Python Matplotlib可视化示例代码-5-坐标系和3维作图

文章目录 [隐藏]

1.matplot的坐标系

import matplotlib.pyplot as plt

# matplot的坐标系projection有这些：
# from matplotlib.projections import axes
# from matplotlib.projections.geo import AitoffAxes, HammerAxes, LambertAxes, MollweideAxes
# from matplotlib.projections.polar import PolarAxes
# from mpl_toolkits.mplot3d import Axes3D
#
# print(axes.Axes.name)  # rectilinear 直角坐标
# print(PolarAxes.name)  # polar 极地坐标图
# print(AitoffAxes.name)  # aitoff 平视地球仪坐标：上下2极收缩
# print(HammerAxes.name)  # hammer 平视地球仪坐标：上下2极收缩
# print(LambertAxes.name)  # lambert 反透视地球仪坐标：上下2极收缩到圆内
# print(MollweideAxes.name)  # mollweide 平视地球仪坐标：数据单位必须是弧度
# print(Axes3D.name)  # 3d 3D坐标图


ax1 = plt.subplot(3, 3, 1, facecolor="#eeefef", projection='rectilinear')
ax1.set_title("直角坐标系")

ax2 = plt.subplot(3, 3, 2, facecolor="#eeefef", projection='polar')
ax2.set_title("极地坐标图系")

ax3 = plt.subplot(3, 3, 3, facecolor="#eeefef", projection='aitoff')
ax3.set_title("平视地球仪坐标系,2极收缩")
ax3.grid()

ax4 = plt.subplot(3, 3, 4, facecolor="#eeefef", projection='hammer')
ax4.set_title("平视地球仪坐标系,2极收缩")
ax4.grid()

ax5 = plt.subplot(3, 3, 5, facecolor="#eeefef", projection='lambert')
ax5.set_title("反透视地球仪坐标系")
ax5.grid()

ax6 = plt.subplot(3, 3, 6, facecolor="#eeefef", projection='mollweide')
ax6.set_title("平视地球仪坐标系,弧度单位")
ax6.grid()

ax7 = plt.subplot(3, 3, 7, facecolor="#eeefef", projection='3d')
ax7.set_title("3D坐标系")

plt.show()

import matplotlib.pyplot as plt

# matplot的坐标系projection有这些：

# from matplotlib.projections import axes

# from matplotlib.projections.geo import AitoffAxes, HammerAxes, LambertAxes, MollweideAxes

# from matplotlib.projections.polar import PolarAxes

# from mpl_toolkits.mplot3d import Axes3D

# print(axes.Axes.name) # rectilinear 直角坐标

# print(PolarAxes.name) # polar 极地坐标图

# print(AitoffAxes.name) # aitoff 平视地球仪坐标：上下2极收缩

# print(HammerAxes.name) # hammer 平视地球仪坐标：上下2极收缩

# print(LambertAxes.name) # lambert 反透视地球仪坐标：上下2极收缩到圆内

# print(MollweideAxes.name) # mollweide 平视地球仪坐标：数据单位必须是弧度

# print(Axes3D.name) # 3d 3D坐标图

ax1 = plt.subplot(3, 3, 1, facecolor="#eeefef", projection='rectilinear')

ax1.set_title("直角坐标系")

ax2 = plt.subplot(3, 3, 2, facecolor="#eeefef", projection='polar')

ax2.set_title("极地坐标图系")

ax3 = plt.subplot(3, 3, 3, facecolor="#eeefef", projection='aitoff')

ax3.set_title("平视地球仪坐标系,2极收缩")

ax3.grid()

ax4 = plt.subplot(3, 3, 4, facecolor="#eeefef", projection='hammer')

ax4.set_title("平视地球仪坐标系,2极收缩")

ax4.grid()

ax5 = plt.subplot(3, 3, 5, facecolor="#eeefef", projection='lambert')

ax5.set_title("反透视地球仪坐标系")

ax5.grid()

ax6 = plt.subplot(3, 3, 6, facecolor="#eeefef", projection='mollweide')

ax6.set_title("平视地球仪坐标系,弧度单位")

ax6.grid()

ax7 = plt.subplot(3, 3, 7, facecolor="#eeefef", projection='3d')

ax7.set_title("3D坐标系")

plt.show()

2.三维空间图形-点

import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator
from mpl_toolkits.mplot3d import Axes3D  # 空间三维画图

# pycharm中的matplotliib 3D图旋转设置
# Settings，找到“Python Scientific”，去除勾选“Show plots in toolwindow”

fig = plt.figure(5)
ax = fig.add_subplot(1, 1, 1, projection='3d')  # 绘制三维图
ax.set_xlim(5, 15)
ax.set_ylim(7, 17)
ax.set_zlim(9, 17)
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.zaxis.set_major_locator(MultipleLocator(1))
ax.set_zlabel('Z轴', fontdict={'size': 15, 'color': 'red'})
ax.set_ylabel('Y轴', fontdict={'size': 15, 'color': 'red'})
ax.set_xlabel('X轴', fontdict={'size': 15, 'color': 'red'})

# 3D空间的点
# x1 = [6]
# y1 = [9]
# z1 = [10]
# ax = Axes3D(fig)
# ax.scatter(x1, y1, z1, marker="D", c='b', label='A3D点')
# ax.legend(loc='best')# 绘制图例

# 3D空间的点
# 2个点数据。(10,10,10) (11,14,15)。默认离得远的会透明化
x = [10, 11]
y = [10, 14]
z = [10, 15]
ax.scatter(x, y, z, marker='P', s=120, c=['#ff3333', '#0000ff'])

plt.show()

import matplotlib.pyplot as plt

from matplotlib.pyplot import MultipleLocator

from mpl_toolkits.mplot3d import Axes3D # 空间三维画图

# pycharm中的matplotliib 3D图旋转设置

# Settings，找到“Python Scientific”，去除勾选“Show plots in toolwindow”

fig = plt.figure(5)

ax = fig.add_subplot(1, 1, 1, projection='3d') # 绘制三维图

ax.set_xlim(5, 15)

ax.set_ylim(7, 17)

ax.set_zlim(9, 17)

ax.xaxis.set_major_locator(MultipleLocator(1))

ax.yaxis.set_major_locator(MultipleLocator(1))

ax.zaxis.set_major_locator(MultipleLocator(1))

ax.set_zlabel('Z轴', fontdict={'size': 15, 'color': 'red'})

ax.set_ylabel('Y轴', fontdict={'size': 15, 'color': 'red'})

ax.set_xlabel('X轴', fontdict={'size': 15, 'color': 'red'})

# 3D空间的点

# x1 = [6]

# y1 = [9]

# z1 = [10]

# ax = Axes3D(fig)

# ax.scatter(x1, y1, z1, marker="D", c='b', label='A3D点')

# ax.legend(loc='best')# 绘制图例

# 3D空间的点

# 2个点数据。(10,10,10) (11,14,15)。默认离得远的会透明化

x = [10, 11]

y = [10, 14]

z = [10, 15]

ax.scatter(x, y, z, marker='P', s=120, c=['#ff3333', '#0000ff'])

plt.show()

3.三维空间图形-线

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')  # 绘制三维图

ax.plot3D(
    xs=[8, 14, 13],  # x 轴坐标
    ys=[12, 11, 10],  # y 轴坐标
    zs=[12, 10, 16],  # z 轴坐标
    ls=':',  # 线条样式
    color='k',  # 线条颜色
    marker='o',  # 标记样式
    mfc='b',  # 标记颜色
    mec='g',  # 标记描边颜色
    ms=10,  # 标记大小
)

plt.show()

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection='3d') # 绘制三维图

ax.plot3D(

xs=[8, 14, 13], # x 轴坐标

ys=[12, 11, 10], # y 轴坐标

zs=[12, 10, 16], # z 轴坐标

ls=':', # 线条样式

color='k', # 线条颜色

marker='o', # 标记样式

mfc='b', # 标记颜色

mec='g', # 标记描边颜色

ms=10, # 标记大小

)

plt.show()

4.三维空间图形-线框

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

# 线段
x1 = [[7, 8]]
y1 = [[8, 10]]
z1 = np.array([[10, 14]]) # 须要转为numpy的列表，否则：axes3d.py", line 1555, in plot_surface if Z.ndim != 2: AttributeError: 'list' object has no attribute 'ndim'

