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.
101 lines
2.7 KiB
Python
101 lines
2.7 KiB
Python
"""Tests for fitter.result."""
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import numpy as np
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from fitter.result import AnalysisResult, FitSummary
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class MockFitResult:
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"""Mock FitResult for testing AnalysisResult."""
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def __init__(self):
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self.success = True
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self.chi2 = 12.5
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self.n_points = 100
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self.n_free = 3
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self.parameters = np.array([5.0, 0.1, 2.0])
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self.covariance = np.array(
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[
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[0.01, 0.0, 0.0],
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[0.0, 0.001, 0.0],
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[0.0, 0.0, 0.04],
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]
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)
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self.model_values = np.ones(100)
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self.residuals = np.zeros(100)
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self.message = "Optimization terminated successfully."
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@property
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def chi2_per_dof(self):
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return self.chi2 / (self.n_points - self.n_free)
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@property
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def p_value(self):
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from scipy.stats import chi2
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return 1.0 - chi2.cdf(self.chi2, self.n_points - self.n_free)
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@property
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def parameters_with_errors(self):
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errors = np.sqrt(np.diag(self.covariance))
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return {
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f"p{i}": (float(self.parameters[i]), float(errors[i]))
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for i in range(len(self.parameters))
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}
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class TestFitSummary:
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"""Test FitSummary."""
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def test_str(self):
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summary = FitSummary(
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nuclide="Tc99",
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converged=True,
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chi2_per_dof=1.234,
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p_value=0.0567,
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parameters={"norm": (5.0, 0.1), "bg": (0.1, 0.01)},
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)
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text = str(summary)
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assert "Tc99" in text
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assert "1.234" in text
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assert "5.0" in text
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assert "Converged: True" in text
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class TestAnalysisResult:
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"""Test AnalysisResult."""
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def test_summary(self):
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mock = MockFitResult()
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result = AnalysisResult(
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fit_result=mock,
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nuclide="Tc99",
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metadata={"param_names": ["norm", "bg", "endpoint"]},
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)
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summary = result.summary
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assert summary.nuclide == "Tc99"
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assert summary.converged is True
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assert summary.chi2_per_dof > 0
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def test_to_dict(self):
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mock = MockFitResult()
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result = AnalysisResult(
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fit_result=mock,
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nuclide="Tc99",
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gv_eff=0.97,
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ga_eff=0.57,
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endpoint_keV=294.0,
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)
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d = result.to_dict()
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assert d["nuclide"] == "Tc99"
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assert d["gv_eff"] == 0.97
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assert d["ga_eff"] == 0.57
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assert d["endpoint_keV"] == 294.0
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assert d["converged"] is True
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def test_empty_result(self):
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mock = MockFitResult()
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result = AnalysisResult(fit_result=mock)
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summary = result.summary
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assert summary.nuclide == ""
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