diff --git a/extreme_data/meteo_france_data/adamont_data/cmip5/temperature_to_year.py b/extreme_data/meteo_france_data/adamont_data/cmip5/temperature_to_year.py
index 6b73a512fb8dd696dee7d11ff1c35db85d115a96..8cd7e69d5f57d610b450c00f46809c1f8555d187 100644
--- a/extreme_data/meteo_france_data/adamont_data/cmip5/temperature_to_year.py
+++ b/extreme_data/meteo_france_data/adamont_data/cmip5/temperature_to_year.py
@@ -8,65 +8,76 @@ from extreme_data.meteo_france_data.adamont_data.adamont_scenario import Adamont
from extreme_data.meteo_france_data.adamont_data.cmip5.climate_explorer_cimp5 import years_and_global_mean_temps
-def temperature_minmax_to_year_minmax(gcm, scenario, temperature_min, temperature_max):
- years, global_mean_temps = years_and_global_mean_temps(gcm, scenario, year_min=2005, year_max=2100, anomaly=True,
+def get_year_min_and_year_max(gcm, scenario, left_limit, right_limit, is_temperature_interval):
+ if is_temperature_interval:
+ years_to_select = _get_year_min_and_year_max_for_temperature_interval(gcm, left_limit,
+ right_limit, scenario)
+ if len(years_to_select) == 0:
+ return None, None
+ year_min, year_max = years_to_select[0], years_to_select[-1]
+ else:
+ year_min, year_max = left_limit, right_limit
+
+ # A minimum of 30 years of data is needed to find a trend
+ if year_max - year_min + 1 >= 30:
+ return year_min, year_max
+ else:
+ return None, None
+
+
+def _get_year_min_and_year_max_for_temperature_interval(gcm, left_limits, right_limits, scenario):
+ years, global_mean_temps = years_and_global_mean_temps(gcm, scenario, year_min=2006, year_max=2100, anomaly=True,
spline=True)
years, global_mean_temps = np.array(years), np.array(global_mean_temps)
- ind = temperature_min < global_mean_temps
- ind &= global_mean_temps < temperature_max
+ ind = left_limits < global_mean_temps
+ ind &= global_mean_temps < right_limits
years_to_select = years[ind]
ind2 = years_to_select[:-1] == years_to_select[1:] - 1
if not all(ind2):
i = list(ind2).index(False)
years_to_select = years_to_select[:i + 1]
- # A minimum of 30 years of data is needed to find a trend
- if len(years_to_select) >= 30:
- year_min, year_max = years_to_select[0], years_to_select[-1]
- assert (year_max - year_min + 1) == len(years_to_select)
- return year_min, year_max
- else:
- return None, None
+ return years_to_select
-def get_nb_data(gcm, scenario, temperature_min, temperature_max):
- year_min, year_max = temperature_minmax_to_year_minmax(gcm, scenario, temperature_min, temperature_max)
+def get_nb_data(gcm, scenario, temperature_min, temperature_max, is_temperature_interval):
+ year_min, year_max = get_year_min_and_year_max(gcm, scenario, temperature_min, temperature_max, is_temperature_interval)
if year_min is None:
return 0
else:
return year_max - year_min + 1
-def plot_nb_data_one_line(ax, gcm, scenario, temp_min, temp_max, first_scenario):
-
- nb_data = [get_nb_data(gcm, scenario, mi, ma) for mi, ma in zip(temp_min, temp_max)]
+def plot_nb_data_one_line(ax, gcm, scenario, left_limits, right_limits, first_scenario, is_temperature_interval):
+ nb_data = [get_nb_data(gcm, scenario, left, right, is_temperature_interval) for left, right in zip(left_limits, right_limits)]
color = gcm_to_color[gcm]
linestyle = get_linestyle_from_scenario(scenario)
# Filter out the zero value
- nb_data, temp_min = np.array(nb_data), np.array(temp_min)
+ nb_data, left_limits = np.array(nb_data), np.array(left_limits)
ind = np.array(nb_data) > 0
- nb_data, temp_min = nb_data[ind], temp_min[ind]
+ nb_data, left_limits = nb_data[ind], left_limits[ind]
# For the legend
if (len(nb_data) > 0) and first_scenario:
- ax.plot(temp_min[0], nb_data[0], color=color, linestyle='solid', label=gcm)
+ ax.plot(left_limits[0], nb_data[0], color=color, linestyle='solid', label=gcm)
- ax.plot(temp_min, nb_data, linestyle=linestyle, color=color, marker='o')
+ ax.plot(left_limits, nb_data, linestyle=linestyle, color=color, marker='o')
-def plot_nb_data():
- temp_min, temp_max = get_temp_min_and_temp_max()
+def plot_nb_data(is_temperature_interval, is_shift_interval):
+ left_limit, right_limit = get_interval_limits(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]):
- plot_nb_data_one_line(ax, gcm, scenario, temp_min, temp_max, first_scenario=i == 0)
+ plot_nb_data_one_line(ax, gcm, scenario, left_limit, right_limit,
+ i == 0, is_temperature_interval)
ax.legend()
- ticks_labels = get_ticks_labels_for_temp_min_and_temp_max()
- ax.set_xticks(temp_min)
+ ticks_labels = get_ticks_labels_for_interval(is_temperature_interval, is_shift_interval)
+ ax.set_xticks(left_limit)
ax.set_xticklabels(ticks_labels)
- ax.set_xlabel('Temperature interval')
+ # ax.set_xlabel('Interval')
ax.set_ylabel('Nb of Maxima')
ax2 = ax.twinx()
legend_elements = [
@@ -75,19 +86,38 @@ def plot_nb_data():
]
ax2.legend(handles=legend_elements, loc='upper center')
ax2.set_yticks([])
- plt.show()
+ # plt.show()
-def get_temp_min_and_temp_max():
- temp_min = np.arange(0, 3, 1)
- temp_max = temp_min + 2
- return temp_min, temp_max
+def get_interval_limits(is_temperature_interval, is_shift_interval):
+ if is_temperature_interval:
+ temp_min = np.arange(0, 3, 1)
+ temp_max = temp_min + 2
+ left_limit, right_limit = temp_min, temp_max
+ else:
+ shift = 25
+ nb = 3
+ year_min = [2006 + shift * i for i in range(nb)]
+ year_max = [2050 + shift * i for i in range(nb)]
+ left_limit, right_limit = year_min, year_max
+ if not is_shift_interval:
+ max_interval_right = max(right_limit)
+ right_limit = [max_interval_right for _ in left_limit]
+ return left_limit, right_limit
+
+
+def get_ticks_labels_for_interval(is_temperature_interval, is_shift_interval):
+ left_limits, right_limits = get_interval_limits(is_temperature_interval, is_shift_interval)
+ ticks_labels = [' +${}^o\mathrm{C}$ and +${}^o\mathrm{C}$'.format(left_limit, right_limit, **{'C': '{C}'})
+ if is_temperature_interval else '{} and {}'.format(left_limit, right_limit)
+ for left_limit, right_limit in zip(left_limits, right_limits)]
+ prefix = 'Maxima occured between \n'
+ ticks_labels = [prefix + l for l in ticks_labels]
+ return ticks_labels
-def get_ticks_labels_for_temp_min_and_temp_max():
- temp_min, temp_max = get_temp_min_and_temp_max()
- return ['Maxima occured between \n' \
- ' +${}^o\mathrm{C}$ and +${}^o\mathrm{C}$'.format(mi, ma, **{'C': '{C}'})
- for mi, ma in zip(temp_min, temp_max)]
if __name__ == '__main__':
- plot_nb_data()
+ for shift_interval in [False, True]:
+ for temp_interval in [True, False]:
+ print("shift = {}, temp_inteval = {}".format(shift_interval, temp_interval))
+ plot_nb_data(is_temperature_interval=temp_interval, is_shift_interval=shift_interval)
diff --git a/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py b/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
index b1230df4f928e7f598af240590697653e826ceeb..4db0401693da80ed46143b10cb2292d7b6f3f386 100644
--- a/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
+++ b/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
@@ -19,5 +19,14 @@ class OneFoldFitMerge(OneFoldFit):
def get_moment(self, altitude, temporal_covariate, order=1):
return self.merge_function([o.get_moment(altitude, temporal_covariate, order) for o in self.one_fold_fit_list])
-
-
+ def changes_of_moment(self, altitudes, order=1):