# 折线
x2 = [[7, 8, 10]]
y2 = [[8, 10, 12]]
z2 = np.array([[15, 10, 13]])  

# 折线...
x2 = [[7, 8, 10, 12]]
y2 = [[8, 10, 12, 14]]
z2 = np.array([[15, 10, 13, 14]]) 

# 封闭的线框
x3 = [[7, 8],  #
      [7, 7]]
y3 = [[8, 10],  #
      [15, 13]]
z3 = np.array([[15, 10], [17, 9]])

# 封闭的线框...
x4 = [[7, 8, 12, 13],  #
      [12, 14, 13, 13]]
y4 = [[8, 10, 9, 11],  #
      [10, 9, 8, 14]]
z4 = np.array([[15, 10, 9, 8],  #
               [9, 14, 12, 10]])

ax1 = fig.add_subplot(2, 2, 1, projection='3d')
ax2 = fig.add_subplot(2, 2, 2, projection='3d')
ax3 = fig.add_subplot(2, 2, 3, projection='3d')
ax4 = fig.add_subplot(2, 2, 4, projection='3d')
ax1.plot_wireframe(X=x1, Y=y1, Z=z1, color='r', label='线段')
ax2.plot_wireframe(X=x2, Y=y2, Z=z2, color='g', label='折线...')
ax3.plot_wireframe(X=x3, Y=y3, Z=z3, color='b', label='线框')
ax4.plot_wireframe(X=x4, Y=y4, Z=z4, color='y', label='线框...')

ax1.legend()
ax2.legend()
ax3.legend()
ax4.legend()
plt.show()

import matplotlib.pyplot as plt

import numpy as np

from matplotlib.pyplot import MultipleLocator

# 线段

x1 = [[7, 8]]

y1 = [[8, 10]]

z1 = np.array([[10, 14]]) # 须要转为numpy的列表，否则：axes3d.py", line 1555, in plot_surface if Z.ndim != 2: AttributeError: 'list' object has no attribute 'ndim'

# 折线

x2 = [[7, 8, 10]]

y2 = [[8, 10, 12]]

z2 = np.array([[15, 10, 13]])

# 折线...

x2 = [[7, 8, 10, 12]]

y2 = [[8, 10, 12, 14]]

z2 = np.array([[15, 10, 13, 14]])

# 封闭的线框

x3 = [[7, 8], #

[7, 7]]

y3 = [[8, 10], #

[15, 13]]

z3 = np.array([[15, 10], [17, 9]])

# 封闭的线框...

x4 = [[7, 8, 12, 13], #

[12, 14, 13, 13]]

y4 = [[8, 10, 9, 11], #

[10, 9, 8, 14]]

z4 = np.array([[15, 10, 9, 8], #

[9, 14, 12, 10]])

ax1 = fig.add_subplot(2, 2, 1, projection='3d')

ax2 = fig.add_subplot(2, 2, 2, projection='3d')

ax3 = fig.add_subplot(2, 2, 3, projection='3d')

ax4 = fig.add_subplot(2, 2, 4, projection='3d')

ax1.plot_wireframe(X=x1, Y=y1, Z=z1, color='r', label='线段')

ax2.plot_wireframe(X=x2, Y=y2, Z=z2, color='g', label='折线...')

ax3.plot_wireframe(X=x3, Y=y3, Z=z3, color='b', label='线框')

ax4.plot_wireframe(X=x4, Y=y4, Z=z4, color='y', label='线框...')

ax1.legend()

ax2.legend()

ax3.legend()

ax4.legend()

plt.show()

