From e02a57674fb24274984288cd1a004e88c03c6903 Mon Sep 17 00:00:00 2001
From: Amira Abdel-Rahman <amira-rahman@aucegypt.edu>
Date: Fri, 30 Apr 2021 17:14:11 -0400
Subject: [PATCH] tendon digital twin, space time topology optimization, mgmg
topology optimization
---
demos/indexTendonDigitalTwin.html | 434 +
demos/indexTutorialGraph.html | 17 -
json/tutorial.json | 8552 +------
json/tutorialInit.json | 2 +-
julia/examples/tendonDigitalTwin.jl | 288 +
jupyter/MetaVoxel_Tutorial.ipynb | 27 +-
package-lock.json | 264 +-
package.json | 27 -
tendonDigitalTwin/runSimulation.jl | 96 +
tendonDigitalTwin/serveDigitalTwin copy.js | 81 +
tendonDigitalTwin/serveDigitalTwin.js | 82 +
tendonDigitalTwin/ws.js | 87 +
voxel_designer/Compliant MMA.ipynb | 1804 +-
voxel_designer/Multigrid CG.ipynb | 14133 ++++++++++++
.../Time Space Topology Optimization.ipynb | 19083 ++++++++++++++++
voxel_designer/julia/include/element.jl | 7 +-
voxel_designer/julia/include/mmaSolver.jl | 406 +
voxel_designer/julia/include/problems.jl | 246 +-
.../julia/include/spaceTimeRobot.jl | 405 +
voxel_designer/julia/include/timeSpace.jl | 813 +
voxel_designer/julia/include/top2D.jl | 1 +
voxel_designer/julia/include/top3D.jl | 146 +-
voxel_designer/julia/include/topmgcg.jl | 229 +
voxel_designer/julia/include/utils.jl | 5 +
voxel_designer/tmp.gif | Bin 55907 -> 0 bytes
25 files changed, 39127 insertions(+), 8108 deletions(-)
create mode 100644 demos/indexTendonDigitalTwin.html
create mode 100644 julia/examples/tendonDigitalTwin.jl
delete mode 100644 package.json
create mode 100644 tendonDigitalTwin/runSimulation.jl
create mode 100644 tendonDigitalTwin/serveDigitalTwin copy.js
create mode 100644 tendonDigitalTwin/serveDigitalTwin.js
create mode 100644 tendonDigitalTwin/ws.js
create mode 100644 voxel_designer/Multigrid CG.ipynb
create mode 100644 voxel_designer/Time Space Topology Optimization.ipynb
create mode 100644 voxel_designer/julia/include/mmaSolver.jl
create mode 100644 voxel_designer/julia/include/spaceTimeRobot.jl
create mode 100644 voxel_designer/julia/include/timeSpace.jl
create mode 100644 voxel_designer/julia/include/topmgcg.jl
delete mode 100644 voxel_designer/tmp.gif
diff --git a/demos/indexTendonDigitalTwin.html b/demos/indexTendonDigitalTwin.html
new file mode 100644
index 0000000..3d25c3a
--- /dev/null
+++ b/demos/indexTendonDigitalTwin.html
@@ -0,0 +1,434 @@
+<head>
+ <style> body { margin: 0; } </style>
+ <title>MetaVoxel</title>
+
+ <script src="../lib/dat.gui.js"></script>
+
+ <script src="//unpkg.com/three"></script>
+
+ <script src="../lib/js-colormaps.js"></script>
+
+ <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"></script>
+ <script src="//unpkg.com/3d-force-graph"></script>
+ <!-- <script src="../lib/3d-force-graph.js"></script> -->
+ <script src="//unpkg.com/three-spritetext"></script>
+
+ <script src="../visualization/utils.js"></script>
+ </head>
+
+ <body>
+ <div id="3d-graph"></div>
+
+ <script>
+ var color1= 0xffffff; /*white*/
+ var color2= '#020227'; /*kohly*/
+ var color3= 0x1c5c61; /*teal*/
+ var color4= "#fa6e70"; //red/orange
+ var color5="#380152"; //purple
+ var color6="#696767"; //grey
+ var color7="#03dbfc"; //blue
+
+ // let socket = new WebSocket("ws://localhost:8000");
+ // // The socket connection needs two event listeners:
+ // socket.onopen = openSocket;
+ // function openSocket() {
+ // // text.textContent="Socket open!";
+ // socket.send("2000,2000");
+ // }
+
+
+ var scale,nodeScale;
+ var simName="tutorial"
+
+ let Graph,gData,gui,guis;
+ let maxNumFiles=0;
+
+ var interaction= {
+ exaggeration:1,
+ speed:100
+ }
+ var simulation={
+ simName:"tutorial",
+ load:1,
+ lowerTendon:3000,
+ upperTendon:3000,
+ prevlowerTendon:3000,
+ prevupperTendon:3000,
+ }
+ var calls={
+ 'Load Simulation':function() {
+ loadSimulation(simulation.simName);
+ },
+
+ }
+
+ // fetch("/", {
+ // method: 'POST',
+ // headers: {
+ // 'Content-Type': 'application/json'
+ // },
+ // body: ""
+ // })
+ // .then(response => response.json())
+ // .then(function(response) {
+ // if (response.ok) {
+ // console.log('got data: ', response.data);
+ // }
+ // throw new Error('Request failed.');
+ // })
+ // .catch(function(error) {
+ // console.log(error);
+ // });
+
+
+ // $.get("/", { data : { foo : JSON.stringify(simulation) } }, function(temp) {
+ // // temp === "I am done";
+ // console.log(temp)
+ // });
+
+
+ /////////////////////////////////////////////////////////////////////
+
+ initializeGraph();
+ setInterval(() => {
+ updateGraphDis()
+
+ }, interaction.speed);
+
+ function loadSimulation(simName){
+ console.log("Load: "+simName);
+ simulation.load=1
+ $.post("/", { data : { foo : JSON.stringify(simulation) } }, function(temp) {
+ // temp === "I am done";
+ console.log(temp);
+ });
+ }
+
+ function runSimulation(){
+ simulation.load=0
+ console.log("Run Sim: "+simName);
+ $.post("/", { data : { foo : JSON.stringify(simulation) } }, function(temp) {
+ // temp === "I am done";
+ console.log(temp)
+ });
+
+
+ }
+
+ function initializeGUI(){
+ gui = new dat.GUI();
+
+ guis=[]
+ gui.add(simulation,'simName').listen();
+ gui.add(calls, "Load Simulation");
+
+
+ // guis.push(gui.add(simulation, 'lowerTendon').min(-6000).max(6000).step(1000))
+ // guis.push(gui.add(simulation, 'upperTendon').min(-6000).max(6000).step(1000))
+
+ // guis.forEach(g => {
+ // g.onChange(function(value) {
+ // runSimulation();
+ // });
+ // });
+
+ var gui1=gui.add(simulation, 'lowerTendon').min(-6000).max(6000).step(1000)
+ var gui2=gui.add(simulation, 'upperTendon').min(-6000).max(6000).step(1000)
+
+ gui1.onChange(function(value) {
+ if (value!=simulation.prevlowerTendon){
+ simulation.prevlowerTendon=value;
+ simulation.upperTendon=value;
+ // socket.send(value);
+ runSimulation();
+
+ }
+ });
+ gui2.onChange(function(value) {
+ if (value!=simulation.prevupperTendon){
+ simulation.prevupperTendon=value;
+ runSimulation();
+
+ }
+ });
+
+
+
+ }
+
+ function initializeGraph(){
+ initializeGUI();
+
+ $.getJSON("../json/"+simulation.simName+"/0.json", function(json0) {
+ maxNumFiles=json0.maxNumFiles-1;
+ $.getJSON("../json/"+simulation.simName+"/"+maxNumFiles+".json", function(json) {
+ var setup=json;
+ var static=false
+ setup.viz.colorMaps=[YlGnBu,coolwarm, winter ,jet];
+
+
+
+ if(setup.hierarchical){
+ scale=setup.scale*0.001;
+ nodeScale=scale*10;
+
+ }else{
+ scale=setup.scale*50;
+ nodeScale=scale*2.0;
+ }
+ if (setup.multiscale){
+
+ scale=setup.scale*0.003;
+ nodeScale=scale*10
+
+ }
+
+
+ var stress=0.0;
+
+ gData = {
+ nodes: setup.nodes.map(node => ({
+ // id: node.id,
+ id: (node.parent=="11")?node.id:(node.parent+node.id),
+ px:node.position.x*scale,
+ py:node.position.y*scale,
+ pz:node.position.z*scale,
+ dx:node.displacement.x*scale,
+ dy:node.displacement.y*scale,
+ dz:node.displacement.z*scale,
+ ax:node.angle.x,
+ ay:node.angle.y,
+ az:node.angle.z,
+ restrained:node.restrained_degrees_of_freedom[0],
+ loaded:!(node.force.x==0.0&&node.force.y==0.0&&node.force.z==0.0),
+ displaced:(node.fixedDisplacement===undefined ||(node.fixedDisplacement.x==0&&node.fixedDisplacement.y==0&&node.fixedDisplacement.z==0))?false:true,
+ size:(node.material===undefined) ? nodeScale:(( setup.multiscale===undefined || !setup.multiscale)?(nodeScale*0.8):(node.scale*nodeScale*0.8**(1/node.scale))),
+ opacity: (node.parent=="11"|| setup.multiscale===undefined ||node.parent=="")?0.8:0.0,
+
+ })),
+ links: setup.edges
+ .filter(edge => edge.id)
+ .map(edge => ({
+ // source: 'n'+edge.source,
+ // target: 'n'+edge.target,
+ source: (setup.multiscale===undefined || !setup.multiscale ||edge.sourceNodalCoordinate==0)?'n'+edge.source:('n'+edge.source+'n'+edge.sourceNodalCoordinate),
+ target: (setup.multiscale===undefined || !setup.multiscale ||edge.targetNodalCoordinate==0)?'n'+edge.target:('n'+edge.target+'n'+edge.targetNodalCoordinate),
+
+ stresses:new Array(maxNumFiles).fill(getColor(setup.viz,edge.stress)),
+ stressesVal:new Array(maxNumFiles).fill(edge.stress),
+ color:getColor(setup.viz,edge.stress)
+ }))
+ };
+
+
+ //////////////////////////////////////////////////////////////////////////
+ Graph = ForceGraph3D({ controlType: 'orbit' }).backgroundColor(color2)
+ (document.getElementById('3d-graph'))
+ .d3Force('center', null)
+ .d3Force('charge', null)
+ .linkWidth(1.0*scale)
+ .linkOpacity(1.0)
+ .nodeThreeObject(({ size,restrained,loaded,displaced,opacity }) => new THREE.Mesh(
+ new THREE.BoxGeometry(size, size, size)
+ ,
+ new THREE.MeshLambertMaterial({
+ color: getNodeColor(restrained,loaded,displaced),
+ transparent: opacity>0.0?false:true,
+ opacity: opacity
+ })
+ ))
+ .d3Force('box', () => {
+
+ gData.nodes.forEach(node => {
+ node.fx=node.px;
+ node.fy=node.py;
+ node.fz=node.pz;
+
+ });
+ })
+ .cooldownTime(Infinity)
+ .graphData(gData);
+
+ var position=new THREE.Vector3(500,100,3000);
+ Graph.camera().position.copy( position );
+ // Graph.camera().lookAt( 500, orbit.target.y, orbit.target.z );
+ // Graph.camera().lookAt( 500, 0, 0 );
+ Graph.controls().target.set( 500, 100, 0 );
+
+ ////////////////////////////////////
+ drawConstraintBoundingBoxes(json0,Graph.scene(),scale);
+
+ });
+ });
+
+ }
+
+ function updateGraphDis(){
+
+ $.getJSON("../json/"+simulation.simName+"/"+maxNumFiles+".json", function(json) {
+ var setup=json;
+ setup.viz.colorMaps=[YlGnBu,coolwarm, winter ,jet];
+
+
+ gData = {
+ nodes: setup.nodes.map(node => ({
+ // id: node.id,
+ id: (node.parent=="11")?node.id:(node.parent+node.id),
+ px:node.position.x*scale,
+ py:node.position.y*scale,
+ pz:node.position.z*scale,
+ dx:node.displacement.x*scale,
+ dy:node.displacement.y*scale,
+ dz:node.displacement.z*scale,
+ ax:node.angle.x,
+ ay:node.angle.y,
+ az:node.angle.z,
+ restrained:node.restrained_degrees_of_freedom[0],
+ loaded:!(node.force.x==0.0&&node.force.y==0.0&&node.force.z==0.0),
+ displaced:(node.fixedDisplacement===undefined ||(node.fixedDisplacement.x==0&&node.fixedDisplacement.y==0&&node.fixedDisplacement.z==0))?false:true,
+ size:(node.material===undefined) ? nodeScale:(( setup.multiscale===undefined || !setup.multiscale)?(nodeScale*0.8):(node.scale*nodeScale*0.8**(1/node.scale))),
+ opacity: (node.parent=="11"|| setup.multiscale===undefined ||node.parent=="")?0.8:0.0,
+
+ })),
+ links: setup.edges
+ .filter(edge => edge.id)
+ .map(edge => ({
+ // source: 'n'+edge.source,
+ // target: 'n'+edge.target,
+ source: (setup.multiscale===undefined || !setup.multiscale ||edge.sourceNodalCoordinate==0)?'n'+edge.source:('n'+edge.source+'n'+edge.sourceNodalCoordinate),
+ target: (setup.multiscale===undefined || !setup.multiscale ||edge.targetNodalCoordinate==0)?'n'+edge.target:('n'+edge.target+'n'+edge.targetNodalCoordinate),
+
+ stresses:new Array(maxNumFiles).fill(getColor(setup.viz,edge.stress)),
+ stressesVal:new Array(maxNumFiles).fill(edge.stress),
+ color:getColor(setup.viz,edge.stress)
+ }))
+ };
+ Graph.graphData(gData);
+ // for (var i=0;i<setup.nodes.length;i++){
+ // gData.nodes[i].dx=setup.nodes[i].displacement.x*scale;
+ // gData.nodes[i].dy=setup.nodes[i].displacement.y*scale;
+ // gData.nodes[i].dz=setup.nodes[i].displacement.z*scale;
+ // }
+
+
+ ///////////////////////////////////////////////////////////
+ Graph.d3Force('box', () => {
+ gData.nodes.forEach(node => {
+
+ node.fx=node.px+node.dx*interaction.exaggeration;
+ node.fy=node.py+node.dy*interaction.exaggeration;
+ node.fz=node.pz+node.dz*interaction.exaggeration;
+
+ });
+ });
+ Graph.nodeThreeObject(({ size,restrained,loaded,displaced,ax,ay,az,opacity }) => getMesh(size,restrained,loaded,displaced,ax,ay,az,opacity) );
+ Graph.d3Force('box', () => {
+ gData.nodes.forEach(node => {
+
+ node.fx=node.px+node.dx*interaction.exaggeration;
+ node.fy=node.py+node.dy*interaction.exaggeration;
+ node.fz=node.pz+node.dz*interaction.exaggeration;
+
+ });
+ });
+ });
+ ///////////////////////////////////////////////////////////
+
+
+ }
+ ////////////////////////////////////////////////////
+
+ function drawConstraintBoundingBoxes(setup,scene,scale){
+
+ //grid helper
+ var helper = new THREE.GridHelper( 100, 100 );
+ helper.position.y = -setup.voxelSize/2.0*scale;
+
+ helper.position.y = -setup.voxelSize/2.0*scale +2*setup.voxelSize*scale;
+ if (setup.hierarchical){
+ helper.position.y=setup.voxelSize*scale*(1.2 -0.1)
+ }
+
+ helper.position.y=10000*setup.voxelSize*scale
+ helper.position.y=0.0
+
+ helper.material.opacity = 0.4;
+ helper.material.transparent = true;
+ helper.scale.x=2.0*scale
+ helper.scale.z=2.0*scale
+ scene.add(helper);
+
+ let supports=setup.supports;
+ let loads=setup.loads;
+ let mat=setup.materials;
+ let disps=setup.fixedDisplacements;
+ if (supports ) {
+ for (var i=0;i< supports.length;i++) {
+ let s=supports[i][0];
+ drawBox(s.min,s.max,color4,scene,scale);
+ }
+ }
+ if (loads ) {
+ for (var i=0;i< loads.length;i++) {
+ let l=loads[i][0];
+ drawBox(l.min,l.max,color7,scene,scale);
+ }
+
+ }
+ if (disps ) {
+ for (var i=0;i< disps.length;i++) {
+ let l=disps[i][0];
+ drawBox(l.min,l.max,color7,scene,scale);
+ }
+
+ }
+ if (mat ) {
+
+ for (var i=0;i< mat.length;i++) {
+ let l=mat[i][0];
+ drawBox(l.min,l.max,color5,scene,scale);
+ }
+
+ }
+
+ };
+
+ function drawBox (min,max,color,scene,scale) {
+ var box = new THREE.Box3(new THREE.Vector3(min.x*scale,min.y*scale,min.z*scale),new THREE.Vector3(max.x*scale,max.y*scale,max.z*scale));
+ var helper = new THREE.Box3Helper( box, color );
+ scene.add( helper );
+ // todo add name??
+ };
+
+ function getNodeColor(restrained,loaded,displaced){
+ if (restrained){
+ return 0xfa6e70;
+ }else if(loaded){
+ return 0x03dbfc;
+ }else if(displaced){
+ return 0x03dbfc;
+ }else{
+ return color3;
+ }
+ }
+
+ function getMesh(size,restrained,loaded,displaced,ax,ay,az,opacity){
+ var m=new THREE.Mesh(
+ new THREE.BoxGeometry(size, size, size),
+ new THREE.MeshLambertMaterial({
+ color: getNodeColor(restrained,loaded,displaced),
+ transparent: opacity>0.0?false:true,
+ opacity: opacity
+ })
+ );
+ m.rotation.x=ax;
+ m.rotation.y=ay;
+ m.rotation.z=az;
+
+ return m;
+
+ }
+
+
+ </script>
+ </body>
\ No newline at end of file
diff --git a/demos/indexTutorialGraph.html b/demos/indexTutorialGraph.html
index 1c6c86c..d652098 100644
--- a/demos/indexTutorialGraph.html
+++ b/demos/indexTutorialGraph.html
@@ -65,7 +65,6 @@
nodeScale=scale*10
}
-
var stress=0.0;
@@ -117,22 +116,6 @@
(document.getElementById('3d-graph'))
.d3Force('center', null)
.d3Force('charge', null)
- // .linkDirectionalParticles(0.5)
- // .linkThreeObject(link => {
- // // extend link with text sprite
- // const sprite = new SpriteText(`${link.source} > ${link.target}`);
- // sprite.color = 'lightgrey';
- // sprite.textHeight = 1.5;
- // return sprite;
- // })
- // .linkPositionUpdate((sprite, { start, end }) => {
- // const middlePos = Object.assign(...['x', 'y', 'z'].map(c => ({
- // [c]: start[c] + (end[c] - start[c]) / 2 // calc middle point
- // })));
-
- // // Position sprite
- // Object.assign(sprite.position, middlePos);
- // })
.linkWidth(1.0*scale)
.linkOpacity(1.0)
// .linkColor(color3)
diff --git a/json/tutorial.json b/json/tutorial.json
index bb8d75d..5d08897 100644
--- a/json/tutorial.json
+++ b/json/tutorial.json
@@ -21,9 +21,9 @@
true
],
"position": {
- "x": 0.0305,
+ "x": 0.0005000000000000004,
"y": 0.0005000000000000004,
- "z": 0.004500000000000004
+ "z": 0.0005000000000000004
},
"force": {
"x": 0,
@@ -48,7 +48,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -74,9 +74,9 @@
true
],
"position": {
- "x": 0.0315,
+ "x": 0.0014999999999999996,
"y": 0.0005000000000000004,
- "z": 0.004500000000000004
+ "z": 0.0005000000000000004
},
"force": {
"x": 0,
@@ -101,7 +101,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -127,9 +127,9 @@
true
],
"position": {
- "x": 0.0325,
+ "x": 0.0005000000000000004,
"y": 0.0005000000000000004,
- "z": 0.004500000000000004
+ "z": 0.0014999999999999996
},
"force": {
"x": 0,
@@ -154,7 +154,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -180,9 +180,9 @@
true
],
"position": {
- "x": 0.0335,
+ "x": 0.0014999999999999996,
"y": 0.0005000000000000004,
- "z": 0.004500000000000004
+ "z": 0.0014999999999999996
},
"force": {
"x": 0,
@@ -207,7 +207,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -233,9 +233,9 @@
true
],
"position": {
- "x": 0.0345,
- "y": 0.0005000000000000004,
- "z": 0.004500000000000004
+ "x": 0.0005000000000000004,
+ "y": 0.0014999999999999996,
+ "z": 0.0005000000000000004
},
"force": {
"x": 0,
@@ -260,7 +260,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -278,17 +278,17 @@
35
],
"restrained_degrees_of_freedom": [
- false,
- false,
- false,
+ true,
+ true,
+ true,
true,
true,
true
],
"position": {
- "x": 0.0305,
- "y": 0.0005000000000000004,
- "z": 0.005500000000000005
+ "x": 0.0014999999999999996,
+ "y": 0.0014999999999999996,
+ "z": 0.0005000000000000004
},
"force": {
"x": 0,
@@ -313,7 +313,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -331,17 +331,17 @@
41
],
"restrained_degrees_of_freedom": [
- false,
- false,
- false,
+ true,
+ true,
+ true,
true,
true,
true
],
"position": {
- "x": 0.0315,
- "y": 0.0005000000000000004,
- "z": 0.005500000000000005
+ "x": 0.0005000000000000004,
+ "y": 0.0014999999999999996,
+ "z": 0.0014999999999999996
},
"force": {
"x": 0,
@@ -366,7 +366,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -384,17 +384,17 @@
47
],
"restrained_degrees_of_freedom": [
- false,
- false,
- false,
+ true,
+ true,
+ true,
true,
true,
true
],
"position": {
- "x": 0.0325,
- "y": 0.0005000000000000004,
- "z": 0.005500000000000005
+ "x": 0.0014999999999999996,
+ "y": 0.0014999999999999996,
+ "z": 0.0014999999999999996
},
"force": {
"x": 0,
@@ -419,7 +419,7 @@
"material": {
"cTE": 0,
"poissonRatio": 0,
- "density": 1000,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -445,9 +445,9 @@
true
],
"position": {
- "x": 0.0335,
- "y": 0.0005000000000000004,
- "z": 0.005500000000000005
+ "x": 0.0005000000000000004,
+ "y": 0.0025000000000000005,
+ "z": 0.0005000000000000004
},
"force": {
"x": 0,
@@ -470,9 +470,9 @@
"z": 0
},
"material": {
- "cTE": 0,
- "poissonRatio": 0,
- "density": 1000,
+ "cTE": 0.1,
+ "poissonRatio": 0.1,
+ "density": 50,
"area": 0.000001,
"stiffness": 1000000
},
@@ -498,9 +498,9 @@
true
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@@ -523,9 +523,9 @@
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@@ -551,9 +551,9 @@
true
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@@ -576,9 +576,9 @@
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@@ -604,9 +604,9 @@
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@@ -629,9 +629,9 @@
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@@ -657,9 +657,9 @@
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@@ -682,9 +682,9 @@
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@@ -710,9 +710,9 @@
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@@ -735,9 +735,9 @@
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@@ -763,9 +763,9 @@
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@@ -788,9 +788,9 @@
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@@ -816,9 +816,9 @@
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@@ -841,9 +841,9 @@
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@@ -869,9 +869,9 @@
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@@ -896,7 +896,7 @@
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@@ -922,9 +922,9 @@
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@@ -949,7 +949,7 @@
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@@ -975,9 +975,9 @@
true
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@@ -1002,7 +1002,7 @@
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@@ -1134,9 +1134,9 @@
true
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@@ -1161,7 +1161,7 @@
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true
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@@ -1214,7 +1214,7 @@
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@@ -1240,9 +1240,9 @@
true
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@@ -1267,7 +1267,7 @@
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@@ -1293,9 +1293,9 @@
true
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@@ -1318,9 +1318,9 @@
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@@ -1346,9 +1346,9 @@
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@@ -1371,9 +1371,9 @@
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@@ -1558,9 +1558,9 @@
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@@ -1742,9 +1742,9 @@
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true
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@@ -1795,9 +1795,9 @@
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true
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true
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@@ -1901,9 +1901,9 @@
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@@ -1929,9 +1929,9 @@
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@@ -1954,9 +1954,9 @@
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@@ -1982,9 +1982,9 @@
true
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@@ -2007,9 +2007,9 @@
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@@ -2035,9 +2035,9 @@
true
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@@ -2060,9 +2060,9 @@
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@@ -2088,9 +2088,9 @@
true
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@@ -2112,6870 +2112,205 @@
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diff --git a/json/tutorialInit.json b/json/tutorialInit.json
index d834709..3f314d7 100644
--- a/json/tutorialInit.json
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\ No newline at end of file
+{"setup":{"materials":[],"poisson":false,"rhino":false,"latticeSizeY":9,"scale":10000.0,"supports":[[{"max":{"x":0.002,"z":0.002,"y":0.002},"min":{"x":0,"z":0,"y":0.0}},[true,true,true,true,true,true]]],"thermal":true,"loads":[],"maxNumFiles":3,"voxelSize":0.001,"numTimeSteps":100,"fixedDisplacements":[],"linear":true,"latticeSizeX":2,"gridSize":10,"latticeSizeZ":2,"useVoxelList":true,"voxelList":[[[0,0,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,0,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,0,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,0,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,1,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,1,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,1,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,1,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,2,0],{"cTE":0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,2,1],{"cTE":0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,3,0],{"cTE":0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,3,1],{"cTE":0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,2,0],{"cTE":-0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,2,1],{"cTE":-0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,3,0],{"cTE":-0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,3,1],{"cTE":-0.1,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,4,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,4,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,5,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,5,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,4,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,4,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,5,0],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,5,1],{"cTE":0.0,"poissonRatio":0.0,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,6,0],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,6,1],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,7,0],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,7,1],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,8,0],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,8,1],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,9,0],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[0,9,1],{"cTE":-0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,6,0],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,6,1],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,7,0],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,7,1],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,8,0],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,8,1],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,9,0],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}],[[1,9,1],{"cTE":0.2,"poissonRatio":0.1,"density":50.0,"area":1.0e-6,"stiffness":1.0e6}]],"hierarchical":true,"globalDamping":0.01}}
\ No newline at end of file
diff --git a/julia/examples/tendonDigitalTwin.jl b/julia/examples/tendonDigitalTwin.jl
new file mode 100644
index 0000000..98fe1ed
--- /dev/null
+++ b/julia/examples/tendonDigitalTwin.jl
@@ -0,0 +1,288 @@
+# Amira Abdel-Rahman
+# (c) Massachusetts Institute of Technology 2021
+
+######################### 1. Voxel Design ###########################
+setup = Dict()
+
+### 1.b Draw Lattice
+
+rhino=false
+
+voxelSize=0.001
+latticeSizeX=2
+latticeSizeY=9
+latticeSizeZ=2
+
+gridSize=10
+
+setup["gridSize"]=gridSize
+
+setup["rhino"]=false
+setup["useVoxelList"]=true
+
+
+setup["latticeSizeX"]=latticeSizeX
+setup["latticeSizeY"]=latticeSizeY
+setup["latticeSizeZ"]=latticeSizeZ
+
+######################### 2. Boundary Conditions #########################
+
+######################### 2.a. Global Settings #########################
+
+#scaling params
+setup["voxelSize"]=voxelSize; #voxel size
+setup["scale"]=1e4; #scale for visualization
+setup["hierarchical"]=true; #hierachical simualtion
+
+
+#simulation params
+setup["numTimeSteps"]=100; #num of timesteps for simulation
+
+setup["poisson"]=false; # account for poisson ration (only for hierarchical)
+setup["linear"]=true; # linear vs non-linear
+setup["thermal"]=true; #if there is change in temperature
+setup["globalDamping"]=0.15; # (usually from 0.1 to 0.4)
+setup["globalDamping"]=0.01; # (usually from 0.1 to 0.4)
+
+
+#visualization params
+setup["maxNumFiles"]=3; #num of saved timesteps for visualization
+
+
+######################### 2.b. Materials #########################
+
+density=1e3
+density=50.0
+stiffness=1e6
+
+
+
+#default neural material
+material0= Dict()
+material0["area"]=voxelSize*voxelSize
+material0["density"]=density
+material0["stiffness"]=stiffness
+material0["poissonRatio"]=0.0
+material0["cTE"]=0.0 #coefficient of thermal expansion
+
+#second material leg1
+material11= Dict()
+material11["area"]=voxelSize*voxelSize
+material11["density"]=density
+material11["stiffness"]=stiffness
+material11["poissonRatio"]=0.1
+material11["cTE"]=0.1 #coefficient of thermal expansion
+
+#second material leg1
+material12= Dict()
+material12["area"]=voxelSize*voxelSize
+material12["density"]=density
+material12["stiffness"]=stiffness
+material12["poissonRatio"]=0.1
+material12["cTE"]=-0.1 #coefficient of thermal expansion
+
+
+#second material leg1
+material21= Dict()
+material21["area"]=voxelSize*voxelSize
+material21["density"]=density
+material21["stiffness"]=stiffness
+material21["poissonRatio"]=0.1
+material21["cTE"]=-0.2 #coefficient of thermal expansion
+
+#second material leg1
+material22= Dict()
+material22["area"]=voxelSize*voxelSize
+material22["density"]=density
+material22["stiffness"]=stiffness
+material22["poissonRatio"]=0.1
+material22["cTE"]=0.2 #coefficient of thermal expansion
+
+
+setup["materials"]=[
+];
+
+# height=-1.5
+height=0
+
+
+#//allowed x, yand z are from -gridSize to +gridSize (floor is at 0)
+setup["voxelList"]=[
+
+ [[0,0+height,0],material0],#top
+ [[1,0+height,0],material0],#top
+ [[0,0+height,1],material0],#top
+ [[1,0+height,1],material0],#top
+
+ [[0,1+height,0],material0],#top
+ [[1,1+height,0],material0],#top
+ [[0,1+height,1],material0],#top
+ [[1,1+height,1],material0],#top
+ ########################lower##################################
+
+ [[0,2+height,0],material11],#leg1 front
+ [[0,2+height,1],material11],#leg1 front
+
+ [[0,3+height,0],material11],#leg1 front
+ [[0,3+height,1],material11],#leg1 front
+
+ [[1,2+height,0],material12],#leg1 back
+ [[1,2+height,1],material12],#leg1 back
+
+ [[1,3+height,0],material12],#leg1 back
+ [[1,3+height,1],material12],#leg1 back
+
+ ########################middle##################################
+ [[0,4+height,0],material0],#leg1 front
+ [[0,4+height,1],material0],#leg1 front
+
+ [[0,5+height,0],material0],#leg1 front
+ [[0,5+height,1],material0],#leg1 front
+
+ [[1,4+height,0],material0],#leg1 back
+ [[1,4+height,1],material0],#leg1 back
+
+ [[1,5+height,0],material0],#leg1 back
+ [[1,5+height,1],material0],#leg1 back
+
+ ########################upper##################################
+
+ [[0,6+height,0],material21],#leg1 front
+ [[0,6+height,1],material21],#leg1 front
+
+ [[0,7+height,0],material21],#leg1 front
+ [[0,7+height,1],material21],#leg1 front
+
+ [[0,8+height,0],material21],#leg1 front
+ [[0,8+height,1],material21],#leg1 front
+
+ [[0,9+height,0],material21],#leg1 front
+ [[0,9+height,1],material21],#leg1 front
+
+ [[1,6+height,0],material22],#leg1 back
+ [[1,6+height,1],material22],#leg1 back
+
+ [[1,7+height,0],material22],#leg1 back
+ [[1,7+height,1],material22],#leg1 back
+
+ [[1,8+height,0],material22],#leg1 back
+ [[1,8+height,1],material22],#leg1 back
+
+ [[1,9+height,0],material22],#leg1 back
+ [[1,9+height,1],material22],#leg1 back
+
+
+];
+######################### 2.c. Supports #########################
+
+#x,y,z,rx,ry,rz (default is pinned joing i.e [false,false,false,true,true,true])
+dof=[true,true,true,true,true,true]
+# dof=[false,true,false,true,true,true]
+
+
+boundingBoxSupport1=Dict()
+boundingBoxSupport1["min"]=Dict()
+boundingBoxSupport1["max"]=Dict()
+
+
+boundingBoxSupport1["min"]["x"]=0;
+boundingBoxSupport1["min"]["y"]=voxelSize*(0+height);
+boundingBoxSupport1["min"]["z"]=0;
+
+boundingBoxSupport1["max"]["x"]=voxelSize*(latticeSizeX);
+boundingBoxSupport1["max"]["y"]=voxelSize*(2+height);
+boundingBoxSupport1["max"]["z"]=voxelSize*(latticeSizeZ);
+
+setup["supports"]=[
+ [boundingBoxSupport1,dof]
+ ];
+
+######################### 2.d. Loads #########################
+#### 2.d.1 Static Loads
+load1=Dict()
+load1["x"]=0.0
+load1["y"]=0.0
+load1["z"]=0.0
+
+boundingBoxLoad1=Dict()
+boundingBoxLoad1["min"]=Dict()
+boundingBoxLoad1["max"]=Dict()
+
+boundingBoxLoad1["min"]["x"]=0
+boundingBoxLoad1["min"]["y"]=voxelSize*(latticeSizeY-1+height);
+boundingBoxLoad1["min"]["z"]=0;
+
+boundingBoxLoad1["max"]["x"]=voxelSize*(latticeSizeX);
+boundingBoxLoad1["max"]["y"]=voxelSize*(latticeSizeY+height);
+boundingBoxLoad1["max"]["z"]=voxelSize*(latticeSizeZ);
+
+
+setup["loads"]=[
+ # [boundingBoxLoad1,load1]
+ ];
+
+setup["fixedDisplacements"]=[];
+
+
+#### 2.d.2 Dynamic Loads
+function floorEnabled()
+ return false
+end
+
+function gravityEnabled()
+ return false
+end
+
+function externalDisplacement(currentTimeStep,N_position,N_fixedDisplacement)
+ return N_fixedDisplacement
+end
+
+function externalForce(currentTimeStep,N_position,N_currentPosition,N_force)
+ return N_force
+end
+
+# if no temperature:
+# function updateTemperature(currentRestLength,currentTimeStep,mat)
+# return currentRestLength
+# end
+
+# function updateTemperature(currentRestLength,currentTimeStep,mat)
+# if currentTimeStep<1000
+# temp=-5.0*currentTimeStep/1000
+# currentRestLength=0.5*mat.L*(2.0+temp*mat.cTE)
+# elseif currentTimeStep==2500
+# temp=0
+# currentRestLength=0.5*mat.L*(2.0+temp*mat.cTE)
+# end
+# return currentRestLength
+# end
+
+function updateTemperature(currentRestLength,currentTimeStep,mat)
+ maxcte=0.1
+ maxtemp=5
+ tempFactor1=factor1/6000/2.0
+ tempFactor2=factor2/6000
+ temp=0.0
+ # print(factor1)
+ if abs(mat.cTE)==0.1 #lower
+ if mat.cTE>0
+ temp=tempFactor1*maxtemp
+ else
+ temp=-tempFactor1*maxtemp
+ end
+ elseif abs(mat.cTE)==0.2
+ if mat.cTE>0
+ temp=tempFactor2*maxtemp
+ else
+ temp=-tempFactor2*maxtemp
+ end
+ else
+ return currentRestLength
+ end
+ currentRestLength=0.5*mat.L*(2.0+temp*maxcte)
+ return currentRestLength
+end
+
+
+
+
diff --git a/jupyter/MetaVoxel_Tutorial.ipynb b/jupyter/MetaVoxel_Tutorial.ipynb
index 3b3fa17..a9aba19 100644
--- a/jupyter/MetaVoxel_Tutorial.ipynb
+++ b/jupyter/MetaVoxel_Tutorial.ipynb
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -467,7 +467,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -475,8 +475,7 @@
"output_type": "stream",
"text": [
"Loaded MetaVoxels Functions!\n",
- "C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\node\n",
- "Success! Created structure with 28 nodes and 52 edges.\n"
+ "Success! Created structure with 40 nodes and 76 edges.\n"
]
},
{
@@ -485,7 +484,7 @@
"Process(`\u001b[4mnode\u001b[24m \u001b[4m../node/app1.js\u001b[24m \u001b[4mtutorial\u001b[24m`, ProcessExited(0))"
]
},
- "execution_count": 21,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -495,7 +494,7 @@
"\n",
"\n",
"# get a saved setup from an external julia file\n",
- "include(\"../julia/examples/thermalTest.jl\") #template for multimaterial hierarchical voxels with different thermal coefficient of thermal expansion \n",
+ "# include(\"../julia/examples/thermalTest.jl\") #template for multimaterial hierarchical voxels with different thermal coefficient of thermal expansion \n",
"# include(\"../julia/examples/poissonTest.jl\") #template for hierarchical voxels with global poisson ratio\n",
"# include(\"../julia/examples/latticeTest.jl\") #template for hiearcical lattice voxel (small scale)\n",
"# include(\"../julia/examples/latticeTest2.jl\") #template for lattice voxel (big scale with real params)\n",
@@ -506,8 +505,8 @@
"# include(\"../julia/examples/walkingRobot.jl\") #2 legged robot\n",
"# include(\"../julia/examples/voxelDesigner.jl\") #inversly designed voxels\n",
"# include(\"../julia/examples/puppy.jl\") #4 legged puppy simulation\n",
- "\n",
"# include(\"../julia/examples/billELoad.jl\") #template bill-e load on lattice structure\n",
+ "include(\"../julia/examples/tendonDigitalTwin.jl\") #template for multimaterial hierarchical voxels with different thermal coefficient of thermal expansion \n",
"\n",
"\n",
"## recompile these just for sanity check for dynamic loads\n",
@@ -526,23 +525,23 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "dt: 1.4235251564292887e-5, s: 0.001, mass: 8.0e-6, momentInertiaInverse: 1.8749999999999997e11\n",
- "first timestep took 0.881576999 seconds\n",
- "ran 28 nodes and 52 edges for 5000 time steps took 7.7723041 seconds\n"
+ "dt: 3.183099017456911e-6, s: 0.001, mass: 4.0e-7, momentInertiaInverse: 3.75e12\n",
+ "first timestep took 0.000144301 seconds\n",
+ "ran 40 nodes and 76 edges for 100 time steps took 0.0152215 seconds\n"
]
}
],
"source": [
"setupSim=getSetup(simName); #get simulation from\"./json/$(simName).json\"\n",
"savedDataFolderPath=\"../json/$(simName)/\" # make sure this folder exists, this is where the simulation result will be saved\n",
- "runMetaVoxels!(setupSim,savedDataFolderPath,\"CPU\")"
+ "runMetaVoxels!(setupSim,savedDataFolderPath,\"CPU\");"
]
},
{
@@ -555,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 190,
"metadata": {},
"outputs": [
{
@@ -581,7 +580,7 @@
" [5] success at .\\process.jl:483 [inlined]",
" [6] run(::Cmd; wait::Bool) at .\\process.jl:440",
" [7] run(::Cmd) at .\\process.jl:438",
- " [8] top-level scope at In[14]:3",
+ " [8] top-level scope at In[190]:3",
" [9] include_string(::Function, ::Module, ::String, ::String) at .\\loading.jl:1091",
" [10] execute_code(::String, ::String) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:27",
" [11] execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:86",
diff --git a/package-lock.json b/package-lock.json
index bcfb1d0..5a6642b 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,9 +1,66 @@
{
- "name": "metavoxels-code",
- "version": "1.0.0",
- "lockfileVersion": 1,
"requires": true,
+ "lockfileVersion": 1,
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+ "accepts": {
+ "version": "1.3.7",
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+ "requires": {
+ "mime-types": "~2.1.24",
+ "negotiator": "0.6.2"
+ }
+ },
+ "array-flatten": {
+ "version": "1.1.1",
+ "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz",
+ "integrity": "sha1-ml9pkFGx5wczKPKgCJaLZOopVdI="
+ },
+ "body-parser": {
+ "version": "1.19.0",
+ "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.19.0.tgz",
+ "integrity": "sha512-dhEPs72UPbDnAQJ9ZKMNTP6ptJaionhP5cBb541nXPlW60Jepo9RV/a4fX4XWW9CuFNK22krhrj1+rgzifNCsw==",
+ "requires": {
+ "bytes": "3.1.0",
+ "content-type": "~1.0.4",
+ "debug": "2.6.9",
+ "depd": "~1.1.2",
+ "http-errors": "1.7.2",
+ "iconv-lite": "0.4.24",
+ "on-finished": "~2.3.0",
+ "qs": "6.7.0",
+ "raw-body": "2.4.0",
+ "type-is": "~1.6.17"
+ }
+ },
+ "bytes": {
+ "version": "3.1.0",
+ "resolved": "https://registry.npmjs.org/bytes/-/bytes-3.1.0.tgz",
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+ },
+ "content-disposition": {
+ "version": "0.5.3",
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+ "requires": {
+ "safe-buffer": "5.1.2"
+ }
+ },
+ "content-type": {
+ "version": "1.0.4",
+ "resolved": "https://registry.npmjs.org/content-type/-/content-type-1.0.4.tgz",
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+ },
+ "cookie": {
+ "version": "0.4.0",
+ "resolved": "https://registry.npmjs.org/cookie/-/cookie-0.4.0.tgz",
+ "integrity": "sha512-+Hp8fLp57wnUSt0tY0tHEXh4voZRDnoIrZPqlo3DPiI4y9lwg/jqx+1Om94/W6ZaPDOUbnjOt/99w66zk+l1Xg=="
+ },
+ "cookie-signature": {
+ "version": "1.0.6",
+ "resolved": "https://registry.npmjs.org/cookie-signature/-/cookie-signature-1.0.6.tgz",
+ "integrity": "sha1-4wOogrNCzD7oylE6eZmXNNqzriw="
+ },
"debug": {
"version": "2.6.9",
"resolved": "https://registry.npmjs.org/debug/-/debug-2.6.9.tgz",
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"resolved": "https://registry.npmjs.org/destroy/-/destroy-1.0.4.tgz",
"integrity": "sha1-l4hXRCxEdJ5CBmE+N5RiBYJqvYA="
},
- "edit-json-file": {
- "version": "1.5.0",
- "resolved": "https://registry.npmjs.org/edit-json-file/-/edit-json-file-1.5.0.tgz",
- "integrity": "sha512-YysmqOok2zzUAF6g/nydRZ+o6W8Iol1nQxkdqxwNt4k7MRjTIydrt9fWSzJ0wC14t651z0ofRjQkTjip66re2g==",
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- "iterate-object": "^1.3.2",
- "r-json": "^1.2.5",
- "set-value": "^3.0.1",
- "w-json": "^1.3.5"
- }
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"version": "1.1.1",
"resolved": "https://registry.npmjs.org/ee-first/-/ee-first-1.1.1.tgz",
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"resolved": "https://registry.npmjs.org/etag/-/etag-1.8.1.tgz",
"integrity": "sha1-Qa4u62XvpiJorr/qg6x9eSmbCIc="
},
+ "express": {
+ "version": "4.17.1",
+ "resolved": "https://registry.npmjs.org/express/-/express-4.17.1.tgz",
+ "integrity": "sha512-mHJ9O79RqluphRrcw2X/GTh3k9tVv8YcoyY4Kkh4WDMUYKRZUq0h1o0w2rrrxBqM7VoeUVqgb27xlEMXTnYt4g==",
+ "requires": {
+ "accepts": "~1.3.7",
+ "array-flatten": "1.1.1",
+ "body-parser": "1.19.0",
+ "content-disposition": "0.5.3",
+ "content-type": "~1.0.4",
+ "cookie": "0.4.0",
+ "cookie-signature": "1.0.6",
+ "debug": "2.6.9",
+ "depd": "~1.1.2",
+ "encodeurl": "~1.0.2",
+ "escape-html": "~1.0.3",
+ "etag": "~1.8.1",
+ "finalhandler": "~1.1.2",
+ "fresh": "0.5.2",
+ "merge-descriptors": "1.0.1",
+ "methods": "~1.1.2",
+ "on-finished": "~2.3.0",
+ "parseurl": "~1.3.3",
+ "path-to-regexp": "0.1.7",
+ "proxy-addr": "~2.0.5",
+ "qs": "6.7.0",
+ "range-parser": "~1.2.1",
+ "safe-buffer": "5.1.2",
+ "send": "0.17.1",
+ "serve-static": "1.14.1",
+ "setprototypeof": "1.1.1",
+ "statuses": "~1.5.0",
+ "type-is": "~1.6.18",
+ "utils-merge": "1.0.1",
+ "vary": "~1.1.2"
+ }
+ },
"finalhandler": {
"version": "1.1.2",
"resolved": "https://registry.npmjs.org/finalhandler/-/finalhandler-1.1.2.tgz",
@@ -68,21 +150,16 @@
"unpipe": "~1.0.0"
}
},
- "find-value": {
- "version": "1.0.12",
- "resolved": "https://registry.npmjs.org/find-value/-/find-value-1.0.12.tgz",
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+ "forwarded": {
+ "version": "0.1.2",
+ "resolved": "https://registry.npmjs.org/forwarded/-/forwarded-0.1.2.tgz",
+ "integrity": "sha1-mMI9qxF1ZXuMBXPozszZGw/xjIQ="
},
"fresh": {
"version": "0.5.2",
"resolved": "https://registry.npmjs.org/fresh/-/fresh-0.5.2.tgz",
"integrity": "sha1-PYyt2Q2XZWn6g1qx+OSyOhBWBac="
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- "http": {
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- "resolved": "https://registry.npmjs.org/http/-/http-0.0.1-security.tgz",
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"version": "1.7.2",
"resolved": "https://registry.npmjs.org/http-errors/-/http-errors-1.7.2.tgz",
@@ -95,39 +172,67 @@
"toidentifier": "1.0.0"
}
},
+ "iconv-lite": {
+ "version": "0.4.24",
+ "resolved": "https://registry.npmjs.org/iconv-lite/-/iconv-lite-0.4.24.tgz",
+ "integrity": "sha512-v3MXnZAcvnywkTUEZomIActle7RXXeedOR31wwl7VlyoXO4Qi9arvSenNQWne1TcRwhCL1HwLI21bEqdpj8/rA==",
+ "requires": {
+ "safer-buffer": ">= 2.1.2 < 3"
+ }
+ },
"inherits": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/inherits/-/inherits-2.0.3.tgz",
"integrity": "sha1-Yzwsg+PaQqUC9SRmAiSA9CCCYd4="
},
- "is-plain-object": {
- "version": "2.0.4",
- "resolved": "https://registry.npmjs.org/is-plain-object/-/is-plain-object-2.0.4.tgz",
- "integrity": "sha512-h5PpgXkWitc38BBMYawTYMWJHFZJVnBquFE57xFpjB8pJFiF6gZ+bU+WyI/yqXiFR5mdLsgYNaPe8uao6Uv9Og==",
- "requires": {
- "isobject": "^3.0.1"
- }
+ "ipaddr.js": {
+ "version": "1.9.1",
+ "resolved": "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-1.9.1.tgz",
+ "integrity": "sha512-0KI/607xoxSToH7GjN1FfSbLoU0+btTicjsQSWQlh/hZykN8KpmMf7uYwPW3R+akZ6R/w18ZlXSHBYXiYUPO3g=="
+ },
+ "media-typer": {
+ "version": "0.3.0",
+ "resolved": "https://registry.npmjs.org/media-typer/-/media-typer-0.3.0.tgz",
+ "integrity": "sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g="
},
- "isobject": {
- "version": "3.0.1",
- "resolved": "https://registry.npmjs.org/isobject/-/isobject-3.0.1.tgz",
- "integrity": "sha1-TkMekrEalzFjaqH5yNHMvP2reN8="
+ "merge-descriptors": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/merge-descriptors/-/merge-descriptors-1.0.1.tgz",
+ "integrity": "sha1-sAqqVW3YtEVoFQ7J0blT8/kMu2E="
},
- "iterate-object": {
- "version": "1.3.4",
- "resolved": "https://registry.npmjs.org/iterate-object/-/iterate-object-1.3.4.tgz",
- "integrity": "sha512-4dG1D1x/7g8PwHS9aK6QV5V94+ZvyP4+d19qDv43EzImmrndysIl4prmJ1hWWIGCqrZHyaHBm6BSEWHOLnpoNw=="
+ "methods": {
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+ "resolved": "https://registry.npmjs.org/methods/-/methods-1.1.2.tgz",
+ "integrity": "sha1-VSmk1nZUE07cxSZmVoNbD4Ua/O4="
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},
+ "mime-db": {
+ "version": "1.47.0",
+ "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.47.0.tgz",
+ "integrity": "sha512-QBmA/G2y+IfeS4oktet3qRZ+P5kPhCKRXxXnQEudYqUaEioAU1/Lq2us3D/t1Jfo4hE9REQPrbB7K5sOczJVIw=="
+ },
+ "mime-types": {
+ "version": "2.1.30",
+ "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.30.tgz",
+ "integrity": "sha512-crmjA4bLtR8m9qLpHvgxSChT+XoSlZi8J4n/aIdn3z92e/U47Z0V/yl+Wh9W046GgFVAmoNR/fmdbZYcSSIUeg==",
+ "requires": {
+ "mime-db": "1.47.0"
+ }
+ },
"ms": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz",
"integrity": "sha1-VgiurfwAvmwpAd9fmGF4jeDVl8g="
},
+ "negotiator": {
+ "version": "0.6.2",
+ "resolved": "https://registry.npmjs.org/negotiator/-/negotiator-0.6.2.tgz",
+ "integrity": "sha512-hZXc7K2e+PgeI1eDBe/10Ard4ekbfrrqG8Ep+8Jmf4JID2bNg7NvCPOZN+kfF574pFQI7mum2AUqDidoKqcTOw=="
+ },
"on-finished": {
"version": "2.3.0",
"resolved": "https://registry.npmjs.org/on-finished/-/on-finished-2.3.0.tgz",
@@ -141,20 +246,50 @@
"resolved": "https://registry.npmjs.org/parseurl/-/parseurl-1.3.3.tgz",
"integrity": "sha512-CiyeOxFT/JZyN5m0z9PfXw4SCBJ6Sygz1Dpl0wqjlhDEGGBP1GnsUVEL0p63hoG1fcj3fHynXi9NYO4nWOL+qQ=="
},
- "r-json": {
- "version": "1.2.10",
- "resolved": "https://registry.npmjs.org/r-json/-/r-json-1.2.10.tgz",
- "integrity": "sha512-hu9vyLjSlHXT62NAS7DjI9WazDlvjN0lgp3n431dCVnirVcLkZIpzSwA3orhZEKzdDD2jqNYI+w0yG0aFf4kpA=="
+ "path-to-regexp": {
+ "version": "0.1.7",
+ "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.7.tgz",
+ "integrity": "sha1-32BBeABfUi8V60SQ5yR6G/qmf4w="
+ },
+ "proxy-addr": {
+ "version": "2.0.6",
+ "resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.6.tgz",
+ "integrity": "sha512-dh/frvCBVmSsDYzw6n926jv974gddhkFPfiN8hPOi30Wax25QZyZEGveluCgliBnqmuM+UJmBErbAUFIoDbjOw==",
+ "requires": {
+ "forwarded": "~0.1.2",
+ "ipaddr.js": "1.9.1"
+ }
+ },
+ "qs": {
+ "version": "6.7.0",
+ "resolved": "https://registry.npmjs.org/qs/-/qs-6.7.0.tgz",
+ "integrity": "sha512-VCdBRNFTX1fyE7Nb6FYoURo/SPe62QCaAyzJvUjwRaIsc+NePBEniHlvxFmmX56+HZphIGtV0XeCirBtpDrTyQ=="
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"range-parser": {
"version": "1.2.1",
"resolved": "https://registry.npmjs.org/range-parser/-/range-parser-1.2.1.tgz",
"integrity": "sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg=="
},
- "rhino3dm": {
- "version": "0.13.0",
- "resolved": "https://registry.npmjs.org/rhino3dm/-/rhino3dm-0.13.0.tgz",
- "integrity": "sha512-rgK4x3fsOGOQav2RiPiOikGBPu0KF+FOcxPtgyBvG9SZ34PZDGvEvG7BMRUBivZjZzN0z9WqNcAlulETBE5oMA=="
+ "raw-body": {
+ "version": "2.4.0",
+ "resolved": "https://registry.npmjs.org/raw-body/-/raw-body-2.4.0.tgz",
+ "integrity": "sha512-4Oz8DUIwdvoa5qMJelxipzi/iJIi40O5cGV1wNYp5hvZP8ZN0T+jiNkL0QepXs+EsQ9XJ8ipEDoiH70ySUJP3Q==",
+ "requires": {
+ "bytes": "3.1.0",
+ "http-errors": "1.7.2",
+ "iconv-lite": "0.4.24",
+ "unpipe": "1.0.0"
+ }
+ },
+ "safe-buffer": {
+ "version": "5.1.2",
+ "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz",
+ "integrity": "sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g=="
+ },
+ "safer-buffer": {
+ "version": "2.1.2",
+ "resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz",
+ "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg=="
},
"send": {
"version": "0.17.1",
@@ -194,14 +329,6 @@
"send": "0.17.1"
}
},
- "set-value": {
- "version": "3.0.2",
- "resolved": "https://registry.npmjs.org/set-value/-/set-value-3.0.2.tgz",
- "integrity": "sha512-npjkVoz+ank0zjlV9F47Fdbjfj/PfXyVhZvGALWsyIYU/qrMzpi6avjKW3/7KeSU2Df3I46BrN1xOI1+6vW0hA==",
- "requires": {
- "is-plain-object": "^2.0.4"
- }
- },
"setprototypeof": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/setprototypeof/-/setprototypeof-1.1.1.tgz",
@@ -212,25 +339,34 @@
"resolved": "https://registry.npmjs.org/statuses/-/statuses-1.5.0.tgz",
"integrity": "sha1-Fhx9rBd2Wf2YEfQ3cfqZOBR4Yow="
},
- "three": {
- "version": "0.123.0",
- "resolved": "https://registry.npmjs.org/three/-/three-0.123.0.tgz",
- "integrity": "sha512-KNnx/IbilvoHRkxOtL0ouozoDoElyuvAXhFB21RK7F5IPWSmqyFelICK6x3hJerLNSlAdHxR0hkuvMMhH9pqXg=="
- },
"toidentifier": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.0.tgz",
"integrity": "sha512-yaOH/Pk/VEhBWWTlhI+qXxDFXlejDGcQipMlyxda9nthulaxLZUNcUqFxokp0vcYnvteJln5FNQDRrxj3YcbVw=="
},
+ "type-is": {
+ "version": "1.6.18",
+ "resolved": "https://registry.npmjs.org/type-is/-/type-is-1.6.18.tgz",
+ "integrity": "sha512-TkRKr9sUTxEH8MdfuCSP7VizJyzRNMjj2J2do2Jr3Kym598JVdEksuzPQCnlFPW4ky9Q+iA+ma9BGm06XQBy8g==",
+ "requires": {
+ "media-typer": "0.3.0",
+ "mime-types": "~2.1.24"
+ }
+ },
"unpipe": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
"integrity": "sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw="
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- "w-json": {
- "version": "1.3.10",
- "resolved": "https://registry.npmjs.org/w-json/-/w-json-1.3.10.tgz",
- "integrity": "sha512-XadVyw0xE+oZ5FGApXsdswv96rOhStzKqL53uSe5UaTadABGkWIg1+DTx8kiZ/VqTZTBneoL0l65RcPe4W3ecw=="
+ "utils-merge": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz",
+ "integrity": "sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM="
+ },
+ "vary": {
+ "version": "1.1.2",
+ "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
+ "integrity": "sha1-IpnwLG3tMNSllhsLn3RSShj2NPw="
}
}
}
diff --git a/package.json b/package.json
deleted file mode 100644
index 51d10bc..0000000
--- a/package.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
- "name": "metavoxels-code",
- "version": "1.0.0",
- "description": "Code for the design, simulation and optimization of MetaVoxels.",
- "main": "app.js",
- "directories": {
- "lib": "lib"
- },
- "dependencies": {
- "edit-json-file": "^1.5.0",
- "finalhandler": "^1.1.2",
- "http": "^0.0.1-security",
- "rhino3dm": "^0.13.0",
- "serve-static": "^1.14.1",
- "three": "^0.123.0"
- },
- "devDependencies": {},
- "scripts": {
- "test": "echo \"Error: no test specified\" && exit 1"
- },
- "repository": {
- "type": "git",
- "url": "ssh://git@gitlab.cba.mit.edu:846/amiraa/metavoxels-code.git"
- },
- "author": "Amira Abdel-Rahman",
- "license": "ISC"
-}
diff --git a/tendonDigitalTwin/runSimulation.jl b/tendonDigitalTwin/runSimulation.jl
new file mode 100644
index 0000000..0b2c58e
--- /dev/null
+++ b/tendonDigitalTwin/runSimulation.jl
@@ -0,0 +1,96 @@
+GPU=false # if you want to use the gpu or not
+plotting=false # if going to plot graphs
+logging=false # if you want the simulation to be verbose
+
+simName= "tutorial" # set name for simulation
+factor1=3000*1
+factor2=3000*1
+
+simName= ARGS[1] # set name for simulation
+
+load=parse(Bool,ARGS[2])
+
+factor1=parse(Int64,ARGS[3])
+factor2=parse(Int64,ARGS[4])
+
+if load
+ include("../julia/examples/tendonDigitalTwin.jl") #template for multimaterial hierarchical voxels with different thermal coefficient of thermal expansion
+end
+include("../julia/MetaVoxels.jl")
+
+function exportJuliaSettingsUsingNode(setup,name)
+ #export prev. settings to json
+ fileName="./json/$(name)Init.json"
+ setup1=Dict()
+ setup1["setup"]=setup
+ stringdata = JSON.json(setup1)
+ open(fileName, "w") do f
+ write(f, stringdata)
+ end
+ #run node.js to draw the gerometry using rhino3dm
+ mycommand = `node ./node/app1.js $(name)`
+ run(mycommand)
+end
+
+function getSetup(fileName)
+ setup = Dict()
+ name=string("./json/$(fileName)",".json")
+ open(name, "r") do f
+ #global setup
+ dicttxt = String(read(f)) # file information to string
+ setup=JSON.parse(dicttxt) # parse and transform data
+ end
+ setup=setup["setup"]
+ setup["viz"]["colorMaps"]=""
+ return setup
+end
+
+function floorEnabled()
+ return false
+end
+
+function gravityEnabled()
+ return false
+end
+
+function externalDisplacement(currentTimeStep,N_position,N_fixedDisplacement)
+ return N_fixedDisplacement
+end
+
+function externalForce(currentTimeStep,N_position,N_currentPosition,N_force)
+ return N_force
+end
+
+function updateTemperature(currentRestLength,currentTimeStep,mat)
+ maxcte=0.1
+ maxtemp=5
+ tempFactor1=factor1/6000/2.0
+ tempFactor2=factor2/6000
+ temp=0.0
+ # print(factor1)
+ if abs(mat.cTE)==0.1 #lower
+ if mat.cTE>0
+ temp=tempFactor1*maxtemp
+ else
+ temp=-tempFactor1*maxtemp
+ end
+ elseif abs(mat.cTE)==0.2
+ if mat.cTE>0
+ temp=tempFactor2*maxtemp
+ else
+ temp=-tempFactor2*maxtemp
+ end
+ else
+ return currentRestLength
+ end
+ currentRestLength=0.5*mat.L*(2.0+temp*maxcte)
+ return currentRestLength
+end
+
+if load
+ exportJuliaSettingsUsingNode(setup,simName)
+end
+
+setupSim=getSetup(simName); #get simulation from"./json/$(simName).json"
+savedDataFolderPath="./json/$(simName)/" # make sure this folder exists, this is where the simulation result will be saved
+runMetaVoxels!(setupSim,savedDataFolderPath,"CPU");
\ No newline at end of file
diff --git a/tendonDigitalTwin/serveDigitalTwin copy.js b/tendonDigitalTwin/serveDigitalTwin copy.js
new file mode 100644
index 0000000..2cb4829
--- /dev/null
+++ b/tendonDigitalTwin/serveDigitalTwin copy.js
@@ -0,0 +1,81 @@
+// Amira Abdel-Rahman
+// (c) Massachusetts Institute of Technology 2021
+
+//to run call 'node ./tendonDigitalTwin/serveDigitalTwin.js' from "./metavoxels-code"
+
+const { exec } = require("child_process");
+
+var express = require('express');
+var fs = require('fs');
+var app = express();
+var path = require('path');
+var setup,cmods;
+
+// will parse incoming JSON data and convert it into an object literal for you
+app.use(express.json({limit: '50mb', extended: true}));
+app.use(express.urlencoded({limit: '50mb', extended: true}));
+
+//serve the html
+app.use(express.static(__dirname + '/../')); // exposes index.html, per below
+
+
+app.post("/", function(req, res) {
+ // each key in req.body will match the keys in the data object that you passed in
+ var myObject = req.body.data;
+ setup=JSON.parse(myObject.foo);
+
+ // console.log(setup)
+ runSimulation(setup)
+ // cmods=setup.cmods;
+
+ // if(setup.run){
+ // console.log("run CMODS!");
+ // runCMODS();
+ // }else{
+ // console.log("reading data");
+ // }
+
+ // console.log(setup);
+ // myObject.foo === "bar"
+ res.send("I am done");
+});
+
+port = 8000;
+app.listen(port);
+console.log(`Open http://localhost:${port}/demos/indexTendonDigitalTwin.html in your browser`);
+
+function runSimulation(setup){
+
+ var command="julia ./tendonDigitalTwin/runSimulation.jl tutorial 1 -3000 3000"
+
+ var juliaLoc="\"C:/Users/amira/AppData/Local/Programs/Julia\ 1.5.2/bin/julia\""
+ var command=juliaLoc+" ./tendonDigitalTwin/runSimulation.jl "+setup.simName+" "+setup.load+" "+setup.lowerTendon+" "+setup.upperTendon+""
+ console.log(command)
+
+ exec(command, (error, stdout, stderr) => {
+ if (error) {
+ console.log(`error: ${error.message}`);
+ return;
+ }
+ if (stderr) {
+ console.log(`stderr: ${stderr}`);
+ return;
+ }
+ console.log(`stdout: ${stdout}`);
+ console.log("done sim")
+
+ // app.post("/", function(req, res) {
+ // // each key in req.body will match the keys in the data object that you passed in
+
+ // res.send(setup);
+ // });
+ // app.get('/', function(req, res){
+ // res.send("Hello world!");
+ // });
+
+
+ });
+
+
+}
+
diff --git a/tendonDigitalTwin/serveDigitalTwin.js b/tendonDigitalTwin/serveDigitalTwin.js
new file mode 100644
index 0000000..e2b2203
--- /dev/null
+++ b/tendonDigitalTwin/serveDigitalTwin.js
@@ -0,0 +1,82 @@
+// Amira Abdel-Rahman
+// (c) Massachusetts Institute of Technology 2021
+
+//to run call 'node ./tendonDigitalTwin/serveDigitalTwin.js' from "./metavoxels-code"
+
+const { exec } = require("child_process");
+// var julia = require('node-julia');
+
+var express = require('express');
+var fs = require('fs');
+var app = express();
+var path = require('path');
+var setup,cmods;
+
+// will parse incoming JSON data and convert it into an object literal for you
+app.use(express.json({limit: '50mb', extended: true}));
+app.use(express.urlencoded({limit: '50mb', extended: true}));
+
+//serve the html
+app.use(express.static(__dirname + '/../')); // exposes index.html, per below
+
+
+app.post("/", function(req, res) {
+ // each key in req.body will match the keys in the data object that you passed in
+ var myObject = req.body.data;
+ setup=JSON.parse(myObject.foo);
+
+ // console.log(setup)
+ runSimulation(setup)
+ // cmods=setup.cmods;
+
+ // if(setup.run){
+ // console.log("run CMODS!");
+ // runCMODS();
+ // }else{
+ // console.log("reading data");
+ // }
+
+ // console.log(setup);
+ // myObject.foo === "bar"
+ res.send("I am done");
+});
+
+port = 8000;
+app.listen(port);
+console.log(`Open http://localhost:${port}/demos/indexTendonDigitalTwin.html in your browser`);
+
+function runSimulation(setup){
+
+ var command="julia ./tendonDigitalTwin/runSimulation.jl tutorial 1 -3000 3000"
+
+ var juliaLoc="\"C:/Users/amira/AppData/Local/Programs/Julia\ 1.5.2/bin/julia\""
+ var command=juliaLoc+" ./tendonDigitalTwin/runSimulation.jl "+setup.simName+" "+setup.load+" "+setup.lowerTendon+" "+setup.upperTendon+""
+ console.log(command)
+
+ exec(command, (error, stdout, stderr) => {
+ if (error) {
+ console.log(`error: ${error.message}`);
+ return;
+ }
+ if (stderr) {
+ console.log(`stderr: ${stderr}`);
+ return;
+ }
+ console.log(`stdout: ${stdout}`);
+ console.log("done sim")
+
+ // app.post("/", function(req, res) {
+ // // each key in req.body will match the keys in the data object that you passed in
+
+ // res.send(setup);
+ // });
+ // app.get('/', function(req, res){
+ // res.send("Hello world!");
+ // });
+
+
+ });
+
+
+}
+
diff --git a/tendonDigitalTwin/ws.js b/tendonDigitalTwin/ws.js
new file mode 100644
index 0000000..c52032d
--- /dev/null
+++ b/tendonDigitalTwin/ws.js
@@ -0,0 +1,87 @@
+/*
+Serial-to-websocket Server
+using serialport.js
+
+To call this type the following on the command line:
+node wsServer.js portName
+
+where portname is the name of your serial port, e.g. /dev/tty.usbserial-xxxx (on OSX)
+
+created 28 Aug 2015
+modified 6 Aug 2020
+by Tom Igoe
+
+*/
+
+// include the various libraries that you'll use:
+// let SerialPort = require('serialport'); // include the serialport library
+// let portName = process.argv[2]; // get the port name from the command line
+let WebSocketServer = require('ws').Server; // include the webSocket library
+
+// configure the webSocket server:
+const SERVER_PORT = 8000; // port number for the webSocket server
+let wss = new WebSocketServer({port: SERVER_PORT}); // the webSocket server
+let connections = new Array; // list of connections to the server
+
+// let myPort = new SerialPort(portName, 9600);// open the port
+// let Readline = SerialPort.parsers.Readline; // make instance of Readline parser
+// let parser = new Readline(); // make a new parser to read ASCII lines
+// myPort.pipe(parser); // pipe the serial stream to the parser
+
+// these are the definitions for the serial events:
+// myPort.on('open', showPortOpen); // called when the serial port opens
+// myPort.on('close', showPortClose); // called when the serial port closes
+// myPort.on('error', showError); // called when there's an error with the serial port
+// parser.on('data', readSerialData); // called when there's new data incoming
+
+
+// ------------------------ Serial event functions:
+// this is called when the serial port is opened:
+function showPortOpen() {
+// console.log('port open. Data rate: ' + myPort.baudRate);
+}
+
+// this is called when new data comes into the serial port:
+function readSerialData(data) {
+ // if there are webSocket connections, send the serial data
+ // to all of them:
+ console.log(data);
+ if (connections.length > 0) {
+ broadcast(data);
+ }
+}
+
+function showPortClose() {
+ console.log('port closed.');
+}
+// this is called when the serial port has an error:
+function showError(error) {
+ console.log('Serial port error: ' + error);
+}
+
+function sendToSerial(data) {
+ console.log("sending to serial: " + data);
+// myPort.write(data);
+}
+
+// ------------------------ webSocket Server event functions
+wss.on('connection', handleConnection);
+
+function handleConnection(client) {
+ console.log("New Connection"); // you have a new client
+ connections.push(client); // add this client to the connections array
+
+ client.on('message', sendToSerial); // when a client sends a message,
+
+ client.on('close', function() { // when a client closes its connection
+ console.log("connection closed"); // print it out
+ let position = connections.indexOf(client); // get the client's position in the array
+ connections.splice(position, 1); // and delete it from the array
+ });
+}
+// This function broadcasts messages to all webSocket clients
+function broadcast(data) {
+ for (c in connections) { // iterate over the array of connections
+ connections[c].send(JSON.stringify(data)); // send the data to each connection
+ }
+}
diff --git a/voxel_designer/Compliant MMA.ipynb b/voxel_designer/Compliant MMA.ipynb
index 383bc86..f74dd6f 100644
--- a/voxel_designer/Compliant MMA.ipynb
+++ b/voxel_designer/Compliant MMA.ipynb
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 308,
"metadata": {},
"outputs": [
{
@@ -40,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 232,
"metadata": {},
"outputs": [
{
@@ -49,7 +49,7 @@
"1"
]
},
- "execution_count": 93,
+ "execution_count": 232,
"metadata": {},
"output_type": "execute_result"
}
@@ -63,7 +63,7 @@
"penal=3.0\n",
"maxIter=200\n",
"getProblem=inverter\n",
- "getProblem=gripper\n",
+ "# getProblem=gripper\n",
"# getProblem=gripper_inv\n",
"\n",
"voxels=1"
@@ -71,21 +71,18 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 233,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Compliance sythesyz problem with OC\n",
- "ndes: 40 x 20 x 2\n",
- "volfrac: 0.5 rmin: 1.5 penal: 3.0\n",
- "It:1 Obj:-0.007 Vol:0.49 ch:0.1 \n",
- "It:2 Obj:-0.071 Vol:0.47 ch:0.1 \n",
- "It:3 Obj:-0.333 Vol:0.46 ch:0.1 \n",
- "It:4 Obj:-0.898 Vol:0.44 ch:0.1 \n",
- "It:5 Obj:-1.473 Vol:0.43 ch:0.1 \n"
+ "It:1 Obj:0.329 Vol:0.5 ch:0.1 \n",
+ "It:2 Obj:0.085 Vol:0.5 ch:0.1 \n",
+ "It:3 Obj:0.012 Vol:0.5 ch:0.1 \n",
+ "It:4 Obj:0.0 Vol:0.47 ch:0.1 \n",
+ "It:5 Obj:-0.0 Vol:0.43 ch:0.1 \n"
]
},
{
@@ -99,7 +96,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"Scene (400px, 400px):\n",
"events:\n",
@@ -128,11 +125,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "It:6 Obj:-1.859 Vol:0.44 ch:0.1 \n",
- "It:7 Obj:-1.885 Vol:0.45 ch:0.1 \n",
- "It:8 Obj:-1.913 Vol:0.48 ch:0.1 \n",
- "It:9 Obj:-1.925 Vol:0.5 ch:0.1 \n",
- "It:10 Obj:-1.938 Vol:0.5 ch:0.1 \n"
+ "It:6 Obj:-0.007 Vol:0.38 ch:0.1 \n",
+ "It:7 Obj:-0.075 Vol:0.36 ch:0.1 \n",
+ "It:8 Obj:-0.213 Vol:0.39 ch:0.1 \n",
+ "It:9 Obj:-0.383 Vol:0.43 ch:0.1 \n",
+ "It:10 Obj:-0.599 Vol:0.47 ch:0.1 \n"
]
},
{
@@ -146,7 +143,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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UZRU9m9REifM7OSeoC5xp9sGXykpGA/ny7+uC30iPYaoQRVFUMSUWcohj9A0RHx7RETS+89Sq9daVf5ALqkrrVlGXNVq3ilpTqgedlG3I7dpaxyJcicaKo4EYjrf88VcKP0HLTdu6AOipLIUM+HC6RpIfjuyZ6w89dYf91pV/5BdUjembeV3S2KCb8U1ZCDsxU5dSjel1Up4rSxQ8GkjrIhV8mWwYC+OtesYvNKn7KgZ+rKeD+eGYUKn2wDN3um+9+/fsnKrxQTeriRIfdDNOkJvxRkQFS6sS6dk+WxIkY/oQKnTMCHF36Tj97Hgmg3GA3HSopYfHxq7c/U/f47355j/ic6rEB92MF+V60ElYQZKT9b4oCpqS9xyH5cowMBkD5KtrgdwB8u5Ebms2EWqMoRTD3ituhpQ0HgjA3fvcvfFbb/4jXNXkWthJuKYsRJ2IbUiiQTuoNTSN9C2b4U8fyRgg/Qf9oEeAfCK3NZu2W3sIlGE4CAXSCalz9080uqPvfvbB9M23/u6vqLIQdWKuKTeTjYhh4/OMaxC+rrbosGfQ3GmPFccAadE34n4OAHFXrR2/lz3EJKeH3ZQCyGeEWpgMCEDf+cx38jff+thbUeVm3IlYQZaTDTdalkTa0wu+rihc3NNPOwoeA+T/zEw62Nw/N1/vL7DUzGOIMdod8O29Q06P9CFkI6Mu3D3xb5q+46nvkbfe/LuzrG2aR0NSgn7B1AWJ87s5V5fUetp1Y/40o+AxQK6s1BrdcMurxN1ldrvJ5o1lf3GbxY/BARAjoFYmBwLQ5596nP78Dx9bS5ospRsB06Bz1TWomiDVgk7G1ZpaM+7Z0SmGXGOAPHFpsc93ttsr76r8vl/yMVabKsbhACjLo+68e6pKUKs/+zb3xUsfG4uarGRdL6YviHD7qDXkRuj4bE3QpFR3Ti8KHpc6uWpJXzlkh8h6f3/ffmxUMpoeHw9TNV24n+Wn64eplZ88UvvipY/6bU1S8p4t1psS7emEb8h8bHss39CUwu2fVhQ8Doj6T5VazwHsuC31UAA8fyrZjnWMLprV/6VtmpgyWqWWfvyo8OWLH/cWWlKKwqBqTZl1ewVXlxqp7TC1uipTvh5MW+5cNA4ILsbmzc19pocYyZ6WmxOV7ICzGl4whcdFherb9JQBINV+4vvS1y+812vR8gpcA7wg17xOwdXFZm5bNF9TZMbr+cXJR8FjgeDvOd/f/voAdvv2QV90LlQG/C6HlEvJYoNuyqTnMVMOXKnWD3+o3HjhfVdbFUV6qxPxnZCrNUXiGBTPywof9NycO+EoeCyQaz2qK/lb+d4BffvEjXdkDSqWuZMGGFHKuiE75YMNSv3BE8r1P3zUURaaEuPqBd9QuNAO2Log0Y4OnhfVRmhaycnmgscCUVP1IvVAb+uByGi3ta/xjoPKoULpFRoAWKmZdKNpW64oVh+7o/v8++tSuylzXq/gGnI9sn2Gb8ica6ccL7SE2DZPNAqe5Jl68I3Jve1uX7D5T+3hsRPUN7/0MXyVvVW+T9v+n9cL1d2VoxPULOylLY3uvvDKO5d+9uuHOOJagabWEFkh02pSxLMyQa2hcHWXqAsnNiNiokkOWedm9uXG1iFEWy1MSQ9POt9spudbY566b5YZp3vmnBC3m7a0SR1X3PekBR4A+i++dK32018/zBHPyutqHYnlUy2RJoGZ1rU6iK/bhdQ+oRkRE04DMq9F7DtbRrLdUrXvT0Zks2nn/9SRIH73gct7ARROl7S0SXrh3DT59s6ybOulF7/kf/qr7/IkMNNaq47UdtGSaIRmzGsCEBhGKrZPZHXc+CgLANBQUu+ivv1kfaK+fUebnzmGbEvybqzsSy1SdZXRbWrs8KFw18P28i7K+refXuxfefV6bbkh10I74GqCBNugeF4SYstjOF5WOM9wyAlEwRMCAaewzmJ2INoa27cfbMK5MomvxDiY66UbKroeM3pYEm1Y0rl9u3mg9uDT54x3X75WW9rs19maINGOk/O82Mwtj+I4Sa0Ffef4o+Ap5vb2bobslYNua8K+fZ/m473Iuw7A/8+H+4zE7AsLwxfnZIbVGLjCLXj9+Y/w41/9oI7ICtmWQBWuRUSFQ2a7lCLTyBzdY1ut0etWj6pJLQRAU0n9Ow66rWmNBNjnvY4w0bf4gmDgE3VGlNONaMiwJHfWkuXFgfExd+8zd3pXXv6SXW6IQmq5dK2usJ6VcpwgwbYLnm2ozdQ85ih4CiDgVMZp5/vd1sDk1gSiKIoaOJFk8hL0GEOmOLCSFHeSAU+aSLDhaKuNYXdl737m7vDKH7+gVhpiM7dsulaTed9KOK4hM46Vc2xdEzPbCI4RyTRAQNeFSL6rv2+QOJuRAAA5Wj9PSesYOueEk4VwIz+YBw71Xv3cyM0dmEvP3Je8+4fPyWqjKRLLovmaXAvMiONqMueaKcfWVBmu7jHHlQueCggYYYDbGpXcGq4t6zgCE74fj5gExEuNoFvsRZKZnWKlPa6m9MWnH8jff++DYqUhSJTtEp6XG5EZsDyv1Hwz5lhekVmv55LasSCZDgjAqexhtzVpALyrvc5qVkOh+C7uGf5EnapJnK9ju9mItx4srEwycqIv/Pyh4IN3PshWGw0JtkN4XhIS02c4Xq5HRsBxnKTwQd/JascQBU8LBHS9GYt394/Utx/uO2ZiQq2RkVMcqLrIeH2apwAEXUNcnXRdNH3uZw8n7739QbLcaMi0bRccLzYz02M4ThISw2N5VlTroWkl858RMTUQMIKS++f7h9zWxEYypCuf3lDo62TMFAe6IcKxGD4zOsxqa4rfM7Xyk8fyD978IF5p1GXGsXKea0qF6dA8K4qZ4TIcI2hCYlrRvGdETA8E4FTiLxxyWxMaSTGq2adjQrnB2CkOtCDka309X1qeNju/9OT3qb+98dd4WajLrGdlPCfIxLIpnm1KxLBpjmloYmr1Q26uIdcsQECLUiReMmbo24uxLT4FE0r1lsZPcWCo2IxUZfofMtV+4gfsx6/9NVxq1mTeM1OOa8iUbYFnGzJtWhTH1DRJ29B9ao4z1mbdWiO8oVOfH0gAjx23j92aYVcTVut+ZdwnEsMW2jBsbabF0OTq8y9v3PeLZ5doBGbcVGsoPCuXFRaFZ2WSxgKBoa+vnp/GH47UzHudBHqnqP/5QCZlZE5+ChybjTHBZ5oPjv5tFqZBL0oU4JtuqzWLuyff/P6PN+69fHmVRmhGDa0O4pupqHEgvpk2Wxzg/KNWCNrYgHoyzeSyAIBrSn5wT2eKvn1as57EeWXNkd7CXXdbmwNzXqp7vVmmkVDK4z+Wv3jpPact8VIjMgOO4xXeN2Oe5ZVa0I9qTELuaQSGkdXnYSVH2Z4putE77LameHA1mUZXkBJWG9yQ+b+x7soLu5UpXCtpKzON5jov/OHzbz/53AUGseXzLQEIzbCxUAMiy6/RySVkTs9jWktTPLEc9oWOknoN9fWi/uaBh7sTP7iaXCPrSNGQF+TDTHKzzy/u31Qgt51iQZ6pA+699NI/H3jyF3cySCyPazWByAr4dh1IrKvcAw0gd3UXamtspzZGR9tRLveu+o23DhGZJSc/RmPshK7fuX9rBuL28rZ6yBwyy0NbxCwy/vDix/f++Jd3MUhth201KSSWx7YFkC8Y0JpMgXi6XSiadqQvf9Q9F6OrJr44drcFYLzv2osk0j11YWAfnpoBszDbIw3r5Rc/uPTTX9zDIrMduiVSSG2Hade+Pl+zbSgaBRL0rVTSFo6Q5TryJpiRvp4PcFvHYCQY57t2kGSG0Vgc2uixEdWmmZ6yR+4rL7x//me/uJ9F7lhUS6KQOXYSPcijcM1CbtFAaBpRvb04c/9+9G1iC/eaX/vLQSLHYyQYzYRS7qUA4nSptjLq9xAaab0124YBwasvXFn+2XMPcigci2gyjfzmjRVVo0A8MxXbDJBYesAvaDM+WJzHRsrOukEOzhI6jr59WyOq/FATQd9vtcaNCXwjb040PeWwwjd+/7b69OXvcCg8M5dbdCdWrFxu0SCBGQuLLJDaus+0NGmW4ueys3Wsr2WHjeSY3BaAYUyI/ktimGJ7An9EPDNqK7M5lvjN378pPn35ER7EMzPJExeJZ6bSAgOEZlhf4oDc0V1qppBr5oHhXrGyGASXegcGiTM/SpxAgweN+fUL6/ny4iQ3pWoy5xj0TGuL2bue+pb/8is3a4tsTeadb3iZqck1r5/UaU5qxrrH8HRDEwu7H2LqndLmAgSoC4W7sjNL6IjP2yfTACZFX11cmbQJqLpM2xY7UwWZO596MH71D9/wSywv+LwVNxheqQf9qM6wopgbLlWnaqpEOX0bU+6UNrfjKpL+jYx/+8T69m3tq3726X+aboyRWS6z0Jzqkt1r3//96/FTv/pBtn53YoaNRQ6IzKC2xAOZbVOqBiAw+0m9rU4T0s3JQgBGEt3ozoNua7bn7VNox042tx/41nQjAFoQMyuYbb0Bff6n3yVv/e4rqrXISUKkB3WGlZqp7tE1WpApyywaFCerXGhYGTv5HeYGBKhLhbt8yG3lXe2Y17dvMqEAUE1u2lE40xQiK5rtSSy9+pNH6bf/skavcKzUTHW/xm46LLpGNxTGNjKBYiWtHhtWQk/qKOYIBPynBTl33jjct7fZY12wC2yXzsyQ3WOlemClU64c3RK9/J2L+PC3nzArPCuKme7RNaYpE9Oi6lRd4Tw9YVmmqQmJbcbUZFWb76FgX7S+8blPDg9JHpGOaQY8gJ0HLQRg7pkttxf0M1mdAUmhW5r21fOvrH3/P/xIA1LboTUFyB2LqC0Avhk1NAEoXN0hYrs1QZHzPqUtWOuxwmuH+/ZHtk32WM/epR+YcXxMPLNQpk7NR510WQLI18+/fP3Rf3miRSGzbUpTgcKxcmWBAkLLr2kiQLy+lQmLytj8wNzPMcyMa5ny6kEiO0ay+fXnfM8d0c3zs6VyAeJY0KYayRZut741dYJcf/6VLx/51yfaFHLHgtICCs/MpDYNxJbLaTJAAsOcIOQ6hpM+zesev+O2BhkJcFxMmHbOtWeNswvbZrTJeeY9p7Wwu3/wzd+//Om3n/3NEoXCsQqlRYH4ZiyqdSC1HFpTAUSGEY3Lch3H0avhWpcRXj9sJD840FTzvzXzIOl7bW3WVGtuOdyku5CHG8XKvhEM2XjxD39/4PJ/s0KhcM1cblNAYESC1gAyx4KyACCxeiE7Mst1LGfh5v2rmfz6aLe19S3me2PmHpm4ZrYoz1pAanr1hQlMLHd6zaVDg4vuSy99dPflf79Kg3jGpsMKzaCuNYHcNQtZ4YHU6fmU0paG9VfHdFq0dd1jPx3ntjY1zwow98hAbnrsJPnFwUqMoDl2ekrW9doDHwz2//DSB3dc/g8X6E2HpdWA2PR4TQIKz0wltbaZeITYHrKH83Gdpx6tb9DCnyYxEmAWKENW+lxYAYC4nwozJtcBRP1YVkde7XewMqwjMP/4h/dWn/nXizTgm5sOK7EcVlMA4puxoDWAwtOdXGgPDLmO7YD7vH8tlQ4TGWgkwHwMhbjbq6Q9I1mcbTYDAAT9TFGHxsC5pcujMsrOKy9eWXz6v7tEA6ERNjQBSG17s1sPzKChNQHi9610YMh1bEAA+xub/eyQ2zrct++251HvmH36P2z/6ArLoRdnPyXEM/LWEKBpJ1jUxlz92otvq0/9pwsNIDL9Wqu52a2rMgtEps9rEoDQ6Cf84RWL80ydHFBdobz2tw5OpUPeGZaTP3J+hUp2kotUQ0jseOYFHLzMmPbADW28m+T8uEeB/P1P3e388cW1ZYFjpWZieHSNbsiMZRUcw0piZjgUT3GyWossMz5wl2O0EKDoX03FNyZ3W5s6So0e3BuJBv1wUZ2ZcWFbTPvg7zczDWWyCQzRn1/4M//z//5ic7MPaclA4ZqZrPJAatuUKrNAavV9Wt4Xch0rEMC9bjGfD3Bbj4weE89aKeqB/UMD26LaMz7uAJBbNre4z8uHvWh54mxZ/Pbv3yA/u/wjcXdoSDwzEdU6kDkWURQOyBzdhbi4G3IdMxAkaxto/HlaI8GsTJoP7AedGX59YfYDKVLLrS3uXu50+OXR8TTZ/u2BpoDk3effSH5y+ccSkNkWpWkACYyoqTWAwjFzWakBhavbxW6W67iBoDCuxc0dIpP07bvfbvq7MY8c9CeRnsnq7BPXEsMXtkKqrG9p7VElkcJx8yxMAa7BMq0mBWTvP/8n78fP/lwGctsiqsLuibQ8IxG1+mbiMW1shVzHDgTwrln0F9O7rc3vOOW9DvgsACCuWSzOmnIEEBmRuMAAQTddHpUBIP71fSeYUs1VmaaQffj86+YTl59RNkfrisIBkenVNGnTXARNABD0jXQz5DoBIEjW10njzRnc1uYXnepeB30WAOSmW5skHzJMgZHImtutL48og/jX/YM1pejGqkxT+cfPv9754eXn1D3demy5rCbRQGj6tVaTSj5Hw4s5rSWeBBCgfzVqfjSjkWAqJgNMBEDcT8SZU44AvK5JLo5wV4NwbFaHbqzKdPHJ86/d+P6zv9YA4hmpqNWA1LIZVWaAyHLZ+kYCil4wc0Y5GSDwr7ms8MqsRoIpmAwyEQCegdlmEm5Wv+OxjaWDqEkBIEWM0PCH14+iG6syPv/9q1cfe+bfySyIb8SiVgcy24KqsCDW2tblVNOJTwgI0t43RPvjISKT9O3bX3+yjw02EaCwbG7GlCMx9OYS65j00p6hPykcL0gIUow/VZaiG6vi1Rde+eKRZ/+9ygK+GQqaAOSOWSjs2h6aFH1SQADjatT4eHa3BUzIhHtoSK427YfiLCnHtOcstiggt2x2uxshwbXtYwQna0CKbrQ7r73+6befeeZObjvSIkWx7h4wrpMDguCawwivHsFtTfj1uXuHTRYM+rk2dcrR62D7SVRmOrVlDqRw1vceIzip7dIAJ/3LPT/9xaUm0mgtYJtBesi4ThAIMv163vrjgCHJFEYyyfenhItDkBDHZKc7mJj0dWnPk6jE8OvSjcNnbE7eirvHZ6MgAy48SSCAeTVo/P2w25rOSCb4/pRwsT54JWhm+MIUU8CTrr+oUdsj8BQuQm/wGZuzt+OpuSwACK/ZdPPVo/TtE35/iq4PMZOon2jKWIvcJOBt5EsMMuRp7iIFMKoDnw+SEwaCXL+Way8f2W1N0gCUsCphkJ24BtUeMfODFCncKM+QINp6adIzsmc/VHa31icMBLCuZtQ/5uC2JmgAioakSOAOYsktp97eG4tt5wTTMCe5F6YTE5i2RuOvPHkgiLob1OHJjTMZySQ9PA0OUo0FAx7A1kgk6UeqQkcAEuTIsgx5mG6WNzOLSWs08kpyCkCQ96+l6quH3dZMRjJZC1Aj8737GRy9RWb3XPE3pwEEcL7KyOHJjTMayVFa4LjKm60A4v7txFInBxR317E7ufHIRnI2kMR/c8lp9CEANh+3K68dJjJ9AHyUJjje4qYsIb4S41Q69S25X6VzdVvTt8DxlzdNAZs8ThEIku4a2Z3cOAe3NW0TnERxQxbUF3v/oins8DhNICD9qyz71/kaCebNZN6eixRIzDBDitgHhDo4ipUXqG0epwoE8LoGVx/wlORIRlJmMyHumpWg2HmVAkBRlLjyZbz91U8VCNLuGll4Ya59+8E2mIfmg4QUxjWXbC8Z3gZCgQK2EmU4dSAgxlWa/WCs2yJT79gIcsBTA6Bnd4RHR0Lcz/xi6xDbEgMB/G4/jf45rG8nBWBHuInzdSiTNykpEHtWhiwG4KMJQtXAsUqT4memcqSWIu6nLtn+grtLkreB7PAoARB8WVBR/cVBbusRwbDy1AAKspn8WEa7Wdtq0UO//y1cpEDsbXgxtn+Om5/ePIuBpnixVZ+C7P5WnfEbksK46u7ZCHyvgZQPyP9x7gcLXzPMILdFUTmh9jsrikarrQJhFGRIEVHwATQBvwWOVVu7LPYXtr3AhwJAA0pdVlGbHssMrUUK46q/v0IUDhhIqYAAQNAx6MaApyTFdoX3iaIBUpD9H916hxxI1u4A2Tl/kewUUoPWah0zE+J+6u+r0kAguzxOcNbJSGW9m/VBbqsYBGRvs4xbRUrtvHwAyOYfFN28ODWTyZtst+fYW6HhXQi9IpUECGD1PPpvh9xWsfMFhjTK1EAOvTsLk4kabW/Psf+OO0UcACI9wdRLAwRhV6carxwwkiMD2frACCAAKLp535S5gUHNth1m+IiRIuv4A5517fNYB4BIz6S1C+UBgrx3o157aT+RYvOfw411qNce1lAjgexdsT0tkkO3ir/JMkQItt7IB0/2xX4gFEBt8/h5UbtQHpcFAHbPpXbnZIOaAMg4HrtAhg4tt81EOpLnIs7HMbX35cENuwVkgIFIPy9qd4glibK2FXV7EPa6rWL7JN2hbUENfhn73x8FZNdK6OZFcbqB48794r851J57jeExAIj0VMHfIZYl7N1RoX/D83smN54oEAAUzTcVFhxqAAbN2qYHd1rxOzHofUAGI6FAszsGAgogpNjHo2xAAKfrkd052bvDh0HNcPiNIV3IgD5kT3aMOvD6gAkRpAmgRoFjhUMpHLLJg6IwzmXRLHXpfB11SMBCH3ARIf5sI9nxVyghEMTdXoNsz0khQyxkYiD70xT7PjgMyGHt7bAoGoBSF1hwaAF+jBTptXjvKvvBQGi2/cQlsAdp5+lHr2a7PEoIBEW/n3JXNo3kyBYyDMjhzwAAoQYBGeQeKXrPW3mBcUDolWeX2MFzkXLnndUdHmUEArhdh6+96GILCCX7NIWC5APaaDgQZrPN9gHJDiQkKbK3iCFNMexeu+8RAux0IYN40Cs/ZJiFYVsyxdf9HR7lBIKk2200fucCBDTXPq+0zmMN/eu6v01lDBCGoi9d5FBzIah0C8C2w76h+3upbAU81IEyBzX64XvtvEcwGgi98twKnVkWO/iU9308SgoEpK+n7LsbBXXvdy7R8eY09pwUPr66fg1ZAbJlAA+23Q93ryo0D6KKel2+u0lTDICoV+xdh4Yi83F9w3bgAciKve0zwaz2UUCGeix65bkVGgBS064N2EY7+ibgd3mUFQjgdR2RfP2dlQNmnpPC1z/WvUsXOdQhLYaNPV4o+RTAt3hswthsF9NqLh4oI1rLlgo46N24lu9cPckqgyHBNyEYBoRm28/tfoXYcIWD64X8taB2Yc8y+tICQdrr1BcGL0Uusr1tvreBisMDhbiXLe1bfOD2alvbnxfZtbd7213TeI81DRACAPTKc+39PXlo+OK+030O8igxEBBDj+XVo+/lTyyzsbSDr+jbrT278xXhJ//sbJnJkDlUO/8bxgP7gOzw2PFV++T3Y1nb2T7FvxnU9/EoMxDA79rionL0cmI92V5kHnfy5f2LdXLS++MIJBMYyL4+ffu1wTgAEK+fqlurgQ/zKDcQZF2TWmgf/QBNYpu1ZQaA02ssHV5hWHT+2M+2xj2HLt397yggFMMSkm+/RjOXnlgZugKCOP1CU5mBPEoOBMTqxeLq0c/PRKLHS81Cd9rqwLeL7NrbnWJGIASA9MtLySduz8GCrmDx/CV29IIU20BLCQfwKDsQIOzYwtIc3BacPoAR5+8UnT/q2eGZLCOA7BoIvfIfJSDffkLFjt8OKrPMNMGe8cfOTcoOBHnXRGv2cwF31f8Kd7VHfaDI+n/ZP3A8sED2wOd3gEj/cWX6Hbmsz2iNvuvQy+UHAljdWF6afYfRTeW6u8Dq7OroNepF5uO6EUdwABg48Axqb/IGu0DE/3GGbW38GxEtJ/cdev1WAIKoYzcW1SMWUZyrIdMDdfwhHkV24IU+NjMvSd92PGB//oZe/teZeIQD/BVuESDI9T5pTXQc3hDZveYyDQCeTp+buZycFIDnQHfjCFcJcAn1uvTdGY6w8m8Gg3ncIkAAuxvKy7O6rbznLWzvfZz3XaU9Yzk75ZECxDaZtoAhKYMxGs7jlgGCuONwS7Ptwht2cG7PzqR+jzo307lsB5QajjjbjqdD/RVuISAo9H6hLc3gbiy9ubIPZG448uI8qhTqsaZNH1+N4nELAQGcbtRcnvaQqbzrHx4MBj1ybvbdfHdFnD7VnrZL929EtQtDt0m9lYAg6TjsojaV2wo3qEFdT2HY4vI8qpQbdn26nQP9G2F9OI9bCwiKfj9Xl6boAExdWh4MMOzlU/+2ByrWA2XsceG7Gm0ftxoQwOuEwvKk27ln3WBI7goAMc3m8kxBwkG5etGedOfAcTxuOSBIujbbbk309YMOtTLKm0S9bGHm06r2qjBNvj1R5xZcj0fzuPWAgBi9Ql4e77aI2ZfGWACxjMbqXIwk6XvSwvg6jedxCwIB/I7fXBq3m3vWDSZ4thX3koV5pJIBX09b4zb9n4DHLQkEaddi2gsjf9p+h1meJPghtlE7NxcjIXafaY9s7eB6wt8xrv+7JYGAmL1MXh4+kiCGIU8a1Sa9qKVN+NnRyvqOMOKc0Yl43KJAgKDj15eHha1pN5zmUbzd5y7MxUgQ6pE2bNP/yXjcskCQ9QxGWxzotP0OszLVZJVUD7RJjtYeL+LqWBgYAgfX0kl43LpAALOXiSuH3RYxDKU9bYLJ7bFHOGRvr3LTrLcPFxVcy7hJeNzKQBB23MYht5V2oqUZBheZ7h85K7+lWA/kg4e3TszjlgaCvGfQyvI+a/A63PJsc+u8Hr04beZyaFH5grK3WpPzuLWBAFYvkvZMbiR9Q53aXW0r111lLll5oLAMdnEXQHAtZyfkcasDQdRxaztndSWdeBZ3tSO/h8XZjz3cp7TviO2tDi5YDyfmccsDQa4blLRKA4Db4Wd0VzuFGbY0l6w8gKCXtFQGQLARTM7j1gey+dxqtY6ib2oLsx9YuKWgd6Qz9vaK2H2qLU/J4ywAQdxx+JbUSZbn8XyjMCxxZU4VywxLEKypeJwJICj0fhytrs7joSyAsJu355KVBxDdWCtWpuGBI9t4GUQvWUE9LY5eEACgcYfSXUuPXg4AFHTzDtGa6rvMt2lOS4tYiK9351QYvXAhv27Po6RgI2yufB3+X1NcciZcFgCQvp7J5+cwJ3uzNKvfWDyyCwzWY/qOJv73/2WKa84MEMDrhI32fJ42AYh78YJ6tCKCjYieqv8AzhQQpB2bk+cVIYHYfX7pKMOamXicKSAgRi+Tzh9hTvZ+Jb2wNXtWfjYeZwsI4Hd8YU7P/wDA1rmlGdfTzcjjrAFB2jM5aWU+z/8ApL1gltm7s/M4c0BAzF4iXZib24KjMzNk5YONuL40U57yzAHZfNx+BN9/UJnuqtPmyML1WXmcRSDIegbU+cztAQC4PXq6rHy4Htcmnu96QGcRCGD2kua8krYAsr6jLEw+5gzXE35WHmcUCMKO21Dn9IwcAPwuJgZ8JB5nFQjynkHkOc22AoC8b0vtiUKFcD3hVmZ/7nhWgQB2N2ouzmUFyKaCbjFJcUfkcYaBIOq4NWVpfuUVhim2x81wD9dT9ig8zjIQFD0jly/M8QFD2M3GZC+PzONMAwGcTjzHBDBQmIYwKisfrqfs6tEmd51tIIg7Dt2aWwIYQNRN2sqwWGEOPM46EBR9PZNm3gJigIjZbywOzsrPg8eZBwK43WCeCWAg7kYLg9Zmh+sZc2QetwEQJB2b0s7NsUBi9fnDWfn58LgdgID09VSceguIUUp6QetAVj5cz+k58LgtgABeJ2hoC3MskDg9bt+eanOyj9sFCNKuSann51piz9daO0YyNx63CxAQo5cJY9dSTyWnx2xvmT0/HrcNEMDv+OziHBPAQKq76gKDOcW7W7p9gCDrmhi1lnoGuT0sifO0j9sKCIjVTYTFec2jBgBkfVsRehl9bm4x3O0EBAg6HtOeYwIYgHejLzbnx+OsTLaeUMIdbdK9Fs2zSIblY3YuM7M3dXsBAbt6nndvzrH9wvVCi5NkfgXeXi4LAMKON78EcLg+z/4DuB2BIO8Z+ZwSwOF6ysyXx+0IBLA7Mb80hwRwtJYyR1qHPUC3JRBEHZdWZ9kDeH8paxk9l4Wme3V7AkGh67k442TPbUVrGT1v+7htgQBOJ+IWj5IAPh4ety8QxB2HUibYTHOIjonHbQwERV/PZ04AHxeP2xkI4HYipj3TdhzRWkbN5WSsQ7qtgSDpWlBmSABHayl9PDxucyAgfT1tTO16js8+bnsggL8RMO3pNj07Th4VEKRdk8gTbbq8pWPlUQEBiNnN+OWJWzi6mVNHPDJulCogAIKNgFqY8OzKY+ZRAQEAZF2zmCwBfNw8KiBbMrsJP8EhcNHNjD5WHhWQbYUbHj32ELjoZk615zlx+7AqINvKe/2iOXr17AnwqIDskdVJ2cURpymdBI8KyF5FGx7UoQng6GZO5vGccbQqIHuV6/2ivjI4AXwyPCogB2R3ksEJ4OhmThbnt6XNUFVADijecCEf3rH8pHhUQA6p0PW8vnwgARzdzHAiPCogA+R2QmZh32lKJ2YfFZCBSjr2vgRwdDMn7XmuiBuhCsggFUYv55bVrb9OkkcFZIi8jWg7AXyiPCogw5R2LCKuNID4xknyqIAMFTG6Gb+kJjdysjDXpYmjVQEZLr/j082E4CR5VEBGKe1+47PMAyfJ43ZbQTWduOLC/d7q5yd6zwrIKLEs/h3mdLbhhKpcVslUWUjJVAEpmSogJVMFpGSqgJRMFZCSqQJSMlVASqYKSMlUASmZKiAlUwWkZKqAlEwVkJKpAlIyVUBKpgpIyVQBKZkqICVTBaRkqoCUTBWQkqkCUjJVQEqmCkjJVAEpmSogJVMFpGSqgJRMFZCSqQJSMlVASqYKSMlUASmZKiAl0/8PtZRLHrEAqKMAAAAASUVORK5CYII=",
"text/plain": [
"Scene (400px, 400px):\n",
"events:\n",
@@ -175,36 +172,1410 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "It:11 Obj:-1.952 Vol:0.5 ch:0.1 \n",
- "It:12 Obj:-1.965 Vol:0.5 ch:0.1 \n",
- "It:13 Obj:-1.978 Vol:0.5 ch:0.1 \n",
- "It:14 Obj:-1.991 Vol:0.5 ch:0.1 \n"
+ "It:11 Obj:-0.803 Vol:0.5 ch:0.1 \n",
+ "It:12 Obj:-1.03 Vol:0.5 ch:0.1 \n",
+ "It:13 Obj:-1.224 Vol:0.5 ch:0.1 \n",
+ "It:14 Obj:-1.399 Vol:0.5 ch:0.1 \n",
+ "It:15 Obj:-1.444 Vol:0.5 ch:0.1 \n"
]
},
{
- "ename": "LoadError",
- "evalue": "\u001b[91mInterruptException:\u001b[39m",
- "output_type": "error",
- "traceback": [
- "\u001b[91mInterruptException:\u001b[39m",
- "",
- "Stacktrace:",
- " [1] sparse(::Array{Int64,1}, ::Array{Int64,1}, ::Array{Float64,1}, ::Int64, ::Int64, ::Function) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/sparsematrix.jl:703",
- " [2] sparse at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/sparsematrix.jl:892 [inlined]",
- " [3] sparse(::Array{Int64,1}, ::Array{Int64,1}, ::Array{Float64,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/sparsematrix.jl:888",
- " [4] compliantOptimization3d(::Int64, ::Int64, ::Int64, ::Float64, ::Float64, ::Float64, ::Int64, ::typeof(gripper), ::Int64) at /Users/amiraabdel-rahman/Dropbox (MIT)/CBA/MetaVoxels/metavoxels-code/voxel_designer/julia/include/top3D.jl:710",
- " [5] top-level scope at In[94]:1",
- " [6] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:16 Obj:-1.484 Vol:0.5 ch:0.1 \n",
+ "It:17 Obj:-1.499 Vol:0.5 ch:0.1 \n",
+ "It:18 Obj:-1.514 Vol:0.5 ch:0.1 \n",
+ "It:19 Obj:-1.518 Vol:0.5 ch:0.1 \n",
+ "It:20 Obj:-1.523 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:21 Obj:-1.526 Vol:0.5 ch:0.1 \n",
+ "It:22 Obj:-1.526 Vol:0.5 ch:0.1 \n",
+ "It:23 Obj:-1.526 Vol:0.5 ch:0.1 \n",
+ "It:24 Obj:-1.529 Vol:0.5 ch:0.1 \n",
+ "It:25 Obj:-1.529 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:26 Obj:-1.532 Vol:0.5 ch:0.1 \n",
+ "It:27 Obj:-1.531 Vol:0.5 ch:0.1 \n",
+ "It:28 Obj:-1.535 Vol:0.5 ch:0.1 \n",
+ "It:29 Obj:-1.534 Vol:0.5 ch:0.1 \n",
+ "It:30 Obj:-1.538 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:31 Obj:-1.538 Vol:0.5 ch:0.1 \n",
+ "It:32 Obj:-1.541 Vol:0.5 ch:0.1 \n",
+ "It:33 Obj:-1.54 Vol:0.5 ch:0.1 \n",
+ "It:34 Obj:-1.542 Vol:0.5 ch:0.1 \n",
+ "It:35 Obj:-1.541 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:36 Obj:-1.544 Vol:0.5 ch:0.1 \n",
+ "It:37 Obj:-1.542 Vol:0.5 ch:0.1 \n",
+ "It:38 Obj:-1.544 Vol:0.5 ch:0.1 \n",
+ "It:39 Obj:-1.543 Vol:0.5 ch:0.1 \n",
+ "It:40 Obj:-1.546 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:41 Obj:-1.544 Vol:0.5 ch:0.1 \n",
+ "It:42 Obj:-1.547 Vol:0.5 ch:0.1 \n",
+ "It:43 Obj:-1.546 Vol:0.5 ch:0.1 \n",
+ "It:44 Obj:-1.549 Vol:0.5 ch:0.1 \n",
+ "It:45 Obj:-1.547 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:46 Obj:-1.549 Vol:0.5 ch:0.1 \n",
+ "It:47 Obj:-1.548 Vol:0.5 ch:0.1 \n",
+ "It:48 Obj:-1.551 Vol:0.5 ch:0.1 \n",
+ "It:49 Obj:-1.549 Vol:0.5 ch:0.1 \n",
+ "It:50 Obj:-1.551 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "([1.0 1.0 … 0.958408993545227 1.0; 0.978198927906208 0.9681589686087783 … 0.8715946931027082 1.0; … ; 1.0 1.0 … 1.0000000000000105e-139 1.0000000000000108e-139; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000108e-139]\n",
+ "\n",
+ "[1.0 1.0 … 0.8561226842216597 1.0; 0.8510994973644505 0.8603627340113739 … 0.8456223326741049 1.0; … ; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000108e-139; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000112e-139], Animation(\"/var/folders/f1/6d3kkmqj2n38tl1j1qkg8pwr0000gn/T/jl_KgQ9UF\", String[]))"
+ ]
+ },
+ "execution_count": 233,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xPhys,anim=compliantOptimization3d(nelx,nely,nelz,volfrac,rmin,penal,50,getProblem,voxels)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 277,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "scene = Scene(resolution = (400, 400))\n",
+ "scene= volume!(vcat(xPhys[end:-1:1,:,:],xPhys), algorithm = :iso,isorange = 0.5, isovalue = 1.0,colormap=:grays)\n",
+ "display(scene)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 285,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:1 Obj:0.528 Vol:0.5 ch:0.1 \n",
+ "It:2 Obj:0.147 Vol:0.5 ch:0.1 \n",
+ "It:3 Obj:0.022 Vol:0.5 ch:0.1 \n",
+ "It:4 Obj:0.001 Vol:0.47 ch:0.1 \n",
+ "It:5 Obj:-0.0 Vol:0.43 ch:0.1 \n",
+ "It:6 Obj:-0.013 Vol:0.38 ch:0.1 \n",
+ "It:7 Obj:-0.138 Vol:0.36 ch:0.1 \n",
+ "It:8 Obj:-0.386 Vol:0.38 ch:0.1 \n",
+ "It:9 Obj:-0.7 Vol:0.42 ch:0.1 \n",
+ "It:10 Obj:-1.027 Vol:0.46 ch:0.1 \n",
+ "It:11 Obj:-1.247 Vol:0.49 ch:0.1 \n",
+ "It:12 Obj:-1.662 Vol:0.5 ch:0.1 \n",
+ "It:13 Obj:-1.897 Vol:0.5 ch:0.1 \n",
+ "It:14 Obj:-2.139 Vol:0.5 ch:0.1 \n",
+ "It:15 Obj:-2.204 Vol:0.5 ch:0.1 \n",
+ "It:16 Obj:-2.243 Vol:0.5 ch:0.1 \n",
+ "It:17 Obj:-2.265 Vol:0.5 ch:0.1 \n",
+ "It:18 Obj:-2.288 Vol:0.5 ch:0.1 \n",
+ "It:19 Obj:-2.299 Vol:0.5 ch:0.1 \n",
+ "It:20 Obj:-2.31 Vol:0.5 ch:0.1 \n",
+ "It:21 Obj:-2.313 Vol:0.5 ch:0.1 \n",
+ "It:22 Obj:-2.317 Vol:0.5 ch:0.1 \n",
+ "It:23 Obj:-2.317 Vol:0.5 ch:0.1 \n",
+ "It:24 Obj:-2.319 Vol:0.5 ch:0.1 \n",
+ "It:25 Obj:-2.317 Vol:0.5 ch:0.1 \n",
+ "It:26 Obj:-2.319 Vol:0.5 ch:0.1 \n",
+ "It:27 Obj:-2.318 Vol:0.5 ch:0.1 \n",
+ "It:28 Obj:-2.32 Vol:0.5 ch:0.1 \n",
+ "It:29 Obj:-2.319 Vol:0.5 ch:0.1 \n",
+ "It:30 Obj:-2.322 Vol:0.5 ch:0.1 \n",
+ "It:31 Obj:-2.321 Vol:0.5 ch:0.1 \n",
+ "It:32 Obj:-2.324 Vol:0.5 ch:0.1 \n",
+ "It:33 Obj:-2.323 Vol:0.5 ch:0.1 \n",
+ "It:34 Obj:-2.326 Vol:0.5 ch:0.1 \n",
+ "It:35 Obj:-2.325 Vol:0.5 ch:0.1 \n",
+ "It:36 Obj:-2.327 Vol:0.5 ch:0.1 \n",
+ "It:37 Obj:-2.326 Vol:0.5 ch:0.1 \n",
+ "It:38 Obj:-2.328 Vol:0.5 ch:0.1 \n",
+ "It:39 Obj:-2.326 Vol:0.5 ch:0.1 \n",
+ "It:40 Obj:-2.328 Vol:0.5 ch:0.1 \n",
+ "It:41 Obj:-2.327 Vol:0.5 ch:0.1 \n",
+ "It:42 Obj:-2.329 Vol:0.5 ch:0.1 \n",
+ "It:43 Obj:-2.328 Vol:0.5 ch:0.1 \n",
+ "It:44 Obj:-2.33 Vol:0.5 ch:0.1 \n",
+ "It:45 Obj:-2.329 Vol:0.5 ch:0.1 \n",
+ "It:46 Obj:-2.331 Vol:0.5 ch:0.1 \n",
+ "It:47 Obj:-2.329 Vol:0.5 ch:0.1 \n",
+ "It:48 Obj:-2.331 Vol:0.5 ch:0.1 \n",
+ "It:49 Obj:-2.329 Vol:0.5 ch:0.1 \n",
+ "It:50 Obj:-2.331 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "([1.0 1.0 … 1.0000000000000107e-139 1.000000000000011e-139; 1.0 1.0 … 1.0000000000000105e-139 1.0000000000000108e-139; … ; 1.0 1.0 … 1.0000000000000105e-139 1.0000000000000108e-139; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000108e-139]\n",
+ "\n",
+ "[1.0 1.0 … 1.0000000000000108e-139 1.000000000000011e-139; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000108e-139; … ; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000108e-139; 1.0 1.0 … 1.0000000000000108e-139 1.0000000000000112e-139], Animation(\"/var/folders/f1/6d3kkmqj2n38tl1j1qkg8pwr0000gn/T/jl_bqVhg0\", String[]))"
+ ]
+ },
+ "execution_count": 285,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nelx=40\n",
+ "nely=40\n",
+ "nelz=2\n",
+ "volfrac=0.5\n",
+ "rmin=1.5\n",
+ "penal=3.0\n",
+ "maxIter=200\n",
+ "getProblem=inverterFull\n",
+ "# getProblem=gripper\n",
+ "# getProblem=gripper_inv\n",
+ "voxels=1\n",
+ "\n",
