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t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let 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o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: 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x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let 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t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let 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o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: 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