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"danfojs": "^1.1.2", "echarts-for-react": "^3.0.2", "react": "^18.2.0", "react-dom": "^18.2.0", diff --git a/src/analysis/computeAccuracy.ts b/src/analysis/computeAccuracy.ts index e1c38f7..924c2a5 100644 --- a/src/analysis/computeAccuracy.ts +++ b/src/analysis/computeAccuracy.ts @@ -1,15 +1,18 @@ -import { DataFrame } from "danfojs"; +import { tidy, filter, tally } from "@tidyjs/tidy"; +import { LongSessionRow } from "../types"; + +const computeAccuracy = (session: LongSessionRow[]): number => { + const correct: number = tidy( + session, + filter(({ correct }) => correct === true), + tally() + )[0]["n"]; + const incorrect: number = tidy( + session, + filter(({ correct, key }) => correct === false && key !== "Backspace"), + tally() + )[0]["n"]; -const computeAccuracy = (session: DataFrame): number => { - // get everything that ins't backspace - const mask = session["key"].values.map((k: string) => k !== "Backspace"); - const correctMask = session.query(mask)["correct"].values; - const incorrectMask = session - .query(mask) - ["correct"].values.map((v: boolean) => v === false); - const correct = session.query(correctMask).values.length; - const incorrect = session.query(incorrectMask).values.length; - // console.log(correctMask, incorrectMask); return +(correct / (correct + incorrect)).toFixed(2); }; diff --git a/src/analysis/computeNGram.ts b/src/analysis/computeNGram.ts index 3b49f3c..8f0e340 100644 --- a/src/analysis/computeNGram.ts +++ b/src/analysis/computeNGram.ts @@ -1,31 +1,49 @@ -import { DataFrame } from "danfojs"; -import { nGramFeedback, WordPerformance } from "../types"; +import { tidy, filter, select } from "@tidyjs/tidy"; +import { createFinalOutput } from "../bin/createFinalOutput"; +import { pullColumns } from "../bin/pullColumns"; +import { LongSessionRow, nGramFeedback, WordPerformance } from "../types"; -const transformIntoWords = (input: DataFrame): WordPerformance[] => { - // create list of words w/ array of targetID's +interface NGramTarget { + nGram: string; + startIndex: number; + endIndex: number; +} + +const transformIntoWords = (input: LongSessionRow[]): WordPerformance[] => { + // create list of words w/ list of each words instance's targetID's const targets: { [key: string]: number[] } = {}; - input.values.forEach((rv: any, rowIndex: number) => { - const row = input.iloc({ rows: [rowIndex] }); - const { target: targetValues, targetID: targetIDValues } = row; - const target = targetValues.values[0]; - const targetID = targetIDValues.values[0]; - if (targets[target] === undefined) targets[target] = [targetID]; + // create separate array of words so we can easily iterate through + const targetWords: string[] = []; + input.forEach((row: LongSessionRow) => { + const { target, targetID } = row; + // if this is a new word, then create a new key/value pair in the targets object + if (targets[target] === undefined) { + targets[target] = [targetID]; + targetWords.push(target); + } // only add targetID to array once else if (targets[target].indexOf(targetID) < 0) targets[target] = [...targets[target], targetID]; }); const words: WordPerformance[] = []; - Object.keys(targets).forEach((target) => { + // create array that contains each word and every attempt made to type the word + targetWords.forEach((target) => { const thisWordPerf: WordPerformance = { target, attempts: [] }; targets[target].forEach((targetID) => { - const row = input.query(input["targetID"].eq(targetID)); - const { correct, timestamp, key } = row; + const row = tidy( + input, + filter(({ targetID: searchID }) => searchID === targetID) + ); + const [key, correct, timestamp] = pullColumns( + tidy(row, select(["key", "correct", "timestamp"])), + ["key", "correct", "timestamp"] + ) as [string[], boolean[], number[]]; thisWordPerf.attempts.push({ - key: key.values, - correct: correct.values, - timestamps: timestamp.values, + key, + correct, + timestamp, }); }); words.push(thisWordPerf); @@ -34,29 +52,6 @@ const transformIntoWords = (input: DataFrame): WordPerformance[] => { return words; }; -interface FinalOutputItem { - key: string; - timestamp: number; -} -// we need to get the final output i.e. the key events minus the ones that were followed by backspace -const createFinalOutput = ( - keys: string[], - timestamp: number[] -): FinalOutputItem[] => { - let out: FinalOutputItem[] = []; - keys.forEach((k, i) => { - if (k !== "Backspace") out.push({ key: k, timestamp: timestamp[i] }); - else out = out.slice(0, out.length - 1); - }); - return out; -}; - -interface NGramTarget { - nGram: string; - startIndex: number; - endIndex: number; -} - // get corresponding performance data for a given nGram const dumpNGrams = ( nGramsInTarget: NGramTarget[], @@ -97,18 +92,18 @@ const createNGramsFromWords = ( } ); const individualNGramPerformance = attempts.flatMap( - ({ key, correct, timestamps }): nGramFeedback[] => { + ({ key, correct, timestamp }): nGramFeedback[] => { // if ever key press was correct, then we can just dump that into the nGram object // if there was a miss, we need to figure out what we can work with // the simplest is when there's just a mistyped character but the string is still the same length as the target if (correct.indexOf(false) <= -1 || target.length === key.length) { - return dumpNGrams(nGramsInTarget, correct, timestamps); + return dumpNGrams(nGramsInTarget, correct, timestamp); } // if there's backspaces, then we need to create the full visual history so we can calculate the nGram duration - const finalTyped = createFinalOutput(key, timestamps); + const finalTyped = createFinalOutput(key, timestamp); // if the lengths are the same, then it can be treated like a simply mistype if (finalTyped.length === target.length) { - return dumpNGrams(nGramsInTarget, correct, timestamps); + return dumpNGrams(nGramsInTarget, correct, timestamp); } // if the lengths differ, then there's probably a way we could handle it, // but i don't want to implement it right now :-P @@ -137,10 +132,11 @@ const createNGramsFromWords = ( }; const computeNGram = ( - session: DataFrame, + session: LongSessionRow[], nGramSize: number ): nGramFeedback[] => { const words = transformIntoWords(session); + console.log(words); const nGrams = createNGramsFromWords(words, nGramSize); return nGrams; }; diff --git a/src/analysis/computeRunningWPM.ts b/src/analysis/computeRunningWPM.ts index 27cee67..e7359c7 100644 --- a/src/analysis/computeRunningWPM.ts +++ b/src/analysis/computeRunningWPM.ts @@ -1,58 +1,54 @@ -import { DataFrame } from "danfojs"; +import { + tidy, + filter, + mutate, + groupBy, + summarize, + first, + last, +} from "@tidyjs/tidy"; +import { LongSessionRow } from "../types"; -const computeRunningWPM = (session: DataFrame): number[] => { +const computeRunningWPM = (session: LongSessionRow[]): number[] => { // filter out incorrect key presses - const correctEvents = session.query( - session["key"].values.map((k: string) => k !== "Backspace") + const correctEvents = tidy( + session, + filter(({ key }) => key !== "Backspace") ); // if there are not enough correct events, then stop and return -1 - if (correctEvents.values.length < 5) return [-1]; + if (correctEvents.length < 5) return [-1]; // create groups of 5 characters and measure how long each one took - const totalRows = correctEvents.shape[0]; - // combine the last two groups so there's enough data - const remainder = totalRows % 5; - const wpmGroupLabelsWithoutRemainder = Array.from(Array(totalRows)).map( - (_, i) => Math.trunc(i / 5) + // to prevent having too small a last group, we'll only take n rows up to a number divisible by 5 + const lastGroup = Math.trunc(correctEvents.length / 5); + let rowNum = 0; + const annotatedSession = tidy( + correctEvents, + mutate({ + wpmGroup: () => { + const group = Math.trunc(rowNum / 5); + rowNum += 1; + return group; + }, + }), + filter(({ wpmGroup }) => wpmGroup < lastGroup) ); - const wpmGroupLabels = [ - ...wpmGroupLabelsWithoutRemainder.slice( - 0, - wpmGroupLabelsWithoutRemainder.length - remainder - ), - ...Array.from(Array(remainder)).map(() => Math.trunc(totalRows / 5) - 1), - ]; - const annotatedSession = correctEvents.addColumn("wpmGroup", wpmGroupLabels); - const maxLabel = wpmGroupLabels.slice(-1)[0]; - // iterate through row groups and get duration - const runningWPMValues = Array.from(new Array(maxLabel)).map( - (_, i): number => { - // grab the timestamp col for the 5 character group - const mask = annotatedSession["wpmGroup"].eq(i); - let queryRes; - try { - queryRes = annotatedSession.query(mask); - } catch (err) { - console.error("failed to query dataframe!", { - err, - targetedWPMGroup: i, - mask, - annotatedSession, - }); - return -1; - } - const group = queryRes["timestamp"]; - // compute group duration in seconds - const tStart = group.min({ axis: 1 }); - const tEnd = group.max({ axis: 1 }); - // this is the duration it took to type 5 characters - const duration = (tEnd - tStart) / 60000; - // so we can extrapolate that out to 1 minute to get the instantaneous WPM - const groupWPM = duration * 6000; - return Math.round(groupWPM); - } + const runningWPMValues = tidy( + annotatedSession, + groupBy("wpmGroup", [ + summarize({ start: first("timestamp"), end: last("timestamp") }), + ]), + mutate({ + // this is the time it took to type 5 characters ie one word + duration: ({ start, end }) => (end - start) / 1000, + }), + mutate({ + // so we can extrapolate that out to 60 seconds to get WPM + wpm: ({ duration }) => Math.round(duration * 60), + }) ); - return runningWPMValues; + + return runningWPMValues.map(({ wpm }) => wpm); }; export default computeRunningWPM; diff --git a/src/analysis/computeWPM.ts b/src/analysis/computeWPM.ts index 0eb6fc8..1589336 100644 --- a/src/analysis/computeWPM.ts +++ b/src/analysis/computeWPM.ts @@ -1,14 +1,15 @@ -import { DataFrame } from "danfojs"; +import { tidy, select } from "@tidyjs/tidy"; +import { LongSessionRow } from "../types"; -const computeWPM = (session: DataFrame): number => { - const first = session.head(1)["timestamp"]; - const last = session.tail(1)["timestamp"]; +const computeWPM = (session: LongSessionRow[]): number => { + const first = session[0]["timestamp"]; + const last = session[session.length - 1]["timestamp"]; // get the total duration of the test - const duration = last.sub(first).values[0]; + const duration = last - first; // divide to get minutes const durationInMin = duration / 60000; // get the number of correct characters typed - const chars = session.query(session["correct"]).shape[0]; + const chars = tidy(session, select("correct")).length; // assume an average word is 5 characters const words = chars / 5; const wpm = words / durationInMin; diff --git a/src/analysis/sessionPostProcessing.ts b/src/analysis/sessionPostProcessing.ts index b14e30f..5c0e89a 100644 --- a/src/analysis/sessionPostProcessing.ts +++ b/src/analysis/sessionPostProcessing.ts @@ -1,4 +1,3 @@ -import * as dfd from "danfojs"; import { v4 as uuidv4 } from "uuid"; import { LongSessionRow, Word } from "../types"; import computeAccuracy from "./computeAccuracy"; @@ -25,7 +24,7 @@ export const dumpSession = async (session: Word[]): Promise => { }; // create a dataframe where each typing event takes up one row -export const pivotSessionLong = (session: Word[]): dfd.DataFrame => { +export const pivotSessionLong = (session: Word[]): LongSessionRow[] => { const rows = session.flatMap(({ target, history }) => history.map((e) => ({ target, @@ -33,6 +32,6 @@ export const pivotSessionLong = (session: Word[]): dfd.DataFrame => { })) ) as LongSessionRow[]; - const out = new dfd.DataFrame(rows); + const out = rows; return out; }; diff --git a/src/bin/createFinalOutput.ts b/src/bin/createFinalOutput.ts new file mode 100644 index 0000000..2a991e4 --- /dev/null +++ b/src/bin/createFinalOutput.ts @@ -0,0 +1,16 @@ +interface FinalOutputItem { + key: string; + timestamp: number; +} +// we need to get the final output i.e. the key events minus the ones that were followed by backspace +export const createFinalOutput = ( + keys: string[], + timestamp: number[] +): FinalOutputItem[] => { + let out: FinalOutputItem[] = []; + keys.forEach((k, i) => { + if (k !== "Backspace") out.push({ key: k, timestamp: timestamp[i] }); + else out = out.slice(0, out.length - 1); + }); + return out; +}; diff --git a/src/bin/pullColumns.ts b/src/bin/pullColumns.ts new file mode 100644 index 0000000..4542628 --- /dev/null +++ b/src/bin/pullColumns.ts @@ -0,0 +1,12 @@ +// returns array of each column's data as an array +// assume that all objects contain the same number of keys +export const pullColumns = (items: Object[], columns: string[]): any[][] => { + const colData: { [key: string]: any[] } = {}; + columns.forEach((c) => (colData[c] = [])); + items.forEach((i: { [key: string]: any }) => + columns.forEach((c) => { + colData[c].push(i[c]); + }) + ); + return columns.map((c) => colData[c]); +}; diff --git a/src/components/Accuracy.tsx b/src/components/Accuracy.tsx index 8d212a0..2aa4fd2 100644 --- a/src/components/Accuracy.tsx +++ b/src/components/Accuracy.tsx @@ -1,7 +1,7 @@ -import { DataFrame } from "danfojs"; import computeAccuracy from "../analysis/computeAccuracy"; +import { LongSessionRow } from "../types"; -const Accuracy = ({ session }: { session: DataFrame }) => { +const Accuracy = ({ session }: { session: LongSessionRow[] }) => { return (
