Metadata-Version: 2.1
Name: autogalaxy
Version: 2021.8.12.1
Summary: Open Source Galaxy Model-Fitting
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
        
            """
            Load Imaging data of the strong lens from the dataset folder of the workspace.
            """
            imaging = ag.Imaging.from_fits(
                image_path="/path/to/dataset/image.fits",
                noise_map_path="/path/to/dataset/noise_map.fits",
                psf_path="/path/to/dataset/psf.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 the galaxy using an EllSersic LightProfile.
            """
            light_profile = ag.lp.EllSersic
        
            """
            We next setup this profile as model components whose parameters are free & fitted for
            by setting up a Galaxy as a Model.
            """
            galaxy_model = af.Model(ag.Galaxy, redshift=1.0, light=light_profile)
            model = af.Collection(galaxy=_galaxy_model)
        
            """
            We define the non-linear search used to fit the model to the data (in this case, Dynesty).
            """
            search = af.DynestyStatic(name="search[example]", nlive=50)
            
            """
            We next set up the `Analysis`, which contains the `log likelihood function` that the
            non-linear search calls to fit the lens model to the data.
            """
            analysis = ag.AnalysisImaging(dataset=masked_imaging)
        
            """
            To perform the model-fit we pass the model and analysis to the search's fit method. This will
            output results (e.g., dynesty samples, model parameters, visualization) to hard-disk.
            """
            result = search.fit(model=model, analysis=analysis)
        
            """
            The results contain information on the fit, for example the maximum likelihood
            model from the Dynesty parameter space search.
            """
            print(result.samples.max_log_likelihood_instance)
        
        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
