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@apio/timeframes

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Comparing version 0.1.35 to 0.1.37

102

build/main/lib/timeframe.d.ts

@@ -1,3 +0,3 @@

import { TimeSerie } from './timeserie';
import { AggregationConfiguration, DateLike, FromTimeseriesOptions, Index, Metadata, PointValue, ReindexOptions, Row, TelemetryV1Output, TimeFrameInternal, PartitionOptions, TimeFrameResampleOptions, TimeframeRowsIterator, TimeserieIterator, TimeFrameReduceOptions, ProjectionOptions, PipelineStage } from './types';
import { TimeSerie } from "./timeserie";
import { AggregationConfiguration, DateLike, FromTimeseriesOptions, Index, Metadata, ReindexOptions, Row, TelemetryV1Output, PartitionOptions, TimeFrameResampleOptions, TimeframeRowsIterator, TimeserieIterator, TimeFrameReduceOptions, ProjectionOptions, PipelineStage, SplitOptions, BetweenTimeOptions } from "./types";
interface TimeFrameOptions {

@@ -16,2 +16,3 @@ data: Row[];

private _indexes;
private _columns;
/**

@@ -23,8 +24,8 @@ * Creates a Timeframe instance from a list of rows. It infers the list of column names from each row's fields.

constructor(options: TimeFrameOptions);
private buildTimeCheckpoints;
private buildTimeTree;
/**
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate(data: Row[]): TimeFrame;

@@ -46,13 +47,11 @@ /**

/**
*
* Creates a TimeFrame from a Telemetry Output Object (Apio private method)
* @param data An object which is telemetry V1 output (Apio Internal)
* @returns
*/
static fromTelemetryV1Output(data?: TelemetryV1Output, metadata?: Metadata): TimeFrame;
static fromInternalFormat(data: TimeFrameInternal, metadata?: Metadata): TimeFrame;
private static fromInternalFormat;
/**
*
* Returns a new TimeFrame, where each input timeserie is used as column
* @param timeseries An array of TimeSerie objects
* @param options.fill Value to use as filler when a column does not hold a value for a specific time
* @returns A new TimeFrame, where each timeserie represent a column
*/

@@ -67,2 +66,8 @@ static fromTimeseries(timeseries: TimeSerie[], options?: FromTimeseriesOptions): TimeFrame;

/**
* Merges together rows and columns of the specified timeframes.
* If two or more timeframes present a value for the same column at the same time, the first timeframe in the array has priority.
* @param timeframes Array of timeframes to merge
*/
static merge(timeframes: TimeFrame[]): TimeFrame;
/**
* Joins multiple timeframes by adding the columns together and merging indexes (time)

@@ -80,13 +85,17 @@ * @param timeframes Array of timeframes to join together

/**
*
* Returns the column as timeseries
* @param name The name of the wanted column
* @returns The column as timeseries
*/
column(name: string): TimeSerie;
/**
* Returns every column as array of timeseries
*/
columns(): TimeSerie[];
/**
*
* @returns Array of rows
* Returns all the rows in an array
*/
rows(): Row[];
/**
* Returns the time index array
*/
indexes(): DateLike[];

@@ -98,27 +107,27 @@ /**

/**
*
* Returns a row at a given time or null
* @param time
* @returns A row at a given time or null
*/
atTime(time: string): Row | null;
/**
*
* @returns The row at the given index (position, not time)
* Get the row at the given index (position, not time)
*/
atIndex(index: number): PointValue;
atIndex(index: number): Row;
/**
* Returns the number of rows
*/
length(): number;
/**
* Returns the shape of the timeframe
* @returns Array<Number> The shape of the timeframe expressed as [rows, columns] where columns excludes the time column
* Returns the shape of the timeframe expressed as [rows, columns] where columns excludes the time column
*/
shape(): number[];
/**
*
* @returns The first row
*/
*
* Returns the first row
*/
first(): Row;
/**
*
* @returns The last row
*/
*
* Returns the last row
*/
last(): Row;

@@ -154,11 +163,7 @@ /**

/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from: DateLike, to: DateLike, options?: {
includeInferior: boolean;
includeSuperior: boolean;
}): TimeFrame;
* Returns the subset of points between the two dates. Extremes are included.
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
betweenTime(from: DateLike, to: DateLike, options?: BetweenTimeOptions): TimeFrame;
/**

@@ -175,3 +180,3 @@ * Applies transformations to TimeFrame. Each transformation is defined as an operation between columns. Allows, for example, to

* tf = tf.aggregate({ output: 'power1', columns: ['voltage1', 'current1'], operation: 'mul' })
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
*/

@@ -188,2 +193,7 @@ aggregate(agg: AggregationConfiguration): TimeFrame;

reduce(options: TimeFrameReduceOptions): TimeFrame;
/**
* Resamples the timeframe by the specified time interval. Each row
* of the result TimeFrame will be the result of the selected aggregation.
* @param options
*/
resample(options: TimeFrameResampleOptions): TimeFrame;

@@ -197,6 +207,6 @@ /**

/**
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map(fn: TimeframeRowsIterator): TimeFrame;

@@ -213,6 +223,10 @@ /**

* @param options
* @returns
*/
partition(options: PartitionOptions): TimeFrame[];
/**
* Splits a timeframe into multiple timeframes where each timeframe has
* a maximum of `options.chunks` rows.
*/
split(options: SplitOptions): TimeFrame[];
/**
* Runs a series of transformations defined as an object. Useful in automation.

@@ -219,0 +233,0 @@ * A stage is an object with a single key and a value, the key is the name of the method, the value is the params object

295

build/main/lib/timeframe.js

@@ -21,2 +21,3 @@ "use strict";

};
const makeTree = require("functional-red-black-tree");
/**

@@ -36,2 +37,3 @@ * @class TimeFrame

this.metadata = {};
this._columns = {};
const { data, metadata = {} } = options;

@@ -45,7 +47,7 @@ // get a list of unique column names excluding the time key

else {
this.columnNames = [...new Set(data
this.columnNames = [
...new Set(data
.filter((row) => !!row)
.map((row) => Object.keys(row))
.flat())]
.filter((name) => name !== 'time');
.flatMap((row) => Object.keys(row))),
].filter((name) => name !== "time");
this.data = data

@@ -66,3 +68,6 @@ .concat([])

const { time, ...rest } = row;
acc[row.time] ? acc[row.time] = { ...acc[row.time], ...rest } : acc[row.time] = rest;
const fTime = utils_1.DateLikeToString(time);
acc[fTime]
? (acc[fTime] = { ...acc[fTime], ...rest })
: (acc[fTime] = rest);
return acc;

@@ -73,21 +78,22 @@ }, {});

time: Object.keys(this.data).sort(),
checkpoints: null
tree: null,
};
}
buildTimeCheckpoints() {
if (!this._indexes.checkpoints) {
this._indexes.checkpoints = {};
const o = utils_1.getOrderOfMagnitude(this._indexes.time.length);
this._indexes.time.forEach((el, i) => {
if (i % (o / 100) === 0) {
this._indexes.checkpoints[el] = i;
}
buildTimeTree() {
if (!this._indexes.tree) {
let tree = makeTree(function (a, b) {
return a - b;
});
this._indexes.time.forEach((time) => {
const t = new Date(time).getTime();
tree = tree.insert(t, t);
});
this._indexes.tree = tree;
}
}
/**
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate(data) {

@@ -117,5 +123,4 @@ return new TimeFrame({ data, metadata: this.metadata });

/**
*
* Creates a TimeFrame from a Telemetry Output Object (Apio private method)
* @param data An object which is telemetry V1 output (Apio Internal)
* @returns
*/

@@ -128,3 +133,3 @@ static fromTelemetryV1Output(data = {}, metadata = {}) {

if (!_data[time]) {
_data[time] = {};
_data[utils_1.DateLikeToString(time)] = {};
}

@@ -134,5 +139,5 @@ const column = `${deviceId}:${propertyName}`;

deviceId,
propertyName
propertyName,
};
_data[time][column] = value;
_data[utils_1.DateLikeToString(time)][column] = value;
}

@@ -142,3 +147,3 @@ }

const rows = Object.keys(_data).map((time) => {
return { time, ..._data[time] };
return { time, ..._data[utils_1.DateLikeToString(time)] };
});

@@ -154,6 +159,5 @@ return new TimeFrame({ data: rows, metadata });

/**
*
* Returns a new TimeFrame, where each input timeserie is used as column
* @param timeseries An array of TimeSerie objects
* @param options.fill Value to use as filler when a column does not hold a value for a specific time
* @returns A new TimeFrame, where each timeserie represent a column
*/

@@ -163,9 +167,10 @@ static fromTimeseries(timeseries, options) {

const metadata = {};
timeseries.forEach(ts => {
const idx = [...new Set(timeseries.flatMap((ts) => ts.indexes()))];
timeseries.forEach((ts) => {
metadata[ts.name] = ts.metadata;
ts.toArray().forEach((point) => {
data[point[0]] = data[point[0]] || {};
data[point[0]][ts.name] = point[1] || (options === null || options === void 0 ? void 0 : options.fill) || null;
});
});
idx.forEach((i) => {
data[i] = {};
timeseries.forEach((ts) => (data[i][ts.name] = ts.atTime(i) || (options === null || options === void 0 ? void 0 : options.fill) || null));
});
return TimeFrame.fromInternalFormat(data, metadata);

@@ -180,7 +185,18 @@ }

return new TimeFrame({
metadata: Object.assign({}, ...timeframes.map(tf => tf.metadata)),
data: timeframes.map((tf) => tf.rows()).flat()
metadata: Object.assign({}, ...timeframes.map((tf) => tf.metadata)),
data: timeframes.flatMap((tf) => tf.rows()),
});
}
/**
* Merges together rows and columns of the specified timeframes.
* If two or more timeframes present a value for the same column at the same time, the first timeframe in the array has priority.
* @param timeframes Array of timeframes to merge
*/
static merge(timeframes) {
if (timeframes.length < 2) {
throw new Error("merge() requires at least two timeframes");
}
return timeframes[0].join(timeframes.slice(1));
}
/**
* Joins multiple timeframes by adding the columns together and merging indexes (time)

@@ -191,3 +207,16 @@ * @param timeframes Array of timeframes to join together

join(timeframes) {
return TimeFrame.fromInternalFormat(Object.assign({}, ...(timeframes.map(tf => tf.data).concat([this.data]))));
const allTf = timeframes.concat([this]);
// Todo should support a filler value, at the moment it just does not define values in rows
// when a row misses a certain column's value
// const allColumns: string[] = [
// ...new Set(allTf.flatMap((tf) => tf.columnNames)),
// ];
const mergedIndex = [...new Set(allTf.flatMap((tf) => tf.indexes()))];
const rows = mergedIndex.map((idx) => ({
time: idx,
...allTf
.map((tf) => tf.atTime(idx))
.reduce((prev, acc) => Object.assign(acc, prev), this.atTime(idx)),
}));
return this.recreate(rows);
}

@@ -203,5 +232,4 @@ /**

/**
*
* Returns the column as timeseries
* @param name The name of the wanted column
* @returns The column as timeseries
*/

@@ -212,6 +240,16 @@ column(name) {

}
const data = Object.entries(this.data).map(([time, values]) => ([time, values[name]]));
const metadata = this.metadata[name] || {};
return new timeserie_1.TimeSerie(name, data, metadata);
// we cache the column to make subsequent reads faster
if (!this._columns[name]) {
const data = Object.entries(this.data).map(([time, values]) => [
time,
values[name],
]);
const metadata = this.metadata[name] || {};
this._columns[name] = new timeserie_1.TimeSerie(name, data, metadata);
}
return this._columns[name];
}
/**
* Returns every column as array of timeseries
*/
columns() {

@@ -221,8 +259,13 @@ return this.columnNames.map((column) => this.column(column));

/**
*
* @returns Array of rows
* Returns all the rows in an array
*/
rows() {
return Object.entries(this.data).map(([time, values]) => ({ time, ...values }));
return Object.entries(this.data).map(([time, values]) => ({
time,
...values,
}));
}
/**
* Returns the time index array
*/
indexes() {

@@ -237,3 +280,3 @@ return this._indexes.time;

if (nonExisting.length > 0) {
throw new Error(`Non existing columns ${nonExisting.join(',')}`);
throw new Error(`Non existing columns ${nonExisting.join(",")}`);
}

@@ -245,19 +288,20 @@ const tf = TimeFrame.fromTimeseries(config.columns.map((columnName) => this.column(columnName)));

/**
*
* Returns a row at a given time or null
* @param time
* @returns A row at a given time or null
*/
atTime(time) {
return { time, ...this.data[time] } || null;
return { time, ...this.data[utils_1.DateLikeToString(time)] } || null;
}
/**
*
* @returns The row at the given index (position, not time)
* Get the row at the given index (position, not time)
*/
atIndex(index) {
if (index >= this.rows().length) {
throw new Error('Index out of bounds');
throw new Error("Index out of bounds");
}
return this.rows()[index];
}
/**
* Returns the number of rows
*/
length() {

@@ -267,4 +311,3 @@ return this._indexes.time.length;

/**
* Returns the shape of the timeframe
* @returns Array<Number> The shape of the timeframe expressed as [rows, columns] where columns excludes the time column
* Returns the shape of the timeframe expressed as [rows, columns] where columns excludes the time column
*/

@@ -275,5 +318,5 @@ shape() {

/**
*
* @returns The first row
*/
*
* Returns the first row
*/
first() {

@@ -287,5 +330,5 @@ var _a;

/**
*
* @returns The last row
*/
*
* Returns the last row
*/
last() {

@@ -306,3 +349,6 @@ if (this.length() === 0) {

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.sum()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.sum()[1];
return acc;
}, { time });
}

@@ -317,3 +363,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.avg()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.avg()[1];
return acc;
}, { time });
}

@@ -328,3 +377,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.delta()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.delta()[1];
return acc;
}, { time });
}

@@ -339,3 +391,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.max()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.max()[1];
return acc;
}, { time });
}

@@ -350,3 +405,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.min()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.min()[1];
return acc;
}, { time });
}

@@ -366,40 +424,27 @@ /**

/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from, to, options = { includeInferior: true, includeSuperior: true }) {
* Returns the subset of points between the two dates. Extremes are included.
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
betweenTime(from, to, options = {
includeInferior: true,
includeSuperior: true,
}) {
/**
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys
* we index the array by mapping a certain number of timestamps to positions in the time index.
*
* This sparse index is smaller than the full index and fester to use for scanning ranges like in this case.
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys. To get better performances we index timestamps with a RBtree in the buildTimeTree funciton
*/
this.buildTimeCheckpoints();
this.buildTimeTree();
const { includeInferior, includeSuperior } = options;
const f = new Date(from).getTime();
const t = new Date(to).getTime();
const keys = Object.keys(this._indexes.checkpoints);
// Indice della prima chiave che va oltre il from
const startingPointValueIndex = keys.findIndex((key) => new Date(key).getTime() > from);
// Ultimo timestamp prima di quell'indice
let startingPoint = this._indexes.checkpoints[keys[startingPointValueIndex - 1]];
if (!startingPoint) {
// Siamo oltre l'ultimo checkpoint
const lastCheckpoint = keys[keys.length - 1];
startingPoint = this._indexes.checkpoints[lastCheckpoint];
}
const goodRows = [];
for (let i = startingPoint; i < this._indexes.time.length; i++) {
const curr = new Date(this._indexes.time[i]).getTime();
if (curr < f) {
continue;
const iter = this._indexes.tree.ge(f);
while (iter && new Date(iter.key).getTime() <= t) {
if (test(iter.key, f, t, includeSuperior, includeInferior)) {
goodRows.push({
time: new Date(iter.key).toISOString(),
...this.data[utils_1.DateLikeToString(iter.key)],
});
}
if (curr > t) {
break;
}
if (test(curr, f, t, includeSuperior, includeInferior)) {
goodRows.push({ time: this._indexes.time[i], ...this.data[this._indexes.time[i]] });
}
iter.next();
}

@@ -419,3 +464,3 @@ return this.recreate(goodRows);

* tf = tf.aggregate({ output: 'power1', columns: ['voltage1', 'current1'], operation: 'mul' })
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
*/

@@ -427,10 +472,11 @@ aggregate(agg) {

let newColumn;
if (typeof agg.operation === 'function') {
if (typeof agg.operation === "function") {
newColumn = timeserie_1.TimeSerie.internals.combine(columnsToAggregate, agg.operation, { name: agg.output });
}
else if (typeof agg.operation === 'string' && agg.operation in timeserie_1.TimeSerie.internals.combiners) {
else if (typeof agg.operation === "string" &&
agg.operation in timeserie_1.TimeSerie.internals.combiners) {
newColumn = timeserie_1.TimeSerie.internals.combine(columnsToAggregate, timeserie_1.TimeSerie.internals.combiners[agg.operation], { name: agg.output });
}
else {
throw new Error('Wrong type for aggregation operation');
throw new Error("Wrong type for aggregation operation");
}

@@ -457,2 +503,7 @@ return this.recreateFromSeries([newColumn].concat(this.columns()));

}
/**
* Resamples the timeframe by the specified time interval. Each row
* of the result TimeFrame will be the result of the selected aggregation.
* @param options
*/
resample(options) {

@@ -462,10 +513,9 @@ var _a, _b;

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || ((_b = this.last()) === null || _b === void 0 ? void 0 : _b.time);
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
return TimeFrame.concat(this.partition(options)
.map((chunk) => chunk.reduce(options)));
return TimeFrame.concat(this.partition(options).map((chunk) => chunk.reduce(options)));
}

@@ -478,11 +528,17 @@ /**

filter(fn) {
return new TimeFrame({ data: this.rows().filter(fn), metadata: this.metadata });
return new TimeFrame({
data: this.rows().filter(fn),
metadata: this.metadata,
});
}
/**
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map(fn) {
return new TimeFrame({ data: this.rows().map(fn), metadata: this.metadata });
return new TimeFrame({
data: this.rows().map(fn),
metadata: this.metadata,
});
}

@@ -496,5 +552,7 @@ /**

apply(fn, columns = this.columnNames) {
const unmodifiedColumns = this.columnNames.filter((columnName) => !columns.includes(columnName)).map((columnName) => this.column(columnName));
const unmodifiedColumns = this.columnNames
.filter((columnName) => !columns.includes(columnName))
.map((columnName) => this.column(columnName));
const series = columns
.map((columnName) => (this.column(columnName)))
.map((columnName) => this.column(columnName))
.map(fn);

@@ -506,3 +564,2 @@ return TimeFrame.fromTimeseries(unmodifiedColumns.concat(series));

* @param options
* @returns
*/

@@ -513,11 +570,14 @@ partition(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || ((_b = this.last()) === null || _b === void 0 ? void 0 : _b.time);
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = types_1.TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false });
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});

@@ -537,2 +597,9 @@ return partitions.map((p, idx) => {

/**
* Splits a timeframe into multiple timeframes where each timeframe has
* a maximum of `options.chunks` rows.
*/
split(options) {
return utils_1.chunk(this.rows(), options.chunks).map((rows) => this.recreate(rows));
}
/**
* Runs a series of transformations defined as an object. Useful in automation.

@@ -556,2 +623,2 @@ * A stage is an object with a single key and a value, the key is the name of the method, the value is the params object

exports.TimeFrame = TimeFrame;
//# 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640

build/main/lib/timeframe.spec.js

@@ -9,19 +9,20 @@ "use strict";

const timeserie_1 = require("./timeserie");
ava_1.default('TimeFrame.column() should return the correct timeserie', (t) => {
const utils_1 = require("./utils");
ava_1.default("TimeFrame.column() should return the correct timeserie", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new timeframe_1.TimeFrame({ data });
const energy = tf.column('energy');
const energy = tf.column("energy");
t.is(true, energy instanceof timeserie_1.TimeSerie);
t.is(2, energy.length());
t.is(energy.atTime('2021-01-01'), 1);
t.is(energy.atTime('2021-01-02'), 2);
t.is(energy.atTime("2021-01-01"), 1);
t.is(energy.atTime("2021-01-02"), 2);
});
ava_1.default('TimeFrame.length() should return the correct value', (t) => {
ava_1.default("TimeFrame.length() should return the correct value", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];

@@ -31,7 +32,7 @@ const tf = new timeframe_1.TimeFrame({ data });

});
ava_1.default('TimeFrame.shape() should return the correct value', (t) => {
ava_1.default("TimeFrame.shape() should return the correct value", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 1, power: 5 },
{ time: '2021-01-03', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 1, power: 5 },
{ time: "2021-01-03", energy: 2, power: 8 },
];

@@ -41,25 +42,29 @@ const tf = new timeframe_1.TimeFrame({ data });

});
ava_1.default('TimeFrame.indexes() should return the correct value', (t) => {
ava_1.default("TimeFrame.indexes() should return the correct value", (t) => {
const data = [
{ time: '2021-01-03', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-03", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new timeframe_1.TimeFrame({ data });
t.deepEqual(tf.indexes(), ['2021-01-01', '2021-01-02', '2021-01-03']);
t.deepEqual(tf.indexes(), [
utils_1.DateLikeToString("2021-01-01"),
utils_1.DateLikeToString("2021-01-02"),
utils_1.DateLikeToString("2021-01-03"),
]);
});
ava_1.default('TimeFrame.atTime() should return the correct row', (t) => {
ava_1.default("TimeFrame.atTime() should return the correct row", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new timeframe_1.TimeFrame({ data });
const row = tf.atTime('2021-01-02');
const row = tf.atTime("2021-01-02");
t.is(row.energy, 2);
t.is(row.power, 8);
});
ava_1.default('TimeFrame.toArray() should return an array of rows', (t) => {
ava_1.default("TimeFrame.toArray() should return an array of rows", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 2, power: 8 },
];

@@ -71,189 +76,252 @@ const tf = new timeframe_1.TimeFrame({ data });

});
ava_1.default('TimeFrame.fromTelemetryV1Output() should return the correct timeframe', (t) => {
ava_1.default("TimeFrame.fromTelemetryV1Output() should return the correct timeframe", (t) => {
const data = {
device1: {
energy: [['2021-01-01', 1], ['2021-01-02', 2]],
power: [['2021-01-01', 4], ['2021-01-02', 8]]
energy: [
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
],
power: [
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 8],
],
},
device2: {
energy: [['2021-01-01', 1], ['2021-01-02', 2]],
power: [['2021-01-01', 4], ['2021-01-02', 8]]
}
energy: [
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
],
power: [
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 8],
],
},
};
const tf = timeframe_1.TimeFrame.fromTelemetryV1Output(data);
const row = tf.atTime('2021-01-01');
t.is(row['device1:energy'], 1);
t.is(row['device1:power'], 4);
const d2energy = tf.column('device2:energy');
t.is(d2energy.metadata.deviceId, 'device2');
t.is(d2energy.metadata.propertyName, 'energy');
const row = tf.atTime("2021-01-01T00:00:00.000Z");
t.is(row["device1:energy"], 1);
t.is(row["device1:power"], 4);
const d2energy = tf.column("device2:energy");
t.is(d2energy.metadata.deviceId, "device2");
t.is(d2energy.metadata.propertyName, "energy");
});
ava_1.default('TimeFrame.fromTimeseries() should return the correct timeframe', (t) => {
ava_1.default("TimeFrame.fromTimeseries() should return the correct timeframe", (t) => {
var _a, _b, _c, _d;
const energyData = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 4],
['2021-01-03T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 4],
["2021-01-03T00:00:00.000Z", 4],
];
const powerData = [
['2021-01-01T00:00:00.000Z', 16],
['2021-01-02T00:00:00.000Z', 16],
['2021-01-03T00:00:00.000Z', 16]
["2021-01-01T00:00:00.000Z", 16],
["2021-01-02T00:00:00.000Z", 16],
["2021-01-03T00:00:00.000Z", 16],
];
const energyTS = new timeserie_1.TimeSerie('energy', energyData, { deviceId: 'd1' });
const powerTS = new timeserie_1.TimeSerie('power', powerData, { deviceId: 'd2' });
const energyTS = new timeserie_1.TimeSerie("energy", energyData, { deviceId: "d1" });
const powerTS = new timeserie_1.TimeSerie("power", powerData, { deviceId: "d2" });
const tf = timeframe_1.TimeFrame.fromTimeseries([energyTS, powerTS]);
const row = tf.atTime('2021-01-01T00:00:00.000Z');
const row = tf.atTime("2021-01-01T00:00:00.000Z");
t.is(row.energy, 4);
t.is(row.power, 16);
// We ensure that metadata is propagated to each timeserie
t.is((_b = (_a = tf.metadata) === null || _a === void 0 ? void 0 : _a.energy) === null || _b === void 0 ? void 0 : _b.deviceId, 'd1');
t.is((_d = (_c = tf.metadata) === null || _c === void 0 ? void 0 : _c.power) === null || _d === void 0 ? void 0 : _d.deviceId, 'd2');
t.is((_b = (_a = tf.metadata) === null || _a === void 0 ? void 0 : _a.energy) === null || _b === void 0 ? void 0 : _b.deviceId, "d1");
t.is((_d = (_c = tf.metadata) === null || _c === void 0 ? void 0 : _c.power) === null || _d === void 0 ? void 0 : _d.deviceId, "d2");
});
ava_1.default('TimeFrame.filter() should return the correct timeframe', (t) => {
ava_1.default("TimeFrame.filter() should return the correct timeframe", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new timeframe_1.TimeFrame({ data });
const filtered = tf.filter(row => { return row.power > 4; });
const filtered = tf.filter((row) => {
return row.power > 4;
});
t.is(filtered.length(), 2);
});
ava_1.default('TimeFrame.join() should return the correct timeframe', (t) => {
const data1 = [
{ time: '2021-01-01', energy: 1, power: 4 }
ava_1.default("TimeFrame.join() should return the correct timeframe", (t) => {
const data1 = [{ time: "2021-01-01", energy: 1, power: 4, voltage1: 7 }];
const data2 = [
{ time: "2021-01-02", energy: 1, power: 5, voltage2: 4 },
{ time: "2021-01-03", energy: 2, power: 8, voltage3: 5 },
];
const data3 = [
{ time: "2021-01-01", cosphi: 1 },
{ time: "2021-01-04", energy: 2, power: 2, cosphi: 1 },
];
const tf1 = new timeframe_1.TimeFrame({ data: data1 });
const tf2 = new timeframe_1.TimeFrame({ data: data2 });
const tf3 = new timeframe_1.TimeFrame({ data: data3 });
// We want join to be not be dependant to the ordering of timeframes
const joined1 = tf1.join([tf2, tf3]);
const joined2 = tf2.join([tf1, tf3]);
const joined3 = tf3.join([tf1, tf2]);
[joined1, joined2, joined3].forEach((joined) => {
t.is(joined.length(), 4);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").energy, 1);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").power, 4);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage1, 7);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage2, undefined);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage3, undefined);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").energy, 1);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").power, 5);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage1, undefined);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage2, 4);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage3, undefined);
});
});
ava_1.default("TimeFrame.merge() should return the correct timeframe", (t) => {
const data1 = [{ time: "2021-01-01", energy: 1, power: 4, voltage1: 7 }];
const data2 = [
{ time: '2021-01-02', energy: 1, power: 5 },
{ time: '2021-01-03', energy: 2, power: 8 }
{ time: "2021-01-02", energy: 1, power: 5, voltage2: 4 },
{ time: "2021-01-03", energy: 2, power: 8, voltage3: 5 },
];
const data3 = [
{ time: "2021-01-01", cosphi: 1, energy: 41 },
{ time: "2021-01-02", power: 11 },
{ time: "2021-01-04", energy: 2, power: 2, cosphi: 1 },
];
const tf1 = new timeframe_1.TimeFrame({ data: data1 });
const tf2 = new timeframe_1.TimeFrame({ data: data2 });
const joined = tf1.join([tf2]);
t.is(joined.length(), 3);
const tf3 = new timeframe_1.TimeFrame({ data: data3 });
// We want join to be not be dependant to the ordering of timeframes
const merged = timeframe_1.TimeFrame.merge([tf3, tf2, tf1]);
t.is(merged.length(), 4);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").energy, 41);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").cosphi, 1);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").power, 4);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").voltage1, 7);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").energy, 1);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").power, 11);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").voltage2, 4);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").energy, 2);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").power, 8);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").voltage3, 5);
});
ava_1.default('TimeFrame.apply() should correctly modify columns', (t) => {
ava_1.default("TimeFrame.apply() should correctly modify columns", (t) => {
const energyData = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 4],
['2021-01-03T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 4],
["2021-01-03T00:00:00.000Z", 4],
];
const powerData = [
['2021-01-01T00:00:00.000Z', 16],
['2021-01-02T00:00:00.000Z', 16],
['2021-01-03T00:00:00.000Z', 16]
["2021-01-01T00:00:00.000Z", 16],
["2021-01-02T00:00:00.000Z", 16],
["2021-01-03T00:00:00.000Z", 16],
];
const energyTS = new timeserie_1.TimeSerie('energy', energyData, { deviceId: 'd1' });
const powerTS = new timeserie_1.TimeSerie('power', powerData, { deviceId: 'd2' });
const energyTS = new timeserie_1.TimeSerie("energy", energyData, { deviceId: "d1" });
const powerTS = new timeserie_1.TimeSerie("power", powerData, { deviceId: "d2" });
const tf = timeframe_1.TimeFrame.fromTimeseries([energyTS, powerTS]);
const tf2 = tf.apply(ts => ts.map((p) => [p[0], 0]), ['power']);
t.is(tf.column('energy').sum()[1], 12);
t.is(tf2.column('power').sum()[1], 0);
t.is(tf2.metadata.energy.deviceId, 'd1');
t.is(tf2.metadata.power.deviceId, 'd2');
const tf2 = tf.apply((ts) => ts.map((p) => [p[0], 0]), ["power"]);
t.is(tf.column("energy").sum()[1], 12);
t.is(tf2.column("power").sum()[1], 0);
t.is(tf2.metadata.energy.deviceId, "d1");
t.is(tf2.metadata.power.deviceId, "d2");
});
ava_1.default('TimeFrame.resample(sum) should correctly resample and aggregate data', t => {
ava_1.default("TimeFrame.resample(sum) should correctly resample and aggregate data", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T00:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T00:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T01:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T01:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:00:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T03:01:00.000Z', energy: 1, power: 1 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T00:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T00:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T01:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T01:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:00:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T03:01:00.000Z", energy: 1, power: 1 },
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const resampled = tf.resample({
interval: 1000 * 60 * 60,
from: '2021-01-01T00:00:00.000Z',
to: '2021-01-01T04:00:00.000Z',
operation: 'sum'
from: "2021-01-01T00:00:00.000Z",
to: "2021-01-01T04:00:00.000Z",
operation: "sum",
});
t.is(resampled.length(), 4);
t.is(resampled.rows()[0].time, '2021-01-01T00:00:00.000Z');
t.is(resampled.rows()[0].time, "2021-01-01T00:00:00.000Z");
t.is(resampled.rows()[0].power, 3);
t.is(resampled.rows()[0].energy, 3);
t.is(resampled.rows()[1].time, '2021-01-01T01:00:00.000Z');
t.is(resampled.rows()[1].time, "2021-01-01T01:00:00.000Z");
t.is(resampled.rows()[1].power, 2);
t.is(resampled.rows()[1].energy, 2);
t.is(resampled.rows()[2].time, '2021-01-01T02:00:00.000Z');
t.is(resampled.rows()[2].time, "2021-01-01T02:00:00.000Z");
t.is(resampled.rows()[2].power, 3);
t.is(resampled.rows()[2].energy, 3);
t.is(resampled.rows()[3].time, '2021-01-01T03:00:00.000Z');
t.is(resampled.rows()[3].time, "2021-01-01T03:00:00.000Z");
t.is(resampled.rows()[3].power, 1);
t.is(resampled.rows()[3].energy, 1);
t.is(resampled.metadata.hello, 'world');
t.is(resampled.metadata.hello, "world");
});
ava_1.default('TimeFrame.sum() should correctly sum all columns', t => {
ava_1.default("TimeFrame.sum() should correctly sum all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 1, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 1, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new timeframe_1.TimeFrame({ data }).sum();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 5);
t.is(row.power, 18);
});
ava_1.default('TimeFrame.delta() should correctly delta all columns', t => {
ava_1.default("TimeFrame.delta() should correctly delta all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, expenergy: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 2, expenergy: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy: 3, expenergy: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy: 4, expenergy: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, expenergy: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 2, expenergy: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy: 3, expenergy: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy: 4, expenergy: 16 },
];
const row = new timeframe_1.TimeFrame({ data }).delta();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 3);
t.is(row.expenergy, 12);
});
ava_1.default('TimeFrame.max() should correctly max() all columns', t => {
ava_1.default("TimeFrame.max() should correctly max() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 7, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 7, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new timeframe_1.TimeFrame({ data }).max();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 7);
t.is(row.power, 9);
});
ava_1.default('TimeFrame.min() should correctly min() all columns', t => {
ava_1.default("TimeFrame.min() should correctly min() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 3, power: 1 },
{ time: '2021-01-02T00:00:00.000Z', energy: 7, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 3, power: 1 },
{ time: "2021-01-02T00:00:00.000Z", energy: 7, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new timeframe_1.TimeFrame({ data }).min();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 1);
t.is(row.power, 1);
});
ava_1.default('TimeFrame.avg() should correctly avg() all columns', t => {
ava_1.default("TimeFrame.avg() should correctly avg() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 4, power: 1 },
{ time: '2021-01-02T00:00:00.000Z', energy: 4, power: 1 },
{ time: '2021-01-03T00:00:00.000Z', energy: 8, power: 11 },
{ time: '2021-01-04T00:00:00.000Z', energy: 8, power: 11 }
{ time: "2021-01-01T00:00:00.000Z", energy: 4, power: 1 },
{ time: "2021-01-02T00:00:00.000Z", energy: 4, power: 1 },
{ time: "2021-01-03T00:00:00.000Z", energy: 8, power: 11 },
{ time: "2021-01-04T00:00:00.000Z", energy: 8, power: 11 },
];
const row = new timeframe_1.TimeFrame({ data }).avg();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 6);
t.is(row.power, 6);
});
ava_1.default('TimeFrame.aggregate() should correctly aggregate columns', t => {
ava_1.default("TimeFrame.aggregate() should correctly aggregate columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const agg = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } })
.aggregate({ output: 'totalenergy', columns: ['energy1', 'energy2'], operation: 'add' });
const agg = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } }).aggregate({
output: "totalenergy",
columns: ["energy1", "energy2"],
operation: "add",
});
t.is(agg.atIndex(0).totalenergy, 5);

