Jun 03, 2024python matplotlib

Intermediate

Ben Gorman

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Colormaps¶

A Colormap maps values between 0 and 1 to colors. Colormaps come in two flavors:

ListedColormap - best for a discrete color mapping
LinearSegmentedColormap - best for a continuous color mapping

ListedColormap¶

As an example, Dark2 is a ListedColormap with 8 distinct colors.

We can fetch the colormap using the matplotlib.colormaps registry ( or pyplot.colormaps). For example,

# Get the Colormap
cmap = plt.colormaps["Dark2"]
 
# Check its type
type(cmap) # <class 'matplotlib.colors.ListedColormap'>

Deprecation of get_cmap() function

Lots of old code use matplotlib.cm.get_cmap(), but this technique will soon be deprecated.

We can inspect its colors via the .colors attribute.

# Inspect the colors (RGB tuples)
 
cmap.colors
# (
#     (0.10588235294117647, 0.6196078431372549, 0.4666666666666667),
#     (0.8509803921568627, 0.37254901960784315, 0.00784313725490196), 
#     (0.4588235294117647, 0.4392156862745098, 0.7019607843137254), 
#     (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),
#     (0.4, 0.6509803921568628, 0.11764705882352941), 
#     (0.9019607843137255, 0.6705882352941176, 0.00784313725490196), 
#     (0.6509803921568628, 0.4627450980392157, 0.11372549019607843), 
#     (0.4, 0.4, 0.4)
# )

When we make a scatter plot, we can use cmap=plt.colormaps["Dark2"] to apply the Dark2 Colormap.

import matplotlib.pyplot as plt
 
fig, ax = plt.subplots()
ax.scatter(
    x=[1,2,3,4,5], 
    y=[1,2,3,4,5], 
    c=[0,1,2,0,1], 
    cmap=plt.colormaps["Dark2"], 
    s=1000
)

How it works

The c values are normalized to range [0, 1] using the default formula

c_{normalized} = \frac{c - c_{min}}{c_{max} - c_{min}}

The normalized c values are then mapped to colors using the chosen color map.

In the example above, the c=[0,1,2,0,1] is normalized into cnorm = [0., 0.5, 1., 0., 0.5]. Those normalized values map to the following colors:

# Get the Colormap
cmap = plt.colormaps["Dark2"]
print(cmap([0., 0.5, 1., 0., 0.5]))
 
#   Red      Green    Blue     Alpha
# [[0.105882 0.619608 0.466667 1.      ]
#  [0.4      0.65098  0.117647 1.      ]
#  [0.4      0.4      0.4      1.      ]
#  [0.105882 0.619608 0.466667 1.      ]
#  [0.4      0.65098  0.117647 1.      ]]

LinearSegmentedColormap¶

LinearSegmentedColormaps behave much like ListedColormaps, except they don't have a .colors attribute. For example, consider the LinearSegmentedColormap rainbow.

# Get the Colormap
cmap = plt.colormaps["rainbow"]
 
# Check its type
type(cmap) # <class 'matplotlib.colors.LinearSegmentedColormap'>
 
# Inspect the colors (RGB tuples)
cmap.colors
# AttributeError: 'LinearSegmentedColormap' object has no attribute 'colors'

We can use cmap=plt.colormaps["rainbow"] to apply the rainbow Colormap to a scatter plot.

import matplotlib.pyplot as plt
 
fig, ax = plt.subplots()
ax.scatter(
    x=[1,2,3,4,5], 
    y=[1,2,3,4,5], 
    c=[0,1,2,0,1], 
    cmap=plt.colormaps["rainbow"], 
    s=1000
)

How it works

The c values are normalized to range [0, 1] using the default formula $c_{normalized} = \frac{c - c_{min}}{c_{max} - c_{min}}$
The normalized c values are then mapped to colors using the chosen color map.

In the example above, the c=[0,1,2,0,1] is normalized into cnorm = [0., 0.5, 1., 0., 0.5]. Those normalized values map to the following colors:

# Get the Colormap
cmap = plt.colormaps["rainbow"]
print(cmap([0., 0.5, 1., 0., 0.5]))
 
#   Red      Green    Blue     Alpha
# [[0.5      0.       1.       1.      ]
#  [0.503922 0.999981 0.704926 1.      ]
#  [1.       0.       0.       1.      ]
#  [0.5      0.       1.       1.      ]
#  [0.503922 0.999981 0.704926 1.      ]]