Interactive matplotlib plot with two sliders

Interactive matplotlib plot with two sliders

Creating an interactive Matplotlib plot with two sliders can be achieved using the matplotlib library along with the ipywidgets library, which allows you to create interactive widgets in a Jupyter Notebook or JupyterLab environment. In this example, we'll create a simple plot with two sliders to control the parameters of the plot.

First, you need to install the required libraries if you haven't already. You can install them using pip:

pip install matplotlib ipywidgets

Here's an example of creating an interactive Matplotlib plot with two sliders:

import numpy as np import matplotlib.pyplot as plt from ipywidgets import interact, widgets # Create a function that will be called when sliders are adjusted def update_plot(a, b): x = np.linspace(0, 10, 100) y = a * x + b plt.figure(figsize=(8, 4)) plt.plot(x, y, label=f'y = {a}x + {b}') plt.xlabel('x') plt.ylabel('y') plt.legend() plt.grid(True) plt.show() # Create sliders a_slider = widgets.FloatSlider(value=1.0, min=0.1, max=2.0, step=0.1, description='a:') b_slider = widgets.FloatSlider(value=0.0, min=-5.0, max=5.0, step=0.5, description='b:') # Create an interactive plot interact(update_plot, a=a_slider, b=b_slider)

In this code:

  1. We create a function update_plot(a, b) that updates the plot based on the values of a and b. It generates a linear plot y = ax + b and displays it using Matplotlib.

  2. We create two sliders (a_slider and b_slider) using ipywidgets.FloatSlider, which allows users to interactively adjust the values of a and b. You can customize the slider's initial value, minimum, maximum, step size, and description.

  3. We use ipywidgets.interact to create an interactive plot. When the sliders are adjusted, the update_plot function is called with the current values of a and b, and the plot is updated accordingly.

When you run this code in a Jupyter Notebook or JupyterLab environment, you'll see an interactive plot with two sliders that allow you to control the parameters a and b. As you adjust the sliders, the plot will update in real-time to reflect the changes.

Examples

  1. "Matplotlib plot with two sliders example"

    • Description: Users often search for examples demonstrating how to create interactive Matplotlib plots with two sliders. This query indicates a desire to see a practical implementation showcasing how to use sliders to manipulate visualizations.
    • Code:
      import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import Slider # Create some data x = np.linspace(0, 10, 1000) y = np.sin(x) # Create the plot fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.25) line, = ax.plot(x, y) # Add sliders ax_slider1 = plt.axes([0.1, 0.1, 0.65, 0.03]) ax_slider2 = plt.axes([0.1, 0.05, 0.65, 0.03]) slider1 = Slider(ax_slider1, 'Amp', 0.1, 10.0, valinit=1) slider2 = Slider(ax_slider2, 'Freq', 0.1, 10.0, valinit=1) # Update function def update(val): amp = slider1.val freq = slider2.val line.set_ydata(amp * np.sin(freq * x)) fig.canvas.draw_idle() slider1.on_changed(update) slider2.on_changed(update) plt.show()

  2. "Matplotlib interactive plot with dual sliders tutorial"

    • Description: This query suggests that users are seeking a tutorial-style resource that guides them through creating interactive Matplotlib plots with two sliders. They likely want step-by-step instructions to understand the process thoroughly.
    • Code:
      # Import necessary libraries import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import Slider # Generate data x = np.linspace(0, 10, 1000) y = np.sin(x) # Create initial plot fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.25) line, = ax.plot(x, y) # Define slider locations and ranges ax_slider1 = plt.axes([0.1, 0.1, 0.65, 0.03]) ax_slider2 = plt.axes([0.1, 0.05, 0.65, 0.03]) # Create sliders slider1 = Slider(ax_slider1, 'Amp', 0.1, 10.0, valinit=1) slider2 = Slider(ax_slider2, 'Freq', 0.1, 10.0, valinit=1) # Update function for sliders def update(val): amp = slider1.val freq = slider2.val line.set_ydata(amp * np.sin(freq * x)) fig.canvas.draw_idle() slider1.on_changed(update) slider2.on_changed(update) plt.show()

  3. "Python matplotlib interactive plot with two adjustable parameters"

    • Description: This query indicates a search for Python code snippets demonstrating how to create interactive Matplotlib plots where users can adjust two parameters. It reflects a specific interest in interactivity and parameter manipulation.
    • Code:
      import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import Slider # Generate some data x = np.linspace(0, 10, 1000) y = np.sin(x) # Create the plot fig, ax = plt.subplots() plt.subplots_adjust(bottom=0.25) line, = ax.plot(x, y) # Add sliders ax_slider1 = plt.axes([0.1, 0.1, 0.65, 0.03]) ax_slider2 = plt.axes([0.1, 0.05, 0.65, 0.03]) slider1 = Slider(ax_slider1, 'Amp', 0.1, 10.0, valinit=1) slider2 = Slider(ax_slider2, 'Freq', 0.1, 10.0, valinit=1) # Update function def update(val): amp = slider1.val freq = slider2.val line.set_ydata(amp * np.sin(freq * x)) fig.canvas.draw_idle() slider1.on_changed(update) slider2.on_changed(update) plt.show()

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