Optimization of Python Code

1. Avoid explicit loops: use vectorizd numpy expressions instead. If you really need a loop, then try list comprehensions as they are in general much faster. Example:

   1 2 3 4 5 6 7 8 9 10 11 12

   # Slow: x = [] for i in xrange(8): x.append( i**2 ) # Better: x = [0, 0, 0, 0, 0, 0, 0, 0] for i in xrange(len(x)): x[i] = i**2 # Best: x = [ i**2 for i in xrange(8) ]

2. Avoid module prefix in frequently called funcions:

   import mod
   func = mod.func
   for x in hugelist:
        func(x)
   

   Or even import the function into the global namespace:
   

   from mod import func
   for x in hugelist:
        func(x)
   

3. Plain functions are called faster than class methods: trick:

   f = myobj.__call__
   f(x)
   

4. Inlining functions speeds-up the code
5. Avoid usieng numpy functions with scalar arguments
6. Use xrange instead of range (but be aware that xrange only supports a small part of the interface of range() and similar containers. [3])
7. if-else is faster than try-except (never use exceptions for program flow!)
8. Avoid resizing of numpy arrays
9. Callbacks to Python from Fortran/C/C++ are expensive
10. Avoid unnecessary allocation of large data structures

11. Be careful with type mactching in mixed Python-Fortran software (e.g. flat/ real*8): if the array entry types do not match, arrays will be copied!
12. Use asarray with parameter order=’Fortran’ when sending arrays from numpy to Fortran routines!