def get_esmf_field_from_ocgis_field(ofield, esmf_field_name=None, **kwargs):
"""
:param ofield: The OCGIS field to convert to an ESMF field.
:type ofield: :class:`ocgis.Field`
:param str esmf_field_name: An optional ESMF field name. If ``None``, use the name of the data variable on
``ofield``.
:param dict kwargs: Any keyword arguments to :func:`ocgis.regrid.base.get_esmf_grid`.
:return: An ESMF field object.
:rtype: :class:`ESMF.api.field.Field`
:raises: ValueError
"""
if len(ofield.data_variables) > 1:
msg = 'Only one data variable may be converted.'
raise ValueError(msg)
if len(ofield.data_variables) == 1:
target_variable = ofield.data_variables[0]
if esmf_field_name is None:
esmf_field_name = target_variable.name
else:
target_variable = None
egrid = get_esmf_grid(ofield.grid, **kwargs)
# Find any dimension lengths that are not associated with the grid. These are considered "extra" dimensions in an
# ESMF field.
if target_variable is None:
dimension_names = get_dimension_names(ofield.grid.dimensions)
else:
dimension_names = get_dimension_names(target_variable.dimensions)
grid_dimension_names = get_dimension_names(ofield.grid.dimensions)
if dimension_names != grid_dimension_names:
if tuple(dimension_names[-2:]) != grid_dimension_names:
raise ValueError(
'Grid dimensions must be last two dimensions of the data variable dimensions.'
)
ndbounds = target_variable.shape[0:-2]
else:
# OCGIS field has no extra dimensions.
ndbounds = None
efield = ESMF.Field(egrid, name=esmf_field_name, ndbounds=ndbounds)
if target_variable is not None:
efield.data[:] = target_variable.get_value()
return efield
def get_ocgis_field_from_esmf_field(efield, field=None):
"""
:param efield: The ESMPy field object to convert to an OCGIS field.
:type efield: :class:`ESMF.Field`
:param field: If provided, use this as the template field for OCGIS field creation.
:type field: :class:`~ocgis.Field`
:return: :class:`~ocgis.Field`
"""
ometa = efield._ocgis
dimnames = ometa.get('dimnames')
dimnames_backref = ometa.get('dimnames_backref')
ogrid = ometa.get('ocgis_grid')
if ogrid is None:
ogrid = get_ocgis_grid_from_esmf_grid(efield.grid)
ovar = None
if dimnames is not None and efield.name is not None:
ovar = Variable(name=efield.name, value=efield.data, dimensions=dimnames, dtype=efield.data.dtype)
broadcast_variable(ovar, get_dimension_names(dimnames_backref))
ovar.set_dimensions(dimnames_backref, force=True)
if field is None:
field = Field(grid=ogrid)
else:
field.set_grid(ogrid)
if ovar is not None:
field.add_variable(ovar, is_data=True, force=True)
if ogrid.has_mask:
field.grid.set_mask(ogrid.get_mask(), cascade=True)
return field
def get_field_slice(self, dslice, strict=True, distributed=False):
"""
Slice the field using a dictionary. Keys are dimension map standard names defined by
:class:`ocgis.constants.DimensionMapKey`. Dimensions are temporarily renamed for the duration of the slice.
:param dict dslice: The dictionary slice.
:param strict: If ``True`` (the default), any dimension names in ``dslice`` are required to be in the target
field.
:param bool distributed: If ``True``, this is should be considered a parallel/global slice.
:return: A shallow copy of the sliced field.
:rtype: :class:`~ocgis.Field`
"""
name_mapping = get_name_mapping(self.dimension_map)
with renamed_dimensions_on_variables(self, name_mapping) as mapping_meta:
# When strict is False, we don't care about extra dimension names in the slice. This is useful for a general
# slicing operation such as slicing for time with or without the dimension.
if not strict:
to_pop = [dname for dname in list(dslice.keys()) if dname not in self.dimensions]
for dname in to_pop:
dslice.pop(dname)
if distributed:
data_variable = self.data_variables[0]
data_variable_dimensions = data_variable.dimensions
data_variable_dimension_names = get_dimension_names(data_variable_dimensions)
the_slice = []
for key in data_variable_dimension_names:
try:
the_slice.append(dslice[key])
except KeyError:
if strict:
raise
else:
the_slice.append(None)
else:
the_slice = dslice
if distributed:
ret = self.data_variables[0].get_distributed_slice(the_slice).parent
else:
ret = super(Field, self).__getitem__(the_slice)
revert_renamed_dimensions_on_variables(mapping_meta, ret)
return ret
def _execute_(self):
for variable, calculation_name in self.iter_calculation_targets():
crosswalk = self._get_dimension_crosswalk_(variable)
fill_dimensions = variable.dimensions
# Some calculations introduce a temporal group dimension during initialization and perform the temporal
# aggregation implicit to the function.
if self.tgd is not None and not self.should_temporally_aggregate:
time_dimension_name = self.field.time.dimensions[0].name
dimension_name = get_dimension_names(fill_dimensions)
time_index = dimension_name.index(time_dimension_name)
fill_dimensions = list(variable.dimensions)
fill_dimensions[time_index] = self.tgd.dimensions[0]
fill = self.get_fill_variable(variable, calculation_name,
fill_dimensions, self.file_only)
if not self.file_only:
arr = self.get_variable_value(variable)
arr_fill = self.get_variable_value(fill)
for yld in self._iter_conformed_arrays_(
crosswalk, variable.shape, arr, arr_fill, None):
carr, carr_fill = yld
res_calculation = self.calculate(carr, **self.parms)
carr_fill.data[:] = res_calculation.data
carr_fill.mask[:] = res_calculation.mask
# Setting the values ensures the mask is updated on the output variables.
fill.set_value(arr_fill)
if self.tgd is not None and self.should_temporally_aggregate:
fill = self._get_temporal_agg_fill_(fill,
calculation_name,
self.file_only,
f=self.aggregate_temporal,
parms={})
else:
fill = {'fill': fill}
self._add_to_collection_(fill)
def get_ocgis_field_from_esmf_field(efield, field=None):
"""
:param efield: The ESMPy field object to convert to an OCGIS field.
:type efield: :class:`ESMF.Field`
:param field: If provided, use this as the template field for OCGIS field creation.
:type field: :class:`~ocgis.Field`
:return: :class:`~ocgis.Field`
"""
ometa = efield._ocgis
dimnames = ometa.get('dimnames')
dimnames_backref = ometa.get('dimnames_backref')
ogrid = ometa.get('ocgis_grid')
if ogrid is None:
ogrid = get_ocgis_grid_from_esmf_grid(efield.grid)
ovar = None
if dimnames is not None and efield.name is not None:
ovar = Variable(name=efield.name,
value=efield.data,
dimensions=dimnames,
dtype=efield.data.dtype)
broadcast_variable(ovar, get_dimension_names(dimnames_backref))
ovar.set_dimensions(dimnames_backref, force=True)
if field is None:
field = Field(grid=ogrid)
else:
field.set_grid(ogrid)
if ovar is not None:
field.add_variable(ovar, is_data=True, force=True)
if ogrid.has_mask:
field.grid.set_mask(ogrid.get_mask(), cascade=True)
return field
def _execute_(self):
for variable, calculation_name in self.iter_calculation_targets():
crosswalk = self._get_dimension_crosswalk_(variable)
fill_dimensions = variable.dimensions
# Some calculations introduce a temporal group dimension during initialization and perform the temporal
# aggregation implicit to the function.
if self.tgd is not None and not self.should_temporally_aggregate:
time_dimension_name = self.field.time.dimensions[0].name
dimension_name = get_dimension_names(fill_dimensions)
time_index = dimension_name.index(time_dimension_name)
fill_dimensions = list(variable.dimensions)
fill_dimensions[time_index] = self.tgd.dimensions[0]
fill = self.get_fill_variable(variable, calculation_name, fill_dimensions, self.file_only)
if not self.file_only:
arr = self.get_variable_value(variable)
arr_fill = self.get_variable_value(fill)
for yld in self._iter_conformed_arrays_(crosswalk, variable.shape, arr, arr_fill, None):
carr, carr_fill = yld
res_calculation = self.calculate(carr, **self.parms)
carr_fill.data[:] = res_calculation.data
carr_fill.mask[:] = res_calculation.mask
# Setting the values ensures the mask is updated on the output variables.
fill.set_value(arr_fill)
if self.tgd is not None and self.should_temporally_aggregate:
fill = self._get_temporal_agg_fill_(fill, calculation_name, self.file_only, f=self.aggregate_temporal,
parms={})
else:
fill = {'fill': fill}
self._add_to_collection_(fill)
def get_unioned(self,
dimensions=None,
union_dimension=None,
spatial_average=None,
root=0):
"""
Unions _unmasked_ geometry objects. Collective across the current :class:`~ocgis.OcgVM`.
"""
# TODO: optimize!
# Get dimension names and lengths for the dimensions to union.
if dimensions is None:
dimensions = self.dimensions
dimension_names = get_dimension_names(dimensions)
dimension_lengths = [
len(self.parent.dimensions[dn]) for dn in dimension_names
]
# Get the variables to spatial average.
if spatial_average is not None:
variable_names_to_weight = get_variable_names(spatial_average)
else:
variable_names_to_weight = []
# Get the new dimensions for the geometry variable. The union dimension is always the last dimension.
if union_dimension is None:
from ocgis.variable.dimension import Dimension
union_dimension = Dimension(
constants.DimensionName.UNIONED_GEOMETRY, 1)
new_dimensions = []
for dim in self.dimensions:
if dim.name not in dimension_names:
new_dimensions.append(dim)
new_dimensions.append(union_dimension)
# Configure the return variable.
ret = self.copy()
if spatial_average is None:
ret = ret.extract()
ret.set_mask(None)
ret.set_value(None)
ret.set_dimensions(new_dimensions)
ret.allocate_value()
# Destination indices in the return variable are filled with non-masked, unioned geometries.
for dst_indices in product(
*
[list(range(dl)) for dl in get_dimension_lengths(new_dimensions)]):
dst_slc = {
new_dimensions[ii].name: dst_indices[ii]
for ii in range(len(new_dimensions))
}
# Select the geometries to union skipping any masked geometries.
to_union = deque()
for indices in product(
*[list(range(dl)) for dl in dimension_lengths]):
dslc = {
dimension_names[ii]: indices[ii]
for ii in range(len(dimension_names))
}
sub = self[dslc]
sub_mask = sub.get_mask()
if sub_mask is None:
to_union.append(sub.get_value().flatten()[0])
else:
if not sub_mask.flatten()[0]:
to_union.append(sub.get_value().flatten()[0])
# Execute the union operation.
processed_to_union = deque()
for geom in to_union:
if isinstance(geom, MultiPolygon) or isinstance(
geom, MultiPoint):
for element in geom:
processed_to_union.append(element)
else:
processed_to_union.append(geom)
unioned = cascaded_union(processed_to_union)
# Pull unioned geometries and union again for the final unioned geometry.
if vm.size > 1:
unioned_gathered = vm.gather(unioned)
if vm.rank == root:
unioned = cascaded_union(unioned_gathered)
# Fill the return geometry variable value with the unioned geometry.
to_fill = ret[dst_slc].get_value()
to_fill[0] = unioned
# Spatial average shared dimensions.
if spatial_average is not None:
# Get source data to weight.
for var_to_weight in filter(
lambda ii: ii.name in variable_names_to_weight,
list(self.parent.values())):
# Holds sizes of dimensions to iterate. These dimension are not squeezed by the weighted averaging.
range_to_itr = []
# Holds the names of dimensions to squeeze.
names_to_itr = []
# Dimension names that are squeezed. Also the dimensions for the weight matrix.
names_to_slice_all = []
for dn in var_to_weight.dimensions:
if dn.name in self.dimension_names:
names_to_slice_all.append(dn.name)
else:
range_to_itr.append(len(dn))
names_to_itr.append(dn.name)
# Reference the weights on the source geometry variable.
weights = self[{
nsa: slice(None)
for nsa in names_to_slice_all
}].weights
# Path if there are iteration dimensions. Checks for axes ordering in addition.
if len(range_to_itr) > 0:
# New dimensions for the spatially averaged variable. Unioned dimension is always last. Remove the
# dimensions aggregated by the weighted average.
new_dimensions = [
dim for dim in var_to_weight.dimensions
if dim.name not in dimension_names
]
new_dimensions.append(union_dimension)
# Prepare the spatially averaged variable.
target = ret.parent[var_to_weight.name]
target.set_mask(None)
target.set_value(None)
target.set_dimensions(new_dimensions)
target.allocate_value()
# Swap weight axes to make sure they align with the target variable.
swap_chain = get_swap_chain(dimension_names,
names_to_slice_all)
if len(swap_chain) > 0:
weights = weights.copy()
for sc in swap_chain:
weights = weights.swapaxes(*sc)
# The main weighting loop. Can get quite intensive with many, large iteration dimensions.
len_names_to_itr = len(names_to_itr)
slice_none = slice(None)
squeeze_out = [
ii for ii, dim in enumerate(var_to_weight.dimensions)
if dim.name in names_to_itr
]
should_squeeze = True if len(squeeze_out) > 0 else False
np_squeeze = np.squeeze
np_atleast_1d = np.atleast_1d
np_ma_average = np.ma.average
for nonweighted_indices in product(
*[list(range(ri)) for ri in range_to_itr]):
w_slc = {
names_to_itr[ii]: nonweighted_indices[ii]
for ii in range(len_names_to_itr)
}
for nsa in names_to_slice_all:
w_slc[nsa] = slice_none
data_to_weight = var_to_weight[w_slc].get_masked_value(
)
if should_squeeze:
data_to_weight = np_squeeze(
data_to_weight, axis=tuple(squeeze_out))
weighted_value = np_atleast_1d(
np_ma_average(data_to_weight, weights=weights))
target[w_slc].get_value()[:] = weighted_value
else:
target_to_weight = var_to_weight.get_masked_value()
# Sort to minimize floating point sum errors.
target_to_weight = target_to_weight.flatten()
weights = weights.flatten()
sindices = np.argsort(target_to_weight)
target_to_weight = target_to_weight[sindices]
weights = weights[sindices]
weighted_value = np.atleast_1d(
np.ma.average(target_to_weight, weights=weights))
target = ret.parent[var_to_weight.name]
target.set_mask(None)
target.set_value(None)
target.set_dimensions(new_dimensions)
target.set_value(weighted_value)
# Collect areas of live ranks and convert to weights.
if vm.size > 1:
# If there is no area information (points for example, we need to use counts).
if ret.area.data[0].max() == 0:
weight_or_proxy = float(self.size)
else:
weight_or_proxy = ret.area.data[0]
if vm.rank != root:
vm.comm.send(weight_or_proxy, dest=root)
else:
live_rank_areas = [weight_or_proxy]
for tner in vm.ranks:
if tner != vm.rank:
recv_area = vm.comm.recv(source=tner)
live_rank_areas.append(recv_area)
live_rank_areas = np.array(live_rank_areas)
rank_weights = live_rank_areas / np.max(live_rank_areas)
for var_to_weight in filter(
lambda ii: ii.name in variable_names_to_weight,
list(ret.parent.values())):
dimensions_to_itr = [
dim.name for dim in var_to_weight.dimensions
if dim.name != union_dimension.name
]
slc = {union_dimension.name: 0}
for idx_slc in var_to_weight.iter_dict_slices(
dimensions=dimensions_to_itr):
idx_slc.update(slc)
to_weight = var_to_weight[idx_slc].get_value().flatten(
)[0]
if vm.rank == root:
collected_to_weight = [to_weight]
if not vm.rank == root:
vm.comm.send(to_weight, dest=root)
else:
for tner in vm.ranks:
if not tner == root:
recv_to_weight = vm.comm.recv(source=tner)
collected_to_weight.append(recv_to_weight)
# Sort to minimize floating point sum errors.
collected_to_weight = np.array(collected_to_weight)
sindices = np.argsort(collected_to_weight)
collected_to_weight = collected_to_weight[sindices]
rank_weights = rank_weights[sindices]
weighted = np.atleast_1d(
np.ma.average(collected_to_weight,
weights=rank_weights))
var_to_weight[idx_slc].get_value()[:] = weighted
if vm.rank == root:
return ret
else:
return
def __init__(self,
variable,
followers=None,
value=None,
mask=None,
allow_masked=True,
primary_mask=None,
slice_remap=None,
shape=None,
melted=None,
repeaters=None,
formatter=None,
clobber_masked=True):
if melted is not None and followers is None:
raise ValueError(
'"melted" must be None if there are no "followers".')
self.variable = variable
self.formatter = formatter
self.allow_masked = allow_masked
self.slice_remap = slice_remap
self.clobber_masked = clobber_masked
if variable.repeat_record is not None:
if repeaters is None:
repeaters = variable.repeat_record
else:
repeaters += variable.repeat_record
self.repeaters = repeaters
self._is_lead = True
if melted is None:
melted_repeaters = None
else:
melted_repeaters = {}
for m in melted:
try:
if m.repeat_record is not None:
melted_repeaters[m.name] = m.repeat_record
except AttributeError:
if m.repeaters is not None:
melted_repeaters[m.name] = m.repeat_record
self.melted_repeaters = melted_repeaters
if melted is not None:
melted = get_variable_names(melted)
self.melted = melted
if shape is None:
shape = self.variable.shape
self.shape = shape
if primary_mask is None:
primary_mask = variable.name
else:
primary_mask = get_variable_names(primary_mask)[0]
self.primary_mask = primary_mask
if value is None:
self.value = variable.get_value()
else:
self.value = value
if mask is None:
self.mask = variable.get_mask()
else:
self.mask = mask
if followers is not None:
dimensions = get_dimension_names(self.variable.dimensions)
followers = get_followers(followers)
for fidx, follower in enumerate(followers):
if isinstance(follower, self.__class__):
iterator = follower
follower = follower.variable
else:
iterator = Iterator(follower,
allow_masked=allow_masked,
primary_mask=primary_mask)
follower_dimensions = get_dimension_names(follower.dimensions)
set_follower_dimensions = set(follower_dimensions)
set_dimensions = set(dimensions)
if not set_follower_dimensions.issubset(set_dimensions):
msg = 'Follower variable "{}" dimensions are not a subset of the lead variable.'.format(
follower.name)
raise ValueError(msg)
if follower_dimensions != dimensions:
follower_slice_remap = []
for d in follower_dimensions:
if d in set_dimensions:
follower_slice_remap.append(dimensions.index(d))
iterator.slice_remap = follower_slice_remap
iterator.shape = self.shape
iterator._is_lead = False
followers[fidx] = iterator
self.iterators = [self] + followers
self.followers = followers
else:
self.iterators = [self]
self.followers = None
self._is_recursing = False
def set_variable(self, entry_key, variable, dimension=None, bounds=None, attrs=None, pos=None, dimensionless=False,
section=None):
"""
Set coordinate variable information for ``entry_key``.
:param str entry_key: See :class:`ocgis.constants.DimensionMapKey` for valid entry keys.
:param variable: The variable to set. Use a variable object to auto-fill additional fields if they are ``None``.
:type variable: :class:`str` | :class:`~ocgis.Variable`
:param dimension: A sequence of dimension names. If ``None``, they will be pulled from ``variable`` if it is a
variable object.
:param bounds: See :meth:`~ocgis.DimensionMap.set_bounds`.
:param dict attrs: Default attributes for the coordinate variables. If ``None``, they will be pulled from
``variable`` if it is a variable object.
:param int pos: The representative dimension position in ``variable`` if ``variable`` has more than one
dimension. For example, a latitude variable may have two dimensions ``(lon, lat)``. The mapper must determine
which dimension position is representative for the latitude variable when slicing.
:param section: A slice-like tuple used to extract the data out of its source variable into a single variable
format.
:type section: tuple
>>> section = (None, 0)
>>> # This will be converted to a slice.
>>> [slice(None), slice(0, 1)]
:param bool dimensionless: If ``True``, this variable has no canonical dimension.
:raises: DimensionMapError
"""
if entry_key in self._special_entry_keys:
raise DimensionMapError(entry_key, "The entry '{}' has a special set method.".format(entry_key))
if section is not None and (pos is None and dimension is None):
raise DimensionMapError(entry_key, "If a section is provided, position or dimension must be defined.")
entry = self._get_entry_(entry_key)
if variable is None:
self._storage.pop(entry_key)
return
try:
if bounds is None:
bounds = variable.bounds
if dimension is None:
if variable.ndim > 1:
if pos is None and not dimensionless:
msg = "A position (pos) is required if no dimension is provided and target variable has " \
"greater than one dimension."
raise DimensionMapError(entry_key, msg)
elif variable.ndim == 1:
pos = 0
else:
pos = None
# We can have scalar dimensions.
if pos is not None and not dimensionless:
dimension = variable.dimensions[pos]
except AttributeError:
# Assume string type.
pass
value = get_variable_names(variable)[0]
if bounds is not None:
bounds = get_variable_names(bounds)[0]
if dimension is None:
dimension = []
else:
dimension = list(get_dimension_names(dimension))
if attrs is None:
try:
attrs = self._storage.__class__(deepcopy(DIMENSION_MAP_TEMPLATE[entry_key][DMK.ATTRS]))
except KeyError:
# Default attributes are empty.
attrs = self._storage.__class__()
# Allow for any variable attributes.
if hasattr(variable, 'attrs'):
attrs.update(variable.attrs)
# Dimension map attributes always take precedence. Dimension map attrs > Variable Attributes > Default Attributes
current_attrs = self.get_attrs(entry_key)
if current_attrs is None:
current_attrs = self._storage.__class__()
attrs.update(current_attrs)
entry[DMK.VARIABLE] = value
entry[DMK.BOUNDS] = bounds
entry[DMK.DIMENSION] = dimension
entry[DMK.ATTRS] = attrs
if section is not None:
entry[DMK.SECTION] = section
def create_esmf_grid(ogrid, regrid_method='auto', value_mask=None):
"""
Create an ESMF :class:`~ESMF.driver.grid.Grid` object from an OCGIS :class:`ocgis.Grid` object.
:param ogrid: The target OCGIS grid to convert to an ESMF grid.
:type ogrid: :class:`~ocgis.Grid`
:param regrid_method: If ``'auto'`` or :attr:`ESMF.api.constants.RegridMethod.CONSERVE`, use corners/bounds from the
grid object. If :attr:`ESMF.api.constants.RegridMethod.CONSERVE`, the corners/bounds must be present on the grid.
:param value_mask: If an ``bool`` :class:`numpy.array` with same shape as ``ogrid``, use a logical *or* operation
with the OCGIS field mask when creating the input grid's mask. Values of ``True`` in ``value_mask`` are assumed to
be masked. If ``None`` is provided (the default) then the mask will be set using the spatial mask on ``ogrid``.
:type value_mask: :class:`numpy.array`
:rtype: :class:`ESMF.driver.grid.Grid`
"""
assert isinstance(ogrid, Grid)
assert create_crs(ogrid.crs) == Spherical()
pkwds = get_periodicity_parameters(ogrid)
# Fill ESMPy grid coordinate values. ###############################################################################
dimensions = ogrid.parent.dimensions
get_dimension = ogrid.dimension_map.get_dimension
y_dimension = get_dimension(DMK.Y, dimensions=dimensions)
x_dimension = get_dimension(DMK.X, dimensions=dimensions)
# ESMPy has index 0 = x-coordinate and index 1 = y-coordinate.
if vm.size == 1:
max_index = np.array([x_dimension.size, y_dimension.size],
dtype=np.int32)
else:
max_index = np.array(
[x_dimension.size_global, y_dimension.size_global], dtype=np.int32)
pkwds['coord_sys'] = ESMF.CoordSys.SPH_DEG
pkwds['staggerloc'] = ESMF.StaggerLoc.CENTER
pkwds['max_index'] = max_index
egrid = ESMF.Grid(**pkwds)
# msg = (egrid.lower_bounds, egrid.upper_bounds)
# ocgis_lh(msg, level=logging.WARN, logger='ocli.chunked_rwg', force=True)
egrid._ocgis['dimnames'] = (x_dimension.name, y_dimension.name)
egrid._ocgis['dimnames_backref'] = get_dimension_names(ogrid.dimensions)
egrid._ocgis['dimension_map'] = deepcopy(ogrid.dimension_map)
ogrid_dimnames = (y_dimension.name, x_dimension.name)
is_yx_order = get_dimension_names(ogrid.dimensions) == ogrid_dimnames
ovalue_stacked = ogrid.get_value_stacked()
row = egrid.get_coords(1, staggerloc=ESMF.StaggerLoc.CENTER)
orow = ovalue_stacked[0, ...]
if is_yx_order:
orow = np.swapaxes(orow, 0, 1)
row[:] = orow
col = egrid.get_coords(0, staggerloc=ESMF.StaggerLoc.CENTER)
ocol = ovalue_stacked[1, ...]
if is_yx_order:
ocol = np.swapaxes(ocol, 0, 1)
col[:] = ocol
####################################################################################################################
# Use a logical or operation to merge with value_mask if present
if value_mask is not None:
# convert to boolean to make sure
value_mask = value_mask.astype(bool)
# do the logical or operation selecting values
value_mask = np.logical_or(value_mask, ogrid.get_mask(create=True))
else:
value_mask = ogrid.get_mask()
# Follows SCRIP convention where 1 is unmasked and 0 is masked.
if value_mask is not None:
esmf_mask = np.invert(value_mask).astype(np.int32)
esmf_mask = np.swapaxes(esmf_mask, 0, 1)
egrid.add_item(ESMF.GridItem.MASK,
staggerloc=ESMF.StaggerLoc.CENTER,
from_file=False)
egrid.mask[0][:] = esmf_mask
# Attempt to access corners if requested.
if regrid_method == 'auto' and ogrid.has_bounds:
regrid_method = ESMF.RegridMethod.CONSERVE
if regrid_method == ESMF.RegridMethod.CONSERVE:
# Conversion to ESMF objects requires an expanded grid (non-vectorized).
# TODO: Create ESMF grids from vectorized OCGIS grids.
expand_grid(ogrid)
# Convert to ESMF corners from OCGIS corners.
corners_esmf = np.zeros([2] + [element + 1 for element in max_index],
dtype=col.dtype)
get_esmf_corners_from_ocgis_corners(ogrid.y.bounds.get_value(),
fill=corners_esmf[0, :, :])
get_esmf_corners_from_ocgis_corners(ogrid.x.bounds.get_value(),
fill=corners_esmf[1, :, :])
# adding corners. first tell the grid object to allocate corners
egrid.add_coords(staggerloc=[ESMF.StaggerLoc.CORNER])
# get the coordinate pointers and set the coordinates
grid_corner = egrid.coords[ESMF.StaggerLoc.CORNER]
# Note indexing is reversed for ESMF v. OCGIS. In ESMF, the x-coordinate (longitude) is the first
# coordinate. If this is periodic, the last column corner wraps/connect to the first. This coordinate must
# be removed.
if pkwds['num_peri_dims'] is not None:
grid_corner[0][:] = corners_esmf[1][0:-1, :]
grid_corner[1][:] = corners_esmf[0][0:-1, :]
else:
grid_corner[0][:] = corners_esmf[1]
grid_corner[1][:] = corners_esmf[0]
return egrid
def create_esmf_grid(ogrid, regrid_method='auto', value_mask=None):
"""
Create an ESMF :class:`~ESMF.driver.grid.Grid` object from an OCGIS :class:`ocgis.Grid` object.
:param ogrid: The target OCGIS grid to convert to an ESMF grid.
:type ogrid: :class:`~ocgis.Grid`
:param regrid_method: If ``'auto'`` or :attr:`ESMF.api.constants.RegridMethod.CONSERVE`, use corners/bounds from the
grid object. If :attr:`ESMF.api.constants.RegridMethod.CONSERVE`, the corners/bounds must be present on the grid.
:param value_mask: If an ``bool`` :class:`numpy.array` with same shape as ``ogrid``, use a logical *or* operation
with the OCGIS field mask when creating the input grid's mask. Values of ``True`` in ``value_mask`` are assumed to
be masked. If ``None`` is provided (the default) then the mask will be set using the spatial mask on ``ogrid``.
:type value_mask: :class:`numpy.array`
:rtype: :class:`ESMF.driver.grid.Grid`
"""
assert isinstance(ogrid, Grid)
assert create_crs(ogrid.crs) == Spherical()
pkwds = get_periodicity_parameters(ogrid)
# Fill ESMPy grid coordinate values. ###############################################################################
dimensions = ogrid.parent.dimensions
get_dimension = ogrid.dimension_map.get_dimension
y_dimension = get_dimension(DMK.Y, dimensions=dimensions)
x_dimension = get_dimension(DMK.X, dimensions=dimensions)
# ESMPy has index 0 = x-coordinate and index 1 = y-coordinate.
if vm.size == 1:
max_index = np.array([x_dimension.size, y_dimension.size], dtype=np.int32)
else:
max_index = np.array([x_dimension.size_global, y_dimension.size_global], dtype=np.int32)
pkwds['coord_sys'] = ESMF.CoordSys.SPH_DEG
pkwds['staggerloc'] = ESMF.StaggerLoc.CENTER
pkwds['max_index'] = max_index
egrid = ESMF.Grid(**pkwds)
# msg = (egrid.lower_bounds, egrid.upper_bounds)
# ocgis_lh(msg, level=logging.WARN, logger='ocli.chunked_rwg', force=True)
egrid._ocgis['dimnames'] = (x_dimension.name, y_dimension.name)
egrid._ocgis['dimnames_backref'] = get_dimension_names(ogrid.dimensions)
egrid._ocgis['dimension_map'] = deepcopy(ogrid.dimension_map)
ogrid_dimnames = (y_dimension.name, x_dimension.name)
is_yx_order = get_dimension_names(ogrid.dimensions) == ogrid_dimnames
ovalue_stacked = ogrid.get_value_stacked()
row = egrid.get_coords(1, staggerloc=ESMF.StaggerLoc.CENTER)
orow = ovalue_stacked[0, ...]
if is_yx_order:
orow = np.swapaxes(orow, 0, 1)
row[:] = orow
col = egrid.get_coords(0, staggerloc=ESMF.StaggerLoc.CENTER)
ocol = ovalue_stacked[1, ...]
if is_yx_order:
ocol = np.swapaxes(ocol, 0, 1)
col[:] = ocol
####################################################################################################################
# Use a logical or operation to merge with value_mask if present
if value_mask is not None:
# convert to boolean to make sure
value_mask = value_mask.astype(bool)
# do the logical or operation selecting values
value_mask = np.logical_or(value_mask, ogrid.get_mask(create=True))
else:
value_mask = ogrid.get_mask()
# Follows SCRIP convention where 1 is unmasked and 0 is masked.
if value_mask is not None:
esmf_mask = np.invert(value_mask).astype(np.int32)
esmf_mask = np.swapaxes(esmf_mask, 0, 1)
egrid.add_item(ESMF.GridItem.MASK, staggerloc=ESMF.StaggerLoc.CENTER, from_file=False)
egrid.mask[0][:] = esmf_mask
# Attempt to access corners if requested.
if regrid_method == 'auto' and ogrid.has_bounds:
regrid_method = ESMF.RegridMethod.CONSERVE
if regrid_method == ESMF.RegridMethod.CONSERVE:
# Conversion to ESMF objects requires an expanded grid (non-vectorized).
# TODO: Create ESMF grids from vectorized OCGIS grids.
expand_grid(ogrid)
# Convert to ESMF corners from OCGIS corners.
corners_esmf = np.zeros([2] + [element + 1 for element in max_index], dtype=col.dtype)
get_esmf_corners_from_ocgis_corners(ogrid.y.bounds.get_value(), fill=corners_esmf[0, :, :])
get_esmf_corners_from_ocgis_corners(ogrid.x.bounds.get_value(), fill=corners_esmf[1, :, :])
# adding corners. first tell the grid object to allocate corners
egrid.add_coords(staggerloc=[ESMF.StaggerLoc.CORNER])
# get the coordinate pointers and set the coordinates
grid_corner = egrid.coords[ESMF.StaggerLoc.CORNER]
# Note indexing is reversed for ESMF v. OCGIS. In ESMF, the x-coordinate (longitude) is the first
# coordinate. If this is periodic, the last column corner wraps/connect to the first. This coordinate must
# be removed.
if pkwds['num_peri_dims'] is not None:
grid_corner[0][:] = corners_esmf[1][0:-1, :]
grid_corner[1][:] = corners_esmf[0][0:-1, :]
else:
grid_corner[0][:] = corners_esmf[1]
grid_corner[1][:] = corners_esmf[0]
return egrid
def __init__(self, variable, followers=None, value=None, mask=None, allow_masked=True, primary_mask=None,
slice_remap=None, shape=None, melted=None, repeaters=None, formatter=None, clobber_masked=True):
if melted is not None and followers is None:
raise ValueError('"melted" must be None if there are no "followers".')
self.variable = variable
self.formatter = formatter
self.allow_masked = allow_masked
self.slice_remap = slice_remap
self.clobber_masked = clobber_masked
if variable.repeat_record is not None:
if repeaters is None:
repeaters = variable.repeat_record
else:
repeaters += variable.repeat_record
self.repeaters = repeaters
self._is_lead = True
if melted is None:
melted_repeaters = None
else:
melted_repeaters = {}
for m in melted:
try:
if m.repeat_record is not None:
melted_repeaters[m.name] = m.repeat_record
except AttributeError:
if m.repeaters is not None:
melted_repeaters[m.name] = m.repeat_record
self.melted_repeaters = melted_repeaters
if melted is not None:
melted = get_variable_names(melted)
self.melted = melted
if shape is None:
shape = self.variable.shape
self.shape = shape
if primary_mask is None:
primary_mask = variable.name
else:
primary_mask = get_variable_names(primary_mask)[0]
self.primary_mask = primary_mask
if value is None:
self.value = variable.get_value()
else:
self.value = value
if mask is None:
self.mask = variable.get_mask()
else:
self.mask = mask
if followers is not None:
dimensions = get_dimension_names(self.variable.dimensions)
followers = get_followers(followers)
for fidx, follower in enumerate(followers):
if isinstance(follower, self.__class__):
iterator = follower
follower = follower.variable
else:
iterator = Iterator(follower, allow_masked=allow_masked, primary_mask=primary_mask)
follower_dimensions = get_dimension_names(follower.dimensions)
set_follower_dimensions = set(follower_dimensions)
set_dimensions = set(dimensions)
if not set_follower_dimensions.issubset(set_dimensions):
msg = 'Follower variable "{}" dimensions are not a subset of the lead variable.'.format(
follower.name)
raise ValueError(msg)
if follower_dimensions != dimensions:
follower_slice_remap = []
for d in follower_dimensions:
if d in set_dimensions:
follower_slice_remap.append(dimensions.index(d))
iterator.slice_remap = follower_slice_remap
iterator.shape = self.shape
iterator._is_lead = False
followers[fidx] = iterator
self.iterators = [self] + followers
self.followers = followers
else:
self.iterators = [self]
self.followers = None
self._is_recursing = False
