Recurrence analysis in a massively parallel manner using the OpenCL framework.

## Project description

## 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.
- Use univariate time series or pre-embedded time series as input data.
- 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 platforms that support OpenCL.
- Use multiple computing devices of the same or different type in parallel.

## Table of Contents

## 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 create 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.

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 are 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*:

*Intel*:

- https://software.intel.com/en-us/articles/opencl-drivers
- https://software.intel.com/en-us/articles/sdk-for-opencl-gsg

*NVIDIA*:

*Vendor-independent*:

## Usage

The following subsections depict the usage of PyRQA. Each subsection focuses on specific functionality that is provided by the package. The presentation of expected output shall ensure the reproducibility of the analytical results.

### Basic Computations

RQA computations are conducted as follows.

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 created likewise. Note that the
parameter `theiler_corrector` is ignored regarding the creation of the
plot.

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 parameter `analysis_type` has to be changed
from `Classic` to `Cross`, when creating the `Settings` object. A
*CRQA* example is given below.

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 created likewise.

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 using the parameter `neighbourhood` to
the constructor of a `Settings` object. The creation of a fixed radius
and a radius corridor neighbourhood is presented below.

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. PyRQA provides
functionality to normalize these distances to values between `0` and
`1`. Additionally, the normalized matrix can 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.

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 quantification analysis (*JRQA*) as well as
joint recurrence plot (*JRP*). 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. Regarding PyRQA, 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.

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 created likewise.

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')

### Time Series Embedding

The previous examples refer to one or multiple univariate time series as
input data. These `TimeSeries` are embedded in multi-dimensional space
using the time delay method. In addition, PyRQA allows to analyze time
series that are already embedded in multi-dimensional space. These
`EmbeddedSeries` are represented as a list of *m*-dimensional arrays,
where *m* refers to the embedding dimension. An example of using a
pre-embedded series considering recurrence quantification analysis is
given below.

from pyrqa.time_series import EmbeddedSeries data_points = [[0.1, 1.3], [0.5, 0.7], [1.3, 0.8], [0.7, 1.4], [0.8, 1.6], [1.4, 1.2], [1.6, 0.4], [1.2, 1.1], [0.4, 0.8], [1.1, 0.2], [0.8, 1.3]] embedded_series = EmbeddedSeries(data_points) settings = Settings(embedded_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

Note that this example corresponds to the introductory example, which
demonstrates the basic usage. It differs regarding the representation of
the multi-dimensional vectors. `EmbeddedSeries` objects can be used
while conducting recurrence analysis, cross recurrence analysis as well
as joint recurrence analysis. This refers to performing quantitative
analyses as well as creating recurrence plots. Regarding cross and joint
recurrence analysis `TimeSeries` objects may be combined with
`EmbeddedSeries` objects.

### 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 parameter `command_line` has to be set to `True`, when
creating an `OpenCL` object.

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.

opencl = OpenCL(platform_id=0, device_ids=(0,))

The `OpenCL` object generated is passed as a parameter while creating
a computation object.

computation = RPComputation.create(settings, verbose=True, opencl=opencl)

### Floating Point Precision

PyRQA allows 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), 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.

import numpy as np 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, dtype=np.float64)

## Performance Tuning

PyRQA offers the opportunity to reduce the runtime of analytical computations using performance tuning. There is a distinction between manual as well as automatic performance tuning. Both aspects are highlighted in the following subsections. Note that every analytical method implemented by PyRQA can be executed without conducting performance tuning. Nonetheless, it may enable significant runtime reductions regarding the analysis of very long time series.

### 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.

from pyrqa.variants.rqa.radius.column_no_overlap_materialisation_bit_no_recycling import ColumnNoOverlapMaterialisationBitNoRecycling from pyrqa.variants.rqa.radius.column_no_overlap_materialisation_bit_recycling import ColumnNoOverlapMaterialisationBitRecycling from pyrqa.variants.rqa.radius.column_no_overlap_materialisation_byte_no_recycling import ColumnNoOverlapMaterialisationByteNoRecycling from pyrqa.variants.rqa.radius.column_no_overlap_materialisation_byte_recycling import ColumnNoOverlapMaterialisationByteRecycling from pyrqa.variants.rqa.radius.column_no_overlap_no_materialisation import ColumnNoOverlapNoMaterialisation from pyrqa.variants.rqa.radius.column_overlap_materialisation_bit_no_recycling import ColumnOverlapMaterialisationBitNoRecycling from pyrqa.variants.rqa.radius.column_overlap_materialisation_bit_recycling import ColumnOverlapMaterialisationBitRecycling from pyrqa.variants.rqa.radius.column_overlap_materialisation_byte_no_recycling import ColumnOverlapMaterialisationByteNoRecycling from pyrqa.variants.rqa.radius.column_overlap_materialisation_byte_recycling import ColumnOverlapMaterialisationByteRecycling from pyrqa.variants.rqa.radius.column_overlap_no_materialisation import ColumnOverlapNoMaterialisation 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=(ColumnNoOverlapMaterialisationBitNoRecycling, ColumnNoOverlapMaterialisationBitRecycling, ColumnNoOverlapMaterialisationByteNoRecycling, ColumnNoOverlapMaterialisationByteRecycling, ColumnNoOverlapNoMaterialisation, ColumnOverlapMaterialisationBitNoRecycling, ColumnOverlapMaterialisationBitRecycling, ColumnOverlapMaterialisationByteNoRecycling, ColumnOverlapMaterialisationByteRecycling, ColumnOverlapNoMaterialisation))

### Input Data Overlapping

PyRQA comprises a set of `variants` for each analytical method, which
differ regarding certain implementation details. The specific
characteristics of each variant are reflected in its name. The naming
convention is applied accordingly to all analytical methods.

One criterion is the way how the input data is represented within the
memory of the computing devices. The multi-dimensional vectors may
either be stored column-wise or row-wise. Considering `TimeSeries`
objects, to which the time delay method is applied, the columns may
additionally be stored in an overlapped fashion. This allows leveraging
data compression as well as potential runtime reductions. Using
`EmbeddedSeries` objects as input data, the multi-dimensional vectors
may only be stored non-overlapping. As an example, variants referring to
the joint recurrence quantification analysis are mentioned below.

from pyrqa.variants.jrqa.radius.column_no_overlap_materialisation_byte_no_recycling import ColumnNoOverlapMaterialisationByteNoRecycling from pyrqa.variants.jrqa.radius.row_no_overlap_materialisation_byte_no_recycling import RowNoOverlapMaterialisationByteNoRecycling from pyrqa.variants.jrqa.radius.column_overlap_materialisation_byte_no_recycling import ColumnOverlapMaterialisationByteNoRecycling

Note that these variants have the same implementation characteristics,
except from the representation of the input data. Only those variants
that store the input data non-overlapping may be applied to
`EmbeddedSeries` objects. By default, non-overlapping variants are
selected.

### OpenCL Compiler Optimisations

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`.

computation = RQAComputation.create(settings, selector=EpsilonGreedySelector(explore=10), variants=(ColumnNoOverlapMaterialisationBitNoRecycling, ColumnNoOverlapMaterialisationBitRecycling, ColumnNoOverlapMaterialisationByteNoRecycling, ColumnNoOverlapMaterialisationByteRecycling, ColumnNoOverlapNoMaterialisation, ColumnOverlapMaterialisationBitNoRecycling, ColumnOverlapMaterialisationBitRecycling, ColumnOverlapMaterialisationByteNoRecycling, ColumnOverlapMaterialisationByteRecycling, ColumnOverlapNoMaterialisation), variants_kwargs={'optimisations_enabled': True})

### Loop Unrolling

Besides gaining drastic performance improvements using parallel computing techniques, parts of the PyRQA kernel processing rely on loops. OpenCL offers the opportunity to leverage additional performance improvements by unrolling these loops. This is conducted by the OpenCL compiler that is applied to the kernel functions. Note that loop unrolling may not be supported by all OpenCL platforms.

PyRQA offers the opportunity to manually specify loop unrolling factors that can be passed as a tuple when creating a selector. While creating a computation object, each factor is combined with each implementation variant. The resulting combinations are further used regarding adaptive implementation selection. An example on how to specify loop unrolling factors is given below.

from pyrqa.selector import EpsilonFirstSelector selector = EpsilonFirstSelector(explore=15, loop_unroll_factors=(1,2,4,8,16,32))

### Submatrix Edge Length

During the analytical processing, PyRQA subdivides the recurrence matrix
into a number of submatrices. To get more information on the recurrence
matrix partitioning, please refer to the publications mentioned below.
The corresponding parameter `edge_length` can be set while creating a
computation object.

computation = RQAComputation.create(settings, edge_length=12800)

Note that the edge length has to be chosen such that a submatrix fits in
the memory of the OpenCL computing devices. Experiments have shown that
values between `10000` and `20000` are a reasonable choice.
Nonetheless, values outside of these boundaries may lead to lower
runtimes for specific computational scenarios. Furthermore, recurrence
matrices with a smaller extent than the value of `edge_length` are
processed without being partitioned.

## 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.

python -m pyrqa.test

The complete set of tests can be executed by adding the option
`--all`.

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.

## Development Status

The development status of the PyRQA package is considered as *Beta*.
Please send feature requests and bug reports to the email address that
is listed in the project metadata.

## Release Notes

### 8.0.0

- Usage of pre-embedded time series as input data.
- Addition of variants referring to non-overlapping column-wise storage of input data.
- Refactoring of the public API.
- Updated documentation.

### 7.0.1

- Modification of the build configuration.
- Updated documentation.

### 7.0.0

- Addition of two variants regarding joint recurrence quantification analysis computations.
- Removal of obsolete source code.
- Refactoring of the public API.
- Updated documentation.

### 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.

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