Pythonic Method Calling

I have an aversion to hard-coding. Hard coding is when you write out a long, elaborate code that could also be written with a dynamic loop. This usually limits the ability to easily adjust your own code in case you want to change something later (or re-use it). “Hard coding” refers to “rigidly” writing out things instead of keeping them dynamic.

This practice is frowned upon by most of the programmers I’ve met (especially the Python programmers). In Python, code is ‘pythonic’ if it is well-written and concise. This link is to an excellent article on ‘What is Pythonic?’, I highly recommend reading this for a more in-depth explanation of the term: http://blog.startifact.com/posts/older/what-is-pythonic.html

Today, while I was revising my Prime Number Algorithm program (an earlier post), I figured out how to pass functions as strings. The eval function allows me to run python code within itself. This definition of eval is vague and unhelpful.

Let me show you to make a calling method, ‘execute,’ pythonic in order to explain the purpose of the eval function.

Let’s assume that we want a program that executes methods p1(n), p2(n), …, p5(n) given some string n. I will refer to these methods collectively as ‘p’-methods.

We could hard-code this as such:
______________________

class Test():
    def execute(self, n):
        self.p1(n)
        self.p2(n)
        self.p3(n)
        self.p4(n)
        self.p5(n)

    def p1(self,n):
        print “Hello”+n
    def p2(self,n):
        print “Hello2″+n
    def p3(self, n):
        print “Hello3″+n
    def p4(self,n):
        print “Hello4″+n
    def p5(self,n):
        print “Hello5″+n

if __name__ == ‘__main__’:
    test = Test()
    test.execute(‘ World!’)

________________________

The above program will run all 5 ‘p’-methods and print the appropriate strings. However, what if there were 1000 different ‘p’-methods we needed to run? Must we type out each method in order to call these ‘p’-methods within the execute method?

At first, I created a forloop to iteratively call execute, providing a new method each time. If you run the code below, execute will treat the ‘method’ parameter as a String type (instead of a function) and throws an error.

_________________________(NON-FUNCTIONAL)
class Test():
    def execute(self, method_name):
        self.method_name(‘ World!’)   

    def send_method(self):
        for version in xrange(1,6):
            version = str(version) #run each ‘p’-method version = 1, 2, 3, 4
            method = ‘self.’+’p’+version #note that type(method == String
            self.execute(method)

    def p1(self,n):
        print “Hello”+n
    def p2(self,n):
        print “Hello2″+n
    def p3(self, n):
        print “Hello3″+n
    def p4(self,n):
        print “Hello4″+n
    def p5(self,n):
        print “Hello5″+n

if __name__ == ‘__main__’:
    test = Test()
    test.send_method()
_________________________

We are so very close to having a pythonic solution. In order to fix this error, we must make the String into a method call. With one extra line, we evaluate the String input (‘method_name’) as a function and run all ‘p’-methods! The final version of the execute method is:

_________________________

class Test():
    def execute(self, method_name):
        method = eval(method_name)
        methodrun = method(‘ World!’)   

    def send_method(self):
        for version in xrange(1,5):
            version = str(version) #test each version of the algorithm, version = 1, 2, 3, 4
            method = ‘self.’+’p’+version
            self.execute(method)

    def p1(self,n):
        print “Hello”+n
    def p2(self,n):
        print “Hello2″+n
    def p3(self, n):
        print “Hello3″+n
    def p4(self,n):
        print “Hello4″+n
    def p5(self,n):
        print “Hello5″+n

if __name__ == ‘__main__’:
    test = Test()
    test.send_method()

_________________________

I hope this gives you an idea of: why hard-coding is bad, what ‘pythonic’ means, how ‘eval’ comes in handy and what I do in my free time.

Implementation of Prime Number Algorithms

I’m currently reading Algorithms Unplugged, lent to me by my friend and professor, Dr. Marr.

The text provided 4 versions of an algorithm and claimed that each new version had a noticeable increase in runtime efficiency.

These algorithms take some number n and return a list of all prime numbers up to and including n. For example, if n = 13, this algorithm returns [2,3,5,7,11,13].

I was skeptical of their claims $\Rightarrow$ I took the psuedocode and converted it into functional Python, then wrote a timing function: this tests each algorithm by letting n = 2^6 to 2^20 and repeating each calculation 1000 times (in order to find the average runtime).

I had to wrestle with Python in order to stop it from rounding off the calculated runtimes (treating runtime as a float), which lead to an interesting adventure into the time module man page.

tl;dr

  • take psuedocode from a book (p.121-127 of Algorithms Unplugged by various German computer scientists*)
  • translate said psuedocode into functional code, and
  • test the run-times of each algorithm

'''
Catherine Ray
prime_runtime0.py

'Algorithms Unplugged' Demonstration
Program uses algorithms listed in psuedocode from pg.119-127. Each algorithm returns a list of all primes up to some number n.
There are 4 versions of this algorithm; this program computes the average runtimes of each version and prints to stdout.
'''

import math
import time as t

class Runtime():
 def execute(self):
  repeat = 1000 #number of times to repeat runtime calculation
  n = [2**x for x in xrange(6, 21, 7)] #produce list of large numbers to test algorithms
  #n = [2**6, 2**20]
  for v in xrange(1,5):
   method = 'self.prime_number'+str(v) #test each version of the algorithm, version = 1, 2, 3, 4
   print 'ntVersion ' + str(v), 'nHighest Prime Number Computed : Average Runtimen'  #find runtime
   [self.runtime(method, number, repeat) for number in n] 
   
  

 def runtime(self, method, n, repeat):
 #function = function to test
 #n = highest prime to compute
 #repeat = number of trials to run
  method = eval(method)
  total = 0 #reset total
  for r in xrange(0, repeat+1): 
   start = t.time() #get start time
   methodrun=method(n) 
   end = t.time() #get end time
  total = total + end-start #add to previously calculated time
  average_time = total/repeat #find average runtime
  print  '2^%d :' %(int(math.log(n)/math.log(2))), average_time
  
 

 def prime_number1(self, n): #version 1, pg. 121
  list = [i for i in xrange(2,n+1)] #write down all numbers between 2 and n in a list
  for i in xrange(2, n+1):
   for k in xrange(2, n+1):
    if i*k in list: list.remove(i*k) #if i*k is in list, remove
    else: pass #if i*k isn't in list, do nothing
  return list


 def prime_number2(self, n): #version 2, pg. 123
  list = [i for i in xrange(2,n+1)] #write down all numbers between 2 and n in a list
  for i in xrange(2, int(math.floor(math.sqrt(n)))+1):
   for k in xrange(2, int(math.floor(n/i))+1):
    if i*k in list: list.remove(i*k) #if i*k is in list, remove
    else: pass #if i*k isn't in list, do nothing
  return list

 def prime_number3(self, n): #version 3, pg. 125
  list = [i for i in xrange(2,n+1)] #write down all numbers  between 2 and n in a list
  for i in xrange(2, int(math.floor(math.sqrt(n)))+1):
   if i in list:
    for k in xrange(2, int(math.floor(n/i))+1):
     if i*k in list: list.remove(i*k) #if i*k is in list, remove
     else: pass #if i*k isn't in list, do nothing
  return list


 def prime_number4(self, n): #final version, pg.127
  list = [i for i in xrange(2,n+1)] #write down all numbers between 2 and n in a list
  for i in xrange(2, int(math.floor(math.sqrt(n)))+1):
   if i in list:
    for k in xrange(int(math.floor(n/i)), i-1, -1):
     if k in list:
      if i*k in list: list.remove(i*k)  #if i*k is in list, remove
      else: pass #if i*k isn't in list, do nothing
  return list

if __name__ == '__main__':
 runtime = Runtime()
 runtime.execute()

I left the code to run overnight.

Taking a brief glance at the output, I found confirmation of the authors’ claims. The runtime each version is less than the previous version (version4 should take less time to run than version1).


*Berthold Vöcking, Helmut Alt, Martin Dietzfelbinger, Rüdiger Reischuk, Christian Scheideler, Heribert Vollmer, Dorothea Wagner

Introduction to FractalCows

I am creating this blog in order to keep my family and friends updated on my adventures. I often neglect to update those close to me on my daily life and changing interests, because of my tendency to get caught up in my school work and research.
I expect this blog an eclectic patchwork of explanations, project updates, doodles, and code.

Here goes. I’ll start with a short introduction.

Hello. I’m Rin, or Catherine. I’m a junior at George Mason University – my major is Computational Physics. In other words, I study scientific simulation, algorithm development, and data analysis. I thrive in the intersections of fields: especially computer science, maths, and physics. This leads me to call myself a compumathemaphysicist.

In my free time, I program in python, cube, rock climb, run, do math, play trombone, read, and draw.

I have 3 main philosophies, based on those held by one of my favorite modern scientists (Neil Degrasse Tyson):
0. Stay healthy.
1. Know more about the world than I did yesterday.
2. Lessen the suffering of others.