nicopal 0.4.5

Nicopal: colour palettes designed by a protanopic student.

Nicopal

Context

These color palettes were created by Nicolas Tharaud inspired by the method of Fabio Crameri [1]. Their purpose is to represent climatic variables as accurately as possible, without visual mis-interpretation. A color palette must be careffuly chosen according to the variable being represented, so that it is interpreted in the same way by every observer. The color palettes were created by a protanope color-blind student (red color deficiency). Furthermore, the contrast of some palettes will be more visible to a protanope color-blind person than to someone with "normal" vision or a different type of color blindness, and vice versa.

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I. Installation

In a Python console or terminal :

pip install nicopal

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II. Usage

0. Loading the package

import nicopal as ncp

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1. Display the package version

print(ncp.version)

Displays the version and a very short description of the package.

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2. Quickly test all palettes

print(ncp.pal_test())

Checks that all palettes load correctly. Displays a confirmation message if everything is valid

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3. List available palettes

print(ncp.pal_list())

Displays all available palette names in Nicopal.

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4. Visualize a palette

print(ncp.pal_show("Lithium"))

Displays a gradient of the chosen palette. pal_show can be used with any palette from pal_list().

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5. Visualiser toutes les palettes

print(ncp.pal_all())

Displays all palettes and their associated names.

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6. Charger une palette en liste HEX

print(ncp.pal_hex("Lithium"))

Returns the palette as the corresponding HEX color codes.

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7. Use a palette as a colormap

colormap = ncp.pal("Lithium")
colormap_r = ncp.pal("Lithium", reverse=True)

ax.contourf(x, y, z, cmap=colormap)

reverse=True allows the colormap to be reversed.
ax.contourf can be replaced by any other Matplotlib function that accepts a colormap.
x, y and z are your data to visualize.

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8. Extract colors from a palette

sample = ncp.pal_sample("Carbon", 6)
x = ["A","B","C","D","E","F"]
y = [3,7,5,6,4,8]
plt.bar(x, y, color=sample)
plt.show()

Returns n discrete colors extracted from the palette.

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9. Palette demonstration

print(ncp.pal_demo("Lithium")

Displays example visualizations using the chosen palette

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10. Palette names

Boron | Carbon | Cesium | Chlorine | Cobalt | Iodine | Iron | Lithium | Magnesium | Manganese | Neon | Nitrogen | Oxygen | Rubidium | Selenium | Silicon | Sodium | Sulfur | Uranium | Vanadium | Zinc |

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III. Methodology

Documentation in progress...

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Bibliography :

[1] Crameri, Fabio, Grace E. Shephard, and Philip J. Heron. 2020. ‘The Misuse of Colour in Science Communication’. Nature Communications 11(1): 5444. doi:10.1038/s41467-020-19160-7.

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:) NT

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