create-2d-composition

name	create-2d-composition
locale	caveman
source_locale	en
source_commit	75ded7a1
translator	Julius Brussee homage — caveman
translation_date	2026-04-19
description	Compose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.
license	MIT
allowed-tools	Read Write Edit Bash Grep Glob
metadata	{"author":"Philipp Thoss","version":"1.0","domain":"visualization","complexity":"intermediate","language":"Python","tags":"svg, 2d, graphics, composition, diagrams, scripting, batch-processing"}

Create 2D Composition

Make 2D graphics by code. SVG building, diagram layout, image compositing, batch workflows. Covers vector, raster, typography, auto-production of charts, diagrams, infographics.

When Use

• Making diagrams, flowcharts, infographics by code
• Building reproducible scientific figures
• Auto-producing badges, icons, assets
• Compositing images or data visualizations
• Building custom chart types not in standard libs
• Batch graphics with param variations
• SVG templates for web or print

Inputs

Input	Type	Description	Example
Layout specification	Configuration	Dimensions, margins, grid layout	Canvas 800x600px, 20px margins
Visual elements	Data/Assets	Shapes, text, images, data points	Rectangle coordinates, labels, icons
Style parameters	CSS/Attributes	Colors, fonts, stroke widths, opacity	fill="#3366cc", stroke-width="2"
Data sources	Files/Arrays	Values to visualize or annotate	CSV data, JSON configuration
Output format	String	SVG, PNG, PDF, composite formats	output.svg, 300 DPI PNG

Steps

1. Set Up Python Environment

Install libs for 2D composition:

# Core libraries
pip install svgwrite pillow cairosvg

# Optional: advanced features
pip install drawsvg reportlab pycairo

# For data-driven graphics
pip install matplotlib numpy pandas

Got: Libraries installed fine. If fail: Check Python version (3.7+). Use virtual env

2. Create Basic SVG Graphics

Generate SVG with svgwrite:

import svgwrite
from svgwrite import cm, mm

def create_basic_svg(output_path):
    """Create a simple SVG graphic."""
    # Initialize drawing (use mm for precise dimensions)
    dwg = svgwrite.Drawing(output_path, size=('180mm', '120mm'), profile='full')

    # Add background rectangle
    dwg.add(dwg.rect(
        insert=(0, 0),
        size=('100%', '100%'),
        fill='white'
    ))

    # Add shapes
    dwg.add(dwg.circle(
        center=(90*mm, 60*mm),
        r=30*mm,
        fill='lightblue',
        stroke='navy',
        stroke_width=2
    ))

    dwg.add(dwg.rect(
        insert=(30*mm, 30*mm),
        size=(60*mm, 40*mm),
        fill='lightgreen',
        stroke='darkgreen',
        stroke_width=2,
        rx=5,  # Rounded corners
        ry=5
    ))

    # Add text
    dwg.add(dwg.text(
        'Example Graphic',
        insert=(90*mm, 20*mm),
        text_anchor='middle',
        font_size='18pt',
        font_family='Arial',
        fill='black'
    ))

    dwg.save()
    print(f"Saved: {output_path}")

Got: SVG file made with shapes and text. If fail: Check svgwrite version. Confirm output dir writable

3. Build Diagrams with Layout Logic

Build structured diagrams with calculated positions:

def create_flowchart(steps, output_path):
    """Generate a flowchart from list of steps."""
    dwg = svgwrite.Drawing(output_path, size=('800px', '600px'))

    # Layout parameters
    box_width = 120
    box_height = 60
    spacing_y = 100
    start_x = 340
    start_y = 50

    for i, step in enumerate(steps):
        y_pos = start_y + i * spacing_y

        # Draw box
        box = dwg.add(dwg.g(id=f'step_{i}'))

        box.add(dwg.rect(
            insert=(start_x, y_pos),
            size=(box_width, box_height),
            fill='lightblue',
            stroke='navy',
            stroke_width=2,
            rx=5,
            ry=5
        ))

        # Add text (wrapped if needed)
        text_lines = wrap_text(step, max_width=16)
        text_y = y_pos + box_height/2 - (len(text_lines)-1) * 7

        for j, line in enumerate(text_lines):
            box.add(dwg.text(
                line,
                insert=(start_x + box_width/2, text_y + j*14),
                text_anchor='middle',
                font_size='12pt',
                font_family='Arial',
                fill='black'
            ))

        # Draw arrow to next step
        if i < len(steps) - 1:
            arrow_start_y = y_pos + box_height
            arrow_end_y = y_pos + spacing_y

            dwg.add(dwg.line(
                start=(start_x + box_width/2, arrow_start_y),
                end=(start_x + box_width/2, arrow_end_y),
                stroke='black',
                stroke_width=2,
                marker_end=dwg.marker(
                    id='arrow',
                    viewBox='0 0 10 10',
                    refX=5,
                    refY=5,
                    markerWidth=6,
                    markerHeight=6,
                    orient='auto'
                )
            ))

    dwg.save()

def wrap_text(text, max_width=20):
    """Simple text wrapping."""
    words = text.split()
    lines = []
    current_line = []

    for word in words:
        test_line = ' '.join(current_line + [word])
        if len(test_line) <= max_width:
            current_line.append(word)
        else:
            if current_line:
                lines.append(' '.join(current_line))
            current_line = [word]

    if current_line:
        lines.append(' '.join(current_line))

    return lines

Got: Flowchart with connected boxes and arrows. If fail: Tune layout math. Verify arrow marker definitions

4. Composite Raster Images

Combine many images with Pillow:

Full Pillow composite example (grid, horizontal, vertical layout + image annotate) live in references/EXAMPLES.md.

