From ac422cd7b72d6d2f17ee0351b3381b4c184203bb Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Fri, 21 May 2021 02:24:02 +0000
Subject: [PATCH] Resolve "Get rid off `Prior.test_valid_for_rescaling`?"
---
bilby/core/prior/analytical.py | 17 -----------------
bilby/core/prior/base.py | 17 -----------------
bilby/core/prior/interpolated.py | 1 -
bilby/core/prior/joint.py | 1 -
bilby/gw/prior.py | 1 -
examples/tutorials/making_priors.ipynb | 1 -
test/core/prior/prior_test.py | 5 -----
7 files changed, 43 deletions(-)
diff --git a/bilby/core/prior/analytical.py b/bilby/core/prior/analytical.py
index 722d4553f..96b8a9e92 100644
--- a/bilby/core/prior/analytical.py
+++ b/bilby/core/prior/analytical.py
@@ -40,7 +40,6 @@ class DeltaFunction(Prior):
=======
float: Rescaled probability, equivalent to peak
"""
- self.test_valid_for_rescaling(val)
return self.peak * val ** 0
def prob(self, val):
@@ -105,7 +104,6 @@ class PowerLaw(Prior):
=======
Union[float, array_like]: Rescaled probability
"""
- self.test_valid_for_rescaling(val)
if self.alpha == -1:
return self.minimum * np.exp(val * np.log(self.maximum / self.minimum))
else:
@@ -206,7 +204,6 @@ class Uniform(Prior):
=======
Union[float, array_like]: Rescaled probability
"""
- self.test_valid_for_rescaling(val)
return self.minimum + val * (self.maximum - self.minimum)
def prob(self, val):
@@ -314,7 +311,6 @@ class SymmetricLogUniform(Prior):
=======
Union[float, array_like]: Rescaled probability
"""
- self.test_valid_for_rescaling(val)
if isinstance(val, (float, int)):
if val < 0.5:
return -self.maximum * np.exp(-2 * val * np.log(self.maximum / self.minimum))
@@ -401,7 +397,6 @@ class Cosine(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
norm = 1 / (np.sin(self.maximum) - np.sin(self.minimum))
return np.arcsin(val / norm + np.sin(self.minimum))
@@ -456,7 +451,6 @@ class Sine(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
norm = 1 / (np.cos(self.minimum) - np.cos(self.maximum))
return np.arccos(np.cos(self.minimum) - val / norm)
@@ -515,7 +509,6 @@ class Gaussian(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return self.mu + erfinv(2 * val - 1) * 2 ** 0.5 * self.sigma
def prob(self, val):
@@ -602,7 +595,6 @@ class TruncatedGaussian(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return erfinv(2 * val * self.normalisation + erf(
(self.minimum - self.mu) / 2 ** 0.5 / self.sigma)) * 2 ** 0.5 * self.sigma + self.mu
@@ -695,7 +687,6 @@ class LogNormal(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return np.exp(self.mu + np.sqrt(2 * self.sigma ** 2) * erfinv(2 * val - 1))
def prob(self, val):
@@ -790,7 +781,6 @@ class Exponential(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return -self.mu * log1p(-val)
def prob(self, val):
@@ -887,7 +877,6 @@ class StudentT(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
if isinstance(val, (float, int)):
if val == 0:
rescaled = -np.inf
@@ -977,7 +966,6 @@ class Beta(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return btdtri(self.alpha, self.beta, val) * (self.maximum - self.minimum) + self.minimum
def prob(self, val):
@@ -1070,7 +1058,6 @@ class Logistic(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
if isinstance(val, (float, int)):
if val == 0:
rescaled = -np.inf
@@ -1151,7 +1138,6 @@ class Cauchy(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
rescaled = self.alpha + self.beta * np.tan(np.pi * (val - 0.5))
if isinstance(val, (float, int)):
if val == 1:
@@ -1233,7 +1219,6 @@ class Gamma(Prior):
This maps to the inverse CDF. This has been analytically solved for this case.
"""
- self.test_valid_for_rescaling(val)
return gammaincinv(self.k, val) * self.theta
def prob(self, val):
@@ -1385,8 +1370,6 @@ class FermiDirac(Prior):
.. [1] M. Pitkin, M. Isi, J. Veitch & G. Woan, `arXiv:1705.08978v1
<https:arxiv.org/abs/1705.08978v1>`_, 2017.
