Metadata-Version: 2.1
Name: stumpy
Version: 1.3.1
Summary: A powerful and scalable library that can be used for a variety of time series data mining tasks
Home-page: https://github.com/TDAmeritrade/stumpy
Author: Sean M. Law
Author-email: seanmylaw@gmail.com
Maintainer: Sean M. Law
Maintainer-email: seanmylaw@gmail.com
License: BSD-3
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        ======
        STUMPY
        ======
        
        STUMPY is a powerful and scalable library that efficiently computes something called the `matrix profile <https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html>`__, which can be used for a variety of time series data mining tasks such as:
        
        * pattern/motif (approximately repeated subsequences within a longer time series) discovery
        * anomaly/novelty (discord) discovery
        * shapelet discovery
        * semantic segmentation 
        * density estimation
        * time series chains (temporally ordered set of subsequence patterns)
        * `and more ... <https://www.cs.ucr.edu/~eamonn/100_Time_Series_Data_Mining_Questions__with_Answers.pdf>`__
        
        Whether you are an academic, data scientist, software developer, or time series enthusiast, STUMPY is straightforward to install and our goal is to allow you to get to your time series insights faster. See `documentation <https://stumpy.readthedocs.io/en/latest/>`__ for more information.
        
        -------------------------
        How to use STUMPY
        -------------------------
        
        Please see our `API documentation <https://stumpy.readthedocs.io/en/latest/api.html>`__ for a complete list of available functions and see our informative `tutorials <https://stumpy.readthedocs.io/en/latest/tutorials.html>`__ for more comprehensive example use cases. Below, you will find code snippets that quickly demonstrate how to use STUMPY.
        
        Typical usage (1-dimensional time series data) with `STUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
            
            your_time_series = np.random.rand(10000)
            window_size = 50  # Approximately, how many data points might be found in a pattern 
            
            matrix_profile = stumpy.stump(your_time_series, m=window_size)
        
        Distributed usage for 1-dimensional time series data with Dask Distributed via `STUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
            from dask.distributed import Client
            dask_client = Client()
            
            your_time_series = np.random.rand(10000)
            window_size = 50  # Approximately, how many data points might be found in a pattern 
            
            matrix_profile = stumpy.stumped(dask_client, your_time_series, m=window_size)
        
        GPU usage for 1-dimensional time series data with `GPU-STUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
            from numba import cuda
        
            your_time_series = np.random.rand(10000)
            window_size = 50  # Approximately, how many data points might be found in a pattern
            all_gpu_devices = [device.id for device in cuda.list_devices()]  # Get a list of all available GPU devices
        
            matrix_profile = stumpy.gpu_stump(your_time_series, m=window_size, device_id=all_gpu_devices)
        
        Multi-dimensional time series data with `MSTUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.mstump>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
        
            your_time_series = np.random.rand(3, 1000)  # Each row represents data from a different dimension while each column represents data from the same dimension
            window_size = 50  # Approximately, how many data points might be found in a pattern
        
            matrix_profile, matrix_profile_indices = stumpy.mstump(your_time_series, m=window_size)
        
        Distributed multi-dimensional time series data analysis with Dask Distributed `MSTUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.mstumped>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
            from dask.distributed import Client
            dask_client = Client()
        
            your_time_series = np.random.rand(3, 1000)   # Each row represents data from a different dimension while each column represents data from the same dimension
            window_size = 50  # Approximately, how many data points might be found in a pattern
        
            matrix_profile, matrix_profile_indices = stumpy.mstumped(dask_client, your_time_series, m=window_size)
        
        Time Series Chains with `Anchored Time Series Chains (ATSC) <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.atsc>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
            
            your_time_series = np.random.rand(10000)
            window_size = 50  # Approximately, how many data points might be found in a pattern 
            
            matrix_profile = stumpy.stump(your_time_series, m=window_size)
        
            left_matrix_profile_index = matrix_profile[:, 2]
            right_matrix_profile_index = matrix_profile[:, 3]
            idx = 10  # Subsequence index for which to retrieve the anchored time series chain for
        
            anchored_chain = stumpy.atsc(left_matrix_profile_index, right_matrix_profile_index, idx)
        
            all_chain_set, longest_unanchored_chain = stumpy.allc(left_matrix_profile_index, right_matrix_profile_index)
        
        Semantic Segmentation with `Fast Low-cost Unipotent Semantic Segmentation (FLUSS) <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.fluss>`__:
        
        .. code:: python
        
            import stumpy
            import numpy as np
        
            your_time_series = np.random.rand(10000)
            window_size = 50  # Approximately, how many data points might be found in a pattern
        
            matrix_profile = stumpy.stump(your_time_series, m=window_size)
        
            subseq_len = 50
            correct_arc_curve, regime_locations = stumpy.fluss(matrix_profile[:, 1], 
                                                               L=subseq_len, 
                                                               n_regimes=2, 
                                                               excl_factor=1
                                                              )
        
        ------------
        Dependencies
        ------------
        
        * `NumPy <http://www.numpy.org/>`__
        * `Numba <http://numba.pydata.org/>`__
        * `SciPy <https://www.scipy.org/>`__
        
        ---------------
        Where to get it
        ---------------
        
        Conda install (preferred):
        
        .. code:: bash
            
            conda install -c conda-forge stumpy
        
        PyPI install, presuming you have numpy, scipy, and numba installed: 
        
        .. code:: bash
        
            python -m pip install stumpy
        
        To install stumpy from source, see the instructions in the `documentation <https://stumpy.readthedocs.io/en/latest/install.html>`__.
        
        -------------
        Documentation
        -------------
        
        In order to fully understand and appreciate the underlying algorithms and applications, it is imperative that you read the original publications_. For a more detailed example of how to use STUMPY please consult the latest `documentation <https://stumpy.readthedocs.io/en/latest/>`__ or explore the following tutorials:
        
        1. `The Matrix Profile <https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html>`__
        2. `STUMPY Basics <https://stumpy.readthedocs.io/en/latest/Tutorial_STUMPY_Basics.html>`__
        3. `Time Series Chains <https://stumpy.readthedocs.io/en/latest/Tutorial_Time_Series_Chains.html>`__
        4. `Semantic Segmentation <https://stumpy.readthedocs.io/en/latest/Tutorial_Semantic_Segmentation.html>`__
        
        -----------
        Performance
        -----------
        
        We tested the performance of computing the exact matrix profile using the Numba JIT compiled version of the code on randomly generated time series data with various lengths (i.e., ``np.random.rand(n)``) along with different `CPU and GPU hardware resources <hardware_>`_. 
        
        .. image:: https://raw.githubusercontent.com/TDAmeritrade/stumpy/master/docs/images/performance.png
            :alt: STUMPY Performance Plot
        
        The raw results are displayed in the table below as Hours:Minutes:Seconds.Milliseconds and with a constant window size of `m = 50`. Note that these reported runtimes include the time that it takes to move the data from the host to all of the GPU device(s). You may need to scroll to the right side of the table in order to see all of the runtimes.
        
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        |    i     |  n = 2\ :sup:`i`  | GPU-STOMP    | STUMP.2     | STUMP.16    | STUMPED.128 | STUMPED.256 | GPU-STUMP.1 | GPU-STUMP.2 | GPU-STUMP.DGX1 | GPU-STUMP.DGX2 |
        +==========+===================+==============+=============+=============+=============+=============+=============+=============+================+================+
        | 6        | 64                | 00:00:10.00  | 00:00:00.00 | 00:00:00.00 | 00:00:05.77 | 00:00:06.08 | 00:00:00.03 | 00:00:01.63 | NaN            | NaN            |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 7        | 128               | 00:00:10.00  | 00:00:00.00 | 00:00:00.00 | 00:00:05.93 | 00:00:07.29 | 00:00:00.04 | 00:00:01.66 | NaN            | NaN            |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 8        | 256               | 00:00:10.00  | 00:00:00.00 | 00:00:00.01 | 00:00:05.95 | 00:00:07.59 | 00:00:00.08 | 00:00:01.69 | 00:00:06.68    | 00:00:25.68    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 9        | 512               | 00:00:10.00  | 00:00:00.00 | 00:00:00.02 | 00:00:05.97 | 00:00:07.47 | 00:00:00.13 | 00:00:01.66 | 00:00:06.59    | 00:00:27.66    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 10       | 1024              | 00:00:10.00  | 00:00:00.02 | 00:00:00.04 | 00:00:05.69 | 00:00:07.64 | 00:00:00.24 | 00:00:01.72 | 00:00:06.70    | 00:00:30.49    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 11       | 2048              | NaN          | 00:00:00.05 | 00:00:00.09 | 00:00:05.60 | 00:00:07.83 | 00:00:00.53 | 00:00:01.88 | 00:00:06.87    | 00:00:31.09    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 12       | 4096              | NaN          | 00:00:00.22 | 00:00:00.19 | 00:00:06.26 | 00:00:07.90 | 00:00:01.04 | 00:00:02.19 | 00:00:06.91    | 00:00:33.93    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 13       | 8192              | NaN          | 00:00:00.50 | 00:00:00.41 | 00:00:06.29 | 00:00:07.73 | 00:00:01.97 | 00:00:02.49 | 00:00:06.61    | 00:00:33.81    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 14       | 16384             | NaN          | 00:00:01.79 | 00:00:00.99 | 00:00:06.24 | 00:00:08.18 | 00:00:03.69 | 00:00:03.29 | 00:00:07.36    | 00:00:35.23    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 15       | 32768             | NaN          | 00:00:06.17 | 00:00:02.39 | 00:00:06.48 | 00:00:08.29 | 00:00:07.45 | 00:00:04.93 | 00:00:07.02    | 00:00:36.09    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 16       | 65536             | NaN          | 00:00:22.94 | 00:00:06.42 | 00:00:07.33 | 00:00:09.01 | 00:00:14.89 | 00:00:08.12 | 00:00:08.10    | 00:00:36.54    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 17       | 131072            | 00:00:10.00  | 00:01:29.27 | 00:00:19.52 | 00:00:09.75 | 00:00:10.53 | 00:00:29.97 | 00:00:15.42 | 00:00:09.45    | 00:00:37.33    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 18       | 262144            | 00:00:18.00  | 00:05:56.50 | 00:01:08.44 | 00:00:33.38 | 00:00:24.07 | 00:00:59.62 | 00:00:27.41 | 00:00:13.18    | 00:00:39.30    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 19       | 524288            | 00:00:46.00  | 00:25:34.58 | 00:03:56.82 | 00:01:35.27 | 00:03:43.66 | 00:01:56.67 | 00:00:54.05 | 00:00:19.65    | 00:00:41.45    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 20       | 1048576           | 00:02:30.00  | 01:51:13.43 | 00:19:54.75 | 00:04:37.15 | 00:03:01.16 | 00:05:06.48 | 00:02:24.73 | 00:00:32.95    | 00:00:46.14    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 21       | 2097152           | 00:09:15.00  | 09:25:47.64 | 03:05:07.64 | 00:13:36.51 | 00:08:47.47 | 00:20:27.94 | 00:09:41.43 | 00:01:06.51    | 00:01:02.67    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 22       | 4194304           | NaN          | 36:12:23.74 | 10:37:51.21 | 00:55:44.43 | 00:32:06.70 | 01:21:12.33 | 00:38:30.86 | 00:04:03.26    | 00:02:23.47    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 23       | 8388608           | NaN          | 143:16:09.94| 38:42:51.42 | 03:33:30.53 | 02:00:49.37 | 05:11:44.45 | 02:33:14.60 | 00:15:46.26    | 00:08:03.76    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 24       | 16777216          | NaN          | NaN         | NaN         | 14:39:11.99 | 07:13:47.12 | 20:43:03.80 | 09:48:43.42 | 01:00:24.06    | 00:29:07.84    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | NaN      | 17729800          | 09:16:12.00  | NaN         | NaN         | 15:31:31.75 | 07:18:42.54 | 23:09:22.43 | 10:54:08.64 | 01:07:35.39    | 00:32:51.55    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 25       | 33554432          | NaN          | NaN         | NaN         | 56:03:46.81 | 26:27:41.29 | 83:29:21.06 | 39:17:43.82 | 03:59:32.79    | 01:54:56.52    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 26       | 67108864          | NaN          | NaN         | NaN         | 211:17:37.60| 106:40:17.17| 328:58:04.68| 157:18:30.50| 15:42:15.94    | 07:18:52.91    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | NaN      | 100000000         | 291:07:12.00 | NaN         | NaN         | NaN         | 234:51:35.39| NaN         | NaN         | 35:03:44.61    | 16:22:40.81    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        | 27       | 134217728         | NaN          | NaN         | NaN         | NaN         | NaN         | NaN         | NaN         | 64:41:55.09    | 29:13:48.12    |
        +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+
        
        ^^^^^^^^^^^^^^^^^^
        Hardware Resources
        ^^^^^^^^^^^^^^^^^^
        
        .. _hardware:
        
        GPU-STOMP: These results are reproduced from the original `Matrix Profile II <https://ieeexplore.ieee.org/abstract/document/7837898>`__ paper - NVIDIA Tesla K80 (contains 2 GPUs) and serves as the performance benchmark to compare against.
            
        STUMP.2: `stumpy.stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>`__ executed with 2 CPUs in Total - 2x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz processors parallelized with Numba on a single server without Dask.
        
