numpy meshgrid用法

最新推荐文章于 2025-01-15 17:00:30 发布
原创 最新推荐文章于 2025-01-15 17:00:30 发布 · 193 阅读 · 0 ·
CC 4.0 BY-SA版权
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。

import numpy as np
# 坐标向量
a = np.array([1,2,3])
# 坐标向量
b = np.array([7,8])
# 从坐标向量中返回坐标矩阵
# 返回list,有两个元素,第一个元素是X轴的取值,第二个元素是Y轴的取值
res = np.meshgrid(a,b)
print(res)

 

#result

[array([[1, 2, 3],
       [1, 2, 3]]), array([[7, 7, 7],
       [8, 8, 8]])]

#2另例子import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,500,30)
print("x的维度:{},shape:{}".format(x.ndim, x.shape))
print(x)
y = np.linspace(0,500,20)
print("y的维度:{},shape:{}".format(y.ndim, y.shape))
print(y)
 
xv,yv = np.meshgrid(x, y)
print("xv的维度:{},shape:{}".format(xv.ndim, xv.shape))
print("yv的维度:{},shape:{}".format(yv.ndim, yv.shape))
 
plt.plot(xv, yv, '.')
plt.grid(True)
plt.show()
print("xv=",xv)
print("yv=",yv)

 

#result

x的维度:1,shape:(30,)
[  0.          17.24137931  34.48275862  51.72413793  68.96551724
  86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
 172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
 258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
 344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
 431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
y的维度:1,shape:(20,)
[  0.          26.31578947  52.63157895  78.94736842 105.26315789
 131.57894737 157.89473684 184.21052632 210.52631579 236.84210526
 263.15789474 289.47368421 315.78947368 342.10526316 368.42105263
 394.73684211 421.05263158 447.36842105 473.68421053 500.        ]
xv的维度:2,shape:(20, 30)
yv的维度:2,shape:(20, 30)


