FERC1 2022

.gitignore

@@ -37,3 +37,4 @@ notebooks/*.tgz
 **.tfstate.*
 terraform/.terraform/*
 .env
+.hypothesis/

pyproject.toml

@@ -17,7 +17,7 @@ dependencies = [
     "boto3>=1.28.55",
     "bottleneck>=1.3.4",  # pandas[performance]
     "catalystcoop.dbfread>=3.0,<3.1",
-    "catalystcoop.ferc-xbrl-extractor>=1.1.1,<1.2",
+    "catalystcoop.ferc-xbrl-extractor>=1.2.0,<2",
     "coloredlogs>=14.0,<15.1",  # Dagster requires 14.0
     "dagster-webserver>=1.4,<1.6",
     "dagster>=1.4,<1.6",
@@ -30,6 +30,7 @@ dependencies = [
     "grpcio==1.57.0",  # Required by dagster. Version works with MacOS
     "grpcio-health-checking==1.57.0",  # Required by dagster. Version works with MacOS
     "grpcio-status==1.57.0",  # Required by dagster. Version works with MacOS
+    "hypothesis>=6.87,<7.0",
     "jellyfish>=1.0.1,<1.1",  # recordlinkage dependency
     "jinja2>=3,<3.2",
     "matplotlib>=3.6.1,<3.9",
@@ -39,6 +40,7 @@ dependencies = [
     "numpy>=1.24,<2.0a0",
     "openpyxl>=3.0.10",  # pandas[excel]
     "pandas[parquet,excel,fss,gcp,compression]>=2,<2.2",
+    "pandera>=0.17,<1.0",
     "pyarrow>=13,<14",  # pandas[parquet]
     "pydantic>=1.7,<2",
     "python-dotenv>=1,<1.1",

src/pudl/helpers.py

@@ -1798,3 +1798,31 @@ def scale_by_ownership(
         gens["fraction_owned"], axis="index"
     )
     return gens
+
+
+def assert_cols_areclose(
+    df: pd.DataFrame,
+    a_cols: list[str],
+    b_cols: list[str],
+    mismatch_threshold: float,
+    message: str,
+):
+    """Check if two column sets of a dataframe are close to each other.
+
+    Ignores NANs and raises if there are too many mismatches.
+    """
+    # we use df.loc, so if we use a debugger in here we can see the actual data
+    # instead of just whether or not there are matches.
+    mismatch = df.loc[
+        ~np.isclose(
+            np.ma.masked_where(np.isnan(df[a_cols]), df[a_cols]),
+            np.ma.masked_where(np.isnan(df[b_cols]), df[b_cols]),
+            equal_nan=True,
+        ).filled()
+    ]
+    mismatch_ratio = len(mismatch) / len(df)
+    if mismatch_ratio > mismatch_threshold:
+        raise AssertionError(
+            f"{message} Mismatch ratio {mismatch_ratio:.01%} > "
+            f"threshold {mismatch_threshold:.01%}."
+        )

src/pudl/io_managers.py

@@ -1,4 +1,5 @@
 """Dagster IO Managers."""
+import json
 import re
 from pathlib import Path
 from sqlite3 import sqlite_version
@@ -643,6 +644,7 @@ def load_input(self, context: InputContext) -> pd.DataFrame:
             context: dagster keyword that provides access output information like asset
                 name.
         """
+        # TODO (daz): this is hard-coded to FERC1, though this is nominally for all FERC datasets.
         ferc1_settings = context.resources.dataset_settings.ferc1
 
         table_name = self._get_table_name(context)
@@ -683,6 +685,105 @@ class FercXBRLSQLiteIOManager(FercSQLiteIOManager):
     metadata.
     """
 
