1 # Copyright 2014 The Android Open Source Project 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 15 import os 16 import time 17 18 import its.caps 19 import its.device 20 import its.image 21 import its.objects 22 import its.target 23 import matplotlib 24 from matplotlib import pylab 25 import numpy 26 27 NAME = os.path.basename(__file__).split('.')[0] 28 N = 20 # Number of samples averaged together, in the plot. 29 MEAN_THRESH = 0.01 # PASS/FAIL threshold for gyro mean drift 30 VAR_THRESH = 0.001 # PASS/FAIL threshold for gyro variance drift 31 32 33 def main(): 34 """Test if the gyro has stable output when device is stationary. 35 """ 36 with its.device.ItsSession() as cam: 37 props = cam.get_camera_properties() 38 # Only run test if the appropriate caps are claimed. 39 its.caps.skip_unless(its.caps.sensor_fusion(props) and 40 cam.get_sensors().get("gyro")) 41 42 print 'Collecting gyro events' 43 cam.start_sensor_events() 44 time.sleep(5) 45 gyro_events = cam.get_sensor_events()['gyro'] 46 47 nevents = (len(gyro_events) / N) * N 48 gyro_events = gyro_events[:nevents] 49 times = numpy.array([(e['time'] - gyro_events[0]['time'])/1000000000.0 50 for e in gyro_events]) 51 xs = numpy.array([e['x'] for e in gyro_events]) 52 ys = numpy.array([e['y'] for e in gyro_events]) 53 zs = numpy.array([e['z'] for e in gyro_events]) 54 55 # Group samples into size-N groups and average each together, to get rid 56 # of individual random spikes in the data. 57 times = times[N/2::N] 58 xs = xs.reshape(nevents/N, N).mean(1) 59 ys = ys.reshape(nevents/N, N).mean(1) 60 zs = zs.reshape(nevents/N, N).mean(1) 61 62 pylab.plot(times, xs, 'r', label='x') 63 pylab.plot(times, ys, 'g', label='y') 64 pylab.plot(times, zs, 'b', label='z') 65 pylab.xlabel('Time (seconds)') 66 pylab.ylabel('Gyro readings (mean of %d samples)'%(N)) 67 pylab.legend() 68 matplotlib.pyplot.savefig('%s_plot.png' % (NAME)) 69 70 for samples in [xs, ys, zs]: 71 mean = samples.mean() 72 var = numpy.var(samples) 73 assert mean < MEAN_THRESH, 'mean: %.3f, TOL=%.2f' % (mean, MEAN_THRESH) 74 assert var < VAR_THRESH, 'var: %.4f, TOL=%.3f' % (var, VAR_THRESH) 75 76 if __name__ == '__main__': 77 main() 78 79
