Pin elf version and bump own version

cluster_tools/affinities/insert_affinities.py

@@ -206,7 +206,7 @@ def _insert_affinities_block(block_id, blocking, ds_in, ds_out, objects, offsets
 
     # zero out some affs if necessary
     if zero_objects_list is not None:
-        zero_ids = obj_ids[np.in1d(obj_ids, zero_objects_list)]
+        zero_ids = obj_ids[np.isin(obj_ids, zero_objects_list)]
         if zero_ids.size:
             for zero_id in zero_ids:
                 # erode the mask to avoid ugly boundary artifacts

cluster_tools/costs/probs_to_costs.py

@@ -132,7 +132,7 @@ def _apply_node_labels(costs, uv_ids, mode, labels,
         # isolate mode: set all edges that connect to a node with label to node without label to max repulsive
         fu.log("Node-label mode: isolate")
         # ignore mode: set all edges that connect two node with label to max attractive
-        edges_with_label = np.in1d(uv_ids, with_label).reshape(uv_ids.shape)
+        edges_with_label = np.isin(uv_ids, with_label).reshape(uv_ids.shape)
         label_sum = edges_with_label.sum(axis=1)
         att_edges = label_sum == 2
         rep_edges = label_sum == 1

cluster_tools/lifted_multicut/solve_lifted_subproblems.py

@@ -132,11 +132,11 @@ class SolveLiftedSubproblemsLSF(SolveLiftedSubproblemsBase, LSFTask):
 def _find_lifted_edges(lifted_uv_ids, node_list):
     lifted_indices = np.arange(len(lifted_uv_ids), dtype='uint64')
     # find overlap of node_list with u-edges
-    inner_us = np.in1d(lifted_uv_ids[:, 0], node_list)
+    inner_us = np.isin(lifted_uv_ids[:, 0], node_list)
     inner_indices = lifted_indices[inner_us]
     inner_uvs = lifted_uv_ids[inner_us]
     # find overlap of node_list with v-edges
-    inner_vs = np.in1d(inner_uvs[:, 1], node_list)
+    inner_vs = np.isin(inner_uvs[:, 1], node_list)
     return inner_indices[inner_vs]
 
 

cluster_tools/mutex_watershed/two_pass_mws.py

@@ -248,7 +248,7 @@ def _mws_block_pass2(block_id, blocking,
             with vu.file_reader(save_path) as f:
                 # first, load the edges and see if they have overlap with our seed ids
                 ngb_edges = f['edges'][:]
-                ngb_edge_mask = np.in1d(ngb_edges, seed_ids).reshape(ngb_edges.shape)
+                ngb_edge_mask = np.isin(ngb_edges, seed_ids).reshape(ngb_edges.shape)
                 ngb_edge_mask = ngb_edge_mask.all(axis=1)
 
                 # if we have edges, load the corresponding weights
@@ -292,7 +292,7 @@ def _mws_block_pass2(block_id, blocking,
 
     # filter the assignments from ids that are not in the crop
     crop_ids = np.unique(seg_crop)
-    filter_mask = np.in1d(assignments[:, 1], crop_ids)
+    filter_mask = np.isin(assignments[:, 1], crop_ids)
     assignments = assignments[filter_mask]
 
     # store assignments to tmp folder

cluster_tools/postprocess/background_size_filter.py

@@ -118,7 +118,7 @@ def apply_block(block_id, blocking, ds_in, ds_out, discard_ids):
         fu.log_block_success(block_id)
         return
 
-    discard_mask = np.in1d(labels, discard_ids).reshape(labels.shape)
+    discard_mask = np.isin(labels, discard_ids).reshape(labels.shape)
     # check if the discard-mask is empty
     if np.sum(discard_mask) == 0:
         ds_out[bb] = labels

cluster_tools/postprocess/filling_size_filter.py

@@ -122,7 +122,7 @@ def apply_block(block_id, blocking, ds_hmap, ds_in, ds_out, discard_ids, preserv
         fu.log_block_success(block_id)
         return
 
-    discard_mask = np.in1d(labels, discard_ids).reshape(labels.shape)
+    discard_mask = np.isin(labels, discard_ids).reshape(labels.shape)
     # check if the discard-mask is empty
     if np.sum(discard_mask) == 0:
         ds_out[bb] = labels

cluster_tools/postprocess/filter_blocks.py

@@ -117,7 +117,7 @@ def _filter_block(blocking, block_id,
         return
 
     # check for filter_ids
-    filter_mask = np.in1d(seg, filter_ids).reshape(seg.shape)
+    filter_mask = np.isin(seg, filter_ids).reshape(seg.shape)
     seg[filter_mask] = 0
     ds_out[bb] = seg
     fu.log_block_success(block_id)
@@ -138,7 +138,7 @@ def _filter_block_inplace(blocking, block_id,
         return
 
     # check for filter_ids
-    filter_mask = np.in1d(seg, filter_ids).reshape(seg.shape)
+    filter_mask = np.isin(seg, filter_ids).reshape(seg.shape)
 
     # check if we filter any ids
     if filter_mask.sum() == 0:

cluster_tools/postprocess/graph_watershed_assignments.py

@@ -163,7 +163,7 @@ def graph_watershed_assignments(job_id, config_path):
     # map zero label to new id
     assignments[assignments == 0] = seed_offset
 
-    discard_mask = np.in1d(assignments, discard_ids)
+    discard_mask = np.isin(assignments, discard_ids)
     assignments[discard_mask] = 0
 
     n_discard = int(discard_mask.sum())

cluster_tools/postprocess/id_filter.py

@@ -98,7 +98,7 @@ def id_filter(job_id, config_path):
         node_labels = f[node_label_key][:]
 
     # find the node ids that overlap with the filter labels
-    filter_mask = np.in1d(node_labels, filter_labels)
+    filter_mask = np.isin(node_labels, filter_labels)
     filter_ids = np.where(filter_mask)[0].tolist()
 
     fu.log("%i ids will be filtered" % len(filter_ids))

cluster_tools/version.py

@@ -1 +1 @@
-__version__ = "0.5.1"
+__version__ = "0.5.2"

cluster_tools/write/write.py

@@ -174,7 +174,7 @@ def _apply_node_labels(seg, node_labels, allow_empty_assignments):
             else:
                 this_assignment = {label: node_labels[label] for label in this_labels}
         else:
-            this_assignment = node_labels[:, 1][np.in1d(node_labels[:, 0], this_labels)]
+            this_assignment = node_labels[:, 1][np.isin(node_labels[:, 0], this_labels)]
             this_assignment = {label: this_assignment[ii] for ii, label in enumerate(this_labels)}
         # FIXME this casts to uint32 which can lead to nasty over-flows
         seg = nt.takeDict(this_assignment, seg)

environment.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
   - affogato >=0.4.2
   - nifty >=1.2.4
-  - python-elf >=0.4.1
+  - python-elf >=0.4.1,<0.9.0
   - luigi
   - pybdv >=0.5.0
   - gdown

test/lifted_features/sparse_lifted_neighborhood.py

@@ -40,7 +40,7 @@ def compute_nh(self, graph_depth, label_path, label_key):
         # filter by nodes which have a node labeling
         node_ids = np.arange(len(node_labels))
         nodes_with_label = node_ids[node_labels != 0]
-        nh_mask = np.in1d(nh, nodes_with_label).reshape(nh.shape)
+        nh_mask = np.isin(nh, nodes_with_label).reshape(nh.shape)
         nh_mask = nh_mask.all(axis=1)
         nh = nh[nh_mask]
         # need to lexsort - awkward in numpy ...