Matplotlib内存泄漏在循环中保存数字时

发布于 2025-01-29 18:49:09 字数 11439 浏览 3 评论 0原文

我正在研究一个大数据集，我需要在每个数据点创建和导出图像。我在过程中间的记忆饱和度。我应用了一些内存管理工具和库，发现内存泄漏，显然来自matplotlib。我已经尝试了垃圾收集器，删除了循环中的所有变量，甚至在保存后删除了数字，但是记忆在每次迭代时都会不断增加。在这里，代码，按线内存分析和前10名对内存的影响“ tracemalloc”

displacement=['Double sinus','Linear','Sinus in X','Sinus in Y']
os.chdir('E:\\Quality\\Magnitude')
disp_axis=[1,1,0,1]
import gc
from matplotlib import figure

import tracemalloc
tracemalloc.start(10)




for a in range(len(Folder_u)):
    for b in range(len(Folder_u[a])):
        u_exp = pd.read_csv(Folder_u[a][b],header=None,delimiter=' ',engine='pyarrow')
        v_exp = pd.read_csv(Folder_v[a][b],header=None,delimiter=' ',engine='pyarrow')
        TH_u = pd.read_csv(u_th[b],sep=',',header=None,engine='pyarrow')
        TH_v = pd.read_csv(v_th[b],sep=',',header=None,engine='pyarrow')
        M_exp = np.sqrt(u_exp.dropna(axis=1)**2+v_exp.dropna(axis=1)**2)
        M_th = np.sqrt(TH_u**2+TH_v**2)
        Error = abs(M_exp-M_th)/M_th *100
        MM_E = Error.mean(axis=disp_axis[b])
        MM_exp = M_exp.mean(axis=disp_axis[b])
        MM_th = M_th.mean(axis=disp_axis[b])
        
        name = N[a]
        title=name.replace('.json','')
        
        xth = np.linspace(0,len(MM_th),len(MM_th))
        xe = np.linspace(0,len(MM_exp),len(MM_exp))
        ## mean error 
        
        #fig=figure.Figure(figsize=(15,10))
        #ax = fig.subplots(1)
        
        fig,ax = plt.subplots(figsize=(15,10))
        
        plt.title('displacement = '+displacement[b]+', Config = ' + title ,fontsize=20,fontweight='bold')
        #plt.title(Pyr_Type[l]+displacement[j])
        ax.plot(MM_E,'b')
        plt.ylim([0,100])
        plt.yticks(np.linspace(0,100,11))
        plt.ylabel('mean magnitude error [%]',fontsize=14)
        plt.xlabel('Pixels',fontsize=14)
        plt.grid()
        #plt.ylim([0,5])
        
        ax1=ax.twinx()
        ax1.plot(xth,MM_th,'k',xe,MM_exp,'r-.')
        plt.ylim([0,23])
        plt.ylabel('mean displacement magnitude [Pixels]',fontsize=14)
        plt.xlabel('Pixels',fontsize=14)
        #plt.ylim([4,23])
        #fig.set_dpi(300)
        plt.grid()
        fig.legend(['Mean error','Theoretical','Results'],fontsize=14,loc='upper right')
        fig.set_dpi(250)
        plt.show(fig)
        plt.savefig('Mean_error_'+title+'_'+str(b)+'.png')
        
        print(a)
    
        
        del u_exp
        del v_exp
        del TH_u
        del TH_v
        del M_exp 
        del M_th
        del Error 
        del MM_E
        del MM_exp 
        del MM_th 
        del fig
        del ax, ax1, name, title, xth,xe
        gc.collect()
        print('deleted')
        snapshot = tracemalloc.take_snapshot()
        top_stats = snapshot.statistics('lineno')
        tracemalloc.clear_traces()

