[NON STATIONARY] fix constant gev params. also some refactoring for the...

[NON STATIONARY] fix constant gev params. also some refactoring for the regions, the colors, and functions.

experiment/eurocode_data/departementalpesfrancaises.py

@@ -51,11 +51,6 @@ class Drome(AbstractDepartementAlpesFrancaises):
         super().__init__(C2)
 
 
-"""
-Quand c'est à cheval, je mets les deux massifs
-Quand juste un bout du massif est dans un autre departement 
-(Chartreuse, Belledonne sont un peu en Savoie
-"""
 massif_name_to_departements = {
     'Chablais': [HauteSavoie],
     'Aravis': [HauteSavoie, Savoie],
@@ -63,8 +58,8 @@ massif_name_to_departements = {
     'Bauges': [HauteSavoie, Savoie],
     'Beaufortain': [HauteSavoie, Savoie],
     'Haute-Tarentaise': [Savoie],
-    'Chartreuse': [Isere],
-    'Belledonne': [Isere],
+    'Chartreuse': [Isere, Savoie],
+    'Belledonne': [Isere, Savoie],
     'Maurienne': [Savoie],
     'Vanoise': [Savoie],
     'Haute-Maurienne': [Savoie],

experiment/meteo_france_data/scm_models_data/abstract_study.py

@@ -229,7 +229,7 @@ class AbstractStudy(object):
     def visualize_study(cls, ax=None, massif_name_to_value: Union[None, Dict[str, float]] = None, show=True, fill=True,
                         replace_blue_by_white=True,
                         label=None, add_text=False, cmap=None, vmax=100, vmin=0,
-                        default_color_for_missing_massif='grey',
+                        default_color_for_missing_massif='gainsboro',
                         default_color_for_nan_values='w',
                         massif_name_to_color=None,
                         show_label=True,

experiment/meteo_france_data/scm_models_data/visualization/study_visualization/main_study_visualizer.py

@@ -24,7 +24,7 @@ SCM_STUDY_NAME_TO_SCM_STUDY = dict(zip(SCM_STUDIES_NAMES, SCM_STUDIES))
 SCM_STUDY_CLASS_TO_ABBREVIATION = {
     SafranSnowfall: 'SF3',
     CrocusTotalSwe: 'TSWE',
-    CrocusRecentSwe: 'RSWE',
+    CrocusRecentSwe: 'SWE3',
     CrocusDepth: 'SD',
 }
 

experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py

@@ -14,7 +14,6 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
     VisualizationParameters
 from experiment.meteo_france_data.stations_data.comparison_analysis import ComparisonAnalysis, MASSIF_COLUMN_NAME, \
     REANALYSE_STR, ALTITUDE_COLUMN_NAME, STATION_COLUMN_NAME
-from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import GevLocationChangePointTest
 from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
 from experiment.trend_analysis.univariate_test.utils import compute_gev_change_point_test_results
 from extreme_estimator.extreme_models.result_from_fit import ResultFromIsmev
@@ -259,21 +258,3 @@ class ComparisonsVisualization(VisualizationParameters):
         ax.grid()
         ax.plot(years, maxima, label=label, color=plot_color)
         return ordered_dict
-
-    def visualize_gev(self):
-        return self._visualize_main(self.plot_gev)
-
-    def plot_gev(self, ax, ax2, years, maxima, label, plot_color):
-        # todo should I normalize here ?
-        # fit gev
-        data = maxima
-        res = safe_run_r_estimator(function=r('gev.fit'), xdat=ro.FloatVector(data),
-                                   use_start=True)
-        res = ResultFromIsmev(res, {})
-        gev_params = res.constant_gev_params
-
-        lim = 1.5 * max(data)
-        x = np.linspace(0, lim, 1000)
-        y = gev_params.density(x)
-        # display the gev distribution that was obtained
-        ax.plot(x, y, label=label, color=plot_color)

experiment/paper1_steps/hard extreme evolution - annual maxima/main1_good_stationary_gev_fit.py

@@ -8,14 +8,15 @@ from experiment.meteo_france_data.scm_models_data.visualization.study_visualizat
 def maxima_analysis():
     save_to_file = False
     only_first_one = False
-    durand_altitude = [900, 1500, 2100, 2700]
+    durand_altitude = [900, 1500, 1800, 2100, 2700][2:-2]
     altitudes = durand_altitude
     study_classes = [CrocusRecentSwe][:]
     for study in study_iterator_global(study_classes, only_first_one=only_first_one, altitudes=altitudes):
         study_visualizer = StudyVisualizer(study, save_to_file=save_to_file,
                                            verbose=True,
                                            multiprocessing=True)
-        study_visualizer.visualize_summary_of_annual_values_and_stationary_gev_fit()
+        # study_visualizer.visualize_summary_of_annual_values_and_stationary_gev_fit()
+        study_visualizer.visualize_all_mean_and_max_graphs()
 
 
 if __name__ == '__main__':

experiment/paper1_steps/hard extreme evolution - annual maxima/main3_non_stationary_strength_evolution.py

