advanced-rendering

# Advanced Rendering Skill

## Overview

Master high-performance rendering for large datasets with Datashader and optimization techniques. This skill covers handling 100M+ point datasets, performance tuning, and efficient visualization strategies.

## Dependencies

- datashader >= 0.15.0
- colorcet >= 3.1.0
- holoviews >= 1.18.0
- pandas >= 1.0.0
- numpy >= 1.15.0

## Core Capabilities

### 1. Datashader Fundamentals

Datashader is designed for rasterizing large datasets:

```python
import datashader as ds
from datashader.mpl_ext import _colorize
import holoviews as hv

# Load large dataset (can handle 100M+ points)
df = pd.read_csv('large_dataset.csv')  # Millions or billions of rows

# Create datashader canvas
canvas = ds.Canvas(plot_width=800, plot_height=600)

# Rasterize aggregation
agg = canvas.points(df, 'x', 'y')

# Convert to image
img = agg.to_array(True)
```

### 2. Efficient Point Rendering

```python
from holoviews.operation.datashader import datashade, aggregate, shade

# Quick datashading with HoloViews
scatter = hv.Scatter(df, 'x', 'y')
shaded = datashade(scatter)

# With custom aggregation
agg = aggregate(scatter, width=800, height=600)
colored = shade(agg, cmap='viridis')

# Control rasterization
from holoviews.operation import rasterize

rasterized = rasterize(
    scatter,
    aggregator=ds.count(),
    pixel_ratio=2,
    upsample_method='interp'
)
```

### 3. Color Mapping and Aggregation

```python
import datashader as ds
from colorcet import cm

# Count aggregation (heatmap)
canvas = ds.Canvas()
agg = canvas.points(df, 'x', 'y', agg=ds.count())

# Weighted aggregation
agg = canvas.points(df, 'x', 'y', agg=ds.sum('value'))

# Mean aggregation
agg = canvas.points(df, 'x', 'y', agg=ds.mean('value'))

# Custom colormapping
import datashader.transfer_functions as tf

shaded = tf.shade(agg, cmap=cm['viridis'])
shaded_with_spread = tf.spread(shaded, px=2)
```

### 4. Image Compositing

```python
# Combine multiple datasets
canvas = ds.Canvas(x_range=