def calcs(self):
data = {}
idx = {}
self.link_map = lambda x: '/snapshot/glsnapshots/reconcile/%s/%s?date=%s' % (self.snapshot.id, x, self.snapshot.closing_date.isoformat())
snapshot_strategy = QueryManager(gl_strategy=self.qm_strategy) if self.qm_strategy else self.query_manager
snap_bals = snapshot_strategy.pd_acct_balances(self.company_id,{'snapshot': self.date}).fillna(DZERO)
curr_bals = self.query_manager.pd_acct_balances(self.company_id,{'current': self.date}).fillna(DZERO)
self.column_order = ['snapshot', 'current','diff']
bals = pd.concat([snap_bals, curr_bals], axis=1).fillna(0.0)
bals['diff'] = bals['current'] - bals['snapshot']
bals.loc['Total'] = bals.apply(sum, axis=0)
accts = api_func('gl', 'account')
acct_map = dict((a['id'], a['display_name']) for a in accts)
label_map = lambda x: x + ': ' + acct_map[x] if x in acct_map else x
bals['fmt_tag'] = 'item'
bals['label'] = bals.index.map(label_map)
bals['link'] = bals.index.map(self.link_map)
bals.loc['Total', 'fmt_tag'] = 'major_total'
table_data = bals.to_dict(orient='records')
for row in table_data:
if row['fmt_tag'] != 'header':
for col in self.column_order:
row[col] = {'text': row[col], 'link': row['link']}
return table_data
def run_primecache(*args, **kwargs):
year = datetime.datetime.now().year
dates_list = utils.end_of_months(year) + utils.end_of_months(year-1)
dates = dict((dt.isoformat(), dt) for dt in dates_list)
company_ids = [c['id'] for c in Company.objects.filter(cmpy_type='ALO').values('id')]
tags = [None, ['yearend']]
query_manager = QueryManager()
for cmpy in company_ids:
for tag in tags:
exclude_cps = [None]
exclude_cps.append([c for c in company_ids if c!=cmpy])
for exclude_cp in exclude_cps:
logger.info('priming balance cache with %s, %s, %s' %(str(cmpy), str(tag), str(exclude_cp)))
throw_away = query_manager.pd_acct_balances(cmpy, dates, excl_contra=exclude_cp, excl_tags=tag)
def download_tranlines(request):
company_ID = utils.get_company(request)
trans = QueryManager().tranlines(company_ID)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="tranlines.csv"'
writer = csv.writer(response)
header_row = ['TranLine ID', 'Transaction ID', 'Amount', 'Account Name', 'Counterparty Name', 'Counterparty ID']
writer.writerow(header_row)
for ex in trans:
writer.writerow(ex)
return response
def expense_trends(request):
# dynamically generate the dates
today = datetime.datetime.now().date()
last_year = datetime.date(today.year-1, today.month, today.day)
all_dts = ['%sM%02d' % (x[0], x[1]) for x in list(monthrange(last_year,today))]
cols = dict(zip(all_dts, all_dts))
raw_data = QueryManager().path_drilldown('SAV', cols, 'equity.retearnings.opexp', excl_contra=['4150'])
# pull out top 5
total_exp = raw_data.sum(axis=1)
total_exp.sort()
tbl_data = raw_data.loc[total_exp.index[:5]]
tbl_data.loc['rest'] = raw_data.loc[total_exp.index[5:]].sum(axis=0)
tbl_data.index = tbl_data.index.map(display_name)
data = {}
data['chart_data'] = {}
data['chart_data']['dates'] = list(tbl_data.columns)
data['chart_data']['values'] = dict((pth, [-int(tbl_data.loc[pth, x]) for x in data['chart_data']['dates']]) for pth in tbl_data.index)
return HttpResponse(json.dumps(data), content_type='application/json')