pyValEIA.stats.tables
=====================

.. py:module:: pyValEIA.stats.tables

.. autoapi-nested-parse::

   Functions to create tables of statistics.

   ..
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Functions
---------

.. autoapisummary::

   pyValEIA.stats.tables.decision_table_sat
   pyValEIA.stats.tables.style_df_table
   pyValEIA.stats.tables.lss_table_sat
   pyValEIA.stats.tables.style_lss_table


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

.. py:function:: decision_table_sat(states, sats=None, sat_key='Sat', eia_type='eia', model_name='Model', const_name='Swarm')

   
   Decision table summing hit/miss/corr-neg/false-pos states by satellite.


   :Parameters:

       **states** : pd.DataFrame
           DataFrame of model data including skill and local times built by
           multiday_states_report

       **sats** : list-like or NoneType
           List of satellites specified by `states[sat_key]` to include in table,
           if None all will be included (default=None)

       **sat_key** : str
           Key in `states` to access the satellite separator (default='Sat')

       **eia_type** : str
           EIA state, e.g. 'eia', 'peak', etc., that declairs what is considered a
           hit.

       **model_name** : str
           Model name for decision table label (default='Model')

       **const_name** : str
           Satellite constellation name (default='Swarm')



   :Returns:

       **table_frame** : pd.DataFrame
           DataFrame in table format separated by satellite and event state
           (state, non-state). Index using: table_frame.loc[(
               f'{const_name} {satellite}', eia_type), (model_name, eia_type)]











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.. py:function:: style_df_table(df_table, eia_type, sat_names=None)

   
   Style decision table.


   :Parameters:

       **df_table** : pd.DataFrame
           DataFrame created by decision_table_sat

       **eia_type** : str
           string designating which eia type is being reported

       **sat_names** : list-like or NoneType
           List of satellite names in `df_table` or None to use Swarm defaults.



   :Returns:

       **styled_frame** : pd.DataFrame
           Styled DataFrame




   :Raises:

       ValueError
           For unknown constellation name







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.. py:function:: lss_table_sat(model1, model2, model1_name='Model1', model2_name='Model2', sats=None, sat_key='Sat', const_name='Swarm')

   
   Create table including the Liemohn Skill Scores 1-4.


   :Parameters:

       **model1** : pd.DataFrame
           DataFrame of 1st model data including skill and local times built by
           multiday_states_report

       **model2** : pd.DataFrame
           DataFrame of 2nd model data including skill and local times built by
           multiday_states_report

       **model1_name** : str
           String of name of model1 (default='Model1')

       **model2_name** : str
           String of name for model2 (default='Model2')

       **sats** : list of strings kwarg
           swarm satellites 'A', 'B', and 'C' as default
           can specify just 1 or 2

       **sats** : list-like or NoneType
           List of satellites specified by `states[sat_key]` to include in table,
           if None all will be included (default=None)

       **sat_key** : str
           Key in `model1` and `model2` used to access the satellite separator
           (default='Sat')

       **const_name** : str
           Satellite constellation name (default='Swarm')



   :Returns:

       **lss_df** : pd.DataFrame
           DataFrame in table format separated by satellite
           and Liemohn skill score







   .. seealso::

       
       :obj:`io.load.multiday_states_report`
           ..
       



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.. py:function:: style_lss_table(lss_df, sat_names=None)

   
   Style the LSS decision table.


   :Parameters:

       **lss_df** : pd.DataFrame
           DataFrame created by lss_table_sat

       **sat_names** : list-like or NoneType
           List of satellite names in `lss_df` or None to use Swarm defaults.



   :Returns:

       **styled_table** : pd.DataFrame
           LSS table with dividers











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