def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
if (sys.version_info < (2, 6) or
(sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
else:
with warnings.catch_warnings(record=True) as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
self.assertFalse(
len(w) != 1, 'Failed to create neighbour warning')
self.assertFalse(('Searching' not in str(
w[0].message)), 'Failed to create correct neighbour warning')
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(res[0], expected_res, 5,
'Failed to calculate gaussian weighting with uncertainty')
self.assertAlmostEqual(stddev[0], expected_stddev, 5,
'Failed to calculate uncertainty for gaussian weighting')
self.assertEqual(
count[0], expected_count, 'Wrong data point count for gaussian weighting with uncertainty')
def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
if (sys.version_info < (2, 6) or
(sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
else:
with warnings.catch_warnings(record=True) as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
self.assertFalse(
len(w) != 1, 'Failed to create neighbour warning')
self.assertFalse(('Searching' not in str(
w[0].message)), 'Failed to create correct neighbour warning')
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(res[0], expected_res, 5,
'Failed to calculate gaussian weighting with uncertainty')
self.assertAlmostEqual(stddev[0], expected_stddev, 5,
'Failed to calculate uncertainty for gaussian weighting')
self.assertEqual(
count[0], expected_count, 'Wrong data point count for gaussian weighting with uncertainty')
def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
with catch_warnings() as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath,
self.tdata,
self.tgrid,
100000,
sigma,
with_uncert=True)
self.assertTrue(len(w) > 0, 'Failed to create neighbour warning')
self.assertTrue((any('Searching' in str(_w.message) for _w in w)),
'Failed to create correct neighbour warning')
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(
res[0], expected_res, 5,
'Failed to calculate gaussian weighting with uncertainty')
self.assertAlmostEqual(
stddev[0], expected_stddev, 5,
'Failed to calculate uncertainty for gaussian weighting')
self.assertEqual(
count[0], expected_count,
'Wrong data point count for gaussian weighting with uncertainty')
def test_gauss_fwhm(self):
data = numpy.fromfunction(lambda y, x: (y + x)*10**-5, (5000, 100))
lons = numpy.fromfunction(lambda y, x: 3 + (10.0/100)*x, (5000, 100))
lats = numpy.fromfunction(lambda y, x: 75 - (50.0/5000)*y, (5000, 100))
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
if sys.version_info < (2, 6):
res = kd_tree.resample_gauss(swath_def, data.ravel(),\
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
else:
with warnings.catch_warnings(record=True) as w:
res = kd_tree.resample_gauss(swath_def, data.ravel(),\
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
self.failIf(len(w) != 1, 'Failed to create neighbour radius warning')
self.failIf(('Possible more' not in str(w[0].message)), 'Failed to create correct neighbour radius warning')
cross_sum = res.sum()
expected = 4872.81050892
self.assertAlmostEqual(cross_sum, expected,\
msg='Swath resampling gauss failed')
def test_gauss_fwhm(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
lons = numpy.fromfunction(
lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
lats = numpy.fromfunction(
lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
if (sys.version_info < (2, 6) or
(sys.version_info >= (3, 0) and sys.version_info < (3, 4))):
res = kd_tree.resample_gauss(swath_def, data.ravel(),
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
else:
with warnings.catch_warnings(record=True) as w:
res = kd_tree.resample_gauss(swath_def, data.ravel(),
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
self.assertFalse(
len(w) != 1, 'Failed to create neighbour radius warning')
self.assertFalse(('Possible more' not in str(
w[0].message)), 'Failed to create correct neighbour radius warning')
cross_sum = res.sum()
expected = 4872.81050892
self.assertAlmostEqual(cross_sum, expected,
msg='Swath resampling gauss failed')
def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
with catch_warnings() as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
self.assertTrue(len(w) > 0)
self.assertTrue((any('Searching' in str(_w.message) for _w in w)))
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(res[0], expected_res, 5)
self.assertAlmostEqual(stddev[0], expected_stddev, 5)
self.assertEqual(count[0], expected_count)
def test_gauss_fwhm(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
lons = numpy.fromfunction(
lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
lats = numpy.fromfunction(
lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def, data.ravel(),
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 4872.8100353517921
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
with catch_warnings(UserWarning) as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
self.assertTrue(len(w) > 0)
self.assertTrue((any('Searching' in str(_w.message) for _w in w)))
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(res[0], expected_res, 5)
self.assertAlmostEqual(stddev[0], expected_stddev, 5)
self.assertEqual(count[0], expected_count)
def test_gauss_fwhm(self):
data = np.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100))
lons = np.fromfunction(
lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
lats = np.fromfunction(
lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_gauss(swath_def, data.ravel(),
self.area_def, 50000, utils.fwhm2sigma(41627.730557884883), segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 4872.8100353517921
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_uncert(self):
sigma = utils.fwhm2sigma(41627.730557884883)
with catch_warnings() as w:
res, stddev, count = kd_tree.resample_gauss(self.tswath, self.tdata,
self.tgrid, 100000, sigma,
with_uncert=True)
self.assertTrue(
len(w) > 0, 'Failed to create neighbour warning')
self.assertTrue((any('Searching' in str(_w.message) for _w in w)),
'Failed to create correct neighbour warning')
expected_res = 2.20206560694
expected_stddev = 0.707115076173
expected_count = 3
self.assertAlmostEqual(res[0], expected_res, 5,
'Failed to calculate gaussian weighting with uncertainty')
self.assertAlmostEqual(stddev[0], expected_stddev, 5,
'Failed to calculate uncertainty for gaussian weighting')
self.assertEqual(
count[0], expected_count, 'Wrong data point count for gaussian weighting with uncertainty')
