import { Heat } from "../Heat";
import * as df from "../../../dataflow/dist/index";

const columns = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23"];

const data = [["Sat", 0, 0.18998719125426], ["Sat", 1, 0.21651075448881343], ["Sat", 2, 0.29360917730014136], ["Sat", 3, 0.1651116355055364], ["Sat", 4, 0.11885237728786953], ["Sat", 5, 0.09315256213264339], ["Sat", 6, 0.19081093933358728], ["Sat", 7, 0.26505462252549605], ["Sat", 8, 0.31304625721290896], ["Sat", 9, 0.18326323889972618], ["Sat", 10, 0.5427922043058862], ["Sat", 11, 0.33890867440986455], ["Sat", 12, 0.22287115322174472], ["Sat", 13, 0.2587162108611005], ["Sat", 14, 0.28441551468915144], ["Sat", 15, 0.19982154681583783], ["Sat", 16, 0.2930569439508717], ["Sat", 17, 0.47723750380299584], ["Sat", 18, 0.22207706211858186], ["Sat", 19, 0.33558402511639085], ["Sat", 20, 0.3690611266071652], ["Sat", 21, 0.5573599155287506], ["Sat", 22, 0.6831520252391093], ["Sat", 23, 0.5778902129422021], ["Fri", 0, 0.22511997013849297], ["Fri", 1, 0.12872968433216836], ["Fri", 2, 0.14983215685472429], ["Fri", 3, 0.2927501476457219], ["Fri", 4, 0.10428671137370601], ["Fri", 5, 0.15568583035698308], ["Fri", 6, 0.24599132277783603], ["Fri", 7, 0.2731898378837191], ["Fri", 8, 0.44567225462048016], ["Fri", 9, 0.20923559143936044], ["Fri", 10, 0.37126545805966676], ["Fri", 11, 0.25775900638903304], ["Fri", 12, 0.3814741051135274], ["Fri", 13, 0.34006938709768136], ["Fri", 14, 0.27593924410503684], ["Fri", 15, 0.4419477474759613], ["Fri", 16, 0.38965124930012096], ["Fri", 17, 0.3172503892478122], ["Fri", 18, 0.46400589048905894], ["Fri", 19, 0.2558543126612278], ["Fri", 20, 0.3590657029853837], ["Fri", 21, 0.15632447799886998], ["Fri", 22, 0.26420326277870526], ["Fri", 23, 0.297398622995917], ["Thur", 0, 0.18232751016901919], ["Thur", 1, 0.10522908735769126], ["Thur", 2, 0.2871142995201194], ["Thur", 3, 0.13264031456847822], ["Thur", 4, 0.18082727623683653], ["Thur", 5, 0.09944495435126643], ["Thur", 6, 0.11995531000488316], ["Thur", 7, 0.32298185555518627], ["Thur", 8, 0.2721533776994879], ["Thur", 9, 0.27001193948954205], ["Thur", 10, 0.4749544279655059], ["Thur", 11, 0.2692331882016368], ["Thur", 12, 0.4508438176709558], ["Thur", 13, 0.4492663733353105], ["Thur", 14, 0.3835056079807946], ["Thur", 15, 0.3464395010469424], ["Thur", 16, 0.34637609647721146], ["Thur", 17, 0.28212732557990894], ["Thur", 18, 0.19046579349029374], ["Thur", 19, 0.3223897386862471], ["Thur", 20, 0.2504833320124048], ["Thur", 21, 0.28639128289431637], ["Thur", 22, 0.4919882701545998], ["Thur", 23, 0.11120547938200998], ["Wed", 0, 0.26539414376986187], ["Wed", 1, 0.1915083896006279], ["Wed", 2, 0.1144094554621247], ["Wed", 3, 0.1915083896006279], ["Wed", 4, 0.1915083896006279], ["Wed", 5, 0.06301033647884767], ["Wed", 6, 0.18917162440973667], ["Wed", 7, 0.2034494131242346], ["Wed", 8, 0.355351933711545], ["Wed", 9, 0.3451218109163239], ["Wed", 10, 0.3226346644031917], ["Wed", 11, 0.20270338677554525], ["Wed", 12, 0.12560445263704206], ["Wed", 13, 0.2519610675488765], ["Wed", 14, 0.30086133062670817], ["Wed", 15, 0.24946221164343108], ["Wed", 16, 0.31534569552000696], ["Wed", 17, 0.4939159736052912], ["Wed", 18, 0.31544642697353115], ["Wed", 19, 0.24156016147712192], ["Wed", 20, 0.27236557847721654], ["Wed", 21, 0.3740930973387977], ["Wed", 22, 0.1085307269282787], ["Wed", 23, 0.13101787344141086], ["Tue", 0, 0.38368457249213195], ["Tue", 1, 0.11328658098527632], ["Tue", 2, 0.14541071077033996], ["Tue", 3, 0.06827291576651778], ["Tue", 4, 0.17107115373307188], ["Tue", 5, 0.11538864700272793], ["Tue", 6, 0.11217648968780919], ["Tue", 7, 0.13902321169712045], ["Tue", 8, 0.3094408893002232], ["Tue", 9, 0.40688502041473745], ["Tue", 10, 0.36313484464602097], ["Tue", 11, 0.4601351437724185], ["Tue", 12, 0.4095924978076847], ["Tue", 13, 0.22557709663316622], ["Tue", 14, 0.3283753345997203], ["Tue", 15, 0.2706332020074705], ["Tue", 16, 0.24600410595721722], ["Tue", 17, 0.5543993311840548], ["Tue", 18, 0.2738964920399142], ["Tue", 19, 0.2781803911141563], ["Tue", 20, 0.17569661936038083], ["Tue", 21, 0.48912841726342815], ["Tue", 22, 0.38632966796969875], ["Tue", 23, 0.18644420525695468], ["Mon", 0, 0.6359227793699938], ["Mon", 1, 0.5221023728137567], ["Mon", 2, 0.37279943958541595], ["Mon", 3, 0.34710013575736504], ["Mon", 4, 0.28285085353288725], ["Mon", 5, 0.25751868261666566], ["Mon", 6, 0.28428921687686476], ["Mon", 7, 0.17089474585761072], ["Mon", 8, 0.36545166807707746], ["Mon", 9, 0.21043056304791902], ["Mon", 10, 0.3696194958825379], ["Mon", 11, 0.3105586505053191], ["Mon", 12, 0.4402250862225449], ["Mon", 13, 0.2582289160630875], ["Mon", 14, 0.2996172716093256], ["Mon", 15, 0.32338989464103557], ["Mon", 16, 0.3249376820005164], ["Mon", 17, 0.22535620329345835], ["Mon", 18, 0.2782090254359703], ["Mon", 19, 0.14818108140584293], ["Mon", 20, 0.32807825351090025], ["Mon", 21, 0.5908569587793598], ["Mon", 22, 0.5080470114204925], ["Mon", 23, 0.2530276960364474], ["Sun", 0, 0.6469792068804184], ["Sun", 1, 0.4528553787783882], ["Sun", 2, 0.540662526620971], ["Sun", 3, 0.48754995027343223], ["Sun", 4, 0.2125643313502361], ["Sun", 5, 0.1999907961108455], ["Sun", 6, 0.19768419922329403], ["Sun", 7, 0.1666001089126883], ["Sun", 8, 0.39061458969829144], ["Sun", 9, 0.3091105719450119], ["Sun", 10, 0.3605096909282889], ["Sun", 11, 0.23504738724596624], ["Sun", 12, 0.2493108587995572], ["Sun", 13, 0.27608139305975626], ["Sun", 14, 0.301086314583818], ["Sun", 15, 0.4038845525503721], ["Sun", 16, 0.46976394580954595], ["Sun", 17, 0.5597125318620746], ["Sun", 18, 0.3975047233847814], ["Sun", 19, 0.19067492630497088], ["Sun", 20, 0.348541567064394], ["Sun", 21, 0.7699334507681412], ["Sun", 22, 0.606812411955852], ["Sun", 23, 1]];
