reconcile: Add further typing info; update tests.
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2 changed files with 75 additions and 41 deletions
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@ -58,7 +58,7 @@ from typing import Callable, Dict, List, Tuple, TextIO
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from beancount import loader
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from beancount.query.query import run_query
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from colorama import Fore, Style
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from colorama import Fore, Style # type: ignore
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if not sys.warnoptions:
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import warnings
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@ -96,7 +96,7 @@ JUNK_WORDS = [
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JUNK_WORDS_RES = [re.compile(word, re.IGNORECASE) for word in JUNK_WORDS]
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ZERO_RE = re.compile('^0+')
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def remove_duplicate_words(text):
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def remove_duplicate_words(text: str) -> str:
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unique_words = []
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known_words = set()
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for word in text.split():
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@ -123,18 +123,19 @@ def remove_payee_junk(payee: str) -> str:
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payee.strip()
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return payee
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# NOTE: Statement doesn't seem to give us a running balance or a final total.
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def read_transactions_from_csv(f: TextIO, standardize_statement_record: Callable) -> list:
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reader = csv.DictReader(f)
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return sort_records([standardize_statement_record(row, reader.line_num) for row in reader])
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# The reader.line_num is the source line number, not the spreadsheet row
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# number due to multi-line records.
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return sort_records([standardize_statement_record(row, i) for i, row in enumerate(reader, 2)])
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# Does the account you entered match the CSV?
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# Is the CSV in the format we expect? (ie. did they download through the right interface?)
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# Logical CSV line numbers
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# CSV reconciliation report
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# NOTE: Statement doesn't seem to give us a running balance or a final total.
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# CSV reconciliation report.
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# Merge helper script.
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def standardize_amex_record(row: Dict, line: int) -> Dict:
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"""Turn an AMEX CSV row into a standard dict format representing a transaction."""
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return {
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@ -148,6 +149,20 @@ def standardize_amex_record(row: Dict, line: int) -> Dict:
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}
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def validate_amex_csv(sample: str, account: str) -> None:
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required_cols = {'Date', 'Amount', 'Description', 'Card Member'}
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reader = csv.DictReader(io.StringIO(sample))
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if reader.fieldnames and not required_cols.issubset(reader.fieldnames):
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sys.exit(f"This CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}")
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def validate_fr_csv(sample: str, account: str) -> None:
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required_cols = {'Date', 'Amount', 'Detail', 'Serial Num'}
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reader = csv.DictReader(io.StringIO(sample))
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if reader.fieldnames and not required_cols.issubset(reader.fieldnames):
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sys.exit(f"This CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}")
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def standardize_fr_record(row: Dict, line: int) -> Dict:
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return {
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'date': datetime.datetime.strptime(row['Date'], '%m/%d/%Y').date(),
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@ -181,7 +196,7 @@ def format_record(record: dict) -> str:
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return output
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def format_multirecord(r1s, r2s, note):
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def format_multirecord(r1s: list[dict], r2s: list[dict], note: str) -> list[list]:
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total = sum(x['amount'] for x in r2s)
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assert len(r1s) == 1
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assert len(r2s) > 1
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@ -191,26 +206,29 @@ def format_multirecord(r1s, r2s, note):
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match_output.append([r1s[0]['date'], f'{r1s[0]["date"].isoformat()}: ↳ → {format_record(r2)} ✓ Matched{note}'])
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return match_output
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def sort_records(records: List) -> List:
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return sorted(records, key=lambda x: (x['date'], x['amount']))
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def first_word_exact_match(a, b):
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def first_word_exact_match(a: str, b: str) -> float:
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if len(a) == 0 or len(b) == 0:
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return 0
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return 0.0
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first_a = a.split()[0].strip()
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first_b = b.split()[0].strip()
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if first_a.casefold() == first_b.casefold():
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return min(1.0, 0.2 * len(first_a))
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else:
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return 0;
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return 0.0;
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def payee_match(a, b):
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fuzzy_match = fuzz.token_set_ratio(a, b) / 100.00
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def payee_match(a: str, b: str) -> float:
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fuzzy_match = float(fuzz.token_set_ratio(a, b) / 100.00)
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first_word_match = first_word_exact_match(a, b)
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return max(fuzzy_match, first_word_match)
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def records_match(r1: Dict, r2: Dict) -> Tuple[bool, str]:
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def records_match(r1: Dict, r2: Dict) -> Tuple[float, List[str]]:
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"""Do these records represent the same transaction?"""
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date_score = date_proximity(r1['date'], r2['date'])
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@ -254,7 +272,7 @@ def records_match(r1: Dict, r2: Dict) -> Tuple[bool, str]:
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return overall_score, overall_message
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def match_statement_and_books(statement_trans: list, books_trans: list):
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def match_statement_and_books(statement_trans: List[Dict], books_trans: List[Dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:
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"""
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Runs through all the statement transactions to find a matching transaction
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in the books. If found, the books transaction is marked off so that it can
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@ -266,9 +284,9 @@ def match_statement_and_books(statement_trans: list, books_trans: list):
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remaining_statement_trans = []
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for r1 in statement_trans:
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best_match_score = 0
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best_match_score = 0.0
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best_match_index = None
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best_match_note = ''
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best_match_note = []
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matches_found = 0
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for i, r2 in enumerate(books_trans):
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score, note = records_match(r1, r2)
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@ -280,7 +298,8 @@ def match_statement_and_books(statement_trans: list, books_trans: list):
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if best_match_score > 0.5 and matches_found == 1 and 'check-id mismatch' not in best_match_note or best_match_score > 0.8:
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matches.append(([r1], [books_trans[best_match_index]], best_match_note))
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# Don't try to make a second match against this books entry.
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del books_trans[best_match_index]
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if best_match_index is not None:
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del books_trans[best_match_index]
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else:
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remaining_statement_trans.append(r1)
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for r2 in books_trans:
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@ -288,7 +307,9 @@ def match_statement_and_books(statement_trans: list, books_trans: list):
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return matches, remaining_statement_trans, remaining_books_trans
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def format_matches(matches, csv_statement: str, show_reconciled_matches):
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# TODO: Return list of tuples (instead of list of lists).
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def format_matches(matches: List, csv_statement: str, show_reconciled_matches: bool) -> List[List]:
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match_output = []
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for r1s, r2s, note in matches:
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note = ', '.join(note)
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@ -306,14 +327,15 @@ def format_matches(matches, csv_statement: str, show_reconciled_matches):
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return match_output
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def date_proximity(d1, d2):
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diff = abs((d1 - d2).days)
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def date_proximity(d1: datetime.date, d2: datetime.date) -> float:
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diff = abs(int((d1 - d2).days))
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if diff > 60:
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return 0
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return 0.0
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else:
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return 1.0 - (diff / 60.0)
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def metadata_for_match(match, statement_filename, csv_filename):
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def metadata_for_match(match: Tuple[List, List, List], statement_filename: str, csv_filename: str) -> List[Tuple[str, int, str]]:
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# Can we really ever have multiple statement entries? Probably not.
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statement_filename = get_repo_relative_path(statement_filename)
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csv_filename = get_repo_relative_path(csv_filename)
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@ -361,15 +383,18 @@ def write_metadata_to_books(metadata_to_apply: List[Tuple[str, int, str]]) -> No
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f.writelines(contents)
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print(f'Wrote {filename}.')
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def get_repo_relative_path(path):
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def get_repo_relative_path(path: str) -> str:
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return os.path.relpath(path, start=os.getenv('CONSERVANCY_REPOSITORY'))
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def parse_path(path):
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def parse_path(path: str) -> str:
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if not os.path.exists(path):
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raise argparse.ArgumentTypeError(f'File {path} does not exist.')
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return path
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def parse_repo_relative_path(path):
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def parse_repo_relative_path(path: str) -> str:
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if not os.path.exists(path):
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raise argparse.ArgumentTypeError(f'File {path} does not exist.')
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repo = os.getenv('CONSERVANCY_REPOSITORY')
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@ -379,7 +404,8 @@ def parse_repo_relative_path(path):
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raise argparse.ArgumentTypeError(f'File {path} does not share a common prefix with $CONSERVANCY_REPOSITORY {repo}.')
