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()
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)
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)
