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概要と使いどころ
Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.
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Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.
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The Lancet figure preparation: resolution (300+ DPI at 120%), preferred editable formats (PowerPoint/Word/SVG), column widths (75/154 mm), Times New Roman, in-house redraw policy.
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NEB-IRC activation energy pipeline for reaction barriers using GFN2-xTB and pysisyphus. Optimize reactant and product geometries, run CI-NEB path search, optimize the transition state with a Hessian, verify with IRC (one imaginary mode, endpoints matching reactant/product, single NEB maximum), and report the electronic and Gibbs barriers. Use when you need a transition state, reaction barrier, activation energy, minimum energy path, or intrinsic reaction coordinate. Covers reactant/product atom-ordering pitfalls, feasibility sizing for single-core runs, and thermochemistry corrections. Renders an IRC energy-profile plot and an animated TS imaginary-mode HTML viewer. For 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
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Per-feature NaN-safe Spearman/Pearson correlation across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail, correct pairwise deletion, degenerate input filtering, and large-dataset performance. Use statistical-analysis for test choice; shap-model-explainability for interpretability.
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Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.
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MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.
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PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.
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Science (AAAS) figure preparation: resolution (150-300+ DPI), formats (PDF/EPS/TIFF), RGB color, Myriad/Helvetica fonts, strict image manipulation policies including gamma adjustment disclosure.
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LLM-driven hypothesis generation/testing on tabular data. Three methods: HypoGeniC (data-driven), HypoRefine (literature+data), Union. Iterative refinement, Redis caching, multi-hypothesis inference. Manual: hypothesis-generation; ideation: scientific-brainstorming.
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Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
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Pipeline for Neuropixels extracellular electrophysiology: probe geometry (ProbeInterface), Kilosort sorting via SpikeInterface, quality metrics, unit curation (ISI, firing rate, SNR), post-sort analysis (PSTH, tuning curves, population decoding). Supports Neuropixels 1.0/2.0/Ultra in rodent/primate experiments.
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Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
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PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.
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Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.
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Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.
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Universal QA checklist for generated scientific plots: overlapping labels, clipped text, missing axes/legends, overcrowded data, and cross-journal resolution/format guidance.
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Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
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Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
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Unified Python framework for extracellular electrophysiology. Load 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess, run 10+ sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) via one API, compute quality metrics (SNR, ISI, firing rate), compare sorters, export NWB/Phy. For format-agnostic multi-sorter workflows. For Neuropixels-specific PSTH/decoding use neuropixels.
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Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).
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Python Materials Genomics library for structure analysis, thermodynamics, and electronic properties. Parse/create crystal structures (CIF, POSCAR), query Materials Project for DFT-computed properties, analyze phase and Pourbaix diagrams, compute XRD patterns, generate DFT inputs for VASP, Quantum ESPRESSO, CP2K. Alternatives: ASE (MD/geometry), AFLOW (high-throughput), OVITO (visualization).
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Python toolkit for neurophysiological signal processing: ECG (HR, HRV, R-peaks), EEG (complexity, PSD), EMG (activation onset), EDA/GSR (SCR decomposition), PPG, and RSP. Includes synthetic signal simulation. Alternatives: BioSPPy (less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP).
日本語の概要は準備中です。原文の説明を表示しています。