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_molinity
values.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_molinity
values.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.