nan values won't break charts down now

frontend/package.json

@@ -31,11 +31,13 @@
         "build": "./scripts/build.sh",
         "build:core": "yarn workspace @visualdl/core build",
         "build:demo": "yarn workspace @visualdl/demo build",
+        "build:wasm": "yarn workspace @visualdl/wasm build",
         "clean": "rimraf output packages/*/dist packages/wasm/target",
         "dev": "yarn dev:core",
         "dev:core": "yarn workspace @visualdl/core dev",
         "dev:demo": "yarn workspace @visualdl/server dev:demo",
         "dev:server": "yarn workspace @visualdl/server dev",
+        "dev:wasm": "yarn workspace @visualdl/wasm dev",
         "lint": "eslint --ext .tsx,.jsx.ts,.js,.mjs .",
         "format": "prettier --write \"**/*.{ts,tsx,js,jsx}\"",
         "test": "yarn workspaces run test",
@@ -44,8 +46,8 @@
         "version": "yarn format && git add -A"
     },
     "devDependencies": {
-        "@typescript-eslint/eslint-plugin": "4.26.0",
-        "@typescript-eslint/parser": "4.26.0",
+        "@typescript-eslint/eslint-plugin": "4.26.1",
+        "@typescript-eslint/parser": "4.26.1",
         "eslint": "7.28.0",
         "eslint-config-prettier": "8.3.0",
         "eslint-plugin-license-header": "0.2.0",

frontend/packages/core/package.json

@@ -95,30 +95,30 @@
         "@types/chai": "4.2.18",
         "@types/d3": "6.7.0",
         "@types/d3-format": "2.0.0",
-        "@types/echarts": "4.9.7",
+        "@types/echarts": "4.9.8",
         "@types/file-saver": "2.0.2",
         "@types/lodash": "4.14.170",
         "@types/mime-types": "2.1.0",
         "@types/nprogress": "0.2.0",
         "@types/numeric": "1.2.1",
-        "@types/react": "17.0.9",
-        "@types/react-dom": "17.0.6",
+        "@types/react": "17.0.10",
+        "@types/react-dom": "17.0.7",
         "@types/react-helmet": "6.1.1",
         "@types/react-rangeslider": "2.2.3",
         "@types/react-redux": "7.1.16",
         "@types/react-router-dom": "5.1.7",
         "@types/react-table": "7.7.1",
         "@types/snowpack-env": "2.3.3",
-        "@types/styled-components": "5.1.9",
+        "@types/styled-components": "5.1.10",
         "@types/three": "0.129.1",
         "@visualdl/mock": "2.2.0-1",
-        "@web/test-runner": "0.13.5",
+        "@web/test-runner": "0.13.6",
         "chai": "4.3.4",
         "chalk": "4.1.1",
         "dotenv": "10.0.0",
         "enhanced-resolve": "5.8.2",
         "html-minifier": "4.0.0",
-        "snowpack": "3.5.5",
+        "snowpack": "3.5.6",
         "snowpack-plugin-copy": "1.0.1",
         "typescript": "4.3.2"
     },

frontend/packages/core/src/components/ScalarPage/ScalarChart.tsx

@@ -85,7 +85,14 @@ const ScalarChart: FunctionComponent<ScalarChartProps> = ({
 
     const xAxisType = useMemo(() => (xAxis === XAxis.WallTime ? XAxisType.time : XAxisType.value), [xAxis]);
 
-    const transformParams = useMemo(() => [datasets?.map(data => data ?? []) ?? [], smoothing], [datasets, smoothing]);
+    const transformParams = useMemo(
+        () => [
+            datasets?.map(data => data?.map(row => [row[0], row[1], Number.isFinite(row[2]) ? row[2] : null]) ?? []) ??
+                [],
+            smoothing
+        ],
+        [datasets, smoothing]
+    );
     const {data: smoothedDatasetsOrUndefined} = useWebAssembly<Dataset[]>('scalar_transform', transformParams);
     const smoothedDatasets = useMemo<NonNullable<typeof smoothedDatasetsOrUndefined>>(
         () => smoothedDatasetsOrUndefined ?? [],

frontend/packages/core/src/resource/scalar/chart.ts

@@ -158,7 +158,7 @@ export const tooltip = (data: TooltipData[], stepLength: number, i18n: typeof I1
         ],
         data: data.map(({min, max, item}) => [
             valueFormatter(item[3] ?? Number.NaN),
-            valueFormatter(item[2] ?? Number.NaN),
+            valueFormatter(Number.isFinite(item[2]) ? (item[2] as number) : Number.NaN),
             item[1],
             valueFormatter(min ?? Number.NaN),
             valueFormatter(max ?? Number.NaN),

