Diamondback digital signal processing package.
Diamondback is a 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.
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 :
- Log 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 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 JSON or binary 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 singleton instance which encodes and decodes an instance or collection with JSON or BSON, base-85 encoded gzip JSON, format 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.
- 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. 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. 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 zero, supporting decimation and 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.
- IA, IAsset, IB, ICache, IClear, ICompress, IConfigure, IConnect, ICount, IData, IDate, IDispose, IDuration, IEmulate, IEncoding, IEqual, IFrequency, IHeader, IIdentity, IInterval, ILabel, ILatency, ILive, IModel, IPath, IPeriod, IPhase, IProxy, IQ, IRate, IReady, IReset, IResolution, IRotation, IS, IStream, ITimeOut, ITimeZone, IUpdate, IUrl, IUser, IValid, and IVersion, interfaces facilitate mix-in design.
- 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.
- 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.
Diamondback depends upon external packages :
Diamondback jupyter notebook depends upon additional external packages :
Diamondback is a public repository hosted at PyPI and GitHub.
pip install diamondback pip install git+https://github.com/larryturner/diamondback.git
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.
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.
A test solution is provided to exercise and verify components. A nox session is defined and pytest is used to execute unit and scenario tests.
pytest --capture=no --verbose
Diamondback documentation is generated from the source, indexed, and searchable from GitHub.
Â© 2018 - 2021 Larry Turner, Schneider Electric Industries SAS. All rights reserved.
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