Remove use of deprecated np.complex

aesara/scalar/basic.py

@@ -2159,7 +2159,7 @@ class Mod(BinaryScalarOp):
     )
 
     def impl(self, x, y):
-        if isinstance(x, np.complex) or isinstance(y, np.complex):
+        if isinstance(x, builtins.complex) or isinstance(y, builtins.complex):
             raise self.complex_error
         return x % y
 
@@ -3936,7 +3936,7 @@ class Complex(BinaryScalarOp):
             return [complex64]
 
     def impl(self, x, y):
-        return np.complex(x, y)
+        return builtins.complex(x, y)
 
     def grad(self, inputs, gout):
         (x, y) = inputs
@@ -3979,9 +3979,9 @@ class ComplexFromPolar(BinaryScalarOp):
         x = r * np.cos(theta)
         y = r * np.sin(theta)
         if x.dtype == "float32":
-            return np.complex64(np.complex(x, y))
+            return np.complex64(builtins.complex(x, y))
         else:
-            return np.complex128(np.complex(x, y))
+            return np.complex128(builtins.complex(x, y))
 
     def grad(self, inputs, gout):
         (r, theta) = inputs

tests/tensor/test_complex.py

@@ -4,7 +4,8 @@ import pytest
 import aesara
 from aesara.gradient import GradientError
 from aesara.tensor.basic import cast
-from aesara.tensor.math import complex, complex_from_polar, imag, real
+from aesara.tensor.math import complex as at_complex
+from aesara.tensor.math import complex_from_polar, imag, real
 from aesara.tensor.type import cvector, dvector, fmatrix, fvector, imatrix, zvector
 from tests import unittest_tools as utt
 
@@ -15,8 +16,7 @@ class TestRealImag:
         rng = np.random.default_rng(23)
         xval = np.asarray(
             list(
-                np.complex(rng.standard_normal(), rng.standard_normal())
-                for i in range(10)
+                complex(rng.standard_normal(), rng.standard_normal()) for i in range(10)
             )
         )
         assert np.all(xval.real == aesara.function([x], real(x))(xval))
@@ -42,7 +42,7 @@ class TestRealImag:
     def test_complex(self):
         rng = np.random.default_rng(2333)
         m = fmatrix()
-        c = complex(m[0], m[1])
+        c = at_complex(m[0], m[1])
         assert c.type == cvector
         r, i = [real(c), imag(c)]
         assert r.type == fvector
@@ -57,7 +57,7 @@ class TestRealImag:
     @pytest.mark.skip(reason="Complex grads not enabled, see #178")
     def test_complex_grads(self):
         def f(m):
-            c = complex(m[0], m[1])
+            c = at_complex(m[0], m[1])
             return 0.5 * real(c) + 0.9 * imag(c)
 
         rng = np.random.default_rng(9333)
@@ -67,7 +67,7 @@ class TestRealImag:
     @pytest.mark.skip(reason="Complex grads not enabled, see #178")
     def test_mul_mixed0(self):
         def f(a):
-            ac = complex(a[0], a[1])
+            ac = at_complex(a[0], a[1])
             return abs((ac) ** 2).sum()
 
         rng = np.random.default_rng(9333)
@@ -82,7 +82,7 @@ class TestRealImag:
     @pytest.mark.skip(reason="Complex grads not enabled, see #178")
     def test_mul_mixed1(self):
         def f(a):
-            ac = complex(a[0], a[1])
+            ac = at_complex(a[0], a[1])
             return abs(ac).sum()
 
         rng = np.random.default_rng(9333)
@@ -97,7 +97,7 @@ class TestRealImag:
     @pytest.mark.skip(reason="Complex grads not enabled, see #178")
     def test_mul_mixed(self):
         def f(a, b):
-            ac = complex(a[0], a[1])
+            ac = at_complex(a[0], a[1])
             return abs((ac * b) ** 2).sum()
 
         rng = np.random.default_rng(9333)
@@ -123,7 +123,7 @@ class TestRealImag:
     @pytest.mark.skip(reason="Complex grads not enabled, see #178")
     def test_abs_grad(self):
         def f(m):
-            c = complex(m[0], m[1])
+            c = at_complex(m[0], m[1])
             return 0.5 * abs(c)
 
         rng = np.random.default_rng(9333)

tests/tensor/test_math.py

@@ -1832,12 +1832,8 @@ class TestDivimpl:
         assert np.allclose(function([i, ii], ii // i)(5, 3), (3 // 5))
         assert np.allclose(function([i, ii], true_div(i, ii))(5, 3), (5.0 / 3.0))
         assert np.allclose(function([i, ii], true_div(ii, i))(5, 3), (3.0 / 5.0))
-        assert np.allclose(
-            function([i, c], i / c)(5, np.complex(5, 3)), (5.0 / (5 + 3j))
-        )
-        assert np.allclose(
-            function([i, c], c / i)(5, np.complex(5, 3)), ((5 + 3j) / 5.0)
-        )
+        assert np.allclose(function([i, c], i / c)(5, complex(5, 3)), (5.0 / (5 + 3j)))
+        assert np.allclose(function([i, c], c / i)(5, complex(5, 3)), ((5 + 3j) / 5.0))
 
 
 class TestMean:
@@ -1929,17 +1925,17 @@ class TestDot:
                 # This strange way of doing things is the only way that worked
                 # on NumPy 1.4.1.
                 if r.ndim == 0:
-                    return np.asarray(np.complex(1.1, 2.1), dtype=r.dtype)
+                    return np.asarray(complex(1.1, 2.1), dtype=r.dtype)
                 if r.ndim == 1:
                     if r.dtype == "complex64":
-                        return np.complex64([np.complex(1.2, 2.2)])
+                        return np.complex64([complex(1.2, 2.2)])
                     elif r.dtype == "complex128":
-                        return np.complex128([np.complex(1.2, 2.2)])
+                        return np.complex128([complex(1.2, 2.2)])
                 elif r.ndim == 2:
                     if r.dtype == "complex64":
-                        return np.complex64([[np.complex(1.3, 2.3)]])
+                        return np.complex64([[complex(1.3, 2.3)]])
                     elif r.dtype == "complex128":
-                        return np.complex128([[np.complex(1.3, 2.3)]])
+                        return np.complex128([[complex(1.3, 2.3)]])
 
             if r.ndim == 0:
                 return np.asarray(1.1, dtype=r.dtype)