I have an array:
arr = [(1,1,1), (1,1,2), (1,1,3), (1,1,4)...(35,1,22),(35,1,23)] I want to split my array according to the third value in each ordered pair. I want each third value of 1 to be the start of a new array. The results should be:
[(1,1,1), (1,1,2),...(1,1,35)][(1,2,1), (1,2,2),...(1,2,46)] and so on. I know numpy.split should do the trick but I'm lost as to how to write the condition for the split.
I cannot think of any numpy functions or tricks to do this . A simple solution using for loop would be -
In [48]: arr = [(1,1,1), (1,1,2), (1,1,3), (1,1,4),(1,2,1),(1,2,2),(1,2,3),(1,3,1),(1,3,2),(1,3,3),(1,3,4),(1,3,5)] In [49]: result = [] In [50]: for i in arr: ....: if i[2] == 1: ....: tempres = [] ....: result.append(tempres) ....: tempres.append(i) ....: In [51]: result Out[51]: [[(1, 1, 1), (1, 1, 2), (1, 1, 3), (1, 1, 4)], [(1, 2, 1), (1, 2, 2), (1, 2, 3)], [(1, 3, 1), (1, 3, 2), (1, 3, 3), (1, 3, 4), (1, 3, 5)]] Here's a quick idea, working with a 1d array. It can be easily extended to work with your 2d array:
In [385]: x=np.arange(10) In [386]: I=np.where(x%3==0) In [387]: I Out[387]: (array([0, 3, 6, 9]),) In [389]: np.split(x,I[0]) Out[389]: [array([], dtype=float64), array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([9])] The key is to use where to find the indecies where you want split to act.
For a 2d arr
First make a sample 2d array, with something interesting in the 3rd column:
In [390]: arr=np.ones((10,3)) In [391]: arr[:,2]=np.arange(10) In [392]: arr Out[392]: array([[ 1., 1., 0.], [ 1., 1., 1.], ... [ 1., 1., 9.]]) Then use the same where and boolean to find indexes to split on:
In [393]: I=np.where(arr[:,2]%3==0) In [395]: np.split(arr,I[0]) Out[395]: [array([], dtype=float64), array([[ 1., 1., 0.], [ 1., 1., 1.], [ 1., 1., 2.]]), array([[ 1., 1., 3.], [ 1., 1., 4.], [ 1., 1., 5.]]), array([[ 1., 1., 6.], [ 1., 1., 7.], [ 1., 1., 8.]]), array([[ 1., 1., 9.]])] From looking at the documentation it seems like specifying the index of where to split on will work best. For your specific example the following works if arr is already a 2dimensional numpy array:
np.split(arr, np.where(arr[:,2] == 1)[0]) arr[:,2] returns a list of the 3rd entry in each tuple. The colon says to take every row and the 2 says to take the 3rd column, which is the 3rd component.
We then use np.where to return all the places where the 3rd coordinate is a 1. We have to do np.where()[0] to get at the array of locations directly.
We then plug in the indices we've found where the 3rd coordinate is 1 to np.split which splits at the desired locations.
Note that because the first entry has a 1 in the 3rd coordinate it will split before the first entry. This gives us one extra "split" array which is empty.