Dev

.github/workflows/gh-pages.yml

@@ -4,6 +4,7 @@ on:
   push:
     branches:
       - main
+      - dev
 
 jobs:
   build-deploy:
@@ -12,6 +13,9 @@ jobs:
       # 1. Checkout repo (code + docs)
       - name: Checkout repository
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          token: ${{ secrets.GH_PAGES_TOKEN || github.token }}
 
       # 2. Set up Python
       - name: Set up Python
@@ -23,19 +27,19 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install .[dev]   # installs mkdocs and all extras
+          pip install .[dev] mike
 
-      # 4. Build doc
-      - name: Build MkDocs site
+      # 4. Deploy MkDocs site with mike
+      - name: Deploy with mike 🚀
         env:
           PYTHONPATH: ${{ github.workspace }}/PyEyesWeb
-        run: mkdocs build --verbose
-
-      # 5. Push to gh-pages
-      - name: Deploy to GitHub Pages 🚀
-        uses: peaceiris/actions-gh-pages@v3
-        with:
-          github_token: ${{ secrets.GH_PAGES_TOKEN }}
-          publish_dir: ./site
-          publish_branch: gh-pages
-          force_orphan: true
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          
+          if [ "${{ github.ref_name }}" = "main" ]; then
+            mike deploy --push --update-aliases main latest
+            mike set-default --push latest
+          else
+            mike deploy --push ${{ github.ref_name }}
+          fi

.gitignore

@@ -27,12 +27,9 @@ Thumbs.db
 *.swp
 
 # Test data or large media files
-*.mp4
-*.avi
-*.mov
-*.csv
 *.npy
 *.h5
+*.pytest.py
 
 # Python virtualenv/poetry
 poetry.lock
@@ -44,4 +41,10 @@ dist/
 *.egg
 
 demos/Backup
-demos/Lib
+demos/Lib
+
+# Ignore specific files or directories
+__*
+
+# Ignore the site directory
+site/

README.md

@@ -28,13 +28,20 @@ from pyeyesweb.data_models import SlidingWindow
 from pyeyesweb.low_level import Smoothness
 
 # Movement smoothness analysis
+# 1. Initialize the feature extractor (e.g., 50Hz sampling rate)
 smoothness = Smoothness(rate_hz=50.0)
-window = SlidingWindow(max_length=100, n_columns=1)
-window.append([motion_data]) 
-# here `motion_data` is a float representing a single sample of motion data
-# (e.g., the x coordinate of the left hand at time t).
 
