work around a limitation of numpy 1.11

nifty2go/random.py

@@ -35,11 +35,13 @@ class Random(object):
     def normal(dtype, shape, mean=0., std=1.):
         if issubclass(dtype, (complex, np.complexfloating)):
             x = np.empty(shape, dtype=dtype)
-            x.real = np.random.normal(mean, std*np.sqrt(0.5), shape)
-            x.imag = np.random.normal(0., std*np.sqrt(0.5), shape)
+            x.real = np.random.normal(loc=0., scale=np.sqrt(0.5), size=shape)
+            x.imag = np.random.normal(loc=0., scale=np.sqrt(0.5), size=shape)
         else:
-            x = np.random.normal(mean, std, shape)
+            x = np.random.normal(loc=0., scale=1., size=shape)
             x = x.astype(dtype, copy=False)
+        x *= std
+        x += mean
         return x
 
     @staticmethod

setup.py

@@ -32,8 +32,8 @@ setup(name="nifty2go",
       dependency_links=[
                'git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git@setuptools_test#egg=pyHealpix-0.0.1'],
       license="GPLv3",
-      setup_requires=['future', 'pyHealpix>=0.0.1', 'numpy>=1.13', 'pyfftw>=0.10.4'],
-      install_requires=['future', 'pyHealpix>=0.0.1', 'numpy>=1.13', 'pyfftw>=0.10.4'],
+      setup_requires=['future', 'pyHealpix>=0.0.1', 'numpy', 'pyfftw>=0.10.4'],
+      install_requires=['future', 'pyHealpix>=0.0.1', 'numpy', 'pyfftw>=0.10.4'],
       classifiers=[
         "Development Status :: 4 - Beta",
         "Topic :: Utilities",