# Jupyter¶

To get these examples to work in Jupyter, you will need to install the following.

## Widgets¶

Let’s see how we can use zava to work with ipywidgets. First, we got to get some data.

[1]:

import numpy as np
import pandas as pd

M = np.array([
[1, 1, 1, 1],
[2, 2, 2, 1],
[3, 3, 3, 3],
[1, 2, 3, 4],
[2, 2, 1, 1],
[1, 1, 3, 3]
])


Now we create an instance of GrandTour with the data and also specifying the minimum c and maximum d values for scaling.

[2]:

from zava.core import GrandTour

c = 0
d = 1
grand_tour = GrandTour(M, c, d)


Finally, we use a function f annotated with @interact to create an interactive visualization with parallel coordinates and Grand Tour.

[3]:

import matplotlib.pyplot as plt
from ipywidgets import interact

@interact(degree=(0, 360 * 4, 0.5))
def f(degree=0):
S = grand_tour.rotate(degree)

fig, ax = plt.subplots(figsize=(15, 3))

params = {
'kind': 'line',
'ax': ax,
'color': 'r',
'marker': 'h',
'markeredgewidth': 1,
'markersize': 5,
'linewidth': 0.8
}

_ = S.plot(**params)
_ = ax.get_legend().remove()
_ = ax.set_xticks(np.arange(len(S.index)))
_ = ax.set_xticklabels(S.index)
_ = ax.get_yaxis().set_ticks([])
_ = ax.set_title('Grand Tour')


## Animations¶

Now let’s see how we can create HTML5 animations in a notebook using matplotlib.animation. Again, start with some data.

[4]:

import numpy as np
import pandas as pd

M = np.array([
[1, 1, 1, 1],
[2, 2, 2, 1],
[3, 3, 3, 3],
[1, 2, 3, 4],
[2, 2, 1, 1],
[1, 1, 3, 3]
])


Create a GrandTour instance with the data.

[5]:

from zava.core import GrandTour

c = 0
d = 1
grand_tour = GrandTour(M, c, d)


We have to wrap the GrandTour instance with a SinglePlotter. The SinglePlotter plots only a single set of data with an axis and does not concern itself with the greater plot (e.g. the title). The params argument is a dictionary that you can override to change the line drawings.

[6]:

from zava.plot import SinglePlotter

single_plotter = SinglePlotter(grand_tour, params={'color': 'r'})


The MultiPlotter controls all the plots and takes in a list of SinglePlotters as well as an axis. You can then use an instance of this object with animation.FuncAnimation() as usual to produce an animation.

[7]:

from zava.plot import MultiPlotter
from matplotlib import animation

fig, ax = plt.subplots(figsize=(5, 3))

multi_plotter = MultiPlotter([single_plotter], ax=ax)

params = {
'fig': fig,
'func': multi_plotter,
'frames': np.linspace(0, 360, 360),
'interval': 20,
'init_func': multi_plotter.init
}
anim = animation.FuncAnimation(**params)

plt.close(fig)


Finally, render the video.

[8]:

%%time

from IPython.display import HTML

HTML(anim.to_html5_video())

CPU times: user 21.4 s, sys: 574 ms, total: 22 s
Wall time: 22.1 s

[8]:


## Animation, colors¶

You might find yourself doing cluster analysis of high-dimensional data. If you recover some clusters, you can break the data apart according to the clusters and visualize them with different colors. Here’s a full working example (without the clustering) of how to visualize two datasets.

[9]:

%%time

# 1. here are your two datasets, M1 and M2

columns = ['v0', 'v1', 'v2', 'v3']

M1 = np.array([
[1, 1, 1, 1],
[2, 2, 2, 1],
[3, 3, 3, 3]
])
M2 = np.array([
[1, 2, 3, 4],
[2, 2, 1, 1],
[1, 1, 3, 3]
])

M1 = pd.DataFrame(M1, columns=columns)
M2 = pd.DataFrame(M2, columns=columns)

# 2. create your GrandTour instances

c = 0.0
d = 100.0

gt1 = GrandTour(M1, c, d)
gt2 = GrandTour(M2, c, d)

# 3. create corresponding SinglePlotters

sp1 = SinglePlotter(gt1, params={'color': 'r'})
sp2 = SinglePlotter(gt2, params={'color': 'g'})

# 4. create a MultiPlotter from the SinglePlotters
fig, ax = plt.subplots(figsize=(5, 3))
mp = MultiPlotter([sp1, sp2], ax=ax)

params = {
'fig': fig,
'func': mp,
'frames': np.linspace(0, 360, 360),
'interval': 20,
'init_func': mp.init
}
anim = animation.FuncAnimation(**params)

plt.close(fig)

# 5. display the animation
HTML(anim.to_html5_video())

CPU times: user 25 s, sys: 608 ms, total: 25.6 s
Wall time: 25.7 s

[9]: