Decrypting the matplotlib color table: revealing the story behind the colors

Detailed explanation of matplotlib color table: Revealing the secrets behind colors

Introduction:
As one of the most commonly used data visualization tools in Python, matplotlib has powerful drawing capabilities Features and rich color table. This article will introduce the color table in matplotlib and explore the secrets behind colors. We will delve into the color tables commonly used in matplotlib and give specific code examples.

1. Color table in Matplotlib

1. How colors are represented
   In matplotlib, colors can be represented in different ways. A common way is to use RGB values ​​to represent colors, that is, using the values ​​of the three channels of red (R), green (G), and blue (B) to represent the depth of the color. For example, pure red can be represented by (1, 0, 0). Another common way is to use hexadecimal values ​​to represent colors. For example, pure red can be represented by "#FF0000".
2. Color Mapping
   Color mapping is the process of mapping numerical values ​​to colors. In matplotlib, we can use different color maps to present changes in data. Common color mappings include single-color mapping and multi-color mapping.

2.1 Monochrome mapping
Monochrome mapping maps data to a single color. Among them, the most commonly used is grayscale mapping. In matplotlib, we can use "gray" or "Greys" to represent grayscale mapping. Another common monochrome mapping is heat map mapping. In matplotlib, we can use "hot" to represent heat map mapping.

The following is a code example using monochrome mapping:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y, color="gray")
plt.plot(x, y+1, color="hot")

plt.show()

In the above code, we use two different color mappings, one is the grayscale mapping "gray", and the other is Is the heat map mapping "hot".

2.2 Multi-color mapping
Multi-color mapping is to map data to a series of colors. In matplotlib, we can use different color tables to implement multi-color mapping. matplotlib provides a rich set of built-in color tables, such as "viridis", "autumn", "cool", etc.

The following is a code example using multi-color mapping:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y, color="viridis")
plt.plot(x, y+1, color="autumn")

plt.show()

In the above code, we use two different color tables, one is "viridis" and the other is "autumn ".

2. Customized color table
In addition to using the built-in color table, we can also customize the color table. In matplotlib, we can use "ListedColormap" to customize the color map. The following is an example of a custom color table:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

x = np.linspace(0, 10, 100)
y = np.sin(x)

colors = ["#FF0000", "#00FF00", "#0000FF"]
cmap = ListedColormap(colors)

plt.scatter(x, y, c=x, cmap=cmap)

plt.colorbar()
plt.show()

In the above code, we use three colors to customize the color table and map the data x to these three colors. Use the plt.colorbar() function to display the color table.

Conclusion:
In this article, we introduced the color table in matplotlib in detail and revealed the secrets behind the colors. We learned about how colors are represented and discussed the concept of color mapping. We also give specific code examples that demonstrate how to use different colormaps. I hope this article can help readers better understand and use color tables in matplotlib.

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