Merge pull request #22295 from oscargus/replacenpmatrixwithnparray

Removed deprecated numpy.matrix

pytest.ini

@@ -4,13 +4,6 @@
 # collect tests from e.g. the bin/ directory as well.
 testpaths = sympy doc/src
 
-# np.matrix is deprecated but still used in sympy.physics.quantum:
-#    https://github.com/sympy/sympy/issues/15563
-# This prevents ~800 warnings showing up running the tests under pytest.
-filterwarnings =
-    ignore:.*the matrix subclass is not the recommended way.*:PendingDeprecationWarning
-    ignore:IPython.utils.signatures backport for Python 2 is deprecated.*:DeprecationWarning
-
 # Normalise output of doctests.
 doctest_optionflags =
     NORMALIZE_WHITESPACE

sympy/physics/quantum/matrixutils.py

@@ -49,7 +49,7 @@ def sympy_to_numpy(m, **options):
         raise ImportError
     dtype = options.get('dtype', 'complex')
     if isinstance(m, MatrixBase):
-        return np.matrix(m.tolist(), dtype=dtype)
+        return np.array(m.tolist(), dtype=dtype)
     elif isinstance(m, Expr):
         if m.is_Number or m.is_NumberSymbol or m == I:
             return complex(m)
@@ -62,7 +62,7 @@ def sympy_to_scipy_sparse(m, **options):
         raise ImportError
     dtype = options.get('dtype', 'complex')
     if isinstance(m, MatrixBase):
-        return sparse.csr_matrix(np.matrix(m.tolist(), dtype=dtype))
+        return sparse.csr_matrix(np.array(m.tolist(), dtype=dtype))
     elif isinstance(m, Expr):
         if m.is_Number or m.is_NumberSymbol or m == I:
             return complex(m)
@@ -183,7 +183,7 @@ def _numpy_eye(n):
     """numpy version of complex eye."""
     if not np:
         raise ImportError
-    return np.matrix(np.eye(n, dtype='complex'))
+    return np.array(np.eye(n, dtype='complex'))
 
 
 def _scipy_sparse_eye(n):

sympy/physics/quantum/qubit.py

@@ -209,7 +209,7 @@ def _represent_ZGate(self, basis, **options):
             return Matrix(result)
         elif _format == 'numpy':
             import numpy as np
-            return np.matrix(result, dtype='complex').transpose()
+            return np.array(result, dtype='complex').transpose()
         elif _format == 'scipy.sparse':
             from scipy import sparse
             return sparse.csr_matrix(result, dtype='complex').transpose()

sympy/physics/quantum/represent.py

@@ -139,6 +139,8 @@ def _represent_FooBasis(self, e, basis, **options)
     """
 
     format = options.get('format', 'sympy')
+    if format == 'numpy':
+        import numpy as np
     if isinstance(expr, QExpr) and not isinstance(expr, OuterProduct):
         options['replace_none'] = False
         temp_basis = get_basis(expr, **options)
@@ -181,20 +183,22 @@ def _represent_FooBasis(self, e, basis, **options)
             from scipy.sparse.linalg import inv
             exp = - exp
             base = inv(base.tocsc()).tocsr()
+        if format == 'numpy':
+            return np.linalg.matrix_power(base, exp)
         return base ** exp
     elif isinstance(expr, TensorProduct):
         new_args = [represent(arg, **options) for arg in expr.args]
         return TensorProduct(*new_args)
     elif isinstance(expr, Dagger):
         return Dagger(represent(expr.args[0], **options))
     elif isinstance(expr, Commutator):
-        A = represent(expr.args[0], **options)
-        B = represent(expr.args[1], **options)
-        return A*B - B*A
+        A = expr.args[0]
+        B = expr.args[1]
+        return represent(Mul(A, B) - Mul(B, A), **options)
     elif isinstance(expr, AntiCommutator):
-        A = represent(expr.args[0], **options)
-        B = represent(expr.args[1], **options)
-        return A*B + B*A
+        A = expr.args[0]
+        B = expr.args[1]
+        return represent(Mul(A, B) + Mul(B, A), **options)
     elif isinstance(expr, InnerProduct):
         return represent(Mul(expr.bra, expr.ket), **options)
     elif not isinstance(expr, (Mul, OuterProduct)):
@@ -228,7 +232,12 @@ def _represent_FooBasis(self, e, basis, **options)
         elif isinstance(last_arg, KetBase) and isinstance(arg, BraBase):
             options["unities"].append(options["index"])
 
-        result = represent(arg, **options)*result
+        next_arg = represent(arg, **options)
+        if format == 'numpy' and isinstance(next_arg, np.ndarray):
+            # Must use np.matmult to "matrix multiply" two np.ndarray
+            result = np.matmul(next_arg, result)
+        else:
+            result = next_arg*result
         last_arg = arg
 
     # All three matrix formats create 1 by 1 matrices when inner products of

sympy/physics/quantum/tests/test_dagger.py

@@ -62,7 +62,7 @@ def test_numpy_dagger():
     if not np:
         skip("numpy not installed.")
 
-    a = np.matrix([[1.0, 2.0j], [-1.0j, 2.0]])
+    a = np.array([[1.0, 2.0j], [-1.0j, 2.0]])
     adag = a.copy().transpose().conjugate()
     assert (Dagger(a) == adag).all()
 

sympy/physics/quantum/tests/test_density.py

@@ -174,7 +174,7 @@ def test_entropy():
         #do this test only if 'numpy' is available on test machine
         np_mat = represent(d, format='numpy')
         ent = entropy(np_mat)
-        assert isinstance(np_mat, np.matrixlib.defmatrix.matrix)
+        assert isinstance(np_mat, np.ndarray)
         assert ent.real == 0.69314718055994529
         assert ent.imag == 0
 

sympy/physics/quantum/tests/test_matrixutils.py

@@ -29,7 +29,7 @@ def test_to_numpy():
     if not np:
         skip("numpy not installed.")
 
-    result = np.matrix([[1, 2], [3, 4]], dtype='complex')
+    result = np.array([[1, 2], [3, 4]], dtype='complex')
     assert (to_numpy(m) == result).all()
 
 
@@ -49,8 +49,8 @@ def test_matrix_tensor_product():
     vec = Matrix([1, 2, 3])
 
     #test for Matrix known 4x4 matricies
-    numpyl1 = np.matrix(l1.tolist())
-    numpyl2 = np.matrix(l2.tolist())
+    numpyl1 = np.array(l1.tolist())
+    numpyl2 = np.array(l2.tolist())
     numpy_product = np.kron(numpyl1, numpyl2)
     args = [l1, l2]
     sympy_product = matrix_tensor_product(*args)
@@ -61,7 +61,7 @@ def test_matrix_tensor_product():
     assert numpy_product.tolist() == sympy_product.tolist()
 
     #test for other known matrix of different dimensions
-    numpyl2 = np.matrix(l3.tolist())
+    numpyl2 = np.array(l3.tolist())
     numpy_product = np.kron(numpyl1, numpyl2)
     args = [l1, l3]
     sympy_product = matrix_tensor_product(*args)
@@ -72,7 +72,7 @@ def test_matrix_tensor_product():
     assert numpy_product.tolist() == sympy_product.tolist()
 
     #test for non square matrix
-    numpyl2 = np.matrix(vec.tolist())
+    numpyl2 = np.array(vec.tolist())
     numpy_product = np.kron(numpyl1, numpyl2)
     args = [l1, vec]
     sympy_product = matrix_tensor_product(*args)

sympy/printing/tests/test_numpy.py

@@ -88,9 +88,9 @@ def test_codegen_einsum():
     cg = convert_matrix_to_array(M * N)
     f = lambdify((M, N), cg, 'numpy')
 
-    ma = np.matrix([[1, 2], [3, 4]])
-    mb = np.matrix([[1,-2], [-1, 3]])
-    assert (f(ma, mb) == ma*mb).all()
+    ma = np.array([[1, 2], [3, 4]])
+    mb = np.array([[1,-2], [-1, 3]])
+    assert (f(ma, mb) == np.matmul(ma, mb)).all()
 
 
 def test_codegen_extra():
@@ -101,10 +101,10 @@ def test_codegen_extra():
     N = MatrixSymbol("N", 2, 2)
     P = MatrixSymbol("P", 2, 2)
     Q = MatrixSymbol("Q", 2, 2)
-    ma = np.matrix([[1, 2], [3, 4]])
-    mb = np.matrix([[1,-2], [-1, 3]])
-    mc = np.matrix([[2, 0], [1, 2]])
-    md = np.matrix([[1,-1], [4, 7]])
+    ma = np.array([[1, 2], [3, 4]])
+    mb = np.array([[1,-2], [-1, 3]])
+    mc = np.array([[2, 0], [1, 2]])
+    md = np.array([[1,-1], [4, 7]])
 
     cg = ArrayTensorProduct(M, N)
     f = lambdify((M, N), cg, 'numpy')