It turns out that bank statement "description" fields are

    difficult to fuzzy match on because they're long and

    noisey. Truncating them (see standardize_XXX_record fns) and

    removing the common junk helps significantly.

    """

    for r in JUNK_WORDS_RES:

        payee = r.sub('', payee)

    payee = ZERO_RE.sub('', payee)

    payee = payee.replace(' - ', ' ')

    payee = re.sub(r'\.0\.\d+', ' ', payee)

    payee = payee.replace('.0', ' ')

    payee = payee.replace('/', ' ')

    payee = re.sub(re.escape('.com'), ' ', payee, flags=re.IGNORECASE)

    payee = re.sub(re.escape('.net'), ' ', payee, flags=re.IGNORECASE)

    payee = payee.replace('*', ' ')

    payee = ' '.join([i for i in payee.split(' ') if len(i) > 2])

    payee = payee.replace('-', ' ')

    payee = remove_duplicate_words(payee)

    payee.strip()

    return payee

def parse_amount(amount: str) -> decimal.Decimal:

    """Parse amounts and handle comma separators as seen in some FR statements."""

    return decimal.Decimal(amount.replace('$', '').replace(',', ''))

def validate_amex_csv(sample: str) -> None:

    required_cols = {'Date', 'Amount', 'Description', 'Card Member'}

    reader = csv.DictReader(io.StringIO(sample))

    if reader.fieldnames and not required_cols.issubset(reader.fieldnames):

        sys.exit(f"This AMEX CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}. Please use an unmodified statement direct from the institution.")

def standardize_amex_record(row: Dict, line: int) -> Dict:

    """Turn an AMEX CSV row into a standard dict format representing a transaction."""

    # NOTE: Statement doesn't seem to give us a running balance or a final total.

    return {

        'date': datetime.datetime.strptime(row['Date'], '%m/%d/%Y').date(),

        'amount': -1 * parse_amount(row['Amount']),

        # Descriptions have too much noise, so taking just the start

        # significantly assists the fuzzy matching.

        'payee': remove_payee_junk(row['Description'] or '')[:20],

        'check_id': '',

        'line': line,

    }

def read_amex_csv(f: TextIO) -> list:

    reader = csv.DictReader(f)

    # The reader.line_num is the source line number, not the spreadsheet row

    # number due to multi-line records.

    return sort_records([standardize_amex_record(row, i) for i, row in enumerate(reader, 2)])

def validate_fr_csv(sample: str) -> None:

    # No column headers in FR statements

    reader = csv.reader(io.StringIO(sample))

    next(reader)  # First row is previous statement ending balance

    row = next(reader)

    date = None

    try:

        date = datetime.datetime.strptime(row[1], '%m/%d/%Y')

    except ValueError:

        pass

    amount_found = '$' in row[4] and '$' in row[5]

    if len(row) != 6 or not date or not amount_found:

        sys.exit("This First Republic CSV doesn't seem to have the 6 columns we're expecting, including a date in column 2 and an amount in columns 5 and 6. Please use an unmodified statement direct from the institution.")

def standardize_fr_record(line, row):

    record = {

        'date': datetime.datetime.strptime(row[1], '%m/%d/%Y').date(),

        'amount': parse_amount(row[4]),

        'payee': remove_payee_junk(row[3] or '')[:20],

        'check_id': row[2].replace('CHECK  ', '') if 'CHECK  ' in row[2] else '',

        'line': line,

    }

    return record

def read_fr_csv(f: TextIO) -> list:

    reader = csv.reader(f)

    # The reader.line_num is the source line number, not the spreadsheet row

    # number due to multi-line records.

    return sort_records(

        standardize_fr_record(i, row) for i, row in enumerate(reader, 1)

        if len(row) == 6 and row[2] not in {'LAST STATEMENT', 'THIS STATEMENT'}

    )

def standardize_beancount_record(row) -> Dict:  # type: ignore[no-untyped-def]

    """Turn a Beancount query result row into a standard dict representing a transaction."""

    return {

        'date': row.date,

        'amount': row.number_cost_position,

        'payee': remove_payee_junk(f'{row.payee or ""} {row.entity or ""} {row.narration or ""}'),

        'check_id': str(row.check_id or ''),

        'filename': row.filename,

        'line': row.line,

        'bank_statement': row.bank_statement,

    }

def format_record(record: dict) -> str:

    """Generate output lines for a standard 1:1 match."""

    if record['payee'] and record['check_id']:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} {record['payee'][:25]} #{record['check_id']}".ljust(59)

    elif record['payee']:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} {record['payee'][:35]}".ljust(59)

    else:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} #{record['check_id']}".ljust(59)

    return output

def format_multirecord(r1s: List[dict], r2s: List[dict], note: str) -> List[list]:

    """Generates output lines for one statement:multiple books transaction match."""

    assert len(r1s) == 1

    assert len(r2s) > 1

    match_output = []

    match_output.append([r1s[0]['date'], f'{format_record(r1s[0])}  →  {format_record(r2s[0])}  ✓ Matched{note}'])

    for r2 in r2s[1:]:

        match_output.append([r1s[0]['date'], f'{r1s[0]["date"].isoformat()}:             ↳                                    →  {format_record(r2)}  ✓ Matched{note}'])

    return match_output

def sort_records(records: List) -> List:

    return sorted(records, key=lambda x: (x['date'], x['amount']))

def first_word_exact_match(a: str, b: str) -> float:

    """Score a payee match based first word.

    We get a whole lot of good matches this way. Helps in the

    situation where the first word or two of a transaction description

    is useful and the rest is garbage.

    """

    if len(a) == 0 or len(b) == 0:

        return 0.0

    first_a = a.split()[0].strip()

    first_b = b.split()[0].strip()

    if first_a.casefold() == first_b.casefold():

        return min(1.0, 0.2 * len(first_a))

    else:

        return 0.0

def payee_match(a: str, b: str) -> float:

    """Score a match between two payees."""

    fuzzy_match = float(fuzz.token_set_ratio(a, b) / 100.00)

    first_word_match = first_word_exact_match(a, b)

    return max(fuzzy_match, first_word_match)

def records_match(r1: Dict, r2: Dict) -> Tuple[float, List[str]]:

    """Do these records represent the same transaction?"""

    date_score = date_proximity(r1['date'], r2['date'])

    if r1['date'] == r2['date']:

        date_message = ''

    elif date_score > 0.0:

        diff = abs((r1['date'] - r2['date']).days)

        date_message = f'+/- {diff} days'

    else:

        date_message = 'date mismatch'

    if r1['amount'] == r2['amount']:

        amount_score, amount_message = 2.0, ''

    else:

        amount_score, amount_message = 0.0, 'amount mismatch'

    # We never consider payee if there's a check_id in the books.

    check_message = ''

    payee_message = ''

    # Sometimes we get unrelated numbers in the statement column with check-ids,

    # so we can't match based on the existence of a statement check-id.

    if r2['check_id']:

        payee_score = 0.0

        if r1['check_id'] and r2['check_id'] and r1['check_id'] == r2['check_id']:

            check_score = 1.0

        else:

            check_message = 'check-id mismatch'

            check_score = 0.0

    else:

        check_score = 0.0

        payee_score = payee_match(r1['payee'], r2['payee'])

        if payee_score > FULL_MATCH_THRESHOLD:

            payee_message = ''

        elif payee_score > PARTIAL_MATCH_THRESHOLD:

            payee_message = 'partial payee match'

        else:

            payee_message = 'payee mismatch'

    overall_score = (date_score + amount_score + check_score + payee_score) / 4

    overall_message = [m for m in [date_message, amount_message, check_message, payee_message] if m]

    return overall_score, overall_message

def match_statement_and_books(statement_trans: List[Dict], books_trans: List[Dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:

    """Match transactions between the statement and books.

    If matched, the books transaction is marked off so that it can

    only be matched once. Some transactions will be matched, some will

    be on the statement but not the books and some on the books but

    not the statement.

    Passes through any unmatched transactions.

    Currently we use the same matching logic for all types of

    statements. It's conceivable that you could have special cases to

    accurately match some types of statements, but that would be more

    work to maintain and test.