+ "xPhys,anim=compliantOptimization3d(nelx,nely,nelz,volfrac,rmin,penal,50,getProblem,voxels)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 310,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"nS: 2\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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6yfGwrZDZbYCarKEYgBACbGpysSgVPQj3f0KEGpWzyrXT3v3Rb8y9b37vUOXbVJojkmDGkmFFkJkkR7erDY3LLyntcRlASKda7wYTLZNA/ST3j5kW3xkBQqxSpYT3CAVwPyiQTk3LpJ2+XgYx5drHx9BNQi06jKFoZI7G87IpmstGWECpqekMMzY10xNNAb//4a/quVffeN4URcSTAZlXw8mYIYomlwyqfEUOZ5eyZ9rlgRhSWSRRVgZAxI+U0L/skITxX0daCNl7drqJvCdQdOEHHACTrzNbnIsbmrp08nFdN7D4MGY6L5uiWT1dSeZDQLerGNCh9X8TAAsmzLFA5a23P4+//OarwRavRVMRRZDpFEfLvBpKR02pIiC1hOShywLpNityIGG0CQD1I5GQ0D/JxdH4H41BzQMk8tp0axY+CP1poV96rS7EXfSLplDXk9z53ZpuLkyETOVl6+zQzFkEPPtJLvSvf3ovcuP176Q7fCeYipmSYHLJoCK0mHSc7tTqanzrsjsPXQoIadfq5zuJVu8J1HdVgNA/uC5I+i9GgUznrgHkyRsXrcJUqj1b4UCmWNPT6SBg6rV3qu15brFdyUno1VEivYXTcyuLBhsK/jv14BvfO9IFiU4kaJnvhtMRQxKMeDrY5SuN1pcut/PQJYAYYoUHG5P677n6rgqAULdeCKaCb380eHIQWAHBc2PF1mSb3nRCQxy9f+ly7W59ESaEIHHn4naOpuCHVUaDDQUfPRtvyUY8GVIkmU1xkAQlHKKrXVoIpreSi1uuhYGozYpMbUnDZZniR2rvL6F/m5R+yX4wTH8myL5oASR3OF79ep2PzktMM6WqnpoItJr68cc1ee4AckKEAHcGvoYrHMOKowHSjSMOJqyLShKoBGASQI+3zOh2blHLtRgQ0q7WtUCsPfyy2gveAiChf/63v7yf/HeiiBYAzSSw6NStYhadWjs1ywJb4ej9Qpdx8tAVFUJAjm5OBaHdq9f1EDIauDPjisLmcm4clQstAkQXKwLioxNPPXPVe1TmbvXwlT+5qQMtiBAa77FftlohMDl7CsBo1cxcysZuEYlvp2ZMQ5i6jJOSUMT8cAt6QPaeYQdbPFxGfaDjuw0y4TrDFRy78yNyD0RtVNojtgoj5goACLWL7+dHKqr0zoFVHTM3EhbXFhuhLauA+Dwcg5vpQBFCQ1UgAmiM/3YEFCEg28+z/S1pLqn+ELVPpj+PrHMtPZxPR91ezCUQIlcbY7YKIMJn4ug/mev/bPzFKJ9a3thq9hRQaq3poTuReDntYpLO1Mf+2QC6RZmKU41huJIQkNir1NKmYnvto0+m30wIkyiGsk5clbF6cQNEF8sCxdFjOMQH4tgl4sPhxUC1U8tAus1MhClX9exYWyetppy69MyD0mzG0iFKFoSHx9ANAsK+GrvkNUfVbx+9JtcnQ+igSMULDh36nlwAUZoVmR6zVaOdeV/cv57qy8SHlkDsZk+hifXARY9IWk05lV5GUEJp8PFMjIKpy7WfSDAD3xk6f0sJ6PUbxrjlMuKyFtrOOEfvFIgpVxtacMxWgZiN+8r4xyx4gH9sCcR+9hRtXuR6BxgRudlaDg6ghyQbAwDp/2mR5FetwoGXYjM2Tz2wXCx3zqbyKYeWyxkQXagIVDQwjkO6L5tk7Ov96NRkNfx+GggFIHbLtoxErqmZDLNcHADQafJcJgZDfPJLyRrISCHcX95gxlLDBgFpKighWkg6ehAnQJR6tT1pq6B+JJGJUnM/KFj5eca9IZDxGOutGQ1ZF2tMrCsnM8teON5p8lywo6bVv8y94DBg7o5Mv2EMnK2+5eLkbjDjJIY6t0ymXGnowUybnyjkQ2Hy/bYyVwCg2URlyal9EwGbwcmj7PWUq7pwokgkclLJHmXMgmPvZ/BBZ2DoXsNge2T6fzBtRLlihSsk5o3g57QQTSyJ9IStAgDo73Qm9oWx4wHjns1AbVYTkZtiKih30tllp9rofD3AdYVEhv//v+ro2iOTIxcPazVDPJZ3NuoGDy1XSDIjW3OWXM8E0qlXO1O2qv9cE0CY/A/s2qP4yC5/JHDb5n1pN8RUJgyz2cBWcplpHUSoG9kUg05DiL+/P//KxGjwhgYFQC8wEQMQCoCNhSPBmRP3E8PE3h9mvKOw6e1Zy3/sgZitSlMPRdtWHyCUflfBxe4XYL/7VMq2aMe83a+sibQbWijde4+6zWYkt7zhQrsuD4adnTNx7seJeL9l2s7ch8LhXDzYr1uL9KPxYeIwqMKVaG47ZRshsAOiCWWRjrFtO17kw9oIEO5fqK1UziZyUz6zf2QLT6vTUEOZi2YtN2SHs+5zpTVG6cr3531e/UAEZk5LUjR6aTUz+5dJyxWWjHDW7jWzBEI6DVtbNZDw3hAIXfgBB7HC5q2tI/nA/jJT3cgEDgBEqJm51OWTOky+Pmb/jF/MMYbqXbUHY042ReLWcO309IOOm6yh5VI7VKoQt+rDLIAYrUrTCEU6s7t78p7YA0IXvlegAXQr7Vzasuyz3sVxo9VpKhM4AEBvNEJbcacVb6NWTRn3ELpv5WZ26uQdEU6AAEi8NBbYGa+4cZN1YbnKiG1bjLGmgHT5ikjHBwvQ7DMzKfUdlQKoPg4AhK9Gt62My0zjMEJEaVjhAAClJiUudfawWhfiE9Nf5z+9PROI8C5xCmRiPrj3s5G/UhZ/gAq39GA2N5kWMQ6EdOpVhR7semGjQfnUd1U6/73RsWCnrOUtgurGT7ZnPNOAiNJQQvbRarGmZdOLBmeNZp3dmtyRsftX27Ni4+QdEQ6BEJDtlyibX/VkubaLxNUOEvnxocnoB41WpWEG0x3J9tZjdw3d+e33nxqro8hh/bw9bQiM6qzRhPb72wFAaXaChRkVlIjxNWHLwTaXFs8tVXUrmNL2rG8ZF/NvZP5d64b1Ew6GBuxE6NGkAVAywpEGH82NDk0urtPlyxITY+U27C89rtDX28r4shU6Gym381P+Q1xOwl7aw1vdxmwcAJgsVz+LL7AdjFJtJS13S1bL12fdLyYunvdlUXX9ATzT+4MeLGJqhSLyyXkqHxu8MMyfAwBIu/S4rGeJ3ktwG7jQ/YRoiqJsWmRDjATGEp6DiW6VTCzoNv5OnBmnULoivZWdmxXAcFGpbobc+Vt6rcjsWjZQ41fK3iyPlisOgcz4WH9VXm53HrthJU5eU0OSEisiAj0kvdNLpTJvBjMXtmqwDeh88yk/iO6PtRJ2K1Jt58e8B/bmJ4a9zdJZprvvbOwXuyZUBTdDdyJUULDLydEVcVbDTSQEgKFoWAAZnbUnAJV4zlmZKBgMCCYtFxWONsRwLhulep16uSLR4bA8/W1noqJ5biyIoJaVrbHwf9E+bKSzmBP3HZfWaES3nE5UtxtSxjaPpfuX58lXZz2j8L7+SpJFGBwFZAD0JulFKFAbJzKhdLOP4xk307S9FciTHRMVEQ06nU+BBX5djtJnz/d4DOceL747V0R+RIdHm0moIFXa2yMVkd+38Y90lgXmxH3HFcgn6sdpR6cWafVm/GhmnyMbsxzfRP67+YloeKH/H0xFOtNpSS1JiB+5mzWnAYCa2CAapM38/uC8mwKLL9R09LfN+muh3gdBDQ6xIZRTc00M+cFoMwlmI+Xj/EWwUdm3/JrBDB633XYer4rsSWVx/tDd5CuB3TlVZcx0NqhnWcbu+3pDvpbuUPVtHcwiPX/PMMBghvlc6l2181wCYP48c4TWF4ZR2mEHHc/8rsxCGl8VLjr4QIRUjMig0piaxbjGoEfqVMk6vxsVSlEVOTC7kbTKfG5n5m4xxr0WpL2Zt21NuCwDmdKpuUWflxoyaMdLtcZE9xwmGoTu1bl6V0Vk+yAC5s/xeUkTFBilnUvmbRNtxOdio6GGFOpdkUiNqQ+P4QD0hJv4IR1NdMta0N7eqJVyZC8xuxEZx3V05yzp0BoiG5jwMIkpVY/jLaGpkMvMv/fWvA5GnuJvVOCQ3GJBETz+exL9AEDoZc5N12F9mxHLJfOtbJYCaZ9OxYyn0zdjjnuRvlqNds5m6G40q+H5sa/uX90HZnfrAEDR4RQDFhwACToMuaPD+Q4tjqR8IBKAfv7pWxRY4Ojagw9pE1DfSXw57KbrsBCRH9HhbDDEgqZiYbHS3jascExXpGK6jIvEo0LZeuguNdS8k+hwGIA40/MFAGLI7dGLacvdFdNgAOWuCgBsNEn1xiH//o/DbBcAEe6+Fr7s6LT/ACwX5RAtPO5M4TBpq6o3pZTLG9HpeOOUm7I5Sl1M7zoxvmwYAPnd3CYCjC41XUru6YgYELXHAzk5jh4Q2sw3iwAA9e5rS8jyIAZgqBQNwJwc3Zi09RtGGu6nagP5RP1RNjPaleiNWvyas2EKvf+eOc/znZRJL3+7rAEPeqcQRg/IX+inZ7TZ/+0yiAAYf68uHsi2wXfc2iwAiOyJ/KOLoTsRm/qe41HBEdud5/lOaNnNAwAR3+8nqrNakkV/mHKqhQfviXpXWezKM2X2/z/D/uq6w2uNiUrup4on/Xho+/Q8duS8obE5AOTUcf9MsHweyjvvXCzkSAB9IMimhh9R7wrL34+Gxhwci4vZeirwuKQBWukxfbTlwgDRX6MBNJxudKOtYDsmZaSyj3aig6rC0afG0fBD6jvvrKCRmPO2IDElZxeaVmj3sPuoXj/rHOy5M7dHrnK+VrDBdd+9AgDQn8aDwKCFlI/JRaCDCEs3W+b8vUdIffGGGd+Pff4Je83tGjI25/ijjveLcqMxHtdvixQwmA+JHm4HpIsNEI1SYZkJg4Ry0tq15MImulvlc2lJD7osNLV9jyBw4MCSKsH+gyyzVsTfXPDg7uD5WBoYzBgeQTGZk9Iwyr9EZ8shDpDKSwvewOBF+iiKdv1RzuWse6FQhIPVNCbdrwxqiUh6o/O+uFfwUj85oT9jCDaJ0LXGsJEYpXRoKfd2iAMwSrcXy2GQyq1sPggEEkGxybpKTaGu3aOuz4+xjDS8pSFR7o6M0LiX2euD8wAHQMAkGWm/dkHkrJa7vNmaxmH7PGb5+UWAKOVqYrc3F0OF40SQXdmtQKSdn9NZk4mFiMtBMtp9gHsp+vQw2n1hQJm96/ijpXbtVrvA2j6M+y18AejVc3J9e4iA3dqhHlVcDGjYtDan49KtHuHS44IxHvEXkrdSw3+N9GhU/unAl0c8lUuOEV1vyttyfQfhVMrtj3V24b39zmMXA6m9ec6sZXu7LJLx9vHC9s0RsznmYiSfir24JCKucTRUd58HOqfn3LSryx2kG0/aTq/B7s+yk/bOrnMkRNd1XR+brRVG3d3dL+0/NTrPPJFKKj+sfzwyQgu97HKV9eCerr9FPq3/0+tuvqA15IhNVny3rkTmZxX1H3hGmqt6qaXYxAAEKILCI4VIAun+EQ/j7tWL7MHu2Dsx0SRjN1ky6v72pkjcFsWpZzUio+Nq7GUKPJ23m4cP7rRrjy6zI0//KRbnQYxuq6bw6O+9VQdVpIFQJLwbr3862n18hbmWn0g/mWx62kkJ7482kuWNSGZJ/6D53Vcdf1quq7PTs4jQpHIOEubNt+0us9AWQb17G43z+sSk4uAmFK2N/Dz+5cj17MS3p5cjGMUz+t01EBk3a/ov9J0/c/hOd+ud2Ny1h3qjFc7NHfsbf+VgXZurpzIaj1vTM7zDm4xu58i9kLgxFfyffi5mnz35yt9fEFnmqN2yjAPVdGfbkzaFwM78FF92O1E7S6TmjG2kA8sfuz6IBEDPUFnRsBH3pez1aatrcWdqi37y5Q9WT2Rcyfp1R7UgNZBzlgQf3pfqcnp2xJFjLLsu1zyI0UWzVrfNfpj2cujC4c6RRb1a3ZrdCj564d4okQWdLVei951E+epdznnAiovyVSHn9m1ylXTT96VqLdVRKsrQYnEvMYUDq27K8l2g04GHX7q0s+VKTKQ9/w5GQwrvuvF9mCxXL3Jp+xdemm4gzpxdYgCq0vNq4YDFxLiM+yNqf8fyxbIpa/xmIPjMb4aNpJePskpR+3MHoURsMHm3S6SDO3JdTts38E8nth+c5+xegOiocELi4psjo8n4y8zRlnWZbNepd08qoV+v0f0lZ/9otinq1PTUQmMLwvOBjHUmCvnr1tNj9WLn7BIDUKGgJbsGcSEKpJddEH8pdiNt9yHbK3eKFfq9tRDp93jX0zM+ozXk2MJ7FOt1my83H1nHFAYZM20ACjTostKBMxAzYkbUgMcL6RvcjA/ZqVs+M/9+CXEUp7LdaAMgfDNwqcPNO/Vu+qJ5ERPQQhSpVPr7ifcAGEy7X/+qPrqdhmvLBFwcFzRQb42PCYD70vZ1+9SxWXud6PUT5cMRIlS/a7c/+2ah1PyhAgec9dS7XDcyCcpik/yh5k/ZSw0qHQM0dLuG2u1CS0cag6xK9bgHYIH6n7pP70gUHO2FEQYHZOuABAXK2WPopgm6cLh/OKOfmrn5jCk+lO6NZoOEns4Euy1+4v1Bb0+WMAK9PVkWxkLR4RQTDMIcHLVKTADdRjucMqDD0A0dXQAj0aAQgCBYJhREMGANhpiA1oWidbq9HfYn6puIH7qONM/gQQdyr9zA9Jkopo7H9x4f5QJ7e7OGOXO2Z5Ie1UfcX4BiZ78/FA2E0rnMJZgAQADBSIyDJBu6KYcG1TXvxggEgyEGLIIAQiqALnQYarerzfi2+oFIljUzS2g293pQZNI2R0/qjRP9sDDTM5m3o1znUYP9zUgjceTmUHT8KB68nP0CRS9iOqZT9+dchHz2eEmrn2k2uvVajqVh8DxliUSvnVDX5yxNmrvFX+tJIzzi/jr1Oyka2Z3FG8rgCVZxUuDELe6fLQEIzeLoS1vDc810wQpJt1QMWu9WPKL5ey4qxRL9G/dEAFB0fC+NxVuKVQzD/bXmPKD+jnw5IDSL2NaXjiZ6DV1o0hNIOmeV+I25UwIONsHUSmfGxQyJu6EZRSMU751ntwiX1bjZY4+s320vdhuaBbCHxI2tGKyONZtE0jrh00fzXXcnu5IatWN16P4usr88DSDVOzctDGDMCZ/itPow5qiI8Qvd3S0HIHYS2AMw44g5XWjSw9FP67NO/sDB/IKzfXubD6XjvrPlGMjUkHWivx14yqkMGGrys5c7Q8K5CKU7BeIcxIV0nu8j4R+q+7tOHsrhztbSF/Kga3e8eN3hi0fRyN4ej8osNYd2jvS7M/oQKiHTAOLYRzjjAsTI5XmeziRQf0QmJ8/tbunQsxQfS5FfSYAbIM5NQejVtUzcT5aQwgSQYbwjC3AIh5Fk99Hrh12CuJDO88RoBqYmz23keDP+dvXD0GcSxnIdx43Q+Dmcro4ZJKGvDaMJ67JWfZ9av38KhgVwpCGRGcQ7+g+oLOXQ9PYDJRc8dPhh56cjfI4y8xsJoHvvUTyZ2GbHwzV6RTzDIIxGAOdEyDCWvE5rBQDQ7xe/mk0gGcclWsFMdU7qsShzw+GnXRxXoZeemO9LoHdf7JWfIZMBTcoAziA+fAy9Pz52WL0EJLyedKOBhmMccnKnsBIOA0mPpew158/m5kAXs3qsPMKbc8tv6nj88WPophsg2P/SuhtH/97ZwvKPqb9Q83FnZ9/FRI67I48an+uHO45svKnj8d2abjqrZAKC3IvLX+Rqd7uRUtE7lBDNr6zfqj00DgsrOmEHAPiHinPepl67e2wSBzmiBAS5L68+cGUhej+r1JWki92IXMisHOP6lqtLuz2lTXos5lxYRFOXHz+uzj1hkICA/e5mgFxLAVKdZBOXvtSU9PPTwFNpd99xfWxe57ievO4m98OAIeOkoUCBBKDe+2mv4VCJXib4GoFMetX0tRQAs8kHC8te+KyeluLX3XJ2f45h97QUecr16zQ8ya6Ono9sPK5iK7qd+qDnkm3OZPVX0nXrMre9VLvVPm6kj1Z9jiHQa4g3MpctrgEDjJSkTL39ycOqbpJ1ALEI6NPDpY3tmpZxaV9mSXrYyh+4998WOXrVrD6iXHZV9tLqLS5n1P4/aUNu7wUQEKHBLO2wkuZDdfbkuY0WO5y4/lB358zZyeSbgVwUgPS/brg04WPem5PJFusgAD26+FdviNHlDBOrj8xr+UWutODx3cJDd8MdG7VqZra/QqLzezdfJEaD1zQoaCOKMAKB1KLTxanDsRdLqSkp5ztu2MksH9M3FvOkFz1PXX4kuHF/LaXW24mB5SOfyY6/R4zGI9kYLzhFZ24slMc3AQSQarisC6yfnofmTp7baOED7pXjmjv3d1JGkw9fLBXgHzsuiPK7hlUmCaESz9oisQ8hZ/cmf2XwzdCl0iTV4yo3f/LcRgsDgfakHJ5ekeVURKqPvoguGohyV7XOx71IrXSjbYtt9C/nArcfCtlrCwNdHAiM0hPmhsNpl0lNmmrj4/mH0gPob4lnBwQYmVe50KwVONSB5RPINX1RF1h8KO/sLx6WuwQQkOojHC3yJukNITZ+pIdTi9Vb5G0PBInX3JWHtslDJ3yDcbKMd0rNLzRnk+c2usxgjNpmHz7UnEV/R59VqDM7489KGs54kA+EOZ+Y2oh33oyXzbtMpbn6eSzrdmBHao/IdWeT5za6nNOduRU5OXa5bUz7SS19NPHumQ738BDnHgZJPp1AO69ybN9INr9vPKm520rOPP+Cftr5IbuuyuNMiVsPi90jFz2pVpfiham3UtccfXlQ27Peencb8QK6fQOP7Es1MefCBdZKp5fwc3q6bPwoevNx8/Mjp8bW5BuB3cV95fkNZHwjXifrabuz1mokYs2K4NgFVh8J3KVGAsBlTRaA0FN7rQfzLHtfrZNm9ppViVuOupApczTvQw4ezpxtcJncAXNadtZ+5fv19NOXDoVdPsLK7rEnD5y4v2pdTlgvE3S4I+l4AxlfM2aak9beUf4KKb045xOh3VbtJONgtan4eWdv7vZb87WEkDe9E3j4ub41p8hGkw/uX2b8C1IaeffZ3DO9HKos6r35lZ/1ZlyoG27OozFq89PA4lGhLsx1gRsPtaNlxFuXMgeRY7/4Yo77K9XIlm13R207GYYYA8tBs1tfpdPDw6l3sAPAuP4/SyYAZsGh6gzR6XhjjgtMqo/x1FJmJJYzKZR65vPj7ozhqVLrpGbspwDOzfiU+9McS7erp9tjzh2T+lelt0oOx/tuFcgnaidp+z09jPIT9vql5+x6z/HnS7lMMEnq7ZiNBdVrlcDOTCtMKg6AmJ9poADuz3IMhQCn1anxnWwp7oUbX3S35h7xOPad4A1nHw8kWF5ggtYf1opn4adSS6nIS4VOxtQtlqOWe+ASsUbn5uwCzj9ycAf91zIocH82uJZUC0zt8Sr9rxsBV5vHOD9BEUazGbZ0gZViLe5+8txGSwMC4/wsYDEJ0K6p6bn79xg/cRBu0H9ugCn86QVbrapsTZJ2N8/lCgig1toWfqL8RTe1wOS5jZaX1srsHukPJs/H00qn7GFurvNhVJ2FKJg3/+UIgcBupno+/kVy4qrM5NxVuD60t9M5bk70U+J9aftoecmoS8z0oPPsw8+10YlkU6izu45G8Uor5aA+2vnnxt/PVKRykh81FpK7jW2deL1jikf5CRe48YV+Y6HJcxstNfE7eytQOrvYWFo+qWcOnUVVzN85MZ3Mi5PVF9pPnI84BC7mHXtyPbCmM9fCxeLFqTiVz8nNpWbPLzcTP3k7XhlEf7vFs/C1jLPrszc7DsJUGerWVIOmsrudk379aMV62CUQ5xP5QwUK+/pJPwpsFB+zt3JLTV9a8tKI2EG68UUHgFE/1vYdJ2fSWXN+nIphLfe+jOxHTusAIJ2aOcdbWvcvub+IyY4ebAvHIgG0s7PIyLbtS9GyswXDB2zp8yNOqhluzj1Hkp4fNqduP2v5+jDb0Wp7h6pL6fRnLhsI9epCLySVjDVLQo4qNhKLT57baOnpm4E99vTTBJOcu7HumJJOzq+j7RpcPFz9jIocBGWXDWRxsVtc/TMFuUtMnts95NLLyuxoHSO67Y503Fls3faeQa3Nstojl5cgTxbPLAvHDa1ztvw1RitYXvcf//MHSt1tVCkGee5QhNgd5KaeijefJ48fOZu3uJDBL37YV/0Bc4Pa/YtL1tW0ljdSH5V43s7tuDrT9OfvUq/NtVmxm5bdklAPF2iQs5rbZ9Grbyz4ipPKMbVoCtRsrWYBauIgXjl2s00bfUA7sFkdq2ann9cyuzSAtut3i5AFu1CjeBy8tRIeKwKC6GGm+dBNF3vELmizWk/0aykAaLvu0cmDbyz2+N3TYvTmJZMZ7LSqJdqhg3zrcxeHd7I5GHNHadNRerNa5g5YAHDdowOGuViIVnlSTV5+8txGK1szz+7tqw+azsvxNSc2a7IVdJ7IB72MVOK6RwfkxaazWidC7vrKdjlY3SYGdOG6eVZ1/No6s1lnY4c5NYqha/1e2b3BWtTRFk7b+YMVHIzb1wp3laByN5niudO30JHNGqv37hm/NcgSXMBgwWgtsgykfqrtXiZ3d55Wus1H6iBSPnV4rCD9Jm0+mb+R94VlEk9x1K9RIt93b7AWslhm+SG1t9zFupP1sMJrA/GDRPWxQ/e3UKAcHAym9ateL1XSgwNHtM8+W4AH+ZS4jkMZxSeh/eUkM9hptUAQOcjyDt1f+mt0x8HHtPsyAeQn6lF67CeuJYoIuRyGdE/LsadXsOPDqFa9Njx4EDh/suPoIY5YR21J+yx6TeKTW71/EVM6XaB5AORTQjs5uXtEnaKQXN7kuY1Wvlif2WUrpztOViOxOWcpwkT+rLsb7u0k3uE7ZLHgjyi6OeAeAFrFdm535fW1+t0TqDxbOtUdTKvRr/2Nw0tqdLn/twVp9H1eVyuk+HMtv8zJcxutY3+RLHte7DrIe3Weyr8wh6FEEThy8/C1MtldzR5O41rLhi9J9qyq784dTM0+KXipIp8S0C84f9/NSpXdSa2jZKtvgwAQO0zU57u/9FfWttuJKALskeOP68ViaD+1lpKtBwjCBznpydyBOLem0vR6EOcWq3taSR1wTj99Oa2rCgJ7hfYTB6k+M7TEqTRRhAuL1Tlppi63tMWF1gUEzM6OftqY/RlpZp0vz56RT4kLiyWdtLaurW2DzvXt4kZtsedn+qxFLY4WJSxDoggg5/DR+XN9Z97ysCVqndvqpdmzc22G+2s6CZ0sQeQJAeg3HVUyqZeo3dVM1lprfegBcIex6oxAh7SmBmIAQKHg5KNmucjsr5PHWlsIED0oNo1dm9m2tVksNJw2EP28Edlb1WSttdbaQoDQQbZ1YuP+LjDpt6C6DhtI97TOHa6Xx7qBgN3bVp9YBhHHp2dXqa7DBtI+4TMH6z7YZO175dJ5tnSqW5jl9TWQtrMGIhXVraXs9OlK69+8mMqx50V9ahp0kVnxBaUYThpI89woLGsvXBfaxG7SKbZYnthnwOw8WNvtSYXszG0gpFam9lY7WWutjWzvHT8o1o3R6K/5tnL6wrqstVGnX597BEq5Gtxd8WSttTYCBJGDc17fuyCgP2ji7oqPax+KyTOqOLuy9fNGdG9ZK8/daTXZ7/NllOqhveGMXfc/dQEq8fKaGsltucnNipyoRZHbW/XkuY3W7fYOxOzmuyPubwwAEe66W9S88DMHc3udE/vJgPaJmD7cEI+NAQGV3zVP+4eJgL0GAFDWRASIHsaKVZtEOelE2VpoMehStDEgQHafPu9nmtI7vXKshwiRACZfEJ9YBjObT/T8zuaqZYNAkDwMVs56yYrJ/hup3BVW36n1gmbcYeB0endaUj2j9leaKzpHmwSC2CFXPxnf9FC5u4ZG0gsKBHa3GmcTWy6apVJgTZPnNtooEIQPMuJJB8DFcqR1dO2DsFnqwDwZi6vpp5XI4Zomz220WSAI7G23T1rjKWtr6EgGcbPQQbp8fpGerz5pcofrmjy30YaBgCnsaE/q6yYyjCxT2X31uNX/aftETB+ubfLcRpsGAmprzyyXMbaEcvVd+0VoOXrAFSsGAIgnndzm3N1hfWxopD4qqaimP/5gvFirHrXTtwPDd7FVZrajaJxT2fymq8IbQNAutkI/m1iyu+LTo7Va+JVh761VWxmzwmyvdfLcRp4AAvW8Jn2yTiLKXQW3/vTCXjc+awdeXvNkrbU23ocAAEJmWvnGhLu5yq5duasA1Qv3SmuxwUJ107UAwCtAQCovs9/cHS/MyohMDXXUJ83MnYbD1akrljdMFgBSLwM/Hzdb4ZVMkSjv905EHpos+ayT3lv75LmNPNJCACq3Rxt/Mm62VhJHGfjU9GARr/hEyR14hYd3gACpg5D8xjiRFcRRhoaQ3e792XjSze9tMJo4IQ8BQfwwrvzxirv2yeuRSpHZ9cDwYygvAUHkIN39zkqJjF4tCcA4LwULlz/7donyFBAE97fMb04RWVocZcoCaqfVaG+3Lc/IM15WX6RaVj6d2LBxWXGUgXvVU+bfBJWixB1uPHo1Lm+1EIDa3ou+/A9X0rVPNTX5REy5Whq9DnkNCJA5CJrfX0FHMnmNpPikkzv03PN7rkBA4iDS/t7SiUxdIXneze97x90dyINAELuWUF9fbtc+bfW4HLXraBHVmuW1Tr0n7bxJ/XIi+nuZrn28OwcA7pX0dmrTj2klbwKBWa52P5ncHXvhgPyUuaILz+Tym01msJNHgYDUSoR+ezlTJFM8uDuBZGEzudRz5VUgQPNcj/x4GUSmeXyLpLznXvXlXSCQzpTwW5cmQsT3J3l8ncnub/rhbOVhIGgXW4GfTxJxOUVi0Z2/Ftza2fSj2cvLQNA9E5hfXMrZmh6/cHei+a1NP9gMeRoI9FLd+NXkDvIuzJYFj1fiu042gNyYvA0EZqWifrqw+2vl7qb2vOnuDuRxIECtZEyNER0SuVLu7kCeBwK+2I38dIGu3cK9+iZSO5taquZU3geC1lkn+Deuu3YL9+rrTOZg0w8zV1cACJQziX3bZddu0Z2/GvKyuzvQVQACrdgk/9sVESt3N1Lwsrs70JUAAqNc1e66IGLhXt2O72xipwzXuhpAQKrlzu+m3F+brt2iO3+NiWxmpwzXuiJAgMZ5l/o7R127RXf+HSO65o3hFtaVAQLxrBv8mQOzZdF9fBPJnXVvRLaorg4QyGdt+pdziVjw+Bqb3lndKV5L1hUCAvVMxN/NIWLp7ubynsmlnqurBARaqaH/epJI6CsjB7crv5rc8p+7E8lvb7rgLnSlgMAsV6djjVRy0LUT4f3paCJ3Ndzd4dNcKSAgtZJh0ZF8LQwAyvvC1GQUE95d0aG1K9IVAwI0i3roLUsiyq+nuo9vm1fG3R3oygGBdKZYRLa+Frbg8U1we1fF3R3o6gFB51S2cLae+mLa3WUyV8fdHegKAkG3yJu/mns0OPdqMLf+fZAvrasIBHqpPh1rnBD3SiS/yY3IFtWVBAKzUun8vjTjcGG68Ex8xws7ZbjW1QQC1M/16SmSoa6guzvQVQUCoahOZdENxH3bjOy5OsbTO7qyQNA660wPEQEA3DcobnfDG8MtrKsLBMqZZGm1uNfY1O6mN4ZbWFcYCLTzpoWzxd0JXUV3d6CrDARGudqdiDXShduR/BpPuVu6rjQQkGrZoP52pJFw30DAWzszuNXVBgI0zrWR3QG517XgbmrTZbqUrjoQiGfqcMkC9y1yZd3dga48ELRP2/1YI/cNKr53Vd3dga4+EKhFwfi1dNXd3YH+AIBAP29035G4V4PZgtd2LnGvPwQgMMvVtpCMbm9fYXd3oKv/SgGgC3pLZeT8FYy2Tz/LpguwFNWr+wdb9Ommi7EM/UGYLKCI3RPK+6txHOgPBMgfjv4wTNYfkHwgHpMPxGPygXhMPhCPyQfiMflAPCYfiMfkA/GYfCAekw/EY/KBeEw+EI/JB+Ix+UA8Jh+Ix+QD8Zh8IB6TD8Rj8oF4TD4Qj8kH4jH5QDym/wN+9MGAO9gGfQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:1 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:2 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:3 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:4 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:5 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:6 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:7 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:8 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:9 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:10 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:11 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:12 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:13 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:14 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:15 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:16 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:17 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:18 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:19 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:20 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:21 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:22 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:23 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:24 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:25 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:26 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:27 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:28 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:29 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:30 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:31 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:32 Obj:0.0 Vol:0.5 ch:0.1 \n",
+ "It:33 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:34 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:35 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:36 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:37 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:38 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:39 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:40 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:41 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:42 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:43 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:44 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:45 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:46 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:47 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:48 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:49 Obj:-0.0 Vol:0.5 ch:0.1 \n",
+ "It:50 Obj:-0.0 Vol:0.5 ch:0.1 \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "([1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0; … ; 1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0]\n",
+ "\n",
+ "[1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0; … ; 1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0], Animation(\"/var/folders/f1/6d3kkmqj2n38tl1j1qkg8pwr0000gn/T/jl_P95ByJ\", String[]))"
+ ]
+ },
+ "execution_count": 310,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nelx=40\n",
+ "nely=40\n",
+ "nelz=2\n",
+ "volfrac=0.5\n",
+ "rmin=3\n",
+ "penal=3.0\n",
+ "maxIter=200\n",
+ "# getProblem=inverterFullM\n",
+ "# getProblem=gripperFullM\n",
+ "getProblem=gripper_invFullM\n",
+ "voxels=1\n",
+ "\n",
+ "xPhys,anim=compliantOptimization3dMultiple(nelx,nely,nelz,volfrac,rmin,penal,50,getProblem,voxels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 307,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "compliantOptimization3d(nelx,nely,nelz,volfrac,rmin,penal,50,getProblem,voxels)\n"
+ "scene = Scene(resolution = (400, 400))\n",
+ "scene= volume!(xPhys, algorithm = :iso,isorange = 0.5, isovalue = 1.0,colormap=:grays)\n",
+ "display(scene)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MMA"
]
},
{
"cell_type": "code",
- "execution_count": 143,
+ "execution_count": 214,
"metadata": {},
"outputs": [
{
@@ -213,7 +1584,7 @@
"0.05"
]
},
- "execution_count": 143,
+ "execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
@@ -230,8 +1601,8 @@
"# dofs:\n",
"ndof = 3*(nelx+1)*(nely+1)*(nelz+1)\n",
"# Allocate design variables (as array), initialize and allocate sens.\n",
- "# x=volfrac .* ones(Float64,nely,nelx,nelz)\n",
- "x= ones(Float64,nely,nelx,nelz)\n",
+ "x=volfrac .* ones(Float64,nely,nelx,nelz)\n",
+ "# x= ones(Float64,nely,nelx,nelz)\n",
"\n",
"xold=copy(x)\n",
"xPhys=copy(x)\n",
@@ -292,16 +1663,16 @@
},
{
"cell_type": "code",
- "execution_count": 144,
+ "execution_count": 222,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "FA (generic function with 1 method)"
+ "G (generic function with 1 method)"
]
},
- "execution_count": 144,
+ "execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
@@ -309,13 +1680,13 @@
"source": [
"###################################################\n",
"\n",
- "function FA(x::Vector, grad::Vector)\n",
+ "function G(x::Vector, grad::Vector)\n",
" \n",
- " x[(x.-xOld) .>move] .= xOld[(x.-xOld) .>move] .+move\n",
- " x[(xOld.-x) .>move] .= xOld[(xOld.-x) .>move] .-move\n",
- " xOld.=copy(x)\n",
+ "# x[(x.-xOld) .>move] .= xOld[(x.-xOld) .>move] .+move\n",
+ "# x[(xOld.-x) .>move] .= xOld[(xOld.-x) .>move] .-move\n",
+ "# xOld.=copy(x)\n",
" \n",
- " x[:] = (H*x[:])./Hs\n",
+ "# x[:] = (H*x[:])./Hs\n",
" \n",
" xPhys=reshape(x,nely,nelx,nelz)\n",
" # xPhys[Bool.(1 .-voxels)] .=Emin\n",
@@ -323,8 +1694,8 @@
" # FE-ANALYSIS\n",
" sK = reshape(KE[:]*(Emin.+xPhys[:]'.^penal*(Emax-Emin)),24*24*nelx*nely*nelz,1)\n",
" K = sparse(vec(iK),vec(jK),vec(sK)) \n",
- " K[din,din] = K[din,din] .+ 0.001\n",
- " K[dout,dout] = K[dout,dout] .+ 0.001\n",
+ " K[din,din] = K[din,din] .+ 0.1\n",
+ " K[dout,dout] = K[dout,dout] .+ 0.1\n",
" K = (K+K')/2\n",
"\n",
" @timed U[freedofs,:] = K[freedofs,freedofs] \\ Array(F[freedofs,:])\n",
@@ -379,22 +1750,22 @@
},
{
"cell_type": "code",
- "execution_count": 145,
+ "execution_count": 223,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "G (generic function with 1 method)"
+ "FA (generic function with 1 method)"
]
},
- "execution_count": 145,
+ "execution_count": 223,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "function G(x::Vector, grad::Vector)\n",
+ "function FA(x::Vector, grad::Vector)\n",
" \n",
"# x[:] = (H*x[:])./Hs\n",
" \n",
@@ -416,63 +1787,56 @@
},
{
"cell_type": "code",
- "execution_count": 146,
+ "execution_count": 224,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " Objective: -0.4808364165415227 Volume: 0.5\n",
- " Objective: -0.5207945397291679 Volume: 0.4996175563575938\n",
- " Objective: -0.5207945397291679 Volume: 0.4996175563575938\n",
- " Objective: -0.5175123015975391 Volume: 0.49927218156547926\n",
- " Objective: -0.5063119764682154 Volume: 0.4991138413788071\n",
- " Objective: -0.49408643023590276 Volume: 0.49936780969551536\n",
- " Objective: -0.48702473181688316 Volume: 0.49965785145529473\n",
- " Objective: -0.4836610659790498 Volume: 0.49983316113040344\n",
- " Objective: -0.4821206737563421 Volume: 0.4999218605683827\n",
- " Objective: -0.48142004369898495 Volume: 0.4999640107348101\n",
- " Objective: -0.4811016579861766 Volume: 0.49998354435877007\n",
- " Objective: -0.4809569702054603 Volume: 0.4999925001878785\n",
- " Objective: -0.48089121119224415 Volume: 0.4999965868721631\n",
- " Objective: -0.4808613226657061 Volume: 0.4999984477299131\n",
- " Objective: -0.4808477374740597 Volume: 0.49999929424694356\n",
- " Objective: -0.4808415623873138 Volume: 0.4999996791652056\n",
- " Objective: -0.4808387556949349 Volume: 0.4999998541574331\n",
- " Objective: -0.48083747988448894 Volume: 0.499999933702199\n",
- " Objective: -0.4808368999268388 Volume: 0.49999996986164336\n",
- " Objective: -0.4808366362852114 Volume: 0.4999999862993104\n",
- " Objective: -0.4808365164356044 Volume: 0.4999999937717441\n",
- " Objective: -0.48083646195285085 Volume: 0.49999999716866694\n",
- " Objective: -0.48083643718519825 Volume: 0.4999999987128902\n",
- " Objective: -0.4808364259260363 Volume: 0.49999999941488754\n",
- " Objective: -0.4808364208075422 Volume: 0.4999999997340109\n",
- " Objective: -0.48083641848090264 Volume: 0.49999999987908356\n",
- " Objective: -0.4808364174231124 Volume: 0.49999999994503214\n",
- " Objective: -0.4808364169422262 Volume: 0.49999999997501227\n",
- " Objective: -0.4808364167236778 Volume: 0.4999999999886414\n",
- " Objective: -0.4808364166242863 Volume: 0.49999999999483663\n",
- " Objective: -0.4808364165791133 Volume: 0.4999999999976535\n",
- " Objective: -0.48083641655849946 Volume: 0.499999999998934\n",
- " Objective: -0.48083641654909826 Volume: 0.4999999999995165\n",
- " Objective: -0.4808364165449258 Volume: 0.49999999999978256\n",
- " Objective: -0.48083641654300785 Volume: 0.4999999999999025\n",
- " Objective: -0.4808364165421957 Volume: 0.49999999999995665\n",
- " Objective: -0.4808364165417963 Volume: 0.4999999999999805\n",
- " Objective: -0.48083641654163994 Volume: 0.4999999999999918\n",
- " Objective: -0.480836416541568 Volume: 0.49999999999999645\n",
- " Objective: -0.48083641654155407 Volume: 0.49999999999999856\n",
- " Objective: -0.48083641654145237 Volume: 0.4999999999999995\n",
- " Objective: -0.4808364165414987 Volume: 0.49999999999999983\n",
- " Objective: -0.4808364165413826 Volume: 0.5\n",
- " Objective: -0.4808364165415984 Volume: 0.5\n"
+ " Volume: 0.600000000000001\n",
+ " Objective: -0.010714406293094777 Volume: 0.5981717021149646\n",
+ " Objective: -0.01079661836784341 Volume: 0.5922899664908623\n",
+ " Objective: -0.012026906954324852 Volume: 0.5641822333937914\n",
+ " Objective: -0.031237268512103938 Volume: 0.521708040590111\n",
+ " Objective: -0.05642819375732256 Volume: 0.1362950973552888\n",
+ " Objective: -1.7717186529120213e-7 Volume: 0.13631862130094366\n",
+ " Objective: -1.9234343772679491e-7 Volume: 0.13945572513824783\n",
+ " Objective: -0.01495301077256873 Volume: 0.3826336765891994\n",
+ " Objective: -0.054030903302380286 Volume: 0.48424731855810493\n",
+ " Objective: -0.05683886374421657 Volume: 0.4640027337982137\n",
+ " Objective: -0.05678458960949248 Volume: 0.4796452605317069\n",
+ " Objective: -0.056845023789349025 Volume: 0.48320116531122287\n",
+ " Objective: -0.05684118426818692 Volume: 0.48400950395218645\n",
+ " Objective: -0.056839438390109645 Volume: 0.4841932578485897\n",
+ " Objective: -0.05683899680540885 Volume: 0.4842350293200155\n",
+ " Objective: -0.05683889411764342 Volume: 0.4842445249326758\n",
+ " Objective: -0.05683887065527781 Volume: 0.4842466835030283\n",
+ " Objective: -0.05683886531558888 Volume: 0.48424717419553365\n",
+ " Objective: -0.056838864101444966 Volume: 0.4842472857411845\n",
+ " Objective: -0.05683886382542011 Volume: 0.48424731109806685\n",
+ " Objective: -0.0568388637626811 Volume: 0.48424731686226735\n",
+ " Objective: -0.05683886374841821 Volume: 0.48424731817260214\n",
+ " Objective: -0.056838863745166134 Volume: 0.4842473184704714\n",
+ " Objective: -0.05683886374443256 Volume: 0.48424731853818387\n",
+ " Objective: -0.056838863744259505 Volume: 0.48424731855357633\n",
+ " Objective: -0.05683886374422262 Volume: 0.48424731855707565\n",
+ " Objective: -0.05683886374421918 Volume: 0.48424731855787084\n",
+ " Objective: -0.056838863744208934 Volume: 0.4842473185580515\n",
+ " Objective: -0.056838863744209545 Volume: 0.48424731855809305\n",
+ " Objective: -0.056838863744212036 Volume: 0.4842473185581024\n",
+ " Objective: -0.05683886374421474 Volume: 0.48424731855810416\n",
+ " Objective: -0.05683886374422061 Volume: 0.48424731855810466\n",
+ " Objective: -0.056838863744218246 Volume: 0.48424731855810477\n",
+ " Objective: -0.05683886374421403 Volume: 0.48424731855810493\n",
+ " Objective: -0.05683886374421657 Volume: 0.48424731855810477\n",
+ " Objective: -0.05683886374422061 Volume: 0.48424731855810477\n"
]
},
{
"data": {
"text/plain": [
- "(-0.48083641654170894, [0.4999999999999997, 0.49999999999999967, 0.49999999999999967, 0.49999999999999967, 0.4999999999999998, 0.4999999999999999, 0.5, 0.5, 0.5, 0.5 … 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], :XTOL_REACHED)"
+ "(25.204290307032124, [0.9648295054107214, 0.6162234667080891, 0.43970259203888806, 0.45451106138694064, 0.46662751786827517, 0.4732579575650261, 0.47699438140257433, 0.4794211143635314, 0.4812364384515705, 0.48276508618577463 … 0.4676622553459592, 0.47646663181738247, 0.4867112774681026, 0.4989248379862383, 0.5140413474993113, 0.5335551421877566, 0.5838433129589278, 0.6196892787392223, 0.6801833825040511, 0.6603623595047727], :XTOL_REACHED)"
]
},
"metadata": {},
@@ -481,7 +1845,7 @@
{
"data": {
"text/plain": [
- "\"got -0.48083641654170894 after 45 iterations (returned XTOL_REACHED)\""
+ "\"got 25.204290307032124 after 38 iterations (returned XTOL_REACHED)\""
]
},
"metadata": {},
@@ -491,8 +1855,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " Objective: -0.48083641654170894 Volume: 0.5\n",
- " 4.447158 seconds (102.84 k allocations: 4.385 GiB, 9.83% gc time)\n"
+ " Objective: -0.05683886374421771 Volume: 0.48424731855810493\n",
+ " Objective: -0.05683886374421657 3.805577 seconds (87.40 k allocations: 3.695 GiB, 9.49% gc time)\n"
]
},
{
@@ -506,7 +1870,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"Scene (400px, 400px):\n",
"events:\n",
@@ -536,7 +1900,7 @@
"# FA(ones(nel)*volfrac, fill(volfrac,nel))\n",
"# G(ones(nel)*volfrac, fill(volfrac,nel))\n",
"\n",
- "opt = Opt(:LD_MMA, nel)\n",
+ "opt = Opt(:LD_CCSAQ, nel) #LD_CCSAQ LD_MMA\n",
"opt.lower_bounds = fill(Emin,nel)\n",
"opt.upper_bounds = fill(1,nel)\n",
"opt.xtol_rel = 1e-12\n",
@@ -547,29 +1911,29 @@
"inequality_constraint!(opt, (x,gg) -> G(x,gg), 1e-12)\n",
"\n",
"\n",
- "display(@time (minf,minx,ret) = optimize(opt, ones(nel)*volfrac))\n",
+ "display(@time (minf,minx,ret) = optimize(opt, x[:].*1.2))\n",
"numevals = opt.numevals # the number of function evaluations\n",
"display(\"got $minf after $numevals iterations (returned $ret)\")\n",
"\n",
"xPhys=reshape(minx,nely,nelx,nelz)\n",
"# display(heatmap(xPhys, aspect_ratio=:equal, legend=false, axis=nothing, foreground_color_subplot=colorant\"white\",fc=:grays))\n",
"scene = Scene(resolution = (400, 400))\n",
- "scene= volume!(xPhys, algorithm = :iso,isorange = 0.5, isovalue = 1.0,colormap=:grays)\n",
+ "scene= volume!(xPhys, algorithm = :iso,isorange = 0.6, isovalue = 1.0,colormap=:grays)\n",
"display(scene)\n"
]
},
{
"cell_type": "code",
- "execution_count": 851,
+ "execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "436.66073023449866"
+ "800.0092096884318"
]
},
- "execution_count": 851,
+ "execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
@@ -580,7 +1944,7 @@
},
{
"cell_type": "code",
- "execution_count": 130,
+ "execution_count": 218,
"metadata": {},
"outputs": [
{
@@ -594,7 +1958,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"Scene (400px, 400px):\n",
"events:\n",
@@ -622,10 +1986,220 @@
],
"source": [
"scene = Scene(resolution = (400, 400))\n",
- "scene= volume!(vcat(xPhys[end:-1:1,:,:],xPhys), algorithm = :iso,isorange = 0.4, isovalue = 1.0,colormap=:grays)\n",
+ "scene= volume!(vcat(xPhys[end:-1:1,:,:],xPhys), algorithm = :iso,isorange = 0.5, isovalue = 1.0,colormap=:grays)\n",
"display(scene)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Objective: -0.007275544620535555\n",
+ "Objective: -0.007275544620535555\n",
+ "Objective: -0.007273690948596735\n",
+ "Objective: -0.007275359219261054\n",
+ "Objective: -0.0072755260800677615\n",
+ "Objective: -0.007275542766484379\n",
+ "Objective: -0.007275544435131216\n",
+ "Objective: -0.007275544601995257\n",
+ "Objective: -0.007275544618683052\n",
+ "Objective: -0.007275544620350478\n",
+ "Objective: -0.007275544620516567\n",
+ "Objective: -0.0072755446205340505\n",
+ "Objective: -0.007275544620534965\n",
+ "Objective: -0.007275544620535158\n",
+ "Objective: -0.007275544620535624\n",
+ "Objective: -0.0072755446205358095\n",
+ "Objective: -0.007275544620535498\n",
