self.old_frame,
self.p0_nose
)
row['timestamp'] = time.time()
return row
def stop(self):
BaseSensor.stop(self)
self.camera.release()
def filter(self, df):
return df
def process(self,df):
return df
def metric(self, df, init_values):
pass
if __name__ == '__main__':
sensors = SensorMaster()
sensors.add_sensor(
CameraSensor('test', 0)
)
import pdb; pdb.set_trace()
sensors.sample_sensors()
def process(self, df):
"""
Pre-process data before the behavorial analysis
"""
df["gz"] = df["gz"] / self.gyro_coef
df["theta"] = util.integrate_trapezoid_col(df["gz"], df["time_diff"], 0)
return df
def metric(self, df):
"""
After the data is processed and filtered,
run the metric computation on it
"""
assert init_values.has_key("theta")
return np.sum(np.power(df["theta"] - init_values["theta"], 2)) / (len(df) - 1)
if __name__ == "__main__":
PORT = "/dev/cu.usbmodem1411"
sensors = SensorMaster()
sensors.add_sensor(WheelSensor("test", PORT))
import pdb
pdb.set_trace()
sensors.sample_sensors()
