def calculate(A, n, p, H, dist):
"""
A: Alphabet (unicode string)
n: length of random text (int)
p: desired number of occurences of pattern words (int)
H: pattern (dict - see above)
dist: description of probability distribution (dict - see above)
"""
if H["type"]=="list": mode = 0
elif H["type"]=="random": mode = 1
elif H["type"]=="pssm/cutoff": mode = 2
elif H["type"]=="pssm/footprints": mode = 3
elif H["type"]=="word/mismatches": mode = 4
elif H["type"]=="consensus": mode = 5
if dist["type"]=="markov": order = int(dist["order"])
elif dist["type"]=="bernoulli": order = 0
elif dist["type"]=="dhmm": order = -1
elif dist["type"]=="hmm": order = -2
#sys.stderr.write("Order: "+str(order)+"\n"+\
# "Mode: "+str(mode)+"\n"+\
# "n: "+str(n)+"\n"+\
# "p: "+str(p)+"\n")
ret = sf.set_input_data(order, mode, n, p)
if ret!=0:
return __error_text__(ret), 0.0, "", []
if dist["type"]=="markov":
ret = sf.analis_alp_mark_data(A, dist["probs"], dist["initial_probs"])
elif dist["type"]=="bernoulli":
ret = sf.analis_alp_bern_data(A, dist["probs"])
elif dist["type"]=="dhmm":
ret = sf.analis_alp_dhhm_data(A, len(dist["probs"]), dist["probs"], dist["trans"])
elif dist["type"]=="hmm":
ret = sf.analis_alp_hhm_data(A, len(dist["probs"]), dist["probs"])
if ret!=0:
return __error_text__(ret), 0.0, "", []
if H["type"]=="list":
ret = sf.analis_pattern_data_0(H["words"])
elif H["type"]=="random":
ret = sf.analis_pattern_data_1(H["word_count"], H["word_length"], H["probs"])
elif H["type"].startswith("pssm"):
if H["type"]=="pssm/footprints":
footprints = H["footprints"]
cutoff = 0.0
else:
footprints = []
cutoff = H["cutoff"]
ret = sf.analis_pattern_data_2_3(len(H["pssm"]), footprints, H["pssm"], cutoff)
elif H["type"]=="word/mismatches":
ret = sf.analis_pattern_data_4(H["word"], int(H["mismatches"]), H["constpositions"])
elif H["type"]=="consensus":
ret = sf.analis_pattern_data_5(H["word"], H["alphabet"])
if ret!=0:
return __error_text__(ret), 0.0, "", []
err_no, result, report, res_words = sf.main()
return __error_text__(err_no), result, report, res_words
def func_test(noccur,tlen,mode,patparam,order,alpfile):
import sf
sf.set_input_data(order,mode,tlen,noccur)
f = open(alpfile,"r")
if order == 0:
order,alpmas,bernprob = read_alp_Bern(f)
print bernprob
sf.analis_alp_bern_data(alpmas, bernprob)
elif order > 0:
order,alpmas,markovprob = read_alp_Mark(f)
print order
print alpmas
print "Probs"
for i in range(len(markovprob)):
print markovprob[i]
sf.analis_alp_mark_data(alpmas,markovprob,[0.3,0.3,0.2,0.2])
elif order == -1:
order,alpmas,nstates,dhhmprob,dhhmtrans = read_alp_DHHM(f)
print nstates
print "Probs"
for i in range(nstates):
print dhhmprob[i]
print "Trans"
for i in range(nstates):
print dhhmtrans[i]
sf.analis_alp_dhhm_data(alpmas, nstates, dhhmprob, dhhmtrans)
elif order == -2:
order,alpmas,nstates,hhmprob = read_alp_HHM(f)
print order
print alpmas
print nstates
for i in range(nstates):
print "State",i
for j in range(nstates):
print hhmprob[i][j]
sf.analis_alp_hhm_data(alpmas,nstates,hhmprob)
f.close()
param = patparam.split()
print param
if mode == 0:
f = open(patparam,"r")
words = read_pat_0(f)
f.close()
sf.analis_pattern_data_0(words)
elif mode == 1:
nwords = int(param[0])
wordlen = int(param[1])
sf.analis_pattern_data_1(nwords, wordlen, [])
elif mode == 2:
f = open(param[0],"r")
wordlen,pssmmas = read_pat_2(f)
f.close()
print wordlen
for i in range(wordlen):
print pssmmas[i]
thr = float(param[1])
sf. analis_pattern_data_2_3(wordlen,[],pssmmas,thr)
elif mode == 4:
motif = param[0]
nreplace = int(param[1])
constpos = map(lambda i: int(param[i]), range(2,len(param)))
print "ConstPos ",constpos
sf.analis_pattern_data_4(motif, nreplace, constpos)
elif mode == 5:
consensus = patparam
consalp = [u'SATC', u'LMAGC', u'NACGT']#['NACTG','SAC','LTG','PATG']
sf.analis_pattern_data_5(consensus, consalp)
err,prob,report,words = sf.main()
print "Prob ",prob
return prob