# merge transects for critical land blockages into
# critical_land_blockages.geojson
fcCritBlockages = gf.read(componentName='ocean',
objectType='transect',
tags=['Critical_Land_Blockage'])
else:
fcCritBlockages = FeatureCollection()
if options.custom_land_blockages is not None:
fcCritBlockages.merge(
read_feature_collection(options.custom_land_blockages))
# create masks from the transects
dsCritBlockMask = conversion.mask(dsBaseMesh, fcMask=fcCritBlockages)
dsLandMask = add_critical_land_blockages(dsLandMask, dsCritBlockMask)
fcCritPassages = FeatureCollection()
dsPreserve = []
if critical_passages:
if options.with_critical_passages:
# merge transects for critical passages into critical_passages.geojson
fcCritPassages.merge(
gf.read(componentName='ocean',
objectType='transect',
tags=['Critical_Passage']))
if options.custom_critical_passages is not None:
fcCritPassages.merge(
read_feature_collection(options.custom_critical_passages))
def saveesm(path,
geom,
mesh,
preserve_floodplain=False,
floodplain_elevation=20.0,
do_inject_elevation=False,
with_cavities=False,
lat_threshold=43.00,
with_critical_passages=True):
"""
SAVEESM: export a jigsaw mesh obj. to MPAS-style output.
1. Writes "mesh_triangles.nc" and "base_mesh.nc" files.
2. (Optionally) injects elevation + floodplain data.
3. Calls MPAS-Tools + Geometric-Data to cull mesh into
ocean/land partitions.
4. Writes "culled_mesh.nc" (ocean) and "invert_mesh.nc"
(land) MPAS-spec. output files.
Data is written to "../path/out/" and/or "../path/tmp/".
"""
# Authors: Darren Engwirda
ttic = time.time()
print("")
print("Running MPAS mesh-tools...")
inject_edge_tags(mesh)
# adapted from BUILD_MESH.py
if (geom.mshID.lower() == "ellipsoid-mesh"):
print("Forming mesh_triangles.nc")
jigsaw_mesh_to_netcdf(mesh=mesh,
on_sphere=True,
sphere_radius=np.mean(geom.radii) * 1e3,
output_name=os.path.join(path, "tmp",
"mesh_triangles.nc"))
if (geom.mshID.lower() == "euclidean-mesh"):
print("Forming mesh_triangles.nc")
jigsaw_mesh_to_netcdf(mesh=mesh,
on_sphere=False,
output_name=os.path.join(path, "tmp",
"mesh_triangles.nc"))
print("Forming base_mesh.nc")
write_netcdf(convert(
xarray.open_dataset(os.path.join(path, "tmp", "mesh_triangles.nc"))),
fileName=os.path.join(path, "out", "base_mesh.nc"))
"""
if do_inject_elevation:
print("Injecting cell elevations")
inject_elevation(
cell_elev=mesh.value,
mesh_file=os.path.join(
path, "out", "base_mesh.nc"))
"""
if preserve_floodplain:
print("Injecting floodplain flag")
inject_preserve_floodplain(mesh_file=os.path.join(
path, "out", "base_mesh.nc"),
floodplain_elevation=floodplain_elevation)
args = [
"paraview_vtk_field_extractor.py", "--ignore_time", "-l", "-d",
"maxEdges=0", "-v", "allOnCells", "-f",
os.path.join(path, "out", "base_mesh.nc"), "-o",
os.path.join(path, "out", "base_mesh_vtk")
]
print("")
print("running:", " ".join(args))
subprocess.check_call(args, env=os.environ.copy())
# adapted from CULL_MESH.py
# required for compatibility with MPAS
netcdfFormat = "NETCDF3_64BIT"
gf = GeometricFeatures(cacheLocation="{}".format(
os.path.join(HERE, "..", "data", "geometric_data")))
# start with the land coverage from Natural Earth
fcLandCoverage = gf.read(componentName="natural_earth",
objectType="region",
featureNames=["Land Coverage"])
# remove the region south of 60S so we can replace
# it based on ice-sheet topography
fcSouthMask = gf.read(componentName="ocean",
objectType="region",
featureNames=["Global Ocean 90S to 60S"])
fcLandCoverage = \
fcLandCoverage.difference(fcSouthMask)
# add land coverage from either the full ice sheet
# or just the grounded part
if with_cavities:
fcAntarcticLand = gf.read(
componentName="bedmap2",
objectType="region",
featureNames=["AntarcticGroundedIceCoverage"])
else:
fcAntarcticLand = gf.read(componentName="bedmap2",
objectType="region",
featureNames=["AntarcticIceCoverage"])
fcLandCoverage.merge(fcAntarcticLand)
# save the feature collection to a geojson file
fcLandCoverage.to_geojson(
os.path.join(path, "tmp", "land_coverage.geojson"))
# Create the land mask based on the land coverage,
# i.e. coastline data.
dsBaseMesh = xarray.open_dataset(os.path.join(path, "out", "base_mesh.nc"))
dsLandMask = mask(dsBaseMesh, fcMask=fcLandCoverage)
dsLandMask = add_land_locked_cells_to_mask(
dsLandMask, dsBaseMesh, latitude_threshold=lat_threshold, nSweeps=20)
if with_critical_passages:
# merge transects for critical passages into
# critical_passages.geojson
fcCritPassages = gf.read(componentName="ocean",
objectType="transect",
tags=["Critical_Passage"])
# create masks from the transects
dsCritPassMask = \
mask(dsBaseMesh, fcMask=fcCritPassages)
# Alter critical passages to be at least two
# cells wide, to avoid sea ice blockage.
dsCritPassMask = widen_transect_edge_masks(
dsCritPassMask, dsBaseMesh, latitude_threshold=lat_threshold)
dsLandMask = subtract_critical_passages(dsLandMask, dsCritPassMask)
# merge transects for critical land blockages
# into critical_land_blockages.geojson
fcCritBlockages = gf.read(componentName="ocean",
objectType="transect",
tags=["Critical_Land_Blockage"])
# create masks from the transects for critical
# land blockages
dsCritBlockMask = \
mask(dsBaseMesh, fcMask=fcCritBlockages)
dsLandMask = add_critical_land_blockages(dsLandMask, dsCritBlockMask)
# create seed points for a flood fill of the ocean
# use all points in the ocean directory, on the
# assumption that they are, in fact *in* the ocean
fcSeed = gf.read(componentName="ocean",
objectType="point",
tags=["seed_point"])
# update the land mask to ensure all ocean cells really
# are "reachable" from the rest of the global ocean
dsLandMask = mask_reachable_ocean(dsMesh=dsBaseMesh,
dsMask=dsLandMask,
fcSeed=fcSeed)
# cull the (ocean) mesh based on the land mask, and a
# cull the (land) mesh using the inverse mask
if preserve_floodplain:
# with "preserve_floodplains", the (ocean) mesh will
# contain overlap with the (land) mesh, otherwise the
# two are "perfectly" disjoint
dsCulledMesh = cull(dsBaseMesh,
dsMask=dsLandMask,
dsPreserve=dsBaseMesh,
graphInfoFileName=os.path.join(
path, "out", "culled_graph.info"))
dsInvertMesh = cull(dsBaseMesh,
dsInverse=dsLandMask,
graphInfoFileName=os.path.join(
path, "out", "invert_graph.info"))
else:
dsCulledMesh = cull(dsBaseMesh,
dsMask=dsLandMask,
graphInfoFileName=os.path.join(
path, "out", "culled_graph.info"))
dsInvertMesh = cull(dsBaseMesh,
dsInverse=dsLandMask,
graphInfoFileName=os.path.join(
path, "out", "invert_graph.info"))
write_netcdf(dsCulledMesh, os.path.join(path, "out", "culled_mesh.nc"),
netcdfFormat)
write_netcdf(dsInvertMesh, os.path.join(path, "out", "invert_mesh.nc"),
netcdfFormat)
args = [
"paraview_vtk_field_extractor.py", "--ignore_time", "-d", "maxEdges=",
"-v", "allOnCells", "-f",
os.path.join(path, "out", "culled_mesh.nc"), "-o",
os.path.join(path, "out", "culled_mesh_vtk")
]
print("")
print("running", " ".join(args))
subprocess.check_call(args, env=os.environ.copy())
args = [
"paraview_vtk_field_extractor.py", "--ignore_time", "-d", "maxEdges=",
"-v", "allOnCells", "-f",
os.path.join(path, "out", "invert_mesh.nc"), "-o",
os.path.join(path, "out", "invert_mesh_vtk")
]
print("running", " ".join(args))
subprocess.check_call(args, env=os.environ.copy())
ttoc = time.time()
print("CPUSEC =", (ttoc - ttic))
return
parser = \
argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-f",
"--input_mask_file",
dest="input_mask_filename",
help="Mask file that includes cell and edge masks.",
metavar="INPUTMASKFILE",
required=True)
parser.add_argument("-o",
"--output_mask_file",
dest="output_mask_filename",
help="Mask file that includes cell and edge masks.",
metavar="OUTPUTMASKFILE",
required=True)
parser.add_argument("-b",
"--blockage_file",
dest="blockage_file",
help="Masks for each transect identifying critical "
"land blockage.",
metavar="BLOCKFILE",
required=True)
args = parser.parse_args()
dsMask = xarray.open_dataset(args.input_mask_filename)
dsBlockages = xarray.open_dataset(args.blockage_file)
dsMask = add_critical_land_blockages(dsMask, dsBlockages)
dsMask.to_netcdf(args.output_mask_filename)
def _cull_mesh_with_logging(logger, with_cavities, with_critical_passages,
custom_critical_passages, custom_land_blockages,
preserve_floodplain, use_progress_bar,
process_count):
""" Cull the mesh once the logger is defined for sure """
critical_passages = with_critical_passages or \
(custom_critical_passages is not None)
land_blockages = with_critical_passages or \
(custom_land_blockages is not None)
gf = GeometricFeatures()
# start with the land coverage from Natural Earth
fcLandCoverage = gf.read(componentName='natural_earth',
objectType='region',
featureNames=['Land Coverage'])
# remove the region south of 60S so we can replace it based on ice-sheet
# topography
fcSouthMask = gf.read(componentName='ocean', objectType='region',
featureNames=['Global Ocean 90S to 60S'])
fcLandCoverage = fcLandCoverage.difference(fcSouthMask)
# Add "land" coverage from either the full ice sheet or just the grounded
# part
if with_cavities:
fcAntarcticLand = gf.read(
componentName='bedmachine', objectType='region',
featureNames=['AntarcticGroundedIceCoverage'])
else:
fcAntarcticLand = gf.read(
componentName='bedmachine', objectType='region',
featureNames=['AntarcticIceCoverage'])
fcLandCoverage.merge(fcAntarcticLand)
# save the feature collection to a geojson file
fcLandCoverage.to_geojson('land_coverage.geojson')
# these defaults may have been updated from config options -- pass them
# along to the subprocess
netcdf_format = mpas_tools.io.default_format
netcdf_engine = mpas_tools.io.default_engine
# Create the land mask based on the land coverage, i.e. coastline data
args = ['compute_mpas_region_masks',
'-m', 'base_mesh.nc',
'-g', 'land_coverage.geojson',
'-o', 'land_mask.nc',
'-t', 'cell',
'--process_count', '{}'.format(process_count),
'--format', netcdf_format,
'--engine', netcdf_engine]
check_call(args, logger=logger)
dsBaseMesh = xarray.open_dataset('base_mesh.nc')
dsLandMask = xarray.open_dataset('land_mask.nc')
dsLandMask = add_land_locked_cells_to_mask(dsLandMask, dsBaseMesh,
latitude_threshold=43.0,
nSweeps=20)
# create seed points for a flood fill of the ocean
# use all points in the ocean directory, on the assumption that they are,
# in fact, in the ocean
fcSeed = gf.read(componentName='ocean', objectType='point',
tags=['seed_point'])
if land_blockages:
if with_critical_passages:
# merge transects for critical land blockages into
# critical_land_blockages.geojson
fcCritBlockages = gf.read(
componentName='ocean', objectType='transect',
tags=['Critical_Land_Blockage'])
else:
fcCritBlockages = FeatureCollection()
if custom_land_blockages is not None:
fcCritBlockages.merge(read_feature_collection(
custom_land_blockages))
# create masks from the transects
fcCritBlockages.to_geojson('critical_blockages.geojson')
args = ['compute_mpas_transect_masks',
'-m', 'base_mesh.nc',
'-g', 'critical_blockages.geojson',
'-o', 'critical_blockages.nc',
'-t', 'cell',
'-s', '10e3',
'--process_count', '{}'.format(process_count),
'--format', netcdf_format,
'--engine', netcdf_engine]
check_call(args, logger=logger)
dsCritBlockMask = xarray.open_dataset('critical_blockages.nc')
dsLandMask = add_critical_land_blockages(dsLandMask, dsCritBlockMask)
fcCritPassages = FeatureCollection()
dsPreserve = []
if critical_passages:
if with_critical_passages:
# merge transects for critical passages into fcCritPassages
fcCritPassages.merge(gf.read(componentName='ocean',
objectType='transect',
tags=['Critical_Passage']))
if custom_critical_passages is not None:
fcCritPassages.merge(read_feature_collection(
custom_critical_passages))
# create masks from the transects
fcCritPassages.to_geojson('critical_passages.geojson')
args = ['compute_mpas_transect_masks',
'-m', 'base_mesh.nc',
'-g', 'critical_passages.geojson',
'-o', 'critical_passages.nc',
'-t', 'cell', 'edge',
'-s', '10e3',
'--process_count', '{}'.format(process_count),
'--format', netcdf_format,
'--engine', netcdf_engine]
check_call(args, logger=logger)
dsCritPassMask = xarray.open_dataset('critical_passages.nc')
# Alter critical passages to be at least two cells wide, to avoid sea
# ice blockage
dsCritPassMask = widen_transect_edge_masks(dsCritPassMask, dsBaseMesh,
latitude_threshold=43.0)
dsPreserve.append(dsCritPassMask)
if preserve_floodplain:
dsPreserve.append(dsBaseMesh)
# cull the mesh based on the land mask
dsCulledMesh = cull(dsBaseMesh, dsMask=dsLandMask,
dsPreserve=dsPreserve, logger=logger)
# create a mask for the flood fill seed points
dsSeedMask = compute_mpas_flood_fill_mask(dsMesh=dsCulledMesh,
fcSeed=fcSeed,
logger=logger)
# cull the mesh a second time using a flood fill from the seed points
dsCulledMesh = cull(dsCulledMesh, dsInverse=dsSeedMask,
graphInfoFileName='culled_graph.info', logger=logger)
write_netcdf(dsCulledMesh, 'culled_mesh.nc')
if critical_passages:
# make a new version of the critical passages mask on the culled mesh
fcCritPassages.to_geojson('critical_passages.geojson')
args = ['compute_mpas_transect_masks',
'-m', 'culled_mesh.nc',
'-g', 'critical_passages.geojson',
'-o', 'critical_passages_mask_final.nc',
'-t', 'cell',
'-s', '10e3',
'--process_count', '{}'.format(process_count),
'--format', netcdf_format,
'--engine', netcdf_engine]
check_call(args, logger=logger)
if with_cavities:
fcAntarcticIce = gf.read(
componentName='bedmachine', objectType='region',
featureNames=['AntarcticIceCoverage'])
fcAntarcticIce.to_geojson('ice_coverage.geojson')
args = ['compute_mpas_region_masks',
'-m', 'culled_mesh.nc',
'-g', 'ice_coverage.geojson',
'-o', 'ice_coverage.nc',
'-t', 'cell',
'--process_count', '{}'.format(process_count),
'--format', netcdf_format,
'--engine', netcdf_engine]
check_call(args, logger=logger)
dsMask = xarray.open_dataset('ice_coverage.nc')
landIceMask = dsMask.regionCellMasks.isel(nRegions=0)
dsLandIceMask = xarray.Dataset()
dsLandIceMask['landIceMask'] = landIceMask
write_netcdf(dsLandIceMask, 'land_ice_mask.nc')
dsLandIceCulledMesh = cull(dsCulledMesh, dsMask=dsMask, logger=logger)
write_netcdf(dsLandIceCulledMesh, 'no_ISC_culled_mesh.nc')
extract_vtk(ignore_time=True, dimension_list=['maxEdges='],
variable_list=['allOnCells'],
filename_pattern='culled_mesh.nc',
out_dir='culled_mesh_vtk',
use_progress_bar=use_progress_bar)
if with_cavities:
extract_vtk(ignore_time=True, dimension_list=['maxEdges='],
variable_list=['allOnCells'],
filename_pattern='no_ISC_culled_mesh.nc',
out_dir='no_ISC_culled_mesh_vtk',
use_progress_bar=use_progress_bar)
