Merge branch 'dev' of https://github.com/EthoML/VAME into docs

.github/workflows/release-vame-package.yaml

@@ -0,0 +1,31 @@
+name: Build and Release
+
+on:
+  workflow_run:
+    workflows: [Testing]
+    branches: [main]
+    types:
+      - completed
+jobs:
+  pypi-release:
+    name: Release VAME in pypi
+    runs-on: ubuntu-latest
+    if: ${{ github.event.workflow_run.conclusion == 'success' }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+          pip install twine
+      - name: Build and publish to PyPI
+        run: |
+          python -m build
+          twine upload dist/*
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}

.github/workflows/testing.yaml

@@ -0,0 +1,39 @@
+name: Testing
+
+on:
+  push:
+    branches:
+      - main
+      - dev
+
+
+jobs:
+  run:
+    name: ${{ matrix.os }} Python ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        os: [ubuntu-latest, macos-13, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+      - run: git fetch --prune --unshallow --tags
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install tests dependencies
+        run: |
+          python -m pip install -U pip
+          pip install . --no-cache-dir
+          pip install -r tests/requirements-tests.txt --no-cache-dir
+
+      - name: Run tests.
+        run: pytest --cov=src/vame --cov-report=xml --cov-report=term-missing -v
+
+      - name: Upload coverage reports to Codecov
+        uses: codecov/[email protected]
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -1,3 +1,7 @@
+# Sample files
+examples/*.mp4
+
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -7,7 +11,7 @@ __pycache__/
 
 # C extensions
 *.so
-*.mp4
+
 
 # Distribution / packaging
 .Python
@@ -95,8 +99,6 @@ celerybeat-schedule
 venv/
 ENV/
 venv_docs/
-examples/sample_data
-
 
 # Spyder project settings
 .spyderproject

MANIFEST.in

@@ -0,0 +1,4 @@
+prune tests
+prune docs
+prune examples
+prune Images

README.md

@@ -1,10 +1,16 @@
 ![image](https://github.com/EthoML/VAME/assets/844306/0f08424f-06ab-48e4-8094-da0f0c78a08d)
 
+<p align="center">
+<a href="https://codecov.io/gh/EthoML/VAME" >
+ <img src="https://codecov.io/gh/EthoML/VAME/graph/badge.svg?token=J1CUXB4N0E"/>
+ </a>
+</p>
+
 🌟 Welcome to EthoML/VAME (Variational Animal Motion Encoding), an open-source machine learning tool for behavioral segmentation and analyses.
 
 We are a group of behavioral enthusiasts, comprising the original VAME developers Kevin Luxem and Pavol Bauer, behavioral neuroscientists Stephanie R. Miller and Jorge J. Palop, and computer scientists and statisticians Alex Pico, Reuben Thomas, and Katie Ly). Our aim is to provide scalable, unbiased and sensitive approaches for assessing mouse behavior using computer vision and machine learning approaches.
 
-We are focused on the expanding the analytical capabilities of VAME segmentation by providing curated scripts for VAME implementation and tools for data processing, visualization, and statistical analyses. 
+We are focused on the expanding the analytical capabilities of VAME segmentation by providing curated scripts for VAME implementation and tools for data processing, visualization, and statistical analyses.
 
 ## Recent Improvements to VAME
 * Curated scripts for VAME implementation

VAME.yaml

@@ -4,27 +4,9 @@
 # update:  conda env update -f VAME.yaml
 name: vame
 channels:
-  - pytorch
   - defaults
 dependencies:
   - python=3.11
   - pip
-  - torchvision
-  - jupyter
-  - nb_conda
   - pip:
-      - pytest-shutil
-      - scipy
-      - numpy
-      - matplotlib
-      - pathlib
-      - pandas
-      - ruamel.yaml
-      - scikit-learn
-      - pyyaml
-      - opencv-python-headless
-      - h5py
-      - umap-learn
-      - networkx
-      - tqdm
-      - hmmlearn
+      - -r requirements.txt

examples/demo.py

@@ -14,10 +14,11 @@
 
 # These paths have to be set manually
 working_directory = './'
-project = 'first_vame_project'
+project = 'my-vame-project'
 videos = ['./video-1.mp4']
 poses_estimations = ['./video-1.csv']
 
+
 # Step 1.1: Initialize your project
 config = vame.init_new_project(project=project, videos=videos, poses_estimations=poses_estimations, working_directory=working_directory, videotype='.mp4')
 
@@ -27,7 +28,7 @@
 # As our config.yaml is sometimes still changing a little due to updates, we have here a small function
 # to update your config.yaml to the current state. Be aware that this will overwrite your current config.yaml
 # and make sure to back up your version if you did parameter changes!
-vame.update_config(config)
+vame.update_config(config, force_update=False)
 
 # Step 1.2: Align your behavior videos egocentric and create training dataset
 # pose_ref_index: list of reference coordinate indices for alignment
@@ -41,13 +42,13 @@
 # Step 1.3: create the training set for the VAME model
 vame.create_trainset(config, check_parameter=False, pose_ref_index=[0,5])
 
-# Step 2: Train VAME
+# # Step 2: Train VAME
 vame.train_model(config)
 
-# Step 3: Evaluate model # -> produces an image
+# # Step 3: Evaluate model
 vame.evaluate_model(config)
 
-# Step 4: Segment motifs/pose # -> dont
+# # Step 4: Segment motifs/pose
 vame.pose_segmentation(config)
 
 

pyproject.toml

@@ -1,5 +1,6 @@
 [project]
-name = "vame"
+name = "vame-py"
+version = '0.1.0'
 dynamic = ["dependencies"]
 description = "Variational Animal Motion Embedding."
 authors = [
@@ -8,7 +9,6 @@ authors = [
 ]
 requires-python = ">=3.11"
 keywords = ["vame", "auto-encoder"]
-version = '0.1.0'
 
 [project.urls]
 homepage = "https://github.com/EthoML/VAME/"
@@ -27,3 +27,11 @@ package-dir = { "" = "src" }
 [tool.setuptools.packages.find]
 where = ["src"]
 
+[tool.pytest.ini_options]
+pythonpath = [
+  ".", "src"
+]
+testpaths = [
+"tests"
+]
+

requirements.txt

@@ -1,66 +1,12 @@
-contextlib2==21.6.0
-contourpy==1.2.1
-cycler==0.12.1
-exceptiongroup==1.2.1
-execnet==2.1.1
-filelock==3.14.0
-fonttools==4.51.0
-fsspec==2024.3.1
-h5py==3.11.0
-hmmlearn==0.3.2
-iniconfig==2.0.0
-install==1.3.5
-Jinja2==3.1.4
-joblib==1.4.2
-kiwisolver==1.4.5
-llvmlite==0.42.0
-MarkupSafe==2.1.5
-matplotlib==3.8.4
-mock==5.1.0
-mpmath==1.3.0
-networkx==3.3
-numba==0.59.1
-numpy==1.26.4
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==8.9.2.26
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.20.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.1.105
-opencv-python-headless==4.9.0.80
-packaging==24.0
-pandas==2.2.2
-path==16.14.0
-path.py==12.5.0
-pathlib==1.0.1
-pillow==10.3.0
-pluggy==1.5.0
-pynndescent==0.5.12
-pyparsing==3.1.2
-pytest==8.2.0
-pytest-shutil==1.7.0
-python-dateutil==2.9.0.post0
-pytz==2024.1
-PyYAML==6.0.1
-ruamel.yaml==0.18.6
-ruamel.yaml.clib==0.2.8
-scikit-learn==1.4.2
-scipy==1.13.0
-six==1.16.0
-sympy==1.12
-termcolor==2.4.0
-threadpoolctl==3.5.0
-tomli==2.0.1
-torch==2.3.0
-torchvision==0.18.0
-tqdm==4.66.4
-triton==2.3.0
-typing_extensions==4.11.0
-tzdata==2024.1
-umap-learn==0.5.6
+PyYAML>=6.0.0
+ruamel.yaml>=0.18.0
+numpy>=1.26.0
+pandas>=2.2.0
+scipy>=1.13.0
+matplotlib>=3.9.0
+torch==2.2.2
+tqdm>=4.66.0
+hmmlearn>=0.3.0
+opencv-python-headless>=4.9.0.0
+umap-learn>=0.5.0
+h5py>=3.11.0