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# Copyright 2014 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import its.image
import its.device
import its.objects
import its.target
import time
import math
import pylab
import os.path
import matplotlib
import matplotlib.pyplot
import json
import numpy
def main():
"""Test if the gyro has stable output when device is stationary.
"""
NAME = os.path.basename(__file__).split(".")[0]
# Number of samples averaged together, in the plot.
N = 20
# Pass/fail thresholds for gyro drift
MEAN_THRESH = 0.01
VAR_THRESH = 0.001
with its.device.ItsSession() as cam:
print "Collecting gyro events"
cam.start_sensor_events()
time.sleep(5)
gyro_events = cam.get_sensor_events()["gyro"]
nevents = (len(gyro_events) / N) * N
gyro_events = gyro_events[:nevents]
times = numpy.array([(e["time"] - gyro_events[0]["time"])/1000000000.0
for e in gyro_events])
xs = numpy.array([e["x"] for e in gyro_events])
ys = numpy.array([e["y"] for e in gyro_events])
zs = numpy.array([e["z"] for e in gyro_events])
# Group samples into size-N groups and average each together, to get rid
# of individual rnadom spikes in the data.
times = times[N/2::N]
xs = xs.reshape(nevents/N, N).mean(1)
ys = ys.reshape(nevents/N, N).mean(1)
zs = zs.reshape(nevents/N, N).mean(1)
pylab.plot(times, xs, 'r', label="x")
pylab.plot(times, ys, 'g', label="y")
pylab.plot(times, zs, 'b', label="z")
pylab.xlabel("Time (seconds)")
pylab.ylabel("Gyro readings (mean of %d samples)"%(N))
pylab.legend()
matplotlib.pyplot.savefig("%s_plot.png" % (NAME))
for samples in [xs,ys,zs]:
assert(samples.mean() < MEAN_THRESH)
assert(numpy.var(samples) < VAR_THRESH)
if __name__ == '__main__':
main()
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