Visualising the results¶
After running calkulate() (or calibrate() and solve()) on your data, Calkulate contains some plotting functions to help visualise the results. More will be added in time, and contributions are welcome!
Dataset plots¶
Calibrated titrant_molinity¶
fig, ax = calk.plot.titrant_molinity(
ds, xvar=None, show_bad=True, show_batches=True, figure_fname=None,
)
The required argument ds is the metadata table as a pandas DataFrame or Calkulate Dataset.
Optional inputs:
xvar: name of column to use as the x-axis variable.show_bad: whether or not to show values whereds.reference_good == False.show_batches: whether or not to show batch-averagedtitrant_molinityvalues.figure_fname: if provided, save figure to this filename.
Measured − certified alkalinity_offset¶
fig, ax = calk.plot.alkalinity_offset(
ds, xvar=None, show_bad=True, show_batches=True, figure_fname=None,
)
The required argument ds is the metadata table as a pandas DataFrame or Calkulate Dataset.
Optional inputs:
xvar: name of column to use as the x-axis variable.show_bad: whether or not to show values whereds.reference_good == False.show_batches: whether or not to show batch-averagedtitrant_molinityvalues.figure_fname: if provided, save figure to this filename.
Titration plots¶
To investigate an individual titration in more detail, first generate a Titration from the relevant row of your Dataset:
tt = ds.to_Titration(index)
where index is the index value for the row you are interested in.
A series of figures can then be plotted for the titration in question:
tt.plot_emf(): how EMF changes through the titration.tt.plot_pH(): how pH changes through the titration.tt.plot_gran_alkalinity(): the Gran-plot initial alkalinity estimate.tt.plot_gran_emf0(): the Gran-plot initial EMF0 estimate.tt.plot_alkalinity(): the total alkalinity calculated from each titration data point.tt.plot_components(): how every equilibrating component of the solution changes throughout the titration.