scidraw-ai 0.1.1

Publication-ready matplotlib styles and figure helpers by SciDraw AI — colorblind-safe palettes, journal figure sizes, and panel labels for Nature, Science, Cell, PLOS, and ACS.

scidraw-ai

Publication-ready matplotlib styles and figure helpers, maintained by SciDraw AI — the AI scientific illustration and data visualization platform.

The color cycles and journal specs in this package are the same ones used by the SciDraw AI chart tools, so Python code exported from the platform reproduces identically on your machine.

Install

pip install scidraw-ai

Quick start

import matplotlib.pyplot as plt
import scidraw_ai  # registers the styles

plt.style.use('scidraw')                      # publication (light)
plt.style.use('scidraw-dark')                 # slides & screens
plt.style.use(['scidraw', 'scidraw-poster'])  # posters & talks

The scidraw style gives you:

• Okabe-Ito colorblind-safe color cycle — the standard recommended for scientific publishing (pair the yellow with distinct markers on white backgrounds)
• Clean axes: no top/right spines, outward ticks, frameless legends
• Journal-friendly type sizes (7–9 pt) with Arial/Helvetica preferred
• 300 DPI tight-bbox saving out of the box
• Editable text in exports — pdf.fonttype 42 / svg.fonttype none, so labels stay editable in Illustrator, Inkscape, or PowerPoint

Journal figure sizes

Widths follow each journal's author guidelines (Nature, Science, Cell, PLOS, ACS):

fig, ax = plt.subplots(figsize=scidraw_ai.figsize('nature', 'single'))
fig, ax = plt.subplots(figsize=scidraw_ai.figsize('science', 'double'))

Panel labels

Each journal formats panel labels differently — Nature wants bold lowercase a, Science wants (A), PLOS wants A.:

fig, axs = plt.subplots(1, 3)
scidraw_ai.label_panels(axs, journal='nature')

Palettes

Four colorblind-safe palettes, identical to the SciDraw AI platform:

scidraw_ai.set_palette('okabe-ito')   # default
scidraw_ai.set_palette('wong')
scidraw_ai.set_palette('tol-bright')
scidraw_ai.set_palette('tol-muted')

Generate figures from data, no code

Prefer not to write plotting code at all? SciDraw AI turns CSV/Excel data into journal-quality charts — box plots, heatmaps, regression and violin plots — and exports editable SVG/PDF plus the matplotlib code to reproduce them:

• Scientific figure maker
• Free scientific diagram & chart tools

License

MIT