def empty3d_twt(request):
iln, xln, n, dom = request.param
return create3d_dataset((iln, xln, n),
sample_rate=1,
first_iline=1,
first_xline=1,
vert_domain=dom)
def test_full_stack_dataset_xysel(self, d, translate, scale, shear, rotate,
samp):
dataset = create3d_dataset(dims=d)
xlines_, ilines_ = np.meshgrid(dataset.xline, dataset.iline)
ix_pairs = np.dstack([ilines_, xlines_])
tr = Affine.translation(*translate)
sc = Affine.scale(scale)
sh = Affine.shear(*shear)
rt = Affine.rotation(rotate)
trsfm = lambda x: tr * sc * sh * rt * x
ix_pairs = np.apply_along_axis(trsfm, 1, ix_pairs.reshape(
-1, 2)).reshape(ix_pairs.shape)
dataset["cdp_x"] = (DimensionKeyField.cdp_3d, ix_pairs[:, :, 0])
dataset["cdp_y"] = (DimensionKeyField.cdp_3d, ix_pairs[:, :, 1])
dataset["data"] = (DimensionKeyField.threed_twt, np.random.rand(*d))
test_points = np.dstack([
np.random.random(samp) * (d[0] - 0.1) + 0.1,
np.random.random(samp) * (d[1] - 0.1) + 0.1,
])[0]
# make sure at least one point is in the box
test_points[0, :] = d[0] / 2, d[1] / 2
xys = np.apply_along_axis(trsfm, 1, test_points)
res = dataset.seis.xysel(xys[:, 0], xys[:, 1], method="linear")
assert isinstance(res, xr.Dataset)
res = dataset.seis.xysel(xys[:, 0], xys[:, 1], method="nearest")
assert isinstance(res, xr.Dataset)
def zeros3d(request):
iln, xln, n, dom = request.param
seisnc = create3d_dataset((iln, xln, n),
sample_rate=1,
first_iline=1,
first_xline=1,
vert_domain=dom)
seisnc = seisnc.seis.zeros_like()
return seisnc
def empty3d_gath(request):
iln, xln, n, off, dom = request.param
return create3d_dataset(
(iln, xln, n, off),
sample_rate=1,
first_iline=1,
first_xline=1,
vert_domain=dom,
first_offset=2,
offset_step=2,
)
def test_angle_stack_dataset_xysel(self, o, f, s, translate, scale, shear,
rotate, samp):
dims = (50, 30, 5, o)
dataset = create3d_dataset(dims, first_offset=f, offset_step=s)
xlines_, ilines_ = np.meshgrid(dataset.xline, dataset.iline)
ix_pairs = np.dstack([ilines_, xlines_])
tr = Affine.translation(*translate)
sc = Affine.scale(scale)
sh = Affine.shear(*shear)
rt = Affine.rotation(rotate)
trsfm = lambda x: tr * sc * sh * rt * x
ix_pairs = np.apply_along_axis(trsfm, 1, ix_pairs.reshape(
-1, 2)).reshape(ix_pairs.shape)
dataset['cdp_x'] = (DimensionKeyField.cdp_3d, ix_pairs[:, :, 0])
dataset['cdp_y'] = (DimensionKeyField.cdp_3d, ix_pairs[:, :, 1])
if dataset.seis.is_depth():
dataset['data'] = (DimensionKeyField.threed_ps_depth,
np.random.rand(*dims))
else:
dataset['data'] = (DimensionKeyField.threed_ps_twt,
np.random.rand(*dims))
test_points = np.dstack([
np.random.random(samp) * (dims[0] - 0.1) + 0.1,
np.random.random(samp) * (dims[1] - 0.1) + 0.1
])[0]
# make sure at least one point is in the box
test_points[0, :] = dims[0] / 2, dims[1] / 2
xys = np.apply_along_axis(trsfm, 1, test_points)
res = dataset.seis.xysel(xys[:, 0], xys[:, 1], method='linear')
assert isinstance(res, xr.Dataset)
res = dataset.seis.xysel(xys[:, 0], xys[:, 1], method='nearest')
assert isinstance(res, xr.Dataset)
