How to count consecutive increases in a 1d array

• I have a 1d numpy array
• It's mostly decreasing, but it increases in a few places
• I'm interested in the places where it increases in several consecutive elements, and how many consecutive elements it increases for in each case
• In other words, I'm interested in the lengths of increasing contiguous sub-arrays
• I'd like to compute and store this information in an array with the same shape as the input (EG that I could use for plotting)
• This could be achieved using cumsum on a binary mask, except I want to reset the accumulation every time the array starts decreasing again
• See example input and expected output below
• How do I do that?

import numpy as np

def count_consecutive_increases(y: np.ndarray) -> np.ndarray:
    ...

y = np.array([9, 8, 7, 9, 6, 5, 6, 7, 8, 4, 3, 1, 2, 3, 0])
c = count_consecutive_increases(y)

print(y)
print(c)

# >>> [9 8 7 9 6 5 6 7 8 4 3 1 2 3 0]
# >>> [0 0 0 1 0 0 1 2 3 0 0 0 1 2 0]

Here is another solution:

import numpy as np

def count_consecutive_increases(y: np.ndarray) -> np.ndarray:
    increases = np.diff(y, prepend=y[0]) > 0
    all_summed = np.cumsum(increases)
    return all_summed - np.maximum.accumulate(all_summed * ~increases)

y = np.array([9, 8, 7, 9, 6, 5, 6, 7, 8, 4, 3, 1, 2, 3, 0])
c = count_consecutive_increases(y)

print(y)  # >>> [9 8 7 9 6 5 6 7 8 4 3 1 2 3 0]
print(c)  # >>> [0 0 0 1 0 0 1 2 3 0 0 0 1 2 0]

The idea is the same as with the solution proposed by OP, albeit a bit shorter: Naively count (by summing over) all indices that have been marked as increasing, then subtract, for each consecutive increasing segment, the count right before its start (the value of which we get by a cumulative maximum over the naive counts at the positions marked as not increasing).