Metadata-Version: 1.1
Name: PyRQA
Version: 4.1.0
Summary: A tool to conduct recurrence analysis in a massively parallel manner using the OpenCL framework.
Home-page: UNKNOWN
Author: Tobias Rawald
Author-email: pyrqa@gmx.net
License: UNKNOWN
Description: PyRQA
        =====
        
        Highlights
        ----------
        
        -  Perform recurrence analysis on long time series in a time efficient
           manner using the OpenCL framework.
        -  Conduct recurrence quantification analysis (*RQA*) or cross
           recurrence quantification analysis (*CRQA*).
        -  Compute recurrence plots (*RP*) or cross recurrence plots (*CRP*).
        -  Employ the fixed radius or radius corridor neighbourhood condition
           for determining state similarity.
        -  Apply the computing capabilities of GPUs, CPUs or other computing
           platforms that support OpenCL.
        -  Use multiple computing devices of the same or different type in
           parallel.
        -  Leverage machine learning techniques that automatically choose the
           fastest implementation from a set of variants.
        -  Select either the half, single or double floating point precision for
           conducting the analytical computations.
        
        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.  `Testing <#testing>`__
        7.  `Origin <#origin>`__
        8.  `Acknowledgements <#acknowledgements>`__
        9.  `Publications <#publications>`__
        10. `Release Notes <#release-notes>`__
        
        General Information
        -------------------
        
        PyRQA is a tool to conduct recurrence analysis 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 quantitative 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*)
        
        PyRQA additionaly allows to compute the corresponding recurrence plot,
        which can be exported as an image file.
        
        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 and all of its dependencies can be installed via the following
        command.
        
        .. code:: bash
        
            pip install PyRQA
        
        OpenCL Setup
        ------------
        
        It may be required to install additional software, e.g., runtimes or
        drivers, to execute PyRQA on OpenCL devices such as GPUs and CPUs.
        References to 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
        -  https://github.com/RadeonOpenCompute/ROCm
        
        *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
        
        *Vendor-independent*:
        
        -  http://portablecl.org
        
        Usage
        -----
        
        Basic Computations
        ~~~~~~~~~~~~~~~~~~
        
        RQA computations are conducted as follows.
        
        .. code:: python
        
            from pyrqa.time_series import TimeSeries
            from pyrqa.settings import Settings
            from pyrqa.computing_type import ComputingType
            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 = TimeSeries(data_points,
                                     embedding_dimension=2,
                                     time_delay=2)
            settings = Settings(time_series,
                                computing_type=ComputingType.Classic,
                                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
        
        The corresponding recurrence plot is computed likewise.
        
        .. code:: python
        
            from pyrqa.computation import RPComputation
            from pyrqa.image_generator import ImageGenerator
            computation = RPComputation.create(settings)
            result = computation.run()
            ImageGenerator.save_recurrence_plot(result.recurrence_matrix_reverse,
                                                'recurrence_plot.png')
        
        Cross Recurrence Analysis
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In addition to the classic recurrence analysis (*RQA* and *RP*), PyRQA
        offers the opportunity to conduct cross recurrence analysis (*CRQA* and
        *CRP*). For this purpose, two time series of potentially different
        length are provided as input. Note that the corresponding computations
        require to set the same values regarding the embedding dimension. Two
        different time delay values may be used regarding the first and the
        second time series. To enable cross recurrence processing, the
        ``computing_type`` argument has to the changed from
        ``ComputingType.Classic`` to ``ComputingType.Cross``, when creating the
        ``Settings`` object. A *CRQA* example is given below.
        
        .. code:: python
        
            data_points_x = [0.9, 0.1, 0.2, 0.3, 0.5, 1.7, 0.4, 0.8, 1.5]
            time_series_x = TimeSeries(data_points_x,
                                       embedding_dimension=2,
                                       time_delay=1)
            data_points_y = [0.3, 1.3, 0.6, 0.2, 1.1, 1.9, 1.3, 0.4, 0.7, 0.9, 1.6]
            time_series_y = TimeSeries(data_points_y,
                                       embedding_dimension=2,
                                       time_delay=2)
            time_series = (time_series_x,
                           time_series_y)
            settings = Settings(time_series,
                                computing_type=ComputingType.Cross,
                                neighbourhood=FixedRadius(0.73),
                                similarity_measure=EuclideanMetric,
                                theiler_corrector=0)
            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.
        
        ::
        
            CRQA 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): 0.319444
            Determinism (DET): 0.521739
            Average diagonal line length (L): 2.400000
            Longest diagonal line length (L_max): 3
            Divergence (DIV): 0.333333
            Entropy diagonal lines (L_entr): 0.673012
            Laminarity (LAM): 0.434783
            Trapping time (TT): 2.500000
            Longest vertical line length (V_max): 3
            Entropy vertical lines (V_entr): 0.693147
            Average white vertical line length (W): 3.500000
            Longest white vertical line length (W_max): 8
            Longest white vertical line length inverse (W_div): 0.125000
            Entropy white vertical lines (W_entr): 1.424130
        
            Ratio determinism / recurrence rate (DET/RR): 1.633270
            Ratio laminarity / determinism (LAM/DET): 0.833333
        
        The corresponding cross recurrence plot is computed likewise.
        
        .. code:: python
        
            from pyrqa.computation import RPComputation
            from pyrqa.image_generator import ImageGenerator
            computation = RPComputation.create(settings)
            result = computation.run()
            ImageGenerator.save_recurrence_plot(result.recurrence_matrix_reverse,
                                                'cross_recurrence_plot.png')
        
        Neighbourhood Condition Selection
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        PyRQA currently supports the fixed radius as well as the radius corridor
        neighbourhood condition. While the first refers to a single radius, the
        latter requires the assignment of an inner and outer radius. The
        condition selected is passed as ``neighbourhood`` argument to the
        ``Settings`` object. The creation of a fixed radius and a radius
        corridor neighbourhood is presented below.
        
        .. code:: python
        
            from pyrqa.neighbourhood import FixedRadius, RadiusCorridor
            fixed_radius = FixedRadius(radius=0.43)
            radius_corridor = RadiusCorridor(inner_radius=0.32, 
                                             outer_radius=0.86)          
        
        Custom OpenCL Environment
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The previous examples use the default OpenCL environment. A custom
        environment can also be created via command line input. For this
        purpose, the ``command_line`` argument has to be set to ``True``.
        
        .. 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 identifiers.
        
        .. 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 by passing the corresponding keyword
        argument with the value ``True``.
        
        .. 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. The approach dynamically adapts the selection to the
        current computational scenario as well as the OpenCL devices employed.
        The selection is performed using one of multiple 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``. Note that the same
        ``variants`` and selection strategies are used for *RQA*, *CRQA*, *RP*
        and *CRP* computations.
        
        .. 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
        in turn determines the precision of the computations conducted by the
        OpenCL devices. Currently, the following precisions are supported:
        
        -  Half precision (16 bit)
        -  Single precision (32 bit)
        -  Double precision (64 bit)
        
        By default, the single precision is applied. Note that not all
        precisions may be supported by the OpenCL devices employed. Furthermore,
        the precision selected influences the performance of the computations on
        a particular device. The precision is set by specifying the
        corresponding data type of the time series data. The following example
        depicts the usage of double precision floating point values.
        
        .. code:: python
        
            import numpy as np
            time_series = TimeSeries(data_points,
                                     embedding_dimension=2,
                                     time_delay=2,
                                     dtype=np.float64)
        
        Testing
        -------
        
        The basic tests for all supported analytical methods can be executed
        cumulatively.
        
        .. code:: bash
        
            python -m pyrqa.test
        
        The complete set of tests can be executed by adding the option
        ``--extended``.
        
        .. code:: bash
        
            python -m pyrqa.test --extended
        
        Origin
        ------
        
        The PyRQA package was initiated by computer scientists from the
        Humboldt-Universität zu Berlin (https://www.hu-berlin.de) and the GFZ
        German Research Centre for Geosciences (https://www.gfz-potsdam.de).
        
        Acknowledgements
        ----------------
        
        We would like to thank Norbert Marwan from the Potsdam Institute for
        Climate Impact Research (https://www.pik-potsdam.de) 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
        -------------
        
        4.1.0
        ~~~~~
        
        -  Usage of two different time delay values regarding the cross
           recurrence plot (*CRP*) and cross recurrence quantification analysis
           (*CRQA*).
        -  Updated documentation.
        
        4.0.0
        ~~~~~
        
        -  Addition of the cross recurrence plot (*CRP*) and cross recurrence
           quantification analysis (*CRQA*) computations.
        -  Addition of the radius corridor neighbourhood condition for
           determining state similarity.
        -  Addition of an additional variant regarding recurrence plot
           computations.
        -  Renaming of directories and classes referring to recurrence plot
           computations.
        -  Removal of obsolete source code.
        -  Updated documentation.
        
        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,cross recurrence quantification analysis,CRQA,recurrence plot,RP,cross recurrence plot,CRP
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 :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Manufacturing
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
