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)
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)