How to use the atomate.vasp.database.VaspCalcDb.from_db_file function in atomate

def run_task(self, fw_spec):

        db_file = env_chk(self["db_file"], fw_spec)
        wf_uuid = self["wf_uuid"]
        mc_settings = self.get("mc_settings", {})

        # Get Heisenberg models from db
        mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
        mmdb.collection = mmdb.db["exchange"]

        # Get documents
        docs = list(
            mmdb.collection.find(
                {"wf_meta.wf_uuid": wf_uuid}, ["heisenberg_model", "nn_cutoff"]
            )
        )

        hmodels = [HeisenbergModel.from_dict(d["heisenberg_model"]) for d in docs]
        cutoffs = [hmodel.cutoff for hmodel in hmodels]
        ordered_hmodels = [h for _, h in sorted(zip(cutoffs, hmodels), reverse=False)]
        # Take the model with smallest NN cutoff
        hmodel = ordered_hmodels[0]

        # Get a converged Heisenberg model if one was found

# assimilate (i.e., parse)
        task_doc = drone.assimilate(calc_dir)

        # Check for additional keys to set based on the fw_spec
        if self.get("fw_spec_field"):
            task_doc.update(fw_spec[self.get("fw_spec_field")])

        # get the database connection
        db_file = env_chk(self.get('db_file'), fw_spec)

        # db insertion or taskdoc dump
        if not db_file:
            with open("task.json", "w") as f:
                f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
        else:
            mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
            t_id = mmdb.insert_task(
                task_doc, use_gridfs=self.get("parse_dos", False)
                or bool(self.get("bandstructure_mode", False))
                or self.get("parse_chgcar", False)  # deprecated
                or self.get("parse_aeccar", False)  # deprecated
                or bool(self.get("store_volumetric_data", STORE_VOLUMETRIC_DATA)))
            logger.info("Finished parsing with task_id: {}".format(t_id))

        defuse_children = False
        if task_doc["state"] != "successful":
            defuse_unsuccessful = self.get("defuse_unsuccessful",
                                           DEFUSE_UNSUCCESSFUL)
            if defuse_unsuccessful is True:
                defuse_children = True
            elif defuse_unsuccessful is False:
                pass

def run_task(self, fw_spec):

        wfid = list(filter(lambda x: 'wfid' in x, fw_spec['tags'])).pop()
        db_file = env_chk(self.get("db_file"), fw_spec)
        vaspdb = VaspCalcDb.from_db_file(db_file, admin=True)

        # ferroelectric workflow groups calculations by generated wfid tag
        polarization_tasks = vaspdb.collection.find({"tags": wfid, "task_label": {"$regex": ".*polarization"}})

        tasks = []
        outcars = []
        structure_dicts = []
        sort_weight = []
        energies_per_atom = []
        energies = []
        zval_dicts = []

        for p in polarization_tasks:
            # Grab data from each polarization task
            energies_per_atom.append(p['calcs_reversed'][0]['output']['energy_per_atom'])
            energies.append(p['calcs_reversed'][0]['output']['energy'])

"number": sg.get_space_group_number(),
                           "point_group": sg.get_point_group_symbol(),
                           "source": "spglib",
                           "crystal_system": sg.get_crystal_system(),
                           "hall": sg.get_hall()}

        d["created_at"] = datetime.utcnow()

        db_file = env_chk(self.get('db_file'), fw_spec)

        if not db_file:
            del d["dos"]
            with open(os.path.join(btrap_dir, "boltztrap.json"), "w") as f:
                f.write(json.dumps(d, default=DATETIME_HANDLER))
        else:
            mmdb = VaspCalcDb.from_db_file(db_file, admin=True)

            # dos gets inserted into GridFS
            dos = json.dumps(d["dos"], cls=MontyEncoder)
            fsid, compression = mmdb.insert_gridfs(dos, collection="dos_boltztrap_fs",
                                                   compress=True)
            d["dos_boltztrap_fs_id"] = fsid
            del d["dos"]

            mmdb.db.boltztrap.insert(d)

def __init__(self):
        with open("/global/homes/s/sivonxay/.conda/envs/knl_env/config/my_launchpad.yaml", "r") as f:
            t = yaml.load(f)
        client = MongoClient(t['host'])
        db = client.fw_es_vasp
        db.authenticate(t['username'], t['password'])
        self.tasks = db.tasks

        db_file = "/global/homes/s/sivonxay/.conda/envs/knl_env/config/db.json"
        self.mmdb = VaspCalcDb.from_db_file(db_file, admin=True)

def run_task(self, fw_spec):

        db_file = env_chk(self["db_file"], fw_spec)
        wf_uuid = self["exchange_wf_uuid"]
        formula_pretty = self["parent_structure"].composition.reduced_formula
        mc_settings = self["mc_settings"]

        # Get Heisenberg models from db
        mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
        mmdb.collection = mmdb.db["exchange"]

        task_doc = {"wf_meta": {"wf_uuid": wf_uuid}, "formula_pretty": formula_pretty}

        if fw_spec.get("tags", None):
            task_doc["tags"] = fw_spec["tags"]

        # Vampire monte carlo settings
        mc_box_size = mc_settings["mc_box_size"]
        equil_timesteps = mc_settings["equil_timesteps"]
        mc_timesteps = mc_settings["mc_timesteps"]

        # Get a converged Heisenberg model if one was found
        if fw_spec["converged_heisenberg_model"]:
            hmodel = HeisenbergModel.from_dict(fw_spec["converged_heisenberg_model"])
            vc = VampireCaller(

def run_task(self, fw_spec):
        db_file = env_chk(self["db_file"], fw_spec)
        wf_uuid = self["exchange_wf_uuid"]

        # Get magnetic orderings collection from db
        mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
        mmdb.collection = mmdb.db["magnetic_orderings"]

        formula = self["parent_structure"].formula
        formula_pretty = self["parent_structure"].composition.reduced_formula

        # Get documents
        docs = list(
            mmdb.collection.find(
                {"wf_meta.wf_uuid": wf_uuid},
                ["task_id", "structure", "energy_per_atom"],
            )
        )

        # Get structures and energy / unit cell
        structures = [Structure.from_dict(d["structure"]) for d in docs]
        epas = [d["energy_per_atom"] for d in docs]

# Check for additional keys to set based on the fw_spec
        if self.get("fw_spec_field"):
            task_doc.update(fw_spec[self.get("fw_spec_field")])

        task_doc["state"] = "successful"

        task_doc = jsanitize(task_doc)
        # get the database connection
        db_file = env_chk(self.get("db_file"), fw_spec)

        # db insertion or taskdoc dump
        if not db_file:
            with open("task_lobster.json", "w") as f:
                f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
        else:
            db = VaspCalcDb.from_db_file(db_file, admin=True)
            db.collection = db.db["lobster"]
            additional_outputs = self.get("additional_outputs", None)
            if additional_outputs:
                for filename in additional_outputs:

                    fs_id = None
                    if os.path.isfile(filename):
                        fs_id = put_file_in_gridfs(
                            filename, db, collection_name="lobster_files", compress=True
                        )
                    elif os.path.isfile(filename + ".gz"):
                        fs_id = put_file_in_gridfs(
                            filename + ".gz",
                            db,
                            collection_name="lobster_files",
                            compress=False,

def run_task(self, fw_spec):

        db_file = env_chk(self["db_file"], fw_spec)
        wf_uuid = self["wf_uuid"]
        vampire_output = loadfn("vampire_output.json")

        mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
        mmdb.collection = mmdb.db["exchange"]

        task_doc = {"wf_meta": {"wf_uuid": wf_uuid}, "task_name": "vampire caller"}

        if fw_spec.get("tags", None):
            task_doc["tags"] = fw_spec["tags"]

        task_doc["vampire_output"] = vampire_output.as_dict()
        mmdb.collection.insert_one(task_doc)

omega = nm_frequencies[k]
            if nm_eigenvals[k] > 0:
                logger.warning("Mode: {} is UNSTABLE. Freq(cm^-1) = {}".format(k, -omega))
            raman_tensor = scale * raman_tensor * np.sum(nm_norms[k]) / np.sqrt(omega)
            raman_tensor_dict[str(k)] = raman_tensor.tolist()

        d["raman_tensor"] = raman_tensor_dict
        d["state"] = "successful"

        # store the results
        db_file = env_chk(self.get("db_file"), fw_spec)
        if not db_file:
            with open("raman.json", "w") as f:
                f.write(json.dumps(d, default=DATETIME_HANDLER))
        else:
            db = VaspCalcDb.from_db_file(db_file, admin=True)
            db.collection = db.db["raman"]
            db.collection.insert_one(d)
            logger.info("Raman tensor calculation complete.")
        return FWAction()