File: //proc/self/root/lib/mysqlsh/lib/python3.8/site-packages/oci/stack_monitoring/models/metric_data.py
# coding: utf-8
# Copyright (c) 2016, 2025, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
# NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20210330
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class MetricData(object):
"""
Metric Details
"""
def __init__(self, **kwargs):
"""
Initializes a new MetricData object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param dimensions:
The value to assign to the dimensions property of this MetricData.
:type dimensions: dict(str, str)
:param training_data_points:
The value to assign to the training_data_points property of this MetricData.
:type training_data_points: list[oci.stack_monitoring.models.DataPoint]
:param evaluation_data_points:
The value to assign to the evaluation_data_points property of this MetricData.
:type evaluation_data_points: list[oci.stack_monitoring.models.DataPoint]
"""
self.swagger_types = {
'dimensions': 'dict(str, str)',
'training_data_points': 'list[DataPoint]',
'evaluation_data_points': 'list[DataPoint]'
}
self.attribute_map = {
'dimensions': 'dimensions',
'training_data_points': 'trainingDataPoints',
'evaluation_data_points': 'evaluationDataPoints'
}
self._dimensions = None
self._training_data_points = None
self._evaluation_data_points = None
@property
def dimensions(self):
"""
Gets the dimensions of this MetricData.
list of dimensions for the metric
:return: The dimensions of this MetricData.
:rtype: dict(str, str)
"""
return self._dimensions
@dimensions.setter
def dimensions(self, dimensions):
"""
Sets the dimensions of this MetricData.
list of dimensions for the metric
:param dimensions: The dimensions of this MetricData.
:type: dict(str, str)
"""
self._dimensions = dimensions
@property
def training_data_points(self):
"""
**[Required]** Gets the training_data_points of this MetricData.
list of data points for the metric for training of baseline
:return: The training_data_points of this MetricData.
:rtype: list[oci.stack_monitoring.models.DataPoint]
"""
return self._training_data_points
@training_data_points.setter
def training_data_points(self, training_data_points):
"""
Sets the training_data_points of this MetricData.
list of data points for the metric for training of baseline
:param training_data_points: The training_data_points of this MetricData.
:type: list[oci.stack_monitoring.models.DataPoint]
"""
self._training_data_points = training_data_points
@property
def evaluation_data_points(self):
"""
**[Required]** Gets the evaluation_data_points of this MetricData.
list of data points for the metric for evaluation of anomalies
:return: The evaluation_data_points of this MetricData.
:rtype: list[oci.stack_monitoring.models.DataPoint]
"""
return self._evaluation_data_points
@evaluation_data_points.setter
def evaluation_data_points(self, evaluation_data_points):
"""
Sets the evaluation_data_points of this MetricData.
list of data points for the metric for evaluation of anomalies
:param evaluation_data_points: The evaluation_data_points of this MetricData.
:type: list[oci.stack_monitoring.models.DataPoint]
"""
self._evaluation_data_points = evaluation_data_points
def __repr__(self):
return formatted_flat_dict(self)
def __eq__(self, other):
if other is None:
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other