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-rw-r--r--numpy/core/fromnumeric.py13
-rw-r--r--numpy/core/numeric.py8
-rw-r--r--numpy/core/tests/test_api.py13
-rw-r--r--numpy/core/tests/test_indexing.py13
-rw-r--r--numpy/core/tests/test_multiarray.py37
-rw-r--r--numpy/core/tests/test_nditer.py23
-rw-r--r--numpy/core/tests/test_numeric.py11
-rw-r--r--numpy/core/tests/test_shape_base.py4
-rw-r--r--numpy/core/tests/test_ufunc.py7
-rw-r--r--numpy/core/tests/test_umath.py7
-rw-r--r--numpy/matrixlib/tests/test_interaction.py155
-rw-r--r--numpy/polynomial/tests/test_polyutils.py9
12 files changed, 202 insertions, 98 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 75bcedd81..58903d411 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -1336,10 +1336,10 @@ def diagonal(a, offset=0, axis1=0, axis2=1):
Returns
-------
array_of_diagonals : ndarray
- If `a` is 2-D and not a `matrix`, a 1-D array of the same type as `a`
- containing the diagonal is returned. If `a` is a `matrix`, a 1-D
+ If `a` is 2-D, a 1-D array of the same type as `a` containing the
+ diagonal is returned (except if `a` is a `matrix`, in which case a 1-D
array containing the diagonal is returned in order to maintain
- backward compatibility.
+ backward compatibility).
If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`
are removed, and a new axis inserted at the end corresponding to the
diagonal.
@@ -1496,10 +1496,9 @@ def ravel(a, order='C'):
Returns
-------
y : array_like
- If `a` is a matrix, y is a 1-D ndarray, otherwise y is an array of
- the same subtype as `a`. The shape of the returned array is
- ``(a.size,)``. Matrices are special cased for backward
- compatibility.
+ y is an array of the same subtype as `a`, with shape ``(a.size,)``
+ (Note: matrices are special-cases for backward compatibility: if `a`
+ is a matrix, y is a 1-D ndarray.)
See Also
--------
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 1108d4667..cd783d242 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -489,9 +489,9 @@ def asarray(a, dtype=None, order=None):
Contrary to `asanyarray`, ndarray subclasses are not passed through:
- >>> issubclass(np.matrix, np.ndarray)
+ >>> issubclass(np.recarray, np.ndarray)
True
- >>> a = np.matrix([[1, 2]])
+ >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
@@ -545,7 +545,7 @@ def asanyarray(a, dtype=None, order=None):
Instances of `ndarray` subclasses are passed through as-is:
- >>> a = np.matrix([1, 2])
+ >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asanyarray(a) is a
True
@@ -2280,7 +2280,7 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
-
+
.. warning:: The default `atol` is not appropriate for comparing numbers
that are much smaller than one (see Notes).
diff --git a/numpy/core/tests/test_api.py b/numpy/core/tests/test_api.py
index a927968a4..9755e7b36 100644
--- a/numpy/core/tests/test_api.py
+++ b/numpy/core/tests/test_api.py
@@ -223,22 +223,25 @@ def test_array_astype():
b = a.astype('f4', subok=0, copy=False)
assert_(a is b)
- a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
+ class MyNDArray(np.ndarray):
+ pass
- # subok=True passes through a matrix
+ a = np.array([[0, 1, 2], [3, 4, 5]], dtype='f4').view(MyNDArray)
+
+ # subok=True passes through a subclass
b = a.astype('f4', subok=True, copy=False)
assert_(a is b)
# subok=True is default, and creates a subtype on a cast
b = a.astype('i4', copy=False)
assert_equal(a, b)
- assert_equal(type(b), np.matrix)
+ assert_equal(type(b), MyNDArray)
- # subok=False never returns a matrix
+ # subok=False never returns a subclass
b = a.astype('f4', subok=False, copy=False)
assert_equal(a, b)
assert_(not (a is b))
- assert_(type(b) is not np.matrix)
+ assert_(type(b) is not MyNDArray)
# Make sure converting from string object to fixed length string
# does not truncate.
diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py
index 65852e577..88f5deabc 100644
--- a/numpy/core/tests/test_indexing.py
+++ b/numpy/core/tests/test_indexing.py
@@ -576,19 +576,6 @@ class TestSubclasses(object):
assert_(isinstance(s[[0, 1, 2]], SubClass))
assert_(isinstance(s[s > 0], SubClass))
- def test_matrix_fancy(self):
- # The matrix class messes with the shape. While this is always
- # weird (getitem is not used, it does not have setitem nor knows
- # about fancy indexing), this tests gh-3110
- m = np.matrix([[1, 2], [3, 4]])
-
- assert_(isinstance(m[[0,1,0], :], np.matrix))
-
- # gh-3110. Note the transpose currently because matrices do *not*
- # support dimension fixing for fancy indexing correctly.
- x = np.asmatrix(np.arange(50).reshape(5,10))
- assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
-
def test_finalize_gets_full_info(self):
# Array finalize should be called on the filled array.
class SubClass(np.ndarray):
diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py
index 3c5f90cfc..1900de2a3 100644
--- a/numpy/core/tests/test_multiarray.py
+++ b/numpy/core/tests/test_multiarray.py
@@ -1745,13 +1745,6 @@ class TestMethods(object):
assert_equal(r, np.array([('a', 1), ('c', 3), ('b', 255), ('d', 258)],
dtype=mydtype))
- def test_sort_matrix_none(self):
- a = np.matrix([[2, 1, 0]])
- actual = np.sort(a, axis=None)
- expected = np.matrix([[0, 1, 2]])
- assert_equal(actual, expected)
- assert_(type(expected) is np.matrix)
-
def test_argsort(self):
# all c scalar argsorts use the same code with different types
# so it suffices to run a quick check with one type. The number
@@ -2497,14 +2490,6 @@ class TestMethods(object):
assert_array_equal(np.partition(d, kth)[kth], tgt,
err_msg="data: %r\n kth: %r" % (d, kth))
- def test_partition_matrix_none(self):
- # gh-4301
- a = np.matrix([[2, 1, 0]])
- actual = np.partition(a, 1, axis=None)
- expected = np.matrix([[0, 1, 2]])
- assert_equal(actual, expected)
- assert_(type(expected) is np.matrix)
-
def test_argpartition_gh5524(self):
# A test for functionality of argpartition on lists.
d = [6,7,3,2,9,0]
@@ -5279,13 +5264,6 @@ class TestDot(object):
assert_equal(np.dot(b, a), res)
assert_equal(np.dot(b, b), res)
- def test_dot_scalar_and_matrix_of_objects(self):
- # Ticket #2469
- arr = np.matrix([1, 2], dtype=object)
- desired = np.matrix([[3, 6]], dtype=object)
- assert_equal(np.dot(arr, 3), desired)
- assert_equal(np.dot(3, arr), desired)
-
def test_accelerate_framework_sgemv_fix(self):
def aligned_array(shape, align, dtype, order='C'):
@@ -5641,21 +5619,6 @@ class TestInner(object):
assert_equal(np.inner(vec, sca), desired)
assert_equal(np.inner(sca, vec), desired)
- def test_inner_scalar_and_matrix(self):
- for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
- sca = np.array(3, dtype=dt)[()]
- arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
- desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
- assert_equal(np.inner(arr, sca), desired)
- assert_equal(np.inner(sca, arr), desired)
-
- def test_inner_scalar_and_matrix_of_objects(self):
- # Ticket #4482
- arr = np.matrix([1, 2], dtype=object)
- desired = np.matrix([[3, 6]], dtype=object)
- assert_equal(np.inner(arr, 3), desired)
- assert_equal(np.inner(3, arr), desired)
-
def test_vecself(self):
