Results for “3d”

Use when global skill for BDB Synapse (3D Codebase Visualizer) integration.

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Build augmented reality experiences with RealityKit and ARKit on iOS. Use when adding 3D content with RealityView, loading entities and models, placing objects via raycasting, configuring AR camera sessions, handling world tracking, scene understanding, or implementing entity interactions and gestures.

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Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.

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p5.js sketches: gen art, shaders, interactive, 3D.

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Create, edit, or debug parametric 3D CAD parts in CadQuery (Python) that export to STEP for SolidWorks. Use when the request involves CadQuery, .step/.stl export, or modelling a physical part — enclosure, bracket, PCB, motor mount, cam, gear. Also use when a STEP will not open in CAD, or geometry looks correct in STL but is invalid as a solid. Applies a verification discipline before export and documented OCC pitfalls.

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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.

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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.

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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.

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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.

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Publication-quality physics plots — vector fields, streamlines, contour maps, 3D surfaces, phase space, spectrograms, and animations. Optimized for journal submission with LaTeX labels, proper colormaps, and multi-panel layouts.

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

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Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.

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Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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

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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.

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Three.js (three, WebGL / WebGPU 3D グラフィクス) r186 リファレンス。 Scene, Group, PerspectiveCamera, OrthographicCamera, WebGLRenderer, WebGPURenderer, RenderPipeline、 BufferGeometry, BufferAttribute, StorageBufferAttribute, Mesh, InstancedMesh, SkinnedMesh、 MeshStandardMaterial, MeshPhysicalMaterial, ShaderMaterial, node-materials (TSL)、 DirectionalLight, SpotLight, PointLight, AmbientLight, PMREMGenerator、 GLTFLoader, FBXLoader, DRACOLoader, TextureLoader、 AnimationMixer, AnimationClip, AnimationAction, KeyframeTrack, Timer、 AudioListener, PositionalAudio、 Vector3, Matrix4, Quaternion, Euler, Color, Box3, Raycaster、 OrbitControls, TransformControls, WebXRManager。

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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。

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visionOS / iOS 空間コンピューティング。RealityKit、ARKit、visionOS SwiftUI、USDKit、Spatial Preview。 Entity, ModelEntity, AnchorEntity, Component, System, RealityView, MeshResource, Material, EntityAction, MeshDeformerComponent, GaussianSplatComponent, ComputeGraphComponent, ComputeNodeGraph, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, USDStage, SpatialPreviewSession, ImmersiveSpace, ImmersionStyle, WindowGroup, VolumetricWindowStyle, Model3D, ARKitSession, ARSession, ARWorldTrackingConfiguration, WorldTrackingProvider, HandTrackingProvider, ImageTrackingProvider, AccessoryTrackingProvider, CameraFrameProvider, ObjectTrackingProvider, RoomTrackingProvider, SceneReconstructionProvider。

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Raycast from Camera.main through a Game View coordinate. Use when you need to check what a screenshot coordinate would hit in 3D physics before clicking or long-pressing with simulate-mouse-input.

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Turn character reference images into reusable, rigged voxel 3D assets for Blender and Three.js through an imagegen turnaround and user-approval workflow. Use when Codex needs to preserve a character's likeness across front, side, and back views; create or revise Minecraft-like cube-headed models; prevent missing rear hair or clothing; split features between block geometry and face/clothing textures; generate GLB and editable .blend files; add walk/smash-compatible rigid-part pivots; or diagnose why a voxel conversion is not recognizable.

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Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.

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Detect distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).

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Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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