# create a GeometricFeatures object that points to a local cache of geometric
# data and knows which branch of geometric_feature to use to download
# missing data
gf = GeometricFeatures('./geometric_data')
# create a FeatureCollection containing all iceshelf regions wtih one of the
# 27 IMBIE basin tags tags
fc = FeatureCollection()
for basin in range(1, 28):
print('Adding feature from IMBIE basin {:d}'.format(basin))
basinName = 'Antarctica_IMBIE{:d}'.format(basin)
tags = [basinName]
# load the iceshelf regions for one IMBIE basin
fcBasin = gf.read(componentName='iceshelves',
objectType='region',
tags=tags)
# combine all regions in the basin into a single feature
fcBasin = fcBasin.combine(featureName=basinName)
# merge the feature for the basin into the collection of all basins
fc.merge(fcBasin)
# save the feature collection to a geojson file
fc.to_geojson('Extended_Antarctic_Basins.geojson')
if plot:
fc.plot(projection='southpole')
plt.show()
iceMask = numpy.logical_or(iceMask, bedMask)
groundedMask = numpy.logical_or(groundedMask, bedMask)
masks = dict()
masks['AntarcticIceCoverage'] = iceMask
masks['AntarcticGroundedIceCoverage'] = groundedMask
fc = FeatureCollection()
for name in masks:
properties = dict()
properties['name'] = name
properties['component'] = 'bedmachine'
properties['author'] = \
'Morlighem et al. (2019) doi:10.1038/s41561-019-0510-8'
properties['object'] = 'region'
properties['tags'] = ''
feature = dict()
feature['properties'] = properties
feature['geometry'] = extract_geometry(masks[name])
fc.add_feature(feature)
fc.to_geojson(out_file_name)
gf = GeometricFeatures(cacheLocation='./geometric_data')
gf.split(fc)
write_feature_names_and_tags(gf.cacheLocation)
os.rename('features_and_tags.json',
'geometric_features/features_and_tags.json')
def main():
author = 'Xylar Asay-Davis, Alice Barthel, Nicolas Jourdain'
tags = 'Antarctic;ISMIP6'
# make a geometric features object that knows about the geometric data
# cache up a couple of directories
gf = GeometricFeatures('../../geometric_data')
bedmap2_bin_to_netcdf('bedmap2.nc')
fcContour1500 = get_longest_contour(contourValue=-1500., author=author)
fc = FeatureCollection()
lons = [-65., -25., -25., -65.]
lats = [-80., -80., -77., -71.]
fc.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Weddell Sea',
author=author,
tags=tags,
fcContour=fcContour1500))
lons = [-128., -128., -90., -90.]
lats = [-76., -69., -69., -76.]
fc.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Amundsen Sea',
author=author,
tags=tags,
fcContour=fcContour1500))
lons = [45., 45., 90., 90.]
lats = [-70., -60., -60., -70.]
fc.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Amery Sector',
author=author,
tags=tags,
fcContour=fcContour1500))
lons = [-22.5, -22.5, 22.5, 22.5]
lats = [-75., -65., -65., -75.]
fc.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Dronning Maud Land',
author=author,
tags=tags,
fcContour=fcContour1500))
lons = [110., 110., 130., 130.]
lats = [-70., -60., -60., -70.]
fc.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Totten Region',
author=author,
tags=tags,
fcContour=fcContour1500))
lons = [165., 165., 180., 180.]
lats = [-80., -71., -73., -80.]
fc_ross = shelf_polygon(lons,
lats,
name='ISMIP6 Western Ross Sea',
author=author,
tags=tags,
fcContour=fcContour1500)
lons = [-180., -180., -150., -150.]
lats = [-80., -73., -77., -80.]
fc_ross.merge(
shelf_polygon(lons,
lats,
name='ISMIP6 Eastern Ross Sea',
author=author,
tags=tags,
fcContour=fcContour1500))
old_props = fc_ross.features[0]['properties']
fc_ross = fc_ross.combine('ISMIP6 Ross Sea')
props = fc_ross.features[0]['properties']
for prop in ['tags', 'zmin', 'zmax']:
props[prop] = old_props[prop]
fc.merge(fc_ross)
fc.plot(projection='southpole')
fc.to_geojson('ismip6_antarctic_ocean_regions.geojson')
# "split" these features into individual files in the geometric data cache
gf.split(fc)
# update the database of feature names and tags
write_feature_names_and_tags(gf.cacheLocation)
# move the resulting file into place
shutil.copyfile('features_and_tags.json',
'../../geometric_features/features_and_tags.json')
plt.show()
def main():
author = 'Xylar Asay-Davis'
timTags = 'Antarctic;Timmermann'
orsiTags = 'Antarctic;Orsi'
# make a geometric fieatures object that knows about the geometric data
# cache up a couple of directories
gf = GeometricFeatures('../../geometric_data')
bedmap2_bin_to_netcdf('bedmap2.nc')
fcContour700 = get_longest_contour(contourValue=-700., author=author)
fcContour800 = get_longest_contour(contourValue=-800., author=author)
fc = FeatureCollection()
fcWeddell = split_rectangle(lon0=-63.,
lon1=0.,
lat0=-80.,
lat1=-65.,
name='Weddell Sea',
author=author,
tags=timTags,
fcContour=fcContour800)
# get rid of the Weddell Sea because we're not that happy with this
# definition, but keep the deep/shelf ones
fcWeddell.features = fcWeddell.features[1:]
fc.merge(fcWeddell)
fcEW = split_rectangle(lon0=-20.,
lon1=25.,
lat0=-80.,
lat1=-55.,
name='Eastern Weddell Sea',
author=author,
tags=orsiTags,
fcContour=fcContour800)
fc.merge(fcEW)
fcWW = split_rectangle(lon0=-63.,
lon1=-20.,
lat0=-80.,
lat1=-60.,
name='Western Weddell Sea',
author=author,
tags=orsiTags,
fcContour=fcContour800)
fc.merge(fcWW)
# The Weddell feature is the sum of the Eastern and Western features before
# splitting into shelf/deep
fcWeddell = FeatureCollection()
fcWeddell.features.append(fcEW.features[0])
fcWeddell.features.append(fcWW.features[0])
# now combine these into a single feature
fcWeddell = fcWeddell.combine('Weddell Sea')
props = fcWeddell.features[0]['properties']
props['tags'] = orsiTags
props['zmin'] = -1000.
props['zmax'] = -400.
fc.merge(fcWeddell)
# add the Weddell Sea back as the sum of Eastern and Western
fc.merge(
make_rectangle(lon0=-63.,
lon1=45.,
lat0=-80.,
lat1=-58.,
name='Weddell Sea',
author=author,
tags=orsiTags))
fc.merge(
split_rectangle(lon0=-100.,
lon1=-63.,
lat0=-80.,
lat1=-67.,
name='Bellingshausen Sea',
author=author,
tags=timTags,
fcContour=fcContour700))
fc.merge(
split_rectangle(lon0=-140.,
lon1=-100.,
lat0=-80.,
lat1=-67.,
name='Amundsen Sea',
author=author,
tags=timTags,
fcContour=fcContour800))
fc.merge(
split_rectangle(lon0=-180.,
lon1=-140.,
lat0=-80.,
lat1=-67.,
name='Eastern Ross Sea',
author=author,
tags=timTags,
fcContour=fcContour700))
fc.merge(
split_rectangle(lon0=160.,
lon1=180.,
lat0=-80.,
lat1=-67.,
name='Western Ross Sea',
author=author,
tags=timTags,
fcContour=fcContour700))
fc.merge(
split_rectangle(lon0=25.,
lon1=160.,
lat0=-80.,
lat1=-62.,
name='East Antarctic Seas',
author=author,
tags=orsiTags,
fcContour=fcContour800))
fc.merge(
make_rectangle(lon0=-180.,
lon1=180.,
lat0=-80.,
lat1=-60.,
name='Southern Ocean 60S',
author=author,
tags=timTags))
fcSO = gf.read('ocean', 'region', ['Southern Ocean'])
props = fcSO.features[0]['properties']
props['zmin'] = -1000.
props['zmax'] = -400.
fc.merge(fcSO)
fc.plot(projection='southpole')
fc.to_geojson('antarctic_ocean_regions.geojson')
# "split" these features into individual files in the geometric data cache
gf.split(fc)
# update the database of feature names and tags
write_feature_names_and_tags(gf.cacheLocation)
# move the resulting file into place
shutil.copyfile('features_and_tags.json',
'../../geometric_features/features_and_tags.json')
plt.show()
tags=subNames,
allTags=False)
print(' * combining features')
fcShelf = fcShelf.combine(featureName=shelfName)
# merge the feature for the basin into the collection of all basins
fc.merge(fcShelf)
# build ice shelves from regions with the appropriate tags
for shelfName in iceShelfNames:
print(shelfName)
print(' * merging features')
fcShelf = gf.read(componentName='iceshelves',
objectType='region',
tags=[shelfName])
print(' * combining features')
fcShelf = fcShelf.combine(featureName=shelfName)
# merge the feature for the basin into the collection of all basins
fc.merge(fcShelf)
# save the feature collection to a geojson file
fc.to_geojson('iceShelves20200621.geojson')
if plot:
fc.plot(projection='southpole')
plt.show()
tags=subNames,
allTags=False)
print(' * combining features')
fcShelf = fcShelf.combine(featureName=shelfName)
# merge the feature for the basin into the collection of all basins
fc.merge(fcShelf)
# build ice shelves from regions with the appropriate tags
for shelfName in iceShelfNames:
print(shelfName)
print(' * merging features')
fcShelf = gf.read(componentName='iceshelves',
objectType='region',
tags=[shelfName])
print(' * combining features')
fcShelf = fcShelf.combine(featureName=shelfName)
# merge the feature for the basin into the collection of all basins
fc.merge(fcShelf)
# save the feature collection to a geojson file
fc.to_geojson('iceShelves.geojson')
if plot:
fc.plot(projection='southpole')
plt.show()
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)