unzhakov
38b44b56e6
Separate monolithic beta_spectrum package into:
beta_spectrum — pure theory (components, spectrum, CLI, visualize)
exp_data — experimental data handling (spectrum, calibration,
fitters, corrections)
fitter — analysis framework (model, fit_engine, extractor,
result)
beta_spectrum.visualize — plotting (BetaSpectrumAnalyzer)
Phase 1 — exp_data module:
ExpSpectrum dataclass with metadata, normalization, serialization
EnergyCalibrator (linear/quadratic calibration)
GaussianFitter + PeakFitter for calibration sources
DeadTimeCorrection, PileUpCorrection, BackgroundSubtractor
43 new tests, ruff + mypy clean
Phase 2 — fitter module:
SpectrumModel wraps BetaSpectrum + DetectorResponse with convolution
SpectrumFitter with χ² minimization, covariance, profile likelihood,
confidence intervals, robust loss functions
CWExtractor + GVAExtractor for C(W) and g_V/g_A extraction
AnalysisResult + FitSummary for physics-specific results
CurveFitter general-purpose callable model fitter
34 new tests, ruff + mypy clean
Phase 3 — clean up beta_spectrum:
Remove cw_extractor.py and fitter.py (moved to fitter/)
Clean __init__.py to export only theory classes
Add backward-compat methods to CWExtractor
Fix BetaSpectrumAnalyzer._get_branch_spectrum returning BetaSpectrum
objects instead of numpy arrays (plot fix)
Phase 4 — package structure:
Auto-discovered by setuptools (4 packages)
All imports updated across 26 source files
Black formatting applied
322 tests pass (262 original + 60 new)
ruff + mypy clean on all source files
Bug fixes:
- Fix multi-branch spectrum plotting: universal component keys
(Fermi, Screening, etc.) don't have per-branch prefix; fixed
fallback lookup.
- Fix negative phase space beyond endpoint: BetaSpectrum returns
negative values for W > W0; clamp branch spectra to zero.
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
"""Tests for fitter.model."""
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import numpy as np
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import pytest
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from beta_spectrum import BetaSpectrum, SpectrumConfig
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from fitter.model import SpectrumModel
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class TestSpectrumModel:
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"""Test SpectrumModel."""
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def _make_model(self, with_detector: bool = False):
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"""Create a test SpectrumModel."""
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config = SpectrumConfig(
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Z_parent=43,
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Z_daughter=44,
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A_number=99,
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endpoint_MeV=0.294,
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)
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spectrum = BetaSpectrum.from_config(config)
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detector = None
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if with_detector:
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detector = BetaSpectrum.create_detector_from_config(
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SpectrumConfig(
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Z_parent=43,
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Z_daughter=44,
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A_number=99,
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endpoint_MeV=0.294,
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detector_model="gaussian",
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detector_sigma_a_keV=1.0,
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detector_n_channels=100,
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)
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)
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model = SpectrumModel(spectrum, detector)
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return model, config
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def test_evaluate_unconvolved(self):
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model, config = self._make_model()
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energies_keV = np.linspace(0.01, 0.29, 50)
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values = model.evaluate_unconvolved(energies_keV)
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assert len(values) == 50
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assert np.all(np.isfinite(values))
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assert np.all(values >= 0)
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def test_evaluate_no_detector(self):
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model, config = self._make_model()
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energies_keV = np.linspace(0.01, 0.29, 50)
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values = model.evaluate(energies_keV, normalization=1.0, background=0.0)
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assert len(values) == 50
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assert np.all(np.isfinite(values))
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def test_evaluate_with_normalization(self):
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model, config = self._make_model()
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energies_keV = np.linspace(0.01, 0.29, 50)
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values1 = model.evaluate(energies_keV, normalization=1.0, background=0.0)
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values2 = model.evaluate(energies_keV, normalization=2.0, background=0.0)
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np.testing.assert_allclose(values2, values1 * 2.0)
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def test_evaluate_with_background(self):
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model, config = self._make_model()
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energies_keV = np.linspace(0.01, 0.29, 50)
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values1 = model.evaluate(energies_keV, normalization=1.0, background=0.0)
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values2 = model.evaluate(energies_keV, normalization=1.0, background=5.0)
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np.testing.assert_allclose(values2, values1 + 5.0)
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def test_convolve_requires_detector(self):
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model, _ = self._make_model()
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energies_keV = np.linspace(0.01, 0.29, 50)
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with pytest.raises(RuntimeError):
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model.convolve(energies_keV)
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def test_convolve_with_detector(self):
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model, _ = self._make_model(with_detector=True)
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energies_keV = model.detector_response.channel_energies
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values = model.convolve(energies_keV)
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assert len(values) == model.detector_response.n_channels
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assert np.all(np.isfinite(values))
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# Convolution can produce small negative values due to numerical effects
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# — check that most values are non-negative
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assert np.sum(values >= 0) > len(values) * 0.9
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def test_default_params(self):
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from exp_data.spectrum import ExpSpectrum
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energies = np.linspace(0, 100, 200)
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counts = 50 * np.exp(-0.5 * ((energies - 50) / 5) ** 2) + 10
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exp_spec = ExpSpectrum(energies=energies, counts=counts)
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norm, bg = SpectrumModel.default_params(exp_spec)
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assert norm > 0
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assert bg >= 0
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def test_default_bounds(self):
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lower, upper = SpectrumModel.default_bounds(100)
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assert len(lower) == 2
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assert len(upper) == 2
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assert lower[0] < upper[0]
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assert lower[1] < upper[1]
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class TestSpectrumModelKeVToW:
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"""Test energy unit conversion in SpectrumModel."""
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def test_keV_to_W(self):
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model = SpectrumModel(None) # type: ignore
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# 0 keV → W = 1.0
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W = model._keV_to_W(np.array([0.0]))
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assert np.isclose(W[0], 1.0)
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# 511 keV → W = 2.0
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W = model._keV_to_W(np.array([510.998950]))
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assert np.isclose(W[0], 2.0)
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# 294 keV → W ≈ 1.575
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W = model._keV_to_W(np.array([294.0]))
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assert np.isclose(W[0], 294.0 / 510.998950 + 1.0)
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