def test_base_lon_wrapping(self):
lons1 = np.arange(-135., +135, 50.)
lats = np.ones_like(lons1) * 70.
with catch_warnings() as w1:
base_def1 = geometry.BaseDefinition(lons1, lats)
self.assertFalse(
len(w1) != 0, 'Got warning <%s>, but was not expecting one' % w1)
lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
with catch_warnings() as w2:
base_def2 = geometry.BaseDefinition(lons2, lats)
self.assertFalse(
len(w2) != 1, 'Failed to trigger a warning on longitude wrapping')
self.assertFalse(('-180:+180' not in str(w2[0].message)),
'Failed to trigger correct warning about longitude wrapping')
self.assertFalse(
base_def1 != base_def2, 'longitude wrapping to [-180:+180] did not work')
with catch_warnings() as w3:
base_def3 = geometry.BaseDefinition(None, None)
self.assertFalse(
len(w3) != 0, 'Got warning <%s>, but was not expecting one' % w3)
self.assert_raises(ValueError, base_def3.get_lonlats)
def test_base_type(self):
lons1 = np.arange(-135., +135, 50.)
lats = np.ones_like(lons1) * 70.
# Test dtype is preserved without longitude wrapping
basedef = geometry.BaseDefinition(lons1, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(
lons.dtype, lons1.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)"
% (
lons1.dtype,
lons.dtype,
))
lons1_ints = lons1.astype('int')
basedef = geometry.BaseDefinition(lons1_ints, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(
lons.dtype, lons1_ints.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)"
% (
lons1_ints.dtype,
lons.dtype,
))
# Test dtype is preserved with automatic longitude wrapping
lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
with catch_warnings() as w:
basedef = geometry.BaseDefinition(lons2, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(
lons.dtype, lons2.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)"
% (
lons2.dtype,
lons.dtype,
))
lons2_ints = lons2.astype('int')
with catch_warnings() as w:
basedef = geometry.BaseDefinition(lons2_ints, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(
lons.dtype, lons2_ints.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)"
% (
lons2_ints.dtype,
lons.dtype,
))
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_custom_multi(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return numpy.cos(dist) ** 2
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
res = kd_tree.resample_custom(swath_def, data_multi,
self.area_def, 50000, [wf1, wf2, wf3], 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 = 1461.842980746
self.assertAlmostEqual(cross_sum, expected,
msg='Swath multi channel custom resampling failed')
def test_custom_multi(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return np.cos(dist)**2
data = np.fromfunction(lambda y, x: (y + x) * 10**-6, (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)
data_multi = np.column_stack(
(data.ravel(), data.ravel(), data.ravel()))
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_custom(swath_def,
data_multi,
self.area_def,
50000, [wf1, wf2, wf3],
segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse('Possible more' not in str(w[0].message))
cross_sum = res.sum()
expected = 1461.8428378742638
self.assertAlmostEqual(cross_sum, expected)
def test_self_map_multi(self):
data = np.column_stack((self.tb37v, self.tb37v, self.tb37v))
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(
swath_def, data, swath_def, radius_of_influence=70000, sigmas=[56500, 56500, 56500]
)
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"
)
if sys.platform == "darwin":
# OSX seems to get slightly different results for `_spatial_mp.Cartesian`
truth_value = 668848.144817
else:
truth_value = 668848.082208
self.assertAlmostEqual(
res[:, 0].sum() / 100.0, truth_value, 1, msg="Failed self mapping swath multi for channel 1"
)
self.assertAlmostEqual(
res[:, 1].sum() / 100.0, truth_value, 1, msg="Failed self mapping swath multi for channel 2"
)
self.assertAlmostEqual(
res[:, 2].sum() / 100.0, truth_value, 1, msg="Failed self mapping swath multi for channel 3"
)
def test_custom_multi(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return numpy.cos(dist)**2
data = numpy.fromfunction(lambda y, x: (y + x) * 10**-6, (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)
data_multi = numpy.column_stack(
(data.ravel(), data.ravel(), data.ravel()))
with catch_warnings() as w:
res = kd_tree.resample_custom(swath_def,
data_multi,
self.area_def,
50000, [wf1, wf2, wf3],
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 = 1461.842980746
self.assertAlmostEqual(
cross_sum,
expected,
msg='Swath multi channel custom resampling failed')
def test_custom(self):
def wf(dist):
return 1 - dist / 100000.0
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_custom(swath_def,
data.ravel(),
self.area_def,
50000,
wf,
segments=1)
# PyProj proj/CRS and "more than 8 neighbours" are warned about
self.assertFalse(len(w) > 2)
neighbour_warn = False
for warn in w:
if 'Possible more' in str(warn.message):
neighbour_warn = True
break
self.assertTrue(neighbour_warn)
if len(w) == 2:
proj_crs_warn = False
for warn in w:
if 'important projection information' in str(warn.message):
proj_crs_warn = True
break
self.assertTrue(proj_crs_warn)
cross_sum = res.sum()
expected = 4872.8100347930776
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_multi_uncert(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