5.三维空间图形-面和投影

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

#  面的着色支持的颜色有：
cmaps = cm.cmap_d.keys()
for color in cmaps:
    print(color)

# 3D空间的面。至少4个顶点。要求顶点个数为 2n 或 n^2
# 四边形的4个顶点(a, h, m]) (b, i, n) (c, j, o) (d, k, p)
x = [
    # x轴的4个点，数值大体上相同，即3d面是(大体上)垂直于x轴的yz面
    # 如果要用contourf方法对面进行投影，数值完全相同时，作为zdird的轴，会导致：art3d.py, line 769, in do_3d_projection zip(*z_segments_2d) ValueError: not enough values to unpack (expected 5, got 0)
    [10.5, 10.5],  # a, b
    [10.5, 10.4]]  # c, d
y = [  # y轴4个点
    [8, 10],   # h, i
    [15, 13]]  # j, k
z = np.array( # 须要转为numpy的列表，否则：axes3d.py", line 1555, in plot_surface if Z.ndim != 2: AttributeError: 'list' object has no attribute 'ndim'
    [  # z轴4个点。
        [15, 10],  # m, n
        [17, 9]])  # o, p
ax.plot_surface(  #
    X=x,
    Y=y,
    Z=z,
    color='g',
    alpha=0.6,
    # rstride=1,  # 对应x轴，行之间的跨度。大于0整数，默认1。影响面的平滑度，值越大越粗糙。
    # cstride=1,  # 对应y轴，列之间的跨度。简单理解：某个轴有n个点，n/r(c)stride即为显示的个数
    cmap='rainbow'  # 面的着色。彩虹 rainbow、热度 coolwarm
)
# 对surface面进行投影
ax.contourf(#
    x, y, z,#
    alpha=0.6, #
    zdir='x',  # zdir指以哪个轴投影。如z，即将影子投在xy平面上
    offset=7,  # 将影子投射到 zdir 轴的某个刻度。影子是个平面，垂直于zdir轴
    cmap='rainbow')

plt.show()

import numpy as np

import matplotlib.pyplot as plt

from matplotlib import cm

# 面的着色支持的颜色有：

cmaps = cm.cmap_d.keys()

for color in cmaps:

print(color)

# 3D空间的面。至少4个顶点。要求顶点个数为 2n 或 n^2

# 四边形的4个顶点(a, h, m]) (b, i, n) (c, j, o) (d, k, p)

x = [

# x轴的4个点，数值大体上相同，即3d面是(大体上)垂直于x轴的yz面

# 如果要用contourf方法对面进行投影，数值完全相同时，作为zdird的轴，会导致：art3d.py, line 769, in do_3d_projection zip(*z_segments_2d) ValueError: not enough values to unpack (expected 5, got 0)

[10.5, 10.5], # a, b

[10.5, 10.4]] # c, d

y = [ # y轴4个点

[8, 10], # h, i

[15, 13]] # j, k

z = np.array( # 须要转为numpy的列表，否则：axes3d.py", line 1555, in plot_surface if Z.ndim != 2: AttributeError: 'list' object has no attribute 'ndim'

[ # z轴4个点。

[15, 10], # m, n

[17, 9]]) # o, p

ax.plot_surface( #

X=x,

Y=y,

Z=z,

color='g',

alpha=0.6,

# rstride=1, # 对应x轴，行之间的跨度。大于0整数，默认1。影响面的平滑度，值越大越粗糙。

# cstride=1, # 对应y轴，列之间的跨度。简单理解：某个轴有n个点，n/r(c)stride即为显示的个数

cmap='rainbow' # 面的着色。彩虹 rainbow、热度 coolwarm

)

# 对surface面进行投影

ax.contourf(#

x, y, z,#

alpha=0.6, #

zdir='x', # zdir指以哪个轴投影。如z，即将影子投在xy平面上

offset=7, # 将影子投射到 zdir 轴的某个刻度。影子是个平面，垂直于zdir轴

cmap='rainbow')

plt.show()

6.三维空间图形-立体柱子

import matplotlib.pyplot as plt

x_pos = [8, 12, 6]
y_pos = [9, 7, 8]
z_pos = [9, 9, 9]  # 都在xy轴的平面上
x_width = 0.5      # x轴上的长度
y_width = 1        # y轴上的宽度
z_height = [4, 2, 6] # z轴上的高度。顶部位置 = z_pos + z_height
ax.bar3d(  #
    x=x_pos,  #
    y=y_pos,  #
    z=z_pos,  #
    dx=x_width,  #
    dy=y_width,  #
    dz=z_height, #
    color='m',   #
    alpha=0.5)

plt.show()

import matplotlib.pyplot as plt

x_pos = [8, 12, 6]

y_pos = [9, 7, 8]

z_pos = [9, 9, 9] # 都在xy轴的平面上

x_width = 0.5 # x轴上的长度

y_width = 1 # y轴上的宽度

z_height = [4, 2, 6] # z轴上的高度。顶部位置 = z_pos + z_height

ax.bar3d( #

x=x_pos, #

y=y_pos, #

z=z_pos, #

dx=x_width, #

dy=y_width, #

dz=z_height, #

color='m', #

alpha=0.5)

plt.show()

