sovabids.datasets

Module with dataset utilities.

Functions

lemon_prepare()	Download and prepare a few files of the LEMON dataset.
lemon_bidscoin_prepare(src_path)	Download and prepare a few files of the LEMON dataset to be used with BIDSCOIN.
make_dummy_dataset(EXAMPLE[, PATTERN, DATASET, NSUBS, ...])	Create a dummy dataset given some parameters.
generate_1_over_f_noise(n_channels, n_times[, ...])
get_dummy_raw([NCHANNELS, SFREQ, STOP, NUMEVENTS])	Create a dummy MNE Raw file given some parameters.
save_dummy_vhdr(fpath[, dummy_args])	Save a dummy vhdr file.
save_dummy_cnt(fpath)	Save a dummy cnt file.
replace_brainvision_filename(fpath, newname)

Module Contents

sovabids.datasets.lemon_prepare()

Download and prepare a few files of the LEMON dataset.

Notes

See the LEMON dataset .

sovabids.datasets.lemon_bidscoin_prepare(src_path)

Download and prepare a few files of the LEMON dataset to be used with BIDSCOIN.

Parameters:

src_path (str) – The path where the BIDSCOIN-ready LEMON files will be

See also

datasets.lemon_prepare

sovabids.datasets.make_dummy_dataset(EXAMPLE, PATTERN='T%task%/S%session%/sub%subject%_%acquisition%_%run%', DATASET='DUMMY', NSUBS=2, NSESSIONS=2, NTASKS=2, NACQS=2, NRUNS=2, PREFIXES={'subject': 'SU', 'session': 'SE', 'task': 'TA', 'acquisition': 'AC', 'run': 'RU'}, ROOT=None)

Create a dummy dataset given some parameters.

Parameters:

• EXAMPLE (str,PathLike|list , required) – Path of the file to replicate as each file in the dummy dataset. If a list, it is assumed each item is a file. All of these items are replicated.

• PATTERN (str, optional) – The pattern in placeholder notation using the following fields: %dataset%, %task%, %session%, %subject%, %run%, %acquisition%

• DATASET (str, optional) – Name of the dataset.

• NSUBS (int, optional) – Number of subjects.

• NSESSIONS (int, optional) – Number of sessions.

• NTASKS (int, optional) – Number of tasks.

• NACQS (int, optional) – Number of acquisitions.

• NRUNS (int, optional) – Number of runs.

• PREFIXES (dict, optional) – Dictionary with the following keys:’subject’, ‘session’, ‘task’ and ‘acquisition’. The values are the corresponding prefix. RUN is not present because it has to be a number.

• ROOT (str, optional) – Path where the files will be generated. If None, the _data subdir will be used.

sovabids.datasets.generate_1_over_f_noise(n_channels, n_times, exponent=1.0, random_state=None)

sovabids.datasets.get_dummy_raw(NCHANNELS=5, SFREQ=200, STOP=10, NUMEVENTS=10)

Create a dummy MNE Raw file given some parameters.

Parameters:

• NCHANNELS (int, optional) – Number of channels.

• SFREQ (float, optional) – Sampling frequency of the data.

• STOP (float, optional) – Time duration of the data in seconds.

• NUMEVENTS (int, optional) – Number of events along the duration.

sovabids.datasets.save_dummy_vhdr(fpath, dummy_args={})

Save a dummy vhdr file.

Parameters:

• fpath (str, required) – Path where to save the file.

• kwargs (dict, optional) – Dictionary with the arguments of the get_dummy_raw function.

Returns:

• List with the Paths of the desired vhdr file, if those were succesfully created,

• None otherwise.

sovabids.datasets.save_dummy_cnt(fpath)

Save a dummy cnt file.

Parameters:

fpath (str, required) – Path where to save the file.

Returns:

• Path of the desired file if the file was succesfully created,

• None otherwise.

sovabids.datasets.replace_brainvision_filename(fpath, newname)