data-visualization

name

data-visualization

description

Create data visualizations, design figures, and plot analysis results. Specialized for observational cosmology (spectra, covariances, posteriors, sky maps) but capable of creative/expressive output for outreach. Use when: plotting data, designing figures for papers/talks, visualizing uncertainty, creating publication-quality graphics, or any task involving matplotlib/seaborn/plotly. Triggers on: "plot", "figure", "visualize", "chart", "graph", "histogram", "scatter", "heatmap", "contour", "power spectrum", "corner plot", or any request to create or improve data graphics.

Data Visualization

Decision framework for translating data into effective visual form. Synthesizes Bertin, Cleveland, Tufte, Cairo, Wilke, and Knaflic — optimized for scientific work with cosmology-specific conventions.

The Intake Protocol

Before plotting, establish two dimensions:

1. Data Structure Analysis

Identify what you're visualizing:

Data Type	Description	Likely Forms
Amounts	Values across categories	Bar, dot plot, heatmap
Distributions	Spread/shape of values	Histogram, KDE, violin, ridgeline
X-Y Relationships	Continuous variables	Scatter, line, confidence bands
Uncertainty	Error on measurements	Error bars, bands, gradient ribbons
Proportions	Parts of whole	Stacked bar, pie (rarely)
Spatial/Maps	Geographic or sky data	Mollweide, healpix, choropleth
Correlations	Variable relationships	Covariance matrix, triangle plot

2. Communication Mode

Determine the venue—this switches the entire rule set:

Mode A: Analytical/Paper

• Audience: Expert peers, reviewers
• Optimize for: Precision, black/white printing, convention
• Philosophy: Tufte/Cleveland/Wilke—density is permitted, accuracy is paramount
• Color: Restrained, colorblind-safe, grayscale-compatible
• Default: This mode unless otherwise specified

Mode B: Presentation/Outreach

• Audience: Mixed expertise, attention-competitive
• Optimize for: Impact, engagement, narrative clarity
• Philosophy: Cairo/McCandless/Knaflic—preattentive pop, visual hierarchy
• Color: Bold accent colors, clear entry points
• Use when: Talks, posters, press releases, social media

The Decision Framework

Route from data to visualization form:

Step 1: Analyze Variables (Bertin)

For each variable, classify:

• Quantitative: Continuous numeric (position, intensity, redshift)
• Ordered: Categorical with sequence (low/med/high, redshift bins)
• Categorical: Nominal groups (experiments, instruments, sky regions)

Check for uncertainty: Is there error on mean (discrete bars) or intrinsic spread (continuous band)?

Step 2: Select Encoding (Cleveland)

Match importance to perceptual accuracy:

Rank	Encoding	Use For
1	Position on common scale	Primary comparisons, precise values
2	Position on non-aligned scales	Secondary comparisons
3	Length	Bar charts (amounts only)
4	Angle/Slope	Avoid for precise reading
5	Area	Gestalt impressions, bubble charts
6	Color saturation	Tertiary encoding, density

Rule: If precise comparison is needed, use position. If gestalt impression is needed, use color/area.

Step 3: Select Form (Wilke)

Consult viz-catalog.md for the specific form. Key mappings:

You Have	Consider
Spectrum (continuous x, continuous y, uncertainty)	Line + confidence band, residual subplot
Correlation/covariance matrix	Heatmap, diverging colormap, white at zero
Parameter posteriors	Triangle plot, ridgeline, violin
Comparison across groups	Small multiples > overlay when groups > 4
Time series	Line, banking to 45 degrees
Amounts across categories	Dot plot (Cleveland) > bar chart

Step 4: Apply Mode-Specific Rules

If Mode A (Paper):

• Enforce strict linear/log scaling
• No bubble charts for precise quantities
• No dual y-axes
• Redundant encoding (shape + color) for colorblind safety
• Direct labeling over legends when <=4 series
• Light grid lines, subordinate to data

If Mode B (Outreach):

• Establish visual hierarchy—most important data most salient
• One clear entry point (where does eye go first?)
• Bolder colors, but maintain accuracy
• Annotations that guide reading
• Title states the takeaway, not the topic

