def __init__(self):
QThread.__init__(self)
self.current_challenge = None
self.challenges = defaultdict(list)
self.progress = ProgressBarThread.ProgressBarThread()
self.progress.signal_progress.connect(self.change_progress)
self.progress.signal_toggle_lights.connect(self.prompter_toggle_lights)
self.progress.signal_start_new_stage.connect(self.start_new_stage)
#self.camera_thread = Camera.WebcamRecorder() # Main Camera thread
self.eeg_thread = EEG.EEG() # Main EEG client thread
self.eeg_thread.sample_signal.connect(self.get_samples)
self.mediaPlayer = QMediaPlayer(None, QMediaPlayer.StreamPlayback)
self.beeper_thread = Beeper.Beeper() # beeper thread
self.current_samples = []
self.current_original_duration = 0
self.current_eyes_open = True
self.current_movie = ""
self.used_challenges = []
self.current_state_filename = ""
self.session_labels_dict = dict()
self.symbols_count = 15 # number of repetitions for each class
self.symbols_status = defaultdict(lambda: 0) # keep track
# EEG samples collection
self.current_log_saving = False
self.current_log_filename = ""
def __init__(self):
QThread.__init__(self)
self.current_challenge = None
self.challenges = defaultdict(list)
self.progress = ProgressBarThread.ProgressBarThread()
self.progress.signal_progress.connect(self.change_progress)
self.progress.signal_toggle_lights.connect(self.prompter_toggle_lights)
self.progress.signal_start_new_stage.connect(self.start_new_stage)
self.camera_thread = Camera.WebcamRecorder() # Main Camera thread
self.eeg_thread = EEG.EEG() # Main EEG client thread
self.eeg_thread.sample_signal.connect(self.get_samples)
self.mediaPlayer = QMediaPlayer(None, QMediaPlayer.StreamPlayback)
self.current_samples = []
self.current_original_duration = 0
self.current_eyes_open = True
self.current_movie = ""
self.used_challenges = []
# EEG samples collection
self.current_log_saving = False
self.current_log_filename = ""
import numpy as np
#import pygtk
import EEG
#p = EEG.EEG('Dog_2', 'interictal', 17)
#p = EEG.EEG('Dog_2', 'preictal', 17)
#p = EEG.EEG('Patient_2', 'interictal', 17)
p = EEG.EEG('Patient_2', 'preictal', 17)
p.load()
print p
#p.normalize_channels()
p.normalize_overall()
#print np.shape(p.data) == (16, ~240k)
data = p.data
eeg = np.rollaxis(data, 1)
# see p797 of matplotlib pdf : matshow() for 2d colour-map of (say) correlation matrix
#import matplotlib
#matplotlib.use('Qt4Agg') # Needs PySide - difficult to install, large
#matplotlib.use('GTKAgg') # Needs pygtk - found script on Gist.github.com
#matplotlib.use('TkAgg') # Needs Tkinter - difficult within VirtualEnv
import matplotlib.pyplot as plt
#data = p.data
n = p.n_channels
spacing = 5
trace_levels = spacing * np.arange(n, 0, -1)
to_hickle = dict(
features=all_params,
signal_period_starts=signal_period_starts,
)
# Dump data, with compression
f = "data/feat/%s/%s_%s_segment_%04d.hickle" % (p.subject, p.subject,
p.desc, p.num)
hickle.dump(to_hickle, f, mode='w', compression='gzip')
if (args.fft > 0):
if False: # Just do one sample
#p = EEG.EEG(_subject, 'interictal', 17)
p = EEG.EEG(_subject, 'preictal', 18)
preprocess(p)
exit(1)
if True: # Load in the survey, and do the fft thing for everything
#d = "data/orig/%s/" % (_subject, )
csv = open("data/survey.%s.csv" % (_subject, ), 'r')
headers = csv.readline()
for line in csv.readlines():
p = EEG.EEG(_subject, '', '') # Nonsense entries
p.survey_line_read(line)
preprocess(p)
if (args.scale > 0):
## concatinate entries (NB, must do train first, to generate min/max meta-data)
import argparse
parser = argparse.ArgumentParser(description='Survey the data')
parser.add_argument('--subject',
type=str,
required=True,
help="Dog_{1,2,3,4,5}, Patient_{1,2}")
args = parser.parse_args()
# 'Dog_2', 'Patient_2'
_subject = args.subject
d = "data/orig/%s/" % (_subject, )
csv = open("data/survey.%s.csv" % (_subject, ), 'w')
csv.write(EEG.EEG.survey_header() + "\n")
f_match = re.compile(r'%s_(.*?)_segment_(\d*?)\.mat' % (_subject, ))
for f in sorted(os.listdir(d)):
m = re.match(f_match, f)
if m is None: continue
desc, num = m.group(1), int(m.group(2))
print desc, num
p = EEG.EEG(_subject, desc, num)
p.load()
csv.write(p.survey_line_write())
csv.close()
