Added Moller validation analysis

moller-scattering/validation/makefile

+name=validation
+figures=xicut.pdf kfac
+include ../../tools/makefile.conf

moller-scattering/validation/validation.py

+# vim: foldmethod=marker
+## Init{{{
+# We begin by loading pyMule
+from pymule import *
+
+# and point it to the correct folder
+setup(folder='ee2ee0504191/out.tar.bz2', obs='8')
+##########################################################################}}}
+## Load data{{{
+# Load the LO with the correct factors of alpha and conv
+lo = scaleset(mergefks(sigma('ee2ee0')), alpha**2*conv)
+
+
+# Load the NLO, either with plot or without
+#nlo = scaleset(mergefks(sigma('ee2eeF'), sigma('ee2eeR')), alpha**3*conv)
+figxi, nlo = mergefkswithplot(
+    [[sigma('ee2eeF')], [sigma('ee2eeR')]],
+    scale=alpha**3*conv
+)
+figxi.savefig("xicut.pdf")
+
+##########################################################################}}}
+## Make plots{{{
+### Simple plots{{{
+# Let's start with a quick LO plot. Because we already have a figure
+# open (the $\xi_c$-study), we first need a new one
+figure()
+errorband(lo['thcms'])
+# Next, we look at the tK factor
+printnumber(dividenumbers(nlo['value'], lo['value']))
+# Now we can make a K-plot
+figK, (ax1, ax2) = kplot(
+    {'lo': mergebins(lo['thcms'], 4), 'nlo': mergebins(nlo['thcms'], 4)},
+    labelx='$\\theta_{CMS}$',
+    labelsigma='$\\D\\sigma/\\D\\theta_{CMS}\ / \ {\\rm \upmu b}$',
+    legend={
+        'lo': '$\\sigma^{(0)}$',
+        'nlo': '$\\sigma^{(1)}$'
+    },
+    legendopts={'what': 'u', 'loc': 'upper center', 'ncol': 2}
+)
+ax1.set_yscale('log')
+figK.savefig("kfac.pdf")
+###########################################################}}}
+### bin-wise $\chi^2${{{
+# Let's also look at the bin-wise $\chi^2$ of the FKS merge. For this
+# we have to do the FKS merge manually. Let's begin by loading the
+# relevant data.
+dataF = sigma('ee2eeF')
+dataR = sigma('ee2eeR')
+# Next, we need to know the FKS parameters used. These are the keys of
+# the sigma result. We will assume they are the same for R and F,
+# otherwise we would have to use an intersection routine such as
+# pymule.loader.multiintersect
+xicuts = dataF.keys()
+# Now we can add the thcms plot of ee2eeF and ee2eeF for each $\xi_c$
+# and merge the results.
+pl, chi = mergeplots([
+    addplots(
+        sigma('ee2eeF')[xic]['thcms'],
+        sigma('ee2eeR')[xic]['thcms']
+    ) for xic in xicuts],
+    True
+)
+figure()
+scatter(chi[:, 0], chi[:, 1])
+xlabel('$\\theta_{CMS}$')
+ylabel('$\\chi^2$')
+###########################################################}}}
+##########################################################################}}}