def likelihood(self, mapping_data, database='default.db.dists', db_obj=None):
frags = self.explode()
if not db_obj:
db = pickle.load(open('%s/models/%s' % (settings.MAPPING_DATABASE_PATH, database)))
else:
db = db_obj
data = mapping.normalize(mapping_data)
probs = array([1.]*len(self.dbn))
for k in frags:
if len(frags[k]) > 0 and k in db:
g = stats.gamma(db[k][0], db[k][1], db[k][2])
for frag in frags[k]:
for i in frag:
if g.pdf(data[i]) > 0:
probs[i] = g.pdf(data[i])
return probs, prod(probs)
from matplotlib.pylab import *
from rdatkit.datahandlers import RDATFile
from rdatkit.view import VARNA
from rdatkit.secondary_structure import fold
from rdatkit.mapping import MappingData, normalize
from analysis import eigen_reactivities
import sys
rdat = RDATFile()
rdat.load(open(sys.argv[1]))
vals = array(rdat.values.values()[0])
for i in xrange(shape(vals)[0]):
vals[i, :] = normalize(vals[i, :])
eigenrs = eigen_reactivities(vals)
matshow(vals)
#mshow(vals, cmap=get_cmap('Greys'), vmin=0, vmax=vals.mean(), aspect='auto', interpolation='nearest')
matshow(eigenrs)
#imshow(eigenrs, cmap=get_cmap('Greys'), vmin=eigenrs.min(), vmax=eigenrs.mean(), aspect='auto', interpolation='nearest')
show()
construct = rdat.constructs.values()[0]
for i, e in enumerate(eigenrs[:35]):
sequence = construct.sequence
md = MappingData(
data=e, seqpos=[s - construct.offset - 1 for s in construct.seqpos])
print fold(sequence, mapping_data=md)
structure = fold(sequence, mapping_data=md)[0].dbn
VARNA.cmd(sequence, structure, 'test_results/eigen_struct%s.png' % i)
from matplotlib.pylab import *
from rdatkit.datahandlers import RDATFile
from rdatkit.view import VARNA
from rdatkit.secondary_structure import fold
from rdatkit.mapping import MappingData, normalize
from analysis import eigen_reactivities
import sys
rdat = RDATFile()
rdat.load(open(sys.argv[1]))
vals = array(rdat.values.values()[0])
for i in xrange(shape(vals)[0]):
vals[i,:] = normalize(vals[i,:])
eigenrs = eigen_reactivities(vals)
matshow(vals)
#mshow(vals, cmap=get_cmap('Greys'), vmin=0, vmax=vals.mean(), aspect='auto', interpolation='nearest')
matshow(eigenrs)
#imshow(eigenrs, cmap=get_cmap('Greys'), vmin=eigenrs.min(), vmax=eigenrs.mean(), aspect='auto', interpolation='nearest')
show()
construct = rdat.constructs.values()[0]
for i, e in enumerate(eigenrs[:35]):
sequence = construct.sequence
md = MappingData(data=e, seqpos=[s - construct.offset - 1 for s in construct.seqpos])
print fold(sequence, mapping_data=md)
structure = fold(sequence, mapping_data=md)[0].dbn
VARNA.cmd(sequence, structure, 'test_results/eigen_struct%s.png' % i)
