semvec/pypi-packages huggingface dataset:
graph-clustering 0.2
Clusters objects found in astronomical images by their visual similarity
License: MIT

# **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