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-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/__init__.py0
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py453
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_interaction.py354
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_masked_matrix.py231
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py93
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_multiarray.py16
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_numeric.py17
-rw-r--r--.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_regression.py31
8 files changed, 1195 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/__init__.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/__init__.py
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py
new file mode 100644
index 00000000..4cb5f3a3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py
@@ -0,0 +1,453 @@
+import collections.abc
+
+import numpy as np
+from numpy import matrix, asmatrix, bmat
+from numpy.testing import (
+ assert_, assert_equal, assert_almost_equal, assert_array_equal,
+ assert_array_almost_equal, assert_raises
+ )
+from numpy.linalg import matrix_power
+from numpy.matrixlib import mat
+
+class TestCtor:
+ def test_basic(self):
+ A = np.array([[1, 2], [3, 4]])
+ mA = matrix(A)
+ assert_(np.all(mA.A == A))
+
+ B = bmat("A,A;A,A")
+ C = bmat([[A, A], [A, A]])
+ D = np.array([[1, 2, 1, 2],
+ [3, 4, 3, 4],
+ [1, 2, 1, 2],
+ [3, 4, 3, 4]])
+ assert_(np.all(B.A == D))
+ assert_(np.all(C.A == D))
+
+ E = np.array([[5, 6], [7, 8]])
+ AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
+ assert_(np.all(bmat([A, E]) == AEresult))
+
+ vec = np.arange(5)
+ mvec = matrix(vec)
+ assert_(mvec.shape == (1, 5))
+
+ def test_exceptions(self):
+ # Check for ValueError when called with invalid string data.
+ assert_raises(ValueError, matrix, "invalid")
+
+ def test_bmat_nondefault_str(self):
+ A = np.array([[1, 2], [3, 4]])
+ B = np.array([[5, 6], [7, 8]])
+ Aresult = np.array([[1, 2, 1, 2],
+ [3, 4, 3, 4],
+ [1, 2, 1, 2],
+ [3, 4, 3, 4]])
+ mixresult = np.array([[1, 2, 5, 6],
+ [3, 4, 7, 8],
+ [5, 6, 1, 2],
+ [7, 8, 3, 4]])
+ assert_(np.all(bmat("A,A;A,A") == Aresult))
+ assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
+ assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
+ assert_(
+ np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
+ b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
+ assert_(np.all(b2 == mixresult))
+
+
+class TestProperties:
+ def test_sum(self):
+ """Test whether matrix.sum(axis=1) preserves orientation.
+ Fails in NumPy <= 0.9.6.2127.
+ """
+ M = matrix([[1, 2, 0, 0],
+ [3, 4, 0, 0],
+ [1, 2, 1, 2],
+ [3, 4, 3, 4]])
+ sum0 = matrix([8, 12, 4, 6])
+ sum1 = matrix([3, 7, 6, 14]).T
+ sumall = 30
+ assert_array_equal(sum0, M.sum(axis=0))
+ assert_array_equal(sum1, M.sum(axis=1))
+ assert_equal(sumall, M.sum())
+
+ assert_array_equal(sum0, np.sum(M, axis=0))
+ assert_array_equal(sum1, np.sum(M, axis=1))
+ assert_equal(sumall, np.sum(M))
+
+ def test_prod(self):
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(x.prod(), 720)
+ assert_equal(x.prod(0), matrix([[4, 10, 18]]))
+ assert_equal(x.prod(1), matrix([[6], [120]]))
+
+ assert_equal(np.prod(x), 720)
+ assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
+ assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
+
+ y = matrix([0, 1, 3])
+ assert_(y.prod() == 0)
+
+ def test_max(self):
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(x.max(), 6)
+ assert_equal(x.max(0), matrix([[4, 5, 6]]))
+ assert_equal(x.max(1), matrix([[3], [6]]))
+
+ assert_equal(np.max(x), 6)
+ assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
+ assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
+
+ def test_min(self):
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(x.min(), 1)
+ assert_equal(x.min(0), matrix([[1, 2, 3]]))
+ assert_equal(x.min(1), matrix([[1], [4]]))
+
+ assert_equal(np.min(x), 1)
+ assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
+ assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
+
+ def test_ptp(self):
+ x = np.arange(4).reshape((2, 2))
+ assert_(x.ptp() == 3)
+ assert_(np.all(x.ptp(0) == np.array([2, 2])))
+ assert_(np.all(x.ptp(1) == np.array([1, 1])))
+
+ def test_var(self):
+ x = np.arange(9).reshape((3, 3))
+ mx = x.view(np.matrix)
+ assert_equal(x.var(ddof=0), mx.var(ddof=0))
+ assert_equal(x.var(ddof=1), mx.var(ddof=1))
+
+ def test_basic(self):
+ import numpy.linalg as linalg
+
+ A = np.array([[1., 2.],
+ [3., 4.]])
