Working with Merger-Trees¶
Arbor class is responsible for loading
and providing access to merger-tree data. Below, we demonstrate how
to load data and what can be done with it.
Loading Merger-Tree Data¶
ytree can load merger-tree data from multiple sources using
import ytree a = ytree.load("consistent_trees/tree_0_0_0.dat")
This command will determine the correct format and read in the data accordingly. For examples of loading each format, see below.
Working with Merger-Tree Data¶
Very little happens immediately after a dataset has been loaded. All tree construction and data access occurs only on demand. After loading, information such as the simulation box size, cosmological parameters, and the available fields can be accessed.
>>> print (a.box_size) 100.0 Mpc/h >>> print (a.hubble_constant, a.omega_matter, a.omega_lambda) 0.695 0.285 0.715 >>> print (a.field_list) ['scale', 'id', 'desc_scale', 'desc_id', 'num_prog', ...]
How many trees are there?¶
As soon as any information about the collection of trees within the loaded
dataset is requested, an array will be constructed containing objects
representing the root of each tree, i.e., the last descendent halo. This
structure is accessed by querying the loaded
Arbor directly. It can
also be accessed as
>>> print (a.size) Loading tree roots: 100%|██████| 5105985/5105985 [00:00<00:00, 505656111.95it/s] 327
Field data for all tree roots is accessed by querying the
Arbor in a
>>> print (a["mass"]) Getting root fields: 100%|██████████████████| 327/327 [00:00<00:00, 9108.67it/s] [ 6.57410072e+14 5.28489209e+14 5.18129496e+14 4.88920863e+14, ..., 8.68489209e+11 8.68489209e+11 8.68489209e+11] Msun
ytree uses yt’s system for symbolic units, allowing for simple unit conversion.
>>> print (a["virial_radius"].to("Mpc/h")) [ 1.583027 1.471894 1.462154 1.434253 1.354779 1.341322 1.28617, ..., 0.173696 0.173696 0.173696 0.173696 0.173696] Mpc/h
When dealing with cosmological simulations, care must be taken to distinguish between comoving and proper reference frames. Please read An Important Note on Comoving and Proper Units before your magical ytree journey begins.
Accessing Individual Trees¶
Individual trees can be accessed by indexing the
>>> print (a) TreeNode
TreeNode is one halo in a merger-tree.
The number is the universal identifier associated with halo. It is unique
to the whole arbor. Fields can be accessed for any given
the same dictionary-like fashion.
>>> print (a["mass"]) 657410071942446.1 Msun
The full lineage of the tree can be accessed by querying any
with the tree keyword.
>>> my_tree = a >>> print (my_tree["tree"]) [TreeNode TreeNode TreeNode TreeNode ... TreeNode]
Fields can be queried for the tree by including the field name.
>>> print (my_tree["tree", "virial_radius"]) [ 2277.73669065 2290.65899281 2301.43165468 2311.47625899 2313.99280576 ... 434.59856115 410.13381295 411.25755396] kpc
A halo’s ancestors are stored as a list in the
The descendents are stored in a similar fashion.
>>> print (my_tree.ancestors) [TreeNode] >>> print (my_tree.ancestors.descendent) TreeNode
Accessing the Progenitor Lineage of a Tree¶
Similar to the tree keyword, the prog keyword can be used to access the line of main progenitors.
>>> print (my_tree["prog"]) [TreeNode TreeNode TreeNode TreeNode ... TreeNode] >>> print (my_tree["prog", "mass"]) [ 6.57410072e+14 6.57410072e+14 6.53956835e+14 6.50071942e+14 ... 8.29496403e+13 7.72949640e+13 6.81726619e+13 5.99280576e+13] Msun
Customizing the Progenitor Line¶
By default, the progenitor line is defined as the line of the most
massive ancestors. This can be changed by calling the
>>> a.set_selector("max_field_value", "virial_radius")
New selector functions can also be supplied. These functions should
minimally accept a list of ancestors and return a single
>>> def max_value(ancestors, field): ... vals = np.array([a[field] for a in ancestors]) ... return ancestors[np.argmax(vals)] ... >>> ytree.add_tree_node_selector("max_field_value", max_value) >>> >>> a.set_selector("max_field_value", "mass") >>> print (a["prog"])
Saving Arbors and Trees¶
Arbors of any type can be saved to a universal file format with the
save_arbor function. These can be
reloaded with the
load command. This
format is optimized for fast tree-building and field-access and so is
recommended for most situations.
>>> fn = a.save_arbor() Setting up trees: 100%|███████████████████| 327/327 [00:00<00:00, 483787.45it/s] Getting fields [1/1]: 100%|████████████████| 327/327 [00:00<00:00, 36704.51it/s] Creating field arrays [1/1]: 100%|█| 613895/613895 [00:00<00:00, 7931878.47it/s] >>> a2 = ytree.load(fn)
By default, all trees and all fields will be saved, but this can be
customized with the
For convenience, individual trees can also be saved by calling
>>> fn = a.save_tree() Creating field arrays [1/1]: 100%|████| 4897/4897 [00:00<00:00, 13711286.17it/s] >>> a2 = ytree.load(fn)
An Important Note on Comoving and Proper Units¶
yt are likely familiar with conversion from proper to comoving
reference frames by adding “cm” to a unit. For example, proper “Mpc”
becomes comoving with “Mpccm”. This conversion relies on all the data
being associated with a single redshift. This is not possible here
because the dataset has values for multiple redshifts. To account for
this, the proper and comoving unit systems are set to be equal to each
>>> print (a.box_size) 100.0 Mpc/h >>> print (a.box_size.to("Mpccm/h")) 100.0 Mpccm/h
Data should be assumed to be in the reference frame in which it was saved. For length scales, this is typically the comoving frame. When in doubt, the safest unit to use for lengths is “unitary”, which a system normalized to the box size.
>>> print (a.box_size.to("unitary")) 1.0 unitary