try almost plain requirements.txt

README.md

@@ -1,7 +1,6 @@
 # pylevy
 
 ## Levy distributions for Python
-======
 
 To install, type:
 

docs/source/conf.py

@@ -14,7 +14,7 @@
 #
 import os
 import sys
-from unittest.mock import MagicMock
+import unittest.mock as mock
 sys.path.insert(0, os.path.abspath('../../../'))
 
 # -- Project information -----------------------------------------------------
@@ -45,14 +45,16 @@
 ]
 
 
-class Mock(MagicMock):
-    @classmethod
-    def __getattr__(cls, name):
-        return MagicMock()
+# class Mock(MagicMock):
+#     @classmethod
+#     def __getattr__(cls, name):
+#         return MagicMock()
 
 
 MOCK_MODULES = ['numpy', 'scipy']
-sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
+for mod_name in MOCK_MODULES:
+    sys.modules[mod_name] = mock.Mock()
+
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']

docs/source/levy.rst

@@ -1,8 +1,11 @@
 Module Levy
-==========
+============
 
 .. automodule:: pylevy.levy
 
+Functions
+----------
+
 .. autofunction:: pylevy.levy.change_par
 
 .. autofunction:: pylevy.levy.random

levy/__init__.py

@@ -26,9 +26,9 @@
 
 It operates by interpolating values from a table, as direct computation
 of these distributions requires a lengthy numerical integration. This
-interpolation scheme allows fast fitting of data by Maximum Likelihood .
+interpolation scheme allows fast fitting of data by Maximum Likelihood.
 
-Does not support alpha values less than 0.5.
+It does not support alpha values less than 0.5.
 """
 
 from __future__ import print_function, division

test.py

@@ -9,13 +9,13 @@
 import levy
 import numpy as np
 from builtins import range
-# from matplotlib import pyplot
 
 
 def get_quantiles(l):
     n = float(len(l))
     return l[int(n * 0.5)], l[int(n * 0.05)], l[int(n * 0.95)]
 
+
 alpha = 1.0
 beta = 0.0
 mu = 0.0