Metadata-Version: 2.1
Name: radioactivedecay
Version: 0.3.4
Summary: A Python package for radioactive decay modelling that supports 1252 radionuclides, decay chains, branching, and metastable states.
Home-page: https://github.com/alexmalins/radioactivedecay
Author: Alex Malins
Author-email: radioactivedecay@REMOVETHISalexmalins.com
License: MIT
Project-URL: Bug Tracker, https://github.com/alexmalins/radioactivedecay/issues
Project-URL: Documentation, https://alexmalins.com/radioactivedecay
Project-URL: Source Code, https://github.com/alexmalins/radioactivedecay
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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.
        
        It solves the radioactive decay differential equations analytically using NumPy
        and SciPy linear algebra routines. There is also a high numerical precision
        decay mode using SymPy routines, useful for when there are orders of magnitude
        differences between half-lives of radionuclides in the same decay chain.
        
        - **Full Documentation**: 
        [https://alexmalins.com/radioactivedecay](https://alexmalins.com/radioactivedecay/)
        
        
        ## Installation
        
        ``radioactivedecay`` requires Python 3.6+. Install ``radioactivedecay`` from
        the [Python Package Index](https://pypi.org/project/radioactivedecay/) using
        ``pip``:
        
        ```console
        $ pip install radioactivedecay
        ```
        
        or from [conda-forge](https://anaconda.org/conda-forge/radioactivedecay):
        
        ```console
        $ 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:
        
        ```pycon
        >>> import radioactivedecay as rd
        >>> inv_t0 = rd.Inventory({'Mo-99': 2.0})
        >>> inv_t1 = inv_t0.decay(20.0, 'h')
        >>> inv_t1.contents
        {'Mo-99': 1.6207863893776937,
        '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.
        
        Note we did not have to specify the units of the initial Mo-99 activity. This
        is because the output activity units are the same as the input units. So the
        above calculation could have represented the decay of 2.0 Ci of Mo-99, or of
        2.0 dpm, 2.0 kBq, etc.
        
        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()``.
        
        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 <sup>222</sup>Rn or <sup>192n</sup>Ir.
        
        
        ### Plotting decay graphs
        
        Use the ``plot()`` method to graph of the decay of an ``Inventory`` over time:
        
        ```pycon
        >>> inv_t0.plot(20, 'd')
        ```
        
        <img src="https://alexmalins.com/radioactivedecay/Mo-99_decay.png" alt="Mo-99 decay graph" width="450"/>
        
        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:
        
        ```pycon
        >>> nuc = rd.Radionuclide('Rn-222')
        >>> nuc.half_life('d')
        3.8235
        >>> nuc.progeny()
        ['Po-218']
        >>> nuc.branching_fractions()
        [1.0]
        >>> nuc.decay_modes()
        ['α']
        ```
        
        Likewise similar methods exist for ``Inventory`` instances:
        
        ```pycon
        >>> inv_t1.half_lives('readable')
        {'Mo-99': '65.94 h', 'Tc-99': '0.2111 My', 'Tc-99m': '6.015 h'}
        >>> inv_t1.progeny()
        {'Mo-99': ['Tc-99m', 'Tc-99'], 'Tc-99': ['Ru-99'], 'Tc-99m': ['Tc-99', 'Ru-99']}
        >>> inv_t1.branching_fractions()
        {'Mo-99': [0.8773, 0.1227], 'Tc-99': [1.0], 'Tc-99m': [0.99996, 3.7e-05]}
        >>> inv_t1.decay_modes()
        {'Mo-99': ['β-', 'β-'], 'Tc-99': ['β-'], 'Tc-99m': ['IT', 'β-']}
        ```
        
        
        ### Decay chain diagrams
        
        The ``Radionuclide`` class includes a `plot()` method for drawing decay chain
        diagrams:
        
        ```pycon
        >>> nuc = rd.Radionuclide('Mo-99')
        >>> nuc.plot()
        ```
        
        <img src="https://alexmalins.com/radioactivedecay/Mo-99_chain.png" alt="Mo-99 decay chain" width="300"/>
        
        These diagrams are drawn using NetworkX and Matplotlib.
        
        ### High numerical precision decay calculations
        
        ``radioactivedecay`` includes a high numerical precision decay mode. This can
        give more reliable results for decay chains containing both long- and
        short-lived radionuclides:
        
        ```pycon
        >>> inv_t0 = rd.Inventory({'U-238': 1.0})
        >>> inv_t1 = inv_t0.decay_high_precision(10.0, 'd')
        >>> inv_t1.contents
        {'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-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)](https://doi.org/10.1016/j.cpc.2009.08.011). See the
        [theory docpage](https://alexmalins.com/radioactivedecay/theory.html) 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)](https://journals.sagepub.com/doi/pdf/10.1177/ANIB_38_3).
        
        The [notebooks
        directory](https://github.com/alexmalins/radioactivedecay/tree/main/notebooks)
        in the GitHub repository contains Jupyter Notebooks for creating the decay
        datasets that are read in by ``radioactivedecay``, e.g.
        [ICRP
        107](https://github.com/alexmalins/radioactivedecay/tree/main/notebooks/icrp107_dataset/icrp107_dataset.ipynb).
        It also contains some comparisons against decay calculations made with
        [PyNE](https://github.com/alexmalins/radioactivedecay/tree/main/notebooks/comparisons/pyne/rd_pyne_truncated_compare.ipynb)
        and
        [Radiological
        Toolbox](https://github.com/alexmalins/radioactivedecay/tree/main/notebooks/comparisons/radiological_toolbox/radiological_toolbox_compare.ipynb).
        
        
        ## Tests
        
        From the base directory run:
        
        ```console
        $ 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](https://github.com/alexmalins/radioactivedecay/blob/main/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](https://github.com/alexmalins/radioactivedecay).
        
        
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