"""
- self.test_valid_for_rescaling(val)
-
inv = (-np.exp(-1. * self.r) + (1. + np.exp(self.r)) ** -val +
np.exp(-1. * self.r) * (1. + np.exp(self.r)) ** -val)
diff --git a/bilby/core/prior/base.py b/bilby/core/prior/base.py
index 023f1609f..3edeec29b 100644
--- a/bilby/core/prior/base.py
+++ b/bilby/core/prior/base.py
@@ -202,23 +202,6 @@ class Prior(object):
"""
return (val >= self.minimum) & (val <= self.maximum)
- @staticmethod
- def test_valid_for_rescaling(val):
- """Test if 0 < val < 1
-
- Parameters
- ==========
- val: Union[float, int, array_like]
-
- Raises
- =======
- ValueError: If val is not between 0 and 1
- """
- valarray = np.atleast_1d(val)
- tests = (valarray < 0) + (valarray > 1)
- if np.any(tests):
- raise ValueError("Number to be rescaled should be in [0, 1]")
-
def __repr__(self):
"""Overrides the special method __repr__.
diff --git a/bilby/core/prior/interpolated.py b/bilby/core/prior/interpolated.py
index ece311a5a..187e8a60a 100644
--- a/bilby/core/prior/interpolated.py
+++ b/bilby/core/prior/interpolated.py
@@ -86,7 +86,6 @@ class Interped(Prior):
This maps to the inverse CDF. This is done using interpolation.
"""
- self.test_valid_for_rescaling(val)
rescaled = self.inverse_cumulative_distribution(val)
if rescaled.shape == ():
rescaled = float(rescaled)
diff --git a/bilby/core/prior/joint.py b/bilby/core/prior/joint.py
index a10a0add2..393a32b9c 100644
--- a/bilby/core/prior/joint.py
+++ b/bilby/core/prior/joint.py
@@ -710,7 +710,6 @@ class JointPrior(Prior):
A sample from the prior paramter.
"""
- self.test_valid_for_rescaling(val)
self.dist.rescale_parameters[self.name] = val
if self.dist.filled_rescale():
diff --git a/bilby/gw/prior.py b/bilby/gw/prior.py
index 739cbf6a0..f9b6c182b 100644
--- a/bilby/gw/prior.py
+++ b/bilby/gw/prior.py
@@ -369,7 +369,6 @@ class UniformInComponentsMassRatio(Prior):
return (self._integral(val) - self._integral(self.minimum)) / self.norm
def rescale(self, val):
- self.test_valid_for_rescaling(val)
resc = self.icdf(val)
if resc.ndim == 0:
return resc.item()
diff --git a/examples/tutorials/making_priors.ipynb b/examples/tutorials/making_priors.ipynb
index 7f9d5719e..8daef3bc1 100644
--- a/examples/tutorials/making_priors.ipynb
+++ b/examples/tutorials/making_priors.ipynb
@@ -127,7 +127,6 @@
" self.alpha = alpha\n",
" \n",
" def rescale(self, val):\n",
- " bilby.prior.Prior.test_valid_for_rescaling(val)\n",
" return np.log((np.exp(self.alpha * self.maximum) - np.exp(self.alpha * self.minimum)) * val\n",
" + np.exp(self.alpha * self.minimum)) / self.alpha\n",
" \n",
diff --git a/test/core/prior/prior_test.py b/test/core/prior/prior_test.py
index 6afdc9933..924194543 100644
--- a/test/core/prior/prior_test.py
+++ b/test/core/prior/prior_test.py
@@ -239,11 +239,6 @@ class TestPriorClasses(unittest.TestCase):
all((many_samples >= prior.minimum) & (many_samples <= prior.maximum))
)
- def test_out_of_bounds_rescaling(self):
- """Test the the rescaling works as expected."""
- for prior in self.priors:
- self.assertRaises(ValueError, lambda: prior.rescale(-1))
-
def test_least_recently_sampled(self):
for prior in self.priors:
least_recently_sampled_expected = prior.sample()
--
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