def getTransformation(self, s_from, s_to):
if s_from == s_to:
# null smoothing
return smoothing.SmoothingNull()
try:
return self.transformations[s_from][s_to]
except KeyError:
return None
def addDataToTrafo(tr_data,
run_0,
run_1,
spl_aligner,
multipeptides,
realign_method,
max_rt_diff,
topN=5,
sd_max_data_length=1000):
id_0 = run_0.get_id()
id_1 = run_1.get_id()
if id_0 == id_1:
null = smoothing.SmoothingNull()
tr_data.addTrafo(id_0, id_1, null)
tr_data.addTrafo(id_1, id_0, null)
return
# Data
data_0, data_1 = spl_aligner._getRTData(run_0, run_1, multipeptides)
tr_data.addData(id_0, data_0, id_1, data_1)
# import pylab
# pylab.scatter(data_0, data_1)
# pylab.savefig('data_%s_%s.pdf' % (run_0, run_1) )
# pylab.clf()
# pylab.scatter(data_0, data_1)
# pylab.xlim(2300, 2600)
# pylab.ylim(2300, 2600)
# pylab.savefig('data_%s_%s_zoom.pdf' % (run_0, run_1) )
# pylab.clf()
if len(data_0) == 0:
null = smoothing.SmoothingNull()
tr_data.addTrafo(id_0, id_1, null)
tr_data.addTrafo(id_1, id_0, null)
return
# Smoothers
sm_0_1 = smoothing.getSmoothingObj(realign_method,
topN=topN,
max_rt_diff=max_rt_diff,
min_rt_diff=0.1,
removeOutliers=False,
tmpdir=None)
sm_1_0 = smoothing.getSmoothingObj(realign_method,
topN=topN,
max_rt_diff=max_rt_diff,
min_rt_diff=0.1,
removeOutliers=False,
tmpdir=None)
# Initialize smoother
sm_0_1.initialize(data_0, data_1)
sm_1_0.initialize(data_1, data_0)
# Compute error for alignment (standard deviation)
stdev_0_1 = 0.0
stdev_1_0 = 0.0
if sd_max_data_length > 0:
sample_idx = random.sample(xrange(len(data_0)),
min(sd_max_data_length, len(data_0)))
data_0_s = [data_0[i] for i in sample_idx]
data_1_s = [data_1[i] for i in sample_idx]
data0_aligned = sm_0_1.predict(data_0_s)
stdev_0_1 = numpy.std(
numpy.array(data_1_s) - numpy.array(data0_aligned))
data1_aligned = sm_1_0.predict(data_1_s)
stdev_1_0 = numpy.std(
numpy.array(data_0_s) - numpy.array(data1_aligned))
print "stdev for", id_0, id_1, stdev_0_1, " / ", stdev_1_0, "on data length", len(
data_0_s)
# Add data
tr_data.addTrafo(id_0, id_1, sm_0_1, stdev_0_1)
tr_data.addTrafo(id_1, id_0, sm_1_0, stdev_1_0)
def addDataToTrafo(tr_data,
run_0,
run_1,
spl_aligner,
multipeptides,
realign_method,
max_rt_diff,
topN=5,
sd_max_data_length=5000,
force=False):
id_0 = run_0.get_id()
id_1 = run_1.get_id()
if id_0 == id_1:
null = smoothing.SmoothingNull()
tr_data.addTrafo(id_0, id_1, null)
tr_data.addTrafo(id_1, id_0, null)
return
# Data
data_0, data_1 = spl_aligner._getRTData(run_0, run_1, multipeptides)
tr_data.addData(id_0, data_0, id_1, data_1)
# import pylab
# pylab.scatter(data_0, data_1)
# pylab.savefig('data_%s_%s.pdf' % (run_0, run_1) )
# pylab.clf()
# pylab.scatter(data_0, data_1)
# pylab.xlim(2300, 2600)
# pylab.ylim(2300, 2600)
# pylab.savefig('data_%s_%s_zoom.pdf' % (run_0, run_1) )
# pylab.clf()
if len(data_0) == 0:
print("Warning, zero data!")
if force:
null = smoothing.SmoothingNull()
tr_data.addTrafo(id_0, id_1, null)
tr_data.addTrafo(id_1, id_0, null)
return
else:
raise Exception("No data available for alignment %s vs %s" %
(id_0, id_1))
# Smoothers
sm_0_1 = smoothing.getSmoothingObj(realign_method,
topN=topN,
max_rt_diff=max_rt_diff,
min_rt_diff=0.1,
removeOutliers=False,
tmpdir=None)
sm_1_0 = smoothing.getSmoothingObj(realign_method,
topN=topN,
max_rt_diff=max_rt_diff,
min_rt_diff=0.1,
removeOutliers=False,
tmpdir=None)
# Initialize smoother
sm_0_1.initialize(data_0, data_1)
sm_1_0.initialize(data_1, data_0)
# Compute error for alignment (standard deviation)
stdev_0_1 = 0.0
stdev_1_0 = 0.0
if sd_max_data_length > 0:
sample_idx = random.sample(xrange(len(data_0)),
min(sd_max_data_length, len(data_0)))
data_0_s = [data_0[i] for i in sample_idx]
data_1_s = [data_1[i] for i in sample_idx]
data0_aligned = sm_0_1.predict(data_0_s)
stdev_0_1 = numpy.std(
numpy.array(data_1_s) - numpy.array(data0_aligned))
data1_aligned = sm_1_0.predict(data_1_s)
stdev_1_0 = numpy.std(
numpy.array(data_0_s) - numpy.array(data1_aligned))
print("stdev for", id_0, id_1, stdev_0_1, " / ", stdev_1_0,
"on data length", len(data_0_s))
# Add data and trafo description.
# The CyLightTransformationData actually requires to get a specific type of
# transformation, the CyLinearInterpolateWrapper which may not be directly
# passed to this function. We will try to recover the underlying linear
# wrapper and then stick it into the tr_data object. If this fails, we just
# revert to the regular behavior.
try:
sm_0_1_lwp = sm_0_1.internal_interpolation.getLWP()
sm_1_0_lwp = sm_1_0.internal_interpolation.getLWP()
tr_data.addTrafo(id_0, id_1, sm_0_1_lwp, stdev_0_1)
tr_data.addTrafo(id_1, id_0, sm_1_0_lwp, stdev_1_0)
except Exception:
tr_data.addTrafo(id_0, id_1, sm_0_1, stdev_0_1)
tr_data.addTrafo(id_1, id_0, sm_1_0, stdev_1_0)
