README for classify.py / graph_clustering

Update history

September 2019: - HSC DR1 UDEEP catalogue added

Near future: - Add DR2 DEEP/UDEEP catalogues with varying k - Add code to run the algorithm w/ examples and documentation

Reference: Martin 2019b

Contact: garrethmartin@arizona.edu

Purpose:

Clusters objects found in a list of astronomical images by their visual similarity. Objects are sorted into k groups and a catalogue containing object centroids, group number, size in pixels and silhouette score is output.

Prerequisites:

• numpy
• scipy
• astropy
• configobj
• sklearn
• joblib
• matplotlib
• dotnetcore SDK

Installation on Python 2.7 (3 not tested):

pip install graph_clustering

or build from source

python setup.py install graph_clustering

Usage:

Using the built-in script:

usage: classify.py [-h] [-bd BASE_DIR] [-dd DATA_DIR] [-im IMG_NAMES]
                   [-il IMG_LIST] [-ib BOUNDS] [-wl BANDS] [-oi OUT_ID]
                   [-pd PATCH_DIR] [-ps PATCH_SIZE] [-nn N_NODES]
                   [-ct HC_TARGET] [-nt N_THREADS] [-ns N_SAMPLES]
                   [-ni N_ITERATIONS] [-mt METRIC] [-pe] [-pl] [-ng] [-hc]
                   [-cc] [-gt]

optional arguments:
  -h, --help            show this help message and exit
  -bd BASE_DIR, --base_dir BASE_DIR
                        Base directory
  -dd DATA_DIR, --data_dir DATA_DIR
                        Data directory
  -im IMG_NAMES, --img_names IMG_NAMES
                        Image filenames
  -il IMG_LIST, --img_list IMG_LIST
                        File containing list of image filenames
  -ib BOUNDS, --bounds BOUNDS
                        Image bounds
  -wl BANDS, --bands BANDS
                        Image bands
  -oi OUT_ID, --out_id OUT_ID
                        Output id for the model
  -pd PATCH_DIR, --patch_dir PATCH_DIR
                        Patch sub-directory
  -ps PATCH_SIZE, --patch_size PATCH_SIZE
                        Patch radius in pixels
  -nn N_NODES, --n_nodes N_NODES
                        Number of nodes in the GNG graph
  -ct HC_TARGET, --HC_target HC_TARGET
                        Target clusters of HC clustering
  -nt N_THREADS, --n_threads N_THREADS
                        Number of CPU threads
  -ns N_SAMPLES, --n_samples N_SAMPLES
                        Number of samples to process before adding a new node
  -ni N_ITERATIONS, --n_iterations N_ITERATIONS
                        Total number of GNG iterations
  -mt METRIC, --metric METRIC