valid_f = Function(Q)
valid_f_v = valid_f.vector().array()
valid_f_v[valid] = 1.0
valid_f.vector().set_local(valid_f_v)
valid_f.vector().apply('insert')
rignot = DataFactory.get_gre_rignot()
drg = DataInput(rignot, gen_space=False)
betaMax = 200.0
ini_f = Function(Q)
ini_f.vector()[:] = ini
plotIce(drg, ini_f, name='ini', direc='images/stats/' + file_n,
cmap='gist_yarg', scale='log', numLvls=12, tp=False,
tpAlpha=0.5, show=False, umin=1.0, umax=betaMax)
Ubar_avg_f = Function(Q)
Ubar_avg_f.vector()[:] = Ubar_avg
plotIce(drg, Ubar_avg_f, name='Ubar_avg', direc='images/stats/' + file_n,
title=r'$\Vert \mathbf{\bar{u}}_{\bar{bv}} \Vert$', cmap='gist_yarg',
scale='log', numLvls=12, tp=False,
tpAlpha=0.5, show=False, umin=1.0, umax=4000.0)
plotIce(drg, valid_f, name='valid', direc='images/stats/' + file_n,
cmap='gist_yarg', scale='bool', numLvls=12, tp=False,
tpAlpha=0.5, show=False)
#===============================================================================
# cell declustering :
a_resi_f = Function(a_Q)
a_yhat_f.vector()[a_valid] = yhat[a_conv]
a_resi_f.vector()[a_valid] = resid[a_conv]
g_yhat_f = Function(g_Q)
g_resi_f = Function(g_Q)
g_yhat_f.vector()[g_valid] = yhat[g_conv]
g_resi_f.vector()[g_valid] = resid[g_conv]
plotIce(dm,
a_yhat_f,
name='GLM_beta',
direc=a_fn,
title=r'$\hat{\beta}$',
cmap='gist_yarg',
scale='log',
extend='max',
umin=1.0,
umax=betaMax,
numLvls=12,
tp=False,
tpAlpha=0.5,
show=False)
plotIce(dm,
a_resi_f,
name='GLM_resid',
direc=a_fn,
title=r'$d$',
cmap='RdGy',
scale='lin',
extend='both',
yhat_f = Function(Q)
y_f = Function(Q)
resi_f = Function(Q)
yhat_f.vector()[valid] = yhat
y_f.vector()[valid] = y
resi_f.vector()[valid] = resid
plotIce(dm,
yhat_f,
name='GLM_beta',
direc='images/stats/' + file_n,
title=r'$\hat{\beta}$',
cmap='gist_yarg',
scale='log',
umin=1.0,
umax=betaMax,
numLvls=12,
tp=False,
tpAlpha=0.5,
show=False)
plotIce(dm,
y_f,
name='beta',
direc='images/stats/' + file_n,
title=r'$\beta$',
cmap='gist_yarg',
scale='log',
umin=1.0,
bamber = DataFactory.get_bamber(thklim)
# load a mesh :
#mesh = MeshFactory.get_greenland_2D_1H()
mesh = Mesh('dump/meshes/greenland_2D_1H_mesh.xml.gz')
# create data objects to use with varglas :
dbm = DataInput(bamber, mesh=mesh)
model = D2Model(mesh)
model.init_Ubar(in_dir + 'Ubar.xml')
plotIce(dbm,
model.Ubar,
name='Ubar',
direc=in_dir + 'plot/',
title=r'$\bar{\mathbf{u}}$',
cmap='gist_yarg',
scale='log',
numLvls=12,
tp=False,
tpAlpha=0.5,
umin=1.0,
umax=4000,
show=False)
#def plotIce(di, u, name, direc, title='', cmap='gist_yarg', scale='lin',
# umin=None, umax=None, numLvls=12, tp=False, tpAlpha=0.5,
# extend='neither', show=True):
mask = logical_or(m1, m2)
# create data objects to use with varglas :
dsr = DataInput(searise, gen_space=False)
dbm = DataInput(bamber, gen_space=False)
drg = DataInput(rignot, gen_space=False)
dbm.data['M'] = mask
plotIce(dbm,
'M',
name='M',
direc='greenland/',
title='$M$',
cmap='gist_yarg',
scale='lin',
umin=None,
umax=None,
numLvls=3,
tp=False,
tpAlpha=0.5,
extend='neither',
show=False)
#plotIce(dsr, 'adot', name='adot', direc='greenland/', title='$\dot{a}$',
# cmap='gist_yarg', scale='lin', umin=None, umax=None,
# numLvls=12, tp=False, tpAlpha=0.5, extend='neither', show=True)
#
#plotIce(dsr, 'T', name='T', direc='greenland/', title='$T$',
# cmap='gist_yarg', scale='lin', umin=None, umax=None,
# numLvls=12, tp=False, tpAlpha=0.5, extend='neither', show=True)
#
# extend='both', show=False)
#plotIce(dm, Ubar50, name='Ubar_bv_50', direc='images/model/', cmap='gist_yarg',
# title=r'$\Vert\mathbf{\bar{u}}_{bv50}\Vert$', scale='log',
# umin=1.0, umax=4000, numLvls=12, tp=False, tpAlpha=0.5,
# extend='both', show=False)
#plotIce(dm, U_ob, name='U_ob', direc='images/model/', cmap='gist_yarg',
# title=r'$\Vert \mathbf{u}_{ob} \Vert$', scale='log',
# umin=1.0, umax=4000, numLvls=12, tp=False, tpAlpha=0.5,
# extend='both', show=False)
plotIce(dm,
beta,
name='beta',
direc='images/model/',
cmap='gist_yarg',
title=r'$\beta$',
scale='lin',
umin=0.0,
umax=200,
numLvls=12,
tp=False,
tpAlpha=0.5,
extend='max',
show=False)
#plotIce(dm, Mb, name='Mb', direc='images/model/', cmap='gist_yarg',
# title=r'$M_B$', scale='log', umin=0.003, umax=1.0,
# numLvls=13, tp=False, tpAlpha=0.5, extend='both', show=False)
#plotIce(dm, tau_id, name='tau_id', direc='images/model/', cmap='RdGy',
# title=r'$\tau_{id}$', scale='lin', umin=-1e5, umax=1e5,
# numLvls=13, tp=False, tpAlpha=0.5, extend='both', show=False)
#plotIce(dm, tau_jd, name='tau_jd', direc='images/model/', cmap='RdGy',
# title=r'$\tau_{jd}$', scale='lin', umin=-1e5, umax=1e5,
# numLvls=13, tp=False, tpAlpha=0.5, extend='both', show=False)
vs_v = vs.vector().array()
ws_v = ws.vector().array()
Us_mag = sqrt(us_v**2 + vs_v**2 + ws_v**2 + 1e-16)
Us_mag_f = Function(Q)
Us_mag_f.vector()[:] = Us_mag
measures = DataFactory.get_ant_measures(res=900)
dm = DataInput(measures, gen_space=False)
plotIce(dm,
beta,
name='beta',
direc=out_dir,
cmap='gist_yarg',
title=r'$\beta$',
scale='lin',
umin=0.0,
umax=200,
numLvls=12,
tp=False,
tpAlpha=0.5,
extend='max',
show=False)
plotIce(dm,
Us_mag_f,
name='Us',
direc=out_dir,
cmap='gist_yarg',
title=r'$\Vert\mathbf{u}_S\Vert$',
scale='log',
umin=1.0,
#for i in y_valid:
# for j in x_valid:
# dbm.data['H'][i,j] = 1.0
#plotIce(dbm, 'mask', name='mask', direc='images/data/', cmap='gist_yarg',
# title=r'', scale='lin', umin=None, umax=None, numLvls=num_mask)
#
#plotIce(dbm, 'gM', name='gM', direc='images/data/', cmap='gist_yarg',
# title=r'', scale='lin', umin=None, umax=None, numLvls=25)
#
#plotIce(dbm, 'ref', name='ref', direc='images/data/', cmap='gist_yarg',
# title=r'', scale='lin', umin=None, umax=None, numLvls=3)
#
plotIce(dbm,
'H',
name='H',
direc='images/data/',
cmap='gist_yarg',
title=r'$H$',
scale='lin',
umin=None,
umax=None,
numLvls=25)
#plotIce(dbm, 'S', name='S', direc='images/data/', cmap='gist_yarg',
# title=r'$S$', scale='lin', umin=0.0, umax=None, numLvls=25)
#
#plotIce(dsr, 'U_sar', name='U_ob', direc='images/data/', cmap='gist_yarg',
# title=r'$\Vert \mathbf{u}_{ob} \Vert$', scale='log',
# umin=1.0, umax=4000, numLvls=25)
from varglas import *
from varglas.helper import plotIce
thklim = 10.0
in_dir = 'dump/balance_velocity/'
# collect the raw data :
bamber = DataFactory.get_bamber(thklim)
# load a mesh :
#mesh = MeshFactory.get_greenland_2D_1H()
mesh = Mesh('dump/meshes/greenland_2D_1H_mesh.xml.gz')
# create data objects to use with varglas :
dbm = DataInput(bamber, mesh=mesh)
model = D2Model(mesh)
model.init_Ubar(in_dir + 'Ubar.xml')
plotIce(dbm, model.Ubar, name='Ubar', direc=in_dir + 'plot/',
title=r'$\bar{\mathbf{u}}$', cmap='gist_yarg',
scale='log', numLvls=12, tp=False, tpAlpha=0.5,
umin=1.0, umax=4000, show=False)
#def plotIce(di, u, name, direc, title='', cmap='gist_yarg', scale='lin',
# umin=None, umax=None, numLvls=12, tp=False, tpAlpha=0.5,
# extend='neither', show=True):
File('antarctica/dump/bed/balance_water/q.xml') >> a_qbar
#===============================================================================
# plotting :
bedmap1 = DataFactory.get_bedmap1(thklim=1.0)
d1 = DataInput(bedmap1, mesh=a_mesh)
cmap = 'gist_earth'
cmap = 'RdYlGn'
cmap = 'RdGy'
cmap = 'gist_gray'
cmap = 'Purples'
cmap = 'Reds'
cmap = 'Oranges'
plotIce(d1, 'B', '.', 'gist_yarg', scale='lin', name=r'$B$',
numLvls=12, tp=False, tpAlpha=0.5)
#===============================================================================
a_dSdx = project(a_S.dx(0), a_Q)
a_dSdy = project(a_S.dx(1), a_Q)
a_dBdx = project(a_B.dx(0), a_Q)
a_dBdy = project(a_B.dx(1), a_Q)
# vectors :
a_beta_v = a_beta.vector().array()
a_S_v = a_S.vector().array()
a_B_v = a_B.vector().array()
a_adot_v = a_adot.vector().array()
a_qgeo_v = a_qgeo.vector().array()
model.config['model_order'] = 'SSA'
model.set_mesh(mesh)
#model.set_surface_and_bed(S, B)
model.initialize_variables()
model.init_U_ob(u, v)
#plotIce(dbm, 'mask', name='mask', direc='images/data/', cmap='gist_yarg',
# title=r'', scale='lin', umin=None, umax=None, numLvls=num_mask)
#
#plotIce(dbm, 'ref', name='ref', direc='images/data/', cmap='gist_yarg',
# title=r'', scale='lin', umin=None, umax=None, numLvls=3)
#
#plotIce(dbm, 'H', name='H', direc='images/data/', cmap='gist_yarg',
# title=r'$H$', scale='lin', umin=None, umax=None, numLvls=25)
#
#plotIce(dbm, 'S', name='S', direc='images/data/', cmap='gist_yarg',
# title=r'$S$', scale='lin', umin=0.0, umax=None, numLvls=25)
#
plotIce(dbm,
model.U_ob,
name='U_ob',
direc='images/data/',
cmap='gist_yarg',
title=r'$\Vert \mathbf{u}_{ob} \Vert$',
scale='log',
umin=1.0,
umax=4000,
numLvls=25,
tp=True)
ubar_v = ubar.vector().array()
vbar_v = vbar.vector().array()
Ubar_mag = sqrt(ubar_v**2 + vbar_v**2 + 1e-16)
Ubar_mag_f = Function(Q)
Ubar_mag_f.vector()[:] = Ubar_mag
etabar_v = etabar.vector().array()
etabar_v /= 1e6
etabar.vector()[:] = etabar_v
rignot = DataFactory.get_gre_rignot()
drg = DataInput(rignot, gen_space=False)
plotIce(drg, mask, name='mask', direc='images/model/', cmap='gist_yarg',
title=r'', scale='bool', umin=0.0, umax=1.0,
numLvls=12, tp=False, tpAlpha=0.5, extend='neither', show=False)
plotIce(drg, S, name='S', direc='images/model/', cmap='gist_yarg',
title=r'$S$', scale='lin', umin=0, umax=None,
numLvls=12, tp=False, tpAlpha=0.5, extend='neither', show=False)
plotIce(drg, B, name='B', direc='images/model/', cmap='gist_yarg',
title=r'$B$', scale='lin', umin=-1000, umax=None,
numLvls=12, tp=False, tpAlpha=0.5, extend='min', show=False)
plotIce(drg, gradS, name='gradS', direc='images/model/', cmap='gist_yarg',
title=r'$\Vert \nabla S \Vert$', scale='log', umin=1e-3, umax=1.0,
numLvls=12, tp=False, tpAlpha=0.5, extend='both', show=False)
plotIce(drg, gradB, name='gradB', direc='images/model/', cmap='gist_yarg',
title=r'$\Vert \nabla B \Vert$', scale='log', umin=1e-3, umax=1.0,
numLvls=12, tp=False, tpAlpha=0.5, extend='both', show=False)
plotIce(drg, H, name='H', direc='images/model/', cmap='gist_yarg',
title=r'$H$', scale='lin', umin=None, umax=None,
