# Our image plane will be normal to some vector. For things like collapsing
# objects, you could set it the way you would a cutting plane -- but for this
# dataset, we'll just choose an off-axis value at random. This gets normalized
# automatically.
L = [0.5, 0.4, 0.7]
# Our "width" is the width of the image plane as well as the depth.
# The first element is the left to right width, the second is the
# top-bottom width, and the last element is the back-to-front width
# (all in code units)
W = [0.04,0.04,0.4]
# The number of pixels along one side of the image.
# The final image will have Npixel^2 pixels.
Npixels = 512
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
yt.write_projection(image, "offaxis_projection_colorbar.png",
colorbar_label="Column Density (cm$^{-2}$)")
stepAlpha=np.pi/100
stepBeta=np.pi/100
frames=math.ceil(2*np.pi/stepAlpha)
for i in np.arange(0, frames):
L = yksikkovektori(alpha, beta)
N = yksikkovektori(alpha+stepAlpha, beta+stepBeta)
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, tiheysmaksimi, L, W, Npixels, "density", north_vector=N, no_ghost=False, steady_north=True)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
#yt.write_projection(image, "slice/kaanto/offaxis_projection_colorbar%04i.png" %frame, colorbar_label="Column Density (cm$^{-2}$)")
yt.write_projection(image, "kuvat/%04i.png" %frame, cmap_name='hot')
frame+=1
alpha+=stepAlpha
beta+=stepBeta
# dataset, we'll just choose an off-axis value at random. This gets normalized
# automatically.
L = [0.5, 0.4, 0.7]
# Our "width" is the width of the image plane as well as the depth.
# The first element is the left to right width, the second is the
# top-bottom width, and the last element is the back-to-front width
# (all in code units)
W = [0.04, 0.04, 0.4]
# The number of pixels along one side of the image.
# The final image will have Npixel^2 pixels.
Npixels = 512
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
yt.write_projection(
image,
"offaxis_projection_colorbar.png",
colorbar_label="Column Density (cm$^{-2}$)",
)
frames = math.ceil(2 * np.pi / stepAlpha)
for i in np.arange(0, frames):
L = yksikkovektori(alpha, beta)
N = yksikkovektori(alpha + stepAlpha, beta + stepBeta)
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds,
tiheysmaksimi,
L,
W,
Npixels,
"density",
north_vector=N,
no_ghost=False,
steady_north=True)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
#yt.write_projection(image, "slice/kaanto/offaxis_projection_colorbar%04i.png" %frame, colorbar_label="Column Density (cm$^{-2}$)")
yt.write_projection(image, "kuvat/%04i.png" % frame, cmap_name='hot')
frame += 1
alpha += stepAlpha
beta += stepBeta
