Software Guide2 min read

Data Visualizations

This document describes data visualization types for mouse movement analysis, including 2D density heatmaps of spatial position, trajectory paths, cumulative distance tracking for individual body parts, and distributions of distances between body part pairs.

Updated Feb 7, 2026
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Data Visualizations

01Oct2024

2D Density Heatmap Representations of Mouse Position

Spatial heatmap of mouse position across all frames

  • X and Y axes are position in the open field (units in pixels)
  • The X and Y positions for each body part across each frame were used to determine mouse position across time
  • Contours in the plot (i.e., the concentric polygons) represent the frequency with which the coordinate values are represented in the dataset
  • The contours are filled with a color gradient, which represents the density levels: coordinates with the lowest value density are blue, highest density values are red

Mouse Trajectory

Path of mouse across time

  • X and Y axes are open field coordinates (units in pixels)
  • Used XBody and YBody to represent the mouse position (omitted zeros)
  • Smoothed the data by applying a rolling mean across every 3 frames to reduce noise

Cumulative Distance Over Time for Each Body Part

Faceted grid of line plots for each tracked body part across time

  • Cumulative distance for each body part is represented by a line with LOESS smoothing (omitted zeros)
  • Shading represents 95% confidence interval
  • Each plot has the same Y axis (easy comparison)
  • No legend (legends are redundant in faceted grids)

Distance Between Body Parts

Violin plot visualizes the distribution of distances between various pairs of body parts (units in centimeters)

  • Each violin corresponds to a different body part pair, and is essentially a histogram superimposed on a box and whiskers plot (though omitting the box & whiskers)
  • X axis labeling should be cleaner (e.g., "Body - Tail" instead of BodyTail)
  • Different color for each violin (rainbow gradient like shown here works well)

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