"""Generalized DBF extractor for FERC data."""
import contextlib
import csv
import importlib.resources
import warnings
import zipfile
from collections import defaultdict
from collections.abc import Iterator
from functools import lru_cache
from pathlib import Path
from typing import IO, Any, Protocol, Self
import pandas as pd
import sqlalchemy as sa
from dbfread import DBF, FieldParser
import pudl
import pudl.logging_helpers
from pudl.metadata.classes import DataSource
from pudl.settings import FercToSqliteSettings, GenericDatasetSettings
from pudl.workspace.datastore import Datastore
[docs]
logger = pudl.logging_helpers.get_logger(__name__)
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class DbcFileMissingError(Exception):
"""This is raised when the DBC index file is missing."""
pass
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class DbfTableSchema:
"""Simple data-wrapper for the fox-pro table schema."""
def __init__(self, table_name: str):
"""Creates new instance of the table schema setting.
The table name will be set as table_name and table will have no columns.
"""
self.name = table_name
self._columns = []
self._column_types = {}
self._short_name_map = {} # short_name_map[short_name] -> long_name
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def add_column(
self,
col_name: str,
col_type: sa.types.TypeEngine,
short_name: str | None = None,
):
"""Adds a new column to this table schema."""
assert col_name not in self._columns
self._columns.append(col_name)
self._column_types[col_name] = col_type
if short_name is not None:
self._short_name_map[short_name] = col_name
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def get_columns(self) -> Iterator[tuple[str, sa.types.TypeEngine]]:
"""Itereates over the (column_name, column_type) pairs."""
for col_name in self._columns:
yield (col_name, self._column_types[col_name])
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def get_column_names(self) -> set[str]:
"""Returns set of long column names."""
return set(self._columns)
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def get_column_rename_map(self) -> dict[str, str]:
"""Returns dictionary that maps from short to long column names."""
return dict(self._short_name_map)
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def create_sa_table(self, sa_meta: sa.MetaData) -> sa.Table:
"""Creates SQLAlchemy table described by this instance.
Args:
sa_meta: new table will be written to this MetaData object.
"""
table = sa.Table(self.name, sa_meta)
for col_name, col_type in self.get_columns():
table.append_column(sa.Column(col_name, col_type))
return table
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class FercDbfArchive:
"""Represents API for accessing files within a single DBF archive.
Typically, archive contains data for a single year and single FERC form dataset
(e.g. FERC Form 1 or FERC Form 2).
"""
def __init__(
self,
zipfile: zipfile.ZipFile,
dbc_path: Path,
table_file_map: dict[str, str],
partition: dict[str, Any],
field_parser: FieldParser,
):
"""Constructs new instance of FercDbfArchive."""
self.zipfile = zipfile
self.partition = dict(partition)
self.root_path: Path = dbc_path.parent
self.dbc_path: Path = dbc_path
self._table_file_map = table_file_map
self.field_parser = field_parser
self._table_schemas: dict[str, list[str]] = {}
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def get_file(self, filename: str) -> IO[bytes]:
"""Opens the file within this archive."""
path = self.root_path / filename
try:
return self.zipfile.open(path.as_posix())
except KeyError:
raise KeyError(f"{path} not available for {self.partition}.")
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def get_db_schema(self) -> dict[str, list[str]]:
"""Returns dict with table names as keys, and list of column names as values."""
if not self._table_schemas:
# TODO(janrous): this should be locked to ensure multi-thread safety
try:
dbf = DBF(
"",
ignore_missing_memofile=True,
filedata=self.zipfile.open(self.dbc_path.as_posix()),
)
except KeyError:
raise DbcFileMissingError(
f"DBC file {self.dbc_path} for {self.partition} is missing."
)
table_names: dict[Any, str] = {}
table_columns = defaultdict(list)
for row in dbf:
obj_id = row.get("OBJECTID")
obj_name = row.get("OBJECTNAME")
obj_type = row.get("OBJECTTYPE", None)
if obj_type == "Table":
table_names[obj_id] = obj_name
elif obj_type == "Field":
parent_id = row.get("PARENTID")
table_columns[parent_id].append(obj_name)
# Remap table ids to table names.
self._table_schemas = {
table_names[tid]: cols for tid, cols in table_columns.items()
}
return self._table_schemas
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def get_table_dbf(self, table_name: str) -> DBF:
"""Opens the DBF for a given table."""
fname = self._table_file_map[table_name]
return DBF(
fname,
encoding="latin1",
parserclass=self.field_parser,
ignore_missing_memofile=True,
filedata=self.get_file(fname),
)
@lru_cache
[docs]
def get_table_schema(self, table_name: str) -> DbfTableSchema:
"""Returns TableSchema for a given table and a given year."""
table_columns = self.get_db_schema()[table_name]
dbf = self.get_table_dbf(table_name)
dbf_fields = [field for field in dbf.fields if field.name != "_NullFlags"]
if len(table_columns) != len(table_columns):
return ValueError(
f"Number of DBF fields in {table_name} does not match what was "
f"found in the DBC index file for {self.partition}."
)
schema = DbfTableSchema(table_name)
for long_name, dbf_col in zip(table_columns, dbf_fields):
if long_name[:8] != dbf_col.name.lower()[:8]:
raise ValueError(
f"DBF field name mismatch: {dbf_col.name} != {long_name}"
)
col_type = DBF_TYPES[dbf_col.type]
if col_type == sa.String:
col_type = sa.String(length=dbf_col.length)
schema.add_column(long_name, col_type, short_name=dbf_col.name)
return schema
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def load_table(self, table_name: str) -> pd.DataFrame:
"""Returns dataframe that holds data for a table contained within this archive.
Args:
table_name: name of the table.
"""
sch = self.get_table_schema(table_name)
df = pd.DataFrame(iter(self.get_table_dbf(table_name)))
df = df.drop("_NullFlags", axis=1, errors="ignore").rename(
sch.get_column_rename_map(), axis=1
)
return df
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class AbstractFercDbfReader(Protocol):
"""This is the interface definition for dealing with fox-pro datastores."""
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def get_dataset(self: Self) -> str:
"""Returns name of the dataset that this datastore provides access to."""
...
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def get_table_names(self: Self) -> list[str]:
"""Returns list of all available table names."""
...
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def get_archive(self: Self, **filters) -> FercDbfArchive:
"""Returns single archive matching specific filters."""
...
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def get_table_schema(self: Self, table_name: str, year: int) -> DbfTableSchema:
"""Returns schema for a given table and a given year."""
...
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def load_table_dfs(
self: Self, table_name: str, partitions: list[dict[str, Any]]
) -> pd.DataFrame | None:
"""Returns dataframe that contains data for a given table across given years."""
...
# TODO(rousik): we should have a class for accessing single archive.
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class FercFieldParser(FieldParser):
"""A custom DBF parser to deal with bad FERC data types."""
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def parseN(self, field, data: bytes) -> int | float | None: # noqa: N802
"""Augments the Numeric DBF parser to account for bad FERC data.
There are a small number of bad entries in the backlog of FERC Form 1
data. They take the form of leading/trailing zeroes or null characters
in supposedly numeric fields, and occasionally a naked '.'
Accordingly, this custom parser strips leading and trailing zeros and
null characters, and replaces a bare '.' character with zero, allowing
all these fields to be cast to numeric values.
Args:
field: The DBF field being parsed.
data: Binary data (bytes) read from the DBF file.
""" # noqa: D417
# Strip whitespace, null characters, and zeroes
data = data.strip().strip(b"*\x00").lstrip(b"0")
# Replace bare periods (which are non-numeric) with zero.
if data == b".":
data = b"0"
return super().parseN(field, data)
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DBF_TYPES = {
"C": sa.String,
"D": sa.Date,
"F": sa.Float,
"I": sa.Integer,
"L": sa.Boolean,
"M": sa.Text, # 10 digit .DBT block number, stored as a string...
"N": sa.Float,
"T": sa.DateTime,
"0": sa.Integer, # based on dbf2sqlite mapping
"B": "XXX", # .DBT block number, binary string
"@": "XXX", # Timestamp... Date = Julian Day, Time is in milliseconds?
"+": "XXX", # Autoincrement (e.g. for IDs)
"O": "XXX", # Double, 8 bytes
"G": "XXX", # OLE 10 digit/byte number of a .DBT block, stored as string
}
"""Dict: A mapping of DBF field types to SQLAlchemy Column types.
This dictionary maps the strings which are used to denote field types in the DBF objects
to the corresponding generic SQLAlchemy Column types: These definitions come from a
combination of the dbfread example program dbf2sqlite and this DBF file format
documentation page:
http://www.dbase.com/KnowledgeBase/int/db7_file_fmt.htm
: http: //www.dbase.com/KnowledgeBase/int/db7_file_fmt.htm Unmapped types left as 'XXX'
which should result in an error if encountered.
"""
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class PartitionedDataFrame:
"""This class bundles pandas.DataFrame with partition information."""
def __init__(self, df: pd.DataFrame, partition: dict[str, Any]):
"""Constructs new instance of PartitionedDataFrame."""
self.df = df
self.partition = partition
# TODO(rousik): instead of using class-level constants, we could pass the params in the constructor, which should
# allow us to instantiate these dataset-specific datastores in the extractor code.
# That may make the manipulations little easier.
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class FercDbfReader:
"""Wrapper to provide standardized access to FERC DBF databases."""
def __init__(
self: Self,
datastore: Datastore,
dataset: str,
field_parser: FieldParser = FercFieldParser,
):
"""Create a new instance of FercDbfReader.
