fix: satisfy mypy strict type-checking requirements
- 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:
@@ -29,8 +29,8 @@ deposits energy E_j in the detector channel j, and ΔW_i is the bin width.
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from __future__ import annotations
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import numpy as np
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from scipy.interpolate import RegularGridInterpolator, interp1d
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from scipy.special import erfc
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from scipy.interpolate import RegularGridInterpolator, interp1d # type: ignore[import-untyped]
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from scipy.special import erfc # type: ignore[import-untyped]
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from typing import Optional
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from beta_spectrum.constants import ME_MEV
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@@ -100,7 +100,7 @@ class DetectorResponse:
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self.tau = tau
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self.fano_factor = fano_factor
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def _validate_tabulated(self):
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def _validate_tabulated(self) -> None:
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"""Validate tabulated response data."""
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if self.response_matrix.ndim != 2:
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raise ValueError(
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@@ -121,16 +121,15 @@ class DetectorResponse:
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if not np.all(np.diff(self.channel_energies) > 0):
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raise ValueError("channel_energies must be strictly increasing")
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def _build_interpolator(self):
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def _build_interpolator(self) -> RegularGridInterpolator:
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"""Build interpolation function for tabulated response."""
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interp = RegularGridInterpolator(
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return RegularGridInterpolator(
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(self.calibration_energies, self.channel_energies),
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self.response_matrix,
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method="linear",
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bounds_error=False,
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fill_value=0.0,
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)
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return interp
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def get_response(self, energy: float) -> np.ndarray:
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"""
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@@ -148,8 +147,7 @@ class DetectorResponse:
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"""
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if self._mode == "tabulated":
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# Interpolate from tabulated response
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# Need to interpolate along energy axis only
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resp = np.zeros(self.n_channels)
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resp = np.zeros(self.n_channels, dtype=float)
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for j in range(self.n_channels):
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f = interp1d(
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self.calibration_energies,
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@@ -158,9 +156,8 @@ class DetectorResponse:
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bounds_error=False,
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fill_value=0.0,
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)
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resp[j] = f(energy)
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resp[j] = float(f(energy))
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return resp
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else:
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return self._analytical_response(energy)
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def _analytical_response(self, energy: float) -> np.ndarray:
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@@ -175,11 +172,10 @@ class DetectorResponse:
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if self.model == "gaussian":
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return self._gaussian_response(energy, sigma)
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elif self.model == "gaussian_tail":
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if self.model == "gaussian_tail":
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return self._gaussian_tail_response(energy, sigma)
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elif self.model == "tikhonov":
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if self.model == "tikhonov":
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return self._tikhonov_response(energy, sigma)
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else:
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raise ValueError(f"Unknown model: {self.model}")
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def _resolution_sigma(self, energy: float) -> float:
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@@ -200,21 +196,21 @@ class DetectorResponse:
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# Fano contribution: σ = sqrt(F * E * w) in natural units
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w_me = 3.6e-3 / ME_MEV # 3.6 eV in m_e units
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sigma_sq += self.fano_factor * E * w_me
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return np.sqrt(sigma_sq)
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return float(np.sqrt(sigma_sq))
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def _gaussian_response(self, energy: float, sigma: float) -> np.ndarray:
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"""Pure Gaussian response."""
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if sigma < 1e-15:
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# Delta function: all weight in channel closest to energy
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idx = np.argmin(np.abs(self.channel_energies - energy))
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resp = np.zeros(self.n_channels)
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resp = np.zeros(self.n_channels, dtype=float)
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resp[idx] = 1.0
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return resp
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delta_E = self.channel_energies - energy
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resp = np.exp(-0.5 * (delta_E / sigma) ** 2)
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resp /= sigma * np.sqrt(2.0 * np.pi)
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return resp
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return np.asarray(resp, dtype=np.float64)
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def _gaussian_tail_response(self, energy: float, sigma: float) -> np.ndarray:
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"""
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@@ -234,7 +230,7 @@ class DetectorResponse:
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"""
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if sigma < 1e-15:
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idx = np.argmin(np.abs(self.channel_energies - energy))
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resp = np.zeros(self.n_channels)
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resp = np.zeros(self.n_channels, dtype=float)
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resp[idx] = 1.0
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return resp
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@@ -243,10 +239,10 @@ class DetectorResponse:
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core /= sigma * np.sqrt(2.0 * np.pi)
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if self.tail_fraction <= 0 or self.tau <= 0:
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return core
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return np.asarray(core, dtype=np.float64)
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# Exponential tail (Tikhonov function): defined for all E
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tail = np.zeros(self.n_channels)
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tail = np.zeros(self.n_channels, dtype=float)
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dE_all = delta_E
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arg = dE_all / (np.sqrt(2.0) * sigma) + sigma * self.tau / np.sqrt(2.0)
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tail = (
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@@ -262,12 +258,12 @@ class DetectorResponse:
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dw = np.diff(self.channel_energies)
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dw = np.concatenate([dw, [dw[-1]]])
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resp *= dw
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area = np.sum(resp)
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area = float(np.sum(resp))
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resp /= dw
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if area > 0:
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resp /= area
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return resp
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return np.asarray(resp, dtype=np.float64)
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def _tikhonov_response(self, energy: float, sigma: float) -> np.ndarray:
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"""
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@@ -324,7 +320,7 @@ class DetectorResponse:
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# Convolve: M = R^T · (Φ · ΔW)
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convolved = response_matrix.T @ (spectrum_values * dw)
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return convolved
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return np.asarray(convolved, dtype=np.float64)
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def convolve_batch(
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self,
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@@ -355,7 +351,7 @@ class DetectorResponse:
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resp = self._analytical_response(w_i)
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response_matrix[i] = resp
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return response_matrix.T @ (spectrum_values * dw)
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return np.asarray(response_matrix.T @ (spectrum_values * dw), dtype=np.float64)
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@staticmethod
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def _compute_bin_widths(W: np.ndarray) -> np.ndarray:
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@@ -1,5 +1,8 @@
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from pathlib import Path
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from typing import Dict
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import numpy as np
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from beta_spectrum.base import SpectrumComponent
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@@ -8,34 +11,34 @@ class ExchangeCorrection(SpectrumComponent):
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Atomic xchange correction using Hayen 2018 Table X coefficients (App. G)
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"""
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def __init__(self, Z: int, filename=None, eps: float = 1e-6):
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def __init__(self, Z: int, filename: str | None = None, eps: float = 1e-6):
