The Kitchin Research Group

yasnippets for jasp, ase and python

Mon, 17 Feb 2014 09:03:50 EST

1. Tangle the snippets, and add them to yasnippet
2. Using the snippets
3. Appendix

In using [[http://github.com/jkitchin/jasp for calculations, I find there are lots of small python phrases I use over and over. Today I will examine using yasnippet to save time and keystrokes. yasnippet is a template expansion module, where you type a small set of characters, press Tab, and the characters "expand" to the full text. It is pretty sophisticated, and allows you to define "tab-stops" which you interactively fill in, and tab between like filling in a form.

All the snippets are defined in the

*Appendix

1 Tangle the snippets, and add them to yasnippet

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)

snippets/org-mode/iase

snippets/org-mode/imp

snippets/org-mode/inp

snippets/org-mode/ij

snippets/org-mode/pl

snippets/org-mode/pyl

snippets/org-mode/pxl

snippets/org-mode/pp

snippets/org-mode/npa

snippets/org-mode/awt

snippets/org-mode/avw

snippets/org-mode/agf

snippets/org-mode/ape

snippets/org-mode/atms

snippets/org-mode/atm

snippets/org-mode/cga

snippets/org-mode/cc

snippets/org-mode/wjn

snippets/org-mode/wjl

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")

c:/Users/jkitchin/Dropbox/blogofile-jkitchin.github.com/_blog/snippets

~/.emacs.d/snippets

c:/users/jkitchin/Dropbox/kitchingroup/jmax/elpa/yasnippet-20140106.1009/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")

2 Using the 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?

3 Appendix

3.1 jasp snippets

# -*- mode: snippet -*-
# --
with jasp('$1') as calc:
    $0

# -*- mode: snippet -*-
# --
with jasp('$1',$0) as calc:
    calc.calculate(atoms)

# -*- mode: snippet -*-
# --
calc.calculate(atoms)

# -*- mode: snippet -*-
# --
atoms = calc.get_atoms()

3.2 ase snippets

Template for an ase.Atom

# -*- mode: snippet -*-
# --
Atom('$1', $2)

# -*- mode: snippet -*-
# --
atoms = Atoms([$1], cell=$2)

# -*- mode: snippet -*-
# --
atoms.get_potential_energy()

# -*- mode: snippet -*-
# --
atoms.get_forces()

# -*- mode: snippet -*-
# --
from ase.visualize import view
view(${1:atoms})

# -*- mode: snippet -*-
# --
from ase.io import write
write('$1.png', ${2:atoms}, show_unit_cell=${3:2})

3.3 python snippets

# -*- mode: snippet -*-
# --
import numpy as np

# -*- mode: snippet -*-
# --
import matplotlib.pyplot as plt

# -*- mode: snippet -*-
# --
from ase import Atom, Atoms

# -*- mode: snippet -*-
# --
np.array($0)

# -*- mode: snippet -*-
# --
plt.plot($1, $2)

# -*- mode: snippet -*-
# --
plt.xlabel($1)

# -*- mode: snippet -*-
# --
plt.ylabel($1)

# -*- mode: snippet -*-
# --
plt.legend($1)

# -*- mode: snippet -*-
# --
from jasp import *

org-mode source

Org-mode version = 8.2.5h

Serializing jasp calculations as json data

Sat, 19 Oct 2013 14:33:51 EDT

1. The simplest case of a simple calculation
2. Including extra information in the JSON file
3. Limitations?

We use VASPto calculate materials properties in our research We use the jasppython module we have developed to setup, run and analyze those calculations. One of the things we have worked on developing recently is to more transparently share how do this kind of work by using org-mode supporting information files. Doing this should make our research more reproducible, and allow others to build off of it more easily.

We have run into the following problem trying to share VASP results however. The VASP license prohibits us from sharing the POTCAR files that are used to run the calculations. That is unfortunate, but since these files are also what give VASP some competitive advantage, they are protected, and we agreed to that when we bought the license. The problem is that the jasp module requires the POTCAR files to work, so without them, our scripts are not reproducible by researchers without a VASP license.

So, we have been looking at new ways to share the data from our calculations. In this post, we consider representing the calculation as a JSON file. We will look at a couple of new features built into the development branch of jasp

1 The simplest case of a simple calculation

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]]

2 Including extra information in the JSON file

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).

3 Limitations?

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.