[projection swe] move projected swe to archive folder

projects/projected_swe/model_as_truth_visualizer/main_model_as_truth.py → projects/archive/projected_swe/model_as_truth_visualizer/main_model_as_truth.py

@@ -4,11 +4,10 @@ from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf impo
 from extreme_data.meteo_france_data.scm_models_data.utils_function import ReturnLevelBootstrap
 from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
     AbstractExtractEurocodeReturnLevel
-from projects.projected_swe.model_as_truth_visualizer.model_as_truth import ModelAsTruth
-from projects.projected_swe.weight_solver.default_weight_solver import EqualWeight
-from projects.projected_swe.weight_solver.indicator import AnnualMaximaMeanIndicator, ReturnLevel30YearsIndicator
-from projects.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
-from projects.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
+from projects.archive.projected_swe.model_as_truth_visualizer import ModelAsTruth
+from projects.archive.projected_swe.weight_solver.indicator import AnnualMaximaMeanIndicator
+from projects.archive.projected_swe.weight_solver import KnuttiWeightSolver
+from projects.archive.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
     KnuttiWeightSolverWithBootstrapVersion1, KnuttiWeightSolverWithBootstrapVersion2
 
 
@@ -44,33 +43,37 @@ def main():
         sigma_list = [10]
 
     else:
-        altitudes = [900, 1800, 2700, 3600][:2]
-        year_couples = [(1982, 2011), (2012, 2041), (2042, 2071), (2070, 2099)][:-1]
+        altitudes = [900, 1800, 2700, 3600][:]
+        year_couples = [(1982, 2011), (2012, 2041), (2042, 2071), (2070, 2099)][:]
         massif_names = None
         AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
         knutti_weight_solver_classes = [KnuttiWeightSolver][:]
         gcm_rcm_couples = gcm_rcm_couples[:]
-        sigma_list = [i + 1 for i in range(10)]
+        sigma_list = [i * 0.25 + 1 for i in range(13)]
 
     for altitude in altitudes:
-        for year_couple in year_couples:
-            year_min_projected, year_max_projected = year_couple
             observation_study = SafranSnowfall2020(altitude=altitude, year_min=year_min_histo, year_max=year_max_histo)
             couple_to_historical_study = {c: AdamontSnowfall(altitude=altitude, scenario=scenario,
                                                              year_min=year_min_histo, year_max=year_max_histo,
                                                              gcm_rcm_couple=c) for c in gcm_rcm_couples}
-            if (year_min_projected, year_max_projected) == (year_min_histo, year_max_histo):
-                couple_to_projected_study = couple_to_historical_study
-            else:
-                couple_to_projected_study = {c: AdamontSnowfall(altitude=altitude, scenario=scenario,
-                                                                year_min=year_min_projected, year_max=year_max_projected,
-                                                                gcm_rcm_couple=c) for c in gcm_rcm_couples
-                                             }
 
-            model_as_truth = ModelAsTruth(observation_study, couple_to_projected_study, couple_to_historical_study,
+
+            model_as_truth = ModelAsTruth(observation_study, couple_to_historical_study,
                                           indicator_class, knutti_weight_solver_classes, massif_names,
                                           add_interdependence_weight=False)
-            model_as_truth.plot_against_sigma(sigma_list)
+
+            for year_couple in year_couples:
+                year_min_projected, year_max_projected = year_couple
+                if (year_min_projected, year_max_projected) == (year_min_histo, year_max_histo):
+                    couple_to_projected_study = couple_to_historical_study
+                else:
+                    couple_to_projected_study = {c: AdamontSnowfall(altitude=altitude, scenario=scenario,
+                                                                    year_min=year_min_projected,
+                                                                    year_max=year_max_projected,
+                                                                    gcm_rcm_couple=c) for c in gcm_rcm_couples
+                                                 }
+
+                model_as_truth.plot_against_sigma(couple_to_projected_study, sigma_list)
 
 
 if __name__ == '__main__':

projects/projected_swe/model_as_truth_visualizer/model_as_truth.py → projects/archive/projected_swe/model_as_truth_visualizer/model_as_truth.py

@@ -2,18 +2,16 @@ import matplotlib.pyplot as plt
 from typing import Dict, Tuple, List
 
 from matplotlib.lines import Line2D
-from scipy.special import softmax
 import numpy as np
 
 from extreme_data.meteo_france_data.adamont_data.adamont_scenario import scenario_to_str
 from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
 from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import StudyVisualizer
-from projects.projected_swe.old_weight_computer.utils import save_to_filepath
-from projects.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
-from projects.projected_swe.weight_solver.default_weight_solver import EqualWeight
-from projects.projected_swe.weight_solver.indicator import AbstractIndicator, WeightComputationException
-from projects.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
-from projects.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
+from projects.archive.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
+from projects.archive.projected_swe.weight_solver.default_weight_solver import EqualWeight
+from projects.archive.projected_swe.weight_solver.indicator import WeightComputationException
+from projects.archive.projected_swe.weight_solver import KnuttiWeightSolver
+from projects.archive.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
     KnuttiWeightSolverWithBootstrapVersion2, KnuttiWeightSolverWithBootstrapVersion1
 from root_utils import get_display_name_from_object_type
 
@@ -21,7 +19,6 @@ from root_utils import get_display_name_from_object_type
 class ModelAsTruth(object):
 
     def __init__(self, observation_study: AbstractStudy,
-                 couple_to_study_projected: Dict[Tuple[str, str], AbstractStudy],
                  couple_to_study_historical: Dict[Tuple[str, str], AbstractStudy],
                  indicator_class: type,
                  knutti_weight_solver_classes,
@@ -32,7 +29,6 @@ class ModelAsTruth(object):
         self.add_interdependence_weight = add_interdependence_weight
         self.indicator_class = indicator_class
         self.couple_to_study_historical = couple_to_study_historical
-        self.couple_to_study_projected = couple_to_study_projected
         self.observation_study = observation_study
         # Parameters
         self.width = 2
@@ -44,7 +40,7 @@ class ModelAsTruth(object):
         }
 
         # Update massif names
-        study_list = [observation_study] + list(couple_to_study_historical.values()) + list(couple_to_study_projected.values())
+        study_list = [observation_study] + list(couple_to_study_historical.values())
         self.massif_names = self.get_massif_names_subset_from_study_list(study_list)
         print('Nb of massifs')
         print(len(self.massif_names))
@@ -52,9 +48,9 @@ class ModelAsTruth(object):
         for study in study_list:
             study._massif_names_for_cache = self.massif_names
 
-    def plot_against_sigma(self, sigma_list):
+    def plot_against_sigma(self, couple_to_study_projected, sigma_list):
         ax = plt.gca()
-        solver_class_to_score_list = self.get_solver_class_to_score_list(sigma_list)
+        solver_class_to_score_list = self.get_solver_class_to_score_list(couple_to_study_projected, sigma_list)
         all_x = []
         labels = []
         colors = []
@@ -75,14 +71,14 @@ class ModelAsTruth(object):
         custom_lines = [Line2D([0], [0], color=color, lw=4) for color in colors]
         ax.legend(custom_lines, labels, prop={'size': 8}, loc='upper left')
         ax.set_xlim(min(all_x) - self.width, max(all_x) + self.width)
-        study_projected = list(self.couple_to_study_projected.values())[0]
+        study_projected = list(couple_to_study_projected.values())[0]
         title = 'crpss between a weighted forecast and an unweighted forecast \n' \
                       'at {} m for {} of snowfall for {}-{} (%)'.format(self.observation_study.altitude,
                                                                         self.indicator_class.str_indicator(),
                                                                         study_projected.year_min,
                                                                         study_projected.year_max)
         ax2 = ax.twiny()
-        ax2.set_xlabel('{} for {} GCM/RCM couples'.format(scenario_to_str(study_projected.scenario), len(self.couple_to_study_projected)))
+        ax2.set_xlabel('{} for {} GCM/RCM couples'.format(scenario_to_str(study_projected.scenario), len(couple_to_study_projected)))
         ax.set_xlabel('sigma skill parameter')
         ax.set_ylabel(title)
 
@@ -102,19 +98,19 @@ class ModelAsTruth(object):
         x_list = [((j + 1) * (self.width * 1.1)) + shift * i * self.width for i in range(len(sigma_list))]
         return x_list
 
-    def get_solver_class_to_score_list(self, sigma_list):
-        return {solver_class: [self.compute_score(solver_class, sigma) for sigma in sigma_list]
+    def get_solver_class_to_score_list(self, couple_to_study_projected, sigma_list):
+        return {solver_class: [self.compute_score(couple_to_study_projected, solver_class, sigma) for sigma in sigma_list]
                 for solver_class in self.knutti_weight_solver_classes}
 
-    def compute_score(self, solver_class, sigma):
+    def compute_score(self, couple_to_study_projected, solver_class, sigma):
         # return [sigma, sigma*2]
         score_list = []
         for gcm_rcm_couple in self.couple_to_study_historical.keys():
             historical_observation_study = self.couple_to_study_historical[gcm_rcm_couple]
-            projected_observation_study = self.couple_to_study_projected[gcm_rcm_couple]
+            projected_observation_study = couple_to_study_projected[gcm_rcm_couple]
             couple_to_study_historical = {c: s for c, s in self.couple_to_study_historical.items() if
                                           c != gcm_rcm_couple}
-            couple_to_study_projected = {c: s for c, s in self.couple_to_study_projected.items() if c != gcm_rcm_couple}
+            couple_to_study_projected = {c: s for c, s in couple_to_study_projected.items() if c != gcm_rcm_couple}
 
             try:
                 if issubclass(solver_class, KnuttiWeightSolver):

projects/projected_swe/old_weight_computer/abstract_weight_computer.py → projects/archive/projected_swe/old_weight_computer/abstract_weight_computer.py

