((num_pairs * model_order, ), dtype=transfer_function.dtype) + +# # reshape ports into one axis of length num_pairs +# TF = transfer_function.reshape(K, -1) # K x (q*m) + +# def process_one(V): +# # D @ phi1 == phi1 * V[:, None] (avoid building a KxK diag) +# Dphi1 = phi1 * V[:, None] +# # build real-stacked block matrix +# A_top = jnp.hstack([jnp.real(phi0), -jnp.real(Dphi1)]) +# A_bot = jnp.hstack([jnp.imag(phi0), -jnp.imag(Dphi1)]) +# A_block = jnp.vstack([A_top, A_bot]) # (2K) x (N+1+N) + +# Q, R = jnp.linalg.qr(A_block, mode="reduced") +# # partition +# R22 = R[(model_order+1):, (model_order+1):] # N x N +# Q2 = Q[:, (model_order+1):] # (2K) x N + +# b = jnp.concatenate([jnp.real(V), jnp.imag(V)]) # (2K,) +# rhs = Q2.T @ b # (N,) +# return R22, rhs + +# R_blocks, rhs_blocks = jax.vmap(process_one, in_axes=1)(TF) # over port-pairs axis + +# # stack into big M and B +# M = R_blocks.reshape((-1, model_order)) +# B = rhs_blocks.reshape((-1,)) +# return M, B + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# num_ports = transfer_function.shape[1] + +# # Precompute QR of A1 since it’s constant +# Q1, R11 = jnp.linalg.qr(phi0) + +# def process_pair(V): +# # Avoid jnp.diag by elementwise multiply +# A2 = -phi1 * V[:, None] +# R12 = Q1.conj().T @ A2 +# Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) +# b_row = Q2.conj().T @ V +# return R22, b_row + +# # Flatten all (i,j) pairs into a single axis +# transfer_pairs = transfer_function.reshape(transfer_function.shape[0], -1) +# R_blocks, b_blocks = jax.vmap(process_pair, in_axes=1)(transfer_pairs) + +# # Stack results +# M = R_blocks.reshape((-1, model_order)) +# B = b_blocks.reshape((-1,)) + +# return M, B + + +def _weight_error(frequency, poles_prev, weight_coeffs): + s = 2j * jnp.pi * frequency + terms = weight_coeffs / (s[:, None] - poles_prev) + weights = 1.0 + jnp.sum(terms, axis=1) + return jnp.sqrt(1 / weights.shape[0] * jnp.sum(jnp.abs(weights - 1) ** 2)) + + +# @jax.jit +def _fit_to_poles(transfer_function, frequency, poles): + model_order = poles.shape[0] + num_ports = transfer_function.shape[1] + phi0, _ = _phi_matrices(frequency, poles) + transfer_pairs = transfer_function.reshape( + transfer_function.shape[0], -1 + ) # shape: (num_freq, num_ports*num_ports) + + # Define a function to solve lstsq for one port pair vector V (shape num_freq,) + + def solve_lstsq(V): + sol, *_ = jnp.linalg.lstsq(phi0, V, rcond=None) + return sol # shape (model_order + 1,) + + # Vectorize over all port pairs (along axis=1) + solutions = jax.vmap(solve_lstsq, in_axes=1)( + transfer_pairs + ) # shape (num_ports*num_ports, model_order + 1) + + # Reshape solutions back to (num_ports, num_ports, model_order + 1) + solutions = solutions.reshape((num_ports, num_ports, model_order + 1)) + + # Extract feedthrough (constant term) + feedthrough = solutions[:, :, 0] # shape (num_ports, num_ports) + + # Extract residues (remaining terms) + residues = solutions[:, :, 1:].transpose( + 2, 0, 1 + ) # shape (model_order, num_ports, num_ports) + + return residues, feedthrough + + +# @jax.jit +def pole_residue_response(frequency, poles, residues, feedthrough): + s = 2j * jnp.pi * (frequency) + frequency_response = feedthrough[None, :, :] + jnp.sum( + residues[None, :, :, :] / (s[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + +# @jax.jit +def _mean_squared_error(transfer_function, frequency, poles, residues, feedthrough): + fit = pole_residue_response(frequency, poles, residues, feedthrough) + error = jnp.mean(jnp.abs(transfer_function - fit) ** 2) + + return error + + +def _enforce_conjugacy(poles): + """Force conjugate symmetry by pairing poles with +/- imag parts. + + Keeps real poles as real. + """ + # sort by imag part + idx = jnp.argsort(jnp.imag(poles)) + p_sorted = poles[idx] + + # average small imag parts to pure real + eps = 1e-12 + is_real = jnp.isclose(jnp.imag(p_sorted), 0.0, atol=1e-10) + p_sorted = jnp.where(is_real, jnp.real(p_sorted) + 0j, p_sorted) + + # enforce conjugate pairs: take positive imag half and mirror them + pos_mask = jnp.imag(p_sorted) > eps + p_pos = p_sorted[pos_mask] + p_neg = jnp.conj(p_pos) + p_real = p_sorted[ + ~pos_mask & ~(~pos_mask & (jnp.imag(p_sorted) < -eps)) + ] # approximately real left over + + # rebuild: interleave for stability (pos, conj), then reattach reals + rebuilt = jnp.concatenate( + [ + jnp.ravel(jnp.column_stack([p_pos, p_neg])) + if p_pos.size + else jnp.array([], poles.dtype), + p_real, + ] + ) + # if sizes mismatch due to odd order, pad/truncate to original length + if rebuilt.size < poles.size: + pad = jnp.zeros((poles.size - rebuilt.size,), dtype=poles.dtype) + rebuilt = jnp.concatenate([rebuilt, pad + (-1.0 + 0j)]) + elif rebuilt.size > poles.size: + rebuilt = rebuilt[: poles.size] + return rebuilt + + +def vector_fitting( + model_order, + transfer_function, + frequency, + max_iterations=10, + alpha=0.01, + weight_threshold=0.0, +): + # baseband_frequency = frequency + + def poles_not_converged(state): + _, weight_error, iteration = state + + return (iteration < max_iterations) & (weight_error > weight_threshold) + + def relocate_poles(state): + previous_poles, _, iteration = state + phi0, phi1 = _phi_matrices(frequency, previous_poles) + M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) + weight_coeffs, *_ = jnp.linalg.lstsq(M, B) + # M_prime, B_prime = _full_lstsq_matrices(transfer_function, phi0, phi1) + # coeffs, *_ = jnp.linalg.lstsq(M_prime, B_prime) + # weight_coeffs = coeffs[transfer_function.shape[1]*transfer_function.shape[2]*(model_order+1):] + weight_error = _weight_error(frequency, previous_poles, weight_coeffs) + + A = jnp.diag(previous_poles) + current_poles, _ = jnp.linalg.eig( + A - jnp.ones((model_order, 1)) @ weight_coeffs[:, None].T + ) + mask = current_poles.real > 0 + current_poles = jnp.where( + mask, -current_poles.real + 1j * current_poles.imag, current_poles + ) + # current_poles = _enforce_conjugacy(current_poles) + + return (current_poles, weight_error, iteration + 1) + + initial_poles = _initial_poles(model_order, frequency, alpha) + initial_state = (initial_poles, jnp.inf, 0) + # final_poles, *_ = jax.lax.while_loop(poles_not_converged, relocate_poles, initial_state) + final_poles, *_ = python_based_while_loop( + poles_not_converged, relocate_poles, initial_state + ) + # final_poles = initial_poles + residues, feedthrough = _fit_to_poles(transfer_function, frequency, final_poles) + error = _mean_squared_error( + transfer_function, frequency, final_poles, residues, feedthrough + ) + return final_poles, residues, feedthrough, error + + +def optimize_order(bias_fn, min_order, max_order): + """bias_fn is a function of order which returns the MSE: + + https://ieeexplore.ieee.org/abstract/document/10274284?casa_token=rnFq1k0dt48AAAAA:nWbftIlFFN_x_a5oZ_CER3WTMeCXcAsvapSF8-SiLfi7seo-6rWv0TPWPLQkIaxEgtUr-w + """ + C_min, *_ = bias_fn(min_order) + C_max, *_ = bias_fn(max_order) + C_max_minus_1, *_ = bias_fn(max_order - 1) + lambda_lower = jnp.abs(C_max_minus_1 - C_max) + lambda_upper = C_min - C_max + l = jnp.log10(lambda_lower) + u = jnp.log10(lambda_upper) + complexity_penalty = 10 ** (0.5 * (u + l)) + + # TODO: implement Golden Section Search + # to minimize C - complexity_penalty * order + golden_ratio = (jnp.sqrt(5) - 1) / 2 + a = min_order + b = max_order + c = int(b - golden_ratio * (b - a)) + d = int(a + golden_ratio * (b - a)) + + fc = bias_fn(c)[0] + complexity_penalty * d + fd = bias_fn(d)[0] + complexity_penalty * d + while abs(b - a) > 1: + if fc < fd: # minimum is in [a, d] + b, d, fd = d, c, fc + c = int(b - golden_ratio * (b - a)) + fc = bias_fn(c)[0] + complexity_penalty * c + else: # minimum is in [c, b] + a, c, fc = c, d, fd + d = int(a + golden_ratio * (b - a)) + fd = bias_fn(d)[0] + complexity_penalty * d + + best_order = int(round((a + b) / 2)) + + return bias_fn(best_order) + + +def vector_fitting_optimize_order( + min_order, + max_order, + transfer_function, + frequency, + max_iterations=10, + alpha=0.01, + weight_threshold=0.0, +): + def bias_fn(model_order): + poles, residues, feedthrough, mean_squared_error = vector_fitting( + model_order, + transfer_function, + frequency, + max_iterations=max_iterations, + alpha=alpha, + weight_threshold=weight_threshold, + ) + return mean_squared_error, poles, residues, feedthrough + + mean_squared_error, poles, residues, feedthrough = optimize_order( + bias_fn, min_order, max_order + ) + + return poles, residues, feedthrough, mean_squared_error + + +def main(): + pass + + import sax + + from simphony.libraries import siepic + from simphony.utils import dict_to_matrix + + poles = jnp.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = jnp.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = jnp.reshape(residues[1:], (10, 1, 1)) + feedthrough = jnp.zeros((1, 1), dtype=complex) + N = len(poles) + + f = jnp.linspace(0.001, 10 / (2 * jnp.pi), 100) + aortic_response = pole_residue_response(f, poles, residues, feedthrough) + + _mzi, info = sax.circuit( + netlist={ + "instances": { + "gc_in": "gc", + "splitter": "ybranch", + "long_wg": "waveguide", + "short_wg": "waveguide", + "combiner": "ybranch", + # "gc_out": "gc", + }, + "connections": { + "splitter,port 2": "long_wg,o0", + "splitter,port 3": "short_wg,o0", + "long_wg,o1": "combiner,port 2", + "short_wg,o1": "combiner,port 3", + # "combiner,port 1": "gc_out,o0", + }, + "ports": { + "in": "splitter,port 1", + "out": "combiner,port 1", + }, + }, + models={ + "ybranch": siepic.y_branch, + "waveguide": siepic.waveguide, + # "gc": siepic.grating_coupler, + }, + ) + + def mzi(wl=1.55): + return _mzi(wl=wl, long_wg={"length": 10.0}, short_wg={"length": 40.0}) + + f_min = speed_of_light / 1.6e-6 + f_max = speed_of_light / 1.50e-6 + # f_min = speed_of_light / 1.565e-6 + # f_max = speed_of_light / 1.5350e-6 + f_center = 0.5 * (f_min + f_max) + frequency = jnp.linspace(f_min, f_max, 1000) + s_params = jnp.reshape( + dict_to_matrix(mzi(wl=1e6 * speed_of_light / frequency))[:, 0, 1], (1000, 1, 1) + ) + phase = jnp.unwrap(jnp.angle(s_params)) + s_params = s_params + s_params = jnp.exp(-2j * phase) * s_params + # s_params = s_params.at[:, 0, 0].set(0) + # s_params = s_params.at[:, 1, 1].set(0) + poles1, residues1, feedthrough1, error = vector_fitting( + 100, s_params, 1e-12 * (frequency), max_iterations=15, alpha=0.01 + ) + + # tic = time() + + # toc = time() + # elapsed_time_1 = toc - tic + # f_prime = jnp.linspace(f_min - 100e12, f_max+100e12, 1000) + + H = pole_residue_response(1e-12 * (frequency), poles1, residues1, feedthrough1) + plt.plot(frequency, jnp.angle(H[:, 0, 1])) + plt.plot(frequency, jnp.angle(s_params[:, 0, 1]), "r--") + plt.show() + + +def main2(): + poles = jnp.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = jnp.