# Correlation fails on high sampling rate vector

I am trying to find the time shift between two vectors.

My function succeeds on randomly generated vectors, but fails on small shifted, high sampling rate vectors.

my code:

class MapperDumpsValidator(object):
@staticmethod
def cross_correlation_shift_from_overlapping_position(static, moving):
from scipy.signal import correlate
correlation = correlate(static, moving, mode='full')
shift = np.argmax(correlation)
return shift, correlation

@staticmethod
static /= np.linalg.norm(static)
moving /= np.linalg.norm(moving)

shift, correlation = MapperDumpsValidator.cross_correlation_shift_from_overlapping_position(static, moving)
# shift -=1
augmented_shift = shift - (len(moving) - 1)

augmented_static = static[augmented_shift: augmented_shift + len(moving)]
moved = moving
diff = augmented_static - moved

plt.figure()
plt.plot(static[augmented_shift: augmented_shift + len(moving)])
plt.plot(moving)
plt.plot(diff)
plt.show()

assert np.count_nonzero(diff) == 0

return diff


And the test:

def correlate_missing_signal(v):
start = 100
end = len(v)-start
reference = v
moving = v[start:end]

plt.figure()
plt.plot(diff)
plt.show()


run with

v = np.random.rand(10000, 1)
correlate_missing_signal(v)


This passes the assertion, and the diff is zero as expected (same input vector, augmented by size).

The Problem:

When I pass in a vector which was samples at high rate, and is shifted by 1, the output augmented_shift is 0, instead of 1.

This is probably due to very small contributions by every single element, which are all smaller than the single contribution of the leftmost and rightmost elements, which are not present if the shift is 1, and are present if the shift is 0.

My Question:

1. Did I use the correlation correctly?
2. If so, How can I get the correct result for vectors of high sampling rate, and small shift?

graphs for illustration of input:

input signals (reference, moving) and diff (in red) zoom in on input signals, notice shift by 1 frame zoom in on diff, notice scale much smaller than input 