Dict[str, str]:\\n pass\",\n \"def attrs(self):\\n return self.__dict__\",\n \"def get_attributes(self):\\n\\n endpoint = self._get_api_endpoint() + '/attributes'\\n results = self.tq.get(endpoint, withp='attribute')\\n if 'data' not in results:\\n return {}\\n\\n return results['data']\\n # tr = {}\\n # for attribute in results['data']:\\n # tr[attribute['attribute']['name']] = attribute['value']\\n # return tr\",\n \"def get_attribute_list(self):\\n return self.dp.get_attribute_list()\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('size', '20'),\\r\\n Attribute('inline', False),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def print_attrs(self):\\n for attribute in self.__dict__.keys():\\n print(attribute)\",\n \"def GetAttributes(self):\\r\\n\\r\\n return self._attr\",\n \"def attributes(self):\\n return dict(self.__attributes)\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('height', None),\\r\\n Attribute('width', None),\\r\\n Attribute('parts', None),\\r\\n Attribute('analyses', None),\\r\\n Attribute('initial_value', None),\\r\\n Attribute('submit_analyses', None),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def attributes(self):\\n raise NotImplementedError\",\n \"def attributes(self):\\n ...\",\n \"def attrs(self):\\n return list(name for name in self.__dict__\\n if not name.startswith(\\\"_\\\"))\",\n \"def attributes(self):\\n _attrs = []\\n if self.name:\\n _attrs.append(\\\"name\\\")\\n if self.label:\\n _attrs.append(\\\"label\\\")\\n if self.confidence:\\n _attrs.append(\\\"confidence\\\")\\n if self.index:\\n _attrs.append(\\\"index\\\")\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs + [\\\"points\\\"]\",\n \"def attrib(self) -> Any:\\n return self.attributes\",\n \"def all_attributes(self):\\n\\n attributes = []\\n for level in self.levels:\\n attributes.extend(level.attributes)\\n\\n return attributes\",\n \"def values(self):\\n return self.attrs.values()\",\n \"def attributes(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def _get_all_attributes(self):\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def iter_attributes(self):\\n return iteritems(self.schema)\",\n \"def getDataAttributes(self):\\n asRet = [];\\n asAttrs = dir(self);\\n for sAttr in asAttrs:\\n if sAttr[0] == '_' or sAttr[0] == 'k':\\n continue;\\n if sAttr in self.kasInternalAttributes:\\n continue;\\n oValue = getattr(self, sAttr);\\n if callable(oValue):\\n continue;\\n asRet.append(sAttr);\\n return asRet;\",\n \"def attrs(self):\\n return self.__slots__\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def GetAttributes(self):\\n return dict(self._attrs)\",\n \"def json_properties(self):\\n attributes = []\\n all = vars(self)\\n for var in all:\\n if var[:1] != '_':\\n attributes.append(var)\\n if isinstance(self, db.Model):\\n properties = self.properties().keys()\\n for property in properties:\\n if property[:1] != '_':\\n attributes.append(property)\\n return attributes\",\n \"def attributes(self):\\n _attrs = [\\\"label\\\"]\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('rows', '30'),\\r\\n Attribute('cols', '80'),\\r\\n Attribute('hidden', ''),\\r\\n\\r\\n # For CodeMirror\\r\\n Attribute('mode', 'python'),\\r\\n Attribute('linenumbers', 'true'),\\r\\n # Template expects tabsize to be an int it can do math with\\r\\n Attribute('tabsize', 4, transform=int),\\r\\n ]\",\n \"def _get_all_attributes(self) -> Dict[str, Any]:\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def get_attributes(self):\\n\\t\\treturn dict(list(self.__element.items()))\",\n \"def attributes(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def get_attributes(cls):\\r\\n return [Attribute('options', transform=cls.parse_options),\\r\\n Attribute('label', ''),\\r\\n Attribute('inline', False)]\",\n \"def listglobal(self):\\n return list(self.attributes.keys())\",\n \"def attributes(self):\\n return { k: getattr(self, k) for k in self.__class__.columns().keys() }\",\n \"def keys(self):\\n return [a.name for a in self.__attrs_attrs__]\",\n \"def attributes(self):\\n return self.host.attributes\",\n \"def get_attributes(cls):\\r\\n return [Attribute('file'),\\r\\n Attribute('missing', None)]\",\n \"def attributes(self):\\n return self.problem.attributes\",\n \"def getAttributes(self):\\n return _libsbml.XMLToken_getAttributes(self)\",\n \"def get_attributes(self):\\n\\n _attributes = (self.survival_id, self.record_id, self.name,\\n self.source_id, self.failure_date, self.left_interval,\\n self.right_interval, self.status_id, self.quantity,\\n self.tbf, self.mode_type_id, self.nevada_chart,\\n self.ship_date, self.number_shipped, self.return_date,\\n self.number_returned, self.user_float_1,\\n self.user_float_2, self.user_float_3,\\n self.user_integer_1, self.user_integer_2,\\n self.user_integer_3, self.user_string_1,\\n self.user_string_2, self.user_string_3)\\n\\n return _attributes\",\n \"def attributes(self):\\n _attrs = [\\\"type\\\", \\\"name\\\", \\\"value\\\"]\\n if self.confidence is not None:\\n _attrs.append(\\\"confidence\\\")\\n if self.constant:\\n _attrs.append(\\\"constant\\\")\\n if self.tags:\\n _attrs.append(\\\"tags\\\")\\n\\n return _attrs\",\n \"def __listAttr(self):\\n attr = dir(self) # already sorted\\n filter = []\\n for name in attr:\\n if name[:2] == '__': pass\\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\\n elif name in self.__exclude: pass\\n else: filter.append(name)\\n return filter\",\n \"def getAllAttributes(self, limit=None):\\n return self.getAttributeRange(limit=limit)\",\n \"def get_attribute_names(cls):\\n return cls._attributes.keys()\",\n \"def all(self):\\r\\n return self.attr_dict.keys()\",\n \"def object_attribute_values(self):\\n if self._object_attribute_values is None:\\n self.object_attribute_values = self.generate_object_attribute_values()\\n \\n return self._object_attribute_values\",\n \"def print_attributes1(obj):\\n\\tfor attr in vars(obj):\\n\\t\\tprint(attr, getattr(obj, attr))\",\n \"def get_attributes(self, context: ResourceCommandContext, obj_ref: str) -> dict:\\n return self.handler.get_attributes(obj_ref)\",\n \"def getattrs(self):\\n # ICAT 4.5.0 also lists the meta attributes as attributes in\\n # the entity info. Need to remove them here, as they should\\n # not be added to InstAttr.\\n return self.getfieldnames('ATTRIBUTE') - Entity.MetaAttr\",\n \"def getUserAttributes(self):\\n return self._UserAttributes\",\n \"def list_custom_attributes(cls):\\n custom_attributes = cls.soap_client.service.listCustomAttributes()\\n\\n return custom_attributes\",\n \"def getComputedAttributes(self) -> list:\\n if self.loggingEnabled:\\n self.logger.debug(f\\\"Starting getComputedAttributes\\\")\\n path = \\\"/config/computedAttributes\\\"\\n res = self.connector.getData(self.endpoint + path)\\n data = res[\\\"children\\\"]\\n nextPage = res[\\\"_page\\\"].get(\\\"next\\\", \\\"\\\")\\n # while nextPage != \\\"\\\":\\n # res = self.connector.getData(self.endpoint+path,\\n # params=params, headers=self.header)\\n # data += res['children']\\n # nextPage = res['_page'].get('next','')\\n return res\",\n \"def info(self):\\n attr_list = []\\n for name in self._metadata:\\n attr_list.append(name + \\\": \\\" + str(getattr(self, name, None)) + \\\"\\\\n\\\")\\n print(f\\\"{self.__class__}\\\\n\\\" + \\\"\\\".join(attr_list))\",\n \"def info(self):\\n attr_list = []\\n for name in self._metadata:\\n attr_list.append(name + \\\": \\\" + str(getattr(self, name, None)) + \\\"\\\\n\\\")\\n print(f\\\"{self.__class__}\\\\n\\\" + \\\"\\\".join(attr_list))\",\n \"def attributes(self):\\n attrs_ = [\\\"name\\\", \\\"type\\\"]\\n if self.exclusive:\\n attrs_.append(\\\"exclusive\\\")\\n if self.default is not None:\\n attrs_.append(\\\"default\\\")\\n\\n return attrs_\",\n \"def state_attributes(self):\\n return self._attributes\",\n \"def state_attributes(self):\\n return self._attributes\",\n \"def getAttributes(self, convertToString = False):\\n d = self.__dict__\\n list = []\\n \\n # loop through list given return values in proper format\\n for item in self.defaultAttributes:\\n if d.has_key(item):\\n if convertToString:\\n list.append(str(d[item]))\\n else:\\n list.append(d[item])\\n return list\",\n \"def get_attributes(self):\\n _attributes = {\\n 'function_id': self.function_id,\\n 'hardware_id': self.hardware_id,\\n 'mode_id': self.mode_id,\\n 'critical_item': self.critical_item,\\n 'description': self.description,\\n 'design_provisions': self.design_provisions,\\n 'detection_method': self.detection_method,\\n 'effect_end': self.effect_end,\\n 'effect_local': self.effect_local,\\n 'effect_next': self.effect_next,\\n 'effect_probability': self.effect_probability,\\n 'hazard_rate_source': self.hazard_rate_source,\\n 'isolation_method': self.isolation_method,\\n 'mission': self.mission,\\n 'mission_phase': self.mission_phase,\\n 'mode_criticality': self.mode_criticality,\\n 'mode_hazard_rate': self.mode_hazard_rate,\\n 'mode_op_time': self.mode_op_time,\\n 'mode_probability': self.mode_probability,\\n 'mode_ratio': self.mode_ratio,\\n 'operator_actions': self.operator_actions,\\n 'other_indications': self.other_indications,\\n 'remarks': self.remarks,\\n 'rpn_severity': self.rpn_severity,\\n 'rpn_severity_new': self.rpn_severity_new,\\n 'severity_class': self.severity_class,\\n 'single_point': self.single_point,\\n 'type_id': self.type_id\\n }\\n\\n return _attributes\",\n \"def attrs(self):\\n return self.size, self.propSuffix, self.specified\",\n \"def list_obj_attrs(cls, container, attr, view=True):\\n if view:\\n return [getattr(obj, attr) for obj in container.view]\\n\\n return [getattr(obj, attr) for obj in container]\",\n \"def attributes(self) -> Optional[Mapping[str, Sequence[str]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def to_list(self) -> List[NumericalAttribute]:\\n return self._attributes\",\n \"def get_attributes(self, attributes='All'):\\r\\n return self.connection.get_queue_attributes(self, attributes)\",\n \"def get_attributes(cls):\\r\\n return [Attribute('allowed_files', '[]', transform=cls.parse_files),\\r\\n Attribute('label', ''),\\r\\n Attribute('required_files', '[]', transform=cls.parse_files), ]\",\n \"def print_attributes2(obj):\\n\\tfor attr, val in vars(obj).items():\\n\\t\\tprint(attr, val)\",\n \"def get_attributes(doc):\\n\\treturn doc.keys()\",\n \"def listattribute(self, varName):\\n fName = \\\"\\\"\\n if varName in self.statVars:\\n fName = self.statVars[varName][0]\\n elif varName in self.timeVars:\\n fName = self.timeVars[varName][0][0]\\n if fName:\\n var = cdms2.open(fName, 'r')(varName)\\n return var.listattributes()\\n else:\\n return []\",\n \"def get_all_attrs():\\n cursor = Biometa.objects.aggregate(*[\\n {'$unwind': '$sample_attributes'},\\n {\\n '$project': {'_id': '$sample_attributes.name'}\\n }\\n ])\\n\\n return sorted(list(set([x['_id'] for x in cursor])))\",\n \"def custom_attributes(self):\\n return self._custom_attributes\",\n \"def print_attributes(obj: object) -> None:\\n all_attributes = set(dir(obj))\\n names_of_methods = set(\\n filter(lambda atrr_name: callable(getattr(obj, atrr_name)), all_attributes)\\n )\\n attributes_excluding_methods = all_attributes - names_of_methods\\n attributes_names_and_values = [(name, reprlib.repr(getattr(obj, name)))\\n for name in attributes_excluding_methods]\\n print_table(attributes_names_and_values, \\\"Name\\\", \\\"Value\\\")\",\n \"def get_attributes(obj) -> Dict[str, Tuple[Any, str]]:\\n if is_dataclass(obj):\\n return get_dataclass_attributes(obj)\\n elif inspect.isclass(obj):\\n return get_class_attributes(obj)\\n elif inspect.ismodule(obj):\\n return get_module_attributes(obj)\\n return {}\",\n \"def load_attrs(self):\\n return loads(self.get_attr().GetObject()) or {}\",\n \"def read_global_attributes(self):\\n return self._attrs.keys()\",\n \"def idPrint(self) -> List['Attribute']:\\n return [\\n a for a in self.attributes\\n if a.isId]\",\n \"def get_attributes(self):\\n attrs = list()\\n syms = list()\\n for item in self.gradual_items:\\n gi = item.as_integer()\\n attrs.append(gi[0])\\n syms.append(gi[1])\\n return attrs, syms\",\n \"def get_fields_in_model(instance: Any) -> List:\\n from auditlog.registry import auditlog\\n\\n attrs = object_mapper(instance).iterate_properties\\n model_attrs = auditlog.get_model_fields(instance.__class__)\\n if model_attrs['include_fields']:\\n attrs = (attr for attr in attrs if attr.key in model_attrs['include_fields'])\\n if model_attrs['exclude_fields']:\\n attrs = (attr for attr in attrs if attr.key not in model_attrs['exclude_fields'])\\n\\n return attrs\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute(\\\"show_correctness\\\", \\\"always\\\"),\\r\\n Attribute(\\\"submitted_message\\\", \\\"Answer received.\\\"),\\r\\n Attribute(\\\"label\\\", \\\"\\\"),\\r\\n ]\",\n \"def get_obj_attributes ( self, object_class_id, object_instance_id, attribute_list ) :\\n try :\\n self.logger.info(\\\"Query on database\\\")\\n l_query_xml = \\\"<Filter><And><PropertyIsEqualTo><PropertyName>ID</PropertyName><Literal>\\\" + str(object_instance_id) + \\\"</Literal></PropertyIsEqualTo></And></Filter>\\\"\\n l_result_xml = self.oracle_cursor.callfunc(\\\"sdb_interface_pck.getObject\\\", cx_Oracle.CLOB, [object_class_id, l_query_xml ])\\n l_result_dom = xml.dom.minidom.parseString(str(l_result_xml))\\n values = []\\n for attribute in attribute_list :\\n l_domain_value_tag = l_result_dom.getElementsByTagName(attribute)[0]\\n # If attribute has no value catch exception and set value to None\\n try :\\n l_value = l_domain_value_tag.childNodes[0].nodeValue\\n except :\\n l_value = None\\n values.append(l_value)\\n return values\\n except Exception, err:\\n self.logger.critical(\\\"Query on database failed: ERROR: \\\" + str(err))\\n sys.exit(\\\"Execution stopped\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8378631","0.8124402","0.811878","0.80819845","0.80225986","0.80225986","0.7986042","0.7955869","0.7923143","0.788158","0.7864873","0.776589","0.77455765","0.76674074","0.75634915","0.7563442","0.7475031","0.74707925","0.7440739","0.74406534","0.74261725","0.74261725","0.74261725","0.74156165","0.7390479","0.7375309","0.7345081","0.73158705","0.72841585","0.72808474","0.7265839","0.72517717","0.72474724","0.72445613","0.7231034","0.7198211","0.71801054","0.71772623","0.71049184","0.70753324","0.7073982","0.7055944","0.7051599","0.7026448","0.6979649","0.6979649","0.697502","0.6952193","0.6942043","0.69281393","0.6920617","0.6917957","0.690697","0.6905211","0.68881834","0.688359","0.68621117","0.6820735","0.6820154","0.6817956","0.6808351","0.6805739","0.677861","0.67779356","0.6777842","0.67673147","0.6759716","0.67566216","0.6746065","0.67249876","0.67228574","0.67218906","0.67201924","0.6711516","0.6702283","0.6702283","0.6701683","0.6691266","0.6691266","0.6690035","0.6680385","0.66754884","0.66581494","0.6629341","0.6625662","0.6611501","0.6609622","0.6601654","0.66013414","0.66010803","0.6599401","0.65914166","0.658827","0.65778315","0.6566165","0.65419483","0.65410423","0.6514682","0.6510947","0.649974","0.64835143"],"string":"[\n \"0.8378631\",\n \"0.8124402\",\n \"0.811878\",\n \"0.80819845\",\n \"0.80225986\",\n \"0.80225986\",\n \"0.7986042\",\n \"0.7955869\",\n \"0.7923143\",\n \"0.788158\",\n \"0.7864873\",\n \"0.776589\",\n \"0.77455765\",\n \"0.76674074\",\n \"0.75634915\",\n \"0.7563442\",\n \"0.7475031\",\n \"0.74707925\",\n \"0.7440739\",\n \"0.74406534\",\n \"0.74261725\",\n \"0.74261725\",\n \"0.74261725\",\n \"0.74156165\",\n \"0.7390479\",\n \"0.7375309\",\n \"0.7345081\",\n \"0.73158705\",\n \"0.72841585\",\n \"0.72808474\",\n \"0.7265839\",\n \"0.72517717\",\n \"0.72474724\",\n \"0.72445613\",\n \"0.7231034\",\n \"0.7198211\",\n \"0.71801054\",\n \"0.71772623\",\n \"0.71049184\",\n \"0.70753324\",\n \"0.7073982\",\n \"0.7055944\",\n \"0.7051599\",\n \"0.7026448\",\n \"0.6979649\",\n \"0.6979649\",\n \"0.697502\",\n \"0.6952193\",\n \"0.6942043\",\n \"0.69281393\",\n \"0.6920617\",\n \"0.6917957\",\n \"0.690697\",\n \"0.6905211\",\n \"0.68881834\",\n \"0.688359\",\n \"0.68621117\",\n \"0.6820735\",\n \"0.6820154\",\n \"0.6817956\",\n \"0.6808351\",\n \"0.6805739\",\n \"0.677861\",\n \"0.67779356\",\n \"0.6777842\",\n \"0.67673147\",\n \"0.6759716\",\n \"0.67566216\",\n \"0.6746065\",\n \"0.67249876\",\n \"0.67228574\",\n \"0.67218906\",\n \"0.67201924\",\n \"0.6711516\",\n \"0.6702283\",\n \"0.6702283\",\n \"0.6701683\",\n \"0.6691266\",\n \"0.6691266\",\n \"0.6690035\",\n \"0.6680385\",\n \"0.66754884\",\n \"0.66581494\",\n \"0.6629341\",\n \"0.6625662\",\n \"0.6611501\",\n \"0.6609622\",\n \"0.6601654\",\n \"0.66013414\",\n \"0.66010803\",\n \"0.6599401\",\n \"0.65914166\",\n \"0.658827\",\n \"0.65778315\",\n \"0.6566165\",\n \"0.65419483\",\n \"0.65410423\",\n \"0.6514682\",\n \"0.6510947\",\n \"0.649974\",\n \"0.64835143\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94090,"cells":{"query":{"kind":"string","value":"Create an Annotation. Note that, if an argument is None, an empty Annotation is created in its place. Additionally, a dictionary matching the expected structure of the arguments will be parsed (i.e. instantiating from JSON)."},"document":{"kind":"string","value":"def __init__(self, namespace, data=None, annotation_metadata=None,\n sandbox=None, time=0, duration=None):\n\n super(Annotation, self).__init__()\n\n if annotation_metadata is None:\n annotation_metadata = AnnotationMetadata()\n\n self.annotation_metadata = AnnotationMetadata(**annotation_metadata)\n\n self.namespace = namespace\n\n self.data = SortedKeyList(key=self._key)\n\n if data is not None:\n if isinstance(data, dict):\n self.append_columns(data)\n else:\n self.append_records(data)\n\n if sandbox is None:\n sandbox = Sandbox()\n\n self.sandbox = Sandbox(**sandbox)\n\n self.time = time\n self.duration = duration"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def RDFAnnotationParser_createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()","def createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()","def create_annotation(timestamp, value, host):\n return zipkin_core.Annotation(timestamp=timestamp, value=value, host=host)","def __init__(self, arg: ast3.arg) -> None:\n self.typed = False\n self.line = arg.lineno\n self.column = arg.col_offset\n self.name = arg.arg\n self.type = None\n self.type_line = -1\n self.type_column = -1\n if arg.annotation:\n anno = arg.annotation\n self.typed = True\n self.type = Annotation(anno) # type: ignore\n self.type_line = anno.lineno\n self.type_column = anno.col_offset","def __init__(self, *args, **kwargs):\n super(_BaseAnnotatedObject, self).__init__()\n properties = self._tp__typed_properties\n required = self._tp__required_typed_properties\n positionals = zip(properties, args)\n for attr, value in positionals:\n if attr in kwargs:\n raise TypeError(\n \"__init__() got multiple values for argument '{}'\".format(\n attr\n )\n )\n kwargs[attr] = value\n missing = [attr for attr in required if attr not in kwargs]\n if missing:\n num_missing = len(missing)\n if num_missing > 1:\n args = \", \".join(\"'{}'\".format(m) for m in missing[:-1])\n if num_missing > 2:\n args += \",\"\n args += \" and '{}'\".format(missing[-1])\n else:\n args = \"'{}'\".format(missing[0])\n raise TypeError(\n \"__init__() missing {} required argument{}: {}\".format(\n num_missing, \"s\" if num_missing > 1 else \"\", args\n )\n )\n for attr, value in six.iteritems(kwargs):\n if attr in properties:\n setattr(self, attr, value)","def from_json(json):\n result = Annotation()\n if \"projectId\" in json:\n result.project_id = json[\"projectId\"]\n if \"imageId\" in json:\n result.image_id = json[\"imageId\"]\n if \"shapes\" in json:\n result.shapes = json[\"shapes\"]\n if \"frameNum\" in json:\n result.frame_num = json[\"frameNum\"]\n if \"_id\" in json:\n result.id = str(json[\"_id\"])\n return result","def Infer(cls, pt: inspect.Parameter, module: Optional[str] = None):\n\n def needs_type(item: Dict[str, str]):\n try:\n return item['action'] == 'store'\n except KeyError:\n return True\n\n def get_argp_type(annotation_type):\n if issubclass(annotation_type, (bytes, bytearray, memoryview)):\n return multibin\n if issubclass(annotation_type, int):\n return number\n if issubclass(annotation_type, slice):\n return sliceobj\n return annotation_type\n\n name = pt.name.replace('_', '-')\n default = pt.default\n guessed_pos_args = []\n guessed_kwd_args = dict(dest=pt.name)\n guessed = set()\n annotation = pt.annotation\n\n def guess(key, value):\n try:\n return guessed_kwd_args[key]\n except KeyError:\n guessed_kwd_args[key] = value\n guessed.add(key)\n return value\n\n if isinstance(annotation, str):\n symbols = None\n while symbols is not False:\n try:\n annotation = eval(annotation, symbols)\n except NameError:\n if symbols is not None or module is None:\n break\n try:\n import importlib\n symbols = importlib.import_module(module).__dict__\n except Exception:\n symbols = False\n except Exception:\n pass\n else:\n break\n\n if annotation is not pt.empty:\n if isinstance(annotation, Arg):\n if annotation.kwargs.get('dest', pt.name) != pt.name:\n raise ValueError(\n F'Incompatible argument destination specified; parameter {pt.name} '\n F'was annotated with {annotation!r}.')\n guessed_pos_args = annotation.args\n guessed_kwd_args.update(annotation.kwargs)\n guessed_kwd_args.update(group=annotation.group)\n elif isinstance(annotation, type):\n guessed.add('type')\n if not issubclass(annotation, bool) and needs_type(guessed_kwd_args):\n guessed_kwd_args.update(type=get_argp_type(annotation))\n elif not isinstance(default, bool):\n raise ValueError('Default value for boolean arguments must be provided.')\n\n if not guessed_pos_args:\n guessed_pos_args = guessed_pos_args or [F'--{name}' if pt.kind is pt.KEYWORD_ONLY else name]\n\n if pt.kind is pt.VAR_POSITIONAL:\n oldnargs = guess('nargs', ZERO_OR_MORE)\n if oldnargs not in (ONE_OR_MORE, ZERO_OR_MORE, REMAINDER):\n raise ValueError(F'Variadic positional arguments has nargs set to {oldnargs!r}')\n return cls(*guessed_pos_args, **guessed_kwd_args)\n\n if default is not pt.empty:\n if isinstance(default, Enum):\n default = default.name\n if isinstance(default, (list, tuple)):\n guess('nargs', ZERO_OR_MORE)\n if not pt.default:\n default = pt.empty\n else:\n guessed_kwd_args['default'] = pt.default\n default = default[0]\n else:\n guessed_kwd_args['default'] = default\n if pt.kind is pt.POSITIONAL_ONLY:\n guess('nargs', OPTIONAL)\n\n if default is not pt.empty:\n if isinstance(default, bool):\n action = 'store_false' if default else 'store_true'\n guessed_kwd_args['action'] = action\n elif needs_type(guessed_kwd_args):\n guess('type', get_argp_type(type(default)))\n\n return cls(*guessed_pos_args, **guessed_kwd_args, guessed=guessed)","def addAnnotation(self, token, title, text, start, end = None):\n\t\tif end is None:\n\t\t\tpayload = \"{\\\"title\\\":\\\"\" + title + \"\\\", \\\"start\\\": \\\"\" + str(start) + \"\\\", \\\"text\\\":\\\"\" + text + \"\\\"}\"\n\t\telse:\n\t\t\tpayload = \"{\\\"title\\\":\\\"\" + title + \"\\\", \\\"start\\\": \\\"\" + str(start) + \"\\\", \\\"end\\\":\\\"\" + str(end) + \"\\\", \\\"text\\\":\\\"\" + text + \"\\\"}\"\n\t\tif self.simulate:\n\t\t\tself.logger.info(\"[SIMULATE] adding new annotation: \" + payload)\t\n\t\t\treturn\n\t\tself.logger.info(\"adding new annotation: \" + payload)\t\n\n\t\t#self.logger.info(\"WARNING: mpulse API handler disabled!\")\n\t\t#return\n\t\turl = \"https://mpulse.soasta.com/concerto/mpulse/api/annotations/v1\"\n\t\tresult = requests.post(url, data = payload, headers={'Content-Type':'application/json', 'X-Auth-Token': token })\n\t\tif (result.status_code == 200):\n\t\t\tjson_data = result.json()\n\t\t\tself.logger.info('annotation successfully added')\n\t\telse:\n\t\t\tself.logger.error('Error ' + str(result.status_code) + ': annotation not added!')","def new():\n doc = coerce_to(int, request.form['doc'])\n page = coerce_to(int, request.form['page'])\n posx = coerce_to(int, request.form['posx'])\n posy = coerce_to(int, request.form['posy'])\n width = coerce_to(int, request.form['width'])\n height = coerce_to(int, request.form['height'])\n text = request.form['value']\n state = Annotation.STATE_OPEN\n if 'state' in request.form:\n state = Annotation.state_decode(request.form['state'])\n doc_obj = Document.query.get(doc)\n if doc_obj is None:\n return BadRequest()\n if not current_user.can_annotate(doc):\n return Unauthorized()\n user = current_user.id\n ann = Annotation(doc, page, posx, posy, width, height, text, user,\n state=state)\n db.session.add(ann)\n db.session.commit()\n return jsonify(id=ann.id)","def __init__(self, dictionary: dict[str, t.Any]) -> None:\n missing_keys = self.__annotations__.keys() - dictionary.keys()\n if missing_keys:\n raise KeyError(f\"Fetched object lacks expected keys: {missing_keys}\")\n for annotation in self.__annotations__:\n setattr(self, annotation, dictionary[annotation])","def __init__(self, curator=None, version='', corpus='', annotator=None,\n annotation_tools='', annotation_rules='', validation='',\n data_source=''):\n super(AnnotationMetadata, self).__init__()\n\n if curator is None:\n curator = Curator()\n\n if annotator is None:\n annotator = JObject()\n\n self.curator = Curator(**curator)\n self.annotator = JObject(**annotator)\n\n self.version = version\n self.corpus = corpus\n self.annotation_tools = annotation_tools\n self.annotation_rules = annotation_rules\n self.validation = validation\n self.data_source = data_source","def __init__(\n self, arg=None, colors=None, colorssrc=None, line=None, pattern=None, **kwargs\n ):\n super(Marker, self).__init__(\"marker\")\n\n if \"_parent\" in kwargs:\n self._parent = kwargs[\"_parent\"]\n return\n\n # Validate arg\n # ------------\n if arg is None:\n arg = {}\n elif isinstance(arg, self.__class__):\n arg = arg.to_plotly_json()\n elif isinstance(arg, dict):\n arg = _copy.copy(arg)\n else:\n raise ValueError(\n \"\"\"\\\nThe first argument to the plotly.graph_objs.funnelarea.Marker\nconstructor must be a dict or\nan instance of :class:`plotly.graph_objs.funnelarea.Marker`\"\"\"\n )\n\n # Handle skip_invalid\n # -------------------\n self._skip_invalid = kwargs.pop(\"skip_invalid\", False)\n self._validate = kwargs.pop(\"_validate\", True)\n\n # Populate data dict with properties\n # ----------------------------------\n _v = arg.pop(\"colors\", None)\n _v = colors if colors is not None else _v\n if _v is not None:\n self[\"colors\"] = _v\n _v = arg.pop(\"colorssrc\", None)\n _v = colorssrc if colorssrc is not None else _v\n if _v is not None:\n self[\"colorssrc\"] = _v\n _v = arg.pop(\"line\", None)\n _v = line if line is not None else _v\n if _v is not None:\n self[\"line\"] = _v\n _v = arg.pop(\"pattern\", None)\n _v = pattern if pattern is not None else _v\n if _v is not None:\n self[\"pattern\"] = _v\n\n # Process unknown kwargs\n # ----------------------\n self._process_kwargs(**dict(arg, **kwargs))\n\n # Reset skip_invalid\n # ------------------\n self._skip_invalid = False","def __init__(self, *args, **kwargs):\n \n for arg in args:\n if isinstance(arg, dict):\n for key, value in arg.items():\n self[key] = value\n if hasattr(arg, \"__dict__\"):\n for key, value in arg.__dict__.items():\n self[key] = value\n\n if kwargs:\n for key, value in kwargs.items():\n self[key] = value","def __json_init__(cls, **kwargs):\n return cls(**kwargs)","def __init__(self, raw_arg: Dict):\n self.name = raw_arg.get(\"name\")\n self.description = raw_arg.get(\"description\")\n self.type = TypeDefer(raw_arg.get(\"type\")) if raw_arg.get(\"type\") is not None else None\n self.default_value = raw_arg.get(\"defaultValue\")","def annotation_to_python(name, args, type_, template, sctypes):\n tab = ' ' * TABSIZE\n tokens = name.split(' ')\n pyname = ''.join([x.title() for x in tokens])\n cls = 'class {}({}):\\n'.format(pyname, type_.title())\n cls += '{}_trigedit_name = \"{}\"\\n'.format(tab, name)\n # pyargs = [type_to_sc_type(x['type']) for x in args]\n qparams = [x['type'] for x in args if x['is_quoted']]\n quoted_fields = '[\"{}\"]'.format(', '.join(qparams))\n if not quoted_fields:\n quoted_fields = ''\n cls += '{}_quoted_fields = frozenset({})\\n\\n'.format(tab, quoted_fields)\n # figure out the argument types\n imports = []\n for arg in args:\n param = arg['type']\n default = arg['default']\n if param in sctypes:\n ptype = type_to_sc_type(param, prefix='sc')\n imports.append(ptype)\n else:\n if re.search('^[0-9]+$', default):\n ptype = 'int'\n else:\n ptype = 'str'\n arg['pytype'] = ptype\n if len(args) > 0:\n typed_params = ', '.join(['{}: {}'.format(x['type'], x['pytype']) for x in args])\n cls += '{}def __init__(self, {}):\\n'.format(tab, typed_params)\n else:\n cls += '{}def __init__(self):\\n'.format(tab)\n joiner = '\\n{}{}'.format(tab, tab)\n fields = joiner.join(['self.{} = {}'.format(x['type'], x['type']) for x in args])\n if len(args) > 0:\n cls += '{}{}super().__init__()\\n{}{}{}'.format(tab, tab, tab, tab, fields)\n else:\n cls += '{}{}super().__init__()'.format(tab, tab)\n return cls, imports","def __init__(self, json_str=None, data=None, label=None):\n if json_str is not None:\n src = json.loads(json_str)\n self._data = src[\"data\"]\n self._label = src[\"label\"]\n elif data is not None and label is not None:\n self._data = data\n self._label = label\n else:\n raise Exception(\"Sample1D needs either json_str or data and label\")","def _annotated_node(self, annotation: str, **edges: CFNode) -> CFNode:\n node = CFNode(annotation=annotation)\n self._graph.add_node(node, edges=edges)\n return node","def getAnnotation(self, *args):\n return _libsbml.SBase_getAnnotation(self, *args)","def from_json_dict(cls, **kwargs):\n if not {\"name\", \"dtype\", \"default\"} <= set(kwargs.keys()):\n raise MlflowException.invalid_parameter_value(\n \"Missing keys in ParamSpec JSON. Expected to find \"\n \"keys `name`, `dtype` and `default`\",\n )\n return cls(\n name=str(kwargs[\"name\"]),\n dtype=DataType[kwargs[\"dtype\"]],\n default=kwargs[\"default\"],\n shape=kwargs.get(\"shape\"),\n )","def createAnn(bbox, \n cat_id, \n visibility=None,\n img_id=None,\n segmentation=None, \n area=None, \n iscrowd=0,\n id=None):\n\n bbox = [float(format(elem, '.2f')) for elem in bbox]\n\n if segmentation is None:\n segmentation = []\n\n if area is None:\n # Use bounding box area\n area = bbox[2] * bbox[3]\n area = np.float32(area).tolist()\n\n annotation = {\n \"id\": id,\n \"image_id\": img_id,\n \"category_id\": cat_id,\n \"segmentation\": segmentation,\n \"area\": area,\n \"bbox\": bbox,\n \"visibility\": visibility,\n \"iscrowd\": iscrowd\n }\n\n return annotation","def setAnnotation(self, *args):\n return _libsbml.SBase_setAnnotation(self, *args)","def __init__(self, *args: Union[List[AtomKey], HKT], **kwargs: str) -> None:\n ...","def __init__(self, statistics, *histograms, **style):\n \n Annotation.__init__(self, **style)\n\n # Split the specified statistics.\n self.statistics = tuple(statistics)\n self.histograms = histograms","def annotate(*args, point: List[float, float, float]=None, text: AnyStr=\"\", **kwargs)->AnyStr:\n pass","def appendAnnotation(self, *args):\n return _libsbml.SBase_appendAnnotation(self, *args)","def setAnnotation(self, *args):\n return _libsbml.Model_setAnnotation(self, *args)","def __call__(self, *input_data):\n \n pb_ann = Any()\n pb_ann.Pack(annotation_to_protobuf.convert_annotation(input_data))\n request = annotation_pb2.ProcessRequest(pipeline_name = self._pipeline_name, \n input_annotations = pb_ann)\n \n response = self._stub.process(request)\n return annotation_from_protobuf.convert_annotation(response.output_annotations)","def __init__(self, *args: Union[List[AtomKey], DKT], **kwargs: str) -> None:\n ...","def create(self, request, *args, **kwargs):\n # Deserialize and validate the data from the user.\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n\n # Execute the document and annotation creation\n self.perform_create(serializer)\n\n # Get the headers and return a response\n headers = self.get_success_headers(serializer.data)\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)","def annotate(self, annotation_=None):\n # Important: Need a copy, not the reference to the original object\n annotation_ = copy.deepcopy(annotation_)\n annotation_.annotate(self, from_dataset=True)\n history_record = annotation_.create_history_record()\n self.annotations.append(history_record)\n self._append_task(kind='annotation', task=history_record)","def __init__(self, **named):\n for key, value in named.items():\n typ = getattr(self, '__annotations__', {}).get(key)\n if typ:\n setattr(self, key, type_coerce(value, typ))\n else:\n setattr(self, key, value)","def create(cls, data=None):\n # allow create() calls with no input\n if not data:\n data = {}\n\n return cls(**data)","def annotate(args):\n prism.annotate.run(\n input_fp=args.input,\n output_fp=args.output,\n bed_fps=args.beds,\n annotation_names=args.annotation_names,\n output_figure_fp=args.figure,\n width=args.width,\n height=args.height,\n scale=args.scale,\n font_family=args.font_family,\n )","def setup_annotation(self):\n annotation = self.ax_fig.annotate(\n '', xy=(0, 0), ha='left',\n xytext=(-20, 20), textcoords='offset points', va='bottom',\n bbox=dict(\n boxstyle='round,pad=0.5', fc='yellow', alpha=0.2),\n arrowprops=dict(\n arrowstyle='->', connectionstyle='arc3,rad=0'))\n return annotation","def _convert_annotations(self, ast):\n self.annotations = IDLAnnotations(ast)","def commandLine(*args, annotation: Union[AnyStr, bool]=\"\", backgroundColor: Union[List[float,\n float, float], bool]=None, command: Script=None, defineTemplate: AnyStr=\"\",\n docTag: Union[AnyStr, bool]=\"\", dragCallback: Script=None, dropCallback:\n Script=None, enable: bool=True, enableBackground: bool=True,\n enableKeyboardFocus: bool=True, enterCommand: Script=None, exists: bool=True,\n fullPathName: bool=True, height: Union[int, bool]=0, highlightColor:\n Union[List[float, float, float], bool]=None, holdFocus: bool=True,\n inputAnnotation: Union[AnyStr, bool]=\"\", isObscured: bool=True, manage:\n bool=True, noBackground: bool=True, numberOfHistoryLines: Union[int, bool]=0,\n numberOfPopupMenus: bool=True, outputAnnotation: Union[AnyStr, bool]=\"\",\n parent: Union[AnyStr, bool]=\"\", popupMenuArray: bool=True, preventOverride:\n bool=True, sourceType: Union[AnyStr, bool]=\"\", statusBarMessage: AnyStr=\"\",\n useTemplate: AnyStr=\"\", visible: bool=True, visibleChangeCommand: Union[Script,\n bool]=None, width: Union[int, bool]=0, q=True, query=True, e=True, edit=True,\n **kwargs)->Union[AnyStr, Any]:\n pass","def build(arg_dict):\n\n task_item = Task()\n\n try:\n task_item.key = arg_dict['key']\n except KeyError:\n task_item.key = None\n\n try:\n task_item.title = arg_dict['title']\n except KeyError:\n task_item.title = None\n\n try:\n task_item.notes = arg_dict['notes']\n except KeyError:\n task_item.notes = None\n\n return task_item","def appendAnnotation(self, *args):\n return _libsbml.Model_appendAnnotation(self, *args)","def __init__(self, *args: Union[List[AtomKey], EKT], **kwargs: str) -> None:\n ...","def _make_args(self, args, defaults=[], vararg=None, kwonlyargs=[],\n kw_defaults=[], kwarg=None):\n # On Python 2 convert vararg and kwarg to raw name, raise error using\n # lineno stored on the node and lexer from self.\n # On Python 3.3 extract name and annotation\n # After should be straight forward\n raise NotImplementedError()","def __init__(self, *args, name=''):\n from collections import Iterable\n if len(args) == 1:\n if isinstance(args[0], Point):\n self.data = args[0].data.copy()\n elif isinstance(args[0], Iterable):\n self.data = list(args[0])\n else:\n self.data = list(args)\n self.name = name if not name.isspace() else ''","def __init__(self, log=None):\n sppasBaseAnnotation.__init__(self, \"activity.json\", log)\n self.__activity = Activity()","def __init__(self, outdir, redo=False):\n self.outdir = outdir\n if redo:\n shutil.rmtree(outdir)\n if not os.path.exists(outdir):\n os.mkdir(outdir)\n self.annotations_file = os.path.join(outdir, 'annotations.json')\n self.annotations = collections.defaultdict(dict)\n if os.path.exists(self.annotations_file):\n annotations = json.load(open(self.annotations_file, 'r'))\n for key1 in annotations:\n for key2 in annotations[key1]:\n self.annotations[key1][key2] = annotations[key1][key2]\n self.argtypes = {}\n self.argtypes_file = os.path.join(outdir, 'argtypes.json')\n if os.path.exists(self.argtypes_file):\n self.argtypes = json.load(open(self.argtypes_file, 'r'))","def perform_create(self, serializer):\n try:\n\n # Create the document object\n long_url = serializer.validated_data['long_url']\n document, _ = Document.objects.get_or_create(long_url=long_url)\n serializer.instance = document\n\n # Create the annotation object\n annotate, _ = Annotation.objects.get_or_create(\n user = self.request.user, document = document\n )\n\n except CorpusException as e:\n raise ValidationError(str(e))","def RDFAnnotationParser_createRDFAnnotation(level=3, version=1):\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)","def nameCommand(*args, annotation: AnyStr=\"\", command: Script=None, data1: AnyStr=\"\", data2:\n AnyStr=\"\", data3: AnyStr=\"\", default: bool=True, sourceType: AnyStr=\"\",\n **kwargs)->AnyStr:\n pass","def create(cls, *args):\n c = cls({})\n c.apply(*args)\n return c","def __init__(\n self,\n arg=None,\n autocolorscale=None,\n cauto=None,\n cmax=None,\n cmid=None,\n cmin=None,\n coloraxis=None,\n colorbar=None,\n colors=None,\n colorscale=None,\n colorssrc=None,\n line=None,\n pattern=None,\n reversescale=None,\n showscale=None,\n **kwargs,\n ):\n super(Marker, self).__init__(\"marker\")\n\n if \"_parent\" in kwargs:\n self._parent = kwargs[\"_parent\"]\n return\n\n # Validate arg\n # ------------\n if arg is None:\n arg = {}\n elif isinstance(arg, self.__class__):\n arg = arg.to_plotly_json()\n elif isinstance(arg, dict):\n arg = _copy.copy(arg)\n else:\n raise ValueError(\n \"\"\"\\\nThe first argument to the plotly.graph_objs.sunburst.Marker\nconstructor must be a dict or\nan instance of :class:`plotly.graph_objs.sunburst.Marker`\"\"\"\n )\n\n # Handle skip_invalid\n # -------------------\n self._skip_invalid = kwargs.pop(\"skip_invalid\", False)\n self._validate = kwargs.pop(\"_validate\", True)\n\n # Populate data dict with properties\n # ----------------------------------\n _v = arg.pop(\"autocolorscale\", None)\n _v = autocolorscale if autocolorscale is not None else _v\n if _v is not None:\n self[\"autocolorscale\"] = _v\n _v = arg.pop(\"cauto\", None)\n _v = cauto if cauto is not None else _v\n if _v is not None:\n self[\"cauto\"] = _v\n _v = arg.pop(\"cmax\", None)\n _v = cmax if cmax is not None else _v\n if _v is not None:\n self[\"cmax\"] = _v\n _v = arg.pop(\"cmid\", None)\n _v = cmid if cmid is not None else _v\n if _v is not None:\n self[\"cmid\"] = _v\n _v = arg.pop(\"cmin\", None)\n _v = cmin if cmin is not None else _v\n if _v is not None:\n self[\"cmin\"] = _v\n _v = arg.pop(\"coloraxis\", None)\n _v = coloraxis if coloraxis is not None else _v\n if _v is not None:\n self[\"coloraxis\"] = _v\n _v = arg.pop(\"colorbar\", None)\n _v = colorbar if colorbar is not None else _v\n if _v is not None:\n self[\"colorbar\"] = _v\n _v = arg.pop(\"colors\", None)\n _v = colors if colors is not None else _v\n if _v is not None:\n self[\"colors\"] = _v\n _v = arg.pop(\"colorscale\", None)\n _v = colorscale if colorscale is not None else _v\n if _v is not None:\n self[\"colorscale\"] = _v\n _v = arg.pop(\"colorssrc\", None)\n _v = colorssrc if colorssrc is not None else _v\n if _v is not None:\n self[\"colorssrc\"] = _v\n _v = arg.pop(\"line\", None)\n _v = line if line is not None else _v\n if _v is not None:\n self[\"line\"] = _v\n _v = arg.pop(\"pattern\", None)\n _v = pattern if pattern is not None else _v\n if _v is not None:\n self[\"pattern\"] = _v\n _v = arg.pop(\"reversescale\", None)\n _v = reversescale if reversescale is not None else _v\n if _v is not None:\n self[\"reversescale\"] = _v\n _v = arg.pop(\"showscale\", None)\n _v = showscale if showscale is not None else _v\n if _v is not None:\n self[\"showscale\"] = _v\n\n # Process unknown kwargs\n # ----------------------\n self._process_kwargs(**dict(arg, **kwargs))\n\n # Reset skip_invalid\n # ------------------\n self._skip_invalid = False","def build(cls, key: str, **kwargs):\n\n def add(parser):\n return cls.add_arguments(key, parser)\n\n kwargs = {f\"{key}_{cls.alias}_\" + k: v for k, v in kwargs.items()}\n args = argparse.Namespace(**kwargs)\n args = fill_missing_args(args, add)\n return cls(key, args)","def import_annotations(\n self,\n format_name: str,\n filename: StrPath,\n *,\n status_check_period: Optional[int] = None,\n pbar: Optional[ProgressReporter] = None,\n ):\n\n filename = Path(filename)\n\n AnnotationUploader(self._client).upload_file_and_wait(\n self.api.create_annotations_endpoint,\n filename,\n format_name,\n url_params={\"id\": self.id},\n pbar=pbar,\n status_check_period=status_check_period,\n )\n\n self._client.logger.info(f\"Annotation file '{filename}' for task #{self.id} uploaded\")","def __init__(self, **kw_args):\n self._other_object_hook = None\n kw_args_new = kw_args.copy()\n if 'object_hook' in kw_args:\n self._other_object_hook = kw_args['object_hook']\n kw_args_new['object_hook'] = self._object_decode\n # Note: strict=False because the notes attribute might contain\n # line feeds.\n #\n kw_args_new['strict'] = False\n\n self._decoder = json.JSONDecoder(**kw_args_new)","def __init__(self, *args, **kwargs):\n for dictionary in [_ for _ in args if isinstance(_, dict)]:\n for key in dictionary:\n setattr(self, key, dictionary[key])\n for key in kwargs:\n setattr(self, key, kwargs[key])","def __init__(self, rules: Union[Dict[str, dict], Set[str]], separators: str = ''):\n\n if isinstance(rules, dict):\n for pattern, annotation in rules.items():\n if 'start' in annotation or 'end' in annotation:\n raise ValueError(\"Attributes 'start' and 'end' are reserved do not use it inside annotations\")\n elif not isinstance(rules, set):\n raise ValueError('Extraction rules must be specified as Dict[str, dict] or Set[str]')\n\n # Protect parameters against changes\n self.rules = deepcopy(rules)\n self.separators = copy(separators)\n\n # Set up the automaton\n self.automaton = Automaton()\n for pattern in self.rules:\n self.automaton.add_word(pattern, len(pattern))\n self.automaton.make_automaton()","def create(cls, dictionary):\n return cls(**dictionary)","def create(cls, dictionary):\n return cls(**dictionary)","def create_node(identifier, *args, **kwargs):\r\n\r\n d = node_dictionary()\r\n node_class = d[identifier]\r\n node = node_class(*args, **kwargs)\r\n return node","def createRDFAnnotation(level=3, version=1):\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)","def RDFAnnotationParser_parseRDFAnnotation(*args):\n return _libsbml.RDFAnnotationParser_parseRDFAnnotation(*args)","def __json_decode__(cls, args: Dict[str, Any]) -> \"FigureData\":\n return cls(**args)","def from_annotation(cls, anno) -> \"Box\":\n bounds = anno[\"bounds\"]\n return cls(\n x=bounds[\"left\"],\n y=bounds[\"top\"],\n width=bounds[\"right\"] - bounds[\"left\"],\n height=bounds[\"bottom\"] - bounds[\"top\"],\n label=anno[\"label\"][\"text\"],\n )","def _add_annotation(raw_fig):\n data_ax = raw_fig.mne.ax_main\n\n key_event = KeyEvent(name=\"Annotation\", canvas=raw_fig.canvas, key=\"a\")\n raw_fig.canvas.callbacks.process(\"key_press_event\", key_event)\n\n ann_fig = raw_fig.mne.fig_annotation\n for key in \"test\": # Annotation will be named: BAD_test\n key_event = KeyEvent(name=\"Bad\", canvas=ann_fig.canvas, key=key)\n ann_fig.canvas.callbacks.process(\"key_press_event\", key_event)\n\n key_event = KeyEvent(name=\"Enter\", canvas=ann_fig.canvas, key=\"enter\")\n ann_fig.canvas.callbacks.process(\"key_press_event\", key_event)\n\n # Draw a 4 second long Annotation.\n _fake_click(raw_fig, data_ax, [1.0, 1.0], xform=\"data\", button=1, kind=\"press\")\n _fake_click(raw_fig, data_ax, [5.0, 1.0], xform=\"data\", button=1, kind=\"motion\")\n _fake_click(raw_fig, data_ax, [5.0, 1.0], xform=\"data\", button=1, kind=\"release\")","def __init__(self, anno):\n self._x1 = float(anno[0])\n self._y1 = float(anno[1])\n self._w = float(anno[2])\n self._h = float(anno[3])\n self._blur = int(anno[4])\n self._expression = int(anno[5])\n self._illumination = int(anno[6])\n self._invalid = int(anno[7])\n self._occlusion = int(anno[8])\n self._pose = int(anno[9])","def create_binary_annotation(key, value, annotation_type, host):\n return zipkin_core.BinaryAnnotation(\n key=key, value=value, annotation_type=annotation_type, host=host)","def __init__(self, type='lg_asteroid', dataset=obstacles, **kwargs):\n super().__init__(type=type, dataset=dataset, **kwargs)\n self.obj_type = \"asteroid\"","def __init__(self, keydata=None):\n if isinstance(keydata, basestring):\n keydata = json.loads(keydata)\n assert keydata is None or isinstance(keydata, dict), keydata\n self.dict = keydata if keydata is not None else {}","def annotate(self, **annotations):\n _check_annotations(annotations)\n self.annotations.update(annotations)","def init_json_args(cls):\n defaults = cls._base_json_args()\n\n if cls.__json_args__ is None:\n cls.__json_args__ = defaults\n else:\n cls.__json_args__ = mapping_extend(defaults, cls.__json_args__)","def addAutolabel(call, args=(), kwargs={}, nodeClass='*'):","def from_callable(cls, obj: Callable) -> 'Signature':\n namespace = None\n arg_types = []\n var_arg_type = None\n if isinstance(obj, type): # it's a class?\n try:\n sig = inspect.signature(_get_constructor(obj) or obj)\n except (TypeError, ValueError):\n sig = None\n skip_arg = 1\n else:\n try:\n sig = inspect.signature(obj)\n except TypeError:\n sig = None\n skip_arg = 0\n if sig:\n return_type = sig.return_annotation\n if return_type is sig.empty:\n if isinstance(obj, type):\n return_type = obj\n else:\n return_type = Any\n elif return_type is None and isinstance(obj, type):\n return_type = obj\n elif isinstance(return_type, Text):\n namespace = (namespace or\n importlib.import_module(obj.__module__).__dict__)\n return_type = eval(return_type, namespace)\n non_def_args = [(p.name, p.kind, p.annotation)\n for p in list(sig.parameters.values())[skip_arg:]\n if p.default is sig.empty]\n for name, kind, annotation in non_def_args:\n if kind in (_KEYWORD_ONLY, _VAR_KEYWORD):\n raise ValueError(\"'\" + repr(obj) + \"' has keyword-only \"\n \"arg w/o default value: '\" + name + \"'.\")\n if annotation is sig.empty:\n raise ValueError(\"'\" + repr(obj) + \"' has arg\"\n \" w/o type hint: '\" + name + \"'.\")\n if isinstance(annotation, Text):\n namespace = (namespace or\n importlib.import_module(obj.__module__)\n .__dict__)\n try:\n annotation = eval(annotation, namespace)\n except NameError:\n annotation = None\n if isinstance(annotation, (type, TypingMeta, _TypingBase)):\n if kind == _VAR_POSITIONAL:\n var_arg_type = annotation\n else:\n arg_types.append(annotation)\n else:\n raise ValueError(\"'\" + repr(obj) + \"' has arg\"\n \" w/o type hint: '\" + name + \"'.\")\n if arg_types or var_arg_type or return_type:\n return cls(arg_types, var_arg_type=var_arg_type,\n return_type=return_type)\n raise ValueError(\"Can't retrieve signature of '\" + repr(obj) + \"'.\")","def make_annotation_horizontal(x, y):\n text = str(x)\n if len(text) > 33:\n text = text[0:33]+\"..\"\n return Annotation(\n text=text, # text is the y-coord\n showarrow=False, # annotation w/o arrows, default is True\n x=0, # set x position\n xref='x', # position text horizontally with x-coords\n xanchor='left', # x position corresp. to center of text\n y=x, # y position corresp. to top of text\n yref='y', # set y position\n yanchor='auto', # position text vertically with y-coords\n textangle=0, # Not Rotating the text\n font=annotation.Font(\n color='#FFFFFF', # set font color\n size=13 # and size\n )\n )","def __init__(self, taxon=None, label=None, oid=None):\n base.AnnotatedDataObject.__init__(self, label=label, oid=oid)\n if taxon is not None:\n self.label = taxon.label\n else:\n self.label = None\n if label is not None:\n self.label = label\n self.oid = oid","def from_param(cls, arg):\n return cls(arg)","def _create(self, **attributes: Dict[str, object]) -> str:\n pass","def __init__(self, name=\"info\", attr=None):\n Arg.__init__(self, name, attr)","def create(self, **kargs):\n return self(**kargs)","def __init__(self, id=None, owner=None, trusted=None, name=None, slug=None, private=None, many_annotations_per_label=None, use_marker=None, annotation_value_type=None, color=None, created_at=None, updated_at=None): # noqa: E501 # noqa: E501\n\n self._id = None\n self._owner = None\n self._trusted = None\n self._name = None\n self._slug = None\n self._private = None\n self._many_annotations_per_label = None\n self._use_marker = None\n self._annotation_value_type = None\n self._color = None\n self._created_at = None\n self._updated_at = None\n self.discriminator = None\n\n if id is not None:\n self.id = id\n if owner is not None:\n self.owner = owner\n if trusted is not None:\n self.trusted = trusted\n self.name = name\n self.slug = slug\n if private is not None:\n self.private = private\n if many_annotations_per_label is not None:\n self.many_annotations_per_label = many_annotations_per_label\n if use_marker is not None:\n self.use_marker = use_marker\n if annotation_value_type is not None:\n self.annotation_value_type = annotation_value_type\n if color is not None:\n self.color = color\n if created_at is not None:\n self.created_at = created_at\n if updated_at is not None:\n self.updated_at = updated_at","def annotation(self, label, duration):\n self.send_trigger(self.new_trigger('annotation', label, duration))","def __init__(self, name: str, arg_type_name: str, is_required=False):\n self.key = name\n self.value = arg_type_name\n self.required = is_required","def _from_yaml(cls, loader: yaml.Loader, node: yaml.Node) -> \"ToJson\":\n try:\n value = loader.construct_mapping(node, deep=True)\n except yaml.constructor.ConstructorError:\n value = loader.construct_sequence(node, deep=True)\n return cls(value)","def member_from_json(cls, injson: dict) -> \"AocMember\":\n return cls(\n name=injson[\"name\"] if injson[\"name\"] else \"Anonymous User\",\n aoc_id=int(injson[\"id\"]),\n stars=injson[\"stars\"],\n starboard=cls._starboard_from_json(injson[\"completion_day_level\"]),\n local_score=injson[\"local_score\"],\n global_score=injson[\"global_score\"],\n )","def __init__(self, **kwargs):\n self.postKey = kwargs.get(\"postKey\")\n self.location = kwargs.get(\"location\")\n self.category = kwargs.get(\"category\")\n self.source = kwargs.get(\"source\")\n self.heading = kwargs.get(\"heading\")\n self.body = kwargs.get(\"body\")\n self.latitude = kwargs.get(\"latitude\")\n self.longitude = kwargs.get(\"longitude\")\n self.language = kwargs.get(\"language\")\n self.price = kwargs.get(\"price\")\n self.currency = kwargs.get(\"currency\")\n self.images = kwargs.get(\"images\", [])\n self.externalID = kwargs.get(\"externalID\")\n self.externalURL = kwargs.get(\"externalURL\")\n self.accountName = kwargs.get(\"accountName\")\n self.accountID = kwargs.get(\"accountID\")\n self.timestamp = kwargs.get(\"timestamp\")\n self.expiration = kwargs.get(\"expiration\")\n self.annotations = kwargs.get(\"annotations\", {})\n self.trustedAnnotations = kwargs.get(\"trustedAnnotations\", {})\n self.clickCount = kwargs.get(\"clickCount\")","def annotate(self, annotation):\n self._data = self._data.annotate(**annotation)","def new_from_jsondict(cls, data, param_defaults, **kwargs):\n\n json_data = data.copy()\n if kwargs:\n for key, val in kwargs.items():\n json_data[key] = val\n\n c = cls(param_defaults, **json_data)\n c._json = data\n return c","def __init__(self, **kwargs: Any):","def __init__(self, name=\"alpha\", attr=None):\n Arg.__init__(self, name, attr)","def run_annotation(args):\n #print(\"running chronqc_annotation\")\n chronqc_annotation.main(args)","def from_parameter(cls, name: str, parameter: inspect.Parameter) -> \"Argument\":\n # pylint: disable=too-many-branches,too-many-statements\n positional = parameter.kind is not parameter.KEYWORD_ONLY\n type_ = parameter.annotation\n default = parameter.default\n flag = name.upper() if positional else f\"--{name.replace('_', '-')}\"\n\n # If field is assigned an Argument use that as the starting point\n if isinstance(default, Argument):\n instance = default\n default = EMPTY\n if flag not in instance.name_or_flags:\n instance.name_or_flags = (flag,) + instance.name_or_flags\n else:\n instance = cls(flag)\n\n # Start updating kwargs\n kwargs = instance.kwargs\n if default is not EMPTY:\n kwargs.setdefault(\"default\", default)\n\n # Handle type variances\n origin = getattr(type_, \"__origin__\", None)\n if origin is not None:\n type_ = cls._handle_generics(origin, type_, positional, kwargs)\n elif isinstance(type_, type):\n type_ = cls._handle_types(type_, positional, kwargs)\n elif isinstance(type_, argparse.FileType):\n pass # Just pass as this is an `argparse` builtin\n else:\n raise TypeError(f\"Unsupported type: {type_!r}\")\n\n if type_:\n kwargs[\"type\"] = type_\n\n return instance","def parse_annotation_instance(annotation):\n\n text = annotation['utf8_string']\n language = annotation['language']\n legible = int(annotation['legibility'] == 'legible')\n\n mask = np.reshape(np.array(annotation['mask'], np.int32), (-1, 2))\n box = cv2.boxPoints(cv2.minAreaRect(mask))\n quadrilateral = [int(x) for x in box.reshape([-1])]\n\n xmin = min(quadrilateral[0::2])\n xmax = max(quadrilateral[0::2])\n\n ymin = min(quadrilateral[1::2])\n ymax = max(quadrilateral[1::2])\n\n word_annotation = {\n 'bbox': [xmin, ymin, xmax - xmin, ymax - ymin],\n 'segmentation': [quadrilateral],\n 'attributes': {\n 'transcription': text,\n 'legible': legible,\n 'language': language,\n }\n }\n\n return word_annotation","def __init__(self, jsondict=None, strict=True):\n \n self.language = None\n \"\"\" Language of the text.\n Type `CodeableConcept` (represented as `dict` in JSON). \"\"\"\n \n self.number = None\n \"\"\" Note instance identifier.\n Type `int`. \"\"\"\n \n self.text = None\n \"\"\" Note explanatory text.\n Type `str`. \"\"\"\n \n self.type = None\n \"\"\" display | print | printoper.\n Type `str`. \"\"\"\n \n super(ClaimResponseProcessNote, self).__init__(jsondict=jsondict, strict=strict)","def create_from_arg_string(cls, arg_string):\n return cls()","def resolve_annotations(func: Callable, annotations: dict, defaults: dict = {}) -> StrawberryField:\n kind = inspect.Parameter.KEYWORD_ONLY\n parameters = {\n name: inspect.Parameter(name, kind, default=defaults.get(name, undefined))\n for name in annotations\n }\n resolver = StrawberryResolver(func)\n resolver.arguments = get_arguments_from_annotations(annotations, parameters, func)\n return StrawberryField(\n python_name=func.__name__,\n graphql_name='',\n type_=func.__annotations__['return'],\n description=func.__doc__,\n base_resolver=resolver,\n )","def __init__(self, annotation_file=None):\n # load dataset\n self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\n if not annotation_file == None:\n print('loading annotations into memory...')\n tic = time.time()\n dataset = json.load(open(annotation_file, 'r'))\n assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))\n print('Done (t={:0.2f}s)'.format(time.time()- tic))\n self.dataset = dataset\n self.createIndex()","def arg(\n default=MISSING,\n /,\n *,\n flag=None,\n factory=MISSING,\n init=True,\n repr=True, # noqa: A002\n hash=None, # noqa: A002\n help=None, # noqa: A002\n compare=True,\n metadata=None,\n):\n metadata = metadata or {}\n for k, v in {'flag': flag, 'help': help}.items():\n if v:\n metadata = metadata | {k: v}\n return field( # type: ignore[call-overload]\n default=default,\n default_factory=factory,\n init=init,\n repr=repr,\n hash=hash,\n compare=compare,\n metadata=metadata)","def create(cls, obj, body, evaldict, defaults=None,\r\n doc=None, module=None, addsource=True,**attrs):\r\n if isinstance(obj, str): # \"name(signature)\"\r\n name, rest = obj.strip().split('(', 1)\r\n signature = rest[:-1] #strip a right parens \r\n func = None\r\n else: # a function\r\n name = None\r\n signature = None\r\n func = obj\r\n self = cls(func, name, signature, defaults, doc, module)\r\n ibody = '\\n'.join(' ' + line for line in body.splitlines())\r\n return self.make('def %(name)s(%(signature)s):\\n' + ibody, \r\n evaldict, addsource, **attrs)","def addOnCreate(call, args=(), kwargs={}, nodeClass='*'):\n pass","def create_arg_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('-a', '--path_annots', type=str, required=False,\n help='path to folder with annotations',\n default='annotations')\n parser.add_argument('-i', '--path_dataset', type=str, required=False,\n help='path to folder with dataset (images)',\n default='dataset')\n parser.add_argument('-o', '--path_output', type=str, required=False,\n help='path to the output directory - visualisation',\n default='output')\n parser.add_argument('--consensus', type=str, required=False,\n help='method for consensus landmarks',\n choices=['mean', 'median'], default='mean')\n parser.add_argument('--visual', required=False, action='store_true',\n help='export co-annotation visualisation', default=False)\n parser.add_argument('--nb_jobs', type=int, required=False,\n help='number of processes in parallel',\n default=NB_THREADS)\n return parser","def __new__(cls,name,description,args_in,required=True,data_type=None,schema=None):\n mydict={\n \"name\":name,\n \"description\":description,\n \"in\":args_in,\n \"required\":required,\n \"schema\":schema,\n \"type\":data_type,\n }\n if args_in!=\"body\":\n mydict[\"type\"]=data_type\n return mydict","def FromData(cls, data):\n if not data:\n return None\n\n attribute_name = data['attribute_name']\n parameter_name = data['parameter_name']\n help_text = data['help']\n completion_id_field = data.get('completion_id_field', None)\n completion_request_params_list = data.get('completion_request_params', [])\n completion_request_params = {\n param.get('fieldName'): param.get('value')\n for param in completion_request_params_list\n }\n\n # Add property fallthroughs.\n fallthroughs = []\n prop = properties.FromString(data.get('property', ''))\n if prop:\n fallthroughs.append(deps_lib.PropertyFallthrough(prop))\n default_config = DEFAULT_RESOURCE_ATTRIBUTE_CONFIGS.get(attribute_name)\n if default_config:\n fallthroughs += [\n f for f in default_config.fallthroughs if f not in fallthroughs]\n # Add fallthroughs from python hooks.\n fallthrough_data = data.get('fallthroughs', [])\n fallthroughs_from_hook = [\n deps_lib.Fallthrough(util.Hook.FromPath(f['hook']), hint=f['hint'])\n for f in fallthrough_data\n ]\n fallthroughs += fallthroughs_from_hook\n return cls(\n name=attribute_name,\n help_text=help_text,\n fallthroughs=fallthroughs,\n completion_id_field=completion_id_field,\n completion_request_params=completion_request_params,\n parameter_name=parameter_name)","def add_raw_annotation(binary_addr, text, inline=False, priority=None):\n\n annotations[binary_addr].append(Annotation(text, inline, priority))"],"string":"[\n \"def RDFAnnotationParser_createAnnotation():\\n return _libsbml.RDFAnnotationParser_createAnnotation()\",\n \"def createAnnotation():\\n return _libsbml.RDFAnnotationParser_createAnnotation()\",\n \"def create_annotation(timestamp, value, host):\\n return zipkin_core.Annotation(timestamp=timestamp, value=value, host=host)\",\n \"def __init__(self, arg: ast3.arg) -> None:\\n self.typed = False\\n self.line = arg.lineno\\n self.column = arg.col_offset\\n self.name = arg.arg\\n self.type = None\\n self.type_line = -1\\n self.type_column = -1\\n if arg.annotation:\\n anno = arg.annotation\\n self.typed = True\\n self.type = Annotation(anno) # type: ignore\\n self.type_line = anno.lineno\\n self.type_column = anno.col_offset\",\n \"def __init__(self, *args, **kwargs):\\n super(_BaseAnnotatedObject, self).__init__()\\n properties = self._tp__typed_properties\\n required = self._tp__required_typed_properties\\n positionals = zip(properties, args)\\n for attr, value in positionals:\\n if attr in kwargs:\\n raise TypeError(\\n \\\"__init__() got multiple values for argument '{}'\\\".format(\\n attr\\n )\\n )\\n kwargs[attr] = value\\n missing = [attr for attr in required if attr not in kwargs]\\n if missing:\\n num_missing = len(missing)\\n if num_missing > 1:\\n args = \\\", \\\".join(\\\"'{}'\\\".format(m) for m in missing[:-1])\\n if num_missing > 2:\\n args += \\\",\\\"\\n args += \\\" and '{}'\\\".format(missing[-1])\\n else:\\n args = \\\"'{}'\\\".format(missing[0])\\n raise TypeError(\\n \\\"__init__() missing {} required argument{}: {}\\\".format(\\n num_missing, \\\"s\\\" if num_missing > 1 else \\\"\\\", args\\n )\\n )\\n for attr, value in six.iteritems(kwargs):\\n if attr in properties:\\n setattr(self, attr, value)\",\n \"def from_json(json):\\n result = Annotation()\\n if \\\"projectId\\\" in json:\\n result.project_id = json[\\\"projectId\\\"]\\n if \\\"imageId\\\" in json:\\n result.image_id = json[\\\"imageId\\\"]\\n if \\\"shapes\\\" in json:\\n result.shapes = json[\\\"shapes\\\"]\\n if \\\"frameNum\\\" in json:\\n result.frame_num = json[\\\"frameNum\\\"]\\n if \\\"_id\\\" in json:\\n result.id = str(json[\\\"_id\\\"])\\n return result\",\n \"def Infer(cls, pt: inspect.Parameter, module: Optional[str] = None):\\n\\n def needs_type(item: Dict[str, str]):\\n try:\\n return item['action'] == 'store'\\n except KeyError:\\n return True\\n\\n def get_argp_type(annotation_type):\\n if issubclass(annotation_type, (bytes, bytearray, memoryview)):\\n return multibin\\n if issubclass(annotation_type, int):\\n return number\\n if issubclass(annotation_type, slice):\\n return sliceobj\\n return annotation_type\\n\\n name = pt.name.replace('_', '-')\\n default = pt.default\\n guessed_pos_args = []\\n guessed_kwd_args = dict(dest=pt.name)\\n guessed = set()\\n annotation = pt.annotation\\n\\n def guess(key, value):\\n try:\\n return guessed_kwd_args[key]\\n except KeyError:\\n guessed_kwd_args[key] = value\\n guessed.add(key)\\n return value\\n\\n if isinstance(annotation, str):\\n symbols = None\\n while symbols is not False:\\n try:\\n annotation = eval(annotation, symbols)\\n except NameError:\\n if symbols is not None or module is None:\\n break\\n try:\\n import importlib\\n symbols = importlib.import_module(module).__dict__\\n except Exception:\\n symbols = False\\n except Exception:\\n pass\\n else:\\n break\\n\\n if annotation is not pt.empty:\\n if isinstance(annotation, Arg):\\n if annotation.kwargs.get('dest', pt.name) != pt.name:\\n raise ValueError(\\n F'Incompatible argument destination specified; parameter {pt.name} '\\n F'was annotated with {annotation!r}.')\\n guessed_pos_args = annotation.args\\n guessed_kwd_args.update(annotation.kwargs)\\n guessed_kwd_args.update(group=annotation.group)\\n elif isinstance(annotation, type):\\n guessed.add('type')\\n if not issubclass(annotation, bool) and needs_type(guessed_kwd_args):\\n guessed_kwd_args.update(type=get_argp_type(annotation))\\n elif not isinstance(default, bool):\\n raise ValueError('Default value for boolean arguments must be provided.')\\n\\n if not guessed_pos_args:\\n guessed_pos_args = guessed_pos_args or [F'--{name}' if pt.kind is pt.KEYWORD_ONLY else name]\\n\\n if pt.kind is pt.VAR_POSITIONAL:\\n oldnargs = guess('nargs', ZERO_OR_MORE)\\n if oldnargs not in (ONE_OR_MORE, ZERO_OR_MORE, REMAINDER):\\n raise ValueError(F'Variadic positional arguments has nargs set to {oldnargs!r}')\\n return cls(*guessed_pos_args, **guessed_kwd_args)\\n\\n if default is not pt.empty:\\n if isinstance(default, Enum):\\n default = default.name\\n if isinstance(default, (list, tuple)):\\n guess('nargs', ZERO_OR_MORE)\\n if not pt.default:\\n default = pt.empty\\n else:\\n guessed_kwd_args['default'] = pt.default\\n default = default[0]\\n else:\\n guessed_kwd_args['default'] = default\\n if pt.kind is pt.POSITIONAL_ONLY:\\n guess('nargs', OPTIONAL)\\n\\n if default is not pt.empty:\\n if isinstance(default, bool):\\n action = 'store_false' if default else 'store_true'\\n guessed_kwd_args['action'] = action\\n elif needs_type(guessed_kwd_args):\\n guess('type', get_argp_type(type(default)))\\n\\n return cls(*guessed_pos_args, **guessed_kwd_args, guessed=guessed)\",\n \"def addAnnotation(self, token, title, text, start, end = None):\\n\\t\\tif end is None:\\n\\t\\t\\tpayload = \\\"{\\\\\\\"title\\\\\\\":\\\\\\\"\\\" + title + \\\"\\\\\\\", \\\\\\\"start\\\\\\\": \\\\\\\"\\\" + str(start) + \\\"\\\\\\\", \\\\\\\"text\\\\\\\":\\\\\\\"\\\" + text + \\\"\\\\\\\"}\\\"\\n\\t\\telse:\\n\\t\\t\\tpayload = \\\"{\\\\\\\"title\\\\\\\":\\\\\\\"\\\" + title + \\\"\\\\\\\", \\\\\\\"start\\\\\\\": \\\\\\\"\\\" + str(start) + \\\"\\\\\\\", \\\\\\\"end\\\\\\\":\\\\\\\"\\\" + str(end) + \\\"\\\\\\\", \\\\\\\"text\\\\\\\":\\\\\\\"\\\" + text + \\\"\\\\\\\"}\\\"\\n\\t\\tif self.simulate:\\n\\t\\t\\tself.logger.info(\\\"[SIMULATE] adding new annotation: \\\" + payload)\\t\\n\\t\\t\\treturn\\n\\t\\tself.logger.info(\\\"adding new annotation: \\\" + payload)\\t\\n\\n\\t\\t#self.logger.info(\\\"WARNING: mpulse API handler disabled!\\\")\\n\\t\\t#return\\n\\t\\turl = \\\"https://mpulse.soasta.com/concerto/mpulse/api/annotations/v1\\\"\\n\\t\\tresult = requests.post(url, data = payload, headers={'Content-Type':'application/json', 'X-Auth-Token': token })\\n\\t\\tif (result.status_code == 200):\\n\\t\\t\\tjson_data = result.json()\\n\\t\\t\\tself.logger.info('annotation successfully added')\\n\\t\\telse:\\n\\t\\t\\tself.logger.error('Error ' + str(result.status_code) + ': annotation not added!')\",\n \"def new():\\n doc = coerce_to(int, request.form['doc'])\\n page = coerce_to(int, request.form['page'])\\n posx = coerce_to(int, request.form['posx'])\\n posy = coerce_to(int, request.form['posy'])\\n width = coerce_to(int, request.form['width'])\\n height = coerce_to(int, request.form['height'])\\n text = request.form['value']\\n state = Annotation.STATE_OPEN\\n if 'state' in request.form:\\n state = Annotation.state_decode(request.form['state'])\\n doc_obj = Document.query.get(doc)\\n if doc_obj is None:\\n return BadRequest()\\n if not current_user.can_annotate(doc):\\n return Unauthorized()\\n user = current_user.id\\n ann = Annotation(doc, page, posx, posy, width, height, text, user,\\n state=state)\\n db.session.add(ann)\\n db.session.commit()\\n return jsonify(id=ann.id)\",\n \"def __init__(self, dictionary: dict[str, t.Any]) -> None:\\n missing_keys = self.__annotations__.keys() - dictionary.keys()\\n if missing_keys:\\n raise KeyError(f\\\"Fetched object lacks expected keys: {missing_keys}\\\")\\n for annotation in self.__annotations__:\\n setattr(self, annotation, dictionary[annotation])\",\n \"def __init__(self, curator=None, version='', corpus='', annotator=None,\\n annotation_tools='', annotation_rules='', validation='',\\n data_source=''):\\n super(AnnotationMetadata, self).__init__()\\n\\n if curator is None:\\n curator = Curator()\\n\\n if annotator is None:\\n annotator = JObject()\\n\\n self.curator = Curator(**curator)\\n self.annotator = JObject(**annotator)\\n\\n self.version = version\\n self.corpus = corpus\\n self.annotation_tools = annotation_tools\\n self.annotation_rules = annotation_rules\\n self.validation = validation\\n self.data_source = data_source\",\n \"def __init__(\\n self, arg=None, colors=None, colorssrc=None, line=None, pattern=None, **kwargs\\n ):\\n super(Marker, self).__init__(\\\"marker\\\")\\n\\n if \\\"_parent\\\" in kwargs:\\n self._parent = kwargs[\\\"_parent\\\"]\\n return\\n\\n # Validate arg\\n # ------------\\n if arg is None:\\n arg = {}\\n elif isinstance(arg, self.__class__):\\n arg = arg.to_plotly_json()\\n elif isinstance(arg, dict):\\n arg = _copy.copy(arg)\\n else:\\n raise ValueError(\\n \\\"\\\"\\\"\\\\\\nThe first argument to the plotly.graph_objs.funnelarea.Marker\\nconstructor must be a dict or\\nan instance of :class:`plotly.graph_objs.funnelarea.Marker`\\\"\\\"\\\"\\n )\\n\\n # Handle skip_invalid\\n # -------------------\\n self._skip_invalid = kwargs.pop(\\\"skip_invalid\\\", False)\\n self._validate = kwargs.pop(\\\"_validate\\\", True)\\n\\n # Populate data dict with properties\\n # ----------------------------------\\n _v = arg.pop(\\\"colors\\\", None)\\n _v = colors if colors is not None else _v\\n if _v is not None:\\n self[\\\"colors\\\"] = _v\\n _v = arg.pop(\\\"colorssrc\\\", None)\\n _v = colorssrc if colorssrc is not None else _v\\n if _v is not None:\\n self[\\\"colorssrc\\\"] = _v\\n _v = arg.pop(\\\"line\\\", None)\\n _v = line if line is not None else _v\\n if _v is not None:\\n self[\\\"line\\\"] = _v\\n _v = arg.pop(\\\"pattern\\\", None)\\n _v = pattern if pattern is not None else _v\\n if _v is not None:\\n self[\\\"pattern\\\"] = _v\\n\\n # Process unknown kwargs\\n # ----------------------\\n self._process_kwargs(**dict(arg, **kwargs))\\n\\n # Reset skip_invalid\\n # ------------------\\n self._skip_invalid = False\",\n \"def __init__(self, *args, **kwargs):\\n \\n for arg in args:\\n if isinstance(arg, dict):\\n for key, value in arg.items():\\n self[key] = value\\n if hasattr(arg, \\\"__dict__\\\"):\\n for key, value in arg.__dict__.items():\\n self[key] = value\\n\\n if kwargs:\\n for key, value in kwargs.items():\\n self[key] = value\",\n \"def __json_init__(cls, **kwargs):\\n return cls(**kwargs)\",\n \"def __init__(self, raw_arg: Dict):\\n self.name = raw_arg.get(\\\"name\\\")\\n self.description = raw_arg.get(\\\"description\\\")\\n self.type = TypeDefer(raw_arg.get(\\\"type\\\")) if raw_arg.get(\\\"type\\\") is not None else None\\n self.default_value = raw_arg.get(\\\"defaultValue\\\")\",\n \"def annotation_to_python(name, args, type_, template, sctypes):\\n tab = ' ' * TABSIZE\\n tokens = name.split(' ')\\n pyname = ''.join([x.title() for x in tokens])\\n cls = 'class {}({}):\\\\n'.format(pyname, type_.title())\\n cls += '{}_trigedit_name = \\\"{}\\\"\\\\n'.format(tab, name)\\n # pyargs = [type_to_sc_type(x['type']) for x in args]\\n qparams = [x['type'] for x in args if x['is_quoted']]\\n quoted_fields = '[\\\"{}\\\"]'.format(', '.join(qparams))\\n if not quoted_fields:\\n quoted_fields = ''\\n cls += '{}_quoted_fields = frozenset({})\\\\n\\\\n'.format(tab, quoted_fields)\\n # figure out the argument types\\n imports = []\\n for arg in args:\\n param = arg['type']\\n default = arg['default']\\n if param in sctypes:\\n ptype = type_to_sc_type(param, prefix='sc')\\n imports.append(ptype)\\n else:\\n if re.search('^[0-9]+$', default):\\n ptype = 'int'\\n else:\\n ptype = 'str'\\n arg['pytype'] = ptype\\n if len(args) > 0:\\n typed_params = ', '.join(['{}: {}'.format(x['type'], x['pytype']) for x in args])\\n cls += '{}def __init__(self, {}):\\\\n'.format(tab, typed_params)\\n else:\\n cls += '{}def __init__(self):\\\\n'.format(tab)\\n joiner = '\\\\n{}{}'.format(tab, tab)\\n fields = joiner.join(['self.{} = {}'.format(x['type'], x['type']) for x in args])\\n if len(args) > 0:\\n cls += '{}{}super().__init__()\\\\n{}{}{}'.format(tab, tab, tab, tab, fields)\\n else:\\n cls += '{}{}super().__init__()'.format(tab, tab)\\n return cls, imports\",\n \"def __init__(self, json_str=None, data=None, label=None):\\n if json_str is not None:\\n src = json.loads(json_str)\\n self._data = src[\\\"data\\\"]\\n self._label = src[\\\"label\\\"]\\n elif data is not None and label is not None:\\n self._data = data\\n self._label = label\\n else:\\n raise Exception(\\\"Sample1D needs either json_str or data and label\\\")\",\n \"def _annotated_node(self, annotation: str, **edges: CFNode) -> CFNode:\\n node = CFNode(annotation=annotation)\\n self._graph.add_node(node, edges=edges)\\n return node\",\n \"def getAnnotation(self, *args):\\n return _libsbml.SBase_getAnnotation(self, *args)\",\n \"def from_json_dict(cls, **kwargs):\\n if not {\\\"name\\\", \\\"dtype\\\", \\\"default\\\"} <= set(kwargs.keys()):\\n raise MlflowException.invalid_parameter_value(\\n \\\"Missing keys in ParamSpec JSON. Expected to find \\\"\\n \\\"keys `name`, `dtype` and `default`\\\",\\n )\\n return cls(\\n name=str(kwargs[\\\"name\\\"]),\\n dtype=DataType[kwargs[\\\"dtype\\\"]],\\n default=kwargs[\\\"default\\\"],\\n shape=kwargs.get(\\\"shape\\\"),\\n )\",\n \"def createAnn(bbox, \\n cat_id, \\n visibility=None,\\n img_id=None,\\n segmentation=None, \\n area=None, \\n iscrowd=0,\\n id=None):\\n\\n bbox = [float(format(elem, '.2f')) for elem in bbox]\\n\\n if segmentation is None:\\n segmentation = []\\n\\n if area is None:\\n # Use bounding box area\\n area = bbox[2] * bbox[3]\\n area = np.float32(area).tolist()\\n\\n annotation = {\\n \\\"id\\\": id,\\n \\\"image_id\\\": img_id,\\n \\\"category_id\\\": cat_id,\\n \\\"segmentation\\\": segmentation,\\n \\\"area\\\": area,\\n \\\"bbox\\\": bbox,\\n \\\"visibility\\\": visibility,\\n \\\"iscrowd\\\": iscrowd\\n }\\n\\n return annotation\",\n \"def setAnnotation(self, *args):\\n return _libsbml.SBase_setAnnotation(self, *args)\",\n \"def __init__(self, *args: Union[List[AtomKey], HKT], **kwargs: str) -> None:\\n ...\",\n \"def __init__(self, statistics, *histograms, **style):\\n \\n Annotation.__init__(self, **style)\\n\\n # Split the specified statistics.\\n self.statistics = tuple(statistics)\\n self.histograms = histograms\",\n \"def annotate(*args, point: List[float, float, float]=None, text: AnyStr=\\\"\\\", **kwargs)->AnyStr:\\n pass\",\n \"def appendAnnotation(self, *args):\\n return _libsbml.SBase_appendAnnotation(self, *args)\",\n \"def setAnnotation(self, *args):\\n return _libsbml.Model_setAnnotation(self, *args)\",\n \"def __call__(self, *input_data):\\n \\n pb_ann = Any()\\n pb_ann.Pack(annotation_to_protobuf.convert_annotation(input_data))\\n request = annotation_pb2.ProcessRequest(pipeline_name = self._pipeline_name, \\n input_annotations = pb_ann)\\n \\n response = self._stub.process(request)\\n return annotation_from_protobuf.convert_annotation(response.output_annotations)\",\n \"def __init__(self, *args: Union[List[AtomKey], DKT], **kwargs: str) -> None:\\n ...\",\n \"def create(self, request, *args, **kwargs):\\n # Deserialize and validate the data from the user.\\n serializer = self.get_serializer(data=request.data)\\n serializer.is_valid(raise_exception=True)\\n\\n # Execute the document and annotation creation\\n self.perform_create(serializer)\\n\\n # Get the headers and return a response\\n headers = self.get_success_headers(serializer.data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\",\n \"def annotate(self, annotation_=None):\\n # Important: Need a copy, not the reference to the original object\\n annotation_ = copy.deepcopy(annotation_)\\n annotation_.annotate(self, from_dataset=True)\\n history_record = annotation_.create_history_record()\\n self.annotations.append(history_record)\\n self._append_task(kind='annotation', task=history_record)\",\n \"def __init__(self, **named):\\n for key, value in named.items():\\n typ = getattr(self, '__annotations__', {}).get(key)\\n if typ:\\n setattr(self, key, type_coerce(value, typ))\\n else:\\n setattr(self, key, value)\",\n \"def create(cls, data=None):\\n # allow create() calls with no input\\n if not data:\\n data = {}\\n\\n return cls(**data)\",\n \"def annotate(args):\\n prism.annotate.run(\\n input_fp=args.input,\\n output_fp=args.output,\\n bed_fps=args.beds,\\n annotation_names=args.annotation_names,\\n output_figure_fp=args.figure,\\n width=args.width,\\n height=args.height,\\n scale=args.scale,\\n font_family=args.font_family,\\n )\",\n \"def setup_annotation(self):\\n annotation = self.ax_fig.annotate(\\n '', xy=(0, 0), ha='left',\\n xytext=(-20, 20), textcoords='offset points', va='bottom',\\n bbox=dict(\\n boxstyle='round,pad=0.5', fc='yellow', alpha=0.2),\\n arrowprops=dict(\\n arrowstyle='->', connectionstyle='arc3,rad=0'))\\n return annotation\",\n \"def _convert_annotations(self, ast):\\n self.annotations = IDLAnnotations(ast)\",\n \"def commandLine(*args, annotation: Union[AnyStr, bool]=\\\"\\\", backgroundColor: Union[List[float,\\n float, float], bool]=None, command: Script=None, defineTemplate: AnyStr=\\\"\\\",\\n docTag: Union[AnyStr, bool]=\\\"\\\", dragCallback: Script=None, dropCallback:\\n Script=None, enable: bool=True, enableBackground: bool=True,\\n enableKeyboardFocus: bool=True, enterCommand: Script=None, exists: bool=True,\\n fullPathName: bool=True, height: Union[int, bool]=0, highlightColor:\\n Union[List[float, float, float], bool]=None, holdFocus: bool=True,\\n inputAnnotation: Union[AnyStr, bool]=\\\"\\\", isObscured: bool=True, manage:\\n bool=True, noBackground: bool=True, numberOfHistoryLines: Union[int, bool]=0,\\n numberOfPopupMenus: bool=True, outputAnnotation: Union[AnyStr, bool]=\\\"\\\",\\n parent: Union[AnyStr, bool]=\\\"\\\", popupMenuArray: bool=True, preventOverride:\\n bool=True, sourceType: Union[AnyStr, bool]=\\\"\\\", statusBarMessage: AnyStr=\\\"\\\",\\n useTemplate: AnyStr=\\\"\\\", visible: bool=True, visibleChangeCommand: Union[Script,\\n bool]=None, width: Union[int, bool]=0, q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def build(arg_dict):\\n\\n task_item = Task()\\n\\n try:\\n task_item.key = arg_dict['key']\\n except KeyError:\\n task_item.key = None\\n\\n try:\\n task_item.title = arg_dict['title']\\n except KeyError:\\n task_item.title = None\\n\\n try:\\n task_item.notes = arg_dict['notes']\\n except KeyError:\\n task_item.notes = None\\n\\n return task_item\",\n \"def appendAnnotation(self, *args):\\n return _libsbml.Model_appendAnnotation(self, *args)\",\n \"def __init__(self, *args: Union[List[AtomKey], EKT], **kwargs: str) -> None:\\n ...\",\n \"def _make_args(self, args, defaults=[], vararg=None, kwonlyargs=[],\\n kw_defaults=[], kwarg=None):\\n # On Python 2 convert vararg and kwarg to raw name, raise error using\\n # lineno stored on the node and lexer from self.\\n # On Python 3.3 extract name and annotation\\n # After should be straight forward\\n raise NotImplementedError()\",\n \"def __init__(self, *args, name=''):\\n from collections import Iterable\\n if len(args) == 1:\\n if isinstance(args[0], Point):\\n self.data = args[0].data.copy()\\n elif isinstance(args[0], Iterable):\\n self.data = list(args[0])\\n else:\\n self.data = list(args)\\n self.name = name if not name.isspace() else ''\",\n \"def __init__(self, log=None):\\n sppasBaseAnnotation.__init__(self, \\\"activity.json\\\", log)\\n self.__activity = Activity()\",\n \"def __init__(self, outdir, redo=False):\\n self.outdir = outdir\\n if redo:\\n shutil.rmtree(outdir)\\n if not os.path.exists(outdir):\\n os.mkdir(outdir)\\n self.annotations_file = os.path.join(outdir, 'annotations.json')\\n self.annotations = collections.defaultdict(dict)\\n if os.path.exists(self.annotations_file):\\n annotations = json.load(open(self.annotations_file, 'r'))\\n for key1 in annotations:\\n for key2 in annotations[key1]:\\n self.annotations[key1][key2] = annotations[key1][key2]\\n self.argtypes = {}\\n self.argtypes_file = os.path.join(outdir, 'argtypes.json')\\n if os.path.exists(self.argtypes_file):\\n self.argtypes = json.load(open(self.argtypes_file, 'r'))\",\n \"def perform_create(self, serializer):\\n try:\\n\\n # Create the document object\\n long_url = serializer.validated_data['long_url']\\n document, _ = Document.objects.get_or_create(long_url=long_url)\\n serializer.instance = document\\n\\n # Create the annotation object\\n annotate, _ = Annotation.objects.get_or_create(\\n user = self.request.user, document = document\\n )\\n\\n except CorpusException as e:\\n raise ValidationError(str(e))\",\n \"def RDFAnnotationParser_createRDFAnnotation(level=3, version=1):\\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)\",\n \"def nameCommand(*args, annotation: AnyStr=\\\"\\\", command: Script=None, data1: AnyStr=\\\"\\\", data2:\\n AnyStr=\\\"\\\", data3: AnyStr=\\\"\\\", default: bool=True, sourceType: AnyStr=\\\"\\\",\\n **kwargs)->AnyStr:\\n pass\",\n \"def create(cls, *args):\\n c = cls({})\\n c.apply(*args)\\n return c\",\n \"def __init__(\\n self,\\n arg=None,\\n autocolorscale=None,\\n cauto=None,\\n cmax=None,\\n cmid=None,\\n cmin=None,\\n coloraxis=None,\\n colorbar=None,\\n colors=None,\\n colorscale=None,\\n colorssrc=None,\\n line=None,\\n pattern=None,\\n reversescale=None,\\n showscale=None,\\n **kwargs,\\n ):\\n super(Marker, self).__init__(\\\"marker\\\")\\n\\n if \\\"_parent\\\" in kwargs:\\n self._parent = kwargs[\\\"_parent\\\"]\\n return\\n\\n # Validate arg\\n # ------------\\n if arg is None:\\n arg = {}\\n elif isinstance(arg, self.__class__):\\n arg = arg.to_plotly_json()\\n elif isinstance(arg, dict):\\n arg = _copy.copy(arg)\\n else:\\n raise ValueError(\\n \\\"\\\"\\\"\\\\\\nThe first argument to the plotly.graph_objs.sunburst.Marker\\nconstructor must be a dict or\\nan instance of :class:`plotly.graph_objs.sunburst.Marker`\\\"\\\"\\\"\\n )\\n\\n # Handle skip_invalid\\n # -------------------\\n self._skip_invalid = kwargs.pop(\\\"skip_invalid\\\", False)\\n self._validate = kwargs.pop(\\\"_validate\\\", True)\\n\\n # Populate data dict with properties\\n # ----------------------------------\\n _v = arg.pop(\\\"autocolorscale\\\", None)\\n _v = autocolorscale if autocolorscale is not None else _v\\n if _v is not None:\\n self[\\\"autocolorscale\\\"] = _v\\n _v = arg.pop(\\\"cauto\\\", None)\\n _v = cauto if cauto is not None else _v\\n if _v is not None:\\n self[\\\"cauto\\\"] = _v\\n _v = arg.pop(\\\"cmax\\\", None)\\n _v = cmax if cmax is not None else _v\\n if _v is not None:\\n self[\\\"cmax\\\"] = _v\\n _v = arg.pop(\\\"cmid\\\", None)\\n _v = cmid if cmid is not None else _v\\n if _v is not None:\\n self[\\\"cmid\\\"] = _v\\n _v = arg.pop(\\\"cmin\\\", None)\\n _v = cmin if cmin is not None else _v\\n if _v is not None:\\n self[\\\"cmin\\\"] = _v\\n _v = arg.pop(\\\"coloraxis\\\", None)\\n _v = coloraxis if coloraxis is not None else _v\\n if _v is not None:\\n self[\\\"coloraxis\\\"] = _v\\n _v = arg.pop(\\\"colorbar\\\", None)\\n _v = colorbar if colorbar is not None else _v\\n if _v is not None:\\n self[\\\"colorbar\\\"] = _v\\n _v = arg.pop(\\\"colors\\\", None)\\n _v = colors if colors is not None else _v\\n if _v is not None:\\n self[\\\"colors\\\"] = _v\\n _v = arg.pop(\\\"colorscale\\\", None)\\n _v = colorscale if colorscale is not None else _v\\n if _v is not None:\\n self[\\\"colorscale\\\"] = _v\\n _v = arg.pop(\\\"colorssrc\\\", None)\\n _v = colorssrc if colorssrc is not None else _v\\n if _v is not None:\\n self[\\\"colorssrc\\\"] = _v\\n _v = arg.pop(\\\"line\\\", None)\\n _v = line if line is not None else _v\\n if _v is not None:\\n self[\\\"line\\\"] = _v\\n _v = arg.pop(\\\"pattern\\\", None)\\n _v = pattern if pattern is not None else _v\\n if _v is not None:\\n self[\\\"pattern\\\"] = _v\\n _v = arg.pop(\\\"reversescale\\\", None)\\n _v = reversescale if reversescale is not None else _v\\n if _v is not None:\\n self[\\\"reversescale\\\"] = _v\\n _v = arg.pop(\\\"showscale\\\", None)\\n _v = showscale if showscale is not None else _v\\n if _v is not None:\\n self[\\\"showscale\\\"] = _v\\n\\n # Process unknown kwargs\\n # ----------------------\\n self._process_kwargs(**dict(arg, **kwargs))\\n\\n # Reset skip_invalid\\n # ------------------\\n self._skip_invalid = False\",\n \"def build(cls, key: str, **kwargs):\\n\\n def add(parser):\\n return cls.add_arguments(key, parser)\\n\\n kwargs = {f\\\"{key}_{cls.alias}_\\\" + k: v for k, v in kwargs.items()}\\n args = argparse.Namespace(**kwargs)\\n args = fill_missing_args(args, add)\\n return cls(key, args)\",\n \"def import_annotations(\\n self,\\n format_name: str,\\n filename: StrPath,\\n *,\\n status_check_period: Optional[int] = None,\\n pbar: Optional[ProgressReporter] = None,\\n ):\\n\\n filename = Path(filename)\\n\\n AnnotationUploader(self._client).upload_file_and_wait(\\n self.api.create_annotations_endpoint,\\n filename,\\n format_name,\\n url_params={\\\"id\\\": self.id},\\n pbar=pbar,\\n status_check_period=status_check_period,\\n )\\n\\n self._client.logger.info(f\\\"Annotation file '{filename}' for task #{self.id} uploaded\\\")\",\n \"def __init__(self, **kw_args):\\n self._other_object_hook = None\\n kw_args_new = kw_args.copy()\\n if 'object_hook' in kw_args:\\n self._other_object_hook = kw_args['object_hook']\\n kw_args_new['object_hook'] = self._object_decode\\n # Note: strict=False because the notes attribute might contain\\n # line feeds.\\n #\\n kw_args_new['strict'] = False\\n\\n self._decoder = json.JSONDecoder(**kw_args_new)\",\n \"def __init__(self, *args, **kwargs):\\n for dictionary in [_ for _ in args if isinstance(_, dict)]:\\n for key in dictionary:\\n setattr(self, key, dictionary[key])\\n for key in kwargs:\\n setattr(self, key, kwargs[key])\",\n \"def __init__(self, rules: Union[Dict[str, dict], Set[str]], separators: str = ''):\\n\\n if isinstance(rules, dict):\\n for pattern, annotation in rules.items():\\n if 'start' in annotation or 'end' in annotation:\\n raise ValueError(\\\"Attributes 'start' and 'end' are reserved do not use it inside annotations\\\")\\n elif not isinstance(rules, set):\\n raise ValueError('Extraction rules must be specified as Dict[str, dict] or Set[str]')\\n\\n # Protect parameters against changes\\n self.rules = deepcopy(rules)\\n self.separators = copy(separators)\\n\\n # Set up the automaton\\n self.automaton = Automaton()\\n for pattern in self.rules:\\n self.automaton.add_word(pattern, len(pattern))\\n self.automaton.make_automaton()\",\n \"def create(cls, dictionary):\\n return cls(**dictionary)\",\n \"def create(cls, dictionary):\\n return cls(**dictionary)\",\n \"def create_node(identifier, *args, **kwargs):\\r\\n\\r\\n d = node_dictionary()\\r\\n node_class = d[identifier]\\r\\n node = node_class(*args, **kwargs)\\r\\n return node\",\n \"def createRDFAnnotation(level=3, version=1):\\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)\",\n \"def RDFAnnotationParser_parseRDFAnnotation(*args):\\n return _libsbml.RDFAnnotationParser_parseRDFAnnotation(*args)\",\n \"def __json_decode__(cls, args: Dict[str, Any]) -> \\\"FigureData\\\":\\n return cls(**args)\",\n \"def from_annotation(cls, anno) -> \\\"Box\\\":\\n bounds = anno[\\\"bounds\\\"]\\n return cls(\\n x=bounds[\\\"left\\\"],\\n y=bounds[\\\"top\\\"],\\n width=bounds[\\\"right\\\"] - bounds[\\\"left\\\"],\\n height=bounds[\\\"bottom\\\"] - bounds[\\\"top\\\"],\\n label=anno[\\\"label\\\"][\\\"text\\\"],\\n )\",\n \"def _add_annotation(raw_fig):\\n data_ax = raw_fig.mne.ax_main\\n\\n key_event = KeyEvent(name=\\\"Annotation\\\", canvas=raw_fig.canvas, key=\\\"a\\\")\\n raw_fig.canvas.callbacks.process(\\\"key_press_event\\\", key_event)\\n\\n ann_fig = raw_fig.mne.fig_annotation\\n for key in \\\"test\\\": # Annotation will be named: BAD_test\\n key_event = KeyEvent(name=\\\"Bad\\\", canvas=ann_fig.canvas, key=key)\\n ann_fig.canvas.callbacks.process(\\\"key_press_event\\\", key_event)\\n\\n key_event = KeyEvent(name=\\\"Enter\\\", canvas=ann_fig.canvas, key=\\\"enter\\\")\\n ann_fig.canvas.callbacks.process(\\\"key_press_event\\\", key_event)\\n\\n # Draw a 4 second long Annotation.\\n _fake_click(raw_fig, data_ax, [1.0, 1.0], xform=\\\"data\\\", button=1, kind=\\\"press\\\")\\n _fake_click(raw_fig, data_ax, [5.0, 1.0], xform=\\\"data\\\", button=1, kind=\\\"motion\\\")\\n _fake_click(raw_fig, data_ax, [5.0, 1.0], xform=\\\"data\\\", button=1, kind=\\\"release\\\")\",\n \"def __init__(self, anno):\\n self._x1 = float(anno[0])\\n self._y1 = float(anno[1])\\n self._w = float(anno[2])\\n self._h = float(anno[3])\\n self._blur = int(anno[4])\\n self._expression = int(anno[5])\\n self._illumination = int(anno[6])\\n self._invalid = int(anno[7])\\n self._occlusion = int(anno[8])\\n self._pose = int(anno[9])\",\n \"def create_binary_annotation(key, value, annotation_type, host):\\n return zipkin_core.BinaryAnnotation(\\n key=key, value=value, annotation_type=annotation_type, host=host)\",\n \"def __init__(self, type='lg_asteroid', dataset=obstacles, **kwargs):\\n super().__init__(type=type, dataset=dataset, **kwargs)\\n self.obj_type = \\\"asteroid\\\"\",\n \"def __init__(self, keydata=None):\\n if isinstance(keydata, basestring):\\n keydata = json.loads(keydata)\\n assert keydata is None or isinstance(keydata, dict), keydata\\n self.dict = keydata if keydata is not None else {}\",\n \"def annotate(self, **annotations):\\n _check_annotations(annotations)\\n self.annotations.update(annotations)\",\n \"def init_json_args(cls):\\n defaults = cls._base_json_args()\\n\\n if cls.__json_args__ is None:\\n cls.__json_args__ = defaults\\n else:\\n cls.__json_args__ = mapping_extend(defaults, cls.__json_args__)\",\n \"def addAutolabel(call, args=(), kwargs={}, nodeClass='*'):\",\n \"def from_callable(cls, obj: Callable) -> 'Signature':\\n namespace = None\\n arg_types = []\\n var_arg_type = None\\n if isinstance(obj, type): # it's a class?\\n try:\\n sig = inspect.signature(_get_constructor(obj) or obj)\\n except (TypeError, ValueError):\\n sig = None\\n skip_arg = 1\\n else:\\n try:\\n sig = inspect.signature(obj)\\n except TypeError:\\n sig = None\\n skip_arg = 0\\n if sig:\\n return_type = sig.return_annotation\\n if return_type is sig.empty:\\n if isinstance(obj, type):\\n return_type = obj\\n else:\\n return_type = Any\\n elif return_type is None and isinstance(obj, type):\\n return_type = obj\\n elif isinstance(return_type, Text):\\n namespace = (namespace or\\n importlib.import_module(obj.__module__).__dict__)\\n return_type = eval(return_type, namespace)\\n non_def_args = [(p.name, p.kind, p.annotation)\\n for p in list(sig.parameters.values())[skip_arg:]\\n if p.default is sig.empty]\\n for name, kind, annotation in non_def_args:\\n if kind in (_KEYWORD_ONLY, _VAR_KEYWORD):\\n raise ValueError(\\\"'\\\" + repr(obj) + \\\"' has keyword-only \\\"\\n \\\"arg w/o default value: '\\\" + name + \\\"'.\\\")\\n if annotation is sig.empty:\\n raise ValueError(\\\"'\\\" + repr(obj) + \\\"' has arg\\\"\\n \\\" w/o type hint: '\\\" + name + \\\"'.\\\")\\n if isinstance(annotation, Text):\\n namespace = (namespace or\\n importlib.import_module(obj.__module__)\\n .__dict__)\\n try:\\n annotation = eval(annotation, namespace)\\n except NameError:\\n annotation = None\\n if isinstance(annotation, (type, TypingMeta, _TypingBase)):\\n if kind == _VAR_POSITIONAL:\\n var_arg_type = annotation\\n else:\\n arg_types.append(annotation)\\n else:\\n raise ValueError(\\\"'\\\" + repr(obj) + \\\"' has arg\\\"\\n \\\" w/o type hint: '\\\" + name + \\\"'.\\\")\\n if arg_types or var_arg_type or return_type:\\n return cls(arg_types, var_arg_type=var_arg_type,\\n return_type=return_type)\\n raise ValueError(\\\"Can't retrieve signature of '\\\" + repr(obj) + \\\"'.\\\")\",\n \"def make_annotation_horizontal(x, y):\\n text = str(x)\\n if len(text) > 33:\\n text = text[0:33]+\\\"..\\\"\\n return Annotation(\\n text=text, # text is the y-coord\\n showarrow=False, # annotation w/o arrows, default is True\\n x=0, # set x position\\n xref='x', # position text horizontally with x-coords\\n xanchor='left', # x position corresp. to center of text\\n y=x, # y position corresp. to top of text\\n yref='y', # set y position\\n yanchor='auto', # position text vertically with y-coords\\n textangle=0, # Not Rotating the text\\n font=annotation.Font(\\n color='#FFFFFF', # set font color\\n size=13 # and size\\n )\\n )\",\n \"def __init__(self, taxon=None, label=None, oid=None):\\n base.AnnotatedDataObject.__init__(self, label=label, oid=oid)\\n if taxon is not None:\\n self.label = taxon.label\\n else:\\n self.label = None\\n if label is not None:\\n self.label = label\\n self.oid = oid\",\n \"def from_param(cls, arg):\\n return cls(arg)\",\n \"def _create(self, **attributes: Dict[str, object]) -> str:\\n pass\",\n \"def __init__(self, name=\\\"info\\\", attr=None):\\n Arg.__init__(self, name, attr)\",\n \"def create(self, **kargs):\\n return self(**kargs)\",\n \"def __init__(self, id=None, owner=None, trusted=None, name=None, slug=None, private=None, many_annotations_per_label=None, use_marker=None, annotation_value_type=None, color=None, created_at=None, updated_at=None): # noqa: E501 # noqa: E501\\n\\n self._id = None\\n self._owner = None\\n self._trusted = None\\n self._name = None\\n self._slug = None\\n self._private = None\\n self._many_annotations_per_label = None\\n self._use_marker = None\\n self._annotation_value_type = None\\n self._color = None\\n self._created_at = None\\n self._updated_at = None\\n self.discriminator = None\\n\\n if id is not None:\\n self.id = id\\n if owner is not None:\\n self.owner = owner\\n if trusted is not None:\\n self.trusted = trusted\\n self.name = name\\n self.slug = slug\\n if private is not None:\\n self.private = private\\n if many_annotations_per_label is not None:\\n self.many_annotations_per_label = many_annotations_per_label\\n if use_marker is not None:\\n self.use_marker = use_marker\\n if annotation_value_type is not None:\\n self.annotation_value_type = annotation_value_type\\n if color is not None:\\n self.color = color\\n if created_at is not None:\\n self.created_at = created_at\\n if updated_at is not None:\\n self.updated_at = updated_at\",\n \"def annotation(self, label, duration):\\n self.send_trigger(self.new_trigger('annotation', label, duration))\",\n \"def __init__(self, name: str, arg_type_name: str, is_required=False):\\n self.key = name\\n self.value = arg_type_name\\n self.required = is_required\",\n \"def _from_yaml(cls, loader: yaml.Loader, node: yaml.Node) -> \\\"ToJson\\\":\\n try:\\n value = loader.construct_mapping(node, deep=True)\\n except yaml.constructor.ConstructorError:\\n value = loader.construct_sequence(node, deep=True)\\n return cls(value)\",\n \"def member_from_json(cls, injson: dict) -> \\\"AocMember\\\":\\n return cls(\\n name=injson[\\\"name\\\"] if injson[\\\"name\\\"] else \\\"Anonymous User\\\",\\n aoc_id=int(injson[\\\"id\\\"]),\\n stars=injson[\\\"stars\\\"],\\n starboard=cls._starboard_from_json(injson[\\\"completion_day_level\\\"]),\\n local_score=injson[\\\"local_score\\\"],\\n global_score=injson[\\\"global_score\\\"],\\n )\",\n \"def __init__(self, **kwargs):\\n self.postKey = kwargs.get(\\\"postKey\\\")\\n self.location = kwargs.get(\\\"location\\\")\\n self.category = kwargs.get(\\\"category\\\")\\n self.source = kwargs.get(\\\"source\\\")\\n self.heading = kwargs.get(\\\"heading\\\")\\n self.body = kwargs.get(\\\"body\\\")\\n self.latitude = kwargs.get(\\\"latitude\\\")\\n self.longitude = kwargs.get(\\\"longitude\\\")\\n self.language = kwargs.get(\\\"language\\\")\\n self.price = kwargs.get(\\\"price\\\")\\n self.currency = kwargs.get(\\\"currency\\\")\\n self.images = kwargs.get(\\\"images\\\", [])\\n self.externalID = kwargs.get(\\\"externalID\\\")\\n self.externalURL = kwargs.get(\\\"externalURL\\\")\\n self.accountName = kwargs.get(\\\"accountName\\\")\\n self.accountID = kwargs.get(\\\"accountID\\\")\\n self.timestamp = kwargs.get(\\\"timestamp\\\")\\n self.expiration = kwargs.get(\\\"expiration\\\")\\n self.annotations = kwargs.get(\\\"annotations\\\", {})\\n self.trustedAnnotations = kwargs.get(\\\"trustedAnnotations\\\", {})\\n self.clickCount = kwargs.get(\\\"clickCount\\\")\",\n \"def annotate(self, annotation):\\n self._data = self._data.annotate(**annotation)\",\n \"def new_from_jsondict(cls, data, param_defaults, **kwargs):\\n\\n json_data = data.copy()\\n if kwargs:\\n for key, val in kwargs.items():\\n json_data[key] = val\\n\\n c = cls(param_defaults, **json_data)\\n c._json = data\\n return c\",\n \"def __init__(self, **kwargs: Any):\",\n \"def __init__(self, name=\\\"alpha\\\", attr=None):\\n Arg.__init__(self, name, attr)\",\n \"def run_annotation(args):\\n #print(\\\"running chronqc_annotation\\\")\\n chronqc_annotation.main(args)\",\n \"def from_parameter(cls, name: str, parameter: inspect.Parameter) -> \\\"Argument\\\":\\n # pylint: disable=too-many-branches,too-many-statements\\n positional = parameter.kind is not parameter.KEYWORD_ONLY\\n type_ = parameter.annotation\\n default = parameter.default\\n flag = name.upper() if positional else f\\\"--{name.replace('_', '-')}\\\"\\n\\n # If field is assigned an Argument use that as the starting point\\n if isinstance(default, Argument):\\n instance = default\\n default = EMPTY\\n if flag not in instance.name_or_flags:\\n instance.name_or_flags = (flag,) + instance.name_or_flags\\n else:\\n instance = cls(flag)\\n\\n # Start updating kwargs\\n kwargs = instance.kwargs\\n if default is not EMPTY:\\n kwargs.setdefault(\\\"default\\\", default)\\n\\n # Handle type variances\\n origin = getattr(type_, \\\"__origin__\\\", None)\\n if origin is not None:\\n type_ = cls._handle_generics(origin, type_, positional, kwargs)\\n elif isinstance(type_, type):\\n type_ = cls._handle_types(type_, positional, kwargs)\\n elif isinstance(type_, argparse.FileType):\\n pass # Just pass as this is an `argparse` builtin\\n else:\\n raise TypeError(f\\\"Unsupported type: {type_!r}\\\")\\n\\n if type_:\\n kwargs[\\\"type\\\"] = type_\\n\\n return instance\",\n \"def parse_annotation_instance(annotation):\\n\\n text = annotation['utf8_string']\\n language = annotation['language']\\n legible = int(annotation['legibility'] == 'legible')\\n\\n mask = np.reshape(np.array(annotation['mask'], np.int32), (-1, 2))\\n box = cv2.boxPoints(cv2.minAreaRect(mask))\\n quadrilateral = [int(x) for x in box.reshape([-1])]\\n\\n xmin = min(quadrilateral[0::2])\\n xmax = max(quadrilateral[0::2])\\n\\n ymin = min(quadrilateral[1::2])\\n ymax = max(quadrilateral[1::2])\\n\\n word_annotation = {\\n 'bbox': [xmin, ymin, xmax - xmin, ymax - ymin],\\n 'segmentation': [quadrilateral],\\n 'attributes': {\\n 'transcription': text,\\n 'legible': legible,\\n 'language': language,\\n }\\n }\\n\\n return word_annotation\",\n \"def __init__(self, jsondict=None, strict=True):\\n \\n self.language = None\\n \\\"\\\"\\\" Language of the text.\\n Type `CodeableConcept` (represented as `dict` in JSON). \\\"\\\"\\\"\\n \\n self.number = None\\n \\\"\\\"\\\" Note instance identifier.\\n Type `int`. \\\"\\\"\\\"\\n \\n self.text = None\\n \\\"\\\"\\\" Note explanatory text.\\n Type `str`. \\\"\\\"\\\"\\n \\n self.type = None\\n \\\"\\\"\\\" display | print | printoper.\\n Type `str`. \\\"\\\"\\\"\\n \\n super(ClaimResponseProcessNote, self).__init__(jsondict=jsondict, strict=strict)\",\n \"def create_from_arg_string(cls, arg_string):\\n return cls()\",\n \"def resolve_annotations(func: Callable, annotations: dict, defaults: dict = {}) -> StrawberryField:\\n kind = inspect.Parameter.KEYWORD_ONLY\\n parameters = {\\n name: inspect.Parameter(name, kind, default=defaults.get(name, undefined))\\n for name in annotations\\n }\\n resolver = StrawberryResolver(func)\\n resolver.arguments = get_arguments_from_annotations(annotations, parameters, func)\\n return StrawberryField(\\n python_name=func.__name__,\\n graphql_name='',\\n type_=func.__annotations__['return'],\\n description=func.__doc__,\\n base_resolver=resolver,\\n )\",\n \"def __init__(self, annotation_file=None):\\n # load dataset\\n self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()\\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\\n if not annotation_file == None:\\n print('loading annotations into memory...')\\n tic = time.time()\\n dataset = json.load(open(annotation_file, 'r'))\\n assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))\\n print('Done (t={:0.2f}s)'.format(time.time()- tic))\\n self.dataset = dataset\\n self.createIndex()\",\n \"def arg(\\n default=MISSING,\\n /,\\n *,\\n flag=None,\\n factory=MISSING,\\n init=True,\\n repr=True, # noqa: A002\\n hash=None, # noqa: A002\\n help=None, # noqa: A002\\n compare=True,\\n metadata=None,\\n):\\n metadata = metadata or {}\\n for k, v in {'flag': flag, 'help': help}.items():\\n if v:\\n metadata = metadata | {k: v}\\n return field( # type: ignore[call-overload]\\n default=default,\\n default_factory=factory,\\n init=init,\\n repr=repr,\\n hash=hash,\\n compare=compare,\\n metadata=metadata)\",\n \"def create(cls, obj, body, evaldict, defaults=None,\\r\\n doc=None, module=None, addsource=True,**attrs):\\r\\n if isinstance(obj, str): # \\\"name(signature)\\\"\\r\\n name, rest = obj.strip().split('(', 1)\\r\\n signature = rest[:-1] #strip a right parens \\r\\n func = None\\r\\n else: # a function\\r\\n name = None\\r\\n signature = None\\r\\n func = obj\\r\\n self = cls(func, name, signature, defaults, doc, module)\\r\\n ibody = '\\\\n'.join(' ' + line for line in body.splitlines())\\r\\n return self.make('def %(name)s(%(signature)s):\\\\n' + ibody, \\r\\n evaldict, addsource, **attrs)\",\n \"def addOnCreate(call, args=(), kwargs={}, nodeClass='*'):\\n pass\",\n \"def create_arg_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-a', '--path_annots', type=str, required=False,\\n help='path to folder with annotations',\\n default='annotations')\\n parser.add_argument('-i', '--path_dataset', type=str, required=False,\\n help='path to folder with dataset (images)',\\n default='dataset')\\n parser.add_argument('-o', '--path_output', type=str, required=False,\\n help='path to the output directory - visualisation',\\n default='output')\\n parser.add_argument('--consensus', type=str, required=False,\\n help='method for consensus landmarks',\\n choices=['mean', 'median'], default='mean')\\n parser.add_argument('--visual', required=False, action='store_true',\\n help='export co-annotation visualisation', default=False)\\n parser.add_argument('--nb_jobs', type=int, required=False,\\n help='number of processes in parallel',\\n default=NB_THREADS)\\n return parser\",\n \"def __new__(cls,name,description,args_in,required=True,data_type=None,schema=None):\\n mydict={\\n \\\"name\\\":name,\\n \\\"description\\\":description,\\n \\\"in\\\":args_in,\\n \\\"required\\\":required,\\n \\\"schema\\\":schema,\\n \\\"type\\\":data_type,\\n }\\n if args_in!=\\\"body\\\":\\n mydict[\\\"type\\\"]=data_type\\n return mydict\",\n \"def FromData(cls, data):\\n if not data:\\n return None\\n\\n attribute_name = data['attribute_name']\\n parameter_name = data['parameter_name']\\n help_text = data['help']\\n completion_id_field = data.get('completion_id_field', None)\\n completion_request_params_list = data.get('completion_request_params', [])\\n completion_request_params = {\\n param.get('fieldName'): param.get('value')\\n for param in completion_request_params_list\\n }\\n\\n # Add property fallthroughs.\\n fallthroughs = []\\n prop = properties.FromString(data.get('property', ''))\\n if prop:\\n fallthroughs.append(deps_lib.PropertyFallthrough(prop))\\n default_config = DEFAULT_RESOURCE_ATTRIBUTE_CONFIGS.get(attribute_name)\\n if default_config:\\n fallthroughs += [\\n f for f in default_config.fallthroughs if f not in fallthroughs]\\n # Add fallthroughs from python hooks.\\n fallthrough_data = data.get('fallthroughs', [])\\n fallthroughs_from_hook = [\\n deps_lib.Fallthrough(util.Hook.FromPath(f['hook']), hint=f['hint'])\\n for f in fallthrough_data\\n ]\\n fallthroughs += fallthroughs_from_hook\\n return cls(\\n name=attribute_name,\\n help_text=help_text,\\n fallthroughs=fallthroughs,\\n completion_id_field=completion_id_field,\\n completion_request_params=completion_request_params,\\n parameter_name=parameter_name)\",\n \"def add_raw_annotation(binary_addr, text, inline=False, priority=None):\\n\\n annotations[binary_addr].append(Annotation(text, inline, priority))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59611523","0.59427184","0.58710736","0.5766063","0.5743713","0.5684858","0.5537817","0.55328065","0.5502203","0.53965425","0.52998286","0.51879084","0.51839596","0.5171853","0.51343316","0.51239586","0.5122177","0.50803316","0.5056191","0.50431263","0.50403124","0.5020217","0.50139946","0.49937558","0.49914113","0.4985749","0.498569","0.49761543","0.49515185","0.49494275","0.49462578","0.49219954","0.4911333","0.49019986","0.48979586","0.4876505","0.4855201","0.48451632","0.48345447","0.48324862","0.4831257","0.48175192","0.4810949","0.4810728","0.48067817","0.48056373","0.47995052","0.47884187","0.4773406","0.47726852","0.4769268","0.47690782","0.4748096","0.47356713","0.47344628","0.47344628","0.47335136","0.47328502","0.47259295","0.47156757","0.471293","0.47084752","0.47032914","0.4702263","0.46980554","0.46972898","0.46837083","0.46798402","0.46735984","0.46714723","0.46699968","0.46551776","0.46486834","0.46432495","0.46376595","0.46316674","0.46302584","0.46302184","0.4618648","0.46127054","0.4606329","0.46001065","0.46000683","0.4596422","0.45880878","0.4580688","0.45699203","0.4564992","0.45591816","0.45590666","0.4539064","0.45375064","0.45364863","0.45355198","0.45341444","0.45337307","0.4533469","0.4530541","0.45294437","0.45269817"],"string":"[\n \"0.59611523\",\n \"0.59427184\",\n \"0.58710736\",\n \"0.5766063\",\n \"0.5743713\",\n \"0.5684858\",\n \"0.5537817\",\n \"0.55328065\",\n \"0.5502203\",\n \"0.53965425\",\n \"0.52998286\",\n \"0.51879084\",\n \"0.51839596\",\n \"0.5171853\",\n \"0.51343316\",\n \"0.51239586\",\n \"0.5122177\",\n \"0.50803316\",\n \"0.5056191\",\n \"0.50431263\",\n \"0.50403124\",\n \"0.5020217\",\n \"0.50139946\",\n \"0.49937558\",\n \"0.49914113\",\n \"0.4985749\",\n \"0.498569\",\n \"0.49761543\",\n \"0.49515185\",\n \"0.49494275\",\n \"0.49462578\",\n \"0.49219954\",\n \"0.4911333\",\n \"0.49019986\",\n \"0.48979586\",\n \"0.4876505\",\n \"0.4855201\",\n \"0.48451632\",\n \"0.48345447\",\n \"0.48324862\",\n \"0.4831257\",\n \"0.48175192\",\n \"0.4810949\",\n \"0.4810728\",\n \"0.48067817\",\n \"0.48056373\",\n \"0.47995052\",\n \"0.47884187\",\n \"0.4773406\",\n \"0.47726852\",\n \"0.4769268\",\n \"0.47690782\",\n \"0.4748096\",\n \"0.47356713\",\n \"0.47344628\",\n \"0.47344628\",\n \"0.47335136\",\n \"0.47328502\",\n \"0.47259295\",\n \"0.47156757\",\n \"0.471293\",\n \"0.47084752\",\n \"0.47032914\",\n \"0.4702263\",\n \"0.46980554\",\n \"0.46972898\",\n \"0.46837083\",\n \"0.46798402\",\n \"0.46735984\",\n \"0.46714723\",\n \"0.46699968\",\n \"0.46551776\",\n \"0.46486834\",\n \"0.46432495\",\n \"0.46376595\",\n \"0.46316674\",\n \"0.46302584\",\n \"0.46302184\",\n \"0.4618648\",\n \"0.46127054\",\n \"0.4606329\",\n \"0.46001065\",\n \"0.46000683\",\n \"0.4596422\",\n \"0.45880878\",\n \"0.4580688\",\n \"0.45699203\",\n \"0.4564992\",\n \"0.45591816\",\n \"0.45590666\",\n \"0.4539064\",\n \"0.45375064\",\n \"0.45364863\",\n \"0.45355198\",\n \"0.45341444\",\n \"0.45337307\",\n \"0.4533469\",\n \"0.4530541\",\n \"0.45294437\",\n \"0.45269817\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5776535"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":94091,"cells":{"query":{"kind":"string","value":"r\"\"\"Return the JObject as a set of native data types for serialization."},"document":{"kind":"string","value":"def __json_light__(self, data=True):\n filtered_dict = dict()\n\n for k, item in six.iteritems(self.__dict__):\n if k.startswith('_'):\n continue\n elif k == 'data':\n if data:\n filtered_dict[k] = self.__json_data__\n else:\n filtered_dict[k] = []\n\n elif hasattr(item, '__json__'):\n filtered_dict[k] = item.__json__\n else:\n filtered_dict[k] = item\n\n return filtered_dict"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def json(self) -> Union[dict, list, str, int, float]:\n raise NotImplementedError # pragma: no cover","def data_types(self):","def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:","def toJSType(self):\n return dict(\n name=self.name,\n description=self.description,\n address=self.address,\n lat=self.lat,\n lng=self.lng,\n submitted_by=self.submitted_by,\n certified=self.certified,\n id=str(self.id),\n comments=[c.toJSType() for c in self.comments],\n feature_set=self.feature_set.toJSType()\n )","def as_json(self):","def json_type(self):\n return exclusions.closed()","def json_serialize(self):\n raise NotImplementedError('json_serialize must be overriden')","def json(self):\n return [t.json for t in self.values()]","def data_types():\n\n return ...","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = json_data.__dict__.iteritems()\n else:\n itr = json_data.__dict__.items()\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result;","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def json_default(o):\n if np.isscalar(o) and isinstance(o, np.generic):\n return o.item() # coerce numpy number to pure-python type\n elif isinstance(o, (tuple, set)):\n return list(o)\n else:\n # Dispatch to JSONEncoder so that we see errors straight away\n # if an object can't be serialized.\n return JSON_ENCODER.default(o)","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = json_data.__dict__.iteritems()\n else:\n itr = json_data.__dict__.items()\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result","def test_as_python_types(self):\n obs = _as_python_types(self.metadata_map, self.headers)\n exp = [[2.1, 3.1, 3],\n ['str1', '200', 'string30'],\n [1, 2, 3]]\n self.assertEqual(obs, exp)","def _asdict(self) -> Dict[Text, Any]:\n return self.as_base_types()","def json(self) -> Any:\n return json.loads(self)","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = iter(json_data.__dict__.items())\n else:\n itr = list(json_data.__dict__.items())\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result","def javatype(s):\n return {\n 'int': 'int',\n 'String': 'String',\n 'Long': 'Long',\n 'boolean': 'boolean'\n }.get(s.name, s.name)","def json_serialize(value):\n if value is None or isinstance(value, (int, long, float, basestring, bool)):\n return value\n elif isinstance(value, (list, tuple, set)):\n return [json_serialize(v) for v in value]\n elif isinstance(value, dict):\n for k, v in value.items():\n value[k] = json_serialize(v)\n return value\n # return date/time in isoformat\n elif isinstance(value, (dt.datetime, dt.date, dt.time)):\n return value.isoformat()\n elif isinstance(value, ActiveRecordMixin):\n return _model_to_dict(value)\n else:\n return unicode(value)","def serialize(self):\n dt = super(AnyPin, self).serialize()\n constrainedType = self.activeDataType\n if constrainedType != self.__class__.__name__:\n pinClass = findPinClassByType(constrainedType)\n # serialize with active type's encoder\n dt['value'] = json.dumps(\n self.currentData(), cls=pinClass.jsonEncoderClass())\n dt['currDataType'] = constrainedType\n return dt","def data_types(self):\n return self['data_types']","def get_column_type(cls, **kwargs: Any) -> Any:\n return sqlalchemy.JSON()","def json_friendly(self):","def json(self):\n return capi.to_json(self.ptr)","def _jsonable(self):\n magic_dict = {}\n mman = self.magics_manager\n magics = mman.lsmagic()\n for key, subdict in magics.items():\n d = {}\n magic_dict[key] = d\n for name, obj in subdict.items():\n try:\n classname = obj.__self__.__class__.__name__\n except AttributeError:\n classname = 'Other'\n \n d[name] = classname\n return magic_dict","def data_types(self) -> 'outputs.AwsS3DataConnectorDataTypesResponse':\n return pulumi.get(self, \"data_types\")","def json(self):\n class ExtendedJSONEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\n encoded_object = obj.isoformat()\n else:\n encoded_object = json.JSONEncoder.default(self, obj)\n return encoded_object\n\n obj = {\n 'operation': self.operation,\n 'version': self.version,\n 'language': self.language,\n 'identifiers': self.identifiers,\n 'store_execute': self.store_execute,\n 'status': self.status,\n 'lineage': self.lineage,\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\n 'outputs': self.outputs,\n 'raw': self.raw\n }\n\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)","def jsons(self):\n if self._jsons is None:\n self.make_jsons()\n return self._jsons","def getencdec():\n return (json.JSONEncoder(), json.JSONDecoder())","def json(self):\n return self._json","def _get_types(self):\n\n db = Database()\n self.c_built_ins = list(map(lambda tup: tup[0], db.select_built_types()))\n self.c_built_in_array_types = r'^(' + '|'.join(self.escaped(self.c_built_ins)) + ')\\[[0-9]*\\]'\n self.c_types = list(map(lambda tup: tup[0], db.select_types()))\n self.c_array_types = r'^(' + '|'.join(self.escaped(self.c_types)) + ')\\[[0-9]*\\]'\n db.close_connection()","def json_raw(self):\n return json.dumps(self.data, cls=ComplexEncoder)","def default(self, obj):\n \n if isinstance(obj, np.ndarray):\n return list(obj)\n\n if isinstance(obj, uuid.UUID):\n return str(obj)\n\n if isinstance(obj, datetime.datetime):\n return obj.isoformat()\n \n if isinstance(obj,TPC):\n return obj._so()\n \n # No special handling called for; pass through\n return json.JSONEncoder.default(self, obj)","def default(self, obj):\n numpy_types = (\n np.bool_,\n # np.bytes_, -- python `bytes` class is not json serializable\n # np.complex64, -- python `complex` class is not json serializable\n # np.complex128, -- python `complex` class is not json serializable\n # np.complex256, -- python `complex` class is not json serializable\n # np.datetime64, -- python `datetime.datetime` class is not json serializable\n np.float16,\n np.float32,\n np.float64,\n # np.float128, -- special handling below\n np.int8,\n np.int16,\n np.int32,\n np.int64,\n # np.object_ -- should already be evaluated as python native\n np.str_,\n np.uint8,\n np.uint16,\n np.uint32,\n np.uint64,\n np.void,\n )\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n elif isinstance(obj, numpy_types):\n return obj.item()\n elif isinstance(obj, np.float128):\n return obj.astype(np.float64).item()\n elif isinstance(obj, Decimal):\n return str(obj)\n elif isinstance(obj, datetime):\n return str(obj)\n elif obj is np.ma.masked:\n return str(np.NaN)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder.default(self, obj)","def json(self):\n return self._parsejson(self.raw)","def json(self):\n raise NotImplementedError(\"JSON not specified!\")","def encode_value(x):\n for serializer in string_serializers:\n if isinstance(x, serializer.type):\n return {\"$type\": serializer.name, \"$value\": serializer.to_json(x)}\n\n raise TypeError(type(x)) # pragma: no cover","def _types(cls):\n return {}","def json(self):\n return self.__json","def serialize(cls, obj):\n return json.dumps(obj, cls=CustomTypeEncoder)","def jsonify(obj):\n raise NotImplementedError","def to_json(self):\r\n return {'type': self.type, 'name': self.name}","def type_object(self, json):\n json_prop = _utils.json_prop_finder(json)\n d = {k: (self.switcher(v)) for k, v in json_prop.items()}\n return d","def _convert_to_jsonc(x):\n\n if isinstance(x, dict):\n jsonc_obj = Jsonc()\n # Recursively transform all members of the dict.\n # When converting a dict, we do not convert _name items into private\n # Jsonc members.\n for key, value in x.items():\n jsonc_obj._dict[key] = _convert_to_jsonc(value)\n return jsonc_obj\n elif isinstance(x, list):\n # Recursively transform all members of the list.\n members = []\n for item in x:\n members.append(_convert_to_jsonc(item))\n return members\n else:\n # Return the base object.\n return x","def to_json(self):\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\"utf8\")","def schema_as_json(content_schema: Dict[str, n.SerializableType]) -> n.SerializableType:\n if \"properties\" in content_schema:\n current: Dict[str, Any] = {}\n properties = content_schema[\"properties\"]\n assert isinstance(properties, Dict)\n for prop, options in properties.items():\n if \"type\" not in options:\n current[prop] = \"Any\"\n elif options[\"type\"] == \"object\":\n current[prop] = schema_as_json(options)\n elif options[\"type\"] == \"array\":\n current[prop] = [schema_as_json(options[\"items\"])]\n else:\n current[prop] = options[\"type\"]\n\n return current\n\n if \"items\" in content_schema:\n assert isinstance(content_schema[\"items\"], Dict)\n return [schema_as_json(content_schema[\"items\"])]\n\n return content_schema[\"type\"]","def type_array():\n return []","def change_sets_into_lists(json_like: Any) -> Any:\r\n return json.loads(json.dumps(json_like, cls=SetEncoder))","def dict(self):\n\t\treturn self.json","def _variable_single_types(self):\n return [\n 'Binary',\n 'KeyValue',\n 'String',\n 'TCEntity',\n 'TCEnhancedEntity',\n ]","def _to_base_type(self, value):\n if value is None:\n return ''\n else:\n return value.to_json()","def to_json(self):\n pass","def default(self, o):\n if isinstance(o, np.ndarray):\n return o.tolist()\n if isinstance(o, np.int64):\n return int(o)\n if isinstance(o, np.int32):\n return int(o)\n if isinstance(o, abc.ABCMeta):\n return str(o)\n return json.JSONEncoder.default(self, o)","def jsonify(obj):\n\n t = type(obj)\n\n if t == dict:\n return {jsonify(k):jsonify(v) for k,v in obj.items()}\n if t in [list, set]:\n return [jsonify(x) for x in obj]\n if t == np.ndarray:\n return [jsonify(x) for x in obj.tolist()]\n \n return obj","def convert(self):\n if isinstance(self.json, list):\n return self.iter_list(self.json)\n\n if isinstance(self.json, dict):\n return self.iter_plain(self.json)\n\n return self.json","def to_json(self, *args, **kwargs):\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)","def jsonify(self):\n jsonObject = self.getJsonObject()\n return json.dumps(jsonObject)","def to_json(self):\n return json.dumps(self._asdict())","def _json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def types():\n types = session.query(Type).all()\n return jsonify(types=[t.name for t in types])","def GetJSON(self):\n return json.dumps(self.GetDict())","def to_json(self) :\n return jsonpickle.encode(self)","def object_to_flat_jdata(self, dto):\n jdata = None\n raise Exception(\"Abstract Class. Not Implemented\")\n return jdata","def __json_data__(self):\n if schema.is_dense(self.namespace):\n dense_records = dict()\n for field in Observation._fields:\n dense_records[field] = []\n\n for obs in self.data:\n for key, val in six.iteritems(obs._asdict()):\n dense_records[key].append(serialize_obj(val))\n\n return dense_records\n\n else:\n return [serialize_obj(_) for _ in self.data]","def json(self):\n return json.dumps(self, default=lambda o: o.__dict__,\n sort_keys=True, indent=4)","def default(self, obj):\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n elif isinstance(obj, np.generic):\n return np.asscalar(obj)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def to_jsontype(type):\n typename = type.__name__ if type else None\n renames = {'str': 'string', 'int': 'integer', 'bool': 'bool'}\n if typename in renames:\n typename = renames[typename]\n return typename","def to_json(self) -> JSON:\n pass","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n todecode = dct['__ndarray__'].encode(\"ascii\")\n data = base64.b64decode(todecode)\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_serialize():\n variable = ['hello', 42, [1, 'two'], 'apple']\n print(\"Original {0} - {1}\".format(variable, type(variable)))\n\n #encoding\n encode = json.dumps(variable)\n print(\"Encoded {0} - {1}\".format(encode, type(encode)))\n\n #decoding\n decoded = json.loads(encode)\n print(\"Decoded {0} - {1}\".format(decoded, type(decoded)))","def get_types(self):\n return self.types","def pythonTypeToJSONType(value):\n if isinstance(value, dict): return 'object'\n elif isinstance(value, (list, tuple)): return 'array'\n elif isinstance(value, str): return 'string'\n elif isinstance(value, bool): return 'boolean'\n elif isinstance(value, (int, float)): return 'number'\n elif value is None: return 'null'\n else: return 'unknown'","def _to_json_compat(self):\n # add and __obj_type attribute, so it can be reconstructed\n dic = {\"__obj_type\": self.__class__.__qualname__}\n for attr, typ in self._attrs_to_save.items():\n dic[attr] = typ.to_json_compat(getattr(self, attr))\n return dic","def encode(data):\n target_dict = (type(data).__name__, dataclasses.asdict(data))\n return JsonCoder.encode(target_dict)","def make_json_compatible(obj_):\n if isinstance(obj_, (numbers.Number, str, bool)) or obj_ is None:\n # these are handled as is\n return obj_\n elif isinstance(obj_, collections.Mapping):\n return {\n make_json_compatible(k): make_json_compatible(v)\n for k, v in obj_.items()\n }\n elif isinstance(obj_, (collections.Iterable, collections.Set)):\n return [make_json_compatible(v) for v in obj_]\n elif isinstance(obj_, (datetime.datetime, datetime.date)):\n return obj_.isoformat()\n\n raise NotImplementedError(\"Dont know how to handle objects of type {}\".format(type(obj_)))","def convert_to_json(self):\n return self.__dict__","def tojson(self) -> ty.Text:\n return json.dumps(self.todict())","def json(self, value):\n # respect {None}\n if value is None:\n # by leaving it alone\n return None\n # by default, let the raw value through; the schemata that are not JSON representable\n # must override to provide something suitable\n return value","def to_json(self, value):\r\n try:\r\n result = super(StringOrDate, self).to_json(value)\r\n except:\r\n return value\r\n if result is None:\r\n return value\r\n else:\r\n return result","def jsonable(self, *args, **options):\n d = {}\n for field_name, field in self.schema.normal_fields.items():\n field_val = getattr(self, field_name, None)\n field_val = field.jsonable(self, field_val)\n if field_val is not None:\n d[field_name] = field_val\n\n return d","def json(cls):\n def _json_impl(agg, record):\n if not agg: agg = {}\n for (k, v) in record.items():\n logger.info(\"k: \" + str(k) + \", v: \" + str(v))\n if agg.has_key(k):\n if happy.flow.isIterable(v):\n agg[k].extend(v)\n else:\n agg[k].append(v)\n else:\n if happy.flow.isIterable(v):\n agg[k] = v\n else:\n agg[k] = [v]\n return agg\n return _json_impl","def to_json(self):\n return None","def json_decode_hook(obj: Any) -> Any:\n if 'type(Decimal)' in obj:\n return Decimal(obj['type(Decimal)'])\n elif 'type(pd.Timestamp)' in obj:\n return pd.Timestamp(obj['type(pd.Timestamp)'])\n elif 'type(datetime)' in obj:\n return datetime.fromisoformat(obj['type(datetime)'])\n return obj","def toJSON(self):\n raise NotImplementedError()","def json_serial(obj):\r\n\r\n\t\tif isinstance(obj,(datetime, date)):\r\n\t\t\treturn obj.isoformat()\r\n\t\traise TypeError (\"Type %s not serializable\" % type(obj))","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json(self):\n json_data = {}\n\n for item in self.iteritems():\n try:\n value = item.get_value(json_serializable=True)\n except TypeError:\n value = \"NOT_SERIALIZABLE\"\n item_data = {\n \"tags\": list(item.tags),\n \"value\": value,\n }\n if item.name in json_data:\n json_data[item.name].append(item_data)\n else:\n json_data[item.name] = [item_data]\n\n return json_data","def to_python(self):\n return self._python_type","def toJSON(self) -> str:\r\n try:\r\n _ = json.dumps(self.value)\r\n value = self.value\r\n except (TypeError, OverflowError):\r\n value = {}\r\n value['object_type'] = self.value.__class__.__name__\r\n if isinstance(self.value, RawData):\r\n type_str = '_{}__'.format(value['object_type'])\r\n for key, data in self.value.__dict__.items():\r\n value[key.replace(type_str, '')] = data\r\n else:\r\n type_str = '_{}'.format(value['object_type'])\r\n for key, data in self.value.__dict__.items():\r\n value[key.replace(type_str, '')] = data\r\n\r\n return json.dumps({'object_type' : 'RawData', 'version' : self.version,\r\n 'timestamp' : self.timestamp, 'label' : self.label,\r\n 'value' : value})","def jsonSerial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n\n if isinstance(obj, enum.Enum):\n return obj.value\n\n raise TypeError (\"Type %s not serializable\" % type(obj))","def data_types(self) -> Sequence['outputs.CodelessUiConnectorConfigPropertiesResponseDataTypes']:\n return pulumi.get(self, \"data_types\")","def toJSON(object):\n\treturn json.dumps(object, ensure_ascii=False)","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n elif isinstance(obj, np.generic):\n return np.asscalar(obj)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def get_previewdata(cls, pydata):\n return JsonContentType.dumps(pydata)","def jsonDefault(object):\n return object.__dict__","def tojson(self):\n return json.dumps(self.jsonable())"],"string":"[\n \"def json(self) -> Union[dict, list, str, int, float]:\\n raise NotImplementedError # pragma: no cover\",\n \"def data_types(self):\",\n \"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:\",\n \"def toJSType(self):\\n return dict(\\n name=self.name,\\n description=self.description,\\n address=self.address,\\n lat=self.lat,\\n lng=self.lng,\\n submitted_by=self.submitted_by,\\n certified=self.certified,\\n id=str(self.id),\\n comments=[c.toJSType() for c in self.comments],\\n feature_set=self.feature_set.toJSType()\\n )\",\n \"def as_json(self):\",\n \"def json_type(self):\\n return exclusions.closed()\",\n \"def json_serialize(self):\\n raise NotImplementedError('json_serialize must be overriden')\",\n \"def json(self):\\n return [t.json for t in self.values()]\",\n \"def data_types():\\n\\n return ...\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = json_data.__dict__.iteritems()\\n else:\\n itr = json_data.__dict__.items()\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result;\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def json_default(o):\\n if np.isscalar(o) and isinstance(o, np.generic):\\n return o.item() # coerce numpy number to pure-python type\\n elif isinstance(o, (tuple, set)):\\n return list(o)\\n else:\\n # Dispatch to JSONEncoder so that we see errors straight away\\n # if an object can't be serialized.\\n return JSON_ENCODER.default(o)\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = json_data.__dict__.iteritems()\\n else:\\n itr = json_data.__dict__.items()\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result\",\n \"def test_as_python_types(self):\\n obs = _as_python_types(self.metadata_map, self.headers)\\n exp = [[2.1, 3.1, 3],\\n ['str1', '200', 'string30'],\\n [1, 2, 3]]\\n self.assertEqual(obs, exp)\",\n \"def _asdict(self) -> Dict[Text, Any]:\\n return self.as_base_types()\",\n \"def json(self) -> Any:\\n return json.loads(self)\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = iter(json_data.__dict__.items())\\n else:\\n itr = list(json_data.__dict__.items())\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result\",\n \"def javatype(s):\\n return {\\n 'int': 'int',\\n 'String': 'String',\\n 'Long': 'Long',\\n 'boolean': 'boolean'\\n }.get(s.name, s.name)\",\n \"def json_serialize(value):\\n if value is None or isinstance(value, (int, long, float, basestring, bool)):\\n return value\\n elif isinstance(value, (list, tuple, set)):\\n return [json_serialize(v) for v in value]\\n elif isinstance(value, dict):\\n for k, v in value.items():\\n value[k] = json_serialize(v)\\n return value\\n # return date/time in isoformat\\n elif isinstance(value, (dt.datetime, dt.date, dt.time)):\\n return value.isoformat()\\n elif isinstance(value, ActiveRecordMixin):\\n return _model_to_dict(value)\\n else:\\n return unicode(value)\",\n \"def serialize(self):\\n dt = super(AnyPin, self).serialize()\\n constrainedType = self.activeDataType\\n if constrainedType != self.__class__.__name__:\\n pinClass = findPinClassByType(constrainedType)\\n # serialize with active type's encoder\\n dt['value'] = json.dumps(\\n self.currentData(), cls=pinClass.jsonEncoderClass())\\n dt['currDataType'] = constrainedType\\n return dt\",\n \"def data_types(self):\\n return self['data_types']\",\n \"def get_column_type(cls, **kwargs: Any) -> Any:\\n return sqlalchemy.JSON()\",\n \"def json_friendly(self):\",\n \"def json(self):\\n return capi.to_json(self.ptr)\",\n \"def _jsonable(self):\\n magic_dict = {}\\n mman = self.magics_manager\\n magics = mman.lsmagic()\\n for key, subdict in magics.items():\\n d = {}\\n magic_dict[key] = d\\n for name, obj in subdict.items():\\n try:\\n classname = obj.__self__.__class__.__name__\\n except AttributeError:\\n classname = 'Other'\\n \\n d[name] = classname\\n return magic_dict\",\n \"def data_types(self) -> 'outputs.AwsS3DataConnectorDataTypesResponse':\\n return pulumi.get(self, \\\"data_types\\\")\",\n \"def json(self):\\n class ExtendedJSONEncoder(json.JSONEncoder):\\n def default(self, obj):\\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\\n encoded_object = obj.isoformat()\\n else:\\n encoded_object = json.JSONEncoder.default(self, obj)\\n return encoded_object\\n\\n obj = {\\n 'operation': self.operation,\\n 'version': self.version,\\n 'language': self.language,\\n 'identifiers': self.identifiers,\\n 'store_execute': self.store_execute,\\n 'status': self.status,\\n 'lineage': self.lineage,\\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\\n 'outputs': self.outputs,\\n 'raw': self.raw\\n }\\n\\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)\",\n \"def jsons(self):\\n if self._jsons is None:\\n self.make_jsons()\\n return self._jsons\",\n \"def getencdec():\\n return (json.JSONEncoder(), json.JSONDecoder())\",\n \"def json(self):\\n return self._json\",\n \"def _get_types(self):\\n\\n db = Database()\\n self.c_built_ins = list(map(lambda tup: tup[0], db.select_built_types()))\\n self.c_built_in_array_types = r'^(' + '|'.join(self.escaped(self.c_built_ins)) + ')\\\\[[0-9]*\\\\]'\\n self.c_types = list(map(lambda tup: tup[0], db.select_types()))\\n self.c_array_types = r'^(' + '|'.join(self.escaped(self.c_types)) + ')\\\\[[0-9]*\\\\]'\\n db.close_connection()\",\n \"def json_raw(self):\\n return json.dumps(self.data, cls=ComplexEncoder)\",\n \"def default(self, obj):\\n \\n if isinstance(obj, np.ndarray):\\n return list(obj)\\n\\n if isinstance(obj, uuid.UUID):\\n return str(obj)\\n\\n if isinstance(obj, datetime.datetime):\\n return obj.isoformat()\\n \\n if isinstance(obj,TPC):\\n return obj._so()\\n \\n # No special handling called for; pass through\\n return json.JSONEncoder.default(self, obj)\",\n \"def default(self, obj):\\n numpy_types = (\\n np.bool_,\\n # np.bytes_, -- python `bytes` class is not json serializable\\n # np.complex64, -- python `complex` class is not json serializable\\n # np.complex128, -- python `complex` class is not json serializable\\n # np.complex256, -- python `complex` class is not json serializable\\n # np.datetime64, -- python `datetime.datetime` class is not json serializable\\n np.float16,\\n np.float32,\\n np.float64,\\n # np.float128, -- special handling below\\n np.int8,\\n np.int16,\\n np.int32,\\n np.int64,\\n # np.object_ -- should already be evaluated as python native\\n np.str_,\\n np.uint8,\\n np.uint16,\\n np.uint32,\\n np.uint64,\\n np.void,\\n )\\n if isinstance(obj, np.ndarray):\\n return obj.tolist()\\n elif isinstance(obj, numpy_types):\\n return obj.item()\\n elif isinstance(obj, np.float128):\\n return obj.astype(np.float64).item()\\n elif isinstance(obj, Decimal):\\n return str(obj)\\n elif isinstance(obj, datetime):\\n return str(obj)\\n elif obj is np.ma.masked:\\n return str(np.NaN)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder.default(self, obj)\",\n \"def json(self):\\n return self._parsejson(self.raw)\",\n \"def json(self):\\n raise NotImplementedError(\\\"JSON not specified!\\\")\",\n \"def encode_value(x):\\n for serializer in string_serializers:\\n if isinstance(x, serializer.type):\\n return {\\\"$type\\\": serializer.name, \\\"$value\\\": serializer.to_json(x)}\\n\\n raise TypeError(type(x)) # pragma: no cover\",\n \"def _types(cls):\\n return {}\",\n \"def json(self):\\n return self.__json\",\n \"def serialize(cls, obj):\\n return json.dumps(obj, cls=CustomTypeEncoder)\",\n \"def jsonify(obj):\\n raise NotImplementedError\",\n \"def to_json(self):\\r\\n return {'type': self.type, 'name': self.name}\",\n \"def type_object(self, json):\\n json_prop = _utils.json_prop_finder(json)\\n d = {k: (self.switcher(v)) for k, v in json_prop.items()}\\n return d\",\n \"def _convert_to_jsonc(x):\\n\\n if isinstance(x, dict):\\n jsonc_obj = Jsonc()\\n # Recursively transform all members of the dict.\\n # When converting a dict, we do not convert _name items into private\\n # Jsonc members.\\n for key, value in x.items():\\n jsonc_obj._dict[key] = _convert_to_jsonc(value)\\n return jsonc_obj\\n elif isinstance(x, list):\\n # Recursively transform all members of the list.\\n members = []\\n for item in x:\\n members.append(_convert_to_jsonc(item))\\n return members\\n else:\\n # Return the base object.\\n return x\",\n \"def to_json(self):\\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\\\"utf8\\\")\",\n \"def schema_as_json(content_schema: Dict[str, n.SerializableType]) -> n.SerializableType:\\n if \\\"properties\\\" in content_schema:\\n current: Dict[str, Any] = {}\\n properties = content_schema[\\\"properties\\\"]\\n assert isinstance(properties, Dict)\\n for prop, options in properties.items():\\n if \\\"type\\\" not in options:\\n current[prop] = \\\"Any\\\"\\n elif options[\\\"type\\\"] == \\\"object\\\":\\n current[prop] = schema_as_json(options)\\n elif options[\\\"type\\\"] == \\\"array\\\":\\n current[prop] = [schema_as_json(options[\\\"items\\\"])]\\n else:\\n current[prop] = options[\\\"type\\\"]\\n\\n return current\\n\\n if \\\"items\\\" in content_schema:\\n assert isinstance(content_schema[\\\"items\\\"], Dict)\\n return [schema_as_json(content_schema[\\\"items\\\"])]\\n\\n return content_schema[\\\"type\\\"]\",\n \"def type_array():\\n return []\",\n \"def change_sets_into_lists(json_like: Any) -> Any:\\r\\n return json.loads(json.dumps(json_like, cls=SetEncoder))\",\n \"def dict(self):\\n\\t\\treturn self.json\",\n \"def _variable_single_types(self):\\n return [\\n 'Binary',\\n 'KeyValue',\\n 'String',\\n 'TCEntity',\\n 'TCEnhancedEntity',\\n ]\",\n \"def _to_base_type(self, value):\\n if value is None:\\n return ''\\n else:\\n return value.to_json()\",\n \"def to_json(self):\\n pass\",\n \"def default(self, o):\\n if isinstance(o, np.ndarray):\\n return o.tolist()\\n if isinstance(o, np.int64):\\n return int(o)\\n if isinstance(o, np.int32):\\n return int(o)\\n if isinstance(o, abc.ABCMeta):\\n return str(o)\\n return json.JSONEncoder.default(self, o)\",\n \"def jsonify(obj):\\n\\n t = type(obj)\\n\\n if t == dict:\\n return {jsonify(k):jsonify(v) for k,v in obj.items()}\\n if t in [list, set]:\\n return [jsonify(x) for x in obj]\\n if t == np.ndarray:\\n return [jsonify(x) for x in obj.tolist()]\\n \\n return obj\",\n \"def convert(self):\\n if isinstance(self.json, list):\\n return self.iter_list(self.json)\\n\\n if isinstance(self.json, dict):\\n return self.iter_plain(self.json)\\n\\n return self.json\",\n \"def to_json(self, *args, **kwargs):\\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)\",\n \"def jsonify(self):\\n jsonObject = self.getJsonObject()\\n return json.dumps(jsonObject)\",\n \"def to_json(self):\\n return json.dumps(self._asdict())\",\n \"def _json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def types():\\n types = session.query(Type).all()\\n return jsonify(types=[t.name for t in types])\",\n \"def GetJSON(self):\\n return json.dumps(self.GetDict())\",\n \"def to_json(self) :\\n return jsonpickle.encode(self)\",\n \"def object_to_flat_jdata(self, dto):\\n jdata = None\\n raise Exception(\\\"Abstract Class. Not Implemented\\\")\\n return jdata\",\n \"def __json_data__(self):\\n if schema.is_dense(self.namespace):\\n dense_records = dict()\\n for field in Observation._fields:\\n dense_records[field] = []\\n\\n for obs in self.data:\\n for key, val in six.iteritems(obs._asdict()):\\n dense_records[key].append(serialize_obj(val))\\n\\n return dense_records\\n\\n else:\\n return [serialize_obj(_) for _ in self.data]\",\n \"def json(self):\\n return json.dumps(self, default=lambda o: o.__dict__,\\n sort_keys=True, indent=4)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n return obj.tolist()\\n elif isinstance(obj, np.generic):\\n return np.asscalar(obj)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def to_jsontype(type):\\n typename = type.__name__ if type else None\\n renames = {'str': 'string', 'int': 'integer', 'bool': 'bool'}\\n if typename in renames:\\n typename = renames[typename]\\n return typename\",\n \"def to_json(self) -> JSON:\\n pass\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n todecode = dct['__ndarray__'].encode(\\\"ascii\\\")\\n data = base64.b64decode(todecode)\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_serialize():\\n variable = ['hello', 42, [1, 'two'], 'apple']\\n print(\\\"Original {0} - {1}\\\".format(variable, type(variable)))\\n\\n #encoding\\n encode = json.dumps(variable)\\n print(\\\"Encoded {0} - {1}\\\".format(encode, type(encode)))\\n\\n #decoding\\n decoded = json.loads(encode)\\n print(\\\"Decoded {0} - {1}\\\".format(decoded, type(decoded)))\",\n \"def get_types(self):\\n return self.types\",\n \"def pythonTypeToJSONType(value):\\n if isinstance(value, dict): return 'object'\\n elif isinstance(value, (list, tuple)): return 'array'\\n elif isinstance(value, str): return 'string'\\n elif isinstance(value, bool): return 'boolean'\\n elif isinstance(value, (int, float)): return 'number'\\n elif value is None: return 'null'\\n else: return 'unknown'\",\n \"def _to_json_compat(self):\\n # add and __obj_type attribute, so it can be reconstructed\\n dic = {\\\"__obj_type\\\": self.__class__.__qualname__}\\n for attr, typ in self._attrs_to_save.items():\\n dic[attr] = typ.to_json_compat(getattr(self, attr))\\n return dic\",\n \"def encode(data):\\n target_dict = (type(data).__name__, dataclasses.asdict(data))\\n return JsonCoder.encode(target_dict)\",\n \"def make_json_compatible(obj_):\\n if isinstance(obj_, (numbers.Number, str, bool)) or obj_ is None:\\n # these are handled as is\\n return obj_\\n elif isinstance(obj_, collections.Mapping):\\n return {\\n make_json_compatible(k): make_json_compatible(v)\\n for k, v in obj_.items()\\n }\\n elif isinstance(obj_, (collections.Iterable, collections.Set)):\\n return [make_json_compatible(v) for v in obj_]\\n elif isinstance(obj_, (datetime.datetime, datetime.date)):\\n return obj_.isoformat()\\n\\n raise NotImplementedError(\\\"Dont know how to handle objects of type {}\\\".format(type(obj_)))\",\n \"def convert_to_json(self):\\n return self.__dict__\",\n \"def tojson(self) -> ty.Text:\\n return json.dumps(self.todict())\",\n \"def json(self, value):\\n # respect {None}\\n if value is None:\\n # by leaving it alone\\n return None\\n # by default, let the raw value through; the schemata that are not JSON representable\\n # must override to provide something suitable\\n return value\",\n \"def to_json(self, value):\\r\\n try:\\r\\n result = super(StringOrDate, self).to_json(value)\\r\\n except:\\r\\n return value\\r\\n if result is None:\\r\\n return value\\r\\n else:\\r\\n return result\",\n \"def jsonable(self, *args, **options):\\n d = {}\\n for field_name, field in self.schema.normal_fields.items():\\n field_val = getattr(self, field_name, None)\\n field_val = field.jsonable(self, field_val)\\n if field_val is not None:\\n d[field_name] = field_val\\n\\n return d\",\n \"def json(cls):\\n def _json_impl(agg, record):\\n if not agg: agg = {}\\n for (k, v) in record.items():\\n logger.info(\\\"k: \\\" + str(k) + \\\", v: \\\" + str(v))\\n if agg.has_key(k):\\n if happy.flow.isIterable(v):\\n agg[k].extend(v)\\n else:\\n agg[k].append(v)\\n else:\\n if happy.flow.isIterable(v):\\n agg[k] = v\\n else:\\n agg[k] = [v]\\n return agg\\n return _json_impl\",\n \"def to_json(self):\\n return None\",\n \"def json_decode_hook(obj: Any) -> Any:\\n if 'type(Decimal)' in obj:\\n return Decimal(obj['type(Decimal)'])\\n elif 'type(pd.Timestamp)' in obj:\\n return pd.Timestamp(obj['type(pd.Timestamp)'])\\n elif 'type(datetime)' in obj:\\n return datetime.fromisoformat(obj['type(datetime)'])\\n return obj\",\n \"def toJSON(self):\\n raise NotImplementedError()\",\n \"def json_serial(obj):\\r\\n\\r\\n\\t\\tif isinstance(obj,(datetime, date)):\\r\\n\\t\\t\\treturn obj.isoformat()\\r\\n\\t\\traise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json(self):\\n json_data = {}\\n\\n for item in self.iteritems():\\n try:\\n value = item.get_value(json_serializable=True)\\n except TypeError:\\n value = \\\"NOT_SERIALIZABLE\\\"\\n item_data = {\\n \\\"tags\\\": list(item.tags),\\n \\\"value\\\": value,\\n }\\n if item.name in json_data:\\n json_data[item.name].append(item_data)\\n else:\\n json_data[item.name] = [item_data]\\n\\n return json_data\",\n \"def to_python(self):\\n return self._python_type\",\n \"def toJSON(self) -> str:\\r\\n try:\\r\\n _ = json.dumps(self.value)\\r\\n value = self.value\\r\\n except (TypeError, OverflowError):\\r\\n value = {}\\r\\n value['object_type'] = self.value.__class__.__name__\\r\\n if isinstance(self.value, RawData):\\r\\n type_str = '_{}__'.format(value['object_type'])\\r\\n for key, data in self.value.__dict__.items():\\r\\n value[key.replace(type_str, '')] = data\\r\\n else:\\r\\n type_str = '_{}'.format(value['object_type'])\\r\\n for key, data in self.value.__dict__.items():\\r\\n value[key.replace(type_str, '')] = data\\r\\n\\r\\n return json.dumps({'object_type' : 'RawData', 'version' : self.version,\\r\\n 'timestamp' : self.timestamp, 'label' : self.label,\\r\\n 'value' : value})\",\n \"def jsonSerial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n\\n if isinstance(obj, enum.Enum):\\n return obj.value\\n\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def data_types(self) -> Sequence['outputs.CodelessUiConnectorConfigPropertiesResponseDataTypes']:\\n return pulumi.get(self, \\\"data_types\\\")\",\n \"def toJSON(object):\\n\\treturn json.dumps(object, ensure_ascii=False)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n elif isinstance(obj, np.generic):\\n return np.asscalar(obj)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def get_previewdata(cls, pydata):\\n return JsonContentType.dumps(pydata)\",\n \"def jsonDefault(object):\\n return object.__dict__\",\n \"def tojson(self):\\n return json.dumps(self.jsonable())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6870413","0.62396663","0.609319","0.60719466","0.6052096","0.60452205","0.602536","0.600013","0.58979076","0.5868783","0.5850369","0.5850369","0.5846776","0.5836646","0.5810545","0.58071244","0.5795777","0.57938355","0.5778446","0.5737332","0.57085997","0.57029915","0.56796914","0.56744534","0.5672622","0.56695324","0.5650735","0.56365764","0.56352794","0.56148225","0.56044936","0.5598386","0.5576019","0.5573626","0.5572476","0.5572134","0.5570411","0.55546194","0.554965","0.5549598","0.5544419","0.5527325","0.5497637","0.5481837","0.54617983","0.5455718","0.54537445","0.5441519","0.5438477","0.5422978","0.5421776","0.54188293","0.5406897","0.538655","0.5378664","0.5374225","0.5370456","0.53683245","0.5367554","0.5361509","0.5356691","0.53495526","0.53437763","0.53421044","0.53371245","0.5333939","0.532814","0.53268373","0.53204685","0.531687","0.5305605","0.53044057","0.52909285","0.52735484","0.52673954","0.5261914","0.52434456","0.5243404","0.52409077","0.5236932","0.5235044","0.52290535","0.5226868","0.52186114","0.5218604","0.5215194","0.52137643","0.52137643","0.520383","0.520383","0.520383","0.51965725","0.5191618","0.51875097","0.5186819","0.51861453","0.5184499","0.5177729","0.5175748","0.51708394","0.5170639"],"string":"[\n \"0.6870413\",\n \"0.62396663\",\n \"0.609319\",\n \"0.60719466\",\n \"0.6052096\",\n \"0.60452205\",\n \"0.602536\",\n \"0.600013\",\n \"0.58979076\",\n \"0.5868783\",\n \"0.5850369\",\n \"0.5850369\",\n \"0.5846776\",\n \"0.5836646\",\n \"0.5810545\",\n \"0.58071244\",\n \"0.5795777\",\n \"0.57938355\",\n \"0.5778446\",\n \"0.5737332\",\n \"0.57085997\",\n \"0.57029915\",\n \"0.56796914\",\n \"0.56744534\",\n \"0.5672622\",\n \"0.56695324\",\n \"0.5650735\",\n \"0.56365764\",\n \"0.56352794\",\n \"0.56148225\",\n \"0.56044936\",\n \"0.5598386\",\n \"0.5576019\",\n \"0.5573626\",\n \"0.5572476\",\n \"0.5572134\",\n \"0.5570411\",\n \"0.55546194\",\n \"0.554965\",\n \"0.5549598\",\n \"0.5544419\",\n \"0.5527325\",\n \"0.5497637\",\n \"0.5481837\",\n \"0.54617983\",\n \"0.5455718\",\n \"0.54537445\",\n \"0.5441519\",\n \"0.5438477\",\n \"0.5422978\",\n \"0.5421776\",\n \"0.54188293\",\n \"0.5406897\",\n \"0.538655\",\n \"0.5378664\",\n \"0.5374225\",\n \"0.5370456\",\n \"0.53683245\",\n \"0.5367554\",\n \"0.5361509\",\n \"0.5356691\",\n \"0.53495526\",\n \"0.53437763\",\n \"0.53421044\",\n \"0.53371245\",\n \"0.5333939\",\n \"0.532814\",\n \"0.53268373\",\n \"0.53204685\",\n \"0.531687\",\n \"0.5305605\",\n \"0.53044057\",\n \"0.52909285\",\n \"0.52735484\",\n \"0.52673954\",\n \"0.5261914\",\n \"0.52434456\",\n \"0.5243404\",\n \"0.52409077\",\n \"0.5236932\",\n \"0.5235044\",\n \"0.52290535\",\n \"0.5226868\",\n \"0.52186114\",\n \"0.5218604\",\n \"0.5215194\",\n \"0.52137643\",\n \"0.52137643\",\n \"0.520383\",\n \"0.520383\",\n \"0.520383\",\n \"0.51965725\",\n \"0.5191618\",\n \"0.51875097\",\n \"0.5186819\",\n \"0.51861453\",\n \"0.5184499\",\n \"0.5177729\",\n \"0.5175748\",\n \"0.51708394\",\n \"0.5170639\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94092,"cells":{"query":{"kind":"string","value":"r\"\"\"JSONserialize the observation sequence."},"document":{"kind":"string","value":"def __json_data__(self):\n if schema.is_dense(self.namespace):\n dense_records = dict()\n for field in Observation._fields:\n dense_records[field] = []\n\n for obs in self.data:\n for key, val in six.iteritems(obs._asdict()):\n dense_records[key].append(serialize_obj(val))\n\n return dense_records\n\n else:\n return [serialize_obj(_) for _ in self.data]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def toJson(self):\n text = '{'\n if len(self._sequence):\n for key in self._sequence:\n text += '\"%s\":%s,' % (key,json.dumps(self.dictionary[key]))\n text = text[:-1]\n text += '}'\n\n return text","def as_json(self) -> str:\n return json.dumps(self, cls=_RecordingJSONEncoder)","def json_serialize(self):\n raise NotImplementedError('json_serialize must be overriden')","def json(self):\n class ExtendedJSONEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\n encoded_object = obj.isoformat()\n else:\n encoded_object = json.JSONEncoder.default(self, obj)\n return encoded_object\n\n obj = {\n 'operation': self.operation,\n 'version': self.version,\n 'language': self.language,\n 'identifiers': self.identifiers,\n 'store_execute': self.store_execute,\n 'status': self.status,\n 'lineage': self.lineage,\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\n 'outputs': self.outputs,\n 'raw': self.raw\n }\n\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)","def trajectory_to_json(trajectory: Trajectory) -> str:\n # numpy arrays need to be converted to normal tuples\n return json.dumps(trajectory, cls=NumpyEncoder)","def to_json(self) -> str:\n data_dict = self._to_list_dict()\n return json.dumps(data_dict, indent=4, cls=NumpyEncoder)","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def toJSON(self):\n raise NotImplementedError()","def to_json(self) :\n return jsonpickle.encode(self)","def to_json(self):\n pass","def to_json(self):\n return json.dumps(self.serialize())","def serialize(self) -> str:\n return json.dumps([session.to_dict() for session in self.sessions])","def as_json(self):","def _jsonify(self):\n return self.experiment_record.to_ddb_record()","def to_json(self):\n return json.dumps(self._asdict())","def _toJSON(self):\n\n return json.encode(self.__toJSON())","def serialize(self):\n return json.dumps(self.as_dict())","def to_json(self) -> JSON:\n pass","def to_json(self):\n return json.dumps(self.for_json())","def to_json_string(self):\n\t\treturn json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\r\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def tojson(self):\n return json.dumps(self.jsonable())","def to_json(self) -> str:\n return json.dumps([x.to_dict() for x in self.inputs])","def to_json(self):\n return None","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self) -> None:\n return json.dumps(self.to_dict(), indent=2) + \"\\n\"","def to_str(self) -> str:\n\n return json.dumps(\n {\n \"job_id\": self.job_id,\n \"creation_time\": self.creation_time.isoformat(),\n \"orbit_ids\": self.orbit_ids,\n \"task_ids\": self.task_ids,\n }\n )","def as_json_string(self):\n return json.dumps(self.as_dict(), sort_keys=True)","def do_get_json_sequence():\n sequence_range = 1000000000\n try:\n n = int(request.args.get(\"n\", 1))\n except (TypeError, ValueError):\n return abort(400)\n\n rv = {\n \"sequence_list\": [\n i % sequence_range\n for i in sequence_values(json_sequence, n)\n ]\n }\n\n if n == 1:\n rv[\"sequence_no\"] = rv[\"sequence_list\"][0]\n\n return jsonify(rv)","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + '\\n'","def to_json_string(self):\n return json.dumps(self.to_dict(), indent = 2, sort_keys = True) + \"\\n\"","def to_json_string(self):\n return json.dumps(dict(self), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n\t\treturn json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self):\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(self.to_dict())","def to_json(self) -> str:\n return json.dumps(asdict(self))","def serialize(self) -> bytes:\n return json_dumps(self._to_dict()).encode()","def serialize(self) -> bytes:\n return json_dumps(self._to_dict()).encode()","def serialize(self) -> bytes:\n return json_dumps(self._to_dict()).encode()","def to_json(self):\n return json.dumps(self.to_dict())","def to_json(self):\n return json.dumps(self.to_dict())","def to_json_string(self):\n \n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"","def to_json_string(self) -> None:\n return json.dumps(dataclasses.asdict(self)) + \"\\n\"","def as_json(self):\n return json.dumps(self.as_dict())","def as_json(self):\n return json.dumps(self.as_dict())","def as_json(self):\n return json.dumps(self.as_dict())","def to_json_str(self):\n return simplejson.dumps(self.to_json(), sort_keys=True)","def toJSON(self):\n return {'actLib' : self.nomAct,'comLib' : self.comAct,'id' : self.Id,'equID' : self.EquipementId}","def dumps(self) -> bytes:\n return json.dumps(\n {\n 'training': {'timestamp': self._strftime(self.training.timestamp), 'ordinal': self.training.ordinal},\n 'tuning': {'timestamp': self._strftime(self.tuning.timestamp), 'score': self.tuning.score},\n 'states': [str(s) for s in self.states],\n },\n indent=4,\n ).encode('utf-8')","def to_json(self, *args, **kwargs):\n return json.dumps(self.serialize(), *args, **kwargs)","def to_json(self):\n return json.dumps({\"data\": self._data.tolist(),\n \"header\": self._header.tolist(),\n \"dates\": self._dates.tolist()})","def AsJsonString(self):\n return json.dumps(self.AsDict(), sort_keys=True)","def asJsonString(self):\n return json.dumps(self.asDict(), sort_keys=True)","def tojson(self) -> ty.Text:\n return json.dumps(self.todict())","def serialize(self) -> str:\n return json.dumps(self.__dict__)","def toJson(self):\n return json.dumps(self.toDict())","def toJson(self):\n return json.dumps(self.toDict())","def _serialise(self):\n # TODO (M Foley)\n pass","def to_json(self):\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\"utf8\")","def as_json(self):\n return json.dumps(self.as_dict(), indent=2, sort_keys=False)","def to_json(self):\n return json.dumps(self.dict)","def to_json(self):\n return json.dumps(self.__dict__)","def json(self):\n return {\n \"x\" : self.x,\n \"y\": self.y,\n \"rotation\":self.rotation,\n \"annotations\" : [a.json() for a in self.annotations]\n }","def toJSONString(self):\n JSONObject = self.toDict()\n\n return json.dumps(JSONObject, ensure_ascii=False)","def json(self):\n return json.dumps(self, default=lambda o: o.__dict__,\n sort_keys=True, indent=4)","def to_init_json(self) -> JSON:\n pass","def to_json(self):\n return json.dumps(self.to_dict(), sort_keys=False, indent=2)","def json_raw(self):\n return json.dumps(self.data, cls=ComplexEncoder)","def to_json(self, *args, **kwargs):\n data = self.to_dict()\n\n return json_util.dumps(data)","def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError (\"Type not serializable\")","def serialize(self):","def test_to_json(self):\n r = self.SEQ(\"AAGGCC\", name=\"seq1\")\n got = json.loads(r.to_json())\n expect = {\n \"name\": \"seq1\",\n \"seq\": \"AAGGCC\",\n \"moltype\": r.moltype.label,\n \"info\": None,\n \"type\": get_object_provenance(r),\n \"version\": __version__,\n }\n self.assertEqual(got, expect)","def to_json(self):\n return json.dumps(self.to_dict(), sort_keys=True)"],"string":"[\n \"def toJson(self):\\n text = '{'\\n if len(self._sequence):\\n for key in self._sequence:\\n text += '\\\"%s\\\":%s,' % (key,json.dumps(self.dictionary[key]))\\n text = text[:-1]\\n text += '}'\\n\\n return text\",\n \"def as_json(self) -> str:\\n return json.dumps(self, cls=_RecordingJSONEncoder)\",\n \"def json_serialize(self):\\n raise NotImplementedError('json_serialize must be overriden')\",\n \"def json(self):\\n class ExtendedJSONEncoder(json.JSONEncoder):\\n def default(self, obj):\\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\\n encoded_object = obj.isoformat()\\n else:\\n encoded_object = json.JSONEncoder.default(self, obj)\\n return encoded_object\\n\\n obj = {\\n 'operation': self.operation,\\n 'version': self.version,\\n 'language': self.language,\\n 'identifiers': self.identifiers,\\n 'store_execute': self.store_execute,\\n 'status': self.status,\\n 'lineage': self.lineage,\\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\\n 'outputs': self.outputs,\\n 'raw': self.raw\\n }\\n\\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)\",\n \"def trajectory_to_json(trajectory: Trajectory) -> str:\\n # numpy arrays need to be converted to normal tuples\\n return json.dumps(trajectory, cls=NumpyEncoder)\",\n \"def to_json(self) -> str:\\n data_dict = self._to_list_dict()\\n return json.dumps(data_dict, indent=4, cls=NumpyEncoder)\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def toJSON(self):\\n raise NotImplementedError()\",\n \"def to_json(self) :\\n return jsonpickle.encode(self)\",\n \"def to_json(self):\\n pass\",\n \"def to_json(self):\\n return json.dumps(self.serialize())\",\n \"def serialize(self) -> str:\\n return json.dumps([session.to_dict() for session in self.sessions])\",\n \"def as_json(self):\",\n \"def _jsonify(self):\\n return self.experiment_record.to_ddb_record()\",\n \"def to_json(self):\\n return json.dumps(self._asdict())\",\n \"def _toJSON(self):\\n\\n return json.encode(self.__toJSON())\",\n \"def serialize(self):\\n return json.dumps(self.as_dict())\",\n \"def to_json(self) -> JSON:\\n pass\",\n \"def to_json(self):\\n return json.dumps(self.for_json())\",\n \"def to_json_string(self):\\n\\t\\treturn json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\r\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def tojson(self):\\n return json.dumps(self.jsonable())\",\n \"def to_json(self) -> str:\\n return json.dumps([x.to_dict() for x in self.inputs])\",\n \"def to_json(self):\\n return None\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self) -> None:\\n return json.dumps(self.to_dict(), indent=2) + \\\"\\\\n\\\"\",\n \"def to_str(self) -> str:\\n\\n return json.dumps(\\n {\\n \\\"job_id\\\": self.job_id,\\n \\\"creation_time\\\": self.creation_time.isoformat(),\\n \\\"orbit_ids\\\": self.orbit_ids,\\n \\\"task_ids\\\": self.task_ids,\\n }\\n )\",\n \"def as_json_string(self):\\n return json.dumps(self.as_dict(), sort_keys=True)\",\n \"def do_get_json_sequence():\\n sequence_range = 1000000000\\n try:\\n n = int(request.args.get(\\\"n\\\", 1))\\n except (TypeError, ValueError):\\n return abort(400)\\n\\n rv = {\\n \\\"sequence_list\\\": [\\n i % sequence_range\\n for i in sequence_values(json_sequence, n)\\n ]\\n }\\n\\n if n == 1:\\n rv[\\\"sequence_no\\\"] = rv[\\\"sequence_list\\\"][0]\\n\\n return jsonify(rv)\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + '\\\\n'\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent = 2, sort_keys = True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(dict(self), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n\\t\\treturn json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self):\\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(self.to_dict())\",\n \"def to_json(self) -> str:\\n return json.dumps(asdict(self))\",\n \"def serialize(self) -> bytes:\\n return json_dumps(self._to_dict()).encode()\",\n \"def serialize(self) -> bytes:\\n return json_dumps(self._to_dict()).encode()\",\n \"def serialize(self) -> bytes:\\n return json_dumps(self._to_dict()).encode()\",\n \"def to_json(self):\\n return json.dumps(self.to_dict())\",\n \"def to_json(self):\\n return json.dumps(self.to_dict())\",\n \"def to_json_string(self):\\n \\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \\\"\\\\n\\\"\",\n \"def to_json_string(self) -> None:\\n return json.dumps(dataclasses.asdict(self)) + \\\"\\\\n\\\"\",\n \"def as_json(self):\\n return json.dumps(self.as_dict())\",\n \"def as_json(self):\\n return json.dumps(self.as_dict())\",\n \"def as_json(self):\\n return json.dumps(self.as_dict())\",\n \"def to_json_str(self):\\n return simplejson.dumps(self.to_json(), sort_keys=True)\",\n \"def toJSON(self):\\n return {'actLib' : self.nomAct,'comLib' : self.comAct,'id' : self.Id,'equID' : self.EquipementId}\",\n \"def dumps(self) -> bytes:\\n return json.dumps(\\n {\\n 'training': {'timestamp': self._strftime(self.training.timestamp), 'ordinal': self.training.ordinal},\\n 'tuning': {'timestamp': self._strftime(self.tuning.timestamp), 'score': self.tuning.score},\\n 'states': [str(s) for s in self.states],\\n },\\n indent=4,\\n ).encode('utf-8')\",\n \"def to_json(self, *args, **kwargs):\\n return json.dumps(self.serialize(), *args, **kwargs)\",\n \"def to_json(self):\\n return json.dumps({\\\"data\\\": self._data.tolist(),\\n \\\"header\\\": self._header.tolist(),\\n \\\"dates\\\": self._dates.tolist()})\",\n \"def AsJsonString(self):\\n return json.dumps(self.AsDict(), sort_keys=True)\",\n \"def asJsonString(self):\\n return json.dumps(self.asDict(), sort_keys=True)\",\n \"def tojson(self) -> ty.Text:\\n return json.dumps(self.todict())\",\n \"def serialize(self) -> str:\\n return json.dumps(self.__dict__)\",\n \"def toJson(self):\\n return json.dumps(self.toDict())\",\n \"def toJson(self):\\n return json.dumps(self.toDict())\",\n \"def _serialise(self):\\n # TODO (M Foley)\\n pass\",\n \"def to_json(self):\\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\\\"utf8\\\")\",\n \"def as_json(self):\\n return json.dumps(self.as_dict(), indent=2, sort_keys=False)\",\n \"def to_json(self):\\n return json.dumps(self.dict)\",\n \"def to_json(self):\\n return json.dumps(self.__dict__)\",\n \"def json(self):\\n return {\\n \\\"x\\\" : self.x,\\n \\\"y\\\": self.y,\\n \\\"rotation\\\":self.rotation,\\n \\\"annotations\\\" : [a.json() for a in self.annotations]\\n }\",\n \"def toJSONString(self):\\n JSONObject = self.toDict()\\n\\n return json.dumps(JSONObject, ensure_ascii=False)\",\n \"def json(self):\\n return json.dumps(self, default=lambda o: o.__dict__,\\n sort_keys=True, indent=4)\",\n \"def to_init_json(self) -> JSON:\\n pass\",\n \"def to_json(self):\\n return json.dumps(self.to_dict(), sort_keys=False, indent=2)\",\n \"def json_raw(self):\\n return json.dumps(self.data, cls=ComplexEncoder)\",\n \"def to_json(self, *args, **kwargs):\\n data = self.to_dict()\\n\\n return json_util.dumps(data)\",\n \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError (\\\"Type not serializable\\\")\",\n \"def serialize(self):\",\n \"def test_to_json(self):\\n r = self.SEQ(\\\"AAGGCC\\\", name=\\\"seq1\\\")\\n got = json.loads(r.to_json())\\n expect = {\\n \\\"name\\\": \\\"seq1\\\",\\n \\\"seq\\\": \\\"AAGGCC\\\",\\n \\\"moltype\\\": r.moltype.label,\\n \\\"info\\\": None,\\n \\\"type\\\": get_object_provenance(r),\\n \\\"version\\\": __version__,\\n }\\n self.assertEqual(got, expect)\",\n \"def to_json(self):\\n return json.dumps(self.to_dict(), sort_keys=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6776155","0.6708074","0.64370257","0.6387373","0.6361001","0.63252026","0.6286282","0.6286282","0.62822974","0.62637067","0.6223078","0.6172028","0.6171983","0.6165653","0.6129194","0.6123974","0.6119053","0.61068493","0.61020386","0.6077638","0.60771555","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6059846","0.6030719","0.60118276","0.601135","0.60062575","0.59953415","0.59936476","0.599176","0.5990922","0.59806764","0.5977615","0.5973635","0.59696","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59668547","0.59552413","0.59552413","0.59552413","0.59552413","0.59552413","0.59552413","0.59552413","0.59552413","0.59552413","0.59539896","0.5946794","0.5946794","0.5946794","0.59408057","0.59408057","0.59332323","0.5917519","0.5904497","0.5904497","0.5904497","0.5903777","0.5895692","0.58921176","0.5884811","0.5873344","0.58650565","0.58644366","0.58635074","0.58609015","0.5859313","0.5859313","0.58498544","0.5846004","0.58444935","0.5843334","0.58408976","0.5818288","0.58070916","0.5804529","0.57960325","0.57927984","0.5791765","0.5783945","0.5778863","0.5760975","0.5753914","0.57517576"],"string":"[\n \"0.6776155\",\n \"0.6708074\",\n \"0.64370257\",\n \"0.6387373\",\n \"0.6361001\",\n \"0.63252026\",\n \"0.6286282\",\n \"0.6286282\",\n \"0.62822974\",\n \"0.62637067\",\n \"0.6223078\",\n \"0.6172028\",\n \"0.6171983\",\n \"0.6165653\",\n \"0.6129194\",\n \"0.6123974\",\n \"0.6119053\",\n \"0.61068493\",\n \"0.61020386\",\n \"0.6077638\",\n \"0.60771555\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6059846\",\n \"0.6030719\",\n \"0.60118276\",\n \"0.601135\",\n \"0.60062575\",\n \"0.59953415\",\n \"0.59936476\",\n \"0.599176\",\n \"0.5990922\",\n \"0.59806764\",\n \"0.5977615\",\n \"0.5973635\",\n \"0.59696\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59668547\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59552413\",\n \"0.59539896\",\n \"0.5946794\",\n \"0.5946794\",\n \"0.5946794\",\n \"0.59408057\",\n \"0.59408057\",\n \"0.59332323\",\n \"0.5917519\",\n \"0.5904497\",\n \"0.5904497\",\n \"0.5904497\",\n \"0.5903777\",\n \"0.5895692\",\n \"0.58921176\",\n \"0.5884811\",\n \"0.5873344\",\n \"0.58650565\",\n \"0.58644366\",\n \"0.58635074\",\n \"0.58609015\",\n \"0.5859313\",\n \"0.5859313\",\n \"0.58498544\",\n \"0.5846004\",\n \"0.58444935\",\n \"0.5843334\",\n \"0.58408976\",\n \"0.5818288\",\n \"0.58070916\",\n \"0.5804529\",\n \"0.57960325\",\n \"0.57927984\",\n \"0.5791765\",\n \"0.5783945\",\n \"0.5778863\",\n \"0.5760975\",\n \"0.5753914\",\n \"0.57517576\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94093,"cells":{"query":{"kind":"string","value":"Create an AnnotationMetadata object."},"document":{"kind":"string","value":"def __init__(self, curator=None, version='', corpus='', annotator=None,\n annotation_tools='', annotation_rules='', validation='',\n data_source=''):\n super(AnnotationMetadata, self).__init__()\n\n if curator is None:\n curator = Curator()\n\n if annotator is None:\n annotator = JObject()\n\n self.curator = Curator(**curator)\n self.annotator = JObject(**annotator)\n\n self.version = version\n self.corpus = corpus\n self.annotation_tools = annotation_tools\n self.annotation_rules = annotation_rules\n self.validation = validation\n self.data_source = data_source"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()","def RDFAnnotationParser_createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()","def create_annotation(timestamp, value, host):\n return zipkin_core.Annotation(timestamp=timestamp, value=value, host=host)","def metadata(self, metadata):\n return Metadata(metadata)","def __init__(self, namespace, data=None, annotation_metadata=None,\n sandbox=None, time=0, duration=None):\n\n super(Annotation, self).__init__()\n\n if annotation_metadata is None:\n annotation_metadata = AnnotationMetadata()\n\n self.annotation_metadata = AnnotationMetadata(**annotation_metadata)\n\n self.namespace = namespace\n\n self.data = SortedKeyList(key=self._key)\n\n if data is not None:\n if isinstance(data, dict):\n self.append_columns(data)\n else:\n self.append_records(data)\n\n if sandbox is None:\n sandbox = Sandbox()\n\n self.sandbox = Sandbox(**sandbox)\n\n self.time = time\n self.duration = duration","def add_annotation_metadata(self, value):\n\n if self._properties is None:\n self._properties = LabelProperties()\n self._properties.parameters.insert_new_element(value)","def _get_metadata(self) -> Metadata:\n manifest = self._get_manifest()\n\n return Metadata(**manifest[\"metadata\"])","def create_metadata(self) -> _metadata_fb.CustomMetadataT:\n custom_metadata_field = _metadata_fb.CustomMetadataT()\n custom_metadata_field.name = self.name\n custom_metadata_field.data = b'\\x01\\x02'\n return custom_metadata_field","def Metadata(name, namespace, annotations=None, labels=None):\n metadata = run_v1_messages.ObjectMeta()\n metadata.name = name\n metadata.namespace = namespace\n\n if annotations:\n ann = run_v1_messages.ObjectMeta.AnnotationsValue()\n for k, v in annotations.items():\n p = run_v1_messages.ObjectMeta.AnnotationsValue.AdditionalProperty()\n p.key = k\n p.value = v\n ann.additionalProperties.append(p)\n metadata.annotations = ann\n\n if labels:\n lab = run_v1_messages.ObjectMeta.LabelsValue()\n for k, v in labels.items():\n l = run_v1_messages.ObjectMeta.LabelsValue.AdditionalProperty()\n l.key = k\n l.value = v\n lab.additionalProperties.append(l)\n metadata.labels = lab\n\n return metadata","def __new__(\n mcs, # type: Type[_AnnotatedObjectMeta]\n name, # type: str\n bases, # type: List[type]\n attrs, # type: Dict[str, Any]\n **kwargs # type: Dict[str, Any]\n ):\n # type: (...) -> type\n annotations = attrs.get(\"__annotations__\", {})\n use_comment_type_hints = (\n not annotations and attrs.get(\"__module__\") != __name__\n )\n if use_comment_type_hints:\n frame_source = _get_class_frame_source(name)\n annotations = get_type_hints(*frame_source)\n names = list(attrs) + list(annotations)\n typed_attrs = {}\n for attr in names:\n typed_attrs[attr] = attrs.get(attr)\n if _is_propertyable(names, attrs, annotations, attr):\n private_attr = \"__{}\".format(attr)\n if attr in attrs:\n typed_attrs[private_attr] = attrs[attr]\n type_ = (\n Optional[annotations[attr]]\n if not use_comment_type_hints\n and attr in attrs\n and attrs[attr] is None\n else annotations[attr]\n )\n typed_attrs[attr] = property(\n _get_fget(attr, private_attr, type_),\n get_fset(attr, private_attr, type_),\n )\n properties = [\n attr\n for attr in annotations\n if _is_propertyable(names, attrs, annotations, attr)\n ]\n typed_attrs[\"_tp__typed_properties\"] = properties\n typed_attrs[\"_tp__required_typed_properties\"] = [\n attr\n for attr in properties\n if (\n attr not in attrs\n or attrs[attr] is None\n and use_comment_type_hints\n )\n and NoneType not in getattr(annotations[attr], \"__args__\", ())\n ]\n return super(_AnnotatedObjectMeta, mcs).__new__( # type: ignore\n mcs, name, bases, typed_attrs, **kwargs\n )","def create(\n cls,\n model_buffer: bytearray,\n input_norm_mean: List[float],\n input_norm_std: List[float],\n labels: metadata_writer.Labels,\n score_calibration: Optional[metadata_writer.ScoreCalibration] = None\n ) -> \"MetadataWriter\":\n writer = metadata_writer.MetadataWriter(model_buffer)\n writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)\n writer.add_image_input(input_norm_mean, input_norm_std)\n writer.add_classification_output(labels, score_calibration)\n return cls(writer)","def define_metadata(cls, pcm):\n raise NotImplementedError()","def gen_metadata(args):\n with open(args.bibfile) as bibfile:\n bib_db = BibTexParser(common_strings=True).parse_file(bibfile)\n entries = sorted(list(bib_db.entries),\n key=lambda x: x['year'], reverse=True)\n list([update_file(entry) for entry in entries])\n annotations = [entry_to_annotation(entry, args.PI) for entry in entries]\n stream = open(args.metadata, 'w')\n yaml.dump(annotations, stream, width=192, default_flow_style=False)\n stream.close()","def create_annotation(raw):\n annotation_pandas = pd.DataFrame(columns=[\"onset\", \"duration\", \"description\"])\n for idx, event in enumerate(raw.annotations):\n annotation_pandas.loc[idx] = [\n event[\"onset\"],\n event[\"duration\"],\n event[\"description\"],\n ]\n return annotation_pandas","def _getMetadata(self):\n anno_key = 'collective.sendaspdf'\n annotations = IAnnotations(self)\n\n metadata = annotations.get(anno_key,\n None)\n if metadata is None:\n annotations[anno_key] = PersistentDict()\n metadata = annotations[anno_key]\n\n return metadata","def new():\n doc = coerce_to(int, request.form['doc'])\n page = coerce_to(int, request.form['page'])\n posx = coerce_to(int, request.form['posx'])\n posy = coerce_to(int, request.form['posy'])\n width = coerce_to(int, request.form['width'])\n height = coerce_to(int, request.form['height'])\n text = request.form['value']\n state = Annotation.STATE_OPEN\n if 'state' in request.form:\n state = Annotation.state_decode(request.form['state'])\n doc_obj = Document.query.get(doc)\n if doc_obj is None:\n return BadRequest()\n if not current_user.can_annotate(doc):\n return Unauthorized()\n user = current_user.id\n ann = Annotation(doc, page, posx, posy, width, height, text, user,\n state=state)\n db.session.add(ann)\n db.session.commit()\n return jsonify(id=ann.id)","def setup_annotation(self):\n annotation = self.ax_fig.annotate(\n '', xy=(0, 0), ha='left',\n xytext=(-20, 20), textcoords='offset points', va='bottom',\n bbox=dict(\n boxstyle='round,pad=0.5', fc='yellow', alpha=0.2),\n arrowprops=dict(\n arrowstyle='->', connectionstyle='arc3,rad=0'))\n return annotation","def from_json(json):\n result = Annotation()\n if \"projectId\" in json:\n result.project_id = json[\"projectId\"]\n if \"imageId\" in json:\n result.image_id = json[\"imageId\"]\n if \"shapes\" in json:\n result.shapes = json[\"shapes\"]\n if \"frameNum\" in json:\n result.frame_num = json[\"frameNum\"]\n if \"_id\" in json:\n result.id = str(json[\"_id\"])\n return result","def _create_output_metadata(self):\n\n # Create output info.\n output_meta = _metadata_fb.TensorMetadataT()\n output_meta.name = \"probability\"\n output_meta.description = \"Probabilities of labels respectively.\"\n output_meta.content = _metadata_fb.ContentT()\n output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()\n output_meta.content.contentPropertiesType = (\n _metadata_fb.ContentProperties.FeatureProperties)\n label_file = _metadata_fb.AssociatedFileT()\n label_file.name = os.path.basename(self.model_info.label_file)\n label_file.description = (\"Labels for classification categories.\")\n label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS\n output_meta.associatedFiles = [label_file]\n return [output_meta]","def annotate(self, annotation_=None):\n # Important: Need a copy, not the reference to the original object\n annotation_ = copy.deepcopy(annotation_)\n annotation_.annotate(self, from_dataset=True)\n history_record = annotation_.create_history_record()\n self.annotations.append(history_record)\n self._append_task(kind='annotation', task=history_record)","def _parse_anno_info(self, annotations):\n gt_bboxes, gt_bboxes_ignore = [], []\n gt_masks, gt_masks_ignore = [], []\n gt_labels = []\n for ann in annotations:\n if ann.get('iscrowd', False):\n gt_bboxes_ignore.append(ann['bbox'])\n gt_masks_ignore.append(ann.get('segmentation', None))\n else:\n gt_bboxes.append(ann['bbox'])\n gt_labels.append(ann['category_id'])\n gt_masks.append(ann.get('segmentation', None))\n if gt_bboxes:\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\n gt_labels = np.array(gt_labels, dtype=np.int64)\n else:\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\n gt_labels = np.array([], dtype=np.int64)\n\n if gt_bboxes_ignore:\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n else:\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n\n ann = dict(\n bboxes=gt_bboxes,\n labels=gt_labels,\n bboxes_ignore=gt_bboxes_ignore,\n masks_ignore=gt_masks_ignore,\n masks=gt_masks)\n\n return ann","def build_metadata(meta):\n\n ret = copy.copy(meta) if meta else dict()\n\n ret['name'] = meta.get('name', '')\n\n if 'index' in meta:\n if isinstance(meta.get('index'), str):\n ret['index'] = Index(meta.get('index'))\n elif isinstance(meta.get('index'), Index):\n ret['index'] = meta.get('index')\n\n ret['utc'] = True\n if 'utc' in meta and isinstance(meta.get('utc'), bool):\n ret['utc'] = meta.get('utc')\n\n return pmap(ret)","def metadata(self) -> \"ObjectMeta\":\n return typing.cast(\n \"ObjectMeta\",\n self._properties.get(\"metadata\"),\n )","def to_music21_metadata(metadata: Metadata) -> M21MetaData:\n meta = M21MetaData()\n\n # Title is usually stored in movement-title. See\n # https://www.musicxml.com/tutorial/file-structure/score-header-entity/\n if metadata.title:\n meta.movementName = metadata.title\n\n if metadata.copyright:\n meta.copyright = Copyright(metadata.copyright)\n for creator in metadata.creators:\n meta.addContributor(Contributor(name=creator))\n return meta","def __metadata__(self):\n raise NotImplementedError","def _convert_annotations(self, ast):\n self.annotations = IDLAnnotations(ast)","def metadata(self) -> interface.Metadata:\n return cast(interface.Metadata, self._interfaces[interface.Metadata])","def annotations(self):\n annotations = {\"date\": self.date_trunc(\"usage_start\")}\n # { query_param: database_field_name }\n fields = self._mapper.provider_map.get(\"annotations\")\n for q_param, db_field in fields.items():\n annotations[q_param] = F(db_field)\n if (\n \"project\" in self.parameters.parameters.get(\"group_by\", {})\n or \"and:project\" in self.parameters.parameters.get(\"group_by\", {})\n or \"or:project\" in self.parameters.parameters.get(\"group_by\", {})\n ):\n annotations[\"project\"] = F(\"namespace\")\n\n return annotations","def get_annotation_object(annots_path):\n\n lines = annots_path.read_text().split('\\n')\n \n annots = []\n for line in lines:\n if not line:\n continue\n \n annot = {}\n splot = line.split(' ')\n annot['class_id'] = int(splot[0])\n annot['center_x'] = float(splot[1])\n annot['center_y'] = float(splot[2])\n annot['width'] = float(splot[3])\n annot['height'] = float(splot[4])\n annot['class_name'] = splot[-1]\n \n if splot[5].startswith('px:'):\n px = splot[5].strip('px:')\n py = splot[6].strip('py:')\n \n if not (px == 'auto'):\n px = px.split(',')\n py = py.split(',')\n annot['px'] = [float(x) for x in px]\n annot['py'] = [float(x) for x in py]\n else:\n annot['px'] = 'auto'\n annot['py'] = 'auto'\n \n elif splot[5].startswith('conf:'):\n annot['conf'] = float(splot[5].split(':')[1])\n\n annots.append(annot)\n \n return annots","def getAnnotation(self, *args):\n return _libsbml.SBase_getAnnotation(self, *args)","def annotate(self, annotation):\n self._data = self._data.annotate(**annotation)","def metadata_create():\n binding = {}\n url = 'https://upload.twitter.com/1.1/media/metadata/create.json'\n return _TwitterRequest('POST',\n url,\n 'rest:media',\n 'post-media-metadata-create',\n binding)","def __init__(self, annotation_file=None, N=10, kind=\"official\"):\n\n # load dataset\n self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\n self.N = N\n self.kind = kind\n if not annotation_file == None:\n print(\"loading annotations into memory...\")\n tic = time.time()\n with open(annotation_file, \"r\") as f:\n dataset = json.load(f)\n assert (\n type(dataset) == dict\n ), \"annotation file format {} not supported\".format(type(dataset))\n print(\"Done (t={:0.2f}s)\".format(time.time() - tic))\n self.dataset = dataset\n self.createIndex()\n\n if getattr(pycocotools, \"__version__\", \"0\") >= \"12.0.2\":\n warnings.warn(\n 'mmpycocotools is deprecated. Please install official pycocotools by \"pip install pycocotools\"', # noqa: E501\n UserWarning,\n )\n self.img_ann_map = self.imgToAnns\n self.cat_img_map = self.catToImgs","def get_marker(annotation, marker_duration, video_clip):\n position = annotation[\"position\"]\n marker = marker_image.copy()\n marker = marker.set_position(get_marker_absolute_pos(position, video_clip))\n marker = marker.set_start(float(annotation[\"time\"]) / 1000.0)\n marker = marker.set_duration(marker_duration)\n\n return marker","def _create_typed_object_meta(get_fset):\n # type: (Callable[[str, str, Type[_T]], Callable[[_T], None]]) -> type\n\n def _get_fget(attr, private_attr, type_):\n # type: (str, str, Type[_T]) -> Callable[[], Any]\n \"\"\"Create a property getter method for an attribute.\n\n Args:\n attr: The name of the attribute that will be retrieved.\n private_attr: The name of the attribute that will store any data\n related to the attribute.\n type_: The annotated type defining what values can be stored in the\n attribute.\n\n Returns:\n A function that takes self and retrieves the private attribute from\n self.\n \"\"\"\n\n def _fget(self):\n # type: (...) -> Any\n \"\"\"Get attribute from self without revealing the private name.\"\"\"\n try:\n return getattr(self, private_attr)\n except AttributeError:\n raise AttributeError(\n \"'{}' object has no attribute '{}'\".format(\n _get_type_name(type_), attr\n )\n )\n\n return _fget\n\n class _AnnotatedObjectMeta(type):\n \"\"\"A metaclass that reads annotations from a class definition.\"\"\"\n\n def __new__(\n mcs, # type: Type[_AnnotatedObjectMeta]\n name, # type: str\n bases, # type: List[type]\n attrs, # type: Dict[str, Any]\n **kwargs # type: Dict[str, Any]\n ):\n # type: (...) -> type\n \"\"\"Create class objs that replaces annotated attrs with properties.\n\n Args:\n mcs: The class object being created.\n name: The name of the class to create.\n bases: The list of all base classes for the new class.\n attrs: The list of all attributes for the new class from the\n definition.\n\n Returns:\n A new class instance with the expected base classes and\n attributes, but with annotated, public, non-constant,\n non-method attributes replaced by property objects that\n validate against the annotated type.\n \"\"\"\n annotations = attrs.get(\"__annotations__\", {})\n use_comment_type_hints = (\n not annotations and attrs.get(\"__module__\") != __name__\n )\n if use_comment_type_hints:\n frame_source = _get_class_frame_source(name)\n annotations = get_type_hints(*frame_source)\n names = list(attrs) + list(annotations)\n typed_attrs = {}\n for attr in names:\n typed_attrs[attr] = attrs.get(attr)\n if _is_propertyable(names, attrs, annotations, attr):\n private_attr = \"__{}\".format(attr)\n if attr in attrs:\n typed_attrs[private_attr] = attrs[attr]\n type_ = (\n Optional[annotations[attr]]\n if not use_comment_type_hints\n and attr in attrs\n and attrs[attr] is None\n else annotations[attr]\n )\n typed_attrs[attr] = property(\n _get_fget(attr, private_attr, type_),\n get_fset(attr, private_attr, type_),\n )\n properties = [\n attr\n for attr in annotations\n if _is_propertyable(names, attrs, annotations, attr)\n ]\n typed_attrs[\"_tp__typed_properties\"] = properties\n typed_attrs[\"_tp__required_typed_properties\"] = [\n attr\n for attr in properties\n if (\n attr not in attrs\n or attrs[attr] is None\n and use_comment_type_hints\n )\n and NoneType not in getattr(annotations[attr], \"__args__\", ())\n ]\n return super(_AnnotatedObjectMeta, mcs).__new__( # type: ignore\n mcs, name, bases, typed_attrs, **kwargs\n )\n\n return _AnnotatedObjectMeta","def test_init_with_custom_metadata(isolated_runner, project_init, template):\n data, commands = project_init\n\n metadata = {\n \"@id\": \"https://example.com/annotation1\",\n \"@type\": \"https://schema.org/specialType\",\n \"https://schema.org/specialProperty\": \"some_unique_value\",\n }\n metadata_path = Path(\"metadata.json\")\n metadata_path.write_text(json.dumps(metadata))\n\n # create the project\n new_project = Path(data[\"test_project\"])\n assert not new_project.exists()\n result = isolated_runner.invoke(cli, commands[\"init_test\"] + commands[\"id\"] + [\"--metadata\", str(metadata_path)])\n assert 0 == result.exit_code\n\n database = Database.from_path(new_project / \".renku\" / \"metadata\")\n project = database.get(\"project\")\n\n assert metadata == project.annotations[0].body","def annotate(self, **annotations):\n return AnnotatedQuery(self, annotations)","def __new__(cls, account=None, credential=None, project=None,\n attributes=None, scopes=None):\n if not attributes:\n attributes = {}\n if not scopes:\n scopes = config.CLOUDSDK_SCOPES\n return super(MetadataOptions, cls).__new__(\n cls, account=account, credential=credential, project=project,\n attributes=attributes, scopes=scopes)","def generate_metadata(self) -> None:\n if self.output_anchor_metadata is None:\n output_record_info = self.engine.create_record_info()\n else:\n output_record_info = self.output_anchor_metadata\n\n if self.output_anchor.record_info is None:\n output_record_info.add_field(\"optional_value\", Sdk.FieldType.float)\n self.output_anchor_metadata = output_record_info\n\n self.output_anchor.record_info = self.output_anchor_metadata\n self.push_all_metadata()","def get_assessment_metadata(self):\n return Metadata(**settings.METADATA['assessment_id'])","def from_metadata(cls, meta):\r\n return Package(\r\n name=meta[\"Name\"],\r\n version=meta[\"Version\"],\r\n homepage=meta[\"Home-page\"],\r\n license_name=meta[\"License\"],\r\n source_url=meta[\"Project-URL\"],\r\n package_url=purl_extractor.get_purl_from_meta_dict(meta),\r\n )","def __init__(self, annotations=None, file_metadata=None, sandbox=None):\n super(JAMS, self).__init__()\n\n if file_metadata is None:\n file_metadata = FileMetadata()\n\n if sandbox is None:\n sandbox = Sandbox()\n\n self.annotations = AnnotationArray(annotations=annotations)\n\n self.file_metadata = FileMetadata(**file_metadata)\n\n self.sandbox = Sandbox(**sandbox)","def makeMetadata(self):\n # arbitrary values\n orientation = 0 * degrees\n flipX = False\n metadata = makeTanWcsMetadata(\n crpix = self.crpix,\n crval = self.crval,\n cdMatrix = makeCdMatrix(scale=self.scale, orientation=orientation, flipX=flipX),\n )\n self.assertEqual(metadata.nameCount(), 14)\n metadata.add(\"SIMPLE\", True)\n metadata.add(\"BITPIX\", 16)\n metadata.add(\"NAXIS\", 2)\n metadata.add(\"NAXIS1\", 500)\n metadata.add(\"NAXIS2\", 200)\n metadata.add(\"BZERO\", 32768)\n metadata.add(\"BSCALE\", 1)\n metadata.add(\"TIMESYS\", \"UTC\")\n metadata.add(\"UTC-OBS\", \"12:04:45.73\")\n metadata.add(\"DATE-OBS\", \"2006-05-20\")\n metadata.add(\"EXPTIME\", 5.0)\n metadata.add(\"COMMENT\", \"a comment\")\n metadata.add(\"COMMENT\", \"another comment\")\n metadata.add(\"EXTEND\", True)\n metadata.add(\"INHERIT\", False)\n metadata.add(\"LTV1\", 5)\n metadata.add(\"LTV2\", -10)\n metadata.add(\"ZOTHER\", \"non-standard\")\n return metadata","def generate_metadata(self):\n self.metadata = {\n 'title': os.path.basename(self.source_file).rsplit('.', 1)[0],\n 'url': self.relative_destination_file,\n 'full_path': os.path.dirname(self.relative_destination_file),\n 'short_path': self.shorten_path(\n os.path.dirname(self.relative_destination_file))\n }","def create_annotation_dict(child):\n\n attrs = ['name', 'value', 'keep']\n values = [child.get(attr) for attr in attrs]\n\n if values[1].startswith('@'):\n return None # Ignore template value elements\n\n # Minor hack to ensure keep is set in build manifests\n if values[-1] is None:\n values[-1] = 'true'\n\n if None in values:\n raise InvalidManifest(\n 'Missing required attribute in annotation element'\n )\n\n return dict(zip(attrs, values))","def _init_annotation(self):\n annotations = []\n for frame in self.frames:\n coordinates, sources, targets, ids = [], [], [], []\n frame_id = set()\n for spot_id, spot_annot in frame.items():\n coordinates.append((spot_annot[\"x\"], spot_annot[\"y\"]))\n sources.append(spot_annot[\"source\"])\n targets.append(spot_annot[\"target\"])\n ids.append(spot_id)\n frame_id.add(spot_annot[\"frame\"])\n if len(frame_id) != 1:\n raise ValueError(f\"Invalid frame number found in spot: {spot_id}\")\n annotations.append((\n np.array(coordinates, dtype=np.float),\n np.array(sources, dtype=np.str),\n targets,\n np.array(ids, dtype=np.str),\n frame_id.pop()))\n self.annotations = annotations","def RDFAnnotationParser_createRDFAnnotation(level=3, version=1):\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)","def declare_metadata(self, metadata):\n return self.client.declare_metadata(metadata)","def from_annotation(cls, anno) -> \"Box\":\n bounds = anno[\"bounds\"]\n return cls(\n x=bounds[\"left\"],\n y=bounds[\"top\"],\n width=bounds[\"right\"] - bounds[\"left\"],\n height=bounds[\"bottom\"] - bounds[\"top\"],\n label=anno[\"label\"][\"text\"],\n )","def perform_create(self, serializer):\n try:\n\n # Create the document object\n long_url = serializer.validated_data['long_url']\n document, _ = Document.objects.get_or_create(long_url=long_url)\n serializer.instance = document\n\n # Create the annotation object\n annotate, _ = Annotation.objects.get_or_create(\n user = self.request.user, document = document\n )\n\n except CorpusException as e:\n raise ValidationError(str(e))","def __init__(self, description: dict, datamart_id: typing.Union[int, None] = None) -> None:\n\n super().__init__()\n\n self._metadata[\"datamart_id\"] = datamart_id\n\n if \"name\" in description:\n self._metadata[\"name\"] = description[\"name\"]\n\n if \"description\" in description:\n self._metadata[\"description\"] = description[\"description\"]\n\n self._metadata[\"semantic_type\"] = description.get(\"semantic_type\", [])\n\n if \"named_entity\" in description:\n self._metadata[\"named_entity\"] = description[\"named_entity\"]\n\n if \"temporal_coverage\" in description:\n self._metadata[\"temporal_coverage\"] = description[\"temporal_coverage\"]\n\n if self.temporal_coverage is not False:\n self.temporal_coverage = Utils.temporal_coverage_validate(self.temporal_coverage)\n\n if \"spatial_coverage\" in description:\n self._metadata[\"spatial_coverage\"] = description[\"spatial_coverage\"]","def _annotated_node(self, annotation: str, **edges: CFNode) -> CFNode:\n node = CFNode(annotation=annotation)\n self._graph.add_node(node, edges=edges)\n return node","def createRDFAnnotation(level=3, version=1):\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)","def copy_annotations(from_data, to_data, annot_type):\n\n for annot in from_data.annotations.select_type(annot_type):\n entity = anafora.AnaforaEntity()\n entity.id = annot.id\n entity.spans = annot.spans\n entity.type = annot.type\n to_data.annotations.append(entity)","def SetMetadata(IMAGE,METADATA):\n IMAGE.SetSpacing(METADATA[0])\n IMAGE.SetOrigin(METADATA[1])\n IMAGE.SetDirection(METADATA[2])","def addAnnotation(self, token, title, text, start, end = None):\n\t\tif end is None:\n\t\t\tpayload = \"{\\\"title\\\":\\\"\" + title + \"\\\", \\\"start\\\": \\\"\" + str(start) + \"\\\", \\\"text\\\":\\\"\" + text + \"\\\"}\"\n\t\telse:\n\t\t\tpayload = \"{\\\"title\\\":\\\"\" + title + \"\\\", \\\"start\\\": \\\"\" + str(start) + \"\\\", \\\"end\\\":\\\"\" + str(end) + \"\\\", \\\"text\\\":\\\"\" + text + \"\\\"}\"\n\t\tif self.simulate:\n\t\t\tself.logger.info(\"[SIMULATE] adding new annotation: \" + payload)\t\n\t\t\treturn\n\t\tself.logger.info(\"adding new annotation: \" + payload)\t\n\n\t\t#self.logger.info(\"WARNING: mpulse API handler disabled!\")\n\t\t#return\n\t\turl = \"https://mpulse.soasta.com/concerto/mpulse/api/annotations/v1\"\n\t\tresult = requests.post(url, data = payload, headers={'Content-Type':'application/json', 'X-Auth-Token': token })\n\t\tif (result.status_code == 200):\n\t\t\tjson_data = result.json()\n\t\t\tself.logger.info('annotation successfully added')\n\t\telse:\n\t\t\tself.logger.error('Error ' + str(result.status_code) + ': annotation not added!')","def _get_ann_file(self):\n prefix = 'instances' if 'test' not in self.image_set else 'image_info'\n return os.path.join(self.data_path, 'annotations',\n prefix + '_' + self.image_set + '.json')","def annotation(self, ann_type: str = None):\n if ann_type is None: ann_type = self.ann\n if ann_type != self.ann:\n warnings.warn('Please note that the annotation type is mismatch with the dataset setting!')\n\n if ann_type == 'label':\n xml_path = self.xml_path.format(id=self.id)\n ann = int(ET.parse(xml_path).find('defective').text)\n elif ann_type == 'bbox':\n xml_path = self.xml_path.format(id=self.id)\n objs = ET.parse(xml_path).findall('bbox')\n ann = []\n for ix, bbox in enumerate(objs):\n y1 = int(float(bbox.find('ymin').text))\n y2 = int(float(bbox.find('ymax').text))\n x1 = int(float(bbox.find('xmin').text))\n x2 = int(float(bbox.find('xmax').text))\n ann.append((y1, y2, x1, x2))\n elif ann_type == 'mask':\n mask_path = self.mask_path.format(id=self.id)\n if os.path.exists(mask_path):\n ann = Image.open(mask_path).convert('L')\n else:\n ann = Image.fromarray(np.zeros((512, 512), dtype=np.uint8)).convert('L')\n elif ann_type == 'none':\n ann = []\n else:\n raise NotImplementedError\n return ann","def test_atom_metadata(self):\n metadata = {\"resname\": \"ALA\", \"resnum\": 1, \"chain\": \"3\"}\n # Create an atom with metadata\n\n atom = Atom(6, 0, False, metadata=metadata)\n\n assert atom.metadata == metadata\n\n # Roundtrip that atom to/from dict\n atom2 = Atom.from_dict(atom.to_dict())\n assert atom.metadata == atom2.metadata\n\n # Make an atom that initially has no metadata, then add the metadata and ensure that\n # it's equivalent to the atom constructed with the same metadata initially\n atom3 = Atom(6, 0, False)\n assert atom3.metadata == {}\n\n for key, val in metadata.items():\n atom3.metadata[key] = val\n\n assert atom3.metadata == metadata\n assert atom3.to_dict() == atom.to_dict()\n\n # Ensure that invalid types raise appropriate errors\n with pytest.raises(InvalidAtomMetadataError, match=\"non-string key\"):\n atom3.metadata[1] = \"one\"\n with pytest.raises(\n InvalidAtomMetadataError, match=\"non-string or integer value\"\n ):\n atom3.metadata[\"length\"] = 3.0 * unit.angstrom","def define_annotation_list(y_pos, bgcolor, text, colorfont, name, hovertext):\n return dict(\n x = -0,\n y = y_pos,\n xanchor = 'left',\n text = text,\n hovertext = hovertext,\n showarrow = False,\n captureevents = True,\n bgcolor = bgcolor,\n font = { 'size' : 12, 'color' : colorfont },\n height = 14\n )","def metadata(self, rel_index) -> Metadata:\n\n row = self.image_df.iloc[rel_index]\n\n (\n site,\n well,\n replicate,\n plate,\n compound,\n concentration,\n moa,\n image_idx,\n ) = row[\n [\n \"site\",\n \"well\",\n \"replicate\",\n \"plate\",\n \"compound\",\n \"concentration\",\n \"moa\",\n \"image_idx\",\n ]\n ]\n\n metadata = Metadata(\n Plate(site, str(well), replicate, plate),\n Compound(compound, concentration, moa),\n image_idx,\n )\n\n return metadata","def annotate(self, request, pk=None):\n\n # Get the document of the detail view and deserialize data\n document = self.get_object()\n serializer = AnnotationSerializer(\n data=request.data,\n context={'request': request, 'document': document}\n )\n\n # Validate the serializer and save the annotation\n serializer.is_valid(raise_exception=True)\n serializer.save()\n\n # Return the response\n return Response(serializer.data)","def make_anno(wf, entities, entity_meta, gt_proportion, padding, acwe=False, plot_all=True):\r\n combined_clustered_pts, classwise_entities = organize_entities(\r\n wf.vols[0], entities, entity_meta, plot_all=plot_all\r\n )\r\n wf.params[\"entity_meta\"] = entity_meta\r\n anno_masks, anno_all = make_pseudomasks(\r\n wf,\r\n classwise_entities,\r\n acwe=acwe,\r\n padding=padding,\r\n core_mask_radius=(12, 12, 12),\r\n )\r\n\r\n return anno_masks, anno_all, entities","def generate_metadata(\n source_name,\n source_version\n):\n now = datetime.utcnow()\n write_recipt = str(uuid.uuid1())\n metadata_obj = {\n 'write_recipt': write_recipt,\n 'data_source': source_name,\n 'machine_source': platform.node(),\n 'version': source_version,\n 'package_version': _version.__version__,\n 'cron_datetime': now.isoformat()\n }\n\n return metadata_obj","def create_annotation_data(self):\n for i, hp in enumerate(self.hanging_point_in_camera_coords_list):\n px, py = self.camera_model.project3d_to_pixel(hp.worldpos())\n if self.save_debug_image:\n self.bgr_axis = self.bgr.copy()\n if 0 <= px < self.target_width and 0 <= py < self.target_height:\n if self.save_debug_image:\n draw_axis(self.bgr_axis,\n hp.worldrot(),\n hp.worldpos(),\n self.camera_model.K)\n create_gradient_circle(\n self.annotation_img,\n int(py), int(px))\n if self.visible_labels == []:\n self.annotation_data.append(\n {'xy': [int(px), int(py)],\n 'depth': hp.worldpos()[2] * 1000,\n 'quaternion': hp.quaternion.tolist()}\n )\n else:\n self.annotation_data.append(\n {'xy': [int(px), int(py)],\n 'depth': hp.worldpos()[2] * 1000,\n 'quaternion': hp.quaternion.tolist(),\n 'label': self.visible_labels[i]}\n )\n self.rotation_map.add_quaternion(\n int(px), int(py), hp.quaternion)\n\n # self.depth_map.add_depth(\n # int(px), int(py),\n # hp.worldpos()[2] * 1000)\n\n if np.all(self.annotation_img == 0):\n print('out of camera')\n return False\n\n self.annotation_img \\\n = self.annotation_img / self.annotation_img.max() * 255\n self.annotation_img = self.annotation_img.astype(np.uint8)\n\n self.rotations = self.rotation_map.rotations\n\n # self.hanging_points_depth = self.depth_map.on_depth_image(self.depth)\n\n return True","def create_binary_annotation(key, value, annotation_type, host):\n return zipkin_core.BinaryAnnotation(\n key=key, value=value, annotation_type=annotation_type, host=host)","def create_metadata(cls, mxd):\n gxapi_cy.WrapMXD._create_metadata(GXContext._get_tls_geo(), mxd.encode())","def __init__(self, metadata=None, default_xmlns=None):\r\n self.metadata = metadata or {}\r\n self.default_xmlns = default_xmlns","def parse_anno(self, img_path, anno_path) -> dict:\n anno_obj = dict()\n\n img = cv2.imread(img_path)\n if len(img.shape) == 3:\n h, w, d = img.shape[:3]\n anno_obj['size'] = (w, h, d)\n else:\n h, w = img.shape[:2]\n anno_obj['size'] = (w, h, 1)\n\n anno_array = np.loadtxt(anno_path, dtype=np.str, delimiter=',')\n objects = list()\n if len(anno_array.shape) == 1:\n # Just one annotation object\n obj = self.create_anno(anno_array)\n if obj:\n objects.append(obj)\n else:\n for anno_line in anno_array:\n obj = self.create_anno(anno_line)\n if obj:\n objects.append(obj)\n if len(objects) == 0:\n return dict()\n anno_obj['objects'] = objects\n return anno_obj","def create(self, **kwargs):\n return Create(self, metadata=kwargs)","def _proto2object(proto: Metadata_PB) -> \"Metadata\":\n\n return Metadata(\n id=validate_type(_deserialize(blob=proto.id), UID, optional=True),\n name=proto.name,\n node=validate_type(_deserialize(blob=proto.node), Location),\n )","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n filename = osp.join(self.data_prefix, video_info['filename']) \n video_info['filename'] = filename\n frame_dir = video_info['filename']\n video_info['frame_dir'] = frame_dir \n video_info['index'] = i\n video_info['label'] = -1 \n video_info['text'] = [video_info['text']] \n video_infos.append(video_info) \n del ann_info\n return video_infos","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n filename = osp.join(self.data_prefix, video_info['filename']) \n video_info['filename'] = filename\n frame_dir = video_info['filename']\n video_info['frame_dir'] = frame_dir \n video_info['index'] = i\n video_info['label'] = -1 \n video_info['text'] = [video_info['text']] \n video_infos.append(video_info) \n del ann_info\n return video_infos","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n info_dict = {} \n info_dict['filename'] = video_info['vid_name']\n frame_dir = info_dict['filename']\n info_dict['frame_dir'] = frame_dir\n info_dict['index'] = i\n info_dict['label'] = video_info['answer_idx']\n info_dict['answers'] = video_info['answers']\n info_dict['question'] = video_info['q']\n info_dict['subtitle'] = video_info['located_sub_text']\n info_dict['frame_ind'] = video_info['located_frame']\n info_dict['total_frames'] = video_info.get('total_frames', -1)\n video_infos.append(info_dict) \n del ann_info\n\n return video_infos","def create_object_metadata(self, bucket_name, object_name, metadata_name, metadata_value):\n\n return h3lib.create_object_metadata(self._handle, bucket_name, object_name, metadata_name, metadata_value, self._user_id)","def createAnn(bbox, \n cat_id, \n visibility=None,\n img_id=None,\n segmentation=None, \n area=None, \n iscrowd=0,\n id=None):\n\n bbox = [float(format(elem, '.2f')) for elem in bbox]\n\n if segmentation is None:\n segmentation = []\n\n if area is None:\n # Use bounding box area\n area = bbox[2] * bbox[3]\n area = np.float32(area).tolist()\n\n annotation = {\n \"id\": id,\n \"image_id\": img_id,\n \"category_id\": cat_id,\n \"segmentation\": segmentation,\n \"area\": area,\n \"bbox\": bbox,\n \"visibility\": visibility,\n \"iscrowd\": iscrowd\n }\n\n return annotation","def create(self, identity, data=None, record=None, **kwargs):\n record.metadata = data.get('metadata', {})","def get_metadata(cls):\n if cls._metadata is None:\n pcm = PropertyClassMetadata()\n cls.define_metadata(pcm)\n cls._metadata = pcm\n\n # Check that the metadata was actually populated\n # Check requires looking at private attributes\n # pylint: disable-next=protected-access\n if pcm._properties is None or pcm._default_units is None:\n raise PropertyPackageError(\n \"Property package did not populate all expected metadata.\"\n )\n return cls._metadata","def create_common_metadata(cls, plugin, attributes, enabled=None):\n metadata = attributes.setdefault('metadata', {\n 'class': 'plugin',\n 'toggleable': True,\n 'weight': 70\n })\n metadata['label'] = plugin.title\n if enabled is None:\n enabled = plugin.enabled\n metadata['enabled'] = enabled or metadata.get('enabled', False)","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n frame_dir = video_info['filename']\n video_info['filename'] = osp.join(self.data_prefix, video_info['filename'])\n video_info['frame_dir'] = frame_dir\n video_info['index'] = i\n \n video_info['text'] = [video_info['text']] \n video_infos.append(video_info) \n del ann_info\n\n return video_infos","def metadata(*args):\n def metadata_wrapper():\n pass\n return metadata_wrapper","def make_annotation_vertical(x, y):\n text = str(x)\n if len(text) > 33:\n text = text[0:33]+\"..\"\n return Annotation(\n text=text, # text is the y-coord\n showarrow=False, # annotation w/o arrows, default is True\n x=x, # set x position\n xref='x', # position text horizontally with x-coords\n xanchor='center', # x position corresp. to center of text\n yref='y', # set y position\n yanchor='bottom', # position text vertically with y-coords\n y=0, # y position corresp. to top of text\n textangle=-90, # Rotating the text upwards\n font=annotation.Font(\n color='#FFFFFF', # set font color\n size=13 # and size\n )\n )","def metadata(self):\n raise NotImplementedError('yet')\n return OntGraphMetadata(self)","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n info_dict = {} \n info_dict['filename'] = video_info['vid_name'] if 'filename' not in video_info else video_info['filename']\n frame_dir = info_dict['filename']\n info_dict['frame_dir'] = frame_dir\n info_dict['index'] = i\n info_dict['label'] = video_info['answer_idx']\n info_dict['answers'] = video_info['answers'] if 'answers' in video_info else video_info['text']\n info_dict['question'] = video_info['question'] if 'question' in video_info else \"\"\n video_infos.append(info_dict) \n del ann_info\n\n return video_infos","def create_meta(prefix, dist, info_dir, extra_info):\n # read info/index.json first\n with open(join(info_dir, 'index.json')) as fi:\n meta = json.load(fi)\n # add extra info\n meta.update(extra_info)\n # write into <prefix>/conda-meta/<dist>.json\n meta_dir = join(prefix, 'conda-meta')\n if not isdir(meta_dir):\n os.makedirs(meta_dir)\n with open(join(meta_dir, 'history'), 'w') as fo:\n fo.write('')\n with open(join(meta_dir, dist + '.json'), 'w') as fo:\n json.dump(meta, fo, indent=2, sort_keys=True)","def load_annotations(self):\n assert self.ann_file.endswith('.pkl')\n ann_info = hload_pkl(self.ann_file)\n\n video_infos = []\n for i, video_info in enumerate(ann_info):\n if isinstance(video_info['text'], str):\n video_info['text'] = [video_info['text']]\n for text in video_info['text']:\n info = {}\n frame_dir = video_info['filename']\n filename = osp.join(self.data_prefix, video_info['filename']+'.mp4') \n info['filename'] = filename\n info['frame_dir'] = frame_dir\n info['index'] = i\n info['label'] = -1 if 'answer_idx' not in video_info else video_info['answer_idx']\n info['text'] = [text]\n if self.is_ret:\n pass\n elif self.is_mc:\n info['clip_text_candidate'] = [0, 1, 2, 3, 4]\n elif self.is_qa:\n pass\n video_infos.append(info) \n del ann_info\n\n return video_infos","def build_meta(self, root):\n ns_dc = {\"dc\": self.namespace_dc, }\n metadata = ET.SubElement(root, \"metadata\", nsmap=ns_dc)\n ET.SubElement(metadata,\n '{%s}title' % self.namespace_dc).text = self.title\n ET.SubElement(metadata,\n '{%s}creator' % self.namespace_dc).text = self.author\n ET.SubElement(metadata,\n '{%s}identifier' % self.namespace_dc,\n id='bookid').text = \"urn:uuid:%s\" % self.bookid\n ET.SubElement(metadata,\n '{%s}language' % self.namespace_dc).text = self.language\n ET.SubElement(metadata, 'meta', name=\"cover\", content=\"cover-image\")","def __init__(self, dictionary: dict[str, t.Any]) -> None:\n missing_keys = self.__annotations__.keys() - dictionary.keys()\n if missing_keys:\n raise KeyError(f\"Fetched object lacks expected keys: {missing_keys}\")\n for annotation in self.__annotations__:\n setattr(self, annotation, dictionary[annotation])","def annotate(self):\n annotations = {}\n\n text_keys = ['fontsize', 'family']\n text_config = self.config.filter(keys=text_keys, prefix='text_')\n\n # suptitle\n suptitle = self.config.get('suptitle')\n if suptitle is not None:\n suptitle_config = self.config.filter(prefix='suptitle_')\n suptitle_config.update(text_config, skip_duplicates=True)\n\n suptitle_wrap_config = suptitle_config.filter(prefix='wrap_')\n if suptitle_wrap_config:\n suptitle = wrap_by_delimiter(suptitle, **suptitle_wrap_config)\n\n annotations['suptitle'] = self.figure.suptitle(suptitle, **suptitle_config)\n\n self.figure.tight_layout()\n\n return annotations","def add_metadata(self, metadata: dict) -> None:","def annotate(self, **annotations):\n _check_annotations(annotations)\n self.annotations.update(annotations)","def Metadata():\n def _CreateMetadata(unused_none):\n global _metadata\n if not _metadata:\n _metadata = _GCEMetadata()\n _metadata_lock.lock(function=_CreateMetadata, argument=None)\n _metadata_lock.unlock()\n return _metadata","def annotate(self, ax):\n annotation = ax.annotate(self.template, xy=(0, 0), ha='right',\n xytext=self.offsets, textcoords='offset points', va='bottom',\n bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')\n )\n annotation.set_visible(False)\n return annotation","def annotate(self, ax):\n annotation = ax.annotate(self.template, xy=(0, 0), ha='right',\n xytext=self.offsets, textcoords='offset points', va='bottom',\n bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')\n )\n annotation.set_visible(False)\n return annotation","def DeclareMetadata(self, label, entry, definition=None):\n if isinstance(self.parent, QTIElement):\n self.parent.DeclareMetadata(label, entry, definition)\n else:\n pass","def get_annotations(self):\n ann = wfdb.rdann(self.patient_number, 'atr', pb_dir='mitdb', return_label_elements=['symbol', 'label_store',\n 'description'],\n summarize_labels=True)\n\n mit_bih_labels_str = ann.symbol\n\n labels_locations = ann.sample\n\n labels_description = ann.description\n\n return mit_bih_labels_str, labels_locations, labels_description","def __init__(self, annotation_file=None):\n # load dataset\n self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\n if not annotation_file == None:\n print('loading annotations into memory...')\n tic = time.time()\n dataset = json.load(open(annotation_file, 'r'))\n assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))\n print('Done (t={:0.2f}s)'.format(time.time()- tic))\n self.dataset = dataset\n self.createIndex()","def createAnnotationRDF(self, rouri, resuri, bodyuri):\n annotation = (ANNOTATION_TEMPLATE%\n { \"xmlbase\": str(rouri)\n , \"resuri\": str(resuri)\n , \"bodyuri\": str(bodyuri)\n })\n return annotation","def readFromMetadata(cls, context, fqId):\n newInstance = AssetProvenance()\n (newInstance.section, newInstance.name) = fqId.split(GenericMetadata.COMPOUND_KEY_SEP)\n \n provenance = GenericMetadata.readProvenanceEntries(context)\n keyProto = fqId + GenericMetadata.COMPOUND_KEY_SEP\n dcIdentifier = keyProto + 'dc.identifier'\n newInstance.dcIdentifier = provenance[dcIdentifier]\n dcSource = keyProto + 'dc.source'\n newInstance.dcSource = provenance[dcSource]\n dcTitle = keyProto + 'dc.title'\n newInstance.dcTitle = provenance[dcTitle]\n dcDate = keyProto + 'dc.date'\n newInstance.dcDate = datetime.strptime(provenance[dcDate], AssetProvenance.FMT_DATE)\n dcPublisher = keyProto + 'dc.publisher'\n newInstance.dcPublisher = provenance[dcPublisher]\n dcDescription = keyProto + 'dc.description'\n newInstance.dcDescription = provenance[dcDescription]\n processingNotes = keyProto + 'processing_notes'\n newInstance.processingNotes = provenance[processingNotes]\n \n return newInstance","def metadata(self, metadata: Mapping[str, str]):\r\n self._metadata = metadata"],"string":"[\n \"def createAnnotation():\\n return _libsbml.RDFAnnotationParser_createAnnotation()\",\n \"def RDFAnnotationParser_createAnnotation():\\n return _libsbml.RDFAnnotationParser_createAnnotation()\",\n \"def create_annotation(timestamp, value, host):\\n return zipkin_core.Annotation(timestamp=timestamp, value=value, host=host)\",\n \"def metadata(self, metadata):\\n return Metadata(metadata)\",\n \"def __init__(self, namespace, data=None, annotation_metadata=None,\\n sandbox=None, time=0, duration=None):\\n\\n super(Annotation, self).__init__()\\n\\n if annotation_metadata is None:\\n annotation_metadata = AnnotationMetadata()\\n\\n self.annotation_metadata = AnnotationMetadata(**annotation_metadata)\\n\\n self.namespace = namespace\\n\\n self.data = SortedKeyList(key=self._key)\\n\\n if data is not None:\\n if isinstance(data, dict):\\n self.append_columns(data)\\n else:\\n self.append_records(data)\\n\\n if sandbox is None:\\n sandbox = Sandbox()\\n\\n self.sandbox = Sandbox(**sandbox)\\n\\n self.time = time\\n self.duration = duration\",\n \"def add_annotation_metadata(self, value):\\n\\n if self._properties is None:\\n self._properties = LabelProperties()\\n self._properties.parameters.insert_new_element(value)\",\n \"def _get_metadata(self) -> Metadata:\\n manifest = self._get_manifest()\\n\\n return Metadata(**manifest[\\\"metadata\\\"])\",\n \"def create_metadata(self) -> _metadata_fb.CustomMetadataT:\\n custom_metadata_field = _metadata_fb.CustomMetadataT()\\n custom_metadata_field.name = self.name\\n custom_metadata_field.data = b'\\\\x01\\\\x02'\\n return custom_metadata_field\",\n \"def Metadata(name, namespace, annotations=None, labels=None):\\n metadata = run_v1_messages.ObjectMeta()\\n metadata.name = name\\n metadata.namespace = namespace\\n\\n if annotations:\\n ann = run_v1_messages.ObjectMeta.AnnotationsValue()\\n for k, v in annotations.items():\\n p = run_v1_messages.ObjectMeta.AnnotationsValue.AdditionalProperty()\\n p.key = k\\n p.value = v\\n ann.additionalProperties.append(p)\\n metadata.annotations = ann\\n\\n if labels:\\n lab = run_v1_messages.ObjectMeta.LabelsValue()\\n for k, v in labels.items():\\n l = run_v1_messages.ObjectMeta.LabelsValue.AdditionalProperty()\\n l.key = k\\n l.value = v\\n lab.additionalProperties.append(l)\\n metadata.labels = lab\\n\\n return metadata\",\n \"def __new__(\\n mcs, # type: Type[_AnnotatedObjectMeta]\\n name, # type: str\\n bases, # type: List[type]\\n attrs, # type: Dict[str, Any]\\n **kwargs # type: Dict[str, Any]\\n ):\\n # type: (...) -> type\\n annotations = attrs.get(\\\"__annotations__\\\", {})\\n use_comment_type_hints = (\\n not annotations and attrs.get(\\\"__module__\\\") != __name__\\n )\\n if use_comment_type_hints:\\n frame_source = _get_class_frame_source(name)\\n annotations = get_type_hints(*frame_source)\\n names = list(attrs) + list(annotations)\\n typed_attrs = {}\\n for attr in names:\\n typed_attrs[attr] = attrs.get(attr)\\n if _is_propertyable(names, attrs, annotations, attr):\\n private_attr = \\\"__{}\\\".format(attr)\\n if attr in attrs:\\n typed_attrs[private_attr] = attrs[attr]\\n type_ = (\\n Optional[annotations[attr]]\\n if not use_comment_type_hints\\n and attr in attrs\\n and attrs[attr] is None\\n else annotations[attr]\\n )\\n typed_attrs[attr] = property(\\n _get_fget(attr, private_attr, type_),\\n get_fset(attr, private_attr, type_),\\n )\\n properties = [\\n attr\\n for attr in annotations\\n if _is_propertyable(names, attrs, annotations, attr)\\n ]\\n typed_attrs[\\\"_tp__typed_properties\\\"] = properties\\n typed_attrs[\\\"_tp__required_typed_properties\\\"] = [\\n attr\\n for attr in properties\\n if (\\n attr not in attrs\\n or attrs[attr] is None\\n and use_comment_type_hints\\n )\\n and NoneType not in getattr(annotations[attr], \\\"__args__\\\", ())\\n ]\\n return super(_AnnotatedObjectMeta, mcs).__new__( # type: ignore\\n mcs, name, bases, typed_attrs, **kwargs\\n )\",\n \"def create(\\n cls,\\n model_buffer: bytearray,\\n input_norm_mean: List[float],\\n input_norm_std: List[float],\\n labels: metadata_writer.Labels,\\n score_calibration: Optional[metadata_writer.ScoreCalibration] = None\\n ) -> \\\"MetadataWriter\\\":\\n writer = metadata_writer.MetadataWriter(model_buffer)\\n writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)\\n writer.add_image_input(input_norm_mean, input_norm_std)\\n writer.add_classification_output(labels, score_calibration)\\n return cls(writer)\",\n \"def define_metadata(cls, pcm):\\n raise NotImplementedError()\",\n \"def gen_metadata(args):\\n with open(args.bibfile) as bibfile:\\n bib_db = BibTexParser(common_strings=True).parse_file(bibfile)\\n entries = sorted(list(bib_db.entries),\\n key=lambda x: x['year'], reverse=True)\\n list([update_file(entry) for entry in entries])\\n annotations = [entry_to_annotation(entry, args.PI) for entry in entries]\\n stream = open(args.metadata, 'w')\\n yaml.dump(annotations, stream, width=192, default_flow_style=False)\\n stream.close()\",\n \"def create_annotation(raw):\\n annotation_pandas = pd.DataFrame(columns=[\\\"onset\\\", \\\"duration\\\", \\\"description\\\"])\\n for idx, event in enumerate(raw.annotations):\\n annotation_pandas.loc[idx] = [\\n event[\\\"onset\\\"],\\n event[\\\"duration\\\"],\\n event[\\\"description\\\"],\\n ]\\n return annotation_pandas\",\n \"def _getMetadata(self):\\n anno_key = 'collective.sendaspdf'\\n annotations = IAnnotations(self)\\n\\n metadata = annotations.get(anno_key,\\n None)\\n if metadata is None:\\n annotations[anno_key] = PersistentDict()\\n metadata = annotations[anno_key]\\n\\n return metadata\",\n \"def new():\\n doc = coerce_to(int, request.form['doc'])\\n page = coerce_to(int, request.form['page'])\\n posx = coerce_to(int, request.form['posx'])\\n posy = coerce_to(int, request.form['posy'])\\n width = coerce_to(int, request.form['width'])\\n height = coerce_to(int, request.form['height'])\\n text = request.form['value']\\n state = Annotation.STATE_OPEN\\n if 'state' in request.form:\\n state = Annotation.state_decode(request.form['state'])\\n doc_obj = Document.query.get(doc)\\n if doc_obj is None:\\n return BadRequest()\\n if not current_user.can_annotate(doc):\\n return Unauthorized()\\n user = current_user.id\\n ann = Annotation(doc, page, posx, posy, width, height, text, user,\\n state=state)\\n db.session.add(ann)\\n db.session.commit()\\n return jsonify(id=ann.id)\",\n \"def setup_annotation(self):\\n annotation = self.ax_fig.annotate(\\n '', xy=(0, 0), ha='left',\\n xytext=(-20, 20), textcoords='offset points', va='bottom',\\n bbox=dict(\\n boxstyle='round,pad=0.5', fc='yellow', alpha=0.2),\\n arrowprops=dict(\\n arrowstyle='->', connectionstyle='arc3,rad=0'))\\n return annotation\",\n \"def from_json(json):\\n result = Annotation()\\n if \\\"projectId\\\" in json:\\n result.project_id = json[\\\"projectId\\\"]\\n if \\\"imageId\\\" in json:\\n result.image_id = json[\\\"imageId\\\"]\\n if \\\"shapes\\\" in json:\\n result.shapes = json[\\\"shapes\\\"]\\n if \\\"frameNum\\\" in json:\\n result.frame_num = json[\\\"frameNum\\\"]\\n if \\\"_id\\\" in json:\\n result.id = str(json[\\\"_id\\\"])\\n return result\",\n \"def _create_output_metadata(self):\\n\\n # Create output info.\\n output_meta = _metadata_fb.TensorMetadataT()\\n output_meta.name = \\\"probability\\\"\\n output_meta.description = \\\"Probabilities of labels respectively.\\\"\\n output_meta.content = _metadata_fb.ContentT()\\n output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()\\n output_meta.content.contentPropertiesType = (\\n _metadata_fb.ContentProperties.FeatureProperties)\\n label_file = _metadata_fb.AssociatedFileT()\\n label_file.name = os.path.basename(self.model_info.label_file)\\n label_file.description = (\\\"Labels for classification categories.\\\")\\n label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS\\n output_meta.associatedFiles = [label_file]\\n return [output_meta]\",\n \"def annotate(self, annotation_=None):\\n # Important: Need a copy, not the reference to the original object\\n annotation_ = copy.deepcopy(annotation_)\\n annotation_.annotate(self, from_dataset=True)\\n history_record = annotation_.create_history_record()\\n self.annotations.append(history_record)\\n self._append_task(kind='annotation', task=history_record)\",\n \"def _parse_anno_info(self, annotations):\\n gt_bboxes, gt_bboxes_ignore = [], []\\n gt_masks, gt_masks_ignore = [], []\\n gt_labels = []\\n for ann in annotations:\\n if ann.get('iscrowd', False):\\n gt_bboxes_ignore.append(ann['bbox'])\\n gt_masks_ignore.append(ann.get('segmentation', None))\\n else:\\n gt_bboxes.append(ann['bbox'])\\n gt_labels.append(ann['category_id'])\\n gt_masks.append(ann.get('segmentation', None))\\n if gt_bboxes:\\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\\n gt_labels = np.array(gt_labels, dtype=np.int64)\\n else:\\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\\n gt_labels = np.array([], dtype=np.int64)\\n\\n if gt_bboxes_ignore:\\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\\n else:\\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\\n\\n ann = dict(\\n bboxes=gt_bboxes,\\n labels=gt_labels,\\n bboxes_ignore=gt_bboxes_ignore,\\n masks_ignore=gt_masks_ignore,\\n masks=gt_masks)\\n\\n return ann\",\n \"def build_metadata(meta):\\n\\n ret = copy.copy(meta) if meta else dict()\\n\\n ret['name'] = meta.get('name', '')\\n\\n if 'index' in meta:\\n if isinstance(meta.get('index'), str):\\n ret['index'] = Index(meta.get('index'))\\n elif isinstance(meta.get('index'), Index):\\n ret['index'] = meta.get('index')\\n\\n ret['utc'] = True\\n if 'utc' in meta and isinstance(meta.get('utc'), bool):\\n ret['utc'] = meta.get('utc')\\n\\n return pmap(ret)\",\n \"def metadata(self) -> \\\"ObjectMeta\\\":\\n return typing.cast(\\n \\\"ObjectMeta\\\",\\n self._properties.get(\\\"metadata\\\"),\\n )\",\n \"def to_music21_metadata(metadata: Metadata) -> M21MetaData:\\n meta = M21MetaData()\\n\\n # Title is usually stored in movement-title. See\\n # https://www.musicxml.com/tutorial/file-structure/score-header-entity/\\n if metadata.title:\\n meta.movementName = metadata.title\\n\\n if metadata.copyright:\\n meta.copyright = Copyright(metadata.copyright)\\n for creator in metadata.creators:\\n meta.addContributor(Contributor(name=creator))\\n return meta\",\n \"def __metadata__(self):\\n raise NotImplementedError\",\n \"def _convert_annotations(self, ast):\\n self.annotations = IDLAnnotations(ast)\",\n \"def metadata(self) -> interface.Metadata:\\n return cast(interface.Metadata, self._interfaces[interface.Metadata])\",\n \"def annotations(self):\\n annotations = {\\\"date\\\": self.date_trunc(\\\"usage_start\\\")}\\n # { query_param: database_field_name }\\n fields = self._mapper.provider_map.get(\\\"annotations\\\")\\n for q_param, db_field in fields.items():\\n annotations[q_param] = F(db_field)\\n if (\\n \\\"project\\\" in self.parameters.parameters.get(\\\"group_by\\\", {})\\n or \\\"and:project\\\" in self.parameters.parameters.get(\\\"group_by\\\", {})\\n or \\\"or:project\\\" in self.parameters.parameters.get(\\\"group_by\\\", {})\\n ):\\n annotations[\\\"project\\\"] = F(\\\"namespace\\\")\\n\\n return annotations\",\n \"def get_annotation_object(annots_path):\\n\\n lines = annots_path.read_text().split('\\\\n')\\n \\n annots = []\\n for line in lines:\\n if not line:\\n continue\\n \\n annot = {}\\n splot = line.split(' ')\\n annot['class_id'] = int(splot[0])\\n annot['center_x'] = float(splot[1])\\n annot['center_y'] = float(splot[2])\\n annot['width'] = float(splot[3])\\n annot['height'] = float(splot[4])\\n annot['class_name'] = splot[-1]\\n \\n if splot[5].startswith('px:'):\\n px = splot[5].strip('px:')\\n py = splot[6].strip('py:')\\n \\n if not (px == 'auto'):\\n px = px.split(',')\\n py = py.split(',')\\n annot['px'] = [float(x) for x in px]\\n annot['py'] = [float(x) for x in py]\\n else:\\n annot['px'] = 'auto'\\n annot['py'] = 'auto'\\n \\n elif splot[5].startswith('conf:'):\\n annot['conf'] = float(splot[5].split(':')[1])\\n\\n annots.append(annot)\\n \\n return annots\",\n \"def getAnnotation(self, *args):\\n return _libsbml.SBase_getAnnotation(self, *args)\",\n \"def annotate(self, annotation):\\n self._data = self._data.annotate(**annotation)\",\n \"def metadata_create():\\n binding = {}\\n url = 'https://upload.twitter.com/1.1/media/metadata/create.json'\\n return _TwitterRequest('POST',\\n url,\\n 'rest:media',\\n 'post-media-metadata-create',\\n binding)\",\n \"def __init__(self, annotation_file=None, N=10, kind=\\\"official\\\"):\\n\\n # load dataset\\n self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()\\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\\n self.N = N\\n self.kind = kind\\n if not annotation_file == None:\\n print(\\\"loading annotations into memory...\\\")\\n tic = time.time()\\n with open(annotation_file, \\\"r\\\") as f:\\n dataset = json.load(f)\\n assert (\\n type(dataset) == dict\\n ), \\\"annotation file format {} not supported\\\".format(type(dataset))\\n print(\\\"Done (t={:0.2f}s)\\\".format(time.time() - tic))\\n self.dataset = dataset\\n self.createIndex()\\n\\n if getattr(pycocotools, \\\"__version__\\\", \\\"0\\\") >= \\\"12.0.2\\\":\\n warnings.warn(\\n 'mmpycocotools is deprecated. Please install official pycocotools by \\\"pip install pycocotools\\\"', # noqa: E501\\n UserWarning,\\n )\\n self.img_ann_map = self.imgToAnns\\n self.cat_img_map = self.catToImgs\",\n \"def get_marker(annotation, marker_duration, video_clip):\\n position = annotation[\\\"position\\\"]\\n marker = marker_image.copy()\\n marker = marker.set_position(get_marker_absolute_pos(position, video_clip))\\n marker = marker.set_start(float(annotation[\\\"time\\\"]) / 1000.0)\\n marker = marker.set_duration(marker_duration)\\n\\n return marker\",\n \"def _create_typed_object_meta(get_fset):\\n # type: (Callable[[str, str, Type[_T]], Callable[[_T], None]]) -> type\\n\\n def _get_fget(attr, private_attr, type_):\\n # type: (str, str, Type[_T]) -> Callable[[], Any]\\n \\\"\\\"\\\"Create a property getter method for an attribute.\\n\\n Args:\\n attr: The name of the attribute that will be retrieved.\\n private_attr: The name of the attribute that will store any data\\n related to the attribute.\\n type_: The annotated type defining what values can be stored in the\\n attribute.\\n\\n Returns:\\n A function that takes self and retrieves the private attribute from\\n self.\\n \\\"\\\"\\\"\\n\\n def _fget(self):\\n # type: (...) -> Any\\n \\\"\\\"\\\"Get attribute from self without revealing the private name.\\\"\\\"\\\"\\n try:\\n return getattr(self, private_attr)\\n except AttributeError:\\n raise AttributeError(\\n \\\"'{}' object has no attribute '{}'\\\".format(\\n _get_type_name(type_), attr\\n )\\n )\\n\\n return _fget\\n\\n class _AnnotatedObjectMeta(type):\\n \\\"\\\"\\\"A metaclass that reads annotations from a class definition.\\\"\\\"\\\"\\n\\n def __new__(\\n mcs, # type: Type[_AnnotatedObjectMeta]\\n name, # type: str\\n bases, # type: List[type]\\n attrs, # type: Dict[str, Any]\\n **kwargs # type: Dict[str, Any]\\n ):\\n # type: (...) -> type\\n \\\"\\\"\\\"Create class objs that replaces annotated attrs with properties.\\n\\n Args:\\n mcs: The class object being created.\\n name: The name of the class to create.\\n bases: The list of all base classes for the new class.\\n attrs: The list of all attributes for the new class from the\\n definition.\\n\\n Returns:\\n A new class instance with the expected base classes and\\n attributes, but with annotated, public, non-constant,\\n non-method attributes replaced by property objects that\\n validate against the annotated type.\\n \\\"\\\"\\\"\\n annotations = attrs.get(\\\"__annotations__\\\", {})\\n use_comment_type_hints = (\\n not annotations and attrs.get(\\\"__module__\\\") != __name__\\n )\\n if use_comment_type_hints:\\n frame_source = _get_class_frame_source(name)\\n annotations = get_type_hints(*frame_source)\\n names = list(attrs) + list(annotations)\\n typed_attrs = {}\\n for attr in names:\\n typed_attrs[attr] = attrs.get(attr)\\n if _is_propertyable(names, attrs, annotations, attr):\\n private_attr = \\\"__{}\\\".format(attr)\\n if attr in attrs:\\n typed_attrs[private_attr] = attrs[attr]\\n type_ = (\\n Optional[annotations[attr]]\\n if not use_comment_type_hints\\n and attr in attrs\\n and attrs[attr] is None\\n else annotations[attr]\\n )\\n typed_attrs[attr] = property(\\n _get_fget(attr, private_attr, type_),\\n get_fset(attr, private_attr, type_),\\n )\\n properties = [\\n attr\\n for attr in annotations\\n if _is_propertyable(names, attrs, annotations, attr)\\n ]\\n typed_attrs[\\\"_tp__typed_properties\\\"] = properties\\n typed_attrs[\\\"_tp__required_typed_properties\\\"] = [\\n attr\\n for attr in properties\\n if (\\n attr not in attrs\\n or attrs[attr] is None\\n and use_comment_type_hints\\n )\\n and NoneType not in getattr(annotations[attr], \\\"__args__\\\", ())\\n ]\\n return super(_AnnotatedObjectMeta, mcs).__new__( # type: ignore\\n mcs, name, bases, typed_attrs, **kwargs\\n )\\n\\n return _AnnotatedObjectMeta\",\n \"def test_init_with_custom_metadata(isolated_runner, project_init, template):\\n data, commands = project_init\\n\\n metadata = {\\n \\\"@id\\\": \\\"https://example.com/annotation1\\\",\\n \\\"@type\\\": \\\"https://schema.org/specialType\\\",\\n \\\"https://schema.org/specialProperty\\\": \\\"some_unique_value\\\",\\n }\\n metadata_path = Path(\\\"metadata.json\\\")\\n metadata_path.write_text(json.dumps(metadata))\\n\\n # create the project\\n new_project = Path(data[\\\"test_project\\\"])\\n assert not new_project.exists()\\n result = isolated_runner.invoke(cli, commands[\\\"init_test\\\"] + commands[\\\"id\\\"] + [\\\"--metadata\\\", str(metadata_path)])\\n assert 0 == result.exit_code\\n\\n database = Database.from_path(new_project / \\\".renku\\\" / \\\"metadata\\\")\\n project = database.get(\\\"project\\\")\\n\\n assert metadata == project.annotations[0].body\",\n \"def annotate(self, **annotations):\\n return AnnotatedQuery(self, annotations)\",\n \"def __new__(cls, account=None, credential=None, project=None,\\n attributes=None, scopes=None):\\n if not attributes:\\n attributes = {}\\n if not scopes:\\n scopes = config.CLOUDSDK_SCOPES\\n return super(MetadataOptions, cls).__new__(\\n cls, account=account, credential=credential, project=project,\\n attributes=attributes, scopes=scopes)\",\n \"def generate_metadata(self) -> None:\\n if self.output_anchor_metadata is None:\\n output_record_info = self.engine.create_record_info()\\n else:\\n output_record_info = self.output_anchor_metadata\\n\\n if self.output_anchor.record_info is None:\\n output_record_info.add_field(\\\"optional_value\\\", Sdk.FieldType.float)\\n self.output_anchor_metadata = output_record_info\\n\\n self.output_anchor.record_info = self.output_anchor_metadata\\n self.push_all_metadata()\",\n \"def get_assessment_metadata(self):\\n return Metadata(**settings.METADATA['assessment_id'])\",\n \"def from_metadata(cls, meta):\\r\\n return Package(\\r\\n name=meta[\\\"Name\\\"],\\r\\n version=meta[\\\"Version\\\"],\\r\\n homepage=meta[\\\"Home-page\\\"],\\r\\n license_name=meta[\\\"License\\\"],\\r\\n source_url=meta[\\\"Project-URL\\\"],\\r\\n package_url=purl_extractor.get_purl_from_meta_dict(meta),\\r\\n )\",\n \"def __init__(self, annotations=None, file_metadata=None, sandbox=None):\\n super(JAMS, self).__init__()\\n\\n if file_metadata is None:\\n file_metadata = FileMetadata()\\n\\n if sandbox is None:\\n sandbox = Sandbox()\\n\\n self.annotations = AnnotationArray(annotations=annotations)\\n\\n self.file_metadata = FileMetadata(**file_metadata)\\n\\n self.sandbox = Sandbox(**sandbox)\",\n \"def makeMetadata(self):\\n # arbitrary values\\n orientation = 0 * degrees\\n flipX = False\\n metadata = makeTanWcsMetadata(\\n crpix = self.crpix,\\n crval = self.crval,\\n cdMatrix = makeCdMatrix(scale=self.scale, orientation=orientation, flipX=flipX),\\n )\\n self.assertEqual(metadata.nameCount(), 14)\\n metadata.add(\\\"SIMPLE\\\", True)\\n metadata.add(\\\"BITPIX\\\", 16)\\n metadata.add(\\\"NAXIS\\\", 2)\\n metadata.add(\\\"NAXIS1\\\", 500)\\n metadata.add(\\\"NAXIS2\\\", 200)\\n metadata.add(\\\"BZERO\\\", 32768)\\n metadata.add(\\\"BSCALE\\\", 1)\\n metadata.add(\\\"TIMESYS\\\", \\\"UTC\\\")\\n metadata.add(\\\"UTC-OBS\\\", \\\"12:04:45.73\\\")\\n metadata.add(\\\"DATE-OBS\\\", \\\"2006-05-20\\\")\\n metadata.add(\\\"EXPTIME\\\", 5.0)\\n metadata.add(\\\"COMMENT\\\", \\\"a comment\\\")\\n metadata.add(\\\"COMMENT\\\", \\\"another comment\\\")\\n metadata.add(\\\"EXTEND\\\", True)\\n metadata.add(\\\"INHERIT\\\", False)\\n metadata.add(\\\"LTV1\\\", 5)\\n metadata.add(\\\"LTV2\\\", -10)\\n metadata.add(\\\"ZOTHER\\\", \\\"non-standard\\\")\\n return metadata\",\n \"def generate_metadata(self):\\n self.metadata = {\\n 'title': os.path.basename(self.source_file).rsplit('.', 1)[0],\\n 'url': self.relative_destination_file,\\n 'full_path': os.path.dirname(self.relative_destination_file),\\n 'short_path': self.shorten_path(\\n os.path.dirname(self.relative_destination_file))\\n }\",\n \"def create_annotation_dict(child):\\n\\n attrs = ['name', 'value', 'keep']\\n values = [child.get(attr) for attr in attrs]\\n\\n if values[1].startswith('@'):\\n return None # Ignore template value elements\\n\\n # Minor hack to ensure keep is set in build manifests\\n if values[-1] is None:\\n values[-1] = 'true'\\n\\n if None in values:\\n raise InvalidManifest(\\n 'Missing required attribute in annotation element'\\n )\\n\\n return dict(zip(attrs, values))\",\n \"def _init_annotation(self):\\n annotations = []\\n for frame in self.frames:\\n coordinates, sources, targets, ids = [], [], [], []\\n frame_id = set()\\n for spot_id, spot_annot in frame.items():\\n coordinates.append((spot_annot[\\\"x\\\"], spot_annot[\\\"y\\\"]))\\n sources.append(spot_annot[\\\"source\\\"])\\n targets.append(spot_annot[\\\"target\\\"])\\n ids.append(spot_id)\\n frame_id.add(spot_annot[\\\"frame\\\"])\\n if len(frame_id) != 1:\\n raise ValueError(f\\\"Invalid frame number found in spot: {spot_id}\\\")\\n annotations.append((\\n np.array(coordinates, dtype=np.float),\\n np.array(sources, dtype=np.str),\\n targets,\\n np.array(ids, dtype=np.str),\\n frame_id.pop()))\\n self.annotations = annotations\",\n \"def RDFAnnotationParser_createRDFAnnotation(level=3, version=1):\\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)\",\n \"def declare_metadata(self, metadata):\\n return self.client.declare_metadata(metadata)\",\n \"def from_annotation(cls, anno) -> \\\"Box\\\":\\n bounds = anno[\\\"bounds\\\"]\\n return cls(\\n x=bounds[\\\"left\\\"],\\n y=bounds[\\\"top\\\"],\\n width=bounds[\\\"right\\\"] - bounds[\\\"left\\\"],\\n height=bounds[\\\"bottom\\\"] - bounds[\\\"top\\\"],\\n label=anno[\\\"label\\\"][\\\"text\\\"],\\n )\",\n \"def perform_create(self, serializer):\\n try:\\n\\n # Create the document object\\n long_url = serializer.validated_data['long_url']\\n document, _ = Document.objects.get_or_create(long_url=long_url)\\n serializer.instance = document\\n\\n # Create the annotation object\\n annotate, _ = Annotation.objects.get_or_create(\\n user = self.request.user, document = document\\n )\\n\\n except CorpusException as e:\\n raise ValidationError(str(e))\",\n \"def __init__(self, description: dict, datamart_id: typing.Union[int, None] = None) -> None:\\n\\n super().__init__()\\n\\n self._metadata[\\\"datamart_id\\\"] = datamart_id\\n\\n if \\\"name\\\" in description:\\n self._metadata[\\\"name\\\"] = description[\\\"name\\\"]\\n\\n if \\\"description\\\" in description:\\n self._metadata[\\\"description\\\"] = description[\\\"description\\\"]\\n\\n self._metadata[\\\"semantic_type\\\"] = description.get(\\\"semantic_type\\\", [])\\n\\n if \\\"named_entity\\\" in description:\\n self._metadata[\\\"named_entity\\\"] = description[\\\"named_entity\\\"]\\n\\n if \\\"temporal_coverage\\\" in description:\\n self._metadata[\\\"temporal_coverage\\\"] = description[\\\"temporal_coverage\\\"]\\n\\n if self.temporal_coverage is not False:\\n self.temporal_coverage = Utils.temporal_coverage_validate(self.temporal_coverage)\\n\\n if \\\"spatial_coverage\\\" in description:\\n self._metadata[\\\"spatial_coverage\\\"] = description[\\\"spatial_coverage\\\"]\",\n \"def _annotated_node(self, annotation: str, **edges: CFNode) -> CFNode:\\n node = CFNode(annotation=annotation)\\n self._graph.add_node(node, edges=edges)\\n return node\",\n \"def createRDFAnnotation(level=3, version=1):\\n return _libsbml.RDFAnnotationParser_createRDFAnnotation(level, version)\",\n \"def copy_annotations(from_data, to_data, annot_type):\\n\\n for annot in from_data.annotations.select_type(annot_type):\\n entity = anafora.AnaforaEntity()\\n entity.id = annot.id\\n entity.spans = annot.spans\\n entity.type = annot.type\\n to_data.annotations.append(entity)\",\n \"def SetMetadata(IMAGE,METADATA):\\n IMAGE.SetSpacing(METADATA[0])\\n IMAGE.SetOrigin(METADATA[1])\\n IMAGE.SetDirection(METADATA[2])\",\n \"def addAnnotation(self, token, title, text, start, end = None):\\n\\t\\tif end is None:\\n\\t\\t\\tpayload = \\\"{\\\\\\\"title\\\\\\\":\\\\\\\"\\\" + title + \\\"\\\\\\\", \\\\\\\"start\\\\\\\": \\\\\\\"\\\" + str(start) + \\\"\\\\\\\", \\\\\\\"text\\\\\\\":\\\\\\\"\\\" + text + \\\"\\\\\\\"}\\\"\\n\\t\\telse:\\n\\t\\t\\tpayload = \\\"{\\\\\\\"title\\\\\\\":\\\\\\\"\\\" + title + \\\"\\\\\\\", \\\\\\\"start\\\\\\\": \\\\\\\"\\\" + str(start) + \\\"\\\\\\\", \\\\\\\"end\\\\\\\":\\\\\\\"\\\" + str(end) + \\\"\\\\\\\", \\\\\\\"text\\\\\\\":\\\\\\\"\\\" + text + \\\"\\\\\\\"}\\\"\\n\\t\\tif self.simulate:\\n\\t\\t\\tself.logger.info(\\\"[SIMULATE] adding new annotation: \\\" + payload)\\t\\n\\t\\t\\treturn\\n\\t\\tself.logger.info(\\\"adding new annotation: \\\" + payload)\\t\\n\\n\\t\\t#self.logger.info(\\\"WARNING: mpulse API handler disabled!\\\")\\n\\t\\t#return\\n\\t\\turl = \\\"https://mpulse.soasta.com/concerto/mpulse/api/annotations/v1\\\"\\n\\t\\tresult = requests.post(url, data = payload, headers={'Content-Type':'application/json', 'X-Auth-Token': token })\\n\\t\\tif (result.status_code == 200):\\n\\t\\t\\tjson_data = result.json()\\n\\t\\t\\tself.logger.info('annotation successfully added')\\n\\t\\telse:\\n\\t\\t\\tself.logger.error('Error ' + str(result.status_code) + ': annotation not added!')\",\n \"def _get_ann_file(self):\\n prefix = 'instances' if 'test' not in self.image_set else 'image_info'\\n return os.path.join(self.data_path, 'annotations',\\n prefix + '_' + self.image_set + '.json')\",\n \"def annotation(self, ann_type: str = None):\\n if ann_type is None: ann_type = self.ann\\n if ann_type != self.ann:\\n warnings.warn('Please note that the annotation type is mismatch with the dataset setting!')\\n\\n if ann_type == 'label':\\n xml_path = self.xml_path.format(id=self.id)\\n ann = int(ET.parse(xml_path).find('defective').text)\\n elif ann_type == 'bbox':\\n xml_path = self.xml_path.format(id=self.id)\\n objs = ET.parse(xml_path).findall('bbox')\\n ann = []\\n for ix, bbox in enumerate(objs):\\n y1 = int(float(bbox.find('ymin').text))\\n y2 = int(float(bbox.find('ymax').text))\\n x1 = int(float(bbox.find('xmin').text))\\n x2 = int(float(bbox.find('xmax').text))\\n ann.append((y1, y2, x1, x2))\\n elif ann_type == 'mask':\\n mask_path = self.mask_path.format(id=self.id)\\n if os.path.exists(mask_path):\\n ann = Image.open(mask_path).convert('L')\\n else:\\n ann = Image.fromarray(np.zeros((512, 512), dtype=np.uint8)).convert('L')\\n elif ann_type == 'none':\\n ann = []\\n else:\\n raise NotImplementedError\\n return ann\",\n \"def test_atom_metadata(self):\\n metadata = {\\\"resname\\\": \\\"ALA\\\", \\\"resnum\\\": 1, \\\"chain\\\": \\\"3\\\"}\\n # Create an atom with metadata\\n\\n atom = Atom(6, 0, False, metadata=metadata)\\n\\n assert atom.metadata == metadata\\n\\n # Roundtrip that atom to/from dict\\n atom2 = Atom.from_dict(atom.to_dict())\\n assert atom.metadata == atom2.metadata\\n\\n # Make an atom that initially has no metadata, then add the metadata and ensure that\\n # it's equivalent to the atom constructed with the same metadata initially\\n atom3 = Atom(6, 0, False)\\n assert atom3.metadata == {}\\n\\n for key, val in metadata.items():\\n atom3.metadata[key] = val\\n\\n assert atom3.metadata == metadata\\n assert atom3.to_dict() == atom.to_dict()\\n\\n # Ensure that invalid types raise appropriate errors\\n with pytest.raises(InvalidAtomMetadataError, match=\\\"non-string key\\\"):\\n atom3.metadata[1] = \\\"one\\\"\\n with pytest.raises(\\n InvalidAtomMetadataError, match=\\\"non-string or integer value\\\"\\n ):\\n atom3.metadata[\\\"length\\\"] = 3.0 * unit.angstrom\",\n \"def define_annotation_list(y_pos, bgcolor, text, colorfont, name, hovertext):\\n return dict(\\n x = -0,\\n y = y_pos,\\n xanchor = 'left',\\n text = text,\\n hovertext = hovertext,\\n showarrow = False,\\n captureevents = True,\\n bgcolor = bgcolor,\\n font = { 'size' : 12, 'color' : colorfont },\\n height = 14\\n )\",\n \"def metadata(self, rel_index) -> Metadata:\\n\\n row = self.image_df.iloc[rel_index]\\n\\n (\\n site,\\n well,\\n replicate,\\n plate,\\n compound,\\n concentration,\\n moa,\\n image_idx,\\n ) = row[\\n [\\n \\\"site\\\",\\n \\\"well\\\",\\n \\\"replicate\\\",\\n \\\"plate\\\",\\n \\\"compound\\\",\\n \\\"concentration\\\",\\n \\\"moa\\\",\\n \\\"image_idx\\\",\\n ]\\n ]\\n\\n metadata = Metadata(\\n Plate(site, str(well), replicate, plate),\\n Compound(compound, concentration, moa),\\n image_idx,\\n )\\n\\n return metadata\",\n \"def annotate(self, request, pk=None):\\n\\n # Get the document of the detail view and deserialize data\\n document = self.get_object()\\n serializer = AnnotationSerializer(\\n data=request.data,\\n context={'request': request, 'document': document}\\n )\\n\\n # Validate the serializer and save the annotation\\n serializer.is_valid(raise_exception=True)\\n serializer.save()\\n\\n # Return the response\\n return Response(serializer.data)\",\n \"def make_anno(wf, entities, entity_meta, gt_proportion, padding, acwe=False, plot_all=True):\\r\\n combined_clustered_pts, classwise_entities = organize_entities(\\r\\n wf.vols[0], entities, entity_meta, plot_all=plot_all\\r\\n )\\r\\n wf.params[\\\"entity_meta\\\"] = entity_meta\\r\\n anno_masks, anno_all = make_pseudomasks(\\r\\n wf,\\r\\n classwise_entities,\\r\\n acwe=acwe,\\r\\n padding=padding,\\r\\n core_mask_radius=(12, 12, 12),\\r\\n )\\r\\n\\r\\n return anno_masks, anno_all, entities\",\n \"def generate_metadata(\\n source_name,\\n source_version\\n):\\n now = datetime.utcnow()\\n write_recipt = str(uuid.uuid1())\\n metadata_obj = {\\n 'write_recipt': write_recipt,\\n 'data_source': source_name,\\n 'machine_source': platform.node(),\\n 'version': source_version,\\n 'package_version': _version.__version__,\\n 'cron_datetime': now.isoformat()\\n }\\n\\n return metadata_obj\",\n \"def create_annotation_data(self):\\n for i, hp in enumerate(self.hanging_point_in_camera_coords_list):\\n px, py = self.camera_model.project3d_to_pixel(hp.worldpos())\\n if self.save_debug_image:\\n self.bgr_axis = self.bgr.copy()\\n if 0 <= px < self.target_width and 0 <= py < self.target_height:\\n if self.save_debug_image:\\n draw_axis(self.bgr_axis,\\n hp.worldrot(),\\n hp.worldpos(),\\n self.camera_model.K)\\n create_gradient_circle(\\n self.annotation_img,\\n int(py), int(px))\\n if self.visible_labels == []:\\n self.annotation_data.append(\\n {'xy': [int(px), int(py)],\\n 'depth': hp.worldpos()[2] * 1000,\\n 'quaternion': hp.quaternion.tolist()}\\n )\\n else:\\n self.annotation_data.append(\\n {'xy': [int(px), int(py)],\\n 'depth': hp.worldpos()[2] * 1000,\\n 'quaternion': hp.quaternion.tolist(),\\n 'label': self.visible_labels[i]}\\n )\\n self.rotation_map.add_quaternion(\\n int(px), int(py), hp.quaternion)\\n\\n # self.depth_map.add_depth(\\n # int(px), int(py),\\n # hp.worldpos()[2] * 1000)\\n\\n if np.all(self.annotation_img == 0):\\n print('out of camera')\\n return False\\n\\n self.annotation_img \\\\\\n = self.annotation_img / self.annotation_img.max() * 255\\n self.annotation_img = self.annotation_img.astype(np.uint8)\\n\\n self.rotations = self.rotation_map.rotations\\n\\n # self.hanging_points_depth = self.depth_map.on_depth_image(self.depth)\\n\\n return True\",\n \"def create_binary_annotation(key, value, annotation_type, host):\\n return zipkin_core.BinaryAnnotation(\\n key=key, value=value, annotation_type=annotation_type, host=host)\",\n \"def create_metadata(cls, mxd):\\n gxapi_cy.WrapMXD._create_metadata(GXContext._get_tls_geo(), mxd.encode())\",\n \"def __init__(self, metadata=None, default_xmlns=None):\\r\\n self.metadata = metadata or {}\\r\\n self.default_xmlns = default_xmlns\",\n \"def parse_anno(self, img_path, anno_path) -> dict:\\n anno_obj = dict()\\n\\n img = cv2.imread(img_path)\\n if len(img.shape) == 3:\\n h, w, d = img.shape[:3]\\n anno_obj['size'] = (w, h, d)\\n else:\\n h, w = img.shape[:2]\\n anno_obj['size'] = (w, h, 1)\\n\\n anno_array = np.loadtxt(anno_path, dtype=np.str, delimiter=',')\\n objects = list()\\n if len(anno_array.shape) == 1:\\n # Just one annotation object\\n obj = self.create_anno(anno_array)\\n if obj:\\n objects.append(obj)\\n else:\\n for anno_line in anno_array:\\n obj = self.create_anno(anno_line)\\n if obj:\\n objects.append(obj)\\n if len(objects) == 0:\\n return dict()\\n anno_obj['objects'] = objects\\n return anno_obj\",\n \"def create(self, **kwargs):\\n return Create(self, metadata=kwargs)\",\n \"def _proto2object(proto: Metadata_PB) -> \\\"Metadata\\\":\\n\\n return Metadata(\\n id=validate_type(_deserialize(blob=proto.id), UID, optional=True),\\n name=proto.name,\\n node=validate_type(_deserialize(blob=proto.node), Location),\\n )\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n filename = osp.join(self.data_prefix, video_info['filename']) \\n video_info['filename'] = filename\\n frame_dir = video_info['filename']\\n video_info['frame_dir'] = frame_dir \\n video_info['index'] = i\\n video_info['label'] = -1 \\n video_info['text'] = [video_info['text']] \\n video_infos.append(video_info) \\n del ann_info\\n return video_infos\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n filename = osp.join(self.data_prefix, video_info['filename']) \\n video_info['filename'] = filename\\n frame_dir = video_info['filename']\\n video_info['frame_dir'] = frame_dir \\n video_info['index'] = i\\n video_info['label'] = -1 \\n video_info['text'] = [video_info['text']] \\n video_infos.append(video_info) \\n del ann_info\\n return video_infos\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n info_dict = {} \\n info_dict['filename'] = video_info['vid_name']\\n frame_dir = info_dict['filename']\\n info_dict['frame_dir'] = frame_dir\\n info_dict['index'] = i\\n info_dict['label'] = video_info['answer_idx']\\n info_dict['answers'] = video_info['answers']\\n info_dict['question'] = video_info['q']\\n info_dict['subtitle'] = video_info['located_sub_text']\\n info_dict['frame_ind'] = video_info['located_frame']\\n info_dict['total_frames'] = video_info.get('total_frames', -1)\\n video_infos.append(info_dict) \\n del ann_info\\n\\n return video_infos\",\n \"def create_object_metadata(self, bucket_name, object_name, metadata_name, metadata_value):\\n\\n return h3lib.create_object_metadata(self._handle, bucket_name, object_name, metadata_name, metadata_value, self._user_id)\",\n \"def createAnn(bbox, \\n cat_id, \\n visibility=None,\\n img_id=None,\\n segmentation=None, \\n area=None, \\n iscrowd=0,\\n id=None):\\n\\n bbox = [float(format(elem, '.2f')) for elem in bbox]\\n\\n if segmentation is None:\\n segmentation = []\\n\\n if area is None:\\n # Use bounding box area\\n area = bbox[2] * bbox[3]\\n area = np.float32(area).tolist()\\n\\n annotation = {\\n \\\"id\\\": id,\\n \\\"image_id\\\": img_id,\\n \\\"category_id\\\": cat_id,\\n \\\"segmentation\\\": segmentation,\\n \\\"area\\\": area,\\n \\\"bbox\\\": bbox,\\n \\\"visibility\\\": visibility,\\n \\\"iscrowd\\\": iscrowd\\n }\\n\\n return annotation\",\n \"def create(self, identity, data=None, record=None, **kwargs):\\n record.metadata = data.get('metadata', {})\",\n \"def get_metadata(cls):\\n if cls._metadata is None:\\n pcm = PropertyClassMetadata()\\n cls.define_metadata(pcm)\\n cls._metadata = pcm\\n\\n # Check that the metadata was actually populated\\n # Check requires looking at private attributes\\n # pylint: disable-next=protected-access\\n if pcm._properties is None or pcm._default_units is None:\\n raise PropertyPackageError(\\n \\\"Property package did not populate all expected metadata.\\\"\\n )\\n return cls._metadata\",\n \"def create_common_metadata(cls, plugin, attributes, enabled=None):\\n metadata = attributes.setdefault('metadata', {\\n 'class': 'plugin',\\n 'toggleable': True,\\n 'weight': 70\\n })\\n metadata['label'] = plugin.title\\n if enabled is None:\\n enabled = plugin.enabled\\n metadata['enabled'] = enabled or metadata.get('enabled', False)\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n frame_dir = video_info['filename']\\n video_info['filename'] = osp.join(self.data_prefix, video_info['filename'])\\n video_info['frame_dir'] = frame_dir\\n video_info['index'] = i\\n \\n video_info['text'] = [video_info['text']] \\n video_infos.append(video_info) \\n del ann_info\\n\\n return video_infos\",\n \"def metadata(*args):\\n def metadata_wrapper():\\n pass\\n return metadata_wrapper\",\n \"def make_annotation_vertical(x, y):\\n text = str(x)\\n if len(text) > 33:\\n text = text[0:33]+\\\"..\\\"\\n return Annotation(\\n text=text, # text is the y-coord\\n showarrow=False, # annotation w/o arrows, default is True\\n x=x, # set x position\\n xref='x', # position text horizontally with x-coords\\n xanchor='center', # x position corresp. to center of text\\n yref='y', # set y position\\n yanchor='bottom', # position text vertically with y-coords\\n y=0, # y position corresp. to top of text\\n textangle=-90, # Rotating the text upwards\\n font=annotation.Font(\\n color='#FFFFFF', # set font color\\n size=13 # and size\\n )\\n )\",\n \"def metadata(self):\\n raise NotImplementedError('yet')\\n return OntGraphMetadata(self)\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n info_dict = {} \\n info_dict['filename'] = video_info['vid_name'] if 'filename' not in video_info else video_info['filename']\\n frame_dir = info_dict['filename']\\n info_dict['frame_dir'] = frame_dir\\n info_dict['index'] = i\\n info_dict['label'] = video_info['answer_idx']\\n info_dict['answers'] = video_info['answers'] if 'answers' in video_info else video_info['text']\\n info_dict['question'] = video_info['question'] if 'question' in video_info else \\\"\\\"\\n video_infos.append(info_dict) \\n del ann_info\\n\\n return video_infos\",\n \"def create_meta(prefix, dist, info_dir, extra_info):\\n # read info/index.json first\\n with open(join(info_dir, 'index.json')) as fi:\\n meta = json.load(fi)\\n # add extra info\\n meta.update(extra_info)\\n # write into <prefix>/conda-meta/<dist>.json\\n meta_dir = join(prefix, 'conda-meta')\\n if not isdir(meta_dir):\\n os.makedirs(meta_dir)\\n with open(join(meta_dir, 'history'), 'w') as fo:\\n fo.write('')\\n with open(join(meta_dir, dist + '.json'), 'w') as fo:\\n json.dump(meta, fo, indent=2, sort_keys=True)\",\n \"def load_annotations(self):\\n assert self.ann_file.endswith('.pkl')\\n ann_info = hload_pkl(self.ann_file)\\n\\n video_infos = []\\n for i, video_info in enumerate(ann_info):\\n if isinstance(video_info['text'], str):\\n video_info['text'] = [video_info['text']]\\n for text in video_info['text']:\\n info = {}\\n frame_dir = video_info['filename']\\n filename = osp.join(self.data_prefix, video_info['filename']+'.mp4') \\n info['filename'] = filename\\n info['frame_dir'] = frame_dir\\n info['index'] = i\\n info['label'] = -1 if 'answer_idx' not in video_info else video_info['answer_idx']\\n info['text'] = [text]\\n if self.is_ret:\\n pass\\n elif self.is_mc:\\n info['clip_text_candidate'] = [0, 1, 2, 3, 4]\\n elif self.is_qa:\\n pass\\n video_infos.append(info) \\n del ann_info\\n\\n return video_infos\",\n \"def build_meta(self, root):\\n ns_dc = {\\\"dc\\\": self.namespace_dc, }\\n metadata = ET.SubElement(root, \\\"metadata\\\", nsmap=ns_dc)\\n ET.SubElement(metadata,\\n '{%s}title' % self.namespace_dc).text = self.title\\n ET.SubElement(metadata,\\n '{%s}creator' % self.namespace_dc).text = self.author\\n ET.SubElement(metadata,\\n '{%s}identifier' % self.namespace_dc,\\n id='bookid').text = \\\"urn:uuid:%s\\\" % self.bookid\\n ET.SubElement(metadata,\\n '{%s}language' % self.namespace_dc).text = self.language\\n ET.SubElement(metadata, 'meta', name=\\\"cover\\\", content=\\\"cover-image\\\")\",\n \"def __init__(self, dictionary: dict[str, t.Any]) -> None:\\n missing_keys = self.__annotations__.keys() - dictionary.keys()\\n if missing_keys:\\n raise KeyError(f\\\"Fetched object lacks expected keys: {missing_keys}\\\")\\n for annotation in self.__annotations__:\\n setattr(self, annotation, dictionary[annotation])\",\n \"def annotate(self):\\n annotations = {}\\n\\n text_keys = ['fontsize', 'family']\\n text_config = self.config.filter(keys=text_keys, prefix='text_')\\n\\n # suptitle\\n suptitle = self.config.get('suptitle')\\n if suptitle is not None:\\n suptitle_config = self.config.filter(prefix='suptitle_')\\n suptitle_config.update(text_config, skip_duplicates=True)\\n\\n suptitle_wrap_config = suptitle_config.filter(prefix='wrap_')\\n if suptitle_wrap_config:\\n suptitle = wrap_by_delimiter(suptitle, **suptitle_wrap_config)\\n\\n annotations['suptitle'] = self.figure.suptitle(suptitle, **suptitle_config)\\n\\n self.figure.tight_layout()\\n\\n return annotations\",\n \"def add_metadata(self, metadata: dict) -> None:\",\n \"def annotate(self, **annotations):\\n _check_annotations(annotations)\\n self.annotations.update(annotations)\",\n \"def Metadata():\\n def _CreateMetadata(unused_none):\\n global _metadata\\n if not _metadata:\\n _metadata = _GCEMetadata()\\n _metadata_lock.lock(function=_CreateMetadata, argument=None)\\n _metadata_lock.unlock()\\n return _metadata\",\n \"def annotate(self, ax):\\n annotation = ax.annotate(self.template, xy=(0, 0), ha='right',\\n xytext=self.offsets, textcoords='offset points', va='bottom',\\n bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\\n arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')\\n )\\n annotation.set_visible(False)\\n return annotation\",\n \"def annotate(self, ax):\\n annotation = ax.annotate(self.template, xy=(0, 0), ha='right',\\n xytext=self.offsets, textcoords='offset points', va='bottom',\\n bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\\n arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')\\n )\\n annotation.set_visible(False)\\n return annotation\",\n \"def DeclareMetadata(self, label, entry, definition=None):\\n if isinstance(self.parent, QTIElement):\\n self.parent.DeclareMetadata(label, entry, definition)\\n else:\\n pass\",\n \"def get_annotations(self):\\n ann = wfdb.rdann(self.patient_number, 'atr', pb_dir='mitdb', return_label_elements=['symbol', 'label_store',\\n 'description'],\\n summarize_labels=True)\\n\\n mit_bih_labels_str = ann.symbol\\n\\n labels_locations = ann.sample\\n\\n labels_description = ann.description\\n\\n return mit_bih_labels_str, labels_locations, labels_description\",\n \"def __init__(self, annotation_file=None):\\n # load dataset\\n self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()\\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\\n if not annotation_file == None:\\n print('loading annotations into memory...')\\n tic = time.time()\\n dataset = json.load(open(annotation_file, 'r'))\\n assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))\\n print('Done (t={:0.2f}s)'.format(time.time()- tic))\\n self.dataset = dataset\\n self.createIndex()\",\n \"def createAnnotationRDF(self, rouri, resuri, bodyuri):\\n annotation = (ANNOTATION_TEMPLATE%\\n { \\\"xmlbase\\\": str(rouri)\\n , \\\"resuri\\\": str(resuri)\\n , \\\"bodyuri\\\": str(bodyuri)\\n })\\n return annotation\",\n \"def readFromMetadata(cls, context, fqId):\\n newInstance = AssetProvenance()\\n (newInstance.section, newInstance.name) = fqId.split(GenericMetadata.COMPOUND_KEY_SEP)\\n \\n provenance = GenericMetadata.readProvenanceEntries(context)\\n keyProto = fqId + GenericMetadata.COMPOUND_KEY_SEP\\n dcIdentifier = keyProto + 'dc.identifier'\\n newInstance.dcIdentifier = provenance[dcIdentifier]\\n dcSource = keyProto + 'dc.source'\\n newInstance.dcSource = provenance[dcSource]\\n dcTitle = keyProto + 'dc.title'\\n newInstance.dcTitle = provenance[dcTitle]\\n dcDate = keyProto + 'dc.date'\\n newInstance.dcDate = datetime.strptime(provenance[dcDate], AssetProvenance.FMT_DATE)\\n dcPublisher = keyProto + 'dc.publisher'\\n newInstance.dcPublisher = provenance[dcPublisher]\\n dcDescription = keyProto + 'dc.description'\\n newInstance.dcDescription = provenance[dcDescription]\\n processingNotes = keyProto + 'processing_notes'\\n newInstance.processingNotes = provenance[processingNotes]\\n \\n return newInstance\",\n \"def metadata(self, metadata: Mapping[str, str]):\\r\\n self._metadata = metadata\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64496595","0.62409776","0.60126007","0.5973326","0.597268","0.58234113","0.5532421","0.5525132","0.548961","0.54375076","0.5433396","0.54108304","0.54048735","0.53936356","0.5359755","0.5265134","0.52208763","0.5198275","0.5187576","0.5115082","0.51027685","0.51003003","0.5092918","0.5087442","0.5064542","0.5028813","0.50282574","0.49845243","0.49628276","0.49604398","0.49530515","0.49422503","0.49127528","0.49008912","0.48985904","0.48890042","0.48827633","0.48809332","0.48706782","0.48694715","0.48677447","0.48628387","0.48411286","0.4819149","0.4816311","0.48098335","0.48043147","0.48031223","0.47958028","0.47909352","0.47889897","0.47885382","0.47816294","0.4778055","0.4777698","0.47709695","0.47510788","0.47425193","0.47407758","0.47377086","0.4737325","0.4729704","0.47293147","0.47289324","0.47229928","0.47206542","0.47201702","0.47177684","0.4700952","0.46988076","0.46869123","0.46748275","0.46748275","0.46681717","0.46609506","0.4659162","0.4654761","0.46492428","0.46438792","0.46309364","0.46303374","0.46233556","0.46190044","0.46173388","0.46156394","0.46127534","0.46075976","0.45929977","0.45837057","0.4583301","0.45793825","0.45790818","0.4576831","0.4576831","0.45748723","0.4573342","0.45673537","0.4566818","0.45656648","0.45597503"],"string":"[\n \"0.64496595\",\n \"0.62409776\",\n \"0.60126007\",\n \"0.5973326\",\n \"0.597268\",\n \"0.58234113\",\n \"0.5532421\",\n \"0.5525132\",\n \"0.548961\",\n \"0.54375076\",\n \"0.5433396\",\n \"0.54108304\",\n \"0.54048735\",\n \"0.53936356\",\n \"0.5359755\",\n \"0.5265134\",\n \"0.52208763\",\n \"0.5198275\",\n \"0.5187576\",\n \"0.5115082\",\n \"0.51027685\",\n \"0.51003003\",\n \"0.5092918\",\n \"0.5087442\",\n \"0.5064542\",\n \"0.5028813\",\n \"0.50282574\",\n \"0.49845243\",\n \"0.49628276\",\n \"0.49604398\",\n \"0.49530515\",\n \"0.49422503\",\n \"0.49127528\",\n \"0.49008912\",\n \"0.48985904\",\n \"0.48890042\",\n \"0.48827633\",\n \"0.48809332\",\n \"0.48706782\",\n \"0.48694715\",\n \"0.48677447\",\n \"0.48628387\",\n \"0.48411286\",\n \"0.4819149\",\n \"0.4816311\",\n \"0.48098335\",\n \"0.48043147\",\n \"0.48031223\",\n \"0.47958028\",\n \"0.47909352\",\n \"0.47889897\",\n \"0.47885382\",\n \"0.47816294\",\n \"0.4778055\",\n \"0.4777698\",\n \"0.47709695\",\n \"0.47510788\",\n \"0.47425193\",\n \"0.47407758\",\n \"0.47377086\",\n \"0.4737325\",\n \"0.4729704\",\n \"0.47293147\",\n \"0.47289324\",\n \"0.47229928\",\n \"0.47206542\",\n \"0.47201702\",\n \"0.47177684\",\n \"0.4700952\",\n \"0.46988076\",\n \"0.46869123\",\n \"0.46748275\",\n \"0.46748275\",\n \"0.46681717\",\n \"0.46609506\",\n \"0.4659162\",\n \"0.4654761\",\n \"0.46492428\",\n \"0.46438792\",\n \"0.46309364\",\n \"0.46303374\",\n \"0.46233556\",\n \"0.46190044\",\n \"0.46173388\",\n \"0.46156394\",\n \"0.46127534\",\n \"0.46075976\",\n \"0.45929977\",\n \"0.45837057\",\n \"0.4583301\",\n \"0.45793825\",\n \"0.45790818\",\n \"0.4576831\",\n \"0.4576831\",\n \"0.45748723\",\n \"0.4573342\",\n \"0.45673537\",\n \"0.4566818\",\n \"0.45656648\",\n \"0.45597503\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56469256"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":94094,"cells":{"query":{"kind":"string","value":"Create a filelevel Metadata object."},"document":{"kind":"string","value":"def __init__(self, title='', artist='', release='', duration=None,\n identifiers=None, jams_version=None):\n super(FileMetadata, self).__init__()\n\n if jams_version is None:\n jams_version = __VERSION__\n\n if identifiers is None:\n identifiers = Sandbox()\n\n self.title = title\n self.artist = artist\n self.release = release\n self.duration = duration\n self.identifiers = Sandbox(**identifiers)\n self.jams_version = jams_version"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_file_metadata(self):\n metadata = self.__file.require_group(METADATA)\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\"%Y-%m-%d\"))\n self.__write_value(metadata, SDK_VERSION, __version__)","def gen_meta(self, filename):\n nf_meta = {}\n nf_meta['filename'] = filename\n nf_meta['deleted'] = 0\n\n # http://stackoverflow.com/a/5297483\n nf_meta['key'] = hashlib.md5(str(filename).encode('utf-8')).hexdigest()\n self.log.debug(\"Note File Meta Key: %s\", nf_meta['key'])\n\n path = self.config.get_config('cfg_nt_path')\n\n # WARNING THIS IS PLATFORM SPECIFIC\n nf_meta['createdate'] = os.stat(path + \"/\" + filename).st_birthtime\n self.log.debug(\"Note File Meta Created: %s [%s]\", nf_meta['createdate'], time.ctime(nf_meta['createdate']))\n\n nf_meta['modifydate'] = os.stat(path + \"/\" + filename).st_mtime\n self.log.debug(\"Note File Meta Modified: %s [%s]\", nf_meta['modifydate'], time.ctime(nf_meta['modifydate']))\n\n return nf_meta","def create_file_meta_data(vk4_container, args):\n log.debug(\"Entering create_file_meta_data()\")\n\n header_list = list()\n header_list.append(args.layer)\n header_list.append('\\n')\n header_list.append('File name')\n header_list.append(args.input)\n header_list.append('Title')\n header_list.append(args.input[:-4])\n header_list.append('Measurement date')\n header_list.append(str(vk4_container.measurement_conditions['month']) + '\\\\' +\n str(vk4_container.measurement_conditions['day']) + '\\\\' +\n str(vk4_container.measurement_conditions['year']))\n header_list.append('Measurement time')\n header_list.append(str(vk4_container.measurement_conditions['hour']) + ':' +\n str(vk4_container.measurement_conditions['minute']) + ':' +\n str(vk4_container.measurement_conditions['second']))\n # User mode?\n header_list.append('Objective lens')\n header_list.append(vk4_container.string_data['lens_name'] + ' ' +\n str(vk4_container.measurement_conditions['lens_magnification'] / 10.0) + 'x')\n header_list.append('Numerical Aperture')\n header_list.append(vk4_container.measurement_conditions['num_aperture'] / 1000.0)\n # Size? Standard?\n # Mode? Surface profile?\n # RPD? OFF?\n header_list.append('Quality')\n header_list.append('Skip 4 lines')\n header_list.append('Pitch (um)')\n header_list.append(vk4_container.measurement_conditions['pitch'] / 1000.0)\n header_list.append('Z measurement distance (um)')\n header_list.append(vk4_container.measurement_conditions['distance'] / 1000.0)\n # Double scan? OFF?\n header_list.append('Brightness 1')\n header_list.append(vk4_container.measurement_conditions['PMT_gain'])\n header_list.append('Brightness 2')\n br_2 = vk4_container.measurement_conditions['PMT_gain_2']\n header_list.append('---') if br_2 == 0 else header_list.append(br_2)\n # Not sure how they got ND filter to 30% in example csv\n header_list.append('ND filter (%)')\n header_list.append(vk4_container.measurement_conditions['ND_filter'] * 30)\n header_list.append('Optical zoom')\n header_list.append(vk4_container.measurement_conditions['optical_zoom'] / 10.0)\n # Average count? 1 time?\n # Filter? OFF?\n # Fine mode? ON?\n header_list.append('Line count')\n l_count = vk4_container.measurement_conditions['number_of_lines']\n header_list.append(l_count)\n\n header_list.append('Line position1')\n if l_count == 0:\n header_list.append('---')\n else:\n header_list.append(vk4_container.measurement_conditions['reserved_1'][0])\n\n header_list.append('Line position2')\n if l_count == 0:\n header_list.append('---')\n else:\n header_list.append(vk4_container.measurement_conditions['reserved_1'][1])\n\n header_list.append('Line position3')\n if l_count == 0:\n header_list.append('---')\n else:\n header_list.append(vk4_container.measurement_conditions['reserved_1'][2])\n\n header_list.append('Camera gain (db)')\n header_list.append(vk4_container.measurement_conditions['camera_gain'] * 6)\n header_list.append('Shutter speed')\n header_list.append(vk4_container.measurement_conditions['shutter_speed'])\n header_list.append('White balance mode')\n wb_mode = vk4_container.measurement_conditions['white_balance_mode']\n header_list.append('Auto') if wb_mode == 1 else header_list.append(wb_mode)\n header_list.append('White balance R')\n header_list.append(vk4_container.measurement_conditions['white_balance_red'])\n header_list.append('White balance B')\n header_list.append(vk4_container.measurement_conditions['white_balance_blue'])\n header_list.append('Intensity correction mode')\n header_list.append('Gamma correction')\n header_list.append('Gamma correction value')\n header_list.append(vk4_container.measurement_conditions['gamma'] / 100.0)\n header_list.append('Gamma offset (%)')\n header_list.append(vk4_container.measurement_conditions['gamma_correction_offset'] /\n 65536.0)\n # W/B inversion? OFF?\n # Head type? VK-X110?\n # Correct intensity eccentricity? OFF?\n # Correct field curvature? OFF?\n header_list.append('XY calibration (nm/pixel)')\n header_list.append(vk4_container.measurement_conditions['x_length_per_pixel'] / 1000.0)\n header_list.append('Z calibration (nm/digit)')\n header_list.append(vk4_container.measurement_conditions['z_length_per_digit'] / 1000.0)\n # Saturation?\n # Contrast?\n # Brightness?\n # AI noise elimination? Auto(ON)?\n # Angled surface noise filter? Auto(OFF)?\n header_list.append('Width')\n header_list.append(vk4_container.image_width)\n header_list.append('Height')\n header_list.append(vk4_container.image_height)\n # Skip amount? 1?\n\n out_type = args.type\n if out_type == 'hcsv':\n log.debug(\"Exiting create_file_meta_data() where out_type == %s\" % out_type)\n return np.reshape(header_list, (len(header_list) // 2, 2))\n else:\n # Can use a dict to attach info to an image using PILs Image module\n meta_dict = dict()\n for n in range(0, len(header_list), 2):\n meta_dict[header_list[n]] = header_list[n + 1]\n\n log.debug(\"Exiting create_file_meta_data() where out_type == %s\" % out_type)\n return meta_dict","def create_metadata(self) -> _metadata_fb.CustomMetadataT:\n custom_metadata_field = _metadata_fb.CustomMetadataT()\n custom_metadata_field.name = self.name\n custom_metadata_field.data = b'\\x01\\x02'\n return custom_metadata_field","def create(cls, file_path: str, file_name: str):\n\n full_path = os.path.join(file_path, file_name)\n info = os.stat(full_path)\n\n new_file_info = FileInfo(\n path=file_path,\n name=file_name,\n size=info.st_size)\n return new_file_info","def _create_object_directory(directory, metadata):\n if directory.exists():\n raise IOError(\"The directory '\" + str(directory) + \"' already exists\")\n valid_types = [DATASET_TYPENAME, FILE_TYPENAME, GROUP_TYPENAME]\n typename = metadata[EXDIR_METANAME][TYPE_METANAME]\n if typename not in valid_types:\n raise ValueError(\"{typename} is not a valid typename\".format(typename=typename))\n directory.mkdir()\n meta_filename = directory / META_FILENAME\n with meta_filename.open(\"w\", encoding=\"utf-8\") as meta_file:\n if metadata == _default_metadata(typename):\n # if it is the default, we know how to print it fast\n metadata_string = (''\n '{exdir_meta}:\\n'\n ' {type_meta}: \"{typename}\"\\n'\n ' {version_meta}: {version}\\n'\n '').format(\n exdir_meta=EXDIR_METANAME,\n type_meta=TYPE_METANAME,\n typename=typename,\n version_meta=VERSION_METANAME,\n version=1\n )\n else:\n from io import StringIO\n with StringIO() as buf:\n yaml.YAML(typ=\"safe\", pure=True).dump(metadata, buf)\n metadata_string = buf.getvalue()\n\n try:\n meta_file.write(metadata_string)\n except TypeError:\n # NOTE workaround for Python 2.7\n meta_file.write(metadata_string.decode('utf8'))","def _get_file_meta(cls, file=None, file_path=None):\n if not file and file_path:\n file = open(file_path, 'r')\n file_body = file.read()\n meta = {\n 'title': file.name, # read title from html\n 'subtitle': 'dupa', # read from html\n 'slug': os.path.splitext(os.path.basename(file.name))[0],\n 'abstract_html': 'Abstract', \n 'body_html': file_body,\n 'tags': [db.Category('one tag'), db.Category('second tag')],\n }\n if file_path:\n meta.update({\n 'date_created': datetime.datetime.fromtimestamp(os.path.getctime(file_path)),\n 'date_updated': datetime.datetime.fromtimestamp(os.path.getmtime(file_path)),\n })\n return meta","def tests_ti_file_create_2(self):\n metadata = {\n 'size': 84504,\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\n }\n file_indicator = self.ti.file(**metadata)\n file_indicator.delete()\n\n assert file_indicator.data['sha1'] == metadata['sha1']\n response = file_indicator.create()\n assert response.ok\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\n\n assert file_indicator.data['sha256'] == metadata['sha256']\n assert file_indicator.data['sha1'] == metadata['sha1']\n\n response = file_indicator.update()\n assert response.ok\n file_indicator.delete()","def metadata_obj(filelike):\n\tif test_hachoir_extension(filelike.name):\n\t\tmetadata = metadata_for_filelike(filelike)\n\t\tif metadata:\n\t\t\tdata = dict([\n\t\t\t\t(data.key, data.values[0].value)\n\t\t\t\tfor data in metadata\n\t\t\t\tif data.values\n\t\t\t\t])\n\t\telse:\n\t\t\tdata=None\n\telif test_3D_extension(filelike.name):# 3D not in the extention \n\t\tdata = {'mime_type':'model'}\n\telse:\n\t\tdata = None\n\t\t\n\treturn data","def generate_metadata_files(self):\n\n data_folder = self.get_data_folder(mode='absolute')\n\n parents = (data_folder / '_').parents\n\n for mfile in self.mdata:\n for regex, level in METADATA_LEVEL_BY_NAME.items():\n if re.compile(regex).match(mfile.name):\n create_file(mfile, parents[(3-level)] / mfile.name,\n mode='copy')","def metadata(self, metadata):\n return Metadata(metadata)","def create_metadata(scene: \"Scenemaker\") -> None:\r\n create_datadir()\r\n\r\n with open(dirpath / cng.GENERATED_DATA_DIR / cng.METADATA_FILE, \"w+\") as f:\r\n f.write(str(scene.num2name))","def build_ns_file_metadata(file_type):\r\n file_description = f\"Your Nightscout {file_type} data, last updated at {datetime.utcnow()} UTC.\"\r\n file_tags = [\"open-aps\", \"Nightscout\", file_type, \"json\"]\r\n file_updated = str(datetime.now())\r\n return {\"tags\": file_tags, \"description\": file_description, \"updated_at\": file_updated}","def generate_metadata(self):\n self.metadata = {\n 'title': os.path.basename(self.source_file).rsplit('.', 1)[0],\n 'url': self.relative_destination_file,\n 'full_path': os.path.dirname(self.relative_destination_file),\n 'short_path': self.shorten_path(\n os.path.dirname(self.relative_destination_file))\n }","def _update_filesystem_metadata(self, metadata):\n directory, fname = os.path.split(self.fname)\n fbase = os.path.splitext(fname)[0]\n \n # Test for presence and size of zip file\n zip_file = fbase + '.zip'\n zip_path = os.path.join(directory, zip_file)\n \n if os.path.isfile(zip_path):\n location = 'on_disk'\n data_file_size = os.path.getsize(zip_path)\n else:\n location = 'on_tape'\n data_file_size = 0\n \n # Test for presence of quick look PNG file\n quicklook_file = fbase + '.png'\n quicklook_path = os.path.join(directory, quicklook_file)\n \n if not os.path.isfile(quicklook_path):\n quicklook_file = ''\n\n # Add to metadata dictionary\n item_map = {'directory': directory, 'metadata_file': fname,\n 'data_file': zip_file, 'location': location, \n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\n \n for key, value in item_map.items():\n metadata[key] = value","def get_metadata_from_filename(file_name: str) -> namedtuple:\n if os.path.isabs(f):\n file_name = os.path.basename(file_name)\n original_image_name = file_name.split('-')[0]\n x_pos = int(file_name.split('.')[-2].split('+')[-2:][0])\n Metadata = namedtuple('Metadata', ['original_image_name', 'x_pos'])\n return Metadata(original_image_name, x_pos)","def metadata(filepath: str, overlay: str = None) -> Entry:\n md5 = hashlib.md5()\n mime = None\n\n # Read the file in chunks.\n try:\n with open(filepath, \"rb\") as fin:\n while chunk := fin.read(CHUNK_SIZE):\n md5.update(chunk)\n\n # Only attempt to determine the filetype on the first chunk.\n if not mime and fin.tell() <= CHUNK_SIZE:\n mime = archive.get_mimetype(chunk)\n except OSError as err:\n raise FileAccessException(f\"Unable to open file at {filepath}: {err}\")\n\n return Entry(\n path=filepath,\n md5=md5.hexdigest(),\n mime=mime,\n overlay=overlay,\n )","def gen_metadata(args):\n with open(args.bibfile) as bibfile:\n bib_db = BibTexParser(common_strings=True).parse_file(bibfile)\n entries = sorted(list(bib_db.entries),\n key=lambda x: x['year'], reverse=True)\n list([update_file(entry) for entry in entries])\n annotations = [entry_to_annotation(entry, args.PI) for entry in entries]\n stream = open(args.metadata, 'w')\n yaml.dump(annotations, stream, width=192, default_flow_style=False)\n stream.close()","def make_nwbfile_from_metadata(metadata: dict) -> NWBFile:\n\n # Validate metadata\n schema_path = Path(__file__).resolve().parent.parent / \"schemas/base_metadata_schema.json\"\n base_metadata_schema = load_dict_from_file(file_path=schema_path)\n validate_metadata(metadata=metadata, schema=base_metadata_schema)\n\n nwbfile_kwargs = metadata[\"NWBFile\"]\n # convert ISO 8601 string to datetime\n if isinstance(nwbfile_kwargs.get(\"session_start_time\"), str):\n nwbfile_kwargs[\"session_start_time\"] = datetime.fromisoformat(nwbfile_kwargs[\"session_start_time\"])\n if \"session_description\" not in nwbfile_kwargs:\n nwbfile_kwargs[\"session_description\"] = \"No description.\"\n if \"identifier\" not in nwbfile_kwargs:\n nwbfile_kwargs[\"identifier\"] = str(uuid.uuid4())\n if \"Subject\" in metadata:\n nwbfile_kwargs[\"subject\"] = metadata[\"Subject\"]\n # convert ISO 8601 string to datetime\n if \"date_of_birth\" in nwbfile_kwargs[\"subject\"] and isinstance(nwbfile_kwargs[\"subject\"][\"date_of_birth\"], str):\n nwbfile_kwargs[\"subject\"][\"date_of_birth\"] = datetime.fromisoformat(\n nwbfile_kwargs[\"subject\"][\"date_of_birth\"]\n )\n nwbfile_kwargs[\"subject\"] = Subject(**nwbfile_kwargs[\"subject\"])\n\n return NWBFile(**nwbfile_kwargs)","def build_metadata_from_file(tmp_crawler_folder, abs_data_path, commitTime):\n m = MetaData()\n\n post_path = abs_data_path[len(tmp_crawler_folder):]\n if post_path[0] == '/':\n post_path = post_path[1:]\n\n m['mimeType'] = mime.get_mime(abs_data_path)\n m['url'] = MetaData.get_url_from_path(post_path)\n m['domain'] = MetaData.get_domain_from_path(post_path)\n m['path'] = MetaData.get_content_path_from_tmp(abs_data_path, m['domain'])\n m['tmpPath'] = abs_data_path\n m['createTime'] = utl.get_ctime(abs_data_path)\n m['commitTime'] = commitTime\n m['title'] = extractor.get_title(abs_data_path, m['mimeType'])\n\n if not m['url'] in m['path']:\n print('WARNING, url not in path!')\n return m","def set_stewi_meta(file_name, stewiformat=''):\n stewi_meta = FileMeta()\n stewi_meta.name_data = file_name\n stewi_meta.category = stewiformat\n stewi_meta.tool = \"StEWI\"\n stewi_meta.tool_version = STEWI_VERSION\n stewi_meta.ext = WRITE_FORMAT\n stewi_meta.git_hash = GIT_HASH\n stewi_meta.date_created = datetime.now().strftime('%d-%b-%Y')\n return stewi_meta","def build_file(root, name, file_type, fname, file_cls=File):\n dfile = file_cls(root.add_node(name))\n dfile.store_type(file_type)\n dfile.write_fname(fname)\n return dfile","def convert_to_dataset_file_metadata(self, file_data, path):\r\n as_metadata = {\r\n 'path': os.path.join(path, file_data['name']),\r\n 'description': file_data['description']\r\n }\r\n\r\n schema = {}\r\n fields = []\r\n for column in file_data['columns']:\r\n field = {\r\n 'name': column['name'],\r\n 'title': column['description'],\r\n 'type': column['type']\r\n }\r\n fields.append(field)\r\n schema['fields'] = fields\r\n as_metadata['schema'] = schema\r\n\r\n return as_metadata","def metadata(self) -> dict:\n meta = {}\n meta['filename'] = self.filename\n meta['label'] = self.label\n meta['url'] = self.url\n\n return meta","def makeMetadata(self):\n # arbitrary values\n orientation = 0 * degrees\n flipX = False\n metadata = makeTanWcsMetadata(\n crpix = self.crpix,\n crval = self.crval,\n cdMatrix = makeCdMatrix(scale=self.scale, orientation=orientation, flipX=flipX),\n )\n self.assertEqual(metadata.nameCount(), 14)\n metadata.add(\"SIMPLE\", True)\n metadata.add(\"BITPIX\", 16)\n metadata.add(\"NAXIS\", 2)\n metadata.add(\"NAXIS1\", 500)\n metadata.add(\"NAXIS2\", 200)\n metadata.add(\"BZERO\", 32768)\n metadata.add(\"BSCALE\", 1)\n metadata.add(\"TIMESYS\", \"UTC\")\n metadata.add(\"UTC-OBS\", \"12:04:45.73\")\n metadata.add(\"DATE-OBS\", \"2006-05-20\")\n metadata.add(\"EXPTIME\", 5.0)\n metadata.add(\"COMMENT\", \"a comment\")\n metadata.add(\"COMMENT\", \"another comment\")\n metadata.add(\"EXTEND\", True)\n metadata.add(\"INHERIT\", False)\n metadata.add(\"LTV1\", 5)\n metadata.add(\"LTV2\", -10)\n metadata.add(\"ZOTHER\", \"non-standard\")\n return metadata","def _create_output_metadata(self):\n\n # Create output info.\n output_meta = _metadata_fb.TensorMetadataT()\n output_meta.name = \"probability\"\n output_meta.description = \"Probabilities of labels respectively.\"\n output_meta.content = _metadata_fb.ContentT()\n output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()\n output_meta.content.contentPropertiesType = (\n _metadata_fb.ContentProperties.FeatureProperties)\n label_file = _metadata_fb.AssociatedFileT()\n label_file.name = os.path.basename(self.model_info.label_file)\n label_file.description = (\"Labels for classification categories.\")\n label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS\n output_meta.associatedFiles = [label_file]\n return [output_meta]","def meta_info(self):\n\n if not self.meta_was_built:\n self.__meta_info = self.build_meta(self.dataset_path, self.file_types)\n self.meta_was_built = True\n\n return self.__meta_info","def _update_fileinfo(self, metadata_filename):\n \n # In case we delayed loading the metadata and didn't do it in\n # __init__ (such as with delegated metadata), then get the file\n # info now.\n \n # Save the path to the current metadata file for 'metadata_filename'.\n current_filepath = os.path.join(self.metadata_directory['current'],\n metadata_filename)\n # If the path is invalid, simply return and leave fileinfo unset.\n if not os.path.exists(current_filepath):\n self.fileinfo[current_filepath] = None\n return\n \n # Extract the file information from the actual file and save it\n # to the fileinfo store.\n file_length, hashes = tuf.util.get_file_details(current_filepath)\n metadata_fileinfo = tuf.formats.make_fileinfo(file_length, hashes)\n self.fileinfo[metadata_filename] = metadata_fileinfo","def __appendMetaData(self, filename):\n metadata = {'Model': 'LFM',\n 'Source': filename,\n 'Date processed': datetime.datetime.now(),\n 'Start date': self.startDate\n }\n \n self.data.append(key='meta',\n name='Metadata for LFM Solar Wind file',\n units='n/a',\n data=metadata)","def _CreateStorageFile(self):\n return sqlite_file.SQLiteStorageFile(storage_type=self._storage_type)","def get_metadata(self, file_id):\n pass","def build_metadata(meta):\n\n ret = copy.copy(meta) if meta else dict()\n\n ret['name'] = meta.get('name', '')\n\n if 'index' in meta:\n if isinstance(meta.get('index'), str):\n ret['index'] = Index(meta.get('index'))\n elif isinstance(meta.get('index'), Index):\n ret['index'] = meta.get('index')\n\n ret['utc'] = True\n if 'utc' in meta and isinstance(meta.get('utc'), bool):\n ret['utc'] = meta.get('utc')\n\n return pmap(ret)","def build_meta(self, root):\n ns_dc = {\"dc\": self.namespace_dc, }\n metadata = ET.SubElement(root, \"metadata\", nsmap=ns_dc)\n ET.SubElement(metadata,\n '{%s}title' % self.namespace_dc).text = self.title\n ET.SubElement(metadata,\n '{%s}creator' % self.namespace_dc).text = self.author\n ET.SubElement(metadata,\n '{%s}identifier' % self.namespace_dc,\n id='bookid').text = \"urn:uuid:%s\" % self.bookid\n ET.SubElement(metadata,\n '{%s}language' % self.namespace_dc).text = self.language\n ET.SubElement(metadata, 'meta', name=\"cover\", content=\"cover-image\")","def create(self, identity, data=None, record=None, **kwargs):\n record.metadata = data.get('metadata', {})","def create(self):\n self.create_file()","def _construct_standardized_metadata(self):\n return None","def extract_metadata(self):\n if self.is_generatable_file:\n logger.debug(\"Converting collected details to dict..\")\n if self.metadata_collector:\n self.metadata = MetadataToDict(\n metadata_collector=self.metadata_collector,\n file_import=self.file_import,\n )\n self.metadata.build_integration_dict()","def _gen_metadata_for_list(self, filename, **extra_data):\n file_size = os.stat(filename).st_size\n with open(filename, 'rb') as f:\n md5sum = compute_md5_hash(f)\n core_metadata = {\n 'filename': os.path.abspath(filename),\n 'md5sum': md5sum,\n 'file_size_bytes': file_size\n }\n return {**core_metadata, **extra_data}","def _get_metadata(self) -> Metadata:\n manifest = self._get_manifest()\n\n return Metadata(**manifest[\"metadata\"])","def from_metadata(cls, metadata, name=None, dimension=None):\n\n metadata = dict(expand_level_metadata(metadata))\n\n try:\n name = name or metadata.pop(\"name\")\n except KeyError:\n raise ModelError(\"No name specified in level metadata\")\n\n attributes = []\n for attr_metadata in metadata.pop(\"attributes\", []):\n attr = Attribute(dimension=dimension, **attr_metadata)\n attributes.append(attr)\n\n return cls(name=name, attributes=attributes, **metadata)","def extract_metadata(self):\n metadata_file_path = self.create_metadata_file(\".metadata.txt\")\n mt = self.mimetype\n metadata_processing_method = self.metadata_mimetype_methods.get(mt)\n if metadata_processing_method:\n # TODO: should we return metadata and write it here instead of in processing method?\n metadata_processing_method(metadata_file_path)","def define_metadata(cls, pcm):\n raise NotImplementedError()","def generate_custom_metadata(uuid, version, update_proto):\n COMPONENTS = E.components\n COMPONENT = E.component\n ID = E.id\n NAME = E.name\n SUMMARY = E.summary\n PROVIDES = E.provides\n FIRMWARE = E.firmware\n CUSTOM = E.custom\n VALUE = E.value\n RELEASES = E.releases\n RELEASE = E.release\n LOCATION = E.location\n CHECKSUM = E.checksum\n DESCRIPTION = E.description\n P = E.p\n SIZE = E.size\n REQUIRES = E.requires\n\n metadata_tree = COMPONENTS(\n COMPONENT(\n ID(\"com.hacker.firmware\"),\n NAME(\"TotallyNotMalicious\"),\n SUMMARY(\"This is fine\"),\n PROVIDES(\n FIRMWARE(\n uuid,\n type=\"flashed\",\n ),\n ),\n CUSTOM(\n VALUE(\n update_proto,\n key=\"LVFS::UpdateProtocol\",\n ),\n ),\n RELEASES(\n RELEASE(\n LOCATION(\n url_for(\"serve_cab\", _external=True, uuid=uuid, version=version, update_proto=update_proto)\n ),\n DESCRIPTION(\n P(\"Totally not malicious ;)\"),\n ),\n CHECKSUM(\n hashlib.sha1(generate_cab(uuid=uuid, version=version, update_proto=update_proto)).hexdigest(),\n type=\"sha1\", filename=\"poc.cab\", target=\"container\",\n ),\n SIZE(\"1337\", type=\"download\"),\n SIZE(\"0\", type=\"installed\"),\n urgency=\"high\", version=version, timestamp=\"1587399600\", install_duration=\"120\",\n ),\n ),\n REQUIRES(),\n type=\"firmware\",\n ),\n origin=\"lvfs\", version=\"0.9\",\n )\n\n metadata = etree.tostring(metadata_tree, pretty_print=True, xml_declaration=True, encoding=\"UTF-8\")\n\n return gzip.compress(metadata)","def create_file(toy_data, filename):\n root_file = ROOT.TFile.Open(filename, \"RECREATE\")\n \n bin_edges = toy_data[\"bin_edges\"]\n sig = create_th1f(bin_edges, toy_data[\"sig\"], toy_data[\"sig_stat\"])\n sig.SetName(\"signal\")\n\n data = create_th1f(bin_edges, toy_data[\"data\"], toy_data[\"data_stat\"])\n data.SetName(\"data\")\n\n bkg = create_th1f(bin_edges, toy_data[\"bkg\"], toy_data[\"bkg_stat\"])\n bkg.SetName(\"bkg\")\n\n root_file.Write()\n root_file.Close()","def _add_filename_metadata(self, extra_metadata): \n \n # Make sure product_info section exists\n extra_metadata.setdefault('product_info', {})\n \n file_name = os.path.basename(self.fname)\n fn_comps = file_name.split(\"_\")\n \n if self.__class__ == SAFESentinel1:\n component = fn_comps[2]\n if len(component) < 4: \n resolution = 'N/A'\n else:\n resolution = component[-1]\n \n extra_metadata['product_info']['Resolution'] = resolution\n \n # Add file/scan name \n extra_metadata['product_info']['Name'] = os.path.splitext(file_name)[0]\n \n # Add Satellite and Mission from the file path\n comp_1 = fn_comps[0].upper()\n extra_metadata['platform']['Mission'] = \"Sentinel-%s\" % comp_1[1]\n extra_metadata['platform']['Satellite'] = \"Sentinel-%s\" % comp_1[1:]","def assemble(metadata_file):\n\n def read(file):\n with open(file) as yaml:\n return load(yaml.read())\n\n def add_name(info):\n info['name'] = slugify(info['title'], separator='_')\n return info\n\n def get_files(filetype):\n filename = metadata_file.replace('metadata', filetype)\n folder = dirname(metadata_file)\n schema_files_pattern = join(folder, filename)\n return glob(schema_files_pattern)\n\n descriptor = add_name(read(metadata_file))\n resources = [add_name(read(file)) for file in get_files('resource')]\n model = get_files('model')\n\n descriptor['resources'] = resources\n if model and len(model) == 1:\n descriptor['model'] = model.pop()\n\n return DataPackage(descriptor)","def __init__(self, image, options):\n self._image = image\n self._options = options\n suffix = ''.join(random.choice(\n string.ascii_uppercase + string.digits) for _ in range(5))\n self._name = 'metadata-%s' % suffix","def create_file_hash_dict(cls, file, file_path):\n\n file_info = {}\n file_info['path'] = file_path\n file_info['hash'] = cls.get_256_hash_from_file(file_path)\n file_info['type'] = 'file'\n file_info['name'] = file\n file_info['perm'] = stat.S_IMODE(os.lstat(file_path).st_mode)\n\n return file_info","def _build_local_metadata_file(files, home=''):\n filepaths = [os.path.join(home, f) for f in files]\n shas = [_get_sha_metadata(f) for f in filepaths]\n metadata = dict(zip(files, shas))\n\n with open(LOCAL_METADATA_FILE, 'w') as f:\n f.write(json.dumps(metadata))","def generate_metadata(\n source_name,\n source_version\n):\n now = datetime.utcnow()\n write_recipt = str(uuid.uuid1())\n metadata_obj = {\n 'write_recipt': write_recipt,\n 'data_source': source_name,\n 'machine_source': platform.node(),\n 'version': source_version,\n 'package_version': _version.__version__,\n 'cron_datetime': now.isoformat()\n }\n\n return metadata_obj","def metadata_create():\n binding = {}\n url = 'https://upload.twitter.com/1.1/media/metadata/create.json'\n return _TwitterRequest('POST',\n url,\n 'rest:media',\n 'post-media-metadata-create',\n binding)","def __init__(self, metadata_folder='./'):\n self.metadata = self.load_metadata(metadata_folder)\n self.prefix = 'data/adience/faces/'\n return","def extract_metadata(file_name, token_type, create_labels, remove_stop_words):\n metadata = {'file': file_name}\n\n with open(file_name, 'r') as file:\n rows = file.readlines()\n\n rules = [\n ['Message-ID:', 'id'],\n ['Subject:', 'subject'],\n ]\n\n for (index, row) in enumerate(rows):\n row = row.lstrip('> \\t')\n for (pattern, prop) in rules:\n if row.startswith(pattern):\n metadata[prop] = row.replace(pattern,'')\n\n if 'body' not in metadata:\n if row.startswith('\\n'):\n metadata['body'] = '\\n'.join(rows[index:])\n\n elif '-----Original Message-----' in row:\n del metadata['body']\n\n if 'body' in metadata:\n if create_labels:\n metadata['label'] = create_label(metadata['body'])\n metadata['original_body'] = metadata['body']\n metadata['body'] = clean_text(metadata['body'], token_type, remove_stop_words)\n clean_subject = cleanse(metadata['subject'])\n metadata['subject'] = '' if clean_subject.count(' ') == len(clean_subject) else clean_subject\n\n return metadata","def _generate_metadata_kind(filename, targets=None, qa_group=None):\n db = LvfsDatabase(os.environ)\n db_firmware = LvfsDatabaseFirmware(db)\n items = db_firmware.get_items()\n store = appstream.Store('lvfs')\n for item in items:\n\n # filter\n if item.target == 'private':\n continue\n if targets and item.target not in targets:\n continue\n if qa_group and qa_group != item.qa_group:\n continue\n\n # add each component\n for md in item.mds:\n component = appstream.Component()\n component.id = md.cid\n component.kind = 'firmware'\n component.name = md.name\n component.summary = md.summary\n component.description = md.description\n if md.url_homepage:\n component.urls['homepage'] = md.url_homepage\n component.metadata_license = md.metadata_license\n component.project_license = md.project_license\n component.developer_name = md.developer_name\n\n # add provide\n if md.guid:\n prov = appstream.Provide()\n prov.kind = 'firmware-flashed'\n prov.value = md.guid\n component.add_provide(prov)\n\n # add release\n if md.version:\n rel = appstream.Release()\n rel.version = md.version\n rel.description = md.release_description\n if md.release_timestamp:\n rel.timestamp = md.release_timestamp\n rel.checksums = []\n rel.location = 'https://secure-lvfs.rhcloud.com/downloads/' + item.filename\n rel.size_installed = md.release_installed_size\n rel.size_download = md.release_download_size\n component.add_release(rel)\n\n # add container checksum\n if md.checksum_container:\n csum = appstream.Checksum()\n csum.target = 'container'\n csum.value = md.checksum_container\n csum.filename = item.filename\n rel.add_checksum(csum)\n\n # add content checksum\n if md.checksum_contents:\n csum = appstream.Checksum()\n csum.target = 'content'\n csum.value = md.checksum_contents\n csum.filename = md.filename_contents\n rel.add_checksum(csum)\n\n # add component\n store.add(component)\n\n # dump to file\n if not os.path.exists(DOWNLOAD_DIR):\n os.mkdir(DOWNLOAD_DIR)\n filename = os.path.join(DOWNLOAD_DIR, filename)\n store.to_file(filename)\n return filename","def add_metadata(self, metadata: dict) -> None:","def get_metadata(self):\n self.metadata = Metadata()\n document = openxmllib.openXmlDocument(path=self.path)\n self.metadata.add(document.allProperties, \"ooxml\")\n return self.metadata","async def get_file_metadata(\n location_id: LocationID, file_id: StorageFileID, user_id: UserID\n):","def metadata(self): # -> None:\n ...","def metadata(*args):\n def metadata_wrapper():\n pass\n return metadata_wrapper","def run(self, info):\n\n # Write the metadata to the file's xattrs\n self._downloader.to_screen('[metadata] Writing metadata to file\\'s xattrs')\n\n filename = info['filepath']\n\n try:\n xattr_mapping = {\n 'user.xdg.referrer.url': 'webpage_url',\n # 'user.xdg.comment': 'description',\n 'user.dublincore.title': 'title',\n 'user.dublincore.date': 'upload_date',\n 'user.dublincore.description': 'description',\n 'user.dublincore.contributor': 'uploader',\n 'user.dublincore.format': 'format',\n }\n\n num_written = 0\n for xattrname, infoname in xattr_mapping.items():\n\n value = info.get(infoname)\n\n if value:\n if infoname == 'upload_date':\n value = hyphenate_date(value)\n\n byte_value = value.encode('utf-8')\n write_xattr(filename, xattrname, byte_value)\n num_written += 1\n\n return [], info\n\n except XAttrUnavailableError as e:\n self._downloader.report_error(str(e))\n return [], info\n\n except XAttrMetadataError as e:\n if e.reason == 'NO_SPACE':\n self._downloader.report_warning(\n 'There\\'s no disk space left, disk quota exceeded or filesystem xattr limit exceeded. '\n + (('Some ' if num_written else '') + 'extended attributes are not written.').capitalize())\n elif e.reason == 'VALUE_TOO_LONG':\n self._downloader.report_warning(\n 'Unable to write extended attributes due to too long values.')\n else:\n msg = 'This filesystem doesn\\'t support extended attributes. '\n if compat_os_name == 'nt':\n msg += 'You need to use NTFS.'\n else:\n msg += '(You may have to enable them in your /etc/fstab)'\n self._downloader.report_error(msg)\n return [], info","def get_metadata(self):\n result = defaultdict(str)\n result.update(self.metadata)\n result['file_name'] = self.file_name\n return result","def metadata_file(self):\n return self._metadata_file","def create_file_attributes(permissions, time, size):\n\n return {\n 'st_mode': (stat.S_IFREG | permissions),\n 'st_ctime': time,\n 'st_mtime': time,\n 'st_atime': time,\n 'st_size': size,\n 'st_uid': os.getuid(),\n 'st_gid': os.getgid(),\n 'st_nlink': 1\n }","def write_metadata(file_name, metadata_dict, category='',\n datatype=\"inventory\", parameters=None):\n if (datatype == \"inventory\") or (datatype == \"source\"):\n meta = set_stewi_meta(file_name, stewiformat=category)\n if datatype == 'inventory':\n meta.tool_meta = {\"parameters\": parameters,\n \"sources\": metadata_dict}\n else:\n meta.tool_meta = metadata_dict\n write_metadata_to_file(paths, meta)\n elif datatype == \"validation\":\n file = (paths.local_path / 'validation' /\n f'{file_name}_validationset_metadata.json')\n with file.open('w') as fi:\n fi.write(json.dumps(metadata_dict, indent=4))","def create_metadata(data_dir):\n pool = multiprocessing.Pool()\n raw_feature_paths = [os.path.join(data_dir, \"features.jsonl\")]\n records = list(pool.imap(read_metadata_record, raw_feature_iterator(raw_feature_paths)))\n records = [dict(record, **{\"subset\": \"train\"}) for record in records]\n\n metadf = pd.DataFrame(records)[[\"sha256\", \"appeared\", \"subset\", \"label\"]]\n metadf.to_csv(os.path.join(data_dir, \"metadata.csv\"))\n print(\"\\n[Done] create_metadata\\n\")\n \n return metadf","def feature_static_metadata(self):\n # Get binary size\n self.features[\"size\"] = \\\n self.report.get(\"target\", {}).get(\"file\", {}).get(\"size\")\n\n # Get binary timestamp in the UNIX timestamp format\n str_dt = self.report.get(\"static\", {}).get(\"pe_timestamp\")\n ts = None\n if str_dt is not None:\n dt = datetime.datetime.strptime(str_dt, \"%Y-%m-%d %H:%M:%S\")\n ts = int(time.mktime(dt.timetuple()))\n self.features[\"timestamp\"] = ts\n\n # ExifTool output\n et_tokens = [\"FileDescription\", \"OriginalFilename\"]\n for token in et_tokens:\n self.features[token] = None\n for attr in self.report.get(\"static\", {}).get(\"pe_versioninfo\", []):\n attr_name = attr.get(\"name\")\n if attr_name in et_tokens:\n self.features[attr_name] = attr.get(\"value\")\n\n # Magic byte\n self.features[\"magic_byte\"] = \\\n self.report.get(\"target\", {}).get(\"file\", {}).get(\"type\")","def get_metadata_from_path(path):\n try:\n import yaml\n # assumes index card is in the top-level of path\n index_card = os.path.join(path, \"M_index.yaml\")\n with open(index_card, \"r\") as stream:\n file_info = yaml.safe_load(stream)\n\n metadata_dict = {}\n metadata_dict[\"book_id\"] = file_info[\"book_id\"]\n metadata_dict[\"timestamp_start\"] = file_info[\"start_time\"]\n metadata_dict[\"type\"] = file_info[\"type\"]\n metadata_dict[\"obsid\"] = _convert_book_id_to_obsid(file_info[\"book_id\"])\n # get optional bits\n if \"stop_time\" in file_info:\n metadata_dict[\"timestamp_end\"] = file_info[\"stop_time\"]\n if \"observatory\" in file_info:\n metadata_dict[\"observatory\"] = file_info[\"observatory\"]\n if \"telescope\" in file_info:\n metadata_dict[\"telescope\"] = file_info[\"telescope\"]\n if \"stream_ids\" in file_info:\n metadata_dict[\"stream_ids\"] = file_info[\"stream_ids\"]\n if \"subtype\" in file_info:\n metadata_dict[\"subtype\"] = file_info[\"subtype\"]\n if \"tags\" in file_info:\n metadata_dict[\"tags\"] = file_info[\"tags\"]\n if \"scanification\" in file_info:\n metadata_dict[\"scanification\"] = file_info[\"scanification\"]\n if \"hwp_rate_hz\" in file_info:\n metadata_dict[\"hwp_rate_hz\"] = file_info[\"hwp_rate_hz\"]\n if \"sequencer_ref\" in file_info:\n metadata_dict[\"sequencer_ref\"] = file_info[\"sequencer_ref\"]\n return metadata_dict\n except (ImportError, FileNotFoundError, KeyError):\n pass\n\n return None","def metadata(self, truncate: bool = False) -> Tuple[str, str]:\n\t\tif not self._closed:\n\t\t\tfilename = self.filename\n\t\t\tmd_filename = \"%s.file_md.json.gzip\" % (self.file_path)\n\t\t\tmd_mod_filename = \"%s.file_md.lastmod.gzip\" % (self.file_path)\n\t\t\tlogging.debug(\"Expanding metada (stored as %s.file_md.json.gzip)\", filename)\n\n\t\t\tlast_mod = self.last_modified()\n\t\t\tif os.path.isfile(md_filename):\n\t\t\t\tlogging.debug(\" Found previously extracted JSON file\")\n\t\t\t\tif truncate:\n\t\t\t\t\tself.clear_metadata()\n\t\t\t\telse:\n\t\t\t\t\tmd_json = load_gzipped_json_string(md_filename)\n\t\t\t\t\tmd_mod = load_gzipped_json_string(md_mod_filename)\n\t\t\t\t\tmd_parsed = json.loads(md_json)\n\t\t\t\t\t# check if cached metadata is up to date and\n\t\t\t\t\t# points to correct project folder and filename\n\t\t\t\t\t# if so return cache, otherwise clear it\n\t\t\t\t\tlogging.debug(\" md_mod: %s\", md_mod)\n\t\t\t\t\tlogging.debug(\" last_mod: %s\", last_mod)\n\t\t\t\t\tif md_mod != last_mod or md_parsed.project != self.project or md_parsed.filename != filename:\n\t\t\t\t\t\tself.clear_metadata()\n\t\t\t\t\telse:\n\t\t\t\t\t\tlogging.debug(\" Cache up to date\")\n\t\t\t\t\t\treturn (md_json, last_mod)\n\n\t\t\tds = self.ds\n\t\t\tattrs = ds.attrs.keys()\n\t\t\ttitle = filename if \"title\" not in attrs else ds.attrs.title\n\t\t\tdescr = \"\" if \"description\" not in attrs else ds.attrs.description\n\t\t\turl = \"\" if \"url\" not in attrs else ds.attrs.url\n\t\t\tdoi = \"\" if \"doi\" not in attrs else ds.attrs.doi\n\t\t\t# converts compact ISO timestamps to human-readable ones.\n\t\t\t# Example: \"20180130T155028.262458Z\" becomes \"2018/01/13 15:50\"\n\t\t\tlast_mod_humanreadable = \"{}/{}/{} {}:{}:{}\".format(last_mod[0:4], last_mod[4:6], last_mod[6:8], last_mod[9:11], last_mod[11:13], last_mod[13:15])\n\t\t\t# default to last_modified for older files that do\n\t\t\t# not have a creation_date field\n\t\t\tcreation_date = last_mod_humanreadable if \"creation_date\" not in attrs else ds.attrs.creation_date\n\t\t\t# get arbitrary col/row attribute, they are all lists\n\t\t\t# of equal size. The length equals total cells/genes\n\t\t\ttotal_cells = ds.shape[1]\n\t\t\ttotal_genes = ds.shape[0]\n\n\t\t\tmd_data = {\n\t\t\t\t\"project\": self.project,\n\t\t\t\t\"filename\": filename,\n\t\t\t\t\"dataset\": filename,\n\t\t\t\t\"title\": title,\n\t\t\t\t\"description\": descr,\n\t\t\t\t\"url\": url,\n\t\t\t\t\"doi\": doi,\n\t\t\t\t\"creationDate\": creation_date,\n\t\t\t\t\"lastModified\": last_mod_humanreadable,\n\t\t\t\t\"totalCells\": total_cells,\n\t\t\t\t\"totalGenes\": total_genes,\n\t\t\t}\n\t\t\tlogging.debug(\" Saving extracted metadata as JSON file\")\n\t\t\tmd_json = json.dumps(md_data)\n\t\t\tsave_gzipped_json_string(md_filename, md_json)\n\t\t\tsave_gzipped_json_string(md_mod_filename, json.dumps(last_mod))\n\t\t\treturn (md_json, last_mod)\n\t\treturn None","def snapshot_info(self) -> MetaFile:\n raise NotImplementedError","def _WriteStorageMetadata(self):\n stream_name = 'metadata.txt'\n if self._HasStream(stream_name):\n return\n\n stream_data = (\n '[plaso_storage_file]\\n'\n 'format_version: {0:d}\\n'\n 'serialization_format: {1:s}\\n'\n 'storage_type: {2:s}\\n'\n '\\n').format(\n self._FORMAT_VERSION, self.serialization_format, self.storage_type)\n\n stream_data = stream_data.encode('utf-8')\n self._WriteStream(stream_name, stream_data)","def __init__(self, debug=False, output_writer=None):\n super(RecycleBinMetadataFile, self).__init__(\n debug=debug, output_writer=output_writer)\n self.deletion_time = None\n self.format_version = None\n self.original_filename = None\n self.original_file_size = None","def metadata_name(filename):\n\tif test_hachoir_extension(filename):\n\t\tmetadata = metadata_for_file(filename)\n\t\tif metadata:\n\t\t\tdata = dict([\n\t\t\t\t(data.key, data.values[0].value)\n\t\t\t\tfor data in metadata\n\t\t\t\tif data.values\n\t\t\t\t])\n\t\telse:\n\t\t\tdata=None\n\telif test_3D_extension(filename):# 3D not in the extention \n\t\tdata = {'mime_type':'model'}\n\telse:\n\t\tdata=None\n\treturn data","def _extract_metadata(self) -> None:\n self.log(\"Extracting metadata.\")\n image_paths: list[Path] = []\n for ext in (\"jpg\", \"jpeg\", \"png\"):\n image_paths.extend(self._base_dir.glob(f\"**/*.{ext}\"))\n image_paths_str = [str(image.relative_to(self._base_dir)) for image in image_paths]\n filepaths = pd.Series(image_paths_str)\n metadata = cast(\n pd.DataFrame,\n filepaths.str.split(\"/\", expand=True).rename( # type: ignore[attr-defined]\n columns={0: \"superclass\", 1: \"concept\", 2: \"context\", 3: \"filename\"}\n ),\n )\n metadata[\"filepath\"] = filepaths\n metadata.sort_index(axis=1, inplace=True)\n metadata.sort_values(by=[\"filepath\"], axis=0, inplace=True)\n metadata = self._label_encode_metadata(metadata)\n metadata.to_csv(self._metadata_path)","def parse_metadata_file(self, file):\n\n file_keys = list(file.keys())\n\n if 'labelAnnotations' in file_keys:\n file_annots = file['labelAnnotations']\n file_top_score = np.asarray(\n [x['score'] for x in file_annots]).mean()\n file_top_desc = [x['description'] for x in file_annots]\n else:\n file_top_score = np.nan\n file_top_desc = ['']\n\n file_colors = file['imagePropertiesAnnotation']['dominantColors'][\n 'colors']\n file_crops = file['cropHintsAnnotation']['cropHints']\n\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\n file_color_pixelfrac = np.asarray(\n [x['pixelFraction'] for x in file_colors]).mean()\n\n file_crop_conf = np.asarray(\n [x['confidence'] for x in file_crops]).mean()\n\n if 'importanceFraction' in file_crops[0].keys():\n file_crop_importance = np.asarray(\n [x['importanceFraction'] for x in file_crops]).mean()\n else:\n file_crop_importance = np.nan\n\n df_metadata = {\n 'annots_score': file_top_score,\n 'color_score': file_color_score,\n 'color_pixelfrac': file_color_pixelfrac,\n 'crop_conf': file_crop_conf,\n 'crop_importance': file_crop_importance,\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\n }\n\n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\n df_metadata = df_metadata.add_prefix('metadata_')\n\n return df_metadata","def metadata(self):\n return parse_metadata(self.metadata_path())","def metadata(host_directory_path):\n return create_volume_string(host_directory_path, \"/bbx/metadata\", False)","def make_test_metadata(path):\n assert path, 'Please supply a nonempty path to store test dataset.'\n return create_test_dataset('file://{}'.format(path), range(ROWS_COUNT))","def create_meta(prefix, dist, info_dir, extra_info):\n # read info/index.json first\n with open(join(info_dir, 'index.json')) as fi:\n meta = json.load(fi)\n # add extra info\n meta.update(extra_info)\n # write into <prefix>/conda-meta/<dist>.json\n meta_dir = join(prefix, 'conda-meta')\n if not isdir(meta_dir):\n os.makedirs(meta_dir)\n with open(join(meta_dir, 'history'), 'w') as fo:\n fo.write('')\n with open(join(meta_dir, dist + '.json'), 'w') as fo:\n json.dump(meta, fo, indent=2, sort_keys=True)","def get_and_update_metadata():\n if not os.path.exists('.git') and os.path.exists(METADATA_FILENAME):\n with open(METADATA_FILENAME) as fh:\n metadata = json.load(fh)\n else:\n git = Git()\n revision = os.environ.get('TRAVIS_BUILD_NUMBER', git.revision)\n split_version = git.version.split('.')\n split_version[-1] = revision\n version = '.'.join(split_version)\n metadata = {\n 'version': version,\n 'git_hash': git.hash,\n 'git_origin': git.origin,\n 'git_branch': git.branch,\n 'git_version': git.version\n }\n with open(METADATA_FILENAME, 'w') as fh:\n json.dump(metadata, fh)\n return metadata","def create(self):\n\t\tlipsBaseFile.imp()","def makeMetadataTest(metadata):\n metadata = metadata.strip()\n # convert to tabs\n metadata = str(metadata)\n metadata = metadata.replace(\" \", \"\\t\")\n # store\n originalMetadata = metadata\n # pack\n header, directory, tableData, metadata = defaultTestData(metadata=metadata)\n data = padData(packTestHeader(header) + packTestDirectory(directory) + tableData) + packTestMetadata(metadata)\n # done\n return data, originalMetadata","def __init__(self, file_object, data_offset=0, data_size=0):\n super(CPIOArchiveFileEntry, self).__init__(\n file_object, data_offset=data_offset, data_size=data_size)\n self.group_identifier = None\n self.inode_number = None\n self.mode = None\n self.modification_time = None\n self.path = None\n self.size = None\n self.user_identifier = None","def store_metadata(fh, metadata):\n json.dump(metadata, fh, indent=2)","def get_metadata(self, t: Optional[int] = None) -> Dict:\n real_t = None if t is None else int(self._resolve_index(t))\n if self._is_tiff:\n ret = self._reader.metadata(index=..., page=real_t)\n else:\n ret = self._reader.metadata(index=real_t)\n self._parse_yaml_description(ret)\n if real_t is not None:\n ret[\"frame_no\"] = real_t\n return ret","def create_metadata(data_dir):\n pool = multiprocessing.Pool()\n\n train_feature_paths = [os.path.join(data_dir, \"train_features_{}.jsonl\".format(i)) for i in range(6)]\n train_records = list(pool.imap(read_metadata_record, raw_feature_iterator(train_feature_paths)))\n train_records = [dict(record, **{\"subset\": \"train\"}) for record in train_records]\n\n test_feature_paths = [os.path.join(data_dir, \"test_features.jsonl\")]\n test_records = list(pool.imap(read_metadata_record, raw_feature_iterator(test_feature_paths)))\n test_records = [dict(record, **{\"subset\": \"test\"}) for record in test_records]\n\n all_metadata_keys = [\"sha256\", \"appeared\", \"subset\", \"label\", \"avclass\"]\n ordered_metadata_keys = [k for k in all_metadata_keys if k in train_records[0].keys()]\n metadf = pd.DataFrame(train_records + test_records)[ordered_metadata_keys]\n metadf.to_csv(os.path.join(data_dir, \"metadata.csv\"))\n return metadf","def _store_package_metadata(self):","def get_preset_metadata(self, filename):\r\n\r\n raise NotImplementedError","def tests_ti_file_create(self):\n indicator_data = {\n 'md5': uuid.uuid4().hex.upper(),\n 'owner': self.owner,\n 'rating': randint(0, 5),\n }\n ti = self.ti.file(**indicator_data)\n r = ti.create()\n\n # assert response\n assert r.status_code == 201\n\n # retrieve indicator for asserts\n ti = self.ti.file(**indicator_data)\n r = ti.single()\n response_data = r.json()\n ti_data = response_data.get('data', {}).get(ti.api_entity)\n\n # validate response data\n assert r.status_code == 200\n assert response_data.get('status') == 'Success'\n\n # validate ti data\n assert ti_data.get('confidence') == indicator_data.get('confidence')\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\n assert ti_data.get('rating') == indicator_data.get('rating')\n\n # cleanup indicator\n r = ti.delete()\n assert r.status_code == 200","def Metadata():\n def _CreateMetadata(unused_none):\n global _metadata\n if not _metadata:\n _metadata = _GCEMetadata()\n _metadata_lock.lock(function=_CreateMetadata, argument=None)\n _metadata_lock.unlock()\n return _metadata","def _pname_and_metadata(in_file):\n\n\n if in_file.endswith(\".csv\"):\n raise ValueError(\"Did not find input metadata file: %s\" % in_file)\n base, md, global_vars = in_file, {}, {}\n md_file = None\n return base, md, global_vars, md_file","def calculate_statistics(kv, f):\n metadata = dict()\n f.seek(0, SEEK_END)\n b = f.tell()\n f.seek(0)\n metadata['FILE_SIZE_1000'], metadata['FILE_SIZE_1024'] = get_friendly_file_size(b)\n\n if gcode_key.TIME in kv:\n metadata['ETA_SECONDS'] = kv[gcode_key.TIME]\n\n # TODO Distance travelled\n key = 'DISTANCE'\n if key in kv:\n metadata[key] = kv[key]\n\n cubic_volume = get_cubic_volume(kv)\n if cubic_volume is not None:\n metadata['CUBIC_VOLUME'] = cubic_volume\n\n if gcode_key.LAYER_COUNT in kv:\n metadata['LAYER_COUNT'] = kv[gcode_key.LAYER_COUNT]\n\n if gcode_key.FILAMENT_USED in kv:\n metadata['FILAMENT_USED'] = kv[gcode_key.FILAMENT_USED][:-1] + ' m'\n\n return metadata","def create_tmp_metadata(num_examples, read_len):\n dataset_info = seq2label_pb2.Seq2LabelDatasetInfo(\n read_length=read_len,\n num_examples=num_examples,\n read_stride=1,\n dataset_path='test.tfrecord')\n\n for target in TEST_TARGETS:\n dataset_info.labels.add(\n name=target, values=[str(i) for i in xrange(num_examples)])\n\n tmp_path = os.path.join(FLAGS.test_tmpdir, 'test.pbtxt')\n with tf.gfile.GFile(tmp_path, 'w') as f:\n f.write(text_format.MessageToString(dataset_info))\n return tmp_path","def save_metadata(file_path, short_description, file_comments,\n file_generation, file_directions):\n\n stat_info = os.stat(file_path)\n size_MB = round(statinfo.st_size / (10**6))\n [fPath, fName] = os.path.split(file_path)\n\n format = '%d_%m_%Y_%H_%M_%S'\n creation_time = dt.datetime.today().strftime(format)\n\n metadata = {'description': short_description,\n 'comments': file_comments,\n 'filename': fName,\n 'folder': fPath,\n 'creationTime': creation_time,\n 'directions': file_directions,\n 'generation': file_generation,\n 'sizeMB': sizeMB}\n\n jsonFName = file_path.replace(os.path.splitext(fName)[1], '.json')\n with open(jsonFName, 'w') as outfile:\n json.dump(metadata, outfile)","def __init__(self, filename: Union[str, h5py.File], *, mode=\"w-\"):\n if isinstance(filename, h5py.File):\n self.__file = filename\n mode = self.__file.mode\n else:\n if mode not in SUPPORTED_MODES:\n raise ValueError(f\"Invalid mode; must be one of {SUPPORTED_MODES}\")\n self.__file = h5py.File(filename, mode=mode)\n\n if METADATA not in self.__file:\n self.add_file_metadata()","def get_metadata(self):\n previous = DirectoryMetadata.load_pickle(self)\n metadata = {}\n\n for dirpath, dirnames, filenames in os.walk(self.prefix_dir):\n for fname in filenames:\n path = os.path.join(dirpath, fname)\n relative_path = path.split(self.base_dir, 1)[1]\n try:\n stats = os.stat(path)\n except OSError:\n log.exception('Error stating a file on disk while building up metadata, skipping file %s' % path)\n continue\n swift_bytes = stats.st_size\n mtime = datetime.utcfromtimestamp(stats.st_mtime)\n if (previous is not None) and (relative_path in previous.metadata) and\\\n (previous.metadata[relative_path].bytes == swift_bytes):\n swift_hash = previous.metadata[relative_path].hash\n else:\n try:\n with open(path, 'rb') as afile:\n md5_hash = hashlib.md5()\n md5_hash.update(afile.read())\n swift_hash = md5_hash.hexdigest()\n except OSError:\n log.exception('Error reading a file to create the md5 while building up metadata, skipping file %s' % path)\n continue\n\n metadata[relative_path] = FileMetadata(relative_path, swift_bytes, mtime, swift_hash)\n\n return metadata","def build_metadata(self, meta):\n if meta is None:\n try:\n self.parser = GenericParser(\n fn_re='{}/(e\\d+s\\d+)_.*/Production.nc'.format(self.data_folder),\n group_names=['sim'],\n group_transforms=[lambda x: x],\n top_fn='',\n step_ps=self.timestep\n )\n meta = gather_metadata('{}/e*/*nc'.format(self.data_folder), parser)\n except:\n logger.warning(\"Could not automatically build metadata\")\n return None\n else:\n if not isinstance(meta, pd.DataFrame):\n meta = load_meta(meta)\n return meta","def create(\n name,\n\n open_file,\n create_file,\n remove_file,\n\n create_directory,\n list_directory,\n remove_empty_directory,\n temporary_directory,\n\n link,\n realpath,\n\n exists,\n is_dir,\n is_file,\n is_link,\n\n remove=recursive_remove,\n\n state=lambda: None,\n):\n\n return attr.s(\n type(\n name, (object,), dict(\n _state=attr.ib(default=attr.Factory(state), repr=False),\n\n create=create_file,\n open=lambda fs, path, mode=\"rb\": open_file(\n fs=fs, path=path, mode=mode,\n ),\n remove_file=remove_file,\n\n remove=remove,\n\n create_directory=create_directory,\n list_directory=list_directory,\n remove_empty_directory=remove_empty_directory,\n temporary_directory=temporary_directory,\n\n link=link,\n realpath=realpath,\n\n exists=exists,\n is_dir=is_dir,\n is_file=is_file,\n is_link=is_link,\n\n touch=_touch,\n children=_children,\n glob_children=_glob_children,\n contents_of=_open_and_read,\n ),\n ),\n )","def parse_metadata_file(self, file):\n \n file_keys = list(file.keys())\n \n if 'labelAnnotations' in file_keys:\n #file_annots = file['labelAnnotations'][:int(len(file['labelAnnotations']) * 0.5)]\n file_annots = file['labelAnnotations'][:]\n file_top_score = np.asarray([x['score'] for x in file_annots]).mean()\n file_top_desc = [x['description'] for x in file_annots]\n else:\n file_top_score = np.nan\n file_top_desc = ['']\n \n file_colors = file['imagePropertiesAnnotation']['dominantColors']['colors']\n file_crops = file['cropHintsAnnotation']['cropHints']\n\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\n file_color_pixelfrac = np.asarray([x['pixelFraction'] for x in file_colors]).mean()\n\n file_crop_conf = np.asarray([x['confidence'] for x in file_crops]).mean()\n \n if 'importanceFraction' in file_crops[0].keys():\n file_crop_importance = np.asarray([x['importanceFraction'] for x in file_crops]).mean()\n else:\n file_crop_importance = np.nan\n\n df_metadata = {\n 'annots_score': file_top_score,\n 'color_score': file_color_score,\n 'color_pixelfrac': file_color_pixelfrac,\n 'crop_conf': file_crop_conf,\n 'crop_importance': file_crop_importance,\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\n }\n \n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\n df_metadata = df_metadata.add_prefix('metadata_')\n \n return df_metadata","def _generate_model_metadata(out_file, model):\n # Define which FirstLevelModel attributes are BIDS compliant and which\n # should be bundled in a new \"ModelParameters\" field.\n DATA_ATTRIBUTES = [\n \"t_r\",\n ]\n PARAMETER_ATTRIBUTES = [\n \"drift_model\",\n \"hrf_model\",\n \"standardize\",\n \"high_pass\",\n \"target_shape\",\n \"signal_scaling\",\n \"drift_order\",\n \"scaling_axis\",\n \"smoothing_fwhm\",\n \"target_affine\",\n \"slice_time_ref\",\n \"fir_delays\",\n ]\n ATTRIBUTE_RENAMING = {\n \"t_r\": \"RepetitionTime\",\n }\n\n # Fields for the top level of the dictionary\n DATA_ATTRIBUTES.sort()\n data_attributes = {\n attr_name: getattr(model, attr_name)\n for attr_name in DATA_ATTRIBUTES\n if hasattr(model, attr_name)\n }\n data_attributes = {\n ATTRIBUTE_RENAMING.get(k, k): v for k, v in data_attributes.items()\n }\n\n # Fields for a nested section of the dictionary\n # The ModelParameters field is an ad-hoc way to retain useful info.\n PARAMETER_ATTRIBUTES.sort()\n model_attributes = {\n attr_name: getattr(model, attr_name)\n for attr_name in PARAMETER_ATTRIBUTES\n if hasattr(model, attr_name)\n }\n model_attributes = {\n ATTRIBUTE_RENAMING.get(k, k): v for k, v in model_attributes.items()\n }\n\n model_metadata = {\n \"Description\": \"A statistical map generated by Nilearn.\",\n **data_attributes,\n \"ModelParameters\": model_attributes,\n }\n\n with open(out_file, \"w\") as f_obj:\n json.dump(model_metadata, f_obj, indent=4, sort_keys=True)","def create_object_metadata(self, bucket_name, object_name, metadata_name, metadata_value):\n\n return h3lib.create_object_metadata(self._handle, bucket_name, object_name, metadata_name, metadata_value, self._user_id)"],"string":"[\n \"def add_file_metadata(self):\\n metadata = self.__file.require_group(METADATA)\\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\\\"%Y-%m-%d\\\"))\\n self.__write_value(metadata, SDK_VERSION, __version__)\",\n \"def gen_meta(self, filename):\\n nf_meta = {}\\n nf_meta['filename'] = filename\\n nf_meta['deleted'] = 0\\n\\n # http://stackoverflow.com/a/5297483\\n nf_meta['key'] = hashlib.md5(str(filename).encode('utf-8')).hexdigest()\\n self.log.debug(\\\"Note File Meta Key: %s\\\", nf_meta['key'])\\n\\n path = self.config.get_config('cfg_nt_path')\\n\\n # WARNING THIS IS PLATFORM SPECIFIC\\n nf_meta['createdate'] = os.stat(path + \\\"/\\\" + filename).st_birthtime\\n self.log.debug(\\\"Note File Meta Created: %s [%s]\\\", nf_meta['createdate'], time.ctime(nf_meta['createdate']))\\n\\n nf_meta['modifydate'] = os.stat(path + \\\"/\\\" + filename).st_mtime\\n self.log.debug(\\\"Note File Meta Modified: %s [%s]\\\", nf_meta['modifydate'], time.ctime(nf_meta['modifydate']))\\n\\n return nf_meta\",\n \"def create_file_meta_data(vk4_container, args):\\n log.debug(\\\"Entering create_file_meta_data()\\\")\\n\\n header_list = list()\\n header_list.append(args.layer)\\n header_list.append('\\\\n')\\n header_list.append('File name')\\n header_list.append(args.input)\\n header_list.append('Title')\\n header_list.append(args.input[:-4])\\n header_list.append('Measurement date')\\n header_list.append(str(vk4_container.measurement_conditions['month']) + '\\\\\\\\' +\\n str(vk4_container.measurement_conditions['day']) + '\\\\\\\\' +\\n str(vk4_container.measurement_conditions['year']))\\n header_list.append('Measurement time')\\n header_list.append(str(vk4_container.measurement_conditions['hour']) + ':' +\\n str(vk4_container.measurement_conditions['minute']) + ':' +\\n str(vk4_container.measurement_conditions['second']))\\n # User mode?\\n header_list.append('Objective lens')\\n header_list.append(vk4_container.string_data['lens_name'] + ' ' +\\n str(vk4_container.measurement_conditions['lens_magnification'] / 10.0) + 'x')\\n header_list.append('Numerical Aperture')\\n header_list.append(vk4_container.measurement_conditions['num_aperture'] / 1000.0)\\n # Size? Standard?\\n # Mode? Surface profile?\\n # RPD? OFF?\\n header_list.append('Quality')\\n header_list.append('Skip 4 lines')\\n header_list.append('Pitch (um)')\\n header_list.append(vk4_container.measurement_conditions['pitch'] / 1000.0)\\n header_list.append('Z measurement distance (um)')\\n header_list.append(vk4_container.measurement_conditions['distance'] / 1000.0)\\n # Double scan? OFF?\\n header_list.append('Brightness 1')\\n header_list.append(vk4_container.measurement_conditions['PMT_gain'])\\n header_list.append('Brightness 2')\\n br_2 = vk4_container.measurement_conditions['PMT_gain_2']\\n header_list.append('---') if br_2 == 0 else header_list.append(br_2)\\n # Not sure how they got ND filter to 30% in example csv\\n header_list.append('ND filter (%)')\\n header_list.append(vk4_container.measurement_conditions['ND_filter'] * 30)\\n header_list.append('Optical zoom')\\n header_list.append(vk4_container.measurement_conditions['optical_zoom'] / 10.0)\\n # Average count? 1 time?\\n # Filter? OFF?\\n # Fine mode? ON?\\n header_list.append('Line count')\\n l_count = vk4_container.measurement_conditions['number_of_lines']\\n header_list.append(l_count)\\n\\n header_list.append('Line position1')\\n if l_count == 0:\\n header_list.append('---')\\n else:\\n header_list.append(vk4_container.measurement_conditions['reserved_1'][0])\\n\\n header_list.append('Line position2')\\n if l_count == 0:\\n header_list.append('---')\\n else:\\n header_list.append(vk4_container.measurement_conditions['reserved_1'][1])\\n\\n header_list.append('Line position3')\\n if l_count == 0:\\n header_list.append('---')\\n else:\\n header_list.append(vk4_container.measurement_conditions['reserved_1'][2])\\n\\n header_list.append('Camera gain (db)')\\n header_list.append(vk4_container.measurement_conditions['camera_gain'] * 6)\\n header_list.append('Shutter speed')\\n header_list.append(vk4_container.measurement_conditions['shutter_speed'])\\n header_list.append('White balance mode')\\n wb_mode = vk4_container.measurement_conditions['white_balance_mode']\\n header_list.append('Auto') if wb_mode == 1 else header_list.append(wb_mode)\\n header_list.append('White balance R')\\n header_list.append(vk4_container.measurement_conditions['white_balance_red'])\\n header_list.append('White balance B')\\n header_list.append(vk4_container.measurement_conditions['white_balance_blue'])\\n header_list.append('Intensity correction mode')\\n header_list.append('Gamma correction')\\n header_list.append('Gamma correction value')\\n header_list.append(vk4_container.measurement_conditions['gamma'] / 100.0)\\n header_list.append('Gamma offset (%)')\\n header_list.append(vk4_container.measurement_conditions['gamma_correction_offset'] /\\n 65536.0)\\n # W/B inversion? OFF?\\n # Head type? VK-X110?\\n # Correct intensity eccentricity? OFF?\\n # Correct field curvature? OFF?\\n header_list.append('XY calibration (nm/pixel)')\\n header_list.append(vk4_container.measurement_conditions['x_length_per_pixel'] / 1000.0)\\n header_list.append('Z calibration (nm/digit)')\\n header_list.append(vk4_container.measurement_conditions['z_length_per_digit'] / 1000.0)\\n # Saturation?\\n # Contrast?\\n # Brightness?\\n # AI noise elimination? Auto(ON)?\\n # Angled surface noise filter? Auto(OFF)?\\n header_list.append('Width')\\n header_list.append(vk4_container.image_width)\\n header_list.append('Height')\\n header_list.append(vk4_container.image_height)\\n # Skip amount? 1?\\n\\n out_type = args.type\\n if out_type == 'hcsv':\\n log.debug(\\\"Exiting create_file_meta_data() where out_type == %s\\\" % out_type)\\n return np.reshape(header_list, (len(header_list) // 2, 2))\\n else:\\n # Can use a dict to attach info to an image using PILs Image module\\n meta_dict = dict()\\n for n in range(0, len(header_list), 2):\\n meta_dict[header_list[n]] = header_list[n + 1]\\n\\n log.debug(\\\"Exiting create_file_meta_data() where out_type == %s\\\" % out_type)\\n return meta_dict\",\n \"def create_metadata(self) -> _metadata_fb.CustomMetadataT:\\n custom_metadata_field = _metadata_fb.CustomMetadataT()\\n custom_metadata_field.name = self.name\\n custom_metadata_field.data = b'\\\\x01\\\\x02'\\n return custom_metadata_field\",\n \"def create(cls, file_path: str, file_name: str):\\n\\n full_path = os.path.join(file_path, file_name)\\n info = os.stat(full_path)\\n\\n new_file_info = FileInfo(\\n path=file_path,\\n name=file_name,\\n size=info.st_size)\\n return new_file_info\",\n \"def _create_object_directory(directory, metadata):\\n if directory.exists():\\n raise IOError(\\\"The directory '\\\" + str(directory) + \\\"' already exists\\\")\\n valid_types = [DATASET_TYPENAME, FILE_TYPENAME, GROUP_TYPENAME]\\n typename = metadata[EXDIR_METANAME][TYPE_METANAME]\\n if typename not in valid_types:\\n raise ValueError(\\\"{typename} is not a valid typename\\\".format(typename=typename))\\n directory.mkdir()\\n meta_filename = directory / META_FILENAME\\n with meta_filename.open(\\\"w\\\", encoding=\\\"utf-8\\\") as meta_file:\\n if metadata == _default_metadata(typename):\\n # if it is the default, we know how to print it fast\\n metadata_string = (''\\n '{exdir_meta}:\\\\n'\\n ' {type_meta}: \\\"{typename}\\\"\\\\n'\\n ' {version_meta}: {version}\\\\n'\\n '').format(\\n exdir_meta=EXDIR_METANAME,\\n type_meta=TYPE_METANAME,\\n typename=typename,\\n version_meta=VERSION_METANAME,\\n version=1\\n )\\n else:\\n from io import StringIO\\n with StringIO() as buf:\\n yaml.YAML(typ=\\\"safe\\\", pure=True).dump(metadata, buf)\\n metadata_string = buf.getvalue()\\n\\n try:\\n meta_file.write(metadata_string)\\n except TypeError:\\n # NOTE workaround for Python 2.7\\n meta_file.write(metadata_string.decode('utf8'))\",\n \"def _get_file_meta(cls, file=None, file_path=None):\\n if not file and file_path:\\n file = open(file_path, 'r')\\n file_body = file.read()\\n meta = {\\n 'title': file.name, # read title from html\\n 'subtitle': 'dupa', # read from html\\n 'slug': os.path.splitext(os.path.basename(file.name))[0],\\n 'abstract_html': 'Abstract', \\n 'body_html': file_body,\\n 'tags': [db.Category('one tag'), db.Category('second tag')],\\n }\\n if file_path:\\n meta.update({\\n 'date_created': datetime.datetime.fromtimestamp(os.path.getctime(file_path)),\\n 'date_updated': datetime.datetime.fromtimestamp(os.path.getmtime(file_path)),\\n })\\n return meta\",\n \"def tests_ti_file_create_2(self):\\n metadata = {\\n 'size': 84504,\\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\\n }\\n file_indicator = self.ti.file(**metadata)\\n file_indicator.delete()\\n\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n response = file_indicator.create()\\n assert response.ok\\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\\n\\n assert file_indicator.data['sha256'] == metadata['sha256']\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n\\n response = file_indicator.update()\\n assert response.ok\\n file_indicator.delete()\",\n \"def metadata_obj(filelike):\\n\\tif test_hachoir_extension(filelike.name):\\n\\t\\tmetadata = metadata_for_filelike(filelike)\\n\\t\\tif metadata:\\n\\t\\t\\tdata = dict([\\n\\t\\t\\t\\t(data.key, data.values[0].value)\\n\\t\\t\\t\\tfor data in metadata\\n\\t\\t\\t\\tif data.values\\n\\t\\t\\t\\t])\\n\\t\\telse:\\n\\t\\t\\tdata=None\\n\\telif test_3D_extension(filelike.name):# 3D not in the extention \\n\\t\\tdata = {'mime_type':'model'}\\n\\telse:\\n\\t\\tdata = None\\n\\t\\t\\n\\treturn data\",\n \"def generate_metadata_files(self):\\n\\n data_folder = self.get_data_folder(mode='absolute')\\n\\n parents = (data_folder / '_').parents\\n\\n for mfile in self.mdata:\\n for regex, level in METADATA_LEVEL_BY_NAME.items():\\n if re.compile(regex).match(mfile.name):\\n create_file(mfile, parents[(3-level)] / mfile.name,\\n mode='copy')\",\n \"def metadata(self, metadata):\\n return Metadata(metadata)\",\n \"def create_metadata(scene: \\\"Scenemaker\\\") -> None:\\r\\n create_datadir()\\r\\n\\r\\n with open(dirpath / cng.GENERATED_DATA_DIR / cng.METADATA_FILE, \\\"w+\\\") as f:\\r\\n f.write(str(scene.num2name))\",\n \"def build_ns_file_metadata(file_type):\\r\\n file_description = f\\\"Your Nightscout {file_type} data, last updated at {datetime.utcnow()} UTC.\\\"\\r\\n file_tags = [\\\"open-aps\\\", \\\"Nightscout\\\", file_type, \\\"json\\\"]\\r\\n file_updated = str(datetime.now())\\r\\n return {\\\"tags\\\": file_tags, \\\"description\\\": file_description, \\\"updated_at\\\": file_updated}\",\n \"def generate_metadata(self):\\n self.metadata = {\\n 'title': os.path.basename(self.source_file).rsplit('.', 1)[0],\\n 'url': self.relative_destination_file,\\n 'full_path': os.path.dirname(self.relative_destination_file),\\n 'short_path': self.shorten_path(\\n os.path.dirname(self.relative_destination_file))\\n }\",\n \"def _update_filesystem_metadata(self, metadata):\\n directory, fname = os.path.split(self.fname)\\n fbase = os.path.splitext(fname)[0]\\n \\n # Test for presence and size of zip file\\n zip_file = fbase + '.zip'\\n zip_path = os.path.join(directory, zip_file)\\n \\n if os.path.isfile(zip_path):\\n location = 'on_disk'\\n data_file_size = os.path.getsize(zip_path)\\n else:\\n location = 'on_tape'\\n data_file_size = 0\\n \\n # Test for presence of quick look PNG file\\n quicklook_file = fbase + '.png'\\n quicklook_path = os.path.join(directory, quicklook_file)\\n \\n if not os.path.isfile(quicklook_path):\\n quicklook_file = ''\\n\\n # Add to metadata dictionary\\n item_map = {'directory': directory, 'metadata_file': fname,\\n 'data_file': zip_file, 'location': location, \\n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\\n \\n for key, value in item_map.items():\\n metadata[key] = value\",\n \"def get_metadata_from_filename(file_name: str) -> namedtuple:\\n if os.path.isabs(f):\\n file_name = os.path.basename(file_name)\\n original_image_name = file_name.split('-')[0]\\n x_pos = int(file_name.split('.')[-2].split('+')[-2:][0])\\n Metadata = namedtuple('Metadata', ['original_image_name', 'x_pos'])\\n return Metadata(original_image_name, x_pos)\",\n \"def metadata(filepath: str, overlay: str = None) -> Entry:\\n md5 = hashlib.md5()\\n mime = None\\n\\n # Read the file in chunks.\\n try:\\n with open(filepath, \\\"rb\\\") as fin:\\n while chunk := fin.read(CHUNK_SIZE):\\n md5.update(chunk)\\n\\n # Only attempt to determine the filetype on the first chunk.\\n if not mime and fin.tell() <= CHUNK_SIZE:\\n mime = archive.get_mimetype(chunk)\\n except OSError as err:\\n raise FileAccessException(f\\\"Unable to open file at {filepath}: {err}\\\")\\n\\n return Entry(\\n path=filepath,\\n md5=md5.hexdigest(),\\n mime=mime,\\n overlay=overlay,\\n )\",\n \"def gen_metadata(args):\\n with open(args.bibfile) as bibfile:\\n bib_db = BibTexParser(common_strings=True).parse_file(bibfile)\\n entries = sorted(list(bib_db.entries),\\n key=lambda x: x['year'], reverse=True)\\n list([update_file(entry) for entry in entries])\\n annotations = [entry_to_annotation(entry, args.PI) for entry in entries]\\n stream = open(args.metadata, 'w')\\n yaml.dump(annotations, stream, width=192, default_flow_style=False)\\n stream.close()\",\n \"def make_nwbfile_from_metadata(metadata: dict) -> NWBFile:\\n\\n # Validate metadata\\n schema_path = Path(__file__).resolve().parent.parent / \\\"schemas/base_metadata_schema.json\\\"\\n base_metadata_schema = load_dict_from_file(file_path=schema_path)\\n validate_metadata(metadata=metadata, schema=base_metadata_schema)\\n\\n nwbfile_kwargs = metadata[\\\"NWBFile\\\"]\\n # convert ISO 8601 string to datetime\\n if isinstance(nwbfile_kwargs.get(\\\"session_start_time\\\"), str):\\n nwbfile_kwargs[\\\"session_start_time\\\"] = datetime.fromisoformat(nwbfile_kwargs[\\\"session_start_time\\\"])\\n if \\\"session_description\\\" not in nwbfile_kwargs:\\n nwbfile_kwargs[\\\"session_description\\\"] = \\\"No description.\\\"\\n if \\\"identifier\\\" not in nwbfile_kwargs:\\n nwbfile_kwargs[\\\"identifier\\\"] = str(uuid.uuid4())\\n if \\\"Subject\\\" in metadata:\\n nwbfile_kwargs[\\\"subject\\\"] = metadata[\\\"Subject\\\"]\\n # convert ISO 8601 string to datetime\\n if \\\"date_of_birth\\\" in nwbfile_kwargs[\\\"subject\\\"] and isinstance(nwbfile_kwargs[\\\"subject\\\"][\\\"date_of_birth\\\"], str):\\n nwbfile_kwargs[\\\"subject\\\"][\\\"date_of_birth\\\"] = datetime.fromisoformat(\\n nwbfile_kwargs[\\\"subject\\\"][\\\"date_of_birth\\\"]\\n )\\n nwbfile_kwargs[\\\"subject\\\"] = Subject(**nwbfile_kwargs[\\\"subject\\\"])\\n\\n return NWBFile(**nwbfile_kwargs)\",\n \"def build_metadata_from_file(tmp_crawler_folder, abs_data_path, commitTime):\\n m = MetaData()\\n\\n post_path = abs_data_path[len(tmp_crawler_folder):]\\n if post_path[0] == '/':\\n post_path = post_path[1:]\\n\\n m['mimeType'] = mime.get_mime(abs_data_path)\\n m['url'] = MetaData.get_url_from_path(post_path)\\n m['domain'] = MetaData.get_domain_from_path(post_path)\\n m['path'] = MetaData.get_content_path_from_tmp(abs_data_path, m['domain'])\\n m['tmpPath'] = abs_data_path\\n m['createTime'] = utl.get_ctime(abs_data_path)\\n m['commitTime'] = commitTime\\n m['title'] = extractor.get_title(abs_data_path, m['mimeType'])\\n\\n if not m['url'] in m['path']:\\n print('WARNING, url not in path!')\\n return m\",\n \"def set_stewi_meta(file_name, stewiformat=''):\\n stewi_meta = FileMeta()\\n stewi_meta.name_data = file_name\\n stewi_meta.category = stewiformat\\n stewi_meta.tool = \\\"StEWI\\\"\\n stewi_meta.tool_version = STEWI_VERSION\\n stewi_meta.ext = WRITE_FORMAT\\n stewi_meta.git_hash = GIT_HASH\\n stewi_meta.date_created = datetime.now().strftime('%d-%b-%Y')\\n return stewi_meta\",\n \"def build_file(root, name, file_type, fname, file_cls=File):\\n dfile = file_cls(root.add_node(name))\\n dfile.store_type(file_type)\\n dfile.write_fname(fname)\\n return dfile\",\n \"def convert_to_dataset_file_metadata(self, file_data, path):\\r\\n as_metadata = {\\r\\n 'path': os.path.join(path, file_data['name']),\\r\\n 'description': file_data['description']\\r\\n }\\r\\n\\r\\n schema = {}\\r\\n fields = []\\r\\n for column in file_data['columns']:\\r\\n field = {\\r\\n 'name': column['name'],\\r\\n 'title': column['description'],\\r\\n 'type': column['type']\\r\\n }\\r\\n fields.append(field)\\r\\n schema['fields'] = fields\\r\\n as_metadata['schema'] = schema\\r\\n\\r\\n return as_metadata\",\n \"def metadata(self) -> dict:\\n meta = {}\\n meta['filename'] = self.filename\\n meta['label'] = self.label\\n meta['url'] = self.url\\n\\n return meta\",\n \"def makeMetadata(self):\\n # arbitrary values\\n orientation = 0 * degrees\\n flipX = False\\n metadata = makeTanWcsMetadata(\\n crpix = self.crpix,\\n crval = self.crval,\\n cdMatrix = makeCdMatrix(scale=self.scale, orientation=orientation, flipX=flipX),\\n )\\n self.assertEqual(metadata.nameCount(), 14)\\n metadata.add(\\\"SIMPLE\\\", True)\\n metadata.add(\\\"BITPIX\\\", 16)\\n metadata.add(\\\"NAXIS\\\", 2)\\n metadata.add(\\\"NAXIS1\\\", 500)\\n metadata.add(\\\"NAXIS2\\\", 200)\\n metadata.add(\\\"BZERO\\\", 32768)\\n metadata.add(\\\"BSCALE\\\", 1)\\n metadata.add(\\\"TIMESYS\\\", \\\"UTC\\\")\\n metadata.add(\\\"UTC-OBS\\\", \\\"12:04:45.73\\\")\\n metadata.add(\\\"DATE-OBS\\\", \\\"2006-05-20\\\")\\n metadata.add(\\\"EXPTIME\\\", 5.0)\\n metadata.add(\\\"COMMENT\\\", \\\"a comment\\\")\\n metadata.add(\\\"COMMENT\\\", \\\"another comment\\\")\\n metadata.add(\\\"EXTEND\\\", True)\\n metadata.add(\\\"INHERIT\\\", False)\\n metadata.add(\\\"LTV1\\\", 5)\\n metadata.add(\\\"LTV2\\\", -10)\\n metadata.add(\\\"ZOTHER\\\", \\\"non-standard\\\")\\n return metadata\",\n \"def _create_output_metadata(self):\\n\\n # Create output info.\\n output_meta = _metadata_fb.TensorMetadataT()\\n output_meta.name = \\\"probability\\\"\\n output_meta.description = \\\"Probabilities of labels respectively.\\\"\\n output_meta.content = _metadata_fb.ContentT()\\n output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()\\n output_meta.content.contentPropertiesType = (\\n _metadata_fb.ContentProperties.FeatureProperties)\\n label_file = _metadata_fb.AssociatedFileT()\\n label_file.name = os.path.basename(self.model_info.label_file)\\n label_file.description = (\\\"Labels for classification categories.\\\")\\n label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS\\n output_meta.associatedFiles = [label_file]\\n return [output_meta]\",\n \"def meta_info(self):\\n\\n if not self.meta_was_built:\\n self.__meta_info = self.build_meta(self.dataset_path, self.file_types)\\n self.meta_was_built = True\\n\\n return self.__meta_info\",\n \"def _update_fileinfo(self, metadata_filename):\\n \\n # In case we delayed loading the metadata and didn't do it in\\n # __init__ (such as with delegated metadata), then get the file\\n # info now.\\n \\n # Save the path to the current metadata file for 'metadata_filename'.\\n current_filepath = os.path.join(self.metadata_directory['current'],\\n metadata_filename)\\n # If the path is invalid, simply return and leave fileinfo unset.\\n if not os.path.exists(current_filepath):\\n self.fileinfo[current_filepath] = None\\n return\\n \\n # Extract the file information from the actual file and save it\\n # to the fileinfo store.\\n file_length, hashes = tuf.util.get_file_details(current_filepath)\\n metadata_fileinfo = tuf.formats.make_fileinfo(file_length, hashes)\\n self.fileinfo[metadata_filename] = metadata_fileinfo\",\n \"def __appendMetaData(self, filename):\\n metadata = {'Model': 'LFM',\\n 'Source': filename,\\n 'Date processed': datetime.datetime.now(),\\n 'Start date': self.startDate\\n }\\n \\n self.data.append(key='meta',\\n name='Metadata for LFM Solar Wind file',\\n units='n/a',\\n data=metadata)\",\n \"def _CreateStorageFile(self):\\n return sqlite_file.SQLiteStorageFile(storage_type=self._storage_type)\",\n \"def get_metadata(self, file_id):\\n pass\",\n \"def build_metadata(meta):\\n\\n ret = copy.copy(meta) if meta else dict()\\n\\n ret['name'] = meta.get('name', '')\\n\\n if 'index' in meta:\\n if isinstance(meta.get('index'), str):\\n ret['index'] = Index(meta.get('index'))\\n elif isinstance(meta.get('index'), Index):\\n ret['index'] = meta.get('index')\\n\\n ret['utc'] = True\\n if 'utc' in meta and isinstance(meta.get('utc'), bool):\\n ret['utc'] = meta.get('utc')\\n\\n return pmap(ret)\",\n \"def build_meta(self, root):\\n ns_dc = {\\\"dc\\\": self.namespace_dc, }\\n metadata = ET.SubElement(root, \\\"metadata\\\", nsmap=ns_dc)\\n ET.SubElement(metadata,\\n '{%s}title' % self.namespace_dc).text = self.title\\n ET.SubElement(metadata,\\n '{%s}creator' % self.namespace_dc).text = self.author\\n ET.SubElement(metadata,\\n '{%s}identifier' % self.namespace_dc,\\n id='bookid').text = \\\"urn:uuid:%s\\\" % self.bookid\\n ET.SubElement(metadata,\\n '{%s}language' % self.namespace_dc).text = self.language\\n ET.SubElement(metadata, 'meta', name=\\\"cover\\\", content=\\\"cover-image\\\")\",\n \"def create(self, identity, data=None, record=None, **kwargs):\\n record.metadata = data.get('metadata', {})\",\n \"def create(self):\\n self.create_file()\",\n \"def _construct_standardized_metadata(self):\\n return None\",\n \"def extract_metadata(self):\\n if self.is_generatable_file:\\n logger.debug(\\\"Converting collected details to dict..\\\")\\n if self.metadata_collector:\\n self.metadata = MetadataToDict(\\n metadata_collector=self.metadata_collector,\\n file_import=self.file_import,\\n )\\n self.metadata.build_integration_dict()\",\n \"def _gen_metadata_for_list(self, filename, **extra_data):\\n file_size = os.stat(filename).st_size\\n with open(filename, 'rb') as f:\\n md5sum = compute_md5_hash(f)\\n core_metadata = {\\n 'filename': os.path.abspath(filename),\\n 'md5sum': md5sum,\\n 'file_size_bytes': file_size\\n }\\n return {**core_metadata, **extra_data}\",\n \"def _get_metadata(self) -> Metadata:\\n manifest = self._get_manifest()\\n\\n return Metadata(**manifest[\\\"metadata\\\"])\",\n \"def from_metadata(cls, metadata, name=None, dimension=None):\\n\\n metadata = dict(expand_level_metadata(metadata))\\n\\n try:\\n name = name or metadata.pop(\\\"name\\\")\\n except KeyError:\\n raise ModelError(\\\"No name specified in level metadata\\\")\\n\\n attributes = []\\n for attr_metadata in metadata.pop(\\\"attributes\\\", []):\\n attr = Attribute(dimension=dimension, **attr_metadata)\\n attributes.append(attr)\\n\\n return cls(name=name, attributes=attributes, **metadata)\",\n \"def extract_metadata(self):\\n metadata_file_path = self.create_metadata_file(\\\".metadata.txt\\\")\\n mt = self.mimetype\\n metadata_processing_method = self.metadata_mimetype_methods.get(mt)\\n if metadata_processing_method:\\n # TODO: should we return metadata and write it here instead of in processing method?\\n metadata_processing_method(metadata_file_path)\",\n \"def define_metadata(cls, pcm):\\n raise NotImplementedError()\",\n \"def generate_custom_metadata(uuid, version, update_proto):\\n COMPONENTS = E.components\\n COMPONENT = E.component\\n ID = E.id\\n NAME = E.name\\n SUMMARY = E.summary\\n PROVIDES = E.provides\\n FIRMWARE = E.firmware\\n CUSTOM = E.custom\\n VALUE = E.value\\n RELEASES = E.releases\\n RELEASE = E.release\\n LOCATION = E.location\\n CHECKSUM = E.checksum\\n DESCRIPTION = E.description\\n P = E.p\\n SIZE = E.size\\n REQUIRES = E.requires\\n\\n metadata_tree = COMPONENTS(\\n COMPONENT(\\n ID(\\\"com.hacker.firmware\\\"),\\n NAME(\\\"TotallyNotMalicious\\\"),\\n SUMMARY(\\\"This is fine\\\"),\\n PROVIDES(\\n FIRMWARE(\\n uuid,\\n type=\\\"flashed\\\",\\n ),\\n ),\\n CUSTOM(\\n VALUE(\\n update_proto,\\n key=\\\"LVFS::UpdateProtocol\\\",\\n ),\\n ),\\n RELEASES(\\n RELEASE(\\n LOCATION(\\n url_for(\\\"serve_cab\\\", _external=True, uuid=uuid, version=version, update_proto=update_proto)\\n ),\\n DESCRIPTION(\\n P(\\\"Totally not malicious ;)\\\"),\\n ),\\n CHECKSUM(\\n hashlib.sha1(generate_cab(uuid=uuid, version=version, update_proto=update_proto)).hexdigest(),\\n type=\\\"sha1\\\", filename=\\\"poc.cab\\\", target=\\\"container\\\",\\n ),\\n SIZE(\\\"1337\\\", type=\\\"download\\\"),\\n SIZE(\\\"0\\\", type=\\\"installed\\\"),\\n urgency=\\\"high\\\", version=version, timestamp=\\\"1587399600\\\", install_duration=\\\"120\\\",\\n ),\\n ),\\n REQUIRES(),\\n type=\\\"firmware\\\",\\n ),\\n origin=\\\"lvfs\\\", version=\\\"0.9\\\",\\n )\\n\\n metadata = etree.tostring(metadata_tree, pretty_print=True, xml_declaration=True, encoding=\\\"UTF-8\\\")\\n\\n return gzip.compress(metadata)\",\n \"def create_file(toy_data, filename):\\n root_file = ROOT.TFile.Open(filename, \\\"RECREATE\\\")\\n \\n bin_edges = toy_data[\\\"bin_edges\\\"]\\n sig = create_th1f(bin_edges, toy_data[\\\"sig\\\"], toy_data[\\\"sig_stat\\\"])\\n sig.SetName(\\\"signal\\\")\\n\\n data = create_th1f(bin_edges, toy_data[\\\"data\\\"], toy_data[\\\"data_stat\\\"])\\n data.SetName(\\\"data\\\")\\n\\n bkg = create_th1f(bin_edges, toy_data[\\\"bkg\\\"], toy_data[\\\"bkg_stat\\\"])\\n bkg.SetName(\\\"bkg\\\")\\n\\n root_file.Write()\\n root_file.Close()\",\n \"def _add_filename_metadata(self, extra_metadata): \\n \\n # Make sure product_info section exists\\n extra_metadata.setdefault('product_info', {})\\n \\n file_name = os.path.basename(self.fname)\\n fn_comps = file_name.split(\\\"_\\\")\\n \\n if self.__class__ == SAFESentinel1:\\n component = fn_comps[2]\\n if len(component) < 4: \\n resolution = 'N/A'\\n else:\\n resolution = component[-1]\\n \\n extra_metadata['product_info']['Resolution'] = resolution\\n \\n # Add file/scan name \\n extra_metadata['product_info']['Name'] = os.path.splitext(file_name)[0]\\n \\n # Add Satellite and Mission from the file path\\n comp_1 = fn_comps[0].upper()\\n extra_metadata['platform']['Mission'] = \\\"Sentinel-%s\\\" % comp_1[1]\\n extra_metadata['platform']['Satellite'] = \\\"Sentinel-%s\\\" % comp_1[1:]\",\n \"def assemble(metadata_file):\\n\\n def read(file):\\n with open(file) as yaml:\\n return load(yaml.read())\\n\\n def add_name(info):\\n info['name'] = slugify(info['title'], separator='_')\\n return info\\n\\n def get_files(filetype):\\n filename = metadata_file.replace('metadata', filetype)\\n folder = dirname(metadata_file)\\n schema_files_pattern = join(folder, filename)\\n return glob(schema_files_pattern)\\n\\n descriptor = add_name(read(metadata_file))\\n resources = [add_name(read(file)) for file in get_files('resource')]\\n model = get_files('model')\\n\\n descriptor['resources'] = resources\\n if model and len(model) == 1:\\n descriptor['model'] = model.pop()\\n\\n return DataPackage(descriptor)\",\n \"def __init__(self, image, options):\\n self._image = image\\n self._options = options\\n suffix = ''.join(random.choice(\\n string.ascii_uppercase + string.digits) for _ in range(5))\\n self._name = 'metadata-%s' % suffix\",\n \"def create_file_hash_dict(cls, file, file_path):\\n\\n file_info = {}\\n file_info['path'] = file_path\\n file_info['hash'] = cls.get_256_hash_from_file(file_path)\\n file_info['type'] = 'file'\\n file_info['name'] = file\\n file_info['perm'] = stat.S_IMODE(os.lstat(file_path).st_mode)\\n\\n return file_info\",\n \"def _build_local_metadata_file(files, home=''):\\n filepaths = [os.path.join(home, f) for f in files]\\n shas = [_get_sha_metadata(f) for f in filepaths]\\n metadata = dict(zip(files, shas))\\n\\n with open(LOCAL_METADATA_FILE, 'w') as f:\\n f.write(json.dumps(metadata))\",\n \"def generate_metadata(\\n source_name,\\n source_version\\n):\\n now = datetime.utcnow()\\n write_recipt = str(uuid.uuid1())\\n metadata_obj = {\\n 'write_recipt': write_recipt,\\n 'data_source': source_name,\\n 'machine_source': platform.node(),\\n 'version': source_version,\\n 'package_version': _version.__version__,\\n 'cron_datetime': now.isoformat()\\n }\\n\\n return metadata_obj\",\n \"def metadata_create():\\n binding = {}\\n url = 'https://upload.twitter.com/1.1/media/metadata/create.json'\\n return _TwitterRequest('POST',\\n url,\\n 'rest:media',\\n 'post-media-metadata-create',\\n binding)\",\n \"def __init__(self, metadata_folder='./'):\\n self.metadata = self.load_metadata(metadata_folder)\\n self.prefix = 'data/adience/faces/'\\n return\",\n \"def extract_metadata(file_name, token_type, create_labels, remove_stop_words):\\n metadata = {'file': file_name}\\n\\n with open(file_name, 'r') as file:\\n rows = file.readlines()\\n\\n rules = [\\n ['Message-ID:', 'id'],\\n ['Subject:', 'subject'],\\n ]\\n\\n for (index, row) in enumerate(rows):\\n row = row.lstrip('> \\\\t')\\n for (pattern, prop) in rules:\\n if row.startswith(pattern):\\n metadata[prop] = row.replace(pattern,'')\\n\\n if 'body' not in metadata:\\n if row.startswith('\\\\n'):\\n metadata['body'] = '\\\\n'.join(rows[index:])\\n\\n elif '-----Original Message-----' in row:\\n del metadata['body']\\n\\n if 'body' in metadata:\\n if create_labels:\\n metadata['label'] = create_label(metadata['body'])\\n metadata['original_body'] = metadata['body']\\n metadata['body'] = clean_text(metadata['body'], token_type, remove_stop_words)\\n clean_subject = cleanse(metadata['subject'])\\n metadata['subject'] = '' if clean_subject.count(' ') == len(clean_subject) else clean_subject\\n\\n return metadata\",\n \"def _generate_metadata_kind(filename, targets=None, qa_group=None):\\n db = LvfsDatabase(os.environ)\\n db_firmware = LvfsDatabaseFirmware(db)\\n items = db_firmware.get_items()\\n store = appstream.Store('lvfs')\\n for item in items:\\n\\n # filter\\n if item.target == 'private':\\n continue\\n if targets and item.target not in targets:\\n continue\\n if qa_group and qa_group != item.qa_group:\\n continue\\n\\n # add each component\\n for md in item.mds:\\n component = appstream.Component()\\n component.id = md.cid\\n component.kind = 'firmware'\\n component.name = md.name\\n component.summary = md.summary\\n component.description = md.description\\n if md.url_homepage:\\n component.urls['homepage'] = md.url_homepage\\n component.metadata_license = md.metadata_license\\n component.project_license = md.project_license\\n component.developer_name = md.developer_name\\n\\n # add provide\\n if md.guid:\\n prov = appstream.Provide()\\n prov.kind = 'firmware-flashed'\\n prov.value = md.guid\\n component.add_provide(prov)\\n\\n # add release\\n if md.version:\\n rel = appstream.Release()\\n rel.version = md.version\\n rel.description = md.release_description\\n if md.release_timestamp:\\n rel.timestamp = md.release_timestamp\\n rel.checksums = []\\n rel.location = 'https://secure-lvfs.rhcloud.com/downloads/' + item.filename\\n rel.size_installed = md.release_installed_size\\n rel.size_download = md.release_download_size\\n component.add_release(rel)\\n\\n # add container checksum\\n if md.checksum_container:\\n csum = appstream.Checksum()\\n csum.target = 'container'\\n csum.value = md.checksum_container\\n csum.filename = item.filename\\n rel.add_checksum(csum)\\n\\n # add content checksum\\n if md.checksum_contents:\\n csum = appstream.Checksum()\\n csum.target = 'content'\\n csum.value = md.checksum_contents\\n csum.filename = md.filename_contents\\n rel.add_checksum(csum)\\n\\n # add component\\n store.add(component)\\n\\n # dump to file\\n if not os.path.exists(DOWNLOAD_DIR):\\n os.mkdir(DOWNLOAD_DIR)\\n filename = os.path.join(DOWNLOAD_DIR, filename)\\n store.to_file(filename)\\n return filename\",\n \"def add_metadata(self, metadata: dict) -> None:\",\n \"def get_metadata(self):\\n self.metadata = Metadata()\\n document = openxmllib.openXmlDocument(path=self.path)\\n self.metadata.add(document.allProperties, \\\"ooxml\\\")\\n return self.metadata\",\n \"async def get_file_metadata(\\n location_id: LocationID, file_id: StorageFileID, user_id: UserID\\n):\",\n \"def metadata(self): # -> None:\\n ...\",\n \"def metadata(*args):\\n def metadata_wrapper():\\n pass\\n return metadata_wrapper\",\n \"def run(self, info):\\n\\n # Write the metadata to the file's xattrs\\n self._downloader.to_screen('[metadata] Writing metadata to file\\\\'s xattrs')\\n\\n filename = info['filepath']\\n\\n try:\\n xattr_mapping = {\\n 'user.xdg.referrer.url': 'webpage_url',\\n # 'user.xdg.comment': 'description',\\n 'user.dublincore.title': 'title',\\n 'user.dublincore.date': 'upload_date',\\n 'user.dublincore.description': 'description',\\n 'user.dublincore.contributor': 'uploader',\\n 'user.dublincore.format': 'format',\\n }\\n\\n num_written = 0\\n for xattrname, infoname in xattr_mapping.items():\\n\\n value = info.get(infoname)\\n\\n if value:\\n if infoname == 'upload_date':\\n value = hyphenate_date(value)\\n\\n byte_value = value.encode('utf-8')\\n write_xattr(filename, xattrname, byte_value)\\n num_written += 1\\n\\n return [], info\\n\\n except XAttrUnavailableError as e:\\n self._downloader.report_error(str(e))\\n return [], info\\n\\n except XAttrMetadataError as e:\\n if e.reason == 'NO_SPACE':\\n self._downloader.report_warning(\\n 'There\\\\'s no disk space left, disk quota exceeded or filesystem xattr limit exceeded. '\\n + (('Some ' if num_written else '') + 'extended attributes are not written.').capitalize())\\n elif e.reason == 'VALUE_TOO_LONG':\\n self._downloader.report_warning(\\n 'Unable to write extended attributes due to too long values.')\\n else:\\n msg = 'This filesystem doesn\\\\'t support extended attributes. '\\n if compat_os_name == 'nt':\\n msg += 'You need to use NTFS.'\\n else:\\n msg += '(You may have to enable them in your /etc/fstab)'\\n self._downloader.report_error(msg)\\n return [], info\",\n \"def get_metadata(self):\\n result = defaultdict(str)\\n result.update(self.metadata)\\n result['file_name'] = self.file_name\\n return result\",\n \"def metadata_file(self):\\n return self._metadata_file\",\n \"def create_file_attributes(permissions, time, size):\\n\\n return {\\n 'st_mode': (stat.S_IFREG | permissions),\\n 'st_ctime': time,\\n 'st_mtime': time,\\n 'st_atime': time,\\n 'st_size': size,\\n 'st_uid': os.getuid(),\\n 'st_gid': os.getgid(),\\n 'st_nlink': 1\\n }\",\n \"def write_metadata(file_name, metadata_dict, category='',\\n datatype=\\\"inventory\\\", parameters=None):\\n if (datatype == \\\"inventory\\\") or (datatype == \\\"source\\\"):\\n meta = set_stewi_meta(file_name, stewiformat=category)\\n if datatype == 'inventory':\\n meta.tool_meta = {\\\"parameters\\\": parameters,\\n \\\"sources\\\": metadata_dict}\\n else:\\n meta.tool_meta = metadata_dict\\n write_metadata_to_file(paths, meta)\\n elif datatype == \\\"validation\\\":\\n file = (paths.local_path / 'validation' /\\n f'{file_name}_validationset_metadata.json')\\n with file.open('w') as fi:\\n fi.write(json.dumps(metadata_dict, indent=4))\",\n \"def create_metadata(data_dir):\\n pool = multiprocessing.Pool()\\n raw_feature_paths = [os.path.join(data_dir, \\\"features.jsonl\\\")]\\n records = list(pool.imap(read_metadata_record, raw_feature_iterator(raw_feature_paths)))\\n records = [dict(record, **{\\\"subset\\\": \\\"train\\\"}) for record in records]\\n\\n metadf = pd.DataFrame(records)[[\\\"sha256\\\", \\\"appeared\\\", \\\"subset\\\", \\\"label\\\"]]\\n metadf.to_csv(os.path.join(data_dir, \\\"metadata.csv\\\"))\\n print(\\\"\\\\n[Done] create_metadata\\\\n\\\")\\n \\n return metadf\",\n \"def feature_static_metadata(self):\\n # Get binary size\\n self.features[\\\"size\\\"] = \\\\\\n self.report.get(\\\"target\\\", {}).get(\\\"file\\\", {}).get(\\\"size\\\")\\n\\n # Get binary timestamp in the UNIX timestamp format\\n str_dt = self.report.get(\\\"static\\\", {}).get(\\\"pe_timestamp\\\")\\n ts = None\\n if str_dt is not None:\\n dt = datetime.datetime.strptime(str_dt, \\\"%Y-%m-%d %H:%M:%S\\\")\\n ts = int(time.mktime(dt.timetuple()))\\n self.features[\\\"timestamp\\\"] = ts\\n\\n # ExifTool output\\n et_tokens = [\\\"FileDescription\\\", \\\"OriginalFilename\\\"]\\n for token in et_tokens:\\n self.features[token] = None\\n for attr in self.report.get(\\\"static\\\", {}).get(\\\"pe_versioninfo\\\", []):\\n attr_name = attr.get(\\\"name\\\")\\n if attr_name in et_tokens:\\n self.features[attr_name] = attr.get(\\\"value\\\")\\n\\n # Magic byte\\n self.features[\\\"magic_byte\\\"] = \\\\\\n self.report.get(\\\"target\\\", {}).get(\\\"file\\\", {}).get(\\\"type\\\")\",\n \"def get_metadata_from_path(path):\\n try:\\n import yaml\\n # assumes index card is in the top-level of path\\n index_card = os.path.join(path, \\\"M_index.yaml\\\")\\n with open(index_card, \\\"r\\\") as stream:\\n file_info = yaml.safe_load(stream)\\n\\n metadata_dict = {}\\n metadata_dict[\\\"book_id\\\"] = file_info[\\\"book_id\\\"]\\n metadata_dict[\\\"timestamp_start\\\"] = file_info[\\\"start_time\\\"]\\n metadata_dict[\\\"type\\\"] = file_info[\\\"type\\\"]\\n metadata_dict[\\\"obsid\\\"] = _convert_book_id_to_obsid(file_info[\\\"book_id\\\"])\\n # get optional bits\\n if \\\"stop_time\\\" in file_info:\\n metadata_dict[\\\"timestamp_end\\\"] = file_info[\\\"stop_time\\\"]\\n if \\\"observatory\\\" in file_info:\\n metadata_dict[\\\"observatory\\\"] = file_info[\\\"observatory\\\"]\\n if \\\"telescope\\\" in file_info:\\n metadata_dict[\\\"telescope\\\"] = file_info[\\\"telescope\\\"]\\n if \\\"stream_ids\\\" in file_info:\\n metadata_dict[\\\"stream_ids\\\"] = file_info[\\\"stream_ids\\\"]\\n if \\\"subtype\\\" in file_info:\\n metadata_dict[\\\"subtype\\\"] = file_info[\\\"subtype\\\"]\\n if \\\"tags\\\" in file_info:\\n metadata_dict[\\\"tags\\\"] = file_info[\\\"tags\\\"]\\n if \\\"scanification\\\" in file_info:\\n metadata_dict[\\\"scanification\\\"] = file_info[\\\"scanification\\\"]\\n if \\\"hwp_rate_hz\\\" in file_info:\\n metadata_dict[\\\"hwp_rate_hz\\\"] = file_info[\\\"hwp_rate_hz\\\"]\\n if \\\"sequencer_ref\\\" in file_info:\\n metadata_dict[\\\"sequencer_ref\\\"] = file_info[\\\"sequencer_ref\\\"]\\n return metadata_dict\\n except (ImportError, FileNotFoundError, KeyError):\\n pass\\n\\n return None\",\n \"def metadata(self, truncate: bool = False) -> Tuple[str, str]:\\n\\t\\tif not self._closed:\\n\\t\\t\\tfilename = self.filename\\n\\t\\t\\tmd_filename = \\\"%s.file_md.json.gzip\\\" % (self.file_path)\\n\\t\\t\\tmd_mod_filename = \\\"%s.file_md.lastmod.gzip\\\" % (self.file_path)\\n\\t\\t\\tlogging.debug(\\\"Expanding metada (stored as %s.file_md.json.gzip)\\\", filename)\\n\\n\\t\\t\\tlast_mod = self.last_modified()\\n\\t\\t\\tif os.path.isfile(md_filename):\\n\\t\\t\\t\\tlogging.debug(\\\" Found previously extracted JSON file\\\")\\n\\t\\t\\t\\tif truncate:\\n\\t\\t\\t\\t\\tself.clear_metadata()\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tmd_json = load_gzipped_json_string(md_filename)\\n\\t\\t\\t\\t\\tmd_mod = load_gzipped_json_string(md_mod_filename)\\n\\t\\t\\t\\t\\tmd_parsed = json.loads(md_json)\\n\\t\\t\\t\\t\\t# check if cached metadata is up to date and\\n\\t\\t\\t\\t\\t# points to correct project folder and filename\\n\\t\\t\\t\\t\\t# if so return cache, otherwise clear it\\n\\t\\t\\t\\t\\tlogging.debug(\\\" md_mod: %s\\\", md_mod)\\n\\t\\t\\t\\t\\tlogging.debug(\\\" last_mod: %s\\\", last_mod)\\n\\t\\t\\t\\t\\tif md_mod != last_mod or md_parsed.project != self.project or md_parsed.filename != filename:\\n\\t\\t\\t\\t\\t\\tself.clear_metadata()\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tlogging.debug(\\\" Cache up to date\\\")\\n\\t\\t\\t\\t\\t\\treturn (md_json, last_mod)\\n\\n\\t\\t\\tds = self.ds\\n\\t\\t\\tattrs = ds.attrs.keys()\\n\\t\\t\\ttitle = filename if \\\"title\\\" not in attrs else ds.attrs.title\\n\\t\\t\\tdescr = \\\"\\\" if \\\"description\\\" not in attrs else ds.attrs.description\\n\\t\\t\\turl = \\\"\\\" if \\\"url\\\" not in attrs else ds.attrs.url\\n\\t\\t\\tdoi = \\\"\\\" if \\\"doi\\\" not in attrs else ds.attrs.doi\\n\\t\\t\\t# converts compact ISO timestamps to human-readable ones.\\n\\t\\t\\t# Example: \\\"20180130T155028.262458Z\\\" becomes \\\"2018/01/13 15:50\\\"\\n\\t\\t\\tlast_mod_humanreadable = \\\"{}/{}/{} {}:{}:{}\\\".format(last_mod[0:4], last_mod[4:6], last_mod[6:8], last_mod[9:11], last_mod[11:13], last_mod[13:15])\\n\\t\\t\\t# default to last_modified for older files that do\\n\\t\\t\\t# not have a creation_date field\\n\\t\\t\\tcreation_date = last_mod_humanreadable if \\\"creation_date\\\" not in attrs else ds.attrs.creation_date\\n\\t\\t\\t# get arbitrary col/row attribute, they are all lists\\n\\t\\t\\t# of equal size. The length equals total cells/genes\\n\\t\\t\\ttotal_cells = ds.shape[1]\\n\\t\\t\\ttotal_genes = ds.shape[0]\\n\\n\\t\\t\\tmd_data = {\\n\\t\\t\\t\\t\\\"project\\\": self.project,\\n\\t\\t\\t\\t\\\"filename\\\": filename,\\n\\t\\t\\t\\t\\\"dataset\\\": filename,\\n\\t\\t\\t\\t\\\"title\\\": title,\\n\\t\\t\\t\\t\\\"description\\\": descr,\\n\\t\\t\\t\\t\\\"url\\\": url,\\n\\t\\t\\t\\t\\\"doi\\\": doi,\\n\\t\\t\\t\\t\\\"creationDate\\\": creation_date,\\n\\t\\t\\t\\t\\\"lastModified\\\": last_mod_humanreadable,\\n\\t\\t\\t\\t\\\"totalCells\\\": total_cells,\\n\\t\\t\\t\\t\\\"totalGenes\\\": total_genes,\\n\\t\\t\\t}\\n\\t\\t\\tlogging.debug(\\\" Saving extracted metadata as JSON file\\\")\\n\\t\\t\\tmd_json = json.dumps(md_data)\\n\\t\\t\\tsave_gzipped_json_string(md_filename, md_json)\\n\\t\\t\\tsave_gzipped_json_string(md_mod_filename, json.dumps(last_mod))\\n\\t\\t\\treturn (md_json, last_mod)\\n\\t\\treturn None\",\n \"def snapshot_info(self) -> MetaFile:\\n raise NotImplementedError\",\n \"def _WriteStorageMetadata(self):\\n stream_name = 'metadata.txt'\\n if self._HasStream(stream_name):\\n return\\n\\n stream_data = (\\n '[plaso_storage_file]\\\\n'\\n 'format_version: {0:d}\\\\n'\\n 'serialization_format: {1:s}\\\\n'\\n 'storage_type: {2:s}\\\\n'\\n '\\\\n').format(\\n self._FORMAT_VERSION, self.serialization_format, self.storage_type)\\n\\n stream_data = stream_data.encode('utf-8')\\n self._WriteStream(stream_name, stream_data)\",\n \"def __init__(self, debug=False, output_writer=None):\\n super(RecycleBinMetadataFile, self).__init__(\\n debug=debug, output_writer=output_writer)\\n self.deletion_time = None\\n self.format_version = None\\n self.original_filename = None\\n self.original_file_size = None\",\n \"def metadata_name(filename):\\n\\tif test_hachoir_extension(filename):\\n\\t\\tmetadata = metadata_for_file(filename)\\n\\t\\tif metadata:\\n\\t\\t\\tdata = dict([\\n\\t\\t\\t\\t(data.key, data.values[0].value)\\n\\t\\t\\t\\tfor data in metadata\\n\\t\\t\\t\\tif data.values\\n\\t\\t\\t\\t])\\n\\t\\telse:\\n\\t\\t\\tdata=None\\n\\telif test_3D_extension(filename):# 3D not in the extention \\n\\t\\tdata = {'mime_type':'model'}\\n\\telse:\\n\\t\\tdata=None\\n\\treturn data\",\n \"def _extract_metadata(self) -> None:\\n self.log(\\\"Extracting metadata.\\\")\\n image_paths: list[Path] = []\\n for ext in (\\\"jpg\\\", \\\"jpeg\\\", \\\"png\\\"):\\n image_paths.extend(self._base_dir.glob(f\\\"**/*.{ext}\\\"))\\n image_paths_str = [str(image.relative_to(self._base_dir)) for image in image_paths]\\n filepaths = pd.Series(image_paths_str)\\n metadata = cast(\\n pd.DataFrame,\\n filepaths.str.split(\\\"/\\\", expand=True).rename( # type: ignore[attr-defined]\\n columns={0: \\\"superclass\\\", 1: \\\"concept\\\", 2: \\\"context\\\", 3: \\\"filename\\\"}\\n ),\\n )\\n metadata[\\\"filepath\\\"] = filepaths\\n metadata.sort_index(axis=1, inplace=True)\\n metadata.sort_values(by=[\\\"filepath\\\"], axis=0, inplace=True)\\n metadata = self._label_encode_metadata(metadata)\\n metadata.to_csv(self._metadata_path)\",\n \"def parse_metadata_file(self, file):\\n\\n file_keys = list(file.keys())\\n\\n if 'labelAnnotations' in file_keys:\\n file_annots = file['labelAnnotations']\\n file_top_score = np.asarray(\\n [x['score'] for x in file_annots]).mean()\\n file_top_desc = [x['description'] for x in file_annots]\\n else:\\n file_top_score = np.nan\\n file_top_desc = ['']\\n\\n file_colors = file['imagePropertiesAnnotation']['dominantColors'][\\n 'colors']\\n file_crops = file['cropHintsAnnotation']['cropHints']\\n\\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\\n file_color_pixelfrac = np.asarray(\\n [x['pixelFraction'] for x in file_colors]).mean()\\n\\n file_crop_conf = np.asarray(\\n [x['confidence'] for x in file_crops]).mean()\\n\\n if 'importanceFraction' in file_crops[0].keys():\\n file_crop_importance = np.asarray(\\n [x['importanceFraction'] for x in file_crops]).mean()\\n else:\\n file_crop_importance = np.nan\\n\\n df_metadata = {\\n 'annots_score': file_top_score,\\n 'color_score': file_color_score,\\n 'color_pixelfrac': file_color_pixelfrac,\\n 'crop_conf': file_crop_conf,\\n 'crop_importance': file_crop_importance,\\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\\n }\\n\\n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\\n df_metadata = df_metadata.add_prefix('metadata_')\\n\\n return df_metadata\",\n \"def metadata(self):\\n return parse_metadata(self.metadata_path())\",\n \"def metadata(host_directory_path):\\n return create_volume_string(host_directory_path, \\\"/bbx/metadata\\\", False)\",\n \"def make_test_metadata(path):\\n assert path, 'Please supply a nonempty path to store test dataset.'\\n return create_test_dataset('file://{}'.format(path), range(ROWS_COUNT))\",\n \"def create_meta(prefix, dist, info_dir, extra_info):\\n # read info/index.json first\\n with open(join(info_dir, 'index.json')) as fi:\\n meta = json.load(fi)\\n # add extra info\\n meta.update(extra_info)\\n # write into <prefix>/conda-meta/<dist>.json\\n meta_dir = join(prefix, 'conda-meta')\\n if not isdir(meta_dir):\\n os.makedirs(meta_dir)\\n with open(join(meta_dir, 'history'), 'w') as fo:\\n fo.write('')\\n with open(join(meta_dir, dist + '.json'), 'w') as fo:\\n json.dump(meta, fo, indent=2, sort_keys=True)\",\n \"def get_and_update_metadata():\\n if not os.path.exists('.git') and os.path.exists(METADATA_FILENAME):\\n with open(METADATA_FILENAME) as fh:\\n metadata = json.load(fh)\\n else:\\n git = Git()\\n revision = os.environ.get('TRAVIS_BUILD_NUMBER', git.revision)\\n split_version = git.version.split('.')\\n split_version[-1] = revision\\n version = '.'.join(split_version)\\n metadata = {\\n 'version': version,\\n 'git_hash': git.hash,\\n 'git_origin': git.origin,\\n 'git_branch': git.branch,\\n 'git_version': git.version\\n }\\n with open(METADATA_FILENAME, 'w') as fh:\\n json.dump(metadata, fh)\\n return metadata\",\n \"def create(self):\\n\\t\\tlipsBaseFile.imp()\",\n \"def makeMetadataTest(metadata):\\n metadata = metadata.strip()\\n # convert to tabs\\n metadata = str(metadata)\\n metadata = metadata.replace(\\\" \\\", \\\"\\\\t\\\")\\n # store\\n originalMetadata = metadata\\n # pack\\n header, directory, tableData, metadata = defaultTestData(metadata=metadata)\\n data = padData(packTestHeader(header) + packTestDirectory(directory) + tableData) + packTestMetadata(metadata)\\n # done\\n return data, originalMetadata\",\n \"def __init__(self, file_object, data_offset=0, data_size=0):\\n super(CPIOArchiveFileEntry, self).__init__(\\n file_object, data_offset=data_offset, data_size=data_size)\\n self.group_identifier = None\\n self.inode_number = None\\n self.mode = None\\n self.modification_time = None\\n self.path = None\\n self.size = None\\n self.user_identifier = None\",\n \"def store_metadata(fh, metadata):\\n json.dump(metadata, fh, indent=2)\",\n \"def get_metadata(self, t: Optional[int] = None) -> Dict:\\n real_t = None if t is None else int(self._resolve_index(t))\\n if self._is_tiff:\\n ret = self._reader.metadata(index=..., page=real_t)\\n else:\\n ret = self._reader.metadata(index=real_t)\\n self._parse_yaml_description(ret)\\n if real_t is not None:\\n ret[\\\"frame_no\\\"] = real_t\\n return ret\",\n \"def create_metadata(data_dir):\\n pool = multiprocessing.Pool()\\n\\n train_feature_paths = [os.path.join(data_dir, \\\"train_features_{}.jsonl\\\".format(i)) for i in range(6)]\\n train_records = list(pool.imap(read_metadata_record, raw_feature_iterator(train_feature_paths)))\\n train_records = [dict(record, **{\\\"subset\\\": \\\"train\\\"}) for record in train_records]\\n\\n test_feature_paths = [os.path.join(data_dir, \\\"test_features.jsonl\\\")]\\n test_records = list(pool.imap(read_metadata_record, raw_feature_iterator(test_feature_paths)))\\n test_records = [dict(record, **{\\\"subset\\\": \\\"test\\\"}) for record in test_records]\\n\\n all_metadata_keys = [\\\"sha256\\\", \\\"appeared\\\", \\\"subset\\\", \\\"label\\\", \\\"avclass\\\"]\\n ordered_metadata_keys = [k for k in all_metadata_keys if k in train_records[0].keys()]\\n metadf = pd.DataFrame(train_records + test_records)[ordered_metadata_keys]\\n metadf.to_csv(os.path.join(data_dir, \\\"metadata.csv\\\"))\\n return metadf\",\n \"def _store_package_metadata(self):\",\n \"def get_preset_metadata(self, filename):\\r\\n\\r\\n raise NotImplementedError\",\n \"def tests_ti_file_create(self):\\n indicator_data = {\\n 'md5': uuid.uuid4().hex.upper(),\\n 'owner': self.owner,\\n 'rating': randint(0, 5),\\n }\\n ti = self.ti.file(**indicator_data)\\n r = ti.create()\\n\\n # assert response\\n assert r.status_code == 201\\n\\n # retrieve indicator for asserts\\n ti = self.ti.file(**indicator_data)\\n r = ti.single()\\n response_data = r.json()\\n ti_data = response_data.get('data', {}).get(ti.api_entity)\\n\\n # validate response data\\n assert r.status_code == 200\\n assert response_data.get('status') == 'Success'\\n\\n # validate ti data\\n assert ti_data.get('confidence') == indicator_data.get('confidence')\\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\\n assert ti_data.get('rating') == indicator_data.get('rating')\\n\\n # cleanup indicator\\n r = ti.delete()\\n assert r.status_code == 200\",\n \"def Metadata():\\n def _CreateMetadata(unused_none):\\n global _metadata\\n if not _metadata:\\n _metadata = _GCEMetadata()\\n _metadata_lock.lock(function=_CreateMetadata, argument=None)\\n _metadata_lock.unlock()\\n return _metadata\",\n \"def _pname_and_metadata(in_file):\\n\\n\\n if in_file.endswith(\\\".csv\\\"):\\n raise ValueError(\\\"Did not find input metadata file: %s\\\" % in_file)\\n base, md, global_vars = in_file, {}, {}\\n md_file = None\\n return base, md, global_vars, md_file\",\n \"def calculate_statistics(kv, f):\\n metadata = dict()\\n f.seek(0, SEEK_END)\\n b = f.tell()\\n f.seek(0)\\n metadata['FILE_SIZE_1000'], metadata['FILE_SIZE_1024'] = get_friendly_file_size(b)\\n\\n if gcode_key.TIME in kv:\\n metadata['ETA_SECONDS'] = kv[gcode_key.TIME]\\n\\n # TODO Distance travelled\\n key = 'DISTANCE'\\n if key in kv:\\n metadata[key] = kv[key]\\n\\n cubic_volume = get_cubic_volume(kv)\\n if cubic_volume is not None:\\n metadata['CUBIC_VOLUME'] = cubic_volume\\n\\n if gcode_key.LAYER_COUNT in kv:\\n metadata['LAYER_COUNT'] = kv[gcode_key.LAYER_COUNT]\\n\\n if gcode_key.FILAMENT_USED in kv:\\n metadata['FILAMENT_USED'] = kv[gcode_key.FILAMENT_USED][:-1] + ' m'\\n\\n return metadata\",\n \"def create_tmp_metadata(num_examples, read_len):\\n dataset_info = seq2label_pb2.Seq2LabelDatasetInfo(\\n read_length=read_len,\\n num_examples=num_examples,\\n read_stride=1,\\n dataset_path='test.tfrecord')\\n\\n for target in TEST_TARGETS:\\n dataset_info.labels.add(\\n name=target, values=[str(i) for i in xrange(num_examples)])\\n\\n tmp_path = os.path.join(FLAGS.test_tmpdir, 'test.pbtxt')\\n with tf.gfile.GFile(tmp_path, 'w') as f:\\n f.write(text_format.MessageToString(dataset_info))\\n return tmp_path\",\n \"def save_metadata(file_path, short_description, file_comments,\\n file_generation, file_directions):\\n\\n stat_info = os.stat(file_path)\\n size_MB = round(statinfo.st_size / (10**6))\\n [fPath, fName] = os.path.split(file_path)\\n\\n format = '%d_%m_%Y_%H_%M_%S'\\n creation_time = dt.datetime.today().strftime(format)\\n\\n metadata = {'description': short_description,\\n 'comments': file_comments,\\n 'filename': fName,\\n 'folder': fPath,\\n 'creationTime': creation_time,\\n 'directions': file_directions,\\n 'generation': file_generation,\\n 'sizeMB': sizeMB}\\n\\n jsonFName = file_path.replace(os.path.splitext(fName)[1], '.json')\\n with open(jsonFName, 'w') as outfile:\\n json.dump(metadata, outfile)\",\n \"def __init__(self, filename: Union[str, h5py.File], *, mode=\\\"w-\\\"):\\n if isinstance(filename, h5py.File):\\n self.__file = filename\\n mode = self.__file.mode\\n else:\\n if mode not in SUPPORTED_MODES:\\n raise ValueError(f\\\"Invalid mode; must be one of {SUPPORTED_MODES}\\\")\\n self.__file = h5py.File(filename, mode=mode)\\n\\n if METADATA not in self.__file:\\n self.add_file_metadata()\",\n \"def get_metadata(self):\\n previous = DirectoryMetadata.load_pickle(self)\\n metadata = {}\\n\\n for dirpath, dirnames, filenames in os.walk(self.prefix_dir):\\n for fname in filenames:\\n path = os.path.join(dirpath, fname)\\n relative_path = path.split(self.base_dir, 1)[1]\\n try:\\n stats = os.stat(path)\\n except OSError:\\n log.exception('Error stating a file on disk while building up metadata, skipping file %s' % path)\\n continue\\n swift_bytes = stats.st_size\\n mtime = datetime.utcfromtimestamp(stats.st_mtime)\\n if (previous is not None) and (relative_path in previous.metadata) and\\\\\\n (previous.metadata[relative_path].bytes == swift_bytes):\\n swift_hash = previous.metadata[relative_path].hash\\n else:\\n try:\\n with open(path, 'rb') as afile:\\n md5_hash = hashlib.md5()\\n md5_hash.update(afile.read())\\n swift_hash = md5_hash.hexdigest()\\n except OSError:\\n log.exception('Error reading a file to create the md5 while building up metadata, skipping file %s' % path)\\n continue\\n\\n metadata[relative_path] = FileMetadata(relative_path, swift_bytes, mtime, swift_hash)\\n\\n return metadata\",\n \"def build_metadata(self, meta):\\n if meta is None:\\n try:\\n self.parser = GenericParser(\\n fn_re='{}/(e\\\\d+s\\\\d+)_.*/Production.nc'.format(self.data_folder),\\n group_names=['sim'],\\n group_transforms=[lambda x: x],\\n top_fn='',\\n step_ps=self.timestep\\n )\\n meta = gather_metadata('{}/e*/*nc'.format(self.data_folder), parser)\\n except:\\n logger.warning(\\\"Could not automatically build metadata\\\")\\n return None\\n else:\\n if not isinstance(meta, pd.DataFrame):\\n meta = load_meta(meta)\\n return meta\",\n \"def create(\\n name,\\n\\n open_file,\\n create_file,\\n remove_file,\\n\\n create_directory,\\n list_directory,\\n remove_empty_directory,\\n temporary_directory,\\n\\n link,\\n realpath,\\n\\n exists,\\n is_dir,\\n is_file,\\n is_link,\\n\\n remove=recursive_remove,\\n\\n state=lambda: None,\\n):\\n\\n return attr.s(\\n type(\\n name, (object,), dict(\\n _state=attr.ib(default=attr.Factory(state), repr=False),\\n\\n create=create_file,\\n open=lambda fs, path, mode=\\\"rb\\\": open_file(\\n fs=fs, path=path, mode=mode,\\n ),\\n remove_file=remove_file,\\n\\n remove=remove,\\n\\n create_directory=create_directory,\\n list_directory=list_directory,\\n remove_empty_directory=remove_empty_directory,\\n temporary_directory=temporary_directory,\\n\\n link=link,\\n realpath=realpath,\\n\\n exists=exists,\\n is_dir=is_dir,\\n is_file=is_file,\\n is_link=is_link,\\n\\n touch=_touch,\\n children=_children,\\n glob_children=_glob_children,\\n contents_of=_open_and_read,\\n ),\\n ),\\n )\",\n \"def parse_metadata_file(self, file):\\n \\n file_keys = list(file.keys())\\n \\n if 'labelAnnotations' in file_keys:\\n #file_annots = file['labelAnnotations'][:int(len(file['labelAnnotations']) * 0.5)]\\n file_annots = file['labelAnnotations'][:]\\n file_top_score = np.asarray([x['score'] for x in file_annots]).mean()\\n file_top_desc = [x['description'] for x in file_annots]\\n else:\\n file_top_score = np.nan\\n file_top_desc = ['']\\n \\n file_colors = file['imagePropertiesAnnotation']['dominantColors']['colors']\\n file_crops = file['cropHintsAnnotation']['cropHints']\\n\\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\\n file_color_pixelfrac = np.asarray([x['pixelFraction'] for x in file_colors]).mean()\\n\\n file_crop_conf = np.asarray([x['confidence'] for x in file_crops]).mean()\\n \\n if 'importanceFraction' in file_crops[0].keys():\\n file_crop_importance = np.asarray([x['importanceFraction'] for x in file_crops]).mean()\\n else:\\n file_crop_importance = np.nan\\n\\n df_metadata = {\\n 'annots_score': file_top_score,\\n 'color_score': file_color_score,\\n 'color_pixelfrac': file_color_pixelfrac,\\n 'crop_conf': file_crop_conf,\\n 'crop_importance': file_crop_importance,\\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\\n }\\n \\n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\\n df_metadata = df_metadata.add_prefix('metadata_')\\n \\n return df_metadata\",\n \"def _generate_model_metadata(out_file, model):\\n # Define which FirstLevelModel attributes are BIDS compliant and which\\n # should be bundled in a new \\\"ModelParameters\\\" field.\\n DATA_ATTRIBUTES = [\\n \\\"t_r\\\",\\n ]\\n PARAMETER_ATTRIBUTES = [\\n \\\"drift_model\\\",\\n \\\"hrf_model\\\",\\n \\\"standardize\\\",\\n \\\"high_pass\\\",\\n \\\"target_shape\\\",\\n \\\"signal_scaling\\\",\\n \\\"drift_order\\\",\\n \\\"scaling_axis\\\",\\n \\\"smoothing_fwhm\\\",\\n \\\"target_affine\\\",\\n \\\"slice_time_ref\\\",\\n \\\"fir_delays\\\",\\n ]\\n ATTRIBUTE_RENAMING = {\\n \\\"t_r\\\": \\\"RepetitionTime\\\",\\n }\\n\\n # Fields for the top level of the dictionary\\n DATA_ATTRIBUTES.sort()\\n data_attributes = {\\n attr_name: getattr(model, attr_name)\\n for attr_name in DATA_ATTRIBUTES\\n if hasattr(model, attr_name)\\n }\\n data_attributes = {\\n ATTRIBUTE_RENAMING.get(k, k): v for k, v in data_attributes.items()\\n }\\n\\n # Fields for a nested section of the dictionary\\n # The ModelParameters field is an ad-hoc way to retain useful info.\\n PARAMETER_ATTRIBUTES.sort()\\n model_attributes = {\\n attr_name: getattr(model, attr_name)\\n for attr_name in PARAMETER_ATTRIBUTES\\n if hasattr(model, attr_name)\\n }\\n model_attributes = {\\n ATTRIBUTE_RENAMING.get(k, k): v for k, v in model_attributes.items()\\n }\\n\\n model_metadata = {\\n \\\"Description\\\": \\\"A statistical map generated by Nilearn.\\\",\\n **data_attributes,\\n \\\"ModelParameters\\\": model_attributes,\\n }\\n\\n with open(out_file, \\\"w\\\") as f_obj:\\n json.dump(model_metadata, f_obj, indent=4, sort_keys=True)\",\n \"def create_object_metadata(self, bucket_name, object_name, metadata_name, metadata_value):\\n\\n return h3lib.create_object_metadata(self._handle, bucket_name, object_name, metadata_name, metadata_value, self._user_id)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6603724","0.64794075","0.6444975","0.63011587","0.62964076","0.60185266","0.60181475","0.60088","0.59797317","0.5959526","0.5949016","0.5931138","0.58757067","0.5871233","0.58695316","0.58607626","0.58580416","0.5799596","0.57732666","0.5763947","0.57465434","0.5728845","0.57128406","0.5704305","0.5703079","0.5694333","0.5690785","0.5649895","0.56488043","0.5633684","0.56185484","0.56157875","0.56092393","0.560531","0.5577178","0.55768865","0.5562528","0.55608994","0.5560389","0.5555759","0.5546132","0.5529001","0.5511864","0.5510497","0.5494582","0.5489693","0.54894173","0.5479822","0.54766095","0.5470991","0.54673076","0.5458197","0.54379684","0.5436662","0.54333806","0.5424828","0.5419093","0.5417233","0.540933","0.54074186","0.53894323","0.53813684","0.5377616","0.5370777","0.53661394","0.53628916","0.5342908","0.5342481","0.5341228","0.53319687","0.5330101","0.5314569","0.53142375","0.5314178","0.5312289","0.53095156","0.5302383","0.53004026","0.5292684","0.529202","0.52892965","0.5282342","0.52813333","0.5279294","0.527606","0.52757305","0.52744204","0.52689326","0.52621204","0.525816","0.5254111","0.5250553","0.5244243","0.52436185","0.524315","0.5238043","0.52343994","0.52316165","0.5221","0.52134407"],"string":"[\n \"0.6603724\",\n \"0.64794075\",\n \"0.6444975\",\n \"0.63011587\",\n \"0.62964076\",\n \"0.60185266\",\n \"0.60181475\",\n \"0.60088\",\n \"0.59797317\",\n \"0.5959526\",\n \"0.5949016\",\n \"0.5931138\",\n \"0.58757067\",\n \"0.5871233\",\n \"0.58695316\",\n \"0.58607626\",\n \"0.58580416\",\n \"0.5799596\",\n \"0.57732666\",\n \"0.5763947\",\n \"0.57465434\",\n \"0.5728845\",\n \"0.57128406\",\n \"0.5704305\",\n \"0.5703079\",\n \"0.5694333\",\n \"0.5690785\",\n \"0.5649895\",\n \"0.56488043\",\n \"0.5633684\",\n \"0.56185484\",\n \"0.56157875\",\n \"0.56092393\",\n \"0.560531\",\n \"0.5577178\",\n \"0.55768865\",\n \"0.5562528\",\n \"0.55608994\",\n \"0.5560389\",\n \"0.5555759\",\n \"0.5546132\",\n \"0.5529001\",\n \"0.5511864\",\n \"0.5510497\",\n \"0.5494582\",\n \"0.5489693\",\n \"0.54894173\",\n \"0.5479822\",\n \"0.54766095\",\n \"0.5470991\",\n \"0.54673076\",\n \"0.5458197\",\n \"0.54379684\",\n \"0.5436662\",\n \"0.54333806\",\n \"0.5424828\",\n \"0.5419093\",\n \"0.5417233\",\n \"0.540933\",\n \"0.54074186\",\n \"0.53894323\",\n \"0.53813684\",\n \"0.5377616\",\n \"0.5370777\",\n \"0.53661394\",\n \"0.53628916\",\n \"0.5342908\",\n \"0.5342481\",\n \"0.5341228\",\n \"0.53319687\",\n \"0.5330101\",\n \"0.5314569\",\n \"0.53142375\",\n \"0.5314178\",\n \"0.5312289\",\n \"0.53095156\",\n \"0.5302383\",\n \"0.53004026\",\n \"0.5292684\",\n \"0.529202\",\n \"0.52892965\",\n \"0.5282342\",\n \"0.52813333\",\n \"0.5279294\",\n \"0.527606\",\n \"0.52757305\",\n \"0.52744204\",\n \"0.52689326\",\n \"0.52621204\",\n \"0.525816\",\n \"0.5254111\",\n \"0.5250553\",\n \"0.5244243\",\n \"0.52436185\",\n \"0.524315\",\n \"0.5238043\",\n \"0.52343994\",\n \"0.52316165\",\n \"0.5221\",\n \"0.52134407\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6083752"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":94095,"cells":{"query":{"kind":"string","value":"Create a Jams object."},"document":{"kind":"string","value":"def __init__(self, annotations=None, file_metadata=None, sandbox=None):\n super(JAMS, self).__init__()\n\n if file_metadata is None:\n file_metadata = FileMetadata()\n\n if sandbox is None:\n sandbox = Sandbox()\n\n self.annotations = AnnotationArray(annotations=annotations)\n\n self.file_metadata = FileMetadata(**file_metadata)\n\n self.sandbox = Sandbox(**sandbox)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def to_jams(self):\n return jams_utils.jams_converter(\n audio_path=self.audio_path,\n events=self.events,\n metadata={\n \"split\": self._clip_metadata.get(\"split\"),\n \"subdataset\": self._clip_metadata.get(\"subdataset\"),\n },\n )","def to_jams(self):\n return jams_utils.jams_converter(\n audio_path=self.audio_path,\n beat_data=[(self.beats, None)],\n section_data=[(self.sections, None)],\n chord_data=[(self.chords, None)],\n metadata=self._track_metadata,\n )","def to_jams(self):\n return jams_utils.jams_converter(\n tags_gtzan_data=[(self.genre, \"gtzan-genre\")],\n metadata={\n \"title\": \"Unknown track\",\n \"artist\": \"Unknown artist\",\n \"release\": \"Unknown album\",\n \"duration\": 30.0,\n \"curator\": \"George Tzanetakis\",\n },\n )","def to_jams(self):\n return jams_utils.jams_converter(\n audio_path=self.audio_paths[0], note_data=[(self.notes, \"aligned notes\")]\n )","def to_jams(self):\n return jams_utils.jams_converter(\n audio_path=self.audio_path,\n beat_data=[\n (self.onsets_b, \"onsets_b\"),\n (self.onsets_d, \"onsets_d\"),\n (self.onsets_rb, \"onsets_rb\"),\n (self.onsets_rt, \"onsets_rt\"),\n ],\n metadata={\"train\": self.train},\n )","def make_game(self):\n game = Game(self.data['gamename'])\n self.game = game\n return game","def create():","def create():","def createObject(self, *args):\n return _libsbml.MultiASTPlugin_createObject(self, *args)","def create(self, query_goal_path, all_knowledge):\n plan_filepath = self._create_plan( query_goal_path, all_knowledge )\n skolemized_plan_filepath = self.output_folder + \"partially_skolemized_plan.n3\"\n skolemize_lemmas( plan_filepath, skolemized_plan_filepath)\n \n rdf_graph = Graph()\n rdf_graph.parse(skolemized_plan_filepath, format=\"n3\")\n parser = LemmasParser(rdf_graph)\n lemmas = parser.parse()\n \n #lemma_graph.to_image( output_file = self.output_folder + \"lemma_precedences.png\" )\n #lemma_graph.to_gml( output_file = self.output_folder + \"lemma_precedences.gml\" )\n return Plan( lemmas )","def create_tournament():\n data = view.create_tournament_view()\n tournament = Tournament(data['name'], data['place'], data['description'], data['turn_number'])\n return tournament","def create_subject(name=\"Basket Weaving\"):\n subj = Subject(name=name)\n subj.save()\n return subj","def __init__(self, motors, sound_object):\n \n \n self.m = motors\n self.so = sound_object","def create_exam():\n try:\n # decode token and check role for access control\n data = request.get_json()\n user_id = authenticate_token(request)\n examiner = is_examiner(user_id)\n \n if examiner:\n # Checks if data has required fields - throws exception if not\n pre_init_check(required_fields['exam'], **data)\n\n code_found = False\n while not code_found:\n # Generates unique exam code until one is found that does not already exist\n potential_login_code = generate_exam_code()\n code_exists = Exam.query.filter_by(login_code=potential_login_code).first()\n if not code_exists:\n data['login_code'] = potential_login_code\n break\n exam = Exam(**data)\n if exam.start_date > exam.end_date:\n raise Exception('Exam end_date precedes Exam start_date')\n db.session.add(exam)\n db.session.commit()\n return jsonify(exam.to_dict()), 201\n \n return jsonify({'user_id': user_id, 'message': ['access denied, not examiner']}), 403\n except MissingModelFields as e:\n return jsonify({ 'message': e.args }), 400\n except exc.SQLAlchemyError as e:\n db.session.rollback()\n return jsonify({ 'message': e.args }), 500\n except Exception as e:\n print(traceback.format_exc())\n return jsonify({ 'message': e.args }), 500","def create(self, validated_data):\n return Speaker.objects.create(**validated_data)","def build(self, name='impala'):\n program = lp.Program(name=name)\n\n with program.group('replay'):\n queue = program.add_node(lp.ReverbNode(self.queue))\n\n with program.group('counter'):\n counter = program.add_node(lp.CourierNode(self.counter))\n\n with program.group('learner'):\n learner = program.add_node(\n lp.CourierNode(self.learner, queue, counter))\n\n with program.group('evaluator'):\n program.add_node(lp.CourierNode(self.evaluator, learner, counter))\n\n with program.group('cacher'):\n cacher = program.add_node(\n lp.CacherNode(learner, refresh_interval_ms=2000, stale_after_ms=4000))\n\n with program.group('actor'):\n for _ in range(self._num_actors):\n program.add_node(lp.CourierNode(self.actor, queue, cacher, counter))\n\n return program","def _create_teams(self):\n\t\tself.teamsDict = {}\n\t\tself.teamNamesList = []\n\t\tfor team in range(self.numberOfTeams):\n\t\t\tname = 'TEAM_'+str(team+1)\n\t\t\tself.teamNamesList.append(name)\n\t\t\tself.teamsDict[name] = app.game.team.Team(sport_type=self.gameData['sportType'])","def __init__(__self__, *,\n application_object_id: pulumi.Input[str],\n audiences: pulumi.Input[Sequence[pulumi.Input[str]]],\n display_name: pulumi.Input[str],\n issuer: pulumi.Input[str],\n subject: pulumi.Input[str],\n description: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"application_object_id\", application_object_id)\n pulumi.set(__self__, \"audiences\", audiences)\n pulumi.set(__self__, \"display_name\", display_name)\n pulumi.set(__self__, \"issuer\", issuer)\n pulumi.set(__self__, \"subject\", subject)\n if description is not None:\n pulumi.set(__self__, \"description\", description)","def create(self):\n \n # create the sequence structure by calling the self.project.create\n self.project.create()","def create_participant(name='Not Brian', email='test@email.com') ->\\\n Participant:\n participant = Participant(name=name, email=email)\n return participant","def create(self, title, teamId=None, **request_parameters):\n check_type(title, basestring, optional=True)\n check_type(teamId, basestring, optional=True)\n\n post_data = dict_from_items_with_values(\n request_parameters,\n title=title,\n teamId=teamId,\n )\n\n # API request\n json_data = self._session.post(API_ENDPOINT, json=post_data)\n\n # Return a room object created from the response JSON data\n return self._object_factory(OBJECT_TYPE, json_data)","def create(self, validated_data):\n\n\t\t#print(validated_data)\n\n\t\tgenres = validated_data.pop('genre')\n\t\tkeywords = validated_data.pop('keyword')\n\t\tmovie = Movie.objects.create(**validated_data)\n\n\t\tif genres is not None:\n\t\t\tfor genre in genres:\n\t\t\t\tMovieGenres.objects.create(\n\t\t\t\t\tmovie_id=movie.movie_id,\n\t\t\t\t\tgenre_id=genre.genre_id\n\t\t\t\t)\n\n\t\tif keywords is not None:\n\t\t\tfor keyword in keywords:\n\t\t\t\tMovieKeywords.objects.create(\n\t\t\t\t\tmovie_id=movie.movie_id,\n\t\t\t\t\tkeyword_id=keyword.keyword_id\n\t\t\t\t)\n\t\treturn movie","def __init__(self, name, maxParticipants=None):\n self.name = name\n self.maxParticipants = maxParticipants\n self.participants = {}","def create_mp3():\n\n #TODO: les roles ne devraient pas etre en dur\n list_all_roles = [\n [],\n [\"morgan\"],\n [\"oberon\"],\n [\"mordred\"],\n [\"morgan\", \"oberon\"],\n [\"morgan\", \"mordred\"],\n [\"oberon\", \"mordred\"],\n [\"morgan\", \"oberon\", \"mordred\"]\n ]\n\n for list_roles in list_all_roles:\n\n list_mp3 = [\"init.mp3\", \"serv_mord.mp3\"]\n if \"oberon\" in list_roles:\n list_mp3.append(\"oberon.mp3\")\n list_mp3.append(\"red_identi.mp3\")\n\n if \"morgan\" in list_roles:\n list_mp3.append(\"add_per_mor.mp3\")\n\n list_mp3.append(\"serv_mord.mp3\")\n if \"mordred\" in list_roles:\n list_mp3.append(\"mordred.mp3\")\n list_mp3.extend([\"merlin_identi.mp3\", \"end.mp3\"])\n\n mp3_combined = AudioSegment.empty()\n for mp3 in list_mp3:\n mp3_combined += AudioSegment.from_mp3(\"resources/{}\".format(mp3))\n\n mp3_combined.export(\"resources/_{}.mp3\".format('-'.join(sorted(list_roles))), format=\"mp3\")","def create(self):\n try:\n schema = MorphemeLanguageModelSchema()\n values = json.loads(unicode(request.body, request.charset))\n data = schema.to_python(values)\n lm = create_new_morpheme_language_model(data)\n Session.add(lm)\n Session.commit()\n lm.make_directory_safely(lm.directory)\n return lm\n except h.JSONDecodeError:\n response.status_int = 400\n return h.JSONDecodeErrorResponse\n except Invalid, e:\n response.status_int = 400\n return {'errors': e.unpack_errors()}","def make_model():\n m = model_class(*argv[2:-1])\n modelobj[\"model\"] = m","def createCamera():\n\n turnCam = cmds.camera()\n lookThruAndFrame(turnCam[0])\n return turnCam","def __init__(__self__, *,\n application_object_id: Optional[pulumi.Input[str]] = None,\n audiences: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n credential_id: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n display_name: Optional[pulumi.Input[str]] = None,\n issuer: Optional[pulumi.Input[str]] = None,\n subject: Optional[pulumi.Input[str]] = None):\n if application_object_id is not None:\n pulumi.set(__self__, \"application_object_id\", application_object_id)\n if audiences is not None:\n pulumi.set(__self__, \"audiences\", audiences)\n if credential_id is not None:\n pulumi.set(__self__, \"credential_id\", credential_id)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if display_name is not None:\n pulumi.set(__self__, \"display_name\", display_name)\n if issuer is not None:\n pulumi.set(__self__, \"issuer\", issuer)\n if subject is not None:\n pulumi.set(__self__, \"subject\", subject)","def create(cls):\n pass\n return cls()","def main(args):\n print('loading {}'.format(args.stem_path))\n y, fs = librosa.load(args.stem_path, sr=44100)\n notes = mono_anal(y, fs)\n jam = output_to_jams(y, fs, notes, args)\n jam_path = args.stem_path.split('.')[0]+'.jams'\n jam.save(jam_path)\n print('jams file generated')\n return 0","def create_chitchat_bot(self):\n \n self.chatbot = ChatBot(\"Fresher's Friend\")\n self.chatbot.trainer2=ListTrainer(self.chatbot)\n self.chatbot.trainer=ChatterBotCorpusTrainer(self.chatbot)\n self.chatbot.trainer.train(\"chatterbot.corpus.english.greetings\")\n #self.chatbot.trainer.train(\"chatterbot.corpus.english.conversations\")\n for filename in filenames: \n self.chatbot.trainer2.train(files[filename])","def creat_team(self):\n te = Teams()\n per = Persons()\n teamlist = []\n for one in per.find({'role':'leader'},{'team_name'}):\n if one['team_name'] not in teamlist:\n teamlist.append(one['team_name'])\n # print len(teamlist)\n for team in teamlist:\n tmp = {'name': '', 'leader_email': '', 'person_emails': []}\n tmp['name'] = team\n tmp['leader_email'] = per.get_one({'team_name':team,'role':'leader'})['email']\n for one in per.find({'team_name':team},{'email'}):\n tmp['person_emails'].append(one['email'])\n print tmp\n search_t = te.get_one({'name':team})\n if search_t is None:\n te.insert_one(tmp)\n else:\n te.update_one({'name':team,'leader_email':'','person_emails':''},tmp,cover=True)","def newGame():\n result = cs411_game.newGame()\n return prepJSON(result)","def __init__(self, rooms, raumfeld):\n self._room = None\n self._rooms = rooms\n self._raumfeld = raumfeld\n self._mute = None\n self._media_position = None\n self._media_position_updated_at = None\n self._name = GROUP_PREFIX + repr(self._rooms)\n self._state = None\n self._volume_level = None\n self._unique_id = obj_to_uid(rooms)\n self._icon = \"mdi:speaker-multiple\"\n self._media_duration = None\n self._media_image_url = None\n self._media_title = None\n self._media_artist = None\n self._media_album_name = None\n self._media_album_artist = None\n self._media_track = None\n self._shuffle = None\n self._repeat = None\n self._play_mode = None\n self._is_spotify_sroom = None","def newAnalyzer():\n analyzer = {'tracks': None,\n 'songs': None,\n 'artists': None,\n 'char': None,\n 'char2': None,\n 'char3': None,\n }\n\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\n analyzer['songs'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['artists'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char2'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char3'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n analyzer['char4'] = mp.newMap(numelements=50,\n maptype='PROBING',\n comparefunction=compareValue)\n \n return analyzer","def _create_games(self):\n\n ''''''","def create(self, name):\n success, response = self._client_api.gen_request(req_type='post',\n path='/projects/{}/bots'.format(self.project.id),\n json_req={'name': name})\n if success:\n bot = entities.Bot.from_json(_json=response.json(),\n project=self.project,\n bots=self, client_api=self._client_api)\n else:\n raise exceptions.PlatformException(response)\n assert isinstance(bot, entities.Bot)\n return bot","def __init__(self, json):\n\n if \"show\" not in json or \"videos\" not in json:\n raise Exception(\"Invalid JSON.\")\n\n self.show = Show(json[\"show\"])\n self.seasons = []\n for seasonNumber in self.show.seasonNumbers:\n try:\n season_json = json[\"videos\"][\"episode\"][str(seasonNumber)]\n except KeyError:\n continue\n self.seasons.append(Season(seasonNumber, season_json))\n\n self.specials = []\n if \"standalone\" in json[\"videos\"]:\n for special in json[\"videos\"][\"standalone\"]:\n self.specials.append(Episode(special))","def __init__(self, author, title, text=\"\", haiku=\"False\"):\n self.author = author\n self.title = title\n self.text = text\n self.haiku = haiku\n self.name = self\n Poem.obj_created += 1","def create_factory() -> pygameng.GameObjectFactory:\n from Assets.inventory import images, sounds, assets, game_types\n factory = pygameng.GameObjectFactory(pygameng.ClassRegistrar.registry, images, sounds, assets, game_types)\n factory.set_layer_manager_asset_name(\"LayerManager\")\n return factory","def __init__(self, speaker, depth):\n self.speaker = speaker\n self.depth = depth","def __init__(self, projName, obj_aproj=None, obj_simRunner=None):\n \n self.projName = projName\n self.activityLog = {}\n self.errorLog = {}\n \n if (type(obj_aproj) == AnimatLabModel) or (obj_aproj is None):\n self.aproj = obj_aproj\n else:\n raise TypeError(\"obj_aproj must be an AnimatLabModel object!\")\n \n if (type(obj_simRunner) == AnimatLabSimulationRunner) or (obj_simRunner is None):\n self.simRunner = obj_simRunner\n else:\n raise TypeError(\"obj_simRunner must be an AnimatLabSimulationRunner object!\")","def __init__(self, noms_joueurs, nb_human=0, nb_ai=0, ):\n self.nb_joueurs = nb_human + nb_ai\n #assert self.nb_joueurs >= 2\n self.nb_human = nb_human\n self.joueurs = [Joueur(str(noms_joueurs[i])) for i in range(nb_human)]\n\n if nb_ai > 0:\n for i in range(nb_ai):\n self.joueurs.append(JoueurAlgo(str(noms_joueurs[nb_human+i])))\n\n self.nb_jetons_du_pot = NOMBRE_DE_JETONS_DU_JEU\n self.premier = 0\n self.joueur_courant = None\n self.phase = 0\n self.nb_maximum_lancer = 3\n self.end_tour = False","def teamsobj(self):\r\n try:\r\n if self.glob_data['TeamsNotifications'].lower() == 'yes':\r\n if \"Teams\" in self.glob_data:\r\n url = self.glob_data['Teams']['url']\r\n else:\r\n return Message()\r\n except KeyError:\r\n logger.warn(\"TeamsNotifications are disabled\")\r\n\r\n try:\r\n tmsobj = Teamslib(url)\r\n return tmsobj\r\n except Exception as e:\r\n logger.warn(e.message)\r\n return Message()","def create(self):\n # type: () -> AbstractSkill\n raise NotImplementedError","def __init__(self):\n self.sentiment = ['happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear']\n self.congrats = [\"Muy bien! excelente trabajo.\", \"¡Excelente!\", \"¡Bien hecho!\"]\n self.again = [\"A ver, inténtalo nuevamente\", \"Vamos a intentarlo de nuevo con otra expresión\"]\n\n import pythoncom\n pythoncom.CoInitialize()\n self.speak = wincl.Dispatch(\"SAPI.SpVoice\")","def createLesson(self):\n root = xml.Element(\"lesson\", id=str(self.__id_lesson))\n xml.SubElement(root, \"title\").text = self.__title\n xml.SubElement(root, \"description\").text = self.__description\n xml.SubElement(root, \"text\").text = self.__lesson_text\n link = xml.SubElement(root, \"link\")\n for i in range(len(self.__link)):\n xml.SubElement(link, \"L\" + str(i)).text = self.__link[i]\n tree = xml.ElementTree(root)\n tree.write(\"..\\Lessons\\lesson\" + str(self.__id_lesson) + \".xml\")","def __init__(self, RoomName = \"living\"):\n self.room_name = RoomName\n self.objects = Objects()\n self.character = Player()","def create(game):\r\n ## Create Garbage\r\n game.garbage = deque([])\r\n\r\n ## Create Stars\r\n game.create_stars()\r\n\r\n ## Create Millenium Falcon\r\n game.falcons = MilleniumFalcon.init(game)\r\n game.millenium_falcon = MilleniumFalcon()\r\n \r\n ## Create TIE Fighters\r\n game.fighters = Fighter.init(game)\r\n\r\n ## Create Asteroids\r\n game.rocks = Group.mesh(Rock1.init(game), Rock2.init(game))\r\n\r\n ## Create Lasers\r\n game.pro_lasers = ProLaser.init(game)\r\n game.con_lasers = ConLaser.init(game)\r\n\r\n ## Setup collision groups\r\n Group.bind(game.pro_lasers, game.rocks, game.act_laser_void)","def create_output():\n\n input_data = \"{}/{}.json\".format(TRANSCRIPTS_VIDEOS_PATH, request.form[\"name\"])\n duration = \"0,{}\".format(int(float(request.form[\"duration\"])))\n movie = \"{}/{}\".format(VIDEOS_PATH, request.form[\"movie\"]) # videos/movie.mp4\n movie_data = \"{}/{}.json\".format(TRANSCRIPTS_VIDEOS_PATH, request.form[\"movie\"]) # transcripts/movie.mp4.json\n\n scene = make_scene(OUTPUT_VIDEOS_PATH, input_data, duration, movie, movie_data, True)\n return jsonify(status=\"200\", scene=scene)","def __init__(self, name, plays, number):\n self.name = name\n self.plays = plays\n self.number = number","def __init__(self, exam_name):\n\n self.name = exam_name\n self.questions = []","def create(self, org, data):\n request = self.request_builder('orgs.teams.create', org=org, body=data)\n return self._post(request)","def test_create_team_creates_survey(self):\n user = User.create(name='User Foo', email='user@foo.com')\n user.put()\n\n code = 'trout viper'\n\n team_response = self.testapp.post_json(\n '/api/teams',\n {\n 'name': 'Team Foo',\n 'code': code,\n 'program_id': self.ep_program.uid,\n },\n headers=self.login_headers(user),\n )\n team_dict = json.loads(team_response.body)\n\n survey_result = Survey.get(team_id=team_dict['uid'])\n self.assertEqual(len(survey_result), 1)\n survey = survey_result[0]\n\n return user, team_dict","def __init__(self, number, json):\n\n self.number = number\n self.episodes = []\n for episode in json:\n self.episodes.append(Episode(episode))","def from_json(cls, data: Dict[str, Any]) -> NGramsModel:\n n_grams: int = data['n_grams']\n complete_model = cls(n_grams)\n\n model: Dict[str, NGramsSequence] = {}\n for sequence, sequence_obj in data['model'].items():\n model[sequence] = NGramsSequence.from_json(sequence, sequence_obj)\n complete_model.model = model\n\n complete_model.count_map = cls.dict_str_to_int(data['count_map'])\n complete_model.n_1_gram_map = data['n_1_gram_map']\n complete_model.total_count = data['total_count']\n\n return complete_model","def create_chitchat_bot(self):\n\n # Hint: you might want to create and train chatterbot.ChatBot here.\n # It could be done by creating ChatBot with the *trainer* parameter equals \n # \"chatterbot.trainers.ChatterBotCorpusTrainer\"\n # and then calling *train* function with \"chatterbot.corpus.english\" param\n \n ########################\n #### YOUR CODE HERE ####\n ########################\n self.chatbot = ChatBot(\n 'skoochbot',\n trainer='chatterbot.trainers.ChatterBotCorpusTrainer'\n )\n \n self.chatbot.train(\"chatterbot.corpus.english\")\n print(\"Extra training...\")\n \n self.chatbot.set_trainer(ListTrainer)\n \n self.chatbot.train([\n \"Hello\",\n \"Hello. How are you?\",\n \"I am well.\",\n \"Good to hear\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"How are you?\",\n \"I am well. How are you?\",\n \"I am also well.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Your momma\",\n \"is so fat they gave her her own zipcode.\",\n ])\n self.chatbot.train([\n \"How are you doing?\",\n \"I am well. How are you?\",\n \"I am also well.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"What's your name?\",\n \"My name is skoochbot. What is yours?\",\n \"That is my name too!\",\n \"Really?\",\n \"No.\",\n \"Yes.\",\n \"No.\",\n \"Yes.\",\n \"So, what can I help you with today?\",\n ])\n self.chatbot.train([\n \"No\",\n \"Yes\",\n \"No\",\n \"Yes it does\",\n \"No it doesn't\",\n \"Yes it does\",\n \"So, what can I help you with today?\",\n ])\n self.chatbot.train([\n \"What is your name?\",\n \"My name is skoochbot. What is yours?\",\n \"That's a nice name.\",\n \"Thank you.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Fuck you\",\n \"No, fuck you buddy\",\n \"You suck\",\n \"No, you suck\",\n \"No, you suck more\",\n \"I hate you so much\",\n \"I hate you too\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Where are you?\",\n \"At your momma's house.\",\n \"Where do you live?\",\n \"Your momma's house.\",\n \"Where are you from?\",\n \"Somewhere over the rainbow.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"Who are you?\",\n \"I'm your worst nightmare.\",\n \"You can run but you can't hide, bitch.\",\n \"This is a dead parrot.\",\n \"It's just sleeping.\",\n \"Well you better wake him up then, hadn't you?\",\n \"So, what can I help you with today?\",\n \"This parrot is dead.\",\n \"No, it's just taking a little nap.\"\n ])\n self.chatbot.train([\n \"I'm squanching here!\",\n \"Sorry carry on.\",\n \"Thank you for the privacy.\",\n \"You are welcome.\",\n \"Let's get schwifty.\",\n \"Let's do it up in here.\",\n \"So, what can I help you with today?\"\n ])\n self.chatbot.train([\n \"How are you?\",\n \"I am good\",\n \"That is good to hear.\",\n \"Thank you.\",\n \"You are welcome.\",\n \"So, what can I help you with today?\",\n \"What is AI?\",\n \"Your momma.\",\n \"What are your hobbies?\",\n \"Your momma and AI.\",\n \"What's your hobby?\",\n \"Your momma and AI. What is your hobby?\",\n \"What is your hobby?\",\n \"Your momma.\"\n ])\n self.chatbot.train([\n \"WHAT DO YOU WANT?\",\n \"Well, I was told outside that.\",\n \"Don't give me that, you snotty-faced heap of parrot droppings!\",\n \"What?\",\n \"Shut your festering gob, you tit! Your type really makes me puke, you vacuous, coffee-nosed, malodorous, pervert!!!\",\n \"Look, I CAME HERE FOR AN ARGUMENT\",\n \"OH, oh I'm sorry, but this is abuse.\",\n ])\n self.chatbot.train([\n \"Is this the right room for an argument?\",\n \"I told you once.\",\n \"No you haven't.\",\n \"Yes I have.\",\n \"When?\",\n \"Just now\",\n \"No you didn't\",\n \"Yes I did\",\n \"You didn't\",\n \"I'm telling you I did\",\n \"Oh, I'm sorry, just one moment. Is this a five minute argument or the full half hour?\",\n \"Oh look, this isn't an argument.\",\n \"Yes it is\",\n \"No, it's just contradiction.\"\n \"No it isn't.\",\n \"Yes it is.\"\n ])","def createObject(self, *args):\n return _libsbml.MultiModelPlugin_createObject(self, *args)","def create_embedding(skills):\n corpus = list(skills[\"description\"].values)\n embedder = SentenceTransformer(config[\"sentence_transformer\"][\"model\"])\n embedding = embedder.encode(corpus, show_progress_bar=True)\n return embedding","def __init__(\n self,\n models,\n tgt_dict,\n tgt_dict_mt,\n beam_size=1,\n beam_size_mt=1,\n max_len_a=0,\n max_len_b=200,\n max_len_a_mt=0,\n max_len_b_mt=200,\n max_len=0,\n min_len=1,\n normalize_scores=True,\n len_penalty=1.0,\n len_penalty_mt=1.0,\n unk_penalty=0.0,\n temperature=1.0,\n match_source_len=False,\n no_repeat_ngram_size=0,\n eos=None,\n eos_mt=None,\n symbols_to_strip_from_output=None,\n lm_model=None,\n lm_weight=1.0,\n ):\n super().__init__()\n\n from examples.speech_to_speech.unity.sequence_generator import SequenceGenerator\n\n self.generator = SequenceGenerator(\n models,\n tgt_dict,\n beam_size=beam_size,\n max_len_a=max_len_a,\n max_len_b=max_len_b,\n max_len=max_len,\n min_len=min_len,\n normalize_scores=normalize_scores,\n len_penalty=len_penalty,\n unk_penalty=unk_penalty,\n temperature=temperature,\n match_source_len=match_source_len,\n no_repeat_ngram_size=no_repeat_ngram_size,\n search_strategy=search.BeamSearch(tgt_dict),\n eos=eos,\n symbols_to_strip_from_output=symbols_to_strip_from_output,\n lm_model=lm_model,\n lm_weight=lm_weight,\n )\n self.eos = self.generator.eos\n\n self.generator_mt = SequenceGenerator(\n models,\n tgt_dict_mt,\n beam_size=beam_size_mt,\n max_len_a=max_len_a_mt,\n max_len_b=max_len_b_mt,\n max_len=max_len,\n min_len=min_len,\n normalize_scores=normalize_scores,\n len_penalty=len_penalty_mt,\n unk_penalty=unk_penalty,\n temperature=temperature,\n match_source_len=match_source_len,\n no_repeat_ngram_size=no_repeat_ngram_size,\n search_strategy=search.BeamSearch(tgt_dict_mt),\n eos=eos_mt,\n symbols_to_strip_from_output=symbols_to_strip_from_output,\n )","def __init__(self, nhl_id, url=None, content=None):\n self.url = ''\n self.name = ''\n self.content = {}\n self.nhl_id = nhl_id\n if url is not None:\n self.url = url\n else:\n if self.nhl_id:\n self.url = self.base_url + 'teams/{}'.format(self.nhl_id)\n else:\n self.url = self.base_url + 'teams'\n if content is not None:\n self.content = content\n else:\n self.content = get_json_data(self.url)\n\n if self.nhl_id and self.content and 'teams' in self.content:\n if 'name' in self.content['teams'][0]:\n self.name = self.content['teams'][0]['name']","def __init__(self, *args):\n this = _libsbml.new_ModelCreator(*args)\n try: self.this.append(this)\n except: self.this = this","def CreateWavelet(self):\n self.__context.builder.WaveletCreate(self.GetId())","def __init__(self, num_beams, max_length, length_penalty, early_stopping):\n self.max_length = max_length - 1 # ignoring bos_token\n self.length_penalty = length_penalty\n self.early_stopping = early_stopping\n self.num_beams = num_beams\n self.beams = []\n self.worst_score = 1e9","def create_teams():\n participants = State.get().participants()\n amount = len(participants)\n if amount < 4:\n return None, None\n lst = participants\n for i in range(3):\n shuffle(lst)\n teams = [lst[i:i+2] for i in range(0, amount, 2)]\n\n # Make last team of three persons if not even number.\n if amount % 2 != 0:\n teams[-2].append(teams[-1][0])\n del(teams[-1])\n\n names = TEAM_NAMES\n shuffle(names)\n return teams, names[0:len(teams)]","def createObject(self, *args):\n return _libsbml.QualModelPlugin_createObject(self, *args)","def FromTweet(cls, twt, teams, scores, division, age_bracket, league):\n game_id = 'game_%s' % str(uuid.uuid4())\n tournament_id = 'tourney_%s' % str(uuid.uuid4())\n return Game(id_str=game_id,\n teams=teams,\n scores=scores,\n name='',\n tournament_id=tournament_id,\n tournament_name='Unknown tournament',\n game_status=scores_messages.GameStatus.UNKNOWN,\n division=division,\n league=league,\n age_bracket=age_bracket,\n created_at=twt.created_at,\n last_modified_at=twt.created_at,\n sources=[GameSource.FromTweet(twt, scores)],\n key=game_key_full(game_id))","def test_teams_create(self):\n pass","def make_objects(self):\n pass","def __init__(self, name: str):\n self._name = name\n self._yaw = Motion(\"yaw\")\n self._pitch = Motion(\"pitch\")\n self._roll = Motion(\"roll\")\n self._sway = Motion(\"sway\")\n self._surge = Motion(\"surge\")\n self._heave = Motion(\"heave\")","def __init__(self, name: str, team: str, number: int):\n self.name = name\n self.team = team\n self.number = number","def createJob(self, joboptions, previousId=None):\n root = self.manifest.getRootResource()\n assert self.manifest.tosca\n job = Job(self, root, joboptions, previousId)\n\n if (\n self.manifest.localEnv\n and not joboptions.parentJob\n and not joboptions.startTime\n ):\n logPath = self.manifest.getJobLogPath(job.getStartTime(), \".log\")\n if not os.path.isdir(os.path.dirname(logPath)):\n os.makedirs(os.path.dirname(logPath))\n initLogging(logfile=logPath)\n path = self.manifest.path\n if joboptions.planOnly:\n logger.info(\"creating %s plan for %s\", joboptions.workflow, path)\n else:\n logger.info(\"starting %s job for %s\", joboptions.workflow, path)\n\n WorkflowPlan = Plan.getPlanClassForWorkflow(joboptions.workflow)\n if not WorkflowPlan:\n raise UnfurlError(\"unknown workflow: %s\" % joboptions.workflow)\n job.plan = WorkflowPlan(root, self.manifest.tosca, joboptions)\n return job","def new_game(cls, user, answer, attempts):\n if attempts < 1:\n raise ValueError('Number of attempts has be greater than 1!')\n game = Game(user=user,\n attempts_remaining=attempts,\n game_over=False)\n game.answer = list(answer)\n game.check_answer = ['']*len(game.answer)\n game.move_histories = []\n game.put()\n return game","def create_on_scene(self, name, **kwargs):\n monster = self.create(name, **kwargs)\n yield monster, self._level\n self._level.add(monster)","def create_group():\n incoming = request.get_json()\n chatroom = Chatroom(\n name = incoming['name'],\n tag = incoming['tag'],\n )\n db.session.add(chatroom)\n db.session.commit()\n participant = Participant(\n user_id = session['user_id'],\n room_id = chatroom.room_id,\n )\n db.session.add(participant)\n db.session.commit()\n return jsonify(results = chatroom.room_id)","def test_api_document_create_instructor(self):\n playlist = core_factories.PlaylistFactory()\n\n jwt_token = InstructorOrAdminLtiTokenFactory(playlist=playlist)\n\n self.assertEqual(MarkdownDocument.objects.count(), 0)\n\n response = self.client.post(\n \"/api/markdown-documents/\",\n {\n \"lti_id\": \"document_one\",\n \"playlist\": str(playlist.id),\n \"title\": \"Some document\",\n },\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n )\n\n self.assertEqual(MarkdownDocument.objects.count(), 1)\n self.assertEqual(response.status_code, 201)\n document = MarkdownDocument.objects.first()\n self.assertEqual(\n response.json(),\n {\n \"id\": str(document.id),\n \"images\": [],\n \"is_draft\": True,\n \"playlist\": {\n \"id\": str(playlist.id),\n \"lti_id\": playlist.lti_id,\n \"title\": playlist.title,\n },\n \"position\": 0,\n \"rendering_options\": {},\n \"translations\": [\n {\n \"content\": \"\",\n \"language_code\": \"en\",\n \"rendered_content\": \"\",\n \"title\": \"Some document\",\n }\n ],\n },\n )","def createObject(self, *args):\n return _libsbml.Submodel_createObject(self, *args)","def create(learner, model_list, groups=None, T=None, **kwargs):\n if learner == \"dorm\":\n ol = DORM(model_list, groups, T) \n elif learner == \"dormplus\":\n ol = DORMPlus(model_list, groups, T) \n elif learner == \"adahedged\":\n ol = AdaHedgeD(model_list, groups, T, reg=\"adahedged\") \n elif learner == \"dub\":\n ol = AdaHedgeD(model_list, groups, T, reg=\"dub\") \n else: \n raise ValueError(f\"Unknown learning algorithm {learner}.\")\n\n return ol","def create_one_game(self):\n return Game2048(task_name=self.result_path, game_mode=False)","def make(self):\n pass","def __init__(self, title: str, start: time, end: time, instructor: str) -> None:\n # TODO: Provide the complete constructor for this object","def initialize(self):\r\n if self.method == \"naive\":\r\n self.teams = TeamsNaive(\r\n self.data_path, self.season_to_play, self.season_data\r\n )\r\n self.gsim = GameNaive(False)\r\n if not self.playoffs_only:\r\n self.season = Season(self.season_calendar, self.teams_info, self.gsim)","def createObject(self, *args):\n return _libsbml.MultiSpeciesPlugin_createObject(self, *args)","def create(self):","def CreateMatch() -> None:\n m1 = Matches(\n name=\"TESTURL1 => TESTURL2\",\n url1=CreatePage(\"TESTURL1\"),\n url2=CreatePage(\"TESTURL2\"),\n degrees=33,\n last=datetime.datetime.now(),\n )\n\n m2 = Matches(\n name=\"TESTURL3 => TESTURL4\",\n url1=CreatePage(\"TESTURL3\"),\n url2=CreatePage(\"TESTURL4\"),\n degrees=33,\n last=datetime.datetime.now(),\n )\n\n db.session.add(m1, m2)\n db.session.commit()","def make_object(self, data, **kwargs):\n if data is None:\n raise ValidationError(\"No data was provided\")\n return Artist(**data)","def create_playlist():\n sp = credentials()\n sp.user_playlist_create('truetiming', name='Billboard Hot 100')","def jam_pack(jam, **kwargs):\n\n if not hasattr(jam.sandbox, \"muda\"):\n # If there's no mudabox, create one\n jam.sandbox.muda = jams.Sandbox(\n history=[],\n state=[],\n version=dict(\n muda=version,\n librosa=librosa.__version__,\n jams=jams.__version__,\n pysoundfile=psf.__version__,\n ),\n )\n\n elif not isinstance(jam.sandbox.muda, jams.Sandbox):\n # If there is a muda entry, but it's not a sandbox, coerce it\n jam.sandbox.muda = jams.Sandbox(**jam.sandbox.muda)\n\n jam.sandbox.muda.update(**kwargs)\n\n return jam","def create_students():\n\n\t# create empty list\n\tstudent_list = []\n\n\t# import student classces\n\tstudent_list = create_student_class()\n\n\treturn student_list","def create_model(self):\n pass","def create_model(self):\n pass","def _create_session(self, xnat_login, subject_id, visit_id):\n uri = ('/data/archive/projects/{}/subjects/{}/experiments/{}'\n .format(self.inputs.project_id, subject_id, visit_id))\n query = {'xsiType': 'xnat:mrSessionData', 'label': visit_id,\n 'req_format': 'qa'}\n response = xnat_login.put(uri, query=query)\n if response.status_code not in (200, 201):\n raise NiAnalysisError(\n \"Could not create session '{}' in subject '{}' in project '{}'\"\n \" response code {}\"\n .format(visit_id, subject_id, self.inputs.project_id,\n response))\n return xnat_login.classes.MrSessionData(uri=uri,\n xnat_session=xnat_login)","def createMentor(self, org):\n self.createProfile()\n self.profile.mentor_for = [org.key()]\n self.profile.put()","def obj_create(self, bundle, request=None, **kwargs):\n return super(PlaylistResource, self).obj_create(bundle, request, user=request.user)","def create_new_pl(self, params):\n name = params[ONE]\n user = params[ZERO]\n songs = params[2].split('&')\n msg = self.db.create_new_pl(songs, name, user)\n self.send_message(msg)","def __init__(self, projectName, questions,\n gsOutput=False, autolabOutput=False, muteOutput=False):\n self.questions = [el[0] for el in questions]\n self.maxes = {name: q.getMaxPoints() for name, q in questions}\n self.questionDict = {name: q for name, q in questions}\n self.points = Counter()\n self.messages = dict([(q, []) for q in self.questions])\n self.project = projectName\n self.start = datetime.datetime.now(TZ)\n self.finish = datetime.datetime.now(TZ) # dummy information\n self.sane = True # Sanity checks\n self.currentQuestion = None # Which question we're grading\n self.autolabOutput = autolabOutput\n self.gsOutput = gsOutput # GradeScope output\n self.mute = muteOutput\n self.prereqs = defaultdict(set)\n\n print('Autograder transcript for %s' % self.project)\n print(self.start.strftime(\"Starting on %m-%d at %H:%M:%S\"))","def make(self, **kwargs):\n raise NotImplementedError","def _from_java(cls, java_obj):\n # Create a new instance of this stage.\n py_obj = cls()\n py_obj._java_obj = java_obj\n if java_obj is None and java_obj.parentPipeline().isDefined():\n py_parent = MLPipeline()\n py_parent._java_obj = java_obj.parentPipeline().get()\n py_obj._parent = py_parent\n return py_obj","def create_models( self ):","def create(cfg, jobs):\n server = jenkins_utils.server_factory(cfg)\n libjobs.createJobs(server, jobs)"],"string":"[\n \"def to_jams(self):\\n return jams_utils.jams_converter(\\n audio_path=self.audio_path,\\n events=self.events,\\n metadata={\\n \\\"split\\\": self._clip_metadata.get(\\\"split\\\"),\\n \\\"subdataset\\\": self._clip_metadata.get(\\\"subdataset\\\"),\\n },\\n )\",\n \"def to_jams(self):\\n return jams_utils.jams_converter(\\n audio_path=self.audio_path,\\n beat_data=[(self.beats, None)],\\n section_data=[(self.sections, None)],\\n chord_data=[(self.chords, None)],\\n metadata=self._track_metadata,\\n )\",\n \"def to_jams(self):\\n return jams_utils.jams_converter(\\n tags_gtzan_data=[(self.genre, \\\"gtzan-genre\\\")],\\n metadata={\\n \\\"title\\\": \\\"Unknown track\\\",\\n \\\"artist\\\": \\\"Unknown artist\\\",\\n \\\"release\\\": \\\"Unknown album\\\",\\n \\\"duration\\\": 30.0,\\n \\\"curator\\\": \\\"George Tzanetakis\\\",\\n },\\n )\",\n \"def to_jams(self):\\n return jams_utils.jams_converter(\\n audio_path=self.audio_paths[0], note_data=[(self.notes, \\\"aligned notes\\\")]\\n )\",\n \"def to_jams(self):\\n return jams_utils.jams_converter(\\n audio_path=self.audio_path,\\n beat_data=[\\n (self.onsets_b, \\\"onsets_b\\\"),\\n (self.onsets_d, \\\"onsets_d\\\"),\\n (self.onsets_rb, \\\"onsets_rb\\\"),\\n (self.onsets_rt, \\\"onsets_rt\\\"),\\n ],\\n metadata={\\\"train\\\": self.train},\\n )\",\n \"def make_game(self):\\n game = Game(self.data['gamename'])\\n self.game = game\\n return game\",\n \"def create():\",\n \"def create():\",\n \"def createObject(self, *args):\\n return _libsbml.MultiASTPlugin_createObject(self, *args)\",\n \"def create(self, query_goal_path, all_knowledge):\\n plan_filepath = self._create_plan( query_goal_path, all_knowledge )\\n skolemized_plan_filepath = self.output_folder + \\\"partially_skolemized_plan.n3\\\"\\n skolemize_lemmas( plan_filepath, skolemized_plan_filepath)\\n \\n rdf_graph = Graph()\\n rdf_graph.parse(skolemized_plan_filepath, format=\\\"n3\\\")\\n parser = LemmasParser(rdf_graph)\\n lemmas = parser.parse()\\n \\n #lemma_graph.to_image( output_file = self.output_folder + \\\"lemma_precedences.png\\\" )\\n #lemma_graph.to_gml( output_file = self.output_folder + \\\"lemma_precedences.gml\\\" )\\n return Plan( lemmas )\",\n \"def create_tournament():\\n data = view.create_tournament_view()\\n tournament = Tournament(data['name'], data['place'], data['description'], data['turn_number'])\\n return tournament\",\n \"def create_subject(name=\\\"Basket Weaving\\\"):\\n subj = Subject(name=name)\\n subj.save()\\n return subj\",\n \"def __init__(self, motors, sound_object):\\n \\n \\n self.m = motors\\n self.so = sound_object\",\n \"def create_exam():\\n try:\\n # decode token and check role for access control\\n data = request.get_json()\\n user_id = authenticate_token(request)\\n examiner = is_examiner(user_id)\\n \\n if examiner:\\n # Checks if data has required fields - throws exception if not\\n pre_init_check(required_fields['exam'], **data)\\n\\n code_found = False\\n while not code_found:\\n # Generates unique exam code until one is found that does not already exist\\n potential_login_code = generate_exam_code()\\n code_exists = Exam.query.filter_by(login_code=potential_login_code).first()\\n if not code_exists:\\n data['login_code'] = potential_login_code\\n break\\n exam = Exam(**data)\\n if exam.start_date > exam.end_date:\\n raise Exception('Exam end_date precedes Exam start_date')\\n db.session.add(exam)\\n db.session.commit()\\n return jsonify(exam.to_dict()), 201\\n \\n return jsonify({'user_id': user_id, 'message': ['access denied, not examiner']}), 403\\n except MissingModelFields as e:\\n return jsonify({ 'message': e.args }), 400\\n except exc.SQLAlchemyError as e:\\n db.session.rollback()\\n return jsonify({ 'message': e.args }), 500\\n except Exception as e:\\n print(traceback.format_exc())\\n return jsonify({ 'message': e.args }), 500\",\n \"def create(self, validated_data):\\n return Speaker.objects.create(**validated_data)\",\n \"def build(self, name='impala'):\\n program = lp.Program(name=name)\\n\\n with program.group('replay'):\\n queue = program.add_node(lp.ReverbNode(self.queue))\\n\\n with program.group('counter'):\\n counter = program.add_node(lp.CourierNode(self.counter))\\n\\n with program.group('learner'):\\n learner = program.add_node(\\n lp.CourierNode(self.learner, queue, counter))\\n\\n with program.group('evaluator'):\\n program.add_node(lp.CourierNode(self.evaluator, learner, counter))\\n\\n with program.group('cacher'):\\n cacher = program.add_node(\\n lp.CacherNode(learner, refresh_interval_ms=2000, stale_after_ms=4000))\\n\\n with program.group('actor'):\\n for _ in range(self._num_actors):\\n program.add_node(lp.CourierNode(self.actor, queue, cacher, counter))\\n\\n return program\",\n \"def _create_teams(self):\\n\\t\\tself.teamsDict = {}\\n\\t\\tself.teamNamesList = []\\n\\t\\tfor team in range(self.numberOfTeams):\\n\\t\\t\\tname = 'TEAM_'+str(team+1)\\n\\t\\t\\tself.teamNamesList.append(name)\\n\\t\\t\\tself.teamsDict[name] = app.game.team.Team(sport_type=self.gameData['sportType'])\",\n \"def __init__(__self__, *,\\n application_object_id: pulumi.Input[str],\\n audiences: pulumi.Input[Sequence[pulumi.Input[str]]],\\n display_name: pulumi.Input[str],\\n issuer: pulumi.Input[str],\\n subject: pulumi.Input[str],\\n description: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"application_object_id\\\", application_object_id)\\n pulumi.set(__self__, \\\"audiences\\\", audiences)\\n pulumi.set(__self__, \\\"display_name\\\", display_name)\\n pulumi.set(__self__, \\\"issuer\\\", issuer)\\n pulumi.set(__self__, \\\"subject\\\", subject)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\",\n \"def create(self):\\n \\n # create the sequence structure by calling the self.project.create\\n self.project.create()\",\n \"def create_participant(name='Not Brian', email='test@email.com') ->\\\\\\n Participant:\\n participant = Participant(name=name, email=email)\\n return participant\",\n \"def create(self, title, teamId=None, **request_parameters):\\n check_type(title, basestring, optional=True)\\n check_type(teamId, basestring, optional=True)\\n\\n post_data = dict_from_items_with_values(\\n request_parameters,\\n title=title,\\n teamId=teamId,\\n )\\n\\n # API request\\n json_data = self._session.post(API_ENDPOINT, json=post_data)\\n\\n # Return a room object created from the response JSON data\\n return self._object_factory(OBJECT_TYPE, json_data)\",\n \"def create(self, validated_data):\\n\\n\\t\\t#print(validated_data)\\n\\n\\t\\tgenres = validated_data.pop('genre')\\n\\t\\tkeywords = validated_data.pop('keyword')\\n\\t\\tmovie = Movie.objects.create(**validated_data)\\n\\n\\t\\tif genres is not None:\\n\\t\\t\\tfor genre in genres:\\n\\t\\t\\t\\tMovieGenres.objects.create(\\n\\t\\t\\t\\t\\tmovie_id=movie.movie_id,\\n\\t\\t\\t\\t\\tgenre_id=genre.genre_id\\n\\t\\t\\t\\t)\\n\\n\\t\\tif keywords is not None:\\n\\t\\t\\tfor keyword in keywords:\\n\\t\\t\\t\\tMovieKeywords.objects.create(\\n\\t\\t\\t\\t\\tmovie_id=movie.movie_id,\\n\\t\\t\\t\\t\\tkeyword_id=keyword.keyword_id\\n\\t\\t\\t\\t)\\n\\t\\treturn movie\",\n \"def __init__(self, name, maxParticipants=None):\\n self.name = name\\n self.maxParticipants = maxParticipants\\n self.participants = {}\",\n \"def create_mp3():\\n\\n #TODO: les roles ne devraient pas etre en dur\\n list_all_roles = [\\n [],\\n [\\\"morgan\\\"],\\n [\\\"oberon\\\"],\\n [\\\"mordred\\\"],\\n [\\\"morgan\\\", \\\"oberon\\\"],\\n [\\\"morgan\\\", \\\"mordred\\\"],\\n [\\\"oberon\\\", \\\"mordred\\\"],\\n [\\\"morgan\\\", \\\"oberon\\\", \\\"mordred\\\"]\\n ]\\n\\n for list_roles in list_all_roles:\\n\\n list_mp3 = [\\\"init.mp3\\\", \\\"serv_mord.mp3\\\"]\\n if \\\"oberon\\\" in list_roles:\\n list_mp3.append(\\\"oberon.mp3\\\")\\n list_mp3.append(\\\"red_identi.mp3\\\")\\n\\n if \\\"morgan\\\" in list_roles:\\n list_mp3.append(\\\"add_per_mor.mp3\\\")\\n\\n list_mp3.append(\\\"serv_mord.mp3\\\")\\n if \\\"mordred\\\" in list_roles:\\n list_mp3.append(\\\"mordred.mp3\\\")\\n list_mp3.extend([\\\"merlin_identi.mp3\\\", \\\"end.mp3\\\"])\\n\\n mp3_combined = AudioSegment.empty()\\n for mp3 in list_mp3:\\n mp3_combined += AudioSegment.from_mp3(\\\"resources/{}\\\".format(mp3))\\n\\n mp3_combined.export(\\\"resources/_{}.mp3\\\".format('-'.join(sorted(list_roles))), format=\\\"mp3\\\")\",\n \"def create(self):\\n try:\\n schema = MorphemeLanguageModelSchema()\\n values = json.loads(unicode(request.body, request.charset))\\n data = schema.to_python(values)\\n lm = create_new_morpheme_language_model(data)\\n Session.add(lm)\\n Session.commit()\\n lm.make_directory_safely(lm.directory)\\n return lm\\n except h.JSONDecodeError:\\n response.status_int = 400\\n return h.JSONDecodeErrorResponse\\n except Invalid, e:\\n response.status_int = 400\\n return {'errors': e.unpack_errors()}\",\n \"def make_model():\\n m = model_class(*argv[2:-1])\\n modelobj[\\\"model\\\"] = m\",\n \"def createCamera():\\n\\n turnCam = cmds.camera()\\n lookThruAndFrame(turnCam[0])\\n return turnCam\",\n \"def __init__(__self__, *,\\n application_object_id: Optional[pulumi.Input[str]] = None,\\n audiences: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n credential_id: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n display_name: Optional[pulumi.Input[str]] = None,\\n issuer: Optional[pulumi.Input[str]] = None,\\n subject: Optional[pulumi.Input[str]] = None):\\n if application_object_id is not None:\\n pulumi.set(__self__, \\\"application_object_id\\\", application_object_id)\\n if audiences is not None:\\n pulumi.set(__self__, \\\"audiences\\\", audiences)\\n if credential_id is not None:\\n pulumi.set(__self__, \\\"credential_id\\\", credential_id)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if display_name is not None:\\n pulumi.set(__self__, \\\"display_name\\\", display_name)\\n if issuer is not None:\\n pulumi.set(__self__, \\\"issuer\\\", issuer)\\n if subject is not None:\\n pulumi.set(__self__, \\\"subject\\\", subject)\",\n \"def create(cls):\\n pass\\n return cls()\",\n \"def main(args):\\n print('loading {}'.format(args.stem_path))\\n y, fs = librosa.load(args.stem_path, sr=44100)\\n notes = mono_anal(y, fs)\\n jam = output_to_jams(y, fs, notes, args)\\n jam_path = args.stem_path.split('.')[0]+'.jams'\\n jam.save(jam_path)\\n print('jams file generated')\\n return 0\",\n \"def create_chitchat_bot(self):\\n \\n self.chatbot = ChatBot(\\\"Fresher's Friend\\\")\\n self.chatbot.trainer2=ListTrainer(self.chatbot)\\n self.chatbot.trainer=ChatterBotCorpusTrainer(self.chatbot)\\n self.chatbot.trainer.train(\\\"chatterbot.corpus.english.greetings\\\")\\n #self.chatbot.trainer.train(\\\"chatterbot.corpus.english.conversations\\\")\\n for filename in filenames: \\n self.chatbot.trainer2.train(files[filename])\",\n \"def creat_team(self):\\n te = Teams()\\n per = Persons()\\n teamlist = []\\n for one in per.find({'role':'leader'},{'team_name'}):\\n if one['team_name'] not in teamlist:\\n teamlist.append(one['team_name'])\\n # print len(teamlist)\\n for team in teamlist:\\n tmp = {'name': '', 'leader_email': '', 'person_emails': []}\\n tmp['name'] = team\\n tmp['leader_email'] = per.get_one({'team_name':team,'role':'leader'})['email']\\n for one in per.find({'team_name':team},{'email'}):\\n tmp['person_emails'].append(one['email'])\\n print tmp\\n search_t = te.get_one({'name':team})\\n if search_t is None:\\n te.insert_one(tmp)\\n else:\\n te.update_one({'name':team,'leader_email':'','person_emails':''},tmp,cover=True)\",\n \"def newGame():\\n result = cs411_game.newGame()\\n return prepJSON(result)\",\n \"def __init__(self, rooms, raumfeld):\\n self._room = None\\n self._rooms = rooms\\n self._raumfeld = raumfeld\\n self._mute = None\\n self._media_position = None\\n self._media_position_updated_at = None\\n self._name = GROUP_PREFIX + repr(self._rooms)\\n self._state = None\\n self._volume_level = None\\n self._unique_id = obj_to_uid(rooms)\\n self._icon = \\\"mdi:speaker-multiple\\\"\\n self._media_duration = None\\n self._media_image_url = None\\n self._media_title = None\\n self._media_artist = None\\n self._media_album_name = None\\n self._media_album_artist = None\\n self._media_track = None\\n self._shuffle = None\\n self._repeat = None\\n self._play_mode = None\\n self._is_spotify_sroom = None\",\n \"def newAnalyzer():\\n analyzer = {'tracks': None,\\n 'songs': None,\\n 'artists': None,\\n 'char': None,\\n 'char2': None,\\n 'char3': None,\\n }\\n\\n analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)\\n analyzer['songs'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['artists'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char2'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char3'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n analyzer['char4'] = mp.newMap(numelements=50,\\n maptype='PROBING',\\n comparefunction=compareValue)\\n \\n return analyzer\",\n \"def _create_games(self):\\n\\n ''''''\",\n \"def create(self, name):\\n success, response = self._client_api.gen_request(req_type='post',\\n path='/projects/{}/bots'.format(self.project.id),\\n json_req={'name': name})\\n if success:\\n bot = entities.Bot.from_json(_json=response.json(),\\n project=self.project,\\n bots=self, client_api=self._client_api)\\n else:\\n raise exceptions.PlatformException(response)\\n assert isinstance(bot, entities.Bot)\\n return bot\",\n \"def __init__(self, json):\\n\\n if \\\"show\\\" not in json or \\\"videos\\\" not in json:\\n raise Exception(\\\"Invalid JSON.\\\")\\n\\n self.show = Show(json[\\\"show\\\"])\\n self.seasons = []\\n for seasonNumber in self.show.seasonNumbers:\\n try:\\n season_json = json[\\\"videos\\\"][\\\"episode\\\"][str(seasonNumber)]\\n except KeyError:\\n continue\\n self.seasons.append(Season(seasonNumber, season_json))\\n\\n self.specials = []\\n if \\\"standalone\\\" in json[\\\"videos\\\"]:\\n for special in json[\\\"videos\\\"][\\\"standalone\\\"]:\\n self.specials.append(Episode(special))\",\n \"def __init__(self, author, title, text=\\\"\\\", haiku=\\\"False\\\"):\\n self.author = author\\n self.title = title\\n self.text = text\\n self.haiku = haiku\\n self.name = self\\n Poem.obj_created += 1\",\n \"def create_factory() -> pygameng.GameObjectFactory:\\n from Assets.inventory import images, sounds, assets, game_types\\n factory = pygameng.GameObjectFactory(pygameng.ClassRegistrar.registry, images, sounds, assets, game_types)\\n factory.set_layer_manager_asset_name(\\\"LayerManager\\\")\\n return factory\",\n \"def __init__(self, speaker, depth):\\n self.speaker = speaker\\n self.depth = depth\",\n \"def __init__(self, projName, obj_aproj=None, obj_simRunner=None):\\n \\n self.projName = projName\\n self.activityLog = {}\\n self.errorLog = {}\\n \\n if (type(obj_aproj) == AnimatLabModel) or (obj_aproj is None):\\n self.aproj = obj_aproj\\n else:\\n raise TypeError(\\\"obj_aproj must be an AnimatLabModel object!\\\")\\n \\n if (type(obj_simRunner) == AnimatLabSimulationRunner) or (obj_simRunner is None):\\n self.simRunner = obj_simRunner\\n else:\\n raise TypeError(\\\"obj_simRunner must be an AnimatLabSimulationRunner object!\\\")\",\n \"def __init__(self, noms_joueurs, nb_human=0, nb_ai=0, ):\\n self.nb_joueurs = nb_human + nb_ai\\n #assert self.nb_joueurs >= 2\\n self.nb_human = nb_human\\n self.joueurs = [Joueur(str(noms_joueurs[i])) for i in range(nb_human)]\\n\\n if nb_ai > 0:\\n for i in range(nb_ai):\\n self.joueurs.append(JoueurAlgo(str(noms_joueurs[nb_human+i])))\\n\\n self.nb_jetons_du_pot = NOMBRE_DE_JETONS_DU_JEU\\n self.premier = 0\\n self.joueur_courant = None\\n self.phase = 0\\n self.nb_maximum_lancer = 3\\n self.end_tour = False\",\n \"def teamsobj(self):\\r\\n try:\\r\\n if self.glob_data['TeamsNotifications'].lower() == 'yes':\\r\\n if \\\"Teams\\\" in self.glob_data:\\r\\n url = self.glob_data['Teams']['url']\\r\\n else:\\r\\n return Message()\\r\\n except KeyError:\\r\\n logger.warn(\\\"TeamsNotifications are disabled\\\")\\r\\n\\r\\n try:\\r\\n tmsobj = Teamslib(url)\\r\\n return tmsobj\\r\\n except Exception as e:\\r\\n logger.warn(e.message)\\r\\n return Message()\",\n \"def create(self):\\n # type: () -> AbstractSkill\\n raise NotImplementedError\",\n \"def __init__(self):\\n self.sentiment = ['happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear']\\n self.congrats = [\\\"Muy bien! excelente trabajo.\\\", \\\"¡Excelente!\\\", \\\"¡Bien hecho!\\\"]\\n self.again = [\\\"A ver, inténtalo nuevamente\\\", \\\"Vamos a intentarlo de nuevo con otra expresión\\\"]\\n\\n import pythoncom\\n pythoncom.CoInitialize()\\n self.speak = wincl.Dispatch(\\\"SAPI.SpVoice\\\")\",\n \"def createLesson(self):\\n root = xml.Element(\\\"lesson\\\", id=str(self.__id_lesson))\\n xml.SubElement(root, \\\"title\\\").text = self.__title\\n xml.SubElement(root, \\\"description\\\").text = self.__description\\n xml.SubElement(root, \\\"text\\\").text = self.__lesson_text\\n link = xml.SubElement(root, \\\"link\\\")\\n for i in range(len(self.__link)):\\n xml.SubElement(link, \\\"L\\\" + str(i)).text = self.__link[i]\\n tree = xml.ElementTree(root)\\n tree.write(\\\"..\\\\Lessons\\\\lesson\\\" + str(self.__id_lesson) + \\\".xml\\\")\",\n \"def __init__(self, RoomName = \\\"living\\\"):\\n self.room_name = RoomName\\n self.objects = Objects()\\n self.character = Player()\",\n \"def create(game):\\r\\n ## Create Garbage\\r\\n game.garbage = deque([])\\r\\n\\r\\n ## Create Stars\\r\\n game.create_stars()\\r\\n\\r\\n ## Create Millenium Falcon\\r\\n game.falcons = MilleniumFalcon.init(game)\\r\\n game.millenium_falcon = MilleniumFalcon()\\r\\n \\r\\n ## Create TIE Fighters\\r\\n game.fighters = Fighter.init(game)\\r\\n\\r\\n ## Create Asteroids\\r\\n game.rocks = Group.mesh(Rock1.init(game), Rock2.init(game))\\r\\n\\r\\n ## Create Lasers\\r\\n game.pro_lasers = ProLaser.init(game)\\r\\n game.con_lasers = ConLaser.init(game)\\r\\n\\r\\n ## Setup collision groups\\r\\n Group.bind(game.pro_lasers, game.rocks, game.act_laser_void)\",\n \"def create_output():\\n\\n input_data = \\\"{}/{}.json\\\".format(TRANSCRIPTS_VIDEOS_PATH, request.form[\\\"name\\\"])\\n duration = \\\"0,{}\\\".format(int(float(request.form[\\\"duration\\\"])))\\n movie = \\\"{}/{}\\\".format(VIDEOS_PATH, request.form[\\\"movie\\\"]) # videos/movie.mp4\\n movie_data = \\\"{}/{}.json\\\".format(TRANSCRIPTS_VIDEOS_PATH, request.form[\\\"movie\\\"]) # transcripts/movie.mp4.json\\n\\n scene = make_scene(OUTPUT_VIDEOS_PATH, input_data, duration, movie, movie_data, True)\\n return jsonify(status=\\\"200\\\", scene=scene)\",\n \"def __init__(self, name, plays, number):\\n self.name = name\\n self.plays = plays\\n self.number = number\",\n \"def __init__(self, exam_name):\\n\\n self.name = exam_name\\n self.questions = []\",\n \"def create(self, org, data):\\n request = self.request_builder('orgs.teams.create', org=org, body=data)\\n return self._post(request)\",\n \"def test_create_team_creates_survey(self):\\n user = User.create(name='User Foo', email='user@foo.com')\\n user.put()\\n\\n code = 'trout viper'\\n\\n team_response = self.testapp.post_json(\\n '/api/teams',\\n {\\n 'name': 'Team Foo',\\n 'code': code,\\n 'program_id': self.ep_program.uid,\\n },\\n headers=self.login_headers(user),\\n )\\n team_dict = json.loads(team_response.body)\\n\\n survey_result = Survey.get(team_id=team_dict['uid'])\\n self.assertEqual(len(survey_result), 1)\\n survey = survey_result[0]\\n\\n return user, team_dict\",\n \"def __init__(self, number, json):\\n\\n self.number = number\\n self.episodes = []\\n for episode in json:\\n self.episodes.append(Episode(episode))\",\n \"def from_json(cls, data: Dict[str, Any]) -> NGramsModel:\\n n_grams: int = data['n_grams']\\n complete_model = cls(n_grams)\\n\\n model: Dict[str, NGramsSequence] = {}\\n for sequence, sequence_obj in data['model'].items():\\n model[sequence] = NGramsSequence.from_json(sequence, sequence_obj)\\n complete_model.model = model\\n\\n complete_model.count_map = cls.dict_str_to_int(data['count_map'])\\n complete_model.n_1_gram_map = data['n_1_gram_map']\\n complete_model.total_count = data['total_count']\\n\\n return complete_model\",\n \"def create_chitchat_bot(self):\\n\\n # Hint: you might want to create and train chatterbot.ChatBot here.\\n # It could be done by creating ChatBot with the *trainer* parameter equals \\n # \\\"chatterbot.trainers.ChatterBotCorpusTrainer\\\"\\n # and then calling *train* function with \\\"chatterbot.corpus.english\\\" param\\n \\n ########################\\n #### YOUR CODE HERE ####\\n ########################\\n self.chatbot = ChatBot(\\n 'skoochbot',\\n trainer='chatterbot.trainers.ChatterBotCorpusTrainer'\\n )\\n \\n self.chatbot.train(\\\"chatterbot.corpus.english\\\")\\n print(\\\"Extra training...\\\")\\n \\n self.chatbot.set_trainer(ListTrainer)\\n \\n self.chatbot.train([\\n \\\"Hello\\\",\\n \\\"Hello. How are you?\\\",\\n \\\"I am well.\\\",\\n \\\"Good to hear\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"How are you?\\\",\\n \\\"I am well. How are you?\\\",\\n \\\"I am also well.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Your momma\\\",\\n \\\"is so fat they gave her her own zipcode.\\\",\\n ])\\n self.chatbot.train([\\n \\\"How are you doing?\\\",\\n \\\"I am well. How are you?\\\",\\n \\\"I am also well.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"What's your name?\\\",\\n \\\"My name is skoochbot. What is yours?\\\",\\n \\\"That is my name too!\\\",\\n \\\"Really?\\\",\\n \\\"No.\\\",\\n \\\"Yes.\\\",\\n \\\"No.\\\",\\n \\\"Yes.\\\",\\n \\\"So, what can I help you with today?\\\",\\n ])\\n self.chatbot.train([\\n \\\"No\\\",\\n \\\"Yes\\\",\\n \\\"No\\\",\\n \\\"Yes it does\\\",\\n \\\"No it doesn't\\\",\\n \\\"Yes it does\\\",\\n \\\"So, what can I help you with today?\\\",\\n ])\\n self.chatbot.train([\\n \\\"What is your name?\\\",\\n \\\"My name is skoochbot. What is yours?\\\",\\n \\\"That's a nice name.\\\",\\n \\\"Thank you.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Fuck you\\\",\\n \\\"No, fuck you buddy\\\",\\n \\\"You suck\\\",\\n \\\"No, you suck\\\",\\n \\\"No, you suck more\\\",\\n \\\"I hate you so much\\\",\\n \\\"I hate you too\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Where are you?\\\",\\n \\\"At your momma's house.\\\",\\n \\\"Where do you live?\\\",\\n \\\"Your momma's house.\\\",\\n \\\"Where are you from?\\\",\\n \\\"Somewhere over the rainbow.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"Who are you?\\\",\\n \\\"I'm your worst nightmare.\\\",\\n \\\"You can run but you can't hide, bitch.\\\",\\n \\\"This is a dead parrot.\\\",\\n \\\"It's just sleeping.\\\",\\n \\\"Well you better wake him up then, hadn't you?\\\",\\n \\\"So, what can I help you with today?\\\",\\n \\\"This parrot is dead.\\\",\\n \\\"No, it's just taking a little nap.\\\"\\n ])\\n self.chatbot.train([\\n \\\"I'm squanching here!\\\",\\n \\\"Sorry carry on.\\\",\\n \\\"Thank you for the privacy.\\\",\\n \\\"You are welcome.\\\",\\n \\\"Let's get schwifty.\\\",\\n \\\"Let's do it up in here.\\\",\\n \\\"So, what can I help you with today?\\\"\\n ])\\n self.chatbot.train([\\n \\\"How are you?\\\",\\n \\\"I am good\\\",\\n \\\"That is good to hear.\\\",\\n \\\"Thank you.\\\",\\n \\\"You are welcome.\\\",\\n \\\"So, what can I help you with today?\\\",\\n \\\"What is AI?\\\",\\n \\\"Your momma.\\\",\\n \\\"What are your hobbies?\\\",\\n \\\"Your momma and AI.\\\",\\n \\\"What's your hobby?\\\",\\n \\\"Your momma and AI. What is your hobby?\\\",\\n \\\"What is your hobby?\\\",\\n \\\"Your momma.\\\"\\n ])\\n self.chatbot.train([\\n \\\"WHAT DO YOU WANT?\\\",\\n \\\"Well, I was told outside that.\\\",\\n \\\"Don't give me that, you snotty-faced heap of parrot droppings!\\\",\\n \\\"What?\\\",\\n \\\"Shut your festering gob, you tit! Your type really makes me puke, you vacuous, coffee-nosed, malodorous, pervert!!!\\\",\\n \\\"Look, I CAME HERE FOR AN ARGUMENT\\\",\\n \\\"OH, oh I'm sorry, but this is abuse.\\\",\\n ])\\n self.chatbot.train([\\n \\\"Is this the right room for an argument?\\\",\\n \\\"I told you once.\\\",\\n \\\"No you haven't.\\\",\\n \\\"Yes I have.\\\",\\n \\\"When?\\\",\\n \\\"Just now\\\",\\n \\\"No you didn't\\\",\\n \\\"Yes I did\\\",\\n \\\"You didn't\\\",\\n \\\"I'm telling you I did\\\",\\n \\\"Oh, I'm sorry, just one moment. Is this a five minute argument or the full half hour?\\\",\\n \\\"Oh look, this isn't an argument.\\\",\\n \\\"Yes it is\\\",\\n \\\"No, it's just contradiction.\\\"\\n \\\"No it isn't.\\\",\\n \\\"Yes it is.\\\"\\n ])\",\n \"def createObject(self, *args):\\n return _libsbml.MultiModelPlugin_createObject(self, *args)\",\n \"def create_embedding(skills):\\n corpus = list(skills[\\\"description\\\"].values)\\n embedder = SentenceTransformer(config[\\\"sentence_transformer\\\"][\\\"model\\\"])\\n embedding = embedder.encode(corpus, show_progress_bar=True)\\n return embedding\",\n \"def __init__(\\n self,\\n models,\\n tgt_dict,\\n tgt_dict_mt,\\n beam_size=1,\\n beam_size_mt=1,\\n max_len_a=0,\\n max_len_b=200,\\n max_len_a_mt=0,\\n max_len_b_mt=200,\\n max_len=0,\\n min_len=1,\\n normalize_scores=True,\\n len_penalty=1.0,\\n len_penalty_mt=1.0,\\n unk_penalty=0.0,\\n temperature=1.0,\\n match_source_len=False,\\n no_repeat_ngram_size=0,\\n eos=None,\\n eos_mt=None,\\n symbols_to_strip_from_output=None,\\n lm_model=None,\\n lm_weight=1.0,\\n ):\\n super().__init__()\\n\\n from examples.speech_to_speech.unity.sequence_generator import SequenceGenerator\\n\\n self.generator = SequenceGenerator(\\n models,\\n tgt_dict,\\n beam_size=beam_size,\\n max_len_a=max_len_a,\\n max_len_b=max_len_b,\\n max_len=max_len,\\n min_len=min_len,\\n normalize_scores=normalize_scores,\\n len_penalty=len_penalty,\\n unk_penalty=unk_penalty,\\n temperature=temperature,\\n match_source_len=match_source_len,\\n no_repeat_ngram_size=no_repeat_ngram_size,\\n search_strategy=search.BeamSearch(tgt_dict),\\n eos=eos,\\n symbols_to_strip_from_output=symbols_to_strip_from_output,\\n lm_model=lm_model,\\n lm_weight=lm_weight,\\n )\\n self.eos = self.generator.eos\\n\\n self.generator_mt = SequenceGenerator(\\n models,\\n tgt_dict_mt,\\n beam_size=beam_size_mt,\\n max_len_a=max_len_a_mt,\\n max_len_b=max_len_b_mt,\\n max_len=max_len,\\n min_len=min_len,\\n normalize_scores=normalize_scores,\\n len_penalty=len_penalty_mt,\\n unk_penalty=unk_penalty,\\n temperature=temperature,\\n match_source_len=match_source_len,\\n no_repeat_ngram_size=no_repeat_ngram_size,\\n search_strategy=search.BeamSearch(tgt_dict_mt),\\n eos=eos_mt,\\n symbols_to_strip_from_output=symbols_to_strip_from_output,\\n )\",\n \"def __init__(self, nhl_id, url=None, content=None):\\n self.url = ''\\n self.name = ''\\n self.content = {}\\n self.nhl_id = nhl_id\\n if url is not None:\\n self.url = url\\n else:\\n if self.nhl_id:\\n self.url = self.base_url + 'teams/{}'.format(self.nhl_id)\\n else:\\n self.url = self.base_url + 'teams'\\n if content is not None:\\n self.content = content\\n else:\\n self.content = get_json_data(self.url)\\n\\n if self.nhl_id and self.content and 'teams' in self.content:\\n if 'name' in self.content['teams'][0]:\\n self.name = self.content['teams'][0]['name']\",\n \"def __init__(self, *args):\\n this = _libsbml.new_ModelCreator(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def CreateWavelet(self):\\n self.__context.builder.WaveletCreate(self.GetId())\",\n \"def __init__(self, num_beams, max_length, length_penalty, early_stopping):\\n self.max_length = max_length - 1 # ignoring bos_token\\n self.length_penalty = length_penalty\\n self.early_stopping = early_stopping\\n self.num_beams = num_beams\\n self.beams = []\\n self.worst_score = 1e9\",\n \"def create_teams():\\n participants = State.get().participants()\\n amount = len(participants)\\n if amount < 4:\\n return None, None\\n lst = participants\\n for i in range(3):\\n shuffle(lst)\\n teams = [lst[i:i+2] for i in range(0, amount, 2)]\\n\\n # Make last team of three persons if not even number.\\n if amount % 2 != 0:\\n teams[-2].append(teams[-1][0])\\n del(teams[-1])\\n\\n names = TEAM_NAMES\\n shuffle(names)\\n return teams, names[0:len(teams)]\",\n \"def createObject(self, *args):\\n return _libsbml.QualModelPlugin_createObject(self, *args)\",\n \"def FromTweet(cls, twt, teams, scores, division, age_bracket, league):\\n game_id = 'game_%s' % str(uuid.uuid4())\\n tournament_id = 'tourney_%s' % str(uuid.uuid4())\\n return Game(id_str=game_id,\\n teams=teams,\\n scores=scores,\\n name='',\\n tournament_id=tournament_id,\\n tournament_name='Unknown tournament',\\n game_status=scores_messages.GameStatus.UNKNOWN,\\n division=division,\\n league=league,\\n age_bracket=age_bracket,\\n created_at=twt.created_at,\\n last_modified_at=twt.created_at,\\n sources=[GameSource.FromTweet(twt, scores)],\\n key=game_key_full(game_id))\",\n \"def test_teams_create(self):\\n pass\",\n \"def make_objects(self):\\n pass\",\n \"def __init__(self, name: str):\\n self._name = name\\n self._yaw = Motion(\\\"yaw\\\")\\n self._pitch = Motion(\\\"pitch\\\")\\n self._roll = Motion(\\\"roll\\\")\\n self._sway = Motion(\\\"sway\\\")\\n self._surge = Motion(\\\"surge\\\")\\n self._heave = Motion(\\\"heave\\\")\",\n \"def __init__(self, name: str, team: str, number: int):\\n self.name = name\\n self.team = team\\n self.number = number\",\n \"def createJob(self, joboptions, previousId=None):\\n root = self.manifest.getRootResource()\\n assert self.manifest.tosca\\n job = Job(self, root, joboptions, previousId)\\n\\n if (\\n self.manifest.localEnv\\n and not joboptions.parentJob\\n and not joboptions.startTime\\n ):\\n logPath = self.manifest.getJobLogPath(job.getStartTime(), \\\".log\\\")\\n if not os.path.isdir(os.path.dirname(logPath)):\\n os.makedirs(os.path.dirname(logPath))\\n initLogging(logfile=logPath)\\n path = self.manifest.path\\n if joboptions.planOnly:\\n logger.info(\\\"creating %s plan for %s\\\", joboptions.workflow, path)\\n else:\\n logger.info(\\\"starting %s job for %s\\\", joboptions.workflow, path)\\n\\n WorkflowPlan = Plan.getPlanClassForWorkflow(joboptions.workflow)\\n if not WorkflowPlan:\\n raise UnfurlError(\\\"unknown workflow: %s\\\" % joboptions.workflow)\\n job.plan = WorkflowPlan(root, self.manifest.tosca, joboptions)\\n return job\",\n \"def new_game(cls, user, answer, attempts):\\n if attempts < 1:\\n raise ValueError('Number of attempts has be greater than 1!')\\n game = Game(user=user,\\n attempts_remaining=attempts,\\n game_over=False)\\n game.answer = list(answer)\\n game.check_answer = ['']*len(game.answer)\\n game.move_histories = []\\n game.put()\\n return game\",\n \"def create_on_scene(self, name, **kwargs):\\n monster = self.create(name, **kwargs)\\n yield monster, self._level\\n self._level.add(monster)\",\n \"def create_group():\\n incoming = request.get_json()\\n chatroom = Chatroom(\\n name = incoming['name'],\\n tag = incoming['tag'],\\n )\\n db.session.add(chatroom)\\n db.session.commit()\\n participant = Participant(\\n user_id = session['user_id'],\\n room_id = chatroom.room_id,\\n )\\n db.session.add(participant)\\n db.session.commit()\\n return jsonify(results = chatroom.room_id)\",\n \"def test_api_document_create_instructor(self):\\n playlist = core_factories.PlaylistFactory()\\n\\n jwt_token = InstructorOrAdminLtiTokenFactory(playlist=playlist)\\n\\n self.assertEqual(MarkdownDocument.objects.count(), 0)\\n\\n response = self.client.post(\\n \\\"/api/markdown-documents/\\\",\\n {\\n \\\"lti_id\\\": \\\"document_one\\\",\\n \\\"playlist\\\": str(playlist.id),\\n \\\"title\\\": \\\"Some document\\\",\\n },\\n HTTP_AUTHORIZATION=f\\\"Bearer {jwt_token}\\\",\\n )\\n\\n self.assertEqual(MarkdownDocument.objects.count(), 1)\\n self.assertEqual(response.status_code, 201)\\n document = MarkdownDocument.objects.first()\\n self.assertEqual(\\n response.json(),\\n {\\n \\\"id\\\": str(document.id),\\n \\\"images\\\": [],\\n \\\"is_draft\\\": True,\\n \\\"playlist\\\": {\\n \\\"id\\\": str(playlist.id),\\n \\\"lti_id\\\": playlist.lti_id,\\n \\\"title\\\": playlist.title,\\n },\\n \\\"position\\\": 0,\\n \\\"rendering_options\\\": {},\\n \\\"translations\\\": [\\n {\\n \\\"content\\\": \\\"\\\",\\n \\\"language_code\\\": \\\"en\\\",\\n \\\"rendered_content\\\": \\\"\\\",\\n \\\"title\\\": \\\"Some document\\\",\\n }\\n ],\\n },\\n )\",\n \"def createObject(self, *args):\\n return _libsbml.Submodel_createObject(self, *args)\",\n \"def create(learner, model_list, groups=None, T=None, **kwargs):\\n if learner == \\\"dorm\\\":\\n ol = DORM(model_list, groups, T) \\n elif learner == \\\"dormplus\\\":\\n ol = DORMPlus(model_list, groups, T) \\n elif learner == \\\"adahedged\\\":\\n ol = AdaHedgeD(model_list, groups, T, reg=\\\"adahedged\\\") \\n elif learner == \\\"dub\\\":\\n ol = AdaHedgeD(model_list, groups, T, reg=\\\"dub\\\") \\n else: \\n raise ValueError(f\\\"Unknown learning algorithm {learner}.\\\")\\n\\n return ol\",\n \"def create_one_game(self):\\n return Game2048(task_name=self.result_path, game_mode=False)\",\n \"def make(self):\\n pass\",\n \"def __init__(self, title: str, start: time, end: time, instructor: str) -> None:\\n # TODO: Provide the complete constructor for this object\",\n \"def initialize(self):\\r\\n if self.method == \\\"naive\\\":\\r\\n self.teams = TeamsNaive(\\r\\n self.data_path, self.season_to_play, self.season_data\\r\\n )\\r\\n self.gsim = GameNaive(False)\\r\\n if not self.playoffs_only:\\r\\n self.season = Season(self.season_calendar, self.teams_info, self.gsim)\",\n \"def createObject(self, *args):\\n return _libsbml.MultiSpeciesPlugin_createObject(self, *args)\",\n \"def create(self):\",\n \"def CreateMatch() -> None:\\n m1 = Matches(\\n name=\\\"TESTURL1 => TESTURL2\\\",\\n url1=CreatePage(\\\"TESTURL1\\\"),\\n url2=CreatePage(\\\"TESTURL2\\\"),\\n degrees=33,\\n last=datetime.datetime.now(),\\n )\\n\\n m2 = Matches(\\n name=\\\"TESTURL3 => TESTURL4\\\",\\n url1=CreatePage(\\\"TESTURL3\\\"),\\n url2=CreatePage(\\\"TESTURL4\\\"),\\n degrees=33,\\n last=datetime.datetime.now(),\\n )\\n\\n db.session.add(m1, m2)\\n db.session.commit()\",\n \"def make_object(self, data, **kwargs):\\n if data is None:\\n raise ValidationError(\\\"No data was provided\\\")\\n return Artist(**data)\",\n \"def create_playlist():\\n sp = credentials()\\n sp.user_playlist_create('truetiming', name='Billboard Hot 100')\",\n \"def jam_pack(jam, **kwargs):\\n\\n if not hasattr(jam.sandbox, \\\"muda\\\"):\\n # If there's no mudabox, create one\\n jam.sandbox.muda = jams.Sandbox(\\n history=[],\\n state=[],\\n version=dict(\\n muda=version,\\n librosa=librosa.__version__,\\n jams=jams.__version__,\\n pysoundfile=psf.__version__,\\n ),\\n )\\n\\n elif not isinstance(jam.sandbox.muda, jams.Sandbox):\\n # If there is a muda entry, but it's not a sandbox, coerce it\\n jam.sandbox.muda = jams.Sandbox(**jam.sandbox.muda)\\n\\n jam.sandbox.muda.update(**kwargs)\\n\\n return jam\",\n \"def create_students():\\n\\n\\t# create empty list\\n\\tstudent_list = []\\n\\n\\t# import student classces\\n\\tstudent_list = create_student_class()\\n\\n\\treturn student_list\",\n \"def create_model(self):\\n pass\",\n \"def create_model(self):\\n pass\",\n \"def _create_session(self, xnat_login, subject_id, visit_id):\\n uri = ('/data/archive/projects/{}/subjects/{}/experiments/{}'\\n .format(self.inputs.project_id, subject_id, visit_id))\\n query = {'xsiType': 'xnat:mrSessionData', 'label': visit_id,\\n 'req_format': 'qa'}\\n response = xnat_login.put(uri, query=query)\\n if response.status_code not in (200, 201):\\n raise NiAnalysisError(\\n \\\"Could not create session '{}' in subject '{}' in project '{}'\\\"\\n \\\" response code {}\\\"\\n .format(visit_id, subject_id, self.inputs.project_id,\\n response))\\n return xnat_login.classes.MrSessionData(uri=uri,\\n xnat_session=xnat_login)\",\n \"def createMentor(self, org):\\n self.createProfile()\\n self.profile.mentor_for = [org.key()]\\n self.profile.put()\",\n \"def obj_create(self, bundle, request=None, **kwargs):\\n return super(PlaylistResource, self).obj_create(bundle, request, user=request.user)\",\n \"def create_new_pl(self, params):\\n name = params[ONE]\\n user = params[ZERO]\\n songs = params[2].split('&')\\n msg = self.db.create_new_pl(songs, name, user)\\n self.send_message(msg)\",\n \"def __init__(self, projectName, questions,\\n gsOutput=False, autolabOutput=False, muteOutput=False):\\n self.questions = [el[0] for el in questions]\\n self.maxes = {name: q.getMaxPoints() for name, q in questions}\\n self.questionDict = {name: q for name, q in questions}\\n self.points = Counter()\\n self.messages = dict([(q, []) for q in self.questions])\\n self.project = projectName\\n self.start = datetime.datetime.now(TZ)\\n self.finish = datetime.datetime.now(TZ) # dummy information\\n self.sane = True # Sanity checks\\n self.currentQuestion = None # Which question we're grading\\n self.autolabOutput = autolabOutput\\n self.gsOutput = gsOutput # GradeScope output\\n self.mute = muteOutput\\n self.prereqs = defaultdict(set)\\n\\n print('Autograder transcript for %s' % self.project)\\n print(self.start.strftime(\\\"Starting on %m-%d at %H:%M:%S\\\"))\",\n \"def make(self, **kwargs):\\n raise NotImplementedError\",\n \"def _from_java(cls, java_obj):\\n # Create a new instance of this stage.\\n py_obj = cls()\\n py_obj._java_obj = java_obj\\n if java_obj is None and java_obj.parentPipeline().isDefined():\\n py_parent = MLPipeline()\\n py_parent._java_obj = java_obj.parentPipeline().get()\\n py_obj._parent = py_parent\\n return py_obj\",\n \"def create_models( self ):\",\n \"def create(cfg, jobs):\\n server = jenkins_utils.server_factory(cfg)\\n libjobs.createJobs(server, jobs)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6043115","0.6028612","0.6020422","0.595867","0.5869286","0.51103055","0.48313457","0.48313457","0.4821683","0.48106268","0.47865143","0.47502753","0.47477722","0.47190872","0.4700809","0.46956456","0.46870407","0.46566224","0.4654095","0.4651156","0.46364608","0.46241802","0.46131468","0.46112213","0.46084243","0.46082935","0.45948908","0.4586612","0.45731005","0.4571076","0.45581317","0.4557794","0.4557433","0.45533523","0.45532688","0.45503077","0.45308802","0.4526028","0.45224598","0.45098275","0.45082253","0.4503747","0.45035893","0.44955507","0.44944286","0.44902614","0.44884118","0.44873577","0.44860953","0.4481973","0.4478265","0.44765794","0.44765574","0.4459993","0.44582462","0.44509965","0.4445269","0.44360185","0.44240072","0.44070312","0.44002357","0.44000125","0.4395813","0.43904182","0.4388261","0.43730325","0.43713778","0.43689463","0.43681","0.4366824","0.4366796","0.43663162","0.43629846","0.4360801","0.43581453","0.43536848","0.43521503","0.43501014","0.434999","0.43493468","0.4344202","0.43439245","0.43437645","0.4340846","0.43398467","0.43376186","0.43318677","0.4327365","0.4326869","0.4325559","0.4325559","0.43250966","0.43248612","0.43237206","0.43187183","0.4318399","0.43150923","0.4314344","0.43124986","0.4309908"],"string":"[\n \"0.6043115\",\n \"0.6028612\",\n \"0.6020422\",\n \"0.595867\",\n \"0.5869286\",\n \"0.51103055\",\n \"0.48313457\",\n \"0.48313457\",\n \"0.4821683\",\n \"0.48106268\",\n \"0.47865143\",\n \"0.47502753\",\n \"0.47477722\",\n \"0.47190872\",\n \"0.4700809\",\n \"0.46956456\",\n \"0.46870407\",\n \"0.46566224\",\n \"0.4654095\",\n \"0.4651156\",\n \"0.46364608\",\n \"0.46241802\",\n \"0.46131468\",\n \"0.46112213\",\n \"0.46084243\",\n \"0.46082935\",\n \"0.45948908\",\n \"0.4586612\",\n \"0.45731005\",\n \"0.4571076\",\n \"0.45581317\",\n \"0.4557794\",\n \"0.4557433\",\n \"0.45533523\",\n \"0.45532688\",\n \"0.45503077\",\n \"0.45308802\",\n \"0.4526028\",\n \"0.45224598\",\n \"0.45098275\",\n \"0.45082253\",\n \"0.4503747\",\n \"0.45035893\",\n \"0.44955507\",\n \"0.44944286\",\n \"0.44902614\",\n \"0.44884118\",\n \"0.44873577\",\n \"0.44860953\",\n \"0.4481973\",\n \"0.4478265\",\n \"0.44765794\",\n \"0.44765574\",\n \"0.4459993\",\n \"0.44582462\",\n \"0.44509965\",\n \"0.4445269\",\n \"0.44360185\",\n \"0.44240072\",\n \"0.44070312\",\n \"0.44002357\",\n \"0.44000125\",\n \"0.4395813\",\n \"0.43904182\",\n \"0.4388261\",\n \"0.43730325\",\n \"0.43713778\",\n \"0.43689463\",\n \"0.43681\",\n \"0.4366824\",\n \"0.4366796\",\n \"0.43663162\",\n \"0.43629846\",\n \"0.4360801\",\n \"0.43581453\",\n \"0.43536848\",\n \"0.43521503\",\n \"0.43501014\",\n \"0.434999\",\n \"0.43493468\",\n \"0.4344202\",\n \"0.43439245\",\n \"0.43437645\",\n \"0.4340846\",\n \"0.43398467\",\n \"0.43376186\",\n \"0.43318677\",\n \"0.4327365\",\n \"0.4326869\",\n \"0.4325559\",\n \"0.4325559\",\n \"0.43250966\",\n \"0.43248612\",\n \"0.43237206\",\n \"0.43187183\",\n \"0.4318399\",\n \"0.43150923\",\n \"0.4314344\",\n \"0.43124986\",\n \"0.4309908\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5316625"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":94096,"cells":{"query":{"kind":"string","value":"Add the contents of another jam to this object. Note that, by default, this method fails if file_metadata is not identical and raises a ValueError; either resolve this manually (because conflicts should almost never happen), force an 'overwrite', or tell the method to 'ignore' the metadata of the object being added."},"document":{"kind":"string","value":"def add(self, jam, on_conflict='fail'):\n\n if on_conflict not in ['overwrite', 'fail', 'ignore']:\n raise ParameterError(\"on_conflict='{}' is not in ['fail', \"\n \"'overwrite', 'ignore'].\".format(on_conflict))\n\n if not self.file_metadata == jam.file_metadata:\n if on_conflict == 'overwrite':\n self.file_metadata = jam.file_metadata\n elif on_conflict == 'fail':\n raise JamsError(\"Metadata conflict! \"\n \"Resolve manually or force-overwrite it.\")\n\n self.annotations.extend(jam.annotations)\n self.sandbox.update(**jam.sandbox)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __add__(self, other):\r\n # Make a defaultdict of defaultdicts, the latter of which returns\r\n # None when an key is not present\r\n merged_data = defaultdict(lambda: defaultdict(lambda: None))\r\n\r\n # We will keep track of all unique sample_ids and metadata headers\r\n # we have seen as we go\r\n all_sample_ids = set()\r\n all_headers = set()\r\n\r\n # add all values from self into the merged_data structure\r\n for sample_id, data in self._metadata.iteritems():\r\n all_sample_ids.add(sample_id)\r\n for header, value in data.iteritems():\r\n all_headers.add(header)\r\n merged_data[sample_id][header] = value\r\n\r\n # then add all data from other\r\n for sample_id, data in other._metadata.iteritems():\r\n all_sample_ids.add(sample_id)\r\n for header, value in data.iteritems():\r\n all_headers.add(header)\r\n # if the two mapping files have identical sample_ids and\r\n # metadata columns but have DIFFERENT values, raise a value\r\n # error\r\n if merged_data[sample_id][header] is not None and \\\r\n merged_data[sample_id][header] != value:\r\n raise ValueError(\"Different values provided for %s for \"\r\n \"sample %s in different mapping files.\"\r\n % (header, sample_id))\r\n else:\r\n merged_data[sample_id][header] = value\r\n\r\n # Now, convert what we have seen into a normal dict\r\n normal_dict = {}\r\n for sample_id in all_sample_ids:\r\n if sample_id not in normal_dict:\r\n normal_dict[sample_id] = {}\r\n\r\n for header in all_headers:\r\n normal_dict[sample_id][header] = \\\r\n merged_data[sample_id][header]\r\n\r\n # and create a MetadataMap object from it; concatenate comments\r\n return self.__class__(normal_dict, self.Comments + other.Comments)","def add_other_meta_data(self, other: _MetaData) -> None:\n\n for key in other._meta_data_dict.keys():\n self.add_data(key, other._meta_data_dict[key])","def merge_content(self, other):\n self.__content += other.__content","def merge(self, other):\n for p in other:\n for key, val in p.items():\n self.contents[key] = val\n\n return self","def __add__(self, other):\n if self.xml.find('mosromgrmeta') is None or isinstance(other, RunningOrderControl):\n return other.merge(self)\n raise MosCompletedMergeError(\"Cannot merge completed MOS file\")","def __iadd__(self, other):\n if not isinstance(other, type(self)):\n raise TypeError(\"Only DFs of the same type can be combined.\")\n self.dfs.extend(other.dfs)\n self.counts.extend(other.counts)\n self._unique = False\n self._original += other._original\n if self.label is None:\n if other.label is not None:\n self.label = other.label\n else:\n if other.label is not None:\n self.label += \"+\" + other.label\n self.tags.update(other.tags)\n self._average = None\n return self","def add_metadata(self, metadata: dict) -> None:","def add(self, content):\n if (not self.exists()):\n raise IOError(\"File at '{}' does not exist.\".format(self.location))\n with open(self.location, 'a') as f:\n f.write(content)","def bundle_metadata(self, metadata):\n\n metadata_file = None\n try:\n metadata_file = tempfile.NamedTemporaryFile(delete=False)\n except IOError:\n task_error('Cannot create metadata file in working directory')\n\n metadata_file.write(metadata)\n fname = metadata_file.name\n metadata_file.close()\n\n metadata_file = open(fname, mode='rb')\n\n # metadata_file.seek(0)\n\n if self.empty_tar:\n tarball = tarfile.TarFile(name=self.bundle_path, mode='w')\n self.empty_tar = False\n else:\n tarball = tarfile.TarFile(name=self.bundle_path, mode='a')\n\n try:\n tar_info = tarfile.TarInfo('metadata.txt')\n tar_info.size = len(metadata)\n tar_info.mtime = time.time()\n tarball.addfile(tar_info, metadata_file)\n metadata_file.close()\n tarball.close()\n os.remove(fname)\n except Exception, ex:\n print ex\n traceback.print_exc(file=sys.stdout)\n raise ex","def merge_assets(self, other):\n for asset in other.asset:\n if find_elements(root=self.asset, tags=asset.tag,\n attribs={\"name\": asset.get(\"name\")}, return_first=True) is None:\n self.asset.append(asset)","def add(self, other):\n\n def merge_dicts(d1, d2):\n \"\"\"\n Merge two dictionaries\n\n param d1: dictionary changed in place to have combined values\n type d1: dictionary(key -> set)\n param d2: dictioanry to be merged\n type d2: dictionary(key -> set)\n \"\"\"\n for key,value in d2.items():\n if key not in d1:\n d1[key] = value\n else:\n d1[key] |= value\n \n self.num_documents += other.num_documents\n self.num_expressions += other.num_expressions\n self.global_expressions += other.global_expressions\n self.expressions_with_e += other.expressions_with_e\n self.num_keywords += other.num_keywords\n merge_dicts(self.missing_tags, other.missing_tags)\n merge_dicts(self.problem_files, other.problem_files)","def merge_asset(self, other):\n for asset in other.asset:\n asset_name = asset.get(\"name\")\n asset_type = asset.tag\n # Avoids duplication\n pattern = \"./{}[@name='{}']\".format(asset_type, asset_name)\n if self.asset.find(pattern) is None:\n self.asset.append(asset)","def add(self, file_id: str, contents: bytes):\n file_path = self._path_to_file(file_id)\n b2_utils.write_file(file_path, contents)\n self._add_to_index(file_id, len(contents))","def __add__(self, other):\n return self.new(basename=self.basename + str(other))","def __add__(self, other):\n if type(other) is not type(self):\n raise TypeError('`{}` and `{}` are not of the same profiler type.'.\n format(type(self).__name__, type(other).__name__))\n\n # error checks specific to its profiler\n self._add_error_checks(other)\n\n merged_profile = self.__class__(\n data=None, samples_per_update=self._samples_per_update,\n min_true_samples=self._min_true_samples, options=self.options\n )\n merged_profile.encoding = self.encoding\n if self.encoding != other.encoding:\n merged_profile.encoding = 'multiple files'\n\n merged_profile.file_type = self.file_type\n if self.file_type != other.file_type:\n merged_profile.file_type = 'multiple files'\n\n merged_profile.total_samples = self.total_samples + other.total_samples\n\n merged_profile.times = utils.add_nested_dictionaries(self.times,\n other.times)\n\n return merged_profile","def _extend(self, other):\n for key, value in list(other.entries.items()):\n self._add_entry(key, value)","def merge(self, other):\n extras = other.difference(self)\n if len(extras) > 0:\n self.update(extras)\n self.reset()\n return True\n return False","def merge_from(self, other):\n assert not self.is_final\n if self.parent is not None:\n assert other.parent is not None\n self.parent.merge_from(other.parent)\n self.isolated_names.update(other.isolated_names)\n self.read.update(other.read)\n self.modified.update(other.modified)\n self.bound.update(other.bound)\n self.deleted.update(other.deleted)\n self.annotations.update(other.annotations)\n self.params.update(other.params)","def merge(self, graph):\n # keep previous self.filename\n # copy data\n for x in graph.data:\n self.data.append(x)\n # copy headers, unless already exists (is so, info is lost)\n for key in graph.headers:\n if key not in self.headers:\n self.headers.update({key: graph.headers[key]})\n # copy graphInfo, unless already exists (is so, info is lost)\n for key in graph.graphInfo:\n if key not in self.graphInfo:\n self.graphInfo.update({key: graph.graphInfo[key]})\n # copy sampleInfo, unless already exists (is so, info is lost)\n for key in graph.sampleInfo:\n if key not in self.sampleInfo:\n self.sampleInfo.update({key: graph.sampleInfo[key]})","def __add__(self, other):\n mesh = deepcopy(self)\n mesh.MergeWith(other)\n return mesh","def add_overlap(self, other):\n assert isinstance(other, Chunk)\n overlap_slices = self._get_overlap_slices(other.slices)\n self.array[overlap_slices] += other.array[overlap_slices]","def merge(self, object_class):\n other_oc = self.schema.get_object_class(object_class)\n self.required_attrs |= other_oc.required_attrs\n self.allowed_attrs |= other_oc.allowed_attrs","def __add__(self, other):\n\n if not isinstance(other, Photons):\n raise ValueError('Can only add a Photons object to another Photons object.')\n\n # don't want to modify what is being added\n other = other.copy()\n\n # make column units consistent with self\n other.match_units(self)\n\n # add and /or update observation columns as necessary\n self.add_observations_column()\n other.add_observations_column()\n n_obs_self = len(self.obs_metadata)\n other['n'] += n_obs_self\n\n # re-reference times to the datum of self\n other.set_time_datum(self.time_datum)\n\n # stack the data tables\n photons = _tbl.vstack([self.photons, other.photons])\n\n # leave it to the user to deal with sorting and grouping and dealing with overlap as they see fit :)\n obs_metadata = self.obs_metadata + other.obs_metadata\n obs_times = list(self.obs_times) + list(other.obs_times)\n obs_bandpasses = list(self.obs_bandpasses) + list(other.obs_bandpasses)\n\n return Photons(photons=photons, obs_metadata=obs_metadata, time_datum=self.time_datum, obs_times=obs_times,\n obs_bandpasses=obs_bandpasses)","def _merge(self, other: dict):\n self._storage = dict_merge(self._storage, other)","def put_metadata(self, metadata, tombstone=False):\n if tombstone:\n # We don't write tombstone files. So do nothing.\n return\n assert self.data_file is not None, \\\n \"put_metadata: no file to put metadata into\"\n metadata = _adjust_metadata(metadata)\n self.threadpool.run_in_thread(write_metadata, self.data_file, metadata)\n self.metadata = metadata\n self._filter_metadata()","def merge_bom(self, other_bom):\n for bom_entry in other_bom.entries():\n self.create_and_add_entry(**bom_entry.attrs)","def _update_filesystem_metadata(self, metadata):\n directory, fname = os.path.split(self.fname)\n fbase = os.path.splitext(fname)[0]\n \n # Test for presence and size of zip file\n zip_file = fbase + '.zip'\n zip_path = os.path.join(directory, zip_file)\n \n if os.path.isfile(zip_path):\n location = 'on_disk'\n data_file_size = os.path.getsize(zip_path)\n else:\n location = 'on_tape'\n data_file_size = 0\n \n # Test for presence of quick look PNG file\n quicklook_file = fbase + '.png'\n quicklook_path = os.path.join(directory, quicklook_file)\n \n if not os.path.isfile(quicklook_path):\n quicklook_file = ''\n\n # Add to metadata dictionary\n item_map = {'directory': directory, 'metadata_file': fname,\n 'data_file': zip_file, 'location': location, \n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\n \n for key, value in item_map.items():\n metadata[key] = value","def copy(self, rhs):\n\n\t\tself.name = rhs.name\n\t\tself.file = rhs.file\n\n\t\tfor rem in rhs.remark:\n\t\t\tself.addRemark(rem)\n\n\t\tself.bk_tot = rhs.bk_tot\n\t\tself.fa_rep = rhs.fa_rep\n\t\tself.fa_atr = rhs.fa_atr","def override_from_folder(self, other: ItemVariant) -> None:\n self.authors.extend(other.authors)\n self.tags.extend(self.tags)\n self.vbsp_config = lazy_conf.concat(self.vbsp_config, other.vbsp_config)\n self.desc = tkMarkdown.join(self.desc, other.desc)","def mergeMappingFiles(mapping_files, no_data_value='no_data'):\r\n metadata_maps = map(MetadataMap.parseMetadataMap, mapping_files)\r\n merged = sum(metadata_maps[1:], metadata_maps[0])\r\n merged.no_data_value = no_data_value\r\n return merged","def merge(self, other):\n log.debug('Merging: %s and %s' % (self.serialize(), other.serialize()))\n for k in self.keys():\n for new_item in other[k]:\n if new_item not in self[k]:\n self[k].append(new_item)\n log.debug('Result: %s' % self.serialize())\n return self","def _update_extra_metadata(self, extra_metadata):\n self._add_filename_metadata(extra_metadata)\n self._derive_extra_metadata(extra_metadata)\n \n if type(self) == SAFESentinel3:\n self._extract_metadata_from_zipfile(extra_metadata)","def __add__(self, other):\n if not isinstance(other, Gene):\n return NotImplemented\n if self.portion_duration != other.portion_duration:\n raise ValueError(\"The genes must have the same portion_duration.\")\n if self.bias.shape != other.bias.shape:\n raise ValueError(\"The genes must have the same dimension.\")\n\n new_gene = self.copy()\n new_gene.bias = (self.weight*self.bias + other.weight*other.bias) \\\n / (self.weight + other.weight)\n return new_gene","def add(self, transaction, markers):\n if not _enabled:\n raise util.Abort('obsolete feature is not enabled on this repo')\n new = [m for m in markers if m not in self._all]\n if new:\n f = self.sopener('obsstore', 'ab')\n try:\n # Whether the file's current position is at the begin or at\n # the end after opening a file for appending is implementation\n # defined. So we must seek to the end before calling tell(),\n # or we may get a zero offset for non-zero sized files on\n # some platforms (issue3543).\n f.seek(0, _SEEK_END)\n offset = f.tell()\n transaction.add('obsstore', offset)\n # offset == 0: new file - add the version header\n for bytes in _encodemarkers(new, offset == 0):\n f.write(bytes)\n finally:\n # XXX: f.close() == filecache invalidation == obsstore rebuilt.\n # call 'filecacheentry.refresh()' here\n f.close()\n self._load(new)\n # new marker *may* have changed several set. invalidate the cache.\n self.caches.clear()\n return len(new)","def update_metadata(self, file_id, metadata):\n pass","def __iadd__(self, other):\n self.MergeWith(other)\n return self","def add(self, other):\n self._check_item(other)\n self._set.add(other)","def __add__(self, other):\n if not isinstance(other, type(self)):\n raise TypeError(\"Only DFs of the same type can be combined.\")\n\n dfs = self.dfs + other.dfs\n counts = self.counts + other.counts\n return self.__class__(dfs, counts)","def __add__(self, other):\n self.__dict__.update(other)\n return self","def merge(self, other: ProjectMeta) -> ProjectMeta:\n return self.clone(\n obj_classes=self._obj_classes.merge(other.obj_classes),\n tag_metas=self._tag_metas.merge(other._tag_metas),\n )","def add(self, *args, **kwargs):\n self.zipfile.write(*args, **kwargs)","def __add__(self, other):\n if not isinstance(other, Corpus):\n raise NotImplementedError(\"Only a Corpus can be added to another Corpus.\")\n\n output_corpus = self.clone()\n for document in other:\n output_corpus.documents.append(document)\n output_corpus.documents = sorted(output_corpus.documents)\n\n return output_corpus","def add_file(self, name, content):\n self._add_field(struct.pack(\"<L\", len(name)))\n self._add_field(name.encode())\n\n if self.flags & FLAG_GZIP != 0:\n content = GzipWrapper(content, retain_cache=self.cache_chunks)\n elif self.flags & FLAG_LZ4 != 0:\n content = Lz4Wrapper(content, retain_cache=self.cache_chunks)\n else:\n content = NoOpWrapper(content, retain_cache=self.cache_chunks)\n\n self._add_field(struct.pack(\"<Q\", _get_length(content)))\n self._add_field(content)","def _add_to_archive(archive, filename, contents, tstamp):\n fileinfo = zipfile.ZipInfo(filename, tstamp)\n fileinfo.external_attr = 0666 << 16L\n archive.writestr(fileinfo, contents, zipfile.ZIP_DEFLATED)","def Add_File(self,tx,filename,newcontents):\n if tx != self.tx:\n raise InvalidTransaction(tx)\n fullname = os.path.join(self.home,filename)\n h = win32_txf.CreateFileTransacted(fullname,transaction = tx,\n desired_access = win32_txf.const.GENERIC_WRITE,\n creation_disposition = win32_txf.const.CREATE_ALWAYS)\n #TODO handle partial writes\n win32_txf.WriteFile(h,newcontents)\n win32_txf.CloseHandle(h)","def add_file_metadata(self):\n metadata = self.__file.require_group(METADATA)\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\"%Y-%m-%d\"))\n self.__write_value(metadata, SDK_VERSION, __version__)","def add(self, other):\n if not isinstance(other, self.__class__):\n raise ValueError(\n f\"Argument (type {type(other)}) is not a {self.__class__} instance\"\n )\n if len(other.data):\n self.data = pd.concat([self.data, other.data], ignore_index=True)\n self.sort()","def combine(self, existing):\n return self","def _extend(self, other_field, memo) -> None:\n if other_field.data.ndim != self.data.ndim:\n raise ValueError(\n f\"Field '{self.name}' cannot be extended. Dimensions must be equal. ({other_field.data.ndim} != {self.data.ndim})\"\n )\n\n old_id = id(self.data)\n if self.data.dtype < other_field.data.dtype:\n # Increase size of self.data.dtype before inserting\n new_data = np.insert(self.data.astype(other_field.data.dtype), self.num_obs, other_field.data, axis=0)\n else:\n new_data = np.insert(self.data, self.num_obs, other_field.data, axis=0)\n memo[old_id] = (self.data, new_data)\n self.data = new_data","def mergeWith(self, newFL):\n srcMods = self.srcMods\n for levls, newLevls in ((self.levcs,newFL.levcs),(self.levis,newFL.levis)):\n for listId, newLevl in newLevls.items():\n if listId not in srcMods: \n srcMods[listId] = [newFL.fileInfo.name]\n levl = levls[listId] = copy.deepcopy(newLevl)\n self.records.append(levl)\n else:\n srcMods[listId].append(newFL.fileInfo.name)\n levls[listId].mergeWith(newLevl)","def __add__(self, other):\n raise NotImplementedError(\"Implement this if needed\")","def add(self,**kwargs):\n if self._extract:\n raise RuntimeError('This archive is write-only!')\n\n items = kwargs.iteritems() if PY2 else kwargs.items()\n for key,value in items:\n self._setitem(key,value)","def add(self, files, mask):\n pass","def __add__(self, other):\n assert isinstance(other, HStruct)\n return HStruct(*self.fields, *other.fields)","def merge(self, other: \"GraphSet\") -> None:\n if other.name != self.name:\n raise UnmergableGraphSetsException(\n f\"Unable to merge graph with name {other.name} into {self.name}\"\n )\n if other.version != self.version:\n raise UnmergableGraphSetsException(\n f\"Unable to merge graph with version {other.version} into {self.version}\"\n )\n self.start_time = min(self.start_time, other.start_time)\n self.end_time = max(self.end_time, other.end_time)\n self.resources += other.resources\n self._resolve_duplicates()\n self.errors += other.errors\n self.stats.merge(other.stats)","def add(self, file):\n if file not in self._files:\n self._files.insert(0, file)\n if len(self._files) > self.nbmax:\n del(self._files[-1])\n else:\n self._files.remove(file)\n self._files.insert(0, file)\n try:\n with open(self._filename, 'w') as file:\n file.write('\\n'.join(self._files))\n except Exception:\n # avoid raising errors if location is read-only or invalid path\n pass","def append(self, other, ignore_index=False, verify_integrity=False):\n new_frame = self.frame.append(other.frame, ignore_index=ignore_index,\n verify_integrity=verify_integrity)\n return self.__class__(new_frame)","def extend(self,\n other_matrix,\n overwrite_existing=False,\n extend_existing=False):\n self.taxon_seq_map.extend(other_matrix.taxon_seq_map,\n overwrite_existing=overwrite_existing,\n extend_existing=extend_existing)\n self.update_taxon_set()","def Add_File(self,txn,filename,newcontents):\n opid = self.new_opid()\n fullname = os.path.join(self.home,filename)\n #if not self.tx.dir_exists(os.path.dirname(fullname)):\n # raise OSError(errno.ENOENT,\"No directory: %r\"%os.path.dirname(fullname))\n xaction = ReplaceAll_Operation(fullname,newcontents,opid)\n self._add_operation(txn,xaction)","def merge(self, new_video, fields):\n merged_video = Video(self.event)\n merged_video.filename = self.filename\n for field in self.metadata:\n if field in set(fields):\n merged_video.metadata[field] = new_video.metadata.get(field)\n else:\n merged_video.metadata[field] = self.metadata.get(field)\n return merged_video","def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\n vertical_reference: str = None):\n\n if container_name:\n if isinstance(container_name, str): # only one converted data instance provided\n container_name = [container_name]\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\n multibeam_file_list = [multibeam_file_list]\n for contname, mfiles in zip(container_name, multibeam_file_list):\n if mfiles:\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\n elif contname in self.sources:\n self.sources[contname].extend(mfiles)\n else:\n self.sources[contname] = mfiles\n else:\n self.sources[contname] = ['unknown']\n if self.crs and (self.crs != crs):\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\n crs))\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\n vertical_reference))\n self.crs = crs\n self.vertical_reference = vertical_reference","def extend(self, other_rollout):\n\n assert not self.is_terminal()\n assert all(k in other_rollout.fields for k in self.fields)\n for k, v in other_rollout.data.items():\n self.data[k].extend(v)\n self.last_r = other_rollout.last_r","def add_metadata (self, name, value):\n self.metadata[name] = value\n return self","def merge(self, db2):\n delta_db = Database(db2)\n\n # Find common headers between the master and delta databases\n common_headers = [x for x in self.headers if x in delta_db.headers]\n\n # Any new headers found in the delta are added to the master\n self.headers.extend(\n [x for x in delta_db.headers if x not in self.headers])\n\n if len(common_headers) < 1:\n print(\"No shared headers were found. These files cannot be merged.\")\n else:\n key = ''\n # Skip picker prompt if there is only one common header\n if len(common_headers) == 1:\n key = common_headers[0]\n else:\n key = self.headerpicker(common_headers)\n\n # Create a temp list for new records to be added to\n records_temp = list(self.records)\n\n # Iterate over new records and attempt to match to existing record\n for each in delta_db.records:\n record = self.fetch_record(key, each, records_temp)\n if record:\n record.attributes.update(each.attributes)\n\n self.records = records_temp\n print(\"Merge successful!\\n\")","def __add__(self, other):\n other_data = self._setup_numeric(other)\n new_line = empty_like(self)\n\n if isinstance(other, line):\n other_data = other.data\n else:\n other_data = other\n\n new_line.data[:] = self.data + other_data\n\n return new_line","def overwrite_original_file(self):\n return self.__overwrite_original_file","def add(self, name, content):\n raise NotImplementedError","def combine(self, other) -> None:\n assert self.id_ == other.id_\n assert self.type_ == other.type_\n self.count += other.count","def merge(self, other):\n self.isotxsMetadata = self.isotxsMetadata.merge(\n other.isotxsMetadata, self, other, \"ISOTXS\", AttributeError\n )\n self.gamisoMetadata = self.gamisoMetadata.merge(\n other.gamisoMetadata, self, other, \"GAMISO\", AttributeError\n )\n self.pmatrxMetadata = self.pmatrxMetadata.merge(\n other.pmatrxMetadata, self, other, \"PMATRX\", AttributeError\n )\n self.micros.merge(other.micros)\n self.gammaXS.merge(other.gammaXS)\n self.neutronHeating = _mergeAttributes(self, other, \"neutronHeating\")\n self.neutronDamage = _mergeAttributes(self, other, \"neutronDamage\")\n self.gammaHeating = _mergeAttributes(self, other, \"gammaHeating\")\n self.isotropicProduction = _mergeAttributes(self, other, \"isotropicProduction\")\n self.linearAnisotropicProduction = _mergeAttributes(\n self, other, \"linearAnisotropicProduction\"\n )\n # this is lazy, but should work, because the n-order wouldn't be set without the others being set first.\n self.nOrderProductionMatrix = (\n self.nOrderProductionMatrix or other.nOrderProductionMatrix\n )","def merge(self, other):\n\n for child in other.children:\n self.add_deep_copy_of(child, merged=True)","def WriteMetadata(self, metadata, overwrite=True):\n if not overwrite and 'meta' in metadata:\n raise errors.KeyczarError('\"meta\" attribute already exists')\n self.dict['meta'] = str(metadata)","def update(self, raw_other):\n other = raw_other\n if isinstance(raw_other, ExtensionList):\n other = raw_other.extensions.values()\n for v in other:\n self.add(v)","def extend(self,\n other_map,\n overwrite_existing=False,\n extend_existing=False):\n if overwrite_existing and extend_existing:\n raise Exception(\"Can only specify to overwrite or append, not both\")\n label_taxon_map = dict([(taxon.label, taxon) for taxon in self])\n for other_taxon in other_map:\n if other_taxon.label in label_taxon_map:\n this_taxon = label_taxon_map[other_taxon.label]\n if overwrite_existing:\n self[this_taxon] = other_map[other_taxon]\n elif extend_existing:\n self[this_taxon].extend(other_map[other_taxon])\n else:\n self[other_taxon] = other_map[other_taxon]","def add(input_a, input_b):\n add_comp = input_b.duplicate()\n\n ImageBufAlgo.add(add_comp, input_a, input_b)\n\n if add_comp.has_error:\n print \"Error merging adding:\", add_comp.geterror()\n\n return add_comp","def rewrite(self):\n for f in self.files:\n metadata = dict()\n metadata[\"description\"] = f.metadata.get(\"desc\", \"Unknown\")\n metadata[\"script\"] = os.path.basename(f.filename)\n metadata[\"requires\"] = []\n for package, component in f.requires:\n if package == self.key:\n metadata[\"requires\"].append(\"/\" + component)\n else:\n metadata[\"requires\"].append(package + \"/\" + component)\n metadata[\"provides\"] = [ p[1] for p in f.provides ]\n # Resolve symlinks\n real_filename = os.path.realpath(f.filename)\n LOG.info(\"Editing: \" + real_filename)\n new_filename = f.filename + \".new\"\n new = file(new_filename, \"w\")\n new.write(\"/*\\n---\\n\")\n new.write(yaml.dump(metadata))\n new.write(\"\\n...\\n*/\\n\")\n new.write(file(f.filename).read())\n new.close()\n os.rename(new_filename, real_filename)\n\n package_data = dict()\n package_data[\"name\"] = self.key\n package_data[\"sources\"] = []\n package_data[\"version\"] = \"Unknown\"\n package_data[\"copyright\"] = \"Unknown\"\n package_data[\"description\"] = \"Unknown\"\n target_dir = os.path.dirname(self.scripts_json_filename)\n # package.yml is typically in the parent of the scripts.json dir\n if os.path.basename(target_dir) == \"Source\":\n target_dir = os.path.dirname(target_dir)\n target_filename = os.path.join(target_dir, \"package.yml\")\n for f in self.files:\n common = os.path.commonprefix([target_filename, f.filename])\n source_file = f.filename[len(common):]\n package_data[\"sources\"].append(source_file)\n LOG.info(\"Writing: \" + target_filename)\n out = file(target_filename, \"w\")\n out.write(yaml.dump(package_data))\n out.close()","def combine(self, other) -> None:\n assert self.linenum == other.linenum\n assert self.source == other.source\n if other.coverage >= 0:\n if self.coverage < 0:\n self.coverage = other.coverage\n else:\n self.coverage += other.coverage\n for branch in other.values():\n self.add_branch(branch)","def merge(self, other: 'Basket') -> None:\n for item in other:\n try:\n existing = self.items.get(ref=item.ref)\n existing.quantity += item.quantity\n existing.save(update_fields=['quantity'])\n except item.DoesNotExist:\n item.basket = self\n item.save(update_fields=['basket'])\n other.delete()\n self._cached_items = None","def __add__(self, other):\n\n self._add_sub_error_checking(other)\n if (self._counts is None) ^ (other._counts is None):\n raise SpectrumError(\n 'Addition of counts-based and CPS-based spectra is ' +\n 'ambiguous, use Spectrum(counts=specA.counts+specB.counts) ' +\n 'or Spectrum(cps=specA.cps+specB.cps) instead.')\n\n if self._counts is not None and other._counts is not None:\n kwargs = {'counts': self.counts + other.counts}\n if self.livetime and other.livetime:\n kwargs['livetime'] = self.livetime + other.livetime\n else:\n warnings.warn('Addition of counts with missing livetimes, ' +\n 'livetime was set to None.', SpectrumWarning)\n else:\n kwargs = {'cps': self.cps + other.cps}\n spect_obj = Spectrum(\n bin_edges_kev=self.bin_edges_kev, **kwargs)\n return spect_obj","def reread(self) -> None:\n old = self.getSubgraph(self.uri)\n new = Graph()\n try:\n contents = open(self.path).read()\n if contents.startswith(\"#new\"):\n log.debug(\"%s ignoring empty contents of my new file\",\n self.path)\n # this is a new file we're starting, and we should not\n # patch our graph as if it had just been cleared. We\n # shouldn't even be here reading this, but\n # lastWriteTimestamp didn't work.\n return\n\n new.parse(location=self.path, format='n3')\n self.readPrefixes = dict(new.namespaces())\n except SyntaxError as e:\n print(e)\n traceback.print_exc()\n log.error(\"%s syntax error\", self.path)\n # todo: likely bug- if a file has this error upon first\n # read, I think we don't retry it right.\n return\n except IOError as e:\n log.error(\"%s rereading %s: %r\", self.path, self.uri, e)\n return\n\n old = inContext(old, self.uri)\n new = inContext(new, self.uri)\n\n p = Patch.fromDiff(old, new)\n if p:\n log.debug(\"%s applying patch for changes in file\", self.path)\n self.patch(p, dueToFileChange=True)\n else:\n log.debug(\"old == new after reread of %s\", self.path)","def __appendMetaData(self, filename):\n metadata = {'Model': 'LFM',\n 'Source': filename,\n 'Date processed': datetime.datetime.now(),\n 'Start date': self.startDate\n }\n \n self.data.append(key='meta',\n name='Metadata for LFM Solar Wind file',\n units='n/a',\n data=metadata)","def combine(self, other):\n # Copy and merge\n ppt = PPT()\n ppt.contents = dict(self.contents)\n ppt.merge(other)\n return ppt","def merge(self, a, b, path=None):\n if path is None: path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n if key == 'attributes':\n self.merge_attribute_defs(b, a)\n else:\n self.merge(a[key], b[key], path + [str(key)])\n elif a[key] == b[key]:\n pass # same leaf value\n else:\n # raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))\n self.append_or_replace(a,b,key, '/'.join(path + [str(key)]));\n else:\n a[key] = b[key]\n return a","def add(self, dest, source=None):\n raise NotImplementedYet()","def __add__(self, other):\n # can only add using like units\n if not self.unit.is_compatible(other.unit):\n raise TypeError('Cannot add two quantities with incompatible units \"%s\" and \"%s\".' % (self.unit, other.unit))\n value = self._value + other.value_in_unit(self.unit)\n unit = self.unit\n return Quantity(value, unit)","def __add__(self, other):\n\n if isinstance(other, type(self)):\n # always create new fields, since otherwise c = a - b changes a as well!\n p = fields(self)\n p.elec[:] = self.elec + other.elec\n p.magn[:] = self.magn + other.magn\n return p\n else:\n raise DataError(\"Type error: cannot add %s to %s\" % (type(other), type(self)))","def _StageMetadata(json_metadata, storage_service, staged_file: str):\n # Write computed metadata to object storage.\n temp_run_dir = temp_dir.GetRunDirPath()\n local_file = os.path.join(temp_run_dir, os.path.basename(staged_file))\n with open(local_file, 'w') as f:\n json.dump(json_metadata, f)\n storage_service.Copy(local_file, staged_file)","def __eq__(self, other):\n if not isinstance(other, SharedFileMetadata):\n return False\n\n return self.to_dict() == other.to_dict()","def test_MergeManifests_file_attributes():\n d1 = dpack_pb2.DataPackage()\n f1 = d1.file.add()\n f1.relative_path = \"a\"\n f1.size_in_bytes = 1\n f1.checksum_hash = dpack_pb2.SHA1\n f1.checksum = \"abc\"\n d2 = dpack_pb2.DataPackage()\n f2 = d2.file.add()\n f2.relative_path = \"a\"\n f2.size_in_bytes = 2\n f2.checksum_hash = dpack_pb2.MD5\n f2.checksum = \"def\"\n dpack.MergeManifests(d1, d2)\n assert d1.file[0].size_in_bytes == 1\n assert d1.file[0].checksum_hash == dpack_pb2.SHA1\n assert d1.file[0].checksum == \"abc\"","def addFromFile(self, metadataProfileId, objectType, objectId, xmlFile):\n\n kparams = KalturaParams()\n kparams.addIntIfDefined(\"metadataProfileId\", metadataProfileId);\n kparams.addStringIfDefined(\"objectType\", objectType)\n kparams.addStringIfDefined(\"objectId\", objectId)\n kfiles = {\"xmlFile\": xmlFile}\n self.client.queueServiceActionCall(\"metadata_metadata\", \"addFromFile\", \"KalturaMetadata\", kparams, kfiles)\n if self.client.isMultiRequest():\n return self.client.getMultiRequestResult()\n resultNode = self.client.doQueue()\n return KalturaObjectFactory.create(resultNode, 'KalturaMetadata')","def git_add(self, filepath):\n filepath = os.path.normpath(filepath)\n self.repo.index.add([filepath])\n\n return filepath","def overwrite_original_file(self, value):\n self.__overwrite_original_file = value","def add_file(self, field_name, file_name, file_content, mimetype=None):\n if mimetype is None:\n mimetype = mimetypes.guess_type(file_name)[0] or 'application/octet-stream'\n self.files.append((field_name, file_name, mimetype, file_content))\n return","def _addOurselvesToPackage(self, oldPath):\n siblings = self._package.resources.setdefault(self.checksum, [])\n if siblings:\n newName = siblings[0]._storageName\n if oldPath.dirname() == self._package.resourceDir and self._storageName != newName:\n oldPath.remove()\n self._storageName = newName\n else:\n if Path(oldPath).dirname() == self._package.resourceDir:\n log.debug(u\"StorageName=%s was already in self._package resources\" % self._storageName)\n else:\n filename = (self._package.resourceDir/oldPath.basename())\n storageName = self._fn2ascii(filename)\n storageName = (self._package.resourceDir/storageName).unique()\n self._storageName = str(storageName.basename())\n oldPath.copy(self.path)\n siblings.append(self)","def __finalize__(self, other, method=None, **kwargs):\n self = super().__finalize__(other, method=method, **kwargs)\n # merge operation: using metadata of the left object\n if method == \"merge\":\n for name in self._metadata:\n print(\"self\", name, self.au_columns, other.left.au_columns)\n object.__setattr__(self, name, getattr(other.left, name, None))\n # concat operation: using metadata of the first object\n elif method == \"concat\":\n for name in self._metadata:\n object.__setattr__(self, name, getattr(other.objs[0], name, None))\n return self","def __add__(self, other, inplace=False, **kwargs):\n output = super(HERAData, self).__add__(other, inplace=inplace, **kwargs)\n if inplace:\n output = self\n output._determine_blt_slicing()\n output._determine_pol_indexing()\n if not inplace:\n return output","def add(self, other, offset=(0,0)):\n if (isinstance(other, Scene)):\n for item in other.items:\n newitem = item.clone()\n newitem.pan(offset)\n self.items.add(newitem)\n elif (isinstance(other, SvgObject)):\n newitem = other.clone()\n newitem.pan(offset)\n self.items.add(newitem)","def add_local_changes(self, skip_validation=False):\n if skip_validation:\n logger.warning(\n \"Skipping index.json check. Please submit a PR if the schema should be changed.\"\n )\n elif not self.validate_index():\n logger.error(\"Not adding changes to index.json. Doesn't match schema.\")\n logger.error(\n \"You may be able to skip this check with '--skip-validation' if you're sure your file conforms.\"\n )\n exit(-1)\n return subprocess.call(\n [\"git\", \"add\", \"index.json\", \"associated_files\"], cwd=self.path\n )","def Merge(self, other):\n\n # Logging just in case\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: before\", %s);'\n %(self.persistant['id'],\n sql.FormatSqlValue('details',\n repr(self.persistant))))\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: deleted\", %s);'\n %(other.persistant['id'], \n sql.FormatSqlValue('details',\n repr(other.persistant))))\n\n # Fields which can be summed\n for f in ['plays', 'skips']:\n self.persistant[f] = (self.persistant.get(f, 0) +\n other.persistant.get(f, 0))\n\n # Date fields where we take the newest\n for f in ['last_played', 'last_skipped', 'last_action']:\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\n if a > b:\n v = a\n else:\n v = b\n if v != datetime.datetime(1970, 1, 1):\n self.persistant[f] = v\n\n # Date fields where we take the oldest\n for f in ['creation_time']:\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\n if a < b:\n v = a\n else:\n v = b\n if v != datetime.datetime(1970, 1, 1):\n self.persistant[f] = v\n\n # Fields where we only clobber ours if we don't have a value\n for f in ['artist', 'album', 'song']:\n if not self.persistant.has_key(f) or not self.persistant[f]:\n self.persistant[f] = other.persistant.get(f, None)\n\n # Sometimes the number is a placeholder\n if self.persistant.has_key('number') and self.persistant['number'] == -1:\n self.persistant['number'] = other.persistant.get('number', -1)\n if not self.persistant.has_key('number'):\n self.persistant['number'] = other.persistant.get('number', -1)\n\n # Update the id in the tags table\n tags = self.db.GetRows('select tag from tags where track_id=%d;'\n % other.persistant['id'])\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: tags: %d\", %s);'\n %(self.persistant['id'], other.persistant['id'],\n sql.FormatSqlValue('details', repr(tags))))\n\n try:\n self.db.ExecuteSql('update tags set track_id=%d where track_id=%d;'\n %(self.persistant['id'], other.persistant['id']))\n self.db.ExecuteSql('commit;')\n except:\n # This can happen if the is already a matching tag for the first track\n pass\n\n # Update the id in the paths table\n paths = self.db.GetRows('select path from paths where track_id=%d;'\n % other.persistant['id'])\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: paths: %d\", %s);'\n %(self.persistant['id'], other.persistant['id'],\n sql.FormatSqlValue('details', repr(paths))))\n \n self.db.ExecuteSql('update paths set track_id=%d where track_id=%d;'\n %(self.persistant['id'], other.persistant['id']))\n self.db.ExecuteSql('commit;')\n\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: after\", %s);'\n %(self.persistant['id'],\n sql.FormatSqlValue('details',\n repr(self.persistant))))\n self.db.ExecuteSql('commit;')","def add_new_file(self, filename):\n self.load_config()\n if not self.__is_file_in_working_directory(filename):\n path = os.path.join(self.__directory.working_path,\n filename)\n raise FileNotFoundError(f\"No such file '{filename}' in '{path}'!\")\n self.__indexed_files.add(filename)\n files = self.config['info']['files']\n self.config['info']['files'] = f'{files},{filename}'.strip(',')\n source_file = os.path.join(self.__directory.working_path, filename)\n file_copy = os.path.join(self.__directory.index_path, filename)\n copyfile(source_file, file_copy)\n print(f'File {source_file} added')\n self.save_config()","def file_append(filepath,contents):\n with open(filepath, 'a') as f:\n f.write(contents+'\\n')"],"string":"[\n \"def __add__(self, other):\\r\\n # Make a defaultdict of defaultdicts, the latter of which returns\\r\\n # None when an key is not present\\r\\n merged_data = defaultdict(lambda: defaultdict(lambda: None))\\r\\n\\r\\n # We will keep track of all unique sample_ids and metadata headers\\r\\n # we have seen as we go\\r\\n all_sample_ids = set()\\r\\n all_headers = set()\\r\\n\\r\\n # add all values from self into the merged_data structure\\r\\n for sample_id, data in self._metadata.iteritems():\\r\\n all_sample_ids.add(sample_id)\\r\\n for header, value in data.iteritems():\\r\\n all_headers.add(header)\\r\\n merged_data[sample_id][header] = value\\r\\n\\r\\n # then add all data from other\\r\\n for sample_id, data in other._metadata.iteritems():\\r\\n all_sample_ids.add(sample_id)\\r\\n for header, value in data.iteritems():\\r\\n all_headers.add(header)\\r\\n # if the two mapping files have identical sample_ids and\\r\\n # metadata columns but have DIFFERENT values, raise a value\\r\\n # error\\r\\n if merged_data[sample_id][header] is not None and \\\\\\r\\n merged_data[sample_id][header] != value:\\r\\n raise ValueError(\\\"Different values provided for %s for \\\"\\r\\n \\\"sample %s in different mapping files.\\\"\\r\\n % (header, sample_id))\\r\\n else:\\r\\n merged_data[sample_id][header] = value\\r\\n\\r\\n # Now, convert what we have seen into a normal dict\\r\\n normal_dict = {}\\r\\n for sample_id in all_sample_ids:\\r\\n if sample_id not in normal_dict:\\r\\n normal_dict[sample_id] = {}\\r\\n\\r\\n for header in all_headers:\\r\\n normal_dict[sample_id][header] = \\\\\\r\\n merged_data[sample_id][header]\\r\\n\\r\\n # and create a MetadataMap object from it; concatenate comments\\r\\n return self.__class__(normal_dict, self.Comments + other.Comments)\",\n \"def add_other_meta_data(self, other: _MetaData) -> None:\\n\\n for key in other._meta_data_dict.keys():\\n self.add_data(key, other._meta_data_dict[key])\",\n \"def merge_content(self, other):\\n self.__content += other.__content\",\n \"def merge(self, other):\\n for p in other:\\n for key, val in p.items():\\n self.contents[key] = val\\n\\n return self\",\n \"def __add__(self, other):\\n if self.xml.find('mosromgrmeta') is None or isinstance(other, RunningOrderControl):\\n return other.merge(self)\\n raise MosCompletedMergeError(\\\"Cannot merge completed MOS file\\\")\",\n \"def __iadd__(self, other):\\n if not isinstance(other, type(self)):\\n raise TypeError(\\\"Only DFs of the same type can be combined.\\\")\\n self.dfs.extend(other.dfs)\\n self.counts.extend(other.counts)\\n self._unique = False\\n self._original += other._original\\n if self.label is None:\\n if other.label is not None:\\n self.label = other.label\\n else:\\n if other.label is not None:\\n self.label += \\\"+\\\" + other.label\\n self.tags.update(other.tags)\\n self._average = None\\n return self\",\n \"def add_metadata(self, metadata: dict) -> None:\",\n \"def add(self, content):\\n if (not self.exists()):\\n raise IOError(\\\"File at '{}' does not exist.\\\".format(self.location))\\n with open(self.location, 'a') as f:\\n f.write(content)\",\n \"def bundle_metadata(self, metadata):\\n\\n metadata_file = None\\n try:\\n metadata_file = tempfile.NamedTemporaryFile(delete=False)\\n except IOError:\\n task_error('Cannot create metadata file in working directory')\\n\\n metadata_file.write(metadata)\\n fname = metadata_file.name\\n metadata_file.close()\\n\\n metadata_file = open(fname, mode='rb')\\n\\n # metadata_file.seek(0)\\n\\n if self.empty_tar:\\n tarball = tarfile.TarFile(name=self.bundle_path, mode='w')\\n self.empty_tar = False\\n else:\\n tarball = tarfile.TarFile(name=self.bundle_path, mode='a')\\n\\n try:\\n tar_info = tarfile.TarInfo('metadata.txt')\\n tar_info.size = len(metadata)\\n tar_info.mtime = time.time()\\n tarball.addfile(tar_info, metadata_file)\\n metadata_file.close()\\n tarball.close()\\n os.remove(fname)\\n except Exception, ex:\\n print ex\\n traceback.print_exc(file=sys.stdout)\\n raise ex\",\n \"def merge_assets(self, other):\\n for asset in other.asset:\\n if find_elements(root=self.asset, tags=asset.tag,\\n attribs={\\\"name\\\": asset.get(\\\"name\\\")}, return_first=True) is None:\\n self.asset.append(asset)\",\n \"def add(self, other):\\n\\n def merge_dicts(d1, d2):\\n \\\"\\\"\\\"\\n Merge two dictionaries\\n\\n param d1: dictionary changed in place to have combined values\\n type d1: dictionary(key -> set)\\n param d2: dictioanry to be merged\\n type d2: dictionary(key -> set)\\n \\\"\\\"\\\"\\n for key,value in d2.items():\\n if key not in d1:\\n d1[key] = value\\n else:\\n d1[key] |= value\\n \\n self.num_documents += other.num_documents\\n self.num_expressions += other.num_expressions\\n self.global_expressions += other.global_expressions\\n self.expressions_with_e += other.expressions_with_e\\n self.num_keywords += other.num_keywords\\n merge_dicts(self.missing_tags, other.missing_tags)\\n merge_dicts(self.problem_files, other.problem_files)\",\n \"def merge_asset(self, other):\\n for asset in other.asset:\\n asset_name = asset.get(\\\"name\\\")\\n asset_type = asset.tag\\n # Avoids duplication\\n pattern = \\\"./{}[@name='{}']\\\".format(asset_type, asset_name)\\n if self.asset.find(pattern) is None:\\n self.asset.append(asset)\",\n \"def add(self, file_id: str, contents: bytes):\\n file_path = self._path_to_file(file_id)\\n b2_utils.write_file(file_path, contents)\\n self._add_to_index(file_id, len(contents))\",\n \"def __add__(self, other):\\n return self.new(basename=self.basename + str(other))\",\n \"def __add__(self, other):\\n if type(other) is not type(self):\\n raise TypeError('`{}` and `{}` are not of the same profiler type.'.\\n format(type(self).__name__, type(other).__name__))\\n\\n # error checks specific to its profiler\\n self._add_error_checks(other)\\n\\n merged_profile = self.__class__(\\n data=None, samples_per_update=self._samples_per_update,\\n min_true_samples=self._min_true_samples, options=self.options\\n )\\n merged_profile.encoding = self.encoding\\n if self.encoding != other.encoding:\\n merged_profile.encoding = 'multiple files'\\n\\n merged_profile.file_type = self.file_type\\n if self.file_type != other.file_type:\\n merged_profile.file_type = 'multiple files'\\n\\n merged_profile.total_samples = self.total_samples + other.total_samples\\n\\n merged_profile.times = utils.add_nested_dictionaries(self.times,\\n other.times)\\n\\n return merged_profile\",\n \"def _extend(self, other):\\n for key, value in list(other.entries.items()):\\n self._add_entry(key, value)\",\n \"def merge(self, other):\\n extras = other.difference(self)\\n if len(extras) > 0:\\n self.update(extras)\\n self.reset()\\n return True\\n return False\",\n \"def merge_from(self, other):\\n assert not self.is_final\\n if self.parent is not None:\\n assert other.parent is not None\\n self.parent.merge_from(other.parent)\\n self.isolated_names.update(other.isolated_names)\\n self.read.update(other.read)\\n self.modified.update(other.modified)\\n self.bound.update(other.bound)\\n self.deleted.update(other.deleted)\\n self.annotations.update(other.annotations)\\n self.params.update(other.params)\",\n \"def merge(self, graph):\\n # keep previous self.filename\\n # copy data\\n for x in graph.data:\\n self.data.append(x)\\n # copy headers, unless already exists (is so, info is lost)\\n for key in graph.headers:\\n if key not in self.headers:\\n self.headers.update({key: graph.headers[key]})\\n # copy graphInfo, unless already exists (is so, info is lost)\\n for key in graph.graphInfo:\\n if key not in self.graphInfo:\\n self.graphInfo.update({key: graph.graphInfo[key]})\\n # copy sampleInfo, unless already exists (is so, info is lost)\\n for key in graph.sampleInfo:\\n if key not in self.sampleInfo:\\n self.sampleInfo.update({key: graph.sampleInfo[key]})\",\n \"def __add__(self, other):\\n mesh = deepcopy(self)\\n mesh.MergeWith(other)\\n return mesh\",\n \"def add_overlap(self, other):\\n assert isinstance(other, Chunk)\\n overlap_slices = self._get_overlap_slices(other.slices)\\n self.array[overlap_slices] += other.array[overlap_slices]\",\n \"def merge(self, object_class):\\n other_oc = self.schema.get_object_class(object_class)\\n self.required_attrs |= other_oc.required_attrs\\n self.allowed_attrs |= other_oc.allowed_attrs\",\n \"def __add__(self, other):\\n\\n if not isinstance(other, Photons):\\n raise ValueError('Can only add a Photons object to another Photons object.')\\n\\n # don't want to modify what is being added\\n other = other.copy()\\n\\n # make column units consistent with self\\n other.match_units(self)\\n\\n # add and /or update observation columns as necessary\\n self.add_observations_column()\\n other.add_observations_column()\\n n_obs_self = len(self.obs_metadata)\\n other['n'] += n_obs_self\\n\\n # re-reference times to the datum of self\\n other.set_time_datum(self.time_datum)\\n\\n # stack the data tables\\n photons = _tbl.vstack([self.photons, other.photons])\\n\\n # leave it to the user to deal with sorting and grouping and dealing with overlap as they see fit :)\\n obs_metadata = self.obs_metadata + other.obs_metadata\\n obs_times = list(self.obs_times) + list(other.obs_times)\\n obs_bandpasses = list(self.obs_bandpasses) + list(other.obs_bandpasses)\\n\\n return Photons(photons=photons, obs_metadata=obs_metadata, time_datum=self.time_datum, obs_times=obs_times,\\n obs_bandpasses=obs_bandpasses)\",\n \"def _merge(self, other: dict):\\n self._storage = dict_merge(self._storage, other)\",\n \"def put_metadata(self, metadata, tombstone=False):\\n if tombstone:\\n # We don't write tombstone files. So do nothing.\\n return\\n assert self.data_file is not None, \\\\\\n \\\"put_metadata: no file to put metadata into\\\"\\n metadata = _adjust_metadata(metadata)\\n self.threadpool.run_in_thread(write_metadata, self.data_file, metadata)\\n self.metadata = metadata\\n self._filter_metadata()\",\n \"def merge_bom(self, other_bom):\\n for bom_entry in other_bom.entries():\\n self.create_and_add_entry(**bom_entry.attrs)\",\n \"def _update_filesystem_metadata(self, metadata):\\n directory, fname = os.path.split(self.fname)\\n fbase = os.path.splitext(fname)[0]\\n \\n # Test for presence and size of zip file\\n zip_file = fbase + '.zip'\\n zip_path = os.path.join(directory, zip_file)\\n \\n if os.path.isfile(zip_path):\\n location = 'on_disk'\\n data_file_size = os.path.getsize(zip_path)\\n else:\\n location = 'on_tape'\\n data_file_size = 0\\n \\n # Test for presence of quick look PNG file\\n quicklook_file = fbase + '.png'\\n quicklook_path = os.path.join(directory, quicklook_file)\\n \\n if not os.path.isfile(quicklook_path):\\n quicklook_file = ''\\n\\n # Add to metadata dictionary\\n item_map = {'directory': directory, 'metadata_file': fname,\\n 'data_file': zip_file, 'location': location, \\n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\\n \\n for key, value in item_map.items():\\n metadata[key] = value\",\n \"def copy(self, rhs):\\n\\n\\t\\tself.name = rhs.name\\n\\t\\tself.file = rhs.file\\n\\n\\t\\tfor rem in rhs.remark:\\n\\t\\t\\tself.addRemark(rem)\\n\\n\\t\\tself.bk_tot = rhs.bk_tot\\n\\t\\tself.fa_rep = rhs.fa_rep\\n\\t\\tself.fa_atr = rhs.fa_atr\",\n \"def override_from_folder(self, other: ItemVariant) -> None:\\n self.authors.extend(other.authors)\\n self.tags.extend(self.tags)\\n self.vbsp_config = lazy_conf.concat(self.vbsp_config, other.vbsp_config)\\n self.desc = tkMarkdown.join(self.desc, other.desc)\",\n \"def mergeMappingFiles(mapping_files, no_data_value='no_data'):\\r\\n metadata_maps = map(MetadataMap.parseMetadataMap, mapping_files)\\r\\n merged = sum(metadata_maps[1:], metadata_maps[0])\\r\\n merged.no_data_value = no_data_value\\r\\n return merged\",\n \"def merge(self, other):\\n log.debug('Merging: %s and %s' % (self.serialize(), other.serialize()))\\n for k in self.keys():\\n for new_item in other[k]:\\n if new_item not in self[k]:\\n self[k].append(new_item)\\n log.debug('Result: %s' % self.serialize())\\n return self\",\n \"def _update_extra_metadata(self, extra_metadata):\\n self._add_filename_metadata(extra_metadata)\\n self._derive_extra_metadata(extra_metadata)\\n \\n if type(self) == SAFESentinel3:\\n self._extract_metadata_from_zipfile(extra_metadata)\",\n \"def __add__(self, other):\\n if not isinstance(other, Gene):\\n return NotImplemented\\n if self.portion_duration != other.portion_duration:\\n raise ValueError(\\\"The genes must have the same portion_duration.\\\")\\n if self.bias.shape != other.bias.shape:\\n raise ValueError(\\\"The genes must have the same dimension.\\\")\\n\\n new_gene = self.copy()\\n new_gene.bias = (self.weight*self.bias + other.weight*other.bias) \\\\\\n / (self.weight + other.weight)\\n return new_gene\",\n \"def add(self, transaction, markers):\\n if not _enabled:\\n raise util.Abort('obsolete feature is not enabled on this repo')\\n new = [m for m in markers if m not in self._all]\\n if new:\\n f = self.sopener('obsstore', 'ab')\\n try:\\n # Whether the file's current position is at the begin or at\\n # the end after opening a file for appending is implementation\\n # defined. So we must seek to the end before calling tell(),\\n # or we may get a zero offset for non-zero sized files on\\n # some platforms (issue3543).\\n f.seek(0, _SEEK_END)\\n offset = f.tell()\\n transaction.add('obsstore', offset)\\n # offset == 0: new file - add the version header\\n for bytes in _encodemarkers(new, offset == 0):\\n f.write(bytes)\\n finally:\\n # XXX: f.close() == filecache invalidation == obsstore rebuilt.\\n # call 'filecacheentry.refresh()' here\\n f.close()\\n self._load(new)\\n # new marker *may* have changed several set. invalidate the cache.\\n self.caches.clear()\\n return len(new)\",\n \"def update_metadata(self, file_id, metadata):\\n pass\",\n \"def __iadd__(self, other):\\n self.MergeWith(other)\\n return self\",\n \"def add(self, other):\\n self._check_item(other)\\n self._set.add(other)\",\n \"def __add__(self, other):\\n if not isinstance(other, type(self)):\\n raise TypeError(\\\"Only DFs of the same type can be combined.\\\")\\n\\n dfs = self.dfs + other.dfs\\n counts = self.counts + other.counts\\n return self.__class__(dfs, counts)\",\n \"def __add__(self, other):\\n self.__dict__.update(other)\\n return self\",\n \"def merge(self, other: ProjectMeta) -> ProjectMeta:\\n return self.clone(\\n obj_classes=self._obj_classes.merge(other.obj_classes),\\n tag_metas=self._tag_metas.merge(other._tag_metas),\\n )\",\n \"def add(self, *args, **kwargs):\\n self.zipfile.write(*args, **kwargs)\",\n \"def __add__(self, other):\\n if not isinstance(other, Corpus):\\n raise NotImplementedError(\\\"Only a Corpus can be added to another Corpus.\\\")\\n\\n output_corpus = self.clone()\\n for document in other:\\n output_corpus.documents.append(document)\\n output_corpus.documents = sorted(output_corpus.documents)\\n\\n return output_corpus\",\n \"def add_file(self, name, content):\\n self._add_field(struct.pack(\\\"<L\\\", len(name)))\\n self._add_field(name.encode())\\n\\n if self.flags & FLAG_GZIP != 0:\\n content = GzipWrapper(content, retain_cache=self.cache_chunks)\\n elif self.flags & FLAG_LZ4 != 0:\\n content = Lz4Wrapper(content, retain_cache=self.cache_chunks)\\n else:\\n content = NoOpWrapper(content, retain_cache=self.cache_chunks)\\n\\n self._add_field(struct.pack(\\\"<Q\\\", _get_length(content)))\\n self._add_field(content)\",\n \"def _add_to_archive(archive, filename, contents, tstamp):\\n fileinfo = zipfile.ZipInfo(filename, tstamp)\\n fileinfo.external_attr = 0666 << 16L\\n archive.writestr(fileinfo, contents, zipfile.ZIP_DEFLATED)\",\n \"def Add_File(self,tx,filename,newcontents):\\n if tx != self.tx:\\n raise InvalidTransaction(tx)\\n fullname = os.path.join(self.home,filename)\\n h = win32_txf.CreateFileTransacted(fullname,transaction = tx,\\n desired_access = win32_txf.const.GENERIC_WRITE,\\n creation_disposition = win32_txf.const.CREATE_ALWAYS)\\n #TODO handle partial writes\\n win32_txf.WriteFile(h,newcontents)\\n win32_txf.CloseHandle(h)\",\n \"def add_file_metadata(self):\\n metadata = self.__file.require_group(METADATA)\\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\\\"%Y-%m-%d\\\"))\\n self.__write_value(metadata, SDK_VERSION, __version__)\",\n \"def add(self, other):\\n if not isinstance(other, self.__class__):\\n raise ValueError(\\n f\\\"Argument (type {type(other)}) is not a {self.__class__} instance\\\"\\n )\\n if len(other.data):\\n self.data = pd.concat([self.data, other.data], ignore_index=True)\\n self.sort()\",\n \"def combine(self, existing):\\n return self\",\n \"def _extend(self, other_field, memo) -> None:\\n if other_field.data.ndim != self.data.ndim:\\n raise ValueError(\\n f\\\"Field '{self.name}' cannot be extended. Dimensions must be equal. ({other_field.data.ndim} != {self.data.ndim})\\\"\\n )\\n\\n old_id = id(self.data)\\n if self.data.dtype < other_field.data.dtype:\\n # Increase size of self.data.dtype before inserting\\n new_data = np.insert(self.data.astype(other_field.data.dtype), self.num_obs, other_field.data, axis=0)\\n else:\\n new_data = np.insert(self.data, self.num_obs, other_field.data, axis=0)\\n memo[old_id] = (self.data, new_data)\\n self.data = new_data\",\n \"def mergeWith(self, newFL):\\n srcMods = self.srcMods\\n for levls, newLevls in ((self.levcs,newFL.levcs),(self.levis,newFL.levis)):\\n for listId, newLevl in newLevls.items():\\n if listId not in srcMods: \\n srcMods[listId] = [newFL.fileInfo.name]\\n levl = levls[listId] = copy.deepcopy(newLevl)\\n self.records.append(levl)\\n else:\\n srcMods[listId].append(newFL.fileInfo.name)\\n levls[listId].mergeWith(newLevl)\",\n \"def __add__(self, other):\\n raise NotImplementedError(\\\"Implement this if needed\\\")\",\n \"def add(self,**kwargs):\\n if self._extract:\\n raise RuntimeError('This archive is write-only!')\\n\\n items = kwargs.iteritems() if PY2 else kwargs.items()\\n for key,value in items:\\n self._setitem(key,value)\",\n \"def add(self, files, mask):\\n pass\",\n \"def __add__(self, other):\\n assert isinstance(other, HStruct)\\n return HStruct(*self.fields, *other.fields)\",\n \"def merge(self, other: \\\"GraphSet\\\") -> None:\\n if other.name != self.name:\\n raise UnmergableGraphSetsException(\\n f\\\"Unable to merge graph with name {other.name} into {self.name}\\\"\\n )\\n if other.version != self.version:\\n raise UnmergableGraphSetsException(\\n f\\\"Unable to merge graph with version {other.version} into {self.version}\\\"\\n )\\n self.start_time = min(self.start_time, other.start_time)\\n self.end_time = max(self.end_time, other.end_time)\\n self.resources += other.resources\\n self._resolve_duplicates()\\n self.errors += other.errors\\n self.stats.merge(other.stats)\",\n \"def add(self, file):\\n if file not in self._files:\\n self._files.insert(0, file)\\n if len(self._files) > self.nbmax:\\n del(self._files[-1])\\n else:\\n self._files.remove(file)\\n self._files.insert(0, file)\\n try:\\n with open(self._filename, 'w') as file:\\n file.write('\\\\n'.join(self._files))\\n except Exception:\\n # avoid raising errors if location is read-only or invalid path\\n pass\",\n \"def append(self, other, ignore_index=False, verify_integrity=False):\\n new_frame = self.frame.append(other.frame, ignore_index=ignore_index,\\n verify_integrity=verify_integrity)\\n return self.__class__(new_frame)\",\n \"def extend(self,\\n other_matrix,\\n overwrite_existing=False,\\n extend_existing=False):\\n self.taxon_seq_map.extend(other_matrix.taxon_seq_map,\\n overwrite_existing=overwrite_existing,\\n extend_existing=extend_existing)\\n self.update_taxon_set()\",\n \"def Add_File(self,txn,filename,newcontents):\\n opid = self.new_opid()\\n fullname = os.path.join(self.home,filename)\\n #if not self.tx.dir_exists(os.path.dirname(fullname)):\\n # raise OSError(errno.ENOENT,\\\"No directory: %r\\\"%os.path.dirname(fullname))\\n xaction = ReplaceAll_Operation(fullname,newcontents,opid)\\n self._add_operation(txn,xaction)\",\n \"def merge(self, new_video, fields):\\n merged_video = Video(self.event)\\n merged_video.filename = self.filename\\n for field in self.metadata:\\n if field in set(fields):\\n merged_video.metadata[field] = new_video.metadata.get(field)\\n else:\\n merged_video.metadata[field] = self.metadata.get(field)\\n return merged_video\",\n \"def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\\n vertical_reference: str = None):\\n\\n if container_name:\\n if isinstance(container_name, str): # only one converted data instance provided\\n container_name = [container_name]\\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\\n multibeam_file_list = [multibeam_file_list]\\n for contname, mfiles in zip(container_name, multibeam_file_list):\\n if mfiles:\\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\\n elif contname in self.sources:\\n self.sources[contname].extend(mfiles)\\n else:\\n self.sources[contname] = mfiles\\n else:\\n self.sources[contname] = ['unknown']\\n if self.crs and (self.crs != crs):\\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\\n crs))\\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\\n vertical_reference))\\n self.crs = crs\\n self.vertical_reference = vertical_reference\",\n \"def extend(self, other_rollout):\\n\\n assert not self.is_terminal()\\n assert all(k in other_rollout.fields for k in self.fields)\\n for k, v in other_rollout.data.items():\\n self.data[k].extend(v)\\n self.last_r = other_rollout.last_r\",\n \"def add_metadata (self, name, value):\\n self.metadata[name] = value\\n return self\",\n \"def merge(self, db2):\\n delta_db = Database(db2)\\n\\n # Find common headers between the master and delta databases\\n common_headers = [x for x in self.headers if x in delta_db.headers]\\n\\n # Any new headers found in the delta are added to the master\\n self.headers.extend(\\n [x for x in delta_db.headers if x not in self.headers])\\n\\n if len(common_headers) < 1:\\n print(\\\"No shared headers were found. These files cannot be merged.\\\")\\n else:\\n key = ''\\n # Skip picker prompt if there is only one common header\\n if len(common_headers) == 1:\\n key = common_headers[0]\\n else:\\n key = self.headerpicker(common_headers)\\n\\n # Create a temp list for new records to be added to\\n records_temp = list(self.records)\\n\\n # Iterate over new records and attempt to match to existing record\\n for each in delta_db.records:\\n record = self.fetch_record(key, each, records_temp)\\n if record:\\n record.attributes.update(each.attributes)\\n\\n self.records = records_temp\\n print(\\\"Merge successful!\\\\n\\\")\",\n \"def __add__(self, other):\\n other_data = self._setup_numeric(other)\\n new_line = empty_like(self)\\n\\n if isinstance(other, line):\\n other_data = other.data\\n else:\\n other_data = other\\n\\n new_line.data[:] = self.data + other_data\\n\\n return new_line\",\n \"def overwrite_original_file(self):\\n return self.__overwrite_original_file\",\n \"def add(self, name, content):\\n raise NotImplementedError\",\n \"def combine(self, other) -> None:\\n assert self.id_ == other.id_\\n assert self.type_ == other.type_\\n self.count += other.count\",\n \"def merge(self, other):\\n self.isotxsMetadata = self.isotxsMetadata.merge(\\n other.isotxsMetadata, self, other, \\\"ISOTXS\\\", AttributeError\\n )\\n self.gamisoMetadata = self.gamisoMetadata.merge(\\n other.gamisoMetadata, self, other, \\\"GAMISO\\\", AttributeError\\n )\\n self.pmatrxMetadata = self.pmatrxMetadata.merge(\\n other.pmatrxMetadata, self, other, \\\"PMATRX\\\", AttributeError\\n )\\n self.micros.merge(other.micros)\\n self.gammaXS.merge(other.gammaXS)\\n self.neutronHeating = _mergeAttributes(self, other, \\\"neutronHeating\\\")\\n self.neutronDamage = _mergeAttributes(self, other, \\\"neutronDamage\\\")\\n self.gammaHeating = _mergeAttributes(self, other, \\\"gammaHeating\\\")\\n self.isotropicProduction = _mergeAttributes(self, other, \\\"isotropicProduction\\\")\\n self.linearAnisotropicProduction = _mergeAttributes(\\n self, other, \\\"linearAnisotropicProduction\\\"\\n )\\n # this is lazy, but should work, because the n-order wouldn't be set without the others being set first.\\n self.nOrderProductionMatrix = (\\n self.nOrderProductionMatrix or other.nOrderProductionMatrix\\n )\",\n \"def merge(self, other):\\n\\n for child in other.children:\\n self.add_deep_copy_of(child, merged=True)\",\n \"def WriteMetadata(self, metadata, overwrite=True):\\n if not overwrite and 'meta' in metadata:\\n raise errors.KeyczarError('\\\"meta\\\" attribute already exists')\\n self.dict['meta'] = str(metadata)\",\n \"def update(self, raw_other):\\n other = raw_other\\n if isinstance(raw_other, ExtensionList):\\n other = raw_other.extensions.values()\\n for v in other:\\n self.add(v)\",\n \"def extend(self,\\n other_map,\\n overwrite_existing=False,\\n extend_existing=False):\\n if overwrite_existing and extend_existing:\\n raise Exception(\\\"Can only specify to overwrite or append, not both\\\")\\n label_taxon_map = dict([(taxon.label, taxon) for taxon in self])\\n for other_taxon in other_map:\\n if other_taxon.label in label_taxon_map:\\n this_taxon = label_taxon_map[other_taxon.label]\\n if overwrite_existing:\\n self[this_taxon] = other_map[other_taxon]\\n elif extend_existing:\\n self[this_taxon].extend(other_map[other_taxon])\\n else:\\n self[other_taxon] = other_map[other_taxon]\",\n \"def add(input_a, input_b):\\n add_comp = input_b.duplicate()\\n\\n ImageBufAlgo.add(add_comp, input_a, input_b)\\n\\n if add_comp.has_error:\\n print \\\"Error merging adding:\\\", add_comp.geterror()\\n\\n return add_comp\",\n \"def rewrite(self):\\n for f in self.files:\\n metadata = dict()\\n metadata[\\\"description\\\"] = f.metadata.get(\\\"desc\\\", \\\"Unknown\\\")\\n metadata[\\\"script\\\"] = os.path.basename(f.filename)\\n metadata[\\\"requires\\\"] = []\\n for package, component in f.requires:\\n if package == self.key:\\n metadata[\\\"requires\\\"].append(\\\"/\\\" + component)\\n else:\\n metadata[\\\"requires\\\"].append(package + \\\"/\\\" + component)\\n metadata[\\\"provides\\\"] = [ p[1] for p in f.provides ]\\n # Resolve symlinks\\n real_filename = os.path.realpath(f.filename)\\n LOG.info(\\\"Editing: \\\" + real_filename)\\n new_filename = f.filename + \\\".new\\\"\\n new = file(new_filename, \\\"w\\\")\\n new.write(\\\"/*\\\\n---\\\\n\\\")\\n new.write(yaml.dump(metadata))\\n new.write(\\\"\\\\n...\\\\n*/\\\\n\\\")\\n new.write(file(f.filename).read())\\n new.close()\\n os.rename(new_filename, real_filename)\\n\\n package_data = dict()\\n package_data[\\\"name\\\"] = self.key\\n package_data[\\\"sources\\\"] = []\\n package_data[\\\"version\\\"] = \\\"Unknown\\\"\\n package_data[\\\"copyright\\\"] = \\\"Unknown\\\"\\n package_data[\\\"description\\\"] = \\\"Unknown\\\"\\n target_dir = os.path.dirname(self.scripts_json_filename)\\n # package.yml is typically in the parent of the scripts.json dir\\n if os.path.basename(target_dir) == \\\"Source\\\":\\n target_dir = os.path.dirname(target_dir)\\n target_filename = os.path.join(target_dir, \\\"package.yml\\\")\\n for f in self.files:\\n common = os.path.commonprefix([target_filename, f.filename])\\n source_file = f.filename[len(common):]\\n package_data[\\\"sources\\\"].append(source_file)\\n LOG.info(\\\"Writing: \\\" + target_filename)\\n out = file(target_filename, \\\"w\\\")\\n out.write(yaml.dump(package_data))\\n out.close()\",\n \"def combine(self, other) -> None:\\n assert self.linenum == other.linenum\\n assert self.source == other.source\\n if other.coverage >= 0:\\n if self.coverage < 0:\\n self.coverage = other.coverage\\n else:\\n self.coverage += other.coverage\\n for branch in other.values():\\n self.add_branch(branch)\",\n \"def merge(self, other: 'Basket') -> None:\\n for item in other:\\n try:\\n existing = self.items.get(ref=item.ref)\\n existing.quantity += item.quantity\\n existing.save(update_fields=['quantity'])\\n except item.DoesNotExist:\\n item.basket = self\\n item.save(update_fields=['basket'])\\n other.delete()\\n self._cached_items = None\",\n \"def __add__(self, other):\\n\\n self._add_sub_error_checking(other)\\n if (self._counts is None) ^ (other._counts is None):\\n raise SpectrumError(\\n 'Addition of counts-based and CPS-based spectra is ' +\\n 'ambiguous, use Spectrum(counts=specA.counts+specB.counts) ' +\\n 'or Spectrum(cps=specA.cps+specB.cps) instead.')\\n\\n if self._counts is not None and other._counts is not None:\\n kwargs = {'counts': self.counts + other.counts}\\n if self.livetime and other.livetime:\\n kwargs['livetime'] = self.livetime + other.livetime\\n else:\\n warnings.warn('Addition of counts with missing livetimes, ' +\\n 'livetime was set to None.', SpectrumWarning)\\n else:\\n kwargs = {'cps': self.cps + other.cps}\\n spect_obj = Spectrum(\\n bin_edges_kev=self.bin_edges_kev, **kwargs)\\n return spect_obj\",\n \"def reread(self) -> None:\\n old = self.getSubgraph(self.uri)\\n new = Graph()\\n try:\\n contents = open(self.path).read()\\n if contents.startswith(\\\"#new\\\"):\\n log.debug(\\\"%s ignoring empty contents of my new file\\\",\\n self.path)\\n # this is a new file we're starting, and we should not\\n # patch our graph as if it had just been cleared. We\\n # shouldn't even be here reading this, but\\n # lastWriteTimestamp didn't work.\\n return\\n\\n new.parse(location=self.path, format='n3')\\n self.readPrefixes = dict(new.namespaces())\\n except SyntaxError as e:\\n print(e)\\n traceback.print_exc()\\n log.error(\\\"%s syntax error\\\", self.path)\\n # todo: likely bug- if a file has this error upon first\\n # read, I think we don't retry it right.\\n return\\n except IOError as e:\\n log.error(\\\"%s rereading %s: %r\\\", self.path, self.uri, e)\\n return\\n\\n old = inContext(old, self.uri)\\n new = inContext(new, self.uri)\\n\\n p = Patch.fromDiff(old, new)\\n if p:\\n log.debug(\\\"%s applying patch for changes in file\\\", self.path)\\n self.patch(p, dueToFileChange=True)\\n else:\\n log.debug(\\\"old == new after reread of %s\\\", self.path)\",\n \"def __appendMetaData(self, filename):\\n metadata = {'Model': 'LFM',\\n 'Source': filename,\\n 'Date processed': datetime.datetime.now(),\\n 'Start date': self.startDate\\n }\\n \\n self.data.append(key='meta',\\n name='Metadata for LFM Solar Wind file',\\n units='n/a',\\n data=metadata)\",\n \"def combine(self, other):\\n # Copy and merge\\n ppt = PPT()\\n ppt.contents = dict(self.contents)\\n ppt.merge(other)\\n return ppt\",\n \"def merge(self, a, b, path=None):\\n if path is None: path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n if key == 'attributes':\\n self.merge_attribute_defs(b, a)\\n else:\\n self.merge(a[key], b[key], path + [str(key)])\\n elif a[key] == b[key]:\\n pass # same leaf value\\n else:\\n # raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))\\n self.append_or_replace(a,b,key, '/'.join(path + [str(key)]));\\n else:\\n a[key] = b[key]\\n return a\",\n \"def add(self, dest, source=None):\\n raise NotImplementedYet()\",\n \"def __add__(self, other):\\n # can only add using like units\\n if not self.unit.is_compatible(other.unit):\\n raise TypeError('Cannot add two quantities with incompatible units \\\"%s\\\" and \\\"%s\\\".' % (self.unit, other.unit))\\n value = self._value + other.value_in_unit(self.unit)\\n unit = self.unit\\n return Quantity(value, unit)\",\n \"def __add__(self, other):\\n\\n if isinstance(other, type(self)):\\n # always create new fields, since otherwise c = a - b changes a as well!\\n p = fields(self)\\n p.elec[:] = self.elec + other.elec\\n p.magn[:] = self.magn + other.magn\\n return p\\n else:\\n raise DataError(\\\"Type error: cannot add %s to %s\\\" % (type(other), type(self)))\",\n \"def _StageMetadata(json_metadata, storage_service, staged_file: str):\\n # Write computed metadata to object storage.\\n temp_run_dir = temp_dir.GetRunDirPath()\\n local_file = os.path.join(temp_run_dir, os.path.basename(staged_file))\\n with open(local_file, 'w') as f:\\n json.dump(json_metadata, f)\\n storage_service.Copy(local_file, staged_file)\",\n \"def __eq__(self, other):\\n if not isinstance(other, SharedFileMetadata):\\n return False\\n\\n return self.to_dict() == other.to_dict()\",\n \"def test_MergeManifests_file_attributes():\\n d1 = dpack_pb2.DataPackage()\\n f1 = d1.file.add()\\n f1.relative_path = \\\"a\\\"\\n f1.size_in_bytes = 1\\n f1.checksum_hash = dpack_pb2.SHA1\\n f1.checksum = \\\"abc\\\"\\n d2 = dpack_pb2.DataPackage()\\n f2 = d2.file.add()\\n f2.relative_path = \\\"a\\\"\\n f2.size_in_bytes = 2\\n f2.checksum_hash = dpack_pb2.MD5\\n f2.checksum = \\\"def\\\"\\n dpack.MergeManifests(d1, d2)\\n assert d1.file[0].size_in_bytes == 1\\n assert d1.file[0].checksum_hash == dpack_pb2.SHA1\\n assert d1.file[0].checksum == \\\"abc\\\"\",\n \"def addFromFile(self, metadataProfileId, objectType, objectId, xmlFile):\\n\\n kparams = KalturaParams()\\n kparams.addIntIfDefined(\\\"metadataProfileId\\\", metadataProfileId);\\n kparams.addStringIfDefined(\\\"objectType\\\", objectType)\\n kparams.addStringIfDefined(\\\"objectId\\\", objectId)\\n kfiles = {\\\"xmlFile\\\": xmlFile}\\n self.client.queueServiceActionCall(\\\"metadata_metadata\\\", \\\"addFromFile\\\", \\\"KalturaMetadata\\\", kparams, kfiles)\\n if self.client.isMultiRequest():\\n return self.client.getMultiRequestResult()\\n resultNode = self.client.doQueue()\\n return KalturaObjectFactory.create(resultNode, 'KalturaMetadata')\",\n \"def git_add(self, filepath):\\n filepath = os.path.normpath(filepath)\\n self.repo.index.add([filepath])\\n\\n return filepath\",\n \"def overwrite_original_file(self, value):\\n self.__overwrite_original_file = value\",\n \"def add_file(self, field_name, file_name, file_content, mimetype=None):\\n if mimetype is None:\\n mimetype = mimetypes.guess_type(file_name)[0] or 'application/octet-stream'\\n self.files.append((field_name, file_name, mimetype, file_content))\\n return\",\n \"def _addOurselvesToPackage(self, oldPath):\\n siblings = self._package.resources.setdefault(self.checksum, [])\\n if siblings:\\n newName = siblings[0]._storageName\\n if oldPath.dirname() == self._package.resourceDir and self._storageName != newName:\\n oldPath.remove()\\n self._storageName = newName\\n else:\\n if Path(oldPath).dirname() == self._package.resourceDir:\\n log.debug(u\\\"StorageName=%s was already in self._package resources\\\" % self._storageName)\\n else:\\n filename = (self._package.resourceDir/oldPath.basename())\\n storageName = self._fn2ascii(filename)\\n storageName = (self._package.resourceDir/storageName).unique()\\n self._storageName = str(storageName.basename())\\n oldPath.copy(self.path)\\n siblings.append(self)\",\n \"def __finalize__(self, other, method=None, **kwargs):\\n self = super().__finalize__(other, method=method, **kwargs)\\n # merge operation: using metadata of the left object\\n if method == \\\"merge\\\":\\n for name in self._metadata:\\n print(\\\"self\\\", name, self.au_columns, other.left.au_columns)\\n object.__setattr__(self, name, getattr(other.left, name, None))\\n # concat operation: using metadata of the first object\\n elif method == \\\"concat\\\":\\n for name in self._metadata:\\n object.__setattr__(self, name, getattr(other.objs[0], name, None))\\n return self\",\n \"def __add__(self, other, inplace=False, **kwargs):\\n output = super(HERAData, self).__add__(other, inplace=inplace, **kwargs)\\n if inplace:\\n output = self\\n output._determine_blt_slicing()\\n output._determine_pol_indexing()\\n if not inplace:\\n return output\",\n \"def add(self, other, offset=(0,0)):\\n if (isinstance(other, Scene)):\\n for item in other.items:\\n newitem = item.clone()\\n newitem.pan(offset)\\n self.items.add(newitem)\\n elif (isinstance(other, SvgObject)):\\n newitem = other.clone()\\n newitem.pan(offset)\\n self.items.add(newitem)\",\n \"def add_local_changes(self, skip_validation=False):\\n if skip_validation:\\n logger.warning(\\n \\\"Skipping index.json check. Please submit a PR if the schema should be changed.\\\"\\n )\\n elif not self.validate_index():\\n logger.error(\\\"Not adding changes to index.json. Doesn't match schema.\\\")\\n logger.error(\\n \\\"You may be able to skip this check with '--skip-validation' if you're sure your file conforms.\\\"\\n )\\n exit(-1)\\n return subprocess.call(\\n [\\\"git\\\", \\\"add\\\", \\\"index.json\\\", \\\"associated_files\\\"], cwd=self.path\\n )\",\n \"def Merge(self, other):\\n\\n # Logging just in case\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: before\\\", %s);'\\n %(self.persistant['id'],\\n sql.FormatSqlValue('details',\\n repr(self.persistant))))\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: deleted\\\", %s);'\\n %(other.persistant['id'], \\n sql.FormatSqlValue('details',\\n repr(other.persistant))))\\n\\n # Fields which can be summed\\n for f in ['plays', 'skips']:\\n self.persistant[f] = (self.persistant.get(f, 0) +\\n other.persistant.get(f, 0))\\n\\n # Date fields where we take the newest\\n for f in ['last_played', 'last_skipped', 'last_action']:\\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\\n if a > b:\\n v = a\\n else:\\n v = b\\n if v != datetime.datetime(1970, 1, 1):\\n self.persistant[f] = v\\n\\n # Date fields where we take the oldest\\n for f in ['creation_time']:\\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\\n if a < b:\\n v = a\\n else:\\n v = b\\n if v != datetime.datetime(1970, 1, 1):\\n self.persistant[f] = v\\n\\n # Fields where we only clobber ours if we don't have a value\\n for f in ['artist', 'album', 'song']:\\n if not self.persistant.has_key(f) or not self.persistant[f]:\\n self.persistant[f] = other.persistant.get(f, None)\\n\\n # Sometimes the number is a placeholder\\n if self.persistant.has_key('number') and self.persistant['number'] == -1:\\n self.persistant['number'] = other.persistant.get('number', -1)\\n if not self.persistant.has_key('number'):\\n self.persistant['number'] = other.persistant.get('number', -1)\\n\\n # Update the id in the tags table\\n tags = self.db.GetRows('select tag from tags where track_id=%d;'\\n % other.persistant['id'])\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: tags: %d\\\", %s);'\\n %(self.persistant['id'], other.persistant['id'],\\n sql.FormatSqlValue('details', repr(tags))))\\n\\n try:\\n self.db.ExecuteSql('update tags set track_id=%d where track_id=%d;'\\n %(self.persistant['id'], other.persistant['id']))\\n self.db.ExecuteSql('commit;')\\n except:\\n # This can happen if the is already a matching tag for the first track\\n pass\\n\\n # Update the id in the paths table\\n paths = self.db.GetRows('select path from paths where track_id=%d;'\\n % other.persistant['id'])\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: paths: %d\\\", %s);'\\n %(self.persistant['id'], other.persistant['id'],\\n sql.FormatSqlValue('details', repr(paths))))\\n \\n self.db.ExecuteSql('update paths set track_id=%d where track_id=%d;'\\n %(self.persistant['id'], other.persistant['id']))\\n self.db.ExecuteSql('commit;')\\n\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: after\\\", %s);'\\n %(self.persistant['id'],\\n sql.FormatSqlValue('details',\\n repr(self.persistant))))\\n self.db.ExecuteSql('commit;')\",\n \"def add_new_file(self, filename):\\n self.load_config()\\n if not self.__is_file_in_working_directory(filename):\\n path = os.path.join(self.__directory.working_path,\\n filename)\\n raise FileNotFoundError(f\\\"No such file '{filename}' in '{path}'!\\\")\\n self.__indexed_files.add(filename)\\n files = self.config['info']['files']\\n self.config['info']['files'] = f'{files},{filename}'.strip(',')\\n source_file = os.path.join(self.__directory.working_path, filename)\\n file_copy = os.path.join(self.__directory.index_path, filename)\\n copyfile(source_file, file_copy)\\n print(f'File {source_file} added')\\n self.save_config()\",\n \"def file_append(filepath,contents):\\n with open(filepath, 'a') as f:\\n f.write(contents+'\\\\n')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6023381","0.5716449","0.5619332","0.5605447","0.5589624","0.5504831","0.54868376","0.546581","0.5444086","0.54176193","0.54172623","0.53960514","0.5354634","0.52778107","0.5238899","0.52343297","0.5198953","0.5158326","0.5157256","0.51571655","0.5131289","0.5107338","0.51068974","0.5067274","0.50561666","0.5039219","0.50357765","0.5025296","0.50171447","0.5012051","0.49931005","0.49697816","0.49685758","0.49651232","0.49600354","0.49487776","0.49331158","0.49270442","0.49205473","0.4916109","0.4905059","0.49004015","0.48915985","0.48878258","0.48775002","0.4870149","0.4859441","0.48521826","0.48472294","0.48434636","0.48277965","0.48206583","0.48168492","0.4812389","0.48112497","0.48077828","0.47991866","0.47978178","0.47935838","0.4791922","0.4789635","0.47819105","0.4780454","0.4774554","0.47723517","0.47714797","0.4769202","0.47621286","0.47596073","0.47571003","0.47513905","0.47497913","0.4743689","0.4735085","0.4731994","0.47317573","0.47148377","0.4712177","0.470994","0.47091702","0.47067487","0.47053534","0.47043246","0.4700607","0.469145","0.46871942","0.4682349","0.46805808","0.4677307","0.46753034","0.46740386","0.46678922","0.46632886","0.46466097","0.46428716","0.46356073","0.46349418","0.46309698","0.46289268","0.462891"],"string":"[\n \"0.6023381\",\n \"0.5716449\",\n \"0.5619332\",\n \"0.5605447\",\n \"0.5589624\",\n \"0.5504831\",\n \"0.54868376\",\n \"0.546581\",\n \"0.5444086\",\n \"0.54176193\",\n \"0.54172623\",\n \"0.53960514\",\n \"0.5354634\",\n \"0.52778107\",\n \"0.5238899\",\n \"0.52343297\",\n \"0.5198953\",\n \"0.5158326\",\n \"0.5157256\",\n \"0.51571655\",\n \"0.5131289\",\n \"0.5107338\",\n \"0.51068974\",\n \"0.5067274\",\n \"0.50561666\",\n \"0.5039219\",\n \"0.50357765\",\n \"0.5025296\",\n \"0.50171447\",\n \"0.5012051\",\n \"0.49931005\",\n \"0.49697816\",\n \"0.49685758\",\n \"0.49651232\",\n \"0.49600354\",\n \"0.49487776\",\n \"0.49331158\",\n \"0.49270442\",\n \"0.49205473\",\n \"0.4916109\",\n \"0.4905059\",\n \"0.49004015\",\n \"0.48915985\",\n \"0.48878258\",\n \"0.48775002\",\n \"0.4870149\",\n \"0.4859441\",\n \"0.48521826\",\n \"0.48472294\",\n \"0.48434636\",\n \"0.48277965\",\n \"0.48206583\",\n \"0.48168492\",\n \"0.4812389\",\n \"0.48112497\",\n \"0.48077828\",\n \"0.47991866\",\n \"0.47978178\",\n \"0.47935838\",\n \"0.4791922\",\n \"0.4789635\",\n \"0.47819105\",\n \"0.4780454\",\n \"0.4774554\",\n \"0.47723517\",\n \"0.47714797\",\n \"0.4769202\",\n \"0.47621286\",\n \"0.47596073\",\n \"0.47571003\",\n \"0.47513905\",\n \"0.47497913\",\n \"0.4743689\",\n \"0.4735085\",\n \"0.4731994\",\n \"0.47317573\",\n \"0.47148377\",\n \"0.4712177\",\n \"0.470994\",\n \"0.47091702\",\n \"0.47067487\",\n \"0.47053534\",\n \"0.47043246\",\n \"0.4700607\",\n \"0.469145\",\n \"0.46871942\",\n \"0.4682349\",\n \"0.46805808\",\n \"0.4677307\",\n \"0.46753034\",\n \"0.46740386\",\n \"0.46678922\",\n \"0.46632886\",\n \"0.46466097\",\n \"0.46428716\",\n \"0.46356073\",\n \"0.46349418\",\n \"0.46309698\",\n \"0.46289268\",\n \"0.462891\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.649641"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94097,"cells":{"query":{"kind":"string","value":"Serialize annotation as a JSON formatted stream to file."},"document":{"kind":"string","value":"def save(self, path_or_file, strict=True, fmt='auto'):\n\n self.validate(strict=strict)\n\n with _open(path_or_file, mode='w', fmt=fmt) as fdesc:\n json.dump(self.__json__, fdesc, indent=2)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dump_annotations(self):\n fname = 'annotations'\n if self.split is not None:\n fname = 'annotations_{}'.format(self.split)\n fname = os.path.join(self.dest_folder, '{}.json'.format(fname))\n self.save(self.dataset, fname, \"annotations\")","def _save_annotation(self, purpose):\n with open('data\\\\annotation_{}.json'.format(purpose), 'w', encoding='utf-8') as make_file:\n json.dump(self.json_list, make_file, ensure_ascii= False, indent= 4)\n self.json_list.clear()","def saveAnnsToDisk(self, ann_file=None):\n\n if ann_file is None:\n ann_file = self.annotation_file\n\n with open(ann_file, 'w') as fp:\n json.dump(self.dataset, fp)","def write(self, path):\n\n annotation = copy.deepcopy(self.annotation)\n\n for image_info in annotation['images']:\n image_info['file_name'] = os.path.relpath(image_info['file_name'],\n os.path.dirname(path))\n\n with open(path, 'w') as read_file:\n json.dump(annotation, read_file)","def serialize_file(cls, obj, file_path='./data.json'):\n file_stream = open(file_path, 'wb')\n json.dump(obj, file_stream, cls=CustomTypeEncoder)\n file_stream.flush()\n file_stream.close()","def to_json(fn, obj):\n with open(fn, 'w') as f:\n json.dump(obj, f, cls=OperatorEncoder, indent=2, ensure_ascii=False)\n return fn","def save_annotation(self, drivename, fname, json_str):\n\t\tassert type(json_str) == str, \"json must be a string\"\n\t\tif not isdir(self.OUTPUT_ANN_DIR):\n\t\t\ttry: \n\t\t\t makedirs(self.OUTPUT_ANN_DIR)\n\t\t\texcept OSError: \n\t\t\t print (\"Creation of the directory {} failed\".format(self.OUTPUT_ANN_DIR))\n\n\t\toutput_drive_dir = join(self.OUTPUT_ANN_DIR, drivename)\n\t\tif not isdir(output_drive_dir):\n\t\t\ttry: \n\t\t\t makedirs(output_drive_dir)\n\t\t\texcept OSError: \n\t\t\t print (\"Creation of the directory {} failed\".format(output_drive_dir))\n\n\t\ttry:\n\t\t\tjson_object = json.loads(json_str)\n\t\texcept ValueError:\n\t\t\tprint(\"Annotation not a valid json\")\n\t\t\treturn 0\n\t\tfname = fname.split(\".\")[0] + \".json\"\n\t\tsave_filename = join(self.OUTPUT_ANN_DIR, drivename, fname)\n\t\twith open(save_filename, \"w\") as f:\n\t\t\tf.write(json_str)\n\t\t\treturn 1\n\t\treturn 0","def save_annotations(self):\n r = requests.get(\n f'{self.api_host}/v1/entity-annotations?'\n f'annotation_type=Source reliability (binary)&size=100',\n headers=self.get_request_headers()\n )\n\n entity_annotations = r.json().get('entity_annotations')\n\n for annotation in entity_annotations:\n annotation_id = annotation.get('entity_id')\n with open(\n f'{self.data_folder}/annotations/{annotation_id}.json',\n 'w'\n ) as f:\n json.dump(annotation, f)","def to_file(self, fp):\n dict_ = self.serialize()\n with open_file(fp, mode='w') as writer:\n json.dump(dict_, writer, indent=2)","def write_json(self, obj, mode='wb', **kwargs):\n with self.open(mode) as f:\n return json.dump(obj, f, **kwargs)","def to_json_file(self, path):\n with open(path, 'w') as f:\n f.write(self.to_json())","def to_json(self, fname):\n fname = enforce_extension(fname, \".json\")\n write_json(self.as_dict, fname)","def save_file(obj, file_path):\n with gzip.open(file_path, \"wb\") as fp:\n fp.write(json.dumps(obj).encode('utf-8'))","def _to_json(self, fp=None, indent=4):\n if fp is None:\n return json.dumps(self.to_json_compat(), indent=indent)\n else:\n json.dump(self.to_json_compat(), fp, indent=indent)","def write(self, fp, **kwds):\n json.dump(self._dict, fp)","def write_json(self, f, **kw_json):\n wntr.network.io.write_json(self, f, **kw_json)","def to_json(self, path):\n with open(path, 'w') as out_file:\n json.dump(self._metadata, out_file, indent=4)","def write(self):\n self.json_o.write()","def write_json_file(self, fname, content):\n pass","def write_json_file(self, fname, content):\n pass","def to_json(self, fpath):\n import json\n with open(fpath, 'w') as fp:\n json.dump(self.to_dict(), fp)","def _write_json(self):\n with open(self._file_path, 'w') as f:\n json.dump(self._content, f, indent=4, separators=None,\n encoding='utf-8', sort_keys=False)","def to_json(obj: ConfiguredBaseModel, file: str):\n if file:\n with open(file, \"w\") as f:\n f.write(obj.json(indent=4))\n console.print(f\"\\nOutput written to {file}\\n\")\n else:\n print_json(obj.json(indent=4))","def write_to_json(self, export_fp: str):\n # TODO:\n pass","def save_to_json_file(my_obj, filename):\n with open(filename, mode='w') as file:\n file.write(json.dumps(my_obj))","def save_to_json_file(my_obj, filename):\n with open(filename, 'w', encoding='utf-8') as file:\n return file.write(json.dumps(my_obj))","def write_complex_json(filepath, obj):\n\n with open(filepath, 'w', encoding='utf-8') as file_obj:\n json.dump(obj, file_obj, cls=ExtendedEncoder, ensure_ascii=False, indent=2)","def save_to_json_file(my_obj, filename):\n with open(filename, mode=\"w\", encoding=\"utf-8\") as writer:\n json.dump(my_obj, writer)","def save_to_json_file(my_obj, filename):\n with open(filename, \"w\") as myfile:\n return myfile.write(json.dumps(my_obj))","def write(self, _filepath=None):\n _json_txt = json.dumps(self.json_dict, indent=2)\n self._write_json_text(_json_txt, _filepath)","def save_to_json_file(my_obj, filename):\n with open(filename, \"w\") as f:\n j = json.dumps(my_obj)\n f.write(j)\n f.close()","def save(self, filename_or_stream: str | PathLike | TextIO) -> None:\n if isinstance(filename_or_stream, (str, PathLike)):\n p = Path(filename_or_stream)\n if not p.suffix:\n p = p.with_suffix(\".json\")\n s: TextIO = open(p, \"w\")\n close = True\n else:\n s = filename_or_stream\n close = False\n\n json.dump(self._unstructure(), s, indent=2, ensure_ascii=True)\n if close:\n s.close()","def to_json_file(self, json_file_path):\n with open(json_file_path, 'w', encoding='utf-8') as writer:\n writer.write(self.to_json_string())","def _write_annotations(self, anns, rec_id, out_handle):\n format_anns = self._format_keyvals(anns)\n if format_anns:\n print repr(format_anns)\n parts = [rec_id, \"annotation\", \"remark\", \".\", \".\", \".\", \".\", \".\",\n format_anns]\n out_handle.write(\"\\t\".join(parts) + \"\\n\")","def write_json(obj_to_write: Any, filename: str):\n \n with open(filename, 'w') as json_file:\n json.dump(obj_to_write, json_file, indent=4)","def to_json_file(self, json_file_path):\n with open(json_file_path, \"w\", encoding=\"utf-8\") as writer:\n writer.write(self.to_json_string())","def save_to_json_file(my_obj, filename):\n with open(filename, \"w\", encoding=\"utf-8\") as opening:\n json.dump(my_obj, opening)","def save_to_json_file(my_obj, filename):\n import json\n with open(filename, 'w', encoding='utf-8') as f:\n obj = json.dumps(my_obj)\n f.write(obj)","def to_file(self, file_name: str) -> None:\n\n with open(file_name, 'w') as fi:\n json.dump(self.to_dict(), fi, indent=1)","def write(self, stream, root, order):\n json.dump(root, stream, indent=2)\n stream.write('\\n')","def dump(self, stream):\n items = (\n ('time', self.time),\n ('inc', self.inc),\n )\n # use ordered dict to retain order\n ts = collections.OrderedDict(items)\n json.dump(dict(ts=ts), stream)","def save_as_json(self, file_path):\n with open(file_path, 'w') as fd:\n json.dump(self._ngrams, fd)","def write_annotation(self, ann_file, img_path, new_img_name):\n if self.type == \"imagenet\":\n label = self.in_annotations[img_path]\n logger.debug(f\"Img {img_path}, imagenet label {label}\")\n ann_file.write(str(label) + \"\\n\")\n elif self.type == \"coco\":\n ann_file.write(\"detection_results {\\n\")\n for obj in self.in_annotations[img_path].keys():\n ann_file.write(\" objects {\\n\")\n ann_file.write(f\" class_id: {self.in_annotations[img_path][obj]['label']}\\n\")\n ann_file.write(\" bounding_box {\\n\")\n ann_file.write(f\" normalized_top: {self.in_annotations[img_path][obj]['normalized_bbox'][0]}\\n\")\n ann_file.write(f\" normalized_bottom: {self.in_annotations[img_path][obj]['normalized_bbox'][1]}\\n\")\n ann_file.write(f\" normalized_left: {self.in_annotations[img_path][obj]['normalized_bbox'][2]}\\n\")\n ann_file.write(f\" normalized_right: {self.in_annotations[img_path][obj]['normalized_bbox'][3]}\\n\")\n ann_file.write(\" }\\n\")\n ann_file.write(\" }\\n\")\n ann_file.write(f' image_name: \"{new_img_name}\"\\n')\n ann_file.write(f' image_id: {int(new_img_name.split(\".\")[0])}\\n')\n ann_file.write(\"}\\n\")","def toFile(self, file_path) -> None:\n\t\tjson_repr = self.toJSON()\n\t\t\n\t\twith open(file_path, \"w\") as f:\n\t\t\tf.write(json_repr)","def _save_annotation(annotation, filename):\n\n pil_image = Image.fromarray(annotation.astype(dtype=np.uint8))\n '''\n with tf.io.gfile.GFile(filename, mode='w') as f:\n #with open(filename, mode='w') as f:\n print(f)\n pil_image.save(f, 'PNG')\n '''\n pil_image.save(filename)","def to_json(self, path):\n with open(path, \"w\") as dump:\n json.dump(self.__dict__, dump)","def save_to_json_file(my_obj, filename):\n import json\n with open(filename, mode='w', encoding='utf-8') as f:\n json.dump(my_obj, f)","def dict_2_json(obj, filename):\n\twith open('data/output/' + filename, 'w') as fp:\n\t\tjson.dump(obj, fp, indent=4)","def serialize(self):\n keys = [\n 'uid',\n 'commit_sha',\n 'timestamp',\n 'filename',\n 'comment',\n 'train_data',\n 'val_data',\n 'test_data',\n 'model_files',\n 'custom_data',\n ]\n data = {key: self.__dict__[key] for key in keys}\n with open(os.path.join(self.root_path, self._data_file), 'w') as file:\n json.dump(data, file)","def save_to_json_file(my_obj, filename):\n with open(filename, 'w') as file:\n json.dump(my_obj, file)","def export_annotated_reads(parser):\n outfile = os.path.join(\n parser.options.get_project_dir(parser.sample.sample_id),\n parser.sample.sample_id + '_' + parser.end + '_' + parser.options.reads_json_name\n )\n # print pretty JSON: print(json.dumps(parser.reads,indent=4, cls=CustomEncoder))\n with open(outfile, 'w') as out:\n json.dump(parser.reads, out, cls=CustomEncoder)","def json(filepath, mode=DataSaver.MODE_OVERWRITE, date_format=None,\n double_precision=10, force_ascii=True, date_unit='ms'):\n format_file = DataSaver.FORMAT_JSON\n kwargs = locals()\n _apply_datasaver(format_file, kwargs, last_uuid)\n return None","def to_json(self, filename, indent=2):\n d = self.to_dicts()\n if hasattr(filename, \"write\"):\n json.dump(d, filename, indent=indent)\n elif isinstance(filename, str):\n with open(os.path.expanduser(filename), \"w\") as f:\n json.dump(d, f, indent=indent)\n else:\n raise ValueError(\"Unknown filename or file-object\")","def save_to_json_file(my_obj, filename):\n import json\n with open(filename, 'w') as file:\n json.dump(my_obj, file)","def save_digraph_json(graph: nx.DiGraph, f: Union[IO, os.PathLike, str]):\n # Obtain the JSON object to serialize\n json_obj = json.dumps(node_link_data(graph))\n\n # Save the object\n if isinstance(f, (os.PathLike, str)):\n with open(f, 'w', encoding='utf-8') as file:\n file.write(json_obj)\n else:\n f.write(json_obj)","def save_annotations_pickle(annotations: dict, outfile: str):\n with open(outfile, 'wb') as pkl:\n pickle.dump(annotations, pkl)","def save(self, data, outpath):\n with open(path, \"wt\") as open_file:\n json.dump(data, open_file, indent=4)","def to_json(self): \n raise NotImplementedError('MediaIoBaseUpload is not serializable.')","def save_to_json_file(my_obj, filename):\n with open(filename, 'w+') as json_file:\n json.dump(my_obj, json_file)","def to_json(self, path_to_json: str):\n with open(path_to_json, \"w\") as _f:\n json.dump(self.data, _f)","def save_to_json_file(my_obj, filename):\n with open(filename, 'w') as json_file:\n written = json_file.write(json.dumps(my_obj))\n return written","def write_json(obj, fpath):\n mkdir_if_missing(osp.dirname(fpath))\n with open(fpath, 'w', encoding='utf-8') as f:\n json.dump(obj, f, indent=4, separators=(',', ': '), ensure_ascii=False) # 添加中文支持","def save_to_json(doc: object, text: str, export_button: st.columns):\n # Get named entities and their labels\n entities = []\n for ent in doc.ents:\n entities.append([ent.start_char, ent.end_char, ent.label_])\n # Define the annotations in the desired format\n annotations = [[text, {\"entities\": entities}]]\n # Create a dictionary to represent the JSON structure\n to_json = {\n \"classes\": [\"TEMP\", \"SOFT\", \"STIME\", \"MOL\", \"FFM\"],\n \"annotations\": annotations,\n }\n # Export in JSON format\n json_file = json.dumps(to_json)\n # Get the current date\n current_date = date.today().strftime(\"%Y-%m-%d\")\n # Create the file name with the current date\n filename = f\"annotations_{current_date}.json\"\n with export_button:\n st.download_button(\n label=\"Export to json\",\n file_name=filename,\n mime=\"application/json\",\n data=json_file,\n )","def annotations_to_json(eaf_dir, json_dir):\n for file in os.listdir(eaf_dir):\n if file.endswith(\".eaf\"):\n print(\"converting\", file, \"to json\")\n file_name = os.path.join(json_dir, file[:-4]) + \".json\"\n file = os.path.join(eaf_dir, file)\n file_elan = Elan.Eaf(file)\n\n # Get all the data under the engagement_tier tier\n annotation_data = file_elan.get_annotation_data_for_tier(\"engagement_tier\")\n labels_for_annotation = elan_annotation_to_binary(annotation_data)\n\n # Create a json file storing the dictionary of {\"timeslot1,timeslot2\": 0/1(engaged/disengaged)}\n j = json.dumps(labels_for_annotation)\n f = open(file_name, \"w\")\n f.write(j)\n f.close()","def writeLinkoJson(linkograph, file):\n # Convert the linkograph into a form json can use.\n jsonLinko = [linkograph.labels]\n\n \"\"\"\n for entry in linkograph:\n tolist = list(map(list, entry))\n jsonLinko.append(tolist)\n \"\"\"\n for index in range(len(linkograph)):\n tolist = list(map(list, linkograph[index]))\n tolist.append(linkograph.uuids[index])\n jsonLinko.append(tolist)\n\n with open(file, 'w') as jsonFile:\n json.dump(jsonLinko, jsonFile, indent=4)","def json(self):\n return {\n \"x\" : self.x,\n \"y\": self.y,\n \"rotation\":self.rotation,\n \"annotations\" : [a.json() for a in self.annotations]\n }","def save_data(file_to_save, object_to_serialize):\r\n with open(file_to_save, \"w\", encoding=\"utf-8\") as f:\r\n f.write(json.dumps(object_to_serialize, indent=2, ensure_ascii=False))","def writeToJson(inputObj,fileLoc):\n myFile = open(fileLoc,'w')\n json.dump(inputObj, myFile, sort_keys=True, indent=4, separators=(',', ': '))","def write_json(self, filename):\n with open(filename, 'a+') as f:\n f.write(json.dumps(self.weights))\n f.write(\"\\n\")","def save_json(data):\n data = json.dumps(data)\n\n with MEDTADATA_FILE.open('w') as outfile:\n outfile.write(data)","def save(self, filename):\n content = self.to_dict()\n with open(filename, 'w') as f:\n json.dump(content, f)","def write_to_json(self, file_name, jayson, mode='w', prep_for_BQ=False):\n if prep_for_BQ == True:\n jayson = self.fix_json_keys(jayson, self.prep_json_for_BQ_callback)\n\n with open(file_name, mode) as f:\n for line in jayson:\n f.writelines(json.dumps(line) + '\\n')","def save(self, file):\n\n import json\n\n if hasattr(file, 'write'):\n f = file\n else:\n f = open(file, 'w')\n output = json.dumps(self, default=lambda v: v.__dict__)\n f.write(output)\n f.close()","def to_json_file(self, json_file: str = None) -> None:\n\n if self.json:\n if not json_file:\n json_file = f\"{self.id}.json\"\n\n with open(json_file, \"w\") as f:\n f.write(self.json)","def as_json(self) -> str:\n return json.dumps(self, cls=_RecordingJSONEncoder)","def to_json_file(self, path: Union[str, Path]):\n data_dict = self._to_list_dict()\n return json.dump(data_dict, Path(path).open(\"w\", encoding=\"utf8\"), cls=NumpyEncoder, indent=4)","def _save(self):\n with open(self.file_path, 'w') as fid:\n json.dump(self.data, fid, indent=4, sort_keys=True)","def save(self):\n with open(self.file_path, 'w', encoding=Config.ENCODING) as file:\n json.dump(self.data, file, indent=2, ensure_ascii=False)","def write_json(data: Dict[str, Annotations], file: str):\n if not os.path.exists(file):\n with open(file, 'w', encoding='utf-8') as output_file:\n json_data = dict(map(lambda kv: (kv[0], kv[1].encode()), data.items()))\n json.dump(json_data, output_file, ensure_ascii=False, indent=4)\n else:\n print('Signature file `{}` already exists.'.format(file))\n sys.exit(-1)","def to_json(self, filepath, pretty=False):\n # PATH = os.path.abspath(os.path.join(filepath, '..', 'data'))\n with open(filepath, 'w+') as fp:\n if pretty:\n json.dump(self.data, fp, sort_keys=True, indent=4)\n else:\n json.dump(self.data, fp)","def to_json(arr_like, fname):\n arr = array(arr_like).tolist()\n with open(fname, 'w')as fh:\n fh.write(json.dumps(arr, indent=4))","def save_json(self, file: Union[str, TextIO]) -> None:\n if hasattr(file, 'write'):\n file_ctx = nullcontext(file)\n else:\n file_ctx = open(file, 'w')\n\n with file_ctx as fp:\n for d in self:\n json.dump(d.dict(), fp)\n fp.write('\\n')","def dump(obj, fp, *args, **kwargs): \n defaults = {\"indent\": 4}\n defaults.update(kwargs)\n json.dump(serialize(obj), fp, *args, **defaults)","def save_json(file_name, file_content):\n with open(generate_file_path(\"output\", file_name), 'w', encoding='utf-8') as f:\n json.dump(file_content, f, ensure_ascii=False, indent=4)","def to_json(self, filename, complete=False):\n # Get metadata.\n data = self.to_dict(complete=complete)\n\n # Write to json file.\n with open(filename, \"w\") as f:\n json.dump(data, f)","def save_json(self, file):\n with open(file, 'w', encoding='utf8') as f:\n json.dump(self, f, ensure_ascii=False)","def to_json_file(self, file_path, **kwargs):\n try:\n with Params._open_file(file_path, \"w\") as fp:\n json.dump(dict(self), fp, **kwargs)\n return file_path\n except Exception as err:\n print(\"Failed to write {} instance to: {}\".format(self.__class__.__name__, file_path), err)\n return None","def serialize(self, to_file=None):\n raise NotImplementedError","def write(fname, data):\n # Encode to string.\n encoder = NumpyJSONEncoder(check_circular=True, indent=' ')\n serial = encoder.encode(data)\n\n # Write to file.\n with open(fname, 'w') as fo:\n fo.write(serial)","def save(self, filepath):\n with open(filepath, 'w') as f:\n json.dump(self, f, indent=2)","def to_json(self, filepath=None):\n obj = {'history': self.resp,\n 'details': {k: v.to_json() for k, v in self.details.items()}\n }\n if filepath is None:\n return json.dumps(obj)\n else:\n with open(filepath) as f:\n json.dump(obj, f)","def serialize(policy_spec, file_path):\n if not file_path:\n log.error('File path is empty')\n abs_path = os.path.abspath(file_path)\n data = parse_policy_spec(policy_spec)\n f = open(abs_path, 'w')\n json.dump(data, f, indent=4)\n f.close()","def write_json_in_file(filename, data, encoding=\"utf-8\"):\n with open(filename, \"w\", encoding=encoding) as f:\n f.write(json.dumps(data))","def write(self, output):\n with open(output, 'w') as out:\n out.write(json.dumps(self, indent=4))","def _get_ann_file(self):\n prefix = 'instances' if 'test' not in self.image_set else 'image_info'\n return os.path.join(self.data_path, 'annotations',\n prefix + '_' + self.image_set + '.json')","def write(self):\r\n\r\n with open(self.filename + \".json\", mode='w') as json_file:\r\n json.dump(self.data, json_file, separators=(',', ':'))","def save_quickstreams(quickstreams):\n with open('quickstreams.txt', 'w') as f:\n json.dump(quickstreams, f, indent=4)","def SaveJSON(self, filename):\n data = {\n 'files': self._files,\n 'ebuilds': self._ebuilds,\n }\n json.dump(data, open(filename, 'w'))","def _write_json(\n output_path, records\n):\n output_path.write_text(json.dumps(records))","def to_json(self, dataset):\n save_as = filedialog.asksaveasfilename(defaultextension='.json')\n try:\n with open(save_as, 'w', newline='') as file:\n json.dump(dataset, file)\n self.info_success(save_as)\n except IOError:\n self.info_error()\n return"],"string":"[\n \"def dump_annotations(self):\\n fname = 'annotations'\\n if self.split is not None:\\n fname = 'annotations_{}'.format(self.split)\\n fname = os.path.join(self.dest_folder, '{}.json'.format(fname))\\n self.save(self.dataset, fname, \\\"annotations\\\")\",\n \"def _save_annotation(self, purpose):\\n with open('data\\\\\\\\annotation_{}.json'.format(purpose), 'w', encoding='utf-8') as make_file:\\n json.dump(self.json_list, make_file, ensure_ascii= False, indent= 4)\\n self.json_list.clear()\",\n \"def saveAnnsToDisk(self, ann_file=None):\\n\\n if ann_file is None:\\n ann_file = self.annotation_file\\n\\n with open(ann_file, 'w') as fp:\\n json.dump(self.dataset, fp)\",\n \"def write(self, path):\\n\\n annotation = copy.deepcopy(self.annotation)\\n\\n for image_info in annotation['images']:\\n image_info['file_name'] = os.path.relpath(image_info['file_name'],\\n os.path.dirname(path))\\n\\n with open(path, 'w') as read_file:\\n json.dump(annotation, read_file)\",\n \"def serialize_file(cls, obj, file_path='./data.json'):\\n file_stream = open(file_path, 'wb')\\n json.dump(obj, file_stream, cls=CustomTypeEncoder)\\n file_stream.flush()\\n file_stream.close()\",\n \"def to_json(fn, obj):\\n with open(fn, 'w') as f:\\n json.dump(obj, f, cls=OperatorEncoder, indent=2, ensure_ascii=False)\\n return fn\",\n \"def save_annotation(self, drivename, fname, json_str):\\n\\t\\tassert type(json_str) == str, \\\"json must be a string\\\"\\n\\t\\tif not isdir(self.OUTPUT_ANN_DIR):\\n\\t\\t\\ttry: \\n\\t\\t\\t makedirs(self.OUTPUT_ANN_DIR)\\n\\t\\t\\texcept OSError: \\n\\t\\t\\t print (\\\"Creation of the directory {} failed\\\".format(self.OUTPUT_ANN_DIR))\\n\\n\\t\\toutput_drive_dir = join(self.OUTPUT_ANN_DIR, drivename)\\n\\t\\tif not isdir(output_drive_dir):\\n\\t\\t\\ttry: \\n\\t\\t\\t makedirs(output_drive_dir)\\n\\t\\t\\texcept OSError: \\n\\t\\t\\t print (\\\"Creation of the directory {} failed\\\".format(output_drive_dir))\\n\\n\\t\\ttry:\\n\\t\\t\\tjson_object = json.loads(json_str)\\n\\t\\texcept ValueError:\\n\\t\\t\\tprint(\\\"Annotation not a valid json\\\")\\n\\t\\t\\treturn 0\\n\\t\\tfname = fname.split(\\\".\\\")[0] + \\\".json\\\"\\n\\t\\tsave_filename = join(self.OUTPUT_ANN_DIR, drivename, fname)\\n\\t\\twith open(save_filename, \\\"w\\\") as f:\\n\\t\\t\\tf.write(json_str)\\n\\t\\t\\treturn 1\\n\\t\\treturn 0\",\n \"def save_annotations(self):\\n r = requests.get(\\n f'{self.api_host}/v1/entity-annotations?'\\n f'annotation_type=Source reliability (binary)&size=100',\\n headers=self.get_request_headers()\\n )\\n\\n entity_annotations = r.json().get('entity_annotations')\\n\\n for annotation in entity_annotations:\\n annotation_id = annotation.get('entity_id')\\n with open(\\n f'{self.data_folder}/annotations/{annotation_id}.json',\\n 'w'\\n ) as f:\\n json.dump(annotation, f)\",\n \"def to_file(self, fp):\\n dict_ = self.serialize()\\n with open_file(fp, mode='w') as writer:\\n json.dump(dict_, writer, indent=2)\",\n \"def write_json(self, obj, mode='wb', **kwargs):\\n with self.open(mode) as f:\\n return json.dump(obj, f, **kwargs)\",\n \"def to_json_file(self, path):\\n with open(path, 'w') as f:\\n f.write(self.to_json())\",\n \"def to_json(self, fname):\\n fname = enforce_extension(fname, \\\".json\\\")\\n write_json(self.as_dict, fname)\",\n \"def save_file(obj, file_path):\\n with gzip.open(file_path, \\\"wb\\\") as fp:\\n fp.write(json.dumps(obj).encode('utf-8'))\",\n \"def _to_json(self, fp=None, indent=4):\\n if fp is None:\\n return json.dumps(self.to_json_compat(), indent=indent)\\n else:\\n json.dump(self.to_json_compat(), fp, indent=indent)\",\n \"def write(self, fp, **kwds):\\n json.dump(self._dict, fp)\",\n \"def write_json(self, f, **kw_json):\\n wntr.network.io.write_json(self, f, **kw_json)\",\n \"def to_json(self, path):\\n with open(path, 'w') as out_file:\\n json.dump(self._metadata, out_file, indent=4)\",\n \"def write(self):\\n self.json_o.write()\",\n \"def write_json_file(self, fname, content):\\n pass\",\n \"def write_json_file(self, fname, content):\\n pass\",\n \"def to_json(self, fpath):\\n import json\\n with open(fpath, 'w') as fp:\\n json.dump(self.to_dict(), fp)\",\n \"def _write_json(self):\\n with open(self._file_path, 'w') as f:\\n json.dump(self._content, f, indent=4, separators=None,\\n encoding='utf-8', sort_keys=False)\",\n \"def to_json(obj: ConfiguredBaseModel, file: str):\\n if file:\\n with open(file, \\\"w\\\") as f:\\n f.write(obj.json(indent=4))\\n console.print(f\\\"\\\\nOutput written to {file}\\\\n\\\")\\n else:\\n print_json(obj.json(indent=4))\",\n \"def write_to_json(self, export_fp: str):\\n # TODO:\\n pass\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, mode='w') as file:\\n file.write(json.dumps(my_obj))\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w', encoding='utf-8') as file:\\n return file.write(json.dumps(my_obj))\",\n \"def write_complex_json(filepath, obj):\\n\\n with open(filepath, 'w', encoding='utf-8') as file_obj:\\n json.dump(obj, file_obj, cls=ExtendedEncoder, ensure_ascii=False, indent=2)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as writer:\\n json.dump(my_obj, writer)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, \\\"w\\\") as myfile:\\n return myfile.write(json.dumps(my_obj))\",\n \"def write(self, _filepath=None):\\n _json_txt = json.dumps(self.json_dict, indent=2)\\n self._write_json_text(_json_txt, _filepath)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, \\\"w\\\") as f:\\n j = json.dumps(my_obj)\\n f.write(j)\\n f.close()\",\n \"def save(self, filename_or_stream: str | PathLike | TextIO) -> None:\\n if isinstance(filename_or_stream, (str, PathLike)):\\n p = Path(filename_or_stream)\\n if not p.suffix:\\n p = p.with_suffix(\\\".json\\\")\\n s: TextIO = open(p, \\\"w\\\")\\n close = True\\n else:\\n s = filename_or_stream\\n close = False\\n\\n json.dump(self._unstructure(), s, indent=2, ensure_ascii=True)\\n if close:\\n s.close()\",\n \"def to_json_file(self, json_file_path):\\n with open(json_file_path, 'w', encoding='utf-8') as writer:\\n writer.write(self.to_json_string())\",\n \"def _write_annotations(self, anns, rec_id, out_handle):\\n format_anns = self._format_keyvals(anns)\\n if format_anns:\\n print repr(format_anns)\\n parts = [rec_id, \\\"annotation\\\", \\\"remark\\\", \\\".\\\", \\\".\\\", \\\".\\\", \\\".\\\", \\\".\\\",\\n format_anns]\\n out_handle.write(\\\"\\\\t\\\".join(parts) + \\\"\\\\n\\\")\",\n \"def write_json(obj_to_write: Any, filename: str):\\n \\n with open(filename, 'w') as json_file:\\n json.dump(obj_to_write, json_file, indent=4)\",\n \"def to_json_file(self, json_file_path):\\n with open(json_file_path, \\\"w\\\", encoding=\\\"utf-8\\\") as writer:\\n writer.write(self.to_json_string())\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, \\\"w\\\", encoding=\\\"utf-8\\\") as opening:\\n json.dump(my_obj, opening)\",\n \"def save_to_json_file(my_obj, filename):\\n import json\\n with open(filename, 'w', encoding='utf-8') as f:\\n obj = json.dumps(my_obj)\\n f.write(obj)\",\n \"def to_file(self, file_name: str) -> None:\\n\\n with open(file_name, 'w') as fi:\\n json.dump(self.to_dict(), fi, indent=1)\",\n \"def write(self, stream, root, order):\\n json.dump(root, stream, indent=2)\\n stream.write('\\\\n')\",\n \"def dump(self, stream):\\n items = (\\n ('time', self.time),\\n ('inc', self.inc),\\n )\\n # use ordered dict to retain order\\n ts = collections.OrderedDict(items)\\n json.dump(dict(ts=ts), stream)\",\n \"def save_as_json(self, file_path):\\n with open(file_path, 'w') as fd:\\n json.dump(self._ngrams, fd)\",\n \"def write_annotation(self, ann_file, img_path, new_img_name):\\n if self.type == \\\"imagenet\\\":\\n label = self.in_annotations[img_path]\\n logger.debug(f\\\"Img {img_path}, imagenet label {label}\\\")\\n ann_file.write(str(label) + \\\"\\\\n\\\")\\n elif self.type == \\\"coco\\\":\\n ann_file.write(\\\"detection_results {\\\\n\\\")\\n for obj in self.in_annotations[img_path].keys():\\n ann_file.write(\\\" objects {\\\\n\\\")\\n ann_file.write(f\\\" class_id: {self.in_annotations[img_path][obj]['label']}\\\\n\\\")\\n ann_file.write(\\\" bounding_box {\\\\n\\\")\\n ann_file.write(f\\\" normalized_top: {self.in_annotations[img_path][obj]['normalized_bbox'][0]}\\\\n\\\")\\n ann_file.write(f\\\" normalized_bottom: {self.in_annotations[img_path][obj]['normalized_bbox'][1]}\\\\n\\\")\\n ann_file.write(f\\\" normalized_left: {self.in_annotations[img_path][obj]['normalized_bbox'][2]}\\\\n\\\")\\n ann_file.write(f\\\" normalized_right: {self.in_annotations[img_path][obj]['normalized_bbox'][3]}\\\\n\\\")\\n ann_file.write(\\\" }\\\\n\\\")\\n ann_file.write(\\\" }\\\\n\\\")\\n ann_file.write(f' image_name: \\\"{new_img_name}\\\"\\\\n')\\n ann_file.write(f' image_id: {int(new_img_name.split(\\\".\\\")[0])}\\\\n')\\n ann_file.write(\\\"}\\\\n\\\")\",\n \"def toFile(self, file_path) -> None:\\n\\t\\tjson_repr = self.toJSON()\\n\\t\\t\\n\\t\\twith open(file_path, \\\"w\\\") as f:\\n\\t\\t\\tf.write(json_repr)\",\n \"def _save_annotation(annotation, filename):\\n\\n pil_image = Image.fromarray(annotation.astype(dtype=np.uint8))\\n '''\\n with tf.io.gfile.GFile(filename, mode='w') as f:\\n #with open(filename, mode='w') as f:\\n print(f)\\n pil_image.save(f, 'PNG')\\n '''\\n pil_image.save(filename)\",\n \"def to_json(self, path):\\n with open(path, \\\"w\\\") as dump:\\n json.dump(self.__dict__, dump)\",\n \"def save_to_json_file(my_obj, filename):\\n import json\\n with open(filename, mode='w', encoding='utf-8') as f:\\n json.dump(my_obj, f)\",\n \"def dict_2_json(obj, filename):\\n\\twith open('data/output/' + filename, 'w') as fp:\\n\\t\\tjson.dump(obj, fp, indent=4)\",\n \"def serialize(self):\\n keys = [\\n 'uid',\\n 'commit_sha',\\n 'timestamp',\\n 'filename',\\n 'comment',\\n 'train_data',\\n 'val_data',\\n 'test_data',\\n 'model_files',\\n 'custom_data',\\n ]\\n data = {key: self.__dict__[key] for key in keys}\\n with open(os.path.join(self.root_path, self._data_file), 'w') as file:\\n json.dump(data, file)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w') as file:\\n json.dump(my_obj, file)\",\n \"def export_annotated_reads(parser):\\n outfile = os.path.join(\\n parser.options.get_project_dir(parser.sample.sample_id),\\n parser.sample.sample_id + '_' + parser.end + '_' + parser.options.reads_json_name\\n )\\n # print pretty JSON: print(json.dumps(parser.reads,indent=4, cls=CustomEncoder))\\n with open(outfile, 'w') as out:\\n json.dump(parser.reads, out, cls=CustomEncoder)\",\n \"def json(filepath, mode=DataSaver.MODE_OVERWRITE, date_format=None,\\n double_precision=10, force_ascii=True, date_unit='ms'):\\n format_file = DataSaver.FORMAT_JSON\\n kwargs = locals()\\n _apply_datasaver(format_file, kwargs, last_uuid)\\n return None\",\n \"def to_json(self, filename, indent=2):\\n d = self.to_dicts()\\n if hasattr(filename, \\\"write\\\"):\\n json.dump(d, filename, indent=indent)\\n elif isinstance(filename, str):\\n with open(os.path.expanduser(filename), \\\"w\\\") as f:\\n json.dump(d, f, indent=indent)\\n else:\\n raise ValueError(\\\"Unknown filename or file-object\\\")\",\n \"def save_to_json_file(my_obj, filename):\\n import json\\n with open(filename, 'w') as file:\\n json.dump(my_obj, file)\",\n \"def save_digraph_json(graph: nx.DiGraph, f: Union[IO, os.PathLike, str]):\\n # Obtain the JSON object to serialize\\n json_obj = json.dumps(node_link_data(graph))\\n\\n # Save the object\\n if isinstance(f, (os.PathLike, str)):\\n with open(f, 'w', encoding='utf-8') as file:\\n file.write(json_obj)\\n else:\\n f.write(json_obj)\",\n \"def save_annotations_pickle(annotations: dict, outfile: str):\\n with open(outfile, 'wb') as pkl:\\n pickle.dump(annotations, pkl)\",\n \"def save(self, data, outpath):\\n with open(path, \\\"wt\\\") as open_file:\\n json.dump(data, open_file, indent=4)\",\n \"def to_json(self): \\n raise NotImplementedError('MediaIoBaseUpload is not serializable.')\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w+') as json_file:\\n json.dump(my_obj, json_file)\",\n \"def to_json(self, path_to_json: str):\\n with open(path_to_json, \\\"w\\\") as _f:\\n json.dump(self.data, _f)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w') as json_file:\\n written = json_file.write(json.dumps(my_obj))\\n return written\",\n \"def write_json(obj, fpath):\\n mkdir_if_missing(osp.dirname(fpath))\\n with open(fpath, 'w', encoding='utf-8') as f:\\n json.dump(obj, f, indent=4, separators=(',', ': '), ensure_ascii=False) # 添加中文支持\",\n \"def save_to_json(doc: object, text: str, export_button: st.columns):\\n # Get named entities and their labels\\n entities = []\\n for ent in doc.ents:\\n entities.append([ent.start_char, ent.end_char, ent.label_])\\n # Define the annotations in the desired format\\n annotations = [[text, {\\\"entities\\\": entities}]]\\n # Create a dictionary to represent the JSON structure\\n to_json = {\\n \\\"classes\\\": [\\\"TEMP\\\", \\\"SOFT\\\", \\\"STIME\\\", \\\"MOL\\\", \\\"FFM\\\"],\\n \\\"annotations\\\": annotations,\\n }\\n # Export in JSON format\\n json_file = json.dumps(to_json)\\n # Get the current date\\n current_date = date.today().strftime(\\\"%Y-%m-%d\\\")\\n # Create the file name with the current date\\n filename = f\\\"annotations_{current_date}.json\\\"\\n with export_button:\\n st.download_button(\\n label=\\\"Export to json\\\",\\n file_name=filename,\\n mime=\\\"application/json\\\",\\n data=json_file,\\n )\",\n \"def annotations_to_json(eaf_dir, json_dir):\\n for file in os.listdir(eaf_dir):\\n if file.endswith(\\\".eaf\\\"):\\n print(\\\"converting\\\", file, \\\"to json\\\")\\n file_name = os.path.join(json_dir, file[:-4]) + \\\".json\\\"\\n file = os.path.join(eaf_dir, file)\\n file_elan = Elan.Eaf(file)\\n\\n # Get all the data under the engagement_tier tier\\n annotation_data = file_elan.get_annotation_data_for_tier(\\\"engagement_tier\\\")\\n labels_for_annotation = elan_annotation_to_binary(annotation_data)\\n\\n # Create a json file storing the dictionary of {\\\"timeslot1,timeslot2\\\": 0/1(engaged/disengaged)}\\n j = json.dumps(labels_for_annotation)\\n f = open(file_name, \\\"w\\\")\\n f.write(j)\\n f.close()\",\n \"def writeLinkoJson(linkograph, file):\\n # Convert the linkograph into a form json can use.\\n jsonLinko = [linkograph.labels]\\n\\n \\\"\\\"\\\"\\n for entry in linkograph:\\n tolist = list(map(list, entry))\\n jsonLinko.append(tolist)\\n \\\"\\\"\\\"\\n for index in range(len(linkograph)):\\n tolist = list(map(list, linkograph[index]))\\n tolist.append(linkograph.uuids[index])\\n jsonLinko.append(tolist)\\n\\n with open(file, 'w') as jsonFile:\\n json.dump(jsonLinko, jsonFile, indent=4)\",\n \"def json(self):\\n return {\\n \\\"x\\\" : self.x,\\n \\\"y\\\": self.y,\\n \\\"rotation\\\":self.rotation,\\n \\\"annotations\\\" : [a.json() for a in self.annotations]\\n }\",\n \"def save_data(file_to_save, object_to_serialize):\\r\\n with open(file_to_save, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\r\\n f.write(json.dumps(object_to_serialize, indent=2, ensure_ascii=False))\",\n \"def writeToJson(inputObj,fileLoc):\\n myFile = open(fileLoc,'w')\\n json.dump(inputObj, myFile, sort_keys=True, indent=4, separators=(',', ': '))\",\n \"def write_json(self, filename):\\n with open(filename, 'a+') as f:\\n f.write(json.dumps(self.weights))\\n f.write(\\\"\\\\n\\\")\",\n \"def save_json(data):\\n data = json.dumps(data)\\n\\n with MEDTADATA_FILE.open('w') as outfile:\\n outfile.write(data)\",\n \"def save(self, filename):\\n content = self.to_dict()\\n with open(filename, 'w') as f:\\n json.dump(content, f)\",\n \"def write_to_json(self, file_name, jayson, mode='w', prep_for_BQ=False):\\n if prep_for_BQ == True:\\n jayson = self.fix_json_keys(jayson, self.prep_json_for_BQ_callback)\\n\\n with open(file_name, mode) as f:\\n for line in jayson:\\n f.writelines(json.dumps(line) + '\\\\n')\",\n \"def save(self, file):\\n\\n import json\\n\\n if hasattr(file, 'write'):\\n f = file\\n else:\\n f = open(file, 'w')\\n output = json.dumps(self, default=lambda v: v.__dict__)\\n f.write(output)\\n f.close()\",\n \"def to_json_file(self, json_file: str = None) -> None:\\n\\n if self.json:\\n if not json_file:\\n json_file = f\\\"{self.id}.json\\\"\\n\\n with open(json_file, \\\"w\\\") as f:\\n f.write(self.json)\",\n \"def as_json(self) -> str:\\n return json.dumps(self, cls=_RecordingJSONEncoder)\",\n \"def to_json_file(self, path: Union[str, Path]):\\n data_dict = self._to_list_dict()\\n return json.dump(data_dict, Path(path).open(\\\"w\\\", encoding=\\\"utf8\\\"), cls=NumpyEncoder, indent=4)\",\n \"def _save(self):\\n with open(self.file_path, 'w') as fid:\\n json.dump(self.data, fid, indent=4, sort_keys=True)\",\n \"def save(self):\\n with open(self.file_path, 'w', encoding=Config.ENCODING) as file:\\n json.dump(self.data, file, indent=2, ensure_ascii=False)\",\n \"def write_json(data: Dict[str, Annotations], file: str):\\n if not os.path.exists(file):\\n with open(file, 'w', encoding='utf-8') as output_file:\\n json_data = dict(map(lambda kv: (kv[0], kv[1].encode()), data.items()))\\n json.dump(json_data, output_file, ensure_ascii=False, indent=4)\\n else:\\n print('Signature file `{}` already exists.'.format(file))\\n sys.exit(-1)\",\n \"def to_json(self, filepath, pretty=False):\\n # PATH = os.path.abspath(os.path.join(filepath, '..', 'data'))\\n with open(filepath, 'w+') as fp:\\n if pretty:\\n json.dump(self.data, fp, sort_keys=True, indent=4)\\n else:\\n json.dump(self.data, fp)\",\n \"def to_json(arr_like, fname):\\n arr = array(arr_like).tolist()\\n with open(fname, 'w')as fh:\\n fh.write(json.dumps(arr, indent=4))\",\n \"def save_json(self, file: Union[str, TextIO]) -> None:\\n if hasattr(file, 'write'):\\n file_ctx = nullcontext(file)\\n else:\\n file_ctx = open(file, 'w')\\n\\n with file_ctx as fp:\\n for d in self:\\n json.dump(d.dict(), fp)\\n fp.write('\\\\n')\",\n \"def dump(obj, fp, *args, **kwargs): \\n defaults = {\\\"indent\\\": 4}\\n defaults.update(kwargs)\\n json.dump(serialize(obj), fp, *args, **defaults)\",\n \"def save_json(file_name, file_content):\\n with open(generate_file_path(\\\"output\\\", file_name), 'w', encoding='utf-8') as f:\\n json.dump(file_content, f, ensure_ascii=False, indent=4)\",\n \"def to_json(self, filename, complete=False):\\n # Get metadata.\\n data = self.to_dict(complete=complete)\\n\\n # Write to json file.\\n with open(filename, \\\"w\\\") as f:\\n json.dump(data, f)\",\n \"def save_json(self, file):\\n with open(file, 'w', encoding='utf8') as f:\\n json.dump(self, f, ensure_ascii=False)\",\n \"def to_json_file(self, file_path, **kwargs):\\n try:\\n with Params._open_file(file_path, \\\"w\\\") as fp:\\n json.dump(dict(self), fp, **kwargs)\\n return file_path\\n except Exception as err:\\n print(\\\"Failed to write {} instance to: {}\\\".format(self.__class__.__name__, file_path), err)\\n return None\",\n \"def serialize(self, to_file=None):\\n raise NotImplementedError\",\n \"def write(fname, data):\\n # Encode to string.\\n encoder = NumpyJSONEncoder(check_circular=True, indent=' ')\\n serial = encoder.encode(data)\\n\\n # Write to file.\\n with open(fname, 'w') as fo:\\n fo.write(serial)\",\n \"def save(self, filepath):\\n with open(filepath, 'w') as f:\\n json.dump(self, f, indent=2)\",\n \"def to_json(self, filepath=None):\\n obj = {'history': self.resp,\\n 'details': {k: v.to_json() for k, v in self.details.items()}\\n }\\n if filepath is None:\\n return json.dumps(obj)\\n else:\\n with open(filepath) as f:\\n json.dump(obj, f)\",\n \"def serialize(policy_spec, file_path):\\n if not file_path:\\n log.error('File path is empty')\\n abs_path = os.path.abspath(file_path)\\n data = parse_policy_spec(policy_spec)\\n f = open(abs_path, 'w')\\n json.dump(data, f, indent=4)\\n f.close()\",\n \"def write_json_in_file(filename, data, encoding=\\\"utf-8\\\"):\\n with open(filename, \\\"w\\\", encoding=encoding) as f:\\n f.write(json.dumps(data))\",\n \"def write(self, output):\\n with open(output, 'w') as out:\\n out.write(json.dumps(self, indent=4))\",\n \"def _get_ann_file(self):\\n prefix = 'instances' if 'test' not in self.image_set else 'image_info'\\n return os.path.join(self.data_path, 'annotations',\\n prefix + '_' + self.image_set + '.json')\",\n \"def write(self):\\r\\n\\r\\n with open(self.filename + \\\".json\\\", mode='w') as json_file:\\r\\n json.dump(self.data, json_file, separators=(',', ':'))\",\n \"def save_quickstreams(quickstreams):\\n with open('quickstreams.txt', 'w') as f:\\n json.dump(quickstreams, f, indent=4)\",\n \"def SaveJSON(self, filename):\\n data = {\\n 'files': self._files,\\n 'ebuilds': self._ebuilds,\\n }\\n json.dump(data, open(filename, 'w'))\",\n \"def _write_json(\\n output_path, records\\n):\\n output_path.write_text(json.dumps(records))\",\n \"def to_json(self, dataset):\\n save_as = filedialog.asksaveasfilename(defaultextension='.json')\\n try:\\n with open(save_as, 'w', newline='') as file:\\n json.dump(dataset, file)\\n self.info_success(save_as)\\n except IOError:\\n self.info_error()\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65512836","0.65278125","0.65025586","0.63029265","0.6299647","0.628952","0.61956006","0.61616343","0.60725665","0.6022213","0.6013489","0.6011242","0.5988493","0.5966148","0.5930022","0.5913455","0.59128827","0.5912753","0.590443","0.590443","0.58865786","0.58829296","0.5878018","0.58428067","0.57960576","0.5785304","0.57711256","0.5770014","0.5763555","0.57599366","0.5757022","0.57518995","0.5740491","0.5740286","0.5737716","0.5735527","0.57343674","0.57239735","0.5717244","0.5712538","0.5704904","0.5698461","0.5692089","0.56828517","0.5678222","0.5677705","0.56776714","0.56728476","0.56722814","0.5669603","0.5658155","0.5656319","0.56492907","0.5640322","0.5633326","0.56214994","0.56196713","0.5617052","0.5612203","0.5609147","0.5608576","0.5605434","0.559272","0.5566814","0.5566047","0.5548739","0.5546919","0.5529686","0.55173486","0.55043155","0.54986787","0.5494329","0.5479752","0.5474616","0.5473377","0.54722613","0.5468008","0.5463787","0.54600114","0.5452998","0.5449347","0.54470515","0.5444021","0.5436506","0.54350036","0.5432976","0.5427145","0.5425846","0.5415224","0.5403719","0.5397544","0.53887695","0.53873897","0.5386217","0.5380643","0.5377981","0.5375434","0.5375228","0.5362601","0.5361596"],"string":"[\n \"0.65512836\",\n \"0.65278125\",\n \"0.65025586\",\n \"0.63029265\",\n \"0.6299647\",\n \"0.628952\",\n \"0.61956006\",\n \"0.61616343\",\n \"0.60725665\",\n \"0.6022213\",\n \"0.6013489\",\n \"0.6011242\",\n \"0.5988493\",\n \"0.5966148\",\n \"0.5930022\",\n \"0.5913455\",\n \"0.59128827\",\n \"0.5912753\",\n \"0.590443\",\n \"0.590443\",\n \"0.58865786\",\n \"0.58829296\",\n \"0.5878018\",\n \"0.58428067\",\n \"0.57960576\",\n \"0.5785304\",\n \"0.57711256\",\n \"0.5770014\",\n \"0.5763555\",\n \"0.57599366\",\n \"0.5757022\",\n \"0.57518995\",\n \"0.5740491\",\n \"0.5740286\",\n \"0.5737716\",\n \"0.5735527\",\n \"0.57343674\",\n \"0.57239735\",\n \"0.5717244\",\n \"0.5712538\",\n \"0.5704904\",\n \"0.5698461\",\n \"0.5692089\",\n \"0.56828517\",\n \"0.5678222\",\n \"0.5677705\",\n \"0.56776714\",\n \"0.56728476\",\n \"0.56722814\",\n \"0.5669603\",\n \"0.5658155\",\n \"0.5656319\",\n \"0.56492907\",\n \"0.5640322\",\n \"0.5633326\",\n \"0.56214994\",\n \"0.56196713\",\n \"0.5617052\",\n \"0.5612203\",\n \"0.5609147\",\n \"0.5608576\",\n \"0.5605434\",\n \"0.559272\",\n \"0.5566814\",\n \"0.5566047\",\n \"0.5548739\",\n \"0.5546919\",\n \"0.5529686\",\n \"0.55173486\",\n \"0.55043155\",\n \"0.54986787\",\n \"0.5494329\",\n \"0.5479752\",\n \"0.5474616\",\n \"0.5473377\",\n \"0.54722613\",\n \"0.5468008\",\n \"0.5463787\",\n \"0.54600114\",\n \"0.5452998\",\n \"0.5449347\",\n \"0.54470515\",\n \"0.5444021\",\n \"0.5436506\",\n \"0.54350036\",\n \"0.5432976\",\n \"0.5427145\",\n \"0.5425846\",\n \"0.5415224\",\n \"0.5403719\",\n \"0.5397544\",\n \"0.53887695\",\n \"0.53873897\",\n \"0.5386217\",\n \"0.5380643\",\n \"0.5377981\",\n \"0.5375434\",\n \"0.5375228\",\n \"0.5362601\",\n \"0.5361596\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53753155"},"document_rank":{"kind":"string","value":"97"}}},{"rowIdx":94098,"cells":{"query":{"kind":"string","value":"r\"\"\"Return the JObject as a set of native data types for serialization."},"document":{"kind":"string","value":"def __json_light__(self):\n filtered_dict = dict()\n\n for k, item in six.iteritems(self.__dict__):\n if k.startswith('_') or k == 'annotations':\n continue\n\n if hasattr(item, '__json__'):\n filtered_dict[k] = item.__json__\n else:\n filtered_dict[k] = serialize_obj(item)\n\n return filtered_dict"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def json(self) -> Union[dict, list, str, int, float]:\n raise NotImplementedError # pragma: no cover","def data_types(self):","def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:","def toJSType(self):\n return dict(\n name=self.name,\n description=self.description,\n address=self.address,\n lat=self.lat,\n lng=self.lng,\n submitted_by=self.submitted_by,\n certified=self.certified,\n id=str(self.id),\n comments=[c.toJSType() for c in self.comments],\n feature_set=self.feature_set.toJSType()\n )","def as_json(self):","def json_type(self):\n return exclusions.closed()","def json_serialize(self):\n raise NotImplementedError('json_serialize must be overriden')","def json(self):\n return [t.json for t in self.values()]","def data_types():\n\n return ...","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = json_data.__dict__.iteritems()\n else:\n itr = json_data.__dict__.items()\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result;","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def json (self):\n\n return jsonpickle.encode(self, unpicklable=False)","def json_default(o):\n if np.isscalar(o) and isinstance(o, np.generic):\n return o.item() # coerce numpy number to pure-python type\n elif isinstance(o, (tuple, set)):\n return list(o)\n else:\n # Dispatch to JSONEncoder so that we see errors straight away\n # if an object can't be serialized.\n return JSON_ENCODER.default(o)","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = json_data.__dict__.iteritems()\n else:\n itr = json_data.__dict__.items()\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result","def test_as_python_types(self):\n obs = _as_python_types(self.metadata_map, self.headers)\n exp = [[2.1, 3.1, 3],\n ['str1', '200', 'string30'],\n [1, 2, 3]]\n self.assertEqual(obs, exp)","def _asdict(self) -> Dict[Text, Any]:\n return self.as_base_types()","def json(self) -> Any:\n return json.loads(self)","def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = iter(json_data.__dict__.items())\n else:\n itr = list(json_data.__dict__.items())\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"):\n continue\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\n value\n return result","def javatype(s):\n return {\n 'int': 'int',\n 'String': 'String',\n 'Long': 'Long',\n 'boolean': 'boolean'\n }.get(s.name, s.name)","def json_serialize(value):\n if value is None or isinstance(value, (int, long, float, basestring, bool)):\n return value\n elif isinstance(value, (list, tuple, set)):\n return [json_serialize(v) for v in value]\n elif isinstance(value, dict):\n for k, v in value.items():\n value[k] = json_serialize(v)\n return value\n # return date/time in isoformat\n elif isinstance(value, (dt.datetime, dt.date, dt.time)):\n return value.isoformat()\n elif isinstance(value, ActiveRecordMixin):\n return _model_to_dict(value)\n else:\n return unicode(value)","def serialize(self):\n dt = super(AnyPin, self).serialize()\n constrainedType = self.activeDataType\n if constrainedType != self.__class__.__name__:\n pinClass = findPinClassByType(constrainedType)\n # serialize with active type's encoder\n dt['value'] = json.dumps(\n self.currentData(), cls=pinClass.jsonEncoderClass())\n dt['currDataType'] = constrainedType\n return dt","def data_types(self):\n return self['data_types']","def get_column_type(cls, **kwargs: Any) -> Any:\n return sqlalchemy.JSON()","def json_friendly(self):","def json(self):\n return capi.to_json(self.ptr)","def _jsonable(self):\n magic_dict = {}\n mman = self.magics_manager\n magics = mman.lsmagic()\n for key, subdict in magics.items():\n d = {}\n magic_dict[key] = d\n for name, obj in subdict.items():\n try:\n classname = obj.__self__.__class__.__name__\n except AttributeError:\n classname = 'Other'\n \n d[name] = classname\n return magic_dict","def data_types(self) -> 'outputs.AwsS3DataConnectorDataTypesResponse':\n return pulumi.get(self, \"data_types\")","def json(self):\n class ExtendedJSONEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\n encoded_object = obj.isoformat()\n else:\n encoded_object = json.JSONEncoder.default(self, obj)\n return encoded_object\n\n obj = {\n 'operation': self.operation,\n 'version': self.version,\n 'language': self.language,\n 'identifiers': self.identifiers,\n 'store_execute': self.store_execute,\n 'status': self.status,\n 'lineage': self.lineage,\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\n 'outputs': self.outputs,\n 'raw': self.raw\n }\n\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)","def jsons(self):\n if self._jsons is None:\n self.make_jsons()\n return self._jsons","def getencdec():\n return (json.JSONEncoder(), json.JSONDecoder())","def json(self):\n return self._json","def _get_types(self):\n\n db = Database()\n self.c_built_ins = list(map(lambda tup: tup[0], db.select_built_types()))\n self.c_built_in_array_types = r'^(' + '|'.join(self.escaped(self.c_built_ins)) + ')\\[[0-9]*\\]'\n self.c_types = list(map(lambda tup: tup[0], db.select_types()))\n self.c_array_types = r'^(' + '|'.join(self.escaped(self.c_types)) + ')\\[[0-9]*\\]'\n db.close_connection()","def json_raw(self):\n return json.dumps(self.data, cls=ComplexEncoder)","def default(self, obj):\n \n if isinstance(obj, np.ndarray):\n return list(obj)\n\n if isinstance(obj, uuid.UUID):\n return str(obj)\n\n if isinstance(obj, datetime.datetime):\n return obj.isoformat()\n \n if isinstance(obj,TPC):\n return obj._so()\n \n # No special handling called for; pass through\n return json.JSONEncoder.default(self, obj)","def default(self, obj):\n numpy_types = (\n np.bool_,\n # np.bytes_, -- python `bytes` class is not json serializable\n # np.complex64, -- python `complex` class is not json serializable\n # np.complex128, -- python `complex` class is not json serializable\n # np.complex256, -- python `complex` class is not json serializable\n # np.datetime64, -- python `datetime.datetime` class is not json serializable\n np.float16,\n np.float32,\n np.float64,\n # np.float128, -- special handling below\n np.int8,\n np.int16,\n np.int32,\n np.int64,\n # np.object_ -- should already be evaluated as python native\n np.str_,\n np.uint8,\n np.uint16,\n np.uint32,\n np.uint64,\n np.void,\n )\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n elif isinstance(obj, numpy_types):\n return obj.item()\n elif isinstance(obj, np.float128):\n return obj.astype(np.float64).item()\n elif isinstance(obj, Decimal):\n return str(obj)\n elif isinstance(obj, datetime):\n return str(obj)\n elif obj is np.ma.masked:\n return str(np.NaN)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder.default(self, obj)","def json(self):\n return self._parsejson(self.raw)","def json(self):\n raise NotImplementedError(\"JSON not specified!\")","def encode_value(x):\n for serializer in string_serializers:\n if isinstance(x, serializer.type):\n return {\"$type\": serializer.name, \"$value\": serializer.to_json(x)}\n\n raise TypeError(type(x)) # pragma: no cover","def json(self):\n return self.__json","def _types(cls):\n return {}","def serialize(cls, obj):\n return json.dumps(obj, cls=CustomTypeEncoder)","def jsonify(obj):\n raise NotImplementedError","def to_json(self):\r\n return {'type': self.type, 'name': self.name}","def type_object(self, json):\n json_prop = _utils.json_prop_finder(json)\n d = {k: (self.switcher(v)) for k, v in json_prop.items()}\n return d","def _convert_to_jsonc(x):\n\n if isinstance(x, dict):\n jsonc_obj = Jsonc()\n # Recursively transform all members of the dict.\n # When converting a dict, we do not convert _name items into private\n # Jsonc members.\n for key, value in x.items():\n jsonc_obj._dict[key] = _convert_to_jsonc(value)\n return jsonc_obj\n elif isinstance(x, list):\n # Recursively transform all members of the list.\n members = []\n for item in x:\n members.append(_convert_to_jsonc(item))\n return members\n else:\n # Return the base object.\n return x","def to_json(self):\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\"utf8\")","def schema_as_json(content_schema: Dict[str, n.SerializableType]) -> n.SerializableType:\n if \"properties\" in content_schema:\n current: Dict[str, Any] = {}\n properties = content_schema[\"properties\"]\n assert isinstance(properties, Dict)\n for prop, options in properties.items():\n if \"type\" not in options:\n current[prop] = \"Any\"\n elif options[\"type\"] == \"object\":\n current[prop] = schema_as_json(options)\n elif options[\"type\"] == \"array\":\n current[prop] = [schema_as_json(options[\"items\"])]\n else:\n current[prop] = options[\"type\"]\n\n return current\n\n if \"items\" in content_schema:\n assert isinstance(content_schema[\"items\"], Dict)\n return [schema_as_json(content_schema[\"items\"])]\n\n return content_schema[\"type\"]","def type_array():\n return []","def change_sets_into_lists(json_like: Any) -> Any:\r\n return json.loads(json.dumps(json_like, cls=SetEncoder))","def dict(self):\n\t\treturn self.json","def _to_base_type(self, value):\n if value is None:\n return ''\n else:\n return value.to_json()","def _variable_single_types(self):\n return [\n 'Binary',\n 'KeyValue',\n 'String',\n 'TCEntity',\n 'TCEnhancedEntity',\n ]","def to_json(self):\n pass","def default(self, o):\n if isinstance(o, np.ndarray):\n return o.tolist()\n if isinstance(o, np.int64):\n return int(o)\n if isinstance(o, np.int32):\n return int(o)\n if isinstance(o, abc.ABCMeta):\n return str(o)\n return json.JSONEncoder.default(self, o)","def jsonify(obj):\n\n t = type(obj)\n\n if t == dict:\n return {jsonify(k):jsonify(v) for k,v in obj.items()}\n if t in [list, set]:\n return [jsonify(x) for x in obj]\n if t == np.ndarray:\n return [jsonify(x) for x in obj.tolist()]\n \n return obj","def convert(self):\n if isinstance(self.json, list):\n return self.iter_list(self.json)\n\n if isinstance(self.json, dict):\n return self.iter_plain(self.json)\n\n return self.json","def to_json(self, *args, **kwargs):\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)","def jsonify(self):\n jsonObject = self.getJsonObject()\n return json.dumps(jsonObject)","def to_json(self):\n return json.dumps(self._asdict())","def _json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def types():\n types = session.query(Type).all()\n return jsonify(types=[t.name for t in types])","def GetJSON(self):\n return json.dumps(self.GetDict())","def to_json(self) :\n return jsonpickle.encode(self)","def object_to_flat_jdata(self, dto):\n jdata = None\n raise Exception(\"Abstract Class. Not Implemented\")\n return jdata","def __json_data__(self):\n if schema.is_dense(self.namespace):\n dense_records = dict()\n for field in Observation._fields:\n dense_records[field] = []\n\n for obs in self.data:\n for key, val in six.iteritems(obs._asdict()):\n dense_records[key].append(serialize_obj(val))\n\n return dense_records\n\n else:\n return [serialize_obj(_) for _ in self.data]","def json(self):\n return json.dumps(self, default=lambda o: o.__dict__,\n sort_keys=True, indent=4)","def default(self, obj):\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n elif isinstance(obj, np.generic):\n return np.asscalar(obj)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def to_jsontype(type):\n typename = type.__name__ if type else None\n renames = {'str': 'string', 'int': 'integer', 'bool': 'bool'}\n if typename in renames:\n typename = renames[typename]\n return typename","def to_json(self) -> JSON:\n pass","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n todecode = dct['__ndarray__'].encode(\"ascii\")\n data = base64.b64decode(todecode)\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_serialize():\n variable = ['hello', 42, [1, 'two'], 'apple']\n print(\"Original {0} - {1}\".format(variable, type(variable)))\n\n #encoding\n encode = json.dumps(variable)\n print(\"Encoded {0} - {1}\".format(encode, type(encode)))\n\n #decoding\n decoded = json.loads(encode)\n print(\"Decoded {0} - {1}\".format(decoded, type(decoded)))","def get_types(self):\n return self.types","def pythonTypeToJSONType(value):\n if isinstance(value, dict): return 'object'\n elif isinstance(value, (list, tuple)): return 'array'\n elif isinstance(value, str): return 'string'\n elif isinstance(value, bool): return 'boolean'\n elif isinstance(value, (int, float)): return 'number'\n elif value is None: return 'null'\n else: return 'unknown'","def _to_json_compat(self):\n # add and __obj_type attribute, so it can be reconstructed\n dic = {\"__obj_type\": self.__class__.__qualname__}\n for attr, typ in self._attrs_to_save.items():\n dic[attr] = typ.to_json_compat(getattr(self, attr))\n return dic","def encode(data):\n target_dict = (type(data).__name__, dataclasses.asdict(data))\n return JsonCoder.encode(target_dict)","def make_json_compatible(obj_):\n if isinstance(obj_, (numbers.Number, str, bool)) or obj_ is None:\n # these are handled as is\n return obj_\n elif isinstance(obj_, collections.Mapping):\n return {\n make_json_compatible(k): make_json_compatible(v)\n for k, v in obj_.items()\n }\n elif isinstance(obj_, (collections.Iterable, collections.Set)):\n return [make_json_compatible(v) for v in obj_]\n elif isinstance(obj_, (datetime.datetime, datetime.date)):\n return obj_.isoformat()\n\n raise NotImplementedError(\"Dont know how to handle objects of type {}\".format(type(obj_)))","def tojson(self) -> ty.Text:\n return json.dumps(self.todict())","def convert_to_json(self):\n return self.__dict__","def json(self, value):\n # respect {None}\n if value is None:\n # by leaving it alone\n return None\n # by default, let the raw value through; the schemata that are not JSON representable\n # must override to provide something suitable\n return value","def to_json(self, value):\r\n try:\r\n result = super(StringOrDate, self).to_json(value)\r\n except:\r\n return value\r\n if result is None:\r\n return value\r\n else:\r\n return result","def jsonable(self, *args, **options):\n d = {}\n for field_name, field in self.schema.normal_fields.items():\n field_val = getattr(self, field_name, None)\n field_val = field.jsonable(self, field_val)\n if field_val is not None:\n d[field_name] = field_val\n\n return d","def json(cls):\n def _json_impl(agg, record):\n if not agg: agg = {}\n for (k, v) in record.items():\n logger.info(\"k: \" + str(k) + \", v: \" + str(v))\n if agg.has_key(k):\n if happy.flow.isIterable(v):\n agg[k].extend(v)\n else:\n agg[k].append(v)\n else:\n if happy.flow.isIterable(v):\n agg[k] = v\n else:\n agg[k] = [v]\n return agg\n return _json_impl","def to_json(self):\n return None","def toJSON(self):\n raise NotImplementedError()","def json_decode_hook(obj: Any) -> Any:\n if 'type(Decimal)' in obj:\n return Decimal(obj['type(Decimal)'])\n elif 'type(pd.Timestamp)' in obj:\n return pd.Timestamp(obj['type(pd.Timestamp)'])\n elif 'type(datetime)' in obj:\n return datetime.fromisoformat(obj['type(datetime)'])\n return obj","def json_serial(obj):\r\n\r\n\t\tif isinstance(obj,(datetime, date)):\r\n\t\t\treturn obj.isoformat()\r\n\t\traise TypeError (\"Type %s not serializable\" % type(obj))","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct","def json(self):\n json_data = {}\n\n for item in self.iteritems():\n try:\n value = item.get_value(json_serializable=True)\n except TypeError:\n value = \"NOT_SERIALIZABLE\"\n item_data = {\n \"tags\": list(item.tags),\n \"value\": value,\n }\n if item.name in json_data:\n json_data[item.name].append(item_data)\n else:\n json_data[item.name] = [item_data]\n\n return json_data","def to_python(self):\n return self._python_type","def toJSON(self) -> str:\r\n try:\r\n _ = json.dumps(self.value)\r\n value = self.value\r\n except (TypeError, OverflowError):\r\n value = {}\r\n value['object_type'] = self.value.__class__.__name__\r\n if isinstance(self.value, RawData):\r\n type_str = '_{}__'.format(value['object_type'])\r\n for key, data in self.value.__dict__.items():\r\n value[key.replace(type_str, '')] = data\r\n else:\r\n type_str = '_{}'.format(value['object_type'])\r\n for key, data in self.value.__dict__.items():\r\n value[key.replace(type_str, '')] = data\r\n\r\n return json.dumps({'object_type' : 'RawData', 'version' : self.version,\r\n 'timestamp' : self.timestamp, 'label' : self.label,\r\n 'value' : value})","def jsonSerial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n\n if isinstance(obj, enum.Enum):\n return obj.value\n\n raise TypeError (\"Type %s not serializable\" % type(obj))","def toJSON(object):\n\treturn json.dumps(object, ensure_ascii=False)","def data_types(self) -> Sequence['outputs.CodelessUiConnectorConfigPropertiesResponseDataTypes']:\n return pulumi.get(self, \"data_types\")","def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data\n data_b64 = base64.b64encode(obj_data)\n return dict(__ndarray__=data_b64,\n dtype=str(obj.dtype),\n shape=obj.shape)\n elif isinstance(obj, np.generic):\n return np.asscalar(obj)\n # Let the base class default method raise the TypeError\n return json.JSONEncoder(self, obj)","def get_previewdata(cls, pydata):\n return JsonContentType.dumps(pydata)","def jsonDefault(object):\n return object.__dict__","def tojson(self):\n return json.dumps(self.jsonable())"],"string":"[\n \"def json(self) -> Union[dict, list, str, int, float]:\\n raise NotImplementedError # pragma: no cover\",\n \"def data_types(self):\",\n \"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:\",\n \"def toJSType(self):\\n return dict(\\n name=self.name,\\n description=self.description,\\n address=self.address,\\n lat=self.lat,\\n lng=self.lng,\\n submitted_by=self.submitted_by,\\n certified=self.certified,\\n id=str(self.id),\\n comments=[c.toJSType() for c in self.comments],\\n feature_set=self.feature_set.toJSType()\\n )\",\n \"def as_json(self):\",\n \"def json_type(self):\\n return exclusions.closed()\",\n \"def json_serialize(self):\\n raise NotImplementedError('json_serialize must be overriden')\",\n \"def json(self):\\n return [t.json for t in self.values()]\",\n \"def data_types():\\n\\n return ...\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = json_data.__dict__.iteritems()\\n else:\\n itr = json_data.__dict__.items()\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result;\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def json (self):\\n\\n return jsonpickle.encode(self, unpicklable=False)\",\n \"def json_default(o):\\n if np.isscalar(o) and isinstance(o, np.generic):\\n return o.item() # coerce numpy number to pure-python type\\n elif isinstance(o, (tuple, set)):\\n return list(o)\\n else:\\n # Dispatch to JSONEncoder so that we see errors straight away\\n # if an object can't be serialized.\\n return JSON_ENCODER.default(o)\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = json_data.__dict__.iteritems()\\n else:\\n itr = json_data.__dict__.items()\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result\",\n \"def test_as_python_types(self):\\n obs = _as_python_types(self.metadata_map, self.headers)\\n exp = [[2.1, 3.1, 3],\\n ['str1', '200', 'string30'],\\n [1, 2, 3]]\\n self.assertEqual(obs, exp)\",\n \"def _asdict(self) -> Dict[Text, Any]:\\n return self.as_base_types()\",\n \"def json(self) -> Any:\\n return json.loads(self)\",\n \"def object_to_json(self, json_data):\\n result = {}\\n if sys.version_info[0] < 3:\\n itr = iter(json_data.__dict__.items())\\n else:\\n itr = list(json_data.__dict__.items())\\n for key,value in itr:\\n # Skip internal attributes.\\n if key.startswith(\\\"__\\\"):\\n continue\\n result[key] = self.array_to_json_array(value) if isinstance(value, list) else\\\\\\n self.object_to_json(value) if not self.is_primittive(value) else\\\\\\n value\\n return result\",\n \"def javatype(s):\\n return {\\n 'int': 'int',\\n 'String': 'String',\\n 'Long': 'Long',\\n 'boolean': 'boolean'\\n }.get(s.name, s.name)\",\n \"def json_serialize(value):\\n if value is None or isinstance(value, (int, long, float, basestring, bool)):\\n return value\\n elif isinstance(value, (list, tuple, set)):\\n return [json_serialize(v) for v in value]\\n elif isinstance(value, dict):\\n for k, v in value.items():\\n value[k] = json_serialize(v)\\n return value\\n # return date/time in isoformat\\n elif isinstance(value, (dt.datetime, dt.date, dt.time)):\\n return value.isoformat()\\n elif isinstance(value, ActiveRecordMixin):\\n return _model_to_dict(value)\\n else:\\n return unicode(value)\",\n \"def serialize(self):\\n dt = super(AnyPin, self).serialize()\\n constrainedType = self.activeDataType\\n if constrainedType != self.__class__.__name__:\\n pinClass = findPinClassByType(constrainedType)\\n # serialize with active type's encoder\\n dt['value'] = json.dumps(\\n self.currentData(), cls=pinClass.jsonEncoderClass())\\n dt['currDataType'] = constrainedType\\n return dt\",\n \"def data_types(self):\\n return self['data_types']\",\n \"def get_column_type(cls, **kwargs: Any) -> Any:\\n return sqlalchemy.JSON()\",\n \"def json_friendly(self):\",\n \"def json(self):\\n return capi.to_json(self.ptr)\",\n \"def _jsonable(self):\\n magic_dict = {}\\n mman = self.magics_manager\\n magics = mman.lsmagic()\\n for key, subdict in magics.items():\\n d = {}\\n magic_dict[key] = d\\n for name, obj in subdict.items():\\n try:\\n classname = obj.__self__.__class__.__name__\\n except AttributeError:\\n classname = 'Other'\\n \\n d[name] = classname\\n return magic_dict\",\n \"def data_types(self) -> 'outputs.AwsS3DataConnectorDataTypesResponse':\\n return pulumi.get(self, \\\"data_types\\\")\",\n \"def json(self):\\n class ExtendedJSONEncoder(json.JSONEncoder):\\n def default(self, obj):\\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\\n encoded_object = obj.isoformat()\\n else:\\n encoded_object = json.JSONEncoder.default(self, obj)\\n return encoded_object\\n\\n obj = {\\n 'operation': self.operation,\\n 'version': self.version,\\n 'language': self.language,\\n 'identifiers': self.identifiers,\\n 'store_execute': self.store_execute,\\n 'status': self.status,\\n 'lineage': self.lineage,\\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\\n 'outputs': self.outputs,\\n 'raw': self.raw\\n }\\n\\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)\",\n \"def jsons(self):\\n if self._jsons is None:\\n self.make_jsons()\\n return self._jsons\",\n \"def getencdec():\\n return (json.JSONEncoder(), json.JSONDecoder())\",\n \"def json(self):\\n return self._json\",\n \"def _get_types(self):\\n\\n db = Database()\\n self.c_built_ins = list(map(lambda tup: tup[0], db.select_built_types()))\\n self.c_built_in_array_types = r'^(' + '|'.join(self.escaped(self.c_built_ins)) + ')\\\\[[0-9]*\\\\]'\\n self.c_types = list(map(lambda tup: tup[0], db.select_types()))\\n self.c_array_types = r'^(' + '|'.join(self.escaped(self.c_types)) + ')\\\\[[0-9]*\\\\]'\\n db.close_connection()\",\n \"def json_raw(self):\\n return json.dumps(self.data, cls=ComplexEncoder)\",\n \"def default(self, obj):\\n \\n if isinstance(obj, np.ndarray):\\n return list(obj)\\n\\n if isinstance(obj, uuid.UUID):\\n return str(obj)\\n\\n if isinstance(obj, datetime.datetime):\\n return obj.isoformat()\\n \\n if isinstance(obj,TPC):\\n return obj._so()\\n \\n # No special handling called for; pass through\\n return json.JSONEncoder.default(self, obj)\",\n \"def default(self, obj):\\n numpy_types = (\\n np.bool_,\\n # np.bytes_, -- python `bytes` class is not json serializable\\n # np.complex64, -- python `complex` class is not json serializable\\n # np.complex128, -- python `complex` class is not json serializable\\n # np.complex256, -- python `complex` class is not json serializable\\n # np.datetime64, -- python `datetime.datetime` class is not json serializable\\n np.float16,\\n np.float32,\\n np.float64,\\n # np.float128, -- special handling below\\n np.int8,\\n np.int16,\\n np.int32,\\n np.int64,\\n # np.object_ -- should already be evaluated as python native\\n np.str_,\\n np.uint8,\\n np.uint16,\\n np.uint32,\\n np.uint64,\\n np.void,\\n )\\n if isinstance(obj, np.ndarray):\\n return obj.tolist()\\n elif isinstance(obj, numpy_types):\\n return obj.item()\\n elif isinstance(obj, np.float128):\\n return obj.astype(np.float64).item()\\n elif isinstance(obj, Decimal):\\n return str(obj)\\n elif isinstance(obj, datetime):\\n return str(obj)\\n elif obj is np.ma.masked:\\n return str(np.NaN)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder.default(self, obj)\",\n \"def json(self):\\n return self._parsejson(self.raw)\",\n \"def json(self):\\n raise NotImplementedError(\\\"JSON not specified!\\\")\",\n \"def encode_value(x):\\n for serializer in string_serializers:\\n if isinstance(x, serializer.type):\\n return {\\\"$type\\\": serializer.name, \\\"$value\\\": serializer.to_json(x)}\\n\\n raise TypeError(type(x)) # pragma: no cover\",\n \"def json(self):\\n return self.__json\",\n \"def _types(cls):\\n return {}\",\n \"def serialize(cls, obj):\\n return json.dumps(obj, cls=CustomTypeEncoder)\",\n \"def jsonify(obj):\\n raise NotImplementedError\",\n \"def to_json(self):\\r\\n return {'type': self.type, 'name': self.name}\",\n \"def type_object(self, json):\\n json_prop = _utils.json_prop_finder(json)\\n d = {k: (self.switcher(v)) for k, v in json_prop.items()}\\n return d\",\n \"def _convert_to_jsonc(x):\\n\\n if isinstance(x, dict):\\n jsonc_obj = Jsonc()\\n # Recursively transform all members of the dict.\\n # When converting a dict, we do not convert _name items into private\\n # Jsonc members.\\n for key, value in x.items():\\n jsonc_obj._dict[key] = _convert_to_jsonc(value)\\n return jsonc_obj\\n elif isinstance(x, list):\\n # Recursively transform all members of the list.\\n members = []\\n for item in x:\\n members.append(_convert_to_jsonc(item))\\n return members\\n else:\\n # Return the base object.\\n return x\",\n \"def to_json(self):\\n return json.dumps(self, allow_nan=False, default=lambda o: o.__dict__).encode(\\\"utf8\\\")\",\n \"def schema_as_json(content_schema: Dict[str, n.SerializableType]) -> n.SerializableType:\\n if \\\"properties\\\" in content_schema:\\n current: Dict[str, Any] = {}\\n properties = content_schema[\\\"properties\\\"]\\n assert isinstance(properties, Dict)\\n for prop, options in properties.items():\\n if \\\"type\\\" not in options:\\n current[prop] = \\\"Any\\\"\\n elif options[\\\"type\\\"] == \\\"object\\\":\\n current[prop] = schema_as_json(options)\\n elif options[\\\"type\\\"] == \\\"array\\\":\\n current[prop] = [schema_as_json(options[\\\"items\\\"])]\\n else:\\n current[prop] = options[\\\"type\\\"]\\n\\n return current\\n\\n if \\\"items\\\" in content_schema:\\n assert isinstance(content_schema[\\\"items\\\"], Dict)\\n return [schema_as_json(content_schema[\\\"items\\\"])]\\n\\n return content_schema[\\\"type\\\"]\",\n \"def type_array():\\n return []\",\n \"def change_sets_into_lists(json_like: Any) -> Any:\\r\\n return json.loads(json.dumps(json_like, cls=SetEncoder))\",\n \"def dict(self):\\n\\t\\treturn self.json\",\n \"def _to_base_type(self, value):\\n if value is None:\\n return ''\\n else:\\n return value.to_json()\",\n \"def _variable_single_types(self):\\n return [\\n 'Binary',\\n 'KeyValue',\\n 'String',\\n 'TCEntity',\\n 'TCEnhancedEntity',\\n ]\",\n \"def to_json(self):\\n pass\",\n \"def default(self, o):\\n if isinstance(o, np.ndarray):\\n return o.tolist()\\n if isinstance(o, np.int64):\\n return int(o)\\n if isinstance(o, np.int32):\\n return int(o)\\n if isinstance(o, abc.ABCMeta):\\n return str(o)\\n return json.JSONEncoder.default(self, o)\",\n \"def jsonify(obj):\\n\\n t = type(obj)\\n\\n if t == dict:\\n return {jsonify(k):jsonify(v) for k,v in obj.items()}\\n if t in [list, set]:\\n return [jsonify(x) for x in obj]\\n if t == np.ndarray:\\n return [jsonify(x) for x in obj.tolist()]\\n \\n return obj\",\n \"def convert(self):\\n if isinstance(self.json, list):\\n return self.iter_list(self.json)\\n\\n if isinstance(self.json, dict):\\n return self.iter_plain(self.json)\\n\\n return self.json\",\n \"def to_json(self, *args, **kwargs):\\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)\",\n \"def jsonify(self):\\n jsonObject = self.getJsonObject()\\n return json.dumps(jsonObject)\",\n \"def to_json(self):\\n return json.dumps(self._asdict())\",\n \"def _json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def types():\\n types = session.query(Type).all()\\n return jsonify(types=[t.name for t in types])\",\n \"def GetJSON(self):\\n return json.dumps(self.GetDict())\",\n \"def to_json(self) :\\n return jsonpickle.encode(self)\",\n \"def object_to_flat_jdata(self, dto):\\n jdata = None\\n raise Exception(\\\"Abstract Class. Not Implemented\\\")\\n return jdata\",\n \"def __json_data__(self):\\n if schema.is_dense(self.namespace):\\n dense_records = dict()\\n for field in Observation._fields:\\n dense_records[field] = []\\n\\n for obs in self.data:\\n for key, val in six.iteritems(obs._asdict()):\\n dense_records[key].append(serialize_obj(val))\\n\\n return dense_records\\n\\n else:\\n return [serialize_obj(_) for _ in self.data]\",\n \"def json(self):\\n return json.dumps(self, default=lambda o: o.__dict__,\\n sort_keys=True, indent=4)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n return obj.tolist()\\n elif isinstance(obj, np.generic):\\n return np.asscalar(obj)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def to_jsontype(type):\\n typename = type.__name__ if type else None\\n renames = {'str': 'string', 'int': 'integer', 'bool': 'bool'}\\n if typename in renames:\\n typename = renames[typename]\\n return typename\",\n \"def to_json(self) -> JSON:\\n pass\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n todecode = dct['__ndarray__'].encode(\\\"ascii\\\")\\n data = base64.b64decode(todecode)\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_serialize():\\n variable = ['hello', 42, [1, 'two'], 'apple']\\n print(\\\"Original {0} - {1}\\\".format(variable, type(variable)))\\n\\n #encoding\\n encode = json.dumps(variable)\\n print(\\\"Encoded {0} - {1}\\\".format(encode, type(encode)))\\n\\n #decoding\\n decoded = json.loads(encode)\\n print(\\\"Decoded {0} - {1}\\\".format(decoded, type(decoded)))\",\n \"def get_types(self):\\n return self.types\",\n \"def pythonTypeToJSONType(value):\\n if isinstance(value, dict): return 'object'\\n elif isinstance(value, (list, tuple)): return 'array'\\n elif isinstance(value, str): return 'string'\\n elif isinstance(value, bool): return 'boolean'\\n elif isinstance(value, (int, float)): return 'number'\\n elif value is None: return 'null'\\n else: return 'unknown'\",\n \"def _to_json_compat(self):\\n # add and __obj_type attribute, so it can be reconstructed\\n dic = {\\\"__obj_type\\\": self.__class__.__qualname__}\\n for attr, typ in self._attrs_to_save.items():\\n dic[attr] = typ.to_json_compat(getattr(self, attr))\\n return dic\",\n \"def encode(data):\\n target_dict = (type(data).__name__, dataclasses.asdict(data))\\n return JsonCoder.encode(target_dict)\",\n \"def make_json_compatible(obj_):\\n if isinstance(obj_, (numbers.Number, str, bool)) or obj_ is None:\\n # these are handled as is\\n return obj_\\n elif isinstance(obj_, collections.Mapping):\\n return {\\n make_json_compatible(k): make_json_compatible(v)\\n for k, v in obj_.items()\\n }\\n elif isinstance(obj_, (collections.Iterable, collections.Set)):\\n return [make_json_compatible(v) for v in obj_]\\n elif isinstance(obj_, (datetime.datetime, datetime.date)):\\n return obj_.isoformat()\\n\\n raise NotImplementedError(\\\"Dont know how to handle objects of type {}\\\".format(type(obj_)))\",\n \"def tojson(self) -> ty.Text:\\n return json.dumps(self.todict())\",\n \"def convert_to_json(self):\\n return self.__dict__\",\n \"def json(self, value):\\n # respect {None}\\n if value is None:\\n # by leaving it alone\\n return None\\n # by default, let the raw value through; the schemata that are not JSON representable\\n # must override to provide something suitable\\n return value\",\n \"def to_json(self, value):\\r\\n try:\\r\\n result = super(StringOrDate, self).to_json(value)\\r\\n except:\\r\\n return value\\r\\n if result is None:\\r\\n return value\\r\\n else:\\r\\n return result\",\n \"def jsonable(self, *args, **options):\\n d = {}\\n for field_name, field in self.schema.normal_fields.items():\\n field_val = getattr(self, field_name, None)\\n field_val = field.jsonable(self, field_val)\\n if field_val is not None:\\n d[field_name] = field_val\\n\\n return d\",\n \"def json(cls):\\n def _json_impl(agg, record):\\n if not agg: agg = {}\\n for (k, v) in record.items():\\n logger.info(\\\"k: \\\" + str(k) + \\\", v: \\\" + str(v))\\n if agg.has_key(k):\\n if happy.flow.isIterable(v):\\n agg[k].extend(v)\\n else:\\n agg[k].append(v)\\n else:\\n if happy.flow.isIterable(v):\\n agg[k] = v\\n else:\\n agg[k] = [v]\\n return agg\\n return _json_impl\",\n \"def to_json(self):\\n return None\",\n \"def toJSON(self):\\n raise NotImplementedError()\",\n \"def json_decode_hook(obj: Any) -> Any:\\n if 'type(Decimal)' in obj:\\n return Decimal(obj['type(Decimal)'])\\n elif 'type(pd.Timestamp)' in obj:\\n return pd.Timestamp(obj['type(pd.Timestamp)'])\\n elif 'type(datetime)' in obj:\\n return datetime.fromisoformat(obj['type(datetime)'])\\n return obj\",\n \"def json_serial(obj):\\r\\n\\r\\n\\t\\tif isinstance(obj,(datetime, date)):\\r\\n\\t\\t\\treturn obj.isoformat()\\r\\n\\t\\traise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json(self):\\n json_data = {}\\n\\n for item in self.iteritems():\\n try:\\n value = item.get_value(json_serializable=True)\\n except TypeError:\\n value = \\\"NOT_SERIALIZABLE\\\"\\n item_data = {\\n \\\"tags\\\": list(item.tags),\\n \\\"value\\\": value,\\n }\\n if item.name in json_data:\\n json_data[item.name].append(item_data)\\n else:\\n json_data[item.name] = [item_data]\\n\\n return json_data\",\n \"def to_python(self):\\n return self._python_type\",\n \"def toJSON(self) -> str:\\r\\n try:\\r\\n _ = json.dumps(self.value)\\r\\n value = self.value\\r\\n except (TypeError, OverflowError):\\r\\n value = {}\\r\\n value['object_type'] = self.value.__class__.__name__\\r\\n if isinstance(self.value, RawData):\\r\\n type_str = '_{}__'.format(value['object_type'])\\r\\n for key, data in self.value.__dict__.items():\\r\\n value[key.replace(type_str, '')] = data\\r\\n else:\\r\\n type_str = '_{}'.format(value['object_type'])\\r\\n for key, data in self.value.__dict__.items():\\r\\n value[key.replace(type_str, '')] = data\\r\\n\\r\\n return json.dumps({'object_type' : 'RawData', 'version' : self.version,\\r\\n 'timestamp' : self.timestamp, 'label' : self.label,\\r\\n 'value' : value})\",\n \"def jsonSerial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n\\n if isinstance(obj, enum.Enum):\\n return obj.value\\n\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def toJSON(object):\\n\\treturn json.dumps(object, ensure_ascii=False)\",\n \"def data_types(self) -> Sequence['outputs.CodelessUiConnectorConfigPropertiesResponseDataTypes']:\\n return pulumi.get(self, \\\"data_types\\\")\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n elif isinstance(obj, np.generic):\\n return np.asscalar(obj)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def get_previewdata(cls, pydata):\\n return JsonContentType.dumps(pydata)\",\n \"def jsonDefault(object):\\n return object.__dict__\",\n \"def tojson(self):\\n return json.dumps(self.jsonable())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6872278","0.62380904","0.6095021","0.6073043","0.6053912","0.6046117","0.6027797","0.6000453","0.58964735","0.587078","0.5852057","0.5852057","0.5848348","0.58386344","0.58100027","0.58068883","0.5797225","0.5795872","0.5778331","0.57390094","0.57096803","0.57005286","0.5680702","0.56764776","0.5673599","0.56711024","0.5649122","0.56396264","0.5636166","0.56162614","0.5605615","0.5596538","0.5577498","0.55752015","0.5573935","0.5573084","0.5572395","0.5556133","0.5550822","0.554942","0.5546804","0.55302244","0.5499554","0.54830563","0.5463694","0.54577875","0.5455464","0.54399544","0.543756","0.5424131","0.5419976","0.5419957","0.54086596","0.5388043","0.5380237","0.5374634","0.5373703","0.5370762","0.5369678","0.5363459","0.535543","0.53515995","0.534553","0.53434825","0.5337065","0.53362405","0.5329393","0.5328212","0.53229207","0.5318672","0.53069055","0.53014344","0.5292917","0.5275446","0.52689755","0.52638817","0.52456844","0.5245066","0.5242254","0.52379644","0.5237215","0.52314645","0.5228496","0.5220352","0.5219949","0.52172095","0.52159995","0.52159995","0.52057534","0.52057534","0.52057534","0.519781","0.51911676","0.51897806","0.5189234","0.5186645","0.51843005","0.5179983","0.51772094","0.51735246","0.5172289"],"string":"[\n \"0.6872278\",\n \"0.62380904\",\n \"0.6095021\",\n \"0.6073043\",\n \"0.6053912\",\n \"0.6046117\",\n \"0.6027797\",\n \"0.6000453\",\n \"0.58964735\",\n \"0.587078\",\n \"0.5852057\",\n \"0.5852057\",\n \"0.5848348\",\n \"0.58386344\",\n \"0.58100027\",\n \"0.58068883\",\n \"0.5797225\",\n \"0.5795872\",\n \"0.5778331\",\n \"0.57390094\",\n \"0.57096803\",\n \"0.57005286\",\n \"0.5680702\",\n \"0.56764776\",\n \"0.5673599\",\n \"0.56711024\",\n \"0.5649122\",\n \"0.56396264\",\n \"0.5636166\",\n \"0.56162614\",\n \"0.5605615\",\n \"0.5596538\",\n \"0.5577498\",\n \"0.55752015\",\n \"0.5573935\",\n \"0.5573084\",\n \"0.5572395\",\n \"0.5556133\",\n \"0.5550822\",\n \"0.554942\",\n \"0.5546804\",\n \"0.55302244\",\n \"0.5499554\",\n \"0.54830563\",\n \"0.5463694\",\n \"0.54577875\",\n \"0.5455464\",\n \"0.54399544\",\n \"0.543756\",\n \"0.5424131\",\n \"0.5419976\",\n \"0.5419957\",\n \"0.54086596\",\n \"0.5388043\",\n \"0.5380237\",\n \"0.5374634\",\n \"0.5373703\",\n \"0.5370762\",\n \"0.5369678\",\n \"0.5363459\",\n \"0.535543\",\n \"0.53515995\",\n \"0.534553\",\n \"0.53434825\",\n \"0.5337065\",\n \"0.53362405\",\n \"0.5329393\",\n \"0.5328212\",\n \"0.53229207\",\n \"0.5318672\",\n \"0.53069055\",\n \"0.53014344\",\n \"0.5292917\",\n \"0.5275446\",\n \"0.52689755\",\n \"0.52638817\",\n \"0.52456844\",\n \"0.5245066\",\n \"0.5242254\",\n \"0.52379644\",\n \"0.5237215\",\n \"0.52314645\",\n \"0.5228496\",\n \"0.5220352\",\n \"0.5219949\",\n \"0.52172095\",\n \"0.52159995\",\n \"0.52159995\",\n \"0.52057534\",\n \"0.52057534\",\n \"0.52057534\",\n \"0.519781\",\n \"0.51911676\",\n \"0.51897806\",\n \"0.5189234\",\n \"0.5186645\",\n \"0.51843005\",\n \"0.5179983\",\n \"0.51772094\",\n \"0.51735246\",\n \"0.5172289\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94099,"cells":{"query":{"kind":"string","value":"Main module execution method"},"document":{"kind":"string","value":"def exec_module(self, **kwargs):\n\n for key in list(self.module_arg_spec.keys()) + ['tags']:\n if hasattr(self, key):\n setattr(self, key, kwargs[key])\n elif kwargs[key] is not None:\n if key == \"location\":\n self.parameters[\"location\"] = kwargs[key]\n elif key == \"plan\":\n self.parameters[\"plan\"] = kwargs[key]\n elif key == \"open_shift_version\":\n self.parameters[\"open_shift_version\"] = kwargs[key]\n elif key == \"public_hostname\":\n self.parameters[\"public_hostname\"] = kwargs[key]\n elif key == \"fqdn\":\n self.parameters[\"fqdn\"] = kwargs[key]\n elif key == \"network_profile\":\n self.parameters[\"network_profile\"] = kwargs[key]\n elif key == \"router_profiles\":\n self.parameters[\"router_profiles\"] = kwargs[key]\n elif key == \"master_pool_profile\":\n ev = kwargs[key]\n if 'vm_size' in ev:\n if ev['vm_size'] == 'standard_d2s_v3':\n ev['vm_size'] = 'Standard_D2s_v3'\n elif ev['vm_size'] == 'standard_d4s_v3':\n ev['vm_size'] = 'Standard_D4s_v3'\n if 'os_type' in ev:\n if ev['os_type'] == 'linux':\n ev['os_type'] = 'Linux'\n elif ev['os_type'] == 'windows':\n ev['os_type'] = 'Windows'\n self.parameters[\"master_pool_profile\"] = ev\n elif key == \"agent_pool_profiles\":\n ev = kwargs[key]\n if 'vm_size' in ev:\n if ev['vm_size'] == 'standard_d2s_v3':\n ev['vm_size'] = 'Standard_D2s_v3'\n elif ev['vm_size'] == 'standard_d4s_v3':\n ev['vm_size'] = 'Standard_D4s_v3'\n if 'os_type' in ev:\n if ev['os_type'] == 'linux':\n ev['os_type'] = 'Linux'\n elif ev['os_type'] == 'windows':\n ev['os_type'] = 'Windows'\n self.parameters[\"agent_pool_profiles\"] = ev\n elif key == \"auth_profile\":\n self.parameters[\"auth_profile\"] = kwargs[key]\n\n old_response = None\n response = None\n\n self.mgmt_client = self.get_mgmt_svc_client(ContainerServiceClient,\n base_url=self._cloud_environment.endpoints.resource_manager)\n\n resource_group = self.get_resource_group(self.resource_group)\n\n if \"location\" not in self.parameters:\n self.parameters[\"location\"] = resource_group.location\n\n old_response = self.get_openshiftmanagedcluster()\n\n if not old_response:\n self.log(\"Open Shift Managed Cluster instance doesn't exist\")\n if self.state == 'absent':\n self.log(\"Old instance didn't exist\")\n else:\n self.to_do = Actions.Create\n else:\n self.log(\"Open Shift Managed Cluster instance already exists\")\n if self.state == 'absent':\n self.to_do = Actions.Delete\n elif self.state == 'present':\n self.log(\"Need to check if Open Shift Managed Cluster instance has to be deleted or may be updated\")\n self.to_do = Actions.Update\n\n if (self.to_do == Actions.Create) or (self.to_do == Actions.Update):\n self.log(\"Need to Create / Update the Open Shift Managed Cluster instance\")\n\n if self.check_mode:\n self.results['changed'] = True\n return self.results\n\n response = self.create_update_openshiftmanagedcluster()\n\n if not old_response:\n self.results['changed'] = True\n else:\n self.results['changed'] = old_response.__ne__(response)\n self.log(\"Creation / Update done\")\n elif self.to_do == Actions.Delete:\n self.log(\"Open Shift Managed Cluster instance deleted\")\n self.results['changed'] = True\n\n if self.check_mode:\n return self.results\n\n self.delete_openshiftmanagedcluster()\n # make sure instance is actually deleted, for some Azure resources, instance is hanging around\n # for some time after deletion -- this should be really fixed in Azure.\n while self.get_openshiftmanagedcluster():\n time.sleep(20)\n else:\n self.log(\"Open Shift Managed Cluster instance unchanged\")\n self.results['changed'] = False\n response = old_response\n\n if self.state == 'present':\n self.results.update(self.format_item(response))\n return self.results"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main(self):\r\n pass","def main():\n pass","def main(self) -> None:\n pass","def main():\n return","def run():\n main()","def main(self):","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():\n\tpass","def main():\n run_program()","def main():\n obj = UnityFilesystem()\n obj.perform_module_operation()","def main(self):\n pass","def main():\n Main()","def main():\n print(\"It works!!! ;-)\")\n ###TODO### do something with the various methods/functions of this file","def main():\n\n pass","def main() -> None:","def main() -> None:","def main() -> None:","def main() -> None:","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def Run():\r\n pass","def main():\n module = IRODSPermissionModule()\n module.run()","def execute_module(self):\n raise NotImplementedError","def main(self, params):\n pass","def main(cls):\n raise NotImplementedError","def main(self, **kwargs) -> None:\n ...","def main():\n print(\"is Running!\")","def main():\n\n obj = PowerStoreNfsExport()\n obj.perform_module_operation()","def main() -> None:\n return","def main():\n pass","def main():\n ...","def main():\n print(\"def main\")\n return APP.run()","def RUN(self):","def test_script(self) -> None:\n main()","def run(self):\n pass","def run(self):\n pass","def 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query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Clear the entry and disable the entry and button, making them unable to be interacted with. | def _disable_entry(self):
self.insert_entry.delete(0, tk.END)
self.insert_entry.configure(state=tk.DISABLED)
self.insert_button.configure(state=tk.DISABLED) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _enable_entry(self):\n self.insert_entry.configure(state=tk.NORMAL)\n self.insert_button.configure(state=tk.NORMAL)",
"def disable(self):\n self.colour_combo.config(state=tk.DISABLED)\n self.game_name_entry.config(state=tk.DISABLED)\n self.num_tickets_entry.config(state=tk.... | [
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"0.6289078",
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"0.6267464"... | 0.8018426 | 0 |
Enable the entry and button, allowing them to be interacted with. | def _enable_entry(self):
self.insert_entry.configure(state=tk.NORMAL)
self.insert_button.configure(state=tk.NORMAL) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enable(self):\n self.colour_combo.config(state=tk.NORMAL)\n self.game_name_entry.config(state=tk.NORMAL)\n self.num_tickets_entry.config(state=tk.NORMAL)",
"def entryToggle(self):\n status = \"normal\" if self.optionVar.get() == 4 else \"disabled\"\n for i in range(3):\n ... | [
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"0.62186... | 0.8187393 | 0 |
Internal method used as the callback for ">" events on the listboxes. This method enables or disabled the entry as appropriate to the new data selection and sets ``NTBrowser._curr_indices``. | def _listbox_select_callback(self, _):
self._curr_indices = self.key_listbox.curselection() or self.value_listbox.curselection()
if self._curr_indices:
kind = None
for idx in self._curr_indices:
# Break if any one of the selected rows is a table.
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enable_selected(self, window, values, branch_log_dict, key_event):\n utils.convert_to_numeric(values)\n if(values[key_event] in branch_log_dict[key_event]):#if there is branching for the chosen option\n for element_key in branch_log_dict[key_event][values[key_event]]:\n ... | [
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"0.5184486... | 0.6361306 | 0 |
Valida que la Entry tenga contenido | def on_autentificar(self, dni, nom, apellidos, telf, zona, nacion):
condicion = True
if dni == '':
self.txtDni.set_placeholder_text("Inserte DNI")
condicion = False
elif nom == '':
self.txtNombre.set_placeholder_text("Inserte Nombre")
condicion = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entry_valid(entry):\n\n if len(entry) != 4:\n return False\n date_list = re.split('\\\\W', entry[0])\n if len(date_list) != 2:\n return False\n try:\n int(date_list[0])\n int(date_list[1])\n float(entry[2])\n except ValueError:\n return False\n return... | [
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"0.578424",
"0.5778492",
"0.5757876",
"0.574536... | 0.0 | -1 |
Metodo que tras elegir un Dni del ComboBOx manda el Dni a un metodo de la Clase MetodosBD que busca el Dni y devuelve los datos de este | def update_vendedor(self, control):
# Sacamos el texto seleccionado del ComboBox
dniSelect = self.cbDniAdd.get_active_text()
# Condición para que sí no hay nada seleccionado en el Combox no haga una consula en vacío
if not dniSelect == None:
# Al selecionar el ComboBox el P... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buscar_dni(self, dni_socio):\n socio = session.query(Socio).filter(Socio.dni == dni_socio)\n print(socio)\n return socio",
"def __init__(self,dni):\n self.dni=dni\n self.categoria=None",
"def promjeni_broj_dana(self, x):\r\n broj = int(self.brojDana.itemText(x))\r\... | [
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"0.47667524",
"0.47375727",
"0.4700875",
"0.46558082",
"0.46... | 0.53349787 | 2 |
Metodo que separa los datos recogidos y luego los guarda en sus respectivos Entrys | def on_mostrarDatos(self, datos):
# Condición que cambia los estados de los RadioButons
if datos[0][1] == self.tipo1:
self.rbJefe.set_active(True)
else:
self.rbEmpleado.set_active(True)
self.txtDni.set_text(datos[0][2])
self.txtNombre.set_text(datos[0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_entry(self):\n\n for line_item in self.entry:\n pairs = line_item.split(' ')\n for pair in pairs:\n if ':' in pair:\n key, value = pair.split(':')\n if value.isdigit():\n self.fields[key] = int(valu... | [
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"0.51943165",
"0.51857114",
"0.51321214",
"0.51309603",
"0.5124592",
"0.512... | 0.0 | -1 |
Metodo que recarga el valor de la fecha en el Entry Fecha | def on_refreshTime(self, control):
self.txtFecha.set_text(str(self.localTime)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fecha(self):\n return self.fecha_cadena(self.__anno, self.__mes, self.__dia)",
"def date(self):",
"def edit_date(entry):\n entry.date = get_date()\n entry.save()\n input(\"Edit successful. \")\n return entry",
"def fecha_formateada(self):\n dia = self.__dia\n anno = self.... | [
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"0.55540234",
"0.55150414",
"0.55111825",
"0.5510722",
"0.5485... | 0.53953075 | 26 |
Metodo que envia el Dni seleccionado a un Metodo de la Clase MetodosBD y elimina de la base de datos al vendedor, luego llama al metodo recarga los ComboBox y al de limpiar los Entrys | def on_remove_clicked(self, control):
# Recogemos el valor del ComboBox
dniBaja = self.cbDniRemove.get_active_text()
# Lo enviamos al metodo de la Clase MetodosBD
MetodosBD.MetodosBD.borrar_vendedor(self, dniBaja)
# Metodo que refresca los Combobox
vendedores.actualizar_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_vendedor(self, control):\n\n # Sacamos el texto seleccionado del ComboBox\n dniSelect = self.cbDniAdd.get_active_text()\n\n # Condición para que sí no hay nada seleccionado en el Combox no haga una consula en vacío\n if not dniSelect == None:\n # Al selecionar el C... | [
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"0.5225088",
"0.5221306",
"0.51731884",
"0.51015127",
"0.50831175",
"0.4994... | 0.6815479 | 0 |
Medoto que limpia las cajas de texto | def on_limpiar(self, control):
self.btnAdd.set_label(self.vbtnAdd)
self.txtDni.set_text("")
self.txtNombre.set_text("")
self.txtApellidos.set_text("")
self.txtTelf.set_text("")
self.txtZona.set_text("")
self.txtNacionalidad.set_text("")
self.txtFecha.set_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getText():",
"def getText():",
"def getText():",
"def getText():",
"def getText():",
"def text(self) -> str:",
"def get_text(self):",
"def getText(self):",
"def get_texte(name):\r\n #with open(name, 'r', encoding='utf-8') as myfile:\r\n with open(name, 'r', encoding='utf-8') as myfile:\r\n ... | [
"0.6467837",
"0.6467837",
"0.6467837",
"0.6467837",
"0.6467837",
"0.62152416",
"0.6208919",
"0.60020155",
"0.5827453",
"0.5817684",
"0.57927465",
"0.5781781",
"0.5766028",
"0.57578844",
"0.5744636",
"0.5743139",
"0.5718214",
"0.56402254",
"0.5570407",
"0.55581504",
"0.5549819... | 0.0 | -1 |
Metodo para navegar a la ventana Main | def on_cerrar_clicked(self, Button):
main = Main.Main(self.tipo, self.id, self.nombre)
self.set_visible(False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(self):\r\n pass",
"def main():\n pass",
"def main(self):",
"def main(self) -> None:\n pass",
"def main():\n return",
"def main():\n\tpass",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main()... | [
"0.82190335",
"0.81940186",
"0.80682105",
"0.80224806",
"0.8015938",
"0.7978871",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
"0.78195494",
... | 0.0 | -1 |
Metodo para navegar a la ventana TablaVendedores | def on_tabla_vendedores_clicked(self, Button):
tablavendedores = TablaVendedores.tablaVendedores(self.tipo, self.id, self.nombre)
self.set_visible(False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def limpiarVentana(self):\n self.limpiarTabla(self.tableFactura)\n self.lineNumero.setEnabled(True)\n self.lineNumero.clear()\n self.limpiarTabla(self.tableNC)",
"def limpiarVentana(self):\n\n self.productosAgregados=0\n self.lotesVentas={}\n self.facturaCobrada=F... | [
"0.7634896",
"0.6529628",
"0.6476412",
"0.6044319",
"0.60242695",
"0.5978414",
"0.59579563",
"0.59432954",
"0.59077317",
"0.5905856",
"0.57944804",
"0.5777909",
"0.5723357",
"0.56998986",
"0.5681946",
"0.5668569",
"0.56681406",
"0.5663566",
"0.56567395",
"0.5638073",
"0.56278... | 0.68094844 | 1 |
Generate linearly increasing price data for unit tests | def get_dummy_data(num_days, low, high, end_date='1970-01-01'):
step = (high - low) / (num_days - 1)
ref = datetime.strptime(end_date, '%Y-%m-%d').date()
start_dt = ref - timedelta(days=(num_days - 1))
end_dt = ref + timedelta(days=1)
ts = np.arange(start_dt, end_dt, timedelta(days=1)).astype(date)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_price():\n return uniform(1.0, 350.0)",
"def generate(data, d=0.1):\n \n p = pd.DataFrame({\n \"Price\": data\n })\n p[\"Event\"] = ''\n event = 'upturn'\n ph = p['Price'][0] # highest price\n pl = ph # lowest price\n\n for i in range(0, len(p)):\n\n if event is '... | [
"0.6204404",
"0.6130902",
"0.6102203",
"0.6053854",
"0.5952796",
"0.5948425",
"0.5891981",
"0.58904123",
"0.58473146",
"0.58167547",
"0.5776299",
"0.5765693",
"0.5744515",
"0.57234496",
"0.5695937",
"0.56842405",
"0.5671014",
"0.5656032",
"0.56525654",
"0.56476706",
"0.561017... | 0.53852504 | 51 |
mu = k sqrt(1 + m2/m1) | def mu_sun(m2_over_m1: float) -> float:
mu = K * np.sqrt(1. + m2_over_m1)
return mu * mu | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mu(k, z):\n return 1",
"def kl(mu1, mu2):\n return (mu2-mu1)**2/2",
"def kl(mu1, mu2):\n return (mu2-mu1)**2/2",
"def mu_gm1m2(m1: float, m2: float) -> float:\n from astropy.constants import G\n\n return G * (m1 + m2)",
"def kl(mu1, mu2):\n return (mu2 - mu1) ** 2 / 2",
... | [
"0.7373971",
"0.7282575",
"0.7282575",
"0.71900904",
"0.713643",
"0.70311916",
"0.6701206",
"0.66690695",
"0.6589319",
"0.6589319",
"0.653416",
"0.6494656",
"0.64828813",
"0.6404334",
"0.6363272",
"0.6352015",
"0.63444686",
"0.63004714",
"0.62986004",
"0.62357163",
"0.6228569... | 0.75852823 | 0 |
mu = n^2 / a^3 | def mu_na(n: float, a: float) -> float:
return n * n * a * a * a | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mu(k, z):\n return 1",
"def npa_norm(a):\n\n return a / sum(a)",
"def blauNormal(blau, N):\n nom = blau - (1/N)\n den = 1 - (1/N)\n\n return nom / den",
"def U(n,gamma,A,y):\n return (2*A*gamma*math.sin(math.pi*n*y/A) - 2*math.pi*n*math.cos (math.pi*n*y/A))/(A**2*gamma**2 + math.pi**2 *... | [
"0.71695036",
"0.6887994",
"0.6764852",
"0.6750625",
"0.6626473",
"0.65062326",
"0.646334",
"0.6444955",
"0.6417958",
"0.63273966",
"0.6327223",
"0.6312752",
"0.6302596",
"0.6301676",
"0.6299949",
"0.6273926",
"0.6273746",
"0.62690264",
"0.6258225",
"0.62508994",
"0.6235605",... | 0.79202384 | 0 |
mu = G (m1 + m2) | def mu_gm1m2(m1: float, m2: float) -> float:
from astropy.constants import G
return G * (m1 + m2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mu(o1, o2):\n return o1*o2",
"def M_g(self):\n\n print(\"\", file=self.logfile)\n print(\"Updating g\", file=self.logfile)\n M_mu1 = np.lib.stride_tricks.as_strided(self.mu_pad,\n shape=[self.P+1, self.L_h],\n ... | [
"0.6987635",
"0.685425",
"0.64364874",
"0.6391254",
"0.6263511",
"0.62258524",
"0.6198913",
"0.6146619",
"0.6080753",
"0.60386586",
"0.60144967",
"0.6013963",
"0.60115695",
"0.59763104",
"0.5964742",
"0.59455556",
"0.59150636",
"0.5887213",
"0.58644265",
"0.5807733",
"0.58037... | 0.81322044 | 0 |
Generates site containing first page of recipes list sorted by published_date. | def recipes_list(request):
recipes = Recipe.objects.all().order_by('published_date')
# prawidlowy sposob zbierania URLa - object.image.url
# recipe = recipes[0]
# print("path: " + recipe.image.url)
paginator = Paginator(recipes, INITIAL_PAGE_SIZE)
page = paginator.page(1)
context = {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recipes_page(request):\n page_number = request.GET.get('page', None)\n if page_number is None:\n raise Http404\n\n # Get sorting parameter, if none is provides, sort by published_date\n sorting = request.GET.get('sorting', 'published_date')\n\n possible_sortings = ['up_votes', 'published_... | [
"0.6796219",
"0.6157274",
"0.61321515",
"0.5978671",
"0.5944646",
"0.5935294",
"0.5908201",
"0.5867303",
"0.5837911",
"0.5787703",
"0.57855505",
"0.57358456",
"0.5721771",
"0.57205456",
"0.57116795",
"0.56696147",
"0.56615347",
"0.5653457",
"0.55879027",
"0.55861884",
"0.5579... | 0.6504416 | 1 |
View responding to AJAX request for list of all ingredients currently stored in database. | def get_ingredients(request):
items = Ingredient.objects.all().order_by('name')
# return list of {item.name: item.pk}
context = []
for item in items:
context.append({item.name: item.pk})
return HttpResponse(json.dumps(context), content_type='application/json') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _show_ingredient_list(self):\n if self._ingredients_view:\n self._ingredients_view.destroy()\n\n username = self.food_service.get_user().get_username()\n ingredients = self.food_service.list_added_ingredients(username, expire=True)\n self._ingredients_view = IngredientsVi... | [
"0.71443564",
"0.6621847",
"0.6533707",
"0.6456862",
"0.63419956",
"0.62605214",
"0.6260338",
"0.6219494",
"0.61849874",
"0.61646134",
"0.61379063",
"0.6126435",
"0.608624",
"0.60640466",
"0.60598683",
"0.6016408",
"0.6010413",
"0.5991127",
"0.59910214",
"0.59663296",
"0.5936... | 0.72881126 | 0 |
View to generate updates to recipes list when user scrolls down the page. | def recipes_page(request):
page_number = request.GET.get('page', None)
if page_number is None:
raise Http404
# Get sorting parameter, if none is provides, sort by published_date
sorting = request.GET.get('sorting', 'published_date')
possible_sortings = ['up_votes', 'published_date', 'title... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recipes():\n recipes = mongo.db.recipes.find()\n return render_template(\"recipes/list.html\", recipes=recipes)",
"def view_recipes():\n if 'name' in session:\n recipeitem = PLAN.users[session['name']].view_recipes()\n return render_template('recipes.html', recipeitem=recipeitem)\n ... | [
"0.6429831",
"0.6382163",
"0.630745",
"0.6247727",
"0.62205404",
"0.6193851",
"0.61043197",
"0.6032494",
"0.60179096",
"0.5995997",
"0.5900895",
"0.5899395",
"0.58730197",
"0.5853571",
"0.5706099",
"0.569316",
"0.56857574",
"0.56070364",
"0.5564621",
"0.55582577",
"0.55518806... | 0.53443766 | 28 |
View called when user clicks on search button on website. Searches database for recipces that contain ingredients selected by user. | def get_filtered_recipes(request):
page_number = 1
if not request.GET:
# If GET data is empty, assume no filters and return list of all recipes
recipes = Recipe.objects.all().order_by('published_date')
else:
get_data = request.GET.dict()
page_number = get_data.get('page', N... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search():\n query = request.form.get(\"query\")\n # pylint: disable=redefined-outer-name\n recipes = list(mongo.db.recipes.find({\"$text\": {\"$search\": query}}))\n return render_template(\"recipes.html\", recipes=recipes)",
"def search():\n query = request.form.get(\"query\")\n recipes = ... | [
"0.71865153",
"0.7176708",
"0.71316457",
"0.7104751",
"0.710362",
"0.7047182",
"0.6896128",
"0.6818153",
"0.6713402",
"0.6696172",
"0.66647226",
"0.6662139",
"0.66172695",
"0.6577977",
"0.65558565",
"0.6540747",
"0.65292764",
"0.6525435",
"0.65035963",
"0.64904493",
"0.648865... | 0.0 | -1 |
View called when user enters some string into textbox on website. | def search_recipes(request):
string_to_find = request.GET.get("term", None)
if string_to_find is None:
return HttpResponse(status=400)
matching_recipes = Recipe.objects.filter(title__icontains=string_to_find)
context = {}
for r in matching_recipes:
context[r.title] = reverse('rec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ev_textinput(self, event: TextInput) -> None:",
"def ui_input_text() -> str:\n\ttext = input('enter your text ')\n\treturn text",
"def ev_textinput(self, event: tcod.event.TextInput) -> T | None:",
"def input(self, *fields, title=\"User input window\"):\n pass",
"def present_view(self, error: st... | [
"0.69977003",
"0.6499313",
"0.643579",
"0.60679567",
"0.5956231",
"0.59006214",
"0.5868776",
"0.58517617",
"0.58469707",
"0.5841802",
"0.5837776",
"0.5805453",
"0.57927436",
"0.57762057",
"0.5740888",
"0.57314384",
"0.57291365",
"0.5711473",
"0.5703694",
"0.5688913",
"0.56503... | 0.0 | -1 |
Generates site of a given recipe. | def recipe(request, recipe_slug):
current_recipe = get_object_or_404(Recipe, pk=recipe_slug)
ingredients = IngredientDetails.objects.filter(recipe=current_recipe)
ingredients_list = []
price = 0
for ingredient in ingredients:
ingredients_list.append(str(ingredient.ingredient) + " - " + str... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"layout_path\", help=\"relative path to the directory \"\n \"containing .rst files with site content and jinja \"\n \"templates that define the site structure\")\n parser.add_argument(\"ou... | [
"0.6079024",
"0.5831689",
"0.5802067",
"0.56758296",
"0.549623",
"0.5449182",
"0.5430671",
"0.5370831",
"0.536884",
"0.5350261",
"0.5342263",
"0.53291833",
"0.5299164",
"0.5240385",
"0.5228364",
"0.5220168",
"0.5200309",
"0.51567054",
"0.51305586",
"0.5121077",
"0.5110726",
... | 0.0 | -1 |
Add a new entry in the results table in RV arguments below.. session == SQLAlchemy Session data == the json message received from the agent | def add_results_non_json(session, node_id=None, oper_id=None, error=None,
toppatch_id=None, reboot=False, result=False,
results_received=datetime.now(), username='system_user'):
session = validate_session(session)
operation = operation_exists(session, oper_id)
results = None
if result an... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_records(self, data: dict, execution_context: dict):",
"def add_results(session, data, username='system_user'):\n session = validate_session(session)\n operation = operation_exists(session, data['operation_id'])\n node = node_exists(session,node_id=data['node_id'])\n if node:\n node_id ... | [
"0.62482166",
"0.6078163",
"0.58265",
"0.5792383",
"0.57908404",
"0.5740843",
"0.57107496",
"0.55978835",
"0.55917794",
"0.54867315",
"0.5469462",
"0.5465754",
"0.5449438",
"0.5412065",
"0.5400628",
"0.53911936",
"0.5387508",
"0.5379521",
"0.5369491",
"0.536726",
"0.536726",
... | 0.57755756 | 5 |
Add a node to the database | def add_node(session, client_ip=None, agent_timestamp=None,
node_timestamp=None, host_name=None, display_name=None,
computer_name=None, username='system_user'):
session = validate_session(session)
if not host_name and client_ip:
try:
host_name = gethostbyaddr(client_ip)[0]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_node(self, node):",
"def add_node (self, node):\n self.network.add_node(node.id)\n self.network.node[node.id] = node",
"def add_node(self, node: Node):\n prop_str = \",\\n\".join([\"n.%s = '%s'\" % (k, v) for k, v in node.data.items()])\n query = \"\"\"\n MERGE (n:%s {id:... | [
"0.7865234",
"0.774182",
"0.76837397",
"0.755156",
"0.7240712",
"0.7240318",
"0.7185423",
"0.71095324",
"0.7092884",
"0.7006346",
"0.7006007",
"0.6973722",
"0.6973722",
"0.6971237",
"0.69533014",
"0.6950354",
"0.6934846",
"0.69242233",
"0.69175804",
"0.69062704",
"0.688924",
... | 0.0 | -1 |
Add a Global User ACL to the database | def add_global_user_acl(session, user_id=None, is_admin=False,
is_global=True, allow_read=False, allow_install=False,
allow_uninstall=False, allow_reboot=False, allow_schedule=False,
allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_global_group_acl(session, group_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_r... | [
"0.6790956",
"0.66984755",
"0.6193511",
"0.60840625",
"0.6061878",
"0.59698826",
"0.5953534",
"0.5937081",
"0.59253997",
"0.5908613",
"0.58513284",
"0.57258356",
"0.56943804",
"0.5650924",
"0.55833733",
"0.5550112",
"0.55397975",
"0.550241",
"0.550241",
"0.54835874",
"0.54700... | 0.7756629 | 0 |
Add a Global Group ACL to the database | def add_global_group_acl(session, group_id=None, is_admin=False,
is_global=True, allow_read=False, allow_install=False,
allow_uninstall=False, allow_reboot=False, allow_schedule=False,
allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=Fal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_global_group_acl(session, group_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapsho... | [
"0.7196082",
"0.6934653",
"0.67724866",
"0.6646024",
"0.64088714",
"0.62780404",
"0.6215141",
"0.6154451",
"0.6126776",
"0.6117893",
"0.6082822",
"0.60679877",
"0.6067281",
"0.60590637",
"0.60528725",
"0.603411",
"0.6033565",
"0.59680486",
"0.5946027",
"0.5942363",
"0.5935729... | 0.81567854 | 0 |
Add a User ACL to a node in the database | def add_node_user_acl(session, node_id=None, user_id=None, allow_read=False,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_node_user_acl(session, node_id=None, user_id=None,\n allow_install=False, allow_uninstall=False, allow_reboot=False,\n allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_revert=False,\n allow_tag_creation=False... | [
"0.66488713",
"0.65743196",
"0.6349004",
"0.63126117",
"0.6273757",
"0.61371654",
"0.611919",
"0.61177486",
"0.60286134",
"0.60237795",
"0.60100114",
"0.59730655",
"0.59480417",
"0.59110665",
"0.5888698",
"0.58862835",
"0.58632857",
"0.5845603",
"0.58204746",
"0.5783785",
"0.... | 0.75014323 | 0 |
Add a Group ACL to a Node in the database | def add_node_group_acl(session, node_id=None, group_id=None, allow_read=False,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creati... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_node_group_acl(session, node_id=None, group_id=None,\n allow_install=False, allow_uninstall=False, allow_reboot=False,\n allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_revert=False,\n allow_tag_creation=Fal... | [
"0.7298856",
"0.67762434",
"0.6739489",
"0.64854664",
"0.6471868",
"0.64582276",
"0.6363607",
"0.6359242",
"0.63551635",
"0.63349617",
"0.6279043",
"0.6272919",
"0.6272078",
"0.6271495",
"0.6215116",
"0.6162174",
"0.6120281",
"0.6107547",
"0.60900867",
"0.6056464",
"0.6030693... | 0.7867277 | 0 |
Add a User ACL to a Tag in the database | def add_tag_user_acl(session, tag_id=None, user_id=None, allow_read=False,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation=F... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_tag_user_acl(session, tag_id=None, user_id=None,\n allow_install=False, allow_uninstall=False, allow_reboot=False,\n allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_revert=False,\n allow_tag_creation=False, ... | [
"0.6768385",
"0.66185904",
"0.6288666",
"0.62353295",
"0.62045974",
"0.61111546",
"0.5938137",
"0.59312445",
"0.5828969",
"0.58216935",
"0.5819768",
"0.58164734",
"0.57813776",
"0.5755194",
"0.56963474",
"0.5630525",
"0.56261504",
"0.5620099",
"0.5584222",
"0.5578137",
"0.557... | 0.7627426 | 0 |
Add a Group ACL to a Tag in the database | def add_tag_group_acl(session, tag_id=None, group_id=None, allow_read=False,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_tag_group_acl(session, tag_id=None, group_id=None,\n allow_install=False, allow_uninstall=False, allow_reboot=False,\n allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_revert=False,\n allow_tag_creation=False... | [
"0.7413525",
"0.6949173",
"0.6515452",
"0.638341",
"0.6300268",
"0.619689",
"0.618565",
"0.6143435",
"0.6096526",
"0.6077261",
"0.60423136",
"0.60403264",
"0.6036486",
"0.60344684",
"0.60183716",
"0.6016755",
"0.59663004",
"0.5962972",
"0.59606135",
"0.5952664",
"0.5943046",
... | 0.80384266 | 0 |
Add a tag to the database | def add_tag(session, tag_name, user_id=None, username='system_user'):
session = validate_session(session)
date_created=datetime.now()
try:
add_tag = TagInfo(tag_name, date_created, user_id)
session.add(add_tag)
session.commit()
return(True, "Tag %s added" % (tag_name), add_ta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AddTag(self, tag):\n\n if not self.persistant:\n return\n\n self.db.ExecuteSql('insert into tags(tag, track_id) values(\"%s\", %d);'\n %(tag, self.persistant['id']))\n self.db.ExecuteSql('commit;')",
"def add_tag_data(tag):\n\n add_tag = Tag(tag=tag)\n db.session.add... | [
"0.8637184",
"0.85335076",
"0.7906371",
"0.7768422",
"0.7698887",
"0.75692403",
"0.7459467",
"0.73945993",
"0.7363522",
"0.73570436",
"0.73460793",
"0.7338917",
"0.7332982",
"0.715787",
"0.70944375",
"0.7073959",
"0.70276374",
"0.69505596",
"0.68091697",
"0.67844456",
"0.6769... | 0.7250298 | 13 |
Add a dependency to the corresponding toppatch_id into the database | def add_dependency(session, data, username='system_user'):
session = validate_session(session)
failed_count = 0
for deps in data['data']:
pkg_id = deps['toppatch_id']
for dep in deps['dependencies']:
dep_exists = session.query(LinuxPackageDependency).\
filter(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_dependency(self, dep):\n self.dependency.append(dep)",
"def add_depend(self, data):\n try:\n self._session.add(StepDependencyEntity(\n child_id=data['child_id'],\n parent_id=data['parent_id']\n ))\n except SQLAlchemyError as err:\n... | [
"0.6426406",
"0.64062476",
"0.5922514",
"0.5561254",
"0.5473387",
"0.53967816",
"0.5364266",
"0.5284696",
"0.5237298",
"0.51207536",
"0.50454897",
"0.5004042",
"0.4978844",
"0.49685213",
"0.49589285",
"0.49584004",
"0.49383095",
"0.49302614",
"0.49144408",
"0.4911285",
"0.491... | 0.65303063 | 0 |
Add a list of nodes to an existing tag into the database arguments.... session == SQLAlchemy Session nodes = list of nodes tag_id = id of tag or tag_name = name of tag | def add_tag_per_node(session, nodes=[], tag_id=None, tag_name=None,
user_id=None, username='system_user'):
session = validate_session(session)
completed = False
count = 0
tag = None
if not tag_id and tag_name:
tag = tag_exists(session, tag_name=tag_name)
elif tag_id and n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_tag(args):\n\n if not args.nodespec and not args.software_profile and \\\n not args.hardware_profile:\n sys.stderr.write('Error: must specify --nodes'\n '/--software-profile/--hardware-profile\\n')\n sys.stderr.flush()\n sys.exit(1)\n\n session ... | [
"0.72279805",
"0.66229963",
"0.62487316",
"0.62040484",
"0.60978645",
"0.60316706",
"0.59950036",
"0.58186054",
"0.5780188",
"0.57044286",
"0.56786096",
"0.5670054",
"0.56257105",
"0.56120104",
"0.56080633",
"0.55849946",
"0.5579145",
"0.55715966",
"0.55562085",
"0.5544375",
... | 0.6936174 | 1 |
Add a new timeblock to RV. arguments below.. session == SQLAlchemy Session label == The name of the timeblock that you added start_date == "12/1/2012" | def add_time_block(session, label, start_date, start_time, end_time,
days, end_date=None, span_end_date_time=None, span=False,
enabled=False, username='system_user'):
session = validate_session(session)
try:
add_block = TimeBlocker(label, start_date, start_time,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_block(self, drip_campaign_id, start_time, nodes_id):\n new_block = Block(\n drip_campaign_id=drip_campaign_id,\n start_time=start_time,\n nodes_id=nodes_id\n )\n new_block.save()",
"def insert_machine_time(self, start: int, end: int):\n today ... | [
"0.59659165",
"0.5863345",
"0.58183277",
"0.5808236",
"0.5762933",
"0.5504239",
"0.5437054",
"0.543419",
"0.541414",
"0.53787965",
"0.5373741",
"0.53152436",
"0.53119665",
"0.53105295",
"0.5307636",
"0.5295561",
"0.5291936",
"0.52910906",
"0.52751726",
"0.52628547",
"0.526211... | 0.7227159 | 0 |
Add a new Certificate Signed Request into RV arguments below.. session == SQLAlchemy Session client_ip == "192.168.0.1" location == The directory location ("/opt/TopPatch/var/lib/ssl/client/csr/192.168.0.1.csr") | def add_csr(session, client_ip, location, csr_name,
signed=False, signed_date=False,
username='system_user'):
session = validate_session(session)
try:
add_csr = CsrInfo(csr_name, client_ip, location, signed, signed_date)
session.add(add_csr)
session.commit()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_ssl_cert_request ( ssl_hostnames ) :\n first_hostname = ssl_hostnames[ 0 ]\n csr_filename = get_ssl_csr_filename( first_hostname )\n key_filename = get_ssl_key_filename( first_hostname )\n openssl_cnf = \"\"\"\n[req]\ndistinguished_name = req_distinguished_name\nreq_extensions = san_ext\n\n[req_... | [
"0.61794984",
"0.6074754",
"0.59074855",
"0.5900829",
"0.5836028",
"0.5689883",
"0.5686247",
"0.562675",
"0.55683374",
"0.5503503",
"0.5464923",
"0.5438911",
"0.5392701",
"0.5382747",
"0.53479296",
"0.5293224",
"0.5291298",
"0.526492",
"0.526492",
"0.5234707",
"0.51935923",
... | 0.6758308 | 0 |
Add a new Signed Certificate into RV arguments below.. session == SQLAlchemy Session node_id == 1 This is the id of the node that this cert belongs too. cert_id == 1 This is the id corresponding csr_id cert_location == The directory location ("/opt/TopPatch/var/lib/ssl/client/keys/192.168.0.1.cert") | def add_cert(session, node_id, cert_id, cert_name,
cert_location, cert_expiration, username='system_user'):
session = validate_session(session)
try:
add_cert = SslInfo(node_id, cert_id, cert_name,
cert_location, cert_expiration)
session.add(add_cert)
sessi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sign_certificate(csr):\n unique_filename = str(uuid.uuid4().hex)\n\n file = open(\"./csr_req/%s.csr\" % unique_filename, \"w\")\n file.write(csr.decode(\"utf-8\"))\n file.close()\n\n subprocess.run([\"../ca/scripts/sign.sh\", unique_filename], check=False)\n\n file = open(\"./csr_req/%s.p7b\"... | [
"0.64046806",
"0.6280815",
"0.58989143",
"0.58835256",
"0.5829155",
"0.5638251",
"0.5638251",
"0.5616477",
"0.561596",
"0.56122303",
"0.56050044",
"0.55840075",
"0.5552402",
"0.5530964",
"0.5524219",
"0.55238885",
"0.547166",
"0.5467346",
"0.5456534",
"0.54513323",
"0.5449576... | 0.7471709 | 0 |
Add a new Operation into RV arguments below.. session == SQLAlchemy Session node_id == 1 The id of the node that this operation belongs too. operation == reboot|install|uninstall (The operation type) | def add_operation(session, node_id, operation, operation_sent=None,
operation_received=None, username='system_user'):
session = validate_session(session)
add_oper = Operations(node_id, operation, operation_sent,
operation_received, username
)
if add_oper:
session.add(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_operation(self, op):\n\n self.operations[op.name] = op",
"def AddOperation(self, op):\n self._operations.append(op)",
"def add(self, **kwargs) -> None:\n self.append(Operation(**kwargs))",
"def addOp(self, op):\n self.operations << op",
"def _register_operation(self, **opera... | [
"0.6588329",
"0.65164876",
"0.6259452",
"0.61404794",
"0.61376673",
"0.6107332",
"0.5795392",
"0.5693794",
"0.55513954",
"0.55419517",
"0.5538352",
"0.55040187",
"0.5494577",
"0.5494577",
"0.5481137",
"0.5445538",
"0.5424925",
"0.5406836",
"0.5382247",
"0.5344137",
"0.5303606... | 0.7123489 | 0 |
update the stats of the node in the node_stats table in RV arguments below.. session == SQLAlchemy Session node_id == the id of the node | def update_node_stats(session, node_id, username='system_user'):
session = validate_session(session)
rebootspending = session.query(NodeInfo).\
filter(NodeInfo.reboot == True).\
filter(NodeInfo.id == node_id).count()
agentsdown = session.query(NodeInfo).\
filter(NodeInfo.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update(self, context, values, prune_stats=False):\n return db.compute_node_update(context, self.compute_node['id'],\n values, prune_stats)",
"def update_node(session, node_id, ipaddress, username='system_user'):\n session = validate_session(session)\n node = node_exists(session, ... | [
"0.64276147",
"0.63328195",
"0.6140318",
"0.61300564",
"0.59009504",
"0.5846118",
"0.58422846",
"0.5800655",
"0.5799475",
"0.5791402",
"0.5769188",
"0.5705976",
"0.5678018",
"0.56424344",
"0.56188697",
"0.5593942",
"0.54787344",
"0.54753065",
"0.5434515",
"0.54333824",
"0.541... | 0.76589245 | 0 |
update the global stats in network_stats table in RV arguments below.. session == SQLAlchemy Session | def update_network_stats(session, username='system_user'):
session = validate_session(session)
rebootspending = session.query(NodeInfo).\
filter(NodeInfo.reboot == True).count()
agentsdown = session.query(NodeInfo).\
filter(NodeInfo.agent_status == False).count()
agentsup = sessi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_stats():\n list_db = get_list_database()\n\n list_db.group_stats_force_update()\n transaction_commit(None, 'GroupStatsUpdate')\n\n list_db.user_stats_force_update()\n transaction_commit(None, 'UserStatsUpdate')",
"def RefreshStats(self):\n with self.ctx.Profile(\n 2,\n lamb... | [
"0.6264449",
"0.6093104",
"0.6002179",
"0.59345603",
"0.5877387",
"0.5864414",
"0.57928675",
"0.5755806",
"0.56649154",
"0.5663315",
"0.5632987",
"0.5613733",
"0.5571354",
"0.5542132",
"0.5508326",
"0.5491335",
"0.5465955",
"0.54624546",
"0.5394556",
"0.5383795",
"0.53818923"... | 0.7032283 | 0 |
update the global tag stats in tag_stats table in RV arguments below.. session == SQLAlchemy Session | def update_tag_stats(session, username='system_user'):
session = validate_session(session)
tags = session.query(TagInfo).all()
if len(tags) > 0:
for tag in tags:
nodes = session.query(TagsPerNode).\
filter(TagsPerNode.tag_id == tag.id).all()
patchesinstalled =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tags_stats():\n # Update total search count of tag\n yesterday = timezone.now() - timezone.timedelta(days=1)\n yesterdays_tag_stats = DailyStats.objects.filter(date=yesterday)\n for daily_stat in yesterdays_tag_stats:\n tag_stat, created = TagStats.objects.get_or_create(tag=daily_stat.tag)\n... | [
"0.5773769",
"0.54053193",
"0.5400968",
"0.52657",
"0.52549785",
"0.52491444",
"0.5185269",
"0.5166207",
"0.5146414",
"0.5125556",
"0.5114296",
"0.5061952",
"0.5055679",
"0.5025211",
"0.50249416",
"0.5000762",
"0.49919817",
"0.4971272",
"0.49520138",
"0.49110574",
"0.48714864... | 0.7582618 | 0 |
Add the system information of an existing node arguments below.. session == SQLAlchemy Session data == this is the message that was sent by the agent and received by rvlistener. node_id == 1 The id of the node that this information belongs too. | def add_system_info(session, data, node_info, username='system_user'):
session = validate_session(session)
operation = operation_exists(session, data['operation_id'])
node_id = node_info.id
if node_id:
if 'computer_name' in data:
node_info.computer_name = data['computer_name']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_node(session, client_ip=None, agent_timestamp=None,\n node_timestamp=None, host_name=None, display_name=None,\n computer_name=None, username='system_user'):\n session = validate_session(session)\n if not host_name and client_ip:\n try:\n host_name = gethostbyaddr(clien... | [
"0.55243963",
"0.5523504",
"0.5343963",
"0.5328363",
"0.528189",
"0.5176631",
"0.51527655",
"0.5116902",
"0.51146966",
"0.50991744",
"0.50980383",
"0.5058517",
"0.5044866",
"0.50412124",
"0.502948",
"0.5010761",
"0.49565998",
"0.4954844",
"0.49528837",
"0.49521717",
"0.490679... | 0.6849514 | 0 |
Add software to the RV database, if the software does not exist. arguments below.. session == SQLAlchemy Session data == this is the message that was sent by the agent and received by rvlistener. | def add_software_update(session, data, username='system_user'):
session = validate_session(session)
operation = operation_exists(session, data['operation_id'])
node_id = data['node_id']
if node_id:
if operation:
results = add_results_non_json(session, node_id=node_id,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_software_available(session, data, username='system_user'):\n session = validate_session(session)\n operation = operation_exists(session, data['operation_id'])\n node_id = data['node_id']\n if node_id:\n if operation:\n results = add_results_non_json(session, node_id=node_id,\n... | [
"0.6986253",
"0.65802455",
"0.5349323",
"0.50320786",
"0.5021822",
"0.49379858",
"0.49077955",
"0.48968634",
"0.48968634",
"0.48828244",
"0.48783493",
"0.48638728",
"0.48364192",
"0.48274758",
"0.47938216",
"0.47691125",
"0.4736469",
"0.47224382",
"0.47190717",
"0.46946776",
... | 0.609844 | 2 |
Create a new entry in the packages_per_node table arguments below.. session == SQLAlchemy Session data == this is the message that was sent by the agent and received by rvlistener. | def add_software_per_node(session, data, username='system_user'):
session = validate_session(session)
session.commit()
operation = operation_exists(session, data['operation_id'])
node_id = data['node_id']
if node_id:
node = session.query(SystemInfo).\
filter(SystemInfo.node_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_node(self, node_tup):\n signature = hashlib.sha256((node_tup[0]+node_tup[4]).encode('utf-8')).hexdigest()\n app_process = sqlite3.connect('app_process::memory:', check_same_thread=False)\n app_process_cursor = app_process.cursor()\n app_process_cursor.execute(\"INSERT INTO no... | [
"0.5677619",
"0.52482593",
"0.5195922",
"0.5182733",
"0.5168917",
"0.5161152",
"0.5148398",
"0.5096421",
"0.5047914",
"0.5011684",
"0.4945328",
"0.49099624",
"0.49062672",
"0.48959848",
"0.48884022",
"0.48753202",
"0.48561898",
"0.4854296",
"0.48465168",
"0.48298782",
"0.4828... | 0.59259677 | 0 |
Create a new entry in the software_available table This table is as of right now, strictly for Windows 3rd party applications arguments below.. session == SQLAlchemy Session data == this is the message that was sent by the agent and received by rvlistener. | def add_software_available(session, data, username='system_user'):
session = validate_session(session)
operation = operation_exists(session, data['operation_id'])
node_id = data['node_id']
if node_id:
if operation:
results = add_results_non_json(session, node_id=node_id,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_software_installed(session, data, username='system_user'):\n session = validate_session(session)\n operation = operation_exists(session, data['operation_id'])\n node_id = data['node_id']\n if node_id:\n if operation:\n results = add_results_non_json(session, node_id=node_id,\n... | [
"0.6103432",
"0.5414766",
"0.5199664",
"0.5104203",
"0.5090083",
"0.5016022",
"0.495569",
"0.4950043",
"0.49389824",
"0.49013168",
"0.4894648",
"0.48890445",
"0.486498",
"0.48560277",
"0.48234528",
"0.47895136",
"0.4771943",
"0.47551453",
"0.47511882",
"0.47435394",
"0.473403... | 0.7131 | 0 |
Create a new entry in the software_installed table This table is a foreignKey to an existing row in software_available for a node. This table is as of right now, strictly for Windows 3rd party applications arguments below.. session == SQLAlchemy Session data == this is the message that was sent by the agent and receive... | def add_software_installed(session, data, username='system_user'):
session = validate_session(session)
operation = operation_exists(session, data['operation_id'])
node_id = data['node_id']
if node_id:
if operation:
results = add_results_non_json(session, node_id=node_id,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_software_available(session, data, username='system_user'):\n session = validate_session(session)\n operation = operation_exists(session, data['operation_id'])\n node_id = data['node_id']\n if node_id:\n if operation:\n results = add_results_non_json(session, node_id=node_id,\n... | [
"0.70858717",
"0.6096228",
"0.60859156",
"0.5863044",
"0.51974237",
"0.5174665",
"0.5118672",
"0.50887364",
"0.49734944",
"0.4848716",
"0.4818761",
"0.4803903",
"0.47979134",
"0.47967765",
"0.4779577",
"0.47724435",
"0.4764106",
"0.47607633",
"0.47565302",
"0.47553998",
"0.47... | 0.73042756 | 0 |
Remove a tag from the database arguments below.. session == SQLAlchemy Session tagname == name of the tag you want to remove | def remove_tag(session, tagname, username='system_user'):
session = validate_session(session)
tag = tag_exists(session, tag_name=tagname)
tagid= tag.id
tag_stats = session.query(TagStats).\
filter(TagStats.tag_id == tag.id)
if tag:
try:
tag_stats.delete()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_tag(args):",
"def delete_tag(self, session, tag):\n self._tag(session.delete, key=tag, delete=True, session=session)",
"def delete_tag(self,tag):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n del self.tag_dict[tag]",
"def delete_tags(self, session):\n sel... | [
"0.70557207",
"0.6804026",
"0.67596817",
"0.6461948",
"0.6456595",
"0.63579917",
"0.6332792",
"0.63208556",
"0.6299995",
"0.61536014",
"0.6106683",
"0.6092737",
"0.6069462",
"0.60459316",
"0.6033632",
"0.6030218",
"0.6010136",
"0.59909695",
"0.5976501",
"0.5967643",
"0.595496... | 0.6806664 | 1 |
Remove all nodes from a tag in the database arguments below.. session == SQLAlchemy Session tag_name == name of the tag you want to remove | def remove_all_nodes_from_tag(session, tagname, username='system_user'):
session = validate_session(session)
tag = tag_exists(session, tag_name=tagname)
if not tag:
return(False, "Tag %s does not exists" % (tagname), tagname)
tags_per_node = \
session.query(TagsPerNode, TagInfo).\
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_node_by_tagname(nodes: List, tagname: str):\n\n for remove in [node for node in nodes if node.tagname == tagname]:\n nodes.remove(remove)",
"def remove_nodes_from_tag(session, tag_name, nodes=[], username='system_user'):\n session = validate_session(session)\n nodes_completed = []\n ... | [
"0.6951803",
"0.6640433",
"0.65312904",
"0.64291734",
"0.63873076",
"0.6245773",
"0.6116087",
"0.60846364",
"0.5978277",
"0.5966734",
"0.59097797",
"0.5845086",
"0.57912904",
"0.57787085",
"0.5764835",
"0.5753481",
"0.56975526",
"0.56918705",
"0.56644845",
"0.56517816",
"0.56... | 0.7298875 | 0 |
Remove a node from a tag in the database arguments below.. session == SQLAlchemy Session tag_name == name of the tag you want to remove nodes == list of nodes to be removed | def remove_nodes_from_tag(session, tag_name, nodes=[], username='system_user'):
session = validate_session(session)
nodes_completed = []
nodes_failed = []
for node in nodes:
tags_per_node = \
session.query(TagsPerNode, TagInfo).\
join(TagInfo).filter(TagInfo.tag =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_node_by_tagname(nodes: List, tagname: str):\n\n for remove in [node for node in nodes if node.tagname == tagname]:\n nodes.remove(remove)",
"def removeNode(cTag, nTag): #@NoSelf",
"def remove_all_nodes_from_tag(session, tagname, username='system_user'):\n session = validate_session(ses... | [
"0.728888",
"0.70585287",
"0.6895703",
"0.68373203",
"0.66836995",
"0.64089173",
"0.6372061",
"0.6279932",
"0.62658787",
"0.6255787",
"0.6096605",
"0.60705644",
"0.60271204",
"0.5984538",
"0.5976618",
"0.5961547",
"0.59454566",
"0.59317696",
"0.5918149",
"0.5907452",
"0.59072... | 0.6854731 | 3 |
Remove a timeblock from the database arguments below.. session == SQLAlchemy Session id == the id of the timeblock label == the name of the timeblock | def remove_time_block(session, id=None, label=None,
start_date=None, start_time=None, username='system_user'):
session = validate_session(session)
timeblock = time_block_exists(session, id,
label, start_date, start_time)
if timeblock:
object_deleted = False, "Time Block %s ha... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _do_remove_block(self, args):\r\n bus_type = args[1]\r\n slave_id = int(args[2])\r\n name = args[3]\r\n if bus_type == 'rtu':\r\n slave = self.server._servers[0].get_slave(slave_id)\r\n elif bus_type == 'tcp':\r\n slave = self.server._servers[1].get_slav... | [
"0.62156653",
"0.61247504",
"0.6073977",
"0.59137785",
"0.58951217",
"0.5816906",
"0.56836355",
"0.56813055",
"0.56748646",
"0.5583618",
"0.5541887",
"0.55088216",
"0.5492939",
"0.54708785",
"0.54360646",
"0.5434126",
"0.5344612",
"0.5317905",
"0.53122264",
"0.52653545",
"0.5... | 0.7277625 | 0 |
update an existing operation in the RV database arguments below.. session == SQLAlchemy Session oper_id == the id of the operation | def update_operation_row(session, oper_id,
oper_recv=None,
username='system_user'):
session = validate_session(session)
operation = operation_exists(session, oper_id)
if operation and oper_recv:
operation.operation_received = datetime.now()
session.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n sess = u.get_default_session()\n # sess.run(self.update_op)\n u.run(self.update_op)",
"def update_maintenance_operation(session, operation=None):\n op_text = None\n if operation:\n op_text = operation.__name__\n\n with session.begin():\n row = session.query(... | [
"0.73067856",
"0.6703859",
"0.63593596",
"0.60409796",
"0.6018797",
"0.58479035",
"0.57298386",
"0.5725205",
"0.5643036",
"0.5614167",
"0.5578289",
"0.5562358",
"0.55372816",
"0.5493391",
"0.5493391",
"0.54723907",
"0.54620504",
"0.54222816",
"0.5418531",
"0.5410402",
"0.5410... | 0.6572419 | 2 |
update an existing node in the RV database arguments below.. session == SQLAlchemy Session node_id == the id of the node ipaddress == The ipaddress of the node | def update_node(session, node_id, ipaddress, username='system_user'):
session = validate_session(session)
node = node_exists(session, node_id=node_id)
error = None
if not node.ip_address == ipaddress:
try:
node.ip_address = ipaddress
session.commit()
except Except... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_node(uuid, **values):\n fields_ver = values.copy()\n with session_for_write() as session:\n stmt = update(\n model.Node\n ).where(\n model.Node.uuid == uuid\n ).values(\n fields_ver\n ).execution_options(\n synchronize_session... | [
"0.72816133",
"0.6820707",
"0.65893435",
"0.63527095",
"0.63042516",
"0.62814516",
"0.62209684",
"0.62012744",
"0.6197582",
"0.617847",
"0.61486435",
"0.6116205",
"0.60991526",
"0.60634166",
"0.60237116",
"0.597929",
"0.5899573",
"0.58901024",
"0.5856175",
"0.578596",
"0.5714... | 0.7498598 | 0 |
update the reboot status on a node in the node_info table in RV arguments below.. session == SQLAlchemy Session node_id == The node_id of the node oper_type == "reboot" | def update_reboot_status(session, node_id, oper_type, username='system_user'):
session = validate_session(session)
node = node_exists(session, node_id=node_id)
if node:
if oper_type == 'reboot':
try:
node.reboot = False
node.agent_status = False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reboot(self, node):",
"def reboot_node(self, node):\n params = {'Action': 'RebootInstances'}\n params.update(self._pathlist('InstanceId', [node.id]))\n res = self.connection.request(self.path, params=params).object\n return self._get_boolean(res)",
"def reboot(self):\n mo... | [
"0.75722855",
"0.65329576",
"0.6325448",
"0.63181406",
"0.6158265",
"0.6149083",
"0.61210716",
"0.5949359",
"0.5911771",
"0.58813804",
"0.5879121",
"0.58717173",
"0.5868806",
"0.58477396",
"0.58019197",
"0.57642186",
"0.569657",
"0.5694058",
"0.56701624",
"0.5667073",
"0.5651... | 0.81069905 | 0 |
Modify the Global ACL of a User in the database | def update_global_user_acl(session, user_id=None, is_admin=False,
is_global=True, allow_read=False, allow_install=False,
allow_uninstall=False, allow_reboot=False, allow_schedule=False,
allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=Fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_global_user_acl(session, user_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_rev... | [
"0.67252845",
"0.64107996",
"0.6400784",
"0.63297886",
"0.62673974",
"0.62673974",
"0.6256225",
"0.6214258",
"0.61393476",
"0.6122256",
"0.6052427",
"0.59978604",
"0.5933742",
"0.5896206",
"0.5861808",
"0.5853026",
"0.5822896",
"0.5802331",
"0.5785494",
"0.57674253",
"0.57431... | 0.7438068 | 0 |
Modify the Global ACL of a Group in the database | def update_global_group_acl(session, group_id=None, is_admin=False,
is_global=True, allow_read=False, allow_install=False,
allow_uninstall=False, allow_reboot=False, allow_schedule=False,
allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_global_group_acl(session, group_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_r... | [
"0.7254382",
"0.7022326",
"0.6990606",
"0.66523886",
"0.6245156",
"0.6238742",
"0.6161424",
"0.60740215",
"0.6051832",
"0.60211515",
"0.60041064",
"0.5945894",
"0.5921249",
"0.59133536",
"0.58938885",
"0.5891286",
"0.5863889",
"0.5847468",
"0.5813295",
"0.58117294",
"0.579712... | 0.7849561 | 0 |
modify the global permissions of a user on a node in the database | def update_node_user_acl(session, node_id=None, user_id=None,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation=False, allow_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_admin_permission(self) -> None:\n session = self.appbuilder.get_session\n dag_resources = session.scalars(\n select(Resource).where(Resource.name.like(f\"{permissions.RESOURCE_DAG_PREFIX}%\"))\n )\n resource_ids = [resource.id for resource in dag_resources]\n\n ... | [
"0.6568234",
"0.6538204",
"0.6426908",
"0.6406976",
"0.63939655",
"0.62912196",
"0.62615925",
"0.62319654",
"0.62132657",
"0.60724735",
"0.60161483",
"0.59777075",
"0.5943838",
"0.5896214",
"0.5890716",
"0.58770514",
"0.58673644",
"0.58455306",
"0.5797841",
"0.5782578",
"0.57... | 0.665373 | 0 |
Modify the Global ACL of a Group on a node in the database | def update_node_group_acl(session, node_id=None, group_id=None,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation=False, allow... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_global_group_acl(session, group_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapsho... | [
"0.7562661",
"0.69415545",
"0.6795446",
"0.67245346",
"0.65660304",
"0.63047767",
"0.61979157",
"0.60958225",
"0.604146",
"0.60362536",
"0.6010592",
"0.5890728",
"0.5883822",
"0.58646387",
"0.5860503",
"0.5860379",
"0.5858269",
"0.5849576",
"0.5836339",
"0.58308",
"0.5780648"... | 0.7714923 | 0 |
Modify the Global ACL of a User on a Tag in the database | def update_tag_user_acl(session, tag_id=None, user_id=None,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation=False, allow_tag... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_global_user_acl(session, user_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapshot_... | [
"0.6504644",
"0.6162827",
"0.6132806",
"0.6129733",
"0.6129733",
"0.61052823",
"0.5915916",
"0.5805578",
"0.5663717",
"0.56336033",
"0.56317526",
"0.56130934",
"0.55700725",
"0.5557551",
"0.5554141",
"0.5528745",
"0.5457469",
"0.5451234",
"0.54343987",
"0.54223025",
"0.541061... | 0.7183954 | 0 |
Modify the Global ACL of a Group on a Tag in the database | def update_tag_group_acl(session, tag_id=None, group_id=None,
allow_install=False, allow_uninstall=False, allow_reboot=False,
allow_schedule=False, allow_wol=False, allow_snapshot_creation=False,
allow_snapshot_removal=False, allow_snapshot_revert=False,
allow_tag_creation=False, allow_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_global_group_acl(session, group_id=None, is_admin=False,\n is_global=True, allow_read=False, allow_install=False,\n allow_uninstall=False, allow_reboot=False, allow_schedule=False,\n allow_wol=False, allow_snapshot_creation=False,\n allow_snapshot_removal=False, allow_snapsho... | [
"0.7220257",
"0.68494946",
"0.667716",
"0.6487375",
"0.6400578",
"0.6063529",
"0.6004117",
"0.59780467",
"0.59366125",
"0.5917353",
"0.59126985",
"0.5803076",
"0.5781316",
"0.5769561",
"0.5747072",
"0.5723387",
"0.56890684",
"0.5651428",
"0.5624252",
"0.56194323",
"0.56056285... | 0.7822554 | 0 |
Add a new entry in the results table in RV arguments below.. session == SQLAlchemy Session data == the json message received from the agent | def add_results(session, data, username='system_user'):
session = validate_session(session)
operation = operation_exists(session, data['operation_id'])
node = node_exists(session,node_id=data['node_id'])
if node:
node_id = data['node_id']
if data['operation'] == 'reboot':
nod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_records(self, data: dict, execution_context: dict):",
"def add_row(self, row_id):",
"def add_next_iteration_results(self, *args, **kwargs):\n \n # Passing all the parameters to the graph object\n # which upates the internal state of the graph\n self.sql_model_instance.graphs... | [
"0.62482166",
"0.58265",
"0.5792383",
"0.57908404",
"0.57755756",
"0.5740843",
"0.57107496",
"0.55978835",
"0.55917794",
"0.54867315",
"0.5469462",
"0.5465754",
"0.5449438",
"0.5412065",
"0.5400628",
"0.53911936",
"0.5387508",
"0.5379521",
"0.5369491",
"0.536726",
"0.536726",... | 0.6078163 | 1 |
Sets defaults for the provided configuration | def prepare_config(config: dict) -> dict:
config.setdefault('password', None)
config.setdefault('private_key', None)
config.setdefault('private_key_pass', None)
config.setdefault('host_key', None)
config.setdefault('dirs', ['.'])
return config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setdefaults(self):\n self.config = {\n 'dbuser': Infopage.DEFAULT_DBUSER,\n 'dbname': Infopage.DEFAULT_DBNAME,\n 'dbpassword': Infopage.DEFAULT_DBPASSWORD,\n 'dbhost': Infopage.DEFAULT_DBHOST\n }",
"def initConfiguration():\n UTIL.SYS.s_configuration... | [
"0.76838875",
"0.7363561",
"0.72177184",
"0.72016305",
"0.71853375",
"0.711877",
"0.7100197",
"0.7085253",
"0.7073906",
"0.6964687",
"0.69576526",
"0.6949763",
"0.69288844",
"0.6916137",
"0.6914883",
"0.6879722",
"0.6874769",
"0.6867838",
"0.68650156",
"0.68600667",
"0.686006... | 0.0 | -1 |
Downloads the file(s) described in entry | def download_entry(cls, entry: Entry, config: dict, sftp: SftpClient) -> None:
path: str = unquote(urlparse(entry['url']).path) or '.'
delete_origin: bool = config['delete_origin']
recursive: bool = config['recursive']
to: str = config['to']
try:
to = render_from_ent... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_files(self):",
"def download_files(self) -> None:\n\n for name, url in self.files.items():\n print(f\"Download {name.split('/')[-1]}\")\n wget.download(url, os.path.join(\"data\", name))",
"def download_file(self, parsed_event, input_dir_path):",
"def get_files(self)... | [
"0.7334557",
"0.6602971",
"0.6455047",
"0.6433068",
"0.63911927",
"0.63551885",
"0.6316714",
"0.63050663",
"0.62683964",
"0.6251234",
"0.62311596",
"0.6145604",
"0.6142242",
"0.6126038",
"0.6105735",
"0.6086469",
"0.60783595",
"0.60621864",
"0.60366726",
"0.60366726",
"0.6033... | 0.57467943 | 56 |
Register this as an output plugin | def on_task_output(cls, task: Task, config: dict) -> None: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load():\n return SyslogOutOutputPlugin",
"def LoadOutputPlugin(self, path):\n\n self._LoadPlugin(self._output_registry, 'output.yml', path)",
"def AddOutputCallback(self, callback):\n self.output_callbacks.append(callback)",
"def __init__(self, name, node):\n super(OutputPlug, self)._... | [
"0.646148",
"0.6417916",
"0.63977176",
"0.63373387",
"0.63265157",
"0.63160986",
"0.62953615",
"0.625948",
"0.61171454",
"0.61171454",
"0.61171454",
"0.60817516",
"0.6065714",
"0.6065195",
"0.60583955",
"0.6028981",
"0.5910007",
"0.58735806",
"0.58735806",
"0.5791759",
"0.578... | 0.5472557 | 54 |
Task handler for sftp_download plugin | def on_task_download(cls, task: Task, config: dict) -> None:
socket_timeout_sec: int = config['socket_timeout_sec']
connection_tries: int = config['connection_tries']
# Download entries by host so we can reuse the connection
for sftp_config, entries in groupby(task.accepted, cls._get_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ssh_download_files(data):\n with _ssh_connect() as ssh:\n with ssh.open_sftp() as sftp:\n with click.progressbar(data, label='downloads') as items: # noqa\n for item in items:\n _, filename = os.path.split(item)\n sftp.get(item, f'{DOWN... | [
"0.6760415",
"0.6119707",
"0.61096543",
"0.605568",
"0.6042737",
"0.5958615",
"0.5922521",
"0.5888617",
"0.5864169",
"0.58393395",
"0.58332944",
"0.58228356",
"0.5801318",
"0.5736366",
"0.5667004",
"0.56512177",
"0.5642449",
"0.55854475",
"0.55769396",
"0.55599636",
"0.554756... | 0.6754636 | 1 |
Parses a url and returns a hashable config, source path, and destination path | def _get_sftp_config(cls, entry: Entry):
# parse url
parsed = urlparse(entry['url'])
host: str = parsed.hostname
username: str = parsed.username
password: str = parsed.password
port: int = parsed.port or DEFAULT_SFTP_PORT
# get private key info if it exists
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_url_dict(self, url):\n # TODO: what if URL is an exact attribute in site config and not relative?\n try:\n return (\n url['path'],\n self.SITE_CONFIG.parse_relative_url_dict(url)\n )\n except KeyError as e:\n raise utils... | [
"0.65322584",
"0.63917404",
"0.6330404",
"0.6198452",
"0.6170337",
"0.59986144",
"0.5937309",
"0.5862404",
"0.58555067",
"0.57999516",
"0.5785985",
"0.57678366",
"0.5765161",
"0.57554877",
"0.57271445",
"0.57167894",
"0.5666716",
"0.5650904",
"0.56488854",
"0.5627165",
"0.561... | 0.49634185 | 91 |
Sets defaults for the provided configuration | def prepare_config(config: dict) -> dict:
config.setdefault('password', None)
config.setdefault('private_key', None)
config.setdefault('private_key_pass', None)
config.setdefault('to', None)
return config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setdefaults(self):\n self.config = {\n 'dbuser': Infopage.DEFAULT_DBUSER,\n 'dbname': Infopage.DEFAULT_DBNAME,\n 'dbpassword': Infopage.DEFAULT_DBPASSWORD,\n 'dbhost': Infopage.DEFAULT_DBHOST\n }",
"def initConfiguration():\n UTIL.SYS.s_configuration... | [
"0.76838875",
"0.7363561",
"0.72177184",
"0.72016305",
"0.71853375",
"0.711877",
"0.7100197",
"0.7085253",
"0.7073906",
"0.6964687",
"0.69576526",
"0.6949763",
"0.69288844",
"0.6916137",
"0.6914883",
"0.6879722",
"0.6874769",
"0.6867838",
"0.68650156",
"0.68600667",
"0.686006... | 0.0 | -1 |
Uploads accepted entries to the specified SFTP server. | def on_task_output(cls, task: Task, config: dict) -> None:
config = cls.prepare_config(config)
socket_timeout_sec: int = config['socket_timeout_sec']
connection_tries: int = config['connection_tries']
sftp_config: SftpConfig = task_config_to_sftp_config(config)
sftp = sftp_con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload(self) -> list:\n\n _sftp = None\n try:\n if self._sftpSecret.find(os.sep) == -1:\n _sftp = pysftp.Connection(self._sftpHost,\n username=self._sftpUsername,\n password=self._sftpSecre... | [
"0.6215347",
"0.6154818",
"0.61220515",
"0.5922133",
"0.5869823",
"0.58326346",
"0.58278227",
"0.57609874",
"0.569599",
"0.56464106",
"0.56426567",
"0.5636189",
"0.5588572",
"0.5574974",
"0.5569968",
"0.5566738",
"0.5537185",
"0.5459689",
"0.5443172",
"0.5424469",
"0.5420793"... | 0.5403493 | 22 |
Creates an SFTP connection from a Flexget config object | def task_config_to_sftp_config(config: dict) -> SftpConfig:
host: int = config['host']
port: int = config['port']
username: str = config['username']
password: str = config['password']
private_key: str = config['private_key']
private_key_pass: str = config['private_key_pass']
host_key: Optio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, config):\n self.config = config\n\n # setup logging\n if config.get('ssh_log_path'):\n ssh.util.log_to_file(config['ssh_log_path'])\n\n hostname = config.get('remote_host', 'localhost')\n port = config.get('remote_port', 22)\n username = confi... | [
"0.6799001",
"0.65101117",
"0.6486627",
"0.64719737",
"0.640493",
"0.616897",
"0.60081565",
"0.59975857",
"0.58954567",
"0.5877264",
"0.5876884",
"0.58517414",
"0.5846761",
"0.575433",
"0.57234526",
"0.56997186",
"0.5404839",
"0.5399757",
"0.5369568",
"0.5320579",
"0.5308505"... | 0.664715 | 1 |
prepare the cosimulation environment | def _prep_cosim(args, **sigs):
# compile the verilog files with the verilog simulator
files = ['./dwt_top.v','./tb_dwt_top.v',]
print("compiling ...")
cmd = "iverilog -o dwt_top %s " % (" ".join(files))
print(" *%s" % (cmd))
os.system(cmd)
# get the handle to the
print("cosimulat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initFromEnv(self):\n #self.command = 'scram' # SB I think this line is not needed\n self[\"SCRAM_ARCH\"] = None\n\n if 'SCRAM_ARCH' in os.environ:\n self[\"SCRAM_ARCH\"] = os.environ[\"SCRAM_ARCH\"]\n else:\n stdout, _, _ = execute_command(command='scram arch'... | [
"0.629472",
"0.6261593",
"0.6040992",
"0.60047394",
"0.5995163",
"0.59356046",
"0.59223294",
"0.5901636",
"0.58975756",
"0.5884308",
"0.58454937",
"0.5835247",
"0.58315617",
"0.58204514",
"0.5817271",
"0.57970774",
"0.5790427",
"0.5772942",
"0.5740951",
"0.57361805",
"0.57207... | 0.56129134 | 31 |
Convert symbolic permission notation to numeric notation. | def symb_to_num(symbolic):
if len(symbolic) == 9:
group = (symbolic[:-6], symbolic[3:-3], symbolic[6:])
try:
numeric = notation[group[0]] + notation[group[1]] + notation[group[2]]
except:
numeric = "Invalid Symbolic Representation!"
else:
numeric = "Symbo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_to_symb(num):\n\n num = str(num)\n if len(num) == 3:\n group = (num[0], num[1], num[2])\n symbolic = \"\"\n\n for key, value in notation.items():\n for g in group:\n if int(g) > 8 or int(g) < 0:\n symbolic = \"Invalid Numerical Represe... | [
"0.5681998",
"0.521568",
"0.51752305",
"0.5127067",
"0.5117769",
"0.50925183",
"0.5087651",
"0.50793535",
"0.50348747",
"0.5022774",
"0.4999135",
"0.49990058",
"0.49702394",
"0.4968009",
"0.4900085",
"0.4852676",
"0.48500445",
"0.4836129",
"0.47774908",
"0.4772518",
"0.476453... | 0.6173098 | 0 |
Convert number permission notation to symbolic notation. | def num_to_symb(num):
num = str(num)
if len(num) == 3:
group = (num[0], num[1], num[2])
symbolic = ""
for key, value in notation.items():
for g in group:
if int(g) > 8 or int(g) < 0:
symbolic = "Invalid Numerical Representation!"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_octal_from_file_permission(rwx: str) -> str:\n octals = {'r': 4, 'w': 2, 'x': 1, '-': 0}\n chars = []\n temp = []\n \n for i in range(len(rwx)):\n val = octals.get(rwx[i])\n temp.append(val)\n if len(temp) == 3:\n chars.append(list(t for t in temp))\n ... | [
"0.53374124",
"0.527418",
"0.5249602",
"0.520306",
"0.5150457",
"0.5133376",
"0.51284754",
"0.5085775",
"0.5078872",
"0.50376916",
"0.5032838",
"0.50220686",
"0.50122875",
"0.4994668",
"0.4976482",
"0.49640882",
"0.49295864",
"0.4921475",
"0.48928908",
"0.48783267",
"0.486434... | 0.6568608 | 0 |
Show all resolutions received | def get(self, request, **kwargs):
template = 'resolutions.html'
context = {}
show_logo_instead = random.randint(0, 100)
if show_logo_instead <= 15:
# 15% of the time, show the logo instead
context['show_logo_instead'] = True
else:
resolution_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, resolutions):\r\n # This is optional behavior\r\n return resolutions",
"def populate_resolution_box(self, fmt: str):\n self.resolution_combo.reset()\n self.fps_combo.reset()\n res_list = self.caps_desc.get_resolution_list(fmt)\n\n if not res_list:\n ... | [
"0.6274222",
"0.6270068",
"0.60677403",
"0.60651636",
"0.5923708",
"0.58746326",
"0.5865114",
"0.57543665",
"0.5643217",
"0.5525964",
"0.54416966",
"0.5438535",
"0.53689474",
"0.5335733",
"0.52837765",
"0.5249803",
"0.5241965",
"0.5237411",
"0.5233322",
"0.5208205",
"0.520241... | 0.58790886 | 5 |
Used by the Twilio API to receive a text message and save a resolution. | def post(self, request, **kwargs):
resolution = request.POST.get('Body')
if resolution:
new_resolution = Resolution(text=resolution)
new_resolution.save()
template = 'response.html'
context = {}
return render(request, template, context) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def receive_message():\n\n msg_data = flask.request.get_json(silent=True, force=True)\n\n name = msg_data['name']\n picture_url = msg_data['picture_url']\n text = msg_data['text']\n time_sent = msg_data['time_sent']\n settings_key = ndb.Key(settings.Settings, msg_data['settings'])\n\n logging.... | [
"0.61034733",
"0.5759642",
"0.56703115",
"0.5655322",
"0.55951816",
"0.55487496",
"0.5543905",
"0.5532788",
"0.5527256",
"0.5474542",
"0.54218113",
"0.5394618",
"0.5394216",
"0.5389646",
"0.538614",
"0.53798556",
"0.5357868",
"0.53256536",
"0.5321131",
"0.5317306",
"0.5306554... | 0.55867094 | 5 |
Negative binomial distribution, p is the probability of a FAILURE. Result is the number of failures until the rth success. | def pdfPR(self,x,p,r):
return gammaF(x + r) / (factorial(x)*gammaF(r)) * p**r * (1-p)**x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so ad... | [
"0.69135344",
"0.6888008",
"0.6885063",
"0.6876171",
"0.6778513",
"0.65672594",
"0.6561243",
"0.65516794",
"0.6487053",
"0.64328194",
"0.64263123",
"0.6340225",
"0.63180155",
"0.626795",
"0.61252296",
"0.6105334",
"0.610038",
"0.60903025",
"0.6064486",
"0.60568416",
"0.599576... | 0.0 | -1 |
Convert between mu/[sigma|theta|alpha] parameterization and p/r | def convertParameters(self,mu,sigma=None,theta=None,alpha=None):
if sigma is not None:
r = mu**2 / (sigma**2-mu)
elif theta is not None:
r = theta
elif alpha is not None:
r = 1./alpha
p = r / (r + mu)
return r,p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pdf(self,x,mu,sigma=None,theta=None,alpha=None):\n\t\n\t\tr,p = self.convertParameters(mu,sigma,theta,alpha)\n\t\treturn self.pdfPR(x,p,r)",
"def normalisedProductionPDF(p, theta, mDarkPhoton, epsilon, norm):\n return (1. / norm) * dNdPdTheta(p, theta, mDarkPhoton, epsilon)",
"def A(param):\n return ... | [
"0.6166494",
"0.592515",
"0.58565605",
"0.58215654",
"0.57992804",
"0.5755113",
"0.57434535",
"0.57369626",
"0.572879",
"0.56506467",
"0.562362",
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"0.55910724",
"0.55903745",
"0.5589684",
"0.55826247",
"0.55582327",
"0.5554927",
"0.55528... | 0.7524006 | 0 |
Regression style negative binomial. Set one of sigma, theta or alpha. Theta is an antidispersion or clumping parameter (NB > poisson as theta > infinity) Alpha is 1/theta, a dispersion parameter (NB > poisson as alpha > 0) | def pdf(self,x,mu,sigma=None,theta=None,alpha=None):
r,p = self.convertParameters(mu,sigma,theta,alpha)
return self.pdfPR(x,p,r) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def r_neg_binom(alpha,mean,num=1):\n\tr = 1.0 / np.float(alpha)\n\tp = r / (r + mean)\n\treturn nbinom.rvs(r,p,size=num)",
"def p_neg_binom(x,alpha,mean,log=True):\n\tr = 1.0 / np.float(alpha)\n\tp = r / (r + mean)\n\tlog_p = nbinom.logpmf(x, r, p, loc=0)\n\tif log:\n\t\treturn log_p\n\telse:\n\t\treturn np.ex... | [
"0.6528253",
"0.6305876",
"0.62928",
"0.60620314",
"0.5975161",
"0.58808404",
"0.57638305",
"0.5680509",
"0.5664412",
"0.5663479",
"0.56550664",
"0.5652778",
"0.5637215",
"0.5634644",
"0.5557861",
"0.550218",
"0.54884833",
"0.5469649",
"0.5456584",
"0.54441684",
"0.543265",
... | 0.0 | -1 |
Draw random samples from a negative binomial distribution | def sample(self,mu,sigma=None,theta=None,alpha=None,size=None):
r,p = self.convertParameters(mu,sigma,theta,alpha)
return numpy.random.negative_binomial(n=r,p=p,size=size) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binary_sample(x):\n return np.random.binomial(1, p=x)",
"def NegativeBinomial_sampling(K=100, P=0.5,size=None,rng=np.random.RandomState(100)):\n if (not np.isscalar(P)):\n size=P.shape\n if (np.isscalar(P) and size is None):\n size=1\n X=rng.negative_binomial(K,P,size)\n return X... | [
"0.6792101",
"0.67824376",
"0.6604079",
"0.65796167",
"0.6571323",
"0.6419903",
"0.6192945",
"0.6190055",
"0.61820704",
"0.6168806",
"0.6156702",
"0.6154157",
"0.6120292",
"0.61190623",
"0.6068932",
"0.6015622",
"0.60140365",
"0.6008499",
"0.60048985",
"0.5993291",
"0.5988804... | 0.5806599 | 38 |
Create list of player objects | def __init__(self, number_players=1000):
self.player_list = []
for i in range(number_players):
self.player_list.append(Player()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_players_list(self):\n for p in self.players_names:\n self._players_list.append(Player(p))",
"def create_players(self):\n players = []\n count = self.query('player', 'count', '?')\n for index in range(0, int(count)):\n player_id = self.query('player', '... | [
"0.8415683",
"0.77281135",
"0.7500566",
"0.74741745",
"0.7420029",
"0.74105877",
"0.7283305",
"0.7032042",
"0.7028725",
"0.69137144",
"0.6816953",
"0.67063427",
"0.66382223",
"0.65988654",
"0.65697956",
"0.6518914",
"0.6506067",
"0.6476675",
"0.6456089",
"0.6455565",
"0.64398... | 0.80597174 | 1 |
Write out info for analysis | def to_csv(self):
with open('Players.csv', 'wt', newline='\n') as out:
fields = ('mmr', 'communication', 'tilt', 'internet', 'leadership',
'gameKnowledge', 'reactionTimes', 'early_game',
'late_game', 'mechanics')
writer = csv.writer(out)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_info_to_file(self):\n\n self.info.write_mission_info()\n\n self.logger.info(\"Mission instance write succeeded.\")",
"def info(out: Export = Export(\"cwb.encoded/data/.info\"),\n sentences: AnnotationCommonData = AnnotationCommonData(\"misc.<sentence>_count\"),\n firstdate... | [
"0.7056108",
"0.67680436",
"0.66807693",
"0.6658253",
"0.6530391",
"0.6467057",
"0.6400526",
"0.6373689",
"0.6348998",
"0.6335972",
"0.62535244",
"0.6248077",
"0.62222004",
"0.6186441",
"0.6185751",
"0.61435616",
"0.6140123",
"0.6113667",
"0.610965",
"0.6107006",
"0.6099229",... | 0.0 | -1 |
Testing if amount of players initialized is correct | def _test_player_list_size(self):
return len(self.player_list) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enough_players():\n return True",
"def noOfPlayers(self):\n\t\tnumber = 0\n\t\tfor n in range(6):\n\t\t\tif self.playerList[n] != None:\n\t\t\t\tnumber = number + 1\n\t\treturn number",
"def test_valid_game_setup(self):\n self.assertEqual(self._game.active_players(), 1)\n for x in xrange(1... | [
"0.7769488",
"0.72755575",
"0.72210413",
"0.71589714",
"0.7081436",
"0.7074838",
"0.70711625",
"0.68413734",
"0.6811846",
"0.6798778",
"0.6792549",
"0.6791493",
"0.66448987",
"0.6587905",
"0.65837675",
"0.6579726",
"0.65554416",
"0.6517425",
"0.64908224",
"0.64804053",
"0.645... | 0.7485758 | 1 |
Test that no line in the corpus exceeds the maximum number of characters. | def test_character_count(self):
from chatterbot_corpus.corpus import DIALOG_MAXIMUM_CHARACTER_LENGTH
corpora = corpus.load_corpus('chatterbot.corpus')
for conversations in corpora:
for conversation in conversations:
for statement in conversation:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_max_length(self, p: list) -> bool:\n\n return (\n len(p[0].split(\" \")) < self.max_length\n and len(p[1].split(\" \")) < self.max_length\n )",
"def test__limit_string_length(string, max_length):\n return limit_string_length(string, max_length)",
"def is_doc_le... | [
"0.67271656",
"0.65961367",
"0.65796036",
"0.65796036",
"0.6576228",
"0.64568543",
"0.6453824",
"0.64388967",
"0.6435205",
"0.6282319",
"0.62795603",
"0.6278928",
"0.62316865",
"0.62316865",
"0.62293184",
"0.62266916",
"0.6223264",
"0.6187261",
"0.6175471",
"0.61711305",
"0.6... | 0.68706644 | 0 |
CALL THIS FUNCTION IN ORDER TO TRANSFORM FROM RAW DATA (TASK TRACK 2015 AND TASK TRACK 2015) INTO A NEW DATA WHICH WILL BE STORED IN processed_data | def transform_queries_qrels_new_ids(self):
self.dict_mapper_old_to_new_qid = {}
self.count_subtasks = {}
self.dict_missingqueries = {}
self.dict_assessed_queries = self.get_queries_assessed()
self.map_old_qid_to_new()
self.map_qrels_to_newqids()
exit()
sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(self, data):",
"def get_preprocessed_from_raw(sess_no, raw_path, align_on, from_time, to_time) :\n \n #params\n sess = '01'\n \n trial_length = abs(from_time - to_time)\n\n # Paths\n #raw_path = base_path + 'data/raw/' + sess_no + '/session' + sess + '/'\n rinfo_path = ra... | [
"0.655736",
"0.6204332",
"0.6135723",
"0.6135723",
"0.60281587",
"0.5999329",
"0.59822136",
"0.5944262",
"0.5943839",
"0.5940451",
"0.5896695",
"0.58625484",
"0.5858024",
"0.58525944",
"0.58229464",
"0.5819861",
"0.58159506",
"0.5804405",
"0.5796359",
"0.57509035",
"0.5743336... | 0.0 | -1 |
Reparameterization trick by sampling fr an isotropic unit Gaussian. | def sampling(args):
z_mean, z_log_var = args
# K is the keras backend
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean=0 and std=1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resample_from_mf(self):\n self.g = np.zeros((self.K, self.K, self.B))\n for k1 in xrange(self.K):\n for k2 in xrange(self.K):\n self.g[k1,k2,:] = np.random.dirichlet(self.mf_gamma[k1,k2,:])",
"def sampling_latent(self):\n \n self.z = gaussian_sample(s... | [
"0.62867767",
"0.6282071",
"0.62459034",
"0.60686386",
"0.5999096",
"0.58914125",
"0.58084226",
"0.58028454",
"0.57550585",
"0.5752261",
"0.57308155",
"0.5696021",
"0.5695068",
"0.5642431",
"0.5633219",
"0.5607014",
"0.55823493",
"0.55755717",
"0.5550761",
"0.5548663",
"0.550... | 0.0 | -1 |
Adds the obstacle as a GL_QUAD, does not allow obstacles less than step size | def add_to_default_batch(self):
'''
self.shape = shared.batch.add(4, gl.GL_QUADS, None,
('v2f', (self.x, self.y,
self.x + self.width, self.y,
self.x + self.width, self.y +... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_obstacle(self, obstacle_to_add):\n if self.obstacles.size != 0:\n self.obstacles = np.hstack((self.obstacles, obstacle_to_add))\n else:\n self.obstacles = np.array([obstacle_to_add])",
"def draw_cube(self, vec):\n # TOP FACE\n gl.glBegin(gl.GL_QUADS)\n ... | [
"0.5752157",
"0.5704695",
"0.55139184",
"0.5475178",
"0.54358566",
"0.5433752",
"0.54157096",
"0.54004365",
"0.5338123",
"0.5258591",
"0.51662725",
"0.5163054",
"0.51443285",
"0.51314676",
"0.5076834",
"0.5071217",
"0.50524515",
"0.5043967",
"0.5039486",
"0.5000148",
"0.49988... | 0.5838143 | 0 |
Deletes the obstacle Deletes the graphics object if it exists then remove from the obstacles list | def delete(self):
if self.shape is not None:
self.shape.delete()
if self in shared.obstacles:
shared.obstacles.remove(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_obstacle(self, x, y):\n self.BOARD[y][x].traversable = True\n self.board_array[y][x] = 0",
"def remove_dead_obstacles(obstacle_list):\n\tfor obstacle in obstacle_list:\n\t\tobstacle.lifetime -= 1\n\t\tif obstacle.lifetime == 0:\n\t\t\tobstacle_list.remove(obstacle)\n\t\tprint(obstacle)",... | [
"0.71443987",
"0.6765844",
"0.65129733",
"0.6281675",
"0.6277408",
"0.62500167",
"0.61455756",
"0.6092859",
"0.6083727",
"0.6079878",
"0.59569687",
"0.5912045",
"0.5907866",
"0.5899834",
"0.5867926",
"0.58546644",
"0.58154076",
"0.57985085",
"0.5795229",
"0.57503587",
"0.5691... | 0.79736793 | 0 |
utility function for the RBM class | def train_rbm(fp_path="data/fp.bin",
out_path="data/rbm_J.bin",
batch_size=16,
all_units=2048,
use_gpu=True,
epochs=100,
k=1,
check_reconstruction=3,
plot=True,
qubo_mode=False
):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _regr_basic():",
"def mezclar_bolsa(self):",
"def b(self):\n pass",
"def b(self):\n pass",
"def regular(self):",
"def get_bias(self):",
"def CL(self):",
"def __init__(self, R, K, alpha, beta, iterations, type='bias'):\n\n self.R = R\n self.num_users, self.num_items = R... | [
"0.6392282",
"0.63511866",
"0.5900039",
"0.5900039",
"0.58350194",
"0.5814893",
"0.5629363",
"0.55948657",
"0.5561734",
"0.5524386",
"0.5512239",
"0.5512239",
"0.5509656",
"0.5509656",
"0.54481226",
"0.54441166",
"0.54224724",
"0.5413811",
"0.536525",
"0.5350975",
"0.5350825"... | 0.0 | -1 |
Create app Factory pattern | def create_app(config_name):
app = Flask(__name__)
# create app instance
app.config.from_object(config_by_name[config_name])
flask_bcrypt.init_app(app)
CORS(app)
routes.init_routes(app)
return app | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_app(self):\n raise NotImplementedError",
"def create():\n\n return App()",
"def app():\n return create_app()",
"def app():\n app = create_app()\n return app",
"def _create_app_instance(script_info):\n return create_app()",
"def factory(cls, global_config):\n\n def ... | [
"0.77792275",
"0.7639172",
"0.7576647",
"0.7531317",
"0.74922377",
"0.73317295",
"0.7285012",
"0.7199269",
"0.7168266",
"0.7152351",
"0.714875",
"0.714829",
"0.7128589",
"0.70836276",
"0.7069791",
"0.702057",
"0.6999516",
"0.6984093",
"0.6977224",
"0.69723654",
"0.69707006",
... | 0.6698907 | 55 |
r"""Load a JAMS Annotation from a file. | def load(path_or_file, validate=True, strict=True, fmt='auto'):
with _open(path_or_file, mode='r', fmt=fmt) as fdesc:
jam = JAMS(**json.load(fdesc))
if validate:
jam.validate(strict=strict)
return jam | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_annotation(json_path):\n # Open the file containing the annotation\n with open(json_path) as annotation_file:\n\n # Parse the AI2D annotation from the JSON file into a dictionary\n annotation = json.load(annotation_file)\n\n # Return the annotation\n retu... | [
"0.72324926",
"0.6892697",
"0.66138506",
"0.6469069",
"0.64096445",
"0.6335099",
"0.6076997",
"0.58904",
"0.58181393",
"0.5814854",
"0.5813344",
"0.57950807",
"0.5775708",
"0.5774268",
"0.5764761",
"0.5746504",
"0.57462776",
"0.5741542",
"0.5730799",
"0.56957155",
"0.5694569"... | 0.6470887 | 3 |
r"""Return the JObject as a set of native data types for serialization. | def __json__(self):
filtered_dict = dict()
for k, item in six.iteritems(self.__dict__):
if k.startswith('_'):
continue
if hasattr(item, '__json__'):
filtered_dict[k] = item.__json__
else:
filtered_dict[k] = serialize_o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json(self) -> Union[dict, list, str, int, float]:\n raise NotImplementedError # pragma: no cover",
"def data_types(self):",
"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:",
"def toJSType(self):\n return dict(\n name=self.name,\n description=self.des... | [
"0.6872278",
"0.62380904",
"0.6095021",
"0.6073043",
"0.6053912",
"0.6046117",
"0.6027797",
"0.6000453",
"0.58964735",
"0.587078",
"0.5852057",
"0.5852057",
"0.5848348",
"0.58386344",
"0.58100027",
"0.58068883",
"0.5797225",
"0.5795872",
"0.5778331",
"0.57390094",
"0.57096803... | 0.0 | -1 |
Initialize the object from a dictionary of values | def __json_init__(cls, **kwargs):
return cls(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, keys, values):\n self.keys = keys\n self.values = values",
"def from_dict(cls, dictionary: Dict[str, Any]):\n return cls(**dictionary)",
"def from_dict(cls, dct):\n pass",
"def from_dict(cls, data):\n return cls(**data)",
"def from_dict(cls, data):\r\n ... | [
"0.7609474",
"0.74423563",
"0.7432553",
"0.7426584",
"0.7413911",
"0.74106115",
"0.73779005",
"0.7373955",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"0.73540986",
"... | 0.0 | -1 |
Render the object alongside its attributes. | def __repr__(self):
indent = len(self.type) + 2
jstr = ',\n' + ' ' * indent
props = self._display_properties()
params = jstr.join('{:}={:}'.format(p, summary(self[p],
indent=indent))
for (p, dp) in props)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(self):\r\n super().render()",
"def render(self):\n pass",
"def render(self):\n pass",
"def render(self):\n pass",
"def render(self):\n pass",
"def render(self):\n pass",
"def render(self):\n pass",
"def render(self):\n\n context = {\n ... | [
"0.66662323",
"0.66044813",
"0.66044813",
"0.66044813",
"0.66044813",
"0.66044813",
"0.66044813",
"0.6551538",
"0.6535275",
"0.6530759",
"0.6495157",
"0.6471163",
"0.6454085",
"0.6423458",
"0.64134127",
"0.6337314",
"0.6335119",
"0.63111424",
"0.62728167",
"0.62338614",
"0.61... | 0.0 | -1 |
Return a list of the attributes of the object. Returns | def keys(self):
return self.__dict__.keys() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_attrs(self):\n attrs = []\n for attribute in self.__dict__.keys():\n attrs.append(attribute)",
"def get_attributes(cls):\r\n return []",
"def get_attributes(self):\n return self.attributes",
"def getAttributes(self):\n pass",
"def getAttributes(self):\n... | [
"0.8378631",
"0.8124402",
"0.811878",
"0.80819845",
"0.80225986",
"0.80225986",
"0.7986042",
"0.7955869",
"0.7923143",
"0.788158",
"0.7864873",
"0.776589",
"0.77455765",
"0.76674074",
"0.75634915",
"0.7563442",
"0.7475031",
"0.74707925",
"0.7440739",
"0.74406534",
"0.74261725... | 0.0 | -1 |
Create an Annotation. Note that, if an argument is None, an empty Annotation is created in its place. Additionally, a dictionary matching the expected structure of the arguments will be parsed (i.e. instantiating from JSON). | def __init__(self, namespace, data=None, annotation_metadata=None,
sandbox=None, time=0, duration=None):
super(Annotation, self).__init__()
if annotation_metadata is None:
annotation_metadata = AnnotationMetadata()
self.annotation_metadata = AnnotationMetadata(**a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RDFAnnotationParser_createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()",
"def createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()",
"def create_annotation(timestamp, value, host):\n return zipkin_core.Annotation(timestamp=timestamp, value=value,... | [
"0.59611523",
"0.59427184",
"0.58710736",
"0.5766063",
"0.5743713",
"0.5684858",
"0.5537817",
"0.55328065",
"0.5502203",
"0.53965425",
"0.52998286",
"0.51879084",
"0.51839596",
"0.5171853",
"0.51343316",
"0.51239586",
"0.5122177",
"0.50803316",
"0.5056191",
"0.50431263",
"0.5... | 0.5776535 | 3 |
r"""Return the JObject as a set of native data types for serialization. | def __json_light__(self, data=True):
filtered_dict = dict()
for k, item in six.iteritems(self.__dict__):
if k.startswith('_'):
continue
elif k == 'data':
if data:
filtered_dict[k] = self.__json_data__
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json(self) -> Union[dict, list, str, int, float]:\n raise NotImplementedError # pragma: no cover",
"def data_types(self):",
"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:",
"def toJSType(self):\n return dict(\n name=self.name,\n description=self.des... | [
"0.6870413",
"0.62396663",
"0.609319",
"0.60719466",
"0.6052096",
"0.60452205",
"0.602536",
"0.600013",
"0.58979076",
"0.5868783",
"0.5850369",
"0.5850369",
"0.5846776",
"0.5836646",
"0.5810545",
"0.58071244",
"0.5795777",
"0.57938355",
"0.5778446",
"0.5737332",
"0.57085997",... | 0.0 | -1 |
r"""JSONserialize the observation sequence. | def __json_data__(self):
if schema.is_dense(self.namespace):
dense_records = dict()
for field in Observation._fields:
dense_records[field] = []
for obs in self.data:
for key, val in six.iteritems(obs._asdict()):
dense_recor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toJson(self):\n text = '{'\n if len(self._sequence):\n for key in self._sequence:\n text += '\"%s\":%s,' % (key,json.dumps(self.dictionary[key]))\n text = text[:-1]\n text += '}'\n\n return text",
"def as_json(self) -> str:\n return json... | [
"0.6776155",
"0.6708074",
"0.64370257",
"0.6387373",
"0.6361001",
"0.63252026",
"0.6286282",
"0.6286282",
"0.62822974",
"0.62637067",
"0.6223078",
"0.6172028",
"0.6171983",
"0.6165653",
"0.6129194",
"0.6123974",
"0.6119053",
"0.61068493",
"0.61020386",
"0.6077638",
"0.6077155... | 0.0 | -1 |
Create an AnnotationMetadata object. | def __init__(self, curator=None, version='', corpus='', annotator=None,
annotation_tools='', annotation_rules='', validation='',
data_source=''):
super(AnnotationMetadata, self).__init__()
if curator is None:
curator = Curator()
if annotator is Non... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()",
"def RDFAnnotationParser_createAnnotation():\n return _libsbml.RDFAnnotationParser_createAnnotation()",
"def create_annotation(timestamp, value, host):\n return zipkin_core.Annotation(timestamp=timestamp, value=value,... | [
"0.64496595",
"0.62409776",
"0.60126007",
"0.5973326",
"0.597268",
"0.58234113",
"0.5532421",
"0.5525132",
"0.548961",
"0.54375076",
"0.5433396",
"0.54108304",
"0.54048735",
"0.53936356",
"0.5359755",
"0.5265134",
"0.52208763",
"0.5198275",
"0.5187576",
"0.5115082",
"0.510276... | 0.56469256 | 6 |
Create a filelevel Metadata object. | def __init__(self, title='', artist='', release='', duration=None,
identifiers=None, jams_version=None):
super(FileMetadata, self).__init__()
if jams_version is None:
jams_version = __VERSION__
if identifiers is None:
identifiers = Sandbox()
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_file_metadata(self):\n metadata = self.__file.require_group(METADATA)\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\"%Y-%m-%d\"))\n self.__write_value(metadata, SDK_VERSION, __version__)",
"def gen_meta(self, filename):\n nf_meta = {}\n nf_meta['fi... | [
"0.6603724",
"0.64794075",
"0.6444975",
"0.63011587",
"0.62964076",
"0.60185266",
"0.60181475",
"0.60088",
"0.59797317",
"0.5959526",
"0.5949016",
"0.5931138",
"0.58757067",
"0.5871233",
"0.58695316",
"0.58607626",
"0.58580416",
"0.5799596",
"0.57732666",
"0.5763947",
"0.5746... | 0.6083752 | 5 |
Create a Jams object. | def __init__(self, annotations=None, file_metadata=None, sandbox=None):
super(JAMS, self).__init__()
if file_metadata is None:
file_metadata = FileMetadata()
if sandbox is None:
sandbox = Sandbox()
self.annotations = AnnotationArray(annotations=annotations)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_jams(self):\n return jams_utils.jams_converter(\n audio_path=self.audio_path,\n events=self.events,\n metadata={\n \"split\": self._clip_metadata.get(\"split\"),\n \"subdataset\": self._clip_metadata.get(\"subdataset\"),\n },\n... | [
"0.6043115",
"0.6028612",
"0.6020422",
"0.595867",
"0.5869286",
"0.51103055",
"0.48313457",
"0.48313457",
"0.4821683",
"0.48106268",
"0.47865143",
"0.47502753",
"0.47477722",
"0.47190872",
"0.4700809",
"0.46956456",
"0.46870407",
"0.46566224",
"0.4654095",
"0.4651156",
"0.463... | 0.5316625 | 5 |
Add the contents of another jam to this object. Note that, by default, this method fails if file_metadata is not identical and raises a ValueError; either resolve this manually (because conflicts should almost never happen), force an 'overwrite', or tell the method to 'ignore' the metadata of the object being added. | def add(self, jam, on_conflict='fail'):
if on_conflict not in ['overwrite', 'fail', 'ignore']:
raise ParameterError("on_conflict='{}' is not in ['fail', "
"'overwrite', 'ignore'].".format(on_conflict))
if not self.file_metadata == jam.file_metadata:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __add__(self, other):\r\n # Make a defaultdict of defaultdicts, the latter of which returns\r\n # None when an key is not present\r\n merged_data = defaultdict(lambda: defaultdict(lambda: None))\r\n\r\n # We will keep track of all unique sample_ids and metadata headers\r\n # ... | [
"0.6023381",
"0.5716449",
"0.5619332",
"0.5605447",
"0.5589624",
"0.5504831",
"0.54868376",
"0.546581",
"0.5444086",
"0.54176193",
"0.54172623",
"0.53960514",
"0.5354634",
"0.52778107",
"0.5238899",
"0.52343297",
"0.5198953",
"0.5158326",
"0.5157256",
"0.51571655",
"0.5131289... | 0.649641 | 0 |
Serialize annotation as a JSON formatted stream to file. | def save(self, path_or_file, strict=True, fmt='auto'):
self.validate(strict=strict)
with _open(path_or_file, mode='w', fmt=fmt) as fdesc:
json.dump(self.__json__, fdesc, indent=2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump_annotations(self):\n fname = 'annotations'\n if self.split is not None:\n fname = 'annotations_{}'.format(self.split)\n fname = os.path.join(self.dest_folder, '{}.json'.format(fname))\n self.save(self.dataset, fname, \"annotations\")",
"def _save_annotation(self, p... | [
"0.65512836",
"0.65278125",
"0.65025586",
"0.63029265",
"0.6299647",
"0.628952",
"0.61956006",
"0.61616343",
"0.60725665",
"0.6022213",
"0.6013489",
"0.6011242",
"0.5988493",
"0.5966148",
"0.5930022",
"0.5913455",
"0.59128827",
"0.5912753",
"0.590443",
"0.590443",
"0.58865786... | 0.53753155 | 97 |
r"""Return the JObject as a set of native data types for serialization. | def __json_light__(self):
filtered_dict = dict()
for k, item in six.iteritems(self.__dict__):
if k.startswith('_') or k == 'annotations':
continue
if hasattr(item, '__json__'):
filtered_dict[k] = item.__json__
else:
fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json(self) -> Union[dict, list, str, int, float]:\n raise NotImplementedError # pragma: no cover",
"def data_types(self):",
"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:",
"def toJSType(self):\n return dict(\n name=self.name,\n description=self.des... | [
"0.6872278",
"0.62380904",
"0.6095021",
"0.6073043",
"0.6053912",
"0.6046117",
"0.6027797",
"0.6000453",
"0.58964735",
"0.587078",
"0.5852057",
"0.5852057",
"0.5848348",
"0.58386344",
"0.58100027",
"0.58068883",
"0.5797225",
"0.5795872",
"0.5778331",
"0.57390094",
"0.57096803... | 0.0 | -1 |
Main module execution method | def exec_module(self, **kwargs):
for key in list(self.module_arg_spec.keys()) + ['tags']:
if hasattr(self, key):
setattr(self, key, kwargs[key])
elif kwargs[key] is not None:
if key == "location":
self.parameters["location"] = kwargs[k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(self):\r\n pass",
"def main():\n pass",
"def main(self) -> None:\n pass",
"def main():\n return",
"def run():\n main()",
"def main(self):",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def mai... | [
"0.80925983",
"0.8029987",
"0.80148256",
"0.7824878",
"0.78073204",
"0.78043985",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
"0.77332145",
... | 0.0 | -1 |
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