Results for “pymodbus”

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.

Detect command injection attacks against Modbus TCP/RTU protocol in ICS environments by monitoring for unauthorized write operations, anomalous function codes, malformed frames, and deviations from established communication baselines.

Sets up PawBytes Upwork Suite module in a project. Use when the user requests to 'install upwork module', 'configure PawBytes Upwork Suite', or 'setup paw-upwork'.

PyAutoGUI와 OpenCV를 결합하여 화면 고속 캡처, 고정밀 템플릿 매칭, 멀티스레딩 병렬 제어 및 다국어 텍스트 입력 우회를 지원하는 강력한 GUI 자동화 스킬입니다.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Read and write genomic alignment and variant files in Python with pysam (htslib bindings) — SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, plus region extraction and per-base coverage/pileup. Use when scripting NGS data-processing pipelines that parse, filter, index, or compute coverage over BAM/CRAM/VCF files. Part of the AlterLab Academic Skills suite.

Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple spectral comparison and metabolite identification use matchms. Part of the AlterLab Academic Skills suite.

Sets up Prodig Suites module in a project. Use when the user requests to 'install prodig module', 'configure Prodig Suites', 'setup product creation suite', or 'paw-ps setup'.

Read, write, and manipulate genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Debug Python: pdb REPL + debugpy remote (DAP).

Creates, reviews, debugs, and modernizes Python project structure with deterministic tooling including pyproject.toml, uv, Ruff, Pyright, and pytest.

Sets up Pawbytes Tools module in a project. Use when the user requests to 'install paw tools', 'configure Pawbytes Tools', or 'setup utility tools'.

Debug Python with pdb, breakpoint(), post-mortem inspection, and debugpy remote attach.

Debug Python code interactively with pdb and debugpy, covering local breakpoints, post-mortem analysis, and remote attachment to running processes.

Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

Initialize BMad project configuration and load config variables. Use when any skill needs module-specific configuration values, or when setting up a new BMad project.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.