算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
datavis【设计与创意】Comprehensive data visualization toolkit for creating beautiful, mathematically elegant visualizations with D3.js, Chart.js, and custom SVG. Use when (1) building interactive data visualizations, (2) designing color palettes for charts, (3) choosing scales and visual encodings, (4) creating data pipelines from Census/SEC/Wikipedia APIs, (5) crafting …
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Create beautiful, mathematically elegant, emotionally resonant data visualizations.
Every visualization should:
Scale Selection:
| Scale | Use When | Example |
|---|---|---|
| Linear | Evenly distributed data | Temperature |
| Log | Multiple orders of magnitude | Population (100 to 1B) |
| Sqrt | Encoding area (circles) | Bubble chart radius |
| Time | Temporal data | Dates |
Perceptual Honesty - Area scales with square of radius, so use sqrt:
// WRONG: Linear radius exaggerates large values
const badScale = d3.scaleLinear().domain([0, max]).range([0, maxRadius]);
// RIGHT: Sqrt maintains perceptual accuracy
const goodScale = d3.scaleSqrt().domain([0, max]).range([0, maxRadius]);
Palette Types:
Colorblind-Safe Palette (8 colors):
const colorblindSafe = [
'#332288', '#117733', '#44AA99', '#88CCEE',
'#DDCC77', '#CC6677', '#AA4499', '#882255'
];
Always use redundant encoding - don't rely on color alone:
node.attr('fill', d => colorScale(d.category))
.attr('d', d => symbolScale(d.category)); // Shape too!
Force Simulation:
const simulation = d3.forceSimulation(nodes)
.force('charge', d3.forceManyBody().strength(-300))
.force('link', d3.forceLink(links).id(d => d.id))
.force('center', d3.forceCenter(width/2, height/2))
.force('collision', d3.forceCollide().radius(d => d.r + 2));
Responsive SVG:
const svg = d3.select('#chart')
.append('svg')
.attr('viewBox', `0 0 ${width} ${height}`)
.attr('preserveAspectRatio', 'xMidYMid meet');
Touch-Friendly (44x44px minimum):
node.append('circle')
.attr('class', 'hit-area')
.attr('r', Math.max(actualRadius, 22))
.attr('fill', 'transparent');
Three Acts:
Progressive Disclosure:
Level 1: Overview → Level 2: Exploration → Level 3: Detail → Level 4: Context
Structure:
scripts/
├── 01_fetch_raw.py # API calls with caching
├── 02_clean_data.py # Transformation
├── 03_validate.py # Quality checks
└── 04_export.py # Final format
Source Documentation (every dataset needs):
scripts/color-palette.py --type sequential --hue blue --steps 9
scripts/color-palette.py --type categorical --count 6 --colorblind-safe
scripts/color-palette.py --type diverging --low red --high blue
scripts/analyze-distribution.py data.csv --column value
# Outputs: min, max, skew ratio, recommended scale
scripts/d3-scaffold.py my-viz --type force-network
scripts/d3-scaffold.py my-viz --type timeline
scripts/d3-scaffold.py my-viz --type choropleth