def __init__(self, hostname='localhost', port=1972, n_channels=16, window_delay=50):
self.client = Client()
self.n_channels = n_channels
self.data_x = np.zeros((0, n_channels))
self.data_y = []
while not self.client.isConnected:
try:
self.client.connect(hostname, port)
except socket.error:
print("Failed to connect at " + hostname + ":" + str(port))
time.sleep(1)
self.buffer_start_index = self.client.poll()[0]
self.transition_indexes = [self.client.poll()[0] - self.buffer_start_index]
self.transition_classes = []
self.min_rows_per_chunk = sys.float_info.max
self.window_delay = window_delay
def connect(header=True, adress="localhost", port=1972):
"""Connects to the buffer. And waits for a header (unless otherwise
specified). The ftc variable contains the client connection."""
global ftc, event
ftc = Client()
event = Event()
while not ftc.isConnected:
try:
ftc.connect(adress, port)
except socket.error:
print "Failed to connect at " + adress + ":" + str(port)
sleep(1)
if header:
hdr = waitforheader()
return ftc, hdr
return ftc
def connect(header=True, verbose=True):
"""Connects to the buffer. And waits for a header (unless otherwise
specified). The ftc variable contains the client connection."""
global ftc, event
ftc = Client()
event = Event()
adress = "localhost"
port = 1972
if verbose:
adress = raw_input("Buffer adress (default is \"localhost:1972\"):")
if adress == "":
adress = "localhost"
else:
try:
split = adress.split(":")
adress = split[0]
port = int(split(1))
except ValueError:
print "Invalid port formatting " + split[1]
except IndexError:
print "Invalid adress formatting " + adress
while not ftc.isConnected:
try:
ftc.connect(adress, port)
except socket.error:
print "Failed to connect at " + adress + ":" + str(port)
sleep(1)
if header:
hdr = waitforheader(verbose)
return ftc, hdr
return ftc
def connect(header=True, verbose=True):
"""Connects to the buffer. And waits for a header (unless otherwise
specified). The ftc variable contains the client connection."""
global ftc, event
ftc = Client()
event = Event()
adress = "localhost"
port = 1972
if verbose:
adress = raw_input('Buffer adress (default is "localhost:1972"):')
if adress == "":
adress = "localhost"
else:
try:
split = adress.split(":")
adress = split[0]
port = int(split(1))
except ValueError:
print "Invalid port formatting " + split[1]
except IndexError:
print "Invalid adress formatting " + adress
while not ftc.isConnected:
try:
ftc.connect(adress, port)
except socket.error:
print "Failed to connect at " + adress + ":" + str(port)
sleep(1)
if header:
hdr = waitforheader(verbose)
return ftc, hdr
return ftc
text_4 = visual.TextStim(win=win, ori=0, name='text_4',
text=u'Pause', font=u'Arial',
pos=[0, 0], height=0.1, wrapWidth=80,
color=u'white', colorSpace='rgb', opacity=1,
depth=0.0)# buffer_bci Handling the requered imports
import sys
import time
sys.path.append("../../dataAcq/buffer/python/")
from FieldTrip import Client, Event
from time import sleep
# buffer_bci Connecting to the buffer.
host = 'localhost'
port = 1972
ftc = Client()
hdr = None;
while hdr is None :
print 'Trying to connect to buffer on %s:%i ...'%(host,port)
try:
ftc.connect(host, port)
print '\nConnected - trying to read header...'
hdr = ftc.getHeader()
except IOError:
pass
if hdr is None:
print 'Invalid Header... waiting'
time.sleep(1)
else:
print hdr
print hdr.labels
class BCI:
def __init__(self, hostname='localhost', port=1972, n_channels=16, window_delay=50):
self.client = Client()
self.n_channels = n_channels
self.data_x = np.zeros((0, n_channels))
self.data_y = []
while not self.client.isConnected:
try:
self.client.connect(hostname, port)
except socket.error:
print("Failed to connect at " + hostname + ":" + str(port))
time.sleep(1)
self.buffer_start_index = self.client.poll()[0]
self.transition_indexes = [self.client.poll()[0] - self.buffer_start_index]
self.transition_classes = []
self.min_rows_per_chunk = sys.float_info.max
self.window_delay = window_delay
def set_new_class(self, new_class):
poll = self.client.poll()[0]
self.transition_indexes += [poll - self.buffer_start_index]
self.transition_classes += [new_class]
if poll - self.buffer_start_index > 100000 or new_class is None:
self.transition_indexes = self.transition_indexes[:-1]
self.transition_classes = self.transition_classes[:-1]
try:
self.data_x = np.vstack((self.data_x, self.client.getData([self.buffer_start_index + self.window_delay,
self.buffer_start_index +
self.transition_indexes[-1] +
self.window_delay])))
except:
time.sleep(1)
self.data_x = np.vstack((self.data_x, self.client.getData([self.buffer_start_index + self.window_delay,
self.buffer_start_index + 1 +
self.transition_indexes[-1] +
self.window_delay])))
transition_indexes = [cur - prev for cur, prev in
zip(self.transition_indexes[::-1], self.transition_indexes[::-1][1:])][::-1]
min_rows_per_chunk = int(np.median(transition_indexes))
if min_rows_per_chunk < self.min_rows_per_chunk: self.min_rows_per_chunk = min_rows_per_chunk
data_y = [[val] * ind for val, ind in zip(self.transition_classes, transition_indexes)]
self.data_y += [item for sublist in data_y for item in sublist]
self.buffer_start_index = self.client.poll()[0]
self.transition_indexes = [self.client.poll()[0] - self.buffer_start_index]
self.transition_classes = []
def restart(self):
self.buffer_start_index = self.client.poll()[0]
self.transition_indexes = [self.client.poll()[0] - self.buffer_start_index]
self.transition_classes = []
def get_data(self):
segmented_x, segmented_y = self._segmentate(self.data_x, self.data_y, self.min_rows_per_chunk)
return segmented_x, np.array(segmented_y)
def get_recent_data(self, n_samples):
last_sample_index = self.client.poll()[0]
return self.client.getData([last_sample_index - n_samples, last_sample_index - 1]).reshape(n_samples, self.n_channels, 1)
def _segmentate(self, data_x, data_y, rows_per_obs):
cur_row = 0
new_x = np.zeros((rows_per_obs, data_x.shape[1], 0))
new_y = []
while data_x.shape[0] - cur_row >= rows_per_obs:
current_class = [data_y[cur_row]]
current_window = np.zeros((0, data_x.shape[1]))
counter = 0
while counter < rows_per_obs and data_y[cur_row] == current_class[0]:
current_window = np.vstack((current_window, data_x[cur_row, :].reshape((1, data_x.shape[1]))))
cur_row += 1
counter += 1
if counter < rows_per_obs:
current_window = np.zeros((rows_per_obs, data_x.shape[1], 0))
current_class = []
new_x = np.dstack((new_x, current_window))
new_y += current_class
return new_x, new_y
# if __name__ == '__main__':
# bci = BCI()
# print bci.get_recent_data(300).shape
