[refactor] move altitudes_studies.py. create trend test folder inside extreme_trend.

extreme_data/meteo_france_data/adamont_data/adamont/adamont_snowfall.py

-import numpy as np
-
 from extreme_data.meteo_france_data.adamont_data.abstract_adamont_study import AbstractAdamontStudy
-from extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario
 from extreme_data.meteo_france_data.adamont_data.adamont.adamont_variables import \
     SafranSnowfallSimulationVariable
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import YEAR_MIN, YEAR_MAX
-from extreme_data.meteo_france_data.scm_models_data.abstract_variable import AbstractVariable
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
-from extreme_data.meteo_france_data.scm_models_data.safran.safran_variable import SafranSnowfallVariable
+from extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario
 from extreme_data.meteo_france_data.scm_models_data.utils import Season, FrenchRegion
 
 

extreme_data/meteo_france_data/adamont_data/deprecated/ensemble_simulation.py

-import os
-from typing import List
-import os.path as op
-
-import numpy as np
-from cached_property import cached_property
-
-from extreme_data.meteo_france_data.adamont_data.deprecated.single_simulation import SingleSimulation
-
-ADAMONT_PATH = r"/home/erwan/Documents/projects/spatiotemporalextremes/local/spatio_temporal_datasets/ADAMONT"
-
-
-class EnsembleSimulation(object):
-
-    def __init__(self, scenario='HISTO', parameter='SNOWSWE',
-                 first_winter_required_for_histo=1958, last_winter_for_histo=2004):
-        assert False, 'RE-read the code to take into the new dates'
-        self.scenario = scenario
-        self.parameter = parameter
-        self.first_winter_required_for_histo = first_winter_required_for_histo
-        self.last_year_for_histo = last_winter_for_histo
-
-        # Assert value for the parameter
-        assert scenario in ['HISTO', 'RCP45']
-        assert parameter in ['SNOWSWE']
-        assert first_winter_required_for_histo >= 1950
-        assert first_winter_required_for_histo <= 2004
-
-        # Load simulations
-        # todo: so far i am using one ensemble member
-        print('single ensemble member', self.nc_files[0])
-        self.simulations = [SingleSimulation(nc_path, self.parameter,
-                                             self.first_winter_required_for_histo,
-                                             self.last_year_for_histo) for nc_path in self.nc_paths][:1]
-
-    @cached_property
-    def simulations_path(self):
-        return op.join(ADAMONT_PATH, self.parameter, self.scenario)
-
-    @cached_property
-    def nc_paths(self):
-        return [op.join(ADAMONT_PATH, self.parameter, self.scenario, nc_file) for nc_file in self.nc_files]
-
-    @cached_property
-    def nc_files(self) -> List[str]:
-        nc_files = []
-        for file in os.listdir(self.simulations_path):
-            first_year = int(file.split('_')[-3][:4])
-            if first_year <= self.first_winter_required_for_histo:
-                # Also remove the historical simulations that contain "CNRM-CM5"
-                # Problem reported in "limitations" on their website
-                # Ce problème affecte toutes lessimulations HISTORIQUE CORDEX
-                # réalisées en utilisant le forçage CNRM-CM5: CCLM4-8-17: ALADIN53 et RCA4
-                if self.scenario == 'HISTO' and 'CNRM-CM5' in file:
-                    print('here', file)
-                    continue
-                nc_files.append(file)
-        assert len(nc_files) > 0
-        return nc_files
-
-    @cached_property
-    def simulations_names(self):
-        return [' + '.join(file.split('_')[2:-5]) for file in self.nc_files]
-
-    def massif_name_and_altitude_to_mean_return_level(self):
-        return {}
-
-    @property
-    def first_simulation(self):
-        return self.simulations[0]
-
-    @property
-    def massif_name_and_altitude(self):
-        pass
-
-    @cached_property
-    def massif_name_and_altitude_to_mean_average_annual_maxima(self):
-        d = {}
-        for m, a in self.first_simulation.massif_name_and_altitude_to_average_maxima.keys():
-            d[(m, a)] = np.mean([s.massif_name_and_altitude_to_average_maxima[(m, a)] for s in self.simulations])
-        return d
-
-
-if __name__ == '__main__':
-    # np.array(d.variables['SNOWSWE'])
-    ensemble = EnsembleSimulation(first_winter_required_for_histo=1958)
-    print(len(ensemble.simulations))
-    print(ensemble.simulations_names)
-    s = ensemble.first_simulation
-    d = s.dataset
-    # print(s.massif_name_and_altitude_to_annual_maxima_time_series)
-    # print(s.massif_name_and_altitude_to_average_maxima)
-    print(ensemble.massif_name_and_altitude_to_mean_average_annual_maxima)
-    print(s.years)
-    # print(d)

extreme_data/meteo_france_data/adamont_data/deprecated/single_simulation.py

-from datetime import datetime
-
-import numpy as np
-import os.path as op
-from cached_property import cached_property
-from netCDF4._netCDF4 import Dataset
-from datetime import timedelta
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-
-
-class SingleSimulation(object):
-
-    def __init__(self, nc_path, parameter, fist_year, last_year):
-        self.fist_year = fist_year
-        self.last_year = last_year
-        self.parameter = parameter
-        self.nc_path = nc_path
-
-    @cached_property
-    def dataset(self):
-        return Dataset(self.nc_path)
-
-    @cached_property
-    def winter_year(self):
-        start = datetime(year=1900, month=1, day=1, hour=0, minute=0, second=0)
-        seconds_after_start = np.array(self.dataset.variables['TIME'])
-        dates = [start + timedelta(seconds=s) for s in seconds_after_start]
-        winter_year = [date.year - 1 if date.month < 8 else date.year for date in dates]
-        return np.array(winter_year)
-
-    @cached_property
-    def years(self):
-        return sorted([year for year in set(self.winter_year) if self.fist_year <= year <= self.last_year])
-
-    def massif_name_and_altitude_to_return_level(self):
-        return {}
-
-    @property
-    def massif_number_to_massif_name(self):
-        # from adamont_data metadata
-        s = """1	Chablais
-        2	Aravis
-        3	Mont-Blanc
-        4	Bauges
-        5	Beaufortain
-        6	Haute-Tarentaise
-        7	Chartreuse
-        8	Belledonne
-        9	Maurienne
-        10	Vanoise
-        11	Haute-Maurienne
-        12	Grandes-Rousses
-        13	Thabor
-        14	Vercors
-        15	Oisans
-        16	Pelvoux
-        17	Queyras
-        18	Devoluy
-        19	Champsaur
-        20	Parpaillon
-        21	Ubaye
-        22	Haut_Var-Haut_Verdon
-        23	Mercantour"""
-        l = s.split('\n')
-        return dict([e.split() for e in l])
-
-    @cached_property
-    def massif_name_and_altitude_to_annual_maxima_time_series(self):
-        all_values = np.array(self.dataset.variables[self.parameter])
-        zs_list = [int(e) for e in np.array(self.dataset.variables['ZS'])]
-        massif_number_list = np.array(self.dataset.variables['MASSIF_NUMBER'])
-        massif_name_list = [self.massif_number_to_massif_name[str(n)] for n in massif_number_list]
-        d = {}
-        for year in self.years:
-            indexes = np.where(self.winter_year == year)[0]
-            winter_values = all_values[indexes, 0, :]
-            assert len(winter_values) in [365, 366]
-            for time_serie, zs, massif_name in zip(winter_values.transpose(), zs_list, massif_name_list):
-                # print(zs, massif_name, len(time_serie))
-                d[(massif_name, zs)] = time_serie
-        return d
-
-    @cached_property
-    def massif_name_and_altitude_to_average_maxima(self):
-        return {t: np.mean(s) for t, s in self.massif_name_and_altitude_to_annual_maxima_time_series.items()}

extreme_data/meteo_france_data/scm_models_data/case_studies/ribatet/day_for_the_maxima_v1.py

 import pandas as pd
-import numpy as np
-import xlsxwriter
 
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusDepth, CrocusSwe3Days
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days
-from extreme_data.meteo_france_data.scm_models_data.utils import FrenchRegion
+from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
 from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
     SCM_STUDY_CLASS_TO_ABBREVIATION
-from extreme_fit.estimator.margin_estimator.utils import fitted_stationary_gev
-from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
+from extreme_data.meteo_france_data.scm_models_data.altitudes_studies import AltitudesStudies
 
 
 def generate_excel_with_annual_maxima(fast=True, maxima_dates=False):

extreme_data/meteo_france_data/scm_models_data/case_studies/ribatet/day_for_the_maxima_v2.py

 import pandas as pd
 import numpy as np
-import xlsxwriter
 
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusDepth, CrocusSwe3Days
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days
-from extreme_data.meteo_france_data.scm_models_data.utils import FrenchRegion
+from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
 from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
     SCM_STUDY_CLASS_TO_ABBREVIATION
-from extreme_fit.estimator.margin_estimator.utils import fitted_stationary_gev
-from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
+from extreme_data.meteo_france_data.scm_models_data.altitudes_studies import AltitudesStudies
 
 
 def generate_excel_with_annual_maxima(fast=True, maxima_dates=False):

extreme_trend/trend_test_one_parameter/abstract_comparison_non_stationary_model.py → extreme_trend/trend_test/trend_test_one_parameter/abstract_comparison_non_stationary_model.py

 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
-from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
+from extreme_trend.trend_test.trend_test_one_parameter.gev_trend_test_one_parameter import \
     GevTrendTestOneParameter
-from extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters import GevLocationAndScaleTrendTest
+from extreme_trend.trend_test.trend_test_two_parameters import GevLocationAndScaleTrendTest
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod
 from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
     NonStationaryLocationTemporalModel, NonStationaryScaleTemporalModel
-import numpy as np
 
 
 class AbstractComparisonNonStationaryModelOneParameter(GevTrendTestOneParameter):

extreme_trend/trend_test_one_parameter/gev_trend_test_one_parameter.py → extreme_trend/trend_test/trend_test_one_parameter/gev_trend_test_one_parameter.py

 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
+from extreme_trend.trend_test.abstract_gev_trend_test import AbstractGevTrendTest
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod
 from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \

extreme_trend/trend_test_one_parameter/gumbel_trend_test_one_parameter.py → extreme_trend/trend_test/trend_test_one_parameter/gumbel_trend_test_one_parameter.py

 # Comparison with the Gumbel model
 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
-from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
+from extreme_trend.trend_test.trend_test_one_parameter.gev_trend_test_one_parameter import \
     GevTrendTestOneParameter, GevTrendTestOneParameterAgainstStationary
 from extreme_fit.distribution.gev.gev_params import GevParams
 from extreme_fit.model.margin_model.utils import \

extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py → extreme_trend/trend_test/trend_test_three_parameters/gev_trend_test_three_parameters.py

@@ -3,7 +3,7 @@ from extreme_fit.model.margin_model.polynomial_margin_model.polynomial_margin_mo
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod
 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
+from extreme_trend.trend_test.abstract_gev_trend_test import AbstractGevTrendTest
 from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
     NonStationaryLocationAndScaleTemporalModel, GumbelTemporalModel, StationaryTemporalModel, \
     NonStationaryLocationAndScaleAndShapeTemporalModel

extreme_trend/trend_test_two_parameters/gev_trend_test_two_parameters.py → extreme_trend/trend_test/trend_test_two_parameters/gev_trend_test_two_parameters.py

 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
 from extreme_fit.model.margin_model.polynomial_margin_model.polynomial_margin_model import NonStationaryQuadraticLocationModel, \
     NonStationaryQuadraticScaleModel
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_trend.trend_test_one_parameter.gev_trend_test_one_parameter import \
+from extreme_trend.trend_test.abstract_gev_trend_test import AbstractGevTrendTest
+from extreme_trend.trend_test.trend_test_one_parameter.gev_trend_test_one_parameter import \
     GevLocationTrendTest, GevScaleTrendTest
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod

extreme_trend/trend_test_two_parameters/gumbel_test_two_parameters.py → extreme_trend/trend_test/trend_test_two_parameters/gumbel_test_two_parameters.py

 from extreme_data.eurocode_data.utils import EUROCODE_QUANTILE
 from extreme_fit.model.margin_model.polynomial_margin_model.polynomial_margin_model import \
     NonStationaryQuadraticLocationGumbelModel, NonStationaryQuadraticScaleGumbelModel
-from extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters import \
-    GevTrendTestTwoParameters
 from extreme_fit.distribution.gev.gev_params import GevParams
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod
 from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import \
     NonStationaryLocationAndScaleGumbelModel, GumbelTemporalModel
+from extreme_trend.trend_test.trend_test_two_parameters.gev_trend_test_two_parameters import GevTrendTestTwoParameters
 from root_utils import classproperty
 
 

extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py → extreme_trend/trend_test/visualizers/study_visualizer_for_non_stationary_trends.py

 from collections import OrderedDict, Counter
 from enum import Enum
 from multiprocessing.pool import Pool
-from typing import Dict, List, Tuple
+from typing import Dict, List
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -18,15 +18,10 @@ from extreme_data.meteo_france_data.scm_models_data.abstract_study import Abstra
 from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import \
     StudyVisualizer
 from projects.exceeding_snow_loads.utils import NON_STATIONARY_TREND_TEST_PAPER_1, ALTITUDE_TO_GREY_MASSIF
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter import \
-    GumbelLocationTrendTest, GevStationaryVersusGumbel, GumbelScaleTrendTest, GumbelVersusGumbel
-from extreme_trend.trend_test_two_parameters.gumbel_test_two_parameters import \
-    GumbelLocationAndScaleTrendTest
+from extreme_trend.trend_test.abstract_gev_trend_test import AbstractGevTrendTest
 from extreme_fit.model.margin_model.utils import \
     MarginFitMethod
-from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel, \
-    StationaryTemporalModel
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel
 from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
     ConfidenceIntervalMethodFromExtremes
 from extreme_fit.model.result_from_model_fit.result_from_extremes.eurocode_return_level_uncertainties import \

extreme_trend/visualizers/study_visualizer_for_shape_repartition.py → extreme_trend/trend_test/visualizers/study_visualizer_for_shape_repartition.py

@@ -3,7 +3,7 @@ from cached_property import cached_property
 
 from extreme_data.meteo_france_data.scm_models_data.visualization.create_shifted_cmap import get_shifted_map
 from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import \
+from extreme_trend.trend_test.visualizers import \
     StudyVisualizerForNonStationaryTrends