Split array at value in numpy

Question

I have a file containing data in the format:

0.0 x1
0.1 x2
0.2 x3
0.0 x4
0.1 x5
0.2 x6
0.3 x7
...

The data consists of multiple datasets, e

Answer 1

You don't need a python loop to evaluate the locations of each split. Do a difference on the first column and find where the values decrease.

import numpy

# read the array
arry = numpy.fromfile(file, dtype=('float, S2'))

# determine where the data "splits" shoule be
col1 = arry['f0']
diff = col1 - numpy.roll(col1,1)
idxs = numpy.where(diff<0)[0]

# only loop thru the "splits"
strts = idxs
stops = list(idxs[1:])+[None]
groups = [data[strt:stop] for strt,stop in zip(strts,stops)]

Answer 2

def getDataSets(fname):
    data_sets = []
    data = []
    prev = None
    with open(fname) as inf:
        for line in inf:
            index,rem = line.strip().split(None,1)
            if index < prev:
                data_sets.append(data)
                data = []
            data.append(rem)
            prev = index
        data_sets.append(data)
    return data_sets

def main():
    data = getDataSets('split.txt')
    print data

if __name__=="__main__":
    main()

results in

[['x1', 'x2', 'x3'], ['x4', 'x5', 'x6', 'x7']]

Answer 3

Once you have the data in a long numpy array, just do:

import numpy as np

A = np.array([[0.0, 1], [0.1, 2], [0.2, 3], [0.0, 4], [0.1, 5], [0.2, 6], [0.3, 7], [0.0, 8], [0.1, 9], [0.2, 10]])
B = np.split(A, np.argwhere(A[:,0] == 0.0).flatten()[1:])

which will give you B containing three arrays B[0], B[1] and B[2] (in this case; I added a third "section" to prove to myself that it was working correctly).

Answer 4

I actually liked Benjamin's answer, a slightly shorter solution would be:

B= np.split(A, np.where(A[:, 0]== 0.)[0][1:])