Working with Merger Trees

The Arbor class is responsible for loading and providing access to merger tree data. In this document, a loaded merger tree dataset is referred to as an arbor. ytree provides several different ways to navigate, query, and analyze merger trees. It is recommended that you read this entire section to identify the way that is best for what you want to do.

Loading Data

ytree can load merger tree data from multiple sources using the load command.

>>> 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.

Getting Started with Merger Trees

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', ...]

Similar to yt, ytree supports accessing fields by their native names as well as generalized aliases. For more information on fields in ytree, see Fields in ytree.

How many trees are there?

The total number of trees in the arbor can be found using the size attribute. As soon as any information about the collection of trees within the loaded dataset is requested, arrays will be created containing the metadata required for generating the root nodes of every tree.

>>> print (a.size)
Loading tree roots: 100%|██████| 5105985/5105985 [00:00<00:00, 505656111.95it/s]

Root Fields

Field data for all tree roots is accessed by querying the Arbor in a dictionary-like manner.

>>> 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 the unyt package for symbolic units on NumPy arrays.

>>> 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 Arbor object.

>>> print (a[0])

A 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 TreeNode in the same dictionary-like fashion.

>>> my_tree = a[0]
>>> print (my_tree["mass"])
657410071942446.1 Msun

Array slicing can also be used to select multiple TreeNode objects. This will return a generator that can be iterated over or cast to a list.

>>> every_second_tree = list(a[::2])
>>> print (every_second_tree[0]["mass"])
657410071942446.1 Msun

Note, the Arbor object does not store individual TreeNode objects, it only generates them. Thus, one must explicitly keep around any TreeNode object for changes to persist. This is illustrated below:

>>> # this will not work
>>> a[0].thing = 5
>>> print (a[0].thing)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'TreeNode' object has no attribute 'thing'
>>> # this will work
>>> my_tree = a[0]
>>> my_tree.thing = 5
>>> print (my_tree.thing)

The only exception to this is computing the number of nodes in a tree. This information will be propagated back to the Arbor as it can be expensive to compute for large trees.

>>> my_tree = a[0]
print (my_tree.tree_size) # call function to calculate tree size
>>> new_tree = a[0]
print (new_tree.tree_size) # retrieved from a cache

Accessing the Nodes in a Tree or Forest

A node is defined as a single halo at a single time in a merger tree. Throughout these docs, the words halo and node are used interchangeably. Nodes in a given tree can be accessed in three different ways: by Accessing All Nodes in a Tree, Accessing All Nodes in a Forest, or Accessing the Progenitor Lineage of a Tree. Each of these will return a generator of TreeNode objects or field values for all TreeNode objects in the tree, forest, or progenitor line. To get a specific node from a tree, see Accessing a Single Node in a Tree.


Access by forest is supported even for datasets that do not group trees by forest. If you have no requirement for the order in which nodes are to be returned, then access by forest is recommended as it will be considerably faster than access by tree. Access by tree is effectively a depth-first walk through the tree. This requires additional data structures to be built, whereas forest access does not.

Accessing All Nodes in a Tree

The full lineage of the tree can be accessed by querying any TreeNode with the tree keyword. As of ytree version 3.0, this returns a generator that can be used to loop through all nodes in the tree.

>>> print (my_tree["tree"])
<generator object TreeNode._tree_nodes at 0x11bbc1f20>
>>> # loop over nodes
>>> for my_node in my_tree["tree"]:
...     print (my_node, my_node["mass"])
TreeNode[12900] 657410100000000.0 Msun
TreeNode[12539] 657410100000000.0 Msun
TreeNode[12166] 653956900000000.0 Msun
TreeNode[11796] 650071960000000.0 Msun

To store all the nodes in a single structure, convert it to a list:

>>> print (list(my_tree["tree"]))
[TreeNode[12900], TreeNode[12539], TreeNode[12166], TreeNode[11796], ...

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

The above examples will work for any halo in the tree, not just the final halo. The full tree leading up to any given halo can be accessed in the same way.

>>> tree_nodes = list(my_tree["tree"])
>>> # start with the 3rd halo in the above tree
>>> sub_tree = tree_nodes[2]
>>> print (list(sub_tree["tree"]))
[TreeNode[12166], TreeNode[11796], TreeNode[11431], TreeNode[11077], ...
>>> print (sub_tree["tree", "virial_radius"])
[2301.4316  2311.4763  2313.993   2331.413   2345.5454  2349.918 ...
 434.59857  410.13382  411.25757] kpc

Accessing All Nodes in a Forest

The Consistent-Trees-HDF5, LHaloTree, LHaloTree-HDF5, MORIA, TreeFrog formats provide access to halos grouped by forest. A forest is a group of trees with halos that interact in a non-merging way through processes like fly-bys.

>>> import ytree
>>> a = ytree.load("consistent_trees_hdf5/soa/forest.h5",
...                access="forest")
>>> my_forest = a[0]
>>> # all halos in the forest
>>> print (list(my_forest["forest"]))
[TreeNode[90049568], TreeNode[88202573], TreeNode[86292249], ...
 TreeNode[9272027], TreeNode[7435733], TreeNode[5768880]]
>>> # all halo masses in forest
>>> print (my_forest["forest", "mass"])
[3.38352524e+11 3.34071450e+11 3.34071450e+11 3.31709477e+11 ...
 7.24092117e+09 4.34455270e+09] Msun

To find all the roots in that forest, i.e., the roots of all individual trees contained, see Accessing the Root Nodes of a Forest.

Accessing a Halo’s Ancestors and Descendent

The direct ancestors of any TreeNode object can be accessed through the ancestors attribute.

>>> my_ancestors = list(my_tree.ancestors)
>>> print (my_ancestors)

A halo’s descendent can be accessed in a similar fashion.

>>> print (my_ancestors[0].descendent)

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. Just as above, this returns a generator of TreeNode objects.

>>> print (list(my_tree["prog"]))
[TreeNode[12900], TreeNode[12539], TreeNode[12166], TreeNode[11796], ...

Fields for the main progenitors can be accessed just like for the whole tree.

>>> 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

Progenitor lists and fields can be accessed for any halo in the tree.

>>> tree_nodes = list(my_tree["tree"])
>>> # pick a random halo in the tree
>>> my_halo = tree_nodes[42]
>>> print (list(my_halo["prog"]))
[TreeNode[588], TreeNode[446], TreeNode[317], TreeNode[200], TreeNode[105],
>>> print (my_halo["prog", "virial_radius"])
[1404.1354 1381.4087 1392.2404 1363.2145 1310.3842 1258.0159] kpc

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 set_selector.

>>> 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 TreeNode.

>>> 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")
>>> my_tree = a[0]
>>> print (list(my_tree["prog"]))

Accessing a Single Node in a Tree

The get_node functions can be used to retrieve a single node from the forest, tree, or progenitor lists.

>>> my_tree = a[0]
>>> my_node = my_tree.get_node("forest", 5)

This function can be called for any node in a tree. For selection by “tree” or “prog”, the index of the node returned will be relative to the calling node, i.e., calling with 0 will return the original node. For selection by “forest”, the index is the absolute index within the entire forest and not relative to the calling node.

Accessing the Leaf Nodes of a Tree

The leaf nodes of a tree are the nodes with no ancestors. These are the very first halos to form. Accessing them for any tree can be useful for semi-analytical models or any framework where you want to start at the origins of a halo and work forward in time. The get_leaf_nodes function will return a generator of all leaf nodes of a tree’s forest, tree, or progenitor lists.

>>> my_tree = a[0]
>>> my_leaves = my_tree.get_leaf_nodes(selector="forest")
>>> for my_leaf in my_leaves:
...     print (my_leaf)

Similar to the get_node function, calling get_leaf_nodes with selector set to “tree” or “prog” will return only leaf nodes from the tree for which the calling node is the head. With selector set to “forest”, the resulting leaf nodes will be all the leaf nodes in the forest, regardless of the calling node.

Accessing the Root Nodes of a Forest

A forest can have multiple root nodes, i.e., nodes that have no descendent. The get_root_nodes function will return a generator of all the root nodes in the forest. This function can be called from any tree within a forest.

>>> my_tree = a[0]
>>> my_roots = my_tree.get_root_nodes()
>>> for my_root in my_roots:
...     print (my_root)

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 trees and fields keywords.

For convenience, individual trees can also be saved by calling save_tree.

>>> my_tree = a[0]
>>> fn = my_tree.save_tree()
Creating field arrays [1/1]: 100%|████| 4897/4897 [00:00<00:00, 13711286.17it/s]
>>> a2 = ytree.load(fn)

Searching Through Merger Trees (Accessing Like a Database)

There are a couple different ways to search through a merger tree dataset to find halos meeting various criteria, similar to the type of selection done with a relational database. The method discussed in Select Halos can be used with all data loadable with ytree, while the one described in Select Halos with yt is only available for Saved Arbors (ytree format).

Select Halos

The select_halos function can be used to search the Arbor for halos matching a specific set of criteria.

>>> halos = list(a.select_halos("tree['forest', 'mass'].to('Msun') > 5e11"))
Selecting halos (found 3): 100%|███████████████| 32/32 [00:00<00:00, 107.70it/s]
>>> print (halos)
[TreeNode[1457223360], TreeNode[1457381406], TreeNode[1420495006]]

The select_halos function will return a generator of TreeNode objects that can be iterated over or cast to a list, as above. The function will return halos as they are found so the user does not have to wait until the end to begin working with them. The progress bar will continually update to report the number of matches found.

The selection criteria string should be designed to eval correctly with a TreeNode object, named “tree”. More complex criteria can be supplied using & and |.

>>> for halo in a.select_halos("(tree['tree', 'mass'].to('Msun') > 2e11) & (tree['tree', 'redshift'] < 0.2)"):
...     progenitor_pos = halo["prog", "position"]
Selecting halos (found 69): 100%|███████████████| 32/32 [00:01<00:00, 22.50it/s]

Select Halos with yt


This functionality only works with Saved Arbors (ytree format). You will need to save your data in the ytree format.

The get_yt_selection function provides enhanced functionality beyond the capabilities of select_halos by loading the dataset into yt. Given search criteria, get_yt_selection will return a YTCutRegion data container that can then be queried to get the value of any field for all halos meeting the criteria. This YTCutRegion can then be used to generate tree nodes or query fields.

Creating the Selection

Search criteria can be provided using a series of keywords: above, below, equal, and about.

>>> import ytree
>>> a = ytree.load("arbor/arbor.h5")
>>> selection = a.get_yt_selection(,
...     above=[("mass", 1e13, "Msun"),
...            ("redshift", 0.5)])

An individual criterion should be expressed as a tuple (e.g., (field, value, <units>)), and the above keywords accept a list of those tuples. The criteria keywords can be given together and the halos must meet all criteria, i.e., the criteria are combined with an AND operator.

>>> selection = a.get_yt_selection(
...     below=[("mass", 1e13, "Msun")],
...     above=[("redshift", 1)])

For more complex search criteria, a cut region conditional string can be provided instead. These should be of the form described in Cut Regions. These cannot not be given with any of the previously mentioned keywords.

>>> selection = a.get_yt_selection(
...     conditionals=['obj["halos", "mass"] > 1e12'])

Querying the Selection

The selection object returned by get_yt_selection can then be queried to get field values for all matching halos. Fields should be queried as ("halos", <field name>).

>>> # halos with masses of 1e14 Msun +/- 5%
>>> selection = a.get_yt_selection(
        about=[("mass", 1e14, "Msun", 0.05)])

>>> print (selection["halos", "redshift"])
[0.82939091 0.97172537 1.02453741 0.31893065 0.74571856 0.97172537 ...
 0.50455122 0.53499009 0.18907477 0.29567248 0.31893065] dimensionless

Getting Halos from the Selection

The get_nodes_from_selection function will return a generator of TreeNode objects for all halos contained within the selection.

>>> # halos with masses of 1e14 Msun +/- 5%
>>> selection = a.get_yt_selection(
        about=[("mass", 1e14, "Msun", 0.05)])

>>> for node in a.get_nodes_from_selection(selector):
...     print (node["prog", "mass"])

This function can generate TreeNode objects for any yt data container.

Halos and Fields from yt Data Containers


This functionality only works with Saved Arbors (ytree format). You will need to save your data in the ytree format.

For merger tree data in the ytree format, the ytds attribute provides access to the data as a yt dataset. This allows one to analyze the entire dataset using the full range of functionality provided by yt. In this way, a merger tree dataset is very much like any particle dataset, where each particle represent a halo at a single time. For example, this makes it possible to select halos within geometric data containers, like spheres or regions.

>>> import ytree
>>> a = ytree.load("arbor/arbor.h5")

>>> ds = a.ytds
>>> sphere = ds.sphere(ds.domain_center, (5, "Mpc"))
>>> print (sphere["halos", "mass"])

These data containers can then be given to the get_nodes_from_selection function to get the tree nodes for all halos within the container.

>>> sphere = ds.sphere(ds.domain_center, (5, "Mpc"))
>>> for node in a.get_nodes_from_selection(sphere):
...     print (node["position"])