def make_problems_for_BritSiv(): """Brit Siv ønsket oversikt over varslede skredprobelemr og faregrader for Indre Fjordane of Fjordane de to siste årene (2015-2017). """ output_filename = '{0}Skredproblemer Indre Fjordane for BritSiv.csv'.format( env.output_folder) pickle_file_name = '{0}runavalancheproblems_britsiv.pickle'.format( env.local_storage) get_new = False all_dangers = [] if get_new: # Get Fjordane 2015-16 region_id = 121 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 = 3027 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) all_problems = [] for d in all_dangers: all_problems += d.avalanche_problems all_problems.sort(key=lambda AvalancheProblem: AvalancheProblem.date) _save_problems(all_problems, output_filename)
def get_all_ofoten(): """Dangers and problems for Ofoten (former Narvik). Writes file to .csv""" get_new = True get_observations = False write_csv = True plot_dangerlevels_simple = False select_years = [ '2012-13', '2013-14', '2014-15', '2015-16', '2016-17', '2017-18' ] region_id_Narvik = 114 # Narvik used from 2012 until nov 2016 region_id_Ofoten = 3015 # Ofoten introduced in november 2016 warnings_pickle = '{0}allforecasteddangerlevels_Ofoten_201218.pickle'.format( env.local_storage) warnings_csv = '{0}Faregrader Ofoten 2012-18.csv'.format(env.output_folder) warnings_plot = '{0}Faregrader Ofoten 2012-18.png'.format( env.output_folder) if get_new: all_warnings = [] all_evaluations = [] for y in select_years: if y in ['2016-17', '2017-18']: region_id = region_id_Ofoten else: region_id = region_id_Narvik from_date, to_date = gm.get_forecast_dates(year=y) all_warnings += gd.get_forecasted_dangers(region_id, from_date, to_date) if get_observations: all_evaluations += go.get_avalanche_evaluation_3( from_date, to_date, region_id) mp.pickle_anything([all_warnings, all_evaluations], warnings_pickle) else: [all_warnings, all_evaluations] = mp.unpickle_anything(warnings_pickle) if write_csv: # write to csv files _save_danger_and_problem_to_file(all_warnings, warnings_csv) elif plot_dangerlevels_simple: # Make simple plot from_date = gm.get_forecast_dates(select_years[0])[0] to_date = gm.get_forecast_dates(select_years[-1])[1] _make_plot_dangerlevels_simple(all_warnings, all_evaluations, warnings_plot, from_date, to_date) else: print("No output selected") return all_warnings, all_evaluations
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_Sander(): """2018 August: Hei igjen Ragnar. Har du statistikk på varsla faregrad over ein heil sesong for Noreg? Eit snitt. XX dagar med faregrad 1, XX dagar med faregrad 2, XX dagar med faregrad 3.... fordelt på XX varslingsdagar. :return: """ pickle_file_name = '{0}201808_avalanche_forecasts_sander.pickle'.format( env.local_storage) get_new = False all_dangers = [] if get_new: years = ['2012-13', '2013-14', '2014-15', '2015-16'] for y in years: from_date, to_date = gm.get_forecast_dates(y) region_ids = gm.get_forecast_regions(y, get_b_regions=True) all_dangers += gd.get_forecasted_dangers(region_ids, from_date, to_date) years = ['2016-17', '2017-18'] for y in years: from_date, to_date = gm.get_forecast_dates(y) region_ids = gm.get_forecast_regions(y, get_b_regions=True) 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}201808 Faregrader for Sander.txt'.format( env.output_folder) import pandas as pd all_dangers_dict = [] for a in all_dangers: all_dangers_dict.append(a.__dict__) col_names = list(all_dangers_dict[0].keys()) all_dangers_df = pd.DataFrame(all_dangers_dict, columns=col_names, index=range(0, len(all_dangers_dict), 1)) a = 1
