def bin_values(lis, bins, relative=False):
bins = list(bins)
bins.sort()
lis.sort()
maxrb = max(bins)
minlb = min(bins)
maxval = max(lis)
minval = min(lis)
bintup = list(zip(bins, bins[1:]))
if minval < minlb:
lowerunder = (minval, minlb)
bintup.insert(0, (minval, minlb))
else:
lowerunder = ()
if maxval > maxrb:
upperover = (maxrb, maxval)
bintup.insert(0, (maxrb, maxval))
else:
upperover = ()
bincount = []
maxborder = bintup[len(bintup) - 1]
minborder = bintup[0]
for num in lis:
for lb, rb in bintup:
if lb < num <= rb:
bincount.append(((lb, rb), num))
break
elif num > maxrb:
bincount.append((maxborder, num))
break
elif num <= minlb:
bincount.append((minborder, num))
break
bincountdic = dict([(k, len(v)) for k, v in fhutils.keylis2dic(bincount).items()])
bincount = [(k, bincountdic.get(k, 0)) for k in bintup]
bincount = [(str(k[0]) + '-' + str(k[1]), v) for k, v in bincount if k not in (lowerunder, upperover)]
lowerout = bincountdic.get(lowerunder, 0)
upperout = bincountdic.get(upperover, 0)
if lowerout != 0:
bincount.insert(0, ('<' + str(lowerunder[1]), lowerout))
if upperout != 0:
bincount.append(('>' + str(upperover[0]), upperout))
check = sum([ele[1] for ele in bincount])
if check != len(lis):
print('\t'.join(('input:', lis, '\n', 'bins:', bins, '\n', 'binned', bincount, '\n', len(lis), check)))
raise NameError('MISSING OR TO MANY NUMBERS IN BINCOUNT')
if relative:
bincount = [(bino[0], (100.0 / sum([ele[1] for ele in bincount])) * bino[1]) for bino in bincount]
print('\n'.join(['total count (100%): ' + str(sum([ele[1] for ele in bincount]))] + [
bino[0] + ' percent: ' + str((100.0 / sum([ele[1] for ele in bincount])) * bino[1]) for bino in bincount]))
return bincount
def propagatetable(datatable, groupcolumn, measurecolumn, errorcolumn):
"""
primitive aggregate i.e. averaging function
for a table with error values
propagates error using ufloat
mechanism
NA filtered out by default - no control
"""
if isinstance(groupcolumn, str):
groupcolumn = (groupcolumn,)
if isinstance(groupcolumn, unicode):
groupcolumn = (groupcolumn,)
keyvalue = []
for row in datatable.iterrows():
tmp = []
for col in groupcolumn:
tmp.append(row[col])
if row[measurecolumn] in ('NA',):
continue
elif row[errorcolumn] in ('NA',):
value = row[measurecolumn]
else:
value = ufloat(row[measurecolumn], row[errorcolumn])
keyvalue.append((tuple(tmp), value))
keyvalue = fhutils.keylis2dic(keyvalue)
resultable = fhutils.Table('propagate')
resultable.columnames = list(groupcolumn) + [measurecolumn, errorcolumn]
for k, valuelist in keyvalue.items():
valuelist = [ele for ele in valuelist if ele != 'NA'] # NA filtered out
ufavg = ufmeanstd(valuelist, asufloat=True)
row = list(k)
if isinstance(ufavg, uncertainties.UFloat):
row += [ufavg.nominal_value, ufavg.std_dev]
else:
row += [ufavg, 0]
resultable.append(row)
resultable.sortrow()
return resultable