        STUMP.16: `stumpy.stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>`__ executed with 16 CPUs in Total - 16x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz processors parallelized with Numba on a single server without Dask.
        
        STUMPED.128: `stumpy.stumped <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>`__ executed with 128 CPUs in Total - 8x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz processors x 16 servers, parallelized with Numba, and distributed with Dask Distributed.
        
        STUMPED.256: `stumpy.stumped <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>`__ executed with 256 CPUs in Total - 8x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz processors x 32 servers, parallelized with Numba, and distributed with Dask Distributed.
        
        GPU-STUMP.1: `stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>`__ executed with 1x NVIDIA GeForce GTX 1080 Ti GPU, 512 threads per block, 200W power limit, compiled to CUDA with Numba, and parallelized with Python multiprocessing
        
        GPU-STUMP.2: `stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>`__ executed with 2x NVIDIA GeForce GTX 1080 Ti GPU, 512 threads per block, 200W power limit, compiled to CUDA with Numba, and parallelized with Python multiprocessing
        
        GPU-STUMP.DGX1: `stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>`__ executed with 8x NVIDIA Tesla V100, 512 threads per block, compiled to CUDA with Numba, and parallelized with Python multiprocessing
        
        GPU-STUMP.DGX2: `stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>`__ executed with 16x NVIDIA Tesla V100, 512 threads per block, compiled to CUDA with Numba, and parallelized with Python multiprocessing
        
        -------------
        Running Tests
        -------------
        
        Tests are written in the ``tests`` directory and processed using `PyTest <https://docs.pytest.org/en/latest/>`__ and requires ``coverage.py`` for code coverage analysis. Tests can be executed with:
        
        .. code:: bash
        
            ./test.sh
        
        --------------
        Python Version
        --------------
        
        STUMPY supports `Python 3.6+ <https://python3statement.org/>`__ and, due to the use of unicode variable names/identifiers, is not compatible with Python 2.x. Given the small dependencies, STUMPY may work on older versions of Python but this is beyond the scope of our support and we strongly recommend that you upgrade to the most recent version of Python.
        
        ------------
        Getting Help
        ------------
        
        First, please check the `issues on github <https://github.com/TDAmeritrade/stumpy/issues?utf8=%E2%9C%93&q=>`__ to see if your question has already been answered there. If no solution is available there feel free to open a new issue and the authors will attempt to respond in a reasonably timely fashion.
        
        Alternatively, for general questions and comments, you can submit a post to the `STUMPY Discourse Group <https://stumpy.discourse.group/>`__.
        
        ------------
        Contributing
        ------------
        
        We welcome `contributions <https://github.com/TDAmeritrade/stumpy/blob/master/CONTRIBUTING.md>`__ in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please `fork the project <https://github.com/TDAmeritrade/stumpy/fork>`__, make your changes, and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
        
        ------
        Citing
        ------
        
        If you have used this codebase in a scientific publication and wish to cite it, please use the `Journal of Open Source Software article <http://joss.theoj.org/papers/10.21105/joss.01504>`__.
        
            S.M. Law, (2019). *STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining*. Journal of Open Source Software, 4(39), 1504.
        
        .. code:: bibtex
        
            @article{law2019stumpy,
              title={{STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining}},
              author={Law, Sean M.},
              journal={{The Journal of Open Source Software}},
              volume={4},
              number={39},
              pages={1504},
              year={2019}
            }
        
        ----------
        References
        ----------
        
        .. _publications:
        
        Yeh, Chin-Chia Michael, et al. (2016) Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View that Includes Motifs, Discords, and Shapelets. ICDM:1317-1322. `Link <https://ieeexplore.ieee.org/abstract/document/7837992>`__
        
        Zhu, Yan, et al. (2016) Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. ICDM:739-748. `Link <https://ieeexplore.ieee.org/abstract/document/7837898>`__
        
        Yeh, Chin-Chia Michael, et al. (2017) Matrix Profile VI: Meaningful Multidimensional Motif Discovery. ICDM:565-574. `Link <https://ieeexplore.ieee.org/abstract/document/8215529>`__ 
        
        Zhu, Yan, et al. (2017) Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining. ICDM:695-704. `Link <https://ieeexplore.ieee.org/abstract/document/8215542>`__
        
        Gharghabi, Shaghayegh, et al. (2017) Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. ICDM:117-126. `Link <https://ieeexplore.ieee.org/abstract/document/8215484>`__
        
        -------------------
        License & Trademark
        -------------------
        
        | STUMPY
        | Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license.
        | STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
        
Keywords: time series matrix profile motif discord
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/x-rst
Provides-Extra: ci
