# Equilibration

Rule of thumb: All Monte Carlo simulations have to be equilibrated before taking measurements.

## Example: Quantum Monte Carlo (directed worm algorithm) simulations

As an example, we consider a Quantum Monte Carlo simulation implemented in the directed worm algorithm for boson Hubbard model in square lattice geometry of size 202.

### Using command line

The parameter file parm1a:

```LATTICE="square lattice"
MODEL="boson Hubbard"

L=20
Nmax=20

t=1.
U=16.
mu=32.

THERMALIZATION=10000
SWEEPS=100000
SKIP=400

{T=1.0}
```

We first convert the input parameters to XML and then run the application dwa:

```parameter2xml parm1a
dwa parm1a.in.xml
```

Detailed information regarding the Density measurements, for example, can be extracted from:

```h5dump -g /simulation/results/Density parm1a.task1.out.h5
```

or its (binned) timeseries from:

```h5dump -g /simulation/results/Density/timeseries parm1a.task1.out.h5
```

### Using Python

The following describes what is going on within the script file tutorial1a.py.

```import pyalps
```

Set up a python list of parameters (python) dictionaries:

```parms = [{
'LATTICE'         : "square lattice",
'MODEL'           : "boson Hubbard",
'L'               : 20,
'Nmax'            : 20,
't'               : 1.,
'U'               : 16.,
'mu'              : 32.,
'T'               : 1.,
'THERMALIZATION'  : 10000,
'SWEEPS'          : 100000,
'SKIP'            : 400
}]
```

Write into XML input file:

```input_file = pyalps.writeInputFiles('parm1a',parms)
```

and run the application dwa:

```pyalps.runApplication('dwa', input_file, Tmin=10, writexml=True)
```