Memory error for large arrays about kdepy HOT 5 OPEN

import numpy as np
import itertools
from KDEpy import FFTKDE

@profile
def run_code():
    nbins_x, nbins_y, nbins_z, nbins_z2 = 120, 30, 100, 200
    bins_x= np.linspace(0,1.0,nbins_x, dtype = np.float32)
    bins_y = np.linspace(0,1.0, nbins_y, dtype = np.float32)
    bins_z = np.linspace(0,1.0,nbins_z, dtype = np.float32)
    bins_z2 = np.linspace(0,1.0, nbins_z2, dtype = np.float32)

    np.random.seed(1)
    x = np.random.random(int(3e6))
    w = np.random.random(int(3e6))
    #Generate KDE1
    inp_data = np.column_stack((x, x, x, x))

    print('start KDE1')
    KDE = FFTKDE(kernel='gaussian', bw=0.1).fit(inp_data, w)
    print('evaluate bins')
    bins1 = np.array(list(itertools.product(bins_x, bins_y, bins_z, bins_z2)))
    y = KDE.evaluate(bins1)

if __name__ == '__main__':
    run_code()

Line #    Mem usage    Increment   Line Contents
================================================
     5   42.320 MiB   42.320 MiB   @profile
     6                             def run_code():
     7   42.320 MiB    0.000 MiB       nbins_x, nbins_y, nbins_z, nbins_z2 = 110, 30, 100, 200
     8   42.320 MiB    0.000 MiB       bins_x= np.linspace(0,1.0,nbins_x, dtype = np.float32)
     9   42.320 MiB    0.000 MiB       bins_y = np.linspace(0,1.0, nbins_y, dtype = np.float32)
    10   42.320 MiB    0.000 MiB       bins_z = np.linspace(0,1.0,nbins_z, dtype = np.float32)
    11   42.320 MiB    0.000 MiB       bins_z2 = np.linspace(0,1.0, nbins_z2, dtype = np.float32)
    12                             
    13   42.320 MiB    0.000 MiB       np.random.seed(1)
    14   65.293 MiB   22.973 MiB       x = np.random.random(int(3e6))
    15   88.090 MiB   22.797 MiB       w = np.random.random(int(3e6))
    16                                 #Generate KDE1
    17  179.719 MiB   91.629 MiB       inp_data = np.column_stack((x, x, x, x))
    18                             
    19  179.719 MiB    0.000 MiB       print('start KDE1')
    20  205.508 MiB   25.789 MiB       KDE = FFTKDE(kernel='gaussian', bw=0.1).fit(inp_data, w)
    21  205.508 MiB    0.000 MiB       print('evaluate bins')
    22 1226.445 MiB 1020.938 MiB       bins1 = np.array(list(itertools.product(bins_x, bins_y, bins_z, bins_z2)))
    23 3515.254 MiB 2288.809 MiB       y = KDE.evaluate(bins1)

Line #    Mem usage    Increment   Line Contents
================================================
     5   42.320 MiB   42.320 MiB   @profile
     6                             def run_code():
     7   42.320 MiB    0.000 MiB       nbins_x, nbins_y, nbins_z, nbins_z2 = 120, 30, 100, 200
     8   42.320 MiB    0.000 MiB       bins_x= np.linspace(0,1.0,nbins_x, dtype = np.float32)
     9   42.320 MiB    0.000 MiB       bins_y = np.linspace(0,1.0, nbins_y, dtype = np.float32)
    10   42.320 MiB    0.000 MiB       bins_z = np.linspace(0,1.0,nbins_z, dtype = np.float32)
    11   42.320 MiB    0.000 MiB       bins_z2 = np.linspace(0,1.0, nbins_z2, dtype = np.float32)
    12                             
    13   42.320 MiB    0.000 MiB       np.random.seed(1)
    14   65.355 MiB   23.035 MiB       x = np.random.random(int(3e6))
    15   88.086 MiB   22.730 MiB       w = np.random.random(int(3e6))
    16                                 #Generate KDE1
    17  179.781 MiB   91.695 MiB       inp_data = np.column_stack((x, x, x, x))
    18                             
    19  179.781 MiB    0.000 MiB       print('start KDE1')
    20  205.637 MiB   25.855 MiB       KDE = FFTKDE(kernel='gaussian', bw=0.1).fit(inp_data, w)
    21  205.637 MiB    0.000 MiB       print('evaluate bins')
    22 1318.488 MiB 1112.852 MiB       bins1 = np.array(list(itertools.product(bins_x, bins_y, bins_z, bins_z2)))
    23 53852.961 MiB 52534.473 MiB       y = KDE.evaluate(bins1)

import numpy as np

def run_code():
    
    # Bins in each dimension, 50 * 30 * 80 * 100 = 12 million grid points
    nbins_x, nbins_y, nbins_z, nbins_z2 = 50, 30, 80, 100

    # Generate random data and weights 
    np.random.seed(1)
    x = np.random.random(int(3e6))
    w = np.random.random(int(3e6))
    inp_data = np.column_stack((x, x, x, x))
    
    # Utility functions for automatic grids and binning
    from KDEpy.utils import autogrid
    from KDEpy.binning import linear_binning
    
    # Create a grid. Maximum of relative and absolute boundary limits is used
    # Here the grid will go from -0.5 to 1.5, not -0.05 to 1.05
    grid = autogrid(inp_data, boundary_abs=0.5,
                    num_points=(nbins_x, nbins_y, nbins_z, nbins_z2), 
                    boundary_rel=0.05)
    
    print(grid[0, :])  # Check boundaries
    print(grid.shape)
    
    # Bin the data on the grid points, the weights are used
    data_vals = linear_binning(inp_data, grid, weights=w)
    
    # At this point, `data_vals` is a linear approximation to your data, at the points `grid`.
    # You can try to convolve this 4D weighted compression of your data with
    # a kernel, or feed this to a different algorithm, but it must handle weighted data.

if __name__ == '__main__':
    run_code()