.. _arbor: Working with Merger Trees ========================= The :class:`~ytree.data_structures.arbor.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. Example Applications -------------------- Not sure how to get started with something? Check out :ref:`examples` for demonstrations of more complex analysis. Loading Data ------------ ``ytree`` can load merger tree data from multiple sources using the :func:`~ytree.data_structures.load.load` command. .. code-block:: python >>> 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. .. toctree:: :maxdepth: 2 Loading 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. .. code-block:: python >>> 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 :ref:`fields`. Useful Arbor Attributes ^^^^^^^^^^^^^^^^^^^^^^^ A loaded arbor will have several helpful attributes. Creating Arrays and Quantities with Units """"""""""""""""""""""""""""""""""""""""" Support for unitful arrays and quantities is provided by the `unyt `__ package. Arrays and quantities with symbolic units can be created directly with ``unyt``. However, if done this way, they will not have access to any custom unit systems defined by a dataset. The :func:`~ytree.data_structures.arbor.Arbor.arr` and :func:`~ytree.data_structures.arbor.Arbor.quan` attributes allow one to create arrays and scalar quantities using the loaded arbor's unit system. .. code-block:: python >>> my_array = a.arr([1, 2, 3], "Msun") >>> my_quantity = a.quan(106, "km") Cosmology Calculations """""""""""""""""""""" If the dataset defines the cosmological parameters, ``omega_matter``, ``omega_lambda``, and ``hubble_constant``, a :ref:`yt:cosmology-calculator` instance can be accessed via the :class:`~ytree.data_structures.arbor.Arbor.cosmology` attribute. This :class:`~yt.utilities.cosmology.Cosmology` instance will be configured with the previously mentioned cosmology parameters from the arbor as well as the arbor's internal unit system. If the necessary parameters are not defined within the dataset, the cosmology attribute will be unavailable. .. code-block:: python >>> print (a.omega_matter, a.omega_lambda, a.hubble_constant) 0.285 0.715 0.695 >>> print (a.cosmology.comoving_radial_distance(0, 3).to("Mpc/h")) 4516.094598250588 Mpc/h >>> print (a.cosmology.critical_density(0).to("Msun/Mpc**3")) 134065965531.17598 Msun/Mpc**3 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. .. code-block:: python >>> print (a.size) Loading tree roots: 100%|██████| 5105985/5105985 [00:00<00:00, 505656111.95it/s] 327 .. _root-fields: Root Fields ^^^^^^^^^^^ Field data for all tree roots is accessed by querying the :class:`~ytree.data_structures.arbor.Arbor` in a dictionary-like manner. .. code-block:: python >>> 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. .. code-block:: python >>> 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 :ref:`frames` before your magical ``ytree`` journey begins. Accessing Individual Trees ^^^^^^^^^^^^^^^^^^^^^^^^^^ Individual trees can be accessed by indexing the :class:`~ytree.data_structures.arbor.Arbor` object. .. code-block:: python >>> print (a[0]) TreeNode[12900] A :class:`~ytree.data_structures.tree_node.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 :class:`~ytree.data_structures.tree_node.TreeNode` in the same dictionary-like fashion. .. code-block:: python >>> my_tree = a[0] >>> print (my_tree["mass"]) 657410071942446.1 Msun Array slicing can also be used to select multiple :class:`~ytree.data_structures.tree_node.TreeNode` objects. This will return a generator that can be iterated over or cast to a list. .. code-block:: python >>> every_second_tree = list(a[::2]) >>> print (every_second_tree[0]["mass"]) 657410071942446.1 Msun Note, the :class:`~ytree.data_structures.arbor.Arbor` object does not store individual :class:`~ytree.data_structures.tree_node.TreeNode` objects, it only generates them. Thus, one must explicitly keep around any :class:`~ytree.data_structures.tree_node.TreeNode` object for changes to persist. This is illustrated below: .. code-block:: python >>> # this will not work >>> a[0].thing = 5 >>> print (a[0].thing) Traceback (most recent call last): File "", line 1, in AttributeError: 'TreeNode' object has no attribute 'thing' >>> # this will work >>> my_tree = a[0] >>> 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 :class:`~ytree.data_structures.arbor.Arbor` as it can be expensive to compute for large trees. .. code-block:: python >>> my_tree = a[0] print (my_tree.tree_size) # call function to calculate tree size 691 >>> new_tree = a[0] print (new_tree.tree_size) # retrieved from a cache 691 .. _tree-or-forest: 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 :ref:`tree-access`, :ref:`forest-access`, or :ref:`progenitor-access`. Each of these will return a generator of :class:`~ytree.data_structures.tree_node.TreeNode` objects or field values for all :class:`~ytree.data_structures.tree_node.TreeNode` objects in the tree, forest, or progenitor line. To get a specific node from a tree, see :ref:`single-node-access`. .. note:: 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. .. _tree-access: Accessing All Nodes in a Tree ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The full lineage of the tree can be accessed by querying any :class:`~ytree.data_structures.tree_node.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. .. code-block:: python >>> print (my_tree["tree"]) >>> # 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], ... TreeNode[591]] Fields can be queried for the tree by including the field name. .. code-block:: python >>> 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. .. code-block:: python >>> 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], ... TreeNode[591]] >>> 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 .. _forest-access: Accessing All Nodes in a Forest ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Several 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. A forest can contain multiple distinct trees ending in halos with no descendent. See :ref:`forest-support` for a list of frontends which use forests. .. code-block:: python >>> 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 :ref:`getting_root_nodes`. .. _forest-support: Frontends that Support Forests ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Below is table illustrating which frontends make use of forests. Note, all frontends can be used with forest access (see :ref:`tree-or-forest`), but only the ones listed below contain forests with multiple distinct trees. .. list-table:: Frontends Using Forests :widths: 25 25 :header-rows: 1 * - Frontend - Supports Forests? * - :ref:`load-ahf` - no * - :ref:`load-ctrees` - no * - :ref:`load-ctrees-hdf5` - yes * - :ref:`load-gadget4` - yes * - :ref:`load-csv` - no * - :ref:`load-lhalotree` - yes * - :ref:`load-lhalotree-hdf5` - yes * - :ref:`load-moria` - yes * - :ref:`load-rockstar` - no * - :ref:`load-treefarm` - no * - :ref:`load-treefrog` - yes Accessing a Halo's Ancestors and Descendent ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The direct ancestors of any :class:`~ytree.data_structures.tree_node.TreeNode` object can be accessed through the ``ancestors`` attribute. .. code-block:: python >>> my_ancestors = list(my_tree.ancestors) >>> print (my_ancestors) [TreeNode[12539]] A halo's descendent can be accessed in a similar fashion. .. code-block:: python >>> print (my_ancestors[0].descendent) TreeNode[12900] .. _progenitor-access: 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 :class:`~ytree.data_structures.tree_node.TreeNode` objects. .. code-block:: python >>> print (list(my_tree["prog"])) [TreeNode[12900], TreeNode[12539], TreeNode[12166], TreeNode[11796], ... TreeNode[62]] Fields for the main progenitors can be accessed just like for the whole tree. .. code-block:: python >>> 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. .. code-block:: python >>> 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], TreeNode[62]] >>> print (my_halo["prog", "virial_radius"]) [1404.1354 1381.4087 1392.2404 1363.2145 1310.3842 1258.0159] kpc .. _custom-progenitor: 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 :func:`~ytree.data_structures.arbor.Arbor.set_selector`. .. code-block:: python >>> 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 :class:`~ytree.data_structures.tree_node.TreeNode`. .. code-block:: python >>> 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"])) .. _single-node-access: Accessing a Single Node in a Tree ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :func:`~ytree.data_structures.tree_node.TreeNode.get_node` functions can be used to retrieve a single node from the forest, tree, or progenitor lists. .. code-block:: python >>> 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 :func:`~ytree.data_structures.tree_node.TreeNode.get_leaf_nodes` function will return a generator of all leaf nodes of a tree's forest, tree, or progenitor lists. .. code-block:: python >>> 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 :func:`~ytree.data_structures.tree_node.TreeNode.get_node` function, calling :func:`~ytree.data_structures.tree_node.TreeNode.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. .. _getting_root_nodes: Accessing the Root Nodes of a Forest ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A forest can have multiple root nodes, i.e., nodes that have no descendent. The :func:`~ytree.data_structures.tree_node.TreeNode.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. .. code-block:: python >>> my_tree = a[0] >>> my_roots = my_tree.get_root_nodes() >>> for my_root in my_roots: ... print (my_root) .. _saving-trees: Saving Arbors and Trees ----------------------- ``Arbors`` of any type can be saved to a universal file format with the :func:`~ytree.data_structures.arbor.Arbor.save_arbor` function. These can be reloaded with the :func:`~ytree.data_structures.load.load` command. This format is optimized for fast tree-building and field-access and so is recommended for most situations. It can also be used with :ref:`analysis-fields` to save additional fields to a dataset. .. code-block:: python >>> 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 :func:`~ytree.data_structures.tree_node.TreeNode.save_tree`. .. code-block:: python >>> 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) .. _save-in-place: Saving New Fields In Place ^^^^^^^^^^^^^^^^^^^^^^^^^^ If the arbor is of the special :ref:`ytree ` format, new data can be added (see :ref:`analysis-fields`) and saved directly to the existing dataset by setting the ``save_in_place`` keyword argument to True. See :ref:`saving-analysis` for more information. .. _node-container: Arbitrary Collections of TreeNodes ---------------------------------- Occasionally, one may want to hold onto or work with a collection of unrelated :class:`~ytree.data_structures.tree_node.TreeNode` objects. The :func:`~ytree.data_structures.arbor.Arbor.container` attribute provides a function to create such a container. This :class:`~ytree.data_structures.node_container.NodeContainer` object accepts any iterable data structure of :class:`~ytree.data_structures.tree_node.TreeNode` objects. This includes a list of nodes: .. code-block:: python >>> a = ytree.load("tiny_ctrees/locations.dat") >>> first = a[0] >>> last = a[-1] >>> my_container = a.container([first, last]) a slice of the arbor: .. code-block:: python >>> a_slice = a.container(a[::8]) or a generator of nodes: .. code-block:: python >>> leaf_container = a.container(a[0].get_leaf_nodes()) Fields for each node in the container can be queried in the same way as :ref:`root-fields` for the arbor. .. code-block:: python >>> print (leaf_container["mass"]) [2.6503598e+08 4.2388490e+08 5.6964032e+08 5.5640288e+08 3.5769786e+08 1.9870504e+08 6.3597126e+08] Msun These fields will be cached for faster access the next time. Nodes can be accessed by numerical index in the container or iterated over. .. code-block:: python >>> print (leaf_container[2]) TreeNode[278238650] >>> for node in leaf_container: ... print (node) The collection of nodes can be saved to a new arbor. The new arbor will include all nodes belonging to the tree for which the nodes in the container are the roots. .. code-block:: python >>> fn = a.save_arbor(trees=leaf_container) >>> a_new = ytree.load(fn) .. note:: :class:`~ytree.data_structures.node_container.NodeContainer` objects are primarily for convenience. Other than the caching of fields mentioned above, there is no performance benefit from using them. 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 :ref:`select-halos` can be used with all data loadable with ``ytree``, while the one described in :ref:`select-halos-yt` is only available for :ref:`load-ytree`. .. _select-halos: Select Halos ^^^^^^^^^^^^ The :func:`~ytree.data_structures.arbor.Arbor.select_halos` function can be used to search the :class:`~ytree.data_structures.arbor.Arbor` for halos matching a specific set of criteria. .. code-block:: python >>> 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 :func:`~ytree.data_structures.arbor.Arbor.select_halos` function will return a generator of :class:`~ytree.data_structures.tree_node.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 :class:`~ytree.data_structures.tree_node.TreeNode` object, named "tree". More complex criteria can be supplied using \& and \|. .. code-block:: python >>> 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-yt: Select Halos with yt ^^^^^^^^^^^^^^^^^^^^ .. note:: This functionality only works with :ref:`load-ytree`. You will need to :ref:`save your data in the ytree format `. The :func:`~ytree.frontends.ytree.arbor.YTreeArbor.get_yt_selection` function provides enhanced functionality beyond the capabilities of :func:`~ytree.data_structures.arbor.Arbor.select_halos` by loading the dataset into `yt `__. Given search criteria, :func:`~ytree.frontends.ytree.arbor.YTreeArbor.get_yt_selection` will return a :class:`~yt.data_objects.selection_objects.cut_region.YTCutRegion` data container that can then be queried to get the value of any field for all halos meeting the criteria. This :class:`~yt.data_objects.selection_objects.cut_region.YTCutRegion` can then be used to :ref:`generate tree nodes ` or :ref:`query fields `. Creating the Selection ^^^^^^^^^^^^^^^^^^^^^^ Search criteria can be provided using a series of keywords: ``above``, ``below``, ``equal``, and ``about``. .. code-block:: python >>> 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, )``), 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. .. code-block:: python >>> 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 :ref:`yt:cut-regions`. These cannot not be given with any of the previously mentioned keywords. .. code-block:: python >>> selection = a.get_yt_selection( ... conditionals=['obj["halos", "mass"] > 1e12']) Querying the Selection ^^^^^^^^^^^^^^^^^^^^^^ The selection object returned by :func:`~ytree.frontends.ytree.arbor.YTreeArbor.get_yt_selection` can then be queried to get field values for all matching halos. Fields should be queried as ``("halos", )``. .. code-block:: python >>> # 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 .. _halos-from-selection: Getting Halos from the Selection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :func:`~ytree.frontends.ytree.arbor.YTreeArbor.get_nodes_from_selection` function will return a generator of :class:`~ytree.data_structures.tree_node.TreeNode` objects for all halos contained within the selection. .. code-block:: python >>> # 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 :class:`~ytree.data_structures.tree_node.TreeNode` objects for :ref:`any yt data container `. .. _yt-data-containers: Halos and Fields from yt Data Containers ---------------------------------------- .. note:: This functionality only works with :ref:`load-ytree`. You will need to :ref:`save your data in the ytree format `. For merger tree data in the :ref:`ytree format `, the :attr:`~ytree.frontends.ytree.arbor.YTreeArbor.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 :ref:`geometric data containers `, like spheres or regions. .. code-block:: python >>> 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 :func:`~ytree.frontends.ytree.arbor.YTreeArbor.get_nodes_from_selection` function to :ref:`get the tree nodes ` for all halos within the container. .. code-block:: python >>> sphere = ds.sphere(ds.domain_center, (5, "Mpc")) >>> for node in a.get_nodes_from_selection(sphere): ... print (node["position"])