// const data = [["Sat", 0, 0.005], ["Sat", 1, 0.005], ["Sat", 2, 0.005], ["Sat", 3, 0.005], ["Sat", 4, 0.005], ["Sat", 5, 0.005], ["Sat", 6, 0.005], ["Sat", 7, 0.005], ["Sat", 8, 0.005], ["Sat", 9, 0.005], ["Sat", 10, 0.005], ["Sat", 11, 0.005], ["Sat", 12, 0.004], ["Sat", 13, 0.006], ["Sat", 14, 0.005], ["Sat", 15, 0.005], ["Sat", 16, 0.005], ["Sat", 17, 0.005], ["Sat", 18, 0.005], ["Sat", 19, 0.005], ["Sat", 20, 0.005], ["Sat", 21, 0.005], ["Sat", 22, 0.005], ["Sat", 23, 0.005], ["Fri", 0, 0.005], ["Fri", 1, 0.005], ["Fri", 2, 0.005], ["Fri", 3, 0.005], ["Fri", 4, 0.005], ["Fri", 5, 0.005], ["Fri", 6, 0.005], ["Fri", 7, 0.005], ["Fri", 8, 0.005], ["Fri", 9, 0.005], ["Fri", 10, 0.005], ["Fri", 11, 0.005], ["Fri", 12, 0.005], ["Fri", 13, 0.005], ["Fri", 14, 0.005], ["Fri", 15, 0.005], ["Fri", 16, 0.005], ["Fri", 17, 0.005], ["Fri", 18, 0.005], ["Fri", 19, 0.005], ["Fri", 20, 0.005], ["Fri", 21, 0.005], ["Fri", 22, 0.005], ["Fri", 23, 0.005], ["Thur", 0, 0.005], ["Thur", 1, 0.005], ["Thur", 2, 0.005], ["Thur", 3, 0.005], ["Thur", 4, 0.005], ["Thur", 5, 0.005], ["Thur", 6, 0.005], ["Thur", 7, 0.005], ["Thur", 8, 0.005], ["Thur", 9, 0.005], ["Thur", 10, 0.005], ["Thur", 11, 0.005], ["Thur", 12, 0.005], ["Thur", 13, 0.005], ["Thur", 14, 0.005], ["Thur", 15, 0.005], ["Thur", 16, 0.005], ["Thur", 17, 0.005], ["Thur", 18, 0.005], ["Thur", 19, 0.005], ["Thur", 20, 0.005], ["Thur", 21, 0.005], ["Thur", 22, 0.005], ["Thur", 23, 0.005], ["Wed", 0, 0.005], ["Wed", 1, 0.005], ["Wed", 2, 0.005], ["Wed", 3, 0.005], ["Wed", 4, 0.005], ["Wed", 5, 0.005], ["Wed", 6, 0.005], ["Wed", 7, 0.005], ["Wed", 8, 0.005], ["Wed", 9, 0.005], ["Wed", 10, 0.005], ["Wed", 11, 0.005], ["Wed", 12, 0.005], ["Wed", 13, 0.005], ["Wed", 14, 0.005], ["Wed", 15, 0.005], ["Wed", 16, 0.005], ["Wed", 17, 0.005], ["Wed", 18, 0.005], ["Wed", 19, 0.005], ["Wed", 20, 0.005], ["Wed", 21, 0.005], ["Wed", 22, 0.005], ["Wed", 23, 0.005], ["Tue", 0, 0.005], ["Tue", 1, 0.005], ["Tue", 2, 0.005], ["Tue", 3, 0.005], ["Tue", 4, 0.005], ["Tue", 5, 0.005], ["Tue", 6, 0.005], ["Tue", 7, 0.005], ["Tue", 8, 0.005], ["Tue", 9, 0.005], ["Tue", 10, 0.005], ["Tue", 11, 0.005], ["Tue", 12, 0.005], ["Tue", 13, 0.005], ["Tue", 14, 0.005], ["Tue", 15, 0.005], ["Tue", 16, 0.005], ["Tue", 17, 0.005], ["Tue", 18, 0.005], ["Tue", 19, 0.005], ["Tue", 20, 0.005], ["Tue", 21, 0.005], ["Tue", 22, 0.005], ["Tue", 23, 0.005], ["Mon", 0, 0.005], ["Mon", 1, 0.005], ["Mon", 2, 0.005], ["Mon", 3, 0.005], ["Mon", 4, 0.005], ["Mon", 5, 0.005], ["Mon", 6, 0.005], ["Mon", 7, 0.005], ["Mon", 8, 0.005], ["Mon", 9, 0.005], ["Mon", 10, 0.005], ["Mon", 11, 0.005], ["Mon", 12, 0.005], ["Mon", 13, 0.005], ["Mon", 14, 0.005], ["Mon", 15, 0.005], ["Mon", 16, 0.005], ["Mon", 17, 0.005], ["Mon", 18, 0.005], ["Mon", 19, 0.005], ["Mon", 20, 0.005], ["Mon", 21, 0.005], ["Mon", 22, 0.005], ["Mon", 23, 0.005], ["Sun", 0, 0.005], ["Sun", 1, 0.005], ["Sun", 2, 0.005], ["Sun", 3, 0.005], ["Sun", 4, 0.005], ["Sun", 5, 0.005], ["Sun", 6, 0.005], ["Sun", 7, 0.005], ["Sun", 8, 0.005], ["Sun", 9, 0.005], ["Sun", 10, 0.005], ["Sun", 11, 0.005], ["Sun", 12, 0.005], ["Sun", 13, 0.005], ["Sun", 14, 0.005], ["Sun", 15, 0.005], ["Sun", 16, 0.005], ["Sun", 17, 0.005], ["Sun", 18, 0.005], ["Sun", 19, 0.005], ["Sun", 20, 0.005], ["Sun", 21, 0.005], ["Sun", 22, 0.005], ["Sun", 23, 0.005]];

