Fields in ytree

ytree supports multiple types of fields, each representing numerical values associated with each halo in the Arbor. These include the native fields stored on disk, alias fields, derived fields, and analysis fields.

The Field Info Container

Each Arbor contains a dictionary, called field_info, with relevant information for each available field. This information can include the units, type of field, any dependencies or aliases, and things relevant to reading the data from disk.

>>> import ytree
>>> a = ytree.load("tree_0_0_0.dat")
>>> print (a.field_info["Rvir"])
{'description': 'Halo radius (kpc/h comoving).', 'units': 'kpc/h ', 'column': 11,
 'aliases': ['virial_radius']}
>>> print (a.field_info["mass"])
{'type': 'alias', 'units': 'Msun', 'dependencies': ['Mvir']}

Fields on Disk

Every field stored in the dataset’s files should be available within the Arbor. The field_list contains a list of all fields on disk with their native names.

>>> print (a.field_list)
['scale', 'id', 'desc_scale', 'desc_id', 'num_prog', ...]

Alias Fields

Because the various dataset formats use different naming conventions for similar fields, ytree allows fields to be referred to by aliases. This allows for a universal set of names for the most common fields. Many are added by default, including “mass”, “virial_radius”, “position_<xyz>”, and “velocity_<xyz>”. The list of available alias and derived fields can be found in the derived_field_list.

>>> print (a.derived_field_list)
['uid', 'desc_uid', 'scale_factor', 'mass', 'virial_mass', ...]

Additional aliases can be added with add_alias_field.

>>> a.add_alias_field("amount_of_stuff", "mass", units="kg")
>>> print (a["amount_of_stuff"])
[  1.30720461e+45,   1.05085632e+45,   1.03025691e+45, ...
1.72691772e+42,   1.72691772e+42,   1.72691772e+42]) kg

Derived Fields

Derived fields are functions of existing fields, including other derived and alias fields. New derived fields are created by providing a defining function and calling add_derived_field.

>>> def potential_field(field, data):
...     # data.arbor points to the parent Arbor
...     return data["mass"] / data["virial_radius"]
>>> a.add_derived_field("potential", potential_field, units="Msun/Mpc")
[  2.88624262e+14   2.49542426e+14   2.46280488e+14, ...
3.47503685e+12   3.47503685e+12   3.47503685e+12] Msun/Mpc

Field functions should take two arguments. The first is a dictionary that will contain basic information about the field, such as its name. The second argument represents the data container for which the field will be defined. It can be used to access field data for any other available field. This argument will also have access to the parent Arbor as data.arbor.

Vector Fields

For fields that have x, y, and z components, such as position, velocity, and angular momentum, a single field can be queried to return an array with all the components. For example, for fields named “position_x”, “position_y”, and “position_z”, the field “position” will return the full vector.

>>> print (a["position"])
[[0.0440018, 0.0672202, 0.9569643],
 [0.7383264, 0.1961563, 0.0238852],
 [0.7042797, 0.6165487, 0.500576 ],
 [0.1822363, 0.1324423, 0.1722414],
 [0.8649974, 0.4718005, 0.7349876]]) unitary

A list of defined vector fields can be seen by doing:

>>> print (a.field_info.vector_fields)
('position', 'velocity', 'angular_momentum')

For all vector fields, a “_magnitude” field also exists, defined as the quadrature sum of the components.

>>> print (a["velocity_magnitude"])
[ 488.26936644  121.97143067  146.81450507, ...
  200.74057711  166.13782652  529.7336846 ] km/s

Only specifically registered fields will be available as vector fields. For example, saved Analysis Fields with x,y,z components will not automatically be available. However, vector fields can be created with the add_vector_field function.

>>> a.add_vector_field("thing")

The above example assumes that fields named “thing_x”, “thing_y”, and “thing_z” already exist.

Analysis Fields

Analysis fields provide a means for saving the results of complicated analysis for any halo in the Arbor. This would be operations beyond derived fields, for example, things that might require loading the original simulation snapshots. New analysis fields are created with add_analysis_field and are initialized to zero.

>>> a.add_analysis_field("saucer_sections", units="m**2")
>>> my_tree = a[0]
>>> print (my_tree["tree", "saucer_sections"])
[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
  0.,  0.,] m**2
>>> import numpy as np
>>> for halo in my_tree["tree"]:
...     halo["saucer_sections"] = np.random.random() # complicated analysis
>>> print (my_tree["tree", "saucer_sections"])
[ 0.33919263  0.79557815  0.38264336  0.53073945  0.09634924  0.6035886, ...
  0.9506636   0.9094426   0.85436984  0.66779632  0.58816873] m**2

Analysis fields will be saved when the TreeNode objects that have been analyzed are saved with save_arbor or save_tree.

>>> my_trees = list(a[:]) # all trees
>>> for my_tree in my_trees:
...     # do analysis...
>>> a.save_arbor(trees=my_trees)

Note that we do my_trees = list(a[:]) and not just my_trees = a[:]. This is because a[:] is a generator that will return a new set of trees each time. The newly generated trees will not retain changes made to any analysis fields. Thus, we must use list(a[:]) to explicitly store a list of trees.

Re-saving Analysis Fields

All analysis fields are saved to sidecar files with the “-analysis” keyword appended to them. They can be altered and the arbor re-saved as many times as you like. In the very specific case of re-saving all trees and not providing a new filename or custom list of fields (as in the example above), analysis fields will be saved in place (i.e., over-writing the “-analysis” files). The conventional on-disk fields will not be re-saved as they cannot be altered.