The clump finder uses a contouring algorithm to identified topologically disconnected structures within a dataset. This works by first creating a single contour over the full range of the contouring field, then continually increasing the lower value of the contour until it reaches the maximum value of the field. As disconnected structures are identified as separate contours, the routine continues recursively through each object, creating a hierarchy of clumps. Individual clumps can be kept or removed from the hierarchy based on the result of user-specified functions, such as checking for gravitational boundedness. A sample recipe can be found in Identifying Clumps.
Setting up the Clump Finder¶
The clump finder requires a data object (see Data Objects) and a field
over which the contouring is to be performed. The data object is then used
to create the initial
Clump object that
acts as the base for clump finding.
import yt from yt.data_objects.level_sets.api import * ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.], (8, 'kpc'), (1, 'kpc')) master_clump = Clump(data_source, ("gas", "density"))
At this point, every isolated contour will be considered a clump,
whether this is physical or not. Validator functions can be added to
determine if an individual contour should be considered a real clump.
These functions are specified with the
function. Current, two validators exist: a minimum number of cells and gravitational
master_clump.add_validator("min_cells", 20) master_clump.add_validator("gravitationally_bound", use_particles=False)
As many validators as desired can be added, and a clump is only kept if all
return True. If not, a clump is remerged into its parent. Custom validators
can easily be added. A validator function must only accept a
and either return True or False.
def _minimum_gas_mass(clump, min_mass): return (clump["gas", "cell_mass"].sum() >= min_mass) add_validator("minimum_gas_mass", _minimum_gas_mass)
function adds the validator to a registry that can
be accessed by the clump finder. Then, the validator can be added to the
clump finding just like the others.
master_clump.add_validator("minimum_gas_mass", ds.quan(1.0, "Msun"))
Running the Clump Finder¶
Clump finding then proceeds by calling the
This function accepts the
Clump object, the initial
minimum and maximum of the contouring field, and the step size. The lower value
of the contour finder will be continually multiplied by the step size.
c_min = data_source["gas", "density"].min() c_max = data_source["gas", "density"].max() step = 2.0 find_clumps(master_clump, c_min, c_max, step)
Calculating Clump Quantities¶
By default, a number of quantities will be calculated for each clump when the
clump finding process has finished. The default quantities are:
Additional items can be added with the
Just like the validators, custom info items can be added by defining functions
that minimally accept a
Clump object and return
a format string to be printed and the value. These are then added to the list
of available info items by calling
def _mass_weighted_jeans_mass(clump): jeans_mass = clump.data.quantities.weighted_average_quantity( "jeans_mass", ("gas", "cell_mass")).in_units("Msun") return "Jeans Mass (mass-weighted): %.6e Msolar." % jeans_mass add_clump_info("mass_weighted_jeans_mass", _mass_weighted_jeans_mass)
Then, add it to the list:
Once you have run the clump finder, you should be able to access the data for
the info item you have defined via the
info attribute of a
clump = leaf_clumps print(clump.info['mass_weighted_jeans_mass'])
Besides the quantities calculated by default, the following are available:
Working with Clumps¶
After the clump finding has finished, the master clump will represent the top
of a hierarchy of clumps. The
children attribute within a
contains a list of all sub-clumps. Each sub-clump is also a
with its own
children attribute, and so on.
print(master_clump["gas", "density"]) print(master_clump.children) print(master_clump.children["gas", "density"])
The entire clump tree can traversed with a loop syntax:
for clump in master_clump: print(clump.clump_id)
leaves attribute of a
Clump object will return a list of the
individual clumps that have no children of their own (the leaf clumps).
# Get a list of just the leaf nodes. leaf_clumps = master_clump.leaves print(leaf_clumps["gas", "density"]) print(leaf_clumps["all", "particle_mass"]) print(leaf_clumps.quantities.total_mass())
Clumps can be visualized using the
prj = yt.ProjectionPlot(ds, 2, ("gas", "density"), center='c', width=(20,'kpc')) prj.annotate_clumps(leaf_clumps) prj.save('clumps')
Saving and Reloading Clump Data¶
The clump tree can be saved as a reloadable dataset with the
function. This will save all info items that have been calculated as well as
any field values specified with the fields keyword. This function
can be called for any clump in the tree, saving that clump and all those
fn = master_clump.save_as_dataset(fields=["density", "particle_mass"])
The clump tree can then be reloaded as a regular dataset. The
associated with the dataset provides access to the clump tree. The tree can be
iterated over in the same fashion as the original tree.
ds_clumps = yt.load(fn) for clump ds_clumps.tree: print(clump.clump_id)
leaves attribute returns a list of all leaf clumps.
Info items for each clump can be accessed with the clump field type. Gas or grid fields should be accessed using the grid field type and particle fields should be access using the specific particle type.
my_clump = ds_clumps.leaves print(my_clumps["clump", "cell_mass"]) print(my_clumps["grid", "density"]) print(my_clumps["all", "particle_mass"])