| @@ -204,20 +204,10 @@ |
| * timeseries of the annual flow data against the water demand. |
| * @param sortedData a string[][] containing daily average stream flow values in the following format: column1 = dates (yyyy-mm-dd format), |
| * column2 = daily average stream flow values (cfs) |
| - * @param lambdaString contains either the user provided value of lambda or "optimize" which tells the program to optimize lambda based on the dataset |
| - * @param action if "optimize", this only optimizes the lambda and phi values for the model and then reports these back to the user, |
| - * if "all" then it generates data based on the calculated phi values and finishes performing the drought analysis, |
| - * if "useParameters" then it takes the previously generated parameters and uses these to perform the regression |
| - * @param phiValues |
| - * @param thetaValues |
| * @return a String[] containing a tab-delimited summary of the droughts (water supply < water demand) |
| * @throws IOException |
| */ |
| - public String[] generalDroughtAnalysis(String[][] sortedData, |
| - String lambdaString, |
| - String action, |
| - String phiValues, |
| - String thetaValues) throws IOException{ |
| + public String[] generalDroughtAnalysis(String[][] sortedData) throws IOException{ |
| DoubleMath doubleMath = new DoubleMath(); |
| DoubleArray doubleArray = new DoubleArray(); |
| //Calculate total annual flows |
| @@ -229,7 +219,7 @@ |
| |
| |
| //Call built in function to perform the drough analysis and graph the resulting data |
| - String[] droughtInfo = generalDroughtAnalysis(sortedData, droughtLimit, lambdaString, action, phiValues, thetaValues); |
| + String[] droughtInfo = generalDroughtAnalysis(sortedData, droughtLimit); |
| |
| |
| return droughtInfo; |
| @@ -242,168 +232,158 @@ |
| * @param sortedData a string[][] containing daily average stream flow values in the following format: column1 = dates (yyyy-mm-dd format), |
| * column2 = daily average stream flow values (cfs) |
| * @param droughtLimit a double representing the annual drought limit (water demand) on the watershed as specified by the user (in units of acre-ft/year) |
| - * @param lambdaString contains either the user provided value of lambda or "optimize" which tells the program to optimize lambda based on the dataset |
| - * @param action if "optimize", this only optimizes the phi values for the model and then reports these back to the user, |
| - * if "all" then it generates data based on the calculated phi values and finishes performing the drought analysis, |
| - * if "useParameters" then it takes the previously generated parameters and uses these to perform the regression |
| - * @param phiValues |
| - * @param thetaValues |
| * @return a String[] containing a tab-delimited summary of the droughts (water supply < water demand) |
| * @throws IOException |
| */ |
| @SuppressWarnings("unchecked") |
| public String[] generalDroughtAnalysis(String[][] sortedData, |
| - double droughtLimit, |
| - String lambdaString, |
| - String action, |
| - String phiValues, |
| - String thetaValues) throws IOException{ |
| + double droughtLimit) throws IOException{ |
| // //Test AR(p) model on sample data |
| // double[][] annualData = sumAnnualFlows(sortedData); |
| // Object[] returnArray = autoRegression.AR(p, annualData); |
| // annualData = (double[][]) returnArray[0]; |
| // double[][] generatedData = (double[][]) returnArray[1]; |
| // autoRegression.graphAR1Double(annualData, generatedData); |
| - DoubleMath doubleMath = new DoubleMath(); |
| - DoubleArray doubleArray = new DoubleArray(); |
| - AutoRegression autoRegression = new AutoRegression(); |
| + DoubleMath doubleMath = new DoubleMath(); |
| + DoubleArray doubleArray = new DoubleArray(); |
| + AutoRegression autoRegression = new AutoRegression(); |
| |
| - //Calculate total annual flows |
| - double[][] annualData = sumAnnualFlows(sortedData); |
| + //Calculate total annual flows |
| + double[][] annualData = sumAnnualFlows(sortedData); |
| |
| |
| - //Convert the data to only the stoicastic portion to modeled using AR(1) or ARMA(1,1) |
| - double average = doubleMath.Average(doubleArray.getColumn(annualData, 1)); |
| - double stDev = doubleMath.StandardDeviationSample(doubleArray.getColumn(annualData, 1)); |
