'''
Bacterial population modelling

You are using 2 different types of bacterial populations to potentially use for
pharmaceutical synthesis. You want to find out which population is growing the 
fastest. You have decided that the best way to find this out is by doing some 
simulations of the growth.

Note: 'break' and 'continue' are not necessarily required to solve the two exercises.
'''

'''
Exercise: Pseudocode

For each of the exercises below, start by writing 
pseudocode on a piece of paper. Only once you're done with pseudocode should 
you start working on Python code in VS Code.
'''

'''
Exercise A: Which strain grows faster?

Write code in which you follow the growth of two bacterial populations, A and B.

We use the following information:
initial_population - initial population of cells 
division_t - division time in minutes

If the population of B ever becomes greater than population of A,
set is_B_faster to 'True'. If not, set is_B_faster to 'False'.

The maximum time (max_time) you have to simulate is 7 days.
'''

initial_population_A = 120000 # cells 
initial_population_B = 241000 # cells 
division_t_A = 45 # min
division_t_B = 35 # min

# Your code here


'''
Exercise B: Delayed division start

Do the same as in Exercise A (you can also reuse the code you just wrote),
but this time also account for the time before the cells start dividing (start_t),
which is different for two bacterial populations.
'''

start_t_A = 700 # min
start_t_B = 1200 # min

# Your code here


'''
Exercise C: Race to 1 million cells

Repeat the same idea as in Exercise B, trying to find the fastest growing strain.
This time, however, you are given tuples corresponding to bacterial strains. 
Each tuple consists of the following: (initial_population, division_t, start_t).

Return the name of the fastest growing strain in 'best_strain', which is 
defined as the quickest strain to reach 1,000,000 cells.
'''
	
strain_A = (100000, 30, 1000)  
strain_B = (120000, 40, 800)   
strain_C = (90000, 35, 500)    

# Your code here