This example demonstrates FastTextRenderableSeries using SciChart.js. The dataset is an AI analysis of 2100 tweets, extracting the organisations mentioned and the sentiment of the tweet.
drawExample.ts
index.tsx
theme.ts
1import { appTheme } from "../../../theme";
2import {
3 SciChartSurface,
4 NumericAxis,
5 NumberRange,
6 ZoomPanModifier,
7 ZoomExtentsModifier,
8 MouseWheelZoomModifier,
9 LogarithmicAxis,
10 ENumericFormat,
11 XyTextDataSeries,
12 FastTextRenderableSeries,
13} from "scichart";
14
15// tslint:disable:no-empty
16
17export const drawExample = async (rootElement: string | HTMLDivElement) => {
18 // Create a SciChartSurface
19 const { sciChartSurface, wasmContext } = await SciChartSurface.create(rootElement, {
20 theme: appTheme.SciChartJsTheme,
21 });
22 const xAxis = new LogarithmicAxis(wasmContext, {
23 axisTitle: "Number of Tweets",
24 logBase: 2,
25 labelFormat: ENumericFormat.SignificantFigures,
26 growBy: new NumberRange(0, 0.1),
27 });
28 sciChartSurface.xAxes.add(xAxis);
29 sciChartSurface.yAxes.add(
30 new NumericAxis(wasmContext, {
31 axisTitle: "Average Sentiment",
32 labelPrecision: 2,
33 visibleRange: new NumberRange(0, 1.01),
34 visibleRangeLimit: new NumberRange(0, 1.01),
35 })
36 );
37
38 // data is { xValues: number[], yValues: number[], textValues: string[] }
39 const data: { xValues: number[]; yValues: number[]; textValues: string[] } = await fetch(
40 "https://demo.scichart.com/api/tweetData"
41 ).then((r) => r.json());
42 const series = new FastTextRenderableSeries(wasmContext, {
43 dataLabels: { style: { fontFamily: "arial", fontSize: 10 }, calculateTextBounds: false },
44 dataSeries: new XyTextDataSeries(wasmContext, data),
45 });
46 sciChartSurface.renderableSeries.add(series);
47
48 sciChartSurface.chartModifiers.add(
49 new ZoomPanModifier({ enableZoom: true }),
50 new ZoomExtentsModifier(),
51 new MouseWheelZoomModifier()
52 );
53
54 return { sciChartSurface, wasmContext };
55};
56
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