2.4. Command line tools#
In addition to the Python objects presented in previous sections, pyALF offers a set of scripts that make it easy to leverage pyALF from a Unix shell (e.g. Bash or zsh). They are located in the folder py_alf/cli and, as mentioned in Section 1, it is recommended to add this folder to the $PATH environment variable, to conveniently use the scripts: \(\phantom{\xi}\)
export PATH="/path/to/pyALF/py_alf/cli:$PATH"
The list of all command line tools can be found in the reference. Out of those, this section will only introduce two more elaborate scripts, namely alf_run.py and alf_postprocess.py.
When starting a code line in Jupyter with an exclamation mark, the line will be interpreted as a shell command. We will use this feature to demonstrate the shell tools.
2.4.1. alf_run.py#
The script alf_run.py enables most of the features displayed in Section 2.2 to be used directly from the shell. The help text lists all possible arguments:
!alf_run.py -h
usage: alf_run.py [-h] [--alfdir ALFDIR] [--sims_file SIMS_FILE]
[--branch BRANCH] [--machine MACHINE] [--mpi]
[--n_mpi N_MPI] [--mpiexec MPIEXEC]
[--mpiexec_args MPIEXEC_ARGS] [--do_analysis]
Helper script for compiling and running ALF.
optional arguments:
-h, --help show this help message and exit
--alfdir ALFDIR Path to ALF directory. (default: os.getenv('ALF_DIR',
'./ALF')
--sims_file SIMS_FILE
File defining simulations parameters. Each line starts
with the Hamiltonian name and a comma, after wich
follows a dict in JSON format for the parameters. A
line that says stop can be used to interrupt.
(default: './Sims')
--branch BRANCH Git branch to checkout.
--machine MACHINE Machine configuration (default: 'GNU')
--mpi mpi run
--n_mpi N_MPI number of mpi processes (default: 4)
--mpiexec MPIEXEC Command used for starting a MPI run (default:
'mpiexec')
--mpiexec_args MPIEXEC_ARGS
Additional arguments to MPI executable.
--do_analysis, --ana Run default analysis after each simulation.
For example, to run a series of four different simulations of the Kondo model, the first step is to create a file specifying the parameters, with one line per simulation:
!cat Sims_Kondo
Kondo, {"L1": 4, "L2": 4, "Ham_JK": 0.5}
Kondo, {"L1": 4, "L2": 4, "Ham_JK": 1.0}
Kondo, {"L1": 4, "L2": 4, "Ham_JK": 1.5}
Kondo, {"L1": 4, "L2": 4, "Ham_JK": 2.0}
Then, one can execute alf_run.py with options as desired, the script automatically recompiles ALF for each simulation. For understanding some of the options, Section 2.2 might help. The following printout is truncated for brevity.
!alf_run.py --sims_file ./Sims_Kondo --mpi --n_mpi 4 --mpiexec orterun
Number of simulations: 4
Compiling ALF...
Cleaning up Prog/
Cleaning up Libraries/
Cleaning up Analysis/
Compiling Libraries
ar: creating modules_90.a
ar: creating libqrref.a
Compiling Analysis
Compiling Program
Parsing Hamiltonian parameters
filename: Hamiltonians/Hamiltonian_Kondo_smod.F90
filename: Hamiltonians/Hamiltonian_Hubbard_smod.F90
filename: Hamiltonians/Hamiltonian_Hubbard_Plain_Vanilla_smod.F90
filename: Hamiltonians/Hamiltonian_tV_smod.F90
filename: Hamiltonians/Hamiltonian_LRC_smod.F90
filename: Hamiltonians/Hamiltonian_Z2_Matter_smod.F90
Compiling program modules
Link program
Done.
Prepare directory "/scratch/pyalf-docu/doc/source/usage/ALF_data/Kondo_L1=4_L2=4_JK=0.5" for Monte Carlo run.
Create new directory.
Run /home/jschwab/Programs/ALF/Prog/ALF.out
ALF Copyright (C) 2016 - 2021 The ALF project contributors
This Program comes with ABSOLUTELY NO WARRANTY; for details see license.GPL
This is free software, and you are welcome to redistribute it under certain conditions.
No initial configuration
Compiling ALF...
...
...
...
