From 98f7d0d4a54c99eb1fdc2bfc149777e9c5063b37 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 4 Mar 2021 12:28:52 +0100
Subject: [PATCH] [projection snowfall] add together fit, and its usage into
visualizer_for_sensitivity.py
---
.../ensemble_fit/abstract_ensemble_fit.py | 1 +
.../visualizer_non_stationary_ensemble.py | 28 ++++++++--
.../visualizer_for_projection_ensemble.py | 2 +
.../visualizer_for_sensitivity.py | 51 +++++++++++--------
.../main_projections_ensemble.py | 7 ++-
.../main_sensitivity.py | 7 +--
.../coordinates/abstract_coordinates.py | 5 +-
7 files changed, 68 insertions(+), 33 deletions(-)
diff --git a/extreme_trend/ensemble_fit/abstract_ensemble_fit.py b/extreme_trend/ensemble_fit/abstract_ensemble_fit.py
index 5d352ea8..115b0700 100644
--- a/extreme_trend/ensemble_fit/abstract_ensemble_fit.py
+++ b/extreme_trend/ensemble_fit/abstract_ensemble_fit.py
@@ -8,6 +8,7 @@ from extreme_trend.one_fold_fit.one_fold_fit import OneFoldFit
class AbstractEnsembleFit(object):
Median_merge = 'Median'
Mean_merge = 'Mean'
+ Together_merge = 'Together'
def __init__(self, massif_names, gcm_rcm_couple_to_altitude_studies: Dict[Tuple[str, str], AltitudesStudies],
models_classes,
diff --git a/extreme_trend/ensemble_fit/together_ensemble_fit/visualizer_non_stationary_ensemble.py b/extreme_trend/ensemble_fit/together_ensemble_fit/visualizer_non_stationary_ensemble.py
index 8781b583..4d8b1999 100644
--- a/extreme_trend/ensemble_fit/together_ensemble_fit/visualizer_non_stationary_ensemble.py
+++ b/extreme_trend/ensemble_fit/together_ensemble_fit/visualizer_non_stationary_ensemble.py
@@ -1,4 +1,5 @@
from typing import List, Dict, Tuple
+import pandas as pd
from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
from extreme_data.meteo_france_data.scm_models_data.altitudes_studies import AltitudesStudies
@@ -9,6 +10,10 @@ from extreme_trend.one_fold_fit.altitudes_studies_visualizer_for_non_stationary_
AltitudesStudiesVisualizerForNonStationaryModels
from extreme_trend.trend_test.visualizers.study_visualizer_for_non_stationary_trends import \
StudyVisualizerForNonStationaryTrends
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+ AbstractSpatioTemporalObservations
class VisualizerNonStationaryEnsemble(AltitudesStudiesVisualizerForNonStationaryModels):
@@ -19,10 +24,23 @@ class VisualizerNonStationaryEnsemble(AltitudesStudiesVisualizerForNonStationary
super().__init__(studies, *args, **kwargs)
def get_massif_altitudes(self, massif_name):
- # return self._get_massif_altitudes(massif_name, self.studies)
- raise NotImplementedError
+ altitudes_before_intersection = []
+ for studies in self.gcm_rcm_couple_to_studies.values():
+ massif_altitudes = self._get_massif_altitudes(massif_name, studies)
+ altitudes_before_intersection.append(set(massif_altitudes))
+ altitudes_after_intersection = altitudes_before_intersection[0].intersection(*altitudes_before_intersection[1:])
+ altitudes_after_intersection = sorted(list(altitudes_after_intersection))
+ return altitudes_after_intersection
def get_dataset(self, massif_altitudes, massif_name):
- raise NotImplementedError
- # dataset = self.studies.spatio_temporal_dataset(massif_name=massif_name, massif_altitudes=massif_altitudes)
- # return dataset
+ df_coordinates_list = []
+ df_maxima_gev_list = []
+ for studies in self.gcm_rcm_couple_to_studies.values():
+ dataset = studies.spatio_temporal_dataset(massif_name=massif_name, massif_altitudes=massif_altitudes)
+ df_coordinates_list.append(dataset.coordinates.df_coordinates(add_climate_informations=True))