7.三维空间图形-矢量场-风向标

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')  # 绘制三维图

x = [[[2, 4], [6, 9]]]
y = [[[2, 4], [6, 9]]]
z = [[[2, 4.5], [4.5, 2]]]

u = [[[14-2, 7-4], [10-6, 15-9]]]
v = [[[4-2, 17-4], [6-6, 15-9]]]
w = [[[4-2, 7-4.5], [10-4.5, 8-2]]]

ax.quiver(x, y, z,  # 箭头的位置-尾巴的
    u, v, w,        # 箭头的指向-尖头的 x y z
    color=['r', 'g', 'b', 'm'], # 颜色
    length=1,  # 箭头长度百分比。(uvw + xyz) * length
    normalize=False)# 是否将所有箭头规格化为具有相同的长度。默认True，一个刻度长度。

plt.show()

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection='3d') # 绘制三维图

x = [[[2, 4], [6, 9]]]

y = [[[2, 4], [6, 9]]]

z = [[[2, 4.5], [4.5, 2]]]

u = [[[14-2, 7-4], [10-6, 15-9]]]

v = [[[4-2, 17-4], [6-6, 15-9]]]

w = [[[4-2, 7-4.5], [10-4.5, 8-2]]]

ax.quiver(x, y, z, # 箭头的位置-尾巴的

u, v, w, # 箭头的指向-尖头的 x y z

color=['r', 'g', 'b', 'm'], # 颜色

length=1, # 箭头长度百分比。(uvw + xyz) * length

normalize=False)# 是否将所有箭头规格化为具有相同的长度。默认True，一个刻度长度。

plt.show()

8.三维空间图形-等高线

import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator

# 第1维元素：山脊或山坳数量。手动设置的线越多，山势细节越多
# 第2维元素：从山脚到山顶，设置2道等高线。中间的根据contour第4个参数细密度补齐
x = [[3, 5], [15, 13], [9, 8], [3, 5]]
y = [[3, 5], [7, 8], [15, 12], [3, 5]]
z = [[3, 15], [3, 16], [3, 16], [3, 15]]
# 第2维：设置3道等高线。手动设置的线越多，山势细节越多
x = [[3, 5, 8], [15, 13, 8], [9, 8, 8], [3, 5, 8]]
y = [[3, 5, 8], [7, 8, 8], [15, 12, 8], [3, 5, 8]]
z = [[3, 13, 15], [3, 9, 15], [3, 8, 15], [3, 13, 15]]

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')

ax.contour(
    x,  #
    y,  #
    z,  # 顶点位置
    60,  # 细密度。默认5
    cmap='binary') # 着色

plt.show()

import matplotlib.pyplot as plt

from matplotlib.pyplot import MultipleLocator

# 第1维元素：山脊或山坳数量。手动设置的线越多，山势细节越多

# 第2维元素：从山脚到山顶，设置2道等高线。中间的根据contour第4个参数细密度补齐

x = [[3, 5], [15, 13], [9, 8], [3, 5]]

y = [[3, 5], [7, 8], [15, 12], [3, 5]]

z = [[3, 15], [3, 16], [3, 16], [3, 15]]

# 第2维：设置3道等高线。手动设置的线越多，山势细节越多

x = [[3, 5, 8], [15, 13, 8], [9, 8, 8], [3, 5, 8]]

y = [[3, 5, 8], [7, 8, 8], [15, 12, 8], [3, 5, 8]]

z = [[3, 13, 15], [3, 9, 15], [3, 8, 15], [3, 13, 15]]

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection='3d')

ax.contour(

x, #

y, #

z, # 顶点位置

60, # 细密度。默认5

cmap='binary') # 着色

plt.show()

- end

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