def doit(plotfile):
ds = yt.load(plotfile)
ds.periodicity = (True, True, True)
field = ('boxlib', 'radial_velocity')
ds._get_field_info(field).take_log = False
sc = Scene()
# add a volume: select a sphere
center = (0, 0, 0)
R = (5.e8, 'cm')
dd = ds.sphere(center, R)
vol = VolumeSource(dd, field=field)
vol.use_ghost_zones = True
sc.add_source(vol)
# transfer function
vals = [-5.e6, -2.5e6, -1.25e6, 1.25e6, 2.5e6, 5.e6]
sigma = 3.e5
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cm = "coolwarm"
for v in vals:
tf.sample_colormap(v, sigma**2, colormap=cm) #, alpha=0.2)
sc.get_source(0).transfer_function = tf
cam = sc.add_camera(ds, lens_type="perspective")
cam.resolution = (1280, 720)
cam.position = 1.5 * ds.arr(np.array([5.e8, 5.e8, 5.e8]), 'cm')
# look toward the center -- we are dealing with an octant
center = ds.domain_left_edge
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
cam.set_width(ds.domain_width)
#sc.annotate_axes()
#sc.annotate_domain(ds)
sc.render()
sc.save("subchandra_test.png", sigma_clip=6.0)
sc.save_annotated(
"subchandra_test_annotated.png",
text_annotate=[[(0.05, 0.05), "t = {}".format(ds.current_time.d),
dict(horizontalalignment="left")],
[(0.5, 0.95),
"Maestro simulation of He convection on a white dwarf",
dict(color="y",
fontsize="24",
horizontalalignment="center")]])
def assignValues(redVal, greenVal, blueVal):
ctf = yt.ColorTransferFunction((-10.0, -5.0))
ctf.add_layers(8)
ctf.red.y = redVal
ctf.green.y = np.asarray([random.random() for _ in xrange(256)])
ctf.blue.y = np.asarray([random.random() for _ in xrange(256)])
'''
ctf.red.y = smooth(redVal, 20)
ctf.green.y = smooth(greenVal, 20)
ctf.blue.y = smooth(blueVal, 20)
'''
ctf.plot("static/img/result.png")
def doit(plotfile):
ds = yt.load(plotfile)
ds.periodicity = (True, True, True)
cm = "coolwarm"
field = ('boxlib', 'radial_velocity')
ds._get_field_info(field).take_log = False
sc = Scene()
# add a volume: select a sphere
#center = (0, 0, 0)
#R = (5.e8, 'cm')
#dd = ds.sphere(center, R)
vol = VolumeSource(ds, field=field)
sc.add_source(vol)
# transfer function
vals = [-5.e5, -2.5e5, -1.25e5, 1.25e5, 2.5e5, 5.e5]
sigma = 3.e4
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cm = "coolwarm"
for v in vals:
tf.sample_colormap(v, sigma**2, colormap=cm) #, alpha=0.2)
sc.get_source(0).transfer_function = tf
cam = sc.add_camera(ds, lens_type="perspective")
cam.resolution = (1080, 1080)
cam.position = 1.0 * ds.domain_right_edge
# look toward the center -- we are dealing with an octant
center = ds.domain_left_edge
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
cam.set_width(ds.domain_width)
sc.camera = cam
#sc.annotate_axes(alpha=0.05)
#sc.annotate_domain(ds, color=np.array([0.05, 0.05, 0.05, 0.05]))
#sc.annotate_grids(ds, alpha=0.05)
sc.render()
sc.save("{}_radvel".format(plotfile), sigma_clip=4.0)
def ytrender(filename,
vmin=None,
vmax=None,
useLog=False,
nLayer=10,
cmap='gist_rainbow',
colorwidth=0.005,
phi=0.5,
theta=1.570796,
outfile=None,
usedomain=(0.25, 0.6, 0.6),
Xrays=768,
Yrays=512):
cube = fits.getdata(filename)
if not vmin:
vmin = np.percentile(cube, 50)
if not vmax:
vmax = np.percentile(cube, 99.5)
if len(cube.shape) > 3:
cube = cube.squeeze()
if not outfile:
outfile = filename.replace('fits', 'png')
cube[np.isnan(cube)] = np.nanmin(cube)
data = dict(density=cube)
pf = yt.load_uniform_grid(data, cube.shape, 9e16)
tf = yt.ColorTransferFunction((vmin, vmax))
c = (pf.domain_right_edge + pf.domain_left_edge) / 2.0
tf.add_layers(nLayer,
w=colorwidth,
colormap=cmap,
alpha=np.logspace(-1.0, 0, nLayer))
c = [0.51, 0.51, 0.51] # centre of the image
L = [
np.cos(theta),
np.sin(theta) * np.cos(phi),
np.sin(theta) * np.sin(phi)
] # normal vector
W = usedomain
cam = pf.camera(c,
L,
W, (Xrays, Yrays),
tf,
no_ghost=False,
north_vector=[1.0, 0, 0],
fields=['density'],
log_fields=[useLog])
image = cam.snapshot(outfile, 8.0)
def __init__(self,data,**kwargs):
"""
Parameters
----------
data : ndarray
the data to generate transfer function
**kwargs : type
bounds (list):
min/max for transfer function bounds, defaults to
[data.min(), data.max()]
"""
self.data=data[~np.isnan(data)]
self.bounds=kwargs.get('bounds',[self.data.min(),self.data.max()])
self.tf=yt.ColorTransferFunction((self.bounds[0],self.bounds[1]))
self.dvbins= np.linspace(self.bounds[0],self.bounds[1],self.tf.nbins)
self.dvbins_c=(self.dvbins[0:-1]+self.dvbins[1:])/2 # bin centers
self.histData,_=self.calcHist()
return
def setup(self):
if yt.__version__.startswith('3'):
self.ds = yt.load(self.dsname)
self.ad = self.ds.all_data()
self.field_name = "density"
else:
self.ds = load(self.dsname)
self.ad = self.ds.h.all_data()
self.field_name = "Density"
# Warmup hdd
self.ad[self.field_name]
if yt.__version__.startswith('3'):
mi, ma = self.ad.quantities['Extrema'](self.field_name)
self.tf = yt.ColorTransferFunction(
(np.log10(mi) + 1, np.log10(ma)))
else:
mi, ma = self.ad.quantities['Extrema'](self.field_name)[0]
self.tf = ColorTransferFunction((np.log10(mi) + 1, np.log10(ma)))
self.tf.add_layers(5, w=0.02, colormap="spectral")
self.c = [0.5, 0.5, 0.5]
self.L = [0.5, 0.2, 0.7]
self.W = 1.0
self.Npixels = 512
def make_13co_faces(
ytcube=yt13co,
outdir='yt_renders_13CO',
size=1024,
scale=1200.,
nframes=180,
movie=True,
camera_angle=[0, 0, 1],
north_vector=[1, 0, 0],
rot_vector1=[1, 0, 0],
rot_vector2=[0.5, 0.5, 0.0],
rot_vector3=[0.0, 0.5, 0.5],
):
tf = yt.ColorTransferFunction([0, 30], grey_opacity=True)
#tf.map_to_colormap(0.1,5,colormap='Reds')
tf.add_gaussian(2, 1, [1.0, 0.8, 0.0, 1.0])
tf.add_gaussian(3, 2, [1.0, 0.5, 0.0, 1.0])
tf.add_gaussian(5, 3, [1.0, 0.0, 0.0, 1.0])
tf.add_gaussian(10, 5, [1.0, 0.0, 0.0, 0.5])
tf.map_to_colormap(10, 30, colormap=red, scale=1)
center = ytcube.dataset.domain_dimensions / 2.
ims = []
for camera_angle in ([1, 0, 0], [0, 1, 0], [0, 0, 1]):
cam = ytcube.dataset.h.camera(center,
camera_angle,
scale,
size,
tf,
north_vector=north_vector,
fields='flux')
im = cam.snapshot()
ims.append(im)
save_images(ims, 'render_13co_faceon_1024')
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.)
p.annotate_contour(("openPMD", "absE_x"), ncont=5, clim=(1e12, 1e11))
p.show()
# There are a lot of another annotations and additional parameters for the visualisation on the yt website
#Plotting time series
print "3: Plotting time series"
# Only load with yt.load but use ? or * instead of characters creats a time series
timeseries = yt.load("data0000??00.h5")
# Get every dataset from timeseries and plot it
for ds in timeseries:
p = yt.SlicePlot(ds, "y", "E_x")
p.show()
# Volume Rendering of 3D Data
# As OpenPMD 3D Datasets are not tested yet we use other sample Data
#Sample Data from http://yt-project.org/data/
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
#This creates and shows a 3D Model of the sample Data
print "4: Volume Rendering of Example Galaxy"
tf = yt.ColorTransferFunction((-28, -25))
tf.add_layers(4, w=0.03)
cam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20.0, 'kpc'),
512,
tf,
no_ghost=False)
cam.show(clip_ratio=4.0)
tfh.set_field('density')
tfh.set_log(True)
tfh.set_bounds()
tfh.build_transfer_function()
tfh.tf.add_layers(5, colormap='inferno')
# Grab the first render source and set it to use the new transfer function
render_source = sc.get_source()
render_source.transfer_function = tfh.tf
#source.tfh.tf = tfh
#source.tfh.bounds = bounds
elif(1):
# select a continous distribution for values in the chosen range
# transparency is linear
# http://yt-project.org/doc/visualizing/volume_rendering.html
tf = yt.ColorTransferFunction(np.log10(bounds))
def linramp(vals, minval, maxval):
return (vals - vals.min())/(vals.max() - vals.min())
tf.map_to_colormap(np.log10(1.e-1), np.log10(1.e+3), colormap='inferno',scale_func=linramp)
source.tfh.tf = tf
source.tfh.bounds = bounds
else:
# transfer fucntion that select a discrete set of values in the chosen range
# transparency is linear but it is awfu
l
# bounds = (1.E-01, 80.0) # H2 density
#bounds = (1.E-3, 0.1) # H2 fraction
import yt
import numpy as np
# Follow the simple_volume_rendering cookbook for the first part of this.
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # load data
ad = ds.all_data()
mi, ma = ad.quantities.extrema("density")
# Set up transfer function
tf = yt.ColorTransferFunction((np.log10(mi), np.log10(ma)))
tf.add_layers(6, w=0.05)
# Set up camera paramters
c = [0.5, 0.5, 0.5] # Center
L = [1, 1, 1] # Normal Vector
W = 1.0 # Width
Nvec = 512 # Pixels on a side
# Specify a north vector, which helps with rotations.
north_vector = [0., 0., 1.]
# Find the maximum density location, store it in max_c
v, max_c = ds.find_max('density')
# Initialize the Camera
cam = ds.camera(c, L, W, (Nvec, Nvec), tf, north_vector=north_vector)
frame = 0
# Do a rotation over 5 frames
for i, snapshot in enumerate(cam.rotation(np.pi, 5, clip_ratio=8.0)):
snapshot.write_png('camera_movement_%04i.png' % frame)
def doit(plotfile):
ds = yt.load(plotfile)
ds.periodicity = (True, True, True)
field = ('boxlib', 'density')
ds._get_field_info(field).take_log = True
sc = Scene()
# add a volume: select a sphere
vol = VolumeSource(ds, field=field)
vol.use_ghost_zones = True
sc.add_source(vol)
# transfer function
vals = [-1, 0, 1, 2, 3, 4, 5, 6, 7]
#vals = [0.1, 1.0, 10, 100., 1.e4, 1.e5, 1.e6, 1.e7]
sigma = 0.1
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cm = "coolwarm"
cm = "spectral"
for v in vals:
if v < 3:
alpha = 0.1
else:
alpha = 0.5
tf.sample_colormap(v, sigma**2, colormap=cm, alpha=alpha)
sc.get_source(0).transfer_function = tf
# for spherical, youtube recommends an "equirectangular" aspect ratio
# (2:1), suggested resolution of 8192 x 4096
# see: https://support.google.com/youtube/answer/6178631?hl=en
#
# also see: http://yt-project.org/docs/dev/cookbook/complex_plots.html#various-lens-types-for-volume-rendering
# the 2:1 is 2*pi in phi and pi in theta
cam = sc.add_camera(ds, lens_type="spherical")
#cam.resolution = (8192, 4096)
cam.resolution = (4096, 2048)
# look toward the +x initially
cam.focus = ds.arr(np.array([ds.domain_left_edge[0], 0.0, 0.0]), 'cm')
# center of the domain -- eventually we might want to do the
# center of mass
cam.position = ds.arr(np.array([0.0, 0.0, 0.0]), 'cm')
# define up
cam.north_vector = np.array([0., 0., 1.])
normal = (cam.focus - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
# there is no such thing as a camera width -- the entire volume is rendered
#cam.set_width(ds.domain_width)
#sc.annotate_axes()
#sc.annotate_domain(ds)
pid = plotfile.split("plt")[1]
sc.render()
sc.save("wdmerger_{}_spherical.png".format(pid), sigma_clip=6.0)
from yt.visualization.volume_rendering.api import Scene, create_volume_source
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# create a scene and add volume sources to it
sc = Scene()
# Add density
field = "density"
vol = create_volume_source(ds, field=field)
vol.use_ghost_zones = True
tf = yt.ColorTransferFunction([-28, -25])
tf.clear()
tf.add_layers(4, 0.02, alpha=np.logspace(-3, -1, 4), colormap="winter")
vol.set_transfer_function(tf)
sc.add_source(vol)
# Add temperature
field = "temperature"
vol2 = create_volume_source(ds, field=field)
vol2.use_ghost_zones = True
tf = yt.ColorTransferFunction([4.5, 7.5])
tf.clear()
}],
'tf_bounds': [-10., 10.],
'sigma_clip':
.4
}
out_dir = './output/' + fname.split('.')[0] # output directory for figures
# load the model
model = sm.netcdf(fname, settings['interp'])
shortname = fname.split('.')[0]
datafld = settings['interp']['field']
data = {datafld: model.interp['data'][datafld]} # dict container for yt scene
# add the transfer functions
bnds = settings.get('tf_bounds', [-5, 5])
tf = yt.ColorTransferFunction((bnds[0], bnds[1]))
for TFtoadd in settings['tfs']:
v = TFtoadd['values']
if TFtoadd['tftype'] == 'step':
tf.add_step(v[0], v[1], v[2])
elif TFtoadd['tftype'] == 'gaussian':
tf.add_gaussian(v[0], v[1], v[2])
# plot the TF with a histogram
x = np.linspace(bnds[0], bnds[1], tf.nbins)
y = tf.funcs[3].y
w = np.append(x[1:] - x[:-1], x[-1] - x[-2])
colors = np.array([tf.funcs[0].y, tf.funcs[1].y, tf.funcs[2].y,
tf.funcs[3].y]).T
fig = plt.figure(figsize=[6, 3])
ax = fig.add_axes([0.2, 0.2, 0.75, 0.75])
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)
import yt
ds = yt.load("RD0023/RD0023")
sc = yt.create_scene(ds)
im, sc = yt.volume_render(ds)
cam = sc.camera
sc.camera.resolution = (800, 800)
for i in cam.iter_zoom(100.0, 10):
source = sc.sources['source_00']
sc.add_source(source)
tf = yt.ColorTransferFunction((-31, -26))
tf.add_layers(5, w=0.01)
source.set_transfer_function(tf)
sc.render()
sc.save("zoom_%04i.png" % i)
#sc.camera.set_width(ds.quan(1, 'Mpc'))
#sc.show()
#sc.show(sigma_clip=4)
#sc.save('render_001',sigma_clip=4)
def render_13co(
ytcube=yt13co,
outdir='yt_renders_13CO',
size=512,
scale=1200.,
nframes=180,
movie=True,
camera_angle=[0, 0, 1],
north_vector=[1, 0, 0],
rot_vector1=[1, 0, 0],
rot_vector2=[0.5, 0.5, 0.0],
rot_vector3=[0.0, 0.5, 0.5],
):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf = yt.ColorTransferFunction([0, 30], grey_opacity=True)
#tf.map_to_colormap(0.1,5,colormap='Reds')
tf.add_gaussian(2, 1, [1.0, 0.8, 0.0, 1.0])
tf.add_gaussian(3, 2, [1.0, 0.5, 0.0, 1.0])
tf.add_gaussian(5, 3, [1.0, 0.0, 0.0, 1.0])
tf.add_gaussian(10, 5, [1.0, 0.0, 0.0, 0.5])
tf.map_to_colormap(10, 30, colormap=red, scale=1)
center = ytcube.dataset.domain_dimensions / 2.
cam = ytcube.dataset.h.camera(center,
camera_angle,
scale,
size,
tf,
north_vector=north_vector,
fields='flux')
im = cam.snapshot()
#images = [im]
if movie:
pb = ProgressBar(nframes * 2 + 30)
for ii, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir, "%04i.png" % (ii))),
rescale=False)
pb.update(ii)
for jj, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir, "%04i.png" % (ii + jj))),
rescale=False)
pb.update(ii + jj)
for kk, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir, "%04i.png" % (ii + jj + kk))),
rescale=False)
pb.update(ii + jj + kk)
TheBrick = ytcube.world2yt([0.253, 0.016, 35])
for LL, snapshot in enumerate(cam.move_to(TheBrick, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(
os.path.join(outdir, '%04i.png' % (ii + jj + kk + LL))),
rescale=False)
pb.update(ii + jj + kk + LL)
for mm, snapshot in enumerate(cam.zoomin(5, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(
os.path.join(outdir, '%04i.png' % (ii + jj + kk + LL + mm))),
rescale=False)
pb.update(ii + jj + kk + LL + mm)
for nn, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn)
for oo, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn + oo))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn + oo)
for pp, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(
outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn + oo + pp))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn + oo + pp)
#save_images(images, paths.mpath(outdir))
pipe = make_movie(paths.mpath(outdir))
return images
else:
return im
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
def doit(plotfile):
ds = CastroDataset(plotfile)
ds._periodicity = (True, True, True)
field = ('boxlib', 'enuc')
ds._get_field_info(field).take_log = True
sc = Scene()
# add a volume: select a sphere
#center = (0, 0, 0)
#R = (5.e8, 'cm')
#dd = ds.sphere(center, R)
vol = create_volume_source(ds.all_data(), field=field)
sc.add_source(vol)
# transfer function
vals = [16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5]
sigma = 0.1
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cmap = "viridis"
for v in vals:
if v < 19.0:
alpha = 0.25
else:
alpha = 0.75
tf.sample_colormap(v, sigma**2, alpha=alpha, colormap=cmap)
sc.get_source(0).transfer_function = tf
cam = sc.add_camera(ds, lens_type="perspective")
cam.resolution = (1920, 1280)
# view 1
cam.position = [
0.75 * ds.domain_right_edge[0],
0.5 * (ds.domain_left_edge[1] + ds.domain_right_edge[1]),
ds.domain_right_edge[2]
] # + 0.25 * (ds.domain_right_edge[2] - ds.domain_left_edge[2])]
# look toward the center
center = 0.5 * (ds.domain_left_edge + ds.domain_right_edge)
# set the center in the vertical direction to be the height of the underlying base layer
center[-1] = 2000 * cm
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
cam.set_width(3 * ds.domain_width)
cam.zoom(3.0)
sc.camera = cam
sc.save_annotated(
"{}_Hnuc_annotated_side.png".format(plotfile),
text_annotate=[[(0.05, 0.05), "t = {}".format(ds.current_time.d),
dict(horizontalalignment="left")],
[(0.5, 0.95),
"Castro simulation of XRB flame spreading",
dict(color="y",
fontsize="24",
horizontalalignment="center")]])
# view 2
dx = ds.domain_right_edge[0] - ds.domain_left_edge[0]
cam.position = [
0.5 * (ds.domain_left_edge[0] + ds.domain_right_edge[0]) + 0.0001 * dx,
0.5 * (ds.domain_left_edge[1] + ds.domain_right_edge[1]),
ds.domain_right_edge[2]
] # + 0.25 * (ds.domain_right_edge[2] - ds.domain_left_edge[2])]
# look toward the center
center = 0.5 * (ds.domain_left_edge + ds.domain_right_edge)
# set the center in the vertical direction to be the height of the underlying base layer
center[-1] = 2000 * cm
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
cam.set_width(3 * ds.domain_width)
cam.zoom(0.6)
sc.camera = cam
sc.save_annotated(
"{}_Hnuc_annotated_top.png".format(plotfile),
text_annotate=[[(0.05, 0.05), "t = {}".format(ds.current_time.d),
dict(horizontalalignment="left")],
[(0.5, 0.95),
"Castro simulation of XRB flame spreading",
dict(color="y",
fontsize="24",
horizontalalignment="center")]])
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] # Choose bounds and levels wisely for maximum variation bounds = (-1, 1) tf = yt.ColorTransferFunction(bounds) tf.sample_colormap(0., w=.005, alpha =0.2,colormap='RAINBOW') tf.sample_colormap(0.8, w=.005, alpha =0.2,colormap='RAINBOW') tf.sample_colormap(-0.8, w=.005, alpha =0.2,colormap='RAINBOW') source.tfh.tf = tf source.tfh.bounds = bounds sc.save(root+'PhaseMovie/Phase_rotrendering%03d.png'%i,sigma_clip=4.0)
data['dvs'] = np.flipud(data['dvs'])
# data['dvs']=np.transpose(data['dvs'],axes=[0,1,2])
bbox = np.array([x, y, z])
# yt spherical expects R, theta, phi (depth, ~lat,~lon)
sc_mult = 1.0 # scale multiplier
# bbox = model.cart['bbox']
ds = yt.load_uniform_grid(data,
data['dvs'].shape,
sc_mult,
bbox=bbox,
nprocs=1,
periodicity=(False, False, False),
unit_system="mks")
sc = yt.create_scene(ds, 'dvs')
sc.camera.set_width(ds.quan(15 * 1000., 'm'))
# sc.camera.rotate(10*np.pi/180.)
# sc.camera.roll(-5*np.pi/180)
# sc.camera.yaw(-5*np.pi/180)
# cam = sc.add_camera()
# cam.resolution=1000
tf = yt.ColorTransferFunction((0, 3))
tf.add_layers(10, w=0.01)
# Set the bounds of the transfer function
source = sc.sources['source_00']
source.set_transfer_function(tf)
source.tfh.set_log(False)
sc.save(os.path.join(out_dir, 'YS_volume.png'), sigma_clip=.25)
def doit(plotfile):
ds = yt.load(plotfile)
ds.periodicity = (True, True, True)
field = ('boxlib', 'density')
ds._get_field_info(field).take_log = True
sc = Scene()
# add a volume: select a sphere
vol = VolumeSource(ds, field=field)
vol.use_ghost_zones = True
sc.add_source(vol)
# transfer function
vals = [-1, 0, 1, 2, 3, 4, 5, 6, 7]
#vals = [0.1, 1.0, 10, 100., 1.e4, 1.e5, 1.e6, 1.e7]
sigma = 0.1
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cm = "coolwarm"
cm = "spectral"
for v in vals:
if v < 3:
alpha = 0.1
else:
alpha = 0.5
tf.sample_colormap(v, sigma**2, colormap=cm, alpha=alpha)
sc.get_source(0).transfer_function = tf
cam = sc.add_camera(ds, lens_type="perspective")
cam.resolution = (1920, 1080)
cam.position = 1.5 * ds.arr(np.array([0.0, 5.e9, 5.e9]), 'cm')
# look toward the center -- we are dealing with an octant
center = 0.5 * (ds.domain_left_edge + ds.domain_right_edge)
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0., 0., 1.])
cam.set_width(ds.domain_width)
#sc.annotate_axes()
#sc.annotate_domain(ds)
pid = plotfile.split("plt")[1]
sc.render()
sc.save("wdmerger_{}_new.png".format(pid), sigma_clip=6.0)
sc.save_annotated(
"wdmerger_annotated_{}_new.png".format(pid),
text_annotate=
[[(0.05, 0.05), "t = {:.3f} s".format(float(ds.current_time.d)),
dict(horizontalalignment="left")],
[(0.5, 0.95),
"Castro simulation of merging white dwarfs (0.6 $M_\odot$ + 0.9 $M_\odot$)",
dict(color="y", fontsize="22", horizontalalignment="center")],
[(0.95, 0.05), "M. Katz et al.",
dict(color="w", fontsize="16", horizontalalignment="right")]])
def render_chem(yth2co321=yth2co321,
yth2co303=yth2co303,
ytsio=ytsio,
outdir='yt_renders_chem3',
size=512,
scale=1100.,
nframes=60,
north_vector=[1, 0, 0],
rot_vector=[1, 0, 0],
movie=True,
camera_angle=[-0.6, 0.4, 0.6]):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf1 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf1.add_gaussian(0.1,0.05, [1,0,0,1])
#tf1.map_to_colormap(0, 0.5, scale=1, colormap='Reds')
tf1.add_gaussian(0.25, 0.01, [1, 0, 0, 1])
tf1.add_step(0.25, 0.5, [1, 0, 0, 1])
#tf1.add_step(1,2,[1,1,1,1])
tf1.map_to_colormap(0.5, 2, scale=1, colormap=red)
tf2 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf2.add_gaussian(0.1,0.05, [0,1,0,1])
#tf2.map_to_colormap(0, 0.5, scale=1, colormap='Greens')
tf2.add_gaussian(0.25, 0.01, [0, 1, 0, 1])
tf2.add_step(0.25, 0.5, [0, 1, 0, 1])
tf2.map_to_colormap(0.5, 2, scale=1, colormap=green)
tf3 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf3.add_gaussian(0.1,0.05, [0,0,1,1])
#tf3.map_to_colormap(0, 0.5, scale=1, colormap='Blues')
tf3.add_gaussian(0.25, 0.01, [0, 0, 1, 1])
tf3.add_step(0.25, 0.5, [0, 0, 1, 1])
tf3.map_to_colormap(0.5, 2, scale=1, colormap=blue)
center = yth2co303.dataset.domain_dimensions / 2.
