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()
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()
# Obtain a single point and weight for each basis functions.
# This is a simplification and approximate. But it should work
# pretty well as long as grid cells are small. And it will work
# for any kind of grid/mesh.
def inserts():
n=0
for cell in pgridI.cells.values():
if wI[cell.index] > 0:
centroid = cell.centroid()
weight = wI[cell.index]
yield (cell.index, (centroid[0], centroid[1], centroid[0], centroid[1]), wI[cell.index])
n += 1
import icebin from icebin import ibgrid import sys import giss.basemap grid_fname = sys.argv[1] vname = sys.argv[2] # =============== Step 2: Plot grid outlines # Set up the page figure = matplotlib.pyplot.figure(figsize=(11,8.5)) # figure.set_size_inches(11., 8.5)# US Letter figure.set_size_inches(8.267,11.692)# A4 # -------- First plot: grid1 # Read the grid nc = netCDF4.Dataset(grid_fname + '.nc', 'r') grid1 = ibgrid.read_nc(nc, vname) nc.close() # Plot it! ax = figure.add_subplot(111) basemap = giss.basemap.greenland_laea(ax=ax) grid1.plot(basemap, linewidth=.1) # ------------ Render the page figure.savefig(grid_fname + '.pdf', dpi=100, transparent=False)
import icebin from icebin import ibgrid import sys import giss.basemap grid_fname = sys.argv[1] vname = sys.argv[2] # =============== Step 2: Plot grid outlines # Set up the page figure = matplotlib.pyplot.figure(figsize=(11,8.5)) # figure.set_size_inches(11., 8.5)# US Letter figure.set_size_inches(8.267,11.692)# A4 # -------- First plot: grid1 # Read the grid nc = netCDF4.Dataset(grid_fname + '.nc', 'r') grid1 = ibgrid.read_nc(nc, vname) nc.close() # Plot it! ax = figure.add_subplot(111) basemap = giss.basemap.greenland_laea(ax=ax) grid1.plot(basemap, linewidth=.1) # ------------ Render the page figure.savefig(grid_fname + '.pdf', dpi=100, transparent=False)
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import netCDF4
from icebin import ibgrid
from matplotlib import pyplot
import giss.basemap
# Loads an plots grids from an IceBin grid file
# See: https://github.com/citibob/icebin
figure = pyplot.figure(figsize=(11., 8.5))
# -------------- Plot ice grid
with netCDF4.Dataset('modele_ll_g2x2_5-ISSM_mesh.nc') as nc:
grid = ibgrid.read_nc(nc, 'gridI')
ax = figure.add_subplot(121)
basemap = giss.basemap.greenland_laea()
grid.plot(basemap, ax=ax)
# -------------- Plot GCM grid
with netCDF4.Dataset('modele_ll_g2x2_5.nc') as nc:
grid = ibgrid.read_nc(nc, 'grid')
ax = figure.add_subplot(122)
basemap = giss.basemap.greenland_laea()
grid.plot(basemap, ax=ax)
# ---------------- Get a regridding matrix
from icebin import ibgrid, element_l1
ISSM_IN = 'modele_ll_g2x2_5-ISSM_mesh.nc'
with netCDF4.Dataset(ISSM_IN) as nc:
gridA = ibgrid.read_nc(nc, 'gridA') # ISMIP6 Grid
gridI = ibgrid.read_nc(nc, 'gridI') # ISSM Grid
exgrid = ibgrid.read_nc(nc, 'exgrid') # Exchange Grid
AvI,weightsA,weightsI = element_l1.compute_AvI(gridX, gridI)
valueI = ...
valueA = (1. / weightsA) * AvI * valueI
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import netCDF4
from icebin import ibgrid
from matplotlib import pyplot
import giss.basemap
# Loads an plots grids from an IceBin grid file
# See: https://github.com/citibob/icebin
figure = pyplot.figure(figsize=(11.,8.5))
# -------------- Plot ice grid
with netCDF4.Dataset('modele_ll_g2x2_5-ISSM_mesh.nc') as nc:
grid = ibgrid.read_nc(nc, 'gridI')
ax = figure.add_subplot(121)
basemap = giss.basemap.greenland_laea()
grid.plot(basemap, ax=ax)
# -------------- Plot GCM grid
with netCDF4.Dataset('modele_ll_g2x2_5.nc') as nc:
grid = ibgrid.read_nc(nc, 'grid')
ax = figure.add_subplot(122)
basemap = giss.basemap.greenland_laea()
grid.plot(basemap, ax=ax)
# ---------------- Get a regridding matrix