def sample_size_f(a, axis=None, masked=False):
"""Return the sample size.
:Parameters:
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
:Returns:
`numpy.ndarray`
"""
if masked:
N = numpy_sum(~a.mask, axis=axis, dtype=float)
if not numpy_ndim(N):
N = numpy_asanyarray(N)
else:
if axis is None:
N = numpy_array(a.size, dtype=float)
else:
shape = a.shape
N = numpy_empty(shape[:axis] + shape[axis + 1 :], dtype=float)
N[...] = shape[axis]
# --- End: if
return asanyarray(N)
def make_surface(array):
"""pygame.surfarray.make_surface (array): return Surface
Copy an array to a new surface.
Create a new Surface that best resembles the data and format on the
array. The array can be 2D or 3D with any sized integer values.
"""
if isinstance(array, numpy_ndarray) and array.dtype in numpy_floats:
array = numpy_rint(array, numpy_empty(array.shape, dtype=numpy_uint32))
return pix_make_surface (array)
def blit_array (surface, array):
"""pygame.surfarray.blit_array(Surface, array): return None
Blit directly from a array values.
Directly copy values from an array into a Surface. This is faster than
converting the array into a Surface and blitting. The array must be the
same dimensions as the Surface and will completely replace all pixel
values. Only integer, ascii character and record arrays are accepted.
This function will temporarily lock the Surface as the new values are
copied.
"""
if isinstance(array, numpy_ndarray) and array.dtype in numpy_floats:
array = numpy_rint(array, numpy_empty(array.shape, dtype=numpy_uint32))
return array_to_surface(surface, array)
def __getitem__(self, indices):
"""x.__getitem__(indices) <==> x[indices]
Returns a numpy array.
"""
if indices is Ellipsis:
array_shape = self.shape
else:
array_shape = []
for index in parse_indices(self.shape, indices):
if isinstance(index, slice):
step = index.step
if step == 1:
array_shape.append(index.stop - index.start)
elif step == -1:
stop = index.stop
if stop is None:
array_shape.append(index.start + 1)
else:
array_shape.append(index.start - index.stop)
else:
stop = index.stop
if stop is None:
stop = -1
a, b = divmod(stop - index.start, step)
if b:
a += 1
array_shape.append(a)
else:
array_shape.append(len(index))
# --- End: if
if self.fill_value() is cf_masked:
return numpy_ma_masked_all(array_shape, dtype=self.dtype)
elif self.fill_value() is not None:
return numpy_full(array_shape,
fill_value=self.fill_value(),
dtype=self.dtype)
else:
return numpy_empty(array_shape, dtype=self.dtype)
def map_array(surface, array):
"""pygame.numpyarray.map_array(Surface, array3d): return array2d
map a 3d array into a 2d array
Convert a 3D array into a 2D array. This will use the given Surface
format to control the conversion.
Note: arrays do not need to be 3D, as long as the minor axis has
three elements giving the component colours, any array shape can be
used (for example, a single colour can be mapped, or an array of
colours). The array shape is limited to eleven dimensions maximum,
including the three element minor axis.
"""
if array.ndim == 0:
raise ValueError("array must have at least 1 dimension")
shape = array.shape
if shape[-1] != 3:
raise ValueError("array must be a 3d array of 3-value color data")
target = numpy_empty(shape[:-1], numpy.int32)
pix_map_array(target, array, surface)
return target
def map_array(surface, array):
"""pygame.numpyarray.map_array(Surface, array3d): return array2d
map a 3d array into a 2d array
Convert a 3D array into a 2D array. This will use the given Surface
format to control the conversion.
Note: arrays do not need to be 3D, as long as the minor axis has
three elements giving the component colours, any array shape can be
used (for example, a single colour can be mapped, or an array of
colours). The array shape is limited to eleven dimensions maximum,
including the three element minor axis.
"""
if array.ndim == 0:
raise ValueError("array must have at least 1 dimension")
shape = array.shape
if shape[-1] != 3:
raise ValueError("array must be a 3d array of 3-value color data")
target = numpy_empty(shape[:-1], numpy.int32)
pix_map_array(target, array, surface)
return target
def sample_size_f(a, axis=None, masked=False):
'''TODO
:Parameters:
axis: `int`, optional
non-negative
'''
if masked:
N = numpy_sum(~a.mask, axis=axis, dtype=float)
if not numpy_ndim(N):
N = numpy_asanyarray(N)
else:
if axis is None:
N = numpy_array(a.size, dtype=float)
else:
shape = a.shape
N = numpy_empty(shape[:axis] + shape[axis + 1:], dtype=float)
N[...] = shape[axis]
# --- End: if
return asanyarray(N)
def create_bounds(self,
bound=None,
cellsize=None,
flt=0.5,
max=None,
min=None):
"""Create cell bounds.
:Parameters:
bound: optional
If set to a value larger (smaller) than the largest
(smallest) coordinate value then bounds are created which
include this value and for which each coordinate is in the
centre of its bounds.
cellsize: optional
Define the exact size of each cell that is
created. Created cells are allowed to overlap do not have
to be contigious. The *cellsize* parameter may be one of:
* A data-like scalar (see below) that defines the cell size,
either in the same units as the coordinates or in the
units provided. Note that in this case, the position of
each coordinate within the its cell is controlled by the
*flt* parameter.
*Parameter example:*
To specify cellsizes of 10, in the same units as the
coordinates: ``cellsize=10``.
*Parameter example:*
To specify cellsizes of 1 day: ``cellsize=cf.Data(1,
'day')`` (see `cf.Data` for details).
*Parameter example:*
For coordinates ``1, 2, 10``, setting ``cellsize=1``
will result in bounds of ``(0.5, 1.5), (1.5, 2.5),
(9.5, 10.5)``.
*Parameter example:*
For coordinates ``1, 2, 10`` kilometres, setting
``cellsize=cf.Data(5000, 'm')`` will result in bounds
of ``(-1.5, 3.5), (-0.5, 4.5), (7.5, 12.5)`` (see
`cf.Data` for details).
*Parameter example:*
For decreasing coordinates ``2, 0, -12`` setting,
``cellsize=2`` will result in bounds of ``(3, 1),
(1, -1), (-11, -13)``.
* A `cf.TimeDuration` defining the cell size. Only
applicable to reference time coordinates. It is possible
to "anchor" the cell bounds via the `cf.TimeDuration`
parameters. For example, to specify cell size of one
calendar month, starting and ending on the 15th day:
``cellsize=cf.M(day=15)`` (see `cf.M` for details). Note
that the *flt* parameter is ignored in this case.
*Parameter example:*
For coordinates ``1984-12-01 12:00, 1984-12-02
12:00, 2000-04-15 12:00`` setting,
``cellsize=cf.D()`` will result in bounds of
``(1984-12-01, 1984-12-02), (1984-12-02,
1984-12-03), (2000-05-15, 2000-04-16)`` (see `cf.D`
for details).
*Parameter example:*
For coordinates ``1984-12-01, 1984-12-02,
2000-04-15`` setting, ``cellsize=cf.D()`` will
result in bounds of ``(1984-12-01, 1984-12-02),
(1984-12-02, 1984-12-03), (2000-05-15, 2000-04-16)``
(see `cf.D` for details).
*Parameter example:*
For coordinates ``1984-12-01, 1984-12-02,
2000-04-15`` setting, ``cellsize=cf.D(hour=12)``
will result in bounds of ``(1984-11:30 12:00,
1984-12-01 12:00), (1984-12-01 12:00, 1984-12-02
12:00), (2000-05-14 12:00, 2000-04-15 12:00)`` (see
`cf.D` for details).
*Parameter example:*
For coordinates ``1984-12-16 12:00, 1985-01-16
12:00`` setting, ``cellsize=cf.M()`` will result in
bounds of ``(1984-12-01, 1985-01-01), (1985-01-01,
1985-02-01)`` (see `cf.M` for details).