def _axis_date_labels_from_year(year): """For a season (year) get labels for the first day in the month and positions on x axis.""" axis_dates = [] axis_positions = [] from_date, to_date = gm.get_forecast_dates(year) for i in range(0, (to_date - from_date).days + 1, 1): date = from_date + dt.timedelta(days=i) if date.day == 1: axis_dates.append(date.strftime("%b %Y")) axis_positions.append(i) return axis_dates, axis_positions
def _get_all_snow(get_new=False): file_name = '{}observations and forecasts 2012-17.pickle'.format( env.local_storage) if get_new: all_observations = go.get_all_registrations('2012-12-01', '2017-07-01', geohazard_tids=10) years = ['2012-13', '2013-14', '2014-15', '2015-16', '2016-17'] all_forecasts = [] for y in years: from_date, to_date = gm.get_forecast_dates(y) region_ids = gm.get_forecast_regions(y) all_forecasts += gfa.get_avalanche_warnings( region_ids, from_date, to_date) mp.pickle_anything([all_observations, all_forecasts], file_name) else: [all_observations, all_forecasts] = mp.unpickle_anything(file_name) return all_observations, all_forecasts
def get_all_forecasts(year, lang_key=1, max_file_age=23): """Specialized method for getting all forecasts for one season. For the current season (at the time of writing, 2018-19), if a 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' :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 valid_forecasts: [list of AvalancheWarning] """ from_date, to_date = gm.get_forecast_dates(year=year) file_name = '{0}all_forecasts_{1}_lk{2}.pickle'.format( env.local_storage, year, lang_key) file_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)) # Get forecast regions used in the current year region_ids = gm.get_forecast_regions(year, get_b_regions=True) get_new = True if os.path.exists(file_name): # if file contains a season long gone, dont make new. if year == current_season: file_age = dt.datetime.fromtimestamp(os.path.getmtime(file_name)) # If file is newer than the given time limit, dont make new. if file_age > file_date_limit: # If file size larger than that of an nearly empty file, dont make new. if os.path.getsize(file_name) > 100: # 100 bytes limit get_new = False else: get_new = False if get_new: lg.info( "getvarsompickles.py -> get_all_forecasts: Get new {0} forecasts and pickle." .format(year)) all_forecasts = gfa.get_avalanche_warnings(region_ids, from_date, to_date, lang_key=lang_key) # Valid forecasts have a danger level. The other are empty. valid_forecasts = [] for f in all_forecasts: if f.danger_level > 0: valid_forecasts.append(f) mp.pickle_anything(valid_forecasts, file_name) else: valid_forecasts = mp.unpickle_anything(file_name) return valid_forecasts
x + 20, -270, '*** {0} ganger er ett snoeskred med hoeysete index. \n' ' {1} som skredaktivitet og {2} med skjema for \n' ' enkeltskred.'.format(est_num_1, est_num_1_aval_act, est_num_1_aval)) return if __name__ == "__main__": season = '2017-18' ### Get all regions region_ids = gm.get_forecast_regions(season) from_date, to_date = gm.get_forecast_dates(season) # from_date, to_date = '2017-12-01', '2018-02-01' # region_ids = [3014, 3015] ### get and make the data set date_region, forecasted_dangers = step_1_make_data_set( region_ids, from_date, to_date) mp.pickle_anything([date_region, forecasted_dangers], '{0}runforavalancheactivity_step_1.pickle'.format( env.local_storage)) ### Find the observaton of highest value pr region pr date date_region, forecasted_dangers = mp.unpickle_anything( '{0}runforavalancheactivity_step_1.pickle'.format(env.local_storage)) date_region = step_2_find_most_valued(date_region) mp.pickle_anything([date_region, forecasted_dangers],
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
from utilities import fencoding as fe import setenvironment as env __author__ = 'Ragnar Ekker' years = ['2014-15', '2015-16', '2016-17', '2017-18'] 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=1) forecast_dangers += gd.get_forecasted_dangers(region_ids, from_date, to_date, lang_key=1) # 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)) current_years_observed_dangers = gd.get_observed_dangers(region_ids, from_date,
