diff --git a/experiment/meteo_france_SCM_study/abstract_study.py b/experiment/meteo_france_SCM_study/abstract_study.py
index 7fc67358c735f921e7a77bdd0bf3c6f08686e02a..dcb669986930a37e7d31a96c2719f0db28b868de 100644
--- a/experiment/meteo_france_SCM_study/abstract_study.py
+++ b/experiment/meteo_france_SCM_study/abstract_study.py
@@ -45,14 +45,6 @@ class AbstractStudy(object):
""" Load some attributes only once """
- @cached_property
- def year_to_annual_maxima(self) -> OrderedDict:
- # Map each year to an array of size nb_massif
- year_to_annual_maxima = OrderedDict()
- for year, time_serie in self.year_to_daily_time_serie.items():
- year_to_annual_maxima[year] = time_serie.max(axis=0)
- return year_to_annual_maxima
-
@cached_property
def year_to_dataset_ordered_dict(self) -> OrderedDict:
# Map each year to the correspond netCDF4 Dataset
@@ -64,6 +56,23 @@ class AbstractStudy(object):
@cached_property
def year_to_daily_time_serie(self) -> OrderedDict:
+ return self._year_to_daily_time_serie
+
+ @cached_property
+ def year_to_annual_maxima(self) -> OrderedDict:
+ # Map each year to an array of size nb_massif
+ year_to_annual_maxima = OrderedDict()
+ for year, time_serie in self._year_to_max_daily_time_serie.items():
+ year_to_annual_maxima[year] = time_serie.max(axis=0)
+ return year_to_annual_maxima
+
+ def instantiate_variable_object(self, dataset) -> AbstractVariable:
+ return self.variable_class(dataset)
+
+ """ Private methods to be overwritten """
+
+ @property
+ def _year_to_daily_time_serie(self) -> OrderedDict:
# Map each year to a matrix of size 365-nb_days_consecutive+1 x nb_massifs
year_to_variable = {year: self.instantiate_variable_object(dataset) for year, dataset in
self.year_to_dataset_ordered_dict.items()}
@@ -72,19 +81,24 @@ class AbstractStudy(object):
year_to_daily_time_serie[year] = year_to_variable[year].daily_time_serie
return year_to_daily_time_serie
- def instantiate_variable_object(self, dataset) -> AbstractVariable:
- return self.variable_class(dataset)
+ @property
+ def _year_to_max_daily_time_serie(self):
+ return self._year_to_daily_time_serie
##########
@property
def safran_massif_names(self) -> List[str]:
+ return self.original_safran_massif_names
+
+ @property
+ def original_safran_massif_names(self):
# Load the names of the massif as defined by SAFRAN
return safran_massif_names_from_datasets(list(self.year_to_dataset_ordered_dict.values()))
@property
- def safran_massif_id_to_massif_name(self) -> Dict[int, str]:
- return {massif_id: massif_name for massif_id, massif_name in enumerate(self.safran_massif_names)}
+ def original_safran_massif_id_to_massif_name(self) -> Dict[int, str]:
+ return {massif_id: massif_name for massif_id, massif_name in enumerate(self.original_safran_massif_names)}
@cached_property
def massifs_coordinates(self) -> AbstractSpatialCoordinates:
@@ -97,10 +111,8 @@ class AbstractStudy(object):
def load_df_centroid(self) -> pd.DataFrame:
df_centroid = pd.read_csv(op.join(self.map_full_path, 'coordonnees_massifs_alpes.csv'))
- # Assert that the massif names are the same between SAFRAN and the coordinate file
- assert not set(self.safran_massif_names).symmetric_difference(set(df_centroid['NOM']))
df_centroid.set_index('NOM', inplace=True)
- df_centroid = df_centroid.loc[self.safran_massif_names]
+ df_centroid = df_centroid.loc[self.original_safran_massif_names]
return df_centroid
@property
diff --git a/experiment/meteo_france_SCM_study/main_visualize.py b/experiment/meteo_france_SCM_study/main_visualize.py
index d7203624bd618ca1a4919a5b496cee67879b5ff0..822001c8f97305119ac881d1bb56ad3b5eedf80d 100644
--- a/experiment/meteo_france_SCM_study/main_visualize.py
+++ b/experiment/meteo_france_SCM_study/main_visualize.py
@@ -3,6 +3,7 @@ from experiment.meteo_france_SCM_study.crocus.crocus import CrocusDepth, CrocusS
from experiment.meteo_france_SCM_study.safran.safran import Safran
from itertools import product
+from experiment.meteo_france_SCM_study.safran.safran_extended import ExtendedSafran
