From 7e4549fda52158d47e36cd88a0772df49b9eff1b Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 13:56:22 +0200 Subject: [PATCH 01/12] fix(matplotlib): corriger le rendu des histogrammes adaptatifs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Réutiliser les fréquences Khiops pour les valeurs extrêmes et rendre visibles les classes très étroites par défaut. Refuser également le type barstacked non pris en charge. Closes #34 --- CHANGELOG.md | 13 +++++ docs/api_comparison.md | 2 +- src/khisto/matplotlib/hist.py | 72 ++++++++++++++++++++----- tests/plot/test_matplotlib_histogram.py | 51 ++++++++++++++++-- 4 files changed, 119 insertions(+), 19 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6ad6cb0..ad9c087 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,19 @@ All notable changes to Khisto are documented in this file. +## Unreleased + +### Changed + +- Draw bar edges in their face color by default so that very narrow adaptive bins + remain visible. +- Reject ``histtype="barstacked"` because Khisto only accepts a single dataset. + +### Fixed + +- Reuse Khisto frequencies when plotting so values remain assigned to the bins + selected by Khisto, including for extreme finite values. + ## [1.0.2] - 2026-09-15 ### Added diff --git a/docs/api_comparison.md b/docs/api_comparison.md index c936773..4e8b8ed 100644 --- a/docs/api_comparison.md +++ b/docs/api_comparison.md @@ -139,7 +139,7 @@ khisto.matplotlib.hist( | **Reverse cumulative** | Supported with negative `cumulative` | Supported with negative `cumulative` | | **Stacked** | Supported | Not supported | | **Weights** | Supported | Not supported | -| **Unsupported histogram args** | None | `bins`, `stacked`, and `weights` raise a `TypeError` | +| **Unsupported histogram args** | None | `bins`, `stacked`, and `weights` raise a `TypeError`; `histtype="barstacked"` raises a `ValueError` | | **Multiple datasets** | Supported | Not supported; only 1-D arrays are accepted | #### Usage Comparison diff --git a/src/khisto/matplotlib/hist.py b/src/khisto/matplotlib/hist.py index 96dcb40..1e58c2e 100644 --- a/src/khisto/matplotlib/hist.py +++ b/src/khisto/matplotlib/hist.py @@ -9,12 +9,14 @@ from typing import TYPE_CHECKING, Any import numpy as np -from matplotlib.axes import Axes from khisto.histogram import histogram as khisto_histogram if TYPE_CHECKING: - from numpy.typing import ArrayLike + from matplotlib.axes import Axes + from matplotlib.container import BarContainer + from matplotlib.patches import Polygon + from numpy.typing import ArrayLike, NDArray def hist( @@ -25,7 +27,11 @@ def hist( *, ax: Axes | None = None, **kwargs: Any, -) -> tuple[np.ndarray, np.ndarray, Any]: +) -> tuple[ + NDArray[np.float64], + NDArray[np.float64], + BarContainer | list[Polygon], +]: """Compute and plot an optimal histogram. Parameters @@ -49,7 +55,8 @@ def hist( Axes object to plot on. If not provided, the current axes will be used. **kwargs : other keyword arguments are described in ``matplotlib.pyplot.hist``. The ``bins``, - ``weights``, and stacked/multiple dataset features are not supported. + ``weights``, ``stacked``, ``histtype="barstacked"``, and multiple dataset + features are not supported. Returns ------- @@ -58,7 +65,7 @@ def hist( bins : ndarray Bin edges. patches - Container with the bar patches. + Container with the bar patches, or a list containing the step polygon. .. note:: Khiops bins are left-open and right-closed, ``(lower, upper]``, unlike @@ -70,6 +77,11 @@ def hist( matplotlib.pyplot.hist : Matplotlib's histogram function. khisto.histogram : Underlying histogram computation. """ + # optional dependency; only import if strictly needed. + import matplotlib.pyplot as plt + from matplotlib.container import BarContainer + from matplotlib.patches import Polygon + unsupported_kwargs = { "bins": "Use max_bins to limit the number of bins.", "stacked": "Stacked histograms are not supported.", @@ -79,16 +91,48 @@ def hist( if name in kwargs: raise TypeError(f"{name} is not supported. {hint}") - # Compute histogram using khisto - _, bin_edges = khisto_histogram(x, range=range, max_bins=max_bins, density=density) + histtype = kwargs.get("histtype", "bar") + if histtype == "barstacked": + raise ValueError( + "histtype='barstacked' is not supported. Khisto only accepts a single dataset." + ) - if ax is None: - # optional dependency; only import if strictly needed. - import matplotlib.pyplot as plt + # Use frequencies so Matplotlib applies density and cumulative only once. + frequencies, bin_edges = khisto_histogram( + x, + range=range, + max_bins=max_bins, + density=False, + ) + if ax is None: ax = plt.gca() - # Khiops bins are right-closed, whereas Matplotlib bins are left-closed. - # Moving each value down one ULP preserves Khiops assignments at shared edges. - plot_values = np.nextafter(np.asarray(x, dtype=np.float64), -np.inf) - return ax.hist(plot_values, bin_edges, density=density, range=range, **kwargs) + # Weighted left edges preserve Khiops' right-closed bins and [-1e100, 1e100] + # clamping when Matplotlib renders its left-closed bins. + values, edges, patches = ax.hist( + bin_edges[:-1].tolist(), + bin_edges.tolist(), + weights=frequencies.tolist(), + density=density, + range=range, + **kwargs, + ) + if isinstance(values, list): + raise TypeError("Matplotlib unexpectedly returned multiple histograms.") + if isinstance(patches, BarContainer): + histogram_patches: BarContainer | list[Polygon] = patches + elif isinstance(patches, list): + histogram_patches = [patch for patch in patches if isinstance(patch, Polygon)] + if len(histogram_patches) != len(patches): + raise TypeError("Matplotlib returned unexpected histogram patches.") + else: + raise TypeError("Matplotlib returned unexpected histogram patches.") + + if histtype == "bar" and "edgecolor" not in kwargs: + if not isinstance(histogram_patches, BarContainer): + raise TypeError("Matplotlib unexpectedly returned non-bar patches.") + for patch in histogram_patches.patches: + patch.set_edgecolor(patch.get_facecolor()) + + return values, edges, histogram_patches diff --git a/tests/plot/test_matplotlib_histogram.py b/tests/plot/test_matplotlib_histogram.py index eca0bf1..1b8a4c5 100644 --- a/tests/plot/test_matplotlib_histogram.py +++ b/tests/plot/test_matplotlib_histogram.py @@ -81,6 +81,17 @@ def test_histogram_matches_khiops_at_internal_edges(self, density): np.testing.assert_array_equal(bins, expected_bins) np.testing.assert_allclose(values, expected) + def test_histogram_matches_khiops_for_large_values(self): + """Test that plotting reuses counts when Khiops adjusts extreme edges.""" + data = np.array([1e300, np.nextafter(1e300, np.inf), 