Speed up searchsorted calls

[yger]

Quite a lot everywhere in spikeinterface, searchsorted is called twice and thus a speedup can be gained by making a single call

[alejoe91]

Thanks Pierre! Can you check why tests are failing?

[h-mayorquin]

import numpy as np

x = np.arange(int(1e8))
first_frame = 500_000
chunk_size = 100_000
last_frame = first_frame + chunk_size

# First case: Two individual searches
def search_individual():
    left1 = np.searchsorted(x, first_frame)
    right1 = np.searchsorted(x, last_frame)
    return left1, right1

# Second case: A single search with both values
def search_together():
    left2, right2 = np.searchsorted(x, [first_frame, last_frame])
    return left2, right2

# Use the timeit magic command to compare
print("First case:")
%timeit search_individual()

print("\nSecond case:")
%timeit search_together()

Output in my machine:

First case:
2.11 µs ± 124 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Second case:
1.93 µs ± 107 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

[h-mayorquin]

5 % improvement for every chunk acumulates. This looks good, @yger .

Not relevant in this PR but I wish we used better naming for the left and right bounds than i0 , i1 s0 and s1.

They are comfortable for the person that knows the code (like algebraic expressions are comfortable to operate in a mathematical theory that you already dominate) but are difficult, terse and non-obvious for someone who encounters the expression for the first time.

[yger]

I thought it would have been larger, but a gain is a gain :-)

[yger]

This is ready to be merged @alejoe91 @samuelgarcia , I only handled the cases where the searchsorted were of the same type (left or right)