Creating a Reusable Function for Image Overlays
To make our code more reusable, let's create a function that can add an image overlay to any Matplotlib figure. This way, we can easily apply the same effect to different plots.
- Create a new cell in your notebook and enter the following code:
def add_image_overlay(fig, image_path, x_pos=25, y_pos=25, alpha=0.5, zorder=3):
"""
Add an image overlay to a matplotlib figure.
Parameters:
-----------
fig : matplotlib.figure.Figure
The figure to add the image to
image_path : str
Path to the image file
x_pos : int
X position in pixels from the bottom left
y_pos : int
Y position in pixels from the bottom left
alpha : float
Transparency level (0 to 1)
zorder : int
Drawing order (higher numbers are drawn on top)
Returns:
--------
fig : matplotlib.figure.Figure
The figure with the image overlay
"""
## Load the image
with cbook.get_sample_data(image_path) as file:
im = image.imread(file)
## Add the image to the figure
fig.figimage(im, x_pos, y_pos, zorder=zorder, alpha=alpha)
return fig
## Example usage: Create a scatter plot with an image overlay
fig, ax = plt.subplots(figsize=(10, 6))
## Set a random seed for reproducibility
np.random.seed(19680801)
## Generate random data for a scatter plot
x = np.random.rand(50) * 10
y = np.random.rand(50) * 10
## Create a scatter plot
ax.scatter(x, y, s=100, c=np.random.rand(50), cmap='viridis', alpha=0.7)
ax.grid(linestyle='--', alpha=0.7)
ax.set_xlabel('X-axis Label')
ax.set_ylabel('Y-axis Label')
ax.set_title('Scatter Plot with Image Overlay')
## Add the image overlay using our function
add_image_overlay(fig, 'logo2.png', x_pos=50, y_pos=50, alpha=0.4)
## Display the plot
plt.tight_layout()
plt.show()
This code defines a function called add_image_overlay that:
- Takes parameters for the figure, image path, position, transparency, and z-order.
- Loads the specified image.
- Adds the image to the figure using
figimage.
- Returns the modified figure.
After defining the function, we demonstrate its use by creating a scatter plot with random data and adding the Matplotlib logo as an overlay.
- Run the cell by pressing Shift+Enter.
The output should show a scatter plot with randomly positioned and colored points, and the Matplotlib logo overlaid at position (50, 50) with 40% opacity.
- Let's try one more example with a line plot. Create a new cell and enter the following code:
## Example usage: Create a line plot with an image overlay
fig, ax = plt.subplots(figsize=(10, 6))
## Generate data for a line plot
x = np.linspace(0, 10, 100)
y = np.sin(x)
## Create a line plot
ax.plot(x, y, linewidth=2, color='#d62728')
ax.grid(linestyle='--', alpha=0.7)
ax.set_xlabel('X-axis Label')
ax.set_ylabel('Y-axis Label')
ax.set_title('Sine Wave with Image Overlay')
ax.set_ylim(-1.5, 1.5)
## Add the image overlay using our function
## Place it in the bottom right corner
fig_width, fig_height = fig.get_size_inches() * fig.dpi
with cbook.get_sample_data('logo2.png') as file:
im = image.imread(file)
x_pos = fig_width - im.shape[1] - 50 ## 50 pixels from the right edge
add_image_overlay(fig, 'logo2.png', x_pos=x_pos, y_pos=50, alpha=0.6)
## Display the plot
plt.tight_layout()
plt.show()
This code creates a line plot showing a sine wave, and adds the Matplotlib logo in the bottom right corner of the plot.
- Run the cell by pressing Shift+Enter.
The output should show a line plot of a sine wave with the Matplotlib logo overlaid in the bottom right corner at 60% opacity.
These examples demonstrate how our add_image_overlay function can be used to easily add image overlays to different types of plots, making it a versatile tool for customizing visualizations.