xv= [[  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]
 [  0.          17.24137931  34.48275862  51.72413793  68.96551724
   86.20689655 103.44827586 120.68965517 137.93103448 155.17241379
  172.4137931  189.65517241 206.89655172 224.13793103 241.37931034
  258.62068966 275.86206897 293.10344828 310.34482759 327.5862069
  344.82758621 362.06896552 379.31034483 396.55172414 413.79310345
  431.03448276 448.27586207 465.51724138 482.75862069 500.        ]]
yv= [[  0.           0.           0.           0.           0.
    0.           0.           0.           0.           0.
    0.           0.           0.           0.           0.
    0.           0.           0.           0.           0.
    0.           0.           0.           0.           0.
    0.           0.           0.           0.           0.        ]
 [ 26.31578947  26.31578947  26.31578947  26.31578947  26.31578947
   26.31578947  26.31578947  26.31578947  26.31578947  26.31578947
   26.31578947  26.31578947  26.31578947  26.31578947  26.31578947
   26.31578947  26.31578947  26.31578947  26.31578947  26.31578947
   26.31578947  26.31578947  26.31578947  26.31578947  26.31578947
   26.31578947  26.31578947  26.31578947  26.31578947  26.31578947]
 [ 52.63157895  52.63157895  52.63157895  52.63157895  52.63157895
   52.63157895  52.63157895  52.63157895  52.63157895  52.63157895
   52.63157895  52.63157895  52.63157895  52.63157895  52.63157895
   52.63157895  52.63157895  52.63157895  52.63157895  52.63157895
   52.63157895  52.63157895  52.63157895  52.63157895  52.63157895
   52.63157895  52.63157895  52.63157895  52.63157895  52.63157895]
 [ 78.94736842  78.94736842  78.94736842  78.94736842  78.94736842
   78.94736842  78.94736842  78.94736842  78.94736842  78.94736842
   78.94736842  78.94736842  78.94736842  78.94736842  78.94736842
   78.94736842  78.94736842  78.94736842  78.94736842  78.94736842
   78.94736842  78.94736842  78.94736842  78.94736842  78.94736842
   78.94736842  78.94736842  78.94736842  78.94736842  78.94736842]
 [105.26315789 105.26315789 105.26315789 105.26315789 105.26315789
  105.26315789 105.26315789 105.26315789 105.26315789 105.26315789
  105.26315789 105.26315789 105.26315789 105.26315789 105.26315789
  105.26315789 105.26315789 105.26315789 105.26315789 105.26315789
  105.26315789 105.26315789 105.26315789 105.26315789 105.26315789
  105.26315789 105.26315789 105.26315789 105.26315789 105.26315789]
 [131.57894737 131.57894737 131.57894737 131.57894737 131.57894737
  131.57894737 131.57894737 131.57894737 131.57894737 131.57894737
  131.57894737 131.57894737 131.57894737 131.57894737 131.57894737
  131.57894737 131.57894737 131.57894737 131.57894737 131.57894737
  131.57894737 131.57894737 131.57894737 131.57894737 131.57894737
  131.57894737 131.57894737 131.57894737 131.57894737 131.57894737]
 [157.89473684 157.89473684 157.89473684 157.89473684 157.89473684
  157.89473684 157.89473684 157.89473684 157.89473684 157.89473684
  157.89473684 157.89473684 157.89473684 157.89473684 157.89473684
  157.89473684 157.89473684 157.89473684 157.89473684 157.89473684
  157.89473684 157.89473684 157.89473684 157.89473684 157.89473684
  157.89473684 157.89473684 157.89473684 157.89473684 157.89473684]
 [184.21052632 184.21052632 184.21052632 184.21052632 184.21052632
  184.21052632 184.21052632 184.21052632 184.21052632 184.21052632
  184.21052632 184.21052632 184.21052632 184.21052632 184.21052632
  184.21052632 184.21052632 184.21052632 184.21052632 184.21052632
  184.21052632 184.21052632 184.21052632 184.21052632 184.21052632
  184.21052632 184.21052632 184.21052632 184.21052632 184.21052632]
 [210.52631579 210.52631579 210.52631579 210.52631579 210.52631579
  210.52631579 210.52631579 210.52631579 210.52631579 210.52631579
  210.52631579 210.52631579 210.52631579 210.52631579 210.52631579
  210.52631579 210.52631579 210.52631579 210.52631579 210.52631579
  210.52631579 210.52631579 210.52631579 210.52631579 210.52631579
  210.52631579 210.52631579 210.52631579 210.52631579 210.52631579]
 [236.84210526 236.84210526 236.84210526 236.84210526 236.84210526
  236.84210526 236.84210526 236.84210526 236.84210526 236.84210526
  236.84210526 236.84210526 236.84210526 236.84210526 236.84210526
  236.84210526 236.84210526 236.84210526 236.84210526 236.84210526
  236.84210526 236.84210526 236.84210526 236.84210526 236.84210526
  236.84210526 236.84210526 236.84210526 236.84210526 236.84210526]
 [263.15789474 263.15789474 263.15789474 263.15789474 263.15789474
  263.15789474 263.15789474 263.15789474 263.15789474 263.15789474
  263.15789474 263.15789474 263.15789474 263.15789474 263.15789474
  263.15789474 263.15789474 263.15789474 263.15789474 263.15789474
  263.15789474 263.15789474 263.15789474 263.15789474 263.15789474
  263.15789474 263.15789474 263.15789474 263.15789474 263.15789474]
 [289.47368421 289.47368421 289.47368421 289.47368421 289.47368421
  289.47368421 289.47368421 289.47368421 289.47368421 289.47368421
  289.47368421 289.47368421 289.47368421 289.47368421 289.47368421
  289.47368421 289.47368421 289.47368421 289.47368421 289.47368421
  289.47368421 289.47368421 289.47368421 289.47368421 289.47368421
  289.47368421 289.47368421 289.47368421 289.47368421 289.47368421]
 [315.78947368 315.78947368 315.78947368 315.78947368 315.78947368
  315.78947368 315.78947368 315.78947368 315.78947368 315.78947368
  315.78947368 315.78947368 315.78947368 315.78947368 315.78947368
  315.78947368 315.78947368 315.78947368 315.78947368 315.78947368
  315.78947368 315.78947368 315.78947368 315.78947368 315.78947368
  315.78947368 315.78947368 315.78947368 315.78947368 315.78947368]
 [342.10526316 342.10526316 342.10526316 342.10526316 342.10526316
  342.10526316 342.10526316 342.10526316 342.10526316 342.10526316
  342.10526316 342.10526316 342.10526316 342.10526316 342.10526316
  342.10526316 342.10526316 342.10526316 342.10526316 342.10526316
  342.10526316 342.10526316 342.10526316 342.10526316 342.10526316
  342.10526316 342.10526316 342.10526316 342.10526316 342.10526316]
 [368.42105263 368.42105263 368.42105263 368.42105263 368.42105263
  368.42105263 368.42105263 368.42105263 368.42105263 368.42105263
  368.42105263 368.42105263 368.42105263 368.42105263 368.42105263
  368.42105263 368.42105263 368.42105263 368.42105263 368.42105263
  368.42105263 368.42105263 368.42105263 368.42105263 368.42105263
  368.42105263 368.42105263 368.42105263 368.42105263 368.42105263]
 [394.73684211 394.73684211 394.73684211 394.73684211 394.73684211
  394.73684211 394.73684211 394.73684211 394.73684211 394.73684211
  394.73684211 394.73684211 394.73684211 394.73684211 394.73684211
  394.73684211 394.73684211 394.73684211 394.73684211 394.73684211
  394.73684211 394.73684211 394.73684211 394.73684211 394.73684211
  394.73684211 394.73684211 394.73684211 394.73684211 394.73684211]
 [421.05263158 421.05263158 421.05263158 421.05263158 421.05263158
  421.05263158 421.05263158 421.05263158 421.05263158 421.05263158
  421.05263158 421.05263158 421.05263158 421.05263158 421.05263158
  421.05263158 421.05263158 421.05263158 421.05263158 421.05263158
  421.05263158 421.05263158 421.05263158 421.05263158 421.05263158
  421.05263158 421.05263158 421.05263158 421.05263158 421.05263158]
 [447.36842105 447.36842105 447.36842105 447.36842105 447.36842105
  447.36842105 447.36842105 447.36842105 447.36842105 447.36842105
  447.36842105 447.36842105 447.36842105 447.36842105 447.36842105
  447.36842105 447.36842105 447.36842105 447.36842105 447.36842105
  447.36842105 447.36842105 447.36842105 447.36842105 447.36842105
  447.36842105 447.36842105 447.36842105 447.36842105 447.36842105]
 [473.68421053 473.68421053 473.68421053 473.68421053 473.68421053
  473.68421053 473.68421053 473.68421053 473.68421053 473.68421053
  473.68421053 473.68421053 473.68421053 473.68421053 473.68421053
  473.68421053 473.68421053 473.68421053 473.68421053 473.68421053
  473.68421053 473.68421053 473.68421053 473.68421053 473.68421053
  473.68421053 473.68421053 473.68421053 473.68421053 473.68421053]
 [500.         500.         500.         500.         500.
  500.         500.         500.         500.         500.
  500.         500.         500.         500.         500.
  500.         500.         500.         500.         500.
  500.         500.         500.         500.         500.
  500.         500.         500.         500.         500.        ]]

