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_week_prod_totals(daily, sourcetype):
if daily:
end_date = daily.production_date
start_date = end_date - timedelta(days=WEEKDAYS - 1)
sp_query = SourceProduction.objects.filter(
Q(daily__production_date__gte=start_date),
Q(daily__production_date__lte=end_date),
sourcetype=sourcetype,
)
else:
sp_query = None
if not sp_query:
wp = {f'week_{key[:5]}': np.nan for key in source_prod_schema[0:6]}
wp[f'week_{source_prod_schema[6]}'] = np.nan
wp['week_total'] = np.nan
return wp
# only include sources and skips
wp = {
f'week_{key[:5]}':
np.nansum([val[key] for val in sp_query.values()])
for key in source_prod_schema[0:6]
}
# get average of number of sources
wp[f'week_{source_prod_schema[6]}'] = nan_avg_array(
[val[source_prod_schema[6]] for val in sp_query.values()])
# only include sources from terrain types
wp['week_total'] = np.sum(wp[f'week_{key[:5]}']
for key in source_prod_schema[0:5])
return wp
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
def calc_proj_prod_totals(self, daily, sourcetype):
# filter for all days in the project up to and including the production date
if daily:
sp_query = SourceProduction.objects.filter(
daily__production_date__lte=daily.production_date,
sourcetype=sourcetype,
).order_by('daily__production_date')
else:
sp_query = None
if not sp_query:
pp = {f'proj_{key[:5]}': np.nan for key in source_prod_schema[0:6]}
pp[f'proj_{source_prod_schema[6]}'] = np.nan
pp['proj_total'] = np.nan
return pp, {}
p_series = {
f'{key[:5]}_series':
nan_array([val[key] for val in sp_query.values()])
for key in source_prod_schema[0:6]
}
p_series = self.calc_ctm_series(p_series, sourcetype)
p_series[f'{source_prod_schema[6]}_series'] = np.array(
[val.avg_sources for val in sp_query])
p_series['date_series'] = np.array(
[val.daily.production_date for val in sp_query])
# only include sources and skips
pp = {
f'proj_{key[:5]}': np.nansum(p_series[f'{key[:5]}_series'])
for key in source_prod_schema[0:6]
}
# get average of number of sources
pp[f'proj_{source_prod_schema[6]}'] = nan_avg_array(
p_series[f'{source_prod_schema[6]}_series'])
# only include sources from terrain types
pp['proj_total'] = np.sum(pp[f'proj_{key[:5]}']
for key in source_prod_schema[0:5])
return pp, p_series
def calc_month_prod_totals(daily, sourcetype):
# filter for days in the month up to and including the production date
if daily:
sp_query = SourceProduction.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),
sourcetype=sourcetype,
)
else:
sp_query = None
if not sp_query:
mp = {
f'month_{key[:5]}': np.nan
for key in source_prod_schema[0:6]
}
mp[f'month_{source_prod_schema[6]}'] = np.nan
mp['month_total'] = np.nan
return mp
# only include sources and skips
mp = {
f'month_{key[:5]}':
np.nansum([val[key] for val in sp_query.values()])
for key in source_prod_schema[0:6]
}
# get average of number of sources
mp[f'month_{source_prod_schema[6]}'] = nan_avg_array(
[val[source_prod_schema[6]] for val in sp_query.values()])
# for total sp only include sources from terrain type
mp['month_total'] = np.sum(mp[f'month_{key[:5]}']
for key in source_prod_schema[0:5])
return mp