pudl.analysis.epacamd_eia¶

Helper functions for filtering the EPA CAMD crosswalk table.

This filtering was originally designed to filter the crosswalk before making a subplant_id so that the only subplant_id s that are generated are for records that show up in EPA CAMD.

Usage Example:

epacems = pudl.output.epacems.epacems(states=[‘ID’], years=[2020]) # subset for test core_epa__assn_eia_epacamd = pudl_out.epacamd_eia() filtered_crosswalk = filter_crosswalk(core_epa__assn_eia_epacamd, epacems) crosswalk_with_subplant_ids = pudl.etl.make_subplant_ids(filtered_crosswalk)

Functions¶

`_get_unique_keys`(→ pandas.DataFrame)	Get unique unit IDs from CEMS data.
`filter_crosswalk_by_epacems`(→ pandas.DataFrame)	Inner join unique CEMS units with the core_epa__assn_eia_epacamd crosswalk.
`filter_out_boiler_rows`(→ pandas.DataFrame)	Remove rows that represent graph edges between generators and boilers.
`filter_crosswalk`(→ pandas.DataFrame)	Remove unmapped crosswalk rows or duplicates due to m2m boiler relationships.

Module Contents¶

pudl.analysis.epacamd_eia._get_unique_keys(epacems: pandas.DataFrame | dask.dataframe.DataFrame) → pandas.DataFrame[source]¶

Get unique unit IDs from CEMS data.

Parameters:: epacems (Union[pd.DataFrame, dd.DataFrame]) – epacems dataset from pudl.output.epacems.epacems
Returns:: unique keys from the epacems dataset
Return type:: pd.DataFrame

pudl.analysis.epacamd_eia.filter_crosswalk_by_epacems(crosswalk: pandas.DataFrame, epacems: pandas.DataFrame | dask.dataframe.DataFrame) → pandas.DataFrame[source]¶

Inner join unique CEMS units with the core_epa__assn_eia_epacamd crosswalk.

This is essentially an empirical filter on EPA units. Instead of filtering by construction/retirement dates in the crosswalk (thus assuming they are accurate), use the presence/absence of CEMS data to filter the units.

Parameters:

crosswalk – core_epa__assn_eia_epacamd crosswalk
unique_epacems_ids (pd.DataFrame) – unique ids from _get_unique_keys

Returns:

The inner join of the core_epa__assn_eia_epacamd crosswalk and unique epacems units. Adds the global ID column unit_id_epa.

pudl.analysis.epacamd_eia.filter_out_boiler_rows(crosswalk: pandas.DataFrame) → pandas.DataFrame[source]¶

Remove rows that represent graph edges between generators and boilers.

Parameters:

crosswalk (pd.DataFrame) – core_epa__assn_eia_epacamd crosswalk

Returns:

the core_epa__assn_eia_epacamd crosswalk with boiler rows (many/one-to-many): removed

Return type:

pd.DataFrame

pudl.analysis.epacamd_eia.filter_crosswalk(crosswalk: pandas.DataFrame, epacems: pandas.DataFrame | dask.dataframe.DataFrame) → pandas.DataFrame[source]¶

Remove unmapped crosswalk rows or duplicates due to m2m boiler relationships.

Parameters:

crosswalk (pd.DataFrame) – The core_epa__assn_eia_epacamd crosswalk.
epacems (Union[pd.DataFrame, dd.DataFrame]) – Emissions data. Must contain columns named [“plant_id_eia”, “emissions_unit_id_epa”]

Returns:

A filtered copy of core_epa__assn_eia_epacamd crosswalk

Return type:

pd.DataFrame