From sciris
Provides Sciris utilities for Matplotlib: plot styles (simple, fancy), axis formatters (dateticks, SI), colors (parula, gridcolors), 3D plotting, layout helpers, and savers.
npx claudepluginhub sciris/scirisThis skill uses the workspace's default tool permissions.
Reference for Matplotlib extensions and color utilities. See full tutorial: `docs/tutorials/tut_plotting.ipynb`.
Creates publication-quality scientific plots using Matplotlib: line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control; PNG/PDF/SVG export.
Guides Matplotlib usage for custom static, animated, interactive plots including lines, scatters, bars, heatmaps, subplots, 3D, and exports to PNG/PDF/SVG in scientific Python workflows.
Guides Matplotlib for creating line, scatter, bar, histogram, heatmap, 3D plots, subplots; pyplot and OO APIs; exports PNG/PDF/SVG for scientific visualizations.
Share bugs, ideas, or general feedback.
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.
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
with plt.style.context('sciris.simple'): # Clean, minimal style
make_plot()
with plt.style.context('sciris.fancy'): # Seaborn-like style
make_plot()
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)
rows, cols = sc.getrowscols(14) # Auto grid for N subplots
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
colors = sc.gridcolors(n) # n<=9: ColorBrewer, 10-19: Kelly's, 20+: uniform RGB
colors = sc.gridcolors(n, asarray=True) # As numpy array
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')
sc.savefig('fig.png') # Publication quality + metadata
sc.savefig('fig.png', comments='v2') # With custom comments