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"""
Load IRS Business Master File (BMF) CSVs into raw.bmf.
Usage:
python -m scripts.parse.irs_bmf
python -m scripts.parse.irs_bmf data/irs/bmf/eo1.csv data/irs/bmf/eo2.csv
"""
import csv
import os
import sys
from psycopg2.extras import execute_batch
from scripts.common.db import execute_transaction
from scripts.common.ingest import (
start_ingest_run, finish_ingest_run, fail_ingest_run, log_ingest_error,
)
from scripts.common.normalize import normalize_ein
DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "..", "data", "irs", "bmf")
PARSER_NAME = "load_raw_bmf"
SOURCE_SYSTEM = "irs_bmf"
BMF_COLUMNS = [
"ein",
"name",
"ico",
"street",
"city",
"state",
"zip",
"grp",
"subsection",
"affiliation",
"classification",
"ruling",
"deductibility",
"foundation",
"activity",
"organization",
"status",
"tax_period",
"asset_cd",
"income_cd",
"filing_req_cd",
"pf_filing_req_cd",
"acct_pd",
"asset_amt",
"income_amt",
"revenue_amt",
"ntee_cd",
"sort_name",
"source_file",
"ingest_run_id",
]
def parse_bigint(value):
if value is None:
return None
s = str(value).strip()
if not s:
return None
return int(s)
def row_to_record(row, source_file, ingest_run_id):
ein = normalize_ein(row.get("EIN"))
if not ein:
raise ValueError(f"Cannot normalize EIN: {row.get('EIN')!r}")
return {
"ein": ein,
"name": row.get("NAME") or None,
"ico": row.get("ICO") or None,
"street": row.get("STREET") or None,
"city": row.get("CITY") or None,
"state": row.get("STATE") or None,
"zip": row.get("ZIP") or None,
"grp": row.get("GROUP") or None,
"subsection": row.get("SUBSECTION") or None,
"affiliation": row.get("AFFILIATION") or None,
"classification": row.get("CLASSIFICATION") or None,
"ruling": row.get("RULING") or None,
"deductibility": row.get("DEDUCTIBILITY") or None,
"foundation": row.get("FOUNDATION") or None,
"activity": row.get("ACTIVITY") or None,
"organization": row.get("ORGANIZATION") or None,
"status": row.get("STATUS") or None,
"tax_period": row.get("TAX_PERIOD") or None,
"asset_cd": row.get("ASSET_CD") or None,
"income_cd": row.get("INCOME_CD") or None,
"filing_req_cd": row.get("FILING_REQ_CD") or None,
"pf_filing_req_cd": row.get("PF_FILING_REQ_CD") or None,
"acct_pd": row.get("ACCT_PD") or None,
"asset_amt": parse_bigint(row.get("ASSET_AMT")),
"income_amt": parse_bigint(row.get("INCOME_AMT")),
"revenue_amt": parse_bigint(row.get("REVENUE_AMT")),
"ntee_cd": row.get("NTEE_CD") or None,
"sort_name": row.get("SORT_NAME") or None,
"source_file": source_file,
"ingest_run_id": ingest_run_id,
}
def upsert_records(records):
if not records:
return 0
placeholders = ", ".join(["%s"] * len(BMF_COLUMNS))
sql = (
f"INSERT INTO raw.bmf ({', '.join(BMF_COLUMNS)}) "
f"VALUES ({placeholders}) "
"ON CONFLICT (ein) DO UPDATE SET "
"name = EXCLUDED.name, "
"ico = EXCLUDED.ico, "
"street = EXCLUDED.street, "
"city = EXCLUDED.city, "
"state = EXCLUDED.state, "
"zip = EXCLUDED.zip, "
"grp = EXCLUDED.grp, "
"subsection = EXCLUDED.subsection, "
"affiliation = EXCLUDED.affiliation, "
"classification = EXCLUDED.classification, "
"ruling = EXCLUDED.ruling, "
"deductibility = EXCLUDED.deductibility, "
"foundation = EXCLUDED.foundation, "
"activity = EXCLUDED.activity, "
"organization = EXCLUDED.organization, "
"status = EXCLUDED.status, "
"tax_period = EXCLUDED.tax_period, "
"asset_cd = EXCLUDED.asset_cd, "
"income_cd = EXCLUDED.income_cd, "
"filing_req_cd = EXCLUDED.filing_req_cd, "
"pf_filing_req_cd = EXCLUDED.pf_filing_req_cd, "
"acct_pd = EXCLUDED.acct_pd, "
"asset_amt = EXCLUDED.asset_amt, "
"income_amt = EXCLUDED.income_amt, "
"revenue_amt = EXCLUDED.revenue_amt, "
"ntee_cd = EXCLUDED.ntee_cd, "
"sort_name = EXCLUDED.sort_name, "
"source_file = EXCLUDED.source_file, "
"ingest_run_id = EXCLUDED.ingest_run_id"
)
values = [[record.get(col) for col in BMF_COLUMNS] for record in records]
def _do(conn):
with conn.cursor() as cur:
execute_batch(cur, sql, values, page_size=1000)
return len(values)
return execute_transaction(_do)
def discover_files(args):
if args:
return args
return sorted(
os.path.join(DATA_DIR, name)
for name in os.listdir(DATA_DIR)
if name.endswith(".csv")
)
def main():
files = discover_files(sys.argv[1:])
if not files:
print(f"No CSV files found in {DATA_DIR}", file=sys.stderr)
sys.exit(1)
notes = " ".join(os.path.basename(path) for path in files)
ingest_run_id = start_ingest_run(PARSER_NAME, SOURCE_SYSTEM, notes)
files_scanned = 0
files_matched = 0
rows_inserted = 0
try:
for csv_path in files:
basename = os.path.basename(csv_path)
files_scanned += 1
print(f"Loading {basename}...")
batch = []
file_rows = 0
file_errors = 0
with open(csv_path, newline="", encoding="utf-8-sig") as f:
reader = csv.DictReader(f)
for line_number, row in enumerate(reader, start=2):
try:
batch.append(row_to_record(row, basename, ingest_run_id))
except Exception as e:
file_errors += 1
log_ingest_error(
ingest_run_id,
basename,
f"line:{line_number}",
e,
source_document_id=row.get("EIN"),
stage="normalize_row",
)
continue
if len(batch) >= 5000:
rows_inserted += upsert_records(batch)
file_rows += len(batch)
batch = []
if batch:
rows_inserted += upsert_records(batch)
file_rows += len(batch)
files_matched += 1
print(f" {basename}: {file_rows:,} rows loaded, {file_errors} errors")
finish_ingest_run(ingest_run_id, files_scanned, files_matched, rows_inserted)
except Exception:
fail_ingest_run(ingest_run_id)
raise
print(
f"Done. {files_scanned} files scanned, "
f"{files_matched} files loaded, {rows_inserted:,} rows upserted."
)
if __name__ == "__main__":
main()
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