| - double[][] stocaisticData = autoRegression.StoicasticConversion(annualData, average, stDev, true); |
| + //Convert the data to only the stoicastic portion to modeled using AR(1) or ARMA(1,1) |
| + double average = doubleMath.Average(doubleArray.getColumn(annualData, 1)); |
| + double stDev = doubleMath.StandardDeviationSample(doubleArray.getColumn(annualData, 1)); |
| + double[][] stocaisticData = autoRegression.StoicasticConversion(annualData, average, stDev, true); |
| |
| |
| - //Perform a transformation on the stoicastic data to change its distribution to a normal distribution |
| - double lambda = 1.0; |
| - if(lambdaString.equalsIgnoreCase("optimize")){ |
| - lambda = autoRegression.BoxCox(stocaisticData); |
| - }else{ |
| - lambda = Double.parseDouble(lambdaString); |
| - } |
| - double[][] transformedData = autoRegression.BoxCox(lambda, stocaisticData, true); |
| + //Perform a transformation on the stoicastic data to change its distribution to a normal distribution |
| + double lambda = 1.0; |
| + if(lambdaString.equalsIgnoreCase("optimize")){ |
| + lambda = autoRegression.BoxCox(stocaisticData); |
| + }else{ |
| + lambda = Double.parseDouble(lambdaString); |
| + } |
| + double[][] transformedData = autoRegression.BoxCox(lambda, stocaisticData, true); |
| // double[][] transformedData = autoRegression.SalasExampleTransformation(stocaisticData, true); |
| |
| |
| |
| - //Run the user specified auto-regressive model on the data |
| - double[][] fittedData = null; |
| - double[][] predictedData = null; |
| - String methodType = ""; |
| - Object[] returnArray = autoRegression.AutoregressiveModel(mainFolder, action, phiValues, thetaValues, transformedData); |
| - if(action.equalsIgnoreCase("optimizeParameters")){ |
| - //Find the parameters of the regression only and report these back |
| - String[] stringArray = (String[]) returnArray; |
| + //Run the user specified auto-regressive model on the data |
| + double[][] fittedData = null; |
| + double[][] predictedData = null; |
| + String methodType = ""; |
| + Object[] returnArray = autoRegression.AutoregressiveModel(mainFolder, action, phiValues, thetaValues, transformedData); |
| + if(action.equalsIgnoreCase("optimizeParameters")){ |
| + //Find the parameters of the regression only and report these back |
| + String[] stringArray = (String[]) returnArray; |
| |
| |
| - //Perform regression with data and graph the resulting data |
| - Object[] dataArray = autoRegression.AutoregressiveModel(mainFolder, "useParameters", stringArray[0], stringArray[1], transformedData); |
| + //Perform regression with data and graph the resulting data |
| + Object[] dataArray = autoRegression.AutoregressiveModel(mainFolder, "useParameters", stringArray[0], stringArray[1], transformedData); |
| |
| - //Keep the data from the regression and graph it |
| - methodType = (String) dataArray[0]; |
| - transformedData = (double[][]) dataArray[1]; |
| - fittedData = (double[][]) dataArray[2]; |
| - predictedData = (double[][]) dataArray[3]; |
| + //Keep the data from the regression and graph it |
| + methodType = (String) dataArray[0]; |
| + transformedData = (double[][]) dataArray[1]; |
| + fittedData = (double[][]) dataArray[2]; |
| + predictedData = (double[][]) dataArray[3]; |
| |
| - }else if(action.equalsIgnoreCase("optimizeModel")){ |
| - if(thetaValues.equalsIgnoreCase("")){ |
| - //Calculate optimal AR(p) model and return the optimal phi values |
| - ArrayList<Double> aic = (ArrayList<Double>) returnArray[0]; |
| - ArrayList<Double> bic = (ArrayList<Double>) returnArray[1]; |
| - String phi = (String) returnArray[2]; |
| - String theta = (String) returnArray[3]; |
| + }else if(action.equalsIgnoreCase("optimizeModel")){ |
| + if(thetaValues.equalsIgnoreCase("")){ |
| + //Calculate optimal AR(p) model and return the optimal phi values |
| + ArrayList<Double> aic = (ArrayList<Double>) returnArray[0]; |
| + ArrayList<Double> bic = (ArrayList<Double>) returnArray[1]; |
| + String phi = (String) returnArray[2]; |
| + String theta = (String) returnArray[3]; |
| |
| - //Graph the AIC/BIC curve for the user |
| - graphAR_AIC_BIC(aic, bic); |
| + //Graph the AIC/BIC curve for the user |
| + graphAR_AIC_BIC(aic, bic); |
| |
| - String[] optimalValues = {phi + "$$", theta}; |
| - return optimalValues; |
| - |
| - }else{ |
| - //Calculate optimal ARMA(p,q) model and return the optimal phi/theta values |
| - double[][] results = (double[][]) returnArray[0]; |
| - String phi = (String) returnArray[1]; |
| - String theta = (String) returnArray[2]; |
| - |
| - //Graph the AIC/BIC curve for the user |
| - graphARMA_AIC_BIC("AIC", results, 3); |
| - graphARMA_AIC_BIC("BIC" , results, 4); |
| - |
| - String[] optimalValues = {phi + "$$", theta}; |
| - return optimalValues; |
| - } |
| + String[] optimalValues = {phi + "$$", theta}; |
| + return optimalValues; |
| |
| }else{ |
| - //Keep the data from the regression and graph it |
| - methodType = (String) returnArray[0]; |
| - transformedData = (double[][]) returnArray[1]; |
| - fittedData = (double[][]) returnArray[2]; |
| - predictedData = (double[][]) returnArray[3]; |
| + //Calculate optimal ARMA(p,q) model and return the optimal phi/theta values |
| + double[][] results = (double[][]) returnArray[0]; |
| + String phi = (String) returnArray[1]; |
| + String theta = (String) returnArray[2]; |
| + |
| + //Graph the AIC/BIC curve for the user |
| + graphARMA_AIC_BIC("AIC", results, 3); |
| + graphARMA_AIC_BIC("BIC" , results, 4); |
| + |
| + String[] optimalValues = {phi + "$$", theta}; |
| + return optimalValues; |
| } |
| |
| - //Un-transform the stoicastic historic and stoicastic generated data so as to graph real stream flow values |
| - transformedData = autoRegression.BoxCox(lambda, transformedData, false); |
| - fittedData = autoRegression.BoxCox(lambda, fittedData, false); |
| - predictedData = autoRegression.BoxCox(lambda, predictedData, false); |
| + }else{ |
| + //Keep the data from the regression and graph it |
| + methodType = (String) returnArray[0]; |
| + transformedData = (double[][]) returnArray[1]; |
| + fittedData = (double[][]) returnArray[2]; |
| + predictedData = (double[][]) returnArray[3]; |
| + } |
| + |
| + //Un-transform the stoicastic historic and stoicastic generated data so as to graph real stream flow values |
| + transformedData = autoRegression.BoxCox(lambda, transformedData, false); |
| + fittedData = autoRegression.BoxCox(lambda, fittedData, false); |
| + predictedData = autoRegression.BoxCox(lambda, predictedData, false); |
| // transformedData = autoRegression.SalasExampleTransformation(transformedData, false); |
| // fittedData = autoRegression.SalasExampleTransformation(fittedData, false); |
| // predictedData = autoRegression.SalasExampleTransformation(predictedData, false); |
| |
| |
| - //Convert back from the stoicastic component of the data to the entire original data |
| - double[][] originalData = autoRegression.StoicasticConversion(transformedData, average, stDev, false); |
| - fittedData = autoRegression.StoicasticConversion(fittedData, average, stDev, false); |
| - predictedData = autoRegression.StoicasticConversion(predictedData, average, stDev, false); |
| + //Convert back from the stoicastic component of the data to the entire original data |
| + double[][] originalData = autoRegression.StoicasticConversion(transformedData, average, stDev, false); |
| + fittedData = autoRegression.StoicasticConversion(fittedData, average, stDev, false); |
| + predictedData = autoRegression.StoicasticConversion(predictedData, average, stDev, false); |
| |
| |
| - //Calculate the droughts, their accumulated deficits, durations, and intensities |
| - DroughtProperties historicDroughtProps = new DroughtProperties(annualData, droughtLimit); |
| - DroughtProperties predictedDroughtProps = new DroughtProperties(predictedData, droughtLimit); |
| + //Calculate the droughts, their accumulated deficits, durations, and intensities |
| + DroughtProperties historicDroughtProps = new DroughtProperties(annualData, droughtLimit); |
| + DroughtProperties predictedDroughtProps = new DroughtProperties(predictedData, droughtLimit); |
| |
| |
| |
| - //Determine the return periods for the droughts on record |
| - double[][] historicDroughts = recurrenceInterval(historicDroughtProps, droughtLimit, annualData); |
| - double[][] predictedDroughts = recurrenceInterval(predictedDroughtProps, droughtLimit, predictedData); |
| + //Determine the return periods for the droughts on record |
| + double[][] historicDroughts = recurrenceInterval(historicDroughtProps, droughtLimit, annualData); |
| + double[][] predictedDroughts = recurrenceInterval(predictedDroughtProps, droughtLimit, predictedData); |
