How to customize building colors in prettymaps?

Set a color palette in the style dictionary under 'building'—for example, use 'palette': ['#FFC857', '#E9724C', '#C5283D'] to assign sequential colors. You can also adjust edge colors and line widths for finer control.

prettymaps vs Folium for Python mapping?

prettymaps focuses on creating artistic, static visualizations with high customizability from OSM data, using matplotlib. Folium is better for interactive web maps with Leaflet.js, offering zoom and pop-ups but less stylistic control. Choose based on output needs: art vs. interactivity.

How to save a prettymaps plot as an SVG file?

Use the 'plotter' mode to generate SVG output directly, or access the matplotlib figure (plot.fig) and call savefig() with an SVG format. For pen plotter compatibility, enable 'mode = 'plotter'' in the plot function.

Can I use prettymaps for commercial projects?

Yes, it's licensed under AGPL v3.0, which permits commercial use but requires disclosing source code modifications. However, the author strongly discourages use for NFTs due to ethical concerns, as noted in the README.

How to plot multiple regions on one map with prettymaps?

Use the multiplot and Subplot classes—create Subplot instances for each region with specific queries and styles, then pass them to multiplot(). This arranges them on a single canvas, as shown in the tutorial examples.

Does prettymaps work with Google Colab?

Yes, there's a dedicated installation command for Google Colaboratory ('!pip install -e git+...'), but you must restart the runtime after installation. A demo notebook is provided in the README for easy experimentation.

Open-Awesome

Prettymaps

AGPL-3.0Jupyter Notebookv1.4.2

A minimal Python library to draw customized, stylized maps from OpenStreetMap data using osmnx, matplotlib, and shapely.

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What is Prettymaps?

prettymaps is a Python library that generates stylized maps from OpenStreetMap data. It fetches geographic features like buildings, streets, and water bodies, and applies customizable visual styles using matplotlib, allowing users to create artistic and informative map visualizations with minimal code.

Target Audience

Python developers, data visualization enthusiasts, generative artists, and designers who want to create custom map illustrations for projects, presentations, or artistic works.

Value Proposition

It simplifies the process of fetching and styling OpenStreetMap data into a single, intuitive Python library, offering extensive customization through layers and presets without requiring deep GIS expertise.

Overview

Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely

Use Cases

Best For

Creating artistic map illustrations for print or digital media
Generating custom map styles for data visualization projects
Producing pen plotter-compatible SVG maps for physical art
Visualizing geographic areas with tailored color palettes and layer compositions
Teaching geospatial data concepts with visually engaging examples
Prototyping map designs for applications or presentations

Not Ideal For

Projects requiring real-time or interactive map displays with zoom/pan functionality
High-performance geospatial analysis needing optimized data processing and querying
Teams wanting drop-in, pre-styled maps without writing Python code for customization
Applications needing frequent, up-to-the-second map data updates due to OSM fetching delays

Pros & Cons

Pros

Rich Customization Options

Allows granular control over layers and styles using OSM tags and matplotlib parameters, as shown in the extensive layer and style dictionary examples in the README.

Preset System for Reusability

Enables saving and sharing visual styles via JSON presets (e.g., 'minimal', 'barcelona'), facilitating consistent designs and quick experimentation across projects.

Pen Plotter Compatibility

Offers a dedicated 'plotter' mode that exports SVG files compatible with physical pen plotters using vsketch integration, ideal for generative art and physical media.

Direct GeoDataFrame Access

Provides access to underlying GeoDataFrames for each layer, allowing for additional geographic analysis or custom visualizations beyond the built-in plotting.

Cons

Slow Data Fetching

Fetching geodataframes from OpenStreetMap can take several seconds (e.g., 14+ seconds in examples), making it impractical for dynamic or real-time applications.

Complex Customization Setup

Requires understanding of both OSM data structure (tags) and matplotlib styling, with no GUI, leading to a steep learning curve for non-technical users.

Static Output Limitations

Generates only static images or SVGs with no built-in interactivity, restricting use in web-based or interactive mapping scenarios without additional tools.

Heavy Dependency Chain

Relies on multiple large libraries (osmnx, matplotlib, shapely, vsketch), increasing setup complexity and potential version conflicts in production environments.

Frequently Asked Questions

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