#!/usr/bin/env python

""" This script steers the systematics calculations and outputs a LaTeX

table. The actual calculations are performed in the C++ files predict.C

"""

import ROOT as r

import math

import os.path

from LatexTable import Table

from config import path, bin_fmt, bins, systInfo, Muon, Electron

import utils

import math

if __name__ == "__main__":

r.gROOT.LoadMacro("predict.C+")

channel = Electron

ctrl_channel = None

# Load MC pseudo-data in bins

data = utils.getZeroMC(channel, ctrl_channel)

# This also calculates statistical uncertainties

results = utils.makePredictions(data)

# Get the scaled/smeared systematic variations

systs = utils.getLiterallyAllSystematicsBkg(channel, ctrl_channel)

# Loop through systematics and calculate systematic uncertainty

for name, scaled in systs:

utils.addSystematic(name, data, results, scaled)

print name

# LaTeX table

l = Table()

cols = [("value", "Bin", "l")] + [("bin%d" % i, utils.formatBin(i), "c") for i in range(len(bins))]

l.defineColumns(cols)

def fields(f):

return dict([("bin%d" % idx, f(idx)) for idx in range(len(bins))])

l.addRowFields(value = "Events Predicted", **fields(lambda idx : "%.1f" % results[idx].predicted()))

l.addRowFields(value = "Events Observed", **fields(lambda idx : "%.1f" % results[idx].observed()))

l.addRowFields(value = "$N_{\\textrm{signal}}/N_{\\textrm{control}}$", **fields(lambda idx : "%.2f" % data[idx].R()))

l.addHLine()

l.addRowFields(value = "Statistical Uncertainty (\%)",

**fields(lambda idx : "%.2f" % (results[idx].statErrorValue(0)*100/results[idx].predicted())))

l.addHLine()

l.addRowFields(value = "Systematic Uncertainty (\%)", **fields(

lambda idx : "%.2f" % (math.sqrt(results[idx].totalSystError()**2 +

results[idx].statErrorValue("stat_control")**2)*100/results[idx].predicted())))

l.addRowFields(value = "\\hspace{0.5cm}Control Region Stat. (\%)",

**fields(lambda idx : "%.2f" % (results[idx].statErrorValue("stat_control")*100/results[idx].predicted())))

# For each systematic, display percentage uncertainty on predicted number of events

for name, scaled in systs:

l.addRowFields(value = "\\hspace{0.5cm}%s (\%%)" % systInfo[name]["title"],

**fields(lambda idx : "%.2f" % (results[idx].systErrorValue(name)*100/results[idx].predicted())))

l.addRowFields(value = "\\hspace{0.5cm}PDF Uncertainty (\%)")

combined = r.CombinedResult()

for res in results:

combined.addBin(res)

# row = l.getDefaultRow()

# row["bin"] = "Combined"

# row["sig"] = "%.1f" % combined.getChi2()

# l.addRow(row)

# Render table

print l.render()

# combined.getResiduals().Print()

# combined.getCovarianceMatrix().Print()

# print combined.getChi2()

# print math.sqrt(2)*r.TMath.ErfInverse(1 - r.TMath.Prob(combined.getChi2(), len(bin_data)))