Advanced topics

Specifying a custom structure merging strategy

By default, the decision about whether a leaf remains independent (i.e., whether it remains a leaf or its pixels get incorporated into another branch) when merged is made based on the min_delta and min_npix parameters, but in some cases, you may want to use more specialized criteria. For example, you may want only leaves overlapping with a certain position, or you may want leaves with a certain spatial or velocity extent, or a minimum peak value, to be considered independent structures.

In order to accomodate this, the compute() method can optionally take an is_independent argument which should be a function with the following call signature:

def is_independent(structure, index=None, value=None):

where structure is the Structure object that is being considered, and index and value are the pixel index and value of the pixel that is linking the structure to the rest of the tree. These last two values are only set when calling the is_independent function during the tree computation, but the is_independent function is also used at the end of the computation to prune leaves that are not attached to the tree, and in this case index and value are not set.

The following example compares the dendrogram obtained with and without a custom is_independent function:

import matplotlib.pyplot as plt
from import fits
from astrodendro import Dendrogram

image = fits.getdata('PerA_Extn2MASS_F_Gal.fits')

fig = plt.figure(figsize=(15,5))

# Default merging strategy

d1 = Dendrogram.compute(image, min_value=2.0)
p1 = d1.plotter()

ax1 = fig.add_subplot(1, 3, 1)
p1.plot_tree(ax1, color='black')
ax1.hlines(3.5, *ax1.get_xlim(), color='b', linestyle='--')
ax1.set_title("Default merging")

# Require minimum peak value
# this is equivalent to
# custom_independent = astrodendro.pruning.min_peak(3.5)
def custom_independent(structure, index=None, value=None):
    peak_index, peak_value = structure.get_peak()
    return peak_value > 3.5

d2 = Dendrogram.compute(image, min_value=2.0,
p2 = d2.plotter()

ax2 = fig.add_subplot(1, 3, 2)
p2.plot_tree(ax2, color='black')
ax2.hlines(3.5, *ax2.get_xlim(), color='b', linestyle='--')
ax2.set_title("Custom merging")

# For comparison, this is what changing the min_value does:
d3 = Dendrogram.compute(image, min_value=3.5)
p3 = d3.plotter()

ax3 = fig.add_subplot(1, 3, 3)
p3.plot_tree(ax3, color='black')
ax3.hlines(3.5, *ax3.get_xlim(), color='b', linestyle='--')
ax3.set_title("min_value=3.5 merging")

Several pre-implemented functions suitable for use as is_independent tests are provided in astrodendro.pruning. In addition, the astrodendro.pruning.all_true() function can be used to combine several criteria. For example, the following code builds a dendrogram where each leaf contains a pixel whose value >=20, and whose pixels sum to >= 100:

from astrodendro.pruning import all_true, min_peak, min_sum

custom_independent = all_true((min_peak(20), min_sum(100)))
Dendrogram.compute(image, is_independent=custom_independent)

Handling custom adjacency logic

By default, neighbours to a given pixel are considered to be the adjacent pixels in the array. However, not all data are like this. For example, all-sky cartesian maps are periodic along the X axis.

You can specify custom neighbour logic by providing a neighbours function to Dendrogram.compute(). For example, the periodic_neighbours() utility will wrap neighbours across array edges. To correctly compute dendrograms for all-sky Cartesian maps:

periodic_axis = 1  # data wraps along longitude axis
Dendrogram.compute(data, neighbours=periodic_neighbours(periodic_axis))