mirror of https://github.com/triqs/dft_tools
analysis.rst done. Minor change in transport.rst
I also described how one can read a self energy form a data
file. However, this needs to be tested and also included
in the reference manual. Maybe the function should move
back into sumk_dft_tools!?
This commit is contained in:
Manuel Zingl
parent
6a50b57c59
commit
00a775a93d
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@ -7,13 +7,13 @@ This section explains how to use some tools of the package in order to analyse t
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There are two practical tools for which a self energy on the real axis is not needed, namely:
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* :meth:`dos_wannier_basis` for the density of states of the Wannier orbitals and
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* :meth:`partial_charges` for the partial charges according to the Wien2k definition.
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* :meth:`dos_wannier_basis <pytriqs.applications.dft.sumk_dft_tools.dos_wannier_basis>` for the density of states of the Wannier orbitals and
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* :meth:`partial_charges <pytriqs.applications.dft.sumk_dft_tools.partial_charges>` for the partial charges according to the Wien2k definition.
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However, a real frequency self energy has to be provided by the user to use the methods:
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* :meth:`dos_parproj_basis` for the momentum-integrated spectral function including self energy effects and
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* :meth:`spaghettis` for the momentum-resolved spectral function (i.e. ARPES)
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* :meth:`dos_parproj_basis <pytriqs.applications.dft.sumk_dft_tools.dos_parproj_basis>` for the momentum-integrated spectral function including self energy effects and
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* :meth:`spaghettis <pytriqs.applications.dft.sumk_dft_tools.spaghettis>` for the momentum-resolved spectral function (i.e. ARPES)
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.. warning::
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This package does NOT provide an explicit method to do an **analytic continuation** of the
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@ -21,10 +21,6 @@ However, a real frequency self energy has to be provided by the user to use the
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There are methods included e.g. in the :program:`ALPS` package, which can be used for these purposes.
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Keep in mind that all these methods have to be used very carefully!
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.. note::
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Add the doc for loading the self energy from a data file. We have to provide this option, because
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in general the user won't has it stored in h5 file!!
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Initialisation
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--------------
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@ -40,18 +36,40 @@ class::
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Note that all routines available in :class:`SumkDFT` are also available here.
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If required, a self energy is load and initialise in the next step. Most conveniently,
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your self energy is already stored as a real frequency :class:`BlockGf` object
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If required, we have to load and initialise the real frequency self energy. Most conveniently,
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you have your self energy already stored as a real frequency :class:`BlockGf` object
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in a hdf5 file::
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ar = HDFArchive('case.h5', 'a')
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SigmaReFreq = ar['dmft_output']['Sigma_w']
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SK.put_Sigma(Sigma_imp = [SigmaReFreq])
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Additionally, the chemical potential and the double counting
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correction from the DMFT calculation are set, and the archive is closed again::
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You may also have your self energy stored in text files. For this case we provide the function
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:meth:`constr_Sigma_real_axis`, which loads the data and puts it into a real frequency :class:`BlockGf` object::
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from pytriqs.applications.dft.build_sigma_from_txt import *
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SigmaReFreq = constr_Sigma_real_axis(SK, filename, hdf=False, hdf_dataset='SigmaReFreq',n_om=0, orb=0)
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where:
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* `filename`: the name of the hdf5 archive file or the `fname` pattern in text files names as described above,
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* `hdf`: if `True`, the real axis self energy will be read from the hdf5 file, otherwise from the text files,
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* `hdf_dataset`: the name of dataset where the self energy is stored in the hdf5 file,
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* `orb`: index of an inequivalent shell,
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* `n_om`: the number of points in the real-axis mesh (used only if `hdf=False`).
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It is important that some rules concerning the structure of the data is followed:
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* Each data file should contain the three columns: real frequency, real part and imaginary part of the self-energy in exactly this order.
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* If all blocks of your self energy are of dimension 1x1, you store them in `filename_(block)0.dat` files. Here `(block)` is a block name (`up`, `down`, or combined `ud`).
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* In the case when you have matrix blocks, you store them in `(i)_(j).dat` files, where `(i)` and `(j)` are the zero based orbital indices, in the `filename_(block)` directory.