2e300]) + expected, expected_bins = histogram(data, density=False) + _fig, ax = plt.subplots() + + values, bins, _ = hist(data, density=False, ax=ax) + + np.testing.assert_array_equal(bins, expected_bins) + np.testing.assert_array_equal(values, expected) + def test_horizontal_orientation(self, normal_data): """Test horizontal histogram.""" _fig, ax = plt.subplots() @@ -118,6 +129,28 @@ def test_color_parameter(self, normal_data): assert patches is not None + def test_bar_edges_match_face_color_by_default(self, normal_data): + """Test that default bar edges reveal narrow adaptive bins.""" + _fig, ax = plt.subplots() + _, _, patches = hist(normal_data, color="tab:blue", alpha=0.5, ax=ax) + + for patch in patches.patches: + assert patch.get_edgecolor() == patch.get_facecolor() + + def test_explicit_bar_edge_style_is_preserved(self, normal_data): + """Test that explicit edge styling overrides the khisto default.""" + _fig, ax = plt.subplots() + _, _, patches = hist( + normal_data, + edgecolor="red", + linewidth=2.0, + ax=ax, + ) + + for patch in patches.patches: + assert patch.get_edgecolor() == (1.0, 0.0, 0.0, 1.0) + assert patch.get_linewidth() == 2.0 + def test_step_histtype(self, normal_data): """Test histogram with step histtype.""" _fig, ax = plt.subplots() @@ -156,12 +189,22 @@ def test_reverse_cumulative_frequency_histogram(self, normal_data): assert np.isclose(n[0], len(normal_data)) - def test_unsupported_bins_parameter(self, normal_data): - """Test that bins raises a clear error message.""" + @pytest.mark.parametrize( + "name, value", [("bins", 10), ("stacked", False), ("weights", [])] + ) + def test_unsupported_parameter(self, normal_data, name, value): + """Test that unsupported parameters raise clear error messages.""" + _fig, ax = plt.subplots() + + with pytest.raises(TypeError, match=rf"{name} is not supported"): + hist(normal_data, ax=ax, **{name: value}) + + def test_unsupported_barstacked_histtype(self, normal_data): + """Test that barstacked raises a clear error message.""" _fig, ax = plt.subplots() - with pytest.raises(TypeError, match="bins is not supported"): - hist(normal_data, bins=10, ax=ax) + with pytest.raises(ValueError, match="barstacked.*not supported"): + hist(normal_data, histtype="barstacked", ax=ax) class TestHistReturnValues: From 13830b56751b7fd4a2aa84e642234b7f783efb79 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:09:58 +0200 Subject: [PATCH 02/12] feat(api): exposer hist au niveau racine --- src/khisto/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/khisto/__init__.py b/src/khisto/__init__.py index 7b07788..fb4521c 100644 --- a/src/khisto/__init__.py +++ b/src/khisto/__init__.py @@ -31,8 +31,10 @@ from .core import HistogramResult from .histogram import histogram +from .matplotlib import hist __all__ = [ "HistogramResult", + "hist", "histogram", ] From f7c14d4654809d02a8165cb0239329b4fa01ffcf Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:10:18 +0200 Subject: [PATCH 03/12] docs(demo): simplifier le guide et actualiser les figures --- docs/demo.ipynb | 104 +++++++++------------------ docs/images/counts-vs-density.png | Bin 121414 -> 121414 bytes docs/images/gaussian-quick-start.png | Bin 37666 -> 34302 bytes docs/images/pareto-quick-start.png | Bin 31280 -> 32068 bytes sandbox/khisto_demo.ipynb | 2 +- 5 files changed, 35 insertions(+), 71 deletions(-) diff --git a/docs/demo.ipynb b/docs/demo.ipynb index f9e75e5..3be981f 100644 --- a/docs/demo.ipynb +++ b/docs/demo.ipynb @@ -7,26 +7,11 @@ "source": [ "# Khisto — optimal binning histograms\n", "\n", - "A good histogram should reveal the structure of your data without asking you to\n", - "guess the right number of bins first. Khisto chooses the bins for you: it uses\n", - "the **Khiops optimal binning algorithm** (the MODL / Minimum Description Length\n", - "principle) to pick both the number of bins *and* their — possibly unequal —\n", - "widths, so dense regions get fine bins and sparse regions get wide ones.\n", - "\n", - "This notebook goes **from the simplest case to a richer one**:\n", - "\n", - "1. **Quick start** — two textbook distributions (a Gaussian and a heavy-tailed\n", - " Pareto), each in a single call.\n", - "2. **A three-component mixture** — a more realistic example used to tour the\n", - " full API.\n", - "\n", - "Throughout, we plot the **density** rather than raw counts. With variable-width\n", - "bins this is almost always the right choice: a tall-but-narrow bin and a\n", - "short-but-wide bin can hold the *same* number of points, so only the density\n", - "(count divided by bin width) shows the true shape of the distribution.\n", - "\n", - "> 📚 Go further with [Histograms - Khiops](https://khiops.org/learn/histograms/),\n", - "a didactic walk-through from the simplest histogram to the most complex." + "Khisto uses the **Khiops optimal binning algorithm** to choose both the number and\n", + "width of bins. This notebook introduces its plotting API with quick examples.\n", + "\n", + "> New to variable-width histograms? Read the [counts vs density tutorial](counts_vs_density.rst)\n", + "to learn how to interpret them." ] }, { @@ -43,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "id": "d9b9b33b", "metadata": {}, "outputs": [], @@ -70,13 +55,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "id": "9e8eff47", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -112,13 +97,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "id": "e8a48420", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -161,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "id": "8b183e7b", "metadata": {}, "outputs": [ @@ -202,13 +187,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 26, "id": "c8ed6f75", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -250,13 +235,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "id": "48c3b0aa", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -274,13 +259,13 @@ "matplotlib_ax.set_title(\"Fixed-width bins (60)\")\n", "matplotlib_ax.set_xlabel(\"Value\")\n", "matplotlib_ax.set_ylabel(\"Density\")\n", - "matplotlib_ax.set_xscale(\"log\")\n", + "matplotlib_ax.set_xscale(\"symlog\")\n", "matplotlib_ax.set_yscale(\"log\")\n", "\n", "khisto_density, _, _ = hist(data, ax=khisto_ax, color=\"steelblue\", edgecolor=\"white\")\n", "khisto_ax.set_title(f\"Khisto: {len(khisto_density)} adaptive bins\")\n", "khisto_ax.set_xlabel(\"Value\")\n", - "khisto_ax.set_xscale(\"log\")\n", + "khisto_ax.set_xscale(\"symlog\")\n", "khisto_ax.set_yscale(\"log\")\n", "\n", "fig.suptitle(\"Tail behaviour on log-log axes\", y=1.03)\n", @@ -302,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 