### 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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