matplotlib example matplotlib 3d 3d zmf (snippet)

Python matplotlib example 'matplotlib 3d 3d zmf'

Modules used in program:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

python matplotlib 3d 3d zmf

Python matplotlib example: matplotlib 3d 3d zmf

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm,colors
import numpy as np

map = Basemap(llcrnrlon=-110,llcrnrlat=-30,urcrnrlon=110,urcrnrlat=30,)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.add_collection3d(map.drawcoastlines(linewidth=0.25))


beta = np.linspace(0, 2 * np.pi, 360)
r = np.arange(0, 3000, 5)
Beta, R = np.meshgrid(beta, r)
alpha = np.pi * (60 / 180)  # 仰角
alpha1 = np.pi * (59 / 180)  #
alpha2 = np.linspace(np.pi * (30 / 180), np.pi * (60 / 180), 360)

alpha3 = np.random.uniform(np.pi * (50 / 180), np.pi * (60 / 180), size=360)
# Then calculate X, Y, and Z
X = R * np.cos(Beta)
Y = R * np.sin(Beta)
Z = np.tan(alpha) * np.sqrt(X ** 2 + Y ** 2)
Z1 = np.tan(alpha3) * np.sqrt(X ** 2 + Y ** 2)

data = np.loadtxt(open("data5.csv", "rb"), delimiter=",", skiprows=0)  # 读取矩阵数据
data = data[:, 0:600]  # 取前600列，即3000m

dataT = data.T
print(dataT)
maxv = np.max(dataT)
# 对数据进行处理，算法：归一化后乘以2，此算法可能不对。

# for i in range(dataT.shape[0]):
#     for j in range(dataT.shape[1]):
#         dataT[i,j]=(dataT[i,j]/maxv)*2

print(dataT)

cdict = {'red'  : ((0., 1, 1),
                   (0.05, 1, 1),
                   (0.11, 0, 0),
                   (0.66, 1, 1),
                   (0.89, 1, 1),
                   (1, 0.5, 0.5)),
         'green': ((0., 1, 1),
                   (0.05, 1, 1),
                   (0.11, 0, 0),
                   (0.375, 1, 1),
                   (0.64, 1, 1),
                   (0.91, 0, 0),
                   (1, 0, 0)),
         'blue' : ((0., 1, 1),
                   (0.05, 1, 1),
                   (0.11, 1, 1),
                   (0.34, 1, 1),
                   (0.65, 0, 0),
                   (1, 0, 0))}

my_cmap = colors.LinearSegmentedColormap('my_colormap', cdict, 256)

cmp2 = my_cmap(dataT)  ##使用用户自定义颜色映射方案

# cmp = cm.jet(N)##使用内置颜色映射方案
# surf = ax.plot_surface(X, Y, Z, rstride=10, cstride=10, facecolors=cmp2, linewidth=0, antialiased=True)
surf1 = ax.plot_surface(X, Y, Z1, rstride=10, cstride=10, facecolors=cmp2, linewidth=0, antialiased=True)
ax.set_xlabel("X/m")
ax.set_ylabel("Y/m")
ax.set_zlabel("Hight/m")

# 图例:
m = cm.ScalarMappable(cmap=my_cmap)
m.set_array(dataT)
plt.colorbar(m)

plt.show()

Python links

Learn Python: https://pythonbasics.org/
Python Tutorial: https://pythonprogramminglanguage.com