How to use the abipy.data.cif_file function in abipy

def make_scf_nscf_inputs(paral_kgb=0, usepaw=0):
    """Returns two input files: GS run and NSCF on a high symmetry k-mesh."""
    pseudos = abidata.pseudos("14si.pspnc") if usepaw == 0 else data.pseudos("Si.GGA_PBE-JTH-paw.xml")

    # Get structure from cif file.
    multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"), pseudos=pseudos, ndtset=2)

    # Global variables
    ecut = 6
    multi.set_vars(
        ecut=ecut,
        nband=8,
        paral_kgb=paral_kgb,
        iomode=3,
        timopt=-1,
    )

    if multi.ispaw:
        multi.set_vars(pawecutdg=2 * ecut)

    # Dataset 1 (GS run)
    multi[0].set_kmesh(ngkpt=[8, 8, 8], shiftk=[0, 0, 0])

def build_flow(options):
    # Init structure and pseudos.
    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
    pseudos = abidata.pseudos("14si.pspnc")

    # 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(__file__).replace(".py", "").replace("run_", "flow_")

    # Initialize the flow.
    flow = flowtk.Flow(options.workdir, manager=options.manager)

    scf_kppa = 120
    nscf_nband = 40
    ecut, ecuteps, ecutsigx = 6, 2, 4
    #scr_nband = 50
    #sigma_nband = 50

    multi = abilab.g0w0_with_ppmodel_inputs(

def make_inputs(ngkpt, paral_kgb=1):
    # Crystalline silicon
    # Calculation of the GW correction to the direct band gap in Gamma
    # Dataset 1: ground state calculation
    # Dataset 2: NSCF calculation
    # Dataset 3: calculation of the screening
    # Dataset 4-5-6: Self-Energy matrix elements (GW corrections) with different values of nband

    multi = abilab.MultiDataset(structure=data.cif_file("si.cif"), pseudos=data.pseudos("14si.pspnc"), ndtset=6)

    # This grid is the most economical, but does not contain the Gamma point.
    scf_kmesh = dict(
        ngkpt=ngkpt,
        shiftk=[0.5, 0.5, 0.5,
                0.5, 0.0, 0.0,
                0.0, 0.5, 0.0,
                0.0, 0.0, 0.5]
    )

    # This grid contains the Gamma point, which is the point at which
    # we will compute the (direct) band gap.
    gw_kmesh = dict(
        ngkpt=ngkpt,
        shiftk=[0.0, 0.0, 0.0,
                0.0, 0.5, 0.5,

def make_inputs(ngkpt, paral_kgb=1):
    # Crystalline silicon
    # Calculation of the GW correction to the direct band gap in Gamma
    # Dataset 1: ground state calculation
    # Dataset 2: NSCF calculation
    # Dataset 3: calculation of the screening
    # Dataset 4-5-6: Self-Energy matrix elements (GW corrections) with different values of nband

    multi = abilab.MultiDataset(structure=data.cif_file("si.cif"), pseudos=data.pseudos("14si.pspnc"), ndtset=6)

    # This grid is the most economical, but does not contain the Gamma point.
    scf_kmesh = dict(
        ngkpt=ngkpt,
        shiftk=[0.5, 0.5, 0.5,
                0.5, 0.0, 0.0,
                0.0, 0.5, 0.0,
                0.0, 0.0, 0.5]
    )

    # This grid contains the Gamma point, which is the point at which
    # we will compute the (direct) band gap.
    gw_kmesh = dict(
        ngkpt=ngkpt,
        shiftk=[0.0, 0.0, 0.0,
                0.0, 0.5, 0.5,

def make_scf_nscf_inputs(paral_kgb=1):
    """Returns two input files: GS run and NSCF on a high symmetry k-mesh."""
    pseudos = abidata.pseudos("14si.pspnc")
    #pseudos = data.pseudos("Si.GGA_PBE-JTH-paw.xml")

    multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"), pseudos=pseudos, ndtset=2)

