Metadata-Version: 1.1
Name: PyRQA
Version: 3.0.0
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. `Recommended Citation <#recommended-citation>`__
        3. `Installation <#installation>`__
        4. `OpenCL Setup <#opencl-setup>`__
        5. `Usage <#usage>`__
        6. `Origin <#origin>`__
        7. `Acknowledgements <#acknowledgements>`__
        8. `Publications <#publications>`__
        9. `Release Notes <#release-notes>`__
        
        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.
        
        Recommended Citation
        --------------------
        
        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.
        
        Installation
        ------------
        
        PyRQA can be installed via the following command.
        
        .. code:: bash
        
            pip install PyRQA
        
        OpenCL Setup
        ------------
        
        It may be required to install additional software, e.g., device drivers,
        to run PyRQA on OpenCL devices such as GPUs and CPUs. Vendor specific
        information is presented below.
        
        *AMD*:
        
        -  https://community.amd.com/community/devgurus/opencl
        -  https://support.amd.com/en-us/kb-articles/Pages/Installation-Instructions-for-amdgpu-Graphics-Stacks.aspx
        
        *ARM*:
        
        -  https://developer.arm.com/docs/100614/0312
        
        *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
        -  https://developer.nvidia.com/cuda-downloads
        
        Usage
        -----
        
        Basic Computations
        ~~~~~~~~~~~~~~~~~~
        
        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
            data_points = [0.1, 0.5, 1.3, 0.7, 0.8, 1.4, 1.6, 1.2, 0.4, 1.1, 0.8, 0.2, 1.3]
            time_series = SingleTimeSeries(data_points,
                                            embedding_dimension=2,
                                            time_delay=2)
            settings = Settings(time_series,
                                neighbourhood=FixedRadius(0.65),
                                similarity_measure=EuclideanMetric,
                                theiler_corrector=1)
            computation = RQAComputation.create(settings,
                                                verbose=True)
            result = computation.run()
            result.min_diagonal_line_length = 2
            result.min_vertical_line_length = 2
            result.min_white_vertical_line_lelngth = 2
            print(result)
        
        The following output is expected.
        
        ::
        
            RQA Result:
            -----------
            Minimum diagonal line length (L_min): 2
            Minimum vertical line length (V_min): 2
            Minimum white vertical line length (W_min): 2
        
            Recurrence rate (RR): 0.371901
            Determinism (DET): 0.411765
            Average diagonal line length (L): 2.333333
            Longest diagonal line length (L_max): 3
            Divergence (DIV): 0.333333
            Entropy diagonal lines (L_entr): 0.636514
            Laminarity (LAM): 0.400000
            Trapping time (TT): 2.571429
            Longest vertical line length (V_max): 4
            Entropy vertical lines (V_entr): 0.955700
            Average white vertical line length (W): 2.538462
            Longest white vertical line length (W_max): 6
            Longest white vertical line length inverse (W_div): 0.166667
            Entropy white vertical lines (W_entr): 0.839796
        
            Ratio determinism / recurrence rate (DET/RR): 1.107190
            Ratio laminarity / determinism (LAM/DET): 0.971429
        
        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')
        
        Custom OpenCL Environment
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        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()
        
        OpenCL Compiler Optimisations Enablement
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        OpenCL compiler optimisations are disabled by default to ensure the
        comparability of computing results. They can be enabled to leverage
        additional performance improvements.
        
        .. code:: python
        
            computation = RQAComputation.create(settings,
                                                variants_kwargs={'optimisations_enabled': True})
            result = computation.run()
        
        Adaptive Implementation Selection
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Adaptive implementation selection allows to automatically select well
        performing implementations regarding RQA and recurrence plot
        computations. It dynamically adapts to the current computational
        scenario as well as OpenCL devices employed. The selection is performed
        using one of multiple selection strategies, each referred to as
        ``selector``. They rely on a set of customized implementation
        ``variants``, which may be parameterized using a set of keyword
        arguments called ``variants_kwargs``.
        
        .. code:: python
        
            from pyrqa.variants.rqa.fixed_radius.column_materialisation_bit_no_recycling import ColumnMaterialisationBitNoRecycling
            from pyrqa.variants.rqa.fixed_radius.column_materialisation_bit_recycling import ColumnMaterialisationBitRecycling
            from pyrqa.variants.rqa.fixed_radius.column_materialisation_byte_no_recycling import ColumnMaterialisationByteNoRecycling
            from pyrqa.variants.rqa.fixed_radius.column_materialisation_byte_recycling import ColumnMaterialisationByteRecycling
            from pyrqa.variants.rqa.fixed_radius.column_no_materialisation import ColumnNoMaterialisation
            from pyrqa.selector import EpsilonGreedySelector
            computation = RQAComputation.create(settings,
                                                selector=EpsilonGreedySelector(explore=10),
                                                variants=(ColumnMaterialisationBitNoRecycling,
                                                          ColumnMaterialisationBitRecycling,
                                                          ColumnMaterialisationByteNoRecycling,
                                                          ColumnMaterialisationByteRecycling,
                                                          ColumnNoMaterialisation),
                                                variants_kwargs={'optimisations_enabled': True})
            result = computation.run()
        
        Floating Point Precision
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        It is possible to specify the precision of the time series data, which
        determines the precision of the computations using the OpenCL devices.
        Currently, the following precisions are supported:
        
        -  Half precision (16 bit)
        -  Single precision (32 bit)
        -  Double precision (64 bit)
        
        Note that not all precisions may be supported by the OpenCL devices that
        are used to conduct the computations. Furthermore, the precision
        selected influences the performance of those computations. The following
        example depicts the usage of double precision floating point values.
        
        .. code:: python
        
            import numpy as np
            time_series = SingleTimeSeries(data_points,
                                           embedding_dimension=2,
                                           time_delay=2,
                                           dtype=np.float64)
        
        Testing
        -------
        
        The basic tests referring to RQA and recurrence plot computations can be
        executed cumulatively.
        
        .. code:: bash
        
            python -m pyrqa.test
        
        The complete set of tests can be executed by adding ``--extended``.
        
        .. code:: bash
        
            python -m pyrqa.test --extended
        
        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
        ------------
        
        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.
        
        Release Notes
        -------------
        
        3.0.0
        ~~~~~
        
        -  Source code cleanup.
        -  Renaming of the implementation variants regarding RQA and recurrence
           plot processing.
        -  Removal of the module ``file_reader.py``. Please refer for example to
           ``numpy.genfromtxt`` to read data from files (see
           https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html).
        -  Updated documentation.
        
        2.0.1
        ~~~~~
        
        -  Updated documentation.
        
        2.0.0
        ~~~~~
        
        -  Major refactoring.
        -  Removal of operator and variant implementations that do not refer to
           OpenCL brute force computing.
        -  Time series data may be represented using half, single and double
           precision floating point values, which is reflected in the
           computations on the OpenCL devices.
        -  Several changes to the public API.
        
        1.0.6
        ~~~~~
        
        -  Changes to the public API have been made, e.g., to the definition of
           the settings. This leads to an increase in the major version number
           (see https://semver.org/).
        -  Time series objects either consist of one or multiple series. The
           former requires to specify a value for the embedding delay as well as
           the time delay parameter.
        -  Regarding the RQA computations, minimum line lengths are now
           specified on the result object. This allows to compute quantitative
           results using different lengths without having to inspect the matrix
           using the same parametrisation multiple times.
        -  Modules for selecting well-performing implementations based on greedy
           selection strategies have been added. By default, the selection pool
           consists of a single pre-defined implementation.
        -  Operators and implementation variants based on multidimensional
           search trees and grid data structures have been added.
        -  The diagonal line based quantitative measures are modified regarding
           the semantics of the Theiler corrector.
        -  The creation of the OpenCL environment now supports device fission.
        
        0.1.0
        ~~~~~
        
        -  Initial release.
        
Keywords: time series analysis,recurrence quantification analysis,RQA,recurrence plot
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
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
