def mkParameterisations(xmids, ranges, nsignal, order=(3, 0)):
import professor2 as prof
S = prof.sampling.NDSampler(ranges)
X = [S() for _ in range(nsignal)]
Y = np.array([mkSignal(x, xmids) for x in X])
MIN = Y.min(axis=0)
MAX = Y.max(axis=0)
import pyrapp
return [pyrapp.Rapp(X, y, order=order) for y in Y.T], MIN, MAX
def getEigentunes(jd, target):
nparams = jd["NBINS"] - jd["NDF"]
Cinv_tune=np.array(jd["TUNEINVPARAMCOV"]).reshape((nparams, nparams))
Cinv_data=np.array(jd["TUNEINVDATACOV"]).reshape((jd["NBINS"], jd["NBINS"]))
T_fwd, S, T_back = eigenDecomposition(Cinv_tune)
goftarget=np.percentile(jd["VPHI2"], target)
import pyrapp
RAPP = [pyrapp.Rapp(r) for r in jd["RAPP"]]
ETS, AX = mkEigenTunes(T_fwd, np.array(jd["TUNEPARS"]), lambda x:chi2wCov(jd["YVALS"], RAPP, Cinv_data, x), goftarget, prefix="toy-ets-%i-%s"%(jd["NBINS"], jd["CORRMODE"]))
return ETS, AX
def plotEigenTuneHist(jd, ETS):
import pyrapp
R = [pyrapp.Rapp(r) for r in jd["RAPP"]]
TUNED = [r(jd["TUNEPARS"]) for r in R ]
ETD=[]
for ET in ETS:
ETD.append([[r(e) for r in R ] for e in ET])
phibest=jd["WINNERPHI2"]
phibestpars = jd["WINNERPARS"]
W = [r(phibestpars) for r in R]
ERR = np.sqrt(1./np.array(jd["TUNEINVDATACOV"]).reshape((jd["NBINS"],jd["NBINS"])).diagonal())
YV = jd["YVALS"]
from matplotlib import pyplot as plt
import matplotlib as mpl
plt.style.use('ggplot')
plt.figure(figsize=(8,8))
#
X = np.array(range(len(YV)))+0.5
plt.errorbar(X, YV, yerr=ERR, marker="o", linestyle="none", color="k")
plt.yscale("log")
plt.xlabel("# Bin")
plt.ylabel("Entries")
plt.plot(X, TUNED, "r-", label="Central tune (%f)"%jd["TUNECHI2"])
etcols=["b", "m"]
for num, ET in enumerate(ETD):
plt.plot(X, ET[0], "%s-"%etcols[num], label="ET%i+"%(num+1))
plt.plot(X, ET[1], "%s--"%etcols[num], label="ET%i-"%(num+1))
plt.plot(X, W, "g--", label="Phi best(%f)"%phibest)
plt.legend()
plt.ylim((100, 580))
#
plt.savefig("toy-et-dist-%i-%s.pdf"%(jd["NBINS"], jd["CORRMODE"]))
def mkRes(X_train, X_test, order, fn):
import pyrapp
if fn == 1:
Y_train = [f1(x) for x in X_train]
Y_test = [f1(x) for x in X_test]
elif fn == 2:
Y_train = [f2(x) for x in X_train]
Y_test = [f2(x) for x in X_test]
elif fn == 3:
Y_train = [f3(x) for x in X_train]
Y_test = [f3(x) for x in X_test]
elif fn == 4:
Y_train = [f4(x) for x in X_train]
Y_test = [f4(x) for x in X_test]
elif fn == 5:
Y_train = [f5(x) for x in X_train]
Y_test = [f5(x) for x in X_test]
elif fn == 6:
Y_train = [f6(x) for x in X_train]
Y_test = [f6(x) for x in X_test]
elif fn == 7:
Y_train = [f7(x) for x in X_train]
Y_test = [f7(x) for x in X_test]
elif fn == 8:
Y_train = [f8(x) for x in X_train]
Y_test = [f8(x) for x in X_test]
elif fn == 9:
Y_train = [f9(x) for x in X_train]
Y_test = [f9(x) for x in X_test]
elif fn == 10:
Y_train = [f10(x) for x in X_train]
Y_test = [f10(x) for x in X_test]
elif fn == 12:
Y_train = [f12(x) for x in X_train]
Y_test = [f12(x) for x in X_test]
elif fn == 13:
Y_train = [f13(x) for x in X_train]
Y_test = [f13(x) for x in X_test]
elif fn == 14:
Y_train = [f14(x) for x in X_train]
Y_test = [f14(x) for x in X_test]
elif fn == 15:
Y_train = [f15(x) for x in X_train]
Y_test = [f15(x) for x in X_test]
elif fn == 16:
Y_train = [f16(x) for x in X_train]
Y_test = [f16(x) for x in X_test]
elif fn == 17:
Y_train = [f17(x) for x in X_train]
Y_test = [f17(x) for x in X_test]
elif fn == 18:
Y_train = [f18(x) for x in X_train]
Y_test = [f18(x) for x in X_test]
elif fn == 19:
Y_train = [f19(x) for x in X_train]
Y_test = [f19(x) for x in X_test]
elif fn == 20:
Y_train = [f20(x) for x in X_train]
Y_test = [f20(x) for x in X_test]
elif fn == 21:
Y_train = [f21(x) for x in X_train]
Y_test = [f21(x) for x in X_test]
elif fn == 22:
Y_train = [f22(x) for x in X_train]
Y_test = [f22(x) for x in X_test]
elif fn == 23:
Y_train = [f23(x) for x in X_train]
Y_test = [f23(x) for x in X_test]
elif fn == 24:
Y_train = [f24(x) for x in X_train]
Y_test = [f24(x) for x in X_test]
else:
raise Exception("function {} not implemented, exiting".format(fn))
R = pyrapp.Rapp(X_train, Y_train, order=order)
return [abs(R(x) - Y_test[num]) for num, x in enumerate(X_test)]