diff --git a/experiment/meteo_france_SCM_study/crocus/crocus_variables.py b/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
index cd4f2e7238239a09a5979a8e58a741f33370ba1b..9e47d8f0fee2613a6c35ff41dc7bd7a361c9ea80 100644
--- a/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
+++ b/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
@@ -18,7 +18,7 @@ class CrocusVariable(AbstractVariable):
class CrocusSweVariable(CrocusVariable):
NAME = 'Snow Water Equivalent'
- UNIT = 'kg/m2 or mm'
+ UNIT = 'kg per m2 or mm'
def __init__(self, dataset, altitude):
super().__init__(dataset, altitude, 'SWE_1DY_ISBA')
diff --git a/experiment/meteo_france_SCM_study/safran/safran_variable.py b/experiment/meteo_france_SCM_study/safran/safran_variable.py
index 87a7a1d789c92fd3c86d5f3b7b4e7962608e1d77..1116d47c7e0049a7a86a90392a808bdafb8540c4 100644
--- a/experiment/meteo_france_SCM_study/safran/safran_variable.py
+++ b/experiment/meteo_france_SCM_study/safran/safran_variable.py
@@ -22,7 +22,7 @@ class SafranSnowfallVariable(AbstractVariable):
"""
NAME = 'Snowfall'
- UNIT = 'kg/m2 or mm'
+ UNIT = 'kg per m2 or mm'
def __init__(self, dataset, altitude, nb_consecutive_days_of_snowfall=1, keyword='Snowf'):
super().__init__(dataset, altitude)
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index 17ab7a991243e4c190ddf6e00323d6d53bf2a284..fbd7d1854ea5c89b1fcf6520f8217148143ee564 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -12,14 +12,15 @@ SCM_EXTENDED_STUDIES = [ExtendedSafranSnowfall, ExtendedCrocusSwe, ExtendedCrocu
SCM_STUDY_TO_EXTENDED_STUDY = OrderedDict(zip(SCM_STUDIES, SCM_EXTENDED_STUDIES))
-def study_iterator(study_class, only_first_one=False, both_altitude=False, verbose=True):
+def study_iterator(study_class, only_first_one=False, both_altitude=False, verbose=True, altitudes=None):
all_studies = []
is_safran_study = study_class in [SafranSnowfall, ExtendedSafranSnowfall]
nb_days = [1] if is_safran_study else [1]
if verbose:
print('Loading studies....')
for nb_day in nb_days:
- for alti in [1800]:
+ altis = [1800] if altitudes is None else altitudes
+ for alti in altis:
if verbose:
print('alti: {}, nb_day: {}'.format(alti, nb_day))
study = study_class(altitude=alti, nb_consecutive_days=nb_day) if is_safran_study \
@@ -96,12 +97,12 @@ def complete_analysis(only_first_one=False):
def trend_analysis():
- save_to_file = False
+ save_to_file = True
only_first_one = True
- for study_class in [CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][1:2]:
- for study in study_iterator(study_class, only_first_one=only_first_one):
+ for study_class in [CrocusSwe, CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][:3]:
+ for study in study_iterator(study_class, only_first_one=only_first_one, altitudes=[1800, 2100, 2400, 2700]):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file)
- study_visualizer.visualize_temporal_trend_relevance(complete_analysis=False)
+ study_visualizer.visualize_temporal_trend_relevance(complete_analysis=not only_first_one)
if __name__ == '__main__':
# annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=2100)
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
index 89fe43516852ba295aa1179c712d7b5268e08f20..6d30e6628141a1bc640539901de6e3189b907552 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
@@ -1,12 +1,17 @@
+from typing import Union
+
from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
from scipy.stats import chi2
from extreme_estimator.estimator.full_estimator.abstract_full_estimator import \
- FullEstimatorInASingleStepWithSmoothMargin
-from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+ FullEstimatorInASingleStepWithSmoothMargin, AbstractFullEstimator
+from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator, \
+ AbstractMarginEstimator
from extreme_estimator.extreme_models.margin_model.linear_margin_model import \
LinearAllParametersTwoFirstCoordinatesMarginModel, LinearAllTwoStatialCoordinatesLocationLinearMarginModel, \
LinearStationaryMarginModel, LinearNonStationaryLocationMarginModel
+from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from utils import get_display_name_from_object_type
class AbstractNonStationaryTrendTest(object):
@@ -18,37 +23,73 @@ class AbstractNonStationaryTrendTest(object):
self.estimator_class = estimator_class
self.stationary_margin_model_class = stationary_margin_model_class
self.non_stationary_margin_model_class = non_stationary_margin_model_class
-
- def load_estimator(self, margin_model) -> AbstractEstimator:
+ # Compute a dictionary that maps couple (margin model class, starting point)
+ # to the corresponding fitted estimator
+ self._margin_model_class_and_starting_point_to_estimator = {}
+
+ def get_estimator(self, margin_model_class, starting_point) -> Union[
+ AbstractFullEstimator, AbstractMarginEstimator]:
+ if (margin_model_class, starting_point) not in self._margin_model_class_and_starting_point_to_estimator:
+ margin_model = margin_model_class(coordinates=self.dataset.coordinates, starting_point=starting_point)
+ estimator = self._load_estimator(margin_model)
+ estimator.fit()
+ self._margin_model_class_and_starting_point_to_estimator[(margin_model_class, starting_point)] = estimator
+ return self._margin_model_class_and_starting_point_to_estimator[(margin_model_class, starting_point)]
+
+ def _load_estimator(self, margin_model) -> Union[AbstractFullEstimator, AbstractMarginEstimator]:
return self.estimator_class(self.dataset, margin_model)
def get_metric(self, margin_model_class, starting_point):
- margin_model = margin_model_class(coordinates=self.dataset.coordinates, starting_point=starting_point)
- estimator = self.load_estimator(margin_model) # type: AbstractEstimator
- estimator.fit()
+ estimator = self.get_estimator(margin_model_class, starting_point)
metric = estimator.result_from_fit.__getattribute__(self.RESULT_ATTRIBUTE_METRIC)
assert isinstance(metric, float)
return metric
+ def get_mu1(self, starting_point):
+ # for the non stationary model gives the mu1 parameters that was fitted
+ estimator = self.get_estimator(self.non_stationary_margin_model_class, starting_point)
+ margin_function = estimator.margin_function_fitted # type: LinearMarginFunction
+ assert isinstance(margin_function, LinearMarginFunction)
+ return margin_function.mu1_temporal_trend
+
def visualize(self, ax, complete_analysis=True):
# Define the year_min and year_max for the starting point
if complete_analysis:
year_min, year_max, step = 1960, 1990, 1
else:
year_min, year_max, step = 1960, 1990, 10
- # Fit the stationary model
- stationary_metric = self.get_metric(self.stationary_margin_model_class, starting_point=None)
- # Fit the non stationary model
years = list(range(year_min, year_max + 1, step))
+
+ # Plot differences
+ stationary_metric = self.get_metric(self.stationary_margin_model_class, starting_point=None)
non_stationary_metrics = [self.get_metric(self.non_stationary_margin_model_class, year) for year in years]
difference = [m - stationary_metric for m in non_stationary_metrics]
- # Plot some lines
- ax.axhline(y=0, xmin=year_min, xmax=year_max)
- # Significative line
- significative_deviance = chi2.ppf(q=0.95, df=1)
- ax.axhline(y=significative_deviance, xmin=year_min, xmax=year_max)
- # todo: plot the line corresponding to the significance 0.05
- ax.plot(years, difference, 'ro-')
+ color_difference = 'b'
+ ax.plot(years, difference, color_difference + 'o-')
+ ax.set_ylabel(self.RESULT_ATTRIBUTE_METRIC + ' difference', color=color_difference)
+
+ # Plot the mu1 parameter
+ mu1_trends = [self.get_mu1(starting_point=year) for year in years]
+ ax2 = ax.twinx()
+ color_mu1 = 'c'
+ ax.plot(years, mu1_trends, color_mu1 + 'o-')
+ ax2.set_ylabel('mu1 parameter', color=color_mu1)
+
+ # Plot zero line
+ ax.plot(years, [0 for _ in years], 'k-', label='zero line')
+ # Plot significative line corresponding to 0.05 relevance
+ alpha = 0.05
+ significative_deviance = chi2.ppf(q=1 - alpha, df=1)
+ ax.plot(years, [significative_deviance for _ in years], 'g-', label='significative line')
+ # Add some informations about the graph
+
+ ax.set_xlabel('year')
+ ax.set_title(self.display_name)
+ ax.legend()
+
+ @property
+ def display_name(self):
+ raise NotImplementedError
class IndependenceLocationTrendTest(AbstractNonStationaryTrendTest):
@@ -65,6 +106,10 @@ class ConditionalIndedendenceLocationTrendTest(AbstractNonStationaryTrendTest):
stationary_margin_model_class=LinearStationaryMarginModel,
non_stationary_margin_model_class=LinearNonStationaryLocationMarginModel)
+ @property
+ def display_name(self):
+ return get_display_name_from_object_type('conditional independence')
+
class MaxStableLocationTrendTest(AbstractNonStationaryTrendTest):
@@ -75,5 +120,9 @@ class MaxStableLocationTrendTest(AbstractNonStationaryTrendTest):
non_stationary_margin_model_class=LinearNonStationaryLocationMarginModel)
self.max_stable_model = max_stable_model
- def load_estimator(self, margin_model) -> AbstractEstimator:
+ def _load_estimator(self, margin_model) -> AbstractEstimator:
return self.estimator_class(self.dataset, margin_model, self.max_stable_model)
+
+ @property
+ def display_name(self):
+ return get_display_name_from_object_type(type(self.max_stable_model))
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
index 3341bf2efc732c8845cd63d1638ba23a0fc9dcea..dc564da1cdd09d16f947ec998a80bd08d9e0dc89 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
@@ -6,6 +6,7 @@ from extreme_estimator.extreme_models.margin_model.param_function.abstract_coef
from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
from extreme_estimator.extreme_models.margin_model.param_function.param_function import AbstractParamFunction, \
LinearParamFunction
+from extreme_estimator.margin_fits.extreme_params import ExtremeParams
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -58,6 +59,10 @@ class LinearMarginFunction(ParametricMarginFunction):
coef_dict.update(coef.coef_dict(dims, self.idx_to_coefficient_name(self.coordinates)))
return coef_dict
+ @property
+ def mu1_temporal_trend(self):
+ return self.coef_dict[LinearCoef.coef_template_str(ExtremeParams.LOC, AbstractCoordinates.COORDINATE_T).format(1)]
+
@property
def form_dict(self) -> Dict[str, str]:
form_dict = {}