These are the objects that act as physical selections of data, describing a region in space. These are not typically addressed directly; see Available Objects for more information.
These will almost never need to be instantiated on their own.
These objects are defined by some selection method or mechanism. Most are geometric.
These objects typically require some effort to build. Often this means integrating through the simulation in some way, or creating some large or expensive set of intermediate data.
These are objects that either contain and represent or operate on series of datasets.
These objects generate an “index” into multiresolution data.
These classes and functions enable yt’s symbolic unit handling system.
These types are used to sum data up and either return that sum or return an
average. Typically they are more easily used through the ProfilePlot
PhasePlot
interface. We also provide the create_profile
function
to create these objects in a uniform manner.
The HaloCatalog
object is the primary means for performing custom analysis
on cosmological halos. It is also the primary interface for halo finding.
These provide direct access to the halo finders. However, it is strongly recommended
to use the HaloCatalog
.
These functions are designed to create correlations or other results of operations acting on two spatially-distinct points in a data source. See also Two Point Functions.
For volume renderings and fixed resolution buffers the image object returned is
an ImageArray
object, which has useful functions for image saving and
writing to bitmaps.
For the generation of stellar SEDs. (See also Star Particle Analysis.)
Light cone generation and simulation analysis. (See also Light Cone Generator.)
Absorption and X-ray spectra and spectral lines:
Absorption spectra fitting:
Sunrise exporting:
RADMC-3D exporting:
See also Volume Rendering: Making 3D Photorealistic Isocontoured Images.
Here are the primary entry points:
These objects set up the way the image looks:
There are also advanced objects for particular use cases:
See also Streamlines: Tracking the Trajectories of Tracers in your Data.
These functions are all used for fast writing of images directly to disk, without calling matplotlib. This can be very useful for high-cadence outputs where colorbars are unnecessary or for volume rendering.
We also provide a module that is very good for generating EPS figures, particularly with complicated layouts.
See also Plot Modifications: Overplotting Contours, Velocities, Particles, and More.
The first set of functions are all provided by NumPy.
These are yt-provided functions: