Some relatively simple visualizations of merger trees can be made with the ~ytree.visualization.tree_plot.TreePlot
command.
Making merger tree plots with ytree
requires the pydot and graphviz packages. pydot
can be installed with pip
and the graphviz website provides a number of installation options.
The ~ytree.visualization.tree_plot.TreePlot
command can be used to create a digraph depicting halos as filled circles with sizes proportional to their mass. The main progenitor line will be colored red.
>>> import ytree
>>> a = ytree.load("ahf_halos/snap_N64L16_000.parameter",
... hubble_constant=0.7)
>>> p = ytree.TreePlot(a[0], dot_kwargs={'rankdir': 'LR', 'size': '"12,4"'})
>>> p.save('tree.png')
Four ~ytree.visualization.tree_plot.TreePlot
attributes can be set to modify the default plotting behavior. These are:
~ytree.visualization.tree_plot.TreePlot.size_field
: The field to determine the size of each circle. Default: 'mass'.~ytree.visualization.tree_plot.TreePlot.size_log
: Whether to scale circle sizes based on log of size field. Default: True.~ytree.visualization.tree_plot.TreePlot.min_mass
: The minimum halo mass to be included in the plot. If given as a float, units are assumed to be Msun. Default: None.~ytree.visualization.tree_plot.TreePlot.min_mass_ratio
: The minimum ratio between a halo's mass and the mass of the main halo to be included in the plot. Default: None.
>>> import ytree
>>> a = ytree.load("ahf_halos/snap_N64L16_000.parameter",
... hubble_constant=0.7)
>>> p = ytree.TreePlot(a[0], dot_kwargs={'rankdir': 'LR', 'size': '"12,4"'})
>>> p.min_mass_ratio = 0.01
>>> p.save('tree_small.png')
The appearance of the nodes can be customized by providing a function that returns a dictionary of keywords that will be used to create the pydot
node. This should accept a single argument that is a ~ytree.data_structures.tree_node.TreeNode
object representing the halo to be plotted. For example, the following function will add labels of the halo id and mass and make the node shape square. It will also color the most massive progenitor red.
def my_node(halo):
prog = list(halo.find_root()['prog', 'uid'])
if halo['uid'] in prog:
color = 'red'
else:
color = 'black'
label = \
"""
id: %d
mass: %.2e Msun
""" % (halo['uid'], halo['mass'].to('Msun'))
my_kwargs = {"label": label, "fontsize": 8,
"shape": "square", "color": color}
return my_kwargs
This function is then provided with the node_function keyword.
>>> p = ytree.TreePlot(tree, dot_kwargs={'rankdir': "BT"},
... node_function=my_node)
>>> p.save('tree_custom_node.png')
The edges of the plot are the lines connecting each of the nodes. Similar to the nodes, their appearance can be customized by providing a function that returns a dictionary of keywords that will be used to create the pydot
edge. This should accept two ~ytree.data_structures.tree_node.TreeNode
arguments representing the ancestor and descendent halos being connected by the edge. The example below colors the edges blue when the descendent is less massive than its ancestor and green when the descendent is more than 10 times more massive than its ancestor.
def my_edge(ancestor, descendent):
if descendent['mass'] < ancestor['mass']:
color = 'blue'
elif descendent['mass'] / ancestor['mass'] > 10:
color = 'green'
else:
color = 'black'
my_kwargs = {"color": color, "penwidth": 5}
return my_kwargs
This function is then provided with the edge_function keyword.
>>> p = ytree.TreePlot(tree, dot_kwargs={'rankdir': "BT"},
... node_function=my_node,
... edge_function=my_edge)
>>> p.save('tree_custom_edge.png')
Plots can be saved to any format supported by graphviz
by giving a filename with the appropriate extension. See here for a list of currently supported formats.