from experiment.meteo_france_SCM_study.safran.safran_visualizer import StudyVisualizer
@@ -20,7 +21,7 @@ def load_all_studies(study_class, only_first_one=False):
if __name__ == '__main__':
- for study_class in [Safran, CrocusSwe, CrocusDepth][1:2]:
+ for study_class in [ExtendedSafran, Safran, CrocusSwe, CrocusDepth][:1]:
for study in load_all_studies(study_class, only_first_one=True):
study_visualizer = StudyVisualizer(study)
# study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
diff --git a/experiment/meteo_france_SCM_study/safran/safran.py b/experiment/meteo_france_SCM_study/safran/safran.py
index b485bafd1d5eae36a9ce44ab9e0940a8f19175c8..44288118d27806d429d4d4afb2732f665dcd8a89 100644
--- a/experiment/meteo_france_SCM_study/safran/safran.py
+++ b/experiment/meteo_france_SCM_study/safran/safran.py
@@ -15,5 +15,5 @@ class Safran(AbstractStudy):
@property
def variable_name(self):
- return super().variable_name() + 'cumulated over {} days'.format(self.nb_days_of_snowfall)
+ return super().variable_name + ' cumulated over {} days'.format(self.nb_days_of_snowfall)
diff --git a/experiment/meteo_france_SCM_study/safran/safran_extended.py b/experiment/meteo_france_SCM_study/safran/safran_extended.py
index 691d719265136e6f91210869c43e88ea78e521ed..17cc73a364faa86c09308eefc5b5de22c85051f3 100644
--- a/experiment/meteo_france_SCM_study/safran/safran_extended.py
+++ b/experiment/meteo_france_SCM_study/safran/safran_extended.py
@@ -1,36 +1,69 @@
+import numpy as np
from collections import OrderedDict
-import pandas as pd
-
from experiment.meteo_france_SCM_study.safran.safran import Safran
from utils import cached_property
class ExtendedSafran(Safran):
+ @property
+ def region_names(self):
+ return ['Alps', 'Northern Alps', 'Central Alps', 'Southern Alps', 'Extreme South Alps']
+
+ @property
+ def nb_region_names(self):
+ return len(self.region_names)
+
@property
def safran_massif_names(self):
return self.region_names + super().safran_massif_names
- """ Properties """
+ @property
+ def massif_name_to_region_name(self):
+ df_centroid = self.load_df_centroid()
+ return OrderedDict(zip(df_centroid.index, df_centroid['REGION']))
- @cached_property
- def df_annual_maxima(self):
- df_annual_maxima = pd.DataFrame(self.year_to_annual_maxima, index=super().safran_massif_names).T
- # Add the column corresponding to the aggregated massif
+ @property
+ def region_name_to_massif_ids(self):
+ region_name_to_massifs_ids = {}
for region_name_loop in self.region_names:
# We use "is in" so that the "Alps" will automatically regroup all the massif data
- massif_belong_to_the_group = [massif_name
- for massif_name, region_name in self.massif_name_to_region_name.items()
- if region_name_loop in region_name]
- df_annual_maxima[region_name_loop] = df_annual_maxima.loc[:, massif_belong_to_the_group].max(axis=1)
- return df_annual_maxima
+ massif_names_belong_to_the_group = [massif_name
+ for massif_name, region_name in self.massif_name_to_region_name.items()
+ if region_name_loop in region_name]
+ massif_ids_belong_to_the_group = [massif_id
+ for massif_id, massif_name in self.original_safran_massif_id_to_massif_name.items()
+ if massif_name in massif_names_belong_to_the_group]
+ region_name_to_massifs_ids[region_name_loop] = massif_ids_belong_to_the_group
+ return region_name_to_massifs_ids
+
+ """ Properties """
+
+
@property
- def massif_name_to_region_name(self):
- df_centroid = self.load_df_centroid()
- return OrderedDict(zip(df_centroid['NOM'], df_centroid['REGION']))
+ def _year_to_daily_time_serie(self) -> OrderedDict:
+ return self._year_to_extended_time_serie(aggregation_function=np.mean)
@property
- def region_names(self):
- return ['Alps', 'Northern Alps', 'Central Alps', 'Southern Alps', 'Extreme South Alps']
+ def _year_to_max_daily_time_serie(self):
+ return self._year_to_extended_time_serie(aggregation_function=np.max)
+
+ def _year_to_extended_time_serie(self, aggregation_function) -> OrderedDict:
+ year_to_extended_time_serie = OrderedDict()
+ for year, old_time_serie in super()._year_to_daily_time_serie.items():
+ new_time_serie = np.zeros([len(old_time_serie), len(self.safran_massif_names)])
+ new_time_serie[:, self.nb_region_names:] = old_time_serie
+ for i, region_name in enumerate(self.region_names):
+ massifs_ids_belong_to_region = self.region_name_to_massif_ids[region_name]
+ aggregated_time_serie = aggregation_function(old_time_serie[:, massifs_ids_belong_to_region], axis=1)
+ new_time_serie[:, i] = aggregated_time_serie
+
+ year_to_extended_time_serie[year] = new_time_serie
+ return year_to_extended_time_serie
+
+
+
+
+
diff --git a/test/test_meteo_france_SCM_study/test_SCM_study.py b/test/test_meteo_france_SCM_study/test_SCM_study.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd4d7f5578a64298579f660d155fc343691fe92a
--- /dev/null
+++ b/test/test_meteo_france_SCM_study/test_SCM_study.py
@@ -0,0 +1,36 @@
+import os
+import os.path as op
+from collections import OrderedDict
+from typing import List, Dict
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from netCDF4 import Dataset
+
+from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
+from experiment.meteo_france_SCM_study.massif import safran_massif_names_from_datasets
+from experiment.meteo_france_SCM_study.safran.safran import Safran
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
+ AbstractSpatialCoordinates
+from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
+from utils import get_full_path, cached_property
+import unittest
+
+from test.test_utils import load_safran_objects
+
+
+class TestSCMStudy(unittest.TestCase):
+
+ def setUp(self) -> None:
+ super().setUp()
+ self.study = Safran()
+
+ def test_massif_safran(self):
+ df_centroid = pd.read_csv(op.join(self.study.map_full_path, 'coordonnees_massifs_alpes.csv'))
+ # Assert that the massif names are the same between SAFRAN and the coordinate file
+ assert not set(self.study.safran_massif_names).symmetric_difference(set(df_centroid['NOM']))
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/test_meteo_france_SCM_study/test_safran_gev.py b/test/test_meteo_france_SCM_study/test_safran_gev.py
new file mode 100644
index 0000000000000000000000000000000000000000..03430b1334719df507b729c23a8192e2d444b2eb
--- /dev/null
+++ b/test/test_meteo_france_SCM_study/test_safran_gev.py
@@ -0,0 +1,31 @@
+import unittest
+import os
+import os.path as op
+from collections import OrderedDict
+from typing import List, Dict
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from netCDF4 import Dataset
+
+from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
+from experiment.meteo_france_SCM_study.massif import safran_massif_names_from_datasets
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
+ AbstractSpatialCoordinates
+from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
+from utils import get_full_path, cached_property
+#
+# from test.test_utils import load_safran_objects
+#
+#
+# class TestFullEstimators(unittest.TestCase):
+#
+# def test_gev_mle_per_massif(self):
+# safran_1800_one_day = load_safran_objects()[0]
+# df = safran_1800_one_day.df_gev_mle_each_massif
+# self.assertAlmostEqual(df.values.sum(), 1131.4551665871832)
+#
+#
+# if __name__ == '__main__':
+# unittest.main()
diff --git a/test/test_safran_study/test_safran_gev.py b/test/test_safran_study/test_safran_gev.py
deleted file mode 100644
index 8f974fa439e5860a0b164e236be042893420dbaf..0000000000000000000000000000000000000000
--- a/test/test_safran_study/test_safran_gev.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import unittest
-
-from test.test_utils import load_safran_objects
-
-
-class TestFullEstimators(unittest.TestCase):
-
- def test_gev_mle_per_massif(self):
- safran_1800_one_day = load_safran_objects()[0]
- df = safran_1800_one_day.df_gev_mle_each_massif
- self.assertAlmostEqual(df.values.sum(), 1131.4551665871832)
-
-
-if __name__ == '__main__':
- unittest.main()