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-rw-r--r--numpy/lib/npyio.py4
-rw-r--r--numpy/lib/tests/test_type_check.py6
-rw-r--r--numpy/lib/type_check.py19
-rw-r--r--numpy/linalg/linalg.py7
-rw-r--r--numpy/linalg/tests/test_linalg.py24
5 files changed, 31 insertions, 29 deletions
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 078c6d7ca..0cf627e7c 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -798,7 +798,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
Examples
--------
- >>> from StringIO import StringIO # StringIO behaves like a file object
+ >>> from io import StringIO # StringIO behaves like a file object
>>> c = StringIO("0 1\\n2 3")
>>> np.loadtxt(c)
array([[ 0., 1.],
@@ -1420,7 +1420,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
Examples
---------
- >>> from StringIO import StringIO
+ >>> from io import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
diff --git a/numpy/lib/tests/test_type_check.py b/numpy/lib/tests/test_type_check.py
index 7afd1206c..f7430c27d 100644
--- a/numpy/lib/tests/test_type_check.py
+++ b/numpy/lib/tests/test_type_check.py
@@ -18,11 +18,13 @@ def assert_all(x):
class TestCommonType(TestCase):
def test_basic(self):
ai32 = np.array([[1, 2], [3, 4]], dtype=np.int32)
+ af16 = np.array([[1, 2], [3, 4]], dtype=np.float16)
af32 = np.array([[1, 2], [3, 4]], dtype=np.float32)
af64 = np.array([[1, 2], [3, 4]], dtype=np.float64)
acs = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.csingle)
acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.cdouble)
assert_(common_type(ai32) == np.float64)
+ assert_(common_type(af16) == np.float16)
assert_(common_type(af32) == np.float32)
assert_(common_type(af64) == np.float64)
assert_(common_type(acs) == np.csingle)
@@ -186,7 +188,7 @@ class TestIsnan(TestCase):
class TestIsfinite(TestCase):
- # Fixme, wrong place, isfinite now ufunc
+ # Fixme, wrong place, isfinite now ufunc
def test_goodvalues(self):
z = np.array((-1., 0., 1.))
@@ -217,7 +219,7 @@ class TestIsfinite(TestCase):
class TestIsinf(TestCase):
- # Fixme, wrong place, isinf now ufunc
+ # Fixme, wrong place, isinf now ufunc
def test_goodvalues(self):
z = np.array((-1., 0., 1.))
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index 99677b394..2fe4e7d23 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -532,14 +532,15 @@ def typename(char):
#-----------------------------------------------------------------------------
#determine the "minimum common type" for a group of arrays.
-array_type = [[_nx.single, _nx.double, _nx.longdouble],
- [_nx.csingle, _nx.cdouble, _nx.clongdouble]]
-array_precision = {_nx.single: 0,
- _nx.double: 1,
- _nx.longdouble: 2,
- _nx.csingle: 0,
- _nx.cdouble: 1,
- _nx.clongdouble: 2}
+array_type = [[_nx.half, _nx.single, _nx.double, _nx.longdouble],
+ [None, _nx.csingle, _nx.cdouble, _nx.clongdouble]]
+array_precision = {_nx.half: 0,
+ _nx.single: 1,
+ _nx.double: 2,
+ _nx.longdouble: 3,
+ _nx.csingle: 1,
+ _nx.cdouble: 2,
+ _nx.clongdouble: 3}
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
@@ -583,7 +584,7 @@ def common_type(*arrays):
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
- p = 1
+ p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 45e12f1a3..07a7a0d42 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -953,7 +953,7 @@ def eigvalsh(a, UPLO='L'):
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
- The eigenvalues are computed using LAPACK routines _ssyevd, _heevd
+ The eigenvalues are computed using LAPACK routines _syevd, _heevd
Examples
--------
@@ -1151,7 +1151,8 @@ def eigh(a, UPLO='L'):
Returns
-------
w : (..., M) ndarray
- The eigenvalues, not necessarily ordered.
+ The eigenvalues in ascending order, each repeated according to
+ its multiplicity.
v : {(..., M, M) ndarray, (..., M, M) matrix}
The column ``v[:, i]`` is the normalized eigenvector corresponding
to the eigenvalue ``w[i]``. Will return a matrix object if `a` is
@@ -1175,7 +1176,7 @@ def eigh(a, UPLO='L'):
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
- The eigenvalues/eigenvectors are computed using LAPACK routines _ssyevd,
+ The eigenvalues/eigenvectors are computed using LAPACK routines _syevd,
_heevd
The eigenvalues of real symmetric or complex Hermitian matrices are
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py
index c6d84706a..b179f3019 100644
--- a/numpy/linalg/tests/test_linalg.py
+++ b/numpy/linalg/tests/test_linalg.py
@@ -718,28 +718,27 @@ class TestEigvalsh(HermitianTestCase, HermitianGeneralizedTestCase):
# Check default is 'L'
w = np.linalg.eigvalsh(Klo)
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'L'
w = np.linalg.eigvalsh(Klo, UPLO='L')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'l'
w = np.linalg.eigvalsh(Klo, UPLO='l')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'U'
w = np.linalg.eigvalsh(Kup, UPLO='U')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'u'
w = np.linalg.eigvalsh(Kup, UPLO='u')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase):
def do(self, a, b):
- # note that eigenvalue arrays must be sorted since
+ # note that eigenvalue arrays returned by eig must be sorted since
# their order isn't guaranteed.
ev, evc = linalg.eigh(a)
evalues, evectors = linalg.eig(a)
- ev.sort(axis=-1)
evalues.sort(axis=-1)
assert_almost_equal(ev, evalues)
@@ -748,7 +747,6 @@ class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase):
rtol=get_rtol(ev.dtype))
ev2, evc2 = linalg.eigh(a, 'U')
- ev2.sort(axis=-1)
assert_almost_equal(ev2, evalues)
assert_allclose(dot_generalized(a, evc2),
@@ -778,19 +776,19 @@ class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase):
# Check default is 'L'
w, v = np.linalg.eigh(Klo)
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'L'
w, v = np.linalg.eigh(Klo, UPLO='L')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'l'
w, v = np.linalg.eigh(Klo, UPLO='l')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'U'
w, v = np.linalg.eigh(Kup, UPLO='U')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
# Check 'u'
w, v = np.linalg.eigh(Kup, UPLO='u')
- assert_allclose(np.sort(w), tgt, rtol=rtol)
+ assert_allclose(w, tgt, rtol=rtol)
class _TestNorm(object):
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