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101 lines
3.6 KiB
101 lines
3.6 KiB
# Copyright 2014 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Verify if the gyro has stable output when device is stationary."""
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import logging
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import os
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import time
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import matplotlib
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from matplotlib import pylab
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from mobly import test_runner
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import numpy
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import its_base_test
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import camera_properties_utils
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import its_session_utils
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NAME = os.path.basename(__file__).split('.')[0]
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N = 20 # Number of samples averaged together, in the plot.
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NSEC_TO_SEC = 1E-9
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MEAN_THRESH = 0.01 # PASS/FAIL threshold for gyro mean drift
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VAR_THRESH = 0.001 # PASS/FAIL threshold for gyro variance drift
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class GyroBiasTest(its_base_test.ItsBaseTest):
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"""Test if the gyro has stable output when device is stationary.
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"""
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def test_gyro_bias(self):
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with its_session_utils.ItsSession(
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device_id=self.dut.serial,
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camera_id=self.camera_id,
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hidden_physical_id=self.hidden_physical_id) as cam:
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props = cam.get_camera_properties()
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props = cam.override_with_hidden_physical_camera_props(props)
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# Only run test if the appropriate caps are claimed.
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camera_properties_utils.skip_unless(
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camera_properties_utils.sensor_fusion(props) and
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cam.get_sensors().get('gyro'))
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logging.debug('Collecting gyro events')
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cam.start_sensor_events()
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time.sleep(5)
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gyro_events = cam.get_sensor_events()['gyro']
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nevents = (len(gyro_events) // N) * N
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gyro_events = gyro_events[:nevents]
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times = numpy.array([(e['time'] - gyro_events[0]['time'])*NSEC_TO_SEC
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for e in gyro_events])
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xs = numpy.array([e['x'] for e in gyro_events])
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ys = numpy.array([e['y'] for e in gyro_events])
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zs = numpy.array([e['z'] for e in gyro_events])
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# Group samples into size-N groups and average each together, to get rid
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# of individual random spikes in the data.
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times = times[N // 2::N]
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xs = xs.reshape(nevents // N, N).mean(1)
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ys = ys.reshape(nevents // N, N).mean(1)
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zs = zs.reshape(nevents // N, N).mean(1)
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# add y limits so plot doesn't look like amplified noise
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y_min = min([numpy.amin(xs), numpy.amin(ys), numpy.amin(zs), -MEAN_THRESH])
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y_max = max([numpy.amax(xs), numpy.amax(ys), numpy.amax(xs), MEAN_THRESH])
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pylab.plot(times, xs, 'r', label='x')
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pylab.plot(times, ys, 'g', label='y')
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pylab.plot(times, zs, 'b', label='z')
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pylab.title(NAME)
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pylab.xlabel('Time (seconds)')
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pylab.ylabel('Gyro readings (mean of %d samples)'%(N))
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pylab.ylim([y_min, y_max])
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pylab.ticklabel_format(axis='y', style='sci', scilimits=(-3, -3))
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pylab.legend()
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logging.debug('Saving plot')
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matplotlib.pyplot.savefig('%s_plot.png' % os.path.join(self.log_path, NAME))
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for samples in [xs, ys, zs]:
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mean = samples.mean()
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var = numpy.var(samples)
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logging.debug('mean: %.3e', mean)
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logging.debug('var: %.3e', var)
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if mean >= MEAN_THRESH:
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raise AssertionError(f'mean: {mean}.3e, TOL={MEAN_THRESH}')
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if var >= VAR_THRESH:
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raise AssertionError(f'var: {var}.3e, TOL={VAR_THRESH}')
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if __name__ == '__main__':
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test_runner.main()
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