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 its.image
16import its.caps
17import its.device
18import its.objects
19import its.target
20import time
21import pylab
22import os.path
23import matplotlib
24import matplotlib.pyplot
25import numpy
26
27def 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
76if __name__ == '__main__':
77    main()
78
79