def arr_add_spsummary(arrfeats, cutoff):
sps = species_list(arrfeats.dtype.names[3:])
names = [s + '_evidence' for s in sps]
newarr = ut.arr_add_feats(arrfeats, names, newtype='|S20')
for i in range(len(newarr)):
splist = passing_species_separate(arrfeats[i:i+1], cutoff)
ut.arr_assign_row_values(newarr,i,range(len(arrfeats.dtype.names),
len(newarr.dtype.names)), splist)
return newarr
def arrfeats_add_ppis(arrfeats, ppis, name='ppi_score'):
#assert len(arrfeats) == len(ppis), "Mismatched lengths"
pdppis = pd.PairDict(ppis)
newarr = ut.arr_add_feats(arrfeats, [name], newtype='<f2')
notfound = 0
for row in newarr:
ppi = pdppis.find((row[0],row[1]))
if ppi:
row[-1] = pdppis.d[ppi][0]
else:
notfound += 1
print "Not found:", notfound
return newarr
def add_passing_features(arr, threshes):
feat_names = ['passing%s' % t for t in threshes]
newarr = ut.arr_add_feats(arr, feat_names)
for t,name in zip(threshes, feat_names):
newarr[name] = max_fracs_passing(arr, t)
return newarr
