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Evolution is the position along the noise's 4th (time) axis: 0 animates it by itself (0.5 per second), any other value freezes the field there, so automate it to control the motion. Colour runs blue - white - red with the displacement length; the normal is estimated from the displacement field.\",\n  \"CREDIT\": \"ossia score\",\n  \"ISFVSN\": \"2.0\",\n  \"MODE\": \"COMPUTE_SHADER\",\n  \"CATEGORIES\": [\"Geometry Generator\"],\n  \"RESOURCES\": [\n    {\n      \"NAME\": \"geo\",\n      \"TYPE\": \"geometry\",\n      \"VERTEX_COUNT\": \"$gridSize * $gridSize * $gridSize\",\n      \"ATTRIBUTES\": [\n        { \"NAME\": \"position\", \"SEMANTIC\": \"position\", \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"vertex\" },\n        { \"NAME\": \"color\",    \"SEMANTIC\": \"color\",    \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"vertex\" },\n        { \"NAME\": \"normal\",   \"SEMANTIC\": \"normal\",   \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"vertex\" }\n      ]\n    },\n    { \"NAME\": \"gridSize\",    \"TYPE\": \"long\",  \"DEFAULT\": 16,  \"MIN\": 2,   \"MAX\": 128 },\n    { \"NAME\": \"frequency\",   \"TYPE\": \"float\", \"DEFAULT\": 2.0, \"MIN\": 0.1, \"MAX\": 20.0 },\n    { \"NAME\": \"amplitude\",   \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 5.0 },\n    { \"NAME\": \"octaves\",     \"TYPE\": \"long\",  \"DEFAULT\": 3,   \"MIN\": 1,   \"MAX\": 8 },\n    { \"NAME\": \"evolution\",   \"TYPE\": \"float\", \"DEFAULT\": 0.0, \"MIN\": 0.0, \"MAX\": 100.0 },\n    { \"NAME\": \"lacunarity\",  \"TYPE\": \"float\", \"DEFAULT\": 2.0, \"MIN\": 1.0, \"MAX\": 4.0 },\n    { \"NAME\": \"persistence\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 1.0 }\n  ],\n  \"PASSES\": [\n    { \"LOCAL_SIZE\": [64, 1, 1], \"EXECUTION_MODEL\": { \"TYPE\": \"PER_VERTEX\" } }\n  ]\n}*/\n\n// ============================================================\n// Simplex noise 3D & 4D\n// Based on webgl-noise by Ian McEwan, Ashima Arts (MIT License)\n// https://github.com/ashima/webgl-noise\n// ============================================================\n\nvec3 mod289_3(vec3 x) { return x - floor(x * (1.0 / 289.0)) * 289.0; }\nvec4 mod289_4(vec4 x) { return x - floor(x * (1.0 / 289.0)) * 289.0; }\nfloat mod289_1(float x) { return x - floor(x * (1.0 / 289.0)) * 289.0; }\n\nvec4 permute4(vec4 x) { return mod289_4(((x * 34.0) + 10.0) * x); }\nfloat permute1(float x) { return mod289_1(((x * 34.0) + 10.0) * x); }\n\nvec4 taylorInvSqrt4(vec4 r) { return 1.79284291400159 - 0.85373472095314 * r; }\nfloat taylorInvSqrt1(float r) { return 1.79284291400159 - 0.85373472095314 * r; }\n\nfloat snoise3(vec3 v)\n{\n    const vec2 C = vec2(1.0 / 6.0, 1.0 / 3.0);\n    const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\n\n    vec3 i  = floor(v + dot(v, C.yyy));\n    vec3 x0 = v - i + dot(i, C.xxx);\n\n    vec3 g = step(x0.yzx, x0.xyz);\n    vec3 l = 