analysis-graphing

name	analysis-graphing
description	RenoDX workflow for creating readable analysis graphs and plots from shader math, CSVs, EXRs, LUTs, hue sweeps, tone curves, gamut comparisons, energy/scalar maps, and test-pattern statistics. Use when graphing, plotting, visualizing, comparing curves, making dark-theme matplotlib figures, or avoiding repeated one-off plot scripts.
argument-hint	data source, variables to compare, output path, units/axis scale, and whether the graph is scratch or durable

RenoDX Analysis Graphing

Use this small skill for plots and graphs. Larger skills should call this out instead of embedding graphing rules.

Boundaries

• Focus on visualizing data, not generating source test images or editing shaders.
• Keep one-off plots and source scripts in a scratch output path unless the graph becomes a durable analysis artifact.
• Promote repeated graph workflows into tools/analysis/ with the data export beside the image.
• Prefer matplotlib for Python analysis unless an existing script in the same workflow already uses another plotting library.
• Use bt2020-png-generation for final HDR PQ PNG writing and signaling.
• Use hdr-test-pattern-generation for ramps, sweeps, charts, and synthetic image inputs.

Default style

• Use a dark theme by default: plt.style.use("dark_background").
• Save readable static images: usually PNG at dpi=150 to 180.
• Choose figure sizes for labels first, not minimum pixels; common overview plots are 14x9, , or inches.