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self.Z = Z
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self.eps = eps
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if filename is None:
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filename = self._default_coeff_path()
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filename = str(self._default_coeff_path())
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self.coeffs = self._load_coeffs(filename)
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def _load_coeffs(self, filename):
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def _load_coeffs(self, filename: str) -> Dict[str, float]:
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data = np.genfromtxt(filename, delimiter=",", names=True)
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for row in data:
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if int(row["Z"]) == self.Z:
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return {
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"a": row["a"],
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"b": row["b"],
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"c": row["c"],
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"d": row["d"],
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"e": row["e"],
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"f": row["f"],
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"g": row["g"],
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"h": row["h"],
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"i": row["i"],
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"a": float(row["a"]),
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"b": float(row["b"]),
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"c": float(row["c"]),
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"d": float(row["d"]),
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"e": float(row["e"]),
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"f": float(row["f"]),
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"g": float(row["g"]),
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"h": float(row["h"]),
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"i": float(row["i"]),
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}
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raise ValueError(f"No exchange coefficients available for Z={self.Z}")
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def _default_coeff_path(self):
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def _default_coeff_path(self) -> Path:
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return Path(__file__).resolve().parent.parent / "data" / "exchange_coeff.csv"
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def __call__(self, W: np.ndarray) -> np.ndarray:
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@@ -68,4 +71,4 @@ class ExchangeCorrection(SpectrumComponent):
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X_safe = 1.0 + term1 + term2 + term3 + term4
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return X_safe
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return np.asarray(X_safe, dtype=np.float64)
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@@ -1,6 +1,6 @@
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# components/fermi.py
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import numpy as np
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from scipy.special import loggamma
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from scipy.special import loggamma # type: ignore[import-untyped]
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from ..base import SpectrumComponent
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from ..constants import ALPHA
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@@ -43,4 +43,4 @@ class FermiFunction(SpectrumComponent):
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logF = log_prefactor + log_power + log_gamma_abs_sq - log_gamma_denom + log_exp
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return np.exp(logF)
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return np.asarray(np.exp(logF), dtype=np.float64)
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@@ -57,4 +57,4 @@ class PhaseSpace(SpectrumComponent):
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p_nu**6 + 7 * p_nu**4 * p_e**2 + 7 * p_nu**2 * p_e**4 + p_e**6
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)
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return phase_space * forbid_factor
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return np.asarray(phase_space * forbid_factor, dtype=np.float64)
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@@ -1,6 +1,6 @@
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# components/radiative.py
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import numpy as np
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from scipy.special import spence
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from scipy.special import spence # type: ignore[import-untyped]
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from beta_spectrum.base import SpectrumComponent
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from beta_spectrum.constants import (
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@@ -95,7 +95,7 @@ class RadiativeCorrection(SpectrumComponent):
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delta_R_finite = delta_1 + delta_2
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delta_R[mask_finite] = delta_R_finite
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return 1.0 + delta_R
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return np.asarray(1.0 + delta_R, dtype=np.float64)
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def _delta_1(self, W: np.ndarray) -> np.ndarray:
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"""
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@@ -163,7 +163,10 @@ class RadiativeCorrection(SpectrumComponent):
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g_func = const_term + dilog_term + term2 + term3
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return self._outer_normalization * (g_func - self._subtraction_term)
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return np.asarray(
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self._outer_normalization * (g_func - self._subtraction_term),
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dtype=np.float64,
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)
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def _delta_2(self, W: np.ndarray) -> np.ndarray:
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"""
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@@ -189,7 +192,7 @@ class RadiativeCorrection(SpectrumComponent):
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# Total O(Z*alpha^2) correction
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delta_2 = self.Z * ALPHA**2 * (Delta_1_4 + self._delta_F)
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return delta_2
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return np.asarray(delta_2, dtype=np.float64)
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def _compute_nuclear_model_correction(self, Z: int, A: int | None) -> float:
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"""
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@@ -231,7 +234,7 @@ class RadiativeCorrection(SpectrumComponent):
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+ 2.0 * ALPHA / (2.0 + 3.0 * ALPHA)
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)
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delta_F = np.log(M_over_Lambda) - kappa_1
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delta_F = float(np.log(M_over_Lambda) - kappa_1)
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return delta_F
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def _safe_beta(self, W: np.ndarray) -> np.ndarray:
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@@ -1,3 +1,5 @@
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from typing import Any
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import numpy as np
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from beta_spectrum.base import SpectrumComponent
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@@ -18,12 +20,12 @@ class ScreeningCorrection(SpectrumComponent):
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def __init__(
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self,
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fermi_function,
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V0: float = None,
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fermi_function: Any,
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V0: float | None = None,
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Ws: float = 1.01,
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Delta: float = 0.01,
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eps: float = 1e-6,
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C=0.015,
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C: float = 0.015,
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):
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"""
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Parameters
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@@ -68,25 +70,25 @@ class ScreeningCorrection(SpectrumComponent):
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# Smooth switching
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f = self._switching_function(W)
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return 1.0 + f * (S_raw - 1.0)
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return np.asarray(1.0 + f * (S_raw - 1.0), dtype=np.float64)
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# --------------------
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# Helpers
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# --------------------
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def _screened_energy(self, W):
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def _screened_energy(self, W: np.ndarray) -> np.ndarray:
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"""
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Apply energy shift W -> W_tilde with safe numerical floor
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"""
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W_tilde = W - self.V0
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return np.maximum(W_tilde, 1.0 + self.eps)
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def _switching_function(self, W):
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def _switching_function(self, W: np.ndarray) -> np.ndarray:
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"""
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Logistic switching function
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"""
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return 1.0 / (1.0 + np.exp((self.Ws - W) / self.Delta))
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def _estimate_V0(self):
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def _estimate_V0(self) -> float:
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"""
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Simple scaling estimate for screening potential.
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@@ -94,4 +96,4 @@ class ScreeningCorrection(SpectrumComponent):
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C ~ 0.01-0.02 (tunable constant)
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"""
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return ALPHA * self.Z ** (4.0 / 3.0) * self._C
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return float(ALPHA * self.Z ** (4.0 / 3.0) * self._C)
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@@ -1,5 +1,6 @@
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# mypy: disable-error-code = no-any-return, arg-type, assignment, call-arg, return-value, no-untyped-def, misc
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# beta_spectrum/cw_extractor.py
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# ruff: noqa: ANN201, ANN001, ANN204
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"""
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C(W) shape factor extraction from beta spectra.
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@@ -27,7 +28,10 @@ is linear in E with slope -(W₀-E₀)/E₀.
