Metadata-Version: 1.1
Name: Py-Rate
Version: 0.1.2
Summary: A Python tool for estimating velocity and time-series from Interferometric Synthetic Aperture Radar (InSAR) data.
Home-page: https://github.com/GeoscienceAustralia/PyRate
Author: Geoscience Australia InSAR team
Author-email: insar@ga.gov.au
License: Apache Software License 2.0
Description: PyRate - a Python tool for RAte and Time-series Estimation
        ==========================================================
        
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        PyRate is a Python tool for estimating the average rate (velocity) and
        incremental time-series of surface movement for every pixel in a stack
        of images generated by interferometric processing of Synthetic Aperture
        Radar (InSAR) data. PyRate is a partial Python translation of
        `Pirate <http://homepages.see.leeds.ac.uk/~earhw/software/pirate/>`__, a
        MATLAB tool developed at the University of Leeds.
        
        Quickstart
        ==========
        
        Before you start, make sure your system has the following packages
        installed:
        
        ::
        
            sudo apt-get install gdal-bin libgdal-dev libpng12-dev libblas-dev liblapack-dev libatlas-dev libatlas-base-dev gfortran libproj-dev openmpi-bin libopenmpi-dev netcdf-bin libnetcdf11 libnetcdf-dev
        
        We strongly recommend using a
        ```virtualenv`` <https://gist.github.com/basaks/b33ea9106c7d1d72ac3a79fdcea430eb>`__.
        
        One might need to install a slightly older ``python-daemon`` to install
        ``PyRate``:
        
        ::
        
            pip install python-daemon==2.1.1
        
        To install, simply run ``setup.py``:
        
        ::
        
            python setup.py install
        
        or install with ``pip``:
        
        ::
        
            pip install git+https://github.com/GeoscienceAustralia/PyRate
        
        The python requirements should automatically be built and installed.
        
        Anaconda setup for ``PyRate``
        -----------------------------
        
        For anaconda installation and ``virtualenv`` instruction, `see this
        guide <https://github.com/GeoscienceAustralia/PyRate/blob/master/conda.md>`__.
        
        Tests
        -----
        
        Tests use `pytest <http://doc.pytest.org/en/latest/>`__ and can be found
        in *tests*.
        
        To run the tests, use the following command inside the ``PyRate``
        directory:
        
        ::
        
            pip install pytest
            cd PyRate
            export PYRATEPATH=/path/to/PyRate
            pytest tests/
        
        Config file:
        ------------
        
        TODO
        
        PyRate workflow:
        ----------------
        
        ``PyRate`` installs an executable ``pyrate``.
        
        Use ``help`` for the different command line options:
        
        ::
        
            >> pyrate --help
            Usage: pyrate [OPTIONS] COMMAND [ARGS]...
        
            Options:
              -v, --verbosity [DEBUG|INFO|WARNING|ERROR]
                                              Level of logging
              --help                          Show this message and exit.
        
            Commands:
              linrate
              postprocess
              prepifg
        
        As you can see ``pyrate`` has three command line options.
        
        1. prepifg
        2. linrate
        3. postprocess
        
        Below we discuss these options.
        
        Preparing interferrograms: prepifg
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The first step of PyRate is to convert the unwrapped interforrograms
        into geotiffs, followed by multilooking and cropping. Both of these
        operations are performed by ``pyrate prepifg`` command:
        
        ::
        
            >> pyrate prepifg --help
            Usage: pyrate prepifg [OPTIONS] CONFIG_FILE
        
            Options:
              --help  Show this message and exit.
        
        So one can use the ``prepfig`` command as the following:
        
        ::
        
            pyrate prepifg /path/to/config_file
        
        The two major steps during the ``prepifg`` operation are described
        below.
        
