Java tutorial
/** * Copyright (c) 2016, All partners of the iTesla project (http://www.itesla-project.eu/consortium) * This Source Code Form is subject to the terms of the Mozilla Public * License, v. 2.0. If a copy of the MPL was not distributed with this * file, You can obtain one at http://mozilla.org/MPL/2.0/. */ package eu.itesla_project.online.tools; import java.util.Arrays; import java.util.Set; import java.util.stream.Collectors; import org.apache.commons.cli.CommandLine; import org.apache.commons.cli.Option; import org.apache.commons.cli.Options; import org.joda.time.DateTime; import org.joda.time.Interval; import com.google.auto.service.AutoService; import eu.itesla_project.commons.tools.Command; import eu.itesla_project.commons.tools.Tool; import eu.itesla_project.computation.ComputationManager; import eu.itesla_project.computation.local.LocalComputationManager; import eu.itesla_project.iidm.network.Country; import eu.itesla_project.cases.CaseType; import eu.itesla_project.modules.online.TimeHorizon; import eu.itesla_project.offline.forecast_errors.ForecastErrorsAnalysisConfig; import eu.itesla_project.offline.forecast_errors.ForecastErrorsAnalysisParameters; import eu.itesla_project.offline.forecast_errors.ForecastErrorsAnalysis; /** * * @author Quinary <itesla@quinary.com> */ @AutoService(Tool.class) public class RunForecastErrorsAnalysisTool implements Tool { private ForecastErrorsAnalysisParameters defaultParameters; private static Command COMMAND = new Command() { @Override public String getName() { return "run-forecast-errors-analysis"; } @Override public String getTheme() { return Themes.MCLA; } @Override public String getDescription() { return "run forecast errors analysis"; } @Override public Options getOptions() { Options options = new Options(); options.addOption(Option.builder().longOpt("analysis").desc("the analysis id").hasArg().required() .argName("ID").build()); options.addOption(Option.builder().longOpt("time-horizon").desc("time horizon (example DACF)").hasArg() .argName("TH").build()); options.addOption(Option.builder().longOpt("base-case-date") .desc("base case date (example 2013-01-15T18:45:00+01:00)").hasArg().argName("DATE").build()); options.addOption(Option.builder().longOpt("history-interval") .desc("historical time interval (example 2013-01-01T00:00:00+01:00/2013-01-31T23:59:00+01:00)") .hasArg().argName("DATE1/DATE2").build()); options.addOption(Option.builder().longOpt("ir") .desc("fraction of explained variance for PCA (example 0.9)").hasArg().argName("IR").build()); options.addOption(Option.builder().longOpt("flagPQ") .desc("1 = P and Q sampled separately, 0 = P sampled and Q computed from P (constant pf)") .hasArg().argName("FLAGPQ").build()); options.addOption(Option.builder().longOpt("method").desc( "method for missing data imputation: 1 = new method proposed by RSE, 2 = gaussian conditional sampling, 3 = gaussian mixture imputation, 4 = interpolation based method") .hasArg().argName("METHOD").build()); options.addOption(Option.builder().longOpt("nClusters").desc("number of clusters for PCA (example 3)") .hasArg().argName("NCLUSTERS").build()); options.addOption(Option.builder().longOpt("percentileHistorical").desc( "quantile of the distribution of historical data related to Q vars, to set realistic limits of Q samples in case of using a constant power factor to produce Q samples starting from P samples") .hasArg().argName("PERCENTILEHISTORICAL").build()); options.addOption(Option.builder().longOpt("modalityGaussian").desc( "1 = fictitious gaussians simulate forecast errors, 0 = historical data with copula estimation are considered") .hasArg().argName("MODALITY_GAUSSIAN").build()); options.addOption(Option.builder().longOpt("outliers") .desc("0 = outliers are included as valid samples, 1 = outliers are