def test_multi_insert():
z = np.random.randint(10, size=(1, 2))
c = da.from_array(z, chunks=(1, 2))
assert_eq(
np.insert(np.insert(z, [0, 1], -1, axis=0), [1], -1, axis=1),
da.insert(da.insert(c, [0, 1], -1, axis=0), [1], -1, axis=1),
)
def _rmatvec(self, x):
if not self.inplace:
x = x.copy()
if not self.reshape:
y = da.insert(x, self.fill, 0, axis=-1)
else:
x = da.reshape(x, self.dimsd)
y = da.insert(x, self.fill, 0, axis=self.dir)
return y
def count(a):
"""counts number of distinct "on" switches in a boolean 1d array
and assigns them cumulatively
"""
a = a.astype(int)
count = da.cumsum(da.insert(da.maximum(a[1:] - a[:-1], 0), a[0], 0,
axis=0),
axis=0)
return da.where(a == True, count, 0)
def _rmatvec(self, x):
if self.reshape:
x = da.reshape(x,
da.insert([np.prod(self.dims)], 0, self.A.shape[0]))
y = self.A.conj().T.dot(x)
if self.reshape:
return y.ravel()
else:
return y
def weighting(spec_dist, temp_dist, comb_dist, similar_pixels_filtered):
# Assign max weight (1) when the temporal or spectral distance is zero
zero_spec_dist = da.where(spec_dist[:,mid_idx][:,None] == 1, 1, 0)
zero_temp_dist = da.where(temp_dist[:,mid_idx][:,None] == 1, 1, 0)
zero_dist_mid = da.where((zero_spec_dist == 1),
zero_spec_dist, zero_temp_dist)
shape = da.subtract(spec_dist.shape,(0,1))
zero_dist = da.zeros(shape, chunks=(spec_dist.shape[0],shape[1]))
zero_dist = da.insert(zero_dist, [mid_idx], zero_dist_mid, axis=1)
weights = da.where((da.sum(zero_dist,1)[:,None] == 1), zero_dist, comb_dist)
# Calculate weights only for the filtered spectrally similar pixels
weights_filt = weights*similar_pixels_filtered
# Normalize weights
norm_weights = da.rechunk(weights_filt/(da.sum(weights_filt,1)[:,None]),
chunks = spec_dist.chunksize)
print ("Done weighting!", norm_weights)
return norm_weights
def test_insert():
x = np.random.randint(10, size=(10, 10))
a = da.from_array(x, chunks=(5, 5))
y = np.random.randint(10, size=(5, 10))
b = da.from_array(y, chunks=(4, 4))
assert_eq(np.insert(x, 0, -1, axis=0), da.insert(a, 0, -1, axis=0))
assert_eq(np.insert(x, 3, -1, axis=-1), da.insert(a, 3, -1, axis=-1))
assert_eq(np.insert(x, 5, -1, axis=1), da.insert(a, 5, -1, axis=1))
assert_eq(np.insert(x, -1, -1, axis=-2), da.insert(a, -1, -1, axis=-2))
assert_eq(np.insert(x, [2, 3, 3], -1, axis=1),
da.insert(a, [2, 3, 3], -1, axis=1))
assert_eq(np.insert(x, [2, 3, 8, 8, -2, -2], -1, axis=0),
da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0))
assert_eq(np.insert(x, slice(1, 4), -1, axis=1),
da.insert(a, slice(1, 4), -1, axis=1))
assert_eq(np.insert(x, [2] * 3 + [5] * 2, y, axis=0),
da.insert(a, [2] * 3 + [5] * 2, b, axis=0))
assert_eq(np.insert(x, 0, y[0], axis=1), da.insert(a, 0, b[0], axis=1))
assert same_keys(da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0),
da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0))
with pytest.raises(NotImplementedError):
da.insert(a, [4, 2], -1, axis=0)
with pytest.raises(IndexError):
da.insert(a, [3], -1, axis=2)
with pytest.raises(IndexError):
da.insert(a, [3], -1, axis=-3)
def test_multi_insert():
z = np.random.randint(10, size=(1, 2))
c = da.from_array(z, chunks=(1, 2))
assert_eq(np.insert(np.insert(z, [0, 1], -1, axis=0), [1], -1, axis=1),
da.insert(da.insert(c, [0, 1], -1, axis=0), [1], -1, axis=1))
def test_insert():
x = np.random.randint(10, size=(10, 10))
a = da.from_array(x, chunks=(5, 5))
y = np.random.randint(10, size=(5, 10))
b = da.from_array(y, chunks=(4, 4))
assert_eq(np.insert(x, 0, -1, axis=0), da.insert(a, 0, -1, axis=0))
assert_eq(np.insert(x, 3, -1, axis=-1), da.insert(a, 3, -1, axis=-1))
assert_eq(np.insert(x, 5, -1, axis=1), da.insert(a, 5, -1, axis=1))
assert_eq(np.insert(x, -1, -1, axis=-2), da.insert(a, -1, -1, axis=-2))
assert_eq(np.insert(x, [2, 3, 3], -1, axis=1),
da.insert(a, [2, 3, 3], -1, axis=1))
assert_eq(np.insert(x, [2, 3, 8, 8, -2, -2], -1, axis=0),
da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0))
assert_eq(np.insert(x, slice(1, 4), -1, axis=1),
da.insert(a, slice(1, 4), -1, axis=1))
assert_eq(np.insert(x, [2] * 3 + [5] * 2, y, axis=0),
da.insert(a, [2] * 3 + [5] * 2, b, axis=0))
assert_eq(np.insert(x, 0, y[0], axis=1),
da.insert(a, 0, b[0], axis=1))
assert same_keys(da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0),
da.insert(a, [2, 3, 8, 8, -2, -2], -1, axis=0))
with pytest.raises(NotImplementedError):
da.insert(a, [4, 2], -1, axis=0)
with pytest.raises(IndexError):
da.insert(a, [3], -1, axis=2)
with pytest.raises(IndexError):
da.insert(a, [3], -1, axis=-3)
def insert(self, obj, values, axis=0):
return as_dask_column(da.insert(self, obj, values, axis=axis),
info=self.info)