summaryrefslogtreecommitdiff
path: root/numpy/core
diff options
context:
space:
mode:
Diffstat (limited to 'numpy/core')
-rw-r--r--numpy/core/_methods.py29
-rw-r--r--numpy/core/tests/test_numeric.py115
2 files changed, 134 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)
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index 782ddd687..0d02ce6c6 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -3,6 +3,7 @@ from __future__ import division, absolute_import, print_function
import sys
import platform
from decimal import Decimal
+import warnings
import numpy as np
from numpy.core import *
@@ -177,18 +178,51 @@ class TestNonarrayArgs(TestCase):
assert_(all(mean(A,0) == array([2.5,3.5,4.5])))
assert_(all(mean(A,1) == array([2.,5.])))
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(mean([])))
+ assert_(w[0].category is RuntimeWarning)
+
+ def test_nanmean(self):
+ A = [[1, nan, nan], [nan, 4, 5]]
+ assert_(nanmean(A) == (10.0 / 3))
+ assert_(all(nanmean(A,0) == array([1, 4, 5])))
+ assert_(all(nanmean(A,1) == array([1, 4.5])))
+
def test_std(self):
A = [[1,2,3],[4,5,6]]
assert_almost_equal(std(A), 1.707825127659933)
assert_almost_equal(std(A,0), array([1.5, 1.5, 1.5]))
assert_almost_equal(std(A,1), array([0.81649658, 0.81649658]))
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(std([])))
+ assert_(w[0].category is RuntimeWarning)
+
+ def test_nanstd(self):
+ A = [[1, nan, nan], [nan, 4, 5]]
+ assert_almost_equal(nanstd(A), 1.699673171197595)
+ assert_almost_equal(nanstd(A,0), array([0.0, 0.0, 0.0]))
+ assert_almost_equal(nanstd(A,1), array([0.0, 0.5]))
+
def test_var(self):
A = [[1,2,3],[4,5,6]]
assert_almost_equal(var(A), 2.9166666666666665)
assert_almost_equal(var(A,0), array([2.25, 2.25, 2.25]))
assert_almost_equal(var(A,1), array([0.66666667, 0.66666667]))
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(mean([])))
+ assert_(w[0].category is RuntimeWarning)
+
+ def test_nanvar(self):
+ A = [[1, nan, nan], [nan, 4, 5]]
+ assert_almost_equal(nanvar(A), 2.88888888889)
+ assert_almost_equal(nanvar(A,0), array([0.0, 0.0, 0.0]))
+ assert_almost_equal(nanvar(A,1), array([0.0, 0.25]))
+
class TestBoolScalar(TestCase):
def test_logical(self):
@@ -1447,6 +1481,39 @@ class TestIsclose(object):
assert_array_equal(x, array([inf, 1]))
assert_array_equal(y, array([0, inf]))
+class TestNaNMean(TestCase):
+ def setUp(self):
+ self.A = array([1,nan,-1,nan,nan,1,-1])
+ self.B = array([nan, nan, nan, nan])
+ self.real_mean = 0
+
+ def test_basic(self):
+ assert_almost_equal(nanmean(self.A),self.real_mean)
+
+ def test_mutation(self):
+ # Because of the "messing around" we do to replace NaNs with zeros
+ # this is meant to ensure we don't actually replace the NaNs in the
+ # actual array.
+ a_copy = self.A.copy()
+ b_copy = self.B.copy()
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ a_ret = nanmean(self.A)
+ assert_equal(self.A, a_copy)
+ b_ret = nanmean(self.B)
+ assert_equal(self.B, b_copy)
+
+ def test_allnans(self):
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(nanmean(self.B)))
+ assert_(w[0].category is RuntimeWarning)
+
+ def test_empty(self):
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(nanmean(array([]))))
+ assert_(w[0].category is RuntimeWarning)
class TestStdVar(TestCase):
def setUp(self):
@@ -1469,6 +1536,54 @@ class TestStdVar(TestCase):
assert_almost_equal(std(self.A,ddof=2)**2,
self.real_var*len(self.A)/float(len(self.A)-2))
+class TestNaNStdVar(TestCase):
+ def setUp(self):
+ self.A = array([nan,1,-1,nan,1,nan,-1])
+ self.B = array([nan, nan, nan, nan])
+ self.real_var = 1
+
+ def test_basic(self):
+ assert_almost_equal(nanvar(self.A),self.real_var)
+ assert_almost_equal(nanstd(self.A)**2,self.real_var)
+
+ def test_mutation(self):
+ # Because of the "messing around" we do to replace NaNs with zeros
+ # this is meant to ensure we don't actually replace the NaNs in the
+ # actual array.
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ a_copy = self.A.copy()
+ b_copy = self.B.copy()
+ a_ret = nanvar(self.A)
+ assert_equal(self.A, a_copy)
+ b_ret = nanstd(self.B)
+ assert_equal(self.B, b_copy)
+
+ def test_ddof1(self):
+ assert_almost_equal(nanvar(self.A,ddof=1),
+ self.real_var*sum(~isnan(self.A))/float(sum(~isnan(self.A))-1))
+ assert_almost_equal(nanstd(self.A,ddof=1)**2,
+ self.real_var*sum(~isnan(self.A))/float(sum(~isnan(self.A))-1))
+
+ def test_ddof2(self):
+ assert_almost_equal(nanvar(self.A,ddof=2),
+ self.real_var*sum(~isnan(self.A))/float(sum(~isnan(self.A))-2))
+ assert_almost_equal(nanstd(self.A,ddof=2)**2,
+ self.real_var*sum(~isnan(self.A))/float(sum(~isnan(self.A))-2))
+
+ def test_allnans(self):
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(nanvar(self.B)))
+ assert_(isnan(nanstd(self.B)))
+ assert_(w[0].category is RuntimeWarning)
+
+ def test_empty(self):
+ with warnings.catch_warnings(record=True) as w:
+ warnings.filterwarnings('always', '', RuntimeWarning)
+ assert_(isnan(nanvar(array([]))))
+ assert_(isnan(nanstd(array([]))))
+ assert_(w[0].category is RuntimeWarning)
class TestStdVarComplex(TestCase):
def test_basic(self):
View Lorry Controller Status