[Python] Python / Numba create histogram from ranges

I want to create a histogram (1d) from given ranges and I want it to be fast. This is why also cython and numba can be taken into consideration.

Given are two boundaries, weights and a stepsize.

For example:

boundaries1 = np.array([511.4, 517.5, 517.2, 520.1, 519.8])
boundaries2 = np.array([517.3, 517.1, 517.8, 518.2, 515.0])
weights = np.array([0.2, 0.4, 0.6, 0.8, 1])
stepsize = 0.1


Note, that the boundaries are not sorted by their value, meaning that e.g. a boundary1 could be greater or lower as the corresponding boundary2.

The result for this example should return a histogram with the given stepsize, the minimum value and the maximum value.

histogram = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.2, 1.2, 1.2,
1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2,
1.2, 1.2, 1.2, 1.2, 1.2, 1.6, 2.2, 2.2, 2. , 2. , 1.6, 1.6, 1.6,
1. , 1. , 1. , 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8,
1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 0.8, 0.8, 0.8])
minimum = 511.4
maximum = 520.1


Are there creative ideas on how to implement this to run as fast as possible?

This is my current approach:

@njit(cache=True)
def create_histogram(boundaries1, boundaries2, weights, stepsize):
minimum = np.minimum(np.min(boundaries1),np.min(boundaries2))
maximum = np.maximum(np.max(boundaries1),np.max(boundaries2))

num_bins = int(np.round((maximum-minimum+stepsize)/stepsize))
hist = np.zeros(num_bins, dtype=np.float64)

lower = np.round((np.minimum(boundaries1, boundaries2)-minimum)/stepsize).astype(np.int32)
upper = np.round((np.maximum(boundaries1, boundaries2)-minimum)/stepsize).astype(np.int32)

for i in range(lower.shape[0]):
hist[lower:upper+1] += weights

return hist, minimum, maximum

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