def __init__(self, p_mgrs, p_info_source, p_avg_name,
p_samples_to_norm=0, p_avg_len=None):
avg_samples = erp_avg.get_normalised_avgs(p_mgrs, p_samples_to_norm, p_avg_len)
tags_source = read_tags_source.MemoryTagsSource([])
p_info_source.get_params()['epoch_name'] = p_avg_name
samples_source = read_data_source.MemoryDataSource(avg_samples)
super(AveragedEpochs, self).__init__(p_info_source,
samples_source,
tags_source)
def __init__(self,
p_mgrs,
p_info_source,
p_avg_name,
p_samples_to_norm=0,
p_avg_len=None):
avg_samples = erp_avg.get_normalised_avgs(p_mgrs, p_samples_to_norm,
p_avg_len)
tags_source = read_tags_source.MemoryTagsSource([])
p_info_source.get_params()['epoch_name'] = p_avg_name
samples_source = read_data_source.MemoryDataSource(avg_samples)
super(AveragedEpochs, self).__init__(p_info_source, samples_source,
tags_source)
def get_averaged_samples(self, p_train_set, p_num_per_avg, p_start_samples_to_norm=0):
"""
>>> dr = '/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(dr, f2_name+'.obci.filtered.xml'), 'data': os.path.join(dr, f2_name+'_10HZ.obci.bin'), 'tags':os.path.join(dr, f2_name+'.obci.arts_free.svarog.tags')}
>>> t = extract_trials_from_file(f2)
>>> avg = RandomAverager()
>>> s, c = avg.get_averaged_samples(t, 5)
>>> sm = sum([len(i['target']) for i in t])/5+sum([len(i['non-target']) for i in t])/5
>>> len(s[0]) == sm == len(c)
True
>>> len(s) == 24
True
"""
# Gather target and non-target tag to separate collections
target_tags = []
non_target_tags = []
for i_set in p_train_set:
target_tags += i_set['target']
non_target_tags += i_set['non-target']
# shuffle both collections
random.shuffle(target_tags)
random.shuffle(non_target_tags)
# Define some crucial variables
n_target = len(target_tags) / p_num_per_avg
n_non_target = len(non_target_tags) / p_num_per_avg
avg_size = len(target_tags[0].get_samples()[0])
ch_num = len(target_tags[0].get_samples())
# Define to-be-returned variables:
# an array of numbers representing trials classes (target 1, non-target -1)
ret_classes = scipy.zeros(n_non_target + n_target)
# A list of arrays (for every channel)
# One array is a 2dim array with:
ret_arrs = []
ret_arr_index = 0
for i in range(ch_num):
ret_arrs.append(scipy.zeros((n_target + n_non_target, avg_size)))
# Fill ret_arss
i = 0
while i/p_num_per_avg < n_target:
avg_arr = erp_avg.get_normalised_avgs(target_tags[i:(i+p_num_per_avg)],
p_start_samples_to_norm,
avg_size)
for j in range(ch_num):
ret_arrs[j][ret_arr_index, :] = avg_arr[j, :]
ret_classes[ret_arr_index] = 1.0
ret_arr_index += 1
i += p_num_per_avg
i = 0
while i/p_num_per_avg < n_non_target:
avg_arr = erp_avg.get_normalised_avgs(non_target_tags[i:(i+p_num_per_avg)],
p_start_samples_to_norm,
avg_size)
for j in range(ch_num):
ret_arrs[j][ret_arr_index, :] = avg_arr[j, :]
ret_classes[ret_arr_index] = -1.0
ret_arr_index += 1
i += p_num_per_avg
#print("R: ", str(ret_arr_index), ", N: ", str(n_target + n_non_target))
assert(ret_arr_index == n_target + n_non_target)
return ret_arrs, ret_classes
def plot_avgs_for(p_files,
plot_type='all',
p_show=True,
x=None,
y=None,
channel_names=None):
mgr_neu1 = smart_tags_manager.SmartTagsManager(NEU1_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_neu1.kamil_desc = "Neutr1"
mgr_neu3 = smart_tags_manager.SmartTagsManager(NEU3_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_neu3.kamil_desc = "Neutr3"
mgr_neg1 = smart_tags_manager.SmartTagsManager(NEG1_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_neg1.kamil_desc = "N1"
mgr_neg3 = smart_tags_manager.SmartTagsManager(NEG3_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_neg3.kamil_desc = "N3"
mgr_pos1 = smart_tags_manager.SmartTagsManager(POS1_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_pos1.kamil_desc = "P1"
mgr_pos3 = smart_tags_manager.SmartTagsManager(POS3_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_pos3.kamil_desc = "P3"
mgr_dr = smart_tags_manager.SmartTagsManager(DREWNO_TAG_DEF,
p_files['info'],
p_files['data'],
p_files['tags'])
mgr_dr.kamil_desc = "Drewno"
mgrs = []
if plot_type == 'all':
mgrs.append(mgr_neu1)
mgrs.append(mgr_neu3)
mgrs.append(mgr_neg1)
mgrs.append(mgr_neg3)
mgrs.append(mgr_pos1)
mgrs.append(mgr_pos3)
mgrs.append(mgr_dr)
elif plot_type == 'system':
mgr_pos1.kamil_desc = "Aut1 = P1+N1"
mgr_pos1.get_smart_tags()
mgr_pos1._smart_tags += mgr_neg1.get_smart_tags()
mgrs.append(mgr_pos1)
mgr_pos3.kamil_desc = "Refl3 = P3+N3"
mgr_pos3.get_smart_tags()
mgr_pos3._smart_tags += mgr_neg3.get_smart_tags()
mgrs.append(mgr_pos3)
mgr_neu1.kamil_desc = "Neu = Neu1+Neu3"
mgr_neu1.get_smart_tags()
mgr_neu1._smart_tags += mgr_neu3.get_smart_tags()
mgrs.append(mgr_neu1)
mgrs.append(mgr_dr)
elif plot_type == 'sign':
mgr_pos1.kamil_desc = "Pos = P1+P3"
mgr_pos1.get_smart_tags()
mgr_pos1._smart_tags += mgr_pos3.get_smart_tags()
mgrs.append(mgr_pos1)
mgr_neg1.kamil_desc = "Neg = N1+N3"
mgr_neg1.get_smart_tags()
mgr_neg1._smart_tags += mgr_neg3.get_smart_tags()
mgrs.append(mgr_neg1)
mgr_neu1.kamil_desc = "Neu = Neu1+Neu3"
mgr_neu1.get_smart_tags()
mgr_neu1._smart_tags += mgr_neu3.get_smart_tags()
mgrs.append(mgr_neu1)
mgrs.append(mgr_dr)
#mgr_pos3.kamil_desc = "P3+N3"
#mgr_pos3.get_smart_tags()
#mgr_pos3._smart_tags += mgr_neg3.get_smart_tags()
#mgrs.append(mgr_pos3)
if channel_names is None:
import math
channel_names = [mgr_pos.get_param('channels_names')]
x = int(math.sqrt(len(channels_names)))
y = ceil(math.sqrt(len(channels_names)))
for i, channels in enumerate(channel_names):
plotter = erp_plot.Plotter(x, y, i)
plt_id = 0
for i_ch_name in channels:
plt_id += 1
for mgr in mgrs:
avgs_for_channels = erp_avg.get_normalised_avgs(
mgr,
abs(START_SEC_OFFSET) * 1024) * 0.0715
ch = get_channel_data(mgr_dr, avgs_for_channels, i_ch_name)
if ch is None:
LOGGER.warning("Couldn`t find channel for name: " +
i_ch_name)
else:
samp = float(
mgr.get_read_manager().get_param('sampling_frequency'))
plotter.add_plot(ch, mgr.kamil_desc + " " + i_ch_name, [
i for i in range(
START_SEC_OFFSET * samp - 1,
len(ch) - (abs(START_SEC_OFFSET) * samp))
], plt_id)
#store_avgs_for_svarog([ch])
plotter.prepare_to_show()
if p_show:
erp_plot.show()
