/*
 * Complete Experiment 1
 *
 * CSI2510 Algorithmes et Structures de Donnees
 * www.uottawa.ca
 *
 * Robert Laganiere, 2022
 *
*/ 
/*
 * Name - Anisaftab Saiyed
 * ID- 300259073
 * CSI PA2
 */
import java.util.List;
import java.util.ArrayList;

import java.io.*;  
import java.util.Scanner;  
/*
 * Exp1 tests the KDtree with different points to see if it gives
 * similar results to linear arraylists or not. 
 */
public class Exp1 {
  
  
  /** reads a csv file of 3D points and turns into a list of 3dpoints
   * @param filename the csv file which needs to be read.
   * @return List<Point3D> Arraylist of all 3d points from the csv file
   * @throws FileNotFoundException,Exception 
   */
  public static List<Point3D> read(String filename) throws Exception {
	  
    List<Point3D> points =  new ArrayList<Point3D>();

    try {
      File myObj = new File(filename);
        //java forces a try catch since scanner throws file not found errors
        Scanner reader = new Scanner(myObj);
        //first line should have column names
        reader.nextLine();
        //scan through file
        while (reader.hasNextLine()) {
            //get next line and split into x,y,z
            String[] tokens = reader.nextLine().split(",");
            //make into point
            Point3D p = new Point3D(
            Double.parseDouble(tokens[0]), 
            Double.parseDouble(tokens[1]), 
            Double.parseDouble(tokens[2])
            );
            //add point to list
            points.add(p);
        }
        reader.close();
      
    } catch(FileNotFoundException f){
      f.printStackTrace();
    }
    catch (Exception e) {
      e.printStackTrace();
    }
        
     return points;
  }
  
  /** save method saves the neigbors of the given point in a txt file.
   * @param list list of neighbors of the given point
   * @param filename output file name
   * @throws FileNotFoundException
   * @throws UnsupportedEncodingException
   */
  public static void save(List<Point3D> list, String filename) throws FileNotFoundException, UnsupportedEncodingException{
    //initialize writer and set header
    PrintWriter writer = new PrintWriter(filename, "UTF-8");
    
    //write each point with its corresponding color
    for(Point3D p: list){
        String point = p.toString();
        writer.println(point);
    }
    writer.close();
  }

  
  /** This method checks to see if both the linear search and kd tree search are giving the same 
   * results.
   * @param listNeighbors list of neighbors from linear search
   * @param kdNeighbors list of neighbors from kd search
   * @return boolean value saying if both the lists are same or not
   */
  public static boolean checkNeighborsPresent(List<Point3D> listNeighbors, List<Point3D> kdNeighbors){

    for(Point3D p : listNeighbors){
      if( ! kdNeighbors.contains(p)){
        return false;
      }
    }
    return true;
  }
  
  
  
  /** 
   * @param args args[0] = type of search, args[1] = eps value, args[2] = csv file name, args[3,4,5] = x,y &z coordinates of a point whose neighbors we are finding.
   * @throws Exception
   */
  public static void main(String[] args) throws Exception { 
    
    String type = args[0];

      // not reading args[0]
    double eps= Double.parseDouble(args[1]);
    
      // reads the csv file
      List<Point3D> points= Exp1.read(args[2]);
    
    Point3D query= new Point3D(Double.parseDouble(args[3]),
                  Double.parseDouble(args[4]),
                  Double.parseDouble(args[5]));
    
    // creates the NearestNeighbor instance
    NearestNeighbors nn= new NearestNeighbors(points);
    List<Point3D> neighbors= nn.rangeQuery(query,eps);


    NearestNeighborsKD nnKD = new NearestNeighborsKD(points);
    List<Point3D> neighbors2 = nnKD.getNeighbors(query, eps) ;

    

    if ( type.equals("lin")){
      save(neighbors, "pt_lin.txt");
      System.out.println("Number of neighbors from linear search= "+neighbors.size());
      System.out.println(neighbors);
    }
    else if ( type.equals("kd")){
      save(neighbors2, "pt_kd.txt");
      System.out.println("Number of neighbors from kdtree search= "+neighbors2.size());
      System.out.println(neighbors2);
    }
    else{
      save(neighbors, "pt_lin.txt");
      save(neighbors2, "pt_kd.txt");

      System.out.println("Number of neighbors from linear search= "+neighbors.size());
      System.out.println(neighbors);

      System.out.println("Number of neighbors from kdtree search= "+neighbors2.size());
      System.out.println(neighbors2);
      System.out.println("Both linear and kd search are giving same neighbors or not: " + Exp1.checkNeighborsPresent(neighbors, neighbors2));
    }

    
    

    
  }   
}