所以我正在做的是使用多处理(略)加快对matplot lib图像的绘图。在大多数情况下,我拥有的代码都可以正常工作,但不是100%。
这是我的一些绘图代码:
import matplotlib.pyplot as plot
def plot(index):
fig = plot.figure(figsize=(6.5875*2, 6.2125*2))
temperature = patches.Patch(color='red', label="Temperature")
dewpoint = patches.Patch(color='green', label="Dewpoint")
parcel = patches.Patch(color='black', label="Parcel")
self.plot.legend(handles=[temperature,dewpoint],loc=2, fontsize=14)
fig.savefig(filename, bbox_inches = 'tight')
from multiprocessing import Pool
import multiprocessing
past_files = [0,1,2,3,4]
try:
pool = Pool(5)
pool.map(plot,range(len(past_files)))
except:
pool.close()
pool.terminate()
pool.join()
finally:
pool.close()
pool.terminate()
pool.join()
错误的情况:
很好的情况: