Metadata-Version: 1.1
Name: astrodash
Version: 0.2.1
Summary: Deep Learning for Automated Spectral Classification of Supernovae
Home-page: https://github.com/daniel-muthukrishna/DASH
Author: Daniel Muthukrishna
Author-email: daniel.muthukrishna@gmail.com
License: MIT
Description: # DASH
        Supernovae classifying and redshifting software: development stage
        
        
        ## 1. How to install:
        
            1.1 pip install astrodash
        
                or download from github (https://github.com/daniel-muthukrishna/DASH)
        
        ## 2. Get started with the Python Library interface:
            2.1 Use the following example code:
                import dash
                classification = dash.Classify([filenames], [knownRedshifts])
                print classification.list_best_matches(n=1)  # Shows top 'n' matches for each spectrum
        
            2.2 To open the gui from a script use:
                import dash
                dash.run_gui()
        
        
        ## 3. Get started with GUI
            2.1 Run GUI/main.py
        
            2.2 Once open, type in a known redshift
        
            2.3 Browse for any single spectrum FITS, ASCII, dat, or two-column text file.
        
            2.4 Click any of the best matches to view the continuum-subtracted binned spectra.
        
            2.5 If the input spectrum is too noisy, increase the smoothing level, and click 'Re-fit with priors'
        
        
        ## 4. Dependencies:
            Using pip will automatically install numpy, scipy, specutils, pyqtgraph, and tensorflow.
        
            PyQt4
        
                This can be installed with anaconda: "conda install pyqt=4" (or else independently - only needed for the GUI)
        
        ## 5. How to raise issues:
        
        ## 6. Example Usage
            6.1 Example from OzDES Run028:
                This example automatically classifies 11 spectra. The last line plots the first spectrum on the GUI.
                ```
                import dash
        
                filenames = []
                filenames.append('DES16C3elb_C3_combined_161227_v10_b00.dat')
                filenames.append('DES16X3dvb_X3_combined_161225_v10_b00.dat')
                filenames.append('DES16C2ege_C2_combined_161225_v10_b00.dat')
                filenames.append('DES16X3eww_X3_combined_161225_v10_b00.dat')
                filenames.append('DES16X3enk_X3_combined_161225_v10_b00.dat')
                filenames.append('DES16S1ffb_S1_combined_161226_v10_b00.dat')
                filenames.append('DES16C1fgm_C1_combined_161226_v10_b00.dat')
                filenames.append('DES16X2dzz_X2_combined_161226_v10_b00.dat')
                filenames.append('DES16X1few_X1_combined_161227_v10_b00.dat')
                filenames.append('DES16X1chc_X1_combined_161227_v10_b00.dat')
                filenames.append('DES16S2ffk_S2_combined_161227_v10_b00.dat')
        
        
                knownRedshifts = []
                knownRedshifts.append(0.429)
                knownRedshifts.append(0.329)
                knownRedshifts.append(0.348)
                knownRedshifts.append(0.445)
                knownRedshifts.append(0.331)
                knownRedshifts.append(0.164)
                knownRedshifts.append(0.361)
                knownRedshifts.append(0.325)
                knownRedshifts.append(0.311)
                knownRedshifts.append(0.043)
                knownRedshifts.append(0.373)
        
                classification = dash.Classify(filenames, knownRedshifts)
                print classification.list_best_matches(n=3)
                classification.plot_with_gui(indexToPlot=0)
                ```
        
        ## 7. API Usage
        Notes:
            Current version requires an input redshift (inaccurate results if redshift is unknown)
        
        
        
Keywords: supernova spectral classification deep machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