excluded").hasArg() .argName("OUTLIERS").build()); options.addOption(Option.builder().longOpt("conditionalSampling") .desc("1 = activated conditional sampling, 0 = not active").hasArg() .argName("CONDITIONAL_SAMPLING").build()); options.addOption(Option.builder().longOpt("nSamples").desc("number of samples to create, offline") .hasArg().required().argName("NSAMPLES").build()); options.addOption( Option.builder().longOpt("countries").desc("the countries of the base case, separated by comma") .hasArg().argName("COUNTRY,COUNTRY,...").build()); options.addOption(Option.builder().longOpt("case-type").desc("the type (FO/SN) of the base case") .hasArg().argName("case-type").build()); return options; } @Override public String getUsageFooter() { return null; } }; @Override public Command getCommand() { return COMMAND; } @Override public void run(CommandLine line) throws Exception { ComputationManager computationManager = new LocalComputationManager(); String analysisId = line.getOptionValue("analysis"); DateTime baseCaseDate = line.hasOption("base-case-date") ? DateTime.parse(line.getOptionValue("base-case-date")) : getDefaultParameters().getBaseCaseDate(); Interval histoInterval = line.hasOption("history-interval") ? Interval.parse(line.getOptionValue("history-interval")) : getDefaultParameters().getHistoInterval(); double ir = line.hasOption("ir") ? Double.parseDouble(line.getOptionValue("ir")) : getDefaultParameters().getIr(); int flagPQ = line.hasOption("flagPQ") ? Integer.parseInt(line.getOptionValue("flagPQ")) : getDefaultParameters().getFlagPQ(); int method = line.hasOption("method") ? Integer.parseInt(line.getOptionValue("method")) : getDefaultParameters().getMethod(); Integer nClusters = line.hasOption("nClusters") ? Integer.parseInt(line.getOptionValue("nClusters")) : getDefaultParameters().getnClusters(); double percentileHistorical = line.hasOption("percentileHistorical") ? Double.parseDouble(line.getOptionValue("percentileHistorical")) : getDefaultParameters().getPercentileHistorical(); Integer modalityGaussian = line.hasOption("modalityGaussian") ? Integer.parseInt(line.getOptionValue("modalityGaussian")) : getDefaultParameters().getModalityGaussian(); Integer outliers = line.hasOption("outliers") ? Integer.parseInt(line.getOptionValue("outliers")) : getDefaultParameters().getOutliers(); Integer conditionalSampling = line.hasOption("conditionalSampling") ? Integer.parseInt(line.getOptionValue("conditionalSampling")) : getDefaultParameters().getConditionalSampling(); Integer nSamples = line.hasOption("nSamples") ? Integer.parseInt(line.getOptionValue("nSamples")) : getDefaultParameters().getnSamples(); Set<Country> countries = line.hasOption("countries") ? Arrays.stream(line.getOptionValue("countries").split(",")).map(Country::valueOf).collect( Collectors.toSet()) : getDefaultParameters().getCountries(); CaseType caseType = line.hasOption("case-type") ? CaseType.valueOf(line.getOptionValue("case-type")) : getDefaultParameters().getCaseType(); ForecastErrorsAnalysisParameters parameters = new ForecastErrorsAnalysisParameters(baseCaseDate, histoInterval, analysisId, ir, flagPQ, method, nClusters, percentileHistorical, modalityGaussian, outliers, conditionalSampling, nSamples, countries, caseType); ForecastErrorsAnalysis feAnalysis = new ForecastErrorsAnalysis(computationManager, ForecastErrorsAnalysisConfig.load(), parameters); System.out.println("Starting Forecast Errors Analysis"); if (line.hasOption("time-horizon")) { TimeHorizon timeHorizon = TimeHorizon.fromName(line.getOptionValue("time-horizon")); feAnalysis.start(timeHorizon); } else feAnalysis.start(); System.out.println("Forecast Errors Analysis Terminated"); } private ForecastErrorsAnalysisParameters getDefaultParameters() { if (defaultParameters == null) { defaultParameters = ForecastErrorsAnalysisParameters.load(); } return defaultParameters; } }