harvest_qmcpack

Python module containing useful routines to inspect and modify qmcpack objects.

Quick Start

Install

Clone the repository and add it to PYTHONPATH. To use examples, add bin to PATH.

git clone https://github.com/Paul-St-Young/harvest_qmcpack.git ~
export PYTHONPATH=~/harvest_qmcpack:$PYTHONPATH
export PATH=~/harvest_qmcpack/bin:$PATH

Prerequisites can be installed following requirement.txt

cd ~/harvest_qmcpack; pip install --user -r requirements.txt

You can also use pip if you do not intend to change the code

git clone https://github.com/Paul-St-Young/harvest_qmcpack.git ~/harvest_qmcpack
pip install --user ~/harvest_qmcpack

To update to the newest version:

cd ~/harvest_qmcpack
git pull
pip install --user --upgrade ~/harvest_qmcpack

Tests

Unit tests should work with either nosetest or pytest

cd ~/harvest_qmcpack; pytest -v .

Use

The library functions can be used in a python script

# extract all scalar data from a run directory 
#  look for scalar.dat files and collect statistics
#  hint: run dirrctory does not have to be an actual run
import os
from qharv.reel  import scalar_dat, mole
from qharv.sieve import scalar_df
"""
*** Strategy adopted in this script:
 1. use "mole" to dig up the locations of all 
  scalar.dat to be analyzed.
 2. use "reel" to reel in all scalar data 
  without prejudice.
 3. use "sieve" to remove equilibration data 
  and perform averages to shrink the database.
only two human inputs are required: folder, nequil
"""

# folder containing QMCPACK scalar.dat files
folder = './runs'

# define equilibration length and autocorrelation length
nequil = 5
kappa  = 1.0 # None to re-calculate
#  runs should be designed to have short equilibration and
# no autocorrelation. kappa can be calculated on-the-fly
# ,be warned though: kappa calculation is slow. For nequil:
# unfortunately I have yet to find a fast and RELIABLE
# algorithm to determine nequil. For custom nequil, use
# a dictionary in the `for floc in flist` loop.

# generate the list of scalar.dat files to analyze
flist = mole.files_with_regex('*scalar.dat', folder)

# analyze the list of scalar.dat files
data  = []
for floc in flist:
  mydf = scalar_dat.parse(floc)
  mdf  = scalar_df.mean_error_scalar_df(mydf,nequil,kappa=kappa)
  assert len(mdf) == 1 # each scalar.dat should contribute only one entry
  # add metadata to identify runs
  mdf['path'] = os.path.dirname(floc)
  mdf['fdat'] = os.path.basename(floc)
  data.append(mdf)
df = pd.concat(data).reset_index() # index must be unique for the database to be saved

The examples in the "bin" folder can be ran in the shell

$ stalk vmc.in.xml
$ stab vmc.s000.scalar.dat
$ slash-and-burn -y -v nscf
$ rebuild_wf opt.in.xml

Documentation

Documentation is available on github pages. A local copy can be generated using sphinx (pip install --user sphinx). To generate the documentation, first use sphinx-apidoc to convert doc strings to rst documentation:

cd ~/harvest_qmcpack/doc; sphinx-apidoc -o source ../qharv

Next, use the generated Makefile to create html documentation:

cd ~/harvest_qmcpack/doc; make html

Finally, use your favorite browser to view the documentation:

cd ~/harvest_qmcpack/doc/build; firefox index.html

Examples

Example usage of the qharv library are included in the "harvest_qmcpack/bin" folder. Each file in the folder is a Python script that performs a very specific task:

• stalk: Show crystal structure specified in a QMCPACK input e.g. stalk vmc.in.xml
• stab: Scalar TABle (stab) analyzer, analyze one column of a scalar table file, e.g. stab vmc.s000.scalar.dat
• transplant: Backup nexus-generated folders. Allow user to select subfolders to backup. e.g. transplant graphene attic -s opt -s qmc -e will backup the QMC runs in folder "opt" and "qmc" from graphene/results and graphene/runs to attic/graphene/results and attic/graphene/runs. The "scf" and "nscf" folders will not be backed up.
• slash-and-burn: Remove temporary files generated by Quantum Espresso.
• rebuild_wf: Rerun QMCPACK on optimized wavefunctions, e.g. rebuild_wf opt.xml

Description

This module is intended to speed up on-the-fly setup, run, and analysis of QMCPACK calculations. The module should be used as a collection of python equivalents of bash commands. This module is NOT intended to be a full-fledged workflow tool. Please refer to nexus for complete workflow magnagement.

Development Guidelines

sown the seeds, inspect the crop;
crossbreed to improve, transplant to adapt;
reel them in, sieve for good, and refine for the best.
-- qharv maxim

Laws of Programming (fully plagiarized from Asimov)

1. A program may not produce wrong results or, through inaction, allow a user to produce wrong results.
2. A program must accept manual overrides given to it by a user, except where such overrides will conflict with the First Law.
3. A program must be as simple and as readable as possible, as long as doing so does not conflict with the First or the Second Law.

note: the simplest way to satisfy both the First and the Second Law is to abort at an unknown request.