w_3.py

#from __future__ import division
from collections import Counter
import copy
import sys


def reverseComplement(string):
	myStr = []
	for i in range(0, len(string)):
		if string[i] == 'A':
			myStr.append('T')
		elif string[i] == 'T':
			myStr.append('A')
		elif string[i] == 'G':
			myStr.append('C')
		elif string[i] == 'C':
			myStr.append('G')

	myStr = myStr[::-1]
	myStr = "".join(myStr)
	return myStr

def CodonDictionary():
	file = '../Downloads/RNA_codon_table_1.txt'
	d = {}
	with open(file) as f:
		for line in f:
			line = line.strip('\n')
			l = line.split(' ')
			if l[0] not in d:
				d[l[0]] = l[1]

	return d

def IntergerMassDictionary():
	file = '../Downloads/integer_mass_table.txt'
	d ={}
	with open(file) as f:
		for line in f:
			line = line.strip('\n')
			l = line.split(' ')
			if l[0] not in d:
				d[l[0]] = l[1]

	return d


def ProteinTranslation(rna):
	pattern = []
	d = CodonDictionary()
	rnaCodons = [rna[i:i+3] for i in range(0, len(rna), 3)]
	for codon in rnaCodons:
		pattern.append(d[codon])
	pattern = ''.join(pattern)
	return pattern

def SubstringEncodingAminoAcid(rna, aminoAcid):
	substrings = []
	substringLen = len(aminoAcid)*3
	for i in range(0, len(rna) - substringLen + 1):
		substrings.append(rna[i:i+substringLen])
	encoders = []
	for sub in substrings:
		reverse = reverseComplement(sub)
		reverseTranslated = reverse.replace('T','U')
		translated = sub.replace('T','U')
		if ProteinTranslation(translated) == aminoAcid or ProteinTranslation(reverseTranslated) == aminoAcid:
			encoders.append(sub)
	return encoders


def NumberOfSubpeptides(n):
	return n*(n-1)

def SubpeptidesLinear(n):
	return n*(n+1)/2 + 1

def LinearSpectrum(peptide, convolution):
	integer_mass_dict = ExtendedMassDict(convolution)
	peptide = list(peptide)
	PrefixMass = [0 for i in range(len(peptide) + 1)]
	for i in range(1, len(peptide) + 1):
		PrefixMass[i] = PrefixMass[i - 1] + int(integer_mass_dict[peptide[i - 1]])

	LinearSpectrum = []
	LinearSpectrum.append(0)

	for i in range(0, len(PrefixMass) - 1):
		for j in range(i + 1, len(PrefixMass)):
			LinearSpectrum.append(PrefixMass[j] - PrefixMass[i])

	LinearSpectrum.sort()

	return LinearSpectrum

def CircularSpectrum(peptide, convolution):
	integer_mass_dict = ExtendedMassDict(convolution)
	peptide = list(peptide)
	PrefixMass = [0 for i in range(len(peptide) + 1)]
	for i in range(1, len(peptide) + 1):
		PrefixMass[i] = PrefixMass[i - 1] + int(integer_mass_dict[peptide[i - 1]])

	peptideMass = PrefixMass[len(PrefixMass) - 1]

	CyclicSpectrum = []
	CyclicSpectrum.append(0)

	for i in range(0, len(PrefixMass) - 1):
		for j in range(i + 1, len(PrefixMass)):
			CyclicSpectrum.append(PrefixMass[j] - PrefixMass[i])
			if i > 0 and j < len(PrefixMass) - 1:
				CyclicSpectrum.append(peptideMass - PrefixMass[j] + PrefixMass[i])

	CyclicSpectrum.sort()

	return CyclicSpectrum

def Expand(peptide, extended_mass_dict):
	p = [chr(key) for key in extended_mass_dict]
	q = []
	if len(peptide) == 0:
		return p
	else:
		for pep in p:
			for pep_ in peptide:
				q.append(pep + pep_)
		return q

def PeptideMass(peptide, convolution):
	d = ExtendedMassDict(convolution)
	peptide = list(peptide)
	mass = 0
	for pep in peptide:
		mass += int(d[pep])

	return mass

def PeptideMassString(peptide, convolution):
	d = ExtendedMassDict(convolution)
	peptide = list(peptide)
	mass = []
	for pep in peptide:
		mass.append(str(d[pep]))

	mass = '-'.join(mass)
	return mass

def CyclopeptideSequencing(spectrum):
	peptides = ['']
	result = []
	d = ExtendedMassDict()
	while len(peptides) != 0:
		peptides = Expand(peptides)
		for peptide in peptides[:]:
			if PeptideMass(peptide) == spectrum[len(spectrum) - 1]:
				if Counter(CircularSpectrum(peptide)) == Counter(spectrum):
					m = PeptideMassString(peptide)
					if m not in result:
						result.append(m)
				peptides.remove(peptide)

			else:
				l = LinearSpectrum(peptide)
				for mass in l:
					if mass not in spectrum:
						peptides.remove(peptide)
						break
	result = ' '.join(result)
	return result

def CyclopeptideScoring(peptide, experimental_spectrum, convolution):
	spectrum = CircularSpectrum(peptide, convolution)
	peptide_dict = {}
	spectrum_dict = {}
	for val in spectrum:
		if val not in peptide_dict:
			peptide_dict[val] = 0
	for val in spectrum:
		peptide_dict[val] += 1

	for val in experimental_spectrum:
		if val not in spectrum_dict:
			spectrum_dict[val] = 0
	for val in experimental_spectrum:
		spectrum_dict[val] += 1

	score = 0
	for mass in peptide_dict:
		if mass in spectrum_dict:
			score += min(peptide_dict[mass], spectrum_dict[mass])

	return score

def LinearScore(peptide, experimental_spectrum, convolution):
	spectrum = LinearSpectrum(peptide, convolution)
	peptide_dict = {}
	spectrum_dict = {}
	for val in spectrum:
		if val not in peptide_dict:
			peptide_dict[val] = 0
	for val in spectrum:
		peptide_dict[val] += 1

	for val in experimental_spectrum:
		if val not in spectrum_dict:
			spectrum_dict[val] = 0
	for val in experimental_spectrum:
		spectrum_dict[val] += 1

	score = 0
	for mass in peptide_dict:
		if mass in spectrum_dict:
			score += min(peptide_dict[mass], spectrum_dict[mass])

	return score

def Trim(leaderboard, spectrum, N, convolution):
	if len(leaderboard) < N:
		return leaderboard
	linear_scores = []
	for i in range(0, len(leaderboard)):
		linear_scores.append(LinearScore(leaderboard[i], spectrum, convolution))

