7. i-SAS_SensorPostprocessor¶

7.1. interface¶

class sensor_postprocessor.interface.Interface(instance_name, input_names, output_names, postprocess, structural_model_name=None, **kwargs)¶

Bases: object

interface class of sensor postprocessor

package_name = 'sensor_postprocessor'¶

structural_model_required = False¶

__init__(instance_name, input_names, output_names, postprocess, structural_model_name=None, **kwargs)¶

initialization of interface class of sensor postprocessor

Parameters

instance_name (str) – instance name.
input_names (dict) – dict whose keys are names of input quantity and values are names of input data.
output_names (dict) – dict whose keys are names of output quantity and values are names of output data.
postprocess (list) – list of processing class instances which apply to data
structural_model_name (str, optional) – structural model name.
**kwargs – Arbitrary keyword arguments.

Example

>>> input_names = {'strain': ['rosette_strain_x', 'rosette_strain_y', 'rosette_strain_xy']}
>>> output_names =
        {'strain': ['postprocessed_strain_x', 'postprocessed_strain_y', 'postprocessed_strain_xy']}
>>> start_timestamp = 1587772811000000000
>>> end_timestamp = 1587772911000000000
>>> delta_t = 10000000000
>>> postprocess = [Extract(start_timestamp, end_timestamp),
                   TimestampChanging('interpolate', 'linear', start_timestamp, end_timestamp, delta_t),
                   Detrand()]
>>> interface = Interface(input_names, output_names, postprocess)

set_project(project_name)¶

Parameters: project_name (str) – project name.

set_model(output_metadata, sensors, structural_models, structural_model_connections, streaming=False)¶

set structural 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.
streaming (bool, optional) – if true, run as structural_analysis.

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)¶

calculate quantity of state

Parameters

input_data (dict) – data calculated before

Returns

containing:: dict: analysis data. pandas.DataFrame: timestamp used. The length is the same as analysis data.

Return type

tuple

exit()¶: exit solver

7.2. model¶

class sensor_postprocessor.model.Model(input_names, output_names, postprocess, structural_model_name, cfg)¶

Bases: object

model class

__init__(input_names, output_names, postprocess, structural_model_name, cfg)¶

constructor

Parameters

input_names (dict) – dict whose keys are names of input quantity and values are names of input data.
output_names (dict) – dict whose keys are names of output quantity and values are names of output data.
postprocess (list) – list of processing class instances which apply to data
structural_model_name (str, optional) – structural model name.
cfg (dict) – config.

set_model(output_metadata, sensors, structural_models, structural_model_connections, streaming)¶

set model and calculate intermediate values

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.
streaming (bool) – if true, run as structural_analysis.

__call__(input_data)¶

calculate analysis data from input using functions

Parameters

input_data (dict) – input data.

Returns

containing followings,

dict: analysis data. the key is output_quantity_name, and the value is the list, which: has data value (the type is pandas.DataFrame) as element corresponding to its data name.
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

7.3. process¶

processing classes in i-SAS_SensorPostprocessor

class sensor_postprocessor.process.Extract(start_timestamp, end_timestamp)¶

Bases: object

extract data

__init__(start_timestamp, end_timestamp)¶: initialization of extract class :param start_timestamp: extracts the data with timestamps after start_timestamp :type start_timestamp: int :param end_timestamp: extracts the data with timestamps before end_timestamp :type end_timestamp: int

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: extracted data
Return type: pd.DataFrame

class sensor_postprocessor.process.TimestampChanging(start_timestamp, end_timestamp, delta_t, interp_kind='linear', extrap_kind='linear')¶

Bases: object

change timestamp of data

__init__(start_timestamp, end_timestamp, delta_t, interp_kind='linear', extrap_kind='linear')¶

initialization of timestamp changing class

Parameters

start_timestamp (int) – start timestamp of interpolation or extrapolation range.
end_timestamp (int) – end timestamp of interpolation or extrapolation range.
delta_t (int) – time increments for interpolation / extrapolation range.
interp_kind (str, optional) – method of interpolation, the default is ‘linear’.
extrap_kind (str, optional) – method of extrapolation, the default is ‘linear’.

__call__(data)¶

change timestamp by interpolate or extrapolate

Parameters: data (pd.DataFrame) – input data
Returns: interpolated or extrapolated data
Return type: pd.DataFrame

class sensor_postprocessor.process.Thinning(interval)¶

Bases: object

thinning data from input cata

__init__(interval)¶

initialization of thinning class

Parameters: interval (int) – the interval between the rows to be thinned out.

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: thinned data
Return type: pd.DataFrame

class sensor_postprocessor.process.RefDiff(ref_timestamp, ref_data=None)¶

Bases: object

calculate difference between input data and reference data

__init__(ref_timestamp, ref_data=None)¶

initialization of RefDiff class

Parameters

ref_timestamp (int, list) – timestamp of reference data
ref_data (pd.DataFrame, optional) – reference data. Defaults to None.

Example

>>> # if reference data is on specific time.
>>> ref_timestamp = 1587772800000000000
>>> # if reference data is average data of designated time range.
>>> ref_timestamp = [1587772800000000000, 1587772900000000000]

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: calculated data
Return type: pd.DataFrame

class sensor_postprocessor.process.Detrend¶

Bases: object

remove trend from data

__init__()¶: initialization of detrend class

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: data without trend
Return type: pd.DataFrame

class sensor_postprocessor.process.Lowpass¶

Bases: object

apply low-pass filter to data

__init__()¶: initialization of Lowpass class

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: filtered data
Return type: pd.DataFrame

class sensor_postprocessor.process.Kalman¶

Bases: object

apply Kalman filter to data

__init__()¶: initialization of Kalman class

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: filtered data
Return type: pd.DataFrame

class sensor_postprocessor.process.MovingAverage¶

Bases: object

apply moving average filter to data

__init__()¶: initialization of MovingAverage class

__call__(data)¶

execute

Parameters: data (pd.DataFrame) – input data
Returns: filtered data
Return type: pd.DataFrame