Our team demonstrates how to create a React 3D Point Line Chart using SciChart.js, capable of creating detailed 3D JavaScript Charts.
drawExample.ts
index.tsx
Radix2FFT.ts
theme.ts
1import { Radix2FFT } from "../../../FeaturedApps/ScientificCharts/AudioAnalyzer/Radix2FFT";
2import {
3 CameraController,
4 HeatmapLegend,
5 MouseWheelZoomModifier3D,
6 NumericAxis3D,
7 OrbitModifier3D,
8 parseColorToUIntArgb,
9 PointLineRenderableSeries3D,
10 ResetCamera3DModifier,
11 SciChart3DSurface,
12 TGradientStop,
13 Vector3,
14 XyzDataSeries3D,
15} from "scichart";
16import { appTheme } from "../../../theme";
17
18type TMetadata = {
19 vertexColor: number;
20 pointScale: number;
21};
22
23// This function generates some spectral data for the waterfall chart
24const createSpectralData = (n: number) => {
25 const spectraSize = 1024;
26 const timeData = new Array(spectraSize);
27
28 // Generate some random data with spectral components
29 for (let i = 0; i < spectraSize; i++) {
30 timeData[i] =
31 4.0 * Math.sin((2 * Math.PI * i) / (20 + n * 0.2)) +
32 10 * Math.sin((2 * Math.PI * i) / (10 + n * 0.01)) +
33 20 * Math.sin((2 * Math.PI * i) / (5 + n * -0.002)) +
34 3.0 * Math.random();
35 }
36
37 // Do a fourier-transform on the data to get the frequency domain
38 const transform = new Radix2FFT(spectraSize);
39 const yValues = transform.run(timeData);
40 // .slice(0, 300); // We only want the first N points just to make the example cleaner
41
42 // This is just setting a floor to make the data cleaner for the example
43 for (let i = 0; i < yValues.length; i++) {
44 yValues[i] =
45 yValues[i] < -30 || yValues[i] > -5 ? (yValues[i] < -30 ? -30 : Math.random() * 9 - 6) : yValues[i];
46 }
47 yValues[0] = -30;
48
49 // we need x-values (sequential numbers) for the frequency data
50 const xValues = yValues.map((value, index) => index);
51
52 return { xValues, yValues };
53};
54
55// SCICHART CODE
56export const drawExample = async (rootElement: string | HTMLDivElement) => {
57 const { sciChart3DSurface, wasmContext } = await SciChart3DSurface.create(rootElement, {
58 theme: appTheme.SciChartJsTheme,
59 });
60 sciChart3DSurface.worldDimensions = new Vector3(300, 100, 300);
61 sciChart3DSurface.camera = new CameraController(wasmContext, {
62 position: new Vector3(-141.6, 310.29, 393.32),
63 target: new Vector3(0, 50, 0),
64 });
65
66 sciChart3DSurface.chartModifiers.add(new MouseWheelZoomModifier3D());
67 sciChart3DSurface.chartModifiers.add(new OrbitModifier3D());
68 sciChart3DSurface.chartModifiers.add(new ResetCamera3DModifier());
69
70 sciChart3DSurface.xAxis = new NumericAxis3D(wasmContext, {
71 axisTitle: "Frequency (Hz)",
72 drawMinorGridLines: false,
73 drawMajorGridLines: false,
74 tickLabelsOffset: 20,
75 });
76 sciChart3DSurface.yAxis = new NumericAxis3D(wasmContext, {
77 axisTitle: "Power (dB)",
78 drawMinorGridLines: false,
79 drawMajorGridLines: false,
80 tickLabelsOffset: 20,
81 });
82 sciChart3DSurface.zAxis = new NumericAxis3D(wasmContext, {
83 axisTitle: "Time (s)",
84 drawMinorGridLines: false,
85 drawMajorGridLines: false,
86 tickLabelsOffset: 20,
87 });
88
89 for (let i = 0; i < 50; i++) {
90 // Create some data for the example
91 // xValues are frequency values (Hz)
92 // yValues are heights or magnitude
93 const { xValues, yValues } = createSpectralData(i);
94 // zValues are the 3rd dimension where we will spread out our series in time
95 const zValues = Array.from({ length: xValues.length }).map((_) => i * 2);
96
97 // Metadata in scichart.js 3D controls color 3D line series. It can also hold additional optional properties
98 // Below we format the data for yValues into metadata colour coded and scaled depending on the value
99 const metadata = formatMetadata(yValues, [
100 { offset: 1, color: appTheme.VividPink },
101 { offset: 0.9, color: appTheme.VividOrange },
102 { offset: 0.7, color: appTheme.MutedRed },
103 { offset: 0.5, color: appTheme.VividGreen },
104 { offset: 0.3, color: appTheme.VividSkyBlue },
105 { offset: 0.2, color: appTheme.Indigo },
106 { offset: 0, color: appTheme.DarkIndigo },
107 ]);
108
109 // Add a 3D Point-Line chart
110 sciChart3DSurface.renderableSeries.add(
111 new PointLineRenderableSeries3D(wasmContext, {
112 dataSeries: new XyzDataSeries3D(wasmContext, {
113 xValues,
114 yValues,
115 zValues,
116 metadata,
117 }),
118 strokeThickness: 3,
119 opacity: 0.5,
120 })
121 );
122 }
123
124 return { sciChartSurface: sciChart3DSurface, wasmContext };
125};
126
127function formatMetadata(valuesArray: number[], gradientStops: TGradientStop[]): TMetadata[] {
128 const low = Math.min(...valuesArray);
129 const high = Math.max(...valuesArray);
130
131 const sGradientStops = gradientStops.sort((a, b) => (a.offset > b.offset ? 1 : -1));
132 // Compute a scaling factor from 0...1 where values in valuesArray at the lower end correspond to 0 and
133 // values at the higher end correspond to 1
134 return valuesArray.map((x) => {
135 // scale from 0..1 for the values
136 const valueScale = (x - low) / (high - low);
137 // Find the nearest gradient stop index
138 const index = sGradientStops.findIndex((gs) => gs.offset >= valueScale);
139 // const nextIndex = Math.min(index + 1, sGradientStops.length - 1);
140 // work out the colour of this point
141 const color1 = parseColorToUIntArgb(sGradientStops[index].color);
142 // const color2 = parseColorToUIntArgb(sGradientStops[nextIndex].color);
143 // const ratio = (valueScale - sGradientStops[index].offset) / (sGradientStops[nextIndex].offset - sGradientStops[index].offset)
144 // const colorScale = uintArgbColorLerp(color1, color2, ratio)
145 // console.log(`valueScale ${valueScale} low ${sGradientStops[index].offset} high ${sGradientStops[nextIndex].offset} ratio ${ratio}`);
146 return { pointScale: 0.1 + valueScale, vertexColor: color1 };
147 });
148}
149
150export const drawHeatmapLegend = async (rootElement: string | HTMLDivElement) => {
151 const { heatmapLegend, wasmContext } = await HeatmapLegend.create(rootElement, {
152 theme: {
153 ...appTheme.SciChartJsTheme,
154 sciChartBackground: appTheme.DarkIndigo + "BB",
155 loadingAnimationBackground: appTheme.DarkIndigo + "BB",
156 },
157 yAxisOptions: {
158 isInnerAxis: true,
159 labelStyle: {
160 fontSize: 12,
161 color: appTheme.ForegroundColor,
162 },
163 axisBorder: {
164 borderRight: 1,
165 color: appTheme.ForegroundColor + "77",
166 },
167 majorTickLineStyle: {
168 color: appTheme.ForegroundColor,
169 tickSize: 6,
170 strokeThickness: 1,
171 },
172 minorTickLineStyle: {
173 color: appTheme.ForegroundColor,
174 tickSize: 3,
175 strokeThickness: 1,
176 },
177 },
178 colorMap: {
179 minimum: -30,
180 maximum: 0,
181 gradientStops: [
182 { offset: 1, color: appTheme.VividPink },
183 { offset: 0.9, color: appTheme.VividOrange },
184 { offset: 0.7, color: appTheme.MutedRed },
185 { offset: 0.5, color: appTheme.VividGreen },
186 { offset: 0.3, color: appTheme.VividSkyBlue },
187 { offset: 0.15, color: appTheme.Indigo },
188 { offset: 0, color: appTheme.DarkIndigo },
189 ],
190 },
191 });
192
193 heatmapLegend.innerSciChartSurface.sciChartSurface.title = "Power (dB)";
194 heatmapLegend.innerSciChartSurface.sciChartSurface.padding.top = 0;
195 heatmapLegend.innerSciChartSurface.sciChartSurface.titleStyle = { fontSize: 12, color: appTheme.ForegroundColor };
196
197 return { sciChartSurface: heatmapLegend.innerSciChartSurface.sciChartSurface };
198};
199This example demonstrates the implementation of a 3D Point Line Chart in a React application using SciChart.js. The chart is designed to visualize spectral data generated via a Fourier transform, with dynamic color scaling achieved through metadata formatting. The example showcases the use of multiple <SciChartReact/> components to layer both the primary 3D chart and an accompanying heatmap legend.
The 3D chart is initialized using the <SciChartReact/> component, which receives an initialization function (drawExample) that sets up the 3D surface, axes, and chart modifiers such as 3D camera controls (e.g., MouseWheelZoomModifier3D and OrbitModifier3D). Data for the chart is dynamically generated; spectral data is created and processed using a Fourier transform, and a metadata formatting function dynamically assigns color and scaling to each data point. This configuration leverages a WebAssembly context (wasmContext) for optimized performance—a technique further explained in the SciChart.js User Manual.
The example implements advanced charting features including real-time spectral data computation and efficient rendering of multiple 3D series. The dynamic metadata formatting enables smooth color gradients based on data values, while the customizable camera controls provide an intuitive zoom and pan experience. Additionally, the integration of a separately rendered heatmap legend enhances the visual storytelling of the chart by providing context to the power (dB) measurements.
The React integration leverages the <SciChartReact/> component to seamlessly incorporate high-performance WebGL charts into the React ecosystem. The layout management is handled by a custom ChartGroupLoader which organizes the primary chart and legend components, following best practices for React integration with SciChart.js. Developers are encouraged to explore performance optimization techniques, such as minimizing re-rendering and reusing the WebAssembly context, to maintain smooth interactivity even with large datasets. Furthermore, the example demonstrates best practices in layering multiple <SciChartReact/> components to create complex chart layouts, as seen in this React chart layering approach.

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