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| author | Benjamin Root <ben.v.root@gmail.com> | 2013-05-15 22:09:23 -0400 |
|---|---|---|
| committer | Charles Harris <charlesr.harris@gmail.com> | 2013-08-12 22:05:17 -0600 |
| commit | 777b6453e166df252298a47ef4f0e867614ac94a (patch) | |
| tree | 959f025b36c309ca888af08c45b82c35af56b858 /numpy/core/_methods.py | |
| parent | aaac613ea54aed930d54aebe05c0179fc14dc031 (diff) | |
| download | numpy-777b6453e166df252298a47ef4f0e867614ac94a.tar.gz | |
TST: Added tests for nanmean(), nanvar(), nanstd()
Diffstat (limited to 'numpy/core/_methods.py')
| -rw-r--r-- | numpy/core/_methods.py | 29 |
1 files changed, 19 insertions, 10 deletions
diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py index 297d708a8..c317358e1 100644 --- a/numpy/core/_methods.py +++ b/numpy/core/_methods.py @@ -7,7 +7,8 @@ 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 +from numpy.core.numeric import array, asanyarray, isnan, issubdtype +from numpy.core import numerictypes as nt def _amax(a, axis=None, out=None, keepdims=False): return um.maximum.reduce(a, axis=axis, @@ -46,8 +47,9 @@ def _count_reduce_items(arr, axis): def _mean(a, axis=None, dtype=None, out=None, keepdims=False): arr = asanyarray(a) - # Upgrade bool, unsigned int, and int to float64 - if dtype is None and arr.dtype.kind in ['b','u','i']: + # 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: @@ -62,11 +64,14 @@ def _mean(a, axis=None, dtype=None, out=None, keepdims=False): 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) - # Upgrade bool, unsigned int, and int to float64 - if dtype is None and arr.dtype.kind in ['b','u','i']: + # 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: @@ -86,7 +91,8 @@ 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 arr.dtype.kind in ['b','u','i']: + 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: arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True) @@ -101,7 +107,7 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, x = arr - arrmean # (arr - arrmean) ** 2 - if arr.dtype.kind == 'c': + if issubdtype(arr.dtype, nt.complex_): x = um.multiply(x, um.conjugate(x), out=x).real else: x = um.multiply(x, x, out=x) @@ -123,11 +129,14 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, 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 arr.dtype.kind in ['b','u','i']: + 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) @@ -142,10 +151,10 @@ def _nanvar(a, axis=None, dtype=None, out=None, ddof=0, # arr - arrmean x = arr - arrmean - x[mask] = 0.0 + mu.copyto(x, 0.0, where=mask) # (arr - arrmean) ** 2 - if arr.dtype.kind == 'c': + if issubdtype(arr.dtype, nt.complex_): x = um.multiply(x, um.conjugate(x), out=x).real else: x = um.multiply(x, x, out=x) |