scicolor 1.3

Collections of color maps for scientific visualizations

Introduction

This python package contains a collection of nice color maps for scientific visualizations and provides a convinient and unified way to pick the color maps. Some color maps are perceptually uniform and/or color blind friendly.

You can also check out the Scicolor Color Picker, a web app for browsing and picking colors.

Quick start

Install

The package is available through pypi. You can install it using the following command:

pip install scicolor

Import the package

import scicolor

Get a color map

scicolor.get_cmap('viridis')

scicolor.get_cmap() works just like plt.get_cmap(). The returned value can be instances of either matplotlib.colors.ListedColormap or matplotlib.colors.LinearSegmentedColormap.

You can access the colors of matplotlib.colors.ListedColormap instances by .colors.

List available color maps

List all color maps

scicolor.list_cmaps()

List the color maps that are sequential, discrete, and color blind friendly

scicolor.list_cmaps(cm_class='sequential', cm_type="discrete", color_blind_friendly=True)

See below for more on the classes and types of the color maps.

Plot color maps

Plot a color map

scicolor.plot_cmaps('tab20')

Plot multiple color maps

scicolor.plot_cmaps(['tab20', 'tab20b'])

Plot the color maps that are sequential, discrete, and color blind friendly

scicolor.plot_cmaps(cm_class='sequential', cm_type="discrete", color_blind_friendly=True)

Below is a screen shot of scicolor.plot_cmaps(), which shows all available color maps.

Characteristics of the color maps

See the following table for different characteristics of the color maps. You can use the corresponding paramters in scicolor.list_cmaps() and scicolor.plot_cmaps() to filter the results.

Characteristic	Parameter	Values
class	cm_class	'categorical', 'diverging', 'misc', 'multi_sequential', 'sequential'
type	cm_type	'categorical', 'continuous', 'discrete'
perceptually uniform	perceptually_uniform	True, None
color blind friendly	color_blind_friendly	True, None

See the guideline below for more on the class and type.

Guideline for choosing color maps

Figure below contains the guideline for choosing color maps.

The figure was borrowed from The misuse of colour in science communication.

Color maps sources

The color maps come from different sources, see the table below for details

Source	Color maps	Link
matplotlib	tab20, tab20b, tab20c, viridis, inferno, cividis	https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
Scientific colour maps	batlow10, batlow25, acton10, acton25, oslo10, oslo25, turku10, turku25, vik10, vik25, roma10, roma25, broc10, broc25, oleron10, oleron25, batlowS, batlow, acton, oslo, turku, vik, roma, broc, oleron	http://www.fabiocrameri.ch/colourmaps.php
Wes Anderson	Royal2, Zissou1, Darjeeling1, FantasticFox1, Moonrise3, Cavalcanti1, GrandBudapest2, IsleofDogs2	https://github.com/karthik/wesanderson
CET	glasbey, cwr, isolum	https://peterkovesi.com/projects/colourmaps/
ocean	topo	https://matplotlib.org/cmocean/
Met Brewer	Archambault, Cassatt1, Cassatt2, Demuth, Derain, Egypt, Greek, Hiroshige, Hokusai2, Ingres, Isfahan1, Isfahan2, Java, Johnson, Kandinsky, Morgenstern, OKeeffe1, OKeeffe2, Pillement, Tam, Troy, Veronese	https://github.com/BlakeRMills/MetBrewer

Matplotlib

The present package simply offers a wrapper for the selected color maps that are included in matplotlib. To add more matplotlib color maps to scicolor, just add the information to color_info_list in __init__.py.

Scientific colors

Scientific colour maps provides a series of perceptually uniform and color blind friendly color maps. To expand the list included in scicolor:

1. Download the data from zenodo
2. Copy the .txt files to /path/to/scicolor/scicolor/scientific_colors
3. Add the information to color_info_list in __init__.py

Wes Anderson

Wes Anderson Palettes provides a series of categorical colors extract from Wes Anderson movies. Note that the color maps might not be color blind friendly, use with cautions. To expand the list:

1. Copy the color data to __init__.py
2. Add the information to color_info_list in __init__.py

CET

CET (Centre for Exploration Targeting) offers many perceptually uniform color maps. To expand the list

1. Download the color data from github to /path/to/scicolor/scicolor/cet_colors
2. Assgin meaniful names to the color data files. References can be found in their script
3. Add the information to color_info_list in __init__.py

ocean

CMOcean (colormaps inspired by oceanography) provides a series of perceptually uniform color maps. To expand the list

1. Download the color data from github to /path/to/scicolor/scicolor/ocean_colors
2. Add the information to color_info_list in __init__.py

MetBrewer

MetBrewer provides a series of palettes inspired by works at the Metropolitan Museum of Art in New York. Currently the scicolor package only includes the color-blind-friendly colors from MetBrewer.

Requirements

• python3
• matplotlib
• pandas
• numpy

Development and Publishing

For Contributors

This project uses uv for dependency management and packaging.

Setup Development Environment

# Install dependencies and sync virtual environment
uv sync

# Test the package
uv run python -c "import scicolor; print(scicolor.list_cmaps())"

Adding Dependencies

# Add a new dependency
uv add package-name

# Add a development dependency
uv add --dev package-name

Developing locally

Test the package locally with Jupyter notebooks through the following command:

uv run --with jupyter jupyter notebook

Bump the version of the package with the following command:

uv version --bump <patch|minor|major>

Publishing to PyPI

Testing with TestPyPI (Recommended First)

Before publishing to the real PyPI, test with TestPyPI:

1. Create TestPyPI Account

   • Register at https://test.pypi.org/account/register/
   • Verify your email

2. Create API Token

   • Go to https://test.pypi.org/manage/account/
   • Create a new API token (save it immediately)

3. Build and Publish to TestPyPI

   # Clean previous builds
   rm -rf dist/
   
   # Build the package
   uv build
   
   # Publish to TestPyPI
   uv publish --publish-url https://test.pypi.org/legacy/
   
   # When prompted:
   # Username: __token__
   # Password: <paste your TestPyPI token>
   

4. Test Installation from TestPyPI

   pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ scicolor
   

   Note: --extra-index-url is needed because dependencies are on regular PyPI.

Publishing to Production PyPI

Once verified on TestPyPI:

1. Create PyPI Account

   • Register at https://pypi.org/account/register/

2. Create API Token

   • Go to https://pypi.org/manage/account/
   • Create a new API token

3. Build and Publish

   # Clean and build
   rm -rf dist/
   uv build
   
   # Publish to PyPI
   uv publish