From fcb0fef5c673ed0a5442b18bcd8c391907b4f9a7 Mon Sep 17 00:00:00 2001
From: Charles Harris <charlesr.harris@gmail.com>
Date: Thu, 20 Jun 2013 20:44:54 -0600
Subject: MAINT: Separate nan functions into their own module.

New files lib/nanfunctions.py and lib/tests/test_nanfunctions.py are
added and both the previous and new nan functions and tests are moved
into them.

The existing nan functions moved from lib/function_base are:

    nansum, nanmin, nanmax, nanargmin, nanargmax

The added nan functions moved from core/numeric are:

    nanmean, nanvar, nanstd
---
 numpy/core/_methods.py | 84 +-------------------------------------------------
 1 file changed, 1 insertion(+), 83 deletions(-)

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

diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py
index c317358e1..51731b9c2 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 array, asanyarray, isnan, issubdtype
+from numpy.core.numeric import asanyarray, isnan, issubdtype
 from numpy.core import numerictypes as nt
 
 def _amax(a, axis=None, out=None, keepdims=False):
@@ -63,29 +63,6 @@ def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
         ret = ret / float(rcount)
     return ret
 
-def _nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
-    # Using array() instead of asanyarray() because the former always
-    # makes a copy, which is important due to the copyto() action later
-    arr = array(a, subok=True)
-    mask = isnan(arr)
-
-    # Cast bool, unsigned int, and int to float64
-    if dtype is None and (issubdtype(arr.dtype, nt.integer) or
-                          issubdtype(arr.dtype, nt.bool_)):
-        ret = um.add.reduce(arr, axis=axis, dtype='f8',
-                            out=out, keepdims=keepdims)
-    else:
-        mu.copyto(arr, 0.0, where=mask)
-        ret = um.add.reduce(arr, axis=axis, dtype=dtype,
-                            out=out, keepdims=keepdims)
-    rcount = (~mask).sum(axis=axis)
-    if isinstance(ret, mu.ndarray):
-        ret = um.true_divide(ret, rcount,
-                        out=ret, casting='unsafe', subok=False)
-    else:
-        ret = ret / float(rcount)
-    return ret
-
 def _var(a, axis=None, dtype=None, out=None, ddof=0,
                             keepdims=False):
     arr = asanyarray(a)
@@ -127,55 +104,6 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0,
 
     return ret
 
-def _nanvar(a, axis=None, dtype=None, out=None, ddof=0,
-                            keepdims=False):
-    # Using array() instead of asanyarray() because the former always
-    # makes a copy, which is important due to the copyto() action later
-    arr = array(a, subok=True)
-    mask = isnan(arr)
-
-    # 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_)):
-        arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
-    else:
-        mu.copyto(arr, 0.0, where=mask)
-        arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
-                                keepdims=True)
-    rcount = (~mask).sum(axis=axis, keepdims=True)
-    if isinstance(arrmean, mu.ndarray):
-        arrmean = um.true_divide(arrmean, rcount,
-                            out=arrmean, casting='unsafe', subok=False)
-    else:
-        arrmean = arrmean / float(rcount)
-
-    # arr - arrmean
-    x = arr - arrmean
-    mu.copyto(x, 0.0, where=mask)
-
-    # (arr - arrmean) ** 2
-    if issubdtype(arr.dtype, nt.complex_):
-        x = um.multiply(x, um.conjugate(x), out=x).real
-    else:
-        x = um.multiply(x, x, out=x)
-
-    # add.reduce((arr - arrmean) ** 2, axis)
-    ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out,
-                        keepdims=keepdims)
-
-    # add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
-    if not keepdims and isinstance(rcount, mu.ndarray):
-        rcount = rcount.squeeze(axis=axis)
-    rcount -= ddof
-    if isinstance(ret, mu.ndarray):
-        ret = um.true_divide(ret, rcount,
-                        out=ret, casting='unsafe', subok=False)
-    else:
-        ret = ret / float(rcount)
-
-    return ret
-
-
 def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
     ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
                keepdims=keepdims)
@@ -187,13 +115,3 @@ def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
 
     return ret
 
-def _nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
-    ret = _nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
-                  keepdims=keepdims)
-
-    if isinstance(ret, mu.ndarray):
-        ret = um.sqrt(ret, out=ret)
-    else:
-        ret = um.sqrt(ret)
-
-    return ret
-- 
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