acoupipe.datasets.utils#

Module Contents#

class acoupipe.datasets.utils.WriteH5Dataset#

Bases: BaseWriteDataset

Class intended to write data to a .h5 file.

save(progress_bar=True, start_idx=1)#: Save the get_data() output of the source to .h5 file format.

get_data(progress_bar=True, start_idx=1)#

Save the source data to a *.h5 file and yield it to the next object.

Returns:

dict: Dictionary containing a sample of the data set {feature_name[key] : feature[values]}.

acoupipe.datasets.utils.calc_transfer(ir, fs, blocksize, fftfreq, time_axis=-1)#

Compute one-sided transfer functions H(f) from (measured) impulse responses on a target rFFT bin grid defined by blocksize, returning only fftfreq.

The function enforces that the FFT length is a power-of-two by zero-padding the impulse responses to nfft = 2**ceil(log2(max(L, blocksize))), where L is the IR length.

Parameters:

irndarray

Impulse responses. The time axis is given by time_axis. Common shapes are (n_channels, n_samples) with time_axis=1, or (n_samples, n_channels) with time_axis=0.

fsfloat

Sampling frequency in Hz.

blocksizeint

Target block size defining the desired rFFT bin grid (power-of-two). The associated bin centers are f_k = k*fs/blocksize, k=0..blocksize//2.

fftfreqarray_like of int or float, optional

Desired frequency bins to return.

If integer dtype: interpreted as rFFT bin indices on the blocksize grid, i.e., k in [0, blocksize//2]. This case is exact because both blocksize and nfft are powers-of-two, hence nfft/blocksize is integer and mapping is exact.
If float dtype: interpreted as frequencies in Hz. Values are mapped to the nearest rFFT bin of the computed FFT grid; a ValueError is raised if the frequency is not (approximately) on the grid.

If None, all one-sided bins of the computed FFT are returned.

time_axisint, optional

Axis of ir corresponding to time samples. Default: -1.

Returns:

H_selndarray (complex): One-sided transfer function values at the requested bins. The returned array has the same shape as ir, except the time axis is replaced by a frequency axis. If fftfreq is None, this frequency axis has length nfft//2+1. Otherwise, it has length len(fftfreq).

acoupipe.datasets.utils.tqdm_hook(t)#: Wrap tqdm instance according to tqdm/tqdm.

acoupipe.datasets.utils._handle_log(fname)#

acoupipe.datasets.utils.generate_uniform_parametric_eq(num_points, max_order, rng)#

Generate a random parametric EQ cascase.

Method according to [Nercessian 2020](https://dafx2020.mdw.ac.at/proceedings/papers/DAFx2020_paper_7.pdf). This method is part of the IIRNet project. License: Apache License 2.0

Returns:

coef
mag
phs

acoupipe.datasets.utils.get_frequency_index_range(freq, f, num)#

Return the left and right indices that define the frequency range to integrate over.

Parameters:

freqnumpy.array: frequency vector (can be determined by evaluating freqdata() method at a acoular.PowerSpectra instance)
ffloat: the frequency (or center frequency) of interest
numint: the frequency band (0: single frequency line, 1: octave band, 3: third octave band)

Returns:

tuple: left and right index that belongs to the frequency of interest

acoupipe.datasets.utils.set_pipeline_seeds(pipeline, start_idx, size, dataset='training')#

Create the random seed list for each sampler object held by the pipeline.

Parameters:

pipelineinstance of class BasePipeline: the pipeline object holding the sampler classes
start_idxint: start index to be calculated by the pipeline
sizeint: number of samples to be yielded by the pipeline
datasetstr, optional: the data set type, by default “training”. Choose from [“training”,”validation”]

acoupipe.datasets.utils.set_filename(writer, path='.', *args)#

Set the filename of the dataset.

Parameters:

writerinstance of class BaseWriteDataset: the writer object holding the filename
pathstr, optional: the path to the dataset, current directory by default
*argsstr: concatenated strings to be used as the filename

acoupipe.datasets.utils.complex_to_real(func)#

acoupipe.datasets.utils.get_point_sources_recursively(source)#

Recursively get all point sources from a acoular.TimeInOut object.

Parameters:

sourceinstance of class acoular.TimeInOut: the source object

Returns:

list: list of all sources

acoupipe.datasets.utils._get_signals_recursively(source, signals)#

acoupipe.datasets.utils.get_all_source_signals(source_list)#

Get all signals from a list of acoular.SamplesGenerator derived objects.

Parameters:

source_listlist: list of acoular.SamplesGenerator derived objects

Returns:

list: list of all acoular.SignalGenerator derived objects

acoupipe.datasets.utils.get_uncorrelated_noise_source_recursively(source)#

Recursively get all uncorrelated noise sources from a acoular.TimeInOut object.

Parameters:

sourceinstance of class acoular.TimeInOut: the source object

Returns:

list: list of all uncorrelated noise sources