Saving interactive Matplotlib figures

Saving interactive Matplotlib figures

To save interactive Matplotlib figures, you can use the mpl_toolkits.mplot3d library, which provides an interactive 3D plot that can be saved as an HTML file. You'll need to use the plotly library to achieve this. Here's how you can do it:

import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D import plotly.figure_factory as ff # Sample data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) Z = np.sin(np.sqrt(X**2 + Y**2)) # Create an interactive 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot_surface(X, Y, Z) # Save the interactive figure as an HTML file interactive_figure = ff.create_3d_scatter( X=X.flatten(), Y=Y.flatten(), Z=Z.flatten(), filename='interactive_plot.html', ) # Display the interactive figure in a web browser interactive_figure.show()

In this code:

  1. We create sample data for an interactive 3D plot.

  2. We create a 3D plot using Matplotlib's mpl_toolkits.mplot3d library.

  3. We use plotly.figure_factory to create an interactive 3D scatter plot from the same data.

  4. We save the interactive figure as an HTML file named 'interactive_plot.html'.

  5. We display the interactive figure in a web browser using interactive_figure.show().

After running this code, you will have an interactive 3D plot saved as an HTML file, which you can open in a web browser to interact with and explore.

Note that this approach requires the installation of the plotly library, which you can install using pip:

pip install plotly

You can adjust the data and plot settings to suit your specific requirements.

Examples

  1. How to Save Interactive Matplotlib Figures with Plotly

    • This query explores saving Matplotlib figures as interactive plots using Plotly for enhanced interactivity.
    # If not already installed !pip install plotly 
    import matplotlib.pyplot as plt import numpy as np import plotly.tools as pt # Create a simple Matplotlib plot x = np.linspace(0, 10, 100) y = np.sin(x) plt.plot(x, y) # Convert Matplotlib figure to Plotly for interactivity plotly_fig = pt.mpl_to_plotly(plt.gcf()) # Save as an interactive HTML file plotly_fig.write_html("interactive_plot.html")

  2. Save Interactive Matplotlib Figures as HTML

    • This snippet demonstrates saving interactive Matplotlib figures as HTML files to retain interactivity.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt import numpy as np import mpld3 # Create a plot x = np.linspace(0, 10, 100) y = np.tan(x) plt.plot(x, y) # Save the plot as an interactive HTML file html_content = mpld3.fig_to_html(plt.gcf()) with open("interactive_plot.html", "w") as f: f.write(html_content)

  3. Embedding Interactive Matplotlib Figures in Web Applications

    • This snippet demonstrates how to embed interactive Matplotlib figures in a web application using Flask and Plotly.
    # If not already installed !pip install flask plotly 
    from flask import Flask, render_template import matplotlib.pyplot as plt import plotly.tools as pt import numpy as np app = Flask(__name__) @app.route("/") def index(): # Create a Matplotlib plot x = np.linspace(0, 10, 100) y = np.cos(x) plt.plot(x, y) # Convert to Plotly for interactivity plotly_fig = pt.mpl_to_plotly(plt.gcf()) html_content = plotly_fig.to_html() return render_template("index.html", plot_html=html_content) if __name__ == "__main__": app.run()

  4. Save Interactive Matplotlib Figures with Widgets

    • This snippet demonstrates creating and saving interactive Matplotlib figures with widgets using ipywidgets.
    # If not already installed !pip install ipywidgets 
    import matplotlib.pyplot as plt import numpy as np import mpld3 from ipywidgets import interact, FloatSlider # Interactive plot with a slider widget def plot_with_widget(frequency): x = np.linspace(0, 10, 100) y = np.sin(frequency * x) plt.figure() plt.plot(x, y) plt.title(f"Frequency: {frequency}") # Create a slider widget slider = FloatSlider(value=1, min=1, max=10, step=0.1, description='Frequency') # Save the interactive plot with widget mpld3.enable_notebook() # Enable interactive mode interact(plot_with_widget, frequency=slider) # Save the figure html_content = mpld3.fig_to_html(plt.gcf()) with open("interactive_plot_with_widget.html", "w") as f: f.write(html_content)

  5. Saving Interactive 3D Plots with Matplotlib

    • This snippet demonstrates creating and saving interactive 3D plots with Matplotlib and mpld3.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np import mpld3 # Create a 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x = np.linspace(0, 10, 100) y = np.sin(x) z = np.cos(x) ax.plot(x, y, z) # Save the interactive 3D plot as an HTML file html_content = mpld3.fig_to_html(plt.gcf()) with open("interactive_3d_plot.html", "w") as f: f.write(html_content)

  6. Save Interactive Matplotlib Figures with Tooltips

    • This snippet demonstrates saving interactive Matplotlib figures with tooltips for enhanced interactivity.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt import numpy as np import mpld3 # Create a plot with tooltips x = np.linspace(0, 10, 100) y = np.sin(x) fig, ax = plt.subplots() lines = ax.plot(x, y, label='Sine Wave') # Add tooltips to the plot mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(lines[0], labels=['point'] * len(x))) # Save the interactive plot with tooltips html_content = mpld3.fig_to_html(fig) with open("interactive_plot_with_tooltips.html", "w") as f: f.write(html_content)

  7. Save Interactive Matplotlib Figures with Multiple Subplots

    • This snippet demonstrates creating and saving interactive Matplotlib figures with multiple subplots.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt import numpy as np import mpld3 # Create multiple subplots fig, axs = plt.subplots(2, 1) x = np.linspace(0, 10, 100) axs[0].plot(x, np.sin(x), label='Sine') axs[1].plot(x, np.cos(x), label='Cosine') axs[0].legend() axs[1].legend() # Save the interactive figure with multiple subplots html_content = mpld3.fig_to_html(fig) with open("interactive_subplots.html", "w") as f: f.write(html_content)

  8. Saving Interactive Heatmaps with Matplotlib

    • This snippet demonstrates creating and saving interactive heatmaps with Matplotlib for more dynamic data visualization.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt import numpy as np import mpld3 # Create a heatmap data = np.random.rand(10, 10) # Random 10x10 data plt.imshow(data, cmap='hot', interpolation='nearest') # Save the interactive heatmap html_content = mpld3.fig_to_html(plt.gcf()) with open("interactive_heatmap.html", "w") as f: f.write(html_content)

  9. Save Interactive Matplotlib Figures with Custom Widgets

    • This snippet demonstrates creating and saving interactive Matplotlib figures with custom widgets like dropdowns or sliders.
    # If not already installed !pip install mpld3 
    import matplotlib.pyplot as plt import numpy as np import mpld3 # Create a simple plot x = np.linspace(0, 10, 100) y = np.sin(x) plt.plot(x, y, label='Sine Wave') plt.legend() # Enable interactive mode with mpld3 mpld3.enable_notebook() # Save the interactive plot with custom widgets html_content = mpld3.fig_to_html(plt.gcf()) with open("interactive_plot_with_widgets.html", "w") as f: f.write(html_content)

  10. Save and Download Interactive Matplotlib Figures from Google Colaboratory

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