使用 Glumpy 将 NumPy 数组显示为持续更新的图像

我有一个使用 NumPy 和 SciPy 在 Python 中运行的模拟模型，它会生成一个 2D NumPy 数组作为每次迭代的输出。我一直使用 matplotlib 和 imshow 函数将此输出显示为图像。然而，我发现了 Glumpy，在它的文档页面上写着：

借助 IPython shell，glumpy 可以在交互模式下运行，您可以在显示的数组内容更改时体验实时更新。

但是，我似乎无法通过他们给出的示例弄清楚如何做到这一点。基本上，我的模型作为单个函数运行，其中有一个大的 for 循环，用于循环我正在运行的迭代次数。在 for 循环的每次迭代结束时，我想显示该数组。目前，我正在使用 matplotlib 将图像保存到 png 文件，因为通过 matplotlib 将其显示在屏幕上似乎会冻结 python 进程。

我确信有一种方法可以用 Glumpy 来做到这一点，我只是不确定如何做，而且我找不到任何有用的教程。

Glumpy 文档几乎不存在！这是一个简单模拟的示例，将数组可视化与glumpy反对matplotlib:

import numpy as np
import glumpy
from OpenGL import GLUT as glut
from time import time
from matplotlib.pyplot import subplots,close
from matplotlib import cm

def randomwalk(dims=(256,256),n=3,sigma=10,alpha=0.95,seed=1):
    """ A simple random walk with memory """
    M = np.zeros(dims,dtype=np.float32)
    r,c = dims
    gen = np.random.RandomState(seed)
    pos = gen.rand(2,n)*((r,),(c,))
    old_delta = gen.randn(2,n)*sigma
    while 1:
        delta = (1.-alpha)*gen.randn(2,n)*sigma + alpha*old_delta
        pos += delta
        for ri,ci in pos.T:
            if not (0. <= ri < r) : ri = abs(ri % r)
            if not (0. <= ci < c) : ci = abs(ci % c)
            M[ri,ci] += 1
        old_delta = delta
        yield M

def mplrun(niter=1000):
    """ Visualise the simulation using matplotlib, using blit for 
    improved speed"""
    fig,ax = subplots(1,1)
    rw = randomwalk()
    im = ax.imshow(rw.next(),interpolation='nearest',cmap=cm.hot,animated=True)
    fig.canvas.draw()
    background = fig.canvas.copy_from_bbox(ax.bbox) # cache the background

    tic = time()
    for ii in xrange(niter):
        im.set_data(rw.next())          # update the image data
        fig.canvas.restore_region(background)   # restore background
        ax.draw_artist(im)          # redraw the image
        fig.canvas.blit(ax.bbox)        # redraw the axes rectangle

    close(fig)
    print "Matplotlib average FPS: %.2f" %(niter/(time()-tic))

def gprun(niter=1000):
    """ Visualise the same simulation using Glumpy """
    rw = randomwalk()
    M = rw.next()

    # create a glumpy figure
    fig = glumpy.figure((512,512))

    # the Image.data attribute is a referenced copy of M - when M
    # changes, the image data also gets updated
    im = glumpy.image.Image(M,colormap=glumpy.colormap.Hot)

    @fig.event
    def on_draw():
        """ called in the simulation loop, and also when the
        figure is resized """
        fig.clear()
        im.update()
        im.draw( x=0, y=0, z=0, width=fig.width, height=fig.height )

    tic = time()
    for ii in xrange(niter):
        M = rw.next()           # update the array          
        glut.glutMainLoopEvent()    # dispatch queued window events
        on_draw()           # update the image in the back buffer
        glut.glutSwapBuffers()      # swap the buffers so image is displayed

    fig.window.hide()
    print "Glumpy average FPS: %.2f" %(niter/(time()-tic))

if __name__ == "__main__":
    mplrun()
    gprun()

Using matplotlib with GTKAgg作为我的后端并使用blit为了避免每次都绘制背景，我可以达到大约 95 FPS。和Glumpy尽管我目前笔记本电脑上的图形设置相当糟糕，但我的帧速率约为 250-300 FPS。话说回来，Glumpy开始工作有点麻烦，除非您正在处理巨大的矩阵，或者无论出于何种原因您需要非常高的帧速率，否则我会坚持使用matplotlib with blit.