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Merge pull request #43 from childmindresearch/alperkent/clean-up-documentation
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‎.github/workflows/pypi.yaml‎

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name: Publish to PyPI
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permissions:
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actions: write
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contents: write # Required for creating releases and tags
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actions: read
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id-token: write # Required for OIDC
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on:
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workflow_run:
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- main
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jobs:
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check-version:
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name: Check Version Change
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runs-on: ubuntu-latest
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if: ${{ github.event.workflow_run.conclusion == 'success' }}
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outputs:
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version-changed: ${{ steps.version-check.outputs.changed }}
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new-version: ${{ steps.version-check.outputs.version }}
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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with:
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fetch-depth: 2 # Need previous commit for comparison
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- name: Check version change
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id: version-check
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run: |
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# Extract current version from pyproject.toml
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current_version=$(grep -Po '(?<=^version = ")[^"]*' pyproject.toml)
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echo "Current version: $current_version"
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# Check if version changed in last commit
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if git diff HEAD~1 HEAD pyproject.toml | grep -q "^[+-]version ="; then
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echo "Version changed in this commit"
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echo "changed=true" >> "$GITHUB_OUTPUT"
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echo "version=$current_version" >> "$GITHUB_OUTPUT"
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else
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echo "Version unchanged - skipping release"
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echo "changed=false" >> "$GITHUB_OUTPUT"
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fi
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pypi-release:
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name: PyPI Release
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runs-on: ubuntu-latest
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if: ${{ github.event.workflow_run.conclusion == 'success' }}
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needs: check-version
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if: ${{ needs.check-version.outputs.version-changed == 'true' }}
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environment:
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name: pypi
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url: https://pypi.org/project/graphomotor/
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timeout-minutes: 10
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steps:
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- uses: actions/checkout@v4
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- name: Checkout code
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uses: actions/checkout@v4
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with:
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fetch-depth: ${{ github.event_name == 'pull_request' && 2 || 0 }}
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fetch-depth: 1 # Only need current commit for build
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- uses: actions/setup-python@v5
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version-file: pyproject.toml
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- name: Skip release if version unchanged
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run: |
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version_change=$(git diff -r HEAD^1 pyproject.toml | grep -E "^(\+|-)version =")
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if [[ -z "$version_change" ]]; then
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gh run cancel ${{ github.run_id }}
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gh run watch ${{ github.run_id }}
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fi
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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- name: Install uv
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uses: astral-sh/setup-uv@bd01e18f51369d5a26f1651c3cb451d3417e3bba # v6.3.1
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with:
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enable-cache: true
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- name: Build package
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run: uv build
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- name: Cache uv dependencies
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uses: actions/cache@v4
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with:
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path: ~/.cache/uv
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key: ${{ runner.os }}-uv-${{ hashFiles('**/pyproject.toml', '**/uv.lock') }}
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restore-keys: |
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${{ runner.os }}-uv-
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- name: Validate package metadata
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run: |
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echo "Building version: ${{ needs.check-version.outputs.new-version }}"
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uv build --wheel --sdist
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ls -la dist/
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# Validate package contents
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uv run python -c "
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import tarfile
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import zipfile
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import os
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# Check sdist contents
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for f in os.listdir('dist'):
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if f.endswith('.tar.gz'):
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with tarfile.open(f'dist/{f}', 'r:gz') as tar:
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print(f'SDist contents ({f}):')
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for member in tar.getnames()[:10]: # Show first 10 files
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print(f' {member}')
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elif f.endswith('.whl'):
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with zipfile.ZipFile(f'dist/{f}', 'r') as zip_file:
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print(f'Wheel contents ({f}):')
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for member in zip_file.namelist()[:10]: # Show first 10 files
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print(f' {member}')
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"
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- name: Upload build artifacts
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uses: actions/upload-artifact@v4
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with:
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name: python-package-distributions
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path: dist/
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retention-days: 30
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4
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with:
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print-hash: true
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verbose: true
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# OIDC authentication - no API token needed
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- name: Create GitHub Release
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: |
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gh release create "v${{ needs.check-version.outputs.new-version }}" \
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--title "Release v${{ needs.check-version.outputs.new-version }}" \
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--notes "## What's Changed
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Automated release for version ${{ needs.check-version.outputs.new-version }}
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### Package Information
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- **PyPI**: https://pypi.org/project/graphomotor/${{ needs.check-version.outputs.new-version }}/
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- **Changelog**: See commit history for detailed changes
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Full package verification and testing completed via CI pipeline." \
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dist/*

