I want to verify if a numpy array is a continuous sequence in another array.
E.g.
a = np.array([1,2,3,4,5,6,7])
b = np.array([3,4,5])
c = np.array([2,3,4,6])
The expected result would be:
is_sequence_of(b, a) # should return True
is_sequence_of(c, a) # should return False
I want to know if there is a numpy method that does this.
Approach #1
We can use one with np.searchsorted -
def isin_seq(a,b):
# Look for the presence of b in a, while keeping the sequence
sidx = a.argsort()
idx = np.searchsorted(a,b,sorter=sidx)
idx[idx==len(a)] = 0
ssidx = sidx[idx]
return (np.diff(ssidx)==1).all() & (a[ssidx]==b).all()
Note that this assumes that the input arrays have no duplicates.
Sample runs -
In [42]: isin_seq(a,b) # search for the sequence b in a
Out[42]: True
In [43]: isin_seq(c,b) # search for the sequence b in c
Out[43]: False
Approach #2
Another with skimage.util.view_as_windows -
from skimage.util import view_as_windows
def isin_seq_v2(a,b):
return (view_as_windows(a,len(b))==b).all(1).any()
Approach #3
This could also be considered as a template-matching problem and hence, for int numbers, we can use OpenCV's built-in function for template-matching : cv2.matchTemplate (inspired by this post), like so -
import cv2
from cv2 import matchTemplate as cv2m
def isin_seq_v3(arr,seq):
S = cv2m(arr.astype('uint8'),seq.astype('uint8'),cv2.TM_SQDIFF)
return np.isclose(S,0).any()
Approach #4
Our methods could benefit with a short-circuiting based one. So, we will use one with numba for performance-efficiency, like so -
from numba import njit
@njit
def isin_seq_numba(a,b):
m = len(a)
n = len(b)
for i in range(m-n+1):
for j in range(n):
if a[i+j]!=b[j]:
break
if j==n-1:
return True
return False
