List of potential project ideas.
Before working on your GSoC application, please review our list of ideas to see if you find a project which excites you. The list of existing ideas is provided to serve as inspiration and to indicate which directions may be good for stdlib.
If you do find an existing idea that you'd like to pursue, please be sure to contact us in our Element channel to discuss it first! Always be sure to ask about these ideas prior to working on application in order to get the latest information about what is already implemented and what exactly must be done.
Priority, difficulty, technology, and topic area have no bearing on the chances of an idea being accepted. All ideas are equally good, and your chances of being accepted depend solely on the quality of your application.
Project Length
GSoC allows three different project lengths: 90 hours, 175 hours, and 350 hours. Each idea must indicate whether the idea is a better fit for 90, 175, or 350 hours.
In some cases, we may be able to extend a 175 hour project to a 350 hour project by extending the ideas of what can be done. Similarly, in some cases, a 350 hour project can be shortened to a 175 hour project by only implementing part of an idea and leaving the rest for a future project. In either case, if you want to adjust the project length, please be sure to contact us in our Element channel to discuss it first!
If you'd like to submit your own idea, that is also welcome; just be sure to propose your idea to stdlib mentors first! After reaching out, we'll inform you whether the idea has already been implemented, if the idea will entail enough work to last the duration of the GSoC program, if the idea requires too much work to be meaningfully pursued during GSoC, and if the idea is within the scope of stdlib. Unsolicited, undiscussed ideas are less likely to get accepted.
The best project for you is the one you are most interested in and knowledgeable about. Excitement and aptitude are two key ingredients of a successful project and help ensure your commitment and ability to see a project through to completion. So if there is something you are especially passionate about and that you believe aligns with the scope and goals of stdlib, we'd be happy to hear your pitch!
After discussing with us in our Element channel and receiving approval to submit your idea, please open an issue which describes your idea using the idea issue template.
To learn who might mentor one of the projects listed below, consult the list of potential project mentors. For each mentor, the list includes a mentor's preferred project(s) and/or general interest area.
Linked issue: #1
The read-eval-print loop (REPL) is a fixture of data analysis and numerical computing and provides a critical entry-point for individuals seeking to learn and better understand APIs and their associated behavior. While a web browser's JavaScript console is a staple of debugging in web applications, the Node.js REPL is significantly underutilized--an occurrence largely attributable to its underdevelopment and lack of functionality.
This idea aims to implement a suite of enhancements to the stdlib REPL, which is an alternative to the Node.js REPL and analogous to Python's IPython. The overarching goal is to provide a compelling environment for interactive JavaScript computing to further establish Node.js as a platform suitable for numerical computing and data analysis.
Potential improvements include, but are not limited to, the following:
- Auto-completion previews for file paths, function names, and variables
- Fuzzy auto-completion extension
- Less/more documentation pager
- Pretty printing of tabular output
- Terminal syntax highlighting and bracket matching
- Bracketed-paste
- Custom key binding support
- Numerical computing workshops/tutorials using the built-in REPL presentation framework
- Multi-line editing
REPL users will be able to leverage the stdlib REPL in a manner similar to a fully-featured IDE and have access to workshops and tutorials for an integrated interactive learning experience covering machine learning and statistical computing concepts.
No other software is necessary to implement this idea.
JavaScript, Node.js
Intermediate. Difficulties may arise due to escape sequences necessary for syntax highlighting and multi-line editing.
350 hours.
@kgryte @Planeshifter @steff456
Linked issue: #2
The goal of this idea is to implement all distributions found in SciPy stats. Distribution support will entail implementing APIs for computing PDFs, CDFs, quantiles, and other distribution properties. Additionally, stdlib should support APIs for drawing random variates from any implemented distributions.
stdlib users will be able to construct, and compute various properties of, every statistical distribution present in SciPy in JavaScript.
No runtime dependencies should be necessary. SciPy will be necessary in order to provide reference test results.
JavaScript, Node.js. Familiarity with C/C++/Fortran would help.
Intermediate. Difficulties may arise for distributions whose properties and moments have complicated formulations. Developing JavaScript implementations will likely require consulting C/C++ and possibly Fortran code.
350 hours.
