matplotlib ValueError: Image size of 1781907x1084 pixels is too large. It must be less than 2^16 in each direction

In this article, we will explore a common issue when using the popular Python data visualization library Matplotlib. The error occurs when attempting to create a plot with an image size that exceeds the maximum allowed limit of 2^16 pixels in each direction.

Understanding the Limitations of Image Size

Before diving into the solution, it’s essential to understand why Matplotlib has a limit on the image size. In computing, images are typically represented as arrays of pixels, where each pixel is assigned a color value. The number of possible colors that can be used in an image is limited by the color model being used.

For example, when using the RGB (Red, Green, Blue) color model, which is commonly used in digital displays, there are 256 possible values for each of the three color channels, resulting in a total of 16,777,216 (2^16) possible colors. This limit is known as the color depth or bit depth.

When creating an image with Matplotlib, the library uses this same color model to render the plot. As a result, there is a natural limitation on the size of the image that can be created, which is determined by the number of pixels required to represent each color value.

Identifying the Cause of the Error

In the provided question, the user is attempting to create a plot with two y-axes: one for the comments per month and another for the top scoring authors. The error occurs when trying to display this plot, which results in an image size that exceeds the maximum allowed limit.

To identify the cause of the error, we need to examine the code provided by the user and understand how it is creating the image. In this case, the user has created a figure with two axes using plt.figure(figsize=(16, 12)) and then added a scatter plot to the second axis using ax2.scatter(top_authors['created_utc'], top_authors['log_score']).

Examining the Code

Let’s take a closer look at the code used by the user:

# Create a secondary y-axis
ax2 = ax1.twinx()

top_submissions = main_submissions.nlargest(10, 'score')
top_authors = top_submissions[['author', 'created_utc', 'score']]

top_authors['log_score'] = np.log(top_authors['score'])

# Plot the names of the top scoring authors at the positions of their highest scores
for idx, row in top_authors.iterrows():
  ax2.text(row['created_utc'],row['log_score'], s=row['author'])

plt.tight_layout()

Understanding the Impact of Twin X-Axis

When creating a twin x-axis using ax1.twinx(), Matplotlib creates a new axis that shares the same x-values as the original axis. This allows us to create multiple y-axes on top of each other.

However, when plotting data onto this secondary axis, we need to consider the impact of the twin x-axis on the image size. In this case, the user has plotted 10 points using ax2.scatter(top_authors['created_utc'], top_authors['log_score']), resulting in a larger number of pixels required to represent each color value.

Solving the Error

To solve the error, we need to reduce the size of the image that is being created. One way to do this is by reducing the figure size using plt.figure(figsize=(x, y)).

For example:

# Create a secondary y-axis
ax2 = ax1.twinx()

top_submissions = main_submissions.nlargest(10, 'score')
top_authors = top_submissions[['author', 'created_utc', 'score']]

top_authors['log_score'] = np.log(top_authors['score'])

# Plot the names of the top scoring authors at the positions of their highest scores
for idx, row in top_authors.iterrows():
  ax2.text(row['created_utc'],row['log_score'], s=row['author'])

plt.tight_layout()
plt.figure(figsize=(8, 6)) # Reduce the figure size to (x, y)

By reducing the figure size to (8, 6), we can reduce the image size that is being created, which should fix the error.

Conclusion

In this article, we explored a common issue when using Matplotlib: the ValueError: Image size of ... pixels is too large. It must be less than 2^16 in each direction. error. We identified the cause of the error and solved it by reducing the image size that is being created.

By understanding the limitations of image size and how Matplotlib creates images, we can take steps to prevent this error from occurring in the future.

Additional Resources

Last modified on 2024-09-24