def main(mdlname,dispdic,title):
os.system('gpdcreport '+mdlname+' >tmp.mdl')
models, data, nd, ne, nrow = get_models('tmp.mdl')
gmt = GMT(config={'BASEMAP_TYPE':'plain','ANOT_FONT_SIZE':8,
'LABEL_FONT_SIZE':10,'COLOR_BACKGROUND':'255/255/255',
'COLOR_FOREGROUND':'0/0/0','COLOR_NAN':'255/255/255',
'PAGE_ORIENTATION':'landscape',
'HEADER_FONT_SIZE':15} )
xyz=gmt.tempfilename('testxyz.txt')
xyz2=gmt.tempfilename('testxyz2.txt')
grd=gmt.tempfilename('tmp.grd')
grdcpt=gmt.tempfilename('tmp.cpt')
fileout='dens_test.ps'
rng='1/5/0/40'
scl='X4.2/-6'
dreso = 0.2
sreso = 0.05
misfit = 0.1
#grid1, grid2, x, y, smean, dmean = dplot.dplot(models,data,nd,ne,dreso=dreso, sreso=sreso,mf=misfit)
grid1, grid2, x, y, smean, dmean = dplot.dplot(models,data,nd,ne,dreso=dreso, sreso=sreso,mf=misfit)
#grid1, grid2, x, y = dplot.dplotpy(models,data,nd,ne,dreso=dreso, sreso=sreso,mf=misfit)
# matshow(grid1)
# show()
f = open(xyz,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid1[ii,jj]>0.0:
print >>f, x[jj],y[ii], grid1[ii,jj]
f.close()
f = open(xyz2,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid2[ii,jj] > 0:
print >>f, x[jj], y[ii], '0.5'
f.close()
anot = int(grid1.max()/1000.)*1000/2.
tick = anot/2
gmt.xyz2grd(xyz,G=grd,R=rng,I='%f/%f'%(sreso,dreso),out_discard=True)
gmt.grd2cpt(grd,C="wysiwyg",Z=True,out_filename=grdcpt)
gmt.psmask(xyz2,R=rng,T=True,J=scl,I='%f/%f'%(sreso,dreso),G='lightgray')
gmt.grdimage(grd,J=scl,R=rng,Q=True,C=grdcpt)
gmt.psbasemap(R=rng,J=scl,B='a1f.5:S-velocity [km/s]:/a10f5:Depth [km]::.%s:WnSe'%title)
gmt.psxy(R=True,J=True,B=True,W='3,black',in_columns=[smean,dmean])
f = open('/home/behrya/dev/data/mt_fixed_layers_ray_c_u_mean.txt','w')
for _p,_v in zip(dmean,smean):
print >>f,_p,_v
f.close()
gmt.psscale(C=grdcpt,D='1.0/1./4c/.4ch',B='a%df%d:No. of models:/::'%(anot,tick))
### plot dispersion curves
gmt.psbasemap(R='5/30/2.0/5.0',J='X4.2/2.5',X='5',B='a1f.5:Period [s]:/a1f.5:Velocity [km/s]:WnSe')
for _d in dispdic.keys():
vo = load(dispdic[_d][0])
p,v = gpdccurve(mdlname,wtype=dispdic[_d][1],ptype=dispdic[_d][2])
gmt.psxy(R=True,J=True,B=True,W='3,black',in_columns=[p,v])
gmt.psxy(R=True,J=True,B=True,W='3,red',in_columns=[vo[:,0],vo[:,1]])
gmt.save(fileout)
os.system('gv '+fileout+'&')
def plot_rep(repfile,pixfile,realmod):
### read in models from dinver-output
os.system('/usr/local/Geopsy.org/bin/gpdcreport '+repfile+' >tmp.mdl')
models, data, nd, ne, nrow, nlayer = get_models('tmp.mdl')
#os.remove('tmp.mdl')
### calculate density
sreso = 0.05 #horizontal resolution
dreso = 0.5 #vertical resolution
misfit = 1.0 #misfit threshhold
grid1, grid2, x, y, smean, dmean = dplot(models,data,nd,ne,dreso=dreso,sreso=sreso,mf=misfit,nlayer=nlayer)
### write output into temporary files that can be plotted by gmt
xyz = tempfile.mktemp()
xyz = 'xyz.txt'
xyz2 = tempfile.mktemp()
grd = tempfile.mktemp()
grdcpt = tempfile.mktemp()
fout = pixfile
f = open(xyz,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid1[ii,jj]>0.0:
print >>f, x[jj],y[ii], grid1[ii,jj]
f.close()
f = open(xyz2,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid2[ii,jj] > 0:
print >>f, x[jj], y[ii], '0.5'
f.close()
### gmt plot
rng = '-R1/5/0/40'
scl = '-JX10.2/-12'
step='-I%f/%f'%(sreso,dreso)
os.system('xyz2grd %(xyz)s -G%(grd)s %(rng)s %(step)s'%vars())
os.system('grd2cpt %(grd)s -Cwysiwyg -Z > %(grdcpt)s'%vars())
os.system('psmask %(xyz2)s %(rng)s %(scl)s %(step)s -Glightgray -K > %(fout)s'%vars())
os.system('grdimage %(grd)s %(rng)s %(scl)s -Q -C%(grdcpt)s -K -O >> %(fout)s'%vars())
os.system('psmask -C -K -O >> %(fout)s'%vars())
os.system("psbasemap %(rng)s %(scl)s -Ba1f.5:'S-velocity [km/s]':/a10f5:'Depth [km]':WnSe -K -O >> %(fout)s"%vars())
p = Popen('psxy -R -J -B -W3,black -O -K>> %(fout)s'%vars(),shell=True,stdin=PIPE).stdin
### plot weighted average for all models with misfit better than defined threshhold
for _v,_d in zip(smean,dmean):
print >>p,_v,_d
p.close()
p = Popen('psxy -R -J -B -W3,red -O -K >> %(fout)s'%vars(),shell=True,stdin=PIPE).stdin
### plot true model
for _v,_d in zip(realmod[:,0],realmod[:,1]):
print >>p,_v,_d
p.close()
prf = get_best_model(repfile,dreso)
p = Popen('psxy -R -J -B -W3,white -O >> %(fout)s'%vars(),shell=True,stdin=PIPE).stdin
### plot best model
for l in prf:
print >>p,l[0],-l[1]
p.close()
os.system('gv %(fout)s&'%vars())
def plot_rep(repfile, paramfile, wtype, pixfile, show=True, misfit=0.1,
minmax=True):
"""
Plot 1D velocity profile.
"""
# ## read in models from dinver-output
os.system('gpdcreport ' + repfile + ' >tmp.mdl')
models, data, nd, ne, nrow, nlayer = get_models('tmp.mdl')
os.remove('tmp.mdl')
if misfit == 'all':
misfit = data.max()
elif misfit <= data.min():
misfit = median(data)
# ## calculate density
sreso = 0.05 # horizontal resolution
dreso = .5 # vertical resolution
grid1, grid2, x, y, smean, dmean = dplot(models, data, nd, ne, dreso=dreso,
sreso=sreso, mf=misfit, nlayer=nlayer, dmax=120)
# ## write output into temporary files that can be plotted by gmt
xyz = tempfile.mktemp()
xyz2 = tempfile.mktemp()
grd = tempfile.mktemp()
grdcpt = tempfile.mktemp()
fout = pixfile
f = open(xyz, 'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid1[ii, jj] > 0.0:
print >> f, x[jj], -y[ii], grid1[ii, jj]
f.close()
f = open(xyz2, 'w')
for ii in range(len(y)):
for jj in range(len(x)):
if grid2[ii, jj] > 0:
print >> f, x[jj], -y[ii], '0.5'
f.close()
lat, lon = dissect_fname(repfile)
if wtype == 'love':
wt = 'L'
if wtype == 'rayleigh':
wt = 'R'
# print repfile, lat, lon
# ## gmt plot
rng = '1/5/-120/0'
step = '%f/%f' % (sreso, dreso)
anot = int(grid1.max() / 1000.) * 1000 / 2.
tick = anot / 2
gmt = GMT(config={'ANOT_FONT_SIZE':8, 'LABEL_FONT_SIZE':10,
'ANNOT_OFFSET_SECONDARY':'0.1c',
'ANNOT_OFFSET_PRIMARY':'0.1c',
'LABEL_OFFSET':'0.1c',
'FRAME_PEN':'.5p'})
widgets, layout = make_widget()
if 1:
widget = widgets[2]
gmt.xyz2grd(xyz, G=grd, R=rng, I='%f/%f' % (sreso, dreso), out_discard=True)
gmt.grd2cpt(grd, L='0/%d' % 3000, C="wysiwyg", D=True, Z=True, out_filename=grdcpt)
gmt.psbasemap(R=True, B='a1f.5:S-velocity [km/s]:/a10f5:Depth [km]:WnSe', *widget.XYJ())
# gmt.psmask(xyz2, R=True, T=True, I='%f/%f' % (sreso, dreso), G='lightgray', *widget.XYJ())
gmt.grdimage(grd, R=True, Q=True, C=grdcpt, *widget.XYJ())
gmt.psxy(R=True, B=True, W='3,black', in_columns=[smean, -dmean], *widget.XYJ())
bmdl = get_best_model(repfile, dreso, dmax=120., dmin=0.)
gmt.psxy(R=True, B=True, W='3,black,-', in_rows=bmdl, *widget.XYJ())
gmt.psscale(widget.XYJ()[0], widget.XYJ()[1], C=grdcpt, D='1.7c/1.5c/2.5c/.4ch', B='a%df%d10:No. of models:/::' % (1000, 500))
txtstr = "1.2 -7. 8 0 1 LT lat = %3.2f" % lat
gmt.pstext(R=rng, G='0/0/0', N=True, in_string=txtstr, *widget.XYJ())
txtstr = "1.2 -12. 8 0 1 LT lon = %3.2f" % lon
gmt.pstext(R=True, G='0/0/0', N=True, in_string=txtstr, *widget.XYJ())
# plot the minimum and maximum model that is allowed
# by the parameterisation
if minmax:
mdl_min, mdl_max = minmax_mdl(paramfile)
gmt.psxy(R=True, B=True, W='3,red,-', in_rows=mdl_min, *widget.XYJ())
gmt.psxy(R=True, B=True, W='3,red,-', in_rows=mdl_max, *widget.XYJ())
if 1:
p, v, e = get_disp(repfile, wtype='phase')
plot_disp(gmt, repfile, widgets[1], p, v, e, 'phase', ptype='p', wtype=wt, mode=0, misfit=misfit)
p, v, e = get_disp(repfile, wtype='group')
plot_disp(gmt, repfile, widgets[0], p, v, e, 'group', ptype='g', wtype=wt, mode=0, misfit=misfit)
if 0:
p, v, e = get_disp(repfile, wtype='grouponly')
plot_disp(gmt, repfile, widgets[0], p, v, e, 'group', ptype='g', wtype=wt, mode=0, misfit=misfit)
if 0:
plot_hist(gmt, widgets, models, nd, ne)
gmt.save(fout)
meanfout = fout.replace('.eps', '_mean.txt')
savetxt(meanfout, vstack((-dmean, smean)).T)
if show:
os.system('gv %(fout)s&' % vars())