*Parameter example:*
For coordinates ``1984-12-01 12:00, 1985-01-01
12:00`` setting, ``cellsize=cf.M()`` will result in
bounds of ``(1984-12-01, 1985-01-01), (1985-01-01,
1985-02-01)`` (see `cf.M` for details).
*Parameter example:*
For coordinates ``1984-12-01 12:00, 1985-01-01
12:00`` setting, ``cellsize=cf.M(day=20)`` will
result in bounds of ``(1984-11-20, 1984-12-20),
(1984-12-20, 1985-01-20)`` (see `cf.M` for details).
*Parameter example:*
For coordinates ``1984-03-01, 1984-06-01`` setting,
``cellsize=cf.Y()`` will result in bounds of
``(1984-01-01, 1985-01-01), (1984-01-01,
1985-01-01)`` (see `cf.Y` for details). Note that in
this case each cell has the same bounds. This
because ``cf.Y()`` is equivalent to ``cf.Y(month=1,
day=1)`` and the closest 1st January to both
coordinates is 1st January 1984.
{+data-like-scalar} TODO
flt: `float`, optional
When creating cells with sizes specified by the *cellsize*
parameter, define the fraction of the each cell which is
less its coordinate value. By default *flt* is 0.5, so that
each cell has its coordinate at it's centre. Ignored if
*cellsize* is not set.
*Parameter example:*
For coordinates ``1, 2, 10``, setting ``cellsize=1,
flt=0.5`` will result in bounds of ``(0.5, 1.5), (1.5,
2.5), (9.5, 10.5)``.
*Parameter example:*
For coordinates ``1, 2, 10``, setting ``cellsize=1,
flt=0.25`` will result in bounds of ``(0.75, 1.75),
(1.75, 2.75), (9.75, 10.75)``.
*Parameter example:*
For decreasing coordinates ``2, 0, -12``, setting
``cellsize=6, flt=0.9`` will result in bounds of
``(2.6, -3.4), (0.6, -5.4), (-11.4, -17.4)``.
max: optional
Limit the created bounds to be no more than this number.
*Parameter example:*
To ensure that all latitude bounds are at most 90:
``max=90``.
min: optional
Limit the created bounds to be no less than this number.
*Parameter example:*
To ensure that all latitude bounds are at least -90:
``min=-90``.
:Returns:
`Bounds`
The newly-created coordinate cell bounds object.
**Examples:**
>>> c.create_bounds()
>>> c.create_bounds(bound=-9000.0)
"""
array = self.array
size = array.size
if cellsize is not None:
if bound:
raise ValueError(
"bound parameter can't be True when setting the "
"cellsize parameter")
if not isinstance(cellsize, TimeDuration):
# ----------------------------------------------------
# Create bounds based on cell sizes defined by a
# data-like object
#
# E.g. cellsize=10
# cellsize=cf.Data(1, 'day')
# ----------------------------------------------------
cellsize = Data.asdata(abs(cellsize))
if cellsize.Units:
if self.Units.isreftime:
if not cellsize.Units.istime:
raise ValueError("q123423423jhgsjhbd jh ")
cellsize.Units = Units(self.Units._utime.units)
else:
if not cellsize.Units.equivalent(self.Units):
raise ValueError("jhgsjhbd jh ")
cellsize.Units = self.Units
cellsize = cellsize.datum()
cellsize0 = cellsize * flt
cellsize1 = cellsize * (1 - flt)
if not self.direction():
cellsize0, cellsize1 = -cellsize1, -cellsize0
bounds = numpy_empty((size, 2), dtype=array.dtype)
bounds[:, 0] = array - cellsize0
bounds[:, 1] = array + cellsize1
else:
# ----------------------------------------------------
# Create bounds based on cell sizes defined by a
# TimeDuration object
#
# E.g. cellsize=cf.s()
# cellsize=cf.m()
# cellsize=cf.h()
# cellsize=cf.D()
# cellsize=cf.M()
# cellsize=cf.Y()
# cellsize=cf.D(hour=12)
# cellsize=cf.M(day=16)
# cellsize=cf.M(2)
# cellsize=cf.M(2, day=15, hour=12)
# ----------------------------------------------------
if not self.Units.isreftime:
raise ValueError(
"Can't create reference time bounds for "
"non-reference time coordinates: {0!r}".format(
self.Units))
bounds = numpy_empty((size, 2), dtype=object)
cellsize_bounds = cellsize.bounds
direction = bool(self.direction())
for c, b in zip(self.datetime_array, bounds):
b[...] = cellsize_bounds(c, direction=direction)
else:
if bound is None:
# ----------------------------------------------------
# Creat Voronoi bounds
# ----------------------------------------------------
if size < 2:
raise ValueError(
"Can't create bounds for Voronoi cells from one value")
bounds_1d = [array.item(0, ) * 1.5 - array.item(1, ) * 0.5]
bounds_1d.extend((array[0:-1] + array[1:]) * 0.5)
bounds_1d.append(
array.item(-1, ) * 1.5 - array.item(-2, ) * 0.5)
dtype = type(bounds_1d[0])
if max is not None:
if self.direction():
bounds_1d[-1] = max
else:
bounds_1d[0] = max
if min is not None:
if self.direction():
bounds_1d[0] = min
else:
bounds_1d[-1] = min
else:
# ----------------------------------------------------
# Create
# ----------------------------------------------------
direction = self.direction()
if not direction and size > 1:
array = array[::-1]
bounds_1d = [bound]
if bound <= array.item(0, ):
for i in range(size):
bound = (2.0 * array.item(i, ) - bound)
bounds_1d.append(bound)
elif bound >= array.item(-1, ):
for i in range(size - 1, -1, -1):
bound = (2.0 * array.item(i, ) - bound)
bounds_1d.append(bound)
bounds_1d = bounds_1d[::-1]
else:
raise ValueError("bad bound value")
dtype = type(bounds_1d[-1])
if not direction:
bounds_1d = bounds_1d[::-1]
bounds = numpy_empty((size, 2), dtype=dtype)
bounds[:, 0] = bounds_1d[:-1]
bounds[:, 1] = bounds_1d[1:]
# Create coordinate bounds object
bounds = Bounds(data=Data(bounds, units=self.Units), copy=False)
return bounds
def add_partitions(self, adimensions, master_flip, extra_boundaries, axis):
'''Add partition boundaries.
:Parameters:
adimensions: `list`
The ordered axis names of the master array.
master_flip: `list`
extra_boundaries: `list` of `int`
The boundaries of the new partitions.
axis: `str`
The name of the axis to have the new partitions.
'''
def _update_p(matrix, location, master_index, part,
master_axis_to_position, master_flip):
'''TODO
:Parameters:
matrix: numpy array of `Partition` objects
location: `list`
master_index: `int`
part: `list`
master_axis_to_position: `dict`
master_flip: `list`
:Returns:
numpy array of `Partition` objects
'''
for partition in matrix.flat:
partition.location = partition.location[:]
partition.shape = partition.shape[:]
partition.location[master_index] = location
partition.shape[master_index] = shape
partition.new_part(part, master_axis_to_position, master_flip)
# --- End: for
return matrix
# If no extra boundaries have been provided, just return
# without doing anything
if not extra_boundaries:
return
master_index = adimensions.index(axis)
index = self.axes.index(axis)
# Find the position of the extra-boundaries dimension in the
# list of master array dimensions
extra_boundaries = extra_boundaries[:]
# Create the master_axis_to_position dictionary required by
# Partition.new_part
master_axis_to_position = {}
for i, data_axis in enumerate(adimensions):
master_axis_to_position[data_axis] = i
matrix = self.matrix
shape = matrix.shape
# Initialize the new partition matrix
new_shape = list(shape)
new_shape[index] += len(extra_boundaries)
new_matrix = numpy_empty(new_shape, dtype=object)
part = [slice(None)] * len(adimensions)
indices = [slice(None)] * matrix.ndim
new_indices = indices[:]
new_indices[index] = slice(0, 1) # 0
# Find the first extra boundary
x = extra_boundaries.pop(0)
for i in range(shape[index]):
indices[index] = slice(i, i + 1)
sub_matrix = matrix[tuple(indices)]
# (r0, r1) = next(sub_matrix.flat).location[master_index]
(r0, r1) = sub_matrix.item(0, ).location[master_index]
# Could do better, perhaps, by assigning in blocks
if not r0 < x < r1:
# This new boundary is *not* within the span of this
# sub-matrix. Therefore, just copy the sub-matrix
# straight into the new matrix
new_matrix[tuple(new_indices)] = sub_matrix
# new_indices[index] += 1
new_indices[index] = slice(new_indices[index].start + 1,
new_indices[index].stop + 1)
continue
# --------------------------------------------------------
# Still here? Then this new boundary *is* within the span
# of this sub-matrix.
# --------------------------------------------------------
# Find the new extent of the original partition(s)
location = (r0, x)
shape = x - r0
part[master_index] = slice(0, shape)
# Create new partition(s) in place of the original ones(s)
# and set the location, shape and part attributes
new_matrix[tuple(new_indices)] = _update_p(
deepcopy(sub_matrix), location, master_index, part,
master_axis_to_position, master_flip)
# new_indices[index] += 1
new_indices[index] = slice(new_indices[index].start + 1,
new_indices[index].stop + 1)
while x < r1:
# Find the extent of the new partition(s)
if not extra_boundaries:
# There are no more new boundaries, so the new
# partition(s) run to the end of the original
# partition(s) in which they lie.
location1 = r1
else:
# There are more new boundaries, so this
# new partition runs either to the next
# new boundary or to the end of the
# original partition, which comes first.
location1 = min(extra_boundaries[0], r1)
location = (x, location1)
shape = location1 - x
offset = x - r0
part[master_index] = slice(offset, offset + shape)
# Create the new partition(s) and set the
# location, shape and part attributes
new_matrix[tuple(new_indices)] = _update_p(
deepcopy(sub_matrix), location, master_index, part,
master_axis_to_position, master_flip)
new_indices[index] = slice(new_indices[index].start + 1,
new_indices[index].stop + 1)
# new_indices[index] += 1
if not extra_boundaries:
# There are no more extra boundaries, so we can
# return now.
# new_indices[index] = slice(new_indices[index],
# None)
new_indices[index] = slice(new_indices[index].start, None)
indices[index] = slice(i + 1, None)
new_matrix[tuple(new_indices)] = matrix[tuple(indices)]
self.matrix = new_matrix
return
# Still here? Then move on to the next new boundary
x = extra_boundaries.pop(0)
# --- End: while
# --- End: for
self.matrix = new_matrix
from numpy import ndenumerate as numpy_ndenumerate
from numpy import empty as numpy_empty
from numpy import expand_dims as numpy_expand_dims
from numpy import squeeze as numpy_squeeze
from copy import deepcopy
from .partition import Partition
from ..functions import (_DEPRECATION_WARNING_METHOD,
_DEPRECATION_ERROR_METHOD)
from ..decorators import (_inplace_enabled,
_inplace_enabled_define_and_cleanup)
_empty_matrix = numpy_empty((), dtype=object)
class PartitionMatrix:
'''
A hyperrectangular partition matrix of a master data array.
Each of elements (called partitions) span all or part of exactly one
sub-array of the master data array.
Normal numpy basic and advanced indexing is supported, but size 1
dimensions are always removed from the output array, i.e. a partition
rather than a partition matrix is returned if the output array has
size 1.
def create_grid(
coords,
use_bounds,
mask=None,
cartesian=False,
cyclic=False,
coords_2D=False,
coord_order=None,
):
"""Create an ESMPy grid given a sequence of coordinates for use
as a source or destination grid in regridding. Optionally the
grid may have an associated mask.
:Parameters:
coords: sequence
The coordinates if not Cartesian it is assume that the
first is longitude and the second is latitude.
use_bounds: `bool`
Whether to populate the grid corners with information from
the bounds or not.
mask: `numpy.ndarray`, optional
An optional numpy array of booleans containing the grid
points to mask. Where the elements of mask are True the
output grid is masked.
cartesian: `bool`, optional
Whether to create a Cartesian grid or a spherical one,
False by default.
cyclic: `bool`, optional
Whether or not the longitude (if present) is cyclic. If
None the a check for cyclicity is made from the
bounds. None by default.
coords_2D: `bool`, optional
Whether the coordinates are 2D or not. Presently only
works for spherical coordinates. False by default.
coord_order: sequence, optional
Two tuples one indicating the order of the x and y axes
for 2D longitude, one for 2D latitude.
:Returns:
`ESMF.Grid`
The resulting ESMPy grid for use as a source or destination
grid in regridding.
"""
if not cartesian:
lon = coords[0]
lat = coords[1]
if not coords_2D:
# Get the shape of the grid
shape = [lon.size, lat.size]
else:
x_order = coord_order[0]
y_order = coord_order[1]
# Get the shape of the grid
shape = lon.transpose(x_order).shape
if lat.transpose(y_order).shape != shape:
raise ValueError(
"The longitude and latitude coordinates"
" must have the same shape."
)
# --- End: if
if use_bounds:
if not coords_2D:
# Get the bounds
x_bounds = lon.get_bounds()
y_bounds = lat.get_bounds().clip(-90, 90, "degrees").array
# If cyclic not set already, check for cyclicity
if cyclic is None:
cyclic = abs(
x_bounds.datum(-1) - x_bounds.datum(0)
) == Data(360, "degrees")
x_bounds = x_bounds.array
else:
# Get the bounds
x_bounds = lon.get_bounds()
y_bounds = lat.get_bounds().clip(-90, 90, "degrees")
n = x_bounds.shape[0]
m = x_bounds.shape[1]
x_bounds = x_bounds.array
y_bounds = y_bounds.array
tmp_x = numpy_empty((n + 1, m + 1))
tmp_x[:n, :m] = x_bounds[:, :, 0]
tmp_x[:n, m] = x_bounds[:, -1, 1]
tmp_x[n, :m] = x_bounds[-1, :, 3]
tmp_x[n, m] = x_bounds[-1, -1, 2]
tmp_y = numpy_empty((n + 1, m + 1))
tmp_y[:n, :m] = y_bounds[:, :, 0]
tmp_y[:n, m] = y_bounds[:, -1, 1]
tmp_y[n, :m] = y_bounds[-1, :, 3]
tmp_y[n, m] = y_bounds[-1, -1, 2]
x_bounds = tmp_x
y_bounds = tmp_y
else:
if not coords_2D:
# If cyclicity not set already, check for cyclicity
if cyclic is None:
try:
x_bounds = lon.get_bounds()
cyclic = abs(
x_bounds.datum(-1) - x_bounds.datum(0)
) == Data(360, "degrees")
except ValueError:
pass
# --- End: if
# Create empty grid
max_index = numpy_array(shape, dtype="int32")
if use_bounds:
staggerLocs = [ESMF.StaggerLoc.CORNER, ESMF.StaggerLoc.CENTER]
else:
staggerLocs = [ESMF.StaggerLoc.CENTER]
if cyclic:
grid = ESMF.Grid(
max_index, num_peri_dims=1, staggerloc=staggerLocs
)
else:
grid = ESMF.Grid(max_index, staggerloc=staggerLocs)
# Populate grid centres
x, y = 0, 1
gridXCentre = grid.get_coords(x, staggerloc=ESMF.StaggerLoc.CENTER)
gridYCentre = grid.get_coords(y, staggerloc=ESMF.StaggerLoc.CENTER)
if not coords_2D:
gridXCentre[...] = lon.array.reshape((lon.size, 1))
gridYCentre[...] = lat.array.reshape((1, lat.size))
else:
gridXCentre[...] = lon.transpose(x_order).array
gridYCentre[...] = lat.transpose(y_order).array
# Populate grid corners if there are bounds
if use_bounds:
gridCorner = grid.coords[ESMF.StaggerLoc.CORNER]
if not coords_2D:
if cyclic:
gridCorner[x][...] = x_bounds[:, 0].reshape(
lon.size, 1
)
else:
n = x_bounds.shape[0]
tmp_x = numpy_empty(n + 1)
tmp_x[:n] = x_bounds[:, 0]
tmp_x[n] = x_bounds[-1, 1]
gridCorner[x][...] = tmp_x.reshape(lon.size + 1, 1)
n = y_bounds.shape[0]
tmp_y = numpy_empty(n + 1)
tmp_y[:n] = y_bounds[:, 0]
tmp_y[n] = y_bounds[-1, 1]
gridCorner[y][...] = tmp_y.reshape(1, lat.size + 1)
else:
gridCorner = grid.coords[ESMF.StaggerLoc.CORNER]
x_bounds = x_bounds.transpose(x_order)
y_bounds = y_bounds.transpose(y_order)
if cyclic:
x_bounds = x_bounds[:-1, :]
y_bounds = y_bounds[:-1, :]
gridCorner[x][...] = x_bounds
gridCorner[y][...] = y_bounds
# --- End: if
else:
# Test the dimensionality of the list of coordinates
ndim = len(coords)
if ndim < 1 or ndim > 3:
raise ValueError(
"Cartesian grid must have between 1 and 3 dimensions."
