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"""Verify if the gyro has stable output when device is stationary."""
15
16import logging
17import os
18import time
19
20import matplotlib
21from matplotlib import pylab
22from mobly import test_runner
23import numpy
24
25import its_base_test
26import camera_properties_utils
27import its_session_utils
28
29NAME = os.path.basename(__file__).split('.')[0]
30N = 20  # Number of samples averaged together, in the plot.
31NSEC_TO_SEC = 1E-9
32MEAN_THRESH = 0.01  # PASS/FAIL threshold for gyro mean drift
33VAR_THRESH = 0.001  # PASS/FAIL threshold for gyro variance drift
34
35
36class GyroBiasTest(its_base_test.ItsBaseTest):
37  """Test if the gyro has stable output when device is stationary.
38  """
39
40  def test_gyro_bias(self):
41    with its_session_utils.ItsSession(
42        device_id=self.dut.serial,
43        camera_id=self.camera_id,
44        hidden_physical_id=self.hidden_physical_id) as cam:
45      props = cam.get_camera_properties()
46      props = cam.override_with_hidden_physical_camera_props(props)
47      # Only run test if the appropriate caps are claimed.
48      camera_properties_utils.skip_unless(
49          camera_properties_utils.sensor_fusion(props) and
50          cam.get_sensors().get('gyro'))
51
52      logging.debug('Collecting gyro events')
53      cam.start_sensor_events()
54      time.sleep(5)
55      gyro_events = cam.get_sensor_events()['gyro']
56
57    nevents = (len(gyro_events) // N) * N
58    gyro_events = gyro_events[:nevents]
59    times = numpy.array([(e['time'] - gyro_events[0]['time'])*NSEC_TO_SEC
60                         for e in gyro_events])
61    xs = numpy.array([e['x'] for e in gyro_events])
62    ys = numpy.array([e['y'] for e in gyro_events])
63    zs = numpy.array([e['z'] for e in gyro_events])
64
65    # Group samples into size-N groups and average each together, to get rid
66    # of individual random spikes in the data.
67    times = times[N // 2::N]
68    xs = xs.reshape(nevents // N, N).mean(1)
69    ys = ys.reshape(nevents // N, N).mean(1)
70    zs = zs.reshape(nevents // N, N).mean(1)
71
72    # add y limits so plot doesn't look like amplified noise
73    y_min = min([numpy.amin(xs), numpy.amin(ys), numpy.amin(zs), -MEAN_THRESH])
74    y_max = max([numpy.amax(xs), numpy.amax(ys), numpy.amax(xs), MEAN_THRESH])
75
76    pylab.plot(times, xs, 'r', label='x')
77    pylab.plot(times, ys, 'g', label='y')
78    pylab.plot(times, zs, 'b', label='z')
79    pylab.title(NAME)
80    pylab.xlabel('Time (seconds)')
81    pylab.ylabel('Gyro readings (mean of %d samples)'%(N))
82    pylab.ylim([y_min, y_max])
83    pylab.ticklabel_format(axis='y', style='sci', scilimits=(-3, -3))
84    pylab.legend()
85    logging.debug('Saving plot')
86    matplotlib.pyplot.savefig('%s_plot.png' % os.path.join(self.log_path, NAME))
87
88    for samples in [xs, ys, zs]:
89      mean = samples.mean()
90      var = numpy.var(samples)
91      logging.debug('mean: %.3e', mean)
92      logging.debug('var: %.3e', var)
93      if mean >= MEAN_THRESH:
94        raise AssertionError(f'mean: {mean}.3e, TOL={MEAN_THRESH}')
95      if var >= VAR_THRESH:
96        raise AssertionError(f'var: {var}.3e, TOL={VAR_THRESH}')
97
98
99if __name__ == '__main__':
100  test_runner.main()
101