def setUp(self):
self.sheetname = 'greenland'
self.mmO = icebin.GCMRegridder(args.icebin_in)
rmO = self.mmO.regrid_matrices(self.sheetname)
OvI = rmO.matrix('AvI', scale=False, correctA=False) # WeightedSparse
_,_,self.wI = OvI()
with netCDF4.Dataset(args.icebin_in) as nc:
self.indexingI = ibgrid.Indexing(nc, 'm.greenland.gridI.indexing')
self.indexingA = ibgrid.Indexing(nc, 'm.gridA.indexing')
self.indexingHC = ibgrid.Indexing(nc, 'm.indexingHC')
self.hcdefs = nc.variables['m.hcdefs'][:]
self.nhc = len(nc.dimensions['m.nhc'])
indexA_sub = nc.variables['m.gridA.cells.index'][:]
areaA_sub = nc.variables['m.gridA.cells.native_area'][:]
nA1 = getattr(nc.variables['m.gridA.info'], 'cells.nfull')
self.areaA1 = np.zeros((nA1,))
self.areaA1[indexA_sub] = areaA_sub # Native area of full grid cell (on sphere)
self.fcontOp, self.fcontOm = compute_fcontO(self.mmO, self.sheetname)
# Construct a GCMRegridder_Model to/from the Atmosphere Grid
self.mmA = self.mmO.to_modele(1.-self.fcontOp, 1.-self.fcontOm)
def setUp(self):
print('BEGIN RegridTests.setUp(): ', ICEBIN_IN)
self.elevI, self.maskI = giss.pism.read_elevI_maskI(ELEV_MASK)
self.elevmaskI = self.elevI
self.elevmaskI[self.maskI] = np.nan
self.mm = icebin.GCMRegridder(ICEBIN_IN)
self.rm = self.mm.regrid_matrices(ice_sheet, self.elevmaskI)
# Smooth 50km in XY direction, 100m in Z direction
sigma = (50000., 50000., 100.)
self.wIvE, self.IvE, self.IvEw = self.rm.matrix('IvE',
scale=True,
sigma=sigma)()
self.wAvI, self.AvI, self.AvIw = self.rm.matrix('AvI', scale=True)()
self.wIvA, self.IvA, self.IvAw = self.rm.matrix('IvA', scale=True)()
self.wEvI, self.EvI, self.EvIw = self.rm.matrix('EvI', scale=True)()
self.wIvE, self.IvE, self.IvEw = self.rm.matrix('IvE', scale=True)()
self.wIvE, self.IvE, self.IvEw = self.rm.matrix('IvE', scale=True)()
self.wEvA, self.EvA, self.EvAw = self.rm.matrix('EvA', scale=True)()
self.wAvE, self.AvE, self.AvEw = self.rm.matrix('AvE', scale=True)()
self.IvE_smooth_x = self.rm.matrix('IvE', scale=True, sigma=sigma)
with netCDF4.Dataset(ICEBIN_IN) as nc:
self.indexingA = ibgrid.Indexing(nc, 'm.agridA.indexing')
self.indexingHC = ibgrid.Indexing(nc, 'm.indexingHC')
self.indexingI = ibgrid.Indexing(
nc, 'm.{}.agridI.indexing'.format(ice_sheet))
self.plotterI = ibplotter.read_nc(nc,
'm.{}.agridI'.format(ice_sheet))
self.plotterA = ibplotter.read_nc(nc, 'm.agridA')
self.plotterE = ibplotter.PlotterE(ICEBIN_IN, ice_sheet, IvE=self.IvE)
print('END RegridTests.setUp()')
def setUp(self):
self.mm = icebin.GCMRegridder(ICEBIN_IN)
self.rm = self.mm.regrid_matrices(ice_sheet)
with netCDF4.Dataset(ICEBIN_IN) as nc:
self.indexingA = ibgrid.Indexing(nc, 'm.gridA.indexing')
self.indexingHP = ibgrid.Indexing(nc, 'm.indexingHP')
self.indexingI = ibgrid.Indexing(
nc, 'm.{}.gridI.indexing'.format(ice_sheet))
self.plotterI = ibplotter.read_nc(nc,
'm.{}.gridI'.format(ice_sheet))
self.plotterA = ibplotter.read_nc(nc, 'm.gridA')
def _Grid_XY_read_plotter(nc, vname) :
"""Reads an plotter out of a netCDF file for a simple Cartesian grid"""
sproj = nc.variables[vname + '.info'].sproj
xb2 = nc.variables[vname + '.x_boundaries'][:]
yb2 = nc.variables[vname + '.y_boundaries'][:]
indexing = ibgrid.Indexing(nc, vname + '.indexing')
return giss.plot.ProjXYPlotter(xb2, yb2, sproj, indexing.indices[0] == 0)
def setUp(self):
self.mm = icebin.GCMRegridder(ICEBIN_IN)
self.rm = self.mm.regrid_matrices(ice_sheet)
with netCDF4.Dataset(ICEBIN_IN) as nc:
self.indexingA = ibgrid.Indexing(nc, 'm.gridA.indexing')
self.indexingHP = ibgrid.Indexing(nc, 'm.indexingHP')
self.indexingI = ibgrid.Indexing(
nc, 'm.{}.gridI.indexing'.format(ice_sheet))
self.plotterI = ibplotter.read_nc(nc,
'm.{}.gridI'.format(ice_sheet))
self.plotterA = ibplotter.read_nc(nc, 'm.gridA')
self.elevI, self.maskI = giss.pism.read_elevI_maskI('elev_mask.nc')
self.AvI, self.wAvI = self.rm.regrid('AvI', scale=True)
self.IvA, self.wIvA = self.rm.regrid('IvA', scale=True)
self.EvI, self.wEvI = self.rm.regrid('EvI', scale=True)
self.IvE, self.wIvE = self.rm.regrid('IvE', scale=True)
self.EvA, self.wEvA = self.rm.regrid('EvA', scale=True)
self.AvE, self.wAvE = self.rm.regrid('AvE', scale=True)
def setUp(self):
with netCDF4.Dataset(SEARISE_GRID, 'r') as nc:
self.plotterA = ibplotter.read_nc(nc, 'grid')
gridA = ibgrid.read_nc(nc, 'grid')
self.indexingA = ibgrid.Indexing(nc, 'grid.indexing')
with netCDF4.Dataset(OVERLAP_FILE, 'r') as nc:
gridI = ibgrid.read_nc(nc, 'gridI')
exgrid = ibgrid.read_nc(nc, 'exgrid')
nA = gridA.cells_nfull
nI = gridI.vertices_nfull
AvI,weightsA,weightsI = element_l1.compute_AvI(exgrid, nA, gridI)
# Diagonal scale matrices based on regrid weights
scaleA = scipy.sparse.dia_matrix( ([1. / weightsA], [0]), shape=(nA,nA))
scaleI = scipy.sparse.dia_matrix( ([1. / weightsI], [0]), shape=(nI,nI))
self.AvI = (scaleA * AvI).tocoo()
self.IvA = (scaleI * AvI.transpose()).tocoo()
for index,w in out_raw:
out.append((index,w*factor))
return out
# ==============================================================
# Get the regridding matrices
# (This tells us, among other thing, the mask and the weight of each gridcell)
mm = icebin.GCMRegridder(ICEBIN_IN)
rm = mm.regrid_matrices(ice_sheet)
with netCDF4.Dataset(ICEBIN_IN) as nc:
# indexingA = ibgrid.Indexing(nc, 'm.gridA.indexing')
# indexingHP = ibgrid.Indexing(nc, 'm.indexingHP')
indexingI = ibgrid.Indexing(nc, 'm.{}.gridI.indexing'.format(ice_sheet))
plotterI = ibplotter.read_nc(nc, 'm.{}.gridI'.format(ice_sheet))
# plotterA = ibplotter.read_nc(nc, 'm.gridA')
IvE,wIvE = rm.regrid('IvE', scale=True)
wI = wIvE # Numpy array
nI = len(wI)
# Read the grid
nc = netCDF4.Dataset(ICEBIN_IN, 'r')
pgridI = ibgrid.read_nc(nc, 'm.{}.gridI'.format(ice_sheet))
nc.close()
def _Grid_LonLat_read_plotter(nc, vname) :
lonb2 = nc.variables[vname + '.lon_boundaries'][:]
latb2 = nc.variables[vname + '.lat_boundaries'][:]
indexing = ibgrid.Indexing(nc, vname + '.indexing')
return giss.plot.LonLatPlotter(lonb2, latb2, transpose=(indexing.indices[0] == 0), boundaries=True)
odir = '.'
oTOPO = os.path.join(odir, args.topo+'_EC2-{segment}.nc')
oGIC = os.path.join(odir, args.gic+'_EC2.nc')
print('WRITE: oTOPO = {}'.format(oTOPO))
print('WRITE: oGIC = {}'.format(oGIC))
# --------------------------------
# 1. Load up IceBin and create the matrix AvE
mm = icebin.GCMRegridder(args.icebin_in)
rm = mm.regrid_matrices('greenland')
wAvE,AvE,_ = rm.matrix('AvE', scale=True)()
wEvI,EvI,_ = rm.matrix('EvI', scale=True)()
wAvI,AvI,_ = rm.matrix('AvI', scale=True)()
with netCDF4.Dataset(args.icebin_in) as nc:
indexingHC = ibgrid.Indexing(nc, 'm.indexingHC')
hcdefs_ice = nc.variables['m.hcdefs'][:]
nhc_ice = len(nc.dimensions['m.nhc'])
indexA_sub = nc.variables['m.gridA.cells.index'][:]
areaA_sub = nc.variables['m.gridA.cells.native_area'][:]
nA1 = getattr(nc.variables['m.gridA.info'], 'cells.nfull')
areaA1 = np.zeros((nA1,))
areaA1[indexA_sub] = areaA_sub # Native area of full grid cell (on sphere)
# ----------------------------------
# 2. Compute new land surface fractions based on hi-res ice sheet
if args.elev_mask_type == 'pism':
with netCDF4.Dataset(args.elev_mask) as nc:
thk = nc.variables['thk'][:]
import netCDF4
import argparse
#from modele.constants import SHI,LHM,RHOI,RHOS,UI_ICEBIN,UI_NOTHING
# Sample program that tries out IceBin's update_topo() and writes the output to NetCDF.
# The filder ../make_topo must have been run first, for the file z1qx1n_bs1-nogr.nc
file_path = os.environ['MODELE_FILE_PATH'].split(os.pathsep)
TOPOO_IN = giutil.search_file('z1qx1n_bs1-nogr.nc', file_path)
ICEBINO_IN = 'pismsheet_g20_icebin_in.nc'
ELEVMASK_IN = 'pismsheet_elev_mask.nc'
# ---------------------------------------
# Read dimensions
with netCDF4.Dataset(ICEBINO_IN) as nc:
indexingO = ibgrid.Indexing(nc, 'm.gridA.indexing')
indexingHC = ibgrid.Indexing(nc, 'm.indexingHC')
hcdefs_ice = nc.variables['m.hcdefs'][:]
nhc_ice = len(nc.dimensions['m.nhc'])
imO = indexingO.extent[0]
jmO = indexingO.extent[1]
im = imO // 2 # Indices in alphabetical order
jm = jmO // 2 # /2 to convert Ocean to Atmosphere grid
segments = 'legacy,sealand,ec'
nhc_gcm = nhc_ice + 3
# ---------------------------------------
# Create gcmO and wrap to get gcmA. Not yet usable, we haven't done
