pudl.extract.vcerare
====================

.. py:module:: pudl.extract.vcerare

.. autoapi-nested-parse::

   Extract VCE Resource Adequacy Renewable Energy (RARE) Power Dataset.

   This dataset has 1,000s of columns, so we don't want to manually specify a rename on
   import because we'll pivot these to a column in the transform step. We adapt the
   standard extraction infrastructure to simply read in the data.

   Each annual zip folder contains a folder with three files:
   Wind_Power_140m_Offshore_county.csv
   Wind_Power_100m_Onshore_county.csv
   Fixed_SolarPV_Lat_UPV_county.csv

   The drive also contains one more CSV file: vce_county_lat_long_fips_table.csv. This gets
   read in when the fips partition is set to True.



Attributes
----------

.. autoapisummary::

   pudl.extract.vcerare.logger
   pudl.extract.vcerare.VCERARE_PAGES
   pudl.extract.vcerare.raw_vcerare__all_dfs
   pudl.extract.vcerare.raw_vcerare_assets


Classes
-------

.. autoapisummary::

   pudl.extract.vcerare.VCERareMetadata
   pudl.extract.vcerare.Extractor


Functions
---------

.. autoapisummary::

   pudl.extract.vcerare.raw_vcerare_asset_factory
   pudl.extract.vcerare.raw_vcerare__lat_lon_fips


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

.. py:data:: logger

.. py:data:: VCERARE_PAGES
   :value: ['offshore_wind_power_140m', 'onshore_wind_power_100m', 'fixed_solar_pv_lat_upv']


.. py:class:: VCERareMetadata(*args, **kwargs)

   Bases: :py:obj:`pudl.extract.extractor.GenericMetadata`


   Special metadata class for VCE RARE Power Dataset.


   .. py:attribute:: _file_name
      :value: None



   .. py:method:: _load_column_maps(column_map_pkg) -> dict

      There are no column maps to load, so return an empty dictionary.



   .. py:method:: get_all_pages() -> list[str]

      Hard code the page names, which usually are pulled from column rename spreadsheets.



   .. py:method:: get_file_name(page, **partition)

      Returns file name of given partition and page.



.. py:class:: Extractor(*args, **kwargs)

   Bases: :py:obj:`pudl.extract.csv.CsvExtractor`


   Extractor for VCE RARE Power Dataset.


   .. py:attribute:: METADATA

      Instance of metadata object to use with this extractor.


   .. py:method:: get_column_map(page, **partition)

      Return empty dictionary, we don't rename these files.



   .. py:method:: source_filename(page: str, **partition: pudl.extract.extractor.PartitionSelection) -> str

      Produce the CSV file name as it will appear in the archive.

      The files are nested in an additional folder with the year name inside of the
      zipfile, so we add a prefix folder based on the yearly partition to the source
      filename.

      :param page: pudl name for the dataset contents, eg "boiler_generator_assn" or
                   "coal_stocks"
      :param partition: partition to load. Examples:
                        {'year': 2009}
                        {'year_month': '2020-08'}

      :returns: string name of the CSV file



   .. py:method:: load_source(page: str, **partition: pudl.extract.extractor.PartitionSelection) -> pandas.DataFrame

      Produce the dataframe object for the given partition.

      :param page: pudl name for the dataset contents, eg "boiler_generator_assn" or
                   "data"
      :param partition: partition to load. Examples:
                        {'year': 2009}
                        {'year_month': '2020-08'}

      :returns: pd.DataFrame instance containing CSV data



   .. py:method:: process_raw(df: pandas.DataFrame, page: str, **partition: pudl.extract.extractor.PartitionSelection) -> pandas.DataFrame

      Append report year to df to distinguish data from other years.



   .. py:method:: validate(df: pandas.DataFrame, page: str, **partition: pudl.extract.extractor.PartitionSelection) -> pandas.DataFrame

      Skip this step, as we aren't renaming any columns.



   .. py:method:: combine(dfs: list[pandas.DataFrame], page: str) -> pandas.DataFrame

      Concatenate dataframes into one, take any special steps for processing final page.



.. py:data:: raw_vcerare__all_dfs

.. py:function:: raw_vcerare_asset_factory(part: str) -> dagster.AssetsDefinition

   An asset factory for VCE RARE Power Dataset.


.. py:data:: raw_vcerare_assets

.. py:function:: raw_vcerare__lat_lon_fips(context) -> pandas.DataFrame

   Extract lat/lon to FIPS and county mapping CSV.

   This dataframe is static, so it has a distinct partition from the other datasets and
   its extraction is controlled by a boolean in the ETL run.