[TREND ANALYSIS] add test_mean, and test_variance for the GevParams class

experiment/paper1_steps/poster_EVAN2019/some_experiment_EVAN.py

@@ -18,15 +18,15 @@ mpl.rcParams['hatch.linewidth'] = 0.3
 
 
 def main_non_stationary_model_comparison():
-    stop_loop = True
+    stop_loop = False
     for altitude in POSTER_ALTITUDES[:]:
         for trend_test_class in [GevLocationTrendTest, GevScaleTrendTest, GevLocationAndScaleTrendTest,
-                                 ComparisonAgainstMu, ComparisonAgainstSigma][::-1][-1:]:
+                                 ComparisonAgainstMu, ComparisonAgainstSigma][::-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

extreme_estimator/margin_fits/gev/gev_params.py

@@ -2,6 +2,7 @@ from collections import OrderedDict
 from typing import List
 
 from cached_property import cached_property
+from mpmath import euler, pi
 
 from extreme_estimator.extreme_models.utils import r
 from extreme_estimator.margin_fits.extreme_params import ExtremeParams
@@ -78,6 +79,8 @@ class GevParams(ExtremeParams):
             return np.nan
         elif self.shape >= 1:
             return np.inf
+        elif self.shape == 0:
+            return self.location + self.scale * euler
         else:
             return self.location + self.scale * (self.g(k=1) - 1) / self.shape
 
@@ -87,6 +90,8 @@ class GevParams(ExtremeParams):
             return np.nan
         elif self.shape >= 0.5:
             return np.inf
+        elif self.shape == 0.0:
+            return (self.scale * pi) ** 2 / 6
         else:
             return ((self.scale / self.shape) ** 2) * (self.g(k=2) - self.g(k=1) ** 2)
 

test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_params.py

 import unittest
 
+from mpmath import euler
+
 import numpy as np
+from scipy.special.cython_special import gamma
 
 from extreme_estimator.margin_fits.gev.gev_params import GevParams
 
@@ -33,6 +36,24 @@ class TestGevParams(unittest.TestCase):
         self.assertEqual(gev_params.variance, np.inf)
         self.assertEqual(gev_params.std, np.inf)
 
+    def test_mean(self):
+        mu = 1.0
+        sigma = 1.0
+        gev_params = GevParams(loc=mu, shape=0.0, scale=sigma)
+        self.assertEqual(gev_params.mean, mu + sigma * euler)
+        chi = 0.5
+        gev_params = GevParams(loc=mu, shape=chi, scale=sigma)
+        self.assertEqual(gev_params.mean, mu + sigma * (gamma(1 - 0.5) - 1) / chi)
+
+    def test_variance(self):
+        mu = 1.0
+        sigma = 1.0
+        gev_params = GevParams(loc=mu, shape=0.0, scale=sigma)
+        self.assertEqual(gev_params.variance, ((sigma * np.math.pi) ** 2) / 6)
+        chi = 0.25
+        gev_params = GevParams(loc=mu, shape=chi, scale=sigma)
+        self.assertEqual(gev_params.variance, ((sigma / chi) ** 2) * (gamma(1 - 2 * chi) - (gamma(1 - chi) ** 2)))
+
 
 if __name__ == '__main__':
     unittest.main()