+    @staticmethod
+    def filter_for_freshest_data(
+        table: pd.DataFrame, primary_key: list[str]
+    ) -> pd.DataFrame:
+        """Get most updated values for each XBRL context.
+
+        An XBRL context includes an entity ID, the time period the data applies
+        to, and other dimensions such as utility type. Each context has its own
+        ID, but they are frequently redefined with the same contents but
+        different IDs - so we identify them by their actual content.
+
+        Each row in our SQLite database includes all the facts for one
+        context/filing pair.
+
+        If one context is represented in multiple filings, we take the facts from the most recently-published filing.
+
+        This means that if a recently-published filing does not include a value for a fact that was previously reported, then that value will remain null. We do not
+        forward-fill facts on a fact-by-fact basis.
+        """
+        filing_metadata_cols = {"publication_time", "filing_name"}
+        xbrl_context_cols = [c for c in primary_key if c not in filing_metadata_cols]
+        # we do this in multiple stages so we can log the drop-off at each stage.
+        stages = [
+            {
+                "message": "completely duplicated rows",
+                "subset": table.columns,
+            },
+            {
+                "message": "rows that are exactly the same in multiple filings",
+                "subset": [c for c in table.columns if c not in filing_metadata_cols],
+            },
+            {
+                "message": "rows that were updated by later filings",
+                "subset": xbrl_context_cols,
+            },
+        ]
+        original = table.sort_values("publication_time")
+        for stage in stages:
+            deduped = original.drop_duplicates(subset=stage["subset"], keep="last")
+            logger.debug(f"Dropped {len(original) - len(deduped)} {stage['message']}")
+            original = deduped
+
+        return deduped
+
+    @staticmethod
+    def refine_report_year(df: pd.DataFrame, xbrl_years: list[int]) -> pd.DataFrame:
+        """Set a fact's report year by its actual dates.
+
+        Sometimes a fact belongs to a context which has no ReportYear
+        associated with it; other times there are multiple ReportYears
+        associated with a single filing. In these cases the report year of a
+        specific fact may be associated with the other years in the filing.
+
+        In many cases we can infer the actual report year from the fact's
+        associated time period - either duration or instant.
+        """
+        is_duration = len({"start_date", "end_date"} - set(df.columns)) == 0
+        is_instant = "date" in df.columns
+
+        def get_year(df: pd.DataFrame, col: str) -> pd.Series:
+            datetimes = pd.to_datetime(df.loc[:, col])
+            if datetimes.isna().any():
+                raise ValueError(f"{col} has null values!")
+            return datetimes.apply(lambda x: x.year)
+
+        if is_duration:
+            start_years = get_year(df, "start_date")
+            end_years = get_year(df, "end_date")
+            if not (start_years == end_years).all():
+                raise ValueError("start_date and end_date are in different years!")
+            new_report_years = start_years
+        elif is_instant:
+            new_report_years = get_year(df, "date")
+        else:
+            raise ValueError("Attempted to read a non-instant, non-duration table.")
+
+        # we include XBRL data from before our "officially supported" XBRL
+        # range because we want to use it to set start-of-year values for the
+        # first XBRL year.
+        xbrl_years_plus_one_previous = [min(xbrl_years) - 1] + xbrl_years
+        return (
+            df.assign(report_year=new_report_years)
+            .loc[lambda df: df.report_year.isin(xbrl_years_plus_one_previous)]
+            .reset_index(drop=True)
+        )
+
+    def _get_primary_key(self, sched_table_name: str) -> list[str]:
+        # TODO (daz): as of 2023-10-13, our datapackage.json is merely
+        # "frictionless-like" so we manually parse it as JSON. once we make our
+        # datapackage.json conformant, we will need to at least update the
+        # "primary_key" to "primaryKey", but maybe there will be other changes
+        # as well.
+        with (self.base_dir / f"{self.db_name}_datapackage.json").open() as f:
+            datapackage = json.loads(f.read())
+        [table_resource] = [
+            tr for tr in datapackage["resources"] if tr["name"] == sched_table_name
+        ]
+        return table_resource["schema"]["primary_key"]
+
     def handle_output(self, context: OutputContext, obj: pd.DataFrame | str):
         """Handle an op or asset output."""
         raise NotImplementedError("FercXBRLSQLiteIOManager can't write outputs yet.")
@@ -694,6 +795,7 @@ def load_input(self, context: InputContext) -> pd.DataFrame:
             context: dagster keyword that provides access output information like asset
                 name.
         """
+        # TODO (daz): this is hard-coded to FERC1, though this is nominally for all FERC datasets.
         ferc1_settings = context.resources.dataset_settings.ferc1
 