export class Test extends Heat {

    constructor() {
        super();
        this
            .paletteID("Greens")
            .useClonedPalette(true)
            .columns(columns)
            .data(data)
            .xAxisType("ordinal")
            .yAxisType("time")
            .yAxisTypeTimePattern("%H")
            .yAxisTickFormat("%H:00")
            .yAxisTitle("Hour")
            .orientation("vertical")
            .lazyRender()
            ;
    }
}

//  Calculate the standard deviation of the weight column  ---
const extractWeight = df.map(row => row[2]);
export const distribution = df.scalar(df.distribution())(extractWeight(data));
export const quartiles = df.scalar(df.quartile())(extractWeight(data));

//  Convert the weight column to standard deviations  ---
const toStdDevs = df.map(r => {
    return [r[0], r[1], distribution.mean + (r[2] - distribution.mean) / (distribution.deviation || 1)];
});
const stdDevData = toStdDevs(data);

//  Normalize the standard deviations and calculate the new mean and deviation  ---
const range = df.scalar(df.extent())(extractWeight(stdDevData));
const denominator = (range[1] - range[0]) || 1;
const normalize = df.map(r => {
    return [r[0], r[1], (r[2] - range[0]) / denominator];
});
const normalizedStdDevData = normalize(toStdDevs(data));
const normalizedMean = (distribution.mean - range[0]) / denominator;
const normalizedDeviation = (distribution.deviation - range[0]) / denominator;

// Create a new Heat map using the normalized standard deviations  ---
export class StdDevTest extends Heat {

    constructor() {
        super();

        this
            .columns(columns)
            .data([...normalizedStdDevData])
            .paletteID("RdYlGn")
            .reversePalette(true)
            .xAxisType("ordinal")
            .yAxisType("time")
            .yAxisTypeTimePattern("%H")
            .yAxisTickFormat("%H:00")
            .yAxisTitle("Hour")
            .orientation("vertical")
            .maxWeight(normalizedMean + 2 * normalizedDeviation)
            .lazyRender()
            ;
    }
}