frontend/packages/core/src/resource/scalar/data.ts

@@ -16,7 +16,7 @@
 
 // cSpell:words quantile accum debias exponentiated
 
-import type {Dataset, ScalarDataset} from './types';
+import type {Dataset, ScalarDataset, Value} from './types';
 
 import BigNumber from 'bignumber.js';
 import type {Run} from '~/types';
@@ -34,34 +34,38 @@ export const transform = ({datasets, smoothing}: {datasets: ScalarDataset[]; smo
         let startValue = 0;
         const bigSmoothing = new BigNumber(smoothing);
         data.forEach((d, i) => {
-            const nextVal = new BigNumber(d[2]);
             const millisecond = (d[0] = Math.floor(d[0]));
             if (i === 0) {
                 startValue = millisecond;
             }
             // relative time in millisecond.
             d[4] = Math.floor(millisecond - startValue);
-            if (!nextVal.isFinite()) {
-                d[3] = nextVal.toNumber();
+            if (!Number.isFinite(d[2])) {
+                d[3] = null;
             } else {
-                // last = last * smoothing + (1 - smoothing) * nextVal;
-                last = last.multipliedBy(bigSmoothing).plus(bigSmoothing.minus(1).negated().multipliedBy(nextVal));
-                numAccum++;
-                let debiasWeight = new BigNumber(1);
-                if (!bigSmoothing.isEqualTo(1)) {
-                    //debiasWeight = 1.0 - Math.pow(smoothing, numAccum);
-                    debiasWeight = bigSmoothing.exponentiatedBy(numAccum).minus(1).negated();
+                const nextVal = new BigNumber(d[2] as number);
+                if (!nextVal.isFinite()) {
+                    d[3] = nextVal.toNumber();
+                } else {
+                    // last = last * smoothing + (1 - smoothing) * nextVal;
+                    last = last.multipliedBy(bigSmoothing).plus(bigSmoothing.minus(1).negated().multipliedBy(nextVal));
+                    numAccum++;
+                    let debiasWeight = new BigNumber(1);
+                    if (!bigSmoothing.isEqualTo(1)) {
+                        //debiasWeight = 1.0 - Math.pow(smoothing, numAccum);
+                        debiasWeight = bigSmoothing.exponentiatedBy(numAccum).minus(1).negated();
+                    }
+                    // d[3] = last / debiasWeight;
+                    d[3] = last.dividedBy(debiasWeight).toNumber();
                 }
-                // d[3] = last / debiasWeight;
-                d[3] = last.dividedBy(debiasWeight).toNumber();
             }
         });
         return data;
     });
 
-export const singlePointRange = (value: number) => ({
-    min: value ? Math.min(value * 2, 0) : -0.5,
-    max: value ? Math.max(value * 2, 0) : 0.5
+export const singlePointRange = (value: Value) => ({
+    min: Number.isFinite(value) ? Math.min((value as number) * 2, 0) : -0.5,
+    max: Number.isFinite(value) ? Math.max((value as number) * 2, 0) : 0.5
 });
 
 export const range = ({datasets}: {datasets: Dataset[]}) => {
@@ -72,7 +76,7 @@ export const range = ({datasets}: {datasets: Dataset[]}) => {
                 max: Number.NaN
             };
         }
-        const values = dataset.map(v => v[2]);
+        const values = dataset.map(v => v[2]).filter(Number.isFinite) as number[];
         return {
             min: Math.min(...values) ?? Number.NaN,
             max: Math.max(...values) ?? Number.NaN
@@ -86,7 +90,7 @@ export const axisRange = ({datasets, outlier}: {datasets: Dataset[]; outlier: bo
             if (dataset.length === 0) {
                 return void 0;
             }
-            const values = dataset.map(v => v[2]);
+            const values = dataset.map(v => v[2]).filter(Number.isFinite) as number[];
             if (!outlier) {
                 // Get the origin data range.
                 return {
@@ -95,7 +99,10 @@ export const axisRange = ({datasets, outlier}: {datasets: Dataset[]; outlier: bo
                 };
             } else {
                 // Get the quantile range.
-                const sorted = dataset.map(v => v[2]).sort();
+                const sorted = dataset
+                    .map(v => v[2])
+                    .filter(Number.isFinite)
+                    .sort() as number[];
                 return {
                     min: quantile(sorted, 0.05),
                     max: quantile(values, 0.95)
@@ -122,10 +129,13 @@ export const nearestPoint = (data: Dataset[], runs: Run[], idx: number, value: n
         let d = Number.POSITIVE_INFINITY;
         let dv = value;
         for (let i = 0; i < series.length; i++) {
-            const dd = Math.abs(series[i][idx] - value);
-            if (d > dd) {
-                d = dd;
-                dv = series[i][idx];
+            const v = series[i][idx];
+            if (Number.isFinite(v)) {
+                const dd = Math.abs((v as number) - value);
+                if (d > dd) {
+                    d = dd;
+                    dv = v as number;
+                }
             }
         }
         result.push(...series.filter(s => s[idx] === dv).map(item => ({run, item})));

