@nova-ui/dashboards
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Nova Dashboards is a framework designed to provide feature developers with a common solution for presenting data coming from various sources within a single view, as well as a set of predefined widget visualizations that are 100% configuration-driven and
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JavaScript
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// © 2022 SolarWinds Worldwide, LLC. All rights reserved.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to
// deal in the Software without restriction, including without limitation the
// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
// sell copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.
Object.defineProperty(exports, "__esModule", { value: true });
exports.transformChangePoint = transformChangePoint;
const tslib_1 = require("tslib");
const d3 = tslib_1.__importStar(require("d3"));
const cloneDeep_1 = tslib_1.__importDefault(require("lodash/cloneDeep"));
const transformer_loess_1 = require("./transformer-loess");
function transformChangePoint(data, hasPercentile) {
let transformed = (0, cloneDeep_1.default)(data);
const sectionSize = 20; // to change section size
const criticalValue = 1.729; // one-tail 0.05
transformed = (0, transformer_loess_1.transformLoessSmoothing)(transformed, hasPercentile);
let tScore; // The t_score formula enables you to take an individual score and transform it into a standardized form>one which helps you to compare scores.
let meanOfFirstSection = 0;
let standardDeviationOfFirstSection;
let meanOfSecondSection;
let standardDeviationOfSecondSection;
let yValuesOfFirstSection = [];
let yValuesOfSecondSection = [];
let merging = true;
let startIndex = 0;
let endIndex = sectionSize - 1;
let startIndex2 = sectionSize;
let endIndex2 = startIndex2 + endIndex;
while (merging) {
merging = false;
yValuesOfFirstSection = [];
yValuesOfSecondSection = [];
for (let i = startIndex; i <= endIndex; i++) {
yValuesOfFirstSection.push(data[i].y);
}
for (let j = startIndex2; j <= endIndex2; j++) {
yValuesOfSecondSection.push(data[j].y);
}
meanOfFirstSection = d3.mean(yValuesOfFirstSection) ?? 0;
meanOfSecondSection = d3.mean(yValuesOfSecondSection) ?? 0;
standardDeviationOfFirstSection =
d3.deviation(yValuesOfFirstSection) ?? 0;
if (standardDeviationOfFirstSection < 1.0) {
standardDeviationOfFirstSection = 1.0;
}
standardDeviationOfSecondSection =
d3.deviation(yValuesOfSecondSection) ?? 0;
if (standardDeviationOfSecondSection < 1.0) {
standardDeviationOfSecondSection = 1.0;
}
tScore = getT_Score(meanOfFirstSection, meanOfSecondSection, standardDeviationOfFirstSection, standardDeviationOfSecondSection, yValuesOfFirstSection.length, yValuesOfSecondSection.length, criticalValue);
if (tScore > criticalValue) {
for (let k = startIndex; k <= endIndex; k++) {
transformed[k].y = meanOfFirstSection;
}
startIndex = startIndex2;
}
endIndex = endIndex2;
if (endIndex + sectionSize < data.length) {
merging = true;
startIndex2 = endIndex + 1;
endIndex2 = startIndex2 + sectionSize - 1;
}
else {
endIndex = data.length - 1;
}
}
for (let k = startIndex; k <= endIndex; k++) {
transformed[k].y = meanOfFirstSection;
}
return transformed;
}
function getT_Score(meanOfFirstSection1, meanOfSecondSection, standardDeviationOfFirstSection, standardDeviationOfSecondSection, sizeOfFirstSection, sizeOfSecondSection, criticalValue) {
const differenceOfSampleMeans = meanOfFirstSection1 - meanOfSecondSection;
const standardDeviationOfBothSections = Math.pow(standardDeviationOfFirstSection, 2) / sizeOfFirstSection +
Math.pow(standardDeviationOfSecondSection, 2) / sizeOfSecondSection;
const squareRootofResult = Math.sqrt(standardDeviationOfBothSections);
let tScore = differenceOfSampleMeans / squareRootofResult;
if (tScore > criticalValue) {
if (meanOfFirstSection1 <
meanOfSecondSection + 2 * standardDeviationOfSecondSection &&
meanOfFirstSection1 >
meanOfSecondSection - 2 * standardDeviationOfSecondSection &&
meanOfSecondSection <
meanOfFirstSection1 + 2 * standardDeviationOfFirstSection &&
meanOfSecondSection >
meanOfFirstSection1 - 2 * standardDeviationOfFirstSection) {
tScore = 0.0;
}
}
return Math.abs(tScore);
}
//# sourceMappingURL=transformer-change-point.js.map