+ mA = matrix(A)
+ assert_(np.allclose(linalg.inv(A), mA.I))
+ assert_(np.all(np.array(np.transpose(A) == mA.T)))
+ assert_(np.all(np.array(np.transpose(A) == mA.H)))
+ assert_(np.all(A == mA.A))
+
+ B = A + 2j*A
+ mB = matrix(B)
+ assert_(np.allclose(linalg.inv(B), mB.I))
+ assert_(np.all(np.array(np.transpose(B) == mB.T)))
+ assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
+
+ def test_pinv(self):
+ x = matrix(np.arange(6).reshape(2, 3))
+ xpinv = matrix([[-0.77777778, 0.27777778],
+ [-0.11111111, 0.11111111],
+ [ 0.55555556, -0.05555556]])
+ assert_almost_equal(x.I, xpinv)
+
+ def test_comparisons(self):
+ A = np.arange(100).reshape(10, 10)
+ mA = matrix(A)
+ mB = matrix(A) + 0.1
+ assert_(np.all(mB == A+0.1))
+ assert_(np.all(mB == matrix(A+0.1)))
+ assert_(not np.any(mB == matrix(A-0.1)))
+ assert_(np.all(mA < mB))
+ assert_(np.all(mA <= mB))
+ assert_(np.all(mA <= mA))
+ assert_(not np.any(mA < mA))
+
+ assert_(not np.any(mB < mA))
+ assert_(np.all(mB >= mA))
+ assert_(np.all(mB >= mB))
+ assert_(not np.any(mB > mB))
+
+ assert_(np.all(mA == mA))
+ assert_(not np.any(mA == mB))
+ assert_(np.all(mB != mA))
+
+ assert_(not np.all(abs(mA) > 0))
+ assert_(np.all(abs(mB > 0)))
+
+ def test_asmatrix(self):
+ A = np.arange(100).reshape(10, 10)
+ mA = asmatrix(A)
+ A[0, 0] = -10
+ assert_(A[0, 0] == mA[0, 0])
+
+ def test_noaxis(self):
+ A = matrix([[1, 0], [0, 1]])
+ assert_(A.sum() == matrix(2))
+ assert_(A.mean() == matrix(0.5))
+
+ def test_repr(self):
+ A = matrix([[1, 0], [0, 1]])
+ assert_(repr(A) == "matrix([[1, 0],\n [0, 1]])")
+
+ def test_make_bool_matrix_from_str(self):
+ A = matrix('True; True; False')
+ B = matrix([[True], [True], [False]])
+ assert_array_equal(A, B)
+
+class TestCasting:
+ def test_basic(self):
+ A = np.arange(100).reshape(10, 10)
+ mA = matrix(A)
+
+ mB = mA.copy()
+ O = np.ones((10, 10), np.float64) * 0.1
+ mB = mB + O
+ assert_(mB.dtype.type == np.float64)
+ assert_(np.all(mA != mB))
+ assert_(np.all(mB == mA+0.1))
+
+ mC = mA.copy()
+ O = np.ones((10, 10), np.complex128)
+ mC = mC * O
+ assert_(mC.dtype.type == np.complex128)
+ assert_(np.all(mA != mB))
+
+
+class TestAlgebra:
+ def test_basic(self):
+ import numpy.linalg as linalg
+
+ A = np.array([[1., 2.], [3., 4.]])
+ mA = matrix(A)
+
+ B = np.identity(2)
+ for i in range(6):
+ assert_(np.allclose((mA ** i).A, B))
+ B = np.dot(B, A)
+
+ Ainv = linalg.inv(A)
+ B = np.identity(2)
+ for i in range(6):
+ assert_(np.allclose((mA ** -i).A, B))
+ B = np.dot(B, Ainv)
+
+ assert_(np.allclose((mA * mA).A, np.dot(A, A)))
+ assert_(np.allclose((mA + mA).A, (A + A)))
+ assert_(np.allclose((3*mA).A, (3*A)))
+
+ mA2 = matrix(A)
+ mA2 *= 3
+ assert_(np.allclose(mA2.A, 3*A))
+
+ def test_pow(self):
+ """Test raising a matrix to an integer power works as expected."""
