Results for “wit-ai”

Build discrete-event simulations of systems with processes, queues, resources, and time-based events using SimPy in Python.

Analyze NATS clusters with read-only operations: discover JetStream streams and consumers, check cluster health, and inspect message flow.

Perform astronomical data analysis with Astropy: coordinate transformations, unit conversions, FITS I/O, cosmological calculations, time handling, table operations, and WCS transformations.

Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.

Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Run computational fluid dynamics simulations using Python, including Navier-Stokes equations, shallow water, and stratified flows with pseudospectral methods and HPC support.

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface, covering loading, preprocessing, drift correction, spike sorting, quality metrics, and unit curation.

Manages and analyzes Clerk authentication resources, including users, organizations, and sessions, with commands for user growth, auth health, and membership review.

Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.

Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance for reproducible retrieval of scientific, regulatory, or financial facts.

Guides statistical hypothesis testing with assumption checks, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting for research data.

Analyzes AWS Glue ETL jobs, crawlers, Data Catalog, and schema registry using parallel AWS CLI queries with anti-hallucination guardrails.

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.

Generate comprehensive, professional-grade market research reports (50+ pages) with LaTeX formatting, strategic frameworks, and data-driven visualizations.

Automatically detect and analyze scientific data files across 200+ formats, generating detailed markdown reports with quality metrics and analysis recommendations.

Convert files and office documents to Markdown using Microsoft's MarkItDown tool. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.

Process satellite imagery, perform GIS analysis, and apply spatial machine learning across 70+ geospatial topics with code examples in 8 programming languages.

Analyze biological sequences, alignments, phylogenetic trees, and diversity metrics (alpha/beta, UniFrac) with ordination (PCoA) and PERMANOVA for microbiome and community ecology data.

Analyzes AWS billing data with anti-hallucination guardrails, covering cost breakdowns, trends, anomaly detection, RI/SP utilization, forecasting, and multi-account comparisons using Cost Explorer.

Monitors and analyzes Qase test management projects, test cases, runs, and defects via the Qase API, with a discovery-first workflow to avoid assumptions.

Deep-dive analysis of AWS RDS instances using Performance Insights, event subscriptions, proxy health, and global database status, with parallel execution and anti-hallucination guardrails.

Perform constraint-based metabolic modeling with COBRApy: run FBA, FVA, gene knockouts, flux sampling, and manage SBML models for systems biology and metabolic engineering.

Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.

Process and analyze large tabular datasets (billions of rows) that exceed available RAM using lazy, out-of-core DataFrames with fast aggregations, visualization, and machine learning integration.

Create, analyze, and visualize complex networks and graphs in Python with NetworkX, including graph algorithms, community detection, synthetic network generation, and multiple I/O formats.

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.

Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, and packages via the Nia API, with AI-powered research and code advisor capabilities.