Merge branch 'iss42'

LoLab-MSM · Apr 7, 2020 · a49ac83 · a49ac83
2 parents 2bcd8c4 + 4c28547
commit a49ac83
Show file tree

Hide file tree

Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/README.md b/README.md
@@ -2,8 +2,8 @@
 
 ![Python version badge](https://img.shields.io/badge/python-3.6-blue.svg)
 [![license](https://img.shields.io/github/license/LoLab-VU/Gleipnir.svg)](LICENSE)
-![version](https://img.shields.io/badge/version-0.26.1-orange.svg)
-[![release](https://img.shields.io/github/release-pre/LoLab-VU/Gleipnir.svg)](https://github.com/LoLab-VU/Gleipnir/releases/tag/v0.26.1)
+![version](https://img.shields.io/badge/version-0.26.2-orange.svg)
+[![release](https://img.shields.io/github/release-pre/LoLab-VU/Gleipnir.svg)](https://github.com/LoLab-VU/Gleipnir/releases/tag/v0.26.2)
 [![anaconda cloud](https://anaconda.org/blakeaw/gleipnir/badges/version.svg)](https://anaconda.org/blakeaw/gleipnir)
 [![DOI](https://zenodo.org/badge/173688080.svg)](https://zenodo.org/badge/latestdoi/173688080)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/e117a46ae8b241539742ab00f8cd1b38)](https://www.codacy.com/app/blakeaw1102/Gleipnir?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=LoLab-VU/Gleipnir&amp;utm_campaign=Badge_Grade)
@@ -83,7 +83,7 @@ pip install gleipnir-ns
 ```
 or it can be directly sourced from the GitHub repo:
 ```
-pip install -e git+https://github.com/LoLab-VU/Gleipnir@v0.26.1#egg=gleipnir
+pip install -e git+https://github.com/LoLab-VU/Gleipnir@v0.26.2#egg=gleipnir
 ```
 However, sourcing from the GitHub repo will not automatically install the core dependencies. You would have to do that separately:
 ```

diff --git a/gleipnir/nsbase.py b/gleipnir/nsbase.py
@@ -76,7 +76,7 @@ def posterior_moments(self):
             marginal, edges, centers = post[parm]
             width = edges[1] - edges[0]
             # resample from the distribution
-            samples = np.random.choice(centers, size=1000000, p=marginal/(marginal.sum())) + (width*(np.random.random(10000)-0.5))
+            samples = np.random.choice(centers, size=1000000, p=marginal/(marginal.sum())) + (width*(np.random.random(1000000)-0.5))
             mean = np.mean(samples)
             var = np.var(samples)
             skew = scipy.stats.skew(samples)

diff --git a/setup.py b/setup.py
@@ -1,7 +1,7 @@
 from distutils.core import setup
 
 setup(name='gleipnir',
-      version='0.26.1',
+      version='0.26.2',
       description='Python toolkit for Nested Sampling.',
       author='Blake A. Wilson',
       author_email='blake.a.wilson@vanderbilt.edu',