def handle(self, *args, **options):
for fd in FireDepartment.objects.filter(archived=False):
update_performance_score.delay(fd.id, options.get('dry_run'))
def handle(self, *args, **options):
ids = options.get('ids')
dry_run = options.get('dry_run')
df = pd.read_csv(options['file'])
count = 0
cols = [
'fc_dept_id', 'lr.fire', 'mr.fire', 'hr.fires', 'lr.injuries',
'mr.injuries', 'hr.injuries', 'lr.deaths.1se', 'mr.deaths.1se',
'hr.deaths', 'lr.sz2', 'mr.sz2', 'hr.sz2', 'lr.sz3', 'mr.sz3',
'hr.sz3', 'lr_beyond_room', 'lr_beyond_structure',
'mr_beyond_room', 'mr_beyond_structure', 'hr_beyond_room',
'hr_beyond_structure'
]
df['lr_beyond_room'] = df.apply(
lambda row: row['lr.fire'] * row['lr.sz2'], axis=1)
df['lr_beyond_structure'] = df.apply(
lambda row: row['lr.fire'] * row['lr.sz2'] * row['lr.sz3'], axis=1)
df['mr_beyond_room'] = df.apply(
lambda row: row['mr.fire'] * row['mr.sz2'], axis=1)
df['mr_beyond_structure'] = df.apply(
lambda row: row['mr.fire'] * row['mr.sz2'] * row['mr.sz3'], axis=1)
df['hr_beyond_room'] = df.apply(
lambda row: row['hr.fires'] * row['hr.sz2'], axis=1)
df['hr_beyond_structure'] = df.apply(
lambda row: row['hr.fires'] * row['hr.sz2'] * row['hr.sz3'],
axis=1)
items = df[cols]
def valid(num):
return not np.isnan(num) and num != 0
for idx, i in enumerate(items.iterrows()):
cur_id = int(i[1]['fc_dept_id'])
row = i[1]
if ids is None or cur_id in ids:
row = df[df.fc_dept_id == cur_id].to_dict(orient='row')[0]
lr_beyond_room = row['lr_beyond_room']
lr_beyond_structure = row['lr_beyond_structure']
mr_beyond_room = row['mr_beyond_room']
mr_beyond_structure = row['mr_beyond_structure']
hr_beyond_room = row['hr_beyond_room']
hr_beyond_structure = row['hr_beyond_structure']
fd = FireDepartment.objects.filter(id=cur_id).first()
if fd is None:
continue
low, _ = fd.firedepartmentriskmodels_set.get_or_create(level=1)
medium, _ = fd.firedepartmentriskmodels_set.get_or_create(
level=2)
high, _ = fd.firedepartmentriskmodels_set.get_or_create(
level=4)
unknown, _ = fd.firedepartmentriskmodels_set.get_or_create(
level=5)
all_level, _ = fd.firedepartmentriskmodels_set.get_or_create(
level=0)
self.stdout.write(
'Updating predictions for {} ({} of {})'.format(
fd, idx + 1, len(items)),
ending='')
low.risk_model_deaths = row['lr.deaths.1se'] if valid(
row['lr.deaths.1se']) else low.risk_model_deaths
low.risk_model_injuries = row['lr.injuries'] if valid(
row['lr.injuries']) else low.risk_model_injuries
low.risk_model_fires = row['lr.fire'] if valid(
row['lr.fire']) else low.risk_model_fires
low.risk_model_fires_size0 = None
low.risk_model_fires_size1 = lr_beyond_room if valid(
lr_beyond_room) else low.risk_model_fires_size1
low.risk_model_fires_size2 = lr_beyond_structure if valid(
lr_beyond_structure) else low.risk_model_fires_size2
self.calculate_derived_values(low)
medium.risk_model_deaths = row['mr.deaths.1se'] if valid(
row['mr.deaths.1se']) else medium.risk_model_deaths
medium.risk_model_injuries = row['mr.injuries'] if valid(
row['mr.injuries']) else medium.risk_model_injuries
medium.risk_model_fires = row['mr.fire'] if valid(
row['mr.fire']) else medium.risk_model_fires
medium.risk_model_fires_size0 = None
medium.risk_model_fires_size1 = mr_beyond_room if valid(
mr_beyond_room) else medium.risk_model_fires_size1
medium.risk_model_fires_size2 = mr_beyond_structure if valid(
mr_beyond_structure) else medium.risk_model_fires_size2
self.calculate_derived_values(medium)
high.risk_model_deaths = row['hr.deaths'] if valid(
row['hr.deaths']) else high.risk_model_deaths
high.risk_model_injuries = row['hr.injuries'] if valid(
row['hr.injuries']) else high.risk_model_injuries
high.risk_model_fires = row['hr.fires'] if valid(
row['hr.fires']) else high.risk_model_fires
high.risk_model_fires_size0 = None
high.risk_model_fires_size1 = hr_beyond_room if valid(
hr_beyond_room) else high.risk_model_fires_size1
high.risk_model_fires_size2 = hr_beyond_structure if valid(
hr_beyond_structure) else high.risk_model_fires_size2
self.calculate_derived_values(high)
all_level.risk_model_deaths = lenient_summation(
low.risk_model_deaths, medium.risk_model_deaths,
high.risk_model_deaths)
all_level.risk_model_injuries = lenient_summation(
low.risk_model_injuries, medium.risk_model_injuries,
high.risk_model_injuries)
all_level.risk_model_fires = lenient_summation(
low.risk_model_fires, medium.risk_model_fires,
high.risk_model_fires)
all_level.risk_model_fires_size0 = None
all_level.risk_model_fires_size1 = lenient_summation(
low.risk_model_fires_size1, medium.risk_model_fires_size1,
high.risk_model_fires_size1)
all_level.risk_model_fires_size2 = lenient_summation(
low.risk_model_fires_size2, medium.risk_model_fires_size2,
high.risk_model_fires_size2)
self.calculate_derived_values(all_level)
# No data for "unknown" risk level in terms of predictions...
if not dry_run:
count = count + 1
low.save()
medium.save()
high.save()
all_level.save()
update_performance_score.delay(fd.id)
self.stdout.write('...done')
self.stdout.write(
self.style.MIGRATE_SUCCESS(
'Updated/created prediction records for {} departments'.format(
count)))
if not dry_run:
self.stdout.write(
'Creating/refreshing "population_quartiles" view')
refresh_quartile_view_task.delay()
self.stdout.write(
'Creating/refreshing "national_calculations" view')
refresh_national_calculations_view_task.delay()
def handle(self, *args, **options):
for fd in FireDepartment.objects.filter(archived=False):
update_performance_score.delay(fd.id, options.get('dry_run'))
def handle(self, *args, **options):
for fd in FireDepartment.objects.all():
update_performance_score.delay(fd.id, options.get('dry_run'))
