Advanced: ipywidgets

Hate manually parameters and re-running your code for a parameter change? Ever want to interact with your data using a GUI?

ipywidgets allows you to easily link up Python code with user interface widgets.

They provide documentation in the form of tutorials and examples on their Github site.

Let's continue the demonstration of Planck's equation by incorporating some interactivity:

Installation

First, install ipywidgets using either pip or conda:

pip:

pip install ipywidgets

conda:

conda install ipywidgets

Example

First we'll get our code from our basic demonstration:



In [1]:

    
# Import numpy and alias to "np"
import numpy as np
# Import and alias to "plt"
import matplotlib.pyplot as plt


def planck(wavelength, temp):
    """ Return the emitted radiation from a blackbody of a given temp and wavelength
    
    Args:
        wavelength (float): wavelength (m)
        temp (float): temperature of black body (Kelvin)
        
    Returns:
        float: spectral radiance (W / (sr m^3))
        
    """
    k_b = 1.3806488e-23  # J/K  Boltzmann constant
    h = 6.626070040e-34  # J s - Planck's constant
    c = 3e8  # m/s - speed of light
    
    return ((2 * h * c ** 2) / wavelength ** 5 * 
            1 / (np.exp(h * c / (wavelength * k_b * temp)) - 1))

Let's also combine our plotting code into a cohesive function:



In [2]:

    
def plot_planck(temp):
    """ Plot the spectral radiance for a blackbody of a given temperature
    
    Args:
        temp (float): temperature of body
        
    """
    wavelength = np.linspace(1e-8, 10e-6, 1000)
    
    
    rad = planck(wavelength, temp)
    
    text_x = wavelength[rad.argmax()] * 1e6
    text_y = rad.max() / 1e3 / 1e9
    temp_str = '%.2f K' % temp
    
    fig, ax = plt.subplots()
    
    ax.plot(wavelength * 1e6, rad / 1e3 / 1e9)
    ax.text(text_x, text_y, temp_str, ha='center')

    ax.set_xlabel(r'Wavelength ($\mu m$)')
    ax.set_ylabel(r'Spectral radiance ($kW \cdot sr^{-1} \cdot m^{-2} \cdot nm^{-1}$)')
    
    ax.set_xlim([1e-8 * 1e6, 10e-6 * 1e6])

Now we can tie our plot function, plot_planck, to the interact function from ipywidgets:



In [4]:

    
%matplotlib nbagg
from ipywidgets import interactive
from IPython.core.display import display

vis = interactive(plot_planck, temp=(250, 10e3, 100))
display(vis)