| |
| |
| - //Graph the drought data |
| - graphTimeseries(annualData, droughtLimit); //graph1 |
| - graphNegativeBarChart(annualData, droughtLimit); //graph2 |
| - graphFittedData(originalData, fittedData, methodType); //graph3 |
| - graphPredictedData(originalData, predictedData, methodType);//graph4 |
| - graphReturnPeriod(historicDroughts, predictedDroughts); //graph5 |
| + //Graph the drought data |
| + graphTimeseries(annualData, droughtLimit); //graph1 |
| + graphNegativeBarChart(annualData, droughtLimit); //graph2 |
| + graphFittedData(originalData, fittedData, methodType); //graph3 |
| + graphPredictedData(originalData, predictedData, methodType);//graph4 |
| + graphReturnPeriod(historicDroughts, predictedDroughts); //graph5 |
| |
| |
| |
| |
| - //Populate the return array with the information gained from the drought analysis |
| - String[] droughtInfo = new String[historicDroughtProps.numberOfDroughts() + 1]; |
| - droughtInfo[0] = "Deficit [acre-ft]\tLambda\tDuration [years]\tIntensity\tFinal Year"; |
| - for(int i=0; i<historicDroughtProps.numberOfDroughts(); i++){ |
| - droughtInfo[i+1] = String.valueOf(historicDroughtProps.getDeficit(i)) + "\t" + |
| - String.valueOf(historicDroughtProps.getDeficit(i)/droughtLimit) + "\t" + |
| - String.valueOf(historicDroughtProps.getDuration(i)) + "\t" + |
| - String.valueOf(historicDroughtProps.getIntensity(i)) + "\t" + |
| - String.valueOf(historicDroughtProps.getEndYear(i)); |
| - } |
| + //Populate the return array with the information gained from the drought analysis |
| + String[] droughtInfo = new String[historicDroughtProps.numberOfDroughts() + 1]; |
| + droughtInfo[0] = "Deficit [acre-ft]\tLambda\tDuration [years]\tIntensity\tFinal Year"; |
| + for(int i=0; i<historicDroughtProps.numberOfDroughts(); i++){ |
| + droughtInfo[i+1] = String.valueOf(historicDroughtProps.getDeficit(i)) + "\t" + |
| + String.valueOf(historicDroughtProps.getDeficit(i)/droughtLimit) + "\t" + |
| + String.valueOf(historicDroughtProps.getDuration(i)) + "\t" + |
| + String.valueOf(historicDroughtProps.getIntensity(i)) + "\t" + |
| + String.valueOf(historicDroughtProps.getEndYear(i)); |
| + } |
| |
| - if(action.equalsIgnoreCase("optimizeParameters")){ |
| - //Return only the parameters of the regression |
| - String[] stringArray = (String[]) returnArray; |
| - stringArray[0] = stringArray[0] + "$$"; |
| - return stringArray; |
| - }else if(action.equalsIgnoreCase("updateParameters")){ |
| - //Return only the parameters of the regression |
| - String[] stringArray = {phiValues + "$$", thetaValues}; |
| - return stringArray; |
| - } |
| + if(action.equalsIgnoreCase("optimizeParameters")){ |
| + //Return only the parameters of the regression |
| + String[] stringArray = (String[]) returnArray; |
| + stringArray[0] = stringArray[0] + "$$"; |
| + return stringArray; |
| + }else if(action.equalsIgnoreCase("updateParameters")){ |
| + //Return only the parameters of the regression |
| + String[] stringArray = {phiValues + "$$", thetaValues}; |
| + return stringArray; |
| + } |
| |
| - return droughtInfo; |
| + return droughtInfo; |
| } |
| /** |
| * Calculates the average recurrence interval for each drought on record and the lambda coefficient of each drought (deficit = lambda * droughtLimit) |
| @@ -1240,7 +1220,7 @@ |
| |
| //Write the summary to the file |
| for(int i=0; i < dynamicSummary.length; i++) { |
| - print_line.printf("%s" + "%n", dynamicSummary[i]); |
| + print_line.printf("%s" + "%n", dynamicSummary[i]); |
| } |
| print_line.close(); |
| writer.close(); |
| @@ -1308,10 +1288,10 @@ |
| |
| if(Double.compare(droughtLimit, 0) < 0){ |
| //If no drought limit is provided (aka -1 is provided) then calculate the drought limit = average(annual flows) |
| - droughtInfo = generalDroughtAnalysis(sortedData_combined, lambdaString, action, phiValues, thetaValues); |
| + droughtInfo = generalDroughtAnalysis(sortedData_combined); |
| }else{ |
| //If a drought limit is provided (aka greater than or equal to zero) then use this as the drought limit |
| - droughtInfo = generalDroughtAnalysis(sortedData_combined, droughtLimit, lambdaString, action, phiValues, thetaValues); |
| + droughtInfo = generalDroughtAnalysis(sortedData_combined, droughtLimit); |
| } |
| |
| //Get today's date for the source reference |