"""
from varglas.data.data_factory import DataFactory
from varglas.utilities import DataInput, MeshGenerator
from numpy import *
from mpl_toolkits.basemap import Basemap
# Get the Antarctica data sets
bedmap2 = DataFactory.get_bedmap2()
db2 = DataInput(bedmap2)
# Get the grounding line by eliminating the shelves
db2.set_data_val('mask',1,127)
# Create a grounding line countour
mg = MeshGenerator(db2, 'mesh', '')
mg.create_contour('mask', 10, skip_pts=2)
mg.eliminate_intersections(dist=20)
cont = mg.longest_cont
# Convert (x,y) coordinates to (lon,lat)
cont_lons, cont_lats = db2.p(cont[:,0], cont[:,1], inverse = True)
# Convert (x,y) coordinates to (lon,lat)
cont_lons, cont_lats = db2.p(cont[:,0], cont[:,1], inverse = True)
# Convert to basemap coordinates
lat_0 = '-90'
lat_ts = '-71'
lon_0 = '0'
height = 3333500*2
#db2.data['mask'][db2.data['mask'] == 1] = 0 # plot to check : #db2.data['mask'][db2.data['mask'] == 1] = 0 #imshow(db2.data['mask'][::-1,:]) #colorbar() #tight_layout() #show() imshow(db2.data['B'][::-1]) colorbar() #show() print db2.data['B'].max() # generate the contour : m = MeshGenerator(db2, 'mesh', '') # Manually create a circular contour angles = linspace(0, 1, 1000)*2*pi xs = 2999000*cos(angles) ys = 2999000*sin(angles) contour = array(zip(xs, ys)) m.set_contour(contour) m.write_gmsh_contour(1000, boundary_extend=False) m.add_edge_attractor(1) #field, ifield, lcMin, lcMax, distMin, distMax m.add_threshold(2, 1, 5000, 5000, 1, 100000) m.finish(4) m.close_file()
# -*- coding: utf-8 -*-
"""
Create a uniform mesh of Antarctica for plotting.
"""
from varglas.utilities import DataInput, MeshGenerator
from varglas.data.data_factory import DataFactory
from pylab import *
thklim = 0
# create meshgrid for contour :
bedmap2 = DataFactory.get_bedmap2()
# process the data :
dbm = DataInput(bedmap2, gen_space=False)
dbm.set_data_val("H", 32767, thklim)
m = MeshGenerator(dbm, 'mesh', '')
m.create_contour('H', 0.0, 5)
m.eliminate_intersections(dist=20)
m.write_gmsh_contour(1000, boundary_extend=False)
m.add_edge_attractor(1)
#field, ifield, lcMin, lcMax, distMin, distMax
m.add_threshold(2, 1, 3000, 3000, 1, 100000)
m.finish(4)
#m.create_2D_mesh('mesh') #FIXME: fails
#m.convert_msh_to_xml('mesh', 'mesh')
from numpy import *
from pylab import *
thklm = 200.0
bm2 = DataFactory.get_bedmap2(thklm)
bedmap2 = DataInput(bm2)
# Load the domain coordinates
domain_coordinates = loadtxt('domain_coordinates1.out')
mesh_name = 'ant_mesh'
# Create a contour for the domain
#=======================================================
# Create a mesh file in the current directory
m = MeshGenerator(bedmap2, mesh_name, '')
# Manually set the countour instead of calculating it automatically
m.set_contour(domain_coordinates)
# Write the contour points to the mesh file
m.write_gmsh_contour(100000, boundary_extend = False)
# Extrude the flat mesh 10,000 meters in the z dimension. The height of the
# mesh can be automatically scaled to the proper height by the model object
m.extrude(10000, 14)
# We're finished with the flat mesh!
m.close_file()
# Refine the mesh based on ice thickness
#=======================================================
ref_bm = MeshRefiner(bedmap2, 'H', gmsh_file_name = mesh_name)
# Refine the mesh based on the refinement radius
@author: jake
"""
from pylab import *
from varglas.data.data_factory import DataFactory
from varglas.utilities import DataInput, MeshGenerator
from numpy import *
# Get the Antarctica data sets
bedmap2 = DataFactory.get_bedmap2(thklim = 200)
# Create a countour of the continent - without the ice shelves
db2 = DataInput(bedmap2)
db2.set_data_val('mask',1,127)
mg = MeshGenerator(db2, 'mesh', '')
mg.create_contour('mask', 0, skip_pts=4)
mg.eliminate_intersections(dist=40)
# Get the longest contour, which will be the coastline
cont = mg.longest_cont
# Load the glacier data
glacier_data = loadtxt('glacier_data.out', delimiter = '|', dtype = 'str')
names = array(glacier_data[:,0], dtype = 'str')
lons = array(glacier_data[:,1], dtype = 'f')
lats = array(glacier_data[:,2], dtype = 'f')
# Convert lons and lats to x, y coordinates
x, y = db2.p(lons,lats)
# Next, eliminate glaciers that are too far from the coast
[xmax, 0], [xmax, 100000], [xmin, 100000] ]) # Name of refined mesh mesh_name = 'transect_mesh' # Get the Antarctica data sets bedmap2 = DataFactory.get_bedmap2(thklim=200) # Load in a DataInput object. We won't use it, but we need to pass a DataInput # to the MeshGenerator, and we have to fake it out. db2 = DataInput(None, bedmap2) # Create a mesh file in the current directory m = MeshGenerator(db2, mesh_name, '') # Manually set the countour instead of calculating it automatically m.set_contour(cont) # Write the contour points to the mesh file m.write_gmsh_contour(100000, boundary_extend = False) # Extrude the flat mesh 10,000 meters in the z dimension. The height of the # mesh can be automatically scaled to the proper height by the model object m.extrude(20000, 10) # We're finished with the flat mesh! m.close_file() # Refine the mesh based on thickness # Wrap the bed spline in a function that takes two arguments def bed(x,y) :
