我正在使用时空数组，我想导出结果以进行可视化。我可以在特定时间间隔提取图像以显示空间变化。现在，我想粘合这些图像以获得动画。
使用下面的代码，我有两个问题：
- 我获得的动画只显示最后一张图片（所以没有动画！）
- 轮廓（当前已评论）绘制在前一张图像的顶部。
代码是从原始版本简化的，但仍然很长，抱歉！我插入了评论以帮助理解这些步骤。
我在 32 位 Windows 7 机器上使用 Python 2.7.2、matplotlib 1.2.1。
提前致谢！
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tempfile, os
duration = 5
timesteps = np.arange(0, duration, 1)
ncol = 6
nrow = 13
nlay = 2
cmap = plt.cm.gist_rainbow_r
CBlabel = 'values ([-])'
plt_title = 'VALUES'
# create output folder
ws = os.path.join(tempfile.gettempdir(), "__ani")
f = 0
if os.path.exists(ws):
ws1 = ws
while os.path.exists(ws1):
ws1 = '%s%d' % (ws, f)
f +=1
os.makedirs(ws1)
ws = ws1
del ws1
else:
os.makedirs(ws)
# create coordinate arrays
x = np.arange(0.5, ncol+1.5, 1)
y = np.arange(0.5, nrow+1.5, 1)
xg,yg = np.meshgrid(x,y)
x = np.arange(1, ncol+1, 1)
y = np.arange(1, nrow+1, 1)
xg1,yg1 = np.meshgrid(x,y)
# create values to be plotted
V = np.zeros([duration,nrow,ncol,nlay], dtype = float)
for d in timesteps:
for L in range(nlay):
V[d,:,:,L] = ((np.random.rand(nrow, ncol)+0.5)*xg1+(np.random.rand(nrow, ncol)+0.5)*yg1)/(1.0+2.0*L)
Vmax = np.max(V)
Vmin = np.min(V)
ticks = np.linspace(Vmin,Vmax,5)
# plot initialization
ax= []
fig = plt.figure(num=None, figsize=(11.7, 8.27), dpi=30)
figtitle = fig.suptitle('')
for L in range(nlay):
ax.append(fig.add_subplot(1,nlay,L+1, axisbg = 'silver'))
ax[L].xaxis.set_ticks(np.arange(0,ncol+1,1))
ax[L].yaxis.set_ticks(np.arange(0,nrow+1,1))
plt.setp(ax[L].get_xticklabels(), fontsize=8)
plt.setp(ax[L].get_yticklabels(), fontsize=8)
plt.ylabel('row i', fontsize=10)
plt.xlabel('col j', fontsize=10)
ax[L].set_title('layer ' + str(L+1), fontsize = 10)
# plot sequences of grids
ims = []
for i, day in enumerate(timesteps):
ims.append([])
figtitle.set_text(plt_title + '\ntime step %s' % (day))
plt.draw()
for L in range(nlay):
Vtmp = V[day,:,:,L]
ims[i].append(ax[L].pcolormesh(xg, yg, Vtmp, cmap = cmap, vmin = Vmin, vmax = Vmax))
# plot contours with labels
#ims[i].append(ax[L].contour(xg1, yg1[::-1], Vtmp[::-1], ticks, colors = 'gray'))
#ax[L].clabel(ims[i][4*i+L+1], inline=1, fontsize = 6, fmt='%2.2f', colors = 'gray')
del Vtmp
# modify axes range
ax[L].set_ylim(bottom = np.max(yg1), top = np.min(yg1))
ax[L].axis('scaled')
# create color bar
cax = fig.add_axes([0.035, 0.125, 0.025, 0.75])
CB = fig.colorbar(ims[0][0], extend='both', ticks = ticks, format = '%2.2f', cax = cax, orientation = 'vertical')
CB.set_label(CBlabel, fontsize = 12)
cax.yaxis.set_label_position('left')
plt.setp(CB.ax.get_yticklabels(), fontsize = 7)
# save image
plt_export_fn = os.path.join(ws, '_plt_%s_timestep%05d.png' % (plt_title, day))
plt.savefig(plt_export_fn)
# save animation
ani = animation.ArtistAnimation(fig, ims, interval=1000*(timesteps[1]-timesteps[0]), repeat_delay=500, blit=False)
ani.save(os.path.join(ws, '_plt_%s_mov.mp4' % plt_title))
plt.close('all')
print "Done!\nCheck output in:\n%s" % ws