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mirror of https://github.com/triqs/dft_tools synced 2024-11-18 12:03:50 +01:00

Merge pull request #238 from AlynJ/unstable

Updated spectral function routines

original PR message from @AlynJ 

I've updated the spectral function routines in sumk_dft_tools.py. Now there are unique routines which calculate the density of states, spaghettis and spectral function (energy) contours (only for Elk inputs for now). I've tried to condense the code down so that it's (hopefully) clearer and uses more internal routines (for example spaghettis and spectral_contours [which replaces the fs_plot routine] uses the same routine to calculate the k-resolved spectral function). I've updated the documentation which should describe these routines in depth along with the input parameters, output variables and example plots showing what the routines can do.

These routines should have the same functionality as before along with some additional features, such as Wannier projected spaghettis and spectral_contours. Also, they have functionality specific to the dft code inputs. I've commented out the Elk specific DFT+DMFT PDOS for now as I've lost confidence with what I implemented before and I need to revisit this.

Alongside this, I've edited the Elk converter documentation and updated the interface routines which read in the data for the spectral function (energy) contours. I've also included more tests which test the Elk inputs and these new spectral routines.

These new routines pass the pre-existing spectral function tests (I note that I've not altered these .h5 files). I've also tested these new routines with the Elk inputs, but I think they need to be verified with the other dft code inputs.
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@ -30,9 +30,9 @@ jobs:
sudo apt-get install lsb-release wget software-properties-common &&
wget -O /tmp/llvm.sh https://apt.llvm.org/llvm.sh && sudo chmod +x /tmp/llvm.sh && sudo /tmp/llvm.sh 13 &&
sudo apt-get install
clang-13
g++-10
gfortran-10
clang-15
g++-12
gfortran-12
hdf5-tools
libblas-dev
libboost-dev
@ -82,7 +82,7 @@ jobs:
env:
CC: ${{ matrix.cc }}
CXX: ${{ matrix.cxx }}
FC: gfortran-10
FC: gfortran-12
LIBRARY_PATH: /usr/local/opt/llvm/lib
run: |
source $HOME/install/share/triqs/triqsvars.sh

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@ -4,6 +4,12 @@
## unstable
* updated github work flow to match triqs unstable requisites
* sumk_dft_tools.py rewritten to have single routines to calculate DOS, spaghettis and (Elk specific for now) spectral contours
* occupied DOS can be calculated (sumk_dft_tools.occupations() is needed to be calculated first)
* analysis.rst and conv_elk.rst updated to improve routine descriptions and includes example figures
* updated Elk tests and rewritten test scripts (.h5 files remain unchanged)
* New converter routines to read in Elk data for sumk_dft_tools.spectral_contours() (Elk k-mesh generator and checker needs to be optimized as it's currently slow). commented out Elk "bandcharacter" conversion from Elk converter and Elk DFT+DMFT PDOS code which used it (this method needs to be checked)
* SumK requires now to pass a mesh on init to clarify the mesh on which it operates
* rename / unify name of `sumk.Sigma_imp_iw` and `sumk.Sigma_imp_w` -> `sumk.Sigma_imp`
* remove `iw_or_w` arguments

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@ -3,17 +3,13 @@
Tools for analysis
==================
This section explains how to use some tools of the package in order to analyse the data.
This section explains how to use some tools of the package in order to analyse the data. There are certain tools here which are not available for some DFT code interfaces. Please refer to the DFT package interface converter documentation (see :ref:`conversion`) on how to interface the required DFT outputs into the HDF5 files needed for the tools discussed here. This section will assume that the user has converted the required DFT data.
There are two practical tools for which a self energy on the real axis is not needed, namely:
The following routines require a self energy on the real frequency axis if the user specifies the inputs `with_Sigma` and `with_dc`:
* :meth:`dos_wannier_basis <dft.sumk_dft_tools.SumkDFTTools.dos_wannier_basis>` for the density of states of the Wannier orbitals and
* :meth:`partial_charges <dft.sumk_dft_tools.SumkDFTTools.partial_charges>` for the partial charges according to the Wien2k definition.
However, a real-frequency self energy has to be provided by the user for the methods:
* :meth:`dos_parproj_basis <dft.sumk_dft_tools.SumkDFTTools.dos_parproj_basis>` for the momentum-integrated spectral function including self energy effects and
* :meth:`density_of_states <dft.sumk_dft_tools.SumkDFTTools.density_of_states>` for the momentum-integrated spectral function including self energy effects and
* :meth:`spaghettis <dft.sumk_dft_tools.SumkDFTTools.spaghettis>` for the momentum-resolved spectral function (i.e. ARPES)
* :meth:`spectral_contours <dft.sumk_dft_tools.SumkDFTTools.spectral_contours>` for the k-resolved spectral function on a specific k-mesh (i.e., spectral function on a two dimensional k-mesh)
.. note::
This package does NOT provide an explicit method to do an **analytic continuation** of
@ -21,6 +17,11 @@ However, a real-frequency self energy has to be provided by the user for the met
but a list of options available within the TRIQS framework is given :ref:`here <ac>`.
Keep in mind that all these methods have to be used very carefully!
Otherwise, without these options, the spectral functions from the inputs of the interfaced DFT code will be used.
The other routines presented here use the Matsubara self-energy.
Initialisation
--------------
@ -74,32 +75,81 @@ and additionally set the chemical potential and the double counting correction f
SK.set_mu(chemical_potential)
SK.set_dc(dc_imp,dc_energ)
.. _dos_wannier:
Density of states of the Wannier orbitals
-----------------------------------------
Density of states
-----------------
For plotting the density of states of the Wannier orbitals, you type::
For plotting the density of states, you type::
SK.dos_wannier_basis(broadening=0.03, mesh=[om_min, om_max, n_om], with_Sigma=False, with_dc=False, save_to_file=True)
SK.density_of_states(mu, broadening, mesh, with_Sigma, with_dc, proj_type, dosocc, save_to_file)
which produces plots between the real frequencies `om_min` and `om_max`, using a mesh of `n_om` points. The parameter
`broadening` defines an additional Lorentzian broadening, and has the default value of `0.01 eV`. To check the Wannier
density of states after the projection set `with_Sigma` and `with_dc` to `False`. If `save_to_file` is set to `True`
the output is printed into the files
where a description of all of the inputs are given in :meth:`density_of_states <dft.sumk_dft_tools.SumkDFTTools.density_of_states>`:
* `DOS_wannier_(sp).dat`: The total DOS, where `(sp)` stands for `up`, `down`, or combined `ud`. The latter case
is relevant for calculations including spin-orbit interaction.
* `DOS_wannier_(sp)_proj(i).dat`: The DOS projected to an orbital with index `(i)`. The index `(i)` refers to
the indices given in ``SK.shells``.
* `DOS_wannier_(sp)_proj(i)_(m)_(n).dat`: As above, but printed as orbitally-resolved matrix in indices
`(m)` and `(n)`. For `d` orbitals, it gives the DOS separately for, e.g., :math:`d_{xy}`, :math:`d_{x^2-y^2}`, and so on,
.. automethod:: triqs_dft_tools.sumk_dft_tools.SumkDFTTools.density_of_states
:noindex:
.. image:: images_scripts/DFT_Tools_SVO_DFT_DOS.png
:width: 600
:align: center
The figure above shows the DFT SrVO\ :sub:`3`\ density of states generated from 2925 k-points in the irreducible Brillouin zone with the V t\ :sub:`2g`\ Wannier projectors generated within a correlated energy window of [-13.6, 13.6] eV. The `broadening` input has been set to the temperature (i.e., 1/Beta). The total, V t\ :sub:`2g`\ Wannier and occupied total density of states generated from the SK.density_of_states() routine are shown. Note that the noise in the density of states comes from the number of k-points used. This can be removed upon by either using more k-points or using a larger `broadening` value.
Band resolved density matrices
------------------------------
Calculates the band resolved density matrices (occupations) from the Matsubara frequency self-energy.
This is done by calling the following::
SK.occupations(mu, with_Sigma, with_dc, save_occ):
This is required to generate the occupied DOS in SK.density_of_states() when dosocc is set to True. The `save_occ` optional input (True by default) saves these density matrices to the HDF5 file within the misc_data subgroup. The other variables are the same as defined above. See :meth:`occupations <dft.sumk_dft_tools.SumkDFTTools.occupations>`
Momentum resolved spectral function (with real-frequency self energy)
---------------------------------------------------------------------
Another quantity of interest is the calculated momentum-resolved spectral function A(k, :math:`\omega`) or (correlated) band structure which can directly be compared to ARPES experiments.
First we have generate the required files from the DFT code of choice and interface them with DFT_Tools, see the guides of the DFT converters (:ref:`conversion`) on how to do this.
This spectral function is calculated by typing::
SK.spaghettis(mu, broadening, mesh, plot_shift, plot_range, shell_list, with_Sigma, with_dc, proj_type, save_to_file)
.. automethod:: triqs_dft_tools.sumk_dft_tools.SumkDFTTools.spaghettis
:noindex:
.. image:: images_scripts/DFT_Tools_SVO_DFT_spaghettis.png
:width: 1000
:align: center
The figure above shows the DFT SrVO\ :sub:`3`\ spaghetti plot (generated using V t\ :sub:`2g`\ Wannier projectors generated within a correlated energy window of [-13.6, 13.6] eV). As before, the broadening input has been set to the temperature (i.e., 1/Beta). The left panel shows the total A(k, :math:`\omega`) whereas the right gives the Wannier A(k, :math:`\omega`), both generated from this SK.spaghettis().
Energy contours of the k-resolved Spectral function
---------------------------------------------------
Currently, this has only been implemented for Elk DFT inputs only.
This routine calculates the k-resolved spectral function evaluated at the Fermi level or several energy contours on the k-mesh defined in the converter stage::
SK.spectral_contours(mu, broadening, mesh, plot_range, FS, with_Sigma, with_dc, proj_type, save_to_file)
.. automethod:: triqs_dft_tools.sumk_dft_tools.SumkDFTTools.spectral_contours
:noindex:
.. image:: images_scripts/DFT_Tools_SVO_DFT_energy_contours.png
:width: 1000
:align: center
The figure above shows the DFT SrVO\ :sub:`3`\ energy contour plots (again, generated using V t\ :sub:`2g`\ Wannier projectors generated within a correlated energy window of [-13.6, 13,6] eV and broadening of 1/Beta). Both panels have been generated on a k-mesh within the first Brillouin zone on the k\ :sub:`z`\ = 0.0 plane centered at the :math:`\Gamma` point. Here, each panel generated using the outputs from this SK.spectral_contours_plot() routine shows the A(k, :math:`\omega`) evaluated at :math:`\omega` = -0.5 eV (left) and the Fermi level, :math:`\omega` = 0.0 eV, (right).
otherwise, the output is returned by the function for a further usage in :program:`python`.
Partial charges
---------------
Currently, this has only been implemented for Wien2k DFT inputs only.
Since we can calculate the partial charges directly from the Matsubara Green functions, we also do not need a
real-frequency self energy for this purpose. The calculation is done by::
@ -109,38 +159,6 @@ real-frequency self energy for this purpose. The calculation is done by::
which calculates the partial charges using the self energy, double counting, and chemical potential as set in the
`SK` object. On return, `dm` is a list, where the list items correspond to the density matrices of all shells
defined in the list `SK.shells`. This list is constructed by the Wien2k converter routines and stored automatically
in the hdf5 archive. For the structure of `dm`, see also :meth:`reference manual <dft.sumk_dft_tools.SumkDFTTools.partial_charges>`.
in the hdf5 archive. For the structure of `dm`, see also :meth:`partial charges <dft.sumk_dft_tools.SumkDFTTools.partial_charges>`.
Correlated spectral function (with real-frequency self energy)
--------------------------------------------------------------
To produce both the momentum-integrated (total density of states or DOS) and orbitally-resolved (partial/projected DOS) spectral functions
we can execute::
SK.dos_parproj_basis(broadening=0.0, with_Sigma=True, with_dc=True, save_to_file=True)
The variable `broadening` is an additional Lorentzian broadening (default: `0.01 eV`) applied to the resulting spectra.
The output is written in the same way as described above for the :ref:`Wannier density of states <dos_wannier>`, but with filenames
`DOS_parproj_*` instead.
Momentum resolved spectral function (with real-frequency self energy)
---------------------------------------------------------------------
Another quantity of interest is the momentum-resolved spectral function, which can directly be compared to ARPES
experiments. First we have to execute `lapw1`, `lapw2 -almd` and :program:`dmftproj` with the `-band`
option and use the :meth:`convert_bands_input <dft.converters.wien2k.Wien2kConverter.convert_bands_input>`
routine, which converts the required files (for a more detailed description see :ref:`conversion`). The spectral function is then calculated by typing::
SK.spaghettis(broadening=0.01,plot_shift=0.0,plot_range=None,ishell=None,save_to_file='Akw_')
Here, optional parameters are
* `shift`: An additional shift added as `(ik-1)*shift`, where `ik` is the index of the `k` point. This is useful for plotting purposes.
The default value is 0.0.
* `plotrange`: A list with two entries, :math:`\omega_{min}` and :math:`\omega_{max}`, which set the plot
range for the output. The default value is `None`, in which case the full momentum range as given in the self energy is used.
* `ishell`: An integer denoting the orbital index `ishell` onto which the spectral function is projected. The resulting function is saved in
the files. The default value is `None`. Note for experts: The spectra are not rotated to the local coordinate system used in Wien2k.
The output is written as the 3-column files ``Akw(sp).dat``, where `(sp)` is defined as above. The output format is
`k`, :math:`\omega`, `value`.

