def test_temporal_indices(self):
""" Test case for computation of argmax/argmin of NDVI and another band
Cases with and without data masking are tested
"""
# EOPatch
eopatch = EOPatch()
t, h, w, c = 5, 3, 3, 2
# NDVI
ndvi_shape = (t, h, w, 1)
# VAlid data mask
valid_data = np.ones(ndvi_shape, np.bool)
valid_data[0] = 0
valid_data[-1] = 0
# Fill in eopatch
eopatch.add_feature(FeatureType.DATA, 'NDVI',
np.arange(np.prod(ndvi_shape)).reshape(ndvi_shape))
eopatch.add_feature(FeatureType.MASK, 'IS_DATA',
np.ones(ndvi_shape, dtype=np.int16))
eopatch.add_feature(FeatureType.MASK, 'VALID_DATA', valid_data)
# Task
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=False)
# Run task
new_eopatch = add_ndvi(eopatch)
# Asserts
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI'],
np.zeros((h, w, 1))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI'],
(t - 1) * np.ones((h, w, 1))))
del add_ndvi, new_eopatch
# Repeat with valid dat amask
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=True)
new_eopatch = add_ndvi(eopatch)
# Asserts
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI'],
np.ones((h, w, 1))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI'],
(t - 2) * np.ones((h, w, 1))))
del add_ndvi, new_eopatch, valid_data
# BANDS
bands_shape = (t, h, w, c)
eopatch.add_feature(
FeatureType.DATA, 'BANDS',
np.arange(np.prod(bands_shape)).reshape(bands_shape))
add_bands = AddMaxMinTemporalIndicesTask(data_feature='BANDS',
data_index=1,
amax_data_feature='ARGMAX_B1',
amin_data_feature='ARGMIN_B1',
mask_data=False)
new_eopatch = add_bands(eopatch)
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMIN_B1'],
np.zeros((h, w, 1))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['ARGMAX_B1'],
(t - 1) * np.ones((h, w, 1))))
def test_stf_task(self):
""" Test case for computation of spatio-temporal features
The NDVI is a sinusoid over 0-pi over the temporal dimension, while bands is an array with values equal to
the temporal index
"""
# Timestamps
timestamp = perdelta(date(2018, 3, 1), date(2018, 3, 11),
timedelta(days=1))
# EOPatch
eopatch = EOPatch(timestamp=list(timestamp))
# Shape of arrays
t, h, w, c = 10, 3, 3, 2
# NDVI is a sinusoid where max slope is at index 1 and min slope at index 8
ndvi_shape = (t, h, w, 1)
bands_shape = (t, h, w, c)
xx = np.zeros(ndvi_shape, np.float32)
x = np.linspace(0, np.pi, t)
xx[:, :, :, :] = x[:, None, None, None]
# Bands are arrays with values equal to the temporal index
bands = np.ones(bands_shape) * np.arange(t)[:, None, None, None]
# Add features to eopatch
eopatch.add_feature(FeatureType.DATA, 'NDVI', np.sin(xx))
eopatch.add_feature(FeatureType.DATA, 'BANDS', bands)
eopatch.add_feature(FeatureType.MASK, 'IS_DATA',
np.ones(ndvi_shape, np.bool))
# Tasks
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=False)
add_bands = AddMaxMinTemporalIndicesTask(data_feature='BANDS',
data_index=1,
amax_data_feature='ARGMAX_B1',
amin_data_feature='ARGMIN_B1',
mask_data=False)
add_ndvi_slope = AddMaxMinNDVISlopeIndicesTask(mask_data=False)
add_stf = AddSpatioTemporalFeaturesTask(argmax_red='ARGMAX_B1',
data_feature='BANDS',
indices=[0, 1])
# Run tasks
new_eopatch = add_stf(add_ndvi_slope(add_bands(add_ndvi(eopatch))))
# Asserts
self.assertTrue(new_eopatch.data_timeless['STF'].shape == (h, w,
c * 5))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['STF'][:, :, 0:c],
4 * np.ones((h, w, c))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['STF'][:, :, c:2 * c],
9 * np.ones((h, w, c))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['STF'][:, :, 2 * c:3 * c],
np.ones((h, w, c))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['STF'][:, :, 3 * c:4 * c],
8 * np.ones((h, w, c))))
self.assertTrue(
np.array_equal(new_eopatch.data_timeless['STF'][:, :, 4 * c:5 * c],
9 * np.ones((h, w, c))))
def test_ndvi_slope_indices():
timestamp = [date(2018, 3, 1) + timedelta(days=x) for x in range(11)]
eopatch = EOPatch(timestamp=list(timestamp))
t, h, w, = (
10,
3,
3,
)
ndvi_shape = (t, h, w, 1)
xx = np.zeros(ndvi_shape, np.float32)
x = np.linspace(0, np.pi, t)
xx[:, :, :, :] = x[:, None, None, None]
eopatch[FeatureType.DATA, "NDVI"] = np.sin(xx)
valid_data = np.ones(ndvi_shape, np.uint8)
valid_data[1] = 0
valid_data[-1] = 0
valid_data[4] = 0
eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, bool)
eopatch[FeatureType.MASK, "VALID_DATA"] = valid_data
add_ndvi_task = AddMaxMinTemporalIndicesTask(mask_data=False)
add_ndvi_slope_task = AddMaxMinNDVISlopeIndicesTask(mask_data=False)
new_eopatch = add_ndvi_slope_task(add_ndvi_task(eopatch))
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"],
(t - 1) * np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"],
(t // 2 - 1) * np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI_SLOPE"],
(t - 2) * np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI_SLOPE"],
np.ones((h, w, 1)))
add_ndvi_task = AddMaxMinTemporalIndicesTask(mask_data=True)
add_ndvi_slope_task = AddMaxMinNDVISlopeIndicesTask(mask_data=True)
new_eopatch = add_ndvi_slope_task(add_ndvi_task(eopatch))
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"], 0 * np.ones(
(h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"],
(t // 2) * np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI_SLOPE"],
(t - 3) * np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI_SLOPE"],
2 * np.ones((h, w, 1)))
def test_ndvi_slope_indices(self):
""" Test case for computation of argmax/argmin of NDVI slope
The NDVI is a sinusoid over 0-pi over the temporal dimension
Cases with and without data masking are tested
"""
# Slope needs timestamps
timestamp = perdelta(date(2018, 3, 1), date(2018, 3, 11), timedelta(days=1))
# EOPatch
eopatch = EOPatch(timestamp=list(timestamp))
t, h, w, = 10, 3, 3
# NDVI is a sinusoid where max slope is at index 1 and min slope at index 8
ndvi_shape = (t, h, w, 1)
xx = np.zeros(ndvi_shape, np.float32)
x = np.linspace(0, np.pi, t)
xx[:, :, :, :] = x[:, None, None, None]
# Valid data mask
valid_data = np.ones(ndvi_shape, np.uint8)
valid_data[1] = 0
valid_data[-1] = 0
valid_data[4] = 0
# Fill EOPatch
eopatch.add_feature(FeatureType.DATA, 'NDVI', np.sin(xx))
eopatch.add_feature(FeatureType.MASK, 'IS_DATA', np.ones(ndvi_shape, np.bool))
eopatch.add_feature(FeatureType.MASK, 'VALID_DATA', valid_data)
# Tasks
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=False)
add_ndvi_slope = AddMaxMinNDVISlopeIndicesTask(mask_data=False)
# Run
new_eopatch = add_ndvi_slope(add_ndvi(eopatch))
# Assert
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI'], (t-1)*np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI'], (t//2-1)*np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI_SLOPE'], (t-2)*np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI_SLOPE'], np.ones((h, w, 1))))
del add_ndvi_slope, add_ndvi, new_eopatch
# Run on valid data only now
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=True)
add_ndvi_slope = AddMaxMinNDVISlopeIndicesTask(mask_data=True)
# Run
new_eopatch = add_ndvi_slope(add_ndvi(eopatch))
# Assert
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI'], 0 * np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI'], (t // 2) * np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMIN_NDVI_SLOPE'], (t - 3) * np.ones((h, w, 1))))
self.assertTrue(np.array_equal(new_eopatch.data_timeless['ARGMAX_NDVI_SLOPE'], 2 * np.ones((h, w, 1))))
def test_temporal_indices():
eopatch = EOPatch()
t, h, w, c = 5, 3, 3, 2
ndvi_shape = (t, h, w, 1)
eopatch[FeatureType.DATA,
"NDVI"] = np.arange(np.prod(ndvi_shape)).reshape(ndvi_shape)
valid_data = np.ones(ndvi_shape, bool)
valid_data[0] = 0
valid_data[-1] = 0
eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, dtype=np.int16)
eopatch[FeatureType.MASK, "VALID_DATA"] = valid_data
new_eopatch = AddMaxMinTemporalIndicesTask(mask_data=False)(eopatch)
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"],
np.zeros((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"],
(t - 1) * np.ones((h, w, 1)))
new_eopatch = AddMaxMinTemporalIndicesTask(mask_data=True)(eopatch)
assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"],
np.ones((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"],
(t - 2) * np.ones((h, w, 1)))
bands_shape = (t, h, w, c)
eopatch[FeatureType.DATA,
"BANDS"] = np.arange(np.prod(bands_shape)).reshape(bands_shape)
add_bands = AddMaxMinTemporalIndicesTask(
data_feature="BANDS",
data_index=1,
amax_data_feature="ARGMAX_B1",
amin_data_feature="ARGMIN_B1",
mask_data=False,
)
new_eopatch = add_bands(eopatch)
assert_array_equal(new_eopatch.data_timeless["ARGMIN_B1"],
np.zeros((h, w, 1)))
assert_array_equal(new_eopatch.data_timeless["ARGMAX_B1"],
(t - 1) * np.ones((h, w, 1)))
def test_stf_task():
timestamp = [date(2018, 3, 1) + timedelta(days=x) for x in range(11)]
eopatch = EOPatch(timestamp=list(timestamp))
t, h, w, c = 10, 3, 3, 2
# NDVI is a sinusoid where max slope is at index 1 and min slope at index 8
ndvi_shape = (t, h, w, 1)
bands_shape = (t, h, w, c)
xx = np.zeros(ndvi_shape, np.float32)
x = np.linspace(0, np.pi, t)
xx[:, :, :, :] = x[:, None, None, None]
bands = np.ones(bands_shape) * np.arange(t)[:, None, None, None]
eopatch[FeatureType.DATA, "NDVI"] = np.sin(xx)
eopatch[FeatureType.DATA, "BANDS"] = bands
eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, bool)
add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=False)
add_bands = AddMaxMinTemporalIndicesTask(
data_feature="BANDS",
data_index=1,
amax_data_feature="ARGMAX_B1",
amin_data_feature="ARGMIN_B1",
mask_data=False,
)
add_ndvi_slope = AddMaxMinNDVISlopeIndicesTask(mask_data=False)
add_stf = AddSpatioTemporalFeaturesTask(argmax_red="ARGMAX_B1",
data_feature="BANDS",
indices=[0, 1])
new_eopatch = add_stf(add_ndvi_slope(add_bands(add_ndvi(eopatch))))
result = new_eopatch.data_timeless["STF"]
assert result.shape == (h, w, c * 5)
assert_array_equal(result[:, :, 0:c], 4 * np.ones((h, w, c)))
assert_array_equal(result[:, :, c:2 * c], 9 * np.ones((h, w, c)))
assert_array_equal(result[:, :, 2 * c:3 * c], np.ones((h, w, c)))
assert_array_equal(result[:, :, 3 * c:4 * c], 8 * np.ones((h, w, c)))
assert_array_equal(result[:, :, 4 * c:5 * c], 9 * np.ones((h, w, c)))