from mne.filter import notch_filter, low_pass_filter print(__doc__) ############################################################################### # To reduce memory consumption and running time, some of the steps are # precomputed. To run everything from scratch change this to False. With # ``use_precomputed = False`` running time of this script can be several # minutes even on a fast computer. use_precomputed = True ############################################################################### # The data was collected with a CTF 275 system at 2400 Hz and low-pass # filtered at 600 Hz. Here the data and empty room data files are read to # construct instances of :class:`mne.io.Raw`. data_path = bst_auditory.data_path() subject = 'bst_auditory' subjects_dir = op.join(data_path, 'subjects') raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_01.ds') raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_02.ds') erm_fname = op.join(data_path, 'MEG', 'bst_auditory', 'S01_Noise_20131218_01.ds') ############################################################################### # In the memory saving mode we use ``preload=False`` and use the memory # efficient IO which loads the data on demand. However, filtering and some # other functions require the data to be preloaded in the memory.
from mne.io import read_raw_ctf print(__doc__) ############################################################################### # To reduce memory consumption and running time, some of the steps are # precomputed. To run everything from scratch change this to False. With # ``use_precomputed = False`` running time of this script can be several # minutes even on a fast computer. use_precomputed = True ############################################################################### # The data was collected with a CTF 275 system at 2400 Hz and low-pass # filtered at 600 Hz. Here the data and empty room data files are read to # construct instances of :class:`mne.io.Raw`. data_path = bst_auditory.data_path() subject = 'bst_auditory' subjects_dir = op.join(data_path, 'subjects') raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_01.ds') raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_02.ds') erm_fname = op.join(data_path, 'MEG', 'bst_auditory', 'S01_Noise_20131218_01.ds') ############################################################################### # In the memory saving mode we use ``preload=False`` and use the memory # efficient IO which loads the data on demand. However, filtering and some # other functions require the data to be preloaded in the memory.