Metadata-Version: 2.1
Name: globalemu
Version: 1.4.0
Summary: globalemu: Robust and Fast Global 21-cm Signal Emulation
Home-page: https://github.com/htjb/globalemu
Author: Harry T. J. Bevins
Author-email: htjb2@cam.ac.uk
License: MIT
Description: ========================================================
        globalemu: Robust and Fast Global 21-cm Signal Emulation
        ========================================================
        
        Introduction
        ------------
        
        :globalemu: Robust Global 21-cm Signal Emulation
        :Author: Harry Thomas Jones Bevins
        :Version: 1.4.0
        :Homepage: https://github.com/htjb/globalemu
        :Documentation: https://globalemu.readthedocs.io/
        
        .. image:: https://mybinder.org/badge_logo.svg
         :target: https://mybinder.org/v2/gh/htjb/globalemu/master?filepath=notebooks%2F
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         :target: https://globalemu.readthedocs.io/en/latest/?badge=latest
         :alt: Documentation Status
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         :target: https://codecov.io/gh/htjb/globalemu
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         :target: https://github.com/htjb/globalemu/actions?query=workflow%3ACI
         :alt: github CI
        .. image:: https://img.shields.io/badge/license-MIT-blue.svg
         :target: https://pypi.org/project/globalemu/
         :alt: MIT License
        .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4767759.svg
          :target: https://doi.org/10.5281/zenodo.4767759
        .. image:: https://img.shields.io/badge/ascl-2104.028-blue.svg?colorB=262255
         :target: https://ascl.net/2104.028
         :alt: ascl:2104.028
        
        Installation
        ------------
        
        The software can be pip installed from the PYPI repository via,
        
        .. code:: bash
        
          pip install globalemu
        
        or alternatively it can be installed from the git repository via.
        
        .. code:: bash
        
          git clone https://github.com/htjb/globalemu.git # or the equivalent using ssh keys
          cd globalemu
          python setup.py install --user
        
        Emulating the Global 21-cm Signal
        ---------------------------------
        
        ``globalemu`` is a fast and robust approach for emulating the Global or
        sky averaged 21-cm signal and the associated neutral fraction history.
        In the cited MNRAS paper below we show that it is
        a factor of approximately 102 times faster and 2 times as accurate
        as the previous state of the art
        `21cmGEM <https://academic.oup.com/mnras/article/495/4/4845/5850763>`__. The
        code is also flexible enough for it to be retrained on detailed simulations
        containing the most up to date physics. We release two trained networks, one
        for the Global signal and one for the neutral fraction history, details of
        which can be found in the MNRAS paper below.
        
        You can download trained networks with the following code after pip installing
        or installing via the github repository:
        
        .. code:: python
        
          from globalemu.downloads import download
        
          download().model() # Redshift-Temperature Network
          download(xHI=True).model() # Redshift-Neutral Fraction Network
        
        which will produce two files in your working directory 'T_release/' and
        'xHI_release/'. Each file has the respective network model in and related
        pre and post processing files. You can then go on to evaluate each network for
        a set of parameters by running:
        
        .. code:: python
        
          from globalemu.eval import evaluate
        
          # [fstar, vc, fx, tau, alpha, nu_min, R_mfp]
          params = [1e-3, 46.5, 1e-2, 0.0775, 1.25, 1.5, 30]
        
          predictor = evaluate(base_dir='T_release/') # Redshift-Temperature Network
          signal, z = predictor(params)
        
          # note the parameter order is different for the neutral fraction emulator
          # [fstar, vc, fx, nu_min, tau, alpha, R_mfp]
          params = [1e-3, 46.5, 1e-2, 1.5, 0.0775, 1.25, 30]
        
          predictor = evaluate(base_dir='xHI_release/', xHI=True) # Redshift-Neutral Fraction Network
          signal, z = predictor(params)
        
        Results are accessed via 'res.z' and 'res.signal'.
        
        The code can also be used to train a network on your own Global 21-cm signal
        or neutral fraction simulations using the built in ``globalemu`` pre-processing
        techniques. There is some flexibility on the required astrophysical input
        parameters and the pre-processing steps which is detailed in the documentation.
        More details about training your own network can be found in the documentation.
        
        ``globalemu`` GUI
        -----------------
        
        ``globalemu`` also features a GUI that can be invoked from the command line
        and used to explore how the structure of the Global 21-cm signal varies with
        the values of the astrophysical inputs. The GUI needs a configuration file to
        run and this can be generated using a built in ``globalemu`` function.
        **GUI configuration files can be generated for any trained model.** For example,
        if we wanted to generate a configuration file for the released Global signal
        emulator we would run,
        
        .. code:: python
        
          from globalemu.gui_config import config
        
          paramnames = [r'$\log(f_*)$', r'$\log(V_c)$', r'$\log(f_X)$',
                        r'$\tau$', r'$\alpha$', r'$\nu_\mathrm{min}$',
                        r'$R_\mathrm{mfp}$']
        
          config('T_release/', paramnames, 'data/')
        
        where the directory 'data/' contains the training and testing data (in this
        case that corresponding to
        `21cmGEM <https://zenodo.org/record/4541500#.YKOTiibTWWg>`__).
        
