Optimizing Matplotlib Performance: Handling Memory Leaks Efficiently
Tags: matplotlib, datascience, performance, programming
Published: 2024-06-06 17:38:33

## 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:

```python
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)

    # ===================================================
    # Execute one of the following patterns:
    # ===================================================

    # Pattern 1
    plt.clf()

    # Pattern 2
    plt.clf()
    plt.close()

    # Pattern 3
    plt.close()

    # Pattern 4
    plt.close()
    plt.clf()

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

    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:

![Memory Usage Patterns](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3y9m1hgkjnsiiwcb3xpb.png)

### 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:

```python
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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