.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "flow_gallery/run_gwconv_ecuteps.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_flow_gallery_run_gwconv_ecuteps.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_flow_gallery_run_gwconv_ecuteps.py:


G0W0 convergence study
======================

G0W0 convergence study wrt ecuteps and the number of bands in W.

.. GENERATED FROM PYTHON SOURCE LINES 8-144



.. image:: /flow_gallery/images/sphx_glr_run_gwconv_ecuteps_001.png
    :alt: run gwconv ecuteps
    :class: sphx-glr-single-img





.. code-block:: default

    import sys
    import os
    import numpy as np

    import abipy.abilab as abilab
    import abipy.data as abidata
    import abipy.flowtk as flowtk


    def make_inputs(paral_kgb=1):
        """
        Returns a tuple of 4 input files for SCF, NSCF, SCR, SIGMA calculations.
        These files are then used as templates for the convergence study
        wrt ecuteps and the number of bands in W.
        """
        multi = abilab.MultiDataset(abidata.structure_from_ucell("SiC"),
                                    pseudos=abidata.pseudos("14si.pspnc", "6c.pspnc"), ndtset=4)

        ecut = 12
        global_vars = dict(
            ecut=ecut,
            istwfk="*1",
            paral_kgb=paral_kgb,
            gwpara=2,
            iomode=1,
        )

        ecuteps = 4
        ngkpt = [4, 4, 4]
        shiftk = [0, 0, 0]

        multi.set_vars(global_vars)
        multi.set_kmesh(ngkpt=ngkpt, shiftk=shiftk)

        # SCF
        multi[0].set_vars(
            nband=10,
            tolvrs=1.e-8,
        )

        # NSCF
        multi[1].set_vars(
            nband=25,
            tolwfr=1.e-8,
            iscf=-2
        )

        # SCR
        multi[2].set_vars(
            optdriver=3,
            ecutwfn=ecut,
            nband=20,
            symchi=1,
            inclvkb=0,
            ecuteps=ecuteps,
        )

        # SIGMA
        multi[3].set_vars(
            optdriver=4,
            nband=20,
            ecutwfn=ecut,
            ecutsigx=ecut,
            #ecutsigx=(4*ecut), ! This is problematic
            symsigma=1,
            ecuteps=ecuteps,
            )

        multi[3].set_kptgw(kptgw=[[0,0,0], [0.5, 0, 0]], bdgw=[1, 8])

        return multi.split_datasets()


    def build_flow(options):
        # Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
        if not options.workdir:
            options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

        # Get our templates
        scf_inp, nscf_inp, scr_inp, sig_inp = make_inputs()

        ecuteps_list = np.arange(2, 8, 2)
        max_ecuteps = max(ecuteps_list)

        flow = flowtk.Flow(workdir=options.workdir, manager=options.manager)

        # Band structure work to produce the WFK file
        bands = flowtk.BandStructureWork(scf_inp, nscf_inp)
        flow.register_work(bands)

        # Build a work made of two SCR runs with different value of nband
        # Use max_ecuteps for the dielectric matrix (sigma tasks will
        # read a submatrix when we test the convergence wrt to ecuteps.
        scr_work = flowtk.Work()

        for inp in scr_inp.generate(nband=[10, 15]):
            inp.set_vars(ecuteps=max_ecuteps)
            scr_work.register_scr_task(inp, deps={bands.nscf_task: "WFK"})

        flow.register_work(scr_work)

        # Do a convergence study wrt ecuteps, each work is connected to a
        # different SCR file computed with a different value of nband.

        # Build a list of sigma inputs with different ecuteps
        sigma_inputs = list(sig_inp.generate(ecuteps=ecuteps_list))

        for scr_task in scr_work:
            sigma_conv = flowtk.SigmaConvWork(wfk_node=bands.nscf_task, scr_node=scr_task,
                                              sigma_inputs=sigma_inputs)
            flow.register_work(sigma_conv)

        return flow


    # This block generates the thumbnails in the AbiPy gallery.
    # You can safely REMOVE this part if you are using this script for production runs.
    if os.getenv("READTHEDOCS", False):
        __name__ = None
        import tempfile
        options = flowtk.build_flow_main_parser().parse_args(["-w", tempfile.mkdtemp()])
        build_flow(options).graphviz_imshow()


    @flowtk.flow_main
    def main(options):
        """
        This is our main function that will be invoked by the script.
        flow_main is a decorator implementing the command line interface.
        Command line args are stored in `options`.
        """
        return build_flow(options)


    if __name__ == "__main__":
        sys.exit(main())


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.746 seconds)


.. _sphx_glr_download_flow_gallery_run_gwconv_ecuteps.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/abinit/abipy/gh-pages?filepath=notebooks/flow_gallery/run_gwconv_ecuteps.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: run_gwconv_ecuteps.py <run_gwconv_ecuteps.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: run_gwconv_ecuteps.ipynb <run_gwconv_ecuteps.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_