Sample not only from uniform distribution but allow all distributions…

… of scipy.stats in morris_sampling, screening/eee, and eee/see.

docs/source/userguide.rst

@@ -332,16 +332,16 @@ limits `0` and `1`.
 
 The three parameters :math:`x_0, x_1, x_2` of the Ishigami-Homma
 function follow uniform distributions between :math:`-\pi` and
-:math:`+\pi`. Say that :math:`x_1` follows a Gaussian
-distribution around the mean `0` with a standard deviation of 1.81. We
-want to sample between three standard deviations, which includes about
-99.7\% of the total distribution. This means that the lower bound
-would be 0.0015 and the upper bound 0.9985.
+:math:`+\pi`. Say that :math:`x_1` follows a Gaussian distribution
+around the mean `0` with a standard deviation of 1.81. We want to
+sample between plus or minus three standard deviations, which includes
+about 99.7\% of the total distribution. This means that the lower
+bound would be 0.0015 (0.003/2.) and the upper bound 0.9985.
 
 .. code-block:: python
 
     import scipy.stats as stats
-    dist      = [None, stats.normal, stats.uniform]
+    dist      = [None, stats.norm, stats.uniform]
     distparam = [None, (0., 1.81), (-np.pi, 2.*np.pi)]
     lb        = [-np.pi, 0.0015, 0.]
     ub        = [np.pi, 0.9985, 1.]
@@ -361,16 +361,16 @@ This shows that
        :any:`scipy.stats.uniform`, `loc` is the lower limit and
        `loc+scale` the upper limit. This means the combination
        `dist=None`, `lb=a`, `ub=b` corresponds to
-       `dist=scipy.stats.uniform`, `distparam=[a,b-a]`, `lb=0`, `ub=1`.
+       `dist=scipy.stats.uniform`, `distparam=[a,b-a]`, `lb=0`,
+       `ub=1`.
 
 Note also that
 
     5. if `distparam==None`, `loc=0` and `scale=1` will be taken;
     6. `loc` and `scale` are implemented as keywords in
-       :any:`scipy.stats`. Other parameters such as the shape
-       parameter of the gamma distribution
-       :any:`scipy.stats.gamma` must hence be given first,
-       e.g. `(shape,loc,scale)`.
+       :any:`scipy.stats`. Other parameters such as for example the shape
+       parameter of the gamma distribution :any:`scipy.stats.gamma` must
+       hence be given first, i.e. `(shape,loc,scale)`.
 
 Remember that Morris' method of Elementary Effects assumes uniformly
 distributed parameters and that other distributions are an extension

pyeee/eee.py

@@ -116,7 +116,7 @@ def eee(func, *args, **kwargs):
 
         If True, return weighted mean mu*, weighted by sd.
     seed : int or array_like
-        Seed for numpy``s random number generator (default: None).
+        Seed for numpy's random number generator (default: None).
     processes : int, optinal
         The number of processes to use to evaluate objective function and constraints (default: 1).
     pool : `schwimmbad` pool object, optinal

pyeee/morris_method.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 from __future__ import division, absolute_import, print_function
 """
-Module provides the Morris Method of Elementary Effects.
+Module provides the Morris' Method of Elementary Effects.
 It includes optimised sampling of trajectories including optional groups
 as well as calculation of the Morris measures mu, stddev and mu*.
 

pyeee/screening.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python
 from __future__ import division, absolute_import, print_function
 """
-screening : Provides the function screening/ee for Morris' method
-            of Elementary Effects.
+screening : Provides the function screening/ee for Morris' method of Elementary Effects.
 
 This function was written by Matthias Cuntz while at Institut National
 de Recherche en Agriculture, Alimentation et Environnement (INRAE),
@@ -47,7 +46,7 @@ def screening(func, lb, ub, nt, x0=None, mask=None,
               processes=1, pool=None,
               verbose=0):
     """
-    Parameter screening using Morris method of Elementary Effects.
+    Parameter screening using Morris' method of Elementary Effects.
 
     Note, the input function must be callable as `func(x)`.
 
@@ -103,7 +102,7 @@ def screening(func, lb, ub, nt, x0=None, mask=None,
 
         The percent point function ppf is called like this: dist(*distparam).ppf(x)
     seed : int or array_like
-        Seed for numpy``s random number generator (default: None).
+        Seed for numpy's random number generator (default: None).
     processes : int, optional
         The number of processes to use to evaluate objective function and constraints (default: 1).
     pool : `schwimmbad` pool object, optional

pyeee/version.py

@@ -23,7 +23,7 @@
 * v0.9, Renamed morris.py to morris_method.py and adapted args and kwargs to common names in pyeee, Feb 2020, Matthias Cuntz
 * v1.0, Restructured package with functions and utils subpackages, Feb 2020, Matthias Cuntz
 * v1.1, Number of final trajectories is argument instead of keyword, Feb 2020, Matthias Cuntz
-* v1.2, Sample not only from uniform distribution but allow all distributions of scipy.stats, Mar 2020, Matthias Cuntz
+* v1.2, Sample not only from uniform distribution but allow all distributions of scipy.stats, Apr 2020, Matthias Cuntz
 
 .. moduleauthor:: Matthias Cuntz
 """