Source code for sensors.polulu

Import data saved with polulu-sensors, through subclassing "IMU_Base"
These are low-cost IMUS (<20 US$), where acceleration/gyroscope data are not
sampled at the same time as the magnetic field data (just over 100 Hz).
As a result, the interpolated sampling rate has to be set by hand.

Author: Thomas Haslwanter

import numpy as np
import pandas as pd

import abc

# To ensure that the relative path works
import os
import sys

parent_dir = os.path.abspath(os.path.join( os.path.dirname(__file__), '..' ))
if parent_dir not in sys.path:
    sys.path.insert(0, parent_dir)

from imus import IMU_Base

[docs]class Polulu(IMU_Base): """Concrete class based on abstract base class IMU_Base """
[docs] def get_data(self, in_file, in_data=125): '''Get the sampling rate, as well as the recorded data, and assign them to the corresponding attributes of "self". Parameters ---------- in_file : string Filename of the data-file in_data : float Sampling rate (has to be provided!!) Assigns ------- - rate : rate - acc : acceleration - omega : angular_velocity - mag : mag_field_direction ''' try: # The sampling rate has to be provided externally rate = in_data['rate'] # Get the data, and label them data = pd.read_csv(in_file, header=None, delim_whitespace=True, engine='python') data.columns = ['acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z', 'mag_x', 'mag_y', 'mag_z', 'taccgyr', 'tmag'] # interpolate with a manually set rate. Note that this sensor acquires exactly 25 seconds! dt = 1/np.float(rate) t_lin = np.arange(0, 25, dt) data_interp = pd.DataFrame() # Different sampling times for acc/gyr and for mag! for ii in range(6): data_interp[data.keys()[ii]] = np.interp(t_lin*1000, data['taccgyr'], data.iloc[:,ii]) for ii in range(6,9): data_interp[data.keys()[ii]] = np.interp(t_lin*1000, data['tmag'], data.iloc[:,ii]) data_interp['time'] = t_lin # Set the conversion factors by hand, and apply them conversions = {} conversions['mag'] = 1/6842 conversions['acc'] = 0.061/1000 conversions['gyr'] = 4.375/1000 * np.pi/180 data_interp.iloc[:,:3] *= conversions['acc'] data_interp.iloc[:,3:6] *= conversions['gyr'] data_interp.iloc[:,6:9] *= conversions['mag'] except FileNotFoundError: print('{0} does not exist!'.format(in_file)) return -1 # Extract the columns that you want, and pass them on in_data = {'rate':rate, 'acc': data_interp.filter(regex='acc').values, 'omega': data_interp.filter(regex='gyr').values, 'mag': data_interp.filter(regex='mag').values} self._set_data(in_data)
if __name__ == '__main__': inFile = r'..\tests\data\data_polulu.txt' in_data = {'rate':125} my_sensor = Polulu(in_file=inFile, in_data=in_data) import matplotlib.pyplot as plt plt.plot(my_sensor.acc) print(my_sensor.rate) print('Done')