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = jnp.reshape(residues[1:], (10, 1, 1)) + feedthrough = jnp.zeros((1, 1), dtype=complex) + N = len(poles) + + f = jnp.linspace(0.001, 10 / (2 * jnp.pi), 100) + response = pole_residue_response(f, poles, residues, feedthrough) + + poles1, residues1, feedthrough1, error = vector_fitting( + 10, response, f, max_iterations=5 + ) + toc = time() + H = pole_residue_response(f, poles1, residues1, feedthrough1) + plt.plot(f, jnp.abs(H[:, 0, 0]) ** 2) + plt.plot(f, jnp.abs(response[:, 0, 0]) ** 2, "r--") + plt.show() + + plt.scatter(poles.real, poles.imag, marker="x") + plt.scatter(poles1.real, poles1.imag, marker="o") + plt.show() + + +if __name__ == "__main__": + main() + # main2() diff --git a/simphony/time_domain/vector_fitting/old/z_domain_vectorized.py b/simphony/time_domain/vector_fitting/old/z_domain_vectorized.py new file mode 100644 index 00000000..c65a701a --- /dev/null +++ b/simphony/time_domain/vector_fitting/old/z_domain_vectorized.py @@ -0,0 +1,300 @@ +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +from scipy.constants import speed_of_light + + +# @jax.jit +def _initial_poles(model_order, frequency, sampling_frequency, gamma): + f = jnp.linspace(jnp.min(frequency), jnp.max(frequency), model_order) + poles = gamma * jnp.exp(1j * 2 * jnp.pi * f / sampling_frequency) + return poles + + +# @jax.jit +def _phi_matrices(frequency, sampling_frequency, poles): + z = jnp.exp(-1j * 2 * jnp.pi * frequency / sampling_frequency) + phi1 = 1 / (z[:, None] - poles[None, :]) + + unity_column = jnp.ones((len(z), 1)) + + phi0 = jnp.hstack((unity_column, phi1)) + + return phi0, phi1 + + +def _lstsq_matrices(model_order, transfer_function, phi0, phi1): + """Here we perform the modified gram schmidt orthonalization on the block + matrix [A1, A2] described here: + + https://arxiv.org/pdf/2208.06194 This allows us to implement the + Fast Vector Fitting algorithm: + https://scholar.googleusercontent.com/scholar?q=cache:u4aY-dn1tF8J:scholar.google.com/+piero+triverio+vector+fitting&hl=en&as_sdt=0,45 + """ + num_ports = transfer_function.shape[1] + M = jnp.zeros(((num_ports**2) * model_order, model_order), dtype=complex) + B = jnp.zeros(((num_ports**2) * model_order), dtype=complex) + + A1 = phi0 + Q1, R11 = jnp.linalg.qr(A1) + + iter = 0 + for i in range(num_ports): + for j in range(num_ports): + D = jnp.diag(transfer_function[:, i, j]) + A2 = -D @ phi1 + + R12 = Q1.conj().T @ A2 + Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) + + V = transfer_function[:, i, j] + M = M.at[(iter) * model_order : (iter + 1) * model_order, :].set(R22) + B = B.at[(iter) * model_order : (iter + 1) * model_order].set( + Q2.conj().T @ V + ) + iter += 1 + + return M, B + + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# num_ports = transfer_function.shape[1] + +# # Precompute QR of A1 since it’s constant +# Q1, R11 = jnp.linalg.qr(phi0) + +# def process_pair(V): +# # Avoid jnp.diag by elementwise multiply +# A2 = -phi1 * V[:, None] +# R12 = Q1.conj().T @ A2 +# Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) +# b_row = Q2.conj().T @ V +# return R22, b_row + +# # Flatten all (i,j) pairs into a single axis +# transfer_pairs = transfer_function.reshape(transfer_function.shape[0], -1) +# R_blocks, b_blocks = jax.vmap(process_pair, in_axes=1)(transfer_pairs) + +# # Stack results +# M = R_blocks.reshape((-1, model_order)) +# B = b_blocks.reshape((-1,)) + +# return M, B + + +def _weight_error(frequency, sampling_frequency, poles_prev, weight_coeffs): + z = jnp.exp(-1j * 2 * jnp.pi * frequency / sampling_frequency) + terms = weight_coeffs / (z[:, None] - poles_prev) + weights = 1.0 + jnp.sum(terms, axis=1) + return jnp.sqrt(1 / weights.shape[0] * jnp.sum(jnp.abs(weights - 1) ** 2)) + + +# @jax.jit +def _fit_to_poles(transfer_function, frequency, sampling_frequency, poles): + model_order = poles.shape[0] + num_ports = transfer_function.shape[1] + residues = jnp.zeros((model_order, num_ports, num_ports), dtype=complex) + feedthrough = jnp.zeros((num_ports, num_ports), dtype=complex) + for i in range(num_ports): + for j in range(num_ports): + phi0, _ = _phi_matrices(frequency, sampling_frequency, poles) + # Q,R = np.linalg.qr(phi0,mode='reduced') + # solutions = np.linalg.pinv(R)@Q.conj().T@self.S[:, i, j] + solutions, *_ = jnp.linalg.lstsq( + phi0, transfer_function[:, i, j], rcond=None + ) + feedthrough = feedthrough.at[i, j].set(jnp.array(solutions[0])) + residues = residues.at[:, i, j].set(solutions[1:]) + + return residues, feedthrough + + +# @jax.jit +def _pole_residue_response( + frequency, center_frequency, sampling_frequency, poles, residues, feedthrough +): + z = jnp.exp(-1j * 2 * jnp.pi * (frequency - center_frequency) / sampling_frequency) + frequency_response = feedthrough[None, :, :] + jnp.sum( + residues[None, :, :, :] / (z[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + +# @jax.jit +def _worst_case_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + poles, + residues, + feedthrough, +): + fit = _pole_residue_response( + frequency, center_frequency, sampling_frequency, poles, residues, feedthrough + ) + error = jnp.array([0]) + return error + + +def vector_fitting_z( + model_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, +): + baseband_frequency = frequency - center_frequency + + # Initialize poles outside the loop + initial_poles = _initial_poles( + model_order, baseband_frequency, sampling_frequency, gamma + ) + + def poles_not_converged(state): + _, weight_error, iteration = state + + return (iteration < max_iterations) & (weight_error > weight_threshold) + + def relocate_poles(state): + previous_poles, _, iteration = state + phi0, phi1 = _phi_matrices( + baseband_frequency, sampling_frequency, previous_poles + ) + M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) + weight_coeffs, *_ = jnp.linalg.lstsq(M, B) + + weight_error = _weight_error( + baseband_frequency, sampling_frequency, previous_poles, weight_coeffs + ) + + A = jnp.diag(previous_poles) + current_poles, _ = jnp.linalg.eig( + A - jnp.outer(jnp.ones(model_order), weight_coeffs) + ) + mask = jnp.abs(current_poles) > 1 + # current_poles = current_poles.at[mask].set(1 / current_poles[mask]) + current_poles = jnp.where(mask, 1 / current_poles, current_poles) + + return (current_poles, weight_error, iteration + 1) + + initial_state = (initial_poles, jnp.inf, 0) + final_poles, *_ = jax.lax.while_loop( + poles_not_converged, relocate_poles, initial_state + ) + + residues, feedthrough = _fit_to_poles( + transfer_function, baseband_frequency, sampling_frequency, final_poles + ) + error = _worst_case_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + final_poles, + residues, + feedthrough, + ) + + return final_poles, residues, feedthrough, error + + +# @jax.jit +# def vector_fitting_z( +# model_order, +# transfer_function, +# frequency, +# center_frequency, +# sampling_frequency, +# max_iterations = 15, +# gamma = 0.95, +# ): +# baseband_frequency = frequency - center_frequency +# poles = _initial_poles(model_order, baseband_frequency, sampling_frequency, gamma) +# for _ in range(max_iterations): +# phi0, phi1 = _phi_matrices(poles, baseband_frequency) +# M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) +# weights, *_ = jnp.linalg.lstsq(M, B) +# # weights_row = weights.reshape((len(weights), 1)) +# # unity_column = jnp.ones((model_order, 1)) + +# A = jnp.diag(poles) +# poles, _ = jnp.linalg.eig(A - jnp.outer(jnp.ones(model_order), weights)) +# mask = jnp.abs(poles) > 1 +# poles = poles.at[mask].set(1 / (poles[mask])) + +# error = compute_error() + +# if error < tolerable_error: +# break + + +def main(): + from time import time + + import sax + + from simphony.libraries import ideal + from simphony.utils import dict_to_matrix + + netlist = { + "instances": { + "wg": "waveguide", + "hr": "half_ring", + }, + "connections": { + "hr,o2": "wg,o0", + "hr,o3": "wg,o1", + }, + "ports": { + "o0": "hr,o0", + "o1": "hr,o1", + }, + } + + circuit, info = sax.circuit( + netlist=netlist, + models={ + "waveguide": ideal.waveguide, + "half_ring": ideal.coupler, + }, + ) + + f_min = speed_of_light / 1.6e-6 + f_max = speed_of_light / 1.5e-6 + f_center = 0.5 * (f_min + f_max) + frequency = jnp.linspace(f_min, f_max, 1000) + s_params = dict_to_matrix( + circuit(wl=1e6 * speed_of_light / frequency, wg={"length": 77.0, "loss": 100}) + ) + + sampling_frequency = 1e14 + model_order = 50 + + tic = time() + poles, residues, feedthrough, error = vector_fitting_z( + model_order, s_params, frequency, f_center, sampling_frequency + ) + toc = time() + elapsed_time = toc - tic + print(elapsed_time) + tic = time() + poles, residues, feedthrough, error = vector_fitting_z( + model_order + 1, s_params, frequency, f_center, sampling_frequency + ) + toc = time() + elapsed_time = toc - tic + print(elapsed_time) + H = _pole_residue_response( + frequency, f_center, sampling_frequency, poles, residues, feedthrough + ) + plt.plot(jnp.abs(H[:, 0, 1]) ** 2) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/simphony/time_domain/vector_fitting/old/z_domain_works.py b/simphony/time_domain/vector_fitting/old/z_domain_works.py new file mode 100644 index 00000000..f8225ba8 --- /dev/null +++ b/simphony/time_domain/vector_fitting/old/z_domain_works.py @@ -0,0 +1,472 @@ +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +from scipy.constants import speed_of_light + +# from simphony.simulation.jax_tools import python_based_while_loop + + +# @jax.jit +def _initial_poles(model_order, frequency, sampling_frequency, gamma): + f = jnp.linspace(jnp.min(frequency), jnp.max(frequency), model_order) + poles = gamma * jnp.exp(1j * 2 * jnp.pi * f / sampling_frequency) + return poles + + +# @jax.jit +def _phi_matrices(frequency, sampling_frequency, poles): + z = jnp.exp(-1j * 2 * jnp.pi * frequency / sampling_frequency) + phi1 = 1 / (z[:, None] - poles[None, :]) + + unity_column = jnp.ones((len(z), 1)) + + phi0 = jnp.hstack((unity_column, phi1)) + + return phi0, phi1 + + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# """ +# Here we perform the modified gram schmidt orthonalization on the block matrix [A1, A2] described here: +# https://arxiv.org/pdf/2208.06194 +# This allows us to implement the Fast Vector Fitting algorithm: +# https://scholar.googleusercontent.com/scholar?q=cache:u4aY-dn1tF8J:scholar.google.com/+piero+triverio+vector+fitting&hl=en&as_sdt=0,45 + +# """ +# num_ports = transfer_function.shape[1] +# M = jnp.zeros(((num_ports**2) * model_order, model_order), dtype=complex) +# B = jnp.zeros(((num_ports**2) * model_order), dtype=complex) + +# A1 = phi0 +# Q1, R11 = jnp.linalg.qr(A1) + +# iter = 0 +# for i in range(num_ports): +# for j in range(num_ports): +# D = jnp.diag(transfer_function[:, i, j]) +# A2 = -D @ phi1 + +# R12 = Q1.conj().T @ A2 +# Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) + +# V = transfer_function[:, i, j] +# M = M.at[(iter) * model_order : (iter + 1) * model_order, :].set(R22) +# B = B.at[(iter) * model_order : (iter + 1) * model_order].set(Q2.conj().T @ V) +# iter += 1 + +# return M, B + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# num_ports = transfer_function.shape[1] + +# # Precompute QR of A1 since it’s constant +# Q1, R11 = jnp.linalg.qr(phi0) + +# def process_pair(V): +# # Avoid jnp.diag by elementwise multiply +# A2 = -phi1 * V[:, None] +# R12 = Q1.conj().T @ A2 +# Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) +# b_row = Q2.conj().T @ V +# return R22, b_row + +# # Flatten all (i,j) pairs into a single axis +# transfer_pairs = transfer_function.reshape(transfer_function.shape[0], -1) +# R_blocks, b_blocks = jax.vmap(process_pair, in_axes=1)(transfer_pairs) + +# # Stack results +# M = R_blocks.reshape((-1, model_order)) +# B = b_blocks.reshape((-1,)) + +# return M, B + + +def _lstsq_matrices(model_order, transfer_function, phi0, phi1): + """""" + num_ports = transfer_function.shape[1] + M = jnp.zeros(((num_ports**2) * (model_order), (model_order)), dtype=complex) + B = jnp.zeros(((num_ports**2) * (model_order)), dtype=complex) + + A1 = phi0 + Q1, R11 = jnp.linalg.qr(A1) + + iter = 0 + for i in range(num_ports): + for j in range(num_ports): + D = jnp.diag(transfer_function[:, i, j]) + A_block = jnp.hstack([phi0, -D @ phi1]) # never build the big matrix + Q, R = jnp.linalg.qr(A_block, mode="reduced") + + R11 = R[: model_order + 1, : model_order + 1] + R12 = R[: model_order + 1, model_order + 1 :] + R22 = R[model_order + 1 :, model_order + 1 :] + Q2 = Q[:, model_order + 1 :] + + V = transfer_function[:, i, j] + M = M.at[(iter) * (model_order) : (iter + 1) * (model_order), :].set(R22) + B = B.at[(iter) * (model_order) : (iter + 1) * (model_order)].set( + Q2.conj().T @ V + ) + iter += 1 + + return M, B + + +def _weight_error(frequency, sampling_frequency, poles_prev, weight_coeffs): + z = jnp.exp(-1j * 2 * jnp.pi * frequency / sampling_frequency) + terms = weight_coeffs / (z[:, None] - poles_prev) + weights = 1.0 + jnp.sum(terms, axis=1) + return jnp.sqrt(1 / weights.shape[0] * jnp.sum(jnp.abs(weights - 1) ** 2)) + + +# # @jax.jit +# def _fit_to_poles(transfer_function, frequency, sampling_frequency, poles): +# model_order = poles.shape[0] +# num_ports = transfer_function.shape[1] +# residues = jnp.zeros((model_order, num_ports, num_ports), dtype=complex) +# feedthrough = jnp.zeros((num_ports, num_ports), dtype=complex) +# phi0, _ = _phi_matrices(frequency, sampling_frequency, poles) +# for i in range(num_ports): +# for j in range(num_ports): +# # Q,R = np.linalg.qr(phi0,mode='reduced') +# # solutions = np.linalg.pinv(R)@Q.conj().T@self.S[:, i, j] +# solutions, *_ = jnp.linalg.lstsq(phi0, transfer_function[:, i, j], rcond=None) +# feedthrough = feedthrough.at[i, j].set(jnp.array(solutions[0])) +# residues = residues.at[:, i, j].set(solutions[1:]) + +# return residues, feedthrough + + +@jax.jit +def _fit_to_poles(transfer_function, frequency, sampling_frequency, poles): + model_order = poles.shape[0] + num_ports = transfer_function.shape[1] + phi0, _ = _phi_matrices(frequency, sampling_frequency, poles) + transfer_pairs = transfer_function.reshape( + transfer_function.shape[0], -1 + ) # shape: (num_freq, num_ports*num_ports) + + # Define a function to solve lstsq for one port pair vector V (shape num_freq,) + + def solve_lstsq(V): + sol, *_ = jnp.linalg.lstsq(phi0, V, rcond=None) + return sol # shape (model_order + 1,) + + # Vectorize over all port pairs (along axis=1) + solutions = jax.vmap(solve_lstsq, in_axes=1)( + transfer_pairs + ) # shape (num_ports*num_ports, model_order + 1) + + # Reshape solutions back to (num_ports, num_ports, model_order + 1) + solutions = solutions.reshape((num_ports, num_ports, model_order + 1)) + + # Extract feedthrough (constant term) + feedthrough = solutions[:, :, 0] # shape (num_ports, num_ports) + + # Extract residues (remaining terms) + residues = solutions[:, :, 1:].transpose( + 2, 0, 1 + ) # shape (model_order, num_ports, num_ports) + + return residues, feedthrough + + +@jax.jit +def pole_residue_response( + frequency, center_frequency, sampling_frequency, poles, residues, feedthrough +): + z = jnp.exp(-1j * 2 * jnp.pi * (frequency - center_frequency) / sampling_frequency) + frequency_response = feedthrough[None, :, :] + jnp.sum( + residues[None, :, :, :] / (z[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + +# @jax.jit +def _mean_squared_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + poles, + residues, + feedthrough, +): + fit = pole_residue_response( + frequency, center_frequency, sampling_frequency, poles, residues, feedthrough + ) + error = jnp.mean(jnp.abs(transfer_function - fit) ** 2) + + return error + + +def state_space_z(poles, residues, feedthrough): + model_order = poles.shape[0] + num_ports = feedthrough.shape[0] + A = jnp.zeros((model_order * num_ports, model_order * num_ports), dtype=complex) + B = jnp.zeros((model_order * num_ports, num_ports), dtype=complex) + C = jnp.zeros((num_ports, model_order * num_ports), dtype=complex) + for i in range(model_order): + A = A.at[ + i * num_ports : (i + 1) * num_ports, + i * num_ports : (i + 1) * num_ports, + ].set(poles[i] * jnp.eye(num_ports)) + + B = B.at[i * num_ports : (i + 1) * num_ports, :].set(jnp.eye(num_ports)) + C = C.at[:, i * num_ports : (i + 1) * num_ports].set(residues[i, :, :]) + + return A, B, C, feedthrough + + +def vector_fitting_z( + model_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, +): + baseband_frequency = frequency - center_frequency + + def poles_not_converged(state): + _, weight_error, iteration = state + + return (iteration < max_iterations) & (weight_error > weight_threshold) + + def relocate_poles(state): + previous_poles, _, iteration = state + phi0, phi1 = _phi_matrices( + baseband_frequency, sampling_frequency, previous_poles + ) + M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) + weight_coeffs, *_ = jnp.linalg.lstsq(M, B) + + weight_error = _weight_error( + baseband_frequency, sampling_frequency, previous_poles, weight_coeffs + ) + + A = jnp.diag(previous_poles) + current_poles, _ = jnp.linalg.eig( + A - jnp.outer(jnp.ones(model_order), weight_coeffs) + ) + mask = jnp.abs(current_poles) > 1 + # current_poles = current_poles.at[mask].set(1 / current_poles[mask]) + current_poles = jnp.where(mask, 1 / current_poles, current_poles) + + return (current_poles, weight_error, iteration + 1) + + initial_poles = _initial_poles( + model_order, baseband_frequency, sampling_frequency, gamma + ) + initial_state = (initial_poles, jnp.inf, 0) + final_poles, *_ = jax.lax.while_loop( + poles_not_converged, relocate_poles, initial_state + ) + residues, feedthrough = _fit_to_poles( + transfer_function, baseband_frequency, sampling_frequency, final_poles + ) + + # Negative signs convert back to Physicist's Convention + # final_poles *= -1 + # residues *= -1 + + error = _mean_squared_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + final_poles, + residues, + feedthrough, + ) + + return final_poles, residues, feedthrough, error + + +# @jax.jit +# def vector_fitting_z( +# model_order, +# transfer_function, +# frequency, +# center_frequency, +# sampling_frequency, +# max_iterations = 15, +# gamma = 0.95, +# ): +# baseband_frequency = frequency - center_frequency +# poles = _initial_poles(model_order, baseband_frequency, sampling_frequency, gamma) +# for _ in range(max_iterations): +# phi0, phi1 = _phi_matrices(poles, baseband_frequency) +# M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) +# weights, *_ = jnp.linalg.lstsq(M, B) +# # weights_row = weights.reshape((len(weights), 1)) +# # unity_column = jnp.ones((model_order, 1)) + +# A = jnp.diag(poles) +# poles, _ = jnp.linalg.eig(A - jnp.outer(jnp.ones(model_order), weights)) +# mask = jnp.abs(poles) > 1 +# poles = poles.at[mask].set(1 / (poles[mask])) + +# error = compute_error() + +# if error < tolerable_error: +# break + + +def optimize_order(bias_fn, min_order, max_order): + """bias_fn is a function of order which returns the MSE: + + https://ieeexplore.ieee.org/abstract/document/10274284?casa_token=rnFq1k0dt48AAAAA:nWbftIlFFN_x_a5oZ_CER3WTMeCXcAsvapSF8-SiLfi7seo-6rWv0TPWPLQkIaxEgtUr-w + """ + C_min, *_ = bias_fn(min_order) + C_max, *_ = bias_fn(max_order) + C_max_minus_1, *_ = bias_fn(max_order - 1) + lambda_lower = jnp.abs(C_max_minus_1 - C_max) + lambda_upper = C_min - C_max + l = jnp.log10(lambda_lower) + u = jnp.log10(lambda_upper) + complexity_penalty = 10 ** (0.5 * (u + l)) + + # TODO: implement Golden Section Search + # to minimize C - complexity_penalty * order + golden_ratio = (jnp.sqrt(5) - 1) / 2 + a = min_order + b = max_order + c = int(b - golden_ratio * (b - a)) + d = int(a + golden_ratio * (b - a)) + + fc = bias_fn(c)[0] + complexity_penalty * d + fd = bias_fn(d)[0] + complexity_penalty * d + while abs(b - a) > 1: + if fc < fd: # minimum is in [a, d] + b, d, fd = d, c, fc + c = int(b - golden_ratio * (b - a)) + fc = bias_fn(c)[0] + complexity_penalty * c + else: # minimum is in [c, b] + a, c, fc = c, d, fd + d = int(a + golden_ratio * (b - a)) + fd = bias_fn(d)[0] + complexity_penalty * d + + best_order = int(round((a + b) / 2)) + + return bias_fn(best_order) + + +def vector_fitting_z_optimize_order( + min_order, + max_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, +): + def bias_fn(model_order): + poles, residues, feedthrough, mean_squared_error = vector_fitting_z( + model_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, + ) + return mean_squared_error, poles, residues, feedthrough + + mean_squared_error, poles, residues, feedthrough = optimize_order( + bias_fn, min_order, max_order + ) + + return poles, residues, feedthrough, mean_squared_error + + +def main(): + from time import time + + import sax + + from simphony.libraries import ideal + from simphony.utils import dict_to_matrix + + netlist = { + "instances": { + "wg": "waveguide", + "hr": "half_ring", + }, + "connections": { + "hr,o2": "wg,o0", + "hr,o3": "wg,o1", + }, + "ports": { + "o0": "hr,o0", + "o1": "hr,o1", + }, + } + + circuit, info = sax.circuit( + netlist=netlist, + models={ + "waveguide": ideal.waveguide, + "half_ring": ideal.coupler, + }, + ) + + f_min = speed_of_light / 1.6e-6 + f_max = speed_of_light / 1.5e-6 + f_center = 0.5 * (f_min + f_max) + frequency = jnp.linspace(f_min, f_max, 1000) + s_params = dict_to_matrix( + circuit(wl=1e6 * speed_of_light / frequency, wg={"length": 77.0, "loss": 100}) + ) + + sampling_frequency = 1e16 + model_order = 10 + + tic = time() + poles, residues, feedthrough, error = vector_fitting_z_optimize_order( + 10, 50, s_params, frequency, f_center, sampling_frequency + ) + toc = time() + elapsed_time_1 = toc - tic + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_2 = toc - tic + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_3 = toc - tic + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_4 = toc - tic + + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z(model_order, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time = toc - tic + # print(elapsed_time) + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z(model_order+1, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time = toc - tic + # print(elapsed_time) + f = jnp.linspace(-sampling_frequency / 2, sampling_frequency / 2, 100000) + f_center + + H = pole_residue_response( + f, f_center, sampling_frequency, poles, residues, feedthrough + ) + plt.plot(f, jnp.abs(H[:, 0, 1]) ** 2) + # plt.xlim([187e12, 200e12]) + plt.plot(frequency, jnp.abs(s_params[:, 0, 1]) ** 2) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/simphony/time_domain/vector_fitting/order_estimation.py b/simphony/time_domain/vector_fitting/order_estimation.py new file mode 100644 index 00000000..e69de29b diff --git a/simphony/time_domain/vector_fitting/s_domain.py b/simphony/time_domain/vector_fitting/s_domain.py new file mode 100644 index 00000000..03b2062b --- /dev/null +++ b/simphony/time_domain/vector_fitting/s_domain.py @@ -0,0 +1,619 @@ +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +import numpy as np +from scipy.constants import speed_of_light + +from simphony.simulation.jax_tools import python_based_while_loop + +jax.config.update("jax_enable_x64", True) + + +# @jax.jit +def _initial_poles(model_order, frequency, alpha): + f = 1 * jnp.linspace(jnp.min(frequency), jnp.max(frequency), model_order // 2) + poles = (-0.1 + 1j) * (2 * jnp.pi * f) + poles = jnp.concatenate([poles, poles.conj()]) + return poles + + +# @jax.jit +def _phi_matrices(frequency, poles): + s = 2j * jnp.pi * frequency + phi1 = 1 / (s[:, None] - poles[None, :]) + + unity_column = jnp.ones((len(s), 1)) + + phi0 = jnp.hstack((unity_column, phi1)) + + return phi0, phi1 + + +def _lstsq_matrices(model_order, transfer_function, phi0, phi1): + """Here we perform the modified gram schmidt orthonalization on the block + matrix [A1, A2] described here: + + https://arxiv.org/pdf/2208.06194 This allows us to implement the + Fast Vector Fitting algorithm: + https://scholar.googleusercontent.com/scholar?q=cache:u4aY-dn1tF8J:scholar.google.com/+piero+triverio+vector+fitting&hl=en&as_sdt=0,45 + """ + num_ports = transfer_function.shape[1] + M = jnp.zeros(((num_ports**2) * (model_order), (model_order)), dtype=complex) + B = jnp.zeros(((num_ports**2) * (model_order)), dtype=complex) + + iter = 0 + for i in range(num_ports): + for j in range(num_ports): + D = jnp.diag(transfer_function[:, i, j]) + A_block = jnp.hstack([phi0, -D @ phi1]) # never build the big matrix + Q, R = jnp.linalg.qr(A_block, mode="reduced") + + R22 = R[model_order + 1 :, model_order + 1 :] + Q2 = Q[:, model_order + 1 :] + + V = transfer_function[:, i, j] + M = M.at[(iter) * (model_order) : (iter + 1) * (model_order), :].set(R22) + B = B.at[(iter) * (model_order) : (iter + 1) * (model_order)].set( + Q2.conj().T @ V + ) + iter += 1 + + # D = jnp.diag(transfer_function[:, i, j]) + # A2 = -D @ phi1 + + # R12 = Q1.conj().T @ A2 + # Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) + + # lhs = jnp.block([phi0, -D@phi1]) + + # V = transfer_function[:, i, j] + # M = M.at[(iter) * model_order : (iter + 1) * model_order, :].set(R22) + # B = B.at[(iter) * model_order : (iter + 1) * model_order].set(Q2.conj().T @ V) + # iter += 1 + + return M, B + + +def _full_lstsq_matrices(transfer_function, phi0, phi1): + D = [] + V = [] + for i in range(transfer_function.shape[1]): + for j in range(transfer_function.shape[2]): + D.append(jnp.diag(transfer_function[:, i, j])) + V.append(transfer_function[:, i, j]) + + phi_column = [] + for _D in D: + phi_column.append(-_D @ phi1) + + blocks = [phi0] * (transfer_function.shape[1] * transfer_function.shape[2]) + M = jax.scipy.linalg.block_diag(*blocks) + + phi_column = np.vstack(phi_column) + M = np.hstack([M, phi_column]) + + return M, np.hstack(V) + + +# def _full_lstsq_matrices(tf, phi0, phi1): +# """ +# Build the full least-squares matrices M and B from Phi0, Phi1, and a 3D transfer function array. + +# Parameters +# ---------- +# phi0 : ndarray, shape (num_measurements, n0) +# Phi0^(i) block. +# phi1 : ndarray, shape (num_measurements, n1) +# Phi1^(i) block. +# tf : ndarray, shape (num_measurements, q, m) +# Complex transfer function. +# tf[:, j, k] = transfer function for output port j, input port k, +# as a vector over 'num_measurements'. + +# Returns +# ------- +# M : ndarray +# Full LHS matrix in the least squares problem (complex). +# B : ndarray +# Full RHS stacked vector (complex). +# """ +# num_meas, q, m_ports = tf.shape +# m_rows, n0 = phi0.shape +# _, n1 = phi1.shape + +# if num_meas != m_rows: +# raise ValueError("phi0/phi1 first dimension must match num_measurements in tf.") + +# total_cols = q * n0 + n1 +# total_rows = q * num_meas + +# M = np.zeros((total_rows, total_cols), dtype=complex) +# B = np.zeros((total_rows, 1), dtype=complex) + +# for j in range(q): +# row_start = j * num_meas +# row_end = row_start + num_meas + +# # Fill diagonal Phi0 block for c_H_j +# col_start_H = j * n0 +# col_end_H = col_start_H + n0 +# M[row_start:row_end, col_start_H:col_end_H] = phi0 + +# # Transfer function vector for this block row: +# # For now, we'll assume we're fitting one specific input port k (or aggregated), +# # so take one column from tf for fixed k. If you want multiple, adapt this. +# tf_vec = tf[:, j, 0] # <-- pick input port index here if needed + +# # Create D from this transfer function vector +# D = np.diag(tf_vec) + +# # Fill last block column for c_w +# col_start_w = q * n0 +# M[row_start:row_end, col_start_w:] = -D @ phi1 + +# # Fill RHS vector +# B[row_start:row_end, 0] = tf_vec + +# return M, B + + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# """ +# Real-stacked QR like in Triverio: +# A_block = [[ Re(phi0) , -Re(D phi1) ], +# [ Im(phi0) , -Im(D phi1) ]] +# Do QR(A_block), keep R22 and Q2, and build the stacked M and B. + +# transfer_function: (K, q, m) complex +# Returns: +# M: ((q*m)*model_order, model_order) +# B: ((q*m)*model_order,) +# """ +# K, q, m = transfer_function.shape +# num_pairs = q * m +# M = jnp.zeros((num_pairs * model_order, model_order)) +# B = jnp.zeros((num_pairs * model_order, ), dtype=transfer_function.dtype) + +# # reshape ports into one axis of length num_pairs +# TF = transfer_function.reshape(K, -1) # K x (q*m) + +# def process_one(V): +# # D @ phi1 == phi1 * V[:, None] (avoid building a KxK diag) +# Dphi1 = phi1 * V[:, None] +# # build real-stacked block matrix +# A_top = jnp.hstack([jnp.real(phi0), -jnp.real(Dphi1)]) +# A_bot = jnp.hstack([jnp.imag(phi0), -jnp.imag(Dphi1)]) +# A_block = jnp.vstack([A_top, A_bot]) # (2K) x (N+1+N) + +# Q, R = jnp.linalg.qr(A_block, mode="reduced") +# # partition +# R22 = R[(model_order+1):, (model_order+1):] # N x N +# Q2 = Q[:, (model_order+1):] # (2K) x N + +# b = jnp.concatenate([jnp.real(V), jnp.imag(V)]) # (2K,) +# rhs = Q2.T @ b # (N,) +# return R22, rhs + +# R_blocks, rhs_blocks = jax.vmap(process_one, in_axes=1)(TF) # over port-pairs axis + +# # stack into big M and B +# M = R_blocks.reshape((-1, model_order)) +# B = rhs_blocks.reshape((-1,)) +# return M, B + +# def _lstsq_matrices(model_order, transfer_function, phi0, phi1): +# num_ports = transfer_function.shape[1] + +# # Precompute QR of A1 since it’s constant +# Q1, R11 = jnp.linalg.qr(phi0) + +# def process_pair(V): +# # Avoid jnp.diag by elementwise multiply +# A2 = -phi1 * V[:, None] +# R12 = Q1.conj().T @ A2 +# Q2, R22 = jnp.linalg.qr(A2 - Q1 @ R12) +# b_row = Q2.conj().T @ V +# return R22, b_row + +# # Flatten all (i,j) pairs into a single axis +# transfer_pairs = transfer_function.reshape(transfer_function.shape[0], -1) +# R_blocks, b_blocks = jax.vmap(process_pair, in_axes=1)(transfer_pairs) + +# # Stack results +# M = R_blocks.reshape((-1, model_order)) +# B = b_blocks.reshape((-1,)) + +# return M, B + + +def _weight_error(frequency, poles_prev, weight_coeffs): + s = 2j * jnp.pi * frequency + terms = weight_coeffs / (s[:, None] - poles_prev) + weights = 1.0 + jnp.sum(terms, axis=1) + return jnp.sqrt(1 / weights.shape[0] * jnp.sum(jnp.abs(weights - 1) ** 2)) + + +# @jax.jit +def _fit_to_poles(transfer_function, frequency, poles): + model_order = poles.shape[0] + num_ports = transfer_function.shape[1] + phi0, _ = _phi_matrices(frequency, poles) + transfer_pairs = transfer_function.reshape( + transfer_function.shape[0], -1 + ) # shape: (num_freq, num_ports*num_ports) + + # Define a function to solve lstsq for one port pair vector V (shape num_freq,) + + def solve_lstsq(V): + sol, *_ = jnp.linalg.lstsq(phi0, V, rcond=None) + return sol # shape (model_order + 1,) + + # Vectorize over all port pairs (along axis=1) + solutions = jax.vmap(solve_lstsq, in_axes=1)( + transfer_pairs + ) # shape (num_ports*num_ports, model_order + 1) + + # Reshape solutions back to (num_ports, num_ports, model_order + 1) + solutions = solutions.reshape((num_ports, num_ports, model_order + 1)) + + # Extract feedthrough (constant term) + feedthrough = solutions[:, :, 0] # shape (num_ports, num_ports) + + # Extract residues (remaining terms) + residues = solutions[:, :, 1:].transpose( + 2, 0, 1 + ) # shape (model_order, num_ports, num_ports) + + return residues, feedthrough + + +# @jax.jit +def pole_residue_response(frequency, poles, residues, feedthrough): + s = 2j * jnp.pi * (frequency) + frequency_response = feedthrough[None, :, :] + jnp.sum( + residues[None, :, :, :] / (s[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + +# @jax.jit +def _mean_squared_error(transfer_function, frequency, poles, residues, feedthrough): + fit = pole_residue_response(frequency, poles, residues, feedthrough) + error = jnp.mean(jnp.abs(transfer_function - fit) ** 2) + + return error + + +def _enforce_conjugacy(poles): + """Force conjugate symmetry by pairing poles with +/- imag parts. + + Keeps real poles as real. + """ + # sort by imag part + idx = jnp.argsort(jnp.imag(poles)) + p_sorted = poles[idx] + + # average small imag parts to pure real + eps = 1e-12 + is_real = jnp.isclose(jnp.imag(p_sorted), 0.0, atol=1e-10) + p_sorted = jnp.where(is_real, jnp.real(p_sorted) + 0j, p_sorted) + + # enforce conjugate pairs: take positive imag half and mirror them + pos_mask = jnp.imag(p_sorted) > eps + p_pos = p_sorted[pos_mask] + p_neg = jnp.conj(p_pos) + p_real = p_sorted[ + ~pos_mask & ~(~pos_mask & (jnp.imag(p_sorted) < -eps)) + ] # approximately real left over + + # rebuild: interleave for stability (pos, conj), then reattach reals + rebuilt = jnp.concatenate( + [ + jnp.ravel(jnp.column_stack([p_pos, p_neg])) + if p_pos.size + else jnp.array([], poles.dtype), + p_real, + ] + ) + # if sizes mismatch due to odd order, pad/truncate to original length + if rebuilt.size < poles.size: + pad = jnp.zeros((poles.size - rebuilt.size,), dtype=poles.dtype) + rebuilt = jnp.concatenate([rebuilt, pad + (-1.0 + 0j)]) + elif rebuilt.size > poles.size: + rebuilt = rebuilt[: poles.size] + return rebuilt + + +def vector_fitting( + model_order, + transfer_function, + frequency, + max_iterations=10, + alpha=0.01, + weight_threshold=0.0, +): + # baseband_frequency = frequency + + def poles_not_converged(state): + _, weight_error, iteration = state + + return (iteration < max_iterations) & (weight_error > weight_threshold) + + def relocate_poles(state): + previous_poles, _, iteration = state + phi0, phi1 = _phi_matrices(frequency, previous_poles) + M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) + weight_coeffs, *_ = jnp.linalg.lstsq(M, B) + # M_prime, B_prime = _full_lstsq_matrices(transfer_function, phi0, phi1) + # coeffs, *_ = jnp.linalg.lstsq(M_prime, B_prime) + # weight_coeffs = coeffs[transfer_function.shape[1]*transfer_function.shape[2]*(model_order+1):] + weight_error = _weight_error(frequency, previous_poles, weight_coeffs) + + A = jnp.diag(previous_poles) + current_poles, _ = jnp.linalg.eig( + A - jnp.ones((model_order, 1)) @ weight_coeffs[:, None].T + ) + mask = current_poles.real > 0 + current_poles = jnp.where( + mask, -current_poles.real + 1j * current_poles.imag, current_poles + ) + # current_poles = _enforce_conjugacy(current_poles) + + return (current_poles, weight_error, iteration + 1) + + initial_poles = _initial_poles(model_order, frequency, alpha) + initial_state = (initial_poles, jnp.inf, 0) + # final_poles, *_ = jax.lax.while_loop(poles_not_converged, relocate_poles, initial_state) + final_poles, *_ = python_based_while_loop( + poles_not_converged, relocate_poles, initial_state + ) + # final_poles = initial_poles + residues, feedthrough = _fit_to_poles(transfer_function, frequency, final_poles) + error = _mean_squared_error( + transfer_function, frequency, final_poles, residues, feedthrough + ) + return final_poles, residues, feedthrough, error + + +def optimize_order(bias_fn, min_order, max_order): + """bias_fn is a function of order which returns the MSE: + + https://ieeexplore.ieee.org/abstract/document/10274284?casa_token=rnFq1k0dt48AAAAA:nWbftIlFFN_x_a5oZ_CER3WTMeCXcAsvapSF8-SiLfi7seo-6rWv0TPWPLQkIaxEgtUr-w + """ + C_min, *_ = bias_fn(min_order) + C_max, *_ = bias_fn(max_order) + C_max_minus_1, *_ = bias_fn(max_order - 1) + lambda_lower = jnp.abs(C_max_minus_1 - C_max) + lambda_upper = C_min - C_max + lower_log = jnp.log10(lambda_lower) + upper_log = jnp.log10(lambda_upper) + complexity_penalty = 10 ** (0.5 * (upper_log + lower_log)) + + # TODO: implement Golden Section Search + # to minimize C - complexity_penalty * order + golden_ratio = (jnp.sqrt(5) - 1) / 2 + a = min_order + b = max_order + c = int(b - golden_ratio * (b - a)) + d = int(a + golden_ratio * (b - a)) + + fc = bias_fn(c)[0] + complexity_penalty * d + fd = bias_fn(d)[0] + complexity_penalty * d + while abs(b - a) > 1: + if fc < fd: # minimum is in [a, d] + b, d, fd = d, c, fc + c = int(b - golden_ratio * (b - a)) + fc = bias_fn(c)[0] + complexity_penalty * c + else: # minimum is in [c, b] + a, c, fc = c, d, fd + d = int(a + golden_ratio * (b - a)) + fd = bias_fn(d)[0] + complexity_penalty * d + + best_order = int(round((a + b) / 2)) + + return bias_fn(best_order) + + +def vector_fitting_optimize_order( + min_order, + max_order, + transfer_function, + frequency, + max_iterations=10, + alpha=0.01, + weight_threshold=0.0, +): + def bias_fn(model_order): + poles, residues, feedthrough, mean_squared_error = vector_fitting( + model_order, + transfer_function, + frequency, + max_iterations=max_iterations, + alpha=alpha, + weight_threshold=weight_threshold, + ) + return mean_squared_error, poles, residues, feedthrough + + mean_squared_error, poles, residues, feedthrough = optimize_order( + bias_fn, min_order, max_order + ) + + return poles, residues, feedthrough, mean_squared_error + + +def main(): + pass + + import sax + + from simphony.libraries import siepic + from simphony.utils import dict_to_matrix + + poles = jnp.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = jnp.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = jnp.reshape(residues[1:], (10, 1, 1)) + feedthrough = jnp.zeros((1, 1), dtype=complex) + f = jnp.linspace(0.001, 10 / (2 * jnp.pi), 100) + + _mzi, info = sax.circuit( + netlist={ + "instances": { + "gc_in": "gc", + "splitter": "ybranch", + "long_wg": "waveguide", + "short_wg": "waveguide", + "combiner": "ybranch", + # "gc_out": "gc", + }, + "connections": { + "splitter,port 2": "long_wg,o0", + "splitter,port 3": "short_wg,o0", + "long_wg,o1": "combiner,port 2", + "short_wg,o1": "combiner,port 3", + # "combiner,port 1": "gc_out,o0", + }, + "ports": { + "in": "splitter,port 1", + "out": "combiner,port 1", + }, + }, + models={ + "ybranch": siepic.y_branch, + "waveguide": siepic.waveguide, + # "gc": siepic.grating_coupler, + }, + ) + + def mzi(wl=1.55): + return _mzi(wl=wl, long_wg={"length": 10.0}, short_wg={"length": 40.0}) + + f_min = speed_of_light / 1.6e-6 + f_max = speed_of_light / 1.50e-6 + # f_min = speed_of_light / 1.565e-6 + # f_max = speed_of_light / 1.5350e-6 + frequency = jnp.linspace(f_min, f_max, 1000) + + plt.plot( + frequency, + jnp.angle( + siepic.y_branch(1e6 * speed_of_light / frequency)[("port_1", "port_2")] + ), + ) + plt.show() + + s_params = jnp.reshape( + dict_to_matrix(mzi(wl=1e6 * speed_of_light / frequency))[:, 0, 1], (1000, 1, 1) + ) + phase = jnp.unwrap(jnp.angle(s_params)) + s_params = s_params + s_params = jnp.exp(-2j * phase) * s_params + # s_params = s_params.at[:, 0, 0].set(0) + # s_params = s_params.at[:, 1, 1].set(0) + poles1, residues1, feedthrough1, error = vector_fitting( + 50, s_params, 1e-12 * (frequency), max_iterations=15, alpha=0.01 + ) + + # tic = time() + + # toc = time() + # elapsed_time_1 = toc - tic + # f_prime = jnp.linspace(f_min - 100e12, f_max+100e12, 1000) + + H = pole_residue_response(1e-12 * (frequency), poles1, residues1, feedthrough1) + plt.plot(frequency, jnp.angle(H[:, 0, 1])) + plt.plot(frequency, jnp.angle(s_params[:, 0, 1]), "r--") + plt.show() + + +def main2(): + poles = jnp.array( + [ + -1.3578, + -1.2679, + -1.4851 + 0.2443 * 1j, + -1.4851 - 0.2443 * 1j, + -0.8487 + 2.9019 * 1j, + -0.8487 - 2.9019 * 1j, + -0.8587 + 3.1752 * 1j, + -0.8587 - 3.1752 * 1j, + -0.2497 + 6.5369 * 1j, + -0.2497 - 6.5369 * 1j, + ] + ) + residues = jnp.array( + [ + 0.1059, + -0.2808, + 0.1166, + 0.9569 - 0.7639 * 1j, + 0.9569 + 0.7639 * 1j, + 0.9357 - 0.7593 * 1j, + 0.9357 + 0.7593 * 1j, + 0.4579 - 0.7406 * 1j, + 0.4579 + 0.7406 * 1j, + 0.2405 - 0.7437 * 1j, + 0.2405 + 0.7437 * 1j, + ] + ) + residues = jnp.reshape(residues[1:], (10, 1, 1)) + feedthrough = jnp.zeros((1, 1), dtype=complex) + f = jnp.linspace(0.001, 10 / (2 * jnp.pi), 100) + response = pole_residue_response(f, poles, residues, feedthrough) + + poles1, residues1, feedthrough1, error = vector_fitting( + 10, response, f, max_iterations=5 + ) + H = pole_residue_response(f, poles1, residues1, feedthrough1) + plt.plot(f, jnp.abs(H[:, 0, 0]) ** 2) + plt.plot(f, jnp.abs(response[:, 0, 0]) ** 2, "r--") + plt.show() + + plt.scatter( + poles.real, + poles.imag, + marker="o", + facecolors="none", + edgecolors="C0", + label="Optimal Poles", + s=150.0, + ) + plt.scatter( + poles1.real, poles1.imag, marker="x", color="red", label="VF Poles", s=150.0 + ) + plt.xlabel("REAL") + plt.ylabel("IMAG") + plt.legend() + plt.show() + + +if __name__ == "__main__": + # main() + main2() diff --git a/simphony/time_domain/vector_fitting/z_domain.py b/simphony/time_domain/vector_fitting/z_domain.py new file mode 100644 index 00000000..6c4b999c --- /dev/null +++ b/simphony/time_domain/vector_fitting/z_domain.py @@ -0,0 +1,1054 @@ +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +from scipy.constants import speed_of_light +from scipy.optimize import linear_sum_assignment + +from simphony.conventions import ENGINEER, PHYSICIST +from simphony.performance.performance import persistent_cache + +# from simphony.simulation.jax_tools import python_based_while_loop + + +# @jax.jit +def _initial_poles(model_order, frequency, sampling_frequency, gamma, sign_convention): + f = jnp.linspace(jnp.min(frequency), jnp.max(frequency), model_order) + poles = gamma * jnp.exp(sign_convention * 1j * 2 * jnp.pi * f / sampling_frequency) + return poles + + +# @jax.jit +def _phi_matrices(frequency, sampling_frequency, poles, sign_convention): + z = jnp.exp(sign_convention * 1j * 2 * jnp.pi * frequency / sampling_frequency) + phi1 = 1 / (z[:, None] - poles[None, :]) + + unity_column = jnp.ones((len(z), 1)) + + phi0 = jnp.hstack((unity_column, phi1)) + + return phi0, phi1 + + +def _lstsq_matrices(model_order, transfer_function, phi0, phi1): + """""" + # num_ports = transfer_function.shape[1] + num_outputs = transfer_function.shape[1] + num_inputs = transfer_function.shape[2] + # M = jnp.zeros(((num_ports**2) * (model_order), (model_order)), dtype=complex) + # B = jnp.zeros(((num_ports**2) * (model_order)), dtype=complex) + M = jnp.zeros( + ((num_inputs * num_outputs) * (model_order), (model_order)), dtype=complex + ) + B = jnp.zeros(((num_inputs * num_outputs) * (model_order)), dtype=complex) + + iter = 0 + # for i in range(num_ports): + # for j in range(num_ports): + for m in range(num_inputs): + for q in range(num_outputs): + D = jnp.diag(transfer_function[:, q, m]) + A_block = jnp.hstack([phi0, -D @ phi1]) # never build the big matrix + Q, R = jnp.linalg.qr(A_block, mode="reduced") + + R22 = R[model_order + 1 :, model_order + 1 :] + Q2 = Q[:, model_order + 1 :] + + V = transfer_function[:, q, m] + M = M.at[(iter) * (model_order) : (iter + 1) * (model_order), :].set(R22) + B = B.at[(iter) * (model_order) : (iter + 1) * (model_order)].set( + Q2.conj().T @ V + ) + iter += 1 + + return M, B + + +def _weight_error( + frequency, sampling_frequency, poles_prev, weight_coeffs, sign_convention +): + z = jnp.exp(sign_convention * 1j * 2 * jnp.pi * frequency / sampling_frequency) + terms = weight_coeffs / (z[:, None] - poles_prev) + weights = 1.0 + jnp.sum(terms, axis=1) + return jnp.sqrt(1 / weights.shape[0] * jnp.sum(jnp.abs(weights - 1) ** 2)) + + +# # @jax.jit +# def _fit_to_poles(transfer_function, frequency, sampling_frequency, poles): +# model_order = poles.shape[0] +# num_ports = transfer_function.shape[1] +# residues = jnp.zeros((model_order, num_ports, num_ports), dtype=complex) +# feedthrough = jnp.zeros((num_ports, num_ports), dtype=complex) +# phi0, _ = _phi_matrices(frequency, sampling_frequency, poles) +# for i in range(num_ports): +# for j in range(num_ports): +# # Q,R = np.linalg.qr(phi0,mode='reduced') +# # solutions = np.linalg.pinv(R)@Q.conj().T@self.S[:, i, j] +# solutions, *_ = jnp.linalg.lstsq(phi0, transfer_function[:, i, j], rcond=None) +# feedthrough = feedthrough.at[i, j].set(jnp.array(solutions[0])) +# residues = residues.at[:, i, j].set(solutions[1:]) + +# return residues, feedthrough + + +# @jax.jit +def _fit_to_poles( + transfer_function, frequency, sampling_frequency, poles, sign_convention +): + model_order = poles.shape[0] + # num_ports = transfer_function.shape[1] + num_outputs = transfer_function.shape[1] + num_inputs = transfer_function.shape[2] + phi0, _ = _phi_matrices(frequency, sampling_frequency, poles, sign_convention) + # transfer_pairs = transfer_function.reshape(transfer_function.shape[0], -1) # shape: (num_freq, num_ports*num_ports) + transfer_pairs = ( + transfer_function.transpose(1, 2, 0).reshape(-1, transfer_function.shape[0]).T + ) + # Define a function to solve lstsq for one port pair vector V (shape num_freq,) + + def solve_lstsq(V): + sol, *_ = jnp.linalg.lstsq(phi0, V, rcond=None) + return sol # shape (model_order + 1,) + + # Vectorize over all port pairs (along axis=1) + solutions = jax.vmap(solve_lstsq, in_axes=1)( + transfer_pairs + ) # shape (num_ports*num_ports, model_order + 1) + + # Reshape solutions back to (num_ports, num_ports, model_order + 1) + solutions = solutions.reshape((num_outputs, num_inputs, model_order + 1)) + + # Extract feedthrough (constant term) + feedthrough = solutions[:, :, 0] # shape (num_ports, num_ports) + + # Extract residues (remaining terms) + residues = solutions[:, :, 1:].transpose( + 2, 0, 1 + ) # shape (model_order, num_ports, num_ports) + + return residues, feedthrough + + +# @jax.jit +def pole_residue_response_discrete( + frequency, + center_frequency, + sampling_frequency, + poles, + residues, + feedthrough, + sign_convention=PHYSICIST, +): + """Evaluate a discrete pole-residue transfer function. + + Parameters + ---------- + frequency: + Frequency samples, in Hz, where the response is evaluated. + center_frequency: + Frequency, in Hz, used as the baseband expansion point. + sampling_frequency: + Discrete-time sampling frequency, in Hz. + poles: + Discrete poles with shape `(r,)`. + residues: + Residue tensor with shape `(r, q, m)` for `r` poles, `q` outputs, and + `m` inputs. + feedthrough: + Direct term with shape `(q, m)`. + sign_convention: + Complex exponential convention, usually `PHYSICIST` or `ENGINEER`. + + Returns + ------- + jax.Array + Frequency response with shape `(len(frequency), q, m)`. + """ + z = jnp.exp( + sign_convention + * 1j + * 2 + * jnp.pi + * (frequency - center_frequency) + / sampling_frequency + ) + frequency_response = feedthrough[None, :, :] + jnp.sum( + residues[None, :, :, :] / (z[:, None, None, None] - poles[None, :, None, None]), + axis=1, + ) + return frequency_response + + +# @jax.jit +def _mean_squared_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + poles, + residues, + feedthrough, + sign_convention, +): + fit = pole_residue_response_discrete( + frequency, + center_frequency, + sampling_frequency, + poles, + residues, + feedthrough, + sign_convention=PHYSICIST, + ) + error = jnp.mean(jnp.abs(transfer_function - fit) ** 2) + + return error + + +# def state_space_discrete(poles, residues, feedthrough): +# model_order = poles.shape[0] +# num_ports = feedthrough.shape[0] +# A = jnp.zeros( +# (model_order * num_ports, model_order * num_ports), dtype=complex +# ) +# B = jnp.zeros((model_order * num_ports, num_ports), dtype=complex) +# C = jnp.zeros((num_ports, model_order * num_ports), dtype=complex) +# for i in range(model_order): +# A = A.at[ +# i * num_ports : (i + 1) * num_ports, +# i * num_ports : (i + 1) * num_ports, +# ].set(poles[i] * jnp.eye(num_ports)) + +# B = B.at[i * num_ports : (i + 1) * num_ports, :].set(jnp.eye(num_ports)) +# C = C.at[:, i * num_ports : (i + 1) * num_ports].set(residues[i, :, :]) + +# return A, B, C, feedthrough + + +@persistent_cache +def vector_fitting_discrete( + model_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + sign_convention=PHYSICIST, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, +): + """Fit a discrete-time pole-residue model to sampled transfer data. + + This is the core z-domain vector fitting routine. It relocates poles until + the weighting function converges or `max_iterations` is reached, then solves + for residues and feedthrough for all input/output pairs. + + Parameters + ---------- + model_order: + Number of poles to fit. + transfer_function: + Target response with shape `(num_frequency, q, m)`. + frequency: + Frequency samples, in Hz, matching the first axis of + `transfer_function`. + center_frequency: + Center frequency, in Hz, used for basebanding. + sampling_frequency: + Discrete-time sampling frequency, in Hz. + sign_convention: + Complex exponential convention, usually `PHYSICIST` or `ENGINEER`. + max_iterations: + Maximum number of pole-relocation iterations. + gamma: + Radius used for the initial pole placement inside the unit circle. + weight_threshold: + Stop when the weight error is at or below this value. + + Returns + ------- + tuple + `(poles, residues, feedthrough, mean_squared_error)` where `poles` has + shape `(model_order,)`, `residues` has shape `(model_order, q, m)`, + `feedthrough` has shape `(q, m)`, and `mean_squared_error` is the fit + error against `transfer_function`. + """ + # Convert to engineer's Sign Convention + # transfer_function = jnp.conj(transfer_function) + + baseband_frequency = frequency - center_frequency + + def poles_not_converged(state): + _, weight_error, iteration = state + + return (iteration < max_iterations) & (weight_error > weight_threshold) + + def relocate_poles(state): + previous_poles, _, iteration = state + phi0, phi1 = _phi_matrices( + baseband_frequency, sampling_frequency, previous_poles, sign_convention + ) + M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) + weight_coeffs, *_ = jnp.linalg.lstsq(M, B) + + weight_error = _weight_error( + baseband_frequency, + sampling_frequency, + previous_poles, + weight_coeffs, + sign_convention, + ) + + A = jnp.diag(previous_poles) + current_poles, _ = jnp.linalg.eig( + A - jnp.outer(jnp.ones(model_order), weight_coeffs) + ) + mask = jnp.abs(current_poles) > 1 + # current_poles = current_poles.at[mask].set(1 / current_poles[mask]) + current_poles = jnp.where(mask, 1 / current_poles, current_poles) + + return (current_poles, weight_error, iteration + 1) + + initial_poles = _initial_poles( + model_order, baseband_frequency, sampling_frequency, gamma, sign_convention + ) + initial_state = (initial_poles, jnp.inf, 0) + final_poles, *_ = jax.lax.while_loop( + poles_not_converged, relocate_poles, initial_state + ) + residues, feedthrough = _fit_to_poles( + transfer_function, + baseband_frequency, + sampling_frequency, + final_poles, + sign_convention, + ) + + # Convert back to Physicist's Convention + # final_poles = 1/final_poles + # residues = -residues * final_poles[:, None, None] + + error = _mean_squared_error( + transfer_function, + frequency, + center_frequency, + sampling_frequency, + final_poles, + residues, + feedthrough, + sign_convention, + ) + + return final_poles, residues, feedthrough, error + + +# @jax.jit +# def vector_fitting_z( +# model_order, +# transfer_function, +# frequency, +# center_frequency, +# sampling_frequency, +# max_iterations = 15, +# gamma = 0.95, +# ): +# baseband_frequency = frequency - center_frequency +# poles = _initial_poles(model_order, baseband_frequency, sampling_frequency, gamma) +# for _ in range(max_iterations): +# phi0, phi1 = _phi_matrices(poles, baseband_frequency) +# M, B = _lstsq_matrices(model_order, transfer_function, phi0, phi1) +# weights, *_ = jnp.linalg.lstsq(M, B) +# # weights_row = weights.reshape((len(weights), 1)) +# # unity_column = jnp.ones((model_order, 1)) + +# A = jnp.diag(poles) +# poles, _ = jnp.linalg.eig(A - jnp.outer(jnp.ones(model_order), weights)) +# mask = jnp.abs(poles) > 1 +# poles = poles.at[mask].set(1 / (poles[mask])) + +# error = compute_error() + +# if error < tolerable_error: +# break + + +def optimize_order(bias_fn, min_order, max_order): + """Choose a model order by balancing fit error and complexity. + + Parameters + ---------- + bias_fn: + Callable accepting an integer model order and returning a tuple whose + first value is the mean squared error for that order. + min_order: + Minimum model order to consider. + max_order: + Maximum model order to consider. + + Returns + ------- + tuple + The full `bias_fn(best_order)` result for the selected order. + """ + C_min, *_ = bias_fn(min_order) + C_max, *_ = bias_fn(max_order) + C_max_minus_1, *_ = bias_fn(max_order - 1) + lambda_lower = jnp.abs(C_max_minus_1 - C_max) + lambda_upper = C_min - C_max + lower_log = jnp.log10(lambda_lower) + upper_log = jnp.log10(lambda_upper) + complexity_penalty = 10 ** (0.5 * (upper_log + lower_log)) + + # TODO: implement Golden Section Search + # to minimize C - complexity_penalty * order + golden_ratio = (jnp.sqrt(5) - 1) / 2 + a = min_order + b = max_order + c = int(b - golden_ratio * (b - a)) + d = int(a + golden_ratio * (b - a)) + + fc = bias_fn(c)[0] + complexity_penalty * d + fd = bias_fn(d)[0] + complexity_penalty * d + while abs(b - a) > 1: + if fc < fd: # minimum is in [a, d] + b, d, fd = d, c, fc + c = int(b - golden_ratio * (b - a)) + fc = bias_fn(c)[0] + complexity_penalty * c + else: # minimum is in [c, b] + a, c, fc = c, d, fd + d = int(a + golden_ratio * (b - a)) + fd = bias_fn(d)[0] + complexity_penalty * d + + best_order = int(round((a + b) / 2)) + + return bias_fn(best_order) + + +# TODO: Cache the model order, not the model itself to save space +@persistent_cache +def optimize_order_vector_fitting_discrete( + min_order, + max_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + sign_convention=PHYSICIST, + max_iterations=10, + gamma=0.95, + weight_threshold=0.0, +): + """Fit a discrete pole-residue model while selecting the model order. + + This wraps `vector_fitting_discrete` with `optimize_order`, searching between + `min_order` and `max_order`. + + Parameters + ---------- + min_order: + Smallest number of poles to consider. + max_order: + Largest number of poles to consider. + transfer_function: + Target response with shape `(num_frequency, q, m)`. + frequency: + Frequency samples, in Hz. + center_frequency: + Center frequency, in Hz, used for