`Calib`#

class acoular.calib.Calib

Bases: InOut

Processing block for handling calibration data in *.xml or NumPy format.

This class implements the application of calibration factors to the data obtained from its source. The calibrated data can be accessed (e.g. for use in a block chain) via the result() generator. Depending on the source type, calibration can be performed in the time or frequency domain.

Examples

Consider calibrating a time signal by specifying the calibration factors in NumPy format. Assume that the following white noise signal is in Volt and the sensitivity of the virtual sensor is 1e-2 V/Pa. Then, the voltage signal can be converted to a calibrated sound pressure signal by multiplying it with a calibration factor of 100 Pa/V.

>>> import acoular as ac
>>> import numpy as np
>>>
>>> signal = ac.WNoiseGenerator(sample_freq=51200,
>>>                             num_samples=51200,
>>>                             rms=0.01).signal()
>>> ts = ac.TimeSamples(data=signal[:, np.newaxis], sample_freq=51200)
>>> calib = ac.Calib(source=ts)
>>> calib.data = np.array([100])
>>> print(next(calib.result(num=1)))
[[1.76405235]]

The calibrated data can then be further processed, e.g. by calculating the FFT of the calibrated data.

>>> fft = ac.RFFT(source=calib, block_size=16)
>>> print(next(fft.result(num=1)))
[[10.63879909+0.j          3.25957562-1.57652611j -2.27342854-3.39108312j
0.07458428+0.49657939j  1.772696  +3.92233098j  3.19543248+0.17988554j
0.3379041 -3.93342331j  0.93949242+2.5328611j   2.97352574+0.j        ]]

One could also apply the calibration after the FFT calculation.

>>> fft = ac.RFFT(source=ts, block_size=16)
>>> calib = ac.Calib(source=fft)
>>> calib.data = 100 * np.ones(ts.num_channels * fft.num_freqs)
>>> print(next(calib.result(num=1)))
[[10.63879909+0.j          3.25957562-1.57652611j -2.27342854-3.39108312j
0.07458428+0.49657939j  1.772696  +3.92233098j  3.19543248+0.17988554j
0.3379041 -3.93342331j  0.93949242+2.5328611j   2.97352574+0.j        ]]

This class serves as interface to load calibration data for the used microphone array. The calibration factors are stored as [Pa/unit].

file: Name of the .xml file to be imported.

num_mics: Number of microphones in the calibration data, is set automatically when read from file or when data is set.

data: Array of calibration factors, is set automatically when read from file. Can be set manually by specifying a NumPy array with shape (num_channels, ) if source yields time domain signals. For frequency domain signals, the expected shape is (num_channels * num_freqs).

invalid_channels: Channels that are to be treated as invalid.

channels: Channel mask to serve as an index for all valid channels, is set automatically.

digest: A unique identifier for the object, based on its properties. (read-only)

result(num)

Python generator that processes the source data and yields the time-signal block-wise.

This method needs to be implemented by the derived classes.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block)

Yields:

numpy.ndarray: Two-dimensional output data block of shape (num, sourcechannels) where sourcechannels is num_channels if the source data is in the time domain or sourcechannels is num_channels*num_freqs if the source data is in the frequency domain.

source: Data source; Generator or derived object.

sample_freq: Sampling frequency of output signal, as given by source.

num_channels: Number of channels in output, as given by source.

num_freqs: Number of frequencies in output, as given by source.

num_samples: Number of samples / snapshots in output, as given by source.

freqs: 1-D array of frequencies

block_size: The length of the block used to calculate the spectra

Calib#

`Calib`#