Source code for pudl.transform.ferc1
"""Classes & functions to process FERC Form 1 data before loading into the PUDL DB.
Note that many of the classes/objects here inherit from/are instances of classes defined
in :mod:`pudl.transform.classes`. Their design and relationships to each other are
documented in that module.
See :mod:`pudl.transform.params.ferc1` for the values that parameterize many of these
transformations.
"""
import enum
import importlib.resources
import itertools
import json
import re
from abc import abstractmethod
from collections import namedtuple
from collections.abc import Mapping
from typing import Annotated, Any, Literal, Self
import numpy as np
import pandas as pd
import sqlalchemy as sa
from dagster import AssetIn, AssetsDefinition, asset
from pandas.core.groupby import DataFrameGroupBy
from pydantic import BaseModel, Field, field_validator
import pudl
from pudl.extract.ferc1 import TABLE_NAME_MAP_FERC1
from pudl.helpers import assert_cols_areclose, convert_cols_dtypes
from pudl.metadata import PUDL_PACKAGE
from pudl.metadata.fields import apply_pudl_dtypes
from pudl.settings import Ferc1Settings
from pudl.transform.classes import (
AbstractTableTransformer,
InvalidRows,
RenameColumns,
TableTransformParams,
TransformParams,
cache_df,
enforce_snake_case,
)
@asset
[docs]
def _core_ferc1_xbrl__metadata_json(
raw_ferc1_xbrl__metadata_json: dict[str, dict[str, list[dict[str, Any]]]],
) -> dict[str, dict[str, list[dict[str, Any]]]]:
"""Generate cleaned json xbrl metadata.
For now, this only runs :func:`add_source_tables_to_xbrl_metadata`.
"""
return add_source_tables_to_xbrl_metadata(raw_ferc1_xbrl__metadata_json)
[docs]
def add_source_tables_to_xbrl_metadata(
raw_ferc1_xbrl__metadata_json: dict[str, dict[str, list[dict[str, Any]]]],
) -> dict[str, dict[str, list[dict[str, Any]]]]:
"""Add a ``source_tables`` field into metadata calculation components.
When a particular component of a calculation does not originate from the table in
which the calculated field is being reported, label the source table.
"""
def all_fields_in_table(table_meta) -> list:
"""Compile a list of all of the fields reported in a table."""
return [
field["name"] for meta_list in table_meta.values() for field in meta_list
]
def extract_tables_to_fields(xbrl_meta: dict) -> dict[str : list[str]]:
"""Compile a dictionary of table names (keys) to list of fields."""
return {
table_name: all_fields_in_table(table_meta)
for table_name, table_meta in xbrl_meta.items()
}
def label_source_tables(calc_component: dict, tables_to_fields: str) -> dict:
"""Add a ``source_tables`` element to the calculation component."""
calc_component["source_tables"] = [
other_table_name
for other_table_name, fields in tables_to_fields.items()
if calc_component["name"] in fields
]
# weirdly there are a number of nuclear xbrl_factoid calc components that seem
# to have no source_tables.
if not calc_component["source_tables"]:
logger.debug(f"Found no source table for {calc_component['name']}.")
return calc_component
tables_to_fields = extract_tables_to_fields(raw_ferc1_xbrl__metadata_json)
# for each table loop through all of the calculations within each field
for table_name, table_meta in raw_ferc1_xbrl__metadata_json.items():
for list_of_facts in table_meta.values():
for xbrl_fact in list_of_facts:
# all facts have ``calculations``, but they are empty lists when null
for calc_component in xbrl_fact["calculations"]:
# does the calc component show up in the table? if not, add a label
if calc_component["name"] not in tables_to_fields[table_name]:
calc_component = label_source_tables(
calc_component, tables_to_fields
)
else:
calc_component["source_tables"] = [table_name]
return raw_ferc1_xbrl__metadata_json
################################################################################
# FERC 1 Transform Parameter Models
################################################################################
@enum.unique
@enum.unique
[docs]
class TableIdFerc1(enum.Enum):
"""Enumeration of the allowed FERC 1 table IDs.
Hard coding this doesn't seem ideal. Somehow it should be either defined in the
context of the Package, the Ferc1Settings, an etl_group, or DataSource. All of the
table transformers associated with a given data source should have a table_id that's
from that data source's subset of the database. Where should this really happen?
Alternatively, the allowable values could be derived *from* the structure of the
Package. But this works for now.
"""
[docs]
PURCHASED_POWER_AND_EXCHANGES = (
"core_ferc1__yearly_purchased_power_and_exchanges_sched326"
)
[docs]
OTHER_REGULATORY_LIABILITIES = (
"core_ferc1__yearly_other_regulatory_liabilities_sched278"
)
################################################################################
# FERC 1 specific Column, MultiColumn, and Table Transform Functions
################################################################################
[docs]
class RenameColumnsFerc1(TransformParams):
"""Dictionaries for renaming either XBRL or DBF derived FERC 1 columns.
This is FERC 1 specific, because we need to store both DBF and XBRL rename
dictionaires separately. Note that this parameter model does not have its own unique
transform function. Like the generic :class:`pudl.transform.classes.RenameColumns`
it depends on the build in :meth:`pd.rename` method, which is called with the values
DBF or XBRL parameters depending on the context.
Potential parameters validations that could be implemented
* Validate that all keys appear in the original dbf/xbrl sources.
This has to be true, but right now we don't have stored metadata enumerating all
of the columns that exist in the raw data, so we don't have anything to check
against. Implement once when we have schemas defined for after the extract step.
* Validate all values appear in PUDL tables, and all expected PUDL names are mapped.
Actually we can't require that the rename values appear in the PUDL tables,
because there will be cases in which the original column gets dropped or modified,
e.g. in the case of unit conversions with a column rename.
"""
@property
[docs]
def rename_dicts_xbrl(self):
"""Compile all of the XBRL rename dictionaries into an ordered list."""
# add the xbrl last bc it happens after the inst/dur renames
return [self.duration_xbrl, self.instant_xbrl, self.xbrl]
[docs]
class WideToTidy(TransformParams):
"""Parameters for converting a wide table to a tidy table with value types."""
"""List of column names to treat as the table index."""
"""Name of column that will contain the stacked categories."""
"""List of names of value types that will end up being the column names.
Some of the FERC tables have multiple data types spread across many different
categories. In the input dataframe given to :func:`wide_to_tidy`, the value types
must be the suffixes of the column names. If the table does not natively have the
pattern of "{to-be stacked category}_{value_type}", rename the columns using a
``rename_columns.duration_xbrl``, ``rename_columns.instant_xbrl`` or
``rename_columns.dbf`` parameter which will be employed in
:meth:`process_duration_xbrl`, :meth:`process_instant_xbrl` or :meth:`process_dbf`.
"""
"""The number of columns that are expected to be dropped.
:func:`wide_to_tidy_xbrl` will generate a regex pattern assuming the ``value_types``
are the column name's suffixes. If a column does not conform to that pattern, it
will be filtered out. This is helpful for us to not include a bunch of columns from
the input dataframe incorrectly included in the stacking process. We could enumerate
every column that we want to drop, but this could be tedious and potentially error
prone. But this does mean that if a column is incorrectly named - or barely missing
the pattern, it will be dropped. This parameter enables us to lock the number of
expected columns. If the dropped columns are a different number, an error will be
raised.
"""
[docs]
class WideToTidySourceFerc1(TransformParams):
"""Parameters for converting either or both XBRL and DBF table from wide to tidy."""
@property
[docs]
def value_types(self) -> list[str]:
"""Compile a list of all of the ``value_types`` from ``wide_to_tidy``."""
# each wtt source could be either a list of WideToTidy or a WideToTidy.
# so we need to drill in to either grab the value_types
value_types = []
for wide_to_tidy in [self.xbrl, self.dbf]:
if isinstance(wide_to_tidy, WideToTidy):
value_types.append(wide_to_tidy.value_types)
elif isinstance(wide_to_tidy, list):
for rly_wide_to_tidy in wide_to_tidy:
value_types.append(rly_wide_to_tidy.value_types)
# remove None's & flatten/dedupe
value_types = [v for v in value_types if v is not None]
flattened_values = []
for item in value_types:
if isinstance(item, list):
flattened_values += list(item)
elif isinstance(item, str):
flattened_values.append(item)
return flattened_values
[docs]
def wide_to_tidy(df: pd.DataFrame, params: WideToTidy) -> pd.DataFrame:
"""Reshape wide tables with FERC account columns to tidy format.
The XBRL table coming into this method could contain all the data from both the
instant and duration tables in a wide format -- with one column for every
combination of value type (e.g. additions, ending_balance) and value category, which
means ~500 columns for some tables.
We tidy this into a long table with one column for each of the value types in
``params.value_types`` and a new column named ``xbrl_factoid`` that contains
categories that were previously the XBRL column name stems.
This allows aggregations of multiple ``xbrl_factoid`` categories in a columnar
fashion such as aggregation across groups of rows to total up various hierarchical
accounting categories (hydraulic turbines -> hydraulic production plant -> all
production plant -> all electric utility plant) though the categorical columns
required for that aggregation are added later.
For table that have a internal relationship between the values in the
``params.value_types``, such as the :ref:`core_ferc1__yearly_plant_in_service_sched204` table, this also
enables aggregation across columns to calculate the ending balance based on the
starting balance and all of the reported changes.
"""
suffixes = "|".join(params.value_types)
pat = r"(^.*)_(" + suffixes + r"$)"
# filter out any columns that don't match the pattern
df_out = df.set_index(params.idx_cols).filter(regex=pat)
# check if there are unexpected columns being dropped
dropped_cols = [col for col in df if col not in df_out.reset_index().columns]
logger.debug(f"dropping: {dropped_cols}")
if params.expected_drop_cols != len(dropped_cols):
raise AssertionError(
f"Unexpected number of columns dropped: ({len(dropped_cols)}) instead of "
f"({params.expected_drop_cols}). Columns dropped: {dropped_cols}"
)
new_cols = pd.MultiIndex.from_tuples(
[(re.sub(pat, r"\1", col), re.sub(pat, r"\2", col)) for col in df_out.columns],
names=[params.stacked_column_name, "value_type"],
)
df_out.columns = new_cols
df_out = (
df_out.stack(params.stacked_column_name, dropna=False)
.loc[:, params.value_types]
.reset_index()
)
# remove the name of the columns which was the name of the renaming layer of the
# multi-index
df_out.columns.name = None
return df_out
[docs]
class MergeXbrlMetadata(TransformParams):
"""Parameters for merging in XBRL metadata."""
"""Dictionary to rename columns in the normalized metadata before merging.
This dictionary will be passed as :func:`pd.DataFrame.rename` ``columns`` parameter.
"""
"""Column name to merge on in :func:`merge_xbrl_metadata`."""
[docs]
def merge_xbrl_metadata(
df: pd.DataFrame, xbrl_metadata: pd.DataFrame, params: MergeXbrlMetadata
) -> pd.DataFrame:
"""Merge metadata based on params."""
return pd.merge(
df,
xbrl_metadata.rename(columns=params.rename_columns),
on=params.on,
how="left",
validate="many_to_one",
)
[docs]
class DropDuplicateRowsDbf(TransformParams):
"""Parameter for dropping duplicate DBF rows."""
"""Name of table used to grab primary keys of PUDL table to check for duplicates."""
"""List of data column names to ensure primary key duplicates have the same data."""
[docs]
def drop_duplicate_rows_dbf(
df: pd.DataFrame,
params: DropDuplicateRowsDbf,
return_dupes_w_unique_data: bool = False,
) -> pd.DataFrame:
"""Drop duplicate DBF rows if duplicates have indentical data or one row has nulls.
There are several instances of the DBF data reporting the same value on multiple
rows. This function checks to see if all of the duplicate values that have the same
primary keys have reported the same data or have records with null data in any of
the data columns while the other record has complete data. If the duplicates have no
unique data, the duplicates are dropped with ``keep="first"``. If any duplicates do
not contain the same data or half null data, an assertion will be raised.
Args:
df: DBF table containing PUDL primary key columns
params: an instance of :class:`DropDuplicateRowsDbf`
return_dupes_w_unique_data: Boolean flag used for debuging only which returns
the duplicates which contain actually unique data instead of raising
assertion. Default is False.
"""
pks = pudl.metadata.classes.Resource.from_id(
params.table_name.value
).schema.primary_key
# add a column that indicates whether or not any of the data columns contain null data
df.loc[:, "null_data"] = df[params.data_columns].isnull().any(axis="columns")
# checks to make sure the drop is targeted as expected
# of the PK dupes, drop all instances when the data *is also the same*
dupes_w_possible_unique_data = df.drop_duplicates(
pks + params.data_columns, keep=False
)
dupes_w_possible_unique_data = dupes_w_possible_unique_data[
dupes_w_possible_unique_data.duplicated(pks, keep=False)
]
# if there are pk+data dupes, is there one record with some null data
# an other with completely non-null data??
# OR are there any records that have some null data and some actually unique
# data
nunique_data_columns = [f"{col}_nunique" for col in params.data_columns]
dupes_w_possible_unique_data.loc[
:, nunique_data_columns + ["null_data_nunique"]
] = (
dupes_w_possible_unique_data.groupby(pks)[params.data_columns + ["null_data"]]
.transform("nunique")
.add_suffix("_nunique")
)
dupes_w_unique_data = dupes_w_possible_unique_data[
(dupes_w_possible_unique_data.null_data_nunique != 2)
| (
dupes_w_possible_unique_data[
dupes_w_possible_unique_data[nunique_data_columns] != 1
].any(axis="columns")
)
].sort_values(by=pks)
if not dupes_w_unique_data.empty:
if return_dupes_w_unique_data:
logger.warning("Returning duplicate records for debugging.")
return dupes_w_unique_data
raise AssertionError(
"Duplicates have unique data and should not be dropped. Unique data: "
f"{len(dupes_w_unique_data)}: \n{dupes_w_unique_data.sort_values(by=pks)}"
)
len_og = len(df)
df = (
df.sort_values(by=["null_data"], ascending=True)
.drop_duplicates(pks, keep="first")
.drop(columns=["null_data"])
)
logger.info(
f"Dropped {len_og - len(df)} duplicate records: {(len_og - len(df))/len_og:.1%}"
" of total rows."
)
return df
[docs]
class AlignRowNumbersDbf(TransformParams):
"""Parameters for aligning DBF row numbers with metadata from mannual maps."""
"""DBF table to use to grab the row map in :func:`align_row_numbers_dbf`.
Default is ``None``.
"""
[docs]
def align_row_numbers_dbf(df: pd.DataFrame, params: AlignRowNumbersDbf) -> pd.DataFrame:
"""Rename the xbrl_factoid column after :meth:`align_row_numbers_dbf`."""
if params.dbf_table_names:
logger.info(
f"Aligning row numbers from DBF row to XBRL map for {params.dbf_table_names}"
)
row_map = (
read_dbf_to_xbrl_map(dbf_table_names=params.dbf_table_names)
.pipe(fill_dbf_to_xbrl_map)
.drop(columns=["sched_table_name", "row_literal"])
)
if row_map.isnull().any(axis=None):
raise ValueError(
"Filled DBF-XBRL map contains NA values, which should never happen:"
f"{row_map}"
)
df = pd.merge(df, row_map, on=["report_year", "row_number"], how="left")
if df.xbrl_factoid.isna().any():
raise ValueError(
"Found null row labels after aligning DBF/XBRL rows.\n"
f"{df[df.xbrl_factoid.isna()]}"
)
# eliminate the header rows since they (should!) contain no data in either the
# DBF or XBRL records:
df = df[df.xbrl_factoid != "HEADER_ROW"]
return df
[docs]
class SelectDbfRowsByCategory(TransformParams):
"""Parameters for :func:`select_dbf_rows_by_category`."""
"""The column name containing categories to select by."""
"""Boolean flag to indicate whether or not to use the categories in the XBRL table.
If True, :func:`select_dbf_rows_by_category` will find the list of categories that
exist in the passed in ``processed_xbrl`` to select by.
"""
"""List of additional categories to select by.
If ``select_by_xbrl_categories`` is ``True``, these categories will be added to the
XBRL categories and both will be used to select rows from the DBF data. If
``select_by_xbrl_categories`` is ``False``, only the "additional" categories will be
the used to select rows from the DBF data.
"""
"""Number of categories that are expected to be dropped from the DBF data.
This is here to ensure no unexpected manipulations to the categories have occured. A
warning will be flagged if this number is different than the number of categories
that are being dropped.
"""
[docs]
def select_dbf_rows_by_category(
processed_dbf: pd.DataFrame,
processed_xbrl: pd.DataFrame,
params: SelectDbfRowsByCategory,
) -> pd.DataFrame:
"""Select DBF rows with values listed or found in XBRL in a categorical-like column.
The XBRL data often breaks out sub-sections of DBF tables into their own table.
These breakout tables are often messy, unstructured portions of a particular
schedule or page on the FERC1 PDF. We often want to preserve some of the ways the
XBRL data is segmented so we need to be able to select only portions of the DBF
table to be concatenated with the XBRL data.
In mapping DBF data to XBRL data for the tables that rely on their ``row_number``
we map each row to its corresponding ``xbrl_factoid``. The standard use of this
transformer is to use the ``column_name`` that corresponds to the ``xbrl_factoid``
that was merged into the DBF data via :func:`align_row_numbers_dbf` and was
converted into a column in the XBRL data via :func:`wide_to_tidy`.
Note: Often, the unstructured portion of the DBF table that (possibly) sums up into
a single value in structured data has the same ``xbrl_factoid`` name in the XBRL
tables. By convention, we are employing a pattern in the ``dbf_to_xbrl.csv`` map
that involves adding an ``_unstructed`` suffix to the rows that correspond to the
unstructured portion of the table. This enables a simple selection of the structured
part of the table. When processing the unstructured table, you can either rename the
XBRL data's factoid name to include an ``_unstructed`` suffix or you can specify
the categories with ``_unstructed`` suffixes using the ``additional_categories``
parameter.
"""
# compile the list of categories from the possible options.
categories_to_select = []
if params.select_by_xbrl_categories:
categories_to_select = categories_to_select + list(
processed_xbrl[params.column_name].unique()
)
if params.additional_categories:
categories_to_select = categories_to_select + params.additional_categories
# check if we are getting the same number of expected categories to drop
categories_to_drop = [
cat
for cat in processed_dbf[params.column_name].unique()
if cat not in categories_to_select
]
if len(categories_to_drop) != params.len_expected_categories_to_drop:
logger.warning(
f"Dropping {len(categories_to_drop)} DBF categories that contain the "
f"following values in {params.column_name} but expected "
f"{params.len_expected_categories_to_drop}:"
f"{categories_to_drop}"
)
# now select only the rows which contain the categories we want to include in the
# column that we care about. Copy bc this is a slice of the og dataframe.
category_mask = processed_dbf[params.column_name].isin(categories_to_select)
return processed_dbf.loc[category_mask].copy()
[docs]
class UnstackBalancesToReportYearInstantXbrl(TransformParams):
"""Parameters for :func:`unstack_balances_to_report_year_instant_xbrl`."""
"""If True unstack balances to a single year (the report year)."""
[docs]
def unstack_balances_to_report_year_instant_xbrl(
df: pd.DataFrame,
params: UnstackBalancesToReportYearInstantXbrl,
primary_key_cols: list[str],
) -> pd.DataFrame:
"""Turn start year end year rows into columns for each value type.
Called in :meth:`Ferc1AbstractTableTransformer.process_instant_xbrl`.
Some instant tables report year-end data, with their datestamps in different years,
but we want year-start and year-end data within a single report_year (which is
equivalent) stored in two separate columns for compatibility with the DBF data.
This function unstacks that table and adds the suffixes ``_starting_balance`` and
``_ending_balance`` to each of the columns. These can then be used as
``value_types`` in :func:`wide_to_tidy` to normalize the table.
There are two checks in place:
First, it will make sure that there are not duplicate entries for a single year +
other primary key fields. Ex: a row for 2020-12-31 and 2020-06-30 for entitiy_id X
means that the data isn't annually unique. We could just drop these mid-year
values, but we might want to keep them or at least check that there is no funny
business with the data.
We also check that there are no mid-year dates at all. If an entity reports a value
from the middle of the year, we can't identify it as a start/end of year value.
Params:
primary_key_cols: The columns that should be used to check for duplicated data,
and also for unstacking the balance -- these are set to be the index before
unstack is called. These are typically set by the wrapping method and
generated automatically based on other class transformation parameters via
:meth:`Ferc1AbstractTableTransformer.source_table_primary_key`.
"""
if not params.unstack_balances_to_report_year:
return df
# report year always corresponds to the year of "date"
unique_cols = set(primary_key_cols).union({"report_year"})
if df.duplicated(unique_cols).any():
raise AssertionError(
"Looks like there are multiple entries per year--not sure which to use "
f"for the start/end balance. {params=} {primary_key_cols=}"
)
if not pd.to_datetime(df["date"]).dt.is_year_end.all():
raise AssertionError(
"Looks like there are some values in here that aren't from the end of "
"the year. We can't use those to calculate start and end balances."
)
ending_balances = df.assign(balance_type="ending_balance")
starting_balances = df.assign(
report_year=df.report_year + 1, balance_type="starting_balance"
)
all_balances = pd.concat([starting_balances, ending_balances])
# for the first year, we expect no starting balances; for the last year, we expect no ending balances.
first_last_year_stripped = all_balances.loc[
lambda df: ~df.report_year.isin({df.report_year.min(), df.report_year.max()})
]
unstacked_by_year = (
first_last_year_stripped.drop(columns=["date"])
.set_index(primary_key_cols + ["balance_type", "sched_table_name"])
.unstack("balance_type")
)
# munge multi-index into flat index, separated by _
unstacked_by_year.columns = [
"_".join(items) for items in unstacked_by_year.columns.to_flat_index()
]
return unstacked_by_year.reset_index()
[docs]
class CombineAxisColumnsXbrl(TransformParams):
"""Parameters for :func:`combine_axis_columns_xbrl`."""
"""List of axis columns to combine."""
"""The name of the combined axis column -- must end with the suffix ``_axis``!."""
@field_validator("new_axis_column_name")
@classmethod
[docs]
def doesnt_end_with_axis(cls, v):
"""Ensure that new axis column ends in _axis."""
if v is not None and not v.endswith("_axis"):
raise ValueError(
"The new axis column name must end with the suffix '_axis'!"
)
return v
[docs]
def combine_axis_columns_xbrl(
df: pd.DataFrame, params: CombineAxisColumnsXbrl
) -> pd.DataFrame:
"""Combine axis columns from squished XBRL tables into one column with no NAs.
Called in :meth:`Ferc1AbstractTableTransformer.process_xbrl`.
There are instances (ex: sales_by_rate_schedule_ferc1) where the DBF table is equal
to several concatenated XBRL tables. These XBRL tables are extracted together with
the function :func:`extract_xbrl_concat`. Once combined, we need to deal with their
axis columns.
We use the axis columns (the primary key for the raw XBRL tables) in the creation
of ``record_id``s for each of the rows. If each of the concatinated XBRL tables has
the same axis column name then there's no need to fret. However, if the columns have
slightly different names (ex: ``residential_sales_axis`` vs.
``industrial_sales_axis``), we'll need to combine them. We combine them to get rid
of NA values which aren't allowed in primary keys. Otherwise it would look like
this:
+-------------------------+-------------------------+
| residential_sales_axis | industrial_sales_axis |
+=========================+=========================+
| value1 | NA |
+-------------------------+-------------------------+
| value2 | NA |
+-------------------------+-------------------------+
| NA | valueA |
+-------------------------+-------------------------+
| NA | valueB |
+-------------------------+-------------------------+
vs. this:
+-------------------------+
| sales_axis |
+=========================+
| value1 |
+-------------------------+
| value2 |
+-------------------------+
| valueA |
+-------------------------+
| valueB |
+-------------------------+
"""
# First, make sure that the new_axis_column_name param as the word axis in it
if not params.new_axis_column_name.endswith("_axis"):
raise ValueError(
"Your new_axis_column_name must end with the suffix _axis so that it gets "
"included in the record_id."
)
# Now, make sure there are no overlapping axis columns
if (df[params.axis_columns_to_combine].count(axis=1) > 1).any():
raise AssertionError(
"You're trying to combine axis columns, but there's more than one axis "
"column value per row."
)
# Now combine all of the columns into one and remove the old axis columns
df[params.new_axis_column_name] = pd.NA
for col in params.axis_columns_to_combine:
df[params.new_axis_column_name] = df[params.new_axis_column_name].fillna(
df[col]
)
df = df.drop(columns=params.axis_columns_to_combine)
return df
[docs]
class AssignQuarterlyDataToYearlyDbf(TransformParams):
"""Parameters for transfering quarterly reported data to annual columns."""
[docs]
def assign_quarterly_data_to_yearly_dbf(
df: pd.DataFrame, params: AssignQuarterlyDataToYearlyDbf
) -> pd.DataFrame:
"""Transfer 4th quarter reported data to the annual columns.
For some reason in the dbf data for this table reported all of the
balance data as quarterly data between specific years. We already choose
the end of the year in :meth:`select_annual_rows_dbf`. This ensures that
by this point, any quarterly data remaining in the input dataframe pertains
to the 4th quarter.
"""
bad_years_mask = df.report_year.isin(params.quarterly_filed_years)
# ensure this filling in treatment is necessary!
if (
not df.loc[bad_years_mask, list(params.quarterly_to_yearly_column_map.values())]
.isnull()
.all(axis=None)
):
raise AssertionError(
f"We expected that all balance data in years {params.quarterly_filed_years} "
"to be all null. Found non-null records, so the annual columns may no "
"longer need to be filled in with quarterly data."
)
for quarterly_col, yearly_col in params.quarterly_to_yearly_column_map.items():
df.loc[bad_years_mask, yearly_col] = df.loc[bad_years_mask, quarterly_col]
return df
[docs]
class AddColumnWithUniformValue(TransformParams):
"""Parameters for adding a column to a table with a single value."""
[docs]
class AddColumnsWithUniformValues(TransformParams):
"""Parameters for adding columns to a table with a single value."""
"Dictionary of column names (keys) with :class:`AddColumnWithUniformValue` (values)"
@property
[docs]
def assign_cols(self) -> dict[str, str]:
"""Dictionary of column_name (key) to uniform value (value) to use with pd.assign."""
return {
col: col_info.column_value
for (col, col_info) in self.columns_to_add.items()
}
[docs]
def add_columns_with_uniform_values(
df: pd.DataFrame, params: AddColumnsWithUniformValues
) -> pd.DataFrame:
"""Add a column to a table with a single value."""
return df.assign(**params.assign_cols)
[docs]
class IsCloseTolerance(TransformParams):
"""Info for testing a particular check."""
"""Relative tolerance to use in :func:`np.isclose` for determining equality."""
"""Absolute tolerance to use in :func:`np.isclose` for determining equality."""
[docs]
class CalculationIsCloseTolerance(TransformParams):
"""Calc params organized by check type."""
[docs]
class MetricTolerances(TransformParams):
"""Tolerances for all data checks to be preformed within a grouped df."""
"""Fraction of records with non-null reported values and null calculated values."""
# ooof this one is just bad
[docs]
class GroupMetricTolerances(TransformParams):
"""Data quality expectations related to FERC 1 calculations.
We are doing a lot of comparisons between calculated and reported values to identify
reporting errors in the data, errors in FERC's metadata, and bugs in our own code.
This class provides a structure for encoding our expectations about the level of
acceptable (or at least expected) errors, and allows us to pass them around.
In the future we might also want to specify much more granular expectations,
pertaining to individual tables, years, utilities, or facts to ensure that we don't
have low overall error rates, but a problem with the way the data or metadata is
reported in a particular year. We could also define per-filing and per-table error
tolerances to help us identify individual utilities that have e.g. used an outdated
version of Form 1 when filing.
"""
[docs]
ungrouped: MetricTolerances = MetricTolerances(
error_frequency=0.0005,
relative_error_magnitude=0.015,
null_calculated_value_frequency=0.50,
null_reported_value_frequency=0.68,
)
[docs]
xbrl_factoid: MetricTolerances = MetricTolerances(
error_frequency=0.018,
null_calculated_value_frequency=1.0,
)
[docs]
utility_id_ferc1: MetricTolerances = MetricTolerances(
error_frequency=0.038,
null_calculated_value_frequency=1.0,
)
[docs]
report_year: MetricTolerances = MetricTolerances(
error_frequency=0.006,
null_calculated_value_frequency=0.7,
)
[docs]
table_name: MetricTolerances = MetricTolerances(
error_frequency=0.0005,
relative_error_magnitude=0.001,
null_calculated_value_frequency=0.50,
null_reported_value_frequency=0.68,
)
[docs]
class GroupMetricChecks(TransformParams):
"""Input for checking calculations organized by group and test."""
[docs]
groups_to_check: list[
Literal[
"ungrouped", "table_name", "xbrl_factoid", "utility_id_ferc1", "report_year"
]
] = [
"ungrouped",
"report_year",
"xbrl_factoid",
"utility_id_ferc1",
]
[docs]
metrics_to_check: list[str] = [
"error_frequency",
"relative_error_magnitude",
"null_calculated_value_frequency",
"null_reported_value_frequency",
]
# TODO: The mechanics of this validation are a pain, given the bajillion combos
# of tolerances we have in the matrix of checks. It works, but actually specifying
# all of the relative values is not currently ergonomic, so it is disabled for the
# moment.
# @model_validator(mode="after")
[docs]
def grouped_tol_ge_ungrouped_tol(self: Self):
"""Grouped tolerance should always be greater than or equal to ungrouped."""
for group in self.groups_to_check:
metric_tolerances = self.group_metric_tolerances.model_dump().get(group)
for metric_name, tolerance in metric_tolerances.items():
ungrouped_tolerance = self.group_metric_tolerances.model_dump()[
"ungrouped"
].get(metric_name)
if tolerance < ungrouped_tolerance:
raise AssertionError(
f"In {group=}, {tolerance=} for {metric_name} should be "
f"greater than {ungrouped_tolerance=}."
)
return self
[docs]
class ReconcileTableCalculations(TransformParams):
"""Parameters for reconciling xbrl-metadata based calculations within a table."""
"""Name of data column to check.
This will typically be ``dollar_value`` or ``ending_balance`` column for the income
statement and the balance sheet tables.
"""
"""Fraction of calculated values which we allow not to match reported values."""
"""Sub-dimension column name (e.g. utility type) to compare calculations against in
:func:`reconcile_table_calculations`."""
"""Fraction of calculated subdimensions allowed not to match reported values."""
"""For the subdimension calculations, how to merge valiate when merging the data (left)
onto the calculation components (right)."""
[docs]
def reconcile_table_calculations(
df: pd.DataFrame,
calculation_components: pd.DataFrame,
xbrl_metadata: pd.DataFrame,
xbrl_factoid_name: str,
table_name: str,
params: ReconcileTableCalculations,
) -> pd.DataFrame:
"""Ensure intra-table calculated values match reported values within a tolerance.
In addition to checking whether all reported "calculated" values match the output
of our repaired calculations, this function adds a correction record to the
dataframe that is included in the calculations so that after the fact the
calculations match exactly. This is only done when the fraction of records that
don't match within the tolerances of :func:`numpy.isclose` is below a set
threshold.
Note that only calculations which are off by a significant amount result in the
creation of a correction record. Many calculations are off from the reported values
by exaclty one dollar, presumably due to rounding errrors. These records typically
do not fail the :func:`numpy.isclose()` test and so are not corrected.
Args:
df: processed table containing data values to check.
calculation_components: processed calculation component metadata.
xbrl_metadata: A dataframe of fact-level metadata, required for inferring the
sub-dimension total calculations.
xbrl_factoid_name: The name of the column which contains XBRL factoid values in
the processed table.
table_name: name of the PUDL table whose data and metadata is being processed.
This is necessary so we can ensure the metadata has the same structure as
the calculation components, which at a minimum need both ``table_name`` and
``xbrl_factoid`` to identify them.
params: :class:`ReconcileTableCalculations` parameters.
Returns:
A dataframe that includes new ``*_correction`` records with values that ensure
the calculations all match to within the required tolerance. It will also
contain columns created by the calculation checking process like ``abs_diff``
and ``rel_diff``.
"""
# If we don't have this value, we aren't doing any calculation checking:
if params.column_to_check is None:
return df
# Use the calculation components which reference ONLY values within the table
intra_table_calcs = calculation_components[
calculation_components.is_within_table_calc
]
# To interact with the calculation components, we need uniformly named columns
# for xbrl_factoid, and table_name
df = df.rename(columns={xbrl_factoid_name: "xbrl_factoid"}).assign(
table_name=table_name
)
dim_cols = [
dim
for dim in other_dimensions(table_names=list(FERC1_TFR_CLASSES))
if dim in df.columns
]
calc_idx = ["xbrl_factoid", "table_name"] + dim_cols
if dim_cols:
table_dims = df[calc_idx].drop_duplicates(keep="first")
intra_table_calcs = _add_intra_table_calculation_dimensions(
intra_table_calcs=intra_table_calcs,
table_dims=table_dims,
dim_cols=dim_cols,
)
# Check the subdimension totals. add subdimension_corrections!
if params.subdimension_column:
logger.info(
f"Checking total-to-subdimension calculations in {params.subdimension_column}"
)
calc_comps_w_totals = _calculation_components_subdimension_calculations(
intra_table_calcs=intra_table_calcs,
table_dims=table_dims,
xbrl_metadata=xbrl_metadata,
dim_cols=dim_cols,
table_name=table_name,
)
df = reconcile_one_type_of_table_calculations(
data=df,
calculation_components=calc_comps_w_totals[
calc_comps_w_totals.is_total_to_subdimensions_calc
],
calc_idx=calc_idx,
value_col=params.column_to_check,
group_metric_checks=params.group_metric_checks,
table_name=table_name,
is_subdimension=True,
calc_to_data_merge_validation=params.subdimension_merge_validation,
)[df.columns]
calculated_df = reconcile_one_type_of_table_calculations(
data=df,
calculation_components=intra_table_calcs,
calc_idx=calc_idx,
value_col=params.column_to_check,
group_metric_checks=params.group_metric_checks,
table_name=table_name,
is_subdimension=False,
)
# Rename back to the original xbrl_factoid column name before returning:
return calculated_df.rename(columns={"xbrl_factoid": xbrl_factoid_name})
[docs]
def reconcile_one_type_of_table_calculations(
data: pd.DataFrame,
calculation_components: pd.DataFrame,
calc_idx: list[str],
value_col: str,
group_metric_checks: GroupMetricChecks,
table_name: str,
is_subdimension: bool,
calc_to_data_merge_validation: Literal[
"one_to_many", "many_to_many"
] = "one_to_many",
) -> pd.DataFrame:
"""Calculate vales, run metric checks and add corrections.
Args:
data: exploded FERC data to apply the calculations to. Primary key should be
``report_year``, ``utility_id_ferc1``, ``table_name``, ``xbrl_factoid``, and
whatever additional dimensions are relevant to the data.
calculation_components: Table defining the calculations, with each row defining
a single component, including its weight. Groups of rows identified by
``table_name_parent`` and ``xbrl_factoid_parent`` indicate the values being
calculated.
calc_idx: primary key columns that uniquely identify a calculation component
(not including the ``_parent`` columns).
value_col: label of the column in ``data`` that contains the values to apply the
calculations to (typically ``dollar_value`` or ``ending_balance``).
"""
if calculation_components.empty:
return data
calculated_df = (
calculate_values_from_components(
data=data,
calculation_components=calculation_components,
calc_idx=calc_idx,
value_col=value_col,
calc_to_data_merge_validation=calc_to_data_merge_validation,
)
.pipe(
check_calculation_metrics,
group_metric_checks=group_metric_checks,
)
.pipe(
add_corrections,
value_col=value_col,
is_close_tolerance=IsCloseTolerance(),
table_name=table_name,
is_subdimension=is_subdimension,
)
)
return calculated_df
[docs]
def _calculation_components_subdimension_calculations(
intra_table_calcs: pd.DataFrame,
table_dims: pd.DataFrame,
xbrl_metadata: pd.DataFrame,
dim_cols: list[str],
table_name: str,
) -> pd.DataFrame:
"""Add total to subdimension calculations into calculation components."""
meta_w_dims = xbrl_metadata.assign(
**{dim: pd.NA for dim in dim_cols} | {"table_name": table_name}
).pipe(
make_xbrl_factoid_dimensions_explicit,
table_dimensions_ferc1=table_dims,
dimensions=dim_cols,
)
calc_comps_w_totals = infer_intra_factoid_totals(
intra_table_calcs,
meta_w_dims=meta_w_dims,
table_dimensions=table_dims,
dimensions=dim_cols,
)
return calc_comps_w_totals
[docs]
def _add_intra_table_calculation_dimensions(
intra_table_calcs: pd.DataFrame,
table_dims: pd.DataFrame,
dim_cols: list[str],
) -> pd.DataFrame:
"""Add all observed subdimensions into the calculation components."""
######## Add all observed subdimensions into the calculation components!!!
# First determine what dimensions matter in this table:
# - usually params.subdimension_column has THE ONE dimension in the table...
# - BUT some tables have more than one dimension so we grab from all of the
# the dims in the transformers.
# need to add in the correction dimensions. they don't show up in the data at
# this point so we don't have the dimensions yet. NOTE: this could have been
# done by adding the dims into table_dims..... maybe would have been more
# straightforward
correction_mask = intra_table_calcs.xbrl_factoid.str.endswith("_correction")
intra_table_calcs = pd.concat(
[
intra_table_calcs[~correction_mask],
pd.merge(
intra_table_calcs[correction_mask].drop(columns=dim_cols),
table_dims[["table_name"] + dim_cols].drop_duplicates(),
on=["table_name"],
),
]
)
intra_table_calcs = make_xbrl_factoid_dimensions_explicit(
intra_table_calcs,
table_dimensions_ferc1=table_dims,
dimensions=dim_cols,
).pipe(
assign_parent_dimensions,
table_dimensions=table_dims,
dimensions=dim_cols,
)
# this is for the income statement table specifically, but is general:
# remove all the bits where we have a child dim but not a parent dim
# sometimes there are child dimensions that have utility_type == "other2" etc
# where the parent dimension has nothing
for dim in dim_cols:
intra_table_calcs = intra_table_calcs[
~(
intra_table_calcs[dim].notnull()
& intra_table_calcs[f"{dim}_parent"].isnull()
)
]
return intra_table_calcs
[docs]
def calculate_values_from_components(
calculation_components: pd.DataFrame,
data: pd.DataFrame,
calc_idx: list[str],
value_col: str,
calc_to_data_merge_validation: Literal[
"one_to_many", "many_to_many"
] = "one_to_many",
) -> pd.DataFrame:
"""Apply calculations derived from XBRL metadata to reported XBRL data.
Args:
calculation_components: Table defining the calculations, with each row defining
a single component, including its weight. Groups of rows identified by
``table_name_parent`` and ``xbrl_factoid_parent`` indicate the values being
calculated.
data: exploded FERC data to apply the calculations to. Primary key should be
``report_year``, ``utility_id_ferc1``, ``table_name``, ``xbrl_factoid``, and
whatever additional dimensions are relevant to the data.
calc_idx: primary key columns that uniquely identify a calculation component
(not including the ``_parent`` columns).
value_col: label of the column in ``data`` that contains the values to apply the
calculations to (typically ``dollar_value`` or ``ending_balance``).
"""
# Merge the reported data and the calculation component metadata to enable
# validation of calculated values. Here the data table exploded is supplying the
# values associated with individual calculation components, and the table_name
# and xbrl_factoid to which we aggregate are coming from the calculation
# components table. After merging we use the weights to adjust the reported
# values so they can be summed directly. This gives us aggregated calculated
# values that can later be compared to the higher level reported values.
# infer the pks of the data by adding in the util/year
data_idx = calc_idx + ["utility_id_ferc1", "report_year"]
# we are going to merge the data onto the calc components with the _parent
# column names, so the groupby after the merge needs a set of by cols with the
# _parent suffix
gby_parent = [f"{col}_parent" for col in calc_idx] + [
"utility_id_ferc1",
"report_year",
]
try:
calc_df = (
pd.merge(
calculation_components,
data,
validate=calc_to_data_merge_validation,
on=calc_idx,
)
# apply the weight from the calc to convey the sign before summing.
.assign(calculated_value=lambda x: x[value_col] * x.weight)
.groupby(gby_parent, as_index=False, dropna=False)[["calculated_value"]]
.sum(min_count=1)
.assign(is_calc=True)
)
except pd.errors.MergeError as err: # Make debugging easier.
raise pd.errors.MergeError(
"Merge failed, duplicated merge keys in left dataset:\n"
f"{calculation_components[calculation_components.duplicated(calc_idx, keep=False)]}"
) from err
# remove the _parent suffix so we can merge these calculated values back onto
# the data using the original pks
calc_df.columns = calc_df.columns.str.removesuffix("_parent")
calculated_df = pd.merge(
data,
calc_df,
on=data_idx,
how="outer",
validate="1:1",
indicator=True,
)
assert calculated_df[
(calculated_df._merge == "right_only") & (calculated_df[value_col].notnull())
].empty
calculated_df = calculated_df.drop(columns=["_merge"])
# Force value_col to be a float to prevent any hijinks with calculating differences.
# Data types were very messy here, including pandas Float64 for the
# calculated_value columns which did not work with the np.isclose(). Not sure
# why these are cropping up.
calculated_df = calculated_df.convert_dtypes(convert_floating=False).astype(
{value_col: "float64", "calculated_value": "float64"}
)
# For all of these below, only assign values when the record is a calculated record
# Also, make sure we are filling nulls so we capture the differences when there are
# null values in the calculated or reported values.
calculated_df = calculated_df.astype({"is_calc": pd.BooleanDtype()}).assign(
is_calc=lambda x: x.is_calc.fillna(False),
diff=lambda x: np.where(
x.is_calc, x[value_col] - x.calculated_value.fillna(0), np.nan
),
abs_diff=lambda x: np.where(
x.is_calc & (x["diff"] != 0.0), abs(x["diff"]), np.nan
),
rel_diff=lambda x: np.where(
x.is_calc & (x[value_col] != 0.0),
abs(x.abs_diff / x[value_col]),
np.nan,
),
)
# Uniformity here helps keep the error checking functions simpler:
calculated_df["reported_value"] = calculated_df[value_col]
return calculated_df
[docs]
def check_calculation_metrics_by_group(
calculated_df: pd.DataFrame,
group_metric_checks: GroupMetricChecks,
) -> pd.DataFrame:
"""Tabulate the results of the calculation checks by group.
Convert all of the groups' checks into a big df. This will have two indexes: first
for the group name (group) and one for the groups values. the columns will include
three for each test: the test mertic that is the same name as the test (ex:
error_frequency), the tolerance for that group/test and a boolean indicating
whether or not that metric failed to meet the tolerance.
"""
results_dfs = {}
# for each groupby grouping: calculate metrics for each test
# then check if each test is within acceptable tolerance levels
for group_name in group_metric_checks.groups_to_check:
group_metrics = {}
for (
metric_name,
metric_tolerance,
) in group_metric_checks.group_metric_tolerances.model_dump()[
group_name
].items():
if metric_name in group_metric_checks.metrics_to_check:
# this feels icky. the param name for the metrics are all snake_case while
# the metric classes are all TitleCase. So we convert to TitleCase
title_case_test = metric_name.title().replace("_", "")
group_metric_checker = globals()[title_case_test](
by=group_name,
is_close_tolerance=group_metric_checks.is_close_tolerance.model_dump()[
metric_name
],
metric_tolerance=metric_tolerance,
)
group_metric = group_metric_checker.check(
calculated_df=calculated_df
).rename(columns={group_name: "group_value"})
# we want to set the index values as the same for all groups, but both the
# ungrouped and table_name group require a special exception because we need
# to add table_name into the columns
if group_name == "ungrouped":
group_metric = group_metric.assign(table_name="ungrouped")
if group_name == "table_name":
# we end up having two columns w/ table_name values in order to keep all the
# outputs having the same indexes.
group_metric = group_metric.assign(
table_name=lambda x: x["group_value"]
)
# make a uniform multi-index w/ group name and group values
group_metrics[metric_name] = group_metric.set_index(
["group", "table_name", "group_value"]
)
results_dfs[group_name] = pd.concat(group_metrics.values(), axis="columns")
results = pd.concat(results_dfs.values(), axis="index")
return results
[docs]
def check_calculation_metrics(
calculated_df: pd.DataFrame,
group_metric_checks: GroupMetricChecks,
) -> pd.DataFrame:
"""Run the calculation metrics and determine if calculations are within tolerance."""
# DO ERROR CHECKS
results = check_calculation_metrics_by_group(calculated_df, group_metric_checks)
# get the records w/ errors in any! of their checks
errors = results[results.filter(like="is_error").any(axis=1)]
if not errors.empty:
# it miiight be good to isolate just the error columns..
raise AssertionError(
f"Found errors while running tests on the calculations:\n{errors}"
)
return calculated_df
########################################################################################
# Calculation Error Checking Functions
# - These functions all take a dataframe and return a float.
# - They are intended to be used in GroupBy.apply() (or on a whole dataframe).
# - They require a uniform `reported_value` column so that they can all have the same
# call signature, which allows us to iterate over all of them in a matrix.
########################################################################################
[docs]
class ErrorMetric(BaseModel):
"""Base class for checking a particular metric within a group."""
"""Name of group to check the metric based on.
With the exception of the ungrouped case, all groups depend on table_name as well as
the other column specified via by.
If by=="table_name" then that is the only column used in the groupby().
If by=="ungrouped" then all records are included in the "group" (via a dummy column
named ungrouped that contains only the value ungrouped). This allows us to use the
same infrastructure for applying the metrics to grouped and ungrouped data.
"""
"""Inputs for the metric to determine :meth:`is_not_close`. Instance of :class:`IsCloseTolerance`."""
"""Tolerance for checking the metric within the ``by`` group."""
[docs]
required_cols: list[str] = [
"table_name",
"xbrl_factoid",
"report_year",
"utility_id_ferc1",
"reported_value",
"calculated_value",
"abs_diff",
"rel_diff",
]
[docs]
def has_required_cols(self: Self, df: pd.DataFrame):
"""Check that the input dataframe has all required columns."""
missing_required_cols = [
col for col in self.required_cols if col not in df.columns
]
if missing_required_cols:
raise AssertionError(
f"The table is missing the following required columns: {missing_required_cols}"
)
return True
@abstractmethod
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Metric function that will be applied to each group of values being checked."""
...
[docs]
def is_not_close(self, df: pd.DataFrame) -> pd.Series:
"""Flag records where reported and calculated values differ significantly.
We only want to check this metric when there is a non-null ``abs_diff`` because
we want to avoid the instances in which there are either null reported or
calculated values.
"""
return pd.Series(
~np.isclose(
df["calculated_value"],
df["reported_value"],
rtol=self.is_close_tolerance.isclose_rtol,
atol=self.is_close_tolerance.isclose_atol,
)
& df["abs_diff"].notnull()
)
[docs]
def groupby_cols(self: Self) -> list[str]:
"""The list of columns to group by.
We want to default to adding the table_name into all groupby's, but two of our
``by`` options need special treatment.
"""
gb_by = ["table_name", self.by]
if self.by in ["ungrouped", "table_name"]:
gb_by = [self.by]
return gb_by
[docs]
def apply_metric(self: Self, df: pd.DataFrame) -> pd.Series:
"""Generate the metric values within each group through an apply method.
This method adds a column ``is_not_close`` into the df before the groupby
because that column is used in many of the :meth:`metric`.
"""
# return a df instead of a series
df["is_not_close"] = self.is_not_close(df)
return df.groupby(by=self.groupby_cols()).apply(
self.metric, include_groups=False
)
[docs]
def _snake_case_metric_name(self: Self) -> str:
"""Convert the TitleCase class name to a snake_case string."""
class_name = self.__class__.__name__
return re.sub("(?!^)([A-Z]+)", r"_\1", class_name).lower()
[docs]
def check(self: Self, calculated_df) -> pd.DataFrame:
"""Make a df w/ the metric, tolerance and is_error columns."""
self.has_required_cols(calculated_df)
# ungrouped is special because the rest of the stock group names are column
# names.
if self.by == "ungrouped":
calculated_df = calculated_df.assign(
ungrouped="ungrouped",
)
metric_name = self._snake_case_metric_name()
df = (
pd.DataFrame(self.apply_metric(calculated_df), columns=[metric_name])
.assign(
**{ # totolerance_ is just for reporting so you can know of off you are
f"tolerance_{metric_name}": self.metric_tolerance,
f"is_error_{metric_name}": lambda x: x[metric_name]
> self.metric_tolerance,
}
)
.assign(group=self.by)
.reset_index()
)
return df
[docs]
class ErrorFrequency(ErrorMetric):
"""Check error frequency in XBRL calculations."""
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Calculate the frequency with which records are tagged as errors."""
try:
out = gb[gb.is_not_close].shape[0] / gb.shape[0]
except ZeroDivisionError:
# Will only occur if all reported values are NaN when calculated values
# exist, or vice versa.
logger.warning(
"Calculated values have no corresponding reported values in this table."
)
out = np.nan
return out
[docs]
class RelativeErrorMagnitude(ErrorMetric):
"""Check relative magnitude of errors in XBRL calculations."""
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Calculate the mangnitude of the errors relative to total reported value."""
gb_value = np.nan
denom = gb["reported_value"].abs().sum(min_count=1)
if np.isclose(denom, 0) | np.isnan(denom):
gb_value = gb.abs_diff.abs().sum(min_count=1) / denom
return gb_value
[docs]
class AbsoluteErrorMagnitude(ErrorMetric):
"""Check absolute magnitude of errors in XBRL calculations.
These numbers may vary wildly from table to table so no default values for the
expected errors are provided here...
"""
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Calculate the absolute mangnitude of XBRL calculation errors."""
return gb.abs_diff.abs().sum()
[docs]
class NullCalculatedValueFrequency(ErrorMetric):
"""Check the frequency of null calculated values."""
[docs]
def apply_metric(self: Self, df: pd.DataFrame) -> pd.Series:
"""Only apply metric to rows that contain calculated values."""
return (
df[df.row_type_xbrl == "calculated_value"]
.groupby(self.groupby_cols())
.apply(self.metric, include_groups=False)
)
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Fraction of non-null reported values that have null corresponding calculated values."""
non_null_reported = gb["reported_value"].notnull()
null_calculated = gb["calculated_value"].isnull()
try:
return (non_null_reported & null_calculated).sum() / non_null_reported.sum()
except ZeroDivisionError:
return np.nan
[docs]
class NullReportedValueFrequency(ErrorMetric):
"""Check the frequency of null reported values."""
[docs]
def metric(self: Self, gb: DataFrameGroupBy) -> pd.Series:
"""Frequency with which the reported values are Null."""
return gb["reported_value"].isnull().sum() / gb.shape[0]
[docs]
def add_corrections(
calculated_df: pd.DataFrame,
value_col: str,
is_close_tolerance: IsCloseTolerance,
table_name: str,
is_subdimension: bool,
) -> pd.DataFrame:
"""Add corrections to discrepancies between reported & calculated values.
To isolate the sources of error, and ensure that all totals add up as expected in
later phases of the transformation, we add correction records to the dataframe
which compensate for any difference between the calculated and reported values. The
``_correction`` factoids that are added here have already been added to the
calculation components during the metadata processing.
Args:
calculated_df: DataFrame containing the data to correct. Must already have
``abs_diff`` column that was added by :func:`check_calculation_metrics`
value_col: Label of the column whose values are being calculated.
calculation_tolerance: Data structure containing various calculation tolerances.
table_name: Name of the table whose data we are working with. For logging.
is_subdimension: Indicator of whether or not the correction to add is a total to
subdimension calculated value.
"""
corrections = calculated_df[
~np.isclose(
calculated_df["calculated_value"],
calculated_df[value_col],
rtol=is_close_tolerance.isclose_rtol,
atol=is_close_tolerance.isclose_atol,
)
& calculated_df["abs_diff"].notnull()
].copy()
# fill in the nulls with zeros so we get a correction for records
corrections[value_col] = corrections[value_col].fillna(0.0) - corrections[
"calculated_value"
].fillna(0.0)
correction_label = "subdimension_correction" if is_subdimension else "correction"
corrections = corrections.assign(
xbrl_factoid_corrected=lambda x: x["xbrl_factoid"],
xbrl_factoid=lambda x: x["xbrl_factoid"] + "_" + correction_label,
row_type_xbrl=correction_label,
is_within_table_calc=False,
record_id=pd.NA,
)
num_notnull_calcs = sum(calculated_df["abs_diff"].notnull())
num_corrections = corrections.shape[0]
num_records = calculated_df.shape[0]
try:
corrected_fraction = num_corrections / num_notnull_calcs
except ZeroDivisionError:
corrected_fraction = np.nan
logger.info(
f"{table_name}: Correcting {corrected_fraction:.2%} of all non-null reported "
f"values ({num_corrections}/{num_notnull_calcs}) out of a total of "
f"{num_records} original records."
)
return pd.concat(
[calculated_df, corrections.astype(calculated_df.dtypes, errors="ignore")],
axis="index",
)
[docs]
class Ferc1TableTransformParams(TableTransformParams):
"""A model defining what TransformParams are allowed for FERC Form 1.
This adds additional parameter models beyond the ones inherited from the
:class:`pudl.transform.classes.AbstractTableTransformer` class.
"""
[docs]
rename_columns_ferc1: RenameColumnsFerc1 = RenameColumnsFerc1(
dbf=RenameColumns(),
xbrl=RenameColumns(),
instant_xbrl=RenameColumns(),
duration_xbrl=RenameColumns(),
)
[docs]
wide_to_tidy: WideToTidySourceFerc1 = WideToTidySourceFerc1(
dbf=WideToTidy(), xbrl=WideToTidy()
)
[docs]
assign_quarterly_data_to_yearly_dbf: AssignQuarterlyDataToYearlyDbf = (
AssignQuarterlyDataToYearlyDbf()
)
[docs]
unstack_balances_to_report_year_instant_xbrl: UnstackBalancesToReportYearInstantXbrl = UnstackBalancesToReportYearInstantXbrl()
[docs]
add_columns_with_uniform_values: AddColumnsWithUniformValues = (
AddColumnsWithUniformValues()
)
@property
[docs]
def xbrl_factoid_name(self) -> str:
"""Access the column name of the ``xbrl_factoid``."""
return self.merge_xbrl_metadata.on
@property
[docs]
def rename_dicts_xbrl(self):
"""Compile all of the XBRL rename dictionaries into an ordered list."""
return self.rename_columns_ferc1.rename_dicts_xbrl
@property
[docs]
def wide_to_tidy_value_types(self) -> list[str]:
"""Compile a list of all of the ``value_types`` from ``wide_to_tidy``."""
return self.wide_to_tidy.value_types
@property
[docs]
def aligned_dbf_table_names(self) -> list[str]:
"""The list of DBF tables aligned by row number in this transform."""
return self.align_row_numbers_dbf.dbf_table_names
@property
[docs]
def dimension_columns(self) -> list[str]:
"""List of column names of dimensions."""
dims = {
dim
for (
dim,
col_info,
) in self.add_columns_with_uniform_values.columns_to_add.items()
if col_info.is_dimension
}
if self.reconcile_table_calculations.subdimension_column:
dims.update({self.reconcile_table_calculations.subdimension_column})
return list(dims)
################################################################################
# FERC 1 transform helper functions. Probably to be integrated into a class
# below as methods or moved to a different module once it's clear where they belong.
################################################################################
[docs]
def get_ferc1_dbf_rows_to_map(ferc1_engine: sa.Engine) -> pd.DataFrame:
"""Identify DBF rows that need to be mapped to XBRL columns.
Select all records in the ``f1_row_lit_tbl`` where the row literal associated with a
given combination of table and row number is different from the preceeding year.
This is the smallest set of records which we can use to reproduce the whole table by
expanding the time series to include all years, and forward filling the row
literals.
"""
idx_cols = ["sched_table_name", "row_number", "report_year"]
data_cols = ["row_literal"]
row_lit = pd.read_sql(
"f1_row_lit_tbl", con=ferc1_engine, columns=idx_cols + data_cols
).sort_values(idx_cols)
row_lit["shifted"] = row_lit.groupby(
["sched_table_name", "row_number"]
).row_literal.shift()
row_lit["changed"] = row_lit.row_literal != row_lit.shifted
return row_lit.loc[row_lit.changed, idx_cols + data_cols]
[docs]
def update_dbf_to_xbrl_map(ferc1_engine: sa.Engine) -> pd.DataFrame:
"""Regenerate the FERC 1 DBF+XBRL glue while retaining existing mappings.
Reads all rows that need to be mapped out of the ``f1_row_lit_tbl`` and appends
columns containing any previously mapped values, returning the resulting dataframe.
"""
idx_cols = ["sched_table_name", "row_number", "report_year"]
all_rows = get_ferc1_dbf_rows_to_map(ferc1_engine).set_index(idx_cols)
mapped_rows = (
pd.read_csv(
importlib.resources.files("pudl.package_data.ferc1") / "dbf_to_xbrl.csv"
)
.set_index(idx_cols)
.drop(["row_literal"], axis="columns")
)
return (
pd.concat([all_rows, mapped_rows], axis="columns")
.reset_index()
.sort_values(["sched_table_name", "report_year", "row_number"])
)
[docs]
def read_dbf_to_xbrl_map(dbf_table_names: list[str]) -> pd.DataFrame:
"""Read the manually compiled DBF row to XBRL column mapping for a given table.
Args:
dbf_table_name: The original name of the table in the FERC Form 1 DBF database
whose mapping to the XBRL data you want to extract. for example
``f1_plant_in_srvce``.
Returns:
DataFrame with columns ``[sched_table_name, report_year, row_number, row_type, xbrl_factoid]``
"""
row_map = pd.read_csv(
importlib.resources.files("pudl.package_data.ferc1") / "dbf_to_xbrl.csv",
usecols=[
"sched_table_name",
"report_year",
"row_number",
"row_type",
"row_literal",
"xbrl_factoid",
],
)
# Select only the rows that pertain to dbf_table_name
row_map = row_map.loc[row_map.sched_table_name.isin(dbf_table_names)]
return row_map
[docs]
def fill_dbf_to_xbrl_map(
df: pd.DataFrame, dbf_years: list[int] | None = None
) -> pd.DataFrame:
"""Forward-fill missing years in the minimal, manually compiled DBF to XBRL mapping.
The relationship between a DBF row and XBRL column/fact/entity/whatever is mostly
consistent from year to year. To minimize the amount of manual mapping work we have
to do, we only map the years in which the relationship changes. In the end we do
need a complete correspondence for all years though, and this function uses the
minimal information we've compiled to fill in all the gaps, producing a complete
mapping across all requested years.
One complication is that we need to explicitly indicate which DBF rows have headers
in them (which don't exist in XBRL), to differentiate them from null values in the
exhaustive index we create below. We set a ``HEADER_ROW`` sentinel value so we can
distinguish between two different reasons that we might find NULL values in the
``xbrl_factoid`` field:
1. It's NULL because it's between two valid mapped values (the NULL was created
in our filling of the time series) and should thus be filled in, or
2. It's NULL because it was a header row in the DBF data, which means it should
NOT be filled in. Without the ``HEADER_ROW`` value, when a row number from year X
becomes associated with a non-header row in year X+1 the ffill will keep right on
filling, associating all of the new header rows with the value of
``xbrl_factoid`` that was associated with the old row number.
Args:
df: A dataframe containing a DBF row to XBRL mapping for a single FERC 1 DBF
table.
dbf_years: The list of years that should have their DBF row to XBRL mapping
filled in. This defaults to all available years of DBF data for FERC 1. In
general this parameter should only be set to a non-default value for testing
purposes.
