Merge branch 'master' of github.com:seung-lab/kimimaro

.github/workflows/build_wheel.yml

@@ -28,13 +28,13 @@ jobs:
         uses: docker/setup-qemu-action@v1
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.22.0
         # to supply options, put them in 'env', like:
         env:
           CIBW_ARCHS_LINUX: ${{matrix.arch}}
           CIBW_BEFORE_BUILD: pip install numpy setuptools wheel cython 
           CIBW_ARCHS_MACOS: "x86_64 arm64"
 
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v3
         with:
           path: ./wheelhouse/*.whl

.github/workflows/test.yml

@@ -27,7 +27,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install cython numpy setuptools wheel
-        pip install -r requirements.txt pytest 
+        pip install -r requirements.txt -r requirements-dev.txt 
         pip install -e .
     - name: Test with pytest
       run: |

automated_test.py

@@ -8,6 +8,11 @@
 import kimimaro.skeletontricks
 from kimimaro.utility import moving_average
 
+@pytest.fixture
+def connectomics_data():
+  import crackle
+  return crackle.load("benchmarks/connectomics.npy.ckl.gz")
+
 def test_empty_image():
   labels = np.zeros( (256, 256, 256), dtype=bool)  
   skels = kimimaro.skeletonize(labels, fix_borders=True)
@@ -502,7 +507,8 @@ def test_moving_average():
   assert np.all(res == ans)
   assert len(ans) == len(data)
 
-
+def test_no_fix_branching(connectomics_data):
+  kimimaro.skeletonize(connectomics_data[:,:,100], fix_branching=False)
 
 
 

benchmarks/README.md

@@ -1,6 +1,8 @@
 Benchmarks
 ==========
 
+To open `connectomics.npy.ckl.gz` you must use [`crackle-codec`](https://github.com/seung-lab/crackle).
+
 Except where noted, these benchmarks were executed on an 2.8 GHz Dual-Core Intel Core i7 with 1600 MHz DDR3 RAM. The data source used was `connectomics.npy` which can be found in this repository. `connectomics.npy` is a 32-bit 512x512x512 cutout of mouse visual cortex at 16nm x 16nm x 40nm resolution that contains 2124 connected components including a partial cell body and a large glia fragment.
 
 Below, we compared the run time and peak memory usage of Kimimaro across many versions that contained performance significant updates. Due to the annoying length of each run, each value represents a single run, so there is some random perturbation around the true mean that can obscure the value of small improvements. Version 0.4.2 can be considered the first "feature complete" version that includes quality improvements like fix_branches, fix_borders, and a reasonable root selected for the cell body.

benchmarks/benchmark.py

@@ -1,8 +1,9 @@
 import time
 import numpy as np
 import kimimaro
+import crackle
 
-labels = np.load("connectomics.npy")
+labels = crackle.load("connectomics.npy.ckl.gz")
 
 s = time.time()
 skels = kimimaro.skeletonize(

kimimaro/trace.py

@@ -152,7 +152,7 @@ def trace(
   # pointers from each voxel to its parent. Then we can rapidly
   # compute multiple paths by simply hopping pointers using path_from_parents
   if not fix_branching:
-    parents = dijkstra3d.parental_field(PDRF, root, voxel_graph)
+    parents = dijkstra3d.parental_field(PDRF, root, voxel_graph=voxel_graph)
     del PDRF
   else:
     parents = PDRF

requirements-dev.txt

@@ -0,0 +1,2 @@
+pytest
+crackle-codec