def test_init(self):
ompi = OcgDist(size=2)
self.assertEqual(len(ompi.mapping), 2)
dim_x = Dimension('x', 5, dist=False)
dim_y = Dimension('y', 11, dist=True)
var_tas = Variable('tas', value=np.arange(0, 5 * 11).reshape(5, 11), dimensions=(dim_x, dim_y))
thing = Variable('thing', value=np.arange(11) * 10, dimensions=(dim_y,))
vc = VariableCollection(variables=[var_tas, thing])
child = VariableCollection(name='younger')
vc.add_child(child)
childer = VariableCollection(name='youngest')
child.add_child(childer)
dim_six = Dimension('six', 6)
hidden = Variable('hidden', value=[6, 7, 8, 9, 0, 10], dimensions=dim_six)
childer.add_variable(hidden)
ompi.add_dimensions([dim_x, dim_y])
ompi.add_dimension(dim_six, group=hidden.group)
ompi.add_variables([var_tas, thing])
ompi.add_variable(hidden)
var = ompi.get_variable(hidden)
self.assertIsInstance(var, dict)
def get_variable_collection(self, **kwargs):
parent = VariableCollection(**kwargs)
for n, v in list(self.metadata_source['variables'].items()):
SourcedVariable(name=n, request_dataset=self.rd, parent=parent)
GeometryVariable(name=DimensionName.GEOMETRY_DIMENSION,
request_dataset=self.rd,
parent=parent)
crs = self.get_crs(self.metadata_source)
if crs is not None:
parent.add_variable(crs)
return parent
def test_renamed_dimensions_on_variables(self):
vc = VariableCollection()
var1 = Variable(name='ugid', value=[1, 2, 3], dimensions='ocgis_geom')
var2 = Variable(name='state', value=[20, 30, 40], dimensions='ocgis_geom')
vc.add_variable(var1)
vc.add_variable(var2)
with renamed_dimensions_on_variables(vc, {'geom': ['ocgis_geom']}):
for var in list(vc.values()):
self.assertEqual(var.dimensions[0].name, 'geom')
for var in list(vc.values()):
self.assertEqual(var.dimensions[0].name, 'ocgis_geom')
def get_variable_collection(self, **kwargs):
"""
:rtype: :class:`ocgis.new_interface.variable.VariableCollection`
"""
if KeywordArgument.DRIVER not in kwargs:
kwargs[KeywordArgument.DRIVER] = self
dimension = list(self.dist.get_group(rank=vm.rank)['dimensions'].values())[0]
ret = VariableCollection(**kwargs)
for v in list(self.metadata_source['variables'].values()):
nvar = SourcedVariable(name=v['name'], dimensions=dimension, dtype=v['dtype'], request_dataset=self.rd)
ret.add_variable(nvar)
return ret
def test_renamed_dimensions_on_variables(self):
vc = VariableCollection()
var1 = Variable(name='ugid', value=[1, 2, 3], dimensions='ocgis_geom')
var2 = Variable(name='state',
value=[20, 30, 40],
dimensions='ocgis_geom')
vc.add_variable(var1)
vc.add_variable(var2)
with renamed_dimensions_on_variables(vc, {'geom': ['ocgis_geom']}):
for var in list(vc.values()):
self.assertEqual(var.dimensions[0].name, 'geom')
for var in list(vc.values()):
self.assertEqual(var.dimensions[0].name, 'ocgis_geom')
def _get_field_write_target_(cls, field):
"""
Takes field data out of the OCGIS unstructured format (similar to UGRID) converting to the format expected
by ESMF Unstructured metadata.
"""
# The driver for the current field must be NetCDF UGRID to ensure interpretability.
assert field.dimension_map.get_driver() == DriverKey.NETCDF_UGRID
grid = field.grid
# Three-dimensional data is not supported.
assert not grid.has_z
# Number of coordinate dimension. This will be 3 for three-dimensional data.
coord_dim = Dimension('coordDim', 2)
# Transform ragged array to one-dimensional array. #############################################################
cindex = grid.cindex
elements = cindex.get_value()
num_element_conn_data = [e.shape[0] for e in elements.flat]
length_connection_count = sum(num_element_conn_data)
esmf_element_conn = np.zeros(length_connection_count,
dtype=elements[0].dtype)
start = 0
tag_start_index = MPITag.START_INDEX
# Collapse the ragged element index array into a single dimensioned vector. This communication block finds the
# size for the new array. ######################################################################################
if vm.size > 1:
max_index = max([ii.max() for ii in elements.flat])
if vm.rank == 0:
vm.comm.isend(max_index + 1, dest=1, tag=tag_start_index)
adjust = 0
else:
adjust = vm.comm.irecv(source=vm.rank - 1, tag=tag_start_index)
adjust = adjust.wait()
if vm.rank != vm.size - 1:
vm.comm.isend(max_index + 1 + adjust,
dest=vm.rank + 1,
tag=tag_start_index)
# Fill the new vector for the element connectivity. ############################################################
for ii in elements.flat:
if vm.size > 1:
if grid.archetype.has_multi:
mbv = cindex.attrs[OcgisConvention.Name.MULTI_BREAK_VALUE]
replace_breaks = np.where(ii == mbv)[0]
else:
replace_breaks = []
ii = ii + adjust
if len(replace_breaks) > 0:
ii[replace_breaks] = mbv
esmf_element_conn[start:start + ii.shape[0]] = ii
start += ii.shape[0]
# Create the new data representation. ##########################################################################
connection_count = create_distributed_dimension(esmf_element_conn.size,
name='connectionCount')
esmf_element_conn_var = Variable(name='elementConn',
value=esmf_element_conn,
dimensions=connection_count)
esmf_element_conn_var.attrs[
CFName.
LONG_NAME] = 'Node indices that define the element connectivity.'
mbv = cindex.attrs.get(OcgisConvention.Name.MULTI_BREAK_VALUE)
if mbv is not None:
esmf_element_conn_var.attrs['polygon_break_value'] = mbv
esmf_element_conn_var.attrs['start_index'] = grid.start_index
ret = VariableCollection(variables=field.copy().values(), force=True)
# Rename the element count dimension.
original_name = ret[cindex.name].dimensions[0].name
ret.rename_dimension(original_name, 'elementCount')
# Add the element-node connectivity variable to the collection.
ret.add_variable(esmf_element_conn_var)
num_element_conn = Variable(
name='numElementConn',
value=num_element_conn_data,
dimensions=cindex.dimensions[0],
attrs={CFName.LONG_NAME: 'Number of nodes per element.'})
ret.add_variable(num_element_conn)
node_coords = Variable(name='nodeCoords',
dimensions=(grid.node_dim, coord_dim))
node_coords.units = 'degrees'
node_coords.attrs[
CFName.
LONG_NAME] = 'Node coordinate values indexed by element connectivity.'
node_coords.attrs['coordinates'] = 'x y'
fill = node_coords.get_value()
fill[:, 0] = grid.x.get_value()
fill[:, 1] = grid.y.get_value()
ret.pop(grid.x.name)
ret.pop(grid.y.name)
ret.add_variable(node_coords)
ret.attrs['gridType'] = 'unstructured'
ret.attrs['version'] = '0.9'
return ret
def write_subsets(self, src_template, dst_template, wgt_template, index_path):
"""
Write grid subsets to netCDF files using the provided filename templates. The template must contain the full
file path with a single curly-bracer pair to insert the combination counter. ``wgt_template`` should not be a
full path. This name is used when generating weight files.
>>> template_example = '/path/to/data_{}.nc'
:param str src_template: The template for the source subset file.
:param str dst_template: The template for the destination subset file.
:param str wgt_template: The template for the weight filename.
>>> wgt_template = 'esmf_weights_{}.nc'
:param index_path: Path to the output indexing netCDF.
"""
src_filenames = []
dst_filenames = []
wgt_filenames = []
dst_slices = []
# nzeros = len(str(reduce(lambda x, y: x * y, self.nsplits_dst)))
for ctr, (sub_src, sub_dst, dst_slc) in enumerate(self.iter_src_grid_subsets(yield_dst=True), start=1):
# padded = create_zero_padded_integer(ctr, nzeros)
src_path = src_template.format(ctr)
dst_path = dst_template.format(ctr)
wgt_filename = wgt_template.format(ctr)
src_filenames.append(os.path.split(src_path)[1])
dst_filenames.append(os.path.split(dst_path)[1])
wgt_filenames.append(wgt_filename)
dst_slices.append(dst_slc)
for target, path in zip([sub_src, sub_dst], [src_path, dst_path]):
if target.is_empty:
is_empty = True
target = None
else:
is_empty = False
field = Field(grid=target, is_empty=is_empty)
ocgis_lh(msg='writing: {}'.format(path), level=logging.DEBUG)
with vm.scoped_by_emptyable('field.write', field):
if not vm.is_null:
field.write(path)
ocgis_lh(msg='finished writing: {}'.format(path), level=logging.DEBUG)
with vm.scoped('index write', [0]):
if not vm.is_null:
dim = Dimension('nfiles', len(src_filenames))
vname = ['source_filename', 'destination_filename', 'weights_filename']
values = [src_filenames, dst_filenames, wgt_filenames]
grid_splitter_destination = GridSplitterConstants.IndexFile.NAME_DESTINATION_VARIABLE
attrs = [{'esmf_role': 'grid_splitter_source'},
{'esmf_role': grid_splitter_destination},
{'esmf_role': 'grid_splitter_weights'}]
vc = VariableCollection()
grid_splitter_index = GridSplitterConstants.IndexFile.NAME_INDEX_VARIABLE
vidx = Variable(name=grid_splitter_index)
vidx.attrs['esmf_role'] = grid_splitter_index
vidx.attrs['grid_splitter_source'] = 'source_filename'
vidx.attrs[GridSplitterConstants.IndexFile.NAME_DESTINATION_VARIABLE] = 'destination_filename'
vidx.attrs['grid_splitter_weights'] = 'weights_filename'
x_bounds = GridSplitterConstants.IndexFile.NAME_X_BOUNDS_VARIABLE
vidx.attrs[x_bounds] = x_bounds
y_bounds = GridSplitterConstants.IndexFile.NAME_Y_BOUNDS_VARIABLE
vidx.attrs[y_bounds] = y_bounds
vc.add_variable(vidx)
for idx in range(len(vname)):
v = Variable(name=vname[idx], dimensions=dim, dtype=str, value=values[idx], attrs=attrs[idx])
vc.add_variable(v)
bounds_dimension = Dimension(name='bounds', size=2)
xb = Variable(name=x_bounds, dimensions=[dim, bounds_dimension], attrs={'esmf_role': 'x_split_bounds'},
dtype=int)
yb = Variable(name=y_bounds, dimensions=[dim, bounds_dimension], attrs={'esmf_role': 'y_split_bounds'},
dtype=int)
x_name = self.dst_grid.x.dimensions[0].name
y_name = self.dst_grid.y.dimensions[0].name
for idx, slc in enumerate(dst_slices):
xb.get_value()[idx, :] = slc[x_name].start, slc[x_name].stop
yb.get_value()[idx, :] = slc[y_name].start, slc[y_name].stop
vc.add_variable(xb)
vc.add_variable(yb)
vc.write(index_path)
vm.barrier()
def write_chunks(self):
"""
Write grid subsets to netCDF files using the provided filename templates. This will also generate ESMF
regridding weights for each subset if requested.