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@ -3,7 +3,7 @@
Interface with Elk
=====================
This is the first iteration of the Elk-TRIQS interface, so certain inputs may change in later updates. The Elk part of the interface is not currently in the main distribution, but it can be found `here <https://github.com/AlynJ/Elk_interface-TRIQS>`_.
This is the first iteration of the Elk-TRIQS interface, so certain inputs may change in later updates. The Elk part of the interface is not currently in the main distribution, but it can be found `here <https://github.com/UoB-Compton-scattering-group/elk-8.4.21-TRIQS>`_.
We assume that the user has obtained a self-consistent solution of the
Kohn-Sham equations with Elk (a full tutorial can be found here :ref:`Elk SVO tutorial <SrVO3_elk>`). Also, the user needs to be familiar with the main in/output files of Elk, and how to run
@ -113,36 +113,16 @@ The band structure information is converted into TRIQS by using::
Converter.convert_bands_input()
Spectral function from Elk inputs
---------------------------------
Elk does not calculate the theta projectors for partial DOS calculations. Instead, Elk outputs the band characters into the file BC.OUT when using the elk.in task::
Spectral function Energy Contour Plots (Fermi Surfaces) from Elk inputs
-----------------------------------------------------------------------
task
803
The contents of BC.OUT need to be converted into the HDF5 file by using the Elk Converter module::
from triqs_dft_tools.converters.elk import *
Converter = ElkConverter(filename=filename, repacking=True)
Converter.dft_band_characters()
Once these have been saved to the HDF5 file (called "filename" here), the spectral function can be calculated with::
SK.elk_dos(broadening=0.0, with_Sigma=True, with_dc=True, pdos=False, nk=None)
This outputs the total spectral function and the partial spectral function if enabled. Most of the user inputs are similar to the "SK.dos_parproj_basis()" module in :ref:`analysis`. The "pdos" flag when "True" enables the partial dos of each lm value to be calculated. It should be noted that these band characters are in Elk's irreducible lm basis and as such, the user has to check the irreducible representation used in Elk. This information can be found in the file ELMIREP.OUT after running task 10 (the DOS calculating task). The "nk" flag enables the calculation of the occupied spectral funciton. Here, nk needs to be the occupation density matrix (calculated from integrating the Green's function on the Matsubara axis) in the Bloch basis. This input needs to be in the same format as the occupation density matrix "deltaN" calculated in the sumk_DFT.calc_density_correction(dm_type='elk') module.
Spectral function Contour Plots (Fermi Surfaces) from Elk inputs
-----------------------------------------------------------------
Here, we will discuss how to plot the Fermi surface contour or any other non-zero omega spectral function contour plot. This is currently tailored for the Elk inputs. From this point, we will refer to these contours as Fermi surfaces. The energy eigenvalues, projectors and so on required for the Fermi surface plot needs to be outputed from Elk. This is done by using::
Here, we will discuss how to plot the Fermi surface contour or any other non-zero omega spectral function contour plot. The energy eigenvalues, projectors and so on required for the contour plot needs to be outputed from Elk. This is done by using::
task
807
in Elk, but unlike the previous Elk interface tasks, the k-mesh grid needs to be specified. This is done like using the same inputs as the Fermi surface calculations in Elk. In Elk, The user needs to specify the "plot3d" input flag used to generate the k-mesh which the interface variables are evaluated on. A simple example is for SrVO3 where plot3d would look something like::
in Elk, but unlike the previous Elk interface tasks, the k-mesh grid needs to be specified. This is done by using the same inputs as the Fermi surface calculations in Elk. In Elk, The user needs to specify the "plot3d" input flag used to generate the k-mesh which the interface variables are evaluated on. A simple example is for SrVO3 where plot3d would look something like::
plot3d
0.0 0.0 0.0 !1) origin
@ -151,12 +131,12 @@ in Elk, but unlike the previous Elk interface tasks, the k-mesh grid needs to be
0.0 0.0 1.0 !4) vertex 3
32 32 32 !5) k-mesh grid size
Lines 1) to 4) specifies the corners (in lattice coordinates) of the k-grid box and line 5) is the grid size in each direction (see the Elk manual). If the user desires to plot a 2D plane, then the user should define the plane using lines 2) and 3) [relative to line 1)] and define line 4) to be the cross-product of lines 2) and 3) [i.e. the vector in line 4) is normal to the 2D plane]. The outputs will be in terms of the k-dependent quantities in the irreducible Brillouin zone (IBZ). The files needed for the interface are:
Lines 1) to 4) specifies the corners (in lattice coordinates) of the k-grid box and line 5) is the grid size in each direction (see the Elk manual). If the user desires to plot a 2D plane, then the user should define the plane using lines 2) and 3) [relative to line 1)] and define line 4) to be the cross-product of lines 2) and 3) [i.e. the vector in line 4) is normal to the 2D plane]. The outputs will be in terms of the k-dependent quantities in the irreducible Brillouin zone (IBZ). The files needed for this converter routine are:
#. EIGVAL_FS.OUT - same as EIGVAL.OUT but the output is of the Fermi surface calculation.
#. KPOINT_FS.OUT - same as KPOINT.OUT but the output is of the Fermi surface calculation.
#. PROJ_FS.OUT - same as PROJ.OUT but the output is of the Fermi surface calculation.
#. WANPROJ_L**_S**_A****_FS.OUT - same as WANPROJ_L**_S**_A****.OUT but the output is of the Fermi surface calculation.
#. EIGVAL_FS.OUT - same as EIGVAL.OUT but with the corresponding plot3d user defined k-mesh instead.
#. KPOINT_FS.OUT - same as KPOINT.OUT but with the corresponding plot3d user defined k-mesh instead.
#. PROJ_FS.OUT - same as PROJ.OUT but with the corresponding plot3d user defined k-mesh instead.
#. WANPROJ_L**_S**_A****_FS.OUT - same as WANPROJ_L**_S**_A****.OUT but with the corresponding plot3d user defined k-mesh instead.
#. EFERMI.OUT - contains the Fermi energy.
#. SYMCRYS.OUT - has the crystal symmetries used for symmetries observables.
#. LATTICE.OUT - has lattice-Cartesian basis transformation matrices.
@ -167,26 +147,17 @@ These outputs are converted to the HDF5 file by::
from triqs_dft_tools.converters.elk import *
Converter = ElkConverter(filename=filename, repacking=True)
Converter.convert_fs_input()
Converter.convert_contours_input(kgrid,ngrid)
The spectral function for the Fermi surface plots are calculated with::
SK.fs_plot(broadening=0.0, mesh=None, FS=True, plane=True, sym=True, orthvec=None, with_Sigma=True, with_dc=True)
The new flags specify the following:
#. "FS" - determines whether the output will be the Fermi surface and uses the closest omega value to 0.0 in the mesh.
#. "plane" - required to specify whether the Elk input parameters were generated on a k-mesh plane.
#. "sym" - needed if the IBZ will be folded out by using symmetry operations.
#. "orthvec" - (numpy array of length 3) needs to be specified if using "plane" as this input is the orthonormal vector to the 2D plane required for the folding out process.
To give the user a range of output capabilities, This routine can be used in the following ways:
#. If using "with_Sigma", the mesh will be the same as the self-energy. However, by setting FS=False, the user can input a mesh option if they desire the "Fermi surface" plots for each omega value (commensurate with the self-energy mesh) within the input range.
#. If the user is generating the DFT Spectral function plot (i.e. with_Sigma and with_dc both set to False), a mesh needs to be specified if FS=False. This function will output the spectral functions for the input mesh. Otherwise if FS is True, this would return the spectral function at omega=0.0.
The output files will have the form of "Akw_FS_X.dat" (X being either up, down or ud) if FS=True or "Akw_X_omega_Y.dat" (Y being the omega mesh index) otherwise. The latter file will have the omega values within the file (the fourth column). The first three columns of both output file types specifies the cartesian lattice vector (kx, ky, kz) and the last column is the spectral function values.
The kgrid and ngrid are user-defined numpy array inputs containing the plot3d inputs described above.
These inputs are needed to generate the reciprocal lattice coordinates for the output files.
The default for both of these variables is None, which in this case the converter automatically generates the
full Brillouin zone by applying all of the symmetry operators to the read IBZ coordinates. However,
if the plot3d input is a k-mesh not centered around the origin and/or a k-mesh which only requires
a subset of the symmetry operators (say a 2D k-mesh) then the plot3d input needs to be an input to
the converter. Here, kgrid would be a double numpy array of size (4,3) specifying the k-mesh corner,
i.e, which contains lines 1) to 4) of the plot3d input described. The ngrid is an integer numpy
array of size (3) containing the k-mesh grid size - line 5) in plot3d input above.
DFT+DMFT wavefunction dependent quantities

