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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!

Calibrated titrant_molinity

fig, ax = calk.plot.titrant_molinity(
    data, xvar=None, show_bad=True, show_batches=True, figure_fname=None,
)

The required argument data 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 where data.reference_good == False.
  • show_batches: whether or not to show batch-averaged titrant_molinity values.
  • figure_fname: if provided, save figure to this filename.

Measured − certified alkalinity_offset

fig, ax = calk.plot.alkalinity_offset(
    data, xvar=None, show_bad=True, show_batches=True, figure_fname=None,
)

The required argument data 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 where data.reference_good == False.
  • show_batches: whether or not to show batch-averaged titrant_molinity values.
  • figure_fname: if provided, save figure to this filename.