[projection snowfall] fix temperature for HadGem. rely on the merge visualizer.

extreme_data/meteo_france_data/adamont_data/cmip5/climate_explorer_cimp5.py

@@ -93,14 +93,16 @@ def dat_to_csv(csv_filepath, txt_filepath, gcm):
     l = df_temp_until_july.sum(axis=1).values + df_temp_after_august.sum(axis=1).values
     l /= 12
     l = [np.nan] + list(l)
+    l = np.array(l)
     assert len(l) == len(df.index)
+    l[l < 280] = np.nan
 
     # First we compute the standard column
     df = set_anomaly(df, mean_data=l, spline=False)
 
     # Then we regress some cubic spline on the temperature columns
     noisy_data = df[get_column_name(anomaly=False, spline=False)]
-    ind = noisy_data > -50
+    ind = ~noisy_data.isna()
     spline_data = noisy_data.copy()
     spline_data.loc[ind] = apply_cubic_spline(noisy_data.loc[ind].index.values, noisy_data.loc[ind].values, gcm)
     df = set_anomaly(df, mean_data=spline_data, spline=True)

extreme_data/meteo_france_data/adamont_data/cmip5/plot_temperatures.py

@@ -32,7 +32,7 @@ def main_plot_temperature_with_spline_on_top(anomaly=True):
         scenarios = rcp_scenarios
         for scenario in scenarios:
             label = gcm if scenario == scenarios[0] else None
-            plot_temperature_for_rcp_gcm(ax, gcm, scenario, year_min=1951, year_max=2005, linestyle=linestyle,
+            plot_temperature_for_rcp_gcm(ax, gcm, scenario, year_min=1850, year_max=2005, linestyle=linestyle,
                                          label=label, spline=spline, anomaly=anomaly)
             plot_temperature_for_rcp_gcm(ax, gcm, scenario, year_min=2005, year_max=2100, spline=spline, anomaly=anomaly)
 

extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py

@@ -32,3 +32,7 @@ class OneFoldFitMerge(OneFoldFit):
         merged_relative_changes = list(self.merge_function(np.array(all_relative_changes), axis=0))
         assert len(all_relative_changes[0]) == len(merged_relative_changes)
         return merged_relative_changes
+
+    @property
+    def best_shape(self):
+        return self.merge_function([o.best_shape for o in self.one_fold_fit_list])

extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py

@@ -112,7 +112,7 @@ class VisualizerForProjectionEnsemble(object):
         merge_keys = [AbstractEnsembleFit.Median_merge, AbstractEnsembleFit.Mean_merge]
         keys = self.gcm_rcm_couples + merge_keys
         # Only plot Mean for speed
-        keys = [AbstractEnsembleFit.Mean_merge]
+        # keys = [AbstractEnsembleFit.Mean_merge]
         for key in keys:
             visualizer_list = [independent_ensemble_fit.gcm_rcm_couple_to_visualizer[key]
                                if key in self.gcm_rcm_couples

projects/past_extreme_snowfall/section_data_and_results/preliminary_analysis.py

@@ -206,9 +206,9 @@ def main_paper2():
 def main_paper3():
     altitudes = list(chain.from_iterable(altitudes_for_groups))
     # altitudes = [1200, 1500, 1800]
-    for scenario in rcp_scenarios[2:]:
+    for scenario in rcp_scenarios[:]:
         gcm_rcm_couples = get_gcm_rcm_couples(scenario)
-        gcm_rcm_couples =[('CNRM-CM5', 'CCLM4-8-17')]
+        # gcm_rcm_couples =[('CNRM-CM5', 'CCLM4-8-17')]
         for gcm_rcm_couple in gcm_rcm_couples:
             visualizer = PointwiseGevStudyVisualizer(AdamontSnowfall, altitudes=altitudes, scenario=scenario,
                                                      gcm_rcm_couple=gcm_rcm_couple)

projects/projected_extreme_snowfall/data/main_data.py

@@ -20,12 +20,11 @@ from extreme_data.meteo_france_data.scm_models_data.utils import Season
 def main():
     scm_study_class = SafranSnowfall1Day
     adamont_study_class = AdamontSnowfall
-    year_min = 2006
+    year_min = 1950
     year_max = 2100
     massif_names = ['Vanoise']
     season = Season.annual
     scenarios = rcm_scenarios_extended
-    scenarios = rcp_scenarios
     altitudes = [600, 2100, 3600]
     for altitude, adamont_scenario in list(zip(altitudes, scenarios))[:]:
         plt.figure(figsize=(10, 5))
@@ -38,7 +37,7 @@ def main():
                                          season=season, scenario=adamont_scenario)
         print(altitude, adamont_scenario)
         adamont_studies.plot_maxima_time_series_adamont(massif_names=massif_names,
-                                                        scm_study=scm_study, legend_and_labels=True)
+                                                        scm_study=scm_study, legend_and_labels=False)
 
 
 if __name__ == '__main__':

projects/projected_extreme_snowfall/results/main_projections_ensemble.py

@@ -39,7 +39,7 @@ from extreme_data.meteo_france_data.scm_models_data.utils import Season
 def main():
     start = time.time()
     study_class = AdamontSnowfall
-    ensemble_fit_classes = [IndependentEnsembleFit, TogetherEnsembleFit][1:]
+    ensemble_fit_classes = [IndependentEnsembleFit, TogetherEnsembleFit][:1]
     temporal_covariate_for_fit = [TimeTemporalCovariate,
                                   AnomalyTemperatureWithSplineTemporalCovariate][0]
     set_seed_for_test()
@@ -58,12 +58,12 @@ def main():
         gcm_rcm_couples = get_gcm_rcm_couples(scenario)
         if fast is None:
             massif_names = None
-            gcm_rcm_couples = gcm_rcm_couples
+            gcm_rcm_couples = gcm_rcm_couples[:2]
             AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
             altitudes_list = altitudes_for_groups[3:]
         elif fast:
             massif_names = ['Vanoise', 'Haute-Maurienne']
-            gcm_rcm_couples = gcm_rcm_couples[:]
+            gcm_rcm_couples = gcm_rcm_couples[:2]
             AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
             altitudes_list = altitudes_for_groups[:1]
         else: