import matplotlib.pyplot as plt import numpy as np fig=plt.figure(figsize=(4,4),dpi=72) plt.rcParams["font.sans-serif"]=["SimHei"] plt.rcParams["axes.unicode_minus"]=False rw=10 k=np.arange(5) x1=rw*np.sin(2*np.pi/5*k) y1=rw*np.cos(2*np.pi/5*k) plt.plot ( " b o " ) rn=rw*np.sin(np.pi/10)/np.sin(np.pi/5) x2=rn*np.sin(2*np.pi/5*k+np.pi/5) y2=rn*np.cos(2*np.pi/5*k+np.pi/5) plt.fill( c='b' ) x=[x1[0],x2[0],x1[1],x2[1],x1[2],x2[2],x1[3],x2[3],x1[4],x2[4],x1[0]] y=[y1[0],y2[0],y1[1],y2[1],y1[2],y2[2],y1[3],y2[3],y1[4],y2[4],y1[0]] plt.plot(marker ='*',c='r') plt.title ("五角星") plt.show( )

这是一段使用matplotlib库绘制五角星的代码。首先导入必要的库，设置图像大小和字体，接着定义五角星的外部和内部点的坐标，使用plt.plot()函数绘制外部点的连线和plt.fill()函数填充内部区域，最后用plt.plot()函数绘制五角星内部的红色点。最后通过plt.title()函数设置图像标题，并使用plt.show()函数显示图像。

相关问题

解释这行代码import matplotlib.pyplot as plt import numpy as np fig,axe=plt.subplots()

这行代码首先引入了 matplotlib.pyplot 库和 numpy 库，它们都是 Python 中用于科学计算和数据可视化的常用库。接着，创建了一个名为 fig 的 Figure 对象和名为 axe 的 Axes 对象，其中 Figure 表示整个画布，Axes 表示图表中的子图。在此之后，我们可以用 Axes 对象来绘制具体的图形，如折线图、散点图、直方图等。

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()`.