    """

    matches = []

    remaining_books_trans = []

    remaining_statement_trans = []

    for r1 in statement_trans:

        best_match_score = 0.0

        best_match_index = None

        best_match_note = []

        matches_found = 0

        for i, r2 in enumerate(books_trans):

            score, note = records_match(r1, r2)

            if score >= 0.5 and score >= best_match_score:

                matches_found += 1

                best_match_score = score

                best_match_index = i

                best_match_note = note

        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:

            matches.append(([r1], [books_trans[best_match_index]], best_match_note))

            # Don't try to make a second match against this books entry.

            if best_match_index is not None:

                del books_trans[best_match_index]

        else:

            remaining_statement_trans.append(r1)

    for r2 in books_trans:

        remaining_books_trans.append(r2)

    return matches, remaining_statement_trans, remaining_books_trans

def subset_match(statement_trans: List[dict], books_trans: List[dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:

    """Match single statement transactions with multiple books transactions.

    Works similarly to match_statement_and_books in that it returns a

    list of matches and lists of remaining statement and books

    transactions.

    """

    matches = []

    remaining_books_trans = []

    remaining_statement_trans = []

            if show_reconciled_matches or not all(x['bank_statement'] for x in r2s):

                if len(r2s) == 1:

                    entry = [r1s[0]['date'], f'{format_record(r1s[0])}  →  {format_record(r2s[0])}  ✓ Matched{note}']

                    if 'payee mismatch' in note:

                        entry[1] = Fore.YELLOW + Style.BRIGHT + entry[1] + Style.RESET_ALL

                    match_output.append(entry)

                else:

                    match_output.extend(format_multirecord(r1s, r2s, note))

        elif r1s:

            match_output.append([r1s[0]['date'], Fore.RED + Style.BRIGHT + f'{format_record(r1s[0])}  →  {" ":^59}  ✗ NOT IN BOOKS ({os.path.basename(csv_statement)}:{r1s[0]["line"]})' + Style.RESET_ALL])

        else:

            match_output.append([r2s[0]['date'], Fore.RED + Style.BRIGHT + f'{" ":^59}  →  {format_record(r2s[0])}  ✗ NOT ON STATEMENT ({os.path.basename(r2s[0]["filename"])}:{r2s[0]["line"]})' + Style.RESET_ALL])

    return match_output

def date_proximity(d1: datetime.date, d2: datetime.date) -> float:

    """Scores two days based on how close they are together."""

    ZERO_CUTOFF = 60  # Score will be zero for this many days apart.

    diff = abs(int((d1 - d2).days))

    if diff >= ZERO_CUTOFF:

        return 0.0

    else:

        return 1.0 - (diff / ZERO_CUTOFF)

def metadata_for_match(match: Tuple[List, List, List], statement_filename: str, csv_filename: str) -> List[Tuple[str, int, str]]:

    """Returns the bank-statement metadata that should be applied for a match."""

    # TODO: Our data structure would allow multiple statement entries

    # for a match, but would this ever make sense? Probably not.

    statement_filename = get_repo_relative_path(statement_filename)

    csv_filename = get_repo_relative_path(csv_filename)

    metadata = []

    statement_entries, books_entries, _ = match

    for books_entry in books_entries:

        for statement_entry in statement_entries:

            if not books_entry['bank_statement']:

                metadata.append((books_entry['filename'], books_entry['line'], f'    bank-statement: "{statement_filename}"'))

                metadata.append((books_entry['filename'], books_entry['line'], f'    bank-statement-csv: "{csv_filename}:{statement_entry["line"]}"'))

    return metadata

def write_metadata_to_books(metadata_to_apply: List[Tuple[str, int, str]]) -> None:

    """Insert reconciliation metadata in the books files.

    Takes a list of edits to make as tuples of form (filename, lineno, metadata):

    [

        ('2021/main.beancount', 4245, '    bank-statement: statement.pdf'),

        ('2021/main.beancount', 1057, '    bank-statement: statement.pdf'),

        ('2021/payroll.beancount', 257, '    bank-statement: statement.pdf'),

        ...,

    ]

    Beancount doesn't provide any infrastructure for programmatically

    updating the books, only appending in the case of importers. So

    we're on our own here.

    """

    file_contents: dict[str, list] = {}

    file_offsets: dict[str, int] = collections.defaultdict(int)

    # Load each books file into memory and insert the relevant metadata lines.

    # Line numbers change as we do this, so we keep track of the offset for each

    # file. Changes must be sorted by line number first or else the offsets will

    # break because we're jumping around making edits.

    for filename, line, metadata in sorted(metadata_to_apply):

        if filename not in file_contents:

            with open(filename, 'r') as f:

                file_contents[filename] = f.readlines()

        # Insert is inefficient, but fast enough for now in practise.

        file_contents[filename].insert(line + file_offsets[filename], metadata.rstrip() + '\n')

        file_offsets[filename] += 1

    # Writes each updated file back to disk.

    for filename, contents in file_contents.items():

        with open(filename, 'w') as f:

            f.writelines(contents)

            print(f'Wrote {filename}.')

def get_repo_relative_path(path: str) -> str:

    """Chop off the unique per-person CONSERVANCY_REPOSITORY.

