/*
    * The baseCode project
    * 
    * Copyright (c) 2008-2019 University of British Columbia
    * 
    * Licensed under the Apache License, Version 2.0 (the "License");
    * you may not use this file except in compliance with the License.
    * You may obtain a copy of the License at
    *
   *       http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   */
  package ubic.basecode.util.r;
  
  import org.apache.commons.collections4.Transformer;
  import org.rosuda.REngine.REXP;
  import ubic.basecode.dataStructure.matrix.DoubleMatrix;
  import ubic.basecode.dataStructure.matrix.ObjectMatrix;
  import ubic.basecode.math.linearmodels.LinearModelSummary;
  import ubic.basecode.math.linearmodels.OneWayAnovaResult;
  import ubic.basecode.math.linearmodels.TwoWayAnovaResult;
  
  import java.util.List;
  import java.util.Map;
  
  /**
   * Abstraction of a connection to R
   * 
   * @author Paul
   * 
   */
  public interface RClient {
  
      /**
       * @param argName
       * @param arg
       */
      public void assign( String argName, double[] arg );
  
      /*
       * (non-Javadoc)
       * 
       * @see org.rosuda.JRclient.Rconnection#assign(java.lang.String, int[])
       */
      public void assign( String arg0, int[] arg1 );
  
      /*
       * (non-Javadoc)
       * 
       * @see org.rosuda.JRclient.Rconnection#assign(java.lang.String, java.lang.String)
       */
      public void assign( String sym, String ct );
  
      public void assign( String argName, String[] array );
  
      /**
       * @param strings
       * @return the name of the factor generated.
       */
      public String assignFactor( List<String> strings );
  
      /**
       * @param factorName
       * @param list
       * @return the factor name
       */
      public String assignFactor( String factorName, List<String> list );
  
      /**
       * Assign a 2-d matrix.
       * 
       * @param matrix
       * @return the name of the variable by which the R matrix can be referred.
       */
      public String assignMatrix( double[][] matrix );
  
      /**
       * Assign a 2-d matrix.
       * 
       * @param matrix
       * @return the name of the variable by which the R matrix can be referred.
       */
      public String assignMatrix( DoubleMatrix<?, ?> matrix );
  
      /**
       * Assign a 2-d matrix.
       * 
       * @param matrix
       * @param rowNameExtractor
       * @return the name of the variable by which the R matrix can be referred.
       */
      public String assignMatrix( DoubleMatrix<?, ?> matrix, Transformer rowNameExtractor );
  
     /**
      * Define a variable corresponding to a character array in the R context, given a List of Strings.
      * 
      * @param objects, which will be stringified if they are not strings.
      * @return the name of the variable in the R context.
      */
     public String assignStringList( List<?> objects );
 
     /**
      * Run a command that takes a double array as an argument and returns a boolean.
      * 
      * @param command
      * @param argName
      * @param arg
      * @return
      */
     public boolean booleanDoubleArrayEval( String command, String argName, double[] arg );
 
     /**
      * Convert an object matrix into an R data frame. Columns that look numeric are treated as numbers. Booleans and
      * Strings are treated as factors.
      * 
      * @param matrix
      * @return variable name in R-land.
      */
     public String dataFrame( ObjectMatrix<String, String, Object> matrix );
 
     /**
      * Evaluate a command that returns a dataFrame
      * 
      * @param command
      * @return an ObjectMatrix representation of the data frame.
      */
     public ObjectMatrix<String, String, Object> dataFrameEval( String command );
 
     /**
      * Run a command that has a single double array parameter, and returns a double array.
      * 
      * @param command
      * @param argName
      * @param arg
      * @return
      */
     public double[] doubleArrayDoubleArrayEval( String command, String argName, double[] arg );
 
     /**
      * Run a command that returns a double array with no arguments.
      * 
      * @param command
      * @return
      */
     public double[] doubleArrayEval( String command );
 
     /**
      * Run a command that takes two double array arguments and returns a double array.
      * 
      * @param command
      * @param argName
      * @param arg
      * @param argName2
      * @param arg2
      * @return
      */
     public double[] doubleArrayTwoDoubleArrayEval( String command, String argName, double[] arg, String argName2,
             double[] arg2 );
 
     /**
      * Run a command that takes two double arrays as arguments and returns a double value.
      * 
      * @param command
      * @param argName
      * @param arg
      * @param argName2
      * @param arg2
      * @return
      */
     public double doubleTwoDoubleArrayEval( String command, String argName, double[] arg, String argName2, double[] arg2 );
 
     /**
      * Evaluate the given command
      * 
      * @param command
      * @return
      */
     public abstract REXP eval( String command );
 
     /*
      * (non-Javadoc)
      * 
      * @see org.rosuda.JRclient.Rconnection#getLastError()
      */
     public String getLastError();
 
     public int[] intArrayEval( String command );
 
     public boolean isConnected();
 
     /**
      * Lower level access to linear model. Fairly simple. Factors are assigned in turn.
      * 
      * @param data
      * @param factors Map of factorNames to factors (which can be expressed as Strings or Doubles). If you care about
      *        the order the factors are introduced into the model, use a LinkedHashMap.
      */
     public LinearModelSummary linearModel( double[] data, Map<String, List<?>> factors );
 
     /**
      * @param data
      * @param design which will be converted to factors or continuous covariates depending on whether the columns are
      *        booleans, strings or numerical. Names of factors are the column names of the design matrix, and the rows
      *        are assumed to be in the same order as the data.
      * @return
      */
     public LinearModelSummary linearModel( double[] data, ObjectMatrix<String, String, Object> design );
 
     /**
      * @param listEntryType a hint about what type of object you want the list to contain. If you set this to be null,
      *        the method will try to guess, but caution is advised.
      * @param command R command
      * @return
      */
     public List<?> listEval( Class<?> listEntryType, String command );
 
     public boolean loadLibrary( String libraryName );
 
     /**
      * Lower-level access to a simple one-way ANOVA
      * 
      * @param data
      * @param factor
      * @return
      */
     public OneWayAnovaResult oneWayAnova( double[] data, List<String> factor );
 
     public Map<String, OneWayAnovaResult> oneWayAnovaEval( String command );
 
     /**
      * Remove a variable from the R namespace
      * 
      * @param variableName
      */
     public void remove( String variableName );
 
     /**
      * Get a matrix back out of the R context. Row and Column names are filled in for the resulting object, if they are
      * present.
      * 
      * @param variableName
      * @return
      */
     public DoubleMatrix<String, String> retrieveMatrix( String variableName );
 
     /**
      * Run lm with anova on all the rows of a matrix
      * 
      * @param dataMatrixVarName from an assignment of a matrix
      * @param modelFormula and other options that will be passed as the argument to 'lm(...)', that refers to factor
      *        variables that have already been assigned, using x as the outcome. Example might be x ~ f1 + f2.
      * @param names of the factors like {"f1", "f2"}.
      * @return map of row identifiers to populated LinearModelSummaries.
      */
     public Map<String, LinearModelSummary> rowApplyLinearModel( String dataMatrixVarName, String modelFormula,
             String[] factorNames );
 
     /**
      * Evaluate any command and return a string
      * 
      * @param command
      * @return string
      */
     public String stringEval( String command );
 
     public List<String> stringListEval( String command );
 
     /**
      * Lower-level access to two-way ANOVA
      * 
      * @param data
      * @param factor1
      * @param factor2
      * @param includeInteraction
      * @return result with interaction term information null if includeInteraction = false
      */
     public TwoWayAnovaResult twoWayAnova( double[] data, List<String> factor1, List<String> factor2,
             boolean includeInteraction );
 
     /**
      * Evaluates two way anova commands of the form
      * <p>
      * apply(matrix,1,function(x){anova(aov(x~farea+ftreat))}
      * </p>
      * and
      * <p>
      * apply(matrix,1,function(x){anova(aov(x~farea+ftreat+farea*ftreat))}
      * </p>
      * where farea and ftreat have already been transposed and had factor called on them.
      * 
      * @param command
      * @return
      */
     public Map<String, TwoWayAnovaResult> twoWayAnovaEval( String command, boolean withInteractions );
 
     public void voidEval( String command );
 
 }