def plot_canada_map():
d = defaultdict(list)
gmt = GMT(config={'PAGE_ORIENTATION':'landscape'})
rng = '-70/-52/40/52.'
scl = 'M10c'
gmt.pscoast(R=rng,J=scl,B='a0.5wsne',D='l',W='thinnest',m=True,A='2/1/1')
a = gmt.output.getvalue().split('\n')
z = 0.
cnt = 0
connections = list()
for _l in a:
if _l.startswith('#'):continue
if _l.startswith('>'):
cnt += 1
continue
try:
d[cnt].append(map(float,_l.split('\t')))
except ValueError:
print _l
for _k in d.keys():
ar = array(d[_k])
x = (6371)*cos(2*pi*ar[:,1]/360.)*cos(2*pi*ar[:,0]/360.)
y = (6371)*cos(2*pi*ar[:,1]/360.)*sin(2*pi*ar[:,0]/360.)
z = (6371)*sin(2*pi*ar[:,1]/360.)
pts = mlab.plot3d(x,y,z,tube_radius=2.0,color=(0,0,0))
def plot_2d(field,slat,slon,elat,elon,pdepth,new=True):
"""
Calculate delaunay triangulation and subsequently probe velocity
field at points of interest.
"""
ndist = 0.
nd = 0.
values = 0.
### calculate points between start and end point using a GMT's project program
fout = 'profile_points.xyp'
gmt = GMT()
gmt.project(C='%f/%f'%(slon,slat),E='%f/%f'%(elon,elat),G=50,Q=True,out_filename=fout)
lon,lat,dist = np.loadtxt(fout,unpack=True)
os.remove(fout)
cx = []
cy = []
cz = []
cd = []
cdp = []
for _lon,_lat,_dist in zip(lon,lat,dist):
for _d in pdepth:
x,y,z = convert_pt(_lat,_lon,_d)
cx.append(x)
cy.append(y)
cz.append(z)
cd.append(_dist)
cdp.append(_d)
values = mlab.pipeline.probe_data(field,cx,cy,cz)
return np.array(cd), np.array(cdp), values
def grafica_track(satelite, debris):
gmt = GMT(config={'BASEMAP_TYPE': 'fancy'})
gmt.pscoast(
R='0/280/-75/75', #g
J='M6i', #'G70/-51/5i'
B='30g30', # grid
N='1',
S=(173, 216, 230), # wet fill color
G=(144, 238, 144), # dry fill color
W='thinnest') # shoreline pen
gmt.psxy(
satelite, #'../Encuentro/archivos/15482U',
R='',
J='',
O='-',
S='t0.05',
K='K',
G='red')
gmt.psxy(
debris, #'../Encuentro/archivos/27386U',
R='',
J='',
O='',
S='t0.05',
K='K',
G='blue')
gmt.save('../visual/archivos/ploteo_track.ps')
print 'Grafico Terminado'
def plot_pdf(nabfile,fout,gauss=True):
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'} )
data,ok = read_nab(nabfile)
if not ok: return 0
xshift = 1
yshift = 2
# for ii in range(0,4):
# x = data[ii,0,:]; y = data[ii,1,:]
# ### skip layers with zero thickness:
# if x[0] == x[-1]:continue
# rng = '%f/%f/%f/%f'%(floor(x[0]),ceil(x[-1]),y.min(),y.max()+0.1*y.max())
# scl = 'X4c/4c'
# ant = (ceil(x[-1])-floor(x[0]))/5.
# fnt = ant/2
# gmt.psxy(R=rng,J=scl,B='a%ff%f:Layer thickness [km]:weSn'%(ant,fnt),W='3,black',X='a%dc'%xshift,Y='a%dc'%yshift,in_columns=[x,y])
# xshift = xshift + 5
# yshift = 8.5
xshift = 1
for ii in range(10,15):
x = data[ii,0,:]; y = data[ii,1,:]
rng = '%f/%f/%f/%f'%(floor(x[0]),ceil(x[-1]),y.min(),y.max()+0.1*y.max())
scl = 'X4c/4c'
ant = (ceil(x[-1])-floor(x[0]))/5.
fnt = ant/2
gmt.psxy(R=rng,J=scl,B='a%ff%f:S-velocity [km/s]:weSn'%(ant,fnt),W='3,black',X='a%dc'%xshift,Y='a%dc'%yshift,in_columns=[x,y])
xshift = xshift + 5
gmt.save(fout)
os.system('gv '+fout+'&')
return 1
def find_scale(slat,slon,elat,elon):
gmt = GMT()
maxdist = 0.
fout = 'profile_points.xyp'
for _slat,_slon,_elat,_elon in zip(slat,slon,elat,elon):
gmt.project(C='%f/%f'%(_slon,_slat),E='%f/%f'%(_elon,_elat),G=100,Q=True,out_filename=fout)
lon,lat,dist = np.loadtxt(fout,unpack=True)
if dist.max() > maxdist:
maxdist = dist.max()
os.remove(fout)
return maxdist
def gmt_map(event_lats=None,
event_lons=None,
station_lats=None,
station_lons=None,
**kwargs):
with_stations = False
if kwargs.get('stations', False):
with_stations = True
with_events = False
if kwargs.get('events', False):
with_events = True
gmt = GMT(config={'BASEMAP_TYPE': 'fancy'})
lat_min = min(station_lats + event_lats)
lat_max = max(station_lats + event_lats)
lon_min = min(station_lons + event_lons)
lon_max = max(station_lons + event_lons)
lat_offset = (lat_max - lat_min) * 0.3
lon_offset = (lon_max - lon_min) * 0.3
gmt.pscoast(R='%i/%i/%i/%i' % (lon_min - lon_offset, lon_max + lon_offset,
lat_min - lat_offset, lat_max + lat_offset),
J='M10c',
B='4g4',
D='f',
S=(114, 159, 207),
G=(233, 185, 110),
W='thinnest')
if station_lats and station_lons and with_stations:
gmt.psxy('-St0.5c',
R=True,
J=True,
G=(0, 255, 123),
in_columns=[station_lons, station_lats])
if event_lats and event_lons and with_events:
gmt.psxy('-Sa0.2c',
R=True,
J=True,
G=(255, 0, 0),
in_columns=[event_lons, event_lats])
gmt.save('mapplot.pdf')
def plot_moho(nabfile,fout,gauss=True):
data,ok = read_nab(nabfile)
if not ok: return 0
x = data[20,0,:]; y = data[20,1,:]
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'} )
rng = '%f/%f/%f/%f'%(floor(x[0]),ceil(x[-1]),y.min(),y.max()+0.1*y.max())
scl = 'X6c/4c'
ant = int((ceil(x[-1])-floor(x[0]))/10.)
fnt = ant/2
gmt.psxy(R=rng,J=scl,B='a%ff%fweSn'%(ant,fnt),W='3,red',in_columns=[x,y])
if gauss:
### fit a gauss-curve to the pdf
fitfunc = lambda p,x: (1/sqrt(2*pi*p[0]**2))*exp(-(x-p[1])**2/(2*p[0]**2))
errfunc = lambda p,x,y: fitfunc(p,x)-y
gaussian = lambda m,s,x: (1/sqrt(2*pi*s**2))*exp(-(x-m)**2/(2*s**2))
p0 = [10.,50.]
p1, success = optimize.leastsq(errfunc,p0[:],args=(x,y))
sigm,mean = p1
z = gaussian(mean,sigm,x)
gmt.psxy(R=rng,J=scl,B='a%ff%fweSn'%(ant,fnt),W='3,black,- -',in_columns=[x,z])
gmt.save(fout)
os.system('gv '+fout+'&')
return 1
def gmt_map(event_lats=None, event_lons=None, station_lats=None,
station_lons=None, **kwargs):
with_stations = False
if kwargs.get('stations', False):
with_stations = True
with_events= False
if kwargs.get('events', False):
with_events = True
gmt = GMT( config={'BASEMAP_TYPE':'fancy'} )
lat_min = min(station_lats+ event_lats)
lat_max = max(station_lats+ event_lats)
lon_min = min(station_lons+ event_lons)
lon_max = max(station_lons+ event_lons)
lat_offset = (lat_max-lat_min)*0.3
lon_offset = (lon_max-lon_min)*0.3
gmt.pscoast( R='%i/%i/%i/%i'%(lon_min-lon_offset,
lon_max+lon_offset,
lat_min-lat_offset,
lat_max+lat_offset),
J='M10c',
B='4g4',
D='f',
S=(114,159,207),
G=(233,185,110),
W='thinnest')
if station_lats and station_lons and with_stations:
gmt.psxy('-St0.5c', R=True, J=True, G=(0,255,123),
in_columns=[station_lons, station_lats])
if event_lats and event_lons and with_events:
gmt.psxy('-Sa0.2c', R=True, J=True, G=(255,0,0),
in_columns=[event_lons, event_lats])
gmt.save('mapplot.pdf')
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 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_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)
from gmtpy import GMT
gmt = GMT( config={'PAGE_COLOR':'247/247/240'} )
gmt.psbasemap( R=(0,5,0,5),
J='X%gi/%gi' % (5,3),
B='%g:Time:/%g:Amplitude:SWne' % (1,1) )
# Make four different datasets
# (1) a nested list, with the first dim corresponding to columns
data_as_columns = [ [ 0,1,2,3,4,5 ], [0,1,0.5,1,0.5,1] ]
# (2) a nested list, with the first dim corresponding to rows
data_as_rows = [ [0,1], [1,2], [2,3], [3,3.5], [4,3], [5,2] ]
# (3) a string containing an ascii table
data_as_string = '''0 5
1 4
2 3.5
3 4
4 4.5
5 5'''
# (4) write ascii table in a temporary file...
# Get a filename in the private tempdir of the GMT instance.
# Files in that directory get deleted automatically.
filename = gmt.tempfilename('table.txt')
from gmtpy import GMT, cm, GridLayout, FrameLayout, golden_ratio
import numpy as np
# some data to plot...
x = np.linspace(0, 5, 101)
ys = (np.sin(x) + 2.5, np.cos(x) + 2.5)
gmt = GMT(config={'PAGE_COLOR': '247/247/240'})
layout = GridLayout(1, 2)
widgets = []
for iwidget in range(2):
inner_layout = FrameLayout()
layout.set_widget(0, iwidget, inner_layout)
widget = inner_layout.get_widget('center')
widget.set_horizontal(7 * cm)
widget.set_vertical(7 * cm / golden_ratio)
widgets.append(widget)
#gmt.draw_layout( layout )
#print layout
for widget, y in zip(widgets, ys):
gmt.psbasemap(R=(0, 5, 0, 5),
B='%g:Time [ s ]:/%g:Amplitude [ m ]:SWne' % (1, 1),
*widget.XYJ())
gmt.psxy(R=True, W='2p,blue,o', in_columns=(x, y), *widget.XYJ())
gmt.save('example4.pdf', bbox=layout.bbox())
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 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
from gmtpy import GMT, cm
import numpy as np
# Some data to plot...
x = np.linspace(0,5,101)
y = np.sin(x) + 2.5
gmt = GMT( config={'PAGE_COLOR':'247/247/240'} )
# Get a default layout for plotting.
# This produces a FrameLayout, a layout built of five widgets,
# a 'center' widget, surrounded by four widgets for the margins:
#
# +---------------------------+
# | top |
# +---------------------------+
# | | | |
# | left | center | right |
# | | | |
# +---------------------------+
# | bottom |
# +---------------------------+
layout = gmt.default_layout()
# We will plot in the 'center' widget:
plot_widget = layout.get_widget('center')
# Set width of plot area to 8 cm and height of the 'top' margin
# to 1 cm. The other values are calculated automatically.
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())
"""
from gmtpy import GMT
runlat = arange(24.,89.)
runlon = arange(-156.,-43.)
xyfile = 'surface_wave_coord.txt'
xyzfile = 'lith5.0_moho.txt'
f = open(xyfile,'w')
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
from gmtpy import GMT
gmt = GMT(config={'PAGE_COLOR': '247/247/240'})
gmt.psbasemap(R=(0, 5, 0, 5),
J='X%gi/%gi' % (5, 3),
B='%g:Time:/%g:Amplitude:SWne' % (1, 1))
# Make four different datasets
# (1) a nested list, with the first dim corresponding to columns
data_as_columns = [[0, 1, 2, 3, 4, 5], [0, 1, 0.5, 1, 0.5, 1]]
# (2) a nested list, with the first dim corresponding to rows
data_as_rows = [[0, 1], [1, 2], [2, 3], [3, 3.5], [4, 3], [5, 2]]
# (3) a string containing an ascii table
data_as_string = '''0 5
1 4
2 3.5
3 4
4 4.5
5 5'''
# (4) write ascii table in a temporary file...
# Get a filename in the private tempdir of the GMT instance.
# Files in that directory get deleted automatically.
filename = gmt.tempfilename('table.txt')
f = open(filename, 'w')
f.write('0 3\n1 3\n5 1.2\n')
elon = eval(conf.get('ns-profiles','elon'))
anot = eval(conf.get('ns-profiles','anot'))
elif direction == 'we':
slat = eval(conf.get('we-profiles','slat'))
slon = eval(conf.get('we-profiles','slon'))
elat = eval(conf.get('we-profiles','elat'))
elon = eval(conf.get('we-profiles','elon'))
anot = eval(conf.get('we-profiles','anot'))
else:
print "'direction' has to be either 'ns' or 'we'"
sys.exit(1)
#maxdist = find_scale(slat,slon,elat,elon)
gmt = GMT(config={'ANOT_FONT_SIZE':14,'LABEL_FONT_SIZE':14,
'ANNOT_OFFSET_SECONDARY':'0.1c',
'ANNOT_OFFSET_PRIMARY':'0.1c',
'LABEL_OFFSET':'0.1c',
'FRAME_PEN':'.5p'})
#scly = (28.-3.-3.*len(slat))/len(slat)
cptws = gmt.tempfilename('ws.cpt')
gmt.makecpt(C='seis',D=True,T='3.0/4.5/0.05',out_filename=cptws)
gmt.psscale(C=cptws,D='8c/.5c/10c/.2ch',B='a%ff.1:Vs:'%(.5))
cnt = 1
for _slat,_slon,_elat,_elon in zip(slat,slon,elat,elon):
print "plotting profile %d"%cnt
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
from gmtpy import GMT
gmt = GMT(config={'BASEMAP_TYPE': 'fancy'})
gmt.pscoast(
R='5/15/52/58', # region
J='B10/55/55/60/10c', # projection
B='4g4', # grid
D='f', # resolution
S=(114, 159, 207), # wet fill color
G=(233, 185, 110), # dry fill color
W='thinnest') # shoreline pen
gmt.save('example1.pdf')
gmt.save('example1.eps')
from gmtpy import GMT
gmt = GMT( config={'BASEMAP_TYPE':'fancy'})
gmt.pscoast( R='5/15/52/58', # region
J='B10/55/55/60/10c', # projection
B='4g4', # grid
D='f', # resolution
S=(114,159,207), # wet fill color
G=(233,185,110), # dry fill color
W='thinnest' ) # shoreline pen
gmt.save('example1.pdf')
gmt.save('example1.eps')
we_anot = eval(conf.get("we-profiles", "anot"))
foutmap = conf.get("map", "fout")
invres = eval(conf.get("map", "invres"))
slat = ns_slat + we_slat
slon = ns_slon + we_slon
elat = ns_elat + we_elat
elon = ns_elon + we_elon
anot = ns_anot + we_anot
# plot cross-section on map
gmt = GMT(
config={
"ANOT_FONT_SIZE": 14,
"LABEL_FONT_SIZE": 14,
"ANNOT_OFFSET_SECONDARY": "0.1c",
"ANNOT_OFFSET_PRIMARY": "0.1c",
"LABEL_OFFSET": "0.1c",
"FRAME_PEN": ".5p",
"PLOT_DEGREE_FORMAT": "-D",
}
)
rng = "-145/-50/41/85"
scl = "L-97.5/63/41/85/15c"
markers = "markers.xyp"
gmt.psbasemap(R=rng, J=scl, B="a15f5WSne", Y="2c", X="2c")
gmt.pscoast("-N1", R=True, J=True, D="i", W="0.5p,black", N="2")
cnt = 0
for _slon, _slat, _elon, _elat in zip(slon, slat, elon, elat):
gmt.project(C="%f/%f" % (_slon, _slat), E="%f/%f" % (_elon, _elat), G=100, Q=True, out_filename=markers)
gmt.psxy(R=True, J=True, W="2p,red,", in_rows=[[_slon, _slat], [_elon, _elat]])
# gmt.psxy(markers,R=True,J=True,S='y0.1c',W='1p,blue')