TDCRPy 2.17.3

TDCR model

TDCRPy

A Photo-Physical Stochastic Model for Liquid Scintillation Counting

---

📖 Overview

TDCRPy is a Python package developed and maintained by the BIPM (Bureau International des Poids et Mesures). It estimates detection efficiencies of liquid scintillation counters using the TDCR (Triple to Double Coincidence Ratio) or CIEMAT/NIST methods.

The calculation is based on a photo-physical stochastic model, allowing users to address:

• Complex decay schemes (Beta spectra via BetaShape).
• Radionuclide mixtures.
• Ionization quenching (Birks model).
• Micelle effects in scintillator cocktails.
• Dynamic efficiency evolution over time.

Technical details can be found in:

• Coulon et al., Applied Radiation and Isotopes (2024)
• Coulon et al., BIPM Technical Report

---

📦 Installation

TDCRPy requires a standard Python scientific environment.

1. Install Dependencies

You can install the required libraries via pip or conda.

# Using pip
pip install numpy scipy configparser tqdm importlib-resources

# Optional (for visualization and dynamic decay features)
pip install opencv-python radioactivedecay matplotlib

2. Install TDCRPy

pip install TDCRPy

To upgrade to the latest version:

pip install TDCRPy --upgrade

3. Run Tests

Verify the installation by running the unit tests:

python -m unittest tdcrpy.test.test_tdcrpy

---

⚡ Quick Start

Here is a basic example to estimate detection efficiencies for Co-60 using a symmetric PMT configuration.

import tdcrpy

# --- 1. Define Parameters ---
mode = "eff"           # Calculation mode
L = 1.2                # Free parameter in keV^-1
Rad = "Co-60"          # Radionuclide
pmf_1 = "1"            # Relative fraction (100%)
N = 1000               # Number of Monte Carlo trials
kB = 1.0e-5            # Birks constant in cm keV^-1
V = 10                 # Volume of scintillator in mL

# --- 2. Run Calculation ---
result = tdcrpy.TDCRPy.TDCRPy(L, Rad, pmf_1, N, kB, V, mode)

# --- 3. Display Results ---
print(f"Efficiency S (Single): {result[0]:.4f} +/- {result[1]:.4f}")
print(f"Efficiency D (Double): {result[2]:.4f} +/- {result[3]:.4f}")
print(f"Efficiency T (Triple): {result[4]:.4f} +/- {result[5]:.4f}")
print(f"Efficiency D (CIEMAT/NIST): {result[12]:.4f} +/- {result[13]:.4f}")

Calculate Free Parameter from Measured TDCR

If you have an experimental TDCR value ($R_T/R_D$), you can reverse-calculate the free parameter $L$:

TD = 0.9776  # Measured TDCR parameter
result = tdcrpy.TDCRPy.eff(TD, Rad, pmf_1, kB, V)

print(f"Global free parameter L = {result[0]:.4f} keV^-1")

---

🛠 Advanced Features

Asymmetric PMTs

TDCRPy supports calculations where the quantum efficiency differs between PMTs. Pass a tuple for the free parameter $L$:

# L for (PMT A, PMT B, PMT C)
L = (1.1, 1.3, 1.2) 
result = tdcrpy.TDCRPy.TDCRPy(L, "Co-60", "1", 1000, 1.0e-5, 10)

# Efficiencies for specific pairs (AB, BC, AC) are available in the result tuple
print(f"Efficiency AB: {result[6]:.4f}")

Radionuclide Mixtures

Simulate mixtures by providing comma-separated nuclides and their relative fractions.

# Example: 80% Co-60 and 20% H-3
Rad = "Co-60, H-3"
pmf_1 = "0.8, 0.2" 

result = tdcrpy.TDCRPy.TDCRPy(L, Rad, pmf_1, N, kB, V)

Dynamic Decay & Efficiency

Combine TDCRPy with radioactivedecay to simulate how efficiency changes as a sample decays (e.g., Mo-99/Tc-99m).

import radioactivedecay as rd
import tdcrpy as td
import numpy as np

# Define inventory
rad_t0 = rd.Inventory({'Mo-99': 1}, 'Bq')

# Decay for 30 hours
rad_t1 = rad_t0.decay(30.0, 'h')

# Calculate current composition for TDCRPy
A_t1 = rad_t1.activities('Bq')
total_activity = sum(A_t1.values())

# Format strings for TDCRPy
nuclides = ", ".join([k for k, v in A_t1.items() if v > 0])
fractions = ", ".join([str(v/total_activity) for k, v in A_t1.items() if v > 0])

# Run simulation
result = td.TDCRPy.TDCRPy(1.0, nuclides, fractions, 1000, 1e-5, 10, "eff")

---

⚙️ Configuration & Physics

You can customize the underlying physics model using tdcrpy.TDCR_model_lib.

To view current settings:

import tdcrpy as td
td.TDCR_model_lib.readParameters(disp=True)

Common Configurations

Parameter	Method	Description
Electron Bins	modifynE_electron(n)	Integration bins for electrons (default: 1000)
Alpha Bins	modifynE_alpha(n)	Integration bins for alpha particles (default: 1000)
Density	modifyDensity(rho)	Scintillator density in g/cm³ (default: 0.96)
Mean Z / A	modifyZ(z), modifyA(a)	Mean atomic/mass numbers of the cocktail
Micelle Effect	modifyMicCorr(bool)	Activate reverse micelle correction (default: False)
Micelle Size	modifyDiam_micelle(d)	Diameter in nm (default: 2.0)
Dead Time	modifyDeadTime(t)	Extended dead time in µs (default: 10)
Coincidence Time	modifyTau(ns)	Resolving time in ns (default: 50)

Tutorials

• Use the stochastic model for all nuclides
• Use the analytical model for pure beta emitters
• Modify the parameters of the model

---

📚 Citations

If you use TDCRPy in your work, please cite the following:

TDCRPy: A python package for TDCR measurements > R. Coulon, J. Hu
Applied Radiation and Isotopes (2024)
DOI: 10.1016/j.apradiso.2024.111518

---

⚖️ License

This project is licensed under the MIT License.

Copyright © BIPM (Bureau International des Poids et Mesures).