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Examples

Interactive Jupyter notebooks demonstrating the features and capabilities of PyBuenColors.

Available Notebooks

Helper Functions

Comprehensive demonstration of utility functions for creating publication-ready plots:

  • eject_legend() - Move legends outside the plot area to avoid obscuring data
  • rotate_discrete_xticks() - Rotate x-axis labels for better readability
  • grab_legend() - Extract legends to separate figures for independent saving
  • get_density() - Compute point density for colored scatter plots
  • shuffle() - Randomize data order to prevent plotting artifacts
  • number_to_color() - Map numerical values to colors from any palette

Color Palettes

Explore the 117+ scientific color palettes available in PyBuenColors:

  • list_palettes() - Browse and filter available palettes
  • display_palette() - Visualize individual palettes
  • get_palette() - Extract colors for use in plots
  • Wes Anderson-inspired palettes
  • Scientific visualization color schemes
  • Sequential and diverging gradients

Single-Cell Analysis

Specialized functions for single-cell RNA-seq visualization with Scanpy integration:

  • clean_umap() - Create publication-quality UMAP plots with minimal decorations
  • Gene expression visualization with custom colormaps
  • L-shaped axis indicators for dimensional reduction plots
  • Multi-panel figures for publications
  • Integration with Scanpy workflows

Optional Dependencies

The single-cell analysis notebook requires scanpy and anndata: 

pip install buencolors scanpy anndata

Running Locally

To run these notebooks on your own machine:

# Install Jupyter
pip install jupyter

# Clone the repository
git clone https://github.com/austinv11/PyBuenColors.git
cd PyBuenColors/docs/examples

# Launch Jupyter
jupyter notebook

All notebooks are designed to run independently and include detailed explanations of each feature.