Metadata-Version: 2.1
Name: diamondback
Version: 1.0.26
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.
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: >= 3.6
Requires-Dist: jsonpickle (>=1.4.1)
Requires-Dist: numpy (>=1.19.2)
Requires-Dist: scipy (>=1.5.2)

### 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](https://larryturner.github.io/diamondback/diamondback.commons#module-diamondback.commons.Log)
  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](https://larryturner.github.io/diamondback/diamondback.commons#module-diamondback.commons.Serial)
  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](https://larryturner.github.io/diamondback/diamondback.filters#module-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](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.ComplexExponentialFilter)
  instances synthesize complex exponential signals at normalized frequencies of
  interest with contiguous phase.

* [ComplexFrequencyFilter](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.ComplexFrequencyFilter)
  instances adaptively discriminate and estimate a normalized frequency of a
  signal.

* [DerivativeFilter](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.DerivativeFilter)
  instances estimate discrete derivative approximations at several filter orders,
  through extensible factory construction.

* [FirFilter](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.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](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.GoertzelFilter)
  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#module-diamondback.filters.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](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.IntegralFilter)
  instances estimate discrete integral approximations at several filter orders,
  through extensible factory construction.

* [PidFilter](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.PidFilter)
  instances realize discrete difference equations of Proportional Integral
  Derivative ( PID ) form.

* [PolynomialRateFilter](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.PolynomialRateFilter)
  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#module-diamondback.filters.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](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.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](https://larryturner.github.io/diamondback/diamondback.filters#module-diamondback.filters.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](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IA),
  [IB](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IB),
  [ICache](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.ICache),
  [IClear](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IClear),
  [IData](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IData),
  [IDateTime](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IDateTime),
  [IDuration](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IDuration),
  [IEncoding](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IEncoding),
  [IEqual](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IEqual),
  [IFrequency](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IFrequency),
  [IInterval](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IInterval),
  [ILatency](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.ILatency),
  [IPath](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IPath),
  [IPeriod](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IPeriod),
  [IPhase](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IPhase),
  [IProxy](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IProxy),  
  [IQ](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IQ),
  [IRate](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IRate),
  [IReset](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IReset),
  [IResolution](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IResolution),
  [IRotation](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IRotation),
  [IS](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IS),
  [IState](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IState),
  [ITimeZone](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.ITimeZone),
  [IUpdate](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IUpdate),
  [IUrl](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IUrl),
  and [IVersion](https://larryturner.github.io/diamondback/diamondback.interfaces#module-diamondback.interfaces.IVersion)
  interfaces facilitate mix-in design.

#### [models](https://larryturner.github.io/diamondback/diamondback.models)

* [DiversityModel](https://larryturner.github.io/diamondback/diamondback.models#module-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](https://larryturner.github.io/diamondback/diamondback.models#module-diamondback.models.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](https://larryturner.github.io/diamondback/diamondback.transforms#module-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](https://larryturner.github.io/diamondback/diamondback.transforms#module-diamondback.transforms.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](https://larryturner.github.io/diamondback/diamondback.transforms#module-diamondback.transforms.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](https://larryturner.github.io/diamondback/diamondback.transforms#module-diamondback.transforms.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](https://larryturner.github.io/diamondback/diamondback.transforms#module-diamondback.transforms.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)

* [pillow](https://github.com/python-pillow/pillow)

### 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 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.

**Remote**

[![Binder](./images/binder.png)](https://mybinder.org/v2/gh/larryturner/diamondback/master?filepath=jupyter%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.

### Documentation

Diamondback documentation is generated from the source, indexed, and searchable
from GitHub.

[![GitHub](./images/github.png)](https://larryturner.github.io/diamondback/)

### Tests

A simple pytest solution is provided to exercise and verify diamondback
components.

    pytest --verbose

### 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.


