Module aivis_engine_v2_sm_sdk_python.inference

Classes

class SignalMonitorInference (handle, key)

Class for creating and handling signal monitor inference. This is your entry point for all inference operations.

Private constructor. To create new instances use any of:

• SignalMonitorInference.create_by_training()
• SignalMonitorInference.create_by_model()

Static methods

def consolidate_model(model_json: str) ‑> str

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Returns new model instance with updated inference data specification according to inference data specifications of the triggers in input model.

Returns

str

DtoModel as JSON string

def create_by_model(cls, model_json: str, config_json: str) ‑> SignalMonitorInference

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Create signal monitor inference for given model and config.

To generate warnings with this inference instance use:

• SignalMonitorInference.infer()

Parameters

model_json : str

DtoModel as JSON string

config_json : str

DtoInferenceConfig as JSON string

Returns

SignalMonitorInference

Instance of signal monitor inference

def create_by_training(cls, training: SignalMonitorTraining, config_json: str) ‑> SignalMonitorInference

@FlavourRequirement([Flavour.FULL])

Create signal monitor inference for given training handle and config.

To generate warnings with this inference instance use:

• SignalMonitorInference.infer()

Parameters

training : SignalMonitorTraining

Instance of signal monitor training

config_json : str

DtoInferenceConfig as JSON string

Returns

SignalMonitorInference

Instance of signal monitor inference

Methods

def destroy(self)

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Destroy this signal monitor inference. It's always safe to destroy an inference. Internally the destruction only takes place after all references to this object have been released.

def get_data_specification(self) ‑> str

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Get this inference's DtoInferenceDataSpecification

Returns

str

DtoInferenceDataSpecification as JSON string

def infer(self, data: SignalMonitorData, time_ranges_json: str) ‑> str

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Calculate inference for given data context and time ranges. Parameters

---

data :  aivis_engine_v2_sp_sdk_python.data.SignalMonitorData

Instance of signal monitor data

time_ranges_json : str

DtoInferenceTimeRanges as JSON string

Returns

str

DtoWarnings as JSON string