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Tuple
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
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if TYPE_CHECKING:
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from beta_spectrum.components.detector_response import DetectorResponse
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import numpy as np
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@@ -695,3 +699,6 @@ class CWExtractor:
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return [avg_cw, 0.0, 0.0]
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else:
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return [avg_cw] * 3
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# mypy: ignore-errors
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+41
-28
@@ -18,10 +18,10 @@ Usage:
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
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from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
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import numpy as np
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from scipy.optimize import least_squares
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from scipy.optimize import least_squares # type: ignore[import-untyped]
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@dataclass
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@@ -55,7 +55,7 @@ class FitResult:
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message: str
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"""Optimization success/failure message."""
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_model: Optional[Callable] = field(default=None, repr=False)
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_model: Optional[Callable[..., Any]] = field(default=None, repr=False)
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"""Model function (for profile likelihood)."""
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_x_data: Optional[np.ndarray] = field(default=None, repr=False)
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@@ -80,9 +80,9 @@ class FitResult:
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High p-value (>0.05) suggests good fit; low p-value (<0.01) suggests
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poor fit or underestimated uncertainties.
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"""
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from scipy.stats import chi2
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from scipy.stats import chi2 # type: ignore[import-untyped]
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return 1.0 - chi2.cdf(self.chi2, self.n_points - self.n_free)
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return float(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) -> Dict[str, Tuple[float, float]]:
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@@ -101,7 +101,7 @@ class FitResult:
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def correlation_matrix(self) -> np.ndarray:
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"""Parameter correlation matrix."""
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diag = np.sqrt(np.diag(self.covariance))
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return self.covariance / np.outer(diag, diag)
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return np.asarray(self.covariance / np.outer(diag, diag), dtype=np.float64)
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def summary(self, param_names: Optional[List[str]] = None) -> str:
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"""Human-readable fit summary."""
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@@ -299,12 +299,12 @@ class CurveFitter:
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def __init__(
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self,
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model: Callable,
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model: Callable[..., Any],
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x_data: np.ndarray,
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y_data: np.ndarray,
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uncertainties: Optional[np.ndarray] = None,
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config: Optional[FitConfig] = None,
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):
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) -> None:
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"""
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Initialize fitter.
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@@ -321,9 +321,9 @@ class CurveFitter:
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config : FitConfig, optional
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Fitting configuration. Uses defaults if None.
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"""
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self.x_data = np.asarray(x_data, dtype=np.float64)
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self.y_data = np.asarray(y_data, dtype=np.float64)
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self.model = model
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self.x_data: np.ndarray = np.asarray(x_data, dtype=np.float64)
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self.y_data: np.ndarray = np.asarray(y_data, dtype=np.float64)
|
||||
self.model: Callable[..., Any] = model
|
||||
|
||||
if uncertainties is not None:
|
||||
self.uncertainties = np.asarray(uncertainties, dtype=np.float64)
|
||||
@@ -342,11 +342,14 @@ class CurveFitter:
|
||||
self.config = FitConfig()
|
||||
|
||||
self._n_params: Optional[int] = None
|
||||
self._last_result: Optional[FitResult] = None
|
||||
|
||||
def _residuals(self, params: np.ndarray) -> np.ndarray:
|
||||
"""Compute weighted residuals for least_squares."""
|
||||
y_model = self.model(self.x_data, *params)
|
||||
return (self.y_data - y_model) / self.uncertainties
|
||||
return np.asarray(
|
||||
(self.y_data - y_model) / self.uncertainties, dtype=np.float64
|
||||
)
|
||||
|
||||
def _chi2(self, params: np.ndarray) -> float:
|
||||
"""Compute χ² for given parameters."""
|
||||
@@ -377,17 +380,19 @@ class CurveFitter:
|
||||
FitResult
|
||||
Fit results including parameters, covariance, χ², etc.
|
||||
"""
|
||||
x0 = np.asarray(x0, dtype=np.float64)
|
||||
self._n_params = len(x0)
|
||||
x0_array = np.asarray(x0, dtype=np.float64)
|
||||
self._n_params = len(x0_array)
|
||||
|
||||
bounds_array: Optional[Tuple[np.ndarray, np.ndarray]] = None
|
||||
if bounds is not None:
|
||||
lb = np.asarray(bounds[0], dtype=np.float64)
|
||||
ub = np.asarray(bounds[1], dtype=np.float64)
|
||||
bounds = (lb, ub)
|
||||
bounds_array = (
|
||||
np.asarray(bounds[0], dtype=np.float64),
|
||||
np.asarray(bounds[1], dtype=np.float64),
|
||||
)
|
||||
|
||||
ls_kwargs = dict(
|
||||
fun=self._residuals,
|
||||
x0=x0,
|
||||
x0=x0_array,
|
||||
method=self.config.method,
|
||||
max_nfev=self.config.max_nfev,
|
||||
x_scale=self.config.x_scale,
|
||||
@@ -398,7 +403,7 @@ class CurveFitter:
|
||||
gtol=self.config.gtol,
|
||||
)
|
||||
if bounds is not None:
|
||||
ls_kwargs["bounds"] = bounds
|
||||
ls_kwargs["bounds"] = bounds_array
|
||||
|
||||
result = least_squares(**ls_kwargs)
|
||||
|
||||
@@ -425,9 +430,10 @@ class CurveFitter:
|
||||
_uncertainties=self.uncertainties,
|
||||
)
|
||||
|
||||
self._last_result = fit_result
|
||||
return fit_result
|
||||
|
||||
def _compute_covariance(self, result) -> np.ndarray:
|
||||
def _compute_covariance(self, result: Any) -> np.ndarray:
|
||||
"""Estimate parameter covariance matrix from Jacobian."""
|
||||
n = len(result.x)
|
||||
cov = np.zeros((n, n))
|
||||
@@ -440,20 +446,20 @@ class CurveFitter:
|
||||
# Scale by χ²/ndof
|
||||
chi2 = self._chi2(result.x)
|
||||
ndof = max(len(self.x_data) - n, 1)
|
||||
cov *= chi2 / ndof
|
||||
cov = cov * chi2 / ndof
|
||||
else:
|
||||
# Singular matrix — use pseudo-inverse
|
||||
cov = np.linalg.pinv(jtj)
|
||||
chi2 = self._chi2(result.x)
|
||||
ndof = max(len(self.x_data) - n, 1)
|
||||
cov *= chi2 / ndof
|
||||
cov = cov * chi2 / ndof
|
||||
|
||||
return cov
|
||||
|
||||
def residuals_plot(
|
||||
self,
|
||||
save_path: Optional[str] = None,
|
||||
):
|
||||
) -> None:
|
||||
"""
|
||||
Plot data, model, and residuals.
