def get_tal_structs_row(subject, anode_tag, cathode_tag):
# '/Users/m/data/eeg/R1111M/tal/R1111M_talLocs_database_bipol.mat'
# tal_path = join(mount_point,'data/eeg/',subject,'tal',subject+'_talLocs_database_bipol.mat')
tal_path = str(
join(mount_point, 'data/eeg/', subject, 'tal',
subject + '_talLocs_database_monopol.mat'))
tal_reader = TalReader(filename=tal_path, struct_name='talStruct')
tal_structs = tal_reader.read()
# sel = tal_structs[np.where(tal_structs.tagName == anode_tag+'-'+cathode_tag)]
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)
| (tal_structs.tagName == cathode_tag))]
if not len(sel):
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)
| (tal_structs.tagName == cathode_tag))]
if not len(sel):
tal_path = str(
join(mount_point, 'data/eeg/', subject, 'tal',
subject + '_talLocs_database_stimOnly.mat'))
tal_reader = TalStimOnlyReader(filename=tal_path)
tal_structs = tal_reader.read()
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)
| (tal_structs.tagName == cathode_tag))]
if not len(sel):
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)
| (tal_structs.tagName == cathode_tag))]
return sel
def get_tal_structs_row(subject,anode_tag,cathode_tag):
# '/Users/m/data/eeg/R1111M/tal/R1111M_talLocs_database_bipol.mat'
# tal_path = join(mount_point,'data/eeg/',subject,'tal',subject+'_talLocs_database_bipol.mat')
tal_path = str(join(mount_point,'data/eeg/',subject,'tal',subject+'_talLocs_database_monopol.mat'))
tal_reader = TalReader(filename=tal_path,struct_name='talStruct')
tal_structs = tal_reader.read()
# sel = tal_structs[np.where(tal_structs.tagName == anode_tag+'-'+cathode_tag)]
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)|(tal_structs.tagName == cathode_tag))]
if not len(sel):
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)|(tal_structs.tagName == cathode_tag))]
if not len(sel):
tal_path = str(join(mount_point,'data/eeg/',subject,'tal',subject+'_talLocs_database_stimOnly.mat'))
tal_reader = TalStimOnlyReader(filename=tal_path)
tal_structs = tal_reader.read()
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)|(tal_structs.tagName == cathode_tag))]
if not len(sel):
sel = tal_structs[np.where((tal_structs.tagName == anode_tag)|(tal_structs.tagName == cathode_tag))]
return sel
def extract_electrode_positions(tal_path):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(map( lambda loc : loc.startswith('Left'), tal_structs.Loc1))
rh_selector = np.array(map( lambda loc : loc.startswith('Right'), tal_structs.Loc1))
lh_avg_surf = tal_structs.avgSurf[lh_selector]
rh_avg_surf = tal_structs.avgSurf[rh_selector]
return lh_avg_surf, rh_avg_surf
def __init__(self, **kwds):
'''
Constructor:
:param kwds:allowed values are:
-------------------------------------
:param filename {str} - path to tal file
:param struct_name {str} - name of the matlab struct to load
:return: None
'''
TalReader.__init__(self, **kwds)
self.struct_name = 'virtualTalStruct'
def __init__(self, **kwds):
'''
Constructor:
:param kwds:allowed values are:
-------------------------------------
:param filename {str} - path to tal file
:param struct_name {str} - name of the matlab struct to load
:return: None
'''
TalReader.__init__(self, **kwds)
self.struct_name = 'virtualTalStruct'
def get_bp_tal_struct(sub, montage=0):
from ptsa.data.readers import TalReader
#Get electrode information -- bipolar
tal_path = '/protocols/r1/subjects/' + sub + '/localizations/0/montages/' + str(
montage) + '/neuroradiology/current_processed/pairs.json'
tal_reader = TalReader(filename=tal_path)
