ENH: final pre release

.github/workflows/pythonpackage.yml

@@ -0,0 +1,45 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Python package
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        python-version: ['3.10']
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install --user wheel
+        pip install --user packaging
+        ./install.sh
+    - name: Lint with flake8
+      run: |
+        python -m pip install --upgrade pip
+        pip install --user wheel
+        python3 -m pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        python3 -m pip install pytest pytest-console-scripts
+        python3 -m pytest .

README.md

@@ -51,19 +51,19 @@ Streamlines will be colored according to their predicted scores (if saving a `.t
 TractOracle-Net is available through Docker Hub. You can pull the image by running
 
 ```
-docker pull TODO
+docker pull scilus/tractoracle-net:v2024b
 ```
 
 You can then score a tractogram by running
 
 ```
-sudo docker run -v .:/workspace/${TRACTOGRAM_LOCATION} tractoracle-net predictor.py /workspace/${TRACTOGRAM_FILE} ${OUT} [...]
+sudo docker run -v .:/workspace/${TRACTOGRAM_LOCATION} scilus/tractoracle-net:v2024b predictor.py /workspace/${TRACTOGRAM_FILE} ${OUT} [...]
 ```
 
 See [Docker volumes](https://docs.docker.com/storage/volumes/) for an explanation of the `-v` flag. **To use CUDA capabilities with Docker, you will need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)**. You will then be able to use the `--gpus` flag. For example:
 
 ```
-sudo docker run --gpus all -v .:/workspace/${TRACTOGRAM_LOCATION} tractoracle-net predictor.py /workspace/${TRACTOGRAM_FILE} ${OUT} [...]
+sudo docker run --gpus all -v .:/workspace/${TRACTOGRAM_LOCATION} scilus/tractoracle-net:v2024b predictor.py /workspace/${TRACTOGRAM_FILE} ${OUT} [...]
 ```
 
 ## Training
@@ -120,6 +120,6 @@ options:
 
 ## References
 
-See preprint: COMING SOON
+See preprint: https://arxiv.org/abs/2403.17845
 
 See conference paper: (hopefully) COMING SOON

tests/test_runners.py

@@ -0,0 +1,6 @@
+def test_ttl_track(script_runner):
+    # Call 'ttl_track.py' from the command line and assert that it
+    # runs without errors
+
+    ret = script_runner.run('predictor.py', '--help')
+    assert ret.success