wavy.validationmod
==================

.. py:module:: wavy.validationmod

.. autoapi-nested-parse::

   Module to organize the validation procedure
   Consists mostly of functions computing validation metrics



Functions
---------

.. autoapisummary::

   wavy.validationmod.calc_model_activity_ratio
   wavy.validationmod.calc_rmsd
   wavy.validationmod.calc_nrmsd
   wavy.validationmod.calc_drmsd
   wavy.validationmod.calc_scatter_index
   wavy.validationmod.calc_corrcoef
   wavy.validationmod.calc_bias
   wavy.validationmod.calc_nbias
   wavy.validationmod.calc_mad
   wavy.validationmod.disp_validation
   wavy.validationmod.validate
   wavy.validationmod.linreg_evm
   wavy.validationmod.linreg_ievm
   wavy.validationmod.linreg_deming
   wavy.validationmod.linreg_std


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

.. py:function:: calc_model_activity_ratio(a, b)

   computes the model activity ratio of input a (mode) and input b (obs)
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_rmsd(a, b)

   root mean square deviation
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_nrmsd(a, b)

   Normalized root mean square deviation
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_drmsd(a, b)

   debiased root mean square deviation
   if nans exist the prinziple of marginalization is applied


.. py:function:: calc_scatter_index(model, obs)

   Scatter index based on rmse and on std of diff


.. py:function:: calc_corrcoef(a, b)

   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_bias(a, b)

   Bias
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_nbias(a, b)

   Normalized Bias [dimensionless]
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: calc_mad(a, b)

   mean absolute deviation
   if nans exist the prinziple of marginalization is applied
   input: np.arrays with np.nan for invalids


.. py:function:: disp_validation(valid_dict)

   Print to screen validation scores.


.. py:function:: validate(results_dict, boot=None)

.. py:function:: linreg_evm(x, y, **kwargs)

.. py:function:: linreg_ievm(x, y, **kwargs)

.. py:function:: linreg_deming(x, y, **kwargs)

.. py:function:: linreg_std(x, y, **kwargs)