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@ -20,8 +20,7 @@ Wien2k + dmftproj
construct the Wannier functions. For this step, see either sections
:ref:`conversion`, or the extensive :download:`dmftproj manual<images_scripts/TutorialDmftproj.pdf>`.
In the following, we discuss how to use the
:ref:`TRIQS <triqslibs:installation>` tools in combination with the Wien2k program.
In the following, we discuss how to use `TRIQS <https://triqs.github.io>`_ in combination with the Wien2k program.
We can use the DMFT script as introduced in section :ref:`singleshot`,
with just a few simple modifications. First, in order to be compatible with the Wien2k standards,
@ -136,8 +135,8 @@ removed. The shell script, in turn, waits for the VASP process and once
the lock file is created it starts a DMFT iteration. The DMFT iteration
must finish by generating a Kohn-Sham (KS) density matrix (file `GAMMA`)
and removing the lock file. The VASP process then reads in `GAMMA`
and proceeds with the next iteration. PLOVasp interface provides a shell-script :program:`vasp_dmft` (in the triqs bin directory):
::
and proceeds with the next iteration. PLOVasp interface provides a shell-script :program:`vasp_dmft` (in the triqs bin directory)::
vasp_dmft [-n <number of cores>] -i <number of iterations> -j <number of VASP iterations with fixed charge density> [-v <VASP version>] [-p <path to VASP directory>] [<dmft_script.py>]
If the number of cores is not specified it is set to 1 by default.

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@ -168,7 +168,7 @@ Example
-------
Here we present an example calculation of the DFT optical conductivity of SrVO3 comparing the results from the Elk and Wien2k inputs. The DFT codes used 4495 k-points in the
irreducible Brillouin zone with Wanner projectors generated within a correlated energy window of [-8, 7.5] eV. We assume that the required DFT files have been read and saved by the TRIQS
irreducible Brillouin zone with Wannier projectors generated within a correlated energy window of [-8, 7.5] eV. We assume that the required DFT files have been read and saved by the TRIQS
interface routines as discussed previously. Below is an example script to generate the conductivities::
from sumk_dft_tools import *

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@ -226,7 +226,7 @@ self energy on the real axis::
put it into SK class and then calculated the actual DOS::
SK.dos_parproj_basis(broadening=broadening)
SK.density_of_states(broadening, proj_type="wien2k")
We may first increase the number of **k**-points in BZ to 10000 by executing the Wien2k
program :program:`kgen` ::

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@ -28,7 +28,7 @@ for orb in orbs:
for iO in orb:
gf = gf + G_latt['up'][iO,iO]
tm.set_G_iw(gf)
tm.omega =LinearOmegaMesh(w_min=-20, w_max=20, n_points=201)
tm.omega =LinearOmegaMesh(omega_min=-20, omega_max=20, n_points=201)
tm.alpha_mesh = LogAlphaMesh(alpha_min=0.01, alpha_max=20000, n_points=60)
tm.set_error(1.e-3)

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@ -41,7 +41,7 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
dft_subgrp='dft_input', symmcorr_subgrp='dft_symmcorr_input',
bc_subgrp='dft_bandchar_input', symmpar_subgrp='dft_symmpar_input',
bands_subgrp='dft_bands_input', misc_subgrp='dft_misc_input',
transp_subgrp='dft_transp_input',fs_subgrp='dft_fs_input',
transp_subgrp='dft_transp_input',cont_subgrp='dft_contours_input',
repacking=False):
"""
Initialise the class.
@ -88,7 +88,7 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
self.bands_subgrp = bands_subgrp
self.misc_subgrp = misc_subgrp
self.transp_subgrp = transp_subgrp
self.fs_subgrp = fs_subgrp
self.cont_subgrp = cont_subgrp
self.fortran_to_replace = {'D': 'E'}
# Checks if h5 file is there and repacks it if wanted:
@ -512,17 +512,63 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
del ar
mpi.report('Converted the band data')
def convert_fs_input(self):
def convert_contours_input(self,kgrid=None,ngrid=None):
"""
Reads the appropriate files and stores the data for the FS_subgrp in the hdf5 archive.
Reads the appropriate files and stores the data for the cont_subgrp in the hdf5 archive.
Parameters:
kgrid : size (4,3) double numpy array, optional
Numpy array defining the reciprocal lattice vertices used in the Elk Fermi
surface calculation. Each row has the following meaning:
grid3d[0,:] - origin lattice vertex
grid3d[1,:] - b1 lattice vertex
grid3d[2,:] - b2 lattice vertex
grid3d[3,:] - b3 lattice vertex
ngrid : size (3) integer numpy array, optional
Numpy array for the number of points along each (b1,b2,b3) lattice vertices
Note that these inputs relate to the plot3d input of Elk.
"""
# Read and write only on the master node
if not (mpi.is_master_node()):
return
filext='_FS.OUT'
dft_file='PROJ'+filext
mpi.report("Reading %s" % dft_file)
#read the symmetries and k-points first
#read kpoints calculated in the Elk FS calculation
mpi.report("Reading KPOINT_FS.OUT")
[bz_weights,vkl]=read_Elk.read_kpoints(self,filext=filext)
n_k=vkl[:,0].size
#Need lattice symmetries to unfold the irreducible BZ
#Read symmetry files
mpi.report("Reading SYMCRYS.OUT")
[n_symm,spinmat,symlat,tr] = read_Elk.readsym(self)
#generate full vectors for Fermi surface plotting along with index mapping
#to irreducible vector set.
if (ngrid is not None and kgrid is not None):
mpi.report('Using User defined k-mesh')
#check variables are in correct format
if ngrid.size != 3:
assert 0, "The input numpy ngrid is not the required size of 3!"
elif ngrid.dtype != int:
assert 0, "The input numpy ngrid is not an array of integers."
elif kgrid.shape != (4,3):
assert 0, "The input numpy kgrid is not the required size of (4x3)!"
#generate full set of k-points with mapping to reduced set
[BZ_vkl, BZ_iknr, BZ_n_k] = Elk_tools.plotpt3d(self,n_k,vkl,n_symm,symlat,kgrid,ngrid)
elif (ngrid is None and kgrid is None):
mpi.report('No grid dimension input for Fermi surface.')
mpi.report('Calculating k-points by folding out irreducible vectors instead if using symmetries.')
mpi.report('Warning! This may not equate to the same set of vectors used to generate the Fermi surface data.')
[BZ_vkl, BZ_iknr, BZ_n_k] = Elk_tools.bzfoldout(self,n_k,vkl,n_symm,symlat)
else:
assert 0, "Either input both ngrid and kgrid numpy arrays or neither."
#return all threads apart from master
if not (mpi.is_master_node()):
return
# Read and write the following only on the master thread
#Energy conversion - Elk uses Hartrees
energy_unit = 27.2113850560 # Elk uses hartrees
shells=[]
@ -546,9 +592,6 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
#read in the eigenvalues used for the FS calculation
mpi.report("Reading EIGVAL_FS.OUT and EFERMI.OUT")
[en,occ,nstsv]=read_Elk.read_eig(self,filext=filext)
#read kpoints calculated in the Elk FS calculation
mpi.report("Reading KPOINT_FS.OUT")
[bz_weights,vkl]=read_Elk.read_kpoints(self,filext=filext)
#read projectors
proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max([crsh['dim'] for crsh in corr_shells]), nstsv], complex)
@ -556,12 +599,8 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
for ish in range(n_corr_shells):
[n_orbitals,band_window,rep,proj_mat]=read_Elk.read_projector(self,corr_shells,n_spin_blocs,ish,proj_mat,ind,T,basis,filext)
#Need lattice symmetries to unfold the irreducible BZ
#Read symmetry files
mpi.report("Reading SYMCRYS.OUT")
[n_symm,spinmat,symlat,tr] = read_Elk.readsym(self)
mpi.report("Reading LATTICE.OUT")
[amat,amatinv,bmat,bmatinv] = read_Elk.readlat(self)
[amat,amatinv,bmat,bmatinv,cell_vol] = read_Elk.readlat(self)
#Put eigenvalues into array of eigenvalues for the correlated window
#alter arrays for spin-orbit coupling
if(SO==1):
@ -576,79 +615,133 @@ class ElkConverter(ConverterTools,Elk_tools,read_Elk):
# Save it to the HDF:
ar = HDFArchive(self.hdf_file, 'a')
if not (self.fs_subgrp in ar):
ar.create_group(self.fs_subgrp)
if not (self.cont_subgrp in ar):
ar.create_group(self.cont_subgrp)
# The subgroup containing the data. If it does not exist, it is
# created. If it exists, the data is overwritten!
things_to_save = ['n_k', 'n_orbitals', 'proj_mat','bmat',
'hopping', 'vkl','symlat', 'n_symm']
things_to_save = ['n_k','n_orbitals', 'proj_mat','bmat',
'BZ_n_k','BZ_iknr','BZ_vkl','hopping']
for it in things_to_save:
ar[self.fs_subgrp][it] = locals()[it]
ar[self.cont_subgrp][it] = locals()[it]
del ar
mpi.report('Converted the FS data')
mpi.report('Converted the Contours data')
def dft_band_characters(self):
"""
Reads in the band characters generated in Elk to be used for
PDOS and band character band structure plots.
"""
# commented out for now - unsure using this produces DFT+DMFT PDOS.
# The data from BC.OUT are the band-resolved diagonal muffin-tin DFT density matrix elements used in Elk to calculate PDOS
# (the PDOS is calculated from the Trace over the bands indices). Although this is equivalent to using using projectors in DFT and is likely valid for DFT+DMFT,
# the equivalence needs to be thoroughly checked for DFT+DMFT, but would require theta (or similar) projectors from Elk to do so.
# code left here just in case.
if not (mpi.is_master_node()):
return
mpi.report("Reading BC.OUT")
# def dft_band_characters(self):
# """
# Reads in the band-resolved muffin-tin density matrix (band characters) generated in Elk
# to be used for PDOS plots.
# """
# get needed data from hdf file
# from general info
ar = HDFArchive(self.hdf_file, 'a')
things_to_read = ['SP', 'SO','n_k','n_orbitals']
for it in things_to_read:
if not hasattr(self, it):
setattr(self, it, ar[self.dft_subgrp][it])
#from misc info
things_to_read = ['nstsv','band_window']
for it in things_to_read:
if not hasattr(self, it):
setattr(self, it, ar[self.misc_subgrp][it])
#from sym info
things_to_read = ['n_atoms']
symm_subgrp=self.symmcorr_subgrp
for it in things_to_read:
if not hasattr(self, it):
setattr(self, it, ar[symm_subgrp][it])
# #determine file extension
# fileext='.OUT'
# #read number of k-points and eigenstates
# things_to_read = ['n_k','n_orbitals']
# ar = HDFArchive(self.hdf_file, 'r')
# for it in things_to_read:
# setattr(self, it, ar[self.dft_subgrp][it])
# del ar
#read in band characters
[bc,maxlm] = read_Elk.read_bc(self)
#set up SO bc array
if (self.SO):
tmp = numpy.zeros([2*maxlm,1,self.n_atoms,self.nstsv,self.n_k], float)
#put both spinors into the lm array indices.
tmp[0:maxlm,0,:,:,:]=bc[0:maxlm,0,:,:,:]
tmp[maxlm:2*maxlm,0,:,:,:]=bc[0:maxlm,1,:,:,:]
maxlm=2*maxlm
del bc
bc = tmp
del tmp
# if not (mpi.is_master_node()):
# return
# mpi.report("Reading BC%s"%(fileext))
#reduce bc matrix to band states stored in hdf file
n_spin_blocs=self.SP+1-self.SO
tmp = numpy.zeros([maxlm,n_spin_blocs,self.n_atoms,numpy.max(self.n_orbitals),self.n_k], float)
for ik in range(self.n_k):
for isp in range(n_spin_blocs):
nst=self.n_orbitals[ik,isp]
ibot=self.band_window[isp][ik, 0]-1
itop=ibot+nst
tmp[:,isp,:,0:nst,ik]=bc[:,isp,:,ibot:itop,ik]
del bc
bc = tmp
del tmp
# # get needed data from hdf file
# # from general info
# ar = HDFArchive(self.hdf_file, 'a')
# things_to_read = ['SP', 'SO']
# for it in things_to_read:
# if not hasattr(self, it):
# setattr(self, it, ar[self.dft_subgrp][it])
# #from misc info
# things_to_read = ['nstsv','band_window']
# for it in things_to_read:
# if not hasattr(self, it):
# setattr(self, it, ar[self.misc_subgrp][it])
# #from sym info
# things_to_read = ['n_atoms','perm']
# symm_subgrp=self.symmcorr_subgrp
# for it in things_to_read:
# if not hasattr(self, it):
# setattr(self, it, ar[symm_subgrp][it])
# del ar
things_to_save = ['maxlm', 'bc']
if not (self.bc_subgrp in ar):
ar.create_group(self.bc_subgrp)
for it in things_to_save:
ar[self.bc_subgrp][it] = locals()[it]
del ar
mpi.report('Converted the band character data')
# #read in band characters
# [bc,maxlm] = read_Elk.read_bc(self,fileext)
#note that bc is the band resolved inner product of the Elk muffin-tin wave functions in
#a diagonal lm basis (by default). These are used in Elk to calculate the DOS in a diagonal
#irreducible lm basis. This band resolved density matrix in the lm basis will be used
#to project to spectral funtion to get the muffin-tin contributions. There will be an
#interstitial contribution (as Elk uses an APW+lo basis) which is the difference between
#the total and summed muffin-tin contributions. Also note that the bc array should be
#symmetrised within Elk.
#general variables
# lmax = int(numpy.sqrt(maxlm)-1)
# n_spin_blocs = self.SP + 1 - self.SO
# so = self.SO + 1
# #get the sort entry which is just the species index for Elk
# [ns, na, atpos]=read_Elk.read_geometry(self)
# isrt=0
# sort=numpy.zeros([self.n_atoms],int)
# #arrange sort(species) order
# for i in range(ns):
# for ia in range(na[i]):
# sort[isrt]=i
# isrt+=1
# #updating n_shells to include all the atoms and l used in the Elk calculation.
# n_shells = self.n_atoms * (lmax+1)
# shells = []
# shell_entries = ['atom', 'sort', 'l', 'dim']
# for iat in range(self.n_atoms):
# for l in range(lmax+1):
# #sort is not known from Elk outputs
# tmp = [iat+1, sort[iat]+1, l, so*(2*l+1)]
# shells.append({name: int(val) for name, val in zip(shell_entries, tmp)})
# del tmp, ns, na, atpos, isrt, shell_entries
# #overwrite n_shells and shells
# things_to_save = ['n_shells', 'shells']
# ar = HDFArchive(self.hdf_file, 'a')
# for it in things_to_save:
# ar[self.dft_subgrp][it] = locals()[it]
# # Initialise P, here a double list of matrices:
# band_dens_muffin = numpy.zeros([self.n_k, n_spin_blocs, n_shells, so*(2*lmax+1), numpy.max(self.n_orbitals)], float)
# for ik in range(self.n_k):
# for isp in range(n_spin_blocs):
# ish=0
# for iat in range(self.n_atoms):
# for l in range(lmax+1):
# #variables for putting subset of bc in proj_mat_all
# lm_min=l**2
# lm_max=(l+1)**2
# nst=self.n_orbitals[ik,isp]
# ibot=self.band_window[isp][ik, 0]-1
# itop=ibot+nst
# dim=l*2+1
#check use of abs (negative values should be close to 0)
# band_dens_muffin[ik,isp,ish,0:dim,0:nst] = \
# bc[lm_min:lm_max,isp,iat,ibot:itop,ik]
# if(self.SO==1):
# band_dens_muffin[ik,isp,ish,dim:2*dim,0:nst] = \
# bc[lm_min:lm_max,1,iat,ibot:itop,ik]
# ish+=1
# things_to_save = ['band_dens_muffin']
# # Save it all to the HDF:
# with HDFArchive(self.hdf_file, 'a') as ar:
# if not (self.bc_subgrp in ar):
# ar.create_group(self.bc_subgrp)
# # The subgroup containing the data. If it does not exist, it is
# # created. If it exists, the data is overwritten!
# things_to_save = ['band_dens_muffin']
# for it in things_to_save:
# ar[self.bc_subgrp][it] = locals()[it]
def convert_transport_input(self):

View File

@ -296,3 +296,97 @@ class ElkConverterTools:
#return y-rotation matrix
return dy
def plotpt3d(self,n_k,vkl,n_symm,symlat,grid3d,ngrid):
import triqs.utility.mpi as mpi
#import time
#st = time.time()
#default vector tolerance used in Elk. This should not be altered.
epslat=1E-6
tol=int(numpy.log10(1/epslat))
b = numpy.zeros([3,3], float)
b = grid3d[1:4,:] - grid3d[0,:]
nk = ngrid[0]*ngrid[1]*ngrid[2]
BZvkl = numpy.zeros([nk,3], float)
BZvkl[:,:] = None
#array which maps the new vkl to the symmetrically equivalent interface vkl
iknr = numpy.zeros([nk], int)
nk_ = 0
vklIBZ = [self.v3frac(vkl[ik,:],epslat) for ik in range(n_k)]
vklIBZ = numpy.array(vklIBZ)
#generate mesh grid
i0, i1, i2 = numpy.meshgrid(numpy.arange(ngrid[0]), numpy.arange(ngrid[1]),
numpy.arange(ngrid[2]), indexing='ij')
#convert to floats
t0 = i0.astype(float)/ngrid[0]
t1 = i1.astype(float)/ngrid[1]
t2 = i2.astype(float)/ngrid[2]
#Calculate Brillouin zone lattice vectors
BZvkl[:, 0] = (t0*b[0,0]+t1*b[1, 0]+t2*b[2, 0]+grid3d[0, 0]).flatten()
BZvkl[:, 1] = (t0*b[0,1]+t1*b[1, 1]+t2*b[2, 1]+grid3d[0, 1]).flatten()
BZvkl[:, 2] = (t0*b[0,2]+t1*b[1, 2]+t2*b[2, 2]+grid3d[0, 2]).flatten()
#check k-point has equivalent point dft-interfaced k-point list (this is a bottle neck for performance)
for ik in range(nk):
br = None
v1 = self.v3frac(BZvkl[ik,:], epslat)
#see if v1 is symmetrically equivalent to a vector in IBZvkl
for isym in range(n_symm):
v_symm=numpy.matmul(symlat[isym][:,:].transpose(),v1)
v_symm=self.v3frac(v_symm,epslat)
if v_symm.round(tol).tolist() in vklIBZ.round(tol).tolist():
iknr[ik] = vkl.round(tol).tolist().index(v_symm.round(tol).tolist())
#if identity symmetry operation was used, this v1 must be in the IBZ vector set
if numpy.allclose(symlat[isym][:,:],numpy.eye(3)):
nk_+=1
br = 1
break
if br == 1: continue
#if v1 is not symmetrically equivalent, then wrong input mesh.
mpi.report('No identity symmetry operator or symmetrically equivalent vector in interface vkl set')
assert 0, "input grid does not generate interfaced reciprocal vectors"
#check that all the vectors from the interface are in this list of vectors
if(nk_!=n_k):
mpi.report('Incorrect number of irreducible vectors with respect to vkl ')
mpi.report('%s!=%s'%(nk_,n_k))
assert 0, "input grid does not generate interfaced reciprocal vectors"
#et = time.time()
#mpi.report(et-st,nk)
return BZvkl, iknr, nk
def bzfoldout(self,n_k,vkl,n_symm,symlat):
#import triqs.utility.mpi as mpi
epslat=1E-6
tol=int(numpy.log10(1/epslat))
#new temporary arrays for expanding irreducible Brillouin zone
iknr = numpy.arange(n_k)
BZvkl = vkl.copy()
vkl2 = numpy.zeros([n_symm,n_k,3], float)
iknr2 = numpy.zeros([n_symm,n_k], int)
vkl2[0,:,:] = vkl[:,:].copy()
iknr2[0,:] = iknr[:].copy()
#expand irreducible Brillouin zone
for ik in range(n_k):
for isym in range(n_symm):
#find point in BZ by symmetry operation
v=numpy.matmul(symlat[isym][:,:].transpose(),vkl[ik,:])
#alter temporary arrays
vkl2[isym,ik,:] = v[:]
iknr2[isym,ik] = ik
#flatten arrays
BZvkl = vkl2.reshape(n_k*n_symm,3)
iknr = iknr2.reshape(n_k*n_symm)
#remove duplicates with eplats tolerance
[BZvkl,ind]=numpy.unique(BZvkl.round(tol),return_index=True,axis=0)
iknr=iknr[ind]
#new number of k-points
nk=BZvkl.shape[0]
#sort the indices for output in decending order
iksrt=numpy.lexsort(([BZvkl[:,i] for i in range(0,BZvkl.shape[1], 1)]))
#rearrange the vkc and iknr arrays
BZvkl=BZvkl[iksrt]
iknr=iknr[iksrt]
#return new set of lattice vectors, number of vectors and index array which
#maps to original irreducible vector set.
return BZvkl, iknr, nk

View File

@ -21,9 +21,6 @@ install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} DESTINATION
${TRIQS_PYTHON_LIB_DEST_ROOT}/${PROJECT_NAME}/converters/elktools FILES_MATCHING PATTERN "*.so"
PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE
WORLD_READ WORLD_EXECUTE PATTERN "CMakeFiles" EXCLUDE)
message(STATUS "foo include dir: ${CMAKE_CURRENT_BINARY_DIR}")
message(STATUS "foo include dir: ${CMAKE_BINARY_DIR}")
message(STATUS "foo include dir: ${TRIQS_PYTHON_LIB_DEST_ROOT}/${PROJECT_NAME}")
# user warning
message(STATUS "-----------------------------------------------------------------------------")

View File

@ -621,67 +621,68 @@ class readElkfiles:
R.close()
return ns, na, atpos
#band character dependent calculations
def read_bc(self):
"""
Read in the ELK generated band characters from BC.OUT
"""
#commented out for now - unsure this will produce DFT+DMFT PDOS
##band character dependent calculations
# def read_bc(self,fileext):
# """
# Read in the ELK generated band characters from BC.OUT
# """
#import string
file = 'BC.OUT'
R = self.read_elk_file(file, self.fortran_to_replace)
try:
#no. of kpts and number of orbital
gen_entries = ['maxlm', 'nspinor','natmtot','nstsv','nkpt','irep']
gen = {name: int(val) for name, val in zip(gen_entries, R)}
#projector lm size
#check the read in information complies with previous read in data
nspinor=self.SP+1
if(gen['nspinor'] != nspinor):
mpi.report('HDF file nspinor = %s'%nspinor)
mpi.report('BC.OUT nspinor = %s'%gen['nspinor'])
raise IOError("Elk_converter (",file,") : reading nspinor failed!")
return
if(gen['natmtot'] != self.n_atoms):
raise IOError("Elk_converter (",file,") : reading no. of atoms failed!")
return
if(gen['nstsv'] != self.nstsv):
raise IOError("Elk_converter (",file,") : reading all states failed!")
return
if(gen['nkpt'] != self.n_k):
raise IOError("Elk_converter (",file,") : reading kpoints failed failed!")
return
if(gen['irep'] == 0):
raise IOError("Elk_converter (",file,") : Band characters are in spherical hamonics, may have issues with the PDOS!")
return
# #import string
# file = 'BC'+fileext
# R = self.read_elk_file(file, self.fortran_to_replace)
# try:
# #no. of kpts and number of orbital
# gen_entries = ['maxlm', 'nspinor','natmtot','nstsv','nkpt','irep']
# gen = {name: int(val) for name, val in zip(gen_entries, R)}
# #projector lm size
# #check the read in information complies with previous read in data
# nspinor=self.SP+1
# if(gen['nspinor'] != nspinor):
# mpi.report('HDF file nspinor = %s'%nspinor)
# mpi.report('BC.OUT nspinor = %s'%gen['nspinor'])
# raise IOError("Elk_converter (",file,") : reading nspinor failed!")
# return
# if(gen['natmtot'] != self.n_atoms):
# raise IOError("Elk_converter (",file,") : reading no. of atoms failed!")
# return
# if(gen['nstsv'] != self.nstsv):
# raise IOError("Elk_converter (",file,") : reading all states failed!")
# return
# if(gen['nkpt'] != self.n_k):
# raise IOError("Elk_converter (",file,") : reading kpoints failed failed!")
# return
# if(gen['irep'] == 0):
# raise IOError("Elk_converter (",file,") : Band characters are in spherical hamonics, may have issues with the PDOS!")
# return
dim=gen['maxlm']
lmax=numpy.sqrt(dim)-1
bc = numpy.zeros([dim,nspinor,self.n_atoms,self.nstsv,self.n_k], float)
# dim=gen['maxlm']
# lmax=numpy.sqrt(dim)-1
# bc = numpy.zeros([dim,nspinor,self.n_atoms,self.nstsv,self.n_k], float)
for ik in range(0,self.n_k):
for iatom in range(0,self.n_atoms):
for ispn in range(0,nspinor):
entry = ['ispn','ias','is','ia','ik']
ent = {name: int(val) for name, val in zip(entry, R)}
#k-point index and correlated band window indices
#check read in values
if(ent['ispn'] != ispn+1):
raise IOError("Elk_converter (",file,") : reading ispn failed!")
return
if(ent['ias'] != iatom+1):
raise IOError("Elk_converter (",file,") : reading iatom failed!")
return
if(ent['ik'] != ik+1):
raise IOError("Elk_converter (",file,") : reading ik failed!")
return
# for ik in range(0,self.n_k):
# for iatom in range(0,self.n_atoms):
# for ispn in range(0,nspinor):
# entry = ['ispn','ias','is','ia','ik']
# ent = {name: int(val) for name, val in zip(entry, R)}
# #k-point index and correlated band window indices
# #check read in values
# if(ent['ispn'] != ispn+1):
# raise IOError("Elk_converter (",file,") : reading ispn failed!")
# return
# if(ent['ias'] != iatom+1):
# raise IOError("Elk_converter (",file,") : reading iatom failed!")
# return
# if(ent['ik'] != ik+1):
# raise IOError("Elk_converter (",file,") : reading ik failed!")
# return
for ist in range(self.nstsv):
for lm in range(dim):
bc[lm,ispn,iatom,ist,ik] = next(R)
# for ist in range(self.nstsv):
# for lm in range(dim):
# bc[lm,ispn,iatom,ist,ik] = next(R)
except StopIteration: # a more explicit error if the file is corrupted.
raise IOError("Elk_converter (read BC.OUT): reading file failed!")
R.close()
return(bc,dim)
# except StopIteration: # a more explicit error if the file is corrupted.
# raise IOError("Elk_converter (read BC.OUT): reading file failed!")
# R.close()
# return(bc,dim)

View File

@ -46,7 +46,7 @@ class SumkDFT(object):
def __init__(self, hdf_file, h_field=0.0, mesh=None, beta=40, n_iw=1025, use_dft_blocks=False,
dft_data='dft_input', symmcorr_data='dft_symmcorr_input', parproj_data='dft_parproj_input',
symmpar_data='dft_symmpar_input', bands_data='dft_bands_input', transp_data='dft_transp_input',
misc_data='dft_misc_input',bc_data='dft_bandchar_input',fs_data='dft_fs_input'):
misc_data='dft_misc_input',bc_data='dft_bandchar_input',cont_data='dft_contours_input'):
r"""
Initialises the class from data previously stored into an hdf5 archive.
@ -102,7 +102,7 @@ class SumkDFT(object):
self.transp_data = transp_data
self.misc_data = misc_data
self.bc_data = bc_data
self.fs_data = fs_data
self.cont_data = cont_data
self.h_field = h_field
if mesh is None:
@ -534,7 +534,7 @@ class SumkDFT(object):
if mesh is None:
broadening = 0.01
else: # broadening = 2 * \Delta omega, where \Delta omega is the spacing of omega points
broadening = 2.0 * ((mesh[1] - mesh[0]) / (mesh[2] - 1))
broadening = 2.0 * ((mesh.w_max - mesh.w_min) / (len(mesh) - 1))
# Check if G_latt is present
set_up_G_latt = False # Assume not

File diff suppressed because it is too large Load Diff

View File

@ -1,10 +1,12 @@
# load triqs helper to set up tests
# load triqs helper to set up tests (removed bandcharacter test for now)
set(all_tests
elk_convert
elk_equiv_convert
elk_bands_convert
elk_bandcharacter_convert
# elk_bandcharacter_convert
occ_test
elk_transport_convert
elk_spectralcontours_convert
)
file(GLOB all_test_files RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.py)

View File

@ -5,6 +5,7 @@ from triqs.utility.h5diff import h5diff
import triqs.utility.mpi as mpi
from triqs_dft_tools.converters import ElkConverter
from triqs_dft_tools.sumk_dft_tools import *
#get current working directory path
cwd = format(os.getcwd())
#location of test directory
@ -17,6 +18,27 @@ Converter.hdf_file = 'elk_bc_convert.out.h5'
Converter.convert_dft_input()
Converter.dft_band_characters()
SK = SumkDFTTools(hdf_file='elk_bc_convert.out.h5', use_dft_blocks=True)
SK.occupations(with_Sigma=False, with_dc=False)
omin = -1.0
omax = 1.0
oN = 3
mesh = MeshReFreq(omin,omax,oN)
dos_elk = SK.density_of_states(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, proj_type='elk', save_to_file=False)
dos_occ_elk = SK.density_of_states(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, proj_type='elk', dosocc=True, save_to_file=False)
if mpi.is_master_node():
#with HDFArchive('elk_bc_convert.ref.h5', 'a') as ar:
# ar['dos_elk'] = dos_elk
# ar['dos_occ_elk'] = dos_occ_elk
# ar['dos_mesh'] = [omin,omax,oN]
with HDFArchive('elk_bc_convert.out.h5', 'a') as ar:
ar['dos_elk'] = dos_elk
ar['dos_occ_elk'] = dos_occ_elk
ar['dos_mesh'] = [omin,omax,oN]
if mpi.is_master_node():
h5diff('elk_bc_convert.out.h5','elk_bc_convert.ref.h5')

View File

@ -5,6 +5,7 @@ from triqs.utility.h5diff import h5diff
import triqs.utility.mpi as mpi
from triqs_dft_tools.converters import ElkConverter
from triqs_dft_tools.sumk_dft_tools import *
#get current working directory path
cwd = format(os.getcwd())
#location of test directory
@ -17,6 +18,24 @@ Converter.hdf_file = 'elk_bands_convert.out.h5'
Converter.convert_dft_input()
Converter.convert_bands_input()
omin = -1.0
omax = 1.0
oN = 3
mesh = MeshReFreq(omin,omax,oN)
SK = SumkDFTTools(hdf_file='elk_bands_convert.out.h5', use_dft_blocks=True)
spag_wann = SK.spaghettis(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, proj_type='wann', save_to_file=False)
if mpi.is_master_node():
#with HDFArchive('elk_bands_convert.ref.h5', 'a') as ar:
# ar['spag_wann'] = spag_wann
# ar['mesh'] = [omin,omax,oN]
with HDFArchive('elk_bands_convert.out.h5', 'a') as ar:
ar['spag_wann'] = spag_wann
ar['mesh'] = [omin,omax,oN]
if mpi.is_master_node():
h5diff('elk_bands_convert.out.h5','elk_bands_convert.ref.h5')

