def make_forecasts_for_Christian():
"""Christian Jaedicke ønsker oversikt over varsel og skredproblemer siste tre år i Narvik."""
pickle_file_name = '{0}forecasts_ofoten_christian.pickle'.format(
env.local_storage)
get_new = False
all_dangers = []
if get_new:
# Get Narvik 2014-15 and 2015-16
region_id = 114
from_date, to_date = gm.get_forecast_dates('2014-15')
all_dangers += gd.get_forecasted_dangers(region_id, from_date, to_date)
from_date, to_date = gm.get_forecast_dates('2015-16')
all_dangers += gd.get_forecasted_dangers(region_id, from_date, to_date)
# Get Indre fjordane 2016-17
region_id = 3015
from_date, to_date = gm.get_forecast_dates('2016-17')
all_dangers += gd.get_forecasted_dangers(region_id, from_date, to_date)
mp.pickle_anything(all_dangers, pickle_file_name)
else:
all_dangers = mp.unpickle_anything(pickle_file_name)
output_forecast_problems = '{0}Varsel Ofoten for Christian.csv'.format(
env.output_folder)
import collections as coll
# Write forecasts to file
with open(output_forecast_problems, 'w', encoding='utf-8') as f:
make_header = True
for d in all_dangers:
for p in d.avalanche_problems:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(p.date, '%Y-%m-%d')),
('Region id', p.region_regobs_id),
('Region', p.region_name), ('DL', p.danger_level),
('Danger level', p.danger_level_name),
('Problem order', p.order), ('Problem', p.problem),
('Cause/ weaklayer', p.cause_name), ('Type', p.aval_type),
('Size', p.aval_size), ('Trigger', p.aval_trigger),
('Probability', p.aval_probability),
('Distribution', p.aval_distribution)
])
if make_header:
f.write(
' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
def make_forecasts_for_Thea():
"""July 2018: Make list of avalanche forecasts danger levels for regions Voss, Romsdalen, Svartisen
and Salten (and those before them) for Thea Møllerhaug Lunde (Jernbanedirektoratet).
Voss-Bergen ligger i for det meste i Voss-regionen vår.
Mo i Rana-Fauske ligger i Svartisen og Salten.
Åndalsnes-Bjorli ligger i varslingsregionen Romsdalen."""
pickle_file_name = '{0}201807_avalanche_forecasts_thea.pickle'.format(
env.local_storage)
get_new = False
all_dangers = []
if get_new:
# Get Voss. ForecastRegionTID 124 form 2012-2016 and 3031 since.
# Get Romsdalen. ForecastRegionTID 118 from 2012-2016 and 3023 since.
# Get Svartisen. ForecastRegionTID 131 from 2012-2016 and 3017 since.
# Get Salten. ForecastRegionTID 133 form 2012-2016 and 3016 since.
years = ['2012-13', '2013-14', '2014-15', '2015-16']
region_ids = [124, 118, 131, 133]
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
all_dangers += gd.get_forecasted_dangers(region_ids, from_date,
to_date)
years = ['2016-17', '2017-18']
region_ids = [3031, 3023, 3017, 3016]
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
all_dangers += gd.get_forecasted_dangers(region_ids, from_date,
to_date)
mp.pickle_anything(all_dangers, pickle_file_name)
else:
all_dangers = mp.unpickle_anything(pickle_file_name)
output_forecast_problems = '{0}201807 Snøskredvarsel for Thea.txt'.format(
env.output_folder)
import collections as coll
# Write forecasts to file
with open(output_forecast_problems, 'w', encoding='utf-8') as f:
make_header = True
for d in all_dangers:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(d.date, '%Y-%m-%d')),
('Region id', d.region_regobs_id),
('Region', d.region_name),
('DL', d.danger_level),
('Danger level', d.danger_level_name),
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
pass
def make_forecasts_for_Heidi():
"""July 2018: Make list of avalanche forecasts for regions Voss, Svartisen og Fauske (and those before them)
for Heidi Bjordal SVV"""
pickle_file_name = '{0}201807_avalanche_forecasts_heidi.pickle'.format(
env.local_storage)
get_new = False
all_dangers = []
if get_new:
# Get Voss. ForecastRegionTID 124 form 2012-2016 and 3031 since.
# Get Svartisen. ForecastRegionTID 131 form 2012-2016 and 3017 since.
# Get Salten. ForecastRegionTID 133 form 2012-2016 and 3016 since.
years = ['2012-13', '2013-14', '2014-15', '2015-16']
region_ids = [124, 131, 133]
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
all_dangers += gd.get_forecasted_dangers(region_ids, from_date,
to_date)
years = ['2016-17', '2017-18']
region_ids = [3031, 3017, 3016]
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
all_dangers += gd.get_forecasted_dangers(region_ids, from_date,
to_date)
mp.pickle_anything(all_dangers, pickle_file_name)
else:
all_dangers = mp.unpickle_anything(pickle_file_name)
output_forecast_problems = '{0}201807 Snøskredvarsel for Heidi.txt'.format(
env.output_folder)
import collections as coll
# Write forecasts to file
with open(output_forecast_problems, 'w', encoding='utf-8') as f:
make_header = True
for d in all_dangers:
for p in d.avalanche_problems:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(p.date, '%Y-%m-%d')),
('Region id', p.region_regobs_id),
('Region', p.region_name), ('DL', p.danger_level),
('Danger level', p.danger_level_name),
('Problem order', p.order), ('Problem', p.problem),
('Cause/ weaklayer', p.cause_name), ('Type', p.aval_type),
('Size', p.aval_size), ('Trigger', p.aval_trigger),
('Probability', p.aval_probability),
('Distribution', p.aval_distribution)
])
if make_header:
f.write(
' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
pass
def make_avalanche_problemes_for_techel():
"""Gets forecastes and observed avalanche problems and dangers for Frank Techel.
Takes 20-30 min to run a year.
:return:
"""
pickle_file_name = '{0}runavalancheproblems_techel.pickle'.format(
env.local_storage)
years = ['2014-15', '2015-16', '2016-17', '2017-18']
get_new = False
if get_new:
forecast_problems = []
forecast_dangers = []
observed_dangers = []
observed_problems = []
for y in years:
# Get forecast data. Different region ids from year to year.
region_ids = gm.get_forecast_regions(year=y)
from_date, to_date = gm.get_forecast_dates(y)
forecast_problems += gp.get_forecasted_problems(region_ids,
from_date,
to_date,
lang_key=2)
forecast_dangers += gd.get_forecasted_dangers(region_ids,
from_date,
to_date,
lang_key=2)
# Get observed data. All older data in regObs have been mapped to new regions.
region_ids = gm.get_forecast_regions(year='2016-17')
from_date, to_date = gm.get_forecast_dates(
y, padding=dt.timedelta(days=20))
this_years_observed_dangers = gd.get_observed_dangers(region_ids,
from_date,
to_date,
lang_key=2)
this_years_observed_problems = gp.get_observed_problems(region_ids,
from_date,
to_date,
lang_key=2)
# Update observations with forecast region ids and names used the respective years
for od in this_years_observed_dangers:
utm33x = od.metadata['Original data'].UTMEast
utm33y = od.metadata['Original data'].UTMNorth
region_id, region_name = gm.get_forecast_region_for_coordinate(
utm33x, utm33y, y)
od.region_regobs_id = region_id
od.region_name = region_name
for op in this_years_observed_problems:
utm33x = op.metadata['Original data']['UtmEast']
utm33y = op.metadata['Original data']['UtmNorth']
region_id, region_name = gm.get_forecast_region_for_coordinate(
utm33x, utm33y, y)
op.region_regobs_id = region_id
op.region_name = region_name
observed_dangers += this_years_observed_dangers
observed_problems += this_years_observed_problems
mp.pickle_anything([
forecast_problems, forecast_dangers, observed_dangers,
observed_problems
], pickle_file_name)
else:
[
forecast_problems, forecast_dangers, observed_dangers,
observed_problems
] = mp.unpickle_anything(pickle_file_name)
# Run EAWS mapping on all problems
for p in forecast_problems:
p.map_to_eaws_problems()
for p in observed_problems:
p.map_to_eaws_problems()
output_forecast_problems = '{0}Techel forecast problems.csv'.format(
env.output_folder)
output_forecast_dangers = '{0}Techel forecast dangers.csv'.format(
env.output_folder)
output_observed_problems = '{0}Techel observed problems.csv'.format(
env.output_folder)
output_observed_dangers = '{0}Techel observed dangers.csv'.format(
env.output_folder)
import collections as coll
# Write observed dangers to file
with open(output_observed_dangers, 'w', encoding='utf-8') as f:
make_header = True
for d in observed_dangers:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(d.date, '%Y-%m-%d')),
('Reg time',
dt.datetime.strftime(d.registration_time, '%Y-%m-%d %H:%M')),
('Region id', d.region_regobs_id),
('Region', d.region_name),
('Municipal', d.municipal_name),
('Nick', d.nick),
('Competence', d.competence_level),
('DL', d.danger_level),
('Danger level', d.danger_level_name),
('Forecast correct', d.forecast_correct),
# ('Table', d.data_table),
# ('URL', d.url),
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
# Write forecasted dangers to file
with open(output_forecast_dangers, 'w', encoding='utf-8') as f:
make_header = True
for d in forecast_dangers:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(d.date, '%Y-%m-%d')),
('Region id', d.region_regobs_id),
('Region', d.region_name),
('Nick', d.nick),
('DL', d.danger_level),
('Danger level', d.danger_level_name),
# ('Table', d.data_table),
# ('URL', d.url),
('Main message', ' '.join(d.main_message_en.split()))
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
# Write forecasted problems to file
with open(output_forecast_problems, 'w', encoding='utf-8') as f:
make_header = True
for p in forecast_problems:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(p.date, '%Y-%m-%d')),
('Region id', p.region_regobs_id),
('Region', p.region_name),
('Nick', p.nick_name),
('Problem order', p.order),
('Problem', p.problem),
('EAWS problem', p.eaws_problem),
('Cause/ weaklayer', p.cause_name),
# ('TypeTID', p.aval_type_tid),
('Type', p.aval_type),
('Size', p.aval_size),
('Trigger', p.aval_trigger),
('Probability', p.aval_probability),
('Distribution', p.aval_distribution),
('DL', p.danger_level),
('Danger level', p.danger_level_name),
# ('Table', p.regobs_table),
# ('URL', p.url)
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
# Write observed problems to file
with open(output_observed_problems, 'w', encoding='utf-8') as f:
make_header = True
for p in observed_problems:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(p.date, '%Y-%m-%d')),
('Reg time',
dt.datetime.strftime(p.registration_time, '%Y-%m-%d %H:%M')),
('Region id', p.region_regobs_id),
('Region', p.region_name),
('Municipal', p.municipal_name),
('Nick', p.nick_name),
('Competence', p.competence_level),
# ('Problem order', p.order),
('EAWS problem', p.eaws_problem),
('Cause/ weaklayer', p.cause_name),
# ('TypeTID', p.aval_type_tid),
('Type', p.aval_type),
('Catch 1', p.cause_attribute_crystal),
('Catch 2', p.cause_attribute_light),
('Catch 3', p.cause_attribute_soft),
('Catch 4', p.cause_attribute_thin),
('Size', p.aval_size),
('Trigger', p.aval_trigger),
# ('Probability', p.aval_probability),
# ('Distribution', p.aval_distribution),
# ('RegID', p.regid),
# ('Table', p.regobs_table),
# ('URL', p.url)
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
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 make_forecasts_for_Espen_at_sweco():
"""Hei. I forbindelse med et prosjekt i Sørreisa i Troms ønsker vi å gi råd til vår kunde om evakuering av bygg
i skredutsatt terreng. Som en del av vår vurdering hadde det vært veldig nyttig med statistikk for varslingen,
altså statistikk om hvor ofte de ulike faregradene er varslet. Er det mulig å få tak i slik statistikk?
Gjerne så langt tilbake i tid som mulig. Vennlig hilsen Espen Eidsvåg"""
pickle_file_name = '{0}forecasts_sorreisa_espen.pickle'.format(
env.local_storage)
get_new = True
all_dangers = []
if get_new:
years = ['2012-13', '2013-14', '2014-15', '2015-16']
region_ids = [110, 112] # Senja, Bardu
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
for region_id in region_ids:
all_dangers += gd.get_forecasted_dangers(
region_id, from_date, to_date)
years = ['2016-17', '2017-18', '2018-19']
region_ids = [3012, 3013] # Sør Troms, Indre Troms
for y in years:
from_date, to_date = gm.get_forecast_dates(y)
for region_id in region_ids:
all_dangers += gd.get_forecasted_dangers(
region_id, from_date, to_date)
mp.pickle_anything(all_dangers, pickle_file_name)
else:
all_dangers = mp.unpickle_anything(pickle_file_name)
output_forecast_problems = '{0}Varsel for Sørreisa.Espen Eidsvåg Sweco.csv'.format(
env.output_folder)
import collections as coll
# Write forecasts to file
with open(output_forecast_problems, 'w', encoding='utf-8') as f:
make_header = True
for d in all_dangers:
for p in d.avalanche_problems:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(p.date, '%Y-%m-%d')),
('Region id', p.region_regobs_id),
('Region', p.region_name), ('DL', p.danger_level),
('Danger level', p.danger_level_name),
('Problem order', p.order), ('Problem', p.problem),
('Cause/ weaklayer', p.cause_name), ('Type', p.aval_type),
('Size', p.aval_size), ('Trigger', p.aval_trigger),
('Probability', p.aval_probability),
('Distribution', p.aval_distribution)
])
if make_header:
f.write(
' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d)
for d in out_data.values()]) + '\n')
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')
# Write observed dangers to file
with open(output_observed_dangers, 'w', encoding='utf-8') as f:
make_header = True
for d in observed_dangers:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(d.date, '%Y-%m-%d')),
('Reg time',
dt.datetime.strftime(d.registration_time, '%Y-%m-%d %H:%M')),
('Region id', d.region_regobs_id), ('Region', d.region_name),
('Municipal', d.municipal_name), ('Nick', d.nick),
('Competence', d.competence_level), ('DL', d.danger_level),
('Danger level', d.danger_level_name),
('Forecast correct', d.forecast_correct)
])
if make_header:
f.write(' ;'.join([fe.make_str(d)
for d in out_data.keys()]) + '\n')
make_header = False
f.write(' ;'.join([fe.make_str(d) for d in out_data.values()]) + '\n')
# Write forecasted dangers to file
with open(output_forecast_dangers, 'w', encoding='utf-8') as f:
make_header = True
for d in forecast_dangers:
out_data = coll.OrderedDict([
('Date', dt.date.strftime(d.date, '%Y-%m-%d')),
('Region id', d.region_regobs_id), ('Region', d.region_name),
('Nick', d.nick), ('DL', d.danger_level),
('Danger level', d.danger_level_name),
('Main message', ' '.join(d.main_message_en.split()))
])