radioactivedecay 0.0.9

A Python package for radioactive decay calculations that supports 1252 radionuclides, including full decay chains and branching.

radioactivedecay

radioactivedecay is a Python package for radioactive decay calculations. It fully supports radionuclide decay chains, including those with branching decays or chains passing through metastable states. By default radioactivedecay uses the decay data from ICRP Publication 107, which covers 1252 radionuclides of 97 elements.

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

Installation

radioactivedecay requires Python 3.6+, NumPy and SciPy.

The easiest way to install radioactivedecay is via the Python Package Index using pip:

$ pip install radioactivedecay

Usage

Decay calculations

Create an Inventory of radionuclides and decay it as follows:

>>> import radioactivedecay as rd
>>> inv = rd.Inventory({'I-123': 1.0, 'Tc-99m': 2.0})
>>> inv = inv.decay(20.0, 'h')
>>> inv.contents
{'I-123': 0.35180331802323694,
 'Tc-99': 5.852125859801924e-09,
 'Tc-99m': 0.19957172182663926,
 'Te-123': 1.6353735405592892e-18,
 'Te-123m': 1.3312369019952352e-07}

Here we created an inventory of 1.0 Bq of ¹²³I and 2.0 Bq of ^99mTc and decayed it for 20 hours. The decayed inventory contains ⁹⁹Tc, which is the progeny of ^99mTc, and ¹²³Te and ^123mTe, which are progeny of ¹²³I.

Note that radioactivedecay does not require you specify the activity units. This is because its calculations are agnostic of the activity units: units out are the same as units in. So this calculation could have also represented the decay of 1.0 Ci of ¹²³I, or 1.0 dpm, or 1.0 kBq, etc.

However, you have to specify the units of the decay time. In the example we supplied 'h' as an argument to the decay() method to specify the decay time period (20.0) had units of hours. Accepted time units include 'ms', 's', 'm', 'h', 'd', 'y' etc. Note seconds ('s') is the default if you do not supply a time unit to decay()

Radionuclides can be specified in three equivalent ways in radioactivedecay. The strings

• 'Rn-222', 'Rn222' or '222Rn',
• 'Ir-192n', 'Ir192n' or '192nIr' are all equivalent ways of specifying ²²²Rn and ¹⁹²ⁿIr to the program.

Fetching decay data

radioactivedecay includes a Radionuclide class which can be used to fetch decay information for individual radionuclides.

>>> nuc = rd.Radionuclide('I-123')
>>> nuc.half_life('d')
13.27
>>> nuc.progeny()
['Te-123', 'Te-123m']
>>> nuc.branching_fractions()
[0.99996, 4.442e-05]
>>> nuc.decay_modes()
['EC', 'EC']

The half-life for ¹²³I is thus 13.27 days. Its direct progeny are ¹²³Te and ^123mTe, with branching fractions 0.99996 and 4.442e-05 respectively. Both of the decay modes occur via electron capture (EC).

The default decay dataset in radioactivedecay is ICRP-107. Its data can be queried directly as follows:

>>> rd.DEFAULTDATA.dataset
'icrp107'
>>> rd.DEFAULTDATA.half_life('Cs-137', 'y')
30.1671
>>> rd.DEFAULTDATA.branching_fraction('Cs-137', 'Ba-137m')
0.94399
>>> rd.DEFAULTDATA.decay_mode('Cs-137', 'Ba-137m')
'β-'

How radioactivedecay works

radioactivedecay calculates an analytical solution to the decay chain differential equations using matrix multiplications. It implements the method described in this paper: M Amaku, PR Pascholati & VR Vanin, Comp. Phys. Comm. 181, 21-23 (2010). It calls NumPy and SciPy for the matrix operations.

By default radioactivedecay uses decay data from ICRP Publication 107 (2008).

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

Tests

From the base directory run:

$ python -m unittest discover

License

radioactivedecay is open source software released under the MIT License. The ICRP-107 decay data is copyright 2008 A. Endo and K.F. Eckerman. See LICENSE for details.

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.

Acknowledgements

Special thanks to

• Center for Computational Science & e-Systems, Japan Atomic Energy Agency
• Kenny McKee

for their help and support to this project.