	linear_scores, leaderboard = zip(*[(x, y) for x, y in sorted(zip(linear_scores, leaderboard))])
	linear_scores = list(linear_scores)[::-1]
	leaderboard = list(leaderboard)[::-1]

	s = linear_scores[N - 1]

	for i in range(N, len(leaderboard)):
		if linear_scores[i] < s:
			leaderboard = leaderboard[0: i]

	return leaderboard

def LeaderboardCyclopeptideSequencing(spectrum, N, convolution):
	leaderboard = ['']
	leader_peptide = ''
	best_score = 0
	d_scores = {}
	while len(leaderboard) != 0:
		leaderboard = Expand(leaderboard,convolution)
		for peptide in leaderboard[:]:
			print(leaderboard)
			if PeptideMass(peptide, convolution) == spectrum[len(spectrum) - 1]:
				score = CyclopeptideScoring(peptide, spectrum, convolution)
				if score > best_score:
					leader_peptide = peptide
					best_score = score
			elif PeptideMass(peptide, convolution) > spectrum[len(spectrum) - 1]:
				leaderboard.remove(peptide)
				continue
		
		leaderboard = Trim(leaderboard, spectrum, N, convolution)

	print(best_score, '!!!!!!!')
		
	return PeptideMassString(leader_peptide, convolution)

def ExtendedMassDict(convolution):
    d = {}
    for i in convolution:
        d[chr(i)] = i
    return d

def Convolution(spectrum):
	spectrum.sort()
	convolution = []
	for i in range(0, len(spectrum)):
		for j in range(0, len(spectrum)):
			if i != j:
				val = spectrum[j] - spectrum[i]
				if val > 0:
					convolution.append(val)
	d = dict(Counter(convolution))
	print(d)
	return convolution

def RestrictConvolution(convolution):
	for score in convolution[:]:
		if score >=57 and score <=200:
			continue
		else:
			convolution.remove(score)

	return convolution

def RankConvWithTies(convolution, M):
	if len(convolution) < M:
		return convolution
	d = dict(Counter(convolution))
	l = sorted(d, key = d.get)
	l = l[::-1]
	s = d[l[M - 1]]
	for i in range(M, len(l)):
		if d[l[i]] < s:
			l = l[0: i]
			break
		else:
			continue
	convolution = l
	return convolution


def ConvolutionCyclopeptideSequencing(spectrum, N, M):
	convolution = Convolution(spectrum)
	#print len(convolution)
	convolution = RestrictConvolution(convolution)
	convolution = RankConvWithTies(convolution, M)
	leader_peptide = LeaderboardCyclopeptideSequencing(spectrum, N, convolution)
	#leader_peptide = LeaderboardCyclopeptideSequencing(spectrum, N)
	#print len(convolution)
	# I totally misunderstood what ties means
	# focus on the count and then select the number
	# just like the last few lines of leaderboard trimming
	# define restrictConvolution() and BestConvWithTies()

	return leader_peptide

	



#n = 24460
#print NumberOfSubpeptides(n)

