credit.metrics
==============

.. py:module:: credit.metrics


Attributes
----------

.. autoapisummary::

   credit.metrics.logger


Classes
-------

.. autoapisummary::

   credit.metrics.LatWeightedMetrics
   credit.metrics.LatWeightedMetricsClimatology
   credit.metrics.LatWeightedMetricsEnsemble


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

.. py:class:: LatWeightedMetrics(conf, training_mode=True)

   .. py:attribute:: conf


   .. py:attribute:: vars


   .. py:attribute:: w_lat
      :value: None



   .. py:attribute:: w_var
      :value: None



   .. py:method:: __call__(pred, y, clim=None, transform=None, forecast_datetime=0)


.. py:class:: LatWeightedMetricsClimatology(conf, climatology=None)

   .. py:attribute:: conf


   .. py:attribute:: climatology
      :value: None



   .. py:attribute:: vars


   .. py:attribute:: acc_vars


   .. py:attribute:: w_lat
      :value: None



   .. py:attribute:: w_var
      :value: None



   .. py:method:: get_climatology(forecast_datetime, variable)

      Extract the climatology for the given forecast datetime and variable.



   .. py:method:: __call__(pred, y, extras=None, transform=None, forecast_datetime=None)


   .. py:method:: acc(loss_dict, pred, y, extras, transform, forecast_datetime, w_var, w_lat)


   .. py:method:: rmse(error, w_lat, w_var)


   .. py:method:: mse(error, w_lat, w_var)


   .. py:method:: mae(error, w_lat, w_var)


.. py:class:: LatWeightedMetricsEnsemble(conf, training_mode=True)

   metrics for rollout_ens_batcher. will output full xarrays of rmse, std etc


   .. py:attribute:: conf


   .. py:attribute:: vars


   .. py:attribute:: w_lat
      :value: None



   .. py:attribute:: w_var
      :value: None



   .. py:method:: __call__(pred, y, clim=None, transform=None, forecast_datetime=0)


.. py:data:: logger