def __init__(self,
sequence: str,
options: Dict[str, Any],
calc_robusntess: bool = True):
self.sequence = sequence
fold_map = options.get('RNAfold').fold(sequence)
self.fold_type = options.get('fold')
self.energy = fold_map.get("{}_energy".format(options.get('fold')))
self.structure = fold_map.get(options.get('fold'))
if calc_robusntess:
self.mutational_robustness = calculate_neutrality(
self.sequence, self.structure, options)
else:
self.mutational_robustness = None
target_tree = shapiro_tree_aligner.get_tree(
options['target_structure'], options['target_sequence'])
self.result_tree = shapiro_tree_aligner.get_tree(
self.structure, self.sequence)
self.align_tree, self.score = shapiro_tree_aligner.align_trees(
self.result_tree, target_tree, options['alignment_rules'])
# Add energy diff
target_energy = options.get('target_energy')
if target_energy is not None:
self.score += abs(
fold_map['{}_energy'.format(options.get('fold'))] -
target_energy)
# Add mutation robustness diff
target_neutrality = options.get('target_neutrality')
if target_neutrality is not None:
self.score += abs(
calculate_neutrality(sequence, self.structure, options) -
target_neutrality) * 100
self.tree_edit_distance = tree_aligner.get_align_tree_distance(
self.align_tree)
self.bp_dist = bp_distance(self.structure, options['target_structure'])
def single_design(run_no, seed, seq_no):
logging.info("Design - start - run {}, seq_no {}".format(run_no, seq_no))
# send sequences to RNAfbinv
# TODO: use new version of RNAfbinv
designed_sequence = rna_designer.run_rnafbinv(
target_structure,
target_sequence,
iter_no=1000,
seed=seed,
random_seed=random_gen.getrandbits(64))
designed_structure_map = vienna.fold(designed_sequence)
logging.info("Design - compare - run {}, seq_no {}".format(run_no, seq_no))
# generate information from MFE
designed_structure_mfe = designed_structure_map.get('MFE')
designed_tree_mfe = shapiro_tree_aligner.get_tree(designed_structure_mfe,
designed_sequence)
match_tree_mfe, match_score_mfe = shapiro_tree_aligner.align_trees(
designed_tree_mfe, target_tree)
# generate information from centroid
designed_structure_centroid = designed_structure_map.get('centroid')
designed_tree_centroid = shapiro_tree_aligner.get_tree(
designed_structure_centroid, designed_sequence)
match_tree_centroid, match_score_centroid = shapiro_tree_aligner.align_trees(
designed_tree_centroid, target_tree)
# print info to file
out_text = "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}".format(
run_no, seq_no, seed, designed_sequence, designed_tree_mfe,
match_score_mfe, match_tree_mfe, designed_tree_centroid,
match_score_centroid, match_tree_centroid)
logging.info("Design - printing results:\n{}".format(out_text))
design_logger.info(out_text)
return seq_no, designed_sequence
def analyze_res(sequence, target_tree):
structure_map = vienna.fold(sequence)
# generate information from MFE
structure_mfe = structure_map.get('MFE')
tree_mfe = shapiro_tree_aligner.get_tree(structure_mfe, sequence)
match_tree_mfe, match_score_mfe = shapiro_tree_aligner.align_trees(
tree_mfe, target_tree)
# generate information from centroid
structure_centroid = structure_map.get('centroid')
tree_centroid = shapiro_tree_aligner.get_tree(structure_centroid, sequence)
match_tree_centroid, match_score_centroid = shapiro_tree_aligner.align_trees(
tree_centroid, target_tree)
return tree_mfe, match_score_mfe, match_tree_mfe, tree_centroid, match_score_centroid, match_tree_centroid
def get_align_score(identifier: str, sequence: str, cm_path: str, target_tree):
fasta_file = None
try:
fasta_file = infernal.generate_fasta({identifier: sequence})
cm_struct, new_sequence = dive_statistics.get_cm_struct(
cm_path, fasta_file.name)
cm_tree = shapiro_tree_aligner.get_tree(cm_struct, new_sequence)
_, score = shapiro_tree_aligner.align_trees(cm_tree, target_tree)
return score
finally:
if fasta_file is not None:
os.remove(fasta_file.name)
def list_motifs(structure: str) -> List[str]:
if not RNAfbinvCL.is_valid_structure(structure):
raise ValueError("ERROR: Dot bracket structure most be balanced")
tree_root = shapiro_tree_aligner.get_tree(structure, 'N' * len(structure))
tree_stack = [tree_root]
motifs = []
index = 0
while tree_stack:
top = tree_stack.pop()
for child in top.children[::-1]:
tree_stack.append(child)
if index > 0:
motifs.append('{}_{}{}'.format(index, top.value.name,
top.value.size))
index += 1
return motifs
def score_sequence(sequence: str, target_tree: tree_aligner.Tree,
options: Dict[str, Any]):
# Align score tree alignment + sequence alignment
fold_map = options.get('RNAfold').fold(sequence)
structure = fold_map[options.get('fold')]
tree, score = shapiro_tree_aligner.align_trees(
shapiro_tree_aligner.get_tree(structure, sequence), target_tree,
options['alignment_rules'])
# Add energy diff
target_energy = options.get('target_energy')
if target_energy is not None:
score += abs(fold_map['{}_energy'.format(options.get('fold'))] -
target_energy)
# Add mutation robustness diff
target_neutrality = options.get('target_neutrality')
if target_neutrality is not None:
score += abs(
calculate_neutrality(sequence, structure, options) -
target_neutrality) * 100
return tree, score
def run_search(run_code: str, designed_object):
general_run_logger.info('Starting search {}'.format(run_code))
cm_path = os.path.join(output_dir, '{}.cm'.format(run_code))
if not infernal.generate_single_seq_cm(designed_object.sequence, cm_path,
designed_object.structure):
general_run_logger.error(
'Failed to build covariance model. run code: {}\n{}\n{}'.format(
run_code, designed_object.sequence, designed_object.structure))
return
results = infernal.search_cm(cm_path, NT_PATH)
if results is None:
general_run_logger.error('Search failed {} {}\n{}'.format(
run_code, cm_path, designed_object.sequence))
return
general_run_logger.info('Finished search {}, {} results'.format(
run_code, len(results)))
for res_no, res in enumerate(results):
try:
sequence = res.get('sequence')
structure = vienna.fold(sequence)['MFE']
res_tree = shapiro_tree_aligner.get_tree(structure, sequence)
tree, score = shapiro_tree_aligner.align_trees(
res_tree, target_tree)
if score < 300 and score % 100 < 30:
general_run_logger.info(
'Adding result {}, score {} sequence {}'.format(
run_code, score, sequence))
'seq code\tmatch no\tsequence\tstructure\tscore\ttarget id'
result_logger.info('{}\t{}\t{}\t{}\t{}\t{}'.format(
run_code, res_no, sequence, structure, score,
res.get('identifier')))
else:
general_run_logger.warning(
'Score too low {} result no {}, score {}, sequence: {}'.
format(run_code, res_no, score, sequence))
except Exception:
general_run_logger.fatel(
'Exception in search {}, res no {}, {}'.format(
run_code, res_no, res))
if __name__ == "__main__":
folder = "/DB/"
if len(sys.argv) < 4:
print(
"Usage: search_runner.py <input sequence> <input structure> <<<amount of runs> <seeds per run> <designed sequence per seed>> | <sequence list>>"
)
sys.exit(-1)
# gather parameters
target_sequence = sys.argv[1]
target_structure = sys.argv[2]
# init random number generator
random_gen = random.Random()
# find maximum match
target_tree = shapiro_tree_aligner.get_tree(target_structure,
target_sequence)
_, target_score = shapiro_tree_aligner.align_trees(target_tree,
target_tree)
logging.basicConfig(
level=logging.INFO,
format=
'%(levelname)s:%(asctime)s:Name[%(name)s]:Thread[%(thread)d] - %(message)s'
)
logging.info("Target score: {}. Target tree: {}".format(
target_score, target_tree))
output_dir = os.path.join(folder, "Output")
