def make_analysis_polar_map(config, mesh_name, projection):
# {{{
mesh_filename = '../mesh.nc'
upperProj = projection[0].upper() + projection[1:]
inDescriptor = MpasMeshDescriptor(mesh_filename, mesh_name)
comparisonStereoWidth = config.getfloat(
'mapping_analysis', 'comparison{}StereoWidth'.format(upperProj))
comparisonStereoResolution = config.getfloat(
'mapping_analysis', 'comparison{}StereoResolution'.format(upperProj))
outDescriptor = get_polar_descriptor(Lx=comparisonStereoWidth,
Ly=comparisonStereoWidth,
dx=comparisonStereoResolution,
dy=comparisonStereoResolution,
projection=projection)
outGridName = '{}x{}km_{}km_{}_stereo'.format(comparisonStereoWidth,
comparisonStereoWidth,
comparisonStereoResolution,
upperProj)
mappingFileName = 'map_{}_to_{}_bilinear.nc'.format(mesh_name, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
mpiTasks = config.getint('main', 'nprocs')
remapper.build_mapping_file(method='bilinear',
mpiTasks=mpiTasks,
tempdir='.')
def build_remapper(self, sourceDescriptor, destinationDescriptor,
weightFileName):
remapper = Remapper(sourceDescriptor, destinationDescriptor,
weightFileName)
remapper.build_mapping_file(method='bilinear')
assert os.path.exists(remapper.mappingFileName)
return remapper
def _make_mapping_file(mesh_name, outGridName, inDescriptor, outDescriptor,
cores, config, logger):
parallel_executable = config.get('parallel', 'parallel_executable')
mappingFileName = 'map_{}_to_{}_bilinear.nc'.format(mesh_name, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method='bilinear', mpiTasks=cores, tempdir='.',
logger=logger,
esmf_parallel_exec=parallel_executable)
def make_analysis_lat_lon_map(config, mesh_name):
# {{{
mesh_filename = '../mesh.nc'
inDescriptor = MpasMeshDescriptor(mesh_filename, mesh_name)
comparisonLatResolution = config.getfloat('mapping_analysis',
'comparisonLatResolution')
comparisonLonResolution = config.getfloat('mapping_analysis',
'comparisonLonResolution')
# modify the resolution of the global lat-lon grid as desired
outDescriptor = get_lat_lon_descriptor(dLon=comparisonLatResolution,
dLat=comparisonLonResolution)
outGridName = outDescriptor.meshName
mappingFileName = 'map_{}_to_{}_bilinear.nc'.format(mesh_name, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
mpiTasks = config.getint('main', 'nprocs')
remapper.build_mapping_file(method='bilinear', mpiTasks=mpiTasks,
tempdir='.')
def remap_rignot(inFileName,
meshFileName,
meshName,
outFileName,
mappingDirectory='.',
method='conserve',
renormalizationThreshold=None,
inVarName='melt_actual',
mpiTasks=1):
# {{{
"""
Remap the Rignot et al. (2013) melt rates at 1 km resolution to an MPAS
mesh
Parameters
----------
inFileName : str
The original Rignot et al. (2013) melt rates
meshFileName : str
The MPAS mesh
meshName : str
The name of the mesh (e.g. oEC60to30wISC), used in the name of the
mapping file
outFileName : str
The melt rates interpolated to the MPAS mesh with ocean sensible heat
fluxes added on (assuming insulating ice)
mappingDirectory : str
The directory where the mapping file should be stored (if it is to be
computed) or where it already exists (if not)
method : {'bilinear', 'neareststod', 'conserve'}, optional
The method of interpolation used, see documentation for
`ESMF_RegridWeightGen` for details.
renormalizationThreshold : float, optional
The minimum weight of a denstination cell after remapping, below
which it is masked out, or ``None`` for no renormalization and
masking.
inVarName : {'melt_actual', 'melt_steadystate'}
Whether to use the melt rate for the time period covered in Rignot et
al. (2013) with observed thinning/thickening or the melt rates that
would be required if ice shelves were in steady state.
mpiTasks : int, optional
The number of MPI tasks to use to compute the mapping file
"""
ds = xr.open_dataset(inFileName)
lx = np.abs(1e-3 * (ds.xaxis.values[-1] - ds.xaxis.values[0]))
ly = np.abs(1e-3 * (ds.yaxis.values[-1] - ds.yaxis.values[0]))
inGridName = '{}x{}km_1.0km_Antarctic_stereo'.format(lx, ly)
projection = pyproj.Proj('+proj=stere +lat_ts=-71.0 +lat_0=-90 +lon_0=0.0 '
'+k_0=1.0 +x_0=0.0 +y_0=0.0 +ellps=WGS84')
inDescriptor = ProjectionGridDescriptor.read(projection,
inFileName,
xVarName='xaxis',
yVarName='yaxis',
meshName=inGridName)
# convert to the units and variable names expected in MPAS-O
rho_fw = 1000.
s_per_yr = 365. * 24. * 60. * 60.
latent_heat_of_fusion = 3.337e5
ds['prescribedLandIceFreshwaterFlux'] = ds[inVarName] * rho_fw / s_per_yr
ds['prescribedLandIceHeatFlux'] = (latent_heat_of_fusion *
ds['prescribedLandIceFreshwaterFlux'])
ds = ds.drop_vars(['melt_actual', 'melt_steadystate', 'lon', 'lat'])
outDescriptor = MpasMeshDescriptor(meshFileName, meshName)
mappingFileName = '{}/map_{}_to_{}.nc'.format(mappingDirectory, inGridName,
meshName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method=method, mpiTasks=mpiTasks)
dsRemap = remapper.remap(ds,
renormalizationThreshold=renormalizationThreshold)
for field in [
'prescribedLandIceFreshwaterFlux', 'prescribedLandIceHeatFlux'
]:
# zero out the field where it's currently NaN
dsRemap[field] = dsRemap[field].where(dsRemap[field].nonnull(), 0.)
dsRemap.attrs['history'] = ' '.join(sys.argv)
write_netcdf(dsRemap, outFileName) # }}}
def get_remapper(config, sourceDescriptor, comparisonDescriptor,
mappingFilePrefix, method, logger=None): # {{{
"""
Given config options and descriptions of the source and comparison grids,
returns a ``pyremap.Remapper`` object that can be used to remap from source
files or data sets to corresponding data sets on the comparison grid.
If necessary, creates the mapping file containing weights and indices
needed to perform remapping.
Parameters
----------
config : instance of ``MpasAnalysisConfigParser``
Contains configuration options
sourceDescriptor : ``MeshDescriptor`` subclass object
A description of the source mesh or grid
comparisonDescriptor : ``MeshDescriptor`` subclass object
A description of the comparison grid
mappingFilePrefix : str
A prefix to be prepended to the mapping file name
method : {'bilinear', 'neareststod', 'conserve'}
The method of interpolation used.
logger : ``logging.Logger``, optional
A logger to which ncclimo output should be redirected
Returns
-------
remapper : ``pyremap.Remapper`` object
A remapper that can be used to remap files or data sets from the source
grid or mesh to the comparison grid.
"""
# Authors
# -------
# Xylar Asay-Davis
mappingFileName = None
if not _matches_comparison(sourceDescriptor, comparisonDescriptor):
# we need to remap because the grids don't match
mappingBaseName = '{}_{}_to_{}_{}.nc'.format(
mappingFilePrefix,
sourceDescriptor.meshName,
comparisonDescriptor.meshName,
method)
tryCustom = config.get('diagnostics', 'customDirectory') != 'none'
if tryCustom:
# first see if mapping files are in the custom directory
mappingSubdirectory = build_config_full_path(
config, 'diagnostics', 'mappingSubdirectory',
baseDirectoryOption='customDirectory')
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
if not tryCustom or not os.path.exists(mappingFileName):
# second see if mapping files are in the base directory
mappingSubdirectory = build_config_full_path(
config, 'diagnostics', 'mappingSubdirectory',
baseDirectoryOption='baseDirectory')
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
if not os.path.exists(mappingFileName):
# we don't have a mapping file yet, so get ready to create one
# in the output subfolder if needed
mappingSubdirectory = \
build_config_full_path(config, 'output',
'mappingSubdirectory')
make_directories(mappingSubdirectory)
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
remapper = Remapper(sourceDescriptor, comparisonDescriptor,
mappingFileName)
remapper.build_mapping_file(method=method, logger=logger)
return remapper # }}}
from pyremap import LatLonGridDescriptor, Remapper
from mpas_analysis.shared.constants import constants
inputFileName = '/media/xylar/extra_data/analysis/output/GMPAS-QU240/' \
'remap_obs/clim/obs/mld_1.0x1.0degree.nc'
obsDescriptor = LatLonGridDescriptor.read(fileName=inputFileName,
latVarName='lat',
lonVarName='lon')
comparisonLatRes = 4.
comparisonLonRes = 4.
nLat = int((constants.latmax - constants.latmin) / comparisonLatRes) + 1
nLon = int((constants.lonmax - constants.lonmin) / comparisonLonRes) + 1
lat = numpy.linspace(constants.latmin, constants.latmax, nLat)
lon = numpy.linspace(constants.lonmin, constants.lonmax, nLon)
comparisonDescriptor = LatLonGridDescriptor.create(lat, lon, units='degrees')
remapper = Remapper(obsDescriptor,
comparisonDescriptor,
mappingFileName='map.nc')
remapper.build_mapping_file()
remapper.remap_file(inputFileName,
'mld_4.0x4.0degree.nc', ['mld', 'month', 'year'],
renormalize=0.05)
def _remap(config, modelFolder):
res = get_res(config)
hres = get_horiz_res(config)
modelName = config.get('model', 'name')
inFileNames = {}
outFileNames = {}
bothExist = True
for fieldName in ['temperature', 'salinity']:
inFileNames[fieldName] = \
'{}/{}_{}_interp_z.nc'.format(modelFolder, modelName, fieldName)
outFileNames[fieldName] = \
'{}/{}_{}_{}.nc'.format(modelFolder, modelName, fieldName, res)
if not os.path.exists(outFileNames[fieldName]):
bothExist = False
if bothExist:
return
print(' Remapping to {} grid...'.format(res))
for fieldName in inFileNames:
inFileName = inFileNames[fieldName]
outFileName = outFileNames[fieldName]
if os.path.exists(outFileName):
continue
outGridFileName = 'ismip6/{}_grid.nc'.format(hres)
print(' {}'.format(outFileName))
progressDir = '{}/progress_remap_{}'.format(modelFolder, fieldName)
try:
os.makedirs(progressDir)
except OSError:
pass
ds = xarray.open_dataset(inFileName)
if len(ds.lon.dims) == 1:
inDescriptor = LatLonGridDescriptor.read(inFileName,
latVarName='lat',
lonVarName='lon')
else:
assert (len(ds.lon.dims) == 2)
inDescriptor = LatLon2DGridDescriptor.read(inFileName,
latVarName='lat',
lonVarName='lon')
inDescriptor.regional = True
outDescriptor = get_polar_descriptor_from_file(outGridFileName,
projection='antarctic')
mappingFileName = '{}/map_{}_to_{}.nc'.format(modelName.lower(),
inDescriptor.meshName,
outDescriptor.meshName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method='bilinear')
ds = ds.drop_vars(['lat', 'lon'])
nt = ds.sizes['time']
widgets = [
' ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.ETA()
]
print(f' remapping: {fieldName}')
bar = progressbar.ProgressBar(widgets=widgets, maxval=nt).start()
for tIndex in range(nt):
progressFileName = '{}/{}_t_{}.nc'.format(progressDir, modelName,
tIndex)
if os.path.exists(progressFileName):
bar.update(tIndex + 1)
continue
dsIn = ds.isel(time=tIndex)
dsOut = remapper.remap(dsIn, renormalizationThreshold=0.1)
dsOut = dsOut.transpose('z', 'y', 'x')
for attrName in ['units', 'standard_name', 'long_name']:
if attrName in ds[fieldName].attrs:
dsOut[fieldName].attrs[attrName] = \
ds[fieldName].attrs[attrName]
dsOut.z.attrs = ds.z.attrs
dsOut.to_netcdf(progressFileName)
bar.update(tIndex + 1)
bar.finish()
dsOut = xarray.open_mfdataset('{}/{}_t_*.nc'.format(
progressDir, modelName),
combine='nested',
concat_dim='time')
dsOut['z_bnds'] = ds.z_bnds
dsOut.to_netcdf(outFileName)
inGridName = 'oQU240'
# replace with the path to the desired mesh or restart file
# As an example, use:
# https://web.lcrc.anl.gov/public/e3sm/inputdata/ocn/mpas-o/oQU240/ocean.QU.240km.151209.nc
inGridFileName = 'ocean.QU.240km.151209.nc'
inDescriptor = MpasMeshDescriptor(inGridFileName, inGridName)
# modify the size and resolution of the Antarctic grid as desired
outDescriptor = get_polar_descriptor(Lx=6000.,
Ly=6000.,
dx=10.,
dy=10.,
projection='antarctic')
outGridName = outDescriptor.meshName
mappingFileName = 'map_{}_to_{}_conserve.nc'.format(inGridName, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
# conservative remapping with 4 MPI tasks (using mpirun)
remapper.build_mapping_file(method='conserve', mpiTasks=4)
outFileName = 'temp_{}.nc'.format(outGridName)
ds = xarray.open_dataset(inGridFileName)
dsOut = xarray.Dataset()
dsOut['temperature'] = ds['temperature']
dsOut = remapper.remap(dsOut)
dsOut.to_netcdf(outFileName)
inDescriptor = MpasMeshDescriptor(inGridFileName, inGridName)
# modify the size and resolution of the Antarctic grid as desired
outDescriptor = get_polar_descriptor(Lx=6000.,
Ly=5000.,
dx=10.,
dy=10.,
projection='antarctic')
outGridName = outDescriptor.meshName
mappingFileName = 'map_{}_to_{}_bilinear.nc'.format(inGridName, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
# conservative remapping with 4 MPI tasks (using mpirun)
remapper.build_mapping_file(method='bilinear', mpiTasks=4)
# select the SST at the initial time as an example data set
srcFileName = 'temp_{}.nc'.format(inGridName)
ds = xarray.open_dataset(inGridFileName)
dsOut = xarray.Dataset()
dsOut['temperature'] = ds['temperature'].isel(nVertLevels=0, Time=0)
dsOut.to_netcdf(srcFileName)
# do remapping with ncremap
outFileName = 'temp_{}_file.nc'.format(outGridName)
remapper.remap_file(srcFileName, outFileName)
# do remapping again, this time with python remapping
outFileName = 'temp_{}_array.nc'.format(outGridName)
dsOut = remapper.remap(dsOut)
Ly = int((y[-1] - y[0]) / 1000.)
inMeshName = '{}x{}km_{}km_Antarctic_stereo'.format(Lx, Ly, dx)
projection = get_antarctic_stereographic_projection()
inDescriptor = ProjectionGridDescriptor.create(projection, x, y, inMeshName)
outRes = args.resolution * 1e3
nxOut = int((x[-1] - x[0]) / outRes + 0.5) + 1
nyOut = int((y[-1] - y[0]) / outRes + 0.5) + 1
xOut = x[0] + outRes * numpy.arange(nxOut)
yOut = y[0] + outRes * numpy.arange(nyOut)
outMeshName = '{}x{}km_{}km_Antarctic_stereo'.format(Lx, Ly, args.resolution)
outDescriptor = ProjectionGridDescriptor.create(projection, xOut, yOut,
outMeshName)
mappingFileName = 'map_{}_to_{}_{}.nc'.format(inMeshName, outMeshName,
args.method)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method=args.method, mpiTasks=args.mpiTasks)
dsOut = remapper.remap(dsIn, renormalizationThreshold=0.01)
dsOut.to_netcdf(args.outFileName)
