2. i-SAS_PointSensor

2.1. interface

class point_sensor.interface.Interface(instance_name, input_data_names, output_data_names, sensor_name, structural_model_name=None, **kwargs)

Bases: object

interface class

package_name = 'point_sensor'
input_data_num = 1
output_data_num = 1
__init__(instance_name, input_data_names, output_data_names, sensor_name, structural_model_name=None, **kwargs)

initialization of Interface class

Parameters

  • instance_name (str) – name of instance.

  • input_data_names (list[str]) – names of input data.

  • output_data_names (list[str]) – names of output data.

  • sensor_name (str) – sensor name.

  • structural_model_name (str, optional) – structural model name.

  • **kwargs – Arbitrary keyword arguments.

set_project(project_name)

set project

Parameters

project_name (str) – project name.

set_model(output_metadata, sensors, structural_models, structural_model_connections, sensor_structural_model_connections)

set model

Parameters

  • output_metadata (pd.DataFrame) – output metadata.

  • sensors (dict) – sensors.

  • structural_models (dict) – structural models.

  • structural_model_connections (dict) – connections between data and structural model.

  • sensor_structural_model_connections (dict) – connections between sensor and structural model.

Returns

containing followings

dict: information on instance of this model. pandas.DataFrame: output metadata of this model. dict: connections between output data of this model and structural model.

Return type

tuple

__call__(input_data)

simulate measurement data

Parameters

input_data (dict) – input data to simulator.

Returns

containing followings,

dict: measurement data simulated. pandas.DataFrame: used timestamp. The length is the same as measurement data.

Return type

tuple

exit()

exit simulator

2.2. model

class point_sensor.model.Model(input_data_names, output_data_names, cfg)

Bases: object

model class

__init__(input_data_names, output_data_names, cfg)

constructor

Parameters

  • input_data_names (list[str]) – names of input data.

  • output_data_names (list[str]) – names of output data.

  • cfg (dict) – config.

set_model(output_metadata, sensors, structural_models, structural_model_connections)

set model and calculate intermediate values

Parameters

  • output_metadata (pd.DataFrame) – output metadata.

  • sensors (dict) – sensors.

  • structural_models (dict) – structural models.

  • structural_model_connection (dict) – connections between data and structural model.

__call__(input_data)

simulate measurement data added normally distributed noise to input_data

Parameters

input_data (list[pandas.DataFrame]) – time series data as input.

Returns

containing followings,

list[pandas.DataFrame]: measurement data. list[dict]: used timestamp for each input data. Dict has two keys, ‘s’ as strating timestamp and

’e’ as end time stamp, and the value is 1d-array of the timestamp used to caclulate output, whose length is the same as the length of output.

Return type

tuple