    CSV and PDF statement metadata should be relative to

    CONSERVANCY_REPOSITORY ie. without regards to exactly where on

    your computer all the files live.

    """

    return os.path.relpath(path, start=os.getenv('CONSERVANCY_REPOSITORY'))

def parse_path(path: str) -> str:

    """Validate that a file exists for use in argparse."""

    if not os.path.exists(path):

        raise argparse.ArgumentTypeError(f'File {path} does not exist.')

    return path

def parse_repo_relative_path(path: str) -> str:

    """Validate that a file exists and is within $CONSERVANCY_REPOSITORY.

    For use with argparse.

    """

    if not os.path.exists(path):

        raise argparse.ArgumentTypeError(f'File {path} does not exist.')

    repo = os.getenv('CONSERVANCY_REPOSITORY')

    if not repo:

        raise argparse.ArgumentTypeError('$CONSERVANCY_REPOSITORY is not set.')

    if not path.startswith(repo):

        raise argparse.ArgumentTypeError(f'File {path} does not share a common prefix with $CONSERVANCY_REPOSITORY {repo}.')

    return path

def parse_decimal_with_separator(number_text: str) -> decimal.Decimal:

    """decimal.Decimal can't parse numbers with thousands separator."""

    number_text = number_text.replace(',', '')

    return decimal.Decimal(number_text)

def parse_arguments(argv: List[str]) -> argparse.Namespace:

    parser = argparse.ArgumentParser(prog=PROGNAME, description='Reconciliation helper')

    cliutil.add_version_argument(parser)

    cliutil.add_loglevel_argument(parser)

    parser.add_argument('--beancount-file', required=True, type=parse_path)

    parser.add_argument('--csv-statement', required=True, type=parse_repo_relative_path)

    parser.add_argument('--bank-statement', required=True, type=parse_repo_relative_path)

    parser.add_argument('--account', required=True, help='eg. Liabilities:CreditCard:AMEX')

    # parser.add_argument('--report-group-regex')

    parser.add_argument('--show-reconciled-matches', action='store_true')

    parser.add_argument('--non-interactive', action='store_true', help="Don't prompt to write to the books")    # parser.add_argument('--statement-balance', type=parse_decimal_with_separator, required=True, help="A.K.A \"cleared balance\" taken from the end of the period on the PDF statement. Required because CSV statements don't include final or running totals")

    args = parser.parse_args(args=argv)

    return args

def totals(matches: List[Tuple[List, List, List]]) -> Tuple[decimal.Decimal, decimal.Decimal, decimal.Decimal]:

    """Calculate the totals of transactions matched/not-matched."""

    total_matched = decimal.Decimal(0)

    total_missing_from_books = decimal.Decimal(0)

    total_missing_from_statement = decimal.Decimal(0)

    for statement_entries, books_entries, _ in matches:

        if statement_entries and books_entries:

            total_matched += sum(c['amount'] for c in statement_entries)

        elif statement_entries:

            total_missing_from_books += sum(c['amount'] for c in statement_entries)

        else:

            total_missing_from_statement += sum(c['amount'] for c in books_entries)

    return total_matched, total_missing_from_books, total_missing_from_statement

def process_unmatched(statement_trans: List[dict], books_trans: List[dict]) -> List[Tuple[List, List, List]]:

    """Format the remaining unmatched transactions to be added to one single list of matches."""

    matches: List[Tuple[List, List, List]] = []

    for r1 in statement_trans:

        matches.append(([r1], [], ['no match']))

    for r2 in books_trans:

        matches.append(([], [r2], ['no match']))

    return matches

def format_output(matches, begin_date, end_date, csv_statement, show_reconciled_matches) -> str:

    with io.StringIO() as out:

        match_output = format_matches(matches, csv_statement, show_reconciled_matches)

        _, total_missing_from_books, total_missing_from_statement = totals(matches)

        print('-' * 155, file=out)

        statement_heading = f'Statement transactions {begin_date} to {end_date}'

        print(f'{statement_heading:<52}            {"Books transactions":<58}   Notes', file=out)

        print('-' * 155, file=out)

        for _, output in sorted(match_output, key=lambda x: x[0]):

            print(output, file=out)

        print('-' * 155, file=out)

        print(f'Sub-total not on statement: {total_missing_from_statement:12,.2f}', file=out)

        print(f'Sub-total not in books:     {total_missing_from_books:12,.2f}', file=out)

        print(f'Total:                      {total_missing_from_statement + total_missing_from_books:12,.2f}', file=out)

        print('-' * 155, file=out)

        return out.getvalue()

def main(arglist: Optional[Sequence[str]] = None,

         stdout: TextIO = sys.stdout,

         stderr: TextIO = sys.stderr,

         config: Optional[configmod.Config] = None,

         ) -> int:

    args = parse_arguments(arglist)

    cliutil.set_loglevel(logger, args.loglevel)

    if config is None:

        config = configmod.Config()

        config.load_file()

    # Validate and normalise the statement into our standard

    # transaction data structure.

    if 'AMEX' in args.account:

        validate_csv = validate_amex_csv

        read_csv = read_amex_csv

    else:

        validate_csv = validate_fr_csv

        read_csv = read_fr_csv

    with open(args.csv_statement) as f:

        sample = f.read(200)

        # Validate should return true/false and a message.

        validate_csv(sample)

        f.seek(0)

        # TODO: Needs a custom read_transactions_from_csv for each of AMEX and

        # FR since AMEX has a header row and FR doesn't.

        statement_trans = read_csv(f)

    # Dates are taken from the beginning/end of the statement.

    begin_date = statement_trans[0]['date']

    end_date = statement_trans[-1]['date']

    # Query for the Beancount books data for this above period.

    #

    # There are pros and cons for using Beancount's in-memory entries

    # list directly and also for using Beancount Query Language (BQL)

    # to get a list of transactions? Using BQL because it's

    # convenient, but we don't have access to the full transaction

    # entry objects. Feels a bit strange that these approaches are so

    # disconnected.

    #

    # beancount.query.query_compile.compile() and

    # beancount.query.query_execute.filter_entries() look useful in this respect,

    # but I'm not clear on how to use compile(). An example would help.

    entries, _, options = loader.load_file(args.beancount_file)

    # String concatenation looks bad, but there's no SQL injection possible here

    # because BQL can't write back to the Beancount files. I hope!

    query = f"""

        SELECT filename,

        META("lineno") AS line,

        META("bank-statement") AS bank_statement,

        date,

        number(cost(position)),

        payee,

        ENTRY_META("entity") as entity,

        ANY_META("check-id") as check_id,

        narration

        WHERE account = "{args.account}"

            AND date >= {begin_date}

            AND date <= {end_date}"""

    _, result_rows = run_query(entries, options, query)

    books_trans = sort_records([standardize_beancount_record(row) for row in result_rows])

    # Apply two passes of matching, one for standard matches and one

    # for subset matches.

    matches, remaining_statement_trans, remaining_books_trans = match_statement_and_books(statement_trans, books_trans)

    subset_matches, remaining_statement_trans, remaining_books_trans = subset_match(

        remaining_statement_trans, remaining_books_trans)

    matches.extend(subset_matches)

    # Add the remaining unmatched to make one big list of matches, successful or not.

    unmatched = process_unmatched(remaining_statement_trans, remaining_books_trans)

    matches.extend(unmatched)

    # Print out results of our matching.

    print(format_output(matches, begin_date, end_date, args.csv_statement, args.show_reconciled_matches))

    # Write statement metadata back to the books.

    metadata_to_apply = []

    for match in matches:

        metadata_to_apply.extend(metadata_for_match(match, args.bank_statement, args.csv_statement))

    if metadata_to_apply and not args.non_interactive:

        print('Mark matched transactions as reconciled in the books? (y/N) ', end='')

        if input().lower() == 'y':

            write_metadata_to_books(metadata_to_apply)

entry_point = cliutil.make_entry_point(__name__, PROGNAME)

if __name__ == '__main__':

    exit(entry_point())

import datetime

import decimal

import io

import os

import tempfile

import textwrap

from conservancy_beancount.reconcile.statement_reconciler import (

    date_proximity,

    format_output,

    match_statement_and_books,

    metadata_for_match,

    payee_match,

    read_amex_csv,

    read_fr_csv,

    remove_duplicate_words,

    remove_payee_junk,

    subset_match,

    totals,

    write_metadata_to_books,

)

