pastas.plotting.modelplots.Plotting.contributions_pie#
- pastas.plotting.modelplots.Plotting.contributions_pie(tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, ax: pastas.typing.Axes | None = None, figsize: pastas.typing.Figure | None = None, split: bool = True, partition: str = 'std', wedgeprops: dict | None = None, startangle: float = 90.0, autopct: str = '%1.1f%%', **kwargs) pastas.typing.Axes#
Make a pie chart of the contributions. This plot is based on the TNO Groundwatertoolbox.
- Parameters:
tmin (pandas.Timestamp or str, optional) – A string or pandas.Timestamp with the start date for the period (E.g. ‘1980-01-01 00:00:00’). Strings are converted to pandas.Timestamp internally.
tmax (pandas.Timestamp or str, optional) – A string or pandas.Timestamp with the end date for the period (E.g. ‘2020-01-01 00:00:00’). Strings are converted to pandas.Timestamp internally.
ax (matplotlib.axes.Axes, optional) – The Axes to plot the pie chart on. A new figure and axes will be created of not provided.
figsize (tuple, optional) – tuple of size 2 to determine the figure size in inches.
split (bool, optional) – Split the stresses in multiple stresses when possible.
partition (str) – statistic to use to determine contribution of stress, either ‘sum’ or ‘std’ (default).
wedgeprops (dict, optional, default None) – dict containing pie chart wedge properties, default is None, which sets edgecolor to white.
startangle (float) – at which angle to start drawing wedges.
autopct (str) – format string to add percentages to pie chart.
kwargs (dict, optional) – The keyword arguments are passed on to plt.pie.
- Returns:
ax
- Return type: