syncopy.specest.wavelet.WaveletTransform

class syncopy.specest.wavelet.WaveletTransform(*argv, **kwargs)[source]

Compute class that performs time-frequency analysis of AnalogData objects

Sub-class of ComputationalRoutine, see Design Guide: Syncopy Compute Classes for technical details on Syncopy’s compute classes and metafunctions.

See also

syncopy.freqanalysis

parent metafunction

__init__(*argv, **kwargs)

Instantiate a ComputationalRoutine subclass

Parameters
Returns

obj – Usable class instance for processing Syncopy data objects.

Return type

instance of ComputationalRoutine-subclass

Methods

__init__(*argv, **kwargs)

Instantiate a ComputationalRoutine subclass

compute(data, out[, parallel, …])

Central management and processing method

computeFunction(trl_dat, *wrkargs, **kwargs)

Perform time-frequency analysis on multi-channel time series data using a wavelet transform

compute_parallel(data, out)

Concurrent computing kernel

compute_sequential(data, out)

Sequential computing kernel

initialize(data[, chan_per_worker, keeptrials])

Perform dry-run of calculation to determine output shape

preallocate_output(out[, parallel_store])

Storage allocation and provisioning

process_metadata(data, out)

Meta-information manager

write_log(data, out[, log_dict])

Processing of output log

static computeFunction(trl_dat, *wrkargs, **kwargs)

Perform time-frequency analysis on multi-channel time series data using a wavelet transform

Parameters
  • trl_dat (2D numpy.ndarray) – Uniformly sampled multi-channel time-series

  • preselect (slice) – Begin- to end-samples to perform analysis on (trim data to interval). See Notes for details.

  • postselect (list of slices or list of 1D NumPy arrays) – Actual time-points of interest within interval defined by preselect See Notes for details.

  • padbegin (int) – Number of samples to pre-pend to trl_dat

  • padend (int) – Number of samples to append to trl_dat

  • samplerate (float) – Samplerate of trl_dat in Hz

  • toi (1D numpy.ndarray or str) – Either time-points to center wavelets on if toi is a numpy.ndarray, or “all” to center wavelets on all samples in trl_dat. Please refer to freqanalysis() for further details. Note: The value of toi has to agree with provided padding values. See Notes for more information.

  • scales (1D numpy.ndarray) – Set of scales to use in wavelet transform.

  • timeAxis (int) – Index of running time axis in trl_dat (0 or 1)

  • wav (callable) – Wavelet function to use, one of availableWavelets

  • polyremoval (int) – FIXME: Not implemented yet Order of polynomial used for de-trending. A value of 0 corresponds to subtracting the mean (“de-meaning”), polyremoval = 1 removes linear trends (subtracting the least squares fit of a linear function), polyremoval = N for N > 1 subtracts a polynomial of order N (N = 2 quadratic, N = 3 cubic etc.). If polyremoval is None, no de-trending is performed.

  • output_fmt (str) – Output of spectral estimation; one of availableOutputs

  • noCompute (bool) – Preprocessing flag. If True, do not perform actual calculation but instead return expected shape and numpy.dtype of output array.

  • chunkShape (None or tuple) – If not None, represents shape of output object spec (respecting provided values of scales, preselect, postselect etc.)

Returns

spec – Complex or real time-frequency representation of (padded) input data.

Return type

numpy.ndarray

Notes

This method is intended to be used as computeFunction() inside a ComputationalRoutine. Thus, input parameters are presumed to be forwarded from a parent metafunction. Consequently, this function does not perform any error checking and operates under the assumption that all inputs have been externally validated and cross-checked.

For wavelets, data concatenation is performed by first trimming trl_dat to an interval of interest (via preselect), then performing the actual wavelet transform, and subsequently extracting the actually wanted time-points (via postselect).

See also

syncopy.freqanalysis()

parent metafunction

WaveletTransform()

ComputationalRoutine instance that calls this method as computeFunction()

process_metadata(data, out)[source]

Meta-information manager

Parameters
  • data (syncopy data object) – Syncopy data object that has been processed

  • out (syncopy data object) – Syncopy data object holding calculation results

Returns

Nothing

Return type

None

Notes

This routine is an abstract method and is thus intended to be overloaded. Consult the developer documentation (Design Guide: Syncopy Compute Classes) for further details.

See also

write_log()

Logging of calculation parameters

__init__(*argv, **kwargs)

Instantiate a ComputationalRoutine subclass

Parameters
Returns

obj – Usable class instance for processing Syncopy data objects.

Return type

instance of ComputationalRoutine-subclass

compute(data, out, parallel=False, parallel_store=None, method=None, mem_thresh=0.5, log_dict=None, parallel_debug=False)

Central management and processing method

Parameters
  • data (syncopy data object) – Syncopy data object to be processed (has to be the same object that was used by initialize() in the pre-calculation dry-run).

  • out (syncopy data object) – Empty object for holding results

  • parallel (bool) – If True, processing is performed in parallel (i.e., computeFunction() is executed concurrently across trials). If parallel is False, computeFunction() is executed consecutively trial after trial (i.e., the calculation realized in computeFunction() is performed sequentially).

  • parallel_store (None or bool) – Flag controlling saving mechanism. If None, parallel_store = parallel, i.e., the compute-paradigm dictates the employed writing method. Thus, in case of parallel processing, results are written in a fully concurrent manner (each worker saves its own local result segment on disk as soon as it is done with its part of the computation). If parallel_store is False and parallel is True the processing result is saved sequentially using a mutex. If both parallel and parallel_store are False standard single-process HDF5 writing is employed for saving the result of the (sequential) computation.

  • method (None or str) – If None the predefined methods compute_parallel() or compute_sequential() are used to control the actual computation (specifically, calling computeFunction()) depending on whether parallel is True or False, respectively. If method is a string, it has to specify the name of an alternative (provided) class method that is invoked using getattr.

  • mem_thresh (float) – Fraction of available memory required to perform computation. By default, the largest single trial result must not occupy more than 50% (mem_thresh = 0.5) of available single-machine or worker memory (if parallel is False or True, respectively).

  • log_dict (None or dict) – If None, the log properties of out is populated with the employed keyword arguments used in computeFunction(). Otherwise, out’s log properties are filled with items taken from log_dict.

  • parallel_debug (bool) – If True, concurrent processing is performed using a single-threaded scheduler, i.e., all parallel computing task are run in the current Python thread permitting usage of tools like pdb/ipdb, cProfile and the like in computeFunction(). Note that enabling parallel debugging effectively runs the given computation on the calling local machine thereby requiring sufficient memory and CPU capacity.

Returns

Nothing – The result of the computation is available in out once compute() terminated successfully.

Return type

None

Notes

This routine calls several other class methods to perform all necessary pre- and post-processing steps in a fully automatic manner without requiring any user-input. Specifically, the following class methods are invoked consecutively (in the given order):

  1. preallocate_output() allocates a (virtual) HDF5 dataset of appropriate dimension for storing the result

  2. compute_parallel() (or compute_sequential()) performs the actual computation via concurrently (or sequentially) calling computeFunction()

  3. process_metadata() attaches all relevant meta-information to the result out after successful termination of the calculation

  4. write_log() stores employed input arguments in out.cfg and out.log to reproduce all relevant computational steps that generated out.

See also

initialize()

pre-calculation preparations

preallocate_output()

storage provisioning

compute_parallel()

concurrent computation using computeFunction()

compute_sequential()

sequential computation using computeFunction()

process_metadata()

management of meta-information

write_log()

log-entry organization

compute_parallel(data, out)

Concurrent computing kernel

Parameters
  • data (syncopy data object) – Syncopy data object to be processed

  • out (syncopy data object) – Empty object for holding results

Returns

Nothing

Return type

None

Notes

This method mereley acts as a concurrent wrapper for computeFunction() by passing along all necessary information for parallel execution and storage of results using a dask bag of dictionaries. The actual reading of source data and writing of results is managed by the decorator syncopy.shared.parsers.unwrap_io(). Note that this routine first builds an entire parallel instruction tree and only kicks off execution on the cluster at the very end of the calculation command assembly.

See also

compute()

management routine invoking parallel/sequential compute kernels

compute_sequential()

serial processing counterpart of this method

compute_sequential(data, out)

Sequential computing kernel

Parameters
  • data (syncopy data object) – Syncopy data object to be processed

  • out (syncopy data object) – Empty object for holding results

Returns

Nothing

Return type

None

Notes

This method most closely reflects classic iterative process execution: trials in data are passed sequentially to computeFunction(), results are stored consecutively in a regular HDF5 dataset (that was pre-allocated by preallocate_output()). Since the calculation result is immediately stored on disk, propagation of arrays across routines is avoided and memory usage is kept to a minimum.

See also

compute()

management routine invoking parallel/sequential compute kernels

compute_parallel()

concurrent processing counterpart of this method

initialize(data, chan_per_worker=None, keeptrials=True)

Perform dry-run of calculation to determine output shape

Parameters
  • data (syncopy data object) – Syncopy data object to be processed (has to be the same object that is passed to compute() for the actual calculation).

  • chan_per_worker (None or int) – Number of channels to be processed by each worker (only relevant in case of concurrent processing). If chan_per_worker is None (default) by-trial parallelism is used, i.e., each worker processes data corresponding to a full trial. If chan_per_worker > 0, trials are split into channel-groups of size chan_per_worker (+ rest if the number of channels is not divisible by chan_per_worker without remainder) and workers are assigned by-trial channel-groups for processing.

  • keeptrials (bool) – Flag indicating whether to return individual trials or average

Returns

Nothing

Return type

None

Notes

This class method has to be called prior to performing the actual computation realized in computeFunction().

See also

compute()

core routine performing the actual computation

preallocate_output(out, parallel_store=False)

Storage allocation and provisioning

Parameters
  • out (syncopy data object) – Empty object for holding results

  • parallel_store (bool) – If True, a directory for virtual source files is created in Syncopy’s temporary on-disk storage (defined by syncopy.__storage__). Otherwise, a dataset of appropriate type and shape is allocated in a new regular HDF5 file created inside Syncopy’s temporary storage folder.

Returns

Nothing

Return type

None

See also

compute()

management routine controlling memory pre-allocation

write_log(data, out, log_dict=None)

Processing of output log

Parameters
  • data (syncopy data object) – Syncopy data object that has been processed

  • out (syncopy data object) – Syncopy data object holding calculation results

  • log_dict (None or dict) – If None, the log properties of out is populated with the employed keyword arguments used in computeFunction(). Otherwise, out’s log properties are filled with items taken from log_dict.

Returns

Nothing

Return type

None

See also

process_metadata()

Management of meta-information