decay-tools 0.1.1

Python package for fitting single- and double-component nuclear decay time distributions and estimating optimal bins in the logarithmic form.

decay-tools

Tools for analyzing decay time distributions in nuclear physics experiments

Installation

pip install decay-tools

Usage

Prepare data

import numpy as np
from decay_tools import estimate_n_bins, get_hist_and_bins

times_mks = np.array([2, 2, ..., 55098.1, 113904.2])
# convert to ln(Δt)
logt = np.log(times_mks)

# find an “optimal” number of bins (IQR or STD method)
nbins = estimate_n_bins(logt, method="iqr")
print(f"Suggested bins: {nbins}")

# build histogram & get bin centers
data, bins = get_hist_and_bins(logt=logt) # default: n_bins_method="iqr"
# or explicitly:
# data, bins = get_hist_and_bins(logt=logt, n_bins=10)
# data, bins = get_hist_and_bins(logt=logt, n_bins_method="std")

Fit a single-component decay curve

from decay_tools import DecayParameters, fit_single_schmidt, visualize_single_fit

# initial guess: half-life=10 μs, N₀=100, background=0
guess = DecayParameters(half_life_us=10, n0=100, c=0)

result = fit_single_schmidt(
    data,
    bins,
    initial_guess=guess,
    check_chi_square=True, # will print chi-squre test results
)

print(result)
# if you want to see the fit overlayed on the histogram:
visualize_single_fit(data, bins, result)

Fit a double-component decay curve

from decay_tools import DoubleDecayParameters, fit_double_schmidt, visualize_double_fit

# initial guess for short & long components
g = DoubleDecayParameters(
    hl_short_us=5, 
    hl_long_us=50,
    n0_short=50, 
    n0_long=20, 
    c=0
)

result = fit_double_schmidt(
    data, 
    bins, 
    initial_guess=g,
)

print(result)
visualize_double_fit(data, bins, result)

Set boundaries

Both fit_single_schmidt and fit_double_schmidt accept an optional bounds argument to constrain the fit parameters. Pass a tuple (lower, upper), where each bound can be either a DecayParameters (or DoubleDecayParameters) instance—whose fields are automatically converted to the log-domain constants—or a scalar/int, which applies the same limit to all parameters.

Note: The fiting procedure is done over decay constant but not half-life. So, when we set a bound of half-life we actually set the lower bound. If the initial guess for half live is lower than bound, the exception will be raised.

from decay_tools import DecayParameters, fit_single_schmidt

bounds = (
    0,
    DecayParameters(half_life_us=10, n0=1e3,  c=0.1)  # NOTE: we actually limit here the lower bound for half life
)
result = fit_single_schmidt(data, bins, initial_guess=guess, bounds=bounds)

Statistical tests to check if there more than one components in the decay curve

TBD