1.0 - g;\n    vec3 i1 = min(g.xyz, l.zxy);\n    vec3 i2 = max(g.xyz, l.zxy);\n\n    vec3 x1 = x0 - i1 + C.xxx;\n    vec3 x2 = x0 - i2 + C.yyy;\n    vec3 x3 = x0 - D.yyy;\n\n    i = mod289_3(i);\n    vec4 p = permute4(permute4(permute4(\n                 i.z + vec4(0.0, i1.z, i2.z, 1.0))\n               + i.y + vec4(0.0, i1.y, i2.y, 1.0))\n               + i.x + vec4(0.0, i1.x, i2.x, 1.0));\n\n    float n_ = 0.142857142857;\n    vec3 ns = n_ * D.wyz - D.xzx;\n\n    vec4 j = p - 49.0 * floor(p * ns.z * ns.z);\n\n    vec4 x_ = floor(j * ns.z);\n    vec4 y_ = floor(j - 7.0 * x_);\n\n    vec4 x = x_ * ns.x + ns.yyyy;\n    vec4 y = y_ * ns.x + ns.yyyy;\n    vec4 h = 1.0 - abs(x) - abs(y);\n\n    vec4 b0 = vec4(x.xy, y.xy);\n    vec4 b1 = vec4(x.zw, y.zw);\n\n    vec4 s0 = floor(b0) * 2.0 + 1.0;\n    vec4 s1 = floor(b1) * 2.0 + 1.0;\n    vec4 sh = -step(h, vec4(0.0));\n\n    vec4 a0 = b0.xzyw + s0.xzyw * sh.xxyy;\n    vec4 a1 = b1.xzyw + s1.xzyw * sh.zzww;\n\n    vec3 p0 = vec3(a0.xy, h.x);\n    vec3 p1 = vec3(a0.zw, h.y);\n    vec3 p2 = vec3(a1.xy, h.z);\n    vec3 p3 = vec3(a1.zw, h.w);\n\n    vec4 norm = taylorInvSqrt4(vec4(dot(p0, p0), dot(p1, p1), dot(p2, p2), dot(p3, p3)));\n    p0 *= norm.x;\n    p1 *= norm.y;\n    p2 *= norm.z;\n    p3 *= norm.w;\n\n    vec4 m = max(0.5 - vec4(dot(x0, x0), dot(x1, x1), dot(x2, x2), dot(x3, x3)), 0.0);\n    m = m * m;\n    return 105.0 * dot(m * m, vec4(dot(p0, x0), dot(p1, x1), dot(p2, x2), dot(p3, x3)));\n}\n\n// 4D simplex noise for time-evolution\nvec4 grad4(float j, vec4 ip)\n{\n    const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\n    vec4 p, s;\n    p.xyz = floor(fract(vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\n    p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\n    s = vec4(lessThan(p, vec4(0.0)));\n    p.xyz = p.xyz + (s.xyz * 2.0 - 1.0) * s.www;\n    return p;\n}\n\nfloat snoise4(vec4 v)\n{\n    const vec4 C = vec4(0.138196601125011, 0.276393202250021,\n                        0.414589803375032, -0.447213595499958);\n    const float F4 = 0.309016994374947451;\n\n    vec4 i = floor(v + dot(v, vec4(F4)));\n    vec4 x0 = v - i + dot(i, C.xxxx);\n\n    vec4 i0;\n    vec3 isX = step(x0.yzw, x0.xxx);\n    vec3 isYZ = step(x0.zww, x0.yyz);\n    i0.x = isX.x + isX.y + isX.z;\n    i0.yzw = 1.0 - isX;\n    i0.y += isYZ.x + isYZ.y;\n    i0.zw += 1.0 - isYZ.xy;\n    i0.z += isYZ.z;\n    i0.w += 1.0 - isYZ.z;\n\n    vec4 i3 = clamp(i0, 0.0, 1.0);\n    vec4 i2 = clamp(i0 - 1.0, 0.0, 1.0);\n    vec4 i1 = clamp(i0 - 2.0, 0.0, 1.0);\n\n    vec4 x1 = x0 - i1 + C.xxxx;\n    vec4 x2 = x0 - i2 + C.yyyy;\n    vec4 x3 = x0 - i3 + C.zzzz;\n    vec4 x4 = x0 + C.wwww;\n\n    i = mod289_4(i);\n    float j0 = permute1(permute1(permute1(permute1(i.w) + i.z) + i.y) + i.x);\n    vec4 j1 = permute4(permute4(permute4(permute4(\n                 i.w + vec4(i1.w, i2.w, i3.w, 1.0))\n               + i.z + vec4(i1.z, i2.z, i3.z, 1.0))\n               + i.y + vec4(i1.y, i2.y, i3.y, 1.0))\n               + i.x + vec4(i1.x, i2.x, i3.x, 1.0));\n\n    vec4 ip = vec4(1.0 / 294.0, 1.0 / 49.0, 1.0 / 7.0, 0.0);\n\n    vec4 p0 = grad4(j0, ip);\n    vec4 p1 = grad4(j1.x, ip);\n    vec4 p2 = grad4(j1.y, ip);\n    vec4 p3 = grad4(j1.z, ip);\n    vec4 p4 = grad4(j1.w, ip);\n\n    vec4 norm = taylorInvSqrt4(vec4(dot(p0, p0), dot(p1, p1), dot(p2, p2), dot(p3, p3)));\n    p0 *= norm.x;\n    p1 *= norm.y;\n    p2 *= norm.z;\n    p3 *= norm.w;\n    p4 *= taylorInvSqrt1(dot(p4, p4));\n\n    vec3 m0 = max(0.6 - vec3(dot(x0, x0), dot(x1, x1), dot(x2, x2)), 0.0);\n    vec2 m1 = max(0.6 - vec2(dot(x3, x3), dot(x4, x4)), 0.0);\n    m0 = m0 * m0;\n    m1 = m1 * m1;\n    return 49.0 * (dot(m0 * m0, vec3(dot(p0, x0), dot(p1, x1), dot(p2, x2)))\n                 + dot(m1 * m1, vec2(dot(p3, x3), dot(p4, x4))));\n}\n\n// ============================================================\n// Fractal Brownian Motion with 4D noise (3D + time)\n// ============================================================\n\nfloat fbm(vec3 p, float w, int oct, float lac, float pers)\n{\n    float value = 0.0;\n    float amp = 1.0;\n    float freq = 1.0;\n    float maxAmp = 0.0;\n\n    for(int i = 0; i < oct; i++)\n    {\n        value += amp * snoise4(vec4(p * freq, w));\n        maxAmp += amp;\n        freq *= lac;\n        amp *= pers;\n    }\n\n    return value / maxAmp;\n}\n\n// 3D displacement vector from noise gradient\nvec3 noiseDisplacement(vec3 p, float w, int oct, float lac, float pers)\n{\n    return vec3(\n        fbm(p,                           w, oct, lac, pers),\n        fbm(p + vec3(31.416, 0.0, 0.0), w, oct, lac, pers),\n        fbm(p + vec3(0.0, 47.853, 0.0), w, oct, lac, pers)\n    );\n}\n\nvoid main()\n{\n    uint idx = gl_GlobalInvocationID.x;\n    uint count = uint(ISF_WRITE(geo, position).length());\n    if(idx >= count)\n        return;\n\n    uint gs = uint(gridSize);\n\n    // Decompose linear index into 3D grid coordinates\n    uint z = idx / (gs * gs);\n    uint remainder = idx % (gs * gs);\n    uint y = remainder / gs;\n    uint x = remainder % gs;\n\n    // Grid spacing so that the grid spans [-1, 1] on each axis\n    float spacing = 2.0 / float(max(gs - 1u, 1u));\n    vec3 gridPos = vec3(float(x), float(y), float(z)) * spacing - 1.0;\n\n    // Time dimension: use evolution if nonzero, otherwise animate with TIME\n    float w = (evolution > 0.0) ? evolution : TIME * 0.5;\n\n    // Sample noise at grid position\n    vec3 samplePos = gridPos * frequency;\n    vec3 disp = noiseDisplacement(samplePos, w, octaves, lacunarity, persistence);\n    vec3 pos = gridPos + disp * amplitude;\n\n    // Approximate normal via central differences of the displacement field\n    float eps = 0.02;\n    vec3 dx = noiseDisplacement((gridPos + vec3(eps, 0.0, 0.0)) * frequency, w, octaves, lacunarity, persistence)\n            - noiseDisplacement((gridPos - vec3(eps, 0.0, 0.0)) * frequency, w, octaves, lacunarity, persistence);\n    