Metadata-Version: 1.2
Name: echopype
Version: 0.1.21
Summary: Enhancing the interoperability and scalability in analyzing ocean sonar data
Home-page: https://github.com/OSOceanAcoustics/echopype
Author: Wu-Jung Lee
Author-email: leewujung@gmail.com
Maintainer: Wu-Jung Lee
Maintainer-email: leewujung@gmail.com
License: Apache License, Version 2.0
Description: .. image:: https://travis-ci.org/OSOceanAcoustics/echopype.svg?branch=master
            :target: https://travis-ci.org/OSOceanAcoustics/echopype
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            :target: https://echopype.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
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            :target: https://mybinder.org/v2/gh/OSOceanAcoustics/echopype/master
        
        echopype
        ===========
        
        echopype is a package built for enhancing the interoperability and scalability
        in ocean sonar data processing.
        These data are widely used for obtaining information about the distribution and
        abundance of marine animals, such as fish and krill.
        Our ability to collect large volumes of sonar data from a variety of
        ocean platforms has grown significantly in the last decade.
        However, most of the new data remain under-utilized.
        echopype aims to address the root cause of this problem - the lack of
        interoperable data format and scalable analysis workflows that adapt well
        with increasing data volume - by providing open-source tools as entry points for
        scientists to make discovery using these new data.
        
        See the `echopype documentation`_ for more detail.
        
        .. _echopype documentation: https://echopype.readthedocs.io
        
        
        Functionality
        ----------------
        echopype include tools for:
        
        - Converting different manufacturer-specifc data files into an interoperable
          netCDF format.
        
        - Processing large volumes of sonar data in local or cloud storage by leveraging
          Python distributed computing libraries.
        
        The current version supports file conversion for the ``.raw`` data files
        collected by the SimRad EK60 echosounder. Conversion for other types of data
        files, including the ``.01A`` files from AZFP echosounder, the ``.raw`` files
        from the SimRad broadband EK80 echosounder, and the *raw beam* data from ADCP
        (Acoustic Doppler Current Profiler) will be added in future releases.
        
        
        Installation
        --------------
        
        To install echopype, do the following in your terminal:
        
        .. code-block:: console
        
            $ pip install echopype
        
        
        Using echopype
        -------------------
        
        File conversion
        +++++++++++++++++++
        
        To batch convert ``.raw`` files to the interoperable netCDF format in the
        terminal, do:
        
        .. code-block:: console
        
            $ echopype_converter -s ek60 data/*.raw
        
        This will generate corresponding ``.nc`` files with the same leading
        filename as the original ``.raw`` files in the same directory.
        
        To use the EK60 data converter in a Python session, you can do:
        
        .. code-block:: python
        
            # import as part of a submodule
            from echopype.convert import ConvertEK60
            data_tmp = ConvertEK60('FILENAME.raw')
            data_tmp.raw2nc()
        
        Or:
        
        .. code-block:: python
        
            # import the full module
            import echopype as ep
            data_tmp = ep.convert.ConvertEK60('FILENAME.raw')
            data_tmp.raw2nc()
        
        Same as in the command line case, this will generate a ``FILENAME.nc``
        in the same directory as ``FILENAME.raw``.
        
        The ``ConvertEK60`` instance contains all the data unpacked from the
        .raw file, so it is a good idea to clear it from memory once done with
        conversion.
        
        
        Data analysis
        +++++++++++++++++++
        
        The data analysis functionalites of echopype is being developed actively.
        echopype currently supports:
        
        - calibration and echo-integration to obtain volume backscattering strength (Sv)
          from the power data collected by EK60.
        
        - simple noise removal by suppressing data points below an adaptively estimated
          noise floor [1]_.
        
        - binning and averaging to obtain mean volume backscattering strength (MVBS)
          from the calibrated data.
        
        The steps of performing these analysis are summarized below:
        
        .. code-block:: python
        
            from echopype.model import EchoData
            data = EchoData('FILENAME.nc')
            data.calibrate()  # Calibration and echo-integration
            data.remove_noise(save=True)  # Save denoised Sv to FILENAME_Sv_clean.nc
            data.get_MVBS(save=True)  # Save MVBS to FILENAME_MVBS.nc
        
        Note that by default, method ``calibrate`` save the calibrated volume
        backscattering strength (Sv) to ``FILENAME_Sv.nc``, while method ``remove_noise``
        and ``get_MVBS`` by default do not generate new files. The computation results
        from these two methods can be accessed from ``data.Sv_clean`` and ``data.MVBS``
        as xarray DataSets. All outputs are xarray DataSets with proper dimension
        labeling.
        
        
        License
        ----------
        
        echopype is licensed under the open source Apache 2.0 license.
        
        This project is lead by `Wu-Jung Lee <http://leewujung.github.io>`_ (@leewujung).
        Other contributors include:
        
        - `Valentina Staneva <https://escience.washington.edu/people/valentina-staneva/>`_
          (@valentina-s)
        - `Marian Peña <https://www.researchgate.net/profile/Marian_Pena2>`_
          (@marianpena)
        - `Mark Langhirt <https://www.linkedin.com/in/mark-langhirt-7b33ba80>`_ (@bnwkeys)
        - `Erin LaBrecque <https://www.linkedin.com/in/erin-labrecque/>`_
          (@erinann)
        - `Emma Ozanich <https://www.linkedin.com/in/emma-reeves-ozanich-b8671938/>`_
          (@emma-ozanich)
        - `Aaron Marburg <http://apl.uw.edu/people/profile.php?last_name=Marburg&first_name=Aaron>`_
          (@amarburg)
        
        
        References
        ------------
        .. [1] De Robertis and Higginbottoms (2007) A post-processing technique to estimate
                the signal-to-noise ratio and remove echosounder background noise.
                `ICES J. Mar. Sci. 64(6): 1282–1291. <https://academic.oup
                .com/icesjms/article/64/6/1282/616894>`_
        
        
        ---------------
        
        Copyright (c) 2018--, Wu-Jung Lee, Applied Physics Laboratory, University of Washington.
        
Platform: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
