DWA-02 Density Profile
Density profile
As a second example of the dwa QMC code, we will study the density profile of an optical lattice in an harmonic trap.
Column integrated density
In this subsection, we want to mimick the experimental setup.
Preparing and running the simulation from the command line
The parameter file parm2a
sets up Monte Carlo simulation of a $120^3$ optical lattice trap that mimicks the experiment:
LATTICE="inhomogeneous simple cubic lattice"
L=120
MODEL='boson Hubbard"
Nmax=20
t=1.
U=8.11
mu="4.05 - (0.0073752*(x-(L-1)/2.)*(x-(L-1)/2.) + 0.0036849*(y-(L-1)/2.)*(y-(L-1)/2.) + 0.0039068155*(z-(L-1)/2.)*(z-(L-1)/2.))"
THERMALIZATION=1500
SWEEPS=7000
SKIP=50
MEASURE[Local Density]=1
{ T=1. }
Using the standard sequence of commands you can run the simulation using the quantum dwa code
parameter2xml parm2a
dwa parm2a.in.xml
(This simulation roughly takes 3 hours.)
Preparing and running the simulation from Python
To set up and run the simulation in Python we use the script tutorial2a.py
. The first parts of this script imports the required modules and then prepares the input files as a list of Python dictionaries:
import pyalps
parms = [
{
'LATTICE' : 'inhomogeneous simple cubic lattice' ,
'L' : 120 ,
'MODEL' : 'boson Hubbard' ,
'Nmax' : 20 ,
't' : 1. ,
'U' : 8.11 ,
'mu' : '4.05 - (0.0073752*(x-(L-1)/2.)*(x-(L-1)/2.) + 0.0036849*(y-(L-1)/2.)*(y-(L-1)/2.) + 0.0039068155*(z-(L-1)/2.)*(z-(L-1)/2.))' ,
'T' : 1. ,
'THERMALIZATION' : 1500 ,
'SWEEPS' : 7000 ,
'SKIP' : 50 ,
'MEASURE[Local Density]': 1
}
]
We next convert this into a job file in XML format and run the worm simulation:
input_file = pyalps.writeInputFiles('parm2a', parms)
res = pyalps.runApplication('dwa', input_file)
We now have the same output files as in the command line version. (This simulation roughly takes roughly 3 hours.)
Evaluating the simulation and preparing plots using Python
To load the results and prepare the plot for density profile we load the results from the output files from all output files starting with parm2a
. The script is again in tutorial2a.py
import pyalps
data = pyalps.loadMeasurements(pyalps.getResultFiles(prefix='parm2a'), 'Local Density');
To visualize the column integrated density:
import pyalps.plot as aplt;
aplt.plot3D(data, centeredAtOrigin=True)
Cross section density
We want to observe a Mott plateau.
Preparing and running the simulation from the command line
The parameter file parm2a
sets up Monte Carlo simulation of a $80^3$ optical lattice trap that mimicks the Bloch experiment:
LATTICE="inhomogeneous simple cubic lattice"
L=60
MODEL="boson Hubbard"
Nmax=20
t=1.
U=60.
mu="40. - (0.09416*(x-(L-1)/2.)*(x-(L-1)/2.) + 0.12955*(y-(L-1)/2.)*(y-(L-1)/2.) + 0.11496*(z-(L-1)/2.)*(z-(L-1)/2.))"
THERMALIZATION=1000000
SWEEPS=3000000
SKIP=1000
MEASURE[Local Density]=1
{ T=1. }
Using the standard sequence of commands you can run the simulation using the quantum dwa code
parameter2xml parm2a
dwa parm2a.in.xml
Preparing and running the simulation from Python
To set up and run the simulation in Python we use the script tutorial2b.py
. The first parts of this script imports the required modules and then prepares the input files as a list of Python dictionaries:
import pyalps
parms = [
{
'LATTICE' : 'inhomogeneous simple cubic lattice' ,
'L' : 60 ,
'MODEL' : 'boson Hubbard' ,
'Nmax' : 20 ,
't' : 1. ,
'U' : 60. ,
'mu' : '40. - (0.09416*(x-(L-1)/2.)*(x-(L-1)/2.) + 0.12955*(y-(L-1)/2.)*(y-(L-1)/2.) + 0.11496*(z-(L-1)/2.)*(z-(L-1)/2.))' ,
'T' : 1. ,
'THERMALIZATION' : 1000000 ,
'SWEEPS' : 3000000 ,
'SKIP' : 1000 ,
'MEASURE[Local Density]': 1
}
]
We next convert this into a job file in XML format and run the worm simulation:
input_file = pyalps.writeInputFiles('parm2b', parms)
res = pyalps.runApplication('dwa', input_file)
We now have the same output files as in the command line version.
Evaluating the simulation and preparing plots using Python
To load the results and prepare the plot for density profile we load the results from the output files from all output files starting with parm2b
. The script is again in tutorial2b.py
import pyalps
data = pyalps.loadMeasurements(pyalps.getResultFiles(prefix='parm2b'), 'Local Density');
To visualize the cross-section density at the center:
import pyalps.plot as aplt;
aplt.plot3D(data, centeredAtOrigin=True, layer="center")
Contributors
- Matthias Troyer
- Ping Nang Ma