+ df_maxima_gev_list.append(dataset.observations.df_maxima_gev)
+ observations = AbstractSpatioTemporalObservations(df_maxima_gev=pd.concat(df_maxima_gev_list, axis=0))
+ coordinates = AbstractCoordinates(df=pd.concat(df_coordinates_list, axis=0),
+ slicer_class=type(dataset.slicer))
+ dataset = AbstractDataset(observations=observations, coordinates=coordinates)
+ return dataset
diff --git a/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py b/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
index 665edb89..42b38f05 100644
--- a/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
+++ b/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
@@ -120,6 +120,8 @@ class VisualizerForProjectionEnsemble(object):
def plot_together(self):
visualizer_list = [together_ensemble_fit.visualizer
for together_ensemble_fit in self.ensemble_fits(TogetherEnsembleFit)]
+ for v in visualizer_list:
+ v.studies.study.gcm_rcm_couple = ("together", "merge")
self.plot_for_visualizer_list(visualizer_list)
def ensemble_fits(self, ensemble_class):
diff --git a/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py b/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
index eeb69783..bd696188 100644
--- a/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
+++ b/extreme_trend/ensemble_fit/visualizer_for_sensitivity.py
@@ -10,6 +10,7 @@ from extreme_fit.model.margin_model.polynomial_margin_model.spatio_temporal_poly
from extreme_fit.model.margin_model.utils import MarginFitMethod
from extreme_trend.ensemble_fit.abstract_ensemble_fit import AbstractEnsembleFit
from extreme_trend.ensemble_fit.independent_ensemble_fit.independent_ensemble_fit import IndependentEnsembleFit
+from extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit import TogetherEnsembleFit
from extreme_trend.ensemble_fit.visualizer_for_projection_ensemble import VisualizerForProjectionEnsemble
from extreme_trend.one_fold_fit.altitude_group import get_altitude_class_from_altitudes, \
get_linestyle_for_altitude_class
@@ -31,16 +32,16 @@ class VisualizerForSensivity(object):
is_temperature_interval=False,
is_shift_interval=False,
):
+ self.ensemble_fit_classes = ensemble_fit_classes
self.is_shift_interval = is_shift_interval
self.temporal_covariate_for_fit = temporal_covariate_for_fit
self.is_temperature_interval = is_temperature_interval
- self.merge_visualizer_str_list = (AbstractEnsembleFit.Median_merge, AbstractEnsembleFit.Mean_merge)
self.altitudes_list = altitudes_list
self.massif_names = massif_names
self.left_limits, self.right_limits = get_interval_limits(self.is_temperature_interval,
- self.is_shift_interval)
+ self.is_shift_interval)
self.left_limit_to_right_limit = OrderedDict(zip(self.left_limits, self.right_limits))
- self.right_limit_to_visualizer = {} # type: Dict[float, VisualizerForProjectionEnsemble]
+ self.right_limit_to_visualizer = {} # type: Dict[float, VisualizerForProjectionEnsemble]
for left_limit, right_limit in zip(self.left_limits, self.right_limits):
print("Interval is", left_limit, right_limit)
@@ -52,9 +53,9 @@ class VisualizerForSensivity(object):
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
-
+
visualizer = VisualizerForProjectionEnsemble(
- altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
+ altitudes_list, gcm_rcm_couples, study_class, season, scenario,
model_classes=model_classes,
fit_method=fit_method,
ensemble_fit_classes=ensemble_fit_classes,
@@ -73,7 +74,12 @@ class VisualizerForSensivity(object):
# , and not just the plots for the last t_min
# for visualizer in self.temp_min_to_visualizer.values():
# visualizer.plot()
- for merge_visualizer_str in self.merge_visualizer_str_list:
+ merge_visualizer_str_list = []
+ if IndependentEnsembleFit in self.ensemble_fit_classes:
+ merge_visualizer_str_list.extend([AbstractEnsembleFit.Median_merge, AbstractEnsembleFit.Mean_merge])
+ if TogetherEnsembleFit in self.ensemble_fit_classes:
+ merge_visualizer_str_list.append(AbstractEnsembleFit.Together_merge)
+ for merge_visualizer_str in merge_visualizer_str_list:
self.sensitivity_plot(merge_visualizer_str)
def sensitivity_plot(self, merge_visualizer_str):
@@ -89,7 +95,7 @@ class VisualizerForSensivity(object):
ax.set_xticklabels(ticks_labels)
ax.legend(prop={'size': 7}, loc='upper center', ncol=2)
ax.set_ylim((0, 122))
- ax.set_yticks([i*10 for i in range(11)])
+ ax.set_yticks([i * 10 for i in range(11)])
merge_visualizer = self.first_merge_visualizer(merge_visualizer_str)
temp_cov = self.temporal_covariate_for_fit is AnomalyTemperatureWithSplineTemporalCovariate
merge_visualizer.plot_name = 'Sensitivity plot with ' \
@@ -99,19 +105,6 @@ class VisualizerForSensivity(object):
merge_visualizer.show_or_save_to_file(no_title=True)
plt.close()
- def first_merge_visualizer(self, merge_visualizer_str):
- altitude_class = get_altitude_class_from_altitudes(self.altitudes_list[0])
- visualizer_projection = list(self.right_limit_to_visualizer.values())[0]
- return self.get_merge_visualizer(altitude_class, visualizer_projection, merge_visualizer_str)
-
- def get_merge_visualizer(self, altitude_class, visualizer_projection: VisualizerForProjectionEnsemble,
- merge_visualizer_str):
- independent_ensemble_fit = visualizer_projection.altitude_class_to_ensemble_class_to_ensemble_fit[altitude_class][
- IndependentEnsembleFit]
- merge_visualizer = independent_ensemble_fit.merge_function_name_to_visualizer[merge_visualizer_str]
- merge_visualizer.studies.study.gcm_rcm_couple = (merge_visualizer_str, "merge")
- return merge_visualizer
-
def interval_plot(self, ax, altitude_class, merge_visualizer_str):
linestyle = get_linestyle_for_altitude_class(altitude_class)
increasing_key = 'increasing'
@@ -137,3 +130,21 @@ class VisualizerForSensivity(object):
color = label_to_color[label]
ax.plot(self.right_limits, l, label=label_improved, color=color, linestyle=linestyle)
+ def get_merge_visualizer(self, altitude_class, visualizer_projection: VisualizerForProjectionEnsemble,
+ merge_visualizer_str):
+ if merge_visualizer_str in [AbstractEnsembleFit.Median_merge, AbstractEnsembleFit.Mean_merge]:
+ independent_ensemble_fit = \
+ visualizer_projection.altitude_class_to_ensemble_class_to_ensemble_fit[altitude_class][
+ IndependentEnsembleFit]
+ merge_visualizer = independent_ensemble_fit.merge_function_name_to_visualizer[merge_visualizer_str]
+ else:
+ together_ensemble_fit = \
+ visualizer_projection.altitude_class_to_ensemble_class_to_ensemble_fit[altitude_class][TogetherEnsembleFit]
+ merge_visualizer = together_ensemble_fit.visualizer
+ merge_visualizer.studies.study.gcm_rcm_couple = (merge_visualizer_str, "merge")
+ return merge_visualizer
+
+ def first_merge_visualizer(self, merge_visualizer_str):
+ altitude_class = get_altitude_class_from_altitudes(self.altitudes_list[0])
+ visualizer_projection = list(self.right_limit_to_visualizer.values())[0]
+ return self.get_merge_visualizer(altitude_class, visualizer_projection, merge_visualizer_str)
diff --git a/projects/projected_extreme_snowfall/main_projections_ensemble.py b/projects/projected_extreme_snowfall/main_projections_ensemble.py
index 9cb4e279..3de38f49 100644
--- a/projects/projected_extreme_snowfall/main_projections_ensemble.py
