If there are NaNs in the timeseries they count as "available data" in the data availability plot.
For example, the data availability now looks as:
Life looks good, there seem to be no gaps in the data. However, that is because the timesteps with no data contain NaNs.
Dropping these NaNs shows a more realistic figure of the actual data availability:
This PR adds the option dropna to the function pastastore.plots.data_availability(). Default is dropna=True.
If there are NaNs in the timeseries they count as "available data" in the data availability plot.
For example, the data availability now looks as: Life looks good, there seem to be no gaps in the data. However, that is because the timesteps with no data contain NaNs.
Dropping these NaNs shows a more realistic figure of the actual data availability:
This PR adds the option
dropna
to the functionpastastore.plots.data_availability()
. Default isdropna=True
.