TextShape

A high-performance Python library for shaping text into columns using advanced line breaking algorithms and precise character positioning with HarfBuzz. TextShape is built with performance in mind, vectorizing most operations with NumPy for efficient text layout operations.

Features

• Advanced Text Wrapping: Intelligent line breaking with support for hyphenation
• Multi-Column Page Layout: Support for multiple columns on a page with customizable spacing for margins
• Font-Aware Rendering: Precise character positioning using HarfBuzz
• Justification: Full text justification support
• Performance Optimized: Vectorized operations with NumPy for speed
• SVG Output: Generate SVG visualizations of text

Installation

Install TextShape using pip:

pip install textshape

Requirements

• Python 3.9+
• NumPy >= 2.0.0
• vharfbuzz

Optional dependency for hyphenation:

• hyperhyphen

Quick Start

from textshape import FontMeasure, TextFragmenter, TextColumn

# Load a font
font_path = "path/to/your/font.ttf"
font_measure = FontMeasure(font_path)

# Create text fragments
text = "Your text here..."
fragmenter = TextFragmenter(measure=font_measure)
fragments = fragmenter(text)

# Create a text column
column = TextColumn(
    fragments=fragments,
    column_width=300,  # Width in points
    fontsize=12,
    justify=True
)

# Get bounding boxes for rendering
text, x, dx, x_origin, y, dy, y_origin = column.to_bounding_boxes()

Usage Examples

Basic Text Wrapping

from textshape import FontMeasure, TextFragmenter, TextColumn

# Setup
font_measure = FontMeasure("fonts/NotoSans-Regular.ttf")
fragmenter = TextFragmenter(measure=font_measure)

text = """Whether I shall turn out to be the hero of my own life, or whether that 
station will be held by anybody else, these pages must show."""

# Fragment and wrap text
fragments = fragmenter(text)
column = TextColumn(
    fragments=fragments,
    column_width=31 * 12,  # 31 characters at 12pt
    fontsize=12,
    justify=False
)

# Get wrapped lines as strings
lines = column.to_list()
for i, line in enumerate(lines):
    print(f"{i+1:02d}: {line}")

Multi-Column Layout

from textshape import FontMeasure, TextFragmenter, MultiColumn

# Setup for multi-column layout
font_measure = FontMeasure("fonts/NotoSans-Regular.ttf")
fragmenter = TextFragmenter(measure=font_measure)

# Long text content
text = "Your long text content here..."
fragments = fragmenter(text)

# Create multi-column layout
multi_column = MultiColumn(
    fragments=fragments,
    column_width=250,
    fontsize=12,
    justify=True
)

# Get bounding boxes with column information
text, x, dx, x_origin, y, dy, y_origin, column_id = multi_column.to_bounding_boxes(
    max_lines_per_column=20,
    line_spacing=1.2
)

Page Layout with Margins

from textshape import FontMeasure, TextFragmenter, MultiColumn, Layout

# Setup
font_measure = FontMeasure("fonts/NotoSans-Regular.ttf")
fragmenter = TextFragmenter(measure=font_measure)

text = "Your document text..."
fragments = fragmenter(text)

# Create page layout
layout = Layout(
    columns=2,
    column_spacing=15,
    page_size=(600, 800),  # width, height
    margins=50  # uniform margins
)

# Create multi-column text
multi_column = MultiColumn(
    fragments=fragments,
    column_width=layout.column_widths,
    fontsize=12,
    justify=True
)

# Get positioned text for the layout
text, x, dx, x_origin, y, dy, y_origin, page = layout.to_bounding_boxes(multi_column)

SVG Rendering

# Generate SVG output
svg_content = font_measure.render_svg(
    text=text,
    x_origin=x_origin,
    y_origin=y_origin,
    fontsize=12,
    canvas_width=600,
    canvas_height=800
)

# Save to file
with open("output.svg", "w") as f:
    f.write(svg_content)

Note on Coordinate System: TextShape uses the SVG/HTML coordinate system where the origin (0, 0) is at the top-left corner, and the y-axis increases downward. All coordinates returned by to_bounding_boxes() follow this convention.

Line breaking with hyphenation

import re
from hyperhyphen import Hyphenator

hyph = Hyphenator(mode="spans", language="en_US")

# Use custom splitter
fragmenter = TextFragmenter(
    measure=font_measure,
    splitter=hyph,
)

API Reference

Core Classes

FontMeasure

Handles font loading and character measurement using HarfBuzz.

FontMeasure(fontpath: str, features: Optional[dict] = None)

TextFragmenter

Breaks a string of text into atomic fragments. A fragment cannot be split further, and lines can only be broken at fragment boundaries. Fragments can be full words, or parts of words if allowing for hyphenation.

TextFragmenter(
    measure: Optional[Callable] = None,
    splitter: Optional[Callable] = None,
    tab_width: float | int = 4
)

TextColumn

Wraps text fragments into a single column.

TextColumn(
    fragments: TextFragments,
    column_width: int | float | list[float],
    fontsize: int | float,
    justify: bool = False
)

MultiColumn

Extends TextColumn to support multiple columns.

MultiColumn(
    fragments: TextFragments,
    column_width: int | float | list[float],
    fontsize: int | float,
    justify: bool = False
)

Layout

Manages page layout with multiple columns and margins.

Layout(
    columns: int,
    column_spacing: float,
    page_size: tuple[float, float],
    margins: float | tuple[float, ...] 
)

Advanced Features

Text Justification

Enable full justification to align text to both left and right margins:

column = TextColumn(fragments, column_width=300, fontsize=12, justify=True)

Variable Column Widths

Support different widths for each line:

import numpy as np

# Varying column widths
widths = np.linspace(200, 400, num_lines)
column = TextColumn(fragments, column_width=widths, fontsize=12)

Custom Line Spacing

Control spacing between lines:

text, x, dx, x_origin, y, dy, y_origin = column.to_bounding_boxes(line_spacing=1.5)

Performance Tips

1. Reuse FontMeasure objects - Font loading is expensive
2. Use vectorized operations - The library is optimized for batch processing
3. Cache fragments - TextFragmenter results can be reused for different layouts
4. Choose appropriate column widths - Very narrow columns increase computation time

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Acknowledgments

• Built on top of HarfBuzz for text shaping
• Uses NumPy for high-performance array operations
• Inspired by advanced typesetting systems