@@ -263,54 +331,57 @@ t.is(agg.atIndex(1).totalenergy, 10);

t.is(agg.atIndex(3).totalenergy, 20);
t.is(agg.metadata.hello, 'world');
t.is(agg.metadata.hello, "world");
});
ava_1.default('TimeFrame.project() should correctly aggregate columns', t => {
ava_1.default("TimeFrame.project() should correctly aggregate columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } });
const projected = tf.project({ columns: ['energy1'] });
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const projected = tf.project({ columns: ["energy1"] });
t.is(tf.columns().length, 2);
t.is(projected.columns().length, 1);
t.is(projected.metadata.hello, 'world');
t.is(projected.metadata.hello, "world");
});
ava_1.default('TimeFrame.mul() should correctly multiply values', t => {
ava_1.default("TimeFrame.mul() should correctly multiply values", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const multiplied = tf.mul(2);
t.is(multiplied.atTime('2021-01-01T00:00:00.000Z').energy1, 2);
t.is(multiplied.atTime('2021-01-01T00:00:00.000Z').energy2, 8);
t.is(multiplied.atTime('2021-01-02T00:00:00.000Z').energy1, 4);
t.is(multiplied.atTime('2021-01-02T00:00:00.000Z').energy2, 16);
t.is(multiplied.atTime("2021-01-01T00:00:00.000Z").energy1, 2);
t.is(multiplied.atTime("2021-01-01T00:00:00.000Z").energy2, 8);
t.is(multiplied.atTime("2021-01-02T00:00:00.000Z").energy1, 4);
t.is(multiplied.atTime("2021-01-02T00:00:00.000Z").energy2, 16);
});
ava_1.default('TimeFrame.add() should correctly add values', t => {
ava_1.default("TimeFrame.add() should correctly add values", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const added = tf.add(2);
t.is(added.atTime('2021-01-01T00:00:00.000Z').energy1, 3);
t.is(added.atTime('2021-01-01T00:00:00.000Z').energy2, 6);
t.is(added.atTime('2021-01-02T00:00:00.000Z').energy1, 4);
t.is(added.atTime('2021-01-02T00:00:00.000Z').energy2, 10);
t.is(added.atTime("2021-01-01T00:00:00.000Z").energy1, 3);
t.is(added.atTime("2021-01-01T00:00:00.000Z").energy2, 6);
t.is(added.atTime("2021-01-02T00:00:00.000Z").energy1, 4);
t.is(added.atTime("2021-01-02T00:00:00.000Z").energy2, 10);
});
ava_1.default('TimeFrame.reduce() should correctly reduce the timeframe', t => {
ava_1.default("TimeFrame.reduce() should correctly reduce the timeframe", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-03T00:00:00.000Z', energy: 1, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 2 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-03T00:00:00.000Z", energy: 1, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 2 },
];
const tf = new timeframe_1.TimeFrame({ data });
const reduced = tf.reduce({ operation: 'avg', operations: { energy: 'sum' } });
const reduced = tf.reduce({
operation: "avg",
operations: { energy: "sum" },
});
const rows = reduced.rows();

@@ -321,28 +392,223 @@ t.is(rows.length, 1);

});
ava_1.default('TimeFrame.pipeline() should correctly run all the stages', t => {
ava_1.default("TimeFrame.pipeline() should correctly run all the stages", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T00:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T00:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T01:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T01:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:00:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T03:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 }
{
time: "2021-01-01T00:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T03:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const processed = tf.pipeline([
{ aggregate: { columns: ['voltage1', 'current1'], operation: 'mul', output: 'power1' } },
{ aggregate: { columns: ['voltage2', 'current2'], operation: 'mul', output: 'power2' } },
{ aggregate: { columns: ['voltage3', 'current3'], operation: 'mul', output: 'power3' } },
{ aggregate: { columns: ['power1', 'power2', 'power3'], operation: 'add', output: 'powertot' } },
{ project: { columns: ['powertot'] } },
{ resample: { interval: 1000 * 60 * 60, operation: 'avg', from: '2021-01-01T00:00:00.000Z' } }
{
aggregate: {
columns: ["voltage1", "current1"],
operation: "mul",
output: "power1",
},
},
{
aggregate: {
columns: ["voltage2", "current2"],
operation: "mul",
output: "power2",
},
},
{
aggregate: {
columns: ["voltage3", "current3"],
operation: "mul",
output: "power3",
},
},
{
aggregate: {
columns: ["power1", "power2", "power3"],
operation: "add",
output: "powertot",
},
},
{ project: { columns: ["powertot"] } },
{
resample: {
interval: 1000 * 60 * 60,
operation: "avg",
from: "2021-01-01T00:00:00.000Z",
},
},
]);
t.is(processed.length(), 4);
t.is(processed.columnNames.length, 1);
t.is(processed.columnNames[0], 'powertot');
t.is(processed.metadata.hello, 'world');
t.is(processed.columnNames[0], "powertot");
t.is(processed.metadata.hello, "world");
});
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ava_1.default("TimeFrame.split() should split a timeframe into sub timeframes of fixed size", (t) => {
const data = [
{
time: "2021-01-01T00:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T03:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
];
const tf = new timeframe_1.TimeFrame({ data, metadata: { hello: "world" } });
const chunks = tf.split({ chunks: 3 });
t.is(chunks.length, 3);
t.deepEqual(chunks.map((tf) => tf.shape()), [
[3, 6],
[3, 6],
[3, 6],
]);
});
//# 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151

build/main/lib/timeserie.d.ts

@@ -1,2 +0,2 @@

import { DateLike, FromIndexOptions, Index, Metadata, PartitionOptions, Point, PointValue, ReindexOptions, ResampleOptions, TimeseriePointIterator, TimeSerieReduceOptions, TimeSeriesOperationOptions } from './types';
import { BetweenTimeOptions, DateLike, FromIndexOptions, Index, Metadata, PartitionOptions, Point, PointValue, ReindexOptions, ResampleOptions, SplitOptions, TimeseriePointIterator, TimeSerieReduceOptions, TimeSeriesOperationOptions } from "./types";
/**

@@ -14,6 +14,18 @@ * A data structure for a time serie.

};
/**
* Creates a new timeserie.
* @param name {String} The name of the serie
* @param serie {Point[]} The points in the serie
* @param metadata {Metadata} Optional metadata
*/
constructor(name: string, serie: Point[] | ReadonlyArray<Point>, metadata?: Metadata);
/**
* Creates a new TimeSerie from the given Index. The new serie's values are all set to `null` unless `options.fill` is passed.
* @param index
* @param options
*/
static fromIndex(index: Index, options: FromIndexOptions): TimeSerie;
/**
* Recreates the serie's index
* Recreates the serie's index according to `options` and returns the reindexed serie.
*
* @param index The new index to use. Can be created with createIndex()

@@ -27,5 +39,9 @@ * @see createIndex

*
* @returns Array of points, where each point is a tuple with ISO8601 timestamp and value
* Returns the array of points, where each point is a tuple with ISO8601 timestamp and value
*/
toArray(): Point[];
/**
* Updates (in place) the serie's name. **This method does NOT return a new timeserie**.
* @param name
*/
rename(name: string): this;

@@ -40,3 +56,3 @@ /**

*
* @returns The array of time indexes
* Returns the array of time indexes
*/

@@ -46,3 +62,3 @@ indexes(): DateLike[];

*
* @returns The array of values
* Returns the array of values
*/

@@ -56,3 +72,3 @@ values(): PointValue[];

*
* @returns The time of the latest non-NaN value
* Returns the time of the latest non-NaN value
*/

@@ -62,18 +78,16 @@ lastValidIndex(): string | null;

*
* @returns The time of the first non-NaN value
* Returns the time of the first non-NaN value
*/
firstValidIndex(): string | null;
/**
*
* @returns The latest non-NaN value
* Returns the latest non-NaN value
*/
lastValidValue(): PointValue;
/**
*
* @returns The first non-NaN value
* Returns the first non-NaN value
*/
firstValidValue(): PointValue;
/**
*
* @returns {PointValue} The value of the timeseries at the given time
* Returns the value of the timeseries at the given time
* @returns {PointValue}
*/

@@ -83,37 +97,56 @@ atTime(time: DateLike, fillValue?: number): PointValue;

*
* @returns The value at the given index (position, not time)
* Returns the value at the given index.
*/
atIndex(index: number): PointValue;
/**
* Returns the subset of points between the two dates. Extremes are included.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from: DateLike, to: DateLike, options?: {
includeInferior: boolean;
includeSuperior: boolean;
}): TimeSerie;
betweenTime(from: DateLike, to: DateLike, options?: BetweenTimeOptions): TimeSerie;
/**
*
* @param from start positional index
* @param to end positional index
* @returns The subset of points between the two indexes. Extremes are included.
*/
* Returns the subset of points between the two indexes. Extremes are included.
*
* @param from start positional index
* @param to end positional index
*/
betweenIndexes(from: number, to: number): TimeSerie;
/**
* Builds a new serie by applying a filter function the current serie's points
* @params fn {Function}
*/
filter(fn: TimeseriePointIterator): TimeSerie;
/**
* Builds a new serie by applying a map function the current serie's points
* @param fn {Function}
*/
map(fn: TimeseriePointIterator): TimeSerie;
/**
* Returns the number of points in the serie.
*/
length(): number;
/**
* Returns true if the serie has 0 points
*/
isEmpty(): boolean;
/**
* Copies the serie to a new serie
*/
copy(): TimeSerie;
/**
* Returns the sum of the values in the serie
*/
sum(): Point;
/**
*
* @returns The average of point values
* Returns the average of the values in the serie
*/
avg(): Point;
/**
* Returns the difference between the last and the first element by performing last value - first value.
*/
delta(): Point;
/**
* Returns the first point
*
* @returns The firstfirst point
*/

@@ -124,3 +157,2 @@ first(): Point;

* @param time
* @returns
*/

@@ -130,3 +162,3 @@ firstAt(time: DateLike): Point;

*
* @returns The last point
* Returns the last point
*/

@@ -136,3 +168,3 @@ last(): Point;

*
* @returns The point with max value, or null
* Returns the point with max value, or null
*/

@@ -142,3 +174,3 @@ max(): Point | null;

*
* @returns The point with min value or null
* Returns the point with min value or null
*/

@@ -153,24 +185,39 @@ min(): Point | null;

* @param options
* @returns
*/
partition(options: PartitionOptions): TimeSerie[];
/**
* Splits a timeserie into multiple timeseries where each timeserie has
* a maximum of `options.chunks` points.
*/
split(options: SplitOptions): TimeSerie[];
/**
* Resample the timeserie using a new time interval and a point aggregation function
* @param options
* @returns
*/
resample(options: ResampleOptions): TimeSerie;
/**
* Remove the point at the given time and returns a new serie
*/
removeAt(time: DateLike): TimeSerie;
/**
* Remove the point at the given index and returns a new serie
*/
removeAtIndex(index: number): TimeSerie;
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns New timeserie without the removed data. Bounds are removed.
*/
* Returns the new timeserie without the removed data. Bounds are removed.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
removeBetweenTime(from: DateLike, to: DateLike): TimeSerie;
/**
* Removes points with NaN value from the serie
*/
dropNaN(): TimeSerie;
/**
* Removes points with null value from the serie.
*/
dropNull(): TimeSerie;
/**
*
* Rounds the serie's points.
* @param decimals {Number} the number of decimals to keep

@@ -180,7 +227,33 @@ * @returns {TimeSerie}

round(decimals: number): TimeSerie;
/**
* Combine the current serie with an array of series y performing combination operations, such as multiplication, addition ecc.
* @param operation {string}
* @param series {TimeSerie[]}
* @param options {TimeSeriesOperationOptions}
*/
combine(operation: string, series: TimeSerie[], options?: TimeSeriesOperationOptions): TimeSerie;
/**
* Adds values to the timeserie. If a scalar is passed, its value is added to every point in the serie. If another serie
* is passed, the two series are combined by addition.
* @see combine
*/
add(value: number | TimeSerie): TimeSerie;
/**
* Subtracts values from the timeserie. If a scalar is passed, its value is subtracted from every point in the serie. If another serie
* is passed, the two series are combined by subtraction.
* @see combine
*/
sub(value: number | TimeSerie): TimeSerie;
/**
* Multiplies values of the timeserie. If a scalar is passed, every point in the serie is multiplied times that value. If another serie
* is passed, the two series are combined by multiplication.
* @see combine
*/
mul(value: number | TimeSerie): TimeSerie;
/**
* Divides values of the timeserie. If a scalar is passed, every point in the serie is divided by that value. If another serie
* is passed, the two series are combined by division.
* @see combine
*/
div(value: number | TimeSerie): TimeSerie;
}

303

build/main/lib/timeserie.js
"use strict";
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });
}) : (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
o[k2] = m[k];
}));
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
o["default"] = v;
});
var __importStar = (this && this.__importStar) || function (mod) {
if (mod && mod.__esModule) return mod;
var result = {};
if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
__setModuleDefault(result, mod);
return result;
};
Object.defineProperty(exports, "__esModule", { value: true });

@@ -6,12 +25,14 @@ exports.TimeSerie = void 0;

const utils_1 = require("./utils");
const Timsort = __importStar(require("timsort"));
function isNumeric(str) {
if (typeof str === 'number')
if (typeof str === "number")
return !isNaN(str);
if (typeof str !== 'string')
if (typeof str !== "string")
return false; // we only process strings!
return !isNaN(str) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str)); // ...and ensure strings of whitespace fail
return (!isNaN(str) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str))); // ...and ensure strings of whitespace fail
}
function sortPoints(points) {
return [].concat(points).sort((a, b) => {
const arr = [].concat(points);
Timsort.sort(arr, (a, b) => {
if (a[0] > b[0]) {

@@ -24,2 +45,3 @@ return 1;

});
return arr;
}

@@ -33,2 +55,8 @@ function normalizePoint(p) {

class TimeSerie {
/**
* Creates a new timeserie.
* @param name {String} The name of the serie
* @param serie {Point[]} The points in the serie
* @param metadata {Metadata} Optional metadata
*/
constructor(name, serie, metadata = {}) {

@@ -38,3 +66,5 @@ this.data = sortPoints(serie).map(normalizePoint);

this.metadata = metadata;
this.index = [].concat(this.data).reduce((acc, p) => {
this.index = []
.concat(this.data)
.reduce((acc, p) => {
acc[p[0]] = p;

@@ -44,7 +74,13 @@ return acc;

}
/**
* Creates a new TimeSerie from the given Index. The new serie's values are all set to `null` unless `options.fill` is passed.
* @param index
* @param options
*/
static fromIndex(index, options) {
return new TimeSerie(options.name, index.map((i) => ([i, (options === null || options === void 0 ? void 0 : options.fill) || null])), options.metadata);
return new TimeSerie(options.name, index.map((i) => [i, (options === null || options === void 0 ? void 0 : options.fill) || null]), options.metadata);
}
/**
* Recreates the serie's index
* Recreates the serie's index according to `options` and returns the reindexed serie.
*
* @param index The new index to use. Can be created with createIndex()

@@ -58,9 +94,9 @@ * @see createIndex

const idx = [...new Set(this.indexes().concat(index))];
return new TimeSerie(this.name, idx.map((i) => ([i, this.atTime(i) || (options === null || options === void 0 ? void 0 : options.fill) || null])), this.metadata);
return new TimeSerie(this.name, idx.map((i) => [i, this.atTime(i) || (options === null || options === void 0 ? void 0 : options.fill) || null]), this.metadata);
}
return new TimeSerie(this.name, index.map((i) => ([i, this.atTime(i) || (options === null || options === void 0 ? void 0 : options.fill) || null])), this.metadata);
return new TimeSerie(this.name, index.map((i) => [i, this.atTime(i) || (options === null || options === void 0 ? void 0 : options.fill) || null]), this.metadata);
}
/**
*
* @returns Array of points, where each point is a tuple with ISO8601 timestamp and value
* Returns the array of points, where each point is a tuple with ISO8601 timestamp and value
*/

@@ -70,2 +106,6 @@ toArray() {

}
/**
* Updates (in place) the serie's name. **This method does NOT return a new timeserie**.
* @param name
*/
rename(name) {

@@ -85,3 +125,3 @@ this.name = name;

*
* @returns The array of time indexes
* Returns the array of time indexes
*/

@@ -93,3 +133,3 @@ indexes() {

*
* @returns The array of values
* Returns the array of values
*/

@@ -103,10 +143,13 @@ values() {

static concat(series) {
return new TimeSerie(series[0].name, series.map(serie => serie.toArray()).flat(), Object.assign({}, ...series.map(serie => serie.metadata)));
return new TimeSerie(series[0].name, series.flatMap((serie) => serie.toArray()), Object.assign({}, ...series.map((serie) => serie.metadata)));
}
/**
*
* @returns The time of the latest non-NaN value
* Returns the time of the latest non-NaN value
*/
lastValidIndex() {
const result = this.data.concat([]).reverse().find((point) => {
const result = this.data
.concat([])
.reverse()
.find((point) => {
return !!point[1];

@@ -123,3 +166,3 @@ });

*
* @returns The time of the first non-NaN value
* Returns the time of the first non-NaN value
*/

@@ -138,7 +181,9 @@ firstValidIndex() {

/**
*
* @returns The latest non-NaN value
* Returns the latest non-NaN value
*/
lastValidValue() {
const result = this.data.concat([]).reverse().find((point) => {
const result = this.data
.concat([])
.reverse()
.find((point) => {
return !!point[1];

@@ -154,4 +199,3 @@ });

/**
*
* @returns The first non-NaN value
* Returns the first non-NaN value
*/

@@ -170,4 +214,4 @@ firstValidValue() {

/**
*
* @returns {PointValue} The value of the timeseries at the given time
* Returns the value of the timeseries at the given time
* @returns {PointValue}
*/

@@ -180,7 +224,7 @@ atTime(time, fillValue = null) {

*
* @returns The value at the given index (position, not time)
* Returns the value at the given index.
*/
atIndex(index) {
if (index >= this.data.length) {
throw new Error('Index out of bounds');
throw new Error("Index out of bounds");
}

@@ -190,8 +234,11 @@ return this.data[index][1];

/**
* Returns the subset of points between the two dates. Extremes are included.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from, to, options = { includeInferior: true, includeSuperior: true }) {
betweenTime(from, to, options = {
includeInferior: true,
includeSuperior: true,
}) {
const { includeInferior, includeSuperior } = options;

@@ -202,12 +249,16 @@ const f = new Date(from);

if (includeInferior && includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() <= t.getTime();
return (new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() <= t.getTime());
}
else if (includeInferior && !includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() < t.getTime();
return (new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() < t.getTime());
}
else if (!includeInferior && includeSuperior) {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() <= t.getTime();
return (new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() <= t.getTime());
}
else {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() < t.getTime();
return (new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() < t.getTime());
}

@@ -218,25 +269,47 @@ });

/**
*
* @param from start positional index
* @param to end positional index
* @returns The subset of points between the two indexes. Extremes are included.
*/
* Returns the subset of points between the two indexes. Extremes are included.
*
* @param from start positional index
* @param to end positional index
*/
betweenIndexes(from, to) {
return this.filter((_, i) => { return i >= from && i <= to; });
return this.filter((_, i) => {
return i >= from && i <= to;
});
}
/**
* Builds a new serie by applying a filter function the current serie's points
* @params fn {Function}
*/
filter(fn) {
return this.recreate(this.data.filter(fn));
}
/**
* Builds a new serie by applying a map function the current serie's points
* @param fn {Function}
*/
map(fn) {
return this.recreate(this.data.map(fn));
}
/**
* Returns the number of points in the serie.
*/
length() {
return this.data.length;
}
/**
* Returns true if the serie has 0 points
*/
isEmpty() {
return this.data.length === 0;
}
/**
* Copies the serie to a new serie
*/
copy() {
return new TimeSerie(this.name, this.data, this.metadata);
}
/**
* Returns the sum of the values in the serie
*/
sum() {

@@ -247,7 +320,12 @@ if (this.length() === 0) {

const copy = this.dropNaN();
return [this.first()[0], copy.data.map((p) => p[1]).reduce((p1, p2) => p1 + p2, 0)];
let tot = 0;
const l = copy.length();
const data = copy.toArray();
for (let i = l - 1; i >= 0; i--) {
tot += data[i][1];
}
return [this.first()[0], tot];
}
/**
*
* @returns The average of point values
* Returns the average of the values in the serie
*/

@@ -261,2 +339,5 @@ avg() {

}
/**
* Returns the difference between the last and the first element by performing last value - first value.
*/
delta() {

@@ -273,4 +354,4 @@ if (this.length() <= 0) {

/**
* Returns the first point
*
* @returns The firstfirst point
*/

@@ -283,10 +364,11 @@ first() {

* @param time
* @returns
*/
firstAt(time) {
return this.data.find((p) => { return new Date(p[0]).getTime() >= new Date(time).getTime(); });
return this.data.find((p) => {
return new Date(p[0]).getTime() >= new Date(time).getTime();
});
}
/**
*
* @returns The last point
* Returns the last point
*/

@@ -298,3 +380,3 @@ last() {

*
* @returns The point with max value, or null
* Returns the point with max value, or null
*/

@@ -308,7 +390,7 @@ max() {

}
return this.data.reduce((prev, current) => current[1] > prev[1] ? current : prev, this.data[0]);
return this.data.reduce((prev, current) => (current[1] > prev[1] ? current : prev), this.data[0]);
}
/**
*
* @returns The point with min value or null
* Returns the point with min value or null
*/

@@ -322,3 +404,3 @@ min() {

}
return this.data.reduce((prev, current) => current[1] < prev[1] ? current : prev, this.data[0]);
return this.data.reduce((prev, current) => (current[1] < prev[1] ? current : prev), this.data[0]);
}

@@ -334,3 +416,2 @@ /**

* @param options
* @returns
*/

@@ -341,11 +422,14 @@ partition(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || ((_b = this.last()) === null || _b === void 0 ? void 0 : _b[0]);
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = types_1.TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false });
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});

@@ -366,5 +450,13 @@ return partitions.map((p, idx) => {

/**
* Splits a timeserie into multiple timeseries where each timeserie has
* a maximum of `options.chunks` points.
*/
split(options) {
return utils_1.chunk(this.toArray(), options.chunks).map((points) => {
return this.recreate(points);
});
}
/**
* Resample the timeserie using a new time interval and a point aggregation function
* @param options
* @returns
*/

@@ -374,23 +466,32 @@ resample(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()[0];
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
return TimeSerie.concat(this.partition(options)
.map((chunk) => chunk.reduce(options)));
return TimeSerie.concat(this.partition(options).map((chunk) => chunk.reduce(options)));
}
/**
* Remove the point at the given time and returns a new serie
*/
removeAt(time) {
return this.recreate(this.data.filter((p) => { return p[0] !== utils_1.DateLikeToString(time); }));
return this.recreate(this.data.filter((p) => {
return p[0] !== utils_1.DateLikeToString(time);
}));
}
/**
* Remove the point at the given index and returns a new serie
*/
removeAtIndex(index) {
return this.recreate(this.data.filter((_, i) => { return i !== index; }));
return this.recreate(this.data.filter((_, i) => {
return i !== index;
}));
}
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns New timeserie without the removed data. Bounds are removed.
*/
* Returns the new timeserie without the removed data. Bounds are removed.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
removeBetweenTime(from, to) {

@@ -400,9 +501,16 @@ const f = new Date(from);

const data = this.data.filter((point) => {
return new Date(point[0]).getTime() < f.getTime() || new Date(point[0]).getTime() > t.getTime();
return (new Date(point[0]).getTime() < f.getTime() ||
new Date(point[0]).getTime() > t.getTime());
});
return this.recreate(data);
}
/**
* Removes points with NaN value from the serie
*/
dropNaN() {
return this.filter((p) => isNumeric(p[1]));
}
/**
* Removes points with null value from the serie.
*/
dropNull() {

@@ -412,3 +520,3 @@ return this.filter((p) => p[1] !== null);

/**
*
* Rounds the serie's points.
* @param decimals {Number} the number of decimals to keep

@@ -418,5 +526,13 @@ * @returns {TimeSerie}

round(decimals) {
return this.map((p) => ([p[0], Number(Number(p[1]).toFixed(decimals))]));
return this.map((p) => [
p[0],
Number(Number(p[1]).toFixed(decimals)),
]);
}
// Operation between timeseries
/**
* Combine the current serie with an array of series y performing combination operations, such as multiplication, addition ecc.
* @param operation {string}
* @param series {TimeSerie[]}
* @param options {TimeSeriesOperationOptions}
*/
combine(operation, series, options = {}) {

@@ -427,32 +543,52 @@ options.name = options.name || this.name;

}
/**
* Adds values to the timeserie. If a scalar is passed, its value is added to every point in the serie. If another serie
* is passed, the two series are combined by addition.
* @see combine
*/
add(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] + value]);
}
else {
return this.combine('add', [value]);
return this.combine("add", [value]);
}
}
/**
* Subtracts values from the timeserie. If a scalar is passed, its value is subtracted from every point in the serie. If another serie
* is passed, the two series are combined by subtraction.
* @see combine
*/
sub(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] - value]);
}
else {
return this.combine('sub', [value]);
return this.combine("sub", [value]);
}
}
/**
* Multiplies values of the timeserie. If a scalar is passed, every point in the serie is multiplied times that value. If another serie
* is passed, the two series are combined by multiplication.
* @see combine
*/
mul(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] * value]);
}
else {
return this.combine('mul', [value]);
return this.combine("mul", [value]);
}
}
/**
* Divides values of the timeserie. If a scalar is passed, every point in the serie is divided by that value. If another serie
* is passed, the two series are combined by division.
* @see combine
*/
div(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] / value]);
}
else {
return this.combine('div', [value]);
return this.combine("div", [value]);
}

@@ -469,8 +605,7 @@ }

TimeSerie.internals.combine = (series, combiner, options) => {
const points = series[0].data.map((p) => p[0]).map((idx) => {
const points = series[0].data
.map((p) => p[0])
.map((idx) => {
const values = series.map((serie) => serie.atTime(idx, options.fill));
return [
idx,
combiner(values, idx)
];
return [idx, combiner(values, idx)];
});

@@ -483,4 +618,4 @@ return new TimeSerie(options.name, points, options.metadata);

TimeSerie.internals.combiners.div = (points) => points.reduce((a, b) => a / b, points[0] * points[0]);
TimeSerie.internals.combiners.avg = (points) => (TimeSerie.internals.combiners.add(points) / points.length);
TimeSerie.internals.combiners.avg = (points) => TimeSerie.internals.combiners.add(points) / points.length;
TimeSerie.createIndex = types_1.createIndex;
//# 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700

build/main/lib/timeserie.spec.js

@@ -8,28 +8,28 @@ "use strict";

const timeserie_1 = require("./timeserie");
ava_1.default('TimeSerie.atTime() should return the correct point or null', (t) => {
ava_1.default("TimeSerie.atTime() should return the correct point or null", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new timeserie_1.TimeSerie('energy', data);
t.is(ts.atTime('2021-01-02T00:00:00.000Z'), 5);
t.is(ts.atTime('2021-01-22T00:00:00.000Z'), null);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.atTime("2021-01-02T00:00:00.000Z"), 5);
t.is(ts.atTime("2021-01-22T00:00:00.000Z"), null);
});
ava_1.default('TimeSerie.atIndex() should return the correct point', (t) => {
ava_1.default("TimeSerie.atIndex() should return the correct point", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.atIndex(1), 5);
});
ava_1.default('TimeSerie.atIndex() should throw when the index is out of bounds', (t) => {
ava_1.default("TimeSerie.atIndex() should throw when the index is out of bounds", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.throws(() => {

@@ -39,76 +39,76 @@ ts.atIndex(100);

});
ava_1.default('TimeSerie.toArray() should return the whole data', (t) => {
ava_1.default("TimeSerie.toArray() should return the whole data", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.deepEqual(data, ts.toArray());
});
ava_1.default('TimeSerie.firstValidIndex() should return the first valid value index', (t) => {
ava_1.default("TimeSerie.firstValidIndex() should return the first valid value index", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', null],
['2021-01-02T00:00:00.000Z', null],
['2021-01-03T00:00:00.000Z', 6],
['2021-01-04T00:00:00.000Z', 7],
['2021-01-05T00:00:00.000Z', 8],
['2021-01-06T00:00:00.000Z', null]
["2021-01-01T00:00:00.000Z", null],
["2021-01-02T00:00:00.000Z", null],
["2021-01-03T00:00:00.000Z", 6],
["2021-01-04T00:00:00.000Z", 7],
["2021-01-05T00:00:00.000Z", 8],
["2021-01-06T00:00:00.000Z", null],
];
const ts = new timeserie_1.TimeSerie('energy', data);
t.is(ts.firstValidIndex(), '2021-01-03T00:00:00.000Z');
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.firstValidIndex(), "2021-01-03T00:00:00.000Z");
});
ava_1.default('TimeSerie.lastValidIndex() should return the last valid value index', (t) => {
ava_1.default("TimeSerie.lastValidIndex() should return the last valid value index", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new timeserie_1.TimeSerie('energy', data);
t.is(ts.lastValidIndex(), '2021-01-05T00:00:00.000Z');
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.lastValidIndex(), "2021-01-05T00:00:00.000Z");
});
ava_1.default('TimeSerie.firstValidValue() should return the first valid value index or null', (t) => {
ava_1.default("TimeSerie.firstValidValue() should return the first valid value index or null", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.firstValidValue(), 6);
const data2 = [
['2021-01-01', null],
['2021-01-02', null]
["2021-01-01", null],
["2021-01-02", null],
];
const ts2 = new timeserie_1.TimeSerie('energy2', data2);
const ts2 = new timeserie_1.TimeSerie("energy2", data2);
t.is(ts2.firstValidValue(), null);
});
ava_1.default('TimeSerie.lastValidValue() should return the last valid value index', (t) => {
ava_1.default("TimeSerie.lastValidValue() should return the last valid value index", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.lastValidValue(), 8);
});
ava_1.default('TimeSerie.betweenTime() should return the correct timeserie subset', (t) => {
ava_1.default("TimeSerie.betweenTime() should return the correct timeserie subset", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const subset = ts.betweenTime('2021-01-03', '2021-01-05');
const ts = new timeserie_1.TimeSerie("energy", data);
const subset = ts.betweenTime("2021-01-03", "2021-01-05");
t.is(subset.length(), 3);

@@ -118,12 +118,12 @@ t.is(subset.firstValidValue(), 6);

});
ava_1.default('TimeSerie.filter() should allow to pass custom filtering logic', (t) => {
ava_1.default("TimeSerie.filter() should allow to pass custom filtering logic", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.filter((p) => {

@@ -136,12 +136,12 @@ return p[1] % 2 === 0;

});
ava_1.default('TimeSerie.map() should allow to pass custom mapping logic', (t) => {
ava_1.default("TimeSerie.map() should allow to pass custom mapping logic", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const mapped = ts.map((p) => {

@@ -154,125 +154,129 @@ return [p[0], p[1] * 2];

});
ava_1.default('TimeSerie.isEmpty() should behave correctly', (t) => {
const data = [
['2021-01-01', 4]
];
const ts1 = new timeserie_1.TimeSerie('energy', data);
const ts2 = new timeserie_1.TimeSerie('energy', []);
ava_1.default("TimeSerie.isEmpty() should behave correctly", (t) => {
const data = [["2021-01-01", 4]];
const ts1 = new timeserie_1.TimeSerie("energy", data);
const ts2 = new timeserie_1.TimeSerie("energy", []);
t.is(ts1.isEmpty(), false);
t.is(ts2.isEmpty(), true);
});
ava_1.default('Timeserie.sum() should return the sum of the values', (t) => {
ava_1.default("Timeserie.sum() should return the sum of the values", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', null],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", null],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.sum()[1], 33);
});
ava_1.default('Timeserie.avg() should return the average of the values', (t) => {
ava_1.default("Timeserie.avg() should return the average of the values", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 4],
['2021-01-03', null]
["2021-01-01", 4],
["2021-01-02", 4],
["2021-01-03", null],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.avg()[1], 4);
});
ava_1.default('Timeserie.first() should return the first point or null', (t) => {
const ts1 = new timeserie_1.TimeSerie('ts1', [['2021-01-01', 4]]);
const ts2 = new timeserie_1.TimeSerie('ts2', []);
ava_1.default("Timeserie.first() should return the first point or null", (t) => {
const ts1 = new timeserie_1.TimeSerie("ts1", [["2021-01-01", 4]]);
const ts2 = new timeserie_1.TimeSerie("ts2", []);
t.is(ts1.first()[1], 4);
t.is(ts2.first(), null);
});
ava_1.default('Timeserie.firstAt() should return the first point with time >= the given', (t) => {
ava_1.default("Timeserie.firstAt() should return the first point with time >= the given", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 4],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 4],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new timeserie_1.TimeSerie('energy', data);
t.is(ts.firstAt('2021-01-02')[1], 4);
t.is(ts.firstAt('2021-01-03')[1], 8);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.firstAt("2021-01-02")[1], 4);
t.is(ts.firstAt("2021-01-03")[1], 8);
});
ava_1.default('Timeserie.last() should return the last point or null', (t) => {
const ts1 = new timeserie_1.TimeSerie('ts1', [['2021-01-01', 4], ['2021-01-02', 5]]);
const ts2 = new timeserie_1.TimeSerie('ts2', []);
ava_1.default("Timeserie.last() should return the last point or null", (t) => {
const ts1 = new timeserie_1.TimeSerie("ts1", [
["2021-01-01", 4],
["2021-01-02", 5],
]);
const ts2 = new timeserie_1.TimeSerie("ts2", []);
t.is(ts1.last()[1], 5);
t.is(ts2.last(), null);
});
ava_1.default('Timeserie.max() should return the point with maximum value', (t) => {
ava_1.default("Timeserie.max() should return the point with maximum value", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 11],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 11],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.max()[1], 11);
});
ava_1.default('Timeserie.min() should return the point with minimum value', (t) => {
ava_1.default("Timeserie.min() should return the point with minimum value", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 11],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 11],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
t.is(ts.min()[1], 4);
});
ava_1.default('Timeserie.partition() should partition the timeserie', (t) => {
ava_1.default("Timeserie.partition() should partition the timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const chunks = ts.partition({ interval: 1000 * 60 * 60 * 24 });
t.is(chunks.length, 4);
});
ava_1.default('Timeserie.reduce() should reduce the timeserie', (t) => {
ava_1.default("Timeserie.reduce() should reduce the timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const result = ts.reduce({ operation: 'sum' });
const ts = new timeserie_1.TimeSerie("energy", data);
const result = ts.reduce({ operation: "sum" });
t.is(result.length(), 1);
t.is(result.atIndex(0), 40);
});
ava_1.default('Timeserie.resample(operation=sum) should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample(operation=sum) should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'sum' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "sum",
});
t.is(daily.length(), 4);

@@ -284,17 +288,20 @@ t.is(daily.atIndex(0), 8);

});
ava_1.default('Timeserie.resample().avg() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().avg() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'avg' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "avg",
});
t.is(daily.length(), 4);

@@ -306,17 +313,20 @@ t.is(daily.atIndex(0), 4);

});
ava_1.default('Timeserie.resample().first() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().first() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 1],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 1],
['2021-01-03T13:00:00.000Z', 2],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 1],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 1],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 1],
["2021-01-03T13:00:00.000Z", 2],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 1],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'first' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "first",
});
t.is(daily.length(), 4);

@@ -328,17 +338,20 @@ t.is(daily.atIndex(0), 1);

});
ava_1.default('Timeserie.resample().last() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().last() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 1],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 1],
['2021-01-03T13:00:00.000Z', 2],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 1],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 1],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 1],
["2021-01-03T13:00:00.000Z", 2],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 1],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'last' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "last",
});
t.is(daily.length(), 4);

@@ -350,17 +363,20 @@ t.is(daily.atIndex(0), 2);

});
ava_1.default('Timeserie.resample().max() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().max() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 8],
['2021-01-03T13:00:00.000Z', 11],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 100],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 8],
["2021-01-03T13:00:00.000Z", 11],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 100],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'max' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "max",
});
t.is(daily.length(), 4);

@@ -372,17 +388,20 @@ t.is(daily.atIndex(0), 2);

});
ava_1.default('Timeserie.resample().min() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().min() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 8],
['2021-01-03T13:00:00.000Z', 11],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 100],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 8],
["2021-01-03T13:00:00.000Z", 11],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 100],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'min' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "min",
});
t.is(daily.length(), 4);

@@ -394,17 +413,20 @@ t.is(daily.atIndex(0), 1);

});
ava_1.default('Timeserie.resample().delta() should provide the correct timeserie', (t) => {
ava_1.default("Timeserie.resample().delta() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 3],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 5],
['2021-01-03T13:00:00.000Z', 8],
['2021-01-03T20:00:00.000Z', 9],
['2021-01-04T12:00:00.000Z', 10],
['2021-01-04T16:00:00.000Z', 12],
['2021-01-04T20:00:00.000Z', 15]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 3],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 5],
["2021-01-03T13:00:00.000Z", 8],
["2021-01-03T20:00:00.000Z", 9],
["2021-01-04T12:00:00.000Z", 10],
["2021-01-04T16:00:00.000Z", 12],
["2021-01-04T20:00:00.000Z", 15],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'delta' });
const ts = new timeserie_1.TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "delta",
});
t.is(daily.length(), 4);

@@ -417,22 +439,22 @@ t.is(daily.data[0][0], data[0][0]); // The resampling should start at the first time index

});
ava_1.default('Timeserie.removeAt() should remove points from the timeserie', (t) => {
ava_1.default("Timeserie.removeAt() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const filtered = ts.removeAt('2021-01-03T00:00:00.000Z');
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.removeAt("2021-01-03T00:00:00.000Z");
t.is(filtered.length(), 3);
t.is(filtered.atIndex(2), 4);
});
ava_1.default('Timeserie.removeAtIndex() should remove points from the timeserie', (t) => {
ava_1.default("Timeserie.removeAtIndex() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.removeAtIndex(0);

@@ -442,23 +464,23 @@ t.is(filtered.length(), 3);

});
ava_1.default('Timeserie.removeBetweenTime() should remove points from the timeserie', (t) => {
ava_1.default("Timeserie.removeBetweenTime() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const filtered = ts.removeBetweenTime('2021-01-02T00:00:00.000Z', '2021-01-03T00:00:00.000Z');
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.removeBetweenTime("2021-01-02T00:00:00.000Z", "2021-01-03T00:00:00.000Z");
t.is(filtered.length(), 2);
t.is(filtered.atIndex(1), 4);
});
ava_1.default('Timeserie.dropNaN() should remove points from the timeserie', (t) => {
ava_1.default("Timeserie.dropNaN() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-01T00:00:00.000Z', NaN],
['2021-01-02T00:00:00.000Z', 'hello'],
['2021-01-03T00:00:00.000Z', {}],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-01T00:00:00.000Z", NaN],
["2021-01-02T00:00:00.000Z", "hello"],
["2021-01-03T00:00:00.000Z", {}],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.dropNaN();

@@ -468,55 +490,71 @@ t.is(filtered.length(), 2);

});
ava_1.default('Timeserie.dropNull() should remove points from the timeserie', (t) => {
ava_1.default("Timeserie.dropNull() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 'hello'],
['2021-01-03T00:00:00.000Z', null],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", "hello"],
["2021-01-03T00:00:00.000Z", null],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const filtered = ts.dropNull();
t.is(filtered.length(), 3);
t.is(filtered.atIndex(1), 'hello');
t.is(filtered.atIndex(1), "hello");
t.is(filtered.atIndex(2), 4);
});
ava_1.default('Timeserie.indexes() and Timeserie.values() should return correct values', (t) => {
ava_1.default("Timeserie.indexes() and Timeserie.values() should return correct values", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 'hello']
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", "hello"],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const ts = new timeserie_1.TimeSerie("energy", data);
const indexes = ts.indexes();
const values = ts.values();
t.is(indexes[0], '2021-01-01T00:00:00.000Z');
t.is(indexes[1], '2021-01-02T00:00:00.000Z');
t.is(indexes[0], "2021-01-01T00:00:00.000Z");
t.is(indexes[1], "2021-01-02T00:00:00.000Z");
t.is(values[0], 1);
t.is(values[1], 'hello');
t.is(values[1], "hello");
});
ava_1.default('Timeserie.reindex() should correctly replace the series index', (t) => {
ava_1.default("Timeserie.reindex() should correctly replace the series index", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
];
const ts = new timeserie_1.TimeSerie('energy', data);
const reindexed = ts.reindex(timeserie_1.TimeSerie.createIndex({ from: ts.firstValidIndex(), to: ts.lastValidIndex(), interval: '1h' }), { fill: 0 });
const ts = new timeserie_1.TimeSerie("energy", data);
const reindexed = ts.reindex(timeserie_1.TimeSerie.createIndex({
from: ts.firstValidIndex(),
to: ts.lastValidIndex(),
interval: "1h",
}), { fill: 0 });
t.is(reindexed.length(), 25);
});
ava_1.default('Timeserie.fromIndex() should correctly create the series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: 'ts' });
ava_1.default("Timeserie.fromIndex() should correctly create the series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: "ts" });
t.is(ts.length(), 24);
t.is(true, ts.toArray().every((item) => item[1] === 1));
});
ava_1.default('Timeserie.combine() should correctly combine the series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
const result = ts1.combine('add', [ts2]);
ava_1.default("Timeserie.combine() should correctly combine the series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const result = ts1.combine("add", [ts2]);
t.is(result.length(), ts1.length());
t.is(true, result.toArray().every((item) => item[1] === 3));
});
ava_1.default('Timeserie.add() should correctly add the series to numbers and other series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
ava_1.default("Timeserie.add() should correctly add the series to numbers and other series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const ts3 = ts1.add(ts2).add(7);

@@ -526,6 +564,10 @@ t.is(ts3.length(), ts1.length());

});
ava_1.default('Timeserie.sub() should correctly diff the series to numbers and other series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 8, name: 'ts1' });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 4, name: 'ts2' });
ava_1.default("Timeserie.sub() should correctly diff the series to numbers and other series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 8, name: "ts1" });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 4, name: "ts2" });
const ts3 = ts1.sub(ts2).sub(7);

@@ -535,6 +577,10 @@ t.is(ts3.length(), ts1.length());

});
ava_1.default('Timeserie.mul() should correctly multiply the series to numbers and other series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
ava_1.default("Timeserie.mul() should correctly multiply the series to numbers and other series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const ts3 = ts1.mul(ts2).mul(7);

@@ -544,6 +590,10 @@ t.is(ts3.length(), ts1.length());

});
ava_1.default('Timeserie.div() should correctly divide the series to numbers and other series', (t) => {
const idx = timeserie_1.TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 16, name: 'ts1' });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 4, name: 'ts2' });
ava_1.default("Timeserie.div() should correctly divide the series to numbers and other series", (t) => {
const idx = timeserie_1.TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 16, name: "ts1" });
const ts2 = timeserie_1.TimeSerie.fromIndex(idx, { fill: 4, name: "ts2" });
const ts3 = ts1.div(ts2).div(4);

@@ -553,2 +603,20 @@ t.is(ts3.length(), ts1.length());

});
//# 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ava_1.default("Timeserie.split() should split the timeserie", (t) => {
const data = [
["2021-01-01T12:00:00.000Z", 181],
["2021-01-01T20:00:00.000Z", 181],
["2021-01-02T12:00:00.000Z", 181],
["2021-01-02T20:00:00.000Z", 181],
["2021-01-03T12:00:00.000Z", 181],
["2021-01-03T13:00:00.000Z", 181],
["2021-01-03T20:00:00.000Z", 181],
["2021-01-04T12:00:00.000Z", 181],
["2021-01-04T16:00:00.000Z", 181],
["2021-01-04T20:00:00.000Z", 181],
];
const ts = new timeserie_1.TimeSerie("energy", data);
const result = ts.split({ chunks: 3 });
t.is(result.length, 4);
t.deepEqual(result.map((ts) => ts.length()), [3, 3, 3, 1]);
});
//# 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CjC,CAAC,0BAA0B,EAAE,GAAG,CAAC;QACjC,CAAC,0BAA0B,EAAE,GAAG,CAAC;QACjC,CAAC,0BAA0B,EAAE,GAAG,CAAC;QACjC,CAAC,0BAA0B,EAAE,GAAG,CAAC;QACjC,CAAC,0BAA0B,EAAE,GAAG,CAAC;KAClC,CAAC;IAEF,MAAM,EAAE,GAAG,IAAI,qBAAS,CAAC,QAAQ,EAAE,IAAI,CAAC,CAAC;IAEzC,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,EAAE,MAAM,EAAE,CAAC,EAAE,CAAC,CAAC;IACvC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;IACvB,CAAC,CAAC,SAAS,CACT,MAAM,CAAC,GAAG,CAAC,CAAC,EAAa,EAAE,EAAE,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,EAC1C,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAC;AACJ,CAAC,CAAC,CAAC"}

17

build/main/lib/types.d.ts

@@ -1,2 +0,2 @@

import { TimeSerie } from './timeserie';
import { TimeSerie } from "./timeserie";
export declare type PointValue = number | string | boolean | any;

@@ -57,3 +57,3 @@ export declare type DateLike = Date | string | number;

export declare type TimeserieIterator = (value: TimeSerie, index: number, array: ReadonlyArray<TimeSerie>) => any;
export declare type ColumnAggregation = 'avg' | 'last' | 'first' | 'min' | 'max' | 'delta' | 'sum';
export declare type ColumnAggregation = "avg" | "last" | "first" | "min" | "max" | "delta" | "sum";
export declare type ResampleDefaultAggregation = ColumnAggregation;

@@ -88,6 +88,6 @@ export declare type IntervalOptions = {

output: string;
operation: 'add' | 'mul' | 'div' | 'sub' | 'avg' | TimeseriePointCombiner;
operation: "add" | "mul" | "div" | "sub" | "avg" | TimeseriePointCombiner;
columns: string[];
}
export declare type ReduceOperation = 'min' | 'max' | 'first' | 'last' | 'avg' | 'sum' | 'delta';
export declare type ReduceOperation = "min" | "max" | "first" | "last" | "avg" | "sum" | "delta";
export declare type TimeFrameReduceOptions = {

@@ -106,3 +106,3 @@ operation: ReduceOperation;

}
export declare type PipelineStageType = 'aggregate' | 'resample' | 'project' | 'reduce' | 'add' | 'mul';
export declare type PipelineStageType = "aggregate" | "resample" | "project" | "reduce" | "add" | "mul";
export declare type PipelineStage = {

@@ -116,2 +116,9 @@ aggregate?: AggregationConfiguration;

};
export declare type SplitOptions = {
chunks: number;
};
export declare type BetweenTimeOptions = {
includeInferior: boolean;
includeSuperior: boolean;
};
export declare class TimeInterval {

@@ -118,0 +125,0 @@ from: Date;

7

build/main/lib/types.js

@@ -8,5 +8,2 @@ "use strict";

const parse_duration_1 = __importDefault(require("parse-duration"));
;
;
;
class TimeInterval {

@@ -39,3 +36,3 @@ constructor(from, to) {

let size = options.interval;
if (typeof options.interval === 'string') {
if (typeof options.interval === "string") {
size = parse_duration_1.default(options.interval);

@@ -53,2 +50,2 @@ }

exports.createIndex = createIndex;
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4

build/main/lib/utils.d.ts

@@ -1,5 +0,5 @@

import { DateLike } from './types';
import { DateLike } from "./types";
export declare function ms(date: Date | string): number;
export declare function DateLikeToString(d: DateLike): string;
export declare function DateLikeToTimestamp(d: DateLike): number;
export declare function getOrderOfMagnitude(n: number): number;
export declare function chunk(arr: any[], chunk_size: number): any[][];

22

build/main/lib/utils.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.getOrderOfMagnitude = exports.DateLikeToTimestamp = exports.DateLikeToString = exports.ms = void 0;
exports.chunk = exports.DateLikeToTimestamp = exports.DateLikeToString = exports.ms = void 0;
function ms(date) {

@@ -9,5 +9,5 @@ return new Date(date).getTime();

function DateLikeToString(d) {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).toISOString();
}
// if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
// return new Date(Number(d)).toISOString();
// }
return new Date(d).toISOString();

@@ -17,3 +17,3 @@ }

function DateLikeToTimestamp(d) {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).getTime();

@@ -24,8 +24,8 @@ }

exports.DateLikeToTimestamp = DateLikeToTimestamp;
function getOrderOfMagnitude(n) {
const order = Math.floor(Math.log(n) / Math.LN10 +
0.000000001); // because float math sucks like that
return Math.pow(10, order);
function chunk(arr, chunk_size) {
return new Array(Math.ceil(arr.length / chunk_size))
.fill(0)
.map((_, i) => arr.slice(i * chunk_size, (i + 1) * chunk_size));
}
exports.getOrderOfMagnitude = getOrderOfMagnitude;
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exports.chunk = chunk;
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102

build/module/lib/timeframe.d.ts

@@ -1,3 +0,3 @@

import { TimeSerie } from './timeserie';
import { AggregationConfiguration, DateLike, FromTimeseriesOptions, Index, Metadata, PointValue, ReindexOptions, Row, TelemetryV1Output, TimeFrameInternal, PartitionOptions, TimeFrameResampleOptions, TimeframeRowsIterator, TimeserieIterator, TimeFrameReduceOptions, ProjectionOptions, PipelineStage } from './types';
import { TimeSerie } from "./timeserie";
import { AggregationConfiguration, DateLike, FromTimeseriesOptions, Index, Metadata, ReindexOptions, Row, TelemetryV1Output, PartitionOptions, TimeFrameResampleOptions, TimeframeRowsIterator, TimeserieIterator, TimeFrameReduceOptions, ProjectionOptions, PipelineStage, SplitOptions, BetweenTimeOptions } from "./types";
interface TimeFrameOptions {

@@ -16,2 +16,3 @@ data: Row[];

private _indexes;
private _columns;
/**

@@ -23,8 +24,8 @@ * Creates a Timeframe instance from a list of rows. It infers the list of column names from each row's fields.

constructor(options: TimeFrameOptions);
private buildTimeCheckpoints;
private buildTimeTree;
/**
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate(data: Row[]): TimeFrame;

@@ -46,13 +47,11 @@ /**

/**
*
* Creates a TimeFrame from a Telemetry Output Object (Apio private method)
* @param data An object which is telemetry V1 output (Apio Internal)
* @returns
*/
static fromTelemetryV1Output(data?: TelemetryV1Output, metadata?: Metadata): TimeFrame;
static fromInternalFormat(data: TimeFrameInternal, metadata?: Metadata): TimeFrame;
private static fromInternalFormat;
/**
*
* Returns a new TimeFrame, where each input timeserie is used as column
* @param timeseries An array of TimeSerie objects
* @param options.fill Value to use as filler when a column does not hold a value for a specific time
* @returns A new TimeFrame, where each timeserie represent a column
*/

@@ -67,2 +66,8 @@ static fromTimeseries(timeseries: TimeSerie[], options?: FromTimeseriesOptions): TimeFrame;

/**
* Merges together rows and columns of the specified timeframes.
* If two or more timeframes present a value for the same column at the same time, the first timeframe in the array has priority.
* @param timeframes Array of timeframes to merge
*/
static merge(timeframes: TimeFrame[]): TimeFrame;
/**
* Joins multiple timeframes by adding the columns together and merging indexes (time)

@@ -80,13 +85,17 @@ * @param timeframes Array of timeframes to join together

/**
*
* Returns the column as timeseries
* @param name The name of the wanted column
* @returns The column as timeseries
*/
column(name: string): TimeSerie;
/**
* Returns every column as array of timeseries
*/
columns(): TimeSerie[];
/**
*
* @returns Array of rows
* Returns all the rows in an array
*/
rows(): Row[];
/**
* Returns the time index array
*/
indexes(): DateLike[];

@@ -98,27 +107,27 @@ /**

/**
*
* Returns a row at a given time or null
* @param time
* @returns A row at a given time or null
*/
atTime(time: string): Row | null;
/**
*
* @returns The row at the given index (position, not time)
* Get the row at the given index (position, not time)
*/
atIndex(index: number): PointValue;
atIndex(index: number): Row;
/**
* Returns the number of rows
*/
length(): number;
/**
* Returns the shape of the timeframe
* @returns Array<Number> The shape of the timeframe expressed as [rows, columns] where columns excludes the time column
* Returns the shape of the timeframe expressed as [rows, columns] where columns excludes the time column
*/
shape(): number[];
/**
*
* @returns The first row
*/
*
* Returns the first row
*/
first(): Row;
/**
*
* @returns The last row
*/
*
* Returns the last row
*/
last(): Row;

@@ -154,11 +163,7 @@ /**

/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from: DateLike, to: DateLike, options?: {
includeInferior: boolean;
includeSuperior: boolean;
}): TimeFrame;
* Returns the subset of points between the two dates. Extremes are included.
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
betweenTime(from: DateLike, to: DateLike, options?: BetweenTimeOptions): TimeFrame;
/**

@@ -175,3 +180,3 @@ * Applies transformations to TimeFrame. Each transformation is defined as an operation between columns. Allows, for example, to

* tf = tf.aggregate({ output: 'power1', columns: ['voltage1', 'current1'], operation: 'mul' })
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
*/

@@ -188,2 +193,7 @@ aggregate(agg: AggregationConfiguration): TimeFrame;

reduce(options: TimeFrameReduceOptions): TimeFrame;
/**
* Resamples the timeframe by the specified time interval. Each row
* of the result TimeFrame will be the result of the selected aggregation.
* @param options
*/
resample(options: TimeFrameResampleOptions): TimeFrame;

@@ -197,6 +207,6 @@ /**

/**
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map(fn: TimeframeRowsIterator): TimeFrame;

@@ -213,6 +223,10 @@ /**

* @param options
* @returns
*/
partition(options: PartitionOptions): TimeFrame[];
/**
* Splits a timeframe into multiple timeframes where each timeframe has
* a maximum of `options.chunks` rows.
*/
split(options: SplitOptions): TimeFrame[];
/**
* Runs a series of transformations defined as an object. Useful in automation.

@@ -219,0 +233,0 @@ * A stage is an object with a single key and a value, the key is the name of the method, the value is the params object

301

build/module/lib/timeframe.js

@@ -1,4 +0,4 @@

import { TimeSerie } from './timeserie';
import { TimeInterval } from './types';
import { getOrderOfMagnitude } from './utils';
import { TimeSerie } from "./timeserie";
import { TimeInterval, } from "./types";
import { chunk, DateLikeToString } from "./utils";
const test = (r, f, t, includeSuperior, includeInferior) => {

@@ -18,2 +18,3 @@ if (includeInferior && includeSuperior) {

};
const makeTree = require("functional-red-black-tree");
/**

@@ -33,2 +34,3 @@ * @class TimeFrame

this.metadata = {};
this._columns = {};
const { data, metadata = {} } = options;

@@ -42,7 +44,7 @@ // get a list of unique column names excluding the time key

else {
this.columnNames = [...new Set(data
this.columnNames = [
...new Set(data
.filter((row) => !!row)
.map((row) => Object.keys(row))
.flat())]
.filter((name) => name !== 'time');
.flatMap((row) => Object.keys(row))),
].filter((name) => name !== "time");
this.data = data

@@ -63,3 +65,6 @@ .concat([])

const { time, ...rest } = row;
acc[row.time] ? acc[row.time] = { ...acc[row.time], ...rest } : acc[row.time] = rest;
const fTime = DateLikeToString(time);
acc[fTime]
? (acc[fTime] = { ...acc[fTime], ...rest })
: (acc[fTime] = rest);
return acc;

@@ -70,21 +75,22 @@ }, {});

time: Object.keys(this.data).sort(),
checkpoints: null
tree: null,
};
}
buildTimeCheckpoints() {
if (!this._indexes.checkpoints) {
this._indexes.checkpoints = {};
const o = getOrderOfMagnitude(this._indexes.time.length);
this._indexes.time.forEach((el, i) => {
if (i % (o / 100) === 0) {
this._indexes.checkpoints[el] = i;
}
buildTimeTree() {
if (!this._indexes.tree) {
let tree = makeTree(function (a, b) {
return a - b;
});
this._indexes.time.forEach((time) => {
const t = new Date(time).getTime();
tree = tree.insert(t, t);
});
this._indexes.tree = tree;
}
}
/**
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate(data) {

@@ -114,5 +120,4 @@ return new TimeFrame({ data, metadata: this.metadata });

/**
*
* Creates a TimeFrame from a Telemetry Output Object (Apio private method)
* @param data An object which is telemetry V1 output (Apio Internal)
* @returns
*/

@@ -125,3 +130,3 @@ static fromTelemetryV1Output(data = {}, metadata = {}) {

if (!_data[time]) {
_data[time] = {};
_data[DateLikeToString(time)] = {};
}

@@ -131,5 +136,5 @@ const column = `${deviceId}:${propertyName}`;

deviceId,
propertyName
propertyName,
};
_data[time][column] = value;
_data[DateLikeToString(time)][column] = value;
}

@@ -139,3 +144,3 @@ }

const rows = Object.keys(_data).map((time) => {
return { time, ..._data[time] };
return { time, ..._data[DateLikeToString(time)] };
});

@@ -151,6 +156,5 @@ return new TimeFrame({ data: rows, metadata });

/**
*
* Returns a new TimeFrame, where each input timeserie is used as column
* @param timeseries An array of TimeSerie objects
* @param options.fill Value to use as filler when a column does not hold a value for a specific time
* @returns A new TimeFrame, where each timeserie represent a column
*/

@@ -160,9 +164,10 @@ static fromTimeseries(timeseries, options) {

const metadata = {};
timeseries.forEach(ts => {
const idx = [...new Set(timeseries.flatMap((ts) => ts.indexes()))];
timeseries.forEach((ts) => {
metadata[ts.name] = ts.metadata;
ts.toArray().forEach((point) => {
data[point[0]] = data[point[0]] || {};
data[point[0]][ts.name] = point[1] || options?.fill || null;
});
});
idx.forEach((i) => {
data[i] = {};
timeseries.forEach((ts) => (data[i][ts.name] = ts.atTime(i) || options?.fill || null));
});
return TimeFrame.fromInternalFormat(data, metadata);

@@ -177,7 +182,18 @@ }

return new TimeFrame({
metadata: Object.assign({}, ...timeframes.map(tf => tf.metadata)),
data: timeframes.map((tf) => tf.rows()).flat()
metadata: Object.assign({}, ...timeframes.map((tf) => tf.metadata)),
data: timeframes.flatMap((tf) => tf.rows()),
});
}
/**
* Merges together rows and columns of the specified timeframes.
* If two or more timeframes present a value for the same column at the same time, the first timeframe in the array has priority.
* @param timeframes Array of timeframes to merge
*/
static merge(timeframes) {
if (timeframes.length < 2) {
throw new Error("merge() requires at least two timeframes");
}
return timeframes[0].join(timeframes.slice(1));
}
/**
* Joins multiple timeframes by adding the columns together and merging indexes (time)

@@ -188,3 +204,16 @@ * @param timeframes Array of timeframes to join together

join(timeframes) {
return TimeFrame.fromInternalFormat(Object.assign({}, ...(timeframes.map(tf => tf.data).concat([this.data]))));
const allTf = timeframes.concat([this]);
// Todo should support a filler value, at the moment it just does not define values in rows
// when a row misses a certain column's value
// const allColumns: string[] = [
// ...new Set(allTf.flatMap((tf) => tf.columnNames)),
// ];
const mergedIndex = [...new Set(allTf.flatMap((tf) => tf.indexes()))];
const rows = mergedIndex.map((idx) => ({
time: idx,
...allTf
.map((tf) => tf.atTime(idx))
.reduce((prev, acc) => Object.assign(acc, prev), this.atTime(idx)),
}));
return this.recreate(rows);
}

@@ -200,5 +229,4 @@ /**

/**
*
* Returns the column as timeseries
* @param name The name of the wanted column
* @returns The column as timeseries
*/

@@ -209,6 +237,16 @@ column(name) {

}
const data = Object.entries(this.data).map(([time, values]) => ([time, values[name]]));
const metadata = this.metadata[name] || {};
return new TimeSerie(name, data, metadata);
// we cache the column to make subsequent reads faster
if (!this._columns[name]) {
const data = Object.entries(this.data).map(([time, values]) => [
time,
values[name],
]);
const metadata = this.metadata[name] || {};
this._columns[name] = new TimeSerie(name, data, metadata);
}
return this._columns[name];
}
/**
* Returns every column as array of timeseries
*/
columns() {

@@ -218,8 +256,13 @@ return this.columnNames.map((column) => this.column(column));

/**
*
* @returns Array of rows
* Returns all the rows in an array
*/
rows() {
return Object.entries(this.data).map(([time, values]) => ({ time, ...values }));
return Object.entries(this.data).map(([time, values]) => ({
time,
...values,
}));
}
/**
* Returns the time index array
*/
indexes() {

@@ -234,3 +277,3 @@ return this._indexes.time;

if (nonExisting.length > 0) {
throw new Error(`Non existing columns ${nonExisting.join(',')}`);
throw new Error(`Non existing columns ${nonExisting.join(",")}`);
}

@@ -242,19 +285,20 @@ const tf = TimeFrame.fromTimeseries(config.columns.map((columnName) => this.column(columnName)));

/**
*
* Returns a row at a given time or null
* @param time
* @returns A row at a given time or null
*/
atTime(time) {
return { time, ...this.data[time] } || null;
return { time, ...this.data[DateLikeToString(time)] } || null;
}
/**
*
* @returns The row at the given index (position, not time)
* Get the row at the given index (position, not time)
*/
atIndex(index) {
if (index >= this.rows().length) {
throw new Error('Index out of bounds');
throw new Error("Index out of bounds");
}
return this.rows()[index];
}
/**
* Returns the number of rows
*/
length() {

@@ -264,4 +308,3 @@ return this._indexes.time.length;

/**
* Returns the shape of the timeframe
* @returns Array<Number> The shape of the timeframe expressed as [rows, columns] where columns excludes the time column
* Returns the shape of the timeframe expressed as [rows, columns] where columns excludes the time column
*/

@@ -272,5 +315,5 @@ shape() {

/**
*
* @returns The first row
*/
*
* Returns the first row
*/
first() {

@@ -283,5 +326,5 @@ if (this.length() === 0) {

/**
*
* @returns The last row
*/
*
* Returns the last row
*/
last() {

@@ -302,3 +345,6 @@ if (this.length() === 0) {

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.sum()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.sum()[1];
return acc;
}, { time });
}

@@ -313,3 +359,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.avg()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.avg()[1];
return acc;
}, { time });
}

@@ -324,3 +373,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.delta()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.delta()[1];
return acc;
}, { time });
}

@@ -335,3 +387,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.max()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.max()[1];
return acc;
}, { time });
}

@@ -346,3 +401,6 @@ /**

const time = this.first().time;
return this.columns().reduce((acc, column) => { acc[column.name] = column.min()[1]; return acc; }, { time });
return this.columns().reduce((acc, column) => {
acc[column.name] = column.min()[1];
return acc;
}, { time });
}

@@ -362,40 +420,27 @@ /**

/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from, to, options = { includeInferior: true, includeSuperior: true }) {
* Returns the subset of points between the two dates. Extremes are included.
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
betweenTime(from, to, options = {
includeInferior: true,
includeSuperior: true,
}) {
/**
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys
* we index the array by mapping a certain number of timestamps to positions in the time index.
*
* This sparse index is smaller than the full index and fester to use for scanning ranges like in this case.
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys. To get better performances we index timestamps with a RBtree in the buildTimeTree funciton
*/
this.buildTimeCheckpoints();
this.buildTimeTree();
const { includeInferior, includeSuperior } = options;
const f = new Date(from).getTime();
const t = new Date(to).getTime();
const keys = Object.keys(this._indexes.checkpoints);
// Indice della prima chiave che va oltre il from
const startingPointValueIndex = keys.findIndex((key) => new Date(key).getTime() > from);
// Ultimo timestamp prima di quell'indice
let startingPoint = this._indexes.checkpoints[keys[startingPointValueIndex - 1]];
if (!startingPoint) {
// Siamo oltre l'ultimo checkpoint
const lastCheckpoint = keys[keys.length - 1];
startingPoint = this._indexes.checkpoints[lastCheckpoint];
}
const goodRows = [];
for (let i = startingPoint; i < this._indexes.time.length; i++) {
const curr = new Date(this._indexes.time[i]).getTime();
if (curr < f) {
continue;
const iter = this._indexes.tree.ge(f);
while (iter && new Date(iter.key).getTime() <= t) {
if (test(iter.key, f, t, includeSuperior, includeInferior)) {
goodRows.push({
time: new Date(iter.key).toISOString(),
...this.data[DateLikeToString(iter.key)],
});
}
if (curr > t) {
break;
}
if (test(curr, f, t, includeSuperior, includeInferior)) {
goodRows.push({ time: this._indexes.time[i], ...this.data[this._indexes.time[i]] });
}
iter.next();
}

@@ -415,3 +460,3 @@ return this.recreate(goodRows);

* tf = tf.aggregate({ output: 'power1', columns: ['voltage1', 'current1'], operation: 'mul' })
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
*/

@@ -423,10 +468,11 @@ aggregate(agg) {

let newColumn;
if (typeof agg.operation === 'function') {
if (typeof agg.operation === "function") {
newColumn = TimeSerie.internals.combine(columnsToAggregate, agg.operation, { name: agg.output });
}
else if (typeof agg.operation === 'string' && agg.operation in TimeSerie.internals.combiners) {
else if (typeof agg.operation === "string" &&
agg.operation in TimeSerie.internals.combiners) {
newColumn = TimeSerie.internals.combine(columnsToAggregate, TimeSerie.internals.combiners[agg.operation], { name: agg.output });
}
else {
throw new Error('Wrong type for aggregation operation');
throw new Error("Wrong type for aggregation operation");
}

@@ -453,13 +499,17 @@ return this.recreateFromSeries([newColumn].concat(this.columns()));

}
/**
* Resamples the timeframe by the specified time interval. Each row
* of the result TimeFrame will be the result of the selected aggregation.
* @param options
*/
resample(options) {
const from = options.from || this.first()?.time;
if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.time;
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
return TimeFrame.concat(this.partition(options)
.map((chunk) => chunk.reduce(options)));
return TimeFrame.concat(this.partition(options).map((chunk) => chunk.reduce(options)));
}

@@ -472,11 +522,17 @@ /**

filter(fn) {
return new TimeFrame({ data: this.rows().filter(fn), metadata: this.metadata });
return new TimeFrame({
data: this.rows().filter(fn),
metadata: this.metadata,
});
}
/**
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map(fn) {
return new TimeFrame({ data: this.rows().map(fn), metadata: this.metadata });
return new TimeFrame({
data: this.rows().map(fn),
metadata: this.metadata,
});
}

@@ -490,5 +546,7 @@ /**

apply(fn, columns = this.columnNames) {
const unmodifiedColumns = this.columnNames.filter((columnName) => !columns.includes(columnName)).map((columnName) => this.column(columnName));
const unmodifiedColumns = this.columnNames
.filter((columnName) => !columns.includes(columnName))
.map((columnName) => this.column(columnName));
const series = columns
.map((columnName) => (this.column(columnName)))
.map((columnName) => this.column(columnName))
.map(fn);

@@ -500,3 +558,2 @@ return TimeFrame.fromTimeseries(unmodifiedColumns.concat(series));

* @param options
* @returns
*/

@@ -506,11 +563,14 @@ partition(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.time;
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false });
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});

@@ -530,2 +590,9 @@ return partitions.map((p, idx) => {

/**
* Splits a timeframe into multiple timeframes where each timeframe has
* a maximum of `options.chunks` rows.
*/
split(options) {
return chunk(this.rows(), options.chunks).map((rows) => this.recreate(rows));
}
/**
* Runs a series of transformations defined as an object. Useful in automation.

@@ -548,2 +615,2 @@ * A stage is an object with a single key and a value, the key is the name of the method, the value is the params object

}
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646

build/module/lib/timeframe.spec.js

@@ -1,21 +0,22 @@

import test from 'ava';
import { TimeFrame } from './timeframe';
import { TimeSerie } from './timeserie';
test('TimeFrame.column() should return the correct timeserie', (t) => {
import test from "ava";
import { TimeFrame } from "./timeframe";
import { TimeSerie } from "./timeserie";
import { DateLikeToString } from "./utils";
test("TimeFrame.column() should return the correct timeserie", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new TimeFrame({ data });
const energy = tf.column('energy');
const energy = tf.column("energy");
t.is(true, energy instanceof TimeSerie);
t.is(2, energy.length());
t.is(energy.atTime('2021-01-01'), 1);
t.is(energy.atTime('2021-01-02'), 2);
t.is(energy.atTime("2021-01-01"), 1);
t.is(energy.atTime("2021-01-02"), 2);
});
test('TimeFrame.length() should return the correct value', (t) => {
test("TimeFrame.length() should return the correct value", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];

@@ -25,7 +26,7 @@ const tf = new TimeFrame({ data });

});
test('TimeFrame.shape() should return the correct value', (t) => {
test("TimeFrame.shape() should return the correct value", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 1, power: 5 },
{ time: '2021-01-03', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 1, power: 5 },
{ time: "2021-01-03", energy: 2, power: 8 },
];

@@ -35,25 +36,29 @@ const tf = new TimeFrame({ data });

});
test('TimeFrame.indexes() should return the correct value', (t) => {
test("TimeFrame.indexes() should return the correct value", (t) => {
const data = [
{ time: '2021-01-03', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-03", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new TimeFrame({ data });
t.deepEqual(tf.indexes(), ['2021-01-01', '2021-01-02', '2021-01-03']);
t.deepEqual(tf.indexes(), [
DateLikeToString("2021-01-01"),
DateLikeToString("2021-01-02"),
DateLikeToString("2021-01-03"),
]);
});
test('TimeFrame.atTime() should return the correct row', (t) => {
test("TimeFrame.atTime() should return the correct row", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new TimeFrame({ data });
const row = tf.atTime('2021-01-02');
const row = tf.atTime("2021-01-02");
t.is(row.energy, 2);
t.is(row.power, 8);
});
test('TimeFrame.toArray() should return an array of rows', (t) => {
test("TimeFrame.toArray() should return an array of rows", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 2, power: 8 },
];

@@ -65,188 +70,251 @@ const tf = new TimeFrame({ data });

});
test('TimeFrame.fromTelemetryV1Output() should return the correct timeframe', (t) => {
test("TimeFrame.fromTelemetryV1Output() should return the correct timeframe", (t) => {
const data = {
device1: {
energy: [['2021-01-01', 1], ['2021-01-02', 2]],
power: [['2021-01-01', 4], ['2021-01-02', 8]]
energy: [
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
],
power: [
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 8],
],
},
device2: {
energy: [['2021-01-01', 1], ['2021-01-02', 2]],
power: [['2021-01-01', 4], ['2021-01-02', 8]]
}
energy: [
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
],
power: [
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 8],
],
},
};
const tf = TimeFrame.fromTelemetryV1Output(data);
const row = tf.atTime('2021-01-01');
t.is(row['device1:energy'], 1);
t.is(row['device1:power'], 4);
const d2energy = tf.column('device2:energy');
t.is(d2energy.metadata.deviceId, 'device2');
t.is(d2energy.metadata.propertyName, 'energy');
const row = tf.atTime("2021-01-01T00:00:00.000Z");
t.is(row["device1:energy"], 1);
t.is(row["device1:power"], 4);
const d2energy = tf.column("device2:energy");
t.is(d2energy.metadata.deviceId, "device2");
t.is(d2energy.metadata.propertyName, "energy");
});
test('TimeFrame.fromTimeseries() should return the correct timeframe', (t) => {
test("TimeFrame.fromTimeseries() should return the correct timeframe", (t) => {
const energyData = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 4],
['2021-01-03T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 4],
["2021-01-03T00:00:00.000Z", 4],
];
const powerData = [
['2021-01-01T00:00:00.000Z', 16],
['2021-01-02T00:00:00.000Z', 16],
['2021-01-03T00:00:00.000Z', 16]
["2021-01-01T00:00:00.000Z", 16],
["2021-01-02T00:00:00.000Z", 16],
["2021-01-03T00:00:00.000Z", 16],
];
const energyTS = new TimeSerie('energy', energyData, { deviceId: 'd1' });
const powerTS = new TimeSerie('power', powerData, { deviceId: 'd2' });
const energyTS = new TimeSerie("energy", energyData, { deviceId: "d1" });
const powerTS = new TimeSerie("power", powerData, { deviceId: "d2" });
const tf = TimeFrame.fromTimeseries([energyTS, powerTS]);
const row = tf.atTime('2021-01-01T00:00:00.000Z');
const row = tf.atTime("2021-01-01T00:00:00.000Z");
t.is(row.energy, 4);
t.is(row.power, 16);
// We ensure that metadata is propagated to each timeserie
t.is(tf.metadata?.energy?.deviceId, 'd1');
t.is(tf.metadata?.power?.deviceId, 'd2');
t.is(tf.metadata?.energy?.deviceId, "d1");
t.is(tf.metadata?.power?.deviceId, "d2");
});
test('TimeFrame.filter() should return the correct timeframe', (t) => {
test("TimeFrame.filter() should return the correct timeframe", (t) => {
const data = [
{ time: '2021-01-01', energy: 1, power: 4 },
{ time: '2021-01-01', energy: 1, power: 5 },
{ time: '2021-01-02', energy: 2, power: 8 }
{ time: "2021-01-01", energy: 1, power: 4 },
{ time: "2021-01-01", energy: 1, power: 5 },
{ time: "2021-01-02", energy: 2, power: 8 },
];
const tf = new TimeFrame({ data });
const filtered = tf.filter(row => { return row.power > 4; });
const filtered = tf.filter((row) => {
return row.power > 4;
});
t.is(filtered.length(), 2);
});
test('TimeFrame.join() should return the correct timeframe', (t) => {
const data1 = [
{ time: '2021-01-01', energy: 1, power: 4 }
test("TimeFrame.join() should return the correct timeframe", (t) => {
const data1 = [{ time: "2021-01-01", energy: 1, power: 4, voltage1: 7 }];
const data2 = [
{ time: "2021-01-02", energy: 1, power: 5, voltage2: 4 },
{ time: "2021-01-03", energy: 2, power: 8, voltage3: 5 },
];
const data3 = [
{ time: "2021-01-01", cosphi: 1 },
{ time: "2021-01-04", energy: 2, power: 2, cosphi: 1 },
];
const tf1 = new TimeFrame({ data: data1 });
const tf2 = new TimeFrame({ data: data2 });
const tf3 = new TimeFrame({ data: data3 });
// We want join to be not be dependant to the ordering of timeframes
const joined1 = tf1.join([tf2, tf3]);
const joined2 = tf2.join([tf1, tf3]);
const joined3 = tf3.join([tf1, tf2]);
[joined1, joined2, joined3].forEach((joined) => {
t.is(joined.length(), 4);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").energy, 1);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").power, 4);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage1, 7);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage2, undefined);
t.is(joined.atTime("2021-01-01T00:00:00.000Z").voltage3, undefined);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").energy, 1);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").power, 5);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage1, undefined);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage2, 4);
t.is(joined.atTime("2021-01-02T00:00:00.000Z").voltage3, undefined);
});
});
test("TimeFrame.merge() should return the correct timeframe", (t) => {
const data1 = [{ time: "2021-01-01", energy: 1, power: 4, voltage1: 7 }];
const data2 = [
{ time: '2021-01-02', energy: 1, power: 5 },
{ time: '2021-01-03', energy: 2, power: 8 }
{ time: "2021-01-02", energy: 1, power: 5, voltage2: 4 },
{ time: "2021-01-03", energy: 2, power: 8, voltage3: 5 },
];
const data3 = [
{ time: "2021-01-01", cosphi: 1, energy: 41 },
{ time: "2021-01-02", power: 11 },
{ time: "2021-01-04", energy: 2, power: 2, cosphi: 1 },
];
const tf1 = new TimeFrame({ data: data1 });
const tf2 = new TimeFrame({ data: data2 });
const joined = tf1.join([tf2]);
t.is(joined.length(), 3);
const tf3 = new TimeFrame({ data: data3 });
// We want join to be not be dependant to the ordering of timeframes
const merged = TimeFrame.merge([tf3, tf2, tf1]);
t.is(merged.length(), 4);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").energy, 41);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").cosphi, 1);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").power, 4);
t.is(merged.atTime("2021-01-01T00:00:00.000Z").voltage1, 7);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").energy, 1);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").power, 11);
t.is(merged.atTime("2021-01-02T00:00:00.000Z").voltage2, 4);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").energy, 2);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").power, 8);
t.is(merged.atTime("2021-01-03T00:00:00.000Z").voltage3, 5);
});
test('TimeFrame.apply() should correctly modify columns', (t) => {
test("TimeFrame.apply() should correctly modify columns", (t) => {
const energyData = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 4],
['2021-01-03T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 4],
["2021-01-03T00:00:00.000Z", 4],
];
const powerData = [
['2021-01-01T00:00:00.000Z', 16],
['2021-01-02T00:00:00.000Z', 16],
['2021-01-03T00:00:00.000Z', 16]
["2021-01-01T00:00:00.000Z", 16],
["2021-01-02T00:00:00.000Z", 16],
["2021-01-03T00:00:00.000Z", 16],
];
const energyTS = new TimeSerie('energy', energyData, { deviceId: 'd1' });
const powerTS = new TimeSerie('power', powerData, { deviceId: 'd2' });
const energyTS = new TimeSerie("energy", energyData, { deviceId: "d1" });
const powerTS = new TimeSerie("power", powerData, { deviceId: "d2" });
const tf = TimeFrame.fromTimeseries([energyTS, powerTS]);
const tf2 = tf.apply(ts => ts.map((p) => [p[0], 0]), ['power']);
t.is(tf.column('energy').sum()[1], 12);
t.is(tf2.column('power').sum()[1], 0);
t.is(tf2.metadata.energy.deviceId, 'd1');
t.is(tf2.metadata.power.deviceId, 'd2');
const tf2 = tf.apply((ts) => ts.map((p) => [p[0], 0]), ["power"]);
t.is(tf.column("energy").sum()[1], 12);
t.is(tf2.column("power").sum()[1], 0);
t.is(tf2.metadata.energy.deviceId, "d1");
t.is(tf2.metadata.power.deviceId, "d2");
});
test('TimeFrame.resample(sum) should correctly resample and aggregate data', t => {
test("TimeFrame.resample(sum) should correctly resample and aggregate data", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T00:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T00:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T01:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T01:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:00:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:01:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T02:59:00.000Z', energy: 1, power: 1 },
{ time: '2021-01-01T03:01:00.000Z', energy: 1, power: 1 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T00:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T00:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T01:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T01:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:00:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:01:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T02:59:00.000Z", energy: 1, power: 1 },
{ time: "2021-01-01T03:01:00.000Z", energy: 1, power: 1 },
];
const tf = new TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const resampled = tf.resample({
interval: 1000 * 60 * 60,
from: '2021-01-01T00:00:00.000Z',
to: '2021-01-01T04:00:00.000Z',
operation: 'sum'
from: "2021-01-01T00:00:00.000Z",
to: "2021-01-01T04:00:00.000Z",
operation: "sum",
});
t.is(resampled.length(), 4);
t.is(resampled.rows()[0].time, '2021-01-01T00:00:00.000Z');
t.is(resampled.rows()[0].time, "2021-01-01T00:00:00.000Z");
t.is(resampled.rows()[0].power, 3);
t.is(resampled.rows()[0].energy, 3);
t.is(resampled.rows()[1].time, '2021-01-01T01:00:00.000Z');
t.is(resampled.rows()[1].time, "2021-01-01T01:00:00.000Z");
t.is(resampled.rows()[1].power, 2);
t.is(resampled.rows()[1].energy, 2);
t.is(resampled.rows()[2].time, '2021-01-01T02:00:00.000Z');
t.is(resampled.rows()[2].time, "2021-01-01T02:00:00.000Z");
t.is(resampled.rows()[2].power, 3);
t.is(resampled.rows()[2].energy, 3);
t.is(resampled.rows()[3].time, '2021-01-01T03:00:00.000Z');
t.is(resampled.rows()[3].time, "2021-01-01T03:00:00.000Z");
t.is(resampled.rows()[3].power, 1);
t.is(resampled.rows()[3].energy, 1);
t.is(resampled.metadata.hello, 'world');
t.is(resampled.metadata.hello, "world");
});
test('TimeFrame.sum() should correctly sum all columns', t => {
test("TimeFrame.sum() should correctly sum all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 1, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 1, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new TimeFrame({ data }).sum();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 5);
t.is(row.power, 18);
});
test('TimeFrame.delta() should correctly delta all columns', t => {
test("TimeFrame.delta() should correctly delta all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, expenergy: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 2, expenergy: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy: 3, expenergy: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy: 4, expenergy: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, expenergy: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 2, expenergy: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy: 3, expenergy: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy: 4, expenergy: 16 },
];
const row = new TimeFrame({ data }).delta();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 3);
t.is(row.expenergy, 12);
});
test('TimeFrame.max() should correctly max() all columns', t => {
test("TimeFrame.max() should correctly max() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 7, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 7, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new TimeFrame({ data }).max();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 7);
t.is(row.power, 9);
});
test('TimeFrame.min() should correctly min() all columns', t => {
test("TimeFrame.min() should correctly min() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 3, power: 1 },
{ time: '2021-01-02T00:00:00.000Z', energy: 7, power: 3 },
{ time: '2021-01-03T00:00:00.000Z', energy: 2, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 9 }
{ time: "2021-01-01T00:00:00.000Z", energy: 3, power: 1 },
{ time: "2021-01-02T00:00:00.000Z", energy: 7, power: 3 },
{ time: "2021-01-03T00:00:00.000Z", energy: 2, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 9 },
];
const row = new TimeFrame({ data }).min();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 1);
t.is(row.power, 1);
});
test('TimeFrame.avg() should correctly avg() all columns', t => {
test("TimeFrame.avg() should correctly avg() all columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 4, power: 1 },
{ time: '2021-01-02T00:00:00.000Z', energy: 4, power: 1 },
{ time: '2021-01-03T00:00:00.000Z', energy: 8, power: 11 },
{ time: '2021-01-04T00:00:00.000Z', energy: 8, power: 11 }
{ time: "2021-01-01T00:00:00.000Z", energy: 4, power: 1 },
{ time: "2021-01-02T00:00:00.000Z", energy: 4, power: 1 },
{ time: "2021-01-03T00:00:00.000Z", energy: 8, power: 11 },
{ time: "2021-01-04T00:00:00.000Z", energy: 8, power: 11 },
];
const row = new TimeFrame({ data }).avg();
t.is(row.time, '2021-01-01T00:00:00.000Z');
t.is(row.time, "2021-01-01T00:00:00.000Z");
t.is(row.energy, 6);
t.is(row.power, 6);
});
test('TimeFrame.aggregate() should correctly aggregate columns', t => {
test("TimeFrame.aggregate() should correctly aggregate columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const agg = new TimeFrame({ data, metadata: { hello: 'world' } })
.aggregate({ output: 'totalenergy', columns: ['energy1', 'energy2'], operation: 'add' });
const agg = new TimeFrame({ data, metadata: { hello: "world" } }).aggregate({
output: "totalenergy",
columns: ["energy1", "energy2"],
operation: "add",
});
t.is(agg.atIndex(0).totalenergy, 5);

