1"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[6347],{36347:function(A,e,a){a.d(e,{xY:function(){return n}});var o=a(2265),t=a(7956),s=a(77974),B=a(85857),i=a(25049);let Q=`#version 300 es 2precision lowp float; 3 4uniform mediump float u_time; 5uniform mediump vec2 u_resolution; 6uniform mediump float u_pixelRatio; 7 8uniform sampler2D u_noiseTexture; 9 10uniform vec4 u_colorBack; 11uniform vec4 u_colors[7]; 12uniform float u_colorsCount; 13uniform float u_softness; 14uniform float u_intensity; 15uniform float u_noise; 16uniform float u_shape; 17 18uniform mediump float u_originX; 19uniform mediump float u_originY; 20uniform mediump float u_worldWidth; 21uniform mediump float u_worldHeight; 22uniform mediump float u_fit; 23 24uniform mediump float u_scale; 25uniform mediump float u_rotation; 26uniform mediump float u_offsetX; 27uniform mediump float u_offsetY; 28 29in vec2 v_objectUV; 30in vec2 v_patternUV; 31in vec2 v_objectBoxSize; 32in vec2 v_patternBoxSize; 33 34out vec4 fragColor; 35 36${i.Wu} 37${i.ty} 38${i.EO} 39${i.DC} 40 41float valueNoiseR(vec2 st) { 42 vec2 i = floor(st); 43 vec2 f = fract(st); 44 float a = randomR(i); 45 float b = randomR(i + vec2(1.0, 0.0)); 46 float c = randomR(i + vec2(0.0, 1.0)); 47 float d = randomR(i + vec2(1.0, 1.0)); 48 vec2 u = f * f * (3.0 - 2.0 * f); 49 float x1 = mix(a, b, u.x); 50 float x2 = mix(c, d, u.x); 51 return mix(x1, x2, u.y); 52} 53vec4 fbmR(vec2 n0, vec2 n1, vec2 n2, vec2 n3) { 54 float amplitude = 0.2; 55 vec4 total = vec4(0.); 56 for (int i = 0; i < 3; i++) { 57 n0 = rotate(n0, 0.3); 58 n1 = rotate(n1, 0.3); 59 n2 = rotate(n2, 0.3); 60 n3 = rotate(n3, 0.3); 61 total.x += valueNoiseR(n0) * amplitude; 62 total.y += valueNoiseR(n1) * amplitude; 63 total.z += valueNoiseR(n2) * amplitude; 64 total.z += valueNoiseR(n3) * amplitude; 65 n0 *= 1.99; 66 n1 *= 1.99; 67 n2 *= 1.99; 68 n3 *= 1.99; 69 amplitude *= 0.6; 70 } 71 return total; 72} 73 74${i.hX} 75 76vec2 truchet(vec2 uv, float idx){ 77 idx = fract(((idx - .5) * 2.)); 78 if (idx > 0.75) { 79 uv = vec2(1.0) - uv; 80 } else if (idx > 0.5) { 81 uv = vec2(1.0 - uv.x, uv.y); 82 } else if (idx > 0.25) { 83 uv = 1.0 - vec2(1.0 - uv.x, uv.y); 84 } 85 return uv; 86} 87 88void main() { 89 90 const float firstFrameOffset = 7.; 91 float t = .1 * (u_time + firstFrameOffset); 92 93 vec2 shape_uv = vec2(0.); 94 vec2 grain_uv = vec2(0.); 95 96 float r = u_rotation * PI / 180.; 97 float cr = cos(r); 98 float sr = sin(r); 99 mat2 graphicRotation = mat2(cr, sr, -sr, cr); 100 vec2 graphicOffset = vec2(-u_offsetX, u_offsetY); 101 102 if (u_shape > 3.5) { 103 shape_uv = v_objectUV; 104 grain_uv = shape_uv; 105 106 // apply inverse transform to grain_uv so it respects the originXY 107 grain_uv = transpose(graphicRotation) * grain_uv; 108 grain_uv *= u_scale; 109 grain_uv -= graphicOffset; 110 grain_uv *= v_objectBoxSize; 111 grain_uv *= .7; 112 } else { 113 shape_uv = .5 * v_patternUV; 114 grain_uv = 100. * v_patternUV; 115 116 // apply inverse transform to grain_uv so it respects the originXY 117 grain_uv = transpose(graphicRotation) * grain_uv; 118 grain_uv *= u_scale; 119 if (u_fit > 0.) { 120 vec2 givenBoxSize = vec2(u_worldWidth, u_worldHeight); 121 givenBoxSize = max(givenBoxSize, vec2(1.)) * u_pixelRatio; 122 float patternBoxRatio = givenBoxSize.x / givenBoxSize.y; 123 vec2 patternBoxGivenSize = vec2( 124 (u_worldWidth == 0.) ? u_resolution.x : givenBoxSize.x, 125 (u_worldHeight == 0.) ? u_resolution.y : givenBoxSize.y 126 ); 127 patternBoxRatio = patternBoxGivenSize.x / patternBoxGivenSize.y; 128 float patternBoxNoFitBoxWidth = patternBoxRatio * min(patternBoxGivenSize.x / patternBoxRatio, patternBoxGivenSize.y); 129 grain_uv /= (patternBoxNoFitBoxWidth / v_patternBoxSize.x); 130 } 131 vec2 patternBoxScale = u_resolution.xy / v_patternBoxSize; 132 grain_uv -= graphicOffset / patternBoxScale; 133 grain_uv *= 1.6; 134 } 135 136 137 float shape = 0.; 138 139 if (u_shape < 1.5) { 140 // Sine wave 141 142 float wave = cos(.5 * shape_uv.x - 4. * t) * sin(1.5 * shape_uv.x + 2. * t) * (.75 + .25 * cos(6. * t)); 143 shape = 1. - smoothstep(-1., 1., shape_uv.y + wave); 144 145 } else if (u_shape < 2.5) { 146 // Grid (dots) 147 148 float stripeIdx = floor(2. * shape_uv.x / TWO_PI); 149 float rand = hash11(stripeIdx * 100.); 150 rand = sign(rand - .5) * pow(4. * abs(rand), .3); 151 shape = sin(shape_uv.x) * cos(shape_uv.y - 5. * rand * t); 152 shape = pow(abs(shape), 4.); 153 154 } else if (u_shape < 3.5) { 155 // Truchet pattern 156 157 float n2 = valueNoiseR(shape_uv * .4 - 3.75 * t); 158 shape_uv.x += 10.; 159 shape_uv *= .6; 160
161 vec2 tile = truchet(fract(shape_uv), randomR(floor(shape_uv))); 162 163 float distance1 = length(tile); 164 float distance2 = length(tile - vec2(1.)); 165 166 n2 -= .5; 167 n2 *= .1; 168 shape = smoothstep(.2, .55, distance1 + n2) * (1. - smoothstep(.45, .8, distance1 - n2)); 169 shape += smoothstep(.2, .55, distance2 + n2) * (1. - smoothstep(.45, .8, distance2 - n2)); 170 171 shape = pow(shape, 1.5); 172 173 } else if (u_shape < 4.5) { 174 // Corners 175 176 shape_uv *= .6; 177 vec2 outer = vec2(.5); 178 179 vec2 bl = smoothstep(vec2(0.), outer, shape_uv + vec2(.1 + .1 * sin(3. * t), .2 - .1 * sin(5.25 * t))); 180 vec2 tr = smoothstep(vec2(0.), outer, 1. - shape_uv); 181 shape = 1. - bl.x * bl.y * tr.x * tr.y; 182 183 shape_uv = -shape_uv; 184 bl = smoothstep(vec2(0.), outer, shape_uv + vec2(.1 + .1 * sin(3. * t), .2 - .1 * cos(5.25 * t))); 185 tr = smoothstep(vec2(0.), outer, 1. - shape_uv); 186 shape -= bl.x * bl.y * tr.x * tr.y; 187 188 shape = 1. - smoothstep(0., 1., shape); 189 190 } else if (u_shape < 5.5) { 191 // Ripple 192 193 shape_uv *= 2.; 194 float dist = length(.4 * shape_uv); 195 float waves = sin(pow(dist, 1.2) * 5. - 3. * t) * .5 + .5; 196 shape = waves; 197 198 } else if (u_shape < 6.5) { 199 // Blob 200 201 t *= 2.; 202 203 vec2 f1_traj = .25 * vec2(1.3 * sin(t), .2 + 1.3 * cos(.6 * t + 4.)); 204 vec2 f2_traj = .2 * vec2(1.2 * sin(-t), 1.3 * sin(1.6 * t)); 205 vec2 f3_traj = .25 * vec2(1.7 * cos(-.6 * t), cos(-1.6 * t)); 206 vec2 f4_traj = .3 * vec2(1.4 * cos(.8 * t), 1.2 * sin(-.6 * t - 3.)); 207 208 shape = .5 * pow(1. - clamp(0., 1., length(shape_uv + f1_traj)), 5.); 209 shape += .5 * pow(1. - clamp(0., 1., length(shape_uv + f2_traj)), 5.); 210 shape += .5 * pow(1. - clamp(0., 1., length(shape_uv + f3_traj)), 5.); 211 shape += .5 * pow(1. - clamp(0., 1., length(shape_uv + f4_traj)), 5.); 212 213 shape = smoothstep(.0, .9, shape); 214 float edge = smoothstep(.25, .3, shape); 215 shape = mix(.0, shape, edge); 216 217 } else { 218 // Sphere 219 220 shape_uv *= 2.; 221 float d = 1. - pow(length(shape_uv), 2.); 222 vec3 pos = vec3(shape_uv, sqrt(max(d, 0.))); 223 vec3 lightPos = normalize(vec3(cos(1.5 * t), .8, sin(1.25 * t))); 224 shape = .5 + .5 * dot(lightPos, pos); 225 shape *= step(0., d); 226 } 227 228 float baseNoise = snoise(grain_uv * .5); 229 vec4 fbmVals = fbmR( 230 .002 * grain_uv + 10., 231 .003 * grain_uv, 232 .001 * grain_uv, 233 rotate(.4 * grain_uv, 2.) 234 ); 235 float grainDist = baseNoise * snoise(grain_uv * .2) - fbmVals.x - fbmVals.y; 236 float rawNoise = .75 * baseNoise - fbmVals.w - fbmVals.z; 237 float noise = clamp(rawNoise, 0., 1.); 238 239 shape += u_intensity * 2. / u_colorsCount * (grainDist + .5); 240 shape += u_noise * 10. / u_colorsCount * noise; 241 242 float aa = fwidth(shape); 243 244 shape = clamp(shape - .5 / u_colorsCount, 0., 1.); 245 float totalShape = smoothstep(0., u_softness + 2. * aa, clamp(shape * u_colorsCount, 0., 1.)); 246 float mixer = shape * (u_colorsCount - 1.); 247 248 int cntStop = int(u_colorsCount) - 1; 249 vec4 gradient = u_colors[0]; 250 gradient.rgb *= gradient.a; 251 for (int i = 1; i < 7; i++) { 252 if (i > cntStop) break; 253 254 float localT = clamp(mixer - float(i - 1), 0., 1.); 255 localT = smoothstep(.5 - .5 * u_softness - aa, .5 + .5 * u_softness + aa, localT); 256 257 vec4 c = u_colors[i]; 258 c.rgb *= c.a; 259 gradient = mix(gradient, c, localT); 260 } 261 262 vec3 color = gradient.rgb * totalShape; 263 float opacity = gradient.a * totalShape; 264 265 vec3 bgColor = u_colorBack.rgb * u_colorBack.a; 266 color = color + bgColor * (1.0 - opacity); 267 opacity = opacity + u_colorBack.a * (1.0 - opacity); 268 269 fragColor = vec4(color, opacity); 270} 271`,g={wave:1,dots:2,truchet:3,corners:4,ripple:5,blob:6,sphere:7};var c=a(57437);let w={name:"Default",params:{...s.q$,speed:1,frame:0,colorBack:"#000000",colors:["#7300ff","#eba8ff","#00bfff","#2a00ff"],softness:.5,intensity:.5,noise:.25,shape:"corners"}};s.j5,s.j5,s.j5,s.q$,s.q$;let n=(0,o.memo)(function({speed:A=w.params.speed,frame:e=w.params.frame,colorBack:a=w.params.colorBack,colors:o=w.params.colors,softness:i=w.params.softness,intensity:n=w.params.intensity,noise:r=w.params.noise,shape:E=w.params.shape,fit:l=w.params.fit,scale:p=w.params.scale,rotation:C=w.params.rotation,originX:h=w.params.originX,originY:u=w.params.originY,offsetX:m=w.params.offsetX,offsetY:I=w.params.offsetY,worldWidth:f=w.params.worldWidth,worldHeight:D=w.params.worldHeight,...F}
271){let M={u_colorBack:(0,B.f)(a),u_colors:o.map(B.f),u_colorsCount:o.length,u_softness:i,u_intensity:n,u_noise:r,u_shape:g[E],u_noiseTexture:function(){if("undefined"==typeof window)return;let A=new Image;return A.src=
vendor: 23,248 bytes, line 271
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"
271,A}(),u_fit:s.MI[l],u_scale:p,u_rotation:C,u_offsetX:m,u_offsetY:I,u_originX:h,u_originY:u,u_worldWidth:f,u_worldHeight:D};return(0,c.jsx)(t.b,{...F,speed:A,frame:e,fragmentShader:Q,uniforms:M})})}}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.