深入探讨在Matplotlib中自定义颜色映射与标签的实用指南

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

# 创建自定义颜色映射
colors = ["#0000ff", "#00ff00", "#ff0000"]  # 蓝色到绿色到红色
n_bins = 100  # 颜色映射中的颜色数量
cmap_name = 'custom_cmap'

# 创建颜色映射对象
custom_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bins)

# 生成示例数据
data = np.random.rand(10, 10)

# 绘制热图并应用自定义颜色映射
plt.imshow(data, cmap=custom_cmap)
plt.colorbar()
plt.title('Custom Colormap Example')
plt.show()

import matplotlib.pyplot as plt

# 生成示例数据
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 创建图形和子图
fig, ax = plt.subplots()

# 绘制曲线
ax.plot(x, y1, label='Sine Wave', color='blue')
ax.plot(x, y2, label='Cosine Wave', color='red')

# 自定义轴标签
ax.set_xlabel('X-axis Label')
ax.set_ylabel('Y-axis Label')

# 自定义图例
ax.legend(loc='upper right', title='Functions')

# 设置图形标题
ax.set_title('Custom Labels Example')

# 显示图形
plt.show()

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

# 创建自定义颜色映射
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
cmap = ListedColormap(colors)

# 生成示例数据
np.random.seed(0)
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.randint(0, 4, 100)  # 4个类别

# 绘制散点图并应用自定义颜色映射
scatter = plt.scatter(x, y, c=z, cmap=cmap)

# 添加颜色条
cbar = plt.colorbar(scatter, ticks=[0, 1, 2, 3])
cbar.ax.set_yticklabels(['Class 0', 'Class 1', 'Class 2', 'Class 3'])  # 自定义颜色条标签

# 自定义轴标签和标题
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Custom Colormap and Labels Example')

# 显示图形
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap

# 示例数据：城市经纬度和温度
cities = {
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
    'Latitude': [40.7128, 34.0522, 41.8781, 29.7604, 33.4484],
    'Longitude': [-74.0060, -118.2437, -87.6298, -95.3698, -112.0740],
    'Temperature': [85, 90, 75, 95, 105]
}

# 创建自定义颜色映射
colors = ["#0000ff", "#00ffff", "#00ff00", "#ffff00", "#ff0000"]
cmap_name = 'temperature_cmap'
temperature_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=100)

# 创建地图
fig, ax = plt.subplots(figsize=(10, 7))
m = Basemap(projection='merc', llcrnrlat=20, urcrnrlat=50, llcrnrlon=-130, urcrnrlon=-60, resolution='i')

# 绘制海岸线和国家边界
m.drawcoastlines()
m.drawcountries()

# 转换城市坐标
x, y = m(cities['Longitude'], cities['Latitude'])

# 绘制城市点并应用自定义颜色映射
sc = m.scatter(x, y, c=cities['Temperature'], cmap=temperature_cmap, s=100, edgecolor='black', zorder=5)

# 添加颜色条
cbar = m.colorbar(sc, location='right', pad='10%')
cbar.set_label('Temperature (°F)')

# 添加城市标签
for city, xpt, ypt in zip(cities['City'], x, y):
    plt.text(xpt, ypt, city, fontsize=12, ha='right')

# 设置图形标题
plt.title('City Temperature Distribution')

# 显示图形
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.colors import LinearSegmentedColormap

# 创建自定义颜色映射
colors = ["#0000ff", "#00ffff", "#00ff00", "#ffff00", "#ff0000"]
cmap_name = 'dynamic_cmap'
dynamic_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=100)

# 初始化数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图形和子图
fig, ax = plt.subplots()
sc = ax.scatter(x, y, c=y, cmap=dynamic_cmap)
cbar = plt.colorbar(sc, ax=ax)
cbar.set_label('Value')

# 动态更新函数
def update(frame):
    y = np.sin(x + frame / 10.0)  # 动态更新数据
    sc.set_array(y)  # 更新颜色映射
    sc.set_clim(vmin=np.min(y), vmax=np.max(y))  # 更新颜色条范围
    ax.set_title(f'Time: {frame}')

# 创建动画
ani = FuncAnimation(fig, update, frames=range(100), interval=100)

# 显示动画
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
from matplotlib.colors import LinearSegmentedColormap

# 创建自定义颜色映射
colors = ["#0000ff", "#00ffff", "#00ff00", "#ffff00", "#ff0000"]
cmap_name = 'interactive_cmap'
interactive_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=100)

# 生成示例数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图形和子图
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, bottom=0.25)
sc = ax.scatter(x, y, c=y, cmap=interactive_cmap)
cbar = plt.colorbar(sc, ax=ax)
cbar.set_label('Value')

# 创建滑块
ax_slider = plt.axes([0.1, 0.1, 0.8, 0.03])
slider = Slider(ax_slider, 'Phase', 0, 2*np.pi, valinit=0)

# 滑块更新函数
def update(val):
    phase = slider.val
    y = np.sin(x + phase)
    sc.set_array(y)
    sc.set_clim(vmin=np.min(y), vmax=np.max(y))
    ax.set_title(f'Phase: {phase:.2f}')
    fig.canvas.draw_idle()

# 滑块事件绑定
slider.on_changed(update)

# 显示图形
plt.show()