tal_struct = tal_reader.read()
monopolar_channels = tal_reader.get_monopolar_channels()
bipolar_pairs = tal_reader.get_bipolar_pairs()
return tal_struct, bipolar_pairs, monopolar_channels
def extract_electrode_positions(tal_path):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(
map(lambda loc: loc.startswith('Left'), tal_structs.Loc1))
rh_selector = np.array(
map(lambda loc: loc.startswith('Right'), tal_structs.Loc1))
lh_avg_surf = tal_structs.avgSurf[lh_selector]
rh_avg_surf = tal_structs.avgSurf[rh_selector]
return lh_avg_surf, rh_avg_surf
def read_session(subject, root, experiment, rhino_root):
jr = JsonIndexReader(rhino_root + '/protocols/r1.json') # Build JSON reader
pairs_path = jr.get_value('pairs', subject=subject, experiment=experiment)
tal_reader = TalReader(filename=pairs_path)
monopolar_channels = tal_reader.get_monopolar_channels()
bipolar_channels = tal_reader.get_bipolar_pairs()
jr = JsonIndexReader(rhino_root + '/protocols/r1.json') # Build JSON reader
pairs_path = jr.get_value('pairs', subject=subject, experiment=experiment)
tal_reader = TalReader(filename=pairs_path)
monopolar_channels = tal_reader.get_monopolar_channels()
p_reader = ParamsReader(dataroot=root)
params = p_reader.read()
samplerate = params['samplerate']
print samplerate
try:
raw_eeg = EEGReader(session_dataroot=root, channels = monopolar_channels)
raw_eeg = raw_eeg.read()
print raw_eeg.shape
except:
with h5py.File(root, 'r') as hfile:
raw_eeg = H5RawReader.H5RawReader.read_h5file(hfile, monopolar_channels,[0])[0]
print raw_eeg.shape
raw_eeg = TimeSeriesX.TimeSeriesX(raw_eeg,
dims=['channels', 'start_offsets', 'time'],
coords={
'channels': monopolar_channels,
'start_offsets': [0],
'time': np.arange(raw_eeg.shape[2])*1000/samplerate,
'offsets': ('time',np.arange(raw_eeg.shape[2])),
'samplerate': samplerate})
return raw_eeg
def extract_electrode_positions(tal_path,
electrode_types=['D', 'G', 'S'],
hemi=['R', 'L']):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
electrode_types_lower = map(lambda t: t.lower(), electrode_types)
electrode_type_selector = np.array(
map(lambda eType: eType.lower() in electrode_types_lower,
tal_structs.eType))
h_data = tal_structs[['avgSurf', 'eType']]
h_data = h_data[electrode_type_selector]
return h_data
def extract_electrode_positions(tal_path, electrode_types=['D', 'G', 'S'], hemi=['R','L']):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
electrode_types_lower = map(lambda t: t.lower(), electrode_types)
electrode_type_selector = np.array(map(lambda eType: eType.lower() in electrode_types_lower, tal_structs.eType))
h_data = tal_structs[['avgSurf','eType']]
h_data = h_data[electrode_type_selector]
return h_data
def test_talreader_on_database():
old_reader = TalReader(
filename=
'/Volumes/rhino_root/data/eeg/R1234D/tal/R1234D_talLocs_database_bipol.mat'
)
jr = JsonIndexReader('/Volumes/rhino_root/protocols/r1.json')
new_reader = TalReader(
filename=jr.get_value('pairs', subject='R1234D', experiment='FR1'))
assert new_reader.get_bipolar_pairs() == old_reader.get_bipolar_pairs()
assert new_reader.get_monopolar_channels(
) == old_reader.get_monopolar_channels()
def test_talreader_on_database():
old_reader = TalReader(filename=osp.join(
utils.get_rhino_root(),
'data/eeg/R1234D/tal/R1234D_talLocs_database_bipol.mat'))
jr = JsonIndexReader(osp.join(utils.get_rhino_root(), 'protocols/r1.json'))
new_reader = TalReader(
filename=jr.get_value('pairs', subject='R1234D', experiment='FR1'))
assert (new_reader.get_bipolar_pairs() == old_reader.get_bipolar_pairs()
).all()
assert (new_reader.get_monopolar_channels() ==
old_reader.get_monopolar_channels()).all()
def extract_electrode_positions(tal_path, electrode_types=['D', 'G', 'S']):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(map(lambda loc: loc.upper().startswith('L'), tal_structs.tagName))
rh_selector = np.array(map(lambda loc: loc.upper().startswith('R'), tal_structs.tagName))
electrode_types_lower = map(lambda t: t.lower(), electrode_types)
electrode_type_selector = np.array(map(lambda eType: eType.lower() in electrode_types_lower, tal_structs.eType))
lh_data = tal_structs[['avgSurf','eType']]
rh_data = tal_structs[['avgSurf','eType']]
lh_data = lh_data[lh_selector & electrode_type_selector]
rh_data = rh_data[rh_selector & electrode_type_selector]
return lh_data,rh_data
def extract_electrode_positions(tal_path, electrode_types=['D', 'G', 'S']):
from ptsa.data.readers import TalReader
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(map(lambda loc: loc.upper().startswith('L'), tal_structs.tagName))
rh_selector = np.array(map(lambda loc: loc.upper().startswith('R'), tal_structs.tagName))
electrode_types_lower = map(lambda t: t.lower(), electrode_types)
electrode_type_selector = np.array(map(lambda eType: eType.lower() in electrode_types_lower, tal_structs.eType))
lh_data = tal_structs[['avgSurf','eType']]
rh_data = tal_structs[['avgSurf','eType']]
lh_data = lh_data[lh_selector & electrode_type_selector]
rh_data = rh_data[rh_selector & electrode_type_selector]
return lh_data,rh_data
def extract_coordinates_from_talstructs(subject_localization, rootdir="/"):
talstruct_base_path = os.path.join(rootdir,
"data10/RAM/subjects/{}/tal/{}_talLocs_database_{}.mat")
# TODO: Use PathFinder once the matlab talstructs are available
bipol_reader = TalReader(filename=talstruct_base_path.format(subject_localization,
subject_localization,
'bipol'),
struct_type='bi')
bipol_structs = bipol_reader.read()
mono_reader = TalReader(filename=talstruct_base_path.format(subject_localization,
subject_localization,
'monopol'),
struct_type='mono')
mono_structs = mono_reader.read()
mono_start_df = get_coords(mono_structs, 'monopolar')
mono_start_df["contact_name"] = "o" + mono_start_df["contact_name"]
mono_adj_df = get_coords(mono_structs, 'monopolar', atlas='Dykstra')
bipo_adj_df = get_coords(bipol_structs, 'bipolar', atlas='Dykstra')
all_coord_df = pd.concat([mono_start_df, mono_adj_df, bipo_adj_df])
return all_coord_df
def _test_eeg_with_tal_struct(self, subject, experiment, session, struct_type='mono'):
from ptsa.data.readers import JsonIndexReader,TalReader,EEGReader,BaseEventReader
import os
tal_file = 'contacts' if struct_type=='mono' else 'pairs'
jr = JsonIndexReader(os.path.join(get_rhino_root(),'protocols','r1.json'))
events = BaseEventReader(filename=jr.get_value('task_events',subject=subject,experiment=experiment,session=session)).read()
tal_struct = TalReader(filename=jr.get_value(tal_file,subject=subject),struct_type=struct_type).read()
eeg_reader = EEGReader(events=events[:10],channels=tal_struct[:10],start_time=0.0,end_time=0.1)
eeg = eeg_reader.read()
print(eeg)
channel_name = eeg_reader.channel_name
for col in tal_struct.dtype.names:
if col != 'atlases': # Because I don't want to deal with NaNs at the moment
assert_array_equal(eeg[channel_name].data[col],tal_struct[:10][col])
def construct_significant_elec_info(df):
tal_structs = None
subject = ''
lh_selector = None
rh_selector = None
lh_data_combined = None
rh_data_combined = None
for index, row in df.iterrows():
if subject != row['Subject']:
subject = row['Subject']
# '/Users/m/data/eeg/R1111M/tal/R1111M_talLocs_database_bipol.mat'
tal_path = join(mount_point, 'data/eeg/', subject, 'tal',
subject + '_talLocs_database_bipol.mat')
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(
map(lambda loc: loc.upper().startswith('L'),
tal_structs.tagName))
rh_selector = np.array(
map(lambda loc: loc.upper().startswith('R'),
tal_structs.tagName))
lh_data = tal_structs[['avgSurf', 'eType', 'tagName']]
rh_data = tal_structs[['avgSurf', 'eType', 'tagName']]
lh_data = lh_data[lh_selector]
rh_data = rh_data[rh_selector]
lh_sel = get_elec_data_coords_array(
lh_data[np.where(lh_data.tagName == row['stimAnodeTag'] + '-' +
row['stimCathodeTag'])])
lh_sel_flip = get_elec_data_coords_array(
lh_data[np.where(lh_data.tagName == row['stimCathodeTag'] + '-' +
row['stimAnodeTag'])])
rh_sel = get_elec_data_coords_array(
rh_data[np.where(rh_data.tagName == row['stimAnodeTag'] + '-' +
row['stimCathodeTag'])])
rh_sel_flip = get_elec_data_coords_array(
rh_data[np.where(rh_data.tagName == row['stimCathodeTag'] + '-' +
row['stimAnodeTag'])])
lh_data_combined = append_to_combined(lh_data_combined, lh_sel,
lh_sel_flip)
rh_data_combined = append_to_combined(rh_data_combined, rh_sel,
rh_sel_flip)
# lh_sel_stack = vstack_arrays(lh_sel,lh_sel_flip)
# rh_sel_stack = vstack_arrays(rh_sel,rh_sel_flip)
#
#
# lh_tmp = np.vstack((lh_sel,lh_sel_flip))
# rh_tmp = np.vstack((rh_sel,rh_sel_flip))
#
# if not lh_data_combined:
# lh_data_combined = lh_tmp
# else:
# lh_data_combined = np.vstack((lh_data_combined,lh_tmp))
#
# if not rh_data_combined:
# rh_data_combined = rh_tmp
# else:
# rh_data_combined = np.vstack((rh_data_combined,rh_tmp))
print rh_data_combined
print lh_data_combined
return lh_data_combined, rh_data_combined
def __init__(self, **kwds):
TalReader.__init__(self, **kwds)
self.struct_name = 'virtualTalStruct'
def __init__(self, **kwds):
TalReader.__init__(self, **kwds)
self.struct_name = 'virtualTalStruct'
def test_from_dict():
pairs_dict = {
u'R1111M': {
u'code': u'R1111M',
u'id': u'r1.111.0_0',
u'pairs': {
u'LAS1-LAS2': {
u'atlases': {
u'avg': {
u'id': u'avg',
u'region': u'inferiortemporal',
u'x': -42.61,
u'y': 1.125,
u'z': -47.255
},
u'avg.dural': {
u'id': u'avg.dural',
u'region': u'inferiortemporal',
u'x': -40.325014,
u'y': -0.977092,
u'z': -42.874617
},
u'dk': {
u'id': u'dk',
u'region': u'inferiortemporal',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'inferiortemporal',
u'x': -35.445,
u'y': 13.665,
u'z': -61.24
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'inferiortemporal',
u'x': -35.617608,
u'y': 14.180917,
u'z': -61.773298
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -44.2202,
u'y': -3.57164,
u'z': -47.2775337826913
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -44.438,
u'y': -0.9717655,
u'z': -40.04145
}
},
u'channel_1': 89,
u'channel_2': 90,
u'code': u'LAS1-LAS2',
u'id': u'las.1-las.2',
u'is_explicit': False,
u'is_stim_only': False,
u'type_1': u'S',
u'type_2': u'S'
},
u'LAS2-LAS3': {
u'atlases': {
u'avg': {
u'id': u'avg',
u'region': u'middletemporal',
u'x': -50.97,
u'y': 3.555,
u'z': -41.405
},
u'avg.dural': {
u'id': u'avg.dural',
u'region': u'inferiortemporal',
u'x': -47.8082255,
u'y': 2.239497,
u'z': -37.192936
},
u'dk': {
u'id': u'dk',
u'region': u'middletemporal',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'middletemporal',
u'x': -43.275,
u'y': 16.62,
u'z': -56.25
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'middletemporal',
u'x': -43.5149515,
u'y': 16.4580355,
u'z': -55.944128
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -54.7232,
u'y': 5.86805,
u'z': -40.4636384749486
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -53.0634,
u'y': 1.0772995,
u'z': -35.2313
}
},
u'channel_1': 90,
u'channel_2': 91,
u'code': u'LAS2-LAS3',
u'id': u'las.2-las.3',
u'is_explicit': False,
u'is_stim_only': False,
u'type_1': u'S',
u'type_2': u'S'
},
u'LAS3-LAS4': {
u'atlases': {
u'avg': {
u'id': u'avg',
u'region': u'middletemporal',
u'x': -55.865,
u'y': 5.325,
u'z': -32.175
},
u'avg.dural': {
u'id': u'avg.dural',
u'region': u'middletemporal',
u'x': -53.7415735,
u'y': 4.1258245,
u'z': -30.37393
},
u'dk': {
u'id': u'dk',
u'region': u'middletemporal',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'middletemporal',
u'x': -47.9,
u'y': 18.98,
u'z': -48.155
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'middletemporal',
u'x': -48.4478225,
u'y': 18.0682125,
u'z': -47.8244455
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -60.0524,
u'y': 9.01968,
u'z': -30.4733115958087
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -58.06875,
u'y': 2.924265,
u'z': -27.53625
}
},
u'channel_1': 91,
u'channel_2': 92,
u'code': u'LAS3-LAS4',
u'id': u'las.3-las.4',
u'is_explicit': False,
u'is_stim_only': False,
u'type_1': u'S',
u'type_2': u'S'
},
u'LOAD1-LOAD2': {
u'atlases': {
u'avg': {
u'id': u'avg',
u'region': u'cuneus',
u'x': -13.63,
u'y': -66.385,
u'z': 15.19
},
u'avg.dural': {
u'id': u'avg.dural',
u'region': u'cuneus',
u'x': -13.63,
u'y': -66.385,
u'z': 15.19
},
u'dk': {
u'id': u'dk',
u'region': u'cuneus',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'cuneus',
u'x': -11.505,
u'y': -48.95,
u'z': -1.285
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'cuneus',
u'x': -11.505,
u'y': -48.95,
u'z': -1.285
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -11.6775,
u'y': -64.569,
u'z': 16.3181365099509
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -11.52777,
u'y': -60.041,
u'z': 17.80605
},
u'wb': {
u'id': u'wb',
u'region': u'Left Cerebral White Matter',
u'x': None,
u'y': None,
u'z': None
}
},
u'channel_1': 65,
u'channel_2': 66,
u'code': u'LOAD1-LOAD2',
u'id': u'load.1-load.2',
u'is_explicit': False,
u'is_stim_only': False,
u'type_1': u'D',
u'type_2': u'D'
},
u'LOAD2-LOAD3': {
u'atlases': {
u'avg': {
u'id': u'avg',
u'region': u'precuneus',
u'x': -18.59,
u'y': -66.81,
u'z': 14.165
},
u'avg.dural': {
u'id': u'avg.dural',
u'region': u'precuneus',
u'x': -18.59,
u'y': -66.81,
u'z': 14.165
},
u'dk': {
u'id': u'dk',
u'region': u'precuneus',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'precuneus',
u'x': -16.185,
u'y': -49.215,
u'z': -2.245
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'precuneus',
u'x': -16.185,
u'y': -49.215,
u'z': -2.245
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -16.7927,
u'y': -65.7112,
u'z': 13.4931492118311
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -16.62185,
u'y': -60.85595,
u'z': 16.8891
},
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},
u'dk': {
u'id': u'dk',
u'region': u'superiortemporal',
u'x': None,
u'y': None,
u'z': None
},
u'ind': {
u'id': u'ind',
u'region': u'superiortemporal',
u'x': -59.91,
u'y': -12.235,
u'z': -26.325
},
u'ind.dural': {
u'id': u'ind.dural',
u'region': u'superiortemporal',
u'x': -59.692081,
u'y': -12.535966,
u'z': -26.175064
},
u'mni': {
u'id': u'mni',
u'region': None,
u'x': -71.2307,
u'y': -28.8535,
u'z': -9.78374566316842
},
u'tal': {
u'id': u'tal',
u'region': None,
u'x': -67.94285,
u'y': -29.3608,
u'z': -6.578205
}
},
u'channel_1': 10,
u'channel_2': 11,
u'code': u'LPOG10-LPOG11',
u'id': u'lpog.10-lpog.11',
u'is_explicit': False,
u'is_stim_only': False,
u'type_1': u'G',
u'type_2': u'G'
},
}
}
}
pairs_array = TalReader('').from_dict(pairs_dict)
channel_array = np.array(
sorted([[89, 90], [90, 91], [91, 92], [65, 66], [66, 67], [67, 68],
[69, 70], [70, 71], [71, 72], [73, 74], [74, 75], [75, 76],
[76, 77], [77, 78], [78, 79], [79, 80], [1, 2], [1, 9],
[10, 11]]))
assert pairs_array.channel.shape == channel_array.shape
assert (pairs_array.channel == channel_array).all()
def test_with_version_no():
fname = osp.join(osp.dirname(__file__), 'data', 'pairs.json')
tal_reader = TalReader(filename=fname)
tal_reader.read()
def test_talreader():
pairinfo = TalReader(
filename=osp.join(osp.dirname(__file__), 'data', 'pairs.json')).read()
_ = pairinfo.atlases.avg[['x', 'y', 'z']]
_ = pairinfo.atlases.mni.region
osp.join(reference_index_root, 'protocols', 'r1', 'subjects',
subject, 'localizations', '0', 'neuroradiology'),
osp.join(test_index_root, 'protocols', 'r1', 'subjects', subject,
'localizations', '0', 'neuroradiology'))
except OSError as ose:
if ose.errno != 17:
raise ose
return run_montage_import(inputs, force=True)
if __name__ == '__main__':
for subject in bipolar_subjects:
logger.set_subject(subject, 'R1')
sucess, _ = make_bipolar_montage(subject)
if sucess:
tal = TalReader(filename=pairs_path_template.format(
protocols_root=test_index_root, subject=subject))
new_bipolar_pairs = tal.get_bipolar_pairs()
jr = JsonIndexReader(
osp.join(reference_index_root, 'protocols', 'r1.json'))
task_event_files = list(
jr.aggregate_values('task_events', subject=subject, montage=0))
events = CMLEventReader(filename=task_event_files[0]).read()
eeg = EEGReader(events=events[:1],
channels=np.array([]),
start_time=0.0,
end_time=0.1).read()
hdf_bipolar_pairs = eeg.bipolar_pairs.values
new_bipolar_pairs = new_bipolar_pairs.astype(
hdf_bipolar_pairs.dtype)
if not np.in1d(hdf_bipolar_pairs, new_bipolar_pairs).all():
logger.info(
def test_talreader_on_database():
old_reader = TalReader(filename='/Volumes/rhino_root/data/eeg/R1234D/tal/R1234D_talLocs_database_bipol.mat')
jr = JsonIndexReader('/Volumes/rhino_root/protocols/r1.json')
new_reader = TalReader(filename=jr.get_value('pairs',subject='R1234D',experiment='FR1'))
assert new_reader.get_bipolar_pairs() == old_reader.get_bipolar_pairs()
assert new_reader.get_monopolar_channels() == old_reader.get_monopolar_channels()
def test_old_behavior_warning():
with pytest.deprecated_call():
TalReader(filename=osp.join(osp.dirname(__file__), 'data',
'pairs.json'),
unpack=False).read()
def test_old_behavior():
pairinfo = TalReader(filename=osp.join(osp.dirname(__file__), 'data',
'pairs.json'),
unpack=False).read()
assert pairinfo.dtype['atlases'] is np.dtype(object)
def test_old_behavior_warning():
with pytest.warns(FutureWarning):
TalReader(filename=osp.join(osp.dirname(__file__), 'data',
'pairs.json'),
unpack=False).read()
t = time.time()
pow_vec_orig = []
pow_vec_recon = []
print(len(recon_files))
print(len(orig_files))
assert (len(recon_files) == len(events))
experiment = 'FR1'
jr = JsonIndexReader(rhino_root +
'/protocols/r1.json') # Build JSON reader
pairs_path = jr.get_value('pairs',
subject=subject,
experiment=experiment)
tal_reader = TalReader(filename=pairs_path)
monopolar_channels = tal_reader.get_monopolar_channels()
bipolar_channels = tal_reader.get_bipolar_pairs()
recon_files = np.sort(recon_files)
orig_files = np.sort(orig_files)
data_orig_vec = []
data_recon_vec = []
for i, (recon_file,
orig_file) in enumerate(zip(recon_files, orig_files)):
if i % 100 == 0:
print(i)
fname_recon = subject_dir + recon_file
fname_orig = subject_dir + orig_file
def construct_significant_elec_info(df):
tal_structs = None
subject = ''
lh_selector = None
rh_selector = None
lh_data_combined = None
rh_data_combined = None
for index, row in df.iterrows():
if subject != row['Subject']:
subject = row['Subject']
# '/Users/m/data/eeg/R1111M/tal/R1111M_talLocs_database_bipol.mat'
tal_path = join(mount_point,'data/eeg/',subject,'tal',subject+'_talLocs_database_bipol.mat')
tal_reader = TalReader(filename=tal_path)
tal_structs = tal_reader.read()
lh_selector = np.array(map(lambda loc: loc.upper().startswith('L'), tal_structs.tagName))
rh_selector = np.array(map(lambda loc: loc.upper().startswith('R'), tal_structs.tagName))
lh_data = tal_structs[['avgSurf','eType','tagName']]
rh_data = tal_structs[['avgSurf','eType','tagName']]
lh_data = lh_data[lh_selector]
rh_data = rh_data[rh_selector]
lh_sel = get_elec_data_coords_array(lh_data[np.where(lh_data.tagName == row['stimAnodeTag']+'-'+row['stimCathodeTag'])])
lh_sel_flip = get_elec_data_coords_array(lh_data[np.where(lh_data.tagName == row['stimCathodeTag']+'-'+row['stimAnodeTag'])])
rh_sel = get_elec_data_coords_array(rh_data[np.where(rh_data.tagName == row['stimAnodeTag']+'-'+row['stimCathodeTag'])])
rh_sel_flip = get_elec_data_coords_array(rh_data[np.where(rh_data.tagName == row['stimCathodeTag']+'-'+row['stimAnodeTag'])])
lh_data_combined = append_to_combined(lh_data_combined,lh_sel,lh_sel_flip)
rh_data_combined = append_to_combined(rh_data_combined,rh_sel,rh_sel_flip)
# lh_sel_stack = vstack_arrays(lh_sel,lh_sel_flip)
# rh_sel_stack = vstack_arrays(rh_sel,rh_sel_flip)
#
#
# lh_tmp = np.vstack((lh_sel,lh_sel_flip))
# rh_tmp = np.vstack((rh_sel,rh_sel_flip))
#
# if not lh_data_combined:
# lh_data_combined = lh_tmp
# else:
# lh_data_combined = np.vstack((lh_data_combined,lh_tmp))
#
# if not rh_data_combined:
# rh_data_combined = rh_tmp
# else:
# rh_data_combined = np.vstack((rh_data_combined,rh_tmp))
print rh_data_combined
print lh_data_combined
return lh_data_combined, rh_data_combined
os.symlink(osp.join(reference_index_root,'protocols','r1','subjects',subject,'localizations','0','neuroradiology'),
osp.join(test_index_root,'protocols','r1','subjects',subject,'localizations','0','neuroradiology'))
except OSError as ose:
if ose.errno != 17:
raise ose
return run_montage_import(inputs,force=True)
if __name__ == '__main__':
for subject in bipolar_subjects:
logger.set_subject(subject,'R1')
sucess, _ =make_bipolar_montage(subject)
if sucess:
tal = TalReader(filename=pairs_path_template.format(protocols_root=test_index_root,
subject= subject))
new_bipolar_pairs = tal.get_bipolar_pairs()
jr = JsonIndexReader(osp.join(reference_index_root,'protocols','r1.json'))
task_event_files = list(jr.aggregate_values('task_events',subject=subject,montage=0))
events = CMLEventReader(filename=task_event_files[0]).read()
eeg = EEGReader(events=events[:1],channels = np.array([]),start_time=0.0,end_time=0.1).read()
hdf_bipolar_pairs = eeg.bipolar_pairs.values
new_bipolar_pairs = new_bipolar_pairs.astype(hdf_bipolar_pairs.dtype)
if not np.in1d(hdf_bipolar_pairs,new_bipolar_pairs).all():
logger.info('\n\n%s missing from new_bipolar_pairs\n\n'%str(hdf_bipolar_pairs[~np.in1d(hdf_bipolar_pairs,new_bipolar_pairs)]))
if not np.in1d(new_bipolar_pairs,hdf_bipolar_pairs).all():
logger.info('\n\n%s missing from new_bipolar_pairs\n\n'%str(new_bipolar_pairs[~np.in1d(new_bipolar_pairs,hdf_bipolar_pairs)]))