Merge pull request #37 from ilastik/main

sync up with fork

.github/workflows/deploy.yml

@@ -0,0 +1,35 @@
+name: deploy
+
+on:
+  push:
+    tags:
+      - '*'
+
+
+jobs:
+  deploy-to-ilastik-forge:
+    # noarch build - build on linux, only
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - uses: actions/checkout@v3
+        with:
+          repository: ilastik/ilastik-conda-recipes
+          path: ilastik-conda-recipes
+      - uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          activate-environment: ""
+          miniforge-variant: Mambaforge
+          use-mamba: true
+      - name: linux conda build and upload
+        shell: bash -l {0}
+        env:
+          ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_TOKEN }}
+        run: |
+          mamba install -n base -c conda-forge boa setuptools_scm anaconda-client -y
+          mamba config --set anaconda_upload yes
+          conda mambabuild -c ilastik-forge -c conda-forge -m ilastik-conda-recipes/ilastik-pins.yaml --user ilastik-forge conda-recipe

.github/workflows/test.yml

@@ -0,0 +1,40 @@
+name: test
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test-w-conda-recipe:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos-latest, windows-latest, ubuntu-latest]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - uses: conda-incubator/setup-miniconda@v2
+        with:
+          activate-environment: ""
+          auto-activate-base: true
+          auto-update-conda: true
+          miniforge-variant: Mambaforge
+          use-mamba: true
+      - name: install build deps
+        run: mamba install -n base -c conda-forge boa setuptools_scm -y
+      - name: linux conda build test
+        if: matrix.os == 'ubuntu-latest'
+        shell: bash -l {0}
+        run: conda mambabuild -c ilastik-forge -c conda-forge conda-recipe
+      - name: osx test
+        if: matrix.os == 'macos-latest'
+        shell: bash -l {0}
+        run: conda mambabuild -c ilastik-forge -c conda-forge conda-recipe
+      - name: windows conda-build
+        if: matrix.os == 'windows-latest'
+        shell: cmd /C CALL {0}
+        run: conda mambabuild -c ilastik-forge -c conda-forge conda-recipe

.gitignore

@@ -6,6 +6,7 @@
 CMakeCache*
 *.pyc
 *_test
+__pycache__
 tags*
 Testing
 CMakeFiles

Readme.md

@@ -2,7 +2,9 @@
 
 By Carsten Haubold, Steffen Wolf, Letitia Parcalabescu, Bernhard Kausler, Martin Schiegg, Jaime I. Cervantes, Janez Ales and more.
 
-* Build status: [ ![Circle CI](https://circleci.com/gh/chaubold/hytra.png?style=shield&circle-token=27b4fff289dfdb41575cecfab8e865c7cac6a099) ](https://circleci.com/gh/chaubold/hytra)
+
+* build status: [![test](https://github.com/ilastik/hytra/actions/workflows/test.yml/badge.svg)](https://github.com/ilastik/hytra/actions/workflows/test.yml)
+* conda: ![last updated](https://anaconda.org/ilastik-forge/hytra/badges/latest_release_date.svg) ![latest-version](https://anaconda.org/ilastik-forge/hytra/badges/version.svg)
 * Usage documentation can be found in this [Google document](https://docs.google.com/document/d/1jxkYGlTEUCPqH03pip03eDBBX2pVYEhPGHHvbegHiWw/edit?usp=sharing)
 * [API Docs](http://chaubold.github.io/hytra/hytra/index.html)
 * Run tests using `nosetests tests` from the root folder

conda-recipe/conda_build_config.yaml

@@ -1,8 +1,2 @@
 networkx:
-  - 1.11
-python:
-  - 3.6
-
-
-pin_run_as_build:
-  python: x.x
+  - 2

conda-recipe/meta.yaml

@@ -21,18 +21,17 @@ build:
 
 requirements:
   build:
-    - python  {{ python }}
+    - python >=3.6
     - pip
-
   run:
-    - python >=2.7
     - dpct
-    - networkx <={{ networkx }}
-    - yapsy
-    - vigra
-    - scikit-learn
-    - scikit-image
     - h5py
+    - networkx >={{ networkx }}
+    - python >=3.6
+    - scikit-image
+    - scikit-learn
+    - vigra
+    - yapsy
 
