diff --git a/config/exampleArray.js b/config/exampleArray.js index bc319b0..ee5b63b 100644 --- a/config/exampleArray.js +++ b/config/exampleArray.js @@ -1,4 +1,4 @@ -const inputExample = +const inputExample = // eslint-disable-line no-unused-vars //tensor 1 [ // 1 prediction per hour diff --git a/dependencies/monster/monster_class.js b/dependencies/monster/monster_class.js index f2bc370..77e1b49 100644 --- a/dependencies/monster/monster_class.js +++ b/dependencies/monster/monster_class.js @@ -1,746 +1 @@ -/** - * @file Measurement.js - * - * Permission is hereby granted to any person obtaining a copy of this software - * and associated documentation files (the "Software"), to use it for personal - * or non-commercial purposes, with the following restrictions: - * - * 1. **No Copying or Redistribution**: The Software or any of its parts may not - * be copied, merged, distributed, sublicensed, or sold without explicit - * prior written permission from the author. - * - * 2. **Commercial Use**: Any use of the Software for commercial purposes requires - * a valid license, obtainable only with the explicit consent of the author. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR OTHERWISE, ARISING FROM, - * OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - * - * Ownership of this code remains solely with the original author. Unauthorized - * use of this Software is strictly prohibited. - * - * Author: - * - Rene De Ren / Sjoerd Fijnje - * Email: - * - r.de.ren@brabantsedelta.nl - * - */ - -const EventEmitter = require('events'); -const Logger = require('../../../generalFunctions/helper/logger'); -const defaultConfig = require('./monsterConfig.json'); -const ConfigUtils = require('../../../generalFunctions/helper/configUtils'); -const tf = require('@tensorflow/tfjs'); -const tfLoader = require('./modelLoader'); - -class Monster{ - /*------------------- Construct and set vars -------------------*/ - constructor(config={}) { - - //init - this.init = false; // keep track of init - - this.emitter = new EventEmitter(); // Own EventEmitter - this.configUtils = new ConfigUtils(defaultConfig,config.general.logging.enabled,config.general.logging.logLevel); // ConfigUtils - this.config = this.configUtils.initConfig(config); - - // Init after config is set - this.logger = new Logger(this.config.general.logging.enabled,this.config.general.logging.logLevel, this.config.general.name); - - this.modelLoader = new tfLoader(this.logger); // ModelLoader - - // -------------------------------------- fetch dependencies -------------------------- - this.convert = require('../../../convert/dependencies/index'); - this.math = require('mathjs'); - - this.model = null; // TensorFlow model placeholder - - //place holders for output data - this.output = {} ; // object to place all relevant outputs in and preform event change check on - this.child = {} ; // register childs - - //Specific object info - this.aquonSampleName = "112100" ; // aquon sample name to start automatic sampling on the basis of the document - this.monsternametijden = {} ; // json monsternametijden file? - this.rain_data = {} ; // precipitation data - this.aggregatedOutput = {} ; // object that does not contain momentary values but a combination of all kinds of data over a fixed period of time - this.sumRain = 0 ; // total sum of rain over time window + n hours and - n hours - this.avgRain = 0 ; // total divided by number of locations to get average over total time - this.daysPerYear = 0 ; // how many days remaining for this year - - // outputs - this.pulse = false; // output pulse to sampling machine - this.bucketVol = 0; // how full is the sample? - this.sumPuls = 0; // number of pulses so far - this.predFlow = 0; // predicted flow over sampling time in hours, expressed in m3 - this.bucketWeight = 0; // actual weight of bucket - - //inputs - this.q = 0; // influent flow in m3/h - this.i_start = false // when true, the program gets kicked off calculating what it needs to take samples - this.sampling_time = config.constraints.samplingtime; // time expressed in hours over which the sampling will run (currently 24) - this.emptyWeightBucket = config.asset.emptyWeightBucket; // empty weight of the bucket - - // internal vars - this.temp_pulse = 0; // each interval pulses send out 1 and then reset - this.volume_pulse = 0.05; // define volume pulse expressed in L - this.minVolume = config.constraints.minVolume;// define min volume in a sampling cabinet before a sample is declared valid expressed in L - this.maxVolume = 0; // calculated maxvolume depending on own weight - this.maxWeight = config.constraints.maxWeight;// define max volume in a sampling cabinet before a sample is declared invalid expressed in L - this.cap_volume = 55; // abs max capacity of bucket (volume) in liters - this.targetVolume = 0; // volume of sampling cabinet that model aims for - this.minPuls = 0; // calculates the min pulses depending on min_vol and max_vol - this.maxPuls = 0; // calculates the max pulses depending on min_vol and max_vol - this.absMaxPuls = 0; // capacity of sampling cabinet (number of pulses) - this.targetPuls = 0; // keeps track of the desired amount of pulses (+- 50% tolerance), based on aimed volume - this.m3PerPuls = 0; // each pulse is equal to a number of m3 - this.predM3PerSec = 0; // predicted flow in m3 per second - this.m3PerTick = 0; // actual measured flow in m3 per second - this.m3Total = 0; // total measured flow over sampling time in m3 - this.running = false; // define if sampling is running or not - - this.qLineRaw = {}; // see example - this.minSeen = {}; // keeps track of minimum ever seen so far in a time period for each hour (over totals not every value) - this.maxSeen = {}; // keeps track of maximum ever seen so far in a time period for each hour (over totals not every value) - this.qLineRefined = {}; // this should be the ( quantiles? ) classified in the datasets - this.calcTimeShiftDry = 0; // What is the delay after a dry period of minimum n hours - this.calcTimeShiftWet = 0; - this.calcCapacitySewer = 0; - // how much rain goes to the sewage ? -> calculate surface area of hardend / sewage. - - this.minDryHours = 0; // what is the minimum of dry hours before we can calculate timeshift? spot this with moving average? - this.minWetHours = 0; // how long does it take to remove all the rain? - this.resolution = 0; // Number of chunks in qLineRaw / define how big the window is to sum all values ( for now we need to take 1 hour or bigger resolutions but in the future smaller is better to see more accurate correlations) - this.tmpTotQ = 0; // keep track of sum of q within resolution window - - //old prediction factor - this.predFactor = 0.7; // define factor as multiplier for prediction - - //track program start and stop - this.start_time = Date.now(); // default start time - this.stop_time = Date.now(); // default stop time - this.flowTime = 0; //keep track in detail how much time between 2 ticks for more accurate flow measurement - this.timePassed = 0; // time in seconds - this.timeLeft = 0; // time in seconds - this.currHour = new Date().getHours(); // on init define in which hour we are 0 - 23 - - - this.init = true; // end of constructor - - //set boundries and targets after init based on above settings - this.set_boundries_and_targets(); - - - } - - /*------------------- GETTER/SETTERS Dynamics -------------------*/ - set monsternametijden(value){ - - if(this.init){ - if(Object.keys(value).length > 0){ - - //check if push is in valid format and not null - if( - typeof value[0].SAMPLE_NAME !== 'undefined' - && - typeof value[0].DESCRIPTION !== 'undefined' - && - typeof value[0].SAMPLED_DATE !== 'undefined' - && - typeof value[0].START_DATE !== 'undefined' - && - typeof value[0].END_DATE !== 'undefined' - ){ - - //each time this changes we load the next date applicable for this function - this._monsternametijden = value; - - //fetch dates - this.regNextDate(value); - - } - else{ - // Monsternametijden object Wrong format contact AQUON - } - } - else{ - // Monsternametijden object Wrong format contact AQUON - } - } - } - - get monsternametijden(){ - return this._monsternametijden; - } - - set rain_data(value){ - - //retrieve precipitation expected during the coming day and precipitation of yesterday - this._rain_data = value; - - //only update after init and is not running. - if(this.init && !this.running){ - this.updatePredRain(value); - } - - } - - get rain_data(){ - return this._rain_data; - } - - set bucketVol(val){ - - //Put val in local var - this._bucketVol = val; - - //Place into output object - this.output.bucketVol = val; - - // update bucket weight - this.bucketWeight = val + this.emptyWeightBucket; - } - - get bucketVol(){ - return this._bucketVol; - } - - set minVolume(val){ - - //Protect against 0 - val == 0 ? val = 1 : val = val; - - this._minVolume = val; - - //Place into output object - this.output.minVolume = val; - } - - get minVolume(){ - return this._minVolume; - } - - set q(val){ - - //Put val in local var - this._q = val; - - //Place into output object - this.output.q = val; - this.output.qm3sec = this.convert(val).from('m3/h').to('m3/s'); - - } - - get q(){ - return this._q; - } - - /*------------------- FUNCTIONS -------------------*/ - - set_boundries_and_targets(){ - - // define boundries for algorithm - this.maxVolume = this.maxWeight - this.emptyWeightBucket ; // substract bucket weight of max volume assuming they are both on a 1 to 1 ratio - this.minPuls = Math.round(this.minVolume / this.volume_pulse); // minimum pulses we want before we have a valid sample - this.maxPuls = Math.round(this.maxVolume / this.volume_pulse); // maximum pulses we can handle (otherwise sample is too heavy) - this.absMaxPuls = Math.round(this.cap_volume / this.volume_pulse); // number of pulses a sample can contain before overflowing - // define target values - this.targetVolume = this.minVolume * Math.sqrt(this.maxVolume/this.minVolume); - //old way - //this.targetVolume = Math.round( ( ( (this.maxVolume - this.minVolume) / 2 ) + this.minVolume ) * 100) / 100; // calculate middle between min and max - // correct target values - this.targetPuls = Math.round(this.targetVolume / this.volume_pulse) ; // define desired amount of pulses (in this case our prediction can deviate 50% up and 50% down without a problem) - } - - updateArchiveRain(val){ - - } - - updatePredRain(value){ - //make date objects to define relative time window - let now = new Date(Date.now()); - let past = new Date(Date.now()); - let future = new Date(Date.now()); - let totalRaw = {}; - let totalProb = {}; - let totalAvg = {}; - - //refine object with different values - let rain = {}; - rain.hourly = {}; // an object with timestamps and aggreated over all locations summed precipation in mm - rain.hourly.time = []; - rain.hourly.precipationRaw = []; - rain.hourly.precipationProb = []; - - let numberOfLocations = 0; - - //Make timestamp + 24 hours - future.setHours(now.getHours() + 24); - - //Make timestamp - 24hours - past.setHours(now.getHours() - 24); - - //go through all locations and sum up the average precipation of each location so we have summed precipation over every hour - Object.entries(value).forEach(([locationKey, location],locationindex) => { - - //number of locations - numberOfLocations++; - - // make an object to keep track of the dataset we load - this.aggregatedOutput[locationKey] = {}; - this.aggregatedOutput[locationKey].tag = {}; - this.aggregatedOutput[locationKey].tag.latitude = location.latitude; - this.aggregatedOutput[locationKey].tag.longitude = location.longitude; - this.aggregatedOutput[locationKey].precipationRaw = {}; - this.aggregatedOutput[locationKey].precipationProb = {}; - - - //loop through object for each location over all hourlys - Object.entries(location.hourly.time).forEach(([key, time], index) => { - - this.aggregatedOutput[locationKey].precipationRaw[key] = {}; - this.aggregatedOutput[locationKey].precipationProb[key] = {}; - - //convert string output to a date object - let checkdate = new Date(time); - - //convert date to milliseconds timestamps - let currTimestamp = checkdate.getTime(); - let probability = 100; //default probility unless otherwise defined - - if(typeof location.hourly.precipitation_probability !