在这里逐行分析内存的分析：


Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
   241   2261.1 MiB   2261.1 MiB           1   @profile
   242                                         
   243                                         def test():
   244                                             
   245                                             
   246                                             
   247                                             #Pyr_Type = ['Haar (old)','Haar (NPP)','Burt (old)','Burt (new,old conv.)','Burt (new,new conv.)','Burt(NPP)']
   248   2261.1 MiB      0.0 MiB           1       displacement=['Double sinus','Linear','Sinus in X','Sinus in Y']
   249   2261.1 MiB      0.0 MiB           1       os.chdir('E:\\Quality\\Magnitude')
   250   2261.1 MiB      0.0 MiB           1       disp_axis=[1,1,0,1]
   251   2261.1 MiB      0.0 MiB           1       import gc
   252                                            # from matplotlib import figure
   253                                         
   254   2261.1 MiB      0.0 MiB           1       import tracemalloc
   255   2261.1 MiB      0.0 MiB           1       tracemalloc.start(10)
   256                                         
   257                                             
   258                                         
   259                                         
   260                                         
   261   2432.0 MiB      0.0 MiB           2       for a in range(1):
   262   2432.0 MiB      0.0 MiB           5           for b in range(len(Folder_u[a])):
   263   2361.8 MiB    -34.9 MiB           4               u_exp = pd.read_csv(Folder_u[a][b],header=None,delimiter=' ',engine='pyarrow')
   264   2361.0 MiB      2.0 MiB           4               v_exp = pd.read_csv(Folder_v[a][b],header=None,delimiter=' ',engine='pyarrow')
   265   2399.2 MiB    110.1 MiB           4               TH_u = pd.read_csv(u_th[b],sep=',',header=None,engine='pyarrow')
   266   2402.3 MiB      6.8 MiB           4               TH_v = pd.read_csv(v_th[b],sep=',',header=None,engine='pyarrow')
   267   2433.4 MiB    126.2 MiB           4               M_exp = np.sqrt(u_exp.dropna(axis=1)**2+v_exp.dropna(axis=1)**2)
   268   2464.9 MiB    124.5 MiB           4               M_th = np.sqrt(TH_u**2+TH_v**2)
   269   2495.9 MiB    125.0 MiB           4               Error = abs(M_exp-M_th)/M_th *100
   270   2495.9 MiB      0.0 MiB           4               MM_E = Error.mean(axis=disp_axis[b])
   271   2495.9 MiB      0.0 MiB           4               MM_exp = M_exp.mean(axis=disp_axis[b])
   272   2495.9 MiB      0.0 MiB           4               MM_th = M_th.mean(axis=disp_axis[b])
   273                                                     
   274   2495.9 MiB      0.0 MiB           4               name = N[a]
   275   2495.9 MiB      0.0 MiB           4               title=name.replace('.json','')
   276                                                     
   277   2495.9 MiB      0.0 MiB           4               xth = np.linspace(0,len(MM_th),len(MM_th))
   278   2495.9 MiB      0.0 MiB           4               xe = np.linspace(0,len(MM_exp),len(MM_exp))
   279                                                     ## mean error 
   280                                                     
   281                                                     #fig=figure.Figure(figsize=(15,10))
   282                                                     #ax = fig.subplots(1)
   283   2497.9 MiB    -27.5 MiB           4               fig,ax = plt.subplots(figsize=(15,10))
   284                                                     
   285   2497.9 MiB      0.0 MiB           4               plt.title('displacement = '+displacement[b]+', Config = ' + title ,fontsize=20,fontweight='bold')
   286                                                     #plt.title(Pyr_Type[l]+displacement[j])
   287   2497.9 MiB      0.0 MiB           4               ax.plot(MM_E,'b')
   288   2497.9 MiB      0.0 MiB           4               plt.ylim([0,100])
   289   2499.9 MiB     10.0 MiB           4               plt.yticks(np.linspace(0,100,11))
   290   2499.9 MiB      0.0 MiB           4               plt.ylabel('mean magnitude error [%]',fontsize=14)
   291   2499.9 MiB      0.0 MiB           4               plt.xlabel('Pixels',fontsize=14)
   292   2499.9 MiB      0.0 MiB           4               plt.grid()
   293                                                     #plt.ylim([0,5])
   294                                                     
   295   2499.9 MiB      0.0 MiB           4               ax1=ax.twinx()
   296   2499.9 MiB      0.1 MiB           4               ax1.plot(xth,MM_th,'k',xe,MM_exp,'r-.')
   297   2499.9 MiB      0.3 MiB           4               plt.ylim([0,23])
   298   2499.9 MiB      4.0 MiB           4               plt.ylabel('mean displacement magnitude [Pixels]',fontsize=14)
   299   2499.9 MiB      0.0 MiB           4               plt.xlabel('Pixels',fontsize=14)
   300                                         
   301   2499.9 MiB      0.5 MiB           4               plt.grid()
   302   2503.9 MiB     10.1 MiB           4               fig.legend(['Mean error','Theoretical','Results'],fontsize=14,loc='upper right')
   303                                                     
   304   2519.8 MiB     61.8 MiB           4               plt.savefig('Mean_error_'+title+'_'+str(b)+'.png',dpi=300)
   305   2535.6 MiB     71.6 MiB           4               plt.show()
   306   2535.6 MiB      0.0 MiB           4               print(a)
   307                                                    
   308                                                     #fig.clear()
   309                                                     #plt.close()
   310                                                     #plt.cla()
   311   2535.6 MiB      0.0 MiB           4               del u_exp
   312   2535.6 MiB      0.0 MiB           4               del v_exp
   313   2535.6 MiB      0.0 MiB           4               del TH_u
   314   2535.6 MiB      0.0 MiB           4               del TH_v
   315   2504.5 MiB   -124.0 MiB           4               del M_exp 
   316   2473.5 MiB   -124.0 MiB           4               del M_th
   317   2442.5 MiB   -124.0 MiB           4               del Error 
   318   2442.5 MiB      0.0 MiB           4               del MM_E
   319   2442.5 MiB      0.0 MiB           4               del MM_exp 
   320   2442.5 MiB      0.0 MiB           4               del MM_th 
   321   2442.5 MiB      0.0 MiB           4               del fig
   322   2442.5 MiB      0.0 MiB           4               del ax, ax1, name, title, xth,xe
   323   2440.0 MiB    -13.0 MiB           4               gc.collect()
   324   2440.0 MiB      0.0 MiB           4               print('deleted')
   325   2440.0 MiB      7.7 MiB           4               snapshot = tracemalloc.take_snapshot()
   326   2440.0 MiB     -2.8 MiB           4               top_stats = snapshot.statistics('lineno')
   327   2432.0 MiB    -42.0 MiB           4               tracemalloc.clear_traces()
   328                                                     
   329                                                     #break
   330                                                 
   331                                                 #break
   332                                                     #plt.show()