Got: Composite image made with right layout. If fail: Check all input images exist. Verify image modes match

5. Generate Data-Driven Graphics

Make visualizations from data:

import numpy as np

def create_bar_chart_svg(data, labels, output_path):
    """Generate SVG bar chart from data."""
    dwg = svgwrite.Drawing(output_path, size=('600px', '400px'))

    # Chart area
    margin = 50
    chart_width = 500
    chart_height = 300
    bar_spacing = 10

    # Calculate bar dimensions
    n_bars = len(data)
    bar_width = (chart_width - (n_bars - 1) * bar_spacing) / n_bars

    # Scale data to fit chart
    max_value = max(data)
    scale = chart_height / max_value

    # Draw axes
    dwg.add(dwg.line(
        start=(margin, margin),
        end=(margin, margin + chart_height),
        stroke='black',
        stroke_width=2
    ))
    dwg.add(dwg.line(
        start=(margin, margin + chart_height),
        end=(margin + chart_width, margin + chart_height),
        stroke='black',
        stroke_width=2
    ))

    # Draw bars
    for i, (value, label) in enumerate(zip(data, labels)):
        x = margin + i * (bar_width + bar_spacing)
        bar_height = value * scale
        y = margin + chart_height - bar_height

        # Bar
        dwg.add(dwg.rect(
            insert=(x, y),
            size=(bar_width, bar_height),
            fill='steelblue',
            stroke='navy',
            stroke_width=1
        ))

        # Value label
        dwg.add(dwg.text(
            f'{value:.1f}',
            insert=(x + bar_width/2, y - 5),
            text_anchor='middle',
            font_size='10pt',
            fill='black'
        ))

        # X-axis label
        dwg.add(dwg.text(
            label,
            insert=(x + bar_width/2, margin + chart_height + 20),
            text_anchor='middle',
            font_size='10pt',
            fill='black'
        ))

    dwg.save()

Got: SVG bar chart with scaled data. If fail: Handle edge cases (empty data, negatives). Add validation

6. Batch Generate Graphics

Auto-make many graphics:

def batch_generate_badges(users, template_path, output_dir):
    """Generate badge for each user."""
    os.makedirs(output_dir, exist_ok=True)

    for user in users:
        output_path = os.path.join(output_dir, f"{user['id']}_badge.svg")

        dwg = svgwrite.Drawing(output_path, size=('300px', '100px'))

        # Background
        dwg.add(dwg.rect(
            insert=(0, 0),
            size=('100%', '100%'),
            fill='#3366cc',
            rx=10,
            ry=10
        ))

        # User name
        dwg.add(dwg.text(
            user['name'],
            insert=(150, 40),
            text_anchor='middle',
            font_size='20pt',
            font_weight='bold',
            fill='white'
        ))

        # User role
        dwg.add(dwg.text(
            user['role'],
            insert=(150, 70),
            text_anchor='middle',
            font_size='14pt',
            fill='lightblue'
        ))

        dwg.save()
        print(f"Generated badge: {output_path}")

Got: Individual graphic made for each data item If fail: Check data structure. Handle missing fields with defaults

7. Convert SVG to Raster

Export SVG to PNG/PDF for many uses:

import cairosvg

def svg_to_png(svg_path, png_path, dpi=300):
    """Convert SVG to PNG with specified DPI."""
    # Calculate pixel dimensions from DPI
    # Assuming A4 size as example
    width_inches = 8.27
    height_inches = 11.69

    width_px = int(width_inches * dpi)
    height_px = int(height_inches * dpi)

    cairosvg.svg2png(
        url=svg_path,
        write_to=png_path,
        output_width=width_px,
        output_height=height_px
    )
    print(f"Converted to PNG: {png_path}")

def svg_to_pdf(svg_path, pdf_path):
    """Convert SVG to PDF."""
    cairosvg.svg2pdf(url=svg_path, write_to=pdf_path)
    print(f"Converted to PDF: {pdf_path}")

Got: Raster output at right resolution If fail: Install cairo system library if missing. Check SVG validity

Checks

• Graphics render right in target apps
• Text readable, positioned well
• Colors match specs
• Dimensions fit use case
• SVG validates (if needed)
• Raster exports have right DPI
• Layout adapts to data
• Batch processing finishes clean
• Output files organized
• Code has error handling

Pitfalls

1. Unit confusion: SVG units (px, mm, cm) vs screen pixels vs print DPI
2. Text overflow: Text past shape boundaries. Wrap it
3. Font availability: System fonts differ. Embed or use web-safe fonts
4. Coordinate math: Off-by-one errors in grid layouts
5. Color format: SVG uses hex strings (#rrggbb), not tuples
6. SVG validity: Check XML. Close all tags
7. File paths: Handle special chars, spaces in filenames
8. Memory usage: Big batch jobs may need chunking
9. Aspect ratio: Keep proportions when resizing
10. Transparency: PNG supports alpha. JPEG does not

See Also

• render-publication-graphic — publication output
• create-3d-scene — similar programmatic approach for 3D
• generate-quarto-report — integrating graphics into reports