| # Copyright 2014 The Android Open Source Project |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| import os |
| import time |
| |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import its.target |
| import matplotlib |
| from matplotlib import pylab |
| import numpy |
| |
| NAME = os.path.basename(__file__).split('.')[0] |
| N = 20 # Number of samples averaged together, in the plot. |
| MEAN_THRESH = 0.01 # PASS/FAIL threshold for gyro mean drift |
| VAR_THRESH = 0.001 # PASS/FAIL threshold for gyro variance drift |
| |
| |
| def main(): |
| """Test if the gyro has stable output when device is stationary. |
| """ |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| # Only run test if the appropriate caps are claimed. |
| its.caps.skip_unless(its.caps.sensor_fusion(props) and |
| cam.get_sensors().get("gyro")) |
| |
| print 'Collecting gyro events' |
| cam.start_sensor_events() |
| time.sleep(5) |
| gyro_events = cam.get_sensor_events()['gyro'] |
| |
| nevents = (len(gyro_events) / N) * N |
| gyro_events = gyro_events[:nevents] |
| times = numpy.array([(e['time'] - gyro_events[0]['time'])/1000000000.0 |
| for e in gyro_events]) |
| xs = numpy.array([e['x'] for e in gyro_events]) |
| ys = numpy.array([e['y'] for e in gyro_events]) |
| zs = numpy.array([e['z'] for e in gyro_events]) |
| |
| # Group samples into size-N groups and average each together, to get rid |
| # of individual random spikes in the data. |
| times = times[N/2::N] |
| xs = xs.reshape(nevents/N, N).mean(1) |
| ys = ys.reshape(nevents/N, N).mean(1) |
| zs = zs.reshape(nevents/N, N).mean(1) |
| |
| pylab.plot(times, xs, 'r', label='x') |
| pylab.plot(times, ys, 'g', label='y') |
| pylab.plot(times, zs, 'b', label='z') |
| pylab.xlabel('Time (seconds)') |
| pylab.ylabel('Gyro readings (mean of %d samples)'%(N)) |
| pylab.legend() |
| matplotlib.pyplot.savefig('%s_plot.png' % (NAME)) |
| |
| for samples in [xs, ys, zs]: |
| mean = samples.mean() |
| var = numpy.var(samples) |
| assert mean < MEAN_THRESH, 'mean: %.3f, TOL=%.2f' % (mean, MEAN_THRESH) |
| assert var < VAR_THRESH, 'var: %.4f, TOL=%.3f' % (var, VAR_THRESH) |
| |
| if __name__ == '__main__': |
| main() |
| |