def initialise_brainflow():
BoardShim.enable_dev_board_logger()
parser = ArgumentParser()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument('--timeout',
type=int,
help='timeout for device discovery or connection',
required=False,
default=0)
parser.add_argument('--ip-port',
type=int,
help='ip port',
required=False,
default=0)
parser.add_argument('--ip-protocol',
type=int,
help='ip protocol, check IpProtocolType enum',
required=False,
default=0)
parser.add_argument('--ip-address',
type=str,
help='ip address',
required=False,
default='')
parser.add_argument('--serial-port',
type=str,
help='serial port',
required=False,
default='')
parser.add_argument('--mac-address',
type=str,
help='mac address',
required=False,
default='')
parser.add_argument('--other-info',
type=str,
help='other info',
required=False,
default='')
parser.add_argument('--streamer-params',
type=str,
help='streamer params',
required=False,
default='')
parser.add_argument('--serial-number',
type=str,
help='serial number',
required=False,
default='')
parser.add_argument(
'--board-id',
type=int,
help='board id, check docs to get a list of supported boards',
required=True)
parser.add_argument('--file',
type=str,
help='file',
required=False,
default='')
args = parser.parse_args()
params = BrainFlowInputParams()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.serial_number = args.serial_number
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
params.timeout = args.timeout
params.file = args.file
params.board_id = args.board_id
return params, args
def main ():
params = BrainFlowInputParams ()
params.board_id = 1
board_id = 1
params.serial_port = 'COM3'
sampling_rate = BoardShim.get_sampling_rate (board_id)
time_thres = 100
max_val = -100000000000
vals_mean = 0
num_samples = 5000
samples = 0
BoardShim.enable_dev_board_logger ()
board = BoardShim (board_id, params)
board.prepare_session ()
if board_id == brainflow.board_shim.BoardIds.GANGLION_BOARD.value:
board.config_board ('x1060000X')
board.start_stream (45000)
print("starting calibration")
time.sleep(5)
data = board.get_board_data()
print("start blinking")
while(samples < num_samples):
data = board.get_board_data()
if(len(data[1]) > 0):
DataFilter.perform_rolling_filter (data[1], 2, AggOperations.MEAN.value)
vals_mean += sum([data[1,i]/num_samples for i in range(len(data[1]))])
samples += len(data[1])
if(np.amax(data[1]) > max_val):
max_val = np.amax(data[1])
blink_thres = 0.5*((max_val - vals_mean)**2)
print("mean value")
print(vals_mean)
print("max value")
print(max_val)
print("threshold")
print(blink_thres)
print("CALIBRATION DONE START PLAYING")
prev_time = int(round(time.time() * 1000))
while True:
data = board.get_board_data()
if(len(data[1]) > 0):
DataFilter.perform_rolling_filter (data[1], 2, AggOperations.MEAN.value)
if((int(round(time.time() * 1000)) - time_thres) > prev_time):
prev_time = int(round(time.time() * 1000))
for element in data[1]:
if(((element - vals_mean)**2) >= blink_thres):
pydirectinput.press('space')
break
board.stop_stream ()
board.release_session ()
def return_prediction():
#ms = post_id
# Lines 27-65 copied from https://brainflow.readthedocs.io/en/stable/Examples.html
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
params = BrainFlowInputParams()
params.board_id = 1
params.serial_port = "COM4"
params.mac_address = "DC:3D:46:E5:10:99"
board = BoardShim(1, params)
board.prepare_session()
board.start_stream() # use this for default options
data = board.get_current_board_data(
256
) # get latest 256 packages or less, doesnt remove them from internal buffer
board.stop_stream()
board.release_session()
# return 'a string'
# CONNECT THE DATA TO THE MODEL FOR PREDICTIONS
# Test Sample Data
# data = 'https://archlife.org/wp-content/uploads/2020/03/OpenBCI-RAW-right0.txt'
# column_names = ['index', 'channel1','channel2', 'channel3', 'channel4', 'accel1', 'accel2', 'accel3', 'timestamp', 'aux']
# dropped_row_indices = [0, 1, 2, 3, 4, 5]
# df = pd.DataFrame(data=data, columns=column_names)
# df = df.drop(dropped_row_indices, axis=0).reset_index()
# df = df.drop(['level_0', 'index', 'timestamp'], axis=1)
df = pd.DataFrame({
'channel1': data[0],
'channel2': data[1],
'channel3': data[2],
'channel4': data[3],
'accel1': data[4],
'accel2': data[5],
'accel3': data[6],
'aux': data[8]
})
df = df.dropna(axis=0)
model = joblib.load('flask_test/rfc.joblib')
commands_proba = model.predict_proba(df)
commands_pred = model.predict(df)
commands_df = pd.DataFrame({
'index': df.index,
'predictions': commands_pred
})
commands_df['predictions'] = commands_df['predictions'].astype('int64')
command_count = commands_df['predictions'].value_counts()
ccdf = pd.DataFrame({
'index': command_count.index,
'predictions': command_count
})
preds = ccdf['index'].values
command_pred = preds[0]
return str(command_pred)