vec3 dy = noiseDisplacement((gridPos + vec3(0.0, eps, 0.0)) * frequency, w, octaves, lacunarity, persistence)\n            - noiseDisplacement((gridPos - vec3(0.0, eps, 0.0)) * frequency, w, octaves, lacunarity, persistence);\n    vec3 dz = noiseDisplacement((gridPos + vec3(0.0, 0.0, eps)) * frequency, w, octaves, lacunarity, persistence)\n            - noiseDisplacement((gridPos - vec3(0.0, 0.0, eps)) * frequency, w, octaves, lacunarity, persistence);\n\n    // The Jacobian of the displacement maps tangent vectors; the normal\n    // of the displaced surface is approximated by the cross product of\n    // two tangent vectors derived from the Jacobian columns.\n    float inv2eps = amplitude / (2.0 * eps);\n    vec3 tangentX = vec3(1.0, 0.0, 0.0) + dx * inv2eps;\n    vec3 tangentY = vec3(0.0, 1.0, 0.0) + dy * inv2eps;\n\n    vec3 nrm = normalize(cross(tangentX, tangentY));\n\n    // Color mapped from displacement magnitude\n    float dispMag = length(disp);\n    // Cool-to-warm colormap: blue -> white -> red\n    vec3 coolColor = vec3(0.2, 0.4, 0.9);\n    vec3 warmColor = vec3(0.9, 0.3, 0.2);\n    vec3 midColor  = vec3(0.95, 0.95, 0.95);\n    float t = clamp(dispMag * 2.0, 0.0, 1.0);\n    vec3 rgb = (t < 0.5)\n        ? mix(coolColor, midColor, t * 2.0)\n        : mix(midColor, warmColor, (t - 0.5) * 2.0);\n\n    ISF_WRITE(geo, position)[idx] = vec4(pos, 1.0);\n    ISF_WRITE(geo, normal)[idx]   = vec4(nrm, 0.0);\n    ISF_WRITE(geo, color)[idx]    = vec4(rgb, 1.0);\n}\n","Inlets":[{"uuid":"238399a0-7e81-47e3-896f-08e8856e2973","ObjectName":"Inlet","id":0,"Hidden":true,"Custom":"gridSize","Exposed":"gridsize","Value":{"Int":82},"Init":{"Int":16},"Domain":{"Int":{"Min":2,"Max":128}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"frequency","Exposed":"frequency","Value":{"Float":0.10999999940395355},"Init":{"Float":2.0},"Domain":{"Float":{"Min":0.10000000149011612,"Max":20.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":2,"Hidden":true,"Custom":"amplitude","Exposed":"amplitude","Value":{"Float":2.5},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":5.0}}},{"uuid":"238399a0-7e81-47e3-896f-08e8856e2973","ObjectName":"Inlet","id":3,"Hidden":true,"Custom":"octaves","Exposed":"octaves","Value":{"Int":3},"Init":{"Int":3},"Domain":{"Int":{"Min":1,"Max":8}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":4,"Hidden":true,"Custom":"evolution","Exposed":"evolution","Value":{"Float":11.666666984558105},"Init":{"Float":0.0},"Domain":{"Float":{"Min":0.0,"Max":100.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":5,"Hidden":true,"Custom":"lacunarity","Exposed":"lacunarity","Value":{"Float":2.0},"Init":{"Float":2.0},"Domain":{"Float":{"Min":1.0,"Max":4.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":6,"Hidden":true,"Custom":"persistence","Exposed":"persistence","Value":{"Float":1.0},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":1.0}}}],"Outlets":[{"uuid":"848061c5-e8a0-4a13-9985-e8df30ce6d4f","ObjectName":"Outlet","id":0,"Hidden":false,"Custom":"geo_out","Exposed":"geo_out"}]},{"uuid":"dbfc2101-40d7-4807-8804-571e88992e7e","ObjectName":"gfxProcess","id":4,"Metadata":{"ScriptingName":"PointCloud3D.1","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[281.14,137.61400000000003],"Size":[734.0,175.0],"Loops":false,"FoldMode":0,"Vertex":"// Reverse-Z (score project rule, CameraMath.hpp setReverseZPerspective): near -> NDC +1, far -> -1,\n// paired with the engine's GREATER depth compare and 0.0 depth clear. A standard GL projection here\n// makes the farthest fragment win.\nmat4 buildPerspective(float fovDeg, float aspect, float near, float far)\n{\n  float f = 1.0 / tan(radians(fovDeg) * 0.5);\n  float nf = 1.0 / (near - far);\n  return mat4(\n    f / aspect, 0.0, 0.0,                   0.0,\n    0.0,        f,   0.0,                   0.0,\n    0.0,        0.0, -(far + near) * nf,    -1.0,\n    0.0,        0.0, -2.0 * far * near * nf, 0.0\n  );\n}\n\nmat4 buildLookAt(vec3 eye, vec3 target, vec3 up)\n{\n  vec3 f = normalize(target - eye);\n  vec3 s = normalize(cross(f, up));\n  vec3 u = cross(s, f);\n  return mat4(\n     s.x,          u.x,         -f.x,         0.0,\n     s.y,          u.y,         -f.y,         0.0,\n     s.z,          u.z,         -f.z,         0.0,\n    -dot(s, eye), -dot(u, eye),  dot(f, eye), 1.0\n  );\n}\n\nvoid main()\n{\n  isf_vertShaderInit();\n\n  float aspect = RENDERSIZE.x / RENDERSIZE.y;\n  mat4 proj = buildPerspective(fov, aspect, nearPlane, farPlane);\n  mat4 view = buildLookAt(cameraPos, lookAt, vec3(0.0, 1.0, 0.0));\n\n  vec4 worldPos = MODEL_MATRIX * vec4(position.xyz, 1.0);\n  vec4 viewPos = view * worldPos;\n  gl_Position = clipSpaceCorrMatrix * proj * viewPos;\n\n  // Point size with optional distance attenuation\n  if(sizeAttenuation)\n  {\n    float dist = length(viewPos.xyz);\n    gl_PointSize = pointSize * (1.0 / max(dist, 0.01));\n  }\n  else\n  {\n    gl_PointSize = pointSize;\n  }\n\n  v_color = color;\n  // Pass linear depth for potential depth-based effects\n  v_depth = -viewPos.z;\n\n  isf_vertShaderFinish();\n}\n","Fragment":"/*{\n  \"DESCRIPTION\": \"Glowing 3D point cloud renderer with its own perspective camera (cameraPos looking at lookAt, fov in degrees). Draws every point of the incoming geometry as a soft Gaussian sprite (falloff sets how fast it fades toward the edge), coloured by the per-point color attribute (white when the geometry has none) times brightness, faded by opacity. Sprites are added onto what is below (additive: overlapping glows brighten, and the result does not depend on drawing order); they are hidden by nearer depth-writing geometry but write no depth themselves. pointSize is in pixels; with sizeAttenuation it is the size at 1 unit from the camera and shrinks with distance. For opaque points that hide each other use PointCloud3D. Works with every point generator (GridPoints, CurvePoints, NoiseField, RandomScatter...).\",\n  \"CREDIT\": \"ossia score\",\n  \"ISFVSN\": \"2\",\n  \"MODE\": \"RAW_RASTER_PIPELINE\",\n  \"ALPHA\": \"premultiplied\",\n  \"COMPOSITE\": \"add\",\n  \"QUEUE\": \"transparent\",\n  \"PIPELINE_STATE\": { \"TOPOLOGY\": \"points\", \"DEPTH_TEST\": true, \"DEPTH_WRITE\": false },\n  \"CATEGORIES\": [\"Renderer\", \"3D\"],\n  \"VERTEX_INPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"position\" },\n    { \"TYPE\": \"vec4\", \"NAME\": \"color\", \"REQUIRED\": false }\n  ],\n  \"VERTEX_OUTPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"v_color\" },\n    { \"TYPE\": \"float\", \"NAME\": \"v_depth\" }\n  ],\n  \"FRAGMENT_INPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"v_color\" },\n    { \"TYPE\": \"float\", \"NAME\": \"v_depth\" }\n  ],\n  \"FRAGMENT_OUTPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"isf_FragColor\" }\n  ],\n  \"INPUTS\": [\n    {\n      \"NAME\": \"pointSize\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 5.0,\n      \"MIN\": 1.0,\n      \"MAX\": 100.0\n    },\n    {\n      \"NAME\": \"opacity\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 1.0,\n      \"MIN\": 0.0,\n      \"MAX\": 1.0\n    },\n    {\n      \"NAME\": \"falloff\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 4.0,\n      \"MIN\": 0.5,\n      \"MAX\": 20.0\n    },\n    {\n      \"NAME\": \"brightness\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 1.0,\n      \"MIN\": 0.0,\n      \"MAX\": 5.0\n    },\n    {\n      \"NAME\": \"sizeAttenuation\",\n      \"TYPE\": \"bool\",\n      \"DEFAULT\": true\n    },\n    {\n      \"NAME\": \"cameraPos\",\n      \"TYPE\": \"point3D\",\n      \"DEFAULT\": [3.0, 2.0, 3.0],\n      \"MIN\": [-50.0, -50.0, -50.0],\n      \"MAX\": [50.0, 50.0, 50.0]\n    },\n    {\n      \"NAME\": \"lookAt\",\n      \"TYPE\": \"point3D\",\n      \"DEFAULT\": [0.0, 0.0, 0.0],\n      \"MIN\": [-50.0, -50.0, -50.0],\n      \"MAX\": [50.0, 50.0, 50.0]\n    },\n    {\n      \"NAME\": \"fov\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 60.0,\n      \"MIN\": 10.0,\n      \"MAX\": 150.0\n    },\n    {\n      \"NAME\": \"nearPlane\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.01,\n      \"MIN\": 0.001,\n      \"MAX\": 10.0\n    },\n    {\n      \"NAME\": \"farPlane\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 100.0,\n      \"MIN\": 1.0,\n      \"MAX\": 10000.0\n    }\n  ]\n}*/\n\nvoid main()\n{\n  vec2 pc = gl_PointCoord * 2.0 - 1.0;\n  float a = v_color.a * clamp(opacity, 0.0, 1.0) * exp(-dot(pc, pc) * falloff);\n  if(a < 0.002)\n    discard;\n\n  isf_FragColor = vec4(v_color.rgb * brightness * a, a);\n}\n","Inlets":[{"uuid":"f2ab26ea-415d-45a2-bfbc-2968c7c92a33","ObjectName":"Inlet","id":1000,"Hidden":false,"Custom":"Geometry In","Exposed":"geometry 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","AutomaticThumbnail":true}],"Version":5,"Commit":"5bbabca73a6a56659aeedfb7129e6de35e65ba7c","Tag":"3.8.2"}