diff --git a/experiment/paper1_steps/poster_EVAN2019/some_experiment_EVAN.py b/experiment/paper1_steps/poster_EVAN2019/some_experiment_EVAN.py
index e4c731a1952208f6439d598b133472a3279c60cc..6ea0da2462482342c083b7aed7611877034756d5 100644
--- a/experiment/paper1_steps/poster_EVAN2019/some_experiment_EVAN.py
+++ b/experiment/paper1_steps/poster_EVAN2019/some_experiment_EVAN.py
@@ -18,17 +18,19 @@ mpl.rcParams['hatch.linewidth'] = 0.3
def main_non_stationary_model_comparison():
- stop_loop = False
+ stop_loop = True
for altitude in POSTER_ALTITUDES[:]:
- for trend_test_class in [GevLocationTrendTest, GevScaleTrendTest, GevLocationAndScaleTrendTest, ComparisonAgainstMu, ComparisonAgainstSigma][::-1]:
+ for trend_test_class in [GevLocationTrendTest, GevScaleTrendTest, GevLocationAndScaleTrendTest,
+ ComparisonAgainstMu, ComparisonAgainstSigma][::-1][-1:]:
vizualiser = get_full_altitude_visualizer(Altitude_Hypercube_Year_Visualizer, altitude=altitude,
exact_starting_year=1958, reduce_strength_array=False,
trend_test_class=trend_test_class,
)
- # vizualiser.save_to_file = False
+ vizualiser.save_to_file = False
vizualiser.visualize_massif_trend_test_one_altitude(poster_plot=True, write_text_on_massif=False)
if stop_loop:
return
+
if __name__ == '__main__':
main_non_stationary_model_comparison()
diff --git a/experiment/trend_analysis/univariate_test/abstract_comparison_non_stationary_model.py b/experiment/trend_analysis/univariate_test/abstract_comparison_non_stationary_model.py
index cb569655c2e2cf5b918d83a1755148ad946a3735..c9fb5671282201d8e48a28623d4db4897fd23b22 100644
--- a/experiment/trend_analysis/univariate_test/abstract_comparison_non_stationary_model.py
+++ b/experiment/trend_analysis/univariate_test/abstract_comparison_non_stationary_model.py
@@ -2,6 +2,7 @@ from experiment.trend_analysis.univariate_test.abstract_gev_trend_test import Ab
from experiment.trend_analysis.univariate_test.gev_trend_test_two_parameters import GevLocationAndScaleTrendTest
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import \
NonStationaryLocationAndScaleModel, NonStationaryLocationStationModel, NonStationaryScaleStationModel
+import numpy as np
class AbstractComparisonNonStationaryModel(AbstractGevTrendTest):
@@ -17,6 +18,12 @@ class AbstractComparisonNonStationaryModelOneParameter(AbstractComparisonNonStat
def degree_freedom_chi2(self) -> int:
return 1
+ @property
+ def test_sign(self) -> int:
+ # Test sign correspond to the difference between the 2 likelihoods
+ # Therefore, colors sum up which non stationary model explain best the data
+ return np.sign(self.likelihood_ratio)
+
class ComparisonAgainstMu(AbstractComparisonNonStationaryModelOneParameter, GevLocationAndScaleTrendTest):
diff --git a/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
index deae22fc07f4e43c8b80bf2f8338279e3ea728d6..95a4345c8ec09ad0fe76c3d11098661632666f99 100644
--- a/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
@@ -1,5 +1,6 @@
import numpy as np
import pandas as pd
+from cached_property import cached_property
from scipy.stats import chi2
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
@@ -109,11 +110,11 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
def test_sign(self) -> int:
return np.sign(self.test_trend_slope_strength)
- def get_non_stationary_linear_coef(self, gev_param_name):
+ def get_non_stationary_linear_coef(self, gev_param_name: str):
return self.non_stationary_estimator.margin_function_fitted.get_coef(gev_param_name,
AbstractCoordinates.COORDINATE_T)
- @property
+ @cached_property
def non_stationary_constant_gev_params(self) -> GevParams:
# Constant parameters correspond to the gev params in 1958
return self.non_stationary_estimator.margin_function_fitted.get_gev_params(coordinate=np.array([1958]),
@@ -145,8 +146,8 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
def variance_difference_same_sign_as_slope_strenght(self) -> bool:
return False
- def mean_difference(self, zeta0, mu1=0.0, sigma1=0.0):
- return GevParams(loc=mu1, scale=sigma1, shape=zeta0).mean
+ def mean_difference(self, zeta0: float, mu1: float = 0.0, sigma1: float = 0.0) -> float:
+ return GevParams(loc=mu1, scale=sigma1, shape=zeta0, accept_zero_scale_parameter=True).mean
@property
def test_trend_constant_quantile(self):
diff --git a/extreme_estimator/margin_fits/extreme_params.py b/extreme_estimator/margin_fits/extreme_params.py
index a20ace4f1493ebe88aa2ab28b29524b8fae33552..026c40405480753082e75b99b848e6a2e0864f21 100644
--- a/extreme_estimator/margin_fits/extreme_params.py
+++ b/extreme_estimator/margin_fits/extreme_params.py
@@ -14,7 +14,7 @@ class ExtremeParams(AbstractParams, ABC):
self.location = loc
self.scale = scale
self.shape = shape
- # Scale cannot be negative
+ # By default, scale cannot be negative
# (sometimes it happens, when we want to find a quantile for every point of a 2D map
# then it can happen that a corner point that was not used for fitting correspond to a negative scale,
# in the case we set all the parameters as equal to np.nan, and we will not display those points)
diff --git a/extreme_estimator/margin_fits/gev/gev_params.py b/extreme_estimator/margin_fits/gev/gev_params.py
index 769df9fdbef859cf5f3fa552dfa30d01ef335195..395032b551a9f18ff10ae347b2f9d03465ae705c 100644
--- a/extreme_estimator/margin_fits/gev/gev_params.py
+++ b/extreme_estimator/margin_fits/gev/gev_params.py
@@ -1,4 +1,5 @@
from collections import OrderedDict
+from typing import List
from cached_property import cached_property
@@ -15,9 +16,11 @@ class GevParams(ExtremeParams):
SUMMARY_NAMES = PARAM_NAMES + ExtremeParams.QUANTILE_NAMES
NB_SUMMARY_NAMES = len(SUMMARY_NAMES)
- def __init__(self, loc: float, scale: float, shape: float, block_size: int = None):
+ def __init__(self, loc: float, scale: float, shape: float, block_size: int = None, accept_zero_scale_parameter=False):
super().__init__(loc, scale, shape)
self.block_size = block_size
+ if accept_zero_scale_parameter and scale == 0.0:
+ self.has_undefined_parameters = False
def quantile(self, p) -> float:
if self.has_undefined_parameters:
@@ -46,6 +49,7 @@ class GevParams(ExtremeParams):
return self.to_dict().__str__()
def quantile_strength_evolution(self, p=0.99, mu1=0.0, sigma1=0.0):
+ # todo: chagne the name to time derivative
"""
Compute the variation of some quantile with respect to time.
(when mu1 and sigma1 can be modified with time)
@@ -63,13 +67,13 @@ class GevParams(ExtremeParams):
# Compute some indicators (such as the mean and the variance)
- def g(self, k):
+ def g(self, k) -> float:
# Compute the g_k parameters as defined in wikipedia
# https://fr.wikipedia.org/wiki/Loi_d%27extremum_g%C3%A9n%C3%A9ralis%C3%A9e
return gamma(1 - k * self.shape)
@property
- def mean(self):
+ def mean(self) -> float:
if self.has_undefined_parameters:
return np.nan
elif self.shape >= 1:
@@ -78,7 +82,7 @@ class GevParams(ExtremeParams):
return self.location + self.scale * (self.g(k=1) - 1) / self.shape
@property
- def variance(self):
+ def variance(self) -> float:
if self.has_undefined_parameters:
return np.nan
elif self.shape >= 0.5:
@@ -87,11 +91,11 @@ class GevParams(ExtremeParams):
return ((self.scale / self.shape) ** 2) * (self.g(k=2) - self.g(k=1) ** 2)
@property
- def std(self):
+ def std(self) -> float:
return np.sqrt(self.variance)
@classmethod
- def indicator_names(cls):
+ def indicator_names(cls) -> List[str]:
return ['mean', 'std'] + cls.QUANTILE_NAMES[:2]
@cached_property