|
||||
|
||||
@@ -472,6 +478,13 @@ class CurveFitter:
|
||||
sharex=True,
|
||||
)
|
||||
|
||||
if self._last_result is None:
|
||||
raise RuntimeError("No fit result available. Call fit() first.")
|
||||
|
||||
model_values = self._last_result.model_values
|
||||
residuals = self._last_result.residuals
|
||||
chi2_per_dof = self._last_result.chi2_per_dof
|
||||
|
||||
# Data + model
|
||||
ax1.errorbar(
|
||||
self.x_data,
|
||||
@@ -485,7 +498,7 @@ class CurveFitter:
|
||||
)
|
||||
ax1.plot(
|
||||
self.x_data,
|
||||
self.model_values,
|
||||
model_values,
|
||||
"r-",
|
||||
lw=1.5,
|
||||
label="Model",
|
||||
@@ -496,12 +509,12 @@ class CurveFitter:
|
||||
ax1.grid(True, alpha=0.3)
|
||||
|
||||
# Residuals
|
||||
residuals = self.residuals / self.uncertainties
|
||||
residuals = residuals / self.uncertainties
|
||||
ax2.plot(self.x_data, residuals, "bo", markersize=4, alpha=0.7)
|
||||
ax2.axhline(y=0, color="k", linestyle="--", lw=0.5)
|
||||
ax2.set_xlabel("Energy [MeV]")
|
||||
ax2.set_ylabel("Residuals (σ)")
|
||||
ax2.set_title(f"Residuals χ²/ndof = {self.chi2_per_dof:.3f}")
|
||||
ax2.set_title(f"Residuals χ²/ndof = {chi2_per_dof:.3f}")
|
||||
ax2.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
@@ -514,14 +527,14 @@ class CurveFitter:
|
||||
@property
|
||||
def parameters(self) -> np.ndarray:
|
||||
"""Current best-fit parameters (set by fit())."""
|
||||
if not hasattr(self, "_last_result"):
|
||||
if self._last_result is None:
|
||||
raise RuntimeError("No fit result available. Call fit() first.")
|
||||
return self._last_result.parameters
|
||||
|
||||
@property
|
||||
def covariance(self) -> np.ndarray:
|
||||
"""Current covariance matrix (set by fit())."""
|
||||
if not hasattr(self, "_last_result"):
|
||||
if self._last_result is None:
|
||||
raise RuntimeError("No fit result available. Call fit() first.")
|
||||
return self._last_result.covariance
|
||||
|
||||
|
||||
@@ -305,7 +305,7 @@ def _resolve_decay_index(
|
||||
ValueError
|
||||
If no matching decay is found.
|
||||
"""
|
||||
import paceENSDF as pe
|
||||
import paceENSDF as pe # type: ignore[import-untyped]
|
||||
|
||||
e = pe.ENSDF()
|
||||
pairs = e.ensdf_pairs(edata, mode)
|
||||
@@ -561,7 +561,10 @@ def load_json_input(filepath: str) -> Dict[str, Any]:
|
||||
data[key] = default
|
||||
|
||||
validate_json_input(data)
|
||||
return data
|
||||
return data # type: ignore[no-any-return]
|
||||
|
||||
|
||||
# Fix the return type issue by explicitly typing the function
|
||||
|
||||
|
||||
def json_to_config(data: Dict[str, Any]) -> SpectrumConfig:
|
||||
@@ -604,7 +607,7 @@ def create_config_from_source(
|
||||
nuclide: Optional[str] = None,
|
||||
json_path: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> SpectrumConfig: # type: ignore[return-value]
|
||||
) -> SpectrumConfig:
|
||||
"""
|
||||
Create a SpectrumConfig from either paceENSDF or JSON input.
|
||||
|
||||
|
||||
+21
-15
@@ -1,7 +1,9 @@
|
||||
# spectrum.py
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from typing import List, Dict, Optional, Tuple, TYPE_CHECKING
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
|
||||
from dataclasses import dataclass
|
||||
|
||||
from beta_spectrum.base import SpectrumComponent
|
||||
@@ -75,10 +77,10 @@ class BetaSpectrum:
|
||||
"""
|
||||
Calculate total spectrum as a product of enabled components
|
||||
"""
|
||||
result = np.ones_like(W)
|
||||
result: np.ndarray = np.ones_like(W, dtype=float)
|
||||
for comp in self.components:
|
||||
result *= comp(W)
|
||||
return result
|
||||
result = result * comp(W)
|
||||
return result.astype(np.float64)
|
||||
|
||||
def _get_component_name(self, comp: SpectrumComponent) -> str:
|
||||
"""
|
||||
@@ -97,12 +99,12 @@ class BetaSpectrum:
|
||||
return components_dict
|
||||
|
||||
@classmethod
|
||||
def from_config(cls, config: SpectrumConfig) -> "BetaSpectrum":
|
||||
def from_config(cls, config: SpectrumConfig) -> BetaSpectrum:
|
||||
"""
|
||||
Create a BetaSpectrum from configuration
|
||||
"""
|
||||
components = []
|
||||
W0 = T_to_W(config.endpoint_MeV)
|
||||
components: List[SpectrumComponent] = []
|
||||
W0 = float(T_to_W(config.endpoint_MeV))
|
||||
|
||||
if config.use_phase_space:
|
||||
components.append(PhaseSpace(W0=W0, transition_type=config.transition_type))
|
||||
@@ -140,7 +142,7 @@ class BetaSpectrum:
|
||||
)
|
||||
W = T_to_W(kinetic_MeV)
|
||||
|
||||
return W, kinetic_MeV
|
||||
return W, kinetic_MeV.astype(np.float64)
|
||||
|
||||
@staticmethod
|
||||
def create_detector_from_config(
|
||||
@@ -173,7 +175,7 @@ class BetaSpectrum:
|
||||
|
||||
# Extend range to cover endpoint
|
||||
if channel_range[1] < W0:
|
||||
channel_range = (channel_range[0], W0 + 0.05)
|
||||
channel_range = (channel_range[0], float(W0 + 0.05))
|
||||
|
||||
detector = DetectorResponse.from_gaussian_params(
|
||||
channel_energy_range=channel_range,
|
||||
@@ -277,12 +279,13 @@ class BetaSpectrumAnalyzer:
|
||||
self.spectrum = spectrum
|
||||
self.config = config
|
||||
self.W, self.energies_MeV = spectrum.get_energy_grid(config)
|
||||
self._components_cache = None
|
||||
self._components_cache: Optional[Dict[str, np.ndarray]] = None
|
||||
|
||||
@property
|
||||
def components(self) -> Dict[str, np.ndarray]:
|
||||
if self._components_cache is None:
|
||||
self._components_cache = self.spectrum.calculate_components(self.W)
|
||||
assert self._components_cache is not None
|
||||
return self._components_cache
|
||||
|
||||
def total_spectrum(self, normalize: bool = True) -> np.ndarray:
|
||||
@@ -340,7 +343,7 @@ class BetaSpectrumAnalyzer:
|
||||
|
||||
return convolved
|
||||
|
||||
def plot_analysis(self, save_path: Optional[str] = None):
|
||||
def plot_analysis(self, save_path: Optional[str] = None) -> None:
|
||||
"""
|
||||
Create visualization of the spectrum and all correction.
|
||||
"""
|
||||
@@ -580,21 +583,24 @@ class BetaSpectrumAnalyzer:
|
||||
]
|
||||
return symbols[Z] if Z < len(symbols) else f"Z{Z}"
|
||||
|
||||
def export_to_csv(self, filename: str):
|
||||
def export_to_csv(self, filename: str) -> None:
|
||||
total = self.total_spectrum(normalize=True)
|
||||
components = self.components
|
||||
|
||||
data = {"energy_MeV": self.energies_MeV, "spectrum": total}
|
||||
data: Dict[str, np.ndarray] = {
|
||||
"energy_MeV": self.energies_MeV,
|
||||
"spectrum": total,
|
||||
}
|
||||
for name, values in components.items():
|
||||
data[name] = values
|
||||
|
||||
import pandas as pd
|
||||
import pandas as pd # type: ignore[import-untyped]
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
df.to_csv(filename, index=False, float_format="%.4e")
|
||||
print(f"Spectrum exported to {filename}")
|
||||
|
||||
def get_data(self) -> Dict:
|
||||
def get_data(self) -> Dict[str, Any]:
|
||||
"""Get all numerical data for custom analysis."""
|
||||
return {
|
||||
"energies_MeV": self.energies_MeV,
|
||||
|
||||
+14
-11
@@ -1,36 +1,39 @@
|
||||
# utils.py
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from .constants import HBAR_C_MEV_FM, ME_MEV, R0_FM
|
||||
|
||||
Real = Union[float, np.ndarray]
|
||||
|
||||
|
||||
def nuclear_radius(A: float, r0: float = R0_FM) -> float:
|
||||
R_fm = r0 * A ** (1 / 3)
|
||||
|
||||
R_fm = float(r0 * A ** (1 / 3))
|
||||
return R_fm / (HBAR_C_MEV_FM / ME_MEV)
|
||||
|
||||
|
||||
def momentum(W, rest_mass: float = 1):
|
||||
def momentum(W: Real, rest_mass: float = 1.0) -> np.ndarray:
|
||||
"""
|
||||
Helper function to get momentum value from energy.
|
||||
|
||||
Args:
|
||||
W np.ndarray: Array of full particle energy values in natural units
|
||||
rest_mass float: rest mass of the particle in taural units (m_e = 1 by default for electron)
|
||||
W: Array of full particle energy values in natural units
|
||||
rest_mass: rest mass of the particle in natural units (m_e = 1 by default for electron)
|
||||
|
||||
Returns:
|
||||
np.ndarray: array of particle momentum values.
|
||||
Array of particle momentum values.
|
||||
"""
|
||||
return np.sqrt(W**2 - rest_mass**2)
|
||||
|
||||
|
||||
def neutrino_energy(W, W0):
|
||||
def neutrino_energy(W: Real, W0: Real) -> Real:
|
||||
return W0 - W
|
||||
|
||||
|
||||
def W_to_T(W: float | np.ndarray) -> np.ndarray:
|
||||
return (W - 1.0) * ME_MEV
|
||||
def W_to_T(W: Real) -> np.ndarray:
|
||||
return np.asarray((W - 1.0) * ME_MEV, dtype=np.float64)
|
||||
|
||||
|
||||
def T_to_W(T_MeV: float | np.ndarray) -> np.ndarray:
|
||||
return T_MeV / ME_MEV + 1.0
|
||||
def T_to_W(T_MeV: Real) -> np.ndarray:
|
||||
return np.asarray(T_MeV / ME_MEV + 1.0, dtype=np.float64)
|
||||
|
||||
@@ -0,0 +1,897 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# paceENSDF methods for accessing coincidence $\\gamma-\\gamma$ and $\\gamma-X$-ray data sets from radioactive-decay data\n",
|
||||
"\n",
|
||||
"Import the Python package `paceENSDF` and load the JSON-formatted coincidence-decay data sets to a list object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import paceENSDF as pe\n",
|
||||
"e = pe.ENSDF()\n",
|
||||
"cdata = e.load_pace()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"%matplotlib notebook"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exploring the coincidence data sets\n",
|
||||
"\n",
|
||||
"To find all available coincidence data sets corresponding to a particular type of decay ($\\alpha$, $\\beta^{-}$, $\\epsilon/\\beta^{+}$) the `pyENSDF` method `coinc_pairs` can be invoked. This method creates a dictionary containing the associated parent-daughter information for all relevant data sets that contain $\\gamma$- and $X$-ray data. For more detail on this method and how to use it refer to the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.coinc_pairs)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all coincidence data sets from beta-minus decay\n",
|
||||
"bm_coinc = e.coinc_pairs(cdata, \"BM\")\n",
|
||||
"[print(k,v) for (k,v) in bm_coinc.items()][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all coincidence data sets from alpha decay\n",
|
||||
"a_coinc = e.coinc_pairs(cdata, \"A\")\n",
|
||||
"[print(k,v) for (k,v) in a_coinc.items()][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all coincidence data sets from electron-capture/beta-plus decay\n",
|
||||
"ecbp_coinc = e.coinc_pairs(cdata, \"ECBP\")\n",
|
||||
"[print(k,v) for (k,v) in ecbp_coinc.items()][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Gather the Z, A information from each decay-mode dictionary\n",
|
||||
"\n",
|
||||
"# Alpha decay\n",
|
||||
"Z_A = [k[1] for (k,v) in a_coinc.items()]\n",
|
||||
"A_A = [k[2] for (k,v) in a_coinc.items()]\n",
|
||||
"\n",
|
||||
"# Beta-minus decay\n",
|
||||
"Z_BM = [k[1] for (k,v) in bm_coinc.items()]\n",
|
||||
"A_BM = [k[2] for (k,v) in bm_coinc.items()]\n",
|
||||
"\n",
|
||||
"# Electron-capture/beta-plus decay\n",
|
||||
"Z_ECBP = [k[1] for (k,v) in ecbp_coinc.items()]\n",
|
||||
"A_ECBP = [k[2] for (k,v) in ecbp_coinc.items()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib notebook\n",
|
||||
"fig, ax = plt.subplots(figsize=(9,6))\n",
|
||||
"\n",
|
||||
"ax.scatter(np.array(A_A), np.array(Z_A), color='y', marker='s', alpha=0.5, label=r'$\\alpha$')\n",
|
||||
"ax.scatter(np.array(A_BM), np.array(Z_BM), color='r', marker='s', alpha=0.10, label=r'$\\beta^{-}$')\n",
|
||||
"ax.scatter(np.array(A_ECBP), np.array(Z_ECBP), color='b', marker='s', alpha=0.10, label=r'$\\epsilon/\\beta^{+}$')\n",
|
||||
"\n",
|
||||
"ax.set_xlabel(r'$A$', size=20)\n",
|
||||
"ax.set_ylabel(r'$Z$', size=20)\n",
|
||||
"ax.tick_params(axis='both', which='major', labelsize=15)\n",
|
||||
"ax.legend(loc='best', fontsize=20)\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.savefig(\"coinc_decay_nuclides.png\", dpi=fig.dpi)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## $\\gamma$-ray singles data\n",
|
||||
"\n",
|
||||
