syncopy.shared.computational_routine.ComputationalRoutine#

class syncopy.shared.computational_routine.ComputationalRoutine(*argv, **kwargs)[source]#

Abstract class for encapsulating sequential/parallel algorithms

A Syncopy compute class consists of a ComputationalRoutine-subclass that binds a static computeFunction() and provides the class method process_metadata().

Requirements for computeFunction():

First positional argument is a numpy.ndarray, the keywords chunkShape and noCompute are supported
Returns a numpy.ndarray if noCompute is False and expected shape and numerical type of output array otherwise.

Requirements for ComputationalRoutine:

Child of ComputationalRoutine, binds computeFunction() as static method
Provides class method process_metadata()

For details on writing compute classes and metafunctions for Syncopy, please refer to Design Guide: Syncopy Compute Classes.

__init__(*argv, **kwargs)[source]#

Instantiate a ComputationalRoutine subclass

Parameters:

*argv (tuple) – Tuple of positional arguments passed on to computeFunction()
**kwargs (dict) – Keyword arguments passed on to computeFunction()

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`(arr, *argv[, chunkShape, ...])	Computational core routine
`compute_parallel`(data, out)	Concurrent computing kernel
`compute_sequential`(data, out)	Sequential computing kernel
`initialize`(data, out_stackingdim[, ...])	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(arr, *argv, chunkShape=None, noCompute=None, **kwargs)[source]#

Computational core routine

Parameters:

arr (numpy.ndarray) – Numerical data from a single trial
*argv (tuple) – Arbitrary tuple of positional arguments
chunkShape (None or tuple) – Mandatory keyword. If not None, represents global block-size of processed trial.
noCompute (None or bool) – Preprocessing flag. If True, do not perform actual calculation but instead return expected shape and numpy.dtype of output array.
**kwargs (dict) – Other keyword arguments.

Returns:

out Shape (tuple, if noCompute == True) – expected shape of output array
outDtype (numpy.dtype, if noCompute == True) – expected numerical type of output array
res (numpy.ndarray, if noCompute == False) – Result of processing input arr

Notes

This concrete method is a placeholder that is intended to be overloaded.

See also

ComputationalRoutine: Developer documentation: Design Guide: Syncopy Compute Classes.

__init__(*argv, **kwargs)[source]#

Instantiate a ComputationalRoutine subclass

Parameters:

*argv (tuple) – Tuple of positional arguments passed on to computeFunction()
**kwargs (dict) – Keyword arguments passed on to computeFunction()

Returns:

obj – Usable class instance for processing Syncopy data objects.

Return type:

instance of ComputationalRoutine-subclass

initialize(data, out_stackingdim, chan_per_worker=None, keeptrials=True)[source]#

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).
out_stackingdim (int) – Index of data dimension for stacking trials in output object
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

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

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 (starting with the word “compute_”).
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 machine locally 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):

preallocate_output() allocates a (virtual) HDF5 dataset of appropriate dimension for storing the result
compute_parallel() (or compute_sequential()) performs the actual computation via concurrently (or sequentially) calling computeFunction()
process_metadata() attaches all relevant meta-information to the result out after successful termination of the calculation
write_log() stores employed input arguments in out.cfg and out.log to reproduce all relevant computational steps that generated out.

Parallel processing setup and input sanitization is performed by ACME. Specifically, all necessary information for parallel execution and storage of results is propagated to the ParallelMap context manager.

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
acme.ParallelMap: concurrent execution of Python callables

preallocate_output(out, parallel_store=False)[source]#

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

compute_parallel(data, out)[source]#

Concurrent computing kernel

Parameters:

data (syncopy data object) – Syncopy data object, ignored for parallel case. The input data information is already contained in the workerdicts, which are stored in self.pmap (expanded from the inargs in compute()) and can be accessed from it.
out (syncopy data object) – Empty object for holding results

Returns:

Nothing

Return type:

None

Notes

The actual reading of source data and writing of results is managed by the decorator syncopy.shared.kwarg_decorators.process_io().

See also

compute: management routine invoking parallel/sequential compute kernels
compute_sequential: serial processing counterpart of this method

compute_sequential(data, out)[source]#

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

write_log(data, out, log_dict=None)[source]#

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

abstract 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

syncopy.shared.computational_routine.ComputationalRoutine

Contents

syncopy.shared.computational_routine.ComputationalRoutine#