== 'undefined'){ - probability = location.hourly.precipitation_probability[key]; - - } - - if(probability > 0){ - probability /= 100; - } - - // only interested in dates before timeframe and after to make use of - // ( currTimestamp >= now && currTimestamp < future) || ( currTimestamp < now && currTimestamp > past ) - if( true ){ - - typeof totalRaw[currTimestamp] === 'undefined' ? totalRaw[currTimestamp] = 0 : null; - typeof totalProb[currTimestamp] === 'undefined' ? totalProb[currTimestamp] = 0 : null; - - //placed probability into the equation - totalRaw[currTimestamp] += location.hourly.precipitation[key] ; - totalProb[currTimestamp] += ( location.hourly.precipitation[key] * probability ) ; - - //keep track of all requested data - this.aggregatedOutput[locationKey].precipationRaw[key]["val"] = location.hourly.precipitation[key]; // raw data from open weather data - this.aggregatedOutput[locationKey].precipationRaw[key]["time"] = currTimestamp; - - this.aggregatedOutput[locationKey].precipationProb[key]["val"] = probability; // probability of open weather - this.aggregatedOutput[locationKey].precipationProb[key]["time"] = currTimestamp; - - } - - //remove dead info - if(Object.keys(this.aggregatedOutput[locationKey].precipationRaw[key]).length == 0 ){ - delete this.aggregatedOutput[locationKey].precipationRaw[key]; - }; - - if(Object.keys(this.aggregatedOutput[locationKey].precipationProb[key]).length == 0 ){ - delete this.aggregatedOutput[locationKey].precipationProb[key]; - }; - - }); - }); - - //total sum expected over time window (just for ref now not so important anymore) - this.sumRain = Object.values(totalProb).reduce((sum, value) => sum + value, 0); - this.avgRain = this.sumRain / numberOfLocations; - - //make average over prob - Object.entries(totalProb).forEach(([key, sum],index) => { - typeof totalAvg[key] === 'undefined' ? totalAvg[key] = 0 : null; - totalAvg[key] = sum / numberOfLocations; - }); - - //make new prediction - //this.get_model_prediction(); - return this.aggregatedOutput; - } - - get_model_prediction(){ - -// combine 24 hourly predictions to make one daily prediction (for the next 24 hours including the current hour) -let inputs = []; -for (let predHour = 0; predHour <= 23; predHour++) { - - // select 24 timestamps based on hour te be predicted - let now = new Date(); - const lastHour = new Date(now.setHours(now.getHours() + predHour)); - let timestamps = this.rain_data[0].hourly.time.map(ts => new Date(ts)); - let timestamps_24 = timestamps.filter(ts => ts <= lastHour).slice(-24) - - // for each relevant hour calculate the mean precipitation across all areas - let precipitation = []; - for (let i = 0; i < timestamps.length; i++) { - - if(timestamps_24.includes(timestamps[i])) { - - let values = []; - for (let j = 0; j < this.rain_data.length; j++) { - - values.push(this.rain_data[j].hourly.precipitation[i]); - } - let mean = values.reduce((sum, value) => sum + value, 0) / this.rain_data.length; - precipitation.push(mean); - } - } - - // standardize variables for prediction and 'zip' them - let hours = timestamps_24.map(ts => ts.getHours()); - hours = hours.map(hour => (hour - 11.50430734) / 6.92241142); - precipitation = precipitation.map(value => (value - 0.09011861) / 0.43853627); - let zipped = hours.map((value, i) => [value, precipitation[i]]); - - // collect inputdata for model - inputs.push(zipped); -} - const output = this.model_loader(inputs); - console.log('Final output: ' + output); - } - -async model_loader(inputs){ - - let dailyPred = 0; - - try { - const localURL = "http://127.0.0.1:1880/generalFunctions/datasets/lstmData/tfjs_model/model.json"; - - // Could you log the original model JSON to help determine the correct input shape? - const response = await fetch(localURL); - const modelJSON = await response.json(); - console.log('Original model config:', JSON.stringify(modelJSON.modelTopology.model_config.config.layers[0], null, 24, 2)); - - // Try loading with default input shape - const model = await this.modelLoader.loadModel(localURL); - console.log('Model loaded successfully!'); - - // make predictions - for (const input of inputs) { - - const inputTensor = tf.tensor3d([input]); - const predict = model.predict(inputTensor); - let predictValue = await predict.data(); - - // back-transformation because of standardization of the response variable - predictValue = predictValue[0] * 1024.1940942 + 1188.0105115; - dailyPred += predictValue; - } - console.log('Daily prediction: ' + dailyPred); - } catch (error) { - console.error('Failed to load model:', error); - } - return dailyPred; -} - - sampling_program(){ - - // ------------------ Run once on conditions and start sampling - if( ( (this.i_start ) || ( Date.now() >= this.nextDate ) ) && !this.running ){ - - this.running = true; - - // reset persistent vars - this.temp_pulse = 0; - this.pulse = false; - this.bucketVol = 0; - this.sumPuls = 0; - this.m3Total = 0; - this.timePassed = 0; // time in seconds - this.timeLeft = 0; // time in seconds - this.predM3PerSec = 0; - - //run prediction to ensure its value is filled - this.get_model_prediction(); - - // define m3 per pulse for this run and round to int ! - this.m3PerPuls = Math.round(this.predFlow / this.targetPuls); - this.predM3PerSec = this.predFlow / this.sampling_time / 60 / 60; // predicted m3 per time - - // define start and stop time based on calender data - this.start_time = Date.now(); - this.stop_time = Date.now() + (this.sampling_time * 60 * 60 * 1000); // convert to milliseconds - - //reset parameters and look for next date - this.regNextDate(this.monsternametijden); - - // reset start - this.i_start = false; - } - - // ------------------ Run for as long as sampling time is not greater than stop time - if(this.stop_time > Date.now()){ - - // define time vars - this.timePassed = Math.round( ( Date.now() - this.start_time ) / 1000); - this.timeLeft = Math.round( ( this.stop_time - Date.now() ) / 1000); - - // calc temp pulse rate - let update = this.m3PerTick / this.m3PerPuls; - - // update values - this.temp_pulse += update; - this.m3Total += this.m3PerTick; - - // check if we need to send out a pulse (stop sending pulses if capacity is reached) - if(this.temp_pulse >= 1 && this.sumPuls < this.absMaxPuls){ - // reset - this.temp_pulse += -1; - // send out a pulse and add to count - this.pulse = true; - // count pulses - this.sumPuls++; - // update bucket volume each puls - this.bucketVol = Math.round(this.sumPuls * this.volume_pulse * 100) / 100; - - } - else{ - - if( this.sumPuls > this.absMaxPuls){ - - // find out how to reschedule sample automatically? - } - - //update pulse when its true - if(this.pulse){ - this.pulse = false; // continue but don't send out a pulse - } - - } - } - else - { - //after setting once dont do it again - if(this.running){ - // Vars can only be 0 if this is not running - this.m3PerPuls = 0; - this.temp_pulse = 0; - this.pulse = false; - this.bucketVol = 0; - this.sumPuls = 0; - this.timePassed = 0; // time in seconds - this.timeLeft = 0; // time in seconds - this.predFlow = 0; - this.predM3PerSec = 0; - this.m3Total = 0; - this.running = false; // end of sampling program (stop_time reached) - - } - } - } - - flowCalc(){ - //reset timePassed - let timePassed = 0; - - // each tick calc flowtimepassed - this.flowTime > 0 ? timePassed = ( Date.now() - this.flowTime) / 1000 : timePassed = 0 ; - - //conver to m3 per tick - this.m3PerTick = this.q / 60 / 60 * timePassed ; - - // put new timestamp - this.flowTime = Date.now(); - - } - - //goes through time related functions - tick(){ - - //calculate flow based on input - this.flowCalc(); - - //run sampling program - this.sampling_program(); - - //logQ for predictions / forecasts - this.logQoverTime(); - } - - regNextDate(monsternametijden){ - - let next_date = new Date(new Date().setFullYear(new Date().getFullYear() + 1)); - let n_days_remaining = 0; - - if(typeof monsternametijden !== 'undefined'){ - // loop through lines - Object.entries(monsternametijden).forEach(([key, line],index) => { - - //console.log(line.START_DATE); - //check if date is not null - if(line.START_DATE != "NULL"){ - let curr_date_conv = new Date(line.START_DATE); - let curr_date = curr_date_conv.getTime(); - - //check if sample name is this sample and if date is bigger than now. - if(line.SAMPLE_NAME == this.aquonSampleName && curr_date > Date.now() ){ - - //only keep date that is bigger than current but smaller than the ones that follow after it. - if(curr_date < next_date){ next_date = curr_date; } - - // check if its within this year only show those days as days remaining - if( new Date().getFullYear() == curr_date_conv.getFullYear() ){ n_days_remaining++; } - } - } - - }); - } - else{ - //this.warning.push(3); - } - - //store vars remaining - this.daysPerYear = n_days_remaining; - this.nextDate = next_date; - } - - logQoverTime(){ - - //store currHour in temp obj for easy ref - let h = this.currHour; - - // define rain hour of which the correlation is the biggest this doesnt belong in this section do this afterwards - // let rainH = h - this.calcTimeShift ; - - // how much rain fell on rainH (define category) - - // fetch current hour from actual time - const currentHour = new Date().getHours(); - - //on hour change begin log - if(h !== currentHour ){ - - //write current total to object - this.qLineRaw.h = this.tmpTotQ - - //reset tmpTotQ - - //set this.currHour to currentHour - } - - - - - - - } - - //create objects where to push arrays in to keep track of data - createMinMaxSeen(){ - //check which hour it is , then make sum , after sum is complete check which hour it is - //loop over sampling time expressed in hours - for(let h = 1; h < this.sampling_time ; h++){ - this.minSeen = {}; - } - } - - -} // end of class - -module.exports = Monster; - - -const mConfig={ - general: { - name: "Monster", - logging:{ - logLevel: "debug", - enabled: true, - }, - }, - asset: { - emptyWeightBucket: 3, - }, - constraints: { - minVolume: 4, - maxWeight: 23, - }, -} - -let monster = new Monster(mConfig); - - - - -monster.rain_data = 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- -monster.get_model_prediction(); - -/* -// combine 24 hourly predictions to make one daily prediction (for the next 24 hours including the current hour) -let inputs = []; -for (let predHour = 0; predHour <= 23; predHour++) { - - // select 24 timestamps based on hour te be predicted - let now = new Date(); - const lastHour = new Date(now.setHours(now.getHours() + predHour)); - let timestamps = monster.rain_data[0].hourly.time.map(ts => new Date(ts)); - timestamps_24 = timestamps.filter(ts => ts <= lastHour).slice(-24) - - // for each relevant hour calculate the mean precipitation across all areas - let precipitation = []; - for (let i = 0; i < timestamps.length; i++) { - - if(timestamps_24.includes(timestamps[i])) { - - let values = []; - for (let j = 0; j < monster.rain_data.length; j++) { - - values.push(monster.rain_data[j].hourly.precipitation[i]); - } - let mean = values.reduce((sum, value) => sum + value, 0) / monster.rain_data.length; - precipitation.push(mean); - } - } - - // standardize variables for prediction and 'zip' them - let hours = timestamps_24.map(ts => ts.getHours()); - hours = hours.map(hour => (hour - 11.50430734) / 6.92241142); - precipitation = precipitation.map(value => (value - 0.09011861) / 0.43853627); - zipped = hours.map((value, i) => [value, precipitation[i]]); - - // collect inputdata for model - inputs.push(zipped); -} - - (async () => { - try { - const localURL = "http://127.0.0.1:1880/generalFunctions/datasets/lstmData/tfjs_model/model.json"; - - // Could you log the original model JSON to help determine the correct input shape? - const response = await fetch(localURL); - const modelJSON = await response.json(); - console.log('Original model config:', JSON.stringify(modelJSON.modelTopology.model_config.config.layers[0], null, 24, 2)); - - // Try loading with default input shape - const model = await monster.modelLoader.loadModel(localURL); - console.log('Model loaded successfully!'); - - // make predictions - let dailyPred = 0; - for (const input of inputs) { - - const inputTensor = tf.tensor3d([input]); - const predict = model.predict(inputTensor); - let predictValue = await predict.data(); - - // back-transformation because of standardization of the response variable - predictValue = predictValue[0] * 1024.1940942 + 1188.0105115; - dailyPred += predictValue; - } - console.log('Daily prediction: ' + dailyPred); - } catch (error) { - console.error('Failed to load model:', error); - } - })();*/ - +module.exports = require('../../src/specificClass'); diff --git a/monster.html b/monster.html index 12d421f..d9c445b 100644 --- a/monster.html +++ b/monster.html @@ -1,5 +1,5 @@