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.
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
"""Tests for exp_data.fitters."""
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import numpy as np
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from exp_data.fitters import GaussianFitter, PeakFitter
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class TestGaussianFitter:
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"""Test Gaussian peak fitting."""
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def _make_synthetic_peak(
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self,
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center=50.0,
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sigma=2.0,
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amplitude=100.0,
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background=10.0,
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n_points=500,
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energy_range=(0, 100),
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):
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"""Create synthetic Gaussian peak with noise."""
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energies = np.linspace(energy_range[0], energy_range[1], n_points)
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counts = (
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amplitude * np.exp(-0.5 * ((energies - center) / sigma) ** 2) + background
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)
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counts += np.random.RandomState(42).normal(
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0, np.sqrt(np.maximum(counts, 1)), n_points
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)
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return energies, counts
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def test_fit_returns_valid_result(self):
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energies, counts = self._make_synthetic_peak()
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fitter = GaussianFitter(energies, counts)
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result = fitter.fit()
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assert result.success
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assert result.chi2 > 0
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assert result.n_dof > 0
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assert result.covariance.shape == (4, 4)
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def test_fit_recovers_parameters(self):
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center, sigma, amplitude, background = 50.0, 2.0, 100.0, 10.0
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energies, counts = self._make_synthetic_peak(
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center=center, sigma=sigma, amplitude=amplitude, background=background
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)
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fitter = GaussianFitter(energies, counts)
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result = fitter.fit()
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np.testing.assert_allclose(result.center_keV, center, rtol=0.05)
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np.testing.assert_allclose(result.sigma_keV, sigma, rtol=0.1)
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np.testing.assert_allclose(result.amplitude, amplitude, rtol=0.15)
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def test_resolution_property(self):
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energies, counts = self._make_synthetic_peak(sigma=2.0)
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fitter = GaussianFitter(energies, counts)
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result = fitter.fit()
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expected_fwhm = 2.35482 * 2.0
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np.testing.assert_allclose(result.resolution(), expected_fwhm, rtol=0.05)
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def test_resolution_fraction(self):
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energies, counts = self._make_synthetic_peak(center=50.0, sigma=2.0)
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fitter = GaussianFitter(energies, counts)
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result = fitter.fit()
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expected_frac = (2.35482 * 2.0) / 50.0
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np.testing.assert_allclose(
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result.resolution_fraction(), expected_frac, rtol=0.05
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)
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def test_auto_guess(self):
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energies, counts = self._make_synthetic_peak()
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fitter = GaussianFitter(energies, counts)
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result = fitter.fit()
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assert result.success
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class TestPeakFitter:
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"""Test multi-peak detection and fitting."""
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def _make_multi_peak(self):
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"""Create spectrum with two peaks."""
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energies = np.linspace(0, 100, 1000)
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counts = np.zeros_like(energies)
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counts += 50 * np.exp(-0.5 * ((energies - 20) / 2) ** 2)
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counts += 80 * np.exp(-0.5 * ((energies - 70) / 3) ** 2)
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counts += 5 # constant background
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counts += np.random.RandomState(42).normal(
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0, np.sqrt(np.maximum(counts, 1)), len(energies)
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)
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return energies, counts
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def test_find_peaks(self):
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energies, counts = self._make_multi_peak()
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fitter = PeakFitter(energies, counts)
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peaks = fitter.find_peaks(min_height=10.0, min_distance_keV=10.0)
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assert len(peaks) >= 1 # At least one peak should be found
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def test_fit_peaks_with_known_energies(self):
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energies, counts = self._make_multi_peak()
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fitter = PeakFitter(energies, counts)
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results = fitter.fit_peaks(known_energies=np.array([20.0, 70.0]))
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assert len(results) >= 1
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for r in results:
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assert r.success
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assert r.n_dof > 0
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def test_fit_peaks_auto_detect(self):
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energies, counts = self._make_multi_peak()
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fitter = PeakFitter(energies, counts)
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results = fitter.fit_peaks()
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assert len(results) >= 1
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