basebanding. + sampling_frequency: + Discrete-time sampling frequency, in Hz. + sign_convention: + Complex exponential convention, usually `PHYSICIST` or `ENGINEER`. + max_iterations: + Maximum pole-relocation iterations for each attempted order. + gamma: + Initial pole radius used by `vector_fitting_discrete`. + weight_threshold: + Pole-relocation convergence threshold. + + Returns + ------- + tuple + `(poles, residues, feedthrough, mean_squared_error)` for the selected + order. + """ + + def bias_fn(model_order): + poles, residues, feedthrough, mean_squared_error = vector_fitting_discrete( + model_order, + transfer_function, + frequency, + center_frequency, + sampling_frequency, + sign_convention=sign_convention, + max_iterations=max_iterations, + gamma=gamma, + weight_threshold=weight_threshold, + # use_cache=False, ### TODO: Decide whether this be necessary + ) + return mean_squared_error, poles, residues, feedthrough + + mean_squared_error, poles, residues, feedthrough = optimize_order( + bias_fn, min_order, max_order + ) + + return poles, residues, feedthrough, mean_squared_error + + +# def broken_state_space_discrete(poles, residues, feedthrough): +# """ +# poles: array of shape (r,) +# residues: array of shape (r, q, m) +# D: feedthrough, shape (q, m) + +# Returns: A, B, C, D with replicated poles per input +# """ +# r, q, m = residues.shape +# M = r * m + +# # A: block-diagonal with replicated poles +# A = jnp.repeat(jnp.diag(poles), m, axis=0) + +# # B: each input excites its replicated states +# B = jnp.zeros((M, m), dtype=complex) +# for i in range(r): +# for j in range(m): +# B = B.at[i*m + j, j].set(1.0) + +# # C: map states to outputs using residues +# C = jnp.zeros((q, M), dtype=complex) +# for i in range(r): +# for j in range(m): +# C = C.at[:, i*m + j].set(residues[i, :, j]) + +# D = feedthrough +# return A, B, C, D + + +def state_space_discrete(poles, residues, feedthrough): + """Convert a pole-residue model into a discrete state-space realization. + + The realization replicates each pole once per input channel. It is simple + and deterministic, but it is not guaranteed to be minimal. + + Parameters + ---------- + poles: + Discrete poles with shape `(r,)`. + residues: + Residue tensor with shape `(r, q, m)`. + feedthrough: + Direct term with shape `(q, m)`. + + Returns + ------- + tuple[jax.Array, jax.Array, jax.Array, jax.Array] + `(A, B, C, D)` with shapes `(r*m, r*m)`, `(r*m, m)`, `(q, r*m)`, and + `(q, m)`. + """ + r, q, m = residues.shape + M = r * m # total number of states + + # A: block-diagonal, replicate each pole m times + A = jnp.kron(jnp.diag(poles), jnp.eye(m, dtype=complex)) + + # B: each input excites its replicated states + B = jnp.zeros((M, m), dtype=complex) + for i in range(r): + for j in range(m): + B = B.at[i * m + j, j].set(1.0) + + # C: map states to outputs using residues + C = jnp.zeros((q, M), dtype=complex) + for i in range(r): # over poles + for j in range(m): # over inputs + # state index for this replicated pole + idx = i * m + j + # residues[i, :, j] has shape (q,) + C = C.at[:, idx].set(residues[i, :, j]) + + D = feedthrough + return A, B, C, D + + +# def state_space_discrete(poles, residues, feedthrough): +# """ +# Creates a state space model without the need for singular value decomposition + +# This approach is not guaranteed to produce a minimal model see +# Vector Fitting by Piero Triverio∗, August 27, 2019 +# """ +# # TODO: Make sure this works when num_inputs is not equal to num_outputs +# model_order = poles.shape[0] +# num_outputs = feedthrough.shape[0] +# num_inputs = feedthrough.shape[1] + +# A = jnp.zeros( +# (model_order * num_inputs, model_order * num_inputs), dtype=complex +# ) +# B = jnp.zeros((model_order * num_inputs, num_inputs), dtype=complex) +# C = jnp.zeros((num_outputs, model_order * num_inputs), dtype=complex) +# for i in range(model_order): +# A = A.at[i * num_inputs : (i + 1) * num_inputs, i * num_inputs : (i + 1) * num_inputs].set(poles[i] * jnp.eye(num_inputs)) +# B = B.at[i * num_inputs : (i + 1) * num_inputs, :].set(jnp.eye(num_inputs)) +# C = C.at[:, i * num_outputs : (i + 1) * self.state_space_matricesnum_inputs].set(residues[i, :, :]) + +# D = feedthrough +# return A, B, C, D + + +@jax.jit +def _state_space_response_discrete(A, B, C, D, u, x0): + def step(x, u_k): + y_k = C @ x + D @ u_k + x_next = A @ x + B @ u_k + return x_next, (y_k, x_next) + + _, (yout, xout) = jax.lax.scan(step, x0, u) + return yout, xout + + +def state_space_response_discrete(A, B, C, D, u, x0=None): + """Simulate a discrete state-space model over an input sequence. + + Parameters + ---------- + A, B, C, D: + State-space matrices. + u: + Input samples with shape `(T, m)`. + x0: + Optional initial state with shape `(n,)`. Defaults to zeros. + + Returns + ------- + tuple[jax.Array, jax.Array] + Output samples `y` with shape `(T, q)` and state history with shape + `(T, n)`. + """ + if x0 is None: + x0 = jnp.zeros((A.shape[0],), dtype=A.dtype) + + return _state_space_response_discrete(A, B, C, D, u, x0) + + +def state_space_has_optimized_structure(A, B): + """Return true when ABCD matrices match `state_space_discrete` + structure.""" + M = A.shape[0] + m = B.shape[1] + if A.shape != (M, M) or B.shape[0] != M or m == 0 or M % m != 0: + return False + + r = M // m + expected_B = jnp.tile(jnp.eye(m, dtype=B.dtype), (r, 1)) + diagonal_A = jnp.diag(jnp.diag(A)) + + return bool(jnp.allclose(B, expected_B)) and bool(jnp.allclose(A, diagonal_A)) + + +def state_space_discrete_optimized_terms(A, B, C, check_structure=True): + """Precompute the terms used by optimized structured state-space + updates.""" + if check_structure and not state_space_has_optimized_structure(A, B): + raise ValueError( + "ABCD matrices do not match the optimized state-space structure" + ) + + M = A.shape[0] + m = B.shape[1] + r = M // m + q = C.shape[0] + + A_diag = jnp.diag(A) + residues = jnp.transpose(C.reshape(q, r, m), (1, 0, 2)) + return A_diag, residues + + +@jax.jit +def state_space_step_discrete_optimized(A_diag, residues, D, update_constant, u, x): + """Run one batched sample update for the optimized pole-residue + realization. + + `update_constant` may be any per-wavelength multiplier for the state + update, not only a unit-magnitude phase. + """ + r = residues.shape[0] + m = residues.shape[2] + x_by_pole = x.reshape((x.shape[0], r, m)) + y = jnp.einsum("rqm,lrm->lq", residues, x_by_pole) + u @ D.T + x_next = update_constant[:, None] * (A_diag[None, :] * x + jnp.tile(u, (1, r))) + return y, x_next + + +@jax.jit +def _state_space_response_discrete_optimized( + A_diag, residues, D, u, x0, input_constant +): + r = residues.shape[0] + m = residues.shape[2] + + def step(x, u_k): + y_k = jnp.einsum("iqm,im->q", residues, x.reshape((r, m))) + D @ u_k + x_next = A_diag * x + input_constant * jnp.tile(u_k, r) + return x_next, (y_k, x_next) + + _, (yout, xout) = jax.lax.scan(step, x0, u) + return yout, xout + + +def state_space_response_discrete_optimized(A, B, C, D, input_constant, u, x0=None): + """Fast discrete-time response for the structured vector-fitting + realization. + + This assumes the ABCD matrices come from `state_space_discrete`, with: + - A diagonal/block-diagonal replicated-pole structure. + - B equal to the canonical replicated-input selector. + - C ordered so it can be reshaped into residues with shape (r, q, m). + - State ordering grouped by pole, then input. + + `input_constant` is just a multiplicative constant for the replicated input + branch. It is often a phase factor in baseband simulations, but the optimized + update does not require it to be unit magnitude. + + This is not equivalent to `state_space_response_discrete` for arbitrary + state-space realizations for user-defined state space models. This method was + explicitly designed to be used for the pole-residue models generated from user + defined s-parameter matrices. + """ + if x0 is None: + x0 = jnp.zeros((A.shape[0],), dtype=A.dtype) + + A_diag, residues = state_space_discrete_optimized_terms( + A, + B, + C, + check_structure=False, + ) + + return _state_space_response_discrete_optimized( + A_diag, residues, D, u, x0, input_constant + ) + + +# def state_space_response_discrete(A, B, C, D, u, x0=None): +# # Initial state +# if x0 is None: +# x0 = jnp.zeros((A.shape[0],), dtype=A.dtype) + +# def step(x, u_k): +# y_k = C @ x + D @ u_k +# x_next = A @ x + B @ u_k +# return x_next, (y_k, x) + +# # Run scan +# x_final, (yout, xout) = jax.lax.scan(step, x0, u) + +# return yout, jnp.vstack([xout, x_final]) + +# def state_space_response_discrete(A, B, C, D, u, x=None): +# out_samples = len(u) +# # stoptime = (out_samples) * dt + +# xout = jnp.zeros((out_samples, A.shape[0]), dtype=complex) +# yout = jnp.zeros((out_samples, C.shape[0]), dtype=complex) +# # tout = jnp.linspace(0.0, stoptime, num=out_samples) + +# xout = xout.at[0, :].set(jnp.zeros((A.shape[1],), dtype=complex)) + +# if x is not None: +# xout = xout.at[0, :].set(x) + +# u_dt = u + +# # Simulate the system +# for i in range(0, out_samples): +# xout = xout.at[i+1, :].set(jnp.dot(A, xout[i, :]) + jnp.dot(B, u_dt[i, :])) +# yout = yout.at[i, :].set(jnp.dot(C, xout[i, :]) + jnp.dot(D, u_dt[i, :])) + +# # Last point +# yout = yout.at[out_samples - 1, :].set(jnp.dot(C, xout[out_samples - 1, :]) + jnp.dot( +# D, u_dt[out_samples - 1, :] +# )) + +# return yout, xout + + +def state_space_frequency_response_discrete(A, B, C, D, f, f_center, dt): + """Compute the frequency response of a discrete state-space system. + + Parameters + ---------- + A: jnp.ndarray, shape (n, n) + State matrix + B: jnp.ndarray, shape (n, m) + Input matrix + C: jnp.ndarray, shape (q, n) + Output matrix + D: jnp.ndarray, shape (q, m) + Feedthrough matrix + f: + Frequency samples, in Hz. + f_center: + Center frequency, in Hz, used for basebanding. + dt: + Time step, in seconds. + ------- + jax.Array + Frequency response with shape `(len(f), q, m)`. + """ + n_out, n_in = D.shape + n_freq = f.size + H = jnp.zeros((n_freq, n_out, n_in), dtype=complex) + + # Discrete-time z = e^(j*omega*dt) + # TODO: Add in sign conventions + z = jnp.exp(-1j * 2 * jnp.pi * (f - f_center) * dt) + + for k in range(n_freq): + Hk = C @ jnp.linalg.inv(z[k] * jnp.eye(A.shape[0]) - A) @ B + D + H = H.at[k, :, :].set(Hk) + + return H + + return H + + +def main(): + import sax + + from simphony.libraries import ideal + from simphony.utils import dict_to_matrix + + netlist = { + "instances": { + "wg": "waveguide", + "hr": "half_ring", + }, + "connections": { + "hr,o2": "wg,o0", + "hr,o3": "wg,o1", + }, + "ports": { + "o0": "hr,o0", + "o1": "hr,o1", + }, + } + + circuit, info = sax.circuit( + netlist=netlist, + models={ + "waveguide": ideal.waveguide, + "half_ring": ideal.coupler, + }, + ) + + f_min = speed_of_light / 1.6e-6 + f_max = speed_of_light / 1.5e-6 + f_center = 0.5 * (f_min + f_max) + # f_center = 192.9e12 + frequency = jnp.linspace(f_min, f_max, 1000) + s_params = dict_to_matrix( + circuit(wl=1e6 * speed_of_light / frequency, wg={"length": 77.0, "loss": 100}) + ) + + sampling_frequency = 1e14 + model_order = 10 + + poles, residues, feedthrough, error = optimize_order_vector_fitting_discrete( + 10, 50, s_params, frequency, f_center, sampling_frequency + ) + model_order = len(poles) + poles_eng, residues_eng, feedthrough_eng, erro = vector_fitting_discrete( + model_order, + jnp.conj(s_params), + frequency, + f_center, + sampling_frequency, + sign_convention=ENGINEER, + ) + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_2 = toc - tic + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_3 = toc - tic + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z_optimize_order(10, 50, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time_4 = toc - tic + + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z(model_order, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time = toc - tic + # print(elapsed_time) + # tic = time() + # poles, residues, feedthrough, error = vector_fitting_z(model_order+1, s_params, frequency, f_center, sampling_frequency) + # toc = time() + # elapsed_time = toc - tic + # print(elapsed_time) + f = jnp.linspace(-sampling_frequency / 2, sampling_frequency / 2, 100000) + f_center + + plt.scatter(poles.real, poles.imag) + plt.scatter(poles_eng.real, poles_eng.imag) + plt.show() + + plt.scatter(residues[:, 0, 1].real, residues[:, 0, 1].imag) + plt.scatter(residues_eng[:, 0, 1].real, residues_eng[:, 0, 1].imag) + plt.show() + + pole_residue_response_discrete( + f, + f_center, + sampling_frequency, + poles, + residues, + feedthrough, + sign_convention=PHYSICIST, + ) + H_eng = pole_residue_response_discrete( + f, + f_center, + sampling_frequency, + jnp.conj(poles), + jnp.conj(residues), + jnp.conj(feedthrough), + sign_convention=ENGINEER, + ) + # plt.plot(f, jnp.abs(H[:, 0, 1])) + plt.plot(f, jnp.abs(H_eng[:, 0, 1]), "r--") + # plt.plot(frequency, jnp.abs(s_params[:, 0, 1])) + plt.plot(frequency, jnp.abs(s_params[:, 0, 1])) + plt.show() + + # plt.plot(f, jnp.angle(H[:, 0, 1])) + # plt.plot(frequency, jnp.angle(s_params[:, 0, 1])) + plt.plot(f, jnp.angle(H_eng[:, 0, 1]), "r--") + plt.plot(frequency, jnp.angle(jnp.conj(s_params[:, 0, 1]))) + plt.xlim([187e12, 200e12]) + plt.show() + + t = jnp.arange(0, 5000 / sampling_frequency, 1 / sampling_frequency) + A, B, C, D = state_space_discrete(poles, residues, feedthrough) + u = jnp.zeros((t.shape[0], 2), dtype=complex) + u = u.at[:, 0].set(1) + u = u.at[:, 0].set(u[:, 0] * jnp.exp(-1j * 2 * jnp.pi * (194.2e12 - f_center) * t)) + y, x = state_space_response_discrete(A, B, C, D, u) + plt.plot(t, jnp.abs(y[:, 1]) ** 2, label="shifted inputs", linewidth=3.0) + # plt.show() + + u = u.at[:, 0].set(1) + y, x = state_space_response_discrete( + jnp.exp(1j * 2 * jnp.pi * (194.2e12 - f_center) / sampling_frequency) * A, + jnp.exp(1j * 2 * jnp.pi * (194.2e12 - f_center) / sampling_frequency) * B, + C, + D, + u, + ) + plt.plot(t, jnp.abs(y[:, 1]) ** 2, "r--", label="modified ss model") + plt.xlabel("time (s)") + plt.ylabel("mag squared") + plt.legend() + plt.show() + # print(jnp.angle(y)) + # plt.plot(frequency, jnp.angle(s_params[:, 0, 1])) + + +def main2(): + sampling_frequency = 1 + center_frequency = sampling_frequency / 2 + frequency = ( + jnp.linspace(-sampling_frequency / 2, sampling_frequency / 2, 1000) + + center_frequency + ) + + num_poles = 10 + key = jax.random.PRNGKey(42) + pole_radius_key, pole_angle_key = jax.random.split(key) + pole_radii = jax.random.uniform( + pole_radius_key, (num_poles,), minval=0.10, maxval=0.98 + ) + pole_angles = jax.random.uniform( + pole_angle_key, (num_poles,), minval=-jnp.pi, maxval=jnp.pi + ) + poles = pole_radii * jnp.exp(1j * pole_angles) + + port_indices = jnp.arange(10, dtype=jnp.float32) + row_indices = port_indices[:, None] + col_indices = port_indices[None, :] + base_residue = ( + 0.9 + + 0.12 * row_indices + - 0.05 * col_indices + + 0.015 * row_indices * col_indices + + 1j * (0.2 + 0.08 * row_indices + 0.045 * col_indices) + ) + scale_magnitudes = jnp.linspace(1.00, 0.08, num_poles) + scale_phases = jnp.linspace(0.0, 0.75 * jnp.pi, num_poles) + residue_scales = scale_magnitudes * jnp.exp(1j * scale_phases) + residues = residue_scales[:, None, None] * base_residue[None, :, :] + + feedthrough = jnp.zeros(residues.shape[1:], dtype=complex) + + def match_pole_residue_pairs(reference_poles, candidate_poles, candidate_residues): + pole_distance = jnp.abs(reference_poles[:, None] - candidate_poles[None, :]) + row_indices, col_indices = linear_sum_assignment(pole_distance) + assignment_order = jnp.asarray( + col_indices[jnp.argsort(jnp.asarray(row_indices))] + ) + return candidate_poles[assignment_order], candidate_residues[assignment_order] + + original_poles = poles + original_residues = residues + original_feedthrough = feedthrough + + H = pole_residue_response_discrete( + frequency, + center_frequency, + sampling_frequency, + original_poles, + original_residues, + original_feedthrough, + ) + final_poles, final_residues, final_feedthrough, fit_error = vector_fitting_discrete( + model_order=num_poles, + transfer_function=H, + frequency=frequency, + center_frequency=center_frequency, + sampling_frequency=sampling_frequency, + ) + + matched_final_poles, matched_final_residues = match_pole_residue_pairs( + original_poles, + final_poles, + final_residues, + ) + fitted_response = pole_residue_response_discrete( + frequency, + center_frequency, + sampling_frequency, + final_poles, + final_residues, + final_feedthrough, + ) + + pole_error = jnp.max(jnp.abs(matched_final_poles - original_poles)) + residue_error = jnp.max(jnp.abs(matched_final_residues - original_residues)) + feedthrough_error = jnp.max(jnp.abs(final_feedthrough - original_feedthrough)) + response_error = jnp.max(jnp.abs(fitted_response - H)) + relative_response_error = jnp.linalg.norm(fitted_response - H) / jnp.maximum( + jnp.linalg.norm(H), + 1e-12, + ) + + print("Fit MSE:", fit_error) + print("Max pole error:", pole_error) + print("Max residue error:", residue_error) + print("Max feedthrough error:", feedthrough_error) + print("Max response error:", response_error) + print("Relative response error:", relative_response_error) + + +if __name__ == "__main__": + main2() diff --git a/simphony/utils.py b/simphony/utils.py index 5d25cc66..dbe0cedd 100644 --- a/simphony/utils.py +++ b/simphony/utils.py @@ -12,7 +12,7 @@ import sax from jax import Array from jax.typing import ArrayLike -from sax.utils import get_ports +from sax import get_ports from scipy.constants import c as SPEED_OF_LIGHT from scipy.interpolate import CubicSpline, interp1d @@ -440,6 +440,53 @@ def dict_to_matrix(sdict: sax.SDict) -> Array: return smat +# TODO: Maybe maybe not merge this with dict to matrix +from typing import Sequence + +# import jax.numpy as jnp +# from sax import sax +# from sax.utils import get_ports + + +def dict_to_rect_matrix( + sdict: sax.SDict, input_ports: Sequence[str], output_ports: Sequence[str] +) -> jnp.ndarray: + r""" + Converts an s-dict to a rectangular S-matrix of shape (f x N x M), + where M = number of inputs, N = number of outputs. + + Missing port-to-port entries are filled with zeros. + + Parameters + ---------- + sdict : sax.SDict + A dictionary of s-parameters. + input_ports : sequence of str + The ports corresponding to the "columns" of the matrix (inputs). + output_ports : sequence of str + The ports corresponding to the "rows" of the matrix (outputs). + + Returns + ------- + smat : jnp.ndarray + Rectangular matrix of s-parameters with shape (f x N x M) + """ + # Determine the number of frequency points + arr = list(sdict.values())[0] + arr = jnp.asarray(arr).reshape(-1) + n_freq = len(arr) + + smat = jnp.zeros((n_freq, len(output_ports), len(input_ports)), dtype=complex) + + for (port_out, port_in), values in sdict.items(): + if port_in in input_ports and port_out in output_ports: + i = output_ports.index(port_out) + j = input_ports.index(port_in) + smat = smat.at[:, i, j].set(values) + + return smat + + def validate_model(model: sax.saxtypes.Model) -> bool: """Validates a model. @@ -515,3 +562,33 @@ def resample(x: ArrayLike, xp: ArrayLike, sdict: sax.SDict) -> sax.SDict: cs = CubicSpline(xp, v) new_sdict[k] = cs(x) return new_sdict + + +def create_multimode_sax_model(models: dict): + """This function takes individual s-parameter models or individual modes + and combines them into the sax-format for multimode models, assuming + complete orthogonality of modes (no cross polarization)""" + import inspect + + # Build the union of parameters across all sub-models so SAX can inspect + # the signature and forward global settings (e.g. wl) correctly. + merged_params = {} + for model in models.values(): + for name, param in inspect.signature(model).parameters.items(): + if param.kind in (param.VAR_POSITIONAL, param.VAR_KEYWORD): + continue + if name not in merged_params: + merged_params[name] = param + + def multimode_model(**params): + S_mm = {} + for mode, model in models.items(): + S = model(**params) + for (p1, p2), val in S.items(): + S_mm[(f"{p1}@{mode}", f"{p2}@{mode}")] = val + return S_mm + + # Replace **params with the merged explicit signature + multimode_model.__signature__ = inspect.Signature(list(merged_params.values())) + + return multimode_model diff --git a/simulation_data_QAM_signal_generation.npz b/simulation_data_QAM_signal_generation.npz new file mode 100644 index 00000000..74888be8 Binary files /dev/null and b/simulation_data_QAM_signal_generation.npz differ diff --git a/tests/libraries/sipann/test_sipann_models.py b/tests/libraries/sipann/test_sipann_models.py index 1d3089e1..11e46b02 100644 --- a/tests/libraries/sipann/test_sipann_models.py +++ b/tests/libraries/sipann/test_sipann_models.py @@ -1,9 +1,6 @@ import pytest -try: - from simphony.libraries import sipann -except ImportError: - SIPANN_AVAILABLE = False +sipann = pytest.importorskip("simphony.libraries.sipann") class TestGapFuncSymmetric: diff --git a/tox.ini b/tox.ini index 66e90e2c..aebe6b4a 100644 --- a/tox.ini +++ b/tox.ini @@ -29,7 +29,15 @@ exclude = # This contains builds of flake8 that we don't want to check dist, env, + .venv, .ipynb_checkpoints, .tox, extra, - deprecated \ No newline at end of file + deprecated + +per-file-ignores = + simphony/time_domain/SSFM_old.py:E226,F841 + simphony/time_domain/examples/*.py:E203,E402 + simphony/time_domain/old_simulation.py:C901,E402,E711,F541 + simphony/time_domain/quicker_delete.py:E226,E402 + simphony/time_domain/tests/*.py:E402,F841