getiing data from mongoseries AND from influx possible
databases/mongo.js - fetchNoiseAVGData added sensorspecials/noise.js - read also from mongo
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+114
-19
@@ -176,25 +176,120 @@ export const fetchActData = async (opts) => {
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).toArray();
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*/
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export const fetchActDataxx = async (opts) => {
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export const fetchNoiseAVGData = async (opts) => {
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let docs = []
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let ret = {err: null, values: []}
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let sorting = ''
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if(opts.sort) {
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if (opts.sort === 1) {
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sorting = '|> sort(columns: ["_time"], desc: false)'
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} else if (opts.sort === -1) {
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sorting = '|> sort(columns: ["_time"], desc: true)'
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}
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let start = opts.start.slice(7)
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let end = opts.stop.slice(6)
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start = DateTime.fromISO(start).toJSDate()
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end = DateTime.fromISO(end).toJSDate()
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let peak = opts.peak; // threshold for peak count
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let datRange = {sensorid: opts.sensorid, datetime: {$gte: start, $lt: end}}
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let sorting = {datetime: opts.sort};
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let grpId = {$dateToString: {format: '%Y-%m-%dT%H:00:00Z', date: '$datetime'}}
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let client = await connectMongo()
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try {
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docs = await client.db(MONGOBASE).collection('sensors').aggregate([
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{$sort: sorting}, // sort by date
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{$match: datRange}, // select only values in give data range
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{
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$group: {
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_id: grpId,
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n_average: {$avg: "$values.E10tel_eq"}, // calculate the average
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n_sum: {$sum: "$values.E10tel_eq"}, // calculate the sum
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peakcount: {$sum: {$cond: [{$gte: ["$values.noise_LA_max", peak]}, 1, 0]}}, // count peaks
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count: {$sum: 1}, // count entries
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}
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},
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{$sort: {_id: 1}}, // sort by result dates
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{ $addFields: { datetime: "$_id"}}, // change '_id' to 'date'
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{$project: opts.long ? { _id:0, n_AVG: { $multiply: [10, {$log10: "$n_average"}]}, datetime:1, peakcount:1, count:1, n_sum:1} :
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{_id:0, n_AVG: { $multiply: [10, {$log10: "$n_average"}]}, datetime:1, peakcount:1}}
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]).toArray(); // return not all fields, depending on 'long'
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} catch(e) { // if there was an error
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ret.err = e // log it to console
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}
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// build the flux query
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let query = `
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from(bucket: "sensor_data")
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|> range(${opts.start}, ${opts.stop})
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|> filter(fn: (r) => r.sid == "${opts.sensorid}")
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${sorting}
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|> keep(columns: ["_time","_field","_value"])
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|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
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`
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return await fetchFromInflux(ret, query)
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finally {
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client.close()
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}
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ret.values = docs
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return ret
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}
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/*
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// *********************************************
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// getAverageData
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//
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// Calculate different values per hour
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// average of E10tel_eq ( E10tel_eq => 10 ^(LAeq/10) )
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// sum of E10tel_eq, to calculate day, night and eveniung averages
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// count, how many values are used for average/sum
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// paeakcount, how many values of LAmax are over defined peak value in every hour
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//
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// params:
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// db: Database
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// opt: different options (see further down)
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//
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// return
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// depending an calling parameter 'what', not all values will be sent in 'values'
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// JSON
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// {[
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// { datetime: "2019-10-23T00:00:00Z" , n_AVG: 67.22, n_sum: 32783, count: 24, peakcount: 6 }.
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// { datetime: "2019-10-23T01:00:00Z" , n_AVG: 52.89, n_sum: 23561, count: 26, peakcount: 5 }.
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// .........
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// ]}
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//
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// *********************************************
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async function getAverageData(db,opt) {
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let start = opt.start;
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let end = opt.end; // start and ent time for aggregation
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let docs = []; // collect data here
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const collection = db.collection('data_' + opt.sid);;
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let span = opt.span // date range in days
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let peak = opt.peak; // threshold for peak count
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let long = opt.long; // true => give extra output
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let nbrOfHours = opt.end.diff(opt.start,'hours') + 24;
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let datRange = {datetime: {$gte: opt.start.toDate(), $lt: opt.end.toDate()}};
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let sorting = {datetime: opt.sort};
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let grpId = {$dateToString: {format: '%Y-%m-%dT%H:00:00Z', date: '$datetime'}};
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try {
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docs = await collection.aggregate([
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{$sort: sorting}, // sort by date
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{$match: datRange}, // select only values in give data range
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{
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$group: {
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_id: grpId,
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n_average: {$avg: '$E10tel_eq'}, // calculate the average
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n_sum: {$sum: '$E10tel_eq'}, // calculate the sum
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peakcount: {$sum: {$cond: [{$gte: ["$noise_LA_max", peak]}, 1, 0]}}, // count peaks
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count: {$sum: 1}, // count entries
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}
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},
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{$sort: {_id: 1}}, // sort by result dates
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{ $addFields: { datetime: "$_id"}}, // change '_id' to 'date'
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{$project: opt.long ? { _id:0, n_AVG: { $multiply: [10, {$log10: "$n_average"}]}, datetime:1, peakcount:1, count:1, n_sum:1} :
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{_id:0, n_AVG: { $multiply: [10, {$log10: "$n_average"}]}, datetime:1, peakcount:1}}
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]).toArray(); // return not all fields, depending on 'long'
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} catch(e) { // if there was an error
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console.log(e); // log it to console
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}
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// To easily extract the values, we copy the data from docs into a new array, so that the
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// hour in an element in docs becomes the index into the new array (for every new day this
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// index will be incremented by 24). Missing values are marked by: {n_sum=-1, n_AVG=-1}.
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let hoursArr = new Array(nbrOfHours); // generate new array
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let emptyValues = opt.long ? {n_sum: -1, n_AVG:-1} : {n_AVG:-1};
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hoursArr.fill(emptyValues); // fill with 'empty' value
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let startDay = moment.utc(docs[0].datetime).date(); // calc first day
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let k = 0;
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for (let i=0; i<docs.length; i++) { // loop through docs
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let stunde = moment.utc(docs[i].datetime).hours(); // extract current hour
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let day = moment.utc(docs[i].datetime).date(); // and curren t day
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if (day != startDay) { // if date has changed
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k += 24; // increment index by 24
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startDay = day;
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}
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hoursArr[k+stunde] = docs[i]; // copy date into hourArray
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}
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return hoursArr;
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}
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*/
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@@ -432,6 +432,7 @@ const getAPIprops = (opt) => {
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const getNoiseAVGData = async (opts) => {
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let ret = await influx.fetchNoiseAVGData(opts)
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let retM = await mongo.fetchNoiseAVGData(opts)
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if(ret.err) {
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return returnOnError(ret, ret.err, getNoiseAVGData.name)
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