def calc_combined_production(self, day, times_total, prod_total_by_type):
days = get_period_days(day)
prod_total = {}
for ptotal in prod_total_by_type.values():
prod_total = sum_keys(prod_total, ptotal)
for period in ['day', 'week', 'month', 'proj']:
# calculate weighted sum of tcf and ctm
tcf = 0
ctm = 0
for ptotal in prod_total_by_type.values():
tcf += ptotal[f'{period}_tcf'] * ptotal[f'{period}_total']
ctm += ptotal[f'{period}_ctm'] * ptotal[f'{period}_total']
prod_total[f'{period}_tcf'] = (tcf / prod_total[f'{period}_total']
if prod_total[f'{period}_total'] > 0
else np.nan)
prod_total[f'{period}_ctm'] = (round(ctm /
prod_total[f'{period}_total'])
if prod_total[f'{period}_total'] > 0
else np.nan)
prod_total[f'{period}_appctm'] = calc_ratio(
prod_total[f'{period}_total'], prod_total[f'{period}_ctm'])
prod_total[f'{period}_avg'] = int(prod_total[f'{period}_total'] /
days[period])
prod_total[f'{period}_rate'] = self.calc_rate(
day, CTM_METHOD, prod_total[f'{period}_appctm'],
times_total[f'{period}_total_time'],
times_total[f'{period}_standby'])
prod_total[f'{period}_perc_skips'] = calc_ratio(
prod_total[f'{period}_skips'], prod_total[f'{period}_total'])
return prod_total
def month_receiver_total(daily, receivertype):
if daily:
rcvr_query = ReceiverProduction.objects.filter(
Q(daily__production_date__year=daily.production_date.year)
& Q(daily__production_date__month=daily.production_date.month)
&
Q(daily__production_date__day__lte=daily.production_date.day),
receivertype=receivertype,
)
else:
rcvr_query = None
if not rcvr_query:
m_rcvr = {f'month_{key}': 0 for key in receiver_prod_schema}
return m_rcvr
m_rcvr = {
f'month_{key}':
sum(nan_array([val[key] for val in rcvr_query.values()]))
for key in receiver_prod_schema[0:7]
}
m_rcvr['month_qc_field'] = nan_avg_array(
[val['qc_field'] for val in rcvr_query.values()])
m_rcvr['month_qc_teams'] = nan_avg_array(
[val['qc_teams'] for val in rcvr_query.values()])
m_rcvr['month_fc_teams'] = nan_avg_array(
[val['fc_teams'] for val in rcvr_query.values()])
m_rcvr['month_bc_teams'] = nan_avg_array(
[val['bc_teams'] for val in rcvr_query.values()])
m_rcvr['month_perc_node_skips'] = calc_ratio(
m_rcvr['month_node_skips'], daily.project.planned_receivers)
return m_rcvr
def week_receiver_total(daily, receivertype):
if daily:
end_date = daily.production_date
start_date = end_date - timedelta(days=WEEKDAYS - 1)
rcvr_query = ReceiverProduction.objects.filter(
Q(daily__production_date__gte=start_date),
Q(daily__production_date__lte=end_date),
receivertype=receivertype,
)
else:
rcvr_query = None
if not rcvr_query:
w_rcvr = {f'week_{key}': 0 for key in receiver_prod_schema}
return w_rcvr
w_rcvr = {
f'week_{key}':
sum(nan_array([val[key] for val in rcvr_query.values()]))
for key in receiver_prod_schema[0:7]
}
w_rcvr['week_qc_field'] = nan_avg_array(
[val['qc_field'] for val in rcvr_query.values()])
w_rcvr['week_qc_teams'] = nan_avg_array(
[val['qc_teams'] for val in rcvr_query.values()])
w_rcvr['week_fc_teams'] = nan_avg_array(
[val['fc_teams'] for val in rcvr_query.values()])
w_rcvr['week_bc_teams'] = nan_avg_array(
[val['bc_teams'] for val in rcvr_query.values()])
w_rcvr['week_perc_node_skips'] = calc_ratio(
w_rcvr['week_node_skips'], daily.project.planned_receivers)
return w_rcvr
def calc_ctm_series(self, p_series, sourcetype):
terrain_series = list(
zip(*[
val for key, val in p_series.items() if key != 'skips_series'
]))
p_series['tcf_series'] = []
p_series['total_sp_series'] = []
p_series['ctm_series'] = []
p_series['appctm_series'] = []
p_series['rate_series'] = []
for terrain_sp in terrain_series:
sp_total = np.nansum(terrain_sp)
p_series['total_sp_series'].append(sp_total)
if sp_total > 0:
p_series['tcf_series'].append(
terrain_sp[0] / sp_total * TCF_table['sp_t1'] +
terrain_sp[1] / sp_total * TCF_table['sp_t2'] +
terrain_sp[2] / sp_total * TCF_table['sp_t3'] +
terrain_sp[3] / sp_total * TCF_table['sp_t4'] +
