There are a few tools for interactive plots that one may choose from, such as Bokeh, Plotly, and VisPy.
Bokeh allows you to plot matplotlib objects via JavaScript, which enables the interactive part easily. For instance, if one needs a map plot that is interactive, Bokeh can be used. Bokeh uses JavaScript and enables D3.js style plots and targets the visualization via modern web browsers. Bokeh delivers good performance over a large dataset. You can easily install bokeh either via conda or pip, as shown in the following code:
conda install bokeh OR pip install bokeh
import collections
from bokeh.sampledata import us_counties, unemployment
from bokeh.plotting import figure, show, output_file
from bokeh.models import HoverTool
county_coordinate_xs=[
us_counties.data[code]['lons'] for code in us_counties.data
if us_counties.data[code]['state'] == 'ca'
]
county_coordinate_ys=[
us_counties.data[code]['lats'] for code in us_counties.data
if us_counties.data[code]['state'] == 'ca'
]
colors = ["#e6f2ff", "#cce5ff", "#99cbff", "#b2d8ff", "#73abe5", "#5985b2"]
county_colors = []
for county_id in us_counties.data:
if us_counties.data[county_id]['state'] != 'ca':
continue
try:
rate = unemployment.data[county_id]
idx = min(int(rate/2), 5)
county_colors.append(colors[idx])
except KeyError:
county_colors.append("black")
output_file("california.html", title="california.py example")
TOOLS="pan,wheel_zoom,box_zoom,reset,hover,save"
p = figure(title="California Unemployment 2009", width=1000, height=1000, tools=TOOLS)
p.patches(county_coordinate_xs, county_coordinate_ys,
fill_color=county_colors, fill_alpha=0.7,
line_color="white", line_width=0.5)
mouse_hover = p.select(dict(type=HoverTool))
mouse_hover.point_policy = "follow_mouse"
mouse_hover.tooltips = collections.OrderedDict([
("index", "$index"), ("(x,y)", "($x, $y)"),
("fill color", "$color[hex, swatch]:fill_color"),
])
show(p)In order to view the results, you may have to use a browser to open California.html:
Plotly is another option that allows interactive plots, but requires one to be online and have a Plotly account. The plots using Plotly look very nice and is interactive. The following code shows how one can create interactive plots using plotly:
from pylab import *
import plotly
#py = plotly.plotly('me', 'mykey')
def to_plotly(ax=None):
if ax is None:
ax = gca()
lines = []
for line in ax.get_lines():
lines.append({'x': line.get_xdata(),
'y': line.get_ydata(),
'name': line.get_label(),
})
layout = {'title':ax.get_title(),
'xaxis':{'title':ax.get_xlabel()},
'yaxis':{'title':ax.get_ylabel()}
}
filename = ax.get_title() if ax.get_title() != '' else 'Untitled'
print filename
close('all')
#return lines, layout
return py.iplot(lines,layout=layout, filename = filename)
plot(rand(100), label = 'trace1')
plot(rand(100)+1, label = 'trace2')
title('Title')
xlabel('X label')
ylabel('Y label ')
response = to_plotly()
response
VisPy is another high performance interactive tool built using Python and OpenGL; therefore, it delivers the power of modern GPU's. It is fairly new, and as it matures, it leaves users with another good visualization library to choose from. The following example shows that using vispy one can create an image that can be zoomed interactively:
import sys
from vispy import scene
from vispy import app
import numpy as np
canvas = scene.SceneCanvas(keys='interactive')
canvas.size = 800, 800
canvas.show()
# Set up a viewbox to display the image with interactive pan/zoom
view = canvas.central_widget.add_view()
# Create the image
img_data = np.random.normal(size=(100, 100, 3), loc=128,
scale=40).astype(np.ubyte)
image = scene.visuals.Image(img_data, parent=view.scene)
# Set 2D camera (the camera will scale to the contents in the scene)
view.camera = scene.PanZoomCamera(aspect=1)
if __name__ == '__main__' and sys.flags.interactive == 0:
app.run()
The preceding image shows the plot that appears the first time, but as we move the mouse and zoom in on it, it appears as follows:
