Metadata-Version: 1.1
Name: PyRQA
Version: 6.0.0
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: Apache License 2.0
Description: PyRQA
        =====
        
        Highlights
        ----------
        
        -  Perform recurrence analysis on long time series in a time efficient
           manner using the OpenCL framework.
        -  Conduct recurrence quantification analysis (*RQA*) and cross
           recurrence quantification analysis (*CRQA*).
        -  Compute recurrence plots (*RP*) and cross recurrence plots (*CRP*).
        -  Compute unthresholded recurrence plots (*URP*) and unthresholded
           cross recurrence plots (*UCRP*).
        -  Conduct joint recurrence quantification analysis (*JRQA*) and compute
           joint recurrence plots (*JRP*).
        -  Employ the euclidean, maximum or taxicab metric for determining state
           similarity.
        -  Choose the fixed radius or radius corridor neighbourhood condition.
        -  Select either the half, single or double floating point precision for
           conducting the analytical computations.
        -  Leverage machine learning techniques that automatically choose the
           fastest from a set of implementations.
        -  Apply the computing capabilities of GPUs, CPUs and other computing
           platforms that support OpenCL.
        -  Use multiple computing devices of the same or different type in
           parallel.
        
        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.  `Performance Tuning <#performance-tuning>`__
        7.  `Testing <#testing>`__
        8.  `Origin <#origin>`__
        9.  `Acknowledgements <#acknowledgements>`__
        10. `Publications <#publications>`__
        11. `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 additionally 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
        ------------
        
        The analytical implementations provided by PyRQA rely on features that
        are part of OpenCL 1.1, which is a fairly mature standard and supported
        by a large number of platforms. The OpenCL computing devices employed
        need to support at least this version to being able to use PyRQA.
        
        It may be required to install additional software, e.g., runtimes or
        drivers, to execute PyRQA on computing devices such as GPUs and CPUs.
        References to vendor-specific information is presented below.
        
        *AMD*:
        
        -  https://www.amd.com/en/support
        -  https://github.com/RadeonOpenCompute/ROCm
        -  https://community.amd.com/community/devgurus/opencl
        -  https://www.amd.com/en/support/kb/release-notes/amdgpu-installation
        
        *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.analysis_type import Classic
            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,
                                analysis_type=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_length = 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. Note that the
        ``theiler_corrector`` is ignored regarding the creation of the plot.
        
        .. 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
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        PyRQA further offers the opportunity to conduct cross recurrence
        analysis (*CRQA* and *CRP*), in addition to the classic recurrence
        analysis (*RQA* and *RP*). For this purpose, two time series of
        potentially different length are provided as input. Note that the
        corresponding computations require to set the same value regarding the
        embedding dimension. Two different time delay values may be used
        regarding the first and the second time series. To enable cross
        recurrence analysis, the ``analysis_type`` argument has to be changed
        from ``Classic`` to ``Cross``, when creating the ``Settings`` object. A
        *CRQA* example is given below.
        
        .. code:: python
        
            from pyrqa.analysis_type import Cross
            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,
                                analysis_type=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_length = 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
        specific condition is passed as ``neighbourhood`` argument to the
        constructor of a ``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)          
        
        Unthresholded Recurrence Plots
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        PyRQA allows to create unthresholded *RP*\ s and *CRP*\ s by selecting
        the ``Unthresholded`` neighbourhood condition. This results in a
        non-binary matrix, containing the mutual distances between the system
        states, based on the similarity measure selected. Functionality is
        provided to normalize these distances to values between ``0`` and ``1``.
        The normalized matrix can further be represented as a grayscale image.
        Darker shades of grey indicate smaller distances whereas lighter shades
        of grey indicate larger distances. An example on how to create an
        unthresholded cross recurrence plot is given below.
        
        .. code:: python
        
            from pyrqa.neighbourhood import Unthresholded
            settings = Settings(time_series,
                                analysis_type=Cross,
                                neighbourhood=Unthresholded(),
                                similarity_measure=EuclideanMetric)
            computation = RPComputation.create(settings)
            result = computation.run()
            ImageGenerator.save_unthresholded_recurrence_plot(result.recurrence_matrix_reverse_normalized,
                                                              'unthresholded_cross_recurrence_plot.png')
        
        Joint Recurrence Analysis
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In addition to classic and cross recurrence analysis, PyRQA provides
        functionality to conduct joint recurrence analysis. This includes in
        particular joint recurrence plots (*JRP*) as well as joint recurrence
        quantification analysis (*JRQA*). On an abstract level, a joint
        recurrence plot is a combination of two individual plots, both having
        the same extent regarding the *X* and *Y* axis. Each of those two plots
        may either be of the analysis type ``Classic`` or ``Cross``, potentially
        having different characteristics regarding:
        
        -  Time series data,
        -  Embedding dimension,
        -  Time delay,
        -  Neighbourhood condition, and
        -  Similarity measure.
        
        In contrast, the same value for ``theiler_corrector`` is expected
        regarding the quantitative analysis. Note that a joint recurrence plot
        by definition relies on thresholded input plots, eliminating the
        application of the ``Unthresholded`` neighbourhood condition.
        
        The settings of the two individual plots are encapsulated in a
        ``JointSettings`` object. The quantification of joint recurrence plots
        is based on the same measures as for recurrence plots and cross
        recurrence plots. An example on how to conduct *JRQA* is given below.
        
        .. code:: python
        
            from pyrqa.computation import JRQAComputation
            from pyrqa.metric import MaximumMetric, TaxicabMetric
            from pyrqa.settings import JointSettings
            data_points_1 = [1.0, 0.7, 0.5, 0.1, 1.7, 1.5, 1.2, 0.4, 0.6, 1.5, 0.8, 0.3]
            time_series_1 = TimeSeries(data_points,
                                       embedding_dimension=3,
                                       time_delay=1)
            settings_1 = Settings(time_series_1,
                                  analysis_type=Classic,
                                  neighbourhood=RadiusCorridor(inner_radius=0.14,
                                                               outer_radius=0.97),
                                  similarity_measure=MaximumMetric,
                                  theiler_corrector=1)
            data_points_2_x = [0.7, 0.1, 1.1, 1.4, 1.0, 0.5, 1.0, 1.9, 1.7, 0.9, 1.5, 0.6]
            time_series_2_x = TimeSeries(data_points_2_x,
                                         embedding_dimension=2,
                                         time_delay=1)
            data_points_2_y = [0.4, 0.7, 0.9, 0.3, 1.9, 1.3, 1.2, 0.2, 1.1, 0.6, 0.8, 0.1, 0.5]
            time_series_2_y = TimeSeries(data_points_2_y,
                                         embedding_dimension=2,
                                         time_delay=2)
            time_series_2 = (time_series_2_x,
                             time_series_2_y)
            settings_2 = Settings(time_series_2,
                                  analysis_type=Cross,
                                  neighbourhood=FixedRadius(0.83),
                                  similarity_measure=TaxicabMetric,
                                  theiler_corrector=1)
            joint_settings = JointSettings(settings_1,
                                           settings_2)
            computation = JRQAComputation.create(joint_settings,
                                                 verbose=True)
            result = computation.run()
            result.min_diagonal_line_length = 2
            result.min_vertical_line_length = 1
            result.min_white_vertical_line_length = 2
            print(result)
        
        The following output is expected.
        
        ::
        
            JRQA Result:
            ============
        
            Minimum diagonal line length (L_min): 2
            Minimum vertical line length (V_min): 1
            Minimum white vertical line length (W_min): 2
        
            Recurrence rate (RR): 0.157025
            Determinism (DET): 0.263158
            Average diagonal line length (L): 2.500000
            Longest diagonal line length (L_max): 3
            Divergence (DIV): 0.333333
            Entropy diagonal lines (L_entr): 0.693147
            Laminarity (LAM): 1.000000
            Trapping time (TT): 1.000000
            Longest vertical line length (V_max): 1
            Entropy vertical lines (V_entr): 0.000000
            Average white vertical line length (W): 3.960000
            Longest white vertical line length (W_max): 11
            Longest white vertical line length inverse (W_div): 0.090909
            Entropy white vertical lines (W_entr): 1.588760
        
            Ratio determinism / recurrence rate (DET/RR): 1.675900
            Ratio laminarity / determinism (LAM/DET): 3.800000
        
        The corresponding joint recurrence plot is computed likewise.
        
        .. code:: python
        
            from pyrqa.computation import JRPComputation
            computation = JRPComputation.create(joint_settings)
            result = computation.run()
            ImageGenerator.save_recurrence_plot(result.recurrence_matrix_reverse,
                                                'joint_recurrence_plot.png')
        
        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``, when
        creating an ``OpenCL`` object.
        
        .. 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 manually using their identifiers.
        
        .. code:: python
        
            opencl = OpenCL(platform_id=0,
                            device_ids=(0,))
            computation = RPComputation.create(settings,
                                               verbose=True,
                                               opencl=opencl)
        
        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 by
        PyRQA:
        
        -  Half precision (16-bit),
        -  Single precision (32-bit), and
        -  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 selected precision influences the performance of the computations on
        a particular device.
        
        The precision is set by specifying the corresponding data type, short
        ``dtype``, 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)
        
        Performance Tuning
        ------------------
        
        OpenCL Compiler Optimisations Enablement
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        OpenCL compiler optimisations aim at improving the performance of the
        operations conducted by the computing devices. Regarding PyRQA, they are
        disabled by default to ensure the comparability of the analytical
        results. They can be enabled by assigning the value ``True`` to the
        corresponding keyword argument ``optimisations_enabled``.
        
        .. code:: python
        
            computation = RPComputation.create(settings,
                                               variants_kwargs={'optimisations_enabled': True})
        
        Adaptive Implementation Selection
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Adaptive implementation selection allows to automatically select well
        performing implementations regarding RQA and recurrence plot
        computations, provided by PyRQA. The approach dynamically adapts the
        selection to the current computational scenario as well as the
        properties of 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 selection strategies can be used
        for *RQA* and *CRQA*, *RP* and *CRP*, *URP* and *UCRP* as well as *JRQA*
        and *JRP* computations.
        
        .. code:: python
        
            from pyrqa.variants.rqa.radius.column_materialisation_bit_no_recycling import ColumnMaterialisationBitNoRecycling
            from pyrqa.variants.rqa.radius.column_materialisation_bit_recycling import ColumnMaterialisationBitRecycling
            from pyrqa.variants.rqa.radius.column_materialisation_byte_no_recycling import ColumnMaterialisationByteNoRecycling
            from pyrqa.variants.rqa.radius.column_materialisation_byte_recycling import ColumnMaterialisationByteRecycling
            from pyrqa.variants.rqa.radius.column_no_materialisation import ColumnNoMaterialisation
            from pyrqa.selector import EpsilonGreedySelector
            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,
                                analysis_type=Classic,
                                neighbourhood=FixedRadius(0.65),
                                similarity_measure=EuclideanMetric,
                                theiler_corrector=1)
            computation = RQAComputation.create(settings,
                                                selector=EpsilonGreedySelector(explore=10),
                                                variants=(ColumnMaterialisationBitNoRecycling,
                                                          ColumnMaterialisationBitRecycling,
                                                          ColumnMaterialisationByteNoRecycling,
                                                          ColumnMaterialisationByteRecycling,
                                                          ColumnNoMaterialisation),
                                                variants_kwargs={'optimisations_enabled': True})
        
        Testing
        -------
        
        PyRQA provides a single-threaded baseline implementation for each
        analytical method. These implementations do not use OpenCL
        functionality. They serve as a ground truth regarding the analytical
        computations. 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
        ``--all``.
        
        .. code:: bash
        
            python -m pyrqa.test --all
        
        Note that there might occur minor deviations regarding the analytical
        results. These deviations may stem from varying precisions regarding the
        computing devices employed.
        
        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. Initial research and development of PyRQA was funded by the
        Deutsche Forschungsgemeinschaft (https://www.dfg.de/).
        
        Publications
        ------------
        
        The underlying computational approach of PyRQA is described in detail
        within the following thesis, which is openly accessible at
        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
        -------------
        
        6.0.0
        ~~~~~
        
        -  Addition of the joint recurrence quantification analysis (*JRQA*) and
           joint recurrence plot (*JRP*) computations.
        -  Refactoring of the test implementation.
        -  Refactoring of the public API.
        -  Updated documentation.
        
        5.1.0
        ~~~~~
        
        -  Addition of the unthresholded recurrence plot (*URP*) and
           unthresholded cross recurrence plot (*UCRP*) computations.
        -  Updated documentation.
        
        5.0.0
        ~~~~~
        
        -  Refactoring of the public API.
        -  Updated documentation.
        
        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,joint recurrence quantification analysis,JRQA,recurrence plot,RP,cross recurrence plot,CRP,joint recurrence plot,JRP
Platform: UNKNOWN
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: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
