diff --git a/extreme_data/meteo_france_data/adamont_data/adamont_gcm_rcm_couples.py b/extreme_data/meteo_france_data/adamont_data/adamont_gcm_rcm_couples.py
index b3fdfc835d1f70db4977a5209e86eeaba3823747..13dc1f9ee96a8c6342e27105abfc768416f4d01a 100644
--- a/extreme_data/meteo_france_data/adamont_data/adamont_gcm_rcm_couples.py
+++ b/extreme_data/meteo_france_data/adamont_data/adamont_gcm_rcm_couples.py
@@ -52,6 +52,21 @@ def get_gcm_rcm_couple_adamont_to_full_name(version):
return gcm_rcm_couple_adamont_v2_to_full_name
+def get_gcm_list(adamont_version):
+ s = set([gcm for gcm, _ in get_gcm_rcm_couple_adamont_to_full_name(adamont_version).keys()])
+ return list(s)
+
+
+gcm_to_rnumber = \
+ {
+ 'MPI-ESM-LR': 2,
+ 'CNRM-CM5': 11,
+ 'IPSL-CM5A-MR': 1,
+ 'EC-EARTH': 12,
+ 'HadGEM2-ES': 1,
+ 'NorESM1-M': 1
+ }
+
gcm_rcm_couple_adamont_v1_to_full_name = {
('CNRM-CM5', 'ALADIN53'): 'CNRM-ALADIN53_CNRM-CERFACS-CNRM-CM5',
('CNRM-CM5', 'RCA4'): 'SMHI-RCA4_CNRM-CERFACS-CNRM-CM5',
@@ -105,3 +120,6 @@ gcm_rcm_couple_adamont_v2_to_full_name = {
# For this member there is only the historical anyway
# ('ERAINT', 'ALADIN62'): 'CNRM-ALADIN62_ECMWF-ERAINT',
}
+
+if __name__ == '__main__':
+ print(get_gcm_list(adamont_version=2))
diff --git a/extreme_data/meteo_france_data/adamont_data/cmip5/__init__.py b/extreme_data/meteo_france_data/adamont_data/cmip5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/extreme_data/meteo_france_data/adamont_data/cmip5/cmip5_global_temp.py b/extreme_data/meteo_france_data/adamont_data/cmip5/cmip5_global_temp.py
new file mode 100644
index 0000000000000000000000000000000000000000..a405bd1b9ed31747977243c6a16e91c6adcd0c7a
--- /dev/null
+++ b/extreme_data/meteo_france_data/adamont_data/cmip5/cmip5_global_temp.py
@@ -0,0 +1,149 @@
+import os.path as op
+import pandas as pd
+import subprocess
+from datetime import datetime, timedelta
+
+import cdsapi
+import numpy as np
+from netCDF4._netCDF4 import Dataset, OrderedDict
+
+from extreme_data.meteo_france_data.adamont_data.adamont_gcm_rcm_couples import gcm_to_rnumber, get_gcm_list
+from extreme_data.meteo_france_data.adamont_data.adamont_scenario import get_year_min_and_year_max_from_scenario, \
+ AdamontScenario, adamont_scenarios_real
+from extreme_data.utils import DATA_PATH
+
+GLOBALTEMP_WEB_PATH = "https://climexp.knmi.nl/CMIP5/Tglobal/"
+GLOBALTEMP_DATA_PATH = op.join(DATA_PATH, 'CMIP5_global_temp')
+
+
+def get_scenario_name(scenario):
+ if scenario is AdamontScenario.histo:
+ return 'historical'
+ else:
+ return str(scenario).split('.')[-1]
+
+
+def get_year_min_and_year_max_for_global_temp(scenario):
+ if scenario is AdamontScenario.histo:
+ return 1951, 2005
+ else:
+ return 2006, 2100
+
+
+def get_periods(gcm, scenario):
+ if scenario is AdamontScenario.histo:
+ if gcm == 'EC-EARTH':
+ return ['195001-201212']
+ else:
+ return ['185001-200512']
+ else:
+ if gcm == 'CNRM-CM5':
+ return []
+ else:
+ return ['200601-210012']
+
+
+
+
+def year_to_global_mean_temp(gcm, scenario):
+ # Compute everything
+ periods = get_periods(gcm, scenario)
+ ensemble_member = 'r{}i1p1'.format(gcm_to_rnumber[gcm])
+ scenario_name = get_scenario_name(scenario)
+ year_min, year_max = get_year_min_and_year_max_for_global_temp(scenario)
+
+ # Standards
+ mean_annual_column_name = 'Annual mean'
+ zip_filepath = op.join(GLOBALTEMP_DATA_PATH, 'download.zip')
+
+
+ # Create a csv file for each period
+ for period in periods:
+ filename = 'tas_Amon_{}_{}_{}_{}'.format(gcm, scenario_name, ensemble_member, period)
+ nc_filepath = op.join(GLOBALTEMP_DATA_PATH, filename + '.nc')
+ csv_filepath = op.join(GLOBALTEMP_DATA_PATH, filename + '.csv')
+ # Download if needed
+ if not op.exists(nc_filepath):
+ download_nc(ensemble_member, gcm, period, scenario_name, zip_filepath)
+ # Transform nc file into csv file
+ if not op.exists(csv_filepath):
+ nc_to_csv(csv_filepath, mean_annual_column_name, nc_filepath, year_max, year_min)
+
+ # Concatenate all csv together into a single summary_csv_filepath
+
+
+ # Load csv file
+ df = pd.read_csv(csv_filepath, index_col=0)
+ d = OrderedDict(df[mean_annual_column_name])
+ print(gcm, scenario_name, np.mean(list(d.values())))
+ return d
+
+
+def nc_to_csv(csv_filepath, mean_annual_column_name, nc_filepath, year_max, year_min):
+ dataset = Dataset(nc_filepath)
+ tas_list = np.array(dataset.variables['tas'])
+ tas_list = np.mean(tas_list, axis=1)
+ tas_list = np.mean(tas_list, axis=1)
+ # 'days since 1850-1-1 00:00:00'
+ time_list = np.array(dataset.variables['time'])
+ assert len(time_list) == len(tas_list)
+ start = datetime(year=1850, month=1, day=1, hour=0, minute=0, second=0)
+ date_list = [start + timedelta(days=time) for time in time_list]
+ winter_year_list = [date.year if date.month < 8 else date.year + 1 for date in date_list]
+ winter_year_to_tas_list = {winter_year: [] for winter_year in range(year_min, year_max + 1)}
+ for winter_year, tas in zip(winter_year_list, tas_list):
+ if year_min <= winter_year <= year_max:
+ winter_year_to_tas_list[winter_year].append(tas)
+ # we have monthly values
+ for tas_list in winter_year_to_tas_list.values():
+ assert len(tas_list) == 12
+ winter_year_to_mean_tas = OrderedDict()
+ for winter_year, t in winter_year_to_tas_list.items():
+ winter_year_to_mean_tas[winter_year] = np.mean(t)
+ s = pd.Series(winter_year_to_mean_tas)
+ df = pd.DataFrame({mean_annual_column_name: s})
+ df.to_csv(csv_filepath)
+
+
+def download_nc(ensemble_member, gcm, period, scenario_name, zip_filepath):
+ gcm_lower = '_'.join(gcm.lower().split('-'))
+ c = cdsapi.Client()
+ c.retrieve(
+ 'projections-cmip5-monthly-single-levels',
+ {
+ 'ensemble_member': ensemble_member,
+ 'format': 'zip',
+ 'experiment': scenario_name,
+ 'variable': '2m_temperature',
+ 'model': gcm_lower,
+ 'period': period,
+ },
+ zip_filepath)
+ # unzip and delete
+ request_list = [
+ 'unzip {} -d {}'.format(zip_filepath, op.dirname(zip_filepath)),
+ 'rm {}'.format(zip_filepath)
+ ]
+ for request in request_list:
+ print(request)
+ subprocess.run(request, shell=True)
+
+
+def main_example():
+ scenario = AdamontScenario.histo
+ gcm = 'EC-EARTH'
+ year_to_global_mean_temp(gcm, scenario)
+
+
+def main_test_cmip5_loader():
+ for scenario in adamont_scenarios_real[:1]:
+ for gcm in get_gcm_list(adamont_version=2)[:]:
+ if gcm != 'CNRM-CM5':
+ print(gcm, scenario)
+ year_to_global_temp = year_to_global_mean_temp(gcm, scenario)
+ print(year_to_global_temp)
+
+
+if __name__ == '__main__':
+ # main_example()
+ main_test_cmip5_loader()