#rna = 'GTTACCGTAGCCTCGTTCGGTCTTTATGATTAACAAAAACTTCGTGACCCGAGGGGCCGTCCACGCTCGTGTATTTTCGTGTACTGTCACCCCCACGGCAACATTGTTGAGATTAAAATACGGAGCATCTGAGCACGGGTAGTGAGATCTATTAGCAATCGATAGTATCTCGCGCGAAGGTTGCGATTTCTCTTTAGCCGAAGTCGCCTACGTTCCGGTTGAACTAGATGTGAAGCCAGGGTGGACCAGGTTCATACAGGGGAGGCGAGATAAATCGAAAAATTAGTCCCCAAGAGCGAGTACACCAAGTTCCTGAGTATCCAAGAACTGACCGCTCGATTCACCGAACACGAATGCTAACTTTTTATGGCGTCATTTCCGGGGGTGATTTCTACACATGGTAAACCTTCACACCATAGATGAGTCGAGTACGTCAACGTTTGGTGCGCGCGTGACGCACAGACGGTTCGGAATAAACTCAATGCCAGTCCCTATGGAAGGGTAACAACTCGTGAGGGCAGGAGATATCTGGTTCTCATAAAATATTACGGCCTCGATACGTAATCAGGCGAGTGATCTCGGGTGTGGTCACCGCATAATAGCAGTTCTGCATTATCCGCGAATTTTTCGGTTGATAGTGCTGGGTACGAAATGACCTGGCAGAATCGTAACTCACCGCTGTGGAAGGACTGGAACCCTGCACTATGGGATAGCCGCGGAGCCCTAGCTCGTTTGTCCAACCAGGTCATACAGCAGTCATCTGCCCTTCCGTATTCATAAGGCCTCTAGCGCATGGCGCTAGCGCAGCTTACCGGACTTTAGGCATAAACAATCGAAAGAACTTAGTTTCGTTACGACGATCCGCGATCGTACGGGATTTTGTCATAGATGGTAGGAAGTCCCGCACTCATGGCAGTTAATGATCGACCGATCGTCGTGGACGGACGGCACGGCTGGTCTGCCTTGATCGGATCAACTCTATGCCCGTTCCAATGCGGTTTCGAGCCAACCGAACAGTGAGGGAACTTGGCCGTGCAACCGTCAGTGATGGATCCAGCAAGCATAATCACCCTTGTGAGCTGGAAGCCAGTTCGGGCCTCGCTGGAAGGTCATACTCCGGCCCAGGTACAATTTGGCTCGATTCACTACGGTACTCGTTCGTGAGCTCTAGACCTAAGAACTCAGCTGATCTCCACGTCCTCCATACCGCGAAGCGCGTGATAGGCCTGAGAAGCAGTAGCGCCACTCTTAAGTGTGCGTAAATTGCTATTGGCACCCGTCAGCGAATAAGGTAATATGTCGGACCCTGAAACGGAGCTTTAACGACTTCTGGTCGATGTTGGTTGTGAAACTCTATTTTTTCTTGGTTCAGCTGAAACCAAACCTAGCCGGGTACCGTCAGGCGGCACTTGACTACTTGTACGCGATACCTTGGGGTCAGGTTACAGCAGTGTAAGGACCCCGCGAAGTGCAGGTGGAGGAGCCCAAGTTACCGCTCGCGACCATAAGATGGGCGTAGCGAACCCATCTATGATAGCTCCCGCGATTTCAATTTGTGCTCTTTATGAGCCTTACCCAGTCCACCGCAGGGGCTTACCGAGTTCTCGCAGATAGGCAAAATAATTGGGGGGCAATTAATTTTACGATAAATACATGCCTTCGCGGAGTCTGTGGGATCGCAGCAATGCTCACTACACCAGTCTTGCCTGCCTGGGCGAGAATGCGTGGGTGACGATATATTGAGCACCACACTATCGAGCATCGCCGCGATTTTGCCGTGTGGCGGACTGCTCCGAGATCGGGACCGGAAAAGTGGTTGTCCTCTAGATGTCGCACCTACGAGCGCCGCATTGGCACCGGCATACTGTTAATAGTGCATTATGGAAGAATAAGCAGCTATACTCCAGACTTGTGCACTTCCTATAGGGAGTATTTATGGCCGGACCGTAACCCATAGGGACCGGCATACTATTGATATCTCAACTAAACTTGCCACCTAAACCCCCACAAGGCTACATTAAAAACATGGGGCCACAGATGTATTGCATTATGTTTGCAGCAGTAACAGTGGGCGTTCTGGCGCAGTCCAGGTAACTAATGATAGTCATTAGCGGATAGTCACACAGCATATACCTCGACTGCTAAAGGTTACCCTACCGTTGTTTTAAATATCGGACCGGAACCTGCTGTGGTAAAACGCCCAATTACAGCTTAAGTCAATAGGGCATTAATAGTATGCCCGTACCAATGGCGCAACGGTGGGCGTGTGCGGTTAGCAAGGTATCGTTGCAGCTTCTTCAGATCTGATTGGAGTGTCGTCCCCAAAAAATTCATCCGTTTGGGCGATGACAACGGTCCAACAAAGCTTCTCGCGTCCGTAAGAGATTGGATTCAACTCGAGGAACTTTTGACCTAAGATGACCGCACCCAGTACCAATAAAGTGGATCAGTCCTAGTTACTACGGATAAGTGGGGACCACCCAATCAGTACGGACACTTCGACAAGTATAAGAGCACATGCCATGTTACCGATGTGATCCTATAGCGGGCTTAACCCTGTGGCGCCTACTATAAGCGGCATTCTCCGAGGTGCCATGATTTGGATTACCGGCGGTGCGCGGATCTTGGCCAACGGCAGCACGCAAAATACACCTTCCCGTGAAATGCAGGGATACTCATATACGGAGAACACGCATCCCGCACGATATATCTCGGTAGAAAGCAATGGGGCTGGGCAGTAAGCCCGATATTTATCGCACCTCCCCAAGGCAAGTCTAGTCATTCAAATCATACCGCCGTTTACTCTCCACACGCTGTACGGTTTATACCATGATGGTCGGTATGATACAGGGTCGCCTTCTGCTATTACGAACAGCGCGTAGTAATTTTGGAAGATAAATGCGCACAAATGAGATTTCTTGAGCTGGAACCTGGTCCCAAGGTCAGTGCCCCACTGGTGATCATGTATTAGTGAGGAGCCTATTTTCGCACGAGTCACAAGTGGTGACCTAGTGGTCCCTCGCGTTGTTACGCTGGTATCTCAGAGGAAATCCCTCACATTGCTACCGGGGGTGTGCTGCTCTGGCCCCGTGAGTGGCATCAATTCCATGCCCGTACCAATGTTTCGCACCGATCTCATGGGGACTGGCATAGAATTAATTTTCGGCTTTGGGCATTAGAAGCCGATGCAGACATTAACTAAAAAGACGATCCAGCAAGAGTTGGGTTGATACAGATACTAATTCCATAAGAGAAGACGTATCCCCCTTTCGCCAGCGTCTGCAAACTAATAGTCATCACTTGGACCAGTGAGTAGCCCAGGTAGTATTTTACGAGAGTTCGGGGAAAGGTCCTTCTGACACGTCCAGACTCAGTTTTCGCATAAGGTTCAAATCTATGGGTTTCCCTGACTGCTAGGGCCAGGGGTGACTCACGCGTCTTTCCTGATCCTCAGCCCTTTACCCTTCCAGACTACTGAATGACATCGGTACTGGCATTGAGTTAATAGGCGGGTCATACGTATGAGCTCCTTCCCTGGATAGCTATGATCTCTGACGGTCCGACAGGTAGAAGTGATGGGGGGCCCTGCCGCTTCGATGAAGCCAGTAGCTCGGCGGAAGACAATCACCCGTAAGTTGTCTTCCCTGATCGCGGTTCTGTCACCCCGGCCACTGCGGCTAGACGCAATGAGAAACCGCTCTGTCCCAGTTCGATAGCAATAAACAAATACGAACAGCGCACCGGGATTATGGCCCGCGCGCCTTCGGCACCTATAACATCGAGTGAATGCATGGACTACAGGCCTGGGCACCCTATTTGAGACCATCGACGTTTAAGTGAGAGGTGCCAAATTCATCCGGAGCGTGGCTTCCCGGAACTAACCCACAGTCGGCTAATTGAAATGAGATCGTCGACCTGTCTATGACTTAGACATGGCCCTCCAAGGGCCCCGATCTCCATCGCACACCACCTCGAAATTTGTTGGAATTAATTCTATGCCAGTTCCCATGTTTACCGCTTGTGTCCAGCTGATGCCGACTCCCCGTGGTCCAGTGTAATCTATGTGCCCACGAGACCTATAAGTGTCCTTGTACACTAATCCGAGTGCGATTATCGACACACAGCAGGCGGGAGGGGCTGGAAAGCATCTCCTTTCGGTCATTTAGTGAGTATCGTCGTTCATACTGGCTTCCGGTCCATGGTCCTCGCTATGCGAGGGCCTTCAACTATTCAGGTAACCCTATCGCCGCAGAGAAAGTGCAATGCGAACCAAACGATTGAACTATCTAACCTAGGCGGTGAGAACCGCCAGGGTTCATGGCGGATCAGCTAGTTCACCAACTCGCTTGACTCATTAGTCGGCTGCGGACTGTCTAGTACACCCGAAACCGCCATTGGTACAGGCATGGAGTTAATTGTGAGGAAGTCCGAATTAGAAGCGTATGTTGCGCCTGCATATGTCACCACTCCAAACTGTTTTGTTCAGTAACGGTTATGTTACTTGCCAGTACGCTGCGCGGTGACTTTAAACGGATCCTGCGTCAAGGGTTAGGAGTACTTCTGTACGATAACCCATAAACAGATTAGTGGAAGCATCCTGTTTAAGATTCCGCGGTGTTCGAACCGCCCGATGCGTCGAGGGTTCAGGTACACGCCAATAGCCCCATGGGCCATATGCTCGAAACTATTTCGTGTGTTGGTGTGCCAGTGATAACGTTATACAGCCAGTGTCTAAAGACCACGTTTCATTCCCCCGGCGGCCTCTGCGTTGCGAGCCCATAGGATCCTAGTTGCGCGACTCGTAGCCCAAGCGCGTGCTACTCGCTATGACTCCGTGATTCAATTTAATGCCATAGGAGACTGCAGGCACATCCTCGCTGAATCTGATCTCCGACCTGAAGGACGATTCTATGCTCACCTTGTCGTCTATAAGTTGGTGCATTAGTCTAACCGTCGCTAAATCGCTGCGGGACGCTTCCTCTCGCATAGGCACGCGCTCACTCTGTAACCATGGTATTTGATGCTCTAGCGTACATCAATAGCATGCCGGTGCCCATGGAAATGCATACGTCGAGCCGCATCGGGGTACGGAGCCTACACGTTGCTGCGCCCCGCGTCGTATTTACCACTGGCCCGAGTCGAAGGATTGTTTTAGCTGCGACAAGGATGGTCAGGTGGACATTGGCACGGGCATGGAGTTAATTTCCCTCTCACTTTTCACACGAAGTGGACAGCCAACGGGTTCATTGGGACCGGCATGCTGTTAATCGTTGCTTCATGGTTCTGATCATGACCCTTTCGGCTCTAGCCTACAGTCAAAGGCATGTGGTGCGTCACTCAGATTCACATTAGGATTGTTTTTTAAGCAGGATTCTAGTCTCGATTTTTGAGCAGCATTTAGTGTTCTGTGGCTTGCGGGCCAAGAATAGTTAAACTAGTAACAATGGGCAGGTGGAGGGAAGCGTATCTATCGTAGGGAATGTCGTATCCGAGTTGTACCTATCTGAGCCTCTCTCCCAATCCTAACGGCTGGTAACAAACCGCCAACAAGCGGTCTCAGTTTCTATGACTTCTTCAGCCCTGCAGTGTCTAAATTCTTCGGCTTGGTCATGCCAGCCTCTCCTGTGCGCGGTCAAAGCTCCTTACCCGAATATCGCAGACCGGTGTCCTACAGAGTTCAGGATAGTGTGTTACTGATTATCAGATAGGCGGCACCACTGACCGCAGGCTACGACGCGATAAGAAGAAGATGGCGGGGCCCTGCATATCGGCCCTGGCATGATTATCTGATACCCGTAGGTTCCTTCTAGCGTAGGTAAATTACTTCATACACGACCCTGTTTTTAGCACCACCCGCTTGATTGCGGGCGATCAATTCAATGCCGGTCCCTATGAGTTAATGTTTTATGTTCCCCATGTAGGGCAGGGATGTGCAATCACTGGCATAAGAGCAATGTCTTTCCCATAATCTCATATCCCTAACTACAGACTGCAAACATTAAGGGACAATTACCCTTATAAGACAGGTGATCTGATCGATCCGGCTTCAGATATGGTATGATGGTCTGCGTATTATTGCTGCCAACCCGCCAAATACAGTTCTCCCTCACGGGGACGGCGGGTTTGGGATAATGATTGAGTCACAGAAGTATTCGCTTTGCCTGCTTAAAACACCCGAGTTCGACACTGCTACTTTGACAACTCTGGGAATTGAAGCGTACGGCACGTACAGCCCCAATTTAGCGTCGTACATACGGTCCTTACATAGATATTCTGGGTACTCCAAACGGTCCGATGCCGCAAAAACATTAGCGGGCGCTCCGGTAATGTTCCGAAACATAGGCCTGGTAGACGTGCTCAAGCGTCACGCATTGGTACGGGCATCGAATTTATATTCTAAGCGCCCTAGGGCACATTAAAGCCCAATTGATAGATGCACGCGTAAGACATCCCAGCCCAGTTGCGCCAACGGACGCTGACTGAATATTGTAAAGTGCCTACAAACTATACTGCTTGCCAACTGTCTTCTAAGATTTAGGGAAGAAAGAGGATAGGGGAGTCTATAACCGGAGGATACATATAAACTCTATGCCGGTACCAATGTCAGAATATTTGCCCCGTGTGTTTGGATATTAGGTTTTCGTCCATGGTGAGCTGCGGTGTATTAGGGGGGCATCCGCACCGGCATGAAGTGTCCTCATACAAGACAAGCTGATCCAGACTACCCCATGCGTGTCTATTACGCTGGAGCAGCACAGCCCCTAATCCGTGGGGTATGTGACGTCTCCTGTAAAGTTCCCGCTAAGTGGACTCGTTAGTGCATCAGACGCGAGTGCCCGTACCACCGAAGCCTGACTCCCTTGGCTCTTTCACCTTTCCCAATTGGAGTTTGTTGTCCGATAAATACAAAATCAGGCCTATTTGTCGAATTAGAAAGGGGCAGAAAATCCATGAGGGTTTGCGGTACCCCGCTACGACCAATAAAGGTAGGTAGGGCCTCGGCGATCGTAGATTAAACTTGGTGGGTGTGTCGACAGTCCGCTCTGATCTGGCCGTAACAGATAAGCTGTGGGATGCACGCAGGACAGGGGCTGCGAGGTCACACCGATGCGATCACCGTCTTGGGCGTGTGGAGGAGCATAAAGGATTGGGAACAGAGGGTCTTGGGCAGAAATCTGTCATCGCCAGCCCAGAAGGTCGAATGTTCTCCGCGACAGGTGCCGTGAATGAGATGGGACTGGAATTCAGATTATGCGCCTGGTAAGAACTCACTAAAATTCGAAAATGTTTCCAGTATTACCTCGGACCAATGTGCTGACTAACGGCAGTGGTGCGAACTCTGAACGTCAGTAGTGCTCCAGGATCTCCACAACGACATCACGATCCATCACAGGGGCTCATGTCGGTTGTGATACAGTGACCAAACATACCTCCAGGGCGTGCCACCGGTGGACGCTCGCTGACGTAGGGTGGGTAGGTTAATCGCTCACGTATCCTCAGATTAATACGCCATCAGGCCGCTCTCTTTGTCATAAATTCGATGCCGGTGCCGATGCGATGCTCGTAGCTACTGGAGTCGGACGTACATATTGTATGCTATTGGTGGAGGTACCACTAGCAGTGCGGAATAGCACTAAGGACGAAGATATAAGCGATAATGCATACCTCCACGGTGACGCTCAATCCTGGTTCTATAAAACGTTGTGGATCCAAATATGGCAAGAAGGCTAATAAGTAAGGGCGACTCGCCATCTTATCAACTCCATGCCCGTCCCTATGCCCTGGTAGGCTGTATCATAGCCCCCCCGAAAACAACAGAGAATTTTCGGATGCAAAAACTAGTTTGCCTGACCGAATCCGTCCGAAGGCGGTCTGGGACGCCGCGACCTGTTTACAATGTGTCCAAGAGCCTAGGGATTGCGCACCGAGTGCGTGTTTCACGATCTCGAGGTCGAGCGTTTGTGGATCAACAGTATGCCTGTGCCGATGAGAGGAAATTGTCTATTGGCGAACCACATTCCCAACAATCTGTGCGTGGATGCTCATGGGTATGGTGACCGGGGTTGTCGCTGTATAAGCGCCTGTGACGGTTCCTGCTAGCTAGTAGTGGCTACAACCTCGCATGCTGTCTTGGAGGCCTGGGGGATCACCTCGCAAG'
#aminoAcid = 'INSMPVPM'
#encoders = SubstringEncodingAmninoAcid(rna, aminoAcid)
#for encoder in encoders:
#	print encoder