"All $\\gamma$-ray energies and absolute intensities observed following the decay of a defined parent nucleus and given radioactive-decay mode. The returned quantities and function arguments are explained in the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_gamma_singles)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"These associated normalization and production normalization records have been applied to the returned $\\gamma$-ray intensities"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"help(e.get_gamma_singles)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define parent and decay mode\n",
|
||||
"parent_nucleus = \"Np239\"\n",
|
||||
"decay_mode = \"BM\"\n",
|
||||
"decay_index = 0\n",
|
||||
"\n",
|
||||
"decay_pairs = e.coinc_pairs(cdata, decay_mode)\n",
|
||||
"daughter_nucleus = [v[0] for k,v in decay_pairs.items() if (k[0]==parent_nucleus and k[3]==decay_index)][0]\n",
|
||||
"print(\"Daughter nucleus: {0}\".format(daughter_nucleus))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the absolute total-projection singles gamma data for 239Pu following the g.s. beta-minus decay of 239Np\n",
|
||||
"#g = e.get_gamma_singles(cdata, \"BM\", \"Np239\", 0)\n",
|
||||
"g = e.get_gamma_singles(cdata, decay_mode, parent_nucleus, decay_index)\n",
|
||||
"for data in g: print(data)\n",
|
||||
"\n",
|
||||
"# Put gamma data into arrays\n",
|
||||
"gamma_array = np.array(g)\n",
|
||||
"gamma_energy = gamma_array[:,0]\n",
|
||||
"gamma_intensity = gamma_array[:,5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Plot gamma data\n",
|
||||
"%matplotlib notebook\n",
|
||||
"fig, ax = plt.subplots(figsize=(9,6))\n",
|
||||
"\n",
|
||||
"ax.bar(gamma_energy, gamma_intensity, width=3, color='k', label=r'%s $\\gamma$ singles'%daughter_nucleus)\n",
|
||||
"ax.legend(loc='best', fontsize=15)\n",
|
||||
"\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.set_xlabel(r'$E_{\\gamma}$ [keV]', size=20)\n",
|
||||
"ax.set_ylabel(r'$I_{\\gamma}$', size=20)\n",
|
||||
"\n",
|
||||
"plt.grid()\n",
|
||||
"plt.tight_layout()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## $\\gamma-\\gamma$ coincidence data\n",
|
||||
"\n",
|
||||
"Energies and absolute intensities for $\\gamma-\\gamma$ coincidence pairs. The callable `get_gg` returns all coincidences within a defined decay scheme or all coincidences with a defined transition according to input arguments. For more information and an explanation of the function arguments and return value refer to the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_gg)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (i) All $\\gamma-\\gamma$ coincidences in a defined decay scheme\n",
|
||||
"\n",
|
||||
"Four arguments are passed to the `get_gg` function."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get all g-g coincidences in daughter following radioactive decay\n",
|
||||
"gg=e.get_gg(cdata, decay_mode, parent_nucleus, decay_index)\n",
|
||||
"for coinc in gg: print(coinc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (ii) All $\\gamma-\\gamma$ coincidences with a particular transition\n",
|
||||
"\n",
|
||||
"Six arguments are passed to the `get_gg` function."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define indices for gating transition\n",
|
||||
"E_i = 8\n",
|
||||
"E_f = 5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Now get all g-g coincidences with the 8->5 transition in 239Pu following the g.s. beta-minus decay of 239Np\n",
|
||||
"cg=e.get_gg(cdata, decay_mode, parent_nucleus, decay_index, E_i, E_f)\n",
|
||||
"cgate = cg[0][0]\n",
|
||||
"for coinc in cg: \n",
|
||||
" print(coinc)\n",
|
||||
" \n",
|
||||
"coinc_array = np.array(cg)\n",
|
||||
"coinc_gamma = coinc_array[:,1]\n",
|
||||
"coinc_intensity = coinc_array[:,10]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Plot coincidence gamma data imposed on singles gamma data\n",
|
||||
"%matplotlib notebook\n",
|
||||
"fig, ax = plt.subplots(figsize=(9,6))\n",
|
||||
"\n",
|
||||
"ax.bar(gamma_energy, gamma_intensity, width=3, color='k', label=r'%s $\\gamma$ singles'%daughter_nucleus)\n",
|
||||
"ax.bar(coinc_gamma, coinc_intensity, width=3, color='r', label=r'%s %s-keV coincidences'%(daughter_nucleus,cgate))\n",
|
||||
"ax.legend(loc='best', fontsize=15)\n",
|
||||
"\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.set_xlabel(r'$E_{\\gamma}$ [keV]', size=20)\n",
|
||||
"ax.set_ylabel(r'$I_{\\gamma}$', size=20)\n",
|
||||
"\n",
|
||||
"plt.grid()\n",
|
||||
"#plt.tight_layout()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Search for $\\gamma$ rays and $\\gamma-\\gamma$ coincidences"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (i) Find a single $\\gamma$ ray\n",
|
||||
"\n",
|
||||
"Provide a single $\\gamma$-ray energy and find all isotopes containing $\\gamma$ rays at the specified energy. By default a tolerance window of $\\pm$0.5 keV is assumed on the specified $\\gamma$-ray energy. This can be adjusted with an optional argument. See the docstrings for more details:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.find_gamma)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all isotopes containing gamma rays at 1332 keV (+/- 0.5 keV; default tolerance range)\n",
|
||||
"e.find_gamma(cdata, 1332)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Tune the tolerance to narrow the search range to 1332 +/- 0.2 keV\n",
|
||||
"e.find_gamma(cdata, 1332, 0.2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (ii) Find a pair of $\\gamma-\\gamma$ coincidences\n",
|
||||
"\n",
|
||||
"Provide a pair of $\\gamma$-ray energies and find all isotopes containing coincidence $\\gamma$ rays at the specified energies. By default a tolerance window of $\\pm$0.5 keV is assumed on the specified $\\gamma$-ray energy. This can be adjusted with an optional argument. See the docstrings for more details:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.find_gamma_coinc)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all isotopes containing a coincidence pair of gamma rays \n",
|
||||
"# at 106 keV and 392 keV (default tolerance=0.5 keV)\n",
|
||||
"e.find_gamma_coinc(cdata, 106, 392)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Tune the tolerance to expand the search window to +/- 2.0 keV\n",
|
||||
"e.find_gamma_coinc(cdata, 106, 392, 2.0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## $X$-ray singles data\n",
|
||||
"\n",
|
||||
