def test_sigma_clip(self):
ds = fake_random_ds(32)
sc = yt.create_scene(ds)
im = sc.render()
sc.save("raw.png")
sc.save("clip_2.png", sigma_clip=2)
sc.save("clip_4.png", sigma_clip=4.0)
return im, sc
def set_the_scene():
""" creates the yt scene, except for transfer function and rendering """
sc = yt.create_scene(ds,datafld)
# Draw the domain boundary and useful grids
lat_rnge=[np.min(model.data.variables['latitude']),np.max(model.data.variables['latitude'])]
lon_rnge=[np.min(model.data.variables['longitude']),np.max(model.data.variables['longitude'])]
min_dep=0.
max_dep=1200.
R=6371.
r_rnge=[(R-max_dep)*1000.,(R-min_dep)*1000.]
Chunk=sp.sphericalChunk(lat_rnge,lon_rnge,r_rnge)
sc=Chunk.domainExtent(sc,RGBa=[1.,1.,1.,0.002],n_latlon=100,n_rad=50)
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.005])
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.002],radius=(R-410.)*1000.)
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.002],radius=(R-max_dep)*1000.)
sc=Chunk.wholeSphereReference(sc,RGBa=[1.,1.,1.,0.002])
# Add shapefile data
print('adding shapefile data to scene')
shp_bbox=[lon_rnge[0],lat_rnge[0],lon_rnge[1],lat_rnge[1]]
for shpfi in ['us_states']:
thisshp=sp.shapedata(shpfi,bbox=shp_bbox,radius=R*1000.)
sc=thisshp.addToScene(sc)
clrs={
'transform':[0.8,0.,0.8,0.05],
'ridge':[0.,0.,0.8,0.05],
'trench':[0.8,0.,0.,0.05],
'global_volcanos':[0.,0.8,0.,0.05]
}
for bound in ['transform','ridge','trench','global_volcanos']:
tect=sp.shapedata(bound,radius=R*1000.,buildTraces=False)
sc=tect.buildTraces(RGBa=clrs[bound],sc=sc,bbox=shp_bbox)
# some camera settings
pos=sc.camera.position
Rmax=6371*1000.
center_vec=np.array([np.mean(bbox[0])/Rmax,np.mean(bbox[1])/Rmax,np.mean(bbox[2])/Rmax])
sc.camera.set_position(pos,north_vector=center_vec)
source = sc.sources['source_00']
source.tfh.set_log(False)
res=sc.camera.get_resolution()
res_factor=settings.get('res_factor', 1)
new_res=(int(res[0]*res_factor),int(res[1]*res_factor))
sc.camera.set_resolution(new_res)
zoom_factor=0.7 # < 1 zooms in
init_width=sc.camera.width
sc.camera.width = (init_width * zoom_factor)
sc.camera.rotate(0*np.pi/180)
return (sc,source)
def test_save_render(self):
ds = fake_random_ds(ndims=32)
sc = yt.create_scene(ds)
# make sure it renders if nothing exists, even if render = False
sc.save(os.path.join(self.tmpdir, "raw.png"), render=False)
# make sure it re-renders
sc.save(os.path.join(self.tmpdir, "raw_2.png"), render=True)
# make sure sigma clip does not re-render
sc.save(os.path.join(self.tmpdir, "clip_2.png"),
sigma_clip=2.0,
render=False)
sc.save(os.path.join(self.tmpdir, "clip_4.png"),
sigma_clip=4.0,
render=False)
return sc
def loop_one():
global lower_bound
global upper_bound
global homdir
os.chdir("/groups/yshirley/skong/s7_t0p1_g512_4pc_isoth_grav_noTurb_rho0p5_rhoamb0p05_b3_resist0p001_vcol2_vshear0p5_T15")
file_list = os.listdir()
file_list_2 = []
for i in file_list:
if '.athdf' in i:
if '.xdmf' not in i:
file_list_2.append(i)
# #print(file_list_2)
# # loop through first dir
i = 0
theta = 0
while i <= 20:
os.chdir("/groups/yshirley/skong/s7_t0p1_g512_4pc_isoth_grav_noTurb_rho0p5_rhoamb0p05_b3_resist0p001_vcol2_vshear0p5_T15")
data = yt.load(file_list_2[i])
sc = yt.create_scene(data)
# really think about it. it needs to go from 0, 4 to 4, 0.
x = 2
z = 2
coord_array = [x, 0, z]
tfh = TransferFunctionHelper(data)
tfh.set_field('density')
tfh.set_log(True)
tfh.set_bounds()
sc[0].tfh.bounds = [lower_bound, upper_bound]
cam = sc.add_camera()
cam.position = data.arr(coord_array, 'code_length')
cam.zoom(2.5)
sc.render()
#sc.show()
os.chdir(homdir)
sc.save(str(file_list_2[i]+'1')) # weirdest error ever? and i don't know why it's happening? not a big deal, though
# did end up wasting some time but it's the weekend so
print('Done with', str(file_list_2[i]))
i += 1
def img_onestep(filename):
ds = yt.load( filename )
ad = ds.all_data()
iteration=int(filename[-5:])
sc = yt.create_scene(ds, field='Ez')
if use_moving_window:
z_shift = jitter_shift( ds, ad, cfl, iteration )
array_shift = z_shift * np.array([0., 0., 1.])
if plot_mr_patch:
box_patch = yt.visualization.volume_rendering.render_source.BoxSource(
left_edge =ds.index.grids[1].LeftEdge +array_shift,
right_edge=ds.index.grids[1].RightEdge+array_shift,
color=[1.,0.1,0.1,.01] )
sc.add_source(box_patch)
########################
### volume rendering ###
########################
source = sc[0]
source.use_ghost_zones = True
source.grey_opacity = True
source.set_log(False)
tf = yt.ColorTransferFunction(bounds)
if rendering_type == 'smooth':
tf.add_gaussian(-my_max/4, width=15**2*w, height=[0.0, 0.0, 1.0, 1])
tf.add_gaussian( my_max/4, width=15**2*w, height=[1.0, 0.0, 0.0, 1])
if rendering_type == 'layers':
# NEGATIVE
tf.add_gaussian(-.04 *my_max, width=8*w, height=[0.1, 0.1, 1.0, 0.2])
tf.add_gaussian(-.2 *my_max, width=5*w, height=[0.1, 0.1, 1.0, 0.5])
tf.add_gaussian(-.6 *my_max, width=w, height=[0.0, 0.0, 1.0, 1.])
# POSITIVE
tf.add_gaussian(.04 *my_max, width=8*w, height=[1.0, 1.0, 0.2, 0.2])
tf.add_gaussian(.2 *my_max, width=5*w, height=[1.0, 1.0, 0.2, 0.5])
tf.add_gaussian(.6 *my_max, width=w, height=[1.0, 1.0, 0.0, 1.])
