def load_file(file_name, standardize, demean):
bbci_set = BBCIDataset(file_name,
load_sensor_names=get_EEG_sensors_sorted())
log.info("Loading...")
cnt = bbci_set.load()
log.info("Set cz to zero and remove high absolute value trials")
marker_def = dict([(str(i_class), [i_class]) for i_class in xrange(1,5)])
clean_result = MaxAbsCleaner(threshold=800,
marker_def=marker_def,
segment_ival=[0,4000]).clean(cnt)
cnt = restrict_cnt(cnt, marker_def.values(), clean_result.clean_trials,
clean_result.rejected_chan_names, copy_data=False)
cnt = set_channel_to_zero(cnt, 'Cz')
log.info("Resampling...")
cnt = resample_cnt(cnt, newfs=250.0)
log.info("Car filtering...")
cnt = common_average_reference_cnt(cnt)
if standardize:
log.info("Standardizing...")
cnt = exponential_standardize_cnt(cnt)
if demean:
log.info("Demeaning...")
cnt = exponential_demean_cnt(cnt)
return cnt
def load_file(file_name, standardize, demean):
bbci_set = BBCIDataset(file_name,
load_sensor_names=get_EEG_sensors_sorted())
log.info("Loading...")
cnt = bbci_set.load()
log.info("Set cz to zero and remove high absolute value trials")
marker_def = dict([(str(i_class), [i_class]) for i_class in xrange(1, 5)])
clean_result = MaxAbsCleaner(threshold=800,
marker_def=marker_def,
segment_ival=[0, 4000]).clean(cnt)
cnt = restrict_cnt(cnt,
marker_def.values(),
clean_result.clean_trials,
clean_result.rejected_chan_names,
copy_data=False)
cnt = set_channel_to_zero(cnt, 'Cz')
log.info("Resampling...")
cnt = resample_cnt(cnt, newfs=250.0)
log.info("Car filtering...")
cnt = common_average_reference_cnt(cnt)
if standardize:
log.info("Standardizing...")
cnt = exponential_standardize_cnt(cnt)
if demean:
log.info("Demeaning...")
cnt = exponential_demean_cnt(cnt)
return cnt
def send_file_data():
print("Loading Experiment...")
# Use model to get cnt preprocessors
base_name = 'data/models/online/cnt/shallow-uneven-trials/9'
exp = create_experiment(base_name + '.yaml')
print("Loading File...")
offline_execution_set = BBCIDataset('data/four-sec-dry-32-sensors/cabin/'
'MaVo2_sahara32_realMovementS001R02_ds10_1-5.BBCI.mat')
cnt = offline_execution_set.load()
print("Running preprocessings...")
cnt_preprocs = exp.dataset.cnt_preprocessors
assert cnt_preprocs[-1][0].__name__ == 'exponential_standardize_cnt'
# Do not do standardizing as it will be done by coordinator
for preproc, kwargs in cnt_preprocs[:-1]:
cnt = preproc(cnt, **kwargs)
cnt_data = cnt.data.astype(np.float32)
assert not np.any(np.isnan(cnt_data))
assert not np.any(np.isinf(cnt_data))
assert not np.any(np.isneginf(cnt_data))
print("max possible block", np.ceil(len(cnt_data) / 50.0))
y_labels = create_y_labels(cnt).astype(np.float32)
assert np.array_equal(np.unique(y_labels), range(5)), ("Should only have "
"labels 0-4")
print("Done.")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(("127.0.0.1", 1234))
chan_names = ['Fp1', 'Fpz', 'Fp2', 'AF7', 'AF3',
'AFz', 'AF4', 'AF8', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6',
'FC1', 'FCz', 'FC2', 'C3', 'C1', 'Cz', 'C2', 'C4', 'CP3', 'CP1',
'CPz', 'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz', 'marker']
chan_line = " ".join(chan_names) + "\n"
s.send(chan_line)
n_chans = 33
n_samples = 50
s.send(np.array([n_chans], dtype=np.int32).tobytes())
s.send(np.array([n_samples], dtype=np.int32).tobytes())
print("Sending data...")
i_block = 0 # if setting i_block to sth higher, printed results will incorrect
max_stop_block = np.ceil(len(cnt_data) / float(n_samples))
stop_block = 800
assert stop_block < max_stop_block
while i_block < stop_block:
arr = cnt_data[i_block * n_samples:i_block*n_samples + n_samples,:].T
this_y = y_labels[i_block * n_samples:i_block*n_samples + n_samples]
# chan x time
arr = np.concatenate((arr, this_y[np.newaxis, :]), axis=0).astype(np.float32)
s.send(arr.tobytes(order='F'))
assert arr.shape == (n_chans, n_samples)
i_block +=1
gevent.sleep(0.01)
print("Done.")
return cnt
def run(only_create_exp, cleaner, min_freq, max_freq, last_low_freq, low_width,
high_width, low_overlap, high_overlap, filename, restricted_n_trials,
n_folds, segment_ival, save_base_name, orig_params):
starttime = time.time()
marker_def = {'1': [1], '2': [2], '4': [4]}
load_sensor_names = None #['2LA','4Z', '5R'] # None means all
set_loader = BBCIDataset(filename, load_sensor_names=load_sensor_names)
sensor_names_after_cleaning = None # none means all
csp_experiment = CSPExperiment(
set_loader,
sensor_names=sensor_names_after_cleaning,
cleaner=cleaner,
resample_fs=250,
min_freq=min_freq,
max_freq=max_freq,
last_low_freq=last_low_freq,
low_width=low_width,
high_width=high_width,
low_overlap=low_overlap,
high_overlap=high_overlap,
filt_order=4,
segment_ival=segment_ival, # trial interval
standardize_filt_cnt=False,
standardize_epo=False, # standardize the data?..
n_folds=n_folds,
n_top_bottom_csp_filters=
5, # this number times two will be number of csp filters per filterband before feature selection
n_selected_filterbands=None, # how many filterbands to select?
n_selected_features=
20, # how many Features to select with the feature selection?
forward_steps=2, # feature selection param
backward_steps=1, # feature selection param
stop_when_no_improvement=False, # feature selection param
only_last_fold=
True, # Split into number of folds, but only run the last fold (i.e. last fold as test fold)?
restricted_n_trials=
restricted_n_trials, # restrict to certain number of _clean_ trials?
common_average_reference=False,
ival_optimizer=None, # optimize the trial ival with some optimizer?
shuffle=False, # shuffle or do blockwise folds?
marker_def=marker_def)
result_file_name = save_base_name + '.result.pkl'
csp_experiment.run()
endtime = time.time()
result = CSPResult(csp_trainer=csp_experiment,
parameters=orig_params,
training_time=endtime - starttime)
with open(result_file_name, 'w') as resultfile:
log.info("Saving to {:s}...\n".format(result_file_name))
pickle.dump(result, resultfile)
def construct_folder_combined_set(folder_names, sensor_names, cnt_preprocessors,
marker_def, end_marker_def, trial_classes,
trial_start_offset_ms, trial_stop_offset_ms,
min_break_length_ms, max_break_length_ms,
break_start_offset_ms, break_stop_offset_ms,
last_set_split_trial, add_trial_breaks=True,
folder_name_to_extra_args=None):
""" extra_args should be dict filename -> args that are different for
this file."""
default_args = deepcopy(locals())
sets = []
if folder_name_to_extra_args is not None:
for folder_name_with_args in folder_name_to_extra_args:
assert folder_name_with_args in folder_names
for i_folder, folder_name in enumerate(folder_names):
this_args = copy(default_args)
if folder_name_to_extra_args is not None and (
folder_name in folder_name_to_extra_args):
for key in folder_name_to_extra_args[folder_name]:
assert key in this_args
this_args[key] = folder_name_to_extra_args[folder_name][key]
assert key != 'last_set_split_trial', "Does not make sense :)"
marker_segmenter = MarkerSegmenter(segment_ival=[
this_args['trial_start_offset_ms'],
this_args['trial_stop_offset_ms']],
marker_def=this_args['marker_def'],
trial_classes=this_args['trial_classes'],
end_marker_def=this_args['end_marker_def'])
trial_break_adder = AddTrialBreaks(min_length_ms=this_args['min_break_length_ms'],
max_length_ms=this_args['max_break_length_ms'],
start_offset_ms=this_args['break_start_offset_ms'],
stop_offset_ms=this_args['break_stop_offset_ms'],
start_marker_def=this_args['marker_def'],
end_marker_def=this_args['end_marker_def'])
if (i_folder < len(folder_names) - 1) or (
this_args['last_set_split_trial'] is None):
segmenters = [marker_segmenter,]
else:
segmenters = [marker_segmenter,
RestrictTrialRange(0,this_args['last_set_split_trial'])]
if this_args['add_trial_breaks']:
segmenters.append(trial_break_adder)
segmenter = PipelineSegmenter(segmenters)
filenames = sorted(glob(os.path.join(folder_name, '*.BBCI.mat')))
cnt_set = SetWithMarkers(MultipleBBCIDataset(filenames,
load_sensor_names=this_args['sensor_names']),
this_args['cnt_preprocessors'],
segmenter)
sets.append(cnt_set)
# add last set last part as test set if you split apart last set
# we use that this_args is now from last set already
if last_set_split_trial is not None:
segmenters = [marker_segmenter,
RestrictTrialRange(last_set_split_trial,None),]
if this_args['add_trial_breaks']:
segmenters.append(trial_break_adder)
segmenter = PipelineSegmenter(segmenters)
cnt_set = SetWithMarkers(BBCIDataset(filenames[-1], # again last file needed
load_sensor_names=this_args['sensor_names']),
this_args['cnt_preprocessors'],
segmenter)
sets.append(cnt_set)
dataset = CombinedSet(sets)
return dataset
def send_file_data():
print("Loading Experiment...")
# Use model to get cnt preprocessors
base_name = 'data/models/online/cnt/start-end-mrk/125'
exp = create_experiment(base_name + '.yaml')
print("Loading File...")
offline_execution_set = BBCIDataset(
'data/robot-hall/NiRiNBD15_cursor_250Hz.BBCI.mat',
load_sensor_names=get_nico_sensors())
cnt = offline_execution_set.load()
log.info("Preprocessing...")
cnt = resample_cnt(cnt, newfs=100)
cnt = lowpass_cnt(cnt, high_cut_off_hz=40, filt_order=10)
log.info("Done.")
cnt_data = cnt.data.astype(np.float32)
assert not np.any(np.isnan(cnt_data))
assert not np.any(np.isinf(cnt_data))
assert not np.any(np.isneginf(cnt_data))
print("max possible block", np.ceil(len(cnt_data) / 50.0))
segmenter = MarkerSegmenter(
segment_ival=(500, 0),
marker_def={
'Right Hand': [1],
'Feet': [2],
'Rotation': [3],
'Words': [4],
'Rest': [5]
},
trial_classes=['Right Hand', 'Feet', 'Rotation', 'Words', 'Rest'],
end_marker_def={
'Right Hand': [10],
'Feet': [20],
'Rotation': [30],
'Words': [40],
'Rest': [50],
},
)
cnt_y, class_names = segmenter.segment(cnt)
has_marker = np.sum(cnt_y, axis=1) > 0
new_y = np.zeros(cnt_y.shape[0], dtype=np.int32)
new_y[has_marker] = (np.argmax(cnt_y[has_marker], axis=1) + 1)
print("Done.")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(("127.0.0.1", 7987))
chan_names = [
'Fp1', 'Fpz', 'Fp2', 'AF7', 'AF3', 'AF4', 'AF8', 'F7', 'F5', 'F3',
'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz',
'FC2', 'FC4', 'FC6', 'FT8', 'M1', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2',
'C4', 'C6', 'T8', 'M2', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2',
'CP4', 'CP6', 'TP8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6',
'P8', 'PO7', 'PO5', 'PO3', 'POz', 'PO4', 'PO6', 'PO8', 'O1', 'Oz',
'O2', 'marker'
]
chan_line = " ".join(chan_names) + "\n"
s.send(chan_line)
n_chans = 65
n_samples = 50
s.send(np.array([n_chans], dtype=np.int32).tobytes())
s.send(np.array([n_samples], dtype=np.int32).tobytes())
print("Sending data...")
i_block = 0 # if setting i_block to sth higher, printed results will incorrect
max_stop_block = np.ceil(len(cnt_data) / float(n_samples))
stop_block = 800
assert stop_block < max_stop_block
while i_block < stop_block:
arr = cnt_data[i_block * n_samples:i_block * n_samples +
n_samples, :].T
this_y = new_y[i_block * n_samples:i_block * n_samples + n_samples]
# chan x time
arr = np.concatenate((arr, this_y[np.newaxis, :]),
axis=0).astype(np.float32)
s.send(arr.tobytes(order='F'))
assert arr.shape == (n_chans, n_samples)
i_block += 1
gevent.sleep(0.03)
print("Done.")
return cnt
