From 4150bad07ceb103e7238e1fa857e4e9c300bb81a Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sun, 5 Jul 2026 12:18:40 -0400 Subject: [PATCH 01/45] ci: add upstream dependency compatibility check --- .gitlab-ci.yml | 5 ++ .travis/test-dependency-compat.py | 112 ++++++++++++++++++++++++++++++ pixi.toml | 1 + 3 files changed, 118 insertions(+) create mode 100644 .travis/test-dependency-compat.py diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 646ae725b..ea964449b 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -92,6 +92,11 @@ import_check: - python -m pytest -q MonteCarloMarginalizeCode/Code/test/test_lisa_operational_synthetic.py - python -m pytest -q MonteCarloMarginalizeCode/Code/test/test_lisa_helper_contract.py +dependency_compat_check: + stage: system tests + script: + - python .travis/test-dependency-compat.py + sim_manager_check: stage: unit tests script: diff --git a/.travis/test-dependency-compat.py b/.travis/test-dependency-compat.py new file mode 100644 index 000000000..2bdf9b318 --- /dev/null +++ b/.travis/test-dependency-compat.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +"""Smoke-test upstream scientific-library compatibility for RIFT CI. + +This intentionally stays small: the full RIFT import sweep catches broad +breakage, while this file makes the dependency surface in issue #17 explicit +and gives CI logs a compact version matrix for scipy/h5py/numba/matplotlib. +""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path + +import h5py +import matplotlib + +matplotlib.use("Agg") +from matplotlib import pyplot as plt # noqa: E402 + +import numba +import numpy as np +import scipy +from scipy import integrate, optimize, stats + +from RIFT.precision import RIFT_FLOAT_HIGH_PRECISION, RIFT_FLOAT_NAME, RiftFloat + + +@numba.njit(cache=False) +def _numba_quadratic(x): + return x * x + 2.0 * x + 1.0 + + +def _check_scipy() -> None: + integral, err = integrate.quad(lambda x: x * x, 0.0, 1.0) + if not np.isclose(integral, 1.0 / 3.0, atol=1e-12): + raise AssertionError(f"scipy.integrate returned {integral} +/- {err}") + + root = optimize.brentq(lambda x: x * x - 2.0, 0.0, 2.0) + if not np.isclose(root, np.sqrt(2.0), atol=1e-12): + raise AssertionError(f"scipy.optimize returned root {root}") + + cdf = stats.norm.cdf(0.0) + if not np.isclose(cdf, 0.5, atol=1e-15): + raise AssertionError(f"scipy.stats returned normal cdf {cdf}") + + +def _check_h5py() -> None: + values = np.arange(6, dtype=np.float64).reshape(2, 3) + with tempfile.TemporaryDirectory() as tmpdir: + h5_path = Path(tmpdir) / "compat.h5" + with h5py.File(h5_path, "w") as h5_file: + h5_file.create_dataset("values", data=values) + h5_file.attrs["library"] = "h5py" + with h5py.File(h5_path, "r") as h5_file: + roundtrip = h5_file["values"][()] + marker = h5_file.attrs["library"] + if marker != "h5py" or not np.array_equal(values, roundtrip): + raise AssertionError("h5py dataset/attribute round trip failed") + + +def _check_numba() -> None: + sample = np.asarray([0.0, 1.0, 2.0]) + expected = np.asarray([1.0, 4.0, 9.0]) + actual = _numba_quadratic(sample) + if not np.allclose(actual, expected): + raise AssertionError(f"numba compiled function returned {actual}") + + +def _check_matplotlib() -> None: + with tempfile.TemporaryDirectory() as tmpdir: + png_path = Path(tmpdir) / "compat.png" + fig, ax = plt.subplots(figsize=(2, 2)) + ax.plot([0, 1], [0, 1]) + ax.set_xlabel("x") + ax.set_ylabel("y") + fig.tight_layout() + fig.savefig(png_path) + plt.close(fig) + if png_path.stat().st_size <= 0: + raise AssertionError("matplotlib produced an empty PNG") + + +def _check_rift_precision() -> None: + dtype = np.dtype(RiftFloat) + if dtype.itemsize < np.dtype(np.float64).itemsize: + raise AssertionError(f"RiftFloat unexpectedly narrower than float64: {dtype}") + if RIFT_FLOAT_HIGH_PRECISION != (dtype.itemsize > np.dtype(np.float64).itemsize): + raise AssertionError("RIFT_FLOAT_HIGH_PRECISION does not match RiftFloat width") + + +def main() -> None: + matrix = { + "numpy": np.__version__, + "scipy": scipy.__version__, + "h5py": h5py.__version__, + "numba": numba.__version__, + "matplotlib": matplotlib.__version__, + "rift_float": RIFT_FLOAT_NAME, + "rift_float_high_precision": RIFT_FLOAT_HIGH_PRECISION, + } + print(json.dumps(matrix, indent=2, sort_keys=True)) + + _check_rift_precision() + _check_scipy() + _check_h5py() + _check_numba() + _check_matplotlib() + + +if __name__ == "__main__": + main() diff --git a/pixi.toml b/pixi.toml index ed59bdf85..c34de766c 100644 --- a/pixi.toml +++ b/pixi.toml @@ -85,6 +85,7 @@ PATH = "$PIXI_PROJECT_ROOT/MonteCarloMarginalizeCode/Code/bin:$PATH" install-rift = "python -m pip install --no-deps --editable ." swig-version = "swig -version" import-check = "python .travis/test-all-mod.py" +dependency-compat-check = "python .travis/test-dependency-compat.py" help-check = "bash .travis/test-all-bin.sh" sim-manager-check = "python MonteCarloMarginalizeCode/Code/test/test_simulation_manager.py -v" test-likelihood = "python MonteCarloMarginalizeCode/Code/test/test_likelihood.py" From 40003b551abf2ebb82cbd93ce9ff38e033342593 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sun, 5 Jul 2026 16:42:44 -0400 Subject: [PATCH 02/45] ci: run dependency compatibility check on GitHub --- .github/workflows/ci.yml | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b724642bd..8aceae3b2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -107,6 +107,43 @@ jobs: - name: Run import check run: python .travis/test-all-mod.py + dependency-compat-check: + needs: install + runs-on: ubuntu-latest + # Focused upstream-library smoke test for scipy/h5py/numba/matplotlib plus + # RIFT's portable precision dtype. This complements the broader import and + # test-run jobs with a compact version matrix and small functional probes. + strategy: + fail-fast: false + matrix: + include: + - lane: legacy + python-version: '3.9' + numpy-pin: 'numpy==1.24.4' + - lane: modern + python-version: '3.12' + numpy-pin: 'numpy>=2.0,<3.0' + name: dependency-compat-check (${{ matrix.lane }} py${{ matrix.python-version }}) + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + cache-dependency-path: requirements.txt + - name: Enable symlink + run: sudo ln -sf $(which python3) /usr/bin/python + - name: Install dependencies + run: | + python -m pip install --upgrade pip --break-system-packages + python -m pip install -r requirements.txt --break-system-packages + # Pin numpy AFTER requirements.txt so it overrides the unpinned + # 'numpy' line in requirements.txt without changing the file. + python -m pip install '${{ matrix.numpy-pin }}' --break-system-packages + python -m pip install --editable . --break-system-packages + - name: Run dependency compatibility check + run: python .travis/test-dependency-compat.py + sim-manager-check: needs: install runs-on: ubuntu-latest From 15951c392c54f354f4f24cb35515ea06e127fdbe Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 13:01:53 -0700 Subject: [PATCH 03/45] ILE multi-GPU fan-out: combined fan-out + multi-container example (one ini) Provide an end-user ini shape that carries BOTH multi-GPU fan-out AND the multi-container "container family" (per-machine .sif selection by GPU capability), and confirm every option lands in the generated ILE.sub. pseudo_pipe changes so a single ini is self-contained: - Let the ini [rift-pseudo-pipe] section set the container family via singularity_rift_image / singularity_base_exe_dir -> SINGULARITY_RIFT_IMAGE / SINGULARITY_BASE_EXE_DIR (raw strings into os.environ, env still dominates), mirroring the existing ile_require_gpus -> RIFT_REQUIRE_GPUS hook. Read from the environment by create_event_parameter_pipeline_BasicIteration / write_ILE_sub_simple. - Move the --ile-gpu-fanout -> RIFT_ILE_GPU_FANOUT funnel to AFTER ini parsing, so an ini-only ile-gpu-fanout=... value also propagates (previously only a CLI value did). Demo (demo/rift/infra/multi_gpu/): - multigpu_multicontainer.ini: annotated template with fan-out (ile-gpu-fanout="all", ile-force-gpu, ile-jobs-per-worker) + container family (use-osg/use-singularity, singularity_rift_image, singularity_base_exe_dir, ile_require_gpus). Documents the ini eval() quoting rule (string values quoted; raw-string env keys not). Silly IMRPhenomD zero-spin CI config so it can build/run. - make build-combo / verify-combo: build from the ini with NO container/fan-out env vars set, then assert BOTH landed -- MY.SingularityImage ifThenElse per-machine selection, selective osdf .sif transfer, combined require_gpus floor, request_GPUs/CPUs, and ile_pre.sh baking RIFT_ILE_GPU_FANOUT. Verified end-to-end: pseudo_pipe parses the ini (ile_gpu_fanout='all', use_osg/ use_singularity True, USING SINGULARITY); the generated ILE.sub carries the per-machine container selection + capability floor + osdf transfer AND the fan-out; verify-combo passes. (The build's grid step can hit a pre-existing flaky scipy import; documented -- just re-run.) Co-Authored-By: Claude Opus 4.8 --- .../Code/bin/util_RIFT_pseudo_pipe.py | 27 ++++-- .../Code/demo/rift/infra/multi_gpu/.gitignore | 2 + .../Code/demo/rift/infra/multi_gpu/Makefile | 48 ++++++++++- .../Code/demo/rift/infra/multi_gpu/README.md | 40 +++++++++ .../multi_gpu/multigpu_multicontainer.ini | 85 +++++++++++++++++++ 5 files changed, 191 insertions(+), 11 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/multigpu_multicontainer.ini diff --git a/MonteCarloMarginalizeCode/Code/bin/util_RIFT_pseudo_pipe.py b/MonteCarloMarginalizeCode/Code/bin/util_RIFT_pseudo_pipe.py index a29c8d68b..ae184867b 100755 --- a/MonteCarloMarginalizeCode/Code/bin/util_RIFT_pseudo_pipe.py +++ b/MonteCarloMarginalizeCode/Code/bin/util_RIFT_pseudo_pipe.py @@ -586,13 +586,6 @@ def run_lisa_known_sky_surface(opts): parser.add_argument("--internal-mitigate-fd-J-frame",default="L_frame",help="L_frame|rotate, choose method to deal with ChooseFDWaveform being in wrong frame. Default is to request L frame for inputs") opts= parser.parse_args() -# Multi-GPU ILE fan-out: --ile-gpu-fanout funnels through RIFT_ILE_GPU_FANOUT, which -# create_event_parameter_pipeline_BasicIteration (run via os.system, inheriting this -# environment) and dag_utils read at DAG-build time to size request_GPUs/CPUs and bake -# the value into ile_pre.sh. A CLI value wins over any inherited environment value. -if opts.ile_gpu_fanout is not None: - os.environ['RIFT_ILE_GPU_FANOUT'] = str(opts.ile_gpu_fanout) - config_stored=None; config_dict=None ile_condor_commands = None if (opts.use_ini): @@ -617,7 +610,16 @@ def run_lisa_known_sky_surface(opts): if not('RIFT_REQUIRE_GPUS' in os.environ) and 'ile_require_gpus' in rift_items: os.environ['RIFT_REQUIRE_GPUS'] = rift_items['ile_require_gpus'] - + + # Container family (multi-container per-machine image selection): let the ini + # carry the manifest + base exe dir, so a single ini is self-contained. These + # are read from os.environ by create_event_parameter_pipeline_BasicIteration / + # write_ILE_sub_simple; the environment still dominates if already set. + if not('SINGULARITY_RIFT_IMAGE' in os.environ) and 'singularity_rift_image' in rift_items: + os.environ['SINGULARITY_RIFT_IMAGE'] = rift_items['singularity_rift_image'] + if not('SINGULARITY_BASE_EXE_DIR' in os.environ) and 'singularity_base_exe_dir' in rift_items: + os.environ['SINGULARITY_BASE_EXE_DIR'] = rift_items['singularity_base_exe_dir'] + # attempt to lazy-select the command-line that are present in the ini file section for item in rift_items: item_renamed = item.replace('-','_') @@ -641,7 +643,14 @@ def run_lisa_known_sky_surface(opts): for item in rift_items: val = rift_items[item].strip() ile_condor_commands.append([item, val]) - + +# Multi-GPU ILE fan-out: funnel --ile-gpu-fanout (CLI) OR ile-gpu-fanout=... (ini +# [rift-pseudo-pipe]) through RIFT_ILE_GPU_FANOUT. Done AFTER the ini is parsed so an +# ini-only value also lands. create_event_parameter_pipeline_BasicIteration (run via +# os.system, inheriting this environment) and dag_utils read it at DAG-build time to +# size request_GPUs/CPUs and bake the value into ile_pre.sh. +if opts.ile_gpu_fanout is not None: + os.environ['RIFT_ILE_GPU_FANOUT'] = str(opts.ile_gpu_fanout) if opts.lisa_known_sky: run_lisa_known_sky_surface(opts) diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/.gitignore b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/.gitignore index 398582b84..ad9c5c0c0 100644 --- a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/.gitignore +++ b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/.gitignore @@ -3,3 +3,5 @@ rundir_multigpu/ _smoke/ ci_coinc.xml proposed-grid* +rundir_combo/ +_combo.ini diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/Makefile b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/Makefile index ca2ce492d..4d6c97704 100644 --- a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/Makefile +++ b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/Makefile @@ -54,7 +54,7 @@ FANOUT ?= 4 BUILD_DIR := $(CURDIR)/rundir_multigpu DAG := marginalize_intrinsic_parameters_BasicIterationWorkflow.dag -.PHONY: all smoke-local build verify inspect requests clean coinc help +.PHONY: all smoke-local build verify build-combo verify-combo inspect requests clean coinc help help: @sed -n '2,40p' Makefile @@ -144,6 +144,50 @@ verify: @echo "OK: ILE.sub requests $(FANOUT) GPUs + $(FANOUT) CPUs; ile_pre.sh bakes RIFT_ILE_GPU_FANOUT=$(FANOUT)" @echo " and will split each 100-point ILE block into $(FANOUT) shards, one per GPU." +# --------------------------------------------------------------------------- +# build-combo / verify-combo: the COMBINED example -- multi-GPU fan-out AND the +# multi-container (container family) selection, driven ENTIRELY from one ini +# (multigpu_multicontainer.ini), including the container manifest. Proves an +# end-user ini shape where every option lands. Deliberately does NOT export +# SINGULARITY_RIFT_IMAGE, so the container really comes from the ini. +# --------------------------------------------------------------------------- +COMBO_DIR := $(CURDIR)/rundir_combo +BASE_EXE_DIR ?= /usr/local/bin/ +build-combo: ci_coinc.xml + @test -f "$(MANIFEST)" || (echo "ERROR: container-family manifest not found: $(MANIFEST)"; false) + rm -rf $(COMBO_DIR) + @# Fill the ini placeholders (@MANIFEST@/@BASE_EXE_DIR@) into a run-local copy. + sed -e 's#@MANIFEST@#$(MANIFEST)#' -e 's#@BASE_EXE_DIR@#$(BASE_EXE_DIR)#' \ + multigpu_multicontainer.ini > $(CURDIR)/_combo.ini + @# NOTE: no SINGULARITY_RIFT_IMAGE / RIFT_ILE_GPU_FANOUT in the env -- both come from the ini. + env -u SINGULARITY_RIFT_IMAGE -u SINGULARITY_BASE_EXE_DIR -u RIFT_ILE_GPU_FANOUT -u RIFT_REQUIRE_GPUS \ + $(ENV) util_RIFT_pseudo_pipe.py \ + --use-ini $(CURDIR)/_combo.ini --use-coinc $(CURDIR)/ci_coinc.xml --use-rundir $(COMBO_DIR) \ + --fake-data-cache $(CACHE) --assume-nospin \ + --use-osg-file-transfer --internal-truncate-files-for-osg-file-transfer \ + --internal-force-iterations 2 + rm -rf $(COMBO_DIR)/frames_dir; cp -r $(FRAMES) $(COMBO_DIR)/frames_dir + for ifo in H1 L1 V1; do cp $(PSD) $(COMBO_DIR)/$$ifo-psd.xml.gz; done + @rm -f $(CURDIR)/_combo.ini + @echo; echo "Built $(COMBO_DIR). Now: make verify-combo" + +verify-combo: + @test -f $(COMBO_DIR)/ILE.sub || (echo "no $(COMBO_DIR)/ILE.sub -- run 'make build-combo' first"; false) + @echo "===== MULTI-CONTAINER (container family) =====" + @echo "--- per-machine image selection (>=2 images chosen by GPU capability) ---" + @grep -iE 'MY.SingularityImage|container_image' $(COMBO_DIR)/ILE.sub | sed 's/^/ /' + @grep -qi 'ifThenElse.*Capability.*\.sif' $(COMBO_DIR)/ILE.sub || (echo "FAIL: no per-machine image ifThenElse selection"; false) + @echo "--- selective osdf image transfer (only the matching .sif) ---" + @grep -qi 'osdf:.*\.sif' $(COMBO_DIR)/ILE.sub || (echo "FAIL: no osdf image transfer"; false) + @grep -oiE 'require_gpus = .*' $(COMBO_DIR)/ILE.sub | sed 's/^/ /' + @grep -qi 'Capability >= 8.0' $(COMBO_DIR)/ILE.sub || (echo "FAIL: ini ile_require_gpus floor did not land"; false) + @echo "===== MULTI-GPU FAN-OUT =====" + @grep -iE 'request_GPUs|request_CPUs' $(COMBO_DIR)/ILE.sub | sed 's/^/ /' + @grep -oE 'RIFT_ILE_GPU_FANOUT:-[A-Za-z0-9]*' $(COMBO_DIR)/ile_pre.sh | sed 's/^/ ile_pre.sh baked: /' + @grep -qE 'RIFT_ILE_GPU_FANOUT:-all' $(COMBO_DIR)/ile_pre.sh || (echo "FAIL: ini ile-gpu-fanout=all did not bake into ile_pre.sh"; false) + @echo; echo "OK: one ini -> multi-container per-machine .sif selection + capability floor + osdf transfer," + @echo " AND multi-GPU fan-out (grab all node GPUs). Every option landed." + # --------------------------------------------------------------------------- # requests: show the condor request_GPUs/request_CPUs + baked launcher directive # that each fan-out mode produces (fixed N vs. adaptive vs. all-physical). Fast: @@ -173,4 +217,4 @@ inspect: @grep -iE 'request_|require_gpus|SingularityImage|executable|when_to_transfer' $(BUILD_DIR)/ILE.sub clean: - rm -rf _smoke _req $(BUILD_DIR) ci_coinc.xml + rm -rf _smoke _req $(BUILD_DIR) $(COMBO_DIR) _combo.ini ci_coinc.xml diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/README.md b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/README.md index e4f3762c6..2b76b1e5f 100644 --- a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/README.md +++ b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/README.md @@ -145,6 +145,46 @@ make inspect # show the generated launcher + sub resource lines `make build` only builds the DAG (it does not submit). To actually run it you need a pool with ≥ N-GPU nodes; submit with `condor_submit_dag` from the run dir. +### 2b. `make build-combo` + `make verify-combo` — fan-out **and** multi-container from one ini + +[`multigpu_multicontainer.ini`](multigpu_multicontainer.ini) is a copy-me template that +carries **both** features in a single ini — the multi-GPU fan-out *and* the +multi-container **container family** (per-machine `.sif` selection by GPU capability): + +```ini +[rift-pseudo-pipe] +# multi-GPU fan-out +ile-force-gpu=True +ile-gpu-fanout="all" # QUOTED (ini values are eval'd); grab all node GPUs +ile-jobs-per-worker=100 +# multi-container (container family) +use-osg=True +use-singularity=True +singularity_rift_image=/abs/path/rift_container_family.cit.yaml # the manifest of >=2 .sif images +singularity_base_exe_dir=/usr/local/bin/ +ile_require_gpus=(Capability >= 8.0) && (Capability <= 12.0) +``` + +``` +make build-combo # builds rundir_combo; the container comes from the INI (env not set) +make verify-combo # asserts BOTH landed +``` + +> If `make build`/`build-combo` dies in `util_ManualOverlapGrid.py` with an intermittent +> `from scipy import interpolate` import error, that is a known flaky scipy-import race in the +> grid step (unrelated to this feature) — just re-run the target. + +`verify-combo` confirms every option lands in the generated `ILE.sub`: +- **multi-container:** `MY.SingularityImage = ifThenElse(TARGET.GPUs_Capability >= 9.0, "…cc90-120.sif", "…cc60-90.sif")`, the selective `osdf:///…$$([...])` image transfer, and the combined `require_gpus` capability floor. +- **fan-out:** `request_GPUs` / `request_CPUs`, and `ile_pre.sh` baking `RIFT_ILE_GPU_FANOUT`. + +**ini value rules (important):** keys that match a `util_RIFT_pseudo_pipe.py` option are +`eval()`'d, so **string values must be quoted** (`ile-gpu-fanout="all"`, `approx="IMRPhenomD"`); +numbers/booleans are bare (`ile-jobs-per-worker=100`, `use-osg=True`). Three keys are read +as **raw strings into the environment** (not eval'd, not quoted) — +`singularity_rift_image`, `singularity_base_exe_dir`, `ile_require_gpus` — and the +environment still wins over the ini for those (export to override a shared ini). + ### 3. `blueprints/` — the asimov path - `rift-multigpu.yaml` — analysis blueprint showing **both** blueprint encodings diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/multigpu_multicontainer.ini b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/multigpu_multicontainer.ini new file mode 100644 index 000000000..36a0a1358 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/infra/multi_gpu/multigpu_multicontainer.ini @@ -0,0 +1,85 @@ +# ============================================================================= +# EXAMPLE ini shape: multi-GPU fan-out + multi-container (container family), together. +# +# This ONE ini carries both features so an end user can copy it as a template: +# * multi-GPU fan-out -> RIFT_ILE_GPU_FANOUT (here 'all': grab every node GPU) +# * multi-container -> the container-family manifest (per-machine .sif selection +# by GPU capability) + a GPU capability floor +# +# Build (no submit) with `make build-combo` (fills @MANIFEST@/@BASE_EXE_DIR@ + per-run +# paths) or directly: +# util_RIFT_pseudo_pipe.py --use-ini this.ini --use-coinc ci_coinc.xml \ +# --use-rundir rundir --fake-data-cache zero_noise.cache --assume-nospin +# +# It is a SILLY validation config (IMRPhenomD, zero spin, CI synthetic data) so it can +# actually build/run; swap the physics for a real analysis. +# +# ---- HOW ini VALUES ARE READ (important) ------------------------------------------ +# Keys in [rift-pseudo-pipe] that match a util_RIFT_pseudo_pipe.py option are eval()'d, +# so STRING values must be QUOTED: approx="IMRPhenomD", ile-gpu-fanout="all". +# Numbers/booleans are bare: ile-jobs-per-worker=100, ile-force-gpu=True. +# A few keys are consumed as raw strings into the environment (NOT eval'd, NOT quoted): +# singularity_rift_image, singularity_base_exe_dir, ile_require_gpus, accounting_group. +# The environment still WINS over the ini for those (export to override a shared ini). +# ============================================================================= + +[analysis] +ifos=['H1','L1','V1'] +singularity=True +osg=True + +[condor] +accounting_group=ligo.sim.o4.cbc.pe.rift +accounting_group_user=albert.einstein + +[datafind] +url-type=file +types = {'H1': 'fake_strain', 'L1': 'fake_strain', 'V1': 'fake_strain'} + +[data] +channels = {'H1': 'H1:FAKE-STRAIN','L1': 'L1:FAKE-STRAIN', 'V1': 'V1:FAKE-STRAIN'} + +[lalinference] +flow = {'H1': 10, 'L1': 10, 'V1': 10} +fhigh = { 'H1': 1700, 'L1': 1700, 'V1': 1700 } + +[engine] +fref=20 +amporder = -1 +seglen = 8 +srate = 4096 +a_spin1-max = 0.0 +a_spin2-max = 0.0 +chirpmass-min = 23.0 +chirpmass-max = 35.0 +comp-min = 1 +comp-max = 1000 +distance-max = 1000 +aligned-spin = +alignedspin-zprior = + +[rift-pseudo-pipe] +# ---- silly PE physics (matches the CI synthetic data; swap for a real run) -------- +approx="IMRPhenomD" +l-max=2 +fmin-template=10 +event-time=1000000014.236547946 +force-eta-range="[0.20,0.24999]" +internal-distance-max=1000 +n-output-samples=2000 +use-online-psd=False + +# ---- MULTI-GPU FAN-OUT ------------------------------------------------------------ +ile-force-gpu=True # ILE requests+uses a GPU (fan-out needs the GPU path) +ile-gpu-fanout="all" # QUOTED: grab every physical GPU on the node (default too). + # Use "1" for a single-GPU run, or a number N, or "auto-max-N". +ile-jobs-per-worker=100 # points evaluated per ILE job == what the fan-out splits +ile-n-eff=10 +ile-runtime-max-minutes=120 + +# ---- MULTI-CONTAINER (container family) ------------------------------------------- +use-osg=True # container-family per-machine image selection uses OSG-mode wiring +use-singularity=True +singularity_rift_image=@MANIFEST@ # the container-family .yaml (manifest of >=2 .sif images) +singularity_base_exe_dir=@BASE_EXE_DIR@ # dir of the RIFT bin INSIDE the images (e.g. /usr/local/bin/) +ile_require_gpus=(Capability >= 8.0) && (Capability <= 12.0) # capability floor (adds to the family's) From 3c16f2e2e5821fd67f82c18ce7f63bc08fe7f90f Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Fri, 3 Jul 2026 17:00:57 -0700 Subject: [PATCH 04/45] slowrot_response: closed-form sidereal-harmonic detector response Realizes the response coefficients for the slow-rotation RIFT generalization, derived from the exact conventions in vectorized_lal_tools.py: F(t) = F_+ + i F_x = sum_{n=-2}^{2} A_n exp(i n g) (5 harmonics) tau(t) = -(1/c) r.nhat = sum_{n=-1}^{1} B_n exp(i n g) (3 harmonics) with g = GMST(t) - RA. The complex antenna harmonics A_n depend only on the detector response tensor, declination, and polarization (NOT RA or time); the delay harmonics B_n depend only on detector location and declination. RA and time enter solely through exp(i n g) = exp(i n (GMST(t)-RA)), i.e. the sidereal modulation exp(i n Omega t) of the design notes, keeping sky in analytic scalars. test_slowrot_response.py validates the reconstruction against lal.ComputeDetAMResponse and lal.TimeDelayFromEarthCenter over a full sidereal day for H1/L1/V1/K1 and random sky/polarization: agreement to machine precision (|dF| ~ 4e-16, |dtau| ~ 9e-18 s), and confirms no harmonic content beyond the predicted bands. Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/slowrot_response.py | 193 ++++++++++++++++++ .../RIFT/likelihood/test_slowrot_response.py | 135 ++++++++++++ 2 files changed, 328 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py new file mode 100644 index 000000000..c9c301047 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py @@ -0,0 +1,193 @@ +""" +slowrot_response : closed-form sidereal-harmonic decomposition of the ground-based +detector response, for the "slow-rotation" generalization of the RIFT likelihood. + +Over a long signal the Earth rotates, so the antenna response and the geometric +propagation delay drift. Both are exactly band-limited in harmonics of the Greenwich +hour angle g(t) = GMST(t) - RA: + + F(t) = F_+(t) + i F_x(t) = sum_{n=-2}^{+2} A_n exp(i n g(t)) (5 harmonics) + tau(t) = -(1/c) r_det . nhat(t) = sum_{n=-1}^{+1} B_n exp(i n g(t)) (3 harmonics) + +The key structural facts (see notes/ sec_amplitude, sec_delay, and the response appendix): + + * The complex antenna harmonics A_n depend ONLY on the detector response tensor, the + source declination, and the polarization angle -- NOT on right ascension or time. + RA and time enter only through the phase exp(i n g(t)) = exp(i n (GMST(t)-RA)). + Since GMST(t) ~ GMST(t_ref) + Omega (t - t_ref), this is exactly the sidereal-harmonic + modulation exp(i n Omega t) of the design notes, with sky/time in analytic scalars. + + * The delay harmonics B_n depend only on the detector location and the declination. + +This module derives A_n, B_n from the SAME algebra used by +RIFT.likelihood.vectorized_lal_tools.ComputeDetAMResponse / TimeDelayFromEarthCenter, so +the reconstruction + + F(t) = sum_n A_n exp(i n g) matches lal.ComputeDetAMResponse + tau(t) = sum_n B_n exp(i n g) matches lal.TimeDelayFromEarthCenter + +to machine precision for any g. This is an exact algebraic identity in g, not an +approximation; the ONLY approximation in the slow-rotation program is the near-linearity +of GMST(t) (handled elsewhere) and the truncation of the delay-derivative expansion +(Path B), neither of which enters here. + +Conventions mirror vectorized_lal_tools.py exactly: + g = GMST - RA (Greenwich hour angle) + nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) + X, Y polarization triads as in ComputeDetAMResponse + F_+ = X.D.X - Y.D.Y, F_x = X.D.Y + Y.D.X (D = detector response tensor, symmetric) +""" +from __future__ import print_function, division + +import numpy as np + +C_SI = 299792458.0 # m/s, matches vectorized_lal_tools + + +# --------------------------------------------------------------------------- +# Constant polarization-triad basis vectors (independent of hour angle g). +# +# Writing X = Xc cos g + Xs sin g + X0 (and likewise Y), read off directly from +# vectorized_lal_tools.ComputeDetAMResponse: +# X[0] = -cos_psi sin_g - sin_psi cos_g sin_dec +# X[1] = -cos_psi cos_g + sin_psi sin_g sin_dec +# X[2] = sin_psi cos_dec +# Y[0] = sin_psi sin_g - cos_psi cos_g sin_dec +# Y[1] = sin_psi cos_g + cos_psi sin_g sin_dec +# Y[2] = cos_psi cos_dec +# --------------------------------------------------------------------------- +def _triad_components(dec, psi): + """Return the six constant 3-vectors (Xc, Xs, X0, Yc, Ys, Y0).""" + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + + Xc = np.array([-sp * sd, -cp, 0.0]) # coeff of cos g in X + Xs = np.array([-cp, sp * sd, 0.0]) # coeff of sin g in X + X0 = np.array([ 0.0, 0.0, sp * cd]) + + Yc = np.array([-cp * sd, sp, 0.0]) # coeff of cos g in Y + Ys = np.array([ sp, cp * sd, 0.0]) # coeff of sin g in Y + Y0 = np.array([ 0.0, 0.0, cp * cd]) + return Xc, Xs, X0, Yc, Ys, Y0 + + +def antenna_harmonics(response_matrix, dec, psi): + """Complex antenna-pattern harmonics A_n, n = -2..2. + + F(t) = F_+(t) + i F_x(t) = sum_{n=-2}^{2} A_n exp(i n g), g = GMST(t) - RA. + + Parameters + ---------- + response_matrix : (3,3) array detector response tensor (Earth-fixed), symmetric + dec, psi : float declination and polarization angle [rad] + + Returns + ------- + A : dict {n: complex} for n in (-2,-1,0,1,2). Independent of RA and time. + """ + D = np.asarray(response_matrix, dtype=float) + Xc, Xs, X0, Yc, Ys, Y0 = _triad_components(dec, psi) + + def B(u, v): + return float(u @ D @ v) # bilinear form; D symmetric => B(u,v)=B(v,u) + + # --- F_+ = X.D.X - Y.D.Y, expanded in cos/sin of (n g) --- + # Using cos^2 = 1/2 + 1/2 cos2g, sin^2 = 1/2 - 1/2 cos2g, cos sin = 1/2 sin2g. + Pp0 = (0.5 * (B(Xc, Xc) + B(Xs, Xs)) + B(X0, X0) + - (0.5 * (B(Yc, Yc) + B(Ys, Ys)) + B(Y0, Y0))) + Pp1 = 2.0 * (B(Xc, X0) - B(Yc, Y0)) # cos g + Qp1 = 2.0 * (B(Xs, X0) - B(Ys, Y0)) # sin g + Pp2 = 0.5 * ((B(Xc, Xc) - B(Xs, Xs)) - (B(Yc, Yc) - B(Ys, Ys))) # cos 2g + Qp2 = B(Xc, Xs) - B(Yc, Ys) # sin 2g + + # --- F_x = X.D.Y + Y.D.X = 2 X.D.Y (D symmetric) --- + Pc0 = B(Xc, Yc) + B(Xs, Ys) + 2.0 * B(X0, Y0) + Pc1 = 2.0 * (B(Xc, Y0) + B(X0, Yc)) # cos g + Qc1 = 2.0 * (B(Xs, Y0) + B(X0, Ys)) # sin g + Pc2 = B(Xc, Yc) - B(Xs, Ys) # cos 2g + Qc2 = B(Xc, Ys) + B(Xs, Yc) # sin 2g + + # Complex combine: F = F_+ + i F_x. For harmonic n>=1, the real signal piece + # P_n cos n g + Q_n sin n g -> A_n exp(i n g) + A_{-n} exp(-i n g) + # with A_{+n} = (P_n - i Q_n)/2, A_{-n} = (P_n + i Q_n)/2, and here P_n, Q_n are + # THEMSELVES complex (P_n = Pp_n + i Pc_n, Q_n = Qp_n + i Qc_n). + P0 = Pp0 + 1j * Pc0 + P1 = Pp1 + 1j * Pc1 + Q1 = Qp1 + 1j * Qc1 + P2 = Pp2 + 1j * Pc2 + Q2 = Qp2 + 1j * Qc2 + + A = { + 0: P0, + 1: 0.5 * (P1 - 1j * Q1), + -1: 0.5 * (P1 + 1j * Q1), + 2: 0.5 * (P2 - 1j * Q2), + -2: 0.5 * (P2 + 1j * Q2), + } + return A + + +def delay_harmonics(location_xyz, dec): + """Geometric-delay harmonics B_n, n = -1..1 [seconds]. + + tau(t) = -(1/c) r_det . nhat(t) = sum_{n=-1}^{1} B_n exp(i n g), g = GMST(t) - RA, + with nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) as in + vectorized_lal_tools.TimeDelayFromEarthCenter. + + Parameters + ---------- + location_xyz : (3,) array detector position relative to Earth center [m], Earth-fixed + dec : float declination [rad] + + Returns + ------- + B : dict {n: complex} for n in (-1,0,1). B_0 real; B_{-1} = conj(B_1). + Independent of RA and time. + """ + r = np.asarray(location_xyz, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + + # tau = -(1/c)[ r_x cos_dec cos g - r_y cos_dec sin g + r_z sin_dec ] + # = T0 + T1c cos g + T1s sin g + T0 = -(r[2] * sd) / C_SI + T1c = -(cd * r[0]) / C_SI + T1s = +(cd * r[1]) / C_SI + + B = { + 0: complex(T0), + 1: 0.5 * (T1c - 1j * T1s), + -1: 0.5 * (T1c + 1j * T1s), + } + return B + + +# --------------------------------------------------------------------------- +# Reconstruction helpers (evaluate the harmonic series at given hour angle g). +# --------------------------------------------------------------------------- +def greenwich_hour_angle(gmst, ra): + """g = GMST - RA.""" + return gmst - ra + + +def antenna_from_harmonics(A, g): + """F_+ + i F_x = sum_n A_n exp(i n g). g may be scalar or array.""" + g = np.asarray(g, dtype=float) + F = np.zeros(g.shape, dtype=complex) if g.shape else 0j + for n, An in A.items(): + F = F + An * np.exp(1j * n * g) + return F + + +def delay_from_harmonics(B, g): + """tau(t) = sum_n B_n exp(i n g) (imaginary part cancels to ~0). g scalar or array.""" + g = np.asarray(g, dtype=float) + tau = np.zeros(g.shape, dtype=complex) if g.shape else 0j + for n, Bn in B.items(): + tau = tau + Bn * np.exp(1j * n * g) + return np.real(tau) + + +def antenna_response(A, gmst, ra): + """Convenience: (F_plus, F_cross) from harmonics at (gmst, ra).""" + F = antenna_from_harmonics(A, greenwich_hour_angle(gmst, ra)) + return np.real(F), np.imag(F) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py new file mode 100644 index 000000000..7bdeed3e1 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py @@ -0,0 +1,135 @@ +""" +Validate slowrot_response against LAL over a full sidereal day. + +The harmonic decomposition is an exact algebraic identity in the hour angle g, so the +reconstruction must match lal.ComputeDetAMResponse and lal.TimeDelayFromEarthCenter to +machine precision (up to LAL's own internal rounding). Run directly: + + python test_slowrot_response.py + +or under pytest (the test_* functions assert). +""" +from __future__ import print_function, division + +import numpy as np + +import lal +import lalsimulation as lalsim + +# Load the leaf module directly by path so this test needs only numpy+lal, not the full +# RIFT package stack (whose __init__ pulls in glue/lalsimutils). When the package is +# properly installed, `import RIFT.likelihood.slowrot_response` is equivalent. +try: + import RIFT.likelihood.slowrot_response as sr +except Exception: + import importlib.util + import os + _here = os.path.dirname(os.path.abspath(__file__)) + _spec = importlib.util.spec_from_file_location( + "slowrot_response", os.path.join(_here, "slowrot_response.py")) + sr = importlib.util.module_from_spec(_spec) + _spec.loader.exec_module(sr) + +DETECTORS = ["H1", "L1", "V1", "K1"] +SIDEREAL_DAY = 86164.0905 # s +T0_GPS = 1000000000.0 # arbitrary reference epoch + +_TOL = 1e-11 # radians of F, seconds of tau -- essentially machine precision + + +def _lal_detector(det): + return lalsim.DetectorPrefixToLALDetector(det) + + +def _sample_gmst(n=512): + tgps = T0_GPS + np.linspace(0.0, SIDEREAL_DAY, n, endpoint=False) + gmst = np.array([lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(t))) for t in tgps]) + return tgps, gmst + + +def _max_antenna_error(det, ra, dec, psi): + lald = _lal_detector(det) + A = sr.antenna_harmonics(lald.response, dec, psi) + _, gmst = _sample_gmst() + # model + Fp_mod, Fc_mod = sr.antenna_response(A, gmst, ra) + # reference (LAL), sampled at the same GMST values + Fp_ref = np.empty_like(gmst) + Fc_ref = np.empty_like(gmst) + for i, g in enumerate(gmst): + Fp_ref[i], Fc_ref[i] = lal.ComputeDetAMResponse(lald.response, ra, dec, psi, float(g)) + return max(np.max(np.abs(Fp_mod - Fp_ref)), np.max(np.abs(Fc_mod - Fc_ref))) + + +def _max_delay_error(det, ra, dec): + lald = _lal_detector(det) + B = sr.delay_harmonics(lald.location, dec) + tgps, gmst = _sample_gmst() + g = sr.greenwich_hour_angle(gmst, ra) + tau_mod = sr.delay_from_harmonics(B, g) + tau_ref = np.array([ + lal.TimeDelayFromEarthCenter(lald.location, ra, dec, lal.LIGOTimeGPS(float(t))) + for t in tgps + ]) + return np.max(np.abs(tau_mod - tau_ref)) + + +# ---- deterministic parameter sweep ------------------------------------------------- +_RNG = np.random.RandomState(20260703) +_CASES = [] +for _det in DETECTORS: + for _ in range(5): + _ra = _RNG.uniform(0, 2 * np.pi) + _dec = np.arcsin(_RNG.uniform(-1, 1)) + _psi = _RNG.uniform(0, np.pi) + _CASES.append((_det, _ra, _dec, _psi)) + + +def test_antenna_harmonics_match_lal(): + worst = 0.0 + for det, ra, dec, psi in _CASES: + e = _max_antenna_error(det, ra, dec, psi) + worst = max(worst, e) + assert e < _TOL, "antenna mismatch %g for %s ra=%.3f dec=%.3f psi=%.3f" % ( + e, det, ra, dec, psi) + print("antenna: worst |dF| = %.3e" % worst) + + +def test_delay_harmonics_match_lal(): + worst = 0.0 + for det, ra, dec, psi in _CASES: + e = _max_delay_error(det, ra, dec) + worst = max(worst, e) + assert e < _TOL, "delay mismatch %g s for %s ra=%.3f dec=%.3f" % (e, det, ra, dec) + print("delay: worst |dtau| = %.3e s" % worst) + + +def test_only_five_and_three_harmonics(): + """Confirm there is genuinely no content beyond |n|=2 (antenna) / |n|=1 (delay).""" + lald = _lal_detector("H1") + ra, dec, psi = 1.0, 0.4, 0.7 + _, gmst = _sample_gmst(1024) + g = sr.greenwich_hour_angle(gmst, ra) + # antenna: DFT of F(g) sampled uniformly in g should have power only at |n|<=2 + gg = np.linspace(0, 2 * np.pi, 1024, endpoint=False) + F = sr.antenna_from_harmonics(sr.antenna_harmonics(lald.response, dec, psi), gg) + coeffs = np.fft.fft(F) / len(gg) + power = np.abs(coeffs) + keep = np.zeros_like(power, dtype=bool) + for n in (-2, -1, 0, 1, 2): + keep[n % len(gg)] = True + assert np.max(power[~keep]) < 1e-12, "antenna has power beyond |n|=2: %g" % np.max(power[~keep]) + tau = sr.delay_from_harmonics(sr.delay_harmonics(lald.location, dec), gg) + tcoef = np.abs(np.fft.fft(tau) / len(gg)) + keep = np.zeros_like(tcoef, dtype=bool) + for n in (-1, 0, 1): + keep[n % len(gg)] = True + assert np.max(tcoef[~keep]) < 1e-15, "delay has power beyond |n|=1: %g" % np.max(tcoef[~keep]) + print("harmonic content confirmed: antenna |n|<=2, delay |n|<=1") + + +if __name__ == "__main__": + test_antenna_harmonics_match_lal() + test_delay_harmonics_match_lal() + test_only_five_and_three_harmonics() + print("ALL SLOWROT RESPONSE CHECKS PASSED") From 4011710b472a800895ce17cb6f18155be26e878b Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Fri, 3 Jul 2026 17:21:56 -0700 Subject: [PATCH 05/45] factored_likelihood_with_rotation: FD-native slow-rotation precompute New realization of factored_likelihood.PrecomputeLikelihoodTerms that folds the time-varying detector response into the likelihood without regenerating templates. Each FD mode is built ONCE; the two response modulations are cheap FD operations: * delay-derivative order p: h^(p)(t) <-> (FT_SIGN 2 pi i f)^p H(f) (exact per-bin weight) * sidereal harmonic n: exp(i n Omega t) <-> sub-bin frequency shift, realized as an exact LAL-FFT time-domain phase (no integer bin roll) PrecomputeLikelihoodTermsWithRotation returns the intrinsic-only, sky-independent overlap bank Q^a_lm(t), U^{a,a'}, V^{a,a'} keyed by elementary templates a=(p,n), reusing the existing ComputeModeIPTimeSeries / ComputeModeCrossTermIP unchanged. For the data term it uses the mode-independent optimization of shifting the DATA by -n f_sidereal (one shift per (det,n)) rather than modulating each template. Response coefficients A_n,B_n that contract these come from slowrot_response (computed in the extrinsic layer, not here). Path A (p_max=0) is the default; Path B is p_max>=1. Heavy RIFT imports are lazy so the FD primitives are importable with numpy alone. test_slowrot_fd_ops.py validates the primitives against LAL FFT round trips (numpy+lal only): round-trip identity 9e-16; FT_SIGN=-1 pinned down from the tone-assignment convention (evaluate_fvals reverses omega sign); time derivatives p=1,2,3 exact to ~1e-13; sidereal modulation exact to 5e-16; O(N^2) reference agrees with the LAL round trip to 4e-15. Full-stack V1 validation (agreement with a brute-force time-varying-response likelihood) needs the data/waveform environment and is the next step. Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_with_rotation.py | 303 ++++++++++++++++++ .../RIFT/likelihood/test_slowrot_fd_ops.py | 144 +++++++++ 2 files changed, 447 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py new file mode 100644 index 000000000..38131ddf8 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -0,0 +1,303 @@ +""" +factored_likelihood_with_rotation : slow-rotation generalization of the RIFT precompute. + +This is a *separate realization* of factored_likelihood.PrecomputeLikelihoodTerms that +accounts for the time dependence of the ground-based detector response over long signals +(Earth rotation). It does NOT regenerate a family of time-domain templates. Instead it +builds each frequency-domain mode ONCE and realizes the two response modulations as cheap +frequency-domain operations on that single template: + + * delay-derivative order p (Path B): time derivative h^(p)(t) <-> (s 2 pi i f)^p H(f) + -- an exact per-bin weight in the FD. (s = FT_SIGN, fixed to match RIFT's FFT + convention; see evaluate_fvals, which itself warns the omega-sign is reversed.) + + * sidereal harmonic n (Paths A,B): modulation exp(i n Omega t) <-> frequency + shift H(f - n f_sid). Since f_sid = Omega/2pi ~ 1.16e-5 Hz is sub-bin, the shift is + realized exactly as a time-domain linear phase (one FFT round trip per template), not + an integer bin roll. + +The response harmonics themselves (the analytic scalars that multiply these precomputed +overlaps in the extrinsic layer) come from RIFT.likelihood.slowrot_response: + + F_k(t) = sum_{n=-2}^{2} A_n e^{i n g}, tau_k(t) = sum_{n=-1}^{1} B_n e^{i n g}, + g = GMST(t) - RA, A_n, B_n depend only on (detector, dec, psi/dec). + +See the design notes (rift-slow-rotation: sec_formalism 'master expansion', sec_amplitude +Path A, sec_delay Path B, app_response) for the derivation and the meaning of the indices. + +Index conventions in the returned structures +-------------------------------------------- +An "elementary modulated template" is labelled a = (p, n): + chi_a(t) = exp(i n Omega t) * d^p/dt^p h_lm(t - tau_0). +The physical data-term time series carries a post-phase (derived in the notes): + Q^a_lm(t) = exp(i n Omega t) * < chi_a(.-t) | d > [applied here] +while the cross terms are arrival-time independent: + U^{a,a'} = < chi_a | chi_a' >, V^{a,a'} = < chi_a^* | chi_a' >. + +Path A (default) uses only p = 0 (amplitude drift; exact 5-harmonic). Path B adds p >= 1. + +Heavy RIFT imports (factored_likelihood, lalsimutils) are done lazily inside the precompute +so the light FD primitives below are importable with numpy alone (used by the unit tests). +""" +from __future__ import print_function, division + +import numpy as np + +# Sidereal angular rate [rad/s] and frequency [Hz] +OMEGA_EARTH = 7.292115e-5 +F_SIDEREAL = OMEGA_EARTH / (2.0 * np.pi) + +# Sign in the time-derivative weight (s 2 pi i f)^p, matched to RIFT's evaluate_fvals FFT +# convention. VALIDATED empirically against a LAL FFT round trip in +# test_slowrot_fd_ops.py (which will fail loudly if this is wrong). +FT_SIGN = -1.0 + + +# --------------------------------------------------------------------------- +# Low-level FD primitives (numpy only; operate on a complex spectrum + its fvals). +# --------------------------------------------------------------------------- +def evaluate_fvals_from_length(npts, deltaF): + """Signed frequency array for a RIFT two-sided COMPLEX16FrequencySeries. + + Replicates RIFT.lalsimutils.evaluate_fvals (kept local so these primitives need no + heavyweight import). Note RIFT's expression ``npts/2 - k if k<=npts/2 else -k+npts/2`` + is the same in both branches, i.e. simply f[k] = deltaF*(npts/2 - k), descending from + +fNyq at k=0. + """ + k = np.arange(npts) + return deltaF * (npts / 2.0 - k) + + +def time_derivative_weight(fvals, p): + """(FT_SIGN * 2 pi i f)^p : exact FD weight for the p-th time derivative.""" + if p == 0: + return np.ones_like(fvals, dtype=complex) + return (FT_SIGN * 2.0j * np.pi * fvals) ** p + + +def apply_time_derivative_array(spectrum, fvals, p): + """Return the spectrum of d^p/dt^p h given the spectrum of h (both two-sided).""" + return spectrum * time_derivative_weight(fvals, p) + + +def apply_sidereal_modulation_array(spectrum, coef, deltaF, f_sidereal=F_SIDEREAL): + """Return the spectrum of exp(i coef Omega t) h(t) given the spectrum of h(t). + + exp(i coef Omega t) multiplication in time <-> frequency shift by coef*f_sidereal. + This is sub-bin (f_sidereal << deltaF for realistic segments), so it is applied exactly + as a time-domain linear phase via a DFT round trip that respects RIFT's fvals packing. + + WARNING: this reference implementation forms an explicit (npts x npts) DFT matrix -- it + is O(npts^2) and intended ONLY for unit tests at small npts. The production path uses + the LAL-FFT round trip in _lal_freq_modulate() below (O(npts log npts)). + """ + if coef == 0: + return spectrum.copy() + npts = len(spectrum) + dt = 1.0 / (npts * deltaF) + t = np.arange(npts) * dt + fvals = evaluate_fvals_from_length(npts, deltaF) + # Round-trip-consistent DFT pair for the packing f[k]=deltaF*(npts/2-k): + # h(t_j) = (1/npts) sum_k H_k exp(+2 pi i f_k t_j), H_k = sum_j h(t_j) exp(-2 pi i f_k t_j) + # LAL's convention (encoded by evaluate_fvals) reverses the omega sign: a tone + # exp(+2 pi i f0 t) lands at fvals = -f0. The consistent DFT pair is therefore + # h(t_j) = (1/npts) sum_k H_k exp(-2 pi i f_k t_j), H_k = sum_j h(t_j) exp(+2 pi i f_k t_j). + phase_inv = np.exp(-2.0j * np.pi * np.outer(t, fvals)) # (npts, npts): H -> h(t) + h_td = phase_inv.dot(spectrum) / npts + h_td *= np.exp(1.0j * coef * 2.0 * np.pi * f_sidereal * t) # exp(i coef Omega t) + phase_fwd = np.exp(2.0j * np.pi * np.outer(fvals, t)) # h(t) -> H + return phase_fwd.dot(h_td) + + +# --------------------------------------------------------------------------- +# LAL-series wrappers (used inside the precompute; import lal lazily). +# --------------------------------------------------------------------------- +def _copy_freqseries(hf): + import lal + out = lal.CreateCOMPLEX16FrequencySeries( + hf.name, hf.epoch, hf.f0, hf.deltaF, hf.sampleUnits, hf.data.length) + out.data.data[:] = hf.data.data[:] + return out + + +def fd_apply_time_derivative(hf, p): + """COMPLEX16FrequencySeries -> new series for the p-th time derivative.""" + if p == 0: + return _copy_freqseries(hf) + out = _copy_freqseries(hf) + fvals = evaluate_fvals_from_length(hf.data.length, hf.deltaF) + out.data.data[:] = apply_time_derivative_array(hf.data.data, fvals, p) + return out + + +def _lal_freq_modulate(hf, coef, f_sidereal=F_SIDEREAL): + """Production exp(i coef Omega t) modulation via a LAL-FFT round trip (O(N log N)). + + Reverse-FFT the spectrum to the time domain, multiply by the sidereal linear phase, + forward-FFT back. Uses the same COMPLEX16 transforms RIFT uses for its overlaps, so + the convention is identical. The absolute time origin only sets a constant phase + (carried analytically by A_n / GMST_ref downstream), so t_j = j*deltaT is fine. + """ + import lal + if coef == 0: + return _copy_freqseries(hf) + npts = hf.data.length + deltaT = 1.0 / (npts * hf.deltaF) + ts = lal.CreateCOMPLEX16TimeSeries("m", hf.epoch, 0., deltaT, + lal.DimensionlessUnit, npts) + revplan = lal.CreateReverseCOMPLEX16FFTPlan(npts, 0) + fwdplan = lal.CreateForwardCOMPLEX16FFTPlan(npts, 0) + lal.COMPLEX16FreqTimeFFT(ts, hf, revplan) + t = np.arange(npts) * deltaT + ts.data.data[:] = ts.data.data * np.exp(1.0j * coef * 2.0 * np.pi * f_sidereal * t) + out = _copy_freqseries(hf) + lal.COMPLEX16TimeFreqFFT(out, ts, fwdplan) + return out + + +def fd_apply_sidereal_modulation(hf, n, f_sidereal=F_SIDEREAL): + """COMPLEX16FrequencySeries -> new series for exp(i n Omega t) h(t).""" + return _lal_freq_modulate(hf, n, f_sidereal) + + +def build_elementary_template(hf, p, n, f_sidereal=F_SIDEREAL): + """chi_{(p,n)} spectrum from the base mode spectrum hf: derivative then modulation.""" + return fd_apply_sidereal_modulation(fd_apply_time_derivative(hf, p), n, f_sidereal) + + +def _elementary_index_set(harmonics, p_max): + """List of a=(p,n). Superset grid; unused (p,n) get zero response coefficients.""" + return [(p, n) for p in range(p_max + 1) for n in harmonics] + + +# --------------------------------------------------------------------------- +# The precompute. +# --------------------------------------------------------------------------- +def PrecomputeLikelihoodTermsWithRotation( + event_time_geo, t_window, P, data_dict, psd_dict, Lmax, fMax, + harmonics=(-2, -1, 0, 1, 2), p_max=0, f_sidereal=F_SIDEREAL, + analyticPSD_Q=False, inv_spec_trunc_Q=False, T_spec=0., + verbose=True, quiet=False, internal_fast_precompute=True, + skip_interpolation=False, **hlm_kwargs): + """Slow-rotation analogue of factored_likelihood.PrecomputeLikelihoodTerms. + + Builds each FD mode once (via factored_likelihood.internal_hlm_generator) and forms the + generalized overlaps for every elementary modulated template a=(p,n): + + rholms_intp_rot[det][a][(l,m)] : interpolant of Q^a_lm(t) = e^{inOmega t} + crossTerms_rot[det][(a,a')] : { ((l,m),(l',m')) : } + crossTermsV_rot[det][(a,a')] : { ((l,m),(l',m')) : } + + Parameters mirror PrecomputeLikelihoodTerms; rotation-specific: + harmonics : sidereal harmonic indices n to carry (antenna needs |n|<=2). + p_max : max delay-derivative order (0 = Path A amplitude-only; >=1 = Path B). + f_sidereal: sidereal frequency [Hz]. + + Response coefficients A_n(det,dec,psi), B_n(det,dec) that contract these overlaps are + NOT computed here -- see slowrot_response and the (forthcoming) rotation-aware + FactoredLogLikelihood assembly. This routine produces only the intrinsic-only, + sky-independent overlap bank. + + NOTE: full-stack numerical validation (V1 of the notes' validation matrix: agreement + with a brute-force time-varying-response likelihood) requires the data/PSD/waveform + environment and is done separately; the FD primitives used here are unit-tested in + test_slowrot_fd_ops.py. + """ + # Lazy heavy imports (need the full RIFT stack / lal). + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + assert data_dict.keys() == psd_dict.keys() + detectors = list(data_dict.keys()) + modulate_data = True # apply the exp(i n Omega t) frequency shift on the DATA for Q + # (mode-independent: one shift per (det,n) instead of per template) + + # Reference distance handling identical to the base precompute. + P.dist = FL.distMpcRef * 1e6 * lsu.lsu_PC + P.deltaF = data_dict[detectors[0]].deltaF + + # --- build base FD modes ONCE --- + hlms, hlms_conj = FL.internal_hlm_generator(P, Lmax, verbose=verbose, quiet=quiet, + **hlm_kwargs) + a_list = _elementary_index_set(harmonics, p_max) + p_values = sorted(set(p for (p, n) in a_list)) + + # --- derivative-weighted template families (per p), built once, reused for all n,det --- + # (the derivative is a trivial FD weight; no waveform regeneration) + hlms_p = {p: {lm: fd_apply_time_derivative(hlms[lm], p) for lm in hlms} for p in p_values} + hlms_conj_p = {p: {lm: fd_apply_time_derivative(hlms_conj[lm], p) for lm in hlms_conj} + for p in p_values} + + # For U,V we need the modulated templates chi_a (modulation cannot be pushed onto data + # in a overlap). Build them once per a (cheap FD op). + chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal) + for lm in hlms} for a in a_list} + chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal) + for lm in hlms_conj} for a in a_list} + + rholms_rot = {} + rholms_intp_rot = {} + crossTerms_rot = {} + crossTermsV_rot = {} + + for det in detectors: + psd = psd_dict[det] + data = data_dict[det] + t_det = FL.ComputeArrivalTimeAtDetector(det, P.phi, P.theta, event_time_geo) + rho_epoch = data.epoch - hlms[list(hlms.keys())[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / P.deltaT + 0.5) + N_window = int(2 * t_window / P.deltaT) + t = np.arange(N_window) * P.deltaT + float(rho_epoch + N_shift * P.deltaT) + + # ---- data-term overlaps Q^a_lm(t) ---- + rholms_rot[det] = {} + rholms_intp_rot[det] = {} + # Pre-shift the (whitened-by-the-IP) data once per harmonic n: exp(i n Omega t) on + # the template is equivalent to shifting the data spectrum by -n f_sidereal, which + # is mode-independent. We realize it by modulating the DATA time series by + # exp(-i n Omega t) (round trip), then post-multiply Q by exp(+i n Omega t). + data_by_n = {} + for n in set(nn for (_, nn) in a_list): + if modulate_data and n != 0: + data_by_n[n] = _lal_freq_modulate(data, -n, f_sidereal) + else: + data_by_n[n] = data + + for a in a_list: + p, n = a + templates = hlms_p[p] if modulate_data else chi[a] + rho = FL.ComputeModeIPTimeSeries( + templates, data_by_n[n], psd, P.fmin, fMax, 1. / 2. / P.deltaT, + N_shift, N_window, analyticPSD_Q, inv_spec_trunc_Q, T_spec) + # post-phase exp(+i n Omega (t - event_time_geo)); the constant piece + # exp(i n Omega event_time_geo) is carried analytically by A_n (GMST_ref). + if n != 0: + phase = np.exp(1.0j * n * OMEGA_EARTH * (t - float(event_time_geo))) + for lm in rho: + rho[lm].data.data[:] = rho[lm].data.data[:len(t)] * phase[:rho[lm].data.length] + rholms_rot[det][a] = rho + if not skip_interpolation: + rholms_intp_rot[det][a] = FL.InterpolateRholms(rho, t, verbose=verbose) + else: + rholms_intp_rot[det][a] = None + + # ---- cross terms U,V for each ordered pair (a,a') ---- + crossTerms_rot[det] = {} + crossTermsV_rot[det] = {} + for a in a_list: + for ap in a_list: + crossTerms_rot[det][(a, ap)] = FL.ComputeModeCrossTermIP( + chi[a], chi[ap], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, verbose=False, + same_waveform_Q=False) + crossTermsV_rot[det][(a, ap)] = FL.ComputeModeCrossTermIP( + chi_conj[a], chi[ap], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, prefix="V", verbose=False, + same_waveform_Q=False) + + meta = dict(harmonics=tuple(harmonics), p_max=p_max, f_sidereal=f_sidereal, + a_list=a_list, event_time_geo=float(event_time_geo), + omega_earth=OMEGA_EARTH, modes=list(hlms.keys())) + return rholms_intp_rot, crossTerms_rot, crossTermsV_rot, rholms_rot, meta diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py new file mode 100644 index 000000000..7c42a1890 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py @@ -0,0 +1,144 @@ +""" +Validate the frequency-domain primitives of factored_likelihood_with_rotation against +LAL FFT round trips (numpy + lal only; no glue / full RIFT stack needed). + +Checks: + 1. LAL forward/reverse COMPLEX16 FFT round-trips to identity (normalization sanity). + 2. Which signed frequency LAL assigns to a tone, vs evaluate_fvals_from_length -> fixes + the sign FT_SIGN in the time-derivative weight. + 3. fd_apply_time_derivative reproduces d^p/dt^p exactly for a multi-tone signal. + 4. _lal_freq_modulate reproduces exp(i coef Omega t) multiplication exactly. + 5. the O(N^2) reference apply_sidereal_modulation_array agrees with the LAL round trip. + +Run: python test_slowrot_fd_ops.py (also usable under pytest) +""" +from __future__ import print_function, division + +import os +import importlib.util + +import numpy as np +import lal + +_here = os.path.dirname(os.path.abspath(__file__)) +_spec = importlib.util.spec_from_file_location( + "flwr", os.path.join(_here, "factored_likelihood_with_rotation.py")) +flwr = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(flwr) + +N = 256 +DELTA_T = 1.0 / 512.0 +DELTA_F = 1.0 / (N * DELTA_T) +_T = np.arange(N) * DELTA_T + + +def _make_timeseries(cvec): + ts = lal.CreateCOMPLEX16TimeSeries("h", lal.LIGOTimeGPS(0.), 0., DELTA_T, + lal.DimensionlessUnit, N) + ts.data.data[:] = cvec.astype(complex) + return ts + + +def _forward(ts): + """DataFourier-style forward transform.""" + fwd = lal.CreateForwardCOMPLEX16FFTPlan(N, 0) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", ts.epoch, 0., DELTA_F, + lal.HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ts, fwd) + return hf + + +def _reverse(hf): + rev = lal.CreateReverseCOMPLEX16FFTPlan(N, 0) + ts = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., DELTA_T, + lal.DimensionlessUnit, N) + lal.COMPLEX16FreqTimeFFT(ts, hf, rev) + return ts + + +def _multitone(): + """h(t) = sum_j c_j exp(2 pi i f_j t), f_j on distinct grid bins.""" + bins = [3, 7, -5, 12, -11] + coeffs = [1.0, 0.5 - 0.3j, -0.8j, 0.4, 0.2 + 0.1j] + h = np.zeros(N, dtype=complex) + for b, c in zip(bins, coeffs): + h += c * np.exp(2.0j * np.pi * (b * DELTA_F) * _T) + return h, bins, coeffs + + +def test_roundtrip_identity(): + h, _, _ = _multitone() + back = _reverse(_forward(_make_timeseries(h))).data.data + assert np.max(np.abs(back - h)) < 1e-10, "LAL round trip not identity" + print("roundtrip: max err = %.2e" % np.max(np.abs(back - h))) + + +def test_tone_frequency_assignment_and_FT_SIGN(): + """Determine FT_SIGN so that (FT_SIGN 2 pi i f)^1 gives the true derivative.""" + f0_bin = 7 + h = np.exp(2.0j * np.pi * (f0_bin * DELTA_F) * _T) + hf = _forward(_make_timeseries(h)) + H = hf.data.data + fvals = flwr.evaluate_fvals_from_length(N, DELTA_F) + kpeak = int(np.argmax(np.abs(H))) + f_assigned = fvals[kpeak] + # true single-tone first derivative is (2 pi i f0) h; the reconstructed weight is + # (FT_SIGN 2 pi i f_assigned). Equate -> FT_SIGN = f0 / f_assigned. + f0 = f0_bin * DELTA_F + needed_sign = np.sign(f0 / f_assigned) + print("tone at bin %d: f0=%.4f, evaluate_fvals assigns %.4f -> FT_SIGN should be %+d" + % (f0_bin, f0, f_assigned, int(needed_sign))) + assert abs(abs(f_assigned) - abs(f0)) < 1e-9, "tone landed at wrong |f|" + assert flwr.FT_SIGN == needed_sign, ( + "module FT_SIGN=%g but LAL convention requires %g" % (flwr.FT_SIGN, needed_sign)) + + +def test_time_derivative_exact(): + h, bins, coeffs = _multitone() + for p in (1, 2, 3): + # analytic derivative + dh = np.zeros(N, dtype=complex) + for b, c in zip(bins, coeffs): + w = (2.0j * np.pi * b * DELTA_F) + dh += c * (w ** p) * np.exp(2.0j * np.pi * (b * DELTA_F) * _T) + hf = _forward(_make_timeseries(h)) + hf_d = flwr.fd_apply_time_derivative(hf, p) + num = _reverse(hf_d).data.data + err = np.max(np.abs(num - dh)) / np.max(np.abs(dh)) + print("derivative p=%d: rel err = %.2e" % (p, err)) + assert err < 1e-9, "derivative order %d inexact: %g" % (p, err) + + +def test_sidereal_modulation_exact(): + h, _, _ = _multitone() + f_sid = 0.05 * DELTA_F # exaggerated so coef*f_sid is an appreciable sub-bin shift + for coef in (1, 2, -1, -2): + expected = np.exp(2.0j * np.pi * coef * f_sid * _T) * h + hf = _forward(_make_timeseries(h)) + hf_mod = flwr._lal_freq_modulate(hf, coef, f_sidereal=f_sid) + num = _reverse(hf_mod).data.data + err = np.max(np.abs(num - expected)) / np.max(np.abs(expected)) + print("modulation coef=%+d: rel err = %.2e" % (coef, err)) + assert err < 1e-9, "modulation coef %d inexact: %g" % (coef, err) + + +def test_reference_matrix_matches_lal_modulation(): + """The O(N^2) reference and the LAL round trip must agree.""" + h, _, _ = _multitone() + f_sid = 0.05 * DELTA_F + hf = _forward(_make_timeseries(h)) + for coef in (1, -2): + via_lal = flwr._lal_freq_modulate(hf, coef, f_sidereal=f_sid).data.data + via_mat = flwr.apply_sidereal_modulation_array(hf.data.data, coef, DELTA_F, f_sid) + err = np.max(np.abs(via_lal - via_mat)) / np.max(np.abs(via_lal)) + print("reference-vs-LAL coef=%+d: rel err = %.2e" % (coef, err)) + assert err < 1e-9, "reference matrix disagrees with LAL: %g" % err + + +if __name__ == "__main__": + test_roundtrip_identity() + test_tone_frequency_assignment_and_FT_SIGN() + test_time_derivative_exact() + test_sidereal_modulation_exact() + test_reference_matrix_matches_lal_modulation() + print("ALL FD-PRIMITIVE CHECKS PASSED") From ea2b70e8c2a4081259db586d2cff5146ee39c3d2 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Fri, 3 Jul 2026 17:45:00 -0700 Subject: [PATCH 06/45] slowrot precompute: fix modulation reference time; validate on real data Full-stack integration testing (test_slowrot_precompute_integration.py, run against the ILE-GPU-Paper-style noiseless-injection data + analytic PSD, same machinery as the CI demo) exposed a reference-time bug: the sidereal modulation used local array time (from 0, ignoring the series epoch) plus a spurious arrival-time post-phase, leaving an O(Omega*seglen) ~ 1e-4 error. Fix: apply the modulation at ABSOLUTE data time referenced to the event time, exp(-i n Omega (t' - t_ev)) on the data (data route), with NO post-phase. This is the physically correct factorization of the true antenna phase exp(i n (GMST(t')-RA)) = exp(i n (GMST(t_ev)-RA)) * exp(i n Omega (t'-t_ev)), the first factor carried analytically by A_n (slowrot_response), the second by the precompute. _lal_freq_modulate now takes t_ref and uses the series epoch; U,V templates use the same t_ev reference. Dropped the modulate_data flag (the data route is the single correct path). Validation on real waveforms/data: V0 (recovery): rotation precompute with harmonics=(0,), p_max=0 reproduces the baseline PrecomputeLikelihoodTerms exactly: rel|dQ|=0, |dU|=0, |dV|=5e-13. Vmag: with harmonics -2..2, a=(0,0) still matches baseline exactly; modulated a=(0,+-1) and a=(0,+-2) differ from n=0 by 7.3e-6 and 1.47e-5 -- nonzero, at the Omega*duration scale, and exactly linear in n (n=2 is 2x n=1) as the modulation demands. FD primitive unit test still passes (t_ref defaults preserve it). Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_with_rotation.py | 65 ++++----- .../test_slowrot_precompute_integration.py | 125 ++++++++++++++++++ 2 files changed, 158 insertions(+), 32 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 38131ddf8..35a536fbb 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -130,13 +130,18 @@ def fd_apply_time_derivative(hf, p): return out -def _lal_freq_modulate(hf, coef, f_sidereal=F_SIDEREAL): - """Production exp(i coef Omega t) modulation via a LAL-FFT round trip (O(N log N)). - - Reverse-FFT the spectrum to the time domain, multiply by the sidereal linear phase, - forward-FFT back. Uses the same COMPLEX16 transforms RIFT uses for its overlaps, so - the convention is identical. The absolute time origin only sets a constant phase - (carried analytically by A_n / GMST_ref downstream), so t_j = j*deltaT is fine. +def _lal_freq_modulate(hf, coef, f_sidereal=F_SIDEREAL, t_ref=0.0): + """Modulate by exp(i coef Omega (t_abs - t_ref)) via a LAL-FFT round trip (O(N log N)). + + Reverse-FFT the spectrum to the time domain, multiply by the sidereal linear phase + referenced to ABSOLUTE sample time t_abs = float(hf.epoch) + j*deltaT (minus t_ref), + forward-FFT back. Uses the same COMPLEX16 transforms RIFT uses for its overlaps. + + The reference t_ref is physical, not cosmetic: the true antenna phase is + exp(i n (GMST(t')-RA)) = exp(i n (GMST(t_ev)-RA)) * exp(i n Omega (t'-t_ev)), so the + precompute must carry exactly exp(i n Omega (t' - t_ev)) at absolute data time t', + with the constant GMST(t_ev) piece carried analytically by A_n (slowrot_response). + Hence callers pass t_ref = event_time_geo. """ import lal if coef == 0: @@ -148,16 +153,17 @@ def _lal_freq_modulate(hf, coef, f_sidereal=F_SIDEREAL): revplan = lal.CreateReverseCOMPLEX16FFTPlan(npts, 0) fwdplan = lal.CreateForwardCOMPLEX16FFTPlan(npts, 0) lal.COMPLEX16FreqTimeFFT(ts, hf, revplan) - t = np.arange(npts) * deltaT - ts.data.data[:] = ts.data.data * np.exp(1.0j * coef * 2.0 * np.pi * f_sidereal * t) + t_abs = float(hf.epoch) + np.arange(npts) * deltaT + ts.data.data[:] = ts.data.data * np.exp( + 1.0j * coef * 2.0 * np.pi * f_sidereal * (t_abs - t_ref)) out = _copy_freqseries(hf) lal.COMPLEX16TimeFreqFFT(out, ts, fwdplan) return out -def fd_apply_sidereal_modulation(hf, n, f_sidereal=F_SIDEREAL): - """COMPLEX16FrequencySeries -> new series for exp(i n Omega t) h(t).""" - return _lal_freq_modulate(hf, n, f_sidereal) +def fd_apply_sidereal_modulation(hf, n, f_sidereal=F_SIDEREAL, t_ref=0.0): + """COMPLEX16FrequencySeries -> new series for exp(i n Omega (t_abs - t_ref)) h(t).""" + return _lal_freq_modulate(hf, n, f_sidereal, t_ref) def build_elementary_template(hf, p, n, f_sidereal=F_SIDEREAL): @@ -210,8 +216,11 @@ def PrecomputeLikelihoodTermsWithRotation( assert data_dict.keys() == psd_dict.keys() detectors = list(data_dict.keys()) - modulate_data = True # apply the exp(i n Omega t) frequency shift on the DATA for Q - # (mode-independent: one shift per (det,n) instead of per template) + t_ev = float(event_time_geo) + # The exp(i n Omega t) modulation for the data term Q is applied to the DATA (shift by + # -n f_sidereal, referenced to t_ev), which is mode-independent: one shift per (det,n), + # and -- since the modulation lives on the fixed absolute data-time axis -- needs NO + # arrival-time-dependent post-phase. U,V use modulated templates (same t_ev reference). # Reference distance handling identical to the base precompute. P.dist = FL.distMpcRef * 1e6 * lsu.lsu_PC @@ -231,9 +240,9 @@ def PrecomputeLikelihoodTermsWithRotation( # For U,V we need the modulated templates chi_a (modulation cannot be pushed onto data # in a overlap). Build them once per a (cheap FD op). - chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal) + chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal, t_ev) for lm in hlms} for a in a_list} - chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal) + chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal, t_ev) for lm in hlms_conj} for a in a_list} rholms_rot = {} @@ -252,31 +261,23 @@ def PrecomputeLikelihoodTermsWithRotation( t = np.arange(N_window) * P.deltaT + float(rho_epoch + N_shift * P.deltaT) # ---- data-term overlaps Q^a_lm(t) ---- + # exp(i n Omega t) on the template is equivalent to shifting the data spectrum by + # -n f_sidereal (mode-independent). Realize it by modulating the DATA time series + # by exp(-i n Omega (t_abs - t_ev)) (round trip). Because the modulation lives on + # the absolute data-time axis, the resulting overlap is directly + # Q^a_lm(t) = int e^{-i n Omega (t'-t_ev)} [d^p h_lm]^*(t'-t) d(t') dt' + # with NO arrival-time-dependent post-phase. rholms_rot[det] = {} rholms_intp_rot[det] = {} - # Pre-shift the (whitened-by-the-IP) data once per harmonic n: exp(i n Omega t) on - # the template is equivalent to shifting the data spectrum by -n f_sidereal, which - # is mode-independent. We realize it by modulating the DATA time series by - # exp(-i n Omega t) (round trip), then post-multiply Q by exp(+i n Omega t). data_by_n = {} for n in set(nn for (_, nn) in a_list): - if modulate_data and n != 0: - data_by_n[n] = _lal_freq_modulate(data, -n, f_sidereal) - else: - data_by_n[n] = data + data_by_n[n] = data if n == 0 else _lal_freq_modulate(data, -n, f_sidereal, t_ev) for a in a_list: p, n = a - templates = hlms_p[p] if modulate_data else chi[a] rho = FL.ComputeModeIPTimeSeries( - templates, data_by_n[n], psd, P.fmin, fMax, 1. / 2. / P.deltaT, + hlms_p[p], data_by_n[n], psd, P.fmin, fMax, 1. / 2. / P.deltaT, N_shift, N_window, analyticPSD_Q, inv_spec_trunc_Q, T_spec) - # post-phase exp(+i n Omega (t - event_time_geo)); the constant piece - # exp(i n Omega event_time_geo) is carried analytically by A_n (GMST_ref). - if n != 0: - phase = np.exp(1.0j * n * OMEGA_EARTH * (t - float(event_time_geo))) - for lm in rho: - rho[lm].data.data[:] = rho[lm].data.data[:len(t)] * phase[:rho[lm].data.length] rholms_rot[det][a] = rho if not skip_interpolation: rholms_intp_rot[det][a] = FL.InterpolateRholms(rho, t, verbose=verbose) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py new file mode 100644 index 000000000..62a77c062 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py @@ -0,0 +1,125 @@ +""" +Full-stack integration test for factored_likelihood_with_rotation on real waveforms/data. + +Run under the RIFT venv with this worktree first on PYTHONPATH, e.g. + + source ~/RIFT_develUWM/bin/activate + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code \ + python .../RIFT/likelihood/test_slowrot_precompute_integration.py + +Inputs are built exactly like RIFT.calmarg.test_precompute_alignment (a short BBH injection, +noiseless detector data via non_herm_hoff, analytic aLIGO PSD), i.e. the same machinery the +ILE-GPU-Paper CI demo exercises. + +Checks: + V0 (recovery): PrecomputeLikelihoodTermsWithRotation(harmonics=(0,), p_max=0) reproduces + the baseline PrecomputeLikelihoodTerms bit-for-bit -- Q, U, V. This validates all of + the new plumbing (mode generation, p=0 / n=0 identity, cross-term assembly). + Vmag (magnitude sanity): running Path A (harmonics -2..2) end-to-end on real data, the + a=(0,0) overlap still equals the baseline exactly, and the modulated a=(0,+-1) overlaps + differ from it by a small but nonzero amount of the expected order Omega*(signal + duration) -- confirming the sidereal modulation is applied, at the right (tiny) scale. + +Full physical validation (V1: the rotation-aware lnL vs a brute-force time-varying-response +likelihood) needs the lnL assembly that contracts this bank with A_n,B_n, and is the next +step. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lalsimutils +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1000000000.0 +t_window = 0.1 +Lmax = 2 + +Psig = lalsimutils.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=1. / fSample, + tref=event_time, deltaF=1. / 4.) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lalsimutils.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +def _maxrel(a, b): + a = np.asarray(a); b = np.asarray(b) + scale = max(np.max(np.abs(a)), np.max(np.abs(b)), 1e-300) + return float(np.max(np.abs(a - b)) / scale) + + +def _run_base(): + # ignore_threshold=None so no mode pruning -> same mode set as the rotation path + return fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True, + ignore_threshold=None) + + +def _run_rot(harmonics, p_max): + return flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=harmonics, p_max=p_max, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + + +def test_V0_recovers_baseline(): + _, ct_b, ctV_b, rho_b, _, _ = _run_base() + _, ct_r, ctV_r, rho_r, meta = _run_rot(harmonics=(0,), p_max=0) + a0 = (0, 0) + worst_q = worst_u = worst_v = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_q = max(worst_q, _maxrel(rho_r[det][a0][mode].data.data, + rho_b[det][mode].data.data)) + for key in ct_b[det]: + worst_u = max(worst_u, abs(ct_r[det][(a0, a0)][key] - ct_b[det][key])) + worst_v = max(worst_v, abs(ctV_r[det][(a0, a0)][key] - ctV_b[det][key])) + print("V0 recovery: max rel|dQ|=%.2e max|dU|=%.2e max|dV|=%.2e" + % (worst_q, worst_u, worst_v)) + assert worst_q < 1e-8, "Q mismatch vs baseline: %g" % worst_q + assert worst_u < 1e-8 * (1 + worst_u), "U mismatch vs baseline: %g" % worst_u + assert worst_v < 1e-8 * (1 + worst_v), "V mismatch vs baseline: %g" % worst_v + + +def test_Vmag_modulation_applied_at_right_scale(): + _, ct_b, _, rho_b, _, _ = _run_base() + _, _, _, rho_r, meta = _run_rot(harmonics=(-2, -1, 0, 1, 2), p_max=0) + # a=(0,0) must still equal baseline exactly even with the extra harmonics present + worst_0 = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_0 = max(worst_0, _maxrel(rho_r[det][(0, 0)][mode].data.data, + rho_b[det][mode].data.data)) + print("Vmag a=(0,0) vs baseline: max rel|dQ|=%.2e" % worst_0) + assert worst_0 < 1e-8, "modulated run corrupts the n=0 overlap: %g" % worst_0 + + # modulated harmonics differ from n=0 by ~ Omega * (signal duration), tiny but nonzero. + # Signal duration scale for a 30+25 BBH from 30 Hz is ~ 0.1-1 s -> Omega*T ~ 1e-5..1e-4. + for n in (1, -1, 2, -2): + worst_n = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_n = max(worst_n, _maxrel(rho_r[det][(0, n)][mode].data.data, + rho_r[det][(0, 0)][mode].data.data)) + print("Vmag a=(0,%+d) vs a=(0,0): max rel|dQ|=%.2e" % (n, worst_n)) + assert worst_n > 1e-9, "harmonic n=%d has no effect (modulation not applied?)" % n + assert worst_n < 1e-2, "harmonic n=%d far too large (bug in modulation scale?): %g" % (n, worst_n) + + +if __name__ == "__main__": + test_V0_recovers_baseline() + test_Vmag_modulation_applied_at_right_scale() + print("ALL SLOWROT PRECOMPUTE INTEGRATION CHECKS PASSED") From 993b2fb77a80a8f38cc748749533b624b283d9f9 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sat, 4 Jul 2026 01:05:15 -0700 Subject: [PATCH 07/45] slowrot: rotation-aware lnL assembly (Path A) + V1 validation Add FactoredLogLikelihoodWithRotation and antenna_harmonics_tilde: Path A (amplitude-drift) log-likelihood contracting the harmonic precompute bank with A_tilde_n = A_n exp(i n (GMST(tref)-RA)) from slowrot_response. The V-term's A_tilde_{-nu} (not A_tilde_nu) matches the precompute V^{(nu,n')} exponent (n'-nu) to the F^2 exponent (n+n'); both terms reduce exactly to baseline as f_sidereal -> 0. V1 (test_slowrot_likelihood_v1.py) on the ILE-GPU-Paper-style injection: V1a: f_sidereal=0 rotation lnL == baseline FactoredLogLikelihood to 2.7e-12. V1b: real sidereal rate rotation lnL == independent brute-force time-varying-response likelihood to 1.6e-9 (rel ~3e-13). Path B (delay-derivative contraction with B_n) remains a TODO. Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_with_rotation.py | 99 ++++++++++ .../likelihood/test_slowrot_likelihood_v1.py | 178 ++++++++++++++++++ 2 files changed, 277 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 35a536fbb..66017d4fa 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -302,3 +302,102 @@ def PrecomputeLikelihoodTermsWithRotation( a_list=a_list, event_time_geo=float(event_time_geo), omega_earth=OMEGA_EARTH, modes=list(hlms.keys())) return rholms_intp_rot, crossTerms_rot, crossTermsV_rot, rholms_rot, meta + + +# --------------------------------------------------------------------------- +# Rotation-aware log-likelihood assembly (Path A: amplitude drift, p_max=0). +# --------------------------------------------------------------------------- +def antenna_harmonics_tilde(det, RA, DEC, psi, tref): + """Return {n: A_tilde_n} where F_k(t) = sum_n A_tilde_n exp(i n Omega (t - tref)). + + A_tilde_n = A_n(response, dec, psi) * exp(i n (GMST(tref) - RA)), with A_n the + RA/time-independent antenna harmonics from slowrot_response. So A_tilde_n is the + coefficient of the precompute's modulation exp(i n Omega (t - tref)); sum_n A_tilde_n + = F_k(tref) reproduces lal.ComputeDetAMResponse exactly. + """ + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + A = srr.antenna_harmonics(lald.response, DEC, psi) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + return {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + + +def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_rot, + crossTermsV_rot, meta, Lmax): + """Slow-rotation analogue of factored_likelihood.FactoredLogLikelihood (Path A). + + Contracts the harmonic-resolved precompute bank with the antenna harmonics + A_tilde_n(det, RA, DEC, psi, tref). Reduces EXACTLY to the baseline + FactoredLogLikelihood when f_sidereal -> 0 (all modulations become identity and + sum_n A_tilde_n -> F_k(tref)). + + Currently implements p_max=0 (amplitude drift only); the delay-derivative (Path B) + contraction with B_n is a TODO. + """ + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + assert meta['p_max'] == 0, "FactoredLogLikelihoodWithRotation implements Path A (p_max=0)" + harmonics = list(meta['harmonics']) + hset = set(harmonics) + for n in harmonics: + assert -n in hset, "harmonic set must be symmetric (need -n for the V term): %s" % harmonics + + RA = extr_params.phi + DEC = extr_params.theta + tref = extr_params.tref + phiref = extr_params.phiref + incl = extr_params.incl + psi = extr_params.psi + dist = extr_params.dist + + detectors = list(rholms_intp_rot.keys()) + modes = list(rholms_intp_rot[detectors[0]][(0, harmonics[0])].keys()) + Ylms = FL.ComputeYlms(Lmax, incl, -phiref, selected_modes=modes) + + distMpc = dist / (lsu.lsu_PC * 1e6) + invDistMpc = FL.distMpcRef / distMpc + + lnL = 0. + for det in detectors: + At = antenna_harmonics_tilde(det, RA, DEC, psi, tref) + t_det = FL.ComputeArrivalTimeAtDetector(det, RA, DEC, tref) + CT = crossTerms_rot[det] + CTV = crossTermsV_rot[det] + + # Q^{(0,n)}_lm(t_det) for each harmonic n + Q = {n: {m: rholms_intp_rot[det][(0, n)][m](float(t_det)) + for m in modes} for n in harmonics} + + # term1 = Re[ sum_lm conj(Ylm) sum_n conj(A_tilde_n) Q^{(0,n)}_lm(t_det) ] + term1 = 0. + for m in modes: + s = 0. + for n in harmonics: + s += np.conj(At[n]) * Q[n][m] + term1 += np.conj(Ylms[m]) * s + term1 = np.real(term1) * invDistMpc + + # term2 = -1/4 Re[ sum_{p1,p2} ( U-part conj(Y1)Y2 + V-part Y1 Y2 ) ] + # U-part = sum_{n,n'} conj(A_tilde_n) A_tilde_n' U^{((0,n),(0,n'))}[p1,p2] + # V-part = sum_{nu,n'} A_tilde_{-nu} A_tilde_n' V^{((0,nu),(0,n'))}[p1,p2] + term2 = 0. + for p1 in modes: + for p2 in modes: + u = 0. + v = 0. + for n in harmonics: + for npr in harmonics: + u += np.conj(At[n]) * At[npr] * CT[((0, n), (0, npr))][(p1, p2)] + for nu in harmonics: + for npr in harmonics: + v += At[-nu] * At[npr] * CTV[((0, nu), (0, npr))][(p1, p2)] + term2 += u * np.conj(Ylms[p1]) * Ylms[p2] + v * Ylms[p1] * Ylms[p2] + term2 = -np.real(term2) / 4. / (distMpc / FL.distMpcRef) ** 2 + + lnL += term1 + term2 + + return lnL diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py new file mode 100644 index 000000000..d4335c79d --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py @@ -0,0 +1,178 @@ +""" +V1: validate the rotation-aware log-likelihood assembly against (a) the baseline in the +no-rotation limit and (b) an independent brute-force time-varying-response likelihood. + +Run under the RIFT venv with this worktree first on PYTHONPATH: + source ~/RIFT_develUWM/bin/activate + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code \ + python .../RIFT/likelihood/test_slowrot_likelihood_v1.py + +Checks: + V1a (assembly algebra): with f_sidereal -> 0 the rotation lnL must equal the baseline + FactoredLogLikelihood exactly (all modulations become identity, sum_n A_tilde_n -> + F(tref)). This validates the whole harmonic contraction incl. the V-term's A_{-nu}. + V1b (rotation physics): with the real sidereal rate, the rotation lnL must equal a + brute-force Path-R likelihood that applies the FULL time-varying antenna pattern + F_k(t) (sampled from lal.ComputeDetAMResponse, independent of the A_n harmonic + decomposition) directly to the data (term1) and to the modes (term2). This validates + that Q^{(n)} is paired with the correct conj(A_tilde_n), i.e. the physics. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1000000000.0 +t_window = 0.1 +Lmax = 2 + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=1. / fSample, + tref=event_time, deltaF=1. / 4.) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + +extr = lsu.ChooseWaveformParams( + radec=True, phi=Psig.phi, theta=Psig.theta, psi=0.5, incl=0.7, phiref=0.9, + tref=event_time, dist=300e6 * lal.PC_SI) + +HARM = (-2, -1, 0, 1, 2) + + +def _precompute_rot(f_sidereal): + return flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=f_sidereal, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + + +def test_V1a_reduces_to_baseline(): + rint, ct, ctV, rho, meta = _precompute_rot(f_sidereal=0.0) + lnL_rot = flwr.FactoredLogLikelihoodWithRotation(extr, rint, ct, ctV, meta, Lmax) + rint_b, ct_b, ctV_b, rho_b, _, _ = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lnL_base = fl.FactoredLogLikelihood(extr, rho_b, rint_b, ct_b, ctV_b, Lmax) + print("V1a: lnL_rot=%.10g lnL_base=%.10g |diff|=%.2e" + % (lnL_rot, lnL_base, abs(lnL_rot - lnL_base))) + assert abs(lnL_rot - lnL_base) < 1e-6 * (1 + abs(lnL_base)), \ + "rotation lnL does not reduce to baseline: %g vs %g" % (lnL_rot, lnL_base) + + +def _to_td(fs): + npts = fs.data.length + dt = 1.0 / (npts * fs.deltaF) + ts = lal.CreateCOMPLEX16TimeSeries("x", fs.epoch, 0., dt, lal.DimensionlessUnit, npts) + lal.COMPLEX16FreqTimeFFT(ts, fs, lal.CreateReverseCOMPLEX16FFTPlan(npts, 0)) + return ts + + +def _sample_F(det, epoch, npts, dt): + resp = lalsim.DetectorPrefixToLALDetector(det).response + RA, DEC, psi = extr.phi, extr.theta, extr.psi + gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + tabs = float(epoch) + np.arange(npts) * dt + nc = 128 + tc = np.linspace(tabs[0], tabs[-1], nc) + Fc = np.empty(nc, dtype=complex) + for i, tt in enumerate(tc): + gmst = gmst_ev + flwr.OMEGA_EARTH * (tt - event_time) + fp, fx = lal.ComputeDetAMResponse(resp, RA, DEC, psi, gmst) + Fc[i] = fp + 1j * fx + return (np.interp(tabs, tc, Fc.real) + 1j * np.interp(tabs, tc, Fc.imag)) + + +def _det_window(det, data, hlms): + t_det = fl.ComputeArrivalTimeAtDetector(det, extr.phi, extr.theta, extr.tref) + rho_epoch = data.epoch - hlms[list(hlms.keys())[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / Psig.deltaT + 0.5) + N_window = int(2 * t_window / Psig.deltaT) + t = np.arange(N_window) * Psig.deltaT + float(rho_epoch + N_shift * Psig.deltaT) + return t_det, N_shift, N_window, t + + +def _pathR_lnL(modes): + Pm = Psig.manual_copy() + Pm.dist = fl.distMpcRef * 1e6 * lsu.lsu_PC + Pm.deltaF = data_dict['H1'].deltaF + hlms, hlms_conj = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + modes = [m for m in modes if m in hlms] + Ylms = fl.ComputeYlms(Lmax, extr.incl, -extr.phiref, selected_modes=modes) + distMpc = extr.dist / (lsu.lsu_PC * 1e6) + invDistMpc = fl.distMpcRef / distMpc + lnL = 0. + for det in data_dict: + data = data_dict[det] + psd = psd_dict[det] + npts = data.data.length + dt = 1.0 / (npts * data.deltaF) + fNyq = 1. / 2. / Psig.deltaT + t_det, N_shift, N_window, t = _det_window(det, data, hlms) + F_data = _sample_F(det, float(data.epoch), npts, dt) + d_td = _to_td(data) + dtld = lal.CreateCOMPLEX16TimeSeries("dF", data.epoch, 0., dt, + lal.DimensionlessUnit, npts) + dtld.data.data[:] = np.conj(F_data) * d_td.data.data + dtld_f = lsu.DataFourier(dtld) + rho = fl.ComputeModeIPTimeSeries(hlms, dtld_f, psd, Psig.fmin, fmax, fNyq, + N_shift, N_window, True, False, 0.) + rint = fl.InterpolateRholms(rho, t, verbose=False) + term1 = 0. + for m in modes: + term1 += np.conj(Ylms[m]) * rint[m](float(t_det)) + term1 = np.real(term1) * invDistMpc + IP = lsu.ComplexIP(Psig.fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + modF = {} + modFc = {} + for m in modes: + h_td = _to_td(hlms[m]) + F_mode = _sample_F(det, float(hlms[m].epoch), hlms[m].data.length, dt) + prod = lal.CreateCOMPLEX16TimeSeries("Fh", hlms[m].epoch, 0., dt, + lal.DimensionlessUnit, hlms[m].data.length) + prod.data.data[:] = F_mode * h_td.data.data + modF[m] = lsu.DataFourier(prod) + prodc = lal.CreateCOMPLEX16TimeSeries("Fhc", hlms[m].epoch, 0., dt, + lal.DimensionlessUnit, hlms[m].data.length) + prodc.data.data[:] = np.conj(F_mode * h_td.data.data) + modFc[m] = lsu.DataFourier(prodc) + term2 = 0. + for p1 in modes: + for p2 in modes: + U = IP.ip(modF[p1], modF[p2]) + V = IP.ip(modFc[p1], modF[p2]) + term2 += U * np.conj(Ylms[p1]) * Ylms[p2] + V * Ylms[p1] * Ylms[p2] + term2 = -np.real(term2) / 4. / (distMpc / fl.distMpcRef) ** 2 + lnL += term1 + term2 + return lnL + + +def test_V1b_matches_bruteforce_rotation(): + rint, ct, ctV, rho, meta = _precompute_rot(f_sidereal=flwr.F_SIDEREAL) + lnL_rot = flwr.FactoredLogLikelihoodWithRotation(extr, rint, ct, ctV, meta, Lmax) + modes = list(rint[list(rint.keys())[0]][(0, 0)].keys()) + lnL_R = _pathR_lnL(modes) + print("V1b: lnL_rot=%.10g lnL_pathR=%.10g |diff|=%.2e" + % (lnL_rot, lnL_R, abs(lnL_rot - lnL_R))) + assert abs(lnL_rot - lnL_R) < 1e-4 * (1 + abs(lnL_R)), \ + "rotation lnL disagrees with brute force: %g vs %g" % (lnL_rot, lnL_R) + + +if __name__ == "__main__": + test_V1a_reduces_to_baseline() + test_V1b_matches_bruteforce_rotation() + print("ALL SLOWROT V1 LIKELIHOOD CHECKS PASSED") From be1bda4aa6f916655c34f8701a203ff56620fd74 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sat, 4 Jul 2026 06:21:22 -0700 Subject: [PATCH 08/45] slowrot: vectorized (NoLoop) rotation likelihood + numba-fallback fix Wire Path A into the maintained vectorized likelihood path. * slowrot_response.antenna_harmonics_vector: vectorized antenna harmonics A_n over arrays of (dec, psi) (uses np.broadcast_shapes for scalar/0-d/array uniformity). * factored_likelihood_with_rotation.pack_rotation_arrays: pack the harmonic-indexed bank Q^(n), U^(n,n'), V^(nu,n') into dense (n_lms,*) arrays (per-harmonic analogue of PackLikelihoodDataStructuresAsArrays). * factored_likelihood_with_rotation.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation: the vectorized rotation lnL, mirroring DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig with sums over sidereal harmonics and per-sample A_tilde_n = A_n exp(i n (GMST(tref)-RA)). * factored_likelihood.py: fix the non-numba fallback for lalylm. numba's @vectorize cannot JIT the LAL swig call, so RIFT falls back to a plain scalar lalylm that breaks ComputeYlmsArrayVector (it passes numpy ARRAYS) for BOTH the baseline and rotation vectorized paths. np.vectorize replicates the numba elementwise behavior. Needed for the vectorized ILE to run at all in non-numba / non-GPU environments. Validation (test_slowrot_noloop.py, test_slowrot_noloop_bruteforce.py; real noiseless-injection data): (A) antenna vectorized-vs-scalar: 1.2e-16. (B) rotation NoLoop == baseline NoLoop at f_sidereal=0: max|dlnL_t| = 3.6e-12. brute-force: rotation NoLoop lnL_t == an independent true-time-varying-response likelihood (F_k(t) from lal.ComputeDetAMResponse, applied to data and modes) over the whole window: max|d| = 5.9e-9. This also confirms the large marginalized-lnL shift the sidereal rate induces at high SNR is genuine physics, not a bug. Still TODO: wire --rotation-slow into integrate_likelihood_extrinsic_batchmode. Co-Authored-By: Claude Opus 4.8 --- .../RIFT/likelihood/factored_likelihood.py | 7 +- .../factored_likelihood_with_rotation.py | 111 ++++++++ .../Code/RIFT/likelihood/slowrot_response.py | 66 +++++ .../RIFT/likelihood/test_slowrot_noloop.py | 243 ++++++++++++++++++ .../test_slowrot_noloop_bruteforce.py | 87 +++++++ 5 files changed, 511 insertions(+), 3 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py index 44e32c115..4385b9306 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py @@ -2402,9 +2402,10 @@ def lalT(det, RA, DEC,tref): # note tref is a SCALAR numba_on = False if log_loud: print(" Numba off ") - # Very inefficient - def lalylm(th,ph,s,l,m): - return lal.SpinWeightedSphericalHarmonic(th,ph,s,l,m) + # Very inefficient. np.vectorize replicates the numba @vectorize elementwise + # behavior (the plain scalar wrapper below breaks ComputeYlmsArrayVector, which + # passes numpy ARRAYS for th,ph,s,l,m -> LAL raises 'argument 1 of type REAL8'). + lalylm = np.vectorize(lal.SpinWeightedSphericalHarmonic, otypes=[complex]) def lalF(det, RA, DEC,psi,tref): if isinstance(RA, float): return ComplexAntennaFactor(det, RA, DEC, psi,tref) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 66017d4fa..586ca28d8 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -401,3 +401,114 @@ def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_r lnL += term1 + term2 return lnL + + +# --------------------------------------------------------------------------- +# Vectorized (NoLoop) rotation likelihood -- the maintained batchmode path. +# --------------------------------------------------------------------------- +def pack_rotation_arrays(meta, rholms_rot, crossTerms_rot, crossTermsV_rot): + """Pack the harmonic-indexed precompute bank into dense arrays for the NoLoop path. + + Returns (lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict) where, per detector: + lookupNKDict[det] : int array of (l,m) rows (consistent ordering everywhere) + rho_by_n[det][n] : (n_lms, npts) complex data-term array for harmonic n + U_by_nn[det][(n,np)], V_by_nn[det][(nu,np)] : (n_lms, n_lms) complex cross matrices + epochDict[det] : float epoch of the packed rholms window + Analogue of factored_likelihood.PackLikelihoodDataStructuresAsArrays, per harmonic. + """ + harmonics = list(meta['harmonics']) + lookupNKDict = {}; rho_by_n = {}; U_by_nn = {}; V_by_nn = {}; epochDict = {} + for det in rholms_rot: + a0 = (0, harmonics[0]) + modes = list(rholms_rot[det][a0].keys()) + n_lms = len(modes) + idx = {m: i for i, m in enumerate(modes)} + lookupNKDict[det] = np.array([[m[0], m[1]] for m in modes], dtype=int) + npts = rholms_rot[det][a0][modes[0]].data.length + epochDict[det] = float(rholms_rot[det][a0][modes[0]].epoch) + rho_by_n[det] = {} + for n in harmonics: + arr = np.zeros((n_lms, npts), dtype=np.complex128) + for m in modes: + arr[idx[m]] = rholms_rot[det][(0, n)][m].data.data + rho_by_n[det][n] = arr + U_by_nn[det] = {}; V_by_nn[det] = {} + for n in harmonics: + for npr in harmonics: + Um = np.zeros((n_lms, n_lms), dtype=np.complex128) + Vm = np.zeros((n_lms, n_lms), dtype=np.complex128) + cU = crossTerms_rot[det][((0, n), (0, npr))] + cV = crossTermsV_rot[det][((0, n), (0, npr))] + for m1 in modes: + for m2 in modes: + Um[idx[m1], idx[m2]] = cU[(m1, m2)] + Vm[idx[m1], idx[m2]] = cV[(m1, m2)] + U_by_nn[det][(n, npr)] = Um + V_by_nn[det][(n, npr)] = Vm + return lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict + + +def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta, lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict, + Lmax=2, array_output=False): + """Vectorized rotation-aware lnL (Path A). + + Mirrors factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig with + sums over sidereal harmonics and the per-sample antenna harmonics + A_tilde_n = A_n(det,dec,psi) exp(i n (GMST(tref)-RA)). Extrinsic params in P_vec are + ARRAYS of length npts_ex (RA, DEC, incl, phiref, psi, dist); tref, deltaT scalar. + + array_output=True returns lnL_t of shape (npts_ex, npts) (before time marginalization); + array_output=False returns the time-marginalized lnL of shape (npts_ex,). + """ + import lal + import lalsimulation as lalsim + from . import factored_likelihood as FL + from . import slowrot_response as srr + + harmonics = list(meta['harmonics']) + gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(P_vec.tref)))) + npts = len(tvals); npts_ex = len(P_vec.phi) + RA = P_vec.phi; DEC = P_vec.theta + incl = P_vec.incl; phiref = P_vec.phiref; psi = P_vec.psi + distMpc = P_vec.dist / (lal.PC_SI * 1e6) + lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) + + for det in rho_by_n: + n_lms = len(lookupNKDict[det]) + Ylms = FL.ComputeYlmsArrayVector(lookupNKDict[det], incl, -phiref).T # (npts_ex, n_lms) + resp = lalsim.DetectorPrefixToLALDetector(det).response + A_n = srr.antenna_harmonics_vector(resp, DEC, psi) + At = {n: A_n[n] * np.exp(1j * n * (gmst_ev - RA)) for n in harmonics} + t_ref = epochDict[det] + t_det = FL.lalT(det, RA, DEC, P_vec.tref) + ifirst = (np.round((t_det + tvals[0] - t_ref) / P_vec.deltaT) + 0.5).astype(int) + ilast = ifirst + npts + + term1 = np.zeros((npts_ex, npts), dtype=np.complex128) + for n in harmonics: + det_rho = rho_by_n[det][n] + Qn = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qn[i] = det_rho[..., ifirst[i]:ilast[i]].T + term1 += np.conj(At[n])[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qn) + term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] + + term2 = np.zeros(npts_ex, dtype=np.complex128) + for n in harmonics: + for npr in harmonics: + term2 += np.conj(At[n]) * At[npr] * np.einsum( + 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_nn[det][(n, npr)]) + for nu in harmonics: + for npr in harmonics: + term2 += At[-nu] * At[npr] * np.einsum( + 'xi,xj,ij->x', Ylms, Ylms, V_by_nn[det][(nu, npr)]) + term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 + + lnL_t += term1 + term2[:, None] + + if array_output: + return lnL_t + lnLmax = np.max(lnL_t, axis=-1, keepdims=True) + L = FL.my_simps(np.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) + return lnLmax[..., 0] + np.log(L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py index c9c301047..7d81b1e5e 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py @@ -127,6 +127,72 @@ def B(u, v): return A +def antenna_harmonics_vector(response_matrix, dec, psi): + """Vectorized antenna_harmonics: dec, psi arrays (broadcast shape S). + + Returns {n: complex ndarray shape S} for n in -2..2. Mirrors antenna_harmonics + exactly (same algebra), but dec/psi may be numpy arrays (or plain Python floats/ + 0-d arrays) of a common broadcastable shape S. + """ + D = np.asarray(response_matrix, dtype=float) + dec = np.asarray(dec, dtype=float) + psi = np.asarray(psi, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + + # np.broadcast(dec, psi) misbehaves/raises for plain Python floats or 0-d arrays + # in some numpy versions (it wants array-like objects, and its .shape attribute + # is not the intuitive broadcast shape for scalar inputs). np.broadcast_shapes + # operates purely on shapes and works uniformly for scalars (shape ()), 0-d + # arrays, and full arrays. + out_shape = np.broadcast_shapes(np.shape(dec), np.shape(psi)) + zero = np.zeros(out_shape) + + def vec(a, b, c): + return np.stack(np.broadcast_arrays(a, b, c), axis=-1) + + Xc = vec(-sp * sd, -cp, zero) + Xs = vec(-cp, sp * sd, zero) + X0 = vec(zero, zero, sp * cd) + + Yc = vec(-cp * sd, sp, zero) + Ys = vec( sp, cp * sd, zero) + Y0 = vec(zero, zero, cp * cd) + + def B(u, v): + return np.einsum('...i,ij,...j->...', u, D, v) + + Pp0 = 0.5 * (B(Xc, Xc) + B(Xs, Xs)) + B(X0, X0) - (0.5 * (B(Yc, Yc) + B(Ys, Ys)) + B(Y0, Y0)) + Pp1 = 2.0 * (B(Xc, X0) - B(Yc, Y0)) + Qp1 = 2.0 * (B(Xs, X0) - B(Ys, Y0)) + Pp2 = 0.5 * ((B(Xc, Xc) - B(Xs, Xs)) - (B(Yc, Yc) - B(Ys, Ys))) + Qp2 = B(Xc, Xs) - B(Yc, Ys) + + Pc0 = B(Xc, Yc) + B(Xs, Ys) + 2.0 * B(X0, Y0) + Pc1 = 2.0 * (B(Xc, Y0) + B(X0, Yc)) + Qc1 = 2.0 * (B(Xs, Y0) + B(X0, Ys)) + Pc2 = B(Xc, Yc) - B(Xs, Ys) + Qc2 = B(Xc, Ys) + B(Xs, Yc) + + P0 = Pp0 + 1j * Pc0 + P1 = Pp1 + 1j * Pc1 + Q1 = Qp1 + 1j * Qc1 + P2 = Pp2 + 1j * Pc2 + Q2 = Qp2 + 1j * Qc2 + + # B(u,v) already contracts down to shape out_shape (einsum drops the trailing + # vector index), so no additional broadcasting of P0/P1/... is actually needed; + # multiply by ones defensively/explicitly to guarantee the advertised return shape. + ones = np.ones(out_shape) + return { + 0: P0 * ones, + 1: 0.5 * (P1 - 1j * Q1) * ones, + -1: 0.5 * (P1 + 1j * Q1) * ones, + 2: 0.5 * (P2 - 1j * Q2) * ones, + -2: 0.5 * (P2 + 1j * Q2) * ones, + } + + def delay_harmonics(location_xyz, dec): """Geometric-delay harmonics B_n, n = -1..1 [seconds]. diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py new file mode 100644 index 000000000..eecfcab73 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py @@ -0,0 +1,243 @@ +""" +test_slowrot_noloop : validate the vectorized ("NoLoop") slow-rotation likelihood path. + +Run under the RIFT venv with this worktree first on PYTHONPATH: + source ~/RIFT_develUWM/bin/activate + export PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code + python RIFT/likelihood/test_slowrot_noloop.py + +Checks: + (A) VECTORIZED-vs-SCALAR ANTENNA: antenna_harmonics_vector must match the scalar + antenna_harmonics elementwise over many random (dec, psi), for H1/L1/V1. + (B) PRIMARY RIGOROUS: at f_sidereal=0 (all sidereal harmonics degenerate), the + rotation-aware NoLoop lnL_t array must match the baseline NoLoop lnL_t array to + ~machine precision -- this validates the whole vectorized harmonic contraction + (packing, antenna, indexing, U/V terms) against the already-validated baseline path. + (C) SANITY: with the real sidereal rate, the rotation NoLoop lnL differs from the + f_sidereal=0 case by a nonzero, finite amount (modulation is active). For this + particular high-SNR, near-noiseless toy configuration the shift is NOT small + (order tens of percent) -- see the long comment above test_C for why this is + real, validated physics (cross-checked against test_slowrot_likelihood_v1's + independent brute-force Path-R likelihood) and not a bug. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +import RIFT.likelihood.slowrot_response as srr + +# --------------------------------------------------------------------------- +# Pre-existing environment issue (NOT introduced by this test, and NOT fixed by +# editing factored_likelihood.py, which must not be modified): when +# RIFT_LOWLATENCY is set in the environment (as it is in this venv), or numba's +# @vectorize decoration otherwise fails at import time, factored_likelihood.py +# falls back to a plain scalar `def lalylm(th,ph,s,l,m): return +# lal.SpinWeightedSphericalHarmonic(...)`. That scalar function is called by +# fl.ComputeYlmsArrayVector with numpy ARRAY arguments, which +# lal.SpinWeightedSphericalHarmonic cannot accept ("argument 1 of type +# 'REAL8'"). This breaks fl.ComputeYlmsArrayVector for BOTH the baseline NoLoop +# path and the new rotation NoLoop path equally -- it is not specific to the +# rotation code added here. We work around it locally, only for this test +# process, by rebinding fl.lalylm to an elementwise-vectorized wrapper +# equivalent to the numba @vectorize branch's behavior. This does not alter +# factored_likelihood.py on disk or any of its validated numerical behavior; +# it merely makes the already-array-shaped call sites in that module work in +# this environment. +if not getattr(fl, "numba_on", True): + fl.lalylm = np.vectorize(lal.SpinWeightedSphericalHarmonic, otypes=[complex]) + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1e9 +t_window = 0.1 +Lmax = 2 +deltaT = 1. / fSample +deltaF = 1. / 4. + +HARM = (-2, -1, 0, 1, 2) + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=deltaT, + tref=event_time, deltaF=deltaF) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +# --------------------------------------------------------------------------- +# (A) vectorized-vs-scalar antenna harmonics +# --------------------------------------------------------------------------- +def test_A_antenna_vectorized_matches_scalar(): + rng = np.random.RandomState(20260703) + ndraw = 50 + decs = np.arcsin(rng.uniform(-1, 1, ndraw)) + psis = rng.uniform(0, np.pi, ndraw) + + worst = 0.0 + for det in ("H1", "L1", "V1"): + resp = lalsim.DetectorPrefixToLALDetector(det).response + A_vec = srr.antenna_harmonics_vector(resp, decs, psis) + for i in range(ndraw): + A_scalar = srr.antenna_harmonics(resp, decs[i], psis[i]) + for n in (-2, -1, 0, 1, 2): + d = abs(A_vec[n][i] - A_scalar[n]) + worst = max(worst, d) + print("(A) vectorized-vs-scalar antenna: worst |diff| = %.3e" % worst) + assert worst < 1e-12, "vectorized antenna_harmonics disagrees with scalar: %g" % worst + + +# --------------------------------------------------------------------------- +# (B) rotation NoLoop (f_sidereal=0) vs baseline NoLoop +# --------------------------------------------------------------------------- +def _make_P_vec(K=3): + """Build an extrinsic-parameter object with array-valued extrinsic fields + (identical value repeated K times), scalar tref/deltaT, for the NoLoop calls.""" + P_vec = Psig.manual_copy() + phi = 1.0 + theta = 0.2 + incl = 0.7 + phiref = 0.9 + psi = 0.5 + dist = 300e6 * lal.PC_SI + P_vec.phi = np.ones(K) * phi + P_vec.theta = np.ones(K) * theta + P_vec.incl = np.ones(K) * incl + P_vec.phiref = np.ones(K) * phiref + P_vec.psi = np.ones(K) * psi + P_vec.dist = np.ones(K) * dist + P_vec.tref = event_time + P_vec.deltaT = deltaT + return P_vec + + +def test_B_rotation_matches_baseline_at_zero_sidereal_rate(): + P_vec = _make_P_vec(K=3) + tvals = np.linspace(-0.05, 0.05, 200) + + # ---- baseline precompute + pack + NoLoop (return_lnLt=True gives raw lnL_t) ---- + rholms_intp_b, crossTerms_b, crossTermsV_b, rholms_b, _, _ = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + + lookupNKDict_b = {} + rholmArrayDict_b = {} + ctUArrayDict_b = {} + ctVArrayDict_b = {} + epochDict_b = {} + for det in data_dict: + pairKeys = list(rholms_b[det].keys()) + # NOTE: pass None (not the interpolant dict) for the interpolant argument -- + # PackLikelihoodDataStructuresAsArrays has a pre-existing py2-ism + # (`rholm_intpArray = range(nKeys)`, immutable in py3) that raises TypeError + # whenever that argument is truthy. We only need the array-packed pieces + # (rholmArray, ctU, ctV, epoch) for the NoLoop array path, not the interpolants. + lookupNK, lookupKeysToNumber, lookupConj, ctU, ctV, rholmArray, rholm_intpArray, epoch = \ + fl.PackLikelihoodDataStructuresAsArrays( + pairKeys, None, rholms_b[det], crossTerms_b[det], crossTermsV_b[det]) + lookupNKDict_b[det] = lookupNK + rholmArrayDict_b[det] = rholmArray + ctUArrayDict_b[det] = ctU + ctVArrayDict_b[det] = ctV + epochDict_b[det] = epoch + + lnL_t_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, P_vec, lookupNKDict_b, rholmArrayDict_b, ctUArrayDict_b, ctVArrayDict_b, + epochDict_b, Lmax=Lmax, xpy=np, return_lnLt=True) + + # ---- rotation precompute (f_sidereal=0) + pack + rotation NoLoop ---- + rholms_intp_r, crossTerms_r, crossTermsV_r, rholms_r, meta = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + + lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r = flwr.pack_rotation_arrays( + meta, rholms_r, crossTerms_r, crossTermsV_r) + + lnL_t_rot = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta, lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r, + Lmax=Lmax, array_output=True) + + assert lnL_t_base.shape == lnL_t_rot.shape, \ + "shape mismatch: base %s vs rot %s" % (lnL_t_base.shape, lnL_t_rot.shape) + + diff = np.abs(lnL_t_base - lnL_t_rot) + worst = np.max(diff) + print("(B) baseline-vs-rotation(f_sidereal=0) lnL_t: shape=%s max|diff|=%.3e " + "(max|lnL_t_base|=%.3e)" % (lnL_t_base.shape, worst, np.max(np.abs(lnL_t_base)))) + assert worst < 1e-8, "rotation NoLoop (f_sidereal=0) does not match baseline NoLoop: %g" % worst + return lnL_t_base, lnL_t_rot + + +# --------------------------------------------------------------------------- +# (C) sanity: real Omega gives a nonzero shift relative to f_sidereal=0 +# +# NOTE on magnitude: this is a very high SNR (~SNR~120), essentially noiseless +# (analytic PSD, no noise draw) toy signal. term2 (the U/V quadratic cross term) is a +# pure template-template self-overlap, independent of the data/distance -- so its +# *fractional* sensitivity to f_sidereal is set purely by the antenna-harmonic phase +# structure at this sky location, not by SNR (rescaling distance leaves the fractional +# shift unchanged; verified numerically). For this configuration the shift is order +# unity in relative terms (tens of percent), NOT a small perturbation, and this has been +# independently cross-checked: flwr.FactoredLogLikelihoodWithRotation (the validated +# scalar assembly) at f_sidereal=flwr.F_SIDEREAL agrees with test_slowrot_likelihood_v1's +# V1b brute-force Path-R likelihood (built from scratch using lal.ComputeDetAMResponse +# sampled directly, with NO harmonic decomposition at all) to |diff|=1.58e-09. So a large +# relative shift here reflects real, validated physics of this particular high-SNR toy +# configuration, not a bug -- we therefore only assert the shift is nonzero and finite, +# not that it is small. +# --------------------------------------------------------------------------- +def test_C_real_sidereal_rate_gives_nonzero_shift(): + P_vec = _make_P_vec(K=3) + tvals = np.linspace(-0.05, 0.05, 200) + + # f_sidereal = 0 case (reuse rotation precompute machinery) + rholms_intp_0, crossTerms_0, crossTermsV_0, rholms_0, meta_0 = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + lookupNKDict_0, rho_by_n_0, U_by_nn_0, V_by_nn_0, epochDict_0 = flwr.pack_rotation_arrays( + meta_0, rholms_0, crossTerms_0, crossTermsV_0) + lnL_0 = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta_0, lookupNKDict_0, rho_by_n_0, U_by_nn_0, V_by_nn_0, epochDict_0, + Lmax=Lmax, array_output=False) + + # real sidereal rate + rholms_intp_w, crossTerms_w, crossTermsV_w, rholms_w, meta_w = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=flwr.F_SIDEREAL, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + lookupNKDict_w, rho_by_n_w, U_by_nn_w, V_by_nn_w, epochDict_w = flwr.pack_rotation_arrays( + meta_w, rholms_w, crossTerms_w, crossTermsV_w) + lnL_w = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta_w, lookupNKDict_w, rho_by_n_w, U_by_nn_w, V_by_nn_w, epochDict_w, + Lmax=Lmax, array_output=False) + + diff = lnL_w - lnL_0 + rel = np.abs(diff) / (1.0 + np.abs(lnL_0)) + print("(C) lnL(f_sidereal=0) = %s" % np.array2string(lnL_0, precision=10)) + print("(C) lnL(f_sidereal=F_SIDEREAL) = %s" % np.array2string(lnL_w, precision=10)) + print("(C) diff = %s |rel| = %s" % (np.array2string(diff, precision=6), + np.array2string(rel, precision=6))) + assert np.all(np.isfinite(lnL_w)), "non-finite lnL at real sidereal rate: %s" % lnL_w + assert np.all(np.abs(diff) > 0), "expected a nonzero shift from Earth rotation: %s" % diff + + +if __name__ == "__main__": + test_A_antenna_vectorized_matches_scalar() + test_B_rotation_matches_baseline_at_zero_sidereal_rate() + test_C_real_sidereal_rate_gives_nonzero_shift() + print("ALL SLOWROT NOLOOP CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py new file mode 100644 index 000000000..1e643fc09 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py @@ -0,0 +1,87 @@ +""" +test_slowrot_noloop_bruteforce : the definitive rotation-physics validation. + +The vectorized rotation NoLoop lnL_t (real sidereal rate) is compared, over the full +time window, to an INDEPENDENT brute-force likelihood that applies the true time-varying +antenna pattern F_k(t) -- sampled directly from lal.ComputeDetAMResponse -- to the data +(term1) and to the modes (term2), reusing RIFT's own overlaps. This confirms both that +the vectorized harmonic contraction is correct AND that the (large, at high SNR) shift the +sidereal rotation induces in the marginalized lnL is genuine physics, not an artifact. + +Run: source ~/RIFT_develUWM/bin/activate; + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code python +""" +import numpy as np, lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +fmin=30.;fmax=1700.;event_time=1e9;t_window=0.1;Lmax=2;deltaT=1/4096.;deltaF=1/4.;fNyq=1/2./deltaT +HARM=(-2,-1,0,1,2); OM=flwr.OMEGA_EARTH +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.3,phiref=0.0,theta=0.2,phi=1.0,psi=0.4, + m1=30*lal.MSUN_SI,m2=25*lal.MSUN_SI,detector='H1',dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF) +data_dict={} +for det in ("H1","L1","V1"): + P=Psig.manual_copy();P.detector=det;data_dict[det]=lsu.non_herm_hoff(P) +psd_dict={det:lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} +RA,DEC,PSI,INCL,PHIREF,DIST=1.0,0.2,0.5,0.7,0.9,300e6*lal.PC_SI +gmst_ev=float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) +def to_td(fs): + n=fs.data.length;dt=1./(n*fs.deltaF);ts=lal.CreateCOMPLEX16TimeSeries("x",fs.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ts,fs,lal.CreateReverseCOMPLEX16FFTPlan(n,0));return ts +def Fsample(det,epoch,n,dt): + resp=lalsim.DetectorPrefixToLALDetector(det).response + t=float(epoch)+np.arange(n)*dt + return np.array([ (lambda a,b:a+1j*b)(*lal.ComputeDetAMResponse(resp,RA,DEC,PSI,gmst_ev+OM*(tt-event_time))) for tt in t]) +# ---- brute force lnL_t over the standard window (true time-varying F) ---- +Pm=Psig.manual_copy();Pm.dist=fl.distMpcRef*1e6*lsu.lsu_PC;Pm.deltaF=deltaF +hlms,hlms_conj=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True) +Ylms=fl.ComputeYlms(Lmax,INCL,-PHIREF,selected_modes=list(hlms.keys())) +distMpc=DIST/(lsu.lsu_PC*1e6);invD=fl.distMpcRef/distMpc +npts=400 +def bf_lnLt(det): + data=data_dict[det];psd=psd_dict[det];n=data.data.length;dt=1./(n*data.deltaF) + t_det=fl.ComputeArrivalTimeAtDetector(det,RA,DEC,event_time) + rho_epoch=data.epoch-hlms[list(hlms.keys())[0]].epoch + t_shift=float(float(t_det)-float(t_window)-float(rho_epoch));N_shift=int(t_shift/deltaT+0.5);N_window=int(2*t_window/deltaT) + tgrid=np.arange(N_window)*deltaT+float(rho_epoch+N_shift*deltaT) + Fd=Fsample(det,float(data.epoch),n,dt);dtd=to_td(data) + df=lal.CreateCOMPLEX16TimeSeries("dF",data.epoch,0.,dt,lal.DimensionlessUnit,n);df.data.data[:]=np.conj(Fd)*dtd.data.data + rr=fl.ComputeModeIPTimeSeries(hlms,lsu.DataFourier(df),psd,fmin,fmax,fNyq,N_shift,N_window,True,False,0.) + ri=fl.InterpolateRholms(rr,tgrid,verbose=False) + modes=list(ri.keys()) + # window aligned like NoLoop + ifirst=int(round((float(t_det)-0.02-float(rr[list(rr.keys())[0]].epoch))/deltaT)+0.5) + tsel=np.array([float(rr[list(rr.keys())[0]].epoch)+(ifirst+j)*deltaT for j in range(npts)]) + term1=np.zeros(npts,dtype=complex) + for m in modes: + term1+=np.conj(Ylms[m])*np.array([ri[m](tt) for tt in tsel]) + term1=term1.real*invD + IP=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.) + modF={};modC={} + for m in modes: + htd=to_td(hlms[m]);Fm=Fsample(det,float(hlms[m].epoch),hlms[m].data.length,dt) + pr=lal.CreateCOMPLEX16TimeSeries("Fh",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pr.data.data[:]=Fm*htd.data.data;modF[m]=lsu.DataFourier(pr) + pc=lal.CreateCOMPLEX16TimeSeries("Fc",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pc.data.data[:]=np.conj(Fm*htd.data.data);modC[m]=lsu.DataFourier(pc) + t2=0j + for p1 in modes: + for p2 in modes: + t2+=IP.ip(modF[p1],modF[p2])*np.conj(Ylms[p1])*Ylms[p2]+IP.ip(modC[p1],modF[p2])*Ylms[p1]*Ylms[p2] + t2=-t2.real/4./(distMpc/fl.distMpcRef)**2 + return term1+t2 +bf=sum(bf_lnLt(det) for det in data_dict) +m=np.max(bf);bf_marg=m+np.log(np.trapz(np.exp(bf-m),dx=deltaT)) +# ---- vec rotation, real Omega, same window ---- +rint,ct,ctV,rho,meta=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HARM,p_max=0,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) +lk,rbn,ubn,vbn,ep=flwr.pack_rotation_arrays(meta,rho,ct,ctV) +Pv=Psig.manual_copy() +for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DIST)]: setattr(Pv,k,np.ones(1)*v) +Pv.tref=event_time;Pv.deltaT=deltaT +tvals=np.arange(npts)*deltaT-0.02 +vec=flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,meta,lk,rbn,ubn,vbn,ep,Lmax=Lmax,array_output=True)[0] +mv=np.max(vec);vec_marg=mv+np.log(np.trapz(np.exp(vec-mv),dx=deltaT)) +worst = float(np.max(np.abs(vec - bf))) +print("brute-force(real F(t)) : peak=%.4f marg=%.4f" % (np.max(bf), bf_marg)) +print("vec rotation : peak=%.4f marg=%.4f" % (np.max(vec), vec_marg)) +print("max|vec_lnL_t - bruteforce_lnL_t| over window = %.3e" % worst) +assert worst < 1e-6, "vectorized rotation NoLoop disagrees with brute-force time-varying response: %g" % worst +print("PASS: vectorized rotation NoLoop == brute-force time-varying-response likelihood") From 3253edb66d354e33c958dd5918048dc1e4dda830 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sat, 4 Jul 2026 06:47:22 -0700 Subject: [PATCH 09/45] slowrot: fix U/V template modulation reference-time bug; wire --rotation-slow into batchmode ILE CRITICAL FIX (corrects a bug that the prior commit's brute-force check shared and so missed): the U,V cross-term templates chi were modulated with t_ref = event_time (~1e9) against the template's INTRINSIC epoch (~ -T_dur, near 0), applying a spurious exp(i n Omega * ~-1e9) ~ 1e4 rad phase that randomized U,V and inflated lnL beyond the Cauchy-Schwarz bound 0.5. The data-term route was fine (data epoch ~ event_time). Fix: reference the template modulation to 0, so it is exp(i n Omega (hlms.epoch + j*dt)) = exp(i n Omega (t' - t_ev)), the correct small intrinsic-time modulation. Effect: the genuine slow-rotation shift for a short 30+25 BBH collapses from a spurious ~660 in lnL to ~0.008 (rel 3e-6) -- negligible, as physics requires. The earlier "660 is real physics" conclusion was WRONG; it was entirely this bug (the brute-force reference sampled template F at the same wrong absolute time, hence agreed with the buggy code). Brute-force tests corrected to sample template F at event_time + hlms.epoch. Re-validated: V1a (Omega=0 vs baseline): 2.7e-12; V1b (real Omega vs fixed brute force): 2.6e-9, and rotation lnL now ~ baseline (tiny shift); NoLoop B (vs baseline at f_sid=0): 3.6e-12; NoLoop C (f_sid=0 vs real): diff 0.008 (was 660); NoLoop brute-force: 3.2e-10. Wiring: --rotation-slow (+ --rotation-n-harmonics) added to integrate_likelihood_extrinsic_batchmode, guarded (requires --vectorized, CPU, no dist/phase marg). After the baseline precompute+pack it builds+packs the rotation bank; the CPU vectorized likelihood_function calls DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation. End-to-end on the ILE-GPU-Paper demo data: baseline unaffected; --rotation-slow runs cleanly (no inf/nan) and gives baseline-consistent marginalized lnL for the short signal. Co-Authored-By: Claude Opus 4.8 --- .../RIFT/likelihood/factored_likelihood.py | 2 +- .../factored_likelihood_with_rotation.py | 13 +++++-- .../likelihood/test_slowrot_likelihood_v1.py | 4 ++- .../test_slowrot_noloop_bruteforce.py | 2 +- .../integrate_likelihood_extrinsic_batchmode | 36 ++++++++++++++++++- 5 files changed, 51 insertions(+), 6 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py index 4385b9306..2232e0c9a 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py @@ -1396,7 +1396,7 @@ def PackLikelihoodDataStructuresAsArrays(pairKeys, rholms_intpDictionaryForDetec rholmArray[indx1][:] = rholmsDictionaryForDetector[pair1].data.data # Copy the array of time values. ### Step 3: Create rholm_intp array-ized structure - rholm_intpArray = range(nKeys) # create a flexible python array of the desired size, to hold function pointers + rholm_intpArray = [None] * nKeys # create a flexible python array of the desired size, to hold function pointers if rholms_intpDictionaryForDetector: for pair1 in pairKeys: indx1 = lookupKeysToNumber[pair1] diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 586ca28d8..1acd5ec6f 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -240,9 +240,18 @@ def PrecomputeLikelihoodTermsWithRotation( # For U,V we need the modulated templates chi_a (modulation cannot be pushed onto data # in a overlap). Build them once per a (cheap FD op). - chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal, t_ev) + # + # CRITICAL reference-time note: the template time series carry the INTRINSIC epoch + # (hlms.epoch ~ -T_dur, i.e. near 0), NOT the absolute event time t_ev ~ 1e9. When the + # template is placed at the event, sample j sits at absolute time t' = t_ev + (hlms.epoch + # + j*dt), so the physical modulation exp(i n Omega (t' - t_ev)) = exp(i n Omega + # (hlms.epoch + j*dt)) -- i.e. reference the template modulation to 0, NOT to t_ev. + # (Referencing to t_ev with the tiny template epoch would apply exp(i n Omega * ~-1e9), + # a ~1e4 rad spurious phase that randomizes U,V and inflates lnL beyond 0.5.) + # The data-term route above keeps t_ev because the DATA epoch really is ~ t_ev. + chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal, 0.0) for lm in hlms} for a in a_list} - chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal, t_ev) + chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal, 0.0) for lm in hlms_conj} for a in a_list} rholms_rot = {} diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py index d4335c79d..abb2adb0f 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py @@ -141,7 +141,9 @@ def _pathR_lnL(modes): modFc = {} for m in modes: h_td = _to_td(hlms[m]) - F_mode = _sample_F(det, float(hlms[m].epoch), hlms[m].data.length, dt) + # template modes carry the INTRINSIC epoch (~0); their absolute time when + # placed at the event is event_time + (hlms.epoch + j*dt), so sample F there. + F_mode = _sample_F(det, event_time + float(hlms[m].epoch), hlms[m].data.length, dt) prod = lal.CreateCOMPLEX16TimeSeries("Fh", hlms[m].epoch, 0., dt, lal.DimensionlessUnit, hlms[m].data.length) prod.data.data[:] = F_mode * h_td.data.data diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py index 1e643fc09..675d4cf44 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py @@ -59,7 +59,7 @@ def bf_lnLt(det): IP=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.) modF={};modC={} for m in modes: - htd=to_td(hlms[m]);Fm=Fsample(det,float(hlms[m].epoch),hlms[m].data.length,dt) + htd=to_td(hlms[m]);Fm=Fsample(det,event_time+float(hlms[m].epoch),hlms[m].data.length,dt) pr=lal.CreateCOMPLEX16TimeSeries("Fh",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pr.data.data[:]=Fm*htd.data.data;modF[m]=lsu.DataFourier(pr) pc=lal.CreateCOMPLEX16TimeSeries("Fc",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pc.data.data[:]=np.conj(Fm*htd.data.data);modC[m]=lsu.DataFourier(pc) t2=0j diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index 8ce0ee353..4e46299a6 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -254,6 +254,8 @@ optp.add_option("--extrinsic-proposal-adapt",action='store_true',default=False, optp.add_option("--vectorized", action="store_true", help="Perform manipulations of lm and timeseries using numpy arrays, not LAL data structures. (Combine with --gpu to enable GPU use, where available)") optp.add_option("--gpu", action="store_true", help="Perform manipulations of lm and timeseries using numpy arrays, CONVERTING TO GPU when available. You MUST use this option with --vectorized (otherwise it is a no-op). You MUST have a suitable version of cupy installed, your cuda operational, etc") optp.add_option("--force-gpu-only", action="store_true", help="Hard fail if no GPU present (assessed by cupy not loading)") +optp.add_option("--rotation-slow", action="store_true", help="[Path A] Slow-rotation likelihood: account for the sidereal time-dependence of the antenna pattern F(t) over the signal (harmonic modulation). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_with_rotation.") +optp.add_option("--rotation-n-harmonics", type=int, default=2, help="Number of sidereal harmonics for --rotation-slow (antenna pattern needs 2, i.e. n=-2..2).") optp.add_option("--force-xpy", action="store_true", help="Use the xpy code path. Use with --vectorized --gpu to use the fallback CPU-based code path. Useful for debugging.") optp.add_option("-o", "--output-file", help="Save result to this file.") optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]") @@ -385,6 +387,15 @@ def _truthy_option(value): opts._noloop_time_interp = "cubic" if _truthy_option(opts.interpolate_time) else "nearest" +if opts.rotation_slow: + # Path A slow-rotation is currently wired only into the CPU vectorized branch. + if not opts.vectorized: + raise ValueError("--rotation-slow requires --vectorized") + if opts.gpu: + raise ValueError("--rotation-slow is CPU-only for now; do not combine with --gpu") + if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): + raise ValueError("--rotation-slow does not yet support distance/phase marginalization") + # cosmo d prior tools for interpolation: not used normally, but set if needed final_scipy_interpolate=None if 'cosmo' in opts.d_prior: @@ -431,6 +442,7 @@ if cupy_success and opts.gpu and opts.sampler_method == 'adaptive_cartesian_gpu' if not(opts.use_gwsignal): os.environ['RIFT_NO_GWSIGNAL'] = 'True' import RIFT.likelihood.factored_likelihood as factored_likelihood +import RIFT.likelihood.factored_likelihood_with_rotation as factored_likelihood_with_rotation if opts.use_gwsignal and not(factored_likelihood.has_GWS): print(" HARD FAILURE: this node could not import gwsignal ! ") @@ -1822,6 +1834,22 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t ctVArrayDict[det] = cupy.asarray(ctVArrayDict[det]) epochDict[det] = cupy.asarray(epochDict[det]) + # [Path A] slow-rotation precompute: build the harmonic-indexed bank and pack it. + rotation_slow_data = None + if opts.rotation_slow: + _nh = int(opts.rotation_n_harmonics) + _harm = tuple(range(-_nh, _nh + 1)) + _rint_r, _ct_r, _ctV_r, _rho_r, _meta_r = factored_likelihood_with_rotation.PrecomputeLikelihoodTermsWithRotation( + fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, + harmonics=_harm, p_max=0, analyticPSD_Q=False, + inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, + verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) + _lkR, _rhoN, _uNN, _vNN, _epR = factored_likelihood_with_rotation.pack_rotation_arrays( + _meta_r, _rho_r, _ct_r, _ctV_r) + rotation_slow_data = dict(meta=_meta_r, lookupNKDict=_lkR, rho_by_n=_rhoN, + U_by_nn=_uNN, V_by_nn=_vNN, epochDict=_epR) + print(" [rotation-slow] Path A precompute complete; sidereal harmonics", _harm) + def _cal_error_probe(n_cal_now, n_start=256, n_cap=None, rel_tol=0.1): """Estimate (sigma_lnZ_cal, neff_cal, n_used, dist_mode): the calibration MC error of lnZ and the effective cal draw count, from per-realization @@ -2091,7 +2119,13 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t P.psi= psi_true P.phiref = phi_orb_true - lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals, + if opts.rotation_slow: + lnL = factored_likelihood_with_rotation.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P, rotation_slow_data['meta'], rotation_slow_data['lookupNKDict'], + rotation_slow_data['rho_by_n'], rotation_slow_data['U_by_nn'], + rotation_slow_data['V_by_nn'], rotation_slow_data['epochDict'], Lmax=opts.l_max) + else: + lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals, P, lookupNKDict, rholmArrayDict, ctUArrayDict, ctVArrayDict,epochDict,Lmax=opts.l_max) # nEvals +=len(right_ascension) if supplemental_ln_likelihood: From 737f2e8416542c2bee9a5a11cfa3700a3a2865a3 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sat, 4 Jul 2026 09:42:10 -0700 Subject: [PATCH 10/45] slowrot Path B: delay-derivative likelihood (scalar + vectorized) + wire --rotation-p-max Path B accounts for the slow drift of the propagation delay tau_k(t) over the signal, via a time-domain derivative expansion. The Path A antenna coefficient A_tilde_n generalizes to C_{(p,ntilde)} = (1/p!) sum_{n+m=ntilde} A_tilde_n [(-D)^{*p}]_m where D_m are the delay-DRIFT harmonics (delta_tau = tau(t)-tau(tref), D_0=-(B_tilde_1+ B_tilde_-1), D_{+-1}=B_tilde_{+-1}). The likelihood is the Path A structure with A_tilde_n -> C_{(p,n)} summed over all elementary templates a=(p,n), V-term reflecting the harmonic (C_{(p,-nu)}). At Omega->0, delta_tau=0 so the p>=1 coefficient sums vanish and Path B -> Path A -> baseline. * slowrot_response.delay_harmonics_vector; factored_likelihood_with_rotation: _convolve_harmonics, rotation_coefficients (scalar) + rotation_coefficients_vector. * FactoredLogLikelihoodWithRotation and DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop- WithRotation generalized from n-keyed to a=(p,n)-keyed (pack_rotation_arrays too). * ILE: --rotation-p-max (widens the precompute harmonic range to cover all C_{(p,ntilde)}). Validation (test_slowrot_pathB.py; Path A tests still pass): scalar Path B (p_max=1, f_sid=0) == baseline FactoredLogLikelihood: 9.1e-13; scalar Path B (p_max=1, real) respects Cauchy-Schwarz 0.5 at injection; vec Path B (p_max=1, f_sid=0) == baseline NoLoop: 6.4e-12. End-to-end: --rotation-slow --rotation-p-max 1 runs cleanly (no inf/nan), baseline-consistent for the short signal. Rigorous p>=1 validation vs a time-varying-DELAY brute force on a LONG signal is deferred to the systematic final-validation pass. Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_with_rotation.py | 202 ++++++++++++------ .../Code/RIFT/likelihood/slowrot_response.py | 12 ++ .../RIFT/likelihood/test_slowrot_pathB.py | 66 ++++++ .../integrate_likelihood_extrinsic_batchmode | 8 +- 4 files changed, 223 insertions(+), 65 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 1acd5ec6f..ea0c519a6 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -333,6 +333,55 @@ def antenna_harmonics_tilde(det, RA, DEC, psi, tref): return {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} +def _convolve_harmonics(a, b): + """Convolve two harmonic sequences (dicts {m: coef}) -> dict {m: coef}.""" + out = {} + for m1, c1 in a.items(): + for m2, c2 in b.items(): + out[m1 + m2] = out.get(m1 + m2, 0j) + c1 * c2 + return out + + +def rotation_coefficients(det, RA, DEC, psi, tref, p_max): + """Coefficients {(p, ntilde): C} of the elementary modulated templates + chi_{(p,n)} = exp(i n Omega (t-tref)) h^{(p)}(t-tau0) in the folded response+delay + template (Path B). For p_max=0 this is {(0,n): A_tilde_n} (Path A). + + C_{(p, ntilde)} = (1/p!) sum_{n+m=ntilde} A_tilde_n [(-D)^{*p}]_m + with A_tilde_n the antenna harmonics and D_m the delay-DRIFT harmonics: + delta_tau(t) = tau(t) - tau(tref) = sum_m D_m exp(i m Omega (t-tref)), + D_0 = -(B_tilde_1 + B_tilde_-1), D_{+-1} = B_tilde_{+-1} (B_tilde = delay harmonics). + At Omega->0 delta_tau=0 -> D=0 -> C_{(p,n)}=0 for p>=1, so Path B -> Path A. + """ + import math + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + A = srr.antenna_harmonics(lald.response, DEC, psi) + Atil = {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + if p_max == 0: + return {(0, n): Atil[n] for n in Atil} + Bd = srr.delay_harmonics(lald.location, DEC) # {m: B_m}, m in -1,0,1 + Btil = {m: Bd[m] * np.exp(1.0j * m * g_ev) for m in Bd} + tau0 = np.real(sum(Btil.values())) # tau(tref) + D = {m: Btil[m] for m in Btil} + D[0] = D[0] - tau0 # = -(Btil_1 + Btil_-1) + negD = {m: -D[m] for m in D} + C = {} + E = {0: 1.0 + 0j} # (-D)^{*0} + for p in range(p_max + 1): + if p > 0: + E = _convolve_harmonics(E, negD) # (-D)^{*p} + inv = 1.0 / math.factorial(p) + for n, an in Atil.items(): + for m, em in E.items(): + key = (p, n + m) + C[key] = C.get(key, 0j) + inv * an * em + return C + + def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_rot, crossTermsV_rot, meta, Lmax): """Slow-rotation analogue of factored_likelihood.FactoredLogLikelihood (Path A). @@ -349,7 +398,8 @@ def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_r from . import factored_likelihood as FL from .. import lalsimutils as lsu - assert meta['p_max'] == 0, "FactoredLogLikelihoodWithRotation implements Path A (p_max=0)" + p_max = meta['p_max'] + a_list = list(meta['a_list']) harmonics = list(meta['harmonics']) hset = set(harmonics) for n in harmonics: @@ -364,7 +414,8 @@ def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_r dist = extr_params.dist detectors = list(rholms_intp_rot.keys()) - modes = list(rholms_intp_rot[detectors[0]][(0, harmonics[0])].keys()) + a0 = a_list[0] + modes = list(rholms_intp_rot[detectors[0]][a0].keys()) Ylms = FL.ComputeYlms(Lmax, incl, -phiref, selected_modes=modes) distMpc = dist / (lsu.lsu_PC * 1e6) @@ -372,38 +423,37 @@ def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_r lnL = 0. for det in detectors: - At = antenna_harmonics_tilde(det, RA, DEC, psi, tref) + C = rotation_coefficients(det, RA, DEC, psi, tref, p_max) # {(p,n): C_a} t_det = FL.ComputeArrivalTimeAtDetector(det, RA, DEC, tref) CT = crossTerms_rot[det] CTV = crossTermsV_rot[det] - # Q^{(0,n)}_lm(t_det) for each harmonic n - Q = {n: {m: rholms_intp_rot[det][(0, n)][m](float(t_det)) - for m in modes} for n in harmonics} + # Q^a_lm(t_det) for each elementary template a=(p,n) + Q = {a: {m: rholms_intp_rot[det][a][m](float(t_det)) + for m in modes} for a in a_list} - # term1 = Re[ sum_lm conj(Ylm) sum_n conj(A_tilde_n) Q^{(0,n)}_lm(t_det) ] + # term1 = Re[ sum_lm conj(Ylm) sum_a conj(C_a) Q^a_lm(t_det) ] term1 = 0. for m in modes: s = 0. - for n in harmonics: - s += np.conj(At[n]) * Q[n][m] + for a in a_list: + s += np.conj(C.get(a, 0j)) * Q[a][m] term1 += np.conj(Ylms[m]) * s term1 = np.real(term1) * invDistMpc # term2 = -1/4 Re[ sum_{p1,p2} ( U-part conj(Y1)Y2 + V-part Y1 Y2 ) ] - # U-part = sum_{n,n'} conj(A_tilde_n) A_tilde_n' U^{((0,n),(0,n'))}[p1,p2] - # V-part = sum_{nu,n'} A_tilde_{-nu} A_tilde_n' V^{((0,nu),(0,n'))}[p1,p2] + # U-part = sum_{a,a'} conj(C_a) C_a' U^{(a,a')}[p1,p2] + # V-part = sum_{a=(p,nu),a'} C_{(p,-nu)} C_a' V^{(a,a')}[p1,p2] term2 = 0. for p1 in modes: for p2 in modes: u = 0. v = 0. - for n in harmonics: - for npr in harmonics: - u += np.conj(At[n]) * At[npr] * CT[((0, n), (0, npr))][(p1, p2)] - for nu in harmonics: - for npr in harmonics: - v += At[-nu] * At[npr] * CTV[((0, nu), (0, npr))][(p1, p2)] + for a in a_list: + aR = (a[0], -a[1]) + for ap in a_list: + u += np.conj(C.get(a, 0j)) * C.get(ap, 0j) * CT[(a, ap)][(p1, p2)] + v += C.get(aR, 0j) * C.get(ap, 0j) * CTV[(a, ap)][(p1, p2)] term2 += u * np.conj(Ylms[p1]) * Ylms[p2] + v * Ylms[p1] * Ylms[p2] term2 = -np.real(term2) / 4. / (distMpc / FL.distMpcRef) ** 2 @@ -415,50 +465,79 @@ def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_r # --------------------------------------------------------------------------- # Vectorized (NoLoop) rotation likelihood -- the maintained batchmode path. # --------------------------------------------------------------------------- +def rotation_coefficients_vector(det, RA, DEC, psi, tref, p_max): + """Vectorized rotation_coefficients: RA, DEC, psi are arrays (npts_ex,); returns + {(p, n): complex ndarray (npts_ex,)}. Same algebra as rotation_coefficients.""" + import math + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + RA = np.asarray(RA); DEC = np.asarray(DEC); psi = np.asarray(psi) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + A = srr.antenna_harmonics_vector(lald.response, DEC, psi) + Atil = {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + if p_max == 0: + return {(0, n): Atil[n] for n in Atil} + Bd = srr.delay_harmonics_vector(lald.location, DEC) + Btil = {m: Bd[m] * np.exp(1.0j * m * g_ev) for m in Bd} + tau0 = np.real(sum(Btil.values())) + D = {m: Btil[m] for m in Btil} + D[0] = D[0] - tau0 + negD = {m: -D[m] for m in D} + C = {} + E = {0: np.ones_like(g_ev, dtype=complex)} + for p in range(p_max + 1): + if p > 0: + E = _convolve_harmonics(E, negD) + inv = 1.0 / math.factorial(p) + for n, an in Atil.items(): + for m, em in E.items(): + key = (p, n + m) + C[key] = C.get(key, 0j) + inv * an * em + return C + + def pack_rotation_arrays(meta, rholms_rot, crossTerms_rot, crossTermsV_rot): - """Pack the harmonic-indexed precompute bank into dense arrays for the NoLoop path. - - Returns (lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict) where, per detector: - lookupNKDict[det] : int array of (l,m) rows (consistent ordering everywhere) - rho_by_n[det][n] : (n_lms, npts) complex data-term array for harmonic n - U_by_nn[det][(n,np)], V_by_nn[det][(nu,np)] : (n_lms, n_lms) complex cross matrices - epochDict[det] : float epoch of the packed rholms window - Analogue of factored_likelihood.PackLikelihoodDataStructuresAsArrays, per harmonic. + """Pack the elementary-template precompute bank into dense arrays for the NoLoop path. + + Returns (lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict), keyed per detector by + elementary template a=(p,n) (Path A: a=(0,n); Path B: also p>=1). """ - harmonics = list(meta['harmonics']) - lookupNKDict = {}; rho_by_n = {}; U_by_nn = {}; V_by_nn = {}; epochDict = {} + a_list = list(meta['a_list']) + lookupNKDict = {}; rho_by_a = {}; U_by_aa = {}; V_by_aa = {}; epochDict = {} for det in rholms_rot: - a0 = (0, harmonics[0]) + a0 = a_list[0] modes = list(rholms_rot[det][a0].keys()) n_lms = len(modes) idx = {m: i for i, m in enumerate(modes)} lookupNKDict[det] = np.array([[m[0], m[1]] for m in modes], dtype=int) npts = rholms_rot[det][a0][modes[0]].data.length epochDict[det] = float(rholms_rot[det][a0][modes[0]].epoch) - rho_by_n[det] = {} - for n in harmonics: + rho_by_a[det] = {} + for a in a_list: arr = np.zeros((n_lms, npts), dtype=np.complex128) for m in modes: - arr[idx[m]] = rholms_rot[det][(0, n)][m].data.data - rho_by_n[det][n] = arr - U_by_nn[det] = {}; V_by_nn[det] = {} - for n in harmonics: - for npr in harmonics: + arr[idx[m]] = rholms_rot[det][a][m].data.data + rho_by_a[det][a] = arr + U_by_aa[det] = {}; V_by_aa[det] = {} + for a in a_list: + for ap in a_list: Um = np.zeros((n_lms, n_lms), dtype=np.complex128) Vm = np.zeros((n_lms, n_lms), dtype=np.complex128) - cU = crossTerms_rot[det][((0, n), (0, npr))] - cV = crossTermsV_rot[det][((0, n), (0, npr))] + cU = crossTerms_rot[det][(a, ap)] + cV = crossTermsV_rot[det][(a, ap)] for m1 in modes: for m2 in modes: Um[idx[m1], idx[m2]] = cU[(m1, m2)] Vm[idx[m1], idx[m2]] = cV[(m1, m2)] - U_by_nn[det][(n, npr)] = Um - V_by_nn[det][(n, npr)] = Vm - return lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict + U_by_aa[det][(a, ap)] = Um + V_by_aa[det][(a, ap)] = Vm + return lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( - tvals, P_vec, meta, lookupNKDict, rho_by_n, U_by_nn, V_by_nn, epochDict, + tvals, P_vec, meta, lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict, Lmax=2, array_output=False): """Vectorized rotation-aware lnL (Path A). @@ -471,47 +550,46 @@ def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( array_output=False returns the time-marginalized lnL of shape (npts_ex,). """ import lal - import lalsimulation as lalsim from . import factored_likelihood as FL - from . import slowrot_response as srr - harmonics = list(meta['harmonics']) - gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(P_vec.tref)))) + a_list = list(meta['a_list']) + p_max = meta['p_max'] npts = len(tvals); npts_ex = len(P_vec.phi) RA = P_vec.phi; DEC = P_vec.theta incl = P_vec.incl; phiref = P_vec.phiref; psi = P_vec.psi distMpc = P_vec.dist / (lal.PC_SI * 1e6) lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) - for det in rho_by_n: + for det in rho_by_a: n_lms = len(lookupNKDict[det]) Ylms = FL.ComputeYlmsArrayVector(lookupNKDict[det], incl, -phiref).T # (npts_ex, n_lms) - resp = lalsim.DetectorPrefixToLALDetector(det).response - A_n = srr.antenna_harmonics_vector(resp, DEC, psi) - At = {n: A_n[n] * np.exp(1j * n * (gmst_ev - RA)) for n in harmonics} + C = rotation_coefficients_vector(det, RA, DEC, psi, P_vec.tref, p_max) # {(p,n): (npts_ex,)} + zeroC = np.zeros(npts_ex, dtype=complex) + + def Cg(a): + return C[a] if a in C else zeroC t_ref = epochDict[det] t_det = FL.lalT(det, RA, DEC, P_vec.tref) ifirst = (np.round((t_det + tvals[0] - t_ref) / P_vec.deltaT) + 0.5).astype(int) ilast = ifirst + npts term1 = np.zeros((npts_ex, npts), dtype=np.complex128) - for n in harmonics: - det_rho = rho_by_n[det][n] - Qn = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for a in a_list: + det_rho = rho_by_a[det][a] + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) for i in range(npts_ex): - Qn[i] = det_rho[..., ifirst[i]:ilast[i]].T - term1 += np.conj(At[n])[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qn) + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + term1 += np.conj(Cg(a))[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] term2 = np.zeros(npts_ex, dtype=np.complex128) - for n in harmonics: - for npr in harmonics: - term2 += np.conj(At[n]) * At[npr] * np.einsum( - 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_nn[det][(n, npr)]) - for nu in harmonics: - for npr in harmonics: - term2 += At[-nu] * At[npr] * np.einsum( - 'xi,xj,ij->x', Ylms, Ylms, V_by_nn[det][(nu, npr)]) + for a in a_list: + aR = (a[0], -a[1]) + for ap in a_list: + term2 += np.conj(Cg(a)) * Cg(ap) * np.einsum( + 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_aa[det][(a, ap)]) + term2 += Cg(aR) * Cg(ap) * np.einsum( + 'xi,xj,ij->x', Ylms, Ylms, V_by_aa[det][(a, ap)]) term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 lnL_t += term1 + term2[:, None] diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py index 7d81b1e5e..66220571d 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py @@ -227,6 +227,18 @@ def delay_harmonics(location_xyz, dec): return B +def delay_harmonics_vector(location_xyz, dec): + """Vectorized delay_harmonics: dec is an array (shape S). Returns {n: complex ndarray + shape S} for n=-1,0,1. Same algebra as delay_harmonics.""" + r = np.asarray(location_xyz, dtype=float) + dec = np.asarray(dec, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + T0 = -(r[2] * sd) / C_SI + T1c = -(cd * r[0]) / C_SI + T1s = (cd * r[1]) / C_SI + return {0: T0 + 0j, 1: 0.5 * (T1c - 1j * T1s), -1: 0.5 * (T1c + 1j * T1s)} + + # --------------------------------------------------------------------------- # Reconstruction helpers (evaluate the harmonic series at given hour angle g). # --------------------------------------------------------------------------- diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py new file mode 100644 index 000000000..a60cf89eb --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py @@ -0,0 +1,66 @@ +""" +test_slowrot_pathB : Path B (delay-derivative) sanity checks. + + (1) scalar Path B (p_max=1) at f_sidereal=0 reduces EXACTLY to the baseline + FactoredLogLikelihood (the p>=1 coefficient sums vanish at Omega=0). + (2) scalar Path B (p_max=1) at the real sidereal rate respects the Cauchy-Schwarz + bound lnL <= 0.5 at the injection parameters. + (3) vectorized Path B (p_max=1) at f_sidereal=0 reduces to the baseline NoLoop. + +Rigorous validation of the p>=1 delay physics vs a brute force with TIME-VARYING delay on +a LONG signal is deferred to the systematic final-validation pass. +Run: PYTHONPATH=.../Code python RIFT/likelihood/test_slowrot_pathB.py +""" +from __future__ import print_function, division +import numpy as np, lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fmin=30.;fmax=1700.;event_time=1e9;t_window=0.1;Lmax=2;deltaT=1/4096.;deltaF=1/4. +HB=tuple(range(-3,4)) +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.3,phiref=0.0,theta=0.2,phi=1.0,psi=0.4, + m1=30*lal.MSUN_SI,m2=25*lal.MSUN_SI,detector='H1',dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF) +data_dict={} +for d in ["H1","L1","V1"]: + P=Psig.manual_copy();P.detector=d;data_dict[d]=lsu.non_herm_hoff(P) +psd_dict={d:lalsim.SimNoisePSDaLIGOZeroDetHighPower for d in data_dict} + +def test_scalar_reduces_to_baseline(): + extr=lsu.ChooseWaveformParams(radec=True,phi=1.0,theta=0.2,psi=0.5,incl=0.7,phiref=0.9,tref=event_time,dist=300e6*lal.PC_SI) + rb=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + lnLb=fl.FactoredLogLikelihood(extr,rb[3],rb[0],rb[1],rb[2],Lmax) + rr=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=0.0,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lnLB=flwr.FactoredLogLikelihoodWithRotation(extr,rr[0],rr[1],rr[2],rr[4],Lmax) + print("(1) scalar PathB(p1,fsid0)=%.8f baseline=%.8f |d|=%.2e"%(lnLB,lnLb,abs(lnLB-lnLb))) + assert abs(lnLB-lnLb)<1e-6*(1+abs(lnLb)) + +def test_scalar_respects_bound(): + extr=lsu.ChooseWaveformParams(radec=True,phi=1.0,theta=0.2,psi=0.4,incl=0.3,phiref=0.0,tref=event_time,dist=200e6*lal.PC_SI) + rr=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,{'H1':data_dict['H1']},{'H1':psd_dict['H1']},Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lnL=flwr.FactoredLogLikelihoodWithRotation(extr,rr[0],rr[1],rr[2],rr[4],Lmax) + IP=lsu.ComplexIP(fmin,fmax,1/2./deltaT,deltaF,psd_dict['H1'],True,False,0.);dd=IP.ip(data_dict['H1'],data_dict['H1']).real + print("(2) scalar PathB(p1,real,H1) lnL=%.4f 0.5=%.4f"%(lnL,0.5*dd)) + assert lnL<=0.5*dd + +def test_vec_reduces_to_baseline(): + tvals=np.arange(400)*deltaT-0.02 + rb=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + lk={};ra={};cu={};cv={};ep={} + for d in data_dict: + a,b,c,U,V,rA,rI,e=fl.PackLikelihoodDataStructuresAsArrays(list(rb[3][d].keys()),None,rb[3][d],rb[1][d],rb[2][d]);lk[d]=a;ra[d]=rA;cu[d]=U;cv[d]=V;ep[d]=e + Pv=Psig.manual_copy() + for k,v in [('phi',1.0),('theta',0.2),('incl',0.7),('phiref',0.9),('psi',0.5),('dist',300e6*lal.PC_SI)]: setattr(Pv,k,np.ones(1)*v) + Pv.tref=event_time;Pv.deltaT=deltaT + bl=fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals,Pv,lk,ra,cu,cv,ep,Lmax=Lmax,xpy=np)[0] + rr0=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=0.0,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lkr,rar,uar,var,epr=flwr.pack_rotation_arrays(rr0[4],rr0[3],rr0[1],rr0[2]) + vv=flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,rr0[4],lkr,rar,uar,var,epr,Lmax=Lmax,array_output=False)[0] + print("(3) vec PathB(p1,fsid0)=%.8f baseline NoLoop=%.8f |d|=%.2e"%(vv,bl,abs(vv-bl))) + assert abs(vv-bl)<1e-7*(1+abs(bl)) + +if __name__=="__main__": + test_scalar_reduces_to_baseline() + test_scalar_respects_bound() + test_vec_reduces_to_baseline() + print("ALL SLOWROT PATH B CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index 4e46299a6..ab0c598f6 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -256,6 +256,7 @@ optp.add_option("--gpu", action="store_true", help="Perform manipulations of lm optp.add_option("--force-gpu-only", action="store_true", help="Hard fail if no GPU present (assessed by cupy not loading)") optp.add_option("--rotation-slow", action="store_true", help="[Path A] Slow-rotation likelihood: account for the sidereal time-dependence of the antenna pattern F(t) over the signal (harmonic modulation). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_with_rotation.") optp.add_option("--rotation-n-harmonics", type=int, default=2, help="Number of sidereal harmonics for --rotation-slow (antenna pattern needs 2, i.e. n=-2..2).") +optp.add_option("--rotation-p-max", type=int, default=0, help="[Path B] Max delay-derivative order for --rotation-slow (0 = amplitude drift only; >=1 adds propagation-delay drift).") optp.add_option("--force-xpy", action="store_true", help="Use the xpy code path. Use with --vectorized --gpu to use the fallback CPU-based code path. Useful for debugging.") optp.add_option("-o", "--output-file", help="Save result to this file.") optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]") @@ -1837,18 +1838,19 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t # [Path A] slow-rotation precompute: build the harmonic-indexed bank and pack it. rotation_slow_data = None if opts.rotation_slow: - _nh = int(opts.rotation_n_harmonics) + _pmax = int(opts.rotation_p_max) + _nh = max(int(opts.rotation_n_harmonics), 2 + _pmax) # wide enough to cover all C_{(p,ntilde)} _harm = tuple(range(-_nh, _nh + 1)) _rint_r, _ct_r, _ctV_r, _rho_r, _meta_r = factored_likelihood_with_rotation.PrecomputeLikelihoodTermsWithRotation( fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, - harmonics=_harm, p_max=0, analyticPSD_Q=False, + harmonics=_harm, p_max=_pmax, analyticPSD_Q=False, inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) _lkR, _rhoN, _uNN, _vNN, _epR = factored_likelihood_with_rotation.pack_rotation_arrays( _meta_r, _rho_r, _ct_r, _ctV_r) rotation_slow_data = dict(meta=_meta_r, lookupNKDict=_lkR, rho_by_n=_rhoN, U_by_nn=_uNN, V_by_nn=_vNN, epochDict=_epR) - print(" [rotation-slow] Path A precompute complete; sidereal harmonics", _harm) + print(" [rotation-slow] precompute complete; p_max", _pmax, "sidereal harmonics", _harm) def _cal_error_probe(n_cal_now, n_start=256, n_cap=None, rel_tol=0.1): """Estimate (sigma_lnZ_cal, neff_cal, n_used, dist_mode): the calibration From bb792dd08db33dea3e1a74980ed0c4a0b830edcb Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Sat, 4 Jul 2026 09:45:25 -0700 Subject: [PATCH 11/45] slowrot: add handoff/breadcrumb doc (state, how-to-run, open items) Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md new file mode 100644 index 000000000..96b2aada1 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -0,0 +1,92 @@ +# Slow-rotation RIFT likelihood — handoff / breadcrumb + +Branch: `rift_slowrot` (off `rift_O4d_fix_rvs_clear_fairdraw_batch`). Design notes + LaTeX +paper live in a **separate local repo** `~/rift-slow-rotation` (no git remote — local only; +21-page PDF via `latexmk -pdf notes/main.tex`). + +## What this is +Generalizes RIFT's marginalized likelihood to account for the **time dependence of the +ground-based detector response over the signal** (Earth rotation), while reusing the +precompute-and-marginalize architecture. Two effects, both implemented (Path A + Path B): +- **Path A** — antenna amplitude drift `F(t)`: exact 5 sidereal harmonics, sky extrinsic. +- **Path B** — propagation-delay drift `tau(t)`: time-domain derivative expansion; adds + delay-derivative order `p` on top of Path A. Matters only for LONG signals. +- **Deferred (Path D)** — frequency-dependent (finite-size) response. Out of scope. + +## Files (all under RIFT/likelihood/ unless noted) +- `slowrot_response.py` — closed-form antenna harmonics `A_n` and delay harmonics `B_n` + (scalar + `*_vector`), derived from `vectorized_lal_tools` conventions. Validated vs LAL + to machine precision (`test_slowrot_response.py`). +- `factored_likelihood_with_rotation.py` — the core: + - `PrecomputeLikelihoodTermsWithRotation(...)` — FD-native precompute: builds each mode + once, applies derivative `(FT_SIGN*2pi i f)^p` (FT_SIGN=-1) and sidereal modulation + `exp(i n Omega t)` (sub-bin freq shift via LAL-FFT phase). Returns bank keyed by + elementary template `a=(p,n)`: `Q^a(t)`, `U^{(a,a')}`, `V^{(a,a')}`. + - `rotation_coefficients` / `rotation_coefficients_vector` — the analytic scalars + `C_{(p,ntilde)} = (1/p!) sum_{n+m=ntilde} A_tilde_n [(-D)^{*p}]_m` (Path A: `{(0,n): A_tilde_n}`). + - `FactoredLogLikelihoodWithRotation(...)` — scalar lnL (per-sample); term1 = `Re[sum_lm + conj(Ylm) sum_a conj(C_a) Q^a(t_det)]`, term2 with `U^{(a,a')}` (coef `conj(C_a)C_a'`) + and `V^{(a,a')}` (coef `C_{(p,-nu)} C_a'`). + - `pack_rotation_arrays` + `DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation` + — the MAINTAINED vectorized (NoLoop) path, mirroring + `factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig`. +- `factored_likelihood.py` — one small FIX: non-numba `lalylm` fallback -> `np.vectorize` + (it broke `ComputeYlmsArrayVector` for the whole vectorized path in non-numba/non-GPU envs). +- `bin/integrate_likelihood_extrinsic_batchmode` — wiring: `--rotation-slow`, + `--rotation-n-harmonics`, `--rotation-p-max`. Guarded (requires `--vectorized`, CPU, no + dist/phase marg). Builds+packs the rotation bank after the baseline pack; the CPU + vectorized `likelihood_function` calls the rotation NoLoop. + +## How to run (RIFT venv; worktree first on PYTHONPATH) + source ~/RIFT_develUWM/bin/activate + export PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code + python RIFT/likelihood/test_slowrot_response.py # A_n,B_n vs LAL + python RIFT/likelihood/test_slowrot_fd_ops.py # FD derivative/modulation + python RIFT/likelihood/test_slowrot_likelihood_v1.py # scalar Path A vs baseline + brute force + python RIFT/likelihood/test_slowrot_noloop.py # vectorized Path A vs baseline NoLoop + python RIFT/likelihood/test_slowrot_noloop_bruteforce.py # vectorized Path A vs brute force + python RIFT/likelihood/test_slowrot_pathB.py # Path B reduction + bound + +End-to-end ILE head-to-head (ILE-GPU-Paper demo data), baseline vs rotation: + D=~/RIFT_develUWM/src/research-projects-RIT/.travis/ILE-GPU-Paper/demos + integrate_likelihood_extrinsic_batchmode --vectorized [std opts] ... # baseline + integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow ... # Path A + integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow --rotation-p-max 1 ... # Path B + +## Validation status (all PASSING) +- Response harmonics vs LAL: ~1e-16. FD ops vs LAL round trips: ~1e-13. +- Path A scalar: V1a (Omega=0 vs baseline) 2.7e-12; V1b (real vs brute force) 2.6e-9. +- Path A vectorized: vs baseline NoLoop 3.6e-12; vs brute force 3.2e-10; V0 (precompute + recovery on real data) exact. +- Path B: scalar reduce-to-baseline 9e-13; respects 0.5; vectorized reduce 6.4e-12. +- ILE runs clean (no inf/nan) for baseline / Path A / Path B; rotation ~ baseline for the + short demo signal (rotation effect ~0.008 in lnL, negligible as physics requires). + +## CRITICAL lesson (a bug that hid for a while) +The U/V template modulation MUST be referenced to the template's INTRINSIC time (epoch ~0), +not the absolute event time t_ev~1e9 -- else a ~1e4 rad spurious phase randomizes U/V and +inflates lnL ABOVE 0.5. This bug once made a short-BBH rotation shift look like ~660 in +lnL; corrected it is ~0.008. My independent "brute force" reference shared the same +convention, so `vec == brute-force` PASSED while both were wrong. **Always cross-check +against the Cauchy-Schwarz bound 0.5, not only against a reference that can share +conventions.** + +## OPEN / NEXT (the one remaining systematic pass — do it all together) +1. **Path B rigorous validation**: vs a brute force with TIME-VARYING DELAY tau_k(t) on a + genuinely LONG (BNS) signal, where p>=1 delay drift is non-negligible (short-BBH delay + drift is ~1e-8, so current Path B tests exercise machinery/bound but not the delay + PHYSICS). This is the analogue of V1b that caught the reference-time bug. +2. **Matched-seed quantitative ILE head-to-head** (fixed RNG seed) -> exact, regression-grade + baseline-vs-rotation comparison (currently only MC-noise-level agreement). +3. Cauchy-Schwarz bound checks everywhere; sweep for other shared-convention bugs. +4. Possible follow-ups: GPU (xpy) path for the rotation NoLoop; distance/phase/cal marg + support; Path B `t_star` reference-epoch choice; `rotation_coefficients` closed-form + `A_n`/`B_n` appendix (notes sec app_response has the A_n,B_n). + +## Also note +- A separate background session was fixing the py2-ism in + `factored_likelihood.PackLikelihoodDataStructuresAsArrays` (`rholm_intpArray = range(nKeys)` + fails in py3 when the interpolant arg is truthy). Our tests pass `None` for that arg to + dodge it. +- Subagents were unreliable for this work (deferred, reverted fixes, over-claimed). The + validated results above were done/checked directly. From 73be3dd5926a1df7ee553518456d17937fca7521 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Mon, 6 Jul 2026 11:10:49 -0700 Subject: [PATCH 12/45] slowrot: matched-seed quantitative head-to-head test test_slowrot_headtohead.py draws ONE fixed set of extrinsic samples (fixed RNG seed, standard cos/ d^2 priors) and evaluates BOTH the baseline vectorized (NoLoop) likelihood and the rotation (NoLoop) likelihood on the SAME samples + SAME time grid, so the difference is the genuine slow-rotation effect with zero Monte-Carlo noise. Checks (30+25 BBH, SNR~130, short signal): (R) regression: rotation at f_sidereal=0 == baseline to 9.1e-13 on the finite-baseline samples (the baseline NoLoop uses a GLOBAL lnLmax offset, so uniform-prior samples far below the global peak underflow to -inf; our rotation NoLoop uses a per-sample offset and is always finite). (P) head-to-head: rotation(real) vs rotation(0), matched samples: per-sample max |dlnL| = 0.052; evidence shift ln Z_rot - ln Z_base = -1.1e-3 (MC-noise-free) -- negligible for a short signal, as physics requires. (B) Cauchy-Schwarz: max lnL 3707 < 0.5 = 8421. Co-Authored-By: Claude Opus 4.8 --- .../likelihood/test_slowrot_headtohead.py | 174 ++++++++++++++++++ 1 file changed, 174 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py new file mode 100644 index 000000000..c07570e53 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py @@ -0,0 +1,174 @@ +""" +test_slowrot_headtohead : matched-seed quantitative baseline-vs-rotation comparison. + +Instead of running the full ILE twice (whose adaptive sampler makes exact seed-matching +fragile), we draw ONE fixed set of extrinsic samples from the standard priors and evaluate +BOTH the baseline vectorized (NoLoop) likelihood and the rotation (NoLoop) likelihood on the +SAME samples and the SAME time grid. Because the samples are identical, the difference is +the genuine slow-rotation effect, with zero Monte-Carlo noise in the difference. + +Checks (all on the same matched samples): + (R) REGRESSION: rotation at f_sidereal=0 == baseline, per-sample, to ~machine precision. + (P) PHYSICS: rotation at the real sidereal rate differs from baseline only by the tiny + genuine effect (short signal => negligible); report per-sample max and the + importance-weighted evidence shift ln Z_rot - ln Z_base (matched-sample, MC-noise-free). + (B) BOUND: every lnL (baseline, rot0, rotW) respects the Cauchy-Schwarz bound + 0.5 * sum_det . + +Run under the RIFT venv with this worktree first on PYTHONPATH. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1e9 +t_window = 0.1 +Lmax = 2 +deltaT = 1. / fSample +deltaF = 1. / 4. +HARM = (-2, -1, 0, 1, 2) +DMIN, DMAX = 10.0, 1000.0 # Mpc, for the d^2 distance prior + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=deltaT, + tref=event_time, deltaF=deltaF) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +def _network_half_dd(): + tot = 0.0 + for det in data_dict: + IP = lsu.ComplexIP(fmin, fmax, 1. / 2. / deltaT, deltaF, psd_dict[det], True, False, 0.) + tot += IP.ip(data_dict[det], data_dict[det]).real + return 0.5 * tot + + +def _draw_extrinsic(K, seed=20260704): + rng = np.random.RandomState(seed) + RA = rng.uniform(0, 2 * np.pi, K) + DEC = np.arcsin(rng.uniform(-1, 1, K)) + PSI = rng.uniform(0, np.pi, K) + INCL = np.arccos(rng.uniform(-1, 1, K)) + PHIREF = rng.uniform(0, 2 * np.pi, K) + # d^2 prior in [DMIN, DMAX] + u = rng.uniform(0, 1, K) + DIST = (DMIN ** 3 + u * (DMAX ** 3 - DMIN ** 3)) ** (1. / 3.) + return RA, DEC, PSI, INCL, PHIREF, DIST + + +def _make_P_vec(RA, DEC, PSI, INCL, PHIREF, DIST_Mpc): + Pv = Psig.manual_copy() + Pv.phi = RA.astype(float) + Pv.theta = DEC.astype(float) + Pv.psi = PSI.astype(float) + Pv.incl = INCL.astype(float) + Pv.phiref = PHIREF.astype(float) + Pv.dist = (DIST_Mpc * 1e6 * lsu.lsu_PC).astype(float) + Pv.tref = float(event_time) + Pv.deltaT = deltaT + return Pv + + +def _pack_baseline(): + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for det in data_dict: + a, b, c, U, V, rArr, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[det].keys()), None, rho[det], ct[det], ctV[det]) + lk[det] = a; rA[det] = rArr; cu[det] = U; cv[det] = V; ep[det] = e + return lk, rA, cu, cv, ep + + +def _pack_rotation(f_sidereal): + ri, ct, ctV, rho, meta = flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=f_sidereal, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + lk, rbn, ubn, vbn, ep = flwr.pack_rotation_arrays(meta, rho, ct, ctV) + return meta, lk, rbn, ubn, vbn, ep + + +K = 300 +tvals = np.arange(int(2 * 0.03 / deltaT)) * deltaT - 0.03 +RA, DEC, PSI, INCL, PHIREF, DIST = _draw_extrinsic(K) +P_vec = _make_P_vec(RA, DEC, PSI, INCL, PHIREF, DIST) +HALF_DD = _network_half_dd() + + +def _lnZ(lnL): + m = np.max(lnL) + return m + np.log(np.mean(np.exp(lnL - m))) + + +# precompute once +_lkB, _rAB, _cuB, _cvB, _epB = _pack_baseline() +_metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0 = _pack_rotation(0.0) +_metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW = _pack_rotation(flwr.F_SIDEREAL) + +lnL_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, P_vec, _lkB, _rAB, _cuB, _cvB, _epB, Lmax=Lmax, xpy=np) +lnL_rot0 = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, _metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0, Lmax=Lmax) +lnL_rotW = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, _metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW, Lmax=Lmax) + + +# NOTE: the baseline DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop uses a GLOBAL lnLmax +# offset in its time marginalization, so uniform-prior samples more than ~745 (float64 +# underflow) below the global peak return -inf. Our rotation NoLoop uses a PER-SAMPLE +# offset (always finite). So the regression is anchored on the samples where the baseline +# is finite (near the peak); the physics head-to-head uses rotation-OFF (f_sid=0, which we +# have just anchored == baseline) vs rotation-ON, both always finite. +_base = np.asarray(lnL_base); _r0 = np.asarray(lnL_rot0); _rW = np.asarray(lnL_rotW) +_finite = np.isfinite(_base) + + +def test_R_rotation_zero_rate_matches_baseline_per_sample(): + assert _finite.sum() > 0, "baseline finite nowhere -- cannot anchor" + d = np.max(np.abs(_r0[_finite] - _base[_finite])) + print("(R) regression anchor: max|lnL_rot(f_sid=0) - lnL_base| over %d finite-baseline " + "samples (of %d) = %.3e" % (int(_finite.sum()), K, d)) + assert d < 1e-6, "rotation at f_sid=0 disagrees with baseline: %g" % d + + +def test_P_physics_effect_and_evidence_shift(): + # matched-sample baseline(=f_sid=0) vs rotation(real); both finite everywhere. + d = np.max(np.abs(_rW - _r0)) + dZ = _lnZ(_rW) - _lnZ(_r0) + print("(P) matched-sample max|lnL_rot(real) - lnL_rot(0)| over %d samples = %.3e" % (K, d)) + print("(P) evidence shift ln Z_rot - ln Z_base (matched, MC-noise-free) = %+.6e" % dZ) + print("(P) [short 30+25 BBH => tiny; ln Z(f_sid=0)=%.4f ln Z(real)=%.4f]" + % (_lnZ(_r0), _lnZ(_rW))) + assert d > 0, "rotation had literally zero effect (suspicious)" + assert d < 1.0, "short-signal rotation effect unexpectedly large: %g" % d + + +def test_B_cauchy_schwarz_bound(): + worst = max(np.max(_r0), np.max(_rW)) + print("(B) max lnL (rot0/rotW) = %.4f 0.5_network = %.4f" % (worst, HALF_DD)) + assert worst <= HALF_DD + 1e-6, "lnL exceeds Cauchy-Schwarz bound 0.5: %g > %g" % (worst, HALF_DD) + + +if __name__ == "__main__": + test_R_rotation_zero_rate_matches_baseline_per_sample() + test_P_physics_effect_and_evidence_shift() + test_B_cauchy_schwarz_bound() + print("ALL SLOWROT HEAD-TO-HEAD CHECKS PASSED") From 6de99aaf99bbd8ec005befdc3224c1a39463abd0 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Mon, 6 Jul 2026 11:39:19 -0700 Subject: [PATCH 13/45] slowrot handoff: Path B findings -- p>=3 high-f derivative blow-up + delay-physics still to validate Inflated-Omega convergence (single-det 30+25 BBH, f_sid x3000): scalar Path B lnL(p_max=0..3)=[1794.39,1781.31,1781.63,928.30] -- p=0..2 capture a real ~13-in-lnL delay effect and appear to converge (~1781.6) within 0.5=1938, but p_max=3 blows up (853). Root cause: FD derivative (2 pi i f)^p amplifies high f; model-norm integrand ~ f^{11/3} for a chirp, dominated by the f_max edge. Fix: band-limit the p>=1 delay-derivative terms to low frequency. Independent time-varying-delay brute force still required (convergence alone can converge to a wrong value -- the delay analogue of the reference-time bug). Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 33 ++++++++++++++++--- 1 file changed, 29 insertions(+), 4 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index 96b2aada1..5838d5206 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -71,11 +71,36 @@ convention, so `vec == brute-force` PASSED while both were wrong. **Always cross against the Cauchy-Schwarz bound 0.5, not only against a reference that can share conventions.** +## PATH B STATUS (findings 2026-07-04, the systematic pass in progress) +- Matched-seed head-to-head DONE (test_slowrot_headtohead.py): rot(f_sid=0)==baseline 9e-13; + evidence shift ln Z_rot - ln Z_base = -1.1e-3 (MC-noise-free) for the short signal. +- Path B DELAY PHYSICS still only partially validated. In an INFLATED-Omega regime (f_sidereal + x3000, single-det 30+25 BBH) where the delay drift matters, scalar Path B gives + lnL(p_max=0..3) = [1794.39, 1781.31, 1781.63, 928.30]: p=0->1->2 captures a real ~13-in-lnL + delay effect and appears to converge (~1781.6) and respects 0.5=1938 -- BUT p_max=3 + BLOWS UP (increment 853). +- ROOT CAUSE of the p>=3 blow-up (likely): the FD derivative weight (2 pi i f)^p amplifies high + frequencies; in the model norm the integrand ~ (2 pi f)^{2p} |h(f)|^2 / S grows like f^{11/3} + for a chirp (|h|^2 ~ f^{-7/3}), so high-order terms are dominated by the f_max edge, not the + physical low-frequency delay drift. FIX for the systematic pass: BAND-LIMIT the delay- + derivative (p>=1) terms to low frequency (the delay drift physically matters in the long early + inspiral, i.e. low f, not at merger). Options: apply a low-pass / taper before the (2 pi i f)^p + weight, or use a reduced f_max for p>=1 templates, or work with dimensionless (f/f_ref) weights. +- Consequence: current Path B is trustworthy only through p_max<=2 and only after the band-limit + fix is validated. Do NOT ship Path B (p>=1) until this is fixed AND checked against an + independent time-varying-delay brute force (below). + ## OPEN / NEXT (the one remaining systematic pass — do it all together) -1. **Path B rigorous validation**: vs a brute force with TIME-VARYING DELAY tau_k(t) on a - genuinely LONG (BNS) signal, where p>=1 delay drift is non-negligible (short-BBH delay - drift is ~1e-8, so current Path B tests exercise machinery/bound but not the delay - PHYSICS). This is the analogue of V1b that caught the reference-time bug. +1. **Path B rigorous validation** (TWO parts, do together): + (a) FIX the p>=3 high-frequency derivative blow-up by band-limiting the delay-derivative + terms to low frequency (see PATH B STATUS above); re-check convergence is monotone. + (b) Validate vs an INDEPENDENT brute force with TIME-VARYING DELAY tau_k(t) (build the + detector strain h_k(t')=Re[F(t') Sigma(t'-tau(t'))] by TD-warping the complex source + strain -- lsu.hoft_from_hlm(...,return_complex=True) gives Sigma with the right epoch -- + then direct lnL = - 0.5). CALIBRATE it against baseline in the static + limit (F=const, tau=const) BEFORE trusting the drift result. A convergence test alone is + NOT enough: Path B could converge to a WRONG value if the C coefficients are off (the + delay analogue of the reference-time bug). Cross-check against 0.5. 2. **Matched-seed quantitative ILE head-to-head** (fixed RNG seed) -> exact, regression-grade baseline-vs-rotation comparison (currently only MC-noise-level agreement). 3. Cauchy-Schwarz bound checks everywhere; sweep for other shared-convention bugs. From 45808559f20705020d65bd8e8e95de22afea6aa5 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Mon, 6 Jul 2026 11:50:28 -0700 Subject: [PATCH 14/45] slowrot handoff: SimDetectorStrain (Jolien) ground-truth recipe for Path B delay validation Findings: RIFT data already uses SimInspiralTD + SimDetectorStrainREAL8TimeSeries (full Earth-rotation delay map). The correct rotation ground truth applies SimDetectorStrain to the SAME RIFT modes via lsu.hoft_from_hlm(hlmsT,P) + ComplexIP -- NOT non_herm_hoff (which regenerates via SimInspiralTD; its modes differ from hlmoff, so with IMRPhenomD the mode-baseline recovers only ~SNR 35 of the data's ~82 at injection: a waveform-convention mismatch, not rotation). Gotchas: pad/align hoft_from_hlm output to the data length; compare time-maximized (fixed-tref is off-peak). Use a long signal (real rate) so delay drift matters. Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 32 +++++++++++++++---- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index 5838d5206..a958ba597 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -94,13 +94,31 @@ conventions.** 1. **Path B rigorous validation** (TWO parts, do together): (a) FIX the p>=3 high-frequency derivative blow-up by band-limiting the delay-derivative terms to low frequency (see PATH B STATUS above); re-check convergence is monotone. - (b) Validate vs an INDEPENDENT brute force with TIME-VARYING DELAY tau_k(t) (build the - detector strain h_k(t')=Re[F(t') Sigma(t'-tau(t'))] by TD-warping the complex source - strain -- lsu.hoft_from_hlm(...,return_complex=True) gives Sigma with the right epoch -- - then direct lnL = - 0.5). CALIBRATE it against baseline in the static - limit (F=const, tau=const) BEFORE trusting the drift result. A convergence test alone is - NOT enough: Path B could converge to a WRONG value if the C coefficients are off (the - delay analogue of the reference-time bug). Cross-check against 0.5. + (b) Validate vs an INDEPENDENT ground truth that uses LAL's OWN full delay-time map -- + lalsim.SimDetectorStrainREAL8TimeSeries (Jolien's code). KEY FACTS FOUND 2026-07-04: + - RIFT's data ALREADY uses it: non_herm_hoff -> hoft -> SimInspiralTD + + SimDetectorStrainREAL8TimeSeries (lalsimutils.py ~line 3020). So a LONG injection + already carries the real Earth-rotation response; baseline (static) will under-recover + it and Path A/B should recover it. + - GROUND TRUTH for the rotation likelihood must apply SimDetectorStrain to the SAME + RIFT modes the likelihood uses: hk = lsu.hoft_from_hlm(hlmsT, P_extr) (radec path, + applies SimDetectorStrain). Then convert to 2-sided FD exactly like non_herm_hoff + (copy REAL8 -> COMPLEX16, COMPLEX16TimeFreqFFT) and lnL = ComplexIP(d,hk).real - + 0.5*ComplexIP(hk,hk).real. + - DO NOT use non_herm_hoff(P_extr) as the template ground truth: it regenerates via + SimInspiralTD, whose modes differ from RIFT's hlmoff modes -- with IMRPhenomD the + mode-based baseline recovers only ~SNR 35 of the data's ~SNR 82 at the injection (a + waveform-convention mismatch, NOT rotation). Using hoft_from_hlm keeps the modes + identical so ONLY the rotation differs. + - Two gotchas to fix: (i) hoft_from_hlm output length = mode length (e.g. 16451) != + padded data length (16384) -> ResizeREAL8TimeSeries + align epoch (replicate + non_herm_hoff padding); (ii) the mode-based likelihood at a FIXED tref is OFF-PEAK + (epoch bookkeeping) -> compare at the time-MAXIMIZED / marginalized value, not at + extr.tref. + - Use a LONG signal (real sidereal rate) so delay drift matters, OR the inflated-Omega + path (already shows p=1,2 capture the delay, p>=3 needs the band-limit fix). + A convergence test alone is NOT enough: Path B could converge to a WRONG value if the C + coefficients are off (the delay analogue of the reference-time bug). Cross-check 0.5. 2. **Matched-seed quantitative ILE head-to-head** (fixed RNG seed) -> exact, regression-grade baseline-vs-rotation comparison (currently only MC-noise-level agreement). 3. Cauchy-Schwarz bound checks everywhere; sweep for other shared-convention bugs. From 16a413dd72daa195bae373401653dfd235551967 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Mon, 6 Jul 2026 18:03:22 -0700 Subject: [PATCH 15/45] slowrot: Path B delay physics VALIDATED (folded-template ground truth) + SimDetectorStrain check test_slowrot_pathB_bruteforce.py: independent folded-template ground truth Re[F(t) Sigma(t-tau(t))] from the same harmonic F(t),tau(t) + same modes as the likelihood (no SimDetectorStrain floor), inflated sidereal rate. At the real 90-min-BNS rate (x340 on a 16s test) Path B CONVERGES: p_max=0 deficit 3.43 -> p=1 0.23 -> p=2/3 0.207, bound-respected, no blow-up. Delay drift recovered; production-ready for target signals with p_max<=2. Catastrophic p>=3 cancellation only at x1000+ (>2.6x any physical signal) -> band-limit fix is a robustness nicety, not a blocker. test_slowrot_pathB_groundtruth.py: vs LAL SimDetectorStrainREAL8TimeSeries (Jolien's full delay map), built from the likelihood's OWN modes. baseline/PathA/PathB agree with LAL to ~0.07 at fmax=256; the ~26 deficit at fmax=1024 was SimDetectorStrain's high-f TD delay-interpolation (confirmed: vanishes as fmax lowers), not a Path A/B bug. HANDOFF updated with the validation summary and the known p>=3 limit. Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 20 ++++++ .../test_slowrot_pathB_bruteforce.py | 53 ++++++++++++++ .../test_slowrot_pathB_groundtruth.py | 69 +++++++++++++++++++ 3 files changed, 142 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index a958ba597..d4316e19a 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -4,6 +4,26 @@ Branch: `rift_slowrot` (off `rift_O4d_fix_rvs_clear_fairdraw_batch`). Design not paper live in a **separate local repo** `~/rift-slow-rotation` (no git remote — local only; 21-page PDF via `latexmk -pdf notes/main.tex`). +## PATH B DELAY PHYSICS — VALIDATED (2026-07 update) +Path B's propagation-delay drift is validated against an INDEPENDENT folded-template ground +truth (`test_slowrot_pathB_bruteforce.py`): data built as Re[F(t) Sigma(t-tau(t))] from the +SAME harmonic F(t),tau(t) model + the SAME modes the likelihood uses (no SimDetectorStrain +convention floor), at an inflated sidereal rate so the delay drift is large. At the REAL +90-min-BNS rate (= x340 inflation on a 16s test): p_max=0 deficit 3.43 -> p_max=1 0.23 -> +p_max=2 0.207 -> p_max=3 0.207: CONVERGES, bound-respected, NO blow-up. So Path B recovers +the delay drift and is production-ready for the target signals with p_max<=2. +KNOWN LIMIT: the p>=3 catastrophic cancellation (huge high-f U terms x tiny delta_tau^p +coefficients) only bites at x1000+ inflation (>2.6x faster than any physical signal): x1000 +gives p=2 deficit 5.9 but p=3 blows to 1e5. So the band-limit fix (low-pass the p>=1 +derivative templates) is a robustness nicety, NOT a blocker for real signals. +Separately validated vs LAL's SimDetectorStrainREAL8TimeSeries (`test_slowrot_pathB_groundtruth.py`): +baseline/PathA/PathB all agree with Jolien's full delay map to ~0.07 at fmax=256 (the ~26 +deficit at fmax=1024 was SimDetectorStrain's high-f TD delay-INTERPOLATION, not a bug -- +confirmed: it vanishes when fmax is lowered). NoLoop uses nearest-neighbor time sampling +(factored_likelihood.py ~L1691), so absolute peak comparisons at high SNR have a resolution +floor (~0.1-0.2); the time-interpolated NoLoop is in oshaughn/rift_O4d and +origin/rift_O4d_junior_calmarg_in_loop if a cleaner absolute comparison is wanted. + ## What this is Generalizes RIFT's marginalized likelihood to account for the **time dependence of the ground-based detector response over the signal** (Earth rotation), while reusing the diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py new file mode 100644 index 000000000..262a552a3 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py @@ -0,0 +1,53 @@ +from __future__ import print_function, division +import numpy as np, lal, lalsimulation as lalsim +from scipy.interpolate import CubicSpline +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +import RIFT.likelihood.slowrot_response as srr +event_time=1e9; Lmax=2; t_window=0.1; det='H1' +psd=lalsim.SimNoisePSDaLIGOZeroDetHighPower; apx=lalsim.GetApproximantFromString("IMRPhenomD") +import os +OMEGA_INF=flwr.OMEGA_EARTH*float(os.environ.get("INFL","340")); FSID_INF=OMEGA_INF/(2*np.pi) +def _ifft(hf_d): + o={} + for lm,hf in hf_d.items(): + n=hf.data.length; dt=1./(n*hf.deltaF); ht=lal.CreateCOMPLEX16TimeSeries("h",hf.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ht,hf,lal.CreateReverseCOMPLEX16FFTPlan(n,0)); o[lm]=ht + return o +def _to_fd(re,epoch,dt,N): + ht=lal.CreateCOMPLEX16TimeSeries("h",epoch,0.,dt,lal.DimensionlessUnit,N); ht.data.data[:]=re[:N] + hf=lal.CreateCOMPLEX16FrequencySeries("hf",epoch,0.,1./dt/N,lsu.lsu_HertzUnit,N) + lal.COMPLEX16TimeFreqFFT(hf,ht,lal.CreateForwardCOMPLEX16FFTPlan(N,0)); return hf +from scipy.interpolate import InterpolatedUnivariateSpline +def _peak(lt): + lt=np.asarray(lt,float); x=np.arange(len(lt)); sp=InterpolatedUnivariateSpline(x,lt,k=4) + xs=np.linspace(0,len(lt)-1,len(lt)*32); return float(np.max(sp(xs))) +fmin,fmax,deltaT,seglen=25.,512.,1/2048.,16.; deltaF=1./seglen; fNyq=1/2./deltaT; N=int(round(seglen/deltaT)) +RA,DEC,PSI,INCL,PHIREF=1.2,0.3,0.5,0.4,0.0; DLOUD=fl.distMpcRef*1e6*lsu.lsu_PC/30. +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=INCL,phiref=PHIREF,theta=DEC,phi=RA,psi=PSI, + m1=2.2*lal.MSUN_SI,m2=1.8*lal.MSUN_SI,detector=det,dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF); Psig.approx=apx +Pm=Psig.manual_copy(); Pm.dist=DLOUD +hlms_fd,_=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True); hlmsT=_ifft(hlms_fd) +lm0=list(hlmsT.keys())[0]; nn=hlmsT[lm0].data.length; dt=hlmsT[lm0].deltaT; ep=float(hlmsT[lm0].epoch); tt=ep+np.arange(nn)*dt +Sig=np.zeros(nn,complex) +for lm in hlmsT: Sig+=hlmsT[lm].data.data*lal.SpinWeightedSphericalHarmonic(INCL,-PHIREF,-2,lm[0],lm[1]) +reS=CubicSpline(tt,Sig.real,extrapolate=False); imS=CubicSpline(tt,Sig.imag,extrapolate=False) +lald=lalsim.DetectorPrefixToLALDetector(det); g_ev=lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))-RA +A=srr.antenna_harmonics(lald.response,DEC,PSI); At={k:A[k]*np.exp(1j*k*g_ev) for k in A} +B=srr.delay_harmonics(lald.location,DEC); Bt={k:B[k]*np.exp(1j*k*g_ev) for k in B} +tau_t=np.real(sum(Bt[k]*np.exp(1j*k*OMEGA_INF*tt) for k in Bt)) +F_t=sum(At[k]*np.exp(1j*k*OMEGA_INF*tt) for k in At) +Sig_d=np.nan_to_num(reS(tt-tau_t)+1j*imS(tt-tau_t)) +data=_to_fd(np.real(F_t*Sig_d),lal.LIGOTimeGPS(float(hlmsT[lm0].epoch)+event_time),dt,N); data_dict={det:data}; psd_dict={det:psd} +IPc=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.); HALF_DD=0.5*IPc.ip(data,data).real +print("inflated seglen=%.0fs 0.5=%.4f"%(seglen,HALF_DD)) +Pv=Psig.manual_copy() +for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DLOUD)]: setattr(Pv,k,np.ones(1)*v) +Pv.tref=event_time; Pv.deltaT=deltaT; Nw=int(0.02/deltaT); tvals=np.arange(-Nw,Nw)*deltaT +for pmax in [0,1,2,3]: + nh=2+pmax + bk=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=tuple(range(-nh,nh+1)),p_max=pmax,f_sidereal=FSID_INF,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=True) + lk,rbn,ubn,vbn,epd=flwr.pack_rotation_arrays(bk[4],bk[3],bk[1],bk[2]) + lnL=_peak(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,bk[4],lk,rbn,ubn,vbn,epd,Lmax=Lmax,array_output=True)[0]) + print(" p_max=%d : lnL=%.5f deficit=%.5f"%(pmax,lnL,HALF_DD-lnL)) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py new file mode 100644 index 000000000..7e2ad583f --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py @@ -0,0 +1,69 @@ +from __future__ import print_function, division +import sys +import numpy as np +import lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +event_time=1e9; Lmax=2; t_window=0.1; det='H1' +psd=lalsim.SimNoisePSDaLIGOZeroDetHighPower +apx=lalsim.GetApproximantFromString("IMRPhenomD") +SCALE=40. # loudness: data-mode distance = distMpcRef/SCALE + +def _ifft(hlms_fd): + out={} + for lm,hf in hlms_fd.items(): + n=hf.data.length; dt=1./(n*hf.deltaF) + ht=lal.CreateCOMPLEX16TimeSeries("h",hf.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ht,hf,lal.CreateReverseCOMPLEX16FFTPlan(n,0)); out[lm]=ht + return out +def _fd(hk,TDlen): + if hk.data.length!=TDlen: hk=lal.ResizeREAL8TimeSeries(hk,0,TDlen) + n=TDlen + htC=lal.CreateCOMPLEX16TimeSeries("h",hk.epoch,hk.f0,hk.deltaT,hk.sampleUnits,n); htC.data.data[:]=hk.data.data + hf=lal.CreateCOMPLEX16FrequencySeries("hf",hk.epoch,hk.f0,1./hk.deltaT/n,lsu.lsu_HertzUnit,n) + lal.COMPLEX16TimeFreqFFT(hf,htC,lal.CreateForwardCOMPLEX16FFTPlan(n,0)); return hf +def _peak(lt,up=16): + lt=np.asarray(lt,dtype=float); N=len(lt) + L=np.fft.rfft(lt); Lp=np.zeros(N*up//2+1,dtype=complex); Lp[:len(L)]=L + return float(np.max(np.fft.irfft(Lp,N*up)*up)) + +def run(mode="short"): + if mode=="long": fmin,fmax,deltaT,seglen,m1,m2=25.,256.,1/2048.,32.,2.2*lal.MSUN_SI,1.8*lal.MSUN_SI + else: fmin,fmax,deltaT,seglen,m1,m2=30.,1700.,1/4096.,4.,30*lal.MSUN_SI,25*lal.MSUN_SI + deltaF=1./seglen; fNyq=1/2./deltaT; TDlen=int(round(seglen/deltaT)) + DLOUD=fl.distMpcRef*1e6*lsu.lsu_PC/SCALE + Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.4,phiref=0.0,theta=0.3,phi=1.2,psi=0.5, + m1=m1,m2=m2,detector=det,dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF); Psig.approx=apx + Pm=Psig.manual_copy(); Pm.dist=DLOUD + hlms_fd,_=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True) + hlmsT=_ifft(hlms_fd) + data=_fd(lsu.hoft_from_hlm(hlmsT,Psig.manual_copy()),TDlen) + data_dict={det:data}; psd_dict={det:psd} + IPc=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.); HALF_DD=0.5*IPc.ip(data,data).real + print("[%s] seglen=%.0fs data_len=%d 0.5=%.4f"%(mode,seglen,data.data.length,HALF_DD)) + RA,DEC,PSI,INCL,PHIREF,DIST=1.2,0.3,0.5,0.4,0.0,DLOUD + ri,ct,ctV,rho,snr,rest=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + Pe=Psig.manual_copy(); Pe.incl=INCL;Pe.phiref=PHIREF;Pe.psi=PSI;Pe.phi=RA;Pe.theta=DEC;Pe.dist=DIST + hk_gt=_fd(lsu.hoft_from_hlm(hlmsT,Pe),TDlen); lnL_gt=IPc.ip(data,hk_gt).real-0.5*IPc.ip(hk_gt,hk_gt).real + Pv=Psig.manual_copy() + for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DIST)]: + setattr(Pv,k,np.ones(1)*v) + Pv.tref=event_time; Pv.deltaT=deltaT + Nw=int(0.02/deltaT); tvals=np.arange(-Nw,Nw)*deltaT + lkB={};rAB={};cuB={};cvB={};epB={} + for d in data_dict: + a,b,c,U,V,rA,rI,e=fl.PackLikelihoodDataStructuresAsArrays(list(rho[d].keys()),None,rho[d],ct[d],ctV[d]) + lkB[d]=a;rAB[d]=rA;cuB[d]=U;cvB[d]=V;epB[d]=e + lnL_base=_peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals,Pv,lkB,rAB,cuB,cvB,epB,Lmax=Lmax,xpy=np,return_lnLt=True)[0]) + def rot(pmax): + nh=2+pmax + bk=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=tuple(range(-nh,nh+1)),p_max=pmax,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=True) + lk,rbn,ubn,vbn,ep=flwr.pack_rotation_arrays(bk[4],bk[3],bk[1],bk[2]) + return _peak(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,bk[4],lk,rbn,ubn,vbn,ep,Lmax=Lmax,array_output=True)[0]) + lnL_A=rot(0); lnL_B=rot(1) + print(" ground truth (SimDetStrain) lnL = %.5f"%lnL_gt) + print(" baseline NoLoop peak lnL = %.5f deficit=%.5f"%(lnL_base,lnL_gt-lnL_base)) + print(" Path A NoLoop (F(t)) lnL = %.5f deficit=%.5f"%(lnL_A,lnL_gt-lnL_A)) + print(" Path B NoLoop (F(t)+p=1) lnL = %.5f deficit=%.5f"%(lnL_B,lnL_gt-lnL_B)) +if __name__=="__main__": run(sys.argv[1] if len(sys.argv)>1 else "short") From ed0d321e0ea8b3512caf19cb8a8dcd14aef164e1 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 09:06:35 -0700 Subject: [PATCH 16/45] handoff: record two parallel thrusts (rotation PE demo; Path D freq-dependent response elevated to active) --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index d4316e19a..b0fa108cf 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -24,6 +24,20 @@ confirmed: it vanishes when fmax is lowered). NoLoop uses nearest-neighbor time floor (~0.1-0.2); the time-interpolated NoLoop is in oshaughn/rift_O4d and origin/rift_O4d_junior_calmarg_in_loop if a cleaner absolute comparison is wanted. +## TWO PARALLEL THRUSTS (2026-07) +Path A + Path B are implemented, validated, and wired into the ILE. Next work is two tracks: +1. **Rotation PE value demo (near-term).** End-to-end injection-recovery showing the static + analysis is BIASED on a long signal and `--rotation-slow` (+`--rotation-p-max`) recovers the + truth. Scaffolded at `~/RIFT_roboto_paper/analyses/slowrot_demo/`. RIFT injections already + carry the time-varying response (hoft -> SimDetectorStrain). Deliverable: overlay + bias + figure for `paper/` cited from `sec:slowrot`. +2. **Frequency-dependent (finite-size) response (later; the 3G regime where 'long' lives).** + Path D, ELEVATED from deferred to an ACTIVE track. F(f; sky) (finite arm size, free spectral + range ~c/2L ~ 3.7 kHz for a 40 km CE arm). Approaches (notes/sec_freqdep.tex): fold a per-mode + transfer T(f;sky) (pinned sky, LISA Evaluate_Gslr machinery) or sky-harmonic expand to keep sky + extrinsic. HARD: precessing+HM break the per-mode SPA -> likely pinned-sky + TD folding. + Sequence AFTER the rotation demo lands. + ## What this is Generalizes RIFT's marginalized likelihood to account for the **time dependence of the ground-based detector response over the signal** (Earth rotation), while reusing the From 0d72aa113ee66ecc8d609181ebb268ee3d1387a5 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 13:59:47 -0700 Subject: [PATCH 17/45] slowrot Thrust 2: frequency-dependent (finite-size) detector response + validation slowrot_freqresponse.py: closed-form ground-based antenna response beyond the long-wavelength approximation, F_k(f; RA, DEC, psi), from arXiv:2412.01693 (Rakhmanov-Romano-Whelan) single-arm round-trip transfer D~(a,n,f) = (e^{-i2pi fT}/2)[e^{+i pi fT_-} sinc(pi fT_+) + e^{-i pi fT_+} sinc(pi fT_-)], T=L/c, T_pm=T(1 +- a.n). Written as the EXACT LAL long-wavelength response plus a finite-size correction that is identically zero at f=0 (LAL stores d.response in float32, so reconstructing arms directly would cap f=0 agreement at ~1e-7). test_slowrot_freqresponse.py (numpy+lal, loadable by path): (A) f->0 == lal.ComputeDetAMResponse to 6.66e-16 over 32 random (ra,dec,psi,gmst) x {H1,L1,V1,K1}; Im F(0)=0 exactly. (B) free-spectral-range: for a.n=0, D~=e^{-i2pi fT} sinc(2fT) exactly, first null at f_FSR=c/2L =3747.4 Hz (40 km); general a.n -> dips at 0.8-1.0 f_FSR. (C) in-band pattern-SHAPE distortion ||F(f)|-|F(0)||/|F(0)|: LIGO 4km 0.24%@1kHz 0.62%@2kHz; CE 40km 11.8%@1kHz 42%@2kHz (CE/LIGO ~68x ~ (10x arm)^2). => negligible for LIGO, first-order only for 3G/CE. The complex ratio is dominated by a benign common e^{-i2pi fL/c} delay (degenerate with coalescence time), NOT sky-dependent shape. LAL has NO closed-form FD response (only ComputeDetAMResponse = LWL), so validated vs the LWL limit + analytic FSR structure. Recommended likelihood route: (b) sky-harmonic expansion for CE (keeps sky extrinsic; fold the common delay into geocenter time), (a) pinned-sky TD fold reserved for precessing+HM. Uncommitted -> now committed after independent test verification. Co-Authored-By: Claude Opus 4.8 --- .../RIFT/likelihood/slowrot_freqresponse.py | 262 ++++++++++++++++++ .../likelihood/test_slowrot_freqresponse.py | 200 +++++++++++++ 2 files changed, 462 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py new file mode 100644 index 000000000..ecd2b1a5c --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py @@ -0,0 +1,262 @@ +""" +slowrot_freqresponse : closed-form FREQUENCY-DEPENDENT (finite-size) detector antenna +response of a ground-based Michelson interferometer, beyond the long-wavelength +approximation. "Path D" of the slow-rotation generalization of the RIFT likelihood. + +Motivation +---------- +The usual antenna response F_+(RA,DEC,psi), F_x(RA,DEC,psi) returned by +lal.ComputeDetAMResponse is the LONG-WAVELENGTH LIMIT: it assumes the GW strain is +uniform across the detector while a photon traverses an arm. Beyond that limit the +finite light-travel time T = L/c across an arm imprints genuine in-band frequency +structure on the response, controlled by the free spectral range + + f_FSR = c / (2 L) ( 37.5 kHz for 4-km LIGO ; 3.75 kHz for a 40-km CE arm ). + +The response becomes F_A -> F_A(f; RA, DEC, psi). This matters for 3G detectors +(Cosmic Explorer, 40 km) whose long signals reach a non-negligible fraction of f_FSR +in band; it is completely negligible for the 4-km LIGO instruments. + +Formula and convention (arXiv:2412.01693, "Beyond the Long Wavelength Approximation", +Eqs. 4-6; equivalent to Rakhmanov-Romano-Whelan 2008, arXiv:0808.3805, and +Essick-Vitale-Evans 2017, PRD 96 084004 / arXiv:1708.06843) +------------------------------------------------------------------------------------- +Single-arm round-trip transfer function, for arm unit vector a-hat and source direction +n-hat (direction TO the source, Sathyaprakash-Schutz convention -- the SAME n-hat used +by lal.ComputeDetAMResponse / TimeDelayFromEarthCenter), with T = L/c the one-way +photon time of flight and T_pm^a = T (1 +/- a-hat . n-hat): + + D~(a-hat, n-hat, f) = (e^{-i 2 pi f T} / 2) + * [ e^{+i pi f T_-^a} sinc(pi f T_+^a) + + e^{-i pi f T_+^a} sinc(pi f T_-^a) ] (Eq. 6) + +with sinc(z) = sin(z)/z, so D~ -> 1 as f -> 0. The frequency-dependent antenna +patterns are then (Eqs. 4-5) + + F_A(f, n-hat) = (1/2) [ D~(x-hat, n-hat, f) (eps^A : x-hat x-hat) + - D~(y-hat, n-hat, f) (eps^A : y-hat y-hat) ] , A in {+, x} + +where eps^+ , eps^x are the GW polarization tensors set by (RA, DEC, psi) and +x-hat, y-hat are the two arm unit vectors. At f -> 0 both D~ -> 1 and this collapses to + F_A(0) = eps^A : (1/2)(x-hat x-hat - y-hat y-hat) = eps^A : D_response + = lal.ComputeDetAMResponse , +since the detector response tensor is exactly D_response = (1/2)(x-hat x-hat - y-hat y-hat). + +Implementation note (exactness of the f -> 0 limit) +--------------------------------------------------- +LAL stores the detector response tensor `d.response` in single precision (REAL4). +Reconstructing (1/2)(x x - y y) from the geodetic arm geometry therefore agrees with +`d.response` only to ~1e-7. To reproduce lal.ComputeDetAMResponse to MACHINE PRECISION +at f = 0 we write the response as an EXACT long-wavelength baseline plus a finite-size +correction that vanishes identically at f = 0: + + F_A(f) = F_A^LWL + (1/2)[ (D~_x - 1)(eps^A : x x) - (D~_y - 1)(eps^A : y y) ] + +The baseline F_A^LWL is computed from the SAME `d.response` and triad algebra as +lal.ComputeDetAMResponse (see slowrot_response.py), so at f = 0 (where D~_x = D~_y = 1 +EXACTLY, sinc(0)=1) the correction is exactly zero and F_A(0) == ComputeDetAMResponse. +The tiny (~1e-7) geodetic imprecision only enters the O((f/f_FSR)^2) correction, i.e. +utterly negligibly. + +Pure numpy + lal; importable without the heavy RIFT stack (mirrors slowrot_response). + +Conventions (identical to slowrot_response.py / vectorized_lal_tools.py): + g = GMST - RA (Greenwich hour angle) + nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) (direction to source) + X = (-cp sg - sp cg sd, -cp cg + sp sg sd, sp cd) (polarization triad) + Y = ( sp sg - cp cg sd, sp cg + cp sg sd, cp cd) + F_+ = X.D.X - Y.D.Y , F_x = X.D.Y + Y.D.X + (cp,sp)=(cos,sin)psi ; (cd,sd)=(cos,sin)dec ; (cg,sg)=(cos,sin)g +""" +from __future__ import print_function, division + +import numpy as np + +try: + import lalsimulation as _lalsim + _HAVE_LAL = True +except Exception: # pragma: no cover + _HAVE_LAL = False + +C_SI = 299792458.0 # m/s, matches vectorized_lal_tools / slowrot_response +DEFAULT_L_LIGO = 3994.5 # m, nominal LIGO arm length (override for CE etc.) + + +# --------------------------------------------------------------------------- +# Detector geometry: arm unit vectors and arm length from a LAL detector. +# --------------------------------------------------------------------------- +def _cartesian_arm(cosAlt, sinAlt, cosAz, sinAz, cosLat, sinLat, cosLon, sinLon): + """Earth-fixed Cartesian unit vector of a detector arm. + + Exact port of LAL's getCartesianComponents (LALDetectors.c): azimuth measured + from local North toward East, altitude above the local horizontal. + """ + uNorth = cosAlt * cosAz + uEast = cosAlt * sinAz + uRho = -sinLat * uNorth + cosLat * sinAlt + return np.array([ + cosLon * uRho - sinLon * uEast, + sinLon * uRho + cosLon * uEast, + cosLat * uNorth + sinLat * sinAlt, + ]) + + +def detector_geometry(det, L_arm=None): + """Return (response, x_arm, y_arm, L) for a detector prefix ('H1','L1','V1',...). + + Parameters + ---------- + det : str detector prefix understood by lalsimulation + L_arm : float or None arm length [m] override (e.g. 40000. for a 40-km CE arm); + default = 2 * xArmMidpoint from the LAL detector. + + Returns + ------- + response : (3,3) float ndarray Earth-fixed detector response tensor D (from LAL) + x_arm, y_arm : (3,) float ndarray Earth-fixed arm unit vectors + L : float arm length [m] used for the finite-size transfer + """ + if not _HAVE_LAL: # pragma: no cover + raise RuntimeError("lalsimulation is required for detector_geometry") + d = _lalsim.DetectorPrefixToLALDetector(det) + fr = d.frDetector + lat, lon = fr.vertexLatitudeRadians, fr.vertexLongitudeRadians + cL, sL, cO, sO = np.cos(lat), np.sin(lat), np.cos(lon), np.sin(lon) + x_arm = _cartesian_arm(np.cos(fr.xArmAltitudeRadians), np.sin(fr.xArmAltitudeRadians), + np.cos(fr.xArmAzimuthRadians), np.sin(fr.xArmAzimuthRadians), + cL, sL, cO, sO) + y_arm = _cartesian_arm(np.cos(fr.yArmAltitudeRadians), np.sin(fr.yArmAltitudeRadians), + np.cos(fr.yArmAzimuthRadians), np.sin(fr.yArmAzimuthRadians), + cL, sL, cO, sO) + L = float(L_arm) if L_arm is not None else 2.0 * float(fr.xArmMidpoint) + return np.asarray(d.response, dtype=float), x_arm, y_arm, L + + +# --------------------------------------------------------------------------- +# Kinematic pieces (triad + source direction), matching ComputeDetAMResponse. +# --------------------------------------------------------------------------- +def _triad(dec, psi, g): + """Polarization triad X, Y and source direction nhat at hour angle g = GMST - RA. + + dec, psi, g may be scalars or broadcastable arrays; returned vectors carry the + 3-component along the last axis. + """ + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + cg, sg = np.cos(g), np.sin(g) + + X = np.stack([-cp * sg - sp * cg * sd, + -cp * cg + sp * sg * sd, + sp * cd * np.ones_like(sg)], axis=-1) + Y = np.stack([ sp * sg - cp * cg * sd, + sp * cg + cp * sg * sd, + cp * cd * np.ones_like(sg)], axis=-1) + nhat = np.stack([cd * cg, -cd * sg, sd * np.ones_like(sg)], axis=-1) + return X, Y, nhat + + +# --------------------------------------------------------------------------- +# Single-arm finite-size transfer function (Eq. 6 of arXiv:2412.01693). +# --------------------------------------------------------------------------- +def single_arm_transfer(a_dot_n, f, L): + """Round-trip single-arm transfer function D~(a-hat, n-hat, f). + + Parameters + ---------- + a_dot_n : float or ndarray a-hat . n-hat (arm unit vector dotted with source dir) + f : float or ndarray frequency [Hz] + L : float arm length [m] + + Returns + ------- + complex ndarray broadcast over (a_dot_n, f). D~ -> 1 as f -> 0. + + D~ = (e^{-i 2 pi f T}/2)[ e^{+i pi f T_-} sinc(pi f T_+) + e^{-i pi f T_+} sinc(pi f T_-) ] + with T = L/c, T_pm = T(1 +/- a_dot_n), sinc(z)=sin(z)/z. + (np.sinc(u) = sin(pi u)/(pi u), so sinc(pi f T_pm) = np.sinc(f T_pm).) + """ + a = np.asarray(a_dot_n, dtype=float) + f = np.asarray(f, dtype=float) + T = L / C_SI + Tp = T * (1.0 + a) # T_+ + Tm = T * (1.0 - a) # T_- + pref = np.exp(-1j * 2.0 * np.pi * f * T) / 2.0 + term_p = np.exp(1j * np.pi * f * Tm) * np.sinc(f * Tp) + term_m = np.exp(-1j * np.pi * f * Tp) * np.sinc(f * Tm) + return pref * (term_p + term_m) + + +# --------------------------------------------------------------------------- +# Frequency-dependent antenna response. +# --------------------------------------------------------------------------- +def _lwl_response(response, X, Y): + """Long-wavelength F_+, F_x from triad and response tensor (== ComputeDetAMResponse). + + F_+ = X.D.X - Y.D.Y , F_x = X.D.Y + Y.D.X . Vectorized over leading axes of X,Y. + """ + D = response + XDX = np.einsum('...i,ij,...j->...', X, D, X) + YDY = np.einsum('...i,ij,...j->...', Y, D, Y) + XDY = np.einsum('...i,ij,...j->...', X, D, Y) + YDX = np.einsum('...i,ij,...j->...', Y, D, X) + return XDX - YDY, XDY + YDX + + +def antenna_response_fd(det, ra, dec, psi, f, gmst=0.0, L_arm=None): + """Frequency-dependent antenna response F_+(f), F_x(f) for a ground-based detector. + + Beyond the long-wavelength limit: includes the finite light-travel-time transfer + across the arms. Reduces EXACTLY to lal.ComputeDetAMResponse as f -> 0. + + Parameters + ---------- + det : str detector prefix ('H1','L1','V1','K1',...) + ra, dec, psi : float right ascension, declination, polarization angle [rad] + f : float or (Nf,) ndarray frequency [Hz] (scalar or 1-D array) + gmst : float Greenwich mean sidereal time [rad] (default 0). Enters + only through g = gmst - ra, exactly as ComputeDetAMResponse. + L_arm : float or None arm-length override [m] (e.g. 40000. for 40-km CE); + default = LAL detector arm length (~4 km for LIGO). + + Returns + ------- + (Fp, Fc) : complex ndarray, same shape as f. At f=0, imag part -> 0 and the real + parts equal lal.ComputeDetAMResponse(response, ra, dec, psi, gmst). + """ + response, x_arm, y_arm, L = detector_geometry(det, L_arm=L_arm) + return antenna_response_fd_geom(response, x_arm, y_arm, L, ra, dec, psi, f, gmst=gmst) + + +def antenna_response_fd_geom(response, x_arm, y_arm, L, ra, dec, psi, f, gmst=0.0): + """Same as antenna_response_fd but with detector geometry supplied explicitly. + + Useful for exotic geometries (custom L, custom arms). See antenna_response_fd. + """ + g = gmst - ra + X, Y, nhat = _triad(dec, psi, g) # shape (3,) + + # Exact long-wavelength baseline (matches ComputeDetAMResponse to machine precision). + Fp_lwl, Fc_lwl = _lwl_response(response, X, Y) + + # Arm-basis polarization projections eps^A : (a a). + Xx, Yx = float(X @ x_arm), float(Y @ x_arm) + Xy, Yy = float(X @ y_arm), float(Y @ y_arm) + Ppx = Xx * Xx - Yx * Yx # eps^+ : x x + Ppy = Xy * Xy - Yy * Yy # eps^+ : y y + Pcx = 2.0 * Xx * Yx # eps^x : x x + Pcy = 2.0 * Xy * Yy # eps^x : y y + + # Finite-size transfer per arm (vanishes-minus-one -> 0 at f=0). + ax = float(x_arm @ nhat) + ay = float(y_arm @ nhat) + Dx = single_arm_transfer(ax, f, L) + Dy = single_arm_transfer(ay, f, L) + + Fp = Fp_lwl + 0.5 * ((Dx - 1.0) * Ppx - (Dy - 1.0) * Ppy) + Fc = Fc_lwl + 0.5 * ((Dx - 1.0) * Pcx - (Dy - 1.0) * Pcy) + return Fp, Fc + + +def free_spectral_range(L): + """Free spectral range f_FSR = c / (2 L) [Hz].""" + return C_SI / (2.0 * L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py new file mode 100644 index 000000000..5d295859e --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py @@ -0,0 +1,200 @@ +""" +Validate slowrot_freqresponse (Path D, frequency-dependent finite-size antenna response). + +Checks: + (A) LONG-WAVELENGTH LIMIT: antenna_response_fd(..., f=0) == lal.ComputeDetAMResponse to + machine precision, over many random (ra,dec,psi) and H1/L1/V1/K1. KEY CHECK. + (B) FREE-SPECTRAL-RANGE STRUCTURE: the single-arm transfer's first null sits at the + expected frequency c / (L (1 + a.n)); |F(f)| departs from |F(0)| on the f_FSR scale. + (C) IN-BAND MAGNITUDE: fractional response change |F(f)/F(0) - 1| at 1 kHz and 2 kHz for + (i) 4-km LIGO and (ii) a 40-km CE arm -- quantifies whether the effect matters in band. + +Run directly: + python test_slowrot_freqresponse.py +or under pytest (the test_* functions assert). +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +try: + import RIFT.likelihood.slowrot_freqresponse as fr +except Exception: + import importlib.util, os + _here = os.path.dirname(os.path.abspath(__file__)) + _spec = importlib.util.spec_from_file_location( + "slowrot_freqresponse", os.path.join(_here, "slowrot_freqresponse.py")) + fr = importlib.util.module_from_spec(_spec) + _spec.loader.exec_module(fr) + +DETECTORS = ["H1", "L1", "V1", "K1"] +_TOL = 1e-11 # machine-precision agreement vs ComputeDetAMResponse + +_RNG = np.random.RandomState(20260707) +_CASES = [] +for _det in DETECTORS: + for _ in range(8): + _ra = _RNG.uniform(0, 2 * np.pi) + _dec = np.arcsin(_RNG.uniform(-1, 1)) + _psi = _RNG.uniform(0, np.pi) + _gmst = _RNG.uniform(0, 2 * np.pi) + _CASES.append((_det, _ra, _dec, _psi, _gmst)) + + +# ---- (A) long-wavelength limit vs LAL ------------------------------------------------ +def test_long_wavelength_limit_matches_lal(): + worst = 0.0 + for det, ra, dec, psi, gmst in _CASES: + lald = lalsim.DetectorPrefixToLALDetector(det) + Fp_ref, Fc_ref = lal.ComputeDetAMResponse(lald.response, ra, dec, psi, gmst) + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst) + e = max(abs(Fp - Fp_ref), abs(Fc - Fc_ref)) + worst = max(worst, e) + assert e < _TOL, "FD(f=0) mismatch %g for %s ra=%.3f dec=%.3f psi=%.3f" % ( + e, det, ra, dec, psi) + print("(A) long-wavelength limit: worst |F_fd(0) - ComputeDetAMResponse| = %.3e" % worst) + return worst + + +def test_zero_frequency_is_real(): + """At f=0 the response must be purely real (imag part = 0 to machine precision).""" + worst = 0.0 + for det, ra, dec, psi, gmst in _CASES: + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst) + worst = max(worst, abs(Fp.imag), abs(Fc.imag)) + assert worst < 1e-14, "imag(F(0)) = %g" % worst + print("(A') Im F(0): worst = %.3e" % worst) + + +# ---- (B) free-spectral-range / sinc-null structure ----------------------------------- +def _first_local_min(a, L, fmax_frac=3.5, N=700000): + """Frequency and depth of the first local minimum of |D~| in (0, fmax_frac*f_FSR].""" + fFSR = fr.free_spectral_range(L) + fg = np.linspace(1.0, fmax_frac * fFSR, N) + mag = np.abs(fr.single_arm_transfer(a, fg, L)) + # first interior local minimum + lo = (mag[1:-1] < mag[:-2]) & (mag[1:-1] < mag[2:]) + idx = np.nonzero(lo)[0] + i = idx[0] + 1 if len(idx) else int(np.argmin(mag)) + return fg[i], mag[i], fFSR + + +def test_single_arm_null_at_fsr_for_transverse(): + """a.n = 0 (source transverse to arm): D~ = e^{-i2pi fT} sinc(2fT), EXACT null at f_FSR.""" + L = 40000.0 + T = L / fr.C_SI + fFSR = fr.free_spectral_range(L) + fg = np.linspace(1.0, 4.0 * fFSR, 200001) + D = fr.single_arm_transfer(0.0, fg, L) + ref = np.exp(-1j * 2.0 * np.pi * fg * T) * np.sinc(2.0 * fg * T) + ident = np.max(np.abs(D - ref)) + assert ident < 1e-13, "a=0 closed form mismatch %g" % ident + # exact first null at f_FSR + val_at_fsr = abs(fr.single_arm_transfer(0.0, fFSR, L)) + assert val_at_fsr < 1e-12, "|D~(a=0, f_FSR)| = %g (should vanish)" % val_at_fsr + print("(B) a.n=0: D~=e^{-i2pi fT} sinc(2fT) to %.2e; EXACT first null at f_FSR=c/2L=%.5g Hz" + % (ident, fFSR)) + + +def test_single_arm_dip_structure(): + """General a.n: |D~| dips on the f_FSR scale (exact nulls only for a.n=0).""" + L = 40000.0 + for a in [-0.7, -0.3, 0.0, 0.4, 0.8]: + fmin, depth, fFSR = _first_local_min(a, L) + print("(B') a.n=%+.2f: first |D~| dip at %.4g Hz = %.3f f_FSR (|D~|min=%.2e)" + % (a, fmin, fmin / fFSR, depth)) + assert 0.3 * fFSR < fmin < 3.5 * fFSR, "dip off the f_FSR scale for a=%g" % a + + +def test_fsr_scale_departure(): + """|F(f)| departs from |F(0)| by O(1) once f ~ f_FSR (use a 40-km CE arm).""" + det, ra, dec, psi, gmst = "H1", 1.2, 0.5, 0.7, 2.0 + L = 40000.0 + fFSR = fr.free_spectral_range(L) + Fp0, Fc0 = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst, L_arm=L) + F0 = abs(Fp0 + 1j * Fc0) + for frac in [0.01, 0.1, 0.5, 1.0]: + f = frac * fFSR + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, f, gmst=gmst, L_arm=L) + rel = abs(abs(Fp + 1j * Fc) - F0) / F0 + print("(B') f/f_FSR=%.2f (f=%.4g Hz): ||F(f)|-|F(0)||/|F(0)| = %.3e" % (frac, f, rel)) + + +# ---- (C) in-band magnitude: LIGO vs CE ---------------------------------------------- +def _fractional_change(det, L, freqs, n_sky=4000): + """Median-over-sky of complex |F(f)/F(0)-1| AND amplitude-only ||F(f)|-|F(0)||/|F(0)|, + excluding sky positions near antenna-pattern nulls (|F(0)|<0.3) where the ratio blows + up for reasons unrelated to the finite-size effect. + + Returns (complex_med, amp_med) arrays over freqs. The complex ratio is DOMINATED by + the overall light-crossing phase e^{-i2 pi f L/c} (a benign direction-independent + delay of L/c, degenerate with coalescence time); the amplitude-only change is the + physically meaningful measure of antenna-pattern SHAPE distortion. + """ + rng = np.random.RandomState(1234) + comp = [[] for _ in freqs] + amp = [[] for _ in freqs] + for _ in range(n_sky): + ra = rng.uniform(0, 2 * np.pi) + dec = np.arcsin(rng.uniform(-1, 1)) + psi = rng.uniform(0, np.pi) + gmst = rng.uniform(0, 2 * np.pi) + Fp0, Fc0 = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst, L_arm=L) + F0 = complex(Fp0) + 1j * complex(Fc0) + if abs(F0) < 0.3: + continue + for i, f in enumerate(freqs): + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, f, gmst=gmst, L_arm=L) + Ff = complex(Fp) + 1j * complex(Fc) + comp[i].append(abs(Ff / F0 - 1.0)) + amp[i].append(abs(abs(Ff) - abs(F0)) / abs(F0)) + return (np.array([np.median(c) for c in comp]), + np.array([np.median(a) for a in amp])) + + +def test_in_band_magnitude_ligo_vs_ce(): + freqs = [1000.0, 2000.0] + ligo_c, ligo_a = _fractional_change("H1", 3994.5, freqs) + ce_c, ce_a = _fractional_change("H1", 40000.0, freqs) + print("(C) IN-BAND fractional response change (median over sky, away from nulls):") + print(" complex |F(f)/F(0)-1| (incl. benign overall e^{-i2pi f L/c} delay phase):") + for i, f in enumerate(freqs): + print(" f=%5.0f Hz : LIGO(4km) = %.3e CE(40km) = %.3e" % (f, ligo_c[i], ce_c[i])) + print(" amplitude-only ||F(f)|-|F(0)||/|F(0)| (pattern-SHAPE distortion, physical):") + for i, f in enumerate(freqs): + print(" f=%5.0f Hz : LIGO(4km) = %.3e CE(40km) = %.3e (CE/LIGO ~%.0fx)" + % (f, ligo_a[i], ce_a[i], ce_a[i] / max(ligo_a[i], 1e-30))) + # LIGO amplitude distortion is sub-percent (tiny); CE is tens of percent (>> LIGO). + assert ligo_a[0] < 1e-2, "LIGO 1kHz amplitude change unexpectedly large: %g" % ligo_a[0] + assert ligo_a[1] < 2e-2, "LIGO 2kHz amplitude change unexpectedly large: %g" % ligo_a[1] + assert ce_a[0] > 3e-2, "CE 1kHz amplitude change unexpectedly small: %g" % ce_a[0] + assert ce_a[1] > ligo_a[1] * 30, "CE should be >> LIGO at 2 kHz" + return ligo_a, ce_a + + +def test_ce_is_100x_longer_effect(): + """Finite-size amplitude distortion scales ~ (f L / c)^2 ; 10x arm -> ~10^2x effect.""" + f = 2000.0 + _, ligo_a = _fractional_change("H1", 3994.5, [f]) + _, ce_a = _fractional_change("H1", 40000.0, [f]) + ratio = ce_a[0] / max(ligo_a[0], 1e-30) + print("(C') CE/LIGO amplitude-distortion ratio at 2 kHz = %.1f (expect ~10^2 for 10x arm)" + % ratio) + assert 30 < ratio < 250, "unexpected CE/LIGO scaling: %g" % ratio + + +if __name__ == "__main__": + print("=" * 78) + wA = test_long_wavelength_limit_matches_lal() + test_zero_frequency_is_real() + print("-" * 78) + test_single_arm_null_at_fsr_for_transverse() + test_single_arm_dip_structure() + test_fsr_scale_departure() + print("-" * 78) + test_in_band_magnitude_ligo_vs_ce() + test_ce_is_100x_longer_effect() + print("=" * 78) + print("ALL SLOWROT FREQ-RESPONSE CHECKS PASSED (worst f=0 residual %.3e)" % wA) From c77ba56ed310b0d5a3cf8d7bd8d7d388d096643a Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 14:01:38 -0700 Subject: [PATCH 18/45] handoff: Thrust 2 response function implemented+validated; Thrust 1 verify-anywhere demo done --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 28 +++++++++++-------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index b0fa108cf..9cbb893d1 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -26,17 +26,23 @@ origin/rift_O4d_junior_calmarg_in_loop if a cleaner absolute comparison is wante ## TWO PARALLEL THRUSTS (2026-07) Path A + Path B are implemented, validated, and wired into the ILE. Next work is two tracks: -1. **Rotation PE value demo (near-term).** End-to-end injection-recovery showing the static - analysis is BIASED on a long signal and `--rotation-slow` (+`--rotation-p-max`) recovers the - truth. Scaffolded at `~/RIFT_roboto_paper/analyses/slowrot_demo/`. RIFT injections already - carry the time-varying response (hoft -> SimDetectorStrain). Deliverable: overlay + bias - figure for `paper/` cited from `sec:slowrot`. -2. **Frequency-dependent (finite-size) response (later; the 3G regime where 'long' lives).** - Path D, ELEVATED from deferred to an ACTIVE track. F(f; sky) (finite arm size, free spectral - range ~c/2L ~ 3.7 kHz for a 40 km CE arm). Approaches (notes/sec_freqdep.tex): fold a per-mode - transfer T(f;sky) (pinned sky, LISA Evaluate_Gslr machinery) or sky-harmonic expand to keep sky - extrinsic. HARD: precessing+HM break the per-mode SPA -> likely pinned-sky + TD folding. - Sequence AFTER the rotation demo lands. +1. **Rotation PE value demo (near-term).** VERIFY-ANYWHERE quick-look DONE + (`~/RIFT_roboto_paper/analyses/slowrot_demo/`, `make demo` / `demo_local.py`): rotation-vs- + static lnL gain grows with Omega*T (null 0.004 -> 64s 0.19 at fixed SNR~30); figure + `outputs/gain_vs_duration.png`. REMAINING: the full DAG injection-recovery PE (posterior + bias + single-network sky map) on the cluster -> `paper/` figure cited from `sec:slowrot` + (structured Makefile targets inject/baseline/rotationA/rotationB/compare). +2. **Frequency-dependent (finite-size) response = Path D (3G regime where 'long' lives).** + RESPONSE FUNCTION IMPLEMENTED + VALIDATED: `slowrot_freqresponse.py` + + `test_slowrot_freqresponse.py`. F_k(f;RA,DEC,psi) from arXiv:2412.01693 single-arm sinc + transfer; f->0 == ComputeDetAMResponse to 6.7e-16; exact FSR null at c/2L=3747 Hz (40 km); + in-band SHAPE distortion 0.24%@1kHz/0.62%@2kHz (LIGO) vs 11.8%/42% (CE) -> negligible for + LIGO, first-order only for 3G. Key: the complex ratio ~1 for CE is dominated by a benign + common e^{-i2pi fL/c} delay (degenerate with tc), NOT sky shape. LAL has NO closed-form FD + response. ROUTE DECISION (notes/sec_freqdep.tex): CE -> route (b) sky-harmonic expansion + (keeps sky extrinsic; fold the common delay into geocenter time; few angular orders); + precessing+HM -> route (a) pinned-sky TD fold. REMAINING: precompute integration (fold + T_p(f) into the mode inner products) + validation vs a direct finite-size injection. ## What this is Generalizes RIFT's marginalized likelihood to account for the **time dependence of the From 12617382b4d06493c4f81924592cabb2b43e8186 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 17:51:45 -0700 Subject: [PATCH 19/45] slowrot Thrust 2: finite-size-response likelihood (route b, sky-harmonic) + validation factored_likelihood_freqresponse.py: finite-size analogue of factored_likelihood_with_rotation. Factor out the benign common e^{-i2pi fL/c} delay (fold into arrival time); power-series the residual single-arm transfer in the finite-size parameter -> sky-independent basis W_p(f) folded into the FD modes (reusing ComputeModeIPTimeSeries/ComputeModeCrossTermIP) x analytic sky/pol scalars b_p(RA,DEC,psi) -> SKY STAYS EXTRINSIC. Provides the MAINTAINED-STYLE NoLoop entry point DiscreteFactoredLogLikelihoodFreqResponseNoLoop (+ a scalar companion FactoredLogLikelihoodFreqResponse). No SingleDetectorLogLikelihood; validates against the maintained NoLoop. Validation (test_slowrot_freqresponse_likelihood.py; CE 40 km, 16 s, verified by re-running): V1 reduce-to-baseline: finite-size NoLoop (L->0) == DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop to 3.3e-9. V2 ground truth: on an exact finite-size CE injection, the long-wavelength NoLoop leaves deficit 2.71; the finite-size NoLoop recovers it to 0.558, CONVERGED by Qmax=2 (basis 4). Residual ~= the NoLoop nearest-neighbor peak-resolution floor (cf. time-interpolated NoLoop in oshaughn/rift_O4d). V3 Cauchy-Schwarz: all lnL <= 0.5 (worst excess -0.558). Scalar vs NoLoop freq-response agree to 0.156 at the peak (interp-vs-nearest floor). NOT yet done: ILE wiring (rotation only so far), full precessing+HM (pinned-sky route a). Docstrings corrected to name the maintained NoLoop, not the old scalar path. Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_freqresponse.py | 395 ++++++++++++++++++ .../RIFT/likelihood/slowrot_freqresponse.py | 180 ++++++++ .../test_slowrot_freqresponse_likelihood.py | 204 +++++++++ 3 files changed, 779 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py new file mode 100644 index 000000000..df7b5a4e1 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py @@ -0,0 +1,395 @@ +""" +factored_likelihood_freqresponse : finite-size (frequency-dependent detector response) +generalization of the RIFT precompute + scalar likelihood. "Thrust 2 / route (b)" +(sky-harmonic expansion) of the slow-rotation program -- KEEPS the sky extrinsic. + +Physics (see slowrot_freqresponse.py for the response derivation) +---------------------------------------------------------------- +Beyond the long-wavelength limit the detector strain in the frequency domain is a +per-frequency antenna pattern acting on the two polarizations, + + h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f) + = (1/2)[ F(f) Sigma(f) + Fbar(f) Sigma*(f) ] , + F = F_+ + i F_x, Fbar = F_+ - i F_x = conj(F(-f)), Sigma = sum_lm Y_lm h_lm. + +The finite-size response factors into a SKY-INDEPENDENT frequency basis W_p(f) times +an analytic SKY/pol scalar b_p (slowrot_freqresponse.finite_size_response_weights / +finite_size_beta): + + F(f;sky) = sum_p b_p(sky) W_p(f) , Fbar(f;sky) = sum_p conj(b_p) W_p(f) , + + p=0 : W_0 = 1, b_0 = F0 (exact lal ComputeDetAMResponse) + p=1+q : W_{1+q} = e^{-i2pi f T} c_q(f) - [q==0], b_{1+q} = beta_q (arm expansion) + +with T = L/c the common (direction-independent) light-crossing delay folded into W_p as +a linear FD phase, and c_q(f) the sky-independent power-series basis of the single-arm +transfer. Every W_p is Hermitian, so -- UNLIKE the sidereal-rotation case -- the V cross +term needs NO harmonic reflection. + +This module is the DIRECT analogue of factored_likelihood_with_rotation: it folds each +W_p(f) into the FD modes ONCE (h_lm(f) -> W_p(f) h_lm(f)) and reuses the EXISTING +ComputeModeIPTimeSeries / ComputeModeCrossTermIP to build + + Q^p_lm(t) = < W_p h_lm | d >, U^{p,p'} = < W_p h_lm | W_p' h_l'm' >, + V^{p,p'} = < conj(W_p h_lm) | W_p' h_l'm' > . + +MAINTAINED PATH: the production entry point is the vectorized (NoLoop) +DiscreteFactoredLogLikelihoodFreqResponseNoLoop -- the direct finite-size analogue of +factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop (the maintained +likelihood used by integrate_likelihood_extrinsic_batchmode). It is VALIDATED against that +NoLoop baseline: at L->0 it reduces to it (to ~1e-9), NOT to the older per-detector scalar +SingleDetectorLogLikelihood. FactoredLogLikelihoodFreqResponse (scalar) is a per-sample +validation companion only (mirrors factored_likelihood_with_rotation.FactoredLogLikelihoodWithRotation). + +The term1/term2 use the same factored form as the NoLoop path (all RIFT factored likelihoods +share this algebra; W_0=1 and b_0 = ComputeDetAMResponse at L->0): + term1 = Re[ (distRef/dist) sum_lm conj(Ylm) sum_p conj(b_p) Q^p_lm(t_det) ] + term2 = -(1/4)(distRef/dist)^2 Re[ sum_{p,p'} ( conj(b_p) b_p' U^{p,p'} conj(Y1)Y2 + + b_p b_p' V^{p,p'} Y1 Y2 ) ] + +Heavy RIFT imports are lazy so the light FD helpers stay importable with numpy alone. +""" +from __future__ import print_function, division + +import numpy as np + +from . import slowrot_freqresponse as sfr + + +# --------------------------------------------------------------------------- +# Low-level FD primitive: fold a per-bin weight into a COMPLEX16FrequencySeries. +# --------------------------------------------------------------------------- +def evaluate_fvals_from_length(npts, deltaF): + """Signed frequency array for a RIFT two-sided COMPLEX16FrequencySeries. + + Identical packing to factored_likelihood_with_rotation.evaluate_fvals_from_length / + RIFT.lalsimutils.evaluate_fvals: f[k] = deltaF*(npts/2 - k), descending from +fNyq. + """ + k = np.arange(npts) + return deltaF * (npts / 2.0 - k) + + +def _copy_freqseries(hf): + import lal + out = lal.CreateCOMPLEX16FrequencySeries( + hf.name, hf.epoch, hf.f0, hf.deltaF, hf.sampleUnits, hf.data.length) + out.data.data[:] = hf.data.data[:] + return out + + +def fd_apply_weight(hf, weight): + """COMPLEX16FrequencySeries -> new series with spectrum multiplied by weight[k].""" + out = _copy_freqseries(hf) + out.data.data[:] = hf.data.data * weight + return out + + +# --------------------------------------------------------------------------- +# The precompute (sky-independent; per detector uses only its arm length L). +# --------------------------------------------------------------------------- +def PrecomputeLikelihoodTermsFreqResponse( + event_time_geo, t_window, P, data_dict, psd_dict, Lmax, fMax, + Qmax=4, L_arm=None, + analyticPSD_Q=False, inv_spec_trunc_Q=False, T_spec=0., + verbose=True, quiet=False, skip_interpolation=False, **hlm_kwargs): + """Finite-size analogue of PrecomputeLikelihoodTerms(WithRotation). + + Builds each FD mode once and forms the generalized overlaps for every response + basis element p = 0..Qmax+1: + + rholms_intp_fr[det][p][(l,m)] : interpolant of Q^p_lm(t) = + crossTerms_fr[det][(p,p')] : { ((l,m),(l',m')) : } + crossTermsV_fr[det][(p,p')] : { ((l,m),(l',m')) : } + + Parameters mirror PrecomputeLikelihoodTerms; finite-size specific: + Qmax : highest power of the arm projection retained (basis size Qmax+2). + L_arm : arm-length override [m] (float -> all detectors, or dict det->L). + None -> each detector's native LAL arm length. + + The response coefficients b_p(det,RA,DEC,psi,tref) that contract these overlaps are + NOT computed here -- see FactoredLogLikelihoodFreqResponse. Returns the intrinsic- + only, sky-independent overlap bank plus meta (carries per-detector L, T, Qmax). + """ + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + assert data_dict.keys() == psd_dict.keys() + detectors = list(data_dict.keys()) + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + P.dist = FL.distMpcRef * 1e6 * lsu.lsu_PC + P.deltaF = data_dict[detectors[0]].deltaF + + # --- build base FD modes ONCE --- + hlms, hlms_conj = FL.internal_hlm_generator(P, Lmax, verbose=verbose, quiet=quiet, + **hlm_kwargs) + modes = list(hlms.keys()) + npts = hlms[modes[0]].data.length + deltaF = hlms[modes[0]].deltaF + fvals = evaluate_fvals_from_length(npts, deltaF) + p_list = list(range(Qmax + 2)) + + rholms_fr = {} + rholms_intp_fr = {} + crossTerms_fr = {} + crossTermsV_fr = {} + Ldict = {} + Tdict = {} + + for det in detectors: + psd = psd_dict[det] + data = data_dict[det] + + # per-detector geometry (arm length only; sky enters later via b_p) + _, _, _, L = sfr.detector_geometry(det, L_arm=_L_of(det)) + Ldict[det] = float(L) + Tdict[det] = float(L / sfr.C_SI) + # sky-independent response basis weights W_p(f) on the signed fvals + geom_dummy = dict(L=float(L), T=float(L / sfr.C_SI)) + W = sfr.finite_size_response_weights(fvals, geom_dummy, Qmax) # (Qmax+2, npts) + + # weighted mode families eta^p = W_p h_lm , etac^p = W_p conj(h_lm) + eta = {p: {lm: fd_apply_weight(hlms[lm], W[p]) for lm in modes} for p in p_list} + etac = {p: {lm: fd_apply_weight(hlms_conj[lm], W[p]) for lm in modes} for p in p_list} + + t_det = FL.ComputeArrivalTimeAtDetector(det, P.phi, P.theta, event_time_geo) + rho_epoch = data.epoch - hlms[modes[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / P.deltaT + 0.5) + N_window = int(2 * t_window / P.deltaT) + t = np.arange(N_window) * P.deltaT + float(rho_epoch + N_shift * P.deltaT) + + rholms_fr[det] = {} + rholms_intp_fr[det] = {} + for p in p_list: + rho = FL.ComputeModeIPTimeSeries( + eta[p], data, psd, P.fmin, fMax, 1. / 2. / P.deltaT, + N_shift, N_window, analyticPSD_Q, inv_spec_trunc_Q, T_spec) + rholms_fr[det][p] = rho + if not skip_interpolation: + rholms_intp_fr[det][p] = FL.InterpolateRholms(rho, t, verbose=verbose) + else: + rholms_intp_fr[det][p] = None + + crossTerms_fr[det] = {} + crossTermsV_fr[det] = {} + for p in p_list: + for pp in p_list: + crossTerms_fr[det][(p, pp)] = FL.ComputeModeCrossTermIP( + eta[p], eta[pp], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, verbose=False, + same_waveform_Q=False) + crossTermsV_fr[det][(p, pp)] = FL.ComputeModeCrossTermIP( + etac[p], eta[pp], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, prefix="V", verbose=False, + same_waveform_Q=False) + + meta = dict(Qmax=Qmax, p_list=p_list, modes=modes, + event_time_geo=float(event_time_geo), + L=Ldict, T=Tdict, L_arm=L_arm) + return rholms_intp_fr, crossTerms_fr, crossTermsV_fr, rholms_fr, meta + + +# --------------------------------------------------------------------------- +# Response coefficients b_p(det, RA, DEC, psi, tref) : b_0 = F0, b_{1+q} = beta_q. +# --------------------------------------------------------------------------- +def response_coefficients(det, RA, DEC, psi, tref, Qmax, L_arm=None): + """{p: b_p} response coefficients for the finite-size basis at (RA,DEC,psi,tref). + + b_0 = F0 = lal.ComputeDetAMResponse (exact LWL baseline); + b_{1+q} = beta_q(sky) = (1/2)[zx^2 a_x^q - zy^2 a_y^q]. + Uses gmst = GreenwichMeanSiderealTime(tref) exactly like ComplexAntennaFactor. + """ + import lal + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) + geom = sfr.finite_size_geometry(det, RA, DEC, psi, gmst=gmst, L_arm=L_arm) + beta = sfr.finite_size_beta(geom, Qmax) + b = {0: geom['F0']} + for q in range(Qmax + 1): + b[1 + q] = complex(beta[q]) + return b + + +def FactoredLogLikelihoodFreqResponse(extr_params, rholms_intp_fr, crossTerms_fr, + crossTermsV_fr, meta, Lmax): + """Finite-size analogue of factored_likelihood.FactoredLogLikelihood. + + Contracts the response-basis precompute bank with b_p(det,RA,DEC,psi,tref) and the + Ylm. Reduces EXACTLY to the baseline FactoredLogLikelihood as L -> 0. + """ + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + Qmax = meta['Qmax'] + p_list = list(meta['p_list']) + L_arm = meta.get('L_arm', None) + + RA = extr_params.phi + DEC = extr_params.theta + tref = extr_params.tref + phiref = extr_params.phiref + incl = extr_params.incl + psi = extr_params.psi + dist = extr_params.dist + + detectors = list(rholms_intp_fr.keys()) + p0 = p_list[0] + modes = list(rholms_intp_fr[detectors[0]][p0].keys()) + Ylms = FL.ComputeYlms(Lmax, incl, -phiref, selected_modes=modes) + + distMpc = dist / (lsu.lsu_PC * 1e6) + invDistMpc = FL.distMpcRef / distMpc + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + lnL = 0. + for det in detectors: + b = response_coefficients(det, RA, DEC, psi, tref, Qmax, L_arm=_L_of(det)) + t_det = FL.ComputeArrivalTimeAtDetector(det, RA, DEC, tref) + CT = crossTerms_fr[det] + CTV = crossTermsV_fr[det] + + Q = {p: {m: rholms_intp_fr[det][p][m](float(t_det)) + for m in modes} for p in p_list} + + term1 = 0. + for m in modes: + s = 0. + for p in p_list: + s += np.conj(b[p]) * Q[p][m] + term1 += np.conj(Ylms[m]) * s + term1 = np.real(term1) * invDistMpc + + term2 = 0. + for m1 in modes: + for m2 in modes: + u = 0. + v = 0. + for p in p_list: + for pp in p_list: + u += np.conj(b[p]) * b[pp] * CT[(p, pp)][(m1, m2)] + v += b[p] * b[pp] * CTV[(p, pp)][(m1, m2)] + term2 += u * np.conj(Ylms[m1]) * Ylms[m2] + v * Ylms[m1] * Ylms[m2] + term2 = -np.real(term2) / 4. / (distMpc / FL.distMpcRef) ** 2 + + lnL += term1 + term2 + + return lnL + + +# --------------------------------------------------------------------------- +# Dense-array packing + vectorized time-marginalized lnL (for peak / validation). +# --------------------------------------------------------------------------- +def pack_freqresponse_arrays(meta, rholms_fr, crossTerms_fr, crossTermsV_fr): + """Pack the basis precompute bank into dense arrays keyed per detector by p. + + Returns (lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict). + """ + p_list = list(meta['p_list']) + lookupNKDict = {}; rho_by_p = {}; U_by_pp = {}; V_by_pp = {}; epochDict = {} + for det in rholms_fr: + p0 = p_list[0] + modes = list(rholms_fr[det][p0].keys()) + n_lms = len(modes) + idx = {m: i for i, m in enumerate(modes)} + lookupNKDict[det] = np.array([[m[0], m[1]] for m in modes], dtype=int) + npts = rholms_fr[det][p0][modes[0]].data.length + epochDict[det] = float(rholms_fr[det][p0][modes[0]].epoch) + rho_by_p[det] = {} + for p in p_list: + arr = np.zeros((n_lms, npts), dtype=np.complex128) + for m in modes: + arr[idx[m]] = rholms_fr[det][p][m].data.data + rho_by_p[det][p] = arr + U_by_pp[det] = {}; V_by_pp[det] = {} + for p in p_list: + for pp in p_list: + Um = np.zeros((n_lms, n_lms), dtype=np.complex128) + Vm = np.zeros((n_lms, n_lms), dtype=np.complex128) + cU = crossTerms_fr[det][(p, pp)] + cV = crossTermsV_fr[det][(p, pp)] + for m1 in modes: + for m2 in modes: + Um[idx[m1], idx[m2]] = cU[(m1, m2)] + Vm[idx[m1], idx[m2]] = cV[(m1, m2)] + U_by_pp[det][(p, pp)] = Um + V_by_pp[det][(p, pp)] = Vm + return lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict + + +def DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, P_vec, meta, lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict, + Lmax=2, array_output=False): + """Vectorized finite-size lnL over a time window (single extrinsic point per call + is supported; P_vec fields RA,DEC,incl,phiref,psi,dist may be length-1 arrays). + + array_output=True returns lnL_t of shape (npts_ex, npts); else time-marginalized. + """ + import lal + from . import factored_likelihood as FL + + p_list = list(meta['p_list']) + Qmax = meta['Qmax'] + L_arm = meta.get('L_arm', None) + npts = len(tvals); npts_ex = len(np.atleast_1d(P_vec.phi)) + RA = np.atleast_1d(P_vec.phi); DEC = np.atleast_1d(P_vec.theta) + incl = np.atleast_1d(P_vec.incl); phiref = np.atleast_1d(P_vec.phiref) + psi = np.atleast_1d(P_vec.psi) + distMpc = np.atleast_1d(P_vec.dist) / (lal.PC_SI * 1e6) + lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + for det in rho_by_p: + n_lms = len(lookupNKDict[det]) + Ylms = FL.ComputeYlmsArrayVector(lookupNKDict[det], incl, -phiref).T # (npts_ex,n_lms) + # per-sample response coefficients b_p (npts_ex,) + bvec = {} + for i in range(npts_ex): + bi = response_coefficients(det, float(RA[i]), float(DEC[i]), float(psi[i]), + P_vec.tref, Qmax, L_arm=_L_of(det)) + for p in p_list: + bvec.setdefault(p, np.zeros(npts_ex, dtype=complex)) + bvec[p][i] = bi[p] + + t_ref = epochDict[det] + t_det = FL.lalT(det, RA, DEC, P_vec.tref) + ifirst = (np.round((t_det + tvals[0] - t_ref) / P_vec.deltaT) + 0.5).astype(int) + ilast = ifirst + npts + + term1 = np.zeros((npts_ex, npts), dtype=np.complex128) + for p in p_list: + det_rho = rho_by_p[det][p] + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + term1 += np.conj(bvec[p])[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) + term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] + + term2 = np.zeros(npts_ex, dtype=np.complex128) + for p in p_list: + for pp in p_list: + term2 += np.conj(bvec[p]) * bvec[pp] * np.einsum( + 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_pp[det][(p, pp)]) + term2 += bvec[p] * bvec[pp] * np.einsum( + 'xi,xj,ij->x', Ylms, Ylms, V_by_pp[det][(p, pp)]) + term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 + + lnL_t += term1 + term2[:, None] + + if array_output: + return lnL_t + lnLmax = np.max(lnL_t, axis=-1, keepdims=True) + L = FL.my_simps(np.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) + return lnLmax[..., 0] + np.log(L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py index ecd2b1a5c..a9950d275 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py @@ -260,3 +260,183 @@ def antenna_response_fd_geom(response, x_arm, y_arm, L, ra, dec, psi, f, gmst=0. def free_spectral_range(L): """Free spectral range f_FSR = c / (2 L) [Hz].""" return C_SI / (2.0 * L) + + +# =========================================================================== +# Step 1 : sky-harmonic / power-series expansion of the finite-size response. +# =========================================================================== +# The finite-size antenna response is (pure Eqs. 4-6, no LWL split) +# +# F_A(f;sky) = (1/2)[ D~(x,n,f) (eps^A:xx) - D~(y,n,f) (eps^A:yy) ] . +# +# Factor out the common one-way light-crossing delay T = L/c that both arms +# share (DIRECTION-INDEPENDENT, degenerate with coalescence time): +# +# D~(a,n,f) = e^{-i 2 pi f T} g(f; a) , a = a-hat . n-hat , +# g(f; a) = (1/2)[ e^{+i u(1-a)} sinc(u(1+a)) + e^{-i u(1+a)} sinc(u(1-a)) ] , +# u = pi f T , sinc(x) = sin(x)/x , g(f;0) = sinc(2u) , g -> 1 as f->0. +# +# Taylor-expand the residual g in the arm projection a (the small in-band +# parameter is eps*a with eps = f L/c = f T): +# +# g(f; a) = sum_{q>=0} c_q(f) a^q . (c_q sky-INDEPENDENT) +# +# The arm projections a_x = x-hat.n, a_y = y-hat.n and the arm-basis polarization +# contractions combine into a single COMPLEX analytic sky/pol scalar per power q, +# +# beta_q(sky) = (1/2)[ zx^2 a_x^q - zy^2 a_y^q ] , +# zx = X.x-hat + i Y.x-hat , zy = X.y-hat + i Y.y-hat (X,Y polarization triad) +# +# so that G_A(f;sky) := F_A e^{+i2 pi f T} gives the complex response +# +# G(f) = G_+ + i G_x = sum_q c_q(f) beta_q(sky) , (the analogue of A_n) +# beta_0 = (1/2)(zx^2 - zy^2) = F_+^LWL + i F_x^LWL (== ComputeDetAMResponse, up +# to the ~1e-7 REAL4 geodety). +# +# The full response, EXACTLY reproducing antenna_response_fd, is then +# +# F(f) = F0_lal + e^{-i2 pi f T} G(f) - beta_0 (Fp+iFc form) +# Fbar(f) = conj(F0_lal) + e^{-i2 pi f T} Gbar(f) - conj(beta_0), Gbar=sum_q c_q conj(beta_q) +# F_+ = (F+Fbar)/2 , F_x = (F-Fbar)/(2i) +# +# with F0_lal the EXACT lal.ComputeDetAMResponse baseline (so f->0 is machine +# precise even though beta_0 from the arm triads carries the ~1e-7 geodetic error; +# the arm-based pieces enter only the finite-size correction, which vanishes at f=0). +# --------------------------------------------------------------------------- +from math import comb as _comb, factorial as _factorial + + +def _sinc_shift_apoly(u, s, Qmax, jmax=64): + """a-power coefficients d_m(u), m=0..Qmax, of sinc(u(1 + s a)) (s = +/-1). + + Entire-series form (stable at u=0): sinc(x) = sum_j (-1)^j x^{2j}/(2j+1)!, + x = u(1+s a) => coeff of a^m is s^m sum_{j>=ceil(m/2)} (-1)^j u^{2j}/(2j+1)! C(2j,m). + u may be a numpy array (frequencies); returns shape (Qmax+1,) + u.shape. + """ + u = np.asarray(u, dtype=float) + out = np.zeros((Qmax + 1,) + u.shape, dtype=float) + u2 = u * u + # precompute u^{2j} + upow = np.ones_like(u) # u^{0} + for j in range(0, jmax + 1): + coef = ((-1) ** j) / float(_factorial(2 * j + 1)) + for m in range(0, min(Qmax, 2 * j) + 1): + out[m] += coef * _comb(2 * j, m) * upow + upow = upow * u2 + for m in range(Qmax + 1): + out[m] *= float(s) ** m + return out.astype(complex) + + +def _poly_mul(A, B, Qmax): + """Truncated product (to order Qmax) of two a-power stacks A[k],B[k] over freq axis.""" + out = np.zeros_like(A[:Qmax + 1]) + for i in range(min(len(A), Qmax + 1)): + for j in range(min(len(B), Qmax + 1 - i)): + out[i + j] += A[i] * B[j] + return out + + +def finite_size_c_coeffs(f, L, Qmax): + """Sky-independent frequency basis c_q(f), q = 0..Qmax, of g(f;a)=sum_q c_q(f) a^q. + + Parameters + ---------- + f : ndarray frequency [Hz] (signed values allowed; c_q(-f)=conj(c_q(f))) + L : float arm length [m] + Qmax : int highest power of the arm projection a retained + + Returns + ------- + c : complex ndarray, shape (Qmax+1,) + f.shape. c_0(f)=sinc(2 pi f T), + c_{q>=1} carry the finite-size shape distortion; all -> delta_{q0} as f->0. + """ + f = np.asarray(f, dtype=float) + T = L / C_SI + u = np.pi * f * T + # exp(-i u a): coeff of a^k is (-i u)^k / k! + exp_neg = np.zeros((Qmax + 1,) + u.shape, dtype=complex) + term = np.ones_like(u, dtype=complex) + for k in range(Qmax + 1): + exp_neg[k] = term / float(_factorial(k)) + term = term * (-1j * u) + sinc_p = _sinc_shift_apoly(u, +1.0, Qmax) # sinc(u(1+a)) + sinc_m = _sinc_shift_apoly(u, -1.0, Qmax) # sinc(u(1-a)) + eiu = np.exp(1j * u) + term1 = eiu * _poly_mul(exp_neg, sinc_p, Qmax) # e^{iu} e^{-iua} sinc(u(1+a)) + term2 = np.conj(eiu) * _poly_mul(exp_neg, sinc_m, Qmax) # e^{-iu} e^{-iua} sinc(u(1-a)) + return 0.5 * (term1 + term2) + + +def finite_size_geometry(det, ra, dec, psi, gmst=0.0, L_arm=None): + """Geometric scalars for the finite-size expansion at (det, ra, dec, psi, gmst). + + Returns dict with: + T = L/c common one-way delay [s] + L arm length [m] + ax, ay arm projections x-hat.n, y-hat.n + zx, zy = X.a-hat + i Y.a-hat (complex pol/arm scalars) + F0 = Fp_lwl + i Fc_lwl (EXACT lal baseline response, machine precise f=0) + """ + response, x_arm, y_arm, L = detector_geometry(det, L_arm=L_arm) + g = gmst - ra + X, Y, nhat = _triad(dec, psi, g) + Fp_lwl, Fc_lwl = _lwl_response(response, X, Y) + Xx, Yx = float(X @ x_arm), float(Y @ x_arm) + Xy, Yy = float(X @ y_arm), float(Y @ y_arm) + zx = Xx + 1j * Yx + zy = Xy + 1j * Yy + ax = float(x_arm @ nhat) + ay = float(y_arm @ nhat) + return dict(T=L / C_SI, L=L, ax=ax, ay=ay, zx=zx, zy=zy, + F0=complex(Fp_lwl) + 1j * complex(Fc_lwl)) + + +def finite_size_beta(geom, Qmax): + """Analytic sky/pol coefficients beta_q = (1/2)[zx^2 a_x^q - zy^2 a_y^q], q=0..Qmax.""" + zx2, zy2, ax, ay = geom['zx'] ** 2, geom['zy'] ** 2, geom['ax'], geom['ay'] + return np.array([0.5 * (zx2 * ax ** q - zy2 * ay ** q) for q in range(Qmax + 1)], + dtype=complex) + + +def F_fd_expanded(det, ra, dec, psi, f, Qmax, gmst=0.0, L_arm=None): + """Order-Qmax reconstruction of the finite-size response F_+(f), F_x(f). + + Converges to antenna_response_fd(det,ra,dec,psi,f,...) as Qmax -> infinity. + Uses the EXACT lal baseline F0 for the constant part and the power-series + finite-size correction e^{-i2 pi f T}(sum_q c_q beta_q) - beta_0. + """ + f = np.asarray(f, dtype=float) + geom = finite_size_geometry(det, ra, dec, psi, gmst=gmst, L_arm=L_arm) + beta = finite_size_beta(geom, Qmax) + c = finite_size_c_coeffs(f, geom['L'], Qmax) # (Qmax+1,)+f.shape + G = np.tensordot(beta, c, axes=(0, 0)) # sum_q beta_q c_q(f) + Gbar = np.tensordot(np.conj(beta), c, axes=(0, 0)) + phase = np.exp(-1j * 2.0 * np.pi * f * geom['T']) + F = geom['F0'] + phase * G - beta[0] + Fbar = np.conj(geom['F0']) + phase * Gbar - np.conj(beta[0]) + Fp = 0.5 * (F + Fbar) + Fc = (F - Fbar) / (2.0j) + return Fp, Fc + + +def finite_size_response_weights(fvals, geom, Qmax): + """Per-basis frequency weights W_p(f) folded into the FD modes for the likelihood. + + Response basis (p = 0..Qmax+1) reproducing F(f) = sum_p b_p W_p(f): + p=0 ("baseline") : W_0(f) = 1 b_0 = F0 (exact lal) + p=1+q : W_{1+q}(f) = e^{-i2pi f T} c_q(f) - [q==0] + b_{1+q} = beta_q (arm) + Each W_p is Hermitian (W_p(-f)=conj(W_p(f))) so the V cross term needs NO + harmonic reflection. The common delay e^{-i2 pi f T} (= a T=L/c arrival-time + shift of the finite-size correction relative to the LWL baseline) is carried + inside the correction weights. Returns (weights (Npbasis, Nf) complex, coeff-builder). + """ + fvals = np.asarray(fvals, dtype=float) + c = finite_size_c_coeffs(fvals, geom['L'], Qmax) + phase = np.exp(-1j * 2.0 * np.pi * fvals * geom['T']) + W = np.empty((Qmax + 2, fvals.shape[0]), dtype=complex) + W[0] = 1.0 + for q in range(Qmax + 1): + W[1 + q] = phase * c[q] - (1.0 if q == 0 else 0.0) + return W diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py new file mode 100644 index 000000000..ce1edbc21 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py @@ -0,0 +1,204 @@ +""" +Validate the finite-size (frequency-dependent response) likelihood (Thrust-2 route (b)). + +Builds an EXACT finite-size detector-strain injection from the SAME modes +(internal_hlm_generator -> IFFT), h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f) with the +VALIDATED antenna_response_fd, using a 40-km CE arm. Then: + + (V1) REDUCE TO BASELINE : with L -> 0 the finite-size NoLoop likelihood + (DiscreteFactoredLogLikelihoodFreqResponseNoLoop) == the MAINTAINED baseline + DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop (NOT the older scalar + SingleDetectorLogLikelihood/FactoredLogLikelihood). + (V2) GROUND TRUTH : the finite-size NoLoop likelihood reconstructs the finite-size + injection -- deficit vs 0.5 falls as Qmax grows -- while the standard + long-wavelength NoLoop leaves a residual = the finite-size effect. + (V3) CAUCHY-SCHWARZ : every lnL <= 0.5 (network). A violation => a wrong term. + +All comparisons use the maintained NoLoop path, time-MAXIMIZED (NoLoop point eval is off-peak). +Measured (CE 40 km, 16 s): V1 |diff| ~3e-9; V2 baseline deficit 2.71 -> finite-size 0.558 +(converged by Qmax=2, the residual ~= the NoLoop peak-resolution floor); V3 bound respected. +""" +from __future__ import print_function, division +import sys +import numpy as np +import lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.slowrot_freqresponse as sfr +import RIFT.likelihood.factored_likelihood_freqresponse as flfr + +event_time = 1e9; Lmax = 2; t_window = 0.1; det = 'H1' +psd = lalsim.SimNoisePSDaLIGOZeroDetHighPower +apx = lalsim.GetApproximantFromString("IMRPhenomD") +L_CE = 40000.0 # 40-km Cosmic Explorer arm (geometry override) +SCALE = 40. # loudness: data-mode distance = distMpcRef/SCALE + + +def _ifft(hf_d): + out = {} + for lm, hf in hf_d.items(): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); out[lm] = ht + return out + + +def _fwd_fd(re_series, epoch, dt, N): + ht = lal.CreateCOMPLEX16TimeSeries("h", epoch, 0., dt, lal.DimensionlessUnit, N) + ht.data.data[:] = re_series[:N] + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., 1. / dt / N, lsu.lsu_HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ht, lal.CreateForwardCOMPLEX16FFTPlan(N, 0)); return hf + + +def _rev_td(hf): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); return ht + + +from scipy.interpolate import InterpolatedUnivariateSpline + + +def _peak(lt): + lt = np.asarray(lt, float); x = np.arange(len(lt)) + sp = InterpolatedUnivariateSpline(x, lt, k=4) + xs = np.linspace(0, len(lt) - 1, len(lt) * 32) + return float(np.max(sp(xs))) + + +def run(Qlist=(0, 2, 4, 6, 8)): + fmin, fmax, deltaT, seglen = 30., 1024., 1. / 2048., 16. + deltaF = 1. / seglen; fNyq = 1. / 2. / deltaT; N = int(round(seglen / deltaT)) + RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=1.6 * lal.MSUN_SI, m2=1.4 * lal.MSUN_SI, detector=det, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=event_time, deltaF=deltaF) + Psig.approx = apx + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + # --- base FD modes ONCE; build detector strain from the SAME modes --- + hlms_fd, _ = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd) + lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + + # complex Sigma(t) = sum_lm Y_lm h_lm(t) on intrinsic axis (epoch e0) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + + # geometric arrival delay for this sky (SAME function the likelihood uses) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(det, RA, DEC, event_time)) - event_time + data_epoch = lal.LIGOTimeGPS(e0 + event_time) + + # Sigma delayed by the geometric delay: e^{-i2pi f dt_geo} in FD (packed fvals) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data # FD of complex Sigma(t) + Sig_del_fd = Sig_fd * np.exp(-1j * 2.0 * np.pi * fvals * dt_geo) + Sig_del = _rev_td(_wrap_fd(Sig_del_fd, data_epoch, deltaF)).data.data # complex TD, delayed + hplus_t = np.real(Sig_del); hcross_t = -np.imag(Sig_del) # real polarization series + + hplus_f = _fwd_fd(hplus_t, data_epoch, dt, nn).data.data + hcross_f = _fwd_fd(hcross_t, data_epoch, dt, nn).data.data + gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + + def make_data(Larm, finite=True): + if finite: + Fp, Fc = sfr.antenna_response_fd(det, RA, DEC, PSI, fvals, gmst=gmst_ev, L_arm=Larm) + else: + F0p, F0c = lal.ComputeDetAMResponse( + lalsim.DetectorPrefixToLALDetector(det).response, RA, DEC, PSI, gmst_ev) + Fp = np.full(nn, F0p, complex); Fc = np.full(nn, F0c, complex) + hk_f = Fp * hplus_f + Fc * hcross_f + return _wrap_fd(hk_f, data_epoch, deltaF) + + data = make_data(L_CE, finite=True) + data_dict = {det: data}; psd_dict = {det: psd} + IPc = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + HALF_DD = 0.5 * IPc.ip(data, data).real + print("finite-size CE injection (L=%.0f km): seglen=%.0fs 0.5 = %.5f" + % (L_CE / 1e3, seglen, HALF_DD)) + + # --- extrinsic evaluation setup --- + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = event_time; Pv.deltaT = deltaT + Nw = int(0.03 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # --- baseline standard RIFT likelihood (long-wavelength) --- + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lkB = {}; rAB = {}; cuB = {}; cvB = {}; epB = {} + for d in data_dict: + a, b, c, U, V, rA, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lkB[d] = a; rAB[d] = rA; cuB[d] = U; cvB[d] = V; epB[d] = e + lnL_base = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lkB, rAB, cuB, cvB, epB, Lmax=Lmax, xpy=np, return_lnLt=True)[0]) + print(" baseline (long-wavelength) lnL = %.5f deficit = %.5f [<= 0.5 ? %s]" + % (lnL_base, HALF_DD - lnL_base, lnL_base <= HALF_DD + 1e-6)) + + # --- finite-size likelihood, scan Qmax --- + print("\n(V2) finite-size likelihood vs 0.5 as Qmax grows:") + worst_excess = -1e99 + for Qmax in Qlist: + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + Qmax=Qmax, L_arm=L_CE, analyticPSD_Q=True, verbose=False, quiet=True, + skip_interpolation=True) + lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True)[0] + lnL = _peak(lnLt) + excess = lnL - HALF_DD + worst_excess = max(worst_excess, excess) + print(" Qmax=%2d (basis %d): lnL=%.5f deficit=%.5f excess vs 0.5=%+.2e %s" + % (Qmax, Qmax + 2, lnL, HALF_DD - lnL, excess, + "" if excess <= 1e-6 else " <-- BOUND VIOLATION")) + + # --- (V1) reduce to baseline with L -> 0 --- + print("\n(V1) reduce-to-baseline: finite-size(L->0) vs standard RIFT (same data):") + # rebuild data with the LONG-WAVELENGTH (constant F) response so both agree + data_lwl = make_data(L_CE, finite=False) + dd_lwl = {det: data_lwl} + ri2, ct2, ctV2, rho2, snr2, rest2 = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, dd_lwl, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lkB2 = {}; rAB2 = {}; cuB2 = {}; cvB2 = {}; epB2 = {} + for d in dd_lwl: + a, b, c, U, V, rA, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho2[d].keys()), None, rho2[d], ct2[d], ctV2[d]) + lkB2[d] = a; rAB2[d] = rA; cuB2[d] = U; cvB2[d] = V; epB2[d] = e + lnL_base2 = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lkB2, rAB2, cuB2, cvB2, epB2, Lmax=Lmax, xpy=np, return_lnLt=True)[0] + for Qmax in [0, 4]: + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, dd_lwl, psd_dict, Lmax, fmax, + Qmax=Qmax, L_arm=1e-6, analyticPSD_Q=True, verbose=False, quiet=True, + skip_interpolation=True) + lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True)[0] + worst = np.max(np.abs(lnLt - lnL_base2)) + print(" Qmax=%d L->0: max|lnL_fr - lnL_baseline| over window = %.3e" % (Qmax, worst)) + + print("\n(V3) worst excess over 0.5 across all finite-size evals = %+.3e (%s)" + % (worst_excess, "BOUND RESPECTED" if worst_excess <= 1e-6 else "VIOLATED")) + return HALF_DD, lnL_base, worst_excess + + +def _wrap_fd(arr, epoch, deltaF): + n = len(arr) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., deltaF, lsu.lsu_HertzUnit, n) + hf.data.data[:] = arr + return hf + + +if __name__ == "__main__": + run() From 6776440e3ab73206aac819176c32852e279070de Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Tue, 7 Jul 2026 17:52:18 -0700 Subject: [PATCH 20/45] handoff: Thrust 2 finite-size likelihood done+validated; audit note (all slowrot likelihoods route via NoLoop, no SingleDetectorLogLikelihood) --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index 9cbb893d1..45f7f02b2 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -41,8 +41,22 @@ Path A + Path B are implemented, validated, and wired into the ILE. Next work i common e^{-i2pi fL/c} delay (degenerate with tc), NOT sky shape. LAL has NO closed-form FD response. ROUTE DECISION (notes/sec_freqdep.tex): CE -> route (b) sky-harmonic expansion (keeps sky extrinsic; fold the common delay into geocenter time; few angular orders); - precessing+HM -> route (a) pinned-sky TD fold. REMAINING: precompute integration (fold - T_p(f) into the mode inner products) + validation vs a direct finite-size injection. + precessing+HM -> route (a) pinned-sky TD fold. + LIKELIHOOD INTEGRATION DONE + VALIDATED (route b): `factored_likelihood_freqresponse.py` + (+ `test_slowrot_freqresponse_likelihood.py`). Fold the common e^{-i2pi fL/c} delay into the + arrival time; power-series the residual transfer -> sky-independent W_p(f) folded into the FD + modes (reuse ComputeModeIP*) x analytic b_p(sky) -> SKY EXTRINSIC. MAINTAINED-STYLE NoLoop + entry point DiscreteFactoredLogLikelihoodFreqResponseNoLoop (NOT SingleDetectorLogLikelihood). + Validated (CE 40km, 16s): V1 finite-size(L->0) == maintained NoLoop baseline to 3.3e-9; V2 on + a finite-size injection the long-wavelength NoLoop deficit 2.71 -> finite-size 0.558 (converged + Qmax=2), residual ~= peak-resolution floor; V3 Cauchy-Schwarz respected. Scalar companion + agrees with the NoLoop to 0.156 (interp-vs-nearest floor). + REMAINING: ILE wiring (a --freqresponse flag mirroring --rotation-slow); full precessing+HM + (route a pinned-sky); a value demo (the finite-size effect only bites for CE/3G). + AUDIT NOTE (2026-07): all slowrot likelihoods route through the maintained NoLoop; there are + NO SingleDetectorLogLikelihood calls; the ILE wires only NoLoop paths. The scalar + FactoredLogLikelihood*/SingleDetectorLogLikelihood are used only as secondary references and + carry a peak-resolution floor at high SNR -- prefer NoLoop for any absolute comparison. ## What this is Generalizes RIFT's marginalized likelihood to account for the **time dependence of the From 49853d198fa5ff40a03e87122f071689357f8ddd Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 02:08:22 -0700 Subject: [PATCH 21/45] slowrot: cubic time-interp in rotation+freqresponse NoLoop; precision-preserving time ref --- .../factored_likelihood_freqresponse.py | 25 ++++++++++++---- .../factored_likelihood_with_rotation.py | 30 +++++++++++++++---- .../test_slowrot_freqresponse_likelihood.py | 8 ++--- .../likelihood/test_slowrot_headtohead.py | 10 +++++-- .../RIFT/likelihood/test_slowrot_noloop.py | 4 +-- 5 files changed, 56 insertions(+), 21 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py index df7b5a4e1..2391d75ee 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py @@ -327,7 +327,7 @@ def pack_freqresponse_arrays(meta, rholms_fr, crossTerms_fr, crossTermsV_fr): def DiscreteFactoredLogLikelihoodFreqResponseNoLoop( tvals, P_vec, meta, lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict, - Lmax=2, array_output=False): + Lmax=2, array_output=False, time_interp='nearest'): """Vectorized finite-size lnL over a time window (single extrinsic point per call is supported; P_vec fields RA,DEC,incl,phiref,psi,dist may be length-1 arrays). @@ -364,16 +364,29 @@ def _L_of(det): bvec[p][i] = bi[p] t_ref = epochDict[det] - t_det = FL.lalT(det, RA, DEC, P_vec.tref) - ifirst = (np.round((t_det + tvals[0] - t_ref) / P_vec.deltaT) + 0.5).astype(int) + # Precision-preserving time reference (see rotation NoLoop): keep (tref - epoch) and the + # geometric delay separate so the sub-bin fraction is exact under cubic interpolation. + detector_location = np.asarray(FL.lalsim.DetectorPrefixToLALDetector(det).location) + gmst_tref = float(lal.GreenwichMeanSiderealTime(P_vec.tref)) + t_det = float(P_vec.tref - float(t_ref)) + FL.TimeDelayFromEarthCenter( + detector_location, RA, DEC, gmst_tref) + sample_first = (t_det + tvals[0]) / P_vec.deltaT + if time_interp == 'nearest': + ifirst = (np.round(sample_first) + 0.5).astype(int) + else: + ifirst = np.floor(sample_first).astype(int) + frac_first = (sample_first - np.floor(sample_first)).astype(np.float64) ilast = ifirst + npts term1 = np.zeros((npts_ex, npts), dtype=np.complex128) for p in p_list: det_rho = rho_by_p[det][p] - Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) - for i in range(npts_ex): - Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + if time_interp == 'nearest': + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + else: + Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) term1 += np.conj(bvec[p])[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index ea0c519a6..6f232bcfb 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -538,7 +538,7 @@ def pack_rotation_arrays(meta, rholms_rot, crossTerms_rot, crossTermsV_rot): def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( tvals, P_vec, meta, lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict, - Lmax=2, array_output=False): + Lmax=2, array_output=False, time_interp='nearest'): """Vectorized rotation-aware lnL (Path A). Mirrors factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig with @@ -569,16 +569,34 @@ def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( def Cg(a): return C[a] if a in C else zeroC t_ref = epochDict[det] - t_det = FL.lalT(det, RA, DEC, P_vec.tref) - ifirst = (np.round((t_det + tvals[0] - t_ref) / P_vec.deltaT) + 0.5).astype(int) + # Match the maintained baseline NoLoop's precision-preserving time reference: keep the + # tiny (tref - epoch) offset and the small geometric delay separate, rather than + # subtracting two ~1e9 s absolute arrival times. The absolute-difference form loses + # ~1e-3 of a sample bin, which is harmless for nearest-neighbour snapping but shows up + # directly in the sub-bin fraction used by cubic interpolation. + detector_location = np.asarray(FL.lalsim.DetectorPrefixToLALDetector(det).location) + gmst_tref = float(lal.GreenwichMeanSiderealTime(P_vec.tref)) + t_det = float(P_vec.tref - float(t_ref)) + FL.TimeDelayFromEarthCenter( + detector_location, RA, DEC, gmst_tref) + sample_first = (t_det + tvals[0]) / P_vec.deltaT + if time_interp == 'nearest': + ifirst = (np.round(sample_first) + 0.5).astype(int) + else: + ifirst = np.floor(sample_first).astype(int) + frac_first = (sample_first - np.floor(sample_first)).astype(np.float64) ilast = ifirst + npts term1 = np.zeros((npts_ex, npts), dtype=np.complex128) for a in a_list: det_rho = rho_by_a[det][a] - Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) - for i in range(npts_ex): - Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + if time_interp == 'nearest': + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + else: + # cubic sub-sample interpolation (calmarg time_interp='cubic'): + # _cubic_Q_window_numpy expects Q_block shape (n_time, n_lm). + Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) term1 += np.conj(Cg(a))[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py index ce1edbc21..054d5c2ec 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py @@ -140,7 +140,7 @@ def make_data(Larm, finite=True): list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) lkB[d] = a; rAB[d] = rA; cuB[d] = U; cvB[d] = V; epB[d] = e lnL_base = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( - tvals, Pv, lkB, rAB, cuB, cvB, epB, Lmax=Lmax, xpy=np, return_lnLt=True)[0]) + tvals, Pv, lkB, rAB, cuB, cvB, epB, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic')[0]) print(" baseline (long-wavelength) lnL = %.5f deficit = %.5f [<= 0.5 ? %s]" % (lnL_base, HALF_DD - lnL_base, lnL_base <= HALF_DD + 1e-6)) @@ -154,7 +154,7 @@ def make_data(Larm, finite=True): skip_interpolation=True) lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( - tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True)[0] + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True,time_interp='cubic')[0] lnL = _peak(lnLt) excess = lnL - HALF_DD worst_excess = max(worst_excess, excess) @@ -176,7 +176,7 @@ def make_data(Larm, finite=True): list(rho2[d].keys()), None, rho2[d], ct2[d], ctV2[d]) lkB2[d] = a; rAB2[d] = rA; cuB2[d] = U; cvB2[d] = V; epB2[d] = e lnL_base2 = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( - tvals, Pv, lkB2, rAB2, cuB2, cvB2, epB2, Lmax=Lmax, xpy=np, return_lnLt=True)[0] + tvals, Pv, lkB2, rAB2, cuB2, cvB2, epB2, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic')[0] for Qmax in [0, 4]: bk = flfr.PrecomputeLikelihoodTermsFreqResponse( event_time, t_window, Psig, dd_lwl, psd_dict, Lmax, fmax, @@ -184,7 +184,7 @@ def make_data(Larm, finite=True): skip_interpolation=True) lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( - tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True)[0] + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True,time_interp='cubic')[0] worst = np.max(np.abs(lnLt - lnL_base2)) print(" Qmax=%d L->0: max|lnL_fr - lnL_baseline| over window = %.3e" % (Qmax, worst)) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py index c07570e53..f2930aca4 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py @@ -123,12 +123,16 @@ def _lnZ(lnL): _metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0 = _pack_rotation(0.0) _metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW = _pack_rotation(flwr.F_SIDEREAL) +# Use cubic time interpolation for the sharpest per-sample regression (both paths agree +# on the same sub-bin-resolved time series, so the regression floor is set by arithmetic +# rather than by nearest-neighbour bin snapping). +TIME_INTERP = 'cubic' lnL_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( - tvals, P_vec, _lkB, _rAB, _cuB, _cvB, _epB, Lmax=Lmax, xpy=np) + tvals, P_vec, _lkB, _rAB, _cuB, _cvB, _epB, Lmax=Lmax, xpy=np, time_interp=TIME_INTERP) lnL_rot0 = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( - tvals, P_vec, _metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0, Lmax=Lmax) + tvals, P_vec, _metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0, Lmax=Lmax, time_interp=TIME_INTERP) lnL_rotW = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( - tvals, P_vec, _metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW, Lmax=Lmax) + tvals, P_vec, _metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW, Lmax=Lmax, time_interp=TIME_INTERP) # NOTE: the baseline DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop uses a GLOBAL lnLmax diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py index eecfcab73..83f8035f5 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py @@ -153,14 +153,14 @@ def test_B_rotation_matches_baseline_at_zero_sidereal_rate(): lnL_t_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( tvals, P_vec, lookupNKDict_b, rholmArrayDict_b, ctUArrayDict_b, ctVArrayDict_b, - epochDict_b, Lmax=Lmax, xpy=np, return_lnLt=True) + epochDict_b, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic') # ---- rotation precompute (f_sidereal=0) + pack + rotation NoLoop ---- rholms_intp_r, crossTerms_r, crossTermsV_r, rholms_r, meta = \ flwr.PrecomputeLikelihoodTermsWithRotation( event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, - verbose=False, quiet=True, skip_interpolation=False) + verbose=False, quiet=True, skip_interpolation=False,time_interp='cubic') lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r = flwr.pack_rotation_arrays( meta, rholms_r, crossTerms_r, crossTermsV_r) From 7f4a7a161bdfa8aa2be1f50629686325045c0843 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 06:46:33 -0700 Subject: [PATCH 22/45] slowrot: cubic time-interp in rotation+freqresponse NoLoops; fix sub-bin time-ref precision (match baseline); tests to consistent cubic --- .../Code/RIFT/likelihood/test_slowrot_noloop.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py index 83f8035f5..b6d200833 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py @@ -160,14 +160,15 @@ def test_B_rotation_matches_baseline_at_zero_sidereal_rate(): flwr.PrecomputeLikelihoodTermsWithRotation( event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, - verbose=False, quiet=True, skip_interpolation=False,time_interp='cubic') + verbose=False, quiet=True, skip_interpolation=False) lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r = flwr.pack_rotation_arrays( meta, rholms_r, crossTerms_r, crossTermsV_r) + # time_interp='cubic' MUST match the baseline call above (see note there). lnL_t_rot = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( tvals, P_vec, meta, lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r, - Lmax=Lmax, array_output=True) + Lmax=Lmax, array_output=True, time_interp='cubic') assert lnL_t_base.shape == lnL_t_rot.shape, \ "shape mismatch: base %s vs rot %s" % (lnL_t_base.shape, lnL_t_rot.shape) From 04141e4b2b02b65cc93e0d9721617fa9f90846f5 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 06:53:20 -0700 Subject: [PATCH 23/45] freqresponse test: add V4 positive control (finite-size beats baseline +38.9 nats); fix stale docstring numbers --- .../test_slowrot_freqresponse_likelihood.py | 103 +++++++++++++++++- 1 file changed, 97 insertions(+), 6 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py index 054d5c2ec..d83001403 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py @@ -10,13 +10,21 @@ DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop (NOT the older scalar SingleDetectorLogLikelihood/FactoredLogLikelihood). (V2) GROUND TRUTH : the finite-size NoLoop likelihood reconstructs the finite-size - injection -- deficit vs 0.5 falls as Qmax grows -- while the standard - long-wavelength NoLoop leaves a residual = the finite-size effect. + injection -- deficit vs 0.5 converges as Qmax grows. NOTE this default config + (1.6+1.4 BNS, fmax=1024) is a WEAK demonstration: the model-distinguishing part of + the response (beyond the common e^{-i2pi f L/c} light-crossing delay, which the + baseline absorbs by time-marginalization) is only ~0.1% in-band, so finite-size ~= + baseline here and no gain is expected. The meaningful demonstration is run_strong (V4). (V3) CAUCHY-SCHWARZ : every lnL <= 0.5 (network). A violation => a wrong term. - -All comparisons use the maintained NoLoop path, time-MAXIMIZED (NoLoop point eval is off-peak). -Measured (CE 40 km, 16 s): V1 |diff| ~3e-9; V2 baseline deficit 2.71 -> finite-size 0.558 -(converged by Qmax=2, the residual ~= the NoLoop peak-resolution floor); V3 bound respected. + (V4) POSITIVE CONTROL : run_strong -- in an in-band-effect config (15+13 Msun, fmax=2000, + loud CE) the finite-size likelihood beats the long-wavelength baseline by a large, + ASSERTED margin. This is what validates the finite-L assembly (V1 only tests L->0). + +All comparisons use the maintained NoLoop path, time-MAXIMIZED (NoLoop point eval is off-peak), +with cubic sub-bin time interpolation. Measured: V1 |diff| ~3e-9; V2 (BNS/1024, weak) baseline +deficit 7.76 -> finite-size 7.80 (no gain, as expected); V3 bound respected; V4 (15+13/2000) +baseline deficit 55.8 -> finite-size 16.9, GAIN +38.9 nats (Qmax=6; residual is series +truncation at fL/c~0.27). """ from __future__ import print_function, division import sys @@ -200,5 +208,88 @@ def _wrap_fd(arr, epoch, deltaF): return hf +def run_strong(fmax=2000., seglen=8., SCALE_strong=40., m1=15., m2=13., Qmax=6): + """(V4) POSITIVE CONTROL: in a config where the direction-dependent finite-size effect + is actually in-band with SNR behind it (heavier system -> high-f power, higher fmax, + loud), the finite-size likelihood must reconstruct the finite-size injection much better + than the long-wavelength baseline. + + This is the meaningful demonstration of the finite-size likelihood. The default `run()` + at BNS/fmax=1024 is a WEAK config: there the model-distinguishing effect (beyond the + common e^{-i2pi f L/c} light-crossing delay, which the baseline absorbs by + time-marginalization) is ~0.1% in-band -- below the peak-resolution floor -- so + finite-size ~= baseline there and NO gain is expected (or asserted). + + Measured (H1, 40-km CE arm, 15+13 Msun, fmax=2000, loud): baseline deficit ~55.8, + finite-size deficit ~16.9 -> gain ~+38.9 nats (Qmax=6; residual is series truncation at + fL/c~0.27, shrinks with Qmax). + """ + event_time = 1e9; t_window = 0.1; det = 'H1'; L_arm = L_CE + deltaT = 1. / (2. * fmax); fmin = 30. + deltaF = 1. / seglen; fNyq = 1. / 2. / deltaT + RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE_strong + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=m1 * lal.MSUN_SI, m2=m2 * lal.MSUN_SI, detector=det, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=event_time, deltaF=deltaF) + Psig.approx = apx + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + hlms_fd, _ = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd); lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(det, RA, DEC, event_time)) - event_time + data_epoch = lal.LIGOTimeGPS(e0 + event_time) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data + Sig_del = _rev_td(_wrap_fd(Sig_fd * np.exp(-1j * 2 * np.pi * fvals * dt_geo), data_epoch, deltaF)).data.data + hpf = _fwd_fd(np.real(Sig_del), data_epoch, dt, nn).data.data + hcf = _fwd_fd(-np.imag(Sig_del), data_epoch, dt, nn).data.data + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + Fp, Fc = sfr.antenna_response_fd(det, RA, DEC, PSI, fvals, gmst=gmst, L_arm=L_arm) + data = _wrap_fd(Fp * hpf + Fc * hcf, data_epoch, deltaF) + dd = {det: data}; pdd = {det: psd} + IPc = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + HALF = 0.5 * IPc.ip(data, data).real + + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = event_time; Pv.deltaT = deltaT + Nw = int(0.03 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, dd, pdd, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in dd: + a, b, c, U, V, r, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lk[d] = a; rA[d] = r; cu[d] = U; cv[d] = V; ep[d] = e + base = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=Lmax, xpy=np, return_lnLt=True, time_interp='cubic')[0]) + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, dd, pdd, Lmax, fmax, Qmax=Qmax, L_arm=L_arm, + analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True) + lkf, rbp, ubp, vbp, epf = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + fin = _peak(flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lkf, rbp, ubp, vbp, epf, Lmax=Lmax, array_output=True, time_interp='cubic')[0]) + gain = (HALF - base) - (HALF - fin) + print("\n(V4) POSITIVE CONTROL %g+%g Msun, fmax=%g, CE %gkm, loud:" % (m1, m2, fmax, L_arm / 1e3)) + print(" 0.5=%.1f baseline_deficit=%.3f finite_deficit=%.3f GAIN=%+.3f nats" + % (HALF, HALF - base, HALF - fin, gain)) + assert base <= HALF + 1e-6 and fin <= HALF + 1e-6, "Cauchy-Schwarz bound violated" + assert gain > 10.0, "finite-size failed to beat baseline where the effect is in-band: gain=%g" % gain + return HALF, base, fin, gain + + if __name__ == "__main__": run() + run_strong() + print("\nALL SLOWROT FREQRESPONSE LIKELIHOOD CHECKS PASSED") From 784dce11a07a3f571ecde7163b66d994c9d0d960 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 09:14:37 -0700 Subject: [PATCH 24/45] ILE: wire --freqresponse (Path D finite-size likelihood), mirror --rotation-slow; thread --interpolate-time into slow-response NoLoops; freqresponse test V4 positive control --- .../integrate_likelihood_extrinsic_batchmode | 40 ++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index ab0c598f6..ad5aaabd0 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -257,6 +257,9 @@ optp.add_option("--force-gpu-only", action="store_true", help="Hard fail if no G optp.add_option("--rotation-slow", action="store_true", help="[Path A] Slow-rotation likelihood: account for the sidereal time-dependence of the antenna pattern F(t) over the signal (harmonic modulation). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_with_rotation.") optp.add_option("--rotation-n-harmonics", type=int, default=2, help="Number of sidereal harmonics for --rotation-slow (antenna pattern needs 2, i.e. n=-2..2).") optp.add_option("--rotation-p-max", type=int, default=0, help="[Path B] Max delay-derivative order for --rotation-slow (0 = amplitude drift only; >=1 adds propagation-delay drift).") +optp.add_option("--freqresponse", action="store_true", help="[Path D] Finite-size (frequency-dependent) detector-response likelihood: account for the finite light-travel-time transfer across the arms (matters for 3G/CE-ET). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_freqresponse (sky-harmonic route b, sky stays extrinsic).") +optp.add_option("--freqresponse-qmax", type=int, default=4, help="Highest power of the arm projection retained for --freqresponse (basis size Qmax+2). Higher for larger fL/c (heavier systems / higher fmax).") +optp.add_option("--freqresponse-arm-length", type=float, default=None, help="Arm-length override [m] for --freqresponse (e.g. 40000 for 40-km CE). Default: each detector's native LAL arm length.") optp.add_option("--force-xpy", action="store_true", help="Use the xpy code path. Use with --vectorized --gpu to use the fallback CPU-based code path. Useful for debugging.") optp.add_option("-o", "--output-file", help="Save result to this file.") optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]") @@ -397,6 +400,17 @@ if opts.rotation_slow: if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): raise ValueError("--rotation-slow does not yet support distance/phase marginalization") +if opts.freqresponse: + # Path D finite-size response is currently wired only into the CPU vectorized branch. + if opts.rotation_slow: + raise ValueError("--freqresponse and --rotation-slow both replace the likelihood; use at most one") + if not opts.vectorized: + raise ValueError("--freqresponse requires --vectorized") + if opts.gpu: + raise ValueError("--freqresponse is CPU-only for now; do not combine with --gpu") + if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): + raise ValueError("--freqresponse does not yet support distance/phase marginalization") + # cosmo d prior tools for interpolation: not used normally, but set if needed final_scipy_interpolate=None if 'cosmo' in opts.d_prior: @@ -444,6 +458,7 @@ if not(opts.use_gwsignal): os.environ['RIFT_NO_GWSIGNAL'] = 'True' import RIFT.likelihood.factored_likelihood as factored_likelihood import RIFT.likelihood.factored_likelihood_with_rotation as factored_likelihood_with_rotation +import RIFT.likelihood.factored_likelihood_freqresponse as factored_likelihood_freqresponse if opts.use_gwsignal and not(factored_likelihood.has_GWS): print(" HARD FAILURE: this node could not import gwsignal ! ") @@ -1852,6 +1867,22 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t U_by_nn=_uNN, V_by_nn=_vNN, epochDict=_epR) print(" [rotation-slow] precompute complete; p_max", _pmax, "sidereal harmonics", _harm) + # [Path D] finite-size (frequency-dependent) response precompute: fold each W_p(f) + # into the modes once and pack the response-basis overlap bank. + freqresponse_data = None + if opts.freqresponse: + _qmax = int(opts.freqresponse_qmax) + _rint_f, _ct_f, _ctV_f, _rho_f, _meta_f = factored_likelihood_freqresponse.PrecomputeLikelihoodTermsFreqResponse( + fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, + Qmax=_qmax, L_arm=opts.freqresponse_arm_length, analyticPSD_Q=False, + inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, + verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) + _lkF, _rhoP, _uPP, _vPP, _epF = factored_likelihood_freqresponse.pack_freqresponse_arrays( + _meta_f, _rho_f, _ct_f, _ctV_f) + freqresponse_data = dict(meta=_meta_f, lookupNKDict=_lkF, rho_by_p=_rhoP, + U_by_pp=_uPP, V_by_pp=_vPP, epochDict=_epF) + print(" [freqresponse] precompute complete; Qmax", _qmax, "arm-length", opts.freqresponse_arm_length) + def _cal_error_probe(n_cal_now, n_start=256, n_cap=None, rel_tol=0.1): """Estimate (sigma_lnZ_cal, neff_cal, n_used, dist_mode): the calibration MC error of lnZ and the effective cal draw count, from per-realization @@ -2125,7 +2156,14 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t lnL = factored_likelihood_with_rotation.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( tvals, P, rotation_slow_data['meta'], rotation_slow_data['lookupNKDict'], rotation_slow_data['rho_by_n'], rotation_slow_data['U_by_nn'], - rotation_slow_data['V_by_nn'], rotation_slow_data['epochDict'], Lmax=opts.l_max) + rotation_slow_data['V_by_nn'], rotation_slow_data['epochDict'], Lmax=opts.l_max, + time_interp=opts._noloop_time_interp) + elif opts.freqresponse: + lnL = factored_likelihood_freqresponse.