def downsample(mgr, factor):
assert (factor >= 1)
ch_num = len(mgr.get_samples())
ch_len = len(mgr.get_samples()[0])
# To be determined ...
ret_ch_len = 0
i = 0
left_inds = []
# Determine ret_ch_len - a size of returned channel
while i < ch_len:
left_inds.append(i)
ret_ch_len += 1
i += factor
new_samples = array([zeros(ret_ch_len) for i in range(ch_num)])
for i in range(ch_num):
for j, ind in enumerate(left_inds):
new_samples[i, j] = mgr.get_samples()[i, ind]
info_source = copy.deepcopy(mgr.info_source)
info_source.get_params()['number_of_samples'] = str(ret_ch_len * ch_num)
info_source.get_params()['sampling_frequency'] = str(
float(mgr.get_param('sampling_frequency')) / factor)
tags_source = copy.deepcopy(mgr.tags_source)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
def get_fake_manager(number_of_channels, channel_len):
"""
Return read manager with samples:
[
[1 2 3 ... channel_len],
[10 20 30 ... 10*channel_len]
...
[10^number_of_channels ... ]
]
"""
new_samples = zeros((number_of_channels, channel_len))
start_i = 1
for ch in range(number_of_channels):
new_samples[ch, :] = range(start_i, start_i*(channel_len+1), start_i)
start_i = 10*start_i
new_tags = []
new_params = {}
new_params['channels_names'] = ['CH'+str(i) for i in range(number_of_channels)]
new_params['channels_numbers'] = [str(i) for i in range(number_of_channels)]
new_params['channels_gains'] = [str(1+i/10.0) for i in range(number_of_channels)]
new_params['channels_offsets'] = [str(i/10.0) for i in range(number_of_channels)]
new_params['number_of_channels'] = str(number_of_channels)
new_params['number_of_samples'] = str(channel_len*number_of_channels)
new_params['sampling_frequency'] = str(128.0)
info_source = read_info_source.MemoryInfoSource(new_params)
tags_source = read_tags_source.MemoryTagsSource(new_tags)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
def process(self, files=None):
if not self.files is None:
files = self.files
mgrs = []
for f in files:
mgrs.append(
read_manager.ReadManager(f['info'], f['data'], f['tags']))
return mgrs
def montage_csa(mgr):
new_samples = get_montage(
mgr.get_samples(),
get_montage_matrix_csa(int(mgr.get_param('number_of_channels'))))
info_source = copy.deepcopy(mgr.info_source)
tags_source = copy.deepcopy(mgr.tags_source)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
def exclude_channels(mgr, channels):
new_params = copy.deepcopy(mgr.get_params())
samples = mgr.get_samples()
new_tags = copy.deepcopy(mgr.get_tags())
ex_channels_inds = [
new_params['channels_names'].index(ch) for ch in channels
]
assert (-1 not in ex_channels_inds)
new_samples = zeros((int(new_params['number_of_channels']) - len(channels),
len(samples[0])))
# Define new samples and params list values
keys = [
'channels_names', 'channels_numbers', 'channels_gains',
'channels_offsets'
]
keys_to_remove = []
for k in keys:
try:
#Exclude from keys those keys that are missing in mgr
mgr.get_params()[k]
except KeyError:
keys_to_remove.append(k)
continue
new_params[k] = []
for k in keys_to_remove:
keys.remove(k)
new_ind = 0
for ch_ind, ch in enumerate(samples):
if ch_ind in ex_channels_inds:
continue
else:
new_samples[new_ind, :] = ch
for k in keys:
new_params[k].append(mgr.get_params()[k][ch_ind])
new_ind += 1
# Define other new new_params
new_params['number_of_channels'] = str(
int(new_params['number_of_channels']) - len(channels))
new_params['number_of_samples'] = str(int(new_params['number_of_samples']) - \
len(channels)*len(samples[0]))
info_source = read_info_source.MemoryInfoSource(new_params)
tags_source = read_tags_source.MemoryTagsSource(new_tags)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
def montage_ears(mgr, l_ear_channel, r_ear_channel):
left_index = mgr.get_param('channels_names').index(l_ear_channel)
right_index = mgr.get_param('channels_names').index(r_ear_channel)
if left_index < 0 or right_index < 0:
raise Exception("Montage - couldn`t find ears channels: " +
str(l_ear_channel) + ", " + str(r_ear_channel))
new_samples = get_montage(
mgr.get_samples(),
get_montage_matrix_ears(int(mgr.get_param('number_of_channels')),
left_index, right_index))
info_source = copy.deepcopy(mgr.info_source)
tags_source = copy.deepcopy(mgr.tags_source)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
def run():
dr2 = '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/p300_10_12_2010/squares/'
f2_name = 'p300_128hz_laptop_training_6x6_square_CATDOGFISHWATERBOWL_longer_8trials2'
f2 = {
'info': os.path.join(dr2, f2_name+'_10HZ.obci.xml'),
'data': os.path.join(dr2, f2_name+'_10HZ.obci.bin'),
'tags':os.path.join(dr2, f2_name+'.obci.arts_free.svarog.tags')
}
"""dr2 = '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/p300_10_12_2010/numbered_squares/'
f2_name = 'p300_128hz_laptop_training_6x6_squareNUMBERS_CATDOGFISHWATERBOWL_longer_8trials'
f2 = {
'info': os.path.join(dr2, f2_name+'.obci.filtered.xml'),
'data': os.path.join(dr2, f2_name+'_10HZ.obci.bin'),
'tags':os.path.join(dr2, f2_name+'.obci.arts_free.svarog.tags')
}"""
mgr = read_manager.ReadManager(f2['info'], f2['data'], f2['tags'])
train_data_ch, train_labels = prepare.get_train_set_from_mgr(mgr, num_per_avg=10, start_samples_to_norm=0, downsample_level=5)
class MY_SVM(object):
def __init__(self, C):
self.s = svm.SVM(C=C)
def process(self, data):
return self.s.stratifiedCV(data, 5).getBalancedSuccessRate()
def __repr__(self):
return "SVM: C - "+str(self.s.C)
class MY_STD(object):
def process(self, data):
#p = prepare.Standardizer()
#p.train(data)
data.normalize(2)
return data
ch = my_chain.Chain(
my_chain.ChainElement(MY_STD, {}),
my_chain.ChainElement(MY_SVM,
{'C':[0.01, 0.1, 0.5, 1, 10]})
)
l = Labels([str(i) for i in train_labels])
for i, train_data in enumerate(train_data_ch):
train_data = train_data_ch[11]
data = VectorDataSet(train_data, L=l)
cs, res = ch.process(data)
print("CSS: "+str(cs))
print("RES: "+str(res))
break
def filter(mgr,
wp,
ws,
gpass,
gstop,
analog=0,
ftype='ellip',
output='ba',
unit='radians',
use_filtfilt=False):
if unit == 'radians':
b, a = signal.iirdesign(wp, ws, gpass, gstop, analog, ftype, output)
elif unit == 'hz':
sampling = float(mgr.get_param('sampling_frequency'))
try:
wp = wp / sampling
ws = ws / sampling
except TypeError:
wp = [i / sampling for i in wp]
ws = [i / sampling for i in ws]
b, a = signal.iirdesign(wp, ws, gpass, gstop, analog, ftype, output)
if use_filtfilt:
import filtfilt
#samples_source = read_data_source.MemoryDataSource(mgr.get_samples(), False)
for i in range(int(mgr.get_param('number_of_channels'))):
print("FILT FILT CHANNEL " + str(i))
mgr.get_samples()[i, :] = filtfilt.filtfilt(
b, a,
mgr.get_samples()[i])
samples_source = read_data_source.MemoryDataSource(
mgr.get_samples(), False)
else:
print("FILTER CHANNELs")
filtered = signal.lfilter(b, a, mgr.get_samples())
print("FILTER CHANNELs finished")
samples_source = read_data_source.MemoryDataSource(filtered, True)
info_source = copy.deepcopy(mgr.info_source)
tags_source = copy.deepcopy(mgr.tags_source)
new_mgr = read_manager.ReadManager(info_source, samples_source,
tags_source)
return new_mgr
def fix_file(f, from_sample, num_of_samples, sample_value):
mgr = read_manager.ReadManager(f['info'], f['data'], None)
wr = data_file_proxy.DataFileWriteProxy(f['data'] + '.fixed.raw')
for i, s in enumerate(mgr.iter_samples()):
for v in s:
wr.data_received(v)
if i > from_sample:
print("BREAK")
break
ch_no = int(mgr.get_param('number_of_channels'))
for i in range(num_of_samples):
for j in range(ch_no):
wr.data_received(sample_value)
print("FILLED")
for i, s in enumerate(mgr.iter_samples()):
if i > from_sample:
for v in s:
wr.data_received(v)
wr.finish_saving()
def trigger_to_tag(p_new_tag_file_path,
p_exp_file_path,
p_info_file_path,
p_data_file_path,
p_ExpTagClass,
p_ignore_first=0,
p_ignore_last=0,
p_tag_len=1.0,
p_min_trig_len=0.0,
p_ignore_from_sample_number=None):
"""
>>> trigger_to_tag('test.tags', '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/kamil_pilot_25_11_2010/kamil_001.txt', '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/kamil_pilot_25_11_2010/kamil_001.obci.svarog.info', '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/kamil_pilot_25_11_2010/kamil_001.obci.dat', KamilExpTag, 2, 1, 1.0, 0.5)
>>> import os
#>>> os.remove('test.tags')
"""
l_mgr = read_manager.ReadManager(p_info_file_path, p_data_file_path, None)
l_exp_tags = get_experiment_tags_from(p_exp_file_path, p_ExpTagClass)
l_tags = get_tags_from_trigger(l_mgr, l_exp_tags, p_ignore_first,
p_ignore_last, p_tag_len, p_min_trig_len,
p_ignore_from_sample_number)
create_tags_file(l_tags, p_new_tag_file_path)
plot([avg_diff] * len(tss_diff))
title("Received_trigger_ts-Sent_trigger_ts")
legend(("Rcv_ts-Sent_ts", "avg(Rcv_ts-Sent_ts)"))
show()
if __name__ == "__main__":
dr = '/home/mati/bci_dev/openbci_repo/trunk/temp/'
f_name = 'synchro_test_02_12_2010_22ch_1024hz_c++_usb'
f = {
'info': os.path.join(dr, f_name + '.obci.svarog.info'),
'data': os.path.join(dr, f_name + '.obci.dat'),
'tags': os.path.join(dr, f_name + '.obci.svarog.tags')
}
read_manager = read_manager.ReadManager(f['info'], f['data'], f['tags'])
asci_trig = '' #os.path.join(dr, f_name+'.asci_trigger')
if os.path.isfile(asci_trig):
sent_vals, sent_tss = trigger_change.get_trigger_sent_timestamps(
None, asci_trig)
else:
sent_vals, sent_tss = trigger_change.get_trigger_sent_timestamps(
read_manager, None)
rcv_vals, rcv_tss, rcv_len = trigger_change.get_trigger_received_timestamps(
read_manager)
LOGGER.info("*******************************************")
LOGGER.info("Red sent vals: ")
LOGGER.info(sent_vals)
def montage_custom(mgr, montage_matrix):
new_samples = get_montage(mgr.get_samples(), montage_matrix)
info_source = copy.deepcopy(mgr.info_source)
tags_source = copy.deepcopy(mgr.tags_source)
samples_source = read_data_source.MemoryDataSource(new_samples)
return read_manager.ReadManager(info_source, samples_source, tags_source)
