def test_TC(filename, scorer):
fasta_seqs = SeqIO.parse(open('../RV11/' + filename + '.tfa'), 'fasta')
seqs = []
for fs in fasta_seqs:
seqs.append(strToSeqStruct(fs.id, str(fs.seq)))
our_bloc = aligne_multiple(seqs, scorer)
ref = msftoDict('../RV11/' + filename + '.msf')
print(TC(ref, our_bloc))
def alignate():
global SEQS, params, bloc
params["openGap"] = float(oG.get())
params["extendGap"] = float(eG.get())
scorer = aminoAcidScorer(str(sName.get()), params)
log(logs, "scorer : " + scorer.getName() + " " + str(scorer.getParams()))
log(logs, "Alignement des séquences en cours ...")
bloc = aligne_multiple(SEQS, scorer)
ButtonMSF['state'] = 'normal'
showBloc(bloc)
def run(self):
"""Code à exécuter pendant l'exécution du thread."""
self.score = SPS_romainTuned(self.msf,
aligne_multiple(self.seqs, self.scorer))
print(self.label, int(1000 * self.score) / 1000)
def optimize():
global SEQS, globalparams, bloc, SEQSmsf, filename
msfToSeqs()
#globalparams["openGap"] = [float(oGm.get()), float(oGp.get())]
#globalparams["extendGap"] = [float(eGm.get()), float(eGp.get())]
if "enf_mixte" not in globalparams:
globalparams["enf_mixte"] = [0, 4]
if "vrac_mixte" not in globalparams:
globalparams["vrac_mixte"] = [0, 0]
if "helice_mixte" not in globalparams:
globalparams["helice_mixte"] = [0, 0]
if "struct_mixte" not in globalparams:
globalparams["struct_mixte"] = [0, 0]
if "enf_propre" not in globalparams:
globalparams["enf_propre"] = [0, 0]
if "struct_propre" not in globalparams:
globalparams["struct_propre"] = [0, 3]
if "helice_propre" not in globalparams:
globalparams["helice_propre"] = [1, 3]
if "openGap" not in globalparams:
globalparams["openGap"] = [14.3, 14.3]
if "extendGap" not in globalparams:
globalparams["extendGap"] = [2.9, 2.9]
print("Parameters : " + str(globalparams))
PD = pd.DataFrame(columns=["seqName", "SPS", "time", "iter"] +
[k for k in globalparams.keys()],
index=np.arange(0,
int(niter.get()) * len(SEQS)))
SPS = 0
bestparams = dict()
ii = 0
for i in range(0, int(niter.get())):
params = dict()
for k, v in globalparams.items():
params[k] = round(10 *
(v[0] +
(v[1] - v[0]) * random.uniform(0, 1))) / 10
print("Iteration", i, str(params))
if (multithreading.get()):
keys = list(SEQS.keys())
T = [
ALIGNEMENT_SCORE([
SEQSmsf[keys[k]].copy(), SEQS[keys[k]].copy(),
aminoAcidScorer(str(sName.get()), params), keys[k]
]) for k in range(0, len(keys))
]
[T[k].start() for k in range(0, len(keys))]
[T[k].join() for k in range(0, len(keys))]
tmpSPS = sum(T[k].getScore()
for k in range(0, len(keys))) / len(SEQS)
else:
tmpSPS = 0
scorer = aminoAcidScorer(str(sName.get()), params)
for kk in SEQS.keys():
tt = time.time()
b = aligne_multiple(SEQS[kk], scorer)
tmptmpSPS = SPS_romainTuned(SEQSmsf[kk], b)
PD.loc[ii]["seqName"] = kk
PD.loc[ii]["iter"] = i
PD.loc[ii]["SPS"] = tmptmpSPS
PD.loc[ii]["time"] = time.time() - tt
for k in params.keys():
PD.loc[ii][k] = params[k]
ii += 1
print(kk, tmptmpSPS, time.time() - tt)
tmpSPS += tmptmpSPS / len(SEQS)
print("Iteration", i, "SPS =", int(100000 * tmpSPS) / 100000)
if (tmpSPS > SPS):
SPS = tmpSPS
bestparams = params
d = str(datetime.datetime.now())
d = d[:4] + "_" + d[5:7] + "_" + d[8:10] + "_" + d[11:13] + "_" + d[
14:16] + "_" + d[17:19]
PD.to_csv("RESULT_" + d + ".csv", index=False)
print(bestparams, SPS)