def __overlap_fe (self, gold_peaks, sample_peaks, fe_low, fe_up, top, bottom, step):
ret = ["%d-%d" % (fe_low,fe_up)]
gp = PeakIO()
gp.init_from_dict(gold_peaks)
gp.filter_fc(fe_low,fe_up)
gptotal = gp.total()
if gptotal <= 0:
return None
ret.append(gptotal)
for i in xrange(top,bottom,step):
p = PeakIO()
p.init_from_dict(sample_peaks[i])
percent = 100.0*gp.overlap_with_other_peaks(p)/gptotal
ret.append(percent)
del p
return ret
def __overlap(self, gold_peaks, sample_peaks, top=90, bottom=10, step=-10):
"""Calculate the overlap between several fe range for the
golden peaks set and results from sampled data.
"""
gp = PeakIO()
gp.init_from_dict(gold_peaks)
if self.femax:
femax = min(self.femax,
(int(gp.max_fold_enrichment()) // self.festep + 1) *
self.festep)
else:
femax = (int(gp.max_fold_enrichment()) // self.festep +
1) * self.festep
femin = self.femin
diag_result = []
for f in xrange(femin, femax, self.festep):
fe_low = f
fe_up = f + self.festep
self.debug("#3 diag: fe range = %d -- %d" % (fe_low, fe_up))
r = self.__overlap_fe(gold_peaks, sample_peaks, fe_low, fe_up, top,
bottom, step)
if r:
diag_result.append(r)
return diag_result
def __overlap (self, gold_peaks, sample_peaks, top=90,bottom=10,step=-10):
"""Calculate the overlap between several fe range for the
golden peaks set and results from sampled data.
"""
gp = PeakIO()
gp.init_from_dict(gold_peaks)
if self.femax:
femax = min(self.femax, (int(gp.max_fold_enrichment())//self.festep+1)*self.festep)
else:
femax = (int(gp.max_fold_enrichment())//self.festep+1)*self.festep
femin = self.femin
diag_result = []
for f in xrange(femin, femax, self.festep):
fe_low = f
fe_up = f + self.festep
self.debug("#3 diag: fe range = %d -- %d" % (fe_low, fe_up))
r = self.__overlap_fe(gold_peaks, sample_peaks, fe_low, fe_up, top, bottom, step)
if r:
diag_result.append(r)
return diag_result
def __overlap_fe(self, gold_peaks, sample_peaks, fe_low, fe_up, top,
bottom, step):
ret = ["%d-%d" % (fe_low, fe_up)]
gp = PeakIO()
gp.init_from_dict(gold_peaks)
gp.filter_fc(fe_low, fe_up)
gptotal = gp.total()
if gptotal <= 0:
return None
ret.append(gptotal)
for i in xrange(top, bottom, step):
p = PeakIO()
p.init_from_dict(sample_peaks[i])
percent = 100.0 * gp.overlap_with_other_peaks(p) / gptotal
ret.append(percent)
del p
return ret