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-rw-r--r--numpy/lib/polynomial.py5
-rw-r--r--numpy/lib/scimath.py2
-rw-r--r--numpy/lib/tests/test_arraysetops.py2
-rw-r--r--numpy/lib/tests/test_function_base.py21
-rw-r--r--numpy/lib/tests/test_index_tricks.py31
-rw-r--r--numpy/lib/tests/test_nanfunctions.py105
-rw-r--r--numpy/lib/tests/test_shape_base.py22
7 files changed, 64 insertions, 124 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 41b5e2f64..078608bbb 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -113,11 +113,6 @@ def poly(seq_of_zeros):
>>> np.poly(P)
array([ 1. , 0. , 0.16666667])
- Or a square matrix object:
-
- >>> np.poly(np.matrix(P))
- array([ 1. , 0. , 0.16666667])
-
Note how in all cases the leading coefficient is always 1.
"""
diff --git a/numpy/lib/scimath.py b/numpy/lib/scimath.py
index e07caf805..f1838fee6 100644
--- a/numpy/lib/scimath.py
+++ b/numpy/lib/scimath.py
@@ -555,7 +555,7 @@ def arctanh(x):
--------
>>> np.set_printoptions(precision=4)
- >>> np.emath.arctanh(np.matrix(np.eye(2)))
+ >>> np.emath.arctanh(np.eye(2))
array([[ Inf, 0.],
[ 0., Inf]])
>>> np.emath.arctanh([1j])
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index 76c36c53e..984a3b15e 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -74,8 +74,6 @@ class TestSetOps(object):
assert_array_equal([1,7,8], ediff1d(two_elem, to_end=[7,8]))
assert_array_equal([7,1], ediff1d(two_elem, to_begin=7))
assert_array_equal([5,6,1], ediff1d(two_elem, to_begin=[5,6]))
- assert(isinstance(ediff1d(np.matrix(1)), np.matrix))
- assert(isinstance(ediff1d(np.matrix(1), to_begin=1), np.matrix))
def test_isin(self):
# the tests for in1d cover most of isin's behavior
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 43d62a7ff..6c4f58424 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -287,9 +287,6 @@ class TestAverage(object):
assert_almost_equal(y5.mean(0), average(y5, 0))
assert_almost_equal(y5.mean(1), average(y5, 1))
- y6 = np.matrix(rand(5, 5))
- assert_array_equal(y6.mean(0), average(y6, 0))
-
def test_weights(self):
y = np.arange(10)
w = np.arange(10)
@@ -357,14 +354,6 @@ class TestAverage(object):
assert_equal(type(np.average(a)), subclass)
assert_equal(type(np.average(a, weights=w)), subclass)
- # also test matrices
- a = np.matrix([[1,2],[3,4]])
- w = np.matrix([[1,2],[3,4]])
-
- r = np.average(a, axis=0, weights=w)
- assert_equal(type(r), np.matrix)
- assert_equal(r, [[2.5, 10.0/3]])
-
def test_upcasting(self):
types = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')]
@@ -1623,16 +1612,6 @@ class TestTrapz(object):
xm = np.ma.array(x, mask=mask)
assert_almost_equal(trapz(y, xm), r)
- def test_matrix(self):
- # Test to make sure matrices give the same answer as ndarrays
- x = np.linspace(0, 5)
- y = x * x
- r = trapz(y, x)
- mx = np.matrix(x)
- my = np.matrix(y)
- mr = trapz(my, mx)
- assert_almost_equal(mr, r)
-
class TestSinc(object):
diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py
index f934e952a..089a7589a 100644
--- a/numpy/lib/tests/test_index_tricks.py
+++ b/numpy/lib/tests/test_index_tricks.py
@@ -184,37 +184,6 @@ class TestConcatenator(object):
assert_array_equal(d[:5, :], b)
assert_array_equal(d[5:, :], c)
- def test_matrix(self):
- a = [1, 2]
- b = [3, 4]
-
- ab_r = np.r_['r', a, b]
- ab_c = np.r_['c', a, b]
-
- assert_equal(type(ab_r), np.matrix)
- assert_equal(type(ab_c), np.matrix)
-
- assert_equal(np.array(ab_r), [[1,2,3,4]])
- assert_equal(np.array(ab_c), [[1],[2],[3],[4]])
-
- assert_raises(ValueError, lambda: np.r_['rc', a, b])
-
- def test_matrix_scalar(self):
- r = np.r_['r', [1, 2], 3]
- assert_equal(type(r), np.matrix)
- assert_equal(np.array(r), [[1,2,3]])
-
- def test_matrix_builder(self):
- a = np.array([1])
- b = np.array([2])
- c = np.array([3])
- d = np.array([4])
- actual = np.r_['a, b; c, d']
- expected = np.bmat([[a, b], [c, d]])
-
- assert_equal(actual, expected)
- assert_equal(type(actual), type(expected))
-
def test_0d(self):
assert_equal(r_[0, np.array(1), 2], [0, 1, 2])
assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3])
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py
index e69d9dd7d..55877e602 100644
--- a/numpy/lib/tests/test_nanfunctions.py
+++ b/numpy/lib/tests/test_nanfunctions.py
@@ -113,42 +113,46 @@ class TestNanFunctions_MinMax(object):
for f in self.nanfuncs:
assert_(f(0.) == 0.)
- def test_matrices(self):
+ def test_subclass(self):
+ class MyNDArray(np.ndarray):
+ pass
+
# Check that it works and that type and
# shape are preserved
- mat = np.matrix(np.eye(3))
+ mine = np.eye(3).view(MyNDArray)
for f in self.nanfuncs:
- res = f(mat, axis=0)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (1, 3))
- res = f(mat, axis=1)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (3, 1))
- res = f(mat)
- assert_(np.isscalar(res))
+ res = f(mine, axis=0)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == (3,))
+ res = f(mine, axis=1)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == (3,))
+ res = f(mine)
+ assert_(res.shape == ())
+
# check that rows of nan are dealt with for subclasses (#4628)
- mat[1] = np.nan
+ mine[1] = np.nan
for f in self.nanfuncs:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
- res = f(mat, axis=0)
- assert_(isinstance(res, np.matrix))
+ res = f(mine, axis=0)
+ assert_(isinstance(res, MyNDArray))
assert_(not np.any(np.isnan(res)))
assert_(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
- res = f(mat, axis=1)
- assert_(isinstance(res, np.matrix))
- assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
- and not np.isnan(res[2, 0]))
+ res = f(mine, axis=1)
+ assert_(isinstance(res, MyNDArray))
+ assert_(np.isnan(res[1]) and not np.isnan(res[0])
+ and not np.isnan(res[2]))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
- res = f(mat)
- assert_(np.isscalar(res))
+ res = f(mine)
+ assert_(res.shape == ())
assert_(res != np.nan)
assert_(len(w) == 0)
@@ -209,19 +213,22 @@ class TestNanFunctions_ArgminArgmax(object):
for f in self.nanfuncs:
assert_(f(0.) == 0.)
- def test_matrices(self):
+ def test_subclass(self):
+ class MyNDArray(np.ndarray):
+ pass
+
# Check that it works and that type and
# shape are preserved
- mat = np.matrix(np.eye(3))
+ mine = np.eye(3).view(MyNDArray)
for f in self.nanfuncs:
- res = f(mat, axis=0)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (1, 3))
- res = f(mat, axis=1)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (3, 1))
- res = f(mat)
- assert_(np.isscalar(res))
+ res = f(mine, axis=0)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == (3,))
+ res = f(mine, axis=1)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == (3,))
+ res = f(mine)
+ assert_(res.shape == ())
class TestNanFunctions_IntTypes(object):
@@ -381,19 +388,27 @@ class SharedNanFunctionsTestsMixin(object):
for f in self.nanfuncs:
assert_(f(0.) == 0.)
- def test_matrices(self):
+ def test_subclass(self):
+ class MyNDArray(np.ndarray):
+ pass
+
# Check that it works and that type and
# shape are preserved
- mat = np.matrix(np.eye(3))
+ array = np.eye(3)
+ mine = array.view(MyNDArray)
for f in self.nanfuncs:
- res = f(mat, axis=0)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (1, 3))
- res = f(mat, axis=1)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (3, 1))
- res = f(mat)
- assert_(np.isscalar(res))
+ expected_shape = f(array, axis=0).shape
+ res = f(mine, axis=0)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == expected_shape)
+ expected_shape = f(array, axis=1).shape
+ res = f(mine, axis=1)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == expected_shape)
+ expected_shape = f(array).shape
+ res = f(mine)
+ assert_(isinstance(res, MyNDArray))
+ assert_(res.shape == expected_shape)
class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin):
@@ -481,18 +496,6 @@ class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin):
res = f(d, axis=axis)
assert_equal(res.shape, (3, 5, 7, 11))
- def test_matrices(self):
- # Check that it works and that type and
- # shape are preserved
- mat = np.matrix(np.eye(3))
- for f in self.nanfuncs:
- for axis in np.arange(2):
- res = f(mat, axis=axis)
- assert_(isinstance(res, np.matrix))
- assert_(res.shape == (3, 3))
- res = f(mat)
- assert_(res.shape == (1, 3*3))
-
def test_result_values(self):
for axis in (-2, -1, 0, 1, None):
tgt = np.cumprod(_ndat_ones, axis=axis)
diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py
index 080fd066d..f84fcdda6 100644
--- a/numpy/lib/tests/test_shape_base.py
+++ b/numpy/lib/tests/test_shape_base.py
@@ -29,19 +29,21 @@ class TestApplyAlongAxis(object):
[[27, 30, 33], [36, 39, 42], [45, 48, 51]])
def test_preserve_subclass(self):
- # this test is particularly malicious because matrix
- # refuses to become 1d
def double(row):
return row * 2
- m = np.matrix([[0, 1], [2, 3]])
- expected = np.matrix([[0, 2], [4, 6]])
+
+ class MyNDArray(np.ndarray):
+ pass
+
+ m = np.array([[0, 1], [2, 3]]).view(MyNDArray)
+ expected = np.array([[0, 2], [4, 6]]).view(MyNDArray)
result = apply_along_axis(double, 0, m)
- assert_(isinstance(result, np.matrix))
+ assert_(isinstance(result, MyNDArray))
assert_array_equal(result, expected)
result = apply_along_axis(double, 1, m)
- assert_(isinstance(result, np.matrix))
+ assert_(isinstance(result, MyNDArray))
assert_array_equal(result, expected)
def test_subclass(self):
@@ -492,16 +494,10 @@ class TestSqueeze(object):
class TestKron(object):
def test_return_type(self):
- a = np.ones([2, 2])
- m = np.asmatrix(a)
- assert_equal(type(kron(a, a)), np.ndarray)
- assert_equal(type(kron(m, m)), np.matrix)
- assert_equal(type(kron(a, m)), np.matrix)
- assert_equal(type(kron(m, a)), np.matrix)
-
class myarray(np.ndarray):
__array_priority__ = 0.0
+ a = np.ones([2, 2])
ma = myarray(a.shape, a.dtype, a.data)
assert_equal(type(kron(a, a)), np.ndarray)
assert_equal(type(kron(ma, ma)), myarray)
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