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    "info": {
        "author": "James Nightingale and Richard Hayes",
        "author_email": "james.w.nightingale@durham.ac.uk",
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        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
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            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "# PyAutoLens\n\nWhen two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy appears multiple times. This is called strong gravitational lensing, & **PyAutoLens** makes it simple to model strong gravitational lenses, like this one: \n\n![alt text](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/master/gitimage.png)\n\n**PyAutoLens** is based on the following papers:\n\n[Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging](https://arxiv.org/abs/1412.7436)\n\n[AutoLens: Automated Modeling of a Strong Lens's Light, Mass & Source](https://arxiv.org/abs/1708.07377)\n\n## Python Example\n\nWith **PyAutoLens**, you can begin modeling a lens in just a couple of minutes. The example below demonstrates a simple analysis which fits the foreground lens galaxy's mass & the background source galaxy's light.\n\n```python\nimport autofit as af\nimport autolens as al\n\nimport os\n\n# In this example, we'll fit a simple lens galaxy + source galaxy system.\ndata_path = '{}/../data/'.format(os.path.dirname(os.path.realpath(__file__)))\n\nlens_name = 'example_lens'\n\n# Get the relative path to the data in our workspace & load the imaging data.\nimaging_data = al.load_imaging_data_from_fits(\n    image_path=data_path + lens_name + '/image.fits',\n    psf_path=data_path+lens_name+'/psf.fits',\n    noise_map_path=data_path+lens_name+'/noise_map.fits', \n    pixel_scale=0.1)\n\n# Create a mask for the data, which we setup as a 3.0\" circle.\nmask = al.Mask.circular(shape=imaging_data.shape, pixel_scale=imaging_data.pixel_scale, radius_arcsec=3.0)\n\n# We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) & our source galaxy \n# a light profile (an elliptical Sersic).\nlens_mass_profile = al.mass_profiles.EllipticalIsothermal\nsource_light_profile = al.light_profiles.EllipticalSersic\n\n# To setup our model galaxies, we use the GalaxyModel class, which represents a galaxy whose parameters \n# are variable & fitted for by PyAutoLens. The galaxies are also assigned redshifts.\nlens_galaxy_model = al.GalaxyModel(redshift=0.5, mass=lens_mass_profile)\nsource_galaxy_model = al.GalaxyModel(redshift=1.0, light=source_light_profile)\n\n# To perform the analysis we set up a phase, which takes our galaxy models & fits their parameters using a non-linear\n# search (in this case, MultiNest).\nphase = al.PhaseImaging(\n    galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),\n    phase_name='example/phase_example', optimizer_class=af.MultiNest)\n\n# We pass the imaging data and mask to the phase, thereby fitting it with the lens model above & plot the resulting fit.\nresult = phase.run(data=imaging_data, mask=mask)\nal.lens_fit_plotters.plot_fit_subplot(fit=result.most_likely_fit)\n```\n\n## Slack\n\nWe'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 will give you the latest updates on the software & discuss how best to use **PyAutoLens** for your science case.\n\nUnfortunately, Slack is invitation-only, so first send me an [email](https://github.com/Jammy2211) requesting an invite.\n\n## Features\n\n**PyAutoLens's** advanced modeling features include:\n\n- **Galaxies** - Use light & mass profiles to make galaxies & perform lensing calculations.\n- **Pipelines** - Write automated analysis pipelines to fit complex lens models to large samples of strong lenses.\n- **Extended Sources** - Reconstruct complex source galaxy morphologies on a variety of pixel-grids.\n- **Adaption** - Adapt the lensing analysis to the features of the observed strong lens imaging.\n- **Multi-Plane** - Perform multi-plane ray-tracing & model multi-plane lens systems.\n- **Visualization** - Custom visualization libraries for plotting physical lensing quantities & modeling results.\n\n## HowToLens\n\nIncluded with **PyAutoLens** is the **HowToLens** lecture series, which provides an introduction to strong gravitational lens modeling with **PyAutoLens**. It can be found in the workspace & consists of 4 chapters:\n\n- **Introduction** - An introduction to strong gravitational lensing & **PyAutolens**.\n- **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis.\n- **Pipelines** - How to build pipelines & tailor them to your own science case.\n- **Inversions** - How to perform pixelized reconstructions of the source-galaxy.\n\n## Workspace\n\n**PyAutoLens** comes with a workspace, which can be found [here](https://github.com/Jammy2211/autolens_workspace) & which includes:\n\n- **Config** - Configuration files which customize the **PyAutoLens** analysis.\n- **Data** - Your data folder, including example data-sets distributed with **PyAutoLens**.\n- **HowToLens** - The **HowToLens** lecture series.\n- **Output** - Where the **PyAutoLens** analysis & visualization are output.\n- **Pipelines** - Example pipelines for modeling strong lenses or to use a template for your own pipeline.\n- **Plotting** - Scripts enabling customized figures & images.\n- **Runners** - Scripts for running a **PyAutoLens** pipeline.\n- **Tools** - Tools for simulating strong lens data, creating masks & using many other **PyAutoLens** features.\n\nIf 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.\n\n## Depedencies\n\n**PyAutoLens** requires [PyMultiNest](http://johannesbuchner.github.io/pymultinest-tutorial/install.html) & [Numba](https://github.com/numba/numba).\n\n## Installation with conda\n\nWe recommend installation using a conda environment as this circumvents a number of compatibility issues when installing **PyMultiNest**.\n\nFirst, install [conda](https://conda.io/miniconda.html).\n\nCreate a conda environment:\n\n```\nconda create -n autolens python=3.7 anaconda\n```\n\nActivate the conda environment:\n\n```\nconda activate autolens\n```\n\nInstall multinest:\n\n```\nconda install -c conda-forge multinest\n```\n\nInstall autolens:\n\n```\npip install autolens\n```\n\nClone autolens workspace & set WORKSPACE enviroment variable:\n```\ncd /path/where/you/want/autolens_workspace\ngit clone https://github.com/Jammy2211/autolens_workspace\nexport WORKSPACE=/path/to/autolens_workspace/\n```\n\nSet PYTHONPATH to include the autolens_workspace directory:\n```\nexport PYTHONPATH=/path/to/autolens_workspace/\n```\n\nYou can test everything is working by running the example pipeline runner in the autolens_workspace\n```\npython3 /path/to/autolens_workspace/runners/simple/runner__lens_sie__source_inversion.py\n```\n\n## Installation with pip\n\nInstallation 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)\n\n```\n$ pip install autolens\n```\n\nClone autolens workspace & set WORKSPACE enviroment variable:\n```\ncd /path/where/you/want/autolens_workspace\ngit clone https://github.com/Jammy2211/autolens_workspace\nexport WORKSPACE=/path/to/autolens_workspace/\n```\n\nSet PYTHONPATH to include the autolens_workspace directory:\n```\nexport PYTHONPATH=/path/to/autolens_workspace\n```\n\nYou can test everything is working by running the example pipeline runner in the autolens_workspace\n```\npython3 /path/to/autolens_workspace/runners/simple/runner__lens_sie__source_inversion.py\n```\n\n## Support & Discussion\n\nIf 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/).\n\n## Contributing\n\nIf you have any suggestions or would like to contribute please get in touch.\n\n## Publications\n\nThe following papers use **PyAutoLens**:\n\n[The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836](https://arxiv.org/abs/1904.00307)\n\n[Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies](https://arxiv.org/abs/1901.07801)\n\n[Novel Substructure & Superfluid Dark Matter](https://arxiv.org/abs/1901.03694)\n\n[CO, H2O, H2O+ line & dust emission in a z = 3.63 strongly lensed starburst merger at sub-kiloparsec scales](https://arxiv.org/abs/1903.00273)\n\n## Credits\n\n### Developers\n\n[James Nightingale](https://github.com/Jammy2211) - Lead developer & PyAutoLens guru.\n\n[Richard Hayes](https://github.com/rhayes777) - Lead developer & [PyAutoFit](https://github.com/rhayes777/PyAutoFit) guru.\n\n[Ashley Kelly](https://github.com/AshKelly) - Developer of [pyquad](https://github.com/AshKelly/pyquad) for fast deflections computations.\n\n[Amy Etherington](https://github.com/amyetherington) - Magnification, Critical Curves and Caustic Calculations.\n\n[Xiaoyue Cao](https://github.com/caoxiaoyue) - Analytic Ellipitcal Power-Law Deflection Angle Calculations.\n\n[Qiuhan He] - NFW Profile Lensing Calculations.\n\n[Nan Li](https://github.com/linan7788626) - Docker integration & support.\n\n### Code Donors\n\n[Andrew Robertson](https://github.com/Andrew-Robertson) - Critical curve & caustic calculations.\n\nMattia Negrello - Visibility models in the uv-plane via direct Fourier transforms.\n\n[Andrea Enia](https://github.com/AndreaEnia) - Voronoi source-plane plotting tools.\n\n[Aristeidis Amvrosiadis](https://github.com/Sketos) - 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