Metadata-Version: 2.1
Name: graph_clustering
Version: 0.2
Summary: Clusters objects found in astronomical images by their visual similarity
Home-page: https://github.com/garrethmartin/HSC_UML
Author: garrethmartin
Author-email: g.martin4@herts.ac.uk
License: MIT
Description: 
        # **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
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
