Troubleshooting Pyplot: Ensuring Consistent Plot Sizes Despite Figsize Settings

Making Two Plots of Equal Display Size in Matplotlib with Unequal Data

In my recent project, I encountered an interesting challenge while working with data visualization in Python using Matplotlib. I wanted to display two heatmaps: one from a symmetric array and another from an asymmetric array, both needing to be of exactly the same display size for comparative analysis. Despite setting the same figure size for both plots, I noticed a discrepancy in their display sizes. Here’s a detailed account of my findings and the solution to ensure both plots appear identically in terms of size.

The Problem

Initially, I utilized the figsize attribute in Matplotlib to set an identical size for both plots. The symmetric array was a 100×100 grid, and the asymmetric array, a 100×50 grid. Curiously, even after specifying the same figsize for each plot, their display sizes appeared different when rendered. The plot from the asymmetric array looked elongated.

Understanding the Issue

On closer inspection, I realized that the issue stemmed from the aspect ratio of the plots. Matplotlib’s imshow() function, which I used for displaying the arrays as images, adjusts the aspect ratio of the plot based on the data’s inherent dimensions unless explicitly specified. Hence, while the figure’s outer dimensions were set equally using figsize, the displayed images adjusted their aspect ratios based on their respective array dimensions, causing the visual mismatch.

The Solution

To fix this, I needed to manually set the aspect parameter in the imshow() function call to ‘auto’. This setting ensures that the image fits the given axes, disregarding the actual aspect ratio of the data array. Here’s how I modified the code to solve the problem:

import matplotlib.pyplot as plt
import random

symmetric_arr = [[random.random() for _ in range(100)] for _ in range(100)]
asymmetric_arr = [[random.random() for _ in range(50)] for _ in range(100)]

# Plotting the symmetric array
plt.figure(figsize=(4, 4))
plt.imshow(symmetric_arr, cmap='hot', interpolation='nearest', aspect='auto')
plt.show()

# Plotting the asymmetric array
plt.figure(figsize=(4, 4))
plt.imshow(asymmetric_arr, cmap='hot', interpolation='nearest', aspect='auto')
plt.show()

Conclusion

By setting aspect='auto' in the imshow() function, both heatmaps now appear identical in size on the display, regardless of their underlying data dimensions. It’s a simple yet crucial fix for cases where accurate visual comparison between different datasets is necessary.

This experience was a reminder of the nuances of working with data visualization tools like Matplotlib, where understanding the behavior of function parameters can significantly impact the output’s interpretation. Whether you are a beginner or an experienced user, such details can be the key to effective and misleading data presentations.