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///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below.       //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,       //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard        //
// Scheines, Joseph Ramsey, and Clark Glymour.                               //
//                                                                           //
// 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 2 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, write to the Free Software               //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetradapp.model;

import edu.cmu.tetrad.data.*;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.TetradSerializableUtils;

/**
 * Converts a continuous data set to a correlation matrix.
 *
 * @author josephramsey
 * @version $Id: $Id
 */
public class NonparanormalTransform extends DataWrapper {

    private static final long serialVersionUID = 23L;

    //=============================CONSTRUCTORS==============================//

    /**
     * Constructor for NonparanormalTransform.
     *
     * @param wrapper a {@link edu.cmu.tetradapp.model.DataWrapper} object
     * @param params  a {@link edu.cmu.tetrad.util.Parameters} object
     */
    public NonparanormalTransform(DataWrapper wrapper, Parameters params) {
        DataModel dataModel = wrapper.getSelectedDataModel();

        if (dataModel instanceof ICovarianceMatrix) {
            throw new IllegalArgumentException("Data model must be a tabular continuous data set, not a covariance matrix.");
        }

        DataSet nonparanormalTransformed = DataTransforms.getNonparanormalTransformed((DataSet) dataModel);
        nonparanormalTransformed.setKnowledge(dataModel.getKnowledge().copy());

        setDataModel(nonparanormalTransformed);
        setSourceGraph(wrapper.getSourceGraph());

        LogDataUtils.logDataModelList("Conversion of parent data to correlation matrix form.", getDataModelList());
    }

    /**
     * Generates a simple exemplar of this class to test serialization.
     *
     * @return a {@link edu.cmu.tetradapp.model.PcRunner} object
     * @see TetradSerializableUtils
     */
    public static PcRunner serializableInstance() {
        return PcRunner.serializableInstance();
    }

}