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return path
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def parse_args(argv):
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def parse_args(argv: List[str]) -> argparse.Namespace:
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parser = argparse.ArgumentParser(description='Reconciliation helper')
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parser.add_argument('--beancount-file', required=True, type=parse_path)
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parser.add_argument('--csv-statement', required=True, type=parse_repo_relative_path)
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@ -392,7 +418,8 @@ def parse_args(argv):
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parser.add_argument('--non-interactive', action='store_true', help="Don't prompt to write to the books")
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return parser.parse_args(args=argv[1:])
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def totals(matches):
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def totals(matches: List[Tuple[List, List, List]]) -> Tuple[decimal.Decimal, decimal.Decimal, decimal.Decimal]:
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total_matched = decimal.Decimal(0)
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total_missing_from_books = decimal.Decimal(0)
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total_missing_from_statement = decimal.Decimal(0)
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@ -406,16 +433,16 @@ def totals(matches):
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return total_matched, total_missing_from_books, total_missing_from_statement
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def subset_match(statement_trans, books_trans):
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def subset_match(statement_trans: List[dict], books_trans: List[dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:
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matches = []
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remaining_books_trans = []
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remaining_statement_trans = []
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groups = itertools.groupby(books_trans, key=lambda x: (x['date'], x['payee']))
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for k, group in groups:
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best_match_score = 0
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best_match_score = 0.0
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best_match_index = None
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best_match_note = ''
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best_match_note = []
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matches_found = 0
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group_items = list(group)
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@ -430,11 +457,11 @@ def subset_match(statement_trans, books_trans):
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best_match_index = i
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best_match_note = note
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if best_match_score > 0.5 and matches_found == 1 and 'check-id mismatch' not in best_match_note or best_match_score > 0.8:
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if best_match_score <= 0.8:
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best_match_note.append('only one decent match')
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matches.append(([statement_trans[best_match_index]], group_items, best_match_note))
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del statement_trans[best_match_index]
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if best_match_index is not None:
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del statement_trans[best_match_index]
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for item in group_items:
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# TODO: Why?
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books_trans.remove(item)
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else:
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remaining_books_trans.append(r2)
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@ -442,26 +469,33 @@ def subset_match(statement_trans, books_trans):
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remaining_statement_trans.append(r1)
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return matches, remaining_statement_trans, remaining_books_trans
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def process_unmatched(statement_trans, books_trans):
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matches = []
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def process_unmatched(statement_trans: List[dict], books_trans: List[dict]) -> List[Tuple[List, List, List]]:
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matches: List[Tuple[List, List, List]] = []
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for r1 in statement_trans:
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matches.append(([r1], [], ['no match']))
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for r2 in books_trans:
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matches.append(([], [r2], ['no match']))
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return matches
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def main(args):
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def main(args: argparse.Namespace) -> None:
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# TODO: Should put in a sanity check to make sure the statement you're feeding
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# in matches the account you've provided.
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# TODO: Can we open the files first, then pass the streams on to the rest of the program?
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if 'AMEX' in args.account:
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validate_csv = validate_amex_csv
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standardize_statement_record = standardize_amex_record
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else:
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validate_csv = validate_fr_csv
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standardize_statement_record = standardize_fr_record
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with open(args.csv_statement) as f:
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sample = f.read(200)
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validate_csv(sample, args.account)
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f.seek(0)
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statement_trans = read_transactions_from_csv(f, standardize_statement_record)
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begin_date = statement_trans[0]['date']
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@ -218,7 +218,7 @@ def test_payee_mismatch_ok_when_only_one_that_amount_and_date():
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statement = [S3]
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books = [B3_payee_mismatch_1]
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assert match_statement_and_books(statement, books) == (
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[([S3], [B3_payee_mismatch_1], ['payee mismatch', 'only one decent match'])],
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[([S3], [B3_payee_mismatch_1], ['payee mismatch'])],
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[],
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[],
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)
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@ -255,8 +255,8 @@ def test_payee_matches_when_first_word_matches():
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def test_metadata_for_match(monkeypatch):
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monkeypatch.setenv('CONSERVANCY_REPOSITORY', '.')
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assert metadata_for_match(([S1], [B1], []), 'statement.pdf', 'statement.csv') == [
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('2022/imports.beancount', 777, ' bank-statement: statement.pdf'),
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('2022/imports.beancount', 777, ' bank-statement-csv: statement.csv:222'),
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('2022/imports.beancount', 777, ' bank-statement: "statement.pdf"'),
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('2022/imports.beancount', 777, ' bank-statement-csv: "statement.csv:222"'),
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]
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def test_no_metadata_if_no_matches():
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