 test:
   source_files:
@@ -50,6 +49,6 @@ test:
     - nosetests tests
 
 about:
-  home: https://github.com/chaubold/hytra
+  home: https://github.com/ilastik/hytra
   license: MIT
   summary: 'Python tracking framework developed at the IAL lab @ University of Heidelberg'

dev/environment-dev.yaml

@@ -0,0 +1,19 @@
+name: hytra-dev
+channels:
+  - ilastik-forge
+  - conda-forge
+  - defaults
+dependencies:
+  - black
+  - configargparse
+  - dpct
+  - h5py
+  - jinja2
+  - networkx >=2.2
+  - nose
+  - pre_commit
+  - python >=3.7
+  - scikit-image
+  - scikit-learn
+  - vigra
+  - yapsy

empryonic/filter.py

@@ -24,7 +24,7 @@ def filterFeaturesByIntensity(h5In, h5Out, threshold = 1500):
     h5Out: output path; file will be overwritten if already existing
     '''
     def intensityFilter(labelGroup):
-        intMaximum = labelGroup[intminmax].value[1]
+        intMaximum = labelGroup[intminmax][()][1]
         return (intMaximum >= threshold)
     filterFeaturesByPredicate(h5In, h5Out, intensityFilter)
 
@@ -81,17 +81,17 @@ def filterFeaturesByPredicate(h5In, h5Out, predicate):
 
     # supervoxels
     print "labelcount = ", labelcount
-    outFeaturesGroup.create_dataset(labelcount, data=featuresGroup[labelcount].value)
+    outFeaturesGroup.create_dataset(labelcount, data=featuresGroup[labelcount][()])
 
     # featureconfig
-    outFeaturesGroup.create_dataset(featureconfig, data=featuresGroup[featureconfig].value)
+    outFeaturesGroup.create_dataset(featureconfig, data=featuresGroup[featureconfig][()])
 
     # labels
     for labelGroup in validLabelGroups:
         outFile.copy(labelGroup, outFeaturesGroup)
 
     # labelcontent
-    inLabelcontent = featuresGroup[labelcontent].value
+    inLabelcontent = featuresGroup[labelcontent][()]
     outLabelcontent = np.zeros(inLabelcontent.shape, dtype=inLabelcontent.dtype)
 
     validLabels = filter(lambda item: item.isdigit(), outFeaturesGroup.keys())

empryonic/io.py

@@ -15,7 +15,7 @@ def __loadDataset( filename, h5path):
     h5path:   location of the data inside the hdf5 file
     '''
     f = h5py.File( filename, mode='r' )
-    data = f[h5path].value
+    data = f[h5path][()]
     f.close()
     return data
 
@@ -214,25 +214,25 @@ def update_moves( self, mov_pairs ):
 
     def get_moves( self ):
         if self.has_tracking() and _path.basename(self.mov_ds) in self[self.track_gn].keys():
-            return self[self.mov_ds].value
+            return self[self.mov_ds][()]
         else:
             return np.empty(0)
 
     def get_mergers( self ):
         if self.has_tracking() and _path.basename(self.merg_ds) in self[self.track_gn].keys():
-            return self[self.merg_ds].value
+            return self[self.merg_ds][()]
         else:
             return np.empty(0)
 
     def get_multiFrameMoves( self ):
          if self.has_tracking() and _path.basename(self.multi_ds) in self[self.track_gn].keys():
-            return self[self.multi_ds].value
+            return self[self.multi_ds][()]
          else:
             return np.empty(0)
 
     def get_move_energies( self ):
         if _path.basename(self.mov_ener_ds) in self[self.track_gn].keys():
-            e = self[self.mov_ener_ds].value
+            e = self[self.mov_ener_ds][()]
             if isinstance(e, np.ndarray):
                 return e
             else:
@@ -243,7 +243,7 @@ def get_move_energies( self ):
 
     def get_divisions( self ):
         if self.has_tracking() and _path.basename(self.div_ds) in self[self.track_gn].keys():
-            return self[self.div_ds].value
+            return self[self.div_ds][()]
         else:
             return np.empty(0)
 
