Packing Santa's Sleigh (Python Code for MATLAB Benchmark)

 #  
 # This is a translation of the MATLAB benchmark code   
 # given by Kaggle in "Packing Santa's Sleigh" competition  
 #   
 # This file will give a score same as the MATLAB benchmark score  
 #  
 import numpy as np  
 import scipy as sp  
 import pandas as pd  
 def getData():  
      print "reading data using pandas"  
      data = pd.read_table('../presents.csv', sep=',')  
      #print data  
      print "converting data to numpy array"  
      data = np.asarray(data)  
      #print data  
      return data  
 def pack(data):  
      #data = data[:100, :]  
      # the number of presents  
      presents = data[:,1:]  
      numPresents = data.shape[0]  
      print "total presents : ", numPresents  
      # width and length are 1000 units. Height is not fixed for the packing  
      width = 1000  
      length = 1000  
      # Initial coordinates  
      xs = 1  
      ys = 1  
      zs = -1  
      lastRowsInd = np.zeros((100, 1)) # temp array for storing indexes of last few rows   
      lastShelfInd = np.zeros((100,1)) # temp array for storing indexes of last few shelves  
      numInRow = 0     # Store the number of presents in current row  
      numInShelf = 0     # Store the number of presents in current shelf  
      presentCoordinates = np.zeros((numPresents, 25))  
      tempPresentLenRow = []  
      tempPresentHeightShelf = []  
      for i in range(numPresents):  
           # check if there is room in the row, else increase the row  
           if (xs + presents[i,0] > width + 1):  
                ys = ys + np.max(tempPresentLenRow)  
                xs = 1  
                numInRow = 0  
                tempPresentLenRow = []  
           # check if there is room in shelf, else increase the height  
           if (ys + presents[i,1] > length + 1):  
                zs = zs - np.max(tempPresentHeightShelf)  
                xs = 1  
                ys = 1  
                numInShelf = 0  
                tempPresentHeightShelf = []  
           presentCoordinates[i,0] = data[i,0]  
           presentCoordinates[i,[1,7,13,19]] = xs  
           presentCoordinates[i,[4,10,16,22]] = xs + presents[i,0] - 1  
           presentCoordinates[i,[2,5,14,17]] = ys  
           presentCoordinates[i,[8,11,20,23]] = ys + presents[i,1] - 1  
           presentCoordinates[i,[3,6,9,12]] = zs  
           presentCoordinates[i,[15,18,21,24]] = zs - presents[i,2] + 1  
           xs = xs + presents[i,0]  
           numInRow = numInRow + 1  
           numInShelf = numInShelf + 1  
           tempPresentLenRow.append(presents[i,1])  
           tempPresentHeightShelf.append(presents[i,2])  
           if i%1000 == 0: print i  
      zCoords = presentCoordinates[:,3::3]  
      minZ = np.min(zCoords.ravel())  
      presentCoordinates[:,3::3] = zCoords - minZ + 1  
      return presentCoordinates  
 def saveCSV(predictions):  
      datafile = pd.read_table('../presents.csv', sep=',')  
      submission = pd.DataFrame(predictions, columns="PresentID,x1,y1,z1,x2,y2,z2,x3,y3,z3,x4,y4,z4,x5,y5,z5,x6,y6,z6,x7,y7,z7,x8,y8,z8".split(','), dtype = int)  
      submission.to_csv('submission.csv', index = False)  
 if __name__ == '__main__':  
      data = getData()  
      predictions = pack(data)  
      saveCSV(predictions)