28, "id": "d0e40b1c", "metadata": {}, "outputs": [ @@ -331,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 29, "id": "55ee75b7", "metadata": {}, "outputs": [ @@ -354,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 30, "id": "33828aaa", "metadata": {}, "outputs": [ @@ -398,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 31, "id": "247c1571", "metadata": {}, "outputs": [ @@ -408,13 +393,13 @@ "Text(0, 0.5, 'Cumulative probability')" ] }, - "execution_count": 37, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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uXDnNmTNHzzzzjNV7fLiPLaeZYmNjtWLFCn366afZ5rlsyF54cCYAZEFffPGFRowYoRUrVqTr0R7nz59X+fLl9e2336pLly6ZWKH9PvnkE82fP1+HDx/mmWTIFMy5AYAs6Pvvv1eJEiX04osvpqufv7+/vvnmG12+fDmTKsu4pKQkzZ07l2CDTMPIDQBkEXfv3tXMmTO1c+dOLV68WEuWLEnxOVEA0sacGwDIIh7c3bd06dLauHFjsoehArANIzcAAMBUmHMDAABMhXADAABM5amcc5OUlKRz584pb9683GMBAIBswjAMxcbGqmjRomk+xuSpDDfnzp1TQECAs8sAAAB2iIyMVPHixVNd/lSGm7x580q6f3AevdsrAADImmJiYhQQEGD5Hk/NUxluHpyK8vb2JtwAAJDNPG5KCROKAQCAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqWSJcHP+/Hlt3bpV0dHRNveJjIxUeHi4YmJiMrEyAACQ3Tg13ISHh6tr166qUaOGmjZtqr179z62T3x8vLp06aKKFSvqtddek5+fn2bMmPEEqgUAANmBU8PNwYMH1b17d+3YscPmPiEhIdq5c6dOnjypw4cPa9GiRXrvvffStQ0AAGBeTn1wZt++fSVJZ8+etbnP/PnzNWjQIPn7+0uSOnXqpKpVq2r+/Plq0KBBZpQJAACykWz1VPBz587p4sWLqlOnjlV7/fr10zyllZCQoISEBMvvzNMBAMC8slW4uXbtmiTJx8fHqt3Hx8eyLCUTJkxQSEhIptYGPGlN1wQ5uwQASNGW9lOcuv9sFW7c3Nwk3Z9U/LC4uDjlypUr1X7BwcEKCvrfF0FMTIwCAgIyp0g4DV/2AAApm4WbgIAA5ciRQ1FRUVbtUVFRKlGiRKr93N3d5e7untnl4RGEDQCAM2SJ+9yk5cSJE5b5NB4eHmrcuLFWrVplWX7r1i2tX79ebdq0cVaJAAAgC3HqyM2lS5d07NgxXb58WZJ04MAB5cyZUyVKlLCMxEycOFHbt2/XwYMHJUnjxo1TmzZtFBwcrEaNGmnGjBkqUqSIBgwY4LT3AQAAsg6nhpvdu3fr008/lSQ1adJEP/zwg3744Qe9+eabevPNNyVJ5cuX1507dyx9mjVrpo0bN2rmzJnauXOnqlWrpu+++05eXl5OeQ9PI043AQCyMhfDMAxnF/GkxcTEKF++fIqOjpa3t7ezy8l2CDcAgLRk1tVStn5/Z/k5NwAAAOlBuAEAAKZCuAEAAKaSre5zA+dhng0AILtg5AYAAJgK4QYAAJgK4QaPxSkpAEB2QrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmwuMXkCrubwMAyI4YuQEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKaS09kFIOtpuibI2SUAAGA3Rm4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpOD3chIWF6cUXX1TdunX1xhtvKCIiIs3179y5o2nTpum5555TvXr11KVLF/36669PplgAAJDlOTXchIWFqUOHDmratKm++OIL3bhxQ88884xu3LiRap/hw4dr0qRJeuutt/TVV1+pZs2aateundauXfvkCgcAAFmWU8PNRx99pFdffVWjR49Ws2bNFBoaqlu3bmnWrFmp9vn55581YMAAdevWTfXq1dMHH3ygKlWqKCws7AlWDgAAsiqnhZubN29q165dateunaXN3d1drVu31saNG1Pt16RJE23ZskVxcXGSpKNHj+rvv//WM888k+k1AwCArM9p4SYqKkqGYcjf39+q3d/fX5GRkan2mzt3rnx9feXr66ty5cqpTp06mjZtml5++eVU+yQkJCgmJsbqBwAAmFNOZ+343r17kqRcuXJZtbu7u+vu3bup9pswYYI2b96s2bNnq2zZslq7dq2CgoJUuXJlNW7cONU+ISEhjiseAABkWU4LNz4+PpKkq1evWrVfvXpVhQoVSrFPTEyMxo0bp7lz56pHjx6SpPr16ys8PFwhISGpXjUVHBysoKAgq+0EBAQ44m2YTtM1QY9fCQCALMxpp6X8/PxUtGhR7dixw6p927ZtqlOnTop94uLilJiYmCz8FC5cOM1TTe7u7vL29rb6AQAA5uTUq6UGDhyob775RqdOnZIkLViwQMeOHVP//v0t63z00Ufq3LmzJMnX11eBgYGaMWOGbt26JUk6cuSIli9frpYtWz75NwAAALIcp52WkqQxY8YoIiJClSpVUqFChXT79m3NmzdPNWvWtKwTFRWl48ePW35fsmSJ3nzzTfn6+qpIkSKKiopSz5499cEHHzjhHQAAgKzGxTAMw9lFXL9+XVeuXFGJEiXk7u5utezcuXO6ffu2ypUrZ9V+48YNXb16VcWLF0/W53FiYmKUL18+RUdHc4rqEcy5AQBk1Jb2UzJlu7Z+fzt15OaBAgUKqECBAikuK1q0aIrt+fPnV/78+TOxKgAAkB05/dlSAAAAjkS4AQAApkK4AQAApkK4gQWTiQEAZkC4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApmJXuFm+fLnu3r3r6FoAAAAyzK5w07t3bxUrVkxBQUE6ePCgo2sCAACwm13Plrpw4YIWL16sefPmaerUqapfv77efPNN9ejRgwdRZhPc0wYAYFZ2jdx4e3tr4MCB2rFjhw4ePKhnnnlGH374ofz8/PTaa69p06ZNDi4TAADANhmeUFylShUNHTpUAwYMUGJion766Se1bt1atWrV0r59+xxQIgAAgO3sDjcJCQkKDQ3Vc889p9KlS2vdunWaOXOmLly4oDNnzqhBgwZ69dVXHVkrAADAY9k152bIkCFatGiRXFxc1Lt3b02ZMkVVq1a1LPfy8tKMGTPk7u7usEIBAABsYVe4+euvv/Tll1+qS5cuqQYYNzc3bdiwIUPFAQAApJddp6ViYmLUs2fPFINN3bp1La+bN29ud2EAAAD2sCvc7N69O8X2xMREJhEDAACnStdpqfDw8BRfS1JSUpK2bdumEiVKOKYyAAAAO6Qr3NSrVy/F1w8UKlRIM2bMyHhVAAAAdkpXuLl+/bqk+yHmypUrVsvc3Nzk6enpuMoAAADskK5wkz9/fknSvXv3MqMWAACADLM53Gzfvl2S1LBhQ8vr1DRs2DBjVSHT8EwpAIDZ2RxuGjVqJEkyDMPyOjWGYWSsKgAAADvZHG5iY2NTfA0AAJCV2BxuvLy8UnwNAACQlaR7zo0tmHMDAACcJd1zbmzBnBsAAOAsds25AQAAyKrsmnMDAACQVXGfGwAAYCrc5wYAAJgK97kBAACmwn1ungI8cgEA8DRJ14MzH3b69Gl99dVXOnz4sCQpMDBQgwcPVokSJRxWHAAAQHrlsKfTzz//rAoVKujXX3+Vr6+vfH19FRYWpgoVKigsLMzRNQIAANjMrpGb4cOH64MPPtDYsWOt2seNG6fhw4fr+eefd0hxAAAA6WXXyM3Zs2c1ZMiQZO1DhgzRmTNnMlwUAACAvewKN5UrV9bBgweTtR84cECBgYEZLgoAAMBeNp+WejjMvP766+rWrZuCg4NVr149GYah8PBwTZgwQWPGjMmUQgEAAGzhYth4xz0XFxebN5rVb+IXExOjfPnyKTo6Wt7e3s4uJ9NxKTgA4Ena0n5KpmzX1u9vm0duzp8/75DCAAAAMpPN4cbPzy8z6wAAAHAIuyYUAwAAZFV2hZuEhAR98sknql69uvLnzy8vLy+rHwAAAGexK9x89NFHCg0NVVBQkKKjo/XNN99owIABSkxM1NChQx1dIwAAgM3sCjehoaFauHCh+vbtK0nq3r27pkyZonnz5mnLli2OrA8AACBd7Ao3kZGRqlatmiTJ09NT0dHRkqQOHTpo165djqsOAAAgnewKN0lJSXJ1dZUklS1bVhs2bJAk7d27V3nz5nVcdQAAAOlk14MzPT09La+HDh2q3r17q3r16jpw4ABzbgAAgFPZFW5u3rxpef3mm2+qbNmy2rZtm0aPHq1OnTo5qjYAAIB0syvcPKpZs2Zq1qyZIzYFB+GRCwCAp5Xd4eb06dP66quvdPjwYUlSYGCgBg8erBIlSjisOAAAgPSya0Lxzz//rAoVKujXX3+Vr6+vfH19FRYWpgoVKigsLMzRNQIAANjMrpGb4cOH64MPPtDYsWOt2seNG6fhw4fr+eefd0hxAAAA6WXXyM3Zs2c1ZMiQZO1DhgzRmTNnMlwUAACAvewKN5UrV9bBgweTtR84cECBgYEZLgoAAMBeNp+WejjMvP766+rWrZuCg4NVr149GYah8PBwTZgwQWPGjElXAQkJCVq7dq0uXryoatWqqUGDBjb1O3PmjDZv3iwPDw8999xzPLATAABIklwMwzBsWtHFxeaN2rhJXbp0Sc2bN5ck1ahRQ7/++qtefvllffPNN2n2CwkJ0Weffabnn39eHh4e2r9/v1auXKlSpUrZtN+YmBjly5dP0dHR8vb2tqlPdsOl4AAAZ9nSfkqmbNfW72+bR27Onz/vkMIeNnr0aLm5uWn79u3KkyeP9u3bpzp16qhjx47q0KFDin0WLVqkcePGafPmzWrUqJGk+8+6SkpKcnh9AAAg+7E53Pj5+Tl0x0lJSVq6dKlCQkKUJ08eSVLNmjXVuHFj/fDDD6mGm8mTJ6tbt26WYCNJAQEBDq0NAABkXxm6Q3F4eLgOHz4swzAUGBiounXr2tw3MjJSsbGxqly5slV75cqVFR4enmKfW7duad++fXrnnXe0d+9e7dmzR0WLFlXz5s0tASklCQkJSkhIsPweExNjc50AACB7sSvcXL58Wa+++qo2bNhgmch78+ZNtWzZUqGhoSpcuPBjtxEbGytJyp8/v1V7gQIFUg0f169fl2EY+uGHHzR16lTVr19fu3btUmxsrNauXasKFSqk2G/ChAkKCQlJxzsEAADZlV2Xgg8dOlRxcXE6cOCAYmNjFRsbqwMHDuj27ds2PxX8wUjLg5DzQExMjDw8PFLs86D92rVr2rdvn+bNm6d9+/apaNGiGjFiRKr7Cg4OVnR0tOUnMjLSphoBAED2Y9fIzX//+1/t2bNHZcuWtbRVrVpV33//verUqWPTNkqUKKFcuXLp77//tmo/deqUypcvn2KfggULysfHRy1btlTOnPdLd3V1VatWrfT999+nui93d3e5u7vbVBcAAMje7Bq5uXv3bor3lfH09NSdO3ds2oabm5teeOEFLVq0yHLp+NmzZ7Vp0ya99NJLlvU2btyoxYsXW37v3Lmz9uzZY7WtvXv3WgUtAADw9LL5PjcPe/755+Xj46NZs2Ypb968ku6fThowYIBu3Lhh88Mzjx49qsaNG6thw4Zq0KCBvv/+exUrVkzr1q2zjMz0799f27dvt9xE8Pz582rUqJFq1KihJk2aaMeOHVq3bp02btxo86gR97kBACDzZJv73Dxs+vTpeuGFF1S0aFHL1U6HDx9W4cKF9csvv9i8nYoVK+rgwYP67rvvdPHiRY0ZM0a9evWyBBtJatmypcqUKWP53d/fX/v27dN3332n06dPq1mzZvrqq6/k6+trz1sBAAAmY9fIjXT/8uoff/xRhw4dkouLiwIDA9WtW7dsMbeFkRsAADJPthy56dChg1avXq3evXvbXSAAAEBmsGtC8caNG3X79m1H1wIAAJBhdoWbNm3aaNmyZY6uBQAAIMPsOi1VtGhRvfHGG1q5cqUCAwOVK1cuq+Vjx451SHEAAADpZVe42b17t+rWrauzZ8/q7NmzyZYTbgAAgLPYFW62b9/u6DoAAAAcwq45Nw+7e/eu7t6964haAAAAMsyucGMYhmbNmqWKFSsqT548ypMnjypVqqR///vfsvO2OQAAAA5h12mpDz/8UF9++aWGDh2qevXqSZJ27dqlUaNGKSoqSiEhIQ4tEgAAwFZ2hZuvv/5aS5YsUZs2bSxtL774oho3bqxevXoRbgAAgNPYPeemQYMGKbZxWgoAADiTXeGmadOmWrBgQbL2BQsW6Nlnn81wUbBf0zVBPFcKAPBUs/smfu+++66WL1+uevXqyTAMhYeHa+PGjRo8eLDGjRtnWZd73gAAgCfJrqeCN2zY0OZ1s+I9ccz8VHBGbQAAzpYtnwqeFQMLAACA5ICb+AEAAGQlhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqdoebNWvWqHPnzqpRo4albfLkybp27ZpDCgMAALCHXeHmu+++U+/evVW+fHnt37/f0u7m5qaJEyc6rDgAAID0sivcTJo0SUuXLtVnn31m1d6hQwctWrTIIYUBAADYw65wc+LECTVp0kSS5OLiYmkvVKiQLl++7JjKAAAA7GBXuPHz89PRo0clWYebdevWqXTp0o6pDAAAwA52hZv+/furf//+2r59u1xcXHT69GnNnj1bAwYM0MCBAx1dIwAAgM3serbUmDFjdP36dTVr1kyJiYkqVaqUcubMqaFDh2rYsGEOLhEAAMB2doWbHDly6IsvvtAHH3ygAwcOKCkpSdWqVVPBggUdXR8AAEC62BVuevXqpd69e6tNmzZq2rSpo2sCAACwm11zbmJjY9WxY0cVK1ZMQ4cO1a5duxxdFwAAgF3sCjerVq3ShQsXFBISoj179qhBgwaqWLGiPvnkE506dcrRNQIAANjM7scvFCxYUG+//ba2bNmiv//+W3379tV3332nsmXLOrI+AACAdMnwgzPv3r2r/fv3688//9TZs2fl4+PjiLoAAADsYle4MQxDW7du1aBBg+Tv76/u3bvLMAyFhobq/Pnzjq4RAADAZnZdLVW6dGlFRkaqRYsWmjx5srp06aK8efM6ujYAAIB0syvcvPfee+rRo4f8/f0dXQ8AAECG2BVugoKCHF0HAACAQ9gcbkaPHi1JmjhxouV1aiZOnJixqgAAAOxkc7gJDw9P8TUAAEBWYnO4Wb9+veX1f/7zHxUvXjzF9c6ePZvxqpAuTddwmhAAgAfsuhQ8ICDArmUAAACZLcM38XvY7du3lSdPHkduEgAAIF3SdbXU2LFjU3wtSUlJSdqzZ49q1qzpkMIAAADska5ws3Xr1hRfS5Kbm5tKlSqlkSNHOqYyAAAAO6Qr3GzatEmS1LVrVy1dujQz6gEAAMgQu+bcEGwAAEBWZdcdiqX7D8/8+++/debMGd27d89qWevWrTNcGAAAgD3sCjcRERHq2rWrdu/eneJywzAyVBQAAIC97DotNWzYMFWrVk3Xr1+XJMXFxen//u//VLlyZc2aNcuhBQIAAKSHXSM3W7du1YEDB5Q/f/77G8mZU88++6y+//57de/eXQMHDnRkjQAAADaza+Tm6tWr8vf3lyT5+Pjo4sWLkqSKFSvqzJkzjqsOAAAgnTJ8h+I6depo6tSpunDhgqZOnarSpUs7oi4AAAC72HVaqlevXpbX48ePV7t27fTFF1/Iy8tLixcvdlhxAAAA6WVXuPn+++8tr+vUqaOzZ8/q5MmTKl68uLy8vBxWHAAAQHrZfZ+bh7m5ualSpUqO2BQAAECG2BxuRo8ebfNGJ06caFcxAAAAGWVzuAkPD8/MOgAAABzC5nCzfv36zKwDAADAITJ8KbijJCUlpbuPYRiKj49P9mwrAADw9LJrQvGwYcPSXD5t2jSbtzVhwgRNmzZNV65cUWBgoKZPn66WLVva1Hf48OGaPn26hg4dmq59AgAA87Ir3Jw4ccLq96SkJJ04cULHjx9P1xPBZ82apfHjx+unn35Sw4YN9fnnn6t9+/Y6dOjQY28GuGbNGq1fv14VK1a05y0AAACTsivcrFmzJlmbYRgaM2ZMuk4RTZkyRf369bMEon/+85+aP3++Zs2apUmTJqXaLyoqSgMHDtTPP/+sPn36pP8NAAAA03LYnBsXFxeNHDlSS5YssWn9q1ev6vjx42rWrJnVNpo1a6Zt27al2i8pKUm9e/dWUFCQatSokeG6AQCAuTjkJn4PXL9+XdHR0Tat++Bhm4ULF7ZqL1KkiHbu3Jlqv08++USurq4KCgqyua6EhAQlJCRYfo+JibG5LwAAyF7sCjezZs1K1nb9+nV9++23atu2bbq29ehVUklJSXJxcUlx3e3bt2vGjBnasWOHJawYhqHExETFx8crd+7cKfabMGGCQkJC0lUXAADInuwKN5MnT07WVqBAAbVr104ffvihTdvw9/eXJF26dMmq/dKlS/Lz80uxz759+3Tz5k1VqVLF0nbnzh0dOnRIc+bM0a1bt+Tq6pqsX3BwsNVIT0xMjAICAmyqEwAAZC8OuVrKHgUKFFBgYKA2btyorl27Sro/arNx40a98cYblvXu3bunpKQk5cqVS2+//bbefvttq+3UrFlTzZs3T/NScHd3d7m7u2e4ZgAAkPU59SZ+o0aN0rx587Rs2TKdO3dOQUFBunnzpgYNGmRZ5+2331bt2rWdWCUAAMhO7J5QHBERoR07duj69evJlj06upKa119/XTdv3lRwcLAuXryoatWqad26dSpevLhlHTc3tzRHXdzd3eXm5pb+NwAAAEzJxTAMI72d5s6dq7ffflv58uVT/vz5ky13xGmrzBQTE6N8+fIpOjpa3t7ezi4nw5qusf3KMQAAMtuW9lMyZbu2fn/bNXLz0Ucf6ZtvvuEGegAAIMuxa87N9evXLZOAAQAAshK7wk2zZs3022+/OboWAACADLPrtNTMmTPVtGlT/fzzzypbtmyym+6NGDHCIcUBAACkl13hZs6cOYqKilJYWFiKE4oJNwAAwFnsfvzCjz/+yLwbAACQ5dg158bFxUUvvPCCo2sBAADIMLvCTcOGDfXLL784uhYAAIAMs+u0lL+/v3r16qXVq1erXLlyySYUjx071iHFAQAApJdd4ebgwYOqVauWjh49qqNHjyZbTrgBAADOYle42b59u6PrAAAAcAinPhUcAADA0ewauRk2bFiay6dNm2bPZgEAADLMrnDz6FO/k5KSdOLECR0/flytW7d2SGEAAAD2sCvcrFmzJlmbYRgaM2aM7t27l+GiAAAA7OWwOTcuLi4aOXKklixZ4qhNAgAApJtDJxRfv35d0dHRjtwkAABAutj9bKlHXb9+Xd9++63atm2b4aIAAADsZVe4mTx5crK2AgUKqF27dvrwww8zXBQAAIC9HHK1FAAAQFbBTfwAAICppCvc/PHHH2rTpk2qy9u0aaM//vgjw0UBAADYK13h5rPPPtPQoUNTXT506FB9/vnnGS4KAADAXukKN7t371bt2rVTXV6rVi3t3r07w0UBAADYK13h5tKlSypUqFCqywsVKqSLFy9muCgAAAB7pSvc+Pv768CBA6kuP3DggIoVK5bhogAAAOyVrnDz4osvauzYsSk+P+revXv64IMP9OKLLzqsOAAAgPRK131u/t//+3+qXbu2qlSpovfee08VK1aUYRg6duyY/vWvfykmJkbz58/PrFoBAAAeK13hpmjRotq6dasGDRqkIUOGWNpdXFzUunVrzZo1S35+fg4vEqlruibI2SUAAJClpPsOxeXKldO6det06dIlHT9+XC4uLipfvrwKFy6cGfUBAACki12PX5CkIkWKqEiRIo6sBQAAIMN4/AIAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVux+/AOfgQZkAAKSNkRsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqOZ1dQEREhObPn6+LFy+qWrVq6tevn3Lnzp3q+omJiVqxYoX++OMP5cyZU88884w6dOjwBCsGAABZmVNHbg4ePKgaNWror7/+Uvny5TVr1iw1b95cd+/eTXH9pKQkValSRaGhoSpWrJjy5s2r/v37q1evXk+4cgAAkFU5deRm1KhRql+/vn788UdJUu/evVWyZEktWLBA/fr1S7a+i4uL/vvf/6ps2bKWtqZNm6p58+YaMWKEatWq9cRqBwAAWZPTRm7u3LmjdevWqXv37pY2X19ftWzZUqtXr06xj4uLi1WwkaRy5cpJki5fvpx5xQIAgGzDaSM3Z86c0d27d1WqVCmr9lKlSmnLli02b2f27Nny8vJS/fr1U10nISFBCQkJlt9jYmLSXS8AAMgenDZyExcXJ0ny9PS0as+bN69l2eOsXr1a48eP18yZM5U/f/5U15swYYLy5ctn+QkICLC7bgAAkLU5Ldx4e3tLkm7cuGHVfu3aNeXLl++x/detW6dXXnlFkyZN0uuvv57musHBwYqOjrb8REZG2l03AADI2pwWbgICApQvXz4dOnTIqv3gwYOqWrVqmn3Xr1+vjh076uOPP9Y//vGPx+7L3d1d3t7eVj8AAMCcnBZucuTIoe7du2vevHm6efOmJGnHjh3asWOHevbsaVlv/vz5Gjt2rOX3DRs26KWXXlJISIhGjhz5xOsGAABZm1PvczNhwgTlzp1b1apV00svvaQ2bdro3XffVdu2bS3r/P7771qxYoUkKTY2Vh06dFDevHl16NAh9e3b1/KzefNmJ70LAACQlTj1PjcFCxbUzp07tXnzZl28eFHjx49PdkrqzTfftNyBOFeuXJo5c2aK2/L398/0egEAQNbn9Mcv5MyZUy1btkx1eePGjS2v3d3d1bdv3ydQFQAAyK54cCYAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVp9+hGKlruibI2SUAAJDtMHIDAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMJaezC8D/NF0T5OwSAADI9hi5AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApsJTwbMAngYOAIDjMHIDAABMhXADAABMhXADAABMJUuEm8jISIWHhysmJiZT+wAAAPNz6oTi+Ph49erVS7/88otKliyp06dPa9KkSXr33Xcd2ierYQIxAACZx6nhJiQkRDt37tTJkyfl7++vFStWqHPnzqpfv74aNGjgsD4AAODp4dTTUvPnz1f//v3l7+8vSerUqZOqVq2q+fPnO7QPAAB4ejht5ObcuXO6ePGi6tSpY9Vev3597d2712F9JCkhIUEJCQmW36OjoyXpiczXaRsWnOn7AAAgK8ms79cH2zUMI831nBZurl27Jkny8fGxavfx8bEsc0QfSZowYYJCQkKStQcEBKSrZgAA8Hj59FWmbj82Nlb58uVLdbnTwo2bm5uk+xOEHxYXF6dcuXI5rI8kBQcHKyjof5N4k5KSdO3aNfn4+MjFxcWu+rOCmJgYBQQEKDIyUt7e3s4uJ0vjWKUPx8t2HKv04XjZjmOVnGEYio2NVdGiRdNcz2nhJiAgQDly5FBUVJRVe1RUlEqUKOGwPpLk7u4ud3d3q7b8+fPbV3gW5O3tzQffRhyr9OF42Y5jlT4cL9txrKylNWLzgNMmFHt4eKhx48ZatWqVpe3WrVtav3692rRpY2k7ceKEZT6NrX0AAMDTy6lXS40bN04rVqxQcHCwVq1apU6dOqlIkSIaMGCAZZ2JEyfqtddeS1cfAADw9HJquGnWrJk2btyo06dPa/r06apSpYq2bt0qLy8vyzrly5dX7dq109XnaeHu7q6PPvoo2Sk3JMexSh+Ol+04VunD8bIdx8p+LsbjrqcCAADIRrLEs6UAAAAchXADAABMhXADAABMxakPzoT9YmJidOzYMRUpUiTNe/w8je7evavdu3erYMGCqlChQorr3Lp1S0eOHFHBggVVunTpJ1xh1nHv3j0dPXpUuXPnVqlSpeTq6priehEREbpy5YoqVar0VE7ef+DWrVs6duyYfHx8Uv17l5CQoEOHDsnT01MVK1Z8whVmPTdv3tS+ffvk5+encuXKJVseFRWl8+fPq2zZsipQoIATKnS+33//PdnjBMqUKZPsRnX37t3ToUOHlDNnTgUGBmbrm9BmOgPZzsyZM408efIYlSpVMjw8PIyOHTsat2/fdnZZThcTE2OMHTvWKF68uOHl5WX06tUrxfUWLFhgeHl5GRUqVDC8vLyM1q1bG9HR0U+4Wue6c+eOMXbsWKNQoUJGlSpVjOLFixulS5c2fvvtN6v1YmNjjbZt2xqenp5GxYoVDU9PT2P+/PnOKdqJrl27ZvTv398oWLCgUbt2baNgwYJGzZo1jb/++stqvZ9//tnw8fExypQpYxQoUMCoU6eOce7cOSdVnTW88sorRo4cOYx+/fpZtd+5c8fo2bOnkTt3bqNy5cpG7ty5jYkTJzqpSudydXU1qlSpYjRp0sTys2TJEqt1duzYYRQvXtwICAgwfH19jQoVKhiHDx92UsVZH+Emm9m1a5fh4uJiLF++3DAMwzh//rxRvHhxY+TIkU6uzPlOnjxpfPzxx8a5c+eMtm3bphhujhw5YuTMmdOYN2+eYRj3v7TKly9vvPXWW0+6XKeKjo42PvnkE0uoS0xMNIYPH27ky5fPKui9/fbbRvny5Y2rV68ahmEY8+fPN1xdXZN9qZvdX3/9ZSxevNhITEw0DMMw4uPjjTZt2hhNmjSxrHP58mUjb968xscff2wYhmHExcUZDRs2NF544QWn1JwVzJ4922jatKnRqFGjZOFm3LhxRpEiRYyIiAjDMAxj7dq1houLS7KA/TRwdXU1fvnll1SXx8fHG8WLFzcGDBhgGMb9v68vvfSSUb169SdVYrZDuMlmBg8ebFStWtWq7Z///KdRqFAhIykpyUlVZT2phZsxY8YYxYsXt2qbNm2a4eHhYcTHxz+p8rKkI0eOGJKM33//3TCM+/+z9vLyMqZNm2a1XokSJYxRo0Y5o8Qs5cMPPzRKlSpl+f2rr74yPDw8jFu3blnali5dari4uDyVozcHDx40/P39jYiICKNJkybJwk2ZMmWMESNGWLU1bNgw1RFXM3N1dTXmzZtn7Nq1y7hy5Uqy5atWrTIkGZGRkZa27du3G5KMXbt2PclSsw0mFGcze/fuVZ06daza6tevrytXrujs2bNOqir7SO343b59W8eOHXNSVVnDrl275OLiojJlykiSjh8/rps3byY7XnXr1rU8EuVps3//fv3f//2fZs2apdmzZyskJMSybO/evapcubI8PDwsbfXr15dhGNq3b58TqnWeuLg4de/eXZMnT1bJkiWTLY+JidGpU6dS/Lv4tH62RowYoX79+qlYsWJ66aWXdOnSJcuyvXv3ytfXV8WLF7e01a1bVy4uLk/t8Xocwk028+Bp5g978Pu1a9ecUVK2wvFL2dmzZzVixAi99dZb8vPzk/S/45HS8Xpaj9XMmTP1/vvvKzg4WHXq1FGrVq0sy/hs/c+wYcNUs2ZN9ezZM8XlfLasff3117p8+bL+/PNPnThxQidPntQbb7xhWZ7SZ8vV1VX58+d/Ko+XLQg32Yybm5vi4+Ot2uLi4iRJuXLlckZJ2QrHL7nLly+rbdu2qlq1qqZPn25pd3Nzk6QUj9fTeqxmz56tHTt2KCoqSu7u7nruueeUlJQkic/WAxs2bNDChQvVo0cPbd26VVu3blVMTIwuXryorVu3KjExkc/WI9566y3lyHH/67h48eL64IMP9PPPPysmJkZSyp8t6f7xexqPly24FDybKVmypKKioqzaoqKi5OLiooCAACdVlX2ULFky2em7B8fzabyk/sqVK2rZsqV8fX21atUq5c6d27LswemEqKgo1apVy9IeFRX1VB6rh3l4eOidd95R69atdebMGZUqVUolS5bUrl27rNZ7Gj9bCQkJqlmzpiZMmGBpO336tC5duqTRo0crLCxMfn5+cnd3T/HfsqfpWKXG19dXknTu3Dl5e3urZMmSunjxohITEy23a7h27Zri4uI4Xqlg5CabadOmjTZs2KBbt25Z2lauXKmGDRs+1fcfsVWbNm30xx9/6MqVK5a2lStXqnLlyipWrJgTK3vyHgSbwoULa82aNVZzRSTJz89PVatW1apVqyxtV69e1e+//642bdo86XKd6uG/bw+cOHHCcmpAuv/ZOnnypP766y/LOitXrlTBggWtHv5rdi+88IJlxObBT7Vq1dS+fXvLQ45dXV3VokULq89WQkKCwsLC+GxJWrt2rTw9PVWqVClJUuvWrXXr1i399ttvlnVWrlwpNzc3NWvW7EmVmq0wcpPN9O/fXzNnzlTHjh317rvvaseOHVq+fLnWrl3r7NKyhAc3w7px44YMw9DWrVvl7u6uevXqSZJ69OihqVOnqmPHjho5cqQOHDiguXPnatmyZU6u/Mm6ffu22rRpoxs3bmjy5Mnas2ePZVnlypUt5/cnTJigTp06qXjx4qpevbomT56sihUrqnfv3s4q3SmmTp2qkydPqm3btipYsKDCw8M1adIkDRs2zBJuWrVqpeeee07dunVTSEiIzp07p08//VRTp061nIbB/3z88cdq2rSphg8frpYtW2r27Nlyd3fXkCFDnF3aE7V06VL99NNP6tKliwoXLqz169frX//6lz7//HPLSGqlSpX0xhtvqF+/fpo4caLi4+M1YsQIjRw5UoUKFXLyO8iaeCp4NnTp0iVNnDhRf/75p4oUKaLBgweradOmzi4rS2jevLnu3btn1VakSBEtX77c8vv169c1adIkhYeHq2DBgnrrrbeeuv8tRkVFqXv37ikuGzdunJo3b275/bffftPs2bN19epV1alTR6NHj1bBggWfUKVZx7Jly/TTTz/p8uXLKlGihF555ZVkn5vbt29rypQp2rx5szw8PNSrVy9169bNSRVnHYMHD1bJkiU1atQoq/Zdu3bpX//6l86dO6fKlStr9OjRVlcEPS3Wr1+vhQsX6vz58ypdurT69u2rBg0aWK1z7949ffnllwoLC1POnDnVqVMn9evXj7sUp4JwAwAATIU5NwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwBMIy4uTqGhoYqNjXV2KQCciHADwKliYmIUGhqqixcvprj877//1g8//KC7d+8+dltXr15Vjx49kj2QEcDThXADwKk8PT31j3/8Q9OnT09x+YcffqjJkyfzfCYANiPcAHAqV1dX9e3bV99++60SExOtlkVHR2vZsmXq16+fDMNQaGioQkNDtXTpUu3atSvZc8QedeXKFYWGhiopKcnSdufOHYWGhurGjRvJ9hUWFqa1a9fq0qVLDnt/AJ48wg0Ap+vXr5/Onz+vsLAwq/ZFixbJxcVFPXv2lGEYWrFihVasWKHFixfr1VdfVZ06dXT+/PlUt3vkyBH16NFDd+7csbTFxMSoR48eioiIsLQtXrxYpUqV0qRJkzRlyhRVqFBBc+bMcfj7BPBk5HR2AQBQpkwZtWjRQvPmzdOLL75oaZ87d666desmb29vSVJoaKhlWWJiojp16qSPP/5YX3/9td37Pnr0qN566y2tW7dOjRo1kiT98ccfatWqlVq1aqUyZcrYvW0AzkG4AZAl9OvXT3379tXly5dVuHBh7d+/X7t379bUqVOt1jt48KD+/vtv3bp1S76+vtq5c2eG9rtw4UL5+fkpKipKP/74owzDkCTlzZtXv//+O+EGyIYINwCyhJdffllDhgzRd999p6CgIM2dO1cVKlRQ06ZNJUm3bt1Su3bt9Ndff6lu3bry9vZWREREhufHREREKD4+XkuXLrVqb9mypQoUKJChbQNwDsINgCwhd+7c6tWrl+bNm6d33nlH33//vUaNGmVZPnfuXEVFRenMmTPKkyePJGny5MmaNm1aqtvMkeP+tMKHJxTHx8dbrePt7a0iRYpYnfICkL0xoRhAltG/f38dOnRIwcHBiomJUZ8+fSzLLly4oBIlSliCjWEYWr58eZrbK1asmCTpxIkTlraNGzdarfP8889r37592r59u1V7dHS0bt26laH3A8A5GLkBkGXUqFFDdevW1dSpU9W5c2f5+vpalnXo0EGfffaZRowYoYoVK2rp0qU6fPiwPD09U91eyZIl9eyzz6pPnz4aPHiwIiMjtXDhQqt12rdvr9dff11t27bVu+++q9KlS+vw4cNauXKlNm3alOb2AWRNjNwAyFLGjh2r7t27KygoyKq9UaNG2rRpk+Lj47Vt2za9/PLLWrJkiTp27GhZx8PDQ927d7dcXSVJa9as0SuvvKLt27fL09NTGzduVPfu3a3m0/znP//RokWLFB0drT/++EPFihXTjh07LCM/ALIXF+PBpQEAAAAmwMgNAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwlf8P1/CFSE8bzW8AAAAASUVORK5CYII=", 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", 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" ] @@ -446,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 32, "id": "03285190", "metadata": {}, "outputs": [ @@ -486,15 +471,15 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 33, "id": "b93b2015", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -502,41 +487,20 @@ } ], "source": [ - "# Visualize selected resolutions through the public plotting API\n", - "best_result = next(result for result in results if result.is_best)\n", - "coarser_limits = sorted(\n", - " {\n", - " len(result)\n", - " for result in results\n", - " if result.granularity < best_result.granularity\n", - " and len(result) < len(best_result)\n", - " }\n", - ")\n", - "max_bins_values = coarser_limits[-5:] + [None]\n", - "\n", - "fig, axes = plt.subplots(2, 3, figsize=(15, 8))\n", + "# Visualize selected resolutions\n", + "fig, axes = plt.subplots(4, 3, figsize=(15, 15))\n", "axes = axes.flatten()\n", "\n", - "for ax, max_bins in zip(axes, max_bins_values, strict=False):\n", - " density, _, _ = hist(\n", - " data,\n", - " max_bins=max_bins,\n", - " density=True,\n", - " ax=ax,\n", - " color=\"steelblue\",\n", - " alpha=0.7,\n", - " )\n", - " if max_bins is None:\n", - " ax.set_title(f\"Best histogram ({len(density)} bins)\")\n", + "for ax, result in zip(axes, results):\n", + " ax.stairs(result.densities, result.bin_edges, fill=True)\n", + " if result.is_best:\n", + " ax.set_title(f\"Best histogram ({len(result)} bins)\")\n", " ax.set_facecolor(\"aliceblue\")\n", " else:\n", - " ax.set_title(f\"max_bins={max_bins} ({len(density)} bins selected)\")\n", + " ax.set_title(f\"{len(result)} bins\")\n", " ax.set_xlabel(\"Value\")\n", " ax.set_ylabel(\"Density\")\n", "\n", - "for ax in axes[len(max_bins_values):]:\n", - " ax.set_visible(False)\n", - "\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/docs/images/counts-vs-density.png b/docs/images/counts-vs-density.png index 2b8bfbd46c06f622efc1066c9fdd91bcb3fade59..e07dda8d0f6a6b067788146655de72e7e78b0fdf 100644 GIT binary patch delta 46 zcmX@Mh5gtT_6eSHMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}=5?-&}>x27}RzX<@Q CS`yd* delta 46 zcmX@Mh5gtT_6eSHhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H0;tn13{;Z%t>se-i+x C8WSS` diff --git a/docs/images/gaussian-quick-start.png 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zoV1hd0j-w|wmuPV`gW?V4YlyJx3^!G=rGX|Eea(KwDE3`PGy9oJ3va&?>>uA6Grlr png8|?iFaFS}M%he*GtSz5z&fVy9?01Bbj~f60 diff --git a/sandbox/khisto_demo.ipynb b/sandbox/khisto_demo.ipynb index 53aebc5..76092d0 100644 --- a/sandbox/khisto_demo.ipynb +++ b/sandbox/khisto_demo.ipynb @@ -655,7 +655,7 @@ ], "metadata": { "kernelspec": { - "display_name": "khisto-python", + "display_name": "khisto-python (3.12.3)", "language": "python", "name": "python3" }, From 63afdbdd9108d48017d869b512cd6b1e9866bd59 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:25:24 +0200 Subject: [PATCH 04/12] =?UTF-8?q?feat(matplotlib):=20pr=C3=A9ciser=20les?