elasticai.preprocessor.downsampling.downsampling

Module Contents

Classes

SettingsDownSampling	Settings class for configuring the properties of the downsampling module Attributes: sampling_rate: Floating value with input sampling rate of the transient data stream dsr: Integer with downsampling ratio for reducing the input sampling rate (SR_out = SR_in / OSR)
DownSampling

Data

DefaultSettingsDownSampling

API

class elasticai.preprocessor.downsampling.downsampling.SettingsDownSampling

Settings class for configuring the properties of the downsampling module Attributes: sampling_rate: Floating value with input sampling rate of the transient data stream dsr: Integer with downsampling ratio for reducing the input sampling rate (SR_out = SR_in / OSR)

sampling_rate: float

None

dsr: int

None

elasticai.preprocessor.downsampling.downsampling.DefaultSettingsDownSampling

‘SettingsDownSampling(…)’

class elasticai.preprocessor.downsampling.downsampling.DownSampling(settings: elasticai.preprocessor.downsampling.downsampling.SettingsDownSampling)

Initialization

property sampling_rate_out: float

do_subsampling(data: numpy.ndarray, augment: bool = False) → numpy.ndarray

Downsample datasets by taking every dsr-th value along the last axis.

When augment is True, additional samples are generated from the remaining offsets and concatenated along the sample axis. Missing tail values are zero-padded so all generated samples have equal length.

create_design(target: str, bitwidth: int, id: str, path2save: pathlib.Path, signed: bool = True) → None

Generate a C design for subsampling.

do_simple(uin: numpy.ndarray) → numpy.ndarray

Performing a simple downsampling of the adc data stream param uin: Numpy array with transient signal input (high sampling rate) return: Numpy array with transient signal output (low sampling rate)

do_cic(uin: numpy.ndarray, num_stages: int = 5) → numpy.ndarray

Performing the CIC filter at the output of oversampled ADC param uin: Numpy array with transient signal input (high sampling rate) param num_stages: Number of stages to perform the CIC downsampling return: Numpy array with transient signal output (low sampling rate)

do_decimation_polyphase(uin: numpy.ndarray, take_first_order: bool) → numpy.ndarray

Performing Non-Recursive Polyphase Decimation on input (depends on DSR) param uin: Numpy array with transient signal input (high sampling rate) return: Numpy array with transient signal output (low sampling rate)