In engineering and scientific analysis workflows, it is often necessary to compare spatial distributions obtained from different experiments or measurement conditions.
Measurement data are often acquired at scattered spatial locations or under varying experimental conditions.
By generating contour plots under consistent visualization settings, spatial trends and differences between experiments can be more clearly identified.
This repository provides utility scripts for generating contour plots from measured data stored in CSV files. All plotting behavior is controlled via a configuration file, enabling consistent visualization across multiple experiments without modifying the source code.
contour plotter.py
Script to generate contour plots from scattered measurement data stored in CSV files.
settings.csv
Configuration file defining interpolation parameters, contour plot settings, color scale options,
logarithmic scaling, output file names, and visualization controls.
input_data.csv
Example input file containing measured data in Cartesian coordinates (x, y, value).
The script supports interpolation-based contour visualization with extensive configuration options via settings.csv.
The following data and coordinate conventions are used in this repository:
- Coordinates are defined in a 2D Cartesian system
(x, y) - Units are user-defined (e.g. mm, µm, arbitrary length units)
- The value column represents a measured scalar quantity
(e.g. thickness, pressure, temperature)
- Comparing process results across different measurement conditions
- Input data is provided as CSV files with headers
- The CSV file must contain at least three columns:
- x coordinate
- y coordinate
- measured value
- Contour plot image file (PNG)
- Interpolated grid data as a CSV file
- This tool performs numerical interpolation and visualization only;
it does not assess the physical validity of the data
- numpy
- pandas
- matplotlib
- scipy