= =?UTF-8?q?=20types=20de=20retour=20de=20hist?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/khisto/matplotlib/hist.py | 45 ++++++++++++++++++++++++- tests/plot/test_matplotlib_histogram.py | 16 +++++++-- 2 files changed, 57 insertions(+), 4 deletions(-) diff --git a/src/khisto/matplotlib/hist.py b/src/khisto/matplotlib/hist.py index 1e58c2e..9d62687 100644 --- a/src/khisto/matplotlib/hist.py +++ b/src/khisto/matplotlib/hist.py @@ -6,7 +6,7 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Literal, overload import numpy as np @@ -19,6 +19,49 @@ from numpy.typing import ArrayLike, NDArray +@overload +def hist( + x: ArrayLike, + range: tuple[float, float] | None = None, + max_bins: int | None = None, + density: bool = True, + *, + ax: Axes | None = None, + histtype: Literal["bar"] = "bar", + **kwargs: Any, +) -> tuple[NDArray[np.float64], NDArray[np.float64], BarContainer]: ... + + +@overload +def hist( + x: ArrayLike, + range: tuple[float, float] | None = None, + max_bins: int | None = None, + density: bool = True, + *, + ax: Axes | None = None, + histtype: Literal["step", "stepfilled"], + **kwargs: Any, +) -> tuple[NDArray[np.float64], NDArray[np.float64], list[Polygon]]: ... + + +@overload +def hist( + x: ArrayLike, + range: tuple[float, float] | None = None, + max_bins: int | None = None, + density: bool = True, + *, + ax: Axes | None = None, + histtype: str, + **kwargs: Any, +) -> tuple[ + NDArray[np.float64], + NDArray[np.float64], + BarContainer | list[Polygon], +]: ... + + def hist( x: ArrayLike, range: tuple[float, float] | None = None, diff --git a/tests/plot/test_matplotlib_histogram.py b/tests/plot/test_matplotlib_histogram.py index 1b8a4c5..45fde39 100644 --- a/tests/plot/test_matplotlib_histogram.py +++ b/tests/plot/test_matplotlib_histogram.py @@ -6,11 +6,15 @@ from __future__ import annotations +from typing import assert_type + import numpy as np import pytest pytest.importorskip("matplotlib") import matplotlib.pyplot as plt +from matplotlib.container import BarContainer +from matplotlib.patches import Polygon from khisto import histogram from khisto.matplotlib import hist @@ -154,16 +158,20 @@ def test_explicit_bar_edge_style_is_preserved(self, normal_data): def test_step_histtype(self, normal_data): """Test histogram with step histtype.""" _fig, ax = plt.subplots() - n, _, _ = hist(normal_data, histtype="step", ax=ax) + n, _, patches = hist(normal_data, histtype="step", ax=ax) assert isinstance(n, np.ndarray) + assert_type(patches, list[Polygon]) + assert all(isinstance(patch, Polygon) for patch in patches) def test_stepfilled_histtype(self, normal_data): """Test histogram with stepfilled histtype.""" _fig, ax = plt.subplots() - n, _, _ = hist(normal_data, histtype="stepfilled", ax=ax) + n, _, patches = hist(normal_data, histtype="stepfilled", ax=ax) assert isinstance(n, np.ndarray) + assert_type(patches, list[Polygon]) + assert all(isinstance(patch, Polygon) for patch in patches) def test_cumulative_density_histogram(self, normal_data): """Test cumulative density histogram.""" @@ -222,9 +230,11 @@ def test_return_tuple_structure(self, data): assert isinstance(result, tuple) assert len(result) == 3 - n, bins, _ = result + n, bins, patches = result assert isinstance(n, np.ndarray) assert isinstance(bins, np.ndarray) + assert_type(patches, BarContainer) + assert isinstance(patches, BarContainer) def test_bins_edges_count(self, data): """Test that bins has n+1 edges.""" From 731d2d4a6e30be9f7c34d0fcddc91532c2889d42 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:25:37 +0200 Subject: [PATCH 05/12] docs(api): utiliser les imports au niveau racine --- docs/demo.ipynb | 50 +++++++++++++++++++++++-------------------------- docs/index.rst | 4 ++-- 2 files changed, 25 insertions(+), 29 deletions(-) diff --git a/docs/demo.ipynb b/docs/demo.ipynb index 3be981f..dea531f 100644 --- a/docs/demo.ipynb +++ b/docs/demo.ipynb @@ -28,16 +28,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "id": "d9b9b33b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", - "from khisto import histogram\n", - "from khisto.matplotlib import hist\n", + "import khisto\n", "\n", "SEED = 42" ] @@ -55,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "9e8eff47", "metadata": {}, "outputs": [ @@ -74,7 +72,7 @@ "gaussian = np.random.default_rng(SEED).normal(0, 1, 10000)\n", "\n", "fig, ax = plt.subplots(figsize=(7, 4.5))\n", - "hist(gaussian, ax=ax, color=\"steelblue\", edgecolor=\"white\")\n", + "khisto.hist(gaussian, ax=ax, color=\"steelblue\", edgecolor=\"white\")\n", "ax.set_title(\"Adaptive histogram on a standard Gaussian\")\n", "ax.set_xlabel(\"Value\")\n", "ax.set_ylabel(\"Density\")\n", @@ -97,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "id": "e8a48420", "metadata": {}, "outputs": [ @@ -116,7 +114,7 @@ "pareto = np.random.default_rng(SEED).pareto(3, 10000) + 1.0 # shift to start at 1 for log axes\n", "\n", "fig, ax = plt.subplots(figsize=(7, 4.5))\n", - "hist(pareto, ax=ax, color=\"darkorange\", edgecolor=\"white\")\n", + "khisto.hist(pareto, ax=ax, color=\"darkorange\", edgecolor=\"white\")\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "ax.set_title(\"Adaptive histogram on a heavy-tailed Pareto law\")\n", @@ -146,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "id": "8b183e7b", "metadata": {}, "outputs": [ @@ -187,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "id": "c8ed6f75", "metadata": {}, "outputs": [ @@ -212,7 +210,7 @@ "matplotlib_ax.set_ylabel(\"Density\")\n", "\n", "# Khisto: adaptive bins (density by default)\n", - "khisto_density, _, _ = hist(data, ax=khisto_ax, color=\"steelblue\")\n", + "khisto_density, _, _ = khisto.hist(data, ax=khisto_ax, color=\"steelblue\")\n", "khisto_ax.set_title(f\"Khisto: {len(khisto_density)} adaptive bins\")\n", "khisto_ax.set_xlabel(\"Value\")\n", "\n", @@ -235,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "id": "48c3b0aa", "metadata": {}, "outputs": [ @@ -262,7 +260,7 @@ "matplotlib_ax.set_xscale(\"symlog\")\n", "matplotlib_ax.set_yscale(\"log\")\n", "\n", - "khisto_density, _, _ = hist(data, ax=khisto_ax, color=\"steelblue\", edgecolor=\"white\")\n", + "khisto_density, _, _ = khisto.hist(data, ax=khisto_ax, color=\"steelblue\", edgecolor=\"white\")\n", "khisto_ax.set_title(f\"Khisto: {len(khisto_density)} adaptive bins\")\n", "khisto_ax.set_xlabel(\"Value\")\n", "khisto_ax.set_xscale(\"symlog\")\n", @@ -287,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "id": "d0e40b1c", "metadata": {}, "outputs": [ @@ -307,7 +305,7 @@ } ], "source": [ - "hist_counts, bin_edges = histogram(data, density=False)\n", + "hist_counts, bin_edges = khisto.histogram(data, density=False)\n", "\n", "print(f\"Number of bins: {len(hist_counts)}\")\n", "print(f\"Bin edges: {bin_edges}\")\n", @@ -316,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "id": "55ee75b7", "metadata": {}, "outputs": [ @@ -330,7 +328,7 @@ ], "source": [ "# With density normalization the integral over the range is ~1\n", - "density, bin_edges = histogram(data, density=True)\n", + "density, bin_edges = khisto.histogram(data, density=True)\n", "\n", "widths = np.diff(bin_edges)\n", "integral = np.sum(density * widths)\n", @@ -339,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "id": "33828aaa", "metadata": {}, "outputs": [ @@ -354,7 +352,7 @@ ], "source": [ "# Cap the number of bins\n", - "hist_limited, edges_limited = histogram(data, max_bins=5)\n", + "hist_limited, edges_limited = khisto.histogram(data, max_bins=5)\n", "print(f\"Limited to max 5 bins: got {len(hist_limited)} bins\")\n", "print(f\"Bin edges: {edges_limited}\")" ] @@ -383,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "id": "247c1571", "metadata": {}, "outputs": [ @@ -393,7 +391,7 @@ "Text(0, 0.5, 'Cumulative probability')" ] }, - "execution_count": 31, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -410,7 +408,7 @@ ], "source": [ "# Cumulative density (CDF) first — the most interpretable cumulative view\n", - "cdf_n, cdf_bins, _ = hist(\n", + "cdf_n, cdf_bins, _ = khisto.hist(\n", " data, cumulative=True, color=\"mediumseagreen\"\n", ")\n", "plt.title(\"Cumulative density (CDF)\")\n", @@ -431,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "id": "03285190", "metadata": {}, "outputs": [ @@ -458,9 +456,7 @@ } ], "source": [ - "from khisto.core import compute_histograms\n", - "\n", - "results = compute_histograms(data)\n", + "results = khisto.core.compute_histograms(data)\n", "\n", "print(f\"Number of granularity levels: {len(results)}\\n\")\n", "print(\"Granularity levels:\")\n", @@ -471,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "id": "b93b2015", "metadata": {}, "outputs": [ diff --git a/docs/index.rst b/docs/index.rst index cc53208..2917136 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,10 +44,10 @@ Get started .. code-block:: python import numpy as np - from khisto import histogram + import khisto data = np.random.normal(0, 1, 10_000) - hist, bin_edges = histogram(data) # optimal bins, no guessing + hist, bin_edges = khisto.histogram(data) # optimal bins, no guessing .. grid:: 1 1 2 2 :gutter: 3 From aa94a3bf536e70ebdf27ae3a5f4440723810a52b Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:32:47 +0200 Subject: [PATCH 06/12] ci: tester l'installation sans matplotlib --- .github/workflows/ci.yaml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index d1ec1ee..6c81761 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -30,6 +30,35 @@ jobs: - name: Run pre-commit hooks run: uv run pre-commit run --all-files + test-base-install: + name: Test base install without Matplotlib + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + version: "0.11.32" + + - name: Test base API without optional dependencies + run: | + uv run --isolated --no-project --with . python - <<'PY' + import importlib.util + + import numpy as np + + import khisto + + assert importlib.util.find_spec("matplotlib") is None + counts, edges = khisto.histogram( + np.array([1.0, 2.0, 3.0]), + density=False, + ) + assert counts.sum() == 3 + assert len(edges) == len(counts) + 1 + PY + get-python-versions: name: Get Python versions runs-on: ubuntu-latest From 2355d70a4b59bac43e4cd7e0eff6405a3e42b036 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 14:50:47 +0200 Subject: [PATCH 07/12] fix(tests): prendre en charge assert_type sous Python 3.10 --- pyproject.toml | 1 + tests/plot/test_matplotlib_histogram.py | 7 ++++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 869c308..626c08a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,6 +59,7 @@ test = [ "pytest-xdist>=3.6", "pytest-cov>=6", "pytest-sugar>=1.0", + "typing-extensions>=4.0; python_version < '3.11'", ] lint = [ "pre-commit>=4.1", diff --git a/tests/plot/test_matplotlib_histogram.py b/tests/plot/test_matplotlib_histogram.py index 45fde39..5fe8e89 100644 --- a/tests/plot/test_matplotlib_histogram.py +++ b/tests/plot/test_matplotlib_histogram.py @@ -6,7 +6,12 @@ from __future__ import annotations -from typing import assert_type +import sys + +if sys.version_info >= (3, 11): + from typing import assert_type +else: + from typing_extensions import assert_type import numpy as np import pytest From c40fc1207fcdcd68580d86a0da78ff840b3c1967 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 15:07:59 +0200 Subject: [PATCH 08/12] docs(sphinx): masquer les surcharges dans les signatures --- docs/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/conf.py b/docs/conf.py index 62ed8d2..e405e85 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -47,6 +47,7 @@ numpydoc_show_class_members = False ## Autodoc extension config +autodoc_typehints = "none" autodoc_default_options = { "members": True, "inherited-members": False, From fbdb9d303423ea4f342bd672dc2798f8e3cb947d Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 15:35:58 +0200 Subject: [PATCH 09/12] docs(sphinx): restaurer les types dans les signatures --- docs/conf.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index e405e85..5a05bf9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -8,9 +8,12 @@ import os import re import sys +from functools import wraps from pathlib import Path from importlib import metadata +import khisto.matplotlib + DOCS_DIR = Path(__file__).resolve().parent ROOT_DIR = DOCS_DIR.parent @@ -47,7 +50,6 @@ numpydoc_show_class_members = False ## Autodoc extension config -autodoc_typehints = "none" autodoc_default_options = { "members": True, "inherited-members": False, @@ -56,6 +58,18 @@ "special-members": False, } +_runtime_hist = khisto.matplotlib.hist + + +@wraps(_runtime_hist) +def _documented_hist(*args, **kwargs): + return _runtime_hist(*args, **kwargs) + + +_documented_hist.__module__ = khisto.matplotlib.__name__ +_documented_hist.__qualname__ = "hist" +khisto.matplotlib.hist = _documented_hist + ## Intersphinx extension config intersphinx_mapping = { "python": ("https://docs.python.org/3", None), From 89f47ab6ae4fc148b5ebb0f480d4c6e287a5d885 Mon Sep 17 00:00:00 2001 From: ElouenGinat <148534753+ElouenGinat@users.noreply.github.com> Date: Tue, 22 Sep 2026 16:20:25 +0200 Subject: [PATCH 10/12] Potential fix for pull request finding 'Cumulative density is incorrect for unequal-width bins' Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --- src/khisto/matplotlib/hist.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/khisto/matplotlib/hist.py b/src/khisto/matplotlib/hist.py index 9d62687..a2b11d5 100644 --- a/src/khisto/matplotlib/hist.py +++ b/src/khisto/matplotlib/hist.py @@ -153,12 +153,15 @@ def hist( # Weighted left edges preserve Khiops' right-closed bins and [-1e100, 1e100] # clamping when Matplotlib renders its left-closed bins. + cumulative = kwargs.get("cumulative", False) + plot_weights = ( + frequencies / frequencies.sum() if density and cumulative else frequencies + ) values, edges, patches = ax.hist( bin_edges[:-1].tolist(), bin_edges.tolist(), - weights=frequencies.tolist(), - density=density, - range=range, + weights=plot_weights.tolist(), + density=density and not cumulative, **kwargs, ) if isinstance(values, list): From a9e78444280dcb14b7f676bd63aa00f228649f45 Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Tue, 22 Sep 2026 16:29:30 +0200 Subject: [PATCH 11/12] =?UTF-8?q?fix(matplotlib):=20pr=C3=A9server=20l'ali?= =?UTF-8?q?as=20ec=20de=20la=20couleur=20de=20bord?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/khisto/matplotlib/hist.py | 2 +- tests/plot/test_matplotlib_histogram.py | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/src/khisto/matplotlib/hist.py b/src/khisto/matplotlib/hist.py index a2b11d5..f440c88 100644 --- a/src/khisto/matplotlib/hist.py +++ b/src/khisto/matplotlib/hist.py @@ -175,7 +175,7 @@ def hist( else: raise TypeError("Matplotlib returned unexpected histogram patches.") - if histtype == "bar" and "edgecolor" not in kwargs: + if histtype == "bar" and not {"edgecolor", "ec"} & kwargs.keys(): if not isinstance(histogram_patches, BarContainer): raise TypeError("Matplotlib unexpectedly returned non-bar patches.") for patch in histogram_patches.patches: diff --git a/tests/plot/test_matplotlib_histogram.py b/tests/plot/test_matplotlib_histogram.py index 5fe8e89..cceed8c 100644 --- a/tests/plot/test_matplotlib_histogram.py +++ b/tests/plot/test_matplotlib_histogram.py @@ -146,14 +146,15 @@ def test_bar_edges_match_face_color_by_default(self, normal_data): for patch in patches.patches: assert patch.get_edgecolor() == patch.get_facecolor() - def test_explicit_bar_edge_style_is_preserved(self, normal_data): - """Test that explicit edge styling overrides the khisto default.""" + @pytest.mark.parametrize("edgecolor_keyword", ["edgecolor", "ec"]) + def test_explicit_bar_edge_style_is_preserved(self, normal_data, edgecolor_keyword): + """Test that explicit edge styling aliases override the khisto default.""" _fig, ax = plt.subplots() _, _, patches = hist( normal_data, - edgecolor="red", linewidth=2.0, ax=ax, + **{edgecolor_keyword: "red"}, ) for patch in patches.patches: From 6f687db98d412a36acffe08f111b11eec61977ea Mon Sep 17 00:00:00 2001 From: Elouen Ginat Date: Wed, 23 Sep 2026 11:06:26 +0200 Subject: [PATCH 12/12] =?UTF-8?q?fix(matplotlib):=20transmettre=20les=20do?= =?UTF-8?q?nn=C3=A9es=20sans=20conversion=20en=20listes?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/demo.ipynb | 38 ++++++++++++++++------------------- src/khisto/matplotlib/hist.py | 6 +++--- 2 files changed, 20 insertions(+), 24 deletions(-) diff --git a/docs/demo.ipynb b/docs/demo.ipynb index dea531f..913f16d 100644 --- a/docs/demo.ipynb +++ b/docs/demo.ipynb @@ -19,16 +19,12 @@ "id": "5c8142b2", "metadata": {}, "source": [ - "## 1. Quick start\n", - "\n", - "The simplest promise: one call, sensible bins, a readable density. We start with\n", - "two distributions where the \"right\" binning is well understood, so you can see\n", - "that Khisto does the natural thing." + "## 1. Quick start" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "d9b9b33b", "metadata": {}, "outputs": [], @@ -53,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "9e8eff47", "metadata": {}, "outputs": [ @@ -95,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "e8a48420", "metadata": {}, "outputs": [ @@ -144,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "8b183e7b", "metadata": {}, "outputs": [ @@ -185,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "c8ed6f75", "metadata": {}, "outputs": [ @@ -233,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "48c3b0aa", "metadata": {}, "outputs": [ @@ -285,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "d0e40b1c", "metadata": {}, "outputs": [ @@ -314,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "55ee75b7", "metadata": {}, "outputs": [ @@ -337,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "33828aaa", "metadata": {}, "outputs": [ @@ -409,7 +405,7 @@ "source": [ "# Cumulative density (CDF) first — the most interpretable cumulative view\n", "cdf_n, cdf_bins, _ = khisto.hist(\n", - " data, cumulative=True, color=\"mediumseagreen\"\n", + " data, cumulative=True, color=\"mediumseagreen\",\n", ")\n", "plt.title(\"Cumulative density (CDF)\")\n", "plt.xlabel(\"Value\")\n", @@ -429,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "03285190", "metadata": {}, "outputs": [ @@ -467,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "id": "b93b2015", "metadata": {}, "outputs": [ @@ -510,10 +506,10 @@ "\n", "Khisto gives you a better histogram without making you tune bins by hand:\n", "\n", - "1. **`khisto.histogram`** — a NumPy-like API with adaptive bins.\n", - "2. **`khisto.matplotlib.hist`** — readable density plots by default, with the usual\n", + "1. **khisto.histogram** — a NumPy-like API with adaptive bins.\n", + "2. **khisto.matplotlib.hist** — readable density plots by default, with the usual\n", " matplotlib workflow.\n", - "3. **`khisto.core.compute_histograms`** — full control over the granularity\n", + "3. **khisto.core.compute_histograms** — full control over the granularity\n", " series, with `HistogramResult` exposing counts, probabilities and densities.\n", "\n", "To go further, [Histograms - Khiops](https://khiops.org/learn/histograms/)\n", @@ -523,7 +519,7 @@ ], "metadata": { "kernelspec": { - "display_name": "khisto-python (3.12.3)", + "display_name": "khisto (3.12.3)", "language": "python", "name": "python3" }, diff --git a/src/khisto/matplotlib/hist.py b/src/khisto/matplotlib/hist.py index f440c88..219d401 100644 --- a/src/khisto/matplotlib/hist.py +++ b/src/khisto/matplotlib/hist.py @@ -158,9 +158,9 @@ def hist( frequencies / frequencies.sum() if density and cumulative else frequencies ) values, edges, patches = ax.hist( - bin_edges[:-1].tolist(), - bin_edges.tolist(), - weights=plot_weights.tolist(), + x=bin_edges[:-1], + bins=bin_edges.tolist(), + weights=plot_weights, density=density and not cumulative, **kwargs, )