Numpy library crash without console error or warning constantly

In Python Console see notifications either Process finished with exit code -1073741819 (0xC0000005) or Process finished with exit code -1073740791 (0xC0000409)

Tried to solve issue by using python [Python 3.13.2] [Python 3.12.9] [Python 3.12.5]
I also tried multiple versions of NumPy: [1.26.4], [2.2.2], [2.1.1]

I cannot point out exact line of code where it crashes as it vary from time to time. Sometimes it is on np.where, other time in np.maximum.accumulate and so on.

On simple calculations it works fine (i.e. when I apply NumPy to lines less then 100k), but apply to a bigger data it fails. Usually after 4 min of calculations.

Here is the error message:

Faulting Application Name: python.exe, Version: 3.12.9150.1013, Timestamp: 0x67a232aa
Faulting Module Name: _multiarray_umath.cp312-win_amd64.pyd, Version: 0.0.0.0, Timestamp: 0x00000000
Exception Code: 0xc0000005
Fault Offset: 0x000000000007c502
Faulting Process ID: 0x7324
Faulting Application Start Time: 0x1DB7ACE52598435
Faulting Application Path: C:\Users\Tony\AppData\Local\Programs\Python\Python312\python.exe
Faulting Module Path: C:\Users\Tony\AppData\Local\Programs\Python\Python313\TradingData\Lib\site-packages\numpy\core_multiarray_umath.cp312-win_amd64.pyd
Report Identifier: 833671e4-3a45-466e-b46c-693883b70457
Faulting Package Full Name:
Faulting Package App ID:

My PC info:
CPU: CPU Type 8C+16c Intel Core i9-13900KF, 5500 MHz (55 x 100)
Mother: Motherboard Name Asus ROG Strix Z790-A Gaming WiFi II (1 PCI-E x1, 2 PCI-E x16, 4 M.2, 4 DDR5 DIMM, Audio, Video, 2.5GbE LAN, WiFi)
RAM: 128gb 4xG Skill F5-6800J3445G32G 32 GB DDR5-4800 DDR5 SDRAM (42-41-41-77 @ 2403 MHz) (40-40-40-77 @ 2400 MHz) (36-36-36-70 @ 2160 MHz) (32-32-32-62 @ 1920 MHz) (30-30-30-58 @ 1800 MHz) (28-28-28-54 @ 1680 MHz) (22-22-22-43 @ 1320 MHz)

Reproducing code example:

import numpy as np
from numpy.lib.stride_tricks import sliding_window_view

for i in range(0,60):

    w = 60
    x = np.random.rand(9000000)
    window = sliding_window_view(x, window_shape=w)


    def cor_max(row):
        try:
            n = np.where(row >= np.quantile(row, 0.9))[0]
            if len(n) == 0:
                return np.nan
            else:
                r = np.corrcoef(n, range(len(n)))[0][1]
            return r
        except:
            return np.nan


    x1 = np.apply_along_axis(cor_max, axis=1, arr=window)
    x1 = np.append(np.empty(w - 1), x1)


    def cor_min(row):
        try:
            n = np.where(row <= np.quantile(row, 0.1))[0]
            if len(n) == 0:
                return np.nan
            else:
                r = np.corrcoef(n, range(len(n)))[0][1]
            return r
        except:
            return np.nan


    x1 = np.apply_along_axis(cor_min, axis=1, arr=window)
    x1 = np.append(np.empty(w - 1), x1)


    def n_max_change(row):
        try:
            cummax = np.maximum.accumulate(row)
            updates = np.diff(cummax) > 0
            n_updates = np.sum(updates)

            return n_updates / len(row)
        except:
            return np.nan


    x1 = np.apply_along_axis(n_max_change, axis=1, arr=window)
    x1 = np.append(np.empty(w - 1), x1)
    print(i)

I expect help on a ways to solve the issue