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_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 set_x_from_date(self): current_season = gm.get_season_from_date(self.date) from_date, to_date = gm.get_forecast_dates(current_season) x = (self.date - from_date).days return x
def make_dl_incident_markus(): """ From the beginning of time: get all forecasts. and then get how many on dl 3. get all incidents, excpt elrapp, and all in back country all these, get all on days in regions of dl 3. get all with serious caracter on days and in regions on dl 3 :return: """ pickle_file_name = '{0}incident_on_dl3_for_markus.pickle'.format( env.local_storage) years = ['2012-13', '2013-14', '2014-15', '2015-16', '2016-17'] get_new = False all_dangers = [] all_incidents = [] if get_new: for y in years: # get forecast regions used this year from_date, to_date = gm.get_forecast_dates(y) # get incidents for this year and map to this years forecast regions this_year_incidents = go.get_incident(from_date, to_date, geohazard_tids=10) for i in this_year_incidents: utm33x = i.UTMEast utm33y = i.UTMNorth region_id, region_name = gm.get_forecast_region_for_coordinate( utm33x, utm33y, y) i.region_regobs_id = region_id i.region_name = region_name all_incidents += this_year_incidents # get regions and the forecasts used this year region_ids = gm.get_forecast_regions(y) all_dangers += gd.get_forecasted_dangers(region_ids, from_date, to_date) # in the end, pickle it all mp.pickle_anything([all_dangers, all_incidents], pickle_file_name) else: [all_dangers, all_incidents] = mp.unpickle_anything(pickle_file_name) all_dl3 = [] for d in all_dangers: if d.danger_level == 3: all_dl3.append(d) all_back_country_incidents = [] for i in all_incidents: if 'drift@svv' not in i.NickName: # if activity influenced is backcounty og scooter # should probably include 100 which is non specified incidents # giving this dataset the observations not specified if i.ActivityInfluencedTID in [ 100, 110, 111, 112, 113, 114, 115, 116, 117, 130 ]: all_back_country_incidents.append(i) all_back_country_incidents_with_consequence = [] for i in all_back_country_incidents: # If damageextent is nestenulykke, personer skadet eller personer omkommet if i.DamageExtentTID > 28: all_back_country_incidents_with_consequence.append(i) # find incidents in regions on days with danger level 3 # find incidetns in region on day with dl3 all_back_country_incidents_on_region_dl3 = [] all_back_country_incidents_with_consequence_on_region_dl3 = [] for d in all_dl3: danger_date = d.date danger_region_id = d.region_regobs_id for i in all_back_country_incidents: incident_date = i.DtObsTime.date() incident_region_id = i.ForecastRegionTID if incident_date == danger_date and incident_region_id == danger_region_id: all_back_country_incidents_on_region_dl3.append(i) for i in all_back_country_incidents_with_consequence: incident_date = i.DtObsTime.date() incident_region_id = i.ForecastRegionTID if incident_date == danger_date and incident_region_id == danger_region_id: all_back_country_incidents_with_consequence_on_region_dl3.append( i) print('Totalt varsler laget siden tidenes morgen: {}'.format( len(all_dangers))) print('Totalt varsler på fg 3: {}'.format(len(all_dl3))) print('Totalt antall hendelser i baklandet: {}'.format( len(all_back_country_incidents))) print('Totalt antall hendelser i baklandet med konsekvens: {}'.format( len(all_back_country_incidents_with_consequence))) print( 'Totalt antall hendelser i baklandet i regioner på dager med fg3: {}'. format(len(all_back_country_incidents_on_region_dl3))) print( 'Totalt antall hendelser i baklandet i regioner på dager med fg3 med konsekvens: {}' .format( len(all_back_country_incidents_with_consequence_on_region_dl3))) return
