Author: Brian Kearns <bdkea...@gmail.com>
Branch:
Changeset: r67658:63bfeb2e635e
Date: 2013-10-28 13:43 -0400
http://bitbucket.org/pypy/pypy/changeset/63bfeb2e635e/
Log: update numpy.core._methods from upstream
diff --git a/lib_pypy/numpypy/core/_methods.py
b/lib_pypy/numpypy/core/_methods.py
--- a/lib_pypy/numpypy/core/_methods.py
+++ b/lib_pypy/numpypy/core/_methods.py
@@ -1,9 +1,16 @@
-# Array methods which are called by the both the C-code for the method
-# and the Python code for the NumPy-namespace function
+"""
+Array methods which are called by the both the C-code for the method
+and the Python code for the NumPy-namespace function
-import multiarray as mu
-import umath as um
-from numeric import asanyarray
+"""
+from __future__ import division, absolute_import, print_function
+
+import warnings
+
+from . import multiarray as mu
+from . import umath as um
+from .numeric import asanyarray
+from . import numerictypes as nt
def _amax(a, axis=None, out=None, keepdims=False):
return um.maximum.reduce(a, axis=axis,
@@ -31,7 +38,7 @@
def _count_reduce_items(arr, axis):
if axis is None:
- axis = tuple(xrange(arr.ndim))
+ axis = tuple(range(arr.ndim))
if not isinstance(axis, tuple):
axis = (axis,)
items = 1
@@ -42,58 +49,66 @@
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']:
- ret = um.add.reduce(arr, axis=axis, dtype='f8',
- out=out, keepdims=keepdims)
+ rcount = _count_reduce_items(arr, axis)
+ # Make this warning show up first
+ if rcount == 0:
+ warnings.warn("Mean of empty slice.", RuntimeWarning)
+
+
+ # Cast bool, unsigned int, and int to float64 by default
+ if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
+ dtype = mu.dtype('f8')
+
+ ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
+ if isinstance(ret, mu.ndarray):
+ ret = um.true_divide(
+ ret, rcount, out=ret, casting='unsafe', subok=False)
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.dtype.type(ret / rcount)
+
return ret
-def _var(a, axis=None, dtype=None, out=None, ddof=0,
- keepdims=False):
+def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
- # First compute the mean, saving 'rcount' for reuse later
- if dtype is None and arr.dtype.kind in ['b','u','i']:
- arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
+ rcount = _count_reduce_items(arr, axis)
+ # Make this warning show up on top.
+ if ddof >= rcount:
+ warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)
+
+ # Cast bool, unsigned int, and int to float64 by default
+ if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
+ dtype = mu.dtype('f8')
+
+ # Compute the mean.
+ # Note that if dtype is not of inexact type then arraymean will
+ # not be either.
+ arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
+ if isinstance(arrmean, mu.ndarray):
+ arrmean = um.true_divide(
+ arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
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.dtype.type(arrmean / rcount)
- # arr - arrmean
- x = arr - arrmean
-
- # (arr - arrmean) ** 2
- if arr.dtype.kind == 'c':
+ # Compute sum of squared deviations from mean
+ # Note that x may not be inexact and that we need it to be an array,
+ # not a scalar.
+ x = asanyarray(arr - arrmean)
+ 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)
-
- # 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
+ # Compute degrees of freedom and make sure it is not negative.
+ rcount = max([rcount - ddof, 0])
+
+ # divide by degrees of freedom
if isinstance(ret, mu.ndarray):
- ret = um.true_divide(ret, rcount,
- out=ret, casting='unsafe', subok=False)
+ ret = um.true_divide(
+ ret, rcount, out=ret, casting='unsafe', subok=False)
else:
- ret = ret / float(rcount)
+ ret = ret.dtype.type(ret / rcount)
return ret
@@ -104,6 +119,6 @@
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
else:
- ret = um.sqrt(ret)
+ ret = ret.dtype.type(um.sqrt(ret))
return ret
diff --git a/pypy/module/micronumpy/test/test_numarray.py
b/pypy/module/micronumpy/test/test_numarray.py
--- a/pypy/module/micronumpy/test/test_numarray.py
+++ b/pypy/module/micronumpy/test/test_numarray.py
@@ -1230,8 +1230,8 @@
assert (a.mean(2) == array(range(0, 15), dtype=float).reshape(3, 5) *
7 + 3).all()
assert (arange(10).reshape(5, 2).mean(axis=1) == [0.5, 2.5, 4.5, 6.5,
8.5]).all()
assert (a.mean(axis=-1) == a.mean(axis=2)).all()
- raises(ValueError, a.mean, -4)
- raises(ValueError, a.mean, 3)
+ raises(IndexError, a.mean, -4)
+ raises(IndexError, a.mean, 3)
a = arange(10).reshape(5, 2)
assert (a.mean(1) == [0.5, 2.5, 4.5, 6.5, 8.5]).all()
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