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chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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Finally, we put the self energy into the `SK` object::
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SK.put_Sigma(Sigma_imp = [SigmaReFreq])
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and additionally set the chemical potential and the double counting correction from the DMFT calculation::
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chemical_potential, dc_imp, dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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SK.set_mu(chemical_potential)
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SK.set_dc(dc_imp,dc_energ)
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del ar
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@ -65,8 +83,18 @@ For plotting the density of states of the Wannier orbitals, you type::
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SK.dos_wannier_basis(broadening=0.03, mesh=[om_min, om_max, n_om], with_Sigma=False, with_dc=False, save_to_file=True)
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which produces plots between the real frequencies `om_min` and `om_max`, using a mesh of `n_om` points. The parameter
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`broadening` defines an additional Lorentzian broadening, and has the default value of `0.01` eV. To check the Wannier
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density of states after the projection set `with_Sigma` and `with_dc` to `False`.
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`broadening` defines an additional Lorentzian broadening, and has the default value of `0.01 eV`. To check the Wannier
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density of states after the projection set `with_Sigma` and `with_dc` to `False`. If `save_to_file` is set to `True`
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the output is printed into the files
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* `DOS_wannier_(sp).dat`: The total DOS, where `(sp)` stands for `up`, `down`, or combined `ud`. The latter case
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is relevant for calculations including spin-orbit interaction.
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* `DOS_wannier_(sp)_proj(i).dat`: The DOS projected to an orbital with index `(i)`. The index `(i)` refers to
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the indices given in ``SK.shells``.
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* `DOS_wannier_(sp)_proj(i)_(m)_(n).dat`: As above, but printed as orbitally-resolved matrix in indices
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`(m)` and `(n)`. For `d` orbitals, it gives the DOS seperately for, e.g., :math:`d_{xy}`, :math:`d_{x^2-y^2}`, and so on,
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otherwise, the ouptut is returend by the function for further use in python.
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Partial charges
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---------------
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@ -75,7 +103,7 @@ Since we can calculate the partial charges directly from the Matsubara Green's f
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real frequency self energy for this purpose. The calculation is done by::
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SK.put_Sigma(Sigma_imp = SigmaImFreq)
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dm = SK.partial_charges(beta=40.0 with_Sigma=True, with_dc=True)
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dm = SK.partial_charges(beta=40.0, with_Sigma=True, with_dc=True)
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which calculates the partial charges using the self energy, double counting, and chemical potential as set in the
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`SK` object. On return, `dm` is a list, where the list items correspond to the density matrices of all shells
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@ -85,29 +113,24 @@ in the hdf5 archive. For the detailed structure of `dm`, see the reference manua
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Correlated spectral function (with real frequency self energy)
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--------------------------------------------------------------
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With this self energy, we can now execute::
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To produce both the momentum-integrated (total density of states or DOS) and orbitally-resolved (partial/projected DOS) spectral functions
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we can execute::
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SK.dos_parproj_basis(broadening=0.0, with_Sigma=True, with_dc=True, save_to_file=True)
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SK.dos_parproj_basis(broadening=broadening)
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This produces both the momentum-integrated (total density of states or DOS) and orbitally-resolved (partial/projected DOS) spectral functions.
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The variable `broadening` is an additional Lorentzian broadening applied to the resulting spectra.
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The output is printed into the files
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* `DOScorr(sp).dat`: The total DOS, where `(sp)` stands for `up`, `down`, or combined `ud`. The latter case
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is relevant for calculations including spin-orbit interaction.
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* `DOScorr(sp)_proj(i).dat`: The DOS projected to an orbital with index `(i)`. The index `(i)` refers to
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the indices given in ``SK.shells``.
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* `DOScorr(sp)_proj(i)_(m)_(n).dat`: As above, but printed as orbitally-resolved matrix in indices
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`(m)` and `(n)`. For `d` orbitals, it gives the DOS seperately for, e.g., :math:`d_{xy}`, :math:`d_{x^2-y^2}`, and so on.
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The variable `broadening` is an additional Lorentzian broadening (default: `0.01 eV`) applied to the resulting spectra.
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The output is written in the same way as described above for Wannier density of states, but with file names
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`DOS_parproj_*` instead.
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Momentum resolved spectral function (with real frequency self energy)
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---------------------------------------------------------------------
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Another quantity of interest is the momentum-resolved spectral function, which can directly be compared to ARPES
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experiments. We assume here that we already converted the output of the :program:`dmftproj` program with the
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converter routines (see :ref:`conversion`). The spectral function is calculated by::
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experiments. First we have to execute `lapw1`, `lapw2 -almd` and :program:`dmftproj` with the `-band`
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option and use the :meth:`convert_bands_input()` routine to convert the required files. For a detailed description
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see :ref:`conversion`. The spectral function is then calculated by::
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SK.spaghettis(broadening)
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SK.spaghettis(broadening=0.01,plot_shift=0.0,plot_range=None,ishell=None,save_to_file='Akw_')
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Optional parameters are
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@ -81,6 +81,8 @@ First we have to read the Wien2k files and store the relevant information in the
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SK = SumkDFTTools(hdf_file='case.h5', use_dft_blocks=True)
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The converter :meth:`convert_transport_input <pytriqs.applications.dft.converters.wien2k_converter.Wien2kConverter.convert_transport_input>`
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reads the required data of the Wien2k output and stores it in the `dft_transp_input` subgroup of your hdf file.
