Metadata-Version: 2.1
Name: clustar
Version: 1.2.1
Summary: A python package for processing and analyzing protostars/
                   protoplanetary disks in astronomical data in Flexible Image
                   Transport System (FITS) images.
Home-page: https://github.com/clustar/clustar
Author: Pavan Kumar Bondalapati
Author-email: pb7ak@virginia.edu
License: UNKNOWN
Description: # Clustar
        
        Release: 1.2.1
        
        Date: June 27, 2021
        
        ## Overview
        
        A python package for processing and analyzing protostars/protoplanetary disks
        in astronomical data in Flexible Image Transport System (FITS) images. 
        
        These files contain grayscale images represented as two-dimensional arrays,
        with each pixel containing the intensity values, and headers containing the
        telescope observational parameters.
        
        Clustar simplifies and expediates the identification pipeline of FITS files
        by automating the preprocessing, grouping, and fitting for a large amount of
        FITS files.
        
        ## Requirements
        
        Clustar 1.2.1 requires
        
        * GEOS >= 3.3
        * Shapely >= 1.7.1
        
        Both of these dependencies are available on <https://anaconda.org/conda-forge>.
        ```
        conda install -c conda-forge geos
        conda install -c conda-forge shapely 
        ```
        
        ## Installation
        
        Clustar is available on [PyPI](https://pypi.org/project/clustar/) and can be installed using `pip`:
        
        ```
        pip install clustar
        ```
        
        ## Singular Usage
        
        Detect celestial objects in a singular FITS image by creating a `ClustarData`
        object.
        
        ```
        from clustar.core import ClustarData
        
        # Create the 'ClustarData' object by specifying the path to FITS file.
        cd = ClustarData(path='~/data/example.fits', threshold=0.025)
        
        # Visualize the detected groups.
        cd.identify()
        
        # Access individual 'Group' objects.
        cd.groups
        ```
        
        ## Multiple Usage
        
        Detect celestial objects in a directory containing multiple FITS images by
        creating a `Clustar` object.
        
        ```
        from clustar.search import Clustar
        
        # Setup 'Clustar' object.
        cs = Clustar(radius_factor=0.95, threshold=0.025)
        
        # Execute pipeline on directory containing FITS files.
        cs.run(directory='~/data/')
        
        # Access individual 'ClustarData' objects.
        cs.data
        
        # Check which FITS files raised an error.
        cs.errors
        
        # Inspect 'ClustarData' variables for all groups in each FITS file.
        cs.display(category='all')
        ```
        
        ## Modules
        
        1. `base.py`
            
            Internal module for testing clustar modules.
        
        2. `core.py`
            
            Contains the `ClustarData` class, which is responsible for executing
            the entire project pipeline for detecting groups in a single FITS image.
        
        3. `denoise.py`
            
            Clustar module for denoising-related methods.
        
        4. `fit.py`
            
            Clustar module for fitting-related methods.
        
        5. `graph.py`
            
            General module for graphing-related methods.
        
        6. `group.py`
            
            Clustar module for grouping-related methods.
        
        7. `search.py`
            
            Contains the `Clustar` hierarchical class, which is responsible for 
            transforming all available FITS images in a specified directory into their 
            respective `ClustarData` objects.
        
        ## Notes
        
        Visit <https://clustar.github.io/> for additional information.
        
Keywords: cluster,astronomy,protostars
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
Provides-Extra: dev