#rna = 'AUGUAUGGCAAUGCUAUAUGCUUACUGUGCAACCCCUUACACGCCUUAGUACUCAGGGUGCUAUGCGCCCAGUCGGGCGAGCGCUAUUACUAUGGUAUCUUUAAGAAUCACUAUCCAAUGCAUUUUACCUUCUUAAAGACCGGGACCUGUCCGACGUACAAUCAAGUCUCAAGCACCCGUAUUUCAAAAGAAACAAUAGCACUUGUCUGGCCCACUACGCGGAAGCAACAUCGAACCGAUCUUAGAUCUUUUCUCACUAAAGUCGUAUACAGGGCGUAUCAUCAUCGGGCCUCUGCUGUCACUUGCAGUCUACUUAAGAUGUUGGAGCCAGUGGUCCAGGCCGCCGAGGUCUUCCUACCUUGGAAAGGUCCUAUUUUCGCACCGGUGGAGAGUGGAACUUUGUCUAACUUCUUCUUAACGCAGCUUUGGAUUUGUGGCCGCUUACAACGUAAAUUGGUCAUAGAGAUGUACGCUGAAAGAGGAUUAAUCCGGAAGGCCCCAACCAGCCUCUCUCGCAGAUUAAAAUGUGAUAGUGACUCAUUCUACCCAGGUUUUAUGAAAUUCGACACUAAGGUUGCCGAUCGGAAUCGGCCUAAGAAGGAAGAGACGAUCUCCACCACCCUCGACGGCGUGUCGUCUUUGCCAGUCUCCUCCCCAAUCAUAGAUAUCUAUGGCAGAAGCGUCAAGGUUUUGAACUGUGUGACCAAGCAUAUCGUUAUCGGGCGAAAGACUCUAAAGCGCGUUAAGUAUGCAAGACAACUUUCAUGCGUCCUGGUAUGCUGCGCAUCGGAGCUGUUAGACUAUAUUAACCGUACCUAUUCAAUACCGCAAAUCGGUGGGACCAAGGGGCGAAAGAGCGGACUUAGUGGUUAUGCUGUUAAUAUUGGUGCUAACACUACCACCGCGGAUCUGUUCACUUCUGUGAAGACGCCAGAGCUAUUUAGGGAGUGGGUUAACACAAGGAAAACUUACAUCCUUCCCCGGCAUUUUUUACGUAGGAGGUGGUUAGCUCAACGCCUCCCGUGUGGACAUAUCUCUAGUAAAUGUAAGACCGGGGGGCAUCUUCUAGGUUCCAACUCGGUCCCUUGGCAUCUGAAAGCCAGCCACGGCAGCCGCGACAGAAUUAGUCUUCUCUCUAGGUUCGCCGUUCGGUACAAAAUGCUGGGUAGGGUCAGGGAGCCCUACUCGCUACUUCUCCGAGUGCUUUUUCCCCCUAAGAUUCGAACUGUGAUCCUCCGGCAAUGCUCUGUGAAGGUCUUGUCCGUCGGCAGGUUCACACGUAAGGCAACGUUAUCGGCCCGGAAUGAUCCGGUGACCCUGAUCGCGCCCCUAAAACAGCAUGUAGCCUCUAAUAUCACCGUUUCCUACUCCAUGUUGAGGAGAUCUGAUCAGAAAGCCAUUCGCUGCGCCCGCGCGCAGUGCCCCCGCCGCGAACUAUGCGAUAGGUUGAACGCAUUCCCAAGGCCCUCCUCCCUAUGGCCUUAUACGCAGGCCUUGACAUAUCAGGUCCCUGUCGCAGAGCGAUACUUGUCCCACAGCUUGUGGACCGUUGGGGAUAAAUUGUAUAAAACAAUCACAUUAGUGUUAAACUGGGCCGCAUUAUCGUAUCACCUCCUCAGGGGUCAUUAUGCUGAUACUGCUUGGUUACCUAGGAUCUGUGUUCGCACCGGGGCCUUCCACUAUGGACCGCUCAGGGGUCAUCAUUAUUGCUCCCCGAAGUUACAACGAGCCCGCGUUGGACUGUGGAUUACUGACUACCCCAGAUCUUCUAACCUAAAGAGUACCCAUCGAUACCACCGGGAAGUUUUUAUGUCUAGAGCGUUGGGACACUCCAAACGGCCCGGGCGACCUCUGAGCCCAAGAAUGCUAAUCGGCGAUUAUGAAUCACCCUGCUAUUGCAGAGGCUGCAGAGGUAUCAAAUCCUAUAGCGGGAUAAAACCGACUGUCCAGCUAGCUAGAUGUGGCGCGCGGCGAGAGAUACCUUCGAUGCCUAUCUGCCCAGCACCAUUGCCACGGUUUAUGUACAAUGUCACCAUAGCUGCGGUAGCCUCUUCUACCAAUCGGUAUGUAUGCAUCAAUAUAGUGGCUCCGAGUGUUCGAGAGGGGGACGGGAGGAUCCUGUUACAGCUUAUCCGACAGACACCUUCUAAUGGUCCCAGAAGUAUAUCUCGUAACGAAGUAGCGCGGCAUUUGUGGGACGCUCCACCGCACUCACACAUUUUGGAUAAAACAGCAUACUUAUCAGCUUCACCCGGUAGCGACCGUAAUACCGGACGUUUUCUAUCCUUUGUCCCAGUCUACGCUUACCCAGCGACCGGGAUCCAAGCAAAGCUAUGUUGCUUGAAACCAAUGAUUCUACACUGCUUCCUAGGCAAGAUGGUUGAGGAUUCACAAGGGUACUACGGCUUUCUGCGGACGGCACUCAUGCGUGGUAACAGGAGGUUCAGUAUUCCGAGGUACCACACGGCGCUUAAUACCAAGGUACCCCUUCUGGAAAUCAAAAAAGCGUGCUUCUCGGCAUACAAUUCCGGGCGAGCGGAGAGAGACGUCAAGGUAAUUAGCGCAUCCUGUGGAAAUCAAUCUCUUAUUUAUGUUAGCAGGUGCGCCAAAAAGUGUUACCGCGGCGUACCAAGUUGGCUUGCAGGACGGAUCUCUCUCUUAUAUUCCCUGCUUCCUGCGCUCGCUCCACUCGUGAAUGCCGUUCGUACAAGGAAGGGGAAAGACGGCGGGAACUAUUUACAUUGCCACACCUAUUCGGGCGGGUCUCUGACCCAAAAAAAUCGCCCACAGCCUGACUGUACGUCCACAGCGGCUAUCGCUGAAAAGCGACAAAACAUCGCCACUUUAGUCUAUGUUAUACUAGUUCGGUCGCCCCUAGCGAAGGAGUGUGAGAGUAGAACCGGCAAACGCGCUAUGCUGAGCUACAUAAAACUAGCAUCCAAGUUUUUACCCCGGAACCAUGGACGGCGUAGAACCCACCUGGCACCUACGCGACGUGUGGGGAUGGACGUCAGCAGAGGGAAAGGGAGAGGCACUACGCGGGUAGACCACCGGAAAGCCAACCCGUCCCAUUGCACUCUUCGGCCUAUGCCACUGGGGUUUAGAGCCAUAUGUUCUGCUCUCAGGUCGGUACACGCUUGUGAAGAUCUAAACCUUCUCGGUUACGAAGUGGUUACAAUGAGAAGAACGCUGGGCUGCCAGUUCCUGAACUACCUGCCAGGAAGCAGCAGAUGUUCUCUACUUUAUGGAUUGGCUAAUUUGAACGCUUACCUAACACGUCCCCGACCCGCCGUAAAUACAGCGCUCCUGGUUUGGCAGAGUGUCUACCCUUACUUGCACUUCGUCGUCGGGCCAUACGAGGUCCGCUCAUCUCACUUUCCGGAUUACUUGGUUAUCCCCAAGAUCCCAGUUCGUACUCUUGUGCGUCCAUCAUUUAAAGGGUGCUGGAUCGCGAUUCAGGUGGAUUUACUUUUACUGAACCCUUACCGCAGCGCUCACGUCGGCCGGGCCGUAAGUAGACAACGGCUGAAAUAUCAACACCUCAUCGUCCAGCCCCCGGGAACUUCAGGAACUGUGCGAAUCAGGCUAUAUGCGCCUUUUAAGUUGCUCAGGACUGACACUACGAUAUGUCGCUGUGGCCAGGUCGUAGCUAUUCCCUCGCAGAGGCCAACGGAACUUGAGCGGCUACCCAGUCGCGUAAUCGAUCGUGCACGUAGAGAACGACUAGGGGAAUGUAAGAUUCCCCUUGCCCGGACUUUUGCUAUUGAGUCGACUUUGAGCACUGUCGCAUUCCGUUUGGGUAGUUUAAAUAUCUGCGAAAGAGUAUAUAUAAAAUCUGAUAGGUUCUUCGGAACAGAUGUGGACGCCUGGGUGCAACGAGUUGGAGAUGGGCGUAGUUGGGAACCACGCCCGUUGGUCCAUACAGCCACCCUUAGAGUCGUCAUGUGCCCCGUACAUUUGUUUCCGGCAGUCCAUGGUACUCAAUUCACGCGGCGAGGUAAGGGUGUUUCGAAGGUCCCAAAGAAAACAUACAGACGUGAGGACGAAUCCCCUUGCUUAUUGAACUCCUACAAUCCCUGUGCGGCAACUGUGCCGGCCAGGACAGAAUUAUUUGGUAUGUGGAGUGCUGCCCGCCGCGGUCUGGACUCUCACCAUAGGACGGCUGAAUGUUCUAGAUUCAGCCGCAGGUUGGAAGUGACGGGAUUGACCGUAAUCUUAGACUCCAGGACACCGAUCCCACCAUUUUUUGCGCAGGCAGCGGUUCGUAGUCCACUUACUAGUGUGCUUCAAUUUGAGGCACUAUCAUCGUUUCGCAGGCAUGAUCCGGUGGGUAGGACGGCUAGGGCUAGAUGCUUCGGCGUUGCUGUGACGCCUAGUGGCCCGACGCGCACGGUUUGCAAAGCAGAACCAGUACCUUACCUUGUUACGGUCAGCGCAGCUUCGGUGAGAUCUCCCUGGUGCGGGCUGCCAAAGUCUGUGCGACCCUGCAUGCCUUGCAGGCAUCAAUCUAAAGGAUAUCGACAAUGGAAUUGGUAUCAUGAGGCAUUCAGUCCUGGAUACGCCCGGAUAUGUUUAGAGUGCAGCAUAUGUUCGCAUACAGAAUUCAAUACCGAGACUAUGCCUCUAUCGUCGGAUAGUCCUCCGGGCAACGAGGGACAUACCCCACACCUUUAUCAGCGUUCCCAACUUAGUUCAGGUAUGCAGGCUCAAAUGAGUCUCCGGGAAUCGCAGUGCGGCCUUACGGGCCGGGUAAGCAUACAUUUCGUAGAGCUGUUUUUCCAGGACUUGGCCCUCACCGGCCAGCAAGACCUACUUCAGUCCUUAGGGAAUCCGAAUCUUGUAAACACUCGCUGUGAUCCAGUGGGUAUGCGUGAUAUACACCUGAGGCGUCCCGCUAUUAAGCGCCGGUCGAGGUAUGUGGUCCAAGAUAGUGUACCUAAUAAAGAUAUGUCAUUGACGCUGACUCCCGCUAAAACAGCAGGCGUCCACCUCGGUAGCAUGAGCGUUGAGCCUACCCGAUGUUCAGCUUCUCCCACUCGGGUUAACUCGUUUAAGCACAGACACUGGUCUUGCCGACUUAUUCGUGGUCGCACGUCUGAUCGUCAACACUACCUUGCUUACUUCCGGGGACAGUAUUGCCUAGGGGAAGGCGUCGAGCGGCUUCACCUAGUCCCCACUCUUCUGCUAGUUCCAGCAUUUCUAACUGCGAAACCGAGGGCCCGUACAGGGAGCUCGAAAUCGGGAUCACCCGUAACUGAGGGGCUAGAUAAUAUUGGCUUUUAUGAUCUGUUGUCAGAUAACCGUACAAACUCCGACGGAGGUUCACGUCGCUGCCGGGUGAAUAAGAUUGAAUUUAUACACAUCCCAUAUGUCUCAAAAAAACAGCUAAAGAUUUUACAUCCCGGGGGUUGGACUCUUAACAUAGGCAAUGGGUUGCGUUUGCAGUUCCGAGGUUCUUCAUGUAUCCAUCCGGACUGGCUCGAACUGAUGUAUCCCUAUACGCGCAGGCAAAAGCGCCGCGAAAUUAUAACCCUGCGAGCUCGGUGCAAGUACAGUUUCCGACUUACUUAUGCUUGUAAGAAUCGGCGCGCUUCCUUCUAUGCUUUUCAGAGAUCUGUUUCAAUUACGACGCGGACUUUCCUUCCCGUCGAGAGCGAAGUUACUCAUCUAACUUUCGGCAAGAGCUCGAGGCGUGCCGGUAUGGGGCAUUUUAUACCACGAUUGCAUUCGACAUUUGUCCUGGAUGUAGCGUCCCACAGCGCUCAUACCAGUUCCUGUGUUUUUCAAUCCAGGAUGGAUGCUCCCUGUAACAGGGCAAACUCCGCUACUUACUUAUGUAAAUUGCCCGCCCCGCAUGUUGUGUUCUUGGGAAGGUAUCAAUGCACCCCACUCGAGUCAUUAACCGCAGUUUUUGUCUCGUCCCCAGAUCGCGAGGCUUUUUACGACCAGAACAUCGCAGUAUUUUCACGUGCUUUAGUGGAAAGUGAAGACACAGCACAGACCUCUACUUUUUCUCCCAGGGCGGCCCCCGACGCUAAAUUCUACUCAUGGAAAAUCCACGGUAUAUUUCGCUUUGAAAUGAGAGGAAAUCUCGAAUAUUUUAGCUGUAUCACACCACGACAGUCUACACAGUACACCGCCCUUAGAAAGGAAGAAGUUUUCUGGGUUUGCCCCUUACACGUCAUUCAGCCAAAUUCGAUAUUAAUUGAAGCAAUACCACUGAUGAAGGUUAGGGUGGUUACAUAUGAGGUAUUAGCAGUCCGUGUGUCCAGCCGAGAUUUGCCAGAGCCUAUCACUCAUUUAUAUUCUAGUAGGGCUCUCCCAGCGCUUUCUGGUGACCGGAGUAAAGAGAUCACAAGACCUUGGCUAUCCGAGCUCAGAGACGGAAUGUUACUGCGCGUGGGCGGCAUUUCGGGGUCCUACCAAGUUCAGCUUUCUUAUAGACACGGUGUUUCGGCCCUUAACGCUGCUAUCACAAAGCUUGCUGGGUCGCCCGAUGGGAAUCUGACACCGUUAUCGAUCGCGUUGAACCGCCCUCAGUUGCCUUGCCCAGCGUCGCUACGACUGAGGAUCAGCCGCGACCUGCCAGAUUGGCAGGCUGGCUCUUGGAAUCGCUUCGAAGAAAACAUACACUCCGGAUCCGUAGACUUAAGUCCAGGUGUCUCCAUCCAGUGCCCCCGCCCCCUAGUAUUCUACUCUAGUGCAAGCGUUCACGUAAGUAGUGGUGAACGCCGUGGUACAGGCGUAGGUUCUUUAAUAGAGGGUUGGCUCAGGAUACGCGGAAAACUCAAGAACACUUGGGAAGCUCCCAGAGACUAUUGGGUAGUUAAGCUAAUGUCAUGGCGGCACAUCUUGAUGGGGAAUUCAAUUCAACCCGGGUGGACAACCAGGCGGGUGGUCAAACUAGCCAUGAUGUUUUGUCUUAAGUCGGUUGACCGCUUUGGUAAGAGCUCUAUUUGCUACGGGAAAUACACGCACUUGAAUGACAGCAAUUUAAGAUUAGGUAUAGGCUCCACUACCCGUUGUUCAAUUCACCUAAUUCUACUACCGUGGCGGAGGCCCGCGCUGAUGGACACCCAGUUCCCAAAAGCUCGAUCCUCUCUUGGUGCGUGUACCCCUAGGGUACCAAAACCGGUAUUUUAUCCAUGUGCUAUGGCAGCUUCAUCUCAGCAUUGCAUUUGUGGACAUUAUGUAAGUUCUGAAAUAGCUACGUUAUCGCCGGGCCGCCUAACUGCCAAUAAUCUGAAGUCGCUACGUCCUUCGUGGAGCGACUCAAACACACCAAUUGGAUCAUUUUGGGUUUUACUUGGGCACCCACUGUCCCCUCAAGCCUCUCACUUCACAGUUGGCCCCCACCACGACGUAUUACUGCACUUAAUCCAGCUUCCCUGCUGGUCUAUAACGGAUGGUCCCAUUGUGGCCGGCCGGGAAACACGCUGGUAUACAGUGCAGACUGCUAUGCUGGCUCAGCGUACCUAUAUGAACUGGGGCUGCAGUCUAGUGACAUGCCAUGUGGCUACGACCGUGUGUAGUAUAACUCGCAGAACUUACUGUGCGACGCGAAAGCGAAGACGCAGAUCAGAAAGCCCAUCUCUCAUCUCAGUUCACCCUUCAUGGUGUAUUAGAUUUAUACCCCCGGGACGAUAUUGCUUCGCUUCGCGGUUGGGCGCAGACACAAGGGACCCCCAAACGGGGGCCGGUCCGGGAAAGGAGGUCCGCAACCACGUUGCUAGCGGGGUAGUUGACGACUUGAGAUUCUUAACGAUAAUCGGAGCGAGUGUCAAUGCGAUGCAAAGGGAUGGAGUGAUGUUAAGAGAUCCAAACGCCAAAUGGUCAAUCUGCUCCAACCCGGCUCCUAGCAAUGAUAGCUCCGGUGCGUUAUACGUUGGUCUUAAUCCGGCCCCGACGGCCGCUGUUUGCCAAUUUAAUUUCCCCACGCACCCGUCUUUCUCCCCCUGGCGUAGGUCACUAAAACUCCACACAAUCGCUAUUACAGUUCAGAGUCAGCGAUGCGCCCCGCGUCAGUACGCUCUAACAGUUGCUAGGGGGGCUUCGCAACCUGAAGAACGAAUACUCGGGAUGCGUUCUUUGGCUGCAGACCAAGAUUCUCCUUGCCACGGGAGGACUAGGUCGAGGGGAGGAUCAAAAUUUGUCUUGUGGGAAGGAGUAUUAUUCACCUGCGUCUGCGGACGGAGAAAGAACUUUUGCGAGAACAGUUGGCGUGACCCAGCACUGGCGCGAUGCCACGCCGCUGAUAGUCGCUAUCCCUCUACACAUUCGGGAUUCGGCAAUGCGGCAGCCUCAUUCAUUUGCGACUUGAAGAGAGCCGGGGUCCGACCGACGGGAGUCUUAGGAAUGGCCCACGCCUUAAUGAGGCAGAAAGGUCACGUCGCGAUUGAAUUAACAACGUACGCUAAGGACUUCCCGUUAAGCCGGUGCGGAAGCGGGGGCUCGAGUGUGAUCUUGUGUACUCACGGCCAGCGUAUUUAUGAUUCUGUUGUUGCGCAGGCUGAGAGAUUUUAUGUUGGCUGUUGGAAGCUAUGCAUAGACUUGGGGAGGCUCCUGCCUAGAGCGGGGAUAUUAAUUCUCAUUGCUUUUCUCAUAGUGCAGGUAGCUGCUGGUUACUUCUCUAGAGUACGAAAUGGCUCUAAUCUCGUGGUCAGUUACCACCCUGAAUGCUCGAUUCCACGGUUUGCGAGGGCGGAACGAAUGGUUGAGGGUAAGCUGGAAGGGCUUCGGGCCUUCUAUCAUCUCCCAGAGAGGUGGUAUUGGCAUCGGGAGAACACAGCGUUGUCGGCUACAGGUAUAACUUGCGAGAGACACCUACACUUCACAUUCCUUACAUCGACAUACCAGAAUUGGACUUUUGUCGCCCACAUCGCCGCACCGGUGCCGCUGUUAAACAUCAGACGUAAUGACCCGUCUCCCGAUUCCAGUGGGCUCAUUCUACGGGCAGACCGUUCGAUGUUGUGCCCAGUUCGGGCGUUUGGUGUACGCCCCGCUUGUGUGAAACGAGGCCCAUACGAUGUCAUCAAGUUGUUGCCCCUAGCAUCGGGCAAAUUAUUUCCUGCUCAGCCGUCACCCGUCUAUUCCUGUUCCAAAUUAGAGAAUCAGUUGAGCAUAGCUGAGAAGGGUCUCAUAGGUAGGGUGGCCGCGGCCUUAAAUGAUCACCGAAGGUUUCCUUCGUACGUGUGUCGACUUGGCUGGUUGCCUUGUCGCUCAACAAGGACGAACUUCAUAGUUUUAGUUAUAAAACAGCCGAGCCUUUUAAGGAGGCGGGGCAGACGCAGUGGGGACUGGUUGCACAGCCUUGGGGGACAGGGGAACACGAUUGAUAAGUCCGUCAGGUGUAACGUACGACGAAGUUACGGUGACUGCUGGAAGCCAACUUCAAUCAGGGGAGUGGAGUCGCCCGAGGGUUCCCUAGGACGAAGCAUCUGGUCAAGAAGACACGUUCUUCAGUCUCCCAAUACGAUCGCCGAUCUACAUUUCGAAUGCGAUAAGGUGCGUGGCAUAAGAGUCUUAACCGAGAUCGUGGAAAAGACCGCACAACAUAGGCCAGACGCGGCCUGGGAGAGCCACACAUUCCACCCAUGUGCACUUCACAUCAUCCGGAUUAGACUGCUCUAUCGGGUAAAACUCCCGCUAGAUGUCUCCUCGAACUUGGCGGGAGAUGAUAUUAGCACGACCUCCGAUGGAUUUAAACUCACAUGGCGGGUAAUGACACCAGGGAGGGUGCCGAUUCCGUACCUGUAUGGAUAUCAGAGUGCCUGUUCCCAGGCUAGCGCCUCGAAUUGA'
#print ProteinTranslation(rna)

#integer_mass_dict = IntergerMassDictionary()
#peptide = 'MPILEINAWWLLWS'
#l =  CircularSpectrum(peptide, integer_mass_dict)
#l = ' '.join(map(str, l))
#print l
'''
N = 1000
file = '../Downloads/Tyrocidine_B1_Spectrum_25.txt'
spectrum = []
with open(file) as f:
	for line in f:
		line = line.strip('\n')
		l = line.split(' ')
		for element in l:
			spectrum.append(int(element))

print LeaderboardCyclopeptideSequencing(spectrum, N)
'''

'''
#peptide = 'YNYYNHSTDMQRYKFNDTDVYGWHMCTDVYFACCYWCQL'
spectrum = []
file = '../Downloads/dataset_104_7.txt'
with open(file) as f:
	for line in f:
		line = line.strip('\n')
		l = line.split(' ')
		for element in l:
			spectrum.append(int(element))

#print LinearScore(peptide, experimental_spectrum)
'''

'''
leaderboard = []
experimental_spectrum = []
file = '../Downloads/dataset_4913_3.txt'
with open(file) as f:
	for line in f:
		line = line.strip('\n')
		l = line.split(' ')
		for element in l:
			try:
				element = int(element)
				experimental_spectrum.append(element)
			except ValueError:
				leaderboard.append(element)
N = 5

l = Trim(leaderboard, experimental_spectrum, N)
item = ' '.join(l)
print item
'''
'''
M = 16
N = 343
#spectrum = [57, 57, 71, 99, 129, 137, 170, 186, 194, 208, 228, 265, 285, 299, 307, 323, 356, 364, 394, 422, 493]
spectrum.sort()
print spectrum
print ConvolutionCyclopeptideSequencing(spectrum, N, M) #too slow for downloaded dataset; works with sample input
#need to optimize LeaderboardCyclopeptideSequencing
'''