"""
src_filenames = []
dst_filenames = []
wgt_filenames = []
dst_slices = []
src_slices = []
index_path = self.create_full_path_from_template('index_file')
# nzeros = len(str(reduce(lambda x, y: x * y, self.nchunks_dst)))
ctr = 1
ocgis_lh(logger=_LOCAL_LOGGER,
msg='starting self.iter_src_grid_subsets',
level=logging.DEBUG)
for sub_src, src_slc, sub_dst, dst_slc in self.iter_src_grid_subsets(
yield_dst=True):
ocgis_lh(
logger=_LOCAL_LOGGER,
msg='finished iteration {} for self.iter_src_grid_subsets'.
format(ctr),
level=logging.DEBUG)
src_path = self.create_full_path_from_template('src_template',
index=ctr)
dst_path = self.create_full_path_from_template('dst_template',
index=ctr)
wgt_path = self.create_full_path_from_template('wgt_template',
index=ctr)
src_filenames.append(os.path.split(src_path)[1])
dst_filenames.append(os.path.split(dst_path)[1])
wgt_filenames.append(wgt_path)
dst_slices.append(dst_slc)
src_slices.append(src_slc)
# Only write destinations if an iterator is not provided.
if self.iter_dst is None:
zip_args = [[sub_src, sub_dst], [src_path, dst_path]]
else:
zip_args = [[sub_src], [src_path]]
cc = 1
for target, path in zip(*zip_args):
with vm.scoped_by_emptyable('field.write' + str(cc), target):
if not vm.is_null:
ocgis_lh(logger=_LOCAL_LOGGER,
msg='write_chunks:writing: {}'.format(path),
level=logging.DEBUG)
field = Field(grid=target)
field.write(path)
ocgis_lh(
logger=_LOCAL_LOGGER,
msg='write_chunks:finished writing: {}'.format(
path),
level=logging.DEBUG)
cc += 1
# Increment the counter outside of the loop to avoid counting empty subsets.
ctr += 1
# Generate an ESMF weights file if requested and at least one rank has data on it.
if self.genweights and len(
vm.get_live_ranks_from_object(sub_src)) > 0:
vm.barrier()
ocgis_lh(logger=_LOCAL_LOGGER,
msg='write_chunks:writing esmf weights: {}'.format(
wgt_path),
level=logging.DEBUG)
self.write_esmf_weights(src_path,
dst_path,
wgt_path,
src_grid=sub_src,
dst_grid=sub_dst)
vm.barrier()
# Global shapes require a VM global scope to collect.
src_global_shape = global_grid_shape(self.src_grid)
dst_global_shape = global_grid_shape(self.dst_grid)
# Gather and collapse source slices as some may be empty and we write on rank 0.
gathered_src_grid_slice = vm.gather(src_slices)
if vm.rank == 0:
len_src_slices = len(src_slices)
new_src_grid_slice = [None] * len_src_slices
for idx in range(len_src_slices):
for rank_src_grid_slice in gathered_src_grid_slice:
if rank_src_grid_slice[idx] is not None:
new_src_grid_slice[idx] = rank_src_grid_slice[idx]
break
src_slices = new_src_grid_slice
with vm.scoped('index write', [0]):
if not vm.is_null:
dim = Dimension('nfiles', len(src_filenames))
vname = [
'source_filename', 'destination_filename',
'weights_filename'
]
values = [src_filenames, dst_filenames, wgt_filenames]
grid_chunker_destination = GridChunkerConstants.IndexFile.NAME_DESTINATION_VARIABLE
attrs = [{
'esmf_role': 'grid_chunker_source'
}, {
'esmf_role': grid_chunker_destination
}, {
'esmf_role': 'grid_chunker_weights'
}]
vc = VariableCollection()
grid_chunker_index = GridChunkerConstants.IndexFile.NAME_INDEX_VARIABLE
vidx = Variable(name=grid_chunker_index)
vidx.attrs['esmf_role'] = grid_chunker_index
vidx.attrs['grid_chunker_source'] = 'source_filename'
vidx.attrs[GridChunkerConstants.IndexFile.
NAME_DESTINATION_VARIABLE] = 'destination_filename'
vidx.attrs['grid_chunker_weights'] = 'weights_filename'
vidx.attrs[GridChunkerConstants.IndexFile.
NAME_SRC_GRID_SHAPE] = src_global_shape
vidx.attrs[GridChunkerConstants.IndexFile.
