Metadata-Version: 2.1
Name: autolens
Version: 0.16.0
Summary: Automated Strong Gravitational Lens Modeling
Home-page: https://github.com/Jammy2211/PyAutoLens
Author: James Nightingale and Richard Hayes
Author-email: james.w.nightingale@durham.ac.uk
License: MIT License
Description: # PyAutoLens
        
        When two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy is strongly lensed and appears multiple times or as an Einstein ring of light. **PyAutoLens** makes it simple to model strong gravitational lenses, like this one: 
        
        ![alt text](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/master/gitimage.png)
        
        **PyAutoLens** is based on the following papers:
        
        [Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging](https://arxiv.org/abs/1412.7436)
        
        [AutoLens: Automated Modeling of a Strong Lens's Light, Mass and Source](https://arxiv.org/abs/1708.07377)
        
        ## Python Example
        
        With **PyAutoLens**, you can begin modeling a lens in just a couple of minutes. The example below demonstrates a simple analysis which fits a lens galaxy's light, mass and a source galaxy.
        
        ```python
        from autofit import conf
        from autofit.optimize import non_linear as nl
        from autolens.pipeline import phase as ph
        from autolens.data.array import mask as msk
        from autolens.model.galaxy import galaxy_model as gm
        from autolens.data import ccd
        from autolens.model.profiles import light_profiles as lp
        from autolens.model.profiles import mass_profiles as mp
        from autolens.data.plotters import ccd_plotters
        from autolens.lens.plotters import lens_fit_plotters
        
        import os
        
        # In this example, we'll generate a phase which fits a simple lens + source plane system.
        
        # Get the relative path to the data in our workspace and load the ccd imaging data.
        path = '{}/../'.format(os.path.dirname(os.path.realpath(__file__)))
        
        lens_name = 'example_lens'
        
        ccd_data = ccd.load_ccd_data_from_fits(image_path=path + '/data/' + lens_name + '/image.fits', 
                                               psf_path=path+'/data/'+lens_name+'/psf.fits',
                                               noise_map_path=path+'/data/'+lens_name+'/noise_map.fits', 
                                               pixel_scale=0.1)
        
        # Create a mask for the data, which we setup below as a 3.0" circle.
        mask = msk.Mask.circular(shape=ccd_data.shape, pixel_scale=ccd_data.pixel_scale, radius_arcsec=3.0)
        
        # We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) and our source galaxy 
        # a light profile (an elliptical Sersic). We load these profiles from the 'light_profiles (lp)' and 
        # 'mass_profiles (mp)' modules.
        lens_mass_profile = mp.EllipticalIsothermal
        source_light_profile = lp.EllipticalSersic
        
        # To setup our model galaxies, we use the GalaxyModel class, representing a galaxy the parameters of 
        # which are variable and fitted for by the analysis.
        lens_galaxy_model = gm.GalaxyModel(mass=lens_mass_profile)
        source_galaxy_model = gm.GalaxyModel(light=source_light_profile)
        
        # To perform the analysis, we set up a phase using the 'phase' module (imported as 'ph').
        # A phase takes our galaxy models and fits their parameters using a non-linear search 
        # (in this case, MultiNest).
        phase = ph.LensSourcePlanePhase(lens_galaxies=dict(lens=gm.GalaxyModel(mass=mp.EllipticalIsothermal)),
                                        source_galaxies=dict(source=gm.GalaxyModel(light=lp.EllipticalSersic)),
                                        optimizer_class=nl.MultiNest, phase_name='example/phase_example')
        
        # We run the phase on the ccd data, print the results and plot the fit.
        result = phase.run(data=ccd_data)
        lens_fit_plotters.plot_fit_subplot(fit=result.most_likely_fit)
        
        ```
        
        ## Slack
        
        We're building a **PyAutoLens** community on Slack, so you should contact us on our [Slack channel](https://pyautolens.slack.com/) before getting started. Here, I can introduce you to the community, give you the latest update on the software and discuss how best to use **PyAutoLens** for your science case.
        
        Unfortunately, Slack is invitation-only, so first send me an [email](https://github.com/Jammy2211) requesting an invite.
        
        ## Features
        
        **PyAutoLens's** advanced modeling features include:
        
        - **Pipelines** - build automated analysis pipelines to fit complex lens models to large samples of strong lenses.
        - **Inversions** - Reconstruct complex source galaxy morphologies on a variety of pixel-grids.
        - **Adaption** - (February 2019) - Adapt the lensing analysis to the features of the observed strong lens imaging.
        - **Multi-Plane** - (April 2019) Model multi-plane lenses, including systems with multiple lensed source galaxies.
        
        ## HowToLens
        
        Included with **PyAutoLens** is the **HowToLens** eBook, which provides an introduction to strong gravitational lens modeling with **PyAutoLens**. It can be found in the workspace and consists of 4 chapters:
        
        - **Introduction** - An introduction to strong gravitational lensing and **PyAutolens**.
        - **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis.
        - **Pipelines** - How to build pipelines and tailor them to your own science case.
        - **Inversions** - How to perform pixelized reconstructions of the source-galaxy.
        
        ## Workspace
        
        **PyAutoLens** comes with a workspace, which can be found [here](https://github.com/Jammy2211/autolens_workspace) and includes the following:
        
        - **Config** - Configuration files which customize the **PyAutoLens** analysis.
        - **Data** - Your data folder, including example data-sets distributed with **PyAutoLens**.
        - **HowToLens** - The **HowToLens** eBook.
        - **Output** - Where the **PyAutoLens** analysis and visualization are output.
        - **Pipelines** - Example pipelines to model a strong lens or use a template for your own pipeline.
        - **Plotting** - Scripts enabling customized figures and images.
        - **Runners** - Scripts for running a **PyAutoLens** pipeline and analysis.
        - **Tools** - Tools for simulating strong lens data, creating masks and using many other **PyAutoLens** features.
        
        If you install **PyAutoLens** with conda or pip, you will need to download the workspace from the [autolens_workspace](https://github.com/Jammy2211/autolens_workspace) repository, which is described in the installation instructions below.
        
        ## Depedencies
        
        **PyAutoLens** requires [PyMultiNest](http://johannesbuchner.github.io/pymultinest-tutorial/install.html) and [Numba](https://github.com/numba/numba).
        
        ## Installation with conda
        
        We recommend installation using a conda environment as this circumvents a number of compatibility issues when installing **PyMultiNest**.
        
        First, install [conda](https://conda.io/miniconda.html).
        
        Create a conda environment:
        
        ```
        conda create -n autolens python=3.7 anaconda
        ```
        
        Install multinest:
        
        ```
        conda install -c conda-forge multinest
        ```
        
        Tell matplotlib what backend to use:
        
        ```
        echo "backend : TKAgg" > ~/.matplotlib/matplotlibrc
        ```
        
        Install autolens:
        
        ```
        pip install autolens
        ```
        
        Clone autolens workspace and set WORKSPACE enviroment variable:
        ```
        cd /path/where/you/want/autolens_workspace
        git clone https://github.com/Jammy2211/autolens_workspace
        export WORKSPACE=/path/to/autolens_workspace/
        ```
        
        Set PYTHONPATH to include the autolens_workspace directory:
        ```
        export PYTHONPATH=/path/to/autolens_workspace/
        ```
        
        You can test everything is working by running the example pipeline runner in the autolens_workspace
        ```
        python3 /path/to/autolens_workspace/runners/pipeline_runner.py
        ```
        
        ## Installation with pip
        
        Installation is also available via pip, however there are reported issues with installing **PyMultiNest** that can make installation difficult, see the file [INSTALL.notes](https://github.com/Jammy2211/PyAutoLens/blob/master/INSTALL.notes)
        
        ```
        $ pip install autolens
        ```
        
        Clone autolens workspace and set WORKSPACE enviroment variable:
        ```
        cd /path/where/you/want/autolens_workspace
        git clone https://github.com/Jammy2211/autolens_workspace
        export WORKSPACE=/path/to/autolens_workspace/
        ```
        
        Set PYTHONPATH to include the autolens_workspace directory:
        ```
        export PYTHONPATH=/path/to/autolens_workspace/
        ```
        
        You can test everything is working by running the example pipeline runner in the autolens_workspace
        ```
        python3 /path/to/autolens_workspace/runners/pipeline_runner.py
        ```
        
        ## Support & Discussion
        
        If you're having difficulty with installation, lens modeling, or just want a chat, feel free to message us on our [Slack channel](https://pyautolens.slack.com/).
        
        ## Contributing
        
        If you have any suggestions or would like to contribute please get in touch.
        
        ## Publications
        
        The following papers use **PyAutoLens**:
        
        [Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies](https://arxiv.org/abs/1901.07801)
        
        [Novel Substructure and Superfluid Dark Matter](https://arxiv.org/abs/1901.03694)
        
        [CO, H2O, H2O+ line and dust emission in a z = 3.63 strongly lensed starburst merger at sub-kiloparsec scales](https://arxiv.org/abs/1903.00273)
        
        ## Credits
        
        [James Nightingale](https://github.com/Jammy2211) - Lead developer and PyAutoLens guru.
        
        [Richard Hayes](https://github.com/rhayes777) - Lead developer and [PyAutoFit](https://github.com/rhayes777/PyAutoFit) guru.
        
        [Ashley Kelly](https://github.com/AshKelly) - Developer of [pyquad](https://github.com/AshKelly/pyquad) for fast deflections computations.
        
        [Nan Li](https://github.com/linan7788626) - Docker integration & support.
        
        [Andrew Robertson](https://github.com/Andrew-Robertson) - Critical curve and caustic calculations.
        
        [Andrea Enia](https://github.com/AndreaEnia) - Voronoi source-plane plotting tools.
        
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
