pastas.plotting.modelplots.Plotting.contributions_pie
=====================================================

.. py:method:: 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.

   :param tmin: 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.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: 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.
   :type tmax: pandas.Timestamp or str, optional
   :param ax: The Axes to plot the pie chart on. A new figure and axes will be created of
              not provided.
   :type ax: matplotlib.axes.Axes, optional
   :param figsize: tuple of size 2 to determine the figure size in inches.
   :type figsize: tuple, optional
   :param split: Split the stresses in multiple stresses when possible.
   :type split: bool, optional
   :param partition: statistic to use to determine contribution of stress, either 'sum' or
                     'std' (default).
   :type partition: str
   :param wedgeprops: dict containing pie chart wedge properties, default is None, which sets
                      edgecolor to white.
   :type wedgeprops: dict, optional, default None
   :param startangle: at which angle to start drawing wedges.
   :type startangle: float
   :param autopct: format string to add percentages to pie chart.
   :type autopct: str
   :param kwargs: The keyword arguments are passed on to plt.pie.
   :type kwargs: dict, optional

   :returns: **ax**
   :rtype: matplotlib.axes.Axes















   ..
       !! processed by numpydoc !!