Although yt provides a number of built-in visualization methods that can process data and construct from that plots, it is often useful to generate the data by hand and construct plots which can then be combined with other plots, modified in some way, or even (gasp) created and modified in some other tool or program.
When making a slice, a projection or an oblique slice in yt, the resultant
YTSelectionContainer2D object is created and
contains flattened arrays of the finest available data. This means a set of
arrays for the x, y, (possibly z), dx, dy, (possibly dz) and data values, for
every point that constitutes the object.
This presents something of a challenge for visualization, as it will require
the transformation of a variable mesh of points consisting of positions and
sizes into a fixed-size array that appears like an image. This process is that
of pixelization, which yt handles transparently internally. You can access
this functionality by constructing a
supplying to it your
object, as well as some information about how you want the final image to look.
You can specify both the bounds of the image (in the appropriate x-y plane) and
the resolution of the output image. You can then have yt pixelize any
field you like.
YTSelectionContainer2D objects, you can
access them as described in Data Objects, specifically the section
Available Objects. Here is an example of how to window into a slice
of resolution(512, 512) with bounds of (0.3, 0.5) and (0.6, 0.8). The next
step is to generate the actual 2D image array, which is accomplished by
accessing the desired field.
sl = ds.slice(0, 0.5) frb = FixedResolutionBuffer(sl, (0.3, 0.5, 0.6, 0.8), (512, 512)) my_image = frb["density"]
This image may then be used in a hand-constructed Matplotlib image, for instance using
The buffer arrays can be saved out to disk in either HDF5 or FITS format:
frb.save_as_dataset("my_images.h5", fields=["density","temperature"]) frb.export_fits("my_images.fits", fields=["density","temperature"], clobber=True, units="kpc")
In the HDF5 case, the created file can be reloaded just like a regular dataset with
yt.load and will, itself, be a first-class dataset. For more information on
this, see Grid Data Containers.
In the FITS case, there is an option for setting the
units of the coordinate system in
the file. If you want to overwrite a file with the same name, set
FixedResolutionBuffer can even be exported
as a 2D dataset itself, which may be operated on in the same way as any other dataset in yt:
ds_frb = frb.export_dataset(fields=["density","temperature"], nprocs=8) sp = ds_frb.sphere("c", (100.,"kpc"))
nprocs parameter can be used to decompose the image into
nprocs number of grids.
Profiles and histograms can also be generated using the
(described in 1D Profile Plots and
2D Phase Plots). These generate profiles transparently, but the
objects they handle and create can be handled manually, as well, for more
control and access. The
can be used to generate 1, 2, and 3D profiles.
Profile objects can be created from any data object (see Data Objects, specifically the section Available Objects for more information) and are best thought of as distribution calculations. They can either sum up or average one quantity with respect to one or more other quantities, and they do this over all the data contained in their source object. When calculating average values, the variance will also be calculated.
To generate a profile, one need only specify the binning fields and the field
to be profiled. The binning fields are given together in a list. The
create_profile() function will guess the
dimensionality of the profile based on the number of fields given. For example,
a one-dimensional profile of the mass-weighted average temperature as a function of
density within a sphere can be created in the following way:
import yt ds = yt.load("galaxy0030/galaxy0030") source = ds.sphere( "c", (10, "kpc")) profile = yt.create_profile(source, [("gas", "density")], # the bin field [("gas", "temperature"), # profile field ("gas", "radial_velocity")], # profile field weight_field=("gas", "cell_mass"))
The binning, weight, and profile data can now be access as:
print(profile.x) # bin field print(profile.weight) # weight field print(profile["gas", "temperature"]) # profile field print(profile["gas", "radial_velocity"]) # profile field
profile.used attribute gives a boolean array of the bins which actually
If a weight field was given, the profile data will represent the weighted mean of
a field. In this case, the weighted variance will be calculated automatically and
can be access via the
A two-dimensional profile of the total gas mass in bins of density and temperature can be created as follows:
profile2d = yt.create_profile(source, [("gas", "density"), # the x bin field ("gas", "temperature")], # the y bin field [("gas", "cell_mass")], # the profile field weight_field=None)
Accessing the x, y, and profile fields work just as with one-dimensional profiles:
print(profile2d.x) print(profile2d.y) print(profile2d["gas", "cell_mass"])
One of the more interesting things that is enabled with this approach is
the generation of 1D profiles that correspond to 2D profiles. For instance, a
phase plot that shows the distribution of mass in the density-temperature
plane, with the average temperature overplotted. The
pcolormesh() function can be used to manually plot
the 2D profile.
Three-dimensional profiles can be generated and accessed following
the same procedures. Additional keyword arguments are available to control
the following for each of the bin fields: the number of bins, min and max, units,
whether to use a log or linear scale, and whether or not to do accumulation to
create a cumulative distribution function. For more information, see the API
documentation on the
To calculate the values along a line connecting two points in a simulation, you
can use the object
accessible as the
ray property on a index. (See Data Objects
for more information on this.) To do so, you can supply two points and access
fields within the returned object. For instance, this code will generate a ray
between the points (0.3, 0.5, 0.9) and (0.1, 0.8, 0.5) and examine the density
along that ray:
ray = ds.ray((0.3, 0.5, 0.9), (0.1, 0.8, 0.5)) print(ray["density"])
The points are ordered, but the ray is also traversing cells of varying length,
as well as taking a varying distance to cross each cell. To determine the
distance traveled by the ray within each cell (for instance, for integration)
dt is available; this field will sum to 1.0, as the ray’s path
will be normalized to 1.0, independent of how far it travels through the domain.
To determine the value of
t at which the ray enters each cell, the field
t is available. For instance:
These can be used as inputs to, for instance, the Matplotlib function
plot(), or they can be saved to disk.
The volume rendering functionality in yt can also be used to calculate
off-axis plane integrals, using the
in a manner similar to that described in 3D Visualization and Volume Rendering.