/*
* Copyright (C) 2011-2012 Dr. John Lindsay <jlindsay@uoguelph.ca>
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package plugins;
import java.text.DecimalFormat;
import org.apache.commons.math3.distribution.FDistribution;
import org.apache.commons.math3.distribution.TDistribution;
import whitebox.geospatialfiles.WhiteboxRaster;
import whitebox.interfaces.WhiteboxPlugin;
import whitebox.interfaces.WhiteboxPluginHost;
/**
* This tool performs a bivariate linear regression analysis on two input raster images.
*
* @author Dr. John Lindsay email: jlindsay@uoguelph.ca
*/
public class ImageRegression implements WhiteboxPlugin {
private WhiteboxPluginHost myHost = null;
private String[] args;
/**
* Used to retrieve the plugin tool's name. This is a short, unique name
* containing no spaces.
*
* @return String containing plugin name.
*/
@Override
public String getName() {
return "ImageRegression";
}
/**
* Used to retrieve the plugin tool's descriptive name. This can be a longer
* name (containing spaces) and is used in the interface to list the tool.
*
* @return String containing the plugin descriptive name.
*/
@Override
public String getDescriptiveName() {
return "Image Regression";
}
/**
* Used to retrieve a short description of what the plugin tool does.
*
* @return String containing the plugin's description.
*/
@Override
public String getToolDescription() {
return "Performs a linear regression on two images.";
}
/**
* Used to identify which toolboxes this plugin tool should be listed in.
*
* @return Array of Strings.
*/
@Override
public String[] getToolbox() {
String[] ret = { "StatisticalTools", "ChangeDetection" };
return ret;
}
/**
* Sets the WhiteboxPluginHost to which the plugin tool is tied. This is the
* class that the plugin will send all feedback messages, progress updates,
* and return objects.
*
* @param host The WhiteboxPluginHost that called the plugin tool.
*/
@Override
public void setPluginHost(WhiteboxPluginHost host) {
myHost = host;
}
/**
* Used to communicate feedback pop-up messages between a plugin tool and
* the main Whitebox user-interface.
*
* @param feedback String containing the text to display.
*/
private void showFeedback(String message) {
if (myHost != null) {
myHost.showFeedback(message);
} else {
System.out.println(message);
}
}
/**
* Used to communicate a return object from a plugin tool to the main
* Whitebox user-interface.
*
* @return Object, such as an output WhiteboxRaster.
*/
private void returnData(Object ret) {
if (myHost != null) {
myHost.returnData(ret);
}
}
private int previousProgress = 0;
private String previousProgressLabel = "";
/**
* Used to communicate a progress update between a plugin tool and the main
* Whitebox user interface.
*
* @param progressLabel A String to use for the progress label.
* @param progress Float containing the progress value (between 0 and 100).
*/
private void updateProgress(String progressLabel, int progress) {
if (myHost != null && ((progress != previousProgress) ||
(!progressLabel.equals(previousProgressLabel)))) {
myHost.updateProgress(progressLabel, progress);
}
previousProgress = progress;
previousProgressLabel = progressLabel;
}
/**
* Used to communicate a progress update between a plugin tool and the main
* Whitebox user interface.
*
* @param progress Float containing the progress value (between 0 and 100).
*/
private void updateProgress(int progress) {
if (myHost != null && progress != previousProgress) {
myHost.updateProgress(progress);
}
previousProgress = progress;
}
/**
* Sets the arguments (parameters) used by the plugin.
*
* @param args An array of string arguments.
*/
@Override
public void setArgs(String[] args) {
this.args = args.clone();
}
private boolean cancelOp = false;
/**
* Used to communicate a cancel operation from the Whitebox GUI.
*
* @param cancel Set to true if the plugin should be canceled.
*/
@Override
public void setCancelOp(boolean cancel) {
cancelOp = cancel;
}
private void cancelOperation() {
showFeedback("Operation cancelled.");
updateProgress("Progress: ", 0);
}
private boolean amIActive = false;
/**
* Used by the Whitebox GUI to tell if this plugin is still running.
*
* @return a boolean describing whether or not the plugin is actively being
* used.
