fix some numpy datatypes

quagmire/mesh/commonmesh.py

@@ -185,7 +185,7 @@ def get_label(self, label):
             pt_range = np.logical_and(labelIS.indices >= pStart, labelIS.indices < pEnd)
             indices = labelIS.indices[pt_range] - pStart
         else:
-            indices = np.zeros((0,), dtype=np.int)
+            indices = np.zeros((0,), dtype=PETSc.IntType)
 
         return indices
 

quagmire/mesh/pixmesh.py

@@ -259,10 +259,10 @@ def interpolate(self, xi, yi, zdata, order=1):
         """
         grid = np.array(zdata).reshape(self.ny, self.nx)
 
-        icoords = np.array(np.c_[xi,yi], dtype=np.float)
+        icoords = np.array(np.c_[xi,yi], dtype=np.float32)
 
         # check if inside bounds
-        inside_bounds = np.zeros(icoords.shape[0], dtype=np.bool)
+        inside_bounds = np.zeros(icoords.shape[0], dtype=bool)
         inside_bounds += icoords[:,0] < self.minX
         inside_bounds += icoords[:,0] > self.maxX
         inside_bounds += icoords[:,1] < self.minY
@@ -282,7 +282,7 @@ def interpolate(self, xi, yi, zdata, order=1):
         # now interpolate
         ival = _map_coordinates(grid.T, icoords.T, order=order, mode='nearest')
 
-        return ival, inside_bounds.astype(np.int)
+        return ival, inside_bounds.astype(PETSc.IntType)
 
     def derivatives(self, PHI, **kwargs):
         return self.derivative_grad(PHI, **kwargs)
@@ -384,8 +384,8 @@ def construct_natural_neighbour_cloud(self):
         numpy array for efficient lookup.
         """
 
-        natural_neighbours = np.empty((self.npoints, 9), dtype=np.int)
-        nodes = np.arange(0, self.npoints, dtype=np.int).reshape(self.ny,self.nx)
+        natural_neighbours = np.empty((self.npoints, 9), dtype=PETSc.IntType)
+        nodes = np.arange(0, self.npoints, dtype=PETSc.IntType).reshape(self.ny,self.nx)
         index = np.pad(nodes, (1,1), mode='constant', constant_values=-1)
 
 
@@ -441,14 +441,14 @@ def construct_neighbour_cloud(self, size=25):
         corners = [0, self.nx-1, -self.nx, -1]
 
         # interior nodes have 3*3 neighbours (including self)
-        neighbours = np.full(self.npoints, 9, dtype=np.int)
+        neighbours = np.full(self.npoints, 9, dtype=PETSc.IntType)
         neighbours[~self.bmask] = 6 # walls have 3*2 neighbours
         neighbours[corners] = 4 # corners have 4 neighbours
 
         self.near_neighbours = neighbours + 2
         self.extended_neighbours = np.full_like(neighbours, size)
 
-        # self.near_neighbour_mask = np.zeros_like(self.neighbour_cloud, dtype=np.bool)
+        # self.near_neighbour_mask = np.zeros_like(self.neighbour_cloud, dtype=bool)
 
         # for node in range(0,self.npoints):
         #     self.near_neighbour_mask[node, 0:self.near_neighbours[node]] = True

quagmire/mesh/strimesh.py

@@ -494,7 +494,7 @@ def construct_natural_neighbour_cloud(self):
         lend = self.tri.lend
         lptr = self.tri.lptr
 
-        segments_array = np.empty((len(lptr),2),dtype=np.int)
+        segments_array = np.empty((len(lptr),2),dtype=PETSc.IntType)
         segments_array[:,0] = np.abs(lst[:]) - 1
         segments_array[:,1] = np.abs(lst[lptr[:]-1]) - 1
 
@@ -511,7 +511,7 @@ def construct_natural_neighbour_cloud(self):
         istart = np.zeros_like(iend)
         istart[1:] = iend[:-1]
 
-        natural_neighbours = np.full((self.npoints, natural_neighbours_count.max()+1), -1, dtype=np.int)
+        natural_neighbours = np.full((self.npoints, natural_neighbours_count.max()+1), -1, dtype=PETSc.IntType)
 
         for j in range(0,self.npoints):
             natural_neighbours[j, 0] = j

quagmire/mesh/trimesh.py

@@ -358,7 +358,7 @@ def construct_natural_neighbour_cloud(self):
         lend = self.tri.lend
         lptr = self.tri.lptr
 
