norm

#!/usr/bin/env python

from mva.cmd import get_parser

args = get_parser(actions=False).parse_args()

from mva.analysis import get_analysis

analysis = get_analysis(args)

from rootpy.plotting import Hist
from rootpy.stats import histfactory
from mva.plotting import draw_channel
from mva.categories import Category_Rest, Category_Preselection
from mva import norm_cache as cache
from mva.variables import VARIABLES
from mva import CONST_PARAMS

# reference category for initial normalization
#category = Category_Rest
category = Category_Preselection

# distribution to fit
field = 'dEta_tau1_tau2'
if args.year == 2011:
    template = Hist(4, 0, 2)
else:
    template = Hist(5, 0, 2)

qcd_scale, qcd_scale_error, \
ztt_scale, ztt_scale_error = analysis.fit_norms(
    field, template, category, args.target_region)

# update the cache
cache.set_scales(
    args.year,
    category.name,
    args.embedding,
    field,
    args.fakes_region,
    qcd_scale=qcd_scale,
    qcd_scale_error=qcd_scale_error,
    qcd_data_scale=1.,
    qcd_z_scale=1.,
    qcd_others_scale=1.,
    z_scale=ztt_scale,
    z_scale_error=ztt_scale_error)

# draw post-fit plots
analysis.normalize(category)

channels = analysis.make_var_channels(
    template, field, [category],
    args.target_region,
    include_signal=False)

draw_channel(channels[0], name=VARIABLES[field]['root'],
    category=category,
    output_name='{0}_postfit{1}'.format(field, analysis.get_suffix()),
    output_dir='plots/normalization',
    show_pvalue=True,
    top_label="Fakes Model: {0}".format(analysis.fakes_region),
    plot_label='After {0} fit'.format(VARIABLES[field]['root']),
    data_info=analysis.data.info,
    output_formats=('eps', 'png'))