这里是Tracemalloc的前10名：


C:\Users\user\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\blocks.py:402: size=62.0 MiB, count=5, average=12.4 MiB
C:\Users\user\anaconda3\lib\site-packages\numexpr\necompiler.py:836: size=31.0 MiB, count=2, average=15.5 MiB
C:\Users\user\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py:2237: size=194 KiB, count=17, average=11.4 KiB
C:\Users\user\anaconda3\lib\selectors.py:315: size=144 KiB, count=5, average=28.8 KiB
C:\Users\user\anaconda3\lib\site-packages\numpy\core\_asarray.py:102: size=119 KiB, count=387, average=314 B
C:\Users\user\anaconda3\lib\linecache.py:137: size=114 KiB, count=1165, average=100 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\lines.py:377: size=97.0 KiB, count=45, average=2208 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\lines.py:672: size=95.9 KiB, count=28, average=3509 B
<unknown>:0: size=95.8 KiB, count=23, average=4264 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\text.py:265: size=90.6 KiB, count=42, average=2208 B
```````````

原文

I'm working on a big data set and i need to create and export images at each data point. I'm hitting the memory saturation at around the middle of the process. I applied some memory management tools and libraries and found that there's a memory leak and apparently comes from matplotlib. I've already tried the garbage collector, deleting all the variables within the loop, and even deleting the figure after saved but memory keeps increasing at each iteration. Here the code, the line by line memory analysis and the top 10 impact on the memory by "tracemalloc"

displacement=['Double sinus','Linear','Sinus in X','Sinus in Y']
os.chdir('E:\\Quality\\Magnitude')
disp_axis=[1,1,0,1]
import gc
from matplotlib import figure

import tracemalloc
tracemalloc.start(10)




for a in range(len(Folder_u)):
    for b in range(len(Folder_u[a])):
        u_exp = pd.read_csv(Folder_u[a][b],header=None,delimiter=' ',engine='pyarrow')
        v_exp = pd.read_csv(Folder_v[a][b],header=None,delimiter=' ',engine='pyarrow')
        TH_u = pd.read_csv(u_th[b],sep=',',header=None,engine='pyarrow')
        TH_v = pd.read_csv(v_th[b],sep=',',header=None,engine='pyarrow')
        M_exp = np.sqrt(u_exp.dropna(axis=1)**2+v_exp.dropna(axis=1)**2)
        M_th = np.sqrt(TH_u**2+TH_v**2)
        Error = abs(M_exp-M_th)/M_th *100
        MM_E = Error.mean(axis=disp_axis[b])
        MM_exp = M_exp.mean(axis=disp_axis[b])
        MM_th = M_th.mean(axis=disp_axis[b])
        
        name = N[a]
        title=name.replace('.json','')
        
        xth = np.linspace(0,len(MM_th),len(MM_th))
        xe = np.linspace(0,len(MM_exp),len(MM_exp))
        ## mean error 
        
        #fig=figure.Figure(figsize=(15,10))
        #ax = fig.subplots(1)
        
        fig,ax = plt.subplots(figsize=(15,10))
        
        plt.title('displacement = '+displacement[b]+', Config = ' + title ,fontsize=20,fontweight='bold')
        #plt.title(Pyr_Type[l]+displacement[j])
        ax.plot(MM_E,'b')
        plt.ylim([0,100])
        plt.yticks(np.linspace(0,100,11))
        plt.ylabel('mean magnitude error [%]',fontsize=14)
        plt.xlabel('Pixels',fontsize=14)
        plt.grid()
        #plt.ylim([0,5])
        
        ax1=ax.twinx()
        ax1.plot(xth,MM_th,'k',xe,MM_exp,'r-.')
        plt.ylim([0,23])
        plt.ylabel('mean displacement magnitude [Pixels]',fontsize=14)
        plt.xlabel('Pixels',fontsize=14)
        #plt.ylim([4,23])
        #fig.set_dpi(300)
        plt.grid()
        fig.legend(['Mean error','Theoretical','Results'],fontsize=14,loc='upper right')
        fig.set_dpi(250)
        plt.show(fig)
        plt.savefig('Mean_error_'+title+'_'+str(b)+'.png')
        
        print(a)
    
        
        del u_exp
        del v_exp
        del TH_u
        del TH_v
        del M_exp 
        del M_th
        del Error 
        del MM_E
        del MM_exp 
        del MM_th 
        del fig
        del ax, ax1, name, title, xth,xe
        gc.collect()
        print('deleted')
        snapshot = tracemalloc.take_snapshot()
        top_stats = snapshot.statistics('lineno')
        tracemalloc.clear_traces()

Here the analysis of the memory broken down line by line:


Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
   241   2261.1 MiB   2261.1 MiB           1   @profile
   242                                         
   243                                         def test():
   244                                             
   245                                             
   246                                             
   247                                             #Pyr_Type = ['Haar (old)','Haar (NPP)','Burt (old)','Burt (new,old conv.)','Burt (new,new conv.)','Burt(NPP)']
   248   2261.1 MiB      0.0 MiB           1       displacement=['Double sinus','Linear','Sinus in X','Sinus in Y']
   249   2261.1 MiB      0.0 MiB           1       os.chdir('E:\\Quality\\Magnitude')
   250   2261.1 MiB      0.0 MiB           1       disp_axis=[1,1,0,1]
   251   2261.1 MiB      0.0 MiB           1       import gc
   252                                            # from matplotlib import figure
   253                                         
   254   2261.1 MiB      0.0 MiB           1       import tracemalloc
   255   2261.1 MiB      0.0 MiB           1       tracemalloc.start(10)
   256                                         
   257                                             
   258                                         
   259                                         
   260                                         
   261   2432.0 MiB      0.0 MiB           2       for a in range(1):
   262   2432.0 MiB      0.0 MiB           5           for b in range(len(Folder_u[a])):
   263   2361.8 MiB    -34.9 MiB           4               u_exp = pd.read_csv(Folder_u[a][b],header=None,delimiter=' ',engine='pyarrow')
   264   2361.0 MiB      2.0 MiB           4               v_exp = pd.read_csv(Folder_v[a][b],header=None,delimiter=' ',engine='pyarrow')
   265   2399.2 MiB    110.1 MiB           4               TH_u = pd.read_csv(u_th[b],sep=',',header=None,engine='pyarrow')
   266   2402.3 MiB      6.8 MiB           4               TH_v = pd.read_csv(v_th[b],sep=',',header=None,engine='pyarrow')
   267   2433.4 MiB    126.2 MiB           4               M_exp = np.sqrt(u_exp.dropna(axis=1)**2+v_exp.dropna(axis=1)**2)
   268   2464.9 MiB    124.5 MiB           4               M_th = np.sqrt(TH_u**2+TH_v**2)
   269   2495.9 MiB    125.0 MiB           4               Error = abs(M_exp-M_th)/M_th *100
   270   2495.9 MiB      0.0 MiB           4               MM_E = Error.mean(axis=disp_axis[b])
   271   2495.9 MiB      0.0 MiB           4               MM_exp = M_exp.mean(axis=disp_axis[b])
   272   2495.9 MiB      0.0 MiB           4               MM_th = M_th.mean(axis=disp_axis[b])
   273                                                     
   274   2495.9 MiB      0.0 MiB           4               name = N[a]
   275   2495.9 MiB      0.0 MiB           4               title=name.replace('.json','')
   276                                                     
   277   2495.9 MiB      0.0 MiB           4               xth = np.linspace(0,len(MM_th),len(MM_th))
   278   2495.9 MiB      0.0 MiB           4               xe = np.linspace(0,len(MM_exp),len(MM_exp))
   279                                                     ## mean error 
   280                                                     
   281                                                     #fig=figure.Figure(figsize=(15,10))
   282                                                     #ax = fig.subplots(1)
   283   2497.9 MiB    -27.5 MiB           4               fig,ax = plt.subplots(figsize=(15,10))
   284                                                     
   285   2497.9 MiB      0.0 MiB           4               plt.title('displacement = '+displacement[b]+', Config = ' + title ,fontsize=20,fontweight='bold')
   286                                                     #plt.title(Pyr_Type[l]+displacement[j])
   287   2497.9 MiB      0.0 MiB           4               ax.plot(MM_E,'b')
   288   2497.9 MiB      0.0 MiB           4               plt.ylim([0,100])
   289   2499.9 MiB     10.0 MiB           4               plt.yticks(np.linspace(0,100,11))
   290   2499.9 MiB      0.0 MiB           4               plt.ylabel('mean magnitude error [%]',fontsize=14)
