Metadata-Version: 1.1
Name: PyRQA
Version: 1.0.6
Summary: A tool to conduct recurrence quantification analysis and to create recurrence plots in a massively parallel manner using the OpenCL framework.
Home-page: UNKNOWN
Author: Tobias Rawald
Author-email: PyRQA@gmx.net
License: UNKNOWN
Description: PyRQA
        =====
        
        Table of Contents
        -----------------
        
        1.  `General Information <#general-information>`__
        2.  `Contribution <#contribution>`__
        3.  `Contact <#contact>`__
        4.  `Installation <#installation>`__
        5.  `OpenCL Setup <#opencl-setup>`__
        6.  `Usage <#usage>`__
        7.  `Testing <#testing>`__
        8.  `Origin <#origin>`__
        9.  `Acknowledgements <#acknowledgements>`__
        10. `Publications <#publications>`__
        
        General Information
        -------------------
        
        PyRQA is a tool to conduct recurrence quantification analysis (RQA) and
        to create recurrence plots in a massively parallel manner using the
        OpenCL framework. It is designed to efficiently process time series
        consisting of hundreds of thousands of data points.
        
        PyRQA supports the computation of the following RQA measures:
        
        -  Recurrence rate (RR)
        -  Determinism (DET)
        -  Average diagonal line length (L)
        -  Longest diagonal line length (L\_max)
        -  Divergence (DIV)
        -  Entropy diagonal lines (L\_entr)
        -  Laminarity (LAM)
        -  Trapping time (TT)
        -  Longest vertical line length (V\_max)
        -  Entropy vertical lines (V\_entr)
        -  Average white vertical line length (W)
        -  Longest white vertical line length (W\_max)
        -  Longest white vertical line length divergence (W\_div)
        -  Entropy white vertical lines (W\_entr)
        
        In addition, PyRQA allows to compute the corresponding recurrence plot
        and to export it as an image file.
        
        Contribution
        ------------
        
        The code of the PyRQA package is hosted under
        https://gitlab.com/tobiasr/PyRQA.
        
        Contact
        -------
        
        Please write an email to pyrqa@gmx.net, if you have any questions
        related to the PyRQA package.
        
        Installation
        ------------
        
        PyRQA can be installed via the following command.
        
        .. code:: bash
        
            pip install PyRQA
        
        OpenCL Setup
        ------------
        
        It may be required to install hardware vendor specific software, e.g.,
        device drivers, to run PyRQA on OpenCL devices (such as GPUs and CPUs).
        Vendor-specific information is presented below.
        
        -  AMD:
        
           -  https://www.amd.com/en-us/solutions/professional/hpc/opencl
           -  https://support.amd.com/en-us/kb-articles/Pages/Installation-Instructions-for-amdgpu-Graphics-Stacks.aspx
        
        -  Intel:
        
           -  https://software.intel.com/en-us/articles/opencl-drivers
           -  https://software.intel.com/en-us/articles/sdk-for-opencl-gsg
        
        -  NVIDIA:
        
           -  https://developer.nvidia.com/opencl
        
        -  ARM:
        
           -  https://developer.arm.com/docs/100614/latest/introduction/about-opencl
        
        Usage
        -----
        
        RQA computations are conducted as follows.
        
        .. code:: python
        
            from pyrqa.time_series import SingleTimeSeries
            from pyrqa.settings import Settings
            from pyrqa.neighbourhood import FixedRadius
            from pyrqa.metric import EuclideanMetric
            from pyrqa.computation import RQAComputation
            time_series = SingleTimeSeries([0.1, 0.5, 0.3, 1.7, 0.8, 2.4, 0.6, 1.2, 1.4, 2.1, 0.8],
                                            embedding_dimension=3,
                                            time_delay=2)
            settings = Settings(time_series,
                                neighbourhood=FixedRadius(1.0),
                                similarity_measure=EuclideanMetric,
                                theiler_corrector=1)
            computation = RQAComputation.create(settings,
                                                verbose=True)
            result = computation.run()
            result.min_diagonal_line_length = 1
            result.min_vertical_line_length = 2
            result.min_white_vertical_line_length = 1
            print(result)
        
        Recurrence plot computations can be conducted likewise.
        
        .. code:: python
        
            from pyrqa.computation import RecurrencePlotComputation
            from pyrqa.image_generator import ImageGenerator
            computation = RecurrencePlotComputation.create(settings)
            result = computation.run()
            ImageGenerator.save_recurrence_plot(result.recurrence_matrix_reverse, 'recurrence_plot.png')
        
        Moreover, it is possible to read time series data that is stored
        column-wise from a file.
        
        .. code:: python
        
            from pyrqa.file_reader import FileReader
            time_series = SingleTimeSeries(FileReader.file_as_float_array('data.csv',
                                                                          delimiter=';',
                                                                          column=0))
        
        The previous examples use the default OpenCL environment. A custom
        environment using command line input can also be created
        
        .. code:: python
        
            from pyrqa.opencl import OpenCL
            opencl = OpenCL(command_line=True)
        
        The OpenCL platform as well as the computing devices can also be
        selected using their IDs.
        
        .. code:: python
        
            opencl = OpenCL(platform_id=0,
                            device_ids=(0,))
            computation = RQAComputation.create(settings,
                                                verbose=True,
                                                opencl=opencl)
            result = computation.run()
        
        Default OpenCL compiler optimisation are deactivated by default to
        ensure the comparability of computing results. They can be activated to
        leverage additional performance improvements.
        
        .. code:: python
        
            computation = RQAComputation.create(settings,
                                                variants_kwargs={'optimisation_enabled': True})
            result = computation.run()
        
        Adaptive implementation selection is performed using one of multiple
        greedy selection strategies supported. It is conducted based on a
        customized pool of implementation variants. These variants may be
        enriched with a set of keyword arguments.
        
        .. code:: python
        
            from pyrqa.variants.rqa.fixed_radius.brute_force.column_materialisation_uncompressed_bit_no_recycling import ColumnMaterialisationUncompressedBitNoRecycling
            from pyrqa.variants.rqa.fixed_radius.brute_force.column_materialisation_uncompressed_bit_recycling import ColumnMaterialisationUncompressedBitRecycling
            from pyrqa.variants.rqa.fixed_radius.brute_force.column_materialisation_uncompressed_byte_no_recycling import ColumnMaterialisationUncompressedByteNoRecycling
            from pyrqa.variants.rqa.fixed_radius.brute_force.column_materialisation_uncompressed_byte_recycling import ColumnMaterialisationUncompressedByteRecycling
            from pyrqa.variants.rqa.fixed_radius.brute_force.column_no_materialisation import ColumnNoMaterialisation
            from pyrqa.selector import EpsilonGreedySelector
            computation = RQAComputation.create(settings,
                                                selector=EpsilonGreedySelector(explore=10),
                                                variants=(ColumnMaterialisationUncompressedBitNoRecycling,
                                                          ColumnMaterialisationUncompressedBitRecycling,
                                                          ColumnMaterialisationUncompressedByteNoRecycling,
                                                          ColumnMaterialisationUncompressedByteRecycling,
                                                          ColumnNoMaterialisation),
                                                variants_kwargs={'optimisations_enabled': True})
            result = computation.run()
        
        Testing
        -------
        
        All tests available within the PyRQA package can be executed
        cumulatively.
        
        .. code:: bash
        
            python -m pyrqa.test
        
        Note that it might be required to execute the command as super user.
        
        Origin
        ------
        
        The PyRQA package was initiated by computer scientists from the
        Humboldt-Universität zu Berlin and the GFZ German Research Centre for
        Geosciences.
        
        Acknowledgements
        ----------------
        
        We would like to thank Norbert Marwan from the Potsdam Institute for
        Climate Impact Research for his continuous support of the project.
        Please visit his website http://recurrence-plot.tk/ for further
        information on recurrence analysis.
        
        Publications
        ------------
        
        Please acknowledge the use of PyRQA by citing the following publication.
        
        ::
        
            Rawald, T., Sips, M., Marwan, N. (2017): PyRQA - Conducting Recurrence Quantification Analysis on Very Long Time Series Efficiently. - Computers and Geosciences, 104, pp. 101-108.
        
        The underlying computational approach of PyRQA is described in detail
        within the following thesis, which is openly accessible under
        https://edoc.hu-berlin.de/handle/18452/19518.
        
        ::
        
            Rawald, T. (2018): Scalable and Efficient Analysis of Large High-Dimensional Data Sets in the Context of Recurrence Analysis, PhD Thesis, Berlin : Humboldt-Universität zu Berlin, 299 p.
        
        Selected aspects of the computational approach are presented within the
        following publications.
        
        ::
        
            Rawald, T., Sips, M., Marwan, N., Dransch, D. (2014): Fast Computation of Recurrences in Long Time Series. - In: Marwan, N., Riley, M., Guiliani, A., Webber, C. (Eds.), Translational Recurrences. From Mathematical Theory to Real-World Applications, (Springer Proceedings in Mathematics and Statistics ; 103), p. 17-29.
        
            Rawald, T., Sips, M., Marwan, N., Leser, U. (2015): Massively Parallel Analysis of Similarity Matrices on Heterogeneous Hardware. - In: Fischer, P. M., Alonso, G., Arenas, M., Geerts, F. (Eds.), Proceedings of the Workshops of the EDBT/ICDT 2015 Joint Conference (EDBT/ICDT), (CEUR Workshop Proceedings ; 1330), p. 56-62.
        
Keywords: time series analysis,recurrence quantification analysis,RQA,recurrence plot
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
