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 \"DESCRIPTION\": \"FEEDBACK MODIFIER for SphereSplat test — reads particle positions from the generator, samples a feedback texture at each particle's world-XZ projection, and uses the sampled color as each particle's output color. Pipeline: Generator → THIS → SphereSplat → Window, with a feedback edge from SphereSplat output back to this node's 'feedbackTex' input. Without feedback, particles will be nearly black (only the baseColor contribution). With feedback, they pick up colors from where they rendered in previous frames, producing visible rotating color trails.\",\n  \"CREDIT\": \"ossia score\",\n  \"ISFVSN\": \"2.0\",\n  \"MODE\": \"COMPUTE_SHADER\",\n  \"CATEGORIES\": [\"Test\", \"Feedback\"],\n  \"RESOURCES\": [\n    {\n      \"NAME\": \"geoOut\",\n      \"TYPE\": \"geometry\",\n      \"TOPOLOGY\": \"triangles\",\n      \"VERTEX_COUNT\": 6,\n      \"INSTANCE_COUNT\": \"$INSTANCE_COUNT_geoIn\",\n      \"ATTRIBUTES\": [\n        { \"NAME\": \"out_position\",    \"SEMANTIC\": \"position\",    \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"instance\" },\n        { \"NAME\": \"out_color\",       \"SEMANTIC\": \"color\",       \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"instance\" },\n        { \"NAME\": \"out_sprite_size\", \"SEMANTIC\": \"sprite_size\", \"TYPE\": \"vec4\", \"ACCESS\": \"write_only\", \"RATE\": \"instance\" }\n      ]\n    },\n    {\n      \"NAME\": \"geoIn\",\n      \"TYPE\": \"geometry\",\n      \"ATTRIBUTES\": [\n        { \"NAME\": \"in_position\",    \"SEMANTIC\": \"position\",    \"TYPE\": \"vec4\", \"ACCESS\": \"read_only\", \"RATE\": \"instance\" },\n        { \"NAME\": \"in_color\",       \"SEMANTIC\": \"color\",       \"TYPE\": \"vec4\", \"ACCESS\": \"read_only\", \"RATE\": \"instance\" },\n        { \"NAME\": \"in_sprite_size\", \"SEMANTIC\": \"sprite_size\", \"TYPE\": \"vec4\", \"ACCESS\": \"read_only\", \"RATE\": \"instance\" }\n      ]\n    },\n    { \"NAME\": \"feedbackTex\",      \"TYPE\": \"texture\" },\n    { \"NAME\": \"feedbackStrength\", \"TYPE\": \"float\", \"DEFAULT\": 1.5, \"MIN\": 0.0, \"MAX\": 5.0 },\n    { \"NAME\": \"baseColor\",        \"TYPE\": \"float\", \"DEFAULT\": 0.15, \"MIN\": 0.0, \"MAX\": 1.0 },\n    { \"NAME\": \"projectionScale\",  \"TYPE\": \"float\", \"DEFAULT\": 0.18, \"MIN\": 0.01, \"MAX\": 1.0 }\n  ],\n  \"PASSES\": [\n    { \"LOCAL_SIZE\": [64, 1, 1], \"EXECUTION_MODEL\": { \"TYPE\": \"PER_INSTANCE\" } }\n  ]\n}*/\n\nvoid main()\n{\n    uint idx = gl_GlobalInvocationID.x;\n    uint count = uint(ISF_READ(geoIn, in_position).length());\n    if(idx >= count)\n        return;\n\n    vec4 pos    = ISF_READ(geoIn, in_position)[idx];\n    vec4 inCol  = ISF_READ(geoIn, in_color)[idx];\n    vec4 inSize = ISF_READ(geoIn, in_sprite_size)[idx];\n\n    // Rough top-down projection: particle world-XZ -> feedback texture UV [0,1]\n    vec2 uv = pos.xz * projectionScale + 0.5;\n    uv = clamp(uv, 0.0, 1.0);\n\n    vec4 fb = IMG_NORM_PIXEL(feedbackTex, uv);\n\n    // Output color = baseline (from generator) + feedback sample amplified\n    vec3 outCol = inCol.rgb * baseColor + fb.rgb * feedbackStrength;\n\n    ISF_WRITE(geoOut, out_position)[idx]    = pos;\n    ISF_WRITE(geoOut, out_color)[idx]       = vec4(outCol, 1.0);\n    ISF_WRITE(geoOut, out_sprite_size)[idx] = inSize;\n}\n","Inlets":[{"uuid":"f2ab26ea-415d-45a2-bfbc-2968c7c92a33","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"geoIn","Exposed":"geoin"},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":1,"Hidden":false,"Custom":"feedbackTex","Exposed":"feedbacktex","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":2,"Hidden":true,"Custom":"feedbackStrength","Exposed":"feedbackstrength","Value":{"Float":0.4517534673213959},"Init":{"Float":1.5},"Domain":{"Float":{"Min":0.0,"Max":5.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":3,"Hidden":true,"Custom":"baseColor","Exposed":"basecolor","Value":{"Float":0.22228124737739563},"Init":{"Float":0.15000000596046448},"Domain":{"Float":{"Min":0.0,"Max":1.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":4,"Hidden":true,"Custom":"projectionScale","Exposed":"projectionscale","Value":{"Float":0.10999999940395355},"Init":{"Float":0.18000000715255737},"Domain":{"Float":{"Min":0.009999999776482582,"Max":1.0}}}],"Outlets":[{"uuid":"848061c5-e8a0-4a13-9985-e8df30ce6d4f","ObjectName":"Outlet","id":0,"Hidden":false,"Custom":"geoOut_out","Exposed":"geoout_out"}]},{"uuid":"dbfc2101-40d7-4807-8804-571e88992e7e","ObjectName":"gfxProcess","id":2,"Metadata":{"ScriptingName":"SphereSplat","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[-354.86,-243.38599999999997],"Size":[656.0,176.0],"Loops":false,"FoldMode":0,"Vertex":"// Reconstruct a lookAt view matrix from eye position looking at the origin.\nmat4 buildLookAt(vec3 eye, vec3 center, vec3 up)\n{\n  vec3 f = normalize(center - eye);\n  vec3 s = normalize(cross(f, up));\n  vec3 u = cross(s, f);\n\n  mat4 m = mat4(1.0);\n  m[0][0] =  s.x; 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Input: the instanced sprite topology from PointsToSprites or PopToSprites (6 vertices per particle). Phong lighting: one white light toward lightDir, ambient floor, a specular highlight (specularStrength, sharper with higher shininess). Particles with colour alpha 0 (the library's dead mark) are not drawn; alpha between 0 and 1 makes the sphere translucent (straight alpha; no sorting). Its own camera: cameraPos looking at the origin, fov in degrees.\",\n  \"CREDIT\": \"ossia score\",\n  \"ISFVSN\": \"2\",\n  \"MODE\": \"RAW_RASTER_PIPELINE\",\n  \"ALPHA\": \"straight\",\n  \"CATEGORIES\": [\n    \"Renderer\"\n  ],\n  \"VERTEX_INPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"position\" },\n    { \"TYPE\": \"vec4\", \"NAME\": \"color\" },\n    { \"TYPE\": \"vec4\", \"NAME\": \"sprite_size\" }\n  ],\n  \"VERTEX_OUTPUTS\": [\n    { \"TYPE\": \"vec3\", \"NAME\": \"v_worldPos\" },\n    { \"TYPE\": \"vec3\", \"NAME\": \"v_sphereCenter\" },\n    { \"TYPE\": \"float\", \"NAME\": \"v_sphereRadius\" },\n    { \"TYPE\": \"vec4\", \"NAME\": \"v_color\" }\n  ],\n  \"FRAGMENT_INPUTS\": [\n    { \"TYPE\": \"vec3\", \"NAME\": \"v_worldPos\" },\n    { \"TYPE\": \"vec3\", \"NAME\": \"v_sphereCenter\" },\n    { \"TYPE\": \"float\", \"NAME\": \"v_sphereRadius\" },\n    { \"TYPE\": \"vec4\", \"NAME\": \"v_color\" }\n  ],\n  \"FRAGMENT_OUTPUTS\": [\n    { \"TYPE\": \"vec4\", \"NAME\": \"isf_FragColor\" }\n  ],\n  \"INPUTS\": [\n    {\n      \"NAME\": \"lightDir\",\n      \"TYPE\": \"point3D\",\n      \"DEFAULT\": [0.3, 0.7, 0.5]\n    },\n    {\n      \"NAME\": \"ambient\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.2,\n      \"MIN\": 0.0,\n      \"MAX\": 1.0\n    },\n    {\n      \"NAME\": \"shininess\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 64.0,\n      \"MIN\": 1.0,\n      \"MAX\": 256.0\n    },\n    {\n      \"NAME\": \"specularStrength\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.5,\n      \"MIN\": 0.0,\n      \"MAX\": 2.0\n    },\n    {\n      \"NAME\": \"cameraPos\",\n      \"TYPE\": \"point3D\",\n      \"DEFAULT\": [2.0, 2.0, 2.0]\n    },\n    {\n      \"NAME\": \"fov\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 45.0,\n      \"MIN\": 5.0,\n      \"MAX\": 120.0\n    },\n    {\n      \"NAME\": \"nearPlane\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.01,\n      \"MIN\": 0.001,\n      \"MAX\": 1.0\n    },\n    {\n      \"NAME\": \"farPlane\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 100.0,\n      \"MIN\": 1.0,\n      \"MAX\": 1000.0\n    }\n  ]\n}*/\n\n// Reconstruct the same view and projection matrices as the vertex shader\n// so we can write correct depth for ray-sphere hit points.\nmat4 buildLookAt(vec3 eye, vec3 center, vec3 up)\n{\n  vec3 f = normalize(center - eye);\n  vec3 s = normalize(cross(f, up));\n  vec3 u = cross(s, f);\n\n  mat4 m = mat4(1.0);\n  m[0][0] =  s.x; m[1][0] =  s.y; m[2][0] =  s.z; m[3][0] = -dot(s, eye);\n  m[0][1] =  u.x; m[1][1] =  u.y; m[2][1] =  u.z; m[3][1] = -dot(u, eye);\n  m[0][2] = -f.x; m[1][2] = -f.y; m[2][2] = -f.z; m[3][2] =  dot(f, eye);\n  m[0][3] = 0.0;  m[1][3] = 0.0;  m[2][3] = 0.0;  m[3][3] = 1.0;\n  return m;\n}\n\nmat4 buildPerspective(float fovDeg, float aspect, float zNear, float zFar)\n{\n  float tanHalf = tan(radians(fovDeg) * 0.5);\n  mat4 m = mat4(0.0);\n  m[0][0] = 1.0 / (aspect * tanHalf);\n  m[1][1] = 1.0 / tanHalf;\n  // Reverse-Z (score project rule, CameraMath.hpp setReverseZPerspective):\n  // near -> NDC +1, far -> -1, paired with the engine's GREATER depth compare.\n  m[2][2] = (zFar + zNear) / (zFar - zNear);\n  m[2][3] = -1.0;\n  m[3][2] = (2.0 * zFar * zNear) / (zFar - zNear);\n  return m;\n}\n\nvoid main()\n{\n  // Alpha 0 is the library's 'dead particle' mark (KillByAttribute,\n  // SurfaceConstrain Kill, POP death). 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","AutomaticThumbnail":true}],"Version":5,"Commit":"5bbabca73a6a56659aeedfb7129e6de35e65ba7c","Tag":"3.8.2"}