.. image:: https://img.shields.io/pypi/pyversions/diamondback.svg?color=blue :target: https://github.com/larryturner/diamondback .. image:: https://img.shields.io/pypi/v/diamondback.svg?label=pypi%20version&color=lightblue :target: https://pypi.org/larryturner/diamondback .. image:: https://img.shields.io/github/license/larryturner/diamondback?color=lightgray :target: https://github.com/larryturner/diamondback/blob/master/license Description ~~~~~~~~~~~ Diamondback is a package which provides Digital Signal Processing ( DSP ) solutions, and complements AI frameworks, by defining components which filter, model, and transform data. Diamondback complements Artificial Intelligence ( AI ) frameworks, by defining components which filter, model, and transform data into forms which are useful in feature extraction and pattern recognition. Diamondback also supports applications including cancellation, identification, optimization, probabilistic modeling, rate adaptation, and serialization. Details ~~~~~~~ Data collections are consistently expressed in native types, including tuples, sets, lists, and dictionaries, with vector and matrix types expressed in numpy arrays. Complex or real types are supported as appropriate. Diamondback is defined in subpackages commons, filters, models, and transforms. `commons <https://larryturner.github.io/diamondback/diamondback.commons>`_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `Log <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-log-module>`_ singleton instance which formats and writes log entries with a specified level and stream using the loguru package. Log entries contain an ISO-8601 datetime and level. Log uses lazy initialization to coexist with loguru. Dynamic stream redirection and level specification are supported. - `RestClient <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-restclient-module>`_ instances define a client for simple REST service requests using the requests package. An API and an elective dictionary of parameter strings are encoded to build a URL, elective binary or JSON data are defined in the body of a request, and a requests response containing JSON, text, or binary data is returned. Proxy, timeout, and URL definition are supported. - `Serial <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-serial-module>`_ singleton instance which encodes and decodes an instance or collection in BSON or JSON, and generates SHA3-256 codes, using the jsonpickle package. An instance may be an object or a collection, referenced by abstract or concrete types, and the instance will be correctly encoded and decoded, without custom encoding definitions. `filters <https://larryturner.github.io/diamondback/diamondback.filters>`_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `ComplexBandPassFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexbandpassfilter-module>`_ instances adaptively extract or reject signals at a normalized frequency of interest, and may be employed to dynamically track magnitude and phase or demodulate signals. - `ComplexExponentialFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexexponentialfilter-module>`_ instances synthesize complex exponential signals at normalized frequencies of interest with contiguous phase. - `ComplexFrequencyFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexfrequencyfilter-module>`_ instances adaptively discriminate and estimate a normalized frequency of a signal. - `DerivativeFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-derivativefilter-module>`_ instances estimate discrete derivative approximations at several filter orders. - `FirFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-firfilter-module>`_ instances realize discrete difference equations of Finite Impulse Response ( FIR ) form. Instances are defined based on style, normalized frequency, order, cascade count, and complement, or forward coefficients. Root extraction, group delay, and frequency response evaluation are defined. - `GoertzelFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-goertzelfilter-module>`_ instances efficiently evaluate a Discrete Fourier Transform ( DFT ) at a normalized frequency, based on a window filter and normalized frequency. - `IirFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-iirfilter-module>`_ instances realize discrete difference equations of Infinite Impulse Response ( IIR ) form. Instances are defined based on style, normalized frequency, order, cascade count, and