def make_forecasts_at_incidents_for_sander():
"""Lager csv med alle varsomhendelser sammen med faregrad og de aktuelle skredproblemene
(svakt lag, skredtype og skredproblemnavnert). Der det er gjort en regObs observasjon
med «hendelse/ulykke» skjema fylt ut har jeg også lagt på skadeomfangsvurderingen.
August 2018: Hei Jostein.
Som du veit skal eg skriva om: Skredulykker knytt til skredproblem
Du snakka om at det var muleg å få ut data for dette frå NVE sin database. Kan du hjelpa meg med det?
Mvh
Sander
"""
pickle_file_name = '{0}dl_inci_sander.pickle'.format(env.local_storage)
output_incident_and_dl = '{0}Hendelse og faregrad til Sander.csv'.format(
env.output_folder)
get_new = False
if get_new:
varsom_incidents = gm.get_varsom_incidents(add_forecast_regions=True,
add_forecasts=True,
add_observations=True)
mp.pickle_anything(varsom_incidents, pickle_file_name)
else:
varsom_incidents = mp.unpickle_anything(pickle_file_name)
incident_and_dl = []
for i in varsom_incidents:
incident_date = i.date
danger_level = None
problem_1 = None
problem_2 = None
problem_3 = None
avalanche_type_1 = None
avalanche_type_2 = None
avalanche_type_3 = None
weak_layer_1 = None
weak_layer_2 = None
weak_layer_3 = None
dato_regobs = None
damage_extent = None
if i.forecast:
danger_level = i.forecast.danger_level
for p in i.forecast.avalanche_problems:
if p.order == 1:
problem_1 = p.problem
weak_layer_1 = p.cause_name
avalanche_type_1 = p.aval_type
if p.order == 2:
problem_2 = p.problem
weak_layer_2 = p.cause_name
avalanche_type_2 = p.aval_type
if p.order == 3:
problem_3 = p.problem
weak_layer_3 = p.cause_name
avalanche_type_3 = p.aval_type
if i.observations:
dato_regobs = i.observations[0].DtObsTime.date()
for obs in i.observations:
for o in obs.Observations:
if isinstance(o, go.Incident):
damage_extent = o.DamageExtentName
incident_and_dl.append({
'Dato': incident_date,
# 'Dato (regObs)': dato_regobs,
'Region': i.region_name,
'Kommune': i.municipality,
'Dødsfall': i.fatalities,
'Alvorsgrad': damage_extent,
'Involverte': i.people_involved,
'Aktivitet': i.activity,
'Faregrad': danger_level,
'Skredproblem 1': problem_1,
'Skredtype 1': avalanche_type_1,
'Svaktlag 1': weak_layer_1,
'Skredproblem 2': problem_2,
'Skredtype 2': avalanche_type_2,
'Svaktlag 2': weak_layer_2,
'Skredproblem 3': problem_3,
'Skredtype 3': avalanche_type_3,
'Svaktlag 3': weak_layer_3,
'Kommentar': i.comment,
'regObs': '{}'.format(i.regid)
})
# Write observed problems to file
with open(output_incident_and_dl, 'w', encoding='utf-8') as f:
make_header = True
for i in incident_and_dl:
if make_header:
f.write(' ;'.join([fe.make_str(d) for d in i.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d) for d in i.values()]).replace(
'[', '').replace(']', '') + '\n')
def incident_troms_winter_2018_for_markus():
"""Communication dated 2018-11-29
Hei Ragnar og Jostein
Kan en av dere hjelpe meg å ta ut et plott som viser antall registrerte ulykker og hendelser i
varslingsregionene Tromsø, Lyngen, Sør-Troms og Indre-Troms for
perioden 15.02 – 15.05.
...
Er du interessert i det som ligger i registrert i
regObs eller det som er kvalitetssikkert data og ligger på varsom?
Skal du ha hendelser som har hatt konsekvens?
Skal hendelsene plottes i tid eller vises i kart?
...