 

 

 

 

参考

https://www.jb51.net/article/166710.htm

numpy函数
ORIGINAL GEOMETRY
09-12 386
【np.r_和 np.c_】 import numpy as np #np.r_是按列连接两个矩阵，就是把两个矩阵上下相加，要求列数相等 #np.c_是按照行连接两个矩阵，把两个矩阵左右相加，要求行数相等 a = np.array([[1, 2, 3],[7,8,9]]) b = np.array([[4,5,6],[1,2,3]]) c=np.c_[a,b] #Out[7]: arra...
详解numpy.meshgrid()方法使用
sinat_41942180的博客
10-31 1万+
（这个操作把图的线型改为默认状态），就会发现A-D是连接的，B-E是连接的，C-F是连接的，也即，会认为你输入的是3条线，如图。有的，注意到我们练习题中的坐标矩阵，其实有大量的重复——XXX的每一行都一样，YYY的每一列都一样。A,B,C,D,E,F是6个网格点，坐标如图，如何用矩阵形式（坐标矩阵）来批量描述这些点的坐标呢？最直接但是最笨的方法，就是按照上面的方法把横纵坐标矩阵XXX，YYY写出来，就像上面练习题中的。图中，每个交叉点都是网格点，描述这些网格点的坐标的矩阵，就是坐标矩阵。
学习：np.meshgrid的用法
czp_374的博客
06-17 506
添加链接描述
np.meshgrid() 的用法及示例
行子的博客
07-08 2413
是一个用于生成多维坐标网格的函数，它常用于在二维或三维空间中创建网格点坐标。其中，x_valuesy_values和z_values是分别表示 x、y 和 z 坐标轴上的一维数组或列表。返回值XY和Z是与输入数组形状相同的多维数组，它们分别表示在网格上的 x、y 和 z 坐标。具体来说，XYZ保存了所有网格点的 z 坐标。
np.meshgrid
dss875914213的博客
09-20 776
X,Y=np.meshgrid(x,y) 即把x,y的所有交点算出来。 例x=[1,2,3],y=[4,5,6] 交点有3*3=9个，分别为 （1,4），（2,4），（3,4） （1,5），（2,5），（3,5） （1,6），（2,6），（3,6） X=[[1,2,3],[1,2,3],[1,2,3]] Y=[[4,4,4],[5,5,5],[6,6,6]]...
【NumPy】深入解析numpy.meshgrid()函数
2402_83361138的博客
04-20 5154
我坚信知识的力量，希望通过我的分享，能够帮助更多的人掌握这些技术，并在实际项目中发挥作用。函数会根据输入的数组生成网格坐标，返回一组与输入数组形状对应的二维数组，每个数组代表一个维度的坐标。通常情况下，我们可以将其设置为True，以确保返回的是输入数组的独立副本，避免在后续操作中意外修改原始数据。在数据插值和拟合中，我们通常需要根据已知的数据点生成一组规则的网格点，并在这些网格点上计算插值或拟合函数的值。函数之前，我们需要仔细考虑输入数组的选择和设置，以确保生成的网格符合实际需求。
(十一)numpy中的meshgrid使用
机器人视觉感知
08-27 1426
欢迎访问个人网络日志🌹🌹。欢迎访问个人网络日志🌹🌹。
numpy中的meshgrid()方法
将军不下马，各自奔前程
01-15 457
python的meshgrid()方法在很多地方都有应用，例如生成等高线图的数据集。那meshgrid()方法的目的是什么呢？可以看到，meshgrid()方法接受两个一维向量，生成一个坐标矩阵。可以看到，通过meshgrid()方法，可以很简单的生成坐标矩阵。
NumPy meshgrid index
最新发布
02-18
### 使用 NumPy 的 `meshgrid` 进行索引 NumPy 提供了 `meshgrid` 函数来创建坐标矩阵，这在多维数组操作中非常有用。通过该函数可以方便地生成网格点的坐标，进而用于索引或其他计算。 考虑给定的一组一维坐标...
Python numpy.meshgrid函数方法的使用
weixin_42098295的博客
06-28 770
numpy.meshgrid 是 NumPy 库中一个非常有用的函数，它生成坐标矩阵，用于向量化评估在二维或更高维度上的函数。它通常用于创建网格点，然后在这些网格点上进行函数计算或绘图。indexing：{'xy', 'ij'}，可选。返回一个列表，其中包含生成的坐标矩阵。以下是一些使用 numpy.meshgrid 的示例，展示了不同的用法和应用场景。表示是否只生成稀疏网格。copy：布尔值，可选。*xi：一个或多个一维数组，用于生成网格坐标。# 使用 'ij' 索引生成网格。# 定义两个一维数组。
numpy: meshgrid方法
weixin_43901558的博客
