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        "author": "David R. Thompson, Winston Olson-Duvall, Philip G. Brodrick, and Team",
        "author_email": "david.r.thompson@jpl.nasa.gov",
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        "description": "Imaging Spectrometer Optimal FITting (ISOFIT) Overview\n======================================================\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4614338.svg\n   :target: https://doi.org/10.5281/zenodo.4614338\n\n.. image:: https://readthedocs.org/projects/pip/badge/?version=stable\n   :target: https://pip.pypa.io/en/stable/?badge=stable\n\nYou can find documentation for isofit at our `readthedocs site <https://isofit.readthedocs.io/en/latest/index.html>`_.\n\nThis repository has two main branches:\n\n`current-release <https://github.com/isofit/isofit/tree/current-release/>`__: Latest stable, versioned release and\n\n`master <https://github.com/isofit/isofit/>`__: The latest non-breaking changes, sometimes pre-version\n\nThis codebase contains a set of routines and utilities for fitting surface,\natmosphere and instrument models to imaging spectrometer data.  It is\nwritten primarily in Python, with JSON format configuration files and some\ndependencies on widely-available numerical and scientific libraries such as\nscipy, numpy, and scikit-learn.  It is designed for maximum flexibility, so\nthat users can swap in and evaluate model components based on different\nradiative transfer models (RTMs) and various statistical descriptions of\nsurface, instrument, and atmosphere.  It can run on individual radiance\nspectra in text format, or imaging spectrometer data cubes.\n\nThe subdirectories contain:\n\n* bin/       - command line scripts for calling isofit and sunposition\n* data/      - shared data files\n* docs/      - documentation\n* examples/  - example runs packaged with input data and configuration files\n* isofit/    - the isofit Python module including utilities and tests\n* logs/      - Pytest logs\n* recipe/    - conda release recipe\n\nIf you use ISOFIT in your research or production, we ask that you cite the \nprecursor publication:\n\n  Thompson, David R., Vijay Natraj, Robert O. Green, Mark C. Helmlinger, Bo-Cai Gao, and Michael L. Eastwood. \"Optimal estimation for imaging spectrometer atmospheric correction.\" Remote Sensing of Environment 216 (2018): 355-373. \n\n\nInstallation Instructions\n-------------------------\n\nFrom Github\n***********\n\nThe code repository, development branches, and user community are found on\n`GitHub <https://github.com/davidraythompson/isofit>`_. To install:\n\n1. Download or clone the git repo located at https://github.com/isofit/isofit, using either the `current-release <https://github.com/isofit/isofit/tree/current-release>`_ or `master (current-release + reviewed development) <https://github.com/isofit/isofit>`_ branch.\n\n2. Install the ISOFIT using pip - be sure to use a full path reference.\n\n.. code::\n\n    pip install --editable /path/to/isofit --use-feature=2020-resolver\n\nFrom PyPI\n*********\n\nAlso, the latest release is always hosted on `PyPI <https://pypi.python.org/pypi/isofit>`_,\nso if you have `pip` installed, you can install ISOFIT from the command line with\n\n.. code::\n\n    pip install isofit\n\nThis will install the \"isofit\" package into your environment as well as its dependencies.\n\nUsing Utils\n***********\n\nSeveral utilities are provided to facilitate using ISOFIT in different workflows.  Some\nof the utilities (such as `apply_oe.py <https://github.com/isofit/isofit/blob/master/isofit/utils/apply_oe.py>`_)\nrequire GDAL, which is not required in setup.py currently to facilitate diverse compatibility.\nAn example installation is available in the `utils workflow <https://github.com/isofit/isofit/blob/master/.github/workflows/utils-workflow.yml>`_\n\nQuick Start using MODTRAN 6.0\n-----------------------------\n\nThis quick start presumes that you have an installation of the MODTRAN 6.0\nradiative transfer model.  This is the preferred radiative transfer option if available, though we have also included an interface to the open source LibRadTran RT code.  Other open source options and neural network emulators will be integrated in the future. \n\n1. Configure your environment with the variable MODTRAN_DIR pointing to the base MODTRAN 6.0 directory.\n\n2. Run the following code\n\n.. code::\n\n    cd examples/20171108_Pasadena\n    ./run_examples_modtran.sh\n\n3. This will build a surface model and run the retrieval. The default example uses a lookup table approximation, and the code should recognize that the tables do not currently exist.  It will call MODTRAN to rebuild them, which will take a few minutes.\n\n4. Look for output data in examples/20171108_Pasadena/output/.  Each retrieval writes diagnostic images to examples/20171108_Pasadena/images/ as it runs.\n\nQuick Start with LibRadTran 2.0.x\n---------------------------------\n\nThis quick start presumes that you have an installation of the open source libRadTran radiative transfer model (`LibRadTran <http://www.libradtran.org/doku.php>`)_ .  We have tested with the 2.0.2 release.  You will need the \"REPTRAN\" absorption parameterization - follow the instructions on the libradtran installation page to get that data.\n\n1. Configure your environment with the variable LIBRADTRAN_DIR pointing to the base libRadTran directory.\n\n2. Run the following code\n\n.. code::\n\n    cd examples/20171108_Pasadena\n    ./run_example_libradtran.sh\n\n3. This will build a surface model and run the retrieval. The default example uses a lookup table approximation, and the code should recognize that the tables do not currently exist.  It will call libRadTran to rebuild them, which will take a few minutes.\n\n4. Look for output data in examples/20171108_Pasadena/output/.  Diagnostic images are written to examples/20171108_Pasadena/images/.\n\n\nQuick Start with sRTMnet\n------------------------\n\nsRTMnet is an emulator for MODTRAN 6, that works by coupling a neural network with a surrogate RTM (6S v2.1).\nInstallation requires two steps:\n\n1. Download `6S v2.1 <https://salsa.umd.edu/files/6S/6sV2.1.tar>`_, and compile.  If you use a modern system,\nit is likely you will need to specify a legacy compiling configuration by changing line 3 of the Makefile to:\n\n.. code::\n\n    EXTRA   = -O -ffixed-line-length-132 -std=legacy\n\n2. Configure your environment by pointing the SIXS_DIR variable to point to your installation directory.\n\n3. Download the `pre-trained sRTMnet neural network <https://zenodo.org/record/4096627>`_, and (for the example below)\npoint the environment variable EMULATOR_PATH to the base unzipped path.\n\n4. Run the following code\n\n.. code::\n\n    cd examples/image_cube/\n    sh ./run_example_cube.sh\n\n\n\nAdditional Installation Info for Mac OSX\n------------------------------------------\n\n1. Install the command-line compiler\n\n.. code::\n\n  xcode-select --install\n\n2. Download the python3 installer from https://www.python.org/downloads/mac-osx/\n\n\nKnown Incompatibilities\n-----------------------\nRay may have compatability issues with older machines with glibc < 2.14.\n\n\n",
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