💡 Problem Formulation: When working with IPython notebooks, there may be a need to display the same matplotlib figure multiple times either for comparison or repetitive analysis. A user is seeking methods to render an identical plot in several places of the document without recreating the figure from scratch. They desire a simple approach to replicate an already existing figure in their analysis notebook.
Method 1: Using Figure Object References
This method revolves around saving a reference to the original Matplotlib figure object and calling display methods on this reference to show the figure multiple times throughout the notebook. It specifically leverages the power of IPython’s display tools.
Here’s an example:
import matplotlib.pyplot as plt from IPython.display import display # Create a figure fig, ax = plt.subplots() ax.plot([0, 1], [0, 1]) # Display the figure first time display(fig) # Do some other stuff... # Display the figure second time display(fig)
The output will be the figure displayed twice in the notebook, at the locations where the display(fig) call is made.
By capturing the figure in a variable, we can invoke display() from the IPython display module wherever we need to show the figure again. This avoids the need to re-draw or re-plot the data, saving time and computational resources.
Method 2: Reuse Figure with Figure.canvas.draw()
This method calls the draw() method on the figure’s canvas to refresh and display the plot inline again. It’s a good way to show the plot within different sections without having to store or use display calls explicitly.
Here’s an example:
import matplotlib.pyplot as plt # Create and display a figure fig, ax = plt.subplots() ax.plot([0, 1], [0, 1]) plt.show() # Later in the notebook... fig.canvas.draw()
The output is a plot that appears initially, and a duplicate of the same plot appears when fig.canvas.draw() is called.
By calling draw() on the figure’s canvas, the plot is re-rendered within the notebook cell where the method is invoked, making it a convenient option to integrate within the flow of analysis.
Method 3: Deep Copying the Figure
Using Python’s copy.deepcopy(), we can create an exact copy of the figure object. This allows us to display it again without altering the original figure, useful if the state of the figure might change.
Here’s an example:
import matplotlib.pyplot as plt import copy # Create a figure fig, ax = plt.subplots() ax.plot([0, 1], [0, 1]) # Make a deep copy of the figure object fig_copy = copy.deepcopy(fig) plt.show() # Later in the notebook, display the copied figure display(fig_copy)
The output is the initial plot and then a second instance of the same plot displayed later using the copied figure object.
When you deep copy a figure, all its properties and data are duplicated as well. This means that you can modify the original figure without affecting its copies, allowing for flexible manipulation and reusability of the plots.
Method 4: Saving and Loading Image Outputs
Another approach is to save the figure to an image file and then load and display this file in the notebook multiple times. This is especially useful when the notebook is shared with others, as the figure is preserved as an image.
Here’s an example:
import matplotlib.pyplot as plt
from IPython.display import Image
# Create a figure
fig, ax = plt.subplots()
ax.plot([0, 1], [0, 1])
# Save figure to a file
fig.savefig("plot.png")
# Display the image file
Image("plot.png")
# Do some other stuff...
# Display the same image file again
Image("plot.png")The output shows the image of the plot displayed wherever the Image("plot.png") code appears in the notebook.
This approach is great for creating a static and shareable version of your plots, but note that it involves I/O operations and could lead to larger notebook size due to embedded images.
Bonus One-Liner Method 5: Use plt.show() Multiple Times
A quick and simple way to show the same figure more than once is by calling plt.show() repeatedly after the initial plot is created. This method works well if no other figures are created in between.
Here’s an example:
import matplotlib.pyplot as plt # Create a plot plt.plot([0, 1], [0, 1]) # First display plt.show() # Second display plt.show()
The output displays the same plot twice in sequential cells within the notebook.
While this method appears straightforward, remember that plt.show() clears the figure after it’s displayed, so this only works if no alterations are made to the figure in between calls.
Summary/Discussion
- Method 1: Using Figure Object References. Strengths: No duplication or re-plotting necessary. Weaknesses: Relies on IPython’s display system, not suitable for external sharing.
- Method 2: Reuse Figure with
Figure.canvas.draw(). Strengths: Inline refresh without additional display calls. Weaknesses: Figure needs to remain unaltered to maintain consistency. - Method 3: Deep Copying the Figure. Strengths: Allows modifications to original while retaining the copy. Weaknesses: Uses more memory to retain duplicate figures.
- Method 4: Saving and Loading Image Outputs. Strengths: Produces shareable and static figures. Weaknesses: Incurs I/O overhead and can bloat notebook size.
- Method 5: Use
plt.show()Multiple Times. Strengths: Easy to use for quick duplication. Weaknesses: Not flexible, figure cleaned after each show.