complement, or recursive and forward coefficients. Root extraction, group delay, and frequency response evaluation are defined. - `IntegralFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-integralfilter-module>`_ instances estimate discrete integral approximations at several filter orders. - `PidFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-pidfilter-module>`_ instances realize discrete difference equations of Proportional Integral Derivative ( PID ) form. - `PolynomialRateFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-polynomialratefilter-module>`_ instances approximate a signal evaluated at an effective frequency equal to the product of the normalized frequency and a rate greater than zero, supporting decimation and interpolation through localized polynomial approximation with no group delay. - `PolyphaseRateFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-polyphaseratefilter-module>`_ instances approximate a signal evaluated at an effective frequency equal to the product of the normalized frequency and a rate greater than zero, supporting decimation and interpolation through definition and application of a polyphase filter bank, a sequence of low pass filters with a common frequency response and a fractional sample difference in group delay. An appropriate stride is determined to realize the specified effective frequency without bias and with group delay based on order. - `RankFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-rankfilter-module>`_ instances define nonlinear morphological operators, which define functionality based on rank and order, including dilation, median, and erosion, and may be combined in sequences to support close and open. - `WindowFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-windowfilter-module>`_ instances realize discrete window functions useful in Fourier analysis, based on style, order, and normalization. `models <https://larryturner.github.io/diamondback/diamondback.models>`_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `DiversityModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-diversitymodel-module>`_ instances select and retain a state extracted to maximize the minimum distance between state members based on style and order. An opportunistic unsupervised learning model typically improves condition and numerical accuracy and reduces storage relative to alternative approaches including generalized linear inverse. - `GaussianModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-gaussianmodel-module>`_ is a supervised learning probabilistic model instance which uses maximum likelihood estimation and regularization to maximize posterior probability and classify an incident signal. Learns one distribution instance per class. - `GaussianMixtureModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-gaussianmixturemodel-module>`_ is a semi-supervised learning probabilistic model instance which uses maximum likelihood estimation, regularization, and expectation maximization to maximize posterior probability and classify an incident signal. Learns model instances of a specified order per class, where intra-class models capture mixture distributions. `transforms <https://larryturner.github.io/diamondback/diamondback.transforms>`_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `ComplexTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-complextransform-module>`_ is a singleton instance which converts a three-phase real signal to a complex signal, or a complex signal to a three-phase real signal, in equivalent and reversible representations, based on a neutral condition. - `FourierTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-fouriertransform-module>`_ is a singleton instance which converts a real or complex discrete-time signal to a complex discrete-frequency signal, or a complex discrete-frequency signal to a real or complex discrete-time signal, in equivalent and reversible representations, based on a window filter and inverse. - `PowerSpectrumTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-powerspectrumtransform-module>`_ is a singleton instance which converts a real or complex discrete-time signal to a real discrete-frequency signal which estimates a mean power density of the signal, based on a window filter, index, and spectrogram. A spectrogram constructs a time frequency representation. - `WaveletTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-wavelettransform-module>`_ instances realize a temporal spatial frequency transformation through defninition and application of analysis and synthesis filters with complementary frequency responses, combined with downsampling and upsampling operations, in equivalent and reversible representations. Instances are defined based on style and order. - `ZTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-ztransform-module>`_ is a singleton instance which converts continuous s-domain to discrete z-domain difference equations, based on a normalized frequency and application of bilinear or impulse invariant methods. Dependencies ~~~~~~~~~~~~ Diamondback depends upon external packages : - `jsonpickle <https://github.com/jsonpickle/jsonpickle>`_ - `loguru <https://github.com/delgan/loguru>`_ - `numpy <https://github.com/numpy/numpy>`_ - `requests <https://github.com/psf/requests>`_ - `scikit-learn <https://github.com/scikit-learn/scikit-learn>`_ - `scipy <https://github.com/scipy/scipy>`_ Diamondback elective documentation, test, and visualization functionality depends upon additional external packages : - `ipython <https://github.com/ipython/ipython>`_ - `ipywidgets <https://github.com/jupyter-widgets/ipywidgets>`_ - `jupyter <https://github.com/jupyter/notebook>`_ - `matplotlib <https://github.com/matplotlib/matplotlib>`_ - `nox <https://github.com/theacodes/nox>`_ - `pandas <https://github.com/pandas-dev/pandas>`_ - `pillow <https://github.com/python-pillow/pillow>`_ - `pytest <https://github.com/pytest-dev/pytest>`_ - `sphinx <https://github.com/sphinx-doc/sphinx>`_ - `sphinx-rtd-theme <https://github.com/readthedocs/sphinx_rtd_theme>`_ Installation ~~~~~~~~~~~~ Diamondback is a public repository hosted at PyPI and GitHub. :: pip install diamondback or pip install git+https://github.com/larryturner/diamondback.git Demonstration ~~~~~~~~~~~~~ A jupyter notebook defines cells to create and exercise diamondback components. The notebook serves as a tool for visualization, validation, and demonstration of diamondback capabilities. A jupyter notebook may be run on a remote server without installation with Binder, which dynamically builds and deploys a docker container from a GitHub repository, or installed from GitHub and run on a local system. Binder may not be operational, as package dependencies on that service are dated. **Remote** .. image:: https://img.shields.io/badge/Binder-blue :target: https://mybinder.org/v2/gh/larryturner/diamondback/master?labpath=notebooks%2Fdiamondback.ipynb **Local** :: git clone https://github.com/larryturner/diamondback.git cd diamondback pip install --requirement requirements.txt jupyter notebook .\jupyter\diamondback.ipynb Restart the kernel, as the first cell contains common definitions, find cells which exercise components of interest, and manipulate widgets to exercise and visualize functionality. Tests ~~~~~ A test solution is provided to exercise and verify components, pytest is used to execute unit and integration tests. :: pytest --capture=no --verbose Documentation ~~~~~~~~~~~~~ Diamondback documentation is available on GitHub pages. .. image:: https://img.shields.io/badge/GitHub-blue :target: https://larryturner.github.io/diamondback/ License ~~~~~~~ `BSD-3C <https://github.com/larryturner/diamondback/blob/master/license>`_ Author ~~~~~~ `Larry Turner <https://github.com/larryturner>`_

{ "_id": null, "home_page": "", "name": "diamondback", "maintainer": "", "docs_url": null, "requires_python": "<3.12,>=3.8", "maintainer_email": "", "keywords": "BSON,DSP,FFT,FIR,GZIP,IIR,JSON,PSD,REST,derivative,diversity,filter,frequency,gaussian,goertzel,integral,log,model,polynomial,polyphase,rate,request,serial,transform,wavelet", "author": "", "author_email": "Larry Turner <larry.turner@se.com>", "download_url": "https://files.pythonhosted.org/packages/4f/2d/cbc96599ce2b9e3ff56c9be93835219cedb3c66bf64742fe755864d48ac6/diamondback-4.1.14.tar.gz", "platform": null, "description": ".. image:: https://img.shields.io/pypi/pyversions/diamondback.svg?color=blue\r\n :target: https://github.com/larryturner/diamondback\r\n.. image:: https://img.shields.io/pypi/v/diamondback.svg?label=pypi%20version&color=lightblue\r\n :target: https://pypi.org/larryturner/diamondback\r\n.. image:: https://img.shields.io/github/license/larryturner/diamondback?color=lightgray\r\n :target: https://github.com/larryturner/diamondback/blob/master/license\r\n\r\nDescription\r\n~~~~~~~~~~~\r\n\r\nDiamondback is a package which provides Digital Signal Processing ( DSP )\r\nsolutions, and complements AI frameworks, by defining components which filter,\r\nmodel, and transform data.\r\n\r\nDiamondback complements Artificial Intelligence ( AI ) frameworks, by\r\ndefining components which filter, model, and transform data into forms which\r\nare useful in feature extraction and pattern recognition.\r\n\r\nDiamondback also supports applications including cancellation, identification,\r\noptimization, probabilistic modeling, rate adaptation, and serialization.\r\n\r\nDetails\r\n~~~~~~~\r\n\r\nData collections are consistently expressed in native types,\r\nincluding tuples, sets, lists, and dictionaries, with vector and matrix\r\ntypes expressed in numpy arrays. Complex or real types are supported as\r\nappropriate.\r\n\r\nDiamondback is defined in subpackages commons, filters, models, and\r\ntransforms.\r\n\r\n`commons <https://larryturner.github.io/diamondback/diamondback.commons>`_\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n- `Log <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-log-module>`_\r\n singleton instance which formats and writes log entries with a specified\r\n level and stream using the loguru package. Log entries contain an ISO-8601\r\n datetime and level. Log uses lazy initialization to coexist with loguru.\r\n Dynamic stream redirection and level specification are supported.\r\n\r\n- `RestClient <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-restclient-module>`_\r\n instances define a client for simple REST service requests using the\r\n requests package. An API and an elective dictionary of parameter strings\r\n are encoded to build a URL, elective binary or JSON data are defined in the\r\n body of a request, and a requests response containing JSON, text, or binary\r\n data is returned. Proxy, timeout, and URL definition are supported.\r\n\r\n- `Serial <https://larryturner.github.io/diamondback/diamondback.commons#diamondback-commons-serial-module>`_\r\n singleton instance which encodes and decodes an instance or collection in\r\n BSON or JSON, and generates SHA3-256 codes, using the jsonpickle package.\r\n An instance may be an object or a collection, referenced by abstract or\r\n concrete types, and the instance will be correctly encoded and decoded,\r\n without custom encoding definitions.\r\n\r\n`filters <https://larryturner.github.io/diamondback/diamondback.filters>`_\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n- `ComplexBandPassFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexbandpassfilter-module>`_\r\n instances adaptively extract or reject signals at a normalized\r\n frequency of interest, and may be employed to dynamically track\r\n magnitude and phase or demodulate signals.\r\n\r\n- `ComplexExponentialFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexexponentialfilter-module>`_\r\n instances synthesize complex exponential signals at normalized\r\n frequencies of interest with contiguous phase.\r\n\r\n- `ComplexFrequencyFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-complexfrequencyfilter-module>`_\r\n instances adaptively discriminate and estimate a normalized frequency\r\n of a signal.\r\n\r\n- `DerivativeFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-derivativefilter-module>`_\r\n instances estimate discrete derivative approximations at several\r\n filter orders.\r\n\r\n- `FirFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-firfilter-module>`_\r\n instances realize discrete difference equations of Finite Impulse\r\n Response ( FIR ) form. Instances are defined based on style,\r\n normalized frequency, order, cascade count, and complement, or\r\n forward coefficients. Root extraction, group delay, and frequency\r\n response evaluation are defined.\r\n\r\n- `GoertzelFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-goertzelfilter-module>`_\r\n instances efficiently evaluate a Discrete Fourier Transform ( DFT )\r\n at a normalized frequency, based on a window filter and normalized\r\n frequency.\r\n\r\n- `IirFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-iirfilter-module>`_\r\n instances realize discrete difference equations of Infinite Impulse\r\n Response ( IIR ) form. Instances are defined based on style,\r\n normalized frequency, order, cascade count, and complement, or recursive\r\n and forward coefficients. Root extraction, group delay, and frequency\r\n response evaluation are defined.\r\n\r\n- `IntegralFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-integralfilter-module>`_\r\n instances estimate discrete integral approximations at several filter\r\n orders.\r\n\r\n- `PidFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-pidfilter-module>`_\r\n instances realize discrete difference equations of Proportional\r\n Integral Derivative ( PID ) form.\r\n\r\n- `PolynomialRateFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-polynomialratefilter-module>`_\r\n instances approximate a signal evaluated at an effective frequency\r\n equal to the product of the normalized frequency and a rate greater\r\n than zero, supporting decimation and interpolation through localized\r\n polynomial approximation with no group delay.\r\n\r\n- `PolyphaseRateFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-polyphaseratefilter-module>`_\r\n instances approximate a signal evaluated at an effective frequency\r\n equal to the product of the normalized frequency and a rate greater\r\n than zero, supporting decimation and interpolation through\r\n definition and application of a polyphase filter bank, a sequence\r\n of low pass filters with a common frequency response and a fractional\r\n sample difference in group delay. An appropriate stride is determined\r\n to realize the specified effective frequency without bias and with\r\n group delay based on order.\r\n\r\n- `RankFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-rankfilter-module>`_\r\n instances define nonlinear morphological operators, which define\r\n functionality based on rank and order, including dilation, median,\r\n and erosion, and may be combined in sequences to support close and\r\n open.\r\n\r\n- `WindowFilter <https://larryturner.github.io/diamondback/diamondback.filters#diamondback-filters-windowfilter-module>`_\r\n instances realize discrete window functions useful in Fourier\r\n analysis, based on style, order, and normalization.\r\n\r\n`models <https://larryturner.github.io/diamondback/diamondback.models>`_\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n- `DiversityModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-diversitymodel-module>`_\r\n instances select and retain a state extracted to maximize the minimum\r\n distance between state members based on style and order. An\r\n opportunistic unsupervised learning model typically improves condition\r\n and numerical accuracy and reduces storage relative to alternative\r\n approaches including generalized linear inverse.\r\n\r\n- `GaussianModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-gaussianmodel-module>`_\r\n is a supervised learning probabilistic model instance which uses\r\n maximum likelihood estimation and regularization to maximize posterior\r\n probability and classify an incident signal. Learns one distribution\r\n instance per class.\r\n\r\n- `GaussianMixtureModel <https://larryturner.github.io/diamondback/diamondback.models#diamondback-models-gaussianmixturemodel-module>`_\r\n is a semi-supervised learning probabilistic model instance which uses\r\n maximum likelihood estimation, regularization, and expectation\r\n maximization to maximize posterior probability and classify an incident\r\n signal. Learns model instances of a specified order per class, where\r\n intra-class models capture mixture distributions. \r\n\r\n`transforms <https://larryturner.github.io/diamondback/diamondback.transforms>`_\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n- `ComplexTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-complextransform-module>`_\r\n is a singleton instance which converts a three-phase real signal to a\r\n complex signal, or a complex signal to a three-phase real signal, in\r\n equivalent and reversible representations, based on a neutral\r\n condition.\r\n\r\n- `FourierTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-fouriertransform-module>`_\r\n is a singleton instance which converts a real or complex\r\n discrete-time signal to a complex discrete-frequency signal, or a\r\n complex discrete-frequency signal to a real or complex discrete-time\r\n signal, in equivalent and reversible representations, based on a\r\n window filter and inverse.\r\n\r\n- `PowerSpectrumTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-powerspectrumtransform-module>`_\r\n is a singleton instance which converts a real or complex\r\n discrete-time signal to a real discrete-frequency signal which\r\n estimates a mean power density of the signal, based on a window\r\n filter, index, and spectrogram. A spectrogram constructs a time\r\n frequency representation.\r\n\r\n- `WaveletTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-wavelettransform-module>`_\r\n instances realize a temporal spatial frequency transformation through\r\n defninition and application of analysis and synthesis filters with\r\n complementary frequency responses, combined with downsampling and\r\n upsampling operations, in equivalent and reversible representations.\r\n Instances are defined based on style and order.\r\n\r\n- `ZTransform <https://larryturner.github.io/diamondback/diamondback.transforms#diamondback-transforms-ztransform-module>`_\r\n is a singleton instance which converts continuous s-domain to\r\n discrete z-domain difference equations, based on a normalized\r\n frequency and application of bilinear or impulse invariant methods.\r\n\r\nDependencies\r\n~~~~~~~~~~~~\r\n\r\nDiamondback depends upon external packages :\r\n\r\n- `jsonpickle <https://github.com/jsonpickle/jsonpickle>`_\r\n\r\n- `loguru <https://github.com/delgan/loguru>`_\r\n\r\n- `numpy <https://github.com/numpy/numpy>`_\r\n\r\n- `requests <https://github.com/psf/requests>`_\r\n\r\n- `scikit-learn <https://github.com/scikit-learn/scikit-learn>`_\r\n\r\n- `scipy <https://github.com/scipy/scipy>`_\r\n\r\nDiamondback elective documentation, test, and visualization functionality\r\ndepends upon additional external packages :\r\n\r\n- `ipython <https://github.com/ipython/ipython>`_\r\n\r\n- `ipywidgets <https://github.com/jupyter-widgets/ipywidgets>`_\r\n\r\n- `jupyter <https://github.com/jupyter/notebook>`_\r\n\r\n- `matplotlib <https://github.com/matplotlib/matplotlib>`_\r\n\r\n- `nox <https://github.com/theacodes/nox>`_\r\n\r\n- `pandas <https://github.com/pandas-dev/pandas>`_\r\n\r\n- `pillow <https://github.com/python-pillow/pillow>`_\r\n\r\n- `pytest <https://github.com/pytest-dev/pytest>`_\r\n\r\n- `sphinx <https://github.com/sphinx-doc/sphinx>`_\r\n\r\n- `sphinx-rtd-theme <https://github.com/readthedocs/sphinx_rtd_theme>`_\r\n\r\nInstallation\r\n~~~~~~~~~~~~\r\n\r\nDiamondback is a public repository hosted at PyPI and GitHub.\r\n\r\n::\r\n\r\n pip install diamondback\r\n\r\n or\r\n\r\n pip install git+https://github.com/larryturner/diamondback.git\r\n\r\nDemonstration\r\n~~~~~~~~~~~~~\r\n\r\nA jupyter notebook defines cells to create and exercise diamondback components.\r\nThe notebook serves as a tool for visualization, validation, and demonstration\r\nof diamondback capabilities.\r\n\r\nA jupyter notebook may be run on a remote server without installation with\r\nBinder, which dynamically builds and deploys a docker container from a GitHub\r\nrepository, or installed from GitHub and run on a local system. Binder may\r\nnot be operational, as package dependencies on that service are dated.\r\n\r\n**Remote**\r\n\r\n.. image:: https://img.shields.io/badge/Binder-blue\r\n :target: https://mybinder.org/v2/gh/larryturner/diamondback/master?labpath=notebooks%2Fdiamondback.ipynb\r\n\r\n**Local**\r\n\r\n::\r\n\r\n git clone https://github.com/larryturner/diamondback.git\r\n\r\n cd diamondback\r\n\r\n pip install --requirement requirements.txt\r\n\r\n jupyter notebook .\\jupyter\\diamondback.ipynb\r\n\r\nRestart the kernel, as the first cell contains common definitions, find cells\r\nwhich exercise components of interest, and manipulate widgets to exercise and\r\nvisualize functionality.\r\n\r\nTests\r\n~~~~~\r\n\r\nA test solution is provided to exercise and verify components, pytest is\r\nused to execute unit and integration tests.\r\n\r\n::\r\n\r\n pytest --capture=no --verbose\r\n\r\nDocumentation\r\n~~~~~~~~~~~~~\r\n\r\nDiamondback documentation is available on GitHub pages.\r\n\r\n.. image:: https://img.shields.io/badge/GitHub-blue\r\n :target: https://larryturner.github.io/diamondback/\r\n\r\nLicense\r\n~~~~~~~\r\n\r\n`BSD-3C <https://github.com/larryturner/diamondback/blob/master/license>`_\r\n\r\nAuthor\r\n~~~~~~\r\n\r\n`Larry Turner <https://github.com/larryturner>`_\r\n", "bugtrack_url": null, "license": "\u00a9 2018 - 2023 Schneider Electric Industries SAS. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met : 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and / or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES ( INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION ) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT ( INCLUDING NEGLIGENCE OR OTHERWISE ) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 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