def deaths_and_injuries_sum(self):
low, med, high, unknown, all_levels = self._get_risk_model_rows()
def empty_deaths(x):
if x:
return x.risk_model_deaths is None and x.risk_model_injuries is None
def sum_deaths(x):
if x:
return (x.risk_model_deaths or 0) + (x.risk_model_injuries
or 0)
d = AttrDict({
'low':
None if empty_deaths(low) else sum_deaths(low),
'medium':
None if empty_deaths(med) else sum_deaths(med),
'high':
None if empty_deaths(high) else sum_deaths(high),
'unknown':
None if empty_deaths(unknown) else sum_deaths(unknown),
'all':
None if empty_deaths(all_levels) else sum_deaths(all_levels)
})
return d
def national_risk_model_deaths_injuries_sum_quartile(self):
# TODO: Need to optimize...
ret = AttrDict()
results = self.national_calculations.objects.filter(
id=self.firedepartment.id)
for numlevel, level in self.RISK_LEVELS:
ret[level] = None
for result in results:
if result.level == numlevel:
ret[level] = result.risk_model_deaths_injuries_sum_quartile
return ret
def _get_risk_model_field(self, field):
"""
Return low/medium/high risk models
"""
low, med, high, unknown, all_levels = self._get_risk_model_rows()
return AttrDict({
'low': getattr(low, field, None),
'medium': getattr(med, field, None),
'high': getattr(high, field, None),
'unknown': getattr(unknown, field, None),
'all': getattr(all_levels, field, None)
})
def community_fire_risk(self):
return AttrDict({
'low':
self.population_metrics_rows.low.risk_model_fires_quartile,
'medium':
self.population_metrics_rows.medium.risk_model_fires_quartile,
'high':
self.population_metrics_rows.high.risk_model_fires_quartile,
'unknown':
self.population_metrics_rows.unknown.risk_model_fires_quartile,
'all':
self.population_metrics_rows.all.risk_model_fires_quartile
})
def residential_fires_avg_3_years_breaks(self):
"""
Risk model fire count breaks for the bullet chart
"""
ret = AttrDict()
for numlevel, level in self.RISK_LEVELS:
vals = self.quartile_class.objects.get_field_stats(
'residential_fires_avg_3_years',
group_by='residential_fires_avg_3_years_quartile',
population_class=self.firedepartment.population_class,
level=numlevel)
ret[level] = [n['max'] for n in vals]
return ret
def risk_model_deaths_injuries_breaks(self):
"""
Deaths and injuries for the bullet chart
"""
ret = AttrDict()
for numlevel, level in self.RISK_LEVELS:
vals = self.quartile_class.objects.get_field_stats(
'risk_model_deaths_injuries_sum',
group_by='risk_model_deaths_injuries_sum_quartile',
population_class=self.firedepartment.population_class,
level=numlevel)
ret[level] = [n['max'] for n in vals]
return ret
def risk_model_greater_than_size_2_breaks(self):
"""
Size 2 or above fire breaks for the bullet chart
"""
ret = AttrDict()
for numlevel, level in self.RISK_LEVELS:
vals = self.quartile_class.objects.get_field_stats(
'risk_model_size1_percent_size2_percent_sum',
group_by='risk_model_size1_percent_size2_percent_sum_quartile',
population_class=self.firedepartment.population_class,
level=numlevel)
ret[level] = [n['max'] for n in vals]
return ret
def dist_model_residential_fires_quartile_breaks(self):
if not self.peers:
self._get_peers()
def get(df):
if df is not None and not df.empty:
return df.groupby(['dist_model_residential_fires_quartile'
]).max()['dist_model_score'].tolist()
return AttrDict({
'low': get(self.peers.low),
'medium': get(self.peers.medium),
'high': get(self.peers.high),
'unknown': get(self.peers.unknown),
'all': get(self.peers.all)
})
def dist_model_risk_model_deaths_injuries_quartile_avg(self):
if not self.peers:
self._get_peers()
def get(df):
if df is not None and not df.empty:
return df.dist_model_risk_model_deaths_injuries_quartile_avg.mean(
)
return AttrDict({
'low': get(self.peers.low),
'medium': get(self.peers.medium),
'high': get(self.peers.high),
'unknown': get(self.peers.unknown),
'all': get(self.peers.all)
})
def dist_model_residential_fires_quartile(self):
if not self.peers:
self._get_peers()
def get(df):
if df is not None and not df.empty:
return df.loc[
df['id'] == self.firedepartment.
id].dist_model_residential_fires_quartile.values[0]
return AttrDict({
'low': get(self.peers.low),
'medium': get(self.peers.medium),
'high': get(self.peers.high),
'unknown': get(self.peers.unknown),
'all': get(self.peers.all)
})
def dist_model_residential_fires_quartile_avg(self):
"""
Used as the "Assessment of performance score based on the number of fires." Safe Grade
"""
if not self.peers:
self._get_peers()
def get(df):
if df is not None and not df.empty:
return df.dist_model_residential_fires_quartile_avg.mean()
return AttrDict({
'low': get(self.peers.low),
'medium': get(self.peers.medium),
'high': get(self.peers.high),
'unknown': get(self.peers.unknown),
'all': get(self.peers.all)
})
def community_death_and_injury_risk(self):
return AttrDict({
'low':
self.population_metrics_rows.low.
risk_model_deaths_injuries_sum_quartile,
'medium':
self.population_metrics_rows.medium.
risk_model_deaths_injuries_sum_quartile,
'high':
self.population_metrics_rows.high.
risk_model_deaths_injuries_sum_quartile,
'unknown':
self.population_metrics_rows.unknown.
risk_model_deaths_injuries_sum_quartile,
'all':
self.population_metrics_rows.all.
risk_model_deaths_injuries_sum_quartile
})
def residential_structure_fire_counts(self):
ret = AttrDict()
for numlevel, level in self.RISK_LEVELS:
v = self.firedepartment.nfirsstatistic_set.filter(metric='residential_structure_fires', level=numlevel)\
.extra(select={
'year_max': "SELECT MAX(COUNT) FROM firestation_nfirsstatistic b WHERE b.fire_department_id = firestation_nfirsstatistic.fire_department_id AND b.year = firestation_nfirsstatistic.year and b.metric='fire_calls' and b.level = firestation_nfirsstatistic.level"
})\
.extra(select={
'year_min': "SELECT 0"
})
ret[level] = [
dict(year=i.year,
count=i.count,
year_max=i.year_max,
year_min=i.year_min) for i in v
]
return ret
def dist_model_risk_model_deaths_injuries_quartile_breaks(self):
"""
Used as the "Assessment of performance score based on fire spread risk." Safe Grade
"""
if not self.peers:
self._get_peers()
def get(df):
if df is not None and not df.empty:
return df.groupby([
'dist_model_risk_model_deaths_injuries_quartile'
]).max()['dist_model_score'].tolist()
return AttrDict({
'low': get(self.peers.low),
'medium': get(self.peers.medium),
'high': get(self.peers.high),
'unknown': get(self.peers.unknown),
'all': get(self.peers.all)
})
def residential_fires_3_year_avg(self):
def get(level):
return self.firedepartment.nfirsstatistic_set.filter(
metric='residential_structure_fires',
year__gte=2010,
level=level).aggregate(Avg('count')).get('count__avg')
low = get(1)
medium = get(2)
high = get(4)
unknown = get(5)
all_levels = get(0)
return AttrDict({
'low': low,
'medium': medium,
'high': high,
'unknown': unknown,
'all': all_levels
})
def population_metrics_rows(self):
"""
Returns the matching rows from the population metrics table.
"""
return AttrDict({
'low':
self.quartile_class.objects.filter(id=self.firedepartment.id,
level=1).first(),
'medium':
self.quartile_class.objects.filter(id=self.firedepartment.id,
level=2).first(),
'high':
self.quartile_class.objects.filter(id=self.firedepartment.id,
level=4).first(),
'unknown':
self.quartile_class.objects.filter(id=self.firedepartment.id,
level=5).first(),
'all':
self.quartile_class.objects.filter(id=self.firedepartment.id,
level=0).first()
})
def nfirs_deaths_and_injuries_sum(self):
def get(level):
return self.firedepartment.nfirsstatistic_set.filter(
Q(metric='civilian_casualties')
| Q(metric='firefighter_casualties'),
level=level,
year__gte=2010).aggregate(Avg('count')).get('count__avg')
low = get(1)
medium = get(2)
high = get(4)
unknown = get(5)
all_levels = get(0)
return AttrDict({
'low': low,
'medium': medium,
'high': high,
'unknown': unknown,
'all': all_levels
})
def __init__(self, firedepartment, quartile_class, national_calculations):
self.firedepartment = firedepartment
self.quartile_class = quartile_class
self.national_calculations = national_calculations
self.peers = AttrDict()