# Ticket 844.
# Inner product of a vector with itself segfaults or give
diff --git a/numpy/core/tests/test_nditer.py b/numpy/core/tests/test_nditer.py
index b6b1c0f31..8cb536f07 100644
--- a/numpy/core/tests/test_nditer.py
+++ b/numpy/core/tests/test_nditer.py
@@ -1469,26 +1469,25 @@ def test_iter_allocate_output_types_scalar():
def test_iter_allocate_output_subtype():
# Make sure that the subtype with priority wins
+ class MyNDArray(np.ndarray):
+ __array_priority__ = 15
- # matrix vs ndarray
- a = np.matrix([[1, 2], [3, 4]])
+ # subclass vs ndarray
+ a = np.array([[1, 2], [3, 4]]).view(MyNDArray)
b = np.arange(4).reshape(2, 2).T
i = nditer([a, b, None], [],
- [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+ [['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_equal(type(a), type(i.operands[2]))
- assert_(type(b) != type(i.operands[2]))
+ assert_(type(b) is not type(i.operands[2]))
assert_equal(i.operands[2].shape, (2, 2))
- # matrix always wants things to be 2D
- b = np.arange(4).reshape(1, 2, 2)
- assert_raises(RuntimeError, nditer, [a, b, None], [],
- [['readonly'], ['readonly'], ['writeonly', 'allocate']])
- # but if subtypes are disabled, the result can still work
+ # If subtypes are disabled, we should get back an ndarray.
i = nditer([a, b, None], [],
- [['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']])
+ [['readonly'], ['readonly'],
+ ['writeonly', 'allocate', 'no_subtype']])
assert_equal(type(b), type(i.operands[2]))
- assert_(type(a) != type(i.operands[2]))
- assert_equal(i.operands[2].shape, (1, 2, 2))
+ assert_(type(a) is not (i.operands[2]))
+ assert_equal(i.operands[2].shape, (2, 2))
def test_iter_allocate_output_errors():
# Check that the iterator will throw errors for bad output allocations
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index 40cccd404..95e9f8497 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -905,7 +905,7 @@ class TestTypes(object):
fi = np.finfo(dt)
assert_(np.can_cast(fi.min, dt))
assert_(np.can_cast(fi.max, dt))
-
+
# Custom exception class to test exception propagation in fromiter
class NIterError(Exception):
@@ -2201,13 +2201,16 @@ class TestLikeFuncs(object):
self.compare_array_value(dz, value, fill_value)
# Test the 'subok' parameter
- a = np.matrix([[1, 2], [3, 4]])
+ class MyNDArray(np.ndarray):
+ pass
+
+ a = np.array([[1, 2], [3, 4]]).view(MyNDArray)
b = like_function(a, **fill_kwarg)
- assert_(type(b) is np.matrix)
+ assert_(type(b) is MyNDArray)
b = like_function(a, subok=False, **fill_kwarg)
- assert_(type(b) is not np.matrix)
+ assert_(type(b) is not MyNDArray)
def test_ones_like(self):
self.check_like_function(np.ones_like, 1)
diff --git a/numpy/core/tests/test_shape_base.py b/numpy/core/tests/test_shape_base.py
index 1d91a651e..72b3451a4 100644
--- a/numpy/core/tests/test_shape_base.py
+++ b/numpy/core/tests/test_shape_base.py
@@ -364,10 +364,6 @@ def test_stack():
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
- # np.matrix
- m = np.matrix([[1, 2], [3, 4]])
- assert_raises_regex(ValueError, 'shape too large to be a matrix',
- stack, [m, m])
class TestBlock(object):
diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py
index fe40456d5..8479260a3 100644
--- a/numpy/core/tests/test_ufunc.py
+++ b/numpy/core/tests/test_ufunc.py
@@ -892,13 +892,6 @@ class TestUfunc(object):
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
assert_array_equal(arr, out)
- def test_object_scalar_multiply(self):
- # Tickets #2469 and #4482
- arr = np.matrix([1, 2], dtype=object)
- desired = np.matrix([[3, 6]], dtype=object)
- assert_equal(np.multiply(arr, 3), desired)
- assert_equal(np.multiply(3, arr), desired)
-
def test_zerosize_reduction(self):
# Test with default dtype and object dtype
for a in [[], np.array([], dtype=object)]:
diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py
index ea0be1892..06dadc674 100644
--- a/numpy/core/tests/test_umath.py
+++ b/numpy/core/tests/test_umath.py
@@ -1664,13 +1664,16 @@ class TestSpecialMethods(object):
assert_equal(ncu.maximum(a, C()), 0)
def test_ufunc_override(self):
-
+ # check override works even with instance with high priority.
class A(object):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return self, func, method, inputs, kwargs
+ class MyNDArray(np.ndarray):
+ __array_priority__ = 100
+
a = A()
- b = np.matrix([1])
+ b = np.array([1]).view(MyNDArray)
res0 = np.multiply(a, b)
res1 = np.multiply(b, b, out=a)
diff --git a/numpy/matrixlib/tests/test_interaction.py b/numpy/matrixlib/tests/test_interaction.py
new file mode 100644
index 000000000..9b515b620
--- /dev/null
+++ b/numpy/matrixlib/tests/test_interaction.py
@@ -0,0 +1,155 @@
+"""Tests of interaction of matrix with other parts of numpy.
+
+Note that tests with MaskedArray and linalg are done in separate files.
+"""
+from __future__ import division, absolute_import, print_function
+
+import numpy as np
+from numpy.testing import (assert_, assert_equal, assert_raises,
+ assert_raises_regex)
+
+
+def test_fancy_indexing():
+ # The matrix class messes with the shape. While this is always
+ # weird (getitem is not used, it does not have setitem nor knows
+ # about fancy indexing), this tests gh-3110
+ # 2018-04-29: moved here from core.tests.test_index.
+ m = np.matrix([[1, 2], [3, 4]])
+
+ assert_(isinstance(m[[0, 1, 0], :], np.matrix))
+
+ # gh-3110. Note the transpose currently because matrices do *not*
+ # support dimension fixing for fancy indexing correctly.
+ x = np.asmatrix(np.arange(50).reshape(5, 10))
+ assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
+
+
+def test_polynomial_mapdomain():
+ # test that polynomial preserved matrix subtype.
+ # 2018-04-29: moved here from polynomial.tests.polyutils.
+ dom1 = [0, 4]
+ dom2 = [1, 3]
+ x = np.matrix([dom1, dom1])
+ res = np.polynomial.mapdomain(x, dom1, dom2)
+ assert_(isinstance(res, np.matrix))
+
+
+def test_sort_matrix_none():
+ # 2018-04-29: moved here from core.tests.test_multiarray
+ a = np.matrix([[2, 1, 0]])
+ actual = np.sort(a, axis=None)
+ expected = np.matrix([[0, 1, 2]])
+ assert_equal(actual, expected)
+ assert_(type(expected) is np.matrix)
+
+
+def test_partition_matrix_none():
+ # gh-4301
+ # 2018-04-29: moved here from core.tests.test_multiarray
+ a = np.matrix([[2, 1, 0]])
+ actual = np.partition(a, 1, axis=None)
+ expected = np.matrix([[0, 1, 2]])
+ assert_equal(actual, expected)
+ assert_(type(expected) is np.matrix)
+
+
+def test_dot_scalar_and_matrix_of_objects():
+ # Ticket #2469
+ # 2018-04-29: moved here from core.tests.test_multiarray
+ arr = np.matrix([1, 2], dtype=object)
+ desired = np.matrix([[3, 6]], dtype=object)
+ assert_equal(np.dot(arr, 3), desired)
+ assert_equal(np.dot(3, arr), desired)
+
+
+def test_inner_scalar_and_matrix():
+ # 2018-04-29: moved here from core.tests.test_multiarray
+ for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
+ sca = np.array(3, dtype=dt)[()]
+ arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
+ desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
+ assert_equal(np.inner(arr, sca), desired)
+ assert_equal(np.inner(sca, arr), desired)
+
+
+def test_inner_scalar_and_matrix_of_objects(self):
+ # Ticket #4482
+ # 2018-04-29: moved here from core.tests.test_multiarray
+ arr = np.matrix([1, 2], dtype=object)
+ desired = np.matrix([[3, 6]], dtype=object)
+ assert_equal(np.inner(arr, 3), desired)
+ assert_equal(np.inner(3, arr), desired)
+
+
+def test_iter_allocate_output_subtype():
+ # Make sure that the subtype with priority wins
+ # 2018-04-29: moved here from core.tests.test_nditer, given the
+ # matrix specific shape test.
