3D Scatter plots with Backplane Heatmaps

Overview

This tool generates three-dimensional scatter plots from experimental data, with optional heatmaps projected onto XY, YZ, and ZX backplanes.

It is designed for practical analysis of multi-parameter experimental and process data, allowing users to visualize measured data points together with spatial trends in a single 3D view.

Typical applications is multi parameter sweep analysis of validaiton experiments.
All visualization behavior is controlled via an external configuration file, enabling flexible use without modifying the source code.

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Included files

• 3D_scatter.py
  Script to generate 3D scatter plots and backplane heatmaps from input CSV data.
• settings.csv
  Configuration file defining column mappings, axis labels, scales, binning parameters, interpolation options, color scales, and output behavior.
• data_input.csv
  Sample input data template for 3D scatter visualization.
• description_setting.txt
  Detailed description of each configuration parameter defined in settings.csv.

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Typical use cases

• Visualization of multi-parameter experimental data in three dimensions
• Identification of correlations and trends between process variables
• Overlay of measured data points with spatial trends
• Comparison of multiple runs using fixed axis ranges and color scales
• Generation of figures for technical reports and presentation materials

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Input format

data_input.csv

Column	Description
X-axis column	Parameter mapped to X-axis
Y-axis column	Parameter mapped to Y-axis
Z-axis column	Parameter mapped to Z-axis
Size column	Parameter mapped to marker size (optional)

Notes

• Column names are user-defined and mapped via settings.csv.
• Units of each parameter are not constrained by the script.
• Log or linear scaling can be selected independently for each axis.

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Output

• 3D scatter plot image (PNG format)
• Optional XY / YZ / ZX backplane heatmaps representing binned mean values
• Optional contour lines projected onto backplanes
• Output resolution, viewing angle, axis ranges, color scale, and transparency are configurable via settings.csv.

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Limitations and notes

• Backplane heatmaps are generated using 2D binning with mean aggregation.
• Empty bins are filled by interpolation and optional smoothing; extrapolated regions should be interpreted with care.
• The visualization is intended for exploratory and qualitative analysis rather than rigorous physical modeling.
• Apparent trends depend on bin size, interpolation method, and smoothing parameters.

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Requirements

• NumPy
• SciPy
• Matplotlib