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
15import os
16import time
17
18import its.caps
19import its.device
20import its.image
21import its.objects
22import its.target
23import matplotlib
24from matplotlib import pylab
25import numpy
26
27NAME = os.path.basename(__file__).split('.')[0]
28N = 20  # Number of samples averaged together, in the plot.
29MEAN_THRESH = 0.01  # PASS/FAIL threshold for gyro mean drift
30VAR_THRESH = 0.001  # PASS/FAIL threshold for gyro variance drift
31
32
33def 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
76if __name__ == '__main__':
77    main()
78
79