Analyzing Merger Trees

This section describes the preferred method for performing analysis on merger trees that results in modification of the dataset (e.g., by creating or altering Analysis Fields) or writing additional files. The end of this section, Putting it all Together: Parallel Analysis, illustrates an analysis workflow that will run in parallel.

When performing the same analysis on a large number of items, we often think in terms of creating an “analysis pipeline”, where a series of discrete actions, including deciding whether to skip a given item, are embedded within a loop over all the items to analyze. For merger trees, this may look something like the following:

import ytree

a = ytree.load(...)

trees = list(a[:])
for tree in trees:
    for node in tree["forest"]:

        # only analyze above some minimum mass
        if node["mass"] < a.quan(1e11, "Msun"):

        # get simulation snapshot associated with this halo
        snap_fn = get_filename_from_redshift(node["redshift"])
        ds = yt.load(snap_fn)

        # get sphere using halo's center and radius
        center = node["position"].to("unitary")
        radius = node["virial_radius"].to("unitary")
        sp = ds.sphere((center, "unitary"), (radius, "unitary"))

        # calculate gas mass and save to field
        node["gas_mass"] = sp.quantities.total_quantity(("gas", "mass"))

        # make a projection and save an image
        p = yt.ProjectionPlot(ds, "x", ("gas", "density"),

There are a few disadvantages of this approach. The inner loop is very long. It can be difficult to understand the full set of actions, especially if you weren’t the one who wrote it. If there is a section you no longer want to do, a whole block of code needs to be commented out or removed, and it may be tricky to tell if doing that will break something else. Putting the operations into functions will make this simpler, but it can still make for a large code block in the inner loop. As well, if the structure of the loops over trees or nodes is more complicated than the above, there is potential for the code to be non-trivial to digest.

The AnalysisPipeline

The AnalysisPipeline allows you to design the analysis workflow in advance and then use it to process a tree or node with a single function call. Skipping straight to the end, the loop from above will take the form:

for tree in trees:
    for node in tree["forest"]:

In the above example, “ap” is some AnalysisPipeline object that we have defined earlier. We will now take a closer look at how to design a workflow using this method.

Creating an AnalysisPipeline

An AnalysisPipeline is instantiated with no arguments. Only an optional output directory inside which new files will be written can be specified with the output_dir keyword.

import ytree

ap = ytree.AnalysisPipeline(output_dir="my_analysis")

The output directory will be created automatically if it does not already exist.

Creating Pipeline Operations

An analysis pipeline is assembled by creating functions that accept a single TreeNode as an argument.

def say_hello(node):
    print (f"This is node {node}! I will now be analyzed.")

This function can now be added to an existing pipeline with the add_operation function.


Now, when the process_target function is called with a TreeNode object, the say_hello function will be called with that TreeNode. Any additional calls to add_operation will result in those functions also being called with that TreeNode in the same order.

Adding Extra Function Arguments

Functions can take additional arguments and keyword arguments as well.

def print_field_value(node, field, units=None):
    val = node[field]
    if units is not None:
    print (f"Value of {field} for node {node} is {val}.")

The additional arguments and keyword arguments are then provided when calling add_operation.

ap.add_operation(print_field_value, "mass")
ap.add_operation(print_field_value, "virial_radius", units="kpc/h")

Organizing File Output by Operation

In the same way that the AnalysisPipeline object accepts an output_dir keyword, analysis functions can also accept an output_dir keyword.

def save_something(node, output_dir=None):
    # make an HDF5 file named by the unique node ID
    filename = f"node_{node.uid}.h5"
    if output_dir is not None:
        filename = os.path.join(output_dir, filename)

    # do some stuff...

# meanwhile, back in the pipeline...
ap.add_operation(save_something, output_dir="images")

This output_dir keyword will be intercepted by the AnalysisPipeline object to ensure that the directory gets created if it does not already exist. Additionally, if an output_dir keyword was given when the AnalysisPipeline was created, as in the example above, the directory associated with the function will be appended to that. Following the examples here, the resulting directory would be “my_analysis/images”, and the code above will correctly save to that location.

