def _test_exploration_to_CSV(self):
io.exploration_to_CSV(self.dataSet, _tests_path)
csvDataSet = io.float_columns_from_CSV(
io.LATEST_EXPLORATION_CSV_FILE_NAME, ('avgX', 'avgY', 'avgZ'),
_tests_path)
self.assertAlmostEqual(csvDataSet['avgX'][0], -0.5)
self.assertAlmostEqual(csvDataSet['avgY'][4], -0.555555555556)
def _test_exploration_from_CSV(self):
csvDataSet = io.float_columns_from_CSV(
self.testExplorationCSVFileName, ('avgX', 'avgY', 'avgZ', 'stdZ'),
_tests_path)
self.assertEqual(len(csvDataSet.keys()), 4)
self.assertEqual(len(csvDataSet['avgX']), 1250)
self.assertAlmostEqual(csvDataSet['avgX'][0], 542.290000)
self.assertAlmostEqual(csvDataSet['avgX'][3], 503.140000)
self.assertAlmostEqual(csvDataSet['avgY'][23], 404.050000)
self.assertAlmostEqual(csvDataSet['avgZ'][253], 555.210000)
def test_fit_parameters(self):
dataSet = io.float_columns_from_CSV(csvFileName="FongTests.csv",
path=io._test_path)
x, y, z = dataSet['avgX'], dataSet['avgY'], dataSet['avgZ']
measures = utils.build_measures_matrix(x, y, z)
misalignmentsAndScales, biases = fong_accelero.fit(measures)
testing.assert_almost_equal(misalignmentsAndScales,
self.misalignmentsAndScales)
testing.assert_almost_equal(biases, self.biases)
def fit_accelero_parameters_fong():
dataSet = io.float_columns_from_CSV(columnsMask=('longitude', 'latitude',
'avgX', 'avgY', 'avgZ'))
measures = utils.build_measures_matrix(dataSet['avgX'], dataSet['avgY'],
dataSet['avgZ'])
misalignmentsAndScales, biases = fong_accelero.fit(measures)
longitudes, latitudes = dataSet["longitude"], dataSet["latitude"]
correctedMesaures = utils.correct_measures(misalignmentsAndScales, biases,
measures)
residuals = fong_accelero.residuals(
utils.concatenate(misalignmentsAndScales, biases), measures)
return misalignmentsAndScales, biases, residuals, correctedMesaures, longitudes, latitudes
def accelerometer_dataset():
calibration, modelListener = buildCalibration()
modelListener.start()
longitudes = statistics.frange(-90.0, 45.0, 25)
latitudes = statistics.frange(-180.0, 180.0, 50)
# longitudes = statistics.frange(-5.0, 5.0, 5)
# latitudes = statistics.frange(-10.0, 10.0, 5)
dataSet = calibration.exploreUnitSphere(longitudes, latitudes)
io.serialize_exploration(dataSet)
dataSet = io.deserialize_exploration()
print io.exploration_to_CSV(dataSet)
return io.float_columns_from_CSV()
def fit_gyro_parameters_fong():
acceleroDataSet = io.float_columns_from_CSV(columnsMask=('longitude',
'latitude',
'avgX', 'avgY',
'avgZ'))
measures = utils.build_measures_matrix(acceleroDataSet['avgX'],
acceleroDataSet['avgY'],
acceleroDataSet['avgZ'])
acceleroMisalignmentsAndScales, acceleroBiases = fong_accelero.fit(
measures)
gyroDataSet = prepareGyroDatatSet()
misalignmentsAndScales = fong_gyro.fit(gyroDataSet,
acceleroMisalignmentsAndScales,
acceleroBiases)
# io.serialize_results(misalignmentsAndScales, io._pkl_path, "gyro_params_%s.pkl")
plotGyroFits(gyroDataSet, misalignmentsAndScales)
def fit_accelero_parameters_regression():
dataSet = io.float_columns_from_CSV(columnsMask=('longitude', 'latitude',
'avgX', 'avgY', 'avgZ'))
measures = utils.build_measures_matrix(dataSet['avgX'], dataSet['avgY'],
dataSet['avgZ'])
longitudes, latitudes = dataSet["longitude"], dataSet["latitude"]
targets = utils.build_targets_matrix(longitudes, latitudes)
misalignmentsAndScales, biases = regression_accelero.fit(targets, measures)
io.serialize_results((misalignmentsAndScales, biases), io._pkl_path,
"accelero_params_%s.pkl")
correctedMesaures = utils.correct_measures(misalignmentsAndScales, biases,
measures)
residuals = regression_accelero.residuals(
utils.concatenate(misalignmentsAndScales, biases), targets, measures)
# plots.plot_residuals(residuals, longitudes, latitudes)
plots.plot_fit(correctedMesaures, longitudes, latitudes)
plots.show_figures()