Returns:
A complete mapping of DBF row number to XBRL columns for all years of data
within a single FERC 1 DBF table. Has columns of
``[report_year, row_number, xbrl_factoid]``
"""
if not dbf_years:
dbf_years = Ferc1Settings().dbf_years
# If the first year that we're trying to produce isn't mapped, we won't be able to
# forward fill.
if min(dbf_years) not in df.report_year.unique():
raise ValueError(
"Invalid combination of years and DBF-XBRL mapping. The first year cannot\n"
"be filled and **must** be mapped.\n"
f"First year: {min(dbf_years)}, "
f"Mapped years: {sorted(df.report_year.unique())}\n"
f"{df}"
)
if df.loc[(df.row_type == "header"), "xbrl_factoid"].notna().any():
raise ValueError("Found non-null XBRL column value mapped to a DBF header row.")
df.loc[df.row_type == "header", "xbrl_factoid"] = "HEADER_ROW"
if df["xbrl_factoid"].isna().any():
raise ValueError(
"Found NA XBRL values in the DBF-XBRL mapping, which shouldn't happen. \n"
f"{df[df['xbrl_factoid'].isna()]}"
)
df = df.drop(["row_type"], axis="columns")
# Create an index containing all combinations of report_year and row_number
idx_cols = ["report_year", "row_number", "sched_table_name"]
idx = pd.MultiIndex.from_product(
[dbf_years, df.row_number.unique(), df.sched_table_name.unique()],
names=idx_cols,
)
# Concatenate the row map with the empty index, so we have blank spaces to fill:
df = pd.concat(
[
pd.DataFrame(index=idx),
df.set_index(idx_cols),
],
axis="columns",
).reset_index()
# Forward fill missing XBRL column names, until a new definition for the row
# number is encountered:
df.loc[:, ["xbrl_factoid", "row_literal"]] = df.groupby(
["row_number", "sched_table_name"]
)[["xbrl_factoid", "row_literal"]].transform("ffill")
# Drop NA values produced in the broadcasting merge onto the exhaustive index.
df = df.dropna(subset="xbrl_factoid")
# There should be no NA values left at this point:
if df.isnull().any(axis=None):
raise ValueError(
"Filled DBF-XBRL map contains NA values, which should never happen:"
f"\n{df[df.isnull().any(axis='columns')]}"
)
return df
[docs]
def get_data_cols_raw_xbrl(
raw_xbrl_instant: pd.DataFrame,
raw_xbrl_duration: pd.DataFrame,
) -> list[str]:
"""Get a list of all XBRL data columns appearing in a given XBRL table.
Returns:
A list of all the data columns found in the original XBRL DB that correspond to
the given PUDL table. Includes columns from both the instant and duration tables
but excludes structural columns that appear in all XBRL tables.
"""
excluded_cols = [
"date",
"end_date",
"entity_id",
"filing_name",
"index",
"report_year",
"start_date",
]
return sorted(
set(raw_xbrl_instant.columns)
.union(raw_xbrl_duration.columns)
.difference(excluded_cols)
)
[docs]
def read_xbrl_calculation_fixes() -> pd.DataFrame:
"""Read in the table of calculation fixes."""
source = importlib.resources.files("pudl.package_data.ferc1").joinpath(
"xbrl_calculation_component_fixes.csv"
)
with importlib.resources.as_file(source) as file:
calc_fixes = pd.read_csv(file)
return calc_fixes
################################################################################
# FERC 1 specific TableTransformer classes
################################################################################
[docs]
class Ferc1AbstractTableTransformer(AbstractTableTransformer):
"""An abstract class defining methods common to many FERC Form 1 tables.
This subclass remains abstract because it does not define transform_main(), which
is always going to be table-specific.
* Methods that only apply to XBRL data should end with _xbrl
* Methods that only apply to DBF data should end with _dbf
"""
"""True if each record in the transformed table corresponds to one input record.
For tables that have been transformed from wide-to-tidy format, or undergone other
kinds of reshaping, there is not a simple one-to-one relationship between input and
output records, and so we should not expect record IDs to be unique. In those cases
they serve only a forensic purpose, telling us where to find the original source of
the transformed data.
"""
"""Dataframe combining XBRL metadata for both instant and duration table columns."""
"""Dataframe of calculation components.
If ``None``, the calculations have not been instantiated. If the table has been
instantiated but is an empty table, then there are no calculations for that table.
"""
def __init__(
self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]]
| None = None,
params: TableTransformParams | None = None,
cache_dfs: bool = False,
clear_cached_dfs: bool = True,
) -> None:
"""Augment inherited initializer to store XBRL metadata in the class."""
super().__init__(
params=params,
cache_dfs=cache_dfs,
clear_cached_dfs=clear_cached_dfs,
)
if xbrl_metadata_json:
xbrl_metadata_converted = self.convert_xbrl_metadata_json_to_df(
xbrl_metadata_json
)
self.xbrl_calculations = self.process_xbrl_metadata_calculations(
xbrl_metadata_converted
)
self.xbrl_metadata = self.process_xbrl_metadata(
xbrl_metadata_converted, self.xbrl_calculations
)
@cache_df(key="start")
[docs]
def transform_start(
self,
raw_dbf: pd.DataFrame,
raw_xbrl_instant: pd.DataFrame,
raw_xbrl_duration: pd.DataFrame,
) -> pd.DataFrame:
"""Process the raw data until the XBRL and DBF inputs have been unified."""
processed_dbf = self.process_dbf(raw_dbf)
processed_xbrl = self.process_xbrl(raw_xbrl_instant, raw_xbrl_duration)
processed_dbf = self.select_dbf_rows_by_category(processed_dbf, processed_xbrl)
logger.info(f"{self.table_id.value}: Concatenating DBF + XBRL dataframes.")
return pd.concat([processed_dbf, processed_xbrl]).reset_index(drop=True)
@cache_df(key="main")
[docs]
def transform_main(self, df: pd.DataFrame) -> pd.DataFrame:
"""Generic FERC1 main table transformer.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
A single transformed table concatenating multiple years of cleaned data
derived from the raw DBF and/or XBRL inputs.
"""
df = (
self.spot_fix_values(df)
.pipe(self.normalize_strings)
.pipe(self.categorize_strings)
.pipe(self.convert_units)
.pipe(self.strip_non_numeric_values)
.pipe(self.nullify_outliers)
.pipe(self.replace_with_na)
.pipe(self.drop_invalid_rows)
.pipe(PUDL_PACKAGE.encode)
.pipe(self.merge_xbrl_metadata)
.pipe(self.add_columns_with_uniform_values)
)
return df
@cache_df(key="end")
[docs]
def transform_end(self, df: pd.DataFrame) -> pd.DataFrame:
"""Standardized final cleanup after the transformations are done.
Checks calculations. Enforces dataframe schema. Checks for empty dataframes and
null columns.
"""
df = self.reconcile_table_calculations(df).pipe(self.enforce_schema)
if df.empty:
raise ValueError(f"{self.table_id.value}: Final dataframe is empty!!!")
for col in df:
if df[col].isna().all():
raise ValueError(
f"{self.table_id.value}: Column {col} is entirely NULL!"
)
return df
[docs]
def select_dbf_rows_by_category(
self,
processed_dbf: pd.DataFrame,
processed_xbrl: pd.DataFrame,
params: SelectDbfRowsByCategory | None = None,
) -> pd.DataFrame:
"""Wrapper method for :func:`select_dbf_rows_by_category`."""
if not params:
params = self.params.select_dbf_rows_by_category
if params.column_name:
logger.info(
f"{self.table_id.value}: Selecting DBF rows with desired values in {params.column_name}."
)
processed_dbf = select_dbf_rows_by_category(
processed_dbf=processed_dbf,
processed_xbrl=processed_xbrl,
params=params,
)
return processed_dbf
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Normalize the XBRL JSON metadata, turning it into a dataframe.
This process concatenates and deduplicates the metadata which is associated with
the instant and duration tables, since the metadata is only combined with the
data after the instant and duration (and DBF) tables have been merged. This
happens in :meth:`Ferc1AbstractTableTransformer.merge_xbrl_metadata`.
"""
logger.info(f"{self.table_id.value}: Processing XBRL metadata.")
tbl_meta = (
pd.concat(
[
pd.json_normalize(xbrl_metadata_json["instant"]),
pd.json_normalize(xbrl_metadata_json["duration"]),
]
)
.drop("references.form_location", axis="columns")
.drop_duplicates(subset="name")
.rename(
columns={
"name": "xbrl_factoid",
"references.account": "ferc_account",
}
)
.assign(
calculations=lambda x: x.calculations.apply(json.dumps),
)
.astype(
{
"xbrl_factoid": pd.StringDtype(),
"balance": pd.StringDtype(),
"ferc_account": pd.StringDtype(),
"calculations": pd.StringDtype(),
}
)
.assign(
xbrl_factoid_original=lambda x: x.xbrl_factoid,
xbrl_factoid=lambda x: self.rename_xbrl_factoid(x.xbrl_factoid),
)
.pipe(self.deduplicate_xbrl_factoid_xbrl_metadata)
)
return tbl_meta
@cache_df(key="process_xbrl_metadata")
[docs]
def process_xbrl_metadata(
self: Self,
xbrl_metadata_converted: pd.DataFrame,
xbrl_calculations: pd.DataFrame,
) -> pd.DataFrame:
"""Process XBRL metadata after the calculations have been cleaned.
Add ``row_type_xbrl`` and ``is_within_table_calc`` columns and create
``xbrl_factoid`` records for the calculation corrections.
Args:
xbrl_metadata_converted: Dataframe of relatively unprocessed metadata.
Result of :meth:`convert_xbrl_metadata_json_to_df`.
xbrl_calculations: Dataframe of calculation components. Result of
:meth:`process_xbrl_metadata_calculations`.
"""
# drop the calcs bc we never want to use them again. the xbrl_calculations are
# now the main source of truth for the calcs. set index so we can easily
# graph some calc info onto the metadata using an index of xbrl_factoid_parent.
tbl_meta = xbrl_metadata_converted.drop(columns=["calculations"]).set_index(
["xbrl_factoid"]
)
# Flag metadata record types
tbl_meta.loc[:, "row_type_xbrl"] = (
( # if there is nothing in the calc cols for a parent fact - its reported
xbrl_calculations.groupby(["xbrl_factoid_parent"])[
["table_name", "xbrl_factoid"]
].count()
== 0
)
.all(axis="columns")
.replace({True: "reported_value", False: "calculated_value"})
.astype(pd.StringDtype())
)
tbl_meta.loc[:, "row_type_xbrl"] = tbl_meta.loc[:, "row_type_xbrl"].fillna(
"reported_value"
)
# this bool column is created and used within the calculations. but its a
# helpful thing in the metadata table as well.
tbl_meta.loc[:, "is_within_table_calc"] = (
xbrl_calculations.groupby(["xbrl_factoid_parent"])["is_within_table_calc"]
.all()
.astype(pd.BooleanDtype())
)
if self.params.add_columns_with_uniform_values.columns_to_add:
tbl_meta = tbl_meta.assign(
**self.params.add_columns_with_uniform_values.assign_cols
)
tbl_meta = tbl_meta.reset_index().pipe(self.add_metadata_corrections)
return tbl_meta
[docs]
def deduplicate_xbrl_factoid_xbrl_metadata(
self, tbl_meta: pd.DataFrame
) -> pd.DataFrame:
"""De-duplicate the xbrl_metadata based on ``xbrl_factoid``.
Default is to do nothing besides check for duplicate values because almost all
tables have no deduping. Deduplication needs to be applied before the
:meth:`apply_xbrl_calculation_fixes` inside of :meth:`process_xbrl_metadata`.
"""
if tbl_meta.duplicated(subset=["xbrl_factoid"]).any() & (
self.params.merge_xbrl_metadata.on is not None
):
raise AssertionError(
f"Metadata for {self.table_id.value} has duplicative xbrl_factoid records."
)
return tbl_meta
[docs]
def raw_xbrl_factoid_to_pudl_name(
self,
col_name_xbrl: str,
) -> str:
"""Rename a column name from original XBRL name to the transformed PUDL name.
There are several transform params that either explicitly or implicity rename
columns:
* :class:`RenameColumnsFerc1`
* :class:`WideToTidySourceFerc1`
* :class:`UnstackBalancesToReportYearInstantXbrl`
* :class:`ConvertUnits`
This method attempts to use the table params to translate a column name.
Note: Instead of doing this for each individual column name, we could compile a
rename dict for the whole table with a similar processand then apply it for each
group of columns instead of running through this full process every time. If
this took longer than... ~5 ms on a single table w/ lots of calcs this would
probably be worth it for simplicity.
"""
col_name_new = col_name_xbrl
for rename_stage in self.params.rename_dicts_xbrl:
col_name_new = str(rename_stage.columns.get(col_name_new, col_name_new))
for value_type in self.params.wide_to_tidy_value_types:
if col_name_new.endswith(f"_{value_type}"):
col_name_new = re.sub(f"_{value_type}$", "", col_name_new)
if self.params.unstack_balances_to_report_year_instant_xbrl: # noqa: SIM102
# TODO: do something...? add starting_balance & ending_balance suffixes?
if self.params.merge_xbrl_metadata.on:
NotImplementedError(
"We haven't implemented a xbrl_factoid rename for the parameter "
"unstack_balances_to_report_year_instant_xbrl. Since you are trying"
"to merge the metadata on this table that has this treatment, a "
"xbrl_factoid rename will be required."
)
if self.params.convert_units: # noqa: SIM102
# TODO: use from_unit -> to_unit map. but none of the $$ tables have this rn.
if self.params.merge_xbrl_metadata.on:
NotImplementedError(
"We haven't implemented a xbrl_factoid rename for the parameter "
"convert_units. Since you are trying to merge the metadata on this "
"table that has this treatment, a xbrl_factoid rename will be "
"required."
)
return col_name_new
[docs]
def rename_xbrl_factoid(self, col: pd.Series) -> pd.Series:
"""Rename a series of raw to PUDL factoid names via :meth:`raw_xbrl_factoid_to_pudl_name`."""
xbrl_factoid_name_map = {
xbrl_factoid_name_og: self.raw_xbrl_factoid_to_pudl_name(
xbrl_factoid_name_og
)
for xbrl_factoid_name_og in col
}
return col.map(xbrl_factoid_name_map)
[docs]
def rename_xbrl_factoid_other_tables(self, calc_comps):
"""Rename the factoids from calculation components from other tables.
Note: It is probably possible to build an apply style function that takes a
series of factoid names and a series of table names and returns a table-specific
rename_xbrl_factoid.
"""
calc_tables = calc_comps.table_name.dropna().unique()
os_tables = [
tbl
for tbl in calc_tables
if (tbl != self.table_id.value) & (tbl in FERC1_TFR_CLASSES)
]
for tbl in os_tables:
trns = FERC1_TFR_CLASSES[tbl]()
calc_comps = calc_comps.assign(
xbrl_factoid=lambda x, tbl=tbl, trns=trns: np.where(
x.table_name == tbl,
trns.rename_xbrl_factoid(x.xbrl_factoid),
x.xbrl_factoid,
),
)
return calc_comps
[docs]
def add_metadata_corrections(self, tbl_meta: pd.DataFrame) -> pd.DataFrame:
"""Create metadata records for the calculation correction factoids.
Args:
tbl_meta: processed metadata table which contains columns ``row_type_xbrl``.
"""
correction_meta = tbl_meta[tbl_meta.row_type_xbrl == "calculated_value"].assign(
is_within_table_calc=True,
row_type_xbrl="correction",
xbrl_factoid=lambda x: x.xbrl_factoid + "_correction",
)
if self.params.reconcile_table_calculations.subdimension_column:
# add the _subdimension_correction records as well.
# how to determine whether a
subdimension_correction_meta = tbl_meta[
(tbl_meta.row_type_xbrl == "calculated_value")
& tbl_meta.is_within_table_calc
].assign(
row_type_xbrl="correction",
xbrl_factoid=lambda x: x.xbrl_factoid + "_subdimension_correction",
)
else:
subdimension_correction_meta = pd.DataFrame(columns=tbl_meta.columns)
tbl_meta = (
pd.concat(
[
tbl_meta,
correction_meta.astype(tbl_meta.dtypes, errors="ignore"),
subdimension_correction_meta.astype(
tbl_meta.dtypes, errors="ignore"
),
]
)
.reset_index(drop=True)
.convert_dtypes()
)
return tbl_meta
[docs]
def add_calculation_corrections(
self: Self, calc_components: pd.DataFrame
) -> pd.DataFrame:
"""Add correction components and parent-only factoids to calculation metadata.
Args:
tbl_meta: Partially transformed table metadata in dataframe form.
Returns:
An updated version of the table metadata containing calculation definitions
that include a correction component.
"""
# If we haven't provided calculation check parameters, then we can't identify
# a appropriate correction factor.
if self.params.reconcile_table_calculations.column_to_check is None:
return calc_components
# split the calcs from non-calcs/make corrections/append
calcs = calc_components[calc_components.xbrl_factoid.notnull()]
correction_components = (
calcs[["table_name_parent", "xbrl_factoid_parent", "is_within_table_calc"]]
.drop_duplicates()
.assign(
table_name=lambda t: t.table_name_parent,
xbrl_factoid=lambda x: x.xbrl_factoid_parent + "_correction",
weight=1,
)
)
if self.params.reconcile_table_calculations.subdimension_column:
subdimension_correction_components = (
calcs[calcs.is_within_table_calc][
["table_name_parent", "xbrl_factoid_parent", "is_within_table_calc"]
]
.drop_duplicates()
.assign(
table_name=lambda t: t.table_name_parent,
xbrl_factoid=lambda x: x.xbrl_factoid_parent
+ "_subdimension_correction",
weight=1,
)
)
else:
subdimension_correction_components = pd.DataFrame()
return pd.concat(
[calc_components, correction_components, subdimension_correction_components]
)
[docs]
def get_xbrl_calculation_fixes(self: Self) -> pd.DataFrame:
"""Grab the XBRL calculation file."""
calc_fixes = read_xbrl_calculation_fixes()
# grab the fixes from this table only!
calc_fixes = calc_fixes[calc_fixes.table_name_parent == self.table_id.value]
return calc_fixes
[docs]
def apply_xbrl_calculation_fixes(
self: Self, calc_components: pd.DataFrame, calc_fixes: pd.DataFrame
) -> pd.DataFrame:
"""Use the fixes we've compiled to update calculations in the XBRL metadata.
Note: Temp fix. These updates should probably be moved into the table params
and integrated into the calculations via TableCalcs.
"""
calc_comp_idx = [
"table_name_parent",
"xbrl_factoid_parent",
"table_name",
"xbrl_factoid",
]
if not (
dupes := calc_fixes[calc_fixes.duplicated(subset=calc_comp_idx, keep=False)]
).empty:
raise AssertionError(
"Duplicates found in the calculation fixes where none were expected."
f"{dupes}"
)
calc_fixes = calc_fixes.set_index(calc_comp_idx).sort_index()
calc_components = calc_components.set_index(calc_comp_idx).sort_index()
# find the fixes that need to be replaced. We id them
# by finding the fixes that share indexes with the calc components
# Note: we can't just dropna after adding the replacements instead
# of while finding the replacements because we have included all
# factoids in the calculation component table as parent factoids
# even if there are no/null calculation components.
replace_me = calc_fixes.loc[
calc_fixes.index.intersection(calc_components.index)
].dropna(how="all")
calc_components.loc[replace_me.index, list(replace_me.columns)] = replace_me
# find the lines that only show up in the fixes that need to be added
add_me = calc_fixes.loc[calc_fixes.index.difference(calc_components.index)]
calc_components = pd.concat([calc_components, add_me])
# sometimes we add fresh calculations to parent facts that originally didn't
# have any calculation components. So if we are adding those parent facts
# with child facts/calc components, we need to remove the non-calc records
# so make fake little parent facts with null childern from all the add_mes
null_calc_versions_of_add_mes = (
add_me.reset_index()[["table_name_parent", "xbrl_factoid_parent"]]
.assign(table_name=pd.NA, xbrl_factoid=pd.NA)
.set_index(calc_comp_idx)
)
remove_the_non_cals_from_add_mes = calc_components.index.difference(
null_calc_versions_of_add_mes.index.drop_duplicates()
)
calc_components = calc_components.loc[remove_the_non_cals_from_add_mes]
# find the "null" fixes which correspond to records which need to be deleted.
delete_me = calc_fixes[calc_fixes.isnull().all(axis=1)]
calc_components = calc_components.loc[
calc_components.index.difference(delete_me.index)
]
len_fixes_applied = len(replace_me) + len(add_me) + len(delete_me)
logger.debug(
f"We've applied {len_fixes_applied} calculation fixes including "
f"{len(replace_me)} replacements, {len(add_me)} additions and "
f"{len(delete_me)} deletions."
)
if len(calc_fixes) != len_fixes_applied:
raise AssertionError(
f"We've applied {len_fixes_applied} calculation fixes while we started "
f"with {len(calc_fixes)}. Length of applied and original fixes should "
f"be the same.\n{replace_me=}\n{add_me=}\n{delete_me=}"
)
return calc_components.reset_index()
[docs]
def process_xbrl_metadata_calculations(
self, xbrl_metadata_converted: pd.DataFrame
) -> pd.DataFrame:
"""Convert xbrl metadata calculations into a table of calculation components.
This method extracts the calculations from the ``xbrl_metadata_converted``
that are stored as json embedded within the ``calculations``column and convert
those into calculation component records. The resulting table includes columns
pertaining to both the calculation components and the parent factoid that the
components pertain to. The parental columns had suffixes of ``_parent``.
This method also adds fixes to the calculations via
:meth:`apply_xbrl_calculation_fixes`, adds corrections records via
:meth:`add_calculation_corrections` and adds the column
``is_within_table_calc``.
Args:
xbrl_metadata_converted: Dataframe of relatively unprocessed metadata.
Result of :meth:`convert_xbrl_metadata_json_to_df`.
"""
metadata = xbrl_metadata_converted.copy()
metadata.calculations = metadata.calculations.apply(json.loads)
# reset the index post calc explosion so we can merge on index later
metadata = metadata.explode("calculations").reset_index(drop=True)
if all(metadata.calculations.isnull()):
calc_comps = pd.DataFrame(columns=["name", "source_tables"])
else:
calc_comps = pd.json_normalize(metadata.calculations)
calc_comps = (
calc_comps.explode("source_tables")
.rename(
columns={
"name": "xbrl_factoid",
"source_tables": "table_name",
}
)
.merge(
metadata.drop(columns=["calculations"]).rename(
columns={
"xbrl_factoid": "xbrl_factoid_parent",
}
),
left_index=True,
right_index=True,
how="left",
)
.dropna(subset=["xbrl_factoid"])
.reset_index(drop=True)
.assign(
table_name_parent=self.table_id.value,
xbrl_factoid=lambda x: np.where(
x.table_name == self.table_id.value,
self.rename_xbrl_factoid(x.xbrl_factoid),
x.xbrl_factoid,
),
)
.pipe(self.rename_xbrl_factoid_other_tables)
.pipe(
self.apply_xbrl_calculation_fixes,
calc_fixes=self.get_xbrl_calculation_fixes(),
)
.drop_duplicates(keep="first")
)
# this is really a xbrl_factoid-level flag, but we need it while using this
# calc components.
calc_comps["is_within_table_calc"] = (
# make a temp bool col to check if all the componets are intra table
# should the non-calc guys get a null or a true here? rn its true bc fillna
calc_comps.assign(
intra_table_calc_comp_flag=lambda x: (
self.table_id.value == x.table_name.fillna(self.table_id.value)
)
)
.groupby(["table_name_parent", "xbrl_factoid_parent"])[
"intra_table_calc_comp_flag"
]
.transform("all")
.astype(pd.BooleanDtype())
)
# check for uniqueness only when we are reconciling the calculations
# bc that implies we have cleaned the calcs and are intending to use them.
if self.params.reconcile_table_calculations.column_to_check:
calc_comp_idx = [
"table_name_parent",
"xbrl_factoid_parent",
"table_name",
"xbrl_factoid",
]
if not (
dupes := calc_comps[calc_comps.duplicated(subset=calc_comp_idx)]
).empty:
raise AssertionError(
"Duplicates found in the calculation components where none were ."
f"expected {dupes}"
)
return calc_comps.pipe(self.add_calculation_corrections).convert_dtypes()
[docs]
def add_columns_with_uniform_values(
self, df: pd.DataFrame, params: AddColumnsWithUniformValues | None = None
) -> pd.DataFrame:
"""Add a column with a uniform value."""
if not params:
params = self.params.add_columns_with_uniform_values
if params.columns_to_add:
logger.info(
f"{self.table_id.value}: Adding uniform value columns. {params.assign_cols}"
)
df = add_columns_with_uniform_values(df, params)
return df
@cache_df(key="merge_xbrl_metadata")
[docs]
def merge_xbrl_metadata(
self, df: pd.DataFrame, params: MergeXbrlMetadata | None = None
) -> pd.DataFrame:
"""Combine XBRL-derived metadata with the data it pertains to.
While the metadata we're using to annotate the data comes from the more recent
XBRL data, it applies generally to all the historical DBF data as well! This
method reads the normalized metadata out of an attribute.
"""
if not params:
params = self.params.merge_xbrl_metadata
if params.on:
if self.xbrl_metadata.empty:
raise AssertionError(
"Metadata has not yet been generated. Must run process_xbrl_metadata"
"and assign xbrl_metadata before merging metadata."
)
logger.info(f"{self.table_id.value}: Merging metadata")
df = merge_xbrl_metadata(df, self.xbrl_metadata, params)
return df
@cache_df(key="dbf")
[docs]
def align_row_numbers_dbf(
self, df: pd.DataFrame, params: AlignRowNumbersDbf | None = None
) -> pd.DataFrame:
"""Align historical FERC1 DBF row numbers with XBRL account IDs.
Additional Parameterization TBD with additional experience. See:
https://github.com/catalyst-cooperative/pudl/issues/2012
"""
if params is None:
params = self.params.align_row_numbers_dbf
if params.dbf_table_names:
df = align_row_numbers_dbf(df, params=params)
return df
@cache_df(key="dbf")
[docs]
def drop_duplicate_rows_dbf(
self, df: pd.DataFrame, params: DropDuplicateRowsDbf | None = None
) -> pd.DataFrame:
"""Drop the DBF rows where the PKs and data columns are duplicated.
Wrapper function for :func:`drop_duplicate_rows_dbf`.
"""
if params is None:
params = self.params.drop_duplicate_rows_dbf
if params.table_name:
logger.info(
f"{self.table_id.value}: Dropping rows where primary key and data "
"columns are duplicated."
)
df = drop_duplicate_rows_dbf(df, params=params)
return df
@cache_df(key="dbf")
[docs]
def process_dbf(self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""DBF-specific transformations that take place before concatenation."""
logger.info(f"{self.table_id.value}: Processing DBF data pre-concatenation.")
return (
raw_dbf.drop_duplicates()
.pipe(self.select_annual_rows_dbf)
.pipe(self.drop_footnote_columns_dbf)
.pipe(self.align_row_numbers_dbf)
.pipe(self.rename_columns, rename_stage="dbf")
.pipe(self.assign_record_id, source_ferc1=SourceFerc1.DBF)
.pipe(self.drop_unused_original_columns_dbf)
.pipe(self.assign_utility_id_ferc1, source_ferc1=SourceFerc1.DBF)
.pipe(self.wide_to_tidy, source_ferc1=SourceFerc1.DBF)
.pipe(self.drop_duplicate_rows_dbf)
.pipe(self.assign_quarterly_data_to_yearly_dbf)
)
@cache_df(key="xbrl")
[docs]
def process_xbrl(
self,
raw_xbrl_instant: pd.DataFrame,
raw_xbrl_duration: pd.DataFrame,
) -> pd.DataFrame:
"""XBRL-specific transformations that take place before concatenation."""
logger.info(f"{self.table_id.value}: Processing XBRL data pre-concatenation.")
return (
self.merge_instant_and_duration_tables_xbrl(
raw_xbrl_instant, raw_xbrl_duration
)
.pipe(self.wide_to_tidy, source_ferc1=SourceFerc1.XBRL)
.pipe(self.rename_columns, rename_stage="xbrl")
.pipe(self.combine_axis_columns_xbrl)
.pipe(self.assign_record_id, source_ferc1=SourceFerc1.XBRL)
.pipe(self.assign_utility_id_ferc1, source_ferc1=SourceFerc1.XBRL)
)
[docs]
def rename_columns(
self,
df: pd.DataFrame,
rename_stage: Literal["dbf", "xbrl", "xbrl_instant", "xbrl_duration"]
| None = None,
params: RenameColumns | None = None,
):
"""Grab the params based on the rename stage and run default rename_columns.
Args:
df: Table to be renamed.
rename_stage: Name of stage in the transform process. Used to get specific
stage's parameters if None have been passed.
params: Rename column parameters.
"""
if not params:
params = self.params.rename_columns_ferc1.__getattribute__(rename_stage)
df = super().rename_columns(df, params=params)
return df
@cache_df(key="dbf")
[docs]
def select_annual_rows_dbf(self, df):
"""Select only annually reported DBF Rows.
There are some DBF tables that include a mix of reporting frequencies. For now,
the default for PUDL tables is to have only the annual records.
"""
if "report_prd" in df and list(df.report_prd.unique()) != [12]:
len_og = len(df)
df = df[df.report_prd == 12].copy()
logger.info(
f"{self.table_id.value}: After selection of only annual records,"
f" we have {len(df)/len_og:.1%} of the original table."
)
return df
@cache_df(key="dbf")
[docs]
def assign_quarterly_data_to_yearly_dbf(
self, df, params: AssignQuarterlyDataToYearlyDbf | None = None
):
"""Transfer quarterly filed data to annual columns."""
if params is None:
params = self.params.assign_quarterly_data_to_yearly_dbf
if params.quarterly_to_yearly_column_map:
logger.info(
f"{self.table_id.value}: Converting quarterly filed data to annual."
)
df = assign_quarterly_data_to_yearly_dbf(df, params=params)
return df
[docs]
def unstack_balances_to_report_year_instant_xbrl(
self,
df: pd.DataFrame,
params: UnstackBalancesToReportYearInstantXbrl | None = None,
) -> pd.DataFrame:
"""Turn start year end year rows into columns for each value type."""
logger.info(f"{self.table_id.value}: Unstacking balances to the report years.")
if params is None:
params = self.params.unstack_balances_to_report_year_instant_xbrl
if params.unstack_balances_to_report_year:
df = unstack_balances_to_report_year_instant_xbrl(
df,
params=params,
primary_key_cols=self.source_table_primary_key(
source_ferc1=SourceFerc1.XBRL
),
)
return df
[docs]
def wide_to_tidy(
self,
df: pd.DataFrame,
source_ferc1: SourceFerc1,
params: WideToTidy | None = None,
) -> pd.DataFrame:
"""Reshape wide tables with FERC account columns to tidy format.
The XBRL table coming into this method contains all the data from both the
instant and duration tables in a wide format -- with one column for every
combination of value type (e.g. additions, ending_balance) and accounting
category, which means ~500 columns.
We tidy this into a long table with one column for each of the value types (6 in
all), and a new column that contains the accounting categories. This allows
aggregation across columns to calculate the ending balance based on the starting
balance and all of the reported changes, and aggregation across groups of rows
to total up various hierarchical accounting categories (hydraulic turbines ->
hydraulic production plant -> all production plant -> all electric utility
plant) though the categorical columns required for that aggregation are added
later.
"""
if not params:
params = self.params.wide_to_tidy.__getattribute__(source_ferc1.value)
multiple_params = [params] if isinstance(params, WideToTidy) else params
for single_params in multiple_params:
if single_params.idx_cols or single_params.value_types:
logger.info(
f"{self.table_id.value}: applying wide_to_tidy for {source_ferc1.value}"
)
df = wide_to_tidy(df, single_params)
return df
[docs]
def combine_axis_columns_xbrl(
self,
df: pd.DataFrame,
params: CombineAxisColumnsXbrl | None = None,
) -> pd.DataFrame:
"""Combine axis columns from squished XBRL tables into one column with no NA."""
if params is None:
params = self.params.combine_axis_columns_xbrl
if params.axis_columns_to_combine:
logger.info(
f"{self.table_id.value}: Combining axis columns: "
f"{params.axis_columns_to_combine} into {params.new_axis_column_name}"
)
df = combine_axis_columns_xbrl(df, params=params)
return df
@cache_df(key="xbrl")
[docs]
def merge_instant_and_duration_tables_xbrl(
self,
raw_xbrl_instant: pd.DataFrame,
raw_xbrl_duration: pd.DataFrame,
) -> pd.DataFrame:
"""Merge XBRL instant and duration tables, reshaping instant as needed.
FERC1 XBRL instant period signifies that it is true as of the reported date,
while a duration fact pertains to the specified time period. The ``date`` column
for an instant fact corresponds to the ``end_date`` column of a duration fact.
When merging the instant and duration tables, we need to preserve row order.
For the small generators table, row order is how we label and extract
information from header and note rows. Outer merging messes up the order, so we
need to use a one-sided merge. So far, it seems like the duration df contains
all the index values in the instant df. To be sure, there's a check that makes
sure there are no unique intant df index values. If that passes, we merge the
instant table into the duration table, and the row order is preserved.
Note: This should always be applied before :meth:``rename_columns``
Args:
raw_xbrl_instant: table representing XBRL instant facts.
raw_xbrl_duration: table representing XBRL duration facts.
Returns:
A unified table combining the XBRL duration and instant facts, if both types
of facts were present. If either input dataframe is empty, the other
dataframe is returned unchanged, except that several unused columns are
dropped. If both input dataframes are empty, an empty dataframe is returned.
"""
drop_cols = ["filing_name", "index"]
# Ignore errors in case not all drop_cols are present.
instant = raw_xbrl_instant.drop(columns=drop_cols, errors="ignore")
duration = raw_xbrl_duration.drop(columns=drop_cols, errors="ignore")
instant_axes = [
col for col in raw_xbrl_instant.columns if col.endswith("_axis")
]
duration_axes = [
col for col in raw_xbrl_duration.columns if col.endswith("_axis")
]
if (
bool(instant_axes)
& bool(duration_axes)
& (set(instant_axes) != set(duration_axes))
):
raise ValueError(
f"{self.table_id.value}: Instant and Duration XBRL Axes do not match.\n"
f" instant: {instant_axes}\n"
f" duration: {duration_axes}"
)
# Do any table-specific preprocessing of the instant and duration tables
instant = self.process_instant_xbrl(instant)
duration = self.process_duration_xbrl(duration)
if instant.empty:
logger.info(f"{self.table_id.value}: No XBRL instant table found.")
out_df = duration
elif duration.empty:
logger.info(f"{self.table_id.value}: No XBRL duration table found.")
out_df = instant
else:
logger.info(
f"{self.table_id.value}: Both XBRL instant & duration tables found."
)
instant_merge_keys = [
"entity_id",
"report_year",
"sched_table_name",
] + instant_axes
duration_merge_keys = [
"entity_id",
"report_year",
"sched_table_name",
] + duration_axes
# See if there are any values in the instant table that don't show up in the
# duration table.
unique_instant_rows = instant.set_index(
instant_merge_keys
).index.difference(duration.set_index(duration_merge_keys).index)
if unique_instant_rows.empty:
logger.info(
f"{self.table_id.value}: Combining XBRL instant & duration tables "
"using RIGHT-MERGE."
)
# Merge instant into duration.
out_df = pd.merge(
instant,
duration,
how="right",
left_on=instant_merge_keys,
right_on=duration_merge_keys,
validate="1:1",
)
else:
# TODO: Check whether our assumptions about these tables hold before
# concatenating them. May need to be table specific. E.g.
# * What fraction of their index values overlap? (it should be high!)
# * Do the instant/duration columns conform to expected naming conventions?
logger.info(
f"{self.table_id.value}: Combining XBRL instant & duration tables "
"using CONCATENATION."
)
out_df = pd.concat(
[
instant.set_index(instant_merge_keys),
duration.set_index(duration_merge_keys),
],
axis="columns",
).reset_index()
return out_df.loc[out_df.report_year.isin(Ferc1Settings().xbrl_years)]
@cache_df("process_instant_xbrl")
[docs]
def process_instant_xbrl(self, df: pd.DataFrame) -> pd.DataFrame:
"""Pre-processing required to make instant and duration tables compatible.
Column renaming is sometimes required because a few columns in the instant and
duration tables do not have corresponding names that follow the naming
conventions of ~95% of all the columns, which we rely on programmatically when
reshaping and concatenating these tables together.
"""
df = self.rename_columns(df, rename_stage="instant_xbrl").pipe(
self.unstack_balances_to_report_year_instant_xbrl
)
return df
@cache_df("process_duration_xbrl")
[docs]
def process_duration_xbrl(self, df: pd.DataFrame) -> pd.DataFrame:
"""Pre-processing required to make instant and duration tables compatible.
Column renaming is sometimes required because a few columns in the instant and
duration tables do not have corresponding names that follow the naming
conventions of ~95% of all the columns, which we rely on programmatically when
reshaping and concatenating these tables together.
"""
if not df.empty:
df = self.rename_columns(df, rename_stage="duration_xbrl").pipe(
self.select_current_year_annual_records_duration_xbrl
)
return df
[docs]
def select_current_year_annual_records_duration_xbrl(self, df):
"""Select for annual records within their report_year.
Select only records that have a start_date at begining of the report_year and
have an end_date at the end of the report_year.
"""
len_og = len(df)
df = df.astype({"start_date": "datetime64[s]", "end_date": "datetime64[s]"})
df = df[
(df.start_date.dt.year == df.report_year)
& (df.start_date.dt.month == 1)
& (df.start_date.dt.day == 1)
& (df.end_date.dt.year == df.report_year)
& (df.end_date.dt.month == 12)
& (df.end_date.dt.day == 31)
]
len_out = len(df)
logger.info(
f"{self.table_id.value}: After selection of dates based on the report year,"
f" we have {len_out/len_og:.1%} of the original table."
)
return df
@cache_df(key="dbf")
[docs]
def drop_footnote_columns_dbf(self, df: pd.DataFrame) -> pd.DataFrame:
"""Drop DBF footnote reference columns, which all end with _f."""
logger.debug(f"{self.table_id.value}: Dropping DBF footnote columns.")
return df.drop(columns=df.filter(regex=r".*_f$").columns)
[docs]
def source_table_primary_key(self, source_ferc1: SourceFerc1) -> list[str]:
"""Look up the pre-renaming source table primary key columns."""
if source_ferc1 == SourceFerc1.DBF:
pk_cols = [
"report_year",
"report_prd",
"respondent_id",
"spplmnt_num",
"row_number",
]
else:
assert source_ferc1 == SourceFerc1.XBRL # nosec: B101
cols = self.params.rename_columns_ferc1.xbrl.columns
pk_cols = ["report_year", "entity_id"]
# Sort to avoid dependence on the ordering of rename_columns.
# Doing the sorting here because we have a particular ordering
# hard coded for the DBF primary keys.
pk_cols += sorted(col for col in cols if col.endswith("_axis"))
return pk_cols
[docs]
def renamed_table_primary_key(self, source_ferc1: SourceFerc1) -> list[str]:
"""Look up the post-renaming primary key columns."""
if source_ferc1 == SourceFerc1.DBF:
cols = self.params.rename_columns_ferc1.dbf.columns
else:
assert source_ferc1 == SourceFerc1.XBRL # nosec: B101
cols = self.params.rename_columns_ferc1.xbrl.columns
pk_cols = self.source_table_primary_key(source_ferc1=source_ferc1)
# Translate to the renamed columns
return [cols[col] for col in pk_cols]
@cache_df(key="dbf")
[docs]
def drop_unused_original_columns_dbf(self, df: pd.DataFrame) -> pd.DataFrame:
"""Remove residual DBF specific columns."""
unused_cols = [
"report_prd",
"spplmnt_num",
"row_number",
"row_seq",
"row_prvlg",
]
logger.debug(
f"{self.table_id.value}: Dropping unused DBF structural columns: "
f"{unused_cols}"
)
missing_cols = set(unused_cols).difference(df.columns)
if missing_cols:
raise ValueError(
f"{self.table_id.value}: Trying to drop missing original DBF columns:"
f"{missing_cols}"
)
return df.drop(columns=unused_cols)
[docs]
def assign_record_id(
self, df: pd.DataFrame, source_ferc1: SourceFerc1
) -> pd.DataFrame:
"""Add a column identifying the original source record for each row.
It is often useful to be able to tell exactly which record in the FERC Form 1
database a given record within the PUDL database came from.
Within each FERC Form 1 DBF table, each record is supposed to be uniquely
identified by the combination of: report_year, report_prd, utility_id_ferc1_dbf,
spplmnt_num, row_number.
The FERC Form 1 XBRL tables do not have these supplement and row number
columns, so we construct an id based on:
report_year, utility_id_ferc1_xbrl, and the primary key columns of the XBRL table
Args:
df: table to assign `record_id` to
source_ferc1: data source of raw ferc1 database.
Raises:
ValueError: If any of the primary key columns are missing from the DataFrame
being processed.
ValueError: If there are any null values in the primary key columns.
ValueError: If the resulting `record_id` column is non-unique.
"""
logger.debug(
f"{self.table_id.value}: Assigning {source_ferc1.value} source record IDs."
)
pk_cols = self.renamed_table_primary_key(source_ferc1)
missing_pk_cols = set(pk_cols).difference(df.columns)
if missing_pk_cols:
raise ValueError(
f"{self.table_id.value} ({source_ferc1.value}): Missing primary key "
"columns in dataframe while assigning source record_id: "
f"{missing_pk_cols}"
)
if df[pk_cols].isnull().any(axis=None):
raise ValueError(
f"{self.table_id.value} ({source_ferc1.value}): Found null primary key "
"values.\n"
f"{df[pk_cols].isnull().any()}"
)
df = df.assign(
record_id=lambda x: x.sched_table_name.str.cat(
x[pk_cols].astype(str), sep="_"
),
)
if df.sched_table_name.isnull().any():
raise ValueError(
f"{self.table_id.value}: Null sched_table_name's were found where none "
"were expected."
)
df.record_id = enforce_snake_case(df.record_id)
dupe_ids = df.record_id[df.record_id.duplicated()].to_numpy()
if dupe_ids.any() and self.has_unique_record_ids:
logger.warning(
f"{self.table_id.value}: Found {len(dupe_ids)} duplicate record_ids: \n"
f"{dupe_ids}."
)
df = df.drop(columns="sched_table_name")
return df
[docs]
def assign_utility_id_ferc1(
self, df: pd.DataFrame, source_ferc1: SourceFerc1
) -> pd.DataFrame:
"""Assign the PUDL-assigned utility_id_ferc1 based on the native utility ID.
We need to replace the natively reported utility ID from each of the two FERC1
sources with a PUDL-assigned utilty. The mapping between the native ID's and
these PUDL-assigned ID's can be accessed in the database tables
``utilities_dbf_ferc1`` and ``utilities_xbrl_ferc1``.
Args:
df: the input table with the native utilty ID column.
source_ferc1: the
Returns:
an augemented version of the input ``df`` with a new column that replaces
the natively reported utility ID with the PUDL-assigned utility ID.
"""
logger.debug(
f"{self.table_id.value}: Assigning {source_ferc1.value} source utility IDs."
)
utility_map_ferc1 = pudl.glue.ferc1_eia.get_utility_map_ferc1()
# use the source utility ID column to get a unique map and for merging
util_id_col = f"utility_id_ferc1_{source_ferc1.value}"
util_map_series = (
utility_map_ferc1.dropna(subset=[util_id_col])
.set_index(util_id_col)
.utility_id_ferc1
)
df["utility_id_ferc1"] = df[util_id_col].map(util_map_series)
return df
[docs]
def reconcile_table_calculations(
self: Self,
df: pd.DataFrame,
params: ReconcileTableCalculations | None = None,
):
"""Check how well a table's calculated values match reported values."""
if params is None:
params = self.params.reconcile_table_calculations
if params.column_to_check:
if self.xbrl_calculations is None:
raise AssertionError(
"No calculations table has been built. Must run process_xbrl_metadata_calculations"
)
logger.info(
f"{self.table_id.value}: Checking the XBRL metadata-based calculations."
)
df = reconcile_table_calculations(
df=df,
calculation_components=self.xbrl_calculations,
xbrl_factoid_name=self.params.xbrl_factoid_name,
xbrl_metadata=self.xbrl_metadata,
table_name=self.table_id.value,
params=params,
)
return df
[docs]
class SteamPlantsFuelTableTransformer(Ferc1AbstractTableTransformer):
"""A table transformer specific to the :ref:`core_ferc1__yearly_steam_plants_fuel_sched402` table.
The :ref:`core_ferc1__yearly_steam_plants_fuel_sched402` table reports data about fuel consumed by large thermal power
plants in the :ref:`core_ferc1__yearly_steam_plants_sched402` table. Each record in the steam table is
typically associated with several records in the fuel table, with each fuel record
reporting data for a particular type of fuel consumed by that plant over the course
of a year. The fuel table presents several challenges.
The type of fuel, which is part of the primary key for the table, is a freeform
string with hundreds of different nonstandard values. These strings are categorized
manually and converted to ``fuel_type_code_pudl``. Some values cannot be categorized
and are set to ``other``. In other string categorizations we set the unidentifiable
values to NA, but in this table the fuel type is part of the primary key and primary
keys cannot contain NA values.
This simplified categorization occasionally results in records with duplicate
primary keys. In those cases the records are aggregated into a single record if they
have the same apparent physical units. If the fuel units are different, only the
first record is retained.
Several columns have unspecified, inconsistent, fuel-type specific units of measure
associated with them. In order for records to be comparable and aggregatable, we
have to infer and standardize these units.
In the raw FERC Form 1 data there is a ``fuel_units`` column which describes the
units of fuel delivered or consumed. Most commonly this is short tons for solid
fuels (coal), thousands of cubic feet (Mcf) for gaseous fuels, and barrels (bbl) for
liquid fuels. However, the ``fuel_units`` column is also a freeform string with
hundreds of nonstandard values which we have to manually categorize, and many of the
values do not map directly to the most commonly used units for fuel quantities. E.g.
some solid fuel quantities are reported in pounds, or thousands of pounds, not tons;
some liquid fuels are reported in gallons or thousands of gallons, not barrels; and
some gaseous fuels are reported in cubic feet not thousands of cubic feet.
Two additional columns report fuel price per unit of heat content and fuel heat
content per physical unit of fuel. The units of those columns are not explicitly
reported, vary by fuel, and are inconsistent within individual fuel types.
We adopt standardized units and attempt to convert all reported values in the fuel
table into those units. For physical fuel units we adopt those that are used by the
EIA: short tons (tons) for solid fuels, barrels (bbl) for liquid fuels, and
thousands of cubic feet (mcf) for gaseous fuels. For heat content per (physical)
unit of fuel, we use millions of British thermal units (mmbtu). All fuel prices are
converted to US dollars, while many are reported in cents.
Because the reported fuel price and heat content units are implicit, we have to
infer them based on observed values. This is only possible because these quantities
are ratios with well defined ranges of valid values. The common units that we
observe and attempt to standardize include:
* coal: primarily BTU/pound, but also MMBTU/ton and MMBTU/pound.
* oil: primarily BTU/gallon.
* gas: reported in a mix of MMBTU/cubic foot, and MMBTU/thousand cubic feet.
"""
@cache_df(key="main")
[docs]
def transform_main(self, df: pd.DataFrame) -> pd.DataFrame:
"""Table specific transforms for core_ferc1__yearly_steam_plants_fuel_sched402.
Args:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
A single transformed table concatenating multiple years of cleaned data
derived from the raw DBF and/or XBRL inputs.
"""
return (
self.spot_fix_values(df)
.pipe(self.drop_invalid_rows)
.pipe(self.correct_units)
)
@cache_df(key="dbf")
[docs]
def process_dbf(self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""Start with inherited method and do some fuel-specific processing.
We have to do most of the transformation before the DBF and XBRL data have been
concatenated because the fuel type column is part of the primary key and it is
extensively modified in the cleaning process.
"""
df = (
super()
.process_dbf(raw_dbf)
.pipe(self.to_numeric)
.pipe(self.convert_units)
.pipe(self.normalize_strings)
.pipe(self.categorize_strings)
.pipe(self.standardize_physical_fuel_units)
)
return df
@cache_df(key="xbrl")
[docs]
def process_xbrl(
self, raw_xbrl_instant: pd.DataFrame, raw_xbrl_duration: pd.DataFrame
) -> pd.DataFrame:
"""Special pre-concat treatment of the :ref:`core_ferc1__yearly_steam_plants_fuel_sched402` table.
We have to do most of the transformation before the DBF and XBRL data have been
concatenated because the fuel type column is part of the primary key and it is
extensively modified in the cleaning process. For the XBRL data, this means we
can't create a record ID until that fuel type value is clean. In addition, the
categorization of fuel types results in a number of duplicate fuel records which
need to be aggregated.
Args:
raw_xbrl_instant: Freshly extracted XBRL instant fact table.
raw_xbrl_duration: Freshly extracted XBRL duration fact table.
Returns:
Almost fully transformed XBRL data table, with instant and duration facts
merged together.
"""
return (
self.merge_instant_and_duration_tables_xbrl(
raw_xbrl_instant, raw_xbrl_duration
)
.pipe(self.rename_columns, rename_stage="xbrl")
.pipe(self.to_numeric)
.pipe(self.convert_units)
.pipe(self.normalize_strings)
.pipe(self.categorize_strings)
.pipe(self.standardize_physical_fuel_units)
.pipe(self.aggregate_duplicate_fuel_types_xbrl)
.pipe(self.assign_record_id, source_ferc1=SourceFerc1.XBRL)
.pipe(
self.assign_utility_id_ferc1,
source_ferc1=SourceFerc1.XBRL,
)
)
[docs]
def to_numeric(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Convert columns containing numeric strings to numeric types."""
numeric_cols = [
"fuel_consumed_units",
"fuel_cost_per_unit_burned",
"fuel_cost_per_unit_delivered",
"fuel_cost_per_mmbtu",
]
for col in numeric_cols:
df[col] = pd.to_numeric(df[col])
return df
[docs]
def standardize_physical_fuel_units(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Convert reported fuel quantities to standard units depending on fuel type.
Use the categorized fuel type and reported fuel units to convert all fuel
quantities to the following standard units, depending on whether the fuel is a
solid, liquid, or gas. When a single fuel reports its quantity in fundamentally
different units, convert based on typical values. E.g. 19.85 MMBTU per ton of
coal, 1.037 Mcf per MMBTU of natural gas, 7.46 barrels per ton of oil.
* solid fuels (coal and waste): short tons [ton]
* liquid fuels (oil): barrels [bbl]
* gaseous fuels (gas): thousands of cubic feet [mcf]
Columns to which these physical units apply:
* fuel_consumed_units (tons, bbl, mcf)
* fuel_cost_per_unit_burned (usd/ton, usd/bbl, usd/mcf)
* fuel_cost_per_unit_delivered (usd/ton, usd/bbl, usd/mcf)
One remaining challenge in this standardization is that nuclear fuel is reported
in both mass of Uranium and fuel heat content, and it's unclear if there's any
reasonable typical conversion between these units, since available heat content
depends on the degree of U235 enrichement, the type of reactor, and whether the
fuel is just Uranium, or a mix of Uranium and Plutonium from decommissioned
nuclear weapons. See:
https://world-nuclear.org/information-library/facts-and-figures/heat-values-of-various-fuels.aspx
"""
df = df.copy()
FuelFix = namedtuple("FuelFix", ["fuel", "from_unit", "to_unit", "mult"])
fuel_fixes = [
# US average coal heat content is 19.85 mmbtu/short ton
FuelFix("coal", "mmbtu", "ton", (1.0 / 19.85)),
FuelFix("coal", "btu", "ton", (1.0 / 19.85e6)),
# 2000 lbs per short ton
FuelFix("coal", "lbs", "ton", (1.0 / 2000.0)),
FuelFix("coal", "klbs", "ton", (1.0 / 2.0)),
# 42 gallons per barrel. Seriously, who makes up these units?
FuelFix("oil", "gal", "bbl", (1.0 / 42.0)),
FuelFix("oil", "kgal", "bbl", (1000.0 / 42.0)),
# On average a "ton of oil equivalent" is 7.46 barrels
FuelFix("oil", "ton", "bbl", 7.46),
FuelFix("gas", "mmbtu", "mcf", (1.0 / 1.037)),
# Nuclear plants report either heat content or mass of heavy metal
# MW*days thermal to MWh thermal
FuelFix("nuclear", "mwdth", "mwhth", 24.0),
# Straight energy equivalence between BTU and MWh here:
FuelFix("nuclear", "mmbtu", "mwhth", (1.0 / 3.412142)),
FuelFix("nuclear", "btu", "mwhth", (1.0 / 3412142)),
# Unclear if it's possible to convert heavy metal to heat reliably
FuelFix("nuclear", "grams", "kg", (1.0 / 1000)),
]
for fix in fuel_fixes:
fuel_mask = df.fuel_type_code_pudl == fix.fuel
unit_mask = df.fuel_units == fix.from_unit
df.loc[(fuel_mask & unit_mask), "fuel_consumed_units"] *= fix.mult
# Note: The 2 corrections below DIVIDE by the multiplier because the units
# are in the denominator ("per_unit") rather than the numerator.
df.loc[(fuel_mask & unit_mask), "fuel_cost_per_unit_burned"] /= fix.mult
df.loc[(fuel_mask & unit_mask), "fuel_cost_per_unit_delivered"] /= fix.mult
df.loc[(fuel_mask & unit_mask), "fuel_units"] = fix.to_unit
# Set all remaining non-standard units and affected columns to NA.
FuelAllowedUnits = namedtuple("FuelAllowedUnits", ["fuel", "allowed_units"])
fuel_allowed_units = [
FuelAllowedUnits("coal", ("ton",)),
FuelAllowedUnits("oil", ("bbl",)),
FuelAllowedUnits("gas", ("mcf",)),
FuelAllowedUnits("nuclear", ("kg", "mwhth")),
FuelAllowedUnits("waste", ("ton",)),
# All unidentified fuel types ("other") get units set to NA
FuelAllowedUnits("other", ()),
]
physical_units_cols = [
"fuel_consumed_units",
"fuel_cost_per_unit_burned",
"fuel_cost_per_unit_delivered",
]
for fau in fuel_allowed_units:
fuel_mask = df.fuel_type_code_pudl == fau.fuel
invalid_unit_mask = ~df.fuel_units.isin(fau.allowed_units)
df.loc[(fuel_mask & invalid_unit_mask), physical_units_cols] = np.nan
df.loc[(fuel_mask & invalid_unit_mask), "fuel_units"] = pd.NA
return df
@cache_df(key="xbrl")
[docs]
def aggregate_duplicate_fuel_types_xbrl(
self, fuel_xbrl: pd.DataFrame
) -> pd.DataFrame:
"""Aggregate the fuel records having duplicate primary keys."""
pk_cols = self.renamed_table_primary_key(source_ferc1=SourceFerc1.XBRL) + [
"sched_table_name"
]
fuel_xbrl.loc[:, "fuel_units_count"] = fuel_xbrl.groupby(pk_cols, dropna=False)[
"fuel_units"
].transform("nunique")
# split
dupe_mask = fuel_xbrl.duplicated(subset=pk_cols, keep=False)
multi_unit_mask = fuel_xbrl.fuel_units_count != 1
fuel_pk_dupes = fuel_xbrl[dupe_mask & ~multi_unit_mask].copy()
fuel_multi_unit = fuel_xbrl[dupe_mask & multi_unit_mask].copy()
fuel_non_dupes = fuel_xbrl[~dupe_mask & ~multi_unit_mask]
logger.info(
f"{self.table_id.value}: Aggregating {len(fuel_pk_dupes)} rows with "
f"duplicate primary keys out of {len(fuel_xbrl)} total rows."
)
logger.info(
f"{self.table_id.value}: Dropping {len(fuel_multi_unit)} records with "
"inconsistent fuel units preventing aggregation "
f"out of {len(fuel_xbrl)} total rows."
)
agg_row_fraction = (len(fuel_pk_dupes) + len(fuel_multi_unit)) / len(fuel_xbrl)
if agg_row_fraction > 0.15:
logger.error(
f"{self.table_id.value}: {agg_row_fraction:.0%} of all rows are being "
"aggregated. Higher than the allowed value of 15%!"
)
data_cols = [
"fuel_consumed_units",
"fuel_mmbtu_per_unit",
"fuel_cost_per_unit_delivered",
"fuel_cost_per_unit_burned",
"fuel_cost_per_mmbtu",
"fuel_cost_per_mwh",
"fuel_mmbtu_per_mwh",
]
# apply
fuel_pk_dupes = pudl.helpers.sum_and_weighted_average_agg(
df_in=fuel_pk_dupes,
by=pk_cols + ["start_date", "end_date", "fuel_units"],
sum_cols=["fuel_consumed_units"],
wtavg_dict={
k: "fuel_consumed_units"
for k in data_cols
if k != "fuel_consumed_units"
},
)
# We can't aggregate data when fuel units are inconsistent, but we don't want
# to lose the records entirely, so we'll keep the first one.
fuel_multi_unit.loc[:, data_cols] = np.nan
fuel_multi_unit = fuel_multi_unit.drop_duplicates(subset=pk_cols, keep="first")
# combine
return pd.concat([fuel_non_dupes, fuel_pk_dupes, fuel_multi_unit]).drop(
columns=["fuel_units_count"]
)
[docs]
def drop_total_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Drop rows that represent plant totals rather than individual fuels.
This is an imperfect, heuristic process. The rows we identify as probably
representing totals rather than individual fuels:
* have zero or null values in all of their numerical data columns
* have no identifiable fuel type
* have no identifiable fuel units
* DO report a value for MMBTU / MWh (heat rate)
In the case of the core_ferc1__yearly_steam_plants_fuel_sched402 table, we drop any row where all the data columns
are null AND there's a non-null value in the ``fuel_mmbtu_per_mwh`` column, as
it typically indicates a "total" row for a plant. We also require a null value
for the fuel_units and an "other" value for the fuel type.
"""
data_cols = [
"fuel_consumed_units",
"fuel_mmbtu_per_unit",
"fuel_cost_per_unit_delivered",
"fuel_cost_per_unit_burned",
"fuel_cost_per_mmbtu",
"fuel_cost_per_mwh",
]
total_rows_idx = df[
df[data_cols].isna().all(axis="columns") # No normal numerical data
& df.fuel_units.isna() # no recognizable fuel units
& (df.fuel_type_code_pudl == "other") # No recognizable fuel type
& df.fuel_mmbtu_per_mwh.notna() # But it DOES report heat rate!
].index
logger.info(
f"{self.table_id.value}: Dropping "
f"{len(total_rows_idx)}/{len(df)}"
"rows representing plant-level all-fuel totals."
)
return df.drop(index=total_rows_idx)
[docs]
def drop_invalid_rows(
self, df: pd.DataFrame, params: InvalidRows | None = None
) -> pd.DataFrame:
"""Drop invalid rows from the fuel table.
This method both drops rows in which all required data columns are null (using
the inherited parameterized method) and then also drops those rows we believe
represent plant totals. See :meth:`SteamPlantsFuelTableTransformer.drop_total_rows`.
"""
return super().drop_invalid_rows(df, params).pipe(self.drop_total_rows)
[docs]
class SteamPlantsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for the :ref:`core_ferc1__yearly_steam_plants_sched402` table."""
[docs]
class HydroelectricPlantsTableTransformer(Ferc1AbstractTableTransformer):
"""A table transformer specific to the :ref:`core_ferc1__yearly_hydroelectric_plants_sched406` table."""
[docs]
def transform_main(self, df):
"""Add bespoke removal of duplicate record after standard transform_main."""
return super().transform_main(df).pipe(self.targeted_drop_duplicates)
[docs]
def targeted_drop_duplicates(self, df):
"""Targeted removal of known duplicate record.
There are two records in 2019 with a ``utility_id_ferc1`` of 200 and a
``plant_name_ferc1`` of "marmet". The records are nearly duplicates of
eachother, except one have nulls in the capex columns. Surgically remove the
record with the nulls.
"""
null_columns = [
"capex_land",
"capex_structures",
"capex_facilities",
"capex_equipment",
"capex_roads",
"asset_retirement_cost",
"capex_total",
"capex_per_mw",
]
dupe_mask = (
(df.report_year == 2019)
& (df.utility_id_ferc1 == 200)
& (df.plant_name_ferc1 == "marmet")
)
null_maks = df[null_columns].isnull().all(axis="columns")
possible_dupes = df.loc[dupe_mask]
if (len(possible_dupes) != 2) & (2019 in df.report_year.unique()):
raise AssertionError(
f"{self.table_id}: Expected 2 records for found: {possible_dupes}"
)
dropping = df.loc[(dupe_mask & null_maks)]
logger.debug(
f"Dropping {len(dropping)} duplicate record with null data in {null_columns}"
)
df = df.loc[~(dupe_mask & null_maks)].copy()
return df
[docs]
class PumpedStoragePlantsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_pumped_storage_plants_sched408` table."""