 import pandas as pd
 
-from projects.projected_swe.weight_computer.utils import WEIGHT_COLUMN_NAME, save_to_filepath
+from projects.archive.projected_swe import WEIGHT_COLUMN_NAME, save_to_filepath
 from collections import OrderedDict
 
 import numpy as np

projects/projected_swe/old_weight_computer/knutti_weight_computer.py → projects/archive/projected_swe/old_weight_computer/knutti_weight_computer.py

 import numpy as np
 
-from projects.projected_swe.weight_computer.abstract_weight_computer import AbstractWeightComputer
-from projects.projected_swe.weight_computer.non_stationary_weight_computer import NllhWeightComputer
+from projects.archive.projected_swe import AbstractWeightComputer
+from projects.archive.projected_swe import NllhWeightComputer
 
 
 class KnuttiWeightComputer(AbstractWeightComputer):

projects/projected_swe/old_weight_computer/main_weight_computation.py → projects/archive/projected_swe/old_weight_computer/main_weight_computation.py

@@ -11,9 +11,9 @@ from extreme_fit.model.margin_model.polynomial_margin_model.models_based_on_pari
 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.one_fold_fit.altitude_group import altitudes_for_groups
-from projects.projected_swe.weight_computer.abstract_weight_computer import AbstractWeightComputer
-from projects.projected_swe.weight_computer.knutti_weight_computer import KnuttiWeightComputer
-from projects.projected_swe.weight_computer.non_stationary_weight_computer import NllhWeightComputer
+from projects.archive.projected_swe import AbstractWeightComputer
+from projects.archive.projected_swe import KnuttiWeightComputer
+from projects.archive.projected_swe import NllhWeightComputer
 from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import \
     TimeTemporalCovariate
 

projects/projected_swe/old_weight_computer/non_stationary_weight_computer.py → projects/archive/projected_swe/old_weight_computer/non_stationary_weight_computer.py

 import numpy as np
 
 from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import compute_nllh
-from projects.projected_swe.weight_computer.abstract_weight_computer import AbstractWeightComputer
+from projects.archive.projected_swe import AbstractWeightComputer
 from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import \
     TimeTemporalCovariate
 

projects/projected_swe/weight_solver/abtract_weight_solver.py → projects/archive/projected_swe/weight_solver/abtract_weight_solver.py

@@ -5,7 +5,7 @@ from scipy.special import softmax
 import numpy as np
 
 from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from projects.projected_swe.weight_solver.indicator import AbstractIndicator, ReturnLevelComputationException, \
+from projects.archive.projected_swe.weight_solver.indicator import AbstractIndicator, ReturnLevelComputationException, \
     ReturnLevel30YearsIndicator
 
 

projects/projected_swe/weight_solver/default_weight_solver.py → projects/archive/projected_swe/weight_solver/default_weight_solver.py

-from typing import Dict, Tuple
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from projects.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
+from projects.archive.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
 
 
 class EqualWeight(AbstractWeightSolver):

projects/projected_swe/weight_solver/knutti_weight_solver.py → projects/archive/projected_swe/weight_solver/knutti_weight_solver.py

 import numpy as np
 from scipy.stats import norm
 
-from extreme_data.meteo_france_data.scm_models_data.utils_function import ReturnLevelBootstrap
-from projects.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
-from projects.projected_swe.weight_solver.indicator import AbstractIndicator, NllhComputationException, \
+from projects.archive.projected_swe.weight_solver.abtract_weight_solver import AbstractWeightSolver
+from projects.archive.projected_swe.weight_solver.indicator import AbstractIndicator, NllhComputationException, \
     WeightComputationException
 
 

projects/projected_swe/weight_solver/knutti_weight_solver_with_bootstrap.py → projects/archive/projected_swe/weight_solver/knutti_weight_solver_with_bootstrap.py

 import numpy as np
 
-from projects.projected_swe.weight_solver.indicator import AbstractIndicator
-from projects.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
+from projects.archive.projected_swe.weight_solver.indicator import AbstractIndicator
+from projects.archive.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
 
 
 class KnuttiWeightSolverWithBootstrap(KnuttiWeightSolver):

test/test_projects/test_projected_swe/test_model_as_truth.py

@@ -3,15 +3,14 @@ import numpy as np
 
 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
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
 from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2020
 from extreme_data.meteo_france_data.scm_models_data.utils_function import ReturnLevelBootstrap
 from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
     AbstractExtractEurocodeReturnLevel
 from extreme_fit.model.utils import set_seed_for_test
-from projects.projected_swe.weight_solver.indicator import AnnualMaximaMeanIndicator, ReturnLevel30YearsIndicator
-from projects.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
-from projects.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
+from projects.archive.projected_swe.weight_solver.indicator import AnnualMaximaMeanIndicator, ReturnLevel30YearsIndicator
+from projects.archive.projected_swe.weight_solver.knutti_weight_solver import KnuttiWeightSolver
+from projects.archive.projected_swe.weight_solver.knutti_weight_solver_with_bootstrap import \
     KnuttiWeightSolverWithBootstrapVersion1, KnuttiWeightSolverWithBootstrapVersion2