Releases: farhanaugustine/Multi-ROI-Analysis-with-DeepLabCut-CSV-Outputs
Kinetic Analysis System for DeepLabCut (KAS-DLC)
Bug Fixed: "Data becoming inverted when plotted using Matplotlib"
The Kinetic Analysis System for DeepLabCut (KAS-DLC) notebook aims to provide a reliable analytical framework for examining multiple Regions of Interest (ROIs) in CSV datasets generated by DeepLabCut. The toolkit is designed to advance research in behavioral neuroscience. Key features include importing essential Python libraries, loading data, and mapping body part coordinates with likelihood estimations.
The provided notebook facilitates the drawing of multiple ROIs, temporal analysis of spatial occupancy of ROIs, and velocity computations. It also offers comprehensive graphing capabilities for visualizing entry/exit frequencies and average velocities across ROIs. Users can navigate the notebook via a step-by-step code walkthrough, with customization options for experimental specificity.
The data architecture supports intricate distance, velocity, and ROI occupancy calculations alongside tracking visit sequences for nuanced behavior pattern analysis. Visualization components are integrated to elucidate animal movement patterns and behavioral dynamics, with code blocks tailored to enhance accuracy for paired-body part analysis and frame count constraints.
Kinetic Analysis System for DeepLabCut (KAS-DLC)
The Kinetic Analysis System for DeepLabCut (KAS-DLC) notebook aims to provide a reliable analytical framework for examining multiple Regions of Interest (ROIs) in CSV datasets generated by DeepLabCut. The toolkit is designed to advance research in behavioral neuroscience. Key features include importing essential Python libraries, loading data, and mapping body part coordinates with likelihood estimations.
The provided notebook facilitates the drawing of multiple ROIs, temporal analysis of spatial occupancy of ROIs, and velocity computations. It also offers comprehensive graphing capabilities for visualizing entry/exit frequencies and average velocities across ROIs. Users can navigate the notebook via a step-by-step code walkthrough, with customization options for experimental specificity.
The data architecture supports intricate distance, velocity, and ROI occupancy calculations alongside tracking visit sequences for nuanced behavior pattern analysis. Visualization components are integrated to elucidate animal movement patterns and behavioral dynamics, with code blocks tailored to enhance accuracy for paired-body part analysis and frame count constraints.