@@ -256,54 +324,57 @@ t.is(agg.atIndex(1).totalenergy, 10);

t.is(agg.atIndex(3).totalenergy, 20);
t.is(agg.metadata.hello, 'world');
t.is(agg.metadata.hello, "world");
});
test('TimeFrame.project() should correctly aggregate columns', t => {
test("TimeFrame.project() should correctly aggregate columns", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new TimeFrame({ data, metadata: { hello: 'world' } });
const projected = tf.project({ columns: ['energy1'] });
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const projected = tf.project({ columns: ["energy1"] });
t.is(tf.columns().length, 2);
t.is(projected.columns().length, 1);
t.is(projected.metadata.hello, 'world');
t.is(projected.metadata.hello, "world");
});
test('TimeFrame.mul() should correctly multiply values', t => {
test("TimeFrame.mul() should correctly multiply values", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const multiplied = tf.mul(2);
t.is(multiplied.atTime('2021-01-01T00:00:00.000Z').energy1, 2);
t.is(multiplied.atTime('2021-01-01T00:00:00.000Z').energy2, 8);
t.is(multiplied.atTime('2021-01-02T00:00:00.000Z').energy1, 4);
t.is(multiplied.atTime('2021-01-02T00:00:00.000Z').energy2, 16);
t.is(multiplied.atTime("2021-01-01T00:00:00.000Z").energy1, 2);
t.is(multiplied.atTime("2021-01-01T00:00:00.000Z").energy2, 8);
t.is(multiplied.atTime("2021-01-02T00:00:00.000Z").energy1, 4);
t.is(multiplied.atTime("2021-01-02T00:00:00.000Z").energy2, 16);
});
test('TimeFrame.add() should correctly add values', t => {
test("TimeFrame.add() should correctly add values", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy1: 1, energy2: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy1: 2, energy2: 8 },
{ time: '2021-01-03T00:00:00.000Z', energy1: 3, energy2: 12 },
{ time: '2021-01-04T00:00:00.000Z', energy1: 4, energy2: 16 }
{ time: "2021-01-01T00:00:00.000Z", energy1: 1, energy2: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy1: 2, energy2: 8 },
{ time: "2021-01-03T00:00:00.000Z", energy1: 3, energy2: 12 },
{ time: "2021-01-04T00:00:00.000Z", energy1: 4, energy2: 16 },
];
const tf = new TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const added = tf.add(2);
t.is(added.atTime('2021-01-01T00:00:00.000Z').energy1, 3);
t.is(added.atTime('2021-01-01T00:00:00.000Z').energy2, 6);
t.is(added.atTime('2021-01-02T00:00:00.000Z').energy1, 4);
t.is(added.atTime('2021-01-02T00:00:00.000Z').energy2, 10);
t.is(added.atTime("2021-01-01T00:00:00.000Z").energy1, 3);
t.is(added.atTime("2021-01-01T00:00:00.000Z").energy2, 6);
t.is(added.atTime("2021-01-02T00:00:00.000Z").energy1, 4);
t.is(added.atTime("2021-01-02T00:00:00.000Z").energy2, 10);
});
test('TimeFrame.reduce() should correctly reduce the timeframe', t => {
test("TimeFrame.reduce() should correctly reduce the timeframe", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-02T00:00:00.000Z', energy: 1, power: 4 },
{ time: '2021-01-03T00:00:00.000Z', energy: 1, power: 2 },
{ time: '2021-01-04T00:00:00.000Z', energy: 1, power: 2 }
{ time: "2021-01-01T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-02T00:00:00.000Z", energy: 1, power: 4 },
{ time: "2021-01-03T00:00:00.000Z", energy: 1, power: 2 },
{ time: "2021-01-04T00:00:00.000Z", energy: 1, power: 2 },
];
const tf = new TimeFrame({ data });
const reduced = tf.reduce({ operation: 'avg', operations: { energy: 'sum' } });
const reduced = tf.reduce({
operation: "avg",
operations: { energy: "sum" },
});
const rows = reduced.rows();

@@ -314,28 +385,223 @@ t.is(rows.length, 1);

});
test('TimeFrame.pipeline() should correctly run all the stages', t => {
test("TimeFrame.pipeline() should correctly run all the stages", (t) => {
const data = [
{ time: '2021-01-01T00:00:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T00:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T00:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T01:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T01:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:00:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T02:59:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 },
{ time: '2021-01-01T03:01:00.000Z', voltage1: 1, current1: 1, voltage2: 2, current2: 2, voltage3: 3, current3: 3 }
{
time: "2021-01-01T00:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T03:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
];
const tf = new TimeFrame({ data, metadata: { hello: 'world' } });
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const processed = tf.pipeline([
{ aggregate: { columns: ['voltage1', 'current1'], operation: 'mul', output: 'power1' } },
{ aggregate: { columns: ['voltage2', 'current2'], operation: 'mul', output: 'power2' } },
{ aggregate: { columns: ['voltage3', 'current3'], operation: 'mul', output: 'power3' } },
{ aggregate: { columns: ['power1', 'power2', 'power3'], operation: 'add', output: 'powertot' } },
{ project: { columns: ['powertot'] } },
{ resample: { interval: 1000 * 60 * 60, operation: 'avg', from: '2021-01-01T00:00:00.000Z' } }
{
aggregate: {
columns: ["voltage1", "current1"],
operation: "mul",
output: "power1",
},
},
{
aggregate: {
columns: ["voltage2", "current2"],
operation: "mul",
output: "power2",
},
},
{
aggregate: {
columns: ["voltage3", "current3"],
operation: "mul",
output: "power3",
},
},
{
aggregate: {
columns: ["power1", "power2", "power3"],
operation: "add",
output: "powertot",
},
},
{ project: { columns: ["powertot"] } },
{
resample: {
interval: 1000 * 60 * 60,
operation: "avg",
from: "2021-01-01T00:00:00.000Z",
},
},
]);
t.is(processed.length(), 4);
t.is(processed.columnNames.length, 1);
t.is(processed.columnNames[0], 'powertot');
t.is(processed.metadata.hello, 'world');
t.is(processed.columnNames[0], "powertot");
t.is(processed.metadata.hello, "world");
});
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test("TimeFrame.split() should split a timeframe into sub timeframes of fixed size", (t) => {
const data = [
{
time: "2021-01-01T00:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T00:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T01:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:00:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T02:59:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
{
time: "2021-01-01T03:01:00.000Z",
voltage1: 1,
current1: 1,
voltage2: 2,
current2: 2,
voltage3: 3,
current3: 3,
},
];
const tf = new TimeFrame({ data, metadata: { hello: "world" } });
const chunks = tf.split({ chunks: 3 });
t.is(chunks.length, 3);
t.deepEqual(chunks.map((tf) => tf.shape()), [
[3, 6],
[3, 6],
[3, 6],
]);
});
//# 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151

build/module/lib/timeserie.d.ts

@@ -1,2 +0,2 @@

import { DateLike, FromIndexOptions, Index, Metadata, PartitionOptions, Point, PointValue, ReindexOptions, ResampleOptions, TimeseriePointIterator, TimeSerieReduceOptions, TimeSeriesOperationOptions } from './types';
import { BetweenTimeOptions, DateLike, FromIndexOptions, Index, Metadata, PartitionOptions, Point, PointValue, ReindexOptions, ResampleOptions, SplitOptions, TimeseriePointIterator, TimeSerieReduceOptions, TimeSeriesOperationOptions } from "./types";
/**

@@ -14,6 +14,18 @@ * A data structure for a time serie.

};
/**
* Creates a new timeserie.
* @param name {String} The name of the serie
* @param serie {Point[]} The points in the serie
* @param metadata {Metadata} Optional metadata
*/
constructor(name: string, serie: Point[] | ReadonlyArray<Point>, metadata?: Metadata);
/**
* Creates a new TimeSerie from the given Index. The new serie's values are all set to `null` unless `options.fill` is passed.
* @param index
* @param options
*/
static fromIndex(index: Index, options: FromIndexOptions): TimeSerie;
/**
* Recreates the serie's index
* Recreates the serie's index according to `options` and returns the reindexed serie.
*
* @param index The new index to use. Can be created with createIndex()

@@ -27,5 +39,9 @@ * @see createIndex

*
* @returns Array of points, where each point is a tuple with ISO8601 timestamp and value
* Returns the array of points, where each point is a tuple with ISO8601 timestamp and value
*/
toArray(): Point[];
/**
* Updates (in place) the serie's name. **This method does NOT return a new timeserie**.
* @param name
*/
rename(name: string): this;

@@ -40,3 +56,3 @@ /**

*
* @returns The array of time indexes
* Returns the array of time indexes
*/

@@ -46,3 +62,3 @@ indexes(): DateLike[];

*
* @returns The array of values
* Returns the array of values
*/

@@ -56,3 +72,3 @@ values(): PointValue[];

*
* @returns The time of the latest non-NaN value
* Returns the time of the latest non-NaN value
*/

@@ -62,18 +78,16 @@ lastValidIndex(): string | null;

*
* @returns The time of the first non-NaN value
* Returns the time of the first non-NaN value
*/
firstValidIndex(): string | null;
/**
*
* @returns The latest non-NaN value
* Returns the latest non-NaN value
*/
lastValidValue(): PointValue;
/**
*
* @returns The first non-NaN value
* Returns the first non-NaN value
*/
firstValidValue(): PointValue;
/**
*
* @returns {PointValue} The value of the timeseries at the given time
* Returns the value of the timeseries at the given time
* @returns {PointValue}
*/

@@ -83,37 +97,56 @@ atTime(time: DateLike, fillValue?: number): PointValue;

*
* @returns The value at the given index (position, not time)
* Returns the value at the given index.
*/
atIndex(index: number): PointValue;
/**
* Returns the subset of points between the two dates. Extremes are included.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from: DateLike, to: DateLike, options?: {
includeInferior: boolean;
includeSuperior: boolean;
}): TimeSerie;
betweenTime(from: DateLike, to: DateLike, options?: BetweenTimeOptions): TimeSerie;
/**
*
* @param from start positional index
* @param to end positional index
* @returns The subset of points between the two indexes. Extremes are included.
*/
* Returns the subset of points between the two indexes. Extremes are included.
*
* @param from start positional index
* @param to end positional index
*/
betweenIndexes(from: number, to: number): TimeSerie;
/**
* Builds a new serie by applying a filter function the current serie's points
* @params fn {Function}
*/
filter(fn: TimeseriePointIterator): TimeSerie;
/**
* Builds a new serie by applying a map function the current serie's points
* @param fn {Function}
*/
map(fn: TimeseriePointIterator): TimeSerie;
/**
* Returns the number of points in the serie.
*/
length(): number;
/**
* Returns true if the serie has 0 points
*/
isEmpty(): boolean;
/**
* Copies the serie to a new serie
*/
copy(): TimeSerie;
/**
* Returns the sum of the values in the serie
*/
sum(): Point;
/**
*
* @returns The average of point values
* Returns the average of the values in the serie
*/
avg(): Point;
/**
* Returns the difference between the last and the first element by performing last value - first value.
*/
delta(): Point;
/**
* Returns the first point
*
* @returns The firstfirst point
*/

@@ -124,3 +157,2 @@ first(): Point;

* @param time
* @returns
*/

@@ -130,3 +162,3 @@ firstAt(time: DateLike): Point;

*
* @returns The last point
* Returns the last point
*/

@@ -136,3 +168,3 @@ last(): Point;

*
* @returns The point with max value, or null
* Returns the point with max value, or null
*/

@@ -142,3 +174,3 @@ max(): Point | null;

*
* @returns The point with min value or null
* Returns the point with min value or null
*/

@@ -153,24 +185,39 @@ min(): Point | null;

* @param options
* @returns
*/
partition(options: PartitionOptions): TimeSerie[];
/**
* Splits a timeserie into multiple timeseries where each timeserie has
* a maximum of `options.chunks` points.
*/
split(options: SplitOptions): TimeSerie[];
/**
* Resample the timeserie using a new time interval and a point aggregation function
* @param options
* @returns
*/
resample(options: ResampleOptions): TimeSerie;
/**
* Remove the point at the given time and returns a new serie
*/
removeAt(time: DateLike): TimeSerie;
/**
* Remove the point at the given index and returns a new serie
*/
removeAtIndex(index: number): TimeSerie;
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns New timeserie without the removed data. Bounds are removed.
*/
* Returns the new timeserie without the removed data. Bounds are removed.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
removeBetweenTime(from: DateLike, to: DateLike): TimeSerie;
/**
* Removes points with NaN value from the serie
*/
dropNaN(): TimeSerie;
/**
* Removes points with null value from the serie.
*/
dropNull(): TimeSerie;
/**
*
* Rounds the serie's points.
* @param decimals {Number} the number of decimals to keep

@@ -180,7 +227,33 @@ * @returns {TimeSerie}

round(decimals: number): TimeSerie;
/**
* Combine the current serie with an array of series y performing combination operations, such as multiplication, addition ecc.
* @param operation {string}
* @param series {TimeSerie[]}
* @param options {TimeSeriesOperationOptions}
*/
combine(operation: string, series: TimeSerie[], options?: TimeSeriesOperationOptions): TimeSerie;
/**
* Adds values to the timeserie. If a scalar is passed, its value is added to every point in the serie. If another serie
* is passed, the two series are combined by addition.
* @see combine
*/
add(value: number | TimeSerie): TimeSerie;
/**
* Subtracts values from the timeserie. If a scalar is passed, its value is subtracted from every point in the serie. If another serie
* is passed, the two series are combined by subtraction.
* @see combine
*/
sub(value: number | TimeSerie): TimeSerie;
/**
* Multiplies values of the timeserie. If a scalar is passed, every point in the serie is multiplied times that value. If another serie
* is passed, the two series are combined by multiplication.
* @see combine
*/
mul(value: number | TimeSerie): TimeSerie;
/**
* Divides values of the timeserie. If a scalar is passed, every point in the serie is divided by that value. If another serie
* is passed, the two series are combined by division.
* @see combine
*/
div(value: number | TimeSerie): TimeSerie;
}

288

build/module/lib/timeserie.js

@@ -1,13 +0,15 @@

import { createIndex, TimeInterval } from './types';
import { DateLikeToString } from './utils';
import { createIndex, TimeInterval, } from "./types";
import { chunk, DateLikeToString } from "./utils";
import * as Timsort from "timsort";
function isNumeric(str) {
if (typeof str === 'number')
if (typeof str === "number")
return !isNaN(str);
if (typeof str !== 'string')
if (typeof str !== "string")
return false; // we only process strings!
return !isNaN(str) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str)); // ...and ensure strings of whitespace fail
return (!isNaN(str) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str))); // ...and ensure strings of whitespace fail
}
function sortPoints(points) {
return [].concat(points).sort((a, b) => {
const arr = [].concat(points);
Timsort.sort(arr, (a, b) => {
if (a[0] > b[0]) {

@@ -20,2 +22,3 @@ return 1;

});
return arr;
}

@@ -29,2 +32,8 @@ function normalizePoint(p) {

export class TimeSerie {
/**
* Creates a new timeserie.
* @param name {String} The name of the serie
* @param serie {Point[]} The points in the serie
* @param metadata {Metadata} Optional metadata
*/
constructor(name, serie, metadata = {}) {

@@ -34,3 +43,5 @@ this.data = sortPoints(serie).map(normalizePoint);

this.metadata = metadata;
this.index = [].concat(this.data).reduce((acc, p) => {
this.index = []
.concat(this.data)
.reduce((acc, p) => {
acc[p[0]] = p;

@@ -40,7 +51,13 @@ return acc;

}
/**
* Creates a new TimeSerie from the given Index. The new serie's values are all set to `null` unless `options.fill` is passed.
* @param index
* @param options
*/
static fromIndex(index, options) {
return new TimeSerie(options.name, index.map((i) => ([i, options?.fill || null])), options.metadata);
return new TimeSerie(options.name, index.map((i) => [i, options?.fill || null]), options.metadata);
}
/**
* Recreates the serie's index
* Recreates the serie's index according to `options` and returns the reindexed serie.
*
* @param index The new index to use. Can be created with createIndex()

@@ -54,9 +71,9 @@ * @see createIndex

const idx = [...new Set(this.indexes().concat(index))];
return new TimeSerie(this.name, idx.map((i) => ([i, this.atTime(i) || options?.fill || null])), this.metadata);
return new TimeSerie(this.name, idx.map((i) => [i, this.atTime(i) || options?.fill || null]), this.metadata);
}
return new TimeSerie(this.name, index.map((i) => ([i, this.atTime(i) || options?.fill || null])), this.metadata);
return new TimeSerie(this.name, index.map((i) => [i, this.atTime(i) || options?.fill || null]), this.metadata);
}
/**
*
* @returns Array of points, where each point is a tuple with ISO8601 timestamp and value
* Returns the array of points, where each point is a tuple with ISO8601 timestamp and value
*/

@@ -66,2 +83,6 @@ toArray() {

}
/**
* Updates (in place) the serie's name. **This method does NOT return a new timeserie**.
* @param name
*/
rename(name) {

@@ -81,3 +102,3 @@ this.name = name;

*
* @returns The array of time indexes
* Returns the array of time indexes
*/

@@ -89,3 +110,3 @@ indexes() {

*
* @returns The array of values
* Returns the array of values
*/

@@ -99,10 +120,13 @@ values() {

static concat(series) {
return new TimeSerie(series[0].name, series.map(serie => serie.toArray()).flat(), Object.assign({}, ...series.map(serie => serie.metadata)));
return new TimeSerie(series[0].name, series.flatMap((serie) => serie.toArray()), Object.assign({}, ...series.map((serie) => serie.metadata)));
}
/**
*
* @returns The time of the latest non-NaN value
* Returns the time of the latest non-NaN value
*/
lastValidIndex() {
const result = this.data.concat([]).reverse().find((point) => {
const result = this.data
.concat([])
.reverse()
.find((point) => {
return !!point[1];

@@ -119,3 +143,3 @@ });

*
* @returns The time of the first non-NaN value
* Returns the time of the first non-NaN value
*/

@@ -134,7 +158,9 @@ firstValidIndex() {

/**
*
* @returns The latest non-NaN value
* Returns the latest non-NaN value
*/
lastValidValue() {
const result = this.data.concat([]).reverse().find((point) => {
const result = this.data
.concat([])
.reverse()
.find((point) => {
return !!point[1];

@@ -150,4 +176,3 @@ });

/**
*
* @returns The first non-NaN value
* Returns the first non-NaN value
*/

@@ -166,4 +191,4 @@ firstValidValue() {

/**
*
* @returns {PointValue} The value of the timeseries at the given time
* Returns the value of the timeseries at the given time
* @returns {PointValue}
*/

@@ -175,7 +200,7 @@ atTime(time, fillValue = null) {

*
* @returns The value at the given index (position, not time)
* Returns the value at the given index.
*/
atIndex(index) {
if (index >= this.data.length) {
throw new Error('Index out of bounds');
throw new Error("Index out of bounds");
}

@@ -185,8 +210,11 @@ return this.data[index][1];

/**
* Returns the subset of points between the two dates. Extremes are included.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime(from, to, options = { includeInferior: true, includeSuperior: true }) {
betweenTime(from, to, options = {
includeInferior: true,
includeSuperior: true,
}) {
const { includeInferior, includeSuperior } = options;

@@ -197,12 +225,16 @@ const f = new Date(from);

if (includeInferior && includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() <= t.getTime();
return (new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() <= t.getTime());
}
else if (includeInferior && !includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() < t.getTime();
return (new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() < t.getTime());
}
else if (!includeInferior && includeSuperior) {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() <= t.getTime();
return (new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() <= t.getTime());
}
else {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() < t.getTime();
return (new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() < t.getTime());
}

@@ -213,25 +245,47 @@ });

/**
*
* @param from start positional index
* @param to end positional index
* @returns The subset of points between the two indexes. Extremes are included.
*/
* Returns the subset of points between the two indexes. Extremes are included.
*
* @param from start positional index
* @param to end positional index
*/
betweenIndexes(from, to) {
return this.filter((_, i) => { return i >= from && i <= to; });
return this.filter((_, i) => {
return i >= from && i <= to;
});
}
/**
* Builds a new serie by applying a filter function the current serie's points
* @params fn {Function}
*/
filter(fn) {
return this.recreate(this.data.filter(fn));
}
/**
* Builds a new serie by applying a map function the current serie's points
* @param fn {Function}
*/
map(fn) {
return this.recreate(this.data.map(fn));
}
/**
* Returns the number of points in the serie.
*/
length() {
return this.data.length;
}
/**
* Returns true if the serie has 0 points
*/
isEmpty() {
return this.data.length === 0;
}
/**
* Copies the serie to a new serie
*/
copy() {
return new TimeSerie(this.name, this.data, this.metadata);
}
/**
* Returns the sum of the values in the serie
*/
sum() {

@@ -242,7 +296,12 @@ if (this.length() === 0) {

const copy = this.dropNaN();
return [this.first()[0], copy.data.map((p) => p[1]).reduce((p1, p2) => p1 + p2, 0)];
let tot = 0;
const l = copy.length();
const data = copy.toArray();
for (let i = l - 1; i >= 0; i--) {
tot += data[i][1];
}
return [this.first()[0], tot];
}
/**
*
* @returns The average of point values
* Returns the average of the values in the serie
*/

@@ -256,2 +315,5 @@ avg() {

}
/**
* Returns the difference between the last and the first element by performing last value - first value.
*/
delta() {

@@ -268,4 +330,4 @@ if (this.length() <= 0) {

/**
* Returns the first point
*
* @returns The firstfirst point
*/

@@ -278,10 +340,11 @@ first() {

* @param time
* @returns
*/
firstAt(time) {
return this.data.find((p) => { return new Date(p[0]).getTime() >= new Date(time).getTime(); });
return this.data.find((p) => {
return new Date(p[0]).getTime() >= new Date(time).getTime();
});
}
/**
*
* @returns The last point
* Returns the last point
*/

@@ -293,3 +356,3 @@ last() {

*
* @returns The point with max value, or null
* Returns the point with max value, or null
*/

@@ -303,7 +366,7 @@ max() {

}
return this.data.reduce((prev, current) => current[1] > prev[1] ? current : prev, this.data[0]);
return this.data.reduce((prev, current) => (current[1] > prev[1] ? current : prev), this.data[0]);
}
/**
*
* @returns The point with min value or null
* Returns the point with min value or null
*/

@@ -317,3 +380,3 @@ min() {

}
return this.data.reduce((prev, current) => current[1] < prev[1] ? current : prev, this.data[0]);
return this.data.reduce((prev, current) => (current[1] < prev[1] ? current : prev), this.data[0]);
}

@@ -329,3 +392,2 @@ /**

* @param options
* @returns
*/

@@ -335,11 +397,14 @@ partition(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.[0];
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false });
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});

@@ -360,5 +425,13 @@ return partitions.map((p, idx) => {

/**
* Splits a timeserie into multiple timeseries where each timeserie has
* a maximum of `options.chunks` points.
*/
split(options) {
return chunk(this.toArray(), options.chunks).map((points) => {
return this.recreate(points);
});
}
/**
* Resample the timeserie using a new time interval and a point aggregation function
* @param options
* @returns
*/

@@ -368,23 +441,32 @@ resample(options) {

if (!from) {
throw new Error('Cannot infer a lower bound for resample');
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()[0];
if (!to) {
throw new Error('Cannot infer an upper bound for resample');
throw new Error("Cannot infer an upper bound for resample");
}
return TimeSerie.concat(this.partition(options)
.map((chunk) => chunk.reduce(options)));
return TimeSerie.concat(this.partition(options).map((chunk) => chunk.reduce(options)));
}
/**
* Remove the point at the given time and returns a new serie
*/
removeAt(time) {
return this.recreate(this.data.filter((p) => { return p[0] !== DateLikeToString(time); }));
return this.recreate(this.data.filter((p) => {
return p[0] !== DateLikeToString(time);
}));
}
/**
* Remove the point at the given index and returns a new serie
*/
removeAtIndex(index) {
return this.recreate(this.data.filter((_, i) => { return i !== index; }));
return this.recreate(this.data.filter((_, i) => {
return i !== index;
}));
}
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns New timeserie without the removed data. Bounds are removed.
*/
* Returns the new timeserie without the removed data. Bounds are removed.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
removeBetweenTime(from, to) {

@@ -394,9 +476,16 @@ const f = new Date(from);

const data = this.data.filter((point) => {
return new Date(point[0]).getTime() < f.getTime() || new Date(point[0]).getTime() > t.getTime();
return (new Date(point[0]).getTime() < f.getTime() ||
new Date(point[0]).getTime() > t.getTime());
});
return this.recreate(data);
}
/**
* Removes points with NaN value from the serie
*/
dropNaN() {
return this.filter((p) => isNumeric(p[1]));
}
/**
* Removes points with null value from the serie.
*/
dropNull() {

@@ -406,3 +495,3 @@ return this.filter((p) => p[1] !== null);

/**
*
* Rounds the serie's points.
* @param decimals {Number} the number of decimals to keep

@@ -412,5 +501,13 @@ * @returns {TimeSerie}

round(decimals) {
return this.map((p) => ([p[0], Number(Number(p[1]).toFixed(decimals))]));
return this.map((p) => [
p[0],
Number(Number(p[1]).toFixed(decimals)),
]);
}
// Operation between timeseries
/**
* Combine the current serie with an array of series y performing combination operations, such as multiplication, addition ecc.
* @param operation {string}
* @param series {TimeSerie[]}
* @param options {TimeSeriesOperationOptions}
*/
combine(operation, series, options = {}) {

@@ -421,32 +518,52 @@ options.name = options.name || this.name;

}
/**
* Adds values to the timeserie. If a scalar is passed, its value is added to every point in the serie. If another serie
* is passed, the two series are combined by addition.
* @see combine
*/
add(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] + value]);
}
else {
return this.combine('add', [value]);
return this.combine("add", [value]);
}
}
/**
* Subtracts values from the timeserie. If a scalar is passed, its value is subtracted from every point in the serie. If another serie
* is passed, the two series are combined by subtraction.
* @see combine
*/
sub(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] - value]);
}
else {
return this.combine('sub', [value]);
return this.combine("sub", [value]);
}
}
/**
* Multiplies values of the timeserie. If a scalar is passed, every point in the serie is multiplied times that value. If another serie
* is passed, the two series are combined by multiplication.
* @see combine
*/
mul(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] * value]);
}
else {
return this.combine('mul', [value]);
return this.combine("mul", [value]);
}
}
/**
* Divides values of the timeserie. If a scalar is passed, every point in the serie is divided by that value. If another serie
* is passed, the two series are combined by division.
* @see combine
*/
div(value) {
if (typeof value === 'number') {
if (typeof value === "number") {
return this.map((point) => [point[0], point[1] / value]);
}
else {
return this.combine('div', [value]);
return this.combine("div", [value]);
}

@@ -462,8 +579,7 @@ }

TimeSerie.internals.combine = (series, combiner, options) => {
const points = series[0].data.map((p) => p[0]).map((idx) => {
const points = series[0].data
.map((p) => p[0])
.map((idx) => {
const values = series.map((serie) => serie.atTime(idx, options.fill));
return [
idx,
combiner(values, idx)
];
return [idx, combiner(values, idx)];
});

@@ -476,4 +592,4 @@ return new TimeSerie(options.name, points, options.metadata);

TimeSerie.internals.combiners.div = (points) => points.reduce((a, b) => a / b, points[0] * points[0]);
TimeSerie.internals.combiners.avg = (points) => (TimeSerie.internals.combiners.add(points) / points.length);
TimeSerie.internals.combiners.avg = (points) => TimeSerie.internals.combiners.add(points) / points.length;
TimeSerie.createIndex = createIndex;
//# 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704

build/module/lib/timeserie.spec.js

@@ -1,29 +0,29 @@

import test from 'ava';
import { TimeSerie } from './timeserie';
test('TimeSerie.atTime() should return the correct point or null', (t) => {
import test from "ava";
import { TimeSerie } from "./timeserie";
test("TimeSerie.atTime() should return the correct point or null", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new TimeSerie('energy', data);
t.is(ts.atTime('2021-01-02T00:00:00.000Z'), 5);
t.is(ts.atTime('2021-01-22T00:00:00.000Z'), null);
const ts = new TimeSerie("energy", data);
t.is(ts.atTime("2021-01-02T00:00:00.000Z"), 5);
t.is(ts.atTime("2021-01-22T00:00:00.000Z"), null);
});
test('TimeSerie.atIndex() should return the correct point', (t) => {
test("TimeSerie.atIndex() should return the correct point", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.atIndex(1), 5);
});
test('TimeSerie.atIndex() should throw when the index is out of bounds', (t) => {
test("TimeSerie.atIndex() should throw when the index is out of bounds", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.throws(() => {

@@ -33,76 +33,76 @@ ts.atIndex(100);

});
test('TimeSerie.toArray() should return the whole data', (t) => {
test("TimeSerie.toArray() should return the whole data", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 4],
['2021-01-02T00:00:00.000Z', 5],
['2021-01-03T00:00:00.000Z', 6]
["2021-01-01T00:00:00.000Z", 4],
["2021-01-02T00:00:00.000Z", 5],
["2021-01-03T00:00:00.000Z", 6],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.deepEqual(data, ts.toArray());
});
test('TimeSerie.firstValidIndex() should return the first valid value index', (t) => {
test("TimeSerie.firstValidIndex() should return the first valid value index", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', null],
['2021-01-02T00:00:00.000Z', null],
['2021-01-03T00:00:00.000Z', 6],
['2021-01-04T00:00:00.000Z', 7],
['2021-01-05T00:00:00.000Z', 8],
['2021-01-06T00:00:00.000Z', null]
["2021-01-01T00:00:00.000Z", null],
["2021-01-02T00:00:00.000Z", null],
["2021-01-03T00:00:00.000Z", 6],
["2021-01-04T00:00:00.000Z", 7],
["2021-01-05T00:00:00.000Z", 8],
["2021-01-06T00:00:00.000Z", null],
];
const ts = new TimeSerie('energy', data);
t.is(ts.firstValidIndex(), '2021-01-03T00:00:00.000Z');
const ts = new TimeSerie("energy", data);
t.is(ts.firstValidIndex(), "2021-01-03T00:00:00.000Z");
});
test('TimeSerie.lastValidIndex() should return the last valid value index', (t) => {
test("TimeSerie.lastValidIndex() should return the last valid value index", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new TimeSerie('energy', data);
t.is(ts.lastValidIndex(), '2021-01-05T00:00:00.000Z');
const ts = new TimeSerie("energy", data);
t.is(ts.lastValidIndex(), "2021-01-05T00:00:00.000Z");
});
test('TimeSerie.firstValidValue() should return the first valid value index or null', (t) => {
test("TimeSerie.firstValidValue() should return the first valid value index or null", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.firstValidValue(), 6);
const data2 = [
['2021-01-01', null],
['2021-01-02', null]
["2021-01-01", null],
["2021-01-02", null],
];
const ts2 = new TimeSerie('energy2', data2);
const ts2 = new TimeSerie("energy2", data2);
t.is(ts2.firstValidValue(), null);
});
test('TimeSerie.lastValidValue() should return the last valid value index', (t) => {
test("TimeSerie.lastValidValue() should return the last valid value index", (t) => {
const data = [
['2021-01-01', null],
['2021-01-02', null],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', null]
["2021-01-01", null],
["2021-01-02", null],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", null],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.lastValidValue(), 8);
});
test('TimeSerie.betweenTime() should return the correct timeserie subset', (t) => {
test("TimeSerie.betweenTime() should return the correct timeserie subset", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new TimeSerie('energy', data);
const subset = ts.betweenTime('2021-01-03', '2021-01-05');
const ts = new TimeSerie("energy", data);
const subset = ts.betweenTime("2021-01-03", "2021-01-05");
t.is(subset.length(), 3);