# These data structures represent individual transactions as taken from the

# statement ("S") or the books ("B").

# Statement transaction examples.

S1 = {

    'date': datetime.date(2022, 1, 1),

    'amount': decimal.Decimal('10.00'),

    'payee': 'Patreon         / Patreon   / 123456/ ST-A1B2C3D4G5H6       /',

    'check_id': '',

    'line': 222,

}

S2 = {

    'date': datetime.date(2022, 1, 2),

    'amount': decimal.Decimal('20.00'),

    'payee': 'BT*LINODE           PHILADELPHIA        P',

    'check_id': '',

    'line': 333,

}

S3 = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('30.00'),

    'payee': 'USPS PO 4067540039 0PORTLAND            OR',

    'check_id': '',

    'line': 444,

}

S4 = {

    'date': datetime.date(2022, 8, 11),

    'amount': decimal.Decimal('-2260.00'),

    'payee': 'Trust 0000000362 210',

    'check_id': '',

    'line': 555,

}

# Books transaction examples.

B1 = {

    'date': datetime.date(2022, 1, 1),

    'amount': decimal.Decimal('10.00'),

    'payee': 'Patreon',

    'check_id': '',

    'filename': '2022/imports.beancount',

    'line': 777,

    'bank_statement': '',

}

B2 = {

    'date': datetime.date(2022, 1, 2),

    'amount': decimal.Decimal('20.00'),

    'payee': 'Linode',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 888,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_next_day = {

    'date': datetime.date(2022, 1, 4),

    'amount': decimal.Decimal('30.00'),

    'payee': 'USPS',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_next_week = {

    'date': datetime.date(2022, 1, 10),

    'amount': decimal.Decimal('30.00'),

    'payee': 'USPS',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_mismatch_amount = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('31.00'),

    'payee': 'USPS',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_payee_mismatch_1 = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('30.00'),

    'payee': 'Credit X',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_payee_mismatch_2 = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('30.00'),

    'payee': 'Credit Y',

    'check_id': '',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B3_unmatched_check_id = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('30.00'),

    'payee': 'USPS',

    'check_id': '1234',

    'filename': '2022/main.beancount',

    'line': 999,

    'bank_statement': "Financial/Bank-Statements/AMEX/2022-01-12_AMEX_statement.pdf"

}

B4A = {

    'date': datetime.date(2022, 8, 11),

    'amount': decimal.Decimal('-250.00'),

    'payee': 'TRUST 0000000362 ACH Retirement Plan',

    'check_id': '',

    'line': 1000,

}

B4B = {

    'date': datetime.date(2022, 8, 11),

    'amount': decimal.Decimal('-250.00'),

    'payee': 'TRUST 0000000362 ACH Retirement Plan',

    'check_id': '',

    'line': 1000,

}

B4C = {

    'date': datetime.date(2022, 8, 11),

    'amount': decimal.Decimal('-1760.00'),

def test_payee_matches_when_first_word_matches():

    assert payee_match('Gandi San Francisco', 'Gandi example.com renewal 1234567') == 1.0

    assert payee_match('USPS 123456789 Portland', 'USPS John Brown') == 0.8

def test_metadata_for_match(monkeypatch):

    monkeypatch.setenv('CONSERVANCY_REPOSITORY', '.')

    assert metadata_for_match(([S1], [B1], []), 'statement.pdf', 'statement.csv') == [

        ('2022/imports.beancount', 777, '    bank-statement: "statement.pdf"'),

        ('2022/imports.beancount', 777, '    bank-statement-csv: "statement.csv:222"'),

    ]

def test_no_metadata_if_no_matches():

    assert metadata_for_match(([S1], [], ['no match']), 'statement.pdf', 'statement.csv') == []

    assert metadata_for_match(([], [B1], ['no match']), 'statement.pdf', 'statement.csv') == []

    assert metadata_for_match(([S1], [B2], ['no match']), 'statement.pdf', 'statement.csv') == []

def test_write_to_books():

    books = textwrap.dedent("""\

        2021-08-16 txn "Gandi" "transfer seleniumconf.us"

