Is it possible to embed matplotlib.figure.Figure object as subplot of another figure?

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Summary

The question revolves around embedding a matplotlib.figure.Figure object as a subplot of another figure, specifically in the context of analyzing impulse responses of system models using statmodels and matplotlib for plotting. The goal is to combine multiple plots, each generated by VARResults.irf.plot, into a single figure for easier comparison and analysis.

Root Cause

The root cause of the issue lies in understanding how matplotlib handles figure and subplot creation. By default, VARResults.irf.plot returns a matplotlib.figure.Figure object, which is a top-level container for all plot elements. The challenge is in combining these top-level figures into subplots of another figure.

Why This Happens in Real Systems

This issue occurs in real systems due to:

  • The need for complex data visualization, where multiple plots need to be compared side-by-side.
  • The use of libraries like statmodels that return plot objects directly, which may not be straightforward to combine.
  • The complexity of matplotlib, which offers powerful customization but can be challenging to master, especially for combining figures.

Real-World Impact

The real-world impact includes:

  • Difficulty in comparative analysis: Without the ability to easily combine plots, analysts must switch between figures, which can hinder the comparison process.
  • Increased complexity in reporting: Combining figures manually or using workarounds can add unnecessary complexity to the analysis and reporting process.
  • Limitations in presentation: The inability to present data in a concise, comparative manner can limit the effectiveness of presentations and reports.

Example or Code

import matplotlib.pyplot as plt
from statmodels.tsa.api import VAR
import numpy as np

# Sample data
np.random.seed(0)
n_samples = 100
n_variables = 2
data = np.random.rand(n_samples, n_variables)

# Fit VAR model
model = VAR(data)
results = model.fit(maxlags=1)

# Plot IRFs
fig, axes = plt.subplots(n_variables, n_variables, figsize=(10, 10))
for i in range(n_variables):
    for j in range(n_variables):
        irf = results.irf(j, i)
        axes[i, j].plot(irf)
plt.tight_layout()
plt.show()

How Senior Engineers Fix It

Senior engineers address this by:

  • Understanding the structure of matplotlib figures and subplots.
  • Using matplotlib‘s subplot functions to create a grid of subplots and then plotting each IRF within these subplots.
  • Leveraging statmodels and matplotlib documentation to find compatible methods for combining plots.

Why Juniors Miss It

Juniors might miss this solution due to:

  • Lack of experience with matplotlib and its subplot functionality.
  • Insufficient understanding of how statmodels integrates with matplotlib for plotting.
  • Overlooking the documentation or examples provided by statmodels and matplotlib that demonstrate how to create complex, multi-plot figures.