Package: diamondback
DescriptionDiamondbackis a package which provides Digital Signal Processing (DSP)
solutions.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.DetailsData 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.commonsLogsingleton 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.RestClientinstances 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.Serialsingleton 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.filtersComplexBandPassFilterinstances adaptively extract or reject signals at a normalized
frequency of interest, and may be employed to dynamically track
magnitude and phase or demodulate signals.ComplexExponentialFilterinstances synthesize complex exponential signals at normalized
frequencies of interest with contiguous phase.ComplexFrequencyFilterinstances adaptively discriminate and estimate a normalized frequency
of a signal.DerivativeFilterinstances estimate discrete derivative approximations at several
filter orders.FirFilterinstances 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.GoertzelFilterinstances efficiently evaluate a Discrete Fourier Transform (DFT)
at a normalized frequency, based on a window filter and normalized
frequency.IirFilterinstances 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.IntegralFilterinstances estimate discrete integral approximations at several filter
orders.PidFilterinstances realize discrete difference equations of Proportional
Integral Derivative (PID) form.PolynomialRateFilterinstances 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.PolyphaseRateFilterinstances 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.RankFilterinstances 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.WindowFilterinstances realize discrete window functions useful in Fourier
analysis, based on style, order, and normalization.modelsDiversityModelinstances 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.GaussianModelis 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.GaussianMixtureModelis 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.transformsComplexTransformis 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.FourierTransformis 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.PsdTransformis a singleton instance which realizes a Power Spectral Density (PSD)
which converts a real or complex discrete-time signal to a real
discrete-frequency signal which estimates an aggregate power spectrum
of the signal, based on a window filter, index, and spectrogram.
A spectrogram constructs a time frequency representation of the power
spectrum.WaveletTransforminstances 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.ZTransformis 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.DependenciesPartialFullDiamondback depends upon external packages.jsonpicklelogurunumpyrequestsscikit-learnscipyDiamondback elective documentation, test, and visualization functionality
depends upon additional external packages.ipythonipywidgetsjupytermatplotlibnoxpandaspillowpydepspytestsphinxsphinx-rtd-themeInstallationDiamondback is a public repository hosted at PyPI and GitHub.pip install diamondback

or

pip install git+https://github.com/larryturner/diamondback.gitDemonstrationA jupyter notebook defines cells to create and exercise diamondback components.
The notebook serves as a tool for visualization, validation, and demonstration
of diamondback capabilities.git clone https://github.com/larryturner/diamondback.git

cd diamondback

pip install --requirement requirements.txt

jupyter notebook .\jupyter\diamondback.ipynbRestart the kernel, as the first cell contains common definitions, find cells
which exercise components of interest, and manipulate widgets to exercise and
visualize functionality.TestsA test solution is provided to exercise and verify components, pytest is
used to execute unit and integration tests.pytest --capture=no --verboseDocumentationDiamondback documentation is available onGitHub Pages.LicenseBSD-3CAuthorLarry Turner