NAME_DST_GRID_SHAPE] = dst_global_shape
vc.add_variable(vidx)
for idx in range(len(vname)):
v = Variable(name=vname[idx],
dimensions=dim,
dtype=str,
value=values[idx],
attrs=attrs[idx])
vc.add_variable(v)
bounds_dimension = Dimension(name='bounds', size=2)
# TODO: This needs to work with four dimensions.
# Source -----------------------------------------------------------------------------------------------
self.src_grid._gc_create_index_bounds_(RegriddingRole.SOURCE,
vidx, vc, src_slices,
dim, bounds_dimension)
# Destination ------------------------------------------------------------------------------------------
self.dst_grid._gc_create_index_bounds_(
RegriddingRole.DESTINATION, vidx, vc, dst_slices, dim,
bounds_dimension)
vc.write(index_path)
vm.barrier()
def create_merged_weight_file(self, merged_weight_filename, strict=False):
"""
Merge weight file chunks to a single, global weight file.
:param str merged_weight_filename: Path to the merged weight file.
:param bool strict: If ``False``, allow "missing" files where the iterator index cannot create a found file.
It is best to leave these ``False`` as not all source and destinations are mapped. If ``True``, raise an
"""
if vm.size > 1:
raise ValueError(
"'create_merged_weight_file' does not work in parallel")
index_filename = self.create_full_path_from_template('index_file')
ifile = RequestDataset(uri=index_filename).get()
ifile.load()
ifc = GridChunkerConstants.IndexFile
gidx = ifile[ifc.NAME_INDEX_VARIABLE].attrs
src_global_shape = gidx[ifc.NAME_SRC_GRID_SHAPE]
dst_global_shape = gidx[ifc.NAME_DST_GRID_SHAPE]
# Get the global weight dimension size.
n_s_size = 0
weight_filename = ifile[gidx[ifc.NAME_WEIGHTS_VARIABLE]]
wv = weight_filename.join_string_value()
split_weight_file_directory = self.paths['wd']
for wfn in map(
lambda x: os.path.join(split_weight_file_directory,
os.path.split(x)[1]), wv):
ocgis_lh(msg="current merge weight file target: {}".format(wfn),
level=logging.DEBUG,
logger=_LOCAL_LOGGER)
if not os.path.exists(wfn):
if strict:
raise IOError(wfn)
else:
continue
curr_dimsize = RequestDataset(wfn).get().dimensions['n_s'].size
# ESMF writes the weight file, but it may be empty if there are no generated weights.
if curr_dimsize is not None:
n_s_size += curr_dimsize
# Create output weight file.
wf_varnames = ['row', 'col', 'S']
wf_dtypes = [np.int32, np.int32, np.float64]
vc = VariableCollection()
dim = Dimension('n_s', n_s_size)
for w, wd in zip(wf_varnames, wf_dtypes):
var = Variable(name=w, dimensions=dim, dtype=wd)
vc.add_variable(var)
vc.write(merged_weight_filename)
# Transfer weights to the merged file.
sidx = 0
src_indices = self.src_grid._gc_create_global_indices_(
src_global_shape)
dst_indices = self.dst_grid._gc_create_global_indices_(
dst_global_shape)
out_wds = nc.Dataset(merged_weight_filename, 'a')
for ii, wfn in enumerate(
map(lambda x: os.path.join(split_weight_file_directory, x),
wv)):
if not os.path.exists(wfn):
if strict:
raise IOError(wfn)
else:
continue
wdata = RequestDataset(wfn).get()
for wvn in wf_varnames:
odata = wdata[wvn].get_value()
try:
split_grids_directory = self.paths['wd']
odata = self._gc_remap_weight_variable_(
ii,
wvn,
odata,
src_indices,
dst_indices,
ifile,
gidx,
split_grids_directory=split_grids_directory)
except IndexError as e:
msg = "Weight filename: '{}'; Weight Variable Name: '{}'. {}".format(
wfn, wvn, str(e))
raise IndexError(msg)
out_wds[wvn][sidx:sidx + odata.size] = odata
out_wds.sync()
sidx += odata.size
out_wds.close()
def _convert_to_ugrid_(field):
"""
Takes field data out of the OCGIS unstructured format (similar to UGRID) converting to the format expected
by ESMF Unstructured metadata.
"""
# The driver for the current field must be NetCDF UGRID to ensure interpretability.
assert field.dimension_map.get_driver() == DriverKey.NETCDF_UGRID
grid = field.grid
# Three-dimensional data is not supported.
assert not grid.has_z
# Number of coordinate dimension. This will be 3 for three-dimensional data.
coord_dim = Dimension('coordDim', 2)
# Transform ragged array to one-dimensional array. #############################################################
cindex = grid.cindex
elements = cindex.get_value()
num_element_conn_data = [e.shape[0] for e in elements.flat]
length_connection_count = sum(num_element_conn_data)
esmf_element_conn = np.zeros(length_connection_count, dtype=elements[0].dtype)
start = 0
tag_start_index = MPITag.START_INDEX
# Collapse the ragged element index array into a single dimensioned vector. This communication block finds the
# size for the new array. ######################################################################################
if vm.size > 1:
max_index = max([ii.max() for ii in elements.flat])
if vm.rank == 0:
vm.comm.isend(max_index + 1, dest=1, tag=tag_start_index)
adjust = 0
else:
adjust = vm.comm.irecv(source=vm.rank - 1, tag=tag_start_index)
adjust = adjust.wait()
if vm.rank != vm.size - 1:
vm.comm.isend(max_index + 1 + adjust, dest=vm.rank + 1, tag=tag_start_index)
# Fill the new vector for the element connectivity. ############################################################
for ii in elements.flat:
if vm.size > 1:
if grid.archetype.has_multi:
mbv = cindex.attrs[OcgisConvention.Name.MULTI_BREAK_VALUE]
replace_breaks = np.where(ii == mbv)[0]
else:
replace_breaks = []
ii = ii + adjust
if len(replace_breaks) > 0:
ii[replace_breaks] = mbv
esmf_element_conn[start: start + ii.shape[0]] = ii
start += ii.shape[0]
# Create the new data representation. ##########################################################################
connection_count = create_distributed_dimension(esmf_element_conn.size, name='connectionCount')
esmf_element_conn_var = Variable(name='elementConn', value=esmf_element_conn, dimensions=connection_count,
dtype=np.int32)
esmf_element_conn_var.attrs[CFName.LONG_NAME] = 'Node indices that define the element connectivity.'
mbv = cindex.attrs.get(OcgisConvention.Name.MULTI_BREAK_VALUE)
if mbv is not None:
esmf_element_conn_var.attrs['polygon_break_value'] = mbv
esmf_element_conn_var.attrs['start_index'] = grid.start_index
ret = VariableCollection(variables=field.copy().values(), force=True)
# Rename the element count dimension.
original_name = ret[cindex.name].dimensions[0].name
ret.rename_dimension(original_name, 'elementCount')
# Add the element-node connectivity variable to the collection.
ret.add_variable(esmf_element_conn_var)
num_element_conn = Variable(name='numElementConn',
value=num_element_conn_data,
dimensions=cindex.dimensions[0],
attrs={CFName.LONG_NAME: 'Number of nodes per element.'},
dtype=np.int32)
ret.add_variable(num_element_conn)
# Check that the node count dimension is appropriately named.
gn_name = grid.node_dim.name
if gn_name != 'nodeCount':
ret.dimensions[gn_name] = ret.dimensions[gn_name].copy()
ret.rename_dimension(gn_name, 'nodeCount')
node_coords = Variable(name='nodeCoords', dimensions=(ret.dimensions['nodeCount'], coord_dim))
node_coords.units = 'degrees'
node_coords.attrs[CFName.LONG_NAME] = 'Node coordinate values indexed by element connectivity.'
node_coords.attrs['coordinates'] = 'x y'
fill = node_coords.get_value()
fill[:, 0] = grid.x.get_value()
fill[:, 1] = grid.y.get_value()
ret.pop(grid.x.name)
ret.pop(grid.y.name)
ret.add_variable(node_coords)
ret.attrs['gridType'] = 'unstructured'
ret.attrs['version'] = '0.9'
# Remove the coordinate index, this does not matter.
if field.grid.cindex is not None:
ret.remove_variable(field.grid.cindex.name)
return ret
def write_chunks(self):
"""
Write grid subsets to netCDF files using the provided filename templates. This will also generate ESMF
regridding weights for each subset if requested.
"""
src_filenames = []
dst_filenames = []
wgt_filenames = []
dst_slices = []
src_slices = []
index_path = self.create_full_path_from_template('index_file')
# nzeros = len(str(reduce(lambda x, y: x * y, self.nchunks_dst)))
ctr = 1
ocgis_lh(logger='grid_chunker', msg='starting self.iter_src_grid_subsets', level=logging.DEBUG)
for sub_src, src_slc, sub_dst, dst_slc in self.iter_src_grid_subsets(yield_dst=True):
ocgis_lh(logger='grid_chunker', msg='finished iteration {} for self.iter_src_grid_subsets'.format(ctr),
level=logging.DEBUG)
src_path = self.create_full_path_from_template('src_template', index=ctr)
dst_path = self.create_full_path_from_template('dst_template', index=ctr)
wgt_path = self.create_full_path_from_template('wgt_template', index=ctr)
src_filenames.append(os.path.split(src_path)[1])
dst_filenames.append(os.path.split(dst_path)[1])
wgt_filenames.append(wgt_path)
dst_slices.append(dst_slc)
src_slices.append(src_slc)
# Only write destinations if an iterator is not provided.
if self.iter_dst is None:
zip_args = [[sub_src, sub_dst], [src_path, dst_path]]
else:
zip_args = [[sub_src], [src_path]]
cc = 1
for target, path in zip(*zip_args):
with vm.scoped_by_emptyable('field.write' + str(cc), target):
if not vm.is_null:
ocgis_lh(logger='grid_chunker', msg='write_chunks:writing: {}'.format(path),
level=logging.DEBUG)
field = Field(grid=target)
field.write(path)
ocgis_lh(logger='grid_chunker', msg='write_chunks:finished writing: {}'.format(path),
level=logging.DEBUG)
cc += 1
# Increment the counter outside of the loop to avoid counting empty subsets.
ctr += 1
# Generate an ESMF weights file if requested and at least one rank has data on it.
if self.genweights and len(vm.get_live_ranks_from_object(sub_src)) > 0:
vm.barrier()
self.write_esmf_weights(src_path, dst_path, wgt_path, src_grid=sub_src, dst_grid=sub_dst)
vm.barrier()
# Global shapes require a VM global scope to collect.
src_global_shape = global_grid_shape(self.src_grid)
dst_global_shape = global_grid_shape(self.dst_grid)
# Gather and collapse source slices as some may be empty and we write on rank 0.
gathered_src_grid_slice = vm.gather(src_slices)
if vm.rank == 0:
len_src_slices = len(src_slices)
new_src_grid_slice = [None] * len_src_slices
for idx in range(len_src_slices):
for rank_src_grid_slice in gathered_src_grid_slice:
if rank_src_grid_slice[idx] is not None:
new_src_grid_slice[idx] = rank_src_grid_slice[idx]
break
src_slices = new_src_grid_slice
with vm.scoped('index write', [0]):
if not vm.is_null:
dim = Dimension('nfiles', len(src_filenames))
vname = ['source_filename', 'destination_filename', 'weights_filename']
values = [src_filenames, dst_filenames, wgt_filenames]
grid_chunker_destination = GridChunkerConstants.IndexFile.NAME_DESTINATION_VARIABLE
attrs = [{'esmf_role': 'grid_chunker_source'},
{'esmf_role': grid_chunker_destination},
{'esmf_role': 'grid_chunker_weights'}]
vc = VariableCollection()
grid_chunker_index = GridChunkerConstants.IndexFile.NAME_INDEX_VARIABLE
vidx = Variable(name=grid_chunker_index)
vidx.attrs['esmf_role'] = grid_chunker_index
vidx.attrs['grid_chunker_source'] = 'source_filename'
vidx.attrs[GridChunkerConstants.IndexFile.NAME_DESTINATION_VARIABLE] = 'destination_filename'
vidx.attrs['grid_chunker_weights'] = 'weights_filename'
vidx.attrs[GridChunkerConstants.IndexFile.NAME_SRC_GRID_SHAPE] = src_global_shape
vidx.attrs[GridChunkerConstants.IndexFile.NAME_DST_GRID_SHAPE] = dst_global_shape
vc.add_variable(vidx)
for idx in range(len(vname)):
v = Variable(name=vname[idx], dimensions=dim, dtype=str, value=values[idx], attrs=attrs[idx])
vc.add_variable(v)
bounds_dimension = Dimension(name='bounds', size=2)
# TODO: This needs to work with four dimensions.
# Source -----------------------------------------------------------------------------------------------
self.src_grid._gc_create_index_bounds_(RegriddingRole.SOURCE, vidx, vc, src_slices, dim,
bounds_dimension)
# Destination ------------------------------------------------------------------------------------------
self.dst_grid._gc_create_index_bounds_(RegriddingRole.DESTINATION, vidx, vc, dst_slices, dim,
bounds_dimension)
vc.write(index_path)
vm.barrier()
def create_merged_weight_file(self, merged_weight_filename, strict=False):
"""
Merge weight file chunks to a single, global weight file.
:param str merged_weight_filename: Path to the merged weight file.
:param bool strict: If ``False``, allow "missing" files where the iterator index cannot create a found file.
It is best to leave these ``False`` as not all source and destinations are mapped. If ``True``, raise an
"""
if vm.size > 1:
raise ValueError("'create_merged_weight_file' does not work in parallel")
index_filename = self.create_full_path_from_template('index_file')
ifile = RequestDataset(uri=index_filename).get()
ifile.load()
ifc = GridChunkerConstants.IndexFile
gidx = ifile[ifc.NAME_INDEX_VARIABLE].attrs
src_global_shape = gidx[ifc.NAME_SRC_GRID_SHAPE]
dst_global_shape = gidx[ifc.NAME_DST_GRID_SHAPE]
# Get the global weight dimension size.
n_s_size = 0
weight_filename = ifile[gidx[ifc.NAME_WEIGHTS_VARIABLE]]
wv = weight_filename.join_string_value()
split_weight_file_directory = self.paths['wd']
for wfn in map(lambda x: os.path.join(split_weight_file_directory, os.path.split(x)[1]), wv):
if not os.path.exists(wfn):
if strict:
raise IOError(wfn)
else:
continue
n_s_size += RequestDataset(wfn).get().dimensions['n_s'].size
# Create output weight file.
wf_varnames = ['row', 'col', 'S']
wf_dtypes = [np.int32, np.int32, np.float64]
vc = VariableCollection()
dim = Dimension('n_s', n_s_size)
for w, wd in zip(wf_varnames, wf_dtypes):
var = Variable(name=w, dimensions=dim, dtype=wd)
vc.add_variable(var)
vc.write(merged_weight_filename)
# Transfer weights to the merged file.
sidx = 0
src_indices = self.src_grid._gc_create_global_indices_(src_global_shape)
dst_indices = self.dst_grid._gc_create_global_indices_(dst_global_shape)
out_wds = nc.Dataset(merged_weight_filename, 'a')
for ii, wfn in enumerate(map(lambda x: os.path.join(split_weight_file_directory, x), wv)):
if not os.path.exists(wfn):
if strict:
raise IOError(wfn)
else:
continue
wdata = RequestDataset(wfn).get()
for wvn in wf_varnames:
odata = wdata[wvn].get_value()
try:
split_grids_directory = self.paths['wd']
odata = self._gc_remap_weight_variable_(ii, wvn, odata, src_indices, dst_indices, ifile, gidx,
split_grids_directory=split_grids_directory)
except IndexError as e:
msg = "Weight filename: '{}'; Weight Variable Name: '{}'. {}".format(wfn, wvn, str(e))
raise IndexError(msg)
out_wds[wvn][sidx:sidx + odata.size] = odata
out_wds.sync()
sidx += odata.size
out_wds.close()