我正在尝试创建一个网格网格，而网格中的某些点没有指定的坐标和半径.我无法减去落入圆内的网格点. 这是我的代码
I am trying to create a meshgrid without some of the points that falls within the circle having specified coordinates and a radius. I am not able to subtract the grid points falling inside the circle. Here is my code
import math import numpy import matplotlib.pyplot as plt N = 50 x_start, x_end = -2.0, 2.0 y_start, y_end = -1.0, 1.0 x = numpy.linspace(x_start, x_end, N) y = numpy.linspace(y_start, y_end, N) circle_x, circle_y, r= 0.0, 0.0, 0.4 #x.remove((r-circle_x)**2) #need some help with these two lines #y.remove((r-circle_y)**2) X, Y = numpy.meshgrid(x, y) size = 10 fig = plt.figure() plt.xlabel('x', fontsize = 16) plt.ylabel('y', fontsize = 16) plt.scatter(X, Y) plt.show()显示网格点的网格##标题
meshgrid showing the mesh points## Heading
推荐答案如果您希望散点图仅包含圆外的点，请使用布尔索引来从2D网格划分"数组中仅选择那些点:
If you'd like a scatter plot with just the points outside the circle, use boolean indexing to select only those points from your 2D "meshgridded" array:
import numpy as np import matplotlib.pyplot as plt N = 50 x_start, x_end = -2.0, 2.0 y_start, y_end = -1.0, 1.0 x = np.linspace(x_start, x_end, N) y = np.linspace(y_start, y_end, N) x0, y0, radius = 0.0, 0.0, 0.4 x, y = np.meshgrid(x, y) r = np.sqrt((x - x0)**2 + (y - y0)**2) outside = r > radius fig, ax = plt.subplots() ax.set(xlabel='X', ylabel='Y', aspect=1.0) ax.scatter(x[outside], y[outside]) plt.show()
另一方面，如果使用的是类似imshow的东西，需要2D输入，则需要掩盖内部的值(如@JulienSpronck所述，尽管最好将它们设置为np.nan或使用遮罩数组而不是将其设置为0)或在图像上设置剪切路径.
On the other hand, if you were using something like imshow that requires 2D input, you'd need to either mask the values inside (as @JulienSpronck mentions, though it would be better to set them to np.nan or use a masked array than to set them to 0) or set a clip path on the image.
但是，为了分散，您不需要2D输入.
For scatter, however, you don't need 2D input.
在nD数组上的布尔索引将返回1d结果.例如:
Boolean indexing on a nD array will return a 1d result. For example:
In [9]: x = np.arange(100).reshape(10, 10) In [10]: x Out[10]: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [50, 51, 52, 53, 54, 55, 56, 57, 58, 59], [60, 61, 62, 63, 64, 65, 66, 67, 68, 69], [70, 71, 72, 73, 74, 75, 76, 77, 78, 79], [80, 81, 82, 83, 84, 85, 86, 87, 88, 89], [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]) In [11]: x[x > 75] Out[11]: array([76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])因为scatter只是绘制点，所以不关心它们的连接方式，因此我们可以轻松地使用布尔索引的一维结果.
Because scatter is only plotting points, it doesn't care how they're connected, and we can easily use the 1D result of boolean indexing.
另一方面，如果要绘制图像，则需要2D网格.在这种情况下，您需要屏蔽这些值:
On the other hand, if you wanted to plot an image, you'd need a 2D grid. In that case, you'd want to mask the values instead:
In [12]: np.ma.masked_where(x <= 75, x) Out[12]: masked_array(data = [[-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- -- -- -- --] [-- -- -- -- -- -- 76 77 78 79] [80 81 82 83 84 85 86 87 88 89] [90 91 92 93 94 95 96 97 98 99]])请注意，这如何维护输入的2D结构.
Notice how this maintains the 2D structure of the input.
另一方面，如果这是一个浮点数组，则可以很容易地将值设置为np.nan而不是对其进行遮罩.用imshow绘制时，两者的行为相同.在这种情况下，我使用了带掩码的数组，因为x是整数数组，并且不能包含NaN.
On a side note, if this were a floating point array, you could just as easily set the values to np.nan instead of masking them. When plotted with imshow, the two will behave identically. I used a masked array in this case because x was an integer array and can't contain NaN's.
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