Commit 337f0405 authored by Edo's avatar Edo

Updated doc index page, fixed DC cond analysis prefactor.

parent ab7a9e64
Welcome to TiPSi's documentation!
=================================
TiPSi is a package for Python 3 to make large-scale tight-binding Hamiltonians and
run Tight Binding Propagation Method (TBPM) calculations. TiPSi is optimized for
usage on cluster nodes. It uses FORTRAN code to do the number crunching, and
f2py to interface with Python.
In general, a simulation consists of the following steps:
- Make a tight-binding Hamiltonian
- Calculate correlation functions
- Analyze correlation functions
.. toctree::
:maxdepth: 2
:caption: Contents:
......
......@@ -403,11 +403,11 @@ def analyze_corr_DC(config, corr_DOS, corr_DC, \
n_energies = len(QE_indices)
energies = energies_DOS[QE_indices]
dc_prefactor = config.sample['nr_orbitals'] \
/ config.sample['area_unit_cell']
/ config.sample['area_unit_cell'] \
/ (2 * np.pi)
# get DC conductivity
DC = np.zeros((2, n_energies))
DC_int = np.zeros((2, n_energies, tnr))
for i in range(2):
for j in range(n_energies):
......@@ -415,13 +415,12 @@ def analyze_corr_DC(config, corr_DOS, corr_DC, \
dosval = DOS[QE_indices[j]]
dcval = 0.
for k in range(tnr):
W = window_DC(k, tnr)
W = window_DC(k + 1, tnr)
cexp = np.exp(-1j * k * t_step * en)
add_dcv = W * (cexp * corr_DC[i, j, k]).real
dcval += add_dcv
DC_int[i, j, k] = dc_prefactor * t_step * dosval * dcval
DC[i, j] = np.amax(DC_int[i, j, :])
DC[i, j] = dc_prefactor * t_step * dosval * dcval
# correct for spin
if config.generic['correct_spin']:
DC = 2. * DC
......
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