‎README.md‎

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@@ -18,25 +18,25 @@ Welcome to `graphomotor`, a specialized Python library for analyzing graphomotor
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## Feature Extraction Capabilities
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The toolkit extracts 25 clinically relevant metrics from digitized drawing data. Currently implemented feature categories include:
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The toolkit extracts [25 clinically relevant metrics](https://childmindresearch.github.io/graphomotor/graphomotor/features.html) from digitized drawing data. Currently implemented feature categories include:
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- **Velocity Features (15)**: Velocity analysis including linear, radial, and angular velocity components with statistical measures (sum, median, variation, skewness, kurtosis).
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- **Distance Features (8)**: Spatial accuracy measurements using Hausdorff distance metrics with temporal normalizations and segment-specific analysis.
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- **Drawing Error Features (1)**: Area under the curve (AUC) calculations between drawn paths and ideal reference trajectories to quantify spatial accuracy.
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- **Temporal Features (1)**: Task completion duration.
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- **[Velocity Features](https://childmindresearch.github.io/graphomotor/graphomotor/features/velocity.html) (15)**: Velocity analysis including linear, radial, and angular velocity components with statistical measures (sum, median, variation, skewness, kurtosis).
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- **[Distance Features](https://childmindresearch.github.io/graphomotor/graphomotor/features/distance.html) (8)**: Spatial accuracy measurements using Hausdorff distance metrics with temporal normalizations and segment-specific analysis.
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- **[Drawing Error Features](https://childmindresearch.github.io/graphomotor/graphomotor/features/drawing_error.html) (1)**: Area under the curve (AUC) calculations between drawn paths and ideal reference trajectories to quantify spatial accuracy.
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- **[Temporal Features](https://childmindresearch.github.io/graphomotor/graphomotor/features/time.html) (1)**: Task completion duration.
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## Feature Visualization Capabilities
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The toolkit provides several plotting functions to visualize extracted features:
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- **Distribution Plots**: Kernel density estimation plots showing feature distributions grouped by task type and hand.
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- **Trend Plots**: Line plots displaying feature progression across task sequences with individual participant trajectories and group means.
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- **Box Plots**: Box-and-whisker plots comparing feature distributions across different tasks and hand conditions.
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- **Cluster Heatmaps**: Hierarchically clustered heatmaps of z-score standardized features to identify patterns across conditions.
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- **[Distribution Plots](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature_plots.html#plot_feature_distributions)**: Kernel density estimation plots showing feature distributions grouped by task type and hand.
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- **[Trend Plots](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature_plots.html#plot_feature_trends)**: Line plots displaying feature progression across task sequences with individual participant trajectories and group means.
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- **[Box Plots](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature_plots.html#plot_feature_boxplots)**: Box-and-whisker plots comparing feature distributions across different tasks and hand conditions.
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- **[Cluster Heatmaps](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature_plots.html#plot_feature_clusters)**: Hierarchically clustered heatmaps of z-score standardized features to identify patterns across conditions.
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## Installation
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Install `graphomotor` from PyPI:
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Install `graphomotor` from [PyPI](https://pypi.org/project/graphomotor/):
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```sh
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pip install graphomotor
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```
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> [!NOTE]
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> For all available feature plotting options and the list of all 25 features extracted by the toolkit, refer to the [`feature_plots` documentation](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature-plots.html).
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> For all available feature plotting options, refer to the [`feature_plots` documentation](https://childmindresearch.github.io/graphomotor/graphomotor/plot/feature_plots.html).
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## Development Progress
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‎src/graphomotor/__init__.py‎

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""".. include:: ../../README.md""" # noqa: D415
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"""A Python toolkit for analysis of graphomotor data collected via Curious.
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15800191.svg)](https://doi.org/10.5281/zenodo.15800191)
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[![Build](https://github.com/childmindresearch/graphomotor/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/childmindresearch/graphomotor/actions/workflows/test.yaml?query=branch%3Amain)
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[![codecov](https://codecov.io/gh/childmindresearch/graphomotor/branch/main/graph/badge.svg?token=22HWWFWPW5)](https://codecov.io/gh/childmindresearch)
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Welcome to `graphomotor`, a specialized Python library for analyzing graphomotor
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data collected via [Curious](https://www.gettingcurious.com/). This toolkit provides
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comprehensive tools for processing, analyzing, and visualizing data from various
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graphomotor assessment tasks.
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For complete documentation, examples, and API reference, visit the
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[full documentation](https://childmindresearch.github.io/graphomotor/).
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"""

‎src/graphomotor/core/__init__.py‎

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""".. include:: ../../README.md""" # noqa: D415
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"""Core functionality for graphomotor data processing and analysis.
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This module provides the essential infrastructure for the graphomotor toolkit:
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## Data Models and Validation
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- **`Spiral`**: Pydantic model for spiral drawing data validation with strict
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metadata requirements (7-digit participant IDs starting with '5', Dom/NonDom
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hand designation, spiral_trace/spiral_recall tasks numbered 1-5)
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- **`FeatureCategories`**: Registry of all 4 feature extractors (duration,
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velocity, hausdorff, AUC) with dynamic extractor mapping
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## Configuration and Logging
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- **`SpiralConfig`**: Immutable dataclass for Archimedean spiral parameters
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(center coordinates, growth rate, angles, point density) with custom
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parameter override support
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- **Logger**: Centralized logging with configurable verbosity levels
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(WARNING/INFO/DEBUG) and structured formatting
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## Pipeline Orchestration
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- **`run_pipeline()`**: Main entry point supporting both single-file and
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batch directory processing with progress tracking, error handling, and
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configurable feature extraction
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- **Feature Extraction**: Coordinates spiral centering, reference generation,
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and execution of all feature extractors
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- **Export System**: Automated CSV export with metadata preservation and
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timestamp-based naming
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## Command-Line Interface
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- **`extract`**: CLI command for feature extraction with extensive configuration
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options and help documentation
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- **`plot-features`**: CLI command for generating publication-ready visualizations
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from extracted feature datasets
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The core module serves as the foundation for all graphomotor operations, ensuring
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data quality, processing consistency, and user-friendly interfaces.
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"""

‎src/graphomotor/features/__init__.py‎

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""".. include:: ../../README.md""" # noqa: D415
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"""Feature extraction modules for graphomotor data analysis.
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This module contains specialized extractors for computing clinically relevant metrics
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across four categories: velocity (15 features), distance (8 features), drawing error
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(1 feature), and time (1 feature). Each feature extractor follows a standardized
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interface and returns a dictionary of computed metrics with descriptive keys.
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Available Features
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------------------
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The graphomotor pipeline extracts 25 clinically validated features organized into
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four categories. These features quantify different aspects of drawing performance
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and motor control:
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**Distance-based Features (8 features):**
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These features use Hausdorff distance to measure spatial accuracy between drawn and
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reference spirals. The Hausdorff distance captures the maximum distance from any point
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in one set to the nearest point in another set.
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- **hausdorff_distance_maximum**: Maximum directed Hausdorff distance between drawn
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and reference spiral points
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- **hausdorff_distance_sum**: Sum of directed Hausdorff distances
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- **hausdorff_distance_sum_per_second**: Sum of Hausdorff distances normalized by
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total drawing duration
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- **hausdorff_distance_interquartile_range**: Interquartile range of directed
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Hausdorff distances, measuring variability in spatial accuracy
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- **hausdorff_distance_start_segment_maximum_normalized**: Maximum Hausdorff distance
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in beginning segment (0-25%) normalized by segment length
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- **hausdorff_distance_end_segment_maximum_normalized**: Maximum Hausdorff distance
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in ending segment (75-100%) normalized by segment length
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- **hausdorff_distance_middle_segment_maximum**: Maximum Hausdorff distance in middle
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segment (15-85%) without normalization
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- **hausdorff_distance_middle_segment_maximum_per_second**: Middle segment maximum
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Hausdorff distance normalized by total drawing duration
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**Velocity-based Features (15 features):**
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These features analyze movement dynamics by computing velocity in three coordinate
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systems, each providing 5 statistical measures.
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*Linear velocity (5 features):*
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Euclidean velocity magnitude in Cartesian coordinates (pixels/second).
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- **linear_velocity_sum**: Sum of absolute linear velocity values
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- **linear_velocity_median**: Median of absolute linear velocity values
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- **linear_velocity_coefficient_of_variation**: Ratio of standard deviation to mean,
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measuring velocity consistency
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- **linear_velocity_skewness**: Asymmetry of velocity distribution (positive =
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right-skewed)
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- **linear_velocity_kurtosis**: Tailedness of velocity distribution (higher = more
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extreme values)
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*Radial velocity (5 features):*
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Rate of change in distance from center (pixels/second).
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- **radial_velocity_sum**: Sum of absolute radial velocity values
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- **radial_velocity_median**: Median of absolute radial velocity values
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- **radial_velocity_coefficient_of_variation**: Consistency of radial movement speed
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- **radial_velocity_skewness**: Asymmetry of radial velocity distribution (positive =
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right-skewed)
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- **radial_velocity_kurtosis**: Tailedness of radial velocity distribution
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(higher = more extreme values)
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*Angular velocity (5 features):*
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Rate of angular rotation around center (radians/second).
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- **angular_velocity_sum**: Sum of absolute angular velocity values
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- **angular_velocity_median**: Median of absolute angular velocity values
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- **angular_velocity_coefficient_of_variation**: Consistency of rotational speed
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- **angular_velocity_skewness**: Asymmetry of angular velocity distribution (positive =
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right-skewed)
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- **angular_velocity_kurtosis**: Tailedness of angular velocity distribution
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(higher = more extreme values)
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**Time-based Features (1 feature):**
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- **duration**: Total time taken to complete the spiral drawing task (seconds)
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**Drawing Error Features (1 feature):**
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- **area_under_curve**: Total enclosed area between drawn and reference spiral paths,
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computed using polygon intersection algorithms. Lower values indicate better
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adherence to the template spiral.
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"""

