“2d”的搜索结果

Turn an authorized 2D logo into an approved visual treatment and animate a controlled brand reveal while preserving the mark.

中文简介正在准备中。当前显示原文说明。

Knowledge base from NASA NPR 7150.2D — NASA Software Engineering Requirements. Use for NASA's Agency-wide software-engineering requirements: the software classification scheme (Classes A–F), the Requirements Mapping Matrix and class-driven applicability, tailoring and Technical Authority governance, the SWE-### 'shall' requirements across software management (Ch 3) and the engineering life cycle (Ch 4), the supporting disciplines (configuration management, risk, peer reviews/inspections, measurement, non-conformance — Ch 5), safety-critical and cybersecurity requirements, IV&V applicability, traceability, and the Appendix A definitions. Does not cover detailed software-assurance/safety procedures (see NASA-STD-8739.8), implementation how-to and document content (see NASA-HDBK-2203), the systems-engineering process framework (see NPR 7123.1), or program/project management (see NPR 7120.5).

中文简介正在准备中。当前显示原文说明。

AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.

中文简介正在准备中。当前显示原文说明。

Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.

中文简介正在准备中。当前显示原文说明。

Digital-human implementation and demo skill for choosing, prototyping, and QA-ing low-cost 2D photoreal digital humans and high-control 3D custom digital humans. Use when the user wants a digital-human avatar, AI host, customer-service presenter, multimodal voice-to-lip-sync video stream, vendor comparison for services such as Silicon Intelligence / Tencent Zhiying / ZEGO-like real-time providers, or a UE/Unity 3D digital-human architecture and launch-ready demo.

中文简介正在准备中。当前显示原文说明。

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

中文简介正在准备中。当前显示原文说明。

Simulate wave propagation — acoustic, electromagnetic, elastic, and quantum waves. FDTD, spectral methods, and absorbing boundary conditions for 1D/2D/3D wave equations with sources, scattering, and dispersion.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

Train Physics-Informed Neural Networks (PINNs) using DeepXDE. Solve forward and inverse PDE problems by embedding physics equations into the neural network loss function. Supports 1D/2D/3D, time-dependent, and parametric PDEs.

中文简介正在准备中。当前显示原文说明。

Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.

中文简介正在准备中。当前显示原文说明。

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

中文简介正在准备中。当前显示原文说明。

Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.

中文简介正在准备中。当前显示原文说明。

Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

中文简介正在准备中。当前显示原文说明。

Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

中文简介正在准备中。当前显示原文说明。

Publication-quality molecular visualization. 2D structure drawings (PNG/SVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views.

中文简介正在准备中。当前显示原文说明。

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

中文简介正在准备中。当前显示原文说明。

Runs Scenic probabilistic scenarios against a running CARLA server with the `scenic FILE --simulate --2d` CLI — the carlaChallenge1-10 traffic scenarios shipped by ScenarioRunner (`model srunner.scenic.models.model`) and by Scenic itself (`model scenic.simulators.carla.model`) — bounding the run, sampling scenes headlessly first to tell a map problem from a syntax problem, and restoring the world afterwards. Use when the user asks to "run a Scenic scenario", "run carlaChallenge", "test a .scenic file", "scenic --simulate", or reports a Scenic scenario that rejects every sample or cannot find blueprints.

中文简介正在准备中。当前显示原文说明。

Apple Silicon GPU コンピュート／数値計算リファレンス。MSL (address space, atomic, SIMD-group, TensorOps, cooperative_tensor, matmul2d), threadgroup sizing, argument buffer, indirect command buffer, unified memory, GPU counters, MTLTensor, MTL4MachineLearningCommandEncoder, MPS / MPSGraph / MPSMatrix / MPSNDArray / MPSNNGraph, MLX (mx.array, lazy evaluation, grad / vmap, mx.compile, mx.fft, export_function, mlx.nn, distributed)。

中文简介正在准备中。当前显示原文说明。

CadQuery (Python 製 3D CAD スクリプティングライブラリ) リファレンス。 Workplane API (lineTo / extrude / revolve / sweep / loft / fillet / chamfer / shell)、 Sketch API、Assemblies (Constraint solver)、Free Function API (hollow / prism / draft / offset2D, History)、 Selectors、Shape / Vertex / Edge / Wire / Face / Shell / Solid、 Import / Export (STEP / DXF / STL / glTF / SVG)、Visualization (VTK, cadquery.fig 非ブロッキング, Jupyter)、CQGI。

中文简介正在准备中。当前显示原文说明。

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

中文简介正在准备中。当前显示原文说明。

Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

中文简介正在准备中。当前显示原文说明。

Extract and update the EcoSIM output-variable catalog from Fortran history-field registration calls such as hist_addfld1d and hist_addfld2d. Use only when explicitly asked to update, regenerate, refresh, or rebuild the list of EcoSIM output variables from HistDataType.F90 or related F90 source; do not invoke for ordinary EcoSIM output analysis unless the variable list itself must be updated.

中文简介正在准备中。当前显示原文说明。

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

中文简介正在准备中。当前显示原文说明。

Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

中文简介正在准备中。当前显示原文说明。