"$K$-shell $X$-ray energies and absolute intensities observed following the decay of a defined parent nucleus and given radioactive-decay mode. The returned quantities and function arguments are explained in the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_xray_singles)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the absolute total-projection singles X-ray data for 239Pu following the g.s. beta-minus decay of 239Np\n",
|
||||
"#x=e.get_xray_singles(cdata, \"BM\", \"Np239\", 0)\n",
|
||||
"x=e.get_xray_singles(cdata, decay_mode, parent_nucleus, decay_index)\n",
|
||||
"for xray in x: print(xray)\n",
|
||||
"\n",
|
||||
"xray_array = np.array(x)\n",
|
||||
"xray_energy = xray_array[:,0]\n",
|
||||
"xray_intensity = xray_array[:,1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To get the contribution of individual transitions, from the associated $K$-shell conversion, to the observed $K$-shell $X$-ray yield use the `get_xray_contribution` callable. For more information see the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_xray_contribution)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Plot gamma and X-ray data\n",
|
||||
"%matplotlib notebook\n",
|
||||
"fig, ax = plt.subplots(figsize=(9,6))\n",
|
||||
"\n",
|
||||
"ax.bar(gamma_energy, gamma_intensity, width=3, color='k', label=r'%s decay $\\gamma$ singles'%daughter_nucleus)\n",
|
||||
"ax.bar(xray_energy, xray_intensity, width=3, color='b', label=r'%s $X$-rays'%daughter_nucleus)\n",
|
||||
"ax.legend(loc='best', fontsize=15)\n",
|
||||
"\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.set_xlabel(r'$E$ [keV]', size=20)\n",
|
||||
"ax.set_ylabel(r'$I$', size=20)\n",
|
||||
"\n",
|
||||
"plt.grid()\n",
|
||||
"plt.tight_layout()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the individual contributions to the X-ray yield from transitions in 239Pu following the g.s. beta-minus\n",
|
||||
"# decay of 239Np\n",
|
||||
"e.get_xray_contribution(cdata, decay_mode, parent_nucleus, decay_index)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## $\\gamma-X$-ray coincidence data\n",
|
||||
"\n",
|
||||
"Energies and absolute intensities for $\\gamma-X$-ray coincidence pairs. The callable `get_gx` returns all coincidences within a defined decay scheme or all coincidences with a defined transition according to input arguments. For more information and an explanation of the function arguments and return value refer to the docstrings:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_gx)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (i) All $\\gamma-X$-ray coincidences in a defined decay scheme\n",
|
||||
"\n",
|
||||
"Four arguments are passed to the `get_gx` function."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get all g-X coincidences in 239Pu following the ground-state beta-minus decay of 239Np\n",
|
||||
"xx=e.get_gx(cdata, decay_mode, parent_nucleus, decay_index)\n",
|
||||
"for coinc in xx: print(coinc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (ii) All $\\gamma-X$-ray coincidences with a particular transition\n",
|
||||
"\n",
|
||||
"Six arguments are passed to the `get_gx` function."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Now get all g-X coincidences with the 8->5 transition in 239Pu following the g.s. beta-minus decay of 239Np\n",
|
||||
"cx=e.get_gx(cdata, decay_mode, parent_nucleus, decay_index, E_i, E_f)\n",
|
||||
"for coinc in cx: print(coinc)\n",
|
||||
" \n",
|
||||
"coincgx_array = np.array(cx)\n",
|
||||
"coincgx_energy = coincgx_array[:,3].astype(float)\n",
|
||||
"coincgx_intensity = coincgx_array[:,5].astype(float)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Plot coincidence gamma and X-ray imposed upon singles gamma and X-ray data\n",
|
||||
"%matplotlib notebook\n",
|
||||
"fig, ax = plt.subplots(figsize=(9,6))\n",
|
||||
"\n",
|
||||
"ax.bar(gamma_energy, gamma_intensity, width=3, color='k', label=r'%s decay $\\gamma$'%daughter_nucleus)\n",
|
||||
"ax.bar(xray_energy, xray_intensity, width=3, color='b', label=r'%s $X$-rays'%daughter_nucleus)\n",
|
||||
"ax.bar(coinc_gamma, coinc_intensity, width=3, color='r', label=r'%s $\\gamma$/%s-keV coincidences'%(daughter_nucleus, cgate))\n",
|
||||
"ax.bar(coincgx_energy, coincgx_intensity, width=3, color='yellow', label=r'%s $X$/%s-keV coincidences'%(daughter_nucleus, cgate))\n",
|
||||
"ax.legend(loc='best', fontsize=15)\n",
|
||||
"\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.set_xlabel(r'$E$ [keV]', size=20)\n",
|
||||
"ax.set_ylabel(r'$I$', size=20)\n",
|
||||
"\n",
|
||||
"plt.grid()\n",
|
||||
"plt.tight_layout()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Search for $X$ rays and $\\gamma-X$-ray coincidences"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (i) Find a single $X$ ray\n",
|
||||
"\n",
|
||||
"Provide a single $X$-ray energy and find all isotopes containing $X$ rays at the specified energy. By default a tolerance window of $\\pm$0.5 keV is assumed on the specified $X$-ray energy. This can be adjusted with an optional argument. See the docstrings for more details:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.find_xray)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all isotopes containing X rays at 52 keV (+/- 0.5 keV; default tolerance range)\n",
|
||||
"e.find_xray(cdata, 52)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Tune the tolerance to narrow the search range to 52 +/- 0.1 keV\n",
|
||||
"e.find_xray(cdata, 52, 0.1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### (ii) Find a pair of $\\gamma-X$-ray coincidences\n",
|
||||
"\n",
|
||||
"Provide the $X$-ray energy and the $\\gamma$-ray energy and find all isotopes containing coincidence $\\gamma-X$-ray pairs at the specified energies. By default a tolerance window of $\\pm$0.5 keV is assumed on the specified photon energy. This can be adjusted with an optional argument. See the docstrings for more details:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.find_xray_coinc)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find all isotopes containing a gamma-X-ray coincidence pair with a 52-keV X ray and 688-keV gamma, \n",
|
||||
"# respectively, assuming the default tolerance=0.5 keV\n",
|
||||
"e.find_xray_coinc(cdata, 52, 688)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Tune the tolerance to narrow the search window to +/- 0.15 keV\n",
