@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
104 lines • 17 kB
JavaScript
// © 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.
import * as d3 from "d3";
import cloneDeep from "lodash/cloneDeep";
import { transformLoessSmoothing } from "./transformer-loess";
export function transformChangePoint(data, hasPercentile) {
let transformed = cloneDeep(data);
const sectionSize = 20; // to change section size
const criticalValue = 1.729; // one-tail 0.05
transformed = 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);
}
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All rights reserved.\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n//  of this software and associated documentation files (the \"Software\"), to\n//  deal in the Software without restriction, including without limitation the\n//  rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n//  sell copies of the Software, and to permit persons to whom the Software is\n//  furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n//  all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n//  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n//  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n//  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n//  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n//  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n//  THE SOFTWARE.\n\nimport * as d3 from \"d3\";\nimport cloneDeep from \"lodash/cloneDeep\";\n\nimport { ITimeseriesWidgetSeriesData } from \"../types\";\nimport { transformLoessSmoothing } from \"./transformer-loess\";\n\nexport function transformChangePoint(\n    data: ITimeseriesWidgetSeriesData[],\n    hasPercentile?: boolean\n): ITimeseriesWidgetSeriesData[] {\n    let transformed = cloneDeep(data);\n    const sectionSize = 20; // to change section size\n    const criticalValue = 1.729; // one-tail 0.05\n\n    transformed = transformLoessSmoothing(transformed, hasPercentile);\n\n    let tScore: number; // 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.\n    let meanOfFirstSection: number = 0;\n    let standardDeviationOfFirstSection: number;\n    let meanOfSecondSection: number;\n    let standardDeviationOfSecondSection: number;\n    let yValuesOfFirstSection = [];\n    let yValuesOfSecondSection = [];\n    let merging: boolean = true;\n    let startIndex: number = 0;\n    let endIndex: number = sectionSize - 1;\n    let startIndex2: number = sectionSize;\n    let endIndex2: number = startIndex2 + endIndex;\n    while (merging) {\n        merging = false;\n        yValuesOfFirstSection = [];\n        yValuesOfSecondSection = [];\n        for (let i = startIndex; i <= endIndex; i++) {\n            yValuesOfFirstSection.push(data[i].y);\n        }\n        for (let j = startIndex2; j <= endIndex2; j++) {\n            yValuesOfSecondSection.push(data[j].y);\n        }\n        meanOfFirstSection = d3.mean(yValuesOfFirstSection) ?? 0;\n        meanOfSecondSection = d3.mean(yValuesOfSecondSection) ?? 0;\n        standardDeviationOfFirstSection =\n            d3.deviation(yValuesOfFirstSection) ?? 0;\n        if (standardDeviationOfFirstSection < 1.0) {\n            standardDeviationOfFirstSection = 1.0;\n        }\n        standardDeviationOfSecondSection =\n            d3.deviation(yValuesOfSecondSection) ?? 0;\n        if (standardDeviationOfSecondSection < 1.0) {\n            standardDeviationOfSecondSection = 1.0;\n        }\n        tScore = getT_Score(\n            meanOfFirstSection,\n            meanOfSecondSection,\n            standardDeviationOfFirstSection,\n            standardDeviationOfSecondSection,\n            yValuesOfFirstSection.length,\n            yValuesOfSecondSection.length,\n            criticalValue\n        );\n        if (tScore > criticalValue) {\n            for (let k = startIndex; k <= endIndex; k++) {\n                transformed[k].y = meanOfFirstSection;\n            }\n            startIndex = startIndex2;\n        }\n        endIndex = endIndex2;\n        if (endIndex + sectionSize < data.length) {\n            merging = true;\n            startIndex2 = endIndex + 1;\n            endIndex2 = startIndex2 + sectionSize - 1;\n        } else {\n            endIndex = data.length - 1;\n        }\n    }\n    for (let k = startIndex; k <= endIndex; k++) {\n        transformed[k].y = meanOfFirstSection;\n    }\n    return transformed;\n}\n\nfunction getT_Score(\n    meanOfFirstSection1: number,\n    meanOfSecondSection: number,\n    standardDeviationOfFirstSection: number,\n    standardDeviationOfSecondSection: number,\n    sizeOfFirstSection: number,\n    sizeOfSecondSection: number,\n    criticalValue: number\n) {\n    const differenceOfSampleMeans = meanOfFirstSection1 - meanOfSecondSection;\n    const standardDeviationOfBothSections =\n        Math.pow(standardDeviationOfFirstSection, 2) / sizeOfFirstSection +\n        Math.pow(standardDeviationOfSecondSection, 2) / sizeOfSecondSection;\n    const squareRootofResult = Math.sqrt(standardDeviationOfBothSections);\n    let tScore = differenceOfSampleMeans / squareRootofResult;\n    if (tScore > criticalValue) {\n        if (\n            meanOfFirstSection1 <\n                meanOfSecondSection + 2 * standardDeviationOfSecondSection &&\n            meanOfFirstSection1 >\n                meanOfSecondSection - 2 * standardDeviationOfSecondSection &&\n            meanOfSecondSection <\n                meanOfFirstSection1 + 2 * standardDeviationOfFirstSection &&\n            meanOfSecondSection >\n                meanOfFirstSection1 - 2 * standardDeviationOfFirstSection\n        ) {\n            tScore = 0.0;\n        }\n    }\n    return Math.abs(tScore);\n}\n"]}