def read_data(data_dir, shape_keys):
if isinstance(shape_keys, basestring):
shape_keys = [shape_keys]
motors = get_motors('head.yaml')
motor_dict = {motor['name']: motor for motor in motors}
ff_df, signal_df = pd.DataFrame(), pd.DataFrame()
for shape_key in shape_keys:
ff_file = '{}/ff_data_{}.csv'.format(data_dir, shape_key)
signal_file = '{}/signal_data_{}.csv'.format(data_dir, shape_key)
tmp_ff_df = pd.read_csv(ff_file, names=blend_shape_names)
ff_df = pd.concat([ff_df, tmp_ff_df], ignore_index=True)
tmp_signal_df = pd.read_csv(signal_file, names=SHKEY_DEPS[shape_key])
remainder_names = motor_dict.keys()[:]
for name in SHKEY_DEPS[shape_key]:
remainder_names.remove(name)
init_values = [motor_dict[name]['init'] for name in remainder_names]
complementary_signal_df = pd.DataFrame(
[init_values]*len(tmp_signal_df), columns=remainder_names)
tmp_signal_df = pd.concat(
[tmp_signal_df, complementary_signal_df], axis=1)
signal_df = pd.concat([signal_df, tmp_signal_df], ignore_index=True)
return ff_df, signal_df
def read_data(data_dir, shape_keys):
if isinstance(shape_keys, basestring):
shape_keys = [shape_keys]
motors = get_motors('head.yaml')
motor_dict = {motor['name']: motor for motor in motors}
ff_df, signal_df = pd.DataFrame(), pd.DataFrame()
for shape_key in shape_keys:
ff_file = '{}/ff_data_{}.csv'.format(data_dir, shape_key)
signal_file = '{}/signal_data_{}.csv'.format(data_dir, shape_key)
tmp_ff_df = pd.read_csv(ff_file, names=blend_shape_names)
ff_df = pd.concat([ff_df, tmp_ff_df], ignore_index=True)
tmp_signal_df = pd.read_csv(signal_file, names=SHKEY_DEPS[shape_key])
remainder_names = motor_dict.keys()[:]
for name in SHKEY_DEPS[shape_key]:
remainder_names.remove(name)
init_values = [motor_dict[name]['init'] for name in remainder_names]
complementary_signal_df = pd.DataFrame([init_values] *
len(tmp_signal_df),
columns=remainder_names)
tmp_signal_df = pd.concat([tmp_signal_df, complementary_signal_df],
axis=1)
signal_df = pd.concat([signal_df, tmp_signal_df], ignore_index=True)
return ff_df, signal_df
import pandas as pd
from fit import get_motors, init_position, set_servos
import time
from pololu.motors import Maestro
def set_position(motors, pos_series):
active_motors = pos_series.index.tolist()
for motor in motors:
if motor['name'] in active_motors:
pos = pos_series[motor['name']]
if not np.isnan(pos):
motor['pos'] = pos
print "set {} to {}".format(motor['name'], pos)
if __name__ == '__main__':
signal_file = 'signal.csv'
signal_df = pd.read_csv(signal_file)
motors = get_motors('head.yaml')
init_position(motors)
DEVICE = '/dev/ttyACM0'
controller = None
if os.path.exists(DEVICE):
controller = Maestro(DEVICE)
for index, row in signal_df.iterrows():
set_position(motors, row)
if controller:
set_servos(controller, motors)
time.sleep(0.5)
import time
from pololu.motors import Maestro
def set_position(motors, pos_series):
active_motors = pos_series.index.tolist()
for motor in motors:
if motor['name'] in active_motors:
pos = pos_series[motor['name']]
if not np.isnan(pos):
motor['pos'] = pos
print "set {} to {}".format(motor['name'], pos)
if __name__ == '__main__':
signal_file = 'signal.csv'
signal_df = pd.read_csv(signal_file)
motors = get_motors('head.yaml')
init_position(motors)
DEVICE = '/dev/ttyACM0'
controller = None
if os.path.exists(DEVICE):
controller = Maestro(DEVICE)
for index, row in signal_df.iterrows():
set_position(motors, row)
if controller:
set_servos(controller, motors)
time.sleep(0.5)