        The GUI can then be invoked from the terminal via,
        
        .. code:: bash
        
          globalemu /path/to/base_dir/T_release/etc/
        
        An image of the GUI is shown below.
        
        .. image:: https://github.com/htjb/globalemu/raw/master/docs/images/gui.png
          :width: 400
          :align: center
          :alt: graphical user interface
        
        The GUI can also be used to investigate the physics of the neutral fraction
        history by generating a configuration file for the released trained model.
        There is no need to specify that the configuration file is for a neutral
        fraction emulator.
        
        Configuration files for the released models are provided.
        
        Documentation
        -------------
        
        The documentation is available at: https://globalemu.readthedocs.io/
        
        It can be compiled locally after downloading the repo and installing
        the relevant packages (see below) via,
        
        .. code:: bash
        
          cd docs
          sphinx-build source html-build
        
        You can find a tutorial notebook
        `here <https://mybinder.org/v2/gh/htjb/globalemu/master?filepath=notebooks%2F>`__.
        
        T_release/ and xHI_release/
        ---------------------------
        
        The currently released global signal trained model, ``T_release/`` is trained
        on the same training data set as 21cmGEM which is available
        `here <http://doi.org/10.5281/zenodo.4541500>`__. The data used to train the
        neutral fraction history network, ``xHI_release/`` is not publicly available
        but comes from the same large scale simulations used to model the global signal.
        
        For both models the input parameters and ranges are given below.
        
        .. list-table::
          :header-rows: 2
        
          * - Parameter
            - Description
            - ``T_release/``
            - ``xHI_release/``
            - Min
            - Max
          * -
            -
            - Input Order
            - Input Order
            -
            -
          * - f\ :sub:`*`
            - Star Formation Efficiency
            - 1
            - 1
            - 0.0001
            - 0.5
          * - V\ :sub:`c`
            - Minimal Virial Circular Veloity
            - 2
            - 2
            - 4.2 km/s
            - 100 km/s
          * - f\ :sub:`x`
            - X-ray Efficiency
            - 3
            - 3
            - 0
            - 1000
          * - tau
            - CMB Optical Depth
            - 4
            - 5
            - 0.04
            - 0.17
          * - alpha
            - Power of X-ray SED slope
            - 5
            - 6
            - 1.0
            - 1.5
          * - nu :sub:`min`
            - Low Energy Cut Off of X-ray SED
            - 6
            - 4
            - 0.1 keV
            - 3 keV
          * - R\ :sub:`mfp`
            - Mean Free Path of Ionizing Photons
            - 7
            - 7
            - 10.0 Mpc
            - 50.0 Mpc
        
        Licence and Citation
        --------------------
        
        The software is free to use on the MIT open source license. If you use the
        software for academic puposes then we request that you cite the
        ``globalemu`` paper below.
        
        `MNRAS pre-print <https://arxiv.org/abs/2104.04336>`__
        (referred to in the documentation as the ``globalemu`` paper),
        
          Bevins, H., W. J. Handley, A. Fialkov, E. D. L. Acedo and K. Javid.
          “GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm
          signal from the cosmic dawn and epoch of reionisation.” (2021). arXiv:2104.04336
        
        Below is the bibtex,
        
        .. code:: bibtex
        
          @article{Bevins2021,
            title = {{GLOBALEMU}: {A} novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation},
            url = {http://arxiv.org/abs/2104.04336},
            urldate = {2021-04-12},
            journal = {arXiv:2104.04336 [astro-ph]},
            author = {Bevins, H. T. J. and Handley, W. J. and Fialkov, A. and Acedo, E. de Lera and Javid, K.},
            month = apr,
            year = {2021},
            note = {arXiv: 2104.04336}
          }
        
        Requirements
        ------------
        
        To run the code you will need to following additional packages:
        
        - `numpy <https://pypi.org/project/numpy/>`__
        - `tensorflow <https://pypi.org/project/tensorflow/>`__
        - `pandas <https://pypi.org/project/pandas/>`__
        - `matplotlib <https://pypi.org/project/matplotlib/>`__
        - `Pillow <https://pypi.org/project/Pillow/>`__
        
        When installing via pip or from source via setup.py the above packages will
        be installed if absent.
        
        To compile the documentation locally you will need:
        
        - `sphinx <https://pypi.org/project/Sphinx/>`__
        - `numpydoc <https://pypi.org/project/numpydoc/>`__
        
        To run the test suit you will need:
        
        - `pytest <https://docs.pytest.org/en/stable/>`__
        
        Contributing
        ------------
        
        Contributions to ``globalemu`` are very much welcome and can be made via,
        
        - Opening an issue to report a bug/propose a new feature.
        - Making a pull request. Please consider opening an issue first to discuss
          any proposals and ensure the PR will be accepted.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Provides-Extra: docs
