Porting code from Matlab / Octave to Python Numpy

Things to watch out for while porting code from Octave / Matlab to Python Numpy

• The V returned from Numpy U, D, V = svd is not the same V in M / O. In order to access the equivalent, you need to do V.T in Python.
  
• Instead of a = [2 3 4] you use a = [2, 3, 4]
  
• Use * in place of .*
• The -1 in reshape means I don't care what you do with rows, just calculate everything according to column parameter.
  
• D returned from svd call is not in diagonal matrix form. It is simple a vector of values that form the diagonal of Matlab's D. This is most likely done for efficiency reasons, and it makes sense. If you need this data in diagonal form, simple call Numpy diag(D) it will form the square matrix for you.
  
• Use dot() instead of *
• Don't forget Matlab / Octave use 1-based indexing of arrays where Python uses 0. So x(3, :) in Matlab / Octave would become x[2, :].
  
• Oh, [] instead of (). Of course.
  
• ** instead of .^
• Instead of find() you just write the filter condition directly on the matrix, vector var itself, but then you need to call nonzero() to get index values, otherwise you get True, False values. The call ind = find(abs(x(3,:)) > bla) becomes ind = (abs(x[2, :]) > bla).nonzero()
• The constant eps is not defined, I simple hardcode it globally eps=1e-15
• Instead of special index value 'end', you have to use negative index value -1. Same for end-1, end-2. Things get a bit confusing however, when there is a "range" involved, such as all columns including the last one. In that case, you don't use -1 at all, just leave the index blank.
  Example: a = array([[1,3,8],[2,4,0],[9,9,9]])
  print a[:,-1] gives [8 0 9]
  print a[:,1:] gives [[3 8] [4 0][9 9]]
  
  

Reference