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"+c+" invalid path type.")}if(0===t&&0===n||r>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&&(h=Math.floor(255*a)),e&&(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&&(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 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