frontend/packages/core/src/resource/scalar/index.ts

@@ -31,7 +31,8 @@ export const sortingMethodMap: Record<SM, (points: TooltipData[], data: number[]
     [SM.Descending]: (points: TooltipData[]) => sortBy(points, point => point.item[3]).reverse(),
     [SM.Ascending]: (points: TooltipData[]) => sortBy(points, point => point.item[3]),
     // Compare other points width the trigger point, calculate the nearest sort.
-    [SM.Nearest]: (points: TooltipData[], data: number[]) => sortBy(points, point => point.item[3] - data[2])
+    [SM.Nearest]: (points: TooltipData[], data: number[]) =>
+        sortBy(points, point => (point.item[3] ?? Number.NaN) - data[2])
 } as const;
 
 export type {Dataset, ScalarDataset, Range, TooltipData} from './types';

frontend/packages/core/src/resource/scalar/types.ts

@@ -18,10 +18,11 @@ import {Run, TimeMode} from '~/types';
 
 export type {Range} from '~/types';
 
-type Value = number;
+type InvalidValue = 'NaN' | 'Inf' | '-Inf';
+export type Value = number | null | InvalidValue;
 type WallTime = number;
 type Step = number;
-type Smoothed = number;
+type Smoothed = number | null;
 type Relative = number;
 
 export type Dataset = [WallTime, Step, Value, Smoothed, Relative][];

frontend/packages/demo/package.json

@@ -35,7 +35,7 @@
     "devDependencies": {
         "@types/express": "4.17.12",
         "@types/mkdirp": "1.0.1",
-        "@types/node": "15.12.1",
+        "@types/node": "15.12.2",
         "@types/node-fetch": "2.5.10",
         "@types/rimraf": "3.0.0",
         "cpy-cli": "3.1.1",

frontend/packages/netron/package.json

@@ -52,7 +52,7 @@
         "sass-loader": "12.0.0",
         "terser": "5.7.0",
         "webpack": "5.38.1",
-        "webpack-cli": "4.7.0"
+        "webpack-cli": "4.7.2"
     },
     "engines": {
         "node": ">=12",

frontend/packages/server/package.json

@@ -41,12 +41,12 @@
         "enhanced-resolve": "5.8.2",
         "express": "4.17.1",
         "http-proxy-middleware": "2.0.0",
-        "pm2": "4.5.6"
+        "pm2": "5.0.4"
     },
     "devDependencies": {
         "@types/enhanced-resolve": "3.0.6",
         "@types/express": "4.17.12",
-        "@types/node": "15.12.1",
+        "@types/node": "15.12.2",
         "@visualdl/mock": "2.2.0-1",
         "cross-env": "7.0.3",
         "nodemon": "2.0.7",

frontend/packages/wasm/package.json

@@ -33,6 +33,7 @@
     "types": "dist/index.d.ts",
     "scripts": {
         "build": "wasm-pack build --release --out-dir dist --out-name index --target web .",
+        "dev": "wasm-pack build --dev --out-dir dist --out-name index --target web .",
         "test": "echo \"Error: no test specified\" && exit 0"
     },
     "devDependencies": {

frontend/packages/wasm/src/scalar.rs

@@ -1,8 +1,8 @@
 #[derive(Serialize, Deserialize)]
-pub struct Dataset(f64, i64, f64);
+pub struct Dataset(f64, i64, Option<f64>);
 
 #[derive(Serialize, Deserialize)]
-pub struct Smoothed(i64, i64, f64, f64, i64);
+pub struct Smoothed(i64, i64, Option<f64>, Option<f64>, i64);
 
