syncopy.tests.misc.generate_artificial_data#

syncopy.tests.misc.generate_artificial_data(nTrials=2, nChannels=2, equidistant=True, seed=42, overlapping=False, inmemory=True, dimord='default')[source]#

Create AnalogData object with synthetic harmonic signal(s)

Parameters:

  • nTrials (int) – Number of trials to populate synthetic data object with

  • nChannels (int) – Number of channels to populate synthetic object with

  • equidistant (bool) – If True, trials of equal length are defined

  • seed (None or int) – If None, imposed noise is completely random. If seed is an integer, it is used to fix the (initial) state of NumPy’s random number generator numpy.random.default_rng(), i.e., objects created wtih same seed will be populated with identical artificial signals.

  • overlapping (bool) – If True, constructed trials overlap

  • inmemory (bool) – If True, the full data array (all channels across all trials) is allocated in memory (fast but dangerous for large arrays), otherwise the output data object’s corresponding backing HDF5 file in __storage__ is filled with synthetic data in a trial-by-trial manner (slow but safe even for very large datasets).

  • dimord (str or list) – If dimord is “default”, the constructed output object uses the default dimensional layout of a standard AnalogData object. If dimord is a list (i.e., ["channel", "time"]) the provided sequence of dimensions is used.

Returns:

out – Syncopy AnalogData object with specified properties populated with a synthetic multivariate trigonometric signal.

Return type:

AnalogData object

Notes

This is an auxiliary method that is intended purely for internal use. Thus, no error checking is performed.

Examples

Generate small artificial AnalogData object in memory

>>> iAmSmall = generate_artificial_data(nTrials=5, nChannels=10, inmemory=True)
>>> iAmSmall
Syncopy AnalogData object with fields

            cfg : dictionary with keys ''
        channel : [10] element <class 'numpy.ndarray'>
      container : None
           data : 5 trials of length 3000 defined on [15000 x 10] float32 Dataset of size 0.57 MB
         dimord : 2 element list
       filename : /Users/pantaray/.spy/spy_158f_4d4153e3.analog
           mode : r+
     sampleinfo : [5 x 2] element <class 'numpy.ndarray'>
     samplerate : 1000.0
            tag : None
           time : 5 element list
      trialinfo : [5 x 0] element <class 'numpy.ndarray'>
         trials : 5 element iterable

Use `.log` to see object history

Generate artificial AnalogData object of more substantial size on disk

>>> iAmBig = generate_artificial_data(nTrials=50, nChannels=1024, inmemory=False)
>>> iAmBig
Syncopy AnalogData object with fields

            cfg : dictionary with keys ''
        channel : [1024] element <class 'numpy.ndarray'>
      container : None
           data : 200 trials of length 3000 defined on [600000 x 1024] float32 Dataset of size 2.29 GB
         dimord : 2 element list
       filename : /Users/pantaray/.spy/spy_158f_b80715fe.analog
           mode : r+
     sampleinfo : [200 x 2] element <class 'numpy.ndarray'>
     samplerate : 1000.0
            tag : None
           time : 200 element list
      trialinfo : [200 x 0] element <class 'numpy.ndarray'>
         trials : 200 element iterable

Use `.log` to see object history