+ "Objective: -0.0072755446205358095\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(-0.0072755446205358095, [0.4999999999999999, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 … 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], :XTOL_REACHED)"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\"got -0.0072755446205358095 after 17 iterations (returned XTOL_REACHED)\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 3.347879 seconds (20.35 k allocations: 1.593 GiB, 52.19% gc time)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Warning: Showing scene as text. This happens because `AbstractPlotting.inline!(true)` is set.\n",
+ "│ This needs to be false to show a plot in a window when in the REPL.\n",
+ "â”” @ AbstractPlotting /Users/amiraabdel-rahman/.julia/packages/AbstractPlotting/ru2ib/src/display.jl:135\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "Scene (400px, 400px):\n",
+ "events:\n",
+ " window_area: GeometryBasics.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
+ " window_dpi: 100.0\n",
+ " window_open: false\n",
+ " mousebuttons: Set{AbstractPlotting.Mouse.Button}()\n",
+ " mouseposition: (0.0, 0.0)\n",
+ " mousedrag: notpressed\n",
+ " scroll: (0.0, 0.0)\n",
+ " keyboardbuttons: Set{AbstractPlotting.Keyboard.Button}()\n",
+ " unicode_input: Char[]\n",
+ " dropped_files: String[]\n",
+ " hasfocus: false\n",
+ " entered_window: false\n",
+ "plots:\n",
+ " *Axis3D{...}\n",
+ " *Volume{...}\n",
+ "subscenes:"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "([0.4999999999999999 0.5 … 0.5 0.5; 0.5 0.5 … 0.5 0.5; … ; 0.5 0.5 … 0.5 0.5; 0.5 0.5 … 0.5 0.4999999999999999]\n",
+ "\n",
+ "[0.5 0.5 … 0.5 0.5; 0.5 0.5 … 0.5 0.5; … ; 0.5 0.5 … 0.5 0.5; 0.5 0.5 … 0.5 0.5], Animation(\"/var/folders/f1/6d3kkmqj2n38tl1j1qkg8pwr0000gn/T/jl_WX3u5H\", String[]))"
+ ]
+ },
+ "execution_count": 152,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "compliantOptimization3dMMA(nelx,nely,nelz,volfrac,rmin,penal,50,getProblem,voxels)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 288,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2-element Array{Int64,1}:\n",
+ " 1\n",
+ " 4921"
+ ]
+ },
+ "execution_count": 288,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ndof = 3*(nelx+1)*(nely+1)*(nelz+1)\n",
+ "# USER-DEFINED LOAD DOFs\n",
+ "il = [0 nelx]; \n",
+ "jl = [nely nely]; \n",
+ "kl = [0 0];\n",
+ "loadnid = kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl);\n",
+ "loaddof = 3 .*loadnid[:] .- 2\n",
+ "\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 286,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2-element Array{Int64,1}:\n",
+ " 1\n",
+ " 4921"
+ ]
+ },
+ "execution_count": 286,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loaddof=[getIndex3d(1,1,1,1,nelx+1,nely+1,nelz+1,3),\n",
+ " getIndex3d(nelx+1,1,1,1,nelx+1,nely+1,nelz+1,3)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 287,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2-element Array{Int64,1}:\n",
+ " 1\n",
+ " 5042"
+ ]
+ },
+ "execution_count": 287,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loaddof=[getIndex3d(1,1,1,1,nelx+1,nely+1,nelz+1,3),\n",
+ " getIndex3d(nelx+1,nely+1,1,2,nelx+1,nely+1,nelz+1,3)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 291,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2-element Array{Int64,1}:\n",
+ " 2\n",
+ " 5042"
+ ]
+ },
+ "execution_count": 291,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "il = [0 nelx]; \n",
+ "jl = [nely 0]; \n",
+ "kl = [0 0];\n",
+ "loadnid = kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl);\n",
+ "loaddof = 3 .*loadnid[:] .- 1 #-2 if x -1 if y and -0 if z\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/voxel_designer/Multigrid CG.ipynb b/voxel_designer/Multigrid CG.ipynb
new file mode 100644
index 0000000..8c1dea7
--- /dev/null
+++ b/voxel_designer/Multigrid CG.ipynb
@@ -0,0 +1,14133 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Amira Abdel-Rahman\n",
+ "# (c) Massachusetts Institute of Technology 2021"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using LinearAlgebra"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded Topology Optimization Library!\n"
+ ]
+ }
+ ],
+ "source": [
+ "include(\"./julia/include/_topologyOptimization.jl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Prepcoarse (generic function with 1 method)"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "include(\"./julia/include/topmgcg.jl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "100"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# top2dmgcg(360,240,0.2,3.0,2.5,2,4,1e-10,100)\n",
+ "\n",
+ "nelx=360\n",
+ "nely=240\n",
+ "volfrac=0.2\n",
+ "penal=3.0\n",
+ "rmin=2.5\n",
+ "ft=2\n",
+ "nl=4\n",
+ "cgtol=1e-10\n",
+ "cgmax=100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "100"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nelx=36*5\n",
+ "nely=24*5\n",
+ "volfrac=0.2\n",
+ "penal=3.0\n",
+ "rmin=1.5\n",
+ "ft=2\n",
+ "nl=4\n",
+ "cgtol=1e-10\n",
+ "cgtol=1e-4\n",
+ "cgmax=100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "LoadError",
+ "evalue": "\u001b[91mInterruptException:\u001b[39m",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[91mInterruptException:\u001b[39m",
+ "",
+ "Stacktrace:",
+ " [1] Array at ./boot.jl:406 [inlined]",
+ " [2] Array at ./boot.jl:415 [inlined]",
+ " [3] ones at ./array.jl:525 [inlined]",
+ " [4] ones at ./array.jl:521 [inlined]",
+ " [5] _allocres at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/higherorderfns.jl:232 [inlined]",
+ " [6] _noshapecheck_map(::typeof(-), ::SparseMatrixCSC{Float64,Int64}, ::SparseMatrixCSC{Float64,Int64}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/higherorderfns.jl:164",
+ " [7] _shapecheckbc at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/higherorderfns.jl:1025 [inlined]",
+ " [8] _copy at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/higherorderfns.jl:1015 [inlined]",
+ " [9] copy at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/higherorderfns.jl:1011 [inlined]",
+ " [10] materialize at ./broadcast.jl:837 [inlined]",
+ " [11] top2dmgcg(::Int64, ::Int64, ::Float64, ::Float64, ::Float64, ::Int64, ::Int64, ::Float64, ::Int64) at /Users/amiraabdel-rahman/Dropbox (MIT)/CBA/MetaVoxels/metavoxels-code/voxel_designer/julia/include/topmgcg.jl:56",
+ " [12] top-level scope at In[52]:1",
+ " [13] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+ ]
+ }
+ ],
+ "source": [
+ "top2dmgcg(nelx,nely,volfrac,penal,rmin,ft,nl,cgtol,cgmax)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"Minimum compliance problem with OC\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\"ndes: 180 x 120\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\"volfrac: 0.2 rmin: 1.5 penal: 3.0\""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\" It:1 Obj:4254.343020172504 Vol:0.20003270289081262 ch:0.2 \""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "\u001b[91mInterruptException:\u001b[39m",
+ "",
+ "Stacktrace:",
+ " [1] allocate_dense at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SuiteSparse/src/cholmod.jl:414 [inlined]",
+ " [2] SuiteSparse.CHOLMOD.Dense{Float64}(::Array{Float64,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SuiteSparse/src/cholmod.jl:810",
+ " [3] \\ at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SuiteSparse/src/cholmod.jl:1709 [inlined]",
+ " [4] \\(::Hermitian{Float64,SparseMatrixCSC{Float64,Int64}}, ::Array{Float64,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SuiteSparse/src/cholmod.jl:1740",
+ " [5] \\(::SparseMatrixCSC{Float64,Int64}, ::Array{Float64,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/SparseArrays/src/linalg.jl:1474",
+ " [6] macro expansion at ./timing.jl:310 [inlined]",
+ " [7] topologyOptimization(::Int64, ::Int64, ::Float64, ::Float64, ::Float64) at /Users/amiraabdel-rahman/Dropbox (MIT)/CBA/MetaVoxels/metavoxels-code/voxel_designer/julia/include/top2D.jl:73",
+ " [8] top-level scope at In[50]:1",
+ " [9] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+ ]
+ }
+ ],
+ "source": [
+ "topologyOptimization(nelx,nely,volfrac,rmin,penal)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Lfac = cholesky(Hermitian(K[nl,1])).L; Ufac = Lfac';\n",
+ "cgiters,cgres,U = mgcg(K,F,U,Lfac,Ufac,Pu,nl,1,cgtol,cgmax);"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.5.3",
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+ "name": "julia-1.5"
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+ "name": "julia",
+ "version": "1.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/voxel_designer/Time Space Topology Optimization.ipynb b/voxel_designer/Time Space Topology Optimization.ipynb
new file mode 100644
index 0000000..c2e5ae9
--- /dev/null
+++ b/voxel_designer/Time Space Topology Optimization.ipynb
@@ -0,0 +1,19083 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Amira Abdel-Rahman\n",
+ "# (c) Massachusetts Institute of Technology 2021"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Space-Time Topology Optimization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 888,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Based on: https://link.springer.com/article/10.1007/s00158-019-02420-6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1090,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded Topology Optimization Library!\n"
+ ]
+ }
+ ],
+ "source": [
+ "include(\"./julia/include/_topologyOptimization.jl\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1091,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Cal_c_ce_for_weightOfRobot (generic function with 1 method)"
+ ]
+ },
+ "execution_count": 1091,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "include(\"./julia/include/timespace.jl\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gravity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1092,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 21\n",
+ " 1\n",
+ " 2562"
+ ]
+ },
+ "metadata": {},
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+ },
+ {
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+ "It:19 Obj:1577.01 Vol:0.67,min:0.03,max:0.96\n",
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+ "text": [
+ "It:35 Obj:695.59 Vol:0.69,min:0.02,max:1.0\n",
+ "It:36 Obj:712.14 Vol:0.68,min:0.01,max:1.0\n",
+ "It:37 Obj:705.81 Vol:0.69,min:0.01,max:1.0\n",
+ "It:38 Obj:676.5 Vol:0.69,min:0.01,max:1.0\n",
+ "It:39 Obj:682.49 Vol:0.68,min:0.0,max:1.0\n",
+ "beta change."
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+ "It:55 Obj:511.67 Vol:0.74,min:0.02,max:1.0\n",
+ "It:56 Obj:496.36 Vol:0.75,min:0.01,max:0.99\n",
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+ "It:59 Obj:595.05 Vol:0.63,min:-0.37,max:0.97\n",
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "It:60 Obj:765.36 Vol:0.66,min:-1.63,max:0.94\n",
+ "It:61 Obj:641.63 Vol:0.63,min:-4.09,max:0.93\n"
+ ]
+ },
+ {
+ "ename": "LoadError",
+ "evalue": "InterruptException:",
+ "output_type": "error",
+ "traceback": [
+ "InterruptException:",
+ "",
+ "Stacktrace:",
+ " [1] try_yieldto(::typeof(Base.ensure_rescheduled)) at .\\task.jl:656",
+ " [2] wait at .\\task.jl:713 [inlined]",
+ " [3] uv_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:933",
+ " [4] unsafe_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:1005",
+ " [5] unsafe_write at .\\io.jl:622 [inlined]",
+ " [6] unsafe_write(::Base.PipeEndpoint, ::Base.RefValue{UInt8}, ::Int64) at .\\io.jl:620",
+ " [7] write at .\\io.jl:623 [inlined]",
+ " [8] write(::Base.PipeEndpoint, ::UInt8) at .\\stream.jl:1043",
+ " [9] write at .\\io.jl:336 [inlined]",
+ " [10] write at .\\io.jl:676 [inlined]",
+ " [11] print at .\\char.jl:229 [inlined]",
+ " [12] print(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String, ::Char) at .\\strings\\io.jl:46",
+ " [13] println(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String) at .\\strings\\io.jl:73",
+ " [14] println(::String) at .\\coreio.jl:4",
+ " [15] Space_Time_TopOpt_Gravity(::Int64, ::Int64, ::typeof(Bridge), ::Int64, ::Int64, ::Float64, ::StepRangeLen{Float64,Base.TwicePrecision{Float64},Base.TwicePrecision{Float64}}) at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\voxel_designer\\julia\\include\\timespace.jl:263",
+ " [16] top-level scope at In[1092]:19",
+ " [17] include_string(::Function, ::Module, ::String, ::String) at .\\loading.jl:1091",
+ " [18] execute_code(::String, ::String) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:27",
+ " [19] execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:86",
+ " [20] #invokelatest#1 at .\\essentials.jl:710 [inlined]",
+ " [21] invokelatest at .\\essentials.jl:709 [inlined]",
+ " [22] eventloop(::ZMQ.Socket) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\eventloop.jl:8",
+ " [23] (::IJulia.var\"#15#18\")() at .\\task.jl:356"
+ ]
+ }
+ ],
+ "source": [
+ "# nelx=90/2+1\n",
+ "# nely=30/2+1\n",
+ "nelx=60/2\n",
+ "nely=20/2\n",
+ "\n",
+ "nelx=60\n",
+ "nely=20\n",
+ "nloop=300\n",
+ "nStage=8\n",
+ "volfrac=0.6\n",
+ "Theta=range(0, stop=0.0, length=nStage)[end:-1:1]\n",
+ "problem=cantl2D\n",
+ "problem=MBB\n",
+ "problem=Bridge\n",
+ "\n",
+ "# gr(size=(nelx*10,nely*10))\n",
+ "gr(size=(nelx*4*2.0,nely*4))\n",
+ "anim1=Animation();anim2=Animation();anim3=Animation();\n",
+ "xPhys, tPhys,animXphys,animtPhys,animtPhys1=Space_Time_TopOpt_Gravity(nelx, nely, problem,nloop, nStage, volfrac, Theta);\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1093,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Info: Saved animation to \n",
+ "│ fn = C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\voxel_designer\\animXphys_0.0.gif\n",
+ "â”” @ Plots C:\\Users\\amira\\.julia\\packages\\Plots\\D7Ica\\src\\animation.jl:104\n"
+ ]
+ },
+ {
+ "data": {
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\" />"
+ ],
+ "text/plain": [
+ "Plots.AnimatedGif(\"C:\\\\Users\\\\amira\\\\Dropbox (MIT)\\\\CBA\\\\MetaVoxels\\\\metavoxels-code\\\\voxel_designer\\\\animXphys_0.0.gif\")"
+ ]
+ },
+ "execution_count": 1093,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gif(anim1, \"animXphys_$(Theta[1]).gif\", fps = 2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1094,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "┌ Info: Saved animation to \n",
+ "│ fn = C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\voxel_designer\\animtPhys_0.0.gif\n",
+ "â”” @ Plots C:\\Users\\amira\\.julia\\packages\\Plots\\D7Ica\\src\\animation.jl:104\n"
+ ]
+ },
+ {
+ "data": {
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3yfzZ6/7cfZOYsj90xiyKShSHE/UPrrPl2n++UCnX+b6O+u8Mvg7Nl1lsoyh364YU677nU5pA3kf/5ACJTYB0C4CNA5naqS+Ay6MZwq03ur8V74aNSaC0CIgBQ14tPAlUIOBa5f8PNgpsESQVNspoPdO87uXdbBEh3KhAGEIHRkqjzg1VOH/p1joHk/Rj4e88aGPNBbBIeKwhWT5HV0eNULtgRA0N8RQDqPouymWqoo3it4Cs0WeiKTmgduBDOwgkycw9m5rGxvj8FCYpomckYhpfMwad0PFEoWmjlmTo8gEaZoyQuSOTyyiHl/Hxj7ary+AlErdPhOASm6JkAbRUfeggkbo6EZ0ummjH6GCrjjScSqVTOUlTwnHTRYKj56cDShnI8pHdq2QptxSJQMCACH5BAUyAAAALFkACwA2ATQAAAj/AAEIHEiwIAAHCAUiWLjQoMOHBRVIhEiR4IKLFR02aJDRoYODDRyEHCmyJMmTJlOiXKmyJcuQBzsa3CjTIsaaDiUqwEmRYcODCXl2RPgRAIGjR4U+LMBUKcEDUIUmSCCUgVWnWLMStMpAKlWeUA9oZVpA60CkSYEWNVuQqEC0BNgCIKs1rNeqV+Xq7cj1LtioWemyhSvQrV7DBtOaJdvUqV2eU/F2VUu0suXLiDNi3rx5YF/IX3E+Vsq47OC4HoNqzUxQ8VjGWEfXjMzzM+fblmvi3u05L07aogGThn36IWunx9+iXkxcqWyZwGva3o1bN3XOvSf/Dl3zOc7Scl0T/0yOU/VDwmwbOxc+mzvE6+Yrkk+9VjP8jtFleq+pXiv69/XxFF9iaMnV319igTbUfTLNZ5CDbTFYUX4d7SfTgVj9Z1yA8nGYkYZOYdgdew3CN2BWEI53IlYmrkiRhR2JiBNcy1nXoVAgDhcbiQuauFeKAwFpnY8IYiVjTTQK5WJhHlaUo1BHZgQjgETKJaRaP7Yo1JQVRdlRkgI2SZQAAmRYYIjNDdniZXv1OGSb9q3JJk/gOfWkTGSyNmaZdp45nGDlyTknnFS+SSiVgl45UJ1K3dlRnkEp+qGjDybK26Fm5VaipJjSZyl2MlGqlKbhiarip6B2ymJlajap6qaoYv+GpJ9WsqoXmD3GOuirStqaq6u8dqjrrk7SytaKZPJpZo1BChrss0CtCm2wiTqElFbJ1pRtVtdWKue0vC55I7iqVksgs0Jt+yiZ/onH5LfkdiouRfPGi6Kz52LLLrmKdWZvvKT2yum/tQ7qLsF9NksswuH6KnC9DGdpMLoRj+pwxdP6++vAGB97cccWcwzyvQsj+vHI5Z6MMqzArtymxnEG7LK8Ks9ML8Q2rzoszDnDKTLKP/cs7M4lC+1xy+QOoDRyOBsd8m0RVuf0oRwrPQChVjON9NRaQ32q11y/3DRBWcNZtsBh+3ypwmCnXbBTZ2N9dZhbu2333VjytPSrewf/WjfegE/Nad+dEr5p4IgjPvjcvMY9tKyJRz6y1B05zrfhN6cq+eYMU56R5cFiHjO+nJeeubk4iQ6t6qeTbvrrUaNeE+uhM94qvLDn/q7sMtHeUQDAx9tt7MPqjjDRJw4/LfDMC38w8n8bTzXy1h7Ma/BsgV4sxV2a5q3r0vsN/qLez8o9Rdon7Xv150Mk46fhP417QV6yL1T64OJ/nvUUvW9p/A+bH0Hqly+9re9Z+rMfncqnHx7tTnOmo85DuOQQAhZEeb07YO1sFyr+uY+BFXIgZSBYOgk6hIIGsWBrPPiQBCJQgyts31JAKCURmhB2NywICulHw+3dD4Yrw2CMvHr4IhE6ZCoJ4NndNoPEESXoO0SEiBB1N8XubcmIBkGiEu3GRApBZIcDjOL+ZGg0IJ6FjGG84hMz0sTU4G1FbQzhGi8kxguisSAujJcZlQNFNbbHPc16Y5PiWMM5DnFGdyTbHl/YqETOpY4GASMhVSTIh0yyiIbMiAqN4kiB5BFP+zLLIlnIQz9Cx4sj5JrMBnLJL2Jxhoj8IQexkqxQamWUjtykQCSJylXmzJcAaOUEX1lBSJ5RlnIhU0AAACH5BAUyAAAALFkACwA2ATQAAAj/AAEIHEiwIEEHDgwqXLhQgQKGEAkumBhRYYOLFRUmzMixo0eOGz9ebPARwMQFJQs6TAkRIcuPLl8qLFBAJoADOEsm2FmSAQObQIMC8KmT50ecB2zSFDowJtOFToUulYm0aIKeP59q7Uj0486rR3PKnMo06laEGwmolVqTqliPX7FunVuxK1yjHquObQtULQGBaOcGBuCXrU29d8F6tEu3cUHGHOOGTbpXaGEAg7UOvgyULEvEHSUvzuq4tEDIGUV3BJ3Ss8zLmRtzVkrT9eq3ofFqRBvStO/dsQ2q5si6Y23bLGf7Vv7yON/JVlvy/k3dIO/ewnUTx+3RueW1Zc0u/wTfGXnG4sCvV19P93rwhegz1v6e8T1D+wbJ036elztU99ixJ6BNAAaoUHwVzReUfveJ16CB+f0VlIIlIXgQgANmCFSB5/nHEYU2MfgfhAuK+BGID2Ko4Yos1lcgiQShWBJzGdLYnXkFvQhjizxqqKN8OHJko4Ymprhjj0gmWZ16QhbZopPpHanklFSeNV2TEk4JpXVXVunll4J1WdGQPNroIJhoptnYmVv2aKaUasYpZ0lsZvnlm3PmqadMdcoJ25l7BirogwKRWeWfcA6qqJ6btekloItGGimkalIq6aV7Woqmpph2Wmmijg0g6kucemrqo6AWJOoAQa1Kaqqnxv/aY6kGucoirbLmeiusU+Kq668C+torr8AW65uwSiJr7LLtEZukssxGG56i0EprLZ/OhspqR9Ve6y1MoI5qmrggZfvtueV6RO6460bEJLrwsvRuRbZSV29F88arb7n4KXSvb/9K1+++BOcoZkQBcyTAwi+1mxFnKhZcbMSG+rttSgtn3PDFHUHsnsTGUuyoqhx/tHAAKG8848gAONcWhyD7qKJ3HbM8kMMdoayzyh9VPJDLgEUcc7AzH+eRzyS/pHPKLOEcEdIt0wzz0ER/HLWMEEF9c8keMS2T01nbLBDW8CFFGWZCxzy12WcnGOR4YgMA9m9zw23nh28TxHZTaYPKvLbZxuUd4d0Z1c3uyoS7zR9EewdtNdV/W1gQ2WEnjjDXAiZsd0mUM+QhQzsNHCta2kX0OUSdb86R5pkbPpDWYwte0OkKhd5tpgiVznjbeC8+Jsussxe86kD6bjrvEemOtrnJnql82ZzLTljcw1dX/eCBG7/7XSNeit/zByKveM2WQ3Q93a4XBHXq4XMPnPfOK9Zh9Npj/9H5AKdP0PrSD0R7ducCn0H+NxPpwU5u+mMX5p7GMvYNUHygk5+1BDg7CKLOgNRLIEMCAgAh+QQFMgAAACxZAAsANgE0AAAI/wABCBxIsKDBgwgTClTAUKHCBRAhOizYoGLFiQcdAHDQgKPHjiA/igxJcqTJkihPqgy5EaNBixddAogoUSZBhgps6tzJs+fAAkB7Hhg61GaCo0d3Mljqs6lTh0sZ7ESaVCbRojyBFnjKtetOrUKvGqWqlKnXsz6jTiVrVWzWoGjjyhUIlufVA2ORlpVqMKpfvnMx/jXbl7BMqgls3u1ZN7Djp411Lj7M1qbawoMfTxwMuODlvFVdTv4KV7Np0lvtunWJeO9Bzp1Pe8782jDryhhH64ws26aD33J5tyUKOvFEvb2TF0TusPVwrKjl/tb4dHrw0pJXY3TePLRy5cwVcv/Prd2mcK7Wv8vUil10eYPTgaufLzs+9YS6XbJPTX/+/p35wRdffwQ6Zp9DAU70X4HfLajYewUdyOCEXkmIH4QOOVigfC4RQABkBdgnooUUlmgiRiOm6EB7PHno230IcYiRi04BpeKIJ+aoY0Y32sfiTjS6JCNB6QlgpEwefjgRiTs26aRmTCKUpExGCiBQegMVeWSHQSoU5ZNghonegBNN6VKVVw6ppZVcKunQl2LGKadMcBZkJkZobjRkV10SueecgAaqE5YE3Wlin1n+KeiijPII40CGlohomo82aumlhEI66Vxb2nSnopeGaimHmzrU6U6ntpmoqKyySqqbNqX/KlukrTb1F6UpCnRrrVzR+qSvvO5lVo+6+hXsU8A2meyxLu1KLAC7MtvTsprlKW1vtlkW27XVysqTtdyalq1M44bLqbfmpqvuuuy26+678MYr77z01mvvvfjmq+9AAfQbK5v7Bswgugj1a/C/AidMIMEH+avwwxBHLPHEFFfcn4ciWqyuiKVainGdGvPKMaymgVvijSFvqKKOVTLcH8opv7xyji0DbCLMMc+Hs4ku42nzXOddmGimMVsIHUY/xtXzY0vXmDRC0IFcsdF4rcefY00HlrVPQUNdtZ5Ep0y1eVdr/XOtXR+UoEHOIWactG4LNN6FxFkdc9oGrb0cW3Ffedv33F7XrV/Z9FJLEN4F6U0Q4It712h4bONG99EKEq6Q4ZdiTtfTamO4N+Sfiwp646Pn7flBnBukeaOrI06Q4gMxHrvjjJYut+SBU56h5QmtzmjrqSd++uy2tyu78IIjzbuUHYsK/PKdJ5/Q8cbjHr3uCgVfaPNzBQQAIfkEBTIAAAAsWQALADYBNAAACP8AAQgcSLAgQQMGDCpcKFCBQ4YMF0iUCHFhg4sVKzrIyLGjx48cN4IkeLHByIkUQTpUMLIgwpYjBwyAObCAzZEHcuYcmaAnTQYMaAodChIozZ4JcOo8MNJmgaEyiXKMKtSpUp08fcI0KrWrV4JcWyK9uhOkVaFUv6qteRPkUqYgx24NWhSo3bt0h+LdOzJs1qRulzZtu7Yw0bMf3/79mdfjXrxSH9/t2ziu1sSCzRI2zLklYo+KLQNu6bejZLuRT1fmWPqjXMxYNT/tTFPmTK+fAy+eK5m2YdVHL4OG63kzUdtSkeM2jnk36dO+CwOH+Xo4zdxQ064lwB0mduvDdSL/HU9+dPTzUsurD92xbPHZI7kTQC9Qvnfm4NsvVV+evn+h/JHH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/>"
+ ],
+ "text/plain": [
+ "Plots.AnimatedGif(\"C:\\\\Users\\\\amira\\\\Dropbox (MIT)\\\\CBA\\\\MetaVoxels\\\\metavoxels-code\\\\voxel_designer\\\\animtPhys_0.0.gif\")"
+ ]
+ },
+ "execution_count": 1094,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gif(anim3, \"animtPhys_$(Theta[1]).gif\", fps = 2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Robot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 656,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " It:1 Obj:94403.6 Vol:0.61 Cons:[0.01, 0.0, -0.0, -0.05, 0.05, -0.06, 0.06, -0.03, 0.03, -0.01, 0.01, 0.01, -0.01, 0.02, -0.02, 0.01, -0.01, -0.04, 0.04]\n",
+ " It:2 Obj:42883.95 Vol:0.61 Cons:[0.01, 0.0, 0.01, -0.05, 0.05, -0.06, 0.06, -0.03, 0.03, -0.0, 0.0, 0.01, -0.01, 0.02, -0.02, 0.01, -0.01, -0.04, 0.04]\n",
+ " It:3 Obj:21487.07 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.05, 0.05, -0.05, 0.05, -0.02, 0.02, -0.0, 0.0, 0.01, -0.01, 0.02, -0.02, 0.01, -0.02, -0.04, 0.04]\n",
+ " It:4 Obj:10792.96 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.04, 0.04, -0.04, 0.04, -0.01, 0.01, 0.0, -0.0, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, -0.03, 0.03]\n",
+ " It:5 Obj:5809.43 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.04, 0.04, -0.04, 0.04, -0.01, 0.01, 0.0, -0.0, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, -0.03, 0.03]\n",
+ " It:6 Obj:3403.37 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.04, 0.04, -0.03, 0.03, -0.0, 0.0, 0.01, -0.01, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, -0.03, 0.03]\n",
+ " It:7 Obj:2202.27 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.03, 0.03, -0.02, 0.02, 0.01, -0.01, 0.01, -0.01, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, -0.03, 0.03]\n",
+ " It:8 Obj:1584.94 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.02, 0.02, -0.01, 0.01, 0.01, -0.01, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, 0.02, -0.02, -0.02, 0.02]\n",
+ " It:9 Obj:1255.35 Vol:0.62 Cons:[0.02, 0.0, 0.01, -0.01, 0.01, -0.0, 0.0, 0.01, -0.01, 0.02, -0.02, 0.02, -0.02, 0.02, -0.02, 0.02, -0.02, -0.02, 0.02]"
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+ "text": [
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+ ]
+ },
+ {
+ "ename": "LoadError",
+ "evalue": "InterruptException:",
+ "output_type": "error",
+ "traceback": [
+ "InterruptException:",
+ "",
+ "Stacktrace:",
+ " [1] try_yieldto(::typeof(Base.ensure_rescheduled)) at .\\task.jl:656",
+ " [2] wait at .\\task.jl:713 [inlined]",
+ " [3] uv_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:933",
+ " [4] unsafe_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:1005",
+ " [5] unsafe_write at .\\io.jl:622 [inlined]",
+ " [6] unsafe_write(::Base.PipeEndpoint, ::Base.RefValue{UInt8}, ::Int64) at .\\io.jl:620",
+ " [7] write at .\\io.jl:623 [inlined]",
+ " [8] write(::Base.PipeEndpoint, ::UInt8) at .\\stream.jl:1043",
+ " [9] write at .\\io.jl:336 [inlined]",
+ " [10] write at .\\io.jl:676 [inlined]",
+ " [11] print at .\\char.jl:229 [inlined]",
+ " [12] print(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String, ::Char) at .\\strings\\io.jl:46",
+ " [13] println(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String) at .\\strings\\io.jl:73",
+ " [14] println(::String) at .\\coreio.jl:4",
+ " [15] Space_Time_TopOpt_Robot(::Int64, ::Int64, ::typeof(cantl2D), ::Int64, ::Int64, ::Float64, ::Int64, ::Int64, ::Float64) at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\voxel_designer\\julia\\include\\timespace.jl:528",
+ " [16] top-level scope at In[656]:11",
+ " [17] include_string(::Function, ::Module, ::String, ::String) at .\\loading.jl:1091",
+ " [18] execute_code(::String, ::String) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:27",
+ " [19] execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:86",
+ " [20] #invokelatest#1 at .\\essentials.jl:710 [inlined]",
+ " [21] invokelatest at .\\essentials.jl:709 [inlined]",
+ " [22] eventloop(::ZMQ.Socket) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\eventloop.jl:8",
+ " [23] (::IJulia.var\"#15#18\")() at .\\task.jl:356"
+ ]
+ }
+ ],
+ "source": [
+ "nelx=60\n",
+ "nely=20\n",
+ "nloop=100\n",
+ "nStage=8\n",
+ "volfrac=0.6\n",
+ "Theta=0.5\n",
+ "problem=cantl2D\n",
+ "Upper_Lower_Print=0\n",
+ "Upper_And_Lower_Print=1\n",
+ "\n",
+ "xPhysR, tPhysR=Space_Time_TopOpt_Robot(nelx,nely,problem,nloop, nStage, volfrac,Upper_Lower_Print, Upper_And_Lower_Print, Theta);\n",
+ "\n"
+ ]
+ },
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+ "execution_count": 671,
+ "metadata": {},
+ "outputs": [
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+ "text": [
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+ " It:2 Obj:41606.16 Vol:0.61 Cons:[0.01, 0.0, 0.01, -0.05, 0.05, -0.06, 0.06, -0.03, 0.03, -0.0, 0.0, 0.01, -0.01, 0.02, -0.02, 0.01, -0.01, -0.04, 0.04]\n",
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+ " It:4 Obj:10176.11 Vol:0.61 Cons:[0.01, 0.0, 0.01, -0.05, 0.05, -0.05, 0.05, -0.02, 0.02, -0.0, 0.0, 0.01, -0.01, 0.02, -0.02, 0.01, -0.01, -0.04, 0.04]\n",
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+ " It:6 Obj:2993.02 Vol:0.61 Cons:[0.02, 0.0, 0.01, -0.04, 0.04, -0.03, 0.03, -0.01, 0.01, 0.0, -0.0, 0.01, -0.01, 0.01, -0.01, 0.01, -0.01, -0.03, 0.03]\n",
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+ ]
+ },
+ {
+ "ename": "LoadError",
+ "evalue": "InterruptException:",
+ "output_type": "error",
+ "traceback": [
+ "InterruptException:",
+ "",
+ "Stacktrace:",
+ " [1] try_yieldto(::typeof(Base.ensure_rescheduled)) at .\\task.jl:656",
+ " [2] wait at .\\task.jl:713 [inlined]",
+ " [3] uv_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:933",
+ " [4] unsafe_write(::Base.PipeEndpoint, ::Ptr{UInt8}, ::UInt64) at .\\stream.jl:1005",
+ " [5] unsafe_write at .\\io.jl:622 [inlined]",
+ " [6] unsafe_write(::Base.PipeEndpoint, ::Base.RefValue{UInt8}, ::Int64) at .\\io.jl:620",
+ " [7] write at .\\io.jl:623 [inlined]",
+ " [8] write(::Base.PipeEndpoint, ::UInt8) at .\\stream.jl:1043",
+ " [9] write at .\\io.jl:336 [inlined]",
+ " [10] write at .\\io.jl:676 [inlined]",
+ " [11] print at .\\char.jl:229 [inlined]",
+ " [12] print(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String, ::Char) at .\\strings\\io.jl:46",
+ " [13] println(::IJulia.IJuliaStdio{Base.PipeEndpoint}, ::String) at .\\strings\\io.jl:73",
+ " [14] println(::String) at .\\coreio.jl:4",
+ " [15] Space_Time_TopOpt_Robot(::Int64, ::Int64, ::typeof(cantl2D), ::Int64, ::Int64, ::Float64, ::Int64, ::Int64, ::Float64) at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\voxel_designer\\julia\\include\\timespace.jl:528",
+ " [16] top-level scope at In[671]:11",
+ " [17] include_string(::Function, ::Module, ::String, ::String) at .\\loading.jl:1091",
+ " [18] execute_code(::String, ::String) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:27",
+ " [19] execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\execute_request.jl:86",
+ " [20] #invokelatest#1 at .\\essentials.jl:710 [inlined]",
+ " [21] invokelatest at .\\essentials.jl:709 [inlined]",
+ " [22] eventloop(::ZMQ.Socket) at C:\\Users\\amira\\.julia\\packages\\IJulia\\rWZ9e\\src\\eventloop.jl:8",
+ " [23] (::IJulia.var\"#15#18\")() at .\\task.jl:356"
+ ]
+ }
+ ],
+ "source": [
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+ "nloop=100\n",
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+ "problem=cantl2D\n",
+ "Upper_Lower_Print=1\n",
+ "Upper_And_Lower_Print=0\n",
+ "\n",
+ "xPhysR1, tPhysR1=Space_Time_TopOpt_Robot(nelx,nely,problem,nloop, nStage, volfrac,Upper_Lower_Print, Upper_And_Lower_Print, Theta);\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " It:20 Obj:458.69 Vol:0.69 Cons:[0.03, 0.0, 0.0, 0.04, -0.04, 0.04, -0.04, 0.04, -0.04, 0.04, -0.04, 0.04, -0.04, 0.04, -0.04, 0.04, -0.04, -0.01, 0.01]\n"
+ ]
+ }
+ ],
+ "source": [
+ "nelx=60\n",
+ "nely=20\n",
+ "nloop=20\n",
+ "nStage=8\n",
+ "volfrac=0.6\n",
+ "Theta=0.5\n",
+ "problem=cantl2D\n",
+ "Upper_Lower_Print=0\n",
+ "Upper_And_Lower_Print=0\n",
+ "\n",
+ "xPhysR2, tPhysR2=Space_Time_TopOpt_Robot(nelx,nely,problem,nloop, nStage, volfrac,Upper_Lower_Print, Upper_And_Lower_Print, Theta);\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 776,
+ "metadata": {},
+ "outputs": [
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Plots.heatmap(( 0.0.-xPhysR2),xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal))#,clims=(0.0, 1.0)\n",
+ "\n",
+ "display(heatmap(tPhysR2,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal))#,clims=(0.0, 1.0)\n",
+ "display(contourf!(tPhysR2, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 808,
+ "metadata": {},
+ "outputs": [
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+ "</g>\n",
+ "</svg>\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Plots.heatmap(( 0.0.-xPhysR2),xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal))#,clims=(0.0, 1.0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 809,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ ]
+ },
+ "execution_count": 809,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "minimum(xPhysR2)"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "tPhysTemp=copy(tPhysR2)\n",
+ "tPhysTemp[xPhysR2.<0.8].=0.0\n",
+ "d=1/nStage\n",
+ "levels=[0.]\n",
+ "for i in 1:nStage\n",
+ " out= (tPhysTemp.> (i*d-d)) .& (tPhysTemp.<= (i*d))\n",
+ "# println(\"Mask $i from $(round((i*d-d),digits=2)) to $(round(i*d,digits=2)).\")\n",
+ " tPhysTemp[out].=i*d\n",
+ " append!(levels,[i*d])\n",
+ "end\n",
+ "# display(levels)\n",
+ "display(heatmap(tPhysTemp,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal,fc=cscheme))#,clims=(0.0, 1.0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 817,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
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+ "<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\"\n",
+ " width=\"180mm\" height=\"25mm\" viewBox=\"0 0 100 1\" stroke=\"none\"\n",
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+ "<defs>\n",
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+ "<path d=\"M99,0h1v1h-1z\" fill=\"#FEEDB0\" fill-opacity=\"1\" />\n",
+ "</svg>"
+ ],
+ "text/plain": [
+ "PlotUtils.ContinuousColorGradient(ColorScheme{Array{RGBA{Float64},1},String,String}(RGBA{Float64}[RGBA{Float64}(1.0,1.0,1.0,1.0), RGBA{Float64}(0.18517171283533682,0.05913348735199072,0.2430426744218359,1.0), RGBA{Float64}(0.3709636991923587,0.09275965695185397,0.35003090424140904,1.0), RGBA{Float64}(0.562131301163961,0.12041538866080385,0.3899647459795313,1.0), RGBA{Float64}(0.7361960326170549,0.2017373568026121,0.36195633187515086,1.0), RGBA{Float64}(0.8691092343527536,0.3466273293966376,0.32390403553439767,1.0), RGBA{Float64}(0.9380625301294421,0.5367636326753261,0.3756001987948469,1.0), RGBA{Float64}(0.9736279426519555,0.7319007587238466,0.5104919963865966,1.0), RGBA{Float64}(0.9942936149611202,0.930327795323208,0.6910969022498408,1.0)], \"\", \"\"), [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])"
+ ]
+ },
+ "execution_count": 817,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t=:matter\n",
+ "p=cgrad(t, nStage, categorical = true, rev = true)\n",
+ "# theme(t);\n",
+ "cschemeList=[]\n",
+ "append!(cschemeList,[:white])\n",
+ "for i=1:nStage\n",
+ " append!(cschemeList,[p[i]])\n",
+ "end\n",
+ "cscheme=cgrad(cschemeList)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 784,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "<path d=\"M99,0h1v1h-1z\" fill=\"#2F0F3E\" fill-opacity=\"1\" />\n",
+ "</svg>"
+ ],
+ "text/plain": [
+ "PlotUtils.ContinuousColorGradient(ColorScheme{Array{RGBA{Float64},1},String,String}(RGBA{Float64}[RGBA{Float64}(1.0,1.0,1.0,1.0), RGBA{Float64}(0.9942936149611202,0.930327795323208,0.6910969022498408,1.0), RGBA{Float64}(0.9736279426519555,0.7319007587238466,0.5104919963865966,1.0), RGBA{Float64}(0.9380625301294421,0.5367636326753261,0.3756001987948469,1.0), RGBA{Float64}(0.8691092343527536,0.3466273293966376,0.32390403553439767,1.0), RGBA{Float64}(0.7361960326170549,0.2017373568026121,0.36195633187515086,1.0), RGBA{Float64}(0.562131301163961,0.12041538866080385,0.3899647459795313,1.0), RGBA{Float64}(0.3709636991923587,0.09275965695185397,0.35003090424140904,1.0), RGBA{Float64}(0.18517171283533682,0.05913348735199072,0.2430426744218359,1.0)], \"\", \"\"), [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])"
+ ]
+ },
+ "execution_count": 784,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cscheme"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 769,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "<path d=\"M99,0h1v1h-1z\" fill=\"#0B0000\" fill-opacity=\"1\" />\n",
+ "</svg>"
+ ],
+ "text/plain": [
+ "PlotUtils.CategoricalColorGradient(ColorScheme{Array{RGBA{Float64},1},String,String}(RGBA{Float64}[RGBA{Float64}(1.0,1.0,1.0,1.0), RGBA{Float64}(1.0,0.7916666666666664,0.0,1.0), RGBA{Float64}(0.9166666666666665,0.0,0.0,1.0), RGBA{Float64}(0.0416,0.0,0.0,1.0)], \"\", \"\"), [0.0, 0.1, 0.3, 0.8, 1.0])"
+ ]
+ },
+ "execution_count": 769,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cgrad(:hot, [0.1, 0.3, 0.8], rev = true, categorical = true)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cgrad(:matter, 5, categorical = true)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 758,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `C:\\Users\\amira\\.julia\\registries\\General`\n",
+ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K\u001b[36m\u001b[1mFetching:\u001b[22m\u001b[39m [========================================>] 99.9 %0.0 %] 3.8 % ] 6.3 % ] 9.0 % %.9 %19.8 %.4 % %1 % %] 38.1 %> ] 40.9 %5 %5 %] 48.2 %\u001b[36m\u001b[1mFetching:\u001b[22m\u001b[39m [=====================> ] 51.2 % % % %> ] 66.8 %=============================> ] 70.7 % ] 74.3 % %=================================> ] 81.2 %.0 % [====================================> ] 89.7 %.0 % [======================================> ] 94.8 % [=======================================> ] 96.2 %5 %\u001b[?25h"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
+ "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ColorSchemes ─ v3.12.0\n",
+ "\u001b[32m\u001b[1mUpdating\u001b[22m\u001b[39m `C:\\Users\\amira\\.julia\\environments\\v1.5\\Project.toml`\n",
+ " \u001b[90m [35d6a980] \u001b[39m\u001b[92m+ ColorSchemes v3.12.0\u001b[39m\n",
+ "\u001b[32m\u001b[1mUpdating\u001b[22m\u001b[39m `C:\\Users\\amira\\.julia\\environments\\v1.5\\Manifest.toml`\n",
+ " \u001b[90m [35d6a980] \u001b[39m\u001b[93m↑ ColorSchemes v3.10.2 ⇒ v3.12.0\u001b[39m\n"
+ ]
+ }
+ ],
+ "source": [
+ "heatmap(tPhys,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal)#,clims=(0.0, 1.0)\n",
+ " (contourf!(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.5.2",
+ "language": "julia",
+ "name": "julia-1.5"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/voxel_designer/julia/include/element.jl b/voxel_designer/julia/include/element.jl
index 8b27b2d..ea747ed 100644
--- a/voxel_designer/julia/include/element.jl
+++ b/voxel_designer/julia/include/element.jl
@@ -4,6 +4,11 @@
#####################################FEA KE####################################################################
function lk()
+ # A11 = [12 3 -6 -3; 3 12 3 0; -6 3 12 -3; -3 0 -3 12];
+ # A12 = [-6 -3 0 3; -3 -6 -3 -6; 0 -3 -6 3; 3 -6 3 -6];
+ # B11 = [-4 3 -2 9; 3 -4 -9 4; -2 -9 -4 -3; 9 4 -3 -4];
+ # B12 = [ 2 -3 4 -9; -3 2 9 -2; 4 9 2 3; -9 -2 3 2];
+ # KE = 1/(1-nu^2)/24*([A11 A12;A12' A11]+nu*[B11 B12;B12' B11]);
E=1
nu=0.3
k=[1/2-nu/6,1/8+nu/8,-1/4-nu/12,-1/8+3*nu/8,-1/4+nu/12,-1/8-nu/8,nu/6,1/8-3*nu/8]
@@ -15,7 +20,7 @@ function lk()
k[5+1] k[4+1] k[3+1] k[2+1] k[1+1] k[0+1] k[7+1] k[6+1];
k[6+1] k[3+1] k[4+1] k[1+1] k[2+1] k[7+1] k[0+1] k[5+1];
k[7+1] k[2+1] k[1+1] k[4+1] k[3+1] k[6+1] k[5+1] k[0+1] ];
- return (KE)
+ return KE
end
function lk_H8(nu)
diff --git a/voxel_designer/julia/include/mmaSolver.jl b/voxel_designer/julia/include/mmaSolver.jl
new file mode 100644
index 0000000..390ffae
--- /dev/null
+++ b/voxel_designer/julia/include/mmaSolver.jl
@@ -0,0 +1,406 @@
+#-------------------------------------------------------------
+#
+# Copyright (C) 2007, 2008 Krister Svanberg
+#
+# This file, mmasub.m, is part of GCMMA-MMA-code.
+#
+# GCMMA-MMA-code is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License as
+# published by the Free Software Foundation; either version 3 of
+# the License, or (at your option) any later version.
+#
+# This code is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# (file COPYING) along with this file. If not, see
+# <http://www.gnu.org/licenses/>.
+#
+# You should have received a file README along with this file,
+# containing contact information. If not, see
+# <http://www.smoptit.se/> or e-mail mmainfo@smoptit.se or krille@math.kth.se.
+#
+# Version September 2007 (and a small change August 2008)
+#
+#
+function mmasub(m,n,iter,xval,xmin,xmax,xold1,xold2,f0val,df0dx,fval,dfdx,low,upp,a0,a,c,d);
+ #
+ # This function mmasub performs one MMA-iteration, aimed at
+ # solving the nonlinear programming problem:
+ #
+ # Minimize f_0(x) + a_0*z + sum( c_i*y_i + 0.5*d_i*(y_i)^2 )
+ # subject to f_i(x) - a_i*z - y_i <= 0, i = 1,...,m
+ # xmin_j <= x_j <= xmax_j, j = 1,...,n
+ # z >= 0, y_i >= 0, i = 1,...,m
+ #*** INPUT:
+ #
+ # m = The number of general constraints.
+ # n = The number of variables x_j.
+ # iter = Current iteration number ( =1 the first time mmasub is called).
+ # xval = Column vector with the current values of the variables x_j.
+ # xmin = Column vector with the lower bounds for the variables x_j.
+ # xmax = Column vector with the upper bounds for the variables x_j.
+ # xold1 = xval, one iteration ago (provided that iter>1).
+ # xold2 = xval, two iterations ago (provided that iter>2).
+ # f0val = The value of the objective function f_0 at xval.
+ # df0dx = Column vector with the derivatives of the objective function
+ # f_0 with respect to the variables x_j, calculated at xval.
+ # fval = Column vector with the values of the constraint functions f_i,
+ # calculated at xval.
+ # dfdx = (m x n)-matrix with the derivatives of the constraint functions
+ # f_i with respect to the variables x_j, calculated at xval.
+ # dfdx(i,j) = the derivative of f_i with respect to x_j.
+ # low = Column vector with the lower asymptotes from the previous
+ # iteration (provided that iter>1).
+ # upp = Column vector with the upper asymptotes from the previous
+ # iteration (provided that iter>1).
+ # a0 = The constants a_0 in the term a_0*z.
+ # a = Column vector with the constants a_i in the terms a_i*z.
+ # c = Column vector with the constants c_i in the terms c_i*y_i.
+ # d = Column vector with the constants d_i in the terms 0.5*d_i*(y_i)^2.
+ #
+ #*** OUTPUT:
+ #
+ # xmma = Column vector with the optimal values of the variables x_j
+ # in the current MMA subproblem.
+ # ymma = Column vector with the optimal values of the variables y_i
+ # in the current MMA subproblem.
+ # zmma = Scalar with the optimal value of the variable z
+ # in the current MMA subproblem.
+ # lam = Lagrange multipliers for the m general MMA constraints.
+ # xsi = Lagrange multipliers for the n constraints alfa_j - x_j <= 0.
+ # eta = Lagrange multipliers for the n constraints x_j - beta_j <= 0.
+ # mu = Lagrange multipliers for the m constraints -y_i <= 0.
+ # zet = Lagrange multiplier for the single constraint -z <= 0.
+ # s = Slack variables for the m general MMA constraints.
+ # low = Column vector with the lower asymptotes, calculated and used
+ # in the current MMA subproblem.
+ # upp = Column vector with the upper asymptotes, calculated and used
+ # in the current MMA subproblem.
+ #
+ #epsimin = sqrt(m+n)*10^(-9);
+ epsimin = 10^(-7);
+ raa0 = 0.00001;
+ move = 0.5;
+ albefa = 0.1;
+ asyinit = 0.5;
+ asyincr = 1.2;
+ asydecr = 0.7;
+ eeen = ones(n);
+ eeem = ones(m);
+ zeron = zeros(n);
+
+ # Calculation of the asymptotes low and upp :
+ if iter < 2.5
+ low = xval - asyinit*(xmax-xmin);
+ upp = xval + asyinit*(xmax-xmin);
+ else
+ zzz = (xval-xold1).*(xold1-xold2);
+ factor = eeen;
+ factor[findall(zzz .> 0)] .= asyincr;
+ factor[findall(zzz .< 0)] .= asydecr;
+ low = xval - factor.*(xold1 - low);
+ upp = xval + factor.*(upp - xold1);
+ lowmin = xval - 10*(xmax-xmin);
+ lowmax = xval - 0.01*(xmax-xmin);
+ uppmin = xval + 0.01*(xmax-xmin);
+ uppmax = xval + 10*(xmax-xmin);
+ low = max(low,lowmin);
+ low = min(low,lowmax);
+ upp = min(upp,uppmax);
+ upp = max(upp,uppmin);
+ end
+
+ # Calculation of the bounds alfa and beta :
+
+ zzz1 = low + albefa*(xval-low);
+ zzz2 = xval - move*(xmax-xmin);
+ zzz = max(zzz1,zzz2);
+ alfa = max(zzz,xmin);
+ zzz1 = upp - albefa*(upp-xval);
+ zzz2 = xval + move*(xmax-xmin);
+ zzz = min(zzz1,zzz2);
+ beta = min(zzz,xmax);
+
+ # Calculations of p0, q0, P, Q and b.
+
+ xmami = xmax-xmin;
+ xmamieps = 0.00001*eeen;
+
+ xmami = max(xmami,xmamieps);
+ xmamiinv = eeen./xmami;
+ ux1 = upp-xval;
+ ux2 = ux1.*ux1;
+ xl1 = xval-low;
+ xl2 = xl1.*xl1;
+ uxinv = eeen./ux1;
+ xlinv = eeen./xl1;
+ #
+ p0 = zeron;
+ q0 = zeron;
+ p0 = max.(df0dx,0);
+ q0 = max.(-df0dx,0);
+ pq0 = 0.001*(p0 + q0) + raa0*xmamiinv;
+ p0 = p0 + pq0;
+ q0 = q0 + pq0;
+ p0 = p0.*ux2;
+ q0 = q0.*xl2;
+ #
+ P = spzeros(m,n);
+ Q = spzeros(m,n);
+ P = max.(dfdx,0);
+ Q = max.(-dfdx,0);
+ PQ = 0.001*(P + Q) + raa0*eeem*xmamiinv';
+ P = P + PQ;
+ Q = Q + PQ;
+ # P = P * spdiags(ux2,0,n,n);
+ # Q = Q * spdiags(xl2,0,n,n);
+ P = P * sparse(diagm(ux2));
+ Q = Q * sparse(diagm(xl2));
+ b = P*uxinv + Q*xlinv - fval ;
+ #
+ ### Solving the subproblem by a primal-dual Newton method
+ xmma,ymma,zmma,lam,xsi,eta,mu,zet,s = subsolv(m,n,epsimin,low,upp,alfa,beta,p0,q0,P,Q,a0,a,b,c,d);
+ return xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,low,upp
+end
+
+
+#-------------------------------------------------------------
+#
+# Copyright (C) 2006 Krister Svanberg
+#
+# This file, subsolv.m, is part of GCMMA-MMA-code.
+#
+# GCMMA-MMA-code is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License as
+# published by the Free Software Foundation; either version 3 of
+# the License, or (at your option) any later version.
+#
+# This code is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# (file COPYING) along with this file. If not, see
+# <http://www.gnu.org/licenses/>.
+#
+# You should have received a file README along with this file,
+# containing contact information. If not, see
+# <http://www.smoptit.se/> or e-mail mmainfo@smoptit.se or krille@math.kth.se.
+#
+# Version Dec 2006.
+#
+#
+function subsolv(m,n,epsimin,low,upp,alfa,beta,p0,q0,P,Q,a0,a,b,c,d);
+ #
+ # This function subsolv solves the MMA subproblem:
+ #
+ # minimize SUM[ p0j/(uppj-xj) + q0j/(xj-lowj) ] + a0*z +
+ # + SUM[ ci*yi + 0.5*di*(yi)^2 ],
+ #
+ # subject to SUM[ pij/(uppj-xj) + qij/(xj-lowj) ] - ai*z - yi <= bi,
+ # alfaj <= xj <= betaj, yi >= 0, z >= 0.
+ #
+ # Input: m, n, low, upp, alfa, beta, p0, q0, P, Q, a0, a, b, c, d.
+ # Output: xmma,ymma,zmma, slack variables and Lagrange multiplers.
+ #
+
+ een = ones(n);
+ eem = ones(m);
+ epsi = 1;
+ epsvecn = epsi*een;
+ epsvecm = epsi*eem;
+ x = 0.5*(alfa+beta);
+ y = eem;
+ z = 1;
+ lam = eem;
+ xsi = een./(x-alfa);
+ xsi = max(xsi,een);
+ eta = een./(beta-x);
+ eta = max(eta,een);
+ mu = max(eem,0.5*c);
+ zet = 1;
+ s = eem;
+ itera = 0;
+
+ while epsi > epsimin && itera < 500
+ epsvecn = epsi*een;
+ epsvecm = epsi*eem;
+ ux1 = upp-x;
+ xl1 = x-low;
+ ux2 = ux1.*ux1;
+ xl2 = xl1.*xl1;
+ uxinv1 = een./ux1;
+ xlinv1 = een./xl1;
+ plam = p0 + P'*lam ;
+ qlam = q0 + Q'*lam ;
+ gvec = P*uxinv1 + Q*xlinv1;
+ dpsidx = plam./ux2 - qlam./xl2 ;
+ rex = dpsidx - xsi + eta;
+ rey = c + d.*y - mu - lam;
+ rez = a0 - zet - a'*lam;
+ relam = gvec - a*z - y + s - b;
+ rexsi = xsi.*(x-alfa) - epsvecn;
+ reeta = eta.*(beta-x) - epsvecn;
+ remu = mu.*y - epsvecm;
+ rezet = zet*z - epsi;
+ res = lam.*s - epsvecm;
+ residu1 = [rex' rey' rez]';
+ residu2 = [relam' rexsi' reeta' remu' rezet res']';
+ residu = [residu1' residu2']';
+ residunorm = sqrt(residu'*residu);
+ residumax = maximum(abs.(residu));
+ ittt = 0;
+ while residumax > 0.9*epsi && ittt < 200
+ ittt=ittt + 1;
+ itera=itera + 1;
+ ux1 = upp-x;
+ xl1 = x-low;
+ ux2 = ux1.*ux1;
+ xl2 = xl1.*xl1;
+ ux3 = ux1.*ux2;
+ xl3 = xl1.*xl2;
+ uxinv1 = een./ux1;
+ xlinv1 = een./xl1;
+ uxinv2 = een./ux2;
+ xlinv2 = een./xl2;
+ plam = p0 + P'*lam ;
+ qlam = q0 + Q'*lam ;
+ gvec = P*uxinv1 + Q*xlinv1;
+ GG = P*sparse(diagm(uxinv2)) - Q*sparse(diagm(xlinv2));
+ dpsidx = plam./ux2 - qlam./xl2 ;
+ delx = dpsidx - epsvecn./(x-alfa) + epsvecn./(beta-x);
+ dely = c + d.*y - lam - epsvecm./y;
+ delz = a0 - a'*lam - epsi/z;
+ dellam = gvec - a*z - y - b + epsvecm./lam;
+ diagx = plam./ux3 + qlam./xl3;
+ diagx = 2*diagx + xsi./(x-alfa) + eta./(beta-x);
+ diagxinv = een./diagx;
+ diagy = d + mu./y;
+ diagyinv = eem./diagy;
+ diaglam = s./lam;
+ diaglamyi = diaglam+diagyinv;
+ if m < n
+ blam = dellam + dely./diagy - GG*(delx./diagx);
+ bb = [blam' delz]';
+ Alam = sparse(diagm(diaglamyi)) + GG*sparse(diagm(diagxinv))*GG';
+ AA = [Alam a
+ a' -zet/z ];
+ solut = AA\bb;
+ dlam = solut[1:m];
+ dz = solut[m+1];
+ dx = -delx./diagx - (GG'*dlam)./diagx;
+ else
+ diaglamyiinv = eem./diaglamyi;
+ dellamyi = dellam + dely./diagy;
+ Axx = sparse(diagm(diagx)) + GG'*sparse(diagm(diaglamyiinv))*GG;
+ azz = zet/z + a'*(a./diaglamyi);
+ axz = -GG'*(a./diaglamyi);
+ bx = delx + GG'*(dellamyi./diaglamyi);
+ bz = delz - a'*(dellamyi./diaglamyi);
+ AA = [Axx axz
+ axz' azz ];
+ bb = [-bx' -bz]';
+ solut = AA\bb;
+ dx = solut[1:n];
+ dz = solut[n+1];
+ dlam = (GG*dx)./diaglamyi - dz*(a./diaglamyi) + dellamyi./diaglamyi;
+ end
+ #
+ dy = -dely./diagy + dlam./diagy;
+ dxsi = -xsi + epsvecn./(x-alfa) - (xsi.*dx)./(x-alfa);
+ deta = -eta + epsvecn./(beta-x) + (eta.*dx)./(beta-x);
+ dmu = -mu + epsvecm./y - (mu.*dy)./y;
+ dzet = -zet + epsi/z - zet*dz/z;
+ ds = -s + epsvecm./lam - (s.*dlam)./lam;
+ xx = [ y' z lam' xsi' eta' mu' zet s']';
+ dxx = [dy' dz dlam' dxsi' deta' dmu' dzet ds']';
+ #
+ stepxx = -1.01*dxx./xx;
+ stmxx = maximum(stepxx);
+ stepalfa = -1.01*dx./(x-alfa);
+ stmalfa = maximum(stepalfa);
+ stepbeta = 1.01*dx./(beta-x);
+ stmbeta = maximum(stepbeta);
+ stmalbe = max.(stmalfa,stmbeta);
+ stmalbexx = max.(stmalbe,stmxx);
+ stminv = max.(stmalbexx,1);
+ steg = 1/stminv;
+ #
+ xold = x;
+ yold = y;
+ zold = z;
+ lamold = lam;
+ xsiold = xsi;
+ etaold = eta;
+ muold = mu;
+ zetold = zet;
+ sold = s;
+ #
+ itto = 0;
+ resinew = 2*residunorm;
+ while resinew > residunorm && itto < 50
+ itto = itto+1;
+ x = xold + steg*dx;
+ y = yold + steg*dy;
+ z = zold + steg*dz;
+ lam = lamold + steg*dlam;
+ xsi = xsiold + steg*dxsi;
+ eta = etaold + steg*deta;
+ mu = muold + steg*dmu;
+ zet = zetold + steg*dzet;
+ s = sold + steg*ds;
+ ux1 = upp-x;
+ xl1 = x-low;
+ ux2 = ux1.*ux1;
+ xl2 = xl1.*xl1;
+ uxinv1 = een./ux1;
+ xlinv1 = een./xl1;
+ plam = p0 + P'*lam ;
+ qlam = q0 + Q'*lam ;
+ gvec = P*uxinv1 + Q*xlinv1;
+ dpsidx = plam./ux2 - qlam./xl2 ;
+ rex = dpsidx - xsi + eta;
+ rey = c + d.*y - mu - lam;
+ rez = a0 - zet - a'*lam;
+ relam = gvec - a*z - y + s - b;
+ rexsi = xsi.*(x-alfa) - epsvecn;
+ reeta = eta.*(beta-x) - epsvecn;
+ remu = mu.*y - epsvecm;
+ rezet = zet*z - epsi;
+ res = lam.*s - epsvecm;
+ residu1 = [rex' rey' rez]';
+ residu2 = [relam' rexsi' reeta' remu' rezet res']';
+ residu = [residu1' residu2']';
+ resinew = sqrt(residu'*residu);
+ steg = steg/2;
+ end
+ residunorm=resinew;
+ residumax = maximum(abs.(residu));
+ steg = 2*steg;
+ end
+ if ittt > 198
+ epsi
+ ittt
+ end
+ epsi = 0.1*epsi;
+ end
+ # display(itera)
+ xmma = x;
+ ymma = y;
+ zmma = z;
+ lamma = lam;
+ xsimma = xsi;
+ etamma = eta;
+ mumma = mu;
+ zetmma = zet;
+ smma = s;
+ return xmma,ymma,zmma,lamma,xsimma,etamma,mumma,zetmma,smma
+end
+#-------------------------------------------------------------
+
+
+
diff --git a/voxel_designer/julia/include/problems.jl b/voxel_designer/julia/include/problems.jl
index 996e854..0e5aef5 100644
--- a/voxel_designer/julia/include/problems.jl
+++ b/voxel_designer/julia/include/problems.jl
@@ -23,6 +23,19 @@ function poiss(nelx,nely)
return U,F,freedofs
end
+function cantl2D(nelx,nely)
+ ndof= Int(2*(nelx+1)*(nely+1));
+
+ F = sparse( [2 * (nelx+1)*(nely+1)], [1], [-1.], 2*(nely+1)*(nelx+1), 1);
+ fixeddofs = 1 : 2*(nely+1);
+ alldofs = 1 : 2*(nely+1)*(nelx+1);
+ freedofs = setdiff(alldofs, fixeddofs);
+
+ U = zeros(ndof,1);
+ return U,F,freedofs
+
+end
+
function MBB(nelx,nely)
F = sparse([2],[1],[-1.0],2*(nely+1)*(nelx+1),1)
@@ -34,6 +47,25 @@ function MBB(nelx,nely)
return U,F,freedofs
end
+function Bridge(nelx,nely)
+
+ F = sparse([2],[1],[-1.0],2*(nely+1)*(nelx+1),1)
+ U = zeros(2*(nely+1)*(nelx+1),1)
+
+ LeftBottomNode=getIndex(1,nely+1,nelx,nely)
+ Index=vcat(LeftBottomNode,LeftBottomNode)
+ Direction=ones(Int,length(Index)).+1
+ Direction[length(Index)]=1
+ Direction[length(Index)-1]=1
+ Support=[Index Direction]
+ fixeddofs = union(Support,2*(nelx+1)*(nely+1))
+ display(fixeddofs)
+
+ alldofs = 1:(2*(nely+1)*(nelx+1))
+ freedofs = setdiff(alldofs,fixeddofs)
+
+ return U,F,freedofs
+end
function inverter2D(Macro_nelx,Macro_nely)
function getIndex(i,j,nelx,nely)
@@ -228,6 +260,101 @@ function inverter(nelx,nely,nelz)
return U,F,freedofs,din,dout
end
+function inverterFull(nelx,nely,nelz)
+ ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 nely/2];
+ kl = [0 0];
+ loadnid =Int.( kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- 2;
+
+ din = loaddof[1];
+ dout = loaddof[2];
+ F = sparse(loaddof,[1;2],[1;-1],ndof,2);
+
+ # USER-DEFINED SUPPORT FIXED DOFs
+ # Top face
+ # m = Matlab.meshgrid(0:nelx,0:nelz)
+ # iif=m[1]
+ # kf=m[2]
+ # fixednid = kf .*(nelx+1) .*(nely+1) .+iif .*(nely+1) .+1;
+ # fixeddof_t = 3 .*fixednid[:] .-1;
+
+ # Side face
+ m = Matlab.meshgrid(0:nelx,0:nely)
+ iif=m[1]
+ jf=m[2]
+ fixednid = iif .*(nely+1) .+(nely+1 .-jf);
+ fixeddof_s = 3*fixednid[:];
+
+ # four pins
+ iif = [0;0;0;0];
+ jf = [0;1;nely-1 ;nely];
+ kf = [nelz;nelz;nelz;nelz];
+ fixednid = kf .*(nelx+1) .*(nely .+1) .+iif .*(nely .+1) .+(nely+1 .-jf)
+
+
+
+ fixeddof_p = [3 .*fixednid[:]; 3 .*fixednid[:].-1; 3 .*fixednid[:].-2] # DOFs
+ # Fixed DOFs
+ # fixeddof = union(fixeddof_t,fixeddof_s);
+ fixeddof = union(fixeddof_s, fixeddof_p);
+ fixeddof = sort(fixeddof);
+
+ # Displacement vector
+ U = zeros(ndof,2);
+ freedofs = setdiff(1:ndof,fixeddof);
+
+ return U,F,freedofs,din,dout
+end
+
+function inverterFullM(nelx,nely,nelz)
+ ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
+
+ din=[];dout=[];F=[];
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 nely/2];
+ kl = [0 0];
+ loadnid =Int.( kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- 2;
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;-1],ndof,2)]);
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;-1],ndof,2)]);
+
+ # Side face
+ m = Matlab.meshgrid(0:nelx,0:nely)
+ iif=m[1]
+ jf=m[2]
+ fixednid = iif .*(nely+1) .+(nely+1 .-jf);
+ fixeddof_s = 3*fixednid[:];
+
+ # four pins
+ iif = [0;0;0;0];
+ jf = [0;1;nely-1 ;nely];
+ kf = [nelz;nelz;nelz;nelz];
+ fixednid = kf .*(nelx+1) .*(nely .+1) .+iif .*(nely .+1) .+(nely+1 .-jf)
+
+
+
+ fixeddof_p = [3 .*fixednid[:]; 3 .*fixednid[:].-1; 3 .*fixednid[:].-2] # DOFs
+ # Fixed DOFs
+ fixeddof = union(fixeddof_s, fixeddof_p);
+ fixeddof = sort(fixeddof);
+
+ # Displacement vector
+ U = zeros(ndof,2);
+ freedofs = setdiff(1:ndof,fixeddof);
+
+ return U,F,freedofs,din,dout
+end
+
function wing(nelx,nely,nelz)
ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
# USER-DEFINED LOAD DOFs
@@ -394,6 +521,12 @@ function gripper(nelx,nely,nelz)
ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
# USER-DEFINED LOAD DOFs
+ # il = [0 nelx];
+ # jl = [nely 0];
+ # kl = [0 0];
+ # loadnid = kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl);
+ # loaddof = 3 .*loadnid[:] .- 1 #-2 if x -1 if y and -0 if z
+
loaddof=[getIndex3d(1,Int(nely/2),1,1,nelx+1,nely+1,nelz+1,3),
getIndex3d(nelx+1,nely+1,1,2,nelx+1,nely+1,nelz+1,3),
getIndex3d(nelx+1,1,1,2,nelx+1,nely+1,nelz+1,3)]
@@ -437,6 +570,12 @@ function gripper(nelx,nely,nelz)
# loadnid = kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl);
# loaddof = 3 .*loadnid[:] .- 2;
+ # il = [0 nelx];
+ # jl = [nely 0];
+ # kl = [0 0];
+ # loadnid = kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl);
+ # loaddof = 3 .*loadnid[:] .- 1 #-2 if x -1 if y and -0 if z
+
loaddof=[getIndex3d(1,1,1,1,nelx+1,nely+1,nelz+1,3),
getIndex3d(nelx+1,nely+1,1,2,nelx+1,nely+1,nelz+1,3)]
din = loaddof[1]; dout = loaddof[2];
@@ -477,6 +616,112 @@ function gripper(nelx,nely,nelz)
return U,F,freedofs,din,dout
end
+function gripperFullM(nelx,nely,nelz)
+ ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
+
+ din=[];dout=[];F=[];
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 0];
+ kl = [0 0];
+ loadnid = Int.(kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- [2;1] #-2 if x -1 if y and -0 if z
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;-1],ndof,2)]);
+
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 nely];
+ kl = [0 0];
+ loadnid = Int.(kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- [2;1] #-2 if x -1 if y and -0 if z
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;1],ndof,2)]);
+
+ # USER-DEFINED SUPPORT FIXED DOFs
+ # Side face
+ m = Matlab.meshgrid(0:nelx,0:nely)
+ iif=m[1]
+ jf=m[2]
+ fixednid = iif .*(nely+1) .+(nely+1 .-jf);
+ fixeddof_s = 3*fixednid[:];
+
+ # four pins
+ iif = [0;0;0;0];
+ jf = [0;1;nely-1 ;nely];
+ kf = [nelz;nelz;nelz;nelz];
+ fixednid = kf .*(nelx+1) .*(nely .+1) .+iif .*(nely .+1) .+(nely+1 .-jf)
+
+ fixeddof_p = [3 .*fixednid[:]; 3 .*fixednid[:].-1; 3 .*fixednid[:].-2] # DOFs
+
+ # Fixed DOFs
+ fixeddof = union(fixeddof_s, fixeddof_p);
+ fixeddof = sort(fixeddof);
+
+ # Displacement vector
+ U = zeros(ndof,2);
+ freedofs = setdiff(1:ndof,fixeddof);
+
+ return U,F,freedofs,din,dout
+end
+
+function gripper_invFullM(nelx,nely,nelz)
+ ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
+
+ din=[];dout=[];F=[];
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 0];
+ kl = [0 0];
+ loadnid = Int.(kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- [2;1] #-2 if x -1 if y and -0 if z
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;1],ndof,2)]);
+
+ # USER-DEFINED LOAD DOFs
+ il = [0 nelx];
+ jl = [nely/2 nely];
+ kl = [0 0];
+ loadnid = Int.(kl .*(nelx+1) .*(nely+1) .+il .*(nely .+1) .+(nely+1 .-jl));
+ loaddof = 3 .*loadnid[:] .- [2;1] #-2 if x -1 if y and -0 if z
+
+ append!(din,[ loaddof[1]]);
+ append!(dout,[ loaddof[2]]);
+ append!(F,[sparse(loaddof,[1;2],[1;-1],ndof,2)]);
+
+ # USER-DEFINED SUPPORT FIXED DOFs
+ # Side face
+ m = Matlab.meshgrid(0:nelx,0:nely)
+ iif=m[1]
+ jf=m[2]
+ fixednid = iif .*(nely+1) .+(nely+1 .-jf);
+ fixeddof_s = 3*fixednid[:];
+
+ # four pins
+ iif = [0;0;0;0];
+ jf = [0;1;nely-1 ;nely];
+ kf = [nelz;nelz;nelz;nelz];
+ fixednid = kf .*(nelx+1) .*(nely .+1) .+iif .*(nely .+1) .+(nely+1 .-jf)
+
+ fixeddof_p = [3 .*fixednid[:]; 3 .*fixednid[:].-1; 3 .*fixednid[:].-2] # DOFs
+
+ # Fixed DOFs
+ fixeddof = union(fixeddof_s, fixeddof_p);
+ fixeddof = sort(fixeddof);
+
+ # Displacement vector
+ U = zeros(ndof,2);
+ freedofs = setdiff(1:ndof,fixeddof);
+
+ return U,F,freedofs,din,dout
+end
+
function gripper_inv(nelx,nely,nelz)
ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
# USER-DEFINED LOAD DOFs
@@ -581,7 +826,6 @@ function gripperWhole(nelx,nely,nelz)
return U,F,freedofs,din,dout
end
-
function madcat(nelx,nely,nelz)
ndof=3*(nelx+1)*(nely+1)*(nelz+1);
# USER-DEFINED LOAD DOFs
diff --git a/voxel_designer/julia/include/spaceTimeRobot.jl b/voxel_designer/julia/include/spaceTimeRobot.jl
new file mode 100644
index 0000000..f1ba0f9
--- /dev/null
+++ b/voxel_designer/julia/include/spaceTimeRobot.jl
@@ -0,0 +1,405 @@
+# Function: Space-Time Topology Optimization for Additive Manufacturing:
+# Concurrent Optimization of Structural Layout and Fabrication Sequence
+# Author: Weiming Wang (wwmdlut@gmail.com)
+# Version: 2020-05-14
+# Usage: [xPhys, tPhys] = Space_Time_TopOpt_Robot(90, 30, 500, 8, 0.6, 0, 0, 0.5);
+# volfrac: volume constraint
+# nStage: the number of layers
+# nloop: the number of iterations
+# nely: dimension in y-axis
+# nelx: dimension in x-axis
+# Theta: parameter in objective function
+# If Upper_Lower_Print = 1 and Upper_And_Lower_Print = 0, one robot prints up and down from left to right;
+# If Upper_Lower_Print = 0 and Upper_And_Lower_Print = 1, two robots print simultaneously one is up and one is down from left to right;
+# If Upper_Lower_Print = 0 and Upper_And_Lower_Print = 0, one robot prints on the top from left to right.
+# You cannot choose Upper_And_Lower_Print = 1 and Upper_Lower_Print = 1
+
+function Space_Time_TopOpt_Robot(nelx,nely,problem,nloop, nStage, volfrac,Upper_Lower_Print, Upper_And_Lower_Print, Theta)
+ ## Locations of the robots
+ tPrint = range(0, stop=1, length=nStage+1);
+ xRobot = range(1, stop=nelx, length=nStage + 1);
+ yRobot = (zeros(length(xRobot)) .+ 1);
+ fPoint = (floor.(xRobot).-1)*(nely.+1).+yRobot;
+
+ if Upper_Lower_Print == 1
+ fPointLower = fPoint + nely;
+ fPointUpper = fPoint;
+ fPoint = [fPointUpper[1:2:end] [fPointLower[2:2:end]; 0]];
+ fPoint = fPoint';
+ fPoint = fPoint[:];
+ fPoint[end] = [];
+ fPoint[end] = nelx*(nely+1)+1;
+ fPoint = unique(fPoint);
+ xRobot = floor(xRobot[:]);
+ yRobot = [yRobot[1:2:end] [yRobot[2:2:end]+nely; zeros(mod(length(xRobot), 2), 1)]]';
+ yRobot = yRobot[:];
+ yRobot = yRobot[1:length(xRobot), :];
+ PT = tPrint[1:end-1];
+ elseif Upper_And_Lower_Print == 1
+ fPointLower = fPoint .+ nely;
+ fPointUpper = fPoint;
+ fPoint = [fPointUpper fPointLower];
+ xRobot = [xRobot xRobot]';
+ xRobot = xRobot[:];
+ yRobot = [yRobot yRobot.+nely]';
+ yRobot = yRobot[:];
+ PT = [tPrint[1:end-1]' tPrint[1:end-1]']';
+ PT = PT[:];
+
+ else
+ PT = tPrint[1:end-1];
+ fPoint[end] = nelx*(nely+1)+1;
+ end
+
+ rRobot = nely+20; # radius of robot arm
+ s = repeat(1 : nely, 1, nelx);
+ yElement = s[:];
+ t = repeat(1:nelx, 1,nely)'
+ xElement = t[:];
+ V = [xElement yElement];
+
+
+ v = V - repeat([xRobot[1] yRobot[1]], size(V)[1] ,1);
+ distance= sqrt.(sum(v.^2, dims=2)');
+
+ for i = 2 : size(xRobot)[1]
+ v = V - repeat([xRobot[i] yRobot[i]], size(V)[1] ,1);
+ distance=[distance; sqrt.(sum(v.^2, dims=2)')];
+ end
+
+ # eControl = cell(length(PT), 1);
+ eControl =Array{Any}(undef, length(PT));
+ flag = zeros(nely*nelx, length(PT));
+ for i = 1 : length(PT)
+ t = distance[i, :];
+ CI=findall(Bool.(t .< rRobot))
+ yt=[ ii[1] for ii in CI ];
+ temp = unique(yt);
+ eControl[i] = temp;
+ flag[temp, i] .= 1;
+ end
+
+ ## Lower and Upper bounds for elements
+ tUpper = zeros(nely*nelx);
+ tLower = zeros(nely*nelx);
+ for i = 1 : size(flag, 1)
+ CI=findall(Bool.(flag[i, :] .== 1))
+ yy=[ i[1] for i in CI ];
+
+ tt = PT[yy];
+
+ tm = minimum(tt);
+ tM = maximum(tt);
+ tLower[i] = tm;
+ if Bool(Upper_And_Lower_Print)
+ tUpper[i] = tM+PT[3]-PT[1];
+ else
+ tUpper[i] = tM+PT[2]-PT[1];
+ end
+ end
+
+ ## CONNECTIVITY MATRIX / LAPLACE MATRIX
+ lrmin = 2;
+ iH = ones(convert(Int,nelx*nely*(2*(ceil(lrmin)-1)+1)^2),1)
+ jH = ones(Int,size(iH))
+ sH = zeros(size(iH))
+ k = 0;
+ for i1 = 1:nelx
+ for j1 = 1:nely
+ e1 = (i1-1)*nely+j1
+ for i2 = max(i1-(ceil(lrmin)-1),1):min(i1+(ceil(lrmin)-1),nelx)
+ for j2 = max(j1-(ceil(lrmin)-1),1):min(j1+(ceil(lrmin)-1),nely)
+ e2 = (i2-1)*nely+j2
+ if e1 == e2
+ continue;
+ end
+ k = k+1
+ iH[k] = e1
+ jH[k] = e2
+ sH[k] = 1
+ end
+ end
+ end
+ end
+ L = sparse(vec(iH),vec(jH),vec(sH))
+ M = repeat(sum(L, dims=2), 1, size(L)[2]);
+ # E = eye(size(L));
+ E = Matrix(1.0I, size(L)[1], size(L)[2]);
+ L = E - L./M;
+ L = sparse(L)
+
+ ## MATERIAL PROPERTIES
+ Emax = 1;
+ Emin = 1e-9;
+ nu = 0.3;
+
+ ## INITIALIZE SVERAL PARAMETERS
+ penal = 3; # stiffness penalty
+ rmin = 2; # density filter radius
+
+ ## PREPARE FINITE ELEMENT ANALYSIS
+ ## Stiffness matrix for element
+ KE=lk();
+
+ ## PREPARE FILTER DENSITY
+ H ,Hs=make_filter(nelx,nely,rmin);
+
+ ## INITIALIZE ITERATION
+ beta = 1; # beta continuation
+ eta = 0.5; # projection threshold, fixed at 0.5
+ x = fill(volfrac,nely,nelx);
+ xTilde = x;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+
+ ## Initial time field
+ s = repeat(1 : nely, 1, nelx);
+ yElement = s[:].-0.5;
+ t = repeat(1:nelx, 1, nely)';
+ xElement = t[:].-0.5;
+ V = [xElement yElement];
+
+ vect = V - repeat(V[1, :], 1,size(V)[1])';
+ dis2 = sum(vect.*vect, dims=2);
+ tPhys = sqrt.(dis2) / maximum(sqrt.(dis2));
+ tPhys = reshape(tPhys, nely, nelx);
+ t = tPhys;
+
+ ## Freedom of degree and loads
+ U,F,freedofs=problem(nelx,nely)
+
+ ##
+ xold1 = reshape(x,nely*nelx,1);
+ xold2 = reshape(x,nely*nelx,1);
+ xold1 = [xold1; tLower];
+ xold2 = [xold2; tLower];
+ low = 0;
+ upp = 0;
+ rou = 10;
+
+ while loop=1:nloop
+
+ if mod(loop, 30) == 0 && rou < 100
+ rou = rou + 10;
+ end
+
+ if mod(loop, 20) == 0 && beta < 40
+ beta = beta + 2;
+ end
+
+ ## OBJECTIVE FUNCTION AND SENSITIVITY ANALYSIS
+ dv = ones(nely,nelx);
+ dx = beta * (1 .-tanh.(beta*(xTilde .-eta)).*tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) + tanh.(beta*(1-eta)));
+ dv[:] = H*(dv[:].*dx[:]./Hs);
+ dc = zeros(nely, nelx);
+ dt = zeros(nely, nelx);
+
+ ## Objective function
+ c, dcx = Cal_c_ce_whole(nelx, nely, KE, xPhys, Emin, Emax, penal, freedofs, F);
+ obj = c;
+ dc[:] = dc[:] + H*(dcx[:].*dx[:]./Hs);
+
+
+ tP = range(0, stop=1, length=nStage + 1)
+ for i = 1 : nStage - 1
+ fp = [ fPoint[i + 1]];
+ force = [-0.5];
+
+ if Bool(Upper_And_Lower_Print)
+ fp = [fPoint[i+1, 1]; fPoint[i+1, 2]];
+ force = [ -0.5; -0.5];
+ end
+
+ ti = tP[i+1];
+ c, dcx, dct = Cal_c_ce_for_weightOfRobot(nelx, nely, KE, xPhys, tPhys,Emin, Emax, penal, ti, fp, force, rou, freedofs);
+ obj = obj + Theta*c;
+ dc[:] = dc[:] + Theta*H*(dcx[:].*dx[:]./Hs);
+ dt[:] = dt[:] + Theta*H*(dct[:]./Hs);
+ end
+
+ df0 = [dc dt];
+
+ ## UPDATE OF DESIGN VARIABLES AND PHYSICAL DENSITIES
+ df0dx = df0[:];
+ n=length(df0dx);
+ move = 0.01;
+ tmove = 0.01;
+ xmin=max.(0.0, x[:].-move);
+ xmax=min.(1, x[:].+move);
+ tmin = max.(tLower, t[:].-tmove);
+ tmax = min.(tUpper, t[:].+tmove);
+ xmin = [xmin; tmin];
+ xmax = [xmax; tmax];
+ xval = [x[:]; t[:]];
+ f0val = obj;
+
+ ## Global volume constraint
+ fval = sum(sum(xPhys)) / (nelx*nely*volfrac) - 1;
+ dfdx= [dv[:]'/(nelx*nely*volfrac) zeros(1, nely*nelx)];
+
+ ## Continuity constraint
+ LL = L;
+ kk = 2*(nely*nelx); # controlling the smoothness of the time field
+ A = LL*tPhys[:];
+ B = A.^2/(nely*nelx);
+ fval = [fval; kk*(sum(B) .-1.0e-6)];
+ dft = kk*2*LL'*A;
+ dft = H*(dft./Hs)/(nely*nelx);
+ dfdx = [dfdx; zeros(1, nely*nelx) dft'];
+
+ ## Starting point
+ fval = [fval; tPhys[1] - 1.0e-9];
+ dfdx = [dfdx; zeros(1, nely*nelx) (H*(Matrix(1.0I,1, nely*nelx)'./Hs))'];
+
+ ## Volumen constraints per layer
+ percent = 1/nStage;
+ tP = range(0, stop=1, length=nStage+1);
+ for i = 1 : nStage
+ ##
+ ti = tP[i+1];
+ ft = 1 .- (tanh.(rou*ti) .+ tanh.(rou*(tPhys .- ti)))/(tanh.(rou*ti) .+ tanh.(rou*(1 .-ti)));
+ dfdt = -(rou*(tanh.(rou*(tPhys .- ti)).^2 .- 1))/(tanh.(rou*(ti .- 1)) - tanh.(rou*ti));
+ xtJoint = xPhys.*ft;
+ fval = [fval; sum(xtJoint[:])/(nelx*nely*volfrac) - i*percent];
+ dfx = ft/(nelx*nely*volfrac);
+ dfx = H*(dfx[:].*dx[:]./Hs);
+ dft = xPhys.*dfdt/(nelx*nely*volfrac);
+ dft = H*(dft[:]./Hs);
+ dfdx = [dfdx; dfx[:]' dft[:]'];
+
+ #
+ fval = [fval; -sum(xtJoint[:])/(nelx*nely*volfrac) .+ i*percent .- 1.0e-5];
+ dfdx = [dfdx; -dfx[:]' 0.0.-dft[:]'];
+ end
+
+ ## Solving with MMA
+ m=length(fval);
+ mdof = 1:m;
+ a0 = 1;
+ a = zeros(m);
+ c_ = ones(m)*1000;
+ d = zeros(m);
+
+ xmma, ymma, zmma, lam, xsi, eta_, mu, zet, s, low, upp = mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,f0val, df0dx, fval[mdof], dfdx[mdof,:],low, upp, a0, a, c_, d);
+
+ xnew = reshape(xmma, nely, Int(size(xmma)[1]/nely))
+ xold2 = xold1;
+ xold1 = xval;
+ s = xnew[:, 1:nelx];
+ xTilde[:] = (H*s[:])./Hs;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+ x = s;
+
+ ##
+ t = xnew[:, nelx+1 : end];
+ tPhys = t;
+ tPhys[:] = (H*t[:])./Hs;
+
+ ## Pring results
+ fval[2] = fval[2]/kk;
+ println(" It:$loop Obj:$(round.(obj,digits=2)) Vol:$(round.(sum(xPhys)/(nelx*nely),digits=2)) Cons:$(round.(fval,digits=2))")
+
+
+ ## Draw
+ if mod(loop, 10) == 0
+ mc = cgrad(:thermal, nStage, categorical = true)
+
+ (Plots.heatmap(( 0.0.-xPhys),xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal))#,clims=(0.0, 1.0)
+
+ ss = range(0, stop=size(mc)[1], length=nStage+1);
+ if Bool(Upper_Lower_Print)
+ rx = zeros(size(fPoint)[1], 1);
+ rx[2:2:end] = fPoint[2:2:end]/(nely+1) - 0.5;
+ rx[1:2:end] = (fPoint[1:2:end] - 1)/(nely+1) + 0.5;
+ ry = yRobot;
+ ry[1:2:end].=0 .+ 0.5
+ ry[2:2:end].=nely .+ 0.5
+ elseif Bool(Upper_And_Lower_Print)
+ rx = zeros(size(fPoint)[1], 2);
+ rx[:, 1] .= (fPoint[:, 1] .- 1) ./(nely .+1) .+ 0.5;
+ rx[:, 2] .= fPoint[:, 2] ./(nely+1) .- 0.5;
+ rx = rx'; rx = rx[:];
+ ry = yRobot;
+ ry[1:2:end].=0 .+ 0.5
+ ry[2:2:end].=nely .+ 0.5
+ else
+ rx = (fPoint - 1)/(nely+1) + 0.5;
+ ry = yRobot-2.2;
+ ry=0 .+ 0.5
+ end
+
+ for kk = 1:nStage
+ if Bool(Upper_And_Lower_Print)
+ scatter!([rx[2*kk-1]], [ry[2*kk-1]],c=mc[kk])
+ scatter!([rx[2*kk]], [ry[2*kk]],c=mc[kk])
+ else
+ scatter!([rx[kk]], [ry[kk]],c=mc[kk])
+ end
+ end
+
+ display(scatter!([nelx+0.5], [nely+0.5],c=mc[nStage]))
+ # # title('Density Field');
+
+
+
+ heatmap(tPhys,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal)#,clims=(0.0, 1.0)
+ (contourf!(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+ ss = range(0, stop=size(mc)[1], length=nStage+1);
+ for kk = 1:nStage
+ if Bool(Upper_And_Lower_Print)
+ scatter!([rx[2*kk-1]], [ry[2*kk-1]],c=mc[kk])
+ scatter!([rx[2*kk]], [ry[2*kk]],c=mc[kk])
+ else
+ scatter!([rx[kk]], [ry[kk]],c=mc[kk])
+ end
+ end
+ #
+
+ display(scatter!([nelx+0.5], [nely+0.5],c=mc[nStage]))
+ end
+ end
+ return xPhys, tPhys
+
+end
+
+
+
+
+##
+function Cal_c_ce_for_weightOfRobot(nelx, nely, KE, xPhys, tPhys,Emin, Emax, penal, ti, fpoint, force, lamda, freedofs)
+
+ ft = 1 .- (tanh.(lamda .*ti) .+ tanh.(lamda*(tPhys .- ti)))/(tanh.(lamda*ti) .+ tanh.(lamda*(1 .-ti)));
+ dfdt = -(lamda*(tanh.(lamda*(tPhys .- ti)).^2 .- 1))/(tanh.(lamda*(ti .- 1)) .- tanh.(lamda*ti));
+ xtJoint = xPhys.*ft;
+
+ ##
+ nodenrs = reshape(1:(1+nelx)*(1+nely),1+nely,1+nelx);
+ edofVec = reshape(2*nodenrs[1:end-1,1:end-1].+1,nelx*nely,1)
+ edofMat = repeat(edofVec,1,8).+repeat([0 1 2*nely.+[2 3 0 1] -2 -1],nelx*nely,1)
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(8,1))',64*nelx*nely,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,8))',64*nelx*nely,1))
+ sK = reshape(KE[:]*(Emin .+xtJoint[:]'.^penal*(Emax-Emin)),64*nelx*nely,1);
+
+ K = sparse(vec(iK),vec(jK),vec(sK)); K = (K+K')/2
+
+ F = zeros((nely+1)*(nelx+1), 2);
+ F = F'; F = F[:];
+ F[2*fpoint] = F[2*fpoint]+force;
+
+ ##
+ U = zeros(2*(nely+1)*(nelx+1), 1);
+ U[freedofs] = K[freedofs, freedofs]\Array(F[freedofs]);
+
+ ce = zeros(nely, nelx);
+ ce[1 : nely, 1 : nelx] = reshape(sum((U[edofMat]*KE).*U[edofMat],dims=2), nely, nelx);
+ c = sum(sum((Emin.+xtJoint.^penal*(Emax-Emin)).*ce));
+ dcx = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*ft;
+ dct = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*xPhys.*dfdt;
+ return c, dcx, dct
+
+end
+
+# Publication
+# Weiming Wang, Dirk Munro, Charlie C.L. Wang, Fred van keulen, Jun Wu,
+# Space-Time Topology Optimization for Additive Manufacturing:
+# Concurrent Optimization of Structural Layout and Fabrication Sequence.
+# Structural and Multidisciplinary Optimization, 61:1-18 (2020).
\ No newline at end of file
diff --git a/voxel_designer/julia/include/timeSpace.jl b/voxel_designer/julia/include/timeSpace.jl
new file mode 100644
index 0000000..f96feac
--- /dev/null
+++ b/voxel_designer/julia/include/timeSpace.jl
@@ -0,0 +1,813 @@
+# Amira Abdel-Rahman
+# (c) Massachusetts Institute of Technology 2021
+
+# Based on:
+# Function: Space-Time Topology Optimization for Additive Manufacturing:
+# Concurrent Optimization of Structural Layout and Fabrication Sequence
+# Author: Weiming Wang (wwmdlut@gmail.com)
+# Version: 2020-05-14
+# Usage: [xPhys, tPhys] = Space_Time_TopOpt_Gravity(90, 30, 500, 8, 0.6, 0.5);
+# volfrac: volume constraint
+# nStage: the number of layers
+# nloop: the number of iterations
+# nely: dimension in y-axis
+# nelx: dimension in x-axis
+# Theta: parameter in objective function
+include("./mmaSolver.jl");
+
+function Space_Time_TopOpt_Gravity(nelx, nely, problem,nloop, nStage, volfrac, Theta)
+
+
+
+ nelx=Int(nelx);nely=Int(nely) ;
+
+ tt=:matter
+ p=cgrad(tt, nStage, categorical = true, rev = true)
+ # theme(t);
+ cschemeList=[]
+ append!(cschemeList,[:white])
+ for i=1:nStage
+ append!(cschemeList,[p[i]])
+ end
+ cscheme=cgrad(cschemeList)
+
+ ## Connectivity matrix for continuity
+ lrmin = 2;
+ iH = ones(convert(Int,nelx*nely*(2*(ceil(lrmin)-1)+1)^2),1)
+ jH = ones(Int,size(iH))
+ sH = zeros(size(iH))
+ k = 0;
+ for i1 = 1:nelx
+ for j1 = 1:nely
+ e1 = (i1-1)*nely+j1
+ for i2 = max(i1-(ceil(lrmin)-1),1):min(i1+(ceil(lrmin)-1),nelx)
+ for j2 = max(j1-(ceil(lrmin)-1),1):min(j1+(ceil(lrmin)-1),nely)
+ e2 = (i2-1)*nely+j2
+ if e1 == e2
+ continue;
+ end
+ k = k+1
+ iH[k] = e1
+ jH[k] = e2
+ sH[k] = 1
+ end
+ end
+ end
+ end
+ L = sparse(vec(iH),vec(jH),vec(sH))
+ M = repeat(sum(L, dims=2), 1, size(L)[2]);
+
+ # E = eye(size(L));
+ E = Matrix(1.0I, size(L)[1], size(L)[2]);
+ L = E - L./M;
+ L = sparse(L)
+
+ ## Material properties
+ Emax = 1;
+ Emin = 1e-9;
+ nu = 0.3;
+
+ ## Initialization several parameters
+ penal = 5; # stiffness penalty
+ rmin = 2; # density filter radius
+
+ ## Stiffness matrix for element
+ KE=lk();
+
+ ## Density filter
+ H ,Hs=make_filter(nelx,nely,rmin);
+
+ ## Definition of gravity force
+ II = Array{Float64}(undef, 0); ;J = Array{Float64}(undef, 0);S = Array{Float64}(undef, 0);
+ fe = 1 / (nely*nelx);
+ for x = 1 : nely
+ for y = 1 : nelx
+ II = [II; (y-1)*(nely+1) + x; (y-1)*(nely+1)+x+1; y*(nely+1)+x; y*(nely+1)+x+1];
+ J = [J; (y-1)*nely + x; (y-1)*nely + x; (y-1)*nely + x; (y-1)*nely + x];
+ S = [S; fe/4; fe/4; fe/4; fe/4];
+ end
+ end
+ C = sparse(vec(II), vec(J), vec(S));
+
+ ##
+ beta = 1; # projection parameter
+ eta = 0.5; # projection threshold, fixed at 0.5
+
+ ## Initialize density field
+ x = fill(volfrac,nely,nelx);
+ xTilde = x;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+
+ # ## Initialize time field (start from left boundary)
+ tPhys = zeros(nely, nelx);
+ t = range(0, stop=1, length=nelx)
+ for i = 1 : nelx
+ tPhys[:, i] .= t[i];
+ end
+
+ ## Initial time field (start from Lower left)
+ s = repeat(1 : nely, 1, nelx);
+ yElement = s[:].-0.5;
+ t = repeat(1:nelx, 1, nely)';
+ xElement = t[:].-0.5;
+ V = [xElement yElement];
+
+ vect = V - repeat(V[1, :], 1,size(V)[1])';
+ dis2 = sum(vect.*vect, dims=2);
+ tPhys = sqrt.(dis2) / maximum(sqrt.(dis2));
+ tPhys = reshape(tPhys, nely, nelx);
+ # tPhys = ones(nely, nelx);
+
+ ## Freedom of degree and loads
+ U,F,freedofs=problem(nelx,nely)
+
+ ## Initialization
+ t = tPhys;
+ println("tPhys ,min:$(round.(minimum(tPhys),digits=2)),max:$(round.(maximum(tPhys),digits=2))")
+
+ xold1 = x[:];
+ xold2 = x[:];
+ xold1 = [xold1; zeros(nely*nelx, 1)];
+ xold2 = [xold2; zeros(nely*nelx, 1)];
+ low = 0;
+ upp = 0;
+ rou = 10;
+
+ for loop =1:nloop
+ ## Parameter for projection on time field
+ if mod(loop, 30) == 0 && rou < 100
+ println("rou change.")
+ rou = rou + 10;
+ end
+
+ ## Parameter for projection on density field
+ if mod(loop, 20) == 0 && beta < 40
+ println("beta change.")
+ beta = beta + 2;
+ end
+
+ ## Ojectives and sensitivities
+ dc = zeros(nely, nelx);
+ dt = zeros(nely, nelx);
+
+ ## Compliance of the whole structure
+ c, dcx = Cal_c_ce_whole(nelx, nely, KE, xPhys, Emin, Emax, penal, freedofs, F);
+ obj = c;
+
+ dx = beta * (1 .-tanh.(beta*(xTilde .-eta)).*tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) + tanh.(beta*(1-eta)));
+ dc[:] = dc[:] + H*(dcx[:].*dx[:]./Hs);
+
+ ## Compliances of the intermediate structures
+ tP = range(0, stop=1, length=nStage + 1)
+ for i = 1 : nStage
+ ti = tP[i+1];
+ c, dcx, dct = Cal_c_ce_for_gravity(nelx, nely, KE, xPhys, tPhys, Emin, Emax, penal, ti, C, rou, freedofs);
+ obj = obj + Theta[i]*c;
+ dc[:] = dc[:] + Theta[i]*H*(dcx[:].*dx[:]./Hs);
+ dt[:] = dt[:] + Theta[i]*H*(dct[:]./Hs);
+ end
+
+ df0 = [dc dt];
+ df0dx = df0[:];
+ f0val = obj;
+ n=length(df0dx);
+
+ ## Lower and upper bounds for timefield and densityfield
+ move = 0.01;
+ xminx=max.(0.0, x[:].-move);
+ xmaxx=min.(1, x[:].+move);
+ tmove = 0.01;
+ xmint=max.(0.0, t[:] .-tmove);
+ xmaxt=min.(1, t[:] .+tmove);
+ xmin = [xminx; xmint];
+ xmax = [xmaxx; xmaxt];
+ xval = [x[:]; t[:]];
+
+ ## Global volume constraint
+ fval = sum(sum(xPhys)) / (nelx*nely*volfrac) - 1;
+ print_out = fval[end];
+
+ dv = ones(nely,nelx);
+ dv[:] = H*(dv[:].*dx[:]./Hs);
+ dfdx = [dv[:]'/(nelx*nely*volfrac) zeros(1, nely*nelx)];
+
+ ## Continuity constraint
+ Nei = 1:nely;
+ LL = L;
+ kk = 2*(nely*nelx); # controlling the smoothness of the time field
+ A = LL*tPhys[:];
+ B = A.^2/(nely*nelx);
+ fval = [fval; kk*(sum(B)-1.0e-6)];
+ print_out = [print_out; fval[end]/kk];
+ dft = kk*2*LL'*A;
+ dft = H*(dft./Hs)/(nely*nelx);
+ dfdx = [dfdx; zeros(1, nely*nelx) dft'];
+
+ ## Start points
+ fval = [fval; (tPhys[Nei]'[:] .- 1.0e-9)];
+ ss = zeros(length(Nei), nely*nelx);
+ for ii = 1 : length(Nei)
+ ss[ii, Nei[ii]] = 1;
+ end
+ dfdx = [dfdx; zeros(length(Nei), nely*nelx) (H*(ss'./repeat(Hs, 1, length(Nei))))'];
+
+ ## Volume constraints for intermediate structures
+ percent = 1 / nStage;
+ tP = range(0, stop=1, length=nStage+1);
+ for i = 1 : nStage
+ ##
+ ti = tP[i+1];
+ ft = 1 .- (tanh.(rou*ti) .+ tanh.(rou*(tPhys .- ti)))/(tanh.(rou*ti) .+ tanh.(rou*(1 .-ti)));
+ dfdt = -(rou*(tanh.(rou*(tPhys .- ti)).^2 .- 1))/(tanh.(rou*(ti .- 1)) - tanh.(rou*ti));
+ xtJoint = xPhys.*ft;
+ fval = [fval; sum(xtJoint[:])/(nelx*nely*volfrac) - i*percent];
+ print_out = [print_out; fval[end]];
+ dfx = ft/(nelx*nely*volfrac);
+ dfx = H*(dfx[:].*dx[:]./Hs);
+ dft = xPhys.*dfdt/(nelx*nely*volfrac);
+ dft = H*(dft[:]./Hs);
+ dfdx = [dfdx; dfx[:]' dft[:]'];
+
+ #
+ fval = [fval; -sum(xtJoint[:])/(nelx*nely*volfrac) + i*percent - 1.0e-5];
+ print_out = [print_out; fval[end]];
+ dfdx = [dfdx; -dfx[:]' 0.0.-dft[:]'];
+ end
+
+ ## Optimizing with MMA solver
+ m=length(fval);
+ mdof = 1:m;
+ a0 = 1;
+ a = zeros(m);
+ c_ = ones(m).*1000;
+ d = zeros(m);
+
+ xmma, ymma, zmma, lam, xsi, eta_, mu, zet, s, low, upp = mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,f0val, df0dx, fval[mdof], dfdx[mdof,:],low, upp, a0, a, c_, d);
+
+ xnew = reshape(xmma, nely, Int(size(xmma)[1]/nely))
+ xold2 = xold1;
+ xold1 = xval;
+ s = xnew[:, 1:nelx];
+
+ xTilde[:] = (H*s[:])./Hs;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+ x = s;
+
+ ##
+ t = xnew[:, nelx+1 : end];
+ tPhys = t;
+ tPhys[:] = (H*t[:])./Hs;
+
+ ## Display and show results
+ # println("It:$loop Obj:$(round.(obj,digits=2)) Vol:$(round.(sum(xPhys)/(nelx*nely),digits=2)) Cons:$(round.(print_out,digits=2))")
+ println("It:$loop Obj:$(round.(obj,digits=2)) Vol:$(round.(sum(xPhys)/(nelx*nely),digits=2)),min:$(round.(minimum(tPhys),digits=2)),max:$(round.(maximum(tPhys),digits=2))")
+
+ if mod(loop, 5) == 0 ||loop==1
+
+ xPhysTemp=copy(( 0.0.-xPhys))
+ # xPhysTemp=copy(( 0.0.-xPhys))[end:-1:1,:]
+ xPhysTemp=hcat(xPhysTemp,xPhysTemp[:,end:-1:1])
+ display(Plots.heatmap(xPhysTemp,xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal))#,clims=(0.0, 1.0)
+ Plots.heatmap(xPhysTemp,xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal)
+ frame(anim1)
+ # heatmap(tPhys,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal)#,clims=(0.0, 1.0)
+ # display(contour(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+
+
+ display(heatmap(hcat(tPhys,tPhys[:,end:-1:1]),xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal))#,clims=(0.0, 1.0)
+ # display(contourf!(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+
+ # TT=copy(tPhys)
+ # TT[Bool.((xPhys).<0.8)].=1.0
+ # display(heatmap(TT,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal,fc=:hot))#,clims=(0.0, 1.0)
+ # display(contourf!(TT, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+
+ tPhysTemp=copy(max.(tPhys,Emin))
+ tPhysTemp=copy(min.(tPhys,1.0))
+ tPhysTemp[xPhys.<0.7].=0.0
+ d=1.0/nStage
+ # levels=[0.]
+ for i in 1:nStage
+ out= (tPhysTemp.> (i*d-d)) .& (tPhysTemp.<= (i*d))
+ tPhysTemp[out].=i*d
+ # append!(levels,[i*d])
+ end
+ # tPhysTemp=tPhysTemp[end:-1:1,:]
+ tPhysTemp=tPhysTemp
+ tPhysTemp=hcat(tPhysTemp,tPhysTemp[:,end:-1:1])
+ display(heatmap(tPhysTemp,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal,fc=cscheme,clims=(0.0, 1.0)))
+ (heatmap(tPhysTemp,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal,fc=cscheme,clims=(0.0, 1.0)))
+ frame(anim2)
+
+ # tPhysTemp=copy(max.(tPhys,Emin))
+ # tPhysTemp=copy(min.(tPhys,1.0))
+ d=1/nStage
+ tPhysTemp=copy(tPhys)
+ if (length(tPhysTemp[xPhys.>0.7])>0)
+ tPhysTemp=mapValss.(tPhysTemp, minimum(tPhysTemp[xPhys.>0.7]), maximum(tPhysTemp[xPhys.>0.7]), d, 1)
+ else
+ tPhysTemp=mapValss.(tPhysTemp, minimum(tPhysTemp[:]), maximum(tPhysTemp[:]), d, 1)
+ end
+ tPhysTemp[xPhys.<0.7].=0.0
+ # levels=[0.]
+ for i in 1:nStage
+ out= (tPhysTemp.> (i*d-d)) .& (tPhysTemp.<= (i*d))
+ tPhysTemp[out].=i*d
+ # append!(levels,[i*d])
+ end
+ tPhysTemp=hcat(tPhysTemp,tPhysTemp[:,end:-1:1])
+ display(heatmap(tPhysTemp,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal,fc=cscheme,clims=(0.0, 1.0)))
+ heatmap(tPhysTemp,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,aspect_ratio=:equal,fc=cscheme,clims=(0.0, 1.0))
+ frame(anim3)
+
+ end
+
+ end
+ return xPhys, tPhys,anim1,anim2,anim2
+
+end
+
+function Space_Time_TopOpt_Robot(nelx,nely,problem,nloop, nStage, volfrac,Upper_Lower_Print, Upper_And_Lower_Print, Theta)
+ t=:matter
+ p=cgrad(t, nStage, categorical = true, rev = true)
+ # theme(t);
+ cschemeList=[]
+ append!(cschemeList,[:white])
+ for i=1:nStage
+ append!(cschemeList,[p[i]])
+ end
+ cscheme=cgrad(cschemeList)
+
+ ## Locations of the robots
+ tPrint = range(0, stop=1, length=nStage+1);
+ xRobot = range(1, stop=nelx, length=nStage + 1);
+ yRobot = (zeros(length(xRobot)) .+ 1);
+ fPoint = Int.((floor.(xRobot).-1)*(nely.+1).+yRobot);
+
+ if Upper_Lower_Print == 1
+ fPointLower = fPoint .+ nely;
+ fPointUpper = fPoint;
+ fPoint = [fPointUpper[1:2:end] [fPointLower[2:2:end]; 0]];
+ fPoint = fPoint';
+ fPoint = fPoint[:];
+ # fPoint[end] = [];
+ fPoint[end] = nelx*(nely+1)+1;
+ fPoint = unique(fPoint);
+ xRobot = floor.(xRobot[:]);
+ yRobot = [yRobot[1:2:end] [yRobot[2:2:end].+nely; zeros(mod(length(xRobot), 2), 1)]]';
+ yRobot = yRobot[:];
+ yRobot = yRobot[1:length(xRobot), :];
+ PT = tPrint[1:end-1];
+ elseif Upper_And_Lower_Print == 1
+ fPointLower = fPoint .+ nely;
+ fPointUpper = fPoint;
+ fPoint = [fPointUpper fPointLower];
+ xRobot = [xRobot xRobot]';
+ xRobot = xRobot[:];
+ yRobot = [yRobot yRobot.+nely]';
+ yRobot = yRobot[:];
+ PT = [tPrint[1:end-1]' tPrint[1:end-1]']';
+ PT = PT[:];
+
+ else
+ PT = tPrint[1:end-1];
+ fPoint[end] = nelx*(nely+1)+1;
+ end
+
+ rRobot = nely+20; # radius of robot arm
+ s = repeat(1 : nely, 1, nelx);
+ yElement = s[:];
+ t = repeat(1:nelx, 1,nely)'
+ xElement = t[:];
+ V = [xElement yElement];
+
+
+ v = V - repeat([xRobot[1] yRobot[1]], size(V)[1] ,1);
+ distance= sqrt.(sum(v.^2, dims=2)');
+
+ for i = 2 : size(xRobot)[1]
+ v = V - repeat([xRobot[i] yRobot[i]], size(V)[1] ,1);
+ distance=[distance; sqrt.(sum(v.^2, dims=2)')];
+ end
+
+ # eControl = cell(length(PT), 1);
+ eControl =Array{Any}(undef, length(PT));
+ flag = zeros(nely*nelx, length(PT));
+ for i = 1 : length(PT)
+ t = distance[i, :];
+ CI=findall(Bool.(t .< rRobot))
+ yt=[ ii[1] for ii in CI ];
+ temp = unique(yt);
+ eControl[i] = temp;
+ flag[temp, i] .= 1;
+ end
+
+ ## Lower and Upper bounds for elements
+ tUpper = zeros(nely*nelx);
+ tLower = zeros(nely*nelx);
+ for i = 1 : size(flag, 1)
+ CI=findall(Bool.(flag[i, :] .== 1))
+ yy=[ i[1] for i in CI ];
+
+ tt = PT[yy];
+
+ tm = minimum(tt);
+ tM = maximum(tt);
+ tLower[i] = tm;
+ if Bool(Upper_And_Lower_Print)
+ tUpper[i] = tM+PT[3]-PT[1];
+ else
+ tUpper[i] = tM+PT[2]-PT[1];
+ end
+ end
+
+ ## CONNECTIVITY MATRIX / LAPLACE MATRIX
+ lrmin = 2;
+ iH = ones(convert(Int,nelx*nely*(2*(ceil(lrmin)-1)+1)^2),1)
+ jH = ones(Int,size(iH))
+ sH = zeros(size(iH))
+ k = 0;
+ for i1 = 1:nelx
+ for j1 = 1:nely
+ e1 = (i1-1)*nely+j1
+ for i2 = max(i1-(ceil(lrmin)-1),1):min(i1+(ceil(lrmin)-1),nelx)
+ for j2 = max(j1-(ceil(lrmin)-1),1):min(j1+(ceil(lrmin)-1),nely)
+ e2 = (i2-1)*nely+j2
+ if e1 == e2
+ continue;
+ end
+ k = k+1
+ iH[k] = e1
+ jH[k] = e2
+ sH[k] = 1
+ end
+ end
+ end
+ end
+ L = sparse(vec(iH),vec(jH),vec(sH))
+ M = repeat(sum(L, dims=2), 1, size(L)[2]);
+ # E = eye(size(L));
+ E = Matrix(1.0I, size(L)[1], size(L)[2]);
+ L = E - L./M;
+ L = sparse(L)
+
+ ## MATERIAL PROPERTIES
+ Emax = 1;
+ Emin = 1e-9;
+ nu = 0.3;
+
+ ## INITIALIZE SVERAL PARAMETERS
+ penal = 3; # stiffness penalty
+ rmin = 2; # density filter radius
+
+ ## PREPARE FINITE ELEMENT ANALYSIS
+ ## Stiffness matrix for element
+ KE=lk();
+
+ ## PREPARE FILTER DENSITY
+ H ,Hs=make_filter(nelx,nely,rmin);
+
+ ## INITIALIZE ITERATION
+ beta = 1; # beta continuation
+ eta = 0.5; # projection threshold, fixed at 0.5
+ x = fill(volfrac,nely,nelx);
+ xTilde = x;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+
+ ## Initial time field
+ s = repeat(1 : nely, 1, nelx);
+ yElement = s[:].-0.5;
+ t = repeat(1:nelx, 1, nely)';
+ xElement = t[:].-0.5;
+ V = [xElement yElement];
+
+ vect = V - repeat(V[1, :], 1,size(V)[1])';
+ dis2 = sum(vect.*vect, dims=2);
+ tPhys = sqrt.(dis2) / maximum(sqrt.(dis2));
+ tPhys = reshape(tPhys, nely, nelx);
+ t = tPhys;
+
+ ## Freedom of degree and loads
+ U,F,freedofs=problem(nelx,nely)
+
+ ##
+ xold1 = reshape(x,nely*nelx,1);
+ xold2 = reshape(x,nely*nelx,1);
+ xold1 = [xold1; tLower];
+ xold2 = [xold2; tLower];
+ low = 0;
+ upp = 0;
+ rou = 10;
+
+ for loop= 1:nloop
+
+ if mod(loop, 30) == 0 && rou < 100
+ println("rou change.")
+ rou = rou + 10;
+ end
+
+ if mod(loop, 20) == 0 && beta < 40
+ println("beta change.")
+ beta = beta + 2;
+ end
+
+ ## OBJECTIVE FUNCTION AND SENSITIVITY ANALYSIS
+ dv = ones(nely,nelx);
+ dx = beta * (1 .-tanh.(beta*(xTilde .-eta)).*tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) + tanh.(beta*(1-eta)));
+ dv[:] = H*(dv[:].*dx[:]./Hs);
+ dc = zeros(nely, nelx);
+ dt = zeros(nely, nelx);
+
+ ## Objective function
+ c, dcx = Cal_c_ce_whole(nelx, nely, KE, xPhys, Emin, Emax, penal, freedofs, F);
+ obj = c;
+ dc[:] = dc[:] + H*(dcx[:].*dx[:]./Hs);
+
+
+ tP = range(0, stop=1, length=nStage + 1)
+ for i = 1 : nStage - 1
+ fp = [ fPoint[i + 1]];
+ force = [-0.5];
+
+ if Bool(Upper_And_Lower_Print)
+ fp = [fPoint[i+1, 1]; fPoint[i+1, 2]];
+ force = [ -0.5; -0.5];
+ end
+
+ ti = tP[i+1];
+ c, dcx, dct = Cal_c_ce_for_weightOfRobot(nelx, nely, KE, xPhys, tPhys,Emin, Emax, penal, ti, fp, force, rou, freedofs);
+ obj = obj + Theta*c;
+ dc[:] = dc[:] + Theta*H*(dcx[:].*dx[:]./Hs);
+ dt[:] = dt[:] + Theta*H*(dct[:]./Hs);
+ end
+
+ df0 = [dc dt];
+
+ ## UPDATE OF DESIGN VARIABLES AND PHYSICAL DENSITIES
+ df0dx = df0[:];
+ n=length(df0dx);
+ move = 0.01;
+ tmove = 0.01;
+ xmin=max.(0.0, x[:].-move);
+ xmax=min.(1, x[:].+move);
+ tmin = max.(tLower, t[:].-tmove);
+ tmax = min.(tUpper, t[:].+tmove);
+ xmin = [xmin; tmin];
+ xmax = [xmax; tmax];
+ xval = [x[:]; t[:]];
+ f0val = obj;
+
+ ## Global volume constraint
+ fval = sum(sum(xPhys)) / (nelx*nely*volfrac) - 1;
+ dfdx= [dv[:]'/(nelx*nely*volfrac) zeros(1, nely*nelx)];
+
+ ## Continuity constraint
+ LL = L;
+ kk = 2*(nely*nelx); # controlling the smoothness of the time field
+ A = LL*tPhys[:];
+ B = A.^2/(nely*nelx);
+ fval = [fval; kk*(sum(B) .-1.0e-6)];
+ dft = kk*2*LL'*A;
+ dft = H*(dft./Hs)/(nely*nelx);
+ dfdx = [dfdx; zeros(1, nely*nelx) dft'];
+
+ ## Starting point
+ fval = [fval; tPhys[1] - 1.0e-9];
+ dfdx = [dfdx; zeros(1, nely*nelx) (H*(Matrix(1.0I,1, nely*nelx)'./Hs))'];
+
+ ## Volumen constraints per layer
+ percent = 1/nStage;
+ tP = range(0, stop=1, length=nStage+1);
+ for i = 1 : nStage
+ ##
+ ti = tP[i+1];
+ ft = 1 .- (tanh.(rou*ti) .+ tanh.(rou*(tPhys .- ti)))/(tanh.(rou*ti) .+ tanh.(rou*(1 .-ti)));
+ dfdt = -(rou*(tanh.(rou*(tPhys .- ti)).^2 .- 1))/(tanh.(rou*(ti .- 1)) - tanh.(rou*ti));
+ xtJoint = xPhys.*ft;
+ fval = [fval; sum(xtJoint[:])/(nelx*nely*volfrac) - i*percent];
+ dfx = ft/(nelx*nely*volfrac);
+ dfx = H*(dfx[:].*dx[:]./Hs);
+ dft = xPhys.*dfdt/(nelx*nely*volfrac);
+ dft = H*(dft[:]./Hs);
+ dfdx = [dfdx; dfx[:]' dft[:]'];
+
+ #
+ fval = [fval; -sum(xtJoint[:])/(nelx*nely*volfrac) .+ i*percent .- 1.0e-5];
+ dfdx = [dfdx; -dfx[:]' 0.0.-dft[:]'];
+ end
+
+ ## Solving with MMA
+ m=length(fval);
+ mdof = 1:m;
+ a0 = 1;
+ a = zeros(m);
+ c_ = ones(m)*1000;
+ d = zeros(m);
+
+ xmma, ymma, zmma, lam, xsi, eta_, mu, zet, s, low, upp = mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,f0val, df0dx, fval[mdof], dfdx[mdof,:],low, upp, a0, a, c_, d);
+
+ xnew = reshape(xmma, nely, Int(size(xmma)[1]/nely))
+ xold2 = xold1;
+ xold1 = xval;
+ s = xnew[:, 1:nelx];
+ xTilde[:] = (H*s[:])./Hs;
+ xPhys = (tanh.(beta*eta) .+ tanh.(beta*(xTilde .-eta))) / (tanh.(beta*eta) .+ tanh.(beta*(1 .-eta)));
+ x = s;
+
+ ##
+ t = xnew[:, nelx+1 : end];
+ tPhys = t;
+ tPhys[:] = (H*t[:])./Hs;
+
+ ## Pring results
+ fval[2] = fval[2]/kk;
+ println(" It:$loop Obj:$(round.(obj,digits=2)) Vol:$(round.(sum(xPhys)/(nelx*nely),digits=2)) Cons:$(round.(fval,digits=2))")
+
+
+ ## Draw
+ if mod(loop, 10) == 0 || loop==1
+ mc = cgrad(:thermal, nStage, categorical = true)
+
+ (Plots.heatmap(( 0.0.-xPhys),xaxis=nothing,showaxis = false,clims=(-1.0, 0.0),yaxis=nothing,legend=nothing,fc=:grays,aspect_ratio=:equal))#,clims=(0.0, 1.0)
+
+ ss = range(0, stop=size(mc)[1], length=nStage+1);
+ if Bool(Upper_Lower_Print)
+ rx = zeros(size(fPoint)[1], 1);
+ rx[2:2:end] .= fPoint[2:2:end]./(nely.+1) .- 0.5;
+ rx[1:2:end] .= (fPoint[1:2:end] .- 1)./(nely.+1) .+ 0.5;
+ ry = yRobot;
+ ry[1:2:end].=0 .+ 0.5
+ ry[2:2:end].=nely .+ 0.5
+ elseif Bool(Upper_And_Lower_Print)
+ rx = zeros(size(fPoint)[1], 2);
+ rx[:, 1] .= (fPoint[:, 1] .- 1) ./(nely .+1) .+ 0.5;
+ rx[:, 2] .= fPoint[:, 2] ./(nely+1) .- 0.5;
+ rx = rx'; rx = rx[:];
+ ry = yRobot;
+ ry[1:2:end].=0 .+ 0.5
+ ry[2:2:end].=nely .+ 0.5
+ else
+ rx = (fPoint .- 1)/(nely+1) .+ 0.5;
+ ry=0 .+ 0.5
+ end
+
+ for kk = 1:nStage
+ if Bool(Upper_And_Lower_Print)
+ scatter!([rx[2*kk-1]], [ry[2*kk-1]],c=mc[kk])
+ scatter!([rx[2*kk]], [ry[2*kk]],c=mc[kk])
+ elseif Bool(Upper_Lower_Print)
+ scatter!([rx[kk]], [ry[kk]],c=mc[kk])
+ end
+ end
+
+ display(scatter!([nelx+0.5], [nely+0.5],c=mc[nStage]))
+ # # title('Density Field');
+
+
+
+ heatmap(tPhys,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal)#,clims=(0.0, 1.0)
+ (contourf!(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+ ss = range(0, stop=size(mc)[1], length=nStage+1);
+ for kk = 1:nStage
+ if Bool(Upper_And_Lower_Print)
+ scatter!([rx[2*kk-1]], [ry[2*kk-1]],c=mc[kk])
+ scatter!([rx[2*kk]], [ry[2*kk]],c=mc[kk])
+ elseif Bool(Upper_Lower_Print)
+ scatter!([rx[kk]], [ry[kk]],c=mc[kk])
+ end
+ end
+ #
+
+ display(scatter!([nelx+0.5], [nely+0.5],c=mc[nStage]))
+
+
+ # heatmap(tPhys,alpha=xPhys',xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal)#,clims=(0.0, 1.0)
+ # (contourf!(tPhys, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+ TT=copy(tPhys)
+ TT[Bool.((xPhys).<0.8)].=1.0
+ (heatmap(TT,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,clims=(0.0, 1.0),aspect_ratio=:equal,fc=:hot))#,clims=(0.0, 1.0)
+ # (contourf!(TT, levels = minimum(tPhys):(maximum(tPhys)-minimum(tPhys))/nStage:maximum(tPhys),w=0.1, linecolor=:black))
+ ss = range(0, stop=size(mc)[1], length=nStage+1);
+ for kk = 1:nStage
+ if Bool(Upper_And_Lower_Print)
+ scatter!([rx[2*kk-1]], [ry[2*kk-1]],c=mc[kk])
+ scatter!([rx[2*kk]], [ry[2*kk]],c=mc[kk])
+ elseif Bool(Upper_Lower_Print)
+ scatter!([rx[kk]], [ry[kk]],c=mc[kk])
+ end
+ end
+ #
+
+ display(scatter!([nelx+0.5], [nely+0.5],c=mc[nStage]))
+ end
+ end
+ return xPhys, tPhys
+
+end
+## Subfunctions
+## Calculation of the compliance of the entire structure
+function Cal_c_ce_whole(nelx, nely, KE, xPhys, Emin, Emax, penal, freedofs, F)
+
+ nodenrs = reshape(1:(1+nelx)*(1+nely),1+nely,1+nelx);
+ edofVec = reshape(2*nodenrs[1:end-1,1:end-1].+1,nelx*nely,1)
+ edofMat = repeat(edofVec,1,8).+repeat([0 1 2*nely.+[2 3 0 1] -2 -1],nelx*nely,1)
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(8,1))',64*nelx*nely,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,8))',64*nelx*nely,1))
+ sK = reshape(KE[:]*(Emin.+xPhys[:]'.^penal*(Emax-Emin)),64*nelx*nely,1)
+
+ K = sparse(vec(iK),vec(jK),vec(sK)); K = (K+K')/2
+
+ ##
+ U = zeros(2*(nely+1)*(nelx+1), 1);
+ U[freedofs] = K[freedofs, freedofs]\Array(F[freedofs]);
+ ce = zeros(nely, nelx);
+ ce[1 : nely, 1 : nelx] = reshape(sum((U[edofMat]*KE).*U[edofMat],dims=2), nely, nelx);
+ c = sum(sum((Emin.+xPhys.^penal*(Emax-Emin)).*ce))
+ dcx = -penal*(Emax-Emin)*xPhys.^(penal-1).*ce;
+ return c,dcx
+end
+
+## Calculation of the compliance of each intermediate structure
+function Cal_c_ce_for_gravity(nelx, nely, KE, xPhys, tPhys, Emin, Emax, penal, ti, C, lamda, freedofs)
+ ## Projection of time field
+ ft = 1 .- (tanh.(lamda .*ti) .+ tanh.(lamda*(tPhys .- ti)))/(tanh.(lamda*ti) .+ tanh.(lamda*(1 .-ti)));
+ dfdt = -(lamda*(tanh.(lamda*(tPhys .- ti)).^2 .- 1))/(tanh.(lamda*(ti .- 1)) .- tanh.(lamda*ti));
+ xtJoint = xPhys.*ft;
+
+ ##
+ nodenrs = reshape(1:(1+nelx)*(1+nely),1+nely,1+nelx);
+ edofVec = reshape(2*nodenrs[1:end-1,1:end-1].+1,nelx*nely,1)
+ edofMat = repeat(edofVec,1,8).+repeat([0 1 2*nely.+[2 3 0 1] -2 -1],nelx*nely,1)
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(8,1))',64*nelx*nely,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,8))',64*nelx*nely,1))
+ sK = reshape(KE[:]*(Emin .+xtJoint[:]'.^penal*(Emax-Emin)),64*nelx*nely,1);
+
+ K = sparse(vec(iK),vec(jK),vec(sK)); K = (K+K')/2
+ f = -C*xtJoint[:];
+ F = zeros((nely+1)*(nelx+1), 2);
+ F[:, 2] = f;
+ F = F';
+ F = F[:];
+
+ ##
+ U = zeros(2*(nely+1)*(nelx+1), 1);
+ U[freedofs] = K[freedofs, freedofs]\Array(F[freedofs]);
+
+ ##
+ ce = zeros(nely, nelx);
+ ce[1 : nely, 1 : nelx] = reshape(sum((U[edofMat]*KE).*U[edofMat],dims=2), nely, nelx);
+ c = sum(sum((Emin.+xtJoint.^penal*(Emax-Emin)).*ce));
+ dcx1 = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*ft;
+ dct1 = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*xPhys.*dfdt;
+ dcx2 = -(U[2:2:end]'*C)'.*ft[:];
+ dct2 = -(U[2:2:end]'*C)'.*xPhys[:].*dfdt[:];
+ dcx = 2*dcx2 + dcx1[:];
+ dct = 2*dct2 + dct1[:];
+ return c, dcx, dct
+
+end
+
+##
+function Cal_c_ce_for_weightOfRobot(nelx, nely, KE, xPhys, tPhys,Emin, Emax, penal, ti, fpoint, force, lamda, freedofs)
+
+ ft = 1 .- (tanh.(lamda .*ti) .+ tanh.(lamda*(tPhys .- ti)))/(tanh.(lamda*ti) .+ tanh.(lamda*(1 .-ti)));
+ dfdt = -(lamda*(tanh.(lamda*(tPhys .- ti)).^2 .- 1))/(tanh.(lamda*(ti .- 1)) .- tanh.(lamda*ti));
+ xtJoint = xPhys.*ft;
+
+ ##
+ nodenrs = reshape(1:(1+nelx)*(1+nely),1+nely,1+nelx);
+ edofVec = reshape(2*nodenrs[1:end-1,1:end-1].+1,nelx*nely,1)
+ edofMat = repeat(edofVec,1,8).+repeat([0 1 2*nely.+[2 3 0 1] -2 -1],nelx*nely,1)
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(8,1))',64*nelx*nely,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,8))',64*nelx*nely,1))
+ sK = reshape(KE[:]*(Emin .+xtJoint[:]'.^penal*(Emax-Emin)),64*nelx*nely,1);
+
+ K = sparse(vec(iK),vec(jK),vec(sK)); K = (K+K')/2
+
+ F = zeros((nely+1)*(nelx+1), 2);
+ F = F'; F = F[:];
+ F[2*fpoint] = F[2*fpoint]+force;
+
+ ##
+ U = zeros(2*(nely+1)*(nelx+1), 1);
+ U[freedofs] = K[freedofs, freedofs]\Array(F[freedofs]);
+
+ ce = zeros(nely, nelx);
+ ce[1 : nely, 1 : nelx] = reshape(sum((U[edofMat]*KE).*U[edofMat],dims=2), nely, nelx);
+ c = sum(sum((Emin.+xtJoint.^penal*(Emax-Emin)).*ce));
+ dcx = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*ft;
+ dct = -penal*(Emax-Emin)*xtJoint.^(penal-1).*ce.*xPhys.*dfdt;
+ return c, dcx, dct
+
+end
+
+
+# Publication
+# Weiming Wang, Dirk Munro, Charlie C.L. Wang, Fred van keulen, Jun Wu,
+# Space-Time Topology Optimization for Additive Manufacturing:
+# Concurrent Optimization of Structural Layout and Fabrication Sequence.
+# Structural and Multidisciplinary Optimization, 61:1-18 (2020).
+
+
+
+
diff --git a/voxel_designer/julia/include/top2D.jl b/voxel_designer/julia/include/top2D.jl
index 18d3787..4862421 100644
--- a/voxel_designer/julia/include/top2D.jl
+++ b/voxel_designer/julia/include/top2D.jl
@@ -480,3 +480,4 @@ function CompliantTopologyOptimization2D(nelx,nely,prob,volfrac,rmin,penal,maxIt
# Plots.heatmap(xPhys, legend=false,fc=:grays,clims=(0.0, 1.0))
return xPhys,anim
end
+
diff --git a/voxel_designer/julia/include/top3D.jl b/voxel_designer/julia/include/top3D.jl
index 633a4d0..0e13f27 100644
--- a/voxel_designer/julia/include/top3D.jl
+++ b/voxel_designer/julia/include/top3D.jl
@@ -761,7 +761,151 @@ function compliantOptimization3d(nelx,nely,nelz,volfrac,rmin,penal,maxIter=500,g
return xPhys,anim
end
+#multiple output trial
+function compliantOptimization3dMultiple(nelx,nely,nelz,volfrac,rmin,penal,maxIter=500,getProblem=inverterFullM,voxels=true)
+ anim=Animation()
+
+ if length(voxels)==1
+ voxels=ones(nely,nelx,nelz)
+ end
+
+ # Max and min stiffness
+ Emin=1e-9
+ Emax=1.0
+ nu=0.3
+ # dofs:
+ ndof = 3*(nelx+1)*(nely+1)*(nelz+1)
+ # Allocate design variables (as array), initialize and allocate sens.
+ x=volfrac * ones(Float64,nely,nelx,nelz)
+ xold=copy(x)
+ xPhys=copy(x)
+ g=0 # must be initialized to use the NGuyen/Paulino OC approach
+ dc=zeros(Float64,(nely,nelx,nelz))
+
+ # FE: Build the index vectors for the for coo matrix format.
+ KE=lk_H8(nu)
+ nele = nelx*nely*nelz
+
+ nodenrs = reshape(1:(1+nelx)*(1+nely)*(1+nelz),1+nely,1+nelx,1+nelz)
+ edofVec = reshape(3*nodenrs[1:end-1,1:end-1,1:end-1].+1,nelx*nely*nelz,1)
+ edofMat = repeat(edofVec,1,24).+repeat([0 1 2 3*nely.+[3 4 5 0 1 2] -3 -2 -1 3*(nely+1)*(nelx+1).+[0 1 2 3*nely.+[3 4 5 0 1 2] -3 -2 -1]],nelx*nely*nelz,1)
+
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(24,1))',24*24*nele,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,24))',24*24*nele,1))
+
+
+
+
+ # # USER-DEFINED LOAD DOFs
+
+ U,F,freedofs,din,dout=getProblem(nelx,nely,nelz)
+ nS=size(dout)[1]
+ display("nS: $nS")
+
+
+
+ # Prepare filter
+ iH=[];jH=[];sH=[];
+ k = 0
+
+ for k1 = 1:nelz
+ for i1 = 1:nelx
+ for j1 = 1:nely
+ e1 = (k1-1)*nelx*nely + (i1-1)*nely+j1
+ for k2 = max(k1-(ceil(rmin)-1),1):min(k1+(ceil(rmin)-1),nelz)
+ for i2 = max(i1-(ceil(rmin)-1),1):min(i1+(ceil(rmin)-1),nelx)
+ for j2 = max(j1-(ceil(rmin)-1),1):min(j1+(ceil(rmin)-1),nely)
+ e2 = (k2-1)*nelx*nely + (i2-1)*nely+j2;
+ k = k+1;
+ append!(iH, e1 )
+ append!(jH, e2 )
+ append!(sH, max(0.0,rmin-sqrt((i1-i2)^2+(j1-j2)^2+(k1-k2)^2) ))
+
+ end
+ end
+ end
+ end
+ end
+ end
+
+ iH=reshape(iH,length(iH),1)
+ jH=reshape(jH,length(jH),1)
+ sH=reshape(convert(Array{Float64}, sH),length(sH),1)
+ H = sparse(vec(iH),vec(jH),vec(sH))
+ Hs = sum(H,dims=2)
+ ###################################################
+ loop = 0
+ change = 1
+
+ # Start iteration
+ for i =1:maxIter
+ if (change > 0.01)
+ # Start iteration
+ loop += 1
+
+ x[Bool.(1 .-voxels)] .=Emin
+ xPhys = copy(x)
+
+ c=0
+ ce=zeros()
+ # FE-ANALYSIS
+ for i in 1:nS
+ sK = reshape(KE[:]*(Emin.+xPhys[:]'.^penal*(Emax-Emin)),24*24*nelx*nely*nelz,1)
+ K = sparse(vec(iK),vec(jK),vec(sK))
+ K[din[i],din[i]] = K[din[i],din[i]] .+ 0.1
+ K[dout[i],dout[i]] = K[dout[i],dout[i]] .+ 0.1
+ K = (K+K')/2
+ @timed U[freedofs,:] = K[freedofs,freedofs] \ Array(F[i][freedofs,:])
+ U1 = U[:,1]
+ U2 = U[:,2]
+ # Objective function and sensitivity analysis
+ ce = ce .+ reshape(sum((U1[edofMat]*KE).*U2[edofMat],dims=2),nely,nelx,nelz);
+ c = c .+ U[dout[i],1]
+ end
+
+ dc = penal*(Emax-Emin)*xPhys.^(penal-1).*ce
+ dv = ones(nely,nelx,nelz)
+ dc[:] = H*(dc[:]./Hs)
+ dv[:] = H*(dv[:]./Hs)
+ # OPTIMALITY CRITERIA UPDATE OF DESIGN VARIABLES AND PHYSICAL DENSITIES
+ #l1 = 0; l2 = 1e9; move = 0.2; xnew = 0
+ l1 = 0; l2 = 1e9; move = 0.1;xnew = 0
+ #while (l2-l1)/(l1+l2) > 1e-3
+ while (l2-l1)/(l1+l2) > 1e-4 && l2 > 1e-40
+ lmid = 0.5*(l2+l1)
+ #xnew = max.(0,max.(x.-move,min.(1,min.(x.+move,x.*sqrt.((0.0.-dc)./dv./lmid)))))
+ xnew = max.(0,max.(x.-move,min.(1,min.(x.+move,x.*(max.(1e-10,-dc./dv./lmid)).^0.3))));
+
+ xPhys[:] = (H*xnew[:])./Hs
+ if sum(xPhys[:]) > volfrac*nelx*nely*nelz
+ l1 = lmid
+ else
+ l2 = lmid
+ end
+ end
+ change = maximum(abs.(xnew[:].-x[:]))
+ x = xnew
+ m=mean(xPhys[:])
+ println("It:$loop Obj:$(round.(c,digits=3)) Vol:$(round(m,digits=2)) ch:$(round(change,digits=2)) ")
+ if mod(loop,5)==0
+ scene = Scene(resolution = (400, 400))
+ scene= volume!(xPhys, algorithm = :iso,isorange = 0.5, isovalue = 1.0,colormap=:grays)
+ display(scene)
+
+ # display(topplot3d(xPhys))
+ # topplot3d(xPhys)
+ # frame(anim)
+ end
+
+ end
+ end
+
+ return xPhys,anim
+end
+
+
+#not working yet
function compliantOptimization3dMMA(nelx,nely,nelz,volfrac,rmin,penal,maxIter=500,getProblem=inverter,voxels=true)
anim=Animation()
if length(voxels)==1
@@ -876,7 +1020,7 @@ function compliantOptimization3dMMA(nelx,nely,nelz,volfrac,rmin,penal,maxIter=50
FA(ones(nel)*volfrac, fill(volfrac,nel))
G(ones(nel)*volfrac, fill(volfrac,nel))
- opt = Opt(:LD_MMA, nel)
+ opt = Opt(:LD_MMA, nel) #LD_CCSAQ #LD_MMA
opt.lower_bounds = fill(Emin,nel)
opt.upper_bounds = fill(1,nel)
opt.xtol_rel = 1e-6
diff --git a/voxel_designer/julia/include/topmgcg.jl b/voxel_designer/julia/include/topmgcg.jl
new file mode 100644
index 0000000..0dece57
--- /dev/null
+++ b/voxel_designer/julia/include/topmgcg.jl
@@ -0,0 +1,229 @@
+# Amira Abdel-Rahman
+# (c) Massachusetts Institute of Technology 2021
+
+#### 2D TOPOLOGY OPTIMIZATION CODE, MGCG ANALYSIS ####
+# Example: top2dmgcg(360,240,0.2,3.0,2.5,2,4,1e-10,100)
+function top2dmgcg(nelx,nely,volfrac,penal,rmin,ft,nl,cgtol,cgmax)
+ anim=Animation()
+ ## MATERIAL PROPERTIES
+ E0 = 1; Emin = 1e-9;
+ nu = 0.3;
+ ## PREPARE FINITE ELEMENT ANALYSIS
+ KE =lk();
+ # Prepare fine grid
+ nelem = nelx*nely;
+ nx = nelx+1; ny = nely+1;
+ ndof = 2*nx*ny;
+ # nodenrs[1:ny,1:nx] = reshape(1:ny*nx,ny,nx);
+ # edofVec[1:nelem,1] = reshape(2*nodenrs(1:ny-1,1:nx-1)+1,nelem,1);
+
+ nodenrs = reshape(1:ny*nx,ny,nx);
+ edofVec = reshape(2*nodenrs[1:end-1,1:end-1].+1,nelx*nely,1)
+ edofMat = repeat(edofVec,1,8).+repeat([0 1 2*nely.+[2 3 0 1] -2 -1],nelx*nely,1)
+ iK = convert(Array{Int},reshape(kron(edofMat,ones(8,1))',64*nelx*nely,1))
+ jK = convert(Array{Int},reshape(kron(edofMat,ones(1,8))',64*nelx*nely,1))
+
+ # Prologation operators
+ # Pu = cell(nl-1,1);
+ Pu=Array{Any}(undef, nl-1);
+
+ for l = 1:nl-1
+ Pu[l,1] = Prepcoarse(nely/2^(l-1),nelx/2^(l-1));
+ end
+ # Define loads and supports (cantilever)
+ F = sparse([Int.(2*(nelx*(nely+1)+nely/2+1))],[1],[-1],2*(nely+1)*(nelx+1),1);
+ U = zeros(Int.(2*(nely+1)*(nelx+1)),1);
+ fixeddofs = 1:2*(nely+1);
+ # Null space elimination of supports
+ N = ones(ndof);
+ N[fixeddofs] .= 0;
+ # Null = spdiags(N,0,ndof,ndof);
+ Null = sparse(diagm(N));
+ H ,Hs=make_filter(nelx,nely,rmin);
+ x = fill(volfrac,nely,nelx);
+ xPhys = x;
+ loop = 0;
+ change = 1;
+ ## START ITERATION
+ while change > 1e-3 && loop < 500
+ loop = loop+1;
+ ## FE-ANALYSIS
+ K = Array{Any}(undef, nl);
+ sK = reshape(KE[:]*(Emin.+xPhys[:]'.^penal*(E0-Emin)),64*nelx*nely,1)
+
+
+ K[1,1] = sparse(vec(iK),vec(jK),vec(sK));
+ K[1,1] = Null'*K[1,1]*Null - (Null .-sparse(Matrix(1.0I, ndof, ndof)));
+
+ for l = 1:nl-1
+ K[l+1,1] = Pu[l,1]'*(K[l,1]*Pu[l,1]);
+ end
+ Lfac = cholesky(Hermitian(K[nl,1])).L; Ufac = Lfac';
+ cgiters,cgres,U = mgcg(K,F,U,Lfac,Ufac,Pu,nl,1,cgtol,cgmax);
+
+ ## OBJECTIVE FUNCTION AND SENSITIVITY ANALYSIS
+ ce = reshape(sum((U[edofMat]*KE).*U[edofMat],dims=2),nely,nelx);
+ c = sum(sum((Emin .+xPhys.^penal*(E0-Emin)).*ce));
+ dc = -penal*(E0-Emin)*xPhys.^(penal-1).*ce;
+ dv = ones(nely,nelx);
+
+ ## FILTERING/MODIFICATION OF SENSITIVITIES
+ if ft == 1
+ dc[:] = H*(x[:].*dc[:])./Hs./max(1e-3,x[:]);
+ elseif ft == 2
+ dc[:] = H*(dc[:]./Hs);
+ dv[:] = H*(dv[:]./Hs);
+ end
+ ## OPTIMALITY CRITERIA UPDATE OF DESIGN VARIABLES AND PHYSICAL DENSITIES
+ g = mean(xPhys[:]) - volfrac;
+ l1 = 0; l2 = 1e9; move = 0.2;xnew=0;
+ while (l2-l1)/(l1+l2) > 1e-6
+ lmid = 0.5*(l2+l1);
+ xnew = max.(0,max.(x.-move,min.(1,min.(x.+move,x.*sqrt.((0.0.-dc)./dv./lmid)))))
+ gt = g + sum((dv[:].*(xnew[:]-x[:])));
+ if gt > 0
+ l1 = lmid;
+ else
+ l2 = lmid;
+ end
+ end
+ change = maximum(abs.(xnew[:].-x[:]));
+ x = xnew;
+ ## FILTERING OF DESIGN VARIABLES
+ if ft == 1
+ xPhys = xnew;
+ elseif ft == 2
+ xPhys[:] = (H*xnew[:])./Hs;
+ end
+ ## PRINT RESULTS
+ println(" Iter.: $loop Comp.: $c Vol.: $(mean(xPhys[:])) Chg.: $change CGres: $cgres CGits: $cgiters Penal: $penal");
+ ## PLOT DENSITIES
+ if loop<20||mod(loop,10)==0
+ xxx=1 .- clamp.(xPhys,0,1)
+ display(Plots.heatmap(xxx,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,fc=:grays,clims=(0.0, 1.0),aspect_ratio=:equal))
+ Plots.heatmap(xxx,xaxis=nothing,showaxis = false,yaxis=nothing,legend=nothing,fc=:grays,clims=(0.0, 1.0),aspect_ratio=:equal)
+ frame(anim)
+ end
+
+ end
+ return xPhys,anim;
+end
+## FUNCTION mgcg - MULTIGRID PRECONDITIONED CONJUGATE GRADIENTS
+function mgcg(A,b,u,Lfac,Ufac,Pu,nl,nswp,tol,maxiter)
+ r = b - A[1,1]*u;
+ res0 = norm(b);
+ # Jacobi smoother
+ omega = 0.8;
+ invD = Array{Any}(undef, nl-1);
+ for l = 1:nl-1
+ invD[l,1]= 1.0 ./diag(A[l,1],0);
+ end
+
+ z = VCycle(A,r,Lfac,Ufac,Pu,1,nl,invD,omega,nswp);
+ p = z;
+ rho = r'*z;
+ rho_p= rho;
+ ii=1
+ relres=0
+
+
+ for i = 1:1e6
+ z = VCycle(A,r,Lfac,Ufac,Pu,1,nl,invD,omega,nswp);
+ rho = r'*z;
+ if i == 1
+ p = z;
+ else
+ beta = rho/rho_p;
+ p = beta.*p + z;
+ end
+ q = A[1,1]*p;
+ dpr = p'*q;
+ alpha = rho/dpr;
+ u = u .+ alpha.*p;
+ r = r .- alpha.*q;
+ rho_p = rho;
+ relres = norm(r)/res0;
+ if relres < tol || i>=maxiter
+ break
+ end
+ ii=ii+1
+ end
+ return ii,relres,u
+end
+## FUNCTION VCycle - COARSE GRID CORRECTION
+function VCycle(A,r,Lfac,Ufac,Pu,l,nl,invD,omega,nswp)
+ z = 0*r;
+ z = smthdmpjac(z,A[l,1],r,invD[l,1],omega,nswp);
+ Az = A[l,1]*z;
+ d = r - Az;
+ dh2 = Pu[l,1]'*d;
+ if (nl == l+1)
+ vh2 = Ufac \ (Lfac \ dh2);
+ else
+ vh2 = VCycle(A,dh2,Lfac,Ufac,Pu,l+1,nl,invD,omega,nswp);
+ end
+ v = Pu[l,1]*vh2;
+ z = z + v;
+ z = smthdmpjac(z,A[l,1],r,invD[l,1],omega,nswp);
+ return z
+end
+## FUNCTIODN smthdmpjac - DAMPED JACOBI SMOOTHER
+function smthdmpjac(u,A,b,invD,omega,nswp)
+ for i = 1:nswp
+ u = u - omega*invD.*(A*u) + omega*invD.*b;
+ end
+ return u
+end
+## FUNCTION prepcoarse - PREPARE MG PROLONGATION OPERATOR
+function Prepcoarse(ney,nex)
+ # Assemble state variable prolongation
+ maxnum = Int(nex*ney*20);
+ iP = zeros(Int64,maxnum); jP = zeros(Int64,maxnum); sP = zeros(maxnum);
+ nexc = nex/2; neyc = ney/2;
+ # weights for fixed distances to neighbors on a structured grid
+ vals = [1;0.5;0.25];
+ cc = 0;
+ for nx = 1:nexc+1
+ for ny = 1:neyc+1
+ col = Int((nx-1)*(neyc+1)+ny);
+ # Coordinate on fine grid
+ nx1 = nx*2 - 1; ny1 = ny*2 - 1;
+ # Loop over fine nodes within the rectangular domain
+ for k = max(nx1-1,1):min(nx1+1,nex+1)
+ for l = max(ny1-1,1):min(ny1+1,ney+1)
+ row = Int((k-1)*(ney+1)+l);
+ # Based on squared dist assign weights: 1.0 0.5 0.25
+ ind = Int(1+((nx1-k)^2+(ny1-l)^2));
+ cc = Int(cc+1); iP[cc] = Int(2*row-1); jP[cc] = 2*col-1; sP[cc] = vals[ind];
+ cc = Int(cc+1); iP[cc] = Int(2*row); jP[cc] = 2*col; sP[cc] = vals[ind];
+ end
+ end
+ end
+ end
+ # Assemble matrices
+ Pu = sparse(Int.(iP[1:cc]),jP[1:cc],sP[1:cc]);
+ return Pu
+end
+############################################################################
+# This code was written by Niels Aage and Boyan S. Lazarov, #
+# Department of Mechanical Engineering, Technical University of Denmark, #
+# and Oded Amir, Faculty of Civil & Environmental Engineering, #
+# Technion - Israel Institute of Technology. #
+# #
+# Contact: naage@mek.dtu.dk, bsl@mek.dtu.dk, odedamir@cv.technion.ac.il #
+# #
+# Details are discussed in the paper #
+# "On multigrid-CG for efficient topology optimization" accepted October #
+# 2013 to Structural and Multidisciplinary Optimization. #
+# #
+# Disclaimer: #
+# The authors reserve all rights but do not guarantee that the code is #
+# free from errors. Furthermore, the authors shall not be liable in any #
+# event caused by the use of the program. #
+############################################################################
+
+
+
+
+
+
diff --git a/voxel_designer/julia/include/utils.jl b/voxel_designer/julia/include/utils.jl
index e1f11eb..a435b31 100644
--- a/voxel_designer/julia/include/utils.jl
+++ b/voxel_designer/julia/include/utils.jl
@@ -260,4 +260,9 @@ function voxelize(mesh,res)
end
+function mapValss(value, x1, y1, x2, y2)
+ return (value .- x1) .* (y2 .- x2) / (y1 .- x1) .+ x2;
+end
+
+
#################################
diff --git a/voxel_designer/tmp.gif b/voxel_designer/tmp.gif
deleted file mode 100644
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--
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