elasticai.hw_measurements.charac.noise#

Module Contents#

Classes#

CharacterizationNoise

API#

class elasticai.hw_measurements.charac.noise.CharacterizationNoise[source]#

Initialization

Class for analysing transient measurement to extract noise properties

property get_sampling_rate: float#

Returning the sampling rate of the measurement

property get_channels_overview: list#

Returning a list with available channels to analyse

property get_num_channels: int#

Return the number of channels

load_data(time: numpy.ndarray, signal: numpy.ndarray, channels: list) → None[source]#

Function for loading the measurement data into the class

Parameters:

  • time – Numpy array with time information [shape: (num of samples, )]

  • signal – Numpy array with noise information [shape: (num of channels, num of samples)]

  • channels – List of channel name

Returns:

None

exclude_channels_from_spec(exclude_channel: list) → None[source]#

Function for excluding channels to extract the noise spectrum density

Parameters:

exclude_channel – List of channels to exclude

Returns:

None

extract_transient_metrics() → elasticai.hw_measurements.MetricNoise[source]#

Function for extracting some metrics from transient measurement data

Returns:

Dataclass MetricNoise with metrics

extract_noise_power_distribution(scale: float = 1.0, num_segments: int = 16354) → elasticai.hw_measurements.TransientNoiseSpectrum[source]#

Function to extract noise power distribution from transient measurement

Parameters:

  • scale – Floating value to scale the transient measurement, e.g. to scale the digital output to voltage

  • num_segments – Number of samples in the noise spectral density

Returns:

Dataclass of TransientNoiseSpectrum

remove_power_line_noise(tolerance: float = 5.0, num_harmonics: int = 10) → elasticai.hw_measurements.TransientNoiseSpectrum[source]#

Function for removing the power line noise in the spectrum

Parameters:

  • tolerance – Floating tolerance value around the power line frequency (= 50 Hz)

  • num_harmonics – Number of harmonics to remove

Returns:

Dataclass of TransientNoiseSpectrum

extract_noise_rms() → numpy.ndarray[source]#

Function for extracting the output effective noise voltage from the total spectrum

Returns:

Numpy array with noise RMS of all channels

extract_noise_rms_specific(freq_start: float = 0.0, freq_stop: float = 1000.0) → numpy.ndarray[source]#

Function for extracting the output effective noise voltage from the specific range of the spectrum

Returns:

Numpy array with noise RMS of all channels