#!/usr/bin/env python
# coding: utf-8

# # Code Optimization and Execution Time
# <a href="https://colab.research.google.com/github/rambasnet/FDSPython-Notebooks/blob/master/Ch20-CodeOptimization-ExecutionTime.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# ## finding actual running time of code - timeit
# - timeit - measures execution time of small code snippets
# - timeit.timeit(stmt='pass', setup='pass', timer=[default timer], number=1000000, globals=None)
# - returns time in seconds

# In[2]:


import timeit
help(timeit)


# # Python code optimization
# - https://wiki.python.org/moin/PythonSpeed/PerformanceTips

# ## Summary
# 
# - use functions and local variables instead of globals, eventhough function calls are relatively high
# - avoid dot . member access specifier
# - user built-in list.sort and sorted with key using itemgetter function if required
# - avoid string concatenation; use "".join(somelist) instead
# - use list comprehension and map() instead of loops
# - while working with dict (esp. initializing), use try catch instead of key test
# - use defaultdict class from collections
# - doing stuff less often - change sys.setswitchinterval(sys.maxsize) to as high as possible if not running threads and catching signals
# - use addition and subtraction instead of multiplicaiton and divisions
# - avoid recursion; esp. if can't increase system's recursion limit
#     - sys.setrecursionlimit(10**6)

# In[7]:


import sys
sys.getswitchinterval()


# In[8]:


# what is the mazsize of sytem
sys.maxsize


# In[9]:


sys.setswitchinterval(100000)


# In[10]:


sys.getswitchinterval()


# In[2]:


import timeit


# In[9]:


code = '''
x = 47
x = x * 2
'''
timeit.timeit(stmt=code) # runs 1M times


# In[10]:


code = '''
x = 47
x = x << 1
'''
timeit.timeit(stmt=code) # runs 1M times


# In[5]:


code = '''
# x is local variable
x = 47
x = x + x
'''
timeit.timeit(stmt=code) # runs 1M times


# In[4]:


# x is global variable
x = 47
code = '''
global x
x = x + x
'''
timeit.timeit(stmt=code, globals=globals()) # runs 1M times; takes ~12 seconds


# In[59]:


# printing some result n times
# input and output are the major bottlenecks in execution time
code = '''
import sys
n = 100000 #100K takes 5.x seconds on my macbook pro 2019
for i in range(n):
    print(i**2)
'''
timeit.timeit(stmt=code, number=1)


# In[58]:


# concatenating result into a string and printing once
code = '''
n = 100000 # 100K takes 0.056 seconds on my 2019 macbook pro
ans = ''
for i in range(n):
    ans += str(i**2)
print(ans)
'''
timeit.timeit(stmt=code, number=1)


# In[57]:


# concatenating result into a string and printing once
code = '''
n = 10000000 # 100K takes 0.050 seconds on my 2019 macbook pro
ans = ''
for i in range(n):
    ans += str(i**2)
'''
timeit.timeit(stmt=code, number=1)


# In[56]:


# using list to collect answers and print once as string
code = '''
n = 10000000 # 100K takes 0.055 seconds on my 2019 macbook pro
ans = []
for i in range(n):
    ans.append(str(i**2)) #converting and appending string to list
ans = ''.join(ans) #typically print before storing into a variable
'''
timeit.timeit(stmt=code, number=1)


# In[55]:


# using list to collect int answers and print once as string
# not a whole lot of difference compared to collecting string itself
code = '''
n = 10000000 # 100K takes 0.055 seconds on my 2019 macbook pro
ans = []
for i in range(n):
    ans.append(i**2) #appending int to list
#map to string and join to print
ans = ''.join(map(str, ans))
'''
timeit.timeit(stmt=code, number=1)


# In[23]:


def factorial_recurse(n): 
    if(n == 0): 
        return 1
    return n * factorial_recurse(n - 1) 


# In[16]:


sys.getrecursionlimit()


# In[19]:


factorial_recurse(3000)


# In[20]:


sys.setrecursionlimit(10**6)


# In[22]:


factorial_recurse(3000)


# In[23]:


timeit.timeit('factorial_recurse(3000)', number=1, globals=globals())


# In[24]:


def factorial_iter(n):
    fact = 1
    for i in range(1, n+1):
        fact *= i
    return fact


# In[25]:


factorial_iter(3000)


# In[26]:


timeit.timeit('factorial_iter(3000)', number=1, globals=globals())


# ## Time complexity
# - Time complexity of various Python built-in data sturctures and functions 
# - https://wiki.python.org/moin/TimeComplexity