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/*LICENSE_START*/
/*
* Copyright 1995-2002 Washington University School of Medicine
*
* http://brainmap.wustl.edu
*
* This file is part of CARET.
*
* CARET 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 2 of the License, or
* (at your option) any later version.
*
* CARET 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 CARET; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
*/
/*LICENSE_END*/
#include "BrainModelSurfaceMetricClustering.h"
#include "BrainSet.h"
#include "CommandMetricClustering.h"
#include "FileFilters.h"
#include "MetricFile.h"
#include "ProgramParameters.h"
#include "ScriptBuilderParameters.h"
/**
* constructor.
*/
CommandMetricClustering::CommandMetricClustering()
: CommandBase("-metric-clustering",
"METRIC CLUSTERING")
{
}
/**
* destructor.
*/
CommandMetricClustering::~CommandMetricClustering()
{
}
/**
* get the script builder parameters.
*/
void
CommandMetricClustering::getScriptBuilderParameters(ScriptBuilderParameters& paramsOut) const
{
std::vector<QString> values, descriptions;
values.push_back("ANY_SIZE"); descriptions.push_back("Any Size");
values.push_back("MINIMUM_NUMBER_OF_NODES"); descriptions.push_back("Minimum Number of Nodes");
values.push_back("MINIMUM_SURFACE_AREA"); descriptions.push_back("Minimum Surface Area");
paramsOut.clear();
paramsOut.clear();
paramsOut.addFile("Coordinate File Name", FileFilters::getCoordinateGenericFileFilter());
paramsOut.addFile("Topology File Name", FileFilters::getTopologyGenericFileFilter());
paramsOut.addFile("Input Metric File Name", FileFilters::getMetricFileFilter());
paramsOut.addFile("Output Metric File Name", FileFilters::getMetricFileFilter());
paramsOut.addFloat("Minimum Negative Threshold", -5.0);
paramsOut.addFloat("Maximum Negative Threshold", -10.0);
paramsOut.addFloat("Minimum Positive Threshold", 5.0);
paramsOut.addFloat("Maximum Positive Threshold", 10.0);
paramsOut.addListOfItems("Cluster Mode", values, descriptions);
paramsOut.addInt("Minimum Number of Nodes", 25);
paramsOut.addFloat("Minimum Surface Area", 25.0);
}
/**
* get full help information.
*/
QString
CommandMetricClustering::getHelpInformation() const
{
QString helpInfo =
(indent3 + getShortDescription() + "\n"
+ indent6 + parameters->getProgramNameWithoutPath() + " " + getOperationSwitch() + " \n"
+ indent9 + "<coordinate-file-name>\n"
+ indent9 + "<topology-file-name>\n"
+ indent9 + "<input-metric-file-name>\n"
+ indent9 + "<output-metric-file-name>\n"
+ indent9 + "<minimum-negative-threshold>\n"
+ indent9 + "<maximum-negative-threshold>\n"
+ indent9 + "<minimum-positive-threshold>\n"
+ indent9 + "<maximum-positive-threshold>\n"
+ indent9 + "<cluster-mode>\n"
+ indent9 + "<cluster-minimum-number-of-node>\n"
+ indent9 + "<cluster-minimum-surface-area>\n"
+ indent9 + "\n"
+ indent9 + "Perform clustering on all columns in a metric file.\n"
+ indent9 + "\n"
+ indent9 + "\"cluster-mode\" must be one of:\n"
+ indent9 + " ANY_SIZE\n"
+ indent9 + " MINIMUM_NUMBER_OF_NODES\n"
+ indent9 + " MINIMUM_SURFACE_AREA\n"
+ indent9 + "\n"
+ indent9 + "Note: The \"maximum-negative-threshold\" should be\n"
+ indent9 + "LESS THAN (more negative) than the \n"
+ indent9 + "\"minimum-negative-threshold\".\n"
+ indent9 + "\n");
return helpInfo;
}
/**
* execute the command.
*/
void
CommandMetricClustering::executeCommand() throw (BrainModelAlgorithmException,
CommandException,
FileException,
ProgramParametersException,
StatisticException)
{
const QString coordinateFileName =
parameters->getNextParameterAsString("Coordinate File Name");
const QString topologyFileName =
parameters->getNextParameterAsString("Topology File Name");
const QString inputMetricFileName =
parameters->getNextParameterAsString("Input Metric File Name");
const QString outputMetricFileName =
parameters->getNextParameterAsString("Output Metric File Name");
const float minimumNegativeThreshold =
parameters->getNextParameterAsFloat("Minimum Negative Threshold");
const float maximumNegativeThreshold =
parameters->getNextParameterAsFloat("Maximum Negative Threshold");
const float minimumPositiveThreshold =
parameters->getNextParameterAsFloat("Minimum Positive Threshold");
const float maximumPositiveThreshold =
parameters->getNextParameterAsFloat("Maximum Positive Threshold");
const QString modeName =
parameters->getNextParameterAsString("Cluster Mode Name");
const int minimumNumberOfNodes =
parameters->getNextParameterAsInt("Minimum Number of Nodes");
const float minimumSurfaceArea =
parameters->getNextParameterAsFloat("Minimum Surface Area");
checkForExcessiveParameters();
//
// Get algorithm
//
BrainModelSurfaceMetricClustering::CLUSTER_ALGORITHM clusterAlgorithm;
if (modeName == "ANY_SIZE") {
clusterAlgorithm = BrainModelSurfaceMetricClustering::CLUSTER_ALGORITHM_ANY_SIZE;
}
else if (modeName == "MINIMUM_SURFACE_AREA") {
clusterAlgorithm = BrainModelSurfaceMetricClustering::CLUSTER_ALGORITHM_MINIMUM_SURFACE_AREA;
}
else if (modeName == "MINIMUM_NUMBER_OF_NODES") {
clusterAlgorithm = BrainModelSurfaceMetricClustering::CLUSTER_ALGORITHM_MINIMUM_NUMBER_OF_NODES;
}
else {
throw CommandException("Invalid mode: \""
+ modeName
+ "\"");
}
//
// Create a brain set
//
BrainSet brainSet(topologyFileName,
coordinateFileName,
"",
true);
BrainModelSurface* surface = brainSet.getBrainModelSurface(0);
if (surface == NULL) {
throw CommandException("unable to find surface.");
}
const TopologyFile* tf = surface->getTopologyFile();
if (tf == NULL) {
throw CommandException("unable to find topology.");
}
const int numNodes = surface->getNumberOfNodes();
if (numNodes == 0) {
throw CommandException("surface contains no nodes.");
}
//
// Read input metric file
//
MetricFile metricFile;
metricFile.readFile(inputMetricFileName);
//
// Perform clustering
//
const int numberOfColumns = metricFile.getNumberOfColumns();
for (int i = 0; i < numberOfColumns; i++) {
BrainModelSurfaceMetricClustering clustering(&brainSet,
surface,
&metricFile,
clusterAlgorithm,
i,
i,
metricFile.getColumnName(i),
minimumNumberOfNodes,
minimumSurfaceArea,
minimumNegativeThreshold,
maximumNegativeThreshold,
minimumPositiveThreshold,
maximumPositiveThreshold,
true);
clustering.execute();
}
//
// Write the metric file
//
metricFile.writeFile(outputMetricFileName);
}
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