Use builtins

qutip/core/data/csr.pyx

@@ -12,7 +12,7 @@ from cpython cimport mem
 
 import numbers
 import warnings
-
+import builtins
 import numpy as np
 cimport numpy as cnp
 import scipy.sparse
@@ -101,7 +101,8 @@ cdef class CSR(base.Data):
         if len(arg) != 3:
             raise ValueError("arg must be a (data, col_index, row_index) tuple")
         if np.lib.NumpyVersion(np.__version__) < '2.0.0b1':
-            copy = bool(copy)
+            # np2 accept None which act as np1's False
+            copy = builtins.bool(copy)
         data = np.array(arg[0], dtype=np.complex128, copy=copy, order='C')
         col_index = np.array(arg[1], dtype=idxint_dtype, copy=copy, order='C')
         row_index = np.array(arg[2], dtype=idxint_dtype, copy=copy, order='C')

qutip/core/data/dense.pyx

@@ -5,7 +5,7 @@ from libc.string cimport memcpy
 cimport cython
 
 import numbers
-
+import builtins
 import numpy as np
 cimport numpy as cnp
 from scipy.linalg cimport cython_blas as blas
@@ -41,7 +41,8 @@ class OrderEfficiencyWarning(EfficiencyWarning):
 cdef class Dense(base.Data):
     def __init__(self, data, shape=None, copy=True):
         if np.lib.NumpyVersion(np.__version__) < '2.0.0b1':
-            copy = bool(copy)
+            # np2 accept None which act as np1's False
+            copy = builtins.bool(copy)
         base = np.array(data, dtype=np.complex128, order='K', copy=copy)
         # Ensure that the array is contiguous.
         # Non contiguous array with copy=False would otherwise slip through

qutip/core/data/dia.pyx

@@ -12,7 +12,7 @@ from cpython cimport mem
 
 import numbers
 import warnings
-
+import builtins
 import numpy as np
 cimport numpy as cnp
 import scipy.sparse
@@ -81,14 +81,14 @@ cdef class Dia(base.Data):
                     "shapes do not match: ", str(shape), " and ", str(arg.shape),
                 ]))
             shape = arg.shape
-            #
             arg = (arg.data, arg.offsets)
         if not isinstance(arg, tuple):
             raise TypeError("arg must be a scipy matrix or tuple")
         if len(arg) != 2:
             raise ValueError("arg must be a (data, offsets) tuple")
         if np.lib.NumpyVersion(np.__version__) < '2.0.0b1':
-            copy = bool(copy)
+            # np2 accept None which act as np1's False
+            copy = builtins.bool(copy)
         data = np.array(arg[0], dtype=np.complex128, copy=copy, order='C')
         offsets = np.array(arg[1], dtype=idxint_dtype, copy=copy, order='C')