+
+ # matrix vs ndarray
+ a = np.matrix([[1, 2], [3, 4]])
+ b = np.arange(4).reshape(2, 2).T
+ i = np.nditer([a, b, None], [],
+ [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+ assert_(type(i.operands[2]) is np.matrix)
+ assert_(type(i.operands[2]) is not np.ndarray)
+ assert_equal(i.operands[2].shape, (2, 2))
+
+ # matrix always wants things to be 2D
+ b = np.arange(4).reshape(1, 2, 2)
+ assert_raises(RuntimeError, np.nditer, [a, b, None], [],
+ [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+ # but if subtypes are disabled, the result can still work
+ i = np.nditer([a, b, None], [],
+ [['readonly'], ['readonly'],
+ ['writeonly', 'allocate', 'no_subtype']])
+ assert_(type(i.operands[2]) is np.ndarray)
+ assert_(type(i.operands[2]) is not np.matrix)
+ assert_equal(i.operands[2].shape, (1, 2, 2))
+
+
+def like_function():
+ # 2018-04-29: moved here from core.tests.test_numeric
+ a = np.matrix([[1, 2], [3, 4]])
+ for like_function in np.zeros_like, np.ones_like, np.empty_like:
+ b = like_function(a)
+ assert_(type(b) is np.matrix)
+
+ c = like_function(a, subok=False)
+ assert_(type(c) is not np.matrix)
+
+
+def test_array_astype():
+ # 2018-04-29: copied here from core.tests.test_api
+ # subok=True passes through a matrix
+ a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
+ b = a.astype('f4', subok=True, copy=False)
+ assert_(a is b)
+
+ # subok=True is default, and creates a subtype on a cast
+ b = a.astype('i4', copy=False)
+ assert_equal(a, b)
+ assert_equal(type(b), np.matrix)
+
+ # subok=False never returns a matrix
+ b = a.astype('f4', subok=False, copy=False)
+ assert_equal(a, b)
+ assert_(not (a is b))
+ assert_(type(b) is not np.matrix)
+
+
+def test_stack():
+ # 2018-04-29: copied here from core.tests.test_shape_base
+ # check np.matrix cannot be stacked
+ m = np.matrix([[1, 2], [3, 4]])
+ assert_raises_regex(ValueError, 'shape too large to be a matrix',
+ np.stack, [m, m])
+
+
+def test_object_scalar_multiply():
+ # Tickets #2469 and #4482
+ # 2018-04-29: moved here from core.tests.test_ufunc
+ arr = np.matrix([1, 2], dtype=object)
+ desired = np.matrix([[3, 6]], dtype=object)
+ assert_equal(np.multiply(arr, 3), desired)
+ assert_equal(np.multiply(3, arr), desired)
diff --git a/numpy/polynomial/tests/test_polyutils.py b/numpy/polynomial/tests/test_polyutils.py
index 32ea55716..801c558cc 100644
--- a/numpy/polynomial/tests/test_polyutils.py
+++ b/numpy/polynomial/tests/test_polyutils.py
@@ -63,7 +63,7 @@ class TestDomain(object):
dom1 = [0, 4]
dom2 = [1, 3]
tgt = dom2
- res = pu. mapdomain(dom1, dom1, dom2)
+ res = pu.mapdomain(dom1, dom1, dom2)
assert_almost_equal(res, tgt)
# test for complex values
@@ -83,11 +83,14 @@ class TestDomain(object):
assert_almost_equal(res, tgt)
# test that subtypes are preserved.
+ class MyNDArray(np.ndarray):
+ pass
+
dom1 = [0, 4]
dom2 = [1, 3]
- x = np.matrix([dom1, dom1])
+ x = np.array([dom1, dom1]).view(MyNDArray)
res = pu.mapdomain(x, dom1, dom2)
- assert_(isinstance(res, np.matrix))
+ assert_(isinstance(res, MyNDArray))
def test_mapparms(self):
# test for real values
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