+ m = matrix("1. 2.; 3. 4.")
+ m2 = m.copy()
+ m2 **= 2
+ mi = m.copy()
+ mi **= -1
+ m4 = m2.copy()
+ m4 **= 2
+ assert_array_almost_equal(m2, m**2)
+ assert_array_almost_equal(m4, np.dot(m2, m2))
+ assert_array_almost_equal(np.dot(mi, m), np.eye(2))
+
+ def test_scalar_type_pow(self):
+ m = matrix([[1, 2], [3, 4]])
+ for scalar_t in [np.int8, np.uint8]:
+ two = scalar_t(2)
+ assert_array_almost_equal(m ** 2, m ** two)
+
+ def test_notimplemented(self):
+ '''Check that 'not implemented' operations produce a failure.'''
+ A = matrix([[1., 2.],
+ [3., 4.]])
+
+ # __rpow__
+ with assert_raises(TypeError):
+ 1.0**A
+
+ # __mul__ with something not a list, ndarray, tuple, or scalar
+ with assert_raises(TypeError):
+ A*object()
+
+
+class TestMatrixReturn:
+ def test_instance_methods(self):
+ a = matrix([1.0], dtype='f8')
+ methodargs = {
+ 'astype': ('intc',),
+ 'clip': (0.0, 1.0),
+ 'compress': ([1],),
+ 'repeat': (1,),
+ 'reshape': (1,),
+ 'swapaxes': (0, 0),
+ 'dot': np.array([1.0]),
+ }
+ excluded_methods = [
+ 'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
+ 'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
+ 'searchsorted', 'setflags', 'setfield', 'sort',
+ 'partition', 'argpartition',
+ 'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any',
+ 'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
+ 'prod', 'std', 'ctypes', 'itemset',
+ ]
+ for attrib in dir(a):
+ if attrib.startswith('_') or attrib in excluded_methods:
+ continue
+ f = getattr(a, attrib)
+ if isinstance(f, collections.abc.Callable):
+ # reset contents of a
+ a.astype('f8')
+ a.fill(1.0)
+ if attrib in methodargs:
+ args = methodargs[attrib]
+ else:
+ args = ()
+ b = f(*args)
+ assert_(type(b) is matrix, "%s" % attrib)
+ assert_(type(a.real) is matrix)
+ assert_(type(a.imag) is matrix)
+ c, d = matrix([0.0]).nonzero()
+ assert_(type(c) is np.ndarray)
+ assert_(type(d) is np.ndarray)
+
+
+class TestIndexing:
+ def test_basic(self):
+ x = asmatrix(np.zeros((3, 2), float))
+ y = np.zeros((3, 1), float)
+ y[:, 0] = [0.8, 0.2, 0.3]
+ x[:, 1] = y > 0.5
+ assert_equal(x, [[0, 1], [0, 0], [0, 0]])
+
+
+class TestNewScalarIndexing:
+ a = matrix([[1, 2], [3, 4]])
+
+ def test_dimesions(self):
+ a = self.a
+ x = a[0]
+ assert_equal(x.ndim, 2)
+
+ def test_array_from_matrix_list(self):
+ a = self.a
+ x = np.array([a, a])
+ assert_equal(x.shape, [2, 2, 2])
+
+ def test_array_to_list(self):
+ a = self.a
+ assert_equal(a.tolist(), [[1, 2], [3, 4]])
+
+ def test_fancy_indexing(self):
+ a = self.a
+ x = a[1, [0, 1, 0]]
+ assert_(isinstance(x, matrix))
+ assert_equal(x, matrix([[3, 4, 3]]))
+ x = a[[1, 0]]
+ assert_(isinstance(x, matrix))
+ assert_equal(x, matrix([[3, 4], [1, 2]]))
+ x = a[[[1], [0]], [[1, 0], [0, 1]]]
+ assert_(isinstance(x, matrix))
+ assert_equal(x, matrix([[4, 3], [1, 2]]))
+
+ def test_matrix_element(self):
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(x[0][0], matrix([[1, 2, 3]]))
+ assert_equal(x[0][0].shape, (1, 3))
+ assert_equal(x[0].shape, (1, 3))
+ assert_equal(x[:, 0].shape, (2, 1))
+
+ x = matrix(0)
+ assert_equal(x[0, 0], 0)
+ assert_equal(x[0], 0)
+ assert_equal(x[:, 0].shape, x.shape)
+
+ def test_scalar_indexing(self):
+ x = asmatrix(np.zeros((3, 2), float))
+ assert_equal(x[0, 0], x[0][0])
+
