def get_kdv(x_kdv):
'''
Imports a x_kdv view from regObs and returns a dictionary with <key, value> = <ID, Name>
An x_kdv is requested from the regObs api if a pickle file newer than a week exists.
:param x_kdv: [string] x_kdv view
:return dict: {} x_kdv as a dictionary
Ex of use: aval_cause_kdv = get_kdv('AvalCauseKDV')
Ex of url for returning values for IceCoverKDV in norwegian:
http://api.nve.no/hydrology/regobs/v0.9.4/OData.svc/ForecastRegionKDV?$filter=Langkey%20eq%201%20&$format=json
'''
kdv_file = '{0}{1}.pickle'.format(kdv_elements_folder, x_kdv)
dict = {}
if os.path.exists(kdv_file):
### Should be useful to test if the file is old and if so make a new one
# max_file_age = 3
# file_date = time.ctime(os.path.getctime(kdv_file))
# date_limit = datetime.datetime.now() - datetime.timedelta(days=max_file_age)
###
#print("Getting KDV from pickle:{0}".format(kdv_file))
dict = mp.unpickle_anything(kdv_file)
else:
url = 'http://api.nve.no/hydrology/regobs/{0}/OData.svc/{1}?$filter=Langkey%20eq%201%20&$format=json'\
.format(api_version, x_kdv)
print("Getting KDV from URL:{0}".format(url))
kdv = requests.get(url).json()
for a in kdv['d']['results']:
try:
if 'AvalCauseKDV' in url and a['ID'] > 9 and a['ID'] < 26: # this table gets special treatment
dict[a["ID"]] = fe.remove_norwegian_letters(a["Description"])
else:
dict[a["ID"]] = fe.remove_norwegian_letters(a["Name"])
except (RuntimeError, TypeError, NameError):
pass
mp.pickle_anything(dict, kdv_file)
return dict
def add_Inflow_DOP(self, Inflow_DOP_inn):
messages = we.test_for_missing_elements(Inflow_DOP_inn, self.from_date, self.to_date)
self.metadata += messages
self.Inflow_DOP = Inflow_DOP_inn
def add_Inflow_Chla(self, Inflow_Chla_inn):
messages = we.test_for_missing_elements(Inflow_Chla_inn, self.from_date, self.to_date)
self.metadata += messages
self.Inflow_Chla = Inflow_Chla_inn
def add_Inflow_DOC(self, Inflow_DOC_inn):
messages = we.test_for_missing_elements(Inflow_DOC_inn, self.from_date, self.to_date)
self.metadata += messages
self.Inflow_DOC = Inflow_DOC_inn
if __name__ == "__main__":
yesturday = (dt.date.today()-dt.timedelta(days=1)).strftime("%Y-%m-%d")
#harvest_and_save_blindern('2000-01-01', yesturday)
#harvest_and_save_nordnesfjelet('2014-08-01', yesturday)
data = harvest_for_mylake_hakkloa('2013-04-01', '2015-10-01')
mp.pickle_anything(data, data.output_file_path +'.pickle')
data2 = mp.unpickle_anything('{0}HAK_input'.format(env.data_path) +'.pickle')
mfd.write_mylake_inputfile(data2)
def add_Inflow_DOP(self, Inflow_DOP_inn):
messages = we.test_for_missing_elements(Inflow_DOP_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Inflow_DOP = Inflow_DOP_inn
def add_Inflow_Chla(self, Inflow_Chla_inn):
messages = we.test_for_missing_elements(Inflow_Chla_inn,
self.from_date, self.to_date)
self.metadata += messages
self.Inflow_Chla = Inflow_Chla_inn
def add_Inflow_DOC(self, Inflow_DOC_inn):
messages = we.test_for_missing_elements(Inflow_DOC_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Inflow_DOC = Inflow_DOC_inn
if __name__ == "__main__":
yesturday = (dt.date.today() - dt.timedelta(days=1)).strftime("%Y-%m-%d")
#harvest_and_save_blindern('2000-01-01', yesturday)
#harvest_and_save_nordnesfjelet('2014-08-01', yesturday)
data = harvest_for_mylake_hakkloa('2013-04-01', '2015-10-01')
mp.pickle_anything(data, data.output_file_path + '.pickle')
data2 = mp.unpickle_anything('{0}HAK_input'.format(env.data_path) +
'.pickle')
mfd.write_mylake_inputfile(data2)
if __name__ == "__main__":
# regions_kdv = GRO.get_kdv("ForecastRegionKDV")
regions = list(range(106, 134)) # ForecastRegionTID = 133 is the last and is Salten
date_from = "2014-12-01"
date_to = "2015-06-01"
pickle_warnings_file_name = '{0}{1}'.format(data_folder, 'runForMatrix warnings.pickle')
pickle_data_set_file_name = '{0}{1}'.format(data_folder, 'runForMatrix data set.pickle')
#### With it piclked you dont need to read on the api all the time ####
# pickle_warnings(regions, date_from, date_to, pickle_warnings_file_name)
# warnings = RP.unpickle_anything(pickle_warnings_file_name)
# pickle_data_set(warnings, pickle_data_set_file_name, False)
data_set = RP.unpickle_anything(pickle_data_set_file_name)
plot_histogram('frequency of levels', data_set['level']['values'], data_set['level']['keys'],
'{0}Histogram of levels {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram('frequency of sizes', data_set['size']['values'], data_set['size']['keys'],
'{0}Histogram of sizes {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram('frequency of triggers', data_set['trigger']['values'], data_set['trigger']['keys'],
'{0}Histogram of triggers {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram('frequency of probabilities', data_set['probability']['values'], data_set['probability']['keys'],
'{0}Histogram of probabilities {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram('frequency of distribution', data_set['distribution']['values'], data_set['distribution']['keys'],
'{0}Histogram of distribution {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram_on_danger_level('', 'size', data_set,
'{0}Histogram of size on danger level {1} to {2}.png'.format(plot_folder, date_from, date_to))
plot_histogram_on_danger_level('', 'trigger', data_set,
