# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
[docs]class EntireDetectResponse(Model):
"""EntireDetectResponse.
All required parameters must be populated in order to send to Azure.
:param period: Required. Frequency extracted from the series, zero means
no recurrent pattern has been found.
:type period: int
:param expected_values: Required. ExpectedValues contain expected value
for each input point. The index of the array is consistent with the input
series.
:type expected_values: list[float]
:param upper_margins: Required. UpperMargins contain upper margin of each
input point. UpperMargin is used to calculate upperBoundary, which equals
to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response
can be filtered by upperBoundary and lowerBoundary. By adjusting
marginScale value, less significant anomalies can be filtered in client
side. The index of the array is consistent with the input series.
:type upper_margins: list[float]
:param lower_margins: Required. LowerMargins contain lower margin of each
input point. LowerMargin is used to calculate lowerBoundary, which equals
to expectedValue - (100 - marginScale)*lowerMargin. Points between the
boundary can be marked as normal ones in client side. The index of the
array is consistent with the input series.
:type lower_margins: list[float]
:param is_anomaly: Required. IsAnomaly contains anomaly properties for
each input point. True means an anomaly either negative or positive has
been detected. The index of the array is consistent with the input series.
:type is_anomaly: list[bool]
:param is_negative_anomaly: Required. IsNegativeAnomaly contains anomaly
status in negative direction for each input point. True means a negative
anomaly has been detected. A negative anomaly means the point is detected
as an anomaly and its real value is smaller than the expected one. The
index of the array is consistent with the input series.
:type is_negative_anomaly: list[bool]
:param is_positive_anomaly: Required. IsPositiveAnomaly contain anomaly
status in positive direction for each input point. True means a positive
anomaly has been detected. A positive anomaly means the point is detected
as an anomaly and its real value is larger than the expected one. The
index of the array is consistent with the input series.
:type is_positive_anomaly: list[bool]
"""
_validation = {
'period': {'required': True},
'expected_values': {'required': True},
'upper_margins': {'required': True},
'lower_margins': {'required': True},
'is_anomaly': {'required': True},
'is_negative_anomaly': {'required': True},
'is_positive_anomaly': {'required': True},
}
_attribute_map = {
'period': {'key': 'period', 'type': 'int'},
'expected_values': {'key': 'expectedValues', 'type': '[float]'},
'upper_margins': {'key': 'upperMargins', 'type': '[float]'},
'lower_margins': {'key': 'lowerMargins', 'type': '[float]'},
'is_anomaly': {'key': 'isAnomaly', 'type': '[bool]'},
'is_negative_anomaly': {'key': 'isNegativeAnomaly', 'type': '[bool]'},
'is_positive_anomaly': {'key': 'isPositiveAnomaly', 'type': '[bool]'},
}
def __init__(self, *, period: int, expected_values, upper_margins, lower_margins, is_anomaly, is_negative_anomaly, is_positive_anomaly, **kwargs) -> None:
super(EntireDetectResponse, self).__init__(**kwargs)
self.period = period
self.expected_values = expected_values
self.upper_margins = upper_margins
self.lower_margins = lower_margins
self.is_anomaly = is_anomaly
self.is_negative_anomaly = is_negative_anomaly
self.is_positive_anomaly = is_positive_anomaly