def plot_avgs_for(p_files, plot_type='all', p_show=True, x=None, y=None, channel_names=None):
mgr_neu1 = smart_tags_manager.SmartTagsManager(NEU1_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_neu1.kamil_desc = "Neutr1"
mgr_neu3 = smart_tags_manager.SmartTagsManager(NEU3_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_neu3.kamil_desc = "Neutr3"
mgr_neg1 = smart_tags_manager.SmartTagsManager(NEG1_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_neg1.kamil_desc = "N1"
mgr_neg3 = smart_tags_manager.SmartTagsManager(NEG3_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_neg3.kamil_desc = "N3"
mgr_pos1 = smart_tags_manager.SmartTagsManager(POS1_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_pos1.kamil_desc = "P1"
mgr_pos3 = smart_tags_manager.SmartTagsManager(POS3_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_pos3.kamil_desc = "P3"
mgr_dr = smart_tags_manager.SmartTagsManager(DREWNO_TAG_DEF, p_files['info'], p_files['data'], p_files['tags'])
mgr_dr.kamil_desc = "Drewno"
mgrs = []
if plot_type == 'all':
mgrs.append(mgr_neu1)
mgrs.append(mgr_neu3)
mgrs.append(mgr_neg1)
mgrs.append(mgr_neg3)
mgrs.append(mgr_pos1)
mgrs.append(mgr_pos3)
mgrs.append(mgr_dr)
elif plot_type == 'system':
mgr_pos1.kamil_desc = "Aut1 = P1+N1"
mgr_pos1.get_smart_tags()
mgr_pos1._smart_tags += mgr_neg1.get_smart_tags()
mgrs.append(mgr_pos1)
mgr_pos3.kamil_desc = "Refl3 = P3+N3"
mgr_pos3.get_smart_tags()
mgr_pos3._smart_tags += mgr_neg3.get_smart_tags()
mgrs.append(mgr_pos3)
mgr_neu1.kamil_desc = "Neu = Neu1+Neu3"
mgr_neu1.get_smart_tags()
mgr_neu1._smart_tags += mgr_neu3.get_smart_tags()
mgrs.append(mgr_neu1)
mgrs.append(mgr_dr)
elif plot_type == 'sign':
mgr_pos1.kamil_desc = "Pos = P1+P3"
mgr_pos1.get_smart_tags()
mgr_pos1._smart_tags += mgr_pos3.get_smart_tags()
mgrs.append(mgr_pos1)
mgr_neg1.kamil_desc = "Neg = N1+N3"
mgr_neg1.get_smart_tags()
mgr_neg1._smart_tags += mgr_neg3.get_smart_tags()
mgrs.append(mgr_neg1)
mgr_neu1.kamil_desc = "Neu = Neu1+Neu3"
mgr_neu1.get_smart_tags()
mgr_neu1._smart_tags += mgr_neu3.get_smart_tags()
mgrs.append(mgr_neu1)
mgrs.append(mgr_dr)
#mgr_pos3.kamil_desc = "P3+N3"
#mgr_pos3.get_smart_tags()
#mgr_pos3._smart_tags += mgr_neg3.get_smart_tags()
#mgrs.append(mgr_pos3)
if channel_names is None:
import math
channel_names = [mgr_pos.get_param('channels_names')]
x = int(math.sqrt(len(channels_names)))
y = ceil(math.sqrt(len(channels_names)))
for i, channels in enumerate(channel_names):
plotter = erp_plot.Plotter(x, y, i)
plt_id = 0
for i_ch_name in channels:
plt_id += 1
for mgr in mgrs:
avgs_for_channels = erp_avg.get_normalised_avgs(mgr, abs(START_SEC_OFFSET)*1024)*0.0715
ch = get_channel_data(mgr_dr, avgs_for_channels, i_ch_name)
if ch is None:
LOGGER.warning("Couldn`t find channel for name: "+i_ch_name)
else:
samp = float(mgr.get_read_manager().get_param('sampling_frequency'))
plotter.add_plot(ch,
mgr.kamil_desc+" "+i_ch_name,
[i for i in range(START_SEC_OFFSET*samp-1, len(ch)-(abs(START_SEC_OFFSET)*samp))],
plt_id)
#store_avgs_for_svarog([ch])
plotter.prepare_to_show()
if p_show:
erp_plot.show()