/*
* 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 java.util.Date;
import whitebox.geospatialfiles.WhiteboxRaster;
import whitebox.geospatialfiles.WhiteboxRasterInfo;
import whitebox.interfaces.WhiteboxPlugin;
import whitebox.interfaces.WhiteboxPluginHost;
/**
* This tool can be used to extract common descriptive statistics associated with the distribution of some underlying Data Raster Image based on area units defined by a Feature Definition Raster Image.
*
* @author Dr. John Lindsay email: jlindsay@uoguelph.ca
*/
public class ExtractStatistics 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 "ExtractStatistics";
}
/**
* 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 "Extract Statistics";
}
/**
* Used to retrieve a short description of what the plugin tool does.
*
* @return String containing the plugin's description.
*/
@Override
public String getToolDescription() {
return "Extracts descriptive statistics for a group of patches.";
}
/**
* 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;
String dataImageHeader = null;
String featureImageHeader = null;
String outputHeader = null;
WhiteboxRasterInfo dataImage;
WhiteboxRasterInfo featureImage;
WhiteboxRaster output;
int cols, rows;
double imageTotal = 0;
long imageN = 0;
double imageAverage = 0;
double imageTotalDeviation = 0;
double stdDeviation = 0;
float progress = 0;
int col, row;
int i;
String statType = null;
boolean textOutput = false;
if (args.length <= 0) {
showFeedback("Plugin parameters have not been set.");
return;
}
for (i = 0; i < args.length; i++) {
if (i == 0) {
dataImageHeader = args[i];
} else if (i == 1) {
featureImageHeader = args[i];
} else if (i == 2) {
outputHeader = args[i];
} else if (i == 3) {
statType = args[i].toLowerCase();
} else if (i == 4) {
textOutput = Boolean.parseBoolean(args[i]);
}
}
// check to see that the inputHeader and outputHeader are not null.
if ((dataImageHeader == null) || (featureImageHeader == null) || (outputHeader == null)) {
showFeedback("One or more of the input parameters have not been set properly.");
return;
}
try {
dataImage = new WhiteboxRasterInfo(dataImageHeader);
rows = dataImage.getNumberRows();
cols = dataImage.getNumberColumns();
double noData = dataImage.getNoDataValue();
featureImage = new WhiteboxRasterInfo(featureImageHeader);
if (featureImage.getNumberColumns() != cols ||
featureImage.getNumberRows() != rows) {
showFeedback("Input images must have the same dimensions (i.e. rows and columns).");
return;
}
double featureNoData = featureImage.getNoDataValue();
String featureImageShortName = featureImage.getShortHeaderFile();
String dataImageShortName = dataImage.getShortHeaderFile();
//see how many features there are
int numFeatures = 0;
double[] featureData;
double[] data;
int minFeatureID = 99999999;
int maxFeatureID = -99999999;
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
//make sure that the feature ID is an integer value
if (Math.rint(featureData[col]) != featureData[col]) {
showFeedback("The feature definition image should "
+ "contain integer values only.");
return;
}
if ((int)featureData[col] < minFeatureID) {
minFeatureID = (int)featureData[col];
}
if ((int)featureData[col] > maxFeatureID) {
maxFeatureID = (int)featureData[col];
}
}
}
if (cancelOp) { cancelOperation(); return; }
progress = (float) (100f * row / (rows - 1));
updateProgress((int)progress);
}
numFeatures = maxFeatureID - minFeatureID + 1;
// in reality this is only the number of features if there are no
// unused feature IDs between the min and max values
double[] featureTotal = new double[numFeatures];
long[] featureN = new long[numFeatures];
double[] featureAverage = new double[numFeatures];
double[] featureTotalDeviation = new double[numFeatures];
double[] featureStdDeviation = new double[numFeatures];
double[] featureMins = new double[numFeatures];
double[] featureMaxs = new double[numFeatures];
boolean[] featurePresent = new boolean[numFeatures];
for (i = 0; i < numFeatures; i++) {
featureMins[i] = 99999999;
featureMaxs[i] = -99999999;
}
updateProgress("Loop 1 of 2:", 0);
for (row = 0; row < rows; row++) {
data = dataImage.getRowValues(row);
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
featurePresent[i] = true;
if (data[col] != noData) {
featureTotal[i] += data[col];
featureN[i]++;
if (data[col] < featureMins[i]) {
featureMins[i] = data[col];
}
if (data[col] > featureMaxs[i]) {
featureMaxs[i] = data[col];
}
}
}
}
if (cancelOp) {
cancelOperation(); return; }
progress = (float) (100f * row / (rows - 1));
updateProgress("Loop 1 of 2:", (int)progress);
}
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
featureAverage[i] = featureTotal[i] / featureN[i];
}
}
updateProgress("Loop 2 of 2:", (int)progress);
for (row = 0; row < rows; row++) {
data = dataImage.getRowValues(row);
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData && data[col] != noData) {
i = (int)(featureData[col] - minFeatureID);
featureTotalDeviation[i] += (data[col] - featureAverage[i]) *
(data[col] - featureAverage[i]);
}
}
if (cancelOp) { cancelOperation(); return; }
progress = (float) (100f * row / (rows - 1));
updateProgress("Loop 2 of 2:", (int)progress);
}
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
featureStdDeviation[i] = Math.sqrt(featureTotalDeviation[i] / (featureN[i] - 1));
}
}
dataImage.close();
if (!outputHeader.toLowerCase().equals("not specified")) {
output = new WhiteboxRaster(outputHeader, "rw", dataImageHeader,
WhiteboxRaster.DataType.FLOAT, noData);
output.setPreferredPalette(dataImage.getPreferredPalette());
output.setDataScale(WhiteboxRaster.DataScale.CONTINUOUS);
updateProgress("Outputing image data:", (int)progress);
if (statType.equals("average")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureAverage[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else if (statType.equals("minimum")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureMins[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else if (statType.equals("maximum")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureMaxs[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else if (statType.equals("range")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureMaxs[i] - featureMins[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else if (statType.equals("standard deviation")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureStdDeviation[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else if (statType.equals("total")) {
for (row = 0; row < rows; row++) {
featureData = featureImage.getRowValues(row);
for (col = 0; col < cols; col++) {
if (featureData[col] != featureNoData) {
i = (int) (featureData[col] - minFeatureID);
output.setValue(row, col, featureTotal[i]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (float) (100f * row / (rows - 1));
updateProgress("Outputing image data:", (int) progress);
}
} else {
showFeedback("Specified statistic type not recognized");
return;
}
output.addMetadataEntry("Created by the "
+ getDescriptiveName() + " tool.");
output.addMetadataEntry("Created on " + new Date());
output.close();
returnData(outputHeader);
}
if (textOutput) {
DecimalFormat df;
df = new DecimalFormat("0.000");
String retstr = null;
retstr = "EXTRACT STATISTICS\n\n";
retstr += "Data Image:\t" + dataImageShortName + "\n";
retstr += "Feature Image:\t" + featureImageShortName + "\n";
retstr += "NoData Value:\t" + noData + "\n";
retstr += "Output Stat:\t" + statType + "\n\n";
retstr += "ID\t" + "Value" + "\n";
if (statType.equals("average")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format(featureAverage[i]) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else if (statType.equals("minimum")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format(featureMins[i]) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else if (statType.equals("maximum")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format(featureMaxs[i]) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else if (statType.equals("range")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format((featureMaxs[i] - featureMins[i])) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else if (statType.equals("standard deviation")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format(featureStdDeviation[i]) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else if (statType.equals("total")) {
for (i = 0; i < numFeatures; i++) {
if (featureN[i] > 0) {
retstr += (i + minFeatureID) + "\t" + df.format(featureTotal[i]) + "\n";
} else if (featurePresent[i]) {
retstr += (i + minFeatureID) + "\t" + df.format(noData) + "\n";
}
}
} else {
showFeedback("Specified statistic type not recognized");
return;
}
returnData(retstr);
}
featureImage.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();
}
}
}