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Additionally we need to read and set the self energy, the chemical potential and the double counting::
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ar = HDFArchive('case.h5', 'a')
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@ -1,99 +1,99 @@
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def read_fortran_file (filename):
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""" Returns a generator that yields all numbers in the Fortran file as float, one by one"""
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import os.path
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if not(os.path.exists(filename)) : raise IOError, "File %s does not exist."%filename
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for line in open(filename,'r') :
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for x in line.replace('D','E').split() :
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yield string.atof(x)
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def read_fortran_file (filename):
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""" Returns a generator that yields all numbers in the Fortran file as float, one by one"""
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import os.path
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if not(os.path.exists(filename)) : raise IOError, "File %s does not exist."%filename
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for line in open(filename,'r') :
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for x in line.replace('D','E').split() :
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yield string.atof(x)
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def constr_Sigma_real_axis(self, filename, hdf=True, hdf_dataset='SigmaReFreq',n_om=0,orb=0, tol_mesh=1e-6):
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"""Uses Data from files to construct Sigma (or GF) on the real axis."""
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def constr_Sigma_real_axis(self, filename, hdf=True, hdf_dataset='SigmaReFreq',n_om=0,orb=0, tol_mesh=1e-6):
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"""Uses Data from files to construct Sigma (or GF) on the real axis."""
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if not hdf: # then read sigma from text files
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if not hdf: # then read sigma from text files
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# first get the mesh out of any one of the files:
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bl = self.gf_struct_solver[orb].items()[0][0] # block name
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ol = self.gf_struct_solver[orb].items()[0][1] # list of orbital indices
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if (len(ol)==1): # if blocks are of size one
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Fname = filename+'_'+bl+'.dat'
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else:
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Fname = filename+'_'+bl+'/'+str(ol[0])+'_'+str(ol[0])+'.dat'
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# first get the mesh out of any one of the files:
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bl = self.gf_struct_solver[orb].items()[0][0] # block name
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ol = self.gf_struct_solver[orb].items()[0][1] # list of orbital indices
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if (len(ol)==1): # if blocks are of size one
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Fname = filename+'_'+bl+'.dat'
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else:
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Fname = filename+'_'+bl+'/'+str(ol[0])+'_'+str(ol[0])+'.dat'
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R = read_fortran_file(Fname)
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mesh = numpy.zeros([n_om],numpy.float_)
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try:
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for i in xrange(n_om):
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mesh[i] = R.next()
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sk = R.next()
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sk = R.next()
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except StopIteration : # a more explicit error if the file is corrupted.
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raise "SumkDFT.read_Sigma_ME : reading mesh failed!"
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R.close()
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# check whether the mesh is uniform
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bin = (mesh[n_om-1]-mesh[0])/(n_om-1)
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R = read_fortran_file(Fname)
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mesh = numpy.zeros([n_om],numpy.float_)
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try:
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for i in xrange(n_om):
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assert abs(i*bin+mesh[0]-mesh[i]) < tol_mesh, 'constr_Sigma_ME: real-axis mesh is non-uniform!'
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mesh[i] = R.next()
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sk = R.next()
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sk = R.next()
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# construct Sigma
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except StopIteration : # a more explicit error if the file is corrupted.
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raise "SumkDFT.read_Sigma_ME : reading mesh failed!"
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R.close()
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# check whether the mesh is uniform
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bin = (mesh[n_om-1]-mesh[0])/(n_om-1)
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for i in xrange(n_om):
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assert abs(i*bin+mesh[0]-mesh[i]) < tol_mesh, 'constr_Sigma_ME: real-axis mesh is non-uniform!'