def _plot_causes(region_name, causes, year='2018-19', plot_folder=env.plot_folder + 'regionplots/'): """Plots observed and forecasted causes for a region for a given year. :param region_name: :param year: [string] :param causes: :param plot_folder: :return: """ if not os.path.exists(plot_folder): os.makedirs(plot_folder) from_date, to_date = gm.get_forecast_dates(year) filename = '{0} skredproblemer {1}'.format(region_name, year) ml.log_and_print( "[info] plotdangerandproblem.py -> plot_causes: Plotting {0}".format( filename)) aval_cause_kdv = gkdv.get_kdv('AvalCauseKDV') list_of_causes = [ 0, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 ] list_of_causes = [10, 15, 14, 11, 13, 18, 19, 16, 22, 20, 24, 0] # list_of_causes = set([c.cause_tid for c in causes]) list_of_cause_names = [aval_cause_kdv[tid].Name for tid in list_of_causes] dict_of_causes = {} for c in list_of_causes: dict_of_causes[c] = [] for c in causes: dict_of_causes[c.cause_tid].append(c) # Start plotting fsize = (16, 7) plt.figure(figsize=fsize) plt.clf() # plot lines and left and bottom ticks y = 0 for k, values in dict_of_causes.items(): for v in values: x = (v.date - from_date).days if 'Forecast' in v.source: plt.hlines(y - 0.1, x, x + 1, lw=4, color='red') # offset the line 0.1 up if 'Observation' in v.source: plt.hlines(y + 0.1, x, x + 1, lw=4, color='blue') # offset the line 0.1 down y += 1 # Left y-axis labels plt.ylim(len(list_of_causes) - 1, -1) # 12 skredproblemer plt.yticks(range(len(list_of_causes) + 1), list_of_cause_names) # x-axis labels axis_dates = [] axis_positions = [] for i in range(0, (to_date - from_date).days, 1): date = from_date + dt.timedelta(days=i) if date.day == 1: axis_dates.append(date.strftime("%b %Y")) axis_positions.append(i) plt.xticks(axis_positions, axis_dates) # Right hand side y-axis right_ticks = [] correlation_sum = 0. for k, values in dict_of_causes.items(): values_obs = [vo for vo in values if 'Observation' in vo.source] values_fc = [vf for vf in values if 'Forecast' in vf.source] correlation = 0. for obs in values_obs: for fc in values_fc: if obs.date == fc.date and obs.cause_tid == fc.cause_tid: correlation += 1 if len(values_obs) == 0 and len(values_fc) == 0: right_ticks.append("") else: if len(values_obs) == 0: right_ticks.append("v{0} o{1} s{2}%".format( len(values_fc), len(values_obs), 0)) else: right_ticks.append("v{0} o{1} s{2}%".format( len(values_fc), len(values_obs), int(correlation / len(values_obs) * 100))) correlation_sum += correlation right_ticks.reverse() plt.twinx() plt.ylim(-1, len(right_ticks) - 1) plt.yticks(range(len(right_ticks) + 1), right_ticks) # the title num_obs = len([c for c in causes if 'Observation' in c.source]) num_fc = len([c for c in causes if 'Forecast' in c.source]) if num_obs == 0: correlation_prct = 0 else: correlation_prct = int(correlation_sum / num_obs * 100) title = 'Skredproblemer for {0} ({1} - {2}) \n Totalt {3} varslede problemer (rød) og {4} observerte problemer (blå) \n og det er {5}% samsvar mellom det som er observert og det som er varselt.'\ .format(region_name, from_date.strftime('%Y%m%d'), to_date.strftime('%Y%m%d'), num_fc, num_obs, correlation_prct) plt.title(title) # When is the figure made? plt.gcf().text(0.85, 0.02, 'Figur laget {0:%Y-%m-%d %H:%M}'.format(dt.datetime.now()), color='0.5') fig = plt.gcf() fig.subplots_adjust(left=0.2) plt.savefig(u'{0}{1}'.format(plot_folder, filename)) plt.close(fig)