Linked issue: #3
The goal of this idea is to expose a set of Fast Fourier Transform (FFT) interfaces similar to those available in NumPy and as documented in the Data APIs Array API specification. Similar to stdlib's BLAS interfaces, we may want to allow switching out the FFT backend.
One potential reference implementation which could form the basis of this idea is pocketfft, as done in NumPy:
stdlib users would be able to evaluate FFT operations on stdlib ndarrays. Ideally, we'd also provide a set of C APIs.
Will need to consult reference implementations in C/Fortran.
JavaScript, Node.js, C/C++/Fortran
Hard. This may be a straightforward port, or it may not be. More R&D is needed.
350 hours.
@kgryte @Planeshifter @rreusser @Pranavchiku @czgdp1807
Linked issue: #4
The stdlib project encompasses over 3500 repositories which are orchestrated via a centralized repository. While orchestration largely works as intended, build failures do happen, and quickly detecting and resolving build failures in standalone repositories is critical to prevent downstream breakages and ensure ecosystem integrity.
The goal of this idea is to build a developer dashboard to display in real-time standalone repository build failures. We currently have the backend database which collects build results in real-time; however, we have yet to build a frontend for viewing and analyzing such data.
The expected roadmap is as follows:
- Build a Node.js backend for querying a PostgreSQL database.
- Build a frontend dashboard which interfaces with the backend. As this will be a developer facing application, the choice of technologies is greenfield. Potential options may include ESBuild, tailwind, etc.
- Add support for filtering the dashboard based on build status and other features.
- Allow for quick navigation to repository resources and build artifacts.
- Extend the dashboard to support historical overviews and other drill down metrics.
stdlib developers will be able to navigate to a webpage and see the build status for all repositories at once.
This will involve building a frontend application and interfacing with a backend for querying a PostgreSQL database. We may want to try more "cutting edge" technology here, such as ESBuild, tailwind, etc.
JavaScript, Node.js, CSS, HTML, JSX.
Intermediate. Requires a fair amount of frontend engineering knowledge and modern frontend application development.
175/350 hours. A skilled contributor may be able to execute on this faster. If so, scope could be expanded to include analytics and historical overviews.
@kgryte @Planeshifter @steff456
Linked issue: #5
The goal of this idea is to implement a variety of PRNGs for use within stdlib to generate pseudorandom numbers. The project currently uses Mersenne Twister as its default PRNG; however, this PRNG, while common, is not ideal given its comparatively large internal state. Would be great to have a collection of PRNGs, such as PCG, Philox, Xorshift, and more.
stdlib users will have a wide selection of PRNGs from which to choose from based on their individual needs and considerations. Having a large selection of PRNGs will useful when replicating the results of numerical simulations which may use a PRNG which is not one of the currently supported stdlib PRNGs. Additionally, a desired outcome would be if we could replace MT19937 with a new default PRNG.
No other software should be necessary. We may be a bit constrained based on 32-bit limitations in JS. This would not, however, stop us from implementing in C for use in generating arrays of random numbers.
JavaScript, Node.js. Familiarity with C/C++/Fortran would help.
Intermediate/Hard. Depends. Some PRNGs may be straightforward to implement. Others, not so much.
175/350 hours. This idea can be adjusted according to needs and availability.
@kgryte @Planeshifter @Pranavchiku
Linked issue: #6
While we currently support running benchmarks, we have yet to provide a means for easily visualizing and comparing benchmark results. Previously, when wanting to visualize and compare benchmark results, one has needed to manually parse TAP results and then plug into some other software (e.g., vega or Plotly).
The idea for this project would be to 1) implement a TAP parser with support for the latest TAP specification and 2) provide a plot frontend for consuming parsed TAP results. The plot frontend could be as simple as a Unicode bar chart plotter, which would be in line with our existing Unicode plotting facilities.
Developers will be able to run benchmarks and visually compare benchmark results based on the namespace and parameterization. Ideally, the plot would include small multiple/facet visualizations.
No other software or dependencies should be necessary. Will need to consult a reference TAP parser implementation (e.g., node-tap).
JavaScript and Node.js.
Intermediate.
350 hours.
@kgryte @Planeshifter @steff456
Linked issue: #7
Currently, stdlib uses tape. The goal of this idea is to migrate away from tape and develop a test runner in-house, similar to @stdlib/bench/harness. This has long been on our TODO list and would allow us to have a simple test runner which is oriented toward stdlib conventions (e.g., we don't use most of the assertion methods in tape).
Bonus points if we can migrate away from istanbul to nyc or c8; however, this may be tricky if we want to support older Node.js versions.
All unit tests have migrated to the in-house runner.
No additional runtime deps. Will need to consult tape as a reference implementation, along with our existing @stdlib/bench/harness implementation.
JavaScript, Node.js.
Intermediate.
175/350 hours. The scope of this idea can be adjusted depending on availability.
@kgryte @Planeshifter @steff456
Linked issue: #8
Currently, stdlib has a bespoke plot API which is useful for fast static rendering. However, our implementation is quite limited in the types of plots it can produce. The goal of this idea is to refactor our plot API to build atop of vega (or its specifications). For this, we'd need to migrate to an async plot generation API, which is probably necessary regardless if we want to support WebGL or some other async rendering engine.
Ideally, we would retain the same plot API and internally generate a vega specification.
We can generate simple plots using the new plot implementation.
This will involve using vega (or something similar depending on whether vega is sufficiently maintained). We will want to transpile to ES5 and vendor in order to ensure that we can support our supported Node.js versions.
JavaScript, Node.js.
Intermediate/Hard.
350 hours. This project has the potential to spiral out of control, as there are many unknowns we'd need to answer. Mentor would likely need to be actively involved in order to perform R&D and properly scope.
@kgryte @Planeshifter @rreusser
Linked issue: #9
Currently, stdlib has a limited set of dedicated "async" APIs for performing various utility operations. The goal of this idea is to achieve feature parity with async.js, a popular library providing callback-based async APIs.
Motivation for this idea stems from certain advantages afforded by callback-based asynchronous programming. Notable among them is superior performance and the ability to more readily return and inspect status objects.
stdlib will have more or less 1:1 feature parity with async.js APIs.
async.js will serve as a reference implementation for API design. Will want to modify to match stdlib conventions.
JavaScript.
Beginner. Would benefit from someone with JavaScript experience.
175/350 hours. Can be scoped accordingly.
@kgryte @Planeshifter @steff456
Linked issue: #10
Achieve feature parity with Node.js fs package. We currently only support a limited selection of fs methods. Would be useful to support more.
Part of this work involves providing an abstraction layer of Node.js built-ins in order to support newer functionality (e.g., options and/or behavior) not present in older Node.js versions. This is similar in concept to the userland readable-stream package.
stdlib will have complete feature parity with Node.js built-ins.
No other software is necessary.
JavaScript, Node.js.
Intermediate. Could require some creative solutions to ensure that abstractions work for older Node.js versions.
175/350 hours. Can be scoped accordingly.
@kgryte @Planeshifter @steff456
Linked issue: #11
The goal of this idea is to implement the multivariate normal distribution. This distribution is fundamental in a wide variety of statistical applications and will help unblock stdlib in offering additional statistics APIs.
As a starting point, SciPy's multivariate normal distribution API and implementation could provide a suitable point of reference:
Users will be able to evaluate the PDF, CDF, logPDF, and logCDF and be able to draw random variates from a specified distribution.
No other software is necessary. Will require reading reference implementations written in Python, R, and Julia.
JavaScript, Node.js.
Intermediate.
175/350 hours. Can be scoped accordingly. A skilled contributor should be able to complete in 175 hours with the potential of using their implementation to implement higher order statistics APIs.
@kgryte @Planeshifter @Pranavchiku
Linked issue: #13
The goal of this idea is to allow users to call stdlib APIs from within Google Sheets. This will allow users to perform linear algebra and various machine learning operations directly on spreadsheet data and all within the browser.
In order to execute on this idea, we'll want to support
- two-dimensional array broadcasting semantics
- performant element-wise iteration APIs
- input argument validation tailored to the Sheets context
- Fused operations to avoid unnecessary network calls
- documentation and tutorials demonstrating API usage
- good generation and automation for creating extension builds
- testing and performance measurement to guard against regressions
Google Sheets users will be able to install an extension which exposes stdlib functionality, run statistical tests, evaluate mathematical functions, and perform linear algebra operations using stdlib.
No other software is necessary.
JavaScript, Node.js.
Beginner/Intermediate.
175/350 hours. Can be scoped accordingly. A skilled contributor can work on a strategy for performant fused operations.