2.4.2. alf_postprocess.py#
The script alf_postprocess.py enables most of the features discussed in Section 2.3, except for plotting capabilities, to be used directly from the shell. The help text lists all possible arguments:
!alf_postprocess.py -h
usage: alf_postprocess.py [-h] [--check_warmup] [--check_rebin]
[-l CHECK_LIST [CHECK_LIST ...]] [--do_analysis]
[--always] [--gather] [--no_tau]
[--custom_obs CUSTOM_OBS] [--symmetry SYMMETRY]
[directories ...]
Script for postprocessing Monte Carlo bins.
positional arguments:
directories Directories to analyze. If empty, analyzes all
directories containing file "data.h5" it can find.
optional arguments:
-h, --help show this help message and exit
--check_warmup, --warmup
Check warmup.
--check_rebin, --rebin
Check rebinning for controlling autocorrelation.
-l CHECK_LIST [CHECK_LIST ...], --check_list CHECK_LIST [CHECK_LIST ...]
List of observables to check for warmup and rebinning.
--do_analysis, --ana Do analysis.
--always Do not skip analysis if parameters and bins are older
than results.
--gather Gather all analysis results in one file named
"gathered.pkl", representing a pickled pandas
DataFrame.
--no_tau Skip time displaced correlations.
--custom_obs CUSTOM_OBS
File that defines custom observables. This file has to
define the object custom_obs, needed by
py_alf.analysis. (default: os.getenv("ALF_CUSTOM_OBS",
None))
--symmetry SYMMETRY, --sym SYMMETRY
File that defines lattice symmetries. This file has to
define the object symmetry, needed by py_alf.analysis.
(default: None))
To use the symmetrization feature, one needs a file defining the object symmetry, similar to the already used file custom_obs.py defining custom_obs.
!cat sym_c4.py
"""Define C_4 symmetry (=fourfold rotation) for pyALF analysis."""
from math import pi
# Define list of transformations (Lattice, i) -> new_i
# Default analysis will average over all listed elements
def sym_c4_0(latt, i): return i
def sym_c4_1(latt, i): return latt.rotate(i, pi*0.5)
def sym_c4_2(latt, i): return latt.rotate(i, pi)
def sym_c4_3(latt, i): return latt.rotate(i, pi*1.5)
symmetry = [sym_c4_0, sym_c4_1, sym_c4_2, sym_c4_3]
To analyze the results from the Kondo model and gather them all in one file gathered.pkl, we execute the following command. The printout has again been truncated.
!alf_postprocess.py --custom_obs custom_obs.py --symmetry sym_c4.py --ana --gather ALF_data/Kondo*
### Analyzing ALF_data/Kondo_L1=4_L2=4_JK=0.5 ###
/scratch/pyalf-docu/doc/source/usage
Custom observables:
custom E_squared ['Ener_scal']
custom E_pot_kin ['Pot_scal', 'Kin_scal']
custom SpinZ_pipi ['SpinZ_eq']
Scalar observables:
Constraint_scal
Ener_scal
Kin_scal
Part_scal
Pot_scal
Histogram observables:
Equal time observables:
Den_eq
Dimer_eq
Green_eq
SpinZ_eq
Time displaced observables:
Den_tau
Dimer_tau
Green_tau
Greenf_tau
SpinZ_tau
### Analyzing ALF_data/Kondo_L1=4_L2=4_JK=1.0 ###
/scratch/pyalf-docu/doc/source/usage
...
...
...
ALF_data/Kondo_L1=4_L2=4_JK=0.5
ALF_data/Kondo_L1=4_L2=4_JK=1.0
ALF_data/Kondo_L1=4_L2=4_JK=1.5
ALF_data/Kondo_L1=4_L2=4_JK=2.0
The data from gathered.pkl can, for example, be read and plotted like this:
# Import modules
import matplotlib.pyplot as plt
import pandas as pd
# Load pickled DataFrame
res = pd.read_pickle('gathered.pkl')
# Create figure with axis labels
fig, ax = plt.subplots()
ax.set_xlabel(r'Kondo interaction $J_K$')
ax.set_ylabel(r'Energy')
# Plot data
ax.errorbar(res.ham_jk, res.Ener_scal0, res.Ener_scal0_err);
