with open('data/stepic_2e.txt') as input_data:
n, spec = [
int(line.strip()) if i == 0 else map(int,
line.strip().split())
for i, line in enumerate(input_data.readlines())
]
# Create the protein weight dictionary.
weight = ProteinWeightDict()
# Initialize the scores dictionary.
scores = dict()
# Build the intial peptides.
seq = filter(lambda L: L[0] != -1,
[[spectrum_score(peptide, spec), peptide]
for peptide in append_protein(weight.keys())])
# Build the sequence until the masses all grow too large.
while seq != []:
# Store the scores of the current sequence in a dictionary.
scores = dict()
for item in seq:
if item[0] in scores:
scores[item[0]].append(item[1])
else:
scores[item[0]] = [item[1]]
# Get the n leading scores with ties, remove lower scores from dictionary.
leaders, leader_scores = [], []
if sum(len(peptides) for peptides in scores.values()) < n:
leaders = scores[max(scores.keys())]
# Return -1 if the peptide has more mass than exp_spec.
if pep_spec[-1] > exp_spec[-1]:
return -1
return sum([min(pep_spec.count(protein),exp_spec.count(protein)) for protein in set(pep_spec)])
if __name__ == '__main__':
with open('data/stepic_2e.txt') as input_data:
n, spec = [int(line.strip()) if i==0 else map(int,line.strip().split()) for i, line in enumerate(input_data.readlines())]
# Create the protein weight dictionary.
weight = ProteinWeightDict()
# Initialize the scores dictionary.
scores = dict()
# Build the intial peptides.
seq = filter(lambda L: L[0] != -1, [[spectrum_score(peptide,spec), peptide] for peptide in append_protein(weight.keys())])
# Build the sequence until the masses all grow too large.
while seq != []:
# Store the scores of the current sequence in a dictionary.
scores = dict()
for item in seq:
if item[0] in scores:
scores[item[0]].append(item[1])
else:
scores[item[0]] = [item[1]]
# Get the n leading scores with ties, remove lower scores from dictionary.
leaders, leader_scores = [], []
if sum(len(peptides) for peptides in scores.values()) < n:
leaders = scores[max(scores.keys())]
# Return -1 if the peptide has more mass than exp_spec.
if pep_spec[-1] > exp_spec[-1]:
return -1
return sum([min(pep_spec.count(protein),exp_spec.count(protein)) for protein in set(pep_spec)])
if __name__ == '__main__':
with open('data/textbook/rosalind_2e.txt') as input_data:
n, spec = [int(line.strip()) if i==0 else map(int,line.strip().split()) for i, line in enumerate(input_data.readlines())]
# Create the protein weight dictionary.
weight = ProteinWeightDict()
# Initialize the scores dictionary.
scores = dict()
# Build the intial peptides.
seq = filter(lambda L: L[0] != -1, [[spectrum_score(peptide,spec), peptide] for peptide in append_protein(weight.keys())])
# Build the sequence until the masses all grow too large.
while seq != []:
# Store the scores of the current sequence in a dictionary.
scores = dict()
for item in seq:
if item[0] in scores:
scores[item[0]].append(item[1])
else:
scores[item[0]] = [item[1]]
# Get the n leading scores with ties, remove lower scores from dictionary.
leaders, leader_scores = [], []
if sum(len(peptides) for peptides in scores.values()) < n:
leaders = scores[max(scores.keys())]