def _plot_danger_levels(region_name, danger_levels, aval_indexes, year='2018-19', plot_folder=env.plot_folder + 'regionplots/'): """Plots the danger levels as bars and makes a small cake diagram with distribution. :param region_name: [String] Name of forecast region :param year: [string] :param danger_levels: :param aval_indexes: :param plot_folder: :return: """ if not os.path.exists(plot_folder): os.makedirs(plot_folder) from_date, to_date = gm.get_forecast_dates(year) filename = '{0} faregrader {1}'.format(region_name, year) ml.log_and_print( "[info] plotdangerandproblem.py -> plot_danger_levels: Plotting {0}". format(filename)) # Figure dimensions fsize = (16, 16) fig = plt.figure(figsize=fsize) plt.clf() ########################################## # First subplot with avalanche index ########################################## pplt.subplot2grid((6, 1), (0, 0), rowspan=1) index_dates = [] data_indexes = [] index_colors = [] for i in aval_indexes: date = i.date index_dates.append(date) data_indexes.append(i.index) # color on the marker if i.index == 0: index_colors.append('white') elif i.index == 1: index_colors.append('pink') elif i.index >= 2 and i.index <= 5: index_colors.append('green') elif i.index >= 6 and i.index <= 9: index_colors.append('yellow') elif i.index >= 10 and i.index <= 12: index_colors.append('orange') elif i.index >= 13: index_colors.append('red') else: # This option should not happen. index_colors.append('black') lg.warning( "plotdangerandproblem.py -> plot_danger_levels: Illegal avalanche index option." ) index_values = np.asarray(data_indexes, int) plt.scatter(index_dates, index_values, s=50., c=index_colors, alpha=0.5) plt.yticks([1, 4, 6, 11, 17, 22], [ 'Ingen - 1', 'Ett str2 - 4', 'Ett str3 - 6', 'Noen str3 - 11', 'Mange str3 - 17', '' ]) plt.ylabel("Skredindex") plt.xlim(from_date, to_date) title = "Faregrad og skredindeks for {0} ({1})".format(region_name, year) plt.title(title) ########################################## # Second subplot with avalanche danger forecast ########################################## pplt.subplot2grid((6, 1), (1, 0), rowspan=2) # Making the main plot dl_labels = [ '', '1 - Liten', '2 - Moderat', '3 - Betydelig', '4 - Stor', '' ] dl_colors = ['0.5', '#ccff66', '#ffff00', '#ff9900', '#ff0000', 'k'] # Making a new dataset with both warned and evaluated data data_dates = [] data_dangers = [] for d in danger_levels: data_dates.append(d.date) if 'Forecast' in d.source: data_dangers.append(d.danger_level) else: data_dangers.append(0. * d.danger_level) values = np.asarray(data_dangers, int) colors = [] for n in values: if abs(n) == 1: colors.append(dl_colors[1]) elif abs(n) == 2: colors.append(dl_colors[2]) elif abs(n) == 3: colors.append(dl_colors[3]) elif abs(n) == 4: colors.append(dl_colors[4]) elif abs(n) == 5: colors.append(dl_colors[5]) else: colors.append(dl_colors[0]) plt.bar(data_dates, values, color=colors) plt.yticks(range(0, len(dl_labels), 1), dl_labels) #, size='small') plt.ylabel("Varslet faregrad2") plt.xlim(from_date, to_date) ########################################## # Third subplot with avalanche danger observed ########################################## pplt.subplot2grid((6, 1), (3, 0), rowspan=2) dl_labels = [ '', '1 - Liten', '2 - Moderat', '3 - Betydelig', '4 - Stor', '' ] dl_colors = ['0.5', '#ccff66', '#ffff00', '#ff9900', '#ff0000', 'k'] # Making a new dataset with both warned and evaluated data data_dates = [] data_dangers = [] for d in danger_levels: data_dates.append(d.date) if not 'Forecast' in d.source: data_dangers.append(-1. * d.danger_level) else: data_dangers.append(0. * d.danger_level) values = np.asarray(data_dangers, int) colors = [] for n in values: if abs(n) == 1: colors.append(dl_colors[1]) elif abs(n) == 2: colors.append(dl_colors[2]) elif abs(n) == 3: colors.append(dl_colors[3]) elif abs(n) == 4: colors.append(dl_colors[4]) elif abs(n) == 5: colors.append(dl_colors[5]) else: colors.append(dl_colors[0]) plt.bar(data_dates, values, color=colors) plt.yticks(range(0, -len(dl_labels), -1), dl_labels) plt.ylabel('Observert faregrad') plt.xticks([]) plt.xlim(from_date, to_date) ########################################## # Forth subplot with how well the forecast is ########################################## pplt.subplot2grid((6, 1), (5, 0), rowspan=1) plt.xlim(from_date, to_date) forecast_correct_values = [] forecast_correct_colours = [] forecast_correct_dates = [] for d in danger_levels: if 'Observation' in d.source: forecast_correct = d.danger_object.forecast_correct if forecast_correct is not None and not 'Ikke gitt' in forecast_correct: forecast_correct_dates.append(d.date) if 'riktig' in forecast_correct: forecast_correct_values.append(0) forecast_correct_colours.append('green') elif 'for lav' in forecast_correct: forecast_correct_values.append(-1) forecast_correct_colours.append('red') elif 'for høy' in forecast_correct: forecast_correct_values.append(1) forecast_correct_colours.append('red') else: forecast_correct_values.append(0) forecast_correct_colours.append('black') lg.warning( "plotdangerandproblem.py -> plot_danger_levels: Illegal option for markes on forecast correct plot." ) forecast_correct_np_values = np.asarray(forecast_correct_values, int) plt.scatter(forecast_correct_dates, forecast_correct_np_values, s=50., c=forecast_correct_colours, alpha=0.5) plt.yticks(range(-1, 2, 1), ["For lav", "Riktig", " For høy"]) plt.ylabel("Stemmer varslet faregrad?") # this is an inset pie of the distribution of danger levels OVER the main axes xfrac = 0.15 yfrac = (float(fsize[0]) / float(fsize[1])) * xfrac xpos = 0.45 - xfrac ypos = 0.95 - yfrac a = plt.axes([0.8, 0.66, 0.10, 0.10]) # a = plt.axes([xpos, ypos, xfrac, yfrac]) wDistr = np.bincount( [d.danger_level for d in danger_levels if 'Forecast' in d.source]) a.pie(wDistr, colors=dl_colors, autopct='%1.0f%%', shadow=False) plt.setp(a, xticks=[], yticks=[]) # this is an inset pie of the distribution of dangerlevels UNDER the main axes xfrac = 0.15 yfrac = (float(fsize[0]) / float(fsize[1])) * xfrac xpos = 0.95 - xfrac ypos = 0.29 - yfrac b = plt.axes([0.8, 0.24, 0.10, 0.10]) # b = plt.axes([xpos, ypos, xfrac, yfrac]) eDistr = np.bincount( [d.danger_level for d in danger_levels if 'Observation' in d.source]) b.pie(eDistr, colors=dl_colors, autopct='%1.0f%%', shadow=False) plt.setp(b, xticks=[], yticks=[]) # figure text in observed danger levels subplot w_number, e_number, fract_same = _compare_danger_levels(danger_levels) fig.text( 0.15, 0.25, " Totalt {0} varslet faregrader og {1} observerte faregrader \n og det er {2}% samsvar mellom det som er observert og varslet." .format(w_number, e_number, int(round(fract_same * 100, 0))), fontsize=14) # fractions to the right in the forecast correct subplot forecast_correct_distr = {} for f in forecast_correct_values: if f in forecast_correct_distr.keys(): forecast_correct_distr[f] += 1 else: forecast_correct_distr[f] = 1 if 1 in forecast_correct_distr.keys(): fig.text(0.91, 0.19, '{0}%'.format( int( round( forecast_correct_distr[1] / float(len(forecast_correct_values)) * 100, 0))), fontsize=14) if 0 in forecast_correct_distr.keys(): fig.text(0.91, 0.15, '{0}%'.format( int( round( forecast_correct_distr[0] / float(len(forecast_correct_values)) * 100, 0))), fontsize=14) if -1 in forecast_correct_distr.keys(): fig.text(0.91, 0.11, '{0}%'.format( int( round( forecast_correct_distr[-1] / float(len(forecast_correct_values)) * 100, 0))), fontsize=14) # When is the figure made? plt.gcf().text(0.8, 0.02, 'Figur laget {0:%Y-%m-%d %H:%M}'.format(dt.datetime.now()), color='0.5') # This saves the figure to file plt.savefig(u'{0}{1}'.format(plot_folder, filename)) #,dpi=90) plt.close(fig)