Library generated for making plots with better color palette. It uses famous color palettes and adds few helpful functions.
Currently it supports following Color Palettes
- IBM Color Palette (
ibm) - Google Material Design Color Palette (
material) - Google Material Design Accent Color Palette (
material-accent) - ColorBrewer2 Color Palette (
brewer) - VMWare Clarity Color Palette (
clarity) - Tableau Color Palette (
tableau)
You can get output of colors in variety of color formats including hex , rgb, rgba etc.
See changelog to know what's new!
Few sample plots and inspiration behind this library can be found in WeirdData blog.
 |
|---|
| Default base colors in matplotlib and SecretColors palettes. |
 |
|---|
| Simple bar plot with default colors. |
 |
|---|
| Histogram comparison with Magenta and Cyan. You can dramatically change colors by just passing single parameter. |
pip install SecretColors
Full documentation and API reference can be accessed via ReadTheDocs
SecretColors is a very flexible library. You can easily select different color palettes.
from SecretColors import Palette
p = Palette() # Generates Default color palette i.e. IBM Color Palette
ibm = Palette("ibm") # Generates IBM Palette
ibm.red() # Returns '#fb4b53'
material = Palette("material") # Generates Material Palette
material.red() # Returns '#f44336'
Select different types of color modes
p1 = Palette() # Default Color mode (hex)
p1.green() # '#24a148'
p2 = Palette(color_mode="hexa")
p2.green() # '#24a148ff'
p3 = Palette(color_mode="ahex")
p3.green() # '#ff24a148'
p4 = Palette(color_mode="rgb")
p4.green() # (0.141, 0.631, 0.282)
p5 = Palette(color_mode="rgba")
p5.green() # '(0.141, 0.282, 0.631, 1)'
Note: matplotlib can accepts hex, rgb or hexa
Get random colors
p = Palette()
p.random() # '#90dbe9'
p.random(no_of_colors=3) # ['#8fca39', '#64a0fe', '#7430b6']
p.random(no_of_colors=2, shade=20) # ['#b3e6ff', '#c2dbf4']
Unlimited color manipulations
p = Palette()
p.blue() # normal blue [#408bfc]
p.blue(shade=20) # lighter shade of blue [#c9deff]
p.blue(shade=70) # darker shade of blue [#054ada]
p.blue(shade=16.10) # arbitrary shade of blue (between 0 to 100) [#dbe9ff]
p.blue(no_of_colors=3) # Three blue shades ['#b8d4ff', '#408bfc', '#0546d4']
p.blue(no_of_colors=3, starting_shade=30) # Three blue shades with lightest one is 30 ['#64a0fe', '#005ef9', '#052ea8']
p.blue(no_of_colors=3, ending_shade=40) # Three blue shades with darkest one is 40 ['#edf4ff', '#c9deff', '#97c1ff']
p.blue(no_of_colors=3, starting_shade=30, ending_shade=40) # Three blue shades with lightest 30 and darkest 60 ['#8cbaff', '#8cbaff', '#8cbaff']
p.blue(no_of_colors=3, gradient=False) # Three blue shades in random order ['#8cbaff', '#b8d4ff', '#64a0fe']
p.color_mode = "rgba"
p.blue(alpha=0.3) # Blue with alpha (0.251, 0.988, 0.545, 0.3) Only works in color mode which outputs alpha values
Flexible functions
get_complementary("#24a148") # Get complementary color [#a0237c]
hex_to_rgb("#a0237c") # (0.627, 0.137, 0.486)
hex_to_hsl("#a0237c") # (0.881, 0.641, 0.382)
hex_to_hex_a("#a0237c", alpha=0.7) # Get hex with transparency at end [#a0237cb2]
hex_to_ahex("#a0237c", alpha=0.7) # Get hex with transparency at start [#b2a0237c]
color_in_between("#24a148","#a0237c") # Color between two ['#616161']
color_in_between("#24a148","#a0237c", steps=4) # 3 colors between two ['#428154', '#616161', '#80426e'] such that color space is divided into 4 parts
rgb_to_hex(0.181, 0.241, 0.382) # '#2e3d61'
rgb_to_hsl(0.181, 0.241, 0.382) # (0.617, 0.357, 0.281)
hsl_to_hex(0.181, 0.241, 0.382) # '#747849'
Custom and flexible colormaps which can be directly used in matplotlib workflow
import matplotlib
import matplotlib.pylab as plt
import numpy as np
from SecretColors.cmaps import ColorMap
from SecretColors import Palette
p = Palette()
c = ColorMap(matplotlib, p)
data = np.random.rand(100, 100)
plt.pcolor(data, cmap=c.greens())
plt.show()
Create your own colormaps or make it qualitative colormap
color_list = [p.red(), p.blue()]
plt.pcolor(data, cmap=c.from_list(color_list, is_qualitative=True))
plt.show()
Reverse the direction
plt.pcolor(data, cmap=c.greens(is_reversed=True))
- CIE XYZ to CIE Lab conversions
- Color Palettes - Latitude, Polaris, Adobe Spectrum
- Direct support for more RGB to XYZ conversions
- Generate Color Palette from image
- Color blind safe palette
-
Out of the boxLinearSegmentedColormapobject
We can not implement 'out of the box' colormaps for matplotlib because matplotlib checks its
type before its use. Hence we can not make new class which can be directly
act as a substitute.
Note: All CIE-XYZ conversion and colorblind simulation functions are still in beta-testing. Do not use them in your production code
Feel free to provide feedback and criticism through GitHub or you can email me [email protected] . If you want to contribute, please send pull request to this repository.
Colors used in this library are partly taken from IBM Design Language , Google Material Design , ColorBrewer, VMWare Clarity and Tableau .
Color name data is taken from X11 and W3.
RGB colorspace matrices were taken from Here .
Any other specific code or resource used in this library is attributed in respective function or method where it is used. It can be accessible from our online documentation as well.
This library and its code is released under MIT License . Read full statement here.
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