         table_name = self._get_table_name(context)
@@ -709,23 +811,27 @@ def load_input(self, context: InputContext) -> pd.DataFrame:
 
         engine = self.engine
 
-        id_table = "identification_001_duration"
-
         sched_table_name = re.sub("_instant|_duration", "", table_name)
         with engine.connect() as con:
-            return pd.read_sql(
-                f"""
-                SELECT {table_name}.*, {id_table}.report_year FROM {table_name}
-                JOIN {id_table} ON {id_table}.filing_name = {table_name}.filing_name
-                WHERE {id_table}.report_year BETWEEN :min_year AND :max_year;
-                """,  # noqa: S608 - table names not supplied by user
+            df = pd.read_sql(
+                f"SELECT {table_name}.* FROM {table_name}",  # noqa: S608 - table names not supplied by user
                 con=con,
-                params={
-                    "min_year": min(ferc1_settings.xbrl_years),
-                    "max_year": max(ferc1_settings.xbrl_years),
-                },
             ).assign(sched_table_name=sched_table_name)
 
+        primary_key = self._get_primary_key(table_name)
+
+        return (
+            df.pipe(
+                FercXBRLSQLiteIOManager.filter_for_freshest_data,
+                primary_key=primary_key,
+            )
+            .pipe(
+                FercXBRLSQLiteIOManager.refine_report_year,
+                xbrl_years=ferc1_settings.xbrl_years,
+            )
+            .drop(columns=["publication_time"])
+        )
+
 
 @io_manager(required_resource_keys={"dataset_settings"})
 def ferc1_xbrl_sqlite_io_manager(init_context) -> FercXBRLSQLiteIOManager:

src/pudl/metadata/sources.py

@@ -365,7 +365,7 @@
         },
         "field_namespace": "ferc1",
         "working_partitions": {
-            "years": sorted(set(range(1994, 2022))),
+            "years": sorted(set(range(1994, 2023))),
         },
         "contributors": [
             CONTRIBUTORS["catalyst-cooperative"],
@@ -405,7 +405,7 @@
             # Years 1991-1995 use strange formats that need to be investigated further.
             # Years 1996-1999 come in split archives and full archives and we are going
             # to be using the aggregated archives (part=None).
-            "years": sorted(set(range(1996, 2022))),
+            "years": sorted(set(range(1996, 2023))),
         },
         "contributors": [
             CONTRIBUTORS["catalyst-cooperative"],
@@ -463,7 +463,7 @@
         ),
         "field_namespace": "ferc60",
         "working_partitions": {
-            "years": sorted(set(range(2006, 2022))),
+            "years": sorted(set(range(2006, 2023))),
         },
         "contributors": [
             CONTRIBUTORS["catalyst-cooperative"],

src/pudl/package_data/glue/utility_id_ferc1.csv

@@ -151,7 +151,7 @@ utility_id_ferc1,utility_id_ferc1_xbrl,utility_id_ferc1_dbf
 150,,1001
 151,,1003
 152,C000029,231
-153,C000030,447
+153,C000030,255
 154,C000038,250
 155,C000041,161
 156,C000045,294
@@ -402,7 +402,7 @@ utility_id_ferc1,utility_id_ferc1_xbrl,utility_id_ferc1_dbf
 408,R001052,52
 409,R001072,72
 411,R001087,87
-414,R001140,140
+414,C011644,140
 419,R001268,268
 420,R001298,298
 421,R001301,301
@@ -412,10 +412,12 @@ utility_id_ferc1,utility_id_ferc1_xbrl,utility_id_ferc1_dbf
 428,R001419,419
 429,R001422,422
 436,R001445,445
-437,,255
 439,R001515,515
 440,R001520,520
 441,R001521,521
 442,,278
 443,C010845,
 444,C011304,
+445,C011745,447
+446,C004679,
+447,C011785,

src/pudl/package_data/glue/utility_id_pudl.csv

@@ -256,7 +256,7 @@ utility_id_pudl,utility_id_ferc1,utility_name_ferc1,utility_id_eia,utility_name_
 258,111,Pinnacle West Marketing & Trading Co. LLC,,
 259,414,Pioneer Power and Light Company,,
 260,153,"PJM Interconnection, LLC",,
-261,437,"PJM Settlement, Inc.",,
+261,445,"PJM Settlement, Inc.",,
 262,84,Platte-Clay Electric Cooperative Inc.,,
 263,250,Portland General Electric Company,15248,Portland General Electric Co
 264,290,Potomac Electric Power Company,,
@@ -15826,3 +15826,5 @@ utility_id_pudl,utility_id_ferc1,utility_name_ferc1,utility_id_eia,utility_name_
 15868,,,65633,"BKV Barnett, LLC"
 15869,,,65634,Highpeak Solar LLC
 15870,,,1937004,
+15871,446,"Transource Oklahoma, LLC",,
+15872,447,"Mountrail-Williams Electric Cooperative",,

src/pudl/package_data/settings/etl_fast.yml

@@ -5,23 +5,23 @@
 ferc_to_sqlite_settings:
   ferc1_dbf_to_sqlite_settings:
     # What years of original FERC data should be cloned into the SQLite DB?
-    years: [2020]
+    years: [2019, 2020]
   ferc1_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc2_dbf_to_sqlite_settings:
-    years: [2020]
+    years: [2019, 2020]
   ferc2_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc6_dbf_to_sqlite_settings:
-    years: [2020]
+    years: [2019, 2020]
   ferc6_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc60_dbf_to_sqlite_settings:
-    years: [2020]
+    years: [2019, 2020]
   ferc60_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc714_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
 
 ###########################################################################
 # Settings for pudl_etl script
@@ -34,7 +34,7 @@ description: >
 version: 0.1.0
 datasets:
   ferc1:
-    years: [2020, 2021]
+    years: [2020, 2021, 2022]
   ferc714:
     years: [2019, 2020]
   eia:

src/pudl/package_data/settings/etl_full.yml

@@ -38,7 +38,7 @@ ferc_to_sqlite_settings:
       # A list of tables to be loaded into the local SQLite database. These are
       # the table names as they appear in the 2015 FERC Form 1 database.
   ferc1_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
     # A list of tables to be loaded into the local SQLite database. These are
     # the table names as created from the 2022 XBRL taxonomy.
   ferc2_dbf_to_sqlite_settings:
@@ -71,7 +71,7 @@ ferc_to_sqlite_settings:
         2020,
       ]
   ferc2_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc6_dbf_to_sqlite_settings:
     years:
       [
@@ -98,7 +98,7 @@ ferc_to_sqlite_settings:
         2020,
       ]
   ferc6_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc60_dbf_to_sqlite_settings:
     years:
       [
@@ -119,9 +119,9 @@ ferc_to_sqlite_settings:
         2020,
       ]
   ferc60_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
   ferc714_xbrl_to_sqlite_settings:
-    years: [2021]
+    years: [2021, 2022]
 
 ###########################################################################
 # Settings for pudl_etl script
@@ -164,6 +164,7 @@ datasets:
         2019,
         2020,
         2021,
+        2022,
       ]
   ferc714:
     years: