Matplotlib 在循环中绘图时内存不足

Question

我有一个相当简单的绘图例程，如下所示：

from __future__ import division
import datetime
import matplotlib
matplotlib.use('Agg')
from matplotlib.pyplot import figure, plot, show, legend, close, savefig, rcParams
import numpy
from globalconstants import *

    def plotColumns(columnNumbers, t, out, showFig=False, filenamePrefix=None, saveFig=True, saveThumb=True):
        lineProps = ['b', 'r', 'g', 'c', 'm', 'y', 'k', 'b--', 'r--', 'g--', 'c--', 'm--', 'y--', 'k--', 'g--', 'b.-', 'r.-', 'g.-', 'c.-', 'm.-', 'y.-', 'k.-']

        rcParams['figure.figsize'] = (13,11)
        for i in columnNumbers:
            plot(t, out[:,i], lineProps[i])

        legendStrings = list(numpy.zeros(NUMCOMPONENTS)) 
        legendStrings[GLUCOSE] = 'GLUCOSE'
        legendStrings[CELLULOSE] = 'CELLULOSE'
        legendStrings[STARCH] = 'STARCH'
        legendStrings[ACETATE] = 'ACETATE'
        legendStrings[BUTYRATE] = 'BUTYRATE'
        legendStrings[SUCCINATE] = 'SUCCINATE'
        legendStrings[HYDROGEN] = 'HYDROGEN'
        legendStrings[PROPIONATE] = 'PROPIONATE'
        legendStrings[METHANE] = "METHANE"

        legendStrings[RUMINOCOCCUS] = 'RUMINOCOCCUS'
        legendStrings[METHANOBACTERIUM] = "METHANOBACTERIUM"
        legendStrings[BACTEROIDES] = 'BACTEROIDES'
        legendStrings[SELENOMONAS] = 'SELENOMONAS'
        legendStrings[CLOSTRIDIUM] = 'CLOSTRIDIUM'

        legendStrings = [legendStrings[i] for i in columnNumbers]
        legend(legendStrings, loc='best')

        dt = datetime.datetime.now()
        dtAsString = dt.strftime('%d-%m-%Y_%H-%M-%S')

        if filenamePrefix is None:
            filenamePrefix = ''

        if filenamePrefix != '' and filenamePrefix[-1] != '_':
            filenamePrefix += '_'

        if saveFig: 
            savefig(filenamePrefix+dtAsString+'.eps')

        if saveThumb:
            savefig(filenamePrefix+dtAsString+'.png', dpi=300)


        if showFig: f.show()

        close('all')

当我在单次迭代中绘制它时，它工作得很好。然而，当我将它放入循环中时，matplotlib 会发出嘶嘶声……

Traceback (most recent call last):
  File "c4hm_param_variation_h2_conc.py", line 148, in <module>
    plotColumns(columnNumbers, timeVector, out, showFig=False, filenamePrefix='c
4hm_param_variation_h2_conc_'+str(hydrogen_conc), saveFig=False, saveThumb=True)

  File "D:\phdproject\alexander paper\python\v3\plotcolumns.py", line 48, in plo
tColumns
    savefig(filenamePrefix+dtAsString+'.png', dpi=300)
  File "C:\Python25\lib\site-packages\matplotlib\pyplot.py", line 356, in savefi
g
    return fig.savefig(*args, **kwargs)
  File "C:\Python25\lib\site-packages\matplotlib\figure.py", line 1032, in savef
ig
    self.canvas.print_figure(*args, **kwargs)
  File "C:\Python25\lib\site-packages\matplotlib\backend_bases.py", line 1476, i
n print_figure
    **kwargs)
  File "C:\Python25\lib\site-packages\matplotlib\backends\backend_agg.py", line
358, in print_png
    FigureCanvasAgg.draw(self)
  File "C:\Python25\lib\site-packages\matplotlib\backends\backend_agg.py", line
314, in draw
    self.figure.draw(self.renderer)
  File "C:\Python25\lib\site-packages\matplotlib\artist.py", line 46, in draw_wr
apper
    draw(artist, renderer, *kl)
  File "C:\Python25\lib\site-packages\matplotlib\figure.py", line 773, in draw
    for a in self.axes: a.draw(renderer)
  File "C:\Python25\lib\site-packages\matplotlib\artist.py", line 46, in draw_wr
apper
    draw(artist, renderer, *kl)
  File "C:\Python25\lib\site-packages\matplotlib\axes.py", line 1735, in draw
    a.draw(renderer)
  File "C:\Python25\lib\site-packages\matplotlib\artist.py", line 46, in draw_wr
apper
    draw(artist, renderer, *kl)
  File "C:\Python25\lib\site-packages\matplotlib\legend.py", line 374, in draw
    bbox = self._legend_box.get_window_extent(renderer)
  File "C:\Python25\lib\site-packages\matplotlib\offsetbox.py", line 209, in get
_window_extent
    px, py = self.get_offset(w, h, xd, yd)
  File "C:\Python25\lib\site-packages\matplotlib\offsetbox.py", line 162, in get
_offset
    return self._offset(width, height, xdescent, ydescent)
  File "C:\Python25\lib\site-packages\matplotlib\legend.py", line 360, in findof
fset
    return _findoffset(width, height, xdescent, ydescent, renderer)
  File "C:\Python25\lib\site-packages\matplotlib\legend.py", line 325, in _findo
ffset_best
    ox, oy = self._find_best_position(width, height, renderer)
  File "C:\Python25\lib\site-packages\matplotlib\legend.py", line 817, in _find_
best_position
    verts, bboxes, lines = self._auto_legend_data()
  File "C:\Python25\lib\site-packages\matplotlib\legend.py", line 669, in _auto_
legend_data
    tpath = trans.transform_path(path)
  File "C:\Python25\lib\site-packages\matplotlib\transforms.py", line 1911, in t
ransform_path
    self._a.transform_path(path))
  File "C:\Python25\lib\site-packages\matplotlib\transforms.py", line 1122, in t
ransform_path
    return Path(self.transform(path.vertices), path.codes,
  File "C:\Python25\lib\site-packages\matplotlib\transforms.py", line 1402, in t
ransform
    return affine_transform(points, mtx)
MemoryError: Could not allocate memory for path

这种情况发生在迭代 2 上（从 1 开始计数），如果这有什么影响的话。该代码在 Windows XP 32 位上运行，使用 python 2.5 和 matplotlib 0.99.1、numpy 1.3.0 和 scipy 0.7.1。

编辑：代码现已更新，以反映崩溃实际上发生在调用 legend() 时。评论这个呼唤可以解决问题，但显然，我仍然希望能够在我的图表上添加图例......

Answer 1

每个循环都应该生成一个新图形吗？我没有看到您关闭它或从循环到循环创建新的图形实例。

在循环结束时保存当前图形后，此调用将清除当前图形：

pyplot.clf()

不过，我会重构，并使您的代码更加面向对象，并在每个循环上创建一个新的图形实例：

from matplotlib import pyplot

while True:
  fig = pyplot.figure()
  ax = fig.add_subplot(111)
  ax.plot(x,y)
  ax.legend(legendStrings, loc = 'best')
  fig.savefig('himom.png')
  # etc....

Answer 2

我也遇到过这个错误。似乎已经解决了它

while True:
    fig = pyplot.figure()
    ax = fig.add_subplot(111)
    ax.plot(x,y)
    ax.legend(legendStrings, loc = 'best')
    fig.savefig('himom.png')
    #new bit here
    pylab.close(fig) #where f is the figure

现在正在稳定地运行我的循环，内存波动但没有持续增加

Answer 3

ninjasmith 的回答也对我有用 - pyplot.close() 使我的循环能够工作。

从 pyplot 教程中，使用多个图形和轴：

您可以使用 clf() 和当前的
带有 cla() 的轴。如果你觉得这种状态很烦人，那就不要
绝望，这只是一个对象的薄状态包装
面向 API，您可以使用它（请参阅艺术家教程)

如果您要制作一长串图形，则需要注意
还有一件事：一个图形所需的内存并不完全
释放直到使用 close()< 明确关闭该图窗/a>.正在删除
所有对该图的引用，和/或使用窗口管理器来终止
图形出现在屏幕上的窗口还不够，
因为 pyplot 会维护内部引用，直到 close() 被调用。

Answer 4

当我从 jupyter 使用它时，我遇到了类似的问题，将 plt.clf() 和 plt.close() 放入循环中不起作用。

但这有帮助：

import matplotlib
matplotlib.use('Agg')

这会禁用 matplotlib 的交互式后端。

Answer 5

就我而言，matplotlib 版本 3.5.0，正如 Hui Liu san 所说，
以下方法可以保持较低的内存使用率

import matplotlib
print(matplotlib.__version__) #'3.5.0'
import matplotlib.pyplot as plt

plt.savefig('your.png')

# Add both in this order for keeping memory usage low
plt.clf()   
plt.close()

--- 已添加 ----
使用 matplotlib 版本“3.7.2”，
即使使用上述方法，内存仍然继续增加。
按照本网站所述添加以下代码(http://datasideoflife.com/?p=1443) 并通过影射，
内存增加已被消除。

import matplotlib
matplotlib.use('Agg') # for avoiding memory leak