[Python] How to save a plot as an output and perform transformations like mirroring and...

I have a Python function that plots power functions using matplotlib. Here's the code:

import numpy as np
import matplotlib.pyplot as plt
import itertools

r = -10
q = 10
a = -5
b = 5

def plot_power_functions(n_list, x_min=a, x_max=b):
# Create an array of x values
x = np.linspace(x_min, x_max, 400)

# Cyclical color generator
colors = itertools.cycle(plt.cm.Set1.colors) # Colors from the Set1 colormap

# Create the plot
plt.figure(figsize=(10, 6))

# Plot each function
for n in n_list:
y = x ** n
color = next(colors) # Get the next color from the cycle

# Create a proper label
label = f'f(x) = x^{n}'

# Plot the function
plt.plot(x, y, label=label, color=color)

# Add labels and title
plt.axhline(0, color='black', linewidth=1) # x-axis
plt.axvline(0, color='black', linewidth=1) # y-axis
# Add arrows at the end of the axes
plt.annotate('', xy=(x_max, 0), xytext=(x_min, 0),
arrowprops=dict(facecolor='black', width=0.5, headwidth=6)) # Arrow on the x-axis
plt.annotate('', xy=(0, q), xytext=(0, 0),
arrowprops=dict(facecolor='black', width=0.5, headwidth=6)) # Arrow on the y-axis

plt.xlabel('x')
plt.ylabel('f(x)')
plt.title('Plot of power functions f(x) = x^n')

# Set axis limits
plt.xlim(x_min, x_max)
plt.ylim(r, q) # y-axis limits, adjustable based on the functions

# Show the legend
plt.legend()

# Show the plot
plt.grid(True)
plt.show()

# Example usage:
n_list = [-2, -4]
plot_power_functions(n_list)


The function works well and displays the power functions correctly. Now, I would like to modify it so that:

1. The plot is saved as an output.
2. After saving, I want to perform some transformations on the saved image, like:
   • Mirroring the plot along the y-axis.
   • Rotating the plot 90 degrees clockwise.

I am unsure how to modify the function to save the plot as an image, and how to apply these transformations using Python libraries like PIL or matplotlib.

Could someone help me with the following?

• Saving the plot as an image within the function itself.
• Applying the transformations (y-axis mirroring and 90-degree clockwise rotation) to the saved plot image.

Thank you!

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