def regrid(array, source_x_coords, source_y_coords, source_proj, target_proj,
target_x_points, target_y_points, mask_extrapolated=False):
"""
Regrid the data array from the source projection to the target projection.
Parameters
----------
array
The :class:`numpy.ndarray` of data to be regridded to the
target projection.
source_x_coords
A 2-dimensional source projection :class:`numpy.ndarray` of
x-direction sample points.
source_y_coords
A 2-dimensional source projection :class:`numpy.ndarray` of
y-direction sample points.
source_proj
The source :class:`~cartopy.crs.Projection` instance.
target_proj
The target :class:`~cartopy.crs.Projection` instance.
target_x_points
A 2-dimensional target projection :class:`numpy.ndarray` of
x-direction sample points.
target_y_points
A 2-dimensional target projection :class:`numpy.ndarray` of
y-direction sample points.
mask_extrapolated: optional
Assume that the source coordinate is rectilinear and so mask the
resulting target grid values which lie outside the source grid domain.
Defaults to False.
Returns
-------
new_array
The data array regridded in the target projection.
"""
# Stack our original xyz array, this will also wrap coords when necessary
xyz = source_proj.transform_points(source_proj,
source_x_coords.flatten(),
source_y_coords.flatten())
# Transform the target points into the source projection
target_xyz = source_proj.transform_points(target_proj,
target_x_points.flatten(),
target_y_points.flatten())
if _is_pykdtree:
kdtree = pykdtree.kdtree.KDTree(xyz)
# Use sqr_dists=True because we don't care about distances,
# and it saves a sqrt.
_, indices = kdtree.query(target_xyz, k=1, sqr_dists=True)
else:
# Versions of scipy >= v0.16 added the balanced_tree argument,
# which caused the KDTree to hang with this input.
kdtree = scipy.spatial.cKDTree(xyz, balanced_tree=False)
_, indices = kdtree.query(target_xyz, k=1)
mask = indices >= len(xyz)
indices[mask] = 0
desired_ny, desired_nx = target_x_points.shape
# Squash the first two dims of the source array into one
temp_array = array.reshape((-1,) + array.shape[2:])
if np.any(mask):
new_array = np.ma.array(temp_array[indices])
new_array[mask] = np.ma.masked
else:
new_array = temp_array[indices]
new_array.shape = (desired_ny, desired_nx) + (array.shape[2:])
# Do double transform to clip points that do not map back and forth
# to the same point to within a fixed fractional offset.
# NOTE: This only needs to be done for (pseudo-)cylindrical projections,
# or any others which have the concept of wrapping
back_to_target_xyz = target_proj.transform_points(source_proj,
target_xyz[:, 0],
target_xyz[:, 1])
back_to_target_x = back_to_target_xyz[:, 0].reshape(desired_ny,
desired_nx)
back_to_target_y = back_to_target_xyz[:, 1].reshape(desired_ny,
desired_nx)
FRACTIONAL_OFFSET_THRESHOLD = 0.1 # data has moved by 10% of the map
x_extent = np.abs(target_proj.x_limits[1] - target_proj.x_limits[0])
y_extent = np.abs(target_proj.y_limits[1] - target_proj.y_limits[0])
non_self_inverse_points = (((np.abs(target_x_points - back_to_target_x) /
x_extent) > FRACTIONAL_OFFSET_THRESHOLD) |
((np.abs(target_y_points - back_to_target_y) /
y_extent) > FRACTIONAL_OFFSET_THRESHOLD))
if np.any(non_self_inverse_points):
if not np.ma.isMaskedArray(new_array):
new_array = np.ma.array(new_array, mask=False)
new_array[non_self_inverse_points] = np.ma.masked
# Transform the target points to the source projection and mask any points
# that fall outside the original source domain.
if mask_extrapolated:
target_in_source_x = target_xyz[:, 0].reshape(desired_ny,
desired_nx)
target_in_source_y = target_xyz[:, 1].reshape(desired_ny,
desired_nx)
bounds = _determine_bounds(source_x_coords, source_y_coords,
source_proj)
outside_source_domain = ((target_in_source_y >= bounds['y'][1]) |
(target_in_source_y <= bounds['y'][0]))
tmp_inside = np.zeros_like(outside_source_domain)
for bound_x in bounds['x']:
tmp_inside = tmp_inside | ((target_in_source_x <= bound_x[1]) &
(target_in_source_x >= bound_x[0]))
outside_source_domain = outside_source_domain | ~tmp_inside
if np.any(outside_source_domain):
if not np.ma.isMaskedArray(new_array):
new_array = np.ma.array(new_array, mask=False)
new_array[outside_source_domain] = np.ma.masked
return new_array
def regrid(array,
source_x_coords,
source_y_coords,
source_cs,
target_proj,
target_x_points,
target_y_points,
mask_extrapolated=False):
"""
Regrid the data array from the source projection to the target projection.
Parameters
----------
array
The :class:`numpy.ndarray` of data to be regridded to the
target projection.
source_x_coords
A 2-dimensional source projection :class:`numpy.ndarray` of
x-direction sample points.
source_y_coords
A 2-dimensional source projection :class:`numpy.ndarray` of
y-direction sample points.
source_cs
The source :class:`~cartopy.crs.Projection` instance.
target_cs
The target :class:`~cartopy.crs.Projection` instance.
target_x_points
A 2-dimensional target projection :class:`numpy.ndarray` of
x-direction sample points.
target_y_points
A 2-dimensional target projection :class:`numpy.ndarray` of
y-direction sample points.
mask_extrapolated: optional
Assume that the source coordinate is rectilinear and so mask the
resulting target grid values which lie outside the source grid domain.
Defaults to False.
Returns
-------
new_array
The data array regridded in the target projection.
"""
# n.b. source_cs is actually a projection (the coord system of the
# source coordinates), but not necessarily the native projection of
# the source array (i.e. you can provide a warped image with lat lon
# coordinates).
# XXX NB. target_x and target_y must currently be rectangular (i.e.
# be a 2d np array)
geo_cent = source_cs.as_geocentric()
xyz = geo_cent.transform_points(source_cs, source_x_coords.flatten(),
source_y_coords.flatten())
target_xyz = geo_cent.transform_points(target_proj,
target_x_points.flatten(),
target_y_points.flatten())
if _is_pykdtree:
kdtree = pykdtree.kdtree.KDTree(xyz)
# Use sqr_dists=True because we don't care about distances,
# and it saves a sqrt.
_, indices = kdtree.query(target_xyz, k=1, sqr_dists=True)
else:
# Versions of scipy >= v0.16 added the balanced_tree argument,
# which caused the KDTree to hang with this input.
try:
kdtree = scipy.spatial.cKDTree(xyz, balanced_tree=False)
except TypeError:
kdtree = scipy.spatial.cKDTree(xyz)
_, indices = kdtree.query(target_xyz, k=1)
mask = indices >= len(xyz)
indices[mask] = 0
desired_ny, desired_nx = target_x_points.shape
# Squash the first two dims of the source array into one
temp_array = array.reshape((-1, ) + array.shape[2:])
if np.any(mask):
new_array = np.ma.array(temp_array[indices])
new_array[mask] = np.ma.masked
else:
new_array = temp_array[indices]
new_array.shape = (desired_ny, desired_nx) + (array.shape[2:])
# Do double transform to clip points that do not map back and forth
# to the same point to within a fixed fractional offset.
# XXX THIS ONLY NEEDS TO BE DONE FOR (PSEUDO-)CYLINDRICAL PROJECTIONS
# (OR ANY OTHERS WHICH HAVE THE CONCEPT OF WRAPPING)
source_desired_xyz = source_cs.transform_points(target_proj,
target_x_points.flatten(),
target_y_points.flatten())
back_to_target_xyz = target_proj.transform_points(source_cs,
source_desired_xyz[:, 0],
source_desired_xyz[:, 1])
back_to_target_x = back_to_target_xyz[:, 0].reshape(desired_ny, desired_nx)
back_to_target_y = back_to_target_xyz[:, 1].reshape(desired_ny, desired_nx)
FRACTIONAL_OFFSET_THRESHOLD = 0.1 # data has moved by 10% of the map
x_extent = np.abs(target_proj.x_limits[1] - target_proj.x_limits[0])
y_extent = np.abs(target_proj.y_limits[1] - target_proj.y_limits[0])
non_self_inverse_points = (((np.abs(target_x_points - back_to_target_x) /
x_extent) > FRACTIONAL_OFFSET_THRESHOLD) |
((np.abs(target_y_points - back_to_target_y) /
y_extent) > FRACTIONAL_OFFSET_THRESHOLD))
if np.any(non_self_inverse_points):
if not np.ma.isMaskedArray(new_array):
new_array = np.ma.array(new_array, mask=False)
new_array[non_self_inverse_points] = np.ma.masked
# Transform the target points to the source projection and mask any points
# that fall outside the original source domain.
if mask_extrapolated:
target_in_source_xyz = source_cs.transform_points(
target_proj, target_x_points, target_y_points)
target_in_source_x = target_in_source_xyz[..., 0]
target_in_source_y = target_in_source_xyz[..., 1]
bounds = _determine_bounds(source_x_coords, source_y_coords, source_cs)
outside_source_domain = ((target_in_source_y >= bounds['y'][1]) |
(target_in_source_y <= bounds['y'][0]))
tmp_inside = np.zeros_like(outside_source_domain)
for bound_x in bounds['x']:
tmp_inside = tmp_inside | ((target_in_source_x <= bound_x[1]) &
(target_in_source_x >= bound_x[0]))
outside_source_domain = outside_source_domain | ~tmp_inside
if np.any(outside_source_domain):
if not np.ma.isMaskedArray(new_array):
new_array = np.ma.array(new_array, mask=False)
new_array[outside_source_domain] = np.ma.masked
return new_array