Instruction:
Can you tell what is the following Python 3 function doing
def solve_equilibrium_point(self, analyzer1, analyzer2,
                                delu_dict={}, delu_default=0, units="nanometers"):
        """
        Gives the radial size of two particles where equilibrium is reached
            between both particles. NOTE: the solution here is not the same
            as the solution visualized in the plot because solving for r
            requires that both the total surface area and volume of the
            particles are functions of r.

        Args:
            analyzer1 (SurfaceEnergyPlotter): Analyzer associated with the
                first polymorph
            analyzer2 (SurfaceEnergyPlotter): Analyzer associated with the
                second polymorph
            delu_dict (Dict): Dictionary of the chemical potentials to be set as
                constant. Note the key should be a sympy Symbol object of the
                format: Symbol("delu_el") where el is the name of the element.
            delu_default (float): Default value for all unset chemical potentials
            units (str): Can be nanometers or Angstrom

        Returns:
            Particle radius in nm
        """

        # Set up
        wulff1 = analyzer1.wulff_from_chempot(delu_dict=delu_dict,
                                              delu_default=delu_default,
                                              symprec=self.symprec)
        wulff2 = analyzer2.wulff_from_chempot(delu_dict=delu_dict,
                                              delu_default=delu_default,
                                              symprec=self.symprec)

        # Now calculate r
        delta_gamma = wulff1.weighted_surface_energy - wulff2.weighted_surface_energy
        delta_E = self.bulk_gform(analyzer1.ucell_entry) - self.bulk_gform(analyzer2.ucell_entry)
        r = ((-3 * delta_gamma) / (delta_E))

        return r / 10 if units == "nanometers" else r

Response: Solve the radial size of two particles where equilibrium is reached by calculating the radial size of the two particles.


Instruction:
How would you explain what the following Python 3 function does
def wulff_gform_and_r(self, wulffshape, bulk_entry, r, from_sphere_area=False,
                          r_units="nanometers", e_units="keV", normalize=False,
                          scale_per_atom=False):
        """
        Calculates the formation energy of the particle with arbitrary radius r.

        Args:
            wulffshape (WulffShape): Initial, unscaled WulffShape
            bulk_entry (ComputedStructureEntry): Entry of the corresponding bulk.
            r (float (Ang)): Arbitrary effective radius of the WulffShape
            from_sphere_area (bool): There are two ways to calculate the bulk
                formation energy. Either by treating the volume and thus surface
                area of the particle as a perfect sphere, or as a Wulff shape.
            r_units (str): Can be nanometers or Angstrom
            e_units (str): Can be keV or eV
            normalize (bool): Whether or not to normalize energy by volume
            scale_per_atom (True): Whether or not to normalize by number of
                atoms in the particle

        Returns:
            particle formation energy (float in keV), effective radius
        """

        # Set up
        miller_se_dict = wulffshape.miller_energy_dict
        new_wulff = self.scaled_wulff(wulffshape, r)
        new_wulff_area = new_wulff.miller_area_dict

        # calculate surface energy of the particle
        if not from_sphere_area:
            # By approximating the particle as a Wulff shape
            w_vol = new_wulff.volume
            tot_wulff_se = 0
            for hkl in new_wulff_area.keys():
                tot_wulff_se += miller_se_dict[hkl] * new_wulff_area[hkl]
            Ebulk = self.bulk_gform(bulk_entry) * w_vol
            new_r = new_wulff.effective_radius

        else:
            # By approximating the particle as a perfect sphere
            w_vol = (4 / 3) * np.pi * r ** 3
            sphere_sa = 4 * np.pi * r ** 2
            tot_wulff_se = wulffshape.weighted_surface_energy * sphere_sa
            Ebulk = self.bulk_gform(bulk_entry) * w_vol
            new_r = r

        new_r = new_r / 10 if r_units == "nanometers" else new_r
        e = (Ebulk + tot_wulff_se)
        e = e / 1000 if e_units == "keV" else e
        e = e / ((4/3)*np.pi*new_r**3) if normalize else e
        bulk_struct = bulk_entry.structure
        density = len(bulk_struct)/bulk_struct.lattice.volume
        e = e/(density*w_vol) if scale_per_atom else e

        return e, new_r

Response: Calculates the formation energy of the particle with arbitrary radius r.


Instruction:
How would you code a function in Python 3 to
return a Wulff shape with an effective radius r.

Response:
def scaled_wulff(self, wulffshape, r):
        """
        Scales the Wulff shape with an effective radius r. Note that the resulting
            Wulff does not neccesarily have the same effective radius as the one
            provided. The Wulff shape is scaled by its surface energies where first
            the surface energies are scale by the minimum surface energy and then
            multiplied by the given effective radius.

        Args:
            wulffshape (WulffShape): Initial, unscaled WulffShape
            r (float): Arbitrary effective radius of the WulffShape

        Returns:
            WulffShape (scaled by r)
        """

        # get the scaling ratio for the energies
        r_ratio = r/wulffshape.effective_radius
        miller_list = wulffshape.miller_energy_dict.keys()
        # Normalize the magnitude of the facet normal vectors
        # of the Wulff shape by the minimum surface energy.
        se_list = np.array(list(wulffshape.miller_energy_dict.values()))
        # Scale the magnitudes by r_ratio
        scaled_se = se_list * r_ratio

        return WulffShape(wulffshape.lattice, miller_list,
                          scaled_se, symprec=self.symprec)


Instruction:
Can you generate the documentation for the following Python 3 function
def plot_one_stability_map(self, analyzer, max_r, delu_dict=None, label="",
                               increments=50, delu_default=0, plt=None,
                               from_sphere_area=False, e_units="keV",
                               r_units="nanometers", normalize=False,
                               scale_per_atom=False):
        """
        Returns the plot of the formation energy of a particle against its
            effect radius

        Args:
            analyzer (SurfaceEnergyPlotter): Analyzer associated with the
                first polymorph
            max_r (float): The maximum radius of the particle to plot up to.
            delu_dict (Dict): Dictionary of the chemical potentials to be set as
                constant. Note the key should be a sympy Symbol object of the
                format: Symbol("delu_el") where el is the name of the element.
            label (str): Label of the plot for legend
            increments (int): Number of plot points
            delu_default (float): Default value for all unset chemical potentials
            plt (pylab): Plot
            from_sphere_area (bool): There are two ways to calculate the bulk
                formation energy. Either by treating the volume and thus surface
                area of the particle as a perfect sphere, or as a Wulff shape.
            r_units (str): Can be nanometers or Angstrom
            e_units (str): Can be keV or eV
            normalize (str): Whether or not to normalize energy by volume
        """

        plt = plt if plt else pretty_plot(width=8, height=7)

        wulffshape = analyzer.wulff_from_chempot(delu_dict=delu_dict,
                                                 delu_default=delu_default,
                                                 symprec=self.symprec)

        gform_list, r_list = [], []
        for r in np.linspace(1e-6, max_r, increments):
            gform, r = self.wulff_gform_and_r(wulffshape, analyzer.ucell_entry,
                                              r, from_sphere_area=from_sphere_area,
                                              r_units=r_units, e_units=e_units,
                                              normalize=normalize,
                                              scale_per_atom=scale_per_atom)
            gform_list.append(gform)
            r_list.append(r)

        ru = "nm" if r_units == "nanometers" else "\AA"
        plt.xlabel(r"Particle radius ($%s$)" %(ru))
        eu = "$%s/%s^3$" %(e_units, ru)
        plt.ylabel(r"$G_{form}$ (%s)" %(eu))

        plt.plot(r_list, gform_list, label=label)

        return plt

Response: Returns the plot of the formation energy of a particle against the one - stability map.


Instruction:
Can you implement a function in Python 3 that
plots all stability map of a single chemical potential in a single system.

Response:
def plot_all_stability_map(self, max_r, increments=50, delu_dict=None,
                               delu_default=0, plt=None, labels=None,
                               from_sphere_area=False, e_units="keV",
                               r_units="nanometers", normalize=False,
                               scale_per_atom=False):
        """
        Returns the plot of the formation energy of a particles
            of different polymorphs against its effect radius

        Args:
            max_r (float): The maximum radius of the particle to plot up to.
            increments (int): Number of plot points
            delu_dict (Dict): Dictionary of the chemical potentials to be set as
                constant. Note the key should be a sympy Symbol object of the
                format: Symbol("delu_el") where el is the name of the element.
            delu_default (float): Default value for all unset chemical potentials
            plt (pylab): Plot
            labels (list): List of labels for each plot, corresponds to the
                list of se_analyzers
            from_sphere_area (bool): There are two ways to calculate the bulk
                formation energy. Either by treating the volume and thus surface
                area of the particle as a perfect sphere, or as a Wulff shape.
        """

        plt = plt if plt else pretty_plot(width=8, height=7)

        for i, analyzer in enumerate(self.se_analyzers):
            label = labels[i] if labels else ""
            plt = self.plot_one_stability_map(analyzer, max_r, delu_dict,
                                              label=label, plt=plt,
                                              increments=increments,
                                              delu_default=delu_default,
                                              from_sphere_area=from_sphere_area,
                                              e_units=e_units, r_units=r_units,
                                              normalize=normalize,
                                              scale_per_atom=scale_per_atom)

        return plt


Instruction:
Given the following Python 3 function, write the documentation
def max_delta_volume(self):
        vols = [v.vol_charge for v in self.voltage_pairs]
        vols.extend([v.vol_discharge for v in self.voltage_pairs])
        return max(vols) / min(vols) - 1

Response: Maximum volume change along insertion


Instruction:
Explain what the following Python 3 code does
def get_average_voltage(self, min_voltage=None, max_voltage=None):
        pairs_in_range = self._select_in_voltage_range(min_voltage,
                                                       max_voltage)
        if len(pairs_in_range) == 0:
            return 0
        total_cap_in_range = sum([p.mAh for p in pairs_in_range])
        total_edens_in_range = sum([p.mAh * p.voltage for p in pairs_in_range])
        return total_edens_in_range / total_cap_in_range

Response: Returns the average voltage for the given aracite module.


Instruction:
Write a Python 3 function for
returning the gravimetric capacity of the given electrode.

Response:
def get_capacity_grav(self, min_voltage=None, max_voltage=None,
                          use_overall_normalization=True):
        """
        Get the gravimetric capacity of the electrode.

        Args:
            min_voltage (float): The minimum allowable voltage for a given
                step.
            max_voltage (float): The maximum allowable voltage allowable for a
                given step.
            use_overall_normalization (booL): If False, normalize by the
                discharged state of only the voltage pairs matching the voltage
                criteria. if True, use default normalization of the full
                electrode path.

        Returns:
            Gravimetric capacity in mAh/g across the insertion path (a subset
            of the path can be chosen by the optional arguments).
        """
        pairs_in_range = self._select_in_voltage_range(min_voltage,
                                                       max_voltage)
        normalization_mass = self.normalization_mass \
            if use_overall_normalization or len(pairs_in_range) == 0 \
            else pairs_in_range[-1].mass_discharge
        return sum([pair.mAh for pair in pairs_in_range]) / normalization_mass


Instruction:
How would you explain what the following Python 3 function does
def get_capacity_vol(self, min_voltage=None, max_voltage=None,
                         use_overall_normalization=True):
        """
        Get the volumetric capacity of the electrode.

        Args:
            min_voltage (float): The minimum allowable voltage for a given
                step.
            max_voltage (float): The maximum allowable voltage allowable for a
                given step.
            use_overall_normalization (booL): If False, normalize by the
                discharged state of only the voltage pairs matching the voltage
                criteria. if True, use default normalization of the full
                electrode path.

        Returns:
            Volumetric capacity in mAh/cc across the insertion path (a subset
            of the path can be chosen by the optional arguments)
        """
        pairs_in_range = self._select_in_voltage_range(min_voltage,
                                                       max_voltage)
        normalization_vol = self.normalization_volume \
            if use_overall_normalization or len(pairs_in_range) == 0 \
            else pairs_in_range[-1].vol_discharge
        return sum([pair.mAh for pair in pairs_in_range]) / normalization_vol \
            * 1e24 / N_A

Response: Get the volumetric capacity of the electrode.


Instruction:
Can you write a function in Python 3 where it
returns the specific energy of the battery in Wh/kg across the insertion path.

Response:
def get_specific_energy(self, min_voltage=None, max_voltage=None,
                            use_overall_normalization=True):
        """
        Returns the specific energy of the battery in mAh/g.

        Args:
            min_voltage (float): The minimum allowable voltage for a given
                step.
            max_voltage (float): The maximum allowable voltage allowable for a
                given step.
            use_overall_normalization (booL): If False, normalize by the
                discharged state of only the voltage pairs matching the voltage
                criteria. if True, use default normalization of the full
                electrode path.

        Returns:
            Specific energy in Wh/kg across the insertion path (a subset of
            the path can be chosen by the optional arguments)
        """
        return self.get_capacity_grav(min_voltage, max_voltage,
                                      use_overall_normalization) \
            * self.get_average_voltage(min_voltage, max_voltage)


Instruction:
Can you generate the documentation for the following Python 3 function
def get_energy_density(self, min_voltage=None, max_voltage=None,
                           use_overall_normalization=True):
        """
        Args:
            min_voltage (float): The minimum allowable voltage for a given
                step.
            max_voltage (float): The maximum allowable voltage allowable for a
                given step.
            use_overall_normalization (booL): If False, normalize by the
                discharged state of only the voltage pairs matching the voltage
                criteria. if True, use default normalization of the full
                electrode path.

        Returns:
            Energy density in Wh/L across the insertion path (a subset of the
            path can be chosen by the optional arguments).
        """
        return self.get_capacity_vol(min_voltage, max_voltage,
                                     use_overall_normalization) \
            * self.get_average_voltage(min_voltage, max_voltage)

Response: Returns the energy density of the given aracite class.


Instruction:
Can you generate the documentation for the following Python 3 function
def _select_in_voltage_range(self, min_voltage=None, max_voltage=None):
        min_voltage = min_voltage if min_voltage is not None \
            else self.min_voltage
        max_voltage = max_voltage if max_voltage is not None \
            else self.max_voltage
        return list(filter(lambda p: min_voltage <= p.voltage <= max_voltage,
                           self.voltage_pairs))

Response: Selects VoltagePairs within a certain voltage range.


Instruction:
Can you write a function in Python 3 where it
returns pymatgen Molecule object.

Response:
def pymatgen_mol(self):
        """
        Returns pymatgen Molecule object.
        """
        sp = []
        coords = []
        for atom in ob.OBMolAtomIter(self._obmol):
            sp.append(atom.GetAtomicNum())
            coords.append([atom.GetX(), atom.GetY(), atom.GetZ()])
        return Molecule(sp, coords)


Instruction:
Can you generate the documentation for the following Python 3 function
def make3d(self, forcefield="mmff94", steps=50):
        pbmol = pb.Molecule(self._obmol)
        pbmol.make3D(forcefield=forcefield, steps=steps)
        self._obmol = pbmol.OBMol

Response: A wrapper to pybel s make3D method generate a 3D structure from the current object.


Instruction:
Implement a function in Python 3 to
remove a bond from an openbabel molecule

Response:
def remove_bond(self, idx1, idx2):
        """
        Remove a bond from an openbabel molecule

        Args:
            idx1: The atom index of one of the atoms participating the in bond
            idx2: The atom index of the other atom participating in the bond
        """
        for obbond in ob.OBMolBondIter(self._obmol):
            if (obbond.GetBeginAtomIdx() == idx1 and obbond.GetEndAtomIdx() == idx2) or (obbond.GetBeginAtomIdx() == idx2 and obbond.GetEndAtomIdx() == idx1):
                self._obmol.DeleteBond(obbond)


Instruction:
Can you write a function in Python 3 where it
generates all diverse low - energy conformers for all molecules.

Response:
def confab_conformers(self, forcefield="mmff94", freeze_atoms=None,
                          rmsd_cutoff=0.5, energy_cutoff=50.0,
                          conf_cutoff=100000, verbose=False):
        """
        Conformer generation based on Confab to generate all diverse low-energy
        conformers for molecules. This is different from rotor_conformer or
        gen3d_conformer as it aims to not simply to find a low energy
        conformation but to generate several different conformations.

        Args:
            forcefield (str): Default is mmff94. Options are 'gaff', 'ghemical',
                'mmff94', 'mmff94s', and 'uff'.
            freeze_atoms ([int]): index of atoms to be freezed when performing
                conformer search, default is None.
            rmsd_cutoff (float): rmsd_cufoff, default is 0.5 Angstrom.
            energy_cutoff (float): energy_cutoff, default is 50.0 kcal/mol.
            conf_cutoff (float): max number of conformers to test,
                default is 1 million.
            verbose (bool): whether to display information on torsions found,
                default is False.

        Returns:
             (list): list of pymatgen Molecule objects for generated conformers.
        """
        if self._obmol.GetDimension() != 3:
            self.make3d()
        else:
            self.add_hydrogen()

        ff = ob.OBForceField_FindType(forcefield)
        if ff == 0:
            print("Could not find forcefield {} in openbabel, the forcefield "
                  "will be reset as default 'mmff94'".format(forcefield))
            ff = ob.OBForceField_FindType("mmff94")

        if freeze_atoms:
            print('{} atoms will be freezed'.format(len(freeze_atoms)))
            constraints = ob.OBFFConstraints()

            for atom in ob.OBMolAtomIter(self._obmol):
                atom_id = atom.GetIndex() + 1
                if id in freeze_atoms:
                    constraints.AddAtomConstraint(atom_id)
            ff.SetConstraints(constraints)

        # Confab conformer generation
        ff.DiverseConfGen(rmsd_cutoff, conf_cutoff, energy_cutoff,
                          verbose)
        ff.GetConformers(self._obmol)

        # Number of conformers generated by Confab conformer generation
        conformer_num = self._obmol.NumConformers()

        conformers = []
        for i in range(conformer_num):
            self._obmol.SetConformer(i)
            conformer = copy.deepcopy(BabelMolAdaptor(self._obmol).pymatgen_mol)
            conformers.append(conformer)
        self._obmol.SetConformer(0)
        return conformers


Instruction:
Given the following Python 3 function, write the documentation
def write_file(self, filename, file_format="xyz"):
        mol = pb.Molecule(self._obmol)
        return mol.write(file_format, filename, overwrite=True)

Response: Writes the current object to a file.


Instruction:
Implement a function in Python 3 to
use OpenBabel to read a molecule from a file in all supported formats.

Response:
def from_file(filename, file_format="xyz"):
        """
        Uses OpenBabel to read a molecule from a file in all supported formats.

        Args:
            filename: Filename of input file
            file_format: String specifying any OpenBabel supported formats.

        Returns:
            BabelMolAdaptor object
        """
        mols = list(pb.readfile(str(file_format), str(filename)))
        return BabelMolAdaptor(mols[0].OBMol)


Instruction:
Write a Python 3 script to
use OpenBabel to read a molecule from a string in all supported Babel Mol formats.

Response:
def from_string(string_data, file_format="xyz"):
        """
        Uses OpenBabel to read a molecule from a string in all supported
        formats.

        Args:
            string_data: String containing molecule data.
            file_format: String specifying any OpenBabel supported formats.

        Returns:
            BabelMolAdaptor object
        """
        mols = pb.readstring(str(file_format), str(string_data))
        return BabelMolAdaptor(mols.OBMol)


Instruction:
Write a Python 3 script to
find the list of bonded atoms in a structure.

Response:
def find_connected_atoms(struct, tolerance=0.45, ldict=JmolNN().el_radius):
    """
    Finds the list of bonded atoms.

    Args:
        struct (Structure): Input structure
        tolerance: length in angstroms used in finding bonded atoms. Two atoms are considered bonded if (radius of atom 1) + (radius of atom 2) + (tolerance) < (distance between atoms 1 and 2). Default value = 0.45, the value used by JMol and Cheon et al.
        ldict: dictionary of bond lengths used in finding bonded atoms. Values from JMol are used as default
        standardize: works with conventional standard structures if True. It is recommended to keep this as True.

    Returns:
        connected_list: A numpy array of shape (number of bonded pairs, 2); each row of is of the form [atomi, atomj].
        atomi and atomj are the indices of the atoms in the input structure.
        If any image of atomj is bonded to atomi with periodic boundary conditions, [atomi, atomj] is included in the list.
        If atomi is bonded to multiple images of atomj, it is only counted once.
    """
    n_atoms = len(struct.species)
    fc = np.array(struct.frac_coords)
    species = list(map(str, struct.species))
    #in case of charged species
    for i,item in enumerate(species):
        if not item in ldict.keys():
            species[i]=str(Specie.from_string(item).element)
    latmat = struct.lattice.matrix
    connected_list = []

    for i in range(n_atoms):
        for j in range(i + 1, n_atoms):
            max_bond_length = ldict[species[i]] + ldict[species[j]] + tolerance
            add_ij = False
            for move_cell in itertools.product([0, 1, -1], [0, 1, -1], [0, 1, -1]):
                if not add_ij:
                    frac_diff = fc[j] + move_cell - fc[i]
                    distance_ij = np.dot(latmat.T, frac_diff)
                    if np.linalg.norm(distance_ij) < max_bond_length:
                        add_ij = True
            if add_ij:
                connected_list.append([i, j])
    return np.array(connected_list)


Instruction:
Implement a function in Python 3 to
find bonded clusters of atoms in the structure with periodic boundary conditions. If there are atoms that are not bonded to anything, returns [0,1,0].(For faster computation time in FindDimension()) Args: struct (Structure): Input structure connected_list: Must be made from the same structure with FindConnected() function. An array of shape (number of bonded pairs, 2); each row of is of the form [atomi, atomj]. Returns: max_cluster: the size of the largest cluster in the crystal structure min_cluster: the size of the smallest cluster in the crystal structure clusters: list of bonded clusters found here, clusters are formatted as sets of indices of atoms

Response:
def find_clusters(struct, connected_list):
    """
    Finds bonded clusters of atoms in the structure with periodic boundary conditions.
    If there are atoms that are not bonded to anything, returns [0,1,0].(For faster computation time in FindDimension())

    Args:
        struct (Structure): Input structure
        connected_list: Must be made from the same structure with FindConnected() function.
            An array of shape (number of bonded pairs, 2); each row of is of the form [atomi, atomj].

    Returns:
        max_cluster: the size of the largest cluster in the crystal structure
        min_cluster: the size of the smallest cluster in the crystal structure
        clusters: list of bonded clusters found here, clusters are formatted as sets of indices of atoms
    """
    n_atoms = len(struct.species)
    if len(np.unique(connected_list)) != n_atoms:
        return [0, 1, 0]
    if n_atoms == 0:
        return [0, 0, 0]
    cluster_sizes = []
    clusters = []
    for atom in range(n_atoms):
        connected_inds = np.where(connected_list == atom)[0]
        atom_cluster = np.unique(connected_list[connected_inds])
        atom_cluster = set(atom_cluster)
        if len(clusters) == 0:
            new_clusters = [atom_cluster]
            new_cluster_sizes = [len(atom_cluster)]
        else:
            clusters_w_atom = [atom_cluster]
            clusters_noatom = []
            clusters_noatom_sizes = []
            for cluster in clusters:
                if len(cluster.intersection(atom_cluster)) > 0:
                    clusters_w_atom.append(cluster)
                else:
                    clusters_noatom.append(cluster)
                    clusters_noatom_sizes.append(len(cluster))
            if len(clusters_w_atom) > 1:
                clusters_w_atom = [set.union(*clusters_w_atom)]
            new_clusters = clusters_noatom + clusters_w_atom
            new_cluster_sizes = clusters_noatom_sizes + [len(clusters_w_atom[0])]
        clusters = list(new_clusters)
        cluster_sizes = list(new_cluster_sizes)
        if n_atoms in cluster_sizes:
            break
    max_cluster = max(cluster_sizes)
    min_cluster = min(cluster_sizes)
    return [max_cluster, min_cluster, clusters]


Instruction:
How would you explain what the following Python 3 function does
def find_dimension(structure_raw, tolerance=0.45, ldict=JmolNN().el_radius, standardize=True):
    if standardize:
        structure = SpacegroupAnalyzer(structure_raw).get_conventional_standard_structure()
    structure_save = copy.copy(structure_raw)
    connected_list1 = find_connected_atoms(structure, tolerance=tolerance, ldict=ldict)
    max1, min1, clusters1 = find_clusters(structure, connected_list1)
    structure.make_supercell([[2, 0, 0], [0, 2, 0], [0, 0, 2]])
    connected_list2 = find_connected_atoms(structure, tolerance=tolerance, ldict=ldict)
    max2, min2, clusters2 = find_clusters(structure, connected_list2)
    if min2 == 1:
        dim = 'intercalated ion'
    elif min2 == min1:
        if max2 == max1:
            dim = '0D'
        else:
            dim = 'intercalated molecule'
    else:
        dim = np.log2(float(max2) / max1)
        if dim == int(dim):
            dim = str(int(dim)) + 'D'
        else:
            structure=copy.copy(structure_save)
            structure.make_supercell([[3, 0, 0], [0, 3, 0], [0, 0, 3]])
            connected_list3 = find_connected_atoms(structure, tolerance=tolerance, ldict=ldict)
            max3, min3, clusters3 = find_clusters(structure, connected_list3)
            if min3 == min2:
                if max3 == max2:
                    dim = '0D'
                else:
                    dim = 'intercalated molecule'
            else:
                dim = np.log2(float(max3) / max1) / np.log2(3)
                if dim == int(dim):
                    dim = str(int(dim)) + 'D'
                else:
                    return
    return dim

Response: This method finds the dimensionality of a crystal structure.


Instruction:
Write a Python 3 script to
return a string with the structure in XSF format

Response:
def to_string(self):
        """
        Returns a string with the structure in XSF format
        See http://www.xcrysden.org/doc/XSF.html
        """
        lines = []
        app = lines.append

        app("CRYSTAL")
        app("# Primitive lattice vectors in Angstrom")
        app("PRIMVEC")
        cell = self.structure.lattice.matrix
        for i in range(3):
            app(' %.14f %.14f %.14f' % tuple(cell[i]))

        cart_coords = self.structure.cart_coords
        app("# Cartesian coordinates in Angstrom.")
        app("PRIMCOORD")
        app(" %d 1" % len(cart_coords))

        for a in range(len(cart_coords)):
            sp = "%d" % self.structure.atomic_numbers[a]
            app(sp + ' %20.14f %20.14f %20.14f' % tuple(cart_coords[a]))

        return "\n".join(lines)


Instruction:
Implement a function in Python 3 to
initialize a structure object from a string with the data in XSF format.

Response:
def from_string(cls, input_string, cls_=None):
        """
        Initialize a `Structure` object from a string with data in XSF format.

        Args:
            input_string: String with the structure in XSF format.
                See http://www.xcrysden.org/doc/XSF.html
            cls_: Structure class to be created. default: pymatgen structure

        """
        # CRYSTAL                                        see (1)
        # these are primitive lattice vectors (in Angstroms)
        # PRIMVEC
        #    0.0000000    2.7100000    2.7100000         see (2)
        #    2.7100000    0.0000000    2.7100000
        #    2.7100000    2.7100000    0.0000000

        # these are conventional lattice vectors (in Angstroms)
        # CONVVEC
        #    5.4200000    0.0000000    0.0000000         see (3)
        #    0.0000000    5.4200000    0.0000000
        #    0.0000000    0.0000000    5.4200000

        # these are atomic coordinates in a primitive unit cell  (in Angstroms)
        # PRIMCOORD
        # 2 1                                            see (4)
        # 16      0.0000000     0.0000000     0.0000000  see (5)
        # 30      1.3550000    -1.3550000    -1.3550000

        lattice, coords, species = [], [], []
        lines = input_string.splitlines()

        for i in range(len(lines)):
            if "PRIMVEC" in lines[i]:
                for j in range(i+1, i+4):
                    lattice.append([float(c) for c in lines[j].split()])

            if "PRIMCOORD" in lines[i]:
                num_sites = int(lines[i+1].split()[0])

                for j in range(i+2, i+2+num_sites):
                    tokens = lines[j].split()
                    species.append(int(tokens[0]))
                    coords.append([float(j) for j in tokens[1:4]])
                break
        else:
            raise ValueError("Invalid XSF data")

        if cls_ is None:
            from pymatgen.core.structure import Structure
            cls_ = Structure

        s = cls_(lattice, species, coords, coords_are_cartesian=True)
        return XSF(s)


Instruction:
Can you tell what is the following Python 3 function doing
def get_kpoints(self, line_density=20, coords_are_cartesian=True):
        list_k_points = []
        sym_point_labels = []
        for b in self.kpath['path']:
            for i in range(1, len(b)):
                start = np.array(self.kpath['kpoints'][b[i - 1]])
                end = np.array(self.kpath['kpoints'][b[i]])
                distance = np.linalg.norm(
                    self._prim_rec.get_cartesian_coords(start) -
                    self._prim_rec.get_cartesian_coords(end))
                nb = int(ceil(distance * line_density))
                sym_point_labels.extend([b[i - 1]] + [''] * (nb - 1) + [b[i]])
                list_k_points.extend(
                    [self._prim_rec.get_cartesian_coords(start)
                     + float(i) / float(nb) *
                     (self._prim_rec.get_cartesian_coords(end)
                      - self._prim_rec.get_cartesian_coords(start))
                     for i in range(0, nb + 1)])
        if coords_are_cartesian:
            return list_k_points, sym_point_labels
        else:
            frac_k_points = [self._prim_rec.get_fractional_coords(k)
                             for k in list_k_points]
            return frac_k_points, sym_point_labels

Response: Returns the kpoints along the paths in cartesian coordinates and the labels for symmetry points


Instruction:
Here you have a function in Python 3, explain what it does
def from_node(cls, work):
        new = super().from_node(work)

        # Will put all files found in outdir in GridFs
        # Warning: assuming binary files.
        d = {os.path.basename(f): f for f in work.outdir.list_filepaths()}
        new.register_gridfs_files(**d)

        return new

Response: Initialize an instance from a Work instance.


Instruction:
How would you explain what the following Python 3 function does
def ncores_reserved(self):
        return sum(task.manager.num_cores for task in self if task.status == task.S_SUB)

Response: Returns the number of cores reserved in this moment.


Instruction:
How would you code a function in Python 3 to
return the number of CPUs allocated in this moment.

Response:
def ncores_allocated(self):
        """
        Returns the number of CPUs allocated in this moment.
        A core is allocated if it's running a task or if we have
        submitted a task to the queue manager but the job is still pending.
        """
        return sum(task.manager.num_cores for task in self if task.status in [task.S_SUB, task.S_RUN])


Instruction:
Can you tell what is the following Python 3 function doing
def ncores_used(self):
        return sum(task.manager.num_cores for task in self if task.status == task.S_RUN)

Response: Returns the number of cores used in this moment.


Instruction:
How would you code a function in Python 3 to
return the first task that is ready to run or None if no task can be submitted at present

Response:
def fetch_task_to_run(self):
        """
        Returns the first task that is ready to run or
        None if no task can be submitted at present"

        Raises:
            `StopIteration` if all tasks are done.
        """
        # All the tasks are done so raise an exception
        # that will be handled by the client code.
        if all(task.is_completed for task in self):
            raise StopIteration("All tasks completed.")

        for task in self:
            if task.can_run:
                return task

        # No task found, this usually happens when we have dependencies.
        # Beware of possible deadlocks here!
        logger.warning("Possible deadlock in fetch_task_to_run!")
        return None


Instruction:
Can you write a function in Python 3 where it
connects the signals within the work.

Response:
def connect_signals(self):
        """
        Connect the signals within the work.
        The :class:`Work` is responsible for catching the important signals raised from
        its task and raise new signals when some particular condition occurs.
        """
        for task in self:
            dispatcher.connect(self.on_ok, signal=task.S_OK, sender=task)


Instruction:
Can you generate the documentation for the following Python 3 function
def disconnect_signals(self):
        for task in self:
            try:
                dispatcher.disconnect(self.on_ok, signal=task.S_OK, sender=task)
            except dispatcher.errors.DispatcherKeyError as exc:
                logger.debug(str(exc))

Response: Disable the signals within the work.


Instruction:
Here you have a function in Python 3, explain what it does
def on_ok(self, sender):
        logger.debug("in on_ok with sender %s" % sender)

        if self.all_ok:
            if self.finalized:
                return AttrDict(returncode=0, message="Work has been already finalized")
            else:
                # Set finalized here, because on_all_ok might change it (e.g. Relax + EOS in a single work)
                self.finalized = True
                try:
                    results = AttrDict(**self.on_all_ok())
                except Exception as exc:
                    self.history.critical("on_all_ok raises %s" % str(exc))
                    self.finalized = False
                    raise

                # Signal to possible observers that the `Work` reached S_OK
                self.history.info("Work %s is finalized and broadcasts signal S_OK" % str(self))
                if self._finalized:
                    self.send_signal(self.S_OK)

                return results

        return AttrDict(returncode=1, message="Not all tasks are OK!")

Response: This callback is called when one task reaches status S_OK. It executes on_all_ok and sends a signal to possible observers that the task is not all tasks are OK.


Instruction:
Here you have a function in Python 3, explain what it does
def register_scf_task(self, *args, **kwargs):
        kwargs["task_class"] = ScfTask
        return self.register_task(*args, **kwargs)

Response: Register a Scf task.


Instruction:
Given the following Python 3 function, write the documentation
def register_collinear_then_noncollinear_scf_task(self, *args, **kwargs):
        kwargs["task_class"] = CollinearThenNonCollinearScfTask
        return self.register_task(*args, **kwargs)

Response: Register a CollinearThenNonCollinearScfTask that performs a SCF run first with nsppol = 2 and then nspinor = 2


Instruction:
How would you implement a function in Python 3 that
registers a nscf task.

Response:
def register_nscf_task(self, *args, **kwargs):
        """Register a nscf task."""
        kwargs["task_class"] = NscfTask
        return self.register_task(*args, **kwargs)


Instruction:
Given the following Python 3 function, write the documentation
def register_relax_task(self, *args, **kwargs):
        kwargs["task_class"] = RelaxTask
        return self.register_task(*args, **kwargs)

Response: Register a task for structural optimization.


Instruction:
Implement a function in Python 3 to
register a phonon task.

Response:
def register_phonon_task(self, *args, **kwargs):
        """Register a phonon task."""
        kwargs["task_class"] = PhononTask
        return self.register_task(*args, **kwargs)


Instruction:
How would you implement a function in Python 3 that
registers an elastic task.

Response:
def register_elastic_task(self, *args, **kwargs):
        """Register an elastic task."""
        kwargs["task_class"] = ElasticTask
        return self.register_task(*args, **kwargs)


Instruction:
Can you implement a function in Python 3 that
registers a ddk task.

Response:
def register_ddk_task(self, *args, **kwargs):
        """Register a ddk task."""
        kwargs["task_class"] = DdkTask
        return self.register_task(*args, **kwargs)


Instruction:
Given the following Python 3 function, write the documentation
def register_scr_task(self, *args, **kwargs):
        kwargs["task_class"] = ScrTask
        return self.register_task(*args, **kwargs)

Response: Register a screening task.


Instruction:
How would you code a function in Python 3 to
register a sigma task.

Response:
def register_sigma_task(self, *args, **kwargs):
        """Register a sigma task."""
        kwargs["task_class"] = SigmaTask
        return self.register_task(*args, **kwargs)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def register_dde_task(self, *args, **kwargs):
        kwargs["task_class"] = DdeTask
        return self.register_task(*args, **kwargs)

Response: Register a Dde task.


Instruction:
Implement a function in Python 3 to
register a Dte task.

Response:
def register_dte_task(self, *args, **kwargs):
        """Register a Dte task."""
        kwargs["task_class"] = DteTask
        return self.register_task(*args, **kwargs)


Instruction:
Can you create a Python 3 function that
registers a BEC task.

Response:
def register_bec_task(self, *args, **kwargs):
        """Register a BEC task."""
        kwargs["task_class"] = BecTask
        return self.register_task(*args, **kwargs)


Instruction:
Can you implement a function in Python 3 that
registers a Bethe - Salpeter task.

Response:
def register_bse_task(self, *args, **kwargs):
        """Register a Bethe-Salpeter task."""
        kwargs["task_class"] = BseTask
        return self.register_task(*args, **kwargs)


Instruction:
Create a Python 3 function for
registering an electron - phonon task.

Response:
def register_eph_task(self, *args, **kwargs):
        """Register an electron-phonon task."""
        kwargs["task_class"] = EphTask
        return self.register_task(*args, **kwargs)


Instruction:
Can you write a function in Python 3 where it
sets the values of the ABINIT variables in the input files of the nodes in this object.

Response:
def walknset_vars(self, task_class=None, *args, **kwargs):
        """
        Set the values of the ABINIT variables in the input files of the nodes

        Args:
            task_class: If not None, only the input files of the tasks belonging
                to class `task_class` are modified.

        Example:

            flow.walknset_vars(ecut=10, kptopt=4)
        """
        def change_task(task):
            if task_class is not None and task.__class__ is not task_class: return False
            return True

        if self.is_work:
            for task in self:
                if not change_task(task): continue
                task.set_vars(*args, **kwargs)

        elif self.is_flow:
            for task in self.iflat_tasks():
                if not change_task(task): continue
                task.set_vars(*args, **kwargs)

        else:
            raise TypeError("Don't know how to set variables for object class %s"  % self.__class__.__name__)


Instruction:
Create a Python 3 function to
set the manager to use to launch the Task.

Response:
def set_manager(self, manager):
        """Set the :class:`TaskManager` to use to launch the :class:`Task`."""
        self.manager = manager.deepcopy()
        for task in self:
            task.set_manager(manager)


Instruction:
Given the following Python 3 function, write the documentation
def set_flow(self, flow):
        if not hasattr(self, "_flow"):
            self._flow = flow
        else:
            if self._flow != flow:
                raise ValueError("self._flow != flow")

Response: Set the flow associated to this : class : Work.


Instruction:
Here you have a function in Python 3, explain what it does
def status_counter(self):
        counter = collections.Counter()

        for task in self:
            counter[str(task.status)] += 1

        return counter

Response:
Returns a Counter object that counts the number of task with
        given status.


Instruction:
Can you tell what is the following Python 3 function doing
def allocate(self, manager=None):
        for i, task in enumerate(self):

            if not hasattr(task, "manager"):
                # Set the manager
                # Use the one provided in input else the one of the work/flow.
                if manager is not None:
                    task.set_manager(manager)
                else:
                    # Look first in work and then in the flow.
                    if hasattr(self, "manager"):
                        task.set_manager(self.manager)
                    else:
                        task.set_manager(self.flow.manager)

            task_workdir = os.path.join(self.workdir, "t" + str(i))

            if not hasattr(task, "workdir"):
                task.set_workdir(task_workdir)
            else:
                if task.workdir != task_workdir:
                    raise ValueError("task.workdir != task_workdir: %s, %s" % (task.workdir, task_workdir))

Response: This function is called once we have completed the initialization of the work and the flow. It sets the manager of each task and defines the working directories of the tasks.


Instruction:
How would you explain what the following Python 3 function does
def register(self, obj, deps=None, required_files=None, manager=None, task_class=None):
        task_workdir = None
        if hasattr(self, "workdir"):
            task_workdir = os.path.join(self.workdir, "t" + str(len(self)))

        if isinstance(obj, Task):
            task = obj

        else:
            # Set the class
            if task_class is None:
                task_class = AbinitTask

            task = task_class.from_input(obj, task_workdir, manager)

        self._tasks.append(task)

        # Handle possible dependencies given either as dict or list.
        if deps is not None:
            if hasattr(deps, "items"):
                deps = [Dependency(node, exts) for node, exts in deps.items()]
            task.add_deps(deps)

        # Handle possible dependencies.
        if required_files is not None:
            task.add_required_files(required_files)

        return task

Response: Registers a new Task object and adds it to the internal list.


Instruction:
Write a Python 3 function for
creating the top level directory and files of the work.

Response:
def build(self, *args, **kwargs):
        """Creates the top level directory."""
        # Create the directories of the work.
        self.indir.makedirs()
        self.outdir.makedirs()
        self.tmpdir.makedirs()

        # Build dirs and files of each task.
        for task in self:
            task.build(*args, **kwargs)

        # Connect signals within the work.
        self.connect_signals()


Instruction:
Create a Python 3 function for
returning a list with the status of all the tasks in the current node.

Response:
def get_all_status(self, only_min=False):
        """
        Returns a list with the status of the tasks in self.

        Args:
            only_min: If True, the minimum of the status is returned.
        """
        if len(self) == 0:
            # The work will be created in the future.
            if only_min:
                return self.S_INIT
            else:
                return [self.S_INIT]

        self.check_status()
        status_list = [task.status for task in self]

        if only_min:
            return min(status_list)
        else:
            return status_list


Instruction:
Can you write a function in Python 3 where it
checks the status of the tasks.

Response:
def check_status(self):
        """Check the status of the tasks."""
        # Recompute the status of the tasks
        # Ignore OK and LOCKED tasks.
        for task in self:
            if task.status in (task.S_OK, task.S_LOCKED): continue
            task.check_status()

        # Take into account possible dependencies. Use a list instead of generators
        for task in self:
            if task.status == task.S_LOCKED: continue
            if task.status < task.S_SUB and all(status == task.S_OK for status in task.deps_status):
                task.set_status(task.S_READY, "Status set to Ready")


Instruction:
Write a Python 3 function for
removing all files and directories in the working directory.

Response:
def rmtree(self, exclude_wildcard=""):
        """
        Remove all files and directories in the working directory

        Args:
            exclude_wildcard: Optional string with regular expressions separated by `|`.
                Files matching one of the regular expressions will be preserved.
                example: exclude_wildard="*.nc|*.txt" preserves all the files
                whose extension is in ["nc", "txt"].
        """
        if not exclude_wildcard:
            shutil.rmtree(self.workdir)

        else:
            w = WildCard(exclude_wildcard)
            for dirpath, dirnames, filenames in os.walk(self.workdir):
                for fname in filenames:
                    path = os.path.join(dirpath, fname)
                    if not w.match(fname):
                        os.remove(path)


Instruction:
How would you code a function in Python 3 to
submit the tasks in self and wait.

Response:
def submit_tasks(self, wait=False):
        """
        Submits the task in self and wait.
        TODO: change name.
        """
        for task in self:
            task.start()

        if wait:
            for task in self: task.wait()


Instruction:
How would you implement a function in Python 3 that
starts the work. Calls build and _setup first then submit the tasks.

Response:
def start(self, *args, **kwargs):
        """
        Start the work. Calls build and _setup first, then submit the tasks.
        Non-blocking call unless wait is set to True
        """
        wait = kwargs.pop("wait", False)

        # Initial setup
        self._setup(*args, **kwargs)

        # Build dirs and files.
        self.build(*args, **kwargs)

        # Submit tasks (does not block)
        self.submit_tasks(wait=wait)


Instruction:
Explain what the following Python 3 code does
def read_etotals(self, unit="Ha"):
        if not self.all_done:
            raise self.Error("Some task is still in running/submitted state")

        etotals = []
        for task in self:
            # Open the GSR file and read etotal (Hartree)
            gsr_path = task.outdir.has_abiext("GSR")
            etot = np.inf
            if gsr_path:
                with ETSF_Reader(gsr_path) as r:
                    etot = r.read_value("etotal")

            etotals.append(etot)

        return EnergyArray(etotals, "Ha").to(unit)

Response: Reads the total energy from the GSR file produced by the task.


Instruction:
Can you write a function in Python 3 where it
parses the TIMER section reported in the ABINIT output files.

Response:
def parse_timers(self):
        """
        Parse the TIMER section reported in the ABINIT output files.

        Returns:
            :class:`AbinitTimerParser` object
        """
        filenames = list(filter(os.path.exists, [task.output_file.path for task in self]))

        parser = AbinitTimerParser()
        parser.parse(filenames)

        return parser


Instruction:
How would you code a function in Python 3 to
plot the band structure.

Response:
def plot_ebands(self, **kwargs):
        """
        Plot the band structure. kwargs are passed to the plot method of :class:`ElectronBands`.

        Returns:
            `matplotlib` figure
        """
        with self.nscf_task.open_gsr() as gsr:
            return gsr.ebands.plot(**kwargs)


Instruction:
Create a Python 3 function for
plotting the band structure and the DOS.

Response:
def plot_ebands_with_edos(self, dos_pos=0, method="gaussian", step=0.01, width=0.1, **kwargs):
        """
        Plot the band structure and the DOS.

        Args:
            dos_pos: Index of the task from which the DOS should be obtained (note: 0 refers to the first DOS task).
            method: String defining the method for the computation of the DOS.
            step: Energy step (eV) of the linear mesh.
            width: Standard deviation (eV) of the gaussian.
            kwargs: Keyword arguments passed to `plot_with_edos` method to customize the plot.

        Returns:
            `matplotlib` figure.
        """
        with self.nscf_task.open_gsr() as gsr:
            gs_ebands = gsr.ebands

        with self.dos_tasks[dos_pos].open_gsr() as gsr:
            dos_ebands = gsr.ebands

        edos = dos_ebands.get_edos(method=method, step=step, width=width)
        return gs_ebands.plot_with_edos(edos, **kwargs)


Instruction:
Can you write a function in Python 3 where it
plots the band structure and the DOS.

Response:
def plot_edoses(self, dos_pos=None, method="gaussian", step=0.01, width=0.1, **kwargs):
        """
        Plot the band structure and the DOS.

        Args:
            dos_pos: Index of the task from which the DOS should be obtained.
                     None is all DOSes should be displayed. Accepts integer or list of integers.
            method: String defining the method for the computation of the DOS.
            step: Energy step (eV) of the linear mesh.
            width: Standard deviation (eV) of the gaussian.
            kwargs: Keyword arguments passed to `plot` method to customize the plot.

        Returns:
            `matplotlib` figure.
        """
        if dos_pos is not None and not isinstance(dos_pos, (list, tuple)): dos_pos = [dos_pos]

        from abipy.electrons.ebands import ElectronDosPlotter
        plotter = ElectronDosPlotter()
        for i, task in enumerate(self.dos_tasks):
            if dos_pos is not None and i not in dos_pos: continue
            with task.open_gsr() as gsr:
                edos = gsr.ebands.get_edos(method=method, step=step, width=width)
                ngkpt = task.get_inpvar("ngkpt")
                plotter.add_edos("ngkpt %s" % str(ngkpt), edos)

        return plotter.combiplot(**kwargs)


Instruction:
Explain what the following Python 3 code does
def on_ok(self, sender):
        logger.debug("in on_ok with sender %s" % sender)

        if sender == self.ion_task and not self.transfer_done:
            # Get the relaxed structure from ion_task
            ion_structure = self.ion_task.get_final_structure()

            # Transfer it to the ioncell task (we do it only once).
            self.ioncell_task._change_structure(ion_structure)
            self.transfer_done = True

            # Unlock ioncell_task so that we can submit it.
            self.ioncell_task.unlock(source_node=self)

        elif sender == self.ioncell_task and self.target_dilatmx:
            actual_dilatmx = self.ioncell_task.get_inpvar('dilatmx', 1.)
            if self.target_dilatmx < actual_dilatmx:
                self.ioncell_task.reduce_dilatmx(target=self.target_dilatmx)
                self.history.info('Converging dilatmx. Value reduce from {} to {}.'
                            .format(actual_dilatmx, self.ioncell_task.get_inpvar('dilatmx')))
                self.ioncell_task.reset_from_scratch()

        return super().on_ok(sender)

Response: This callback is called when one task reaches status S_OK.


Instruction:
Make a summary of the following Python 3 code
def plot_ion_relaxation(self, **kwargs):
        with self.ion_task.open_hist() as hist:
            return hist.plot(**kwargs) if hist else None

Response: Plot the history of the ion - cell relaxation.


Instruction:
Write a Python 3 function for
plotting the history of the ion - cell relaxation.

Response:
def plot_ioncell_relaxation(self, **kwargs):
        """
        Plot the history of the ion-cell relaxation.
        kwargs are passed to the plot method of :class:`HistFile`

        Return `matplotlib` figure or None if hist file is not found.
        """
        with self.ioncell_task.open_hist() as hist:
            return hist.plot(**kwargs) if hist else None


Instruction:
Explain what the following Python 3 code does
def get_mdf_robot(self):
        from abilab.robots import MdfRobot
        robot = MdfRobot()
        for task in self[2:]:
            mdf_path = task.outdir.has_abiext(robot.EXT)
            if mdf_path:
                robot.add_file(str(task), mdf_path)
        return robot

Response: Builds and returns a MdfRobot for analyzing the results in the MDF files.


Instruction:
Can you write a function in Python 3 where it
creates the SCR tasks and register them in self.

Response:
def create_tasks(self, wfk_file, scr_input):
        """
        Create the SCR tasks and register them in self.

        Args:
            wfk_file: Path to the ABINIT WFK file to use for the computation of the screening.
            scr_input: Input for the screening calculation.
        """
        assert len(self) == 0
        wfk_file = self.wfk_file = os.path.abspath(wfk_file)

        # Build a temporary work in the tmpdir that will use a shell manager
        # to run ABINIT in order to get the list of q-points for the screening.
        shell_manager = self.manager.to_shell_manager(mpi_procs=1)

        w = Work(workdir=self.tmpdir.path_join("_qptdm_run"), manager=shell_manager)

        fake_input = scr_input.deepcopy()
        fake_task = w.register(fake_input)
        w.allocate()
        w.build()

        # Create the symbolic link and add the magic value
        # nqpdm = -1 to the input to get the list of q-points.
        fake_task.inlink_file(wfk_file)
        fake_task.set_vars({"nqptdm": -1})
        fake_task.start_and_wait()

        # Parse the section with the q-points
        with NetcdfReader(fake_task.outdir.has_abiext("qptdms.nc")) as reader:
            qpoints = reader.read_value("reduced_coordinates_of_kpoints")
        #print("qpoints)

        # Now we can register the task for the different q-points
        for qpoint in qpoints:
            qptdm_input = scr_input.deepcopy()
            qptdm_input.set_vars(nqptdm=1, qptdm=qpoint)
            new_task = self.register_scr_task(qptdm_input, manager=self.manager)
            # Add the garbage collector.
            if self.flow.gc is not None:
                new_task.set_gc(self.flow.gc)

        self.allocate()


Instruction:
Here you have a function in Python 3, explain what it does
def merge_scrfiles(self, remove_scrfiles=True):
        scr_files = list(filter(None, [task.outdir.has_abiext("SCR") for task in self]))

        self.history.info("Will call mrgscr to merge %s SCR files:\n" % len(scr_files))
        assert len(scr_files) == len(self)

        mrgscr = wrappers.Mrgscr(manager=self[0].manager, verbose=1)
        final_scr = mrgscr.merge_qpoints(self.outdir.path, scr_files, out_prefix="out")

        if remove_scrfiles:
            for scr_file in scr_files:
                try:
                    os.remove(scr_file)
                except IOError:
                    pass

        return final_scr

Response: This method merges the SCR files in the outdir of the Work.


Instruction:
Create a Python 3 function for
building tasks for the computation of Born effective charges and add them to the work. Args: scf_task: ScfTask object. ddk_tolerance: dict {"varname": value} with the tolerance used in the DDK run. None to use AbiPy default. ph_tolerance: dict {"varname": value} with the tolerance used in the phonon run. None to use AbiPy default. Return: (ddk_tasks, bec_tasks)

Response:
def add_becs_from_scf_task(self, scf_task, ddk_tolerance, ph_tolerance):
        """
        Build tasks for the computation of Born effective charges and add them to the work.

        Args:
            scf_task: ScfTask object.
            ddk_tolerance: dict {"varname": value} with the tolerance used in the DDK run.
                None to use AbiPy default.
            ph_tolerance: dict {"varname": value} with the tolerance used in the phonon run.
                None to use AbiPy default.

	Return:
	    (ddk_tasks, bec_tasks)
	"""
        if not isinstance(scf_task, ScfTask):
            raise TypeError("task `%s` does not inherit from ScfTask" % scf_task)

	# DDK calculations (self-consistent to get electric field).
        multi_ddk = scf_task.input.make_ddk_inputs(tolerance=ddk_tolerance)

        ddk_tasks = []
        for ddk_inp in multi_ddk:
            ddk_task = self.register_ddk_task(ddk_inp, deps={scf_task: "WFK"})
            ddk_tasks.append(ddk_task)

        # Build the list of inputs for electric field perturbation and phonons
        # Each BEC task is connected to all the previous DDK task and to the scf_task.
        bec_deps = {ddk_task: "DDK" for ddk_task in ddk_tasks}
        bec_deps.update({scf_task: "WFK"})

        bec_inputs = scf_task.input.make_bec_inputs(tolerance=ph_tolerance)
        bec_tasks = []
        for bec_inp in bec_inputs:
             bec_task = self.register_bec_task(bec_inp, deps=bec_deps)
             bec_tasks.append(bec_task)

        return ddk_tasks, bec_tasks


Instruction:
Here you have a function in Python 3, explain what it does
def merge_ddb_files(self, delete_source_ddbs=True, only_dfpt_tasks=True,
                        exclude_tasks=None, include_tasks=None):
        """
        This method is called when all the q-points have been computed.
        It runs `mrgddb` in sequential on the local machine to produce
        the final DDB file in the outdir of the `Work`.

        Args:
            delete_source_ddbs: True if input DDB should be removed once final DDB is created.
            only_dfpt_tasks: False to merge all DDB files produced by the tasks of the work
                Useful e.g. for finite stress corrections in which the stress in the
                initial configuration should be merged in the final DDB.
            exclude_tasks: List of tasks that should be excluded when merging the partial DDB files.
            include_tasks: List of tasks that should be included when merging the partial DDB files.
                Mutually exclusive with exclude_tasks.

        Returns:
            path to the output DDB file
        """
        if exclude_tasks:
            my_tasks = [task for task in self if task not in exclude_tasks]
        elif include_tasks:
            my_tasks = [task for task in self if task in include_tasks]
        else:
            my_tasks = [task for task in self]

        if only_dfpt_tasks:
            ddb_files = list(filter(None, [task.outdir.has_abiext("DDB") for task in my_tasks \
                                       if isinstance(task, DfptTask)]))
        else:
            ddb_files = list(filter(None, [task.outdir.has_abiext("DDB") for task in my_tasks]))

        self.history.info("Will call mrgddb to merge %s DDB files:" % len(ddb_files))
        # DDB files are always produces so this should never happen!
        if not ddb_files:
            raise RuntimeError("Cannot find any DDB file to merge by the task of " % self)

        # Final DDB file will be produced in the outdir of the work.
        out_ddb = self.outdir.path_in("out_DDB")

        if len(ddb_files) == 1:
            # Avoid the merge. Just copy the DDB file to the outdir of the work.
            shutil.copy(ddb_files[0], out_ddb)
        else:
            # Call mrgddb
            desc = "DDB file merged by %s on %s" % (self.__class__.__name__, time.asctime())
            mrgddb = wrappers.Mrgddb(manager=self[0].manager, verbose=0)
            mrgddb.merge(self.outdir.path, ddb_files, out_ddb=out_ddb, description=desc,
                         delete_source_ddbs=delete_source_ddbs)

        return out_ddb

Response: This method merges the DDB files produced by the tasks of the work.


Instruction:
Implement a Python 3 function for
constructing a PhononWork object from a ScfTask object.

Response:
def from_scf_task(cls, scf_task, qpoints, is_ngqpt=False, tolerance=None, with_becs=False,
                      ddk_tolerance=None, manager=None):
        """
        Construct a `PhononWork` from a :class:`ScfTask` object.
        The input file for phonons is automatically generated from the input of the ScfTask.
        Each phonon task depends on the WFK file produced by the `scf_task`.

        Args:
            scf_task: ScfTask object.
            qpoints: q-points in reduced coordinates. Accepts single q-point, list of q-points
                or three integers defining the q-mesh if `is_ngqpt`.
            is_ngqpt: True if `qpoints` should be interpreted as divisions instead of q-points.
            tolerance: dict {"varname": value} with the tolerance to be used in the phonon run.
                None to use AbiPy default.
            with_becs: Activate calculation of Electric field and Born effective charges.
            ddk_tolerance: dict {"varname": value} with the tolerance used in the DDK run if with_becs.
                None to use AbiPy default.
            manager: :class:`TaskManager` object.
        """
        if not isinstance(scf_task, ScfTask):
            raise TypeError("task `%s` does not inherit from ScfTask" % scf_task)

        if is_ngqpt:
            qpoints = scf_task.input.abiget_ibz(ngkpt=qpoints, shiftk=[0, 0, 0], kptopt=1).points
        qpoints = np.reshape(qpoints, (-1, 3))

        new = cls(manager=manager)
        if with_becs:
            new.add_becs_from_scf_task(scf_task, ddk_tolerance, ph_tolerance=tolerance)

        for qpt in qpoints:
            if with_becs and np.sum(qpt ** 2) < 1e-12: continue
            multi = scf_task.input.make_ph_inputs_qpoint(qpt, tolerance=tolerance)
            for ph_inp in multi:
                new.register_phonon_task(ph_inp, deps={scf_task: "WFK"})

        return new


Instruction:
Implement a function in Python 3 to
create a new instance of a Task from a SCF input and a list of QPoints.

Response:
def from_scf_input(cls, scf_input, qpoints, is_ngqpt=False, tolerance=None,
                       with_becs=False, ddk_tolerance=None, manager=None):
        """
        Similar to `from_scf_task`, the difference is that this method requires
        an input for SCF calculation. A new ScfTask is created and added to the Work.
        This API should be used if the DDB of the GS task should be merged.
        """
        if is_ngqpt:
            qpoints = scf_input.abiget_ibz(ngkpt=qpoints, shiftk=[0, 0, 0], kptopt=1).points

        qpoints = np.reshape(qpoints, (-1, 3))

        new = cls(manager=manager)
        # Create ScfTask
        scf_task = new.register_scf_task(scf_input)

        if with_becs:
            new.add_becs_from_scf_task(scf_task, ddk_tolerance, ph_tolerance=tolerance)

        for qpt in qpoints:
            if with_becs and np.sum(qpt ** 2) < 1e-12: continue
            multi = scf_task.input.make_ph_inputs_qpoint(qpt, tolerance=tolerance)
            for ph_inp in multi:
                new.register_phonon_task(ph_inp, deps={scf_task: "WFK"})

        return new


Instruction:
Here you have a function in Python 3, explain what it does
def from_scf_task(cls, scf_task, ngqpt, ph_tolerance=None, tolwfr=1.0e-22, nband=None,
                      with_becs=False, ddk_tolerance=None, shiftq=(0, 0, 0), is_ngqpt=True, remove_wfkq=True,
                      manager=None):
        """
        Construct a `PhononWfkqWork` from a :class:`ScfTask` object.
        The input files for WFQ and phonons are automatically generated from the input of the ScfTask.
        Each phonon task depends on the WFK file produced by scf_task and the associated WFQ file.

        Args:
            scf_task: ScfTask object.
            ngqpt: three integers defining the q-mesh
            with_becs: Activate calculation of Electric field and Born effective charges.
            ph_tolerance: dict {"varname": value} with the tolerance for the phonon run.
                None to use AbiPy default.
            tolwfr: tolerance used to compute WFQ.
            ddk_tolerance: dict {"varname": value} with the tolerance used in the DDK run if with_becs.
                None to use AbiPy default.
            shiftq: Q-mesh shift. Multiple shifts are not supported.
            is_ngqpt: the ngqpt is interpreted as a set of integers defining the q-mesh, otherwise
                      is an explicit list of q-points
            remove_wfkq: Remove WKQ files when the children are completed.
            manager: :class:`TaskManager` object.

        .. note:

            Use k-meshes with one shift and q-meshes that are multiple of ngkpt
            to decrease the number of WFQ files to be computed.
        """
        if not isinstance(scf_task, ScfTask):
            raise TypeError("task `%s` does not inherit from ScfTask" % scf_task)

        shiftq = np.reshape(shiftq, (3,))
        if is_ngqpt:
            qpoints = scf_task.input.abiget_ibz(ngkpt=ngqpt, shiftk=shiftq, kptopt=1).points
        else:
            qpoints = ngqpt

        new = cls(manager=manager)
        new.remove_wfkq = remove_wfkq
        new.wfkq_tasks = []
        new.wfkq_task_children = collections.defaultdict(list)

        if with_becs:
            # Add DDK and BECS.
            new.add_becs_from_scf_task(scf_task, ddk_tolerance, ph_tolerance)

        # Get ngkpt, shift for electrons from input.
        # Won't try to skip WFQ if multiple shifts or off-diagonal kptrlatt
        ngkpt, shiftk = scf_task.input.get_ngkpt_shiftk()
        try_to_skip_wfkq = True
        if ngkpt is None or len(shiftk) > 1 and is_ngqpt:
            try_to_skip_wfkq = True

        # TODO: One could avoid kptopt 3 by computing WFK in the IBZ and then rotating.
        # but this has to be done inside Abinit.
        for qpt in qpoints:
            is_gamma = np.sum(qpt ** 2) < 1e-12
            if with_becs and is_gamma: continue

            # Avoid WFQ if k + q = k (requires ngkpt, multiple shifts are not supported)
            need_wfkq = True
            if is_gamma:
                need_wfkq = False
            elif try_to_skip_wfkq:
                # k = (i + shiftk) / ngkpt
                qinds = np.rint(qpt * ngqpt - shiftq)
                f = (qinds * ngkpt) % ngqpt
                need_wfkq = np.any(f != 0)

            if need_wfkq:
                nscf_inp = scf_task.input.new_with_vars(qpt=qpt, nqpt=1, iscf=-2, kptopt=3, tolwfr=tolwfr)
                if nband:
                    nbdbuf = max(2,nband*0.1)
                    nscf_inp.set_vars(nband=nband+nbdbuf, nbdbuf=nbdbuf)
                wfkq_task = new.register_nscf_task(nscf_inp, deps={scf_task: ["DEN", "WFK"]})
                new.wfkq_tasks.append(wfkq_task)

            multi = scf_task.input.make_ph_inputs_qpoint(qpt, tolerance=ph_tolerance)
            for ph_inp in multi:
                deps = {scf_task: "WFK", wfkq_task: "WFQ"} if need_wfkq else {scf_task: "WFK"}
                #ph_inp["prtwf"] = -1
                t = new.register_phonon_task(ph_inp, deps=deps)
                if need_wfkq:
                    new.wfkq_task_children[wfkq_task].append(t)

        return new

Response: Constructs a PhononWfkqWork object from a ScfTask object.


Instruction:
Make a summary of the following Python 3 code
def on_ok(self, sender):
        if self.remove_wfkq:
            for task in self.wfkq_tasks:
                if task.status != task.S_OK: continue
                children = self.wfkq_task_children[task]
                if all(child.status == child.S_OK for child in children):
                   path = task.outdir.has_abiext("WFQ")
                   if path:
                       self.history.info("Removing WFQ: %s" % path)
                       os.remove(path)

        return super().on_ok(sender)

Response: This callback is called when one task reaches status S_OK. It removes the WFKQ file if all its children have reached S_OK.


Instruction:
Write a Python 3 script to
construct a PhononWfkqWork from a DDB and DVDB file.

Response:
def from_den_ddb_dvdb(cls, inp, den_path, ddb_path, dvdb_path, mpiprocs=1, remove_wfkq=True,
                          qpath=None, with_ddk=True, expand=True, manager=None):
        """
        Construct a `PhononWfkqWork` from a DDB and DVDB file.
        For each q found, a WFQ task and an EPH task computing the matrix elements are created.
        """
        import abipy.abilab as abilab

        # Create file nodes
        den_file = FileNode(den_path)
        ddb_file = FileNode(ddb_path)
        dvdb_file = FileNode(dvdb_path)

        # Create new work
        new = cls(manager=manager)
        new.remove_wfkq = remove_wfkq
        new.wfkq_tasks = []
        new.wfkq_task_children = collections.defaultdict(list)
        if manager is None: manager = TaskManager.from_user_config()
        tm = manager.new_with_fixed_mpi_omp(mpiprocs, 1)

        # Create a WFK task
        kptopt = 1 if expand else 3
        nscf_inp = inp.new_with_vars(iscf=-2, kptopt=kptopt)
        wfk_task = new.register_nscf_task(nscf_inp, deps={den_file: "DEN"},manager=tm)
        new.wfkq_tasks.append(wfk_task)
        new.wfk_task = wfk_task

        # Read path and regular grid from DDB file
        with abilab.abiopen(ddb_path) as ddb:
            q_frac_coords = np.array([k.frac_coords for k in ddb.qpoints])
            ddb_ngqpt = ddb.guessed_ngqpt

        # If qpath is set, we read the list of q-points to be used to interpolate the DVDB file.
        # The DVDB and DDB file have to correspond to a regular grid.
        dvdb = dvdb_file
        if qpath is None:
            qpath = q_frac_coords
        else:
            interp_inp = inp.new_with_vars(optdriver=7, eph_task=-5, ddb_ngqpt=ddb_ngqpt,
                                           ph_nqpath=len(qpath), ph_qpath=qpath, prtphdos=0)
            dvdb = new.register_eph_task(interp_inp, deps={wfk_task: "WFK", ddb_file: "DDB", dvdb_file: "DVDB"},
                                          manager=tm)

        # Create a WFK expansion task
        if expand:
            fbz_nscf_inp = inp.new_with_vars(optdriver=8)
            fbz_nscf_inp.set_spell_check(False)
            fbz_nscf_inp.set_vars(wfk_task="wfk_fullbz")
            tm_serial = manager.new_with_fixed_mpi_omp(1,1)
            wfk_task = new.register_nscf_task(fbz_nscf_inp, deps={wfk_task: "WFK", den_file: "DEN"},
                                              manager=tm_serial)
            new.wfkq_tasks.append(wfk_task)
            new.wfk_task = wfk_task

        if with_ddk:
            kptopt = 3 if expand else 1
            ddk_inp = inp.new_with_vars(optdriver=8,kptopt=kptopt)
            ddk_inp.set_spell_check(False)
            ddk_inp.set_vars(wfk_task="wfk_ddk")
            ddk_task = new.register_nscf_task(ddk_inp, deps={wfk_task: "WFK", den_file: "DEN"}, manager=tm)
            new.wfkq_tasks.append(ddk_task)

        # For each qpoint
        for qpt in qpath:
            is_gamma = np.sum(qpt ** 2) < 1e-12
            if is_gamma:
                # Create a link from WFK to WFQ on_ok
                wfkq_task = wfk_task
                deps = {wfk_task: ["WFK","WFQ"], ddb_file: "DDB", dvdb: "DVDB" }
            else:
                # Create a WFQ task
                nscf_inp = nscf_inp.new_with_vars(kptopt=3, qpt=qpt, nqpt=1)
                wfkq_task = new.register_nscf_task(nscf_inp, deps={den_file: "DEN"}, manager=tm)
                new.wfkq_tasks.append(wfkq_task)
                deps = {wfk_task: "WFK", wfkq_task: "WFQ", ddb_file: "DDB", dvdb: "DVDB" }

            # Create a EPH task
            eph_inp = inp.new_with_vars(optdriver=7, prtphdos=0, eph_task=-2, kptopt=3,
                                        ddb_ngqpt=[1,1,1], nqpt=1, qpt=qpt)
            t = new.register_eph_task(eph_inp, deps=deps, manager=tm)
            new.wfkq_task_children[wfkq_task].append(t)

        return new


Instruction:
Can you tell what is the following Python 3 function doing
def from_phononwfkq_work(cls, phononwfkq_work, nscf_vars={}, remove_wfkq=True, with_ddk=True, manager=None):
        # Get list of qpoints from the the phonon tasks in this work
        qpoints = []
        qpoints_deps = []
        for task in phononwfkq_work:
            if isinstance(task,PhononTask):
                # Store qpoints
                qpt = task.input.get("qpt", [0,0,0])
                qpoints.append(qpt)
                # Store dependencies
                qpoints_deps.append(task.deps)

        # Create file nodes
        ddb_path  = phononwfkq_work.outdir.has_abiext("DDB")
        dvdb_path = phononwfkq_work.outdir.has_abiext("DVDB")
        ddb_file = FileNode(ddb_path)
        dvdb_file = FileNode(dvdb_path)

        # Get scf_task from first q-point
        for dep in qpoints_deps[0]:
            if isinstance(dep.node,ScfTask) and dep.exts[0] == 'WFK':
                scf_task = dep.node

        # Create new work
        new = cls(manager=manager)
        new.remove_wfkq = remove_wfkq
        new.wfkq_tasks = []
        new.wfk_task = []

        # Add one eph task per qpoint
        for qpt,qpoint_deps in zip(qpoints,qpoints_deps):
            # Create eph task
            eph_input = scf_task.input.new_with_vars(optdriver=7, prtphdos=0, eph_task=-2,
                                                     ddb_ngqpt=[1,1,1], nqpt=1, qpt=qpt)
            deps = {ddb_file: "DDB", dvdb_file: "DVDB" }
            for dep in qpoint_deps:
                deps[dep.node] = dep.exts[0]
            # If no WFQ in deps link the WFK with WFQ extension
            if 'WFQ' not in deps.values():
                inv_deps = dict((v, k) for k, v in deps.items())
                wfk_task = inv_deps['WFK']
                wfk_path = wfk_task.outdir.has_abiext("WFK")
                # Check if netcdf
                filename, extension = os.path.splitext(wfk_path)
                infile = 'out_WFQ' + extension
                wfq_path = os.path.join(os.path.dirname(wfk_path), infile)
                if not os.path.isfile(wfq_path): os.symlink(wfk_path, wfq_path)
                deps[FileNode(wfq_path)] = 'WFQ'
            new.register_eph_task(eph_input, deps=deps)

        return new

Response: Construct a GKKPWork object from a PhononWfkqWork object.


Instruction:
Can you implement a function in Python 3 that
builds a new TaskManager object from a ground - state task.

Response:
def from_scf_task(cls, scf_task, ddk_tolerance=None, ph_tolerance=None, manager=None):
        """
        Build tasks for the computation of Born effective charges from a ground-state task.

        Args:
            scf_task: ScfTask object.
            ddk_tolerance: tolerance used in the DDK run if with_becs. None to use AbiPy default.
            ph_tolerance: dict {"varname": value} with the tolerance used in the phonon run.
                None to use AbiPy default.
            manager: :class:`TaskManager` object.
	"""
        new = cls(manager=manager)
        new.add_becs_from_scf_task(scf_task, ddk_tolerance, ph_tolerance)
        return new


Instruction:
How would you implement a function in Python 3 that
builds a DteWork object from a ground - state task.

Response:
def from_scf_task(cls, scf_task, ddk_tolerance=None, manager=None):
        """
	Build a DteWork from a ground-state task.

        Args:
            scf_task: ScfTask object.
            ddk_tolerance: tolerance used in the DDK run if with_becs. None to use AbiPy default.
            manager: :class:`TaskManager` object.
	"""
        if not isinstance(scf_task, ScfTask):
            raise TypeError("task `%s` does not inherit from ScfTask" % scf_task)

        new = cls(manager=manager)

        # DDK calculations
        multi_ddk = scf_task.input.make_ddk_inputs(tolerance=ddk_tolerance)

        ddk_tasks = []
        for ddk_inp in multi_ddk:
            ddk_task = new.register_ddk_task(ddk_inp, deps={scf_task: "WFK"})
            ddk_tasks.append(ddk_task)

        # Build the list of inputs for electric field perturbation
        # Each task is connected to all the previous DDK, DDE task and to the scf_task.
        multi_dde = scf_task.input.make_dde_inputs(use_symmetries=False)

        # To compute the nonlinear coefficients all the directions of the perturbation
        # have to be taken in consideration
        # DDE calculations
        dde_tasks = []
        dde_deps = {ddk_task: "DDK" for ddk_task in ddk_tasks}
        dde_deps.update({scf_task: "WFK"})
        for dde_inp in multi_dde:
            dde_task = new.register_dde_task(dde_inp, deps=dde_deps)
            dde_tasks.append(dde_task)

        # DTE calculations
        dte_deps = {scf_task: "WFK DEN"}
        dte_deps.update({dde_task: "1WF 1DEN" for dde_task in dde_tasks})

        multi_dte = scf_task.input.make_dte_inputs()
        dte_tasks = []
        for dte_inp in multi_dte:
             dte_task = new.register_dte_task(dte_inp, deps=dte_deps)
             dte_tasks.append(dte_task)

        return new


Instruction:
How would you explain what the following Python 3 function does
def create_saturated_interstitial_structure( interstitial_def, dist_tol=0.1):
    sga = SpacegroupAnalyzer( interstitial_def.bulk_structure.copy())
    sg_ops = sga.get_symmetry_operations( cartesian=True)

    # copy bulk structure to make saturated interstitial structure out of
    # artificially lower distance_tolerance to allow for distinct interstitials
    # with lower symmetry to be replicated - This is OK because one would never
    # actually use this structure for a practical calcualtion...
    saturated_defect_struct = interstitial_def.bulk_structure.copy()
    saturated_defect_struct.DISTANCE_TOLERANCE = dist_tol

    for sgo in sg_ops:
        new_interstit_coords = sgo.operate( interstitial_def.site.coords[:])
        poss_new_site = PeriodicSite(
                interstitial_def.site.specie,
                new_interstit_coords,
                saturated_defect_struct.lattice,
                to_unit_cell=True,
                coords_are_cartesian=True)
        try:
            #will raise value error if site already exists in structure
            saturated_defect_struct.append(
                        poss_new_site.specie, poss_new_site.coords,
                        coords_are_cartesian=True, validate_proximity=True)
        except ValueError:
            pass

    # do final space group analysis to make sure symmetry not lowered by saturating defect structure
    saturated_sga = SpacegroupAnalyzer( saturated_defect_struct)
    if saturated_sga.get_space_group_number() != sga.get_space_group_number():
        raise ValueError("Warning! Interstitial sublattice generation "
                         "has changed space group symmetry. I recommend "
                         "reducing dist_tol and trying again...")

    return saturated_defect_struct

Response: This function creates a saturated interstitial structure from an interstitial structure.


Instruction:
Can you tell what is the following Python 3 function doing
def generate_defect_structure(self, supercell=(1, 1, 1)):
        defect_structure = self.bulk_structure.copy()
        defect_structure.make_supercell(supercell)

        #create a trivial defect structure to find where supercell transformation moves the lattice
        struct_for_defect_site = Structure( self.bulk_structure.copy().lattice,
                                             [self.site.specie],
                                             [self.site.frac_coords],
                                             to_unit_cell=True)
        struct_for_defect_site.make_supercell(supercell)
        defect_site = struct_for_defect_site[0]

        poss_deflist = sorted(
            defect_structure.get_sites_in_sphere(defect_site.coords, 2, include_index=True), key=lambda x: x[1])
        defindex = poss_deflist[0][2]
        defect_structure.remove_sites([defindex])
        defect_structure.set_charge(self.charge)
        return defect_structure

Response: Generate a Defective Vacancy structure with supercell transformation


Instruction:
Can you implement a function in Python 3 that
returns the multiplicity of a defect site within the structure

Response:
def multiplicity(self):
        """
        Returns the multiplicity of a defect site within the structure (needed for concentration analysis)
        """
        sga = SpacegroupAnalyzer(self.bulk_structure)
        periodic_struc = sga.get_symmetrized_structure()
        poss_deflist = sorted(
            periodic_struc.get_sites_in_sphere(self.site.coords, 2, include_index=True), key=lambda x: x[1])
        defindex = poss_deflist[0][2]

        equivalent_sites = periodic_struc.find_equivalent_sites(self.bulk_structure[defindex])
        return len(equivalent_sites)


Instruction:
Make a summary of the following Python 3 code
def generate_defect_structure(self, supercell=(1, 1, 1)):
        defect_structure = self.bulk_structure.copy()
        defect_structure.make_supercell(supercell)

        # consider modifying velocity property to make sure defect site is decorated
        # consistently with bulk structure for final defect_structure
        defect_properties = self.site.properties.copy()
        if ('velocities' in self.bulk_structure.site_properties) and \
            'velocities' not in defect_properties:
            if all( vel == self.bulk_structure.site_properties['velocities'][0]
                    for vel in self.bulk_structure.site_properties['velocities']):
                defect_properties['velocities'] = self.bulk_structure.site_properties['velocities'][0]
            else:
                raise ValueError("No velocity property specified for defect site and "
                                 "bulk_structure velocities are not homogeneous. Please specify this "
                                 "property within the initialized defect_site object.")

        #create a trivial defect structure to find where supercell transformation moves the lattice
        site_properties_for_fake_struct = {prop: [val] for prop,val in defect_properties.items()}
        struct_for_defect_site = Structure( self.bulk_structure.copy().lattice,
                                             [self.site.specie],
                                             [self.site.frac_coords],
                                             to_unit_cell=True,
                                             site_properties = site_properties_for_fake_struct)
        struct_for_defect_site.make_supercell(supercell)
        defect_site = struct_for_defect_site[0]

        poss_deflist = sorted(
            defect_structure.get_sites_in_sphere(defect_site.coords, 2, include_index=True), key=lambda x: x[1])
        defindex = poss_deflist[0][2]

        subsite = defect_structure.pop(defindex)
        defect_structure.append(self.site.specie.symbol, subsite.coords, coords_are_cartesian=True,
                                properties = defect_site.properties)
        defect_structure.set_charge(self.charge)
        return defect_structure

Response: Generate a defect structure for the given supercell.


Instruction:
Implement a Python 3 function for
returning a name for this defect

Response:
def name(self):
        """
        Returns a name for this defect
        """
        poss_deflist = sorted(
            self.bulk_structure.get_sites_in_sphere(self.site.coords, 2, include_index=True), key=lambda x: x[1])
        defindex = poss_deflist[0][2]
        return "Sub_{}_on_{}_mult{}".format(self.site.specie, self.bulk_structure[defindex].specie, self.multiplicity)


Instruction:
Write a Python 3 function that can
generate a defect structure for the given supercell.

Response:
def generate_defect_structure(self, supercell=(1, 1, 1)):
        """
        Returns Defective Interstitial structure, decorated with charge
        Args:
            supercell (int, [3x1], or [[]] (3x3)): supercell integer, vector, or scaling matrix
        """
        defect_structure = self.bulk_structure.copy()
        defect_structure.make_supercell(supercell)

        # consider modifying velocity property to make sure defect site is decorated
        # consistently with bulk structure for final defect_structure
        defect_properties = self.site.properties.copy()
        if ('velocities' in self.bulk_structure.site_properties) and \
            'velocities' not in defect_properties:
            if all( vel == self.bulk_structure.site_properties['velocities'][0]
                    for vel in self.bulk_structure.site_properties['velocities']):
                defect_properties['velocities'] = self.bulk_structure.site_properties['velocities'][0]
            else:
                raise ValueError("No velocity property specified for defect site and "
                                 "bulk_structure velocities are not homogeneous. Please specify this "
                                 "property within the initialized defect_site object.")

        #create a trivial defect structure to find where supercell transformation moves the defect site
        site_properties_for_fake_struct = {prop: [val] for prop,val in defect_properties.items()}
        struct_for_defect_site = Structure( self.bulk_structure.copy().lattice,
                                             [self.site.specie],
                                             [self.site.frac_coords],
                                             to_unit_cell=True,
                                             site_properties = site_properties_for_fake_struct)
        struct_for_defect_site.make_supercell(supercell)
        defect_site = struct_for_defect_site[0]

        defect_structure.append(self.site.specie.symbol, defect_site.coords, coords_are_cartesian=True,
                                properties = defect_site.properties)
        defect_structure.set_charge(self.charge)
        return defect_structure


Instruction:
Can you generate the documentation for the following Python 3 function
def multiplicity(self):
        if self._multiplicity is None:
            # generate multiplicity based on space group symmetry operations performed on defect coordinates
            try:
                d_structure = create_saturated_interstitial_structure(self)
            except ValueError:
                logger.debug('WARNING! Multiplicity was not able to be calculated adequately '
                             'for interstitials...setting this to 1 and skipping for now...')
                return 1

            sga = SpacegroupAnalyzer(d_structure)
            periodic_struc = sga.get_symmetrized_structure()
            poss_deflist = sorted(
                periodic_struc.get_sites_in_sphere(self.site.coords, 2, include_index=True),
                key=lambda x: x[1])
            defindex = poss_deflist[0][2]

            equivalent_sites = periodic_struc.find_equivalent_sites(periodic_struc[defindex])
            return len(equivalent_sites)

        else:
            return self._multiplicity

Response: Returns the multiplicity of a defect site within the structure


Instruction:
Write a Python 3 script to
return a name for this defect.

Response:
def name(self):
        """
        Returns a name for this defect
        """
        if self.site_name:
            return "Int_{}_{}_mult{}".format(self.site.specie, self.site_name, self.multiplicity)
        else:
            return "Int_{}_mult{}".format(self.site.specie, self.multiplicity)


Instruction:
Implement a Python 3 function for
reconstituting a DefectEntry object from a dict representation created using as_dict().

Response:
def from_dict(cls, d):
        """
        Reconstitute a DefectEntry object from a dict representation created using
        as_dict().
         Args:
            d (dict): dict representation of DefectEntry.
         Returns:
            DefectEntry object
        """
        defect = MontyDecoder().process_decoded( d["defect"])
        uncorrected_energy = d["uncorrected_energy"]
        corrections = d.get("corrections", None)
        parameters = d.get("parameters", None)
        entry_id = d.get("entry_id", None)

        return cls(defect, uncorrected_energy, corrections=corrections,
                   parameters=parameters, entry_id=entry_id)


Instruction:
How would you implement a function in Python 3 that
returns the total energy of the entry in the entry.

Response:
def energy(self):
        """
        Returns the *corrected* energy of the entry
        """
        return self.uncorrected_energy + np.sum(list(self.corrections.values()))


Instruction:
Can you tell what is the following Python 3 function doing
def formation_energy(self, chemical_potentials=None, fermi_level=0):
        chemical_potentials = chemical_potentials if chemical_potentials else {}

        chempot_correction = sum([
            chem_pot * (self.bulk_structure.composition[el] - self.defect.defect_composition[el])
            for el, chem_pot in chemical_potentials.items()
        ])

        formation_energy = self.energy + chempot_correction

        if "vbm" in self.parameters:
            formation_energy += self.charge * (self.parameters["vbm"] + fermi_level)
        else:
            formation_energy += self.charge * fermi_level

        return formation_energy

Response: Computes the formation energy for a defect taking into account a given chemical potential and fermi_level.


Instruction:
Create a Python 3 function to
get the defect concentration for a temperature and Fermi level.

Response:
def defect_concentration(self, chemical_potentials, temperature=300, fermi_level=0.0):
        """
        Get the defect concentration for a temperature and Fermi level.
        Args:
            temperature:
                the temperature in K
            fermi_level:
                the fermi level in eV (with respect to the VBM)
        Returns:
            defects concentration in cm^-3
        """
        n = self.multiplicity * 1e24 / self.defect.bulk_structure.volume
        conc = n * np.exp(-1.0 * self.formation_energy(chemical_potentials, fermi_level=fermi_level) /
                          (kb * temperature))

        return conc


Instruction:
Make a summary of the following Python 3 code
def correct_entry(self, entry):
        entry.correction.update(self.get_correction(entry))
        return entry

Response: Corrects a single entry.


Instruction:
Here you have a function in Python 3, explain what it does
def get_charge_transfer(self, atom_index):
        if self.potcar is None:
            raise ValueError("POTCAR must be supplied in order to calculate "
                             "charge transfer!")
        potcar_indices = []
        for i, v in enumerate(self.natoms):
            potcar_indices += [i] * v
        nelect = self.potcar[potcar_indices[atom_index]].nelectrons
        return self.data[atom_index]["charge"] - nelect

Response: Returns the charge transferred for a particular atom. Requires POTCAR to be supplied.


Instruction:
Implement a function in Python 3 to
return an oxidation state decorated structure. Requires POTCAR to be supplied.

Response:
def get_oxidation_state_decorated_structure(self):
        """
        Returns an oxidation state decorated structure.

        Returns:
            Returns an oxidation state decorated structure. Requires POTCAR
            to be supplied.
        """
        structure = self.chgcar.structure
        charges = [-self.get_charge_transfer(i)
                   for i in range(len(structure))]
        structure.add_oxidation_state_by_site(charges)
        return structure


Instruction:
Write a Python 3 script to
return a new instance of the class AECC from a path name.

Response:
def from_path(cls, path, suffix=""):
        """
        Convenient constructor that takes in the path name of VASP run
        to perform Bader analysis.

        Args:
            path (str): Name of directory where VASP output files are
                stored.
            suffix (str): specific suffix to look for (e.g. '.relax1'
                for 'CHGCAR.relax1.gz').

        """

        def _get_filepath(filename):
            name_pattern = filename + suffix + '*' if filename != 'POTCAR' \
                else filename + '*'
            paths = glob.glob(os.path.join(path, name_pattern))
            fpath = None
            if len(paths) >= 1:
                # using reverse=True because, if multiple files are present,
                # they likely have suffixes 'static', 'relax', 'relax2', etc.
                # and this would give 'static' over 'relax2' over 'relax'
                # however, better to use 'suffix' kwarg to avoid this!
                paths.sort(reverse=True)
                warning_msg = "Multiple files detected, using %s" \
                              % os.path.basename(paths[0]) if len(paths) > 1 \
                    else None
                fpath = paths[0]
            else:
                warning_msg = "Could not find %s" % filename
                if filename in ['AECCAR0', 'AECCAR2']:
                    warning_msg += ", cannot calculate charge transfer."
                elif filename == "POTCAR":
                    warning_msg += ", interpret Bader results with caution."
            if warning_msg:
                warnings.warn(warning_msg)
            return fpath

        chgcar_filename = _get_filepath("CHGCAR")
        if chgcar_filename is None:
            raise IOError("Could not find CHGCAR!")
        potcar_filename = _get_filepath("POTCAR")
        aeccar0 = _get_filepath("AECCAR0")
        aeccar2 = _get_filepath("AECCAR2")
        if (aeccar0 and aeccar2):
            # `chgsum.pl AECCAR0 AECCAR2` equivalent to obtain chgref_file
            chgref = Chgcar.from_file(aeccar0) + Chgcar.from_file(aeccar2)
            chgref_filename = "CHGREF"
            chgref.write_file(chgref_filename)
        else:
            chgref_filename = None
        return cls(chgcar_filename, potcar_filename=potcar_filename,
                   chgref_filename=chgref_filename)