@@ -112,12 +112,12 @@ t.is(subset.firstValidValue(), 6);

});
test('TimeSerie.filter() should allow to pass custom filtering logic', (t) => {
test("TimeSerie.filter() should allow to pass custom filtering logic", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const filtered = ts.filter((p) => {

@@ -130,12 +130,12 @@ return p[1] % 2 === 0;

});
test('TimeSerie.map() should allow to pass custom mapping logic', (t) => {
test("TimeSerie.map() should allow to pass custom mapping logic", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', 6],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", 6],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const mapped = ts.map((p) => {

@@ -148,125 +148,129 @@ return [p[0], p[1] * 2];

});
test('TimeSerie.isEmpty() should behave correctly', (t) => {
const data = [
['2021-01-01', 4]
];
const ts1 = new TimeSerie('energy', data);
const ts2 = new TimeSerie('energy', []);
test("TimeSerie.isEmpty() should behave correctly", (t) => {
const data = [["2021-01-01", 4]];
const ts1 = new TimeSerie("energy", data);
const ts2 = new TimeSerie("energy", []);
t.is(ts1.isEmpty(), false);
t.is(ts2.isEmpty(), true);
});
test('Timeserie.sum() should return the sum of the values', (t) => {
test("Timeserie.sum() should return the sum of the values", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 5],
['2021-01-03', null],
['2021-01-04', 7],
['2021-01-05', 8],
['2021-01-06', 9]
["2021-01-01", 4],
["2021-01-02", 5],
["2021-01-03", null],
["2021-01-04", 7],
["2021-01-05", 8],
["2021-01-06", 9],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.sum()[1], 33);
});
test('Timeserie.avg() should return the average of the values', (t) => {
test("Timeserie.avg() should return the average of the values", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 4],
['2021-01-03', null]
["2021-01-01", 4],
["2021-01-02", 4],
["2021-01-03", null],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.avg()[1], 4);
});
test('Timeserie.first() should return the first point or null', (t) => {
const ts1 = new TimeSerie('ts1', [['2021-01-01', 4]]);
const ts2 = new TimeSerie('ts2', []);
test("Timeserie.first() should return the first point or null", (t) => {
const ts1 = new TimeSerie("ts1", [["2021-01-01", 4]]);
const ts2 = new TimeSerie("ts2", []);
t.is(ts1.first()[1], 4);
t.is(ts2.first(), null);
});
test('Timeserie.firstAt() should return the first point with time >= the given', (t) => {
test("Timeserie.firstAt() should return the first point with time >= the given", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 4],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 4],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new TimeSerie('energy', data);
t.is(ts.firstAt('2021-01-02')[1], 4);
t.is(ts.firstAt('2021-01-03')[1], 8);
const ts = new TimeSerie("energy", data);
t.is(ts.firstAt("2021-01-02")[1], 4);
t.is(ts.firstAt("2021-01-03")[1], 8);
});
test('Timeserie.last() should return the last point or null', (t) => {
const ts1 = new TimeSerie('ts1', [['2021-01-01', 4], ['2021-01-02', 5]]);
const ts2 = new TimeSerie('ts2', []);
test("Timeserie.last() should return the last point or null", (t) => {
const ts1 = new TimeSerie("ts1", [
["2021-01-01", 4],
["2021-01-02", 5],
]);
const ts2 = new TimeSerie("ts2", []);
t.is(ts1.last()[1], 5);
t.is(ts2.last(), null);
});
test('Timeserie.max() should return the point with maximum value', (t) => {
test("Timeserie.max() should return the point with maximum value", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 11],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 11],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.max()[1], 11);
});
test('Timeserie.min() should return the point with minimum value', (t) => {
test("Timeserie.min() should return the point with minimum value", (t) => {
const data = [
['2021-01-01', 4],
['2021-01-02', 11],
['2021-01-04', 8],
['2021-01-05', 8]
["2021-01-01", 4],
["2021-01-02", 11],
["2021-01-04", 8],
["2021-01-05", 8],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
t.is(ts.min()[1], 4);
});
test('Timeserie.partition() should partition the timeserie', (t) => {
test("Timeserie.partition() should partition the timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const chunks = ts.partition({ interval: 1000 * 60 * 60 * 24 });
t.is(chunks.length, 4);
});
test('Timeserie.reduce() should reduce the timeserie', (t) => {
test("Timeserie.reduce() should reduce the timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const result = ts.reduce({ operation: 'sum' });
const ts = new TimeSerie("energy", data);
const result = ts.reduce({ operation: "sum" });
t.is(result.length(), 1);
t.is(result.atIndex(0), 40);
});
test('Timeserie.resample(operation=sum) should provide the correct timeserie', (t) => {
test("Timeserie.resample(operation=sum) should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'sum' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "sum",
});
t.is(daily.length(), 4);

@@ -278,17 +282,20 @@ t.is(daily.atIndex(0), 8);

});
test('Timeserie.resample().avg() should provide the correct timeserie', (t) => {
test("Timeserie.resample().avg() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 4],
['2021-01-01T20:00:00.000Z', 4],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 4],
['2021-01-03T13:00:00.000Z', 4],
['2021-01-03T20:00:00.000Z', 4],
['2021-01-04T12:00:00.000Z', 4],
['2021-01-04T16:00:00.000Z', 4],
['2021-01-04T20:00:00.000Z', 4]
["2021-01-01T12:00:00.000Z", 4],
["2021-01-01T20:00:00.000Z", 4],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 4],
["2021-01-03T13:00:00.000Z", 4],
["2021-01-03T20:00:00.000Z", 4],
["2021-01-04T12:00:00.000Z", 4],
["2021-01-04T16:00:00.000Z", 4],
["2021-01-04T20:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'avg' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "avg",
});
t.is(daily.length(), 4);

@@ -300,17 +307,20 @@ t.is(daily.atIndex(0), 4);

});
test('Timeserie.resample().first() should provide the correct timeserie', (t) => {
test("Timeserie.resample().first() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 1],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 1],
['2021-01-03T13:00:00.000Z', 2],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 1],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 1],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 1],
["2021-01-03T13:00:00.000Z", 2],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 1],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'first' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "first",
});
t.is(daily.length(), 4);

@@ -322,17 +332,20 @@ t.is(daily.atIndex(0), 1);

});
test('Timeserie.resample().last() should provide the correct timeserie', (t) => {
test("Timeserie.resample().last() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 1],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 1],
['2021-01-03T13:00:00.000Z', 2],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 1],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 1],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 1],
["2021-01-03T13:00:00.000Z", 2],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 1],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'last' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "last",
});
t.is(daily.length(), 4);

@@ -344,17 +357,20 @@ t.is(daily.atIndex(0), 2);

});
test('Timeserie.resample().max() should provide the correct timeserie', (t) => {
test("Timeserie.resample().max() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 8],
['2021-01-03T13:00:00.000Z', 11],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 100],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 8],
["2021-01-03T13:00:00.000Z", 11],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 100],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'max' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "max",
});
t.is(daily.length(), 4);

@@ -366,17 +382,20 @@ t.is(daily.atIndex(0), 2);

});
test('Timeserie.resample().min() should provide the correct timeserie', (t) => {
test("Timeserie.resample().min() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 4],
['2021-01-02T20:00:00.000Z', 2],
['2021-01-03T12:00:00.000Z', 8],
['2021-01-03T13:00:00.000Z', 11],
['2021-01-03T20:00:00.000Z', 3],
['2021-01-04T12:00:00.000Z', 100],
['2021-01-04T16:00:00.000Z', 2],
['2021-01-04T20:00:00.000Z', 3]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 4],
["2021-01-02T20:00:00.000Z", 2],
["2021-01-03T12:00:00.000Z", 8],
["2021-01-03T13:00:00.000Z", 11],
["2021-01-03T20:00:00.000Z", 3],
["2021-01-04T12:00:00.000Z", 100],
["2021-01-04T16:00:00.000Z", 2],
["2021-01-04T20:00:00.000Z", 3],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'min' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "min",
});
t.is(daily.length(), 4);

@@ -388,17 +407,20 @@ t.is(daily.atIndex(0), 1);

});
test('Timeserie.resample().delta() should provide the correct timeserie', (t) => {
test("Timeserie.resample().delta() should provide the correct timeserie", (t) => {
const data = [
['2021-01-01T12:00:00.000Z', 1],
['2021-01-01T20:00:00.000Z', 2],
['2021-01-02T12:00:00.000Z', 3],
['2021-01-02T20:00:00.000Z', 4],
['2021-01-03T12:00:00.000Z', 5],
['2021-01-03T13:00:00.000Z', 8],
['2021-01-03T20:00:00.000Z', 9],
['2021-01-04T12:00:00.000Z', 10],
['2021-01-04T16:00:00.000Z', 12],
['2021-01-04T20:00:00.000Z', 15]
["2021-01-01T12:00:00.000Z", 1],
["2021-01-01T20:00:00.000Z", 2],
["2021-01-02T12:00:00.000Z", 3],
["2021-01-02T20:00:00.000Z", 4],
["2021-01-03T12:00:00.000Z", 5],
["2021-01-03T13:00:00.000Z", 8],
["2021-01-03T20:00:00.000Z", 9],
["2021-01-04T12:00:00.000Z", 10],
["2021-01-04T16:00:00.000Z", 12],
["2021-01-04T20:00:00.000Z", 15],
];
const ts = new TimeSerie('energy', data);
const daily = ts.resample({ interval: 1000 * 60 * 60 * 24, operation: 'delta' });
const ts = new TimeSerie("energy", data);
const daily = ts.resample({
interval: 1000 * 60 * 60 * 24,
operation: "delta",
});
t.is(daily.length(), 4);

@@ -411,22 +433,22 @@ t.is(daily.data[0][0], data[0][0]); // The resampling should start at the first time index

});
test('Timeserie.removeAt() should remove points from the timeserie', (t) => {
test("Timeserie.removeAt() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const filtered = ts.removeAt('2021-01-03T00:00:00.000Z');
const ts = new TimeSerie("energy", data);
const filtered = ts.removeAt("2021-01-03T00:00:00.000Z");
t.is(filtered.length(), 3);
t.is(filtered.atIndex(2), 4);
});
test('Timeserie.removeAtIndex() should remove points from the timeserie', (t) => {
test("Timeserie.removeAtIndex() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const filtered = ts.removeAtIndex(0);

@@ -436,23 +458,23 @@ t.is(filtered.length(), 3);

});
test('Timeserie.removeBetweenTime() should remove points from the timeserie', (t) => {
test("Timeserie.removeBetweenTime() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2],
['2021-01-03T00:00:00.000Z', 3],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
["2021-01-03T00:00:00.000Z", 3],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const filtered = ts.removeBetweenTime('2021-01-02T00:00:00.000Z', '2021-01-03T00:00:00.000Z');
const ts = new TimeSerie("energy", data);
const filtered = ts.removeBetweenTime("2021-01-02T00:00:00.000Z", "2021-01-03T00:00:00.000Z");
t.is(filtered.length(), 2);
t.is(filtered.atIndex(1), 4);
});
test('Timeserie.dropNaN() should remove points from the timeserie', (t) => {
test("Timeserie.dropNaN() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-01T00:00:00.000Z', NaN],
['2021-01-02T00:00:00.000Z', 'hello'],
['2021-01-03T00:00:00.000Z', {}],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-01T00:00:00.000Z", NaN],
["2021-01-02T00:00:00.000Z", "hello"],
["2021-01-03T00:00:00.000Z", {}],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const filtered = ts.dropNaN();

@@ -462,55 +484,71 @@ t.is(filtered.length(), 2);

});
test('Timeserie.dropNull() should remove points from the timeserie', (t) => {
test("Timeserie.dropNull() should remove points from the timeserie", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 'hello'],
['2021-01-03T00:00:00.000Z', null],
['2021-01-04T00:00:00.000Z', 4]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", "hello"],
["2021-01-03T00:00:00.000Z", null],
["2021-01-04T00:00:00.000Z", 4],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const filtered = ts.dropNull();
t.is(filtered.length(), 3);
t.is(filtered.atIndex(1), 'hello');
t.is(filtered.atIndex(1), "hello");
t.is(filtered.atIndex(2), 4);
});
test('Timeserie.indexes() and Timeserie.values() should return correct values', (t) => {
test("Timeserie.indexes() and Timeserie.values() should return correct values", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 'hello']
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", "hello"],
];
const ts = new TimeSerie('energy', data);
const ts = new TimeSerie("energy", data);
const indexes = ts.indexes();
const values = ts.values();
t.is(indexes[0], '2021-01-01T00:00:00.000Z');
t.is(indexes[1], '2021-01-02T00:00:00.000Z');
t.is(indexes[0], "2021-01-01T00:00:00.000Z");
t.is(indexes[1], "2021-01-02T00:00:00.000Z");
t.is(values[0], 1);
t.is(values[1], 'hello');
t.is(values[1], "hello");
});
test('Timeserie.reindex() should correctly replace the series index', (t) => {
test("Timeserie.reindex() should correctly replace the series index", (t) => {
const data = [
['2021-01-01T00:00:00.000Z', 1],
['2021-01-02T00:00:00.000Z', 2]
["2021-01-01T00:00:00.000Z", 1],
["2021-01-02T00:00:00.000Z", 2],
];
const ts = new TimeSerie('energy', data);
const reindexed = ts.reindex(TimeSerie.createIndex({ from: ts.firstValidIndex(), to: ts.lastValidIndex(), interval: '1h' }), { fill: 0 });
const ts = new TimeSerie("energy", data);
const reindexed = ts.reindex(TimeSerie.createIndex({
from: ts.firstValidIndex(),
to: ts.lastValidIndex(),
interval: "1h",
}), { fill: 0 });
t.is(reindexed.length(), 25);
});
test('Timeserie.fromIndex() should correctly create the series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts = TimeSerie.fromIndex(idx, { fill: 1, name: 'ts' });
test("Timeserie.fromIndex() should correctly create the series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts = TimeSerie.fromIndex(idx, { fill: 1, name: "ts" });
t.is(ts.length(), 24);
t.is(true, ts.toArray().every((item) => item[1] === 1));
});
test('Timeserie.combine() should correctly combine the series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
const result = ts1.combine('add', [ts2]);
test("Timeserie.combine() should correctly combine the series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const result = ts1.combine("add", [ts2]);
t.is(result.length(), ts1.length());
t.is(true, result.toArray().every((item) => item[1] === 3));
});
test('Timeserie.add() should correctly add the series to numbers and other series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
test("Timeserie.add() should correctly add the series to numbers and other series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const ts3 = ts1.add(ts2).add(7);

@@ -520,6 +558,10 @@ t.is(ts3.length(), ts1.length());

});
test('Timeserie.sub() should correctly diff the series to numbers and other series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = TimeSerie.fromIndex(idx, { fill: 8, name: 'ts1' });
const ts2 = TimeSerie.fromIndex(idx, { fill: 4, name: 'ts2' });
test("Timeserie.sub() should correctly diff the series to numbers and other series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = TimeSerie.fromIndex(idx, { fill: 8, name: "ts1" });
const ts2 = TimeSerie.fromIndex(idx, { fill: 4, name: "ts2" });
const ts3 = ts1.sub(ts2).sub(7);

@@ -529,6 +571,10 @@ t.is(ts3.length(), ts1.length());

});
test('Timeserie.mul() should correctly multiply the series to numbers and other series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: 'ts1' });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: 'ts2' });
test("Timeserie.mul() should correctly multiply the series to numbers and other series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = TimeSerie.fromIndex(idx, { fill: 1, name: "ts1" });
const ts2 = TimeSerie.fromIndex(idx, { fill: 2, name: "ts2" });
const ts3 = ts1.mul(ts2).mul(7);

@@ -538,6 +584,10 @@ t.is(ts3.length(), ts1.length());

});
test('Timeserie.div() should correctly divide the series to numbers and other series', (t) => {
const idx = TimeSerie.createIndex({ from: '2022-01-01', to: '2022-01-01T23:00:00.000Z', interval: '1h' });
const ts1 = TimeSerie.fromIndex(idx, { fill: 16, name: 'ts1' });
const ts2 = TimeSerie.fromIndex(idx, { fill: 4, name: 'ts2' });
test("Timeserie.div() should correctly divide the series to numbers and other series", (t) => {
const idx = TimeSerie.createIndex({
from: "2022-01-01",
to: "2022-01-01T23:00:00.000Z",
interval: "1h",
});
const ts1 = TimeSerie.fromIndex(idx, { fill: 16, name: "ts1" });
const ts2 = TimeSerie.fromIndex(idx, { fill: 4, name: "ts2" });
const ts3 = ts1.div(ts2).div(4);

@@ -547,2 +597,20 @@ t.is(ts3.length(), ts1.length());

});
//# 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test("Timeserie.split() should split the timeserie", (t) => {
const data = [
["2021-01-01T12:00:00.000Z", 181],
["2021-01-01T20:00:00.000Z", 181],
["2021-01-02T12:00:00.000Z", 181],
["2021-01-02T20:00:00.000Z", 181],
["2021-01-03T12:00:00.000Z", 181],
["2021-01-03T13:00:00.000Z", 181],
["2021-01-03T20:00:00.000Z", 181],
["2021-01-04T12:00:00.000Z", 181],
["2021-01-04T16:00:00.000Z", 181],
["2021-01-04T20:00:00.000Z", 181],
];
const ts = new TimeSerie("energy", data);
const result = ts.split({ chunks: 3 });
t.is(result.length, 4);
t.deepEqual(result.map((ts) => ts.length()), [3, 3, 3, 1]);
});
//# 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17

build/module/lib/types.d.ts

@@ -1,2 +0,2 @@

import { TimeSerie } from './timeserie';
import { TimeSerie } from "./timeserie";
export declare type PointValue = number | string | boolean | any;

@@ -57,3 +57,3 @@ export declare type DateLike = Date | string | number;

export declare type TimeserieIterator = (value: TimeSerie, index: number, array: ReadonlyArray<TimeSerie>) => any;
export declare type ColumnAggregation = 'avg' | 'last' | 'first' | 'min' | 'max' | 'delta' | 'sum';
export declare type ColumnAggregation = "avg" | "last" | "first" | "min" | "max" | "delta" | "sum";
export declare type ResampleDefaultAggregation = ColumnAggregation;

@@ -88,6 +88,6 @@ export declare type IntervalOptions = {

output: string;
operation: 'add' | 'mul' | 'div' | 'sub' | 'avg' | TimeseriePointCombiner;
operation: "add" | "mul" | "div" | "sub" | "avg" | TimeseriePointCombiner;
columns: string[];
}
export declare type ReduceOperation = 'min' | 'max' | 'first' | 'last' | 'avg' | 'sum' | 'delta';
export declare type ReduceOperation = "min" | "max" | "first" | "last" | "avg" | "sum" | "delta";
export declare type TimeFrameReduceOptions = {

@@ -106,3 +106,3 @@ operation: ReduceOperation;

}
export declare type PipelineStageType = 'aggregate' | 'resample' | 'project' | 'reduce' | 'add' | 'mul';
export declare type PipelineStageType = "aggregate" | "resample" | "project" | "reduce" | "add" | "mul";
export declare type PipelineStage = {

@@ -116,2 +116,9 @@ aggregate?: AggregationConfiguration;

};
export declare type SplitOptions = {
chunks: number;
};
export declare type BetweenTimeOptions = {
includeInferior: boolean;
includeSuperior: boolean;
};
export declare class TimeInterval {

@@ -118,0 +125,0 @@ from: Date;

9

build/module/lib/types.js

@@ -1,5 +0,2 @@

import parse from 'parse-duration';
;
;
;
import parse from "parse-duration";
export class TimeInterval {

@@ -31,3 +28,3 @@ constructor(from, to) {

let size = options.interval;
if (typeof options.interval === 'string') {
if (typeof options.interval === "string") {
size = parse(options.interval);

@@ -44,2 +41,2 @@ }

}
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4

build/module/lib/utils.d.ts

@@ -1,5 +0,5 @@

import { DateLike } from './types';
import { DateLike } from "./types";
export declare function ms(date: Date | string): number;
export declare function DateLikeToString(d: DateLike): string;
export declare function DateLikeToTimestamp(d: DateLike): number;
export declare function getOrderOfMagnitude(n: number): number;
export declare function chunk(arr: any[], chunk_size: number): any[][];

18

build/module/lib/utils.js

@@ -5,9 +5,9 @@ export function ms(date) {

export function DateLikeToString(d) {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).toISOString();
}
// if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
// return new Date(Number(d)).toISOString();
// }
return new Date(d).toISOString();
}
export function DateLikeToTimestamp(d) {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).getTime();

@@ -17,7 +17,7 @@ }

}
export function getOrderOfMagnitude(n) {
const order = Math.floor(Math.log(n) / Math.LN10 +
0.000000001); // because float math sucks like that
return Math.pow(10, order);
export function chunk(arr, chunk_size) {
return new Array(Math.ceil(arr.length / chunk_size))
.fill(0)
.map((_, i) => arr.slice(i * chunk_size, (i + 1) * chunk_size));
}
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14

CHANGELOG.md

@@ -1,3 +0,15 @@

# 📦 0.1.3 (16 Nov 2022)
# 📦 0.1.3 (28 Dec 2022)
- [fcdd7](https://github.com/fatmatto/timeframes/commit/fcdd7973eaaa8006b3fbff20868c972025457011) chore(release): 0.1.37
- [796cf](https://github.com/fatmatto/timeframes/commit/796cf3319bc2a133ae1d7cc506f2761dd4da7821) chore: drop tests for node 14
- [5ffcd](https://github.com/fatmatto/timeframes/commit/5ffcd3cb60b17f37665f7bafa1d78cb04852f000) feat: move to rome tools, add split() method
- [a13d0](https://github.com/fatmatto/timeframes/commit/a13d04156cdad193c62fc8353702bef478024b36) doc: update docs
- [5f7cf](https://github.com/fatmatto/timeframes/commit/5f7cf594f75efc62d2578728a1f18c9fb28b2982) docs: improve doc template
- [9db5b](https://github.com/fatmatto/timeframes/commit/9db5b379c421a8551af82ef3eef9824d6a2ecce0) docs: fix docs path
- [c9788](https://github.com/fatmatto/timeframes/commit/c9788f9d07a4d7cf4b4bdc3961de79cac168af24) docs: add .nojekyll
- [c1a3b](https://github.com/fatmatto/timeframes/commit/c1a3b04fe2b6e16713ae8936a3bc5d28e3da506e) docs: add params to docsify
- [8d184](https://github.com/fatmatto/timeframes/commit/8d18461696a5620bfa518fdf74bb16737ae39f22) perf: use rb-tree for timeframe indexing
- [9547c](https://github.com/fatmatto/timeframes/commit/9547cdbd21da915d81e3315df4404e8424a7d957) chore: migrate docs to docsify
- [6b7c0](https://github.com/fatmatto/timeframes/commit/6b7c0c9ee23f3a588cc97e35205f2a94e65ac0ba) 📦 Release 0.1.35 standard-version [skip ci]
- [8c367](https://github.com/fatmatto/timeframes/commit/8c367b565436e3f908bc9ba2f05f08132a90802f) chore(release): 0.1.35
- [62dd3](https://github.com/fatmatto/timeframes/commit/62dd3016b6cb0d2468a2f244291e7c095d7ad678) feat: add TimeFrame.pipeline method; fix timeserie reducers behaviour with NaNsw
- [f6013](https://github.com/fatmatto/timeframes/commit/f601353bebc459db53d2625ca6a0f9b56d62b923) refactor: TimeFrame.aggregate() no longer takes arrays

@@ -4,0 +16,0 @@ - [a2408](https://github.com/fatmatto/timeframes/commit/a24080727cf00bd6848c1430c424bc052d1b9e67) 📦 Release 0.1.33 standard-version [skip ci]

26

package.json
{
"name": "@apio/timeframes",
"version": "0.1.35",
"version": "0.1.37",
"description": "Library for dealing with timeseries data",

@@ -18,7 +18,4 @@ "main": "build/main/index.js",

"build:module": "tsc -p tsconfig.module.json",
"fix": "run-s fix:*",
"fix:prettier": "prettier \"src/**/*.ts\" --write",
"fix:lint": "eslint src --ext .ts --fix",
"test": "run-s build test:*",
"test:lint": "eslint src --ext .ts",
"lint": "npx rome check src/**/*.ts",
"test:unit": "nyc ava --fail-fast",

@@ -35,3 +32,3 @@ "check-cli": "run-s test diff-integration-tests check-integration-tests",

"cov:check": "nyc report && nyc check-coverage --lines 100 --functions 100 --branches 100",
"doc": "npx typedoc --out docs src/**/*.ts",
"doc": "bash scripts/build-docs-website.sh",
"doc:json": "typedoc --json docs/docs.json src/**/*.ts",

@@ -50,4 +47,2 @@ "doc:publish": "gh-pages -m \"[ci skip] Updates\" -d docs",

"@types/node": "^18.8.5",
"@typescript-eslint/eslint-plugin": "^5.40.0",
"@typescript-eslint/parser": "^5.40.0",
"ava": "^4.3.3",

@@ -58,10 +53,2 @@ "codecov": "^3.5.0",

"doctrine": "^3.0.0",
"eslint": "^8.25.0",
"eslint-config-prettier": "^6.11.0",
"eslint-config-standard": "^17.0.0",
"eslint-plugin-eslint-comments": "^3.2.0",
"eslint-plugin-functional": "^3.0.2",
"eslint-plugin-import": "^2.26.0",
"eslint-plugin-n": "^15.3.0",
"eslint-plugin-promise": "^6.1.0",
"gh-pages": "^3.1.0",

@@ -71,3 +58,3 @@ "npm-run-all": "^4.1.5",

"open-cli": "^7.1.0",
"prettier": "^2.1.1",
"rome": "^11.0.0",
"standard-version": "^9.0.0",

@@ -117,4 +104,7 @@ "ts-node": "^9.0.0",

"dependencies": {
"parse-duration": "^1.0.2"
"@types/functional-red-black-tree": "^1.0.1",
"functional-red-black-tree": "^1.0.1",
"parse-duration": "^1.0.2",
"timsort": "^0.3.0"
}
}

14

README.md

@@ -5,4 +5,4 @@

<p align="center">
<strong>Library for dealing with timeseries data</strong>
<div align="center"> ⚠️ This is a work in progress</div>
<strong>Dataframe-like API for timeseries-like data</strong>
<div align="center"> ⚠️ This is still a work in progress</div>
</p>

@@ -22,3 +22,9 @@

![Timeseries](images/timeserie.png?raw=true "Timeseries")
```javascript
import { TimeSerie } from '@apio/timeframes'
// Pass an array of points

@@ -36,4 +42,6 @@ // a point is a tuple [DateLike, PointValue]

![Timeframes](images/timeframe.png?raw=true "Timeframes")
```javascript
import { TimeFrame } from '@apio/timeframes'
// Each item is a row

@@ -40,0 +48,0 @@ const rows = [

699

src/lib/timeframe.ts

@@ -1,16 +0,46 @@

import { TimeSerie } from './timeserie'
import { AggregationConfiguration, DateLike, FromTimeseriesOptions, Index, Metadata, Point, PointValue, ReindexOptions, Row, TelemetryV1Output, TimeFrameInternal, PartitionOptions, TimeFrameResampleOptions, TimeframeRowsIterator, TimeInterval, TimeserieIterator, TimeFrameReduceOptions, ProjectionOptions, PipelineStage, PipelineStageType } from './types'
import { getOrderOfMagnitude } from './utils'
import { Tree } from "functional-red-black-tree";
import { TimeSerie } from "./timeserie";
import {
AggregationConfiguration,
DateLike,
FromTimeseriesOptions,
Index,
Metadata,
Point,
ReindexOptions,
Row,
TelemetryV1Output,
TimeFrameInternal,
PartitionOptions,
TimeFrameResampleOptions,
TimeframeRowsIterator,
TimeInterval,
TimeserieIterator,
TimeFrameReduceOptions,
ProjectionOptions,
PipelineStage,
PipelineStageType,
SplitOptions,
BetweenTimeOptions,
} from "./types";
import { chunk, DateLikeToString } from "./utils";
const test = (r, f, t, includeSuperior, includeInferior) => {
if (includeInferior && includeSuperior) {
return r >= f && r <= t
return r >= f && r <= t;
} else if (includeInferior && !includeSuperior) {
return r >= f && r < t
return r >= f && r < t;
} else if (!includeInferior && includeSuperior) {
return r > f && r <= t
return r > f && r <= t;
} else {
return r > f && r < t
return r > f && r < t;
}
};
interface TimeFrameIndexes {
time: DateLike[];
tree: Tree<DateLike, DateLike>;
}
const makeTree = require("functional-red-black-tree");
interface TimeFrameOptions {

@@ -25,6 +55,7 @@ data: Row[];

export class TimeFrame {
private readonly data: TimeFrameInternal = {}
columnNames: string[] = []
metadata: Metadata = {}
private _indexes: any
private readonly data: TimeFrameInternal = {};
columnNames: string[] = [];
metadata: Metadata = {};
private _indexes: TimeFrameIndexes;
private _columns: Record<string, TimeSerie> = {};

@@ -36,29 +67,38 @@ /**

*/
constructor (options: TimeFrameOptions) {
const { data, metadata = {} } = options
constructor(options: TimeFrameOptions) {
const { data, metadata = {} } = options;
// get a list of unique column names excluding the time key
this.metadata = metadata
this.metadata = metadata;
if (data.length === 0) {
this.data = {}
this.columnNames = []
this.data = {};
this.columnNames = [];
} else {
this.columnNames = [...new Set(data
.filter((row: any) => !!row)
.map((row: Row) => Object.keys(row))
.flat())]
.filter((name: string) => name !== 'time')
this.columnNames = [
...new Set(
data
.filter((row: Row) => !!row)
.flatMap((row: Row) => Object.keys(row)),
),
].filter((name: string) => name !== "time");
this.data = data
.concat([])
.filter((row: any) => !!row)
.filter((row: Row) => !!row)
.sort((a, b) => {
const ta = new Date(a.time).getTime()
const tb = new Date(b.time).getTime()
if (ta >= tb) { return 1 } else { return -1 }
const ta = new Date(a.time).getTime();
const tb = new Date(b.time).getTime();
if (ta >= tb) {
return 1;
} else {
return -1;
}
})
.reduce((acc: TimeFrameInternal, row: Row) => {
const { time, ...rest } = row
acc[row.time] ? acc[row.time] = { ...acc[row.time], ...rest } : acc[row.time] = rest
return acc
}, {})
const { time, ...rest } = row;
const fTime = DateLikeToString(time);
acc[fTime]
? (acc[fTime] = { ...acc[fTime], ...rest })
: (acc[fTime] = rest);
return acc;
}, {});
}

@@ -68,14 +108,16 @@

time: Object.keys(this.data).sort(),
checkpoints: null
}
tree: null,
};
}
private buildTimeCheckpoints () {
if (!this._indexes.checkpoints) {
this._indexes.checkpoints = {}
const o = getOrderOfMagnitude(this._indexes.time.length)
this._indexes.time.forEach((el, i) => {
if (i % (o / 100) === 0) { this._indexes.checkpoints[el] = i }
})
private buildTimeTree() {
if (!this._indexes.tree) {
let tree = makeTree(function (a: number, b: number) {
return a - b;
});
this._indexes.time.forEach((time: string) => {
const t = new Date(time).getTime();
tree = tree.insert(t, t);
});
this._indexes.tree = tree;
}

@@ -85,8 +127,8 @@ }

/**
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate (data: Row[]): TimeFrame {
return new TimeFrame({ data, metadata: this.metadata })
* Creates a new timeframe preserving the metadata but replacing data
* @param data The new data to recreate the serie from
* @returns
*/
recreate(data: Row[]): TimeFrame {
return new TimeFrame({ data, metadata: this.metadata });
}

@@ -99,6 +141,6 @@

*/
recreateFromSeries (series: TimeSerie[]) {
const tf = TimeFrame.fromTimeseries(series)
tf.metadata = this.metadata
return tf
recreateFromSeries(series: TimeSerie[]) {
const tf = TimeFrame.fromTimeseries(series);
tf.metadata = this.metadata;
return tf;
}

@@ -113,13 +155,17 @@

*/
reindex (index : Index, options?: ReindexOptions) : TimeFrame {
return this.recreate(index.map((i:string) => this.atTime(i) || options.fill || { time: i }))
reindex(index: Index, options?: ReindexOptions): TimeFrame {
return this.recreate(
index.map((i: string) => this.atTime(i) || options.fill || { time: i }),
);
}
/**
*
* Creates a TimeFrame from a Telemetry Output Object (Apio private method)
* @param data An object which is telemetry V1 output (Apio Internal)
* @returns
*/
static fromTelemetryV1Output (data: TelemetryV1Output = {}, metadata: Metadata = {}): TimeFrame {
const _data: TimeFrameInternal = {}
static fromTelemetryV1Output(
data: TelemetryV1Output = {},
metadata: Metadata = {},
): TimeFrame {
const _data: TimeFrameInternal = {};
for (const deviceId in data) {

@@ -129,10 +175,10 @@ for (const propertyName in data[deviceId]) {

if (!_data[time]) {
_data[time] = {}
_data[DateLikeToString(time)] = {};
}
const column = `${deviceId}:${propertyName}`
const column = `${deviceId}:${propertyName}`;
metadata[column] = {
deviceId,
propertyName
}
_data[time][column] = value
propertyName,
};
_data[DateLikeToString(time)][column] = value;
}

@@ -142,31 +188,40 @@ }

const rows = Object.keys(_data).map((time: string) => {
return { time, ..._data[time] }
})
return new TimeFrame({ data: rows, metadata })
return { time, ..._data[DateLikeToString(time)] };
});
return new TimeFrame({ data: rows, metadata });
}
static fromInternalFormat (data: TimeFrameInternal, metadata?: Metadata): TimeFrame {
private static fromInternalFormat(
data: TimeFrameInternal,
metadata?: Metadata,
): TimeFrame {
const _data: Row[] = Object.keys(data).map((time: string) => {
return { time, ...data[time] }
})
return new TimeFrame({ data: _data, metadata })
return { time, ...data[time] };
});
return new TimeFrame({ data: _data, metadata });
}
/**
*
* Returns a new TimeFrame, where each input timeserie is used as column
* @param timeseries An array of TimeSerie objects
* @param options.fill Value to use as filler when a column does not hold a value for a specific time
* @returns A new TimeFrame, where each timeserie represent a column
*/
static fromTimeseries (timeseries: TimeSerie[], options?: FromTimeseriesOptions): TimeFrame {
const data: TimeFrameInternal = {}
const metadata: Metadata = {}
timeseries.forEach(ts => {
metadata[ts.name] = ts.metadata
ts.toArray().forEach((point: Point) => {
data[point[0]] = data[point[0]] || {}
data[point[0]][ts.name] = point[1] || options?.fill || null
})
})
return TimeFrame.fromInternalFormat(data, metadata)
static fromTimeseries(
timeseries: TimeSerie[],
options?: FromTimeseriesOptions,
): TimeFrame {
const data: TimeFrameInternal = {};
const metadata: Metadata = {};
const idx = [...new Set(timeseries.flatMap((ts) => ts.indexes()))];
timeseries.forEach((ts) => {
metadata[ts.name] = ts.metadata;
});
idx.forEach((i: DateLike) => {
data[i as string] = {};
timeseries.forEach(
(ts) =>
(data[i as string][ts.name] = ts.atTime(i) || options?.fill || null),
);
});
return TimeFrame.fromInternalFormat(data, metadata);
}

@@ -179,10 +234,22 @@

*/
static concat (timeframes: TimeFrame[]) : TimeFrame {
static concat(timeframes: TimeFrame[]): TimeFrame {
return new TimeFrame({
metadata: Object.assign({}, ...timeframes.map(tf => tf.metadata)),
data: timeframes.map((tf: TimeFrame) => tf.rows()).flat()
})
metadata: Object.assign({}, ...timeframes.map((tf) => tf.metadata)),
data: timeframes.flatMap((tf: TimeFrame) => tf.rows()),
});
}
/**
* Merges together rows and columns of the specified timeframes.
* If two or more timeframes present a value for the same column at the same time, the first timeframe in the array has priority.
* @param timeframes Array of timeframes to merge
*/
static merge(timeframes: TimeFrame[]): TimeFrame {
if (timeframes.length < 2) {
throw new Error("merge() requires at least two timeframes");
}
return timeframes[0].join(timeframes.slice(1));
}
/**
* Joins multiple timeframes by adding the columns together and merging indexes (time)

@@ -192,4 +259,20 @@ * @param timeframes Array of timeframes to join together

*/
join (timeframes: TimeFrame[]): TimeFrame {
return TimeFrame.fromInternalFormat(Object.assign({}, ...(timeframes.map(tf => tf.data).concat([this.data]))))
join(timeframes: TimeFrame[]): TimeFrame {
const allTf = timeframes.concat([this]);
// Todo should support a filler value, at the moment it just does not define values in rows
// when a row misses a certain column's value
// const allColumns: string[] = [
// ...new Set(allTf.flatMap((tf) => tf.columnNames)),
// ];
const mergedIndex = [...new Set(allTf.flatMap((tf) => tf.indexes()))];
const rows = mergedIndex.map((idx: DateLike) => ({
time: idx,
...allTf
.map((tf) => tf.atTime(idx as string))
.reduce(
(prev, acc) => Object.assign(acc, prev),
this.atTime(idx as string),
),
}));
return this.recreate(rows);
}

@@ -202,34 +285,49 @@

*/
addColumn (serie: TimeSerie): TimeFrame {
return this.recreateFromSeries(this.columns().concat([serie]))
addColumn(serie: TimeSerie): TimeFrame {
return this.recreateFromSeries(this.columns().concat([serie]));
}
/**
*
* Returns the column as timeseries
* @param name The name of the wanted column
* @returns The column as timeseries
*/
column (name: string): TimeSerie {
column(name: string): TimeSerie {
if (!this.columnNames.includes(name)) {
return null
return null;
}
const data: Point[] = Object.entries(this.data).map(([time, values]) => ([time, values[name]]))
const metadata = this.metadata[name] || {}
return new TimeSerie(name, data, metadata)
// we cache the column to make subsequent reads faster
if (!this._columns[name]) {
const data: Point[] = Object.entries(this.data).map(([time, values]) => [
time,
values[name],
]);
const metadata = this.metadata[name] || {};
this._columns[name] = new TimeSerie(name, data, metadata);
}
return this._columns[name];
}
columns (): TimeSerie[] {
return this.columnNames.map((column: string) => this.column(column))
/**
* Returns every column as array of timeseries
*/
columns(): TimeSerie[] {
return this.columnNames.map((column: string) => this.column(column));
}
/**
*
* @returns Array of rows
* Returns all the rows in an array
*/
rows (): Row[] {
return Object.entries(this.data).map(([time, values]) => ({ time, ...values }))
rows(): Row[] {
return Object.entries(this.data).map(([time, values]) => ({
time,
...values,
}));
}
indexes (): DateLike[] {
return this._indexes.time
/**
* Returns the time index array
*/
indexes(): DateLike[] {
return this._indexes.time;
}

@@ -240,59 +338,69 @@

*/
project (config: ProjectionOptions): TimeFrame {
const nonExisting = config.columns.filter((name: string) => !this.columnNames.includes(name))
if (nonExisting.length > 0) { throw new Error(`Non existing columns ${nonExisting.join(',')}`) }
const tf = TimeFrame.fromTimeseries(config.columns.map((columnName: string) => this.column(columnName)))
tf.metadata = this.metadata
return tf
project(config: ProjectionOptions): TimeFrame {
const nonExisting = config.columns.filter(
(name: string) => !this.columnNames.includes(name),
);
if (nonExisting.length > 0) {
throw new Error(`Non existing columns ${nonExisting.join(",")}`);
}
const tf = TimeFrame.fromTimeseries(
config.columns.map((columnName: string) => this.column(columnName)),
);
tf.metadata = this.metadata;
return tf;
}
/**
*
* Returns a row at a given time or null
* @param time
* @returns A row at a given time or null
*/
atTime (time: string): Row | null {
return { time, ...this.data[time] } || null
atTime(time: string): Row | null {
return { time, ...this.data[DateLikeToString(time)] } || null;
}
/**
*
* @returns The row at the given index (position, not time)
* Get the row at the given index (position, not time)
*/
atIndex (index: number): PointValue {
atIndex(index: number): Row {
if (index >= this.rows().length) {
throw new Error('Index out of bounds')
throw new Error("Index out of bounds");
}
return this.rows()[index]
return this.rows()[index];
}
length (): number {
return this._indexes.time.length
/**
* Returns the number of rows
*/
length(): number {
return this._indexes.time.length;
}
/**
* Returns the shape of the timeframe
* @returns Array<Number> The shape of the timeframe expressed as [rows, columns] where columns excludes the time column
* Returns the shape of the timeframe expressed as [rows, columns] where columns excludes the time column
*/
shape (): number[] {
return [this._indexes.time.length, this.columnNames.length]
shape(): number[] {
return [this._indexes.time.length, this.columnNames.length];
}
/**
*
* @returns The first row
*/
first (): Row {
if (this.length() === 0) { return null }
return this.rows()?.[0] || null
*
* Returns the first row
*/
first(): Row {
if (this.length() === 0) {
return null;
}
return this.rows()?.[0] || null;
}
/**
*
* @returns The last row
*/
last (): Row {
if (this.length() === 0) { return null }
const t = this.rows()
return t?.[t.length - 1] || null
*
* Returns the last row
*/
last(): Row {
if (this.length() === 0) {
return null;
}
const t = this.rows();
return t?.[t.length - 1] || null;
}

@@ -303,6 +411,14 @@

*/
sum (): Row {
if (this.length() === 0) { return null }
const time = this.first().time
return this.columns().reduce((acc, column) => { acc[column.name] = column.sum()[1]; return acc }, { time })
sum(): Row {
if (this.length() === 0) {
return null;
}
const time = this.first().time;
return this.columns().reduce(
(acc, column) => {
acc[column.name] = column.sum()[1];
return acc;
},
{ time },
);
}

@@ -313,6 +429,14 @@

*/
avg (): Row {
if (this.length() === 0) { return null }
const time = this.first().time
return this.columns().reduce((acc, column) => { acc[column.name] = column.avg()[1]; return acc }, { time })
avg(): Row {
if (this.length() === 0) {
return null;
}
const time = this.first().time;
return this.columns().reduce(
(acc, column) => {
acc[column.name] = column.avg()[1];
return acc;
},
{ time },
);
}

@@ -323,6 +447,14 @@

*/
delta (): Row {
if (this.length() === 0) { return null }
const time = this.first().time
return this.columns().reduce((acc, column) => { acc[column.name] = column.delta()[1]; return acc }, { time })
delta(): Row {
if (this.length() === 0) {
return null;
}
const time = this.first().time;
return this.columns().reduce(
(acc, column) => {
acc[column.name] = column.delta()[1];
return acc;
},
{ time },
);
}

@@ -333,6 +465,14 @@

*/
max (): Row {
if (this.length() === 0) { return null }
const time = this.first().time
return this.columns().reduce((acc, column) => { acc[column.name] = column.max()[1]; return acc }, { time })
max(): Row {
if (this.length() === 0) {
return null;
}
const time = this.first().time;
return this.columns().reduce(
(acc, column) => {
acc[column.name] = column.max()[1];
return acc;
},
{ time },
);
}

@@ -343,6 +483,14 @@

*/
min (): Row {
if (this.length() === 0) { return null }
const time = this.first().time
return this.columns().reduce((acc, column) => { acc[column.name] = column.min()[1]; return acc }, { time })
min(): Row {
if (this.length() === 0) {
return null;
}
const time = this.first().time;
return this.columns().reduce(
(acc, column) => {
acc[column.name] = column.min()[1];
return acc;
},
{ time },
);
}

@@ -353,6 +501,6 @@

*/
add (value: number) : TimeFrame {
add(value: number): TimeFrame {
return this.recreateFromSeries(
this.columns().map((c:TimeSerie) => c.add(value))
)
this.columns().map((c: TimeSerie) => c.add(value)),
);
}

@@ -363,48 +511,42 @@

*/
mul (value: number) : TimeFrame {
mul(value: number): TimeFrame {
return this.recreateFromSeries(
this.columns().map((c:TimeSerie) => c.mul(value))
)
this.columns().map((c: TimeSerie) => c.mul(value)),
);
}
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime (from: DateLike, to: DateLike, options = { includeInferior: true, includeSuperior: true }): TimeFrame {
* Returns the subset of points between the two dates. Extremes are included.
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
betweenTime(
from: DateLike,
to: DateLike,
options: BetweenTimeOptions = {
includeInferior: true,
includeSuperior: true,
},
): TimeFrame {
/**
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys
* we index the array by mapping a certain number of timestamps to positions in the time index.
*
* This sparse index is smaller than the full index and fester to use for scanning ranges like in this case.
* Here we might have to scan a huge sorted array. To prevent scanning too many useless keys. To get better performances we index timestamps with a RBtree in the buildTimeTree funciton
*/
this.buildTimeCheckpoints()
const { includeInferior, includeSuperior } = options
const f = new Date(from).getTime()
const t = new Date(to).getTime()
this.buildTimeTree();
const { includeInferior, includeSuperior } = options;
const f = new Date(from).getTime();
const t = new Date(to).getTime();
const keys = Object.keys(this._indexes.checkpoints)
// Indice della prima chiave che va oltre il from
const startingPointValueIndex = keys.findIndex((key) => new Date(key).getTime() > from)
// Ultimo timestamp prima di quell'indice
let startingPoint = this._indexes.checkpoints[keys[startingPointValueIndex - 1]]
if (!startingPoint) {
// Siamo oltre l'ultimo checkpoint
const lastCheckpoint = keys[keys.length - 1]
startingPoint = this._indexes.checkpoints[lastCheckpoint]
}
const goodRows = []
for (let i = startingPoint; i < this._indexes.time.length; i++) {
const curr = new Date(this._indexes.time[i]).getTime()
if (curr < f) { continue }
if (curr > t) {
break
const goodRows = [];
const iter = this._indexes.tree.ge(f);
while (iter && new Date(iter.key).getTime() <= t) {
if (test(iter.key, f, t, includeSuperior, includeInferior)) {
goodRows.push({
time: new Date(iter.key).toISOString(),
...this.data[DateLikeToString(iter.key)],
});
}
if (test(curr, f, t, includeSuperior, includeInferior)) {
goodRows.push({ time: this._indexes.time[i], ...this.data[this._indexes.time[i]] })
}
iter.next();
}
return this.recreate(goodRows)
return this.recreate(goodRows);
}

@@ -423,19 +565,30 @@

* tf = tf.aggregate({ output: 'power1', columns: ['voltage1', 'current1'], operation: 'mul' })
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
* .aggregate({ output: 'power', columns: ['power1', 'power2', 'power3'], operation: 'add'})
*/
aggregate (agg: AggregationConfiguration): TimeFrame {
aggregate(agg: AggregationConfiguration): TimeFrame {
const columnsToAggregate: TimeSerie[] = agg.columns
.filter((colName:string) => this.columnNames.includes(colName))
.map((colName: string) => this.column(colName))
.filter((colName: string) => this.columnNames.includes(colName))
.map((colName: string) => this.column(colName));
let newColumn : TimeSerie
if (typeof agg.operation === 'function') {
newColumn = TimeSerie.internals.combine(columnsToAggregate, agg.operation, { name: agg.output })
} else if (typeof agg.operation === 'string' && agg.operation in TimeSerie.internals.combiners) {
newColumn = TimeSerie.internals.combine(columnsToAggregate, TimeSerie.internals.combiners[agg.operation], { name: agg.output })
let newColumn: TimeSerie;
if (typeof agg.operation === "function") {
newColumn = TimeSerie.internals.combine(
columnsToAggregate,
agg.operation,
{ name: agg.output },
);
} else if (
typeof agg.operation === "string" &&
agg.operation in TimeSerie.internals.combiners
) {
newColumn = TimeSerie.internals.combine(
columnsToAggregate,
TimeSerie.internals.combiners[agg.operation],
{ name: agg.output },
);
} else {
throw new Error('Wrong type for aggregation operation')
throw new Error("Wrong type for aggregation operation");
}
return this.recreateFromSeries([newColumn].concat(this.columns()))
return this.recreateFromSeries([newColumn].concat(this.columns()));
}

@@ -451,24 +604,31 @@

*/
reduce (options: TimeFrameReduceOptions): TimeFrame {
return this.recreateFromSeries(this.columns().map((column:TimeSerie) => {
if (options.operations && column.name in options.operations) {
return column.recreate([column[options.operations[column.name]]()])
} else {
return column.recreate([column[options.operation]()])
}
}))
reduce(options: TimeFrameReduceOptions): TimeFrame {
return this.recreateFromSeries(
this.columns().map((column: TimeSerie) => {
if (options.operations && column.name in options.operations) {
return column.recreate([column[options.operations[column.name]]()]);
} else {
return column.recreate([column[options.operation]()]);
}
}),
);
}
resample (options: TimeFrameResampleOptions): TimeFrame {
const from = options.from || this.first()?.time
/**
* Resamples the timeframe by the specified time interval. Each row
* of the result TimeFrame will be the result of the selected aggregation.
* @param options
*/
resample(options: TimeFrameResampleOptions): TimeFrame {
const from = options.from || this.first()?.time;
if (!from) {
throw new Error('Cannot infer a lower bound for resample')
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.time
const to = options.to || this.last()?.time;
if (!to) {
throw new Error('Cannot infer an upper bound for resample')
throw new Error("Cannot infer an upper bound for resample");
}
return TimeFrame.concat(this.partition(options)
.map((chunk: TimeFrame) => chunk.reduce(options))
)
return TimeFrame.concat(
this.partition(options).map((chunk: TimeFrame) => chunk.reduce(options)),
);
}

@@ -481,13 +641,19 @@

*/
filter (fn: TimeframeRowsIterator): TimeFrame {
return new TimeFrame({ data: this.rows().filter(fn), metadata: this.metadata })
filter(fn: TimeframeRowsIterator): TimeFrame {
return new TimeFrame({
data: this.rows().filter(fn),
metadata: this.metadata,
});
}
/**
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map (fn: TimeframeRowsIterator): TimeFrame {
return new TimeFrame({ data: this.rows().map(fn), metadata: this.metadata })
* Returns a new timeframe where each **row** is mapped by the iterator function. For mapping over columns, use apply()
* @param fn Iterator function
* @returns {TimeFrame}
*/
map(fn: TimeframeRowsIterator): TimeFrame {
return new TimeFrame({
data: this.rows().map(fn),
metadata: this.metadata,
});
}

@@ -501,9 +667,14 @@

*/
apply (fn: TimeserieIterator, columns: string[] = this.columnNames): TimeFrame {
const unmodifiedColumns = this.columnNames.filter((columnName: string) => !columns.includes(columnName)).map((columnName: string) => this.column(columnName))
apply(
fn: TimeserieIterator,
columns: string[] = this.columnNames,
): TimeFrame {
const unmodifiedColumns = this.columnNames
.filter((columnName: string) => !columns.includes(columnName))
.map((columnName: string) => this.column(columnName));
const series: TimeSerie[] = columns
.map((columnName: string) => (this.column(columnName)))
.map(fn)
.map((columnName: string) => this.column(columnName))
.map(fn);
return TimeFrame.fromTimeseries(unmodifiedColumns.concat(series))
return TimeFrame.fromTimeseries(unmodifiedColumns.concat(series));
}

@@ -514,30 +685,44 @@

* @param options
* @returns
*/
partition (options: PartitionOptions): TimeFrame[] {
const from = options.from || this.first()?.time
partition(options: PartitionOptions): TimeFrame[] {
const from = options.from || this.first()?.time;
if (!from) {
throw new Error('Cannot infer a lower bound for resample')
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.time
const to = options.to || this.last()?.time;
if (!to) {
throw new Error('Cannot infer an upper bound for resample')
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = TimeInterval.generate(from, to, options.interval)
const intervals = TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval: TimeInterval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false })
})
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});
return partitions.map((p: TimeFrame, idx: number) => {
if (p.length() === 0) {
return p.recreate([{ time: intervals[idx].from.toISOString() }])
return p.recreate([{ time: intervals[idx].from.toISOString() }]);
} else if (p.first().time !== intervals[idx].from.toISOString()) {
return p.recreate([{ time: intervals[idx].from.toISOString() }].concat(p.rows()))
return p.recreate(
[{ time: intervals[idx].from.toISOString() }].concat(p.rows()),
);
} else {
return p
return p;
}
})
});
}
/**
* Splits a timeframe into multiple timeframes where each timeframe has
* a maximum of `options.chunks` rows.
*/
split(options: SplitOptions): TimeFrame[] {
return chunk(this.rows(), options.chunks).map((rows: Row[]) =>
this.recreate(rows),
);
}
/**
* Runs a series of transformations defined as an object. Useful in automation.

@@ -547,7 +732,7 @@ * A stage is an object with a single key and a value, the key is the name of the method, the value is the params object

*/
pipeline (stages: PipelineStage[]) {
return stages.reduce((tf:TimeFrame, stage: PipelineStage) => {
const fn: PipelineStageType = Object.keys(stage)[0] as PipelineStageType
return tf[fn](stage[fn] as any)
}, this)
pipeline(stages: PipelineStage[]) {
return stages.reduce((tf: TimeFrame, stage: PipelineStage) => {
const fn: PipelineStageType = Object.keys(stage)[0] as PipelineStageType;
return tf[fn](stage[fn] as any);
}, this);
}

@@ -558,5 +743,5 @@

*/
print () {
console.table(this.rows())
print() {
console.table(this.rows());
}
}

672

src/lib/timeserie.ts

@@ -1,23 +0,47 @@

import { createIndex, DateLike, FromIndexOptions, Index, Metadata, PartitionOptions, Point, PointValue, ReindexOptions, ResampleOptions, TimeInterval, TimeseriePointCombiner, TimeseriePointIterator, TimeSerieReduceOptions, TimeSeriesOperationOptions } from './types'
import { DateLikeToString } from './utils'
import {
BetweenTimeOptions,
createIndex,
DateLike,
FromIndexOptions,
Index,
Metadata,
PartitionOptions,
Point,
PointValue,
ReindexOptions,
ResampleOptions,
SplitOptions,
TimeInterval,
TimeseriePointCombiner,
TimeseriePointIterator,
TimeSerieReduceOptions,
TimeSeriesOperationOptions,
} from "./types";
import { chunk, DateLikeToString } from "./utils";
function isNumeric (str: string | number): boolean {
if (typeof str === 'number') return !isNaN(str)
if (typeof str !== 'string') return false // we only process strings!
return !isNaN(str as any) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str)) // ...and ensure strings of whitespace fail
import * as Timsort from "timsort";
function isNumeric(str: string | number): boolean {
if (typeof str === "number") return !isNaN(str);
if (typeof str !== "string") return false; // we only process strings!
return (
!isNaN(str as any) && // use type coercion to parse the _entirety_ of the string (`parseFloat` alone does not do this)...
!isNaN(parseFloat(str))
); // ...and ensure strings of whitespace fail
}
function sortPoints (points: Point[] | ReadonlyArray<Point>) {
return [].concat(points).sort((a: Point, b: Point) => {
function sortPoints(points: Point[] | ReadonlyArray<Point>) {
const arr = [].concat(points);
Timsort.sort(arr, (a: Point, b: Point) => {
if (a[0] > b[0]) {
return 1
return 1;
} else {
return -1
return -1;
}
})
});
return arr;
}
function normalizePoint (p: Point): Point {
return [DateLikeToString(p[0]), p[1]]
function normalizePoint(p: Point): Point {
return [DateLikeToString(p[0]), p[1]];
}

@@ -29,24 +53,47 @@

export class TimeSerie {
public static internals: any = {}
public static createIndex: Function
readonly data: Point[]
name: string
metadata: Metadata
index: {[key: string] : PointValue}
constructor (name: string, serie: Point[] | ReadonlyArray<Point>, metadata: Metadata = {}) {
this.data = sortPoints(serie).map(normalizePoint)
this.name = name
this.metadata = metadata
this.index = [].concat(this.data).reduce((acc: any, p:Point) => {
acc[p[0]] = p
return acc
}, {})
public static internals: any = {};
public static createIndex: Function;
readonly data: Point[];
name: string;
metadata: Metadata;
index: { [key: string]: PointValue };
/**
* Creates a new timeserie.
* @param name {String} The name of the serie
* @param serie {Point[]} The points in the serie
* @param metadata {Metadata} Optional metadata
*/
constructor(
name: string,
serie: Point[] | ReadonlyArray<Point>,
metadata: Metadata = {},
) {
this.data = sortPoints(serie).map(normalizePoint);
this.name = name;
this.metadata = metadata;
this.index = []
.concat(this.data)
.reduce((acc: { [key: string]: PointValue }, p: Point) => {
acc[p[0]] = p;
return acc;
}, {});
}
static fromIndex (index: Index, options: FromIndexOptions) : TimeSerie {
return new TimeSerie(options.name, index.map((i: string) => ([i, options?.fill || null])), options.metadata)
/**
* Creates a new TimeSerie from the given Index. The new serie's values are all set to `null` unless `options.fill` is passed.
* @param index
* @param options
*/
static fromIndex(index: Index, options: FromIndexOptions): TimeSerie {
return new TimeSerie(
options.name,
index.map((i: string) => [i, options?.fill || null]),
options.metadata,
);
}
/**
* Recreates the serie's index
* Recreates the serie's index according to `options` and returns the reindexed serie.
*
* @param index The new index to use. Can be created with createIndex()

@@ -57,8 +104,16 @@ * @see createIndex

*/
reindex (index : Index, options?: ReindexOptions) : TimeSerie {
reindex(index: Index, options?: ReindexOptions): TimeSerie {
if (options.mergeIndexes === true) {
const idx = [...new Set(this.indexes().concat(index))]
return new TimeSerie(this.name, idx.map((i: string) => ([i, this.atTime(i) || options?.fill || null])), this.metadata)
const idx = [...new Set(this.indexes().concat(index))];
return new TimeSerie(
this.name,
idx.map((i: string) => [i, this.atTime(i) || options?.fill || null]),
this.metadata,
);
}
return new TimeSerie(this.name, index.map((i: string) => ([i, this.atTime(i) || options?.fill || null])), this.metadata)
return new TimeSerie(
this.name,
index.map((i: string) => [i, this.atTime(i) || options?.fill || null]),
this.metadata,
);
}