‎src/graphomotor/features/distance.py‎

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is based on the original R script provided with the publication. The Hausdorff
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distance measures the maximum distance of a set to the nearest point in the other
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set. This metric and its derivatives capture various aspects of the spatial
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relationship between the drawn and reference spirals. Calculated features include:
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- hausdorff_distance_maximum: The maximum of the directed Hausdorff distances
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between the data points and the reference data points,
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- hausdorff_distance_sum: The sum of the directed Hausdorff distances,
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- hausdorff_distance_sum_per_second: The sum of the directed Hausdorff distances
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divided by the total drawing duration,
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- hausdorff_distance_interquartile_range: The interquartile range of the
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directed Hausdorff distances,
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- hausdorff_distance_start_segment_maximum_normalized: The maximum of the
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directed Hausdorff distances between the beginning segment (0% to 25%) of
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data points and the beginning segment of reference data points divided by
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the number of data points in the beginning segment,
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- hausdorff_distance_end_segment_maximum_normalized: The maximum of the directed
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Hausdorff distances in the ending segment (75% to 100%) of data points and
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the ending segment of reference data points divided by the number of data
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points in the ending segment,
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- hausdorff_distance_middle_segment_maximum: The maximum of the directed
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Hausdorff distances in the middle segment (15% to 85%) of data points and
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the ending segment of reference data points (this metric is not divided by
61-
the number of data points in the middle segment unlike previous ones),
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- hausdorff_distance_middle_segment_maximum_per_second: The maximum of the
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directed Hausdorff distances in the middle segment divided by the total
64-
drawing duration.
42+
relationship between the drawn and reference spirals.
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44+
Calculated features include:
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- **hausdorff_distance_maximum**: The maximum of the directed Hausdorff distances
47+
between the data points and the reference data points
48+
- **hausdorff_distance_sum**: The sum of the directed Hausdorff distances
49+
- **hausdorff_distance_sum_per_second**: The sum of the directed Hausdorff distances
50+
divided by the total drawing duration
51+
- **hausdorff_distance_interquartile_range**: The interquartile range of the
52+
directed Hausdorff distances
53+
- **hausdorff_distance_start_segment_maximum_normalized**: The maximum of the
54+
directed Hausdorff distances between the beginning segment (0% to 25%) of
55+
data points and the beginning segment of reference data points divided by
56+
the number of data points in the beginning segment
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- **hausdorff_distance_end_segment_maximum_normalized**: The maximum of the directed
58+
Hausdorff distances in the ending segment (75% to 100%) of data points and
59+
the ending segment of reference data points divided by the number of data
60+
points in the ending segment
61+
- **hausdorff_distance_middle_segment_maximum**: The maximum of the directed
62+
Hausdorff distances in the middle segment (15% to 85%) of data points and
63+
the ending segment of reference data points (this metric is not divided by
64+
the number of data points in the middle segment unlike previous ones)
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- **hausdorff_distance_middle_segment_maximum_per_second**: The maximum of the
66+
directed Hausdorff distances in the middle segment divided by the total
67+
drawing duration
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Args:
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spiral: Spiral object with drawing data.

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