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()