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
21from matplotlib import 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