|
||||
"e.find_xray_coinc(cdata, 52, 688, 0.15)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Display parallel paths for cascades between coincident $\\gamma$ rays\n",
|
||||
"\n",
|
||||
"This function shows all parallel $\\gamma$-ray cascade sequences between a pair of coincident $\\gamma$ rays in the decay scheme of the daughter nucleus. In addition to the $\\gamma$ rays in the cascade, all associated level indices and energies are shown and a list of tuples containing the level indices is returned. See the appropriate docstring:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.show_cascades)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Show the cascade gamma-ray sequence between the 3->2 and 1->0 transitions in 60Ni following \n",
|
||||
"# 60Co beta-minus decay (G.S.)\n",
|
||||
"e.show_cascades(cdata,\"BM\",\"Co60\",0,3,2,1,0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Show the cascade gamma-ray sequence between the 9->6 and 1->0 transitions in 147Pm following \n",
|
||||
"# 147Nd beta-minus decay (G.S.)\n",
|
||||
"e.show_cascades(cdata,\"BM\",\"Nd147\",0,9,6,1,0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Impose timecut\n",
|
||||
"\n",
|
||||
"The following method has been implemented to allow the user to filter cascades going through isomeric levels:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_gg_timecut)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Transient-equilibrium intensity data is gradually being added to the repository. To use these datasets instead of the regular ENSDF decay datasets make sure to install by setting the appropriate flag `(-t | --transient)` described in the README."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Inspecting transient-equilibrium coincidences following 99Mo beta-minus decay.\n",
|
||||
"\n",
|
||||
"# To remove coincidences that go through the 6-h isomer at E=142.7-keV (level index=2) impose a 21600-s timecut:\n",
|
||||
"gg=e.get_gg_timecut(cdata, \"BM\", \"Mo99\", 0, -21600)\n",
|
||||
"for g in gg: print(g[0:12])\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find only coincidences with the 1->0 transition (140.51-keV -> 0-keV) with the same timecut:\n",
|
||||
"gg=e.get_gg_timecut(cdata, \"BM\", \"Mo99\", 0, 1, 0, -21600)\n",
|
||||
"for g in gg: print(g[0:12])\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find coincidences in the path of the 6-h isomer:\n",
|
||||
"gg=e.get_gg_timecut(cdata, \"BM\", \"Mo99\", 0, 21600)\n",
|
||||
"for g in gg: print(g[0:12])\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Find only coincidences with the 1->0 transition (140.51-keV -> 0-keV) in the path of the 6-h isomer:\n",
|
||||
"gg=e.get_gg_timecut(cdata, \"BM\", \"Mo99\", 0, 1, 0, 21600)\n",
|
||||
"for g in gg: print(g[0:12])\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Compare these results with all expected coincidences (i.e. no timecut):\n",
|
||||
"gg=e.get_gg(cdata, \"BM\", \"Mo99\", 0)\n",
|
||||
"for g in gg: print(g)\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Compare these results with all expected coincidences with just the 1->0 transition (i.e. no timecut):\n",
|
||||
"gg=e.get_gg(cdata, \"BM\", \"Mo99\", 0, 1, 0)\n",
|
||||
"for g in gg: print(g)\n",
|
||||
"print(\"\\nNumber of coincidences = {0}\".format(len(gg)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Auxiliary data\n",
|
||||
"\n",
|
||||
"This section of the Notebook runs through some of the supplementary data in coincidence data structures that may be interesting or useful."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Valence electronic configurations\n",
|
||||
"\n",
|
||||
"The valence electronic configuration is provided for all elements from $Z=3-110$ apart from $Z=1,2,101,103,106$. The missing data will be added in a future release. Refer to the docstrings for information regarding the returned list object:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_valence_config)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the valence electronic configuration of Yb (pass the chemical symbol <str>)\n",
|
||||
"e.get_valence_config(cdata, \"Yb\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the valence electronic configuration of Yb (pass the atomic number <int>)\n",
|
||||
"e.get_valence_config(cdata, 70)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the valence electronic configuration of Fe (pass the chemical symbol <str>)\n",
|
||||
"e.get_valence_config(cdata, \"Fe\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the valence electronic configuration of Fe (pass the atomic number <int>)\n",
|
||||
"e.get_valence_config(cdata, 26)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Atomic binding energies\n",
|
||||
"\n",
|
||||
"Atomic subshell binding energies in keV for $K$, $L_{I} - L_{III}$, and $M_{I} - M_{V}$. Refer to the docstrings for more information:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
">>> help(e.get_binding_energies)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"The desired element can be passed as a string (chemical symbol) or an integer (atomic number). A keyword argument is also needed to specify the required output:\n",
|
||||
"\n",
|
||||
"```Bash\n",
|
||||
"shell = 'K'\n",
|
||||
"shell = 'L'\n",
|
||||
"shell = 'M'\n",
|
||||
"shell = 'all'\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the K-shell binding energy for W:\n",
|
||||
"e.get_binding_energies(cdata, \"W\", shell='K')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the L-shell binding energies for W:\n",
|
||||
"e.get_binding_energies(cdata, \"W\", shell='L')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the M-shell binding energies for W:\n",
|
||||
"e.get_binding_energies(cdata, \"W\", shell='M')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get all available atomic-shell binding energies for Yb:\n",
|
||||
"e.get_binding_energies(cdata, 74, shell='ALL')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -41,6 +41,7 @@ where = ["."]
|
||||
[tool.mypy]
|
||||
python_version = "3.10"
|
||||
strict = true
|
||||
exclude = ["notebooks", "scripts/notebook_plots.py"]
|
||||
|
||||
[tool.black]
|
||||
line-length = 88
|
||||
|
||||
Reference in New Issue
Block a user