Generating Processed Data

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.

2D Image Arrays

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 FixedResolutionBuffer and supplying to it your YTSelectionContainer2D 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.


In previous versions of yt, there was a special class of FixedResolutionBuffer for off-axis slices. This is no longer necessary.

To create 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 imshow().

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 clobber=True.

The 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"))

where the nprocs parameter can be used to decompose the image into nprocs number of grids.

Profiles and Histograms

Profiles and histograms can also be generated using the ProfilePlot and PhasePlot functions (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 create_profile() function 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 standard deviation 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 = source.profile([("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

The profile.used attribute gives a boolean array of the bins which actually have data.


If a weight field was given, the profile data will represent the weighted mean of a field. In this case, the weighted standard deviation will be calculated automatically and can be access via the profile.standard_deviation attribute.

print(profile.standard_deviation["gas", "temperature"])

A two-dimensional profile of the total gas mass in bins of density and temperature can be created as follows:

profile2d = source.profile([("gas", "density"),      # the x bin field
                            ("gas", "temperature")], # the y bin field
                           [("gas", "cell_mass")],   # the profile field

Accessing the x, y, and profile fields work just as with one-dimensional profiles:

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. If you want to generate a default profile plot, you can simply call::


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 create_profile() function.

For custom bins the other keyword arguments can be overriden using the override_bins keyword argument. This accepts a dictionary with an array for each bin field or None to use the default settings.

custom_bins = np.array([1e-27, 1e-25, 2e-25, 5e-25, 1e-23])
profile2d = source.profile([("gas", "density"), ("gas", "temperature")],
                            [("gas", "cell_mass")],
                            override_bins = {("gas", "density"):custom_bins,
                                             ("gas", "temperature"):None})

Line Queries and Planar Integrals

To calculate the values along a line connecting two points in a simulation, you can use the object YTRay, 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))

The points are not ordered, so you may need to sort the data (see the example in the YTRay docs). Also note, the ray is 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) the field 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 ProjectionTransferFunction in a manner similar to that described in 3D Visualization and Volume Rendering.