From 1b6b8719735ca3e98a0a0c3f492f16ff00ba1aa9 Mon Sep 17 00:00:00 2001
From: Charles Harris <charlesr.harris@gmail.com>
Date: Fri, 21 Jun 2013 10:28:41 -0600
Subject: MAINT: Clean up core/_methods.py and core/fromnumeric.py

Use issubclass instead of issubdtype.
Add some blank lines.
Remove trailing whitespace.
Remove uneeded float casts since true_divide is default.
Clean up documentation a bit.
---
 numpy/core/_methods.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

(limited to 'numpy/core/_methods.py')

diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py
index 51731b9c2..6312d35cf 100644
--- a/numpy/core/_methods.py
+++ b/numpy/core/_methods.py
@@ -7,7 +7,7 @@ from __future__ import division, absolute_import, print_function
 
 from numpy.core import multiarray as mu
 from numpy.core import umath as um
-from numpy.core.numeric import asanyarray, isnan, issubdtype
+from numpy.core.numeric import asanyarray
 from numpy.core import numerictypes as nt
 
 def _amax(a, axis=None, out=None, keepdims=False):
@@ -48,19 +48,20 @@ def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
     arr = asanyarray(a)
 
     # Cast bool, unsigned int, and int to float64
-    if dtype is None and (issubdtype(arr.dtype, nt.integer) or
-                          issubdtype(arr.dtype, nt.bool_)):
+    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
         ret = um.add.reduce(arr, axis=axis, dtype='f8',
                             out=out, keepdims=keepdims)
     else:
         ret = um.add.reduce(arr, axis=axis, dtype=dtype,
                             out=out, keepdims=keepdims)
+
     rcount = _count_reduce_items(arr, axis)
     if isinstance(ret, mu.ndarray):
         ret = um.true_divide(ret, rcount,
                         out=ret, casting='unsafe', subok=False)
     else:
-        ret = ret / float(rcount)
+        ret = ret / rcount
+
     return ret
 
 def _var(a, axis=None, dtype=None, out=None, ddof=0,
@@ -68,23 +69,23 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0,
     arr = asanyarray(a)
 
     # First compute the mean, saving 'rcount' for reuse later
-    if dtype is None and (issubdtype(arr.dtype, nt.integer) or
-                          issubdtype(arr.dtype, nt.bool_)):
+    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
         arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
     else:
         arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
+
     rcount = _count_reduce_items(arr, axis)
     if isinstance(arrmean, mu.ndarray):
         arrmean = um.true_divide(arrmean, rcount,
                             out=arrmean, casting='unsafe', subok=False)
     else:
-        arrmean = arrmean / float(rcount)
+        arrmean = arrmean / rcount
 
     # arr - arrmean
     x = arr - arrmean
 
     # (arr - arrmean) ** 2
-    if issubdtype(arr.dtype, nt.complex_):
+    if issubclass(arr.dtype.type, nt.complexfloating):
         x = um.multiply(x, um.conjugate(x), out=x).real
     else:
         x = um.multiply(x, x, out=x)
@@ -100,7 +101,7 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0,
         ret = um.true_divide(ret, rcount,
                         out=ret, casting='unsafe', subok=False)
     else:
-        ret = ret / float(rcount)
+        ret = ret / rcount
 
     return ret
 
-- 
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