Source code for openapi_client.models.update_dataset_metadata
# coding: utf-8
"""
Amorphic Data Platform
Amorphic Data Platform - API Definition documentation
The version of the OpenAPI document: 0.3.0
Generated by OpenAPI Generator (https://openapi-generator.tech)
Do not edit the class manually.
""" # noqa: E501
from __future__ import annotations
import pprint
import re # noqa: F401
import json
from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from openapi_client.models.advanced_config import AdvancedConfig
from openapi_client.models.column_name_and_type import ColumnNameAndType
from openapi_client.models.data_metrics_collection_options import DataMetricsCollectionOptions
from openapi_client.models.dataset_life_cycle_rules import DatasetLifeCycleRules
from openapi_client.models.malware_detection_options import MalwareDetectionOptions
from typing import Optional, Set
from typing_extensions import Self
[docs]
class UpdateDatasetMetadata(BaseModel):
"""
UpdateDatasetMetadata
""" # noqa: E501
dataset_description: Optional[StrictStr] = Field(default=None, alias="DatasetDescription")
display_name: Optional[StrictStr] = Field(default=None, alias="DisplayName")
data_classification: Optional[List[StrictStr]] = Field(default=None, alias="DataClassification")
keywords: Optional[List[StrictStr]] = Field(default=None, alias="Keywords")
table_update: Optional[StrictStr] = Field(default=None, alias="TableUpdate")
skip_file_header: Optional[StrictBool] = Field(default=None, alias="SkipFileHeader")
malware_detection_options: Optional[MalwareDetectionOptions] = Field(default=None, alias="MalwareDetectionOptions")
target_table_prep_mode: Optional[StrictStr] = Field(default=None, alias="TargetTablePrepMode")
is_data_validation_enabled: Optional[StrictBool] = Field(default=None, alias="IsDataValidationEnabled")
life_cycle_policy_status: Optional[StrictStr] = Field(default=None, alias="LifeCyclePolicyStatus")
life_cycle_rules: Optional[DatasetLifeCycleRules] = Field(default=None, alias="LifeCycleRules")
data_metrics_collection_options: Optional[DataMetricsCollectionOptions] = Field(default=None, alias="DataMetricsCollectionOptions")
advanced_config: Optional[AdvancedConfig] = Field(default=None, alias="AdvancedConfig")
is_data_cleanup_enabled: Optional[StrictBool] = Field(default=None, alias="IsDataCleanupEnabled")
is_data_profiling_enabled: Optional[StrictBool] = Field(default=None, alias="IsDataProfilingEnabled")
skip_lz_process: Optional[StrictBool] = Field(default=None, alias="SkipLZProcess")
update_schema: Optional[StrictStr] = Field(default=None, alias="UpdateSchema")
dataset_schema: Optional[List[ColumnNameAndType]] = Field(default=None, alias="DatasetSchema")
__properties: ClassVar[List[str]] = ["DatasetDescription", "DisplayName", "DataClassification", "Keywords", "TableUpdate", "SkipFileHeader", "MalwareDetectionOptions", "TargetTablePrepMode", "IsDataValidationEnabled", "LifeCyclePolicyStatus", "LifeCycleRules", "DataMetricsCollectionOptions", "AdvancedConfig", "IsDataCleanupEnabled", "IsDataProfilingEnabled", "SkipLZProcess", "UpdateSchema", "DatasetSchema"]
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
protected_namespaces=(),
)
[docs]
def to_str(self) -> str:
"""Returns the string representation of the model using alias"""
return pprint.pformat(self.model_dump(by_alias=True))
[docs]
def to_json(self) -> str:
"""Returns the JSON representation of the model using alias"""
# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
return json.dumps(self.to_dict())
[docs]
@classmethod
def from_json(cls, json_str: str) -> Optional[Self]:
"""Create an instance of UpdateDatasetMetadata from a JSON string"""
return cls.from_dict(json.loads(json_str))
[docs]
def to_dict(self) -> Dict[str, Any]:
"""Return the dictionary representation of the model using alias.
This has the following differences from calling pydantic's
`self.model_dump(by_alias=True)`:
* `None` is only added to the output dict for nullable fields that
were set at model initialization. Other fields with value `None`
are ignored.
"""
excluded_fields: Set[str] = set([
])
_dict = self.model_dump(
by_alias=True,
exclude=excluded_fields,
exclude_none=True,
)
# override the default output from pydantic by calling `to_dict()` of malware_detection_options
if self.malware_detection_options:
_dict['MalwareDetectionOptions'] = self.malware_detection_options.to_dict()
# override the default output from pydantic by calling `to_dict()` of life_cycle_rules
if self.life_cycle_rules:
_dict['LifeCycleRules'] = self.life_cycle_rules.to_dict()
# override the default output from pydantic by calling `to_dict()` of data_metrics_collection_options
if self.data_metrics_collection_options:
_dict['DataMetricsCollectionOptions'] = self.data_metrics_collection_options.to_dict()
# override the default output from pydantic by calling `to_dict()` of advanced_config
if self.advanced_config:
_dict['AdvancedConfig'] = self.advanced_config.to_dict()
# override the default output from pydantic by calling `to_dict()` of each item in dataset_schema (list)
_items = []
if self.dataset_schema:
for _item_dataset_schema in self.dataset_schema:
if _item_dataset_schema:
_items.append(_item_dataset_schema.to_dict())
_dict['DatasetSchema'] = _items
return _dict
[docs]
@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
"""Create an instance of UpdateDatasetMetadata from a dict"""
if obj is None:
return None
if not isinstance(obj, dict):
return cls.model_validate(obj)
_obj = cls.model_validate({
"DatasetDescription": obj.get("DatasetDescription"),
"DisplayName": obj.get("DisplayName"),
"DataClassification": obj.get("DataClassification"),
"Keywords": obj.get("Keywords"),
"TableUpdate": obj.get("TableUpdate"),
"SkipFileHeader": obj.get("SkipFileHeader"),
"MalwareDetectionOptions": MalwareDetectionOptions.from_dict(obj["MalwareDetectionOptions"]) if obj.get("MalwareDetectionOptions") is not None else None,
"TargetTablePrepMode": obj.get("TargetTablePrepMode"),
"IsDataValidationEnabled": obj.get("IsDataValidationEnabled"),
"LifeCyclePolicyStatus": obj.get("LifeCyclePolicyStatus"),
"LifeCycleRules": DatasetLifeCycleRules.from_dict(obj["LifeCycleRules"]) if obj.get("LifeCycleRules") is not None else None,
"DataMetricsCollectionOptions": DataMetricsCollectionOptions.from_dict(obj["DataMetricsCollectionOptions"]) if obj.get("DataMetricsCollectionOptions") is not None else None,
"AdvancedConfig": AdvancedConfig.from_dict(obj["AdvancedConfig"]) if obj.get("AdvancedConfig") is not None else None,
"IsDataCleanupEnabled": obj.get("IsDataCleanupEnabled"),
"IsDataProfilingEnabled": obj.get("IsDataProfilingEnabled"),
"SkipLZProcess": obj.get("SkipLZProcess"),
"UpdateSchema": obj.get("UpdateSchema"),
"DatasetSchema": [ColumnNameAndType.from_dict(_item) for _item in obj["DatasetSchema"]] if obj.get("DatasetSchema") is not None else None
})
return _obj