def make_hist_from_dict_of_counts(values, var_type, hist_label, log='', xmin_force=0.0, xmax_force=0.0, normalize=False, sort=False):
""" Fill a histogram with values from a dictionary (each key will correspond to one bin) """
assert var_type == 'int' or var_type == 'string' # floats should be handled by Hist class in hist.py
if len(values) == 0:
print 'WARNING no values for %s in make_hist' % hist_label
return TH1D(hist_label, '', 1, 0, 1)
bin_labels = sorted(values)
if not sort and var_type == 'string': # for strings, sort so most common value is to left side
bin_labels = sorted(values, key=values.get, reverse=True)
if var_type == 'string':
n_bins = len(values)
else:
n_bins = bin_labels[-1] - bin_labels[0] + 1
hist = None
xbins = [0. for _ in range(n_bins+1)] # NOTE the +1 is 'cause you need the lower edge of the overflow bin
if xmin_force == xmax_force: # if boundaries aren't set explicitly, work out what they should be
if var_type == 'string':
set_bins(bin_labels, n_bins, 'x' in log, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
hist = Hist(n_bins, bin_labels[0] - 0.5, bin_labels[-1] + 0.5) # for integers, just go from the first to the last bin label (they're sorted)
else:
hist = Hist(n_bins, xmin_force, xmax_force)
for ival in range(len(values)):
if var_type == 'string':
label = bin_labels[ival]
ibin = ival + 1
else:
label = ''
ibin = hist.find_bin(bin_labels[ival])
hist.set_ibin(ibin, values[bin_labels[ival]], error=math.sqrt(values[bin_labels[ival]]), label=label)
# make sure there's no overflows
if hist.bin_contents[0] != 0.0 or hist.bin_contents[-1] != 0.0:
for ibin in range(hist.n_bins + 2):
print '%d %f %f' % (ibin, hist.low_edges[ibin], hist.bin_contents[ibin])
raise Exception('overflows in ' + hist_label)
if normalize:
hist.normalize()
hist.ytitle = 'freq'
else:
hist.ytitle = 'counts'
roothist = make_hist_from_my_hist_class(hist, hist_label)
return roothist
def make_hist_from_dict_of_counts(values, var_type, hist_label, log='', xmin_force=0.0, xmax_force=0.0, normalize=False, sort=False):
""" Fill a histogram with values from a dictionary (each key will correspond to one bin) """
assert var_type == 'int' or var_type == 'string' # floats should be handled by Hist class in hist.py
if len(values) == 0:
print 'WARNING no values for %s in make_hist' % hist_label
return Hist(1, 0, 1)
bin_labels = sorted(values)
if not sort and var_type == 'string': # for strings, sort so most common value is to left side
bin_labels = sorted(values, key=values.get, reverse=True)
if var_type == 'string':
n_bins = len(values)
else:
n_bins = bin_labels[-1] - bin_labels[0] + 1
hist = None
xbins = [0. for _ in range(n_bins+1)] # NOTE the +1 is 'cause you need the lower edge of the overflow bin
if xmin_force == xmax_force: # if boundaries aren't set explicitly, work out what they should be
if var_type == 'string':
set_bins(bin_labels, n_bins, 'x' in log, xbins, var_type)
hist = Hist(n_bins, xbins[0], xbins[-1], xbins=xbins)
else:
hist = Hist(n_bins, bin_labels[0] - 0.5, bin_labels[-1] + 0.5) # for integers, just go from the first to the last bin label (they're sorted)
else:
hist = Hist(n_bins, xmin_force, xmax_force)
for ival in range(len(values)):
if var_type == 'string':
label = bin_labels[ival]
ibin = ival + 1
else:
label = ''
ibin = hist.find_bin(bin_labels[ival])
hist.set_ibin(ibin, values[bin_labels[ival]], error=math.sqrt(values[bin_labels[ival]]), label=label)
# make sure there's no overflows
if hist.bin_contents[0] != 0.0 or hist.bin_contents[-1] != 0.0:
for ibin in range(hist.n_bins + 2):
print '%d %f %f' % (ibin, hist.low_edges[ibin], hist.bin_contents[ibin])
raise Exception('overflows in ' + hist_label)
if normalize:
hist.normalize()
hist.ytitle = 'freq'
else:
hist.ytitle = 'counts'
return hist
