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https://github.com/thousandeyes/thousandeyes-sdk-python.git
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* [GitHub Bot] Generated python SDK * Updated README --------- Co-authored-by: API Team <api-team@thousandeyes.com> Co-authored-by: Miguel Pragosa <mpragosa@thousandeyes.com>
96 lines
4.4 KiB
Python
96 lines
4.4 KiB
Python
# coding: utf-8
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"""
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Event Detection API
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Event detection occurs when ThousandEyes identifies that error signals related to a component (proxy, network node, AS, server etc) have deviated from the baselines established by events. * To determine this, ThousandEyes takes the test results from all accounts groups within an organization, and analyzes that data. * Noisy test results (those that have too many errors in a short window) are removed until they stabilize, and the rest of the results are tagged with the components associated with that test result (for example, proxy, network, or server). * Next, any increase in failures from the test results and each component helps in determining the problem domain and which component may be at fault. * When this failure rate increases beyond a pre-defined threshold (set by the algorithm), an event is triggered and an email notification is sent to the user (if they've enabled email alerts). With the Events API, you can perform the following tasks on the ThousandEyes platform: * **Retrieve Events**: Obtain a list of events and detailed information for each event. For more information about events, see [Event Detection](https://docs.thousandeyes.com/product-documentation/event-detection).
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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, Field, StrictInt, 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 Error(BaseModel):
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"""
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Error
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""" # noqa: E501
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type: Optional[StrictStr] = Field(default=None, description="A URI reference that identifies the problem type. When this member is not present, its value is assumed to be \"about:blank\".")
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title: Optional[StrictStr] = Field(default=None, description="A short, human-readable summary of the problem type.")
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status: Optional[StrictInt] = Field(default=None, description="The HTTP status code generated by the origin server for this occurrence of the problem.")
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detail: Optional[StrictStr] = Field(default=None, description="A human-readable explanation specific to this occurrence of the problem.")
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instance: Optional[StrictStr] = Field(default=None, description="A URI reference that identifies the specific occurrence of the problem.")
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__properties: ClassVar[List[str]] = ["type", "title", "status", "detail", "instance"]
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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 Error 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 Error 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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"type": obj.get("type"),
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"title": obj.get("title"),
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"status": obj.get("status"),
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"detail": obj.get("detail"),
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"instance": obj.get("instance")
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})
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return _obj
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