03-10 373
meshgrid方法用于对两个一维数组生成一个数值对(x, y) 例如，对于数组[1, 2]和[3, 4]，可以生成4个数值对，分别为: [1, 3], [2, 3], [1, 4], [2, 4] meshgrid方法返回两个二维矩阵，分别存储所有键值对中的x值和y值： import numpy as np a = [1, 2] b = [3, 4] c, d = np.meshgrid(a, ...
numpy 2: np.meshgrid()
smart boy
05-15 411
import numpy as np import matplotlib.pyplot as plt %matplotlib inline m, n = (5, 3) x = np.linspace(0, 1, m) y = np.linspace(0, 1, n) X, Y = np.meshgrid(x,y) x out: array([ 0. , 0.25, 0.5 , 0.75, 1. ...
一分钟看懂：numpy.meshgrid()
Tian_yi_qi的博客
11-01 608
一分钟看懂 numpy.meshgrid() meshgrid是作图生成网格时经常使用的函数。 基本作用：将传入的两个numpy数组中的每个元素分别配对，并按照传入的顺序返回两个numpy数组。 传入：两个numpy数组无具体要求，常用取点方式为np.arange（），假设分别为 X，Y 返回：返回的两个numpy分别是网格中所有点的x，y坐标，设为XX, YY，两者shape一致，XX.shape = YY.shape = [Y.shape， X.shape] ——————————————————————
python笔记之NUMPY中的meshgrid()——生成以某点为中心指定半径内的
热门推荐
baoqian1993的博客
08-04 7万+
1、meshgrid函数用两个坐标轴上的点在平面上画格。 用法： 　　[X,Y]=meshgrid(x,y) 　　[X,Y]=meshgrid(x)与[X,Y]=meshgrid(x,x)是等同的 　　[X,Y,Z]=meshgrid(x,y,z)生成三维数组，可用来计算三变量的函数和绘制三维立体图 　　例如例题1： 　　x=-3:1:3;y=-2:1:2; 　　[X,Y]
numpy中的meshgrid函数
　ZC的专栏
04-17 1331
语法：x,y = meshgrid(a,b) 作用：输入两个一维数组，输出两个二维数组x,y。其中矩阵x的行向量是向量a的简单复制，而矩阵y的列向量是向量b的简单复制。示例：a = np.linspace(0,2,3) b = np.linspace(0,2,3) c = zip(a.ravel(),b.ravel())#根据a,b的一维数据。打包为元组的列表 print(list(c))#注意在...
无涯教程-numpy - meshgrid()函数
无涯教程
05-18 1534
Python的numpy模块提供函数，该函数借助表示或。MATLAB有点启发了meshgrid()函数。从坐标矢量，meshgrid()函数返回坐标矩阵。在上图中，x轴的范围是-5到5，y轴的范围是-5到5。因此，图中总共标记了121个点，每个点都有x坐标和y坐标。对于平行于x轴的任何直线，标记点的x坐标分别为-5，-4，-3，-2，-1、0、1、2、3、4和5。另一方面，对于平行于y轴的任何线，从下到上的标记点的y坐标为-5，-4，-3，-2，-1、0、1、2、3 ，4和5。
【Numpy】np.meshgird
mjiansun的专栏
09-30 340
[X,Y]=meshgrid(x,y) [X,Y]=meshgrid(x)与[X,Y]=meshgrid(x,x)是等同的 [X,Y,Z]=meshgrid(x,y,z)生成三维数组，可用来计算三变量的函数和绘制三维立体图 这里，主要以[X,Y]=meshgrid(x,y)为例，来对该函数进行介绍。 [X,Y] = meshgrid(x,y) 将向量x和y定义的区域转换成矩阵X和Y,其中矩...
3分钟理解np.meshgrid()
littlehaes的博客
10-30 4万+
欢迎光临我的个人主页 官方解释: np.meshgrid(*xi, **kwargs) Return coordinate matrices from coordinate vectors. 从坐标向量中返回坐标矩阵 不够直观 直观的例子 二维坐标系中,X轴可以取三个值1,2,3, Y轴可以取三个值7,8, 请问可以获得多少个点的坐标? 显而易见是6个: (1,7)(2,7)(3,7) (1,8...
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值