[projections] fix anomaly for temperature

extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py

@@ -64,7 +64,8 @@ class LinearMarginEstimator(AbstractMarginEstimator):
     def nllh(self, split=Split.all):
         nllh = 0
         maxima_values = self.dataset.maxima_gev(split=split)
-        coordinate_values = self.dataset.df_coordinates(split=split).values
+        df = pd.concat([self.df_coordinates_spat(split=split), self.df_coordinates_temp(split=split)], axis=1)
+        coordinate_values = df.values
         for maximum, coordinate in zip(maxima_values, coordinate_values):
             assert len(maximum) == 1, \
                 'So far, only one observation for each coordinate, but code would be easy to change'

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py

@@ -144,7 +144,6 @@ class OneFoldFit(object):
     @cached_property
     def sorted_estimators(self):
         estimators = list(self.model_class_to_estimator.values())
-        print(self.massif_name, self.altitude_group)
         if self.remove_physically_implausible_models:
             estimators = [e for e in estimators if -0.5 < self._compute_shape_for_reference_altitude(e) < 0.5]
             if len(estimators) == 0:

projects/altitude_spatial_model/altitudes_fit/plots/plot_histogram_altitude_studies.py

@@ -115,8 +115,8 @@ def plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list: L
     ax.tick_params(axis='both', which='major', labelsize=labelsize)
     ax.set_xticks(x)
     ax.yaxis.grid()
-    ax.set_ylim([0, 79])
-    ax.set_ylim(bottom=0)
+    _, ylim_max = ax.get_ylim()
+    ax.set_ylim([0, max(ylim_max, 79)])
     ax.set_xticklabels([v.altitude_group.formula_upper for v in visualizer_list])
 
     plot_nb_massif_on_upper_axis(ax, labelsize, legend_fontsize, nb_massifs, x)

projects/projected_snowfall/elevation_temporal_model_for_projections/main_elevation_temporal_for_projections_ensemble.py

@@ -44,12 +44,12 @@ def main():
     set_seed_for_test()
     AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
 
-    fast = False
+    fast = None
     if fast is None:
         massif_names = None
-        gcm_rcm_couples = gcm_rcm_couples[1:2]
+        gcm_rcm_couples = gcm_rcm_couples[:5]
         AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
-        altitudes_list = altitudes_for_groups[:2]
+        altitudes_list = altitudes_for_groups[:]
     elif fast:
         AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
         massif_names = None
@@ -71,6 +71,9 @@ def main_loop(gcm_rcm_couples, altitudes_list, massif_names, study_classes, ense
               temporal_covariate_for_fit):
     assert isinstance(altitudes_list, List)
     assert isinstance(altitudes_list[0], List)
+    gof_test = True
+    print('Goodness of fit test ?', gof_test)
+    print('Covariate is {}'.format(temporal_covariate_for_fit))
     for study_class in study_classes:
         print('Inner loop', study_class)
         model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
@@ -83,7 +86,7 @@ def main_loop(gcm_rcm_couples, altitudes_list, massif_names, study_classes, ense
             massif_names=massif_names,
             temporal_covariate_for_fit=temporal_covariate_for_fit,
             confidence_interval_based_on_delta_method=False,
-            display_only_model_that_pass_gof_test=False,
+            display_only_model_that_pass_gof_test=gof_test,
             remove_physically_implausible_models=True,
         )
         visualizer.plot()

projects/projected_snowfall/elevation_temporal_model_for_projections/visualizer_for_projection_ensemble.py

@@ -83,7 +83,8 @@ class MetaVisualizerForProjectionEnsemble(object):
         with_significance = False
         # Individual plots
         for independent_ensemble_fit in self.ensemble_fits(IndependentEnsembleFit):
-            for v in independent_ensemble_fit.gcm_rcm_couple_to_visualizer.values():
+            for c, v in independent_ensemble_fit.gcm_rcm_couple_to_visualizer.items():
+                print(c)
                 v.plot_moments()
         # Aggregated at gcm_rcm_level plots
         for gcm_rcm_couple in self.gcm_rcm_couples:

spatio_temporal_dataset/coordinates/temporal_coordinates/abstract_temporal_covariate_for_fit.py

@@ -32,5 +32,5 @@ class AnomalyTemperatureTemporalCovariate(AbstractTemporalCovariateForFit):
             d = year_to_global_mean_temp(gcm, scenario, anomaly=True)
             cls.gcm_and_scenario_to_d[(gcm, scenario)] = d
         d = cls.gcm_and_scenario_to_d[(gcm, scenario)]
-        global_mean_temp = d[year] * 1000
+        global_mean_temp = d[year]
         return global_mean_temp

test/test_projects/test_contrasting/test_two_fold_fit.py

@@ -46,7 +46,7 @@ class TestTwoFoldFit(unittest.TestCase):
         massif_fit = two_fold_fit.massif_name_to_massif_fit['Vercors']
         model_fit = massif_fit.sample_id_to_sample_fit[0].model_class_to_model_fit[
             ConstantMarginModel]  # type: TwoFoldModelFit
-        self.assertEqual(461.6710428902022, model_fit.score(score=Score.NLLH_TEST))
+        self.assertEqual(232.1804953450304, model_fit.score(score=Score.NLLH_TEST))
 
 
 if __name__ == '__main__':