import matplotlib.pyplot as plt from matplotlib import cm import numpy as np if __name__ == '__main__': fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 大半球 theta1 = np.arange(1.6, 3.2, 0.025).reshape(64, 1) phi1 = np.arange(0, 6.4, 0.1).reshape(1, 64) X1 = 30*np.sin(theta1)*np.cos(phi1) Y1 = 30*np.sin(theta1)*np.sin(phi1) z1 = 30*np.cos(theta1)+30 ax.plot_surface(X1, Y1, z1, alpha=0.25, cmap=cm.rainbow) # 小半球 theta2 = np.arange(1.6, 3.2, 0.025).reshape(64, 1) phi2 = np.arange(0, 6.4, 0.1).reshape(1, 64) X2 = 0.534 * 30 * np.sin(theta2) @ np.cos(phi2) Y2 = 0.534 * 30 * np.sin(theta2) @ np.sin(phi2) z2 = 0.534 * 30 * np.cos(theta2) + 30 ax.plot_surface(X2, Y2, z2, alpha=0.25, cmap=cm.rainbow) # 抛物面 theta3 = np.arange(0, 6.4, 0.1).reshape(64, 1) u = np.linspace(-15, 15, 64).reshape(1, 64) X3 = u*np.sin(theta3) Y3 = u*np.cos(theta3) z3 = (2-np.sqrt(3))/15*u**2 ax.plot_surface(X3, Y3, z3, cmap=cm.coolwarm) plt.axis('square') plt.show()如何绘制投影
时间: 2023-12-04 07:02:08 浏览: 38
要绘制投影,需要在原始三维图形下添加一个二维投影。可以使用 `ax.contour()` 函数绘制等高线图作为投影。以下是一个简单的例子,假设我们要绘制大半球的投影:
```
# 绘制大半球的投影
X1_projected = X1
Y1_projected = Y1
z1_projected = np.zeros_like(z1)
# 绘制等高线图作为投影
ax.contour(X1_projected, Y1_projected, z1_projected, colors='black')
```
这将在原始三维图形下添加一个黑色的大半球投影。同样的方法也可以用于小半球和抛物面。
相关问题
import matplotlib.pyplot as plt import numpy as np import netCDF4 as nc import cartopy.crs as ccrs from cartopy.util import add_cyclic_point
I'm sorry, as an AI language model, I am not capable of displaying images or visualizations. However, I can provide you with the code for displaying a map using Cartopy and NetCDF4. Here's an example code:
```
import matplotlib.pyplot as plt
import numpy as np
import netCDF4 as nc
import cartopy.crs as ccrs
from cartopy.util import add_cyclic_point
# Load the NetCDF data
data = nc.Dataset('data.nc')
# Extract the variables
lon = data.variables['lon'][:]
lat = data.variables['lat'][:]
temp = data.variables['temp'][:]
# Add a cyclic point for plotting
temp, lon = add_cyclic_point(temp, coord=lon)
# Create a map projection
projection = ccrs.PlateCarree()
# Create a figure and axis
fig, ax = plt.subplots(subplot_kw={'projection': projection})
# Add coastlines
ax.coastlines()
# Plot the data
plt.contourf(lon, lat, temp, cmap='coolwarm', transform=projection)
# Add a colorbar
plt.colorbar()
plt.show()
```
In this example, we first load the NetCDF data and extract the variables we need. We then add a cyclic point to the temperature data to ensure it wraps around the plot correctly. Next, we create a map projection and a figure with an axis that uses the projection. We add coastlines to the plot and use `plt.contourf()` to plot the temperature data on the map. Finally, we add a colorbar to the plot and display it using `plt.show()`.
import numpy as np import pandas as pd import matplotlib.pyplot as plt
这三个库都是Python中常用的数据分析和可视化库。其中,NumPy是Python中用于科学计算的基础库,提供了高性能的多维数组和矩阵计算功能。Pandas是基于NumPy的一个数据处理库,提供了高效的数据结构和数据分析工具。Matplotlib是Python中最流行的数据可视化库之一,提供了各种绘图工具和图表类型,可以用于生成各种静态和动态的图表。
以下是一个简单的例子,展示如何使用这三个库绘制一个柱状图:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 创建数据
bar_positions = [1, 2, 3, 4]
bar_heights = [1, 2, 3, 4]
# 绘制柱状图
fig, ax = plt.subplots()
ax.bar(np.arange(len(bar_positions)), bar_heights)
# 设置x轴标签
ax.set_xticks(np.arange(len(bar_positions)))
ax.set_xticklabels(bar_positions)
# 设置y轴标签
ax.set_ylabel('Bar Heights')
# 设置图表标题
ax.set_title('A Simple Bar Chart')
# 显示图表
plt.show()
```