:py:mod:`src.plot`
==================

.. py:module:: src.plot


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   src.plot.MidpointNormalize



Functions
~~~~~~~~~

.. autoapisummary::

   src.plot.hindcasts
   src.plot.metric_maps
   src.plot.metrics



Attributes
~~~~~~~~~~

.. autoapisummary::

   src.plot.PROJECT_DIR


.. py:data:: PROJECT_DIR
   

   

.. py:class:: MidpointNormalize(vmin=None, vmax=None, midpoint=None, clip=False)

   Bases: :py:obj:`matplotlib.colors.Normalize`

   
   Midpoint normalised colorbar norm
















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   .. py:method:: __call__(value, clip=None)

      
      Normalize *value* data in the ``[vmin, vmax]`` interval into the
      ``[0.0, 1.0]`` interval and return it.


      :Parameters:

          **value**
              Data to normalize.

          **clip** : bool
              If ``None``, defaults to ``self.clip`` (which defaults to
              ``False``).









      .. rubric:: Notes

      If not already initialized, ``self.vmin`` and ``self.vmax`` are
      initialized using ``self.autoscale_None(value)``.





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.. py:function:: hindcasts(hcsts, obsvs=None, hists=None, shade=False, ax=None, figsize=(15, 4))

   
   Plot sets of hindcasts. Where multiple variables are provided, it is
   assumed that all inputs contain the same variables.


   :Parameters:

       **hcsts** : dict
           Dictionary of hindcasts to plot with the format {"name": hcst}, where
           hcst is an xarray.Dataset with dimensions "init" and "lead"

       **obsvs** : dict, optional
           Dictionary of observations to plot with the format {"name": obsv},
           where obsv is an xarray.Dataset with dimension "time"

       **hist** : dict, optional
           Dictionary of historical runs to plot with the format {"name": hist},
           where hist is an xarray.Dataset with dimension "time"

       **shade** : bool, optional
           If True, shade background according to change in bias correction in
           CAFE60v1














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.. py:function:: metric_maps(fields, variable, vrange, headings=None, add_colorbar=True, cbar_bounds=None, cmap='PiYG', central_longitude=180, figsize=(15, 15))

   
   Plot panels of skill score maps


   :Parameters:

       **fields** : list
           List of size n_rows x n_columns containing the fields to plot

       **variable** : str
           The name of the variable

       **vrange** : iterable of length 2
           The vmin and vmax values for all panels

       **headings** : list
           List of the same size as fields containing the headins for each panel

       **figsize** : iterable of length 2
           The total size of the figure














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.. py:function:: metrics(metrics, variable, headings=None, one_legend=True, shade_background=True, figsize=(15, 15))

   
   Plot panels of skill scores. When multiple metrics are provided in a panel,
   cross are shown along the x-axis where both metrics are positive.


   :Parameters:

       **metrics** : list
           List of size n_rows x n_columns containing dictionaries of the model
           metrics to plot in each panel, e.g.
           [[{"model1": {"rXY": metric1, "ri": metric2}, "model2": {"rXY": metric1, "ri": metric2}}]]

       **variable** : str
           The name of the variable

       **headings** : list
           List of the same size as fields containing the headins for each panel

       **one_legend** : boolean, optional
           If True, only add a legend to the first panel

       **shade_background** : boolean, optional
           Of True, shade the background either side of zero

       **figsize** : iterable of length 2
           The total size of the figure














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