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 its.image 16 import its.caps 17 import its.device 18 import its.objects 19 import its.target 20 import time 21 from matplotlib import pylab 22 import os.path 23 import matplotlib 24 import matplotlib.pyplot 25 import numpy 26 27 def main(): 28 """Test if the gyro has stable output when device is stationary. 29 """ 30 NAME = os.path.basename(__file__).split(".")[0] 31 32 # Number of samples averaged together, in the plot. 33 N = 20 34 35 # Pass/fail thresholds for gyro drift 36 MEAN_THRESH = 0.01 37 VAR_THRESH = 0.001 38 39 with its.device.ItsSession() as cam: 40 props = cam.get_camera_properties() 41 # Only run test if the appropriate caps are claimed. 42 its.caps.skip_unless(its.caps.sensor_fusion(props)) 43 44 print "Collecting gyro events" 45 cam.start_sensor_events() 46 time.sleep(5) 47 gyro_events = cam.get_sensor_events()["gyro"] 48 49 nevents = (len(gyro_events) / N) * N 50 gyro_events = gyro_events[:nevents] 51 times = numpy.array([(e["time"] - gyro_events[0]["time"])/1000000000.0 52 for e in gyro_events]) 53 xs = numpy.array([e["x"] for e in gyro_events]) 54 ys = numpy.array([e["y"] for e in gyro_events]) 55 zs = numpy.array([e["z"] for e in gyro_events]) 56 57 # Group samples into size-N groups and average each together, to get rid 58 # of individual random spikes in the data. 59 times = times[N/2::N] 60 xs = xs.reshape(nevents/N, N).mean(1) 61 ys = ys.reshape(nevents/N, N).mean(1) 62 zs = zs.reshape(nevents/N, N).mean(1) 63 64 pylab.plot(times, xs, 'r', label="x") 65 pylab.plot(times, ys, 'g', label="y") 66 pylab.plot(times, zs, 'b', label="z") 67 pylab.xlabel("Time (seconds)") 68 pylab.ylabel("Gyro readings (mean of %d samples)"%(N)) 69 pylab.legend() 70 matplotlib.pyplot.savefig("%s_plot.png" % (NAME)) 71 72 for samples in [xs,ys,zs]: 73 assert(samples.mean() < MEAN_THRESH) 74 assert(numpy.var(samples) < VAR_THRESH) 75 76 if __name__ == '__main__': 77 main() 78 79