@kgryte @Planeshifter @steff456
Linked issue: #14
stdlib is a large (and growing!) project, which can make project navigation challenging. The goal of this idea is to provide a visual representation of an API's dependency graph directly in the stdlib API documentation. Initial thinking is that would be an interactive network diagram in which nodes present package dependencies and allow for navigation; however, other visual representations may be possible.
By providing such a means for navigating the project, users could more readily deepen their understanding of the stdlib code base, identify potential issues, and better understand how underlying APIs are used.
A user will be able to navigate to a package's documentation page, click to display a network graph, and then click on nodes within that graph to explore the documentation of package dependencies.
No other software is necessary.
JavaScript, Node.js, HTML/CSS, JSX.
Beginner/Intermediate.
175 hours.
@kgryte @Planeshifter @steff456
Linked issue: #15
Manually constructing confidence intervals and other statistical properties can be useful when no analytic solution exists. The goal of this idea to implement APIs for bootstrap and jackknife resampling.
Users will be to resample provided datasets.
No other software is necessary.
JavaScript, Node.js.
Beginner/Intermediate.
175/350 hours. Can be scoped accordingly. Scope can be expanded to implement different bootstrap algorithms.
Linked issue: #16
Jupyter is a dominate force in scientific computing. While some effort has been done to expose JavaScript kernels to Jupyter/JupyterLab, most of these kernels are under-developed or lack numerical functionality.
The goal of this idea would be to develop a Jupyter backend based on stdlib.
A JupyterLab user will be able to connect to a stdlib kernel and invoke stdlib operations.
This goal will require interfacing with the Jupyter technology stack, including ZeroMQ and implementing messaging protocols.
JavaScript, Node.js. Experience with Python would be very helpful.
Hard.
350 hours. This idea has many unknowns and will be hard to scope.
@kgryte @Planeshifter
Linked issue: #17
Implement various statistical tests which are not currently implemented in stdlib, but are implemented in other envs such as R, Python (SciPy, statsmodels), Julia, and MATLAB.
stdlib will have a broader array of statistical tests which can operate on ndarrays.
No other software should be necessary. However, we will need to do a needs analysis to determine which prerequisite packages/functionality is necessary in order to allow these tests to be implemented (e.g., BLAS, ndarray slicing, etc).
JavaScript, Node.js. Familiarity with R, Python, C/C++ would be very useful, as will need to consult reference implementations.
Hard. Depends on the reference implementation requirements and algorithmic difficulty.
350 hours.
@kgryte @Planeshifter @Pranavchiku
Linked issue: #19
stdlib relies heavily on JSDoc comments to document its source code. Currently, the project has only rudimentary support for generating HTML docs from those comments. The goal of this idea would be to
- Write an in-house JSDoc parser.
- Generate HTML documentation from the parsed comments which is capable of supporting project conventions and its embrace of radical modularity.
JSDoc comments are oriented toward JavaScript source files; however, stdlib also uses similar documentation practices for documenting C source files and make files. A possible extension to the in-house JSDoc parser would be to support these other source file types. As those file types may require separate AST parsers, supporting other file types is likely to require writing separate comment parsers for each source type.
In addition to the current API documentation, a user will be able to navigate to a package's JSDoc documentation to gain more insight into supported input and output dtypes and implemented algorithms. This would be especially useful for rendering the extended JSDoc comment of elementary mathematical functions.
No other software is necessary.
JavaScript, Node.js, HTML/CSS.
Intermediate.
350 hours. The length can likely be scaled down; however, there are several unknowns, and it may not be straightforward to develop an in-house parser which caters to the unique structure and setup of stdlib. For advanced contributors, possibility to explore support for source file types other than JavaScript (e.g., C and make).
@kgryte @Planeshifter @steff456
Linked issue: #20
Currently, stdlib publishes TypeScript interface documentation in its web-based API documentation. The generated documentation monkey-patches tsdoc to handle generating documentation across the entire mono-repo. The goal of this project is to refactor/rethink this approach and provide a solution capable of addressing the unique constraints of the stdlib project.
At a base level, it would be great if we had a working documentation render which did not require monkey-patching. A more difficult, but potentially more desirable, outcome would be if TypeScript documentation was not rendered as a separate website, but rather was integrated within the docs as simply another page/fragment.
No other software is necessary.
JavaScript, Node.js, HTML/CSS, TypeScript, JSX/React.
Intermediate.
175/350 hours. Length will depend on the nature of the proposed solution (e.g., needing to write a custom TypeScript parser vs modifying the existing tsdoc library).
@kgryte @Planeshifter @steff456
Linked issue: #21
Currently, when generating stdlib API documentation, we generate UMD bundles for unit tests and benchmarks. When a user navigates to our package documentation, they can load unit tests and benchmarks and have those run without needing to setup a local environment. The pain point here is that creating separate bundles for each package is time consuming and adds significant heft to the www repo.
The goal of this idea is to refactor the way we support unit tests and benchmarks to use ES6 modules and potentially skip bundling altogether. This has the downside of not supporting older browsers which don't support the <module> tag, but is probably fine considering that running package unit tests and benchmarks is likely a forward looking concern.
Users will be able to run unit tests and benchmarks directly in their web browsers by navigating to project API documentation and what is loaded are ES6 modules, not UMD bundles.
No other software is necessary.
JavaScript, Node.js, HTML/CSS, JSX/React.
Intermediate.
350 hours.
@kgryte @Planeshifter @steff456
Migrate web API documentation to use matomo and instrument for better understanding user navigation behavior
Linked issue: #22
Currently, the stdlib web-based API docs use GA for analytics and have only minimal integration. E.g., the API docs application is a SPA which uses React and the app does not record changes in page views; we only record first hits.
The goal of this idea is to migrate to using matomo and take advantage of its privacy features. The work will involve instrumenting the API documentation application and integrating with matomo. A potential stretch goal would be to setup dashboards for reporting so that we can better understand user behavior and continue to improve project documentation.
All user interaction data is logged to matomo and stored in a hosted database.
No other software is necessary.
JavaScript, Node.js, HTML/CSS, JSX/React.
Intermediate.
350 hours. Can be adjusted depending on skill and ability.
@kgryte @Planeshifter @steff456
Linked issue: #23
stdlib currently offers a REPL presentation framework for authoring presentations for use directly in the REPL. This is particularly useful for creating interactive tutorials illustrating how to use stdlib functionality for data analysis and visualization from the terminal. Some functionality is missing which would be quite useful. E.g.,
- ASCII plotting
- ASCII animations
- syntax highlighting
- pretty printing tables
- speaker notes
- multiplexing
- theming
The REPL presentation framework will have additional features similar to those in WYSIWYG presentation applications.
No other software is necessary.
JavaScript, Node.js.
Intermediate.
175/350 hours. Can be scoped according to project length.
@kgryte @Planeshifter @steff456
Linked issue: #24
The goal of this idea is to add functions for numerical integration or differentiation to stdlib as building blocks for downstream algorithms. The functions could be ported from permissively licensed open-source libraries in other languages such as C or Fortran or alternatively be implemented from scratch by consulting the literature and reference implementations from various languages.
Some work along these lines has been started in the scijs ecosystem, which can be used for initial inspiration (e.g., https://github.com/scijs/ode45-cash-karp), and more generally in SciPy (e.g., https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html).
stdlib will have a range of robust functions for performing numerical integration or differentiation
No other software is necessary.
JavaScript, Node.js.
Intermediate.
350 hours.
@kgryte @Planeshifter @rreusser @Pranavchiku @czgdp1807
Linked issue: #25
The goal of this idea is to add basic support for symbolic math operations in stdlib.
Users have the ability to perform basic symbolic math operations in JavaScript, such as solving equations, simplifying expressions, and using mathematical functions.
No other software should be necessary.
JavaScript, Node.js, and an understanding of mathematics and calculus.
Intermediate
350 hours.
@kgryte @Planeshifter @rreusser
Linked issue: #26
Currently, all code blocks in the documentation at https://stdlib.io/docs/api/latest are static. To make example code more useful and engaging, it would be nice to have interactive code shells on the website that could be edited and would provide real-time return annotations.
Some initial brainstorming has been done to inform how this would work, but, at minimum, we'd need to
- convert READMEs to structured data to allow for more straightforward transformation
- support dynamic loading of relevant stdlib packages used in example code blocks
- lazily integrate a code editor into documentation pages
- implement security measures to prevent malicious usage
Improved user experience on the website, as the code examples would become editable and interactive. Return annotations would have to update in real-time, and additional contextual help could be provided via overlays etc. Another outcome would be to make it easy to switch between ES5 and ES6 for code blocks.
No other software is necessary.
JavaScript, HTML/CSS, React + JSX
Hard.
350 hours.
@kgryte @Planeshifter @steff456
Linked issue: #27
We currently do not have optimization algorithms in stdlib. Having support for Linear Programming, Convex Optimization, Quadratic Programming, and/or Non-Linear Optimization algorithms would be a great addition.
stdlib will have a broad array of optimization algorithms for solving problems.
No other software should be necessary. However, we will need to do a needs analysis to determine which prerequisite packages/functionality is necessary in order to allow these algorithms to be implemented (e.g., BLAS).
JavaScript, Node.js. Familiarity with R, Python, C/C++ would be very useful, as will need to consult reference implementations.
Hard. Depends on the reference implementation requirements and algorithmic difficulty.
350 hours.
@kgryte @Planeshifter @rreusser @Pranavchiku @czgdp1807
Linked issue: #28
Currently, support for linear algebra operations in stdlib is limited. The goal of this idea would be to implement algorithms for linear algebra operations such as matrix multiplication, calculating the matrix inverse, eigenvalue calculation, singular value decomposition, Cholesky & LU Decomposition, and the like. This overlaps with the goal of increasing the amount of BLAS and LAPACK that is available in stdlib.
stdlib will have extended support for linear algebra operations which can be used to solve problems involving matrices and vectors.
No other software should be necessary. However, we will need to do a needs analysis to determine which prerequisite packages/functionality is necessary in order to allow these operations to be implemented (e.g., BLAS, ndarray slicing, etc).
JavaScript, Node.js. C, Fortran. Familiarity with linear algebra would be very useful, as will need to consult and understand reference implementations.
Hard. Depends on the reference implementation requirements and algorithmic difficulty.
350 hours.
@kgryte @Planeshifter @Pranavchiku @czgdp1807 @rreusser
Linked issue: #33
Built-in JavaScript arrays (and typed arrays) have a number of methods for creating, transforming, and manipulating array contents (e.g., forEach, map, reverse, slice, filter, etc). These APIs provide base level functionality forming a default vocabulary for working with array data.
The goal of this idea is to create functional analogs of array methods for working with ndarrays, which are efficient data structures for operating on multi-dimensional data. The main difficulty in implementing analogs is in ensuring efficient iteration of non-contiguous data. The main patterns for such iteration have been established in stdlib, but work remains to apply such patterns for top-level array-equivalent APIs.
Users will be able to use functional APIs (exposed as part of individual packages) for operating on ndarrays in a manner similar to how users can use prototype methods available on built-in arrays and typed arrays.
No other software is necessary.
JavaScript, Node.js.
For APIs not accepting callbacks, certain kernels can be implemented in C, as time and scope allow.
Intermediate. Writing the loop kernels can be involved, but, once understood, are straightforward to apply.
90/175/350 hours. Can be scoped accordingly. Scope can be expanded to implement additional ndarray kernels outside of Array method equivalents.
@kgryte @Planeshifter @steff456 @rreusser
Linked issue: #34
This idea builds on the work outlined in stdlib-js/stdlib#649. Namely, implementing base special mathematical functions in C. Currently, all special mathematical functions have JavaScript implementations, which are often ports from other languages.
The goal of this idea is to port all JavaScript implementations to C. Having such implementations will allow stdlib to provide Node.js native add-ons for higher performance ndarray computation and is more generally necessary for achieving NumPy/SciPy parity.
Users will be able to leverage C implementations for use in Node.js native add-ons, and stdlib will be able to expose element-wise APIs for evaluating base special math functions over ndarrays.
No other software is necessary.
C, JavaScript, Node.js.
Intermediate. Familiarity with C is beneficial. This idea mainly involves porting existing implementations (many of which are written in C/C++) and doing so in a manner which conforms with stdlib conventions.
90/175/350 hours. Can be scoped accordingly. Scope can be expanded to implement new special mathematical functions.
@kgryte @Planeshifter @steff456 @rreusser @Pranavchiku @czgdp1807
Linked issue: #35
The goal of this idea is to allow users to call stdlib APIs from within Excel. This will allow users to perform linear algebra and various machine learning operations directly on spreadsheet data and all within the browser.
In order to execute on this idea, we'll want to support
- two-dimensional array broadcasting semantics
- performant element-wise iteration APIs
- input argument validation tailored to the Sheets context
- Fused operations to avoid unnecessary network calls
- documentation and tutorials demonstrating API usage
- good generation and automation for creating extension builds
- testing and performance measurement to guard against regressions
This idea is the Excel version of #13.
Excel users will be able to install an extension which exposes stdlib functionality, run statistical tests, evaluate mathematical functions, and perform linear algebra operations using stdlib.
No other software is necessary; however, access to a local copy of Excel will be beneficial. While Microsoft 360 can be used, debugging is more difficult and less stable.
JavaScript, Node.js.
Beginner/Intermediate.
175/350 hours. Can be scoped accordingly. A skilled contributor can work on a strategy for performant fused operations.
@kgryte @Planeshifter @steff456
Linked issue: #36
BLAS routines are standard building blocks for performing basic vector and matrix operations. These building blocks are leveraged by most modern numerical programming languages and libraries, including NumPy, SciPy, Julia, MATLAB, R, and others.
The goal of this idea is to
- reimplement reference BLAS routines in free-form Fortran 95
- port reference BLAS routines to C
- port reference BLAS routines to JavaScript
- write Node.js bindings to allow calling BLAS routines in compiled C/ Fortran from JavaScript
Users will be able to call BLAS routines from JavaScript. In web browsers, BLAS routines will be in JavaScript. In Node.js, provided native bindings have been compiled, BLAS routines will either be ported reference implementations or hardware optimized system libraries.
No other software is necessary apart from standard compilers (GCC, gfortran).
C, Fortran, JavaScript, Node.js.
Intermediate. Familiarity with C and Fortran will be beneficial. This idea mainly involves porting existing implementations and doing so in a manner which conforms with stdlib conventions.
90/175/350 hours. Can be scoped accordingly.
@kgryte @Planeshifter @steff456 @rreusser @Pranavchiku @czgdp1807
Linked issue: #37
The goal of this idea is to implement incremental machine learning algorithms to allow for real-time regression and classification. Such online algorithms would allow for point-by-point data processing and avoid the sometimes costly overhead of batch processing. Online algorithms are particularly useful in data streaming contexts (e.g., user clicks, photon collection, etc).
While stdlib includes some incremental algorithms (binary classification, k-means, and stochastic gradient descent regression), the project would benefit from additional algorithms.
Individuals interested in pursuing this idea should be prepared to research possible algorithms and propose specific APIs.
stdlib will expose one or more additional APIs for incremental machine learning.
No other software is necessary.
JavaScript, Node.js.
Intermediate. In order to implement ML algorithms, individuals will likely need to consult reference implementations written in other languages. Porting from these implementations may not be straightforward depending on the features involved.
90/175/350 hours. Can be scoped accordingly.
@kgryte @Planeshifter
Linked issue: #43
Currently, stdlib supports a variety of array data types; however, most of these data types are numeric. The lone exception is the "generic" data type for use with generic arrays.
A need exists to expand data type support beyond numeric data types (e.g., real and complex floating-point, signed and unsigned integer) to include other non-numeric data types. One such data type is a boolean data type. The rationale for having a dedicated boolean data type is for use in advanced array indexing, where a boolean array can be used to filter/mask another array. See, e.g., the examples in @stdlib/array/to-fancy.
Accordingly, the goal of this project is to create a new typed array called a BooleanArray, which will only support the values true and false. This new array type should follow a similar path to that of @stdlib/array/complex64, which provides a typed array dedicated to single-precision complex floating-point numbers; namely, BooleanArray should support standard typed array methods, as well as provide accessors for getting and setting array elements.
Note, however, that a BooleanArray should be a typed array (meaning, fixed length with a fixed memory layout). A BooleanArray should not wrap a "generic" array. Instead, the array should be backed by a Uint8Array, similar to how @stdlib/array/complex64 is backed by a Float32Array.
As part of this project, not only will a new BooleanArray be added to the project, but it will be integrated throughout stdlib. This will entail adding support for BooleanArrays wherever arrays are accepted/used, following the same precedent established by @stdlib/array/complex64 and other custom array types in stdlib. This includes adding support for boolean arrays in ndarray APIs.
The expected outcomes of this idea should be (1) creation of a new @stdlib/array/bool package exposing a new typed array constructor, (2) support for BooleanArray instances throughout @stdlib/array/*, (3) support for BooleanArray instances as backing arrays for ndarrays (which may involve working with various C APIs), and (4) any other integration opportunities.
Some initial work has been done to create a new @stdlib/array/bool package, but this is incomplete and needs more significant investment. There exists prior art for adding custom typed arrays to stdlib; namely, Complex64Array and Complex128Array.
No special software for initial work. Once work has progressed to ndarray support, will need access to a C compiler, as documented in the project development guide.
JavaScript, C, nodejs, native addons
n/a
Intermediate/Advanced
This project is ambitious, as arrays are fundamental to a lot of stdlib functionality; however, many of the more difficult integration aspects have already addressed given the widespread support for accessor arrays throughout the project. The main project difficulty beyond the creation of a new BooleanArray class will be finding all the various bits of code throughout the project which need to be updated.
Familiarity and comfort with JavaScript would be highly recommended, given that this project will require considerable programming in JavaScript. Some familiarity with C would also be good, especially for boolean array integration with ndarrays.
350hrs, but can scope can be tailored depending on contributor ability. A contributor proficient in JavaScript can probably finish a @stdlib/array/bool package within 90hrs. Integration work is likely to take longer.
@kgryte @Planeshifter
Linked issue: #44
Similar to what's described in #43, a need exists to expand array data type support beyond numeric data types. One such data type is a string data type. The rationale for having a dedicated string data type is for better interoperation between JavaScript and C, and this is particularly paramount for supporting ndarrays having a string data type, as much of ndarray iteration machinery is written in C.
Accordingly, the goal of this project is to add a dedicated string typed array called a StringArray, which will support variable-length strings. This new array type should follow a similar path to that of @stdlib/array/complex64, which provides a typed array dedicated to single-precision complex floating-point numbers; namely, StringArray should support standard typed array methods, as well as provide accessors for getting and setting array elements.
Note, however, that a StringArray should be a typed array. A StringArray should not wrap a "generic" array. Instead, the array should be backed by fixed length memory, similar to how @stdlib/array/complex64 is backed by a Float32Array. One possibility is backing StringArray instances with Node.js Buffer objects, which are, in turn, Uint8Arrays.
There are, however, some design considerations; namely, how to handle setting of array elements. In particular, what happens when a user attempts to update a StringArray element with a larger string? Does that lead to a new memory allocation and data copy? Or should elements have a fixed allocation to allow for elements to grow until some maximum size?
As part of this project, not only will a new StringArray be added to the project, but it will be integrated throughout stdlib. This will entail adding support for StringArrays wherever arrays are accepted/used, following the same precedent established by @stdlib/array/complex64 and other custom array types in stdlib. This includes adding support for string arrays in ndarray APIs.
Prior Art
- Recent work in NumPy adding UTF-8 variable length string support: https://numpy.org/neps/nep-0055-string_dtype.html
The expected outcomes of this idea should be (1) creation of a new @stdlib/array/string package exposing a new typed array constructor, (2) support for StringArray instances throughout @stdlib/array/*, (3) support for StringArray instances as backing arrays for ndarrays (which may involve working with various C APIs), and (4) any other integration opportunities.
While no work has been done to create a new @stdlib/array/string package, there exists prior art for adding custom typed arrays to stdlib; namely, Complex64Array and Complex128Array.
No special software for initial work. Once work has progressed to ndarray support, will need access to a C compiler, as documented in the project development guide.
JavaScript, C, nodejs, native addons
n/a
Intermediate/Advanced
This project is ambitious, as there are many design considerations which need to be addressed in order to ensure performance and allow for efficient JS/C interoperation.
Additionally, there will be difficulty beyond the creation of a new StringArray class in finding all the various bits of code throughout the project which need to be updated in order to more universally support StringArray instances throughout stdlib on equal footing with other array data types.
Familiarity and comfort with JavaScript would be highly recommended, given that this project will require considerable programming in JavaScript. Some familiarity with C would also be good, especially for string array integration with ndarrays.
350hrs, as will likely involve a decent amount of R&D.
@kgryte @Planeshifter