/*
* Copyright (C) 2013 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 org.apache.commons.math3.distribution.NormalDistribution;
import java.text.DecimalFormat;
import whitebox.geospatialfiles.WhiteboxRaster;
import whitebox.interfaces.WhiteboxPlugin;
import whitebox.interfaces.WhiteboxPluginHost;
/**
* Spatial autocorrelation describes the extent to which a variable is either dispersed or clustered through space.
*
* @author Dr. John Lindsay email: jlindsay@uoguelph.ca
*/
public class ImageAutocorrelation 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 "ImageAutocorrelation";
}
/**
* 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 Autocorrelation";
}
/**
* Used to retrieve a short description of what the plugin tool does.
*
* @return String containing the plugin's description.
*/
@Override
public String getToolDescription() {
return "Computes the Spatial autocorrelation (Morans' I) of an image.";
}
/**
* Used to identify which toolboxes this plugin tool should be listed in.
*
* @return Array of Strings.
*/
@Override
public String[] getToolbox() {
String[] ret = {"StatisticalTools"};
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;
WhiteboxRaster image;
int col, row, numImages, x, y;
int cols, rows;
int a = 0;
double noData;
double z, zn;
int progress = 0;
String progressMessage = "";
String inputFilesString = null;
String[] imageFiles;
long[] n;
double[] mean;
String[] shortNames;
String[] units;
double[] I;
double[] stdDev;
double totalDeviation;
int[] dX;
int[] dY;
double numerator, W;
//double recipRoot2 = 1 / Math.sqrt(2);
//double[] wNeighbour = {recipRoot2, 1, recipRoot2, 1, recipRoot2, 1, recipRoot2, 1};
//double[] wNeighbour = {1, 1, 1, 1};
if (args.length <= 0) {
showFeedback("Plugin parameters have not been set.");
return;
}
inputFilesString = args[0];
imageFiles = inputFilesString.split(";");
numImages = imageFiles.length;
if (args[1].toLowerCase().contains("bishop")) {
dX = new int[]{1, 1, -1, -1};
dY = new int[]{-1, 1, 1, -1};
} else if (args[1].toLowerCase().contains("queen")
|| args[1].toLowerCase().contains("king")) {
dX = new int[]{1, 1, 1, 0, -1, -1, -1, 0};
dY = new int[]{-1, 0, 1, 1, 1, 0, -1, -1};
} else {
// go with the rook default
dX = new int[]{1, 0, -1, 0};
dY = new int[]{0, 1, 0, -1};
}
try {
//initialize the image data arrays
double sigmaZ;
n = new long[numImages];
mean = new double[numImages];
I = new double[numImages];
shortNames = new String[numImages];
units = new String[numImages];
stdDev = new double[numImages];
double[] E_I = new double[numImages];
double[] varNormality = new double[numImages];
double[] varRandomization = new double[numImages];
double[] zN = new double[numImages];
double[] zR = new double[numImages];
double[] pValueN = new double[numImages];
double[] pValueR = new double[numImages];
double[] data;
NormalDistribution distribution = new NormalDistribution(0, 1);
for (a = 0; a < numImages; a++) {
progressMessage = "Image " + (a + 1) + " of " + numImages;
image = new WhiteboxRaster(imageFiles[a], "r");
noData = image.getNoDataValue();
rows = image.getNumberRows();
cols = image.getNumberColumns();
shortNames[a] = image.getShortHeaderFile();
if (!image.getZUnits().toLowerCase().equals("not specified")) {
units[a] = image.getZUnits();
} else {
units[a] = "";
}
sigmaZ = 0;
for (row = 0; row < rows; row++) {
data = image.getRowValues(row);
for (col = 0; col < cols; col++) {
if (data[col] != noData) {
sigmaZ += data[col];
n[a]++;
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (int)(row * 100.0 / rows);
updateProgress(progressMessage, progress);
}
mean[a] = sigmaZ / n[a];
E_I[a] = -1.0 / (n[a] - 1);
totalDeviation = 0;
W = 0;
numerator = 0;
double S2 = 0;
double wij;
int numNeighbours = dX.length;
double k = 0;
for (row = 0; row < rows; row++) {
for (col = 0; col < cols; col++) {
z = image.getValue(row, col);
if (z != noData) {
totalDeviation += (z - mean[a]) * (z - mean[a]);
k += (z - mean[a]) * (z - mean[a]) * (z - mean[a]) * (z - mean[a]);
wij = 0;
for (int i = 0; i < numNeighbours; i++) {
x = col + dX[i];
y = row + dY[i];
zn = image.getValue(y, x);
if (zn != noData) { // two valid neighbour pairs
W += 1.0;
numerator += (z - mean[a]) * (zn - mean[a]); //* weight of 1.0
wij += 1;
}
}
S2 += wij * wij;
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (int)(row * 100.0 / rows);
updateProgress(progressMessage, progress);
}
double S1 = 4 * W;
S2 = S2 * 4;
stdDev[a] = Math.sqrt(totalDeviation / (n[a] - 1));
I[a] = n[a] * numerator / (totalDeviation * W);
varNormality[a] = (n[a] * n[a] * S1 - n[a] * S2 + 3 * W * W) /
((W * W) * (n[a] * n[a] - 1));
zN[a] = (I[a] - E_I[a]) / (Math.sqrt(varNormality[a]));
pValueN[a] = 2d * (1.0 - distribution.cumulativeProbability(Math.abs(zN[a])));
k = k / (n[a] * stdDev[a] * stdDev[a] * stdDev[a] * stdDev[a]);
varRandomization[a] = (n[a] * ((n[a] * n[a] - 3 * n[a] + 3) * S1 - n[a] * S2 + 3 * W * W) -
k * (n[a] * n[a] - n[a]) * S1 - 2 * n[a] * S1 + 6 * W * W) /
((n[a] - 1) * (n[a] - 2) * (n[a] - 3) * W * W);
zR[a] = (I[a] - E_I[a]) / (Math.sqrt(varRandomization[a]));
pValueR[a] = 2d * (1.0 - distribution.cumulativeProbability(Math.abs(zR[a])));
image.close();
progress = (int) (100f * (a + 1) / numImages);
updateProgress(progressMessage, progress);
}
StringBuilder retstr = new StringBuilder();
DecimalFormat df1 = new DecimalFormat("###,###,###,###");
DecimalFormat df2 = new DecimalFormat("0.0000");
retstr.append("SPATIAL AUTOCORRELATION\n");
for (a = 0; a < numImages; a++) {
retstr.append("\n");
retstr.append("Input image:\t\t\t").append(shortNames[a]).append("\n");
retstr.append("Number of cells included:\t\t").append(df1.format(n[a])).append("\n");
if (units[a].equals("")) {
retstr.append("Mean of cells included:\t\t").append(df2.format(mean[a])).append("\n");
} else {
retstr.append("Mean of cells included:\t\t").append(df2.format(mean[a])).append(" ").append(units[a]).append("\n");
}
retstr.append("Spatial autocorrelation (Moran's I):\t").append(df2.format(I[a])).append("\n");
retstr.append("Expected value:\t\t").append(df2.format(E_I[a])).append("\n");
retstr.append("Variance of I (normality assumption):\t").append(df2.format(varNormality[a])).append("\n");
retstr.append("z test stat (normality assumption):\t").append(df2.format(zN[a])).append("\n");
retstr.append("p-value (normality assumption):\t").append(df2.format(pValueN[a])).append("\n");
retstr.append("Variance of I (randomization assumption):\t").append(df2.format(varRandomization[a])).append("\n");
retstr.append("z test stat (randomization assumption):\t").append(df2.format(zR[a])).append("\n");
retstr.append("p-value (randomization assumption):\t").append(df2.format(pValueR[a])).append("\n");
}
// System.out.println(retstr.toString());
returnData(retstr.toString());
} 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 method is only used for debugging the tool
// * @param args
// */
//// this is only used for debugging the tool
// public static void main(String[] args) {
// ImageAutocorrelation ia = new ImageAutocorrelation();
// args = new String[2];
// //args[0] = "/Users/johnlindsay/Documents/Data/Vermont DEM/Vermont DEM.dep;/Users/johnlindsay/Documents/Data/Change detection/Data/1992 GTA band4.dep";
//
// args[0] = "/Users/johnlindsay/Documents/Data/Random fields/random1.dep;/Users/johnlindsay/Documents/Data/Random fields/random2.dep;"
// + "/Users/johnlindsay/Documents/Data/Random fields/random3.dep;/Users/johnlindsay/Documents/Data/Random fields/random4.dep;"
// + "/Users/johnlindsay/Documents/Data/Random fields/random5.dep;/Users/johnlindsay/Documents/Data/Random fields/random6.dep;"
// + "/Users/johnlindsay/Documents/Data/Random fields/random7.dep;/Users/johnlindsay/Documents/Data/Random fields/checker board pattern.dep;"
// + "/Users/johnlindsay/Documents/Data/Random fields/random8.dep";
//
//// args[0] = "/Users/johnlindsay/Documents/Data/Random fields/random7.dep";
//
// args[1] = "rook";
// ia.setArgs(args);
// ia.run();
//
// }
}