Working with Merger Trees¶
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.
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.
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', ...]
How many trees are there?¶
The total number of trees in the arbor can be found using the
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] 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 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
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
TreeNode in the same dictionary-like
>>> my_tree = a >>> 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["mass"]) 657410071942446.1 Msun
>>> # this will not work >>> a.thing = 5 >>> print (a.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 >>> my_tree.thing = 5 >>> print (my_tree.thing) 5
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 print (my_tree.tree_size) # call function to calculate tree size 691 >>> new_tree = a print (new_tree.tree_size) # retrieved from a cache 691
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
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
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 657410100000000.0 Msun TreeNode 657410100000000.0 Msun TreeNode 653956900000000.0 Msun TreeNode 650071960000000.0 Msun ...
To store all the nodes in a single structure, convert it to a list:
>>> print (list(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
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 >>> print (list(sub_tree["tree"])) [TreeNode, TreeNode, TreeNode, TreeNode, ... TreeNode] >>> 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 >>> # all halos in the forest >>> print (list(my_forest["forest"])) [TreeNode, TreeNode, TreeNode, ... TreeNode, TreeNode, TreeNode] >>> # 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
>>> my_ancestors = list(my_tree.ancestors) >>> print (my_ancestors) [TreeNode]
A halo’s descendent can be accessed in a similar fashion.
>>> print (my_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. Just as above, this returns a generator
>>> print (list(my_tree["prog"])) [TreeNode, TreeNode, TreeNode, TreeNode, ... TreeNode]
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 >>> print (list(my_halo["prog"])) [TreeNode, TreeNode, TreeNode, TreeNode, TreeNode, TreeNode] >>> 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
>>> 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") >>> my_tree = a >>> print (list(my_tree["prog"]))
Accessing a Single Node in a Tree¶
get_node functions can be
used to retrieve a single node from the forest, tree, or progenitor lists.
>>> my_tree = a >>> 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
function will return a generator of all leaf nodes of a tree’s forest, tree, or
>>> my_tree = a >>> my_leaves = my_tree.get_leaf_nodes(selector="forest") >>> for my_leaf in my_leaves: ... print (my_leaf)
Similar to the
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 >>> 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
For convenience, individual trees can also be saved by calling
>>> my_tree = a >>> 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).
>>> 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, TreeNode, TreeNode]
select_halos function will return a
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
The selection criteria string should be designed to
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¶
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
can then be used to generate tree nodes or
Creating the Selection¶
Search criteria can be provided using a series of keywords:
>>> 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
(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
("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¶
>>> # 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"])
Halos and Fields from yt Data Containers¶
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"])
>>> sphere = ds.sphere(ds.domain_center, (5, "Mpc")) >>> for node in a.get_nodes_from_selection(sphere): ... print (node["position"])