radioactivedecay 0.4.3

A Python package for radioactive decay modelling that supports 1252 radionuclides, decay chains, branching, and metastable states.

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radioactivedecay is a Python package for radioactive decay calculations. It supports decay chains of radionuclides, metastable states and branching decays. By default it uses the decay data from ICRP Publication 107, which contains 1252 radionuclides of 97 elements, and atomic mass data from the Atomic Mass Data Center.

The code solves the radioactive decay differential equations analytically using NumPy and SciPy linear algebra routines. There is also a high numerical precision calculation mode employing SymPy routines. This gives more accurate results for decay chains containing radionuclides with orders of magnitude differences between the half-lives.

This is free-to-use open source software. It was created for engineers, technicians and researchers who work with and study radioactivity, and for educational use.

• Full Documentation: https://alexmalins.com/radioactivedecay

Installation

radioactivedecay requires Python 3.6+. Install radioactivedecay from the Python Package Index using pip:

$ pip install radioactivedecay

or from conda-forge:

$ conda install -c conda-forge radioactivedecay

Either command will attempt to install the dependencies (Matplotlib, NetworkX, NumPy, SciPy & SymPy) if they are not already present in the environment.

Usage

Decay calculations

Create an Inventory of radionuclides and decay it as follows:

>>> import radioactivedecay as rd
>>> Mo99_t0 = rd.Inventory({'Mo-99': 2.0}, 'Bq')
>>> Mo99_t1 = inv_t0.decay(20.0, 'h')
>>> Mo99_t1.activities('Bq')
{'Mo-99': 1.6207863893776937, 'Ru-99': 0.0,
 'Tc-99': 9.05304236308454e-09, 'Tc-99m': 1.3719829376710406}

An Inventory of 2.0 Bq of Mo-99 was decayed for 20 hours, producing the radioactive progeny Tc-99m and Tc-99, and the stable nuclide Ru-99.

We supplied 'h' as an argument to decay() to specify the decay time period had units of hours. Supported time units include 'μs', 'ms', 's', 'm', 'h', 'd', 'y' etc. Note seconds ('s') is the default if no unit is supplied to decay().

Use cumulative_decays() to calculate the total number of atoms of each radionuclide that decay over the decay time period:

>>> Mo99_t0.cumulative_decays(20.0, 'h')
{'Mo-99': 129870.3165339939, 'Tc-99m': 71074.31925850797,
'Tc-99': 0.0002724635511147602}

Radionuclides can be specified in four equivalent ways in radioactivedecay: three variations of nuclide strings or by canonical ids. For example, the following are equivalent ways of specifying ²²²Rn and ¹⁹²ⁿIr:

• 'Rn-222', 'Rn222', '222Rn', 862220000,
• 'Ir-192n', 'Ir192n', '192nIr', 771920002.

Inventories can be created by supplying activity ('Bq', 'Ci', 'dpm'...), mass ('g', 'kg'...), mole ('mol', 'kmol'...) units, or numbers of nuclei ('num') to the Inventory() constructor. Use the methods activities(), masses(), moles(), numbers(), activity_fractions(), mass_fractions() and mole_fractions() to obtain the contents of the inventory in different formats:

>>> H3_t0 = rd.Inventory({'H-3': 3.0}, 'g')
>>> H3_t1 = tritium_t0.decay(12.32, 'y')
>>> H3_t1.masses('g')
{'H-3': 1.5, 'He-3': 1.4999900734297729}
>>> H3_t1.mass_fractions()
{'H-3': 0.5000016544338455, 'He-3': 0.4999983455661545}

>>> C14_t0 = rd.Inventory({'C-14': 3.2E24}, 'num')
>>> C14_t1 = carbon14_t0.decay(3000, 'y')
>>> C14_t1.moles('mol')
{'C-14': 3.6894551567795797, 'N-14': 1.6242698581767292}
>>> c14_t1.mole_fractions()
{'C-14': 0.6943255713073281, 'N-14': 0.3056744286926719}

Plotting decay graphs

Use the plot() method to graph of the decay of an inventory over time:

>>> mo99_t0.plot(20, 'd', yunits='Bq')

The graph shows the decay of Mo-99 over 20 days, leading to the ingrowth of Tc-99m and a trace quantity of Tc-99. Graphs are drawn using Matplotlib.