+ all_changes = [o.changes_of_moment(altitudes, order) for o in self.one_fold_fit_list]
+ merged_changes = list(self.merge_function(np.array(all_changes), axis=0))
+ assert len(all_changes[0]) == len(merged_changes)
+ return merged_changes
+
+ def relative_changes_of_moment(self, altitudes, order=1):
+ all_relative_changes = [o.relative_changes_of_moment(altitudes, order) for o in self.one_fold_fit_list]
+ merged_relative_changes = list(self.merge_function(np.array(all_relative_changes), axis=0))
+ assert len(all_relative_changes[0]) == len(merged_relative_changes)
+ return merged_relative_changes
diff --git a/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py b/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
index 4b9993bf3c7b74bf5b2df3c4bfdce58519d8fc27..222fbc79e9e275c130d5315c0f3fe32385c41e15 100644
--- a/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
+++ b/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
@@ -2,8 +2,8 @@ from collections import OrderedDict
import matplotlib.pyplot as plt
from typing import List, Dict
-from extreme_data.meteo_france_data.adamont_data.cmip5.temperature_to_year import get_temp_min_and_temp_max, \
- temperature_minmax_to_year_minmax, get_ticks_labels_for_temp_min_and_temp_max
+from extreme_data.meteo_france_data.adamont_data.cmip5.temperature_to_year import get_interval_limits, \
+ get_year_min_and_year_max, get_ticks_labels_for_interval
from extreme_data.meteo_france_data.scm_models_data.utils import Season
from extreme_fit.model.margin_model.polynomial_margin_model.spatio_temporal_polynomial_model import \
AbstractSpatioTemporalPolynomialModel
@@ -25,22 +25,28 @@ class VisualizerForSensivity(object):
display_only_model_that_pass_gof_test=False,
confidence_interval_based_on_delta_method=False,
remove_physically_implausible_models=False,
- merge_visualizer_str=IndependentEnsembleFit.Median_merge # if we choose the Mean merge, then it is almost impossible to obtain stationary trends
+ merge_visualizer_str=IndependentEnsembleFit.Median_merge, # if we choose the Mean merge, then it is almost impossible to obtain stationary trends
+ is_temperature_interval=False,
+ is_shift_interval=False,
):
+ self.is_shift_interval = is_shift_interval
+ self.is_temperature_interval = is_temperature_interval
self.merge_visualizer_str = merge_visualizer_str
self.altitudes_list = altitudes_list
self.massif_names = massif_names
- self.temp_min, self.temp_max = get_temp_min_and_temp_max()
- self.temp_min_to_temp_max = OrderedDict(zip(self.temp_min, self.temp_max))
- self.temp_min_to_visualizer = {} # type: Dict[float, VisualizerForProjectionEnsemble]
+ self.left_limits, self.right_limits = get_interval_limits(self.is_temperature_interval,
+ self.is_shift_interval)
+ self.left_limit_to_right_limit = OrderedDict(zip(self.left_limits, self.right_limits))
+ self.left_limit_to_visualizer = {} # type: Dict[float, VisualizerForProjectionEnsemble]
- for temp_min, temp_max in zip(self.temp_min, self.temp_max):
- print("temp min and temp max", temp_min, temp_max)
- # Build
+ for left_limit, right_limit in zip(self.left_limits, self.right_limits):
+ print("Interval is", left_limit, right_limit)
+ # Build gcm_to_year_min_and_year_max
gcm_to_year_min_and_year_max = {}
gcm_list = list(set([g for g, r in gcm_rcm_couples]))
for gcm in gcm_list:
- year_min_and_year_max = temperature_minmax_to_year_minmax(gcm, scenario, temp_min, temp_max)
+ year_min_and_year_max = get_year_min_and_year_max(gcm, scenario, left_limit, right_limit,
+ self.is_temperature_interval)
if year_min_and_year_max[0] is not None:
gcm_to_year_min_and_year_max[gcm] = year_min_and_year_max
@@ -56,7 +62,7 @@ class VisualizerForSensivity(object):
remove_physically_implausible_models=remove_physically_implausible_models,
gcm_to_year_min_and_year_max=gcm_to_year_min_and_year_max
)
- self.temp_min_to_visualizer[temp_min] = visualizer
+ self.left_limit_to_visualizer[left_limit] = visualizer
def plot(self):
# todo: before reactivating the subplot, i should ensure that we can modify the prefix
@@ -70,12 +76,12 @@ class VisualizerForSensivity(object):
ax = plt.gca()
for altitudes in self.altitudes_list:
altitude_class = get_altitude_class_from_altitudes(altitudes)
- self.temperature_interval_plot(ax, altitude_class)
+ self.interval_plot(ax, altitude_class)
- ticks_labels = get_ticks_labels_for_temp_min_and_temp_max()
+ ticks_labels = get_ticks_labels_for_interval(self.is_temperature_interval, self.is_shift_interval)
ax.set_ylabel('Percentages of massifs (\%)')
- ax.set_xlabel('Range of temperatures used to compute the trends ')
- ax.set_xticks(self.temp_min)
+ ax.set_xlabel('Interval used to compute the trends ')
+ ax.set_xticks(self.left_limits)
ax.set_xticklabels(ticks_labels)
ax.legend(prop={'size': 7}, loc='upper center', ncol=2)
ax.set_ylim((0, 122))
@@ -87,7 +93,7 @@ class VisualizerForSensivity(object):
@property
def first_merge_visualizer(self):
altitude_class = get_altitude_class_from_altitudes(self.altitudes_list[0])
- visualizer_projection = list(self.temp_min_to_visualizer.values())[0]
+ visualizer_projection = list(self.left_limit_to_visualizer.values())[0]
return self.get_merge_visualizer(altitude_class, visualizer_projection)
def get_merge_visualizer(self, altitude_class, visualizer_projection: VisualizerForProjectionEnsemble):
@@ -97,7 +103,7 @@ class VisualizerForSensivity(object):
merge_visualizer.studies.study.gcm_rcm_couple = (self.merge_visualizer_str, "merge")
return merge_visualizer
- def temperature_interval_plot(self, ax, altitude_class):
+ def interval_plot(self, ax, altitude_class):
linestyle = get_linestyle_for_altitude_class(altitude_class)
increasing_key = 'increasing'
decreasing_key = 'decreasing'
@@ -109,14 +115,15 @@ class VisualizerForSensivity(object):
increasing_key: 'red',
decreasing_key: 'blue'
}
- for v in self.temp_min_to_visualizer.values():
+ for v in self.left_limit_to_visualizer.values():
merge_visualizer = self.get_merge_visualizer(altitude_class, v)
- _, *trends = merge_visualizer.all_trends(self.massif_names, with_significance=False)
+ _, *trends = merge_visualizer.all_trends(self.massif_names, with_significance=False,
+ with_relative_change=True)
label_to_l[decreasing_key].append(trends[0])
label_to_l[increasing_key].append(trends[2])
altitude_str = altitude_class().formula
for label, l in label_to_l.items():
label_improved = 'with {} trends {}'.format(label, altitude_str)
color = label_to_color[label]
- ax.plot(self.temp_min, l, label=label_improved, color=color, linestyle=linestyle)
+ ax.plot(self.left_limits, l, label=label_improved, color=color, linestyle=linestyle)
diff --git a/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py b/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
index 995470b7690c1071adde49083df899df6d76d510..6263daed5f8e63eb8c4ffed5acc31ba78a989d2f 100644
--- a/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -455,7 +455,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
self.studies.show_or_save_to_file(plot_name=plot_name, show=self.show)
plt.close()
- def all_trends(self, massif_names, with_significance=True):
+ def all_trends(self, massif_names, with_significance=True, with_relative_change=False):
"""return percents which contain decrease, significant decrease, increase, significant increase percentages"""
