Say I have a 1D numpy array of numbers myArray = ([1, 1, 0, 2, 0, 1, 1, 1, 1, 0, 0 ,1, 2, 1, 1, 1])
.
I want to create a 2D numpy array that describe the fir
Approach #1
Here's one approach inspired by this post -
def start_stop(a, trigger_val, len_thresh=2):
# "Enclose" mask with sentients to catch shifts later on
mask = np.r_[False,np.equal(a, trigger_val),False]
# Get the shifting indices
idx = np.flatnonzero(mask[1:] != mask[:-1])
# Get lengths
lens = idx[1::2] - idx[::2]
return idx.reshape(-1,2)[lens>len_thresh]-[0,1]
Sample run -
In [47]: myArray
Out[47]: array([1, 1, 0, 2, 0, 1, 1, 1, 1, 0, 0, 1, 2, 1, 1, 1])
In [48]: start_stop(myArray, trigger_val=1, len_thresh=2)
Out[48]:
array([[ 5, 8],
[13, 15]])
Approach #2
Another with binary_erosion -
from scipy.ndimage.morphology import binary_erosion
mask = binary_erosion(myArray==1,structure=np.ones((3)))
idx = np.flatnonzero(mask[1:] != mask[:-1])
out = idx.reshape(-1,2)+[0,1]