Accuracy: {(100 * computeAccuracy(session)).toFixed(0)}% diff --git a/src/components/NGram.tsx b/src/components/NGram.tsx index 0543d82..efb07c0 100644 --- a/src/components/NGram.tsx +++ b/src/components/NGram.tsx @@ -1,7 +1,6 @@ -import { DataFrame } from "danfojs"; -import { Series } from "danfojs/dist/danfojs-base"; +import { tidy, mean, summarize, filter } from "@tidyjs/tidy"; import computeNGram from "../analysis/computeNGram"; -import { nGramFeedback } from "../types"; +import { LongSessionRow, nGramFeedback } from "../types"; interface ProcessedNGramData { nGram: string; @@ -12,11 +11,14 @@ interface ProcessedNGramData { const processNGramData = (nGrams: nGramFeedback[]): ProcessedNGramData[] => nGrams.map(({ nGram, performance }) => { - const meanDuration = Math.round( - new Series(performance.map(({ duration }) => duration)).mean() - ); - const correctData = performance.flatMap( - ({ correct }) => correct.indexOf(false) <= -1 + const meanDuration = tidy( + performance, + summarize({ meanDuration: mean("duration") }) + )[0]["meanDuration"] as number; + + const correctData = tidy( + performance, + filter(({ correct }) => correct.indexOf(false) <= -1) ); const meanAccuracy = correctData.filter((e) => e).length / correctData.length; @@ -42,12 +44,12 @@ const displayNGramData = ( {nGram} {nGramCount} - {meanDuration}ms + {Math.round(meanDuration)}ms {Math.round(meanAccuracy * 100)}% )); -const NGram = ({ session }: { session: DataFrame }) => { +const NGram = ({ session }: { session: LongSessionRow[] }) => { const sortBy: NGramSortables = "nGramCount"; const nRows = 10; diff --git a/src/components/RunningWPM.tsx b/src/components/RunningWPM.tsx index afa9bea..b6deb2b 100644 --- a/src/components/RunningWPM.tsx +++ b/src/components/RunningWPM.tsx @@ -1,9 +1,13 @@ import { ReactElement } from "react"; import ECharts from "echarts-for-react"; -import { DataFrame } from "danfojs"; import computeRunningWPM from "../analysis/computeRunningWPM"; +import { LongSessionRow } from "../types"; -const RunningWPM = ({ session }: { session: DataFrame }): ReactElement => { +const RunningWPM = ({ + session, +}: { + session: LongSessionRow[]; +}): ReactElement => { // blah const runningWPM = computeRunningWPM(session); const options = { diff --git a/src/components/WPM.tsx b/src/components/WPM.tsx index ebd1441..22d70ce 100644 --- a/src/components/WPM.tsx +++ b/src/components/WPM.tsx @@ -1,7 +1,7 @@ -import { DataFrame } from "danfojs"; import computeWPM from "../analysis/computeWPM"; +import { LongSessionRow } from "../types"; -const WPM = ({ session }: { session: DataFrame }) => { +const WPM = ({ session }: { session: LongSessionRow[] }) => { const wpm = computeWPM(session); return
WPM: {wpm}
; }; diff --git a/src/routes/Analysis.tsx b/src/routes/Analysis.tsx index 8366dfb..7a185ff 100644 --- a/src/routes/Analysis.tsx +++ b/src/routes/Analysis.tsx @@ -1,4 +1,3 @@ -import { DataFrame } from "danfojs"; import { useEffect, useState } from "react"; import { Link } from "react-router-dom"; import { RxDocument } from "rxdb"; @@ -9,7 +8,7 @@ import NGram from "../components/NGram"; import RunningWPM from "../components/RunningWPM"; import WPM from "../components/WPM"; import "../styles/Analysis.scss"; -import { Word } from "../types"; +import { LongSessionRow, Word } from "../types"; import { SessionType } from "../types/sessionSchema"; const Vis = () => { @@ -18,7 +17,7 @@ const Vis = () => { Function ] = useState([]); const [selectedSession, setSelectedSession]: [ - DataFrame | undefined, + LongSessionRow[] | undefined, Function ] = useState(); const [selectedSessionUUID, setSelectedSessionUUID] = useState(""); diff --git a/src/types/index.ts b/src/types/index.ts index fe306b3..cb28020 100644 --- a/src/types/index.ts +++ b/src/types/index.ts @@ -30,7 +30,7 @@ export interface WordPerformance { // array of whether the right key was pressed correct: boolean[]; // array of timestamps so we can know how long the word took to type - timestamps: number[]; + timestamp: number[]; }[]; }