# UserSuppliedPtclCatalog¶

class halotools.sim_manager.UserSuppliedPtclCatalog(**kwargs)[source] [edit on github]

Bases: object

Class used to transform a user-provided particle catalog into the standard form recognized by Halotools.

Random downsamplings of dark matter particles are not especially useful catalogs in their own right. So primary purpose of this class is the add_ptclcat_to_cache method, which sets you up to use the dark matter particle collection together with the associated halo catalog.

See Instructions for Working with Alternative Particle Data for a tutorial on this class.

Parameters: **metadata : float or string Keyword arguments storing catalog metadata. The quantities Lbox and particle_mass are required and must be in Mpc/h and Msun/h units, respectively. redshift is also required metadata. See Examples section for further notes. **ptcl_catalog_columns : sequence of arrays Sequence of length-Nptcls arrays passed in as keyword arguments. Each key will be the column name attached to the input array. At a minimum, there must be columns x, y and z. See Examples section for further notes.

Examples

Here is an example using dummy data to show how to create a new UserSuppliedPtclCatalog and store it in cache for future use with the associated halo catalog. First the setup:

>>> redshift = 0.0
>>> Lbox = 250.
>>> particle_mass = 1e9
>>> num_ptcls = int(1e4)
>>> x = np.random.uniform(0, Lbox, num_ptcls)
>>> y = np.random.uniform(0, Lbox, num_ptcls)
>>> z = np.random.uniform(0, Lbox, num_ptcls)
>>> ptcl_ids = np.arange(0, num_ptcls)
>>> vx = np.random.uniform(-100, 100, num_ptcls)
>>> vy = np.random.uniform(-100, 100, num_ptcls)
>>> vz = np.random.uniform(-100, 100, num_ptcls)


Now we simply pass in both the metadata and the particle catalog columns as keyword arguments:

>>> ptcl_catalog = UserSuppliedPtclCatalog(redshift=redshift, Lbox=Lbox, particle_mass=particle_mass, x=x, y=y, z=z, vx=vx, vy=vy, vz=vz, ptcl_ids=ptcl_ids)


Take note: it is important that the value of the input redshift matches whatever the redshift is of the associated halo catalog. Your redshift should be accurate to four decimal places.

Now that we have built a Halotools-formatted particle catalog, we can add it to the cache as follows.

First choose a relatively permanent location on disk where you will be storing the particle data:

>>> my_fname = 'some_fname.hdf5'


Next choose the simname that matches the simname of the associated halo catalog, for example:

>>> my_simname = 'bolplanck'


Now choose any version name that will help you keep track of potentially different version of the same catalog of particles.

>>> my_version_name = 'any version name'


Finally, give a short, plain-language descriptions of how you obtained your collection of particles:

>>> my_processing_notes = 'This particle catalog was obtained through the following means: ...'


Now we add the particle catalog to cache using the following syntax:

>>> ptcl_catalog.add_ptclcat_to_cache(my_fname, my_simname, my_version_name, my_processing_notes) # doctest: +SKIP


Your particle catalog has now been cached and is accessible whenever you load the associated halo catalog into memory. For example:

>>> from halotools.sim_manager import CachedHaloCatalog
>>> halocat = CachedHaloCatalog(simname=my_simname, halo_finder='some halo-finder', version_name='some version-name', redshift=redshift, ptcl_version_name=my_version_name) # doctest: +SKIP


Note the arguments passed to the CachedHaloCatalog class. The version_name here refers to the halos, not the particles. When loading the CachedHaloCatalog, you specify the version name of the particles with the ptcl_version_name keyword argument. The ptcl_version_name need not agree with the version_name of the associated halos. This allows halo and particle catalogs to evolve independently over time. In fact, for cases where you have supplied your own particles, it is strongly recommended that you choose a version name for your particles that differs from the version name that Halotools uses for its catalogs. This will help avoid future confusion over the where the cached particle catalog came from.

The particle catalog itself is stored in the ptcl_table attribute, with columns accessed as follows:

>>> array_of_x_positions = halocat.ptcl_table['x'] # doctest: +SKIP


If you do not wish to store your particle catalog in cache, see the Using your particle catalog without the cache section of the Instructions for Working with Alternative Particle Data tutorial.

Methods Summary

add_ptclcat_to_cache(fname, simname, …[, …])

Methods Documentation

add_ptclcat_to_cache(fname, simname, version_name, processing_notes, overwrite=False)[source] [edit on github]
Parameters: fname : string Absolute path of the file to be stored in cache. Must conclude with an hdf5 extension. simname : string Nickname of the simulation used as a shorthand way to keep track of the catalogs in your cache. version_name : string Nickname of the version of the particle catalog. The version_name is used as a bookkeeping tool in the cache log. As described in the UserSuppliedPtclCatalog docstring, the version name selected here need not match the version name of the associated halo catalog. processing_notes : string String used to provide supplementary notes that will be attached to the hdf5 file storing your particle data. overwrite : bool, optional If the chosen fname already exists, then you must set overwrite to True in order to write the file to disk. Default is False.