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 plotnad(fnad,fout):
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'} )
grd=gmt.tempfilename('tmp.grd')
grdcpt=gmt.tempfilename('tmp.cpt')
xyz=gmt.tempfilename('xyz.txt')
xyz2=gmt.tempfilename('xyz2.txt')
rng='1/5/0/40'
scl='X4.2/-6'
kosu1,kosu2,x,y,dbest,sbest,dmean,smean = nadplot(fnad,smin=1.5)
f = open(xyz,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if kosu1[ii,jj] > 0:
print >>f, x[jj], y[ii], kosu1[ii,jj]
f.close()
f = open(xyz2,'w')
for ii in range(len(y)):
for jj in range(len(x)):
if kosu2[ii,jj] > 0:
print >>f, x[jj], y[ii], '0.5'
f.close()
gmt.xyz2grd(xyz,G=grd,R=rng,I='.02/.5',out_discard=True)
gmt.grd2cpt(grd,C="wysiwyg",Q='o',Z=True,out_filename=grdcpt)
gmt.psmask(xyz2,R=rng,T=True,J=scl,I='.02/.5',G='lightgray')
gmt.grdimage(grd,J=scl,R=rng,Q=True,P=True,C=grdcpt)
gmt.psbasemap(R=rng,J=scl,B='a1f.5:Velocity [km/s]:/a10f5:Depth [km]:WNse')
gmt.psxy(R=True,J=True,B=True,W='3,red',in_columns=[sbest,dbest])
gmt.psxy(R=True,J=True,B=True,W='3,white',in_columns=[smean,dmean])
gmt.psscale(C=grdcpt,D='1.0/1./4c/.4ch',B='a40f10:No. of models:/::')
gmt.save(fout)
os.system('gv '+fout+'&')
return 1
def gmt_north_america(**kwargs):
'''
Give rf_data as ([values],[time])
'''
from gmtpy import GMT
#get kwargs
fname = kwargs.get('fname','USA_map.pdf')
station_data = kwargs.get('station_data','none')
quake_locs = kwargs.get('quake_locs','none')
rf_data = kwargs.get('rf_data','none')
header = kwargs.get('header','none')
#topo data
etopo='/geo/home/romaguir/utils/ETOPO5.grd'
topo_grad='/geo/home/romaguir/utils/topo_grad.grd'
#colormap
colombia='/geo/home/romaguir/utils/colors/colombia'
region = '-128/-66/24/52'
scale = 'l-100/35/33/45/1:30000000'
#initialize gmt
gmt = GMT(config={'BASEMAP_TYPE':'fancy',
'HEADER_FONT_SIZE':'14'})
#'COLOR_BACKGROUND':'-', :setting this to '-' plots z are transparent
#'COLOR_FOREGROUND':'-'})
#make colormap
cptfile = gmt.tempfilename()
gmt.makecpt(C=colombia,T='-4000/4950/100',Z=True,out_filename=cptfile,D='i')
#make gradient
#topo_grad = gmt.tempfilename()
#gmt.grdgradient(etopo,V=True,A='100',N='e0.8',M=True,G=topo_grad,K=False)
#plot topography
gmt.grdimage( etopo,
R = region,
J = scale,
C = cptfile,
E = '200')
#I = '0.5')#topo_grad)
#plot coastlines
gmt.pscoast( R=region,
J=scale,
B='a10',
D='l',
A='500',
W='thinnest,black,-',
N='all')
#plot stations
if station_data != 'none':
gmt.psxy( R=region,
J=scale,
B='a10',
S='t0.1',
G='red',
in_rows = station_data )
if quake_locs != 'none':
eq_region = 'g'
eq_scale = 'E-100/40/2.25i'
gmt.pscoast( R = eq_region,
J = eq_scale,
B = 'p',
A = '10000',
G = 'lightgrey',
W = 'thinnest',
Y = '3.5i',
X = '-0.5i' )
gmt.psxy( R = eq_region,
J = eq_scale,
S = 'a0.05',
G = 'blue',
in_rows = quake_locs )
#plot receiver function stack
if rf_data != 'none':
rf_region = '-0.20/0.20/0/100'
rf_scale = 'X1i/-5i'
gmt.psxy( R = rf_region,
J = rf_scale,
#G = 'grey', #fill
B = '0.25::/10:time (s):NE',
W = 'thin',
X = '9i',
Y = '-3.5i',
in_columns = rf_data )
#plot shaded area for positive values
vals = rf_data[0]
time = rf_data[1]
poly_vals_pos = []
poly_time_pos = []
for i in range(0,len(vals)):
val_here = max(0,np.float(vals[i]))
poly_time_pos.append(time[i])
poly_vals_pos.append(val_here)
poly_vals_pos.append(0.0)
poly_time_pos.append(time[::-1][0])
poly_vals_pos.append(0.0)
poly_time_pos.append(time[0])
gmt.psxy( R = rf_region,
J = rf_scale,
G = 'red',
B = '0.25::/10:time (s):NE',
W = 'thin',
in_columns = (poly_vals_pos,poly_time_pos) )
#plot shaded area for negative values
'''
vals = rf_data[0]
time = rf_data[1]
poly_vals_neg = []
poly_time_neg = []
for i in range(0,len(vals)):
val_here = min(0,np.float(vals[i]))
poly_time_neg.append(time[i])
poly_vals_neg.append(val_here)
poly_vals_neg.append(0.0)
poly_time_neg.append(time[::-1][0])
poly_vals_neg.append(0.0)
poly_time_neg.append(time[0])
gmt.psxy( R = rf_region,
J = rf_scale,
G = 'blue',
B = '0.25::/10:time (s):NE',
W = 'thin',
in_columns = (poly_vals_neg,poly_time_neg) )
'''
#header_file = open('header_file','w')
#header_file.write('<< EOF')
#header_file.close()
#write header information
if header != 'none':
stations_text = header['N_traces'] + ' traces in stack'
events_text = header['N_events'] + ' events'
freq_text = header['frequency']
decon_text = 'deconvolution method : ' + header['decon']
gmt.pstext( in_rows = [(-96,60,12,0,1,'MC',stations_text)],
R = region,
J = scale,
N = True,
X = '-8.5i' )
gmt.pstext( in_rows = [(-96,59,12,0,1,'MC',events_text)],
R = region,
J = scale,
N = True )
gmt.pstext( in_rows = [(-96,58,12,0,1,'MC',freq_text)],
R = region,
J = scale,
N = True )
gmt.pstext( in_rows = [(-96,57,12,0,1,'MC',decon_text)],
R = region,
J = scale,
N = True )
#save figure
gmt.save(fname)
def plot_vtk_slice(vtk_slice,theta_range=[0,360],depth_range=[0,2885],**kwargs):
#--------------------------------------------------------------------------------
'''
This will plot a cross section of a .vtk file. Open the .vtk file in paraview,
choose a slice, and select 'File -> export data'. This will save the data as
a .csv file, which can be plotted with this function.
args--------------------------------------------------------------------------
vtk_slice: the .csv file
theta_range: the range in degrees you wish to plot.
dtype=tuple
default=[0,360] (whole earth)
depth_range: the range in depth you wish to plot.
dtype=tuple
default=[0,2885] (whole mantle)
kwargs------------------------------------------------------------------------
cmap = colormap to use
dtype=string
default='BlueWhiiteOrangeRed'
data_range = max and min values to use in colorbar.
dtype=tuple
default=[-1.0,1.0]
csteps = number of divisions in colorbar
dtype=int
default=100
cmap_direction = forward or reverse colormap
dtype=string
default='i'
fname = filename
dtype=string
default='slice.pdf'
rotation = number of degrees to rotate figure
dtype=float
default=90.0 (i.e., rotate from lat coor to colat based coor)
contour = True or False
'''
#get kwargs-------------------------------------------------------------------
cmap_dir = '/geo/home/romaguir/utils/colors/'
cmap = kwargs.get('cmap','BlueWhiteOrangeRed')
cmap = cmap_dir+cmap
data_range = kwargs.get('data_range',[-0.25,0.25])
csteps = kwargs.get('csteps',100)
cmap_direction=kwargs.get('cmap_direction','i')
fname = kwargs.get('fname','slice.pdf')
rotation=kwargs.get('rotation',90.0)
contour=kwargs.get('contour',True)
#read csv slice (output of paraview)------------------------------------------
f = pandas.read_csv(vtk_slice)
p1 = f['Points:0']
p2 = f['Points:1']
p3 = f['Points:2']
dvp = f['dVp()']
#transform to polar, earth coordinates----------------------------------------
r,t = cart2polar(p1,p3)
r *= 6371.0
t=np.degrees(t)
print min(dvp),max(dvp)
#setup GMT plot---------------------------------------------------------------
gmt = GMT(config={'BASEMAP_TYPE':'fancy',
'HEADER_FONT_SIZE':'14'})
region = '{}/{}/{}/{}'.format(theta_range[0],theta_range[1],6371.0-depth_range[1],6371.0-depth_range[0])
surf_region = '{}/{}/{}/{}'.format(theta_range[0]-2,theta_range[1]+2,6371.0-depth_range[1],6500.0-depth_range[0])
scale = 'P6i' #Polar, 8 inch radius
cptfile = gmt.tempfilename()
grdfile = gmt.tempfilename()
#gmt.makecpt(C=cmap,T='{}/{}/{}'.format(data_range[0],data_range[1],csteps),Z=False,out_filename=cptfile,D=cmap_direction)
gmt.makecpt(C=cmap,T='-0.25/0.25/0.025',A=100,out_filename=cptfile,D=True)
gmt.surface(in_columns=[t+rotation,r,dvp],G=grdfile,I='0.5/25',T=0.0,R=surf_region,out_discard=True)
'''
#plot the data----------------------------------------------------------------
gmt.psxy( R=region,
J=scale,
#B='a15f15:"":/200f200:""::."":WSne',
B='a300f300',
S='s0.20',
#G='blue',
C=cptfile,
in_columns=[t+rotation,r,dvp] )
'''
#plot the data----------------------------------------------------------------
gmt.grdimage( grdfile,
R=region,
J=scale,
B='a300f300',
C=cptfile,
E='i5' )
#contour the data-------------------------------------------------------------
if contour == True:
gmt.grdcontour( grdfile,
R=region,
J=scale,
C=cptfile,
W='1' )
#plot 660---------------------------------------------------------------------
d660 = np.loadtxt('/geo/home/romaguir/utils/660_polar.dat')
print d660
gmt.psxy( R=region,
J=scale,
W='1',
in_rows=[d660] )
gmt.save(fname)
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())
ndist, nd, values = plot_2d(field,_slat,_slon,_elat,_elon,pdepth,new=True)
lbl1,lbl2 = anot[cnt-1]
fstr = cStringIO.StringIO()
for dist,depth,vs in zip(ndist,nd,values):
fstr.write("%f %f %f\n"%(dist,depth,vs))
sclx = 18.*ndist.max()/xscale
scly = yscale
scl = 'X%fc/%fc'%(sclx,scly)
print scl
rng = '%f/%f/%f/%f'%(ndist.min(),ndist.max(),nd.min(),nd.max())
grdws = gmt.tempfilename('ws.grd')
grdgd = gmt.tempfilename('grdgd.cpt')
gmt.xyz2grd(G=grdws,R=rng,I='50./1.',out_discard=True,in_string=fstr.getvalue())
gmt.grdgradient(grdws,G=grdgd,out_discard=True,A='180',N='e0.8')
if cnt == 1:
gmt.grdimage(grdws,I=grdgd,R=True,J=scl,C=cptws,
B='a500f100:Distance [km]:/a20f10:Depth [km]:WSne',X='1c',Y='3c')
else:
gmt.grdimage(grdws,I=grdgd,R=True,J=scl,C=cptws,
B='a500f100:Distance [km]:/a20f10:Depth [km]:WSne',Y='%fc'%(scly+2.5))
txtstr1 = """%f %f 12 0 1 LB %s"""%(0.0,8.,lbl1)
txtstr2 = """%f %f 12 0 1 RB %s"""%(ndist.max(),8.,lbl2)
gmt.pstext(R=True,J=True,G='0/0/0',N=True,in_string=txtstr1)
gmt.pstext(R=True,J=True,G='0/0/0',N=True,in_string=txtstr2)
cnt += 1
gmt.save(foutprofiles)
#test(field)
def gmt_north_america(**kwargs):
'''
Give rf_data as ([values],[time])
'''
from gmtpy import GMT
#get kwargs
fname = kwargs.get('fname','USA_map.pdf')
station_data = kwargs.get('station_data','none')
quake_locs = kwargs.get('quake_locs','none')
rf_data = kwargs.get('rf_data','none')
header = kwargs.get('header','none')
#topo data
etopo='/geo/home/romaguir/utils/ETOPO5.grd'
topo_grad='/geo/home/romaguir/utils/topo_grad.grd'
#colormap
colombia='/geo/home/romaguir/utils/colors/colombia'
region = '-128/-66/24/52'
scale = 'l-100/35/33/45/1:30000000'
#initialize gmt
gmt = GMT(config={'BASEMAP_TYPE':'fancy',
'HEADER_FONT_SIZE':'14'})
#'COLOR_BACKGROUND':'-', :setting this to '-' plots z are transparent
#'COLOR_FOREGROUND':'-'})
#make colormap
cptfile = gmt.tempfilename()
gmt.makecpt(C=colombia,T='-4000/4950/100',Z=True,out_filename=cptfile,D='i')
#make gradient
#topo_grad = gmt.tempfilename()
#gmt.grdgradient(etopo,V=True,A='100',N='e0.8',M=True,G=topo_grad,K=False)
#plot topography
gmt.grdimage( etopo,
R = region,
J = scale,
C = cptfile,
E = '200')
#I = '0.5')#topo_grad)
#plot coastlines
gmt.pscoast( R=region,
J=scale,
B='a10',
D='l',
A='500',
W='thinnest,black,-',
N='all')
#plot stations
if station_data != 'none':
gmt.psxy( R=region,
J=scale,
B='a10',
S='t0.1',
G='red',
in_rows = station_data )
if quake_locs != 'none':
eq_region = 'g'
eq_scale = 'E-100/40/2.25i'
gmt.pscoast( R = eq_region,
J = eq_scale,
B = 'p',
A = '10000',
G = 'lightgrey',
W = 'thinnest',
Y = '3.5i',
X = '-0.5i' )
gmt.psxy( R = eq_region,
J = eq_scale,
S = 'a0.05',
G = 'blue',
in_rows = quake_locs )
#plot receiver function stack
if rf_data != 'none':
rf_region = '-0.20/0.20/0/100'
rf_scale = 'X1i/-5i'
gmt.psxy( R = rf_region,
J = rf_scale,
#G = 'grey', #fill
B = '0.25::/10:time (s):NE',
W = 'thin',
X = '9i',
Y = '-3.5i',
in_columns = rf_data )
#plot shaded area for positive values
vals = rf_data[0]
time = rf_data[1]
poly_vals_pos = []
poly_time_pos = []
for i in range(0,len(vals)):
val_here = max(0,np.float(vals[i]))
poly_time_pos.append(time[i])
poly_vals_pos.append(val_here)
poly_vals_pos.append(0.0)
poly_time_pos.append(time[::-1][0])
poly_vals_pos.append(0.0)
poly_time_pos.append(time[0])
gmt.psxy( R = rf_region,
J = rf_scale,
G = 'red',
B = '0.25::/10:time (s):NE',
W = 'thin',
in_columns = (poly_vals_pos,poly_time_pos) )
#plot shaded area for negative values
'''
vals = rf_data[0]
time = rf_data[1]
poly_vals_neg = []
poly_time_neg = []
for i in range(0,len(vals)):
val_here = min(0,np.float(vals[i]))
poly_time_neg.append(time[i])
poly_vals_neg.append(val_here)
poly_vals_neg.append(0.0)
poly_time_neg.append(time[::-1][0])
poly_vals_neg.append(0.0)
poly_time_neg.append(time[0])
gmt.psxy( R = rf_region,
J = rf_scale,
G = 'blue',
B = '0.25::/10:time (s):NE',
W = 'thin',
in_columns = (poly_vals_neg,poly_time_neg) )
'''
#header_file = open('header_file','w')
#header_file.write('<< EOF')
#header_file.close()
#write header information
if header != 'none':
stations_text = header['N_traces'] + ' traces in stack'
events_text = header['N_events'] + ' events'
freq_text = header['frequency']
decon_text = 'deconvolution method : ' + header['decon']
gmt.pstext( in_rows = [(-96,60,12,0,1,'MC',stations_text)],
R = region,
J = scale,
N = True,
X = '-8.5i' )
gmt.pstext( in_rows = [(-96,59,12,0,1,'MC',events_text)],
R = region,
J = scale,
N = True )
gmt.pstext( in_rows = [(-96,58,12,0,1,'MC',freq_text)],
R = region,
J = scale,
N = True )
gmt.pstext( in_rows = [(-96,57,12,0,1,'MC',decon_text)],
R = region,
J = scale,
N = True )
#save figure
gmt.save(fname)
for lat in runlat:
for lon in runlon:
print >>f, lon, lat
f.close()
### Plot original
na04_moho = loadtxt('./LITH5.0/NA04_moho.xyf')
gmt = GMT(config={'PAGE_ORIENTATION':'landscape'})
rng = '-145/-50/35/80'
scl = 'L-100/60/45/65/12c'
anot = 'a20f10/a25f5WSne'
fout = 'na04_moho.eps'
tmpgrd = gmt.tempfilename('moho_temp.grd')
mohogrd = gmt.tempfilename('moho.grd')
mohocpt = gmt.tempfilename('moho.cpt')
gmt.xyz2grd(G=mohogrd,I='%f/%f'%(0.25,0.25),R=rng,out_discard=True,in_rows=na04_moho)
gmt.grd2cpt(mohogrd,E=50,L='10/60',C="seis",out_filename=mohocpt)
gmt.grdtrack(xyfile,G=mohogrd,R=True,out_filename=xyzfile)
gmt.grdimage(mohogrd,R=True,J=scl,C=mohocpt)
gmt.pscoast(R=True,J=scl,B=anot,D='i',W='thinnest' )
### Plot resampled
na04_moho = loadtxt(xyzfile)
gmt.xyz2grd(G=mohogrd,I='%f/%f'%(1.,1.),R=rng,out_discard=True,in_rows=na04_moho)
gmt.grdimage(mohogrd,R=True,J=scl,C=mohocpt,X='13c')
gmt.pscoast(R=True,J=scl,B=anot,D='i',W='thinnest' )
gmt.psscale(C=mohocpt,V=True,D='0c/-.7c/6c/.2ch',B='10::/:km:')
gmt.save(fout)
os.system('gv %s&'%fout)
def plot_vtk_slice(vtk_slice,
theta_range=[0, 360],
depth_range=[0, 2885],
**kwargs):
#--------------------------------------------------------------------------------
'''
This will plot a cross section of a .vtk file. Open the .vtk file in paraview,
choose a slice, and select 'File -> export data'. This will save the data as
a .csv file, which can be plotted with this function.
args--------------------------------------------------------------------------
vtk_slice: the .csv file
theta_range: the range in degrees you wish to plot.
dtype=tuple
default=[0,360] (whole earth)
depth_range: the range in depth you wish to plot.
dtype=tuple
default=[0,2885] (whole mantle)
kwargs------------------------------------------------------------------------
cmap = colormap to use
dtype=string
default='BlueWhiiteOrangeRed'
data_range = max and min values to use in colorbar.
dtype=tuple
default=[-1.0,1.0]
csteps = number of divisions in colorbar
dtype=int
default=100
cmap_direction = forward or reverse colormap
dtype=string
default='i'
fname = filename
dtype=string
default='slice.pdf'
rotation = number of degrees to rotate figure
dtype=float
default=90.0 (i.e., rotate from lat coor to colat based coor)
contour = True or False
'''
#get kwargs-------------------------------------------------------------------
cmap_dir = '/geo/home/romaguir/utils/colors/'
cmap = kwargs.get('cmap', 'BlueWhiteOrangeRed')
cmap = cmap_dir + cmap
data_range = kwargs.get('data_range', [-0.25, 0.25])
csteps = kwargs.get('csteps', 100)
cmap_direction = kwargs.get('cmap_direction', 'i')
fname = kwargs.get('fname', 'slice.pdf')
rotation = kwargs.get('rotation', 90.0)
contour = kwargs.get('contour', True)
#read csv slice (output of paraview)------------------------------------------
f = pandas.read_csv(vtk_slice)
p1 = f['Points:0']
p2 = f['Points:1']
p3 = f['Points:2']
dvp = f['dVp()']
#transform to polar, earth coordinates----------------------------------------
r, t = cart2polar(p1, p3)
r *= 6371.0
t = np.degrees(t)
print min(dvp), max(dvp)
#setup GMT plot---------------------------------------------------------------
gmt = GMT(config={'BASEMAP_TYPE': 'fancy', 'HEADER_FONT_SIZE': '14'})
region = '{}/{}/{}/{}'.format(theta_range[0], theta_range[1],
6371.0 - depth_range[1],
6371.0 - depth_range[0])
surf_region = '{}/{}/{}/{}'.format(theta_range[0] - 2, theta_range[1] + 2,
6371.0 - depth_range[1],
6500.0 - depth_range[0])
scale = 'P6i' #Polar, 8 inch radius
cptfile = gmt.tempfilename()
grdfile = gmt.tempfilename()
#gmt.makecpt(C=cmap,T='{}/{}/{}'.format(data_range[0],data_range[1],csteps),Z=False,out_filename=cptfile,D=cmap_direction)
gmt.makecpt(C=cmap,
T='-0.25/0.25/0.025',
A=100,
out_filename=cptfile,
D=True)
gmt.surface(in_columns=[t + rotation, r, dvp],
G=grdfile,
I='0.5/25',
T=0.0,
R=surf_region,
out_discard=True)
'''
#plot the data----------------------------------------------------------------
gmt.psxy( R=region,
J=scale,
#B='a15f15:"":/200f200:""::."":WSne',
B='a300f300',
S='s0.20',
#G='blue',
C=cptfile,
in_columns=[t+rotation,r,dvp] )
'''
#plot the data----------------------------------------------------------------
gmt.grdimage(grdfile, R=region, J=scale, B='a300f300', C=cptfile, E='i5')
#contour the data-------------------------------------------------------------
if contour == True:
gmt.grdcontour(grdfile, R=region, J=scale, C=cptfile, W='1')
#plot 660---------------------------------------------------------------------
d660 = np.loadtxt('/geo/home/romaguir/utils/660_polar.dat')
print d660
gmt.psxy(R=region, J=scale, W='1', in_rows=[d660])
gmt.save(fname)