def histMedian(h, bins):
# Assume you only want to operate on the last dimension
nx, ny = h.shape
median, sigL, sigR, var = np.zeros((4,nx))
binMids = getMids(bins, infvalue=100)
for i in range(nx):
median[i] = histPercentile(h[i], 50, binMids)
sigL[i] = histPercentile(h[i], 16, binMids)
sigR[i] = histPercentile(h[i], 84, binMids)
var[i] = histVar(h[i], binMids)
return median, sigL, sigR, var
try: i = paramList.index([config, str(emin), str(emax)])
except ValueError:
found = False
# If it exists, option to overwrite
if found and not args.overwrite:
print 'Info for', config, emin, emax, 'already exists...'
continue
if found and args.overwrite:
table.remove(table[i])
# Calculate information for energy range
idx0 = np.where(args.eList==emin)[0][0]
idx1 = np.where(args.eList==emax)[0][0]
h = ehists[idx0:idx1].sum(axis=0)
emids = (ebins[:-1] + ebins[1:]) / 2.
median = histPercentile(h, 50, emids)
sigL = histPercentile(h, 16, emids)
sigR = histPercentile(h, 84, emids)
var = histVar(h, emids)
counts = h.sum()
# Append to file
newLine = [config, emin, emax, median, sigL, sigR, var, counts]
newLine = ['%s' % i for i in newLine]
table.append(newLine)
# Write as you go
lines = [' '.join(line) for line in table]
lines = [line + '\n' for line in lines]
lines.sort()