Fetching decay data

The Radionuclide class can be used to fetch decay information for individual radionuclides, e.g. for Rn-222:

>>> nuc = rd.Radionuclide('Rn-222')
>>> nuc.half_life('s')
330350.4
>>> nuc.half_life('readable')
'3.8235 d'
>>> nuc.progeny()
['Po-218']
>>> nuc.branching_fractions()
[1.0]
>>> nuc.decay_modes()
['α']

Likewise similar methods exist for inventory instances:

>>> Mo99_t1.half_lives('readable')
{'Mo-99': '65.94 h', 'Ru-99': 'stable', 'Tc-99': '0.2111 My', 'Tc-99m': '6.015 h'}
>>> Mo99_t1.progeny()
{'Mo-99': ['Tc-99m', 'Tc-99'], 'Ru-99': [], 'Tc-99': ['Ru-99'], 'Tc-99m': ['Tc-99', 'Ru-99']}
>>> Mo99_t1.branching_fractions()
{'Mo-99': [0.8773, 0.1227], 'Ru-99': [], 'Tc-99': [1.0], 'Tc-99m': [0.99996, 3.7e-05]}
>>> Mo99_t1.decay_modes()
{'Mo-99': ['β-', 'β-'], 'Ru-99': [], 'Tc-99': ['β-'], 'Tc-99m': ['IT', 'β-']}

Decay chain diagrams

The Radionuclide class includes a plot() method for drawing decay chain diagrams:

>>> nuc = rd.Radionuclide('Mo-99')
>>> nuc.plot()

These diagrams are drawn using NetworkX and Matplotlib.

High numerical precision decay calculations

radioactivedecay includes an InventoryHP class for high numerical precision calculations. This class can give more reliable decay calculation results for chains containing long- and short-lived radionuclides:

>>> inv_t0 = rd.InventoryHP({'U-238': 1.0})
>>> inv_t1 = inv_t0.decay(10.0, 'd')
>>> inv_t1.activities()
{'At-218': 1.4511675857141352e-25,
 'Bi-210': 1.8093327888942224e-26,
 'Bi-214': 7.09819414496093e-22,
 'Hg-206': 1.9873081129046843e-33,
 'Pa-234': 0.00038581180879502017,
 'Pa-234m': 0.24992285949158477,
 'Pb-206': 0.0,
 'Pb-210': 1.0508864357335218e-25,
 'Pb-214': 7.163682655782086e-22,
 'Po-210': 1.171277829871092e-28,
 'Po-214': 7.096704966148592e-22,
 'Po-218': 7.255923469955255e-22,
 'Ra-226': 2.6127168262000313e-21,
 'Rn-218': 1.4511671865210924e-28,
 'Rn-222': 7.266530698712501e-22,
 'Th-230': 8.690585458641225e-16,
 'Th-234': 0.2499481473619856,
 'Tl-206': 2.579902288672889e-32,
 'Tl-210': 1.4897029111914831e-25,
 'U-234': 1.0119788393651999e-08,
 'U-238': 0.9999999999957525}

How radioactivedecay works

radioactivedecay calculates an analytical solution to the radioactive decay differential equations using linear algebra operations. It implements the method described in this paper: M Amaku, PR Pascholati & VR Vanin, Comp. Phys. Comm. 181, 21-23 (2010). See the theory docpage for more details.

It uses NumPy and SciPy routines for standard decay calculations (double-precision floating-point operations), and SymPy for arbitrary numerical precision calculations.

By default radioactivedecay uses decay data from ICRP Publication 107 (2008) and atomic mass data from the Atomic Mass Data Center (AMDC - AME2020 and Nubase2020 evaluations).

The notebooks directory in the GitHub repository contains Jupyter Notebooks for creating the decay datasets that are read in by radioactivedecay, e.g. ICRP 107. It also contains some comparisons against decay calculations made with PyNE and Radiological Toolbox.

Tests

From the base directory run:

$ python -m unittest discover

License

radioactivedecay is open source software released under the MIT License. See LICENSE file for details.

The default decay data used by radioactivedecay (ICRP-107) is copyright 2008 A. Endo and K.F. Eckerman and distributed under a separate license. The default atomic mass data is from AMDC (license).

Contributing

Contributors are welcome to fix bugs, add new features or make feature requests. Please open a pull request or a new issue on the GitHub repository.