Metadata-Version: 1.2
Name: diamondback
Version: 1.0.16
Summary: Diamondback Digital Signal Processing ( DSP ) package including commons, filters, interfaces, models, and transforms.
Home-page: https://github.com/larryturner/diamondback
Author: Larry Turner
Author-email: larry.turner@se.com
License: BSD-3C.  Copyright (c) 2018, Larry Turner, Schneider Electric.  All rights reserved.
Description: ### Description
        
        Diamondback is a Python package which provides Digital Signal Processing ( DSP )
        solutions, organized in the form of commons, filters, interfaces, models, and
        transforms.
        
        Diamondback was designed to complement Artificial Intelligence ( AI ) frameworks,
        by defining components which analyze, filter, extract, model, and transform data
        into forms which are useful in applications including pattern recognition,
        feature extraction, and optimization.
        
        Diamondback was also designed to provide utility in the context of classical
        signal processing solutions including communications, modeling, signal
        identification and extraction, and noise cancellation.
        
        Documentation is provided in HTML form, extracted from docstrings in the
        diamondback package source, and a Jupyter notebook is provided to dynamically
        construct and exercise diamondback components to facilitate experimentation and
        visualization.
        	
        ### Details
        
        An extensible factory design pattern is expressed in many components, and a
        mix-in design pattern is extensively employed in property definition.  Complex
        or real types, in adaptive or static forms, are supported as appropriate.  Data
        collections are consistently expressed in native types, including tuples, sets,
        lists, and dictionaries, with vector and matrix types expressed in numpy arrays.
        	
        Diamondback is defined in subpackages :
        
        * [commons](https://larryturner.github.io/diamondback/diamondback.commons)
        	
        * [filters](https://larryturner.github.io/diamondback/diamondback.filters)
        	
        * [interfaces](https://larryturner.github.io/diamondback/diamondback.interfaces)
        	
        * [models](https://larryturner.github.io/diamondback/diamondback.models)
        	
        * [transforms](https://larryturner.github.io/diamondback/diamondback.transforms)
        
        #### [commons](https://larryturner.github.io/diamondback/diamondback.commons)
        	
        **Log** is a singleton instance which formats and writes log entries,
        electively using the logger package or directly to a specified stream. Log
        entries are prefaced with an ISO-8601 datetime and log level, and enhancements
        are made to the formatting of datetime, exception, and collection data types.
        Dynamic stream redirection and log level specification are supported.
        	    
        **Serial** is a singleton instance which encodes and decodes an instance or
        collection with JSON text, or base-64 encoded gzip JSON binary format.
        
        #### [filters](https://larryturner.github.io/diamondback/diamondback.filters)
        	
        **ComplexBandPassFilter** 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** instances synthesize complex exponential signals at
        normalized frequencies of interest with contiguous phase.
        	
        **ComplexFrequencyFilter** instances adaptively discriminate and estimate a
        normalized frequency of a signal.
        	
        **DerivativeFilter** instances estimate discrete derivative approximations at
        several filter orders, through extensible factory construction.
        
        **FirFilter** instances realize discrete difference equations of Finite Impulse
        Response ( FIR ) form, in adaptive or static solutions.  A factory electively
        constructs instances based on type, classification, normalized frequency, order,
        cascade count, and complement.  Filters may be readily extended to support new
        types and functionality, while retaining factory support.  Root extraction,
        group delay, and frequency response evaluation are defined.
        
        **GoertzelFilter** instances efficiently evaluate a Discrete Fourier Transform
        ( DFT ) at a normalized frequency, based on a window filter and normalized
        frequency.
        	
        **IirFilter** instances realize discrete difference equations of Infinite Impulse
        Response ( IIR ) form, in adaptive or static solutions.  A factory electively
        constructs instances based on type, classification, normalized frequency, order,
        cascade count, and complement.  Filters may be readily extended to support new
        types and functionality, while retaining factory support.  Root extraction,
        group delay, and frequency response evaluation are defined.
        
        **IntegralFilter** instances estimate discrete integral approximations at several
        filter orders, through extensible factory construction.
        
        **PidFilter** instances realize discrete difference equations of Proportional
        Integral Derivative ( PID ) form.
        
        **PolynomialRateFilter** instances approximate a signal evaluated at an effective
        frequency equal to the product of the normalized frequency and a rate greater
        than or equal to one, supporting interpolation through localized polynomial
        approximation with no group delay.
        		
        **PolyphaseRateFilter** 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 construction 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** 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** instances realize discrete window functions useful in Fourier
        analysis, based on type, classification, order, and normalization, through
        extensible factory construction.
        		
        #### [interfaces](https://larryturner.github.io/diamondback/diamondback.interfaces)
        
        **IA**, **IB**, **IClear**, **IData**, **IDateTime**, **IDuration**,
        **IEncoding**, **IEqual**, **IFrequency**, **IInterval**, **ILatency**,
        **IPath**, **IPeriod**, **IPhase**, **IQ**, **IRate**, **IReset**,
        **IResolution**, **IRotation**, **IS**, **IState**, **ITimeZone**, and
        **IUpdate** interfaces facilitate mix-in design.
        	
        #### [models](https://larryturner.github.io/diamondback/diamondback.models)
        
        **DiversityModel** instances select and retain a state extracted to maximize the
        minimum distance between state members based on classification and order,
        through extensible factory construction.  An opportunistic unsupervised learning
        model typically improves condition and numerical accuracy and reduces storage
        relative to alternative approaches including generalized linear inverse.
        
        **PrincipalComponentModel** instances are supervised learning models which
        analyze an incident signal to learn a mean vector, standard deviation vector,
        and a collection of eigenvectors, and produce a reference signal which is a
        candidate for dimension reduction, in which higher order dimensions are
        discarded, reducing the order of the reference signal, while preserving
        significant and often sufficient information.
        		
        #### [transforms](https://larryturner.github.io/diamondback/diamondback.transforms)
        	
        **ComplexTransform** 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** 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** 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.
        		
        **WaveletTransform** instances realize a temporal spatial frequency transformation
        through construction and application of analysis and synthesis filters with
        complementary frequency responses, combined with downsampling and upsampling
        operations, in equivalent and reversible representations.  A factory constructs
        instances based on type, classification, and order.  Filters may be readily
        extended to support new types and functionality, while retaining factory
        support.
        
        **ZTransform** 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)
            
        * [numpy](https://github.com/numpy/numpy)
            
        * [scipy](https://github.com/scipy/scipy)
        
        Diamondback Jupyter notebook 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)
        
        * [opencv-python](https://github.com/opencv/opencv)
        	
        ### Installation
        
        Diamondback is a public repository hosted at PyPI and GitHub.
        
            pip install diamondback
        
            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 simplified but useful introduction to and
        demonstration of diamondback capabilities.	
        
            pip install ipython ipywidgets jupyter matplotlib opencv-python
            
            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.
            
        ### Documentation
        
        Diamondback documentation is generated from the source, indexed, and searchable.
          
        [GitHub](https://larryturner.github.io/diamondback/)
                
        ### Test
        
        A simple pytest solution is provided to exercise and verify diamondback
        components.
        	
            py.test -v --capture=no .\test
        	
        ### Author
        
        [Larry Turner](https://github.com/larryturner)
        	
        ### License
        
        [BSD-3C](https://github.com/larryturner/diamondback/blob/master/license)
        
        ### Release
        
        [Version](https://github.com/larryturner/diamondback/blob/master/version)
                
        Copyright (c) 2018, Larry Turner, Schneider Electric.  All rights reserved.
        	
Keywords: DSP,FFT,FIR,GZIP,IIR,JSON,PCA,PID,PSD,adaptive,complex,derivative,diversity,exponential,filter,fourier,frequency,goertzel,integral,log,model,polynomial,polyphase,power,serial,transform,wavelet
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >= 3.5