+ def test_row_column_indexing(self):
+ x = asmatrix(np.eye(2))
+ assert_array_equal(x[0,:], [[1, 0]])
+ assert_array_equal(x[1,:], [[0, 1]])
+ assert_array_equal(x[:, 0], [[1], [0]])
+ assert_array_equal(x[:, 1], [[0], [1]])
+
+ def test_boolean_indexing(self):
+ A = np.arange(6)
+ A.shape = (3, 2)
+ x = asmatrix(A)
+ assert_array_equal(x[:, np.array([True, False])], x[:, 0])
+ assert_array_equal(x[np.array([True, False, False]),:], x[0,:])
+
+ def test_list_indexing(self):
+ A = np.arange(6)
+ A.shape = (3, 2)
+ x = asmatrix(A)
+ assert_array_equal(x[:, [1, 0]], x[:, ::-1])
+ assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
+
+
+class TestPower:
+ def test_returntype(self):
+ a = np.array([[0, 1], [0, 0]])
+ assert_(type(matrix_power(a, 2)) is np.ndarray)
+ a = mat(a)
+ assert_(type(matrix_power(a, 2)) is matrix)
+
+ def test_list(self):
+ assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
+
+
+class TestShape:
+
+ a = np.array([[1], [2]])
+ m = matrix([[1], [2]])
+
+ def test_shape(self):
+ assert_equal(self.a.shape, (2, 1))
+ assert_equal(self.m.shape, (2, 1))
+
+ def test_numpy_ravel(self):
+ assert_equal(np.ravel(self.a).shape, (2,))
+ assert_equal(np.ravel(self.m).shape, (2,))
+
+ def test_member_ravel(self):
+ assert_equal(self.a.ravel().shape, (2,))
+ assert_equal(self.m.ravel().shape, (1, 2))
+
+ def test_member_flatten(self):
+ assert_equal(self.a.flatten().shape, (2,))
+ assert_equal(self.m.flatten().shape, (1, 2))
+
+ def test_numpy_ravel_order(self):
+ x = np.array([[1, 2, 3], [4, 5, 6]])
+ assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+ assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+ assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+ assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+ assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+ assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+ assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+
+ def test_matrix_ravel_order(self):
+ x = matrix([[1, 2, 3], [4, 5, 6]])
+ assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
+ assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
+ assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
+ assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
+
+ def test_array_memory_sharing(self):
+ assert_(np.may_share_memory(self.a, self.a.ravel()))
+ assert_(not np.may_share_memory(self.a, self.a.flatten()))
+
+ def test_matrix_memory_sharing(self):
+ assert_(np.may_share_memory(self.m, self.m.ravel()))
+ assert_(not np.may_share_memory(self.m, self.m.flatten()))
+
+ def test_expand_dims_matrix(self):
+ # matrices are always 2d - so expand_dims only makes sense when the
+ # type is changed away from matrix.
+ a = np.arange(10).reshape((2, 5)).view(np.matrix)
+ expanded = np.expand_dims(a, axis=1)
+ assert_equal(expanded.ndim, 3)
+ assert_(not isinstance(expanded, np.matrix))
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_interaction.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_interaction.py
new file mode 100644
index 00000000..5154bd62
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_interaction.py
@@ -0,0 +1,354 @@
+"""Tests of interaction of matrix with other parts of numpy.
+
+Note that tests with MaskedArray and linalg are done in separate files.
