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Global financial entities, clearinghouses, and settlement networks require absolute precision in market scheduling to maintain operational integrity, compliance, and international goodwill. Currently, the open-source ecosystem forces developers and financial institutions to rely on expensive, proprietary APIs for this critical data. This project democratizes global market schedules by significantly expanding Holiday's financial coverage, implementing native, offline calendars for 15-17 new global exchanges across the Americas, EMEA, and APAC regions. Every newly integrated market will be mapped to standard ISO 10383 Market Identifier Codes (MIC), strictly researched for deep historical accuracy, and equipped with native localization (L10N) support.

Pwndbg has supported LLDB internally since 2024, and in general availability as of the 2025.01.20 release. That support is, however, still in its infancy. Currently, all the test code in Pwndbg is still strongly tied to GDB, meaning that, aside from manual checks, there is nothing keeping changes to the codebase from silently breaking the LLDB subsystem. And, while there is support for attaching to and debugging programs running on platforms other than Linux, there are not as many features catering to the specifics of those platforms as there are for traditional Linux distributions. In particular, the process by which mobile debugging is done is still poorly documented, intricate, and not thoroughly tested. These shortcomings hold back LLDB Pwndbg from being as useful to its users as it can be.

This project aims to enhance the FURY documentation experience by making it more reliable, accessible, and visually engaging for both users and developers. Currently, the documentation build process generates multiple warnings, some tutorials fail to produce static images consistently, animated tutorials are not natively supported, and parts of the website layout can be improved for better usability. To address these issues, I plan to streamline the documentation build pipeline by resolving existing warnings and improving the reliability of tutorial image generation. I will also introduce native GIF support to enable animated tutorials and interactive visual examples. In addition, I will redesign key parts of the documentation website’s UI/UX to improve readability, navigation, and overall user experience. The key deliverables of this project include a clean and warning-free documentation build process, robust static image generation for tutorials, native support for GIF-based animated examples, and a refreshed documentation interface that makes FURY easier to learn, explore, and contribute to.

<p>I have started doing the initial work on the EOS Adobe XD plugin and I found the community extremely supportive. For the summer, my goal would be to increase the capabilities of the Adobe XD plugin, and develop the Angular package for EOS. This might touch the sister packages for React library and the original NPM package and I would ensure there are no frictions during development and we are on the same page while moving forward.</p>

<p>The goal of this project is to make the viewport more informative by adding options to visualise liquid simulation grids and improving existing fluid display options in Workbench.</p>

This project in Lpython and LPython is a Python compiler. It is in heavy development, currently in pre-alpha stage. Some of the goals of LPython, my project will add three features in compiler.

During my project, I will successfully implement a module for Correlation-Tensor Magnetic Resonance Imaging (CT-MRI) in DIPY, which provides advanced diffusion acquisition techniques for characterizing healthy and pathological brain alterations. This implementation is significant because DIPY previously provided limited modules to process this type of diffusion MRI data. By expanding DIPY's capabilities, researchers and clinicians can now gain unique insights into tissue microstructure, making DIPY a more complete and useful tool for a wider range of MRI data users.

<p>PyZombies proposed a web application to present all the content of the course, theory, and practical exercises, following the syllabus of Python para Zumbies. This web application intends to be an interactive book, in which the student can be able to run the exercise and get feedback about it.</p>

<p>The goal of this project is to finish the implementation of the formal integral approach for generating spectra which was started during last years ESO Summer of Code in Space (SoCiS). The current implementation will be rewritten in C and features that enable the use of this method for popular configuration settings will be added. Lastly the whole method will be integrated fully into tardis by providing configuration options to control it.</p>

<ul> <li>Improving Guillotina API by making it more compliant to JSON-LD.</li> <li>Implementing WebSocket endpoint for all the API functionalities </li> <li>JSON validation for all the payloads by well-defined JSON schemas.</li> <li>Updating swagger documentation.</li> <li>Versioning of API by adding a version number to the endpoint URLs.</li> <li>And writing kubernetes configurations along with helm charts for Guillotina.</li> </ul>

<h5>coala website and coala-html</h5> <p><em>coala-html</em> is a console application used to display results from coala-json as an interactive web page. Prototype is available at <a href="https://github.com/coala-analyzer/coala-html/tree/tushar-rishav" target="_blank">Github</a>.</p> <p><em>coala website</em> a main website for coala with coala demo integrated. It’d be written from scratch with improved UI and features.</p> <p>Proposed features are:</p> <ul> <li>coala-html<ul> <li>Improve UI for Logs and Results by implementing Material Design.</li> <li>Implement better search feature. Create a Fuzzy Search module.</li> <li>Add UnitTest</li> </ul> </li> <li>coala-website<ul> <li>Implement a demo of coala within coala website allowing users to run coala on their code snippet from within their web browser.</li> <li>Available at <a href="https://gitlab.com/coala/website" target="_blank">Gitlab</a></li> </ul> </li> </ul>

<p>Scrapy uses a signaling mechanism, as documented here: <a href="http://doc.scrapy.org/en/latest/topics/signals.html#topics-signals" target="_blank">http://doc.scrapy.org/en/latest/topics/signals.html#topics-signals</a> to notify various entities when something of interest happens, so callbacks can be triggered accordingly and deferred calls can be processed. Signaling is also exposed via the core API of Scrapy for use in extensions/middleware. The current signaling mechanism is based on the <code>pydispatcher</code> library which although does serve the purpose well often tends to be slower than the actual HTML parsing and tends to bottleneck spiders(see: <a href="https://github.com/scrapy/scrapy/issues/8#issuecomment-112029296" target="_blank">https://github.com/scrapy/scrapy/issues/8#issuecomment-112029296</a>). Django, in its 1.0 release refactored the <code>pydispatcher</code> code which they claim resulted in a speed improvement of up to 90%, and also simplified the API for better. The task at hand is to introduce the same efficiency in Scrapy while also trying to retain as much backward API compatibility as possible and not breaking any user code.</p>

<p>The aim of this project is to bring SpaCy and Gensim functionality to Pharo. Along with that, I plan on integrating all the existing functioning NLP packages into a united library with a uniform API and good documentation.</p>

This proposal aims to develop an information theoretic approach to filter out artificial information from real information in geospatial datasets. The project will use the bitinformation framework to distinguish between real and false information, where real information is defined as the mutual information between adjacent bits. The proposed algorithm will filter out artificial information, which is a consequence of prior compression, from real information, which reflects the underlying signal. The project will involve a theoretical review, test case generation, algorithm development, evaluation, and integration into xbitinfo, a software package that implements the bitinformation framework. The resulting artificial information filter will provide a valuable tool for users who may not have access to high-precision/uncompressed data, and it will enhance the accuracy and reliability of geospatial data analysis. As a participant in the Google Summer of Code, I am excited about the opportunity to work on this project and contribute to the development of an open-source software tool that will benefit the scientific community.

This project introduces a library of 7 data structures to the Pharo ecosystem, replacing O(N) bottlenecks with resilient, mathematically proven structures (including B Trees, K-D Trees, and Persistent Arrays). Following a full design approach, the implementations will be stress-tested using Competitive Programming datasets to guarantee architectural integrity under heavy load. Finally, the project utilizes VM-level tools (TimeProfiler and MemoryProfiler) to empirically benchmark performance, delivering production-ready infrastructure alongside interactive visual inspectors.

<p>The main objective of this project is to create such interfaces (by the means of various visualizations of the library) that makes it easier for user to:</p> <ul> <li>Access the required filter curves</li> <li>Calculate the photometry of a star's spectrum under a certain filter set</li> </ul> <p>To achieve this, I propose to develop several adequate visualizations by enabling interactivity and UI controls, such that they serve as <strong>analytical web interfaces</strong> through which user can access what they need in a couple of clicks! These interfaces will be made accessible to the user by integrating them within living Sphinx docs, such that they will be auto generated when building the docs.</p> <p>Besides, I also aim to well document the wsynphot package after integrating the developed interfaces in it, ultimately reshaping the entire package. Hence, user can <em>auto-generate both the filter curves &amp; photometry</em> directly from the docs as per their requirements, by using these responsive interfaces.</p>

<p>Dimensionality reduction techniques are useful methods that allow us to gain crucial insights about the given dataset. Unfortunately, such methods become computationally intensive when dealing with large scale dataset. To deal with complexity issues, one possible approach is to implement algorithms in a distributed fashion. Ideally, users of LiberTEM can benefit from implementation of these algorithms that they can run through a simple pipeline called User-defined functions, which allow the users to run functions with their desired functionality without having to worry about parallelization, which is done under the hood by LiberTEM. Therefore, my project will be concerning both the distributed implementation of a dimensionality reduction method as well as improving on the User-defined functions framework.</p>

<p>Bringing the functionality of Pandas to Pharo</p>

LPython currently doesn't support classes and object oriented programming paradigm. This goal of this project is to implement classes and OOP features like inheritance and polymorphism in LPython at the ASR and LLVM level. This will enable porting of standard library modules in Python to LPython. The project involves adding class semantics to the Abstract Semantic Representation (ASR) in LPython, allowing for the inclusion of member functions with local scope and enabling seamless integration of object-oriented programming (OOP) features. This entails modifications to the ASR nodes, the AST-to-ASR conversion process, and the ASR-to-LLVM translation to facilitate function calling and method invocation. Additionally, the project includes extending operator functionality, transitioning from dataclass to struct decorators, implementing limited dunder functions, and thorough integration testing.

This project aims to make Tölvera more accessible to artists and researchers by adding a Natural Language Interface (NLI). Currently, Tölvera's reliance on Python coding presents a barrier for those without programming expertise. This project will refine and substantially extend tv.llm, a functional proof-of-concept NLI module, enabling users to generate and modify Tölvera sketches using natural language commands. The core work of this project involves expanding the NLI's architecture, which utilizes Pydantic for configuration validation and Jinja2 for reliable code generation, to encompass a broader range of Tölvera modules (tv.vera, tv.osc, tv.cv, tv.mp, tv.iml), refining prompt engineering strategies, and improving the command-line interface. Deliverables will include the full release of the tv.llm module integrated as a core Tölvera feature, detailed documentation, and an evaluation report. The goal is to help empower a broader community of users to integrate Tölvera into their creative workflows.

<p>Currently the vertex painting lacks the features like vertex masking, don’t have many blend options, color choice is only limited to rgb and not to rgba. With this project I propose to improve vertex painting toolkit, adding lacking tools and functionality so that artists will be able to use vertex painting tools as easily as texture painting tools</p>

<p>DIPY aims at building scifi-like 3D and 2D user interfaces. Dipy.viz provides many visualization capabilities. The goal of the project is to improve DIPY’s current User Interface widgets and create new futuristic ones so as to have a complete library from which users can build interactive applications.</p>

Increase in amount of recording sensors in EEG, MEG and iEEG has brought about a need in new approaches for analysis of the data. Such approaches include source separation and decoding. This project is aimed at (1) implementing a supervised source separation technique called generalised eigendecomposition (GED) with accompanying visualisations, (2) refactoring, modernising and enhancing the implementation of decoding classes already implemented in MNE-Python.