scichart
Version:
Fast WebGL JavaScript Charting Library and Framework
122 lines (121 loc) • 4.88 kB
JavaScript
;
var __extends = (this && this.__extends) || (function () {
var extendStatics = function (d, b) {
extendStatics = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };
return extendStatics(d, b);
};
return function (d, b) {
if (typeof b !== "function" && b !== null)
throw new TypeError("Class extends value " + String(b) + " is not a constructor or null");
extendStatics(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
};
})();
var __assign = (this && this.__assign) || function () {
__assign = Object.assign || function(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
t[p] = s[p];
}
return t;
};
return __assign.apply(this, arguments);
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.XyLinearTrendFilter = void 0;
var DataFilterType_1 = require("../../../types/DataFilterType");
var XyFilterBase_1 = require("./XyFilterBase");
/**
* An XyDataSeries that represents the linear trendline (or linear regression) of the original series
*/
var XyLinearTrendFilter = /** @class */ (function (_super) {
__extends(XyLinearTrendFilter, _super);
function XyLinearTrendFilter(originalSeries, options) {
var _this = _super.call(this, originalSeries, options) || this;
if (_this.getOriginalCount() > 0) {
_this.filterAll();
}
return _this;
}
Object.defineProperty(XyLinearTrendFilter.prototype, "slope", {
/**
* Gets the slope of the trendline
*/
get: function () {
return this.slopeProperty;
},
enumerable: false,
configurable: true
});
Object.defineProperty(XyLinearTrendFilter.prototype, "intercept", {
/**
* Gets the y-intercept of the trendline
*/
get: function () {
return this.interceptProperty;
},
enumerable: false,
configurable: true
});
Object.defineProperty(XyLinearTrendFilter.prototype, "correlation", {
/**
* Gets the correlation coefficient of the trendline
*/
get: function () {
return this.correlationProperty;
},
enumerable: false,
configurable: true
});
// @ts-ignore
XyLinearTrendFilter.prototype.toJSON = function (excludeData) {
if (excludeData === void 0) { excludeData = false; }
var original = _super.prototype.toJSON.call(this, excludeData);
return __assign(__assign({}, original), { filter: {
type: DataFilterType_1.EDataFilterType.XyLinearTrend,
options: { field: this.field, slope: this.slope, intercept: this.intercept }
} });
};
XyLinearTrendFilter.prototype.filterAll = function () {
var sumByIndex = 0;
var sumX = 0;
var sumY = 0;
var sumPowX = 0;
var sumPowY = 0;
var rawX = this.getOriginalXValues();
var rawY = this.getOriginalYValues();
var originalCount = this.getOriginalCount();
for (var i = 0; i < this.getOriginalCount(); i++) {
var dblY = rawY.get(i);
var dblX = rawX.get(i);
sumByIndex += dblX * dblY;
sumX += dblX;
sumY += dblY;
sumPowX += Math.pow(dblX, 2);
sumPowY += Math.pow(dblY, 2);
}
var a = sumByIndex * originalCount;
var b = sumX * sumY;
var c = sumPowX * originalCount;
var d = Math.pow(sumX, 2);
this.correlationProperty = (a - b) / Math.sqrt((c - d) * (sumPowY * originalCount - Math.pow(sumY, 2)));
this.slopeProperty = (a - b) / (c - d); // y = mx + c, m is a slope, c is an intercept
var f = this.slopeProperty * sumX;
this.interceptProperty = (sumY - f) / originalCount; // y = mx + c, m is a slope, c is an intercept
var yValues = [];
var xValues = [];
for (var i = 0; i < originalCount; i++) {
var x = rawX.get(i);
xValues.push(x);
yValues.push(x * this.slopeProperty + this.interceptProperty);
}
this.clear();
this.appendRange(xValues, yValues);
};
return XyLinearTrendFilter;
}(XyFilterBase_1.XyFilterBase));
exports.XyLinearTrendFilter = XyLinearTrendFilter;