+"""
+import pytest
+
+import textwrap
+import warnings
+
+import numpy as np
+from numpy.testing import (assert_, assert_equal, assert_raises,
+ assert_raises_regex, assert_array_equal,
+ assert_almost_equal, assert_array_almost_equal)
+
+
+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.polyutils.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():
+ # 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)
+
+
+def test_nanfunctions_matrices():
+ # Check that it works and that type and
+ # shape are preserved
+ # 2018-04-29: moved here from core.tests.test_nanfunctions
+ mat = np.matrix(np.eye(3))
+ for f in [np.nanmin, np.nanmax]:
+ 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))
+ # check that rows of nan are dealt with for subclasses (#4628)
+ mat[1] = np.nan
+ for f in [np.nanmin, np.nanmax]:
+ with warnings.catch_warnings(record=True) as w:
+ warnings.simplefilter('always')
+ res = f(mat, axis=0)
+ assert_(isinstance(res, np.matrix))
+ 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]))
+ 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))
+ assert_(res != np.nan)
+ assert_(len(w) == 0)
+
+
+def test_nanfunctions_matrices_general():
+ # Check that it works and that type and
+ # shape are preserved
+ # 2018-04-29: moved here from core.tests.test_nanfunctions
+ mat = np.matrix(np.eye(3))
+ for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
+ np.nanmean, np.nanvar, np.nanstd):
+ 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))
+
+ for f in np.nancumsum, np.nancumprod:
+ res = f(mat, axis=0)
+ assert_(isinstance(res, np.matrix))
+ assert_(res.shape == (3, 3))
+ res = f(mat, axis=1)
+ assert_(isinstance(res, np.matrix))
+ assert_(res.shape == (3, 3))
+ res = f(mat)
+ assert_(isinstance(res, np.matrix))
+ assert_(res.shape == (1, 3*3))
+
+
+def test_average_matrix():
+ # 2018-04-29: moved here from core.tests.test_function_base.
+ y = np.matrix(np.random.rand(5, 5))
+ assert_array_equal(y.mean(0), np.average(y, 0))
+
+ 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_trapz_matrix():
+ # Test to make sure matrices give the same answer as ndarrays
+ # 2018-04-29: moved here from core.tests.test_function_base.
+ x = np.linspace(0, 5)
+ y = x * x
+ r = np.trapz(y, x)
+ mx = np.matrix(x)
+ my = np.matrix(y)
+ mr = np.trapz(my, mx)
+ assert_almost_equal(mr, r)
+
+
+def test_ediff1d_matrix():
+ # 2018-04-29: moved here from core.tests.test_arraysetops.
+ assert(isinstance(np.ediff1d(np.matrix(1)), np.matrix))
+ assert(isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix))
+
+
+def test_apply_along_axis_matrix():
+ # this test is particularly malicious because matrix
+ # refuses to become 1d
+ # 2018-04-29: moved here from core.tests.test_shape_base.
+ def double(row):
+ return row * 2
+
+ m = np.matrix([[0, 1], [2, 3]])
+ expected = np.matrix([[0, 2], [4, 6]])
+
+ result = np.apply_along_axis(double, 0, m)
+ assert_(isinstance(result, np.matrix))
+ assert_array_equal(result, expected)
+
+ result = np.apply_along_axis(double, 1, m)
+ assert_(isinstance(result, np.matrix))
+ assert_array_equal(result, expected)
+
+
+def test_kron_matrix():
+ # 2018-04-29: moved here from core.tests.test_shape_base.
+ a = np.ones([2, 2])
+ m = np.asmatrix(a)
+ assert_equal(type(np.kron(a, a)), np.ndarray)
+ assert_equal(type(np.kron(m, m)), np.matrix)
+ assert_equal(type(np.kron(a, m)), np.matrix)
+ assert_equal(type(np.kron(m, a)), np.matrix)
+
+
+class TestConcatenatorMatrix:
+ # 2018-04-29: moved here from core.tests.test_index_tricks.
+ 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_array_equal_error_message_matrix():
+ # 2018-04-29: moved here from testing.tests.test_utils.
+ with pytest.raises(AssertionError) as exc_info:
+ assert_equal(np.array([1, 2]), np.matrix([1, 2]))
+ msg = str(exc_info.value)
+ msg_reference = textwrap.dedent("""\
+
+ Arrays are not equal
+
+ (shapes (2,), (1, 2) mismatch)
+ x: array([1, 2])
+ y: matrix([[1, 2]])""")
+ assert_equal(msg, msg_reference)
+
+
+def test_array_almost_equal_matrix():
+ # Matrix slicing keeps things 2-D, while array does not necessarily.
+ # See gh-8452.
+ # 2018-04-29: moved here from testing.tests.test_utils.
+ m1 = np.matrix([[1., 2.]])
+ m2 = np.matrix([[1., np.nan]])
+ m3 = np.matrix([[1., -np.inf]])
+ m4 = np.matrix([[np.nan, np.inf]])
+ m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
+ for assert_func in assert_array_almost_equal, assert_almost_equal:
+ for m in m1, m2, m3, m4, m5:
+ assert_func(m, m)
+ a = np.array(m)
+ assert_func(a, m)
+ assert_func(m, a)
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_masked_matrix.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
new file mode 100644
index 00000000..d0ce357a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
@@ -0,0 +1,231 @@
+import numpy as np
+from numpy.testing import assert_warns
+from numpy.ma.testutils import (assert_, assert_equal, assert_raises,
+ assert_array_equal)
+from numpy.ma.core import (masked_array, masked_values, masked, allequal,
+ MaskType, getmask, MaskedArray, nomask,
+ log, add, hypot, divide)
+from numpy.ma.extras import mr_
+from numpy.compat import pickle
+
+
+class MMatrix(MaskedArray, np.matrix,):
+
+ def __new__(cls, data, mask=nomask):
+ mat = np.matrix(data)
+ _data = MaskedArray.__new__(cls, data=mat, mask=mask)
+ return _data
+
+ def __array_finalize__(self, obj):
+ np.matrix.__array_finalize__(self, obj)
+ MaskedArray.__array_finalize__(self, obj)
+ return
+
+ @property
+ def _series(self):
+ _view = self.view(MaskedArray)
+ _view._sharedmask = False
+ return _view
+
+
+class TestMaskedMatrix:
+ def test_matrix_indexing(self):
+ # Tests conversions and indexing
+ x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
+ x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
+ x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
+ x4 = masked_array(x1)
+ # test conversion to strings
+ str(x2) # raises?
+ repr(x2) # raises?
+ # tests of indexing
+ assert_(type(x2[1, 0]) is type(x1[1, 0]))
+ assert_(x1[1, 0] == x2[1, 0])
+ assert_(x2[1, 1] is masked)
+ assert_equal(x1[0, 2], x2[0, 2])
+ assert_equal(x1[0, 1:], x2[0, 1:])
+ assert_equal(x1[:, 2], x2[:, 2])
+ assert_equal(x1[:], x2[:])
+ assert_equal(x1[1:], x3[1:])
+ x1[0, 2] = 9
+ x2[0, 2] = 9
+ assert_equal(x1, x2)
+ x1[0, 1:] = 99
+ x2[0, 1:] = 99
+ assert_equal(x1, x2)
+ x2[0, 1] = masked
+ assert_equal(x1, x2)
+ x2[0, 1:] = masked
+ assert_equal(x1, x2)
+ x2[0, :] = x1[0, :]
+ x2[0, 1] = masked
+ assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
+ x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+ assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0])))
+ assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
+ x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+ assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
+ assert_(allequal(x4[1], masked_array([1, 2, 3])))
+ x1 = np.matrix(np.arange(5) * 1.0)
+ x2 = masked_values(x1, 3.0)
+ assert_equal(x1, x2)
+ assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType),
+ x2.mask))
+ assert_equal(3.0, x2.fill_value)
+
+ def test_pickling_subbaseclass(self):
+ # Test pickling w/ a subclass of ndarray
+ a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
+ for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+ a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+ assert_equal(a_pickled._mask, a._mask)
+ assert_equal(a_pickled, a)
+ assert_(isinstance(a_pickled._data, np.matrix))
+
+ def test_count_mean_with_matrix(self):
+ m = masked_array(np.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2)))
+
+ assert_equal(m.count(axis=0).shape, (1, 2))
+ assert_equal(m.count(axis=1).shape, (2, 1))
+
+ # Make sure broadcasting inside mean and var work
+ assert_equal(m.mean(axis=0), [[2., 3.]])
+ assert_equal(m.mean(axis=1), [[1.5], [3.5]])
+
+ def test_flat(self):
+ # Test that flat can return items even for matrices [#4585, #4615]
+ # test simple access
+ test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+ assert_equal(test.flat[1], 2)
+ assert_equal(test.flat[2], masked)
+ assert_(np.all(test.flat[0:2] == test[0, 0:2]))
+ # Test flat on masked_matrices
+ test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+ test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
+ control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
+ assert_equal(test, control)
+ # Test setting
+ test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+ testflat = test.flat
+ testflat[:] = testflat[[2, 1, 0]]
+ assert_equal(test, control)
+ testflat[0] = 9
+ # test that matrices keep the correct shape (#4615)
+ a = masked_array(np.matrix(np.eye(2)), mask=0)
+ b = a.flat
+ b01 = b[:2]
+ assert_equal(b01.data, np.array([[1., 0.]]))
+ assert_equal(b01.mask, np.array([[False, False]]))
+
+ def test_allany_onmatrices(self):
+ x = np.array([[0.13, 0.26, 0.90],
+ [0.28, 0.33, 0.63],
+ [0.31, 0.87, 0.70]])
+ X = np.matrix(x)
+ m = np.array([[True, False, False],
+ [False, False, False],
+ [True, True, False]], dtype=np.bool_)
+ mX = masked_array(X, mask=m)
+ mXbig = (mX > 0.5)
+ mXsmall = (mX < 0.5)
+
+ assert_(not mXbig.all())
+ assert_(mXbig.any())
+ assert_equal(mXbig.all(0), np.matrix([False, False, True]))
+ assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
+ assert_equal(mXbig.any(0), np.matrix([False, False, True]))
+ assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
+
+ assert_(not mXsmall.all())
+ assert_(mXsmall.any())
+ assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
+ assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
+ assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
+ assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
+
+ def test_compressed(self):
+ a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
+ b = a.compressed()
+ assert_equal(b, a)
+ assert_(isinstance(b, np.matrix))
+ a[0, 0] = masked
+ b = a.compressed()
+ assert_equal(b, [[2, 3, 4]])
+
+ def test_ravel(self):
+ a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
+ aravel = a.ravel()
+ assert_equal(aravel.shape, (1, 5))
+ assert_equal(aravel._mask.shape, a.shape)
+
+ def test_view(self):
+ # Test view w/ flexible dtype
+ iterator = list(zip(np.arange(10), np.random.rand(10)))
+ data = np.array(iterator)
+ a = masked_array(iterator, dtype=[('a', float), ('b', float)])
+ a.mask[0] = (1, 0)
+ test = a.view((float, 2), np.matrix)
+ assert_equal(test, data)
+ assert_(isinstance(test, np.matrix))
+ assert_(not isinstance(test, MaskedArray))
+
+
+class TestSubclassing:
+ # Test suite for masked subclasses of ndarray.
+
+ def setup_method(self):
+ x = np.arange(5, dtype='float')
+ mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
+ self.data = (x, mx)
+
+ def test_maskedarray_subclassing(self):
+ # Tests subclassing MaskedArray
+ (x, mx) = self.data
+ assert_(isinstance(mx._data, np.matrix))
+
+ def test_masked_unary_operations(self):
+ # Tests masked_unary_operation
+ (x, mx) = self.data
+ with np.errstate(divide='ignore'):
+ assert_(isinstance(log(mx), MMatrix))
+ assert_equal(log(x), np.log(x))
+
+ def test_masked_binary_operations(self):
+ # Tests masked_binary_operation
+ (x, mx) = self.data
+ # Result should be a MMatrix
+ assert_(isinstance(add(mx, mx), MMatrix))
+ assert_(isinstance(add(mx, x), MMatrix))
+ # Result should work
+ assert_equal(add(mx, x), mx+x)
+ assert_(isinstance(add(mx, mx)._data, np.matrix))
+ with assert_warns(DeprecationWarning):
+ assert_(isinstance(add.outer(mx, mx), MMatrix))
+ assert_(isinstance(hypot(mx, mx), MMatrix))
+ assert_(isinstance(hypot(mx, x), MMatrix))
+
+ def test_masked_binary_operations2(self):
+ # Tests domained_masked_binary_operation
+ (x, mx) = self.data
+ xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+ assert_(isinstance(divide(mx, mx), MMatrix))
+ assert_(isinstance(divide(mx, x), MMatrix))
+ assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+class TestConcatenator:
+ # Tests for mr_, the equivalent of r_ for masked arrays.
+
+ def test_matrix_builder(self):
+ assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
+
+ def test_matrix(self):
+ # Test consistency with unmasked version. If we ever deprecate
+ # matrix, this test should either still pass, or both actual and
+ # expected should fail to be build.
+ actual = mr_['r', 1, 2, 3]
+ expected = np.ma.array(np.r_['r', 1, 2, 3])
+ assert_array_equal(actual, expected)
+
+ # outer type is masked array, inner type is matrix
+ assert_equal(type(actual), type(expected))
+ assert_equal(type(actual.data), type(expected.data))
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
new file mode 100644
index 00000000..106c2e38
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
@@ -0,0 +1,93 @@
+""" Test functions for linalg module using the matrix class."""
