light-curve-preprocessing
Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
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Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
A modular data processing tool for cleaning, validating, and analyzing CSV files with support for custom transformations and automated dependency management.
Find, review, and remove duplicate or near-duplicate images in FiftyOne datasets using computer vision similarity embeddings.
Python library for geospatial vector data analysis. Perform spatial joins, geometric operations, coordinate transformations, and mapping using GeoPandas, shapely, and interactive tools.
Create professional data visualizations with Python using matplotlib, seaborn, and plotly. Includes chart selection guidance, design principles, accessibility standards, and code patterns for publication-quality figures.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Reliably rotate images by 90-degree increments using a deterministic Python script. Supports PNG, JPG, GIF, BMP, and TIFF, preserving quality with automated file handling.
A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.
Generate publication-quality statistical plots from CSV or JSON data files using AI-driven automated visualization.
Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.
Generate and process 16-bit pixel art office assets for the Claude Office Visualizer using Nano Banana MCP and multi-pass ImageMagick workflows.
Python toolkit for mass spectrometry data processing. Enables spectral file importing (mzML, MGF, MSP), metadata harmonization, peak filtering, and calculating spectral similarity scores (cosine, modified cosine) for metabolomics.