######################
### plot particles ###
######################
species_points = {}
for species in species_to_plot:
species_points[ species ] = get_species_ytpoints(ad,
species, species_colors[species])
sc.add_source( species_points[ species ] )
source.tfh.tf = tf
source.tfh.bounds = bounds
#########################
### camera properties ###
#########################
cam = sc.camera
cam.resolution = (2048, 2048)
cam.width = .00018*yt.units.meter
cam.focus = ds.domain_center + \
np.array([0., 0., 10.e-6 ])*yt.units.meter + \
array_shift
cam.position = ds.domain_center + \
np.array([15., 15., -5. ])*yt.units.micrometer + \
array_shift
cam.normal_vector = [-0.3, -0.3, -.2]
cam.switch_orientation()
# save image
if rendering_type == 'smooth':
sc.save('img_' + str(my_number_list[count]).zfill(5), sigma_clip=5.)
if rendering_type == 'layers':
sc.save('img_' + str(my_number_list[count]).zfill(5), sigma_clip=2.)
def test_simple_scene_creation(self):
yt.create_scene(self.ds)
import yt data_dir = "SecondOrderTets" # data_set = "few-element-mesh_out.e" data_set = "tet10_unstructured_out.e" ds = yt.load(data_dir + "/" + data_set, step=-1) # create a default scene # sc = yt.create_scene(ds) sc = yt.create_scene(ds, field='uz') # override the default colormap ms = sc.get_source(0) ms.cmap = 'Eos A' # ms.color_bounds = (.07, .15) # ms.color_bounds = (-.02, .02) ms.color_bounds = (-.01, .2) # adjust the camera position and orientation cam = sc.camera # increase the default resolution cam.resolution = (800, 800) cam.focus = ds.arr([0.5, 0.5, 0.5], 'code_length') cam_pos = ds.arr([0.5, 0.5, -1.0], 'code_length') north_vector = [1,0,0] cam.set_position(cam_pos, north_vector) sc.save(data_set + "_from-minus-z-axis-second-order-embree.png") cam_pos = ds.arr([0.5, 0.5, 4.0], 'code_length')
import numpy as np
import yt
ds = yt.load("MOOSE_sample_data/out.e-s010")
sc = yt.create_scene(ds)
cam = sc.camera
# save an image at the starting position
frame = 0
sc.save("camera_movement_%04i.png" % frame)
frame += 1
# Zoom out by a factor of 2 over 5 frames
for _ in cam.iter_zoom(0.5, 5):
sc.save("camera_movement_%04i.png" % frame)
frame += 1
# Move to the position [-10.0, 10.0, -10.0] over 5 frames
pos = ds.arr([-10.0, 10.0, -10.0], "code_length")
for _ in cam.iter_move(pos, 5):
sc.save("camera_movement_%04i.png" % frame)
frame += 1
# Rotate by 180 degrees over 5 frames
for _ in cam.iter_rotate(np.pi, 5):
sc.save("camera_movement_%04i.png" % frame)
frame += 1
def makemovie(name):
data_name = 'cut_co/xycut_co/' + name
ds = yt.load(data_name, nan_mask=0.0)
sc = yt.create_scene(ds, "intensity")
source = sc[0]
sc.annotate_domain(ds, color=[128 / 255, 128 / 255, 128 / 255, 0.01])
sc.annotate_axes(alpha=0.1)
source.set_log(False)
source.tfh.build_transfer_function()
sc.camera.switch_orientation(normal_vector=[-0.1, -0.1, -9])
sc.camera.set_resolution((1024, 1024))
sc.camera.roll(radians(-180))
header = fits.getheader(data_name)
data = fits.getdata(data_name)
obj1 = header['object']
obj = obj1.strip()
max1 = ds.r['intensity'].max()
max = max1 / K
sig = mad_std(
data, ignore_nan=True
) # Older astropy version does not support ignore_nan argument. Delete it and the code will run.
tf2 = yt.ColorTransferFunction((sig, max))
tf2.add_gaussian(3 * sig, sig / 100, [106 / 255, 90 / 255, 205 / 255, 0.6])
tf2.add_gaussian(5 * sig, sig / 100, [30 / 255, 144 / 255, 1, 1.0])
if max > 18 * sig:
tf2.add_gaussian(0.4 * max, sig / 100, [0, 1, 127 / 255, 1.4])
tf2.add_gaussian(0.54 * max, sig / 100, [1, 215 / 255, 0, 1.8])
tf2.add_gaussian(0.68 * max, sig / 100, [1, 0, 0, 2.8])
tf2.add_gaussian(0.85 * max, sig / 100, [240 / 255, 1, 1, 3.8])
else:
tf2.add_gaussian(7 * sig, sig / 100, [0, 1, 127 / 255, 1.4])
tf2.add_gaussian(9 * sig, sig / 100, [1, 215 / 255, 0, 1.8])
tf2.add_gaussian(11.5 * sig, sig / 100, [1, 0, 0, 2.8])
tf2.add_gaussian(13.5 * sig, sig / 100, [240 / 255, 1, 1, 3.8])
command3 = 'mkdir cut_co/' + obj
command4 = 'mkdir cut_co/' + obj + '/movie'
call(command3, shell=True)
call(command4, shell=True)
save_place = 'cut_co/' + obj + '/movie'
source.set_transfer_function(tf2)
source.tfh.tf = tf2
#-----------------------------makeing the colormap------------------------------------------------
if max > 18 * sig:
white_patch = mpatches.Patch(color='#F0FFFF', label='0.85 max')
red_patch = mpatches.Patch(color='#FF0000', label='0.68 max')
yellow_patch = mpatches.Patch(color='#FFD700', label='0.54 max')
green_patch = mpatches.Patch(color='#00FF7F', label='0.4 max')
blue_patch = mpatches.Patch(color='#1E90FF', label='5 $\sigma$')
purple_patch = mpatches.Patch(color='#6A5ACD', label='3 $\sigma$')
else:
white_patch = mpatches.Patch(color='#F0FFFF', label='13.5 $\sigma$')
red_patch = mpatches.Patch(color='#FF0000', label='11.5 $\sigma$')
yellow_patch = mpatches.Patch(color='#FFD700', label='9 $\sigma$')
green_patch = mpatches.Patch(color='#00FF7F', label='7 $\sigma$')
blue_patch = mpatches.Patch(color='#1E90FF', label='5 $\sigma$')
purple_patch = mpatches.Patch(color='#6A5ACD', label='3 $\sigma$')
source.tfh.plot(save_place + '/transfer_function.png')
img = mpimg.imread(save_place + '/transfer_function.png')
fig, ax = plt.subplots(figsize=(8, 4))
imgnew = np.delete(img, np.arange(0, 77), 1)
imgplot = plt.imshow(imgnew)
ax.axis('off')
plt.legend(handles=[
white_patch, red_patch, yellow_patch, green_patch, blue_patch,
purple_patch
],
loc=(0.91, 0.4),
prop={'size': 9})
plt.savefig('cut_co/colormaps/colormap_' + obj + '.png',
bbox_inches='tight')
#----------------------------------------------------------------------------------------------------
i = 0
j = 1
while i < 180:
sc.camera.yaw(
radians(rotate_angle_per_time)) #rotate about the north vector
sc.save(save_place + '/pic' + str(j) + '.png', sigma_clip=4.5)
i += rotate_angle_per_time
j += 1
#I zoomed in 1.07 times for 9 times/frames
k = 0
l = j
while k <= 8:
sc.camera.zoom(1.07)
sc.camera.yaw(radians(rotate_angle_per_time))
sc.save(save_place + '/pic' + str(l) + '.png', sigma_clip=4.5)
k += 1
l += 1
a = l
b = 0
while b < 360:
sc.camera.yaw(radians(rotate_angle_per_time))
sc.save(save_place + '/pic' + str(a) + '.png', sigma_clip=4.5)
b += rotate_angle_per_time
a += 1
num_of_frames = str(a)
command = "ffmpeg -r " + frames_per_sec + " -f image2 -s 1920x1080 -start_number 1 -i " + save_place + "/pic%d.png -vframes " + num_of_frames + " -vcodec libx264 -crf 15 -b 5000K -vb 20M -pix_fmt yuv420p " + save_place + "/movie.mp4"
command2 = "ffmpeg -i " + save_place + "/movie.mp4 -vf drawtext=\"fontfile=/Library/Fonts/SignPainter.ttc: \\" + obj + ": fontcolor=white: fontsize=33: x=(w-text_w)/8: y=(h-text_h)/8\" -codec:a copy cut_co/movies/" + obj + ".mp4"
call(command, shell=True)
call(command2, shell=True)
bounds = (2e7, 3e9)
# Since this rendering is done in log space, the transfer function needs
# to be specified in log space.
tf = yt.ColorTransferFunction(np.log10(bounds))
tf.sample_colormap(np.log10(2E9), 0.005, alpha=0.1, colormap="arbre")
#tf.sample_colormap(np.log10(1E9), 0.005, alpha=0.5, colormap="arbre")
#tf.sample_colormap(np.log10(6E8), 0.005, alpha=0.1, colormap="arbre")
tf.sample_colormap(np.log10(7.5E8), 0.005, alpha=0.1, colormap="arbre")
tf.sample_colormap(np.log10(2.5E8), 0.005, alpha=0.1, colormap="arbre")
tf.sample_colormap(np.log10(1E8), 0.005, alpha=0.1, colormap="arbre")
tf.sample_colormap(np.log10(3.5E7), 0.005, alpha=0.01, colormap="arbre")
for ds in ts.piter():
sc = yt.create_scene(ds, field='temperature', lens_type='plane-parallel')
render_source = sc.get_source(0)
render_source.transfer_function = tf
render_source.tfh.tf = tf
render_source.tfh.bounds = bounds
render_source.tfh.plot(tfdir +
'/%s_render_transfer_function.png' % ds.basename,
profile_field='density')
cam = sc.add_camera(ds, lens_type='plane-parallel')
cam.resolution = (1920, 1080)
cam.width = ds.arr([100, 56.25, 56.25], 'kpc')
cam.position = ds.arr([50, 0, 0], 'kpc')
cam.switch_orientation(normal_vector=[-1, 0, 0], north_vector=[0, 1, 0])
H2[H2<=0] = np.min(H2[H2>0])
shape_data = H2.shape
bounds = (1.e-3, 1.e+3)
data = dict(density=H2)
# data = dict(density=density*H2)
#data = dict(density=density, H2=H2)
ds = yt.load_uniform_grid(data, shape_data)
#dd = ds.all_data()
# from refined_snapshot28_ytclump_fields import _h2density
# from yt.units import dimensions
# ds.add_field(("stream", "h2density"), function=_h2density, units='code_mass/code_length**3') # units="1/cm**3")
sc = yt.create_scene(ds, lens_type='perspective')
# Get reference to the VolumeSource associated with this scene
# It is the first source associated with the scene, so we can refer to it
# using index 0.
source = sc[0]
source.set_log = True
# Set bounds of transfer function
# # plt.hist((density * H2).flatten(), bins=70)
# plt.hist((H2).flatten(), bins=70)
# plt.xscale('log')
# plt.yscale('log')
# plt.show()
#ds = yt.load(fname)
bounds = (1e-28, 1e-25)
# Since this rendering is done in log space, the transfer function needs
# to be specified in log space.
tf = yt.ColorTransferFunction(np.log10(bounds))
tf.sample_colormap(np.log10(1E-25), 0.005, alpha=0.03, colormap="arbre")
#tf.sample_colormap(np.log10(2E-26), 0.005, alpha=0.05, colormap="arbre")
tf.sample_colormap(np.log10(1E-26), 0.005, alpha=0.2, colormap="arbre")
tf.sample_colormap(np.log10(3E-27), 0.005, alpha=0.5, colormap="arbre")
tf.sample_colormap(np.log10(1E-27), 0.005, alpha=0.9, colormap="arbre")
for ds in ts.piter():
sc = yt.create_scene(ds, field='density', lens_type='plane-parallel')
render_source = sc.get_source(0)
render_source.transfer_function = tf
render_source.tfh.tf = tf
render_source.tfh.bounds = bounds
render_source.tfh.plot(tfdir +
'/%s_render_transfer_function.png' % ds.basename,
profile_field='density')
cam = sc.add_camera(ds, lens_type='plane-parallel')
cam.resolution = (1600, 1600)
cam.width = ds.quan(80, 'kpc')
cam.position = ds.arr([50, 0, 0], 'kpc')
cam.switch_orientation(normal_vector=[-1, 0, 0], north_vector=[0, 0, 1])
def VolumeRender(colorbound, RenderingOperation, outputfile, X1,X2,X3, data, Particles):
bbox = np.array([[X1[0], X1[-1]], [X2[0], X2[-1]], [X3[0], X3[-1]]])
arr=data
data = dict(density = (arr, "g/cm**3"))
ds = yt.load_uniform_grid(data, arr.shape, length_unit="pc", bbox=bbox, nprocs=1)
#**************************************
# Extracting user's specified
# parameters in main.py
#**************************************
zooming = RenderingOperation[0]
Operationalfile = RenderingOperation[1]
Initial_L0 = RenderingOperation[2]
Lmin = RenderingOperation[3]
Lmax = RenderingOperation[4]
dl = RenderingOperation[5]
Rotation = RenderingOperation[6]
RotaAngle = RenderingOperation[7]
RotaSteps = int(RenderingOperation[8])
length = Initial_L0
frame=1
print(">> Performing volume rendering ...")
fout= "%s.%04d.png" % (outputfile,frame)
print(">>",frame,length,fout)
#**************************************
# main part starts
#**************************************
sc = yt.create_scene(ds)
source = sc[0]
sc.camera.resolution = [1024, 1024] #horizontal, verticle
sc.camera.width = (length, 'pc')
sc.camera.switch_orientation(normal_vector=np.array([0,0,-1]),north_vector=np.array([0,1,0]))
#sc.camera.set_position(ds.domain_left_edge)
if len(Particles) > 1:
print(">>Adding particles")
colors = 2 + np.zeros((len(Particles), 4)) #np.random.random([len(Particles), 4])
colors[:, 3] = 0.7 # Opacity of Star particles
points = PointSource(Particles, colors=colors)
sc.add_source(points)
source.set_log(True)
source.tfh.set_bounds((colorbound[0], colorbound[1]))
source.tfh.set_log(True)
source.tfh.grey_opacity = True
source.tfh.plot('%s-transferfunction'% outputfile, profile_field='density')
#sc.annotate_axes(alpha=.02)
#sc.annotate_domain(ds, color=[1, 1, 1, .01]
sc.annotate_axes()
sc.save(fout)
#text_string = '10' #"%s" % (length)
#sc.save_annotated(fout,text_annotate=[[(.8, 0.2), text_string]])
frame = frame + 1
cam = sc.camera
#**************************************
# Zooming-in
#**************************************
if ('%s' % zooming) == 'yes':
print("Zooming in requested!")
# Zoom in
while(length>Lmin):
print(length)
sc.camera.width = (length, 'pc')
sc.render()
sc.save('%s.%04d.png' % (outputfile,frame))
length = length - dl
frame += 1
#**************************************
# Rotation: 180 degree
#**************************************
if ('%s' % Rotation) == 'yes':
print("Rotation is requested! Forward.")
#Rotate by 180 degrees over 5 frames
for _ in cam.iter_rotate(RotaAngle, RotaSteps):
sc.render()
sc.save('%s.%04d.png' % (outputfile,frame))
frame += 1
#**************************************
# Zooming out
#**************************************
if ('%s' % zooming) == 'yes':
print("Zooming out requested!")
length = length + dl
while(length<=Lmax):
print(length)
sc.camera.width = (length, 'pc')
sc.render()
sc.save('%s.%04d.png' % (outputfile,frame))
length = length + dl
frame += 1
#**************************************
# 180 degree rotation to return to
# original position
#**************************************
if ('%s' % Rotation) == 'yes':
print("Rotation is requested! Backward.")
#Rotate by 180 degrees over 5 frames
for _ in cam.iter_rotate(RotaAngle, RotaSteps):
sc.render()
sc.save('%s.%04d.png' % (outputfile,frame))
frame += 1
import yt
# Load the dataset.
ds = yt.load("Enzo_64/DD0043/data0043")
sc = yt.create_scene(ds, ('gas', 'density'))
# You may need to adjust the alpha values to get a rendering with good contrast
# For annotate_domain, the fourth color value is alpha.
# Draw the domain boundary
sc.annotate_domain(ds, color=[1, 1, 1, 0.01])
sc.save("%s_vr_domain.png" % ds, sigma_clip=4)
# Draw the grid boundaries
sc.annotate_grids(ds, alpha=0.01)
sc.save("%s_vr_grids.png" % ds, sigma_clip=4)
# Draw a coordinate axes triad
sc.annotate_axes(alpha=0.01)
sc.save("%s_vr_coords.png" % ds, sigma_clip=4)
def img_onestep(filename):
# Load the data
ds = yt.load(filename)
ad = ds.all_data()
# Calculate the z position of the box.
# You can use ds.domain_right_edge[2] instead. However, if a moving window
# was used in the simulation, the rendering shows some jitter.
# This is because a cell is added in z at some iterations but not all.
# These lines calculate this jitter z_shift and remove it from the camera position and focus
iteration = int(filename[-5:])
dt = 1. / clight * 1. / np.sqrt(
(1. / ad['dx'][-1]**2 + 1. / ad['dy'][-1]**2 + 1. / ad['dz'][-1]**2))
z_front = dt * float(iteration) * clight
z_shift = z_front - ds.domain_right_edge[2]
# Create a yt source object for the level1 patch
box_patch = yt.visualization.volume_rendering.render_source.BoxSource(
left_edge=ds.index.grids[1].LeftEdge +
np.array([0., 0., z_shift]) * yt.units.meter,
right_edge=ds.index.grids[1].RightEdge +
np.array([0., 0., z_shift]) * yt.units.meter,
color=[1., 0.1, 0.1, .01])
# Handle 2 populations of particles: beam and plasma electrons
# Color for each of these particles
colors0_vect = [1., 1., 1., .05] # the last value is overwritten later
colors1_vect = [1., 1., 1., .05] # the last value is overwritten later
# particle0: read data and create a yt source object
x0 = ad['particle0', 'particle_position_x'].v
y0 = ad['particle0', 'particle_position_y'].v
z0 = ad['particle0', 'particle_position_z'].v
vertices0 = np.column_stack((x0, y0, z0))
colors0 = np.tile(colors0_vect, (vertices0.shape[0], 1))
colors0[:, 3] = .01
point0 = yt.visualization.volume_rendering.render_source.PointSource(
vertices0, colors=colors0, radii=2)
# particle1: read data and create a yt source object
x1 = ad['particle1', 'particle_position_x'].v
y1 = ad['particle1', 'particle_position_y'].v
z1 = ad['particle1', 'particle_position_z'].v
# select only some particles
selector = np.abs(x1) < .1e-6
x1 = x1[selector]
y1 = y1[selector]
z1 = z1[selector]
vertices1 = np.column_stack((x1, y1, z1))
colors1 = np.tile(colors1_vect, (vertices1.shape[0], 1))
colors1[:, 3] = .002
point1 = yt.visualization.volume_rendering.render_source.PointSource(
vertices1, colors=colors1, radii=1)
# Set the field rendering and camera attributes
# Max field in the simulation. This is easy to get from a single image, but
# it must be set by hand for a video
my_max = 500000000000
sc = yt.create_scene(ds, field='Ez')
# Set camera properties
cam = sc.camera
cam.set_width(ds.quan(35, yt.units.micrometer))
cam.position = ds.domain_center + np.array([
15., 20., -5.
]) * yt.units.micrometer + np.array([0., 0., z_shift]) * yt.units.meter
cam.focus = ds.domain_center + np.array([0., 0., z_shift]) * yt.units.meter
cam.resolution = (2048, 2048)
# Field rendering properties
source = sc[0]
source.set_field('Ez')
source.set_log(False)
source.use_ghost_zones = True
bounds = (-my_max, my_max)
tf = yt.ColorTransferFunction(bounds)
w = (.01 * my_max)**2
# Define the transfer function for 3d rendering
# 3 isocontours for negative field values
# The sharpness of the contour is controlled by argument width
tf.add_gaussian(-.04 * my_max, width=8 * w, height=[0.1, 0.1, 1.0, 0.02])
tf.add_gaussian(-.2 * my_max, width=5 * w, height=[0.1, 0.1, 1.0, 0.05])
tf.add_gaussian(-.6 * my_max, width=w, height=[0.0, 0.0, 1.0, 0.3])
# 3 isocontours for positive field values
tf.add_gaussian(.04 * my_max, width=8 * w, height=[1.0, 1.0, 0.2, 0.02])
tf.add_gaussian(.2 * my_max, width=5 * w, height=[1.0, 1.0, 0.2, 0.05])
tf.add_gaussian(.6 * my_max, width=w, height=[1.0, 1.0, 0.0, 0.3])
source.tfh.tf = tf
source.tfh.bounds = bounds
source.tfh.set_log(False)
source.tfh.tf.grey_opacity = True
# Plot user-defined sources (here, 2 species for particles and 1 box)
sc.add_source(point0)
sc.add_source(point1)
sc.add_source(box_patch)
# Save file
sc.save(filename + '_quarter.png', sigma_clip=1.)
return 0
import yt
# Load the dataset.
ds = yt.load("Enzo_64/DD0043/data0043")
sc = yt.create_scene(ds, ("gas", "density"))
# You may need to adjust the alpha values to get a rendering with good contrast
# For annotate_domain, the fourth color value is alpha.
# Draw the domain boundary
sc.annotate_domain(ds, color=[1, 1, 1, 0.01])
sc.save("%s_vr_domain.png" % ds, sigma_clip=4)
# Draw the grid boundaries
sc.annotate_grids(ds, alpha=0.01)
sc.save("%s_vr_grids.png" % ds, sigma_clip=4)
# Draw a coordinate axes triad
sc.annotate_axes(alpha=0.01)
sc.save("%s_vr_coords.png" % ds, sigma_clip=4)
idx_start = args.idx_start
idx_end = args.idx_end
didx = args.didx
prefix_in = args.prefix_in
prefix_out = args.prefix_out
yt.enable_parallelism()
ts = yt.load([
prefix_in + '/Data_%06d' % idx
for idx in range(idx_start, idx_end + 1, didx)
])
for ds in ts.piter():
# create a scene
sc = yt.create_scene(ds, field=field, lens_type='perspective')
cam = sc.camera
# get a reference to the VolumeSource associated with this scene
src = sc[0]
# set the target field
src.set_field(field)
# set that the transfer function should be evaluated in log space
src.tfh.set_log(True)
# make underdense regions appear opaque
# src.tfh.grey_opacity = True
# set the image resolution
# Since this rendering is done in log space, the transfer function needs
# to be specified in log space.
tf = yt.ColorTransferFunction(np.log10(bounds))
tf.sample_colormap(-0.35, 0.001, alpha=0.9, colormap="arbre")
tf.sample_colormap(-0.2, 0.001, alpha=0.3, colormap="arbre")
tf.sample_colormap(-0.1, 0.001, alpha=0.2, colormap="arbre")
tf.sample_colormap(0.2, 0.001, alpha=0.4, colormap="arbre")
tf.sample_colormap(0.3, 0.0005, alpha=0.7, colormap="arbre")
for ds in ts[::-1].piter():
figfname = figuredir + '/' + ds.basename + '.png'
#if os.path.exists(figfname): continue
sp = ds.sphere([0, 0, 0], (150, 'kpc'))
sc = yt.create_scene(sp, field='entropy_ratio', lens_type='perspective')
render_source = sc.get_source(0)
render_source.transfer_function = tf
render_source.tfh.tf = tf
render_source.tfh.bounds = bounds
#render_source.set_use_ghost_zones(True)
render_source.tfh.plot(tfdir +
'/%s_render_transfer_function.png' % ds.basename,
profile_field='density')
cam = sc.camera
cam.resolution = (1920, 1080)
cam.width = ds.arr([512, 288, 512], 'kpc')
cam.position = ds.arr([256, 0, 0], 'kpc')
tf.funcs[3].y]).T
fig = plt.figure(figsize=[6, 3])
ax = fig.add_axes([0.2, 0.2, 0.75, 0.75])
d_hist=ax.hist(data['dvs'][~np.isnan(dvs)].ravel(),bins=100,density=True,log=False)
ax.bar(x, tf.funcs[3].y, w, edgecolor=[0.0, 0.0, 0.0, 0.0],
log=False, color=colors, bottom=[0])
plt.savefig(os.path.join(out_dir,'shapeplotter_TF_exploration_tf.png'))
# load the data as a uniform grid, create the 3d scene
sc_mult=1.0 # scale multiplier
bbox = model.cart['bbox'] # list-like [[xmin,xmax],[ymin,ymax],[zmin,zmax]]
print(bbox)
ds = yt.load_uniform_grid(data,data['dvs'].shape,sc_mult,bbox=bbox,nprocs=1,
periodicity=(True,True,True),unit_system="mks")
sc = yt.create_scene(ds,'dvs')
# Draw the domain boundary
# draw true boundary extents
lat_rnge=[np.min(model.data.variables['latitude']),np.max(model.data.variables['latitude'])]
lon_rnge=[np.min(model.data.variables['longitude']),np.max(model.data.variables['longitude'])]
min_dep=0.
max_dep=1200.
R=6371.
r_rnge=[(R-max_dep)*1000.,(R-min_dep)*1000.]
Chunk=sp.sphericalChunk(lat_rnge,lon_rnge,r_rnge)
sc=Chunk.domainExtent(sc,RGBa=[1.,1.,1.,0.005],n_latlon=100,n_rad=50)
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.005])
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.001],radius=(R-410.)*1000.)
sc=Chunk.latlonGrid(sc,RGBa=[1.,1.,1.,0.001],radius=(R-max_dep)*1000.)
def test_sigma_clip(self):
ds = fake_random_ds(32)
sc = yt.create_scene(ds)
sc.save("clip_2.png", sigma_clip=2)
os.chdir(
"/groups/yshirley/skong/s7_t0p01_g512_4pc_isoth_grav_noTurb_rho0p5_rhoamb0p05_b3_resist0p001_vcol2_vshear0p5_T15"
)
file_list = os.listdir()
file_list_2 = []
for i in file_list:
if '.athdf' in i:
if '.xdmf' not in i:
file_list_2.append(i)
i = 0
while i <= 10:
os.chdir(
"/groups/yshirley/skong/s7_t0p01_g512_4pc_isoth_grav_noTurb_rho0p5_rhoamb0p05_b3_resist0p001_vcol2_vshear0p5_T15"
)
data = yt.load(file_list_2[i])
sc = yt.create_scene(data)
tfh = TransferFunctionHelper(data)
tfh.set_field('density')
tfh.set_log(True)
tfh.set_bounds()
sc[0].tfh.bounds = [lower_bound, upper_bound]
cam = sc.add_camera()
cam.position = data.arr([4, 0, 0], 'code_length')
cam.zoom(2.5)
sc.render()
#sc.show()
os.chdir("/home/u20/avichalk/exports/29_3_export")
sc.save(
def make_scene(grid_ds):
sc = yt.create_scene(grid_ds, field=('gas', 'PartType1_density'))
cam = sc.add_camera(grid_ds, lens_type='perspective')
focus = cam.focus
print('-------- Scene created. --------')
return sc, cam, focus
import yt
ds = yt.load("Enzo_64/DD0043/data0043")
sc = yt.create_scene(ds, lens_type="perspective")
source = sc[0]
source.set_field(("gas", "density"))
source.set_log(True)
# Set up the camera parameters: focus, width, resolution, and image orientation
sc.camera.focus = ds.domain_center
sc.camera.resolution = 1024
sc.camera.north_vector = [0, 0, 1]
sc.camera.position = [1.7, 1.7, 1.7]
# You may need to adjust the alpha values to get an image with good contrast.
# For the annotate_domain call, the fourth value in the color tuple is the
# alpha value.
sc.annotate_axes(alpha=0.02)
sc.annotate_domain(ds, color=[1, 1, 1, 0.01])
text_string = f"T = {float(ds.current_time.to('Gyr'))} Gyr"
# save an annotated version of the volume rendering including a representation
# of the transfer function and a nice label showing the simulation time.
sc.save_annotated(
"vol_annotated.png", sigma_clip=6, text_annotate=[[(0.1, 0.95), text_string]]
)
import numpy as np from scipy import stats import numexpr as ne import yt import math root = "/Volumes/External/JanData/" array = np.load(root+"AxDensity4_036.npy") maxdens = array.max() for i in range(36,365): print i array = np.load(root+"AxDensity4_%03d.npy"%i) data = dict(density=array) bbox = np.array([[0., 1.0], [0, 1.0], [0., 1.0]]) ds = yt.load_uniform_grid(data, array.shape,length_unit="cm",bbox=bbox) ad = ds.all_data() sc = yt.create_scene(ds, field="density") sc.camera.width = (0.3,'cm') alpha = i/(365.0-36.0)*2*math.pi sc.camera.position = np.array([0.5,0.5,0.5])+np.array([np.cos(alpha),0,np.sin(alpha)]) sc.camera.focus = np.array([0.5,0.5,0.5]) sc.camera.north_vector = np.array([0,1.0,0]) # sc.camera.focus = np.array(np.unravel_index(array.argmax(),array.shape))/406.0 source =sc[0] bounds = (maxdens/1e3, maxdens) tf = yt.ColorTransferFunction(np.log10(bounds)) tf.sample_colormap(np.log10(maxdens*0.75), w=.005,colormap='RAINBOW') tf.sample_colormap(np.log10(maxdens/2.0e1), w=.005, alpha = 0.2,colormap='RAINBOW') tf.sample_colormap(np.log10(maxdens/1.0e2), w=.005, alpha =
def img_onestep(filename):
# Load the data
ds = yt.load(filename)
ad = ds.all_data()
# Calculate the z position of the box.
# You can use ds.domain_right_edge[2] instead. However, if a moving window
# was used in the simulation, the rendering shows some jitter.
# This is because a cell is added in z at some iterations but not all.
# These lines calculate this jitter z_shift and remove it from the camera position and focus
iteration = int(filename[-5:])
dt = 1. / clight * 1. / np.sqrt(
(1. / ad['dx'][-1]**2 + 1. / ad['dy'][-1]**2 + 1. / ad['dz'][-1]**2))
z_front = dt * float(iteration) * clight
z_shift = z_front - ds.domain_right_edge[2]
# Create a yt source object for the level1 patch
if do_patch:
box_patch = yt.visualization.volume_rendering.render_source.BoxSource(
left_edge=ds.index.grids[1].LeftEdge +
np.array([0., 0., z_shift]) * yt.units.meter,
right_edge=ds.index.grids[1].RightEdge +
np.array([0., 0., z_shift]) * yt.units.meter,
color=[1., 0.1, 0.1, .01])
# Handle 2 populations of particles: beam and plasma electrons
if do_particles:
point0 = plot_species(species0, ad, 2, .01, 1.)
point1 = plot_species(species1, ad, 1, .002, 20.e-6)
sc = yt.create_scene(ds, field=field)
# Set camera properties
cam = sc.camera
dom_length = ds.domain_width[2].v
cam.set_width(ds.quan(dom_length, yt.units.meter))
cam.position = ds.domain_center + camera_position + np.array(
[0., 0., z_shift]) * yt.units.meter
cam.focus = ds.domain_center + np.array([0., 0., z_shift]) * yt.units.meter
cam.resolution = resolution
# Field rendering properties
source = sc[0]
source.set_field(field)
source.set_log(False)
source.use_ghost_zones = True
bounds = (-my_max, my_max)
tf = yt.ColorTransferFunction(bounds)
w = (.01 * my_max)**2
# Define the transfer function for 3d rendering
# 3 isocontours for negative field values
# The sharpness of the contour is controlled by argument width
tf.add_gaussian(-.04 * my_max, width=8 * w, height=[0.1, 0.1, 1.0, 0.02])
tf.add_gaussian(-.2 * my_max, width=5 * w, height=[0.1, 0.1, 1.0, 0.05])
tf.add_gaussian(-.6 * my_max, width=w, height=[0.0, 0.0, 1.0, 0.3])
# 3 isocontours for positive field values
tf.add_gaussian(.04 * my_max, width=8 * w, height=[1.0, 1.0, 0.2, 0.02])
tf.add_gaussian(.2 * my_max, width=5 * w, height=[1.0, 1.0, 0.2, 0.05])
tf.add_gaussian(.6 * my_max, width=w, height=[1.0, 1.0, 0.0, 0.3])
source.tfh.tf = tf
source.tfh.bounds = bounds
source.tfh.set_log(False)
source.tfh.tf.grey_opacity = True
if do_particles:
sc.add_source(point0)
sc.add_source(point1)
if do_patch:
sc.add_source(box_patch)
sc.save('./img_' + filename[-8:] + '.png', sigma_clip=1.)
ax.set_title(r'Isocontour of max($\rho$)/4 = ' + str(float(ISO) / 4.) +
r' $\frac{M_{pl}^2}{m}$')
plt.savefig("%s_Surface.png" % i, dpi=300)
os.system("mv *.png SurfRend2")
#surface.export_obj("%s_VR" % i )
#os.system("mv *.obj *.mtl OBJRend2")
print("===============================================")
print("Time to compute SurfaceRender 2 ")
print("time = ", time.time() - Debug, "s ")
print("===============================================")
#==============================================
# Volume rendering
#==============================================
Debug = time.time()
sc = yt.create_scene(i, "rho")
sc.camera.resolution = 1024
sc.annotate_domain(i, color=[1, 1, 1, 1])
sc.save()
os.system("mv *.png VolRend")
print("===============================================")
print("Time to compute VolumeRender")
print("time = ", time.time() - Debug, "s ")
print("===============================================")
os.mkdir(out_dir)
plt.savefig(os.path.join(out_dir, 'transfer_function.png'))
# load the data as a uniform grid, create the 3d scene
sc_mult = 1.0 # scale multiplier
bbox = model.cart['bbox'] # list-like [[xmin,xmax],[ymin,ymax],[zmin,zmax]]
ds = yt.load_uniform_grid(data,
data[datafld].shape,
sc_mult,
bbox=bbox,
nprocs=1,
periodicity=(True, True, True),
unit_system="mks")
sc = yt.create_scene(ds, datafld)
# Draw the domain boundary and useful grids
lat_rnge = [
np.min(model.data.variables['latitude']),
np.max(model.data.variables['latitude'])
]
lon_rnge = [
np.min(model.data.variables['longitude']),
np.max(model.data.variables['longitude'])
]
min_dep = 0.
max_dep = 1200.
R = 6371.
r_rnge = [(R - max_dep) * 1000., (R - min_dep) * 1000.]
Chunk = sp.sphericalChunk(lat_rnge, lon_rnge, r_rnge)
# Volume renders. These are very finiky, so you need to alter the values case by case.
# Documentation can be found here: http://yt-project.org/doc/visualizing/volume_rendering.html
#Find the min and max of the field
mi, ma = ds.all_data().quantities.extrema('wavefunction')
#Reduce the dynamic range
mi = mi.value + 0.004
#ma = ma.value - 0.002
# Choose a vector representing the viewing direction.
L = [0.4, 0.5, 0.15]
# Define the center of the camera to be the domain center
c = ds.domain_center[0]
# Define the width of the image
W = 1.5*max(ds.domain_width)
sc = yt.create_scene(ds, 'wavefunction')
dd = ds.all_data()
source = sc[0]
source.log_field = False
tf = yt.ColorTransferFunction((mi, ma), grey_opacity=False)
tf.map_to_colormap(mi, ma, scale=0.5, colormap='YlGnBu')
source.set_transfer_function(tf)
sc.add_source(source)
cam = sc.add_camera()
sc.annotate_domain(ds, color=[1, 1, 1, 0.1])
sc.annotate_axes(alpha=0.5)
cam.width = W
cam.resolution = 1024
cam.center = c
cam.normal_vector = L
cam.north_vector = [0, 0, 1]
import yt
import numpy as np
# Load the dataset
ds = yt.load("Enzo_64/DD0043/data0043")
# Create a volume rendering
sc = yt.create_scene(ds, field=('gas', 'density'))
# Modify the transfer function
# First get the render source, in this case the entire domain, with field ('gas','density')
render_source = sc.get_source()
# Clear the transfer function
render_source.transfer_function.clear()
# Map a range of density values (in log space) to the Reds_r colormap
render_source.transfer_function.map_to_colormap(
np.log10(ds.quan(5.0e-31, 'g/cm**3')),
np.log10(ds.quan(1.0e-29, 'g/cm**3')),
scale=30.0,
colormap='RdBu_r')
sc.save('new_tf.png')
ax.scatter(x, y, z, c=cvar, marker='.')