[docs]
class PurchasedPowerAndExchangesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_purchased_power_and_exchanges_sched326`.
This table has data about inter-utility power purchases into the PUDL DB. This
includes how much electricty was purchased, how much it cost, and who it was
purchased from. Unfortunately the field describing which other utility the power was
being bought from is poorly standardized, making it difficult to correlate with
other data. It will need to be categorized by hand or with some fuzzy matching
eventually.
"""
[docs]
class PlantInServiceTableTransformer(Ferc1AbstractTableTransformer):
"""A transformer for the :ref:`core_ferc1__yearly_plant_in_service_sched204` table."""
@cache_df("process_xbrl_metadata")
[docs]
def process_xbrl_metadata(
self: Self,
xbrl_metadata_converted: pd.DataFrame,
xbrl_calculations: pd.DataFrame,
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
We fill in some gaps in the metadata, e.g. for FERC accounts that have been
split across multiple rows, or combined without being calculated. We also need
to rename the XBRL metadata categories to conform to the same naming convention
that we are using in the data itself (since FERC doesn't quite follow their own
naming conventions...). We use the same rename dictionary, but as an argument to
:meth:`pd.Series.replace` instead of :meth:`pd.DataFrame.rename`.
"""
tbl_meta = super().process_xbrl_metadata(
xbrl_metadata_converted, xbrl_calculations
)
# Set pseudo-account numbers for rows that split or combine FERC accounts, but
# which are not calculated values.
tbl_meta.loc[
tbl_meta.xbrl_factoid == "electric_plant_purchased",
["ferc_account", "plant_status"],
] = ["102_purchased", pd.NA]
tbl_meta.loc[
tbl_meta.xbrl_factoid == "electric_plant_sold",
["ferc_account", "plant_status"],
] = ["102_sold", pd.NA]
tbl_meta.loc[
tbl_meta.xbrl_factoid
== "electric_plant_in_service_and_completed_construction_not_classified_electric",
"ferc_account",
] = "101_and_106"
return tbl_meta
[docs]
def deduplicate_xbrl_factoid_xbrl_metadata(
self, tbl_meta: pd.DataFrame
) -> pd.DataFrame:
"""De-duplicate the XBLR metadata.
We deduplicate the metadata on the basis of the ``xbrl_factoid`` name.
This table in particular has multiple ``wide_to_tidy`` ``value_types`` because
there are multiple dollar columns embedded (it has both the standard start/end
balances as well as modifcations like transfers/retirements). In the XBRL
metadata, each xbrl_fact has its own set of metadata and possibly its own set of
calculations. Which means that one ``xbrl_factoid`` for this table natively
could have multiple calculations or other metadata.
For merging, we need the metadata to have one field per ``xbrl_factoid``.
Because we normally only use the start/end balance in calculations, when there
are duplicate renamed ``xbrl_factoid`` s in our processed metadata, we are going
to prefer the one that refers to the start/end balances. In an ideal world, we
would be able to access this metadata based on both the ``xbrl_factoid`` and
any column from ``value_types`` but that would require a larger change in
architecture.
"""
# remove duplication of xbrl_factoid
same_calcs_mask = tbl_meta.duplicated(
subset=["xbrl_factoid", "calculations"], keep=False
)
# if they key values are the same, select the records with values in ferc_account
same_calcs_deduped = (
tbl_meta[same_calcs_mask]
.sort_values(["ferc_account"]) # sort brings the nulls to the bottom
.drop_duplicates(subset=["xbrl_factoid"], keep="first")
)
# when the calcs are different, they are referring to the non-adjustments
suffixes = ("_additions", "_retirements", "_adjustments", "_transfers")
unique_calcs_deduped = tbl_meta[
~same_calcs_mask & (~tbl_meta.xbrl_factoid_original.str.endswith(suffixes))
]
tbl_meta_cleaned = pd.concat([same_calcs_deduped, unique_calcs_deduped])
assert set(tbl_meta_cleaned.xbrl_factoid.unique()) == set(
tbl_meta.xbrl_factoid.unique()
)
assert ~tbl_meta_cleaned.duplicated(["xbrl_factoid"]).all()
return tbl_meta_cleaned
[docs]
def apply_sign_conventions(self, df) -> pd.DataFrame:
"""Adjust rows and column sign conventsion to enable aggregation by summing.
Columns have uniform sign conventions, which we have manually inferred from the
original metadata. This can and probably should be done programmatically in the
future. If not, we'll probably want to store the column_weights as a parameter
rather than hard-coding it in here.
"""
column_weights = {
"starting_balance": 1.0,
"additions": 1.0,
"retirements": -1.0,
"transfers": 1.0,
"adjustments": 1.0,
"ending_balance": 1.0,
}
# Set row weights based on the value of the "balance" field
df.loc[df.balance == "debit", "row_weight"] = 1.0
df.loc[df.balance == "credit", "row_weight"] = -1.0
# Apply column weightings. Can this be done all at once in a vectorized way?
for col in column_weights:
df.loc[:, col] *= column_weights[col]
df.loc[:, col] *= df["row_weight"]
return df
[docs]
def targeted_drop_duplicates_dbf(self, df: pd.DataFrame) -> pd.DataFrame:
"""Drop bad duplicate records from a specific utility in 2018.
This is a very specific fix, meant to get rid of a particular observed set of
duplicate records: FERC Respondent ID 187 in 2018 has two sets of plant in
service records, one of which contains a bunch of null data.
This method is part of the DBF processing because we want to be able to
hard-code a specific value of ``utility_id_ferc1_dbf`` and those IDs are no
longer available later in the process. I think.
"""
# A single utility has double reported data in 2018.
pk = ["report_year", "utility_id_ferc1", "ferc_account_label"]
dupe_mask = (
df.duplicated(subset=pk, keep=False)
& (df.report_year == 2018)
& (df.utility_id_ferc1_dbf == 187)
)
all_dupes = df[dupe_mask]
# The observed pairs of duplicate records have NA values in all of the
# additions, retirements, adjustments, and transfers columns. This selects
# only those duplicates that have *any* non-null value in those rows.
good_dupes = all_dupes[
all_dupes[["additions", "retirements", "adjustments", "transfers"]]
.notnull()
.any(axis="columns")
]
# Make sure that the good and bad dupes have exactly the same indices:
pd.testing.assert_index_equal(
good_dupes.set_index(pk).index,
all_dupes.set_index(pk).index.drop_duplicates(),
)
deduped = pd.concat([df[~dupe_mask], good_dupes], axis="index")
remaining_dupes = deduped[deduped.duplicated(subset=pk)]
logger.info(
f"{self.table_id.value}: {len(remaining_dupes)} dupes remaining after "
"targeted deduplication."
)
return deduped
@cache_df(key="dbf")
[docs]
def process_dbf(self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""Drop targeted duplicates in the DBF data so we can use FERC respondent ID."""
return super().process_dbf(raw_dbf).pipe(self.targeted_drop_duplicates_dbf)
@cache_df(key="main")
[docs]
def transform_main(self, df: pd.DataFrame) -> pd.DataFrame:
"""The main table-specific transformations, affecting contents not structure.
Annotates and alters data based on information from the XBRL taxonomy metadata.
Also assigns utility type, plant status & function for use in table explosions.
Make all electric_plant_sold balances positive.
"""
df = super().transform_main(df).pipe(self.apply_sign_conventions)
# Make all electric_plant_sold values positive
# This could probably be a FERC transformer class function or in the
# apply_sign_conventions function, but it doesn't seem like the best fit for
# now.
neg_values = (df["ferc_account_label"] == "electric_plant_sold") & (
df["ending_balance"] < 0
)
df.loc[neg_values, "ending_balance"] = abs(df["ending_balance"])
logger.info(
f"{self.table_id.value}: Converted {len(df[neg_values])} negative values to positive."
)
# Add two exceptions for plant status
df.loc[
df.ferc_account_label.isin(
["electric_plant_sold", "electric_plant_purchased"]
)
].plant_status = pd.NA
return df
[docs]
class SmallPlantsTableTransformer(Ferc1AbstractTableTransformer):
"""A table transformer specific to the :ref:`core_ferc1__yearly_small_plants_sched410` table."""
@cache_df(key="main")
[docs]
def transform_main(self, df: pd.DataFrame) -> pd.DataFrame:
"""Table specific transforms for core_ferc1__yearly_small_plants_sched410.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
A single transformed table concatenating multiple years of cleaned data
derived from the raw DBF and/or XBRL inputs.
"""
df = (
self.spot_fix_values(df)
.pipe(self.normalize_strings)
.pipe(self.nullify_outliers)
.pipe(self.convert_units)
.pipe(self.extract_ferc1_license)
.pipe(self.label_row_types)
.pipe(self.prep_header_fuel_and_plant_types)
.pipe(self.map_plant_name_fuel_types)
.pipe(self.categorize_strings)
.pipe(self.map_header_fuel_and_plant_types)
.pipe(self.associate_notes_with_values)
.pipe(self.spot_fix_rows)
.pipe(self.drop_invalid_rows)
# Now remove the row_type columns because we've already moved totals to a
# different column
.drop(columns=["row_type"])
)
return df
[docs]
def extract_ferc1_license(self, df: pd.DataFrame) -> pd.DataFrame:
"""Extract FERC license number from ``plant_name_ferc1``.
Many FERC license numbers are embedded in the ``plant_name_ferc1`` column, but
not all numbers in the ``plant_name_ferc1`` column are FERC licenses. Some are
dates, dollar amounts, page numbers, or numbers of wind turbines. This function
extracts valid FERC license numbers and puts them in a new column called
``license_id_ferc1``.
Potential FERC license numbers are valid when:
- Two or more integers were found.
- The found integers were accompanied by key phrases such as:
``["license", "no.", "ferc", "project"]``.
- The accompanying name does not contain phrases such as:
``["page", "pg", "$", "wind", "units"]``.
- The found integers don't fall don't fall within the range of a valid year,
defined as: 1900-2050.
- The plant record is categorized as ``hydro`` or not categorized via the
``plant_type`` and ``fuel_type`` columns.
This function also fills ``other`` fuel types with ``hydro`` for all plants with
valid FERC licenses because only hydro plants have FERC licenses.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
The same input DataFrame but with a new column called ``license_id_ferc1``
that contains FERC 1 license infromation extracted from
``plant_name_ferc1``.
"""
logger.info(f"{self.table_id.value}: Extracting FERC license from plant name")
# Extract all numbers greater than 2 digits from plant_name_ferc1 and put them
# in a new column as integers.
out_df = df.assign(
license_id_ferc1=lambda x: (
x.plant_name_ferc1.str.extract(r"(\d{3,})")
.astype("float")
.astype("Int64")
),
)
# Define what makes a good license
obvious_license = out_df.plant_name_ferc1.str.contains(
r"no\.|license|ferc|project", regex=True
)
not_license = out_df.plant_name_ferc1.str.contains(
r"page|pg|\$|wind|solar|nuclear|nonutility|units|surrendered", regex=True
)
exceptions_to_is_year = out_df.plant_name_ferc1.str.contains(
r"tomahawk|otter rapids|wausau|alexander|hooksett|north umpqua", regex=True
)
is_year = out_df["license_id_ferc1"].between(1900, 2050)
not_hydro = ~out_df["plant_type"].isin(["hydro", np.nan, None]) | ~out_df[
"fuel_type"
].isin(["hydro", "other"])
# Replace all the non-license numbers with NA
out_df.loc[
(not_hydro & ~obvious_license)
| not_license
| (is_year & ~obvious_license & ~exceptions_to_is_year),
"license_id_ferc1",
] = np.nan
# Fill fuel type with hydro
out_df.loc[
out_df["license_id_ferc1"].notna() & (out_df["fuel_type"] == "other"),
"fuel_type",
] = "hydro"
return out_df
[docs]
def _find_possible_header_or_note_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Find and label rows that might be headers or notes.
Called by the coordinating function :func:`label_row_types`.
This function creates a column called ``possible_header_or_note`` that is either
True or False depending on whether a group of columns are all NA. Rows labeled
as True will be further scrutinized in the :func:`_label_header_rows` and
:func:`_label_note_rows` functions to determine whether they are actually
headers or notes.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
The same input DataFrame but with a new column called
``possible_header_or_note`` that flags rows that might contain useful header
or note information.
"""
# Define header qualifications
possible_header_or_note_if_cols_na = [
"construction_year",
"net_generation_mwh",
"total_cost_of_plant",
"capex_total",
"capex_per_mw",
"opex_total",
"opex_fuel",
"opex_maintenance",
"fuel_cost_per_mmbtu",
# "peak_demand_mw",
# "opex_operations"
]
# Label possible header or note rows
df["possible_header_or_note"] = (
df.filter(possible_header_or_note_if_cols_na).isna().all(1)
)
return df
[docs]
def _find_note_clumps(
self, group: DataFrameGroupBy
) -> tuple[DataFrameGroupBy, pd.DataFrame]:
"""Find groups of rows likely to be notes.
Once the :func:`_find_possible_header_or_note_rows` function identifies rows
that are either headers or notes, we must deterine which one they are. As
described in the :func:`_label_note_rows` function, notes rows are usually
adjecent rows with no content.
This function itentifies instances of two or more adjecent rows where
``possible_header_or_note`` = True. It takes individual utility-year groups as a
parameter as opposed to the entire dataset because adjecent rows are only
meaningful if they are from the same reporting entity in the same year. If we
were to run this on the whole dataframe, we would see "note clumps" that are
actually notes from the end of one utility's report and headers from the
beginning of another. For this reason, we run this function from within the
:func:`_label_note_rows_group` function.
The output of this function is not a modified version of the original
utility-year group, rather, it is a DataFrame containing information about the
nature of the ``possible_header_or_note`` = True rows that is used to determine
if that row is a note or not. It also returns the original utility-year-group as
groupby objects seperated by each time ``possible_header_or_note`` changes from
True to False or vice versa.
If you pass in the following df:
+-------------------+-------------------------+
| plant_name_ferc1 | possible_header_or_note |
+===================+=========================+
| HYDRO: | True |
+-------------------+-------------------------+
| rainbow falls (b) | False |
+-------------------+-------------------------+
| cadyville (a) | False |
+-------------------+-------------------------+
| keuka (c) | False |
+-------------------+-------------------------+
| (a) project #2738 | True |
+-------------------+-------------------------+
| (b) project #2835 | True |
+-------------------+-------------------------+
| (c) project #2852 | True |
+-------------------+-------------------------+
You will get the following output (in addition to the groupby objects for each
clump):
+----------------+----------------+
| header_or_note | rows_per_clump |
+================+================+
| True | 1 |
+----------------+----------------+
| False | 3 |
+----------------+----------------+
| True | 3 |
+----------------+----------------+
This shows each clump of adjecent records where ``possible_header_or_note`` is
True or False and how many records are in each clump.
Params:
group: A utility-year grouping of the concatenated FERC XBRL and DBF tables.
This table must have been run through the
:func:`_find_possible_header_or_note_rows` function and contain the
column ``possible_header_or_note``.
Returns:
A tuple containing groupby objects for each of the note and non-note clumps
and a DataFrame indicating the number of rows in each note or non-note
clump.
"""
# Make groups based on consecutive sections where the group_col is alike.
clump_groups = group.groupby(
(
group["possible_header_or_note"].shift()
!= group["possible_header_or_note"]
).cumsum(),
as_index=False,
)
# Identify the first (and only) group_col value for each group and count
# how many rows are in each group.
clump_groups_df = clump_groups.agg(
header_or_note=("possible_header_or_note", "first"),
rows_per_clump=("possible_header_or_note", "count"),
)
return clump_groups, clump_groups_df
[docs]
def _label_header_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Label header rows by adding ``header`` to ``row_type`` column.
Called by the coordinating function :func:`label_row_types`.
Once possible header or notes rows have been identified via the
:func:`_find_possible_header_or_note_rows` function, this function sorts out
which ones are headers. It does this by identifying a list of strings that, when
found in the ``plant_name_ferc1`` column, indicate that the row is or is not a
header.
Sometimes this function identifies a header that is acutally a note. For this
reason, it's important that the function be called before
:func:`_label_note_rows` so that the bad header values get overridden by the
``note`` designation.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
the :func:`_find_possible_header_or_note_rows` function and contains the
column ``possible_header_or_note``.
Returns:
The same input DataFrame but with likely headers rows containing the string
``header`` in the ``row_type`` column.
"""
# Possible headers/note rows that contains these strings are headers
header_strings = [
"hydro",
"hyrdo",
"internal",
"wind",
"solar",
"gas",
"diesel",
"diesal",
"steam",
"other",
"combustion",
"combustine",
"fuel cell",
"hydraulic",
"waste",
"landfill",
"photovoltaic",
"nuclear",
"oil",
"renewable",
"facilities",
"combined cycle",
]
# Possible headers/note rows that contains these strings are not headers
nonheader_strings = [
"#",
r"\*",
"pg",
"solargenix",
"solargennix",
r"\@",
"rockton",
"albany steam",
"other general ops. supervision & engineering",
]
# Any rows that contains these strings are headers
header_exceptions = [
"hydro plants: licensed proj. no.",
"hydro license no.",
"hydro: license no.",
"hydro plants: licensed proj no.",
"photo voltaic generating plants:",
]
logger.info(f"{self.table_id.value}: Labeling header rows")
# Label good header rows (based on whether they contain key strings)
possible_header = df["possible_header_or_note"]
good_header = df["plant_name_ferc1"].str.contains("|".join(header_strings))
bad_header = df["plant_name_ferc1"].str.contains("|".join(nonheader_strings))
df.loc[possible_header & good_header & ~bad_header, "row_type"] = "header"
# There are some headers that don't pass the possible_header test but are
# still definitely headers.
df.loc[df["plant_name_ferc1"].isin(header_exceptions), "row_type"] = "header"
return df
[docs]
def _label_note_rows_group(
self, util_year_group: DataFrameGroupBy
) -> DataFrameGroupBy:
"""Label note rows by adding ``note`` to ``row_type`` column.
Called within the wraper function :func:`_label_note_rows`
This function breaks the data down by reporting unit (utility and year) and
determines whether a ``possible_header_note`` = True row is a note based on two
criteria:
- Clumps of 2 or more adjecent rows where ``possible_header_or_note`` is True.
- Instances where the last row in a utility-year group has
``possible_header_or_note`` as True.
There are a couple of important exceptions that this function also
addresses. Utilities often have multiple headers in a single utility-year
grouping. You might see something like: ``pd.Series([header, plant1, plant2,
note, header, plant3, plant4])``. In this case, a note clump is actually
comprised of a note followed by a header. This function will not override the
header as a note. Unfortunately, there is always the possability that a header
row is followed by a plant that had no values reported. This would look like,
and therefore be categorized as a note clump. I haven't built a work around, but
hopefully there aren't very many of these.
Params:
util_year_group: A groupby object that contains a single year and utility.
Returns:
The same input but with likely note rows containing the string ``note`` in
the ``row_type`` column.
"""
# Create mini groups that count pockets of true and false for each
# utility and year. See _find_note_clumps docstring.
clump_group, clump_count = self._find_note_clumps(util_year_group)
# Used later to enable exceptions
max_df_val = util_year_group.index.max()
# Create a list of the index values where there is a note clump! This also
# includes instances where the last row in a group is a note.
note_clump_idx_list = list(
clump_count[
(clump_count["header_or_note"])
& (
(clump_count["rows_per_clump"] > 1)
| (clump_count.tail(1)["rows_per_clump"] == 1)
)
].index
)
# If there are any clumped/end headers:
if note_clump_idx_list:
for idx in note_clump_idx_list:
# If the last row in a clump looks like a header, and the clump is
# not the last clump in the utility_year group, then drop the last
# row from the note clump index range because it's a header!
note_clump_idx_range = clump_group.groups[idx + 1]
not_last_clump = clump_group.groups[idx + 1].max() < max_df_val
is_good_header = (
util_year_group.loc[
util_year_group.index.isin(clump_group.groups[idx + 1])
]
.tail(1)["row_type"]
.str.contains("header")
.all()
)
if not_last_clump & is_good_header:
note_clump_idx_range = [
x
for x in note_clump_idx_range
if x != note_clump_idx_range.max()
]
# Label the note clump as a note
util_year_group.loc[
util_year_group.index.isin(note_clump_idx_range), "row_type"
] = "note"
return util_year_group
[docs]
def _label_note_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Wrapper for :func:`_label_note_rows_group`.
The small plants table has lots of note rows that contain useful information.
Unfortunately, the notes are in their own row rather than their own column! This
means that useful information pertaining to plant rows is floating around as a
junk row with no other information except the note in the ``plant_name_ferc1``
field. Luckily, the data are reported just like they would be on paper. I.e.,
The headers are at the top, and the notes are at the bottom. See the table in
:func:`label_row_types` for more detail. This function labels note rows.
Note rows are determined by row location within a given report, so we must break
the data into reporting units (utility and year) and then apply note-finding
methodology defined in :func:`_label_note_rows_group` to each group.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
the :func:`_find_possible_header_or_note_rows` function and contains the
column ``possible_header_or_note``.
Returns:
The same input DataFrame but with likely note rows containing the string
``note`` in the ``row_type`` column.
"""
logger.info(f"{self.table_id.value}: Labeling notes rows")
util_groups = df.groupby(["utility_id_ferc1", "report_year"])
return util_groups.apply(lambda x: self._label_note_rows_group(x))
[docs]
def _label_total_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Label total rows by adding ``total`` to ``row_type`` column.
Called within the wraper function :func:`_label_note_rows`
For the most part, when ``plant_name_ferc1`` contains the string ``total``, the
values therein are duplicates of what is already reported, i.e.: a total value.
However, there are some cases where that's not true. For example, the phrase
``amounts are for the total`` appears when chunks of plants (usually but not
always wind) are reported together. It's a total, but it's not double counting
which is the reason for the ``total`` flag.
Similar to :func:`_label_header_rows`, it's important that this be called before
:func:`_label_note_rows` in :func:`label_row_types` so that not clumps can
override certain non-totals that are mistakenly labeled as such.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
The same input DataFrame but with likely total rows containing the string
``total`` in the ``row_type`` column.
"""
# Label totals in row_type in case it overwrites any headers
logger.info(f"{self.table_id.value}: Labeling total rows")
df.loc[
df["plant_name_ferc1"].str.contains("total")
& ~df["plant_name_ferc1"].str.contains("amounts are for the total"),
"row_type",
] = "total"
# This one gets overridden by notes: total solar operation/maintenance
return df
[docs]
def label_row_types(self, df: pd.DataFrame) -> pd.DataFrame:
"""Coordinate labeling of ``row_types`` as headers, notes, or totals.
The small plants table is more like a digitized PDF than an actual data table.
The rows contain all sorts of information in addition to what the columns might
suggest. For instance, there are header rows, note rows, and total rows that
contain useful information, but cause confusion in their current state, mixed in
with the rest of the data.
Here's an example of what you might find in the small plants table:
+-------------------+------------+-----------------+
| plant_name_ferc1 | plant_type | capacity_mw |
+===================+============+=================+
| HYDRO: | NA | NA |
+-------------------+------------+-----------------+
| rainbow falls (b) | NA | 30 |
+-------------------+------------+-----------------+
| cadyville (a) | NA | 100 |
+-------------------+------------+-----------------+
| keuka (c) | NA | 80 |
+-------------------+------------+-----------------+
| total plants | NA | 310 |
+-------------------+------------+-----------------+
| (a) project #2738 | NA | NA |
+-------------------+------------+-----------------+
| (b) project #2835 | NA | NA |
+-------------------+------------+-----------------+
| (c) project #2852 | NA | NA |
+-------------------+------------+-----------------+
Notice how misleading it is to have all this infomration in one column. The
goal of this function is to coordinate labeling functions so that we can
identify which rows contain specific plant information and which rows are
headers, notes, or totals.
Once labeled, other functions can either remove rows that might cause double
counting, extract useful plant or fuel type information from headers, and
extract useful context or license id information from notes.
Coordinates :func:`_label_header_rows`, :func:`_label_total_rows`,
:func:`_label_note_rows`.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
the :func:`_find_possible_header_or_note_rows` function and contains the
column ``possible_header_or_note``.
Returns:
The same input DataFrame but with a column called ``row_type`` containg the
strings ``header``, ``note``, ``total``, or NA to indicate what type of row
it is.
"""
# Add a column to show final row type
df.insert(3, "row_type", np.nan)
# Label the row types
df_labeled = (
df.pipe(self._find_possible_header_or_note_rows)
.pipe(self._label_header_rows)
.pipe(self._label_total_rows)
.pipe(self._label_note_rows)
.drop(columns=["possible_header_or_note"])
)
# Move total lables to a different column
df_labeled.loc[df_labeled["row_type"] == "total", "is_total"] = True
df_labeled["is_total"] = df_labeled.filter(["row_type"]).isin(["total"]).all(1)
return df_labeled
[docs]
def prep_header_fuel_and_plant_types(
self, df: pd.DataFrame, show_unmapped_headers=False
) -> pd.DataFrame:
"""Forward fill header rows to prep for fuel and plant type extraction.
The headers we've identified in :func:`_label_header_rows` can be used to
supplement the values in the ``plant_type`` and ``fuel_type`` columns.
This function groups the data by utility, year, and header; extracts the header
into a new column; and forward fills the headers so that each record in the
header group is associated with that header. Because the headers map to
different fuel types and plant types (ex: ``solar pv`` maps to fuel type
``solar`` and plant type ``photovoltaic``), the new forward-filled header column
is duplicated and called ``fuel_type_from_header`` and
``plant_type_from_header``. In :func:`map_header_fuel_and_plant_types`, these
columns will be mapped to their respective fuel and plant types, used
to fill in blank values in the ``plant_type`` and ``fuel_type``, and then
eventually removed.
Why separate the prep step from the map step?
We trust the values originally reported in the ``fuel_type`` and ``plant_type``
columns more than the extracted and forward filled header values, so we only
want to replace ``fuel_type`` and ``plant_type`` values that are labeled as
``pd.NA`` or ``other``. The values reported to those columns are extremely messy
and must be cleaned via :func:`pudl.transform.classes.categorize_strings` in
order for us to know which are truely ``pd.NA`` or ``other``. Because we also
use :func:`pudl.transform.classes.categorize_strings` to map the headers to fuel
and plant types, it makes sense to clean all four columns at once and then
combine them.
Here's a look at what this function does. It starts with the following table:
+-------------------+------------+------------+----------+
| plant_name_ferc1 | plant_type | fuel_type | row_type |
+===================+============+============+==========+
| HYDRO: | NA | NA | header |
+-------------------+------------+------------+----------+
| rainbow falls (b) | NA | NA | NA |
+-------------------+------------+------------+----------+
| cadyville (a) | NA | NA | NA |
+-------------------+------------+------------+----------+
| keuka (c) | NA | NA | NA |
+-------------------+------------+------------+----------+
| Wind Turbines: | NA | NA | header |
+-------------------+------------+------------+----------+
| sunny grove | NA | NA | NA |
+-------------------+------------+------------+----------+
| green park wind | NA | wind | NA |
+-------------------+------------+------------+----------+
And ends with this:
+-------------------+---------+---------+----------------+--------------------+
| plant_name_ferc1 | plant | fuel | plant_type | fuel_type |
| | _type | _type | _from_header | _from_header |
+===================+=========+=========+================+====================+
| HYDRO: | NA | NA | HYDRO: | HYDRO: |
+-------------------+---------+---------+----------------+--------------------+
| rainbow falls (b) | NA | NA | HYDRO: | HYDRO: |
+-------------------+---------+---------+----------------+--------------------+
| cadyville (a) | NA | NA | HYDRO: | HYDRO: |
+-------------------+---------+---------+----------------+--------------------+
| keuka (c) | NA | NA | HYDRO: | HYDRO: |
+-------------------+---------+---------+----------------+--------------------+
| Wind Turbines: | NA | NA | Wind Turbines: | Wind Turbines: |
+-------------------+---------+---------+----------------+--------------------+
| sunny grove | NA | NA | Wind Turbines: | Wind Turbines: |
+-------------------+---------+---------+----------------+--------------------+
| green park wind | NA | wind | Wind Turbines: | Wind Turbines: |
+-------------------+---------+---------+----------------+--------------------+
NOTE: If a utility's ``plant_name_ferc1`` values look like this: ``["STEAM",
"coal_plant1", "coal_plant2", "wind_turbine1"]``, then this algorythem will
think that last wind turbine is a steam plant. Luckily, when a utility embeds
headers in the data it usually includes them for all plant types: ``["STEAM",
"coal_plant1", "coal_plant2", "WIND", "wind_turbine"]``.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
:func:`_label_row_type` and contains the columns ``row_type``.
Returns:
The same input DataFrame but with new columns ``plant_type_from_header``
and ``fuel_type_from_header`` that forward fill the values in the header
rows by utility, year, and header group.
"""
logger.info(
f"{self.table_id.value}: Forward filling header fuel and plant types"
)
# Create a column of just headers
df.loc[df["row_type"] == "header", "header"] = df["plant_name_ferc1"]
# Make groups based on utility, year, and header.
# The .cumsum() creates a new series with values that go up from 1 whenever
# there is a new header. So imagine row_type["header", NA, NA, "header", NA].
# this creates a series of [1,1,1,2,2] so that the data can be grouped by
# header.
df = df.reset_index(drop=True)
df["header_group"] = (df["row_type"] == "header").cumsum()
df.loc[df["row_type"] != "note", "header"] = df.groupby(
["utility_id_ferc1", "report_year", "header_group"]
).header.ffill()
# Create temporary columns for plant type and fuel type
df["plant_type_from_header"] = df["header"]
df["fuel_type_from_header"] = df["header"]
df = df.drop(columns=["header", "header_group"])
return df
[docs]
def map_header_fuel_and_plant_types(self, df: pd.DataFrame) -> pd.DataFrame:
"""Fill ``pd.NA`` and ``other`` plant and fuel types with cleaned headers.
:func:`prep_header_fuel_and_plant_types` extracted and forward filled the header
values; :func:`pudl.transform.params.categorize_strings` cleaned them according
to both the fuel and plant type parameters. This function combines the
``fuel_type_from_header`` with ``fuel_type`` and ``plant_type_from_header`` with
``plant_type`` when the reported, cleaned values are ``pd.NA`` or ``other``.
To understand more about why these steps are necessary read the docstrings for
:func:`prep_header_fuel_and_plant_types`.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
:func:`prep_header_fuel_and_plant_types` and contains the columns
``fuel_type_from_header`` and ``plant_type_from_header``.
Returns:
The same input DataFrame but with rows with ``pd.NA`` or ``other`` in the
``fuel_type`` and ``plant_type`` columns filled in with the respective
values from ``fuel_type_from_header`` and ``plant_type_from_header`` when
available. ``fuel_type_from_header`` and ``plant_type_from_header`` columns
removed.
"""
logger.info(
f"{self.table_id.value}: Filling NA and 'other' fuel and plant types with"
" header info"
)
# Stash the amount of NA values to check that the filling worked.
old_fuel_type_count = len(
df[~df["fuel_type"].isin([pd.NA, "other"]) & df["row_type"].isna()]
)
old_plant_type_count = len(
df[~df["plant_type"].isin([pd.NA, "other"]) & df["row_type"].isna()]
)
# Fill NA and "other" fields
df.loc[df["plant_type"].isin([pd.NA, "other"]), "plant_type"] = (
df.plant_type_from_header
)
df.loc[df["fuel_type"].isin([pd.NA, "other"]), "fuel_type"] = (
df.fuel_type_from_header
)
# Remove _from_header fields
df = df.drop(columns=["plant_type_from_header", "fuel_type_from_header"])
# Check that this worked!
new_fuel_type_count = len(
df[~df["fuel_type"].isin([pd.NA, "other"]) & df["row_type"].isna()]
)
new_plant_type_count = len(
df[~df["plant_type"].isin([pd.NA, "other"]) & df["row_type"].isna()]
)
if not old_fuel_type_count < new_fuel_type_count:
raise AssertionError("No header fuel types added when there should be")
if not old_plant_type_count < new_plant_type_count:
raise AssertionError("No header plant types added when there should be")
useful_rows_len = len(df[df["row_type"].isna()])
logger.info(
f"Added fuel types to {new_fuel_type_count-old_fuel_type_count} plant rows "
f"({round((new_fuel_type_count-old_fuel_type_count)/useful_rows_len*100)}%). "
f"Added plant types to {new_plant_type_count-old_plant_type_count} plant "
f"rows ({round((new_plant_type_count-old_plant_type_count)/useful_rows_len*100)}%)."
)
return df
[docs]
def map_plant_name_fuel_types(self, df: pd.DataFrame) -> pd.DataFrame:
"""Suppliment ``fuel_type`` with information in ``plant_name_ferc1``.
Sometimes fuel type is embedded in a plant name (not just headers). In this case
we can identify that what that fuel is from the name and fill in empty
``fuel_type`` values. Right now, this only works for hydro plants because the
rest are complicated and have a slew of exceptions. This could probably be
applied to the ``plant_type`` column in the future too.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
The same input DataFrame but with rows with ``other`` in the
``fuel_type`` column filled in notable fuel types extracted from the the
``plant_name_ferc1`` column.
"""
logger.info(f"{self.table_id.value}: Getting fuel type (hydro) from plant name")
df.loc[
(
df["plant_name_ferc1"].str.contains("hydro")
& (df["fuel_type"] == "other")
),
"fuel_type",
] = "hydro"
return df
[docs]
def associate_notes_with_values(self, df: pd.DataFrame) -> pd.DataFrame:
"""Use footnote indicators to map notes and FERC licenses to plant rows.
There are many utilities that report a bunch of mostly empty note rows at the
bottom of their yearly entry. These notes often pertain to specific plant rows
above. Sometimes the notes and their respective plant rows are linked by a
common footnote indicator such as (a) or (1) etc.
This function takes this:
+-------------------+------------+------------------+
| plant_name_ferc1 | row_type | license_id_ferc1 |
+===================+============+==================+
| HYDRO: | header | NA |
+-------------------+------------+------------------+
| rainbow falls (b) | NA | NA |
+-------------------+------------+------------------+
| cadyville (a) | NA | NA |
+-------------------+------------+------------------+
| keuka (c) | NA | NA |
+-------------------+------------+------------------+
| total plants | total | NA |
+-------------------+------------+------------------+
| (a) project #2738 | note | 2738 |
+-------------------+------------+------------------+
| (b) project #2835 | note | 2738 |
+-------------------+------------+------------------+
| (c) project #2852 | note | 2738 |
+-------------------+------------+------------------+
Finds the note rows with footnote indicators, maps the content from the note row
into a new note column that's associated with the value row, and maps any FERC
license extracted from this note column to the ``license_id_ferc1`` column in
the value row.
+-------------------+------------+-------------------+------------------+
| plant_name_ferc1 | row_type | notes | license_id_ferc1 |
+===================+============+===================+==================+
| HYDRO: | header | NA | NA |
+-------------------+------------+-------------------+------------------+
| rainbow falls (b) | NA | (b) project #2835 | 2835 |
+-------------------+------------+-------------------+------------------+
| cadyville (a) | NA | (a) project #2738 | 2738 |
+-------------------+------------+-------------------+------------------+
| keuka (c) | NA | (c) project #2852 | 2752 |
+-------------------+------------+-------------------+------------------+
| total plants | total | NA | NA |
+-------------------+------------+-------------------+------------------+
| (a) project #2738 | note | NA | 2738 |
+-------------------+------------+-------------------+------------------+
| (b) project #2835 | note | NA | 2835 |
+-------------------+------------+-------------------+------------------+
| (c) project #2852 | note | NA | 2752 |
+-------------------+------------+-------------------+------------------+
(Header and note rows are removed later).
NOTE: Note rows that don't have a footnote indicator or note rows with a
footnote indicator that don't have a cooresponding plant row with the same
indicator are not captured. They will ultimately get removed and their content
will not be preserved.
Params:
df: Pre-processed, concatenated XBRL and DBF data that has been run through
:func:`label_row_types` and contains the column ``row_type``.
Returns:
The same input DataFrame but with a column called ``notes`` that contains
notes, reported below, in the same row as the plant values they pertain to.
Also, any further additions to the ``license_id_ferc1`` field as extracted
from these newly associated notes.
"""
logger.info(
f"{self.table_id.value}: Mapping notes and ferc license from notes rows"
)
def associate_notes_with_values_group(group):
"""Map footnotes within a given utility year group.
Because different utilities may use the same footnotes or the same utility
could reuse footnotes each year, we must do the footnote association within
utility-year groups.
"""
regular_row = group["row_type"].isna()
has_note = group["row_type"] == "note"
# Shorten execution time by only looking at groups with discernable
# footnotes
if group.footnote.any():
# Make a df that combines notes and ferc license with the same footnote
footnote_df = (
group[has_note]
.groupby("footnote")
.agg({"plant_name_ferc1": ", ".join, "license_id_ferc1": "first"})
.rename(columns={"plant_name_ferc1": "notes"})
)
# Map these new license and note values onto the original df
updated_ferc_license_col = group.footnote.map(
footnote_df["license_id_ferc1"]
)
notes_col = group.footnote.map(footnote_df["notes"])
# We update the ferc lic col because some were already there from the
# plant name extraction. However, we want to override with the notes
# ferc licenses because they are more likely to be accurate.
group.license_id_ferc1.update(updated_ferc_license_col)
group.loc[regular_row, "notes"] = notes_col
return group
footnote_pattern = r"(\(\d?[a-z]?[A-Z]?\))"
df["notes"] = pd.NA
# Create new footnote column
df.loc[:, "footnote"] = df.plant_name_ferc1.str.extract(
footnote_pattern, expand=False
)
# Group by year and utility and run footnote association
groups = df.groupby(["report_year", "utility_id_ferc1"])
sg_notes = groups.apply(lambda x: associate_notes_with_values_group(x))
# Remove footnote column now that rows are associated
sg_notes = sg_notes.drop(columns=["footnote"])
notes_added = len(
sg_notes[sg_notes["notes"].notna() & sg_notes["row_type"].isna()]
)
logger.info(f"Mapped {notes_added} notes to plant rows.")
return sg_notes
[docs]
def spot_fix_rows(self, df: pd.DataFrame) -> pd.DataFrame:
"""Fix one-off row errors.
In 2004, utility_id_ferc1 251 reports clumps of units together. Each unit clump
looks something like this: ``intrepid wind farm (107 units @ 1.5 mw each)`` and
is followed by a row that looks like this: ``(amounts are for the total of all
107 units)``. For the most part, these rows are useless note rows. However,
there is one instance where important values are reported in this note row
rather than in the actual plant row above.
There are probably plenty of other spot fixes one could add here.
Params:
df: Pre-processed, concatenated XBRL and DBF data.
Returns:
The same input DataFrame but with some spot fixes corrected.
"""
logger.info(f"{self.table_id.value}: Spot fixing some rows")
# Define rows and columns to change
cols_to_change = df.select_dtypes(include=np.number).columns.tolist() + [
"row_type"
]
row_with_info = (df["report_year"] == 2004) & (
df["plant_name_ferc1"] == "(amounts are for the total of all 107 units)"
)
row_missing_info = (df["report_year"] == 2004) & (
df["plant_name_ferc1"] == "intrepid wind farm (107 units @ 1.5 mw each)"
)
# Replace row missing information with data from row containing information
df.loc[row_missing_info, cols_to_change] = df[row_with_info][
cols_to_change
].to_numpy()
# Remove row_with_info so there is no duplicate information
df = df[~row_with_info]
return df
[docs]
class TransmissionLinesTableTransformer(Ferc1AbstractTableTransformer):
"""A table transformer for the :ref:`core_ferc1__yearly_transmission_lines_sched422` table."""
[docs]
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Do some string-to-numeric ninja moves."""
df["num_transmission_circuits"] = pd.to_numeric(df["num_transmission_circuits"])
return super().transform_main(df)
[docs]
class EnergySourcesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_energy_sources_sched401` table.
The raw DBF and XBRL table will be split up into two tables. This transformer
generates the sources of electricity for utilities, dropping the information about
dispositions. For XBRL, this is a duration-only table. Right now we are merging in
the metadata but not actually keeping anything from it. We are also not yet doing
anything with the sign.
"""
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Perform default xbrl metadata processing plus adding 1 new xbrl_factoid.
Note: we should probably parameterize this and add it into the standard
:meth:`process_xbrl_metadata`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
facts_to_add = [
{
"xbrl_factoid": new_fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": new_fact,
"is_within_table_calc": True,
"row_type_xbrl": "reported_value",
}
for new_fact in ["megawatt_hours_purchased", "purchased_mwh"]
]
new_facts = pd.DataFrame(facts_to_add).astype(tbl_meta.dtypes, errors="ignore")
return pd.concat([tbl_meta, new_facts])
[docs]
class EnergyDispositionsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_energy_dispositions_sched401` table."""
[docs]
class UtilityPlantSummaryTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_utility_plant_summary_sched200` table."""
[docs]
def process_xbrl(
self: Self, raw_xbrl_instant: pd.DataFrame, raw_xbrl_duration: pd.DataFrame
) -> pd.DataFrame:
"""Remove the end-of-previous-year instant data."""
all_current_year = raw_xbrl_instant[
raw_xbrl_instant["date"].astype("datetime64[ns]").dt.year
== raw_xbrl_instant["report_year"].astype("int64")
]
return super().process_xbrl(all_current_year, raw_xbrl_duration)
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Do the default metadata processing plus add a new factoid.
The new factoid cooresponds to the aggregated factoid in
:meth:`aggregated_xbrl_factoids`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
# things that could be grabbed from a aggregated_xbrl_factoids param
new_factoid_name = (
"utility_plant_in_service_classified_and_property_under_capital_leases"
)
# point this new aggregated factiod to the PIS table's equivilant when the
# subdimensions line up
calc = [
{
"name": "electric_plant_in_service_and_completed_construction_not_classified_electric",
"weight": 1.0,
"source_tables": ["core_ferc1__yearly_plant_in_service_sched204"],
"utility_type": "electric",
}
]
new_fact = pd.DataFrame(
{
"xbrl_factoid": [new_factoid_name],
"calculations": [json.dumps(calc)],
"balance": ["debit"],
"ferc_account": [pd.NA],
"xbrl_factoid_original": [new_factoid_name],
"is_within_table_calc": [False],
"row_type_xbrl": ["calculated_value"],
}
).convert_dtypes()
tbl_meta = pd.concat(
[tbl_meta, new_fact.astype(tbl_meta.dtypes, errors="ignore")]
).reset_index(drop=True)
return tbl_meta
[docs]
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Default transforming, plus spot fixing and building aggregate xbrl_factoid."""
# we want to aggregate the factoids first here bc merge_xbrl_metadata is done
# at the end of super().transform_main
df = (
self.aggregated_xbrl_factoids(df)
.pipe(super().transform_main)
.pipe(self.spot_fix_bad_signs)
)
return df
[docs]
def aggregated_xbrl_factoids(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Aggregate xbrl_factoids records for linking to :ref:`core_ferc1__yearly_plant_in_service_sched204`.
This table has two ``xbrl_factoid`` which can be linked via calcuations to one
``xbrl_factoid`` in the :ref:`core_ferc1__yearly_plant_in_service_sched204`.
Doing this 2:1 linkage would be fine in theory. But the
:ref:`core_ferc1__yearly_plant_in_service_sched204` is in most senses
the table with the more details and of our desire to build tree-link
relationships between factoids, we need to build a new factoid to link in a 1:1
manner between this table and the :ref:`core_ferc1__yearly_plant_in_service_sched204`.
We'll also add this factoid into the metadata via :meth:`process_xbrl_metadata`
and add the linking calculation via :meth:`apply_xbrl_calculation_fixes`.
"""
# these guys could be params
factoids_to_agg = [
"utility_plant_in_service_classified",
"utility_plant_in_service_property_under_capital_leases",
]
new_factoid_name = (
"utility_plant_in_service_classified_and_property_under_capital_leases"
)
cols_to_agg = ["ending_balance"]
# grab some key infor for the actual aggregation
xbrl_factoid_name = self.params.xbrl_factoid_name
pks = pudl.metadata.classes.Resource.from_id(
self.table_id.value
).schema.primary_key
pks_wo_factoid = [col for col in pks if col != xbrl_factoid_name]
agg_mask = df[xbrl_factoid_name].isin(factoids_to_agg)
agg_df = (
df[agg_mask]
.groupby(pks_wo_factoid, as_index=False, dropna=False)[cols_to_agg]
.sum(min_count=1)
.assign(**{xbrl_factoid_name: new_factoid_name})
)
# note: this results in the "loss" of non-pk columns like record_id - which
# seems appropriate imo. still flag a warning
missing_cols = [
col for col in df.columns if col not in list(agg_df.columns) + ["record_id"]
]
logger.warning(
f"Post-aggregating a new xbrl_factoid, we are missing the following columns: {missing_cols}"
)
# squish em back together
df = pd.concat([df, agg_df]).reset_index(drop=True)
return df
[docs]
def spot_fix_bad_signs(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Spot fix depreciation_utility_plant_in_service records with bad signs."""
primary_keys = [
"report_year",
"utility_id_ferc1",
"utility_type",
"utility_plant_asset_type",
]
spot_fix_pks = [
(
2012,
156,
"total",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2012,
156,
"total",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2012, 156, "total", "depreciation_utility_plant_in_service"),
(
2012,
156,
"electric",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2012,
156,
"electric",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2012, 156, "electric", "depreciation_utility_plant_in_service"),
(
2013,
170,
"total",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2013,
170,
"total",
"amortization_of_other_utility_plant_utility_plant_in_service",
),
(2013, 170, "total", "amortization_of_plant_acquisition_adjustment"),
(
2013,
170,
"total",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2013, 170, "total", "depreciation_utility_plant_in_service"),
(
2013,
170,
"electric",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2013,
170,
"electric",
"amortization_of_other_utility_plant_utility_plant_in_service",
),
(2013, 170, "electric", "amortization_of_plant_acquisition_adjustment"),
(
2013,
170,
"electric",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2013, 170, "electric", "depreciation_utility_plant_in_service"),
(
2007,
393,
"electric",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2007,
393,
"electric",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2007, 393, "electric", "depreciation_utility_plant_in_service"),
(
2007,
393,
"total",
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
),
(
2007,
393,
"total",
"depreciation_amortization_and_depletion_utility_plant_in_service",
),
(2007, 393, "total", "depreciation_utility_plant_in_service"),
]
spot_fix_pks += [
(year, 211, utility_type, column_name)
for year in [2006] + list(range(2009, 2021))
for utility_type in ["electric", "total"]
for column_name in [
"accumulated_provision_for_depreciation_amortization_and_depletion_of_plant_utility",
"amortization_of_other_utility_plant_utility_plant_in_service",
"depreciation_amortization_and_depletion_utility_plant_in_service",
"depreciation_utility_plant_in_service",
]
]
# Par down spot fixes to account for fast tests where not all years are used
df_years = df.report_year.unique().tolist()
spot_fix_pks = [x for x in spot_fix_pks if x[0] in df_years]
logger.info(f"{self.table_id.value}: Spotfixing {len(spot_fix_pks)} records.")
if spot_fix_pks:
# Create a df of the primary key of the records you want to fix
df_keys = pd.DataFrame(spot_fix_pks, columns=primary_keys).set_index(
primary_keys
)
df = df.set_index(primary_keys)
# Flip the signs for the values in "ending balance" all records in the original
# df that appear in the primary key df
df.loc[df_keys.index, "ending_balance"] *= -1
# All of these are flipping negative values to positive values,
# so let's make sure that's what happens
flipped_values = df.loc[df_keys.index]
if (flipped_values["ending_balance"] < 0).any():
raise AssertionError("None of these spot fixes should be negative")
df = df.reset_index()
return apply_pudl_dtypes(df, group="ferc1")
[docs]
class BalanceSheetLiabilitiesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_balance_sheet_liabilities_sched110` table."""
@cache_df(key="main")
[docs]
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Duplicate data that appears in multiple distinct calculations.
There is a one case in which exactly the same data values are referenced in
multiple calculations which can't be resolved by choosing one of the
referenced values as the canonical location for that data. In order to preserve
all of the calculation structure, we need to duplicate those records in the
data, the metadata, and the calculation specifications. Here we duplicate the
data and associated it with newly defined facts, which we will also add to
the metadata and calculations.
"""
df = super().transform_main(df)
facts_to_duplicate = [
"long_term_portion_of_derivative_instrument_liabilities",
"long_term_portion_of_derivative_instrument_liabilities_hedges",
]
new_data = (
df[df.liability_type.isin(facts_to_duplicate)]
.copy()
.assign(liability_type=lambda x: "less_" + x.liability_type)
)
return pd.concat([df, new_data])
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Perform default xbrl metadata processing plus adding 2 new xbrl_factoids.
We add two new factoids which are defined (by PUDL) only for the DBF data, and
also duplicate and redefine several factoids which are referenced in multiple
calculations and need to be distinguishable from each other.
Note: we should probably parameterize this and add it into the standard
:meth:`process_xbrl_metadata`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
facts_to_duplicate = [
"long_term_portion_of_derivative_instrument_liabilities",
"long_term_portion_of_derivative_instrument_liabilities_hedges",
]
duplicated_facts = (
tbl_meta[tbl_meta.xbrl_factoid.isin(facts_to_duplicate)]
.copy()
.assign(
xbrl_factoid=lambda x: "less_" + x.xbrl_factoid,
xbrl_factoid_original=lambda x: "less_" + x.xbrl_factoid_original,
balance="credit",
)
)
facts_to_add = [
{
"xbrl_factoid": new_fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": new_fact,
"is_within_table_calc": True,
"row_type_xbrl": "reported_value",
}
for new_fact in [
"accumulated_deferred_income_taxes",
]
]
new_facts = pd.DataFrame(facts_to_add).astype(tbl_meta.dtypes, errors="ignore")
return pd.concat([tbl_meta, new_facts, duplicated_facts]).reset_index(drop=True)
[docs]
class BalanceSheetAssetsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_balance_sheet_assets_sched110` table."""
@cache_df(key="main")
[docs]
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Duplicate data that appears in multiple distinct calculations.
There is a one case in which exactly the same data values are referenced in
multiple calculations which can't be resolved by choosing one of the
referenced values as the canonical location for that data. In order to preserve
all of the calculation structure, we need to duplicate those records in the
data, the metadata, and the calculation specifications. Here we duplicate the
data and associated it with newly defined facts, which we will also add to
the metadata and calculations.
"""
df = super().transform_main(df)
facts_to_duplicate = [
"noncurrent_portion_of_allowances",
"derivative_instrument_assets_long_term",
"derivative_instrument_assets_hedges_long_term",
]
new_data = (
df[df.asset_type.isin(facts_to_duplicate)]
.copy()
.assign(asset_type=lambda x: "less_" + x.asset_type)
)
return pd.concat([df, new_data])
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Default xbrl metadata processing plus some error correction.
We add two new factoids which are defined (by PUDL) only for the DBF data, and
also duplicate and redefine several factoids which are referenced in multiple
calculations and need to be distinguishable from each other.
Note: we should probably parameterize this and add it into the standard
:meth:`process_xbrl_metadata`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
facts_to_duplicate = [
"noncurrent_portion_of_allowances",
"derivative_instrument_assets_long_term",
"derivative_instrument_assets_hedges_long_term",
]
duplicated_facts = (
tbl_meta[tbl_meta.xbrl_factoid.isin(facts_to_duplicate)]
.copy()
.assign(
xbrl_factoid=lambda x: "less_" + x.xbrl_factoid,
xbrl_factoid_original=lambda x: "less_" + x.xbrl_factoid_original,
balance="credit",
)
)
facts_to_add = [
{
"xbrl_factoid": new_fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": new_fact,
"is_within_table_calc": True,
"row_type_xbrl": "reported_value",
}
for new_fact in [
"special_funds_all",
"nuclear_fuel",
"preliminary_natural_gas_and_other_survey_and_investigation_charges",
]
]
new_facts = pd.DataFrame(facts_to_add).astype(tbl_meta.dtypes, errors="ignore")
return pd.concat([tbl_meta, new_facts, duplicated_facts])
[docs]
class IncomeStatementsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for the :ref:`core_ferc1__yearly_income_statements_sched114` table."""
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Perform default xbrl metadata processing plus adding a new xbrl_factoid.
Note: we should probably parameterize this and add it into the standard
:meth:`process_xbrl_metadata`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
facts_to_add = {
"xbrl_factoid": ["miscellaneous_deductions"],
"calculations": ["[]"],
"balance": ["debit"],
"ferc_account": [pd.NA],
"xbrl_factoid_original": ["miscellaneous_deductions"],
"is_within_table_calc": [True],
"row_type_xbrl": ["reported_value"],
}
new_facts = pd.DataFrame(facts_to_add).astype(tbl_meta.dtypes, errors="ignore")
return pd.concat([tbl_meta, new_facts])
[docs]
def process_dbf(self: Self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""Drop incorrect row numbers from f1_incm_stmnt_2 before standard processing.
In 2003, two rows were added to the ``f1_income_stmnt`` dbf table, which bumped
the starting ``row_number`` of ``f1_incm_stmnt_2`` from 25 to 27. A small
handfull of respondents seem to have not gotten the memo about this this in
2003 and have information on these row numbers that shouldn't exist at all for
this table.
This step necessitates the ability to know which source table each record
actually comes from, which required adding a column (``sched_table_name``) in
the extract step before these two dbf input tables were concatenated.
Right now we are just dropping these bad row numbers. Should we actually be
bumping the whole respondent's row numbers - assuming they reported incorrectly
for the whole table? See: https://github.com/catalyst-cooperative/pudl/issues/471
"""
len_og = len(raw_dbf)
known_bad_income2_rows = [25, 26]
raw_dbf = raw_dbf[
~(
(raw_dbf.sched_table_name == "f1_incm_stmnt_2")
& (raw_dbf.report_year == 2003)
& (raw_dbf.row_number.isin(known_bad_income2_rows))
)
].copy()
logger.info(
f"Dropped {len_og - len(raw_dbf)} records ({(len_og - len(raw_dbf))/len_og:.1%} of"
"total) records from 2003 from the f1_incm_stmnt_2 DBF table that have "
"known incorrect row numbers."
)
raw_dbf = super().process_dbf(raw_dbf)
return raw_dbf
[docs]
def transform_main(self: Self, df: pd.DataFrame) -> pd.DataFrame:
"""Drop duplicate records from f1_income_stmnt.
Because net_utility_operating_income is reported on both page 1 and 2 of the
form, it ends up introducing a bunch of duplicated records, so we need to drop
one of them. Since the value is used in the calculations that are part of the
second page, we'll drop it from the first page.
"""
df = super().transform_main(df)
df = df[
~(
(df.record_id.str.startswith("f1_income_stmnt_"))
& (df.income_type == "net_utility_operating_income")
)
]
return apply_pudl_dtypes(df, group="ferc1")
[docs]
class RetainedEarningsTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_retained_earnings_sched118` table."""
[docs]
current_year_types: set[str] = {
"unappropriated_undistributed_subsidiary_earnings",
"unappropriated_retained_earnings",
}
[docs]
previous_year_types: set[str] = {
"unappropriated_undistributed_subsidiary_earnings_previous_year",
"unappropriated_retained_earnings_previous_year",
}
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
Beyond the standard :meth:`Ferc1AbstractTableTransformer.process_xbrl_metadata`
processing, add FERC account values for a few known values.
"""
meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
meta.loc[
meta.xbrl_factoid
== "transfers_from_unappropriated_undistributed_subsidiary_earnings",
"ferc_account",
] = "216.1"
meta.loc[
meta.xbrl_factoid
== "appropriated_retained_earnings_including_reserve_amortization",
"ferc_account",
] = "215_and_215.1"
meta.loc[
meta.xbrl_factoid == "retained_earnings",
"ferc_account",
] = "215_and_215.1_and_216"
meta.loc[
meta.xbrl_factoid == "unappropriated_retained_earnings",
"ferc_account",
] = "216"
meta.loc[
meta.xbrl_factoid == "equity_in_earnings_of_subsidiary_companies",
"ferc_account",
] = "418.1"
# NOTE: Needs to happen before `process_xbrl_metadata_calculations`
facts_to_add = [
{
"xbrl_factoid": new_fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": new_fact,
"is_within_table_calc": True,
"row_type_xbrl": "reported_value",
}
for new_fact in [
"unappropriated_retained_earnings_previous_year",
"unappropriated_undistributed_subsidiary_earnings_previous_year",
]
]
new_facts = pd.DataFrame(facts_to_add).astype(meta.dtypes, errors="ignore")
return pd.concat([meta, new_facts])
[docs]
def process_dbf(self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""Preform generic :meth:`process_dbf`, plus deal with duplicates.
Along with the standard processing in
:meth:`Ferc1AbstractTableTransformer.process_dbf`, this method runs:
* :meth:`targeted_drop_duplicates_dbf`
* :meth:`reconcile_double_year_earnings_types_dbf`
"""
processed_dbf = (
super()
.process_dbf(raw_dbf)
.pipe(self.targeted_drop_duplicates_dbf)
.pipe(self.reconcile_double_year_earnings_types_dbf)
)
return processed_dbf
@cache_df("main")
[docs]
def transform_main(self, df):
"""Add `_previous_year` factoids after standard transform_main.
Add `_previous_year` factoids for `unappropriated_retained_earnings` and
`unappropriated_undistributed_subsidiary_earnings` after standard
transform_main. This should only affect XBRL data, but we do it after merging to
enable access to DBF data to fill this in as well.
"""
df = super().transform_main(df).pipe(self.add_previous_year_factoid)
return df
[docs]
def transform_end(self, df: pd.DataFrame) -> pd.DataFrame:
"""Check ``_previous_year`` factoids for consistency after the transformation is done."""
return super().transform_end(df).pipe(self.check_double_year_earnings_types)
[docs]
def check_double_year_earnings_types(self, df: pd.DataFrame) -> pd.DataFrame:
"""Check previous year/current year factoids for consistency.
The terminology can be very confusing - here are the expectations:
1. "inter year consistency": earlier year's "current starting/end
balance" == later year's "previous starting/end balance"
2. "intra year consistency": each year's "previous ending balance" ==
"current starting balance"
"""
current_year_facts = df.loc[df.earnings_type.isin(self.current_year_types)]
previous_year_facts = df.loc[
df.earnings_type.isin(self.previous_year_types)
].pipe(
lambda df: df.assign(
earnings_type=df.earnings_type.str.removesuffix("_previous_year")
)
)
# inter year comparison requires us to match the earlier year's current facts
# to the later year's previous facts, so we add 1 to the report year & merge.
earlier_years = current_year_facts.assign(
report_year=current_year_facts.report_year + 1
)
later_years = previous_year_facts
idx = ["utility_id_ferc1", "report_year", "earnings_type"]
inter_year_facts = earlier_years.merge(
later_years,
on=idx,
suffixes=["_earlier", "_later"],
).dropna(
subset=[
"starting_balance_earlier",
"starting_balance_later",
"ending_balance_earlier",
"ending_balance_later",
]
)
intra_year_facts = previous_year_facts.merge(
current_year_facts, on=idx, suffixes=["_previous", "_current"]
)
assert_cols_areclose(
df=inter_year_facts,
a_cols=["starting_balance_earlier"],
b_cols=["starting_balance_later"],
mismatch_threshold=0.05,
message="'Current starting balance' for year X-1 doesn't match "
"'previous starting balance' for year X.",
)
assert_cols_areclose(
df=inter_year_facts,
a_cols=["ending_balance_earlier"],
b_cols=["ending_balance_later"],
mismatch_threshold=0.05,
message="'Current ending balance' for year X-1 doesn't match "
"'previous ending balance' for year X.",
)
assert_cols_areclose(
df=intra_year_facts,
a_cols=["ending_balance_previous"],
b_cols=["starting_balance_current"],
mismatch_threshold=0.05,
message="'Previous year ending balance' should be the same as "
"'current year starting balance' for all years!",
)
return df
[docs]
def targeted_drop_duplicates_dbf(self, df: pd.DataFrame) -> pd.DataFrame:
"""Drop duplicates with truly duplicate data.
There are instances of utilities that reported multiple values for several
earnings types for a specific year (utility_id_ferc1 68 in 1998 &
utility_id_ferc1 296 in 2015). We are taking the largest value reported and
dropping the rest. There very well could be a better strategey here, but there
are only 25 records that have this problem, so we've going with this.
