Python io.read方法代碼示例

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def structure_to_atoms(structure):
    """
    Convert a qmpy.Structure to an ase.Atoms

    Example::

        >>> import qmpy.io as io
        >>> structure = io.read('POSCAR')
        >>> atoms = io.ase_mapper.structure_to_atoms(structure)

    """
    if not qmpy.FOUND_ASE:
        print 'ASE must be installed to convert a Structure to an Atoms object'
        return
    atoms = ase.Atoms(
            structure.name,
            cell=structure.cell,
            scaled_positions=structure.coords, 
            magmoms=structure.magmoms)
    return atoms 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def atoms_to_structure(atoms):
    """
    Convert a qmpy.Structure to an ase.Atoms

    Example::

        >>> import qmpy.io.ase_mapper
        >>> atoms = ase.io.read('POSCAR')
        >>> structure = qmpy.io.ase_mapper.atoms_to_structure(atoms)

    """
    if not qmpy.FOUND_ASE:
        print 'ASE must be installed to convert Atoms object to a Structure'
        return
    struct = Structure()
    struct.cell = atoms.get_cell()
    for a in atoms: 
        atom = Atom()
        atom.coord = a.position
        atom.symbol = a.symbol
        atom.magmom = a.magmom
        atom.direct = False
        struct.add_atom(atom)
    return struct 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def test_minimize(self):
        initial_structure = read('initial_mlmin.traj')
        initial_structure.set_calculator(EMT())

        initial_opt = MLMin(initial_structure,
                            trajectory='mlmin_structures.traj')
        initial_opt.run(fmax=0.01, full_output=True)

        atoms_catlearn = read('mlmin_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn)
        print('Checking number of function calls...')
        self.assertEqual(n_eval_catlearn, 7)
        print('Checking converged energy...')
        e_opt = initial_opt.list_targets[-1]
        e_test = 3.31076
        np.testing.assert_array_almost_equal(e_opt, e_test, decimal=5)
        print('Checking converged fmax...')
        fmax_opt = initial_opt.list_fmax[-1][0]
        fmax_test = 0.00816
        np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def testWithNumericalStressWithPBCEnabled(self):
        filename = os.path.join(path, '../tools/generate-unit-test-expect/others/Benzene.cif')
        benzene = read(filename)
        calculator = self.model.ase()
        benzene.set_calculator(calculator)
        dyn = NPTBerendsen(benzene, timestep=0.1 * units.fs,
                           temperature=300 * units.kB,
                           taut=0.1 * 1000 * units.fs, pressure=1.01325,
                           taup=1.0 * 1000 * units.fs, compressibility=4.57e-5)

        def test_stress():
            stress = benzene.get_stress()
            numerical_stress = calculator.calculate_numerical_stress(benzene)
            diff = torch.from_numpy(stress - numerical_stress).abs().max().item()
            self.assertLess(diff, tol)
        dyn.attach(test_stress, interval=30)
        dyn.run(120) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def read_posfile():
    from ase.io import read

    try:
        atoms = read('POSCAR')
    except IOError:
        print "[__main__]: Couldn't open input file POSCAR, atomic positions will not be written...\n"
        atoms = []

    return atoms

### WRITE DOS0 CONTAINING TOTAL DOS ### 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def read(source_file, *args, **kwargs):
    """
    Read an input file of various formats.

    Arguments:
        source_file:
            The first argument is a structure file of some format.

    The remaining args and kwargs are passed to the methods called to read the
    structure.

    If the structure name contains "cif" or "POSCAR" it will be read as one of
    these formats. Failing that, the file is passed to the ASE read method, and
    the returned Atoms object is then converted to a Structure.

    Examples::

        >>> io.read(INSTALL_PATH+'/io/files/POSCAR_FCC')
        >>> io.read(INSTALL_PATH+'/io/files/fe3o4.cif')

    """
    try:
        if 'cif' in source_file:
            return cif.read(source_file, *args, **kwargs)
        elif ( 'POSCAR' in source_file or
                'CONTCAR' in source_file ):
            return poscar.read(source_file, *args, **kwargs)
        else:
            return ase.io.read(source_file, *args, **kwargs)
    except:
        try:
            return poscar.read(source_file, *args, **kwargs)
        except Exception:
            pass
        try:
            return cif.read(source_file, *args, **kwargs)
        except Exception:
            pass
    raise FormatNotSupportedError('The file %s is in an unrecognized format\
            and cannot be read' % source_file) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def read(filename, **kwargs):
    """
    Uses the ase.io.read method to read in a file, and convert it to a
    qmpy.Structure object. Passes any optional keyword arguments to the
    ase.io.read call.

    """
    if not qmpy.FOUND_ASE:
        print 'ASE must be installed to convert Atoms object to a Structure'
        return

    atoms = ase.io.read(filename, **kwargs)
    return atoms_to_structure(atoms) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def test_path(self):
        """Test ML-NEB algorithm running with an interpolated path"""
        n_images = 8
        images = [initial_structure]
        for i in range(1, n_images-1):
            image = initial_structure.copy()
            image.set_calculator(copy.deepcopy(ase_calculator))
            images.append(image)
        images.append(final_structure)

        neb = NEB(images, climb=True)
        neb.interpolate(method='linear')

        neb_catlearn = MLNEB(start=initial_structure,
                             end=final_structure,
                             interpolation=images,
                             ase_calc=ase_calculator,
                             restart=False
                             )

        neb_catlearn.run(fmax=0.05, trajectory='ML-NEB.traj', max_step=0.2,
                         full_output=True)

        atoms_catlearn = read('evaluated_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn) - 2

        self.assertEqual(n_eval_catlearn, 13)
        print('Checking number of function calls using 8 images...')
        np.testing.assert_array_equal(n_eval_catlearn, 13)
        max_unc = np.max(neb_catlearn.uncertainty_path)
        unc_test = 0.0468
        print('Checking uncertainty on the path (8 images):')
        np.testing.assert_array_almost_equal(max_unc, unc_test, decimal=4) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def read_OUTCAR(path='OUTCAR'):
    """read time and ncores info from OUTCAR"""
    time = 0
    ncore = 0
    for line in open(path, 'r'):
        if line.rfind('running on  ') > -1:
            ncore = int(line.split()[2])
        elif line.rfind('Elapsed time ') > -1:
            time = float(line.split(':')[-1])

    return time, ncore 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def single_optimize(struc, level, pstress, mode, setup):
    """single optmization"""
    struc = symmetrize_cell(struc, mode)
    struc.set_calculator(set_vasp(level, pstress, setup))
    energy = struc.get_potential_energy()
    print(energy)
    time, ncore = read_OUTCAR()
    struc = read('CONTCAR',format='vasp')
    return struc, energy, time 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def __init__(self, procar, poscar, supercell_matrix, ispin=None):
        self.fname = procar
        self.supercell_matrix = supercell_matrix
        self._parse_procar(ispin=ispin)
        self.atoms = read(poscar)
        self.basis = []
        self.positions = [] 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def test_acquisition(self):
        """Test ML-Min acquisition functions"""

        # Test acquisition lcb:
        initial_structure = read('initial_mlmin.traj')
        initial_structure.set_calculator(EMT())

        initial_opt = MLMin(initial_structure,
                            trajectory='mlmin_structures.traj')
        initial_opt.run(fmax=0.01, full_output=True, acq='lcb')

        atoms_catlearn = read('mlmin_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn)
        print('Checking number of function calls...')
        self.assertEqual(n_eval_catlearn, 7)
        print('Checking converged energy...')
        e_opt = initial_opt.list_targets[-1]
        e_test = 3.31076
        np.testing.assert_array_almost_equal(e_opt, e_test, decimal=5)
        print('Checking converged fmax...')
        fmax_opt = initial_opt.list_fmax[-1][0]
        fmax_test = 0.00816
        np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5)

        # Test acquisition ucb:
        initial_structure = read('initial_mlmin.traj')
        initial_structure.set_calculator(EMT())

        initial_opt = MLMin(initial_structure,
                            trajectory='mlmin_structures.traj')
        initial_opt.run(fmax=0.01, full_output=True, acq='ucb')

        atoms_catlearn = read('mlmin_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn)
        print('Checking number of function calls...')
        self.assertEqual(n_eval_catlearn, 7)
        print('Checking converged energy...')
        e_opt = initial_opt.list_targets[-1]
        e_test = 3.31076
        np.testing.assert_array_almost_equal(e_opt, e_test, decimal=5)
        print('Checking converged fmax...')
        fmax_opt = initial_opt.list_fmax[-1][0]
        fmax_test = 0.00782
        np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def test_kernel(self):
        """Test ML-Min kernels"""

        # Test kernel ARD fixed:
        initial_structure = read('initial_mlmin.traj')
        initial_structure.set_calculator(EMT())

        initial_opt = MLMin(initial_structure,
                            trajectory='mlmin_structures.traj')
        initial_opt.run(fmax=0.01, full_output=True, acq='min_energy',
                        kernel='SQE_fixed')

        atoms_catlearn = read('mlmin_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn)
        print('Checking number of function calls...')
        self.assertEqual(n_eval_catlearn, 7)
        print('Checking converged energy...')
        e_opt = initial_opt.list_targets[-1]
        e_test = 3.31076
        np.testing.assert_array_almost_equal(e_opt, e_test, decimal=5)
        print('Checking converged fmax...')
        fmax_opt = initial_opt.list_fmax[-1][0]
        fmax_test = 0.00786
        np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5)

        # Test ARD SQE kernel:
        initial_structure = read('initial_mlmin.traj')
        initial_structure.set_calculator(EMT())

        initial_opt = MLMin(initial_structure,
                            trajectory='mlmin_structures.traj')
        initial_opt.run(fmax=0.01, full_output=True, acq='min_energy',
                        kernel='ARD_SQE')

        atoms_catlearn = read('mlmin_structures.traj', ':')
        n_eval_catlearn = len(atoms_catlearn)
        print('Checking number of function calls...')
        self.assertEqual(n_eval_catlearn, 7)
        print('Checking converged energy...')
        e_opt = initial_opt.list_targets[-1]
        e_test = 3.31076
        np.testing.assert_array_almost_equal(e_opt, e_test, decimal=5)
        print('Checking converged fmax...')
        fmax_opt = initial_opt.list_fmax[-1][0]
        fmax_test = 0.00766
        np.testing.assert_array_almost_equal(fmax_opt, fmax_test, decimal=5) 

# 需要導入模塊: from ase import io [as 別名]
# 或者: from ase.io import read [as 別名]
def optimize(struc, dir1):
    os.mkdir(dir1)
    os.chdir(dir1)
    time0 = 0
    # Step1: ISIF = 2
    struc.set_calculator(set_vasp(level=0, pstress=pstress)) #, setup=setup))
    print(struc.get_potential_energy())
    time, ncore = read_OUTCAR()
    time0 += time
    print('time for vasp calcs0 (seconds):  ', time)

    # Step2: ISIF = 3
    struc = read('CONTCAR',format='vasp')
    struc.set_calculator(set_vasp(level=1, pstress=pstress)) #, setup=setup))
    print(struc.get_potential_energy())
    time, ncore = read_OUTCAR()
    time0 += time
    print('time for vasp calcs1 (seconds):  ', time)

    # Step3: ISIF = 3 with high precision 
    struc = read('CONTCAR',format='vasp')
    if good_lattice(struc):
        struc = symmetrize_cell(struc, mode='C')
        struc.set_calculator(set_vasp(level=2, pstress=pstress)) #, setup=setup))
        print(struc.get_potential_energy())
        time, ncore = read_OUTCAR()
        time0 += time
        print('time for vasp calcs2 (seconds):  ', time)
        struc = read('CONTCAR',format='vasp')

        if good_lattice(struc):
            struc = symmetrize_cell(struc, mode='P')
            struc.set_calculator(set_vasp(level=3, pstress=pstress, setup=setup))
            print(struc.get_potential_energy())
            time, ncore = read_OUTCAR()
            time0 += time
            print('time for vasp calcs3 (seconds):  ', time)
            struc = read('CONTCAR',format='vasp')
           
            if good_lattice(struc):
                struc = symmetrize_cell(struc, mode='P')
                struc.set_calculator(set_vasp(level=4, pstress=pstress, setup=setup))
                struc.get_potential_energy()
                time, ncore = read_OUTCAR()
                print('time for vasp calcs4 (seconds):  ', time)
                time0 += time
                result = vasprun().values
                spg = get_symmetry_dataset(struc, symprec=5e-2)['international']
                print('#####%-10s %-10s %12.6f %6.2f %8.2f %4d %12s' % 
                (dir1, struc.get_chemical_formula(), result['calculation']['energy_per_atom'], result['gap'], time0, ncore, spg))