def setUpClass(cls):
cls.test_cases = [
cls.InterpolationTestCase('linear',
LinearInterpolation(
'ndvi',
result_interval=(-0.3, -0.1),
unknown_value=10),
result_len=180,
img_min=-0.3000,
img_max=10.0,
img_mean=-0.147169,
img_median=-0.21132),
cls.InterpolationTestCase(
'cubic',
CubicInterpolation('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2015-01-01', '2018-01-01',
16),
unknown_value=5),
result_len=69,
img_min=-0.3000,
img_max=5.0,
img_mean=1.30387,
img_median=-0.1007357),
cls.InterpolationTestCase(
'spline',
SplineInterpolation('ndvi',
result_interval=(-10, 10),
resample_range=('2016-01-01', '2018-01-01',
5),
spline_degree=3,
smoothing_factor=0),
result_len=147,
img_min=-0.68187547,
img_max=0.26355705,
img_mean=-0.1886001,
img_median=-0.18121514),
cls.InterpolationTestCase('bspline',
BSplineInterpolation(
'ndvi',
unknown_value=-3,
resample_range=('2017-01-01',
'2017-02-01', 50),
spline_degree=5),
result_len=1,
img_min=-0.20366311,
img_max=0.035470814,
img_mean=-0.04307341,
img_median=-0.040708482),
cls.InterpolationTestCase('akima',
AkimaInterpolation('ndvi',
unknown_value=0),
result_len=180,
img_min=-0.4821199,
img_max=0.2299331,
img_mean=-0.20141865,
img_median=-0.213559),
cls.InterpolationTestCase(
'nearest resample',
NearestResampling('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2016-01-01', '2018-01-01',
5)),
result_len=147,
img_min=-0.3000,
img_max=-0.1000,
img_mean=-0.19651729,
img_median=-0.20000,
nan_replace=-0.2),
cls.InterpolationTestCase(
'linear resample',
LinearResampling('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2016-01-01', '2018-01-01',
5)),
result_len=147,
img_min=-0.3000,
img_max=-0.1000,
img_mean=-0.1946315,
img_median=-0.20000,
nan_replace=-0.2),
cls.InterpolationTestCase(
'cubic resample',
CubicResampling('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2015-01-01', '2018-01-01',
16),
unknown_value=5),
result_len=69,
img_min=-0.3000,
img_max=5.0,
img_mean=1.22359,
img_median=-0.181167,
nan_replace=-0.2)
]
for test_case in cls.test_cases:
test_case.execute()
def setUpClass(cls):
cls.test_cases = [
cls.InterpolationTestCase('linear', LinearInterpolation('NDVI', result_interval=(0.0, 1.0),
mask_feature=(FeatureType.MASK, 'IS_VALID'),
unknown_value=10),
result_len=68, img_min=0.0, img_max=10.0, img_mean=0.720405,
img_median=0.59765935),
cls.InterpolationTestCase('linear_change_timescale',
LinearInterpolation('NDVI', result_interval=(0.0, 1.0),
mask_feature=(FeatureType.MASK, 'IS_VALID'),
unknown_value=10, scale_time=1),
result_len=68, img_min=0.0, img_max=10.0, img_mean=0.720405,
img_median=0.597656965),
cls.InterpolationTestCase('cubic', CubicInterpolation('NDVI', result_interval=(0.0, 1.0),
mask_feature=(FeatureType.MASK, 'IS_VALID'),
resample_range=('2015-01-01', '2018-01-01', 16),
unknown_value=5, bounds_error=False),
result_len=69, img_min=0.0, img_max=5.0, img_mean=1.3592644,
img_median=0.6174331),
cls.InterpolationTestCase('spline', SplineInterpolation('NDVI', result_interval=(-0.3, 1.0),
mask_feature=(FeatureType.MASK, 'IS_VALID'),
resample_range=('2016-01-01', '2018-01-01', 5),
spline_degree=3, smoothing_factor=0,
unknown_value=0),
result_len=147, img_min=-0.3, img_max=1.0, img_mean=0.492752,
img_median=0.53776133),
cls.InterpolationTestCase('bspline', BSplineInterpolation('NDVI', unknown_value=-3,
mask_feature=(FeatureType.MASK, 'IS_VALID'),
resample_range=('2017-01-01', '2017-02-01', 50),
spline_degree=5),
result_len=1, img_min=-0.032482587, img_max=0.701796, img_mean=0.42080238,
img_median=0.42889267),
cls.InterpolationTestCase('akima', AkimaInterpolation('NDVI', unknown_value=0,
mask_feature=(FeatureType.MASK, 'IS_VALID')),
result_len=68, img_min=-0.13793105, img_max=0.860242, img_mean=0.53159297,
img_median=0.59087014),
cls.InterpolationTestCase('kriging interpolation',
KrigingInterpolation('NDVI', result_interval=(-10, 10),
resample_range=('2016-01-01', '2018-01-01', 5)),
result_len=147, img_min=-0.252500534, img_max=0.659086704, img_mean=0.3825493,
img_median=0.39931053),
cls.InterpolationTestCase('nearest resample', NearestResampling('NDVI', result_interval=(0.0, 1.0),
resample_range=('2016-01-01',
'2018-01-01', 5)),
result_len=147, img_min=-0.2, img_max=0.860242, img_mean=0.35143828,
img_median=0.37481314, nan_replace=-0.2),
cls.InterpolationTestCase('linear resample', LinearResampling('NDVI', result_interval=(0.0, 1.0),
resample_range=('2016-01-01',
'2018-01-01', 5)),
result_len=147, img_min=-0.2, img_max=0.8480114, img_mean=0.350186,
img_median=0.3393997, nan_replace=-0.2),
cls.InterpolationTestCase('cubic resample', CubicResampling('NDVI', result_interval=(-0.2, 1.0),
resample_range=('2015-01-01', '2018-01-01', 16),
unknown_value=5),
result_len=69, img_min=-0.2, img_max=5.0, img_mean=1.234881997,
img_median=0.465670556, nan_replace=-0.2),
cls.InterpolationTestCase('linear custom list', LinearInterpolation(
'NDVI', result_interval=(-0.2, 1.0),
resample_range=('2015-09-01', '2016-01-01', '2016-07-01', '2017-01-01', '2017-07-01'),
unknown_value=-2),
result_len=5, img_min=-0.032482587, img_max=0.8427637, img_mean=0.5108417,
img_median=0.5042224),
cls.InterpolationTestCase('linear with bands and multiple masks',
LinearInterpolation('BANDS-S2-L1C', result_interval=(0.0, 1.0), unknown_value=10,
mask_feature=[(FeatureType.MASK, 'IS_VALID'),
(FeatureType.MASK_TIMELESS, 'RANDOM_UINT8'),
(FeatureType.LABEL, 'RANDOM_DIGIT')]),
result_len=68, img_min=0.000200, img_max=10.0, img_mean=0.34815648,
img_median=0.1003600)
]
cls.copy_feature_cases = [
cls.InterpolationTestCase('cubic_copy_success',
CubicInterpolation('NDVI', result_interval=(0.0, 1.0),
mask_feature=(
FeatureType.MASK, 'IS_VALID'),
resample_range=(
'2015-01-01', '2018-01-01', 16),
unknown_value=5, bounds_error=False,
copy_features=[
(FeatureType.MASK, 'IS_VALID'),
(FeatureType.DATA, 'NDVI', 'NDVI_OLD'),
(FeatureType.MASK_TIMELESS, 'LULC'),
]),
result_len=69, img_min=0.0, img_max=5.0, img_mean=1.3592644,
img_median=0.6174331),
cls.InterpolationTestCase('cubic_copy_fail',
CubicInterpolation('NDVI', result_interval=(0.0, 1.0),
mask_feature=(FeatureType.MASK, 'IS_VALID'),
resample_range=(
'2015-01-01', '2018-01-01', 16),
unknown_value=5, bounds_error=False,
copy_features=[
(FeatureType.MASK, 'IS_VALID'),
(FeatureType.DATA, 'NDVI'),
(FeatureType.MASK_TIMELESS, 'LULC'),
]),
result_len=69, img_min=0.0, img_max=5.0, img_mean=1.3592644,
img_median=0.6174331),
]
for test_case in cls.test_cases:
test_case.execute()
for test_case in cls.copy_feature_cases:
try:
test_case.execute()
except ValueError:
pass
def setUpClass(cls):
cls.test_cases = [
cls.InterpolationTestCase('linear',
LinearInterpolation(
'ndvi',
result_interval=(-0.3, -0.1),
unknown_value=10),
result_len=180,
img_min=-0.3000,
img_max=10.0,
img_mean=-0.147169,
img_median=-0.21132),
cls.InterpolationTestCase(
'cubic',
CubicInterpolation('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2015-01-01', '2018-01-01',
16),
unknown_value=5),
result_len=69,
img_min=-0.3000,
img_max=5.0,
img_mean=1.30387,
img_median=-0.1007357),
cls.InterpolationTestCase(
'spline',
SplineInterpolation('ndvi',
result_interval=(-10, 10),
resample_range=('2016-01-01', '2018-01-01',
5),
spline_degree=3,
smoothing_factor=0.5),
result_len=147,
img_min=-0.431703,
img_max=0.116959,
img_mean=-0.1948325,
img_median=-0.1922528),
cls.InterpolationTestCase('bspline',
BSplineInterpolation(
'ndvi',
unknown_value=-3,
resample_range=('2017-01-01',
'2017-02-01', 50),
spline_degree=5),
result_len=1,
img_min=-0.161574,
img_max=-0.0116416,
img_mean=-0.085181,
img_median=-0.0856696),
cls.InterpolationTestCase('akima',
AkimaInterpolation('ndvi',
unknown_value=0),
result_len=180,
img_min=-0.4821199,
img_max=0.2299331,
img_mean=-0.20141865,
img_median=-0.213559),
cls.InterpolationTestCase(
'linear resample',
LinearResampling('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2016-01-01', '2018-01-01',
5)),
result_len=147,
img_min=-0.3000,
img_max=-0.1000,
img_mean=-0.1946315,
img_median=-0.20000,
nan_replace=-0.2),
cls.InterpolationTestCase(
'cubic resample',
CubicResampling('ndvi',
result_interval=(-0.3, -0.1),
resample_range=('2015-01-01', '2018-01-01',
16),
unknown_value=5),
result_len=69,
img_min=-0.3000,
img_max=5.0,
img_mean=1.22359,
img_median=-0.181167,
nan_replace=-0.2)
]
for test_case in cls.test_cases:
test_case.execute()