:py:mod:`src.prepare_data`
==========================

.. py:module:: src.prepare_data


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

Classes
~~~~~~~

.. autoapisummary::

   src.prepare_data._open



Functions
~~~~~~~~~

.. autoapisummary::

   src.prepare_data.maybe_generate_CAFE_grid_files
   src.prepare_data.prepare_dataset
   src.prepare_data.main



Attributes
~~~~~~~~~~

.. autoapisummary::

   src.prepare_data.PROJECT_DIR
   src.prepare_data.DATA_DIR
   src.prepare_data.log_fmt


.. py:data:: PROJECT_DIR
   

   

.. py:data:: DATA_DIR
   

   

.. py:class:: _open

   
   Class containing the dataset-specific code for opening each available dataset
















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   .. py:method:: JRA55(variables, realm, preprocess)
      :staticmethod:

      
      Open JRA55 variables from specified realm
















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   .. py:method:: HadISST(variables, realm, preprocess)
      :staticmethod:

      
      Open HadISST variables from specified realm
















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   .. py:method:: EN422(variables, _, preprocess)
      :staticmethod:

      
      Open EN.4.2.2 variables
















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   .. py:method:: GPCP(variables, _, preprocess)
      :staticmethod:

      
      Open GPCP v2.3 variables
















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   .. py:method:: AGCD(variables, _, preprocess)
      :staticmethod:

      
      Open AGCD variables
















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   .. py:method:: CAFEf6(variables, realm, preprocess)
      :staticmethod:

      
      Open CAFE-f6 variables from specified realm applying preprocess prior to
      concanenating forecasts
















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   .. py:method:: CAFEf5(variables, realm, preprocess)
      :staticmethod:

      
      Open CAFE-f5 variables from specified realm, including appending first
      10 members of CAFE-f6 for 2020 forecast
















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   .. py:method:: CAFE60v1(variables, realm, preprocess)
      :staticmethod:

      
      Open CAFE60v1 variables from specified realm
















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   .. py:method:: CAFE_hist(variables, realm, preprocess)
      :staticmethod:

      
      Open CAFE historical run variables from specified realm
















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   .. py:method:: _cmip6_dcppA_hindcast(model, variant_id, grid, variables, realm, years, members, version)
      :staticmethod:

      
      Open CMIP6 dcppA-hindcast variables from specified monthly realm
















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   .. py:method:: CanESM5(variables, realm, preprocess)
      :staticmethod:

      
      Open CanESM5 dcppA-hindcast variables from specified monthly realm
















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   .. py:method:: EC_Earth3(variables, realm, preprocess)
      :staticmethod:

      
      Open EC-Earth3 dcppA-hindcast variables from specified monthly realm
















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   .. py:method:: HadGEM3(variables, realm, preprocess)
      :staticmethod:

      
      Open HadGEM3-GC31-MM dcppA-hindcast variables from specified monthly realm
















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   .. py:method:: _cmip6(model, experiment, variant_id, grid, variables, realm, members, version)
      :staticmethod:

      
      Open CMIP6 variables from specified realm

      Can specify version='latest' but this is slower as it has to search each
      directory for the latest version















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   .. py:method:: CanESM5_hist(variables, realm, preprocess)
      :staticmethod:

      
      Open CanESM5 historical variables from specified realm
















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   .. py:method:: CanESM5_ctrl(variables, realm, preprocess)
      :staticmethod:

      
      Open CanESM5 piControl variables from specified realm
















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   .. py:method:: EC_Earth3_hist(variables, realm, preprocess)
      :staticmethod:

      
      Open EC-Earth3 historical variables from specified realm
















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   .. py:method:: EC_Earth3_ctrl(variables, realm, preprocess)
      :staticmethod:

      
      Open EC-Earth3 piControl variables from specified realm
















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.. py:function:: maybe_generate_CAFE_grid_files()

   
   Generate files containing CAFE grids
















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.. py:function:: prepare_dataset(config, save_dir, save=True)

   
   Prepare a dataset according to a provided config file


   :Parameters:

       **config** : str
           The name of the config file

       **save_dir** : str
           The directory to save to

       **save** : boolean, optional
           If True (default), save the prepared dataset(s) in zarr format to save_dir. If
           False, return an xarray Dataset containing the prepared data. The latter is
           useful for debugging














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.. py:function:: main(config, config_dir, save_dir)

   
   Spin up a dask cluster and process and save raw data according to a provided config file


   :Parameters:

       **config** : str
           The name of the config file

       **config_dir** : str
           The directory containing the config file

       **save_dir** : str
           The directory to save to














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.. py:data:: log_fmt
   :annotation: = %(asctime)s - %(name)s - %(levelname)s - %(message)s