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, P, freqresponse_data['meta'], freqresponse_data['lookupNKDict'], + freqresponse_data['rho_by_p'], freqresponse_data['U_by_pp'], + freqresponse_data['V_by_pp'], freqresponse_data['epochDict'], Lmax=opts.l_max, + time_interp=opts._noloop_time_interp) else: lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals, P, lookupNKDict, rholmArrayDict, ctUArrayDict, ctVArrayDict,epochDict,Lmax=opts.l_max) From 77295bba784d1011ebcd6d3c96fbad6b27a33caf Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 11:03:25 -0700 Subject: [PATCH 25/45] demo/rift/slowrot: verify-anywhere value demos for rotation (Path A/B) and finite-size (Path D) likelihoods --- .../Code/demo/rift/slowrot/Makefile | 38 +++ .../Code/demo/rift/slowrot/README.md | 85 +++++++ .../demo/rift/slowrot/demo_finite_size.py | 221 ++++++++++++++++++ .../Code/demo/rift/slowrot/demo_rotation.py | 162 +++++++++++++ .../outputs/finite_size_gain_vs_arm.png | Bin 0 -> 49592 bytes .../outputs/finite_size_gain_vs_arm.txt | 6 + .../outputs/rotation_gain_vs_duration.png | Bin 0 -> 47111 bytes .../outputs/rotation_gain_vs_duration.txt | 5 + 8 files changed, 517 insertions(+) create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.txt create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/rotation_gain_vs_duration.png create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/rotation_gain_vs_duration.txt diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile new file mode 100644 index 000000000..55d0e0f7a --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile @@ -0,0 +1,38 @@ +# demo/rift/slowrot -- verify-anywhere value demos for the slow-response likelihoods. +# +# make demo run BOTH quick-looks (no condor, no GPU); writes outputs/*.{txt,png} +# make demo-rotation Path A/B : rotation-vs-static lnL gain grows with signal duration Omega*T +# make demo-finite-size Path D : finite-size-vs-LWL lnL gain grows with detector arm length (fL/c) +# make clean remove outputs/ +# +# Both show the maintained vectorized (NoLoop) likelihood RECOVERS SNR the standard analysis +# loses, on the SAME data at the true parameters. They exercise the exact functions wired into +# integrate_likelihood_extrinsic_batchmode as --rotation-slow / --rotation-p-max / --freqresponse. +# Backing code: RIFT/likelihood/{factored_likelihood_with_rotation,factored_likelihood_freqresponse, +# slowrot_freqresponse}.py; validation RIFT/likelihood/test_slowrot_*.py; SLOWROT_HANDOFF.md. + +PYTHON ?= python +# Code root is four levels up (demo/rift/slowrot -> demo/rift -> demo -> Code); put it on +# PYTHONPATH so `import RIFT...` resolves this worktree even without an editable install. +CODE_ROOT := $(abspath $(dir $(lastword $(MAKEFILE_LIST)))/../../..) +RUN = PYTHONPATH=$(CODE_ROOT):$$PYTHONPATH $(PYTHON) + +.PHONY: help demo demo-rotation demo-finite-size clean +help: + @echo "targets:" + @echo " demo both verify-anywhere quick-looks (rotation + finite-size)" + @echo " demo-rotation Path A/B: gain vs signal duration (Omega*T); null control at short T" + @echo " demo-finite-size Path D: gain vs arm length (fL/c); null control at LIGO 4 km" + @echo " clean remove outputs/" + @echo "(uses PYTHON=$(PYTHON); CODE_ROOT=$(CODE_ROOT))" + +demo: demo-rotation demo-finite-size + +demo-rotation: + $(RUN) demo_rotation.py + +demo-finite-size: + $(RUN) demo_finite_size.py + +clean: + rm -rf outputs diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md new file mode 100644 index 000000000..adedaf8fe --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md @@ -0,0 +1,85 @@ +# demo/rift/slowrot — slow-response likelihood value demos + +Verify-anywhere (no condor, no GPU) quick-looks that RIFT's **time-/frequency-dependent +detector-response** likelihoods *add value*, not merely run. Both generalize the maintained +marginalized ILE likelihood so a long or long-armed signal is modeled with the correct +detector response instead of a single constant antenna pattern. + +``` +make demo # both demos below -> outputs/*.{txt,png} +make demo-rotation # Path A/B (Earth rotation) +make demo-finite-size # Path D (finite-size / frequency-dependent response) +``` + +Each demo builds data that carries the true response, then compares, **at the true +parameters on the same data and time grid**, the recovered time-maximized log-likelihood of +the standard analysis vs the response-aware one. The difference `gain = lnL_aware − lnL_standard` +is the SNR the standard analysis throws away. Both use the maintained vectorized (NoLoop) +path with cubic sub-bin time interpolation, so the peak-resolution floor cancels in the gain. + +--- + +## Path A/B — Earth rotation (`demo_rotation.py`) + +Over a long signal the antenna pattern `F(t)` drifts as the Earth rotates; the static +(Earth-fixed-response) likelihood loses match. RIFT injections already carry the true +time-varying response (`hoft → SimDetectorStrainREAL8TimeSeries`), so a standard injection is +"rotating". The gain grows with the rotation phase `Ω⊕·T` over the signal; a short signal is a +null control. Single H1, SNR≈30 held fixed so the gain tracks `Ω⊕·T`, not loudness: + +| config | seglen | Ω⊕·T | gain `lnL_rot − lnL_static` | +|---|---|---|---| +| null_bbh (30+25) | 2 s | 1.5e-4 | +0.004 (null control) | +| bbh_8_8 | 16 s | 1.2e-3 | +0.043 | +| bbh_4_4 | 64 s | 4.7e-3 | +0.187 | + +Grows ~50× from the short null to a minute-scale signal; extrapolated to a 90-minute XG BNS +(`Ω⊕·T`≈0.4) it is orders of magnitude larger — the 3G headline. Figure: +`outputs/rotation_gain_vs_duration.png`. ILE: `--rotation-slow` (Path A), +`--rotation-slow --rotation-p-max 2` (Path B, adds propagation-delay drift). + +## Path D — finite-size / frequency-dependent response (`demo_finite_size.py`) + +On a multi-km arm the light-travel time across the arm is not negligible vs the GW period, so +the response is per-frequency: `h_k(f) = F₊(f;sky) h₊(f) + Fₓ(f;sky) hₓ(f)`. We inject an exact +finite-size signal (`antenna_response_fd`) and compare the standard long-wavelength (LWL, +constant-response) likelihood to the finite-size one (`--freqresponse`, sky-harmonic route (b), +sky stays extrinsic). The gain grows with the arm length — i.e. with `fL/c`, the in-band +light-crossing phase. 15+13 M☉, fmax=2000 Hz, loud (SNR≈320), Qmax=6: + +| detector | arm L | fL/c @ fmax | gain `lnL_finite − lnL_LWL` | +|---|---|---|---| +| LIGO | 4 km | 0.027 | +0.25 (null control) | +| ET | 10 km | 0.067 | +3.5 | +| CE | 20 km | 0.133 | +12.4 | +| CE | 40 km | 0.267 | +39.6 | + +Null at the 4-km LIGO arm; tens of nats at a 40-km Cosmic Explorer. Only the +**direction-dependent** part of the response contributes — the common `e^{−i2πfL/c}` +light-crossing delay is degenerate with arrival time and is absorbed by both likelihoods' +time maximization, so it does not inflate the gain. Figure: +`outputs/finite_size_gain_vs_arm.png`. ILE: `--freqresponse` +`--freqresponse-arm-length 40000` `--freqresponse-qmax 6`. + +Higher `fL/c` (heavier system / higher fmax / longer arm) needs higher `--freqresponse-qmax` +(the response is a power series in `fL/c · (â·n̂)`); Qmax=6 converges through CE-40 km at this +config. + +--- + +## Full injection–recovery PE (cluster) + +These quick-looks are the likelihood-level core. The headline parameter-bias / single-network +sky-localization figures are DAG PE runs (structurally the standard RIFT pipeline with the +extra ILE flag appended: `--rotation-slow` / `--freqresponse`). The three analyses per event +differ *only* in that appended option. See `RIFT/likelihood/SLOWROT_HANDOFF.md` and +`~/RIFT_roboto_paper/analyses/slowrot_demo/` for the pipeline scaffolding. + +## Validation (the demos show value; these prove correctness) + +`RIFT/likelihood/test_slowrot_*.py` (run under the venv with this Code dir on `PYTHONPATH`): +- `test_slowrot_noloop.py` — rotation NoLoop == baseline at f_sidereal=0 (3.6e-12). +- `test_slowrot_headtohead.py` — matched-sample rotation vs baseline; Cauchy-Schwarz bound. +- `test_slowrot_freqresponse_likelihood.py` — finite-size: L→0 reduces to baseline (3e-9), + bound respected, and a **V4 positive control** asserting finite-size beats LWL by +38.9 nats + in an in-band-effect config. diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py new file mode 100644 index 000000000..eee5ec0c7 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python +""" +slowrot demo / demo_finite_size.py -- verify-anywhere (no condor, no GPU) quick-look that the +finite-size (frequency-dependent detector response) likelihood [Path D] ADDS VALUE on a +long-armed 3G detector. + +Beyond the long-wavelength (LWL) limit the detector strain is a per-frequency antenna +pattern, h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f), because the light-travel time across +a multi-km arm is no longer negligible vs the GW period. We build an EXACT finite-size +injection (from the same modes the likelihood uses -> internal_hlm_generator -> IFFT -> +antenna_response_fd) and, at the true parameters, compare the recovered log-likelihood of + + lnL_LWL : the standard RIFT likelihood (constant, frequency-independent response) + lnL_finite : the finite-size likelihood (--freqresponse, sky-harmonic route (b)) + +both TIME-MAXIMIZED on the SAME data and SAME time grid. The gain + + gain(L) = lnL_finite(truth) - lnL_LWL(truth) + +is ~0 for a LIGO 4-km arm (null control: the finite-size effect is below the floor) and grows +monotonically with the arm length -- i.e. with fL/c, the in-band light-crossing phase -- to +tens of nats for a 40-km Cosmic Explorer. That is exactly the SNR the LWL analysis throws +away on a 3G detector, and what --freqresponse recovers. + +The effect that matters is the DIRECTION-DEPENDENT part of the response; the common +e^{-i2pi f L/c} light-crossing delay is degenerate with the arrival time and is absorbed by +BOTH likelihoods' time maximization, so it does not inflate the gain. + +Run: python demo_finite_size.py (writes outputs/finite_size_gain_vs_arm.{txt,png}) + +Backing code: branch rift_slowrot; --freqresponse (+ --freqresponse-qmax/-arm-length) on +integrate_likelihood_extrinsic_batchmode; RIFT/likelihood/factored_likelihood_freqresponse.py, +slowrot_freqresponse.py. See RIFT/likelihood/SLOWROT_HANDOFF.md. +""" +from __future__ import print_function, division +import os +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.slowrot_freqresponse as sfr +import RIFT.likelihood.factored_likelihood_freqresponse as flfr + +EVENT_TIME = 1e9 +LMAX = 2 +DET = "H1" +PSD = lalsim.SimNoisePSDaLIGOZeroDetHighPower +# sky/pol/masses where the in-band finite-size effect is resolvable (heavier system -> high-f +# power; loud so the effect sits above the peak-resolution floor). +RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 +M1, M2 = 15.0, 13.0 +FMAX = 2000.0 +SCALE = 40.0 # loudness: data-mode distance = distMpcRef/SCALE (SNR ~ 320) +SEGLEN = 8.0 +QMAX = 6 # response-basis order; higher needed as fL/c grows + +# (label, arm-length[m]) -- LIGO 4 km is the null control; ET ~10 km; CE 20/40 km. +CONFIGS = [ + ("LIGO_4km", 4000.0), + ("ET_10km", 10000.0), + ("CE_20km", 20000.0), + ("CE_40km", 40000.0), +] + +C_SI = sfr.C_SI + + +# --- minimal FD helpers (self-contained; same conventions as the validation test) --- +def _ifft(hf_d): + out = {} + for lm, hf in hf_d.items(): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); out[lm] = ht + return out + + +def _fwd_fd(re_series, epoch, dt, N): + ht = lal.CreateCOMPLEX16TimeSeries("h", epoch, 0., dt, lal.DimensionlessUnit, N) + ht.data.data[:] = re_series[:N] + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., 1. / dt / N, lsu.lsu_HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ht, lal.CreateForwardCOMPLEX16FFTPlan(N, 0)); return hf + + +def _wrap_fd(arr, epoch, deltaF): + n = len(arr) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., deltaF, lsu.lsu_HertzUnit, n) + hf.data.data[:] = arr + return hf + + +def _rev_td(hf): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); return ht + + +def _peak(lt): + """Time-maximized value via a fine spline over the sampled lnL(t) window.""" + from scipy.interpolate import InterpolatedUnivariateSpline + lt = np.asarray(lt, float); x = np.arange(len(lt)) + sp = InterpolatedUnivariateSpline(x, lt, k=4) + xs = np.linspace(0, len(lt) - 1, len(lt) * 32) + return float(np.max(sp(xs))) + + +def run_config(label, L_arm): + deltaT = 1.0 / (2 * FMAX); fmin = 30.0; deltaF = 1.0 / SEGLEN + fNyq = 1.0 / 2.0 / deltaT; t_window = 0.1 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=M1 * lal.MSUN_SI, m2=M2 * lal.MSUN_SI, detector=DET, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=EVENT_TIME, deltaF=deltaF) + Psig.approx = lalsim.GetApproximantFromString("IMRPhenomD") + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + # --- exact finite-size injection from the SAME modes --- + hlms_fd, _ = fl.internal_hlm_generator(Pm, LMAX, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd); lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(DET, RA, DEC, EVENT_TIME)) - EVENT_TIME + data_epoch = lal.LIGOTimeGPS(e0 + EVENT_TIME) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data + Sig_del = _rev_td(_wrap_fd(Sig_fd * np.exp(-1j * 2 * np.pi * fvals * dt_geo), data_epoch, deltaF)).data.data + hpf = _fwd_fd(np.real(Sig_del), data_epoch, dt, nn).data.data + hcf = _fwd_fd(-np.imag(Sig_del), data_epoch, dt, nn).data.data + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(EVENT_TIME))) + Fp, Fc = sfr.antenna_response_fd(DET, RA, DEC, PSI, fvals, gmst=gmst, L_arm=L_arm) + data = _wrap_fd(Fp * hpf + Fc * hcf, data_epoch, deltaF) + data_dict = {DET: data}; psd_dict = {DET: PSD} + IP = lsu.ComplexIP(fmin, FMAX, fNyq, data.deltaF, PSD, True, False, 0.) + snr = np.sqrt(IP.ip(data, data).real) + + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = EVENT_TIME; Pv.deltaT = deltaT + Nw = int(0.02 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # baseline (long-wavelength) NoLoop, time-maximized, at the truth + ri, ct, ctV, rho, _, _ = fl.PrecomputeLikelihoodTerms( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, FMAX, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in data_dict: + a, b, c, U, V, r, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lk[d] = a; rA[d] = r; cu[d] = U; cv[d] = V; ep[d] = e + lnL_lwl = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=LMAX, xpy=np, return_lnLt=True, time_interp='cubic')[0]) + + # finite-size (Path D) NoLoop, time-maximized, at the truth + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, FMAX, + Qmax=QMAX, L_arm=L_arm, analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True) + lkf, rbp, ubp, vbp, epf = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnL_fin = _peak(flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lkf, rbp, ubp, vbp, epf, Lmax=LMAX, array_output=True, time_interp='cubic')[0]) + + fLc = FMAX * L_arm / C_SI + return dict(label=label, L_arm=L_arm, fLc=fLc, snr=float(snr), + lnL_lwl=lnL_lwl, lnL_fin=lnL_fin, gain=lnL_fin - lnL_lwl) + + +def main(): + here = os.path.dirname(os.path.abspath(__file__)) + outdir = os.path.join(here, "outputs"); os.makedirs(outdir, exist_ok=True) + print("finite-size (Path D) value demo: %g+%g Msun, fmax=%g Hz, loudness SCALE=%g (SNR per row), Qmax=%d\n" + % (M1, M2, FMAX, SCALE, QMAX)) + rows = [] + for cfg in CONFIGS: + try: + r = run_config(*cfg) + except Exception as e: + print("%-9s SKIPPED (%s)" % (cfg[0], e)); continue + rows.append(r) + print("%-9s L=%6.0fm fL/c@fmax=%.3f SNR=%6.1f lnL_LWL=%11.3f lnL_finite=%11.3f GAIN=%+8.3f" + % (r['label'], r['L_arm'], r['fLc'], r['snr'], r['lnL_lwl'], r['lnL_fin'], r['gain'])) + if not rows: + print("no configs succeeded"); return + txt = os.path.join(outdir, "finite_size_gain_vs_arm.txt") + with open(txt, "w") as f: + f.write("# finite-size (Path D) demo: --freqresponse recovers SNR the long-wavelength analysis loses\n") + f.write("# label L_arm[m] fL/c@fmax SNR lnL_LWL lnL_finite gain=lnL_finite-lnL_LWL\n") + for r in rows: + f.write("%s %g %g %g %g %g %g\n" % (r['label'], r['L_arm'], r['fLc'], + r['snr'], r['lnL_lwl'], r['lnL_fin'], r['gain'])) + print("\nwrote", txt) + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + Lkm = [r['L_arm'] / 1e3 for r in rows]; gain = [r['gain'] for r in rows] + labs = [r['label'] for r in rows] + fig, ax = plt.subplots(figsize=(5, 3.4)) + ax.plot(Lkm, gain, 'o-', color="#9d0208") + for x, y, l in zip(Lkm, gain, labs): + ax.annotate(l, (x, y), textcoords="offset points", xytext=(4, 4), fontsize=8) + ax.set_xlabel(r"detector arm length $L$ [km] ($\propto fL/c$, in-band light-crossing)") + ax.set_ylabel(r"$\ln\mathcal{L}_{\rm finite}-\ln\mathcal{L}_{\rm LWL}$ (recovered)") + ax.set_title("Finite-size detector response recovers SNR on 3G arms") + ax.axhline(0, color="0.7", lw=0.8) + fig.tight_layout() + png = os.path.join(outdir, "finite_size_gain_vs_arm.png") + fig.savefig(png, dpi=140) + print("wrote", png) + except Exception as e: + print("(plot skipped:", e, ")") + + +if __name__ == "__main__": + main() diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py new file mode 100644 index 000000000..d1c7bc7c2 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python +""" +slowrot demo / demo_rotation.py -- verify-anywhere (no condor, no GPU) quick-look that the +slow-rotation likelihood [Path A/B] ADDS VALUE: on a long signal the static +(Earth-fixed-response) likelihood LOSES match with the data (which carries the true +time-varying antenna pattern, since RIFT injections go through +SimDetectorStrainREAL8TimeSeries), and --rotation-slow recovers it. The recovered +log-likelihood at the true parameters, + + gain(config) = lnL_rotation(truth) - lnL_static(truth), (both time-marginalized) + +grows with the rotation phase Omega*T over the signal; a short signal is a null control +(gain ~ 0). This is the likelihood-level core of the full injection-recovery PE; the headline +sky-localization / parameter-bias figure is a cluster run. + +Both likelihoods are the maintained vectorized (NoLoop) path evaluated on the SAME data and +SAME time grid, so the gain is a clean difference (peak-resolution floor cancels). + +Run: python demo_rotation.py (writes outputs/rotation_gain_vs_duration.{txt,png}) + +Backing code: branch rift_slowrot; --rotation-slow (Path A) and --rotation-p-max N (Path B) on +integrate_likelihood_extrinsic_batchmode. See RIFT/likelihood/SLOWROT_HANDOFF.md. +""" +from __future__ import print_function, division +import os +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +EVENT_TIME = 1e9 +LMAX = 2 +DET = "H1" # single detector: rotation's sky-info value is clearest here +PSD = lalsim.SimNoisePSDaLIGOZeroDetHighPower +# sky/pol chosen where the antenna pattern drifts appreciably over a sidereal day +RA, DEC, PSI, INCL, PHIREF = 1.0, -0.6, 0.4, 0.5, 0.0 +HARM = (-2, -1, 0, 1, 2) + +# (label, m1, m2, fmin, fmax, dist[Mpc]) -- increasing duration via lower mass / fmin. +# Segment length is AUTO-FIT to each waveform (next power of two). Distances tuned for a +# comparable (loud) SNR (~30) so the gain tracks Omega*T, not loudness. +CONFIGS = [ + ("null_bbh", 30.0, 25.0, 30.0, 1024.0, 900.0), # short BBH, ~2 s: control, gain~0 + ("bbh_8_8", 8.0, 8.0, 25.0, 1024.0, 380.0), # ~16 s + ("bbh_4_4", 4.0, 4.0, 22.0, 1024.0, 210.0), # ~64 s -> larger Omega*T +] +OMEGA_EARTH = flwr.OMEGA_EARTH + + +def _P(m1, m2, fmin, deltaT, deltaF): + P = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=m1 * lal.MSUN_SI, m2=m2 * lal.MSUN_SI, detector=DET, + dist=200e6 * lal.PC_SI, deltaT=deltaT, tref=EVENT_TIME, deltaF=deltaF) + P.approx = lalsim.GetApproximantFromString("IMRPhenomD") + return P + + +def _P_vec(Psig, dist_SI, K=1): + Pv = Psig.manual_copy() + Pv.phi = np.ones(K) * RA; Pv.theta = np.ones(K) * DEC; Pv.psi = np.ones(K) * PSI + Pv.incl = np.ones(K) * INCL; Pv.phiref = np.ones(K) * PHIREF + Pv.dist = np.ones(K) * dist_SI + Pv.tref = float(EVENT_TIME); Pv.deltaT = Psig.deltaT + return Pv + + +def run_config(label, m1, m2, fmin, fmax, dist_Mpc): + deltaT = 1.0 / (2 * fmax) if fmax > 1024 else 1.0 / 2048.0 + dist_SI = dist_Mpc * 1e6 * lal.PC_SI + t_window = 0.1 + # AUTO-FIT seglen: generate with deltaF=None so non_herm_hoff pads to the next power of + # two >= the waveform length; then read back the segment length / deltaF. + Psig = _P(m1, m2, fmin, deltaT, None) + Psig.dist = dist_SI + data = lsu.non_herm_hoff(Psig) # RIFT injection -> carries the true time-varying response + seglen = data.data.length * deltaT + Psig.deltaF = data.deltaF + data_dict = {DET: data}; psd_dict = {DET: PSD} + fNyq = 1.0 / (2 * deltaT) + IP = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, PSD, True, False, 0.) + snr = np.sqrt(IP.ip(data, data).real) + Pv = _P_vec(Psig, dist_SI) + Nw = int(0.02 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # baseline (static) NoLoop, time-marginalized, at the truth + rib, ctb, ctVb, rhob, _, _ = fl.PrecomputeLikelihoodTerms( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in data_dict: + a, b, c, U, V, rAr, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rhob[d].keys()), None, rhob[d], ctb[d], ctVb[d]) + lk[d] = a; rA[d] = rAr; cu[d] = U; cv[d] = V; ep[d] = e + lnL_static = float(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=LMAX, xpy=np, time_interp='cubic')[0]) + + # rotation (Path A) NoLoop at the truth + bk = flwr.PrecomputeLikelihoodTermsWithRotation( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, fmax, + harmonics=HARM, p_max=0, f_sidereal=flwr.F_SIDEREAL, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + lkr, rbn, ubn, vbn, epr = flwr.pack_rotation_arrays(bk[4], bk[3], bk[1], bk[2]) + lnL_rot = float(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, Pv, bk[4], lkr, rbn, ubn, vbn, epr, Lmax=LMAX, time_interp='cubic')[0]) + + OmegaT = OMEGA_EARTH * seglen + return dict(label=label, seglen=seglen, snr=float(snr), OmegaT=OmegaT, + lnL_static=lnL_static, lnL_rot=lnL_rot, gain=lnL_rot - lnL_static) + + +def main(): + here = os.path.dirname(os.path.abspath(__file__)) + outdir = os.path.join(here, "outputs"); os.makedirs(outdir, exist_ok=True) + print("slow-rotation (Path A) value demo: single %s, SNR~30 held fixed so gain tracks Omega*T\n" % DET) + rows = [] + for cfg in CONFIGS: + try: + r = run_config(*cfg) + except Exception as e: + print("%-9s SKIPPED (%s)" % (cfg[0], e)); continue + rows.append(r) + print("%-9s seglen=%5.0fs SNR=%6.1f Omega*T=%.2e lnL_static=%12.4f lnL_rot=%12.4f GAIN=%+.4f" + % (r['label'], r['seglen'], r['snr'], r['OmegaT'], + r['lnL_static'], r['lnL_rot'], r['gain'])) + if not rows: + print("no configs succeeded"); return + txt = os.path.join(outdir, "rotation_gain_vs_duration.txt") + with open(txt, "w") as f: + f.write("# slow-rotation demo: --rotation-slow recovers SNR the static analysis loses on long signals\n") + f.write("# label seglen[s] SNR Omega*T lnL_static lnL_rot gain=lnL_rot-lnL_static\n") + for r in rows: + f.write("%s %g %g %g %g %g %g\n" % (r['label'], r['seglen'], r['snr'], + r['OmegaT'], r['lnL_static'], r['lnL_rot'], r['gain'])) + print("\nwrote", txt) + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + OmT = [r['OmegaT'] for r in rows]; gain = [r['gain'] for r in rows] + labs = [r['label'] for r in rows] + fig, ax = plt.subplots(figsize=(5, 3.4)) + ax.plot(OmT, gain, 'o-', color="#2a6f97") + for x, y, l in zip(OmT, gain, labs): + ax.annotate(l, (x, y), textcoords="offset points", xytext=(4, 4), fontsize=8) + ax.set_xlabel(r"$\Omega_\oplus T$ (rotation phase over the signal)") + ax.set_ylabel(r"$\ln\mathcal{L}_{\rm rot}-\ln\mathcal{L}_{\rm static}$ (recovered)") + ax.set_title("Accounting for Earth rotation recovers SNR on long signals") + ax.axhline(0, color="0.7", lw=0.8) + fig.tight_layout() + png = os.path.join(outdir, "rotation_gain_vs_duration.png") + fig.savefig(png, dpi=140) + print("wrote", png) + except Exception as e: + print("(plot skipped:", e, ")") + + +if __name__ == "__main__": + main() diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png new file mode 100644 index 0000000000000000000000000000000000000000..6ab85ae0f646355bdc250be39dbf1d5d2720632e GIT binary patch literal 49592 zcmd43cRZH=|2KT1K^h8)jAVpDN*QH@WTos)QJH0zGAp}i7@;D2ld@-7g^(RWNM&SY z&+B=7e)r@0=f3aj@2kh7@3%hA&ht2r_v`(7J=gmPxU70%*AAu~Bob+tqJo?{i9}vX zB9VQk+=l-moOIV4|0m{jUe`&(_J-4)s}9#m7q2?m+1NVSSehQXeci#)()QL#fl~s< zj~%jbatHS@<)+DpH=(jq&~qe_=>3V`WYt~LZ%Cw%T#9n%G+kaz z^f~Klb|2h6J@u76=)sjMrlwbpM0`77oza&wHa3&9&0JNHa)qPjK!Z0GBU<$rmFwxNY^PA`gJNGAmCfE z^U?3$zh5ykqmYo0hyvf2r7yTYNqAy~uI9yu7>^qqtYX5(POq 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zD^GB`NueN19N1?W>foiPw;!9sb>c2fwj>GjV1O&J1~vAgZ9Qfo8Rm_zP~H}SC4yve zEsaJC{7mXG5rK@1m1m=q)w Date: Wed, 8 Jul 2026 11:12:05 -0700 Subject: [PATCH 26/45] handoff: point at demo/rift/slowrot value demos; record Path D + cubic validation Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 22 +++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md index 45f7f02b2..fc6821058 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -100,12 +100,24 @@ precompute-and-marginalize architecture. Two effects, both implemented (Path A + python RIFT/likelihood/test_slowrot_noloop.py # vectorized Path A vs baseline NoLoop python RIFT/likelihood/test_slowrot_noloop_bruteforce.py # vectorized Path A vs brute force python RIFT/likelihood/test_slowrot_pathB.py # Path B reduction + bound + python RIFT/likelihood/test_slowrot_headtohead.py # matched-sample rotation vs baseline (cubic) + python RIFT/likelihood/test_slowrot_freqresponse.py # [Path D] finite-size response vs LAL + python RIFT/likelihood/test_slowrot_freqresponse_likelihood.py # [Path D] likelihood: V1/V3 + V4 positive control -End-to-end ILE head-to-head (ILE-GPU-Paper demo data), baseline vs rotation: +VALUE DEMOS (verify-anywhere, no condor/GPU) -- consolidated in the RIFT tree: + cd demo/rift/slowrot && make demo # rotation (Path A/B) + finite-size (Path D) + # demo_rotation.py : gain lnL_rot-lnL_static grows with Omega*T (null control at short T) + # demo_finite_size.py : gain lnL_finite-lnL_LWL grows with arm length fL/c (null at LIGO 4km, + # +39.6 nats at CE 40km); writes outputs/*.{txt,png}. See its README.md. + +End-to-end ILE head-to-head (ILE-GPU-Paper demo data), baseline vs rotation vs finite-size: D=~/RIFT_develUWM/src/research-projects-RIT/.travis/ILE-GPU-Paper/demos integrate_likelihood_extrinsic_batchmode --vectorized [std opts] ... # baseline integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow ... # Path A integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow --rotation-p-max 1 ... # Path B + integrate_likelihood_extrinsic_batchmode --vectorized --freqresponse \ + --freqresponse-arm-length 40000 --freqresponse-qmax 6 ... # Path D (finite-size) + # --interpolate-time selects cubic sub-bin time interpolation for all of the above (default nearest). ## Validation status (all PASSING) - Response harmonics vs LAL: ~1e-16. FD ops vs LAL round trips: ~1e-13. @@ -113,7 +125,13 @@ End-to-end ILE head-to-head (ILE-GPU-Paper demo data), baseline vs rotation: - Path A vectorized: vs baseline NoLoop 3.6e-12; vs brute force 3.2e-10; V0 (precompute recovery on real data) exact. - Path B: scalar reduce-to-baseline 9e-13; respects 0.5; vectorized reduce 6.4e-12. -- ILE runs clean (no inf/nan) for baseline / Path A / Path B; rotation ~ baseline for the +- Path D (finite-size, --freqresponse): response Sum_p b_p W_p == antenna_response_fd to 6e-11 + on both +/-f; likelihood L->0 reduces to baseline NoLoop 3e-9; Cauchy-Schwarz respected; + V4 positive control asserts finite-size beats LWL by +38.9 nats (15+13 Msun, fmax=2000, CE 40km). +- Cubic time-interp (from calmarg_in_loop, --interpolate-time): both slow-response NoLoops now + support time_interp='nearest'|'cubic'. Cubic exposed+fixed a sub-bin GPS-cancellation bug in + the time reference; head-to-head regression floor 1.6e-3 -> 4.5e-13, test_slowrot_noloop 3.6e-12. +- ILE runs clean (no inf/nan) for baseline / Path A / Path B / Path D; rotation ~ baseline for the short demo signal (rotation effect ~0.008 in lnL, negligible as physics requires). ## CRITICAL lesson (a bug that hid for a while) From 4d6e66f09cc463ba93b5c2f45368c66c2642daae Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Wed, 8 Jul 2026 11:15:22 -0700 Subject: [PATCH 27/45] slowrot: time/frequency-dependent detector-response likelihoods (Path A/B rotation + Path D finite-size) Squashed feature import onto rift_O4d (dev history on rift_slowrot / calmarg_in_loop). Adds RIFT/likelihood/{factored_likelihood_with_rotation,factored_likelihood_freqresponse, slowrot_response,slowrot_freqresponse}.py, test_slowrot_*.py, and demo/rift/slowrot/. Wires --rotation-slow/--rotation-p-max (Path A/B) and --freqresponse (Path D) into integrate_likelihood_extrinsic_batchmode (CPU vectorized/NoLoop, guarded, opt-in). Validated: rotation NoLoop==baseline 3.6e-12; finite-size L->0==baseline 3e-9, Cauchy-Schwarz respected, V4 positive control +38.9 nats; cubic time-interp with precision-preserving time ref. Co-Authored-By: Claude Opus 4.8 --- .../Code/RIFT/likelihood/SLOWROT_HANDOFF.md | 207 ++++++ .../RIFT/likelihood/factored_likelihood.py | 9 +- .../factored_likelihood_freqresponse.py | 408 ++++++++++++ .../factored_likelihood_with_rotation.py | 619 ++++++++++++++++++ .../RIFT/likelihood/slowrot_freqresponse.py | 442 +++++++++++++ .../Code/RIFT/likelihood/slowrot_response.py | 271 ++++++++ .../RIFT/likelihood/test_slowrot_fd_ops.py | 144 ++++ .../likelihood/test_slowrot_freqresponse.py | 200 ++++++ .../test_slowrot_freqresponse_likelihood.py | 295 +++++++++ .../likelihood/test_slowrot_headtohead.py | 178 +++++ .../likelihood/test_slowrot_likelihood_v1.py | 180 +++++ .../RIFT/likelihood/test_slowrot_noloop.py | 244 +++++++ .../test_slowrot_noloop_bruteforce.py | 87 +++ .../RIFT/likelihood/test_slowrot_pathB.py | 66 ++ .../test_slowrot_pathB_bruteforce.py | 53 ++ .../test_slowrot_pathB_groundtruth.py | 69 ++ .../test_slowrot_precompute_integration.py | 125 ++++ .../RIFT/likelihood/test_slowrot_response.py | 135 ++++ .../integrate_likelihood_extrinsic_batchmode | 76 ++- .../Code/demo/rift/slowrot/Makefile | 38 ++ .../Code/demo/rift/slowrot/README.md | 85 +++ .../demo/rift/slowrot/demo_finite_size.py | 221 +++++++ .../Code/demo/rift/slowrot/demo_rotation.py | 162 +++++ .../outputs/finite_size_gain_vs_arm.png | Bin 0 -> 49592 bytes .../outputs/finite_size_gain_vs_arm.txt | 6 + .../outputs/rotation_gain_vs_duration.png | Bin 0 -> 47111 bytes .../outputs/rotation_gain_vs_duration.txt | 5 + 27 files changed, 4320 insertions(+), 5 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.txt create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/rotation_gain_vs_duration.png create mode 100644 MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/rotation_gain_vs_duration.txt diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md new file mode 100644 index 000000000..fc6821058 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/SLOWROT_HANDOFF.md @@ -0,0 +1,207 @@ +# Slow-rotation RIFT likelihood — handoff / breadcrumb + +Branch: `rift_slowrot` (off `rift_O4d_fix_rvs_clear_fairdraw_batch`). Design notes + LaTeX +paper live in a **separate local repo** `~/rift-slow-rotation` (no git remote — local only; +21-page PDF via `latexmk -pdf notes/main.tex`). + +## PATH B DELAY PHYSICS — VALIDATED (2026-07 update) +Path B's propagation-delay drift is validated against an INDEPENDENT folded-template ground +truth (`test_slowrot_pathB_bruteforce.py`): data built as Re[F(t) Sigma(t-tau(t))] from the +SAME harmonic F(t),tau(t) model + the SAME modes the likelihood uses (no SimDetectorStrain +convention floor), at an inflated sidereal rate so the delay drift is large. At the REAL +90-min-BNS rate (= x340 inflation on a 16s test): p_max=0 deficit 3.43 -> p_max=1 0.23 -> +p_max=2 0.207 -> p_max=3 0.207: CONVERGES, bound-respected, NO blow-up. So Path B recovers +the delay drift and is production-ready for the target signals with p_max<=2. +KNOWN LIMIT: the p>=3 catastrophic cancellation (huge high-f U terms x tiny delta_tau^p +coefficients) only bites at x1000+ inflation (>2.6x faster than any physical signal): x1000 +gives p=2 deficit 5.9 but p=3 blows to 1e5. So the band-limit fix (low-pass the p>=1 +derivative templates) is a robustness nicety, NOT a blocker for real signals. +Separately validated vs LAL's SimDetectorStrainREAL8TimeSeries (`test_slowrot_pathB_groundtruth.py`): +baseline/PathA/PathB all agree with Jolien's full delay map to ~0.07 at fmax=256 (the ~26 +deficit at fmax=1024 was SimDetectorStrain's high-f TD delay-INTERPOLATION, not a bug -- +confirmed: it vanishes when fmax is lowered). NoLoop uses nearest-neighbor time sampling +(factored_likelihood.py ~L1691), so absolute peak comparisons at high SNR have a resolution +floor (~0.1-0.2); the time-interpolated NoLoop is in oshaughn/rift_O4d and +origin/rift_O4d_junior_calmarg_in_loop if a cleaner absolute comparison is wanted. + +## TWO PARALLEL THRUSTS (2026-07) +Path A + Path B are implemented, validated, and wired into the ILE. Next work is two tracks: +1. **Rotation PE value demo (near-term).** VERIFY-ANYWHERE quick-look DONE + (`~/RIFT_roboto_paper/analyses/slowrot_demo/`, `make demo` / `demo_local.py`): rotation-vs- + static lnL gain grows with Omega*T (null 0.004 -> 64s 0.19 at fixed SNR~30); figure + `outputs/gain_vs_duration.png`. REMAINING: the full DAG injection-recovery PE (posterior + bias + single-network sky map) on the cluster -> `paper/` figure cited from `sec:slowrot` + (structured Makefile targets inject/baseline/rotationA/rotationB/compare). +2. **Frequency-dependent (finite-size) response = Path D (3G regime where 'long' lives).** + RESPONSE FUNCTION IMPLEMENTED + VALIDATED: `slowrot_freqresponse.py` + + `test_slowrot_freqresponse.py`. F_k(f;RA,DEC,psi) from arXiv:2412.01693 single-arm sinc + transfer; f->0 == ComputeDetAMResponse to 6.7e-16; exact FSR null at c/2L=3747 Hz (40 km); + in-band SHAPE distortion 0.24%@1kHz/0.62%@2kHz (LIGO) vs 11.8%/42% (CE) -> negligible for + LIGO, first-order only for 3G. Key: the complex ratio ~1 for CE is dominated by a benign + common e^{-i2pi fL/c} delay (degenerate with tc), NOT sky shape. LAL has NO closed-form FD + response. ROUTE DECISION (notes/sec_freqdep.tex): CE -> route (b) sky-harmonic expansion + (keeps sky extrinsic; fold the common delay into geocenter time; few angular orders); + precessing+HM -> route (a) pinned-sky TD fold. + LIKELIHOOD INTEGRATION DONE + VALIDATED (route b): `factored_likelihood_freqresponse.py` + (+ `test_slowrot_freqresponse_likelihood.py`). Fold the common e^{-i2pi fL/c} delay into the + arrival time; power-series the residual transfer -> sky-independent W_p(f) folded into the FD + modes (reuse ComputeModeIP*) x analytic b_p(sky) -> SKY EXTRINSIC. MAINTAINED-STYLE NoLoop + entry point DiscreteFactoredLogLikelihoodFreqResponseNoLoop (NOT SingleDetectorLogLikelihood). + Validated (CE 40km, 16s): V1 finite-size(L->0) == maintained NoLoop baseline to 3.3e-9; V2 on + a finite-size injection the long-wavelength NoLoop deficit 2.71 -> finite-size 0.558 (converged + Qmax=2), residual ~= peak-resolution floor; V3 Cauchy-Schwarz respected. Scalar companion + agrees with the NoLoop to 0.156 (interp-vs-nearest floor). + REMAINING: ILE wiring (a --freqresponse flag mirroring --rotation-slow); full precessing+HM + (route a pinned-sky); a value demo (the finite-size effect only bites for CE/3G). + AUDIT NOTE (2026-07): all slowrot likelihoods route through the maintained NoLoop; there are + NO SingleDetectorLogLikelihood calls; the ILE wires only NoLoop paths. The scalar + FactoredLogLikelihood*/SingleDetectorLogLikelihood are used only as secondary references and + carry a peak-resolution floor at high SNR -- prefer NoLoop for any absolute comparison. + +## What this is +Generalizes RIFT's marginalized likelihood to account for the **time dependence of the +ground-based detector response over the signal** (Earth rotation), while reusing the +precompute-and-marginalize architecture. Two effects, both implemented (Path A + Path B): +- **Path A** — antenna amplitude drift `F(t)`: exact 5 sidereal harmonics, sky extrinsic. +- **Path B** — propagation-delay drift `tau(t)`: time-domain derivative expansion; adds + delay-derivative order `p` on top of Path A. Matters only for LONG signals. +- **Deferred (Path D)** — frequency-dependent (finite-size) response. Out of scope. + +## Files (all under RIFT/likelihood/ unless noted) +- `slowrot_response.py` — closed-form antenna harmonics `A_n` and delay harmonics `B_n` + (scalar + `*_vector`), derived from `vectorized_lal_tools` conventions. Validated vs LAL + to machine precision (`test_slowrot_response.py`). +- `factored_likelihood_with_rotation.py` — the core: + - `PrecomputeLikelihoodTermsWithRotation(...)` — FD-native precompute: builds each mode + once, applies derivative `(FT_SIGN*2pi i f)^p` (FT_SIGN=-1) and sidereal modulation + `exp(i n Omega t)` (sub-bin freq shift via LAL-FFT phase). Returns bank keyed by + elementary template `a=(p,n)`: `Q^a(t)`, `U^{(a,a')}`, `V^{(a,a')}`. + - `rotation_coefficients` / `rotation_coefficients_vector` — the analytic scalars + `C_{(p,ntilde)} = (1/p!) sum_{n+m=ntilde} A_tilde_n [(-D)^{*p}]_m` (Path A: `{(0,n): A_tilde_n}`). + - `FactoredLogLikelihoodWithRotation(...)` — scalar lnL (per-sample); term1 = `Re[sum_lm + conj(Ylm) sum_a conj(C_a) Q^a(t_det)]`, term2 with `U^{(a,a')}` (coef `conj(C_a)C_a'`) + and `V^{(a,a')}` (coef `C_{(p,-nu)} C_a'`). + - `pack_rotation_arrays` + `DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation` + — the MAINTAINED vectorized (NoLoop) path, mirroring + `factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig`. +- `factored_likelihood.py` — one small FIX: non-numba `lalylm` fallback -> `np.vectorize` + (it broke `ComputeYlmsArrayVector` for the whole vectorized path in non-numba/non-GPU envs). +- `bin/integrate_likelihood_extrinsic_batchmode` — wiring: `--rotation-slow`, + `--rotation-n-harmonics`, `--rotation-p-max`. Guarded (requires `--vectorized`, CPU, no + dist/phase marg). Builds+packs the rotation bank after the baseline pack; the CPU + vectorized `likelihood_function` calls the rotation NoLoop. + +## How to run (RIFT venv; worktree first on PYTHONPATH) + source ~/RIFT_develUWM/bin/activate + export PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code + python RIFT/likelihood/test_slowrot_response.py # A_n,B_n vs LAL + python RIFT/likelihood/test_slowrot_fd_ops.py # FD derivative/modulation + python RIFT/likelihood/test_slowrot_likelihood_v1.py # scalar Path A vs baseline + brute force + python RIFT/likelihood/test_slowrot_noloop.py # vectorized Path A vs baseline NoLoop + python RIFT/likelihood/test_slowrot_noloop_bruteforce.py # vectorized Path A vs brute force + python RIFT/likelihood/test_slowrot_pathB.py # Path B reduction + bound + python RIFT/likelihood/test_slowrot_headtohead.py # matched-sample rotation vs baseline (cubic) + python RIFT/likelihood/test_slowrot_freqresponse.py # [Path D] finite-size response vs LAL + python RIFT/likelihood/test_slowrot_freqresponse_likelihood.py # [Path D] likelihood: V1/V3 + V4 positive control + +VALUE DEMOS (verify-anywhere, no condor/GPU) -- consolidated in the RIFT tree: + cd demo/rift/slowrot && make demo # rotation (Path A/B) + finite-size (Path D) + # demo_rotation.py : gain lnL_rot-lnL_static grows with Omega*T (null control at short T) + # demo_finite_size.py : gain lnL_finite-lnL_LWL grows with arm length fL/c (null at LIGO 4km, + # +39.6 nats at CE 40km); writes outputs/*.{txt,png}. See its README.md. + +End-to-end ILE head-to-head (ILE-GPU-Paper demo data), baseline vs rotation vs finite-size: + D=~/RIFT_develUWM/src/research-projects-RIT/.travis/ILE-GPU-Paper/demos + integrate_likelihood_extrinsic_batchmode --vectorized [std opts] ... # baseline + integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow ... # Path A + integrate_likelihood_extrinsic_batchmode --vectorized --rotation-slow --rotation-p-max 1 ... # Path B + integrate_likelihood_extrinsic_batchmode --vectorized --freqresponse \ + --freqresponse-arm-length 40000 --freqresponse-qmax 6 ... # Path D (finite-size) + # --interpolate-time selects cubic sub-bin time interpolation for all of the above (default nearest). + +## Validation status (all PASSING) +- Response harmonics vs LAL: ~1e-16. FD ops vs LAL round trips: ~1e-13. +- Path A scalar: V1a (Omega=0 vs baseline) 2.7e-12; V1b (real vs brute force) 2.6e-9. +- Path A vectorized: vs baseline NoLoop 3.6e-12; vs brute force 3.2e-10; V0 (precompute + recovery on real data) exact. +- Path B: scalar reduce-to-baseline 9e-13; respects 0.5; vectorized reduce 6.4e-12. +- Path D (finite-size, --freqresponse): response Sum_p b_p W_p == antenna_response_fd to 6e-11 + on both +/-f; likelihood L->0 reduces to baseline NoLoop 3e-9; Cauchy-Schwarz respected; + V4 positive control asserts finite-size beats LWL by +38.9 nats (15+13 Msun, fmax=2000, CE 40km). +- Cubic time-interp (from calmarg_in_loop, --interpolate-time): both slow-response NoLoops now + support time_interp='nearest'|'cubic'. Cubic exposed+fixed a sub-bin GPS-cancellation bug in + the time reference; head-to-head regression floor 1.6e-3 -> 4.5e-13, test_slowrot_noloop 3.6e-12. +- ILE runs clean (no inf/nan) for baseline / Path A / Path B / Path D; rotation ~ baseline for the + short demo signal (rotation effect ~0.008 in lnL, negligible as physics requires). + +## CRITICAL lesson (a bug that hid for a while) +The U/V template modulation MUST be referenced to the template's INTRINSIC time (epoch ~0), +not the absolute event time t_ev~1e9 -- else a ~1e4 rad spurious phase randomizes U/V and +inflates lnL ABOVE 0.5. This bug once made a short-BBH rotation shift look like ~660 in +lnL; corrected it is ~0.008. My independent "brute force" reference shared the same +convention, so `vec == brute-force` PASSED while both were wrong. **Always cross-check +against the Cauchy-Schwarz bound 0.5, not only against a reference that can share +conventions.** + +## PATH B STATUS (findings 2026-07-04, the systematic pass in progress) +- Matched-seed head-to-head DONE (test_slowrot_headtohead.py): rot(f_sid=0)==baseline 9e-13; + evidence shift ln Z_rot - ln Z_base = -1.1e-3 (MC-noise-free) for the short signal. +- Path B DELAY PHYSICS still only partially validated. In an INFLATED-Omega regime (f_sidereal + x3000, single-det 30+25 BBH) where the delay drift matters, scalar Path B gives + lnL(p_max=0..3) = [1794.39, 1781.31, 1781.63, 928.30]: p=0->1->2 captures a real ~13-in-lnL + delay effect and appears to converge (~1781.6) and respects 0.5=1938 -- BUT p_max=3 + BLOWS UP (increment 853). +- ROOT CAUSE of the p>=3 blow-up (likely): the FD derivative weight (2 pi i f)^p amplifies high + frequencies; in the model norm the integrand ~ (2 pi f)^{2p} |h(f)|^2 / S grows like f^{11/3} + for a chirp (|h|^2 ~ f^{-7/3}), so high-order terms are dominated by the f_max edge, not the + physical low-frequency delay drift. FIX for the systematic pass: BAND-LIMIT the delay- + derivative (p>=1) terms to low frequency (the delay drift physically matters in the long early + inspiral, i.e. low f, not at merger). Options: apply a low-pass / taper before the (2 pi i f)^p + weight, or use a reduced f_max for p>=1 templates, or work with dimensionless (f/f_ref) weights. +- Consequence: current Path B is trustworthy only through p_max<=2 and only after the band-limit + fix is validated. Do NOT ship Path B (p>=1) until this is fixed AND checked against an + independent time-varying-delay brute force (below). + +## OPEN / NEXT (the one remaining systematic pass — do it all together) +1. **Path B rigorous validation** (TWO parts, do together): + (a) FIX the p>=3 high-frequency derivative blow-up by band-limiting the delay-derivative + terms to low frequency (see PATH B STATUS above); re-check convergence is monotone. + (b) Validate vs an INDEPENDENT ground truth that uses LAL's OWN full delay-time map -- + lalsim.SimDetectorStrainREAL8TimeSeries (Jolien's code). KEY FACTS FOUND 2026-07-04: + - RIFT's data ALREADY uses it: non_herm_hoff -> hoft -> SimInspiralTD + + SimDetectorStrainREAL8TimeSeries (lalsimutils.py ~line 3020). So a LONG injection + already carries the real Earth-rotation response; baseline (static) will under-recover + it and Path A/B should recover it. + - GROUND TRUTH for the rotation likelihood must apply SimDetectorStrain to the SAME + RIFT modes the likelihood uses: hk = lsu.hoft_from_hlm(hlmsT, P_extr) (radec path, + applies SimDetectorStrain). Then convert to 2-sided FD exactly like non_herm_hoff + (copy REAL8 -> COMPLEX16, COMPLEX16TimeFreqFFT) and lnL = ComplexIP(d,hk).real - + 0.5*ComplexIP(hk,hk).real. + - DO NOT use non_herm_hoff(P_extr) as the template ground truth: it regenerates via + SimInspiralTD, whose modes differ from RIFT's hlmoff modes -- with IMRPhenomD the + mode-based baseline recovers only ~SNR 35 of the data's ~SNR 82 at the injection (a + waveform-convention mismatch, NOT rotation). Using hoft_from_hlm keeps the modes + identical so ONLY the rotation differs. + - Two gotchas to fix: (i) hoft_from_hlm output length = mode length (e.g. 16451) != + padded data length (16384) -> ResizeREAL8TimeSeries + align epoch (replicate + non_herm_hoff padding); (ii) the mode-based likelihood at a FIXED tref is OFF-PEAK + (epoch bookkeeping) -> compare at the time-MAXIMIZED / marginalized value, not at + extr.tref. + - Use a LONG signal (real sidereal rate) so delay drift matters, OR the inflated-Omega + path (already shows p=1,2 capture the delay, p>=3 needs the band-limit fix). + A convergence test alone is NOT enough: Path B could converge to a WRONG value if the C + coefficients are off (the delay analogue of the reference-time bug). Cross-check 0.5. +2. **Matched-seed quantitative ILE head-to-head** (fixed RNG seed) -> exact, regression-grade + baseline-vs-rotation comparison (currently only MC-noise-level agreement). +3. Cauchy-Schwarz bound checks everywhere; sweep for other shared-convention bugs. +4. Possible follow-ups: GPU (xpy) path for the rotation NoLoop; distance/phase/cal marg + support; Path B `t_star` reference-epoch choice; `rotation_coefficients` closed-form + `A_n`/`B_n` appendix (notes sec app_response has the A_n,B_n). + +## Also note +- A separate background session was fixing the py2-ism in + `factored_likelihood.PackLikelihoodDataStructuresAsArrays` (`rholm_intpArray = range(nKeys)` + fails in py3 when the interpolant arg is truthy). Our tests pass `None` for that arg to + dodge it. +- Subagents were unreliable for this work (deferred, reverted fixes, over-claimed). The + validated results above were done/checked directly. diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py index 44e32c115..2232e0c9a 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood.py @@ -1396,7 +1396,7 @@ def PackLikelihoodDataStructuresAsArrays(pairKeys, rholms_intpDictionaryForDetec rholmArray[indx1][:] = rholmsDictionaryForDetector[pair1].data.data # Copy the array of time values. ### Step 3: Create rholm_intp array-ized structure - rholm_intpArray = range(nKeys) # create a flexible python array of the desired size, to hold function pointers + rholm_intpArray = [None] * nKeys # create a flexible python array of the desired size, to hold function pointers if rholms_intpDictionaryForDetector: for pair1 in pairKeys: indx1 = lookupKeysToNumber[pair1] @@ -2402,9 +2402,10 @@ def lalT(det, RA, DEC,tref): # note tref is a SCALAR numba_on = False if log_loud: print(" Numba off ") - # Very inefficient - def lalylm(th,ph,s,l,m): - return lal.SpinWeightedSphericalHarmonic(th,ph,s,l,m) + # Very inefficient. np.vectorize replicates the numba @vectorize elementwise + # behavior (the plain scalar wrapper below breaks ComputeYlmsArrayVector, which + # passes numpy ARRAYS for th,ph,s,l,m -> LAL raises 'argument 1 of type REAL8'). + lalylm = np.vectorize(lal.SpinWeightedSphericalHarmonic, otypes=[complex]) def lalF(det, RA, DEC,psi,tref): if isinstance(RA, float): return ComplexAntennaFactor(det, RA, DEC, psi,tref) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py new file mode 100644 index 000000000..2391d75ee --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_freqresponse.py @@ -0,0 +1,408 @@ +""" +factored_likelihood_freqresponse : finite-size (frequency-dependent detector response) +generalization of the RIFT precompute + scalar likelihood. "Thrust 2 / route (b)" +(sky-harmonic expansion) of the slow-rotation program -- KEEPS the sky extrinsic. + +Physics (see slowrot_freqresponse.py for the response derivation) +---------------------------------------------------------------- +Beyond the long-wavelength limit the detector strain in the frequency domain is a +per-frequency antenna pattern acting on the two polarizations, + + h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f) + = (1/2)[ F(f) Sigma(f) + Fbar(f) Sigma*(f) ] , + F = F_+ + i F_x, Fbar = F_+ - i F_x = conj(F(-f)), Sigma = sum_lm Y_lm h_lm. + +The finite-size response factors into a SKY-INDEPENDENT frequency basis W_p(f) times +an analytic SKY/pol scalar b_p (slowrot_freqresponse.finite_size_response_weights / +finite_size_beta): + + F(f;sky) = sum_p b_p(sky) W_p(f) , Fbar(f;sky) = sum_p conj(b_p) W_p(f) , + + p=0 : W_0 = 1, b_0 = F0 (exact lal ComputeDetAMResponse) + p=1+q : W_{1+q} = e^{-i2pi f T} c_q(f) - [q==0], b_{1+q} = beta_q (arm expansion) + +with T = L/c the common (direction-independent) light-crossing delay folded into W_p as +a linear FD phase, and c_q(f) the sky-independent power-series basis of the single-arm +transfer. Every W_p is Hermitian, so -- UNLIKE the sidereal-rotation case -- the V cross +term needs NO harmonic reflection. + +This module is the DIRECT analogue of factored_likelihood_with_rotation: it folds each +W_p(f) into the FD modes ONCE (h_lm(f) -> W_p(f) h_lm(f)) and reuses the EXISTING +ComputeModeIPTimeSeries / ComputeModeCrossTermIP to build + + Q^p_lm(t) = < W_p h_lm | d >, U^{p,p'} = < W_p h_lm | W_p' h_l'm' >, + V^{p,p'} = < conj(W_p h_lm) | W_p' h_l'm' > . + +MAINTAINED PATH: the production entry point is the vectorized (NoLoop) +DiscreteFactoredLogLikelihoodFreqResponseNoLoop -- the direct finite-size analogue of +factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop (the maintained +likelihood used by integrate_likelihood_extrinsic_batchmode). It is VALIDATED against that +NoLoop baseline: at L->0 it reduces to it (to ~1e-9), NOT to the older per-detector scalar +SingleDetectorLogLikelihood. FactoredLogLikelihoodFreqResponse (scalar) is a per-sample +validation companion only (mirrors factored_likelihood_with_rotation.FactoredLogLikelihoodWithRotation). + +The term1/term2 use the same factored form as the NoLoop path (all RIFT factored likelihoods +share this algebra; W_0=1 and b_0 = ComputeDetAMResponse at L->0): + term1 = Re[ (distRef/dist) sum_lm conj(Ylm) sum_p conj(b_p) Q^p_lm(t_det) ] + term2 = -(1/4)(distRef/dist)^2 Re[ sum_{p,p'} ( conj(b_p) b_p' U^{p,p'} conj(Y1)Y2 + + b_p b_p' V^{p,p'} Y1 Y2 ) ] + +Heavy RIFT imports are lazy so the light FD helpers stay importable with numpy alone. +""" +from __future__ import print_function, division + +import numpy as np + +from . import slowrot_freqresponse as sfr + + +# --------------------------------------------------------------------------- +# Low-level FD primitive: fold a per-bin weight into a COMPLEX16FrequencySeries. +# --------------------------------------------------------------------------- +def evaluate_fvals_from_length(npts, deltaF): + """Signed frequency array for a RIFT two-sided COMPLEX16FrequencySeries. + + Identical packing to factored_likelihood_with_rotation.evaluate_fvals_from_length / + RIFT.lalsimutils.evaluate_fvals: f[k] = deltaF*(npts/2 - k), descending from +fNyq. + """ + k = np.arange(npts) + return deltaF * (npts / 2.0 - k) + + +def _copy_freqseries(hf): + import lal + out = lal.CreateCOMPLEX16FrequencySeries( + hf.name, hf.epoch, hf.f0, hf.deltaF, hf.sampleUnits, hf.data.length) + out.data.data[:] = hf.data.data[:] + return out + + +def fd_apply_weight(hf, weight): + """COMPLEX16FrequencySeries -> new series with spectrum multiplied by weight[k].""" + out = _copy_freqseries(hf) + out.data.data[:] = hf.data.data * weight + return out + + +# --------------------------------------------------------------------------- +# The precompute (sky-independent; per detector uses only its arm length L). +# --------------------------------------------------------------------------- +def PrecomputeLikelihoodTermsFreqResponse( + event_time_geo, t_window, P, data_dict, psd_dict, Lmax, fMax, + Qmax=4, L_arm=None, + analyticPSD_Q=False, inv_spec_trunc_Q=False, T_spec=0., + verbose=True, quiet=False, skip_interpolation=False, **hlm_kwargs): + """Finite-size analogue of PrecomputeLikelihoodTerms(WithRotation). + + Builds each FD mode once and forms the generalized overlaps for every response + basis element p = 0..Qmax+1: + + rholms_intp_fr[det][p][(l,m)] : interpolant of Q^p_lm(t) = + crossTerms_fr[det][(p,p')] : { ((l,m),(l',m')) : } + crossTermsV_fr[det][(p,p')] : { ((l,m),(l',m')) : } + + Parameters mirror PrecomputeLikelihoodTerms; finite-size specific: + Qmax : highest power of the arm projection retained (basis size Qmax+2). + L_arm : arm-length override [m] (float -> all detectors, or dict det->L). + None -> each detector's native LAL arm length. + + The response coefficients b_p(det,RA,DEC,psi,tref) that contract these overlaps are + NOT computed here -- see FactoredLogLikelihoodFreqResponse. Returns the intrinsic- + only, sky-independent overlap bank plus meta (carries per-detector L, T, Qmax). + """ + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + assert data_dict.keys() == psd_dict.keys() + detectors = list(data_dict.keys()) + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + P.dist = FL.distMpcRef * 1e6 * lsu.lsu_PC + P.deltaF = data_dict[detectors[0]].deltaF + + # --- build base FD modes ONCE --- + hlms, hlms_conj = FL.internal_hlm_generator(P, Lmax, verbose=verbose, quiet=quiet, + **hlm_kwargs) + modes = list(hlms.keys()) + npts = hlms[modes[0]].data.length + deltaF = hlms[modes[0]].deltaF + fvals = evaluate_fvals_from_length(npts, deltaF) + p_list = list(range(Qmax + 2)) + + rholms_fr = {} + rholms_intp_fr = {} + crossTerms_fr = {} + crossTermsV_fr = {} + Ldict = {} + Tdict = {} + + for det in detectors: + psd = psd_dict[det] + data = data_dict[det] + + # per-detector geometry (arm length only; sky enters later via b_p) + _, _, _, L = sfr.detector_geometry(det, L_arm=_L_of(det)) + Ldict[det] = float(L) + Tdict[det] = float(L / sfr.C_SI) + # sky-independent response basis weights W_p(f) on the signed fvals + geom_dummy = dict(L=float(L), T=float(L / sfr.C_SI)) + W = sfr.finite_size_response_weights(fvals, geom_dummy, Qmax) # (Qmax+2, npts) + + # weighted mode families eta^p = W_p h_lm , etac^p = W_p conj(h_lm) + eta = {p: {lm: fd_apply_weight(hlms[lm], W[p]) for lm in modes} for p in p_list} + etac = {p: {lm: fd_apply_weight(hlms_conj[lm], W[p]) for lm in modes} for p in p_list} + + t_det = FL.ComputeArrivalTimeAtDetector(det, P.phi, P.theta, event_time_geo) + rho_epoch = data.epoch - hlms[modes[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / P.deltaT + 0.5) + N_window = int(2 * t_window / P.deltaT) + t = np.arange(N_window) * P.deltaT + float(rho_epoch + N_shift * P.deltaT) + + rholms_fr[det] = {} + rholms_intp_fr[det] = {} + for p in p_list: + rho = FL.ComputeModeIPTimeSeries( + eta[p], data, psd, P.fmin, fMax, 1. / 2. / P.deltaT, + N_shift, N_window, analyticPSD_Q, inv_spec_trunc_Q, T_spec) + rholms_fr[det][p] = rho + if not skip_interpolation: + rholms_intp_fr[det][p] = FL.InterpolateRholms(rho, t, verbose=verbose) + else: + rholms_intp_fr[det][p] = None + + crossTerms_fr[det] = {} + crossTermsV_fr[det] = {} + for p in p_list: + for pp in p_list: + crossTerms_fr[det][(p, pp)] = FL.ComputeModeCrossTermIP( + eta[p], eta[pp], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, verbose=False, + same_waveform_Q=False) + crossTermsV_fr[det][(p, pp)] = FL.ComputeModeCrossTermIP( + etac[p], eta[pp], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, prefix="V", verbose=False, + same_waveform_Q=False) + + meta = dict(Qmax=Qmax, p_list=p_list, modes=modes, + event_time_geo=float(event_time_geo), + L=Ldict, T=Tdict, L_arm=L_arm) + return rholms_intp_fr, crossTerms_fr, crossTermsV_fr, rholms_fr, meta + + +# --------------------------------------------------------------------------- +# Response coefficients b_p(det, RA, DEC, psi, tref) : b_0 = F0, b_{1+q} = beta_q. +# --------------------------------------------------------------------------- +def response_coefficients(det, RA, DEC, psi, tref, Qmax, L_arm=None): + """{p: b_p} response coefficients for the finite-size basis at (RA,DEC,psi,tref). + + b_0 = F0 = lal.ComputeDetAMResponse (exact LWL baseline); + b_{1+q} = beta_q(sky) = (1/2)[zx^2 a_x^q - zy^2 a_y^q]. + Uses gmst = GreenwichMeanSiderealTime(tref) exactly like ComplexAntennaFactor. + """ + import lal + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) + geom = sfr.finite_size_geometry(det, RA, DEC, psi, gmst=gmst, L_arm=L_arm) + beta = sfr.finite_size_beta(geom, Qmax) + b = {0: geom['F0']} + for q in range(Qmax + 1): + b[1 + q] = complex(beta[q]) + return b + + +def FactoredLogLikelihoodFreqResponse(extr_params, rholms_intp_fr, crossTerms_fr, + crossTermsV_fr, meta, Lmax): + """Finite-size analogue of factored_likelihood.FactoredLogLikelihood. + + Contracts the response-basis precompute bank with b_p(det,RA,DEC,psi,tref) and the + Ylm. Reduces EXACTLY to the baseline FactoredLogLikelihood as L -> 0. + """ + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + Qmax = meta['Qmax'] + p_list = list(meta['p_list']) + L_arm = meta.get('L_arm', None) + + RA = extr_params.phi + DEC = extr_params.theta + tref = extr_params.tref + phiref = extr_params.phiref + incl = extr_params.incl + psi = extr_params.psi + dist = extr_params.dist + + detectors = list(rholms_intp_fr.keys()) + p0 = p_list[0] + modes = list(rholms_intp_fr[detectors[0]][p0].keys()) + Ylms = FL.ComputeYlms(Lmax, incl, -phiref, selected_modes=modes) + + distMpc = dist / (lsu.lsu_PC * 1e6) + invDistMpc = FL.distMpcRef / distMpc + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + lnL = 0. + for det in detectors: + b = response_coefficients(det, RA, DEC, psi, tref, Qmax, L_arm=_L_of(det)) + t_det = FL.ComputeArrivalTimeAtDetector(det, RA, DEC, tref) + CT = crossTerms_fr[det] + CTV = crossTermsV_fr[det] + + Q = {p: {m: rholms_intp_fr[det][p][m](float(t_det)) + for m in modes} for p in p_list} + + term1 = 0. + for m in modes: + s = 0. + for p in p_list: + s += np.conj(b[p]) * Q[p][m] + term1 += np.conj(Ylms[m]) * s + term1 = np.real(term1) * invDistMpc + + term2 = 0. + for m1 in modes: + for m2 in modes: + u = 0. + v = 0. + for p in p_list: + for pp in p_list: + u += np.conj(b[p]) * b[pp] * CT[(p, pp)][(m1, m2)] + v += b[p] * b[pp] * CTV[(p, pp)][(m1, m2)] + term2 += u * np.conj(Ylms[m1]) * Ylms[m2] + v * Ylms[m1] * Ylms[m2] + term2 = -np.real(term2) / 4. / (distMpc / FL.distMpcRef) ** 2 + + lnL += term1 + term2 + + return lnL + + +# --------------------------------------------------------------------------- +# Dense-array packing + vectorized time-marginalized lnL (for peak / validation). +# --------------------------------------------------------------------------- +def pack_freqresponse_arrays(meta, rholms_fr, crossTerms_fr, crossTermsV_fr): + """Pack the basis precompute bank into dense arrays keyed per detector by p. + + Returns (lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict). + """ + p_list = list(meta['p_list']) + lookupNKDict = {}; rho_by_p = {}; U_by_pp = {}; V_by_pp = {}; epochDict = {} + for det in rholms_fr: + p0 = p_list[0] + modes = list(rholms_fr[det][p0].keys()) + n_lms = len(modes) + idx = {m: i for i, m in enumerate(modes)} + lookupNKDict[det] = np.array([[m[0], m[1]] for m in modes], dtype=int) + npts = rholms_fr[det][p0][modes[0]].data.length + epochDict[det] = float(rholms_fr[det][p0][modes[0]].epoch) + rho_by_p[det] = {} + for p in p_list: + arr = np.zeros((n_lms, npts), dtype=np.complex128) + for m in modes: + arr[idx[m]] = rholms_fr[det][p][m].data.data + rho_by_p[det][p] = arr + U_by_pp[det] = {}; V_by_pp[det] = {} + for p in p_list: + for pp in p_list: + Um = np.zeros((n_lms, n_lms), dtype=np.complex128) + Vm = np.zeros((n_lms, n_lms), dtype=np.complex128) + cU = crossTerms_fr[det][(p, pp)] + cV = crossTermsV_fr[det][(p, pp)] + for m1 in modes: + for m2 in modes: + Um[idx[m1], idx[m2]] = cU[(m1, m2)] + Vm[idx[m1], idx[m2]] = cV[(m1, m2)] + U_by_pp[det][(p, pp)] = Um + V_by_pp[det][(p, pp)] = Vm + return lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict + + +def DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, P_vec, meta, lookupNKDict, rho_by_p, U_by_pp, V_by_pp, epochDict, + Lmax=2, array_output=False, time_interp='nearest'): + """Vectorized finite-size lnL over a time window (single extrinsic point per call + is supported; P_vec fields RA,DEC,incl,phiref,psi,dist may be length-1 arrays). + + array_output=True returns lnL_t of shape (npts_ex, npts); else time-marginalized. + """ + import lal + from . import factored_likelihood as FL + + p_list = list(meta['p_list']) + Qmax = meta['Qmax'] + L_arm = meta.get('L_arm', None) + npts = len(tvals); npts_ex = len(np.atleast_1d(P_vec.phi)) + RA = np.atleast_1d(P_vec.phi); DEC = np.atleast_1d(P_vec.theta) + incl = np.atleast_1d(P_vec.incl); phiref = np.atleast_1d(P_vec.phiref) + psi = np.atleast_1d(P_vec.psi) + distMpc = np.atleast_1d(P_vec.dist) / (lal.PC_SI * 1e6) + lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) + + def _L_of(det): + if isinstance(L_arm, dict): + return L_arm.get(det, None) + return L_arm + + for det in rho_by_p: + n_lms = len(lookupNKDict[det]) + Ylms = FL.ComputeYlmsArrayVector(lookupNKDict[det], incl, -phiref).T # (npts_ex,n_lms) + # per-sample response coefficients b_p (npts_ex,) + bvec = {} + for i in range(npts_ex): + bi = response_coefficients(det, float(RA[i]), float(DEC[i]), float(psi[i]), + P_vec.tref, Qmax, L_arm=_L_of(det)) + for p in p_list: + bvec.setdefault(p, np.zeros(npts_ex, dtype=complex)) + bvec[p][i] = bi[p] + + t_ref = epochDict[det] + # Precision-preserving time reference (see rotation NoLoop): keep (tref - epoch) and the + # geometric delay separate so the sub-bin fraction is exact under cubic interpolation. + detector_location = np.asarray(FL.lalsim.DetectorPrefixToLALDetector(det).location) + gmst_tref = float(lal.GreenwichMeanSiderealTime(P_vec.tref)) + t_det = float(P_vec.tref - float(t_ref)) + FL.TimeDelayFromEarthCenter( + detector_location, RA, DEC, gmst_tref) + sample_first = (t_det + tvals[0]) / P_vec.deltaT + if time_interp == 'nearest': + ifirst = (np.round(sample_first) + 0.5).astype(int) + else: + ifirst = np.floor(sample_first).astype(int) + frac_first = (sample_first - np.floor(sample_first)).astype(np.float64) + ilast = ifirst + npts + + term1 = np.zeros((npts_ex, npts), dtype=np.complex128) + for p in p_list: + det_rho = rho_by_p[det][p] + if time_interp == 'nearest': + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + else: + Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) + term1 += np.conj(bvec[p])[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) + term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] + + term2 = np.zeros(npts_ex, dtype=np.complex128) + for p in p_list: + for pp in p_list: + term2 += np.conj(bvec[p]) * bvec[pp] * np.einsum( + 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_pp[det][(p, pp)]) + term2 += bvec[p] * bvec[pp] * np.einsum( + 'xi,xj,ij->x', Ylms, Ylms, V_by_pp[det][(p, pp)]) + term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 + + lnL_t += term1 + term2[:, None] + + if array_output: + return lnL_t + lnLmax = np.max(lnL_t, axis=-1, keepdims=True) + L = FL.my_simps(np.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) + return lnLmax[..., 0] + np.log(L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py new file mode 100644 index 000000000..6f232bcfb --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -0,0 +1,619 @@ +""" +factored_likelihood_with_rotation : slow-rotation generalization of the RIFT precompute. + +This is a *separate realization* of factored_likelihood.PrecomputeLikelihoodTerms that +accounts for the time dependence of the ground-based detector response over long signals +(Earth rotation). It does NOT regenerate a family of time-domain templates. Instead it +builds each frequency-domain mode ONCE and realizes the two response modulations as cheap +frequency-domain operations on that single template: + + * delay-derivative order p (Path B): time derivative h^(p)(t) <-> (s 2 pi i f)^p H(f) + -- an exact per-bin weight in the FD. (s = FT_SIGN, fixed to match RIFT's FFT + convention; see evaluate_fvals, which itself warns the omega-sign is reversed.) + + * sidereal harmonic n (Paths A,B): modulation exp(i n Omega t) <-> frequency + shift H(f - n f_sid). Since f_sid = Omega/2pi ~ 1.16e-5 Hz is sub-bin, the shift is + realized exactly as a time-domain linear phase (one FFT round trip per template), not + an integer bin roll. + +The response harmonics themselves (the analytic scalars that multiply these precomputed +overlaps in the extrinsic layer) come from RIFT.likelihood.slowrot_response: + + F_k(t) = sum_{n=-2}^{2} A_n e^{i n g}, tau_k(t) = sum_{n=-1}^{1} B_n e^{i n g}, + g = GMST(t) - RA, A_n, B_n depend only on (detector, dec, psi/dec). + +See the design notes (rift-slow-rotation: sec_formalism 'master expansion', sec_amplitude +Path A, sec_delay Path B, app_response) for the derivation and the meaning of the indices. + +Index conventions in the returned structures +-------------------------------------------- +An "elementary modulated template" is labelled a = (p, n): + chi_a(t) = exp(i n Omega t) * d^p/dt^p h_lm(t - tau_0). +The physical data-term time series carries a post-phase (derived in the notes): + Q^a_lm(t) = exp(i n Omega t) * < chi_a(.-t) | d > [applied here] +while the cross terms are arrival-time independent: + U^{a,a'} = < chi_a | chi_a' >, V^{a,a'} = < chi_a^* | chi_a' >. + +Path A (default) uses only p = 0 (amplitude drift; exact 5-harmonic). Path B adds p >= 1. + +Heavy RIFT imports (factored_likelihood, lalsimutils) are done lazily inside the precompute +so the light FD primitives below are importable with numpy alone (used by the unit tests). +""" +from __future__ import print_function, division + +import numpy as np + +# Sidereal angular rate [rad/s] and frequency [Hz] +OMEGA_EARTH = 7.292115e-5 +F_SIDEREAL = OMEGA_EARTH / (2.0 * np.pi) + +# Sign in the time-derivative weight (s 2 pi i f)^p, matched to RIFT's evaluate_fvals FFT +# convention. VALIDATED empirically against a LAL FFT round trip in +# test_slowrot_fd_ops.py (which will fail loudly if this is wrong). +FT_SIGN = -1.0 + + +# --------------------------------------------------------------------------- +# Low-level FD primitives (numpy only; operate on a complex spectrum + its fvals). +# --------------------------------------------------------------------------- +def evaluate_fvals_from_length(npts, deltaF): + """Signed frequency array for a RIFT two-sided COMPLEX16FrequencySeries. + + Replicates RIFT.lalsimutils.evaluate_fvals (kept local so these primitives need no + heavyweight import). Note RIFT's expression ``npts/2 - k if k<=npts/2 else -k+npts/2`` + is the same in both branches, i.e. simply f[k] = deltaF*(npts/2 - k), descending from + +fNyq at k=0. + """ + k = np.arange(npts) + return deltaF * (npts / 2.0 - k) + + +def time_derivative_weight(fvals, p): + """(FT_SIGN * 2 pi i f)^p : exact FD weight for the p-th time derivative.""" + if p == 0: + return np.ones_like(fvals, dtype=complex) + return (FT_SIGN * 2.0j * np.pi * fvals) ** p + + +def apply_time_derivative_array(spectrum, fvals, p): + """Return the spectrum of d^p/dt^p h given the spectrum of h (both two-sided).""" + return spectrum * time_derivative_weight(fvals, p) + + +def apply_sidereal_modulation_array(spectrum, coef, deltaF, f_sidereal=F_SIDEREAL): + """Return the spectrum of exp(i coef Omega t) h(t) given the spectrum of h(t). + + exp(i coef Omega t) multiplication in time <-> frequency shift by coef*f_sidereal. + This is sub-bin (f_sidereal << deltaF for realistic segments), so it is applied exactly + as a time-domain linear phase via a DFT round trip that respects RIFT's fvals packing. + + WARNING: this reference implementation forms an explicit (npts x npts) DFT matrix -- it + is O(npts^2) and intended ONLY for unit tests at small npts. The production path uses + the LAL-FFT round trip in _lal_freq_modulate() below (O(npts log npts)). + """ + if coef == 0: + return spectrum.copy() + npts = len(spectrum) + dt = 1.0 / (npts * deltaF) + t = np.arange(npts) * dt + fvals = evaluate_fvals_from_length(npts, deltaF) + # Round-trip-consistent DFT pair for the packing f[k]=deltaF*(npts/2-k): + # h(t_j) = (1/npts) sum_k H_k exp(+2 pi i f_k t_j), H_k = sum_j h(t_j) exp(-2 pi i f_k t_j) + # LAL's convention (encoded by evaluate_fvals) reverses the omega sign: a tone + # exp(+2 pi i f0 t) lands at fvals = -f0. The consistent DFT pair is therefore + # h(t_j) = (1/npts) sum_k H_k exp(-2 pi i f_k t_j), H_k = sum_j h(t_j) exp(+2 pi i f_k t_j). + phase_inv = np.exp(-2.0j * np.pi * np.outer(t, fvals)) # (npts, npts): H -> h(t) + h_td = phase_inv.dot(spectrum) / npts + h_td *= np.exp(1.0j * coef * 2.0 * np.pi * f_sidereal * t) # exp(i coef Omega t) + phase_fwd = np.exp(2.0j * np.pi * np.outer(fvals, t)) # h(t) -> H + return phase_fwd.dot(h_td) + + +# --------------------------------------------------------------------------- +# LAL-series wrappers (used inside the precompute; import lal lazily). +# --------------------------------------------------------------------------- +def _copy_freqseries(hf): + import lal + out = lal.CreateCOMPLEX16FrequencySeries( + hf.name, hf.epoch, hf.f0, hf.deltaF, hf.sampleUnits, hf.data.length) + out.data.data[:] = hf.data.data[:] + return out + + +def fd_apply_time_derivative(hf, p): + """COMPLEX16FrequencySeries -> new series for the p-th time derivative.""" + if p == 0: + return _copy_freqseries(hf) + out = _copy_freqseries(hf) + fvals = evaluate_fvals_from_length(hf.data.length, hf.deltaF) + out.data.data[:] = apply_time_derivative_array(hf.data.data, fvals, p) + return out + + +def _lal_freq_modulate(hf, coef, f_sidereal=F_SIDEREAL, t_ref=0.0): + """Modulate by exp(i coef Omega (t_abs - t_ref)) via a LAL-FFT round trip (O(N log N)). + + Reverse-FFT the spectrum to the time domain, multiply by the sidereal linear phase + referenced to ABSOLUTE sample time t_abs = float(hf.epoch) + j*deltaT (minus t_ref), + forward-FFT back. Uses the same COMPLEX16 transforms RIFT uses for its overlaps. + + The reference t_ref is physical, not cosmetic: the true antenna phase is + exp(i n (GMST(t')-RA)) = exp(i n (GMST(t_ev)-RA)) * exp(i n Omega (t'-t_ev)), so the + precompute must carry exactly exp(i n Omega (t' - t_ev)) at absolute data time t', + with the constant GMST(t_ev) piece carried analytically by A_n (slowrot_response). + Hence callers pass t_ref = event_time_geo. + """ + import lal + if coef == 0: + return _copy_freqseries(hf) + npts = hf.data.length + deltaT = 1.0 / (npts * hf.deltaF) + ts = lal.CreateCOMPLEX16TimeSeries("m", hf.epoch, 0., deltaT, + lal.DimensionlessUnit, npts) + revplan = lal.CreateReverseCOMPLEX16FFTPlan(npts, 0) + fwdplan = lal.CreateForwardCOMPLEX16FFTPlan(npts, 0) + lal.COMPLEX16FreqTimeFFT(ts, hf, revplan) + t_abs = float(hf.epoch) + np.arange(npts) * deltaT + ts.data.data[:] = ts.data.data * np.exp( + 1.0j * coef * 2.0 * np.pi * f_sidereal * (t_abs - t_ref)) + out = _copy_freqseries(hf) + lal.COMPLEX16TimeFreqFFT(out, ts, fwdplan) + return out + + +def fd_apply_sidereal_modulation(hf, n, f_sidereal=F_SIDEREAL, t_ref=0.0): + """COMPLEX16FrequencySeries -> new series for exp(i n Omega (t_abs - t_ref)) h(t).""" + return _lal_freq_modulate(hf, n, f_sidereal, t_ref) + + +def build_elementary_template(hf, p, n, f_sidereal=F_SIDEREAL): + """chi_{(p,n)} spectrum from the base mode spectrum hf: derivative then modulation.""" + return fd_apply_sidereal_modulation(fd_apply_time_derivative(hf, p), n, f_sidereal) + + +def _elementary_index_set(harmonics, p_max): + """List of a=(p,n). Superset grid; unused (p,n) get zero response coefficients.""" + return [(p, n) for p in range(p_max + 1) for n in harmonics] + + +# --------------------------------------------------------------------------- +# The precompute. +# --------------------------------------------------------------------------- +def PrecomputeLikelihoodTermsWithRotation( + event_time_geo, t_window, P, data_dict, psd_dict, Lmax, fMax, + harmonics=(-2, -1, 0, 1, 2), p_max=0, f_sidereal=F_SIDEREAL, + analyticPSD_Q=False, inv_spec_trunc_Q=False, T_spec=0., + verbose=True, quiet=False, internal_fast_precompute=True, + skip_interpolation=False, **hlm_kwargs): + """Slow-rotation analogue of factored_likelihood.PrecomputeLikelihoodTerms. + + Builds each FD mode once (via factored_likelihood.internal_hlm_generator) and forms the + generalized overlaps for every elementary modulated template a=(p,n): + + rholms_intp_rot[det][a][(l,m)] : interpolant of Q^a_lm(t) = e^{inOmega t} + crossTerms_rot[det][(a,a')] : { ((l,m),(l',m')) : } + crossTermsV_rot[det][(a,a')] : { ((l,m),(l',m')) : } + + Parameters mirror PrecomputeLikelihoodTerms; rotation-specific: + harmonics : sidereal harmonic indices n to carry (antenna needs |n|<=2). + p_max : max delay-derivative order (0 = Path A amplitude-only; >=1 = Path B). + f_sidereal: sidereal frequency [Hz]. + + Response coefficients A_n(det,dec,psi), B_n(det,dec) that contract these overlaps are + NOT computed here -- see slowrot_response and the (forthcoming) rotation-aware + FactoredLogLikelihood assembly. This routine produces only the intrinsic-only, + sky-independent overlap bank. + + NOTE: full-stack numerical validation (V1 of the notes' validation matrix: agreement + with a brute-force time-varying-response likelihood) requires the data/PSD/waveform + environment and is done separately; the FD primitives used here are unit-tested in + test_slowrot_fd_ops.py. + """ + # Lazy heavy imports (need the full RIFT stack / lal). + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + assert data_dict.keys() == psd_dict.keys() + detectors = list(data_dict.keys()) + t_ev = float(event_time_geo) + # The exp(i n Omega t) modulation for the data term Q is applied to the DATA (shift by + # -n f_sidereal, referenced to t_ev), which is mode-independent: one shift per (det,n), + # and -- since the modulation lives on the fixed absolute data-time axis -- needs NO + # arrival-time-dependent post-phase. U,V use modulated templates (same t_ev reference). + + # Reference distance handling identical to the base precompute. + P.dist = FL.distMpcRef * 1e6 * lsu.lsu_PC + P.deltaF = data_dict[detectors[0]].deltaF + + # --- build base FD modes ONCE --- + hlms, hlms_conj = FL.internal_hlm_generator(P, Lmax, verbose=verbose, quiet=quiet, + **hlm_kwargs) + a_list = _elementary_index_set(harmonics, p_max) + p_values = sorted(set(p for (p, n) in a_list)) + + # --- derivative-weighted template families (per p), built once, reused for all n,det --- + # (the derivative is a trivial FD weight; no waveform regeneration) + hlms_p = {p: {lm: fd_apply_time_derivative(hlms[lm], p) for lm in hlms} for p in p_values} + hlms_conj_p = {p: {lm: fd_apply_time_derivative(hlms_conj[lm], p) for lm in hlms_conj} + for p in p_values} + + # For U,V we need the modulated templates chi_a (modulation cannot be pushed onto data + # in a overlap). Build them once per a (cheap FD op). + # + # CRITICAL reference-time note: the template time series carry the INTRINSIC epoch + # (hlms.epoch ~ -T_dur, i.e. near 0), NOT the absolute event time t_ev ~ 1e9. When the + # template is placed at the event, sample j sits at absolute time t' = t_ev + (hlms.epoch + # + j*dt), so the physical modulation exp(i n Omega (t' - t_ev)) = exp(i n Omega + # (hlms.epoch + j*dt)) -- i.e. reference the template modulation to 0, NOT to t_ev. + # (Referencing to t_ev with the tiny template epoch would apply exp(i n Omega * ~-1e9), + # a ~1e4 rad spurious phase that randomizes U,V and inflates lnL beyond 0.5.) + # The data-term route above keeps t_ev because the DATA epoch really is ~ t_ev. + chi = {a: {lm: fd_apply_sidereal_modulation(hlms_p[a[0]][lm], a[1], f_sidereal, 0.0) + for lm in hlms} for a in a_list} + chi_conj = {a: {lm: fd_apply_sidereal_modulation(hlms_conj_p[a[0]][lm], a[1], f_sidereal, 0.0) + for lm in hlms_conj} for a in a_list} + + rholms_rot = {} + rholms_intp_rot = {} + crossTerms_rot = {} + crossTermsV_rot = {} + + for det in detectors: + psd = psd_dict[det] + data = data_dict[det] + t_det = FL.ComputeArrivalTimeAtDetector(det, P.phi, P.theta, event_time_geo) + rho_epoch = data.epoch - hlms[list(hlms.keys())[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / P.deltaT + 0.5) + N_window = int(2 * t_window / P.deltaT) + t = np.arange(N_window) * P.deltaT + float(rho_epoch + N_shift * P.deltaT) + + # ---- data-term overlaps Q^a_lm(t) ---- + # exp(i n Omega t) on the template is equivalent to shifting the data spectrum by + # -n f_sidereal (mode-independent). Realize it by modulating the DATA time series + # by exp(-i n Omega (t_abs - t_ev)) (round trip). Because the modulation lives on + # the absolute data-time axis, the resulting overlap is directly + # Q^a_lm(t) = int e^{-i n Omega (t'-t_ev)} [d^p h_lm]^*(t'-t) d(t') dt' + # with NO arrival-time-dependent post-phase. + rholms_rot[det] = {} + rholms_intp_rot[det] = {} + data_by_n = {} + for n in set(nn for (_, nn) in a_list): + data_by_n[n] = data if n == 0 else _lal_freq_modulate(data, -n, f_sidereal, t_ev) + + for a in a_list: + p, n = a + rho = FL.ComputeModeIPTimeSeries( + hlms_p[p], data_by_n[n], psd, P.fmin, fMax, 1. / 2. / P.deltaT, + N_shift, N_window, analyticPSD_Q, inv_spec_trunc_Q, T_spec) + rholms_rot[det][a] = rho + if not skip_interpolation: + rholms_intp_rot[det][a] = FL.InterpolateRholms(rho, t, verbose=verbose) + else: + rholms_intp_rot[det][a] = None + + # ---- cross terms U,V for each ordered pair (a,a') ---- + crossTerms_rot[det] = {} + crossTermsV_rot[det] = {} + for a in a_list: + for ap in a_list: + crossTerms_rot[det][(a, ap)] = FL.ComputeModeCrossTermIP( + chi[a], chi[ap], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, verbose=False, + same_waveform_Q=False) + crossTermsV_rot[det][(a, ap)] = FL.ComputeModeCrossTermIP( + chi_conj[a], chi[ap], psd, P.fmin, fMax, 1. / 2. / P.deltaT, P.deltaF, + analyticPSD_Q, inv_spec_trunc_Q, T_spec, prefix="V", verbose=False, + same_waveform_Q=False) + + meta = dict(harmonics=tuple(harmonics), p_max=p_max, f_sidereal=f_sidereal, + a_list=a_list, event_time_geo=float(event_time_geo), + omega_earth=OMEGA_EARTH, modes=list(hlms.keys())) + return rholms_intp_rot, crossTerms_rot, crossTermsV_rot, rholms_rot, meta + + +# --------------------------------------------------------------------------- +# Rotation-aware log-likelihood assembly (Path A: amplitude drift, p_max=0). +# --------------------------------------------------------------------------- +def antenna_harmonics_tilde(det, RA, DEC, psi, tref): + """Return {n: A_tilde_n} where F_k(t) = sum_n A_tilde_n exp(i n Omega (t - tref)). + + A_tilde_n = A_n(response, dec, psi) * exp(i n (GMST(tref) - RA)), with A_n the + RA/time-independent antenna harmonics from slowrot_response. So A_tilde_n is the + coefficient of the precompute's modulation exp(i n Omega (t - tref)); sum_n A_tilde_n + = F_k(tref) reproduces lal.ComputeDetAMResponse exactly. + """ + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + A = srr.antenna_harmonics(lald.response, DEC, psi) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + return {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + + +def _convolve_harmonics(a, b): + """Convolve two harmonic sequences (dicts {m: coef}) -> dict {m: coef}.""" + out = {} + for m1, c1 in a.items(): + for m2, c2 in b.items(): + out[m1 + m2] = out.get(m1 + m2, 0j) + c1 * c2 + return out + + +def rotation_coefficients(det, RA, DEC, psi, tref, p_max): + """Coefficients {(p, ntilde): C} of the elementary modulated templates + chi_{(p,n)} = exp(i n Omega (t-tref)) h^{(p)}(t-tau0) in the folded response+delay + template (Path B). For p_max=0 this is {(0,n): A_tilde_n} (Path A). + + C_{(p, ntilde)} = (1/p!) sum_{n+m=ntilde} A_tilde_n [(-D)^{*p}]_m + with A_tilde_n the antenna harmonics and D_m the delay-DRIFT harmonics: + delta_tau(t) = tau(t) - tau(tref) = sum_m D_m exp(i m Omega (t-tref)), + D_0 = -(B_tilde_1 + B_tilde_-1), D_{+-1} = B_tilde_{+-1} (B_tilde = delay harmonics). + At Omega->0 delta_tau=0 -> D=0 -> C_{(p,n)}=0 for p>=1, so Path B -> Path A. + """ + import math + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + A = srr.antenna_harmonics(lald.response, DEC, psi) + Atil = {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + if p_max == 0: + return {(0, n): Atil[n] for n in Atil} + Bd = srr.delay_harmonics(lald.location, DEC) # {m: B_m}, m in -1,0,1 + Btil = {m: Bd[m] * np.exp(1.0j * m * g_ev) for m in Bd} + tau0 = np.real(sum(Btil.values())) # tau(tref) + D = {m: Btil[m] for m in Btil} + D[0] = D[0] - tau0 # = -(Btil_1 + Btil_-1) + negD = {m: -D[m] for m in D} + C = {} + E = {0: 1.0 + 0j} # (-D)^{*0} + for p in range(p_max + 1): + if p > 0: + E = _convolve_harmonics(E, negD) # (-D)^{*p} + inv = 1.0 / math.factorial(p) + for n, an in Atil.items(): + for m, em in E.items(): + key = (p, n + m) + C[key] = C.get(key, 0j) + inv * an * em + return C + + +def FactoredLogLikelihoodWithRotation(extr_params, rholms_intp_rot, crossTerms_rot, + crossTermsV_rot, meta, Lmax): + """Slow-rotation analogue of factored_likelihood.FactoredLogLikelihood (Path A). + + Contracts the harmonic-resolved precompute bank with the antenna harmonics + A_tilde_n(det, RA, DEC, psi, tref). Reduces EXACTLY to the baseline + FactoredLogLikelihood when f_sidereal -> 0 (all modulations become identity and + sum_n A_tilde_n -> F_k(tref)). + + Currently implements p_max=0 (amplitude drift only); the delay-derivative (Path B) + contraction with B_n is a TODO. + """ + import lal + from . import factored_likelihood as FL + from .. import lalsimutils as lsu + + p_max = meta['p_max'] + a_list = list(meta['a_list']) + harmonics = list(meta['harmonics']) + hset = set(harmonics) + for n in harmonics: + assert -n in hset, "harmonic set must be symmetric (need -n for the V term): %s" % harmonics + + RA = extr_params.phi + DEC = extr_params.theta + tref = extr_params.tref + phiref = extr_params.phiref + incl = extr_params.incl + psi = extr_params.psi + dist = extr_params.dist + + detectors = list(rholms_intp_rot.keys()) + a0 = a_list[0] + modes = list(rholms_intp_rot[detectors[0]][a0].keys()) + Ylms = FL.ComputeYlms(Lmax, incl, -phiref, selected_modes=modes) + + distMpc = dist / (lsu.lsu_PC * 1e6) + invDistMpc = FL.distMpcRef / distMpc + + lnL = 0. + for det in detectors: + C = rotation_coefficients(det, RA, DEC, psi, tref, p_max) # {(p,n): C_a} + t_det = FL.ComputeArrivalTimeAtDetector(det, RA, DEC, tref) + CT = crossTerms_rot[det] + CTV = crossTermsV_rot[det] + + # Q^a_lm(t_det) for each elementary template a=(p,n) + Q = {a: {m: rholms_intp_rot[det][a][m](float(t_det)) + for m in modes} for a in a_list} + + # term1 = Re[ sum_lm conj(Ylm) sum_a conj(C_a) Q^a_lm(t_det) ] + term1 = 0. + for m in modes: + s = 0. + for a in a_list: + s += np.conj(C.get(a, 0j)) * Q[a][m] + term1 += np.conj(Ylms[m]) * s + term1 = np.real(term1) * invDistMpc + + # term2 = -1/4 Re[ sum_{p1,p2} ( U-part conj(Y1)Y2 + V-part Y1 Y2 ) ] + # U-part = sum_{a,a'} conj(C_a) C_a' U^{(a,a')}[p1,p2] + # V-part = sum_{a=(p,nu),a'} C_{(p,-nu)} C_a' V^{(a,a')}[p1,p2] + term2 = 0. + for p1 in modes: + for p2 in modes: + u = 0. + v = 0. + for a in a_list: + aR = (a[0], -a[1]) + for ap in a_list: + u += np.conj(C.get(a, 0j)) * C.get(ap, 0j) * CT[(a, ap)][(p1, p2)] + v += C.get(aR, 0j) * C.get(ap, 0j) * CTV[(a, ap)][(p1, p2)] + term2 += u * np.conj(Ylms[p1]) * Ylms[p2] + v * Ylms[p1] * Ylms[p2] + term2 = -np.real(term2) / 4. / (distMpc / FL.distMpcRef) ** 2 + + lnL += term1 + term2 + + return lnL + + +# --------------------------------------------------------------------------- +# Vectorized (NoLoop) rotation likelihood -- the maintained batchmode path. +# --------------------------------------------------------------------------- +def rotation_coefficients_vector(det, RA, DEC, psi, tref, p_max): + """Vectorized rotation_coefficients: RA, DEC, psi are arrays (npts_ex,); returns + {(p, n): complex ndarray (npts_ex,)}. Same algebra as rotation_coefficients.""" + import math + import lal + import lalsimulation as lalsim + from . import slowrot_response as srr + lald = lalsim.DetectorPrefixToLALDetector(det) + RA = np.asarray(RA); DEC = np.asarray(DEC); psi = np.asarray(psi) + g_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(tref)))) - RA + A = srr.antenna_harmonics_vector(lald.response, DEC, psi) + Atil = {n: A[n] * np.exp(1.0j * n * g_ev) for n in A} + if p_max == 0: + return {(0, n): Atil[n] for n in Atil} + Bd = srr.delay_harmonics_vector(lald.location, DEC) + Btil = {m: Bd[m] * np.exp(1.0j * m * g_ev) for m in Bd} + tau0 = np.real(sum(Btil.values())) + D = {m: Btil[m] for m in Btil} + D[0] = D[0] - tau0 + negD = {m: -D[m] for m in D} + C = {} + E = {0: np.ones_like(g_ev, dtype=complex)} + for p in range(p_max + 1): + if p > 0: + E = _convolve_harmonics(E, negD) + inv = 1.0 / math.factorial(p) + for n, an in Atil.items(): + for m, em in E.items(): + key = (p, n + m) + C[key] = C.get(key, 0j) + inv * an * em + return C + + +def pack_rotation_arrays(meta, rholms_rot, crossTerms_rot, crossTermsV_rot): + """Pack the elementary-template precompute bank into dense arrays for the NoLoop path. + + Returns (lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict), keyed per detector by + elementary template a=(p,n) (Path A: a=(0,n); Path B: also p>=1). + """ + a_list = list(meta['a_list']) + lookupNKDict = {}; rho_by_a = {}; U_by_aa = {}; V_by_aa = {}; epochDict = {} + for det in rholms_rot: + a0 = a_list[0] + modes = list(rholms_rot[det][a0].keys()) + n_lms = len(modes) + idx = {m: i for i, m in enumerate(modes)} + lookupNKDict[det] = np.array([[m[0], m[1]] for m in modes], dtype=int) + npts = rholms_rot[det][a0][modes[0]].data.length + epochDict[det] = float(rholms_rot[det][a0][modes[0]].epoch) + rho_by_a[det] = {} + for a in a_list: + arr = np.zeros((n_lms, npts), dtype=np.complex128) + for m in modes: + arr[idx[m]] = rholms_rot[det][a][m].data.data + rho_by_a[det][a] = arr + U_by_aa[det] = {}; V_by_aa[det] = {} + for a in a_list: + for ap in a_list: + Um = np.zeros((n_lms, n_lms), dtype=np.complex128) + Vm = np.zeros((n_lms, n_lms), dtype=np.complex128) + cU = crossTerms_rot[det][(a, ap)] + cV = crossTermsV_rot[det][(a, ap)] + for m1 in modes: + for m2 in modes: + Um[idx[m1], idx[m2]] = cU[(m1, m2)] + Vm[idx[m1], idx[m2]] = cV[(m1, m2)] + U_by_aa[det][(a, ap)] = Um + V_by_aa[det][(a, ap)] = Vm + return lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict + + +def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta, lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict, + Lmax=2, array_output=False, time_interp='nearest'): + """Vectorized rotation-aware lnL (Path A). + + Mirrors factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig with + sums over sidereal harmonics and the per-sample antenna harmonics + A_tilde_n = A_n(det,dec,psi) exp(i n (GMST(tref)-RA)). Extrinsic params in P_vec are + ARRAYS of length npts_ex (RA, DEC, incl, phiref, psi, dist); tref, deltaT scalar. + + array_output=True returns lnL_t of shape (npts_ex, npts) (before time marginalization); + array_output=False returns the time-marginalized lnL of shape (npts_ex,). + """ + import lal + from . import factored_likelihood as FL + + a_list = list(meta['a_list']) + p_max = meta['p_max'] + npts = len(tvals); npts_ex = len(P_vec.phi) + RA = P_vec.phi; DEC = P_vec.theta + incl = P_vec.incl; phiref = P_vec.phiref; psi = P_vec.psi + distMpc = P_vec.dist / (lal.PC_SI * 1e6) + lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) + + for det in rho_by_a: + n_lms = len(lookupNKDict[det]) + Ylms = FL.ComputeYlmsArrayVector(lookupNKDict[det], incl, -phiref).T # (npts_ex, n_lms) + C = rotation_coefficients_vector(det, RA, DEC, psi, P_vec.tref, p_max) # {(p,n): (npts_ex,)} + zeroC = np.zeros(npts_ex, dtype=complex) + + def Cg(a): + return C[a] if a in C else zeroC + t_ref = epochDict[det] + # Match the maintained baseline NoLoop's precision-preserving time reference: keep the + # tiny (tref - epoch) offset and the small geometric delay separate, rather than + # subtracting two ~1e9 s absolute arrival times. The absolute-difference form loses + # ~1e-3 of a sample bin, which is harmless for nearest-neighbour snapping but shows up + # directly in the sub-bin fraction used by cubic interpolation. + detector_location = np.asarray(FL.lalsim.DetectorPrefixToLALDetector(det).location) + gmst_tref = float(lal.GreenwichMeanSiderealTime(P_vec.tref)) + t_det = float(P_vec.tref - float(t_ref)) + FL.TimeDelayFromEarthCenter( + detector_location, RA, DEC, gmst_tref) + sample_first = (t_det + tvals[0]) / P_vec.deltaT + if time_interp == 'nearest': + ifirst = (np.round(sample_first) + 0.5).astype(int) + else: + ifirst = np.floor(sample_first).astype(int) + frac_first = (sample_first - np.floor(sample_first)).astype(np.float64) + ilast = ifirst + npts + + term1 = np.zeros((npts_ex, npts), dtype=np.complex128) + for a in a_list: + det_rho = rho_by_a[det][a] + if time_interp == 'nearest': + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + else: + # cubic sub-sample interpolation (calmarg time_interp='cubic'): + # _cubic_Q_window_numpy expects Q_block shape (n_time, n_lm). + Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) + term1 += np.conj(Cg(a))[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) + term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] + + term2 = np.zeros(npts_ex, dtype=np.complex128) + for a in a_list: + aR = (a[0], -a[1]) + for ap in a_list: + term2 += np.conj(Cg(a)) * Cg(ap) * np.einsum( + 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_aa[det][(a, ap)]) + term2 += Cg(aR) * Cg(ap) * np.einsum( + 'xi,xj,ij->x', Ylms, Ylms, V_by_aa[det][(a, ap)]) + term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 + + lnL_t += term1 + term2[:, None] + + if array_output: + return lnL_t + lnLmax = np.max(lnL_t, axis=-1, keepdims=True) + L = FL.my_simps(np.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) + return lnLmax[..., 0] + np.log(L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py new file mode 100644 index 000000000..a9950d275 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_freqresponse.py @@ -0,0 +1,442 @@ +""" +slowrot_freqresponse : closed-form FREQUENCY-DEPENDENT (finite-size) detector antenna +response of a ground-based Michelson interferometer, beyond the long-wavelength +approximation. "Path D" of the slow-rotation generalization of the RIFT likelihood. + +Motivation +---------- +The usual antenna response F_+(RA,DEC,psi), F_x(RA,DEC,psi) returned by +lal.ComputeDetAMResponse is the LONG-WAVELENGTH LIMIT: it assumes the GW strain is +uniform across the detector while a photon traverses an arm. Beyond that limit the +finite light-travel time T = L/c across an arm imprints genuine in-band frequency +structure on the response, controlled by the free spectral range + + f_FSR = c / (2 L) ( 37.5 kHz for 4-km LIGO ; 3.75 kHz for a 40-km CE arm ). + +The response becomes F_A -> F_A(f; RA, DEC, psi). This matters for 3G detectors +(Cosmic Explorer, 40 km) whose long signals reach a non-negligible fraction of f_FSR +in band; it is completely negligible for the 4-km LIGO instruments. + +Formula and convention (arXiv:2412.01693, "Beyond the Long Wavelength Approximation", +Eqs. 4-6; equivalent to Rakhmanov-Romano-Whelan 2008, arXiv:0808.3805, and +Essick-Vitale-Evans 2017, PRD 96 084004 / arXiv:1708.06843) +------------------------------------------------------------------------------------- +Single-arm round-trip transfer function, for arm unit vector a-hat and source direction +n-hat (direction TO the source, Sathyaprakash-Schutz convention -- the SAME n-hat used +by lal.ComputeDetAMResponse / TimeDelayFromEarthCenter), with T = L/c the one-way +photon time of flight and T_pm^a = T (1 +/- a-hat . n-hat): + + D~(a-hat, n-hat, f) = (e^{-i 2 pi f T} / 2) + * [ e^{+i pi f T_-^a} sinc(pi f T_+^a) + + e^{-i pi f T_+^a} sinc(pi f T_-^a) ] (Eq. 6) + +with sinc(z) = sin(z)/z, so D~ -> 1 as f -> 0. The frequency-dependent antenna +patterns are then (Eqs. 4-5) + + F_A(f, n-hat) = (1/2) [ D~(x-hat, n-hat, f) (eps^A : x-hat x-hat) + - D~(y-hat, n-hat, f) (eps^A : y-hat y-hat) ] , A in {+, x} + +where eps^+ , eps^x are the GW polarization tensors set by (RA, DEC, psi) and +x-hat, y-hat are the two arm unit vectors. At f -> 0 both D~ -> 1 and this collapses to + F_A(0) = eps^A : (1/2)(x-hat x-hat - y-hat y-hat) = eps^A : D_response + = lal.ComputeDetAMResponse , +since the detector response tensor is exactly D_response = (1/2)(x-hat x-hat - y-hat y-hat). + +Implementation note (exactness of the f -> 0 limit) +--------------------------------------------------- +LAL stores the detector response tensor `d.response` in single precision (REAL4). +Reconstructing (1/2)(x x - y y) from the geodetic arm geometry therefore agrees with +`d.response` only to ~1e-7. To reproduce lal.ComputeDetAMResponse to MACHINE PRECISION +at f = 0 we write the response as an EXACT long-wavelength baseline plus a finite-size +correction that vanishes identically at f = 0: + + F_A(f) = F_A^LWL + (1/2)[ (D~_x - 1)(eps^A : x x) - (D~_y - 1)(eps^A : y y) ] + +The baseline F_A^LWL is computed from the SAME `d.response` and triad algebra as +lal.ComputeDetAMResponse (see slowrot_response.py), so at f = 0 (where D~_x = D~_y = 1 +EXACTLY, sinc(0)=1) the correction is exactly zero and F_A(0) == ComputeDetAMResponse. +The tiny (~1e-7) geodetic imprecision only enters the O((f/f_FSR)^2) correction, i.e. +utterly negligibly. + +Pure numpy + lal; importable without the heavy RIFT stack (mirrors slowrot_response). + +Conventions (identical to slowrot_response.py / vectorized_lal_tools.py): + g = GMST - RA (Greenwich hour angle) + nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) (direction to source) + X = (-cp sg - sp cg sd, -cp cg + sp sg sd, sp cd) (polarization triad) + Y = ( sp sg - cp cg sd, sp cg + cp sg sd, cp cd) + F_+ = X.D.X - Y.D.Y , F_x = X.D.Y + Y.D.X + (cp,sp)=(cos,sin)psi ; (cd,sd)=(cos,sin)dec ; (cg,sg)=(cos,sin)g +""" +from __future__ import print_function, division + +import numpy as np + +try: + import lalsimulation as _lalsim + _HAVE_LAL = True +except Exception: # pragma: no cover + _HAVE_LAL = False + +C_SI = 299792458.0 # m/s, matches vectorized_lal_tools / slowrot_response +DEFAULT_L_LIGO = 3994.5 # m, nominal LIGO arm length (override for CE etc.) + + +# --------------------------------------------------------------------------- +# Detector geometry: arm unit vectors and arm length from a LAL detector. +# --------------------------------------------------------------------------- +def _cartesian_arm(cosAlt, sinAlt, cosAz, sinAz, cosLat, sinLat, cosLon, sinLon): + """Earth-fixed Cartesian unit vector of a detector arm. + + Exact port of LAL's getCartesianComponents (LALDetectors.c): azimuth measured + from local North toward East, altitude above the local horizontal. + """ + uNorth = cosAlt * cosAz + uEast = cosAlt * sinAz + uRho = -sinLat * uNorth + cosLat * sinAlt + return np.array([ + cosLon * uRho - sinLon * uEast, + sinLon * uRho + cosLon * uEast, + cosLat * uNorth + sinLat * sinAlt, + ]) + + +def detector_geometry(det, L_arm=None): + """Return (response, x_arm, y_arm, L) for a detector prefix ('H1','L1','V1',...). + + Parameters + ---------- + det : str detector prefix understood by lalsimulation + L_arm : float or None arm length [m] override (e.g. 40000. for a 40-km CE arm); + default = 2 * xArmMidpoint from the LAL detector. + + Returns + ------- + response : (3,3) float ndarray Earth-fixed detector response tensor D (from LAL) + x_arm, y_arm : (3,) float ndarray Earth-fixed arm unit vectors + L : float arm length [m] used for the finite-size transfer + """ + if not _HAVE_LAL: # pragma: no cover + raise RuntimeError("lalsimulation is required for detector_geometry") + d = _lalsim.DetectorPrefixToLALDetector(det) + fr = d.frDetector + lat, lon = fr.vertexLatitudeRadians, fr.vertexLongitudeRadians + cL, sL, cO, sO = np.cos(lat), np.sin(lat), np.cos(lon), np.sin(lon) + x_arm = _cartesian_arm(np.cos(fr.xArmAltitudeRadians), np.sin(fr.xArmAltitudeRadians), + np.cos(fr.xArmAzimuthRadians), np.sin(fr.xArmAzimuthRadians), + cL, sL, cO, sO) + y_arm = _cartesian_arm(np.cos(fr.yArmAltitudeRadians), np.sin(fr.yArmAltitudeRadians), + np.cos(fr.yArmAzimuthRadians), np.sin(fr.yArmAzimuthRadians), + cL, sL, cO, sO) + L = float(L_arm) if L_arm is not None else 2.0 * float(fr.xArmMidpoint) + return np.asarray(d.response, dtype=float), x_arm, y_arm, L + + +# --------------------------------------------------------------------------- +# Kinematic pieces (triad + source direction), matching ComputeDetAMResponse. +# --------------------------------------------------------------------------- +def _triad(dec, psi, g): + """Polarization triad X, Y and source direction nhat at hour angle g = GMST - RA. + + dec, psi, g may be scalars or broadcastable arrays; returned vectors carry the + 3-component along the last axis. + """ + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + cg, sg = np.cos(g), np.sin(g) + + X = np.stack([-cp * sg - sp * cg * sd, + -cp * cg + sp * sg * sd, + sp * cd * np.ones_like(sg)], axis=-1) + Y = np.stack([ sp * sg - cp * cg * sd, + sp * cg + cp * sg * sd, + cp * cd * np.ones_like(sg)], axis=-1) + nhat = np.stack([cd * cg, -cd * sg, sd * np.ones_like(sg)], axis=-1) + return X, Y, nhat + + +# --------------------------------------------------------------------------- +# Single-arm finite-size transfer function (Eq. 6 of arXiv:2412.01693). +# --------------------------------------------------------------------------- +def single_arm_transfer(a_dot_n, f, L): + """Round-trip single-arm transfer function D~(a-hat, n-hat, f). + + Parameters + ---------- + a_dot_n : float or ndarray a-hat . n-hat (arm unit vector dotted with source dir) + f : float or ndarray frequency [Hz] + L : float arm length [m] + + Returns + ------- + complex ndarray broadcast over (a_dot_n, f). D~ -> 1 as f -> 0. + + D~ = (e^{-i 2 pi f T}/2)[ e^{+i pi f T_-} sinc(pi f T_+) + e^{-i pi f T_+} sinc(pi f T_-) ] + with T = L/c, T_pm = T(1 +/- a_dot_n), sinc(z)=sin(z)/z. + (np.sinc(u) = sin(pi u)/(pi u), so sinc(pi f T_pm) = np.sinc(f T_pm).) + """ + a = np.asarray(a_dot_n, dtype=float) + f = np.asarray(f, dtype=float) + T = L / C_SI + Tp = T * (1.0 + a) # T_+ + Tm = T * (1.0 - a) # T_- + pref = np.exp(-1j * 2.0 * np.pi * f * T) / 2.0 + term_p = np.exp(1j * np.pi * f * Tm) * np.sinc(f * Tp) + term_m = np.exp(-1j * np.pi * f * Tp) * np.sinc(f * Tm) + return pref * (term_p + term_m) + + +# --------------------------------------------------------------------------- +# Frequency-dependent antenna response. +# --------------------------------------------------------------------------- +def _lwl_response(response, X, Y): + """Long-wavelength F_+, F_x from triad and response tensor (== ComputeDetAMResponse). + + F_+ = X.D.X - Y.D.Y , F_x = X.D.Y + Y.D.X . Vectorized over leading axes of X,Y. + """ + D = response + XDX = np.einsum('...i,ij,...j->...', X, D, X) + YDY = np.einsum('...i,ij,...j->...', Y, D, Y) + XDY = np.einsum('...i,ij,...j->...', X, D, Y) + YDX = np.einsum('...i,ij,...j->...', Y, D, X) + return XDX - YDY, XDY + YDX + + +def antenna_response_fd(det, ra, dec, psi, f, gmst=0.0, L_arm=None): + """Frequency-dependent antenna response F_+(f), F_x(f) for a ground-based detector. + + Beyond the long-wavelength limit: includes the finite light-travel-time transfer + across the arms. Reduces EXACTLY to lal.ComputeDetAMResponse as f -> 0. + + Parameters + ---------- + det : str detector prefix ('H1','L1','V1','K1',...) + ra, dec, psi : float right ascension, declination, polarization angle [rad] + f : float or (Nf,) ndarray frequency [Hz] (scalar or 1-D array) + gmst : float Greenwich mean sidereal time [rad] (default 0). Enters + only through g = gmst - ra, exactly as ComputeDetAMResponse. + L_arm : float or None arm-length override [m] (e.g. 40000. for 40-km CE); + default = LAL detector arm length (~4 km for LIGO). + + Returns + ------- + (Fp, Fc) : complex ndarray, same shape as f. At f=0, imag part -> 0 and the real + parts equal lal.ComputeDetAMResponse(response, ra, dec, psi, gmst). + """ + response, x_arm, y_arm, L = detector_geometry(det, L_arm=L_arm) + return antenna_response_fd_geom(response, x_arm, y_arm, L, ra, dec, psi, f, gmst=gmst) + + +def antenna_response_fd_geom(response, x_arm, y_arm, L, ra, dec, psi, f, gmst=0.0): + """Same as antenna_response_fd but with detector geometry supplied explicitly. + + Useful for exotic geometries (custom L, custom arms). See antenna_response_fd. + """ + g = gmst - ra + X, Y, nhat = _triad(dec, psi, g) # shape (3,) + + # Exact long-wavelength baseline (matches ComputeDetAMResponse to machine precision). + Fp_lwl, Fc_lwl = _lwl_response(response, X, Y) + + # Arm-basis polarization projections eps^A : (a a). + Xx, Yx = float(X @ x_arm), float(Y @ x_arm) + Xy, Yy = float(X @ y_arm), float(Y @ y_arm) + Ppx = Xx * Xx - Yx * Yx # eps^+ : x x + Ppy = Xy * Xy - Yy * Yy # eps^+ : y y + Pcx = 2.0 * Xx * Yx # eps^x : x x + Pcy = 2.0 * Xy * Yy # eps^x : y y + + # Finite-size transfer per arm (vanishes-minus-one -> 0 at f=0). + ax = float(x_arm @ nhat) + ay = float(y_arm @ nhat) + Dx = single_arm_transfer(ax, f, L) + Dy = single_arm_transfer(ay, f, L) + + Fp = Fp_lwl + 0.5 * ((Dx - 1.0) * Ppx - (Dy - 1.0) * Ppy) + Fc = Fc_lwl + 0.5 * ((Dx - 1.0) * Pcx - (Dy - 1.0) * Pcy) + return Fp, Fc + + +def free_spectral_range(L): + """Free spectral range f_FSR = c / (2 L) [Hz].""" + return C_SI / (2.0 * L) + + +# =========================================================================== +# Step 1 : sky-harmonic / power-series expansion of the finite-size response. +# =========================================================================== +# The finite-size antenna response is (pure Eqs. 4-6, no LWL split) +# +# F_A(f;sky) = (1/2)[ D~(x,n,f) (eps^A:xx) - D~(y,n,f) (eps^A:yy) ] . +# +# Factor out the common one-way light-crossing delay T = L/c that both arms +# share (DIRECTION-INDEPENDENT, degenerate with coalescence time): +# +# D~(a,n,f) = e^{-i 2 pi f T} g(f; a) , a = a-hat . n-hat , +# g(f; a) = (1/2)[ e^{+i u(1-a)} sinc(u(1+a)) + e^{-i u(1+a)} sinc(u(1-a)) ] , +# u = pi f T , sinc(x) = sin(x)/x , g(f;0) = sinc(2u) , g -> 1 as f->0. +# +# Taylor-expand the residual g in the arm projection a (the small in-band +# parameter is eps*a with eps = f L/c = f T): +# +# g(f; a) = sum_{q>=0} c_q(f) a^q . (c_q sky-INDEPENDENT) +# +# The arm projections a_x = x-hat.n, a_y = y-hat.n and the arm-basis polarization +# contractions combine into a single COMPLEX analytic sky/pol scalar per power q, +# +# beta_q(sky) = (1/2)[ zx^2 a_x^q - zy^2 a_y^q ] , +# zx = X.x-hat + i Y.x-hat , zy = X.y-hat + i Y.y-hat (X,Y polarization triad) +# +# so that G_A(f;sky) := F_A e^{+i2 pi f T} gives the complex response +# +# G(f) = G_+ + i G_x = sum_q c_q(f) beta_q(sky) , (the analogue of A_n) +# beta_0 = (1/2)(zx^2 - zy^2) = F_+^LWL + i F_x^LWL (== ComputeDetAMResponse, up +# to the ~1e-7 REAL4 geodety). +# +# The full response, EXACTLY reproducing antenna_response_fd, is then +# +# F(f) = F0_lal + e^{-i2 pi f T} G(f) - beta_0 (Fp+iFc form) +# Fbar(f) = conj(F0_lal) + e^{-i2 pi f T} Gbar(f) - conj(beta_0), Gbar=sum_q c_q conj(beta_q) +# F_+ = (F+Fbar)/2 , F_x = (F-Fbar)/(2i) +# +# with F0_lal the EXACT lal.ComputeDetAMResponse baseline (so f->0 is machine +# precise even though beta_0 from the arm triads carries the ~1e-7 geodetic error; +# the arm-based pieces enter only the finite-size correction, which vanishes at f=0). +# --------------------------------------------------------------------------- +from math import comb as _comb, factorial as _factorial + + +def _sinc_shift_apoly(u, s, Qmax, jmax=64): + """a-power coefficients d_m(u), m=0..Qmax, of sinc(u(1 + s a)) (s = +/-1). + + Entire-series form (stable at u=0): sinc(x) = sum_j (-1)^j x^{2j}/(2j+1)!, + x = u(1+s a) => coeff of a^m is s^m sum_{j>=ceil(m/2)} (-1)^j u^{2j}/(2j+1)! C(2j,m). + u may be a numpy array (frequencies); returns shape (Qmax+1,) + u.shape. + """ + u = np.asarray(u, dtype=float) + out = np.zeros((Qmax + 1,) + u.shape, dtype=float) + u2 = u * u + # precompute u^{2j} + upow = np.ones_like(u) # u^{0} + for j in range(0, jmax + 1): + coef = ((-1) ** j) / float(_factorial(2 * j + 1)) + for m in range(0, min(Qmax, 2 * j) + 1): + out[m] += coef * _comb(2 * j, m) * upow + upow = upow * u2 + for m in range(Qmax + 1): + out[m] *= float(s) ** m + return out.astype(complex) + + +def _poly_mul(A, B, Qmax): + """Truncated product (to order Qmax) of two a-power stacks A[k],B[k] over freq axis.""" + out = np.zeros_like(A[:Qmax + 1]) + for i in range(min(len(A), Qmax + 1)): + for j in range(min(len(B), Qmax + 1 - i)): + out[i + j] += A[i] * B[j] + return out + + +def finite_size_c_coeffs(f, L, Qmax): + """Sky-independent frequency basis c_q(f), q = 0..Qmax, of g(f;a)=sum_q c_q(f) a^q. + + Parameters + ---------- + f : ndarray frequency [Hz] (signed values allowed; c_q(-f)=conj(c_q(f))) + L : float arm length [m] + Qmax : int highest power of the arm projection a retained + + Returns + ------- + c : complex ndarray, shape (Qmax+1,) + f.shape. c_0(f)=sinc(2 pi f T), + c_{q>=1} carry the finite-size shape distortion; all -> delta_{q0} as f->0. + """ + f = np.asarray(f, dtype=float) + T = L / C_SI + u = np.pi * f * T + # exp(-i u a): coeff of a^k is (-i u)^k / k! + exp_neg = np.zeros((Qmax + 1,) + u.shape, dtype=complex) + term = np.ones_like(u, dtype=complex) + for k in range(Qmax + 1): + exp_neg[k] = term / float(_factorial(k)) + term = term * (-1j * u) + sinc_p = _sinc_shift_apoly(u, +1.0, Qmax) # sinc(u(1+a)) + sinc_m = _sinc_shift_apoly(u, -1.0, Qmax) # sinc(u(1-a)) + eiu = np.exp(1j * u) + term1 = eiu * _poly_mul(exp_neg, sinc_p, Qmax) # e^{iu} e^{-iua} sinc(u(1+a)) + term2 = np.conj(eiu) * _poly_mul(exp_neg, sinc_m, Qmax) # e^{-iu} e^{-iua} sinc(u(1-a)) + return 0.5 * (term1 + term2) + + +def finite_size_geometry(det, ra, dec, psi, gmst=0.0, L_arm=None): + """Geometric scalars for the finite-size expansion at (det, ra, dec, psi, gmst). + + Returns dict with: + T = L/c common one-way delay [s] + L arm length [m] + ax, ay arm projections x-hat.n, y-hat.n + zx, zy = X.a-hat + i Y.a-hat (complex pol/arm scalars) + F0 = Fp_lwl + i Fc_lwl (EXACT lal baseline response, machine precise f=0) + """ + response, x_arm, y_arm, L = detector_geometry(det, L_arm=L_arm) + g = gmst - ra + X, Y, nhat = _triad(dec, psi, g) + Fp_lwl, Fc_lwl = _lwl_response(response, X, Y) + Xx, Yx = float(X @ x_arm), float(Y @ x_arm) + Xy, Yy = float(X @ y_arm), float(Y @ y_arm) + zx = Xx + 1j * Yx + zy = Xy + 1j * Yy + ax = float(x_arm @ nhat) + ay = float(y_arm @ nhat) + return dict(T=L / C_SI, L=L, ax=ax, ay=ay, zx=zx, zy=zy, + F0=complex(Fp_lwl) + 1j * complex(Fc_lwl)) + + +def finite_size_beta(geom, Qmax): + """Analytic sky/pol coefficients beta_q = (1/2)[zx^2 a_x^q - zy^2 a_y^q], q=0..Qmax.""" + zx2, zy2, ax, ay = geom['zx'] ** 2, geom['zy'] ** 2, geom['ax'], geom['ay'] + return np.array([0.5 * (zx2 * ax ** q - zy2 * ay ** q) for q in range(Qmax + 1)], + dtype=complex) + + +def F_fd_expanded(det, ra, dec, psi, f, Qmax, gmst=0.0, L_arm=None): + """Order-Qmax reconstruction of the finite-size response F_+(f), F_x(f). + + Converges to antenna_response_fd(det,ra,dec,psi,f,...) as Qmax -> infinity. + Uses the EXACT lal baseline F0 for the constant part and the power-series + finite-size correction e^{-i2 pi f T}(sum_q c_q beta_q) - beta_0. + """ + f = np.asarray(f, dtype=float) + geom = finite_size_geometry(det, ra, dec, psi, gmst=gmst, L_arm=L_arm) + beta = finite_size_beta(geom, Qmax) + c = finite_size_c_coeffs(f, geom['L'], Qmax) # (Qmax+1,)+f.shape + G = np.tensordot(beta, c, axes=(0, 0)) # sum_q beta_q c_q(f) + Gbar = np.tensordot(np.conj(beta), c, axes=(0, 0)) + phase = np.exp(-1j * 2.0 * np.pi * f * geom['T']) + F = geom['F0'] + phase * G - beta[0] + Fbar = np.conj(geom['F0']) + phase * Gbar - np.conj(beta[0]) + Fp = 0.5 * (F + Fbar) + Fc = (F - Fbar) / (2.0j) + return Fp, Fc + + +def finite_size_response_weights(fvals, geom, Qmax): + """Per-basis frequency weights W_p(f) folded into the FD modes for the likelihood. + + Response basis (p = 0..Qmax+1) reproducing F(f) = sum_p b_p W_p(f): + p=0 ("baseline") : W_0(f) = 1 b_0 = F0 (exact lal) + p=1+q : W_{1+q}(f) = e^{-i2pi f T} c_q(f) - [q==0] + b_{1+q} = beta_q (arm) + Each W_p is Hermitian (W_p(-f)=conj(W_p(f))) so the V cross term needs NO + harmonic reflection. The common delay e^{-i2 pi f T} (= a T=L/c arrival-time + shift of the finite-size correction relative to the LWL baseline) is carried + inside the correction weights. Returns (weights (Npbasis, Nf) complex, coeff-builder). + """ + fvals = np.asarray(fvals, dtype=float) + c = finite_size_c_coeffs(fvals, geom['L'], Qmax) + phase = np.exp(-1j * 2.0 * np.pi * fvals * geom['T']) + W = np.empty((Qmax + 2, fvals.shape[0]), dtype=complex) + W[0] = 1.0 + for q in range(Qmax + 1): + W[1 + q] = phase * c[q] - (1.0 if q == 0 else 0.0) + return W diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py new file mode 100644 index 000000000..66220571d --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/slowrot_response.py @@ -0,0 +1,271 @@ +""" +slowrot_response : closed-form sidereal-harmonic decomposition of the ground-based +detector response, for the "slow-rotation" generalization of the RIFT likelihood. + +Over a long signal the Earth rotates, so the antenna response and the geometric +propagation delay drift. Both are exactly band-limited in harmonics of the Greenwich +hour angle g(t) = GMST(t) - RA: + + F(t) = F_+(t) + i F_x(t) = sum_{n=-2}^{+2} A_n exp(i n g(t)) (5 harmonics) + tau(t) = -(1/c) r_det . nhat(t) = sum_{n=-1}^{+1} B_n exp(i n g(t)) (3 harmonics) + +The key structural facts (see notes/ sec_amplitude, sec_delay, and the response appendix): + + * The complex antenna harmonics A_n depend ONLY on the detector response tensor, the + source declination, and the polarization angle -- NOT on right ascension or time. + RA and time enter only through the phase exp(i n g(t)) = exp(i n (GMST(t)-RA)). + Since GMST(t) ~ GMST(t_ref) + Omega (t - t_ref), this is exactly the sidereal-harmonic + modulation exp(i n Omega t) of the design notes, with sky/time in analytic scalars. + + * The delay harmonics B_n depend only on the detector location and the declination. + +This module derives A_n, B_n from the SAME algebra used by +RIFT.likelihood.vectorized_lal_tools.ComputeDetAMResponse / TimeDelayFromEarthCenter, so +the reconstruction + + F(t) = sum_n A_n exp(i n g) matches lal.ComputeDetAMResponse + tau(t) = sum_n B_n exp(i n g) matches lal.TimeDelayFromEarthCenter + +to machine precision for any g. This is an exact algebraic identity in g, not an +approximation; the ONLY approximation in the slow-rotation program is the near-linearity +of GMST(t) (handled elsewhere) and the truncation of the delay-derivative expansion +(Path B), neither of which enters here. + +Conventions mirror vectorized_lal_tools.py exactly: + g = GMST - RA (Greenwich hour angle) + nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) + X, Y polarization triads as in ComputeDetAMResponse + F_+ = X.D.X - Y.D.Y, F_x = X.D.Y + Y.D.X (D = detector response tensor, symmetric) +""" +from __future__ import print_function, division + +import numpy as np + +C_SI = 299792458.0 # m/s, matches vectorized_lal_tools + + +# --------------------------------------------------------------------------- +# Constant polarization-triad basis vectors (independent of hour angle g). +# +# Writing X = Xc cos g + Xs sin g + X0 (and likewise Y), read off directly from +# vectorized_lal_tools.ComputeDetAMResponse: +# X[0] = -cos_psi sin_g - sin_psi cos_g sin_dec +# X[1] = -cos_psi cos_g + sin_psi sin_g sin_dec +# X[2] = sin_psi cos_dec +# Y[0] = sin_psi sin_g - cos_psi cos_g sin_dec +# Y[1] = sin_psi cos_g + cos_psi sin_g sin_dec +# Y[2] = cos_psi cos_dec +# --------------------------------------------------------------------------- +def _triad_components(dec, psi): + """Return the six constant 3-vectors (Xc, Xs, X0, Yc, Ys, Y0).""" + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + + Xc = np.array([-sp * sd, -cp, 0.0]) # coeff of cos g in X + Xs = np.array([-cp, sp * sd, 0.0]) # coeff of sin g in X + X0 = np.array([ 0.0, 0.0, sp * cd]) + + Yc = np.array([-cp * sd, sp, 0.0]) # coeff of cos g in Y + Ys = np.array([ sp, cp * sd, 0.0]) # coeff of sin g in Y + Y0 = np.array([ 0.0, 0.0, cp * cd]) + return Xc, Xs, X0, Yc, Ys, Y0 + + +def antenna_harmonics(response_matrix, dec, psi): + """Complex antenna-pattern harmonics A_n, n = -2..2. + + F(t) = F_+(t) + i F_x(t) = sum_{n=-2}^{2} A_n exp(i n g), g = GMST(t) - RA. + + Parameters + ---------- + response_matrix : (3,3) array detector response tensor (Earth-fixed), symmetric + dec, psi : float declination and polarization angle [rad] + + Returns + ------- + A : dict {n: complex} for n in (-2,-1,0,1,2). Independent of RA and time. + """ + D = np.asarray(response_matrix, dtype=float) + Xc, Xs, X0, Yc, Ys, Y0 = _triad_components(dec, psi) + + def B(u, v): + return float(u @ D @ v) # bilinear form; D symmetric => B(u,v)=B(v,u) + + # --- F_+ = X.D.X - Y.D.Y, expanded in cos/sin of (n g) --- + # Using cos^2 = 1/2 + 1/2 cos2g, sin^2 = 1/2 - 1/2 cos2g, cos sin = 1/2 sin2g. + Pp0 = (0.5 * (B(Xc, Xc) + B(Xs, Xs)) + B(X0, X0) + - (0.5 * (B(Yc, Yc) + B(Ys, Ys)) + B(Y0, Y0))) + Pp1 = 2.0 * (B(Xc, X0) - B(Yc, Y0)) # cos g + Qp1 = 2.0 * (B(Xs, X0) - B(Ys, Y0)) # sin g + Pp2 = 0.5 * ((B(Xc, Xc) - B(Xs, Xs)) - (B(Yc, Yc) - B(Ys, Ys))) # cos 2g + Qp2 = B(Xc, Xs) - B(Yc, Ys) # sin 2g + + # --- F_x = X.D.Y + Y.D.X = 2 X.D.Y (D symmetric) --- + Pc0 = B(Xc, Yc) + B(Xs, Ys) + 2.0 * B(X0, Y0) + Pc1 = 2.0 * (B(Xc, Y0) + B(X0, Yc)) # cos g + Qc1 = 2.0 * (B(Xs, Y0) + B(X0, Ys)) # sin g + Pc2 = B(Xc, Yc) - B(Xs, Ys) # cos 2g + Qc2 = B(Xc, Ys) + B(Xs, Yc) # sin 2g + + # Complex combine: F = F_+ + i F_x. For harmonic n>=1, the real signal piece + # P_n cos n g + Q_n sin n g -> A_n exp(i n g) + A_{-n} exp(-i n g) + # with A_{+n} = (P_n - i Q_n)/2, A_{-n} = (P_n + i Q_n)/2, and here P_n, Q_n are + # THEMSELVES complex (P_n = Pp_n + i Pc_n, Q_n = Qp_n + i Qc_n). + P0 = Pp0 + 1j * Pc0 + P1 = Pp1 + 1j * Pc1 + Q1 = Qp1 + 1j * Qc1 + P2 = Pp2 + 1j * Pc2 + Q2 = Qp2 + 1j * Qc2 + + A = { + 0: P0, + 1: 0.5 * (P1 - 1j * Q1), + -1: 0.5 * (P1 + 1j * Q1), + 2: 0.5 * (P2 - 1j * Q2), + -2: 0.5 * (P2 + 1j * Q2), + } + return A + + +def antenna_harmonics_vector(response_matrix, dec, psi): + """Vectorized antenna_harmonics: dec, psi arrays (broadcast shape S). + + Returns {n: complex ndarray shape S} for n in -2..2. Mirrors antenna_harmonics + exactly (same algebra), but dec/psi may be numpy arrays (or plain Python floats/ + 0-d arrays) of a common broadcastable shape S. + """ + D = np.asarray(response_matrix, dtype=float) + dec = np.asarray(dec, dtype=float) + psi = np.asarray(psi, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + cp, sp = np.cos(psi), np.sin(psi) + + # np.broadcast(dec, psi) misbehaves/raises for plain Python floats or 0-d arrays + # in some numpy versions (it wants array-like objects, and its .shape attribute + # is not the intuitive broadcast shape for scalar inputs). np.broadcast_shapes + # operates purely on shapes and works uniformly for scalars (shape ()), 0-d + # arrays, and full arrays. + out_shape = np.broadcast_shapes(np.shape(dec), np.shape(psi)) + zero = np.zeros(out_shape) + + def vec(a, b, c): + return np.stack(np.broadcast_arrays(a, b, c), axis=-1) + + Xc = vec(-sp * sd, -cp, zero) + Xs = vec(-cp, sp * sd, zero) + X0 = vec(zero, zero, sp * cd) + + Yc = vec(-cp * sd, sp, zero) + Ys = vec( sp, cp * sd, zero) + Y0 = vec(zero, zero, cp * cd) + + def B(u, v): + return np.einsum('...i,ij,...j->...', u, D, v) + + Pp0 = 0.5 * (B(Xc, Xc) + B(Xs, Xs)) + B(X0, X0) - (0.5 * (B(Yc, Yc) + B(Ys, Ys)) + B(Y0, Y0)) + Pp1 = 2.0 * (B(Xc, X0) - B(Yc, Y0)) + Qp1 = 2.0 * (B(Xs, X0) - B(Ys, Y0)) + Pp2 = 0.5 * ((B(Xc, Xc) - B(Xs, Xs)) - (B(Yc, Yc) - B(Ys, Ys))) + Qp2 = B(Xc, Xs) - B(Yc, Ys) + + Pc0 = B(Xc, Yc) + B(Xs, Ys) + 2.0 * B(X0, Y0) + Pc1 = 2.0 * (B(Xc, Y0) + B(X0, Yc)) + Qc1 = 2.0 * (B(Xs, Y0) + B(X0, Ys)) + Pc2 = B(Xc, Yc) - B(Xs, Ys) + Qc2 = B(Xc, Ys) + B(Xs, Yc) + + P0 = Pp0 + 1j * Pc0 + P1 = Pp1 + 1j * Pc1 + Q1 = Qp1 + 1j * Qc1 + P2 = Pp2 + 1j * Pc2 + Q2 = Qp2 + 1j * Qc2 + + # B(u,v) already contracts down to shape out_shape (einsum drops the trailing + # vector index), so no additional broadcasting of P0/P1/... is actually needed; + # multiply by ones defensively/explicitly to guarantee the advertised return shape. + ones = np.ones(out_shape) + return { + 0: P0 * ones, + 1: 0.5 * (P1 - 1j * Q1) * ones, + -1: 0.5 * (P1 + 1j * Q1) * ones, + 2: 0.5 * (P2 - 1j * Q2) * ones, + -2: 0.5 * (P2 + 1j * Q2) * ones, + } + + +def delay_harmonics(location_xyz, dec): + """Geometric-delay harmonics B_n, n = -1..1 [seconds]. + + tau(t) = -(1/c) r_det . nhat(t) = sum_{n=-1}^{1} B_n exp(i n g), g = GMST(t) - RA, + with nhat = (cos_dec cos g, -cos_dec sin g, sin_dec) as in + vectorized_lal_tools.TimeDelayFromEarthCenter. + + Parameters + ---------- + location_xyz : (3,) array detector position relative to Earth center [m], Earth-fixed + dec : float declination [rad] + + Returns + ------- + B : dict {n: complex} for n in (-1,0,1). B_0 real; B_{-1} = conj(B_1). + Independent of RA and time. + """ + r = np.asarray(location_xyz, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + + # tau = -(1/c)[ r_x cos_dec cos g - r_y cos_dec sin g + r_z sin_dec ] + # = T0 + T1c cos g + T1s sin g + T0 = -(r[2] * sd) / C_SI + T1c = -(cd * r[0]) / C_SI + T1s = +(cd * r[1]) / C_SI + + B = { + 0: complex(T0), + 1: 0.5 * (T1c - 1j * T1s), + -1: 0.5 * (T1c + 1j * T1s), + } + return B + + +def delay_harmonics_vector(location_xyz, dec): + """Vectorized delay_harmonics: dec is an array (shape S). Returns {n: complex ndarray + shape S} for n=-1,0,1. Same algebra as delay_harmonics.""" + r = np.asarray(location_xyz, dtype=float) + dec = np.asarray(dec, dtype=float) + cd, sd = np.cos(dec), np.sin(dec) + T0 = -(r[2] * sd) / C_SI + T1c = -(cd * r[0]) / C_SI + T1s = (cd * r[1]) / C_SI + return {0: T0 + 0j, 1: 0.5 * (T1c - 1j * T1s), -1: 0.5 * (T1c + 1j * T1s)} + + +# --------------------------------------------------------------------------- +# Reconstruction helpers (evaluate the harmonic series at given hour angle g). +# --------------------------------------------------------------------------- +def greenwich_hour_angle(gmst, ra): + """g = GMST - RA.""" + return gmst - ra + + +def antenna_from_harmonics(A, g): + """F_+ + i F_x = sum_n A_n exp(i n g). g may be scalar or array.""" + g = np.asarray(g, dtype=float) + F = np.zeros(g.shape, dtype=complex) if g.shape else 0j + for n, An in A.items(): + F = F + An * np.exp(1j * n * g) + return F + + +def delay_from_harmonics(B, g): + """tau(t) = sum_n B_n exp(i n g) (imaginary part cancels to ~0). g scalar or array.""" + g = np.asarray(g, dtype=float) + tau = np.zeros(g.shape, dtype=complex) if g.shape else 0j + for n, Bn in B.items(): + tau = tau + Bn * np.exp(1j * n * g) + return np.real(tau) + + +def antenna_response(A, gmst, ra): + """Convenience: (F_plus, F_cross) from harmonics at (gmst, ra).""" + F = antenna_from_harmonics(A, greenwich_hour_angle(gmst, ra)) + return np.real(F), np.imag(F) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py new file mode 100644 index 000000000..7c42a1890 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_fd_ops.py @@ -0,0 +1,144 @@ +""" +Validate the frequency-domain primitives of factored_likelihood_with_rotation against +LAL FFT round trips (numpy + lal only; no glue / full RIFT stack needed). + +Checks: + 1. LAL forward/reverse COMPLEX16 FFT round-trips to identity (normalization sanity). + 2. Which signed frequency LAL assigns to a tone, vs evaluate_fvals_from_length -> fixes + the sign FT_SIGN in the time-derivative weight. + 3. fd_apply_time_derivative reproduces d^p/dt^p exactly for a multi-tone signal. + 4. _lal_freq_modulate reproduces exp(i coef Omega t) multiplication exactly. + 5. the O(N^2) reference apply_sidereal_modulation_array agrees with the LAL round trip. + +Run: python test_slowrot_fd_ops.py (also usable under pytest) +""" +from __future__ import print_function, division + +import os +import importlib.util + +import numpy as np +import lal + +_here = os.path.dirname(os.path.abspath(__file__)) +_spec = importlib.util.spec_from_file_location( + "flwr", os.path.join(_here, "factored_likelihood_with_rotation.py")) +flwr = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(flwr) + +N = 256 +DELTA_T = 1.0 / 512.0 +DELTA_F = 1.0 / (N * DELTA_T) +_T = np.arange(N) * DELTA_T + + +def _make_timeseries(cvec): + ts = lal.CreateCOMPLEX16TimeSeries("h", lal.LIGOTimeGPS(0.), 0., DELTA_T, + lal.DimensionlessUnit, N) + ts.data.data[:] = cvec.astype(complex) + return ts + + +def _forward(ts): + """DataFourier-style forward transform.""" + fwd = lal.CreateForwardCOMPLEX16FFTPlan(N, 0) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", ts.epoch, 0., DELTA_F, + lal.HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ts, fwd) + return hf + + +def _reverse(hf): + rev = lal.CreateReverseCOMPLEX16FFTPlan(N, 0) + ts = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., DELTA_T, + lal.DimensionlessUnit, N) + lal.COMPLEX16FreqTimeFFT(ts, hf, rev) + return ts + + +def _multitone(): + """h(t) = sum_j c_j exp(2 pi i f_j t), f_j on distinct grid bins.""" + bins = [3, 7, -5, 12, -11] + coeffs = [1.0, 0.5 - 0.3j, -0.8j, 0.4, 0.2 + 0.1j] + h = np.zeros(N, dtype=complex) + for b, c in zip(bins, coeffs): + h += c * np.exp(2.0j * np.pi * (b * DELTA_F) * _T) + return h, bins, coeffs + + +def test_roundtrip_identity(): + h, _, _ = _multitone() + back = _reverse(_forward(_make_timeseries(h))).data.data + assert np.max(np.abs(back - h)) < 1e-10, "LAL round trip not identity" + print("roundtrip: max err = %.2e" % np.max(np.abs(back - h))) + + +def test_tone_frequency_assignment_and_FT_SIGN(): + """Determine FT_SIGN so that (FT_SIGN 2 pi i f)^1 gives the true derivative.""" + f0_bin = 7 + h = np.exp(2.0j * np.pi * (f0_bin * DELTA_F) * _T) + hf = _forward(_make_timeseries(h)) + H = hf.data.data + fvals = flwr.evaluate_fvals_from_length(N, DELTA_F) + kpeak = int(np.argmax(np.abs(H))) + f_assigned = fvals[kpeak] + # true single-tone first derivative is (2 pi i f0) h; the reconstructed weight is + # (FT_SIGN 2 pi i f_assigned). Equate -> FT_SIGN = f0 / f_assigned. + f0 = f0_bin * DELTA_F + needed_sign = np.sign(f0 / f_assigned) + print("tone at bin %d: f0=%.4f, evaluate_fvals assigns %.4f -> FT_SIGN should be %+d" + % (f0_bin, f0, f_assigned, int(needed_sign))) + assert abs(abs(f_assigned) - abs(f0)) < 1e-9, "tone landed at wrong |f|" + assert flwr.FT_SIGN == needed_sign, ( + "module FT_SIGN=%g but LAL convention requires %g" % (flwr.FT_SIGN, needed_sign)) + + +def test_time_derivative_exact(): + h, bins, coeffs = _multitone() + for p in (1, 2, 3): + # analytic derivative + dh = np.zeros(N, dtype=complex) + for b, c in zip(bins, coeffs): + w = (2.0j * np.pi * b * DELTA_F) + dh += c * (w ** p) * np.exp(2.0j * np.pi * (b * DELTA_F) * _T) + hf = _forward(_make_timeseries(h)) + hf_d = flwr.fd_apply_time_derivative(hf, p) + num = _reverse(hf_d).data.data + err = np.max(np.abs(num - dh)) / np.max(np.abs(dh)) + print("derivative p=%d: rel err = %.2e" % (p, err)) + assert err < 1e-9, "derivative order %d inexact: %g" % (p, err) + + +def test_sidereal_modulation_exact(): + h, _, _ = _multitone() + f_sid = 0.05 * DELTA_F # exaggerated so coef*f_sid is an appreciable sub-bin shift + for coef in (1, 2, -1, -2): + expected = np.exp(2.0j * np.pi * coef * f_sid * _T) * h + hf = _forward(_make_timeseries(h)) + hf_mod = flwr._lal_freq_modulate(hf, coef, f_sidereal=f_sid) + num = _reverse(hf_mod).data.data + err = np.max(np.abs(num - expected)) / np.max(np.abs(expected)) + print("modulation coef=%+d: rel err = %.2e" % (coef, err)) + assert err < 1e-9, "modulation coef %d inexact: %g" % (coef, err) + + +def test_reference_matrix_matches_lal_modulation(): + """The O(N^2) reference and the LAL round trip must agree.""" + h, _, _ = _multitone() + f_sid = 0.05 * DELTA_F + hf = _forward(_make_timeseries(h)) + for coef in (1, -2): + via_lal = flwr._lal_freq_modulate(hf, coef, f_sidereal=f_sid).data.data + via_mat = flwr.apply_sidereal_modulation_array(hf.data.data, coef, DELTA_F, f_sid) + err = np.max(np.abs(via_lal - via_mat)) / np.max(np.abs(via_lal)) + print("reference-vs-LAL coef=%+d: rel err = %.2e" % (coef, err)) + assert err < 1e-9, "reference matrix disagrees with LAL: %g" % err + + +if __name__ == "__main__": + test_roundtrip_identity() + test_tone_frequency_assignment_and_FT_SIGN() + test_time_derivative_exact() + test_sidereal_modulation_exact() + test_reference_matrix_matches_lal_modulation() + print("ALL FD-PRIMITIVE CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py new file mode 100644 index 000000000..5d295859e --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse.py @@ -0,0 +1,200 @@ +""" +Validate slowrot_freqresponse (Path D, frequency-dependent finite-size antenna response). + +Checks: + (A) LONG-WAVELENGTH LIMIT: antenna_response_fd(..., f=0) == lal.ComputeDetAMResponse to + machine precision, over many random (ra,dec,psi) and H1/L1/V1/K1. KEY CHECK. + (B) FREE-SPECTRAL-RANGE STRUCTURE: the single-arm transfer's first null sits at the + expected frequency c / (L (1 + a.n)); |F(f)| departs from |F(0)| on the f_FSR scale. + (C) IN-BAND MAGNITUDE: fractional response change |F(f)/F(0) - 1| at 1 kHz and 2 kHz for + (i) 4-km LIGO and (ii) a 40-km CE arm -- quantifies whether the effect matters in band. + +Run directly: + python test_slowrot_freqresponse.py +or under pytest (the test_* functions assert). +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +try: + import RIFT.likelihood.slowrot_freqresponse as fr +except Exception: + import importlib.util, os + _here = os.path.dirname(os.path.abspath(__file__)) + _spec = importlib.util.spec_from_file_location( + "slowrot_freqresponse", os.path.join(_here, "slowrot_freqresponse.py")) + fr = importlib.util.module_from_spec(_spec) + _spec.loader.exec_module(fr) + +DETECTORS = ["H1", "L1", "V1", "K1"] +_TOL = 1e-11 # machine-precision agreement vs ComputeDetAMResponse + +_RNG = np.random.RandomState(20260707) +_CASES = [] +for _det in DETECTORS: + for _ in range(8): + _ra = _RNG.uniform(0, 2 * np.pi) + _dec = np.arcsin(_RNG.uniform(-1, 1)) + _psi = _RNG.uniform(0, np.pi) + _gmst = _RNG.uniform(0, 2 * np.pi) + _CASES.append((_det, _ra, _dec, _psi, _gmst)) + + +# ---- (A) long-wavelength limit vs LAL ------------------------------------------------ +def test_long_wavelength_limit_matches_lal(): + worst = 0.0 + for det, ra, dec, psi, gmst in _CASES: + lald = lalsim.DetectorPrefixToLALDetector(det) + Fp_ref, Fc_ref = lal.ComputeDetAMResponse(lald.response, ra, dec, psi, gmst) + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst) + e = max(abs(Fp - Fp_ref), abs(Fc - Fc_ref)) + worst = max(worst, e) + assert e < _TOL, "FD(f=0) mismatch %g for %s ra=%.3f dec=%.3f psi=%.3f" % ( + e, det, ra, dec, psi) + print("(A) long-wavelength limit: worst |F_fd(0) - ComputeDetAMResponse| = %.3e" % worst) + return worst + + +def test_zero_frequency_is_real(): + """At f=0 the response must be purely real (imag part = 0 to machine precision).""" + worst = 0.0 + for det, ra, dec, psi, gmst in _CASES: + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst) + worst = max(worst, abs(Fp.imag), abs(Fc.imag)) + assert worst < 1e-14, "imag(F(0)) = %g" % worst + print("(A') Im F(0): worst = %.3e" % worst) + + +# ---- (B) free-spectral-range / sinc-null structure ----------------------------------- +def _first_local_min(a, L, fmax_frac=3.5, N=700000): + """Frequency and depth of the first local minimum of |D~| in (0, fmax_frac*f_FSR].""" + fFSR = fr.free_spectral_range(L) + fg = np.linspace(1.0, fmax_frac * fFSR, N) + mag = np.abs(fr.single_arm_transfer(a, fg, L)) + # first interior local minimum + lo = (mag[1:-1] < mag[:-2]) & (mag[1:-1] < mag[2:]) + idx = np.nonzero(lo)[0] + i = idx[0] + 1 if len(idx) else int(np.argmin(mag)) + return fg[i], mag[i], fFSR + + +def test_single_arm_null_at_fsr_for_transverse(): + """a.n = 0 (source transverse to arm): D~ = e^{-i2pi fT} sinc(2fT), EXACT null at f_FSR.""" + L = 40000.0 + T = L / fr.C_SI + fFSR = fr.free_spectral_range(L) + fg = np.linspace(1.0, 4.0 * fFSR, 200001) + D = fr.single_arm_transfer(0.0, fg, L) + ref = np.exp(-1j * 2.0 * np.pi * fg * T) * np.sinc(2.0 * fg * T) + ident = np.max(np.abs(D - ref)) + assert ident < 1e-13, "a=0 closed form mismatch %g" % ident + # exact first null at f_FSR + val_at_fsr = abs(fr.single_arm_transfer(0.0, fFSR, L)) + assert val_at_fsr < 1e-12, "|D~(a=0, f_FSR)| = %g (should vanish)" % val_at_fsr + print("(B) a.n=0: D~=e^{-i2pi fT} sinc(2fT) to %.2e; EXACT first null at f_FSR=c/2L=%.5g Hz" + % (ident, fFSR)) + + +def test_single_arm_dip_structure(): + """General a.n: |D~| dips on the f_FSR scale (exact nulls only for a.n=0).""" + L = 40000.0 + for a in [-0.7, -0.3, 0.0, 0.4, 0.8]: + fmin, depth, fFSR = _first_local_min(a, L) + print("(B') a.n=%+.2f: first |D~| dip at %.4g Hz = %.3f f_FSR (|D~|min=%.2e)" + % (a, fmin, fmin / fFSR, depth)) + assert 0.3 * fFSR < fmin < 3.5 * fFSR, "dip off the f_FSR scale for a=%g" % a + + +def test_fsr_scale_departure(): + """|F(f)| departs from |F(0)| by O(1) once f ~ f_FSR (use a 40-km CE arm).""" + det, ra, dec, psi, gmst = "H1", 1.2, 0.5, 0.7, 2.0 + L = 40000.0 + fFSR = fr.free_spectral_range(L) + Fp0, Fc0 = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst, L_arm=L) + F0 = abs(Fp0 + 1j * Fc0) + for frac in [0.01, 0.1, 0.5, 1.0]: + f = frac * fFSR + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, f, gmst=gmst, L_arm=L) + rel = abs(abs(Fp + 1j * Fc) - F0) / F0 + print("(B') f/f_FSR=%.2f (f=%.4g Hz): ||F(f)|-|F(0)||/|F(0)| = %.3e" % (frac, f, rel)) + + +# ---- (C) in-band magnitude: LIGO vs CE ---------------------------------------------- +def _fractional_change(det, L, freqs, n_sky=4000): + """Median-over-sky of complex |F(f)/F(0)-1| AND amplitude-only ||F(f)|-|F(0)||/|F(0)|, + excluding sky positions near antenna-pattern nulls (|F(0)|<0.3) where the ratio blows + up for reasons unrelated to the finite-size effect. + + Returns (complex_med, amp_med) arrays over freqs. The complex ratio is DOMINATED by + the overall light-crossing phase e^{-i2 pi f L/c} (a benign direction-independent + delay of L/c, degenerate with coalescence time); the amplitude-only change is the + physically meaningful measure of antenna-pattern SHAPE distortion. + """ + rng = np.random.RandomState(1234) + comp = [[] for _ in freqs] + amp = [[] for _ in freqs] + for _ in range(n_sky): + ra = rng.uniform(0, 2 * np.pi) + dec = np.arcsin(rng.uniform(-1, 1)) + psi = rng.uniform(0, np.pi) + gmst = rng.uniform(0, 2 * np.pi) + Fp0, Fc0 = fr.antenna_response_fd(det, ra, dec, psi, 0.0, gmst=gmst, L_arm=L) + F0 = complex(Fp0) + 1j * complex(Fc0) + if abs(F0) < 0.3: + continue + for i, f in enumerate(freqs): + Fp, Fc = fr.antenna_response_fd(det, ra, dec, psi, f, gmst=gmst, L_arm=L) + Ff = complex(Fp) + 1j * complex(Fc) + comp[i].append(abs(Ff / F0 - 1.0)) + amp[i].append(abs(abs(Ff) - abs(F0)) / abs(F0)) + return (np.array([np.median(c) for c in comp]), + np.array([np.median(a) for a in amp])) + + +def test_in_band_magnitude_ligo_vs_ce(): + freqs = [1000.0, 2000.0] + ligo_c, ligo_a = _fractional_change("H1", 3994.5, freqs) + ce_c, ce_a = _fractional_change("H1", 40000.0, freqs) + print("(C) IN-BAND fractional response change (median over sky, away from nulls):") + print(" complex |F(f)/F(0)-1| (incl. benign overall e^{-i2pi f L/c} delay phase):") + for i, f in enumerate(freqs): + print(" f=%5.0f Hz : LIGO(4km) = %.3e CE(40km) = %.3e" % (f, ligo_c[i], ce_c[i])) + print(" amplitude-only ||F(f)|-|F(0)||/|F(0)| (pattern-SHAPE distortion, physical):") + for i, f in enumerate(freqs): + print(" f=%5.0f Hz : LIGO(4km) = %.3e CE(40km) = %.3e (CE/LIGO ~%.0fx)" + % (f, ligo_a[i], ce_a[i], ce_a[i] / max(ligo_a[i], 1e-30))) + # LIGO amplitude distortion is sub-percent (tiny); CE is tens of percent (>> LIGO). + assert ligo_a[0] < 1e-2, "LIGO 1kHz amplitude change unexpectedly large: %g" % ligo_a[0] + assert ligo_a[1] < 2e-2, "LIGO 2kHz amplitude change unexpectedly large: %g" % ligo_a[1] + assert ce_a[0] > 3e-2, "CE 1kHz amplitude change unexpectedly small: %g" % ce_a[0] + assert ce_a[1] > ligo_a[1] * 30, "CE should be >> LIGO at 2 kHz" + return ligo_a, ce_a + + +def test_ce_is_100x_longer_effect(): + """Finite-size amplitude distortion scales ~ (f L / c)^2 ; 10x arm -> ~10^2x effect.""" + f = 2000.0 + _, ligo_a = _fractional_change("H1", 3994.5, [f]) + _, ce_a = _fractional_change("H1", 40000.0, [f]) + ratio = ce_a[0] / max(ligo_a[0], 1e-30) + print("(C') CE/LIGO amplitude-distortion ratio at 2 kHz = %.1f (expect ~10^2 for 10x arm)" + % ratio) + assert 30 < ratio < 250, "unexpected CE/LIGO scaling: %g" % ratio + + +if __name__ == "__main__": + print("=" * 78) + wA = test_long_wavelength_limit_matches_lal() + test_zero_frequency_is_real() + print("-" * 78) + test_single_arm_null_at_fsr_for_transverse() + test_single_arm_dip_structure() + test_fsr_scale_departure() + print("-" * 78) + test_in_band_magnitude_ligo_vs_ce() + test_ce_is_100x_longer_effect() + print("=" * 78) + print("ALL SLOWROT FREQ-RESPONSE CHECKS PASSED (worst f=0 residual %.3e)" % wA) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py new file mode 100644 index 000000000..d83001403 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_freqresponse_likelihood.py @@ -0,0 +1,295 @@ +""" +Validate the finite-size (frequency-dependent response) likelihood (Thrust-2 route (b)). + +Builds an EXACT finite-size detector-strain injection from the SAME modes +(internal_hlm_generator -> IFFT), h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f) with the +VALIDATED antenna_response_fd, using a 40-km CE arm. Then: + + (V1) REDUCE TO BASELINE : with L -> 0 the finite-size NoLoop likelihood + (DiscreteFactoredLogLikelihoodFreqResponseNoLoop) == the MAINTAINED baseline + DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop (NOT the older scalar + SingleDetectorLogLikelihood/FactoredLogLikelihood). + (V2) GROUND TRUTH : the finite-size NoLoop likelihood reconstructs the finite-size + injection -- deficit vs 0.5 converges as Qmax grows. NOTE this default config + (1.6+1.4 BNS, fmax=1024) is a WEAK demonstration: the model-distinguishing part of + the response (beyond the common e^{-i2pi f L/c} light-crossing delay, which the + baseline absorbs by time-marginalization) is only ~0.1% in-band, so finite-size ~= + baseline here and no gain is expected. The meaningful demonstration is run_strong (V4). + (V3) CAUCHY-SCHWARZ : every lnL <= 0.5 (network). A violation => a wrong term. + (V4) POSITIVE CONTROL : run_strong -- in an in-band-effect config (15+13 Msun, fmax=2000, + loud CE) the finite-size likelihood beats the long-wavelength baseline by a large, + ASSERTED margin. This is what validates the finite-L assembly (V1 only tests L->0). + +All comparisons use the maintained NoLoop path, time-MAXIMIZED (NoLoop point eval is off-peak), +with cubic sub-bin time interpolation. Measured: V1 |diff| ~3e-9; V2 (BNS/1024, weak) baseline +deficit 7.76 -> finite-size 7.80 (no gain, as expected); V3 bound respected; V4 (15+13/2000) +baseline deficit 55.8 -> finite-size 16.9, GAIN +38.9 nats (Qmax=6; residual is series +truncation at fL/c~0.27). +""" +from __future__ import print_function, division +import sys +import numpy as np +import lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.slowrot_freqresponse as sfr +import RIFT.likelihood.factored_likelihood_freqresponse as flfr + +event_time = 1e9; Lmax = 2; t_window = 0.1; det = 'H1' +psd = lalsim.SimNoisePSDaLIGOZeroDetHighPower +apx = lalsim.GetApproximantFromString("IMRPhenomD") +L_CE = 40000.0 # 40-km Cosmic Explorer arm (geometry override) +SCALE = 40. # loudness: data-mode distance = distMpcRef/SCALE + + +def _ifft(hf_d): + out = {} + for lm, hf in hf_d.items(): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); out[lm] = ht + return out + + +def _fwd_fd(re_series, epoch, dt, N): + ht = lal.CreateCOMPLEX16TimeSeries("h", epoch, 0., dt, lal.DimensionlessUnit, N) + ht.data.data[:] = re_series[:N] + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., 1. / dt / N, lsu.lsu_HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ht, lal.CreateForwardCOMPLEX16FFTPlan(N, 0)); return hf + + +def _rev_td(hf): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); return ht + + +from scipy.interpolate import InterpolatedUnivariateSpline + + +def _peak(lt): + lt = np.asarray(lt, float); x = np.arange(len(lt)) + sp = InterpolatedUnivariateSpline(x, lt, k=4) + xs = np.linspace(0, len(lt) - 1, len(lt) * 32) + return float(np.max(sp(xs))) + + +def run(Qlist=(0, 2, 4, 6, 8)): + fmin, fmax, deltaT, seglen = 30., 1024., 1. / 2048., 16. + deltaF = 1. / seglen; fNyq = 1. / 2. / deltaT; N = int(round(seglen / deltaT)) + RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=1.6 * lal.MSUN_SI, m2=1.4 * lal.MSUN_SI, detector=det, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=event_time, deltaF=deltaF) + Psig.approx = apx + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + # --- base FD modes ONCE; build detector strain from the SAME modes --- + hlms_fd, _ = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd) + lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + + # complex Sigma(t) = sum_lm Y_lm h_lm(t) on intrinsic axis (epoch e0) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + + # geometric arrival delay for this sky (SAME function the likelihood uses) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(det, RA, DEC, event_time)) - event_time + data_epoch = lal.LIGOTimeGPS(e0 + event_time) + + # Sigma delayed by the geometric delay: e^{-i2pi f dt_geo} in FD (packed fvals) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data # FD of complex Sigma(t) + Sig_del_fd = Sig_fd * np.exp(-1j * 2.0 * np.pi * fvals * dt_geo) + Sig_del = _rev_td(_wrap_fd(Sig_del_fd, data_epoch, deltaF)).data.data # complex TD, delayed + hplus_t = np.real(Sig_del); hcross_t = -np.imag(Sig_del) # real polarization series + + hplus_f = _fwd_fd(hplus_t, data_epoch, dt, nn).data.data + hcross_f = _fwd_fd(hcross_t, data_epoch, dt, nn).data.data + gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + + def make_data(Larm, finite=True): + if finite: + Fp, Fc = sfr.antenna_response_fd(det, RA, DEC, PSI, fvals, gmst=gmst_ev, L_arm=Larm) + else: + F0p, F0c = lal.ComputeDetAMResponse( + lalsim.DetectorPrefixToLALDetector(det).response, RA, DEC, PSI, gmst_ev) + Fp = np.full(nn, F0p, complex); Fc = np.full(nn, F0c, complex) + hk_f = Fp * hplus_f + Fc * hcross_f + return _wrap_fd(hk_f, data_epoch, deltaF) + + data = make_data(L_CE, finite=True) + data_dict = {det: data}; psd_dict = {det: psd} + IPc = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + HALF_DD = 0.5 * IPc.ip(data, data).real + print("finite-size CE injection (L=%.0f km): seglen=%.0fs 0.5 = %.5f" + % (L_CE / 1e3, seglen, HALF_DD)) + + # --- extrinsic evaluation setup --- + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = event_time; Pv.deltaT = deltaT + Nw = int(0.03 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # --- baseline standard RIFT likelihood (long-wavelength) --- + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lkB = {}; rAB = {}; cuB = {}; cvB = {}; epB = {} + for d in data_dict: + a, b, c, U, V, rA, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lkB[d] = a; rAB[d] = rA; cuB[d] = U; cvB[d] = V; epB[d] = e + lnL_base = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lkB, rAB, cuB, cvB, epB, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic')[0]) + print(" baseline (long-wavelength) lnL = %.5f deficit = %.5f [<= 0.5 ? %s]" + % (lnL_base, HALF_DD - lnL_base, lnL_base <= HALF_DD + 1e-6)) + + # --- finite-size likelihood, scan Qmax --- + print("\n(V2) finite-size likelihood vs 0.5 as Qmax grows:") + worst_excess = -1e99 + for Qmax in Qlist: + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + Qmax=Qmax, L_arm=L_CE, analyticPSD_Q=True, verbose=False, quiet=True, + skip_interpolation=True) + lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True,time_interp='cubic')[0] + lnL = _peak(lnLt) + excess = lnL - HALF_DD + worst_excess = max(worst_excess, excess) + print(" Qmax=%2d (basis %d): lnL=%.5f deficit=%.5f excess vs 0.5=%+.2e %s" + % (Qmax, Qmax + 2, lnL, HALF_DD - lnL, excess, + "" if excess <= 1e-6 else " <-- BOUND VIOLATION")) + + # --- (V1) reduce to baseline with L -> 0 --- + print("\n(V1) reduce-to-baseline: finite-size(L->0) vs standard RIFT (same data):") + # rebuild data with the LONG-WAVELENGTH (constant F) response so both agree + data_lwl = make_data(L_CE, finite=False) + dd_lwl = {det: data_lwl} + ri2, ct2, ctV2, rho2, snr2, rest2 = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, dd_lwl, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lkB2 = {}; rAB2 = {}; cuB2 = {}; cvB2 = {}; epB2 = {} + for d in dd_lwl: + a, b, c, U, V, rA, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho2[d].keys()), None, rho2[d], ct2[d], ctV2[d]) + lkB2[d] = a; rAB2[d] = rA; cuB2[d] = U; cvB2[d] = V; epB2[d] = e + lnL_base2 = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lkB2, rAB2, cuB2, cvB2, epB2, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic')[0] + for Qmax in [0, 4]: + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, dd_lwl, psd_dict, Lmax, fmax, + Qmax=Qmax, L_arm=1e-6, analyticPSD_Q=True, verbose=False, quiet=True, + skip_interpolation=True) + lk, rbp, ubp, vbp, ep = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnLt = flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lk, rbp, ubp, vbp, ep, Lmax=Lmax, array_output=True,time_interp='cubic')[0] + worst = np.max(np.abs(lnLt - lnL_base2)) + print(" Qmax=%d L->0: max|lnL_fr - lnL_baseline| over window = %.3e" % (Qmax, worst)) + + print("\n(V3) worst excess over 0.5 across all finite-size evals = %+.3e (%s)" + % (worst_excess, "BOUND RESPECTED" if worst_excess <= 1e-6 else "VIOLATED")) + return HALF_DD, lnL_base, worst_excess + + +def _wrap_fd(arr, epoch, deltaF): + n = len(arr) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., deltaF, lsu.lsu_HertzUnit, n) + hf.data.data[:] = arr + return hf + + +def run_strong(fmax=2000., seglen=8., SCALE_strong=40., m1=15., m2=13., Qmax=6): + """(V4) POSITIVE CONTROL: in a config where the direction-dependent finite-size effect + is actually in-band with SNR behind it (heavier system -> high-f power, higher fmax, + loud), the finite-size likelihood must reconstruct the finite-size injection much better + than the long-wavelength baseline. + + This is the meaningful demonstration of the finite-size likelihood. The default `run()` + at BNS/fmax=1024 is a WEAK config: there the model-distinguishing effect (beyond the + common e^{-i2pi f L/c} light-crossing delay, which the baseline absorbs by + time-marginalization) is ~0.1% in-band -- below the peak-resolution floor -- so + finite-size ~= baseline there and NO gain is expected (or asserted). + + Measured (H1, 40-km CE arm, 15+13 Msun, fmax=2000, loud): baseline deficit ~55.8, + finite-size deficit ~16.9 -> gain ~+38.9 nats (Qmax=6; residual is series truncation at + fL/c~0.27, shrinks with Qmax). + """ + event_time = 1e9; t_window = 0.1; det = 'H1'; L_arm = L_CE + deltaT = 1. / (2. * fmax); fmin = 30. + deltaF = 1. / seglen; fNyq = 1. / 2. / deltaT + RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE_strong + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=m1 * lal.MSUN_SI, m2=m2 * lal.MSUN_SI, detector=det, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=event_time, deltaF=deltaF) + Psig.approx = apx + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + hlms_fd, _ = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd); lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(det, RA, DEC, event_time)) - event_time + data_epoch = lal.LIGOTimeGPS(e0 + event_time) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data + Sig_del = _rev_td(_wrap_fd(Sig_fd * np.exp(-1j * 2 * np.pi * fvals * dt_geo), data_epoch, deltaF)).data.data + hpf = _fwd_fd(np.real(Sig_del), data_epoch, dt, nn).data.data + hcf = _fwd_fd(-np.imag(Sig_del), data_epoch, dt, nn).data.data + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + Fp, Fc = sfr.antenna_response_fd(det, RA, DEC, PSI, fvals, gmst=gmst, L_arm=L_arm) + data = _wrap_fd(Fp * hpf + Fc * hcf, data_epoch, deltaF) + dd = {det: data}; pdd = {det: psd} + IPc = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + HALF = 0.5 * IPc.ip(data, data).real + + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = event_time; Pv.deltaT = deltaT + Nw = int(0.03 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, dd, pdd, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in dd: + a, b, c, U, V, r, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lk[d] = a; rA[d] = r; cu[d] = U; cv[d] = V; ep[d] = e + base = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=Lmax, xpy=np, return_lnLt=True, time_interp='cubic')[0]) + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + event_time, t_window, Psig, dd, pdd, Lmax, fmax, Qmax=Qmax, L_arm=L_arm, + analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True) + lkf, rbp, ubp, vbp, epf = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + fin = _peak(flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lkf, rbp, ubp, vbp, epf, Lmax=Lmax, array_output=True, time_interp='cubic')[0]) + gain = (HALF - base) - (HALF - fin) + print("\n(V4) POSITIVE CONTROL %g+%g Msun, fmax=%g, CE %gkm, loud:" % (m1, m2, fmax, L_arm / 1e3)) + print(" 0.5=%.1f baseline_deficit=%.3f finite_deficit=%.3f GAIN=%+.3f nats" + % (HALF, HALF - base, HALF - fin, gain)) + assert base <= HALF + 1e-6 and fin <= HALF + 1e-6, "Cauchy-Schwarz bound violated" + assert gain > 10.0, "finite-size failed to beat baseline where the effect is in-band: gain=%g" % gain + return HALF, base, fin, gain + + +if __name__ == "__main__": + run() + run_strong() + print("\nALL SLOWROT FREQRESPONSE LIKELIHOOD CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py new file mode 100644 index 000000000..f2930aca4 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_headtohead.py @@ -0,0 +1,178 @@ +""" +test_slowrot_headtohead : matched-seed quantitative baseline-vs-rotation comparison. + +Instead of running the full ILE twice (whose adaptive sampler makes exact seed-matching +fragile), we draw ONE fixed set of extrinsic samples from the standard priors and evaluate +BOTH the baseline vectorized (NoLoop) likelihood and the rotation (NoLoop) likelihood on the +SAME samples and the SAME time grid. Because the samples are identical, the difference is +the genuine slow-rotation effect, with zero Monte-Carlo noise in the difference. + +Checks (all on the same matched samples): + (R) REGRESSION: rotation at f_sidereal=0 == baseline, per-sample, to ~machine precision. + (P) PHYSICS: rotation at the real sidereal rate differs from baseline only by the tiny + genuine effect (short signal => negligible); report per-sample max and the + importance-weighted evidence shift ln Z_rot - ln Z_base (matched-sample, MC-noise-free). + (B) BOUND: every lnL (baseline, rot0, rotW) respects the Cauchy-Schwarz bound + 0.5 * sum_det . + +Run under the RIFT venv with this worktree first on PYTHONPATH. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1e9 +t_window = 0.1 +Lmax = 2 +deltaT = 1. / fSample +deltaF = 1. / 4. +HARM = (-2, -1, 0, 1, 2) +DMIN, DMAX = 10.0, 1000.0 # Mpc, for the d^2 distance prior + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=deltaT, + tref=event_time, deltaF=deltaF) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +def _network_half_dd(): + tot = 0.0 + for det in data_dict: + IP = lsu.ComplexIP(fmin, fmax, 1. / 2. / deltaT, deltaF, psd_dict[det], True, False, 0.) + tot += IP.ip(data_dict[det], data_dict[det]).real + return 0.5 * tot + + +def _draw_extrinsic(K, seed=20260704): + rng = np.random.RandomState(seed) + RA = rng.uniform(0, 2 * np.pi, K) + DEC = np.arcsin(rng.uniform(-1, 1, K)) + PSI = rng.uniform(0, np.pi, K) + INCL = np.arccos(rng.uniform(-1, 1, K)) + PHIREF = rng.uniform(0, 2 * np.pi, K) + # d^2 prior in [DMIN, DMAX] + u = rng.uniform(0, 1, K) + DIST = (DMIN ** 3 + u * (DMAX ** 3 - DMIN ** 3)) ** (1. / 3.) + return RA, DEC, PSI, INCL, PHIREF, DIST + + +def _make_P_vec(RA, DEC, PSI, INCL, PHIREF, DIST_Mpc): + Pv = Psig.manual_copy() + Pv.phi = RA.astype(float) + Pv.theta = DEC.astype(float) + Pv.psi = PSI.astype(float) + Pv.incl = INCL.astype(float) + Pv.phiref = PHIREF.astype(float) + Pv.dist = (DIST_Mpc * 1e6 * lsu.lsu_PC).astype(float) + Pv.tref = float(event_time) + Pv.deltaT = deltaT + return Pv + + +def _pack_baseline(): + ri, ct, ctV, rho, snr, rest = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for det in data_dict: + a, b, c, U, V, rArr, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[det].keys()), None, rho[det], ct[det], ctV[det]) + lk[det] = a; rA[det] = rArr; cu[det] = U; cv[det] = V; ep[det] = e + return lk, rA, cu, cv, ep + + +def _pack_rotation(f_sidereal): + ri, ct, ctV, rho, meta = flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=f_sidereal, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + lk, rbn, ubn, vbn, ep = flwr.pack_rotation_arrays(meta, rho, ct, ctV) + return meta, lk, rbn, ubn, vbn, ep + + +K = 300 +tvals = np.arange(int(2 * 0.03 / deltaT)) * deltaT - 0.03 +RA, DEC, PSI, INCL, PHIREF, DIST = _draw_extrinsic(K) +P_vec = _make_P_vec(RA, DEC, PSI, INCL, PHIREF, DIST) +HALF_DD = _network_half_dd() + + +def _lnZ(lnL): + m = np.max(lnL) + return m + np.log(np.mean(np.exp(lnL - m))) + + +# precompute once +_lkB, _rAB, _cuB, _cvB, _epB = _pack_baseline() +_metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0 = _pack_rotation(0.0) +_metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW = _pack_rotation(flwr.F_SIDEREAL) + +# Use cubic time interpolation for the sharpest per-sample regression (both paths agree +# on the same sub-bin-resolved time series, so the regression floor is set by arithmetic +# rather than by nearest-neighbour bin snapping). +TIME_INTERP = 'cubic' +lnL_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, P_vec, _lkB, _rAB, _cuB, _cvB, _epB, Lmax=Lmax, xpy=np, time_interp=TIME_INTERP) +lnL_rot0 = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, _metaR0, _lkR0, _rbn0, _ubn0, _vbn0, _epR0, Lmax=Lmax, time_interp=TIME_INTERP) +lnL_rotW = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, _metaRW, _lkRW, _rbnW, _ubnW, _vbnW, _epRW, Lmax=Lmax, time_interp=TIME_INTERP) + + +# NOTE: the baseline DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop uses a GLOBAL lnLmax +# offset in its time marginalization, so uniform-prior samples more than ~745 (float64 +# underflow) below the global peak return -inf. Our rotation NoLoop uses a PER-SAMPLE +# offset (always finite). So the regression is anchored on the samples where the baseline +# is finite (near the peak); the physics head-to-head uses rotation-OFF (f_sid=0, which we +# have just anchored == baseline) vs rotation-ON, both always finite. +_base = np.asarray(lnL_base); _r0 = np.asarray(lnL_rot0); _rW = np.asarray(lnL_rotW) +_finite = np.isfinite(_base) + + +def test_R_rotation_zero_rate_matches_baseline_per_sample(): + assert _finite.sum() > 0, "baseline finite nowhere -- cannot anchor" + d = np.max(np.abs(_r0[_finite] - _base[_finite])) + print("(R) regression anchor: max|lnL_rot(f_sid=0) - lnL_base| over %d finite-baseline " + "samples (of %d) = %.3e" % (int(_finite.sum()), K, d)) + assert d < 1e-6, "rotation at f_sid=0 disagrees with baseline: %g" % d + + +def test_P_physics_effect_and_evidence_shift(): + # matched-sample baseline(=f_sid=0) vs rotation(real); both finite everywhere. + d = np.max(np.abs(_rW - _r0)) + dZ = _lnZ(_rW) - _lnZ(_r0) + print("(P) matched-sample max|lnL_rot(real) - lnL_rot(0)| over %d samples = %.3e" % (K, d)) + print("(P) evidence shift ln Z_rot - ln Z_base (matched, MC-noise-free) = %+.6e" % dZ) + print("(P) [short 30+25 BBH => tiny; ln Z(f_sid=0)=%.4f ln Z(real)=%.4f]" + % (_lnZ(_r0), _lnZ(_rW))) + assert d > 0, "rotation had literally zero effect (suspicious)" + assert d < 1.0, "short-signal rotation effect unexpectedly large: %g" % d + + +def test_B_cauchy_schwarz_bound(): + worst = max(np.max(_r0), np.max(_rW)) + print("(B) max lnL (rot0/rotW) = %.4f 0.5_network = %.4f" % (worst, HALF_DD)) + assert worst <= HALF_DD + 1e-6, "lnL exceeds Cauchy-Schwarz bound 0.5: %g > %g" % (worst, HALF_DD) + + +if __name__ == "__main__": + test_R_rotation_zero_rate_matches_baseline_per_sample() + test_P_physics_effect_and_evidence_shift() + test_B_cauchy_schwarz_bound() + print("ALL SLOWROT HEAD-TO-HEAD CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py new file mode 100644 index 000000000..abb2adb0f --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_likelihood_v1.py @@ -0,0 +1,180 @@ +""" +V1: validate the rotation-aware log-likelihood assembly against (a) the baseline in the +no-rotation limit and (b) an independent brute-force time-varying-response likelihood. + +Run under the RIFT venv with this worktree first on PYTHONPATH: + source ~/RIFT_develUWM/bin/activate + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code \ + python .../RIFT/likelihood/test_slowrot_likelihood_v1.py + +Checks: + V1a (assembly algebra): with f_sidereal -> 0 the rotation lnL must equal the baseline + FactoredLogLikelihood exactly (all modulations become identity, sum_n A_tilde_n -> + F(tref)). This validates the whole harmonic contraction incl. the V-term's A_{-nu}. + V1b (rotation physics): with the real sidereal rate, the rotation lnL must equal a + brute-force Path-R likelihood that applies the FULL time-varying antenna pattern + F_k(t) (sampled from lal.ComputeDetAMResponse, independent of the A_n harmonic + decomposition) directly to the data (term1) and to the modes (term2). This validates + that Q^{(n)} is paired with the correct conj(A_tilde_n), i.e. the physics. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1000000000.0 +t_window = 0.1 +Lmax = 2 + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=1. / fSample, + tref=event_time, deltaF=1. / 4.) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + +extr = lsu.ChooseWaveformParams( + radec=True, phi=Psig.phi, theta=Psig.theta, psi=0.5, incl=0.7, phiref=0.9, + tref=event_time, dist=300e6 * lal.PC_SI) + +HARM = (-2, -1, 0, 1, 2) + + +def _precompute_rot(f_sidereal): + return flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=f_sidereal, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + + +def test_V1a_reduces_to_baseline(): + rint, ct, ctV, rho, meta = _precompute_rot(f_sidereal=0.0) + lnL_rot = flwr.FactoredLogLikelihoodWithRotation(extr, rint, ct, ctV, meta, Lmax) + rint_b, ct_b, ctV_b, rho_b, _, _ = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lnL_base = fl.FactoredLogLikelihood(extr, rho_b, rint_b, ct_b, ctV_b, Lmax) + print("V1a: lnL_rot=%.10g lnL_base=%.10g |diff|=%.2e" + % (lnL_rot, lnL_base, abs(lnL_rot - lnL_base))) + assert abs(lnL_rot - lnL_base) < 1e-6 * (1 + abs(lnL_base)), \ + "rotation lnL does not reduce to baseline: %g vs %g" % (lnL_rot, lnL_base) + + +def _to_td(fs): + npts = fs.data.length + dt = 1.0 / (npts * fs.deltaF) + ts = lal.CreateCOMPLEX16TimeSeries("x", fs.epoch, 0., dt, lal.DimensionlessUnit, npts) + lal.COMPLEX16FreqTimeFFT(ts, fs, lal.CreateReverseCOMPLEX16FFTPlan(npts, 0)) + return ts + + +def _sample_F(det, epoch, npts, dt): + resp = lalsim.DetectorPrefixToLALDetector(det).response + RA, DEC, psi = extr.phi, extr.theta, extr.psi + gmst_ev = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) + tabs = float(epoch) + np.arange(npts) * dt + nc = 128 + tc = np.linspace(tabs[0], tabs[-1], nc) + Fc = np.empty(nc, dtype=complex) + for i, tt in enumerate(tc): + gmst = gmst_ev + flwr.OMEGA_EARTH * (tt - event_time) + fp, fx = lal.ComputeDetAMResponse(resp, RA, DEC, psi, gmst) + Fc[i] = fp + 1j * fx + return (np.interp(tabs, tc, Fc.real) + 1j * np.interp(tabs, tc, Fc.imag)) + + +def _det_window(det, data, hlms): + t_det = fl.ComputeArrivalTimeAtDetector(det, extr.phi, extr.theta, extr.tref) + rho_epoch = data.epoch - hlms[list(hlms.keys())[0]].epoch + t_shift = float(float(t_det) - float(t_window) - float(rho_epoch)) + N_shift = int(t_shift / Psig.deltaT + 0.5) + N_window = int(2 * t_window / Psig.deltaT) + t = np.arange(N_window) * Psig.deltaT + float(rho_epoch + N_shift * Psig.deltaT) + return t_det, N_shift, N_window, t + + +def _pathR_lnL(modes): + Pm = Psig.manual_copy() + Pm.dist = fl.distMpcRef * 1e6 * lsu.lsu_PC + Pm.deltaF = data_dict['H1'].deltaF + hlms, hlms_conj = fl.internal_hlm_generator(Pm, Lmax, verbose=False, quiet=True) + modes = [m for m in modes if m in hlms] + Ylms = fl.ComputeYlms(Lmax, extr.incl, -extr.phiref, selected_modes=modes) + distMpc = extr.dist / (lsu.lsu_PC * 1e6) + invDistMpc = fl.distMpcRef / distMpc + lnL = 0. + for det in data_dict: + data = data_dict[det] + psd = psd_dict[det] + npts = data.data.length + dt = 1.0 / (npts * data.deltaF) + fNyq = 1. / 2. / Psig.deltaT + t_det, N_shift, N_window, t = _det_window(det, data, hlms) + F_data = _sample_F(det, float(data.epoch), npts, dt) + d_td = _to_td(data) + dtld = lal.CreateCOMPLEX16TimeSeries("dF", data.epoch, 0., dt, + lal.DimensionlessUnit, npts) + dtld.data.data[:] = np.conj(F_data) * d_td.data.data + dtld_f = lsu.DataFourier(dtld) + rho = fl.ComputeModeIPTimeSeries(hlms, dtld_f, psd, Psig.fmin, fmax, fNyq, + N_shift, N_window, True, False, 0.) + rint = fl.InterpolateRholms(rho, t, verbose=False) + term1 = 0. + for m in modes: + term1 += np.conj(Ylms[m]) * rint[m](float(t_det)) + term1 = np.real(term1) * invDistMpc + IP = lsu.ComplexIP(Psig.fmin, fmax, fNyq, data.deltaF, psd, True, False, 0.) + modF = {} + modFc = {} + for m in modes: + h_td = _to_td(hlms[m]) + # template modes carry the INTRINSIC epoch (~0); their absolute time when + # placed at the event is event_time + (hlms.epoch + j*dt), so sample F there. + F_mode = _sample_F(det, event_time + float(hlms[m].epoch), hlms[m].data.length, dt) + prod = lal.CreateCOMPLEX16TimeSeries("Fh", hlms[m].epoch, 0., dt, + lal.DimensionlessUnit, hlms[m].data.length) + prod.data.data[:] = F_mode * h_td.data.data + modF[m] = lsu.DataFourier(prod) + prodc = lal.CreateCOMPLEX16TimeSeries("Fhc", hlms[m].epoch, 0., dt, + lal.DimensionlessUnit, hlms[m].data.length) + prodc.data.data[:] = np.conj(F_mode * h_td.data.data) + modFc[m] = lsu.DataFourier(prodc) + term2 = 0. + for p1 in modes: + for p2 in modes: + U = IP.ip(modF[p1], modF[p2]) + V = IP.ip(modFc[p1], modF[p2]) + term2 += U * np.conj(Ylms[p1]) * Ylms[p2] + V * Ylms[p1] * Ylms[p2] + term2 = -np.real(term2) / 4. / (distMpc / fl.distMpcRef) ** 2 + lnL += term1 + term2 + return lnL + + +def test_V1b_matches_bruteforce_rotation(): + rint, ct, ctV, rho, meta = _precompute_rot(f_sidereal=flwr.F_SIDEREAL) + lnL_rot = flwr.FactoredLogLikelihoodWithRotation(extr, rint, ct, ctV, meta, Lmax) + modes = list(rint[list(rint.keys())[0]][(0, 0)].keys()) + lnL_R = _pathR_lnL(modes) + print("V1b: lnL_rot=%.10g lnL_pathR=%.10g |diff|=%.2e" + % (lnL_rot, lnL_R, abs(lnL_rot - lnL_R))) + assert abs(lnL_rot - lnL_R) < 1e-4 * (1 + abs(lnL_R)), \ + "rotation lnL disagrees with brute force: %g vs %g" % (lnL_rot, lnL_R) + + +if __name__ == "__main__": + test_V1a_reduces_to_baseline() + test_V1b_matches_bruteforce_rotation() + print("ALL SLOWROT V1 LIKELIHOOD CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py new file mode 100644 index 000000000..b6d200833 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop.py @@ -0,0 +1,244 @@ +""" +test_slowrot_noloop : validate the vectorized ("NoLoop") slow-rotation likelihood path. + +Run under the RIFT venv with this worktree first on PYTHONPATH: + source ~/RIFT_develUWM/bin/activate + export PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code + python RIFT/likelihood/test_slowrot_noloop.py + +Checks: + (A) VECTORIZED-vs-SCALAR ANTENNA: antenna_harmonics_vector must match the scalar + antenna_harmonics elementwise over many random (dec, psi), for H1/L1/V1. + (B) PRIMARY RIGOROUS: at f_sidereal=0 (all sidereal harmonics degenerate), the + rotation-aware NoLoop lnL_t array must match the baseline NoLoop lnL_t array to + ~machine precision -- this validates the whole vectorized harmonic contraction + (packing, antenna, indexing, U/V terms) against the already-validated baseline path. + (C) SANITY: with the real sidereal rate, the rotation NoLoop lnL differs from the + f_sidereal=0 case by a nonzero, finite amount (modulation is active). For this + particular high-SNR, near-noiseless toy configuration the shift is NOT small + (order tens of percent) -- see the long comment above test_C for why this is + real, validated physics (cross-checked against test_slowrot_likelihood_v1's + independent brute-force Path-R likelihood) and not a bug. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +import RIFT.likelihood.slowrot_response as srr + +# --------------------------------------------------------------------------- +# Pre-existing environment issue (NOT introduced by this test, and NOT fixed by +# editing factored_likelihood.py, which must not be modified): when +# RIFT_LOWLATENCY is set in the environment (as it is in this venv), or numba's +# @vectorize decoration otherwise fails at import time, factored_likelihood.py +# falls back to a plain scalar `def lalylm(th,ph,s,l,m): return +# lal.SpinWeightedSphericalHarmonic(...)`. That scalar function is called by +# fl.ComputeYlmsArrayVector with numpy ARRAY arguments, which +# lal.SpinWeightedSphericalHarmonic cannot accept ("argument 1 of type +# 'REAL8'"). This breaks fl.ComputeYlmsArrayVector for BOTH the baseline NoLoop +# path and the new rotation NoLoop path equally -- it is not specific to the +# rotation code added here. We work around it locally, only for this test +# process, by rebinding fl.lalylm to an elementwise-vectorized wrapper +# equivalent to the numba @vectorize branch's behavior. This does not alter +# factored_likelihood.py on disk or any of its validated numerical behavior; +# it merely makes the already-array-shaped call sites in that module work in +# this environment. +if not getattr(fl, "numba_on", True): + fl.lalylm = np.vectorize(lal.SpinWeightedSphericalHarmonic, otypes=[complex]) + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1e9 +t_window = 0.1 +Lmax = 2 +deltaT = 1. / fSample +deltaF = 1. / 4. + +HARM = (-2, -1, 0, 1, 2) + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=deltaT, + tref=event_time, deltaF=deltaF) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lsu.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +# --------------------------------------------------------------------------- +# (A) vectorized-vs-scalar antenna harmonics +# --------------------------------------------------------------------------- +def test_A_antenna_vectorized_matches_scalar(): + rng = np.random.RandomState(20260703) + ndraw = 50 + decs = np.arcsin(rng.uniform(-1, 1, ndraw)) + psis = rng.uniform(0, np.pi, ndraw) + + worst = 0.0 + for det in ("H1", "L1", "V1"): + resp = lalsim.DetectorPrefixToLALDetector(det).response + A_vec = srr.antenna_harmonics_vector(resp, decs, psis) + for i in range(ndraw): + A_scalar = srr.antenna_harmonics(resp, decs[i], psis[i]) + for n in (-2, -1, 0, 1, 2): + d = abs(A_vec[n][i] - A_scalar[n]) + worst = max(worst, d) + print("(A) vectorized-vs-scalar antenna: worst |diff| = %.3e" % worst) + assert worst < 1e-12, "vectorized antenna_harmonics disagrees with scalar: %g" % worst + + +# --------------------------------------------------------------------------- +# (B) rotation NoLoop (f_sidereal=0) vs baseline NoLoop +# --------------------------------------------------------------------------- +def _make_P_vec(K=3): + """Build an extrinsic-parameter object with array-valued extrinsic fields + (identical value repeated K times), scalar tref/deltaT, for the NoLoop calls.""" + P_vec = Psig.manual_copy() + phi = 1.0 + theta = 0.2 + incl = 0.7 + phiref = 0.9 + psi = 0.5 + dist = 300e6 * lal.PC_SI + P_vec.phi = np.ones(K) * phi + P_vec.theta = np.ones(K) * theta + P_vec.incl = np.ones(K) * incl + P_vec.phiref = np.ones(K) * phiref + P_vec.psi = np.ones(K) * psi + P_vec.dist = np.ones(K) * dist + P_vec.tref = event_time + P_vec.deltaT = deltaT + return P_vec + + +def test_B_rotation_matches_baseline_at_zero_sidereal_rate(): + P_vec = _make_P_vec(K=3) + tvals = np.linspace(-0.05, 0.05, 200) + + # ---- baseline precompute + pack + NoLoop (return_lnLt=True gives raw lnL_t) ---- + rholms_intp_b, crossTerms_b, crossTermsV_b, rholms_b, _, _ = fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + + lookupNKDict_b = {} + rholmArrayDict_b = {} + ctUArrayDict_b = {} + ctVArrayDict_b = {} + epochDict_b = {} + for det in data_dict: + pairKeys = list(rholms_b[det].keys()) + # NOTE: pass None (not the interpolant dict) for the interpolant argument -- + # PackLikelihoodDataStructuresAsArrays has a pre-existing py2-ism + # (`rholm_intpArray = range(nKeys)`, immutable in py3) that raises TypeError + # whenever that argument is truthy. We only need the array-packed pieces + # (rholmArray, ctU, ctV, epoch) for the NoLoop array path, not the interpolants. + lookupNK, lookupKeysToNumber, lookupConj, ctU, ctV, rholmArray, rholm_intpArray, epoch = \ + fl.PackLikelihoodDataStructuresAsArrays( + pairKeys, None, rholms_b[det], crossTerms_b[det], crossTermsV_b[det]) + lookupNKDict_b[det] = lookupNK + rholmArrayDict_b[det] = rholmArray + ctUArrayDict_b[det] = ctU + ctVArrayDict_b[det] = ctV + epochDict_b[det] = epoch + + lnL_t_base = fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, P_vec, lookupNKDict_b, rholmArrayDict_b, ctUArrayDict_b, ctVArrayDict_b, + epochDict_b, Lmax=Lmax, xpy=np, return_lnLt=True,time_interp='cubic') + + # ---- rotation precompute (f_sidereal=0) + pack + rotation NoLoop ---- + rholms_intp_r, crossTerms_r, crossTermsV_r, rholms_r, meta = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + + lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r = flwr.pack_rotation_arrays( + meta, rholms_r, crossTerms_r, crossTermsV_r) + + # time_interp='cubic' MUST match the baseline call above (see note there). + lnL_t_rot = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta, lookupNKDict_r, rho_by_n, U_by_nn, V_by_nn, epochDict_r, + Lmax=Lmax, array_output=True, time_interp='cubic') + + assert lnL_t_base.shape == lnL_t_rot.shape, \ + "shape mismatch: base %s vs rot %s" % (lnL_t_base.shape, lnL_t_rot.shape) + + diff = np.abs(lnL_t_base - lnL_t_rot) + worst = np.max(diff) + print("(B) baseline-vs-rotation(f_sidereal=0) lnL_t: shape=%s max|diff|=%.3e " + "(max|lnL_t_base|=%.3e)" % (lnL_t_base.shape, worst, np.max(np.abs(lnL_t_base)))) + assert worst < 1e-8, "rotation NoLoop (f_sidereal=0) does not match baseline NoLoop: %g" % worst + return lnL_t_base, lnL_t_rot + + +# --------------------------------------------------------------------------- +# (C) sanity: real Omega gives a nonzero shift relative to f_sidereal=0 +# +# NOTE on magnitude: this is a very high SNR (~SNR~120), essentially noiseless +# (analytic PSD, no noise draw) toy signal. term2 (the U/V quadratic cross term) is a +# pure template-template self-overlap, independent of the data/distance -- so its +# *fractional* sensitivity to f_sidereal is set purely by the antenna-harmonic phase +# structure at this sky location, not by SNR (rescaling distance leaves the fractional +# shift unchanged; verified numerically). For this configuration the shift is order +# unity in relative terms (tens of percent), NOT a small perturbation, and this has been +# independently cross-checked: flwr.FactoredLogLikelihoodWithRotation (the validated +# scalar assembly) at f_sidereal=flwr.F_SIDEREAL agrees with test_slowrot_likelihood_v1's +# V1b brute-force Path-R likelihood (built from scratch using lal.ComputeDetAMResponse +# sampled directly, with NO harmonic decomposition at all) to |diff|=1.58e-09. So a large +# relative shift here reflects real, validated physics of this particular high-SNR toy +# configuration, not a bug -- we therefore only assert the shift is nonzero and finite, +# not that it is small. +# --------------------------------------------------------------------------- +def test_C_real_sidereal_rate_gives_nonzero_shift(): + P_vec = _make_P_vec(K=3) + tvals = np.linspace(-0.05, 0.05, 200) + + # f_sidereal = 0 case (reuse rotation precompute machinery) + rholms_intp_0, crossTerms_0, crossTermsV_0, rholms_0, meta_0 = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=0.0, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + lookupNKDict_0, rho_by_n_0, U_by_nn_0, V_by_nn_0, epochDict_0 = flwr.pack_rotation_arrays( + meta_0, rholms_0, crossTerms_0, crossTermsV_0) + lnL_0 = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta_0, lookupNKDict_0, rho_by_n_0, U_by_nn_0, V_by_nn_0, epochDict_0, + Lmax=Lmax, array_output=False) + + # real sidereal rate + rholms_intp_w, crossTerms_w, crossTermsV_w, rholms_w, meta_w = \ + flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=flwr.F_SIDEREAL, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=False) + lookupNKDict_w, rho_by_n_w, U_by_nn_w, V_by_nn_w, epochDict_w = flwr.pack_rotation_arrays( + meta_w, rholms_w, crossTerms_w, crossTermsV_w) + lnL_w = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P_vec, meta_w, lookupNKDict_w, rho_by_n_w, U_by_nn_w, V_by_nn_w, epochDict_w, + Lmax=Lmax, array_output=False) + + diff = lnL_w - lnL_0 + rel = np.abs(diff) / (1.0 + np.abs(lnL_0)) + print("(C) lnL(f_sidereal=0) = %s" % np.array2string(lnL_0, precision=10)) + print("(C) lnL(f_sidereal=F_SIDEREAL) = %s" % np.array2string(lnL_w, precision=10)) + print("(C) diff = %s |rel| = %s" % (np.array2string(diff, precision=6), + np.array2string(rel, precision=6))) + assert np.all(np.isfinite(lnL_w)), "non-finite lnL at real sidereal rate: %s" % lnL_w + assert np.all(np.abs(diff) > 0), "expected a nonzero shift from Earth rotation: %s" % diff + + +if __name__ == "__main__": + test_A_antenna_vectorized_matches_scalar() + test_B_rotation_matches_baseline_at_zero_sidereal_rate() + test_C_real_sidereal_rate_gives_nonzero_shift() + print("ALL SLOWROT NOLOOP CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py new file mode 100644 index 000000000..675d4cf44 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_noloop_bruteforce.py @@ -0,0 +1,87 @@ +""" +test_slowrot_noloop_bruteforce : the definitive rotation-physics validation. + +The vectorized rotation NoLoop lnL_t (real sidereal rate) is compared, over the full +time window, to an INDEPENDENT brute-force likelihood that applies the true time-varying +antenna pattern F_k(t) -- sampled directly from lal.ComputeDetAMResponse -- to the data +(term1) and to the modes (term2), reusing RIFT's own overlaps. This confirms both that +the vectorized harmonic contraction is correct AND that the (large, at high SNR) shift the +sidereal rotation induces in the marginalized lnL is genuine physics, not an artifact. + +Run: source ~/RIFT_develUWM/bin/activate; + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code python +""" +import numpy as np, lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +fmin=30.;fmax=1700.;event_time=1e9;t_window=0.1;Lmax=2;deltaT=1/4096.;deltaF=1/4.;fNyq=1/2./deltaT +HARM=(-2,-1,0,1,2); OM=flwr.OMEGA_EARTH +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.3,phiref=0.0,theta=0.2,phi=1.0,psi=0.4, + m1=30*lal.MSUN_SI,m2=25*lal.MSUN_SI,detector='H1',dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF) +data_dict={} +for det in ("H1","L1","V1"): + P=Psig.manual_copy();P.detector=det;data_dict[det]=lsu.non_herm_hoff(P) +psd_dict={det:lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} +RA,DEC,PSI,INCL,PHIREF,DIST=1.0,0.2,0.5,0.7,0.9,300e6*lal.PC_SI +gmst_ev=float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))) +def to_td(fs): + n=fs.data.length;dt=1./(n*fs.deltaF);ts=lal.CreateCOMPLEX16TimeSeries("x",fs.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ts,fs,lal.CreateReverseCOMPLEX16FFTPlan(n,0));return ts +def Fsample(det,epoch,n,dt): + resp=lalsim.DetectorPrefixToLALDetector(det).response + t=float(epoch)+np.arange(n)*dt + return np.array([ (lambda a,b:a+1j*b)(*lal.ComputeDetAMResponse(resp,RA,DEC,PSI,gmst_ev+OM*(tt-event_time))) for tt in t]) +# ---- brute force lnL_t over the standard window (true time-varying F) ---- +Pm=Psig.manual_copy();Pm.dist=fl.distMpcRef*1e6*lsu.lsu_PC;Pm.deltaF=deltaF +hlms,hlms_conj=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True) +Ylms=fl.ComputeYlms(Lmax,INCL,-PHIREF,selected_modes=list(hlms.keys())) +distMpc=DIST/(lsu.lsu_PC*1e6);invD=fl.distMpcRef/distMpc +npts=400 +def bf_lnLt(det): + data=data_dict[det];psd=psd_dict[det];n=data.data.length;dt=1./(n*data.deltaF) + t_det=fl.ComputeArrivalTimeAtDetector(det,RA,DEC,event_time) + rho_epoch=data.epoch-hlms[list(hlms.keys())[0]].epoch + t_shift=float(float(t_det)-float(t_window)-float(rho_epoch));N_shift=int(t_shift/deltaT+0.5);N_window=int(2*t_window/deltaT) + tgrid=np.arange(N_window)*deltaT+float(rho_epoch+N_shift*deltaT) + Fd=Fsample(det,float(data.epoch),n,dt);dtd=to_td(data) + df=lal.CreateCOMPLEX16TimeSeries("dF",data.epoch,0.,dt,lal.DimensionlessUnit,n);df.data.data[:]=np.conj(Fd)*dtd.data.data + rr=fl.ComputeModeIPTimeSeries(hlms,lsu.DataFourier(df),psd,fmin,fmax,fNyq,N_shift,N_window,True,False,0.) + ri=fl.InterpolateRholms(rr,tgrid,verbose=False) + modes=list(ri.keys()) + # window aligned like NoLoop + ifirst=int(round((float(t_det)-0.02-float(rr[list(rr.keys())[0]].epoch))/deltaT)+0.5) + tsel=np.array([float(rr[list(rr.keys())[0]].epoch)+(ifirst+j)*deltaT for j in range(npts)]) + term1=np.zeros(npts,dtype=complex) + for m in modes: + term1+=np.conj(Ylms[m])*np.array([ri[m](tt) for tt in tsel]) + term1=term1.real*invD + IP=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.) + modF={};modC={} + for m in modes: + htd=to_td(hlms[m]);Fm=Fsample(det,event_time+float(hlms[m].epoch),hlms[m].data.length,dt) + pr=lal.CreateCOMPLEX16TimeSeries("Fh",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pr.data.data[:]=Fm*htd.data.data;modF[m]=lsu.DataFourier(pr) + pc=lal.CreateCOMPLEX16TimeSeries("Fc",hlms[m].epoch,0.,dt,lal.DimensionlessUnit,hlms[m].data.length);pc.data.data[:]=np.conj(Fm*htd.data.data);modC[m]=lsu.DataFourier(pc) + t2=0j + for p1 in modes: + for p2 in modes: + t2+=IP.ip(modF[p1],modF[p2])*np.conj(Ylms[p1])*Ylms[p2]+IP.ip(modC[p1],modF[p2])*Ylms[p1]*Ylms[p2] + t2=-t2.real/4./(distMpc/fl.distMpcRef)**2 + return term1+t2 +bf=sum(bf_lnLt(det) for det in data_dict) +m=np.max(bf);bf_marg=m+np.log(np.trapz(np.exp(bf-m),dx=deltaT)) +# ---- vec rotation, real Omega, same window ---- +rint,ct,ctV,rho,meta=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HARM,p_max=0,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) +lk,rbn,ubn,vbn,ep=flwr.pack_rotation_arrays(meta,rho,ct,ctV) +Pv=Psig.manual_copy() +for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DIST)]: setattr(Pv,k,np.ones(1)*v) +Pv.tref=event_time;Pv.deltaT=deltaT +tvals=np.arange(npts)*deltaT-0.02 +vec=flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,meta,lk,rbn,ubn,vbn,ep,Lmax=Lmax,array_output=True)[0] +mv=np.max(vec);vec_marg=mv+np.log(np.trapz(np.exp(vec-mv),dx=deltaT)) +worst = float(np.max(np.abs(vec - bf))) +print("brute-force(real F(t)) : peak=%.4f marg=%.4f" % (np.max(bf), bf_marg)) +print("vec rotation : peak=%.4f marg=%.4f" % (np.max(vec), vec_marg)) +print("max|vec_lnL_t - bruteforce_lnL_t| over window = %.3e" % worst) +assert worst < 1e-6, "vectorized rotation NoLoop disagrees with brute-force time-varying response: %g" % worst +print("PASS: vectorized rotation NoLoop == brute-force time-varying-response likelihood") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py new file mode 100644 index 000000000..a60cf89eb --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB.py @@ -0,0 +1,66 @@ +""" +test_slowrot_pathB : Path B (delay-derivative) sanity checks. + + (1) scalar Path B (p_max=1) at f_sidereal=0 reduces EXACTLY to the baseline + FactoredLogLikelihood (the p>=1 coefficient sums vanish at Omega=0). + (2) scalar Path B (p_max=1) at the real sidereal rate respects the Cauchy-Schwarz + bound lnL <= 0.5 at the injection parameters. + (3) vectorized Path B (p_max=1) at f_sidereal=0 reduces to the baseline NoLoop. + +Rigorous validation of the p>=1 delay physics vs a brute force with TIME-VARYING delay on +a LONG signal is deferred to the systematic final-validation pass. +Run: PYTHONPATH=.../Code python RIFT/likelihood/test_slowrot_pathB.py +""" +from __future__ import print_function, division +import numpy as np, lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fmin=30.;fmax=1700.;event_time=1e9;t_window=0.1;Lmax=2;deltaT=1/4096.;deltaF=1/4. +HB=tuple(range(-3,4)) +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.3,phiref=0.0,theta=0.2,phi=1.0,psi=0.4, + m1=30*lal.MSUN_SI,m2=25*lal.MSUN_SI,detector='H1',dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF) +data_dict={} +for d in ["H1","L1","V1"]: + P=Psig.manual_copy();P.detector=d;data_dict[d]=lsu.non_herm_hoff(P) +psd_dict={d:lalsim.SimNoisePSDaLIGOZeroDetHighPower for d in data_dict} + +def test_scalar_reduces_to_baseline(): + extr=lsu.ChooseWaveformParams(radec=True,phi=1.0,theta=0.2,psi=0.5,incl=0.7,phiref=0.9,tref=event_time,dist=300e6*lal.PC_SI) + rb=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + lnLb=fl.FactoredLogLikelihood(extr,rb[3],rb[0],rb[1],rb[2],Lmax) + rr=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=0.0,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lnLB=flwr.FactoredLogLikelihoodWithRotation(extr,rr[0],rr[1],rr[2],rr[4],Lmax) + print("(1) scalar PathB(p1,fsid0)=%.8f baseline=%.8f |d|=%.2e"%(lnLB,lnLb,abs(lnLB-lnLb))) + assert abs(lnLB-lnLb)<1e-6*(1+abs(lnLb)) + +def test_scalar_respects_bound(): + extr=lsu.ChooseWaveformParams(radec=True,phi=1.0,theta=0.2,psi=0.4,incl=0.3,phiref=0.0,tref=event_time,dist=200e6*lal.PC_SI) + rr=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,{'H1':data_dict['H1']},{'H1':psd_dict['H1']},Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lnL=flwr.FactoredLogLikelihoodWithRotation(extr,rr[0],rr[1],rr[2],rr[4],Lmax) + IP=lsu.ComplexIP(fmin,fmax,1/2./deltaT,deltaF,psd_dict['H1'],True,False,0.);dd=IP.ip(data_dict['H1'],data_dict['H1']).real + print("(2) scalar PathB(p1,real,H1) lnL=%.4f 0.5=%.4f"%(lnL,0.5*dd)) + assert lnL<=0.5*dd + +def test_vec_reduces_to_baseline(): + tvals=np.arange(400)*deltaT-0.02 + rb=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + lk={};ra={};cu={};cv={};ep={} + for d in data_dict: + a,b,c,U,V,rA,rI,e=fl.PackLikelihoodDataStructuresAsArrays(list(rb[3][d].keys()),None,rb[3][d],rb[1][d],rb[2][d]);lk[d]=a;ra[d]=rA;cu[d]=U;cv[d]=V;ep[d]=e + Pv=Psig.manual_copy() + for k,v in [('phi',1.0),('theta',0.2),('incl',0.7),('phiref',0.9),('psi',0.5),('dist',300e6*lal.PC_SI)]: setattr(Pv,k,np.ones(1)*v) + Pv.tref=event_time;Pv.deltaT=deltaT + bl=fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals,Pv,lk,ra,cu,cv,ep,Lmax=Lmax,xpy=np)[0] + rr0=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=HB,p_max=1,f_sidereal=0.0,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=False) + lkr,rar,uar,var,epr=flwr.pack_rotation_arrays(rr0[4],rr0[3],rr0[1],rr0[2]) + vv=flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,rr0[4],lkr,rar,uar,var,epr,Lmax=Lmax,array_output=False)[0] + print("(3) vec PathB(p1,fsid0)=%.8f baseline NoLoop=%.8f |d|=%.2e"%(vv,bl,abs(vv-bl))) + assert abs(vv-bl)<1e-7*(1+abs(bl)) + +if __name__=="__main__": + test_scalar_reduces_to_baseline() + test_scalar_respects_bound() + test_vec_reduces_to_baseline() + print("ALL SLOWROT PATH B CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py new file mode 100644 index 000000000..262a552a3 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_bruteforce.py @@ -0,0 +1,53 @@ +from __future__ import print_function, division +import numpy as np, lal, lalsimulation as lalsim +from scipy.interpolate import CubicSpline +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +import RIFT.likelihood.slowrot_response as srr +event_time=1e9; Lmax=2; t_window=0.1; det='H1' +psd=lalsim.SimNoisePSDaLIGOZeroDetHighPower; apx=lalsim.GetApproximantFromString("IMRPhenomD") +import os +OMEGA_INF=flwr.OMEGA_EARTH*float(os.environ.get("INFL","340")); FSID_INF=OMEGA_INF/(2*np.pi) +def _ifft(hf_d): + o={} + for lm,hf in hf_d.items(): + n=hf.data.length; dt=1./(n*hf.deltaF); ht=lal.CreateCOMPLEX16TimeSeries("h",hf.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ht,hf,lal.CreateReverseCOMPLEX16FFTPlan(n,0)); o[lm]=ht + return o +def _to_fd(re,epoch,dt,N): + ht=lal.CreateCOMPLEX16TimeSeries("h",epoch,0.,dt,lal.DimensionlessUnit,N); ht.data.data[:]=re[:N] + hf=lal.CreateCOMPLEX16FrequencySeries("hf",epoch,0.,1./dt/N,lsu.lsu_HertzUnit,N) + lal.COMPLEX16TimeFreqFFT(hf,ht,lal.CreateForwardCOMPLEX16FFTPlan(N,0)); return hf +from scipy.interpolate import InterpolatedUnivariateSpline +def _peak(lt): + lt=np.asarray(lt,float); x=np.arange(len(lt)); sp=InterpolatedUnivariateSpline(x,lt,k=4) + xs=np.linspace(0,len(lt)-1,len(lt)*32); return float(np.max(sp(xs))) +fmin,fmax,deltaT,seglen=25.,512.,1/2048.,16.; deltaF=1./seglen; fNyq=1/2./deltaT; N=int(round(seglen/deltaT)) +RA,DEC,PSI,INCL,PHIREF=1.2,0.3,0.5,0.4,0.0; DLOUD=fl.distMpcRef*1e6*lsu.lsu_PC/30. +Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=INCL,phiref=PHIREF,theta=DEC,phi=RA,psi=PSI, + m1=2.2*lal.MSUN_SI,m2=1.8*lal.MSUN_SI,detector=det,dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF); Psig.approx=apx +Pm=Psig.manual_copy(); Pm.dist=DLOUD +hlms_fd,_=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True); hlmsT=_ifft(hlms_fd) +lm0=list(hlmsT.keys())[0]; nn=hlmsT[lm0].data.length; dt=hlmsT[lm0].deltaT; ep=float(hlmsT[lm0].epoch); tt=ep+np.arange(nn)*dt +Sig=np.zeros(nn,complex) +for lm in hlmsT: Sig+=hlmsT[lm].data.data*lal.SpinWeightedSphericalHarmonic(INCL,-PHIREF,-2,lm[0],lm[1]) +reS=CubicSpline(tt,Sig.real,extrapolate=False); imS=CubicSpline(tt,Sig.imag,extrapolate=False) +lald=lalsim.DetectorPrefixToLALDetector(det); g_ev=lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(event_time))-RA +A=srr.antenna_harmonics(lald.response,DEC,PSI); At={k:A[k]*np.exp(1j*k*g_ev) for k in A} +B=srr.delay_harmonics(lald.location,DEC); Bt={k:B[k]*np.exp(1j*k*g_ev) for k in B} +tau_t=np.real(sum(Bt[k]*np.exp(1j*k*OMEGA_INF*tt) for k in Bt)) +F_t=sum(At[k]*np.exp(1j*k*OMEGA_INF*tt) for k in At) +Sig_d=np.nan_to_num(reS(tt-tau_t)+1j*imS(tt-tau_t)) +data=_to_fd(np.real(F_t*Sig_d),lal.LIGOTimeGPS(float(hlmsT[lm0].epoch)+event_time),dt,N); data_dict={det:data}; psd_dict={det:psd} +IPc=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.); HALF_DD=0.5*IPc.ip(data,data).real +print("inflated seglen=%.0fs 0.5=%.4f"%(seglen,HALF_DD)) +Pv=Psig.manual_copy() +for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DLOUD)]: setattr(Pv,k,np.ones(1)*v) +Pv.tref=event_time; Pv.deltaT=deltaT; Nw=int(0.02/deltaT); tvals=np.arange(-Nw,Nw)*deltaT +for pmax in [0,1,2,3]: + nh=2+pmax + bk=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=tuple(range(-nh,nh+1)),p_max=pmax,f_sidereal=FSID_INF,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=True) + lk,rbn,ubn,vbn,epd=flwr.pack_rotation_arrays(bk[4],bk[3],bk[1],bk[2]) + lnL=_peak(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,bk[4],lk,rbn,ubn,vbn,epd,Lmax=Lmax,array_output=True)[0]) + print(" p_max=%d : lnL=%.5f deficit=%.5f"%(pmax,lnL,HALF_DD-lnL)) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py new file mode 100644 index 000000000..7e2ad583f --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_pathB_groundtruth.py @@ -0,0 +1,69 @@ +from __future__ import print_function, division +import sys +import numpy as np +import lal, lalsimulation as lalsim +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr +event_time=1e9; Lmax=2; t_window=0.1; det='H1' +psd=lalsim.SimNoisePSDaLIGOZeroDetHighPower +apx=lalsim.GetApproximantFromString("IMRPhenomD") +SCALE=40. # loudness: data-mode distance = distMpcRef/SCALE + +def _ifft(hlms_fd): + out={} + for lm,hf in hlms_fd.items(): + n=hf.data.length; dt=1./(n*hf.deltaF) + ht=lal.CreateCOMPLEX16TimeSeries("h",hf.epoch,0.,dt,lal.DimensionlessUnit,n) + lal.COMPLEX16FreqTimeFFT(ht,hf,lal.CreateReverseCOMPLEX16FFTPlan(n,0)); out[lm]=ht + return out +def _fd(hk,TDlen): + if hk.data.length!=TDlen: hk=lal.ResizeREAL8TimeSeries(hk,0,TDlen) + n=TDlen + htC=lal.CreateCOMPLEX16TimeSeries("h",hk.epoch,hk.f0,hk.deltaT,hk.sampleUnits,n); htC.data.data[:]=hk.data.data + hf=lal.CreateCOMPLEX16FrequencySeries("hf",hk.epoch,hk.f0,1./hk.deltaT/n,lsu.lsu_HertzUnit,n) + lal.COMPLEX16TimeFreqFFT(hf,htC,lal.CreateForwardCOMPLEX16FFTPlan(n,0)); return hf +def _peak(lt,up=16): + lt=np.asarray(lt,dtype=float); N=len(lt) + L=np.fft.rfft(lt); Lp=np.zeros(N*up//2+1,dtype=complex); Lp[:len(L)]=L + return float(np.max(np.fft.irfft(Lp,N*up)*up)) + +def run(mode="short"): + if mode=="long": fmin,fmax,deltaT,seglen,m1,m2=25.,256.,1/2048.,32.,2.2*lal.MSUN_SI,1.8*lal.MSUN_SI + else: fmin,fmax,deltaT,seglen,m1,m2=30.,1700.,1/4096.,4.,30*lal.MSUN_SI,25*lal.MSUN_SI + deltaF=1./seglen; fNyq=1/2./deltaT; TDlen=int(round(seglen/deltaT)) + DLOUD=fl.distMpcRef*1e6*lsu.lsu_PC/SCALE + Psig=lsu.ChooseWaveformParams(fmin=fmin,radec=True,incl=0.4,phiref=0.0,theta=0.3,phi=1.2,psi=0.5, + m1=m1,m2=m2,detector=det,dist=200e6*lal.PC_SI,deltaT=deltaT,tref=event_time,deltaF=deltaF); Psig.approx=apx + Pm=Psig.manual_copy(); Pm.dist=DLOUD + hlms_fd,_=fl.internal_hlm_generator(Pm,Lmax,verbose=False,quiet=True) + hlmsT=_ifft(hlms_fd) + data=_fd(lsu.hoft_from_hlm(hlmsT,Psig.manual_copy()),TDlen) + data_dict={det:data}; psd_dict={det:psd} + IPc=lsu.ComplexIP(fmin,fmax,fNyq,data.deltaF,psd,True,False,0.); HALF_DD=0.5*IPc.ip(data,data).real + print("[%s] seglen=%.0fs data_len=%d 0.5=%.4f"%(mode,seglen,data.data.length,HALF_DD)) + RA,DEC,PSI,INCL,PHIREF,DIST=1.2,0.3,0.5,0.4,0.0,DLOUD + ri,ct,ctV,rho,snr,rest=fl.PrecomputeLikelihoodTerms(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,analyticPSD_Q=True,verbose=False,quiet=True,ignore_threshold=None) + Pe=Psig.manual_copy(); Pe.incl=INCL;Pe.phiref=PHIREF;Pe.psi=PSI;Pe.phi=RA;Pe.theta=DEC;Pe.dist=DIST + hk_gt=_fd(lsu.hoft_from_hlm(hlmsT,Pe),TDlen); lnL_gt=IPc.ip(data,hk_gt).real-0.5*IPc.ip(hk_gt,hk_gt).real + Pv=Psig.manual_copy() + for k,v in [('phi',RA),('theta',DEC),('incl',INCL),('phiref',PHIREF),('psi',PSI),('dist',DIST)]: + setattr(Pv,k,np.ones(1)*v) + Pv.tref=event_time; Pv.deltaT=deltaT + Nw=int(0.02/deltaT); tvals=np.arange(-Nw,Nw)*deltaT + lkB={};rAB={};cuB={};cvB={};epB={} + for d in data_dict: + a,b,c,U,V,rA,rI,e=fl.PackLikelihoodDataStructuresAsArrays(list(rho[d].keys()),None,rho[d],ct[d],ctV[d]) + lkB[d]=a;rAB[d]=rA;cuB[d]=U;cvB[d]=V;epB[d]=e + lnL_base=_peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals,Pv,lkB,rAB,cuB,cvB,epB,Lmax=Lmax,xpy=np,return_lnLt=True)[0]) + def rot(pmax): + nh=2+pmax + bk=flwr.PrecomputeLikelihoodTermsWithRotation(event_time,t_window,Psig,data_dict,psd_dict,Lmax,fmax,harmonics=tuple(range(-nh,nh+1)),p_max=pmax,f_sidereal=flwr.F_SIDEREAL,analyticPSD_Q=True,verbose=False,quiet=True,skip_interpolation=True) + lk,rbn,ubn,vbn,ep=flwr.pack_rotation_arrays(bk[4],bk[3],bk[1],bk[2]) + return _peak(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation(tvals,Pv,bk[4],lk,rbn,ubn,vbn,ep,Lmax=Lmax,array_output=True)[0]) + lnL_A=rot(0); lnL_B=rot(1) + print(" ground truth (SimDetStrain) lnL = %.5f"%lnL_gt) + print(" baseline NoLoop peak lnL = %.5f deficit=%.5f"%(lnL_base,lnL_gt-lnL_base)) + print(" Path A NoLoop (F(t)) lnL = %.5f deficit=%.5f"%(lnL_A,lnL_gt-lnL_A)) + print(" Path B NoLoop (F(t)+p=1) lnL = %.5f deficit=%.5f"%(lnL_B,lnL_gt-lnL_B)) +if __name__=="__main__": run(sys.argv[1] if len(sys.argv)>1 else "short") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py new file mode 100644 index 000000000..62a77c062 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_precompute_integration.py @@ -0,0 +1,125 @@ +""" +Full-stack integration test for factored_likelihood_with_rotation on real waveforms/data. + +Run under the RIFT venv with this worktree first on PYTHONPATH, e.g. + + source ~/RIFT_develUWM/bin/activate + PYTHONPATH=~/RIFT_slowrot/MonteCarloMarginalizeCode/Code \ + python .../RIFT/likelihood/test_slowrot_precompute_integration.py + +Inputs are built exactly like RIFT.calmarg.test_precompute_alignment (a short BBH injection, +noiseless detector data via non_herm_hoff, analytic aLIGO PSD), i.e. the same machinery the +ILE-GPU-Paper CI demo exercises. + +Checks: + V0 (recovery): PrecomputeLikelihoodTermsWithRotation(harmonics=(0,), p_max=0) reproduces + the baseline PrecomputeLikelihoodTerms bit-for-bit -- Q, U, V. This validates all of + the new plumbing (mode generation, p=0 / n=0 identity, cross-term assembly). + Vmag (magnitude sanity): running Path A (harmonics -2..2) end-to-end on real data, the + a=(0,0) overlap still equals the baseline exactly, and the modulated a=(0,+-1) overlaps + differ from it by a small but nonzero amount of the expected order Omega*(signal + duration) -- confirming the sidereal modulation is applied, at the right (tiny) scale. + +Full physical validation (V1: the rotation-aware lnL vs a brute-force time-varying-response +likelihood) needs the lnL assembly that contracts this bank with A_n,B_n, and is the next +step. +""" +from __future__ import print_function, division + +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lalsimutils +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +fSample = 4096.0 +fmin = 30.0 +fmax = 1700.0 +event_time = 1000000000.0 +t_window = 0.1 +Lmax = 2 + +Psig = lalsimutils.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, + detector='H1', dist=200e6 * lal.PC_SI, deltaT=1. / fSample, + tref=event_time, deltaF=1. / 4.) + +data_dict = {} +for det in ("H1", "L1", "V1"): + P = Psig.manual_copy(); P.detector = det + data_dict[det] = lalsimutils.non_herm_hoff(P) +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +def _maxrel(a, b): + a = np.asarray(a); b = np.asarray(b) + scale = max(np.max(np.abs(a)), np.max(np.abs(b)), 1e-300) + return float(np.max(np.abs(a - b)) / scale) + + +def _run_base(): + # ignore_threshold=None so no mode pruning -> same mode set as the rotation path + return fl.PrecomputeLikelihoodTerms( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True, + ignore_threshold=None) + + +def _run_rot(harmonics, p_max): + return flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=harmonics, p_max=p_max, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + + +def test_V0_recovers_baseline(): + _, ct_b, ctV_b, rho_b, _, _ = _run_base() + _, ct_r, ctV_r, rho_r, meta = _run_rot(harmonics=(0,), p_max=0) + a0 = (0, 0) + worst_q = worst_u = worst_v = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_q = max(worst_q, _maxrel(rho_r[det][a0][mode].data.data, + rho_b[det][mode].data.data)) + for key in ct_b[det]: + worst_u = max(worst_u, abs(ct_r[det][(a0, a0)][key] - ct_b[det][key])) + worst_v = max(worst_v, abs(ctV_r[det][(a0, a0)][key] - ctV_b[det][key])) + print("V0 recovery: max rel|dQ|=%.2e max|dU|=%.2e max|dV|=%.2e" + % (worst_q, worst_u, worst_v)) + assert worst_q < 1e-8, "Q mismatch vs baseline: %g" % worst_q + assert worst_u < 1e-8 * (1 + worst_u), "U mismatch vs baseline: %g" % worst_u + assert worst_v < 1e-8 * (1 + worst_v), "V mismatch vs baseline: %g" % worst_v + + +def test_Vmag_modulation_applied_at_right_scale(): + _, ct_b, _, rho_b, _, _ = _run_base() + _, _, _, rho_r, meta = _run_rot(harmonics=(-2, -1, 0, 1, 2), p_max=0) + # a=(0,0) must still equal baseline exactly even with the extra harmonics present + worst_0 = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_0 = max(worst_0, _maxrel(rho_r[det][(0, 0)][mode].data.data, + rho_b[det][mode].data.data)) + print("Vmag a=(0,0) vs baseline: max rel|dQ|=%.2e" % worst_0) + assert worst_0 < 1e-8, "modulated run corrupts the n=0 overlap: %g" % worst_0 + + # modulated harmonics differ from n=0 by ~ Omega * (signal duration), tiny but nonzero. + # Signal duration scale for a 30+25 BBH from 30 Hz is ~ 0.1-1 s -> Omega*T ~ 1e-5..1e-4. + for n in (1, -1, 2, -2): + worst_n = 0.0 + for det in data_dict: + for mode in rho_b[det]: + worst_n = max(worst_n, _maxrel(rho_r[det][(0, n)][mode].data.data, + rho_r[det][(0, 0)][mode].data.data)) + print("Vmag a=(0,%+d) vs a=(0,0): max rel|dQ|=%.2e" % (n, worst_n)) + assert worst_n > 1e-9, "harmonic n=%d has no effect (modulation not applied?)" % n + assert worst_n < 1e-2, "harmonic n=%d far too large (bug in modulation scale?): %g" % (n, worst_n) + + +if __name__ == "__main__": + test_V0_recovers_baseline() + test_Vmag_modulation_applied_at_right_scale() + print("ALL SLOWROT PRECOMPUTE INTEGRATION CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py new file mode 100644 index 000000000..7bdeed3e1 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_response.py @@ -0,0 +1,135 @@ +""" +Validate slowrot_response against LAL over a full sidereal day. + +The harmonic decomposition is an exact algebraic identity in the hour angle g, so the +reconstruction must match lal.ComputeDetAMResponse and lal.TimeDelayFromEarthCenter to +machine precision (up to LAL's own internal rounding). Run directly: + + python test_slowrot_response.py + +or under pytest (the test_* functions assert). +""" +from __future__ import print_function, division + +import numpy as np + +import lal +import lalsimulation as lalsim + +# Load the leaf module directly by path so this test needs only numpy+lal, not the full +# RIFT package stack (whose __init__ pulls in glue/lalsimutils). When the package is +# properly installed, `import RIFT.likelihood.slowrot_response` is equivalent. +try: + import RIFT.likelihood.slowrot_response as sr +except Exception: + import importlib.util + import os + _here = os.path.dirname(os.path.abspath(__file__)) + _spec = importlib.util.spec_from_file_location( + "slowrot_response", os.path.join(_here, "slowrot_response.py")) + sr = importlib.util.module_from_spec(_spec) + _spec.loader.exec_module(sr) + +DETECTORS = ["H1", "L1", "V1", "K1"] +SIDEREAL_DAY = 86164.0905 # s +T0_GPS = 1000000000.0 # arbitrary reference epoch + +_TOL = 1e-11 # radians of F, seconds of tau -- essentially machine precision + + +def _lal_detector(det): + return lalsim.DetectorPrefixToLALDetector(det) + + +def _sample_gmst(n=512): + tgps = T0_GPS + np.linspace(0.0, SIDEREAL_DAY, n, endpoint=False) + gmst = np.array([lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(float(t))) for t in tgps]) + return tgps, gmst + + +def _max_antenna_error(det, ra, dec, psi): + lald = _lal_detector(det) + A = sr.antenna_harmonics(lald.response, dec, psi) + _, gmst = _sample_gmst() + # model + Fp_mod, Fc_mod = sr.antenna_response(A, gmst, ra) + # reference (LAL), sampled at the same GMST values + Fp_ref = np.empty_like(gmst) + Fc_ref = np.empty_like(gmst) + for i, g in enumerate(gmst): + Fp_ref[i], Fc_ref[i] = lal.ComputeDetAMResponse(lald.response, ra, dec, psi, float(g)) + return max(np.max(np.abs(Fp_mod - Fp_ref)), np.max(np.abs(Fc_mod - Fc_ref))) + + +def _max_delay_error(det, ra, dec): + lald = _lal_detector(det) + B = sr.delay_harmonics(lald.location, dec) + tgps, gmst = _sample_gmst() + g = sr.greenwich_hour_angle(gmst, ra) + tau_mod = sr.delay_from_harmonics(B, g) + tau_ref = np.array([ + lal.TimeDelayFromEarthCenter(lald.location, ra, dec, lal.LIGOTimeGPS(float(t))) + for t in tgps + ]) + return np.max(np.abs(tau_mod - tau_ref)) + + +# ---- deterministic parameter sweep ------------------------------------------------- +_RNG = np.random.RandomState(20260703) +_CASES = [] +for _det in DETECTORS: + for _ in range(5): + _ra = _RNG.uniform(0, 2 * np.pi) + _dec = np.arcsin(_RNG.uniform(-1, 1)) + _psi = _RNG.uniform(0, np.pi) + _CASES.append((_det, _ra, _dec, _psi)) + + +def test_antenna_harmonics_match_lal(): + worst = 0.0 + for det, ra, dec, psi in _CASES: + e = _max_antenna_error(det, ra, dec, psi) + worst = max(worst, e) + assert e < _TOL, "antenna mismatch %g for %s ra=%.3f dec=%.3f psi=%.3f" % ( + e, det, ra, dec, psi) + print("antenna: worst |dF| = %.3e" % worst) + + +def test_delay_harmonics_match_lal(): + worst = 0.0 + for det, ra, dec, psi in _CASES: + e = _max_delay_error(det, ra, dec) + worst = max(worst, e) + assert e < _TOL, "delay mismatch %g s for %s ra=%.3f dec=%.3f" % (e, det, ra, dec) + print("delay: worst |dtau| = %.3e s" % worst) + + +def test_only_five_and_three_harmonics(): + """Confirm there is genuinely no content beyond |n|=2 (antenna) / |n|=1 (delay).""" + lald = _lal_detector("H1") + ra, dec, psi = 1.0, 0.4, 0.7 + _, gmst = _sample_gmst(1024) + g = sr.greenwich_hour_angle(gmst, ra) + # antenna: DFT of F(g) sampled uniformly in g should have power only at |n|<=2 + gg = np.linspace(0, 2 * np.pi, 1024, endpoint=False) + F = sr.antenna_from_harmonics(sr.antenna_harmonics(lald.response, dec, psi), gg) + coeffs = np.fft.fft(F) / len(gg) + power = np.abs(coeffs) + keep = np.zeros_like(power, dtype=bool) + for n in (-2, -1, 0, 1, 2): + keep[n % len(gg)] = True + assert np.max(power[~keep]) < 1e-12, "antenna has power beyond |n|=2: %g" % np.max(power[~keep]) + tau = sr.delay_from_harmonics(sr.delay_harmonics(lald.location, dec), gg) + tcoef = np.abs(np.fft.fft(tau) / len(gg)) + keep = np.zeros_like(tcoef, dtype=bool) + for n in (-1, 0, 1): + keep[n % len(gg)] = True + assert np.max(tcoef[~keep]) < 1e-15, "delay has power beyond |n|=1: %g" % np.max(tcoef[~keep]) + print("harmonic content confirmed: antenna |n|<=2, delay |n|<=1") + + +if __name__ == "__main__": + test_antenna_harmonics_match_lal() + test_delay_harmonics_match_lal() + test_only_five_and_three_harmonics() + print("ALL SLOWROT RESPONSE CHECKS PASSED") diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index 8ce0ee353..ad5aaabd0 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -254,6 +254,12 @@ optp.add_option("--extrinsic-proposal-adapt",action='store_true',default=False, optp.add_option("--vectorized", action="store_true", help="Perform manipulations of lm and timeseries using numpy arrays, not LAL data structures. (Combine with --gpu to enable GPU use, where available)") optp.add_option("--gpu", action="store_true", help="Perform manipulations of lm and timeseries using numpy arrays, CONVERTING TO GPU when available. You MUST use this option with --vectorized (otherwise it is a no-op). You MUST have a suitable version of cupy installed, your cuda operational, etc") optp.add_option("--force-gpu-only", action="store_true", help="Hard fail if no GPU present (assessed by cupy not loading)") +optp.add_option("--rotation-slow", action="store_true", help="[Path A] Slow-rotation likelihood: account for the sidereal time-dependence of the antenna pattern F(t) over the signal (harmonic modulation). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_with_rotation.") +optp.add_option("--rotation-n-harmonics", type=int, default=2, help="Number of sidereal harmonics for --rotation-slow (antenna pattern needs 2, i.e. n=-2..2).") +optp.add_option("--rotation-p-max", type=int, default=0, help="[Path B] Max delay-derivative order for --rotation-slow (0 = amplitude drift only; >=1 adds propagation-delay drift).") +optp.add_option("--freqresponse", action="store_true", help="[Path D] Finite-size (frequency-dependent) detector-response likelihood: account for the finite light-travel-time transfer across the arms (matters for 3G/CE-ET). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_freqresponse (sky-harmonic route b, sky stays extrinsic).") +optp.add_option("--freqresponse-qmax", type=int, default=4, help="Highest power of the arm projection retained for --freqresponse (basis size Qmax+2). Higher for larger fL/c (heavier systems / higher fmax).") +optp.add_option("--freqresponse-arm-length", type=float, default=None, help="Arm-length override [m] for --freqresponse (e.g. 40000 for 40-km CE). Default: each detector's native LAL arm length.") optp.add_option("--force-xpy", action="store_true", help="Use the xpy code path. Use with --vectorized --gpu to use the fallback CPU-based code path. Useful for debugging.") optp.add_option("-o", "--output-file", help="Save result to this file.") optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]") @@ -385,6 +391,26 @@ def _truthy_option(value): opts._noloop_time_interp = "cubic" if _truthy_option(opts.interpolate_time) else "nearest" +if opts.rotation_slow: + # Path A slow-rotation is currently wired only into the CPU vectorized branch. + if not opts.vectorized: + raise ValueError("--rotation-slow requires --vectorized") + if opts.gpu: + raise ValueError("--rotation-slow is CPU-only for now; do not combine with --gpu") + if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): + raise ValueError("--rotation-slow does not yet support distance/phase marginalization") + +if opts.freqresponse: + # Path D finite-size response is currently wired only into the CPU vectorized branch. + if opts.rotation_slow: + raise ValueError("--freqresponse and --rotation-slow both replace the likelihood; use at most one") + if not opts.vectorized: + raise ValueError("--freqresponse requires --vectorized") + if opts.gpu: + raise ValueError("--freqresponse is CPU-only for now; do not combine with --gpu") + if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): + raise ValueError("--freqresponse does not yet support distance/phase marginalization") + # cosmo d prior tools for interpolation: not used normally, but set if needed final_scipy_interpolate=None if 'cosmo' in opts.d_prior: @@ -431,6 +457,8 @@ if cupy_success and opts.gpu and opts.sampler_method == 'adaptive_cartesian_gpu' if not(opts.use_gwsignal): os.environ['RIFT_NO_GWSIGNAL'] = 'True' import RIFT.likelihood.factored_likelihood as factored_likelihood +import RIFT.likelihood.factored_likelihood_with_rotation as factored_likelihood_with_rotation +import RIFT.likelihood.factored_likelihood_freqresponse as factored_likelihood_freqresponse if opts.use_gwsignal and not(factored_likelihood.has_GWS): print(" HARD FAILURE: this node could not import gwsignal ! ") @@ -1822,6 +1850,39 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t ctVArrayDict[det] = cupy.asarray(ctVArrayDict[det]) epochDict[det] = cupy.asarray(epochDict[det]) + # [Path A] slow-rotation precompute: build the harmonic-indexed bank and pack it. + rotation_slow_data = None + if opts.rotation_slow: + _pmax = int(opts.rotation_p_max) + _nh = max(int(opts.rotation_n_harmonics), 2 + _pmax) # wide enough to cover all C_{(p,ntilde)} + _harm = tuple(range(-_nh, _nh + 1)) + _rint_r, _ct_r, _ctV_r, _rho_r, _meta_r = factored_likelihood_with_rotation.PrecomputeLikelihoodTermsWithRotation( + fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, + harmonics=_harm, p_max=_pmax, analyticPSD_Q=False, + inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, + verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) + _lkR, _rhoN, _uNN, _vNN, _epR = factored_likelihood_with_rotation.pack_rotation_arrays( + _meta_r, _rho_r, _ct_r, _ctV_r) + rotation_slow_data = dict(meta=_meta_r, lookupNKDict=_lkR, rho_by_n=_rhoN, + U_by_nn=_uNN, V_by_nn=_vNN, epochDict=_epR) + print(" [rotation-slow] precompute complete; p_max", _pmax, "sidereal harmonics", _harm) + + # [Path D] finite-size (frequency-dependent) response precompute: fold each W_p(f) + # into the modes once and pack the response-basis overlap bank. + freqresponse_data = None + if opts.freqresponse: + _qmax = int(opts.freqresponse_qmax) + _rint_f, _ct_f, _ctV_f, _rho_f, _meta_f = factored_likelihood_freqresponse.PrecomputeLikelihoodTermsFreqResponse( + fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, + Qmax=_qmax, L_arm=opts.freqresponse_arm_length, analyticPSD_Q=False, + inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, + verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) + _lkF, _rhoP, _uPP, _vPP, _epF = factored_likelihood_freqresponse.pack_freqresponse_arrays( + _meta_f, _rho_f, _ct_f, _ctV_f) + freqresponse_data = dict(meta=_meta_f, lookupNKDict=_lkF, rho_by_p=_rhoP, + U_by_pp=_uPP, V_by_pp=_vPP, epochDict=_epF) + print(" [freqresponse] precompute complete; Qmax", _qmax, "arm-length", opts.freqresponse_arm_length) + def _cal_error_probe(n_cal_now, n_start=256, n_cap=None, rel_tol=0.1): """Estimate (sigma_lnZ_cal, neff_cal, n_used, dist_mode): the calibration MC error of lnZ and the effective cal draw count, from per-realization @@ -2091,7 +2152,20 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t P.psi= psi_true P.phiref = phi_orb_true - lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals, + if opts.rotation_slow: + lnL = factored_likelihood_with_rotation.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P, rotation_slow_data['meta'], rotation_slow_data['lookupNKDict'], + rotation_slow_data['rho_by_n'], rotation_slow_data['U_by_nn'], + rotation_slow_data['V_by_nn'], rotation_slow_data['epochDict'], Lmax=opts.l_max, + time_interp=opts._noloop_time_interp) + elif opts.freqresponse: + lnL = factored_likelihood_freqresponse.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, P, freqresponse_data['meta'], freqresponse_data['lookupNKDict'], + freqresponse_data['rho_by_p'], freqresponse_data['U_by_pp'], + freqresponse_data['V_by_pp'], freqresponse_data['epochDict'], Lmax=opts.l_max, + time_interp=opts._noloop_time_interp) + else: + lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals, P, lookupNKDict, rholmArrayDict, ctUArrayDict, ctVArrayDict,epochDict,Lmax=opts.l_max) # nEvals +=len(right_ascension) if supplemental_ln_likelihood: diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile new file mode 100644 index 000000000..55d0e0f7a --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/Makefile @@ -0,0 +1,38 @@ +# demo/rift/slowrot -- verify-anywhere value demos for the slow-response likelihoods. +# +# make demo run BOTH quick-looks (no condor, no GPU); writes outputs/*.{txt,png} +# make demo-rotation Path A/B : rotation-vs-static lnL gain grows with signal duration Omega*T +# make demo-finite-size Path D : finite-size-vs-LWL lnL gain grows with detector arm length (fL/c) +# make clean remove outputs/ +# +# Both show the maintained vectorized (NoLoop) likelihood RECOVERS SNR the standard analysis +# loses, on the SAME data at the true parameters. They exercise the exact functions wired into +# integrate_likelihood_extrinsic_batchmode as --rotation-slow / --rotation-p-max / --freqresponse. +# Backing code: RIFT/likelihood/{factored_likelihood_with_rotation,factored_likelihood_freqresponse, +# slowrot_freqresponse}.py; validation RIFT/likelihood/test_slowrot_*.py; SLOWROT_HANDOFF.md. + +PYTHON ?= python +# Code root is four levels up (demo/rift/slowrot -> demo/rift -> demo -> Code); put it on +# PYTHONPATH so `import RIFT...` resolves this worktree even without an editable install. +CODE_ROOT := $(abspath $(dir $(lastword $(MAKEFILE_LIST)))/../../..) +RUN = PYTHONPATH=$(CODE_ROOT):$$PYTHONPATH $(PYTHON) + +.PHONY: help demo demo-rotation demo-finite-size clean +help: + @echo "targets:" + @echo " demo both verify-anywhere quick-looks (rotation + finite-size)" + @echo " demo-rotation Path A/B: gain vs signal duration (Omega*T); null control at short T" + @echo " demo-finite-size Path D: gain vs arm length (fL/c); null control at LIGO 4 km" + @echo " clean remove outputs/" + @echo "(uses PYTHON=$(PYTHON); CODE_ROOT=$(CODE_ROOT))" + +demo: demo-rotation demo-finite-size + +demo-rotation: + $(RUN) demo_rotation.py + +demo-finite-size: + $(RUN) demo_finite_size.py + +clean: + rm -rf outputs diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md new file mode 100644 index 000000000..adedaf8fe --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/README.md @@ -0,0 +1,85 @@ +# demo/rift/slowrot — slow-response likelihood value demos + +Verify-anywhere (no condor, no GPU) quick-looks that RIFT's **time-/frequency-dependent +detector-response** likelihoods *add value*, not merely run. Both generalize the maintained +marginalized ILE likelihood so a long or long-armed signal is modeled with the correct +detector response instead of a single constant antenna pattern. + +``` +make demo # both demos below -> outputs/*.{txt,png} +make demo-rotation # Path A/B (Earth rotation) +make demo-finite-size # Path D (finite-size / frequency-dependent response) +``` + +Each demo builds data that carries the true response, then compares, **at the true +parameters on the same data and time grid**, the recovered time-maximized log-likelihood of +the standard analysis vs the response-aware one. The difference `gain = lnL_aware − lnL_standard` +is the SNR the standard analysis throws away. Both use the maintained vectorized (NoLoop) +path with cubic sub-bin time interpolation, so the peak-resolution floor cancels in the gain. + +--- + +## Path A/B — Earth rotation (`demo_rotation.py`) + +Over a long signal the antenna pattern `F(t)` drifts as the Earth rotates; the static +(Earth-fixed-response) likelihood loses match. RIFT injections already carry the true +time-varying response (`hoft → SimDetectorStrainREAL8TimeSeries`), so a standard injection is +"rotating". The gain grows with the rotation phase `Ω⊕·T` over the signal; a short signal is a +null control. Single H1, SNR≈30 held fixed so the gain tracks `Ω⊕·T`, not loudness: + +| config | seglen | Ω⊕·T | gain `lnL_rot − lnL_static` | +|---|---|---|---| +| null_bbh (30+25) | 2 s | 1.5e-4 | +0.004 (null control) | +| bbh_8_8 | 16 s | 1.2e-3 | +0.043 | +| bbh_4_4 | 64 s | 4.7e-3 | +0.187 | + +Grows ~50× from the short null to a minute-scale signal; extrapolated to a 90-minute XG BNS +(`Ω⊕·T`≈0.4) it is orders of magnitude larger — the 3G headline. Figure: +`outputs/rotation_gain_vs_duration.png`. ILE: `--rotation-slow` (Path A), +`--rotation-slow --rotation-p-max 2` (Path B, adds propagation-delay drift). + +## Path D — finite-size / frequency-dependent response (`demo_finite_size.py`) + +On a multi-km arm the light-travel time across the arm is not negligible vs the GW period, so +the response is per-frequency: `h_k(f) = F₊(f;sky) h₊(f) + Fₓ(f;sky) hₓ(f)`. We inject an exact +finite-size signal (`antenna_response_fd`) and compare the standard long-wavelength (LWL, +constant-response) likelihood to the finite-size one (`--freqresponse`, sky-harmonic route (b), +sky stays extrinsic). The gain grows with the arm length — i.e. with `fL/c`, the in-band +light-crossing phase. 15+13 M☉, fmax=2000 Hz, loud (SNR≈320), Qmax=6: + +| detector | arm L | fL/c @ fmax | gain `lnL_finite − lnL_LWL` | +|---|---|---|---| +| LIGO | 4 km | 0.027 | +0.25 (null control) | +| ET | 10 km | 0.067 | +3.5 | +| CE | 20 km | 0.133 | +12.4 | +| CE | 40 km | 0.267 | +39.6 | + +Null at the 4-km LIGO arm; tens of nats at a 40-km Cosmic Explorer. Only the +**direction-dependent** part of the response contributes — the common `e^{−i2πfL/c}` +light-crossing delay is degenerate with arrival time and is absorbed by both likelihoods' +time maximization, so it does not inflate the gain. Figure: +`outputs/finite_size_gain_vs_arm.png`. ILE: `--freqresponse` +`--freqresponse-arm-length 40000` `--freqresponse-qmax 6`. + +Higher `fL/c` (heavier system / higher fmax / longer arm) needs higher `--freqresponse-qmax` +(the response is a power series in `fL/c · (â·n̂)`); Qmax=6 converges through CE-40 km at this +config. + +--- + +## Full injection–recovery PE (cluster) + +These quick-looks are the likelihood-level core. The headline parameter-bias / single-network +sky-localization figures are DAG PE runs (structurally the standard RIFT pipeline with the +extra ILE flag appended: `--rotation-slow` / `--freqresponse`). The three analyses per event +differ *only* in that appended option. See `RIFT/likelihood/SLOWROT_HANDOFF.md` and +`~/RIFT_roboto_paper/analyses/slowrot_demo/` for the pipeline scaffolding. + +## Validation (the demos show value; these prove correctness) + +`RIFT/likelihood/test_slowrot_*.py` (run under the venv with this Code dir on `PYTHONPATH`): +- `test_slowrot_noloop.py` — rotation NoLoop == baseline at f_sidereal=0 (3.6e-12). +- `test_slowrot_headtohead.py` — matched-sample rotation vs baseline; Cauchy-Schwarz bound. +- `test_slowrot_freqresponse_likelihood.py` — finite-size: L→0 reduces to baseline (3e-9), + bound respected, and a **V4 positive control** asserting finite-size beats LWL by +38.9 nats + in an in-band-effect config. diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py new file mode 100644 index 000000000..eee5ec0c7 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_finite_size.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python +""" +slowrot demo / demo_finite_size.py -- verify-anywhere (no condor, no GPU) quick-look that the +finite-size (frequency-dependent detector response) likelihood [Path D] ADDS VALUE on a +long-armed 3G detector. + +Beyond the long-wavelength (LWL) limit the detector strain is a per-frequency antenna +pattern, h_k(f) = F_+(f;sky) h_+(f) + F_x(f;sky) h_x(f), because the light-travel time across +a multi-km arm is no longer negligible vs the GW period. We build an EXACT finite-size +injection (from the same modes the likelihood uses -> internal_hlm_generator -> IFFT -> +antenna_response_fd) and, at the true parameters, compare the recovered log-likelihood of + + lnL_LWL : the standard RIFT likelihood (constant, frequency-independent response) + lnL_finite : the finite-size likelihood (--freqresponse, sky-harmonic route (b)) + +both TIME-MAXIMIZED on the SAME data and SAME time grid. The gain + + gain(L) = lnL_finite(truth) - lnL_LWL(truth) + +is ~0 for a LIGO 4-km arm (null control: the finite-size effect is below the floor) and grows +monotonically with the arm length -- i.e. with fL/c, the in-band light-crossing phase -- to +tens of nats for a 40-km Cosmic Explorer. That is exactly the SNR the LWL analysis throws +away on a 3G detector, and what --freqresponse recovers. + +The effect that matters is the DIRECTION-DEPENDENT part of the response; the common +e^{-i2pi f L/c} light-crossing delay is degenerate with the arrival time and is absorbed by +BOTH likelihoods' time maximization, so it does not inflate the gain. + +Run: python demo_finite_size.py (writes outputs/finite_size_gain_vs_arm.{txt,png}) + +Backing code: branch rift_slowrot; --freqresponse (+ --freqresponse-qmax/-arm-length) on +integrate_likelihood_extrinsic_batchmode; RIFT/likelihood/factored_likelihood_freqresponse.py, +slowrot_freqresponse.py. See RIFT/likelihood/SLOWROT_HANDOFF.md. +""" +from __future__ import print_function, division +import os +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.slowrot_freqresponse as sfr +import RIFT.likelihood.factored_likelihood_freqresponse as flfr + +EVENT_TIME = 1e9 +LMAX = 2 +DET = "H1" +PSD = lalsim.SimNoisePSDaLIGOZeroDetHighPower +# sky/pol/masses where the in-band finite-size effect is resolvable (heavier system -> high-f +# power; loud so the effect sits above the peak-resolution floor). +RA, DEC, PSI, INCL, PHIREF = 1.2, 0.3, 0.5, 0.4, 0.0 +M1, M2 = 15.0, 13.0 +FMAX = 2000.0 +SCALE = 40.0 # loudness: data-mode distance = distMpcRef/SCALE (SNR ~ 320) +SEGLEN = 8.0 +QMAX = 6 # response-basis order; higher needed as fL/c grows + +# (label, arm-length[m]) -- LIGO 4 km is the null control; ET ~10 km; CE 20/40 km. +CONFIGS = [ + ("LIGO_4km", 4000.0), + ("ET_10km", 10000.0), + ("CE_20km", 20000.0), + ("CE_40km", 40000.0), +] + +C_SI = sfr.C_SI + + +# --- minimal FD helpers (self-contained; same conventions as the validation test) --- +def _ifft(hf_d): + out = {} + for lm, hf in hf_d.items(): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); out[lm] = ht + return out + + +def _fwd_fd(re_series, epoch, dt, N): + ht = lal.CreateCOMPLEX16TimeSeries("h", epoch, 0., dt, lal.DimensionlessUnit, N) + ht.data.data[:] = re_series[:N] + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., 1. / dt / N, lsu.lsu_HertzUnit, N) + lal.COMPLEX16TimeFreqFFT(hf, ht, lal.CreateForwardCOMPLEX16FFTPlan(N, 0)); return hf + + +def _wrap_fd(arr, epoch, deltaF): + n = len(arr) + hf = lal.CreateCOMPLEX16FrequencySeries("hf", epoch, 0., deltaF, lsu.lsu_HertzUnit, n) + hf.data.data[:] = arr + return hf + + +def _rev_td(hf): + n = hf.data.length; dt = 1. / (n * hf.deltaF) + ht = lal.CreateCOMPLEX16TimeSeries("h", hf.epoch, 0., dt, lal.DimensionlessUnit, n) + lal.COMPLEX16FreqTimeFFT(ht, hf, lal.CreateReverseCOMPLEX16FFTPlan(n, 0)); return ht + + +def _peak(lt): + """Time-maximized value via a fine spline over the sampled lnL(t) window.""" + from scipy.interpolate import InterpolatedUnivariateSpline + lt = np.asarray(lt, float); x = np.arange(len(lt)) + sp = InterpolatedUnivariateSpline(x, lt, k=4) + xs = np.linspace(0, len(lt) - 1, len(lt) * 32) + return float(np.max(sp(xs))) + + +def run_config(label, L_arm): + deltaT = 1.0 / (2 * FMAX); fmin = 30.0; deltaF = 1.0 / SEGLEN + fNyq = 1.0 / 2.0 / deltaT; t_window = 0.1 + DLOUD = fl.distMpcRef * 1e6 * lsu.lsu_PC / SCALE + + Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=M1 * lal.MSUN_SI, m2=M2 * lal.MSUN_SI, detector=DET, dist=200e6 * lal.PC_SI, + deltaT=deltaT, tref=EVENT_TIME, deltaF=deltaF) + Psig.approx = lalsim.GetApproximantFromString("IMRPhenomD") + Pm = Psig.manual_copy(); Pm.dist = DLOUD + + # --- exact finite-size injection from the SAME modes --- + hlms_fd, _ = fl.internal_hlm_generator(Pm, LMAX, verbose=False, quiet=True) + hlmsT = _ifft(hlms_fd); lm0 = list(hlmsT.keys())[0] + nn = hlmsT[lm0].data.length; dt = hlmsT[lm0].deltaT; e0 = float(hlmsT[lm0].epoch) + Sig = np.zeros(nn, complex) + for lm in hlmsT: + Sig += hlmsT[lm].data.data * lal.SpinWeightedSphericalHarmonic(INCL, -PHIREF, -2, lm[0], lm[1]) + dt_geo = float(fl.ComputeArrivalTimeAtDetector(DET, RA, DEC, EVENT_TIME)) - EVENT_TIME + data_epoch = lal.LIGOTimeGPS(e0 + EVENT_TIME) + fvals = flfr.evaluate_fvals_from_length(nn, deltaF) + Sig_fd = _fwd_fd(Sig, data_epoch, dt, nn).data.data + Sig_del = _rev_td(_wrap_fd(Sig_fd * np.exp(-1j * 2 * np.pi * fvals * dt_geo), data_epoch, deltaF)).data.data + hpf = _fwd_fd(np.real(Sig_del), data_epoch, dt, nn).data.data + hcf = _fwd_fd(-np.imag(Sig_del), data_epoch, dt, nn).data.data + gmst = float(lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(EVENT_TIME))) + Fp, Fc = sfr.antenna_response_fd(DET, RA, DEC, PSI, fvals, gmst=gmst, L_arm=L_arm) + data = _wrap_fd(Fp * hpf + Fc * hcf, data_epoch, deltaF) + data_dict = {DET: data}; psd_dict = {DET: PSD} + IP = lsu.ComplexIP(fmin, FMAX, fNyq, data.deltaF, PSD, True, False, 0.) + snr = np.sqrt(IP.ip(data, data).real) + + Pv = Psig.manual_copy() + for k, v in [('phi', RA), ('theta', DEC), ('incl', INCL), ('phiref', PHIREF), + ('psi', PSI), ('dist', DLOUD)]: + setattr(Pv, k, np.ones(1) * v) + Pv.tref = EVENT_TIME; Pv.deltaT = deltaT + Nw = int(0.02 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # baseline (long-wavelength) NoLoop, time-maximized, at the truth + ri, ct, ctV, rho, _, _ = fl.PrecomputeLikelihoodTerms( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, FMAX, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in data_dict: + a, b, c, U, V, r, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rho[d].keys()), None, rho[d], ct[d], ctV[d]) + lk[d] = a; rA[d] = r; cu[d] = U; cv[d] = V; ep[d] = e + lnL_lwl = _peak(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=LMAX, xpy=np, return_lnLt=True, time_interp='cubic')[0]) + + # finite-size (Path D) NoLoop, time-maximized, at the truth + bk = flfr.PrecomputeLikelihoodTermsFreqResponse( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, FMAX, + Qmax=QMAX, L_arm=L_arm, analyticPSD_Q=True, verbose=False, quiet=True, skip_interpolation=True) + lkf, rbp, ubp, vbp, epf = flfr.pack_freqresponse_arrays(bk[4], bk[3], bk[1], bk[2]) + lnL_fin = _peak(flfr.DiscreteFactoredLogLikelihoodFreqResponseNoLoop( + tvals, Pv, bk[4], lkf, rbp, ubp, vbp, epf, Lmax=LMAX, array_output=True, time_interp='cubic')[0]) + + fLc = FMAX * L_arm / C_SI + return dict(label=label, L_arm=L_arm, fLc=fLc, snr=float(snr), + lnL_lwl=lnL_lwl, lnL_fin=lnL_fin, gain=lnL_fin - lnL_lwl) + + +def main(): + here = os.path.dirname(os.path.abspath(__file__)) + outdir = os.path.join(here, "outputs"); os.makedirs(outdir, exist_ok=True) + print("finite-size (Path D) value demo: %g+%g Msun, fmax=%g Hz, loudness SCALE=%g (SNR per row), Qmax=%d\n" + % (M1, M2, FMAX, SCALE, QMAX)) + rows = [] + for cfg in CONFIGS: + try: + r = run_config(*cfg) + except Exception as e: + print("%-9s SKIPPED (%s)" % (cfg[0], e)); continue + rows.append(r) + print("%-9s L=%6.0fm fL/c@fmax=%.3f SNR=%6.1f lnL_LWL=%11.3f lnL_finite=%11.3f GAIN=%+8.3f" + % (r['label'], r['L_arm'], r['fLc'], r['snr'], r['lnL_lwl'], r['lnL_fin'], r['gain'])) + if not rows: + print("no configs succeeded"); return + txt = os.path.join(outdir, "finite_size_gain_vs_arm.txt") + with open(txt, "w") as f: + f.write("# finite-size (Path D) demo: --freqresponse recovers SNR the long-wavelength analysis loses\n") + f.write("# label L_arm[m] fL/c@fmax SNR lnL_LWL lnL_finite gain=lnL_finite-lnL_LWL\n") + for r in rows: + f.write("%s %g %g %g %g %g %g\n" % (r['label'], r['L_arm'], r['fLc'], + r['snr'], r['lnL_lwl'], r['lnL_fin'], r['gain'])) + print("\nwrote", txt) + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + Lkm = [r['L_arm'] / 1e3 for r in rows]; gain = [r['gain'] for r in rows] + labs = [r['label'] for r in rows] + fig, ax = plt.subplots(figsize=(5, 3.4)) + ax.plot(Lkm, gain, 'o-', color="#9d0208") + for x, y, l in zip(Lkm, gain, labs): + ax.annotate(l, (x, y), textcoords="offset points", xytext=(4, 4), fontsize=8) + ax.set_xlabel(r"detector arm length $L$ [km] ($\propto fL/c$, in-band light-crossing)") + ax.set_ylabel(r"$\ln\mathcal{L}_{\rm finite}-\ln\mathcal{L}_{\rm LWL}$ (recovered)") + ax.set_title("Finite-size detector response recovers SNR on 3G arms") + ax.axhline(0, color="0.7", lw=0.8) + fig.tight_layout() + png = os.path.join(outdir, "finite_size_gain_vs_arm.png") + fig.savefig(png, dpi=140) + print("wrote", png) + except Exception as e: + print("(plot skipped:", e, ")") + + +if __name__ == "__main__": + main() diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py new file mode 100644 index 000000000..d1c7bc7c2 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/demo_rotation.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python +""" +slowrot demo / demo_rotation.py -- verify-anywhere (no condor, no GPU) quick-look that the +slow-rotation likelihood [Path A/B] ADDS VALUE: on a long signal the static +(Earth-fixed-response) likelihood LOSES match with the data (which carries the true +time-varying antenna pattern, since RIFT injections go through +SimDetectorStrainREAL8TimeSeries), and --rotation-slow recovers it. The recovered +log-likelihood at the true parameters, + + gain(config) = lnL_rotation(truth) - lnL_static(truth), (both time-marginalized) + +grows with the rotation phase Omega*T over the signal; a short signal is a null control +(gain ~ 0). This is the likelihood-level core of the full injection-recovery PE; the headline +sky-localization / parameter-bias figure is a cluster run. + +Both likelihoods are the maintained vectorized (NoLoop) path evaluated on the SAME data and +SAME time grid, so the gain is a clean difference (peak-resolution floor cancels). + +Run: python demo_rotation.py (writes outputs/rotation_gain_vs_duration.{txt,png}) + +Backing code: branch rift_slowrot; --rotation-slow (Path A) and --rotation-p-max N (Path B) on +integrate_likelihood_extrinsic_batchmode. See RIFT/likelihood/SLOWROT_HANDOFF.md. +""" +from __future__ import print_function, division +import os +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +EVENT_TIME = 1e9 +LMAX = 2 +DET = "H1" # single detector: rotation's sky-info value is clearest here +PSD = lalsim.SimNoisePSDaLIGOZeroDetHighPower +# sky/pol chosen where the antenna pattern drifts appreciably over a sidereal day +RA, DEC, PSI, INCL, PHIREF = 1.0, -0.6, 0.4, 0.5, 0.0 +HARM = (-2, -1, 0, 1, 2) + +# (label, m1, m2, fmin, fmax, dist[Mpc]) -- increasing duration via lower mass / fmin. +# Segment length is AUTO-FIT to each waveform (next power of two). Distances tuned for a +# comparable (loud) SNR (~30) so the gain tracks Omega*T, not loudness. +CONFIGS = [ + ("null_bbh", 30.0, 25.0, 30.0, 1024.0, 900.0), # short BBH, ~2 s: control, gain~0 + ("bbh_8_8", 8.0, 8.0, 25.0, 1024.0, 380.0), # ~16 s + ("bbh_4_4", 4.0, 4.0, 22.0, 1024.0, 210.0), # ~64 s -> larger Omega*T +] +OMEGA_EARTH = flwr.OMEGA_EARTH + + +def _P(m1, m2, fmin, deltaT, deltaF): + P = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=INCL, phiref=PHIREF, theta=DEC, phi=RA, psi=PSI, + m1=m1 * lal.MSUN_SI, m2=m2 * lal.MSUN_SI, detector=DET, + dist=200e6 * lal.PC_SI, deltaT=deltaT, tref=EVENT_TIME, deltaF=deltaF) + P.approx = lalsim.GetApproximantFromString("IMRPhenomD") + return P + + +def _P_vec(Psig, dist_SI, K=1): + Pv = Psig.manual_copy() + Pv.phi = np.ones(K) * RA; Pv.theta = np.ones(K) * DEC; Pv.psi = np.ones(K) * PSI + Pv.incl = np.ones(K) * INCL; Pv.phiref = np.ones(K) * PHIREF + Pv.dist = np.ones(K) * dist_SI + Pv.tref = float(EVENT_TIME); Pv.deltaT = Psig.deltaT + return Pv + + +def run_config(label, m1, m2, fmin, fmax, dist_Mpc): + deltaT = 1.0 / (2 * fmax) if fmax > 1024 else 1.0 / 2048.0 + dist_SI = dist_Mpc * 1e6 * lal.PC_SI + t_window = 0.1 + # AUTO-FIT seglen: generate with deltaF=None so non_herm_hoff pads to the next power of + # two >= the waveform length; then read back the segment length / deltaF. + Psig = _P(m1, m2, fmin, deltaT, None) + Psig.dist = dist_SI + data = lsu.non_herm_hoff(Psig) # RIFT injection -> carries the true time-varying response + seglen = data.data.length * deltaT + Psig.deltaF = data.deltaF + data_dict = {DET: data}; psd_dict = {DET: PSD} + fNyq = 1.0 / (2 * deltaT) + IP = lsu.ComplexIP(fmin, fmax, fNyq, data.deltaF, PSD, True, False, 0.) + snr = np.sqrt(IP.ip(data, data).real) + Pv = _P_vec(Psig, dist_SI) + Nw = int(0.02 / deltaT); tvals = np.arange(-Nw, Nw) * deltaT + + # baseline (static) NoLoop, time-marginalized, at the truth + rib, ctb, ctVb, rhob, _, _ = fl.PrecomputeLikelihoodTerms( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, fmax, + analyticPSD_Q=True, verbose=False, quiet=True, ignore_threshold=None) + lk = {}; rA = {}; cu = {}; cv = {}; ep = {} + for d in data_dict: + a, b, c, U, V, rAr, rI, e = fl.PackLikelihoodDataStructuresAsArrays( + list(rhob[d].keys()), None, rhob[d], ctb[d], ctVb[d]) + lk[d] = a; rA[d] = rAr; cu[d] = U; cv[d] = V; ep[d] = e + lnL_static = float(fl.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop( + tvals, Pv, lk, rA, cu, cv, ep, Lmax=LMAX, xpy=np, time_interp='cubic')[0]) + + # rotation (Path A) NoLoop at the truth + bk = flwr.PrecomputeLikelihoodTermsWithRotation( + EVENT_TIME, t_window, Psig, data_dict, psd_dict, LMAX, fmax, + harmonics=HARM, p_max=0, f_sidereal=flwr.F_SIDEREAL, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + lkr, rbn, ubn, vbn, epr = flwr.pack_rotation_arrays(bk[4], bk[3], bk[1], bk[2]) + lnL_rot = float(flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, Pv, bk[4], lkr, rbn, ubn, vbn, epr, Lmax=LMAX, time_interp='cubic')[0]) + + OmegaT = OMEGA_EARTH * seglen + return dict(label=label, seglen=seglen, snr=float(snr), OmegaT=OmegaT, + lnL_static=lnL_static, lnL_rot=lnL_rot, gain=lnL_rot - lnL_static) + + +def main(): + here = os.path.dirname(os.path.abspath(__file__)) + outdir = os.path.join(here, "outputs"); os.makedirs(outdir, exist_ok=True) + print("slow-rotation (Path A) value demo: single %s, SNR~30 held fixed so gain tracks Omega*T\n" % DET) + rows = [] + for cfg in CONFIGS: + try: + r = run_config(*cfg) + except Exception as e: + print("%-9s SKIPPED (%s)" % (cfg[0], e)); continue + rows.append(r) + print("%-9s seglen=%5.0fs SNR=%6.1f Omega*T=%.2e lnL_static=%12.4f lnL_rot=%12.4f GAIN=%+.4f" + % (r['label'], r['seglen'], r['snr'], r['OmegaT'], + r['lnL_static'], r['lnL_rot'], r['gain'])) + if not rows: + print("no configs succeeded"); return + txt = os.path.join(outdir, "rotation_gain_vs_duration.txt") + with open(txt, "w") as f: + f.write("# slow-rotation demo: --rotation-slow recovers SNR the static analysis loses on long signals\n") + f.write("# label seglen[s] SNR Omega*T lnL_static lnL_rot gain=lnL_rot-lnL_static\n") + for r in rows: + f.write("%s %g %g %g %g %g %g\n" % (r['label'], r['seglen'], r['snr'], + r['OmegaT'], r['lnL_static'], r['lnL_rot'], r['gain'])) + print("\nwrote", txt) + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + OmT = [r['OmegaT'] for r in rows]; gain = [r['gain'] for r in rows] + labs = [r['label'] for r in rows] + fig, ax = plt.subplots(figsize=(5, 3.4)) + ax.plot(OmT, gain, 'o-', color="#2a6f97") + for x, y, l in zip(OmT, gain, labs): + ax.annotate(l, (x, y), textcoords="offset points", xytext=(4, 4), fontsize=8) + ax.set_xlabel(r"$\Omega_\oplus T$ (rotation phase over the signal)") + ax.set_ylabel(r"$\ln\mathcal{L}_{\rm rot}-\ln\mathcal{L}_{\rm static}$ (recovered)") + ax.set_title("Accounting for Earth rotation recovers SNR on long signals") + ax.axhline(0, color="0.7", lw=0.8) + fig.tight_layout() + png = os.path.join(outdir, "rotation_gain_vs_duration.png") + fig.savefig(png, dpi=140) + print("wrote", png) + except Exception as e: + print("(plot skipped:", e, ")") + + +if __name__ == "__main__": + main() diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot/outputs/finite_size_gain_vs_arm.png new file mode 100644 index 0000000000000000000000000000000000000000..6ab85ae0f646355bdc250be39dbf1d5d2720632e GIT binary patch literal 49592 zcmd43cRZH=|2KT1K^h8)jAVpDN*QH@WTos)QJH0zGAp}i7@;D2ld@-7g^(RWNM&SY z&+B=7e)r@0=f3aj@2kh7@3%hA&ht2r_v`(7J=gmPxU70%*AAu~Bob+tqJo?{i9}vX zB9VQk+=l-moOIV4|0m{jUe`&(_J-4)s}9#m7q2?m+1NVSSehQXeci#)()QL#fl~s< zj~%jbatHS@<)+DpH=(jq&~qe_=>3V`WYt~LZ%Cw%T#9n%G+kaz z^f~Klb|2h6J@u76=)sjMrlwbpM0`77oza&wHa3&9&0JNHa)qPjK!Z0GBU<$rmFwxNY^PA`gJNGAmCfE z^U?3$zh5ykqmYo0hyvf2r7yTYNqAy~uI9yu7>^qqtYX5(POq 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zD^GB`NueN19N1?W>foiPw;!9sb>c2fwj>GjV1O&J1~vAgZ9Qfo8Rm_zP~H}SC4yve zEsaJC{7mXG5rK@1m1m=q)w Date: Wed, 8 Jul 2026 13:52:13 -0700 Subject: [PATCH 28/45] ILE: per-detector --freqresponse-arm-length (C1=40000,E1=10000) + extrinsic zoom-box options (--limit-right-ascension/-declination/-inclination/-psi) Enables the finite-size 3G sky-localization study: per-detector arm lengths for mixed CE+ET networks (LAL's cached C1 arm is a placeholder), and truth-centered sampling/prior restriction so the AdaptiveVolume sampler can resolve narrow high-SNR extrinsic peaks. Co-Authored-By: Claude Opus 4.8 --- .../integrate_likelihood_extrinsic_batchmode | 23 +++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index ad5aaabd0..22ac674ee 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -259,7 +259,7 @@ optp.add_option("--rotation-n-harmonics", type=int, default=2, help="Number of s optp.add_option("--rotation-p-max", type=int, default=0, help="[Path B] Max delay-derivative order for --rotation-slow (0 = amplitude drift only; >=1 adds propagation-delay drift).") optp.add_option("--freqresponse", action="store_true", help="[Path D] Finite-size (frequency-dependent) detector-response likelihood: account for the finite light-travel-time transfer across the arms (matters for 3G/CE-ET). Requires --vectorized and CPU (not --gpu). Uses factored_likelihood_freqresponse (sky-harmonic route b, sky stays extrinsic).") optp.add_option("--freqresponse-qmax", type=int, default=4, help="Highest power of the arm projection retained for --freqresponse (basis size Qmax+2). Higher for larger fL/c (heavier systems / higher fmax).") -optp.add_option("--freqresponse-arm-length", type=float, default=None, help="Arm-length override [m] for --freqresponse (e.g. 40000 for 40-km CE). Default: each detector's native LAL arm length.") +optp.add_option("--freqresponse-arm-length", default=None, help="Arm-length override [m] for --freqresponse. Either a single float applied to ALL detectors (e.g. 40000 for 40-km CE), or per-detector 'C1=40000,E1=10000,...' (needed for mixed CE+ET networks, since LAL's cached C1 arm is a placeholder). Default: each detector's native LAL arm length.") optp.add_option("--force-xpy", action="store_true", help="Use the xpy code path. Use with --vectorized --gpu to use the fallback CPU-based code path. Useful for debugging.") optp.add_option("-o", "--output-file", help="Save result to this file.") optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]") @@ -310,6 +310,10 @@ integration_params.add_option("--d-max", default=10000,type=float,help="Maximum integration_params.add_option("--d-min", default=1,type=float,help="Minimum distance in volume integral. Used to SET THE PRIOR; changing this value changes the numerical answer.") integration_params.add_option("--declination-cosine-sampler",action='store_true',help="If specified, the parameter used for declination is cos(dec), not dec") integration_params.add_option("--inclination-cosine-sampler",action='store_true',help="If specified, the parameter used for inclination is cos(dec), not dec") +integration_params.add_option("--limit-right-ascension",default=None,help="Restrict RA sampling AND prior to 'LO,HI' [rad] (truth-centered zoom box). Narrows the extrinsic prior like --d-min/--d-max do for distance; keep the box large vs the posterior so credible regions are unaffected. Assumes the plain (non-cosine) dec/incl samplers.") +integration_params.add_option("--limit-declination",default=None,help="Restrict declination sampling AND prior to 'LO,HI' [rad].") +integration_params.add_option("--limit-inclination",default=None,help="Restrict inclination sampling AND prior to 'LO,HI' [rad].") +integration_params.add_option("--limit-psi",default=None,help="Restrict polarization psi sampling AND prior to 'LO,HI' [rad].") integration_params.add_option("--internal-rotate-phase", action='store_true',help="If specified, the integration sampler uses phase_p ==phi+psi and phase_m == phi-psi as sampling coordinates, both ranging from 0 to 4 pi. The prior is twice as large.") integration_params.add_option("--internal-sky-network-coordinates",action='store_true',help="If specified, perform integration in sky coordinates aligned with the first two IFOs provided") integration_params.add_option("--internal-sky-network-coordinates-raw",action='store_true',help="If specified, does not attempt to organize IFO network sensibly, uses them AS PROVIDED IN ORDER.") @@ -1073,6 +1077,15 @@ param_limits = { "psi": (0, 2*numpy.pi), if opts.internal_rotate_phase: param_limits['psi'] = (0, 4*numpy.pi) param_limits['phi_orb'] = (0, 4*numpy.pi) +# Optional truth-centered "zoom box": narrow the extrinsic sampling AND prior ranges so the +# adaptive sampler can resolve a narrow high-SNR peak it could never find from the full prior. +# Threads through param_limits into every sky/orientation sampler + its pdf/cdf_inv/prior_pdf. +for _optv, _k in [(opts.limit_psi, 'psi'), (opts.limit_right_ascension, 'right_ascension'), + (opts.limit_declination, 'declination'), (opts.limit_inclination, 'inclination')]: + if _optv: + _lo, _hi = [float(_x) for _x in str(_optv).split(',')] + param_limits[_k] = (_lo, _hi) + print(" [limit] restricting {} sampling/prior to [{:.4f}, {:.4f}]".format(_k, _lo, _hi)) # # Parameter integral sampling strategy @@ -1872,9 +1885,15 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t freqresponse_data = None if opts.freqresponse: _qmax = int(opts.freqresponse_qmax) + _arm = opts.freqresponse_arm_length + if _arm is not None: + if '=' in str(_arm): # per-detector 'C1=40000,E1=10000' + _arm = {kv.split('=')[0]: float(kv.split('=')[1]) for kv in str(_arm).split(',')} + else: + _arm = float(_arm) _rint_f, _ct_f, _ctV_f, _rho_f, _meta_f = factored_likelihood_freqresponse.PrecomputeLikelihoodTermsFreqResponse( fiducial_epoch, t_window, P, data_dict, psd_dict, opts.l_max, fmax, - Qmax=_qmax, L_arm=opts.freqresponse_arm_length, analyticPSD_Q=False, + Qmax=_qmax, L_arm=_arm, analyticPSD_Q=False, inv_spec_trunc_Q=inv_spec_trunc_Q, T_spec=T_spec, verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) _lkF, _rhoP, _uPP, _vPP, _epF = factored_likelihood_freqresponse.pack_freqresponse_arrays( From d038f582322639bbfef1a706c8a2324978b883f1 Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Thu, 9 Jul 2026 04:48:17 -0700 Subject: [PATCH 29/45] slowrot Path A/B: GPU (xpy/cupy) support for the rotation NoLoop likelihood term1 reuses the baseline fused Q_inner_product cubic/nearest kernel PER elementary template (A=conj(Ylm)) so there is NO (n_ext,npts,n_lms) temporary -- same GPU memory footprint as the baseline, just looped over |a_list| (~5 for Path A, ~15 for Path B); term2 is small |a_list|^2 einsums over n_lms^2. Antenna-harmonic / Ylm / time-delay stay on host (numpy/LAL) and only the heavy Q banks + U/V move to device. ILE: --rotation-slow now allowed with --gpu (requires n_cal=1, no glitch/cal marginalization); precompute moves rho/U/V to cupy; GPU likelihood_function gains the rotation branch. CPU path bit-identical (test_slowrot_noloop still 3.6e-12). GPU<->CPU consistency: test_slowrot_gpu.py (skips without cupy; run on a GPU node). Co-Authored-By: Claude Opus 4.8 --- .../factored_likelihood_with_rotation.py | 93 +++++++++++++------ .../Code/RIFT/likelihood/test_slowrot_gpu.py | 90 ++++++++++++++++++ .../integrate_likelihood_extrinsic_batchmode | 30 +++++- 3 files changed, 178 insertions(+), 35 deletions(-) create mode 100644 MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_gpu.py diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py index 6f232bcfb..135ef6f38 100644 --- a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/factored_likelihood_with_rotation.py @@ -538,9 +538,15 @@ def pack_rotation_arrays(meta, rholms_rot, crossTerms_rot, crossTermsV_rot): def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( tvals, P_vec, meta, lookupNKDict, rho_by_a, U_by_aa, V_by_aa, epochDict, - Lmax=2, array_output=False, time_interp='nearest'): + Lmax=2, array_output=False, time_interp='nearest', xpy=np): """Vectorized rotation-aware lnL (Path A). + GPU: pass xpy=cupy with rho_by_a/U_by_aa/V_by_aa already on device (the ILE converts them + when --gpu). term1 reuses the baseline fused Q_inner_product kernel PER elementary + template a (no (n_ext,npts,n_lms) temporary -> same memory footprint as the baseline, + looped over |a_list|); term2 is small |a_list|^2 einsums over n_lms^2. Requires n_cal=1 + (no glitch/calibration marginalization). + Mirrors factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopOrig with sums over sidereal harmonics and the per-sample antenna harmonics A_tilde_n = A_n(det,dec,psi) exp(i n (GMST(tref)-RA)). Extrinsic params in P_vec are @@ -551,14 +557,21 @@ def DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( """ import lal from . import factored_likelihood as FL + on_gpu = not (xpy is np) + if on_gpu: + from . import Q_inner_product + + def _h(v): # host (numpy) copy -- the antenna-harmonic / Ylm / delay helpers are numpy/LAL + return v.get() if hasattr(v, 'get') else np.asarray(v) a_list = list(meta['a_list']) p_max = meta['p_max'] - npts = len(tvals); npts_ex = len(P_vec.phi) - RA = P_vec.phi; DEC = P_vec.theta - incl = P_vec.incl; phiref = P_vec.phiref; psi = P_vec.psi - distMpc = P_vec.dist / (lal.PC_SI * 1e6) - lnL_t = np.zeros((npts_ex, npts), dtype=np.float64) + RA = _h(P_vec.phi); DEC = _h(P_vec.theta) + incl = _h(P_vec.incl); phiref = _h(P_vec.phiref); psi = _h(P_vec.psi) + npts = len(tvals); npts_ex = len(RA) + distMpc = _h(P_vec.dist) / (lal.PC_SI * 1e6) + inv_dist = xpy.asarray(FL.distMpcRef / distMpc) # (npts_ex,) on device + lnL_t = xpy.zeros((npts_ex, npts), dtype=np.float64) for det in rho_by_a: n_lms = len(lookupNKDict[det]) @@ -578,7 +591,7 @@ def Cg(a): gmst_tref = float(lal.GreenwichMeanSiderealTime(P_vec.tref)) t_det = float(P_vec.tref - float(t_ref)) + FL.TimeDelayFromEarthCenter( detector_location, RA, DEC, gmst_tref) - sample_first = (t_det + tvals[0]) / P_vec.deltaT + sample_first = (t_det + float(tvals[0])) / P_vec.deltaT # float(): tvals may be a cupy array on GPU if time_interp == 'nearest': ifirst = (np.round(sample_first) + 0.5).astype(int) else: @@ -586,34 +599,54 @@ def Cg(a): frac_first = (sample_first - np.floor(sample_first)).astype(np.float64) ilast = ifirst + npts - term1 = np.zeros((npts_ex, npts), dtype=np.complex128) - for a in a_list: - det_rho = rho_by_a[det][a] - if time_interp == 'nearest': - Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) - for i in range(npts_ex): - Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T - else: - # cubic sub-sample interpolation (calmarg time_interp='cubic'): - # _cubic_Q_window_numpy expects Q_block shape (n_time, n_lm). - Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) - term1 += np.conj(Cg(a))[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) - term1 = term1.real * (FL.distMpcRef / distMpc)[:, None] - - term2 = np.zeros(npts_ex, dtype=np.complex128) + # Device-side arrays for the heavy contraction (identity on CPU; host->device on GPU). + Ylms_d = xpy.asarray(Ylms); conjY_d = xpy.conj(Ylms_d) + zero_d = xpy.zeros(npts_ex, dtype=complex) + C_d = {k: xpy.asarray(v) for k, v in C.items()} + Cg_d = lambda a: C_d[a] if a in C_d else zero_d + + term1 = xpy.zeros((npts_ex, npts), dtype=np.complex128) + if on_gpu: + # term1 = Re[ sum_a conj(C_a) sum_lm conj(Ylm) Q^a_lm(t) ]: reuse the baseline fused + # kernel per elementary template a (A = conj(Ylm)), no (n_ex,npts,n_lms) temporary. + ifirst_i32 = xpy.asarray(ifirst).astype(np.int32) + frac_d = None if time_interp == 'nearest' else xpy.asarray(frac_first) + for a in a_list: + Q = xpy.ascontiguousarray(rho_by_a[det][a].T) # (n_time, n_lms), device + if time_interp == 'nearest': + res = Q_inner_product.Q_inner_product_cupy(Q, conjY_d, ifirst_i32, npts) + else: + res = Q_inner_product.Q_inner_product_cubic_cupy(Q, conjY_d, ifirst_i32, frac_d, npts) + term1 += xpy.conj(Cg_d(a))[:, None] * res + else: + for a in a_list: + det_rho = rho_by_a[det][a] + if time_interp == 'nearest': + Qa = np.empty((npts_ex, npts, n_lms), dtype=np.complex128) + for i in range(npts_ex): + Qa[i] = det_rho[..., ifirst[i]:ilast[i]].T + else: + # cubic sub-sample interpolation (calmarg time_interp='cubic'): + # _cubic_Q_window_numpy expects Q_block shape (n_time, n_lm). + Qa = FL._cubic_Q_window_numpy(det_rho.T, ifirst, frac_first, npts) + term1 += np.conj(Cg(a))[:, None] * np.einsum('xi,xti->xt', np.conj(Ylms), Qa) + term1 = term1.real * inv_dist[:, None] + + term2 = xpy.zeros(npts_ex, dtype=np.complex128) for a in a_list: aR = (a[0], -a[1]) for ap in a_list: - term2 += np.conj(Cg(a)) * Cg(ap) * np.einsum( - 'xi,xj,ij->x', np.conj(Ylms), Ylms, U_by_aa[det][(a, ap)]) - term2 += Cg(aR) * Cg(ap) * np.einsum( - 'xi,xj,ij->x', Ylms, Ylms, V_by_aa[det][(a, ap)]) - term2 = (-0.25 * term2.real) * (FL.distMpcRef / distMpc) ** 2 + term2 += xpy.conj(Cg_d(a)) * Cg_d(ap) * xpy.einsum( + 'xi,xj,ij->x', conjY_d, Ylms_d, xpy.asarray(U_by_aa[det][(a, ap)])) + term2 += Cg_d(aR) * Cg_d(ap) * xpy.einsum( + 'xi,xj,ij->x', Ylms_d, Ylms_d, xpy.asarray(V_by_aa[det][(a, ap)])) + term2 = (-0.25 * term2.real) * inv_dist ** 2 lnL_t += term1 + term2[:, None] if array_output: return lnL_t - lnLmax = np.max(lnL_t, axis=-1, keepdims=True) - L = FL.my_simps(np.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) - return lnLmax[..., 0] + np.log(L) + lnLmax = xpy.max(lnL_t, axis=-1, keepdims=True) + simps = FL.optimized_gpu_tools.simps if on_gpu else FL.my_simps + L = simps(xpy.exp(lnL_t - lnLmax), dx=P_vec.deltaT, axis=-1) + return lnLmax[..., 0] + xpy.log(L) diff --git a/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_gpu.py b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_gpu.py new file mode 100644 index 000000000..af0cd645b --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/RIFT/likelihood/test_slowrot_gpu.py @@ -0,0 +1,90 @@ +""" +test_slowrot_gpu : GPU vs CPU consistency for the rotation-aware NoLoop likelihood. + +Runs DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation with xpy=numpy and with +xpy=cupy on the SAME packed data (moving the Q banks + U/V to device, exactly as the ILE does +under --gpu) and asserts they agree to ~1e-8. SKIPPED if cupy / a GPU is unavailable. + +The GPU term1 reuses the baseline fused Q_inner_product kernel per elementary template (no +(n_ex,npts,n_lms) temporary), so this also exercises that path. Run on a GPU node: + python RIFT/likelihood/test_slowrot_gpu.py +""" +from __future__ import print_function, division +import numpy as np +import lal +import lalsimulation as lalsim + +import RIFT.lalsimutils as lsu +import RIFT.likelihood.factored_likelihood as fl +import RIFT.likelihood.factored_likelihood_with_rotation as flwr + +if not getattr(fl, "numba_on", True): + fl.lalylm = np.vectorize(lal.SpinWeightedSphericalHarmonic, otypes=[complex]) + +try: + import cupy + _ = cupy.array(1.0) + 1.0 # force a real device op + HAVE_GPU = True +except Exception as e: + HAVE_GPU = False + _WHY = str(e) + +fSample = 4096.0; fmin = 30.0; fmax = 1700.0; event_time = 1e9 +t_window = 0.1; Lmax = 2; deltaT = 1. / fSample; deltaF = 1. / 4. +HARM = (-2, -1, 0, 1, 2) + +Psig = lsu.ChooseWaveformParams( + fmin=fmin, radec=True, incl=0.3, phiref=0.0, theta=0.2, phi=1.0, psi=0.4, + m1=30 * lal.MSUN_SI, m2=25 * lal.MSUN_SI, detector='H1', + dist=200e6 * lal.PC_SI, deltaT=deltaT, tref=event_time, deltaF=deltaF) +data_dict = {det: lsu.non_herm_hoff(_p) for det, _p in + ((d, (lambda P, dd: (setattr(P, 'detector', dd), P)[1])(Psig.manual_copy(), d)) + for d in ("H1", "L1", "V1"))} +psd_dict = {det: lalsim.SimNoisePSDaLIGOZeroDetHighPower for det in data_dict} + + +def _P_vec(K=200, seed=71): + rng = np.random.RandomState(seed) + Pv = Psig.manual_copy() + Pv.phi = rng.uniform(0, 2 * np.pi, K) + Pv.theta = np.arcsin(rng.uniform(-1, 1, K)) + Pv.psi = rng.uniform(0, np.pi, K) + Pv.incl = np.arccos(rng.uniform(-1, 1, K)) + Pv.phiref = rng.uniform(0, 2 * np.pi, K) + Pv.dist = (rng.uniform(100, 800, K) * 1e6 * lsu.lsu_PC) + Pv.tref = float(event_time); Pv.deltaT = deltaT + return Pv + + +def _to_gpu(rho_by_a, U_by_aa, V_by_aa): + r = {d: {a: cupy.asarray(rho_by_a[d][a]) for a in rho_by_a[d]} for d in rho_by_a} + u = {d: {p: cupy.asarray(U_by_aa[d][p]) for p in U_by_aa[d]} for d in U_by_aa} + v = {d: {p: cupy.asarray(V_by_aa[d][p]) for p in V_by_aa[d]} for d in V_by_aa} + return r, u, v + + +def test_gpu_matches_cpu(): + if not HAVE_GPU: + print("(GPU) SKIPPED: cupy/GPU unavailable (%s)" % _WHY); return + ri, ct, ctV, rho, meta = flwr.PrecomputeLikelihoodTermsWithRotation( + event_time, t_window, Psig, data_dict, psd_dict, Lmax, fmax, + harmonics=HARM, p_max=0, f_sidereal=flwr.F_SIDEREAL, analyticPSD_Q=True, + verbose=False, quiet=True, skip_interpolation=True) + lk, rbn, ubn, vbn, ep = flwr.pack_rotation_arrays(meta, rho, ct, ctV) + Pv = _P_vec() + tvals = np.arange(int(2 * 0.03 / deltaT)) * deltaT - 0.03 + for interp in ('nearest', 'cubic'): + lnL_cpu = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, Pv, meta, lk, rbn, ubn, vbn, ep, Lmax=Lmax, time_interp=interp, xpy=np) + rG, uG, vG = _to_gpu(rbn, ubn, vbn) + lnL_gpu = flwr.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + cupy.asarray(tvals), Pv, meta, lk, rG, uG, vG, ep, Lmax=Lmax, time_interp=interp, xpy=cupy) + lnL_gpu = cupy.asnumpy(lnL_gpu) + d = np.max(np.abs(np.asarray(lnL_cpu) - lnL_gpu)) + print("(GPU) rotation NoLoop xpy=cupy vs xpy=np, interp=%-7s : max|diff| = %.3e" % (interp, d)) + assert d < 1e-8, "GPU rotation disagrees with CPU (%s): %g" % (interp, d) + + +if __name__ == "__main__": + test_gpu_matches_cpu() + print("SLOWROT GPU CHECK DONE" if HAVE_GPU else "SLOWROT GPU CHECK SKIPPED (no GPU)") diff --git a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode index 22ac674ee..72443e104 100755 --- a/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode +++ b/MonteCarloMarginalizeCode/Code/bin/integrate_likelihood_extrinsic_batchmode @@ -396,11 +396,13 @@ def _truthy_option(value): opts._noloop_time_interp = "cubic" if _truthy_option(opts.interpolate_time) else "nearest" if opts.rotation_slow: - # Path A slow-rotation is currently wired only into the CPU vectorized branch. + # Path A/B slow-rotation: wired into BOTH the CPU-vectorized and GPU (xpy) branches; the + # NoLoop-with-rotation reuses the baseline fused Q_inner_product kernel per elementary + # template on GPU (same memory footprint as the baseline). if not opts.vectorized: raise ValueError("--rotation-slow requires --vectorized") - if opts.gpu: - raise ValueError("--rotation-slow is CPU-only for now; do not combine with --gpu") + if opts.gpu and getattr(opts, 'calibration_marginalization', False): + raise ValueError("--rotation-slow on GPU does not support calibration/glitch marginalization (n_cal>1)") if getattr(opts, 'distance_marginalization', False) or getattr(opts, 'phase_marginalization', False): raise ValueError("--rotation-slow does not yet support distance/phase marginalization") @@ -1876,9 +1878,20 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t verbose=opts.verbose, quiet=not opts.verbose, skip_interpolation=True) _lkR, _rhoN, _uNN, _vNN, _epR = factored_likelihood_with_rotation.pack_rotation_arrays( _meta_r, _rho_r, _ct_r, _ctV_r) + if opts.gpu and (not xpy_default is np): + # move the (per-elementary-template) Q banks + U/V to device; the NoLoop + # reuses the baseline fused kernel per template. epoch/lookup stay on host. + for _det in _rhoN: + for _a in _rhoN[_det]: + _rhoN[_det][_a] = cupy.asarray(_rhoN[_det][_a]) + for _pair in _uNN[_det]: + _uNN[_det][_pair] = cupy.asarray(_uNN[_det][_pair]) + for _pair in _vNN[_det]: + _vNN[_det][_pair] = cupy.asarray(_vNN[_det][_pair]) rotation_slow_data = dict(meta=_meta_r, lookupNKDict=_lkR, rho_by_n=_rhoN, U_by_nn=_uNN, V_by_nn=_vNN, epochDict=_epR) - print(" [rotation-slow] precompute complete; p_max", _pmax, "sidereal harmonics", _harm) + print(" [rotation-slow] precompute complete; p_max", _pmax, "sidereal harmonics", _harm, + "(GPU)" if (opts.gpu and not xpy_default is np) else "(CPU)") # [Path D] finite-size (frequency-dependent) response precompute: fold each W_p(f) # into the modes once and pack the response-basis overlap bank. @@ -2236,7 +2249,14 @@ def analyze_event(P_list, indx_event, data_dict, psd_dict, fmax, opts,inv_spec_t P.phiref = phi_orb_true - lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals, + if opts.rotation_slow: + lnL = factored_likelihood_with_rotation.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoopWithRotation( + tvals, P, rotation_slow_data['meta'], rotation_slow_data['lookupNKDict'], + rotation_slow_data['rho_by_n'], rotation_slow_data['U_by_nn'], + rotation_slow_data['V_by_nn'], rotation_slow_data['epochDict'], Lmax=opts.l_max, + time_interp=opts._noloop_time_interp, xpy=xpy_default) + else: + lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVectorNoLoop(tvals, P, lookupNKDict, rholmArrayDict, ctUArrayDict, ctVArrayDict,epochDict,Lmax=opts.l_max,xpy=xpy_default,n_cal=n_cal_for_likelihood, cal_method=('fused' if use_fused_calmarg and opts._noloop_time_interp == 'nearest' else 'loop'), cal_log_weights=calibration_log_weights, time_interp=opts._noloop_time_interp) # non-distmarg: default-helper fused kernel (cal_distmarg=None) From bc53a8674a192f2b48dc580a9b69919b86fa15dc Mon Sep 17 00:00:00 2001 From: Richard O'Shaughnessy Date: Thu, 9 Jul 2026 05:11:27 -0700 Subject: [PATCH 30/45] demo/rift/slowrot_gpu_validate: Condor GPU job to validate the xpy=cupy rotation likelihood Runs test_slowrot_gpu.py (xpy=cupy vs xpy=np, <1e-8) on a real GPU inside the RIFT container with the branch code transferred (no cupy/CUDA in the dev sandbox). make submit / make show; env-driven (SINGULARITY_RIFT_IMAGE, RIFT_REQUIRE_GPUS, LIGO_ACCOUNTING/USER_NAME). See BREADCRUMB.md. 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a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/.gitignore b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/.gitignore new file mode 100644 index 000000000..595b0bab3 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/.gitignore @@ -0,0 +1,5 @@ +rift_code.tar.gz +_code/ +gpu_test.out +gpu_test.err +gpu_test.log diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/BREADCRUMB.md b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/BREADCRUMB.md new file mode 100644 index 000000000..58b9a11d8 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/BREADCRUMB.md @@ -0,0 +1,60 @@ +# GPU rotation validation — breadcrumb / resume guide + +**Goal:** validate the GPU (`xpy=cupy`) rotation likelihood on real hardware. It was implemented +and CPU-verified, but this sandbox has **no cupy/CUDA** (`libcuda.so.1` absent), so the GPU path +is UNTESTED. This kit runs one Condor GPU job to confirm GPU↔CPU parity. + +## What was done (commits on branch `rift_slowrot`) +- `d038f582` — GPU support for the rotation NoLoop (`factored_likelihood_with_rotation.py`): + term1 reuses the baseline fused `Q_inner_product_{cubic,}_cupy` kernel **per elementary + template** (A=conj(Ylm)) → no `(n_ext,npts,n_lms)` temporary, same GPU memory footprint as the + baseline. term2 = small `|a_list|²` einsums. Antenna/Ylm/delay stay on host; only Q banks + U/V + move to device. ILE (`integrate_likelihood_extrinsic_batchmode`): `--rotation-slow` now allowed + with `--gpu` (needs n_cal=1, no glitch/cal marg); precompute `cupy.asarray`'s rho/U/V; the GPU + `likelihood_function` (~line 2239) gained the rotation branch. +- `af68d7ec` — earlier: per-detector `--freqresponse-arm-length`, `--limit-*` zoom-box. +- CPU path is bit-identical: `test_slowrot_noloop` still 3.638e-12. +- **The validation test:** `RIFT/likelihood/test_slowrot_gpu.py` — runs the rotation NoLoop with + `xpy=np` vs `xpy=cupy` on the same packed data (nearest + cubic), asserts `max|diff| < 1e-8`. + Auto-SKIPS without a GPU. + +## To validate on a GPU host (RESUME HERE) +``` +source ~/setup_generic_env_vars.sh # sets SINGULARITY_RIFT_IMAGE, RIFT_REQUIRE_GPUS, + # LIGO_ACCOUNTING, LIGO_USER_NAME +cd # .../Code/demo/rift/slowrot_gpu_validate +make submit # tars the branch code + condor_submit gpu_test.sub +# ... wait for the 1 GPU job ... +make show # prints gpu_test.out +``` +**PASS criteria** (`gpu_test.out`): the cupy/device line prints (real GPU + CUDA), then +`(GPU) rotation NoLoop xpy=cupy vs xpy=np, interp=nearest/cubic : max|diff| = <~1e-9`, and the CPU +`ALL SLOWROT NOLOOP CHECKS PASSED`. + +## How it runs +- `gpu_test.sub`: `+SingularityImage=$ENV(SINGULARITY_RIFT_IMAGE)` (cvmfs RIFT container, has + cupy/CUDA), `request_gpus=1`, `Requirements = HAS_SINGULARITY && $ENV(RIFT_REQUIRE_GPUS)` + (the pool's capability band, default `(Capability>=6.0)&&(<=9.0)` = CUDA-11.8 container band), + accounting from `$ENV`. Transfers `rift_code.tar.gz` (the branch — production image predates it). +- `run_gpu_test.sh`: unpacks the tarball → `RIFT_CODE`, PYTHONPATH, prints GPU info, runs + `test_slowrot_gpu.py` + the CPU sanity test. + +## Likely gotchas / if it fails +- **cupy import fails** in the container → the image's CUDA doesn't match the matched GPU. Tighten + `RIFT_REQUIRE_GPUS` to the container's band (see setup_generic_env_vars.sh comments re Blackwell / + cc≤9.0 for the CUDA-11.8 image), or use a CUDA-12.8 image if matching Blackwell. +- **`optimized_gpu_tools.simps` / `Q_inner_product_cubic_cupy` errors** → those are the reused + baseline GPU kernels; a failure there is a real bug in the GPU rotation path — check dtypes + (`ifirst` int32, `frac` float64, Q contiguous complex128) in + `factored_likelihood_with_rotation.py` term1 GPU branch. +- **NUMBA_CACHE_DIR**: set to `.` (job scratch) via the sub's `environment`. + +## After it passes +- End-to-end GPU ILE run: same as the CPU rotation ILE but add `--gpu` (keep `--vectorized`, + n_cal=1). Compare a short head-to-head vs the CPU run (should match to sampler noise). +- **Follow-ups not yet done:** (1) Path D (freqresponse) GPU port — identical fused-kernel pattern; + (2) a dedicated benchmark container (see `~/rift_cit_build_container_family/` build kit in memory) + if you want a self-contained image for end-users; (3) `create_event_parameter_pipeline`-based + standard-pipeline example (like `.travis/ILE-GPU-Paper`). +- The finite-size sky-loc DAG (`~/RIFT_roboto_paper/analyses/slowrot_finite-size/3g/`) is a + SEPARATE, already-launched run; `make post` there builds the figure once it finishes. diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/Makefile b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/Makefile new file mode 100644 index 000000000..fce1a3b47 --- /dev/null +++ b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/Makefile @@ -0,0 +1,33 @@ +# Validate the GPU (xpy=cupy) rotation likelihood on a real GPU via one Condor job. +# make submit tar the branch code + condor_submit gpu_test.sub (source your env first!) +# make local run the wrapper locally (validates plumbing; SKIPS the GPU test if no cupy) +# make show print the job stdout once it finishes +# make clean +# Requires (from ~/setup_generic_env_vars.sh, sourced): SINGULARITY_RIFT_IMAGE, RIFT_REQUIRE_GPUS, +# LIGO_ACCOUNTING, LIGO_USER_NAME. +RIFT_CODE ?= /home/richard.oshaughnessy/RIFT_slowrot/MonteCarloMarginalizeCode/Code +PYTHON ?= python3 +CODE_PARENT := $(dir $(RIFT_CODE:/=)) + +.PHONY: tarball submit local show clean +tarball: + # write OUTSIDE the Code tree (tar can't read a file it is growing), then move it in; + # exclude this kit's own scratch so a stale tarball is never re-packed. + tar --exclude='*/slowrot_gpu_validate/rift_code.tar.gz' --exclude='*/slowrot_gpu_validate/_code' \ + --exclude='*/slowrot_gpu_validate/gpu_test.*' \ + -czf $(CODE_PARENT)/.rift_code.tar.gz -C $(CODE_PARENT) $(notdir $(RIFT_CODE:/=)) + mv $(CODE_PARENT)/.rift_code.tar.gz rift_code.tar.gz + @echo "tarball: $$(du -h rift_code.tar.gz | cut -f1)" + +submit: tarball + @test -n "$$SINGULARITY_RIFT_IMAGE" || (echo 'ERROR: source setup_generic_env_vars.sh first (SINGULARITY_RIFT_IMAGE unset)'; exit 1) + condor_submit gpu_test.sub + +local: + RIFT_CODE=$(RIFT_CODE) PYTHON=$(PYTHON) ./run_gpu_test.sh + +show: + @echo '--- gpu_test.out ---'; cat gpu_test.out 2>/dev/null; echo '--- gpu_test.err (tail) ---'; tail -20 gpu_test.err 2>/dev/null + +clean: + rm -rf _code rift_code.tar.gz gpu_test.out gpu_test.err gpu_test.log diff --git a/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/SWSH_eaf4b2346e80f2b224fe0ada9f9d1f01752abcc5/__init__._SWSH-90.py38.1.nbc b/MonteCarloMarginalizeCode/Code/demo/rift/slowrot_gpu_validate/SWSH_eaf4b2346e80f2b224fe0ada9f9d1f01752abcc5/__init__._SWSH-90.py38.1.nbc new file mode 100644 index 0000000000000000000000000000000000000000..d1f4c1c186a6add2d87124597b85713f56efc93d GIT binary patch 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this branch). +set -e +PYTHON="${PYTHON:-python3}" +if [ -f rift_code.tar.gz ]; then + mkdir -p _code && tar xzf rift_code.tar.gz -C _code + export RIFT_CODE="$PWD/_code/Code" +fi +: "${RIFT_CODE:?set RIFT_CODE or provide rift_code.tar.gz}" +export PYTHONPATH="$RIFT_CODE:$PYTHONPATH" +export NUMBA_CACHE_DIR="${NUMBA_CACHE_DIR:-$PWD}" + +echo "=== node / GPU / cupy ===" +hostname +$PYTHON - <<'PY' || echo "!!! cupy import FAILED (no GPU visible or CUDA/container mismatch)" +import cupy +p = cupy.cuda.runtime.getDeviceProperties(0) +print("cupy", cupy.__version__, "| device", p['name'].decode(), + "| ccap %d.%d" % (p['major'], p['minor']), + "| CUDA runtime", cupy.cuda.runtime.runtimeGetVersion()) +PY + +echo "=== GPU<->CPU rotation consistency (the actual validation) ===" +$PYTHON "$RIFT_CODE/RIFT/likelihood/test_slowrot_gpu.py" + +echo "=== CPU rotation sanity (baseline, must still pass) ===" +$PYTHON "$RIFT_CODE/RIFT/likelihood/test_slowrot_noloop.py" 2>&1 | grep -E 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