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 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')
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, 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 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)