Interact Demos¶
This Notebook shows basic demonstrations of IPython interact module. This provides a high-level interface for creating user interface controls to use in exploring code and data interactively.
%pylab inline
from IPython.html.widgets.interact import interact, interactive
from IPython.html import widgets
from IPython.display import clear_output, display, HTML
Basic interact¶
Here is a simple function that displays its arguments as an HTML table:
def show_args(**kwargs):
s = '<h3>Arguments:</h3><table>\n'
for k,v in kwargs.items():
s += '<tr><td>{0}</td><td>{1}</td></tr>\n'.format(k,v)
s += '</table>'
display(HTML(s))
show_args(a=10, b='Hi There', c=True)
Let's use this function to explore how interact works.
interact(show_args,
Temp=(0,10),
Current=(0.,10.,0.01),
z=True,
Text=u'Type here!',
Algorithm=['This','That','Other'],
a=widgets.FloatSliderWidget(min=-10.0, max=10.0, step=0.1, value=5.0)
)
%debug
isinstance(10, bool)
The keyword arguments to interact can be any Widget instance that has a value and description attribute, or one of the shorthand notations shown above.
Factoring polynomials¶
Here is an example that uses SymPy to factor polynomials.
from sympy import Symbol, Eq, factor, init_printing
init_printing(use_latex=True)
x = Symbol('x')
def factorit(n):
display(Eq(x**n-1, factor(x**n-1)))
Notice how the output of the factorit function is properly formatted LaTeX.
interact(factorit, n=(2,40))
A simple image browser¶
This example shows how to browse through a set of images with a slider.
from sklearn import datasets
We will use the digits dataset from scikit-learn.
digits = datasets.load_digits()
def browse_images(digits):
n = len(digits.images)
def view_image(i):
imshow(digits.images[i], cmap=cm.gray_r, interpolation='nearest')
title('Training: %s' % digits.target[i])
show()
interact(view_image, i=(0,n-1))
browse_images(digits)
Explore random graphs¶
In this example, we build a simple UI for exploring random graphs with NetworkX.
import networkx as nx
def plot_random_graph(n, p, generator):
g = generator(n,p)
nx.draw(g)
show()
interact(plot_random_graph, n=(2,30), p=(0.0, 1.0, 0.001),
generator={'gnp': nx.gnp_random_graph,
'erdos_renyi': nx.erdos_renyi_graph,
'binomial': nx.binomial_graph})
Image manipulation¶
This example builds a simple UI for performing basic image manipulation with scikit-image.
import skimage
from skimage import data, filter, io
i = data.coffee()
io.Image(i)
def edit_image(image):
def apply_filter(sigma, r, g, b):
new_image = filter.gaussian_filter(image, sigma=sigma)
new_image[:,:,0] = r*new_image[:,:,0]
new_image[:,:,1] = g*new_image[:,:,1]
new_image[:,:,2] = b*new_image[:,:,2]
new_image = io.Image(new_image)
display(new_image)
return new_image
lims = (0.0,1.0,0.01)
return interactive(apply_filter, sigma=(0.1,10.0,0.01), r=lims, g=lims, b=lims)
w = edit_image(i)
display(w)
w.arguments
w.result
Playing with audio¶
This example uses the Audio object and Matplotlib to explore the phenomenon of beat frequencies.
from IPython.display import Audio
import numpy as np
def beat_freq(f1=220.0, f2=224.0):
max_time = 3
rate = 8000.0
times = np.linspace(0,max_time,rate*max_time)
signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)
print f1, f2, abs(f1-f2)
display(Audio(data=signal, rate=rate))
return signal
v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))
display(v)
plot(v.result[0:6000])