Metadata-Version: 2.1
Name: autolens
Version: 0.35.2
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 appears multiple times. This is called strong gravitational lensing, & **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 & 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 the foreground lens galaxy's mass & the background source galaxy's light.
        
        ```python
        import autofit as af
        import autolens as al
        
        import os
        
        # In this example, we'll fit a simple lens galaxy + source galaxy system.
        dataset_path = '{}/../data/'.format(os.path.dirname(os.path.realpath(__file__)))
        
        lens_name = 'example_lens'
        
        # Get the relative path to the data in our workspace & load the imaging data.
        imaging = al.imaging.from_fits(
            image_path=dataset_path + lens_name + '/image.fits',
            psf_path=dataset_path+lens_name+'/psf.fits',
            noise_map_path=dataset_path+lens_name+'/noise_map.fits',
            pixel_scales=0.1)
        
        # Create a mask for the data, which we setup as a 3.0" circle.
        mask = al.mask.circular(shape=imaging.shape, pixel_scales=imaging.pixel_scales, radius=3.0)
        
        # We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) & our source galaxy 
        # a light profile (an elliptical Sersic).
        lens_mass_profile = al.mp.EllipticalIsothermal
        source_light_profile = al.lp.EllipticalSersic
        
        # To setup our model galaxies, we use the GalaxyModel class, which represents a galaxy whose parameters 
        # are model & fitted for by PyAutoLens. The galaxies are also assigned redshifts.
        lens_galaxy_model = al.GalaxyModel(redshift=0.5, mass=lens_mass_profile)
        source_galaxy_model = al.GalaxyModel(redshift=1.0, light=source_light_profile)
        
        # To perform the analysis we set up a phase, which takes our galaxy models & fits their parameters using a non-linear
        # search (in this case, MultiNest).
        phase = al.PhaseImaging(
            galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
            phase_name='example/phase_example', optimizer_class=af.MultiNest)
        
        # We pass the imaging data and mask to the phase, thereby fitting it with the lens model above & plot the resulting fit.
        result = phase.run(data=imaging, mask=mask)
        al.plot.fit_imaging.subplot_fit_imaging(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 will give you the latest updates on the software & 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:
        
        - **Galaxies** - Use light & mass profiles to make galaxies & perform lensing calculations.
        - **Pipelines** - Write automated analysis pipelines to fit complex lens models to large samples of strong lenses.
        - **Extended Sources** - Reconstruct complex source galaxy morphologies on a variety of pixel-aa.
        - **Adaption** - Adapt the lensing analysis to the features of the observed strong lens imaging.
        - **Multi-Plane** - Perform multi-plane ray-tracing & model multi-plane lens systems.
        - **Visualization** - Custom visualization libraries for plotting physical lensing quantities & modeling results.
        
        ## HowToLens
        
        Included 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:
        
        - **Introduction** - An introduction to strong gravitational lensing & **PyAutolens**.
        - **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis.
        - **Pipelines** - How to build pipelines & 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) & which includes:
        
        - **Aggregator** - Manipulate large suites of model-fits via Jupyter notebooks, using **PyAutoFit**'s in-built results database.
        - **Config** - Configuration files which customize **PyAutoLens**'s behaviour.
        - **Dataset** - Where data is stored, including example datasets distributed with **PyAutoLens**.
        - **HowToLens** - The **HowToLens** lecture series.
        - **Output** - Where the **PyAutoLens** analysis and visualization are output.
        - **Pipelines** - Example pipelines for modeling strong lenses.
        - **Plot** - Example scripts for customizing figures and images.
        - **Preprocessing** - Tools for preprocessing data before an analysis and creating auxilary data like masks.
        - **Quick Start** - A quick start guide, so you can begin modeling your own lens data within hours.
        - **Runners** - Scripts for running a **PyAutoLens** pipeline.
        - **Simulators** - Scripts for simulating strong lens datasets with **PyAutoLens**.
        - **Tools** - Extra tools for 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) & [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
        ```
        
        Activate the conda environment:
        
        ```
        conda activate autolens
        ```
        
        Install multinest:
        
        ```
        conda install -c conda-forge multinest
        ```
        
        Install autolens (v0.35.2 is the most recent stable build):
        
        ```
        pip install autolens==0.35.2
        ```
        
        Clone autolens workspace & set WORKSPACE enviroment model:
        ```
        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/beginner/no_lens_light/lens_sie__source_inversion.py
        ```
        
        ## Installation with pip
        
        Installation is also available via pip (v0.35.2 is the most recent stable build), 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==0.35.2
        ```
        
        Clone autolens workspace & set WORKSPACE enviroment model:
        ```
        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/beginner/no_lens_light/lens_sie__source_inversion.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**:
        
        [Likelihood-free MCMC with Amortized Approximate Likelihood Ratios](https://arxiv.org/abs/1903.04057)
        
        [Deep Learning the Morphology of Dark Matter Substructure](https://arxiv.org/abs/1909.07346)
        
        [The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836](https://arxiv.org/abs/1904.00307)
        
        [Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies](https://arxiv.org/abs/1901.07801)
        
        [Novel Substructure & Superfluid Dark Matter](https://arxiv.org/abs/1901.03694)
        
        [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)
        
        [Subaru FOCAS IFU observations of two z=0.12 strong-lensing elliptical galaxies from SDSS MaNGA](https://arxiv.org/abs/1911.06338)
        
        ## Credits
        
        ### Developers
        
        [James Nightingale](https://github.com/Jammy2211) - Lead developer & PyAutoLens guru.
        
        [Richard Hayes](https://github.com/rhayes777) - Lead developer & [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.
        
        [Amy Etherington](https://github.com/amyetherington) - Magnification, Critical Curves and Caustic Calculations.
        
        [Xiaoyue Cao](https://github.com/caoxiaoyue) - Analytic Ellipitcal Power-Law Deflection Angle Calculations.
        
        [Qiuhan He] - NFW Profile Lensing Calculations.
        
        [Nan Li](https://github.com/linan7788626) - Docker integration & support.
        
        ### Code Donors
        
        [Andrew Robertson](https://github.com/Andrew-Robertson) - Critical curve & caustic calculations.
        
        Mattia Negrello - Visibility models in the uv-plane via direct Fourier transforms.
        
        [Andrea Enia](https://github.com/AndreaEnia) - Voronoi source-plane plotting tools.
        
        [Aristeidis Amvrosiadis](https://github.com/Sketos) - ALMA imaging data loading.
        
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