terrain_sp[4] / sp_total * TCF_table['sp_t5'])
else:
p_series['tcf_series'].append(np.nan)
for tcf, total in zip(p_series['tcf_series'],
p_series['total_sp_series']):
ctm = self.calc_ctm(sourcetype, tcf)
appctm = calc_ratio(total, ctm)
p_series['ctm_series'].append(ctm)
p_series['appctm_series'].append(appctm)
p_series['rate_series'].append(np.nan)
return p_series
def calc_period_totals(self, day, stype, times_total, prod_total):
days = get_period_days(day)
for period in ['day', 'week', 'month', 'proj']:
prod_total[f'{period}_tcf'] = self.calc_tcf(period, prod_total)
prod_total[f'{period}_ctm'] = self.calc_ctm(
stype, prod_total[f'{period}_tcf']) * days[period]
prod_total[f'{period}_appctm'] = calc_ratio(
prod_total[f'{period}_total'], prod_total[f'{period}_ctm'])
prod_total[f'{period}_avg'] = int(prod_total[f'{period}_total'] /
days[period])
prod_total[f'{period}_rate'] = self.calc_rate(
day, CTM_METHOD, prod_total[f'{period}_appctm'],
times_total[f'{period}_total_time'],
times_total[f'{period}_standby'])
prod_total[f'{period}_perc_skips'] = calc_ratio(
prod_total[f'{period}_skips'], prod_total[f'{period}_total'])
return prod_total
def calc_combined_series(self, day, times_total, prod_series_by_type):
prod_series = {}
for pseries in prod_series_by_type.values():
prod_series = sum_keys(prod_series, pseries)
ps_length = len(prod_series['total_sp_series'])
ctm_series = np.zeros(ps_length)
tcf_series = np.zeros(ps_length)
totals = np.array(prod_series['total_sp_series'])
for pseries in prod_series_by_type.values():
ctm = np.array(pseries['ctm_series'])
app = np.array(pseries['total_sp_series'])
tcf = np.array(pseries['tcf_series'])
weights = np.array(
[v1 / v2 if v2 > 0 else 0 for v1, v2 in zip(app, totals)])
ctm_series = ctm_series + ctm * weights
tcf_series = tcf_series + tcf * weights
prod_series['ctm_series'] = ctm_series
prod_series['tcf_series'] = tcf_series
prod_series['appctm_series'] = np.array([
calc_ratio(v1, v2) if v2 > 0 else np.nan
for v1, v2 in zip(totals, ctm_series)
])
rate_series = []
for appctm, total_time, standby in zip(
prod_series['appctm_series'],
self.time_series['total_time_series'],
self.time_series['standby_series']):
rate_series.append(
self.calc_rate(day, CTM_METHOD, appctm, total_time, standby))
prod_series['rate_series'] = np.array(rate_series)
return prod_series
def day_receiver_total(daily, receivertype):
if daily:
try:
rcvr = ReceiverProduction.objects.get(
daily=daily,
receivertype=receivertype,
)
except ReceiverProduction.DoesNotExist:
d_rcvr = {f'day_{key}': 0 for key in receiver_prod_schema}
return d_rcvr
else:
d_rcvr = {f'day_{key}': 0 for key in receiver_prod_schema}
return d_rcvr
d_rcvr = {
f'day_{key}': np.nan_to_num(getattr(rcvr, key))
for key in receiver_prod_schema
}
d_rcvr['day_perc_node_skips'] = calc_ratio(
d_rcvr['day_node_skips'], daily.project.planned_receivers)
return d_rcvr
def project_receiver_total(daily, receivertype):
if daily:
rcvr_query = ReceiverProduction.objects.filter(
daily__production_date__lte=daily.production_date,
receivertype=receivertype,
).order_by('daily__production_date')
else:
rcvr_query = None
if not rcvr_query:
p_rcvr = {f'proj_{key}': 0 for key in receiver_prod_schema}
return p_rcvr, {}
rcvr_series = {
f'{key}_series': [val[key] for val in rcvr_query.values()]
for key in receiver_prod_schema
}
p_rcvr = {
f'proj_{key}':
sum(nan_array([val[key] for val in rcvr_query.values()]))
for key in receiver_prod_schema[0:7]
}
p_rcvr['proj_qc_field'] = nan_avg_array(
[val['qc_field'] for val in rcvr_query.values()])
p_rcvr['proj_qc_teams'] = nan_avg_array(
[val['qc_teams'] for val in rcvr_query.values()])
p_rcvr['proj_fc_teams'] = nan_avg_array(
[val['fc_teams'] for val in rcvr_query.values()])
p_rcvr['proj_bc_teams'] = nan_avg_array(
[val['bc_teams'] for val in rcvr_query.values()])
p_rcvr['proj_perc_node_skips'] = calc_ratio(
p_rcvr['proj_node_skips'], daily.project.planned_receivers)
return p_rcvr, rcvr_series