Using a Function as a Filter

Making an analysis function return True or False allows it to act as a filter. If a function returns False, then any additional operations defined in the pipeline will not be performed. For example, we might create a mass filter like this:

def minimum_mass(node, value):
    return node["mass"] >= value

# later, in the pipeline
ap.add_operation(minimum_mass, a.quan(1e11, "Msun"))

The pipeline will interpret any return value from an operation that is not None in a boolean context to use as a filter.

Adding Operations that Always Run

As discussed above in Using a Function as a Filter, returning False from an operation will prevent all further operations in the pipeline from being performed on that node. However, there may be operations that you want to always run, regardless of previous filters. For example, there may be clean up operations, like freeing up memory, that should run for every node, no matter what. To accomplish this, the always_do keyword can be set to True in the call to add_operation.

def delete_attributes(node, attributes):
    for attr in attributes:
        if hasattr(node, attr):
            delattr(node, attr)

# later, in the pipeline
ap.add_operation(delete_attributes, ["ds", "sphere"], always_do=True)

Modifying a Node

There may be occasions where you want to pass local variables or objects around from one function to the next. The easiest way to do this is by attaching them to the TreeNode object itself as an attribute. For example, say we have a function that returns a simulation snapshot loaded with yt as a function of redshift. We might do something like the the following to then pass it to another function which creates a yt sphere.

def get_yt_dataset(node):
    # assume you have something like this
    filename = get_filename_from_redshift(node["redshift"])
    # attach it to the node for later use
    node.ds = yt.load(filename)

def get_yt_sphere(node):
    # this works if get_yt_dataset has been called first
    ds = node.ds

    center = node["position"].to("unitary")
    radius = node["virial_radius"].to("unitary")
    node.sphere = ds.sphere((center, "unitary"), (radius, "unitary"))

Then, we can add these to the pipeline such that a later function can use the sphere.


To clean things up, we can make a function to remove attributes and add it to the end of the pipeline.

def delete_attributes(node, attributes):
    for attr in attributes:
        if hasattr(node, attr):
            delattr(node, attr)

# later, in the pipeline
ap.add_operation(delete_attributes, ["ds", "sphere"], always_do=True)

See Adding Operations that Always Run for a discussion of the always_do option.

Running the Pipeline

Once the pipeline has been defined through calls to add_operation, it is now only a matter of looping over the nodes we want to analyze and calling process_target with them.

for tree in trees:
    for node in tree["forest"]:

Depending on what you want to do, you may want to call process_target with an entire tree and skip the inner loop. After all, a tree in this context is just another TreeNode object, only one that has no descendent.

Creating a Analysis Recipe

Through the previous examples, we have designed a workflow by defining functions and adding them to our pipeline in the order we want them to be called. Has it resulted in fewer lines of code? No. But it has allowed us to construct a workflow out of a series of reusable parts, so the creation of future pipelines will certainly involve fewer lines of code. It is also possible to define a more complex series of operations as a “recipe” that can be added in one go to the pipeline using the add_recipe function. A recipe should be a function that, minimally, accepts an AnalysisPipeline object as the first argument, but can also accept more. Below, we will define a recipe for calculating the gas mass for a halo. For our purposes, assume the functions we created earlier exist here.

def calculate_gas_mass(node):
    sphere = node.sphere
    node["gas_mass"] = sphere.quantities.total_quantity(("gas", "mass"))

def gas_mass_recipe(pipeline):
    pipeline.add_operation(delete_attributes, ["ds", "sphere"])

Now, our entire analysis pipeline design can look like this.

ap = ytree.AnalysisPipeline()

See the add_recipe docstring for an example of including additional function arguments.

Putting it all Together: Parallel Analysis

To unleash the true power of the AnalysisPipeline, run it in parallel using one of the Parallel Iterators. See Parallel Computing with ytree for more information on using ytree on multiple processors.

import ytree

a = ytree.load("arbor/arbor.h5")
if "test_field" not in a.field_list:
    a.add_analysis_field("gas_mass", default=-1, units="Msun")

ap = ytree.AnalysisPipeline()

trees = list(a[:])
for node in ytree.parallel_nodes(trees):

If you need some inspiration, have a look at some Example Applications.