   291   2499.9 MiB      0.0 MiB           4               plt.xlabel('Pixels',fontsize=14)
   292   2499.9 MiB      0.0 MiB           4               plt.grid()
   293                                                     #plt.ylim([0,5])
   294                                                     
   295   2499.9 MiB      0.0 MiB           4               ax1=ax.twinx()
   296   2499.9 MiB      0.1 MiB           4               ax1.plot(xth,MM_th,'k',xe,MM_exp,'r-.')
   297   2499.9 MiB      0.3 MiB           4               plt.ylim([0,23])
   298   2499.9 MiB      4.0 MiB           4               plt.ylabel('mean displacement magnitude [Pixels]',fontsize=14)
   299   2499.9 MiB      0.0 MiB           4               plt.xlabel('Pixels',fontsize=14)
   300                                         
   301   2499.9 MiB      0.5 MiB           4               plt.grid()
   302   2503.9 MiB     10.1 MiB           4               fig.legend(['Mean error','Theoretical','Results'],fontsize=14,loc='upper right')
   303                                                     
   304   2519.8 MiB     61.8 MiB           4               plt.savefig('Mean_error_'+title+'_'+str(b)+'.png',dpi=300)
   305   2535.6 MiB     71.6 MiB           4               plt.show()
   306   2535.6 MiB      0.0 MiB           4               print(a)
   307                                                    
   308                                                     #fig.clear()
   309                                                     #plt.close()
   310                                                     #plt.cla()
   311   2535.6 MiB      0.0 MiB           4               del u_exp
   312   2535.6 MiB      0.0 MiB           4               del v_exp
   313   2535.6 MiB      0.0 MiB           4               del TH_u
   314   2535.6 MiB      0.0 MiB           4               del TH_v
   315   2504.5 MiB   -124.0 MiB           4               del M_exp 
   316   2473.5 MiB   -124.0 MiB           4               del M_th
   317   2442.5 MiB   -124.0 MiB           4               del Error 
   318   2442.5 MiB      0.0 MiB           4               del MM_E
   319   2442.5 MiB      0.0 MiB           4               del MM_exp 
   320   2442.5 MiB      0.0 MiB           4               del MM_th 
   321   2442.5 MiB      0.0 MiB           4               del fig
   322   2442.5 MiB      0.0 MiB           4               del ax, ax1, name, title, xth,xe
   323   2440.0 MiB    -13.0 MiB           4               gc.collect()
   324   2440.0 MiB      0.0 MiB           4               print('deleted')
   325   2440.0 MiB      7.7 MiB           4               snapshot = tracemalloc.take_snapshot()
   326   2440.0 MiB     -2.8 MiB           4               top_stats = snapshot.statistics('lineno')
   327   2432.0 MiB    -42.0 MiB           4               tracemalloc.clear_traces()
   328                                                     
   329                                                     #break
   330                                                 
   331                                                 #break
   332                                                     #plt.show()

Here the top 10 from tracemalloc:


C:\Users\user\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\blocks.py:402: size=62.0 MiB, count=5, average=12.4 MiB
C:\Users\user\anaconda3\lib\site-packages\numexpr\necompiler.py:836: size=31.0 MiB, count=2, average=15.5 MiB
C:\Users\user\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py:2237: size=194 KiB, count=17, average=11.4 KiB
C:\Users\user\anaconda3\lib\selectors.py:315: size=144 KiB, count=5, average=28.8 KiB
C:\Users\user\anaconda3\lib\site-packages\numpy\core\_asarray.py:102: size=119 KiB, count=387, average=314 B
C:\Users\user\anaconda3\lib\linecache.py:137: size=114 KiB, count=1165, average=100 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\lines.py:377: size=97.0 KiB, count=45, average=2208 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\lines.py:672: size=95.9 KiB, count=28, average=3509 B
<unknown>:0: size=95.8 KiB, count=23, average=4264 B
C:\Users\user\anaconda3\lib\site-packages\matplotlib\text.py:265: size=90.6 KiB, count=42, average=2208 B
```````````

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雨后彩虹 2025-02-05 18:49:09

我遇到了同样的问题，以下解决方案也有效：

import matplotlib
matplotlib.use('Agg')

https：/ /matplotlib.org/stable/faq/howto_faq.html#work-work-with-threads

bug： https://github.com/matplotlib/matplotlib/issues/20300

I had the same issue, the following solution worked:

import matplotlib
matplotlib.use('Agg')

Source: https://matplotlib.org/stable/faq/howto_faq.html#work-with-threads

Bug: https://github.com/matplotlib/matplotlib/issues/20300

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未蓝澄海的烟 2025-02-05 18:49:09

我的问题是您创建大量数字。
您可以尝试将

fig,ax = plt.subplots(figsize=(15,10))

嵌套的循环放在外面。仅创建一个数字。然后在

fig.clear()

内部使用内部清除图形，然后再添加所有内部的东西。

您还可以绘制for循环之外的第一个图，并使用set_data更新图的数据。如果保存图形，则不需要plt.show（图）。

I thing the issue is that you create a large amount of figures.
You can try to put

fig,ax = plt.subplots(figsize=(15,10))

outside the for nested for loop. To create only one figure. And then use

fig.clear()

inside to clear the figure before adding all the stuffs inside.

You could also plot the first figure outside the for loop and update the data of the figure with set_data. The plt.show(fig) is not needed if you save the figure.

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