diff --git a/pyext/professor2/eigentunes.py b/pyext/professor2/eigentunes.py
new file mode 100644
--- /dev/null
+++ b/pyext/professor2/eigentunes.py
@@ -0,0 +1,230 @@
+
+def exploreValley(center_t, direction_t, TRAFO, GOFdef,):
+ exec GOFdef in globals() # Note globals!
+ def getVal(a):
+ temp_t = center_t + a*direction_t
+ temp = TRAFO * temp_t.transpose()
+ temp_r = temp.transpose().tolist()[0]
+ return profGoF(*temp_r) - target
+
+
+def mkNewParamCorrelation(T_trans, T, point, GOFdef, target):
+ import professor2 as prof
+ exec GOFdef in locals() # Note globals!
+
+ from numpy import matrix
+ rv = matrix(point.values())
+ center_t = (T_trans * rv.transpose()).transpose()
+
+ DIM = len(point.values())
+ from scipy.optimize import fsolve
+ from numpy import zeros,diag
+
+ def getVal(a, direction):
+ temp_t = center_t + a*direction
+ temp = T * temp_t.transpose()
+ temp_r = temp.transpose().tolist()[0]
+ return profGoF(*temp_r) - target
+
+ def getX(a, direction):
+ return center_t + a*direction
+
+ newdiag = []
+ for i in xrange(DIM):
+ ev = zeros(DIM)
+ ev[i] = 1
+ temp = fsolve(lambda x:getVal(x, ev), 1)
+ temp2 = fsolve(lambda x:getVal(x, -ev), 1)
+ if temp > temp2:
+ newdiag.append(temp)
+ else:
+ newdiag.append(temp2)
+
+ N = diag([x[0] for x in newdiag])
+ return T_trans*N*T
+
+
+
+
+def calcHistoCov(h, COV_P, result):
+ """
+ Propagate the parameter covariance onto the histogram covariance
+ using the ipol gradients.
+ """
+ IBINS = h.bins
+ from numpy import zeros
+ COV_H = zeros((h.nbins, h.nbins))
+ from numpy import array
+ for i in xrange(len(IBINS)):
+ GRD_i = array(IBINS[i].grad(result))
+ for j in xrange(len(IBINS)):
+ GRD_j = array(IBINS[j].grad(result))
+ pc =GRD_i.dot(COV_P).dot(GRD_j)
+ COV_H[i][j] = pc
+ return COV_H
+
+
+def mkErrorPropagationHistos(IHISTOS, point, COV, combine=False):
+ covs = {}
+ properrs = {}
+ ipolerrs = {}
+ ipolvals = {}
+ from numpy import sqrt
+ for k, v in IHISTOS.iteritems():
+ covs[k] = calcHistoCov(v, COV, point)
+ properrs[k] = sqrt(0.5*covs[k].diagonal())
+ ipolerrs[k] = [b.err for b in v.toDataHisto(point).bins]
+ ipolvals[k] = [b.val for b in v.toDataHisto(point).bins]
+
+ scatters=[]
+ for k, v in IHISTOS.iteritems():
+ T = v.toDataHisto(point)
+ for i in xrange(T.nbins):
+ T.bins[i].errs=properrs[k][i] if combine is False else sqrt(properrs[k][i]**2 + ipolerrs[k][i]**2)
+ scatters.append(T.toScatter2D())
+ return scatters
+
+
+def mkScatters(masterbox, mastercenter, ppoint):
+ import professor2 as prof
+ boxdict=None
+ try:
+ ppoint = ppoint.values()
+ except:
+ pass
+ for box, bdict in masterbox.iteritems():
+ if prof.pInBOX(ppoint, box):
+ boxdict = bdict
+ break
+ if boxdict is None:
+ distances={}
+ for c in mastercenter.keys():
+ distances[prof.pBoxDistance(ppoint, c)] = c
+ winner = min(distances.keys())
+ boxdict = mastercenter[distances[winner]]
+ ipolH=boxdict["IHISTOS"]
+
+ scatters_e =[]
+ for k in sorted(ipolH.keys()):
+ v = ipolH[k]
+ T = v.toDataHisto(ppoint)
+ scatters_e.append(T.toScatter2D())
+ return scatters_e
+
+
+def mkEnvelopes(central, etunes):
+ ret = {}
+ for i in xrange(1, len(etunes.keys())/2 + 1):
+ ret[i] = []
+ Hplus = etunes[i]
+ Hminus = etunes[-i]
+ for num_h, h in enumerate(central):
+ temp = h.clone()
+ for num_p, p in enumerate(temp.points):
+ yplus = Hplus[num_h].points[num_p].y
+ yminus = Hminus[num_h].points[num_p].y
+ if yplus > p.y:
+ eplus = yplus - p.y
+ eminus = p.y - yminus
+ else:
+ eplus = yminus - p.y
+ eminus = p.y - yplus
+ p.yErrs = (eminus, eplus)
+ ret[i].append(temp)
+ return ret
+
+
+def mkTotvelopes(central, etunes):
+ ret = []
+ for num_h, h in enumerate(central):
+ temp = h.clone()
+ allThis = [x[num_h] for x in etunes.values()]
+ for num_p, p in enumerate(temp.points):
+ dybin = [x.points[num_p].y - p.y for x in allThis]
+ pos = [x for x in dybin if x>=0]
+ neg = [x for x in dybin if x<0]
+ eplus = max(pos) if len(pos) > 0 else 0
+ eminus = abs(min(neg)) if len(neg) > 0 else 0
+ p.yErrs = (eminus, eplus)
+ ret.append(temp)
+ return ret
+
+
+def mkAddvelopes(central, etunes, addLinear=False):
+ ret = []
+ for num_h, h in enumerate(central):
+ temp = h.clone()
+ allThis = [x[num_h] for x in etunes.values()]
+ for num_p, p in enumerate(temp.points):
+ dybin = [x.points[num_p].y-p.y for x in allThis]
+ pos = [x for x in dybin if x>=0]
+ neg = [x for x in dybin if x<0]
+ from math import sqrt
+ if addLinear:
+ eplus = sum(pos) if len(pos) > 0 else 0
+ eminus = sum([abs(x) for x in neg]) if len(neg) > 0 else 0
+ else:
+ eplus = sqrt(sum([x*x for x in pos])) if len(pos) > 0 else 0
+ eminus = sqrt(sum([x*x for x in neg])) if len(neg) > 0 else 0
+ p.yErrs = (eminus, eplus)
+ ret.append(temp)
+ return ret
+
+
+def readFromFile(fname):
+ nbins = 0
+ params = []
+ ret = []
+ with open(fname) as f:
+ for line in f:
+ l = line.strip()
+ if l.startswith("#"):
+ if "Nbins" in l:
+ nbins = int(l.split()[-1])
+ elif "Parameters" in l:
+ params = l.split()[2:]
+ else:
+ ret.append(map(float, l.split()))
+ return ret, nbins, params
+
+# https://stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
+def mkTabulate(head, rows):
+ from tabulate import tabulate
+ return tabulate(rows, headers=head, tablefmt='orgtbl')
+
+def mkTable(head, rows):
+ t = head[0]
+ for h in head[1:]:
+ t+="\t%s"%str(h)
+
+ for row in rows:
+ t+="\n%s"%row[0]
+ for r in row[1:]:
+ t+="\t%s"%str(r)
+ t+="\n"
+
+ return t
+
+def mkEigenTable(ETs, rfile, latex=False):
+ import professor2 as prof
+ P_min, OTH = prof.readResult(rfile)
+
+ head = ["Tune"] + P_min.keys()
+
+ rows = [["Central"] + P_min.values()]
+
+ for e in sorted(list(set([abs(x) for x in ETs.keys()]))):
+ rows.append(["%i+"%e] + list(ETs[e]) )
+ rows.append(["%i-"%e] + list(ETs[-e]))
+
+ s="\nEigentune summary:\n\n"
+
+ try:
+ from tabulate import tabulate
+ s+=mkTabulate(head, rows)
+ except ImportError, e:
+ print e
+ print "Fallback to simple table"
+ s+=mkTable(head, rows)
+ return s
+