Python Tools

As a part of the NESSi package, we provide python tools, which include:

• Scripts for pre-processing to assist the use of programs based on libcntr

• Scripts for reading and post-processing Green’s functions from HDF5 format via the h5py python package.

The python tools can be found in libcntr/python (for python2) or libcntr/python3 (for python3 compatibility).

Creation of an input file

An input file for a custom program (see details in Creating and Passing Input Files) can be generated using ReadCNTR.py python module:

Example:

from ReadCNTR import write_input_file

inp = {
  'nt': 1000,                 # number of points on the real axis
  'ntau': 500,                # number of points on the Matsubara (imaginary) axis
  'h': 0.01,                  # timestep interval
  'beta': 10.0,               # inverse temperature
  'Efield': '--field.txt'     # electrical field read from a file
}

write_input_file(inp, 'input.txt')      # creates an input file

Note

To make the python module available to a python script, the module ReadCNTR.py needs to be in the python include path. For python2, this is achieved by setting export PYTHONPATH=<path>/nessi/libcntr/python, while export PYTHONPATH=<path>/nessi/libcntr/python3 makes the python3 version accessible.

Reading Green’s functions from hdf5

The python module unrolls the triangular storage of the ret and les components making it simple to work with time slices.

Example 1:

To store the imaginary part of the retarded Green’s function \(\textrm{Im}[G_{i,j}^\mathrm{R}(t, t^\prime=5 h)]\) at orbital indices \(i=j=0\) as a function of \(t\) into a text file we may use the commands:

import h5py
from ReadCNTRhdf5 import read_group

# read the components of the contour function from hdf5 file
with h5py.File('data.h5', 'r') as fd:
    g = read_group(fd).g

# choose a timeslice for the retarded Green's function
indxt=5
G_ret=g.ret[:,indxt,0,0].imag
with open("data_output.txt",'w') as output:     # create a text file
    output.write("%s \t" % G_ret)               # write data into it

Example 2:

The same way one can save the lesser component of the Green’s function \(\textrm{Im}[G^<(t=5 h, t^\prime)]\) as a function of \(t^\prime\) into a text file:

import h5py
from ReadCNTRhdf5 import read_group

# read the components of the contour function from hdf5 file
with h5py.File('data.h5', 'r') as fd:
    g = read_group(fd).g

# choose a timeslice for the lesser Green's function
indxt=5
G_les=g.les[indxt,:,0,0].imag
with open("data_output.txt",'w') as output:     # create a text file
    output.write("%s \t" % G_les)               # write data into it

Postprocessing analysis

The ReadCNTRhdf5 python module contains also functions for reading parameters and observables from an output file. The following minimal example script is used to interpret the output files in the examples provided in section Example Programs.

Example:

import h5py
from ReadCNTRhdf5 import read_imp_h5file
# Plot

ax0=plt.subplot(1,1,1)
imp_filename = '{}/data.h5'.format(output_file)
data = read_imp_h5file(imp_filename,['obs','parm'])

Nt=data.parm.Nt                                                       # read out the number of timesteps on the real axis from the output file
dt=data.parm.h                                                        # read out the timestep interval from the output file
tpts = np.linspace(0.0,Nt*dt,Nt+1)                                    # define the real time axis
ekin=np.real(data.obs.Ekin.data[1:,0,0]-data.obs.Ekin.data[0,0,0])    # read out the value of an observable (Ekin) from the output file and calculate the difference between the equilibrium and nonequilibrium values

plt.plot(tpts,ekin,label="Ekin")                                      # plot data

ax0.set_xlabel(r't ')                                                 # set x label
ax0.set_ylabel(r"$\mathrm{E}_\mathrm{kin}$")                          # set y label

ax0.legend(loc="best",bbox_to_anchor=(0.45,0.3),frameon=False,fancybox=False,handlelength=1)
plt.savefig('Ekin.pdf')
plt.show()