[DRAFT] NetworkX 3 support

meshparty/skeleton_quality/multicut.py

@@ -1,21 +1,29 @@
 import networkx as nx
-from meshparty.meshwork import Meshwork
-from meshparty.trimesh_io import Mesh
-import pandas as pd
 import numpy as np
+import pandas as pd
 from scipy import sparse
 
+from meshparty.meshwork import Meshwork
+from meshparty.trimesh_io import Mesh
+
 
 def _build_multicut_graph(nrn):
-    G = nx.from_scipy_sparse_matrix(nrn.mesh.csgraph)
+    if nx.__version__ < 3:
+        G = nx.from_scipy_sparse_matrix(nrn.mesh.csgraph)
+    else:
+        G = nx.from_scipy_sparse_array(nrn.mesh.csgraph)
 
-    G.add_node('source')
-    G.add_node('target')
+    G.add_node("source")
+    G.add_node("target")
 
-    source_edges = [('source', ii, np.inf)
-                    for ii in nrn.anno['st_df'].df.query('type == "s"').mesh_index.values]
-    target_edges = [('target', ii, np.inf)
-                    for ii in nrn.anno['st_df'].df.query('type == "t"').mesh_index.values]
+    source_edges = [
+        ("source", ii, np.inf)
+        for ii in nrn.anno["st_df"].df.query('type == "s"').mesh_index.values
+    ]
+    target_edges = [
+        ("target", ii, np.inf)
+        for ii in nrn.anno["st_df"].df.query('type == "t"').mesh_index.values
+    ]
 
     G.add_weighted_edges_from(source_edges)
     G.add_weighted_edges_from(target_edges)
@@ -24,12 +32,14 @@ def _build_multicut_graph(nrn):
 
 
 def _multicut_partitions(G, nrn):
-    _, partition = nx.minimum_cut(G, 'source', 'target', capacity='weight')
+    _, partition = nx.minimum_cut(G, "source", "target", capacity="weight")
 
-    part0 = list(partition[0].difference({'source', 'target'}))
-    part1 = list(partition[1].difference({'source', 'target'}))
+    part0 = list(partition[0].difference({"source", "target"}))
+    part1 = list(partition[1].difference({"source", "target"}))
 
-    return nrn.MeshIndex(part0).to_mesh_mask_base, nrn.MeshIndex(part1).to_mesh_mask_base
+    return nrn.MeshIndex(part0).to_mesh_mask_base, nrn.MeshIndex(
+        part1
+    ).to_mesh_mask_base
 
 
 def _build_nrn_with_st_annos(mesh, source_points, target_points):
@@ -38,30 +48,39 @@ def _build_nrn_with_st_annos(mesh, source_points, target_points):
     if isinstance(target_points, np.ndarray):
         target_points = target_points.tolist()
     nrn = Meshwork(mesh, voxel_resolution=[1, 1, 1])
-    source_df = pd.DataFrame(data={'pt_position': source_points})
-    source_df['type'] = 's'
-    target_df = pd.DataFrame(data={'pt_position': target_points})
-    target_df['type'] = 't'
+    source_df = pd.DataFrame(data={"pt_position": source_points})
+    source_df["type"] = "s"
+    target_df = pd.DataFrame(data={"pt_position": target_points})
+    target_df["type"] = "t"
     st_df = source_df.append(target_df, ignore_index=True)
-    st_df['pt_position'] = np.vstack(st_df['pt_position'].values).tolist()
+    st_df["pt_position"] = np.vstack(st_df["pt_position"].values).tolist()
 
-    nrn.add_annotations('st_df', st_df, point_column='pt_position', anchored=True, overwrite=True)
+    nrn.add_annotations(
+        "st_df", st_df, point_column="pt_position", anchored=True, overwrite=True
+    )
     return nrn
 
 
 def _build_local_mask(nrn, initial_window):
     ds = sparse.csgraph.dijkstra(
-        nrn.mesh.csgraph, indices=nrn.anno['st_df'].mesh_index, limit=initial_window)
-    d_sq = ds[:, nrn.anno['st_df'].mesh_index]
+        nrn.mesh.csgraph, indices=nrn.anno["st_df"].mesh_index, limit=initial_window
+    )
+    d_sq = ds[:, nrn.anno["st_df"].mesh_index]
     if np.any(np.isinf(d_sq.ravel())):
         raise ValueError(
-            "Initial window is too low (default: 10000) or points are in different components")
+            "Initial window is too low (default: 10000) or points are in different components"
+        )
 
     # Centers mask on the point with the lowest mean distance to other points
     ctr_ind = np.argmin(np.mean(d_sq, axis=0))
-    ctr_pt = nrn.anno['st_df'].mesh_index[ctr_ind]
-    local_mask = np.invert(np.isinf(sparse.csgraph.dijkstra(
-        nrn.mesh.csgraph, indices=ctr_pt, limit=np.max(d_sq[ctr_ind])+1)))
+    ctr_pt = nrn.anno["st_df"].mesh_index[ctr_ind]
+    local_mask = np.invert(
+        np.isinf(
+            sparse.csgraph.dijkstra(
+                nrn.mesh.csgraph, indices=ctr_pt, limit=np.max(d_sq[ctr_ind]) + 1
+            )
+        )
+    )
     return local_mask
 