@@ -255,7 +255,7 @@ def update_divisions( self, div_triples ):
 
     def get_division_energies( self ):
         if _path.basename(self.div_ener_ds) in self[self.track_gn].keys():
-            e = self[self.div_ener_ds].value
+            e = self[self.div_ener_ds][()]
             if isinstance(e, np.ndarray):
                 return e
             else:
@@ -265,7 +265,7 @@ def get_division_energies( self ):
 
     def get_disappearances( self ):
         if self.has_tracking() and _path.basename(self.dis_ds) in self[self.track_gn].keys():
-            dis = self[self.dis_ds].value
+            dis = self[self.dis_ds][()]
             if isinstance(dis, np.ndarray):
                 return dis
             else:
@@ -281,7 +281,7 @@ def update_disappearances( self, dis_singlets ):
 
     def get_disappearance_energies( self ):
         if _path.basename(self.dis_ener_ds) in self[self.track_gn].keys():
-            e = self[self.dis_ener_ds].value
+            e = self[self.dis_ener_ds][()]
             if isinstance(e, np.ndarray):
                 return e
             else:
@@ -292,7 +292,7 @@ def get_disappearance_energies( self ):
 
     def get_appearances( self ):
         if self.has_tracking() and _path.basename(self.app_ds) in self[self.track_gn].keys():
-            app = self[self.app_ds].value
+            app = self[self.app_ds][()]
             if isinstance(app, np.ndarray):
                 return app
             else:
@@ -308,7 +308,7 @@ def update_appearances( self, app_singlets ):
 
     def get_appearance_energies( self ):
         if _path.basename(self.app_ener_ds) in self[self.track_gn].keys():
-            e = self[self.app_ener_ds].value
+            e = self[self.app_ener_ds][()]
             if isinstance(e, np.ndarray):
                 return e
             else:
@@ -336,7 +336,7 @@ def rm_disappearance( self, id ):
 
     def get_ids( self ):
         features_group = self[self.feat_gn]
-        labelcontent = features_group["labelcontent"].value
+        labelcontent = features_group["labelcontent"][()]
         valid_labels = (np.arange(len(labelcontent))+1)[labelcontent==1]
         return valid_labels
 
@@ -365,16 +365,16 @@ def cTraxels( self, as_python_list=False, prediction_threshold=None ):
     def _cTraxels_from_objects_group( self , as_python_list = False, prediction_threshold=None):
         objects_g = self["objects"]
         features_g = self["objects/features"]
-        ids = objects_g["meta/id"].value
-        valid = objects_g["meta/valid"].value
+        ids = objects_g["meta/id"][()]
+        valid = objects_g["meta/valid"][()]
         prediction = None
         if "prediction" in objects_g["meta"]:
             prediction = objects_g["meta/prediction"]
         elif prediction_threshold:
             raise Exception("prediction_threshold set, but no prediction dataset found")
         features = {}
         for name in features_g.keys():
-            features[name] = features_g[name].value
+            features[name] = features_g[name][()]
 
         if as_python_list:
             ts = list()
@@ -404,7 +404,7 @@ def _cTraxels_from_objects_group( self , as_python_list = False, prediction_thre
 
     def _cTraxels_from_features_group( self ):
         features_group = self[self.feat_gn]
-        labelcontent = features_group["labelcontent"].value
+        labelcontent = features_group["labelcontent"][()]
         invalid_labels = (np.arange(len(labelcontent))+1)[labelcontent==0]
 
         # note, that we used the ctracklet_from_labelgroup() here before, but had

empryonic/learning/optimal_matching.py

@@ -135,8 +135,8 @@ def _formulate_associations( graph, solved_ilp_variables ):
     for id, var in solved_ilp_variables.items():
         if var.value() == 1:
             match = graph.edges[id]
-            lhs = graph.lhs[match.id_lhs].value
-            rhs = graph.rhs[match.id_rhs].value
+            lhs = graph.lhs[match.id_lhs][()]
+            rhs = graph.rhs[match.id_rhs][()]
             if lhs != None:
                 assoc['lhs'][lhs] = rhs  
             if rhs != None: