It's quite difficult - practically impossible - to perceive data from rows and columns with numerical data. This holds true for complex (scientific) data; but also for much simpler data, e.g. temperature records over time. Graphical plots allow fast perception. That makes plots very important - also for debugging while development.

There are many plot types, you can select from. See https://datavizcatalogue.com/

Gnuplot is a portable command-line tool for creating plots.

Usually, comma seperated value (CSV) files are used as input, see comma separated values. But it's also possible to plot from binary data files.

Following plotting libraries might be used for creating (debug) plot files.

First some meta results, which already give a good overview:

• https://gasparri.org/2019/12/06/best-plot-and-charting-c-packages-for-data-visualization/
• https://www.researchgate.net/post/What_are_the_best_plot_and_charting_C_package_which_can_be_used_for_data_visualization
• https://stackoverflow.com/questions/4283731/plotting-package-for-c

Here some (additional) links:

• sciplot
  • https://sciplot.github.io/
  • https://github.com/sciplot/sciplot
• Matplot++
  • https://alandefreitas.github.io/matplotplusplus/
  • https://github.com/alandefreitas/matplotplusplus
• VTK
  • https://vtk.org/
  • https://gitlab.kitware.com/vtk/vtk
  • https://kitware.github.io/vtk-examples/site/Cxx/Plotting/LinePlot/
• PLplot
  • https://plplot.sourceforge.net/
• https://github.com/ABRG-Models/morphologica
• https://github.com/thclark/cpplot
• Matplotlib-Cpp - requires python
  • https://github.com/lava/matplotlib-cpp
• Immediate Mode Plotting (implot)
  • https://github.com/epezent/implot
  • Dear ImGui: https://github.com/ocornut/imgui

A python script/program allowing interactive commands is better suited for experimenting/development.

However, C++ developers might use python when compiling their code as python module with bindings, e.g. with pybind11. Interface functions might utilize numpy or Eigen, see https://pybind11.readthedocs.io/en/stable/advanced/cast/eigen.html

In python, there are several plot libraries available:

• Matplotlib
  • https://matplotlib.org/
  • https://github.com/matplotlib/matplotlib
• PyQtGraph - builds on Qt
  • https://www.pyqtgraph.org/
  • https://github.com/pyqtgraph/pyqtgraph
• seaborn
  • https://seaborn.pydata.org/
  • https://github.com/mwaskom/seaborn

i really like colored heatmaps:

• https://stackoverflow.com/questions/33282368/plotting-a-2d-heatmap
• https://github.com/DingWB/PyComplexHeatmap
• https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html
• https://seaborn.pydata.org/examples/spreadsheet_heatmap.html

Colormaps are used for visualizing or mapping mostly linear or grayscale data. IMHO, jet is very appealing for linear or logarithmic data. matplotlibs' documentation shows many colormaps: https://matplotlib.org/stable/tutorials/colors/colormaps.html

From technical point of view, turbo is an improved alternative for jet, see https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

Other colormaps like viridis, plasma or inferno provide perceptually uniform sequential lightness.

Here an interesting video on this topic: https://www.youtube.com/watch?v=xAoljeRJ3lU

In SDR and signal processing, such colormaps are used to visualize the power (or energy) in one single pixel to allow spectrogram plots, which already use x and y axes for time and frequency. For sure, there are usages in many other fields.

Circular data, e.g. phase values, should be treated special - as -180° equals +180°. Cyclic colormaps like twilight or hsv are for that case.

These colormaps are ideally viewed on circle like here: https://stackoverflow.com/questions/62531754/how-to-draw-a-hsv-color-wheel-using-matplotlib - and not on a ruler.

• https://blog.habrador.com/2023/04/colormaps-overview-code-implementations-rainbow-virids.html
• https://stackoverflow.com/questions/7706339/grayscale-to-red-green-blue-matlab-jet-color-scale