 #[derive(Serialize, Deserialize)]
 pub struct Range {
@@ -33,6 +33,13 @@ fn quantile(values: &Vec<f64>, p: f64) -> f64 {
     return value0 + (value1 - value0) * (i - (i0 as f64));
 }
 
+fn sort_values(data: &Vec<Smoothed>) -> (Vec<f64>, Vec<f64>) {
+    let values: Vec<f64> = data.iter().filter_map(|x| x.2).collect();
+    let mut sorted: Vec<f64> = values.clone();
+    sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    (sorted, values)
+}
+
 pub fn transform(datasets: &Vec<Vec<Dataset>>, smoothing: f64) -> Vec<Vec<Smoothed>> {
     let mut result: Vec<Vec<Smoothed>> = vec![];
     for dataset in datasets.iter() {
@@ -44,8 +51,7 @@ pub fn transform(datasets: &Vec<Vec<Dataset>>, smoothing: f64) -> Vec<Vec<Smooth
         let mut num_accum: i32 = 0;
         let mut start_value: i64 = 0;
         for (i, d) in dataset.iter().enumerate() {
-            let mut r: Smoothed = Smoothed(0, d.1, d.2, 0.0, 0);
-            let next_val: f64 = d.2;
+            let mut r: Smoothed = Smoothed(0, d.1, d.2, Some(0.0), 0);
             // second to millisecond.
             let millisecond: i64 = d.0.floor() as i64;
             r.0 = millisecond;
@@ -54,16 +60,20 @@ pub fn transform(datasets: &Vec<Vec<Dataset>>, smoothing: f64) -> Vec<Vec<Smooth
             }
             // Relative time in millisecond.
             r.4 = millisecond - start_value;
-            if next_val.is_infinite() {
-                r.3 = next_val;
-            } else {
-                last = last * smoothing + (1.0 - smoothing) * next_val;
-                num_accum += 1;
-                let mut debias_weight: f64 = 1.0_f64;
-                if smoothing != 1.0 {
-                    debias_weight = (1.0_f64 - smoothing.powi(num_accum)).into();
+            if let Some(next_val) = d.2 {
+                if next_val.is_infinite() || next_val.is_nan() {
+                    r.3 = Some(next_val);
+                } else {
+                    last = last * smoothing + (1.0 - smoothing) * next_val;
+                    num_accum += 1;
+                    let mut debias_weight: f64 = 1.0_f64;
+                    if smoothing != 1.0 {
+                        debias_weight = (1.0_f64 - smoothing.powi(num_accum)).into();
+                    }
+                    r.3 = Some(last / debias_weight);
                 }
-                r.3 = last / debias_weight;
+            } else {
+                r.3 = None;
             }
             row.push(r);
         }
@@ -76,17 +86,12 @@ pub fn range(datasets: &Vec<Vec<Smoothed>>) -> Vec<Range> {
     let mut ranges: Vec<Range> = vec![];
 
     for data in datasets.iter() {
-        let n: usize = data.len();
-
-        if n == 0 {
+        if data.len() == 0 {
             ranges.push(Range::new(f64::NAN, f64::NAN));
         }
 
-        let values: Vec<f64> = data.iter().map(|x| x.2).collect();
-        let mut sorted: Vec<f64> = values.clone();
-        sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
-
-        ranges.push(Range::new(sorted[0], sorted[n - 1]));
+        let (sorted, _) = sort_values(data);
+        ranges.push(Range::new(sorted[0], sorted[sorted.len() - 1]));
     }
 
     return ranges;
@@ -96,18 +101,14 @@ pub fn axis_range(datasets: &Vec<Vec<Smoothed>>, outlier: bool) -> Range {
     let mut ranges: Vec<Range> = vec![];
 
     for data in datasets.iter() {
-        let n: usize = data.len();
-
-        if n == 0 {
+        if data.len() == 0 {
             continue;
         }
 
-        let values: Vec<f64> = data.iter().map(|x| x.2).collect();
-        let mut sorted: Vec<f64> = values.clone();
-        sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
+        let (sorted, values) = sort_values(data);
 
         if !outlier {
-            ranges.push(Range::new(sorted[0], sorted[n - 1]));
+            ranges.push(Range::new(sorted[0], sorted[sorted.len() - 1]));
         } else {
             ranges.push(Range::new(
                 quantile(&sorted, 0.05_f64),