-sparc, jerk = smoothness(window)
+# 2. Initialize a sliding window for speed data (1 signal, 1 dimension)
+window = SlidingWindow(max_length=60, n_signals=1, n_dims=1)
+
+# 3. Process data frame by frame (simulating a real-time loop)
+# here `speed_value` is a float representing the instantaneous speed
+window.append(speed_value) 
+
+# 4. Compute the feature (only after the window is full)
+if len(window) >= window.max_length:
+    result = smoothness(window)
+    print(f"SPARC: {result.sparc}, Jerk: {result.jerk_rms}")
 ```
 > [!TIP]
 > For more advanced and complete use cases see the [Documentation](https://infomuscp.github.io/PyEyesWeb/)

docs/javascripts/version-select.js

@@ -0,0 +1,52 @@
+document.addEventListener("DOMContentLoaded", function() {
+    // Determine base URL based on environment (localhost vs GitHub Pages)
+    var isLocalhost = window.location.hostname === "localhost" || window.location.hostname === "127.0.0.1";
+    var baseUrl = isLocalhost ? "" : "/PyEyesWeb";
+
+    // Attempt to fetch the versions.json generated by mike
+    fetch(baseUrl + "/versions.json").then(response => {
+        if (!response.ok) return null;
+        return response.json();
+    }).then(versions => {
+        if (!versions || versions.length === 0) return;
+
+        // Find the current version from the URL path. 
+        var pathSegments = window.location.pathname.split('/').filter(p => p !== "");
+        // If localhost: /dev/ -> pathSegments[0] is "dev"
+        // If GitHub: /PyEyesWeb/dev/ -> pathSegments[1] is "dev"
+        var currentVersion = isLocalhost ? pathSegments[0] : pathSegments[1]; 
+
+        if (!currentVersion) {
+            currentVersion = versions[0].version; // fallback if at root
+        }
+
+        var select = document.createElement("select");
+        select.style.margin = "10px auto";
+        select.style.display = "block";
+        select.style.width = "90%";
+        select.style.padding = "5px";
+        select.style.color = "#000";
+        select.style.borderRadius = "3px";
+        select.style.border = "1px solid #ccc";
+
+        versions.forEach(function(version) {
+            var option = document.createElement("option");
+            option.value = version.version;
+            option.text = "Version: " + version.title;
+            if (version.version === currentVersion || version.aliases.includes(currentVersion)) {
+                option.selected = true;
+            }
+            select.appendChild(option);
+        });
+
+        select.addEventListener("change", function() {
+            window.location.href = baseUrl + "/" + this.value + "/";
+        });
+
+        // Add the dropdown to the readthedocs sidebar search box or navigation container
+        var searchBox = document.querySelector(".wy-side-nav-search");
+        if (searchBox) {
+            searchBox.appendChild(select);
+        }
+    }).catch(e => console.error("Error loading versions.json:", e));
+});

docs/scripts/gen_pages.py

@@ -21,7 +21,7 @@ def format_module_name(name):
 
 
 # Generate individual module pages and build nav
-for path in sorted(SRC_DIR.rglob("*.py")):
+for path in sorted(SRC_DIR.rglob("[!.]*.py")): # Exclude files starting with . (e.g., .pytest.py) from nav
     module_path = path.relative_to(SRC_DIR).with_suffix("")
     doc_path = API_DOCS_PATH / path.relative_to(SRC_DIR).with_suffix(".md")
     module_name = ".".join(module_path.parts)
@@ -38,7 +38,9 @@ def format_module_name(name):
     doc_path.parent.mkdir(parents=True, exist_ok=True)
     with mkdocs_gen_files.open(doc_path, "w") as f:
         f.write(f"# {format_module_name(module_path.name)}\n\n")
-        print(f"::: {PACKAGE_NAME}.{module_name}", file=f)
+        f.write(f"::: {PACKAGE_NAME}.{module_name}\n")
+        f.write(f"    options:\n")
+        f.write(f"      filters: [\"!^_[^_]\"]\n")
 
     mkdocs_gen_files.set_edit_path(doc_path, path)
 

docs/user_guide/getting_started.md

@@ -46,7 +46,7 @@ from pyeyesweb.low_level import Smoothness
 smoothness = Smoothness(rate_hz=50.0)
 
 # 2. Create a sliding window to store the last 100 frames of data
-window = SlidingWindow(max_length=100, n_columns=1)
+window = SlidingWindow(max_length=100, n_signals=1, n_dims=1)
 
 # 3. Simulate a real-time loop reading from a CSV
 with open('velocity_data.csv', 'r') as f:
@@ -57,23 +57,23 @@ with open('velocity_data.csv', 'r') as f:
         velocity_val = float(row[0])
 
         # Append the new frame to the sliding window
-        window.append([velocity_val]) #(1)!
+        window.append(velocity_val) #(1)!
 
         # Compute smoothness features on the current window
-        sparc, jerk = smoothness(window) #(2)!
+        result = smoothness(window) #(2)!
 
         # Check if a valid result was returned 
-        # (feature may return None if window has not enough samples)
-        if sparc is not None and jerk is not None:
-            print(f"SPARC: {sparc:.3f} | Jerk: {jerk:.3f}")
+        # (feature fields may be None if window has not enough samples)
+        if result.sparc is not None and result.jerk_rms is not None:
+            print(f"SPARC: {result.sparc:.3f} | Jerk: {result.jerk_rms:.3f}")
 ```
 
-1. The `SlidingWindow` expects a list of values for every frame (even if there is only 1 sample). 
+1. `SlidingWindow` automatically handles the data shape. Since we initialized it with `n_signals=1` and `n_dims=1`, we can append a scalar value directly.
 As the loop runs, new data is added to the end, and old data is automatically discarded once the max_length is reached.
 
 2. The `smoothness` callable processes the current state of the window. 
-It returns the SPARC (Spectral Arc Length) and Jerk RMS. 
-If the window does not yet contain enough data to compute the metric, it may return None.
+It returns a result object containing the SPARC (Spectral Arc Length) and Jerk RMS. 
+If the window does not yet contain enough data to compute the metric, the result fields will be `None`.
 
 ## Subpackages
 

docs/user_guide/theoretical_framework/analysis_primitives/analysis_primitives.md

@@ -37,23 +37,21 @@ They summarize, transform, or model data at various temporal and spatial scales.
 
 | Primitive Type                | Description                                                                                                  | Implemented      |
 |-------------------------------|--------------------------------------------------------------------------------------------------------------|------------------|
-| **Statistical Moments**       | Unary operators summarizing distributions (mean, variance, skewness, kurtosis).                              | :material-close: |
+| [**Statistical Moments**](statistical_moments.md)       | Unary operators summarizing distributions (mean, variance, skewness, kurtosis).                              | :material-check: |
 | **Shape Descriptors**         | Peaks, slopes, valleys in time-series; geometric descriptors of movement curves.                             | :material-close: |
-| **Entropy** [^1]              | Approximate/sample entropy, recurrence analysis; quantify predictability or irregularity.                    | :material-close: |
+| [**Entropy**](mse_dominance.md) [^1]              | Approximate/sample entropy, recurrence analysis; quantify predictability or irregularity.                    | :material-check: |
 | **Time-Frequency Transforms** | Fourier or wavelet transforms to detect rhythm, periodicity, or temporal structures.                         | :material-close: |
-| **Symmetry** [^2]             | Unary/binary operators measuring geometric or dynamic balance (e.g., left vs. right entropy or energy).      | :material-close: |
-| **Synchronization** [^3][^4]  | Binary/n-ary operators measuring alignment of signals (cross-correlation, phase-locking, group entrainment). | :material-close: |
-| **Causality** [^4]            | Directional relationships (e.g., Granger causality, transfer entropy) to detect leader–follower dynamics.    | :material-close: |
-| **Clusterability** [^5]       | Measures the tendency of data points to form clusters by means of the Hopkins statistics.                    | :material-close: |
+| [**Synchronization**](synchronization.md) [^2][^3]  | Binary/n-ary operators measuring alignment of signals (cross-correlation, phase-locking, group entrainment). | :material-check: |
+| **Causality** [^3]            | Directional relationships (e.g., Granger causality, transfer entropy) to detect leader–follower dynamics.    | :material-close: |
+| [**Clusterability**](clusterability.md) [^4]       | Measures the tendency of data points to form clusters by means of the Hopkins statistics.                    | :material-check: |
 | **Predictive Models**         | Hidden Markov Models, classifiers, neural networks; used for gesture segmentation or quality inference.      | :material-close: |
-| **Saliency / Rarity** [^6]    | Detecting unusual occurrences in movement with respect to most frequent patterns.                            | :material-close: |
+| [**Rarity**](rarity.md) [^5]    | Detecting unusual occurrences in movement with respect to most frequent patterns.                            | :material-check: |
 
 
 ## References
 
 [^1]: Glowinski, D., Mancini, M., & Camurri, A. (2013, March). Studying the effect of creative joint action on musicians’ behavior. In International Conference on Arts and Technology (pp. 113-119). Berlin, Heidelberg: Springer Berlin Heidelberg.
-[^2]: Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., & Scherer, K. (2011). Toward a minimal representation of affective gestures. IEEE Transactions on Affective Computing, 2(2), 106-118.
-[^3]: Varni, G., Volpe, G., & Camurri, A. (2010). A system for real-time multimodal analysis of nonverbal affective social interaction in user-centric media. IEEE Transactions on Multimedia, 12(6), 576-590.
-[^4]: Sabharwal, S. R., Varlet, M., Breaden, M., Volpe, G., Camurri, A., & Keller, P. E. (2022). huSync-A model and system for the measure of synchronization in small groups: A case study on musical joint action. IEEE Access, 10, 92357-92372.
-[^5]: Corbellini, N., Ceccaldi, E., Varni, G., & Volpe, G. (2022, August). An exploratory study on group potency classification from non-verbal social behaviours. In International Conference on Pattern Recognition (pp. 240-255). Cham: Springer Nature Switzerland.
-[^6]: Niewiadomski, R., Mancini, M., Cera, A., Piana, S., Canepa, C., & Camurri, A. (2019). Does embodied training improve the recognition of mid-level expressive movement qualities sonification?. Journal on Multimodal User Interfaces, 13, 191-203.
+[^2]: Varni, G., Volpe, G., & Camurri, A. (2010). A system for real-time multimodal analysis of nonverbal affective social interaction in user-centric media. IEEE Transactions on Multimedia, 12(6), 576-590.
+[^3]: Sabharwal, S. R., Varlet, M., Breaden, M., Volpe, G., Camurri, A., & Keller, P. E. (2022). huSync-A model and system for the measure of synchronization in small groups: A case study on musical joint action. IEEE Access, 10, 92357-92372.
+[^4]: Corbellini, N., Ceccaldi, E., Varni, G., & Volpe, G. (2022, August). An exploratory study on group potency classification from non-verbal social behaviours. In International Conference on Pattern Recognition (pp. 240-255). Cham: Springer Nature Switzerland.
+[^5]: Niewiadomski, R., Mancini, M., Cera, A., Piana, S., Canepa, C., & Camurri, A. (2019). Does embodied training improve the recognition of mid-level expressive movement qualities sonification?. Journal on Multimodal User Interfaces, 13, 191-203.