# Optimizations¶

This is a small set of notes explaining the methods used to optimize different parts of the software code. It is meant as an overview of the changes and thoughts that have gone into the code structure and changes made.

## Timing function¶

In order to quantify whether or not a speed-up has been achieved, one need to time the method in question. This is mostly easily done through the use of the package time from Python, where one simply requests the current time in seconds before and after the method has been running. Subtracting the two then gives an estimate of the time consumption. The reason for the use of the word ‘estimate’ is that this is not the correct time used by the computer. The true time is the actual CPU time or even better, the number of CPU cycles needed to run the method. The reason the time module does not capture this is that if the program does not run as the only process and with a 100% usage of the core then a discrepancy between measured and actual time consumption is created. However, these technicalities are not taken into account when I have performed the profiling of the code as I in no case am able to perform a superb optimization with a background in pure physics.

The decorator used for profiling is given below:

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 def my_timer_N(N=3): if N<0: raise AttributeError('Number of runs need to be bigger or equal to 1 or equal to 0 for no timing, but {} given.'.format(N)) def my_timer(func): import time def newFunc(*args,**kwargs): Time = [] if N ==0: returnval = func(*args,**kwargs) else: for i in range(N): startT = time.time() returnval = func(*args,**kwargs) stopT = time.time() Time.append(stopT-startT) if N>1: print('Function "{}" took: {}s (±{}s)'.format(func.__name__,np.mean(Time),np.std(Time)/np.sqrt(N))) else: print('Function "{}" took: {}s'.format(func.__name__,Time[0])) return returnval return newFunc return my_timer 

With this definition of the decorator, using it to time a function is straight forward, i.e.

 1 2 3 4 5 6 7 8 def untimedFunction(*args,**kwargs): # some calculations return True @my_timer_N(N=5) def timedFunction(*args,**kwargs): # some calculations return True 

Thus, the above code allows for timing the function timedFunction called 5 times, producing the output: Function “timedFunction” took: 1.1920928955078125e-06s (±1.3571023436315258e-06s), where the first time is the average of the N=5 runs, and the parenthesis denotes the uncertainty on the mean, $$std(X)/\sqrt{N}$$.