Metadata-Version: 1.1
Name: ScatPy
Version: 0.1.0
Summary: A Python package for setting up DDSCAT jobs and analysing the results.
Home-page: https://github.com/hohlraum/ScatPy
Author: Andrew G. Mark
Author-email: mark@is.mpg.de
License: GNU GPLv3
Description: 
        **************************************
        ScatPy -- A Python interface to DDSCAT
        **************************************
        
        
        ScatPy is a Python package for interfacing to the popular scattering simulator
        `DDSCAT <http://www.astro.princeton.edu/~draine/DDSCAT.html>`_. ScatPy provides a rich toolset to:
        
        * Create standard DDSCAT scattering targets based on physical (rather than dipole) dimensions
        * Construct and visualize complex custom scattering targets
        * Manage the job parameters found in the ddscat.par file
        * Organize iterative jobs requiring multiple targets or input parameters
        * Script job submission to cluster queue managers
        * Maintain profiles and defaults for deployment on platforms other than the local machine
        * Load, plot and manipulate DDSCAT output tables
        * Manage the output from multiple jobs through results collections
        * Work with and visualize nearfield results as multidimensional numpy arrays
        * Suitable for interactive or scripted use
        
        Documentation
        =============
        
        Complete documentation can be found at:
            http://pythonhosted.org/ScatPy/#
        
        
        Download
        ========
        
        The package can be downloaded for installation via easy_install at
            https://pypi.python.org/pypi/ScatPy
        
        
        Example
        =======
        
        .. code:: python
        
            from ScatPy import *
        
            # Establish target geometry (in um)
            length = 0.100
            radius = 0.020
            target = targets.CYLNDRCAP(length, radius, d=0.005, material='Au_Palik.txt')
        
            # Create a job to be run in the subdirectory tmp/
            job = DDscat(folder = './tmp', target=target)
        
            # Change the range of calculated wavelengths and ambient index
            job.settings.wavelengths = ranges.How_Range(0.300, 0.600, 15)
            job.settings.NAMBIENT = 1.0
        
            # Run the job locally
            job.calculate()
        
            # Open the results qtable, plot Q_sca, and Q_abs, and add a legend
            ans = results.QTable(folder = './tmp')
            ax = ans.plot(['Q_sca', 'Q_abs'])
            ax.legend(loc=0)
Platform: All
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
