1 line
No EOL
191 KiB
JSON
1 line
No EOL
191 KiB
JSON
{"v":"5.5.2","fr":60,"ip":0,"op":140,"w":1125,"h":1500,"nm":"Comp 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","e":1},{"id":"image_1","w":513,"h":513,"u":"","p":"data:image/png;base64,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$bm_rt;\nvar e, g, nMax, n, n, t, v, vl, vu, vu, tCur, segDur, tNext, nb, delta;\ne = 0.2;\ng = 2100;\nnMax = 5;\n$bm_rt = n = 0;\nif (numKeys > 0) {\n $bm_rt = n = nearestKey(time).index;\n if (key(n).time > time)\n n--;\n}\nif (n > 0) {\n t = $bm_sub(time, key(n).time);\n v = $bm_mul($bm_neg(velocityAtTime($bm_sub(key(n).time, 0.001))), e);\n vl = length(v);\n if ($bm_isInstanceOfArray(value)) {\n vu = vl > 0 ? normalize(v) : [\n 0,\n 0,\n 0\n ];\n } else {\n vu = v < 0 ? -1 : 1;\n }\n tCur = 0;\n segDur = $bm_div($bm_mul(2, vl), g);\n tNext = segDur;\n nb = 1;\n while (tNext < t && nb <= nMax) {\n vl *= e;\n segDur *= e;\n tCur = tNext;\n tNext = $bm_sum(tNext, segDur);\n nb++;\n }\n if (nb <= nMax) {\n delta = $bm_sub(t, tCur);\n $bm_rt = $bm_sum(value, $bm_mul($bm_mul(vu, delta), $bm_sub(vl, $bm_div($bm_mul(g, delta), 2))));\n } else {\n $bm_rt = value;\n }\n} else\n $bm_rt = value;"}},"ao":0,"ip":0,"op":141,"st":0,"cp":false,"bm":0},{"ddd":0,"ind":2,"ty":2,"nm":"Accent","refId":"image_1","sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":0,"k":0,"ix":10},"p":{"a":0,"k":[548.5,1066,0],"ix":2},"a":{"a":0,"k":[256.5,256.5,0],"ix":1},"s":{"a":1,"k":[{"i":{"x":[0.833,0.833,0.833],"y":[0.833,0.833,0.833]},"o":{"x":[0.167,0.167,0.167],"y":[0.167,0.167,0.167]},"t":97,"s":[0,0,100]},{"t":105,"s":[100,100,100]}],"ix":6,"x":"var $bm_rt;\nvar e, g, nMax, n, n, t, v, vl, vu, vu, tCur, segDur, tNext, nb, delta;\ne = 0.2;\ng = 2100;\nnMax = 5;\n$bm_rt = n = 0;\nif (numKeys > 0) {\n $bm_rt = n = nearestKey(time).index;\n if (key(n).time > time)\n n--;\n}\nif (n > 0) {\n t = $bm_sub(time, key(n).time);\n v = $bm_mul($bm_neg(velocityAtTime($bm_sub(key(n).time, 0.001))), e);\n vl = length(v);\n if ($bm_isInstanceOfArray(value)) {\n vu = vl > 0 ? normalize(v) : [\n 0,\n 0,\n 0\n ];\n } else {\n vu = v < 0 ? -1 : 1;\n }\n tCur = 0;\n segDur = $bm_div($bm_mul(2, vl), g);\n tNext = segDur;\n nb = 1;\n while (tNext < t && nb <= nMax) {\n vl *= e;\n segDur *= e;\n tCur = tNext;\n tNext = $bm_sum(tNext, segDur);\n nb++;\n }\n if (nb <= nMax) {\n delta = $bm_sub(t, tCur);\n $bm_rt = $bm_sum(value, $bm_mul($bm_mul(vu, delta), $bm_sub(vl, $bm_div($bm_mul(g, delta), 2))));\n } else {\n $bm_rt = value;\n }\n} else\n $bm_rt = value;"}},"ao":0,"ip":0,"op":141,"st":0,"cp":false,"bm":0},{"ddd":0,"ind":3,"ty":4,"nm":"Top Bubble","sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":0,"k":0,"ix":10},"p":{"a":1,"k":[{"i":{"x":0.833,"y":0.833},"o":{"x":0.167,"y":0.167},"t":36,"s":[608.5,960,0],"to":[4.333,-12.333,0],"ti":[-4.333,12.333,0]},{"t":44,"s":[634.5,886,0]}],"ix":2,"x":"var $bm_rt;\nfunction doIt() {\n var easingEquation, val1, val2, val3;\n var ew_one = expoIn;\n var ew_two = expoOut;\n var n = 0;\n if (numKeys > 0) {\n n = nearestKey(time).index;\n if (key(n).time > time) {\n n--;\n }\n if (n < 2) {\n easingEquation = ew_one;\n } else if (n >= $bm_sub(numKeys, 1)) {\n easingEquation = ew_two;\n } else {\n return null;\n }\n }\n try {\n var key1 = key(n);\n var key2 = key($bm_sum(n, 1));\n } catch (e) {\n return null;\n }\n var dim = 1;\n try {\n key(1)[1].length;\n dim = 2;\n key(1)[2].length;\n dim = 3;\n } catch (e) {\n }\n var t = $bm_sub(time, key1.time);\n var d = $bm_sub(key2.time, key1.time);\n var sX = key1[0];\n var eX = $bm_sub(key2[0], key1[0]);\n if (dim >= 2) {\n var sY = key1[1];\n var eY = $bm_sub(key2[1], key1[1]);\n if (dim >= 3) {\n var sZ = key1[2];\n var eZ = $bm_sub(key2[2], key1[2]);\n }\n }\n if (time < key1.time || time > key2.time) {\n return value;\n } else {\n val1 = easingEquation(t, sX, eX, d);\n switch (dim) {\n case 1:\n return val1;\n break;\n case 2:\n val2 = easingEquation(t, sY, eY, d);\n return [\n val1,\n val2\n ];\n break;\n case 3:\n val2 = easingEquation(t, sY, eY, d);\n val3 = easingEquation(t, sZ, eZ, d);\n return [\n val1,\n val2,\n val3\n ];\n break;\n default:\n return null;\n }\n }\n}\nvar IN_EXPO_CORRECTION = 0.000976563;\nvar OUT_EXPO_CORRECTION = 1.000976563;\nvar s = 1.70158;\nvar p = 0.81;\nvar a = 50;\nfunction bounceOut(t, b, c, d) {\n if ((t /= d) < 1 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_mul($bm_mul(7.5625, t), t)), b);\n } else if (t < 2 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 1.5 / 2.75), t), 0.75)), b);\n } else if (t < 2.5 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.25 / 2.75), t), 0.9375)), b);\n } else {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.625 / 2.75), t), 0.984375)), b);\n }\n}\nfunction bounceIn(t, b, c, d) {\n return $bm_sum($bm_sub(c, bounceOut($bm_sub(d, t), 0, c, d)), b);\n}\nfunction bounceInOut(t, b, c, d) {\n if (t < $bm_div(d, 2))\n return $bm_sum($bm_mul(bounceIn($bm_mul(t, 2), 0, c, d), 0.5), b);\n else\n return bounceOut(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b;\n}\nfunction backIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul(c, t /= d), t), $bm_sub($bm_mul($bm_sum(s, 1), t), s)), b);\n}\nfunction backInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_mul($bm_mul(t, t), $bm_sub($bm_mul($bm_sum(s *= 1.525, 1), t), s))), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul(t -= 2, t), $bm_sum($bm_mul($bm_sum(s *= 1.525, 1), t), s)), 2)), b);\n}\nfunction backOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(t = t / d - 1, t), $bm_sum($bm_mul($bm_sum(s, 1), t), s)), 1)), b);\n}\nfunction circIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t /= d, t))), 1)), b);\n}\nfunction circInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t, t))), 1)), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum(Math.sqrt($bm_sub(1, $bm_mul(t -= 2, t))), 1)), b);\n}\nfunction circOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sqrt($bm_sub(1, $bm_mul(t = t / d - 1, t)))), b);\n}\nfunction elasticIn(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_neg($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n}\nfunction elasticInOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d / 2) == 2)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3 * 1.5);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n if (t < 1)\n return $bm_sum($bm_mul(-0.5, $bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n return $bm_sum($bm_sum($bm_mul($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), 0.5), c), b);\n}\nfunction elasticOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_sum($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), c), b);\n}\nfunction expoIn(t, b, c, d) {\n return t == 0 ? b : c * (Math.pow(2, 10 * (t / d - 1)) - IN_EXPO_CORRECTION) + b;\n}\nfunction expoInOut(t, b, c, d) {\n var v;\n if ((t /= d / 2) < 1) {\n v = $bm_sub(Math.pow(2, $bm_mul(10, $bm_sub(t, 1))), IN_EXPO_CORRECTION);\n } else {\n v = $bm_sum($bm_sum($bm_neg(Math.pow(2, $bm_mul(-10, $bm_sub(t, 1)))), 2), IN_EXPO_CORRECTION);\n }\n return $bm_sum(b, $bm_mul($bm_div(v, 2), c));\n}\nfunction expoOut(t, b, c, d) {\n return t == d ? b + c : c * OUT_EXPO_CORRECTION * (-Math.pow(2, -10 * t / d) + 1) + b;\n}\nfunction quadIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul(c, t /= d), t), b);\n}\nfunction quadInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_div(c, 2), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul(--t, $bm_sub(t, 2)), 1)), b);\n}\nfunction quadOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_neg(c), t /= d), $bm_sub(t, 2)), b);\n}\nfunction quartIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), b);\n}\nfunction quartInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), 2)), b);\n}\nfunction quartOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), 1)), b);\n}\nfunction quintIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), t), b);\n}\nfunction quintInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), t), 2)), b);\n}\nfunction quintOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), t), 1)), b);\n}\nfunction sineIn(t, b, c, d) {\n return $bm_sum($bm_sum($bm_mul($bm_neg(c), Math.cos($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), c), b);\n}\nfunction sineInOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.cos($bm_div($bm_mul(Math.PI, t), d)), 1)), b);\n}\nfunction sineOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sin($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), b);\n}\n$bm_rt = doIt() || value;"},"a":{"a":0,"k":[0,0,0],"ix":1},"s":{"a":0,"k":[100,100,100],"ix":6}},"ao":0,"shapes":[{"ty":"gr","it":[{"ty":"rc","d":1,"s":{"a":0,"k":[336,120],"ix":2},"p":{"a":0,"k":[0,0],"ix":3},"r":{"a":0,"k":39,"ix":4},"nm":"Rectangle Path 1","mn":"ADBE Vector Shape - Rect","hd":false},{"ty":"fl","c":{"a":0,"k":[0.909803921569,0.909803921569,0.909803921569,1],"ix":4},"o":{"a":0,"k":100,"ix":5},"r":1,"bm":0,"nm":"Fill 1","mn":"ADBE Vector Graphic - Fill","hd":false},{"ty":"tr","p":{"a":0,"k":[65.813,-744.164],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[94.346,94.346],"ix":3},"r":{"a":0,"k":18.5,"ix":6},"o":{"a":1,"k":[{"i":{"x":[0.833],"y":[0.833]},"o":{"x":[0.167],"y":[0.167]},"t":40,"s":[39]},{"t":42,"s":[0]}],"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transform"}],"nm":"Rectangle 1","np":3,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":142,"st":0,"cp":true,"bm":0},{"ddd":0,"ind":4,"ty":4,"nm":"Middle Bubble","sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":0,"k":0,"ix":10},"p":{"a":1,"k":[{"i":{"x":0.833,"y":0.833},"o":{"x":0.167,"y":0.167},"t":36,"s":[724.5,1202,0],"to":[4.333,-12.333,0],"ti":[-4.333,12.333,0]},{"t":44,"s":[750.5,1128,0]}],"ix":2,"x":"var $bm_rt;\nfunction doIt() {\n var easingEquation, val1, val2, val3;\n var ew_one = expoIn;\n var ew_two = expoOut;\n var n = 0;\n if (numKeys > 0) {\n n = nearestKey(time).index;\n if (key(n).time > time) {\n n--;\n }\n if (n < 2) {\n easingEquation = ew_one;\n } else if (n >= $bm_sub(numKeys, 1)) {\n easingEquation = ew_two;\n } else {\n return null;\n }\n }\n try {\n var key1 = key(n);\n var key2 = key($bm_sum(n, 1));\n } catch (e) {\n return null;\n }\n var dim = 1;\n try {\n key(1)[1].length;\n dim = 2;\n key(1)[2].length;\n dim = 3;\n } catch (e) {\n }\n var t = $bm_sub(time, key1.time);\n var d = $bm_sub(key2.time, key1.time);\n var sX = key1[0];\n var eX = $bm_sub(key2[0], key1[0]);\n if (dim >= 2) {\n var sY = key1[1];\n var eY = $bm_sub(key2[1], key1[1]);\n if (dim >= 3) {\n var sZ = key1[2];\n var eZ = $bm_sub(key2[2], key1[2]);\n }\n }\n if (time < key1.time || time > key2.time) {\n return value;\n } else {\n val1 = easingEquation(t, sX, eX, d);\n switch (dim) {\n case 1:\n return val1;\n break;\n case 2:\n val2 = easingEquation(t, sY, eY, d);\n return [\n val1,\n val2\n ];\n break;\n case 3:\n val2 = easingEquation(t, sY, eY, d);\n val3 = easingEquation(t, sZ, eZ, d);\n return [\n val1,\n val2,\n val3\n ];\n break;\n default:\n return null;\n }\n }\n}\nvar IN_EXPO_CORRECTION = 0.000976563;\nvar OUT_EXPO_CORRECTION = 1.000976563;\nvar s = 1.70158;\nvar p = 0.81;\nvar a = 50;\nfunction bounceOut(t, b, c, d) {\n if ((t /= d) < 1 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_mul($bm_mul(7.5625, t), t)), b);\n } else if (t < 2 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 1.5 / 2.75), t), 0.75)), b);\n } else if (t < 2.5 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.25 / 2.75), t), 0.9375)), b);\n } else {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.625 / 2.75), t), 0.984375)), b);\n }\n}\nfunction bounceIn(t, b, c, d) {\n return $bm_sum($bm_sub(c, bounceOut($bm_sub(d, t), 0, c, d)), b);\n}\nfunction bounceInOut(t, b, c, d) {\n if (t < $bm_div(d, 2))\n return $bm_sum($bm_mul(bounceIn($bm_mul(t, 2), 0, c, d), 0.5), b);\n else\n return bounceOut(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b;\n}\nfunction backIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul(c, t /= d), t), $bm_sub($bm_mul($bm_sum(s, 1), t), s)), b);\n}\nfunction backInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_mul($bm_mul(t, t), $bm_sub($bm_mul($bm_sum(s *= 1.525, 1), t), s))), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul(t -= 2, t), $bm_sum($bm_mul($bm_sum(s *= 1.525, 1), t), s)), 2)), b);\n}\nfunction backOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(t = t / d - 1, t), $bm_sum($bm_mul($bm_sum(s, 1), t), s)), 1)), b);\n}\nfunction circIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t /= d, t))), 1)), b);\n}\nfunction circInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t, t))), 1)), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum(Math.sqrt($bm_sub(1, $bm_mul(t -= 2, t))), 1)), b);\n}\nfunction circOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sqrt($bm_sub(1, $bm_mul(t = t / d - 1, t)))), b);\n}\nfunction elasticIn(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_neg($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n}\nfunction elasticInOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d / 2) == 2)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3 * 1.5);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n if (t < 1)\n return $bm_sum($bm_mul(-0.5, $bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n return $bm_sum($bm_sum($bm_mul($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), 0.5), c), b);\n}\nfunction elasticOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_sum($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), c), b);\n}\nfunction expoIn(t, b, c, d) {\n return t == 0 ? b : c * (Math.pow(2, 10 * (t / d - 1)) - IN_EXPO_CORRECTION) + b;\n}\nfunction expoInOut(t, b, c, d) {\n var v;\n if ((t /= d / 2) < 1) {\n v = $bm_sub(Math.pow(2, $bm_mul(10, $bm_sub(t, 1))), IN_EXPO_CORRECTION);\n } else {\n v = $bm_sum($bm_sum($bm_neg(Math.pow(2, $bm_mul(-10, $bm_sub(t, 1)))), 2), IN_EXPO_CORRECTION);\n }\n return $bm_sum(b, $bm_mul($bm_div(v, 2), c));\n}\nfunction expoOut(t, b, c, d) {\n return t == d ? b + c : c * OUT_EXPO_CORRECTION * (-Math.pow(2, -10 * t / d) + 1) + b;\n}\nfunction quadIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul(c, t /= d), t), b);\n}\nfunction quadInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_div(c, 2), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul(--t, $bm_sub(t, 2)), 1)), b);\n}\nfunction quadOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_neg(c), t /= d), $bm_sub(t, 2)), b);\n}\nfunction quartIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), b);\n}\nfunction quartInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), 2)), b);\n}\nfunction quartOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), 1)), b);\n}\nfunction quintIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), t), b);\n}\nfunction quintInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), t), 2)), b);\n}\nfunction quintOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), t), 1)), b);\n}\nfunction sineIn(t, b, c, d) {\n return $bm_sum($bm_sum($bm_mul($bm_neg(c), Math.cos($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), c), b);\n}\nfunction sineInOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.cos($bm_div($bm_mul(Math.PI, t), d)), 1)), b);\n}\nfunction sineOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sin($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), b);\n}\n$bm_rt = doIt() || value;"},"a":{"a":0,"k":[0,0,0],"ix":1},"s":{"a":0,"k":[100,100,100],"ix":6}},"ao":0,"shapes":[{"ty":"gr","it":[{"ty":"rc","d":1,"s":{"a":0,"k":[336,222.277],"ix":2},"p":{"a":0,"k":[0,0],"ix":3},"r":{"a":0,"k":39,"ix":4},"nm":"Rectangle Path 1","mn":"ADBE Vector Shape - Rect","hd":false},{"ty":"fl","c":{"a":0,"k":[0.909803921569,0.909803921569,0.909803921569,1],"ix":4},"o":{"a":0,"k":100,"ix":5},"r":1,"bm":0,"nm":"Fill 1","mn":"ADBE Vector Graphic - Fill","hd":false},{"ty":"tr","p":{"a":0,"k":[65.813,-744.164],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[94.346,94.346],"ix":3},"r":{"a":0,"k":18.516,"ix":6},"o":{"a":0,"k":100,"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transform"}],"nm":"Rectangle 1","np":3,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":142,"st":0,"cp":true,"bm":0},{"ddd":0,"ind":5,"ty":4,"nm":"Bottom Bubble","sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":0,"k":0,"ix":10},"p":{"a":1,"k":[{"i":{"x":0.667,"y":1},"o":{"x":0.333,"y":0},"t":25,"s":[470.5,1374,0],"to":[4.333,-12.333,0],"ti":[-4.333,12.333,0]},{"t":44,"s":[496.5,1300,0]}],"ix":2,"x":"var $bm_rt;\nfunction doIt() {\n var easingEquation, val1, val2, val3;\n var ew_one = expoIn;\n var ew_two = expoOut;\n var n = 0;\n if (numKeys > 0) {\n n = nearestKey(time).index;\n if (key(n).time > time) {\n n--;\n }\n if (n < 2) {\n easingEquation = ew_one;\n } else if (n >= $bm_sub(numKeys, 1)) {\n easingEquation = ew_two;\n } else {\n return null;\n }\n }\n try {\n var key1 = key(n);\n var key2 = key($bm_sum(n, 1));\n } catch (e) {\n return null;\n }\n var dim = 1;\n try {\n key(1)[1].length;\n dim = 2;\n key(1)[2].length;\n dim = 3;\n } catch (e) {\n }\n var t = $bm_sub(time, key1.time);\n var d = $bm_sub(key2.time, key1.time);\n var sX = key1[0];\n var eX = $bm_sub(key2[0], key1[0]);\n if (dim >= 2) {\n var sY = key1[1];\n var eY = $bm_sub(key2[1], key1[1]);\n if (dim >= 3) {\n var sZ = key1[2];\n var eZ = $bm_sub(key2[2], key1[2]);\n }\n }\n if (time < key1.time || time > key2.time) {\n return value;\n } else {\n val1 = easingEquation(t, sX, eX, d);\n switch (dim) {\n case 1:\n return val1;\n break;\n case 2:\n val2 = easingEquation(t, sY, eY, d);\n return [\n val1,\n val2\n ];\n break;\n case 3:\n val2 = easingEquation(t, sY, eY, d);\n val3 = easingEquation(t, sZ, eZ, d);\n return [\n val1,\n val2,\n val3\n ];\n break;\n default:\n return null;\n }\n }\n}\nvar IN_EXPO_CORRECTION = 0.000976563;\nvar OUT_EXPO_CORRECTION = 1.000976563;\nvar s = 1.70158;\nvar p = 0.81;\nvar a = 50;\nfunction bounceOut(t, b, c, d) {\n if ((t /= d) < 1 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_mul($bm_mul(7.5625, t), t)), b);\n } else if (t < 2 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 1.5 / 2.75), t), 0.75)), b);\n } else if (t < 2.5 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.25 / 2.75), t), 0.9375)), b);\n } else {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.625 / 2.75), t), 0.984375)), b);\n }\n}\nfunction bounceIn(t, b, c, d) {\n return $bm_sum($bm_sub(c, bounceOut($bm_sub(d, t), 0, c, d)), b);\n}\nfunction bounceInOut(t, b, c, d) {\n if (t < $bm_div(d, 2))\n return $bm_sum($bm_mul(bounceIn($bm_mul(t, 2), 0, c, d), 0.5), b);\n else\n return bounceOut(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b;\n}\nfunction backIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul(c, t /= d), t), $bm_sub($bm_mul($bm_sum(s, 1), t), s)), b);\n}\nfunction backInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_mul($bm_mul(t, t), $bm_sub($bm_mul($bm_sum(s *= 1.525, 1), t), s))), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul(t -= 2, t), $bm_sum($bm_mul($bm_sum(s *= 1.525, 1), t), s)), 2)), b);\n}\nfunction backOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(t = t / d - 1, t), $bm_sum($bm_mul($bm_sum(s, 1), t), s)), 1)), b);\n}\nfunction circIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t /= d, t))), 1)), b);\n}\nfunction circInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t, t))), 1)), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum(Math.sqrt($bm_sub(1, $bm_mul(t -= 2, t))), 1)), b);\n}\nfunction circOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sqrt($bm_sub(1, $bm_mul(t = t / d - 1, t)))), b);\n}\nfunction elasticIn(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_neg($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n}\nfunction elasticInOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d / 2) == 2)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3 * 1.5);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n if (t < 1)\n return $bm_sum($bm_mul(-0.5, $bm_mul($bm_mul(a, Math.pow(2, $bm_mul(10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p)))), b);\n return $bm_sum($bm_sum($bm_mul($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t -= 1))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), 0.5), c), b);\n}\nfunction elasticOut(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n if (!a || a < Math.abs(c)) {\n a = c;\n s = $bm_div(p, 4);\n } else\n s = $bm_mul($bm_div(p, $bm_mul(2, Math.PI)), Math.asin($bm_div(c, a)));\n return $bm_sum($bm_sum($bm_mul($bm_mul(a, Math.pow(2, $bm_mul(-10, t))), Math.sin($bm_div($bm_mul($bm_sub($bm_mul(t, d), s), $bm_mul(2, Math.PI)), p))), c), b);\n}\nfunction expoIn(t, b, c, d) {\n return t == 0 ? b : c * (Math.pow(2, 10 * (t / d - 1)) - IN_EXPO_CORRECTION) + b;\n}\nfunction expoInOut(t, b, c, d) {\n var v;\n if ((t /= d / 2) < 1) {\n v = $bm_sub(Math.pow(2, $bm_mul(10, $bm_sub(t, 1))), IN_EXPO_CORRECTION);\n } else {\n v = $bm_sum($bm_sum($bm_neg(Math.pow(2, $bm_mul(-10, $bm_sub(t, 1)))), 2), IN_EXPO_CORRECTION);\n }\n return $bm_sum(b, $bm_mul($bm_div(v, 2), c));\n}\nfunction expoOut(t, b, c, d) {\n return t == d ? b + c : c * OUT_EXPO_CORRECTION * (-Math.pow(2, -10 * t / d) + 1) + b;\n}\nfunction quadIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul(c, t /= d), t), b);\n}\nfunction quadInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_div(c, 2), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul(--t, $bm_sub(t, 2)), 1)), b);\n}\nfunction quadOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_neg(c), t /= d), $bm_sub(t, 2)), b);\n}\nfunction quartIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), b);\n}\nfunction quartInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), 2)), b);\n}\nfunction quartOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), 1)), b);\n}\nfunction quintIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul(c, t /= d), t), t), t), t), b);\n}\nfunction quintInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul($bm_mul($bm_div(c, 2), t), t), t), t), t), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t -= 2, t), t), t), t), 2)), b);\n}\nfunction quintOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul($bm_mul($bm_mul(t = t / d - 1, t), t), t), t), 1)), b);\n}\nfunction sineIn(t, b, c, d) {\n return $bm_sum($bm_sum($bm_mul($bm_neg(c), Math.cos($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), c), b);\n}\nfunction sineInOut(t, b, c, d) {\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.cos($bm_div($bm_mul(Math.PI, t), d)), 1)), b);\n}\nfunction sineOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sin($bm_mul($bm_div(t, d), $bm_div(Math.PI, 2)))), b);\n}\n$bm_rt = doIt() || value;"},"a":{"a":0,"k":[0,0,0],"ix":1},"s":{"a":0,"k":[100,100,100],"ix":6}},"ao":0,"shapes":[{"ty":"gr","it":[{"ty":"rc","d":1,"s":{"a":0,"k":[336,222.277],"ix":2},"p":{"a":0,"k":[0,0],"ix":3},"r":{"a":0,"k":39,"ix":4},"nm":"Rectangle Path 1","mn":"ADBE Vector Shape - Rect","hd":false},{"ty":"fl","c":{"a":0,"k":[0.909803921569,0.909803921569,0.909803921569,1],"ix":4},"o":{"a":0,"k":100,"ix":5},"r":1,"bm":0,"nm":"Fill 1","mn":"ADBE Vector Graphic - Fill","hd":false},{"ty":"tr","p":{"a":0,"k":[65.813,-744.164],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[94.346,94.346],"ix":3},"r":{"a":0,"k":18.516,"ix":6},"o":{"a":0,"k":39,"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transform"}],"nm":"Rectangle 1","np":3,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":142,"st":0,"cp":true,"bm":0},{"ddd":0,"ind":6,"ty":4,"nm":"New Bubble","sr":1,"ks":{"o":{"a":1,"k":[{"i":{"x":[0.833],"y":[0.833]},"o":{"x":[0.167],"y":[0.167]},"t":39,"s":[0]},{"t":42,"s":[100]}],"ix":11,"x":"var $bm_rt;\nfunction easeandwizz_inoutExpo(t, b, c, d) {\n var CORRECTION = 0.000976563;\n var v;\n if ((t /= d / 2) < 1) {\n v = $bm_sub(Math.pow(2, $bm_mul(10, $bm_sub(t, 1))), CORRECTION);\n } else {\n v = $bm_sum($bm_sum($bm_neg(Math.pow(2, $bm_mul(-10, $bm_sub(t, 1)))), 2), CORRECTION);\n }\n return $bm_sum(b, $bm_mul($bm_div(v, 2), c));\n}\nfunction easeAndWizz() {\n var t, d, sX, eX, sY, eY, sZ, eZ, val1, val2, val2, val3;\n var n = 0;\n if (numKeys > 0) {\n n = nearestKey(time).index;\n if (key(n).time > time) {\n n--;\n }\n }\n try {\n var key1 = key(n);\n var key2 = key($bm_sum(n, 1));\n } catch (e) {\n return null;\n }\n var dim = 1;\n try {\n key(1)[1].length;\n dim = 2;\n key(1)[2].length;\n dim = 3;\n } catch (e) {\n }\n t = $bm_sub(time, key1.time);\n d = $bm_sub(key2.time, key1.time);\n sX = key1[0];\n eX = $bm_sub(key2[0], key1[0]);\n if (dim >= 2) {\n sY = key1[1];\n eY = $bm_sub(key2[1], key1[1]);\n if (dim >= 3) {\n sZ = key1[2];\n eZ = $bm_sub(key2[2], key1[2]);\n }\n }\n if (time < key1.time || time > key2.time) {\n return value;\n } else {\n val1 = easeandwizz_inoutExpo(t, sX, eX, d);\n switch (dim) {\n case 1:\n return val1;\n break;\n case 2:\n val2 = easeandwizz_inoutExpo(t, sY, eY, d);\n return [\n val1,\n val2\n ];\n break;\n case 3:\n val2 = easeandwizz_inoutExpo(t, sY, eY, d);\n val3 = easeandwizz_inoutExpo(t, sZ, eZ, d);\n return [\n val1,\n val2,\n val3\n ];\n break;\n default:\n return null;\n }\n }\n}\n$bm_rt = easeAndWizz() || value;"},"r":{"a":0,"k":0,"ix":10},"p":{"a":1,"k":[{"i":{"x":0.833,"y":0.833},"o":{"x":0.167,"y":0.167},"t":25,"s":[402.5,1554,0],"to":[5.333,-12.667,0],"ti":[-5.333,12.667,0]},{"t":44,"s":[434.5,1478,0]}],"ix":2,"x":"var $bm_rt;\nfunction doIt() {\n var easingEquation, val1, val2, val3;\n var ew_one = expoIn;\n var ew_two = expoOut;\n var n = 0;\n if (numKeys > 0) {\n n = nearestKey(time).index;\n if (key(n).time > time) {\n n--;\n }\n if (n < 2) {\n easingEquation = ew_one;\n } else if (n >= $bm_sub(numKeys, 1)) {\n easingEquation = ew_two;\n } else {\n return null;\n }\n }\n try {\n var key1 = key(n);\n var key2 = key($bm_sum(n, 1));\n } catch (e) {\n return null;\n }\n var dim = 1;\n try {\n key(1)[1].length;\n dim = 2;\n key(1)[2].length;\n dim = 3;\n } catch (e) {\n }\n var t = $bm_sub(time, key1.time);\n var d = $bm_sub(key2.time, key1.time);\n var sX = key1[0];\n var eX = $bm_sub(key2[0], key1[0]);\n if (dim >= 2) {\n var sY = key1[1];\n var eY = $bm_sub(key2[1], key1[1]);\n if (dim >= 3) {\n var sZ = key1[2];\n var eZ = $bm_sub(key2[2], key1[2]);\n }\n }\n if (time < key1.time || time > key2.time) {\n return value;\n } else {\n val1 = easingEquation(t, sX, eX, d);\n switch (dim) {\n case 1:\n return val1;\n break;\n case 2:\n val2 = easingEquation(t, sY, eY, d);\n return [\n val1,\n val2\n ];\n break;\n case 3:\n val2 = easingEquation(t, sY, eY, d);\n val3 = easingEquation(t, sZ, eZ, d);\n return [\n val1,\n val2,\n val3\n ];\n break;\n default:\n return null;\n }\n }\n}\nvar IN_EXPO_CORRECTION = 0.000976563;\nvar OUT_EXPO_CORRECTION = 1.000976563;\nvar s = 1.70158;\nvar p = 0.81;\nvar a = 50;\nfunction bounceOut(t, b, c, d) {\n if ((t /= d) < 1 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_mul($bm_mul(7.5625, t), t)), b);\n } else if (t < 2 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 1.5 / 2.75), t), 0.75)), b);\n } else if (t < 2.5 / 2.75) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.25 / 2.75), t), 0.9375)), b);\n } else {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(7.5625, t -= 2.625 / 2.75), t), 0.984375)), b);\n }\n}\nfunction bounceIn(t, b, c, d) {\n return $bm_sum($bm_sub(c, bounceOut($bm_sub(d, t), 0, c, d)), b);\n}\nfunction bounceInOut(t, b, c, d) {\n if (t < $bm_div(d, 2))\n return $bm_sum($bm_mul(bounceIn($bm_mul(t, 2), 0, c, d), 0.5), b);\n else\n return bounceOut(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b;\n}\nfunction backIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_mul($bm_mul(c, t /= d), t), $bm_sub($bm_mul($bm_sum(s, 1), t), s)), b);\n}\nfunction backInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_mul($bm_mul(t, t), $bm_sub($bm_mul($bm_sum(s *= 1.525, 1), t), s))), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum($bm_mul($bm_mul(t -= 2, t), $bm_sum($bm_mul($bm_sum(s *= 1.525, 1), t), s)), 2)), b);\n}\nfunction backOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, $bm_sum($bm_mul($bm_mul(t = t / d - 1, t), $bm_sum($bm_mul($bm_sum(s, 1), t), s)), 1)), b);\n}\nfunction circIn(t, b, c, d) {\n return $bm_sum($bm_mul($bm_neg(c), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t /= d, t))), 1)), b);\n}\nfunction circInOut(t, b, c, d) {\n if ((t /= d / 2) < 1)\n return $bm_sum($bm_mul($bm_div($bm_neg(c), 2), $bm_sub(Math.sqrt($bm_sub(1, $bm_mul(t, t))), 1)), b);\n return $bm_sum($bm_mul($bm_div(c, 2), $bm_sum(Math.sqrt($bm_sub(1, $bm_mul(t -= 2, t))), 1)), b);\n}\nfunction circOut(t, b, c, d) {\n return $bm_sum($bm_mul(c, Math.sqrt($bm_sub(1, $bm_mul(t = t / d - 1, t)))), b);\n}\nfunction elasticIn(t, b, c, d) {\n if (t == 0)\n return b;\n if ((t /= d) == 1)\n return $bm_sum(b, c);\n if (!p)\n p = $bm_mul(d, 0.3);\n 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