)
# For 1D conservative regridding add an extra dimension of size 1
if ndim == 1:
if not use_bounds:
# For 1D nonconservative regridding the extra dimension
# should already have been added in cf.Field.regridc.
raise ValueError(
"Cannot create a Cartesian grid from "
"one dimension coordinate with no bounds."
)
coords = [
DimensionCoordinate(
data=Data(0),
bounds=Data(
[
numpy_finfo("float32").epsneg,
numpy_finfo("float32").eps,
]
),
)
] + coords
if mask is not None:
mask = mask[None, :]
ndim = 2
shape = list()
for coord in coords:
shape.append(coord.size)
# Initialise the grid
max_index = numpy_array(shape, dtype="int32")
if use_bounds:
if ndim < 3:
staggerLocs = [
ESMF.StaggerLoc.CORNER,
ESMF.StaggerLoc.CENTER,
]
else:
staggerLocs = [
ESMF.StaggerLoc.CENTER_VCENTER,
ESMF.StaggerLoc.CORNER_VFACE,
]
else:
if ndim < 3:
staggerLocs = [ESMF.StaggerLoc.CENTER]
else:
staggerLocs = [ESMF.StaggerLoc.CENTER_VCENTER]
# --- End: if
grid = ESMF.Grid(
max_index, coord_sys=ESMF.CoordSys.CART, staggerloc=staggerLocs
)
# Populate the grid centres
for d in range(0, ndim):
if ndim < 3:
gridCentre = grid.get_coords(
d, staggerloc=ESMF.StaggerLoc.CENTER
)
else:
gridCentre = grid.get_coords(
d, staggerloc=ESMF.StaggerLoc.CENTER_VCENTER
)
gridCentre[...] = coords[d].array.reshape(
[shape[d] if x == d else 1 for x in range(0, ndim)]
)
# --- End: for
# Populate grid corners
if use_bounds:
if ndim < 3:
gridCorner = grid.coords[ESMF.StaggerLoc.CORNER]
else:
gridCorner = grid.coords[ESMF.StaggerLoc.CORNER_VFACE]
for d in range(0, ndim):
# boundsD = coords[d].get_bounds(create=True).array
boundsD = coords[d].get_bounds(None)
if boundsD is None:
boundsD = coords[d].create_bounds()
boundsD = boundsD.array
if shape[d] > 1:
tmp = numpy_empty(shape[d] + 1)
tmp[0:-1] = boundsD[:, 0]
tmp[-1] = boundsD[-1, 1]
boundsD = tmp
gridCorner[d][...] = boundsD.reshape(
[shape[d] + 1 if x == d else 1 for x in range(0, ndim)]
)
# --- End: if
# --- End: if
# Add the mask if appropriate
if mask is not None:
gmask = grid.add_item(ESMF.GridItem.MASK)
gmask[...] = 1
gmask[mask] = 0
return grid
