Halo Catalogs

Creating Halo Catalogs

In yt 3.0, operations relating to the analysis of halos (halo finding, merger tree creation, and individual halo analysis) are all brought together into a single framework. This framework is substantially different from the halo analysis machinery available in yt-2.x and is entirely backward incompatible. For a direct translation of various halo analysis tasks using yt-2.x to yt-3.0 please see Getting up to Speed with Halo Analysis in yt-3.0.

A catalog of halos can be created from any initial dataset given to halo catalog through data_ds. These halos can be found using friends-of-friends, HOP, and Rockstar. The finder_method keyword dictates which halo finder to use. The available arguments are FOF, HOP, and Rockstar Halo Finding. For more details on the relative differences between these halo finders see Halo Finding.

The class which holds all of the halo information is the HaloCatalog.

import yt
from yt.analysis_modules.halo_analysis.api import HaloCatalog

data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')
hc = HaloCatalog(data_ds=data_ds, finder_method='hop')

A halo catalog may also be created from already run rockstar outputs. This method is not implemented for previously run friends-of-friends or HOP finders. Even though rockstar creates one file per processor, specifying any one file allows the full catalog to be loaded. Here we only specify the file output by the processor with ID 0. Note that the argument for supplying a rockstar output is halos_ds, not data_ds.

halos_ds = yt.load(path+'rockstar_halos/halos_0.0.bin')
hc = HaloCatalog(halos_ds=halos_ds)

Although supplying only the binary output of the rockstar halo finder is sufficient for creating a halo catalog, it is not possible to find any new information about the identified halos. To associate the halos with the dataset from which they were found, supply arguments to both halos_ds and data_ds.

halos_ds = yt.load(path+'rockstar_halos/halos_0.0.bin')
data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')
hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds)

A data object can also be supplied via the keyword data_source, associated with either dataset, to control the spatial region in which halo analysis will be performed.

Analysis Using Halo Catalogs

Analysis is done by adding actions to the HaloCatalog. Each action is represented by a callback function that will be run on each halo. There are three types of actions:

  • Filters
  • Quantities
  • Callbacks

A list of all available filters, quantities, and callbacks can be found in Halo Analysis. All interaction with this analysis can be performed by importing from halo_analysis.

Filters

A filter is a function that returns True or False. If the return value is True, any further queued analysis will proceed and the halo in question will be added to the final catalog. If the return value False, further analysis will not be performed and the halo will not be included in the final catalog.

An example of adding a filter:

hc.add_filter('quantity_value', 'particle_mass', '>', 1E13, 'Msun')

Currently quantity_value is the only available filter, but more can be added by the user by defining a function that accepts a halo object as the first argument and then adding it as an available filter. If you think that your filter may be of use to the general community, you can add it to yt/analysis_modules/halo_analysis/halo_filters.py and issue a pull request.

An example of defining your own filter:

def my_filter_function(halo):

    # Define condition for filter
    filter_value = True

    # Return a boolean value
    return filter_value

# Add your filter to the filter registry
add_filter("my_filter", my_filter_function)

# ... Later on in your script
hc.add_filter("my_filter")

Quantities

A quantity is a call back that returns a value or values. The return values are stored within the halo object in a dictionary called “quantities.” At the end of the analysis, all of these quantities will be written to disk as the final form of the generated halo catalog.

Quantities may be available in the initial fields found in the halo catalog, or calculated from a function after supplying a definition. An example definition of center of mass is shown below. Currently available quantities are center_of_mass and bulk_velocity. Their definitions are available in yt/analysis_modules/halo_analysis/halo_quantities.py. If you think that your quantity may be of use to the general community, add it to halo_quantities.py and issue a pull request. Default halo quantities are:

  • particle_identifier – Halo ID (e.g. 0 to N)
  • particle_mass – Mass of halo
  • particle_position_x – Location of halo
  • particle_position_y – Location of halo
  • particle_position_z – Location of halo
  • virial_radius – Virial radius of halo

An example of adding a quantity:

hc.add_quantity('center_of_mass')

An example of defining your own quantity:

def my_quantity_function(halo):
    # Define quantity to return
    quantity = 5

    return quantity

# Add your filter to the filter registry
add_quantity('my_quantity', my_quantity_function)


# ... Later on in your script
hc.add_quantity("my_quantity")

This quantity will then be accessible for functions called later via the quantities dictionary that is associated with the halo object.

def my_new_function(halo):
    print halo.quantities["my_quantity"]
add_callback("print_quantity", my_new_function)

# ... Anywhere after "my_quantity" has been called
hc.add_callback("print_quantity")

Callbacks

A callback is actually the super class for quantities and filters and is a general purpose function that does something, anything, to a Halo object. This can include hanging new attributes off the Halo object, performing analysis and writing to disk, etc. A callback does not return anything.

An example of using a pre-defined callback where we create a sphere for each halo with a radius that is twice the saved radius.

hc.add_callback("sphere", factor=2.0)

Currently available callbacks are located in yt/analysis_modules/halo_analysis/halo_callbacks.py. New callbacks may be added by using the syntax shown below. If you think that your callback may be of use to the general community, add it to halo_callbacks.py and issue a pull request.

An example of defining your own callback:

def my_callback_function(halo):
    # Perform some callback actions here
    x = 2
    halo.x_val = x

# Add the callback to the callback registry
add_callback('my_callback', my_callback_function)


# ...  Later on in your script
hc.add_callback("my_callback")

Running Analysis

After all callbacks, quantities, and filters have been added, the analysis begins with a call to HaloCatalog.create.

hc.create()

The save_halos keyword determines whether the actual Halo objects are saved after analysis on them has completed or whether just the contents of their quantities dicts will be retained for creating the final catalog. The looping over halos uses a call to parallel_objects allowing the user to control how many processors work on each halo. The final catalog is written to disk in the output directory given when the HaloCatalog object was created.

All callbacks, quantities, and filters are stored in an actions list, meaning that they are executed in the same order in which they were added. This enables the use of simple, reusable, single action callbacks that depend on each other. This also prevents unecessary computation by allowing the user to add filters at multiple stages to skip remaining analysis if it is not warranted.

Saving and Reloading Halo Catalogs

A HaloCatalog saved to disk can be reloaded as a yt dataset with the standard call to load. Any side data, such as profiles, can be reloaded with a load_profiles callback and a call to load().

hds = yt.load(path+"halo_catalogs/catalog_0046/catalog_0046.0.h5")
hc = HaloCatalog(halos_ds=hds,
                 output_dir="halo_catalogs/catalog_0046")
hc.add_callback("load_profiles", output_dir="profiles",
                filename="virial_profiles")
hc.load()

Worked Example of Halo Catalog in Action

For a full example of how to use these methods together see Worked Example of Halo Analysis.