Results for “fft”

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT using analyzeMFT and Python.

Provides a quick reference for fp-ts pipe and flow functions to chain functions, compose operations, and build data pipelines.

Fuzz C/C++ projects with multi-core support using AFL++, a fork of AFL with better performance and advanced features.

Apply functional programming patterns with fp-ts in React, covering state management, form validation, and data fetching for React 18/19 and Next.js 14/15.

Use when performing multiple sequence alignment on nucleotide or protein sequences, such as preparing alignments for phylogenetic analysis or comparative genomics.

Provides practical patterns for using fp-ts with React, including hooks, state management, form validation, and data fetching.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology, with dual-index tracking and a state machine for rally attempt, FTD qualification, and post-FTD health monitoring.

Store and retrieve files on IPFS (InterPlanetary File System). Use when a user asks to store files in a decentralized way, pin content on IPFS, upload NFT metadata, or use content-addressed storage.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Integrates on-device and cloud machine learning into Flutter apps with TensorFlow Lite and Firebase ML Kit, covering image classification, object detection, OCR, face detection, and barcode scanning.

Practical patterns for using fp-ts with React: Option for optional state, Either for form validation, TaskEither for data fetching, and RemoteData for async state management.

Provides patterns and strategies for migrating imperative TypeScript code to fp-ts functional programming patterns, covering error handling, null checks, callbacks, dependency injection, and loops.

Development with Ffi: tools and best practices

Use when annotating VCF files with flanking sequence information (INFO/FS tag) or masking regions/variants in flanking sequences.

Replace exception-heavy TypeScript code with fp-ts Either and TaskEither for type-safe error handling as values.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Practical patterns for using fp-ts with React, covering hooks, state management, form validation, and data fetching for React 18/19 and Next.js 14/15.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Practical patterns for using fp-ts with React, covering hooks, state management, form validation, and data fetching for React 18/19 and Next.js 14/15.

Read BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link simulations.

Drafts evaluation-ready U.S. federal RFP responses across all standard proposal volumes (cover letter, technical, cost/price, reps and certs). Enforces FAR compliance, CPARS references, and Section L/M alignment. Use when preparing federal solicitation submissions, responding to government RFPs, or drafting procurement bids.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.