    # Global variables
    ecut = 28
    global_vars = dict(ecut=ecut,
                       nband=8,
                       timopt=-1,
                       istwfk="*1",
                       nstep=15,
                       paral_kgb=paral_kgb,
                    )

    if multi.ispaw:
        global_vars.update(pawecutdg=2*ecut)

    multi.set_vars(global_vars)

def all_inputs(paral_kgb=1):
    """
    Build the input files of the calculation.
    Returns: gs_input, nscf_input, scr_input, sigma_input
    """
    ecut = ecutwfn = 4
    global_vars = dict(
        ecut=ecut,
        istwfk="*1",
        paral_kgb=paral_kgb,
    )

    multi = abilab.MultiDataset(structure=data.cif_file("si.cif"),
                                pseudos=data.pseudos("14si.pspnc"), ndtset=4)
    multi.set_vars(global_vars)

    gs, nscf, scr, sigma = multi.split_datasets()

    # This grid is the most economical, but does not contain the Gamma point.
    gs_kmesh = dict(
        ngkpt=[2, 2, 2],
        shiftk=[0.5, 0.5, 0.5,
                0.5, 0.0, 0.0,
                0.0, 0.5, 0.0,
                0.0, 0.0, 0.5]
    )

    # This grid contains the Gamma point, which is the point at which
    # we will compute the (direct) band gap.

def make_ion_ioncell_inputs(paral_kgb=0):

    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))

    # Perturb the structure (random perturbation of 0.1 Angstrom)
    # then compress the volume to trigger dilatmx.
    structure.perturb(distance=0.1)
    structure.scale_lattice(structure.volume * 0.6)

    global_vars = dict(
        ecut=4,
        ngkpt=[4,4,4],
        shiftk=[0,0,0],
        nshiftk=1,
        chksymbreak=0,
        paral_kgb=paral_kgb,
        iomode=3,
        #prtwf=0,
    )

def make_inputs(paw=False):
    # Crystalline silicon
    # Calculation of the GW correction to the direct band gap in Gamma
    # Dataset 1: ground state calculation 
    # Dataset 2: NSCF calculation 
    # Dataset 3: calculation of the screening 
    # Dataset 4: calculation of the Self-Energy matrix elements (GW corrections)
    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
    pseudos = abidata.pseudos("14si.pspnc") if not paw else abidata.pseudos("Si.GGA_PBE-JTH-paw.xml")

    multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=4)

    ecut = 24
    multi.set_vars(
        ecut=ecut,
        pawecutdg=ecut*4 if paw else None,
        timopt=-1,
        istwfk="*1",
        paral_kgb=0,
    )

    gs, nscf, scr, sigma = multi.split_datasets()

    # This grid is the most economical, but does not contain the Gamma point.

def make_relax_flow():
    # Structural relaxation for different k-point samplings.
    ngkpt_list = [(2, 2, 2), (4, 4, 4)]
    multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
                                pseudos=abidata.pseudos("14si.pspnc"), ndtset=len(ngkpt_list))

    # Global variables
    multi.set_vars(
        ecut=10,
        tolvrs=1e-9,
        optcell=1,
        ionmov=3,
        ntime=10,
        dilatmx=1.05,
        ecutsm=0.5,
    )

    for i, ngkpt in enumerate(ngkpt_list):
        multi[i].set_kmesh(ngkpt=ngkpt, shiftk=[0,0,0])

def make_scf_nscf_inputs(paral_kgb=1):
    """Returns two input files: GS run and NSCF on a high symmetry k-mesh."""
    multi = abilab.MultiDataset(structure=abidata.cif_file("si.cif"),
                                pseudos=abidata.pseudos("14si.pspnc"), ndtset=2)

    # Global variables
    ecut = 6
    global_vars = dict(
        ecut=ecut,
        nband=8,
        nstep=15,
        paral_kgb=paral_kgb,
    )

    if multi.ispaw:
        global_vars.update(pawecutdg=2*ecut)

    multi.set_vars(global_vars)