From 7573b488942efc1c1fc33c00f67ffa1eb5cb22b7 Mon Sep 17 00:00:00 2001
From: Eric Giguere <eric.giguere@calculquebec.ca>
Date: Mon, 19 Aug 2024 11:20:45 -0400
Subject: [PATCH] Use positional only for changing parameter name

---
 qutip/tests/core/data/test_expect.py  | 2 +-
 qutip/tests/core/data/test_reshape.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/qutip/tests/core/data/test_expect.py b/qutip/tests/core/data/test_expect.py
index 779d3ba13e..47b8c79ce0 100644
--- a/qutip/tests/core/data/test_expect.py
+++ b/qutip/tests/core/data/test_expect.py
@@ -49,7 +49,7 @@ class TestExpectSuper(BinaryOpMixin):
     def op_numpy(self, op, state):
         n = np.sqrt(state.shape[0]).astype(int)
         out_shape = (n, n)
-        return np.trace(np.reshape(op@state, shape=out_shape))
+        return np.trace(np.reshape(op@state, out_shape))
 
     _dim = 100
     _super_ket = pytest.param((_dim, 1), id="super_ket")
diff --git a/qutip/tests/core/data/test_reshape.py b/qutip/tests/core/data/test_reshape.py
index bf864fee23..f9e2947baa 100644
--- a/qutip/tests/core/data/test_reshape.py
+++ b/qutip/tests/core/data/test_reshape.py
@@ -20,7 +20,7 @@ def op_numpy(self, matrix):
 class TestColumnStack(UnaryOpMixin):
     def op_numpy(self, matrix):
         out_shape = (matrix.shape[0]*matrix.shape[1], 1)
-        return np.reshape(matrix, shape=out_shape, order='F')
+        return np.reshape(matrix, out_shape, order='F')
 
     specialisations = [
         pytest.param(data.column_stack_csr, CSR, CSR),
@@ -32,7 +32,7 @@ def op_numpy(self, matrix):
 class TestColumnUnstack(UnaryOpMixin):
     def op_numpy(self, matrix, rows):
         out_shape = (rows, matrix.shape[0]*matrix.shape[1]//rows)
-        return np.reshape(matrix, shape=out_shape, order='F')
+        return np.reshape(matrix, out_shape, order='F')
 
     shapes = [
         (pytest.param((10, 1), id="ket"), ),
@@ -83,7 +83,7 @@ def generate_incorrect_rows_raises(self, metafunc):
 class TestReshape(UnaryOpMixin):
     def op_numpy(self, matrix, rows, columns):
         out_shape = (rows, columns)
-        return np.reshape(matrix, shape=out_shape, order='C')
+        return np.reshape(matrix, out_shape, order='C')
 
     # All matrices should have the same number of elements in total, so we can
     # use the same (rows, columns) parametrisation for each input.