@@ -13,7 +13,7 @@ from experiment.paper1_steps.utils import get_full_altitude_visualizer, FULL_ALT
 
 
 def main_fast_spatial_risk_evolution():
-    for altitude in FULL_ALTITUDES[-1:]:
+    for altitude in [1800]:
         vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude,
                                                   exact_starting_year=1958, reduce_strength_array=True,
                                                   trend_test_class=GevLocationAndScaleTrendTest)
@@ -31,8 +31,8 @@ def main_full_spatial_risk_evolution():
 
 
 def main_run():
-    main_full_spatial_risk_evolution()
-    # main_fast_spatial_risk_evolution()
+    # main_full_spatial_risk_evolution()
+    main_fast_spatial_risk_evolution()
 
 
 if __name__ == '__main__':

experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py

@@ -113,14 +113,18 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
 
     @property
     def non_stationary_constant_gev_params(self) -> GevParams:
-        return self.non_stationary_estimator.result_from_fit.constant_gev_params
+        # Constant parameters correspond to the gev params in 1958
+        return self.non_stationary_estimator.margin_function_fitted.get_gev_params(coordinate=np.array([1958]),
+                                                                                   is_transformed=False)
 
     @property
     def test_trend_slope_strength(self):
         if self.crashed:
             return 0.0
         else:
+            # Compute the slope strength
             slope = self._slope_strength()
+            # Delta T must in the same unit as were the parameter of slope mu1 and sigma1
             slope *= self.nb_years_for_quantile_evolution * self.coordinates.transformed_distance_between_two_successive_years[0]
             return slope
 

experiment/trend_analysis/univariate_test/abstract_univariate_test.py

@@ -61,8 +61,8 @@ class AbstractUnivariateTest(object):
         d[cls.ALL_TREND] = 'k-'
         d[cls.NON_SIGNIFICATIVE_TREND] = 'b-'
         # d[cls.SIGNIFICATIVE_ALL_TREND] = 'k-'
-        d[cls.SIGNIFICATIVE_POSITIVE_TREND] = 'g-'
-        d[cls.SIGNIFICATIVE_NEGATIVE_TREND] = 'r-'
+        d[cls.SIGNIFICATIVE_POSITIVE_TREND] = 'darkgreen-'
+        d[cls.SIGNIFICATIVE_NEGATIVE_TREND] = 'darkred-'
         # d[cls.NO_TREND] = 'k--'
         return d
 

extreme_estimator/extreme_models/margin_model/temporal_linear_margin_model.py

@@ -37,6 +37,10 @@ class TemporalLinearMarginModel(LinearMarginModel):
     def shl(self):
         return get_null()
 
+    @property
+    def siglink(self):
+        return r('identity')
+
 
 class StationaryStationModel(TemporalLinearMarginModel):
 
@@ -64,6 +68,16 @@ class NonStationaryScaleStationModel(TemporalLinearMarginModel):
         return 1
 
 
+class NonStationaryLogScaleStationModel(NonStationaryScaleStationModel):
+
+    def load_margin_functions(self, gev_param_name_to_dims=None):
+        super().load_margin_functions({GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
+
+    @property
+    def siglink(self):
+        return r('exp')
+
+
 class NonStationaryShapeStationModel(TemporalLinearMarginModel):
 
     def load_margin_functions(self, gev_param_name_to_dims=None):

extreme_estimator/extreme_models/result_from_fit.py

@@ -48,10 +48,6 @@ class ResultFromFit(object):
     def convergence(self) -> str:
         raise NotImplementedError
 
-    @property
-    def constant_gev_params(self) -> GevParams:
-        raise NotImplementedError
-
 
 class ResultFromIsmev(ResultFromFit):
 
@@ -79,12 +75,6 @@ class ResultFromIsmev(ResultFromFit):
                 i += 1
         return coef_dict
 
-    @property
-    def constant_gev_params(self) -> GevParams:
-        params = {k.split('Coeff1')[0]: v for k, v in self.margin_coef_dict.items()
-                  if 'Coeff1' in k and 'temp' not in k}
-        return GevParams.from_dict(params)
-
     @property
     def all_parameters(self):
         return self.margin_coef_dict

test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py

@@ -33,8 +33,6 @@ class TestGevTemporal(unittest.TestCase):
         df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
         observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
         self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
-        #
-        self.margin_models = load_non_stationary_temporal_margin_models(self.coordinates)
 
     def test_gev_temporal_margin_fit_stationary(self):
         # Create estimator
@@ -49,7 +47,8 @@ class TestGevTemporal(unittest.TestCase):
 
     def test_gev_temporal_margin_fit_nonstationary(self):
         # Create estimator
-        for margin_model in self.margin_models:
+        margin_models = load_non_stationary_temporal_margin_models(self.coordinates)
+        for margin_model in margin_models:
             # margin_model = NonStationaryLocationStationModel(self.coordinates)
             estimator = LinearMarginEstimator(self.dataset, margin_model)
             estimator.fit()