ds = yt.load("maestro_subCh_plt00248")
dd = ds.all_data()
# field in the dataset we will visualize
field = ('boxlib', 'radial_velocity')
# the values we wish to highlight in the rendering. We'll put a Gaussian
# centered on these with width sigma
vals = [-1.e7, -5.e6, -2.5e6, 2.5e6, 5.e6, 1.e7]
sigma = 2.e5
mi, ma = min(vals), max(vals)
# Instantiate the ColorTransferfunction.
tf = vr.ColorTransferFunction((mi, ma))
for v in vals:
tf.sample_colormap(v, sigma**2, colormap="coolwarm")
# volume rendering requires periodic boundaries. This dataset has
# solid walls. We need to hack it for now (this will be fixed in
# a later yt)
ds.periodicity = (True, True, True)
# Set up the camera parameters: center, looking direction, width, resolution
c = np.array([0.0, 0.0, 0.0])
L = np.array([1.0, 1.0, 1.2])
W = 1.5*ds.domain_width
def doit(plotfile, fname, view):
ds = yt.load(plotfile)
cm = "gist_rainbow"
# rotate?
Nrot = 0
if fname == "vz":
field = ('gas', 'velocity_z')
use_log = False
vals = [-1.e7, -5.e6, 5.e6, 1.e7]
sigma = 5.e5
cm = "coolwarm"
elif fname == "magvel":
field = ('gas', 'velocity_magnitude')
use_log = False
vals = [1.e6, 2.e6, 4.e6, 8.e6, 1.6e7]
sigma = 2.e5
elif fname == "radvel":
field = ('boxlib', 'radial_velocity')
use_log = False
vals = [-1.e7, -5.e6, -2.5e6, 2.5e6, 5.e6, 1.e7]
sigma = 2.e5
cm = "coolwarm"
dd = ds.all_data()
mi = min(vals)
ma = max(vals)
if use_log:
mi, ma = np.log10(mi), np.log10(ma)
# Instantiate the ColorTransferfunction.
tf = vr.ColorTransferFunction((mi, ma))
# Set up the camera parameters: center, looking direction, width, resolution
#c = np.array([0.0, 0.0, 0.0])
center = 0.5 * (ds.domain_left_edge + ds.domain_right_edge)
xmax, ymax, zmax = ds.domain_right_edge
# this shows something face on
c = np.array([-4.0 * xmax, center[1], center[2]])
L = np.array([1.0, 0.0, 0.0])
# this shows something face on
c = np.array([-2.0 * xmax, -2.0 * ymax, center[2]])
L = np.array([1.0, 1.0, 0.0])
if view == "top":
# this is a view from above
c = np.array([-2.0 * xmax, -2.0 * ymax, center[2] + 0.75 * zmax])
# the normal vector should be pointing back through the center
L = center.d - c
L = L / np.sqrt((L**2).sum())
W = 3.0 * ds.domain_width
N = 1080
north = [0.0, 0.0, 1.0]
for v in vals:
if (use_log):
tf.sample_colormap(math.log10(v), sigma**2,
colormap=cm) #, alpha=0.2)
else:
tf.sample_colormap(v, sigma**2, colormap=cm) #, alpha=0.2)
# alternate attempt
ds.periodicity = (True, True, True)
# loop doing a Matrix transform on the orientation vectors about
# the domain center to simulate rotation
if not Nrot == 0:
dtheta = 2.0 * np.pi / Nrot
for n in range(max(1, Nrot)):
# Create a camera object
if n == 0:
cam = vr.PerspectiveCamera(c,
L,
W,
N,
transfer_function=tf,
ds=ds,
no_ghost=False,
north_vector=north,
fields=[field],
log_fields=[use_log])
if not Nrot == 0:
cam.yaw(dtheta, center)
# make an image
im = cam.snapshot()
# add an axes triad -- note if we do this, we HAVE to do draw
# domain, otherwise the image is blank (likely a bug)
cam.draw_coordinate_vectors(im)
# add the domain box to the image:
nim = cam.draw_domain(im)
# increase the contrast -- for some reason, the enhance default
# to save_annotated doesn't do the trick (likely a bug)
if fname == "vz":
# this normalization comes from looking at im[:,:,:3].std() * 4.0 for
# a 3-d wide XRB visualization near 0.02 s
max_val = 0.0276241228025
elif n == 0:
max_val = im[:, :, :3].std() * 4.0
nim[:, :, :3] /= max_val
f = plt.figure()
plt.text(0.2,
0.15,
"{:.3g} s".format(float(ds.current_time.d)),
transform=f.transFigure,
color="white")
cam._render_figure = f
# save annotated -- this added the transfer function values,
# but this messes up our image size defined above
#cam.save_annotated("{}_{}_{:03d}.png".format(os.path.normpath(plotfile), fname, n),
# nim,
# dpi=145, clear_fig=False)
if view == "top":
ostr = fname + "_top"
else:
ostr = fname
cam.save_annotated("{}_{}_HD{:03d}.png".format(
os.path.normpath(plotfile), ostr, n),
nim,
dpi=218,
clear_fig=False)
plt.close()
def doit(plotfile, fname):
ds = load(plotfile)
cm = "gist_rainbow"
if fname == "vz":
field = ('gas', 'velocity_z')
use_log = False
vals = [-1.e7, -5.e6, 5.e6, 1.e7]
sigma = 5.e5
cm = "coolwarm"
elif fname == "magvel":
field = ('gas', 'velocity_magnitude')
use_log = False
vals = [1.e6, 2.e6, 4.e6, 8.e6, 1.6e7]
sigma = 2.e5
dd = ds.all_data()
mi = min(vals)
ma = max(vals)
if use_log:
mi, ma = np.log10(mi), np.log10(ma)
# Instantiate the ColorTransferfunction.
tf = vr.ColorTransferFunction((mi, ma))
# Set up the camera parameters: center, looking direction, width, resolution
c = (ds.domain_right_edge + ds.domain_left_edge) / 2.0
L = np.array([1.0, 1.0, 1.0])
L = np.array([1.0, 1.0, 1.2])
W = 2.0 * ds.domain_width
N = 720
north = [0.0, 0.0, 1.0]
for v in vals:
if (use_log):
tf.sample_colormap(math.log10(v), sigma**2,
colormap=cm) #, alpha=0.2)
else:
tf.sample_colormap(v, sigma**2, colormap=cm) #, alpha=0.2)
# alternate attempt
ds.periodicity = (True, True, True)
# Create a camera object
cam = vr.Camera(c,
L,
W,
N,
transfer_function=tf,
ds=ds,
no_ghost=False,
north_vector=north,
fields=[field],
log_fields=[use_log])
# make an image
im = cam.snapshot()
# add an axes triad -- note if we do this, we HAVE to do draw
# domain, otherwise the image is blank (likely a bug)
cam.draw_coordinate_vectors(im)
# add the domain box to the image:
nim = cam.draw_domain(im)
# increase the contrast -- for some reason, the enhance default
# to save_annotated doesn't do the trick (likely a bug)
max_val = im[:, :, :3].std() * 4.0
nim[:, :, :3] /= max_val
f = pylab.figure()
pylab.text(0.2,
0.15,
"{:.3g} s".format(float(ds.current_time.d)),
transform=f.transFigure,
color="white")
cam._render_figure = f
# save annotated -- this added the transfer function values,
# but this messes up our image size defined above
cam.save_annotated("{}_{}.png".format(os.path.normpath(plotfile), fname),
nim,
dpi=145,
clear_fig=False)
def doit(plotfile, fname):
ds = yt.load(plotfile)
if fname == "vz":
field = ('gas', 'velocity_z')
use_log = False
vals = [-1.e7, -5.e6, 5.e6, 1.e7]
sigma = 5.e5
fmt = None
elif fname == "magvel":
field = ('gas', 'velocity_magnitude')
use_log = True
vals = [1.e5, 3.16e5, 1.e6, 3.16e6, 1.e7]
sigma = 0.1
elif fname == "enucdot":
field = ('boxlib', 'enucdot')
use_log = True
vals = [1.e16, 3.162e16, 1.e17, 3.162e17, 1.e18]
vals = list(10.0**numpy.array([16.5, 17.0, 17.5, 18.0, 18.5]))
sigma = 0.05
fmt = "%.3g"
dd = ds.all_data()
mi = min(vals)
ma = max(vals)
if use_log:
mi, ma = np.log10(mi), np.log10(ma)
# this is hackish, but there seems to be no better way to set whether
# you are rendering logs
ds._get_field_info("density").take_log = use_log
print mi, ma
# Instantiate the ColorTransferfunction.
tf = vr.ColorTransferFunction((mi, ma))
cm = "gist_rainbow"
for v in vals:
if (use_log):
print v, math.log10(v)
tf.sample_colormap(math.log10(v), sigma**2,
colormap=cm) #, alpha=0.2)
else:
tf.sample_colormap(v, sigma**2, colormap=cm) #, alpha=0.2)
# an attempt to get around the periodic assumption -- suggested by Nathan
#root_dds = pf.domain_width/pf.domain_dimensions
#half_w = pf.domain_width/2.# - root_dds
#half_w[2] -= root_dds[2]
#reg = pf.region(pf.domain_center, pf.domain_center-half_w, pf.domain_center+half_w)
# alternate attempt
ds.periodicity = (True, True, True)
# Create a camera object
# Set up the camera parameters: center, looking direction, width, resolution
c = (ds.domain_right_edge + ds.domain_left_edge) / 2.0
L = np.array([0.0, -1.0, -1.0])
north_vector = [0.0, 0.0, 1.0]
im, sc = yt.volume_render(ds, [field])
source = sc.get_source(0)
source.set_transfer_function(tf)
source.transfer_function.grey_opacity = True
# note: this needs to come AFTER the set_transfer_function(), or
# else we get an AttributeError
#sc.annotate_domain(ds)
cam = sc.camera
cam.resolution = (720, 720)
cam.set_width(3.0 * ds.domain_width)
cam.focus = c
# we cannot seem to switch views and get an image...
#cam.switch_view(north_vector=north_vector)
#cam.switch_view(normal_vector=L)
#cam.switch_orientation(L, north_vector)
# make an image
im = sc.render("test.png")