View File

@ -0,0 +1,60 @@
import os
from h5 import *
from triqs.utility.comparison_tests import *
from triqs.utility.h5diff import h5diff
import triqs.utility.mpi as mpi
from triqs_dft_tools.converters import ElkConverter
from triqs_dft_tools.sumk_dft_tools import *
#get current working directory path
cwd = format(os.getcwd())
#location of test directory
testdir = cwd+'/elk_spectralcontours_convert'
#change to test directory
os.chdir(testdir)
#default k-mesh
Converter = ElkConverter(filename='SrVO3', repacking=True)
Converter.hdf_file = 'elk_spectralcontours_convert.out.h5'
Converter.convert_dft_input()
Converter.convert_contours_input()
omin = -1.0
omax = 1.0
oN = 3
mesh = MeshReFreq(omin,omax,oN)
SK = SumkDFTTools(hdf_file='elk_spectralcontours_convert.out.h5', use_dft_blocks=True)
fs_elk = SK.spectral_contours(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, FS=True, proj_type='wann', save_to_file=False)
omega_elk = SK.spectral_contours(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, FS=False, proj_type='wann', save_to_file=False)
omega_range_elk = SK.spectral_contours(broadening=0.01, mesh=mesh, plot_range=(-0.5,2), with_Sigma=False, with_dc=False, FS=False, proj_type='wann', save_to_file=False)
#user specified k-mesh - has to be same as used in elk.in
Converter = ElkConverter(filename='SrVO3', repacking=True)
Converter.hdf_file = 'elk_spectralcontours_convert.out.h5'
ngrid=np.array([10,10,1],np.int_)
kgrid=np.array([[0.0,0.0,0.0],[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]],np.float_)
Converter.convert_contours_input(kgrid=kgrid,ngrid=ngrid)
SK2 = SumkDFTTools(hdf_file='elk_spectralcontours_convert.out.h5', use_dft_blocks=True)
fs_elk_user = SK2.spectral_contours(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, FS=True, proj_type='wann', save_to_file=False)
if mpi.is_master_node():
#with HDFArchive('elk_spectralcontours_convert.ref.h5', 'a') as ar:
# ar['fs_elk'] = fs_elk
# ar['fs_elk_user'] = fs_elk_user
# ar['omega_elk'] = omega_elk
# ar['omega_range_elk'] = omega_range_elk
# ar['mesh'] = [omin,omax,oN]
with HDFArchive('elk_spectralcontours_convert.out.h5', 'a') as ar:
ar['fs_elk'] = fs_elk
ar['fs_elk_user'] = fs_elk_user
ar['omega_elk'] = omega_elk
ar['omega_range_elk'] = omega_range_elk
ar['mesh'] = [omin,omax,oN]
if mpi.is_master_node():
h5diff('elk_spectralcontours_convert.out.h5','elk_spectralcontours_convert.ref.h5')
#return to cwd
os.chdir(cwd)

View File

@ -0,0 +1 @@
0.3211418521

View File

@ -0,0 +1,452 @@
10 : nkpt
41 : nstsv
1 0.000000000 0.000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080910719 2.000000000
2 -1.131688868 2.000000000
3 -1.131688868 2.000000000
4 -1.131688868 2.000000000
5 -0.9440722281 2.000000000
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12 0.1240403570 2.000000000
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21 0.2787404948 2.000000000
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23 0.2787404948 2.000000000
24 0.3627396356 0.1719262367E-17
25 0.3627396356 0.1719262367E-17
26 0.4679669876 0.000000000
27 0.4679669876 0.000000000
28 0.4816901801 0.000000000
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36 1.065261897 0.000000000
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39 1.106455928 0.000000000
40 1.114323880 0.000000000
41 1.114323880 0.000000000
2 0.2500000000 0.000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080913193 2.000000000
2 -1.132327830 2.000000000
3 -1.131627115 2.000000000
4 -1.131627115 2.000000000
5 -0.9434460805 2.000000000
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19 0.2167722434 2.000000000
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21 0.2810750475 2.000000000
22 0.3212383247 0.9518010834
23 0.3212383247 0.9518010834
24 0.3630289786 0.1287306864E-17
25 0.4141859733 0.000000000
26 0.5095225831 0.000000000
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28 0.5351317989 0.000000000
29 0.6323948955 0.000000000
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32 0.7959842788 0.000000000
33 0.8780219214 0.000000000
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35 0.8898914875 0.000000000
36 0.9650596386 0.000000000
37 1.041840477 0.000000000
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39 1.073097886 0.000000000
40 1.073097886 0.000000000
41 1.117664142 0.000000000
3 0.5000000000 0.000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080912294 2.000000000
2 -1.132926000 2.000000000
3 -1.131564883 2.000000000
4 -1.131564883 2.000000000
5 -0.9428157584 2.000000000
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12 0.6746678864E-01 2.000000000
13 0.1234010884 2.000000000
14 0.1234010884 2.000000000
15 0.1468283741 2.000000000
16 0.1474941442 2.000000000
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18 0.1641425238 2.000000000
19 0.2112615509 2.000000000
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21 0.2834528906 2.000000000
22 0.3519041118 0.8732736605E-13
23 0.3519041118 0.8732736605E-13
24 0.3633785741 0.9075167915E-18
25 0.4481704980 0.000000000
26 0.5555294131 0.000000000
27 0.5580645177 0.000000000
28 0.6281608880 0.000000000
29 0.6332808152 0.000000000
30 0.6924728482 0.000000000
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32 0.7832086939 0.000000000
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38 1.036659717 0.000000000
39 1.044273941 0.000000000
40 1.044273941 0.000000000
41 1.063670948 0.000000000
4 0.2500000000 0.2500000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080910305 2.000000000
2 -1.132253334 2.000000000
3 -1.132203942 2.000000000
4 -1.131564292 2.000000000
5 -0.9429128350 2.000000000
6 -0.3974642421 2.000000000
7 -0.3620613011 2.000000000
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9 -0.2816629848 2.000000000
10 -0.2755380936 2.000000000
11 -0.2700258631 2.000000000
12 0.8547828315E-01 2.000000000
13 0.1039516055 2.000000000
14 0.1197230548 2.000000000
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17 0.1753953421 2.000000000
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19 0.1940737286 2.000000000
20 0.2141345371 2.000000000
21 0.3192041969 1.748188638
22 0.3242569532 0.8497725960E-01
23 0.3326622012 0.1985188022E-04
24 0.3876946347 0.000000000
25 0.4473600939 0.000000000
26 0.5410462795 0.000000000
27 0.5568988187 0.000000000
28 0.5594024471 0.000000000
29 0.6584119709 0.000000000
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31 0.6729354314 0.000000000
32 0.7948725946 0.000000000
33 0.8323960461 0.000000000
34 0.8621206515 0.000000000
35 0.8968767900 0.000000000
36 0.9026311112 0.000000000
37 1.007375063 0.000000000
38 1.029511860 0.000000000
39 1.074335499 0.000000000
40 1.076877573 0.000000000
41 1.128715119 0.000000000
5 0.5000000000 0.2500000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080915142 2.000000000
2 -1.132870627 2.000000000
3 -1.132197689 2.000000000
4 -1.131556232 2.000000000
5 -0.9423759464 2.000000000
6 -0.3957985951 2.000000000
7 -0.3591886159 2.000000000
8 -0.3541237898 2.000000000
9 -0.2803411887 2.000000000
10 -0.2742745845 2.000000000
11 -0.2649434107 2.000000000
12 0.6526613080E-01 2.000000000
13 0.8555139360E-01 2.000000000
14 0.9994023735E-01 2.000000000
15 0.1245618184 2.000000000
16 0.1367849583 2.000000000
17 0.1699818356 2.000000000
18 0.1826235006 2.000000000
19 0.1855674022 2.000000000
20 0.2170412769 2.000000000
21 0.3221154164 0.5483412429
22 0.3512664743 0.1652238713E-12
23 0.3540552435 0.1016074895E-13
24 0.3921830392 0.000000000
25 0.4807900649 0.000000000
26 0.5824822150 0.000000000
27 0.6006494969 0.000000000
28 0.6229834244 0.000000000
29 0.6657915676 0.000000000
30 0.6890583665 0.000000000
31 0.6988887480 0.000000000
32 0.7600570316 0.000000000
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34 0.8243421355 0.000000000
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39 1.034862996 0.000000000
40 1.123680910 0.000000000
41 1.128338774 0.000000000
6 0.5000000000 0.5000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080919469 2.000000000
2 -1.132821084 2.000000000
3 -1.132821084 2.000000000
4 -1.131556349 2.000000000
5 -0.9419324868 2.000000000
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39 1.078257119 0.000000000
40 1.131319957 0.000000000
41 1.131319957 0.000000000
7 0.2500000000 0.2500000000 0.2500000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080913977 2.000000000
2 -1.132241568 2.000000000
3 -1.132172611 2.000000000
4 -1.132172611 2.000000000
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12 0.7777182416E-01 2.000000000
13 0.1043736461 2.000000000
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21 0.3336490597 0.7399751594E-05
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23 0.3371547058 0.2221958444E-06
24 0.4466894689 0.000000000
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36 0.9036846530 0.000000000
37 1.014152150 0.000000000
38 1.014152150 0.000000000
39 1.058268396 0.000000000
40 1.080994750 0.000000000
41 1.080994750 0.000000000
8 0.5000000000 0.2500000000 0.2500000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080917843 2.000000000
2 -1.132810191 2.000000000
3 -1.132178689 2.000000000
4 -1.132133756 2.000000000
5 -0.9419326068 2.000000000
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21 0.3392602725 0.2705826722E-07
22 0.3563641887 0.1009633014E-14
23 0.3578146532 0.2367199304E-15
24 0.4454329801 0.000000000
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39 1.019094714 0.000000000
40 1.021550943 0.000000000
41 1.047281587 0.000000000
9 0.5000000000 0.5000000000 0.2500000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080921208 2.000000000
2 -1.132758919 2.000000000
3 -1.132758919 2.000000000
4 -1.132120892 2.000000000
5 -0.9414857882 2.000000000
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12 0.5632350433E-01 2.000000000
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15 0.1088394444 2.000000000
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21 0.3601440638 0.2304542215E-16
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24 0.4633895546 0.000000000
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36 0.8941930696 0.000000000
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38 0.9781855322 0.000000000
39 1.024091390 0.000000000
40 1.057340734 0.000000000
41 1.057340734 0.000000000
10 0.5000000000 0.5000000000 0.5000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080926396 2.000000000
2 -1.132723022 2.000000000
3 -1.132723022 2.000000000
4 -1.132723022 2.000000000
5 -0.9410358021 2.000000000
6 -0.3473222676 2.000000000
7 -0.3473222676 2.000000000
8 -0.3473222676 2.000000000
9 -0.2965701321 2.000000000
10 -0.2965701321 2.000000000
11 -0.2965701321 2.000000000
12 0.4839222808E-01 2.000000000
13 0.7509750358E-01 2.000000000
14 0.7509750358E-01 2.000000000
15 0.9821261830E-01 2.000000000
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39 1.032575179 2.000000000
40 1.091331552 2.000000000
41 1.118103456 2.000000000
15 0.5000000000 0.2000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080915413 2.000000000
2 -1.132891012 2.000000000
3 -1.132015592 2.000000000
4 -1.131566151 2.000000000
5 -0.9425122411 2.000000000
6 -0.4001227534 2.000000000
7 -0.3563639315 2.000000000
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10 -0.2764149195 2.000000000
11 -0.2635225292 2.000000000
12 0.6600668299E-01 2.000000000
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34 0.8399854218 2.000000000
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38 1.020782934 2.000000000
39 1.022953844 2.000000000
40 1.114830034 2.000000000
41 1.115916337 2.000000000
16 0.3000000000 0.3000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080914971 2.000000000
2 -1.132436968 2.000000000
3 -1.132418318 2.000000000
4 -1.131566223 2.000000000
5 -0.9425907807 2.000000000
6 -0.3941947599 2.000000000
7 -0.3638680315 2.000000000
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10 -0.2731334556 2.000000000
11 -0.2687672089 2.000000000
12 0.7628097937E-01 2.000000000
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31 0.6837327429 2.000000000
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34 0.8480547574 2.000000000
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39 1.068074555 2.000000000
40 1.081333640 2.000000000
41 1.142822231 2.000000000
17 0.4000000000 0.3000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080916149 2.000000000
2 -1.132733541 2.000000000
3 -1.132400746 2.000000000
4 -1.131568612 2.000000000
5 -0.9423366413 2.000000000
6 -0.3914794664 2.000000000
7 -0.3635836581 2.000000000
8 -0.3538096622 2.000000000
9 -0.2801657495 2.000000000
10 -0.2722594797 2.000000000
11 -0.2671308164 2.000000000
12 0.6791777276E-01 2.000000000
13 0.8235645579E-01 2.000000000
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25 0.4861413807 2.000000000
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27 0.6007419184 2.000000000
28 0.6221902279 2.000000000
29 0.6722373150 2.000000000
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31 0.6961463328 2.000000000
32 0.7686733701 2.000000000
33 0.8052301596 2.000000000
34 0.8359038407 2.000000000
35 0.8375336935 2.000000000
36 0.8658094998 2.000000000
37 0.9487850813 2.000000000
38 0.9681844240 2.000000000
39 1.055395650 2.000000000
40 1.100695686 2.000000000
41 1.155650871 2.000000000
18 0.5000000000 0.3000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080915624 2.000000000
2 -1.132850089 2.000000000
3 -1.132388258 2.000000000
4 -1.131547752 2.000000000
5 -0.9422393049 2.000000000
6 -0.3908737675 2.000000000
7 -0.3618869250 2.000000000
8 -0.3553445021 2.000000000
9 -0.2799058677 2.000000000
10 -0.2722492654 2.000000000
11 -0.2661706205 2.000000000
12 0.6414383834E-01 2.000000000
13 0.7660769556E-01 2.000000000
14 0.9093097487E-01 2.000000000
15 0.1251700771 2.000000000
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18 0.1763261642 2.000000000
19 0.1835857744 2.000000000
20 0.2255134950 2.000000000
21 0.3316233696 2.000000000
22 0.3511715339 2.000000000
23 0.3563289914 2.000000000
24 0.3959382304 2.000000000
25 0.4922753552 2.000000000
26 0.5914731571 2.000000000
27 0.6181130599 2.000000000
28 0.6193088746 2.000000000
29 0.6784747967 2.000000000
30 0.6913423708 2.000000000
31 0.7016502917 2.000000000
32 0.7525755071 2.000000000
33 0.8004113737 2.000000000
34 0.8092377498 2.000000000
35 0.8328494307 2.000000000
36 0.8590811936 2.000000000
37 0.9465071345 2.000000000
38 0.9633976262 2.000000000
39 1.048557906 2.000000000
40 1.128880774 2.000000000
41 1.135097909 2.000000000
19 0.4000000000 0.4000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080917731 2.000000000
2 -1.132706790 2.000000000
3 -1.132705960 2.000000000
4 -1.131565401 2.000000000
5 -0.9421058471 2.000000000
6 -0.3834790329 2.000000000
7 -0.3665756857 2.000000000
8 -0.3570864087 2.000000000
9 -0.2794661602 2.000000000
10 -0.2694169315 2.000000000
11 -0.2681876336 2.000000000
12 0.6262103867E-01 2.000000000
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27 0.6101071207 2.000000000
28 0.6613991904 2.000000000
29 0.6670118773 2.000000000
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31 0.7028656497 2.000000000
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33 0.7865876965 2.000000000
34 0.8143653036 2.000000000
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39 1.068976702 2.000000000
40 1.110556071 2.000000000
41 1.159698049 2.000000000
20 0.5000000000 0.4000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080917891 2.000000000
2 -1.132827350 2.000000000
3 -1.132688495 2.000000000
4 -1.131562003 2.000000000
5 -0.9420174680 2.000000000
6 -0.3800462132 2.000000000
7 -0.3660252128 2.000000000
8 -0.3604288618 2.000000000
9 -0.2791973288 2.000000000
10 -0.2691628168 2.000000000
11 -0.2676617893 2.000000000
12 0.5993194062E-01 2.000000000
13 0.6425223388E-01 2.000000000
14 0.7786030826E-01 2.000000000
15 0.1263728510 2.000000000
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20 0.2360688023 2.000000000
21 0.3457297335 2.000000000
22 0.3509995361 2.000000000
23 0.3616826223 2.000000000
24 0.3997580283 2.000000000
25 0.5109441828 2.000000000
26 0.6083858840 2.000000000
27 0.6097125375 2.000000000
28 0.6627027592 2.000000000
29 0.6967353862 2.000000000
30 0.7008363829 2.000000000
31 0.7073978040 2.000000000
32 0.7347418759 2.000000000
33 0.7903798822 2.000000000
34 0.8013347673 2.000000000
35 0.8067730797 2.000000000
36 0.8485275110 2.000000000
37 0.8667098609 2.000000000
38 0.9586620793 2.000000000
39 1.069981813 2.000000000
40 1.131713603 2.000000000
41 1.135135916 2.000000000
21 0.5000000000 0.5000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
1 -2.080919482 2.000000000
2 -1.132821097 2.000000000
3 -1.132821097 2.000000000
4 -1.131556361 2.000000000
5 -0.9419324886 2.000000000
6 -0.3708454340 2.000000000
7 -0.3675374646 2.000000000
8 -0.3675374646 2.000000000
9 -0.2789251682 2.000000000
10 -0.2680364276 2.000000000
11 -0.2680364276 2.000000000
12 0.5665045277E-01 2.000000000
13 0.5993673263E-01 2.000000000
14 0.7465522991E-01 2.000000000
15 0.1269678685 2.000000000
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17 0.1634606715 2.000000000
18 0.1830616667 2.000000000
19 0.1830616667 2.000000000
20 0.2395130119 2.000000000
21 0.3509860264 2.000000000
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25 0.5180680338 2.000000000
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27 0.6159910807 2.000000000
28 0.6990396102 2.000000000
29 0.6990396102 2.000000000
30 0.7103343712 2.000000000
31 0.7103343712 2.000000000
32 0.7116369245 2.000000000
33 0.7967413245 2.000000000
34 0.8017159495 2.000000000
35 0.8017159495 2.000000000
36 0.8135759195 2.000000000
37 0.8435846655 2.000000000
38 0.9580135298 2.000000000
39 1.078257127 2.000000000
40 1.131319960 2.000000000
41 1.131319960 2.000000000

