Dev

.github/workflows/release.yml

@@ -0,0 +1,31 @@
+name: Semantic Release
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: write
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Python Semantic Release
+        id: release
+        uses: python-semantic-release/python-semantic-release@v9.12.0
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Publish package to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        if: steps.release.outputs.released == 'true'
+        with:
+          password: ${{ secrets.PYPI_API_KEY }}

examples/00_quickstart_pipeline.ipynb

@@ -9,18 +9,29 @@
     "\n",
     "Welcome to PyEyesWeb! This library is designed to extract expressive and biomechanical features from motion data in real-time.\n",
     "\n",
-    "In this quickstart, we will build a complete, unified pipeline that extracts:\n",
+    "## Understanding the Architecture: Concepts vs. Implementation\n",
     "\n",
-    "1. A Static Feature: Bounding Box Contraction (How folded/expanded is the body?)\n",
+    "PyEyesWeb separates **what** we measure (the conceptual framework) from **how** it is computed (the implementation API).\n",
     "\n",
-    "2. A Dynamic Feature: Kinetic Energy (How much total energy is in the movement?)\n",
+    "### 1. The Conceptual Framework (What we measure)\n",
+    "*   **Low-Level Features**: Physical biomechanics, including spatial geometry (Contraction, Expansion) and raw kinematics (Kinetic Energy, Velocity).\n",
+    "*   **Mid-Level Features**: Qualitative, expressive aspects of movement that represent behavior or intent (e.g., Smoothness, Directness, Rhythmicity).\n",
+    "*   **Analysis Primitives**: Domain-agnostic mathematical and statistical tools (e.g., Probability/Rarity, Signal Synchronization, Volatility) that can be applied to any data stream.\n",
     "\n",
-    "3. An Analysis Primitive: Smoothness (How fluid is the right hand moving?)"
+    "### 2. The Implementation API (How we compute it)\n",
+    "In the code, every metric is implemented as either a **Static** or **Dynamic** feature, defined purely by its memory requirements:\n",
+    "*   **Static Features**: Require only the instantaneous state of the *current frame*. For example, the spatial Contraction of a single pose, or instantaneous Kinetic Energy. They operate on a sliding window of size 1.\n",
+    "*   **Dynamic Features**: Require a historical trajectory to evaluate changes *over time*. For example, checking if a movement was Smooth over the last second. They require a rolling sliding window to store past frames.\n",
+    "\n",
+    "In this quickstart, we will build a unified pipeline extracting three metrics that cross-reference these categories:\n",
+    "1. **Contraction** (Concept: Low-Level | API: Static Feature)\n",
+    "2. **Kinetic Energy** (Concept: Low-Level | API: Static Feature)\n",
+    "3. **Smoothness** (Concept: Low-Level | API: Dynamic Feature)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "initial_id",
    "metadata": {
     "ExecuteTime": {
@@ -39,15 +50,18 @@
     }
    ],
    "source": [
-    "from utils.data_loader import load_qualisys_tsv\n",
+    "from utils.data_loader import load_motion_data\n",
     "from utils.plot_utils import plot_feature_timeseries\n",
     "\n",
     "# Load data into clean 3D tensors: (Time, Joints, Dimensions)\n",
-    "pos_tensor, vel_tensor, _, marker_names = load_qualisys_tsv(\"data/trial0001_impulsive.tsv\")\n",
+    "pos_tensor, vel_tensor, acc_tensor, marker_names = load_motion_data(\"data/trial0001_impulsive.tsv\")\n",
+    "\n",
+    "# Let's track the Right Hand\n",
     "hand_idx = marker_names.index(\"HAND_RIGHT\")\n",
     "\n",
     "N_frames, N_joints, N_dims = pos_tensor.shape\n",
-    "print(f\"Ready to process {N_frames} frames tracking {N_joints} joints!\")"
+    "print(f\"Loaded {N_frames} frames tracking {N_joints} joints.\")\n",
+    "print(f\"Position Tensor Shape: ({N_frames}, {N_joints}, {N_dims})\")"
    ]
   },
   {
@@ -186,21 +200,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "pwb",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pyeyesweb"
-version = "0.0.1a5"
+version = "1.0.0b1"
 requires-python = ">=3.8"
 authors = [
     {name = "InfoMus Lab - Casa Paganini", email = "cp.infomus@gmail.com"}
@@ -87,4 +87,12 @@ where = ["."]
 [tool.pytest.ini_options]
 pythonpath = [
   "."
-]
+]
+
+[tool.semantic_release]
+version_toml = [
+    "pyproject.toml:project.version",
+]
+branch = "main"
+build_command = "pip install build && python -m build"
+commit_parser = "angular"