# coding: utf-8 """ ThousandEyes for OpenTelemetry API ThousandEyes for OpenTelemetry provides machine-to-machine integration between ThousandEyes and its customers. It allows you to export ThousandEyes telemetry data in OTel format, which is widely used in the industry. With ThousandEyes for OTel, you can leverage frameworks widely used in the observability domain - such as Splunk, Grafana, and Honeycomb - to capture and analyze ThousandEyes data. Any client that supports OTel can use ThousandEyes for OpenTelemetry. ThousandEyes for OTel is made up of the following components: * Data streaming APIs that you can use to configure and enable your ThousandEyes tests with OTel-compatible streams, in particular to configure how ThousandEyes telemetry data is exported to client integrations. * A set of streaming pipelines called _collectors_ that actively fetch ThousandEyes network test data, enrich the data with some additional detail, filter, and push the data to the customer-configured endpoints, depending on what you configure via the public APIs. * Third-party OTel collectors that receive, transform, filter, and export different metrics to client applications such as AppD, or any other OTel-capable client configuration. For more information about ThousandEyes for OpenTelemetry, see the [documentation](https://docs.thousandeyes.com/product-documentation/api/opentelemetry). 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 from typing import Any, ClassVar, Dict, List, Optional from thousandeyes_sdk.streaming.models.stream_self_link import StreamSelfLink from typing import Optional, Set from typing_extensions import Self class StreamLinks(BaseModel): """ StreamLinks """ # noqa: E501 var_self: Optional[StreamSelfLink] = Field(default=None, alias="self") __properties: ClassVar[List[str]] = ["self"] model_config = ConfigDict( populate_by_name=True, validate_assignment=True, protected_namespaces=(), extra="allow", ) def to_str(self) -> str: """Returns the string representation of the model using alias""" return pprint.pformat(self.model_dump(by_alias=True)) 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 self.model_dump_json(by_alias=True, exclude_unset=True, exclude_none=True) @classmethod def from_json(cls, json_str: str) -> Optional[Self]: """Create an instance of StreamLinks from a JSON string""" return cls.from_dict(json.loads(json_str)) 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 var_self if self.var_self: _dict['self'] = self.var_self.to_dict() return _dict @classmethod def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: """Create an instance of StreamLinks from a dict""" if obj is None: return None if not isinstance(obj, dict): return cls.model_validate(obj) _obj = cls.model_validate({ "self": StreamSelfLink.from_dict(obj["self"]) if obj.get("self") is not None else None }) return _obj