def calibrate(r, model=None):
# load default model
if model is None: model = Model.load('models/rover_calibration.pkl')
# calibration movements | left & right using only one motor (easier)
r.init_log()
sleep(1)
r.left(2, motor=1)
sleep(1)
r.left(2, motor=2)
sleep(1) # sequence: 1,3
r.right(2, motor=1)
sleep(1)
r.right(2, motor=2)
sleep(1) # sequence: 5,7
# end of calibration movements
# get log, process data and remove stops
logdata = r.save_log()
logdata['fname'] = 'dropme'
logdata.index.name = 'time'
data = process_df(logdata)
preds = data[model.features].groupby(level=['seq']).apply(
lambda x: model.labels[model.predict_proba(x).sum(axis=0).argmax()])
# check if movements are correctly calibrated
if preds[1] == 'left' and preds[3] == 'left' and preds[
5] == 'right' and preds[7] == 'right':
print "Rover calibrated correctly (Motor1 gpio1_is_fw: %s | Motor2 gpio1_is_fw: %s)" % (
r.m1.gpio1_is_fw, r.m2.gpio1_is_fw)
return True
else:
if preds[1] == 'left' and preds[5] == 'right':
r.m2.gpio1_is_fw = not r.m2.gpio1_is_fw
print "Rover re-calibrated changing direction of Motor 1, testing..."
return calibrate(r, model)
elif preds[3] == 'left' and preds[7] == 'right':
r.m1.gpio1_is_fw = not r.m1.gpio1_is_fw
print "Rover re-calibrated changing direction of Motor 2, testing..."
return calibrate(r, model)
else:
print "Error calibrating"
return False
def train_data():
from navigation import r
r.m1.use_pwm = False
r.m2.use_pwm = False
r.init_log()
sleep(1)
r.left(2)
sleep(1)
r.right(2)
sleep(1)
r.fw(2)
sleep(1)
r.rw(2)
sleep(1)
r.left(2)
sleep(1)
r.right(2)
sleep(1)
r.fw(2)
sleep(1)
r.rw(2)
sleep(1)
r.left(2)
sleep(1)
r.right(2)
sleep(1)
r.fw(2)
sleep(1)
r.rw(2)
sleep(1)
r.save_log(savepath='data/navigation_calibration/')