diff --git a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
index 17bbf6f7a471253e35e83675c4ed006047118fa8..91be36f36cfcfc76af97424480e4ddcaecfd58ce 100644
--- a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
+++ b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
@@ -77,10 +77,6 @@ class AbstractStudy(object):
""" Time """
- @property
- def nb_years(self):
- return self.year_max - self.year_min + 1
-
@cached_property
def year_to_days(self) -> OrderedDict:
# Map each year to the 'days since year-08-01 06:00:00'
@@ -115,19 +111,24 @@ class AbstractStudy(object):
wps = pd.Series(np.concatenate([self.massif_name_to_df_ordered_by_maxima[massif_name]['WP'].values[:nb_top]
for massif_name in massif_names]))
s_normalized = wps.value_counts(normalize=True) * 100
- s_normalized = s_normalized.round()
- s_not_normalized = wps.value_counts()
- # todo: do that, complete the last columns with the mean maxima
- # Add a column that indicate the mean maxima associated to each weather pattern
- # f = {}
- # for wp in s_normalized.index:
- # print(wp)
- # for massif_id
-
-
- # Concatenate all the results in one dataframe
- df = pd.concat([s_normalized, s_not_normalized], axis=1)
- df.columns = ['Percentage', 'Nb massifs concerned']
+ # Add several columns that indicate the strength of the maxima to each weather pattern
+ f = {wp: [] for wp in s_normalized.index}
+ for massif_name in massif_names:
+ df_ordered_by_maxima = self.massif_name_to_df_ordered_by_maxima[massif_name]
+ df_ordered_by_maxima = df_ordered_by_maxima.iloc[:nb_top, :] # type: pd.DataFrame
+ assert len(df_ordered_by_maxima) == nb_top
+ for _, row in df_ordered_by_maxima.iterrows():
+ wp, maxima = row['WP'], row['Maxima']
+ f[wp].append(maxima)
+ df = pd.DataFrame({wp: pd.Series(l).describe() for wp, l in f.items()}).transpose()
+ df = pd.concat([s_normalized, df], axis=1)
+ df.columns = ['%' if i == 0 else c for i, c in enumerate(df.columns)]
+ drop_columns = ['25%', '75%']
+ if df['std'].isnull().any():
+ drop_columns.append('std')
+ df.drop(columns=drop_columns, inplace=True)
+ df.rename(columns={'50%': 'median'}, inplace=True)
+ df = df.astype(int)
df.index.name = 'Number Top={}'.format(nb_top)
return df
@@ -146,6 +147,7 @@ class AbstractStudy(object):
}
df = pd.DataFrame(d)
df.set_index('Year', inplace=True)
+ assert len(df) == self.nb_years
massif_name_to_df_ordered_by_maxima[massif_name] = df
assert set(self.study_massif_names) == set(massif_name_to_df_ordered_by_maxima.keys())
return massif_name_to_df_ordered_by_maxima
@@ -315,6 +317,10 @@ class AbstractStudy(object):
""" Temporal properties """
+ @property
+ def nb_years(self):
+ return len(self.ordered_years)
+
@property
def ordered_years(self):
return self.ordered_years_and_path_files[1]
diff --git a/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py b/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py
index 5018c1b49315301bf9b010276007822cb979c79b..9372f13b45771fbe09760ee51d802cb4931a7d5c 100644
--- a/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py
+++ b/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py
@@ -26,20 +26,23 @@ If we look at the 5 biggest maxima for each massif,
and look to which Weather pattern they correspond
then we do some percentage for each climatic region
- Percentage Nb massifs concerned
+
Northern Alps
-Steady Oceanic 77.0 27
-Atlantic Wave 11.0 4
+ % count mean std min median max
+Steady Oceanic 77 27 105 16 80 104 150
+Atlantic Wave 11 4 111 15 95 111 129
Central Alps
-Steady Oceanic 57.0 20
-South Circulation 17.0 6
-East Return 14.0 5
+ % count mean min median max
+Steady Oceanic 57 20 90 70 86 119
+South Circulation 17 6 85 64 88 104
+East Return 14 5 88 74 93 100
Southern Alps
-South Circulation 43.0 13
-Central Depression 37.0 11
-East Return 17.0 5
+ % count mean min median max
+South Circulation 43 13 99 72 106 122
+Central Depression 36 11 98 68 97 136
+East Return 16 5 90 70 83 121
Extreme South Alps
Central Depression 53.0 8
@@ -55,7 +58,7 @@ def main_temporal_distribution_wps(study_class, year_min=1954, year_max=2008):
for region_name in AbstractExtendedStudy.region_names:
massif_names = AbstractExtendedStudy.region_name_to_massif_names[region_name]
print('\n \n', region_name, '\n')
- for nb_top in [study_before.nb_years, 10, 5, 1][-1:]:
+ for nb_top in [study_before.nb_years, 10][1:]:
print(study_before.df_for_top_annual_maxima(nb_top=nb_top, massif_names=massif_names), '\n')
print(study_after.df_for_top_annual_maxima(nb_top=nb_top, massif_names=massif_names), '\n')