-        segments_array = np.empty((len(lptr),2),dtype=np.int)
+        segments_array = np.empty((len(lptr),2),dtype=PETSc.IntType)
         segments_array[:,0] = np.abs(lst[:]) - 1
         segments_array[:,1] = np.abs(lst[lptr[:]-1]) - 1
 
@@ -375,7 +375,7 @@ def construct_natural_neighbour_cloud(self):
         istart = np.zeros_like(iend)
         istart[1:] = iend[:-1]
 
-        natural_neighbours = np.full((self.npoints, natural_neighbours_count.max()+1), -1, dtype=np.int)
+        natural_neighbours = np.full((self.npoints, natural_neighbours_count.max()+1), -1, dtype=PETSc.IntType)
 
         for j in range(0,self.npoints):
             natural_neighbours[j, 0] = j

quagmire/tools/meshtools.py

@@ -581,10 +581,10 @@ def _create_DMPlex(points, simplices, boundary_vertices=None, refinement_levels=
     mesh_type = {2: "TriMesh", 3: "sTriMesh"}
 
     if PETSc.COMM_WORLD.rank == 0:
-        coords = _np.array(points, dtype=_np.float)
+        coords = _np.array(points, dtype=_np.float32)
         cells  = simplices.astype(PETSc.IntType)
     else:
-        coords = _np.zeros((0,ndim), dtype=_np.float)
+        coords = _np.zeros((0,ndim), dtype=_np.float32)
         cells  = _np.zeros((0,3), dtype=PETSc.IntType)
 
     dim = 2
@@ -1011,6 +1011,6 @@ def map_global_raster_to_strimesh(mesh, latlongrid, order=3, origin="lower", uni
 
     icoords = np.array((ilons, ilats))
 
-    mvals = ndimage.map_coordinates(raster, icoords , order=order, mode='nearest').astype(np.float)
+    mvals = ndimage.map_coordinates(raster, icoords , order=order, mode='nearest').astype(np.float32)
 
     return mvals

quagmire/topomesh/topomesh.py

@@ -677,7 +677,7 @@ def _node_walk_downhill(self, node):
         """
 
         """
-        chain = -np.ones(self.npoints, dtype=np.int32)
+        chain = -np.ones(self.npoints, dtype=PETSc.IntType32)
 
         idx = 0
         max_idx = self.npoints
@@ -717,7 +717,7 @@ def build_node_chains(self):
         """
 
         """
-        self.node_chain_lookup = -np.ones(self.npoints, dtype=np.int32)
+        self.node_chain_lookup = -np.ones(self.npoints, dtype=PETSc.IntType32)
         self.node_chain_list = []
         node_chain_idx = 1
 
@@ -852,7 +852,7 @@ def low_points_swamp_fill(self, its=1000, saddles=None, ref_height=0.0, ref_grad
         my_glow_points = self.lgmap_row.apply(my_low_points.astype(PETSc.IntType))
 
         t = perf_counter()
-        ctmt = self.uphill_propagation(my_low_points,  my_glow_points, its=its, fill=-999999).astype(np.int)
+        ctmt = self.uphill_propagation(my_low_points,  my_glow_points, its=its, fill=-999999).astype(PETSc.IntType)
 
         height = self.topography.data.copy()
 
@@ -1077,7 +1077,7 @@ def identify_low_points(self, include_shadows=False, ref_height=0):
 
         # from petsc4py import PETSc
 
-        nodes = np.arange(0, self.npoints, dtype=np.int)
+        nodes = np.arange(0, self.npoints, dtype=PETSc.IntType)
         gnodes = self.lgmap_row.apply(nodes.astype(PETSc.IntType))
 
         low_nodes = self.down_neighbour[1]
@@ -1142,7 +1142,7 @@ def identify_outflow_points(self):
         Identify the (boundary) outflow points and return an array of (local) node indices
         """
 
-        # nodes = np.arange(0, self.npoints, dtype=np.int)
+        # nodes = np.arange(0, self.npoints, dtype=PETSc.IntType)
         # low_nodes = self.down_neighbour[1]
         # mask = np.logical_and(nodes == low_nodes, self.bmask == False)
         #