aval_triggers = ', '.join("{!s} ({!r})".format(key, val)
for (key, val) in aval_triggers)
s = u'{0};{1};{2};{3};{4};{5};{6};{7}\n'.format(
m.occurrences, danger_levels, problems, aval_triggers, cause_names,
aval_types, m.main_message_no, m.main_message_en)
l.write(s)
l.close()
if __name__ == "__main__":
year = '2017-18'
regions = gm.get_forecast_regions(year=year)
date_from, date_to = gm.get_dates_from_season(year=year)
# file names
file_name_for_warnings_pickle = '{0}{1}'.format(
env.local_storage, 'runmainmessage warnings.pickle')
file_name_for_main_messages_pickle = '{0}{1}'.format(
env.local_storage, 'runmainmessage main messages.pickle')
file_name_for_main_messages_csv = '{0}{1}'.format(
env.output_folder, 'Alle hovedbudskap {}.csv'.format(year))
##### pickle the warnings and dataset with main messages
# pickle_warnings(regions, date_from, date_to, file_name_for_warnings_pickle)
main_messages = select_messages_with_more(file_name_for_warnings_pickle)
mp.pickle_anything(main_messages, file_name_for_main_messages_pickle)
main_messages = mp.unpickle_anything(file_name_for_main_messages_pickle)
def get_all_observations(year,
output='Nest',
geohazard_tids=None,
lang_key=1,
max_file_age=23):
"""Specialized method for getting all observations for one season (1. sept to 31. august).
For the current season (at the time of writing, 2018-19), if request has been made the last 23hrs,
data is retrieved from a locally stored pickle, if not, new request is made to the regObs api. Previous
seasons are not requested if a pickle is found in local storage.
:param year: [string] Eg. season '2017-18' (sept-sept) or one single year '2018'
:param output: [string] 'Nest' or 'List'
:param geohazard_tids: [int or list of ints] Default None gives all. Note, pickle stores all, but this option returns a select
:param lang_key [int] 1 is norwegian, 2 is english
:param max_file_age: [int] hrs how old the file is before new is retrieved
:return:
"""
from_date, to_date = gm.get_dates_from_season(year=year)
file_name_list = '{0}all_observations_list_{1}_lk{2}.pickle'.format(
env.local_storage, year, lang_key)
file_name_nest = '{0}all_observations_nest_{1}_lk{2}.pickle'.format(
env.local_storage, year, lang_key)
get_new = True
date_limit = dt.datetime.now() - dt.timedelta(hours=max_file_age)
# if we are well out of the current season (30 days) its little chance the data set has changed.
current_season = gm.get_season_from_date(dt.date.today() -
dt.timedelta(30))
if geohazard_tids:
if not isinstance(geohazard_tids, list):
geohazard_tids = [geohazard_tids]
if os.path.exists(file_name_list):
# if file contains a season long gone, dont make new.
if year == current_season:
file_age = dt.datetime.fromtimestamp(
os.path.getmtime(file_name_list))
# If file is newer than the given time limit, dont make new.
if file_age > date_limit:
# If file size larger than that of an nearly empty file, dont make new.
if os.path.getsize(file_name_list) > 100: # 100 bytes limit
get_new = False
else:
get_new = False
if get_new:
# When get new, get all geo hazards
nested_observations = go.get_data_as_class(from_date=from_date,
to_date=to_date,
output='Nest',
geohazard_tids=None,
lang_key=lang_key)
mp.pickle_anything(nested_observations, file_name_nest)
listed_observations = []
for d in nested_observations:
for o in d.Observations:
if _observation_is_not_empty(o):
listed_observations.append(o)
for p in d.Pictures:
# p['RegistrationName'] = 'Bilde'
listed_observations.append(p)
mp.pickle_anything(listed_observations, file_name_list)
if output == 'Nest':
all_nested_observations = mp.unpickle_anything(file_name_nest)
nested_observations = []
if geohazard_tids:
for o in all_nested_observations:
if o.GeoHazardTID in geohazard_tids:
nested_observations.append(o)
else:
nested_observations = all_nested_observations
return nested_observations
elif output == 'List':
all_listed_observations = mp.unpickle_anything(file_name_list)
listed_observations = []
if geohazard_tids:
for o in all_listed_observations:
if o.GeoHazardTID in geohazard_tids:
listed_observations.append(o)
else:
listed_observations = all_listed_observations
return listed_observations
else:
ml.log_and_print(
'[warning] getvarsompickles.py -> get_all_registrations: Unknown output option'
)
return []
def _get_weather_obs(year, date=None, days=None, max_file_age=23):
"""
Download data from the weather API:s
:param year: String representation of season. None if a specific date should be fetched.
:param max_file_age: Time to live for cache in hours.
:param date: datetime.date. None if a whole season should be fetched.
