# Clump Finding¶

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.analysis_modules.level_sets.api import *

data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],
(8, 'kpc'), (1, 'kpc'))

master_clump = Clump(data_source, ("gas", "density"))


## Clump Validators¶

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 add_validator() function. Current, two validators exist: a minimum number of cells and gravitational boundedness.

master_clump.add_validator("min_cells", 20)



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 Clump object and either return True or False.

def _minimum_gas_mass(clump, min_mass):
return (clump["gas", "cell_mass"].sum() >= min_mass)


The add_validator() 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 find_clumps() function. 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: total_cells, cell_mass, mass_weighted_jeans_mass, volume_weighted_jeans_mass, max_grid_level, min_number_density, and max_number_density. Additional items can be added with the add_info_item() function.

master_clump.add_info_item("total_cells")


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 add_clump_info():

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


Then, add it to the list:

master_clump.add_info_item("mass_weighted_jeans_mass")


Beside the quantities calculated by default, the following are available: center_of_mass and distance_to_main_clump.

## 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 Clump object contains a list of all sub-clumps. Each sub-clump is also a Clump object with its own children attribute, and so on.

print(master_clump["gas", "density"])
print(master_clump.children)
print(master_clump.children[0]["gas", "density"])


The entire clump tree can traversed with a loop syntax:

for clump in master_clump:
print(clump.clump_id)


The get_lowest_clumps() function 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 = get_lowest_clumps(master_clump)

print(leaf_clumps[0]["gas", "density"])
print(leaf_clumps[0]["all", "particle_mass"])
print(leaf_clumps[0].quantities.total_mass())


## Visualizing Clumps¶

Clumps can be visualized using the annotate_clumps callback.

prj = yt.ProjectionPlot(ds, 2, ("gas", "density"),
center='c', width=(20,'kpc'))
prj.annotate_clumps(leaf_clumps)
prj.save('clumps')


The clump tree can be saved as a reloadable dataset with the save_as_dataset() 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 below it.

fn = master_clump.save_as_dataset(fields=["density", "particle_mass"])


The clump tree can then be reloaded as a regular dataset. The tree attribute 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)


The leaves attribute returns a list of all leaf clumps.

print(ds_clumps.leaves)


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[0]
print(my_clumps["clump", "cell_mass"])
print(my_clumps["grid", "density"])
print(my_clumps["all", "particle_mass"])