Lightweight visualization tools for timsTOF proteomics data.
Project description
imspy-vis
Lightweight visualization tools for timsTOF proteomics data.
Installation
pip install imspy-vis
For Jupyter notebook support:
pip install imspy-vis[notebook]
Features
- Frame Rendering: DDA and DIA frame visualization with annotation overlays
- Point Cloud Visualization: Interactive 3D visualization using Plotly
- Video Generation: Generate preview videos from timsTOF datasets
- Jupyter Integration: Interactive widgets for notebook-based exploration
Quick Start
Frame Rendering
from imspy_vis import DDAFrameRenderer, DIAFrameRenderer, generate_preview_video
# Generate a quick preview video
generate_preview_video(
'/path/to/data.d',
'/path/to/output.mp4',
mode='dda',
max_frames=100,
fps=10
)
# Or use the renderer directly
renderer = DDAFrameRenderer('/path/to/data.d')
renderer.render_to_video('/path/to/output.mp4', max_frames=50)
Point Cloud Visualization (Jupyter)
from imspy_vis import DDAPrecursorPointCloudVis
# In a Jupyter notebook
visualizer = DDAPrecursorPointCloudVis(precursor_data)
visualizer.display_widgets()
Modules
- pointcloud: Interactive 3D point cloud visualization using Plotly
- frame_rendering: Frame-by-frame rendering and video generation
Dependencies
- imspy-core: Core data structures (required)
- plotly: Interactive plotting
- matplotlib: Static plotting and frame rendering
- imageio: Video generation
- ipywidgets: Jupyter notebook widgets
Related Packages
- imspy-core: Core data structures and timsTOF readers
- imspy-predictors: ML-based predictors
- imspy-simulation: TimSim simulation tools
- imspy-search: Database search functionality
License
MIT License - see LICENSE file for details.
Project details
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