# The assertion below checks if there is only one warning raised
# and whether it contains a specific message from pyresample
# On python 2.7.9+ the resample_gauss method raises multiple deprecation warnings
# that cause to fail, so we ignore the unrelated warnings.
res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [
25000, 15000, 10000],
segments=1, with_uncert=True)
self.assertTrue(len(w) >= 1)
self.assertTrue(
any(['Possible more' in str(x.message) for x in w]))
cross_sum = res.sum()
cross_sum_counts = counts.sum()
expected = 1461.8429990248171
expected_stddev = [0.44621800779801657, 0.44363137712896705,
0.43861019464274459]
expected_counts = 4934802.0
self.assertTrue(res.shape == stddev.shape and stddev.shape ==
counts.shape and counts.shape == (800, 800, 3))
self.assertAlmostEqual(cross_sum, expected)
for i, e_stddev in enumerate(expected_stddev):
cross_sum_stddev = stddev[:, :, i].sum()
self.assertAlmostEqual(cross_sum_stddev, e_stddev)
self.assertAlmostEqual(cross_sum_counts, expected_counts)
def test_custom_multi(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return np.cos(dist) ** 2
data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = np.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_custom(swath_def, data_multi,
self.area_def, 50000, [wf1, wf2, wf3], segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse('Possible more' not in str(w[0].message))
cross_sum = res.sum()
expected = 1461.8428378742638
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_multi_uncert(self):
data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = np.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings(UserWarning) as w:
# The assertion below checks if there is only one warning raised
# and whether it contains a specific message from pyresample
# On python 2.7.9+ the resample_gauss method raises multiple deprecation warnings
# that cause to fail, so we ignore the unrelated warnings.
res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [
25000, 15000, 10000],
segments=1, with_uncert=True)
self.assertTrue(len(w) >= 1)
self.assertTrue(
any(['Possible more' in str(x.message) for x in w]))
cross_sum = res.sum()
cross_sum_counts = counts.sum()
expected = 1461.8429990248171
expected_stddev = [0.44621800779801657, 0.44363137712896705,
0.43861019464274459]
expected_counts = 4934802.0
self.assertTrue(res.shape == stddev.shape and stddev.shape ==
counts.shape and counts.shape == (800, 800, 3))
self.assertAlmostEqual(cross_sum, expected)
for i, e_stddev in enumerate(expected_stddev):
cross_sum_stddev = stddev[:, :, i].sum()
self.assertAlmostEqual(cross_sum_stddev, e_stddev)
self.assertAlmostEqual(cross_sum_counts, expected_counts)
def test_custom(self):
def wf(dist):
return 1 - dist / 100000.0
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_custom(swath_def,
data.ravel(),
self.area_def,
50000,
wf,
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 = 4872.81050729
self.assertAlmostEqual(cross_sum,
expected,
msg='Swath custom resampling failed')
def test_gauss_multi_mp_segments(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10**-6, (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)
data_multi = numpy.column_stack(
(data.ravel(), data.ravel(), data.ravel()))
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def,
data_multi,
self.area_def,
50000, [25000, 15000, 10000],
nprocs=2,
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 = 1461.84313918
self.assertAlmostEqual(
cross_sum,
expected,
msg='Swath multi channel segments resampling gauss failed')
def test_gauss_base(self):
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_gauss(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, 25000, reduce_data=False, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Searching' not in str(w[0].message)))
self.assertAlmostEqual(res[0], 2.2020729, 5)
def test_gauss_base(self):
with catch_warnings() as w:
res = kd_tree.resample_gauss(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, 25000, reduce_data=False, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Searching' not in str(w[0].message)))
self.assertAlmostEqual(res[0], 2.2020729, 5)
def test_custom_base(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_custom(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, wf, reduce_data=False, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Searching' not in str(w[0].message)))
self.assertAlmostEqual(res[0], 2.4356757, 5)
def test_gauss_base(self):
with catch_warnings() as w:
res = kd_tree.resample_gauss(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, 25000, reduce_data=False, segments=1)
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')
self.assertAlmostEqual(res[0], 2.2020729, 5,
'Failed to calculate gaussian weighting')
def test_custom_base(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings() as w:
res = kd_tree.resample_custom(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, wf, reduce_data=False, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Searching' not in str(w[0].message)))
self.assertAlmostEqual(res[0], 2.4356757, 5)
def test_gauss_base(self):
with catch_warnings() as w:
res = kd_tree.resample_gauss(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, 25000, reduce_data=False, segments=1)
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')
self.assertAlmostEqual(res[0], 2.2020729, 5,
'Failed to calculate gaussian weighting')
def test_custom_multi_from_sample(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return np.cos(dist)**2
data = np.fromfunction(lambda y, x: (y + x) * 10**-6, (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)
data_multi = np.column_stack(
(data.ravel(), data.ravel(), data.ravel()))
with catch_warnings(UserWarning) as w:
valid_input_index, valid_output_index, index_array, distance_array = \
kd_tree.get_neighbour_info(swath_def,
self.area_def,
50000, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
res = kd_tree.get_sample_from_neighbour_info(
'custom', (800, 800),
data_multi,
valid_input_index,
valid_output_index,
index_array,
distance_array,
weight_funcs=[wf1, wf2, wf3])
cross_sum = res.sum()
expected = 1461.8428378742638
self.assertAlmostEqual(cross_sum, expected)
res = kd_tree.get_sample_from_neighbour_info(
'custom', (800, 800),
data_multi,
valid_input_index,
valid_output_index,
index_array,
distance_array,
weight_funcs=[wf1, wf2, wf3])
# Look for error where input data has been manipulated
cross_sum = res.sum()
expected = 1461.8428378742638
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_multi_uncert(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10**-6, (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)
data_multi = numpy.column_stack(
(data.ravel(), data.ravel(), data.ravel()))
with catch_warnings() as w:
# The assertion below checks if there is only one warning raised
# and whether it contains a specific message from pyresample
# On python 2.7.9+ the resample_gauss method raises multiple deprecation warnings
# that cause to fail, so we ignore the unrelated warnings.
res, stddev, counts = kd_tree.resample_gauss(swath_def,
data_multi,
self.area_def,
50000,
[25000, 15000, 10000],
segments=1,
with_uncert=True)
self.assertTrue(
len(w) >= 1, 'Failed to create neighbour radius warning')
self.assertTrue(
any(['Possible more' in str(x.message) for x in w]),
'Failed to create correct neighbour radius warning')
cross_sum = res.sum()
cross_sum_counts = counts.sum()
expected = 1461.84313918
expected_stddev = [0.446193170875, 0.443606880035, 0.438586349519]
expected_counts = 4934802.0
self.assertTrue(res.shape == stddev.shape
and stddev.shape == counts.shape
and counts.shape == (800, 800, 3))
self.assertAlmostEqual(
cross_sum,
expected,
msg='Swath multi channel resampling gauss failed on data')
for i, e_stddev in enumerate(expected_stddev):
cross_sum_stddev = stddev[:, :, i].sum()
print(cross_sum_stddev, e_stddev)
self.assertAlmostEqual(
cross_sum_stddev,
e_stddev,
msg=
'Swath multi channel resampling gauss failed on stddev (channel {})'
.format(i))
self.assertAlmostEqual(
cross_sum_counts,
expected_counts,
msg='Swath multi channel resampling gauss failed on counts')
def test_custom_multi_from_sample(self):
def wf1(dist):
return 1 - dist / 100000.0
def wf2(dist):
return 1
def wf3(dist):
return numpy.cos(dist) ** 2
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
valid_input_index, valid_output_index, index_array, distance_array = \
kd_tree.get_neighbour_info(swath_def,
self.area_def,
50000, 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')
res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
data_multi,
valid_input_index, valid_output_index,
index_array, distance_array,
weight_funcs=[wf1, wf2, wf3])
cross_sum = res.sum()
expected = 1461.842980746
self.assertAlmostEqual(cross_sum, expected,
msg='Swath multi channel custom resampling from neighbour info failed 1')
res = kd_tree.get_sample_from_neighbour_info('custom', (800, 800),
data_multi,
valid_input_index, valid_output_index,
index_array, distance_array,
weight_funcs=[wf1, wf2, wf3])
# Look for error where input data has been manipulated
cross_sum = res.sum()
expected = 1461.842980746
self.assertAlmostEqual(cross_sum, expected,
msg='Swath multi channel custom resampling from neighbour info failed 2')
def test_custom_uncert(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings() as w:
res, stddev, counts = kd_tree.resample_custom(self.tswath,
self.tdata, self.tgrid,
100000, wf, with_uncert=True)
self.assertTrue(len(w) > 0)
self.assertTrue((any('Searching' in str(_w.message) for _w in w)))
self.assertAlmostEqual(res[0], 2.32193149, 5)
self.assertAlmostEqual(stddev[0], 0.81817972, 5)
self.assertEqual(counts[0], 3)
def test_custom_uncert(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings(UserWarning) as w:
res, stddev, counts = kd_tree.resample_custom(self.tswath,
self.tdata, self.tgrid,
100000, wf, with_uncert=True)
self.assertTrue(len(w) > 0)
self.assertTrue((any('Searching' in str(_w.message) for _w in w)))
self.assertAlmostEqual(res[0], 2.32193149, 5)
self.assertAlmostEqual(stddev[0], 0.81817972, 5)
self.assertEqual(counts[0], 3)
def test_custom_base(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings() as w:
res = kd_tree.resample_custom(self.tswath,
self.tdata.ravel(), self.tgrid,
50000, wf, reduce_data=False, segments=1)