 
@@ -78,51 +97,66 @@ def _faces_to_keep(p1mask, p2mask, nrn):
     return good_faces
 
 
-def _add_expected_edges(G, new_mesh, p1mask, p2mask, local_network_mask, test_split=True):
+def _add_expected_edges(
+    G, new_mesh, p1mask, p2mask, local_network_mask, test_split=True
+):
     "Adds edges that were not included in the faces graph"
-    G.remove_node('source')
-    G.remove_node('target')
+    G.remove_node("source")
+    G.remove_node("target")
 
     new_mesh_filt = new_mesh.apply_mask(local_network_mask)
     p1s = new_mesh_filt.filter_unmasked_boolean(p1mask)
     p2s = new_mesh_filt.filter_unmasked_boolean(p2mask)
 
     # Make matrix without cross-partition edges
-    Gorig = nx.to_scipy_sparse_matrix(G)
+    if nx.__version__ < 3:
+        Gorig = nx.to_scipy_sparse_matrix(G)
+    else:
+        Gorig = nx.to_scipy_sparse_array(G)
     ii, jj, dd = sparse.find(Gorig)
     keep11 = p1s[ii] & p1s[jj]
     keep22 = p2s[ii] & p2s[jj]
     keep_all = keep11 | keep22
 
-    GsplitB = sparse.csr_matrix((dd[keep_all], (ii[keep_all], jj[keep_all]))).toarray() > 0
+    GsplitB = (
+        sparse.csr_matrix((dd[keep_all], (ii[keep_all], jj[keep_all]))).toarray() > 0
+    )
 
     Gnew = new_mesh_filt.csgraph.toarray()
     GnewB = Gnew > 0
 
     # Places where edge in expected Gmat but not in new mesh
-    link_edges_to_add_rough = np.vstack(np.where(np.logical_and(GsplitB == True, GnewB == False))).T
+    link_edges_to_add_rough = np.vstack(
+        np.where(np.logical_and(GsplitB == True, GnewB == False))
+    ).T
     if len(link_edges_to_add_rough) > 0:
         link_edges_to_add = np.unique(
-            [tuple(x) for x in np.sort(link_edges_to_add_rough, axis=1)], axis=0)
+            [tuple(x) for x in np.sort(link_edges_to_add_rough, axis=1)], axis=0
+        )
 
         link_edges_unmasked = new_mesh_filt.map_indices_to_unmasked(link_edges_to_add)
         new_mesh.link_edges = np.vstack(
-            (new_mesh.link_edges, new_mesh.filter_unmasked_indices(link_edges_unmasked)))
+            (new_mesh.link_edges, new_mesh.filter_unmasked_indices(link_edges_unmasked))
+        )
 
     if test_split:
         if len(link_edges_to_add_rough) > 0:
-            new_mesh_filt.link_edges = np.vstack((new_mesh_filt.link_edges, link_edges_to_add))
+            new_mesh_filt.link_edges = np.vstack(
+                (new_mesh_filt.link_edges, link_edges_to_add)
+            )
 
         ncomp = sparse.csgraph.connected_components(new_mesh_filt.csgraph)[0]
         if ncomp > 2:
-            print('Warning: more than 2 local components after split')
+            print("Warning: more than 2 local components after split")
         if ncomp == 1:
-            print('Warning: Only 1 local component after split')
+            print("Warning: Only 1 local component after split")
 
     return new_mesh
 
 
-def mesh_multicut(mesh, source_points, target_points, initial_window=10000, return_masks=False):
+def mesh_multicut(
+    mesh, source_points, target_points, initial_window=10000, return_masks=False
+):
     """Use multi-point source/target split to cut a minimal set of faces from a mesh.
     Warns if the split produces more than 2 graph components in a local cutout, although
     the end result may still be suitable.
@@ -166,8 +200,12 @@ def mesh_multicut(mesh, source_points, target_points, initial_window=10000, retu
 
     keep_faces = _faces_to_keep(p1mask, p2mask, nrn)
 
-    new_mesh = Mesh(vertices=nrn.mesh.vertices,
-                    faces=nrn.mesh.faces[keep_faces], node_mask=nrn.mesh_mask, link_edges=nrn.mesh.link_edges)
+    new_mesh = Mesh(
+        vertices=nrn.mesh.vertices,
+        faces=nrn.mesh.faces[keep_faces],
+        node_mask=nrn.mesh_mask,
+        link_edges=nrn.mesh.link_edges,
+    )
 
     new_mesh = _add_expected_edges(G, new_mesh, p1mask, p2mask, local_network_mask)
 

requirements.txt

@@ -3,7 +3,7 @@ h5py
 numpy
 scipy>=1.3.0
 scikit-learn
-networkx<3
+networkx
 multiwrapper
 cloud-volume>=1.16.0
 trimesh>=3.0.14