目录

官方自带的colormap其他的colormap结果单独绘制一个colorbar

用自己的颜色创建colormap获取图片颜色给定一个基本颜色，通过改变饱和度来创建colorbar

官方自带的colormap

import numpy as np

import matplotlib.pyplot as plt

num_curves = 100 #100条曲线

cm = plt.cm.rainbow(np.linspace(0, 1, num_curves))

sm = plt.cm.ScalarMappable(cmap='rainbow',

norm=plt.Normalize(vmin=0, vmax=num_curves))

fig = plt.figure(figsize=(6,4))

for i in range(num_curves):

curve = np.linspace(0,100+0.4*i,100) #生成曲线

plt.plot(curve,color=cm[i])

plt.title('An example of ColorMap and ColorBar')

cb = plt.colorbar(sm)

cb.set_label('curve number')

plt.show()

其他的colormap

可参考官网链接: https://matplotlib.org/2.0.2/users/colormaps.html

结果

单独绘制一个colorbar

cycle_num = 100

fig, ax = plt.subplots(figsize=(5, 2)) # 设置colorbar的尺寸

fig.subplots_adjust(bottom=0.3, top=0.7,left=0.1,right=0.9) # 调整图的上下边距

sm = plt.cm.ScalarMappable(cmap='rainbow', norm=plt.Normalize(vmin=0, vmax=cycle_num))

cb = plt.colorbar(sm, cax=ax, orientation='horizontal') # 指定colorbar为水平方向

cb.set_label('label', fontsize=14) # 设置colorbar的标签字体大小

plt.title('title', fontsize=14) # 设置colorbar的标题字体大小

plt.show()

结果：

用自己的颜色创建colormap

from matplotlib.colors import LinearSegmentedColormap

def create_colormap(rgb_list,alpha_list=None):

'''

取rgb_list里面的颜色，通过等间隔线性插值，生成colormap

:param rgb_list: 形如[(0.69,0.53,0.81),(0.92,0.10,0.50),(1.00,0.78,0.01)]

list中的颜色为colorbar中插值点的颜色

:param alpha_list: 透明度列表，形如[0.2,0.3]

:return:colorbar

'''

num_of_colors = len(rgb_list)

interpolation_points = np.linspace(0,1,num_of_colors) #插值点位置

red, green, blue = [], [], []

for i in range(num_of_colors):

r = [interpolation_points[i],rgb_list[i][0],rgb_list[i][0]]

g = [interpolation_points[i],rgb_list[i][1],rgb_list[i][1]]

b = [interpolation_points[i],rgb_list[i][2],rgb_list[i][2]]

red.append(r)

green.append(g)

blue.append(b)

if alpha_list is not None: #透明度变化

alpha = []

local = np.linspace(0, 1, len(alpha_list))

for i in range(len(alpha_list)):

alpha.append([local[i],alpha_list[i],alpha_list[i]])

else: #不透明

alpha =[[0,1,1],[1,1,1]]

color_dict = {

'red':red,

'green':green,

'blue':blue,

'alpha':alpha

}

my_colormap = LinearSegmentedColormap(name='my_colormap', segmentdata=color_dict)

plt.register_cmap(cmap=my_colormap)

return my_colormap

测试create_colormap函数

color_list = [(0.69,0.53,0.81),(0.92,0.10,0.50),(1.00,0.78,0.01)]

cmap = create_colormap(color_list)

num_curves = 100 #100条曲线

cm = cmap(np.linspace(0, 1, num_curves))

sm = plt.cm.ScalarMappable(cmap=cmap,norm=plt.Normalize(vmin=0, vmax=num_curves))

fig = plt.figure(figsize=(6,4))

for i in range(num_curves):

curve = np.linspace(0,100+0.4*i,100) #生成曲线

plt.plot(curve,color=cm[i])

plt.title('Another example of ColorMap and ColorBar')

cb = plt.colorbar(sm)

cb.set_label('curve number')

plt.show()

获取图片颜色

可以参考这篇：https://blog.csdn.net/baidu_38963740/article/details/125210583?spm=1001.2014.3001.5501

给定一个基本颜色，通过改变饱和度来创建colorbar

import matplotlib.pyplot as plt

import numpy as np

import matplotlib.colors as mcolors

def generate_colorbar(base_color, num_steps=10):

# Convert the base_color to RGB format if it's not already

base_color_rgb = mcolors.to_rgba(base_color)[:3]

# Create an array of colors with the same hue as the base_color but varying saturation

saturation_values = np.linspace(0.2, 1, num_steps)

colors = [base_color_rgb + (s,) for s in saturation_values]

# Create a custom colormap using the generated colors

cmap = mcolors.ListedColormap(colors)

# Create a colorbar with the custom colormap

norm = mcolors.Normalize(vmin=0, vmax=num_steps)

scalar_map = plt.cm.ScalarMappable(cmap=cmap, norm=norm)

scalar_map.set_array([])

# Plot the colorbar

plt.colorbar(scalar_map)

# Optional: Display a sample image with the colorbar to visualize the gradient

gradient_image = np.linspace(0, 1, 100).reshape(1, -1)

plt.imshow(gradient_image, cmap=cmap, aspect='auto', extent=(0, 1, 0, 0.1))

plt.yticks([])

# Show the plot

plt.show()

# Example:

base_color = 'red'

generate_colorbar(base_color, num_steps=10)

运行结果：

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