View File

@ -0,0 +1,31 @@
scale
1.0
scale1
1.0
scale2
1.0
scale3
1.0
avec
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
atoms
3 : nspecies
'Sr.in' : spfname
1 : natoms; atpos, bfcmt below
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
'V.in' : spfname
1 : natoms; atpos, bfcmt below
0.50000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
'O.in' : spfname
3 : natoms; atpos, bfcmt below
0.00000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
0.50000000 0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
0.50000000 0.50000000 0.00000000 0.00000000 0.00000000 0.00000000

View File

@ -0,0 +1,11 @@
10 : nkpt; k-point, vkl, wkpt, nmat below
1 0.000000000 0.000000000 0.000000000 0.1562500000E-01 401
2 0.2500000000 0.000000000 0.000000000 0.9375000000E-01 397
3 0.5000000000 0.000000000 0.000000000 0.4687500000E-01 372
4 0.2500000000 0.2500000000 0.000000000 0.1875000000 374
5 0.5000000000 0.2500000000 0.000000000 0.1875000000 386
6 0.5000000000 0.5000000000 0.000000000 0.4687500000E-01 392
7 0.2500000000 0.2500000000 0.2500000000 0.1250000000 386
8 0.5000000000 0.2500000000 0.2500000000 0.1875000000 386
9 0.5000000000 0.5000000000 0.2500000000 0.9375000000E-01 388
10 0.5000000000 0.5000000000 0.5000000000 0.1562500000E-01 396

View File

@ -0,0 +1,22 @@
21 : nkpt; k-point, vkl, wkpt, nmat below
1 0.000000000 0.000000000 0.000000000 0.1000000000E-01 401
2 0.1000000000 0.000000000 0.000000000 0.4000000000E-01 389
3 0.2000000000 0.000000000 0.000000000 0.4000000000E-01 385
4 0.3000000000 0.000000000 0.000000000 0.4000000000E-01 393
5 0.4000000000 0.000000000 0.000000000 0.4000000000E-01 388
6 0.5000000000 0.000000000 0.000000000 0.2000000000E-01 372
7 0.1000000000 0.1000000000 0.000000000 0.4000000000E-01 392
8 0.2000000000 0.1000000000 0.000000000 0.8000000000E-01 387
9 0.3000000000 0.1000000000 0.000000000 0.8000000000E-01 392
10 0.4000000000 0.1000000000 0.000000000 0.8000000000E-01 381
11 0.5000000000 0.1000000000 0.000000000 0.4000000000E-01 388
12 0.2000000000 0.2000000000 0.000000000 0.4000000000E-01 392
13 0.3000000000 0.2000000000 0.000000000 0.8000000000E-01 380
14 0.4000000000 0.2000000000 0.000000000 0.8000000000E-01 388
15 0.5000000000 0.2000000000 0.000000000 0.4000000000E-01 390
16 0.3000000000 0.3000000000 0.000000000 0.4000000000E-01 382
17 0.4000000000 0.3000000000 0.000000000 0.8000000000E-01 389
18 0.5000000000 0.3000000000 0.000000000 0.4000000000E-01 386
19 0.4000000000 0.4000000000 0.000000000 0.4000000000E-01 390
20 0.5000000000 0.4000000000 0.000000000 0.4000000000E-01 394
21 0.5000000000 0.5000000000 0.000000000 0.1000000000E-01 392

View File

@ -0,0 +1,41 @@
+----------------------------+
| Real-space lattice vectors |
+----------------------------+
vector a1 : 7.260500000 0.000000000 0.000000000
vector a2 : 0.000000000 7.260500000 0.000000000
vector a3 : 0.000000000 0.000000000 7.260500000
Stored column-wise as a matrix :
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
Inverse of matrix :
0.1377315612 0.000000000 0.000000000
0.000000000 0.1377315612 0.000000000
0.000000000 0.000000000 0.1377315612
Unit cell volume : 382.7362428
+----------------------------------+
| Reciprocal-space lattice vectors |
+----------------------------------+
vector b1 : 0.8653929216 0.000000000 0.000000000
vector b2 : 0.000000000 0.8653929216 0.000000000
vector b3 : 0.000000000 0.000000000 0.8653929216
Stored column-wise as a matrix :
0.8653929216 0.000000000 0.000000000
0.000000000 0.8653929216 0.000000000
0.000000000 0.000000000 0.8653929216
Inverse of matrix :
1.155544464 0.000000000 0.000000000
0.000000000 1.155544464 0.000000000
0.000000000 0.000000000 1.155544464
Brillouin zone volume : 0.6480970070

