wavy.collocation_module
=======================

.. py:module:: wavy.collocation_module

.. autoapi-nested-parse::

   - Module that should take care of collocation of points or swaths
   - Needs input from modules that retrieve from observational platforms
     and models



Attributes
----------

.. autoapisummary::

   wavy.collocation_module.model_dict
   wavy.collocation_module.insitu_dict
   wavy.collocation_module.variable_def


Classes
-------

.. autoapisummary::

   wavy.collocation_module.collocation_class


Functions
---------

.. autoapisummary::

   wavy.collocation_module.collocate_observations
   wavy.collocation_module.collocation_fct
   wavy.collocation_module.get_model_filename
   wavy.collocation_module.find_valid_fc_dates_for_model_and_leadtime
   wavy.collocation_module.check_if_file_is_valid
   wavy.collocation_module.get_closest_date
   wavy.collocation_module.adjust_dict_for_idx
   wavy.collocation_module.validate_collocated_values


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

.. py:data:: model_dict

.. py:data:: insitu_dict

.. py:data:: variable_def

.. py:function:: collocate_observations(co1, co2, twin=5, dist_max=200)

    Collocates observations from two wavy objects, keeping only closest
    data from the second dataset within a given time range around each 
    observation from the first given dataset.

   Args:
       co1 (insitu_class or satellite_class object): first observation dataset
                                                   the data from the second
                                                   dataset will be collocated
                                                   around this one.
       co2 (insitu_class or satellite_class object): second observation 
                                                     dataset
       twin (int): time window length in minutes
       dist_max (int | float): Maximum distance
                               accepted to collocate
                               data, in km

   Returns:
       tuple (co1_filter (insitu_class or satellite_class object),
              co2_filter (insitu_class or satellite_class object)): first and
                                              second objects given as inputs, 
                                              with data filtered to keep only 
                                              collocated observations


.. py:function:: collocation_fct(obs_lons, obs_lats, model_lons, model_lats)

.. py:function:: get_model_filename(nID, d, leadtime, **kwargs)

.. py:function:: find_valid_fc_dates_for_model_and_leadtime(fc_dates, model, leadtime, colloc_time_method, **kwargs)

   Finds valid dates that are close to desired dates at a precision
   of complete hours


.. py:function:: check_if_file_is_valid(fc_date, model, leadtime, **kwargs)

.. py:function:: get_closest_date(overdetermined_lst, target_lst)

.. py:function:: adjust_dict_for_idx(indict, idx, excl_keys_lst)

.. py:class:: collocation_class(oco=None, model=None, poi=None, leadtime=None, varalias=None, **kwargs)

   Bases: :py:obj:`wavy.quicklookmod.quicklook_class_sat`


   draft of envisioned collocation class object


   .. py:attribute:: varalias
      :value: None



   .. py:attribute:: varalias_obs


   .. py:attribute:: varalias_mod
      :value: None



   .. py:attribute:: model
      :value: None



   .. py:attribute:: leadtime
      :value: None



   .. py:attribute:: oco
      :value: None



   .. py:attribute:: nID


   .. py:attribute:: obstype
      :value: ''



   .. py:attribute:: units


   .. py:attribute:: stdvarname


   .. py:attribute:: region


   .. py:attribute:: sd


   .. py:attribute:: ed


   .. py:attribute:: twin


   .. py:attribute:: distlim


   .. py:attribute:: method


   .. py:attribute:: colloc_time_method


   .. py:attribute:: nproc


   .. py:attribute:: res


   .. py:method:: populate(**kwargs)


   .. py:method:: _build_xr_dataset(results_dict, **kwargs)


   .. py:method:: _drop_duplicates(**kwargs)


   .. py:method:: _collocate_field(mco, tmp_dict, **kwargs)

      Some info



   .. py:method:: _collocate_track(**kwargs)

      Some info



   .. py:method:: _collocate_centered_model_value(time, lon, lat, **kwargs)


   .. py:method:: _collocate_regridded_model(**kwargs)


   .. py:method:: collocate(**kwargs)

      get obs value for model value for given
          temporal and spatial constraints



   .. py:method:: validate_collocated_values(**kwargs)


.. py:function:: validate_collocated_values(dtime, obs, mods, **kwargs)