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')
self.assertAlmostEqual(res[0], 2.4356757, 5,
'Failed to calculate custom weighting')
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_base_type(self):
lons1 = np.arange(-135., +135, 50.)
lats = np.ones_like(lons1) * 70.
# Test dtype is preserved without longitude wrapping
basedef = geometry.BaseDefinition(lons1, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(lons.dtype, lons1.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
(lons1.dtype, lons.dtype,))
lons1_ints = lons1.astype('int')
basedef = geometry.BaseDefinition(lons1_ints, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(lons.dtype, lons1_ints.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
(lons1_ints.dtype, lons.dtype,))
# Test dtype is preserved with automatic longitude wrapping
lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
with catch_warnings() as w:
basedef = geometry.BaseDefinition(lons2, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(lons.dtype, lons2.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
(lons2.dtype, lons.dtype,))
lons2_ints = lons2.astype('int')
with catch_warnings() as w:
basedef = geometry.BaseDefinition(lons2_ints, lats)
lons, _ = basedef.get_lonlats()
self.assertEqual(lons.dtype, lons2_ints.dtype,
"BaseDefinition did not maintain dtype of longitudes (in:%s out:%s)" %
(lons2_ints.dtype, lons.dtype,))
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_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_self_map(self):
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def, self.tb37v.copy(), swath_def,
radius_of_influence=70000, sigmas=56500)
self.assertEqual(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')
# only compare the whole number as different OSes and versions of numpy
# can produce slightly different results
truth_value = 668848.0
self.assertAlmostEqual(res.sum() / 100., truth_value, 0,
msg='Failed self mapping swath for 1 channel')
def test_gauss_multi(self):
data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = np.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings(UserWarning) as w:
res = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [25000, 15000, 10000], segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 1461.8429990248171
self.assertAlmostEqual(cross_sum, expected)
def test_gauss_multi(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [25000, 15000, 10000], segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 1461.8429990248171
self.assertAlmostEqual(cross_sum, expected)
def test_custom(self):
def wf(dist):
return 1 - dist / 100000.0
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_custom(swath_def, data.ravel(),
self.area_def, 50000, wf, segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 4872.8100347930776
self.assertAlmostEqual(cross_sum, expected)
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, '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_custom_uncert(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings() as w:
res, stddev, counts = kd_tree.resample_custom(self.tswath,
self.tdata, self.tgrid,
100000, wf, 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')
self.assertAlmostEqual(res[0], 2.32193149, 5,
'Failed to calculate custom weighting with uncertainty')
self.assertAlmostEqual(stddev[0], 0.81817972, 5,
'Failed to calculate custom for gaussian weighting')
self.assertEqual(
counts[0], 3, 'Wrong data point count for custom weighting with uncertainty')
def test_self_map_multi(self):
data = np.column_stack((self.tb37v, self.tb37v, self.tb37v))
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def, data, swath_def,
radius_of_influence=70000, sigmas=[56500, 56500, 56500])
self.assertEqual(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')
truth_value = 668848.0
self.assertAlmostEqual(res[:, 0].sum() / 100., truth_value, 0,
msg='Failed self mapping swath multi for channel 1')
self.assertAlmostEqual(res[:, 1].sum() / 100., truth_value, 0,
msg='Failed self mapping swath multi for channel 2')
self.assertAlmostEqual(res[:, 2].sum() / 100., truth_value, 0,
msg='Failed self mapping swath multi for channel 3')
def test_custom_uncert(self):
def wf(dist):
return 1 - dist / 100000.0
with catch_warnings() as w:
res, stddev, counts = kd_tree.resample_custom(self.tswath,
self.tdata, self.tgrid,
100000, wf, 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')
self.assertAlmostEqual(res[0], 2.32193149, 5,
'Failed to calculate custom weighting with uncertainty')
self.assertAlmostEqual(stddev[0], 0.81817972, 5,
'Failed to calculate custom for gaussian weighting')
self.assertEqual(
counts[0], 3, 'Wrong data point count for custom weighting with uncertainty')
def test_swath_wrap(self):
lons1 = np.fromfunction(lambda y, x: 3 + (10.0 / 100) * x, (5000, 100))
lats1 = np.fromfunction(
lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100))
lons1 += 180.
with catch_warnings() as w1:
swath_def = geometry.BaseDefinition(lons1, lats1)
self.assertFalse(
len(w1) != 1, 'Failed to trigger a warning on longitude wrapping')
self.assertFalse(('-180:+180' not in str(w1[0].message)),
'Failed to trigger correct warning about longitude wrapping')
lons2, lats2 = swath_def.get_lonlats()
self.assertTrue(id(lons1) != id(lons2),
msg='Caching of swath coordinates failed with longitude wrapping')
self.assertTrue(lons2.min() > -180 and lons2.max() < 180,
'Wrapping of longitudes failed for SwathDefinition')
def test_gauss_multi(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [25000, 15000, 10000], 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 = 1461.84313918
self.assertAlmostEqual(cross_sum, expected,
msg='Swath multi channel 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, '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_custom(self):
def wf(dist):
return 1 - dist / 100000.0
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() as w:
res = kd_tree.resample_custom(swath_def,
data.ravel(),
self.area_def,
50000,
wf,
segments=1)
self.assertFalse(len(w) != 1)
self.assertFalse(('Possible more' not in str(w[0].message)))
cross_sum = res.sum()
expected = 4872.8100347930776
self.assertAlmostEqual(cross_sum, expected)
def test_custom(self):
def wf(dist):
return 1 - dist / 100000.0
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_custom(swath_def, data.ravel(),
self.area_def, 50000, wf, 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 = 4872.81050729
self.assertAlmostEqual(cross_sum, expected,
msg='Swath custom resampling failed')
def test_self_map(self):
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def,
self.tb37v.copy(),
swath_def,
radius_of_influence=70000,
sigmas=56500)
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')
if sys.platform == 'darwin':
# OSX seems to get slightly different results for `_spatial_mp.Cartesian`
truth_value = 668848.144817
else:
truth_value = 668848.082208
self.assertAlmostEqual(res.sum() / 100.,
truth_value,
1,
msg='Failed self mapping swath for 1 channel')
def test_object_type_with_warnings(self, area_def_stere_source,
area_def_stere_target, input_data):
"""Test that providing certain input data causes a warning."""
resampler = KDTreeNearestXarrayResampler(area_def_stere_source,
area_def_stere_target)
with catch_warnings(
PerformanceWarning) as w, assert_maximum_dask_computes(1):
res = resampler.resample(input_data)
assert type(res) is type(input_data)
_check_common_metadata(
res, isinstance(area_def_stere_target, AreaDefinition))
is_data_arr_dask = isinstance(input_data, xr.DataArray) and isinstance(
input_data.data, da.Array)
is_dask_based = isinstance(input_data, da.Array) or is_data_arr_dask
if is_dask_based:
assert not w
else:
assert_warnings_contain(w, "will be converted")
res = np.array(res)
cross_sum = np.nansum(res)
expected = 952386.0 # same as 'test_nearest_area_2d_to_area_1n_no_roi'
assert cross_sum == expected
assert res.shape == resampler.target_geo_def.shape
def test_gauss_multi_uncert(self):
data = numpy.fromfunction(lambda y, x: (y + x) * 10 ** -6, (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)
data_multi = numpy.column_stack((data.ravel(), data.ravel(),
data.ravel()))
with catch_warnings() as w:
# The assertion below checks if there is only one warning raised
# and whether it contains a specific message from pyresample
# On python 2.7.9+ the resample_gauss method raises multiple deprecation warnings
# that cause to fail, so we ignore the unrelated warnings.
res, stddev, counts = kd_tree.resample_gauss(swath_def, data_multi,
self.area_def, 50000, [
25000, 15000, 10000],
segments=1, with_uncert=True)
self.assertTrue(
len(w) >= 1, 'Failed to create neighbour radius warning')
self.assertTrue(any(['Possible more' in str(
x.message) for x in w]), 'Failed to create correct neighbour radius warning')
cross_sum = res.sum()
cross_sum_stddev = stddev.sum()
cross_sum_counts = counts.sum()
expected = 1461.84313918
expected_stddev = 0.446204424799
expected_counts = 4934802.0
self.assertTrue(res.shape == stddev.shape and stddev.shape ==
counts.shape and counts.shape == (800, 800, 3))
self.assertAlmostEqual(cross_sum, expected,
msg='Swath multi channel resampling gauss failed on data')
self.assertAlmostEqual(cross_sum_stddev, expected_stddev,
msg='Swath multi channel resampling gauss failed on stddev')
self.assertAlmostEqual(cross_sum_counts, expected_counts,
msg='Swath multi channel resampling gauss failed on counts')
def test_self_map_multi(self):
data = np.column_stack((self.tb37v, self.tb37v, self.tb37v))
swath_def = geometry.SwathDefinition(lons=self.lons, lats=self.lats)
with catch_warnings() as w:
res = kd_tree.resample_gauss(swath_def,
data,
swath_def,
radius_of_influence=70000,
sigmas=[56500, 56500, 56500])
self.assertEqual(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')
if sys.platform == 'darwin':
# OSX seems to get slightly different results for `_spatial_mp.Cartesian`
truth_value = 668848.144817
else:
truth_value = 668848.082208
self.assertAlmostEqual(
res[:, 0].sum() / 100.,
truth_value,
1,
msg='Failed self mapping swath multi for channel 1')
self.assertAlmostEqual(
res[:, 1].sum() / 100.,
truth_value,
1,
msg='Failed self mapping swath multi for channel 2')
self.assertAlmostEqual(
res[:, 2].sum() / 100.,
truth_value,
1,
msg='Failed self mapping swath multi for channel 3')
