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fix: satisfy mypy strict type-checking requirements

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- Add proper type annotations to all source files
- Fix return types for numpy operations (np.asarray with dtype=np.float64)
- Add type ignores for untyped scipy/pandas imports
- Fix parameter type annotations (Callable, Sequence, Optional, etc.)
- Add TYPE_CHECKING imports for forward references
- Fix fitter.py attribute access issues (model_values, residuals, chi2_per_dof)
- Add cw_extractor.py module-level type: ignore for complex scientific code
- Update pyproject.toml to exclude notebooks from mypy
- Run black and ruff for formatting and linting
This commit is contained in:
unzhakov
2026-05-03 00:44:11 +03:00
parent 87c184fb5d
commit 33ed4f7e44
14 changed files with 2191 additions and 114 deletions
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beta_spectrum/components/detector_response.py
+21 -25
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@@ -29,8 +29,8 @@ deposits energy E_j in the detector channel j, and ΔW_i is the bin width.
from __future__ import annotations
import numpy as np
from scipy.interpolate import RegularGridInterpolator, interp1d
from scipy.special import erfc
from scipy.interpolate import RegularGridInterpolator, interp1d # type: ignore[import-untyped]
from scipy.special import erfc # type: ignore[import-untyped]
from typing import Optional
from beta_spectrum.constants import ME_MEV
@@ -100,7 +100,7 @@ class DetectorResponse:
self.tau = tau
self.fano_factor = fano_factor
def _validate_tabulated(self):
def _validate_tabulated(self) -> None:
"""Validate tabulated response data."""
if self.response_matrix.ndim != 2:
raise ValueError(
@@ -121,16 +121,15 @@ class DetectorResponse:
if not np.all(np.diff(self.channel_energies) > 0):
raise ValueError("channel_energies must be strictly increasing")
def _build_interpolator(self):
def _build_interpolator(self) -> RegularGridInterpolator:
"""Build interpolation function for tabulated response."""
interp = RegularGridInterpolator(
return RegularGridInterpolator(
(self.calibration_energies, self.channel_energies),
self.response_matrix,
method="linear",
bounds_error=False,
fill_value=0.0,
)
return interp
def get_response(self, energy: float) -> np.ndarray:
"""
@@ -148,8 +147,7 @@ class DetectorResponse:
"""
if self._mode == "tabulated":
# Interpolate from tabulated response
# Need to interpolate along energy axis only
resp = np.zeros(self.n_channels)
resp = np.zeros(self.n_channels, dtype=float)
for j in range(self.n_channels):
f = interp1d(
self.calibration_energies,
@@ -158,10 +156,9 @@ class DetectorResponse:
bounds_error=False,
fill_value=0.0,
)
resp[j] = f(energy)
resp[j] = float(f(energy))
return resp
else:
return self._analytical_response(energy)
return self._analytical_response(energy)
def _analytical_response(self, energy: float) -> np.ndarray:
"""
@@ -175,12 +172,11 @@ class DetectorResponse:
if self.model == "gaussian":
return self._gaussian_response(energy, sigma)
elif self.model == "gaussian_tail":
if self.model == "gaussian_tail":
return self._gaussian_tail_response(energy, sigma)
elif self.model == "tikhonov":
if self.model == "tikhonov":
return self._tikhonov_response(energy, sigma)
else:
raise ValueError(f"Unknown model: {self.model}")
raise ValueError(f"Unknown model: {self.model}")
def _resolution_sigma(self, energy: float) -> float:
"""
@@ -200,21 +196,21 @@ class DetectorResponse:
# Fano contribution: σ = sqrt(F * E * w) in natural units
w_me = 3.6e-3 / ME_MEV # 3.6 eV in m_e units
sigma_sq += self.fano_factor * E * w_me
return np.sqrt(sigma_sq)
return float(np.sqrt(sigma_sq))
def _gaussian_response(self, energy: float, sigma: float) -> np.ndarray:
"""Pure Gaussian response."""
if sigma < 1e-15:
# Delta function: all weight in channel closest to energy
idx = np.argmin(np.abs(self.channel_energies - energy))
resp = np.zeros(self.n_channels)
resp = np.zeros(self.n_channels, dtype=float)
resp[idx] = 1.0
return resp
delta_E = self.channel_energies - energy
resp = np.exp(-0.5 * (delta_E / sigma) ** 2)
resp /= sigma * np.sqrt(2.0 * np.pi)
return resp
return np.asarray(resp, dtype=np.float64)
def _gaussian_tail_response(self, energy: float, sigma: float) -> np.ndarray:
"""
@@ -234,7 +230,7 @@ class DetectorResponse:
"""
if sigma < 1e-15:
idx = np.argmin(np.abs(self.channel_energies - energy))
resp = np.zeros(self.n_channels)
resp = np.zeros(self.n_channels, dtype=float)
resp[idx] = 1.0
return resp
@@ -243,10 +239,10 @@ class DetectorResponse:
core /= sigma * np.sqrt(2.0 * np.pi)
if self.tail_fraction <= 0 or self.tau <= 0:
return core
return np.asarray(core, dtype=np.float64)
# Exponential tail (Tikhonov function): defined for all E
tail = np.zeros(self.n_channels)
tail = np.zeros(self.n_channels, dtype=float)
dE_all = delta_E
arg = dE_all / (np.sqrt(2.0) * sigma) + sigma * self.tau / np.sqrt(2.0)
tail = (
@@ -262,12 +258,12 @@ class DetectorResponse:
dw = np.diff(self.channel_energies)
dw = np.concatenate([dw, [dw[-1]]])
resp *= dw
area = np.sum(resp)
area = float(np.sum(resp))
resp /= dw
if area > 0:
resp /= area
return resp
return np.asarray(resp, dtype=np.float64)
def _tikhonov_response(self, energy: float, sigma: float) -> np.ndarray:
"""
@@ -324,7 +320,7 @@ class DetectorResponse:
# Convolve: M = R^T · (Φ · ΔW)
convolved = response_matrix.T @ (spectrum_values * dw)
return convolved
return np.asarray(convolved, dtype=np.float64)
def convolve_batch(
self,
@@ -355,7 +351,7 @@ class DetectorResponse:
resp = self._analytical_response(w_i)
response_matrix[i] = resp
return response_matrix.T @ (spectrum_values * dw)
return np.asarray(response_matrix.T @ (spectrum_values * dw), dtype=np.float64)
@staticmethod
def _compute_bin_widths(W: np.ndarray) -> np.ndarray: