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Reference
Find details on the SPL language, toolkits, APIs, commands, and more.
Toolkits
A toolkit is a set of SPL artifacts, which are organized into a package. Toolkits make SPL or native functions and primitive or composite operators reusable across different applications.
Toolkits
Toolkit com.teracloud.streams.timeseries 7.0.0
Functions: com.teracloud.streams.timeseries 7.0.0

Welcome
Learn about the core capabilities of Teracloud® Streams, its architecture, and key concepts.
Installing
Use this information to install, upgrade, and uninstall the Teracloud® Streams product.
Configuring
Create a basic or an enterprise domain which is a single point for configuring and managing common resources, security, and instances.
Administering
Administer the product by using the Teracloud® Streams graphical user interface, APIs, or the streamtool command-line interface.
Developing
Develop stream applications with the Streams Processing Language (SPL), Java, and Python.
Troubleshooting
Resolve problems with Teracloud® Streams using the troubleshooting tools provided with the product as well as the resources offered by Teracloud Support.
Reference
Find details on the SPL language, toolkits, APIs, commands, and more.
- APIs
  Application programming interfaces (APIs) provide functions that simplify applications development.
- Job configuration overlays
  You can specify or change configuration parameters when you use the submitjob or updateoperators operations. The settings control submission constraints, host constraints and locations, and operator status. Parameters are captured and updated in the configuration JSON file. When you change the values in the job configuration overlay file, the changes override any previously set values.
- Commands
  Teracloud® Streams provides a number of commands you use to perform tasks.
- Operator model
  An operator model is an XML document that describes the basic properties of a primitive operator.
- Streams Processing Language (SPL)
  Streams Processing Language (SPL) is a distributed data flow composition language that is used in Teracloud® Streams. SPL has primitive types, program structures, and definitions that are tailored for streaming data.
- Toolkits
  A toolkit is a set of SPL artifacts, which are organized into a package. Toolkits make SPL or native functions and primitive or composite operators reusable across different applications.
  - Specialized toolkit requirements and restrictions
    Several of the specialized toolkits require RPMs that are not required by the Teracloud® Streams product. There are also toolkit restrictions on some platforms.
  - Toolkits
- SPLDOC markup
  You can use SPLDOC markup in the descriptions associated with the SPL artifacts in a toolkit. The spl-make-doc command generates HTML documentation from the marked up artifacts.
Glossary
Use this glossary to find terms and definitions for Teracloud® Streams.

Functions: com.teracloud.streams.timeseries 7.0.0

Functions

convolve(list<float64>, list<float64>): The convolve function generates the convolution of two time series input signals.
crosscorrelate(list<float64>, list<float64>): The crosscorrelate function generates the crosscorrelation of two time series input signals.
dtw(list<float64>, list<float64>): This function uses dynamic time warping (DTW) to measure the similarity between two time series.
dtw_itakura(list<float64>, list<float64>, int32, float64): This function computes the Itakura Parallelogram DTW.
dtw_sakoe_chiba(list<float64>, list<float64>, int32): This function computes the Sakoe-Chiba Band based DTW.
generate_pulsetrain_wave(float64, uint32, float64, uint32): Generates a pulse train representation of a time series, with a specified frequency, duration, sampling rate, and hump size.
generate_sawtooth_wave(float64, uint32, uint32): Generates a sawtooth wave representation of a time series, with a specified frequency, duration, and sampling rate.
generate_sine_wave(float64, uint32, uint32): Generates a sine wave representation of a time series, with a specified frequency, duration, and sampling rate.
generate_square_wave(float64, uint32, uint32): Generates a square wave representation of time series with a specific frequency, duration, and sampling rate.
generate_triangular_wave(float64, uint32, uint32): Generates a triangular wave representation of a time series, with a specified frequency, duration, and sampling rate.
laggedConvolve(list<float64>, list<float64>, enum{STANDARD, FFT}): The laggedConvolve function generates the convolution of two time series input signals.
laggedCrosscorrelate(list<float64>, list<float64>, int32, enum{STANDARD, FFT}): The laggedCrosscorrelate function generates the lagged crosscorrelation of two time series input signals.
lcss(list<float64>, list<float64>, int32, float64): This function computer the longest common subsequence (LCSS) between two time series.
lpNorm(list<float64>, list<float64>, float64): This function computes the Lp distance between two time series.
rms(list<T>): The rms function generates the root mean square value from a list of numerical values.