Cosmology-Specific Overrides

These conventions override general principles for domain consistency:

Power Spectra

• Flatten steeply falling spectra: Multiply by x-axis factor to reveal percent-level features
  • Angular: Plot ell^n C_ell (commonly D_ell = ell(ell+1)C_ell/2pi, but factor varies)
  • Matter: Plot k^3 P(k) or Delta^2(k) to flatten
  • Correlation functions: Plot theta xi(theta) or similar
• Log-linear preferred: Log scale on x (multipole/k), linear on y after flattening
  • Reveals small differences hidden by log-log compression
  • Reserve log-log only when dynamic range is the message
• Label x-axis with actual values (10, 100, 1000), not exponents
• Residual panel: Show (data - model)/sigma or data/model below main panel
• Uncertainty: Confidence bands if dense sampling, error bars if sparse

Covariance Matrices

• Diverging colormap required (RdBu, coolwarm)
• White/neutral at zero (or at 1 for correlation matrices)
• Explicit colorbar with position-based lookup for precise values
• Consider: Showing only upper/lower triangle for symmetry

Triangle/Corner Plots

• Standard layout: 1D posteriors on diagonal, 2D contours off-diagonal
• Contour levels: 68%, 95% (1sigma, 2sigma)
• Consistent axis ranges across all panels showing same parameter
• Direct parameter labels on axes, not legend

Sky Maps (Healpix/Mollweide)

• Projection matters: Mollweide for full-sky, orthographic for regions
• Graticule: RA/Dec grid, labeled at edges
• Sequential colormap for intensity, diverging for residuals

Error Representation

• Asymmetric errors: Make asymmetry visually obvious
• Bands vs bars: Use bands for continuous functions, bars for discrete points
• Multiple sigma levels: Gradient opacity (dark = 1sigma, light = 2sigma)

Encoding Principles

Brief rules from perceptual science:

Preattentive Attributes (Cairo)

These "pop out" in <250ms—use for key distinctions:

• Color (hue)
• Size
• Position
• Orientation

If your main finding should be visible at a glance, encode it preattentively.

Working Memory Limits

Humans hold ~4 chunks in working memory:

• Legends with >4 items require constant back-and-forth
• Direct labeling dramatically reduces cognitive load
• Group by meaningful categories to chunk (8 items -> 2 groups of 4)

Redundant Encoding (Wilke)

Never rely on color alone:

• Shape + color for categories
• Position + color for emphasis
• Ensures colorblind safety and bad projector survival

The Refinement Loop

After generating the plot, inspect against:

The Squint Test (Knaflic)

Squint at the figure. What stands out? If it's not your main finding, you have:

• Clutter competing with signal
• Wrong visual hierarchy
• Preattentive attributes on wrong elements

Data-Ink Ratio (Tufte)

For each element, ask: "Does this earn its ink?"

• Remove chart frames if not essential
• Lighten or remove gridlines
• Replace legends with direct labels
• Remove redundant axis lines

The 1+1=3 Principle (Tufte)

Two elements create emergent visual artifacts (the space between). Check:

• Dense grids creating moire
• Grouped bars creating unintended rhythms
• Close parallel lines creating "third" shapes

Colorblind Check

Verify with simulation (viridis is designed for CVD safety). Test: Would the message survive grayscale printing?

Reference Files

Consult as needed:

• viz-catalog.md — Form directory organized by visualization function
• color-palettes.md — Colormaps, categorical palettes, Porch Morning theme
• design-system.md — Typography, decluttering checklist, styling

Library preference: Use seaborn over raw matplotlib when possible. Seaborn provides cleaner defaults and better statistical visualization primitives.

Quick Reference: Common Mistakes

Mistake	Fix
Jet/rainbow colormap	Use forestdawn (diverging) or mako/rocket (sequential)
>5 colors in legend	Small multiples or direct labeling
Dual y-axes	Two separate plots or faceting
3D effects	Never. Use 2D with color/facets
Pie charts for comparison	Dot plot or bar chart
Bar chart not starting at zero	Start at zero (length encoding) or use dot plot
Truncated axis exaggerating effect	Show full range or use log scale
Heavy matplotlib defaults	Apply decluttering checklist