tree = fin.Get('t')
ncombbad_1 = 0
ncombbad_2 = 0
ncombbad_3 = 0
ncombbad_4 = 0
ncombbad_5 = 0
nglbbad_1 = 0
nglbbad_2 = 0
nglbbad_3 = 0
nglbbad_4 = 0
nglbbad_5 = 0
for e, entry in enumerate(tree):
p = t.pyTree(tree)
glb = t.Track('global', p)
glb_comb = t.Track('global_comb', p)
glb_muOnly = t.Track('global_refit_noUpdate', p)
glb_refit = t.Track('global_refit', p)
tracker = t.Track('tracker', p)
if len(glb_comb.par()) < 5: continue
glbres = (glb.K() - p.gen_K) / p.gen_K
combres = (glb_comb.K() - p.gen_K) / p.gen_K
ptres = combreshist.GetBinContent(combreshist.FindBin(p.gen_pt))
#print p.gen_pt,ptres,glbres/ptres,combres/ptres
if abs(combres / ptres) > 1:
ncombbad_1 += 1
50, 0, 0.1, 50, 0, 2)
'''
# fR vs fT vs mu,sigma(K-Kgen/Kgen)
step = 0.0002
steps = np.arange(0.999,1.001+step,step)
resdict = {fR:{fT:np.array([]) for fT in steps} for fR in steps}
'''
# loop on tree
for l, entry in enumerate(tree):
#if l>2000: continue
sys.stdout.write('Entry {l:>5}\r'.format(**locals()))
p = t.pyTree(tree)
if not p.allOkay: continue
for trackType in tracklist:
track = t.Track(trackType, p)
if track.cov(0, 0) < 0.:
print "Negative covariance? WTF?"
continue
if track.eta() == -999.:
print "track lambda is strange?"
continue
# Fill base histograms
basehists[trackType]['K'].Fill(track.K())
basehists[trackType]['K_res'].Fill((track.K() - p.gen_K) / p.gen_K)
basehists[trackType]['K_b'].Fill(track.K() - p.gen_K)
basehists[trackType]['K_rel_err'].Fill(
math.sqrt(track.cov(0, 0)) / abs(track.K()))
# Fill K_b plots
#outFile = R.TFile(outfilename,'recreate')
RECREATE = args.recreate
#dxy = R.TH1D('dxy','',100,-10,10)
if RECREATE:
rFile = R.TFile(fileName)
tree = rFile.Get('t')
#outFileBase = R.TFile(outfilebasename,'recreate')
for e, entry in enumerate(tree):
p = t.pyTree(tree)
if not p.allOkay: continue
muon = t.Track('refit_noUpdate', p)
full = t.Track('global', p)
tracker = t.Track('tracker', p)
comb = t.Track('comb', p)
refit = t.Track('refit', p)
#res_par,res_cov = t.kalman_prefit_residuals(muon._par,muon._cov,full._par,full._cov)
#print res_par[2], full.dxy()-muon.dxy()
#dxy.Fill(res_par[2])
refit_par, refit_cov = t.kalman_filter_update(muon._par, muon._cov,
full._par, full._cov)
print 'refit'
print refit_par[0], refit.K()
print refit_par[1], refit.Lambda()
print refit_par[2], refit.phi()
print refit_par[3], refit.dxy()
hists2D[track][plot2D] = R.TH2D(name,title,*varList[x]['bins']+varList[y]['bins'])
fracs = [i*0.05 for i in range(21)]
other['global']['frachist'] = R.TH2D('global_frachist','#kappa_{comb} = f #times #kappa_{refit} + (1-f) #times #kappa_{tracker}',21,0,1.05,100,-0.5,0.5)
other['picky']['frachist'] = R.TH2D('picky_frachist','#kappa_{comb} = f #times #kappa_{refit} + (1-f) #times #kappa_{tracker}',21,0,1.05,100,-0.5,0.5)
# loop on tree
for l,entry in enumerate(tree):
#if l>2000: continue
sys.stdout.write('Entry {l:>5}\r'.format(**locals()))
p = t.pyTree(tree)
if not p.allOkay: continue
# Plots for all tracks
for trackType in tracklist:
track = t.Track(trackType,p)
if track.cov(0,0) < 0: continue
hists1D[trackType]['K'].Fill(track.K())
hists1D[trackType]['lambda'].Fill(track.Lambda())
hists1D[trackType]['phi'].Fill(track.phi())
hists1D[trackType]['dxy'].Fill(track.dxy())
hists1D[trackType]['dsz'].Fill(track.dsz())
hists1D[trackType]['eta'].Fill(track.eta())
hists1D[trackType]['pt'].Fill(track.pt())
hists1D[trackType]['K_gen_res'].Fill((track.K()-p.gen_K)/p.gen_K)
hists1D[trackType]['K_rel_err'].Fill(math.sqrt(track.cov(0,0))/abs(track.K()))
hists1D[trackType]['chi2'].Fill(track.chi2())
hists2D[trackType]['K_gen_res_vs_gen_pt'].Fill(p.gen_pt, (track.K()-p.gen_K)/p.gen_K)
hists2D[trackType]['K_gen_res_vs_gen_eta'].Fill(p.gen_eta, (track.K()-p.gen_K)/p.gen_K)
hists2D[trackType]['K_rel_err_vs_gen_pt'].Fill(p.gen_pt, math.sqrt(track.cov(0,0))/abs(track.K()))
hists2D[trackType]['K_rel_err_vs_gen_eta'].Fill(p.gen_eta, math.sqrt(track.cov(0,0))/abs(track.K()))
if 'rcParams' in args.keys():
for k, v in args['rcParams'].items():
plt.rcParams[k] = v
if 'style' in args.keys():
plt.style.use(args['style'])
auth = spotipy.SpotifyOAuth(redirect_uri='http://localhost:8888/callback',
username=args['username'])
sp = spotipy.Spotify(auth_manager=auth)
album = tools.get_album(sp, args['album_id'])
all_data = pd.concat(
tools.Track(t).loudness(sp) for t in album['tracks']['items'])
all_data['Centred Time'] = (
all_data['Time'] -
(all_data.groupby('TrackNo')['Time'].transform('max') / 2))
g = sns.FacetGrid(data=all_data,
sharex=True,
sharey=True,
row='Name',
aspect=8,
height=.8)
g.map_dataframe(tools.plot_waves)
g.set_titles('{row_name}', c='C1', weight='bold', pad=2)
for ax in g.axes.flatten():
import ROOT as R
import numpy as np
import math as math
import tools as t
fin = R.TFile('AnalyzeTracks_muonPT.root')
tree = fin.Get('t')
step = 0.1
uplim = 2.0
slist = np.arange(0, uplim + step, step)
title = 'Comb = %(s)s*ref + trk;#frac{#kappa-#kappa_{gen}}{#kappa_{gen}};Counts'
reshists = {
s: {R.TH1D('comb_scale_' + str(s), title % (str(s)), 200, -1, 1)}
for s in slist
}
for e, entry in enumerate(tree):
p = t.PyTree(tree)
glb = t.Track('global', p)
trk = t.Track('tracker', p)
glb_ref = t.Track('global_refit', p)
glb_comb = t.Track('global_comb', p)
for s in slist:
par, cov = t.chisq_comb(glb_ref.par(), s * glb_ref._cov, trk.par(),
trk._cov)