*/
@Override
public boolean isActive() {
return amIActive;
}
/**
* Used to execute this plugin tool.
*/
@Override
public void run() {
amIActive = true;
String inputHeader1 = null;
String inputHeader2 = null;
String outputHeader = null;
boolean outputResidualImage = false;
double yEstimate;
double residual;
boolean standardizeResiduals = false;
if (args.length <= 0) {
showFeedback("Plugin parameters have not been set.");
return;
}
inputHeader1 = args[0];
inputHeader2 = args[1];
if (!args[2].toLowerCase().equals("not specified")) {
outputHeader = args[2];
outputResidualImage = true;
standardizeResiduals = Boolean.parseBoolean(args[3]);
}
// check to see that the inputHeader1 and outputHeader are not null.
if (inputHeader1 == null || inputHeader2 == null) {
showFeedback("One or more of the input parameters have not been set properly.");
return;
}
try {
int row, col;
double x, y;
float progress = 0;
WhiteboxRaster image1 = new WhiteboxRaster(inputHeader1, "r");
int rows = image1.getNumberRows();
int cols = image1.getNumberColumns();
double noData1 = image1.getNoDataValue();
WhiteboxRaster image2 = new WhiteboxRaster(inputHeader2, "r");
if (rows != image2.getNumberRows() || cols != image2.getNumberColumns()) {
showFeedback("The input images must have the same dimensions (rows and columns).");
return;
}
double noData2 = image2.getNoDataValue();
double sumX = 0, sumY = 0, sumXY = 0, sumXX = 0, sumYY = 0;
long N = 0;
double[] data1, data2;
for (row = 0; row < rows; row++) {
data1 = image1.getRowValues(row);
data2 = image2.getRowValues(row);
for (col = 0; col < cols; col++) {
x = data1[col];
y = data2[col];
if (x != noData1 && y != noData2) {
sumX += x;
sumY += y;
sumXY += x * y;
sumXX += x * x;
sumYY += y * y;
N++;
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress((int) progress);
}
double slope = (N * sumXY - (sumX * sumY)) / (N * sumXX - (sumX * sumX));
double intercept = (sumY - slope * sumX) / N;
double r = (N * sumXY - (sumX * sumY)) / ((Math.sqrt(N * sumXX - (sumX * sumX)) * (Math.sqrt(N * sumYY - (sumY * sumY)))));
double rSqr = r * r;
double yMean = sumY / N;
double xMean = sumX / N;
double SSreg = 0;
double SStotal = 0;
double SSerror = 0;
int dfReg = 1;
int dfError = (int)(N - 2);
for (row = 0; row < rows; row++) {
data1 = image1.getRowValues(row);
data2 = image2.getRowValues(row);
for (col = 0; col < cols; col++) {
x = data1[col];
y = data2[col];
if (x != noData1 && y != noData2) {
yEstimate = slope * x + intercept;
SSerror += (y - yEstimate) * (y - yEstimate);
SStotal += (y - yMean) * (y - yMean);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress((int) progress);
}
SSreg = SStotal - SSerror;
double MSreg = SSreg / dfReg;
double MSerror = SSerror / dfError;
double Fstat = MSreg / MSerror;
double SEofEstimate = Math.sqrt(MSerror);
FDistribution f = new FDistribution(1, dfError);
double pValue = 1.0 - f.cumulativeProbability(Fstat);
double msse = (Math.max(0d, sumYY - sumXY * sumXY / sumXX)) / (N - 2);
double interceptSE = Math.sqrt(msse * ((1d / N) + (xMean * xMean) / sumXX));
double interceptT = intercept / interceptSE;
TDistribution distribution = new TDistribution(N - 2);
double interceptPValue = 2d * (1.0 - distribution.cumulativeProbability(Math.abs(intercept) / interceptSE));
double slopeSE = Math.sqrt(msse / sumXX);
double slopeT = slope / slopeSE;
double slopePValue = 2d * (1.0 - distribution.cumulativeProbability(Math.abs(slope) / slopeSE));
if (outputResidualImage) {
WhiteboxRaster output = new WhiteboxRaster(outputHeader, "rw", inputHeader1, WhiteboxRaster.DataType.FLOAT, noData1);
output.setPreferredPalette("blue_white_red.pal");
if (standardizeResiduals) {
for (row = 0; row < rows; row++) {
data1 = image1.getRowValues(row);
data2 = image2.getRowValues(row);
for (col = 0; col < cols; col++) {
x = data1[col];
y = data2[col];
yEstimate = slope * x + intercept;
residual = (y - yEstimate) / SEofEstimate;
output.setValue(row, col, residual);
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress((int) progress);
}
} else {
for (row = 0; row < rows; row++) {
data1 = image1.getRowValues(row);
data2 = image2.getRowValues(row);
for (col = 0; col < cols; col++) {
x = data1[col];
y = data2[col];
yEstimate = slope * x + intercept;
residual = y - yEstimate;
output.setValue(row, col, residual);
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress((int) progress);
}
}
output.close();
}
DecimalFormat df = new DecimalFormat("###,###,###,##0.000");
DecimalFormat df2 = new DecimalFormat("###,###,###,###");
String retstr = null;
retstr = "IMAGE REGRESSION REPORT\n\n";
retstr += "Input Image 1 (X):\t\t" + image1.getShortHeaderFile() + "\n";
retstr += "Input Image 2 (Y):\t\t" + image2.getShortHeaderFile() + "\n\n";
retstr += "Model Summary:\n";
retstr += "R\tR Square\tStd. Error of the Estimate\n";
retstr += df.format(r) + "\t" + df.format(rSqr) + "\t" + df.format(SEofEstimate) + "\n\n";
String ANOVA = "\nAnalysis of Variance (ANOVA):\n";
ANOVA += "Source\tSS\tdf\tMS\tF\tP\n";
ANOVA += "Regression\t" + df.format(SSreg) + "\t" + df2.format(dfReg) + "\t" + df.format(MSreg) + "\t" + df.format(Fstat) + "\t" + df.format(pValue) + "\n";
ANOVA += "Residual\t" + df.format(SSerror) + "\t" + df2.format(dfError) + "\t" + df.format(MSerror) + "\n";
ANOVA += "Total\t" + df.format(SStotal) + "\n\n";
retstr += ANOVA;
String coefficents = "Coefficients:\n";
coefficents += "Variable\tB\tStd. Error\tt\tSig.\n";
coefficents += "Constant\t" + df.format(intercept) + "\t" + df.format(interceptSE) + "\t" + df.format(interceptT) + "\t" + df.format(interceptPValue) + "\n";
coefficents += "Slope\t" + df.format(slope) + "\t" + df.format(slopeSE) + "\t" + df.format(slopeT) + "\t" + df.format(slopePValue) + "\n\n";
retstr += coefficents;
if (intercept >= 0) {
retstr += "Regression Equation:\t\t" + image2.getShortHeaderFile() + " = "
+ df.format(slope) + " \u00D7 " + image2.getShortHeaderFile() + " + " + df.format(intercept) + "\n";
} else {
retstr += "Regression Equation:\t\t" + image2.getShortHeaderFile() + " = "
+ df.format(slope) + " \u00D7 " + image2.getShortHeaderFile() + " - " + df.format(-intercept) + "\n";
}
returnData(retstr);
if (outputResidualImage) {
returnData(outputHeader);
}
image1.close();
image2.close();
} catch (OutOfMemoryError oe) {
myHost.showFeedback("An out-of-memory error has occurred during operation.");
} catch (Exception e) {
myHost.showFeedback("An error has occurred during operation. See log file for details.");
myHost.logException("Error in " + getDescriptiveName(), e);
} finally {
updateProgress("Progress: ", 0);
// tells the main application that this process is completed.
amIActive = false;
myHost.pluginComplete();
}
}
// // this is only used for debugging the tool
// public static void main(String[] args) {
// ImageRegression ir = new ImageRegression();
// args = new String[3];
// args[0] = "/Users/johnlindsay/Documents/Data/LandsatData/band1.dep";
// args[1] = "/Users/johnlindsay/Documents/Data/LandsatData/band2_cropped.dep";
// args[2]= "not specified";
//
// ir.setArgs(args);
// ir.run();
//
// }
}