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# Author: Eric Larson <larson.eric.d@gmail.com>
# License: BSD Style.
import numpy as np
valid_data_types = ['experimental', 'simulation']
valid_data_formats = ['single-trial', 'evoked', 'raw']
valid_conditions = ['visual', 'auditory', 'somatosensory']
url_root = 'http://cobre.mrn.org/megsim'
urls = ['/empdata/neuromag/visual/subject1_day1_vis_raw.fif',
'/empdata/neuromag/visual/subject1_day2_vis_raw.fif',
'/empdata/neuromag/visual/subject3_day1_vis_raw.fif',
'/empdata/neuromag/visual/subject3_day2_vis_raw.fif',
'/empdata/neuromag/aud/subject1_day1_aud_raw.fif',
'/empdata/neuromag/aud/subject1_day2_aud_raw.fif',
'/empdata/neuromag/aud/subject3_day1_aud_raw.fif',
'/empdata/neuromag/aud/subject3_day2_aud_raw.fif',
'/empdata/neuromag/somato/subject1_day1_median_raw.fif',
'/empdata/neuromag/somato/subject1_day2_median_raw.fif',
'/empdata/neuromag/somato/subject3_day1_median_raw.fif',
'/empdata/neuromag/somato/subject3_day2_median_raw.fif',
'/simdata/neuromag/visual/M87174545_vis_sim1A_4mm_30na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim1B_20mm_50na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim2_4mm_30na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim3A_4mm_30na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim3B_20mm_50na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim4_4mm_30na_neuro_rn.fif',
'/simdata/neuromag/visual/M87174545_vis_sim5_4mm_30na_neuro_rn.fif',
'/simdata_singleTrials/subject1_singleTrials_VisWorkingMem_fif.zip',
'/simdata_singleTrials/subject1_singleTrials_VisWorkingMem_withOsc_fif.zip',
'/simdata_singleTrials/4545_sim_oscOnly_v1_IPS_ILOG_30hzAdded.fif']
data_formats = ['raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'raw',
'evoked',
'evoked',
'evoked',
'evoked',
'evoked',
'evoked',
'evoked',
'single-trial',
'single-trial',
'single-trial',
]
subjects = ['subject_1',
'subject_1',
'subject_3',
'subject_3',
'subject_1',
'subject_1',
'subject_3',
'subject_3',
'subject_1',
'subject_1',
'subject_3',
'subject_3',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1',
'subject_1']
data_types = ['experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'experimental',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation',
'simulation']
conditions = ['visual',
'visual',
'visual',
'visual',
'auditory',
'auditory',
'auditory',
'auditory',
'somatosensory',
'somatosensory',
'somatosensory',
'somatosensory',
'visual',
'visual',
'visual',
'visual',
'visual',
'visual',
'visual',
'visual',
'visual',
'visual']
# turn them into arrays for ease of use
urls = np.atleast_1d(urls)
data_formats = np.atleast_1d(data_formats)
subjects = np.atleast_1d(subjects)
data_types = np.atleast_1d(data_types)
conditions = np.atleast_1d(conditions)
# Useful for testing
#assert len(conditions) == len(data_types) == len(subjects) \
# == len(data_formats) == len(urls)
def url_match(condition, data_format, data_type):
"""Function to match MEGSIM data files"""
inds = np.logical_and(conditions == condition, data_formats == data_format)
inds = np.logical_and(inds, data_types == data_type)
inds = np.logical_and(inds, data_formats == data_format)
good_urls = list(urls[inds])
for gi, g in enumerate(good_urls):
good_urls[gi] = url_root + g
if len(good_urls) == 0:
raise ValueError('No MEGSIM dataset found with condition="%s",\n'
'data_format="%s", data_type="%s"'
% (condition, data_format, data_type))
return good_urls
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