Optimizing Matplotlib Performance: Handling Memory Leaks Efficiently

Introduction

Memory management is a crucial aspect when dealing with large datasets and intensive plotting operations in Python. matplotlib, a popular plotting library, can sometimes exhibit memory leaks if not used correctly. This post discusses effective strategies to prevent memory leaks in matplotlib.pyplot, particularly focusing on the proper use of plt.clf() and plt.close().

Understanding the Problem

When creating numerous plots in a loop, improper handling of figure clearing and closing can lead to memory not being released, ultimately causing an OutOfMemory error. This issue is particularly prominent when plotting large datasets multiple times.

Consider the following example where memory leak issues can occur:

import matplotlib.pyplot as plt

import numpy as np

import psutil

mem_ary = []

# Plot 10 times

for i in range(10):

    x = np.arange(1e7)

    y = np.arange(1e7)

    plt.plot(x, y)

<span class="c1"># ===================================================



    

    




    # Execute one of the following patterns:

    # ===================================================



    # Pattern 1

    plt.clf()

<span class="c1"># Pattern 2



    

    




    plt.clf()

    plt.close()

<span class="c1"># Pattern 3



    

    




    plt.close()

<span class="c1"># Pattern 4



    

    




    plt.close()

    plt.clf()

<span class="c1"># ===================================================



    

    






    mem = psutil.virtual_memory().used / 1e9

    mem = round(mem, 1)

    mem_ary.append(mem)

Experimental Setup

To understand how each method affects memory usage, we plotted graphs with large memory sizes 10 times, recording memory usage at the end of each plot. This experiment was conducted under four different patterns:

1. plt.clf()
2. plt.clf() → plt.close()
3. plt.close()
4. plt.close() → plt.clf()

Each pattern was tested by restarting the kernel to ensure a consistent memory usage baseline.

Results and Conclusions

The memory usage for each pattern is visualized as follows:

Key Observations:

• Pattern 1 (plt.clf()): Memory usage alternates, resembling a mountain-like shape, which indicates incomplete memory clearance.
• Pattern 2 (plt.clf() → plt.close()): Memory usage remains flat, demonstrating effective memory clearance.
• Pattern 3 (plt.close()): Memory usage increases linearly, indicating a memory leak.
• Pattern 4 (plt.close() → plt.clf()): Memory usage increases similarly to Pattern 3, also showing a memory leak.

Effective Solution

The combination of plt.clf() followed by plt.close() (Pattern 2) proved to be the most effective in preventing memory leaks. This pattern ensures that all allocated memory is properly freed after each plot.

Incorrect Order

Reversing the order (plt.close() → plt.clf()) did not release memory effectively. Closing the figure before clearing it prevents the clearing process from freeing up the allocated memory, leading to a leak.

Practical Implementation

Here’s a practical implementation to prevent memory leaks using multiprocessing:

from multiprocessing import Pool

import matplotlib.pyplot as plt

import numpy as np

import psutil

# Plotting method

def plot(args):

    x, y = args

    plt.plot(x, y)

    plt.tight_layout()

    plt.savefig('plot.png')

    plt.clf()

    plt.close()

# Plot values

x = np.arange(1e7)

y = np.arange(1e7)

# Create a process pool and perform plotting

p = Pool(1)

p.map(plot, [(x, y)])

p.close()

# Verify memory release

for i in range(10):

    x = np.arange(1e7)

    y = np.arange(1e7)

    p = Pool(1)

    p.map(plot, [(x, y)])

    p.close()

    mem = psutil.virtual_memory().free / 1e9

    print(i, f'Memory free: {mem} [GB]')

Summary

Proper memory management is critical when working with matplotlib for intensive plotting tasks. The combination of plt.clf() and plt.close() effectively prevents memory leaks, ensuring that memory is properly released after each plot. This method is particularly useful when handling large datasets and generating numerous plots in a loop.

By following these guidelines, you can prevent memory leaks and ensure efficient use of resources in your Python plotting applications.

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