# new method infernal has it's out index
indexed_fasta_map = index_all(os.path.join(folder, "fasta_db"))
# Start runs
if len(sys.argv) == 6:
# init log (result) files
def calc_score(source_sequence: str, source_structure: str) -> float:
source_tree = shapiro_tree_aligner.get_tree(source_structure,
source_sequence)
_, score = shapiro_tree_aligner.align_trees(source_tree, target_tree)
return score
def extend_final_all(folder_path: str, continue_old: bool = True):
def search_cm(target_seq: str,
target_ident: str) -> Tuple[bool, Optional[str]]:
seq_db = None
try:
seq_db = infernal.generate_fasta({target_ident: target_seq})
cm_res = infernal.search_cm(cm_purine_path, seq_db.name, inc_e=10)
finally:
if seq_db is not None and os.path.exists(seq_db.name):
os.remove(seq_db.name)
if cm_res is not None and cm_res:
return True, cm_res[0]['E-value']
else:
return False, None
def calc_score(source_sequence: str, source_structure: str) -> float:
source_tree = shapiro_tree_aligner.get_tree(source_structure,
source_sequence)
_, score = shapiro_tree_aligner.align_trees(source_tree, target_tree)
return score
cm_purine_path = os.path.join(folder_path, 'RF00167.cm')
target_tree = shapiro_tree_aligner.get_tree(
"((((((((...(.(((((.......))))).)........((((((.......))))))..))))))))",
"NNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNYNNNNNNNN"
)
skip_lines = 0
old_exists = os.path.exists(os.path.join(folder_path, 'FINAL_all_ext'))
if continue_old and old_exists:
with open(os.path.join(folder_path, 'FINAL_all_ext'),
'r') as count_lines:
count_lines.readline()
for line in count_lines:
skip_lines += 1
elif old_exists:
os.remove(os.path.join(folder_path, 'FINAL_all_ext'))
with open(os.path.join(folder_path, 'FINAL_all'), 'r') as all_in_file, \
open(os.path.join(folder_path, 'FINAL_all_ext'), 'a+') as all_out_file:
header = all_in_file.readline().strip()
header += "\tis_rfam_matched\trfam_evalue\talignment_score_min\talignment_score_cm\talignment_score_cm_min\t" \
"tax_id\tis_bacteria\tgene_id\tgene_location\tgene_description\n"
if not old_exists:
all_out_file.write(header)
for line in all_in_file:
if skip_lines > 0:
skip_lines -= 1
continue
# 0:design_code, 1:identifier, 2:score, 3:E-value, 4:sequence, 5:round
# Adding 6:is_rfam_matched 7:rfam_evalue 8:alignment_score_min 9:alignment_score_cm 10:alignment_score_cm_min 11:tax_id 12:is_bacteria 13:gene_id 14:gene_location 15:gene_desctription
items = line.strip().split('\t')
design_code = items[0].strip()
identifier = items[1].strip()
sequence = items[4].strip()
# check rfam purine matching
is_found, evalue = search_cm(sequence, identifier)
items.append('1' if is_found else '0')
items.append(str(evalue))
# get scores
new_sequence, cm_struct, min_struct, cm_min_struct = get_structures(
folder_path, sequence, identifier, design_code)
items.append(str(calc_score(new_sequence, min_struct)))
items.append(str(calc_score(new_sequence, cm_struct)))
items.append(str(calc_score(new_sequence, cm_min_struct)))
split_ident = compare_rfam.break_id(identifier)
organism_id = split_ident['code']
start = split_ident['start']
end = split_ident['end']
strand = split_ident['strand']
subtext = ''
gene_id = ''
product = ''
tax_id = None
gff_path = get_gff(organism_id)
if gff_path is not None:
tax_id = tax_from_gff(gff_path)
if tax_id is not None:
tax_id = result_dive.get_tax_id(organism_id)
gene_id, product, subtext = nearby_gene(
gff_path, int(start), int(end), strand)
is_bacteria = None
if tax_id is not None:
is_bacteria = result_dive.check_ancestor('bacteria', tax_id)
items.append(str(tax_id))
items.append(str(is_bacteria))
items.append(gene_id)
items.append(subtext)
items.append(product)
all_out_file.write('{}\n'.format('\t'.join(items)))
all_out_file.flush()
def generate_clusters(match_file_path: str,
design_file_path: str,
target_tree,
is_filter_bacteria: bool = False,
mode: MODE = MODE.OLD) -> List[DesignGroup]:
vienna_folder = None
try:
if mode == MODE.NEW:
vienna_folder = vienna.LiveRNAfold()
vienna_folder.start()
design_group_map = {}
with open(match_file_path,
'r') as match_file, open(design_file_path,
'r') as design_file:
seq_code_map = {}
design_file.readline()
for line in design_file:
if line.strip() == '':
continue
items = line.strip().split('\t')
if mode == MODE.OLD:
seq_code_map[items[0]] = {
'sequence': items[2].strip(),
'structure': items[4].strip()
}
else:
# new doesnt go through a filter so we will filter it now
code = '{}_{}'.format(items[0].strip(), items[1].strip())
sequence = items[3].strip()
structure = vienna_folder.fold(sequence)['MFE']
source_tree = shapiro_tree_aligner.get_tree(
structure, sequence)
_, score = shapiro_tree_aligner.align_trees(
source_tree, target_tree)
if score < FILTER_THRESHOLD:
seq_code_map[code] = {
'sequence': sequence,
'structure': structure
}
match_file.readline()
for line in match_file:
if line.strip() == '':
continue
items = line.strip().split('\t')
design_id = items[0].strip()
if seq_code_map.get(design_id) is None:
continue
design_group = design_group_map.get(
design_id,
DesignGroup(design_id, seq_code_map.get(design_id)))
if mode == MODE.OLD:
if not is_filter_bacteria or not check_ancestor(
'Bacteria',
get_tax_id(items[5].strip().split('/', 1)[0])):
design_group.add_match(
items[5].strip(), {
'identifier': items[5].strip(),
'sequence': items[2].strip(),
'round': 0
})
else:
# new mode didnt save identifier, add just and replace on first search
design_group.add_match(
str(len(design_group.matches)), {
'identifier': len(design_group.matches),
'sequence': items[1].strip(),
'round': 0
})
design_group_map[design_id] = design_group
finally:
if vienna_folder is not None:
vienna_folder.close()
return list(design_group_map.values())
match.get('score'), match.get('E-value'),
match.get('sequence'), match.get('round')))
logging.info(write_str)
out_file.write('{}\n'.format(write_str))
out_file.flush()
final_round.flush()
final_all.flush()
logging.info('All clusters done')
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
# test_taxonomy()
if len(sys.argv) == 1:
target_tree = shapiro_tree_aligner.get_tree(
"((((((((...(.(((((.......))))).)........((((((.......))))))..))))))))",
"NNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNYNNNNNNNN"
)
base_dir = '/DB/Output/SandD'
logging.info(
"No arguments, running on default ({}, 0.01, no filter)".format(
base_dir))
run_dive(base_dir, target_tree, filter_evalue=0.01, mode=MODE)
else:
parser = argparse.ArgumentParser(
description=
"A tool that performs family generation to search_runner.py single"
" results",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"base_dir",
help=
logger_path = os.path.join(folder, logger_name)
handler = logging.FileHandler(logger_path)
handler.setFormatter(formatter)
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
logger.addHandler(handler)
return logger
if __name__ == '__main__':
# setup vienna location
vienna.set_vienna_path('/opt/algorithm/ViennaRNA/bin')
# setup log files
output_dir = '/DB/Output/SandD'
result_logger = setup_logger("match_log", output_dir)
result_logger.info(
"seq code\tmatch no\tsequence\tstructure\tscore\ttarget id")
general_run_logger = setup_logger(
"log", output_dir,
logging.Formatter('%(levelname)s::%(asctime)s - %(message)s'))
design_logger = setup_logger("design_log", output_dir)
design_logger.info(
"sec_code\tseed\tsequence\tscore\tstructure\tbp distance\tmotif distance\tevalue"
)
# setup target tree
PURINE_SEQ = "NNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNYNNNNNNNN"
PURINE_STRUCT = "((((((((...(.(((((.......))))).)........((((((.......))))))..))))))))"
target_tree = shapiro_tree_aligner.get_tree(PURINE_STRUCT, PURINE_SEQ)
# run
run(PURINE_SEQ, PURINE_STRUCT)
def recreate_cm(folder_path: str,
design_code: str,
desc_keywords: List[str],
test_name: str,
sequence: str,
structure: str,
identifier_keywords: List[str] = None,
new_seq_map: Dict[str, str] = {},
top_taxonomy: str = None,
score_cutoff: int = 250):
def keyword_relevant(check_item: str, keymap: List[str]) -> bool:
if keymap is None:
return True
for key in keymap:
if key.lower() in check_item.lower():
return True
return False
def check_taxonomy(tax_id: Optional[int]) -> bool:
if top_taxonomy is None:
return True
elif tax_id is None:
return False
return result_dive.check_ancestor(top_taxonomy, tax_id)
def check_c_or_u(named_tree) -> Tuple[bool, bool]:
_, score = shapiro_tree_aligner.align_trees(named_tree, c_tree)
check_c = score < 1000
_, score = shapiro_tree_aligner.align_trees(named_tree, u_tree)
check_u = score < 1000
return check_c, check_u
tree = shapiro_tree_aligner.get_tree(
".....((((((((...(.(((((.......))))).)........((((((.......))))))..)))))))).....",
"NNNNNNNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNYNNNNNNNNNNNNN"
)
c_tree = shapiro_tree_aligner.get_tree(
".....((((((((...(.(((((.......))))).)........((((((.......))))))..)))))))).....",
"NNNNNNNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNCNNNNNNNNNNNNN"
)
u_tree = shapiro_tree_aligner.get_tree(
".....((((((((...(.(((((.......))))).)........((((((.......))))))..)))))))).....",
"NNNNNNNNNNNNNUNNNNNNNNNNNNNNNNNNNNNNNNUNNNUNNNNNNNNNNNNNNNNNNNNNNUNNNNNNNNNNNNN"
)
gather_results = {}
full_results = {}
with open(os.path.join(folder_path, "FINAL_all_ext"), 'r') as input_all:
input_all.readline()
for line in input_all:
if line.strip() == '':
continue
parts = line.strip().split('\t')
try:
tax_id = int(parts[11])
except ValueError:
tax_id = None
if parts[0].strip() == design_code and keyword_relevant(parts[15], desc_keywords) and\
keyword_relevant(parts[1], identifier_keywords) and check_taxonomy(tax_id):
read_sequence = new_seq_map.get(parts[1], parts[4])
gather_results[parts[1]] = read_sequence
full_results[parts[1]] = {
'sequence': read_sequence,
'rfam_eval': parts[7],
'tax_id': parts[11],
'gene_id': parts[13],
'gene_loc': parts[14],
'gene_desc': parts[15]
}
if len(gather_results) == 0:
print("No results from design {} match keywords {}".format(
design_code, desc_keywords))
exit(0)
try:
cm = NamedTemporaryFile('w', suffix=".cm")
cm_file_name = cm.name
cm.close()
sto = NamedTemporaryFile('w', suffix=".sto")
sto_file_name = sto.name
sto.close()
real_cm = os.path.join(folder_path, "{}.cm".format(test_name))
if not os.path.exists(real_cm):
if not infernal.generate_single_seq_cm(sequence, cm_file_name,
structure):
print(
"Failed to generate single sequence-structure cm file for {}"
.format(design_code))
exit(-1)
shutil.copy(cm_file_name, real_cm)
if not infernal.align_sequences(gather_results,
os.path.join(folder_path, real_cm),
sto_file_name):
print("Failed to generate alignment for {}".format(design_code))
exit(-2)
removed_names = set()
cm_map = read_sto(sto.name)
with open(os.path.join(folder_path, "{}.txt".format(test_name)),
'w') as tbl_out:
tbl_out.write(
'name\tscore\ttax id\trfam evalue\tgene id\tgene location\tgene description\tcm sequence\t'
'cm structure\toriginal sequence\tguanine_bind\tadenine_bind\n'
)
for name, (cmsequence, cmstructure) in cm_map.items():
named_tree = shapiro_tree_aligner.get_tree(
cmstructure, cmsequence)
_, score = shapiro_tree_aligner.align_trees(named_tree, tree)
is_c, is_u = check_c_or_u(named_tree)
if score > score_cutoff:
removed_names.add(name)
print("Removing {} score {}\n{}\n{}".format(
name, score, cmsequence, cmstructure))
else:
res_item = full_results.get(name)
tbl_out.write(
"{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".
format(name, score, res_item.get('tax_id'),
res_item.get('rfam_eval'),
res_item.get('gene_id'),
res_item.get('gene_loc'),
res_item.get('gene_desc'), cmsequence,
cmstructure, res_item.get('sequence'), is_c,
is_u))
clean_sto(removed_names, sto_file_name)
real_sto = os.path.join(folder_path, "{}.sto".format(test_name))
shutil.copy(sto_file_name, real_sto)
dive_statistics.generate_r2r(folder_path, real_sto, force=True)
finally:
try:
os.remove(cm_file_name)
except:
pass
try:
os.remove(sto_file_name)
except:
pass
def get_max_score(seq, struct):
tree = shapiro_tree_aligner.get_tree(struct, seq)
_, score = shapiro_tree_aligner.align_trees(tree, tree)
return tree, score
def simulated_annealing(options: Dict[str, Any]):
if len(options) == 0:
options.get('logger').fatal(
"Options object was not properly initiated. ")
return None
alignment_rules = tree_aligner.AlignmentRules(
delete_func=lambda tree_value, is_target: shapiro_tree_aligner.
delete_shapiro_func(tree_value, is_target, options['reduced_bi']),
cmp_func=shapiro_tree_aligner.cmp_shapiro_tree_values,
merge_func=shapiro_tree_aligner.merge_shapiro_tree_values,
minmax_func=min)
options['alignment_rules'] = alignment_rules
# init rng
rng_seed = options.get('rng')
if rng_seed is not None:
random.seed(rng_seed)
# init loop variables
no_iterations = options.get('iter')
no_lookahead = options.get('look_ahead')
# init initial sequence
current_sequence = options.get('starting_sequence')
if current_sequence is None:
if options.get('random'):
current_sequence = generate_random_start(
len(options['target_structure']),
options['target_sequence'].replace('T', 'U'))
else:
current_sequence = current_sequence.replace('T', 'U')
# Vienna starts the process
vienna_sequence = vienna.inverse(
options['target_structure'],
vienna.inverse_seq_ready(options['target_sequence'], current_sequence))
# vienna might fail initiation + removing any wildcard left
if vienna_sequence is None or vienna_sequence == '':
if options.get('starting_sequence') is None:
current_sequence = generate_random_start(
len(options['target_structure']),
options['target_sequence'].replace('T', 'U'))
else:
current_sequence = generate_random_start(
len(options['target_structure']),
options.get('starting_sequence').replace('T', 'U'))
else:
current_sequence = generate_random_start(
len(options['target_structure']),
vienna_sequence.upper().replace('T', 'U'))
#print("Structure: {}\nsequence: {}\nstart: {}\ninverse: {}".format(options['target_structure'],
# options['target_sequence'],
# options.get('starting_sequence'),
# current_sequence))
final_result = current_sequence
# setup target tree and get initial sequence score (and max score)
target_tree = shapiro_tree_aligner.get_tree(options['target_structure'],
options['target_sequence'])
if not merge_motifs(target_tree, options.get('motifs')):
shapiro_str = shapiro_generator.get_shapiro(
options['target_structure']).shapiro
logging.error(
'Motif list does not match target structure {}\nTarget Shapiro:{}'.
format(options.get('motifs'), shapiro_str))
return None
_, optimal_score = shapiro_tree_aligner.align_trees(
target_tree, target_tree, options['alignment_rules'])
match_tree, current_score = score_sequence(current_sequence, target_tree,
options)
best_score = current_score
options.get('logger').info(
'Initial sequence ({}): {}\nAlign tree: {}'.format(
current_score, current_sequence, match_tree))
updater = options.get('updater')
# main loop
for iter in range(0, no_iterations):
if options.get('stop') is not None:
return None
if best_score == 0:
break
progress = False
for look_ahead in range(0, no_lookahead):
if options.get('stop') is not None:
return None
new_sequence = mutator.perturbate(current_sequence, match_tree,
options)
new_tree, new_score = score_sequence(new_sequence, target_tree,
options)
temperature = calc_temp(iter, no_iterations)
probability = acceptance_probability(current_score, new_score,
temperature,
len(current_sequence))
options.get('logger').debug(
"iteration {} - TEMP: {} PROBABILITY: {}".format(
iter + 1, temperature, probability))
if random.random() < probability:
progress = True
break
''' OLD method, decays very fast (new is boltzman probability)
if new_score < current_score:
progress = True
break
elif random.random() < (2.0 / (iter + 1.0) / no_lookahead):
progress = True
break
'''
if progress:
current_sequence = new_sequence
current_score = new_score
match_tree = new_tree
if current_score <= best_score:
best_score = current_score
final_result = current_sequence
options.get('logger').debug(
'Iteration {} current sequence ({}): {}\nAlign tree: {}'.format(
iter + 1, current_score, current_sequence, match_tree))
if updater is not None:
updater.update(iter + 1)
# final print
return final_result