[projection snowfall] add preliminary analysis for the work on model with...

[projection snowfall] add preliminary analysis for the work on model with effect related to the GCM or RCM type.

extreme_data/meteo_france_data/adamont_data/cmip5/temperature_to_year.py

@@ -69,7 +69,7 @@ def plot_nb_data(is_temperature_interval, is_shift_interval):
 
     ax = plt.gca()
     for gcm in get_gcm_list(adamont_version=2)[:]:
-        for i, scenario in enumerate(rcp_scenarios[:2]):
+        for i, scenario in enumerate(rcp_scenarios[2:]):
             plot_nb_data_one_line(ax, gcm, scenario, left_limit, right_limit,
                                   i == 0, is_temperature_interval)
 
@@ -118,6 +118,6 @@ def get_ticks_labels_for_interval(is_temperature_interval, is_shift_interval):
 
 if __name__ == '__main__':
     for shift_interval in [False, True]:
-        for temp_interval in [False, True]:
+        for temp_interval in [False, True][1:]:
             print("shift = {}, temp_inteval = {}".format(shift_interval, temp_interval))
             plot_nb_data(is_temperature_interval=temp_interval, is_shift_interval=shift_interval)

extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py

+import matplotlib.pyplot as plt
 from collections import OrderedDict
 from typing import List
 
+from extreme_data.meteo_france_data.adamont_data.adamont_gcm_rcm_couples import gcm_rcm_couple_to_color
+from extreme_data.meteo_france_data.adamont_data.adamont_scenario import gcm_rcm_couple_to_str
+from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
+from extreme_fit.distribution.gev.gev_params import GevParams
 from extreme_fit.model.margin_model.polynomial_margin_model.spatio_temporal_polynomial_model import \
     AbstractSpatioTemporalPolynomialModel
 from extreme_fit.model.margin_model.utils import MarginFitMethod
@@ -129,3 +134,40 @@ class VisualizerForProjectionEnsemble(object):
         return [ensemble_class_to_ensemble_fit[ensemble_class]
                 for ensemble_class_to_ensemble_fit
                 in self.altitude_class_to_ensemble_class_to_ensemble_fit.values()]
+
+    def plot_preliminary_first_part(self):
+        if self.massif_names is None:
+            massif_names = AbstractStudy.all_massif_names()
+        else:
+            massif_names = self.massif_names
+        assert isinstance(massif_names, list)
+        # Plot for all parameters
+        for param_name in GevParams.PARAM_NAMES:
+            for degree in [0, 1]:
+                for massif_name in massif_names:
+                    self.plot_preliminary_first_part_for_one_massif(massif_name, param_name, degree)
+
+    def plot_preliminary_first_part_for_one_massif(self, massif_name, param_name, degree):
+        # Retrieve the data
+        ensemble_fit: IndependentEnsembleFit
+        gcm_rcm_couple_to_data = {
+            c: [] for c in self.gcm_rcm_couples
+        }
+        for ensemble_fit in self.ensemble_fits(IndependentEnsembleFit):
+            for gcm_rcm_couple in self.gcm_rcm_couples:
+                visualizer = ensemble_fit.gcm_rcm_couple_to_visualizer[gcm_rcm_couple]
+                one_fold_fit = visualizer.massif_name_to_one_fold_fit[massif_name]
+                coef = one_fold_fit.best_coef(param_name, 0, degree)
+                altitude = visualizer.altitude_group.reference_altitude
+                gcm_rcm_couple_to_data[gcm_rcm_couple].append((altitude, coef))
+        # Plot
+        ax = plt.gca()
+        for gcm_rcm_couple, data in gcm_rcm_couple_to_data.items():
+            altitudes, coefs = list(zip(*data))
+            color = gcm_rcm_couple_to_color[gcm_rcm_couple]
+            label = gcm_rcm_couple_to_str(gcm_rcm_couple)
+            ax.plot(coefs, altitudes, color=color, label=label, marker='o')
+        ax.legend()
+        visualizer.plot_name = '{}/{}_{}'.format(param_name, degree, massif_name)
+        visualizer.show_or_save_to_file(no_title=True)
+        plt.close()
\ No newline at end of file

extreme_trend/one_fold_fit/one_fold_fit.py

@@ -246,8 +246,11 @@ class OneFoldFit(object):
     def best_coef(self, param_name, dim, degree):
         try:
             coef = self.best_function_from_fit.param_name_to_coef[param_name]  # type: PolynomialAllCoef
-            coef = coef.dim_to_polynomial_coef[dim]  # type: PolynomialCoef
-            coef = coef.idx_to_coef[degree]
+            if coef.dim_to_polynomial_coef is None:
+                return coef.intercept
+            else:
+                coef = coef.dim_to_polynomial_coef[dim]  # type: PolynomialCoef
+                coef = coef.idx_to_coef[degree]
             return coef
         except (TypeError, KeyError):
             return None

projects/projected_extreme_snowfall/main_sensitivity.py

@@ -8,17 +8,16 @@ from extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit impo
 
 matplotlib.use('Agg')
 import matplotlib as mpl
+
 mpl.rcParams['text.usetex'] = True
 mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
 
-
 from extreme_trend.ensemble_fit.independent_ensemble_fit.independent_ensemble_fit import IndependentEnsembleFit
 from extreme_trend.ensemble_fit.visualizer_for_projection_ensemble import VisualizerForProjectionEnsemble
 from extreme_trend.ensemble_fit.visualizer_for_sensitivity import VisualizerForSensivity
 from spatio_temporal_dataset.coordinates.temporal_coordinates.temperature_covariate import \
     AnomalyTemperatureWithSplineTemporalCovariate
 
-
 from extreme_fit.model.margin_model.polynomial_margin_model.utils import \
     ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
 
@@ -27,8 +26,8 @@ from extreme_fit.model.utils import set_seed_for_test
 from extreme_data.meteo_france_data.adamont_data.adamont.adamont_safran import AdamontSnowfall
 from extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario, get_gcm_rcm_couples, \
     rcp_scenarios
-from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import TimeTemporalCovariate
-
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import \
+    TimeTemporalCovariate
 
 from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
     AbstractExtractEurocodeReturnLevel
@@ -42,8 +41,6 @@ def main():
     start = time.time()
     study_class = AdamontSnowfall
     ensemble_fit_classes = [IndependentEnsembleFit, TogetherEnsembleFit][1:]
-    temporal_covariate_for_fit = [TimeTemporalCovariate,
-                                  AnomalyTemperatureWithSplineTemporalCovariate][0]
     set_seed_for_test()
     AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
 
@@ -71,29 +68,32 @@ def main():
         assert isinstance(altitudes_list, List)
         assert isinstance(altitudes_list[0], List)
         print('Scenario is', scenario)
-        print('Covariate is {}'.format(temporal_covariate_for_fit))
 
         model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
         assert scenario in rcp_scenarios
         remove_physically_implausible_models = True
+        temp_cov = False
+        temporal_covariate_for_fit = AnomalyTemperatureWithSplineTemporalCovariate if temp_cov else TimeTemporalCovariate
+        print('Covariate is {}'.format(temporal_covariate_for_fit))
 
-        visualizer = VisualizerForSensivity(
-            altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
-            model_classes=model_classes,
-            ensemble_fit_classes=ensemble_fit_classes,
-            massif_names=massif_names,
-            temporal_covariate_for_fit=temporal_covariate_for_fit,
-            remove_physically_implausible_models=remove_physically_implausible_models,
-            is_temperature_interval=False,
-            is_shift_interval=False,
-        )
-        visualizer.plot()
+        for is_temperature_interval in [True, False][1:]:
+            for is_shift_interval in [True, False][1:]:
+                visualizer = VisualizerForSensivity(
+                    altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
+                    model_classes=model_classes,
+                    ensemble_fit_classes=ensemble_fit_classes,
+                    massif_names=massif_names,
+                    temporal_covariate_for_fit=temporal_covariate_for_fit,
+                    remove_physically_implausible_models=remove_physically_implausible_models,
+                    is_temperature_interval=is_temperature_interval,
+                    is_shift_interval=is_shift_interval,
+                )
+                visualizer.plot()
 
     end = time.time()
     duration = str(datetime.timedelta(seconds=end - start))
     print('Total duration', duration)
 
 
-
 if __name__ == '__main__':
     main()

projects/projected_extreme_snowfall/preliminary_works/main_preliminary_works.py

+import datetime
+import time
+from typing import List
+import matplotlib
+
+from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.model.margin_model.polynomial_margin_model.gev_altitudinal_models import StationaryAltitudinal
+from extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit import TogetherEnsembleFit
+
+matplotlib.use('Agg')
+import matplotlib as mpl
+
+mpl.rcParams['text.usetex'] = True
+mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
+
+from extreme_trend.ensemble_fit.independent_ensemble_fit.independent_ensemble_fit import IndependentEnsembleFit
+from extreme_trend.ensemble_fit.visualizer_for_projection_ensemble import VisualizerForProjectionEnsemble
+from extreme_trend.ensemble_fit.visualizer_for_sensitivity import VisualizerForSensivity
+from spatio_temporal_dataset.coordinates.temporal_coordinates.temperature_covariate import \
+    AnomalyTemperatureWithSplineTemporalCovariate
+
+from extreme_fit.model.margin_model.polynomial_margin_model.utils import \
+    ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
+
+from extreme_fit.model.utils import set_seed_for_test
+
+from extreme_data.meteo_france_data.adamont_data.adamont.adamont_safran import AdamontSnowfall
+from extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario, get_gcm_rcm_couples, \
+    rcp_scenarios
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import \
+    TimeTemporalCovariate
+
+from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
+    AbstractExtractEurocodeReturnLevel
+
+from extreme_trend.one_fold_fit.altitude_group import altitudes_for_groups
+
+from extreme_data.meteo_france_data.scm_models_data.utils import Season
+
+
+def main(scenario=AdamontScenario.rcp85):
+    start = time.time()
+    study_class = AdamontSnowfall
+    set_seed_for_test()
+    gcm_rcm_couples = get_gcm_rcm_couples(scenario)
+    model_classes = [StationaryAltitudinal]
+
+    fast = False
+
+    if fast is None:
+        massif_names = ['Vanoise']
+        altitudes_list = altitudes_for_groups[:]
+    elif fast:
+        massif_names = ['Vanoise']
+        gcm_rcm_couples = gcm_rcm_couples[:1]
+        altitudes_list = altitudes_for_groups[2:]
+
+    else:
+        massif_names = None
+        altitudes_list = altitudes_for_groups[:]
+
+    visualizer = VisualizerForProjectionEnsemble(
+        altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
+        model_classes=model_classes,
+        ensemble_fit_classes=[IndependentEnsembleFit],
+        massif_names=massif_names,
+        temporal_covariate_for_fit=None,
+        remove_physically_implausible_models=True,
+        gcm_to_year_min_and_year_max=None,
+    )
+    visualizer.plot_preliminary_first_part()
+
+
+    end = time.time()
+    duration = str(datetime.timedelta(seconds=end - start))
+    print('Total duration', duration)
+
+
+if __name__ == '__main__':
+    main()