Varsom
Ikke nødvendigvis konsekvens
Tid
:return:
"""
pickle_file_name = '{0}incident_troms_winter_2018_for_markus.pickle'.format(
env.local_storage)
from_date = dt.date(2018, 2, 15) # '2018-02-15'
to_date = dt.date(2018, 5, 15) # '2018-05-15'
# Tromsø, Lyngen, Sør-Troms og Indre-Troms
regions = [3011, 3010, 3012, 3013]
get_new = False
if get_new:
all_varsom_incidents = gm.get_varsom_incidents(
add_forecast_regions=True, add_observations=True)
all_regobs_avalobs_and_incidents = go.get_data_as_class(
from_date,
to_date,
registration_types=[11, 26],
region_ids=regions,
output='Nest')
mp.pickle_anything(
[all_varsom_incidents, all_regobs_avalobs_and_incidents],
pickle_file_name)
else:
[all_varsom_incidents, all_regobs_avalobs_and_incidents
] = mp.unpickle_anything(pickle_file_name)
varsom_incidents = mm.make_date_int_dict(start_date=from_date,
end_date=to_date)
regobs_avalobs_and_incidents = mm.make_date_int_dict(start_date=from_date,
end_date=to_date)
for i in all_varsom_incidents:
if from_date <= i.date <= to_date:
if i.region_id in regions:
if i.date in varsom_incidents.keys():
varsom_incidents[i.date] += 1
for i in all_regobs_avalobs_and_incidents:
if from_date <= i.DtObsTime.date() <= to_date:
if i.ForecastRegionTID in regions:
if i.DtObsTime.date() in regobs_avalobs_and_incidents.keys():
regobs_avalobs_and_incidents[i.DtObsTime.date()] += 1
sum_varsom = sum(varsom_incidents.values())
sum_regobs = sum(regobs_avalobs_and_incidents.values())
varsom_incident_troms_winter_2018_for_markus = '{0}varsom_incident_troms_winter_2018_for_markus.csv'.format(
env.output_folder)
regobs_incident_troms_winter_2018_for_markus = '{0}regobs_incident_troms_winter_2018_for_markus.csv'.format(
env.output_folder)
with open(varsom_incident_troms_winter_2018_for_markus,
'w',
encoding='utf-8') as f:
make_header = True
for k, v in varsom_incidents.items():
if make_header:
f.write('date; number\n')
make_header = False
f.write('{}; {}\n'.format(k, v))
with open(regobs_incident_troms_winter_2018_for_markus,
'w',
encoding='utf-8') as f:
make_header = True
for k, v in regobs_avalobs_and_incidents.items():
if make_header:
f.write('date; number\n')
make_header = False
f.write('{}; {}\n'.format(k, v))
pass
def make_forecasts_at_incidents_for_mala(get_new=False):
"""Lager csv med alle varsomhendelser sammen med faregrad og de aktuelle skredproblemene
(svakt lag, skredtype og skredproblemnavnert). Der det er gjort en regObs observasjon
med «hendelse/ulykke» skjema fylt ut har jeg også lagt på skadeomfangsvurderingen.
"""
pickle_file_name = '{0}dl_inci_mala.pickle'.format(env.local_storage)
output_incident_and_dl = '{0}incidents_mala.csv'.format(env.output_folder)
if get_new:
varsom_incidents = gm.get_varsom_incidents(add_forecast_regions=True,
add_forecasts=True,
add_observations=False)
mp.pickle_anything(varsom_incidents, pickle_file_name)
else:
varsom_incidents = mp.unpickle_anything(pickle_file_name)
incident_and_dl = []
for i in varsom_incidents:
incident_date = i.date
danger_level = None
problem_1 = None
problem_2 = None
problem_3 = None
avalanche_type_1 = None
avalanche_type_2 = None
avalanche_type_3 = None
weak_layer_1 = None
weak_layer_2 = None
weak_layer_3 = None
dato_regobs = None
damage_extent = None
if i.forecast:
danger_level = i.forecast.danger_level
for p in i.forecast.avalanche_problems:
if p.order == 1:
problem_1 = p.problem
weak_layer_1 = p.cause_name
avalanche_type_1 = p.aval_type
if p.order == 2:
problem_2 = p.problem
weak_layer_2 = p.cause_name
avalanche_type_2 = p.aval_type
if p.order == 3:
problem_3 = p.problem
weak_layer_3 = p.cause_name
avalanche_type_3 = p.aval_type
if i.observations:
dato_regobs = i.observations[0].DtObsTime.date()
for obs in i.observations:
for o in obs.Observations:
if isinstance(o, go.Incident):
damage_extent = o.DamageExtentName
incident_and_dl.append({
'Date': incident_date,
# 'Dato (regObs)': dato_regobs,
'Region_id': i.region_id,
'Region': i.region_name,
'Fatalities': i.fatalities,
'Damage_extent': damage_extent,
'People_involved': i.people_involved,
'Activity': i.activity,
'Danger_level': danger_level,
'Avalanche_problem_1': problem_1,
'Avalanche_type_1': avalanche_type_1,
'Weak_layer_1': weak_layer_1,
'Avalanche_problem_2': problem_2,
'Avalanche_type_2': avalanche_type_2,
'Weak_layer_2': weak_layer_2,
'Avalanche_problem_3': problem_3,
'Avalanche_type_3': avalanche_type_3,
'Weak_layer_3': weak_layer_3,
'Comment': i.comment,
'regObs_id': '{}'.format(i.regid)
})
# Write observed problems to file
with open(output_incident_and_dl, 'w', encoding='utf-8') as f:
make_header = True
for i in incident_and_dl:
if make_header:
f.write(';'.join([fe.make_str(d) for d in i.keys()]) + '\n')
make_header = False
f.write(';'.join([fe.make_str(d) for d in i.values()]).replace(
'[', '').replace(']', '') + '\n')