Widgets

Since we're typically running our notebooks in a web browser, they are quite well suited for also including more interactive elements. A typical use case could be that you want to communicate some results to a collaborator or to a wider audience, and that you would like them to be able to modify how the results are displayed. It could, for example, be to select which gene to plot for, or to see how some parameter value affects a clustering. Jupyter notebooks has great support for this in the form of widgets.

Widgets are eventful Python objects that have a representation in the browser, often as a control like a slider, textbox, etc. These are implemented in the ipywidgets package.

The easiest way to get started with using widgets are via the interact and interactive functions. These functions autogenerate widgets from functions that you define, and then call those functions when you manipulate the widgets. Too abstract? Let's put it into practice!

Let's try to add sliders that allow us to change the frequency, amplitude and phase of the sine curve we plotted previously.

# Import the interactive function from ipywidgets
from ipywidgets import interactive
# Also import numpy (for calculating the sine curve)
# and pyplot from matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt

# Define the function for plotting the sine curve
def sine_curve(A, f, p):
    # Set up the plot
    plt.figure(1, figsize=(4,4))
    # Create a range of 100 evenly spaced numbers between 0 and 100
    x = np.linspace(0,10,100)
    # Calculate the y values using the supplied parameters
    y = A*np.sin(x*f+p)
    # Plot the x and y values ('r-' specifies color and line style)
    plt.plot(x, y, color='red', linestyle="-")
    plt.show()

# Here we supply the sine_curve function to interactive,
# and set some limits on the input parameters
interactive_plot = interactive(sine_curve,
            A=(1, 5, 1),
            f=(0, 5, 1),
            p=(1, 5, 0.5))

# Display the widgets and the plot
interactive_plot

The code above defines a function called sine_curve which takes three arguments:

• A = the amplitude of the curve
• f = the frequency of the curve
• p = the phase of the curve

The function creates a plot area, generates x-values and calculates y-values using the np.sin function and the supplied parameters. Finally, the x and y values are plotted.

Below the function definition we use interactive with the sine_curve function as the first parameter. This means that the widgets will be tied to the sine_curve function. As you can see we also supply the A, f and p keyword arguments. Importantly, all parameters defined in the sine_curve function must be given in the interactive call and a widget is created for each one.

Depending on the type of the passed argument different types of widgets will be created by interactive. For instance:

• int or float arguments will generate a slider
• bool arguments (True/False) will generate checkbox widgets
• list arguments will generate a dropdown
• str arguments will generate a text-box

By supplying the arguments in the form of tuples we can adjust the properties of the sliders. f=(1, 5, 1) creates a widget with minimum value of 1, maximum value of 5 and a step size of 1. Try adjusting these numbers in the interactive call to see how the sliders change (you have to re-execute the cell).

The final line of the cell (interactive_plot) is where the actual widgets and plot are displayed. This code can be put in a separate cell, so that you can define functions and widgets in one part of your notebook, and reuse them somewhere else.

This is how it should look if everything works. You can now set the frequency amplitude and phase of the sine curve by moving the sliders.

There are lots of widgets, e.g.:

• Dropdown menus
• Toggle buttons
• Range sliders
• File uploader

... and much, much more. Here is a list of all available widgets together with documentation and examples. Some of these widgets cannot be autogenerated by interactive, but fear not! Instead of relying on autogeneration we can define the widget and supply it directly to interactive.

To see this in practice, change out the A argument to a pre-defined IntSlider widget. First define the slider:

from ipywidgets import widgets
A = widgets.IntSlider(value=2, min=1, max=5, step=1)

Then replace the call to interactive so that it looks like this:

interactive_plot = interactive(sine_curve, A=A, f=5, p=5)

Extra challenge#

If you can't get enough of widgets you might want to try this out: see if you can figure out how to add a widget that lets you pick the color for the sine curve line. Search for the appropriate widget in the Widget list. You'll need to update the sine_curve function and pass the new widget as an argument in the call to interactive. If you need help, see the code chunk below:

# Import the interactive function from ipywidgets
from ipywidgets import interactive
# Also import numpy (for calculating the sine curve)
# and pyplot from matplotlib for plotting
import numpy as np
from ipywidgets import widgets ## <- import widgets
import matplotlib.pyplot as plt

# Define the function for plotting the sine curve
def sine_curve(A, f, p, color): ## <- add parameter here
    # Set up the plot
    plt.figure(1, figsize=(4,4))
    # Create a range of 100 evenly spaced numbers between 0 and 100
    x = np.linspace(0,10,100)
    # Calculate the y values using the supplied parameters
    y = A*np.sin(x*f+p)
    # Plot the x and y values
    plt.plot(x, y, color=color) ## <- Use color from widget here
    plt.show()

# Here we supply the sine_curve function to interactive,
# and set some limits on the input parameters
# Define the colorpicker widget
colorpicker = widgets.ColorPicker(description='color',value="red")
interactive_plot = interactive(sine_curve,
            A=(1, 5, 1),
            f=(0, 5, 1),
            p=(1, 5, 0.5),
            color=colorpicker) ## <- Supply the colorpicker to the function

# Display the widgets and the plot
interactive_plot

Other interactive plots#

Jupyter widgets, like we used here, is the most vanilla way of getting interactive graphs in Jupyter notebooks. Some other alternatives are:

• Plotly is actually an API to a web service that renders your graph and returns it for display in your Jupyter notebook. Generates very visually appealing graphs, but from a reproducibility perspective it's maybe not a good idea to be so reliant on a third party.
• Bokeh is another popular tool for interactive graphs. Most plotting packages for Python are built on top of matplotlib, but Bokeh has its own library. This can give a steeper learning curve if you're used to the standard packages.
• mpld3 tries to integrate matplotlib with Javascript and the D3js package. It doesn't scale well for very large datasets, but it's easy to use and works quite seamlessly.