"""
aw = _get_raw_varsom(year, date, days, max_file_age=max_file_age)
file_name = f'{se.local_storage}weather_v{CSV_VERSION}_{year}.pickle'
file_date_limit = dt.datetime.now() - dt.timedelta(hours=max_file_age)
current_season = gm.get_season_from_date(dt.date.today() -
dt.timedelta(30))
get_new = True
if date:
from_date = date - dt.timedelta(days=days)
to_date = date + dt.timedelta(days=1)
get_new = True
else:
from_date, to_date = gm.get_dates_from_season(year)
to_date = to_date + dt.timedelta(days=1)
try:
# Don't fetch new data if old is cached. If older season file doesn't exists we get out via an exception.
if dt.datetime.fromtimestamp(os.path.getmtime(
file_name)) > file_date_limit or year != current_season:
get_new = False
except FileNotFoundError:
pass
if get_new:
futures_tuples = []
weather_api_native = {}
with futures.ThreadPoolExecutor(300) as executor:
while from_date < to_date:
if from_date.month in [7, 8, 9, 10]:
from_date = from_date.replace(day=1,
month=from_date.month + 1)
continue
url = 'http://h-web03.nve.no/APSapi/TimeSeriesReader.svc/MountainWeather/-/{0}/no/true'.format(
from_date.isoformat())
future = executor.submit(lambda: requests.get(url))
futures_tuples.append((from_date, 0, future))
from_date += dt.timedelta(days=1)
while len(futures_tuples):
from_date, retries, future = futures_tuples.pop()
response = future.result()
if response.status_code != requests.codes.ok:
if retries < 5:
url = 'http://h-web03.nve.no/APSapi/TimeSeriesReader.svc/MountainWeather/-/{0}/no/true'.format(
from_date.isoformat())
future = executor.submit(lambda: requests.get(url))
futures_tuples.insert(0,
(from_date, retries + 1, future))
else:
print(
f"Failed to fetch weather for {from_date.isoformat()}, skipping",
file=sys.stderr)
continue
json = response.json()
for obs in json:
region = int(float(obs['RegionId']))
if region not in REGIONS:
continue
if obs['Attribute'] not in weather_api_native:
weather_api_native[obs['Attribute']] = {}
region = int(float(obs['RegionId']))
try:
weather_api_native[obs['Attribute']][(
from_date.isoformat(),
region)] = float(obs['Value'])
except ValueError:
weather_api_native[obs['Attribute']][(
from_date.isoformat(), region)] = obs['Value']
if not date:
with open(file_name, 'wb') as handle:
pickle.dump(weather_api_native,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
else:
try:
with open(file_name, 'rb') as handle:
weather_api_native = pickle.load(handle)
except:
os.remove(file_name)
return _get_weather_obs(year, max_file_age)
weather_api = {}
for key, (orig_key, mapper) in WEATHER_API.items():
weather_api[key] = {}
if orig_key in weather_api_native:
for date_region, value in weather_api_native[orig_key].items():
weather_api[key][date_region] = mapper(value)
weather_varsom = {}
for forecast in aw:
for key, (orig_key, mapper) in WEATHER_VARSOM.items():
if key not in weather_varsom:
weather_varsom[key] = {}
date_region = (forecast.date_valid.isoformat(), forecast.region_id)
weather_varsom[key][date_region] = mapper(
getattr(forecast.mountain_weather, orig_key))
# We prioritize APS before the Varsom data.
# Varsom is manually adjusted by a professional,
# but by the time we download it, the data will be
# 18-24 h old.
return merge(weather_varsom, weather_api)
def _get_regobs_obs(year,
requested_types,
date=None,
days=None,
max_file_age=23):
regions = gm.get_forecast_regions(year=year, get_b_regions=True)
observations = {}
observations_neigh = {}
varsomdata_obses = {}
if len(requested_types) == 0:
return observations
file_name = f'{se.local_storage}regobs_v{CSV_VERSION}_{year}.pickle'
file_date_limit = dt.datetime.now() - dt.timedelta(hours=max_file_age)
current_season = gm.get_season_from_date(dt.date.today() -
dt.timedelta(30))
number_of_records = 50
get_new = True
try:
# Don't fetch new data if old is cached. If older season file doesn't exists we get out via an exception.
if dt.datetime.fromtimestamp(os.path.getmtime(
file_name)) > file_date_limit or year != current_season:
get_new = False
if date:
get_new = True
except FileNotFoundError:
pass
if date:
from_date = date - dt.timedelta(days=days)
to_date = date
else:
from_date, to_date = gm.get_dates_from_season(year=year)
if "AvalancheIndex" in requested_types:
avalanche_index = True
else:
avalanche_index = False
req_set = set(requested_types) & set(REG_ENG_V4.keys())
# Make sure all requested elements from RegObs actually have the information we need specified
if not min(
map(lambda x: set(list(x.keys())).issuperset(req_set),
[REGOBS_CLASSES, REGOBS_SCALARS, REG_ENG])):
raise RegObsRegTypeError()
url = "https://api.regobs.no/v4/Search"
query = {
"LangKey": 1,
"FromDate": from_date.isoformat(),
"ToDate": to_date.isoformat(),
"SelectedRegistrationTypes": None,
"SelectedRegions": regions,
"NumberOfRecords": number_of_records,
"Offset": 0
}
results = []
def send_req(queries):
query = queries.pop()
try:
req = requests.post(url=url, json=query)
return (req, query)
except:
return (None, query)
if get_new:
future_tuples = []
total_matches = requests.post(url=url + "/Count",
json=query).json()["TotalMatches"]
with futures.ThreadPoolExecutor(140) as executor:
queries = []
while query["Offset"] < total_matches:
queries.append(query.copy())
query["Offset"] += number_of_records
for _ in range(0, len(queries)):
future = executor.submit(send_req, queries)
future_tuples.append((0, future))
while len(future_tuples):
retries, future = future_tuples.pop()
try:
response, query = future.result()
raw_obses = response.json()
except:
if retries < 5:
future = executor.submit(send_req, [query])
future_tuples.insert(0, (retries + 1, future))
else:
offset = json.loads(response.request.body)["Offset"]
print(
f"Failed to fetch regobs, offset {offset}, skipping",
file=sys.stderr)
continue
results = results + raw_obses
if not date:
with open(file_name, 'wb') as handle:
pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)
else:
try:
with open(file_name, 'rb') as handle:
results = pickle.load(handle)
except:
os.remove(file_name)
return _get_regobs_obs(regions, year, requested_types,
max_file_age)
for raw_obs in results:
date = dt.datetime.fromisoformat(raw_obs["DtObsTime"]).date()
key = (date.isoformat(), raw_obs["ObsLocation"]["ForecastRegionTID"])
if key not in observations:
observations[key] = {}
observations_neigh[key] = {}
varsomdata_obses[key] = []
for obs_type in req_set:
if REG_ENG_V4[obs_type] not in raw_obs or not raw_obs[
REG_ENG_V4[obs_type]]:
continue
reg = raw_obs[REG_ENG_V4[obs_type]]
# Ignore snow profiles of the old format
if obs_type == "Snøprofil" and "StratProfile" not in reg:
continue
obs = {"competence": raw_obs["Observer"]["CompetenceLevelTID"]}
try:
for attr, categories in REGOBS_CLASSES[obs_type].items():
for cat_id, cat_name in categories.items():
if isinstance(reg, list):
obs[cat_name] = 1 if cat_id in map(
lambda x: x[attr], reg) else 0
else:
obs[cat_name] = 1 if cat_id == reg[attr] else 0
except KeyError:
pass
try:
for regobs_attr, conv in REGOBS_SCALARS[obs_type].values():
obs[regobs_attr] = 0
if isinstance(reg, list) and len(reg) > 0:
obs[regobs_attr] = reg[0][regobs_attr]
elif not isinstance(reg, list):
obs[regobs_attr] = reg[regobs_attr]
if obs[regobs_attr] is None:
obs[regobs_attr] = 0
except KeyError:
pass
if obs_type not in observations[key]:
observations[key][obs_type] = []
observations[key][obs_type].append(obs)
varsomdata_obses[key] += go.Observation(raw_obs).Observations
# We want the most competent observations first
for key, date_region in observations.items():
for reg_key, reg_type in date_region.items():
reg_type.sort(key=lambda x: x['competence'], reverse=True)
observations_neigh[key][reg_key] = reg_type.copy()
rng = np.random.default_rng(1984)
for (date, region), date_region in observations.items():
for reg_key in date_region.keys():
reg_neigh = []
for neighbour in rng.permutation(REGION_NEIGH[region]):
try:
reg_neigh += observations[(date, neighbour)][reg_key]
except KeyError:
pass
reg_neigh.sort(key=lambda x: x['competence'], reverse=True)
observations_neigh[(date, region)][reg_key] += reg_neigh
df_dict = {}
for key, observation in observations_neigh.items():
# Use 2 most competent observations, and list both categories as well as scalars
for obs_idx in range(0, 2):
# One type of observation (test, danger signs etc.) at a time
for regobs_type in req_set:
obses = observation[
regobs_type] if regobs_type in observation else []
# Go through each requested class attribute from the specified observation type
for attr, cat in REGOBS_CLASSES[regobs_type].items():
# We handle categories using 1-hot, so we step through each category
for cat_name in cat.values():
attr_name = f"regobs_{REG_ENG[regobs_type]}_{_camel_to_snake(attr)}_{cat_name}_{obs_idx}"
if attr_name not in df_dict:
df_dict[attr_name] = {}
df_dict[attr_name][key] = obses[obs_idx][
cat_name] if len(obses) > obs_idx else 0
# Go through all requested scalars
for attr, (regobs_attr,
conv) in REGOBS_SCALARS[regobs_type].items():
attr_name = f"regobs_{REG_ENG[regobs_type]}_{_camel_to_snake(attr)}_{obs_idx}"
if attr_name not in df_dict:
df_dict[attr_name] = {}
try:
df_dict[attr_name][key] = conv(
obses[obs_idx]
[regobs_attr]) if len(obses) > obs_idx else 0
except TypeError:
df_dict[attr_name][key] = 0
if "accuracy" not in df_dict:
df_dict["accuracy"] = {}
df_dict['accuracy'][key] = sum(
map(lambda x: {
0: 0,
1: 1,
2: -1,
3: -1
}[x['ForecastCorrectTID']], observation['Skredfarevurdering'])
) if 'Skredfarevurdering' in observation else 0
if avalanche_index:
if "regobs_avalancheidx" not in df_dict:
df_dict["regobs_avalancheidx"] = {}
avalanche_indices = list(
map(lambda x: x.index,
gm.get_avalanche_index(varsomdata_obses[key])))
if avalanche_indices:
df_dict['regobs_avalancheidx'][key] = max(avalanche_indices)
else:
df_dict['regobs_avalancheidx'][key] = 0
return df_dict
def _get_regobs_obs(regions, year, requested_types, max_file_age=23):
observations = {}
if len(requested_types) == 0:
return observations
file_name = f'{se.local_storage}regobs_{year}.pickle'
file_date_limit = dt.datetime.now() - dt.timedelta(hours=max_file_age)
current_season = gm.get_season_from_date(dt.date.today() -
dt.timedelta(30))
number_of_records = 50
get_new = True
try:
# Don't fetch new data if old is cached. If older season file doesn't exists we get out via an exception.
if dt.datetime.fromtimestamp(os.path.getmtime(
file_name)) > file_date_limit or year != current_season:
get_new = False
except FileNotFoundError:
pass
from_date, to_date = gm.get_dates_from_season(year=year)
req_set = set(requested_types)
# Make sure all requested elements from RegObs actually have the information we need specified
if not min(
map(lambda x: set(list(x.keys())).issuperset(req_set),
[REGOBS_CLASSES, REGOBS_SCALARS, REG_ENG])):
raise RegObsRegTypeError()
url = "https://api.nve.no/hydrology/regobs/webapi_v3.2.0/Search/Avalanche"
query = {
"LangKey": 1,
"FromDate": from_date.isoformat(),
"ToDate": to_date.isoformat(),
"SelectedRegistrationTypes": [],
"SelectedRegions": regions,
"NumberOfRecords": number_of_records,
"Offset": 0
}
response = []
if get_new:
while True:
try:
raw_obses = requests.post(url=url, json=query).json()
except requests.exceptions.ConnectionError:
time.sleep(1)
continue
response = response + raw_obses["Results"]
query["Offset"] += number_of_records
if raw_obses["ResultsInPage"] < number_of_records:
with open(file_name, 'wb') as handle:
pickle.dump(response,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
break
else:
try:
with open(file_name, 'rb') as handle:
response = pickle.load(handle)
except:
os.remove(file_name)
return _get_regobs_obs(regions, year, requested_types,
max_file_age)
for raw_obs in response:
for reg in raw_obs["Registrations"]:
obs_type = reg["RegistrationName"]
if obs_type not in requested_types:
continue
# Ignore snow profiles of the old format
if obs_type == "Snøprofil" and "StratProfile" not in reg[
"FullObject"]:
continue
obs = {"competence": raw_obs["CompetenceLevelTid"]}
try:
for attr, categories in REGOBS_CLASSES[obs_type].items():
value = reg["FullObject"][attr]
for cat_id, cat_name in categories.items():
obs[cat_name] = 1 if cat_id == value else 0
except KeyError:
pass
try:
for regobs_attr, conv in REGOBS_SCALARS[obs_type].values():
obs[regobs_attr] = reg["FullObject"][regobs_attr]
except KeyError:
pass
date = dt.datetime.fromisoformat(raw_obs["DtObsTime"]).date()
key = (raw_obs["ForecastRegionTid"], date)
if key not in observations:
observations[key] = {}
if obs_type not in observations[key]:
observations[key][obs_type] = []
observations[key][obs_type].append(obs)
# We want the most competent observations first
for date_region in observations.values():
for reg_type in date_region.values():
reg_type.sort(key=lambda x: x['competence'], reverse=True)
return observations