我正在尝试使用CPU多处理来编码Mandelbrot集，以获得更快的速度。现在，我想渲染一个32768 x32768的图像集。在使用np.vstack（）将我的文件转换为接近结尾的正确尺寸时，我遇到了一个OSError: [WinError 87] The parameter is incorrect。

import numpy as np
import matplotlib.pyplot as plt
import multiprocessing as mp
import warnings
import time

start = time.time() #to time program

warnings.filterwarnings("ignore") #For the overflow and invalid exponent runtime warnings

b = 8192*4 #Length and Width of the 22500

if __name__ == "__main__":
    num_cores = mp.cpu_count() 
    Y, X = np.mgrid[-1 * (b // 2):b // 2, -1 * (b // 2):b // 2]  #inclue all four quadrants about (0,0), not just Quadrant I
    
    chunk_size = b // num_cores  
    chunks = []
    
    for i in range(num_cores):   #create the chunks
        start_row = i * chunk_size
        end_row = (i + 1) * chunk_size if i < num_cores - 1 else b
        chunks.append((Y, X, start_row, end_row))

    pool = mp.Pool(processes=num_cores)
    results = pool.imap(calculate_mandelbrot_chunk, chunks, 3) #this is the memory error for b > 2^14
    pool.close()
    pool.join()
####vvvvvvvvvvvvvvvvvvvvvv##################
    a = np.vstack(results)

我完全傻眼了，为什么会出现这个问题。在16384 x16384或更低的时候一切都很好，但在32 k或更高的时候似乎会崩溃。我自己也不能调试这个，因为它对较小的b值工作得很好。
更新：由于问题是我的电脑不够结实，我将采取不同的方法。下面是@jsbueno请求的代码：

import matplotlib.pyplot as plt
from mpl_interactions import ioff, panhandler, zoom_factory
import warnings
import time

warnings.filterwarnings("ignore")  # For the overflow and invalid exponent runtime warnings

start = time.time()  # to time program

b = 512  # Length and Width of the image
xlims = (-512,512)
ylims = (-512,512)
#(-512, 512)

#scroll code
def on_ylims_change(event_ax):
    global ylims
    ylims = event_ax.get_ylim()

def on_xlims_change(event_ax):
    global xlims
    xlims = event_ax.get_xlim()
    print("Updated xlims:", xlims)
    #blank(xlims, ylims)
    update_plot(xlims, ylims)

#calc mandy's set
def calculate_mandelbrot(xmin, xmax, ymin, ymax, size=512):
    Y, X = np.mgrid[ymin:ymax, xmin:xmax]
    c = (X + Y * 1j) * (4 / size)  # Complex numbers array
    c = c.astype(np.complex64)  # Save memory
    a = np.copy(c) * 0
    for k in range(64):
        a = a ** 2 + c
    a[a < 100] = 1  # Filter divergents out
    return np.real(a)  # Type casting

def update_plot(xlims, ylims):
    a_zoomed = calculate_mandelbrot(xlims[0], xlims[1], ylims[0], ylims[1], size=b)
    ax.imshow(a_zoomed, cmap="gray", extent=(xlims[0], xlims[1], ylims[0], ylims[1]))

def blank(xlims, ylims):
    a_zoomed = np.zeros([32, 32])
    ax.imshow(a_zoomed, cmap="gray", extent=(xlims[0], xlims[1], ylims[0], ylims[1]))

a = calculate_mandelbrot(xlims[0], xlims[1], ylims[0], ylims[1], size=b)

end = time.time()
print(end - start)

#### scroll wheel zoom ####
with plt.ioff():
    fig, ax = plt.subplots()
ax.imshow(a, cmap="gray", extent=(-512,512,-512,512))
disconnect_zoom = zoom_factory(ax)
ax.callbacks.connect('ylim_changed', on_ylims_change)
ax.callbacks.connect('xlim_changed', on_xlims_change)

pan_handler = panhandler(fig)

plt.show()

注意：在这个b值下的每次重绘在我的电脑上只需要0.3055903911590576 seconds。我有一个英特尔i9，所以它可能有点不同的其他人。

编辑2：添加ax.cla()以上更新修复了这个问题！