I have a Numpy 1D vector, for example x = [1, 1, 1, 2, 2, 1, 3, 3, 1]
I have to perform a splitting of it into n subarrays, where each vector must start with a new value and continue for as long as the value is the same, such that the final answer is [[1, 1, 1], [2, 2], [1], [3, 3], [1]].
I do understand that I have to use numpy.split() function, but I have problems with finding the places at which the splitting must be done.
I humbly ask of your help, thanks for your time!
You just need to give numpy.split the indexes you need it to split the array
a = np.array([1,1,1,2,2,1,3,3,1])
np.split(a, np.argwhere(np.diff(a) != 0)[:,0] + 1)
# [array([1, 1, 1]), array([2, 2]), array([1]), array([3, 3]), array([1])]
Details
Using np.diff(a) you get the differences between each consecutive element
np.diff(a)
# array([ 0, 0, 1, 0, -1, 2, 0, -2])
The points where the differences are not equal to 0 is the points where the elements are not consecutively the same. Since you are looking for the indexes where the changes you need to do np.diff(a) != 0 which returns:
np.diff(a) != 0
# array([False, False, True, False, True, True, False, True])
To convert the booleans into the indexes you can use np.argwhere
np.argwhere(np.diff(a) != 0)
# array([[2],[4],[5],[7]])
# since we only need this for 1d arrays
np.argwhere(np.diff(a) != 0)[:,0]
# array([2, 4, 5, 7])
The you just use the aforementioned procedure to provide the correct indexes to np.split
np.split(a, np.argwhere(np.diff(a) != 0)[:,0])
# [array([1, 1]), array([1, 2]), array([2]), array([1, 3]), array([3, 1])]
Woops... wrong indexes... Seems we are off by 1 index. No problem, just add +1 to the results of np.argwhere
np.split(a, np.argwhere(np.diff(a) != 0)[:,0] + 1)
# [array([1, 1, 1]), array([2, 2]), array([1]), array([3, 3]), array([1])]
