How to use the pymatgen.Structure.from_dict function in pymatgen

# handle the "parent structure" option, which is used to intentionally force slightly
        # different structures to contribute to the same "material", e.g. from an ordering scheme
        if "parent_structure" in taskdoc:
            t_struct = Structure.from_dict(taskdoc["parent_structure"]["structure"])
            q = {"formula_reduced_abc": formula, "parent_structure.spacegroup.number": taskdoc[
                "parent_structure"]["spacegroup"]["number"]}
        else:
            sgnum = taskdoc["output"]["spacegroup"]["number"]
            t_struct = Structure.from_dict(taskdoc["output"]["structure"])
            q = {"formula_reduced_abc": formula, "sg_number": sgnum}

        for m in self._materials.find(q, {"parent_structure": 1, "structure": 1, "material_id": 1}):
            s_dict = m["parent_structure"]["structure"] if "parent_structure" in m else m[
                "structure"]
            m_struct = Structure.from_dict(s_dict)
            sm = StructureMatcher(ltol=ltol, stol=stol, angle_tol=angle_tol,
                                  primitive_cell=True, scale=True,
                                  attempt_supercell=False, allow_subset=False,
                                  comparator=ElementComparator())

            if sm.fit(m_struct, t_struct):
                return m["material_id"]

        return None

# for backwards compatibility with older version of mpmorph
            fs_id.append(run["ionic_steps_fs_id"])
            fs.append('previous_runs_gfs')
        elif "input" in run.keys():
            fs_id.append(run["calcs_reversed"][0]["output"]["ionic_steps_fs_id"])
            fs.append('structures_fs')

    for i, v in enumerate(fs_id):
        mmdb = VaspMDCalcDb.from_db_file(db_file, admin=True)
        ionic_steps_dict = load_ionic_steps(v, mmdb.db, fs[i])

        if i == 0:
            trajectory = Trajectory.from_structures([Structure.from_dict(i['structure']) for i in ionic_steps_dict])
        else:
            trajectory.extend(
                Trajectory.from_structures([Structure.from_dict(i['structure']) for i in ionic_steps_dict]))
    return trajectory

tag = self["tag"]
        db_file = env_chk(self.get("db_file"), fw_spec)
        t_step = self.get("t_step", 10)
        t_min = self.get("t_min", 0)
        t_max = self.get("t_max", 1000)
        mesh = self.get("mesh", [20, 20, 20])
        eos = self.get("eos", "vinet")
        qha_type = self.get("qha_type", "debye_model")
        pressure = self.get("pressure", 0.0)
        poisson = self.get("poisson", 0.25)
        gibbs_summary_dict = {}

        mmdb = MMVaspDb.from_db_file(db_file, admin=True)
        # get the optimized structure
        d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)})
        structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
        gibbs_summary_dict["structure"] = structure.as_dict()

        # get the data(energy, volume, force constant) from the deformation runs
        docs = mmdb.collection.find({"task_label": {"$regex": "{} gibbs*".format(tag)},
                                     "formula_pretty": structure.composition.reduced_formula})
        energies = []
        volumes = []
        force_constants = []
        for d in docs:
            s = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
            energies.append(d["calcs_reversed"][-1]["output"]['energy'])
            if qha_type not in ["debye_model"]:
                force_constants.append(d["calcs_reversed"][-1]["output"]['force_constants'])
            volumes.append(s.volume)
        gibbs_summary_dict["energies"] = energies
        gibbs_summary_dict["volumes"] = volumes

Args:
        docs ([dict]): List of docs. Each doc should have the same
            format as one returned from .dft.parse_dir.

    Returns:
        A list of structures, and a DataFrame with energy and force
        data in 'y_orig' column, data type ('energy' or 'force') in
        'dtype' column, No. of atoms in 'n' column sharing the same row
        of energy data while 'n' being 1 for the rows of force data.

    """
    structures, y_orig, n, dtype = [], [], [], []
    for d in docs:
        if isinstance(d['structure'], dict):
            structure = Structure.from_dict(d['structure'])
        else:
            structure = d['structure']
        outputs = d['outputs']
        force_arr = np.array(outputs['forces'])
        assert force_arr.shape == (len(structure), 3), \
            'Wrong force array not matching structure'
        force_arr = force_arr.ravel()
        y = np.concatenate(([outputs['energy']], force_arr))
        y_orig.append(y)
        n.append(np.insert(np.ones(len(y) - 1), 0, d['num_atoms']))
        dtype.extend(['energy'] + ['force'] * len(force_arr))
        structures.append(structure)
    df = pd.DataFrame(dict(y_orig=np.concatenate(y_orig), n=np.concatenate(n),
                           dtype=dtype))
    for k, v in kwargs.items():
        df[k] = v

(approximately 70k).

    Returns:
        formula (input)
        ehull (output)


    References:
        If you need a version WITH structures, use generate_data.py to create
        mp_all.csv, and use filename="sources/mp_all.csv"
    """

    df = pd.read_csv(os.path.join(data_dir, filename))
    df = df.drop("mpid", axis=1)
    if 'structure' in df.columns.values:
        df['structure'] = df['structure'].map(ast.literal_eval).map(Structure.from_dict)
    colmap = {'material_id': 'mpid',
              'pretty_formula': 'formula',
              'band_gap': 'gap pbe',
              'e_above_hull': 'ehull',
              'elasticity.K_VRH': 'bulkmod',
              'elasticity.G_VRH': 'shearmod',
              'elasticity.elastic_anisotropy': 'elastic_anisotropy',
              'total_magnetization': 'mu_B'}
    return df.rename(columns=colmap)

def run_task(self, fw_spec):
        ref_struct = self['structure']
        d = {
            "analysis": {},
            "initial_structure": self['structure'].as_dict()
        }

        # Get optimized structure
        calc_locs_opt = [cl for cl in fw_spec.get('calc_locs', []) if 'optimiz' in cl['name']]
        if calc_locs_opt:
            optimize_loc = calc_locs_opt[-1]['path']
            logger.info("Parsing initial optimization directory: {}".format(optimize_loc))
            drone = VaspDrone()
            optimize_doc = drone.assimilate(optimize_loc)
            opt_struct = Structure.from_dict(optimize_doc["calcs_reversed"][0]["output"]["structure"])
            d.update({"optimized_structure": opt_struct.as_dict()})
            ref_struct = opt_struct
            eq_stress = -0.1*Stress(optimize_doc["calcs_reversed"][0]["output"]["ionic_steps"][-1]["stress"])
        else:
            eq_stress = None

        if self.get("fw_spec_field"):
            d.update({self.get("fw_spec_field"): fw_spec.get(self.get("fw_spec_field"))})

        # Get the stresses, strains, deformations from deformation tasks
        defo_dicts = fw_spec["deformation_tasks"].values()
        stresses, strains, deformations = [], [], []
        for defo_dict in defo_dicts:
            stresses.append(Stress(defo_dict["stress"]))
            strains.append(Strain(defo_dict["strain"]))
            deformations.append(Deformation(defo_dict["deformation_matrix"]))

def write_cfgs(self, filename, cfg_pool):

        lines = []
        for dataset in cfg_pool:
            if isinstance(dataset['structure'], dict):
                structure = Structure.from_dict(dataset['structure'])
            else:
                structure = dataset['structure']
            energy = dataset['outputs']['energy']
            forces = dataset['outputs']['forces']
            virial_stress = dataset['outputs']['virial_stress']

            lines.append(self._line_up(structure, energy, forces, virial_stress))

            # dist = np.unique(structure.distance_matrix.ravel())[1]
            # if self.shortest_distance > dist:
            #     self.shortest_distance = dist

        self.specie = Element(structure.symbol_set[0])

        with open(filename, 'w') as f:
            f.write('\n'.join(lines))

def add_structure(self, source, name=None, identifier=None, fmt=None):
        """add a structure to the mpfile"""
        from pymatgen import Structure, MPRester

        if isinstance(source, Structure):
            structure = source
        elif isinstance(source, dict):
            structure = Structure.from_dict(source)
        elif os.path.exists(source):
            structure = Structure.from_file(source, sort=True)
        elif isinstance(source, six.string_types):
            if fmt is None:
                raise ValueError("Need fmt to get structure from string!")
            structure = Structure.from_str(source, fmt, sort=True)
        else:
            raise ValueError(source, "not supported!")

        if name is not None:
            if not isinstance(name, six.string_types):
                raise ValueError("structure name needs to be a string")
            elif "." in name:
                raise ValueError("structure name cannot contain dots (.)")

        mpr = MPRester()

def process_traj(data):
    i, fs_id, fs, db_file = data[0], data[1], data[2], data[3]
    mmdb = VaspMDCalcDb.from_db_file(db_file, admin=True)
    ionic_steps_dict = load_ionic_steps(fs_id, mmdb.db, fs)

    structure = Structure.from_dict(ionic_steps_dict[0]['structure'])
    positions = [0] * len(ionic_steps_dict)
    for i, step in enumerate(ionic_steps_dict):
        _step = [atom['abc'] for atom in step["structure"]["sites"]]
        positions[i] = _step

    traj = Trajectory(structure.lattice.matrix, structure.species, positions, 0.002)
    return i, traj.as_dict()

gfs_id, compression_type = self.insert_gridfs(ionic_steps_json, "structures_fs")
            task_doc["calcs_reversed"][0]['output']['ionic_steps_compression'] = compression_type
            task_doc["calcs_reversed"][0]['output']['ionic_steps_fs_id'] = gfs_id

            # Aggregate a trajectory
            ## Convert from a list of dictionaries to a dictionary of lists
            ionic_steps_dict = task_doc["calcs_reversed"][0]['output']['ionic_steps']
            del task_doc["calcs_reversed"][0]['output']['ionic_steps']
            ionic_steps_defaultdict = defaultdict(list)
            for d in ionic_steps_dict:
                for key, val in d.items():
                    ionic_steps_defaultdict[key].append(val)
            ionic_steps = dict(ionic_steps_defaultdict.items())

            ## extract structures from dictionary
            structures = [Structure.from_dict(struct) for struct in ionic_steps['structure']]
            del ionic_steps['structure']

            frame_properties = {}
            for key in ['e_fr_energy', 'e_wo_entrp', 'e_0_energy', 'kinetic', 'lattice kinetic', 'nosepot',
                        'nosekinetic', 'total']:
                frame_properties[key] = ionic_steps[key]

            # Create trajectory
            trajectory = Trajectory.from_structures(structures, constant_lattice=True,
                                                    frame_properties=frame_properties,
                                                    time_step=task_doc['input']['incar']['POTIM'])
            traj_dict = json.dumps(trajectory.as_dict(), cls=MontyEncoder)
            gfs_id, compression_type = self.insert_gridfs(traj_dict, "trajectories_fs")

            task_doc['trajectory'] = {
                'formula': trajectory[0].composition.formula.replace(' ', ''),