# sequences for performing a relative rate test

seq1='AACCAAACCATTCACCCAAACGAATTCTAGGCGATAGAAATTACAATCCG'
seq2='AATCAAACCATTTACCCTAATAAAGTATAGGCGATAGAAATTGTAAATCG'
seq3='ATTAAATCATTCTCAAATATTAAAGTATGGGTGATAGAAAGTGTACTCTG'

# lookup table for chisquared results
# find the largest index i for which the critical value is smaller than the
# observed value.   Then the pvalue at that same index i is the most significant
# one you can claim.  Note that chisq values may be less than 1.32,
# meaning the p-value is above 0.25.   Your code should work in this case.
# 
chisq_critical_values = [1.32, 1.64, 2.07, 2.71, 3.84, 5.02, 5.41, 6.63, 7.88]
chisq_assoc_pvalues=[0.25, 0.20, 0.15, 0.10, 0.05, 0.025, 0.020, 0.010, 0.005]

# arguments are observed chisq value, and parallel lists of critical values
# and p-value cutoffs.  should return a string containing the highest 
# significance level identified,  e.g. "p < .01" or "p > .25"
#
def find_signif(observed,critvals,pvals):


# arguments are the count values of m1 and m2,
# should return float chisquared value  (difference, squared, divided by sum).  
#
def chisqval(m1,m2):


 
# To compute x2 value for tajima rel rate test given s1, s2 w/outgroup s3:
# compute m1, counting if s1 == s3 != s2
# compute m2, counting if s2 == s3 != s1
# return the chi-squared value of m1 and m2.
# 
def tajima(s1,s2,s3):


    

print find_signif(tajima(seq1,seq2,seq3) , chisq_critical_values, chisq_assoc_pvalues)