sciris/sciris-plotting
claude_plugin/skills/sciris-plotting/SKILL.md
Use when plotting with Sciris or Matplotlib — sc.options, sc.dateformatter, sc.commaticks, sc.SIticks, sc.boxoff, sc.setylim, sc.figlayout, sc.getrowscols, sc.vectocolor, sc.gridcolors, sc.scatter3d, sc.savefig, plot styles (sciris.simple, sciris.fancy), colormaps (parula, orangeblue), or 3D plotting.
$ install --global
skillsauthnpx skillsauth add sciris/sciris sciris-plottingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: May 31, 2026, 4:42 AM159.5s1 file scanned
SKILL.md
- name:
- sciris-plotting
- description:
- Use when plotting with Sciris or Matplotlib — sc.options, sc.dateformatter, sc.commaticks, sc.SIticks, sc.boxoff, sc.setylim, sc.figlayout, sc.getrowscols, sc.vectocolor, sc.gridcolors, sc.scatter3d, sc.savefig, plot styles (sciris.simple, sciris.fancy), colormaps (parula, orangeblue), or 3D plotting.
Sciris Plotting and Colors
Reference for Matplotlib extensions and color utilities. See full tutorial: docs/tutorials/tut_plotting.ipynb.
If you need more detail, use your MCP tools (Context7 or GitMCP) to look up current Sciris documentation, or consult the other Sciris skills.
Setup and Options
sc.options(jupyter=True) # High-res retina backend for Jupyter
sc.options(dpi=120) # Set figure DPI
sc.options(font='serif') # Change font globally
sc.options(font='default') # Reset font
Plot Styles
with plt.style.context('sciris.simple'): # Clean, minimal style
make_plot()
with plt.style.context('sciris.fancy'): # Seaborn-like style
make_plot()
Axis Formatting
sc.dateformatter() # Auto date formatting on x-axis
sc.commaticks() # Comma-separated tick labels (1,000,000)
sc.SIticks() # SI notation (1M, 2.5k)
sc.setylim() # Auto y-limits (starts at 0)
sc.boxoff() # Remove top/right spines
sc.figlayout() # Tight layout (remove whitespace)
Layout Helpers
rows, cols = sc.getrowscols(14) # Auto grid for N subplots
Colors
Continuous (for ordered data)
colors = sc.vectocolor(n, cmap='turbo') # Map values to colormap
colors = sc.vectocolor(values, cmap='parula') # Custom Sciris colormaps: parula, orangeblue
c = sc.arraytocolor(data_2d) # 2D version
Categorical (for distinct groups)
colors = sc.gridcolors(n) # n<=9: ColorBrewer, 10-19: Kelly's, 20+: uniform RGB
colors = sc.gridcolors(n, asarray=True) # As numpy array
3D Plotting
ax = sc.ax3d(121) # Create 3D subplot
sc.scatter3d(x, y, z, c=colors, ax=ax)
sc.bar3d(data, ax=ax)
sc.surf3d(data, cmap='orangeblue')
Saving Figures
sc.savefig('fig.png') # Publication quality + metadata
sc.savefig('fig.png', comments='v2') # With custom comments
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