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# construct Sigma
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a_list = [a for a,al in self.gf_struct_solver[orb].iteritems()]
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glist = lambda : [ GfReFreq(indices = al, window=(mesh[0],mesh[n_om-1]),n_points=n_om) for a,al in self.gf_struct_solver[orb].iteritems()]
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SigmaME = BlockGf(name_list = a_list, block_list = glist(),make_copies=False)
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#read Sigma
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for i,g in SigmaME:
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mesh=[w for w in g.mesh]
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for iL in g.indices:
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for iR in g.indices:
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if (len(g.indices) == 1):
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Fname = filename+'_%s'%(i)+'.dat'
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else:
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Fname = 'SigmaME_'+'%s'%(i)+'_%s'%(iL)+'_%s'%(iR)+'.dat'
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R = read_fortran_file(Fname)
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try:
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for iom in xrange(n_om):
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sk = R.next()
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rsig = R.next()
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isig = R.next()
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g.data[iom,iL,iR]=rsig+1j*isig
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except StopIteration : # a more explicit error if the file is corrupted.
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raise "SumkDFT.read_Sigma_ME : reading Sigma from file failed!"
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R.close()
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else: # read sigma from hdf
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omega_min=0.0
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omega_max=0.0
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n_om=0
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if (mpi.is_master_node()):
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ar = HDFArchive(filename)
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SigmaME = ar[hdf_dataset]
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del ar
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#OTHER SOLUTION FIXME
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#else:
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# SigmaME=0
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#SigmaME = mpi.broadcast..
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# we need some parameters to construct Sigma on other nodes
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omega_min=SigmaME.mesh.omega_min
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omega_max=SigmaME.mesh.omega_max
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n_om=len(SigmaME.mesh)
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omega_min=mpi.bcast(omega_min)
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omega_max=mpi.bcast(omega_max)
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n_om=mpi.bcast(n_om)
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mpi.barrier()
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# construct Sigma on other nodes
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if (not mpi.is_master_node()):
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a_list = [a for a,al in self.gf_struct_solver[orb].iteritems()]
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glist = lambda : [ GfReFreq(indices = al, window=(mesh[0],mesh[n_om-1]),n_points=n_om) for a,al in self.gf_struct_solver[orb].iteritems()]
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glist = lambda : [ GfReFreq(indices = al, window=(omega_min,omega_max),n_points=n_om) for a,al in self.gf_struct_solver[orb].iteritems()]
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SigmaME = BlockGf(name_list = a_list, block_list = glist(),make_copies=False)
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# pass SigmaME to other nodes
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SigmaME = mpi.bcast(SigmaME)
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mpi.barrier()
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#read Sigma
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for i,g in SigmaME:
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mesh=[w for w in g.mesh]
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for iL in g.indices:
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for iR in g.indices:
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if (len(g.indices) == 1):
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Fname = filename+'_%s'%(i)+'.dat'
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else:
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Fname = 'SigmaME_'+'%s'%(i)+'_%s'%(iL)+'_%s'%(iR)+'.dat'
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R = read_fortran_file(Fname)
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try:
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for iom in xrange(n_om):
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sk = R.next()
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rsig = R.next()
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isig = R.next()
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g.data[iom,iL,iR]=rsig+1j*isig
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except StopIteration : # a more explicit error if the file is corrupted.
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raise "SumkDFT.read_Sigma_ME : reading Sigma from file failed!"
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R.close()
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SigmaME.note='ReFreq'
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else: # read sigma from hdf
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omega_min=0.0
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omega_max=0.0
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n_om=0
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if (mpi.is_master_node()):
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ar = HDFArchive(filename)
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SigmaME = ar[hdf_dataset]
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del ar
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#OTHER SOLUTION FIXME
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#else:
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# SigmaME=0
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#SigmaME = mpi.broadcast..
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# we need some parameters to construct Sigma on other nodes
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omega_min=SigmaME.mesh.omega_min
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omega_max=SigmaME.mesh.omega_max
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n_om=len(SigmaME.mesh)
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omega_min=mpi.bcast(omega_min)
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omega_max=mpi.bcast(omega_max)
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n_om=mpi.bcast(n_om)
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mpi.barrier()
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# construct Sigma on other nodes
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if (not mpi.is_master_node()):
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a_list = [a for a,al in self.gf_struct_solver[orb].iteritems()]
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glist = lambda : [ GfReFreq(indices = al, window=(omega_min,omega_max),n_points=n_om) for a,al in self.gf_struct_solver[orb].iteritems()]
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SigmaME = BlockGf(name_list = a_list, block_list = glist(),make_copies=False)
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# pass SigmaME to other nodes
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SigmaME = mpi.bcast(SigmaME)
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mpi.barrier()
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SigmaME.note='ReFreq'
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return SigmaME
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return SigmaME
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