cmcrameri

All core colormaps are available via cmcrameri.cm.<colormapname>.

Naming conventions:

• Reversed colormaps: Append _r to the name (e.g., batlow_r).
• Categorical colormaps: These have the suffix S (e.g., batlowS).

You can use tab autocompletion on cmcrameri.cm in editors that support it to browse available names.

To use the colormaps in Matplotlib, import cmcrameri.cm as cmc. You can pass the colormap object directly to the cmap argument of plotting functions like plt.imshow().

To use a colormap by its registered name string (e.g., 'cmc.batlow'), you must first import cmcrameri to ensure the colormaps are registered with Matplotlib.

import cmcrameri.cm as cmc
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 1, 100)[np.newaxis, :]

# Using the colormap object directly
plt.imshow(x, aspect='auto', cmap=cmc.batlow)
plt.axis('off')
plt.show()

# Using the registered name string (requires 'import cmcrameri')
import cmcrameri
plt.imshow(x, aspect='auto', cmap='cmc.batlow')

You can install the cmcrameri package using either pip or conda (via the conda-forge channel).

# Using pip
python -m pip install cmcrameri

# Using conda
conda install -c conda-forge cmcrameri

The cmcrameri.cm module automatically loads and registers a collection of perceptually uniform colormaps from Fabio Crameri. When registered, these colormaps are prefixed with cmc. to avoid namespace collisions in Matplotlib.

To use them, you can either access them directly from the cmcrameri.cm namespace or refer to them by their registered names (e.g., 'cmc.batlow') in any Matplotlib plotting function.

import matplotlib.pyplot as plt
import cmcrameri.cm

# Use a colormap by its registered name
plt.imshow(data, cmap='cmc.batlow')

# Or use it directly from the namespace
plt.imshow(data, cmap=cmcrameri.cm.batlow)

For discretized colormaps (like batlow), you can create a version with a specific number of levels using the Matplotlib .resampled(N) method.

plt.imshow(x, aspect='auto', cmap=cmc.batlow.resampled(25))

To quickly see a visual representation of all available colormaps within your Python session, use the show_cmaps() function.

from cmcrameri import show_cmaps

show_cmaps()

The original colormap text files are included in the package. You can find their system paths by accessing paths from cmcrameri.cm.

from cmcrameri.cm import paths

print(paths)

You can use the show_cmaps function to generate a visual grid of all available colormaps, organized by category (Sequential, Diverging, Multi-sequential, and Cyclic). This is useful for inspecting the color scales before choosing one for your plot.

import cmcrameri.cm
import matplotlib.pyplot as plt

cmcrameri.cm.show_cmaps(ncols=6, figwidth=10)
plt.show()

The show_cmaps() function is exported at the package level and can be used to visualize the available colormaps provided by the package.

import cmcrameri

cmcrameri.show_cmaps()

The cm module is the primary entrypoint for accessing the collection of perceptually uniform colormaps. It serves as a wrapper for colormaps created by Fabio Crameri. You can import cm from the top-level cmcrameri package to access the available colormaps.

import cmcrameri

# Access colormaps through the cm module
# Example: cmcrameri.cm.batlow

Colormaps are categorized into four main types. Note that categorical and cyclic versions have specific suffixes in their names:

• Sequential: Standard perceptually uniform scales (e.g., batlow, devon, nuuk).
• Diverging: Scales designed to highlight deviations from a central value (e.g., broc, cork, vik).
• Multi-sequential: Scales with multiple sequential segments (e.g., oleron, bukavu, fes).
• Cyclic: Scales designed for periodic data, suffixed with O (e.g., romaO, bamO).
• Categorical: Discrete scales suffixed with S (e.g., batlowS).

All registered names in Matplotlib are prefixed with cmc..