ax.set_xlim([min(x), 0])
ax.set_ylim([min(y), 0])
ax.set_zlim([max(z), 0])
plt.show()
# https://yt-project.org/doc/examining/loading_data.html#unstructured-grid-data
# model.unstructured['data']['connect1', 'dvs']=np.float64(model.unstructured['data']['connect1', 'dvs'])
# model.unstructured['cnnct']=np.float64(model.unstructured['cnnct'])
ds = yt.load_unstructured_mesh(model.unstructured['cnnct'],
model.unstructured['vertices'],
node_data=model.unstructured['data'])
sc = yt.create_scene(ds, ('connect1', 'depth'))
# # override the default colormap
# ms = sc.get_source()
# ms.cmap = 'Eos A'
#
# # adjust the camera position and orientation
# cam = sc.camera
# cam.focus = ds.arr([0.0, 0.0, 0.0], 'code_length')
# cam_pos = ds.arr([-3.0, 3.0, -3.0], 'code_length')
# north_vector = ds.arr([0.0, -1.0, -1.0], 'dimensionless')
# cam.set_position(cam_pos, north_vector)
#
# # increase the default resolution
# cam.resolution = (800, 800)
# render and save
def visualize_tables(table_path,
geom_file,
slices=True,
projections=True,
plot_3d=True):
tables_dir = dirname(table_path)
table_fname = basename(table_path)
tables_basename = table_fname
for name in [
'survival_prob', 'avg_photon_x', 'avg_photon_y', 'avg_photon_z'
]:
tables_basename = tables_basename.replace('_' + name + '.fits', '')
if tables_basename[-1] == '_':
tables_basename = tables_basename[:-1]
det_string_depth_xyz = np.load(geom_file)
num_doms_in_detector = np.prod(det_string_depth_xyz.shape[:2])
data = {}
fname = '%s_survival_prob.fits' % tables_basename
fpath = join(tables_dir, fname)
with pyfits.open(fpath) as fits_file:
survival_prob = fits_file[0].data
ma = survival_prob.max()
print('Max survival probability :', ma)
mi = survival_prob.min()
print('Min survival probability :', mi)
mi_nonzero = survival_prob[survival_prob != 0].min()
print('Min non-zero survival probability:', mi_nonzero)
data['density'] = (survival_prob, 'kg/m**3')
xyz_shape = fits_file[1].data
lims = fits_file[2].data
doms_used = fits_file[3].data
# If 3D, dims represent: (string numbers, depth indices, (x, y, z))
if len(doms_used.shape) == 3:
doms_used = np.stack(
(doms_used[:, :, 0].flatten(), doms_used[:, :, 1].flatten(),
doms_used[:, :, 2].flatten())).T
nx, ny, nz = xyz_shape
xlims = lims[0, :]
ylims = lims[1, :]
zlims = lims[2, :]
print('x lims:', xlims)
print('y lims:', ylims)
print('z lims:', zlims)
print('(nx, ny, nz):', xyz_shape)
num_doms_used = doms_used.shape[0]
print('num doms used:', num_doms_used)
print('doms used:', doms_used.shape)
if slices:
mask = survival_prob > 0 #(ma / 10000000)
avg_photon_info = {}
for dim in ['x', 'y', 'z']:
fname = '%s_avg_photon_%s.fits' % (tables_basename, dim)
fpath = join(tables_dir, fname)
with pyfits.open(fpath) as fits_file:
d = np.zeros_like(survival_prob)
d[mask] = fits_file[0].data[mask]
#d = -fits_file[0].data
avg_photon_info[dim] = d
data['velocity_' + dim] = (d, 'm/s')
avg_photon_info = Cart3DCoord(**avg_photon_info)
del mask
bbox = lims
ds = yt.load_uniform_grid(data,
domain_dimensions=(nx, ny, nz),
bbox=bbox,
nprocs=4)
savefig_kw = dict(name=join(tables_dir, tables_basename),
suffix='png',
mpl_kwargs=dict(dpi=300))
plots = []
sphere_kwargs = dict(radius=(5 * DOM_RADIUS_M, 'cm'),
coord_system='data',
circle_args=dict(color=(0, 0.8, 0),
linewidth=1,
alpha=0.3))
if projections:
for normal in ['x', 'y', 'z']:
prj = yt.ProjectionPlot(ds, normal, 'density')
prj.set_log('density', False)
prj.set_cmap('density', 'inferno')
# Display all doms in the detector
for depth_xyz in det_string_depth_xyz:
for xyz in depth_xyz:
prj.annotate_sphere(xyz, **sphere_kwargs)
# Display only doms used (if subset of the detector)
if num_doms_used != num_doms_in_detector:
kw = deepcopy(sphere_kwargs)
kw['radius'] = (15 * DOM_RADIUS_M, 'cm')
kw['circle_args']['alpha'] = 1
for depth_xyz in doms_used:
prj.annotate_sphere(depth_xyz, **kw)
prj.save(**savefig_kw)
plots.append(prj)
if plot_3d:
# Choose a vector representing the viewing direction.
L = [-0.5, -0.5, -0.5]
# Define the center of the camera to be the domain center
c = ds.domain_center[0]
#c = (1400*100, 1300*100, 1300*100)
# Define the width of the image
W = 1.0 * ds.domain_width[0]
# Define the number of pixels to render
Npixels = 2048
sc = yt.create_scene(ds, 'density')
source = sc[0]
source.log_field = False
tf = yt.ColorTransferFunction((0, ma), grey_opacity=True)
tf.map_to_colormap(0, ma, scale=1.0, colormap='inferno')
source.set_transfer_function(tf)
sc.add_source(source)
cam = sc.add_camera()
cam.width = W
cam.center = c
cam.normal_vector = L
cam.north_vector = [0, 0, 1]
cam.position = (1400, 1300, 1300)
#sc.show(sigma_clip=4)
sc.save(savefig_kw['name'])
plots.append(sc)
if slices:
skw = deepcopy(sphere_kwargs)
skw['circle_args']['color'] = (0.8, 0, 0)
if num_doms_used != num_doms_in_detector:
center = np.mean(doms_used, axis=0)
else:
center = (0, 0, 0)
#center = det_string_depth_xyz[35, 47]
#cut_plane_strings = [1 - s for s in [6, 12, 27, 36, 45, 54, 62, 69, 75]]
#normal =
#north_vector = (0, 0, 1)
for normal in ['x', 'y', 'z']:
#if normal == 'x':
# plt.
slc = yt.SlicePlot(ds,
normal=normal,
fields='density',
center=center)
slc.set_cmap('density', 'octarine')
#slc.set_log('density', False)
for depth_xyz in det_string_depth_xyz:
for xyz in depth_xyz:
slc.annotate_sphere(xyz, **skw)
if num_doms_used != num_doms_in_detector:
kw = deepcopy(skw)
kw['radius'] = (15 * DOM_RADIUS_M, 'cm')
kw['circle_args']['alpha'] = 1
for depth_xyz in doms_used:
slc.annotate_sphere(depth_xyz, **kw)
nskip = 10
kw = dict(factor=nskip, scale=1e3)
if normal == 'x':
slc.annotate_quiver('velocity_y', 'velocity_z', **kw)
elif normal == 'y':
slc.annotate_quiver('velocity_z', 'velocity_x', **kw)
elif normal == 'z':
slc.annotate_quiver('velocity_x', 'velocity_y', **kw)
#slc.annotate_grids(cmap=None)
slc.save(**savefig_kw)
plots.append(slc)
return ds, plots
import yt
# We load the last time frame
ds = yt.load("/home/lindsayad/yt_data/MOOSE_sample_data/mps_out.e", step=-1,
displacements={'connect2': (10.0, [0.01, 0.0, 0.0])})
# create a default scene
sc = yt.create_scene(ds, ("connect2", "temp"))
# override the default colormap. This time we also override
# the default color bounds
ms = sc.get_source(0)
ms.cmap = 'hot'
ms.color_bounds = (500.0, 1700.0)
# adjust the camera position and orientation
cam = sc.camera
camera_position = ds.arr([-1.0, 1.0, -0.5], 'code_length')
north_vector = ds.arr([0.0, -1.0, -1.0], 'dimensionless')
cam.width = ds.arr([0.05, 0.05, 0.05], 'code_length')
cam.set_position(camera_position, north_vector)
# increase the default resolution
cam.resolution = (800, 800)
# render, draw the element boundaries, and save
sc.render()
sc.annotate_mesh_lines()
sc.save()
import yt from yt.testing import fake_hexahedral_ds ds = fake_hexahedral_ds() sc = yt.create_scene(ds) cam = sc.camera cam.focus = ds.arr([0.0, 0.0, 0.0], 'code_length') cam_pos = ds.arr([-3.0, 3.0, -3.0], 'code_length') north_vector = ds.arr([0.0, -1.0, -1.0], 'dimensionless') cam.set_position(cam_pos, north_vector) # increase the default resolution cam.resolution = (800, 800) sc.show()
def plot_a_vr(path,
header,
cycle,
use_ghost_zones=True,
annotate=True,
rotate=False,
zoom=False):
"""
volume rendering plot
path [string] : the path of data files
header [string]: the header of the data file
cycle [int] : the data cycle
ex. for file: /data/ccsn3d_hdf5_plt_cnt_0100
path = "/data"
header = "ccsn3d"
cycle = 100
"""
fn = get_fn(path, header, cycle)
ds = yt.load(fn)
# register new units for entropy
ds.unit_registry.add('kB',
1.3806488e-16,
dimensions=energy / temperature,
tex_repr='k_{B}')
ds.unit_registry.add('by', 1.674e-24, dimensions=mass, tex_repr='baryon')
# create a new derived field: Entropy
def _entr(field, data):
"""
fixed the entropy units in flash
"""
entr = data["entr"]
kb_by = yt.YTQuantity(1, 'erg') / yt.YTQuantity(
1, 'K') / yt.YTQuantity(1, 'g') * (1.3806488e-16 / 1.674e-24)
return entr * kb_by
ds.add_field("Entr",
function=_entr,
units="kB/by",
display_name="Entropy",
dimensions=energy / temperature / mass)
# create a new derived field: Entropy
def _entrdens(field, data):
"""
create a new derived field to show both entropy and density
if density > PNS_DENSITY:
entropy = PNS_ENTR
else:
entropy = entropy
"""
dens = data["dens"]
entr = data["entr"]
entrdens = entr * (
np.exp(-(dens.in_cgs() / PNS_DENSITY)**5)) + PNS_ENTR
kb_by = yt.YTQuantity(1, 'erg') / yt.YTQuantity(
1, 'K') / yt.YTQuantity(1, 'g') * (1.3806488e-16 / 1.674e-24)
return entrdens * kb_by
ds.add_field("Entropy",
function=_entrdens,
units="kB/by",
display_name="Entropy",
dimensions=energy / temperature / mass)
#debug: also plot a entropy slice
if yt.is_root():
pc = yt.SlicePlot(ds, 'z', 'Entr')
pc.zoom(40)
pc.set_log('Entr', False)
pc.save('fig_slice_z_' + header + '_' + string.zfill(cycle, 4) +
'.png')
# get the entropy range from time
time = ds.current_time.in_cgs().v - TSHIFT
if CUSTOM_EMAX:
emin, emax = get_emin_emax(time)
else:
entropy_max, pe = ds.find_max('Entropy')
emax = entropy_max.v - 1.0
emin = emax - 3.0
if yt.is_root():
print "emin/emax:", emin, emax
# check if emax > emin
if emax <= emin:
print "Error: emax <= emin"
quit()
# only render the region with r < 1.e8 cm
# this is necessary if we want to include ghost zones
sphere = ds.sphere([0, 0, 0], (1.e8, 'cm'))
sc = yt.create_scene(sphere, field='Entropy')
# set the camera resolution and width
sc.camera.north_vector = [0, 0, 1]
sc.camera.resolution = (VR_RESOLUTION, VR_RESOLUTION)
sc.camera.set_width(ds.quan(VR_WIDTH, 'cm'))
#sc.camera.set_width(ds.quan(2.5e7,'cm'))
# define the tranfer function for high entropy region
def linearFunc(values, minv, maxv):
try:
return ((values) - values.min()) / (values.max() - values.min())
#return (na.sqrt(values) - values.min())/(values.max()-values.min())
except:
return 1.0
source = sc.get_source(0)
source.set_use_ghost_zones(use_ghost_zones) # *** SLOW ***
source.set_log(False)
source.grey_opacity = True
# create a transfer function helper
tfh = TransferFunctionHelper(ds)
tfh.set_field('Entropy')
tfh.set_bounds()
tfh.set_log(False)
tfh.build_transfer_function()
# add a thin layer to show the shock front
esh, ew = get_shock_entropy(time, emin, emax)
tfh.tf.add_layers(1,
w=(ew),
mi=(esh),
ma=(esh + 0.5),
col_bounds=[4.0, (esh + 1.0)],
alpha=10.0 * esh * np.ones(1),
colormap='cool_r')
# plot the PNS at entr = PNS_ENTR
tfh.tf.add_layers(1,
w=0.5,
mi=0.49,
ma=0.55,
col_bounds=[0.05, 0.55],
alpha=100.0 * np.ones(1),
colormap='Purples')
# map the high entropy region: version 3
tfh.tf.map_to_colormap(emin,
emax,
scale=100.0,
scale_func=linearFunc,
colormap='autumn')
# version 1: use many layers
#tfh.tf.add_layers(12,w=0.05,mi=emin,ma=emax,col_bounds=[emin,emax],
# alpha=70*np.linspace(0.0,1.5,10),colormap='hot')
tfh.tf.grey_opacity = True
source.set_transfer_function(tfh.tf)
source.grey_opacity = True
# plot the transfer function
#source.tfh.plot('fig_transfer_function_entr.png', profile_field='cell_mass')
# plot volume rendering plot without annotation
if not annotate:
sc.save('fig_vr_' + header + '_' + string.zfill(cycle, 4) + '.png',
sigma_clip=4)
else:
# with annotation
sc.annotate_axes(alpha=0.8)
#sc.annotate_scale() #
# line annotation
#annotate_width = 1.0e7 # [cm]
#annotate_axis = 1.5e7 # [cm]
#annotate_at = (annotate_axis/2.0 - annotate_width/(2.0*np.sqrt(2.0)))
#colors = np.array([[0.45,0.5,0.5,0.7]]) # white
#vertices = np.array([[[annotate_at,0.,0.],[0.,annotate_at,0.]]])*annotate_width*cm
#lines = LineSource(vertices,colors)
#sc.add_source(lines)
#sc.annotate_domain(ds,color=[1,1,1,0.01])
#text_string= "Time = %.1f (ms)" % (float(ds.current_time.to('s')*1.e3))
text_string = "Time = %.1f (ms)" % (float(time * 1.e3))
sc.save_annotated("fig_vr_" + header + "_annotated_" +
string.zfill(cycle, 4) + '.png',
sigma_clip=4.0,
label_fmt="%.1f",
text_annotate=[[(0.05, 0.95), text_string,
dict(color="w",
fontsize="20",
horizontalalignment="left")]])
# rotate the camera
if DO_ROT:
rotate = True
if rotate:
fstart = 1 # modify here for restart
frames = ROT_FRAMES # total number of frames for rotation
cam = sc.camera
i = 0
for _ in cam.iter_rotate(2.0 * np.pi, frames):
i += 1
if i >= fstart:
sc.render()
if not annotate:
sc.save("fig_vr_" + header + "_" + string.zfill(cycle, 4) +
'_rot_' + string.zfill(i, 4) + '.png',
sigma_clip=2)
else:
sc.save_annotated("fig_vr_" + header + "_annotated_" +
string.zfill(cycle, 4) + '_rot_' +
string.zfill(i, 4) + '.png',
sigma_clip=4.0,
text_annotate=[[
(0.05, 0.95), text_string,
dict(color="w",
fontsize="20",
horizontalalignment="left")
]])
# TODO: zoom in or zoom out
if DO_ZOOM:
zoom = True
if zoom:
fstart = 0 # modify here for restart
frames = ZOOM_FRAMES
cam = sc.camera
i = 0
for _ in cam.iter_zoom(ZOOM_FACT, ZOOM_FRAMES):
i += 1
if i >= fstart:
sc.render()
if not annotate:
sc.save("fig_vr_" + header + "_" + string.zfill(cycle, 4) +
'_zoom_' + string.zfill(i, 4) + '.png',
sigma_clip=2)
else:
sc.save_annotated("fig_vr_" + header + "_annotated_" +
string.zfill(cycle, 4) + '_zoom_' +
string.zfill(i, 4) + '.png',
sigma_clip=4.0,
text_annotate=[[
(0.05, 0.95), text_string,
dict(color="w",
fontsize="20",
horizontalalignment="left")
]])
quit()
return
