def __pd_balances_prep(self, company_id, account_ids, excl_contra=None, excl_interco=False, with_counterparties=None):
if api_func('gl', 'company', company_id)['cmpy_type'] == 'CON':
excl_interco = True
cache = accountifie.gl.cache.get_cache(company_id)
entries = cache.get_gl_entries(account_ids)
if entries is None or entries.empty:
return None
if excl_contra:
for contra in excl_contra:
entries = entries[entries.contra_accts.map(lambda x: contra not in x.split(','))]
if excl_interco:
entries = entries[~entries.apply(self.__inter_co, axis=1)]
if with_counterparties:
entries = entries[entries['counterparty'] in with_counterparties]
if entries.empty:
return None
clean_end = lambda row: row['date_end'] if row['date_end'] else row['date']
entries['date_end'] = entries.apply(clean_end, axis=1)
return entries
def transactions(self, company_id, account_ids, from_date, to_date,
chunk_frequency, with_counterparties, excl_interco,
excl_contra):
period_entries = []
from_date = dateutil.parser.parse(from_date).date() if isinstance(
from_date, basestring) else from_date
to_date = dateutil.parser.parse(to_date).date() if isinstance(
to_date, basestring) else to_date
periods = [{'from': from_date, 'to': to_date}]
if chunk_frequency == 'end-of-month':
periods = self.monthly_chunk_periods(from_date, to_date)
else:
raise Exception('Unknown chunk frequency: %s' % chunk_frequency)
cache = accountifie.gl.cache.get_cache(company_id)
clean_end = lambda row: row['date_end'] if row['date_end'] else row[
'date']
clean_id = lambda row: str(row['transaction_id'])
for period in periods:
start = period['from']
end = period['to']
entries = cache.get_gl_entries(account_ids,
from_date=start,
to_date=end)
if entries is not None and not entries.empty:
if with_counterparties:
entries = entries[entries['counterparty'] in
with_counterparties]
if excl_contra and not entries.empty:
for contra in excl_contra:
entries = entries[entries.contra_accts.map(
lambda x: contra not in x.split(','))]
if excl_interco and not entries.empty:
entries = entries[~entries.apply(self.__inter_co, axis=1)]
if entries.empty:
break
entries['transaction_id'] = entries.apply(clean_id, axis=1)
entries['date_end'] = entries.apply(clean_end, axis=1)
sub_entries = self.__depreciation_calcs(start, end, entries)
sub_entries.reset_index(drop=False, inplace=True)
sub_entries.rename(columns={'transaction_id': 'id'},
inplace=True)
period_entries.append(sub_entries)
if len(period_entries) == 0:
return None
else:
all_entries = pd.concat(period_entries, ignore_index=True)
return all_entries.filter(items=[
'date', 'id', 'comment', 'account_id', 'contra_accts',
'counterparty', 'amount'
]).to_dict()
def __pd_balances_prep(self,
company_id,
account_ids,
excl_contra=None,
excl_interco=False,
with_counterparties=None):
if api_func('gl', 'company', company_id)['cmpy_type'] == 'CON':
excl_interco = True
cache = accountifie.gl.cache.get_cache(company_id)
entries = cache.get_gl_entries(account_ids)
if entries is None or entries.empty:
return None
if excl_contra:
for contra in excl_contra:
entries = entries[entries.contra_accts.map(
lambda x: contra not in x.split(','))]
if excl_interco:
entries = entries[~entries.apply(self.__inter_co, axis=1)]
if with_counterparties:
entries = entries[entries['counterparty'] in with_counterparties]
if entries.empty:
return None
clean_end = lambda row: row['date_end'] if row['date_end'] else row[
'date']
entries['date_end'] = entries.apply(clean_end, axis=1)
return entries
def transactions(self, company_id, account_ids, from_date, to_date, chunk_frequency, with_counterparties, excl_interco, excl_contra):
period_entries = []
from_date = dateutil.parser.parse(from_date).date() if isinstance(from_date, basestring) else from_date
to_date = dateutil.parser.parse(to_date).date() if isinstance(to_date, basestring) else to_date
periods = [{'from': from_date, 'to': to_date}]
if chunk_frequency == 'end-of-month':
periods = self.monthly_chunk_periods(from_date, to_date)
else:
raise Exception('Unknown chunk frequency: %s' % chunk_frequency)
cache = accountifie.gl.cache.get_cache(company_id)
clean_end = lambda row: row['date_end'] if row['date_end'] else row['date']
clean_id = lambda row: str(row['transaction_id'])
for period in periods:
start = period['from']
end = period['to']
entries = cache.get_gl_entries(account_ids, from_date=start, to_date=end)
if entries is not None and not entries.empty:
if with_counterparties:
entries = entries[entries['counterparty'] in with_counterparties]
if excl_contra and not entries.empty:
for contra in excl_contra:
entries = entries[entries.contra_accts.map(lambda x: contra not in x.split(','))]
if excl_interco and not entries.empty:
entries = entries[~entries.apply(self.__inter_co, axis=1)]
if entries.empty:
break
entries['transaction_id'] = entries.apply(clean_id, axis=1)
entries['date_end'] = entries.apply(clean_end, axis=1)
sub_entries = self.__depreciation_calcs(start, end, entries)
sub_entries.reset_index(drop=False, inplace=True)
sub_entries.rename(columns={'transaction_id': 'id'}, inplace=True)
period_entries.append(sub_entries)
if len(period_entries) == 0:
return None
else:
all_entries = pd.concat(period_entries, ignore_index=True)
return all_entries.filter(items=['date', 'id', 'comment', 'account_id', 'contra_accts', 'counterparty', 'amount']).to_dict()