data_glob = "*2010-10-14T00:15:40.98*.dat" data_files=glob.glob(data_path + os.sep + data_glob) hdr_file="%s/grid.500m.search.hdr"%lib_path # creat the object to contain the stations pd = tvtk.PolyData() pd.points = [[s.x/1000.0, s.y/1000.0, -s.elev/1000.0] for s in sta.stations.values()] # create the DEM dem_data=tvtk.PolyData() dem_data.points = numpy.array([demx, demy, demz]).T for data_file in data_files : print data_file data=QDGrid() data.read_NLL_hdr_file(hdr_file) data.buf=numpy.fromfile(data_file, dtype=numpy.int16) data.buf=numpy.array(data.buf, dtype=numpy.float) print data.buf.min(), data.buf.max() data.buf.shape = (data.nx, data.ny, data.nz) max_ib=numpy.argmax(data.buf) ix,iy,iz=data.get_ix_iy_iz(max_ib) # 'mayavi' is always defined on the interpreter. e = OffScreenEngine() #e = Engine() e.start()
sta = StationList()
sta.read_from_file(stations_filename)
cha = ChannelList()
cha.populate_from_station_list(sta, comp_string=["HHZ"])
time_grid = QDTimeGrid()
time_grid.read_NLL_hdr_file(search_grid_filename)
load_ttimes_buf = wo.opdict['load_ttimes_buf']
if recalc_grids:
time_grid.populate_from_time_grids(grid_filename_base, cha, out_dir,
load_ttimes_buf)
# set up basic grid information for test
grid_filename = os.path.join(base_path, 'lib', wo.opdict['search_grid'])
dummy_grid = QDGrid()
dummy_grid.read_NLL_hdr_file(grid_filename)
# set up projection information for test
f = open(grid_filename)
lines = f.readlines()
f.close()
proj_line = lines[1]
proj_info = {}
proj_info['orig_lat'] = np.float(proj_line.split()[3])
proj_info['orig_lon'] = np.float(proj_line.split()[5])
proj_info['map_rot'] = np.float(proj_line.split()[7])
print proj_info
event1ll = (44.9235, 11.1418)
event2ll = (44.8833, 11.1350)
# read the time-grid
sta=StationList()
sta.read_from_file(stations_filename)
cha=ChannelList()
cha.populate_from_station_list(sta,comp_string=["HHZ"])
time_grid=QDTimeGrid()
time_grid.read_NLL_hdr_file(search_grid_filename)
load_ttimes_buf=wo.opdict['load_ttimes_buf']
if recalc_grids : time_grid.populate_from_time_grids(grid_filename_base,cha,out_dir,load_ttimes_buf)
# set up basic grid information for test
grid_filename=os.path.join(base_path,'lib',wo.opdict['search_grid'])
dummy_grid=QDGrid()
dummy_grid.read_NLL_hdr_file(grid_filename)
# set up projection information for test
f=open(grid_filename)
lines=f.readlines()
f.close()
proj_line=lines[1]
proj_info={}
proj_info['orig_lat'] = np.float(proj_line.split()[3])
proj_info['orig_lon'] = np.float(proj_line.split()[5])
proj_info['map_rot'] = np.float(proj_line.split()[7])
print proj_info
event1ll=(44.9235 , 11.1418)
data_files = glob.glob(data_path + os.sep + data_glob)
hdr_file = "%s/grid.500m.search.hdr" % lib_path
# creat the object to contain the stations
pd = tvtk.PolyData()
pd.points = [[s.x / 1000.0, s.y / 1000.0, -s.elev / 1000.0]
for s in sta.stations.values()]
# create the DEM
dem_data = tvtk.PolyData()
dem_data.points = numpy.array([demx, demy, demz]).T
for data_file in data_files:
print data_file
data = QDGrid()
data.read_NLL_hdr_file(hdr_file)
data.buf = numpy.fromfile(data_file, dtype=numpy.int16)
data.buf = numpy.array(data.buf, dtype=numpy.float)
print data.buf.min(), data.buf.max()
data.buf.shape = (data.nx, data.ny, data.nz)
max_ib = numpy.argmax(data.buf)
ix, iy, iz = data.get_ix_iy_iz(max_ib)
# 'mayavi' is always defined on the interpreter.
e = OffScreenEngine()
#e = Engine()
e.start()
win = e.new_scene(magnification=1)
win.scene.isometric_view()
def plot_slice_mayavi(dat_filename,output_file,hyp_x,hyp_y,hyp_z,search_grid_file_name,max_stack_value):
base_path=os.getenv('WAVELOC_PATH')
lib_path="%s/lib"%base_path
# grid geometry
hdr_file=lib_path + os.sep + search_grid_file_name
# detection
detection=50
# stations
stations_file="%s/coord_stations_piton"%lib_path
sta=StationList()
sta.read_from_file(stations_file)
# create the object to contain the stations
pd = tvtk.PolyData()
pd.points = [[s.x/1000.0, s.y/1000.0, -s.elev/1000.0] for s in sta.stations.values()]
# create the object to contain the stations
try:
pd_hyp = tvtk.PolyData()
pd_hyp.points=[[hyp_x,hyp_y,hyp_z]]
except TypeError:
pass
# read the dat file
print dat_filename
data=QDGrid()
data.read_NLL_hdr_file(hdr_file)
data.buf=numpy.fromfile(dat_filename, dtype=numpy.int16)
max_ib=numpy.argmax(data.buf)
print max_ib
max_val=data.buf[max_ib]
ix,iy,iz=data.get_ix_iy_iz(max_ib)
#data.buf=numpy.array(data.buf, dtype=numpy.float)
data.buf.shape = (data.nx, data.ny, data.nz)
# 'mayavi' is always defined on the interpreter.
e = OffScreenEngine()
#e = Engine()
e.start()
win = e.new_scene(magnification=1)
e.current_scene.scene.off_screen_rendering = True
win.scene.isometric_view()
# Make the data and add it to the pipeline.
src = ArraySource(transpose_input_array=True)
src.scalar_data = data.buf
src.spacing=(data.dx, data.dy, -data.dz)
src.origin=(data.x_orig, data.y_orig, -data.z_orig)
e.add_source(src)
# Visualize the data.
o = Outline()
e.add_module(o)
lut=e.scenes[0].children[0].children[0].scalar_lut_manager
lut.data_range=[-1,max_stack_value]
lut.show_legend = True
lut.data_name = 'Stack'
# Create one ContourGridPlane normal to the 'x' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the position to the middle of the data.
if max_val > detection:
cgp.grid_plane.position = ix
else:
cgp.grid_plane.position = data.nx/2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output=cgp.grid_plane.outputs[0]
x_data=numpy.array(output.point_data.scalars.to_array())
# Another with filled contours normal to 'y' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the axis and position to the middle of the data.
cgp.grid_plane.axis = 'y'
if max_val > detection:
cgp.grid_plane.position = iy
else:
cgp.grid_plane.position = data.ny/2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output=cgp.grid_plane.outputs[0]
y_data=numpy.array(output.point_data.scalars.to_array())
# Another with filled contours normal to 'z' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the axis and position to the middle of the data.
cgp.grid_plane.axis = 'z'
if max_val > detection:
cgp.grid_plane.position = iz
else:
cgp.grid_plane.position = data.nz/2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output=cgp.grid_plane.outputs[0]
z_data=numpy.array(output.point_data.scalars.to_array())
a=Axes()
e.add_module(a)
d=VTKDataSource()
d.data=pd
e.add_source(d)
g=Glyph()
e.add_module(g)
g.glyph.glyph_source.glyph_source=g.glyph.glyph_source.glyph_list[4]
g.glyph.glyph_source.glyph_source.radius=0.1
d=VTKDataSource()
d.data=pd_hyp
e.add_source(d)
g=Glyph()
e.add_module(g)
g.glyph.glyph_source.glyph_source=g.glyph.glyph_source.glyph_list[4]
g.glyph.glyph_source.glyph_source.radius=0.5
g.actor.property.color=(0.0,0.0,0.0)
#view(azimuth=-60,elevation=60,distance=120)
win.scene.save(output_file,size=(800,800))
e.stop()
del win
del e
return (x_data, y_data, z_data)
def plot_slice_mayavi(dat_filename, output_file, hyp_x, hyp_y, hyp_z,
search_grid_file_name, max_stack_value):
base_path = os.getenv('WAVELOC_PATH')
lib_path = "%s/lib" % base_path
# grid geometry
hdr_file = lib_path + os.sep + search_grid_file_name
# detection
detection = 50
# stations
stations_file = "%s/coord_stations_piton" % lib_path
sta = StationList()
sta.read_from_file(stations_file)
# create the object to contain the stations
pd = tvtk.PolyData()
pd.points = [[s.x / 1000.0, s.y / 1000.0, -s.elev / 1000.0]
for s in sta.stations.values()]
# create the object to contain the stations
try:
pd_hyp = tvtk.PolyData()
pd_hyp.points = [[hyp_x, hyp_y, hyp_z]]
except TypeError:
pass
# read the dat file
print dat_filename
data = QDGrid()
data.read_NLL_hdr_file(hdr_file)
data.buf = numpy.fromfile(dat_filename, dtype=numpy.int16)
max_ib = numpy.argmax(data.buf)
print max_ib
max_val = data.buf[max_ib]
ix, iy, iz = data.get_ix_iy_iz(max_ib)
#data.buf=numpy.array(data.buf, dtype=numpy.float)
data.buf.shape = (data.nx, data.ny, data.nz)
# 'mayavi' is always defined on the interpreter.
e = OffScreenEngine()
#e = Engine()
e.start()
win = e.new_scene(magnification=1)
e.current_scene.scene.off_screen_rendering = True
win.scene.isometric_view()
# Make the data and add it to the pipeline.
src = ArraySource(transpose_input_array=True)
src.scalar_data = data.buf
src.spacing = (data.dx, data.dy, -data.dz)
src.origin = (data.x_orig, data.y_orig, -data.z_orig)
e.add_source(src)
# Visualize the data.
o = Outline()
e.add_module(o)
lut = e.scenes[0].children[0].children[0].scalar_lut_manager
lut.data_range = [-1, max_stack_value]
lut.show_legend = True
lut.data_name = 'Stack'
# Create one ContourGridPlane normal to the 'x' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the position to the middle of the data.
if max_val > detection:
cgp.grid_plane.position = ix
else:
cgp.grid_plane.position = data.nx / 2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output = cgp.grid_plane.outputs[0]
x_data = numpy.array(output.point_data.scalars.to_array())
# Another with filled contours normal to 'y' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the axis and position to the middle of the data.
cgp.grid_plane.axis = 'y'
if max_val > detection:
cgp.grid_plane.position = iy
else:
cgp.grid_plane.position = data.ny / 2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output = cgp.grid_plane.outputs[0]
y_data = numpy.array(output.point_data.scalars.to_array())
# Another with filled contours normal to 'z' axis.
cgp = ContourGridPlane()
e.add_module(cgp)
# Set the axis and position to the middle of the data.
cgp.grid_plane.axis = 'z'
if max_val > detection:
cgp.grid_plane.position = iz
else:
cgp.grid_plane.position = data.nz / 2
cgp.contour.filled_contours = True
cgp.actor.property.opacity = 0.6
output = cgp.grid_plane.outputs[0]
z_data = numpy.array(output.point_data.scalars.to_array())
a = Axes()
e.add_module(a)
d = VTKDataSource()
d.data = pd
e.add_source(d)
g = Glyph()
e.add_module(g)
g.glyph.glyph_source.glyph_source = g.glyph.glyph_source.glyph_list[4]
g.glyph.glyph_source.glyph_source.radius = 0.1
d = VTKDataSource()
d.data = pd_hyp
e.add_source(d)
g = Glyph()
e.add_module(g)
g.glyph.glyph_source.glyph_source = g.glyph.glyph_source.glyph_list[4]
g.glyph.glyph_source.glyph_source.radius = 0.5
g.actor.property.color = (0.0, 0.0, 0.0)
#view(azimuth=-60,elevation=60,distance=120)
win.scene.save(output_file, size=(800, 800))
e.stop()
del win
del e
return (x_data, y_data, z_data)
######### INTERPOLATE TRAVEL TIMES #############
# The time grid will contain as array values just the travel-times needed
# (interpolated from the full NLL files) so we can free up the memory as soon as possible
time_grid=QDTimeGrid()
time_grid.read_NLL_hdr_file(hdr_file)
if options.twoD:
time_grid.populate_from_2D_time_grids(grid_filename_base,cha)
else:
time_grid.populate_from_time_grids(grid_filename_base,cha,out_path,load_buf=True)
print "Getting Grid geometry"
dummy_grid=QDGrid()
dummy_grid.read_NLL_hdr_file(hdr_file)
(nx,ny,nz)=(dummy_grid.nx, dummy_grid.ny, dummy_grid.nz)
if do_hyp:
logging.info("Reading hyp parameters")
hyp_parameters=[]
for hyp_file in hyp_files:
logging.debug("Hyp info from %s"%hyp_file)
(otime,hypo_x,sigma_x,hypo_y,sigma_y,hypo_z,sigma_z)=qd_read_hyp_file(hyp_file)
phase_dict=qd_read_picks_from_hyp_file(hyp_file)
hyp_parameters.append(((otime,hypo_x,sigma_x,hypo_y,sigma_y,hypo_z,sigma_z),phase_dict))
info.debug(hyp_parameters[0])
# read location file
