{"Document":{"ObjectName":"Scenario::ScenarioDocumentModel","id":1,"BaseScenario":{"ObjectName":"Scenario::BaseScenario","id":0,"Constraint":{"ObjectName":"Scenario::IntervalModel","id":0,"Metadata":{"ScriptingName":"volumetric-textures","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Inlet":{"uuid":"a1574bb0-cbd4-4c7d-9417-0c25cfd1187b","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"Audio In","Exposed":"audio in"},"Outlet":{"uuid":"a1d97535-18ac-444a-8417-0cbc1692d897","ObjectName":"Outlet","id":0,"Hidden":false,"Custom":"Audio Out","Exposed":"audio out","Address":"audio:/out/main","GainInlet":{"uuid":"9a13fb32-269a-47bf-99a9-930188c1f19c","ObjectName":"Inlet","id":10000,"Hidden":false,"Custom":"Gain","Exposed":"gain","Value":{},"Init":{},"Domain":{"Float":{"Min":0.0,"Max":1.0}}},"PanInlet":{"uuid":"9a13fb32-269a-47bf-99a9-930188c1f19c","ObjectName":"Inlet","id":10001,"Hidden":false,"Custom":"Pan","Exposed":"pan","Value":{},"Init":{},"Domain":{}},"Gain":1.0,"Pan":[1.0,1.0],"Propagate":true},"Processes":[{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":3,"Metadata":{"ScriptingName":"3d-slice-viewer","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[209.00499585047578,-257.2780774548406],"Size":[160.0,55.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n \"DESCRIPTION\": \"Displays a slice of a 3D volume texture. Connect a 3D texture producer (e.g. '3d-generate-volume') to the 'volume' input. Use 'sliceZ' to scrub through depth slices. Should show a circle cross-section that grows then shrinks as sliceZ goes 0->0.5->1.\",\n \"CREDIT\": \"test\",\n \"ISFVSN\": \"2.0\",\n \"CATEGORIES\": [\"TEST-3D\"],\n \"INPUTS\": [\n { \"NAME\": \"volume\", \"TYPE\": \"image\", \"DIMENSIONS\": 3 },\n { \"NAME\": \"sliceZ\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 1.0 }\n ]\n}*/\n\nvoid main(void)\n{\n vec2 uv = isf_FragNormCoord;\n gl_FragColor = texture(volume, vec3(uv, sliceZ));\n}\n","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"volume","Exposed":"volume","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"sliceZ","Exposed":"slicez","Value":{"Float":0.14166666567325592},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":1.0}}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":5,"Metadata":{"ScriptingName":"Grid","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[591.14,-177.38599999999997],"Size":[253.0,320.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n \"CATEGORIES\": [\n \"Utility\"\n ],\n \"CREDIT\": \"ossia score\",\n \"ISFVSN\": \"2\",\n \"DESCRIPTION\": \"Display up to 16 image inputs in a mosaic grid for previsualization. The grid layout adapts automatically to the number of active inputs.\",\n \"INPUTS\": [\n { \"NAME\": \"t0\", \"LABEL\": \"Texture 1\", \"TYPE\": \"image\" },\n { \"NAME\": \"t1\", \"LABEL\": \"Texture 2\", \"TYPE\": \"image\" },\n { \"NAME\": \"t2\", \"LABEL\": \"Texture 3\", \"TYPE\": \"image\" },\n { \"NAME\": \"t3\", \"LABEL\": \"Texture 4\", \"TYPE\": \"image\" },\n { \"NAME\": \"t4\", \"LABEL\": \"Texture 5\", \"TYPE\": \"image\" },\n { \"NAME\": \"t5\", \"LABEL\": \"Texture 6\", \"TYPE\": \"image\" },\n { \"NAME\": \"t6\", \"LABEL\": \"Texture 7\", \"TYPE\": \"image\" },\n { \"NAME\": \"t7\", \"LABEL\": \"Texture 8\", \"TYPE\": \"image\" },\n { \"NAME\": \"t8\", \"LABEL\": \"Texture 9\", \"TYPE\": \"image\" },\n { \"NAME\": \"t9\", \"LABEL\": \"Texture 10\", \"TYPE\": \"image\" },\n { \"NAME\": \"t10\", \"LABEL\": \"Texture 11\", \"TYPE\": \"image\" },\n { \"NAME\": \"t11\", \"LABEL\": \"Texture 12\", \"TYPE\": \"image\" },\n { \"NAME\": \"t12\", \"LABEL\": \"Texture 13\", \"TYPE\": \"image\" },\n { \"NAME\": \"t13\", \"LABEL\": \"Texture 14\", \"TYPE\": \"image\" },\n { \"NAME\": \"t14\", \"LABEL\": \"Texture 15\", \"TYPE\": \"image\" },\n { \"NAME\": \"t15\", \"LABEL\": \"Texture 16\", \"TYPE\": \"image\" },\n {\n \"NAME\": \"arrangement\",\n \"LABEL\": \"Layout\",\n \"TYPE\": \"long\",\n \"DEFAULT\": 0,\n \"VALUES\": [0, 1, 2],\n \"LABELS\": [\"Grid\", \"Row\", \"Column\"]\n },\n {\n \"NAME\": \"count\",\n \"LABEL\": \"Input Count\",\n \"TYPE\": \"long\",\n \"DEFAULT\": 4,\n \"VALUES\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n },\n {\n \"NAME\": \"gap\",\n \"LABEL\": \"Gap\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 0.005,\n \"MIN\": 0.0,\n \"MAX\": 0.05\n },\n {\n \"NAME\": \"bgColor\",\n \"LABEL\": \"Background\",\n \"TYPE\": \"color\",\n \"DEFAULT\": [0.1, 0.1, 0.1, 1.0]\n }\n ]\n}*/\n\nvec4 sampleTexture(int idx, vec2 uv) {\n switch(idx) {\n case 0: return IMG_NORM_PIXEL(t0, uv);\n case 1: return IMG_NORM_PIXEL(t1, uv);\n case 2: return IMG_NORM_PIXEL(t2, uv);\n case 3: return IMG_NORM_PIXEL(t3, uv);\n case 4: return IMG_NORM_PIXEL(t4, uv);\n case 5: return IMG_NORM_PIXEL(t5, uv);\n case 6: return IMG_NORM_PIXEL(t6, uv);\n case 7: return IMG_NORM_PIXEL(t7, uv);\n case 8: return IMG_NORM_PIXEL(t8, uv);\n case 9: return IMG_NORM_PIXEL(t9, uv);\n case 10: return IMG_NORM_PIXEL(t10, uv);\n case 11: return IMG_NORM_PIXEL(t11, uv);\n case 12: return IMG_NORM_PIXEL(t12, uv);\n case 13: return IMG_NORM_PIXEL(t13, uv);\n case 14: return IMG_NORM_PIXEL(t14, uv);\n case 15: return IMG_NORM_PIXEL(t15, uv);\n default: return bgColor;\n }\n}\n\nvoid main() {\n vec2 uv = isf_FragNormCoord;\n int n = int(count);\n\n int mode = int(arrangement);\n\n // Grid dimensions based on layout mode\n int cols, rows;\n if (mode == 1) {\n // Row: all side by side horizontally\n cols = n; rows = 1;\n } else if (mode == 2) {\n // Column: all stacked vertically\n cols = 1; rows = n;\n } else {\n // Grid: as square as possible\n if (n <= 1) { cols = 1; rows = 1; }\n else if (n <= 2) { cols = 2; rows = 1; }\n else if (n <= 4) { cols = 2; rows = 2; }\n else if (n <= 6) { cols = 3; rows = 2; }\n else if (n <= 9) { cols = 3; rows = 3; }\n else if (n <= 12) { cols = 4; rows = 3; }\n else { cols = 4; rows = 4; }\n }\n\n // Flip Y so row 0 is at the top (reading order: left-to-right, top-to-bottom)\n float flippedY = 1.0 - uv.y;\n\n // Determine which cell we're in\n int col = int(floor(uv.x * float(cols)));\n int row = int(floor(flippedY * float(rows)));\n\n // Clamp to valid range (handles edge at uv = 1.0)\n col = min(col, cols - 1);\n row = min(row, rows - 1);\n\n // Texture index in reading order\n int idx = row * cols + col;\n\n // Beyond the active count: show background\n if (idx >= n) {\n gl_FragColor = bgColor;\n return;\n }\n\n // Local coordinates within the cell [0..1]\n float localU = fract(uv.x * float(cols));\n float localV = fract(flippedY * float(rows));\n\n // Gap as border around each cell (in cell-local coordinates)\n float gapU = gap * float(cols) * 0.5;\n float gapV = gap * float(rows) * 0.5;\n\n if (localU < gapU || localU > 1.0 - gapU ||\n localV < gapV || localV > 1.0 - gapV) {\n gl_FragColor = bgColor;\n return;\n }\n\n // Remap to [0..1] excluding gap, flip V back for correct texture orientation\n vec2 cellUV = vec2(\n (localU - gapU) / (1.0 - 2.0 * gapU),\n 1.0 - (localV - gapV) / (1.0 - 2.0 * gapV)\n );\n\n gl_FragColor = sampleTexture(idx, cellUV);\n}\n","Root":":packages/default/Presets/GLSL_shaders/utility/Grid.fs","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"t0","Exposed":"t0","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":1,"Hidden":false,"Custom":"t1","Exposed":"t1","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":2,"Hidden":false,"Custom":"t2","Exposed":"t2","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":3,"Hidden":false,"Custom":"t3","Exposed":"t3","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":4,"Hidden":false,"Custom":"t4","Exposed":"t4","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":5,"Hidden":false,"Custom":"t5","Exposed":"t5","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":6,"Hidden":false,"Custom":"t6","Exposed":"t6","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":7,"Hidden":false,"Custom":"t7","Exposed":"t7","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":8,"Hidden":false,"Custom":"t8","Exposed":"t8","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":9,"Hidden":false,"Custom":"t9","Exposed":"t9","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":10,"Hidden":false,"Custom":"t10","Exposed":"t10","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":11,"Hidden":false,"Custom":"t11","Exposed":"t11","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":12,"Hidden":false,"Custom":"t12","Exposed":"t12","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":13,"Hidden":false,"Custom":"t13","Exposed":"t13","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":14,"Hidden":false,"Custom":"t14","Exposed":"t14","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":15,"Hidden":false,"Custom":"t15","Exposed":"t15","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"485680cc-b8b9-4a01-acc7-3e8334bdc016","ObjectName":"Inlet","id":16,"Hidden":true,"Custom":"arrangement","Exposed":"arrangement","Value":{"Int":0},"Init":{"Int":0},"Domain":{"Int":{"Values":[0,1,2]}},"Values":[["Grid",{"Int":0}],["Row",{"Int":1}],["Column",{"Int":2}]],"FolderPort":"","FileExtensions":""},{"uuid":"485680cc-b8b9-4a01-acc7-3e8334bdc016","ObjectName":"Inlet","id":17,"Hidden":true,"Custom":"count","Exposed":"count","Value":{"Int":6},"Init":{"Int":4},"Domain":{"Int":{"Values":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},"Values":[["1",{"Int":1}],["2",{"Int":2}],["3",{"Int":3}],["4",{"Int":4}],["5",{"Int":5}],["6",{"Int":6}],["7",{"Int":7}],["8",{"Int":8}],["9",{"Int":9}],["10",{"Int":10}],["11",{"Int":11}],["12",{"Int":12}],["13",{"Int":13}],["14",{"Int":14}],["15",{"Int":15}],["16",{"Int":16}]],"FolderPort":"","FileExtensions":""},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":18,"Hidden":true,"Custom":"gap","Exposed":"gap","Value":{"Float":0.004999999888241291},"Init":{"Float":0.004999999888241291},"Domain":{"Float":{"Min":0.0,"Max":0.05000000074505806}}},{"uuid":"8f38638e-9f9f-48b0-ae36-1cba86ef5703","ObjectName":"Inlet","id":19,"Hidden":true,"Custom":"bgColor","Exposed":"bgcolor","Value":{"Vec4f":[0.10000000149011612,0.10000000149011612,0.10000000149011612,1.0]},"Init":{"Vec4f":[0.10000000149011612,0.10000000149011612,0.10000000149011612,1.0]},"Domain":{}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out","Address":"Window:/"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":6,"Metadata":{"ScriptingName":"3d-slice-viewer.1","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[201.2887468878568,-166.3455795296028],"Size":[168.0,55.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n \"DESCRIPTION\": \"Displays a slice of a 3D volume texture. Connect a 3D texture producer (e.g. '3d-generate-volume') to the 'volume' input. Use 'sliceZ' to scrub through depth slices. Should show a circle cross-section that grows then shrinks as sliceZ goes 0->0.5->1.\",\n \"CREDIT\": \"test\",\n \"ISFVSN\": \"2.0\",\n \"CATEGORIES\": [\"TEST-3D\"],\n \"INPUTS\": [\n { \"NAME\": \"volume\", \"TYPE\": \"image\", \"DIMENSIONS\": 3 },\n { \"NAME\": \"sliceZ\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 1.0 }\n ]\n}*/\n\nvoid main(void)\n{\n vec2 uv = isf_FragNormCoord;\n gl_FragColor = texture(volume, vec3(uv, sliceZ));\n}\n","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"volume","Exposed":"volume","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"sliceZ","Exposed":"slicez","Value":{"Float":0.2666666805744171},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":1.0}}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":7,"Metadata":{"ScriptingName":"3d-slice-viewer.2","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[203.28874688785686,-76.07557123055437],"Size":[168.0,55.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n \"DESCRIPTION\": \"Displays a slice of a 3D volume texture. Connect a 3D texture producer (e.g. '3d-generate-volume') to the 'volume' input. Use 'sliceZ' to scrub through depth slices. Should show a circle cross-section that grows then shrinks as sliceZ goes 0->0.5->1.\",\n \"CREDIT\": \"test\",\n \"ISFVSN\": \"2.0\",\n \"CATEGORIES\": [\"TEST-3D\"],\n \"INPUTS\": [\n { \"NAME\": \"volume\", \"TYPE\": \"image\", \"DIMENSIONS\": 3 },\n { \"NAME\": \"sliceZ\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 1.0 }\n ]\n}*/\n\nvoid main(void)\n{\n vec2 uv = isf_FragNormCoord;\n gl_FragColor = texture(volume, vec3(uv, sliceZ));\n}\n","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"volume","Exposed":"volume","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"sliceZ","Exposed":"slicez","Value":{"Float":0.75},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":1.0}}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":9,"Metadata":{"ScriptingName":"18-06_orbit_raymarch","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[-48.170832641745974,282.88521991594956],"Size":[432.0,252.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n \"DESCRIPTION\": \"Free-camera emission-absorption raymarch of a 3D volume inside a unit cube (front-to-back compositing, Beer-Lambert opacity per step), with ray-start jitter to hide step banding, tint, an ambient colour added to every sample in proportion to its opacity, brightness and gamma. The output is premultiplied (colour already weighted by the accumulated opacity) and transparent outside the volume, so it composites over whatever lies under it. Density is read from the alpha channel by default; set Density from to Luminance or a single channel for scalar volumes that store their field in RGB with alpha = 1 (most of the 01-05 generators), and raise Threshold to cut away low-density fog. Spin orbits the camera over time. This is the general-purpose viewer \\u2014 use it first before reaching for 18-04 or 18-08. Volumes store premultiplied colour: with Density from = Alpha each sample's colour is unpremultiplied (divided by its alpha) before it is weighted by the step opacity, so thin fog keeps its colour instead of being darkened twice; the other modes treat RGB as a field and use it as stored.\",\n \"CREDIT\": \"ossia 3D catalog\",\n \"ISFVSN\": \"2.0\",\n \"ALPHA\": \"premultiplied\",\n \"CATEGORIES\": [\n \"3D-VIEWER\"\n ],\n \"INPUTS\": [\n {\n \"NAME\": \"volume\",\n \"TYPE\": \"image\",\n \"DIMENSIONS\": 3,\n \"LABEL\": \"Volume\"\n },\n {\n \"NAME\": \"yaw\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 0.6,\n \"MIN\": -3.14,\n \"MAX\": 3.14,\n \"LABEL\": \"Yaw\"\n },\n {\n \"NAME\": \"pitch\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 0.3,\n \"MIN\": -1.5,\n \"MAX\": 1.5,\n \"LABEL\": \"Pitch\"\n },\n {\n \"NAME\": \"zoom\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 2.5,\n \"MIN\": 1.2,\n \"MAX\": 6.0,\n \"LABEL\": \"Zoom\"\n },\n {\n \"NAME\": \"steps\",\n \"TYPE\": \"long\",\n \"DEFAULT\": 128,\n \"MIN\": 32,\n \"MAX\": 256,\n \"LABEL\": \"Steps\"\n },\n {\n \"NAME\": \"density\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 3.0,\n \"MIN\": 0.1,\n \"MAX\": 16.0,\n \"LABEL\": \"Density\"\n },\n {\n \"NAME\": \"brightness\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 1.0,\n \"MIN\": 0.0,\n \"MAX\": 4.0,\n \"LABEL\": \"Brightness\"\n },\n {\n \"NAME\": \"gamma\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 1.0,\n \"MIN\": 0.4,\n \"MAX\": 2.4,\n \"LABEL\": \"Gamma\"\n },\n {\n \"NAME\": \"tint\",\n \"TYPE\": \"color\",\n \"DEFAULT\": [\n 1.0,\n 1.0,\n 1.0,\n 1.0\n ],\n \"LABEL\": \"Tint\"\n },\n {\n \"NAME\": \"jitter\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 1.0,\n \"MIN\": 0.0,\n \"MAX\": 1.0,\n \"LABEL\": \"Dither strength\"\n },\n {\n \"NAME\": \"densityFrom\",\n \"TYPE\": \"long\",\n \"DEFAULT\": 0,\n \"VALUES\": [0, 1, 2, 3, 4],\n \"LABELS\": [\"Alpha\", \"Luminance\", \"Red\", \"Green\", \"Blue\"],\n \"LABEL\": \"Density from\"\n },\n {\n \"NAME\": \"threshold\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 0.0,\n \"MIN\": 0.0,\n \"MAX\": 0.95,\n \"LABEL\": \"Threshold\"\n },\n {\n \"NAME\": \"spin\",\n \"TYPE\": \"float\",\n \"DEFAULT\": 0.0,\n \"MIN\": -2.0,\n \"MAX\": 2.0,\n \"LABEL\": \"Spin (rad/s)\"\n },\n {\n \"NAME\": \"ambient\",\n \"TYPE\": \"color\",\n \"DEFAULT\": [\n 0.08,\n 0.1,\n 0.15,\n 1.0\n ],\n \"LABEL\": \"Ambient\"\n }\n ]\n}*/\n\n// Ray-box intersection against unit cube centered at origin\nvec2 rayBox(vec3 ro, vec3 rd){\n vec3 inv = 1.0 / rd;\n vec3 t1 = (-vec3(0.5) - ro) * inv;\n vec3 t2 = ( vec3(0.5) - ro) * inv;\n vec3 tn = min(t1, t2);\n vec3 tf = max(t1, t2);\n return vec2(max(max(tn.x, tn.y), tn.z), min(min(tf.x, tf.y), tf.z));\n}\n\n// Cheap hash-based dither for ray-offset noise (kills stepping banding)\nfloat hash12(vec2 p){\n vec3 p3 = fract(vec3(p.xyx) * 0.1031);\n p3 += dot(p3, p3.yzx + 33.33);\n return fract((p3.x + p3.y) * p3.z);\n}\n\nvoid main()\n{\n vec2 uv = isf_FragNormCoord * 2.0 - 1.0;\n uv.x *= RENDERSIZE.x / RENDERSIZE.y;\n // Camera matrix\n float yw = yaw + spin * TIME;\n vec3 ro = zoom * vec3(cos(pitch)*cos(yw), sin(pitch), cos(pitch)*sin(yw));\n vec3 fwd = -normalize(ro);\n // right = fwd x up: with up x fwd the screen basis is left-handed and\n // the picture comes out mirrored left-right.\n vec3 right = normalize(cross(fwd, vec3(0.0, 1.0, 0.0)));\n vec3 up = cross(right, fwd);\n vec3 rd = normalize(uv.x*right + uv.y*up + 1.5*fwd);\n vec2 tnf = rayBox(ro, rd);\n tnf.x = max(tnf.x, 0.0);\n if(tnf.x >= tnf.y){ isf_FragColor = vec4(0.0); return; }\n\n int N = int(steps);\n float dt = (tnf.y - tnf.x) / float(N);\n // Dither the starting offset by [0,1) voxel so neighbouring rays don't\n // sample the same planes — kills banding at low step counts.\n float jit = jitter * hash12(isf_FragCoord.xy + FRAMEINDEX * 0.0131);\n vec3 acc = vec3(0.0);\n float trans = 1.0;\n for(int i = 0; i < N; i++){\n vec3 wp = ro + rd*(tnf.x + (float(i) + jit) * dt);\n vec3 uvw = wp + 0.5;\n vec4 s = texture(volume, uvw);\n float d = (densityFrom == 0) ? s.a\n : (densityFrom == 1) ? dot(s.rgb, vec3(0.2126, 0.7152, 0.0722))\n : (densityFrom == 2) ? s.r\n : (densityFrom == 3) ? s.g\n : s.b;\n d = max(d - threshold, 0.0) / (1.0 - threshold);\n float a = clamp(d * density * dt, 0.0, 1.0);\n vec3 c = (densityFrom == 0 && s.a > 0.0) ? s.rgb / s.a : s.rgb;\n acc += trans * (c * tint.rgb + ambient.rgb) * a;\n trans *= (1.0 - a);\n if(trans < 0.002) break;\n }\n acc *= brightness;\n acc = pow(max(acc, 0.0), vec3(1.0 / max(gamma, 0.01)));\n isf_FragColor = vec4(acc, 1.0 - trans);\n}\n","Root":":packages/score-csf-testers/shaderlib/volume/18-viewers/18-06_orbit_raymarch.fs","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"volume","Exposed":"volume","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"yaw","Exposed":"yaw","Value":{"Float":0.6000000238418579},"Init":{"Float":0.6000000238418579},"Domain":{"Float":{"Min":-3.140000104904175,"Max":3.140000104904175}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":2,"Hidden":true,"Custom":"pitch","Exposed":"pitch","Value":{"Float":0.30000001192092896},"Init":{"Float":0.30000001192092896},"Domain":{"Float":{"Min":-1.5,"Max":1.5}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":3,"Hidden":true,"Custom":"zoom","Exposed":"zoom","Value":{"Float":2.5},"Init":{"Float":2.5},"Domain":{"Float":{"Min":1.2000000476837158,"Max":6.0}}},{"uuid":"238399a0-7e81-47e3-896f-08e8856e2973","ObjectName":"Inlet","id":4,"Hidden":true,"Custom":"steps","Exposed":"steps","Value":{"Int":128},"Init":{"Int":128},"Domain":{"Int":{"Min":32,"Max":256}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":5,"Hidden":true,"Custom":"density","Exposed":"density","Value":{"Float":3.0},"Init":{"Float":3.0},"Domain":{"Float":{"Min":0.10000000149011612,"Max":16.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":6,"Hidden":true,"Custom":"brightness","Exposed":"brightness","Value":{"Float":2.2666666507720947},"Init":{"Float":1.0},"Domain":{"Float":{"Min":0.0,"Max":4.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":7,"Hidden":true,"Custom":"gamma","Exposed":"gamma","Value":{"Float":1.0},"Init":{"Float":1.0},"Domain":{"Float":{"Min":0.4000000059604645,"Max":2.4000000953674316}}},{"uuid":"8f38638e-9f9f-48b0-ae36-1cba86ef5703","ObjectName":"Inlet","id":8,"Hidden":true,"Custom":"tint","Exposed":"tint","Value":{"Vec4f":[1.0,1.0,1.0,1.0]},"Init":{"Vec4f":[1.0,1.0,1.0,1.0]},"Domain":{}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":9,"Hidden":true,"Custom":"jitter","Exposed":"jitter","Value":{"Float":1.0},"Init":{"Float":1.0},"Domain":{"Float":{"Min":0.0,"Max":1.0}}},{"uuid":"485680cc-b8b9-4a01-acc7-3e8334bdc016","ObjectName":"Inlet","id":10,"Hidden":true,"Custom":"densityFrom","Exposed":"densityfrom","Value":{"Int":0},"Init":{"Int":0},"Domain":{"Int":{"Values":[0,1,2,3,4]}},"Values":[["Alpha",{"Int":0}],["Luminance",{"Int":1}],["Red",{"Int":2}],["Green",{"Int":3}],["Blue",{"Int":4}]],"FolderPort":"","FileExtensions":""},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":11,"Hidden":true,"Custom":"threshold","Exposed":"threshold","Value":{"Float":0.0},"Init":{"Float":0.0},"Domain":{"Float":{"Min":0.0,"Max":0.949999988079071}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":12,"Hidden":true,"Custom":"spin","Exposed":"spin","Value":{"Float":1.0},"Init":{"Float":0.0},"Domain":{"Float":{"Min":-2.0,"Max":2.0}}},{"uuid":"8f38638e-9f9f-48b0-ae36-1cba86ef5703","ObjectName":"Inlet","id":13,"Hidden":true,"Custom":"ambient","Exposed":"ambient","Value":{"Vec4f":[0.07999999821186066,0.10000000149011612,0.15000000596046448,1.0]},"Init":{"Vec4f":[0.07999999821186066,0.10000000149011612,0.15000000596046448,1.0]},"Domain":{}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":11,"Metadata":{"ScriptingName":"18-10_orbit_iso_raymarch","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[-84.06331396888706,572.128146610633],"Size":[466.0,327.0],"Loops":false,"FoldMode":2,"Vertex":"","Fragment":"/*{\n \"DESCRIPTION\": \"Free-camera isosurface raycaster for a 3D volume inside a unit cube. Each ray is clipped to the cube (slab test), marched at a fixed step until the chosen density channel crosses Iso, the hit is refined by linear interpolation between the two bracketing samples plus two bisection steps (Hadwiger et al. 2005), and shaded with a Lambert key light plus a hemispherical fill, using the normalised central-difference gradient of the field as the normal (Levoy 1988). Surface colour is Tint, the volume's own RGB at the hit, or the RGB of an optional second Colour volume at the hit (so a surface taken from one field can be painted by another, e.g. 09-11 axis ramp or 08-14 chromatic split of the same source). Invert makes the solid the region below Iso (for distance-like fields such as Worley F1), Clip X cuts the volume open to show nested structure. Show box draws the cube's silhouette edges for orientation. The 3D counterpart of 18-05, which only looks down +Z. Volumes store premultiplied colour, so Volume RGB and Colour volume RGB are unpremultiplied (divided by the alpha at the hit) to give the surface its straight colour; a volume with alpha 1 is used as is.\",\n \"CREDIT\": \"ossia 3D catalog. M. Levoy, Display of Surfaces from Volume Data, IEEE CG&A 8(3), 1988; M. Hadwiger, C. Sigg, H. Scharsach, K. Buehler, M. Gross, Real-Time Ray-Casting and Advanced Shading of Discrete Isosurfaces, Eurographics 2005; T. Kay, J. Kajiya, Ray Tracing Complex Scenes (slab test), SIGGRAPH 1986.\",\n \"ISFVSN\": \"2.0\",\n \"CATEGORIES\": [ \"3D-VIEWER\" ],\n \"INPUTS\": [\n { \"NAME\": \"volume\", \"TYPE\": \"image\", \"DIMENSIONS\": 3, \"LABEL\": \"Volume\" },\n { \"NAME\": \"colorVolume\", \"TYPE\": \"image\", \"DIMENSIONS\": 3, \"LABEL\": \"Colour volume (optional)\" },\n { \"NAME\": \"iso\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": 0.0, \"MAX\": 1.0, \"LABEL\": \"Iso value\" },\n { \"NAME\": \"densityFrom\", \"TYPE\": \"long\", \"DEFAULT\": 2, \"VALUES\": [0, 1, 2, 3, 4], \"LABELS\": [\"Alpha\", \"Luminance\", \"Red\", \"Green\", \"Blue\"], \"LABEL\": \"Density from\" },\n { \"NAME\": \"invert\", \"TYPE\": \"bool\", \"DEFAULT\": false, \"LABEL\": \"Invert (solid below Iso)\" },\n { \"NAME\": \"clipX\", \"TYPE\": \"float\", \"DEFAULT\": 0.5, \"MIN\": -0.5, \"MAX\": 0.5, \"LABEL\": \"Clip X (cut away x > this)\" },\n { \"NAME\": \"yaw\", \"TYPE\": \"float\", \"DEFAULT\": 0.6, \"MIN\": -3.14, \"MAX\": 3.14, \"LABEL\": \"Yaw\" },\n { \"NAME\": \"pitch\", \"TYPE\": \"float\", \"DEFAULT\": 0.35, \"MIN\": -1.5, \"MAX\": 1.5, \"LABEL\": \"Pitch\" },\n { \"NAME\": \"zoom\", \"TYPE\": \"float\", \"DEFAULT\": 1.8, \"MIN\": 0.9, \"MAX\": 6.0, \"LABEL\": \"Distance\" },\n { \"NAME\": \"spin\", \"TYPE\": \"float\", \"DEFAULT\": 0.0, \"MIN\": -2.0, \"MAX\": 2.0, \"LABEL\": \"Spin (rad/s)\" },\n { \"NAME\": \"steps\", \"TYPE\": \"long\", \"DEFAULT\": 192, \"MIN\": 32, \"MAX\": 512, \"LABEL\": \"Steps\" },\n { \"NAME\": \"lightDir\", \"TYPE\": \"point3D\", \"DEFAULT\": [0.5, 0.8, 0.3], \"LABEL\": \"Light direction (world)\" },\n { \"NAME\": \"colorFrom\", \"TYPE\": \"long\", \"DEFAULT\": 0, \"VALUES\": [0, 1, 2], \"LABELS\": [\"Tint\", \"Volume RGB\", \"Colour volume RGB\"], \"LABEL\": \"Surface colour\" },\n { \"NAME\": \"tint\", \"TYPE\": \"color\", \"DEFAULT\": [0.95, 0.78, 0.5, 1.0], \"LABEL\": \"Tint\" },\n { \"NAME\": \"background\", \"TYPE\": \"color\", \"DEFAULT\": [0.02, 0.025, 0.035, 1.0], \"LABEL\": \"Background\" },\n { \"NAME\": \"showBox\", \"TYPE\": \"bool\", \"DEFAULT\": true, \"LABEL\": \"Show box\" }\n ]\n}*/\n\nvec3 straightColor(vec4 s) { return s.a > 0.0 ? s.rgb / s.a : s.rgb; }\n\n// Slab test against the unit cube centred at the origin.\nvec2 rayBox(vec3 ro, vec3 rd)\n{\n vec3 inv = 1.0 / rd;\n vec3 t1 = (-vec3(0.5) - ro) * inv;\n vec3 t2 = ( vec3(0.5) - ro) * inv;\n vec3 tn = min(t1, t2);\n vec3 tf = max(t1, t2);\n return vec2(max(max(tn.x, tn.y), tn.z), min(min(tf.x, tf.y), tf.z));\n}\n\nfloat field(vec3 wp)\n{\n // Everything past the clip plane is empty, whichever side Invert selects.\n if(wp.x > clipX) return invert ? 1e3 : -1e3;\n vec4 s = texture(volume, wp + 0.5);\n float f = (densityFrom == 0) ? s.a\n : (densityFrom == 1) ? dot(s.rgb, vec3(0.2126, 0.7152, 0.0722))\n : (densityFrom == 2) ? s.r\n : (densityFrom == 3) ? s.g\n : s.b;\n return f;\n}\n\n// Signed so that > 0 is inside the solid.\nfloat inside(vec3 wp) { return invert ? iso - field(wp) : field(wp) - iso; }\n\nvoid main()\n{\n vec2 uv = isf_FragNormCoord * 2.0 - 1.0;\n uv.x *= RENDERSIZE.x / RENDERSIZE.y;\n\n float yw = yaw + spin * TIME;\n vec3 ro = zoom * vec3(cos(pitch) * cos(yw), sin(pitch), cos(pitch) * sin(yw));\n vec3 fwd = -normalize(ro);\n // right = fwd x up: with up x fwd the screen basis is left-handed and\n // the picture comes out mirrored left-right.\n vec3 right = normalize(cross(fwd, vec3(0.0, 1.0, 0.0)));\n vec3 up = cross(right, fwd);\n vec3 rd = normalize(uv.x * right + uv.y * up + 1.5 * fwd);\n\n vec3 col = background.rgb;\n vec2 tnf = rayBox(ro, rd);\n tnf.x = max(tnf.x, 0.0);\n if(tnf.x < tnf.y)\n {\n int N = int(steps);\n float dt = (tnf.y - tnf.x) / float(N);\n float t0 = tnf.x;\n float f0 = inside(ro + rd * t0);\n bool hit = false;\n float th = 0.0;\n // A ray that starts inside the solid shows the cube face as a cut.\n if(f0 > 0.0) { hit = true; th = t0; }\n for(int i = 1; i <= N && !hit; i++)\n {\n float t1 = tnf.x + float(i) * dt;\n float f1 = inside(ro + rd * t1);\n if(f1 > 0.0)\n {\n // Linear estimate, then two bisection refinements.\n float a = t0, b = t1, fa = f0;\n for(int k = 0; k < 2; k++)\n {\n float m = 0.5 * (a + b);\n float fm = inside(ro + rd * m);\n if(fm > 0.0) b = m; else { a = m; fa = fm; }\n }\n float fb = inside(ro + rd * b);\n th = mix(a, b, clamp(-fa / max(fb - fa, 1e-6), 0.0, 1.0));\n hit = true;\n }\n t0 = t1; f0 = f1;\n }\n if(hit)\n {\n vec3 p = ro + rd * th;\n vec3 e = 1.0 / vec3(textureSize(volume, 0));\n vec3 g = vec3(inside(p + vec3(e.x, 0, 0)) - inside(p - vec3(e.x, 0, 0)),\n inside(p + vec3(0, e.y, 0)) - inside(p - vec3(0, e.y, 0)),\n inside(p + vec3(0, 0, e.z)) - inside(p - vec3(0, 0, e.z)));\n // inside() rises into the solid, so the outward normal is -grad.\n vec3 n = -g / max(length(g), 1e-6);\n // On a cut face the field gradient is meaningless: use the face normal.\n if(abs(p.x - clipX) < 1.5 * e.x) n = vec3(1.0, 0.0, 0.0);\n else if(length(g) < 1e-4 || th <= tnf.x + 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","AutomaticThumbnail":true}],"Version":5,"Commit":"5bbabca73a6a56659aeedfb7129e6de35e65ba7c","Tag":"3.8.2"}