如何编写算法以在python中查找numpy数组的特定值 [英] how to write an algorithm for finding specific values of a numpy array in python

问题描述

我有一条线，想要提取其中的一些.这是行数:

I have a serie of lines and want to extract some of them. This is the number of lines:

line_no= np.arange (17, 34)

这些线沿两个垂直方向排列.我在无花果中用蓝色和红色线条显示了它们.我知道方向正在改变，这叫做sep:

These lines are arranged in two perependicular direction. I have shown them with bluw and red lines in the fig. I know where the direction is changing, it is called sep:

sep=25 # lines from 17 to 25 are blue and from 26 to end are red

然后，我有创建线的点数.我称它们为块，因为每个数字都可以是块:

Then, I have the number of the points that create the lines. I call them chunks, because each number can be chunk:

chunk_val=np.array([1,2,3,3,4])

此 chunk_val 说明如何创建行.我需要提取特定的行号，该行号在图中用蓝色圆圈突出显示.对于红线，算法更简单. chunk_val [0] 是 1 ，这表示我那里没有红线. chunk_val [1] 是 2 ，所以我只有一行，并且我希望该行( 26 )，因为前一个块没有给我任何行. chunk_val [2] 是 3 ，所以我在这里有两行，我想要最后一行( 27 中的 28 /code>和 28 )，因为前面的代码块给了我一行. chunk_val [3] 是 3 ，又有两行，但是我什么都不想要，因为在此块中创建的红线数量等于先前的块. chunk_val [3] 是 4 ，它创建了三行，我想从 31中导出最后一个数字( 33 ， 32 和 33 )，因为它比以前的代码块创建的行多了一行.如果 chunk_val [3] 为 5 ，那么我想保存最后两行.

This chunk_val says how lines are created. I need to extract specific line numbers which are highlighted by a blue circle in the fig. For the red lines the algorithm is simpler. chunk_val[0] is 1, it means I have no red line there. chunk_val[1] is 2, so I have one line and I want that line (26) because the previous chunk gave me no line. chunk_val[2] is 3, so I have two lines here and I want the one last line (28 out of 27 and 28), because previous chunk gave me one line. chunk_val[3] is 3, there are again two line but I want none of them because the number of created red lines in this chunk is equal to previous chunk. chunk_val[3] is 4, it creates three line and I want to export the number of last one (33 out of 31, 32 and 33) because it is creating one line more than previous chunk. If chunk_val[3] was 5, then I wanted to save the last two line.

对于连接块的蓝线来说，要复杂一些. chunk_val [0] 为 1 ，而 chunk_val [1] 为 2 ，这意味着存在一条连接线，因为第一个 chunk_value 定义可以有多少行.同时，我需要该行( 17 )，因为只有一行.如果还有更多行，我想选择最后一行.从 chunk_val [1] 到 chunk_val [2] ，我有两行，我希望最后一行( 18 <中的 19 /code>和 19 ).然后， chunk_val [2] 到 chunk_val [3] 共有三行，我希望最后一行( 20中的 22 ， 21 和 22 ).从 chunk_value [3] 到 chunk_value [4] 再次有三行，我希望最后一行( 23中的 25 ， 24 和 25 ).关于如何选择这些连接的(蓝色)线，有一个非常重要的注意事项:我想从第一个 chunk_val 开始选择它们，直到 chunk_val 的值更改为止.在我的示例中，直到最后一个 chunk_val 为止，我看到了一个变化( chunk_val [-2] 为 3 ，而 chunk_val [-1] 是 4 ).如果 chunk_val [-1] 也是 3 ，那么我想在从 chunk_val [1] 转到 chunk_val [2] (我是说我只想拥有 17 和 19 ).这些是我算法的想法，但我不知道用python编写代码.总而言之，我要列出以下数字:

For blue lines, that connect the chunks it is a little bit more complicated. chunk_val[0] is 1and chunk_val[1] is 2, it means there is one connecting line because first chunk_value defines how many lines can be there. Meanwhile, I want that line (17), because there is only one line. If there were more lines, I wanted to pick the last one. From chunk_val[1] to chunk_val[2] I have two lines and I want the last one (19 out of 18 and 19). Then, chunk_val[2] to chunk_val[3] there are three line and I want the last one (22 out of 20, 21 and 22). From chunk_value[3] to chunk_value[4] again there are three line and I want the last one (25 out of 23, 24 and 25). There is one very important note on how to pick these connecting (blue) lines: I want to start picking them from the first chunk_val untill where values of chunk_val change. In my example until the last number of chunk_val I see a change (chunk_val[-2] is 3 while chunk_val[-1] is 4). If chunk_val[-1] was also 3, then I wanted to stop picking the blue lines after going from chunk_val[1] to chunk_val[2] (I mean I only wanted to have 17 and 19). These are the idea of my algorithm but I have no idea to code it in python. In summary I want to have the following list of numbers:

[17, 26, 19, 28, 22, 25, 33]

在此，我感谢您所做的任何贡献.

In advance, I do appreciate any contribution.

推荐答案

通过反思，可以找到一个更简单的解决方案(也可以很容易地将其应用于

Upon reflection, there is a simpler solution to this problem (which can also be easily adapted for its twin problem):

import numpy as np
from scipy.ndimage.interpolation import shift

# Input data
line_no = np.arange (17, 34)
sep = 25  # lines from 17 to 25 are blue and from 26 to end are red
chunk_val = np.array([1,2,3,3,4])

# Blue candidates
blue_A = line_no[:np.where(line_no == sep)[0][0]+1]

# Red candidates
red_A = line_no[np.where(line_no == sep)[0][0]+1:]

# Select blue lines
blue_index = (chunk_val[:-1]).cumsum() - 1
blues = blue_A[blue_index]
blue_shift = (chunk_val[-1] - chunk_val)[:-1]
blues[blue_shift == 0] = 0  # set value of zero if no further jagged lines

# Select red lines
red_index = (chunk_val[1:] - 1).cumsum() - 1
reds = red_A[red_index]
red_shift = (chunk_val - shift(chunk_val, 1, cval=0))[1:]  # increments between successive series of red lines
reds[red_shift == 0] = 0  # set value of zero if no increment

# Merge blue lines and red lines
result = np.ravel(np.column_stack((blues, reds)))
result = result[result != 0]  # remove zeros
print(result)

输出:

[17 26 19 28 22 25 33]

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