+import numpy as np
+
+from numpy.linalg.tests.test_linalg import (
+ LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase,
+ _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base,
+ SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases,
+ PinvCases, DetCases, LstsqCases)
+
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+ LinalgCase("0x0_matrix",
+ np.empty((0, 0), dtype=np.double).view(np.matrix),
+ np.empty((0, 1), dtype=np.double).view(np.matrix),
+ tags={'size-0'}),
+ LinalgCase("matrix_b_only",
+ np.array([[1., 2.], [3., 4.]]),
+ np.matrix([2., 1.]).T),
+ LinalgCase("matrix_a_and_b",
+ np.matrix([[1., 2.], [3., 4.]]),
+ np.matrix([2., 1.]).T),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+ LinalgCase("hmatrix_a_and_b",
+ np.matrix([[1., 2.], [2., 1.]]),
+ None),
+])
+# No need to make generalized or strided cases for matrices.
+
+
+class MatrixTestCase(LinalgTestCase):
+ TEST_CASES = CASES
+
+
+class TestSolveMatrix(SolveCases, MatrixTestCase):
+ pass
+
+
+class TestInvMatrix(InvCases, MatrixTestCase):
+ pass
+
+
+class TestEigvalsMatrix(EigvalsCases, MatrixTestCase):
+ pass
+
+
+class TestEigMatrix(EigCases, MatrixTestCase):
+ pass
+
+
+class TestSVDMatrix(SVDCases, MatrixTestCase):
+ pass
+
+
+class TestCondMatrix(CondCases, MatrixTestCase):
+ pass
+
+
+class TestPinvMatrix(PinvCases, MatrixTestCase):
+ pass
+
+
+class TestDetMatrix(DetCases, MatrixTestCase):
+ pass
+
+
+class TestLstsqMatrix(LstsqCases, MatrixTestCase):
+ pass
+
+
+class _TestNorm2DMatrix(_TestNorm2D):
+ array = np.matrix
+
+
+class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase):
+ pass
+
+
+class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase):
+ pass
+
+
+class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base):
+ pass
+
+
+class TestQRMatrix(_TestQR):
+ array = np.matrix
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_multiarray.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_multiarray.py
new file mode 100644
index 00000000..638d0d15
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_multiarray.py
@@ -0,0 +1,16 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_array_equal
+
+class TestView:
+ def test_type(self):
+ x = np.array([1, 2, 3])
+ assert_(isinstance(x.view(np.matrix), np.matrix))
+
+ def test_keywords(self):
+ x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
+ # We must be specific about the endianness here:
+ y = x.view(dtype='<i2', type=np.matrix)
+ assert_array_equal(y, [[513]])
+
+ assert_(isinstance(y, np.matrix))
+ assert_equal(y.dtype, np.dtype('<i2'))
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_numeric.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_numeric.py
new file mode 100644
index 00000000..a772bb38
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_numeric.py
@@ -0,0 +1,17 @@
+import numpy as np
+from numpy.testing import assert_equal
+
+class TestDot:
+ def test_matscalar(self):
+ b1 = np.matrix(np.ones((3, 3), dtype=complex))
+ assert_equal(b1*1.0, b1)
+
+
+def test_diagonal():
+ b1 = np.matrix([[1,2],[3,4]])
+ diag_b1 = np.matrix([[1, 4]])
+ array_b1 = np.array([1, 4])
+
+ assert_equal(b1.diagonal(), diag_b1)
+ assert_equal(np.diagonal(b1), array_b1)
+ assert_equal(np.diag(b1), array_b1)
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_regression.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_regression.py
new file mode 100644
index 00000000..a54d4402
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_regression.py
@@ -0,0 +1,31 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_raises
+
+
+class TestRegression:
+ def test_kron_matrix(self):
+ # Ticket #71
+ x = np.matrix('[1 0; 1 0]')
+ assert_equal(type(np.kron(x, x)), type(x))
+
+ def test_matrix_properties(self):
+ # Ticket #125
+ a = np.matrix([1.0], dtype=float)
+ assert_(type(a.real) is np.matrix)
+ assert_(type(a.imag) is np.matrix)
+ c, d = np.matrix([0.0]).nonzero()
+ assert_(type(c) is np.ndarray)
+ assert_(type(d) is np.ndarray)
+
+ def test_matrix_multiply_by_1d_vector(self):
+ # Ticket #473
+ def mul():
+ np.mat(np.eye(2))*np.ones(2)
+
+ assert_raises(ValueError, mul)
+
+ def test_matrix_std_argmax(self):
+ # Ticket #83
+ x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
+ assert_equal(x.std().shape, ())
+ assert_equal(x.argmax().shape, ())