This can be used for retrieving data from the legacy FoxPro databases that are
used by FERC Form N datasets up to 2020.
Args:
datastore: provides access to raw files on disk.
dataset: name of the dataset (e.g. ferc1), this is used to load metadata
from package_data/{dataset} subdirectory.
field_parser: FieldParser class to use when loading data
"""
self._cache = {}
self.datastore = datastore
self.dataset = dataset
self.field_parser = field_parser
# dbc_file_map.csv contains path to the DBC file that contains the
# overall database schemas. It is expected that DBF files live in
# the same directory.
self._dbc_path = {}
for row in self._open_csv_resource("dbc_file_map.csv"):
self._dbc_path[int(row["year"])] = Path(row["path"])
# table_file_map holds mapping between tables and their corresponding
# DBF files.
self._table_file_map = {}
for row in self._open_csv_resource("table_file_map.csv"):
self._table_file_map[row["table"]] = row["filename"]
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def get_dataset(self: Self) -> str:
"""Return the name of the dataset this datastore works with."""
return self.dataset
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def _open_csv_resource(self: Self, base_filename: str) -> csv.DictReader:
"""Open the given resource file as :class:`csv.DictReader`."""
csv_path = (
importlib.resources.files(f"pudl.package_data.{self.dataset}")
/ base_filename
)
return csv.DictReader(csv_path.open())
@lru_cache
[docs]
def get_archive(self: Self, year: int, **filters) -> FercDbfArchive:
"""Returns single dbf archive matching given filters."""
nfilters = self._normalize(filters)
return FercDbfArchive(
self.datastore.get_zipfile_resource(self.dataset, year=year, **nfilters),
dbc_path=self._dbc_path[year],
partition=filters,
table_file_map=self._table_file_map,
field_parser=self.field_parser,
)
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def get_table_names(self: Self) -> list[str]:
"""Returns list of tables that this datastore provides access to."""
return list(self._table_file_map)
@staticmethod
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def _normalize(filters: dict[str, Any]) -> dict[str, str]:
"""Casts partition values to lowercase strings."""
return {k: str(v).lower() for k, v in filters.items()}
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def valid_partition_filter(self: Self, fl: dict[str, Any]) -> bool:
"""Returns True if a given filter fl is considered to be valid.
This can be used to eliminate partitions that are not suitable for processing,
e.g. for early years of FERC Form 2, databases marked with part=1 or part=2 are
not suitable.
"""
logger.info(f"running base class valid_partition_filter on {fl}")
return True
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def load_table_dfs(
self: Self, table_name: str, partitions: list[dict[str, Any]]
) -> list[PartitionedDataFrame]:
"""Returns all data for a given table.
Merges data for a given table across all partitions.
Args:
table_name: name of the table to load.
partitions: list of partition filters to use
"""
# Retrieve all archives that match given years
# Then try to simply merge
# There may be need for some first-pass aggregation of tables
# from within the same year.
dfs: list[PartitionedDataFrame] = []
for p in partitions:
archive = self.get_archive(**p)
try:
dfs.append(PartitionedDataFrame(archive.load_table(table_name), p))
except KeyError:
logger.debug(f"Table {table_name} missing for partition {p}")
continue
return dfs
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def add_key_constraints(
meta: sa.MetaData, pk_table: str, column: str, pk_column: str | None = None
) -> sa.MetaData:
"""Adds primary and foreign key to tables present in meta.
Args:
meta: constraints will be applied to this metadata instance
pk_table: name of the table that contains primary-key
column: foreign key column name. Tables that contain this column will
have foreign-key constraint added.
pk_column: (optional) if specified, this is the primary key column name in
the table. If not specified, it is assumed that this is the same as pk_column.
"""
pk_column = pk_column or column
for table in meta.tables.values():
constraint = None
if table.name == pk_table:
constraint = sa.PrimaryKeyConstraint(
pk_column, sqlite_on_conflict="REPLACE"
)
elif column in table.columns:
constraint = sa.ForeignKeyConstraint(
columns=[column],
refcolumns=[f"{pk_table}.{pk_column}"],
)
if constraint:
table.append_constraint(constraint)
return meta
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def deduplicate_by_year(
dfs: list[PartitionedDataFrame], pk_column: str
) -> pd.DataFrame | None:
"""Deduplicate records by year, keeping the most recent version of each record.
It will use pk_column as the primary key column. report_yr column is expected to
either be present, or it will be derived from partition["year"].
"""
yr_dfs: list[pd.DataFrame] = []
for p_df in dfs:
df = p_df.df
if "report_yr" not in df.columns:
df = df.assign(report_yr=p_df.partition["year"])
yr_dfs.append(df)
agg_df = pd.concat(yr_dfs)
return (
agg_df.sort_values(by=["report_yr", pk_column])
.drop_duplicates(subset=pk_column, keep="last")
.drop(columns="report_yr")
)