Plotting

Decay chain diagrams

Use the Nuclide class plot() method to create a diagram of the decay chain originating from a radionuclide:

>>> import radioactivedecay as rd
>>> nuc = rd.Nuclide('Mo-99')
>>> fig, ax = nuc.plot()

The decay chain diagrams are drawn using NetworkX and Matplotlib. The plot() method returns the Matplotlib figure and axes objects containing the decay chain diagram.

For more complicated decay chains, you can avoid decay mode and branching fraction labels overlapping by adjusting the label_pos parameter from the default value of 0.5:

>>> import radioactivedecay as rd
>>> nuc = rd.Nuclide('Rn-222')
>>> fig, ax = nuc.plot(label_pos=0.66)

Inventory decay graphs

The Inventory and InventoryHP class plot() method is for creating graphs of the radioactive decay of the nuclides in an inventory and their progeny over time. At its simplest, supply the decay timespan to the method:

>>> inv = rd.Inventory({'Rn-222': 10.0})
>>> fig, ax = inv.plot(5, 'h')

The graph shows the ingrowth of short-lived Rn-222 progeny. Use parameters such as xscale, yscale, xmin, ymin and yunits to tailor the graph to your own needs:

>>> fig, ax = inv.plot(1000, 'd', xscale='log', yscale='log', xmin=1, ymin=1E-17, yunits='mg')

Now we can see the long-lived Pb-210 radionuclide and its progeny, which form over a period of months. Large numbers of curves can make the graphs difficult to read. Use the display parameter to specify only the nuclides you wish to display. The curves follow the same order as the list you supply:

>>> fig, ax = inv.plot(1000, 'd', display=['Rn-222', 'Pb-210', 'Po-210'], xscale='log', yscale='log', xmin=1, ymin=1E-8)

By default nuclides are plotted according to those highest in the decay chains downwards. If you wish to display nuclides in alphabetical order, use the order parameter:

>>> fig, ax = inv.plot(5, 'h', order='alphabetical')

The plot() method returns the Matplotlib figure and axes objects containing the graph. These can be used to save the figure to the file or to replot it with your own Matplotlib parameters, e.g. to save a PNG image:

>>> fig.savefig('Rn-222.png', dpi=150)

For more information on handling the figure and axes objects, see the Matplotlib documentation.

To control which progeny are plotted, the decay_time_series_pandas() method can be use to extract the data into a pandas dataframe which can be filtered as required then plotted directly. As the plot method of a dataframe is a wrapper around Matplotlib, the returned axes object can be used and manipulated in the usual way (see the Matplotlib documentation).

>>> inv = rd.Inventory({'U-238': 2000.0, 'U-235': 3000.0, 'U-234': 1500.0}, 'num')
>>> df = inv.decay_time_series_pandas(time_period=1E9, time_units='y', decay_units='num')
>>> df.plot(ylabel='Number of Particles')

We can remove the progency we are not interested in and replot.

>>> filtered_df = df.loc[:, df.max() > 100]
>>> filtered_df.plot(ylabel='Number of Particles')
# This could have been done in a single statement, but has been split for demonstration purposes
# df.loc[:, df.max() > 100].plot(ylabel='Number of Particles')