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How to use _create_metadata_file method in avocado

Best Python code snippet using avocado_python

SignalImplanter.py

Source:SignalImplanter.py

...45        processed_repertoires = SignalImplanter._implant_signals(simulation_state, SignalImplanter._process_repertoire, repertoires_path)46        processed_dataset = RepertoireDataset(repertoires=processed_repertoires, labels={**(simulation_state.dataset.labels if simulation_state.dataset.labels is not None else {}),47                                                                                         **{signal.id: [True, False] for signal in simulation_state.signals}},48                                              name=simulation_state.dataset.name,49                                              metadata_file=Path(SignalImplanter._create_metadata_file(processed_repertoires, simulation_state)))50        return processed_dataset51    @staticmethod52    def _implant_signals(simulation_state: SimulationState, process_element_func, output_path: Path):53        processed_elements = []54        simulation_limits = SignalImplanter._prepare_simulation_limits(simulation_state.simulation.implantings,55                                                                       simulation_state.dataset.get_example_count())56        current_implanting_index = 057        current_implanting = simulation_state.simulation.implantings[current_implanting_index]58        for index, element in enumerate(simulation_state.dataset.get_data()):59            if current_implanting is not None and index >= simulation_limits[current_implanting.name]:60                current_implanting_index += 161                if current_implanting_index < len(simulation_limits.keys()):62                    current_implanting = simulation_state.simulation.implantings[current_implanting_index]63                else:64                    current_implanting = None65            processed_element = process_element_func(index, element, current_implanting, simulation_state, output_path)66            processed_elements.append(processed_element)67        return processed_elements68    @staticmethod69    def _process_receptor(index, receptor, implanting, simulation_state, output_path: Path = None) -> Receptor:70        if implanting is not None:71            new_receptor = receptor72            for signal in implanting.signals:73                new_receptor = signal.implant_in_receptor(new_receptor, implanting.is_noise)74        else:75            new_receptor = receptor.clone()76        for signal in simulation_state.signals:77            if signal.id not in new_receptor.metadata:78                new_receptor.metadata[signal.id] = False79        return new_receptor80    @staticmethod81    def _process_repertoire(index, repertoire, current_implanting, simulation_state, output_path: Path = None) -> Repertoire:82        if current_implanting is not None:83            new_repertoire = SignalImplanter._implant_in_repertoire(index, repertoire, current_implanting, simulation_state)84        else:85            new_metadata = {**repertoire.metadata, **{f"{signal.id}": False for signal in simulation_state.signals}}86            new_repertoire = Repertoire.build_from_sequence_objects(repertoire.sequences, simulation_state.result_path / "repertoires",87                                                                    metadata=new_metadata)88        return new_repertoire89    @staticmethod90    def _create_metadata_file(processed_repertoires: List[Repertoire], simulation_state) -> str:91        path = simulation_state.result_path / "metadata.csv"92        new_df = pd.DataFrame([{**repertoire.metadata, **{'identifier': repertoire.identifier}} for repertoire in processed_repertoires])93        new_df.drop('field_list', axis=1, inplace=True)94        new_df["filename"] = [repertoire.data_filename.name for repertoire in processed_repertoires]95        new_df.to_csv(path, index=False)96        return path97    @staticmethod98    def _implant_in_repertoire(index, repertoire, implanting, simulation_state) -> Repertoire:99        new_repertoire = copy.deepcopy(repertoire)100        for signal in implanting.signals:101            new_repertoire = signal.implant_to_repertoire(repertoire=new_repertoire,102                                                          repertoire_implanting_rate=implanting.repertoire_implanting_rate,103                                                          path=simulation_state.result_path / "repertoires/")104        for signal in implanting.signals:...

worker.py

Source:worker.py

...75        dataset_path = self.dataset_dict.get(dataset)76        if dataset_path:77            with open("{}/{}".format(dataset_path, METADATA_FILE_NAME), "r", encoding=ENCODING) as file:78                return json.load(file)79    def _create_metadata_file(self, dataset: str, metadata_dict: dict) -> None:80        if metadata_dict:81            with open("{}/{}/{}".format(self.path, dataset, METADATA_FILE_NAME), "w", encoding=ENCODING) as file:82                json.dump(metadata_dict, file)83        else:84            logger.error("metadata for {} isn't found".format(dataset))85    def init_dataset(self, dataset: str, metadata_dict: dict) -> None:86        if not self.is_exist_dataset(dataset):87            dataset_path = "{}/{}".format(self.path, dataset)88            os.mkdir(dataset_path)89            self.dataset_dict[dataset] = dataset_path90            data_path = "{}/{}".format(dataset_path, DATA_DIR)91            os.mkdir(data_path)92            self._create_metadata_file(dataset, metadata_dict)93    def compress_dataset(self, dataset: str):94        metadata_dict = self._get_metadata_dict(dataset)95        metadata_checker = self.is_correct_metadata(metadata_dict)96        sum_by_row = 097        total_sum = 098        sum_by_key = 099        print(psutil.virtual_memory())100        if metadata_checker:101            logger.info("metadata_checker was successfully checked")102            path = metadata_dict.get("path")103            schema = metadata_dict.get("schema")104            partition = metadata_dict.get("partition_key")105            data_path = "{}/{}/{}".format(self.path, dataset, DATA_DIR)106            partition_analyzer = self._get_partition_dict(schema=schema, key_is_number=True)...

prop_prep.py

Source:prop_prep.py

...33    attrs[PROP_CONFIG_PATH_ATTR] = prop_config_path34    attrs[RANDOM_NETWORK_PROP_CONFIG_PATH_ATTR] = random_network_prop_config_path35    attrs[RANDOM_NETWORK_CONFIG_PATH] = random_network_conf_path36    attrs[METADATA_FILE_PATH_ATTR] = metadata_file_path37def _create_metadata_file(attrs: dict):38    prior_set_source, index_col, prior_set_col = attrs[PRIOR_SET_SOURCE_ATTR], attrs[INDEX_COL_ATTR], attrs[PRIOR_SET_COL_ATTR]39    interactors_series = pd.read_csv(prior_set_source, index_col=index_col)[prior_set_col].dropna().astype(int)40    dict_of_lists = dict()41    for item in interactors_series.iteritems():42        viral_protein, interactor = item[0], item[1]43        if viral_protein not in dict_of_lists:44            dict_of_lists[viral_protein] = []45        dict_of_lists[viral_protein].append(str(interactor))46def _copy_and_patch_conf(attrs: dict):47    print("copying and patching conf")48    conf_to_patch = attrs[CONF_TO_PATCH_ATTR]49    metadata_file_path = attrs[METADATA_FILE_PATH_ATTR]50    virus_res_root = attrs[VIRUS_RES_ROOT_ATTR]51    virus_name = attrs[VIRUS_NAME_ATTR]...

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