@@ -68,11 +123,15 @@

*
* @returns Array of points, where each point is a tuple with ISO8601 timestamp and value
* Returns the array of points, where each point is a tuple with ISO8601 timestamp and value
*/
toArray () {
return this.data
toArray() {
return this.data;
}
rename (name: string) {
this.name = name
return this
/**
* Updates (in place) the serie's name. **This method does NOT return a new timeserie**.
* @param name
*/
rename(name: string) {
this.name = name;
return this;
}

@@ -85,4 +144,4 @@

*/
recreate (serie: Point[] | ReadonlyArray<Point>) {
return new TimeSerie(this.name, serie, this.metadata)
recreate(serie: Point[] | ReadonlyArray<Point>) {
return new TimeSerie(this.name, serie, this.metadata);
}

@@ -92,6 +151,6 @@

*
* @returns The array of time indexes
* Returns the array of time indexes
*/
indexes (): DateLike[] {
return this.data.map((p: Point) => p[0])
indexes(): DateLike[] {
return this.data.map((p: Point) => p[0]);
}

@@ -101,6 +160,6 @@

*
* @returns The array of values
* Returns the array of values
*/
values (): PointValue[] {
return this.data.map((p: Point) => p[1])
values(): PointValue[] {
return this.data.map((p: Point) => p[1]);
}

@@ -111,4 +170,8 @@

*/
static concat (series:TimeSerie[]) : TimeSerie {
return new TimeSerie(series[0].name, series.map(serie => serie.toArray()).flat(), Object.assign({}, ...series.map(serie => serie.metadata)))
static concat(series: TimeSerie[]): TimeSerie {
return new TimeSerie(
series[0].name,
series.flatMap((serie) => serie.toArray()),
Object.assign({}, ...series.map((serie) => serie.metadata)),
);
}

@@ -118,12 +181,15 @@

*
* @returns The time of the latest non-NaN value
* Returns the time of the latest non-NaN value
*/
lastValidIndex (): string | null {
const result = this.data.concat([]).reverse().find((point: Point) => {
return !!point[1]
})
lastValidIndex(): string | null {
const result = this.data
.concat([])
.reverse()
.find((point: Point) => {
return !!point[1];
});
if (result) {
return result[0]
return result[0];
} else {
return null
return null;
}

@@ -134,12 +200,12 @@ }

*
* @returns The time of the first non-NaN value
* Returns the time of the first non-NaN value
*/
firstValidIndex (): string | null {
firstValidIndex(): string | null {
const result = this.data.find((point: Point) => {
return !!point[1]
})
return !!point[1];
});
if (result) {
return result[0]
return result[0];
} else {
return null
return null;
}

@@ -149,13 +215,15 @@ }

/**
*
* @returns The latest non-NaN value
* Returns the latest non-NaN value
*/
lastValidValue (): PointValue {
const result = this.data.concat([]).reverse().find((point: Point) => {
return !!point[1]
})
lastValidValue(): PointValue {
const result = this.data
.concat([])
.reverse()
.find((point: Point) => {
return !!point[1];
});
if (result) {
return result[1]
return result[1];
} else {
return null
return null;
}

@@ -165,13 +233,12 @@ }

/**
*
* @returns The first non-NaN value
* Returns the first non-NaN value
*/
firstValidValue (): PointValue {
firstValidValue(): PointValue {
const result = this.data.find((point: Point) => {
return !!point[1]
})
return !!point[1];
});
if (result) {
return result[1]
return result[1];
} else {
return null
return null;
}

@@ -181,7 +248,7 @@ }

/**
*
* @returns {PointValue} The value of the timeseries at the given time
* Returns the value of the timeseries at the given time
* @returns {PointValue}
*/
atTime (time: DateLike, fillValue: number = null): PointValue {
return this.index?.[DateLikeToString(time)]?.[1] || fillValue
atTime(time: DateLike, fillValue: number = null): PointValue {
return this.index?.[DateLikeToString(time)]?.[1] || fillValue;
}

@@ -191,103 +258,152 @@

*
* @returns The value at the given index (position, not time)
* Returns the value at the given index.
*/
atIndex (index: number): PointValue {
atIndex(index: number): PointValue {
if (index >= this.data.length) {
throw new Error('Index out of bounds')
throw new Error("Index out of bounds");
}
return this.data[index][1]
return this.data[index][1];
}
/**
* Returns the subset of points between the two dates. Extremes are included.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns The subset of points between the two dates. Extremes are included.
*/
betweenTime (from: DateLike, to: DateLike, options = { includeInferior: true, includeSuperior: true }) {
const { includeInferior, includeSuperior } = options
const f = new Date(from)
const t = new Date(to)
betweenTime(
from: DateLike,
to: DateLike,
options: BetweenTimeOptions = {
includeInferior: true,
includeSuperior: true,
},
) {
const { includeInferior, includeSuperior } = options;
const f = new Date(from);
const t = new Date(to);
const data: Point[] = this.data.filter((point: Point) => {
if (includeInferior && includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() <= t.getTime()
return (
new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() <= t.getTime()
);
} else if (includeInferior && !includeSuperior) {
return new Date(point[0]).getTime() >= f.getTime() && new Date(point[0]).getTime() < t.getTime()
return (
new Date(point[0]).getTime() >= f.getTime() &&
new Date(point[0]).getTime() < t.getTime()
);
} else if (!includeInferior && includeSuperior) {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() <= t.getTime()
return (
new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() <= t.getTime()
);
} else {
return new Date(point[0]).getTime() > f.getTime() && new Date(point[0]).getTime() < t.getTime()
return (
new Date(point[0]).getTime() > f.getTime() &&
new Date(point[0]).getTime() < t.getTime()
);
}
})
return this.recreate(data)
});
return this.recreate(data);
}
/**
*
* @param from start positional index
* @param to end positional index
* @returns The subset of points between the two indexes. Extremes are included.
*/
betweenIndexes (from: number, to: number) {
return this.filter((_: Point, i: number) => { return i >= from && i <= to })
* Returns the subset of points between the two indexes. Extremes are included.
*
* @param from start positional index
* @param to end positional index
*/
betweenIndexes(from: number, to: number) {
return this.filter((_: Point, i: number) => {
return i >= from && i <= to;
});
}
filter (fn: TimeseriePointIterator) {
return this.recreate(this.data.filter(fn))
/**
* Builds a new serie by applying a filter function the current serie's points
* @params fn {Function}
*/
filter(fn: TimeseriePointIterator) {
return this.recreate(this.data.filter(fn));
}
map (fn: TimeseriePointIterator) {
return this.recreate(this.data.map(fn))
/**
* Builds a new serie by applying a map function the current serie's points
* @param fn {Function}
*/
map(fn: TimeseriePointIterator) {
return this.recreate(this.data.map(fn));
}
length (): number {
return this.data.length
/**
* Returns the number of points in the serie.
*/
length(): number {
return this.data.length;
}
isEmpty (): boolean {
return this.data.length === 0
/**
* Returns true if the serie has 0 points
*/
isEmpty(): boolean {
return this.data.length === 0;
}
copy (): TimeSerie {
return new TimeSerie(this.name, this.data, this.metadata)
/**
* Copies the serie to a new serie
*/
copy(): TimeSerie {
return new TimeSerie(this.name, this.data, this.metadata);
}
sum (): Point {
/**
* Returns the sum of the values in the serie
*/
sum(): Point {
if (this.length() === 0) {
return [null, null]
return [null, null];
}
const copy = this.dropNaN()
return [this.first()[0], copy.data.map((p: Point) => p[1]).reduce((p1: number, p2: number) => p1 + p2, 0)]
const copy = this.dropNaN();
let tot = 0;
const l = copy.length();
const data = copy.toArray();
for (let i = l - 1; i >= 0; i--) {
tot += data[i][1];
}
return [this.first()[0], tot];
}
/**
*
* @returns The average of point values
* Returns the average of the values in the serie
*/
avg (): Point {
avg(): Point {
if (this.length() === 0) {
return [null, null]
return [null, null];
}
const copy = this.dropNaN()
return [this.first()[0], copy.sum()[1] / copy.length()]
const copy = this.dropNaN();
return [this.first()[0], copy.sum()[1] / copy.length()];
}
delta (): Point {
/**
* Returns the difference between the last and the first element by performing last value - first value.
*/
delta(): Point {
if (this.length() <= 0) {
return [null, null]
return [null, null];
}
const copy = this.dropNaN()
const copy = this.dropNaN();
if (copy.length() === 1) {
return copy.data[0][1]
return copy.data[0][1];
}
return [this.first()[0], copy.last()[1] - copy.first()[1]]
return [this.first()[0], copy.last()[1] - copy.first()[1]];
}
/**
* Returns the first point
*
* @returns The firstfirst point
*/
first (): Point {
return this.data[0] || null
first(): Point {
return this.data[0] || null;
}

@@ -298,6 +414,7 @@

* @param time
* @returns
*/
firstAt (time: DateLike): Point {
return this.data.find((p: Point) => { return new Date(p[0]).getTime() >= new Date(time).getTime() })
firstAt(time: DateLike): Point {
return this.data.find((p: Point) => {
return new Date(p[0]).getTime() >= new Date(time).getTime();
});
}

@@ -307,6 +424,6 @@

*
* @returns The last point
* Returns the last point
*/
last (): Point {
return this.data[this.length() - 1] || null
last(): Point {
return this.data[this.length() - 1] || null;
}

@@ -316,12 +433,15 @@

*
* @returns The point with max value, or null
* Returns the point with max value, or null
*/
max (): Point | null {
max(): Point | null {
if (this.length() === 0) {
return [null, null]
return [null, null];
}
if (this.length() === 1) {
return this.data[0]
return this.data[0];
}
return this.data.reduce((prev, current) => current[1] > prev[1] ? current : prev, this.data[0])
return this.data.reduce(
(prev, current) => (current[1] > prev[1] ? current : prev),
this.data[0],
);
}

@@ -331,12 +451,15 @@

*
* @returns The point with min value or null
* Returns the point with min value or null
*/
min (): Point | null {
min(): Point | null {
if (this.length() === 0) {
return [null, null]
return [null, null];
}
if (this.length() === 1) {
return this.data[0]
return this.data[0];
}
return this.data.reduce((prev, current) => current[1] < prev[1] ? current : prev, this.data[0])
return this.data.reduce(
(prev, current) => (current[1] < prev[1] ? current : prev),
this.data[0],
);
}

@@ -347,4 +470,4 @@

*/
reduce (options: TimeSerieReduceOptions): TimeSerie {
return this.recreate([this[options.operation]()])
reduce(options: TimeSerieReduceOptions): TimeSerie {
return this.recreate([this[options.operation]()]);
}

@@ -355,125 +478,196 @@

* @param options
* @returns
*/
partition (options: PartitionOptions): TimeSerie[] {
const from = options.from || this.first()?.[0]
partition(options: PartitionOptions): TimeSerie[] {
const from = options.from || this.first()?.[0];
if (!from) {
throw new Error('Cannot infer a lower bound for resample')
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()?.[0]
const to = options.to || this.last()?.[0];
if (!to) {
throw new Error('Cannot infer an upper bound for resample')
throw new Error("Cannot infer an upper bound for resample");
}
const intervals = TimeInterval.generate(from, to, options.interval)
const intervals = TimeInterval.generate(from, to, options.interval);
const partitions = intervals.map((interval: TimeInterval) => {
return this.betweenTime(interval.from, interval.to, { includeInferior: true, includeSuperior: false })
})
return this.betweenTime(interval.from, interval.to, {
includeInferior: true,
includeSuperior: false,
});
});
return partitions.map((p: TimeSerie, idx: number) => {
if (p.length() === 0) {
return p.recreate([[intervals[idx].from.toISOString(), null]])
return p.recreate([[intervals[idx].from.toISOString(), null]]);
} else if (p.first()[0] !== intervals[idx].from.toISOString()) {
const newPoint : Point = [intervals[idx].from.toISOString(), null]
return p.recreate([newPoint].concat(p.toArray()))
const newPoint: Point = [intervals[idx].from.toISOString(), null];
return p.recreate([newPoint].concat(p.toArray()));
} else {
return p
return p;
}
})
});
}
/**
* Splits a timeserie into multiple timeseries where each timeserie has
* a maximum of `options.chunks` points.
*/
split(options: SplitOptions): TimeSerie[] {
return chunk(this.toArray(), options.chunks).map((points: Point[]) => {
return this.recreate(points);
});
}
/**
* Resample the timeserie using a new time interval and a point aggregation function
* @param options
* @returns
*/
resample (options: ResampleOptions): TimeSerie {
const from = options.from || this.first()[0]
resample(options: ResampleOptions): TimeSerie {
const from = options.from || this.first()[0];
if (!from) {
throw new Error('Cannot infer a lower bound for resample')
throw new Error("Cannot infer a lower bound for resample");
}
const to = options.to || this.last()[0]
const to = options.to || this.last()[0];
if (!to) {
throw new Error('Cannot infer an upper bound for resample')
throw new Error("Cannot infer an upper bound for resample");
}
return TimeSerie.concat(this.partition(options)
.map((chunk: TimeSerie) => chunk.reduce(options)))
return TimeSerie.concat(
this.partition(options).map((chunk: TimeSerie) => chunk.reduce(options)),
);
}
removeAt (time: DateLike): TimeSerie {
return this.recreate(this.data.filter((p: Point) => { return p[0] !== DateLikeToString(time) }))
/**
* Remove the point at the given time and returns a new serie
*/
removeAt(time: DateLike): TimeSerie {
return this.recreate(
this.data.filter((p: Point) => {
return p[0] !== DateLikeToString(time);
}),
);
}
removeAtIndex (index: number): TimeSerie {
return this.recreate(this.data.filter((_: Point, i: number) => { return i !== index }))
/**
* Remove the point at the given index and returns a new serie
*/
removeAtIndex(index: number): TimeSerie {
return this.recreate(
this.data.filter((_: Point, i: number) => {
return i !== index;
}),
);
}
/**
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
* @returns New timeserie without the removed data. Bounds are removed.
*/
removeBetweenTime (from: DateLike, to: DateLike) {
const f = new Date(from)
const t = new Date(to)
* Returns the new timeserie without the removed data. Bounds are removed.
*
* @param from start date string in ISO8601 format
* @param to end date string in ISO8601 format
*/
removeBetweenTime(from: DateLike, to: DateLike) {
const f = new Date(from);
const t = new Date(to);
const data = this.data.filter((point: Point) => {
return new Date(point[0]).getTime() < f.getTime() || new Date(point[0]).getTime() > t.getTime()
})
return this.recreate(data)
return (
new Date(point[0]).getTime() < f.getTime() ||
new Date(point[0]).getTime() > t.getTime()
);
});
return this.recreate(data);
}
dropNaN () : TimeSerie {
return this.filter((p: Point) => isNumeric(p[1]))
/**
* Removes points with NaN value from the serie
*/
dropNaN(): TimeSerie {
return this.filter((p: Point) => isNumeric(p[1]));
}
dropNull () {
return this.filter((p: Point) => p[1] !== null)
/**
* Removes points with null value from the serie.
*/
dropNull() {
return this.filter((p: Point) => p[1] !== null);
}
/**
*
* Rounds the serie's points.
* @param decimals {Number} the number of decimals to keep
* @returns {TimeSerie}
*/
round (decimals: number) {
return this.map((p: Point) => ([p[0], Number(Number(p[1]).toFixed(decimals))]))
round(decimals: number) {
return this.map((p: Point) => [
p[0],
Number(Number(p[1]).toFixed(decimals)),
]);
}
// Operation between timeseries
combine (operation:string, series: TimeSerie[], options: TimeSeriesOperationOptions = {}) : TimeSerie {
options.name = options.name || this.name
options.metadata = options.metadata || this.metadata
return TimeSerie.internals.combine([this.recreate(this.data)].concat(series), TimeSerie.internals.combiners[operation], options)
/**
* Combine the current serie with an array of series y performing combination operations, such as multiplication, addition ecc.
* @param operation {string}
* @param series {TimeSerie[]}
* @param options {TimeSeriesOperationOptions}
*/
combine(
operation: string,
series: TimeSerie[],
options: TimeSeriesOperationOptions = {},
): TimeSerie {
options.name = options.name || this.name;
options.metadata = options.metadata || this.metadata;
return TimeSerie.internals.combine(
[this.recreate(this.data)].concat(series),
TimeSerie.internals.combiners[operation],
options,
);
}
add (value: number | TimeSerie): TimeSerie {
if (typeof value === 'number') {
return this.map((point:Point) => [point[0], point[1] + value])
/**
* Adds values to the timeserie. If a scalar is passed, its value is added to every point in the serie. If another serie
* is passed, the two series are combined by addition.
* @see combine
*/
add(value: number | TimeSerie): TimeSerie {
if (typeof value === "number") {
return this.map((point: Point) => [point[0], point[1] + value]);
} else {
return this.combine('add', [value])
return this.combine("add", [value]);
}
}
sub (value: number | TimeSerie): TimeSerie {
if (typeof value === 'number') {
return this.map((point:Point) => [point[0], point[1] - value])
/**
* Subtracts values from the timeserie. If a scalar is passed, its value is subtracted from every point in the serie. If another serie
* is passed, the two series are combined by subtraction.
* @see combine
*/
sub(value: number | TimeSerie): TimeSerie {
if (typeof value === "number") {
return this.map((point: Point) => [point[0], point[1] - value]);
} else {
return this.combine('sub', [value])
return this.combine("sub", [value]);
}
}
mul (value: number | TimeSerie): TimeSerie {
if (typeof value === 'number') {
return this.map((point:Point) => [point[0], point[1] * value])
/**
* Multiplies values of the timeserie. If a scalar is passed, every point in the serie is multiplied times that value. If another serie
* is passed, the two series are combined by multiplication.
* @see combine
*/
mul(value: number | TimeSerie): TimeSerie {
if (typeof value === "number") {
return this.map((point: Point) => [point[0], point[1] * value]);
} else {
return this.combine('mul', [value])
return this.combine("mul", [value]);
}
}
div (value: number | TimeSerie): TimeSerie {
if (typeof value === 'number') {
return this.map((point:Point) => [point[0], point[1] / value])
/**
* Divides values of the timeserie. If a scalar is passed, every point in the serie is divided by that value. If another serie
* is passed, the two series are combined by division.
* @see combine
*/
div(value: number | TimeSerie): TimeSerie {
if (typeof value === "number") {
return this.map((point: Point) => [point[0], point[1] / value]);
} else {
return this.combine('div', [value])
return this.combine("div", [value]);
}

@@ -486,21 +680,31 @@ }

// Restituisce la funzione
TimeSerie.internals = {}
TimeSerie.internals.combiners = {}
TimeSerie.internals.combine = (series: TimeSerie[], combiner: TimeseriePointCombiner, options: TimeSeriesOperationOptions) : TimeSerie => {
const points = series[0].data.map((p: Point) => p[0]).map((idx: string) => {
const values = series.map((serie:TimeSerie) => serie.atTime(idx, options.fill))
return [
idx,
combiner(values, idx)
] as Point
})
return new TimeSerie(options.name, points, options.metadata)
}
TimeSerie.internals = {};
TimeSerie.internals.combiners = {};
TimeSerie.internals.combine = (
series: TimeSerie[],
combiner: TimeseriePointCombiner,
options: TimeSeriesOperationOptions,
): TimeSerie => {
const points = series[0].data
.map((p: Point) => p[0])
.map((idx: string) => {
const values = series.map((serie: TimeSerie) =>
serie.atTime(idx, options.fill),
);
return [idx, combiner(values, idx)] as Point;
});
return new TimeSerie(options.name, points, options.metadata);
};
TimeSerie.internals.combiners.add = (points: PointValue[]) => points.reduce((a:PointValue, b:PointValue) => a + b, 0)
TimeSerie.internals.combiners.sub = (points: PointValue[]) => points.reduce((a:PointValue, b:PointValue) => a - b, points[0] * 2)
TimeSerie.internals.combiners.mul = (points: PointValue[]) => points.reduce((a:PointValue, b:PointValue) => a * b, 1)
TimeSerie.internals.combiners.div = (points: PointValue[]) => points.reduce((a:PointValue, b:PointValue) => a / b, points[0] * points[0])
TimeSerie.internals.combiners.avg = (points: PointValue[]) => (TimeSerie.internals.combiners.add(points) / points.length)
TimeSerie.internals.combiners.add = (points: PointValue[]) =>
points.reduce((a: PointValue, b: PointValue) => a + b, 0);
TimeSerie.internals.combiners.sub = (points: PointValue[]) =>
points.reduce((a: PointValue, b: PointValue) => a - b, points[0] * 2);
TimeSerie.internals.combiners.mul = (points: PointValue[]) =>
points.reduce((a: PointValue, b: PointValue) => a * b, 1);
TimeSerie.internals.combiners.div = (points: PointValue[]) =>
points.reduce((a: PointValue, b: PointValue) => a / b, points[0] * points[0]);
TimeSerie.internals.combiners.avg = (points: PointValue[]) =>
TimeSerie.internals.combiners.add(points) / points.length;
TimeSerie.createIndex = createIndex
TimeSerie.createIndex = createIndex;

194

src/lib/types.ts

@@ -1,5 +0,5 @@

import parse from 'parse-duration'
import { TimeSerie } from './timeserie'
import parse from "parse-duration";
import { TimeSerie } from "./timeserie";
export type PointValue = number | string | boolean | any
export type PointValue = number | string | boolean | any;

@@ -10,20 +10,20 @@ export type DateLike = Date | string | number;

*/
export type Point = readonly [string, PointValue]
export type Point = readonly [string, PointValue];
export interface TelemetryV1OutputProperty {
[propertyName: string]: readonly Point[]
};
[propertyName: string]: readonly Point[];
}
export interface TelemetryV1Output {
[id: string]: TelemetryV1OutputProperty
};
[id: string]: TelemetryV1OutputProperty;
}
export type Metadata = {
[key: string]: any
}
[key: string]: any;
};
export type TimeFrameInternalRow = {
[columnName: string]: PointValue
}
[columnName: string]: PointValue;
};
export type TimeFrameInternal = {
[time: string]: TimeFrameInternalRow
}
[time: string]: TimeFrameInternalRow;
};

@@ -36,5 +36,5 @@ export interface TimeSeriesOperationOptions {

export interface FromIndexOptions {
name: string
metadata?: Metadata
fill?: PointValue
name: string;
metadata?: Metadata;
fill?: PointValue;
}

@@ -46,7 +46,7 @@

export interface Row {
readonly time: string
readonly [x: string]: unknown
};
readonly time: string;
readonly [x: string]: unknown;
}
export type Index = string[]
export type Index = string[];

@@ -56,3 +56,7 @@ /**

*/
export type TimeseriePointIterator = (value: Point, index: number, array: ReadonlyArray<Point>) => any
export type TimeseriePointIterator = (
value: Point,
index: number,
array: ReadonlyArray<Point>,
) => any;

@@ -62,3 +66,6 @@ /**

*/
export type TimeseriePointCombiner = (values: PointValue[], index: DateLike) => PointValue
export type TimeseriePointCombiner = (
values: PointValue[],
index: DateLike,
) => PointValue;

@@ -68,3 +75,7 @@ /**

*/
export type TimeframeRowsIterator = (value: Row, index: number, array: ReadonlyArray<Row>) => any
export type TimeframeRowsIterator = (
value: Row,
index: number,
array: ReadonlyArray<Row>,
) => any;

@@ -74,6 +85,17 @@ /**

*/
export type TimeserieIterator = (value: TimeSerie, index: number, array: ReadonlyArray<TimeSerie>) => any
export type TimeserieIterator = (
value: TimeSerie,
index: number,
array: ReadonlyArray<TimeSerie>,
) => any;
export type ColumnAggregation = 'avg' | 'last' | 'first' | 'min' | 'max' | 'delta' | 'sum'
export type ResampleDefaultAggregation = ColumnAggregation
export type ColumnAggregation =
| "avg"
| "last"
| "first"
| "min"
| "max"
| "delta"
| "sum";
export type ResampleDefaultAggregation = ColumnAggregation;

@@ -84,9 +106,9 @@ export type IntervalOptions = {

to?: DateLike;
}
};
export type TimeSerieReduceOptions = {
operation: ColumnAggregation
}
operation: ColumnAggregation;
};
export type PartitionOptions = IntervalOptions
export type PartitionOptions = IntervalOptions;

@@ -96,7 +118,7 @@ export type ResampleOptions = IntervalOptions & {

dropNaN?: boolean;
}
};
export type TimeFrameResampleOptions = ResampleOptions & {
operations?: {[key: string]: ColumnAggregation};
}
operations?: { [key: string]: ColumnAggregation };
};

@@ -110,28 +132,41 @@ export interface IndexCreationOptions {

export interface ProjectionOptions {
columns: string[]
columns: string[];
}
export interface AggregationConfiguration {
output:string;
operation:'add' | 'mul' | 'div' | 'sub' | 'avg' | TimeseriePointCombiner;
columns: string[]
output: string;
operation: "add" | "mul" | "div" | "sub" | "avg" | TimeseriePointCombiner;
columns: string[];
}
export type ReduceOperation = 'min' | 'max' | 'first' | 'last' | 'avg' | 'sum' | 'delta'
export type ReduceOperation =
| "min"
| "max"
| "first"
| "last"
| "avg"
| "sum"
| "delta";
export type TimeFrameReduceOptions = {
operation: ReduceOperation
operations?: {[key:string]:ReduceOperation}
}
operation: ReduceOperation;
operations?: { [key: string]: ReduceOperation };
};
export interface FromTimeseriesOptions {
fill?: PointValue
fill?: PointValue;
}
export interface ReindexOptions {
fill?: PointValue
mergeIndexes?: boolean
fill?: PointValue;
mergeIndexes?: boolean;
}
export type PipelineStageType = 'aggregate' | 'resample' | 'project'| 'reduce'| 'add' | 'mul'
export type PipelineStageType =
| "aggregate"
| "resample"
| "project"
| "reduce"
| "add"
| "mul";
export type PipelineStage = {

@@ -144,25 +179,38 @@ aggregate?: AggregationConfiguration;

mul?: number;
}
};
export type SplitOptions = {
chunks: number;
};
export type BetweenTimeOptions = {
includeInferior: boolean;
includeSuperior: boolean;
};
export class TimeInterval {
from: Date
to: Date
size: number
constructor (from: Date, to: Date) {
this.from = from
this.to = to
this.size = to.getTime() - from.getTime()
from: Date;
to: Date;
size: number;
constructor(from: Date, to: Date) {
this.from = from;
this.to = to;
this.size = to.getTime() - from.getTime();
}
static generate (from: DateLike, to: DateLike, interval: number): TimeInterval[] {
const _to = new Date(to)
let cursor: Date = new Date(from)
const intervals: TimeInterval[] = []
static generate(
from: DateLike,
to: DateLike,
interval: number,
): TimeInterval[] {
const _to = new Date(to);
let cursor: Date = new Date(from);
const intervals: TimeInterval[] = [];
while (cursor.getTime() < _to.getTime()) {
const next = new Date(cursor)
next.setMilliseconds(next.getMilliseconds() + interval)
intervals.push(new TimeInterval(cursor, next))
cursor = new Date(next)
const next = new Date(cursor);
next.setMilliseconds(next.getMilliseconds() + interval);
intervals.push(new TimeInterval(cursor, next));
cursor = new Date(next);
}
return intervals
return intervals;
}

@@ -176,15 +224,15 @@ }

*/
export function createIndex (options: IndexCreationOptions): Index {
let size = options.interval
if (typeof options.interval === 'string') {
size = parse(options.interval)
export function createIndex(options: IndexCreationOptions): Index {
let size = options.interval;
if (typeof options.interval === "string") {
size = parse(options.interval);
}
const _to = new Date(options.to)
const cursor: Date = new Date(options.from)
const index: Index = []
const _to = new Date(options.to);
const cursor: Date = new Date(options.from);
const index: Index = [];
while (cursor.getTime() <= _to.getTime()) {
index.push(cursor.toISOString())
cursor.setMilliseconds(cursor.getMilliseconds() + (size as number))
index.push(cursor.toISOString());
cursor.setMilliseconds(cursor.getMilliseconds() + (size as number));
}
return index
return index;
}

32

src/lib/utils.ts

@@ -1,25 +0,25 @@

import { DateLike } from './types'
import { DateLike } from "./types";
export function ms (date: Date | string): number {
return new Date(date).getTime()
export function ms(date: Date | string): number {
return new Date(date).getTime();
}
export function DateLikeToString (d: DateLike): string {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).toISOString()
}
return new Date(d).toISOString()
export function DateLikeToString(d: DateLike): string {
// if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
// return new Date(Number(d)).toISOString();
// }
return new Date(d).toISOString();
}
export function DateLikeToTimestamp (d: DateLike): number {
if (typeof d === 'string' && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).getTime()
export function DateLikeToTimestamp(d: DateLike): number {
if (typeof d === "string" && !isNaN(new Date(Number(d)).getTime())) {
return new Date(Number(d)).getTime();
}
return new Date(d).getTime()
return new Date(d).getTime();
}
export function getOrderOfMagnitude (n:number):number {
const order = Math.floor(Math.log(n) / Math.LN10 +
0.000000001) // because float math sucks like that
return Math.pow(10, order)
export function chunk(arr: any[], chunk_size: number) {
return new Array(Math.ceil(arr.length / chunk_size))
.fill(0)
.map((_, i: number) => arr.slice(i * chunk_size, (i + 1) * chunk_size));
}
src/types/example.d.ts
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