View File

@ -0,0 +1,8 @@
1 10 1 0 5 : nproj, nkpt, nspinor, spinorb, natmtot
1 : Proj index
2 1 2 3 : Species index, natoms, l, lm submatrix size
1 : Subset no. of equivalent atoms
1 2 : atom, spatom
3 4 5 : lm indices
1 : Cubic Harmonics

View File

@ -0,0 +1,8 @@
1 21 1 0 5 : nproj, nkpt, nspinor, spinorb, natmtot
1 : Proj index
2 1 2 3 : Species index, natoms, l, lm submatrix size
1 : Subset no. of equivalent atoms
1 2 : atom, spatom
3 4 5 : lm indices
1 : Cubic Harmonics

View File

@ -0,0 +1,580 @@
(translation vectors and rotation matrices are in lattice coordinates)
48 : nsymcrys
Crystal symmetry : 1
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
1 0 0
0 1 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 2
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 -1 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 3
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 -1
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 4
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 -1
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 5
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 1
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 6
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 1
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 7
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 1 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 8
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 1 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 9
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
-1 0 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 10
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
-1 0 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 11
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
0 0 -1
-1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 12
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
0 0 -1
1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 13
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
0 0 1
-1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 14
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
0 0 1
1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 15
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
1 0 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 16
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
1 0 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 17
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
-1 0 0
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 18
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
-1 0 0
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 19
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
0 -1 0
-1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 20
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
0 -1 0
1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 21
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
0 1 0
-1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 22
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
0 1 0
1 0 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 23
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
1 0 0
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 24
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 0 -1
1 0 0
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 25
spatial translation :
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spatial translation :
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spatial translation :
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View File

@ -0,0 +1,141 @@
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View File

@ -0,0 +1,50 @@
tasks
0
807
ngridk
4 4 4
! Path for species files
sppath
'/home/elk-6.2.8/species/'
! Maximum length for G+k vectors
rgkmax
7.0
!generate eigenvalues in kz=0 plane
!these inputs are in terms of lattice
!vectors
plot3d
0.00000000 0.00000000 0.00000000
1.00000000 0.00000000 0.00000000
0.00000000 1.00000000 0.00000000
0.00000000 0.00000000 1.00000000
10 10 1
avec
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
atoms
3 : nspecies
'Sr.in' : spfname
1 : natoms; atposl, bfcmt below
0.50000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
'V.in' : spfname
1 : natoms; atposl, bfcmt below
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
'O.in' : spfname
3 : natoms; atposl, bfcmt below
0.50000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
0.00000000 0.50000000 0.00000000 0.00000000 0.00000000 0.00000000
0.00000000 0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
!Wannier projectors
wanproj !projector flag
1 !number of projectors - next 3 lines are repeated for each projector
2 2 3 !species, l, reduced max lm (rlmmax) value
7 8 9 !the lm quanties which will be projected (vector length eq. rlmmax)
-0.294 0.27562 ![-8.0, 7.5] eV t2g correlated energy window

View File

@ -0,0 +1,42 @@
import os
from h5 import *
from triqs.utility.comparison_tests import *
from triqs.utility.h5diff import h5diff
import triqs.utility.mpi as mpi
from triqs_dft_tools.converters import ElkConverter
from triqs_dft_tools.sumk_dft_tools import *
#get current working directory path
cwd = format(os.getcwd())
#location of test directory
testdir = cwd+'/occ_test'
#change to test directory
os.chdir(testdir)
Converter = ElkConverter(filename='SrVO3', repacking=True)
Converter.hdf_file = 'elk_occ_convert.out.h5'
Converter.convert_dft_input()
SK = SumkDFTTools(hdf_file='elk_occ_convert.out.h5', use_dft_blocks=True)
SK.occupations(with_Sigma=False, with_dc=False)
omin = -1.0
omax = 1.0
oN = 3
mesh = MeshReFreq(omin,omax,oN)
dos_occ = SK.density_of_states(broadening=0.01, mesh=mesh, with_Sigma=False, with_dc=False, dosocc=True, save_to_file=False)
if mpi.is_master_node():
with HDFArchive('elk_occ_convert.ref.h5', 'a') as ar:
ar['dos_occ'] = dos_occ
ar['dos_mesh'] = [omin,omax,oN]
with HDFArchive('elk_occ_convert.out.h5', 'a') as ar:
ar['dos_occ'] = dos_occ
ar['dos_mesh'] = [omin,omax,oN]
if mpi.is_master_node():
h5diff('elk_occ_convert.out.h5','elk_occ_convert.ref.h5')
#return to cwd
os.chdir(cwd)

View File

@ -0,0 +1 @@
0.3211418522

View File

@ -0,0 +1,452 @@
10 : nkpt
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(state, eigenvalue and occupancy below)
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41 1.128338774 0.000000000
6 0.5000000000 0.5000000000 0.000000000 : k-point, vkl
(state, eigenvalue and occupancy below)
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2 -1.132821084 2.000000000
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4 -1.131556348 2.000000000
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(state, eigenvalue and occupancy below)
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(state, eigenvalue and occupancy below)
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22 0.3563641889 0.1009632998E-14
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(state, eigenvalue and occupancy below)
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(state, eigenvalue and occupancy below)
1 -2.080926396 2.000000000
2 -1.132723022 2.000000000
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41 1.011396116 0.000000000

View File

@ -0,0 +1,31 @@
scale
1.0
scale1
1.0
scale2
1.0
scale3
1.0
avec
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
atoms
3 : nspecies
'Sr.in' : spfname
1 : natoms; atpos, bfcmt below
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
'V.in' : spfname
1 : natoms; atpos, bfcmt below
0.50000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
'O.in' : spfname
3 : natoms; atpos, bfcmt below
0.00000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
0.50000000 0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
0.50000000 0.50000000 0.00000000 0.00000000 0.00000000 0.00000000

View File

@ -0,0 +1,11 @@
10 : nkpt; k-point, vkl, wkpt, nmat below
1 0.000000000 0.000000000 0.000000000 0.1562500000E-01 401
2 0.2500000000 0.000000000 0.000000000 0.9375000000E-01 397
3 0.5000000000 0.000000000 0.000000000 0.4687500000E-01 372
4 0.2500000000 0.2500000000 0.000000000 0.1875000000 374
5 0.5000000000 0.2500000000 0.000000000 0.1875000000 386
6 0.5000000000 0.5000000000 0.000000000 0.4687500000E-01 392
7 0.2500000000 0.2500000000 0.2500000000 0.1250000000 386
8 0.5000000000 0.2500000000 0.2500000000 0.1875000000 386
9 0.5000000000 0.5000000000 0.2500000000 0.9375000000E-01 388
10 0.5000000000 0.5000000000 0.5000000000 0.1562500000E-01 396

View File

@ -0,0 +1,41 @@
+----------------------------+
| Real-space lattice vectors |
+----------------------------+
vector a1 : 7.260500000 0.000000000 0.000000000
vector a2 : 0.000000000 7.260500000 0.000000000
vector a3 : 0.000000000 0.000000000 7.260500000
Stored column-wise as a matrix :
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
Inverse of matrix :
0.1377315612 0.000000000 0.000000000
0.000000000 0.1377315612 0.000000000
0.000000000 0.000000000 0.1377315612
Unit cell volume : 382.7362428
+----------------------------------+
| Reciprocal-space lattice vectors |
+----------------------------------+
vector b1 : 0.8653929216 0.000000000 0.000000000
vector b2 : 0.000000000 0.8653929216 0.000000000
vector b3 : 0.000000000 0.000000000 0.8653929216
Stored column-wise as a matrix :
0.8653929216 0.000000000 0.000000000
0.000000000 0.8653929216 0.000000000
0.000000000 0.000000000 0.8653929216
Inverse of matrix :
1.155544464 0.000000000 0.000000000
0.000000000 1.155544464 0.000000000
0.000000000 0.000000000 1.155544464
Brillouin zone volume : 0.6480970070

View File

@ -0,0 +1,8 @@
1 10 1 0 5 : nproj, nkpt, nspinor, spinorb, natmtot
1 : Proj index
2 1 2 3 : Species index, natoms, l, lm submatrix size
1 : Subset no. of equivalent atoms
1 2 : atom, spatom
3 4 5 : lm indices
1 : Cubic Harmonics

View File

@ -0,0 +1,580 @@
(translation vectors and rotation matrices are in lattice coordinates)
48 : nsymcrys
Crystal symmetry : 1
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
1 0 0
0 1 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 2
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 -1 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 3
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 -1
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 4
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 -1
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 5
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 1
0 -1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 6
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 0 1
0 1 0
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 7
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 1 0
0 0 -1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 8
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
-1 0 0
0 1 0
0 0 1
global spin rotation :
1 0 0
0 1 0
0 0 1
Crystal symmetry : 9
spatial translation :
0.000000000 0.000000000 0.000000000
spatial rotation :
0 -1 0
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0 0 -1
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View File

@ -0,0 +1,141 @@
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@ -0,0 +1,40 @@
tasks
0
805
ngridk
4 4 4
! Path for species files
sppath
'/home/elk-6.2.8/species/'
! Maximum length for G+k vectors
rgkmax
7.0
avec
7.260500000 0.000000000 0.000000000
0.000000000 7.260500000 0.000000000
0.000000000 0.000000000 7.260500000
atoms
3 : nspecies
'Sr.in' : spfname
1 : natoms; atposl, bfcmt below
0.50000000 0.50000000 0.50000000 0.00000000 0.00000000 0.00000000
'V.in' : spfname
1 : natoms; atposl, bfcmt below
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
'O.in' : spfname
3 : natoms; atposl, bfcmt below
0.50000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
0.00000000 0.50000000 0.00000000 0.00000000 0.00000000 0.00000000
0.00000000 0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
!Wannier projectors
wanproj !projector flag
1 !number of projectors - next 3 lines are repeated for each projector
2 2 3 !species, l, reduced max lm (rlmmax) value
7 8 9 !the lm quanties which will be projected (vector length eq. rlmmax)
-0.294 0.27562 ![-8.0, 7.5] eV t2g correlated energy window

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View File

@ -13,9 +13,10 @@ SK.chemical_potential = chemical_potential
SK.dc_imp = dc_imp
SK.set_Sigma([Sigma])
dos_wannier = SK.dos_wannier_basis(broadening=0.01, with_Sigma=True, with_dc=True, save_to_file=False)
dos_parproj = SK.dos_parproj_basis(broadening=0.01, with_Sigma=True, with_dc=True, save_to_file=False)
spaghetti = SK.spaghettis(broadening=0.01, plot_shift=0.0, plot_range=(-1,1), ishell=None, save_to_file=False)
dos_wannier = SK.density_of_states(broadening=0.01, with_Sigma=True, with_dc=True, proj_type='wann', save_to_file=False)
dos_parproj = SK.density_of_states(broadening=0.01, with_Sigma=True, with_dc=True, proj_type='wien2k', save_to_file=False)
[spaghetti, pAkw, pAkw_orb] = SK.spaghettis(broadening=0.01, plot_shift=0.0, plot_range=(-1,1), shell_list=None, save_to_file=False)
if mpi.is_master_node():