import random, math

# Generate a random sequence of 1000 DNA nucleotides.
# Each character in the generated sequence has
# a 25% chance of being 'A', a 25% chance of being 'C',
# a 25% chance of being 'G', and a 25% chance of being 'T'.
def generateRandomSequence():

    # Enter your code here
    return ""


# The "mutate" function takes a genomic sequence as a parameter
# and returns a new genomic sequence, the same as the input sequence,
# except a single randomly chosen nucleotide from the sequence is changed.
# The function should procede as follows:
# Randomly choose an index between 0 and len(seq)-1,
# and mutate the nucleotide in seq at this randomly chosen index.
# If the nucletide at the randomly chosen index is 'A' then
# randomly choose to mutate the nucleotide to 'C' or 'G' or 'T'.
# If the nucletide at the randomly chosen index is 'C' then
# randomly choose to mutate the nucleotide to 'A' or 'G' or 'T'.
# If the nucletide at the randomly chosen index is 'G' then
# randomly choose to mutate the nucleotide to 'A' or 'C' or 'T'.
# If the nucletide at the randomly chosen index is 'T' then
# randomly choose to mutate the nucleotide to 'A' or 'C' or 'G'.
# The helper function "random.randint(a,b)" may be helpful in
# choosing a random index between 0 and len(seq)-1 to mutate.
# The helper function "random.choice(seq)" may be helpful in
# choosing a random nucleotide to mutate the selected nucleotide to.
def mutate(seq):

    # Enter your code here
    return ""


# Takes two sequences, s1 and s2, of the same length and
# counts the number of nucleotides that differ between the two sequences.
# For instance, if s1="ACCGTGCTA" and s2="GCCGAGCCA" then the function
# should return the number 3 since s1 and s2 contain different
# nucleotides at indices 0, 4, and 7. 
def distanceBetweenSequences(s1, s2):

    # Enter your code here
    return 0


# Given the observed distance, p, between two sequences, this function
# applies the Jukes-Cantor correction in order to estimate the actual
# distance between the two sequences. The acutal distance, K, can be
# estimated using the Jukes-Cantor correction as:
#               K = -3.0/4.0 * (1000.0) * ln(1.0-(4.0/3.0)*p/1000.0)
# where "ln" refers to the natural logarithm.
# In the formula above, the value 1000.0 is used since the sequence
# is 1000 nucleotides in length.
# In Python, the natural logarithm of a number x can be calculated
# as "math.log(x)"
def JukesCantorCorrection(p):

    # Enter your code here
    return 0.0


# Takes a genomic sequence, seq, and mutates randomly chosen
# nucleotides in the sequence 1000 times. After each of the
# 1000 mutations, the function prints out two numbers:
#   (1) p = the distance between the mutated sequence and the
#       original sequence.
#   (2) K = the Jukes-Cantor correction to p, i.e., the estimated
#       number of actual mutations that occurred between the two sequences.
# For example, the first 10 (of 1000) lines printed out might look as follows:
# 1     1.00066725985
# 2 	2.00267141691
# 3 	3.00601604815
# 4 	4.01070474495
# 5 	5.0167411131
# 6 	6.02412877295
# 7 	7.03287135945
# 7 	7.03287135945
# 8 	8.04297252223
# 9 	9.0544359257
def mutagenesis(seq):

    # Enter your code here
    print 0, "\t", 0.0




########################################################
### End of functions ###################################
########################################################

s = generateRandomSequence()
mutagenesis(s)