The Kitchin Research Group

Each snippet definition belongs in a file in a directory. The main directory is called "snippets". Since I anticipate using these snippets in org-mode, each snippet is defined in a directory within snippets called "org-mode". First, we make the directory here. I also want to use the snippets in python mode, so we also create a python-mode directory here. We do not have to duplicate the snippets. We can create a file called .yas-parents , with one line in it containing "org-mode".

mkdir -p snippets/org-mode
mkdir -p snippets/python-mode
echo "org-mode" > snippets/python-mode/.yas-parents

Each snippet is defined in a src block with a :tangle header. So, we can extract them all in one command here.

(org-babel-tangle)

We also need to add our new directory to yasnippets. This is done by adding the directory to the yas-snippet-dirs variable. You could add this to your init.el file to permanently add these snippets.

(add-to-list 'yas-snippet-dirs "c:/Users/jkitchin/Dropbox/blogofile-jkitchin.github.com/_blog/snippets")

Finally, we reload all the snippet definitions, so our new definitions are ready to use.

(yas-reload-all)

[yas] Reloaded everything (snippets will load just-in-time)... (some errors, check *Messages*).

Alternatively, you might just load this directory.

(yas-load-directory "./snippets")

Each of these snippets is for a python phrase, but I usually write my python blocks in org-mode. You would use these by typing the shortcut name, and then pressing tab. Below I show what each shortcut expands to.

wjl → with jasp('') as calc:

wjn → with jasp('',) as calc: calc.calculate(atoms)

cc → calc.calculate(atoms)

cga → atoms = calc.get_atoms()

atm → Atom('', )

atms → atoms = Atoms([], cell)=

ape → atoms.get_potential_energy()

agf → atoms.get_forces()

avw → from ase.visualize import view view(atoms)

awt → from ase.io import write write('.png', atoms, show_unit_cell=2)

npa → np.array()

pp → plt.plot(, )

pxl → plt.xlabel()

pyl → plt.ylabel()

pl → plt.legend()

ij → from jasp import *

inp → import numpy as np

imp → import matplotlib.pyplot as plt

iase → from ase import Atom, Atoms

What other snippets would be handy?

Here we setup and run a simple calculation, and output the JSON file.

from ase import Atoms, Atom
from jasp import *
import numpy as np
np.set_printoptions(precision=3, suppress=True)

co = Atoms([Atom('C',[0,   0, 0]),
            Atom('O',[1.2, 0, 0])],
            cell=(6., 6., 6.))

with jasp('molecules/simple-co', #output dir
          xc='PBE',  # the exchange-correlation functional
          nbands=6,  # number of bands
          encut=350, # planewave cutoff
          ismear=1,  # Methfessel-Paxton smearing
          sigma=0.01,# very small smearing factor for a molecule
          atoms=co) as calc:
    print 'energy = {0} eV'.format(co.get_potential_energy())
    print co.get_forces()
    with open('JSON', 'w') as f:
        f.write(calc.json)

energy = -14.687906 eV
[[ 5.095  0.     0.   ]
 [-5.095  0.     0.   ]]

Now, we can analyze the JSON file independently of jasp. The json data contains all the inputs we used for the VASP calculation, the atomic geometry, and many of the outputs of the calculation. Here is the JSONfile.

import json
with open('molecules/simple-co/JSON', 'rb') as f:
    d = json.loads(f.read())

print('The energy is {0}'.format(d['data']['total_energy']))
print('The forces are {0}'.format(d['data']['forces']))

The energy is -14.687906
The forces are [[5.095488, 0.0, 0.0], [-5.095488, 0.0, 0.0]]

If we use a slightly different syntax, we can also include the total DOS in the JSON file.

from jasp import *

with jasp('molecules/simple-co') as calc:
    with open('JSON-DOS', 'w') as f:
        f.write(calc_to_json(calc, dos=True))

To illustrate that we have done that, let us plot the DOS without using jasp from the JSON-DOSfile.

import json
import matplotlib.pyplot as plt

with open('molecules/simple-co/JSON-DOS', 'rb') as f:
    d = json.loads(f.read())

energies = d['data']['dos']['e']
dos = d['data']['dos']['dos']
plt.plot(energies, dos)
plt.savefig('molecules/simple-co/dos.png')

We are still working on getting atom-projected DOS into the json file, and ensuring that all the spin cases are handled (e.g. the spin-up and spin-down DOS).

JSON is flexible, and can store text and numeric data. It does not store numpy arrays, but rather it is limited to storing lists of data. You would have to convert them back to arrays if you want to do array math. You probably wouldn't want to store a 3d array of electron density in this format, although it probably isn't worse than a CUBE file format. We haven't tested these files very significantly yet at a large scale to see how fast it is to read from lots of them.

Nonetheless, this looks like a reasonable format to share data in human and machine readable form, without violating the VASP licence conditions.