Cast to c_long in RxD on Python side before passing to C++

share/lib/python/neuron/rxd/rxd.py

@@ -61,7 +61,7 @@
 setup_solver.argtypes = [
     ndpointer(ctypes.c_double),
     ctypes.c_int,
-    numpy.ctypeslib.ndpointer(numpy.int_, flags="contiguous"),
+    numpy.ctypeslib.ndpointer(ctypes.c_long, flags="contiguous"),
     ctypes.c_int,
 ]
 
@@ -630,8 +630,8 @@ def _matrix_to_rxd_sparse(m):
     return (
         n,
         len(nonzero_i),
-        numpy.ascontiguousarray(nonzero_i, dtype=numpy.int_),
-        numpy.ascontiguousarray(nonzero_j, dtype=numpy.int_),
+        numpy.ascontiguousarray(nonzero_i, dtype=ctypes.c_long),
+        numpy.ascontiguousarray(nonzero_j, dtype=ctypes.c_long),
         nonzero_values,
     )
 
@@ -709,7 +709,7 @@ def _setup_matrices():
         n = len(_node_get_states())
 
         volumes = node._get_data()[0]
-        zero_volume_indices = (numpy.where(volumes == 0)[0]).astype(numpy.int_)
+        zero_volume_indices = (numpy.where(volumes == 0)[0]).astype(ctypes.c_long)
         if species._has_1d:
             # TODO: initialization is slow. track down why
             for sr in _species_get_all_species():
@@ -1896,7 +1896,7 @@ def _init():
     _setup_matrices()
     # if species._has_1d and species._1d_submatrix_n():
     # volumes = node._get_data()[0]
-    # zero_volume_indices = (numpy.where(volumes == 0)[0]).astype(numpy.int_)
+    # zero_volume_indices = (numpy.where(volumes == 0)[0]).astype(ctypes.c_long)
     # setup_solver(_node_get_states(), len(_node_get_states()), zero_volume_indices, len(zero_volume_indices), h._ref_t, h._ref_dt)
     clear_rates()
     _setup_memb_currents()

share/lib/python/neuron/rxd/species.py

@@ -60,12 +60,12 @@
     ctypes.c_int,
     ctypes.py_object,
     ctypes.c_long,
-    numpy.ctypeslib.ndpointer(dtype=int),
-    numpy.ctypeslib.ndpointer(dtype=int),
+    numpy.ctypeslib.ndpointer(dtype=ctypes.c_long),
+    numpy.ctypeslib.ndpointer(dtype=ctypes.c_long),
     ctypes.c_long,
-    numpy.ctypeslib.ndpointer(dtype=int),
+    numpy.ctypeslib.ndpointer(dtype=ctypes.c_long),
     ctypes.c_long,
-    numpy.ctypeslib.ndpointer(dtype=int),
+    numpy.ctypeslib.ndpointer(dtype=ctypes.c_long),
     ctypes.c_long,
     numpy.ctypeslib.ndpointer(dtype=float),
     ctypes.c_double,
@@ -842,7 +842,7 @@ def line_defs(self, nodes, direction, nodes_length):
 
         # sort list for parallelization
         line_defs.sort(key=lambda x: x[1], reverse=True)
-        line_defs = numpy.asarray(line_defs, dtype=int)
+        line_defs = numpy.asarray(line_defs, dtype=ctypes.c_long)
         line_defs = line_defs.reshape(2 * len(line_defs))
         return line_defs
 
@@ -862,7 +862,7 @@ def ordered_nodes(self, p_line_defs, direction, neighbors):
 
     def create_neighbors_array(self, nodes, nodes_length):
         self._isalive()
-        my_array = numpy.zeros((nodes_length, 3), dtype=int)
+        my_array = numpy.zeros((nodes_length, 3), dtype=ctypes.c_long)
         for n in nodes:
             for i, ele in enumerate(n.neighbors[::2]):
                 my_array[n._index, i] = ele if ele is not None else -1

test/rxd/conftest.py

@@ -1,5 +1,6 @@
 import os.path as osp
 import numpy
+import ctypes
 import pytest
 import gc
 
@@ -81,7 +82,7 @@ def neuron_nosave_instance(neuron_import):
     rxd.rxd.rxd_include_node_flux1D(0, None, None, None)
     rxd.species._has_1d = False
     rxd.species._has_3d = False
-    rxd.rxd._zero_volume_indices = numpy.ndarray(0, dtype=numpy.int_)
+    rxd.rxd._zero_volume_indices = numpy.ndarray(0, dtype=ctypes.c_long)
     rxd.set_solve_type(dimension=1)