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viznoir

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VTK — это всё, что вам нужно. Научная визуализация кинематографического качества для ИИ-агентов.

CI PyPI Python License: MIT Mentioned in Awesome VTK

Science Storytelling

Один промпт → физический анализ → кинематографический рендеринг → уравнения LaTeX → готовая к публикации история.

Что он делает

MCP-сервер, который предоставляет ИИ-агентам полный доступ к конвейеру рендеринга VTK — без графического интерфейса ParaView, без Jupyter-ноутбуков, без сервера отображения. Ваш агент считывает данные моделирования, применяет фильтры, визуализирует изображения кинематографического качества и экспортирует анимации, работая полностью в безголовом режиме.

Работает с: Claude Code · Cursor · Windsurf · Gemini CLI · любым MCP-клиентом

Related MCP server: HoloViz MCP Server

Быстрый старт

pip install mcp-server-viznoir

Добавьте в конфигурацию вашего MCP-клиента:

{
  "mcpServers": {
    "viznoir": {
      "command": "mcp-server-viznoir"
    }
  }
}

Затем спросите своего ИИ-агента:

"Открой cavity.foam, визуализируй поле давления с кинематографическим освещением, а затем создай историю физической декомпозиции."

Возможности

Категория

Инструменты

Рендеринг

render · cinematic_render · batch_render · volume_render

Фильтры

slice · contour · clip · streamlines · pv_isosurface

Анализ

inspect_data · inspect_physics · extract_stats · analyze_data

Зондирование

plot_over_line · integrate_surface · probe_timeseries

Анимация

animate · split_animate

Сравнение

compare · compose_assets

Экспорт

preview_3d · execute_pipeline

22 инструмента · 12 ресурсов · 4 промпта · 50+ форматов файлов (OpenFOAM, VTK, CGNS, Exodus, STL, glTF, …)

Архитектура

  prompt                    "Render pressure from cavity.foam"
    │
  MCP Server                22 tools · 12 resources · 4 prompts
    │
  VTK Engine                readers → filters → renderer → camera
    │                       EGL/OSMesa headless · cinematic lighting
  Physics Layer             topology analysis · context parsing
    │                       vortex detection · stagnation points
  Animation                 7 physics presets · easing · timeline
    │                       transitions · compositor · video export
  Output                    PNG · WebP · MP4 · GLTF · LaTeX

Цифры

22 MCP-инструмента

24 фильтра VTK

10 доменов

19 нативных форматов файлов

6/6 типов данных VTK

50+ форматов через meshio

Документация

Домашняя страница: kimimgo.github.io/viznoir

Документация для разработчиков: kimimgo.github.io/viznoir/docs — полная справка по инструментам, галерея доменов, руководство по архитектуре

Лицензия

MIT

Available Tools

23 tools
analyze_dataA

[DEPRECATED — use inspect_physics instead] Analyze VTK/simulation data.

This tool is deprecated. Use inspect_physics for structured physics data extraction with vortex detection, critical points, and solver metadata.

Inputs: file_path (required), focus, domain. Returns: object

animateA

Create an animation from time series data or camera orbit.

Time mapping (timesteps mode): speed_factor=1.0: real-time (physics 1s = video 1s) speed_factor=5.0: 5x fast-forward speed_factor=0.2: 5x slow-motion (physics 1s = video 5s)

For VTK file series (e.g., PartFluid_0000.vtk through PartFluid_0100.vtk), use 'files' with an explicit list or 'file_pattern' with a glob pattern.

Inputs: file_path (required), field_name (required), mode, colormap, +12 more. Returns: object

auto_postprocessA

Autonomous post-processing: inspect → visualize → evaluate → refine.

Analyzes the file, detects the simulation domain (CFD/FEA/SPH), and produces 3-5 visualizations automatically. With sampling-capable clients, evaluates results and refines parameters iteratively.

Inputs: file_path (required), goal, max_iterations.

batch_renderA

Render multiple fields from the same dataset in one call.

Returns a dict with images list, each containing field name and base64 PNG. Useful for comparing pressure, velocity, temperature, etc. side-by-side.

Inputs: file_path (required), fields (required), colormap, camera, +5 more. Returns: object

cinematic_renderA

Cinematic-quality rendering with auto-framing, 3-point lighting, SSAO, and PBR.

Produces publication/presentation-quality images with:

  • PCA-based auto-camera: analyzes object shape and picks optimal viewing angle

  • 3-point cinematic lighting (key + fill + rim)

  • SSAO (Screen-Space Ambient Occlusion) for contact shadows

  • FXAA anti-aliasing

  • Gradient backgrounds

  • PBR material support (metallic/roughness)

Quality presets:

  • draft: 960x540, no post-processing (fast preview)

  • standard: 1920x1080, SSAO + FXAA

  • cinematic: 1920x1080, all effects + ground plane

  • ultra: 3840x2160, all effects + ground plane

  • publication: 2400x1800, clean lighting, white background

Inputs: file_path (required), field_name, colormap, quality, +15 more.

clipC

Create a clipped visualization.

Inputs: file_path (required), field_name (required), origin, normal, +7 more.

compareA

Compare two simulation results side-by-side or as a difference map.

Renders both datasets with identical camera, colormap, and scalar range for direct visual comparison. Essential for design comparison, mesh convergence studies, and solver validation.

Modes:

  • side_by_side: Two panels with shared colorbar and consistent framing

  • diff: Absolute field difference map (|A - B|) rendered on dataset A's mesh

Inputs: file_a (required), file_b (required), field_name, mode, +10 more.

compose_assetsC

Compose multiple assets into a deliverable format.

Inputs: assets (required), layout, title, width, +3 more.

contourC

Create an iso-surface (contour) visualization.

Inputs: file_path (required), field_name (required), isovalues (required), colormap, +5 more.

execute_pipelineA

Execute a custom pipeline definition (advanced).

Accepts a full PipelineDefinition JSON for maximum flexibility. This is the primary interface for CFD/FEA/CAE specialist agents.

The pipeline JSON structure: { "source": {"file": "/data/file.foam", "timestep": "latest"}, "pipeline": [ {"filter": "Slice", "params": {"origin": [0,0,0], "normal": [1,0,0]}}, {"filter": "Calculator", "params": {"expression": "mag(U)", "result_name": "Umag"}} ], "output": { "type": "image", "render": {"field": "Umag", "colormap": "Viridis"} } }

Available filters: Slice, Clip, Contour, Threshold, StreamTracer, Calculator, Gradient, IntegrateVariables, GenerateSurfaceNormals, ExtractBlock, ExtractSurface, WarpByVector, WarpByScalar, CellDatatoPointData, PlotOverLine, Glyph, ProgrammableFilter, Decimate, Triangulate.

Output types: image, data, csv, animation, export, multi.

Inputs: pipeline (required).

extract_statsB

Extract statistical summary (min/max/mean/std) for fields.

Inputs: file_path (required), fields (required), timestep, blocks. Returns: object

inspect_dataA

Inspect a simulation file and return metadata.

Returns bounds, point/cell arrays with ranges, timestep info, and multiblock structure. Use this first to understand what data is available before rendering or extracting.

Inputs: file_path (required). Returns: object

inspect_physicsA

Extract structured physics data for AI storytelling.

Analyzes simulation data to extract:

  • L2 FieldTopology: vortex detection (Q-criterion), critical points, centerline profiles, gradient statistics per field

  • L3 CaseContext: boundary conditions, transport properties, solver info, mesh quality, derived quantities (Re, Ma, etc.)

Returns structured JSON for LLM to build physics narratives. Replaces analyze_data with quantitative topology data instead of hardcoded heuristics.

Inputs: file_path (required), case_dir, fields, probe_lines, +1 more. Returns: object

integrate_surfaceA

Integrate a field over a surface to compute forces, areas, or fluxes.

Inputs: file_path (required), field_name (required), boundary, timestep. Returns: object

plot_over_lineB

Sample field values along a line between two points.

Returns coordinate arrays and field values for plotting.

Inputs: file_path (required), field_name (required), point1 (required), point2 (required), +2 more. Returns: object

preview_3dA

Export dataset to glTF/glB for interactive 3D viewing in a browser.

Returns the exported file path and a viewer URL hint. Requires VTK >= 9.4 with vtkGLTFExporter support.

Inputs: file_path (required), field_name, timestep, output_filename. Returns: object

probe_timeseriesA

Sample a field value at a fixed point across timesteps.

Useful for monitoring pressure/velocity at a sensor location over time. Returns dict with times and values arrays.

Inputs: file_path (required), field_name (required), point (required), files, +2 more. Returns: object

pv_isosurfaceA

Run DualSPHysics IsoSurface to generate VTK surface mesh files.

Converts bi4 particle data into VTK surface meshes (iso-surfaces). The output files can then be used with animate() or render() via SourceDef(files=[...]) for visualization.

Inputs: bi4_dir (required), output_dir (required), vars, only_type, +1 more. Returns: object

renderB

Render a field visualization and return a PNG screenshot.

Inputs: file_path (required), field_name (required), association, colormap, +8 more.

sliceC

Create a slice (cut plane) visualization.

Inputs: file_path (required), field_name (required), origin, normal, +6 more.

split_animateA

Create a split-pane synchronized animation with multiple views.

Render 2-4 panes in a grid layout, combining 3D visualizations with time-series graphs. All panes are timestep-synchronized and output as a single GIF or PNG sequence.

Inputs: file_path (required), panes (required), layout, fps, +4 more.

streamlinesB

Create a streamline visualization for vector fields.

Inputs: file_path (required), vector_field (required), seed_point1, seed_point2, +8 more.

volume_renderC

Volume render 3D data (CT, MRI, CFD fields) with transfer function presets.

Presets: generic, ct_bone, ct_tissue, mri_brain, thermal, isosurface_like

Inputs: file_path (required), field_name, transfer_preset, colormap, +8 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.2/5.0

    Scored across 23 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose. Overlaps like multiple render tools are differentiated by quality presets and batch capabilities, and inspect_data vs inspect_physics serve different metadata levels. Deprecated tools are clearly marked.

    Naming Consistency2/5

    Naming is inconsistent: some tools are single verbs (animate, render, slice), others are verb_noun (inspect_data, batch_render), and some are nouns (contour, streamlines). The mix of styles and a few odd names like pv_isosurface reduce coherence.

    Tool Count3/5

    With 23 tools, the set is on the high side but still reasonable for a comprehensive simulation visualization server. The tools cover a wide range of operations without feeling excessive, but could be trimmed slightly.

    Completeness4/5

    The tool set offers strong coverage for simulation data analysis and visualization: inspection, multiple render styles, animations, statistics, and pipeline execution. Minor gaps like direct data export to common formats or advanced filtering are present but not critical.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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