+++ b/projects/projected_extreme_snowfall/main_projections_ensemble.py
@@ -45,7 +45,7 @@ def main():
set_seed_for_test()
AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
- fast = True
+ fast = False
scenarios = rcp_scenarios[::-1] if fast is False else [AdamontScenario.rcp85]
for scenario in scenarios:
@@ -57,7 +57,7 @@ def main():
altitudes_list = altitudes_for_groups[3:]
elif fast:
massif_names = ['Vanoise', 'Haute-Maurienne']
- gcm_rcm_couples = gcm_rcm_couples[4:6]
+ gcm_rcm_couples = gcm_rcm_couples[:]
AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
altitudes_list = altitudes_for_groups[:1]
else:
@@ -73,7 +73,6 @@ def main():
model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
assert scenario in rcp_scenarios
- remove_physically_implausible_models = True
visualizer = VisualizerForProjectionEnsemble(
altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
@@ -81,7 +80,7 @@ def main():
ensemble_fit_classes=ensemble_fit_classes,
massif_names=massif_names,
temporal_covariate_for_fit=temporal_covariate_for_fit,
- remove_physically_implausible_models=remove_physically_implausible_models,
+ remove_physically_implausible_models=True,
gcm_to_year_min_and_year_max=None,
)
visualizer.plot()
diff --git a/projects/projected_extreme_snowfall/main_sensitivity.py b/projects/projected_extreme_snowfall/main_sensitivity.py
index 8b88ffa7..39b2d243 100644
--- a/projects/projected_extreme_snowfall/main_sensitivity.py
+++ b/projects/projected_extreme_snowfall/main_sensitivity.py
@@ -4,6 +4,7 @@ from typing import List
import matplotlib
from extreme_trend.ensemble_fit.abstract_ensemble_fit import AbstractEnsembleFit
+from extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit import TogetherEnsembleFit
matplotlib.use('Agg')
import matplotlib as mpl
@@ -40,7 +41,7 @@ from extreme_data.meteo_france_data.scm_models_data.utils import Season
def main():
start = time.time()
study_class = AdamontSnowfall
- ensemble_fit_classes = [IndependentEnsembleFit]
+ ensemble_fit_classes = [IndependentEnsembleFit, TogetherEnsembleFit][1:]
temporal_covariate_for_fit = [TimeTemporalCovariate,
AnomalyTemperatureWithSplineTemporalCovariate][0]
set_seed_for_test()
@@ -53,9 +54,9 @@ def main():
gcm_rcm_couples = get_gcm_rcm_couples(scenario)
if fast is None:
massif_names = None
- gcm_rcm_couples = gcm_rcm_couples
+ gcm_rcm_couples = gcm_rcm_couples[4:6]
AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
- altitudes_list = altitudes_for_groups[1:2]
+ altitudes_list = altitudes_for_groups[3:]
elif fast:
massif_names = ['Vanoise', 'Haute-Maurienne']
gcm_rcm_couples = gcm_rcm_couples[4:6]
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index d78bb441..83d077d6 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -154,11 +154,14 @@ class AbstractCoordinates(object):
# Split
- def df_coordinates(self, split: Split = Split.all, transformed=True) -> pd.DataFrame:
+ def df_coordinates(self, split: Split = Split.all, transformed=True, add_climate_informations=False) -> pd.DataFrame:
if transformed:
df_transformed_coordinates = self.transformation.transform_df(self.df_all_coordinates)
else:
df_transformed_coordinates = self.df_all_coordinates
+ if add_climate_informations:
+ df_transformed_coordinates = pd.concat([df_transformed_coordinates,
+ self.df_coordinate_climate_model], axis=1)
return df_sliced(df=df_transformed_coordinates, split=split, slicer=self.slicer)
def coordinates_values(self, split: Split = Split.all, transformed=True) -> np.ndarray:
--
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