"""
pks = PUDL_PACKAGE.get_resource(self.table_id.value).schema.primary_key
# we are not going to check all of the unstructed earnings types for dupes bc
# we will drop these later
dupe_mask = ~df.earnings_type.str.endswith("_unstructured") & df.duplicated(
subset=pks, keep=False
)
dupes = df[dupe_mask]
if len(dupes) > 25:
raise AssertionError(
f"{self.table_id.value}: Too many duplicates found ({len(dupes)}). "
"Expected 25 or less."
)
# we are simply sorting to get the biggest value and dropping the rest.
dupes = dupes.sort_values(
["starting_balance", "amount"], ascending=False
).drop_duplicates(subset=pks)
df = pd.concat([df[~dupe_mask], dupes])
return df
[docs]
def reconcile_double_year_earnings_types_dbf(
self, df: pd.DataFrame
) -> pd.DataFrame:
"""Reconcile current and past year data reported in 1 report_year.
The DBF table includes two different earnings types that have: "Begining of
Period" and "End of Period" rows. But the table has both an amount column that
corresponds to a balance and a starting balance column. For these two earnings
types, this means that there is in effect two years of data in this table for
each report year: a starting and ending balance for the pervious year and a
starting and ending balance for the current year. The ending balance for the
previous year should be the same as the starting balance for the current year.
We need to keep both pieces of data in order to calculate `ending_balances`,
so we want to check these assumptions, extract as much information from these
two years of data, and keep both records for each of these two earnings
types for each utility.
Raises:
AssertionError: There are a very small number of instances in which the
ending balance from the previous year does not match the starting
balance from the current year. The % of these non-matching instances
should be less than 2% of the records with these date duplicative
earnings types.
"""
logger.info(f"{self.table_id.value}: Reconciling previous year's data.")
# DBF has _current_year suffix while PUDL core version does not
current_year_types = [
"unappropriated_undistributed_subsidiary_earnings_current_year",
"unappropriated_retained_earnings_current_year",
]
previous_year_types = [
"unappropriated_undistributed_subsidiary_earnings_previous_year",
"unappropriated_retained_earnings_previous_year",
]
# assign() copies, so no need to double copy when extracting this slice
current_year = df[df.earnings_type.isin(current_year_types)].assign(
earnings_type=lambda x: x.earnings_type.str.removesuffix("_current_year")
)
previous_year = df[df.earnings_type.isin(previous_year_types)].assign(
earnings_type=lambda x: x.earnings_type.str.removesuffix("_previous_year")
)
idx = [
"utility_id_ferc1_dbf",
"report_year",
"utility_id_ferc1",
"earnings_type",
]
data_columns = ["amount", "starting_balance"]
date_dupe_types = pd.merge(
current_year,
previous_year[idx + data_columns],
on=idx,
how="outer",
suffixes=("", "_previous_year"),
)
date_dupe_types.loc[:, "ending_balance"] = pd.NA
# check if the starting balance from the current year is actually
# the amount from the previous year
date_mismatch = date_dupe_types[
~np.isclose(
date_dupe_types.starting_balance,
date_dupe_types.amount_previous_year,
equal_nan=True,
)
& (date_dupe_types.starting_balance.notnull())
& (date_dupe_types.amount_previous_year.notnull())
]
data_mismatch_ratio = len(date_mismatch) / len(date_dupe_types)
if data_mismatch_ratio > 0.02:
raise AssertionError(
"More records than expected have data that is not the same in "
"the starting_balance vs the amount column for the earnings_type "
"that reports both current and previous year. % of mismatch records: "
f"{data_mismatch_ratio:.01%} (expected less than 1%)"
)
# the amount from the current year values should be the ending balance.
# the amount from the previous year should fill in the starting balance
# then drop all of the _previous_year columns
date_dupe_types = date_dupe_types.assign(
ending_balance=lambda x: x.amount,
amount=pd.NA,
starting_balance=lambda x: x.starting_balance.fillna(
x.amount_previous_year
),
).drop(columns=["amount_previous_year", "starting_balance_previous_year"])
df = pd.concat(
[df[~df.earnings_type.isin(current_year_types)], date_dupe_types]
)
# Since we've created an ending balance column, we should use the 'amount'
# value to fill it across the table and drop the amount column.
df.ending_balance = df.ending_balance.fillna(df.amount)
df = df.drop("amount", axis=1)
return df
[docs]
def add_previous_year_factoid(self, df: pd.DataFrame) -> pd.DataFrame:
"""Create ``*_previous_year`` factoids for XBRL data.
XBRL doesn't include the previous year's data, but DBF does - so we try to
check that year X's ``*_current_year`` factoid has the same value as year X+1's
``*_previous_year`` factoid.
To do this, we need to add some ``*_previous_year`` factoids to the XBRL data.
"""
current_year_facts = df[df.earnings_type.isin(self.current_year_types)]
previous_year_facts = df[df.earnings_type.isin(self.previous_year_types)]
missing_years = set(current_year_facts.report_year.unique()) - set(
previous_year_facts.report_year.unique()
)
to_copy_forward = current_year_facts[
(current_year_facts.report_year + 1).isin(missing_years)
]
idx = [
"utility_id_ferc1",
"earnings_type",
"report_year",
]
inferred_previous_year_facts = (
to_copy_forward.assign(
report_year=to_copy_forward.report_year + 1,
new_earnings_type=to_copy_forward.earnings_type + "_previous_year",
)
.merge(current_year_facts[idx])
.drop(columns=["earnings_type"])
.rename(columns={"new_earnings_type": "earnings_type"})
.assign(row_type_xbrl="reported_value")
)
return pd.concat([df, inferred_previous_year_facts])
[docs]
def deduplicate_xbrl_factoid_xbrl_metadata(self, tbl_meta) -> pd.DataFrame:
"""Deduplicate the xbrl_metadata based on the ``xbrl_factoid``.
The metadata relating to dollar_value column *generally* had the same name as
the renamed xbrl_factoid. we'll double check that we a) didn't remove too many
factoid's by doing this AND that we have a fully deduped output below. In an
ideal world, we would have multiple pieces of metadata information (like
calucations and ferc account #'s), for every single :meth:`wide_to_tidy` value
column.
Note: This is **almost** the same as the method for
:ref:`core_ferc1__yearly_operating_revenues_sched300`. If we wanted to lean into this
version of deduplication more generally this might be a fine way start to an
abstraction, but ideally we wouldn't need to dedupe this at all and instead
enable metadata for every value column from :meth:`wide_to_tidy`.
"""
dupes_masks = tbl_meta.duplicated(subset=["xbrl_factoid"], keep=False)
non_dupes = tbl_meta[~dupes_masks]
dupes = tbl_meta[dupes_masks]
deduped = dupes[dupes.xbrl_factoid == dupes.xbrl_factoid_original]
tbl_meta_cleaned = pd.concat([non_dupes, deduped])
assert ~tbl_meta_cleaned.duplicated(subset=["xbrl_factoid"]).all()
missing = {
factoid
for factoid in tbl_meta.xbrl_factoid.unique()
if factoid not in tbl_meta_cleaned.xbrl_factoid.unique()
}
if missing:
raise AssertionError(
"We expected to find no missing xbrl_factoid's after deduplication "
f"but found {missing}"
)
return tbl_meta_cleaned
[docs]
class DepreciationSummaryTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_depreciation_summary_sched336` table."""
@cache_df("process_xbrl_metadata")
[docs]
def process_xbrl_metadata(
self: Self,
xbrl_metadata_converted: pd.DataFrame,
xbrl_calculations: pd.DataFrame,
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
Beyond the standard :meth:`Ferc1AbstractTableTransformer.process_xbrl_metadata`
processing, add FERC account values for a few known values.
"""
meta = super().process_xbrl_metadata(xbrl_metadata_converted, xbrl_calculations)
# logger.info(meta)
meta.loc[
meta.xbrl_factoid == "depreciation_expense",
"ferc_account",
] = "403"
meta.loc[
meta.xbrl_factoid == "depreciation_expense_asset_retirement",
"ferc_account",
] = "403.1"
meta.loc[
meta.xbrl_factoid == "amortization_limited_term_electric_plant",
"ferc_account",
] = "404"
meta.loc[
meta.xbrl_factoid == "amortization_other_electric_plant",
"ferc_account",
] = "405"
return meta
[docs]
class DepreciationChangesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_depreciation_changes_sched219` table."""
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self, xbrl_metadata_json
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
Warning: The calculations in this table are currently being corrected using
reconcile_table_calculations(), but they still contain high rates of error.
This function replaces the name of the single balance column reported in the
XBRL Instant table with starting_balance / ending_balance. We pull those two
values into their own separate labeled rows, each of which should get the
metadata from the original column. We do this pre-processing before we
call the main function in order for the calculation fixes and renaming to work
as expected.
"""
new_xbrl_metadata_json = xbrl_metadata_json
# Get instant metadata
instant = pd.json_normalize(new_xbrl_metadata_json["instant"])
# Duplicate instant metadata, and add starting/ending suffix
# should just be balance begining of year
instant = pd.concat([instant] * 2).reset_index(drop=True)
instant["name"] = instant["name"] + ["_starting_balance", "_ending_balance"]
# Return to JSON format in order to continue processing
new_xbrl_metadata_json["instant"] = json.loads(
instant.to_json(orient="records")
)
self.xbrl_metadata_json = new_xbrl_metadata_json
tbl_meta = super().convert_xbrl_metadata_json_to_df(new_xbrl_metadata_json)
return tbl_meta
@cache_df("dbf")
[docs]
def process_dbf(self, raw_df: pd.DataFrame) -> pd.DataFrame:
"""Accumulated Depreciation table specific DBF cleaning operations.
The XBRL reports a utility_type which is always electric in this table, but
which may be necessary for differentiating between different values when this
data is combined with other tables. The DBF data doesn't report this value so
we are adding it here for consistency across the two data sources.
Also rename the ``ending_balance_accounts`` to ``ending_balance``
"""
df = super().process_dbf(raw_df).assign(utility_type="electric")
df.loc[
df["depreciation_type"] == "ending_balance_accounts", "depreciation_type"
] = "ending_balance"
return df
@cache_df("process_instant_xbrl")
[docs]
def process_instant_xbrl(self, df: pd.DataFrame) -> pd.DataFrame:
"""Pre-processing required to make the instant and duration tables compatible.
This table has a rename that needs to take place in an unusual spot -- after the
starting / ending balances have been usntacked, but before the instant &
duration tables are merged. This method just reversed the order in which these
operations happen, comapared to the inherited method.
"""
df = self.unstack_balances_to_report_year_instant_xbrl(df).pipe(
self.rename_columns, rename_stage="instant_xbrl"
)
return df
[docs]
class DepreciationByFunctionTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer for :ref:`core_ferc1__yearly_depreciation_by_function_sched219` table."""
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Create a metadata table with the one factoid we've assigned to this table.
Instead of adding facts to the metdata like a lot of the other table-specific
:meth:`convert_xbrl_metadata_json_to_df`, this method creates a metadata table
with one singular ``xbrl_factoid``. We assign that factoid to the table in
:meth:`transform_main`.
"""
fact = "accumulated_depreciation"
single_table_fact = [
{
"xbrl_factoid": fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": fact,
"is_within_table_calc": True,
"row_type_xbrl": "calculated_value",
}
]
tbl_meta = pd.DataFrame(single_table_fact).convert_dtypes()
return tbl_meta
[docs]
def raw_xbrl_factoid_to_pudl_name(
self,
col_name_xbrl: str,
) -> str:
"""Return the one fact name for this table.
We've artificially assigned this table to have one ``xbrl_factoid`` during
:meth:`transform_main`. Because this table only has one value for its
``xbrl_factoid`` column, all ``col_name_xbrl`` should be converted to
"accumulated_depreciation".
"""
return "accumulated_depreciation"
@cache_df("dbf")
[docs]
def process_dbf(self, raw_df: pd.DataFrame) -> pd.DataFrame:
"""Accumulated Depreciation table specific DBF cleaning operations.
The XBRL reports a utility_type which is always electric in this table, but
which may be necessary for differentiating between different values when this
data is combined with other tables. The DBF data doesn't report this value so we
are adding it here for consistency across the two data sources.
"""
return super().process_dbf(raw_df).assign(utility_type="electric")
@cache_df("process_instant_xbrl")
[docs]
def process_instant_xbrl(self, df: pd.DataFrame) -> pd.DataFrame:
"""Pre-processing required to make the instant and duration tables compatible.
This table has a rename that needs to take place in an unusual spot -- after the
starting / ending balances have been usntacked, but before the instant &
duration tables are merged. This method reverses the order in which these
operations happen comapared to the inherited method. We also want to strip the
``accumulated_depreciation`` that appears on every plant functional class.
"""
df = self.unstack_balances_to_report_year_instant_xbrl(df).pipe(
self.rename_columns, rename_stage="instant_xbrl"
)
return df
@cache_df("main")
[docs]
def transform_main(self, df: pd.DataFrame) -> pd.DataFrame:
"""Add ``depreciation_type`` then run default :meth:`transform_main`.
We are adding ``depreciation_type`` as the ``xbrl_factoid`` column for this
table with one value ("accumulated_depreciation") across the whole table. This
table has multiple "dimension" columns such as ``utility_type`` and
``plant_function`` which differentiate what slice of a utility's assets each
record pertains to. We added this new column as the ``xbrl_factoid`` of the
table instead of using one of the dimensions of the table so that the table can
conform to the same patern of treatment for these dimension columns.
"""
df = df.assign(depreciation_type="accumulated_depreciation").pipe(
super().transform_main
)
# convert this **one** utility's depreciation $$ from negative -> +
# this was found through checking the inter-table calculations in the explosion
# process. The one factoid in this table is linked with
# depreciation_utility_plant_in_service in the utility_plant_summary_ferc1 table.
# the values in both tables are almost always postive. Not always & there are
# some logical reasons why depreciation can sometimes be negative. Nonetheless,
# for this one utility, all of its values in utility_plant_summary_ferc1 are
# postive while nearly all of the $s over here are negative. No other utility
# has as many -$ which tells me this is a data entry error.
# see https://github.com/catalyst-cooperative/pudl/issues/2703 for more details
negative_util_mask = df.utility_id_ferc1 == 211
df.loc[negative_util_mask, "ending_balance"] = abs(
df.loc[negative_util_mask, "ending_balance"]
)
return df
[docs]
def transform_end(self, df: pd.DataFrame) -> pd.DataFrame:
"""Run standard :meth:`Ferc1AbstractTableTransformer.transform_end` plus a data validation step.
In :func:`infer_intra_factoid_totals`, we restrict the child calculation components to
only those without "total" in any of the dimension columns (e.g. `plant_status == "total"`).
Because of this, when there is more than one dimension with totals in a table, as in this table,
records with two totals (e.g. `plant_status == "total"` and `plant_function == "total"`) only get
linked to children with no "totals" in any of their subdimensions. This is fine and good because
it avoids possible double counting of mixed total and sub-dimension calculations. But it means
that records with totals in one sub-dimension (e.g. `plant_status == "in_service"` and
`plant_function == "total"`) aren't linked to double-total parent factoids. To ensure that there
aren't many instances of data where most or all of the data is reported in these mixed-total
records, we add a validation step to ward against large-scale data loss in
:class:`pudl.output.ferc1.Exploder`.
"""
df = super().transform_end(df)
dimension_cols = ["plant_function", "plant_status"]
correction_mask = df.depreciation_type.str.endswith("_correction")
double_total_mask = (df[dimension_cols] == "total").all(axis="columns")
single_total_mask = (df[dimension_cols] == "total").any(
axis="columns"
) & ~double_total_mask
gb_idx = ["utility_id_ferc1", "report_year"]
value_col = "ending_balance"
single_total_sum = (
df[single_total_mask & ~correction_mask]
.groupby(gb_idx, as_index=False)[[value_col]]
.sum(min_count=1)
.rename(columns={value_col: f"{value_col}_single_total"})
)
children_sum = (
df[~single_total_mask & ~double_total_mask]
.groupby(gb_idx, as_index=False)[[value_col]]
.sum(min_count=1)
.rename(columns={value_col: f"{value_col}_children"})
)
double_totals = df.loc[
double_total_mask & ~correction_mask, gb_idx + [value_col]
].rename(columns={value_col: f"{value_col}_double_total"})
total_test = double_totals.merge(
single_total_sum, on=gb_idx, validate="one_to_one", how="outer"
).merge(children_sum, on=gb_idx, validate="one_to_one", how="outer")
total_test = total_test[
total_test.ending_balance_double_total.isnull()
& total_test.ending_balance_single_total.notnull()
]
description_of_these_records = "with nulls in the total/total record but non-null data in the total/sub-dimension records."
if len(total_test) > 7:
raise AssertionError(
f"Found more records ({len(total_test)}) than expected (7) {description_of_these_records}"
)
if not total_test[total_test.ending_balance_children.isnull()].empty:
raise AssertionError(
f"We expected that all of the records {description_of_these_records} "
"to have non-null data in the pure sub-dimension records, but some records that don't meet this condition were found."
)
return df
[docs]
class OperatingExpensesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_operating_expenses_sched320` table."""
[docs]
def targeted_drop_duplicates_dbf(self, raw_df: pd.DataFrame) -> pd.DataFrame:
"""Drop incorrect duplicate from 2002.
In 2002, utility_id_ferc1_dbf 96 reported two values for
administrative_and_general_operation_expense. I found the correct value by
looking at the prev_yr_amt value in 2003. This removes the incorrect row.
"""
start_len = len(raw_df)
raw_df = raw_df[
~((raw_df["report_year"] == 2002) & (raw_df["crnt_yr_amt"] == 35990321))
]
if (dropped := start_len - len(raw_df)) > 1:
raise AssertionError(f"More rows dropped than expected: {dropped}")
logger.info("Heyyyy dropping that one row")
return raw_df
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Default XBRL metadata processing and add a DBF-only xblr factoid.
Note: we should probably parameterize this and add it into the standard
:meth:`process_xbrl_metadata`.
"""
tbl_meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
dbf_only_facts = [
{
"xbrl_factoid": dbf_only_fact,
"calculations": "[]",
"balance": "credit",
"ferc_account": pd.NA,
"xbrl_factoid_original": dbf_only_fact,
"is_within_table_calc": True,
"row_type_xbrl": "reported_value",
}
for dbf_only_fact in ["load_dispatching_transmission_expense"]
]
dbf_only_facts = pd.DataFrame(dbf_only_facts).astype(
tbl_meta.dtypes, errors="ignore"
)
return pd.concat([tbl_meta, dbf_only_facts]).assign(utility_type="electric")
@cache_df(key="dbf")
[docs]
def process_dbf(self, raw_dbf: pd.DataFrame) -> pd.DataFrame:
"""Process DBF but drop a bad row that is flagged by drop_duplicates."""
return super().process_dbf(self.targeted_drop_duplicates_dbf(raw_dbf))
[docs]
class OperatingRevenuesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_operating_revenues_sched300` table."""
[docs]
def deduplicate_xbrl_factoid_xbrl_metadata(
self, tbl_meta: pd.DataFrame
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
Employ the standard process for processing metadata. Then remove duplication on
the basis of the ``xbrl_factoid``. This table used :meth:`wide_to_tidy` with three
seperate value columns. Which results in one ``xbrl_factoid`` referencing three
seperate data columns. This method grabs only one piece of metadata for each
renamed ``xbrl_factoid``, preferring the calculated value or the factoid
referencing the dollar columns.
In an ideal world, we would have multiple pieces of metadata information (like
calucations and ferc account #'s), for every single :meth:`wide_to_tidy` value
column. We would probably want to employ that across the board - adding suffixes
or something like that to stack the metadata in a similar fashion that we stack
the data.
"""
dupes_masks = tbl_meta.duplicated(subset=["xbrl_factoid"], keep=False)
non_dupes = tbl_meta[~dupes_masks]
dupes = tbl_meta[dupes_masks]
# the metadata relating to dollar_value column *generally* had the same name as
# the renamed xbrl_factoid. the outliers here are these two that have calcs for
# the factoid we want to keep (we could also id them w/ their og factoid names
# if that would be more straightforward)
deduped = dupes[
(dupes.xbrl_factoid == dupes.xbrl_factoid_original)
| (
dupes.xbrl_factoid.isin(["small_or_commercial", "large_or_industrial"])
& (dupes.calculations != "[]")
)
]
tbl_meta_cleaned = pd.concat([non_dupes, deduped])
assert ~tbl_meta_cleaned.duplicated(subset=["xbrl_factoid"]).all()
# double check that we're getting only the guys we want
missing = {
factoid
for factoid in tbl_meta.xbrl_factoid.unique()
if factoid not in tbl_meta_cleaned.xbrl_factoid.unique()
}
if missing:
raise AssertionError(
"We expected to find no missing xbrl_factoid's after deduplication "
f"but found {missing}"
)
return tbl_meta_cleaned
@cache_df("main")
[docs]
def transform_main(self, df):
"""Add duplicate removal after standard transform_main & assign utility type."""
return super().transform_main(df).pipe(self.targeted_drop_duplicates)
@cache_df("main")
[docs]
def targeted_drop_duplicates(self, df):
"""Drop one duplicate records from 2011, utility_id_ferc1 295."""
dupe_mask = (
(df.utility_id_ferc1 == 295)
& (df.report_year == 2011)
& ((df.dollar_value == 3.33e8) | (df.dollar_value == 3.333e9))
)
return df[~dupe_mask].copy()
[docs]
class CashFlowsTableTransformer(Ferc1AbstractTableTransformer):
"""Transform class for :ref:`core_ferc1__yearly_cash_flows_sched120` table."""
@cache_df("process_instant_xbrl")
[docs]
def process_instant_xbrl(self, df: pd.DataFrame) -> pd.DataFrame:
"""Pre-processing required to make the instant and duration tables compatible.
This table has a rename that needs to take place in an unusual spot -- after the
starting / ending balances have been usntacked, but before the instant &
duration tables are merged. This method just reversed the order in which these
operations happen, comapared to the inherited method.
"""
df = self.unstack_balances_to_report_year_instant_xbrl(df).pipe(
self.rename_columns, rename_stage="instant_xbrl"
)
return df
@cache_df("main")
[docs]
def transform_main(self, df):
"""Add duplicate removal and validation after standard transform_main."""
return (
super()
.transform_main(df)
.pipe(self.targeted_drop_duplicates)
.pipe(self.validate_start_end_balance)
)
@cache_df("main")
[docs]
def targeted_drop_duplicates(self, df):
"""Drop one duplicate record from 2020, utility_id_ferc1 2037.
Note: This step could be avoided if we employed a :meth:`drop_invalid_rows`
transform step with ``required_valid_cols = ["amount"]``
"""
dupe_mask = (
(df.utility_id_ferc1 == 237)
& (df.report_year == 2020)
& (df.amount_type == "dividends_on_common_stock")
& (df.amount.isnull())
)
if (len_dupes := dupe_mask.value_counts().loc[True]) != 1:
raise ValueError(f"Expected to find 1 duplicate record. Found {len_dupes}")
return df[~dupe_mask].copy()
@cache_df("main")
[docs]
def validate_start_end_balance(self, df):
"""Validate of start balance + net = end balance.
Add a quick check to ensure the vast majority of the ending balances are
calculable from the net change + the starting balance = the ending balance.
"""
# calculate ending balance
df.amount = pd.to_numeric(df.amount)
end_bal_calc = (
df[
df.amount_type.isin(
[
"starting_balance",
"net_increase_decrease_in_cash_and_cash_equivalents",
]
)
]
.groupby(["utility_id_ferc1", "report_year"])[["amount"]]
.sum(min_count=2, numeric_only=True)
.add_suffix("_ending_balace")
)
# grab reported ending balance & squish with the calculated version
end_bal = df[df.amount_type == "ending_balance"].set_index(
["utility_id_ferc1", "report_year"]
)
logger.info(end_bal_calc.columns)
end_bal.loc[:, "amount_ending_balace"] = end_bal_calc.amount_ending_balace
# when both exist, are they close?
end_bal_off = end_bal[
~np.isclose(end_bal.amount, end_bal.amount_ending_balace)
& end_bal[["amount", "amount_ending_balace"]].notnull().all(axis="columns")
]
if (end_bal_off_ratio := len(end_bal_off) / len(end_bal)) > 0.005:
raise ValueError(
f"Ahhh!! The ending balance isn't calculable in {end_bal_off_ratio:.2%}"
" of records. Expected under 0.5%."
)
return df
[docs]
def convert_xbrl_metadata_json_to_df(
self: Self,
xbrl_metadata_json: dict[Literal["instant", "duration"], list[dict[str, Any]]],
) -> pd.DataFrame:
"""Transform the metadata to reflect the transformed data.
Replace the name of the balance column reported in the XBRL Instant table with
starting_balance / ending_balance since we pull those two values into their own
separate labeled rows, each of which should get the original metadata for the
Instant column.
"""
meta = super().convert_xbrl_metadata_json_to_df(xbrl_metadata_json)
ending_balance = meta[meta.xbrl_factoid == "starting_balance"].assign(
xbrl_factoid="ending_balance"
)
return pd.concat([meta, ending_balance])
[docs]
class SalesByRateSchedulesTableTransformer(Ferc1AbstractTableTransformer):
"""Transform class for :ref:`core_ferc1__yearly_sales_by_rate_schedules_sched304` table."""
[docs]
def add_axis_to_total_table_rows(self, df: pd.DataFrame):
"""Add total to the axis column for rows from the total table.
Because we're adding the
sales_of_electricity_by_rate_schedules_account_totals_304 table into the mix,
we have a bunch of total values that get mixed in with all the _billed columns
from the individual tables. If left alone, these totals aren't labeled in any
way becuse they don't have the same _axis columns explaining what each of the
values are. In order to distinguish them from the rest of the sub-total data we
use this function to create an _axis value for them noting that they are totals.
It's worth noting that there are also some total values in there already.
Those would be hard to clean. The idea is that if you want the actual totals,
don't try and sum the sub-components, look at the actual labeled total rows.
This function relies on the ``sched_table_name`` column, so it must be called
before that gets dropped.
Args:
df: The sales table with a ``sched_table_name`` column.
"""
logger.info(f"{self.table_id.value}: Labeling total values.")
df.loc[
df["sched_table_name"]
== "sales_of_electricity_by_rate_schedules_account_totals_304",
["sales_axis", "rate_schedule_description"],
] = "total"
return df
@cache_df(key="xbrl")
[docs]
def process_xbrl(
self,
raw_xbrl_instant: pd.DataFrame,
raw_xbrl_duration: pd.DataFrame,
) -> pd.DataFrame:
"""Rename columns before running wide_to_tidy."""
logger.info(f"{self.table_id.value}: Processing XBRL data pre-concatenation.")
return (
self.merge_instant_and_duration_tables_xbrl(
raw_xbrl_instant, raw_xbrl_duration
)
.pipe(self.rename_columns, rename_stage="xbrl")
.pipe(self.combine_axis_columns_xbrl)
.pipe(self.add_axis_to_total_table_rows)
.pipe(self.wide_to_tidy, source_ferc1=SourceFerc1.XBRL)
.pipe(self.assign_record_id, source_ferc1=SourceFerc1.XBRL)
.pipe(self.assign_utility_id_ferc1, source_ferc1=SourceFerc1.XBRL)
)
[docs]
class OtherRegulatoryLiabilitiesTableTransformer(Ferc1AbstractTableTransformer):
"""Transformer class for :ref:`core_ferc1__yearly_other_regulatory_liabilities_sched278` table."""
[docs]
FERC1_TFR_CLASSES: Mapping[str, type[Ferc1AbstractTableTransformer]] = {
"core_ferc1__yearly_steam_plants_fuel_sched402": SteamPlantsFuelTableTransformer,
"core_ferc1__yearly_steam_plants_sched402": SteamPlantsTableTransformer,
"core_ferc1__yearly_small_plants_sched410": SmallPlantsTableTransformer,
"core_ferc1__yearly_hydroelectric_plants_sched406": HydroelectricPlantsTableTransformer,
"core_ferc1__yearly_plant_in_service_sched204": PlantInServiceTableTransformer,
"core_ferc1__yearly_pumped_storage_plants_sched408": PumpedStoragePlantsTableTransformer,
"core_ferc1__yearly_transmission_lines_sched422": TransmissionLinesTableTransformer,
"core_ferc1__yearly_purchased_power_and_exchanges_sched326": PurchasedPowerAndExchangesTableTransformer,
"core_ferc1__yearly_energy_sources_sched401": EnergySourcesTableTransformer,
"core_ferc1__yearly_energy_dispositions_sched401": EnergyDispositionsTableTransformer,
"core_ferc1__yearly_utility_plant_summary_sched200": UtilityPlantSummaryTableTransformer,
"core_ferc1__yearly_operating_expenses_sched320": OperatingExpensesTableTransformer,
"core_ferc1__yearly_balance_sheet_liabilities_sched110": BalanceSheetLiabilitiesTableTransformer,
"core_ferc1__yearly_depreciation_summary_sched336": DepreciationSummaryTableTransformer,
"core_ferc1__yearly_balance_sheet_assets_sched110": BalanceSheetAssetsTableTransformer,
"core_ferc1__yearly_income_statements_sched114": IncomeStatementsTableTransformer,
"core_ferc1__yearly_depreciation_changes_sched219": DepreciationChangesTableTransformer,
"core_ferc1__yearly_depreciation_by_function_sched219": DepreciationByFunctionTableTransformer,
"core_ferc1__yearly_retained_earnings_sched118": RetainedEarningsTableTransformer,
"core_ferc1__yearly_operating_revenues_sched300": OperatingRevenuesTableTransformer,
"core_ferc1__yearly_cash_flows_sched120": CashFlowsTableTransformer,
"core_ferc1__yearly_sales_by_rate_schedules_sched304": SalesByRateSchedulesTableTransformer,
"core_ferc1__yearly_other_regulatory_liabilities_sched278": OtherRegulatoryLiabilitiesTableTransformer,
}
[docs]
def ferc1_transform_asset_factory(
table_name: str,
tfr_class: Ferc1AbstractTableTransformer,
io_manager_key: str = "pudl_io_manager",
convert_dtypes: bool = True,
generic: bool = False,
) -> AssetsDefinition:
"""Create an asset that pulls in raw ferc Form 1 assets and applies transformations.
This is a convenient way to create assets for tables that only depend on raw dbf,
raw xbrl instant and duration tables and xbrl metadata.
Args:
table_name: The name of the table to create an asset for.
tfr_class: A transformer class cooresponding to the table_name.
io_manager_key: the dagster io_manager key to use. None defaults
to the fs_io_manager.
convert_dtypes: convert dtypes of transformed dataframes.
generic: If using GenericPlantFerc1TableTransformer pass table_id to constructor.
Return:
An asset for the clean table.
"""
ins: Mapping[str, AssetIn] = {}
listify = lambda x: x if isinstance(x, list) else [x] # noqa: E731
dbf_tables = listify(TABLE_NAME_MAP_FERC1[table_name]["dbf"])
xbrl_tables = listify(TABLE_NAME_MAP_FERC1[table_name]["xbrl"])
ins = {f"raw_dbf__{tn}": AssetIn(f"raw_ferc1_dbf__{tn}") for tn in dbf_tables}
ins |= {
f"raw_xbrl_instant__{tn}": AssetIn(f"raw_ferc1_xbrl__{tn}_instant")
for tn in xbrl_tables
}
ins |= {
f"raw_xbrl_duration__{tn}": AssetIn(f"raw_ferc1_xbrl__{tn}_duration")
for tn in xbrl_tables
}
ins["_core_ferc1_xbrl__metadata_json"] = AssetIn("_core_ferc1_xbrl__metadata_json")
table_id = TableIdFerc1(table_name)
@asset(name=table_name, ins=ins, io_manager_key=io_manager_key)
def ferc1_transform_asset(**kwargs: dict[str, pd.DataFrame]) -> pd.DataFrame:
"""Transform a FERC Form 1 table.
Args:
raw_dbf: raw dbf table.
raw_xbrl_instant: raw XBRL instant table.
raw_xbrl_duration: raw XBRL duration table.
_core_ferc1_xbrl__metadata_json: XBRL metadata json for all tables.
Returns:
transformed FERC Form 1 table.
"""
# TODO: split the key by __, then groupby, then concatenate
_core_ferc1_xbrl__metadata_json = kwargs["_core_ferc1_xbrl__metadata_json"]
if generic:
transformer = tfr_class(
xbrl_metadata_json=_core_ferc1_xbrl__metadata_json[table_name],
table_id=table_id,
)
else:
transformer = tfr_class(
xbrl_metadata_json=_core_ferc1_xbrl__metadata_json[table_name]
)
raw_dbf = pd.concat(
[df for key, df in kwargs.items() if key.startswith("raw_dbf__")]
)
raw_xbrl_instant = pd.concat(
[df for key, df in kwargs.items() if key.startswith("raw_xbrl_instant__")]
)
raw_xbrl_duration = pd.concat(
[df for key, df in kwargs.items() if key.startswith("raw_xbrl_duration__")]
)
df = transformer.transform(
raw_dbf=raw_dbf,
raw_xbrl_instant=raw_xbrl_instant,
raw_xbrl_duration=raw_xbrl_duration,
)
if convert_dtypes:
df = convert_cols_dtypes(df, data_source="ferc1")
return df
return ferc1_transform_asset
[docs]
def create_ferc1_transform_assets() -> list[AssetsDefinition]:
"""Create a list of transformed FERC Form 1 assets.
Returns:
A list of AssetsDefinitions where each asset is a clean ferc form 1 table.
"""
assets = []
for table_name, tfr_class in FERC1_TFR_CLASSES.items():
assets.append(ferc1_transform_asset_factory(table_name, tfr_class))
return assets
[docs]
def other_dimensions(table_names: list[str]) -> list[str]:
"""Get a list of the other dimension columns across all of the transformers."""
# grab all of the dimensions columns that we are currently verifying as a part of
# reconcile_table_calculations
return pudl.helpers.dedupe_n_flatten_list_of_lists(
[
FERC1_TFR_CLASSES[table_name]().params.dimension_columns
for table_name in table_names
]
)
[docs]
def table_to_xbrl_factoid_name() -> dict[str, str]:
"""Build a dictionary of table name (keys) to ``xbrl_factoid`` column name."""
return {
table_name: transformer().params.xbrl_factoid_name
for (table_name, transformer) in FERC1_TFR_CLASSES.items()
}
[docs]
def table_to_column_to_check() -> dict[str, list[str]]:
"""Build a dictionary of table name (keys) to column_to_check from reconcile_table_calculations."""
return {
table_name: transformer().params.reconcile_table_calculations.column_to_check
for (table_name, transformer) in FERC1_TFR_CLASSES.items()
if transformer().params.reconcile_table_calculations.column_to_check
}
@asset(
ins={
table_name: AssetIn(table_name)
for table_name in FERC1_TFR_CLASSES
if table_name != "plants_steam_ferc1"
}
)
[docs]
def _core_ferc1__table_dimensions(**kwargs) -> pd.DataFrame:
"""Build a table of values of dimensions observed in the transformed data tables.
Compile a dataframe indicating what distinct values are observed in the data for
each dimension column in association with each unique combination of ``table_name``
and ``xbrl_factoid``. E.g. for all factoids found in the
:ref:`core_ferc1__yearly_depreciation_by_function_sched219` table,
the only value observed for ``utility_type`` is ``electric`` and the values observed
for ``plant_status`` include: ``future``, ``in_service``, ``leased`` and ``total``.
We need to include the ``xbrl_factoid`` column because these dimensions can differ
based on the ``xbrl_factoid``. So we first rename all of the columns which
contain the ``xbrl_factoid`` using :func:`table_to_xbrl_factoid_name` rename
dictionary. Then we concatenate all of the tables together and drop duplicates so
we have unique instances of observed ``table_name`` and ``xbrl_factoid`` and the
other dimension columns found in :func:`other_dimensions`.
"""
table_to_xbrl_factoid_name_dict = table_to_xbrl_factoid_name()
tbls = {
name: df.assign(table_name=name).rename(
columns={table_to_xbrl_factoid_name_dict[name]: "xbrl_factoid"}
)
for (name, df) in kwargs.items()
}
dimensions = (
pd.concat(tbls.values())[
["table_name", "xbrl_factoid"]
+ other_dimensions(table_names=list(FERC1_TFR_CLASSES))
]
.drop_duplicates()
.reset_index(drop=True)
)
return dimensions
@asset(
ins={
"_core_ferc1_xbrl__metadata_json": AssetIn("_core_ferc1_xbrl__metadata_json"),
"_core_ferc1__table_dimensions": AssetIn("_core_ferc1__table_dimensions"),
},
io_manager_key=None, # Change to sqlite_io_manager...
)
[docs]
def _core_ferc1_xbrl__metadata(**kwargs) -> pd.DataFrame:
"""Build a table of all of the tables' XBRL metadata."""
_core_ferc1_xbrl__metadata_json = kwargs["_core_ferc1_xbrl__metadata_json"]
_core_ferc1__table_dimensions = kwargs["_core_ferc1__table_dimensions"]
tbl_metas = []
for table_name, trans in FERC1_TFR_CLASSES.items():
tbl_meta = (
trans(xbrl_metadata_json=_core_ferc1_xbrl__metadata_json[table_name])
.xbrl_metadata[
[
"xbrl_factoid",
"xbrl_factoid_original",
"is_within_table_calc",
]
]
.assign(table_name=table_name)
)
tbl_metas.append(tbl_meta)
dimensions = other_dimensions(table_names=list(FERC1_TFR_CLASSES))
metadata_all = (
pd.concat(tbl_metas)
.reset_index(drop=True)
.assign(**{dim: pd.NA for dim in dimensions})
.pipe(
make_xbrl_factoid_dimensions_explicit,
table_dimensions_ferc1=_core_ferc1__table_dimensions,
dimensions=dimensions,
)
)
return metadata_all
@asset(
ins={
"_core_ferc1_xbrl__metadata_json": AssetIn("_core_ferc1_xbrl__metadata_json"),
"_core_ferc1__table_dimensions": AssetIn("_core_ferc1__table_dimensions"),
"_core_ferc1_xbrl__metadata": AssetIn("_core_ferc1_xbrl__metadata"),
},
io_manager_key=None, # Change to sqlite_io_manager...
)
[docs]
def _core_ferc1_xbrl__calculation_components(**kwargs) -> pd.DataFrame:
"""Create calculation-component table from table-level metadata."""
_core_ferc1_xbrl__metadata_json = kwargs["_core_ferc1_xbrl__metadata_json"]
_core_ferc1__table_dimensions = kwargs["_core_ferc1__table_dimensions"]
_core_ferc1_xbrl__metadata = kwargs["_core_ferc1_xbrl__metadata"]
# compile all of the calc comp tables.
calc_metas = []
for table_name, transformer in FERC1_TFR_CLASSES.items():
calc_meta = transformer(
xbrl_metadata_json=_core_ferc1_xbrl__metadata_json[table_name]
).xbrl_calculations.convert_dtypes()
calc_metas.append(calc_meta)
# squish all of the calc comp tables then add in the implicit table dimensions
dimensions = other_dimensions(table_names=list(FERC1_TFR_CLASSES))
calc_components = (
pd.concat(calc_metas)
.astype({dim: pd.StringDtype() for dim in dimensions})
.pipe(
make_xbrl_factoid_dimensions_explicit,
_core_ferc1__table_dimensions,
dimensions=dimensions,
)
.pipe(
assign_parent_dimensions,
table_dimensions=_core_ferc1__table_dimensions,
dimensions=dimensions,
)
.pipe(
infer_intra_factoid_totals,
meta_w_dims=_core_ferc1_xbrl__metadata,
table_dimensions=_core_ferc1__table_dimensions,
dimensions=dimensions,
)
.pipe(add_calculation_component_corrections)
)
child_cols = ["table_name", "xbrl_factoid"]
calc_cols = child_cols + dimensions
calc_and_parent_cols = calc_cols + [f"{col}_parent" for col in calc_cols]
# Defensive testing on this table!
assert calc_components[["table_name", "xbrl_factoid"]].notnull().all(axis=1).all()
# Let's check that all calculated components that show up in our data are
# getting calculated.
def check_calcs_vs_table(
calcs: pd.DataFrame,
checked_table: pd.DataFrame,
idx_calcs: list[str],
idx_table: list[str],
how: Literal["in", "not_in"],
) -> pd.DataFrame:
if how == "in":
idx = calcs.set_index(idx_calcs).index.intersection(
checked_table.set_index(idx_table).index
)
elif how == "not_in":
idx = calcs.set_index(idx_calcs).index.difference(
checked_table.set_index(idx_table).index
)
calcs_vs_table = calcs.set_index(idx_calcs).loc[idx]
return calcs_vs_table.reset_index()
# which calculations are missing from the metadata table?
# ignore the corrections bc they are weird
missing_calcs = check_calcs_vs_table(
calcs=calc_components[
calc_components.table_name.isin(FERC1_TFR_CLASSES.keys())
& ~calc_components.xbrl_factoid.str.endswith("_correction")
],
checked_table=_core_ferc1_xbrl__metadata,
idx_calcs=calc_cols,
idx_table=calc_cols,
how="not_in",
)
# ensure that none of the calculation components that are missing from the metadata
# table are from any of the exploded tables.
if not missing_calcs.empty:
logger.warning(
"Calculations found in calculation components table are missing from the "
"_core_ferc1_xbrl__metadata table."
)
# which of these missing calculations actually show up in the transformed tables?
# This handles dbf-only calculation components, whic are added to the
# _core_ferc1_xbrl__metadata table as part of each table's transformations but aren't
# observed (or therefore present in _core_ferc1__table_dimensions) in the fast ETL or
# in all subsets of years. We only want to flag calculation components as
# missing when they're actually observed in the data.
actually_missing_kids = check_calcs_vs_table(
calcs=missing_calcs,
checked_table=_core_ferc1__table_dimensions,
idx_calcs=child_cols,
idx_table=child_cols,
how="in",
)
logger.warning(
f"{len(actually_missing_kids)} of {len(missing_calcs)} missing calculation components observed in transformed FERC1 data."
)
if not actually_missing_kids.empty:
raise AssertionError(
f"Found missing calculations from the exploded tables:\n{actually_missing_kids=}"
)
check_for_calc_components_duplicates(
calc_components,
table_names_known_dupes=["core_ferc1__yearly_sales_by_rate_schedules_sched304"],
idx=calc_and_parent_cols,
)
# check for parent/child duplicates. again need to remove the
# core_ferc1__yearly_sales_by_rate_schedules_sched304 table. Null hack bc comparing pandas
# nulls
self_refs_mask = calc_components[calc_and_parent_cols].fillna("NULL HACK").apply(
lambda x: all(x[col] == x[f"{col}_parent"] for col in calc_cols), axis=1
) & (
calc_components.table_name
!= "core_ferc1__yearly_sales_by_rate_schedules_sched304"
)
if not (parent_child_dupes := calc_components.loc[self_refs_mask]).empty:
raise AssertionError(
f"Found {len(parent_child_dupes)} calcuations where the parent and child "
f"columns are identical and expected 0.\n{parent_child_dupes=}"
)
if not (
unexpected_totals := unexpected_total_components(
calc_components.convert_dtypes(), dimensions
)
).empty:
raise AssertionError(f"Found unexpected total records: {unexpected_totals}")
# Remove convert_dtypes() once we're writing to the DB using enforce_schema()
return calc_components.convert_dtypes()
[docs]
def unexpected_total_components(
calc_comps: pd.DataFrame, dimensions: list[str]
) -> pd.DataFrame:
"""Find unexpected components in within-fact total calculations.
This doesn't check anything about the calcs we get from the metadata, we
are only looking at within-fact totals which we've added ourselves.
Finds calculation relationships where:
- child components that do not match with parent in non-total dimensions.
- For example, if utility_type_parent is not "total", then utility_type
must be the same as utility_type_parent.
- child components, that share table_name/xbrl_factoid with their parent,
that have "total" for any dimension - these should be represented by
*their* child components
Args:
calc_comps: calculation component join table
dimensions: list of dimensions we resolved "total" values for
"""
parent_dimensions = [f"{dim}_parent" for dim in dimensions]
totals_mask = (
(calc_comps[parent_dimensions] == "total").any(axis="columns")
& (calc_comps["table_name_parent"] == calc_comps["table_name"])
& (calc_comps["xbrl_factoid_parent"] == calc_comps["xbrl_factoid"])
)
calcs_with_totals = calc_comps[totals_mask]
unexpected_links = []
for child_dim in dimensions:
mismatched_non_total = (
calcs_with_totals[f"{child_dim}_parent"] != calcs_with_totals[child_dim]
) & (calcs_with_totals[f"{child_dim}_parent"] != "total")
children_with_totals = calcs_with_totals[child_dim] == "total"
unexpected_links.append(
calcs_with_totals[mismatched_non_total | children_with_totals][
["table_name_parent", "xbrl_factoid_parent"]
+ parent_dimensions
+ ["table_name", "xbrl_factoid"]
+ dimensions
]
)
return pd.concat(unexpected_links)
[docs]
def check_for_calc_components_duplicates(
calc_components: pd.DataFrame, table_names_known_dupes: list[str], idx: list[str]
) -> None:
"""Check for duplicates calculation records.
We need to remove the core_ferc1__yearly_sales_by_rate_schedules_sched304 bc there are
duplicate renamed factoids in that table (originally billed/unbilled).
"""
calc_components_test = (
calc_components[
~calc_components.table_name_parent.isin(table_names_known_dupes)
]
.set_index(idx)
.sort_index()
)
if not calc_components_test.index.is_unique:
raise AssertionError(
f"Found duplicates based on {idx=} when expected none.\n"
f"{calc_components_test[calc_components_test.index.duplicated(keep=False)]}"
)
[docs]
def make_xbrl_factoid_dimensions_explicit(
df_w_xbrl_factoid: pd.DataFrame,
table_dimensions_ferc1: pd.DataFrame,
dimensions: list[str],
parent: bool = False,
) -> pd.DataFrame:
"""Fill in null dimensions w/ the values observed in :func:`_core_ferc1__table_dimensions`.
In the raw XBRL metadata's calculations, there is an implicit assumption that
calculated values are aggregated within categorical columns called Axes or
dimensions, in addition to being grouped by date, utility, table, and fact. The
dimensions and their values don't need to be specified explicitly in the calculation
components because the same calculation is assumed to apply in all cases.
We have extended this calculation system to allow independent calculations to be
specified for different values within a given dimension. For example, the
:ref:`core_ferc1__yearly_utility_plant_summary_sched200` table contains records with a variety of
different ``utility_type`` values (gas, electric, etc.). For many combinations of
fact and ``utility_type``, no more detailed information about the soruce of the data
is available, but for some, and only in the case of electric utilities, much more
detail can be found in the :ref:`core_ferc1__yearly_plant_in_service_sched204` table.
In order to use this additional information when it is available, we sometimes
explicitly specify different calculations for different values of additional
dimension columns.
This function uses the observed associations between ``table_name``,
``xbrl_factoid`` and the other dimension columns compiled by
:func:`_core_ferc1__table_dimensions` to fill in missing (previously implied) dimension
values in the calculation components table.
This is often a broadcast merge because many tables contain many values within these
dimension columns, so it is expected that new calculation component table will have
many more records than the input calculation components table.
Any dimension that was already specified in the calculation fixes will be left
unchanged. If no value of a particular dimension has ever been observed in
association with a given combination of ``table_name`` and ``xbrl_factoid`` it will
remain null.
Args:
calculation_components: a table of calculation component records which have had
some manual calculation fixes applied.
table_dimensions_ferc1: table with all observed values of
:func:`other_dimensions` for each ``table_name`` and ``xbrl_factoid``
dimensions: list of dimension columns to check.
parent: boolean to indicate whether or not the dimensions to be added are
the parental dimensions or the child dimensions.
"""
logger.info(f"Adding {dimensions=} into calculation component table.")
df_w_dims = df_w_xbrl_factoid.copy()
on_cols = ["table_name", "xbrl_factoid"]
if parent:
table_dimensions_ferc1 = table_dimensions_ferc1.rename(
columns={col: f"{col}_parent" for col in on_cols}
| {dim: f"{dim}_parent" for dim in dimensions}
)
on_cols = [f"{col}_parent" for col in on_cols]
dimensions = [f"{dim}_parent" for dim in dimensions]
# for each dimension, use split/apply/combine. when there are no dims explict in
# the calc components, merge in all of the dims.
for dim_col in dimensions:
# extract the unique observed instances of this one dimension column & add the
# _calc suffix so we can merge onto the calculation components.
observed_dim = (
table_dimensions_ferc1[on_cols + [dim_col]]
.drop_duplicates()
.dropna(subset=dim_col)
) # bc there are dupes after we removed the other dim cols
null_dim_mask = df_w_dims[dim_col].isnull()
null_dim = df_w_dims[null_dim_mask].drop(columns=[dim_col])
df_w_implied_dims = pd.merge(
null_dim,
observed_dim,
on=on_cols,
how="left",
)
df_w_explicit_dims = df_w_dims[~null_dim_mask]
# astypes dealing w/ future warning regarding all null columns
df_w_dims = pd.concat(
[
df_w_implied_dims,
df_w_explicit_dims.astype(df_w_implied_dims.dtypes, errors="ignore"),
]
)
return df_w_dims
[docs]
def assign_parent_dimensions(
calc_components: pd.DataFrame, table_dimensions: pd.DataFrame, dimensions: list[str]
) -> pd.DataFrame:
"""Add dimensions to calculation parents.
We now add in parent-dimension values for all of the original calculation component
records using the observed dimensions.
Args:
calc_components: a table of calculation component records which have had some
manual calculation fixes applied.
table_dimensions: table with all observed values of :func:`other_dimensions` for
each ``table_name`` and ``xbrl_factoid``.
dimensions: list of dimension columns to check.
"""
if calc_components.empty:
return calc_components.assign(**{f"{dim}_parent": pd.NA for dim in dimensions})
# desired: add parental dimension columns
for dim in dimensions:
# split the nulls and non-nulls. If the child dim is null, then we can run the
# parent factoid through make_xbrl_factoid_dimensions_explicit to get it's dims.
# if a child fact has dims, we need to merge the dimensions using the dim of the
# child and the dim of the parent bc we don't want to broadcast merge all parent
# dims to all child dims. We are assuming here that if a child is has a dim
null_dim_mask = calc_components[dim].isnull()
calc_components_null = make_xbrl_factoid_dimensions_explicit(
df_w_xbrl_factoid=calc_components[null_dim_mask].assign(
**{f"{dim}_parent": pd.NA}
),
table_dimensions_ferc1=table_dimensions,
dimensions=[dim],
)
parent_dim_idx = ["table_name_parent", "xbrl_factoid_parent", f"{dim}_parent"]
calc_components_non_null = calc_components[~null_dim_mask]
table_dimensions_non_null = table_dimensions.rename(
columns={col: f"{col}_parent" for col in table_dimensions}
)[parent_dim_idx].drop_duplicates()
calc_components_non_null = pd.merge(
left=calc_components_non_null,
right=table_dimensions_non_null,
left_on=["table_name_parent", "xbrl_factoid_parent", dim],
right_on=parent_dim_idx,
how="left",
)
# astypes dealing w/ future warning regarding empty or all null dfs
calc_components = pd.concat(
[
calc_components_null.astype(calc_components_non_null.dtypes),
calc_components_non_null.astype(calc_components_null.dtypes),
]
).reset_index(drop=True)
return calc_components
[docs]
def infer_intra_factoid_totals(
calc_components: pd.DataFrame,
meta_w_dims: pd.DataFrame,
table_dimensions: pd.DataFrame,
dimensions: list[str],
) -> pd.DataFrame:
"""Define dimension total calculations.
Some factoids are marked as a total along some dimension in the metadata,
which means that they are the sum of all the non-total factoids along that
dimension.
We match the parent factoids from the metadata to child factoids from the
table_dimensions. We treat "total" as a wildcard value.
We exclude child factoids that are themselves totals, because that would
result in a double-count.
Here are a few examples:
Imagine a factoid with the following dimensions & values:
- utility types: "total", "gas", "electric";
- plant status: "total", "in_service", "future"
Then the following parents would match/not-match:
- parent: "total", "in_service"
- child: "gas", "in_service" WOULD match.
- child: "electric", "in_service" WOULD match.
- child: "electric", "future" WOULD NOT match.
- parent: "total", "total"
- child: "gas", "in_service" WOULD match.
- child: "electric", "future" WOULD match.
See the unit test in ferc1_test.py for more details.
To be able to define these within-dimension calculations we also add dimension
columns to all of the parent factoids in the table.
Args:
calc_components: a table of calculation component records which have had some
manual calculation fixes applied. Passed through unmodified.
meta_w_dims: metadata table with the dimensions.
table_dimensions: table with all observed values of :func:`other_dimensions` for
each ``table_name`` and ``xbrl_factoid``.
dimensions: list of dimension columns to check.
Returns:
An table associating calculation components with the parents they will be
aggregated into. The components and the parents are each identified by
``table_name``, ``xbrl_factoid``, and columns defining the additional dimensions
(``utility_type``, ``plant_status``, ``plant_function``). The parent columns
have a ``_parent`` suffix.
"""
# grab all of the non-total and non-correction records
child_candidates = table_dimensions[
~(table_dimensions[dimensions] == "total").any(axis="columns")
& ~(table_dimensions.xbrl_factoid.str.endswith("_correction"))
]
total_comps = []
# check *every* combination of dimensions that could have any total values
dim_combos = itertools.chain.from_iterable(
itertools.combinations(dimensions, i + 1) for i in range(len(dimensions))
)
for _total_dims in dim_combos:
total_dims = list(_total_dims)
parents = meta_w_dims.dropna(subset=total_dims).loc[
(meta_w_dims[total_dims] == "total").all(axis="columns")
]
if parents.empty:
continue
# There's no wildcard merge in Pandas, so we just ignore whichever
# columns have "total"
non_total_cols = ["table_name", "xbrl_factoid"] + [
d for d in dimensions if d not in total_dims
]
total_comps.append(
pd.merge(
left=parents,
right=child_candidates,
on=non_total_cols,
how="inner",
suffixes=("_parent", ""),
).assign(
is_within_table_calc=True,
weight=1,
table_name_parent=lambda x: x.table_name,
xbrl_factoid_parent=lambda x: x.xbrl_factoid,
)
)
child_node_pk = ["table_name", "xbrl_factoid"] + dimensions
parent_node_pk = [f"{col}_parent" for col in child_node_pk]
relationship_cols = ["is_within_table_calc", "weight"]
all_expected_cols = parent_node_pk + child_node_pk + relationship_cols
inferred_totals = (
pd.concat(total_comps).reindex(columns=all_expected_cols).reset_index(drop=True)
)
# merge() will have dropped shared columns, so re-fill with child values:
child_values = inferred_totals[["table_name", "xbrl_factoid"] + dimensions].rename(
lambda dim: f"{dim}_parent", axis="columns"
)
inferred_totals = inferred_totals.fillna(child_values)
calcs_with_totals = pd.concat(
[
calc_components.assign(is_total_to_subdimensions_calc=False),
inferred_totals.assign(is_total_to_subdimensions_calc=True),
]
)
# verification + deduping below.
check_for_calc_components_duplicates(
calcs_with_totals,
table_names_known_dupes=[
"core_ferc1__yearly_sales_by_rate_schedules_sched304",
],
idx=parent_node_pk + child_node_pk,
)
# only drop duplicates if the table_name is in known dupes list.
calcs_with_totals = calcs_with_totals.drop_duplicates(
parent_node_pk + child_node_pk, keep="first"
)
assert calcs_with_totals[calcs_with_totals.duplicated()].empty
return calcs_with_totals
[docs]
def add_calculation_component_corrections(
calc_components: pd.DataFrame,
) -> pd.DataFrame:
"""Add records into the calculation components table.
All calculated records should have correction records. All total-to-subdimension
calculations should also have correction records. For the core (non-subdimension)
calculations, all calculation parents require a record with a correction child.
For the total-to-subdimension calculations, we are assuming we can identify those
calculations with the `is_total_to_subdimensions_calc` boolean column. All
total-to-subdimension
All calculaitons will get a correction record in the calculation components table
wether or not there is ever a correction record in the data.
"""
def assign_child_cols(df, is_subdimension_correction):
return df.assign(
table_name=lambda x: x.table_name_parent,
xbrl_factoid=lambda x: x.xbrl_factoid_parent
+ (
"_subdimension_correction"
if is_subdimension_correction
else "_correction"
),
plant_function=lambda x: x.plant_function_parent,
utility_type=lambda x: x.utility_type_parent,
plant_status=lambda x: x.plant_status_parent,
is_total_to_subdimensions_calc=is_subdimension_correction,
weight=1,
)
dimensions = other_dimensions(table_names=list(FERC1_TFR_CLASSES))
parent_idx = [f"{c}_parent" for c in ["table_name", "xbrl_factoid"] + dimensions]
non_corrections = calc_components[
~calc_components.xbrl_factoid.str.endswith("_correction")
]
corrections = calc_components.drop_duplicates(subset=parent_idx).pipe(
assign_child_cols, is_subdimension_correction=False
)
subdimension_corrections = (
calc_components[calc_components.is_total_to_subdimensions_calc]
.drop_duplicates(subset=parent_idx)
.pipe(assign_child_cols, is_subdimension_correction=True)
)
return pd.concat([non_corrections, corrections, subdimension_corrections])
@asset(
ins={
table_name: AssetIn(table_name)
# list of tables that have reconcile_table_calculations params
# minus electric_plant_depreciation_changes_ferc1 bc that table is messy and
# not actually in the explosion work
for table_name in [
"core_ferc1__yearly_plant_in_service_sched204",
"core_ferc1__yearly_utility_plant_summary_sched200",
"core_ferc1__yearly_operating_expenses_sched320",
"core_ferc1__yearly_balance_sheet_liabilities_sched110",
"core_ferc1__yearly_depreciation_summary_sched336",
"core_ferc1__yearly_balance_sheet_assets_sched110",
"core_ferc1__yearly_income_statements_sched114",
"core_ferc1__yearly_depreciation_by_function_sched219",
"core_ferc1__yearly_retained_earnings_sched118",
"core_ferc1__yearly_operating_revenues_sched300",
]
}
| {
"_core_ferc1_xbrl__calculation_components": AssetIn(
"_core_ferc1_xbrl__calculation_components"
)
},
)
[docs]
def _core_ferc1__calculation_metric_checks(**kwargs):
"""Check calculation metrics for all transformed tables which have reconciled calcs."""
calculation_components = kwargs["_core_ferc1_xbrl__calculation_components"]
transformed_ferc1_dfs = {
name: df
for (name, df) in kwargs.items()
if name not in ["_core_ferc1_xbrl__calculation_components"]
}
# standardize the two key columns we are going to use into generic names
xbrl_factoid_name = table_to_xbrl_factoid_name()
columns_to_check = table_to_column_to_check()
tbls = [
df.assign(table_name=name).rename(
columns={
xbrl_factoid_name[name]: "xbrl_factoid",
columns_to_check[name]: "column_to_check",
}
)
for (name, df) in transformed_ferc1_dfs.items()
]
transformed_ferc1 = pd.concat(tbls)
# restrict the calculation components to only the bits we want
calculation_components = calculation_components[
# remove total to subdimensions
~calculation_components.is_total_to_subdimensions_calc
# only the intra table calcs
& calculation_components.is_within_table_calc
# remove corrections (bc they would clean up the calcs so the errors wouldn't show up)
& ~calculation_components.xbrl_factoid.str.contains("_correction")
# remove all of the tables that aren't in this check
& calculation_components.table_name_parent.isin(transformed_ferc1_dfs.keys())
]
calc_idx = ["xbrl_factoid", "table_name"] + other_dimensions(
table_names=list(FERC1_TFR_CLASSES)
)
calculated_df = calculate_values_from_components(
data=transformed_ferc1,
calculation_components=calculation_components,
calc_idx=calc_idx,
value_col="column_to_check",
)
calculation_metrics = check_calculation_metrics_by_group(
calculated_df=calculated_df,
group_metric_checks=GroupMetricChecks(
groups_to_check=[
"ungrouped",
"table_name",
"xbrl_factoid",
"utility_id_ferc1",
"report_year",
]
),
)
errors = calculation_metrics[
calculation_metrics.filter(like="is_error").any(axis=1)
]
if len(errors) > 42:
raise AssertionError(
f"Found {len(errors)} from the results of check_calculation_metrics_by_group"
f"with default group values when less than 41 was expected.\n{errors}"
)
return calculation_metrics