From 8cc64c5b82f36241fbac9f29cb7a11bdaaf1be10 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 25 Feb 2021 10:43:43 +0100
Subject: [PATCH] [refactor] add nan_if_undefined_wrapper. update tests.
---
extreme_fit/distribution/gev/gev_params.py | 47 ++++++++-----------
.../distribution/utils_extreme_params.py | 12 +++++
.../__init__.py | 0
.../test_gev/test_gev_params.py | 7 +++
4 files changed, 38 insertions(+), 28 deletions(-)
create mode 100644 extreme_fit/distribution/utils_extreme_params.py
delete mode 100644 projects/contrasting_trends_in_snow_loads/__init__.py
diff --git a/extreme_fit/distribution/gev/gev_params.py b/extreme_fit/distribution/gev/gev_params.py
index a233f53c..e83e787f 100644
--- a/extreme_fit/distribution/gev/gev_params.py
+++ b/extreme_fit/distribution/gev/gev_params.py
@@ -7,6 +7,7 @@ from mpmath import euler
from extreme_fit.distribution.abstract_extreme_params import AbstractExtremeParams
from extreme_fit.distribution.abstract_params import AbstractParams
+from extreme_fit.distribution.utils_extreme_params import nan_if_undefined_wrapper
from extreme_fit.model.utils import r
import numpy as np
from scipy.special import gamma
@@ -26,37 +27,24 @@ class GevParams(AbstractExtremeParams):
self.has_undefined_parameters = False
self.param_name_to_confidence_interval = None
+ @nan_if_undefined_wrapper
def sample(self, n) -> np.ndarray:
- if self.has_undefined_parameters:
- return np.nan
- else:
- return np.array(r.rgev(n, self.location, self.scale, self.shape))
+ return np.array(r.rgev(n, self.location, self.scale, self.shape))
+ @nan_if_undefined_wrapper
def quantile(self, p) -> float:
- if self.has_undefined_parameters:
- return np.nan
- else:
- return r.qgev(p, self.location, self.scale, self.shape)[0]
+ return r.qgev(p, self.location, self.scale, self.shape)[0]
def return_level(self, return_period):
return self.quantile(1 - 1 / return_period)
+ @nan_if_undefined_wrapper
def density(self, x, log_scale=False):
- if self.has_undefined_parameters:
- return np.nan
- else:
- res = r.dgev(x, self.location, self.scale, self.shape, log_scale)
- if isinstance(x, float):
- return res[0]
- else:
- return np.array(res)
-
- @property
- def param_values(self):
- if self.has_undefined_parameters:
- return [np.nan for _ in range(3)]
+ res = r.dgev(x, self.location, self.scale, self.shape, log_scale)
+ if isinstance(x, float):
+ return res[0]
else:
- return [self.location, self.scale, self.shape]
+ return np.array(res)
def time_derivative_of_return_level(self, p=0.99, mu1=0.0, sigma1=0.0):
"""
@@ -77,6 +65,11 @@ class GevParams(AbstractExtremeParams):
quantile_annual_variation -= (sigma1 / self.shape) * (1 - power)
return quantile_annual_variation
+ @property
+ @nan_if_undefined_wrapper
+ def param_values(self):
+ return [self.location, self.scale, self.shape]
+
# Compute some indicators (such as the mean and the variance)
def g(self, k) -> float:
@@ -85,10 +78,9 @@ class GevParams(AbstractExtremeParams):
return gamma(1 - k * self.shape)
@property
+ @nan_if_undefined_wrapper
def mean(self) -> float:
- if self.has_undefined_parameters:
- mean = np.nan
- elif self.shape >= 1:
+ if self.shape >= 1:
mean = np.inf
elif self.shape == 0:
mean = self.location + self.scale * float(euler)
@@ -98,10 +90,9 @@ class GevParams(AbstractExtremeParams):
return mean
@property
+ @nan_if_undefined_wrapper
def variance(self) -> float:
- if self.has_undefined_parameters:
- return np.nan
- elif self.shape >= 0.5:
+ if self.shape >= 0.5:
return np.inf
elif self.shape == 0.0:
return (self.scale * np.pi) ** 2 / 6
diff --git a/extreme_fit/distribution/utils_extreme_params.py b/extreme_fit/distribution/utils_extreme_params.py
new file mode 100644
index 00000000..ce61e72e
--- /dev/null
+++ b/extreme_fit/distribution/utils_extreme_params.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+from extreme_fit.distribution.abstract_extreme_params import AbstractExtremeParams
+
+
+def nan_if_undefined_wrapper(func):
+ def wrapper(obj: AbstractExtremeParams, *args, **kwargs):
+ if obj.has_undefined_parameters:
+ return np.nan
+ return func(obj, *args, **kwargs)
+
+ return wrapper
diff --git a/projects/contrasting_trends_in_snow_loads/__init__.py b/projects/contrasting_trends_in_snow_loads/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_params.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_params.py
index 52a515f7..607e47a1 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_params.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_params.py
@@ -17,6 +17,13 @@ class TestGevParams(unittest.TestCase):
for quantile_name, p in gev_params.quantile_name_to_p.items():
self.assertAlmostEqual(- 1 / np.log(p), quantile_dict[quantile_name])
+ def test_wrapper(self):
+ gev_params = GevParams(loc=1.0, shape=1.0, scale=-1.0)
+ self.assertTrue(np.isnan(gev_params.quantile(p=0.5)))
+ self.assertTrue(np.isnan(gev_params.sample(n=10)))
+ self.assertTrue(np.isnan(gev_params.param_values))
+ self.assertTrue(np.isnan(gev_params.density(x=1.5)))
+
def test_time_derivative_return_level(self):
p = 0.99
for mu1 in [-1, 0, 1]:
--
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