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https://github.com/thousandeyes/thousandeyes-sdk-python.git
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88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
# coding: utf-8
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"""
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Alerts API
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**Note:** API operations for the creation or retrieval of API, Page Load, or Web-Transaction alert rules are not available for ThousandEyes for Government instance. You can manage the following alert functionalities on the ThousandEyes platform using the Alerts API: * **Alerts**: Retrieve alert details. Alerts are assigned to tests through alert rules. * **Alert Rules**: Conditions that you configure in order to highlight or be notified of events of interest in your ThousandEyes tests. When an alert rule’s conditions are met, the associated alert is triggered and the alert becomes active. It remains active until the alert is cleared. Alert rules are reusable across multiple tests.. * **Alert Suppression Windows**: Suppress alerts for tests during periods such as planned maintenance. Windows can be one-time events or recurring events to handle periodic occurrences such as monthly downtime for maintenance. For more information about the alerts, see [Alerts](https://docs.thousandeyes.com/product-documentation/alerts).
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Generated by OpenAPI Generator (https://openapi-generator.tech)
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Do not edit the class manually.
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""" # noqa: E501
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from __future__ import annotations
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import pprint
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import re # noqa: F401
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import json
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from pydantic import BaseModel, ConfigDict, StrictStr
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from typing import Any, ClassVar, Dict, List, Optional
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from typing import Optional, Set
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from typing_extensions import Self
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class EndAlertMetrics(BaseModel):
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"""
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String representation of the metric or metrics being considered in the alert rule at the point that the alert was cleared. If the alert is not yet cleared, this field reflects the last round of data gathered from the source.
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""" # noqa: E501
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metrics: Optional[StrictStr] = None
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__properties: ClassVar[List[str]] = ["metrics"]
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model_config = ConfigDict(
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populate_by_name=True,
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validate_assignment=True,
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protected_namespaces=(),
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extra="allow",
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)
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def to_str(self) -> str:
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"""Returns the string representation of the model using alias"""
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return pprint.pformat(self.model_dump(by_alias=True))
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def to_json(self) -> str:
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"""Returns the JSON representation of the model using alias"""
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# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
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return self.model_dump_json(by_alias=True, exclude_unset=True, exclude_none=True)
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@classmethod
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def from_json(cls, json_str: str) -> Optional[Self]:
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"""Create an instance of EndAlertMetrics from a JSON string"""
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return cls.from_dict(json.loads(json_str))
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def to_dict(self) -> Dict[str, Any]:
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"""Return the dictionary representation of the model using alias.
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This has the following differences from calling pydantic's
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`self.model_dump(by_alias=True)`:
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* `None` is only added to the output dict for nullable fields that
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were set at model initialization. Other fields with value `None`
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are ignored.
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"""
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excluded_fields: Set[str] = set([
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])
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_dict = self.model_dump(
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by_alias=True,
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exclude=excluded_fields,
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exclude_none=True,
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)
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return _dict
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@classmethod
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def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
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"""Create an instance of EndAlertMetrics from a dict"""
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if obj is None:
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return None
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if not isinstance(obj, dict):
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return cls.model_validate(obj)
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_obj = cls.model_validate({
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"metrics": obj.get("metrics")
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})
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return _obj
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