          Liabilities:CreditCard:AMEX            -15.50 USD

          Expenses:Hosting                        15.50 USD""")

    f = tempfile.NamedTemporaryFile('w', delete=False)

    f.write(books)

    f.close()

    metadata = [(f.name, 2, '    bank-statement: statement.pdf')]

    write_metadata_to_books(metadata)

    with open(f.name) as f:

        output = f.read()

    assert output == textwrap.dedent("""\

        2021-08-16 txn "Gandi" "transfer seleniumconf.us"

          Liabilities:CreditCard:AMEX            -15.50 USD

            bank-statement: statement.pdf

          Expenses:Hosting                        15.50 USD""")

    os.remove(f.name)

def test_totals():

    assert totals([

        ([S1], [B1], []),

        ([S2], [], []),

        ([], [B3_next_day], []),

    ]) == (decimal.Decimal('10'), decimal.Decimal('20'), decimal.Decimal('30'))

def test_payee_not_considered_if_check_id_present():

    # These records match aside from check-id.

    statement = [S3]

    books = [B3_unmatched_check_id]

    assert match_statement_and_books(statement, books) == (

        [],

        [S3],

        [B3_unmatched_check_id],

    )

def test_subset_sum_match():

    statement = [S4]

    books = [B4A, B4B, B4C]

    assert subset_match(statement, books) == (

        [([S4], [B4A, B4B, B4C], [])],

        [],  # No remaining statement trans.

        [],  # No remaining books trans.

    )

def test_subset_passes_through_all_non_matches():

    """This was used to locate a bug where some of the non-matches had

    gone missing due to mutation of books_trans."""

    statement_trans = [

        S1,  # No match

        S4,  # Match

    ]

    books_trans = [

        B2,  # No match

        B4A, B4B, B4C,  # Match

        B3_next_day, B3_next_week,  # No match

    ]

    assert subset_match(statement_trans, books_trans) == (

        [([S4], [B4A, B4B, B4C], [])],  # Matched

        [S1],  # No match: preserved intact

        [B2, B3_next_day, B3_next_week]  # No match: preserved intact

    )

def test_handles_amex_csv():

    CSV = """Date,Receipt,Description,Card Member,Account #,Amount,Extended Details,Appears On Your Statement As,Address,City/State,Zip Code,Country,Reference,Category\n08/19/2021,,Gandi.net           San Francisco,RODNEY R BROWN,-99999,28.15,"00000009999 00000009999999999999\nGandi.net\nSan Francisco\n00000009999999999999",Gandi.net           San Francisco,"NEPTUNUSSTRAAT 41-63\nHOOFDDORP",,2132 JA,NETHERLANDS (THE),'999999999999999999',Merchandise & Supplies-Internet Purchase\n"""

    expected = [

        {

            'date': datetime.date(2021, 8, 19),

            'amount': decimal.Decimal('-28.15'),

            'payee': 'Gandi San Francisco',

            'check_id': '',

            'line': 2,

        },

    ]

    assert read_amex_csv(io.StringIO(CSV)) == expected

def test_handles_fr_csv():

    CSV = """"DD99999999999","03/31/2022","LAST STATEMENT","","","$1,000.00"\n"9999999999999","04/01/2022","INCOMING WIRE","GONDOR S.S. A111111111BCDE0F","$6.50","$1,006.50"\n"DD99999999999","04/18/2022","CHECK  3741","","$-4.50","$1,002.00"\n"DD99999999999","04/30/2022","THIS STATEMENT","","","$102.00"\n"""

    expected = [

        {

            'date': datetime.date(2022, 4, 1),

            'amount': decimal.Decimal('6.50'),

            'payee': 'GONDOR S.S. A1111111',

            'check_id': '',

            'line': 2,

        },

        {

            'date': datetime.date(2022, 4, 18),

            'amount': decimal.Decimal('-4.50'),

            'payee': '',

            'check_id': '3741',

            'line': 3,

        },

    ]

    assert read_fr_csv(io.StringIO(CSV)) == expected

def test_format_output():

    statement = [S1]

    books = [B1]

    matches, _, _ = match_statement_and_books(statement, books)

    output = format_output(matches, datetime.date(2022, 1, 1), datetime.date(2022, 2, 1), 'test.csv', True)

    assert '2022-01-01:       10.00 Patreon         / Patreon   / 12345  →  2022-01-01:       10.00 Patreon                              ✓ Matched' in output