colormaps-styling

# Colormaps & Styling Skill

## Overview

Master color management and visual styling with Colorcet and theme customization. Select appropriate colormaps, create accessible visualizations, and apply consistent application styling.

### What is Colorcet?

Colorcet provides perceptually uniform colormaps designed for scientific visualization:

- **Perceptually uniform**: Changes in data correspond to proportional visual changes
- **Colorblind-friendly**: Palettes designed for accessibility
- **Purpose-built**: Specific colormaps for different data types
- **HoloViz integration**: Seamless use across HoloViews, Panel, and Bokeh

## Quick Start

### Installation

```bash
pip install colorcet
```

### Basic Usage

```python
import colorcet as cc
from colorcet import cm
import holoviews as hv

hv.extension('bokeh')

# Use a colormap
data.hvplot.scatter('x', 'y', c='value', cmap=cm['cet_goertzel'])
```

## Core Concepts

### 1. Colormap Categories

**Sequential**: Single hue, increasing intensity
```python
# Blues, greens, reds, grays
data.hvplot('x', 'y', c='value', cmap=cm['cet_blues'])
```

**Diverging**: Two hues from center point
```python
# Emphasize positive/negative
data.hvplot('x', 'y', c='value', cmap=cm['cet_coolwarm'])
```

**Categorical**: Distinct colors for categories
```python
# Qualitative data
data.hvplot('x', 'y', c='category', cmap=cc.palette['tab10'])
```

**Cyclic**: Wraps around for angular data
```python
# Angles, directions, phases
data.hvplot('x', 'y', c='angle', cmap=cm['cet_cyclic_c1'])
```

**See**: [Colormap Reference](../../references/colormaps/colormap-reference.md) for complete catalog

### 2. Accessibility

**Colorblind-safe palettes**:
```python
# Deuteranopia (red-green)
cmap=cm['cet_d4']

# Protanopia (red-green)
cmap=cm['cet_p3']

# Tritanopia (blue-yellow)
cmap=cm['cet_t10']

# Grayscale-safe
cmap=cm['cet_gray_r']
```

**See**: [Accessibility Guide](../../references/colormaps/accessibility.md) for comprehensive guidelines

### 3. Color