camh2co303 = yth2co303.dataset.h.camera(center,
camera_angle,
scale,
size,
tf3,
north_vector=north_vector,
fields='flux')
camh2co321 = yth2co321.dataset.h.camera(center,
camera_angle,
scale,
size,
tf2,
north_vector=north_vector,
fields='flux')
camsio = ytsio.dataset.h.camera(center,
camera_angle,
scale,
size,
tf1,
north_vector=north_vector,
fields='flux')
imh2co303 = camh2co303.snapshot()
imh2co321 = camh2co321.snapshot()
imsio = camsio.snapshot()
pl.figure(1)
pl.clf()
pl.imshow(imh2co303 + imh2co321 + imsio)
pl.figure(2)
pl.clf()
pl.imshow(imh2co303[:, :, :3] + imh2co321[:, :, :3] + imsio[:, :, :3])
if movie:
images_h2co303 = [imh2co303]
images_h2co321 = [imh2co321]
images_sio = [imsio]
r1 = camh2co303.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r2 = camh2co321.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r3 = camsio.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
pb = ProgressBar(nframes * 3)
for (ii, (imh2co303, imh2co321, imsio)) in enumerate(izip(r1, r2, r3)):
images_h2co303.append(imh2co303)
images_h2co321.append(imh2co321)
images_sio.append(imsio)
imh2co303 = imh2co303.swapaxes(0, 1)
imh2co303.write_png(paths.mpath(
os.path.join(outdir, "h2co303_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3)
imsio = imsio.swapaxes(0, 1)
imsio.write_png(paths.mpath(
os.path.join(outdir, "sio_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3 + 1)
imh2co321 = imh2co321.swapaxes(0, 1)
imh2co321.write_png(paths.mpath(
os.path.join(outdir, "h2co321_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3 + 2)
pb.next()
save_images([
i1 + i2 + i3
for i1, i2, i3 in izip(images_h2co303, images_h2co321, images_sio)
], paths.mpath(outdir))
make_movie(paths.mpath(outdir))
return images_h2co303, images_h2co321, images_sio
else:
return imh2co303, imh2co321, imsio
# We begin by loading up yt, and importing the AMRKDTree
import numpy as np
import yt
from yt.utilities.amr_kdtree.api import AMRKDTree
# Load up a dataset and define the kdtree
ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
kd = AMRKDTree(ds)
# Print out specifics of KD Tree
print("Total volume of all bricks = %i" % kd.count_volume())
print("Total number of cells = %i" % kd.count_cells())
# Define a camera and take an volume rendering.
tf = yt.ColorTransferFunction((-30, -22))
cam = ds.camera([0.5, 0.5, 0.5], [0.2, 0.3, 0.4], 0.10, 256,
tf, volume=kd)
tf.add_layers(4, 0.01, col_bounds=[-27.5, -25.5], colormap='RdBu_r')
cam.snapshot("v1.png", clip_ratio=6.0)
# This rendering is okay, but lets say I'd like to improve it, and I don't want
# to spend the time rendering the high resolution data. What we can do is
# generate a low resolution version of the AMRKDTree and pass that in to the
# camera. We do this by specifying a maximum refinement level of 6.
kd_low_res = AMRKDTree(ds, max_level=6)
print(kd_low_res.count_volume())
print(kd_low_res.count_cells())
# Now we pass this in as the volume to our camera, and render the snapshot
length_unit="Mpc",
bbox=bbox,
nprocs=64)
#slc = yt.SlicePlot(ds, "z", ["density"])
#slc.set_cmap("density", "Blues")
#slc.annotate_grids(cmap=None)
#slc.show()
#Find the min and max of the field
mi, ma = ds.all_data().quantities.extrema('density')
##Reduce the dynamic range
#mi = mi.value + 1.5e7
#ma = ma.value - 0.81e7
tf = yt.ColorTransferFunction((mi, ma), )
# 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]
# Define the width of the image
W = 1.5 * ds.domain_width[0]
# Define the number of pixels to render
Npixels = 512
cam = ds.camera(c,
L,
W,
Npixels,
tf,
annotateddir = os.path.join(figuredir, 'annotated')
tfdir = os.path.join(figuredir, 'transfer_function')
if yt.is_root():
for subdir in [figuredir, tfdir, annotateddir]:
if not os.path.exists(subdir):
os.mkdir(subdir)
#fname = '/home/ychen/d9/FLASH4/stampede/0529_L45_M10_b1_h1/MHD_Jet_hdf5_plt_cnt_0620'
#ds = yt.load(fname)
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
# First we use the simple_volume_rendering.py recipe from above to generate
# a standard volume rendering. The only difference is that we use
# grey_opacity=True with our TransferFunction, as the colored background
# functionality requires images with an opacity between 0 and 1.
# We have removed all the comments from the volume rendering recipe for
# brevity here, but consult the recipe for more details.
import yt
import numpy as np
ds = yt.load("Enzo_64/DD0043/data0043")
ad = ds.all_data()
mi, ma = ad.quantities.extrema("density")
tf = yt.ColorTransferFunction((np.log10(mi) + 1, np.log10(ma)),
grey_opacity=True)
tf.add_layers(5, w=0.02, colormap="spectral")
c = [0.5, 0.5, 0.5]
L = [0.5, 0.2, 0.7]
W = 1.0
Npixels = 512
cam = ds.camera(c, L, W, Npixels, tf)
im = cam.snapshot("original.png" % ds, clip_ratio=8.0)
# Our image array can now be transformed to include different background
# colors. By default, the background color is black. The following
# modifications can be used on any image array.
# write_png accepts a background keyword argument that defaults to 'black'.
# Other choices include:
# black (0.,0.,0.,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 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
def vol_render_density(outfile, ds):
"""Volume render the density given a yt dataset."""
import numpy as np
import yt
import matplotlib
matplotlib.use('agg')
from yt.visualization.volume_rendering.api import Scene, VolumeSource
import matplotlib.pyplot as plt
ds.periodicity = (True, True, True)
field = ('boxlib', 'density')
ds._get_field_info(field).take_log = True
sc = Scene()
# Add a volume: select a sphere
vol = VolumeSource(ds, field=field)
vol.use_ghost_zones = True
sc.add_source(vol)
# Transfer function
vals = [-1, 0, 1, 2, 3, 4, 5, 6, 7]
sigma = 0.1
tf = yt.ColorTransferFunction((min(vals), max(vals)))
tf.clear()
cm = "spectral"
for v in vals:
if v < 3:
alpha = 0.1
else:
alpha = 0.5
tf.sample_colormap(v, sigma**2, colormap=cm, alpha=alpha)
sc.get_source(0).transfer_function = tf
cam = sc.add_camera(ds, lens_type="perspective")
cam.resolution = (1920, 1080)
center = 0.5 * (ds.domain_left_edge + ds.domain_right_edge)
width = ds.domain_width
# Set the camera so that we're looking down on the xy plane from a 45
# degree angle. We reverse the y-coordinate since yt seems to use the
# opposite orientation convention to us (where the primary should be
# on the left along the x-axis). We'll scale the camera position based
# on a zoom factor proportional to the width of the domain.
zoom_factor = 0.75
cam_position = np.array([
center[0], center[1] - zoom_factor * width[1],
center[2] + zoom_factor * width[2]
])
cam.position = zoom_factor * ds.arr(cam_position, 'cm')
# Set the normal vector so that we look toward the center.
normal = (center - cam.position)
normal /= np.sqrt(normal.dot(normal))
cam.switch_orientation(normal_vector=normal, north_vector=[0.0, 0.0, 1.0])
cam.set_width(width)
# Render the image.
sc.render()
# Save the image without annotation.
sc.save(outfile, sigma_clip=6.0)
# Save the image with a colorbar.
sc.save_annotated(outfile.replace(".png", "_colorbar.png"), sigma_clip=6.0)
# Save the image with a colorbar and the current time.
sc.save_annotated(outfile.replace(".png", "_colorbar_time.png"),
sigma_clip=6.0,
text_annotate=[[
(0.05, 0.925),
"t = {:.2f} s".format(float(ds.current_time.d)),
dict(horizontalalignment="left", fontsize="20")
]])
