Metadata-Version: 2.1
Name: autogalaxy
Version: 0.18.1
Summary: Astro modelling
Home-page: https://github.com/jammy2211/PyAutoGalaxy
Author: James Nightingale and Richard Hayes
Author-email: james.w.nightingale@durham.ac.uk
License: MIT License
Description: PyAutoGalaxy
        ==========
        
        The study of a galaxy's light, structure and dynamics is at the heart of modern day Astrophysical research.
        **PyAutoGalaxy** makes it simple to model galaxies, like this one:
        
        Missing for now :(
        
        Example
        -------
        
        With **PyAutoGalaxy**, you can begin modeling a galaxy in just a couple of minutes. The example below demonstrates a
        simple analysis which fits a galaxy's light.
        
        .. code-block:: python
        
            import autofit as af
            import autogalaxy as ag
        
            import os
        
            # In this example, we'll fit an image of a single galaxy .
            dataset_path = '{}/../data/'.format(os.path.dirname(os.path.realpath(__file__)))
        
            galaxy_name = 'example_galaxy'
        
            # Use the relative path to the dataset to load the imaging data.
            imaging = ag.Imaging.from_fits(
                image_path=dataset_path + galaxy_name + '/image.fits',
                psf_path=dataset_path+galaxy_name+'/psf.fits',
                noise_map_path=dataset_path+galaxy_name+'/noise_map.fits',
                pixel_scales=0.1)
        
            # Create a mask for the data, which we setup as a 3.0" circle.
            mask = ag.Mask2D.circular(shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=3.0)
        
            # We model our galaxy using a light profile (an elliptical Sersic).
            light_profile = ag.lp.EllipticalSersic
        
            # To setup our model galaxy, we use the GalaxyModel class, which represents a galaxy whose parameters
            # are free & fitted for by PyAutoGalaxy. The galaxy is also assigned a redshift.
            galaxy_model = ag.GalaxyModel(redshift=1.0, light=light_profile)
        
            # To perform the analysis we set up a phase, which takes our galaxy model & fits its parameters using a non-linear
            # search (in this case, MultiNest).
            phase = ag.PhaseImaging(
                galaxies=dict(galaxy=galaxy_model),
                name='example/phase_example',
                search=af.DynestyStatic()
                )
        
            # We pass the imaging ``data`` and mask to the phase, thereby fitting it with the galaxy model & plot the resulting fit.
            result = phase.run(data=imaging, mask=mask)
            ag.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)
        
        Getting Started
        ---------------
        
        Please contact us via email or on our SLACK channel if you are interested in using **PyAutoGalaxy**, as project
        is still a work in progress whilst we focus n **PyAutoFit** and **PyAutoLens**.
        
        Slack
        -----
        
        We're building a **PyAutoGalaxy** community on Slack, so you should contact us on our
        `Slack channel <https://pyautogalaxy.slack.com/>`_ before getting started. Here, I will give you the latest updates on
        the software & discuss how best to use **PyAutoGalaxy** for your science case.
        
        Unfortunately, Slack is invitation-only, so first send me an `email <https://github.com/Jammy2211>`_ requesting an
        invite.
Keywords: cli
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: test
