Interactive figures in blog posts with mpld3

Posted February 08, 2016 at 07:33 AM | categories: plotting, interactive, python | tags:

Continuing the exploration of interactive figures, today we consider the Python plotting library mpld3 . We will again use our own published data. We wrote this great paper on core level shifts (CLS) in Cu-Pd alloys boes-2015-core-cu. I want an interactive figure that shows the name of the calculation on each point as a tooltip. This data is all stored in the supporting information file, and you can see how we use it here. This figure shows how the core level shift of a Cu atom changes depending on the number of nearest neighbor Cu atoms. Just hover your mouse over a point to see the name and CLS for that point.

1 Data and code

You can check out our preprint at https://github.com/KitchinHUB/kitchingroup-51 . We are going to adapt the code to make Figure 6a in the manuscript interactive. The code needed a somewhat surprising amount of adaptation. Apparently the ase database interface has changed a lot since we write that paper, so the code here looks a bit different than what we published. The biggest difference is due to name-mangling so each key that started with a number now starts with _, and and periods are replaced by _ also. The rest of the script is nearly unchanged. At the end is the very small bit of mpld3 code that generates the figure for html. We will add tooltips onto datapoints to indicate what the name associated with each data point is. Here is the code. 

import matplotlib.pyplot as plt
from ase.db import connect

# loads the ASE database and select certain keywords
db = connect('~/Desktop/cappa/kitchingroup-51/supporting-information/data.json')

keys = ['bcc', 'GS', '_54atom', 'ensam']

CLS, IMP, labels = [], [], []
for k in db.select(keys + ['_1cl']):
    name = k.keywords[-2]

    Cu0 = db.select('bcc,GS,_72atom,_0cl,_1_00Cu').next().energy
    Cu1 = db.select('bcc,GS,_72atom,_1cl,_1_00Cu').next().energy
    x0 = db.select(','.join(keys + [name, '_0cl'])).next().energy
    x1 = k.energy

    cls0 = x0 - Cu0
    cls1 = x1 - Cu1

    IMP.append(int(name[1]))
    CLS.append(cls1 - cls0)
    labels += ['{0} ({1}, {2})'.format(name, int(name[1]), cls1 - cls0)]

Cu0 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_0cl', '_1_00Cu'])).next().energy
Cu1 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_1cl', '_1_00Cu'])).next().energy

x0 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_0cl', '_1'])).next().energy
x1 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_1cl', '_1'])).next().energy

cls0 = x0 - Cu0
cls1 = x1 - Cu1

IMP.append(1)
CLS.append(cls1 - cls0)
labels += ['(1, {0})'.format(cls1 - cls0)]

Cu0 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_0cl', '_1_00Cu'])).next().energy
Cu1 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_1cl', '_1_00Cu'])).next().energy

x0 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_0cl', '_0'])).next().energy
x1 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_1cl', '_0'])).next().energy

cls0 = x0 - Cu0
cls1 = x1 - Cu1

IMP.append(0)
CLS.append(cls1 - cls0)
labels += ['(0, {0})'.format(cls1 - cls0)]

fig = plt.figure()

p = plt.scatter(IMP, CLS, c='g', marker='o', s=25)
ax1 = plt.gca()
ax1.set_ylim(-1.15, -0.6)
ax1.set_xlim(-0.1, 5.1)

ax1.set_xlabel('# Cu Nearest neighbors')
ax1.set_ylabel('Cu 2p(3/2) Core Level Shift (eV)')

ax1.set_title('Hover over a point to see the calculation name')

# Now the mpld3 stuff.
import mpld3
from mpld3 import plugins

tooltip = plugins.PointHTMLTooltip(p, labels, voffset=0, hoffset=10)
plugins.connect(fig, tooltip)

print mpld3.fig_to_html(fig)

I like this workflow pretty well. It seems less functional than plotly and Bokeh (e.g. it does not look like it you can export the data from the html here), but it is well integrated with Matplotlib, with my blogging style, and does not require a server, oran account. The code outputs html that is self-contained in the body of the html. The smooth integration with Matplotlib means I could have static images in org-mode, and dynamic images in HTML potentially. Overall, this is a nice tool for making interactive plots in blog posts.

Copyright (C) 2016 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 8.2.10

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