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emergent-agent-e1
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16 KiB
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1 line
16 KiB
Plaintext
{"dependencies":[],"output":[{"data":{"code":"__d(function (global, require, _$$_IMPORT_DEFAULT, _$$_IMPORT_ALL, module, exports, _dependencyMap) {\n /**\n * Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * This source code is licensed under the MIT license found in the\n * LICENSE file in the root directory of this source tree.\n *\n * \n * @format\n */\n\n /**\n * BezierEasing - use bezier curve for transition easing function\n * https://github.com/gre/bezier-easing\n * @copyright 2014-2015 Gaëtan Renaudeau. MIT License.\n */\n\n 'use strict';\n\n // These values are established by empiricism with tests (tradeoff: performance VS precision)\n Object.defineProperty(exports, '__esModule', {\n value: true\n });\n Object.defineProperty(exports, \"default\", {\n enumerable: true,\n get: function () {\n return bezier;\n }\n });\n var NEWTON_ITERATIONS = 4;\n var NEWTON_MIN_SLOPE = 0.001;\n var SUBDIVISION_PRECISION = 0.0000001;\n var SUBDIVISION_MAX_ITERATIONS = 10;\n var kSplineTableSize = 11;\n var kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);\n var float32ArraySupported = typeof Float32Array === 'function';\n function A(aA1, aA2) {\n return 1.0 - 3.0 * aA2 + 3.0 * aA1;\n }\n function B(aA1, aA2) {\n return 3.0 * aA2 - 6.0 * aA1;\n }\n function C(aA1) {\n return 3.0 * aA1;\n }\n\n // Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2.\n function calcBezier(aT, aA1, aA2) {\n return ((A(aA1, aA2) * aT + B(aA1, aA2)) * aT + C(aA1)) * aT;\n }\n\n // Returns dx/dt given t, x1, and x2, or dy/dt given t, y1, and y2.\n function getSlope(aT, aA1, aA2) {\n return 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1);\n }\n function binarySubdivide(aX, _aA, _aB, mX1, mX2) {\n var currentX,\n currentT,\n i = 0,\n aA = _aA,\n aB = _aB;\n do {\n currentT = aA + (aB - aA) / 2.0;\n currentX = calcBezier(currentT, mX1, mX2) - aX;\n if (currentX > 0.0) {\n aB = currentT;\n } else {\n aA = currentT;\n }\n } while (Math.abs(currentX) > SUBDIVISION_PRECISION && ++i < SUBDIVISION_MAX_ITERATIONS);\n return currentT;\n }\n function newtonRaphsonIterate(aX, _aGuessT, mX1, mX2) {\n var aGuessT = _aGuessT;\n for (var i = 0; i < NEWTON_ITERATIONS; ++i) {\n var currentSlope = getSlope(aGuessT, mX1, mX2);\n if (currentSlope === 0.0) {\n return aGuessT;\n }\n var currentX = calcBezier(aGuessT, mX1, mX2) - aX;\n aGuessT -= currentX / currentSlope;\n }\n return aGuessT;\n }\n function bezier(mX1, mY1, mX2, mY2) {\n if (!(mX1 >= 0 && mX1 <= 1 && mX2 >= 0 && mX2 <= 1)) {\n throw new Error('bezier x values must be in [0, 1] range');\n }\n\n // Precompute samples table\n var sampleValues = float32ArraySupported ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);\n if (mX1 !== mY1 || mX2 !== mY2) {\n for (var i = 0; i < kSplineTableSize; ++i) {\n sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);\n }\n }\n function getTForX(aX) {\n var intervalStart = 0.0;\n var currentSample = 1;\n var lastSample = kSplineTableSize - 1;\n for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) {\n intervalStart += kSampleStepSize;\n }\n --currentSample;\n\n // Interpolate to provide an initial guess for t\n var dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]);\n var guessForT = intervalStart + dist * kSampleStepSize;\n var initialSlope = getSlope(guessForT, mX1, mX2);\n if (initialSlope >= NEWTON_MIN_SLOPE) {\n return newtonRaphsonIterate(aX, guessForT, mX1, 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