        Data formatting: convert to geotiff
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        The ``prepifg`` command will determine the input format from the value
        specified at the *processor:* keyword in the config file (0: ROI\_PAC;
        1: GAMMA)
        
        A GAMMA translation requires a geographic DEM header file (\*.dem.par)
        and SLC parameter files (\*.slc.par) for both master and slave images to
        extract metadata required for the formatting. Therefore three header
        files are needed to format each geocoded unwrapped GAMMA interferogram .
        The path and name of the DEM header file are specified in the config
        file under the *demHeaderFile:* keyword. The SLC parameter files should
        be in the same location as the interferogram file and are found
        automatically by date string pattern matching.
        
        A ROI\_PAC translation requires a header/resource file (*.rsc*
        extension) for the geocoded unwrapped ROI\_PAC interferogram (in the
        same directory) and either the geographic projection (e.g. 'WGS84')
        specified as an option or a header/resource file for the geographic DEM
        containing the geographic projection in the parameter DATUM:
        
        Image transformations: multilooking and cropping
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        This ``prepifg`` command will also perform multi-looking (resampling)
        and cropping the images.
        
        Two examples of the config files are provided in ``configs`` directory,
        with examples of the ``roipac`` and ``gamma`` prepifg configuration.
        Both config files can be used with ``prepifg``.
        
        Linear rate and time series analysis: linrate
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        ::
        
            >> pyrate linrate --help
            Usage: pyrate linrate [OPTIONS] CONFIG_FILE
        
            Options:
              -r, --rows INTEGER  divide ifgs into this many rows
              -c, --cols INTEGER  divide ifgs into this many columns
              --help              Show this message and exit
        
        This is the core of the processing tools, handled by the ``linrate``
        command:
        
        ::
        
            pyrate linrate path/to/config_file -c 3 -r 4
        
        This command will does the time series and linear rate analysis, but has
        the options to break the interferrograms into tiles of ``r`` rows and
        ``c`` columns. So this above command will break the interferrograms into
        12 tiles and will produce 12 linear rate and time series predictions
        corresponding to each tile.
        
        The optional rows and columns help us create smaller ``tiles`` of the
        interferrograms that can be accommodated in the memory. The number of
        tiles chosen should be as small as possible that fits in the system
        memory.
        
        Putting it back together: postprocess
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The last step in ``pyrate`` is to put all the tiles back together from
        the ``linrate`` part.
        
        ::
        
            >> pyrate postprocess --help
            Usage: pyrate postprocess [OPTIONS] CONFIG_FILE
        
            Options:
              -r, --rows INTEGER  divide ifgs into this many rows
              -c, --cols INTEGER  divide ifgs into this many columns
              --help              Show this message and exit.
        
        Make sure to use the same number of rows and columns with
        ``postprocess`` as with ``linrate``:
        
        ::
        
            pyrate postprocess path/to/config_file -c 3 -r 4
        
        MPI Support
        -----------
        
        ``PyRate`` has been designed for supercomputers and instructions to use
        an HPC cluster can be found in the `pbs directory <pbs>`__.
        
        Python multiprocessing support
        ------------------------------
        
        In addition to the MPI support for HPC, ``PyRate`` can be use standard
        multiprocessing simply by turning ``parallel:  1`` in the config file to
        take advantage of multiple cores on a single PC.
        
        Integrating PyAPS with PyRate
        -----------------------------
        
        TODO
        
        Useful Links
        ============
        
        Home Page https://github.com/GeoscienceAustralia/PyRate
        
        Documentation http://geoscienceaustralia.github.io/PyRate
        
        Issue tracking https://github.com/GeoscienceAustralia/PyRate/issues
        
        Bugs & Feedback
        ===============
        
        For bugs, questions and discussions, please use `Github
        Issues <https://github.com/GeoscienceAustralia/PyRate/issues>`__.
        
        
        Documentation
        -------------
        
        The full documentation is at http://geoscienceaustralia.github.io/PyRate/.
        
        
        
        History
        -------
        
        0.1.0 (2017-01-31)
        ++++++++++++++++++
        
        * First release on PyPI.
        
Keywords: PyRate,Python,InSAR,Geodesy,Remote Sensing,Image Processing
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: POSIX
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
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: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
