To join two matplotlib figures, you can use the add_subplot function to create a new subplot in a single figure that combines both of the original figures. You can then use the imshow function to display each figure in its respective subplot. This allows you to visually compare or analyze the data in both figures simultaneously within a single plot. Additionally, you can customize the appearance of the combined plot by adjusting the layout, color scheme, labels, and other properties using various matplotlib functions. By merging two figures together, you can create a more comprehensive and informative visualization for your data analysis or presentation needs.
How to plot two matplotlib figures on the same graph?
To plot two matplotlib figures on the same graph, you can simply call the plotting functions for each figure within the same plot call. Here is an example code snippet:
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import matplotlib.pyplot as plt # Create the first figure x1 = [1, 2, 3, 4, 5] y1 = [2, 3, 5, 7, 11] # Create the second figure x2 = [1, 2, 3, 4, 5] y2 = [1, 4, 9, 16, 25] # Plot both figures on the same graph plt.plot(x1, y1, label='Figure 1') plt.plot(x2, y2, label='Figure 2') # Add labels and title plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Two Figures on the Same Graph') # Add legend plt.legend() # Show the plot plt.show() |
This code will plot the two figures on the same graph with different line styles/colors and add a legend to differentiate between the two figures.
How to overlay two matplotlib figures?
You can overlay two matplotlib figures by creating a new figure and using the add_subplot method to add subplots from each of the original figures. Here's an example:
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import matplotlib.pyplot as plt # Create two subplots in separate figures fig1, ax1 = plt.subplots() ax1.plot([1, 2, 3, 4], [10, 20, 25, 30]) fig2, ax2 = plt.subplots() ax2.plot([1, 2, 3, 4], [5, 10, 15, 20]) # Create a new figure and add subplots from the original figures fig_combined = plt.figure() ax_combined = fig_combined.add_subplot(111) ax_combined.plot([1, 2, 3, 4], [10, 20, 25, 30], label='Figure 1') ax_combined.plot([1, 2, 3, 4], [5, 10, 15, 20], label='Figure 2') plt.legend() plt.show() |
In this example, we create two separate figures (fig1 and fig2) with their own subplots (ax1 and ax2). We then create a new figure fig_combined and add a subplot to it that overlays the plots from the original figures using add_subplot. Finally, we plot the data from both original figures onto the new combined figure and display it using plt.show().
What is the code to combine matplotlib plots?
To combine matplotlib plots, you can use the subplot function to create multiple plots in the same figure. Here is an example of how you can combine two plots in a single figure:
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import numpy as np import matplotlib.pyplot as plt # Create some data for the plots x = np.linspace(0, 10, 100) y1 = np.sin(x) y2 = np.cos(x) # Create a figure and two subplots plt.figure(figsize=(10, 5)) plt.subplot(1, 2, 1) # 1 row, 2 columns, position 1 plt.plot(x, y1) plt.title('Sin(x)') plt.subplot(1, 2, 2) # 1 row, 2 columns, position 2 plt.plot(x, y2) plt.title('Cos(x)') plt.tight_layout() # Adjust the spacing between plots plt.show() |
In this example, we create a figure with two subplots using the subplot function. The first argument of subplot specifies the number of rows, the second argument specifies the number of columns, and the third argument specifies the position of the subplot in the grid. We then plot the data on each subplot and display the figure using plt.show().