valid_massif_names = self.get_valid_names(massif_names)
@@ -464,10 +464,14 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
for one_fold in [one_fold for m, one_fold in self.massif_name_to_one_fold_fit.items()
if m in valid_massif_names]:
# Compute nb of non stationary models
- if one_fold.change_in_return_level_for_reference_altitude == 0:
+ if with_relative_change:
+ change_value = one_fold.relative_change_in_return_level_for_reference_altitude
+ else:
+ change_value = one_fold.change_in_return_level_for_reference_altitude
+ if change_value == 0:
continue
# Compute nbs
- idx = 0 if one_fold.change_in_return_level_for_reference_altitude < 0 else 2
+ idx = 0 if change_value < 0 else 2
nbs[idx] += 1
if with_significance and one_fold.is_significant:
nbs[idx + 1] += 1
diff --git a/projects/projected_extreme_snowfall/main_elevation_temporal_for_projections_ensemble.py b/projects/projected_extreme_snowfall/main_elevation_temporal_for_projections_ensemble.py
index 09f9bdb3ba74e52f6a754bdc329df49157fc2d2f..5e1eac74b84f6e07368d55afd041b62f0a4cf27c 100644
--- a/projects/projected_extreme_snowfall/main_elevation_temporal_for_projections_ensemble.py
+++ b/projects/projected_extreme_snowfall/main_elevation_temporal_for_projections_ensemble.py
@@ -38,11 +38,12 @@ def main():
start = time.time()
study_class = AdamontSnowfall
ensemble_fit_class = [IndependentEnsembleFit]
- temporal_covariate_for_fit = [TimeTemporalCovariate, AnomalyTemperatureWithSplineTemporalCovariate][1]
+ temporal_covariate_for_fit = [TimeTemporalCovariate,
+ AnomalyTemperatureWithSplineTemporalCovariate][0]
set_seed_for_test()
AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
- fast = False
+ fast = True
sensitivity_plot = True
scenarios = rcp_scenarios[::-1] if fast is False else [AdamontScenario.rcp85]
@@ -57,7 +58,7 @@ def main():
massif_names = ['Vanoise', 'Haute-Maurienne']
gcm_rcm_couples = gcm_rcm_couples[4:6]
AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
- altitudes_list = altitudes_for_groups[:]
+ altitudes_list = altitudes_for_groups[:1]
else:
massif_names = None
altitudes_list = altitudes_for_groups[:]
diff --git a/test/test_extreme_trend/test_one_fold_fit.py b/test/test_extreme_trend/test_one_fold_fit.py
index 31da639f893060240425e2e8443254c7eba1bbd1..30829f586c9dfbf231f42f49f9092f5c8c1af5fd 100644
--- a/test/test_extreme_trend/test_one_fold_fit.py
+++ b/test/test_extreme_trend/test_one_fold_fit.py
@@ -2,7 +2,7 @@ import unittest
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
-from extreme_data.meteo_france_data.adamont_data.cmip5.temperature_to_year import temperature_minmax_to_year_minmax
+from extreme_data.meteo_france_data.adamont_data.cmip5.temperature_to_year import get_year_min_and_year_max
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
from extreme_fit.model.margin_model.polynomial_margin_model.gev_altitudinal_models import StationaryAltitudinal
from extreme_fit.model.margin_model.polynomial_margin_model.models_based_on_pariwise_analysis.gev_with_constant_shape_wrt_altitude import \
@@ -84,8 +84,8 @@ class TestOneFoldFit(unittest.TestCase):
self.altitudes = [3000, 3300, 3600]
gcm_rcm_couple = ('HadGEM2-ES', 'RegCM4-6')
scenario = AdamontScenario.rcp85
- year_min, year_max = temperature_minmax_to_year_minmax(gcm_rcm_couple[0], scenario, temperature_min=1.0,
- temperature_max=2.5)
+ year_min, year_max = get_year_min_and_year_max(gcm_rcm_couple[0], scenario, left_limit=1.0,
+ right_limit=2.5, is_temperature_interval=True)
dataset = self.load_dataset(AdamontSnowfall,
scenario=scenario, gcm_rcm_couple=gcm_rcm_couple,
year_min=year_min, year_max=year_max)