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Write_MET_binned_histogram.py
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Write_MET_binned_histogram.py
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import math
import numpy as np
import matplotlib.pyplot as plt
from collections import OrderedDict
def Write_MET_binned_histogram(Predict_array, Gen_array, bin_number, bin_minimum, bin_median, bin_maximum, name='title'):
import ROOT as rt
# book histograms
def book(h, name, n, a, b, title=""):
h[name] = rt.TH1F(name, title, n, a, b)
h[name].Sumw2()
def book2(h, name, nx, ax, bx, ny, ay, by, title=""):
h[name] = rt.TH2F(name, title, nx, ax, bx, ny, ay, by)
h[name].Sumw2()
def SgnPara(para, z):
return para.Mod()*(1. if abs(para.DeltaPhi(z)) > rt.TMath.Pi()/2 else -1.)
def SgnPerp(perp, z):
return perp.Mod()*(1. if perp.DeltaPhi(z) > 0 else -1.)
# Separate MET-bin
bin_ = bin_number
bin_mini = bin_minimum
bin_medi = bin_median
bin_maxi = bin_maximum
binning_le = (bin_medi - bin_mini)/(bin_/2.)
binning_gr = (bin_maxi - bin_medi)/(bin_/2.)
hists = OrderedDict()
for i in range(int(bin_/2.)):
book(hists, "predict_perp_"+str(int(i*binning_le))+"-"+str(int((i+1)*binning_le))+"", 100, -bin_maxi, bin_maxi)
book(hists, "predict_para_"+str(int(i*binning_le))+"-"+str(int((i+1)*binning_le))+"", 100, -bin_maxi, bin_maxi)
book(hists, "v_gen_"+str(int(i*binning_le))+"-"+str(int((i+1)*binning_le))+"", 1000, i*binning_le, (i+1)*binning_le)
for i in range(int(bin_/2.)):
book(hists, "predict_perp_"+str(int(i*binning_gr+bin_medi))+"-"+str(int((i+1)*binning_gr+bin_medi))+"", 100, -bin_maxi, bin_maxi)
book(hists, "predict_para_"+str(int(i*binning_gr+bin_medi))+"-"+str(int((i+1)*binning_gr+bin_medi))+"", 100, -bin_maxi, bin_maxi)
book(hists, "v_gen_"+str(int(i*binning_gr+bin_medi))+"-"+str(int((i+1)*binning_gr+bin_medi))+"", 1000, i*binning_gr+bin_medi, (i+1)*binning_gr+bin_medi)
v_gen = rt.TVector2()
v_para_PUPPI = rt.TVector2()
v_perp_PUPPI = rt.TVector2()
v_predict = rt.TVector2()
v_para_predict = rt.TVector2()
v_perp_predict = rt.TVector2()
test_events = Predict_array.shape[0]
for i in range(test_events):
v_predict.SetMagPhi(Predict_array[i, 0], Predict_array[i, 1])
v_gen.SetMagPhi(Gen_array[i, 0], Gen_array[i, 1])
v_para_predict = v_predict.Proj(v_gen)
v_perp_predict = v_predict.Norm(v_gen)
for j in range(int(bin_/2.)):
if (j*binning_le < v_gen.Mod()) and (v_gen.Mod() <= (j+1)*binning_le):
hists["v_gen_"+str(int(j*binning_le))+"-"+str(int((j+1)*binning_le))+""].Fill(v_gen.Mod())
hists["predict_para_"+str(int(j*binning_le))+"-"+str(int((j+1)*binning_le))+""].Fill(SgnPara(v_para_predict, v_gen))
hists["predict_perp_"+str(int(j*binning_le))+"-"+str(int((j+1)*binning_le))+""].Fill(SgnPerp(v_perp_predict, v_gen))
for j in range(int(bin_/2.)):
if (j*binning_gr+bin_medi < v_gen.Mod()) and (v_gen.Mod() <= (j+1)*binning_gr+bin_medi):
hists["v_gen_"+str(int(j*binning_gr+bin_medi))+"-"+str(int((j+1)*binning_gr+bin_medi))+""].Fill(v_gen.Mod())
hists["predict_para_"+str(int(j*binning_gr+bin_medi))+"-"+str(int((j+1)*binning_gr+bin_medi))+""].Fill(SgnPara(v_para_predict, v_gen))
hists["predict_perp_"+str(int(j*binning_gr+bin_medi))+"-"+str(int((j+1)*binning_gr+bin_medi))+""].Fill(SgnPerp(v_perp_predict, v_gen))
fout = rt.TFile(name, "recreate")
for n in hists:
hists[n].Write()
def response_ab(predict_met, gen_met, bin_number, bin_minimum, bin_median, bin_maximum, path_, name='response.pdf'):
# Separate MET-bin
bin_ = bin_number
bin_mini = bin_minimum
bin_medi = bin_median
bin_maxi = bin_maximum
binning_le = (bin_medi - bin_mini)/(bin_/2.)
binning_gr = (bin_maxi - bin_medi)/(bin_/2.)
predict_array = np.zeros(bin_)
predict_RMS = np.zeros(bin_)
gen_array = np.zeros(bin_)
gen_RMS = np.zeros(bin_)
gen_array_number = np.zeros(bin_)
for i in range(int(bin_/2.)):
print(i)
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_array[i] += predict_met[j, 0]
gen_array[i] += gen_met[j, 0]
gen_array_number[i] += 1
for i in range(int(bin_/2.)):
print(i)
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_array[i + 10] += predict_met[j, 0]
gen_array[i + 10] += gen_met[j, 0]
gen_array_number[i + 10] += 1
predict_mean = predict_array / gen_array_number
gen_mean = gen_array / gen_array_number
for i in range(int(bin_/2.)):
print(i)
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_RMS[i] += (predict_mean[i] - predict_met[j, 0]) ** 2
gen_RMS[i] += (gen_mean[i] - gen_met[j, 0]) ** 2
for i in range(int(bin_/2.)):
print(i)
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_RMS[i + 10] += (predict_mean[i + 10] - predict_met[j, 0]) ** 2
gen_RMS[i + 10] += (gen_mean[i + 10] - gen_met[j, 0]) ** 2
predict_RMS = np.sqrt(predict_RMS / gen_array_number)
gen_RMS = np.sqrt(gen_RMS / gen_array_number)
response = predict_mean / gen_mean
response_error = response * np.sqrt((predict_RMS/predict_mean) ** 2 + (gen_RMS/gen_mean) ** 2)
plt.figure()
plt.errorbar(x=gen_mean, y=response, yerr=response_error, xerr=gen_RMS)
plt.xlabel("Gen MET [GeV]", fontsize=16)
plt.ylabel("response", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
feat = open(''+path_+'response_dat.txt', 'w')
for i in range(bin_):
data = '%f, %f, %f, %f' % (gen_mean[i], gen_RMS[i], response[i], response_error[i])
feat.write(data)
feat.write('\n')
def response_parallel(predict_met, gen_met, bin_number, bin_minimum, bin_median, bin_maximum, path_, name='response.pdf'):
# phi calc
def phidiff(phi1, phi2):
phi_r = phi1 - phi2
if phi_r > math.pi:
phi_r = phi_r - 2 * math.pi
if phi_r < -math.pi:
phi_r = phi_r + 2 * math.pi
return phi_r
# Separate MET-bin
bin_ = bin_number
bin_mini = bin_minimum
bin_medi = bin_median
bin_maxi = bin_maximum
binning_le = (bin_medi - bin_mini)/(bin_/2.)
binning_gr = (bin_maxi - bin_medi)/(bin_/2.)
predict_array = np.zeros(bin_)
predict_RMS = np.zeros(bin_)
gen_array = np.zeros(bin_)
gen_RMS = np.zeros(bin_)
gen_array_number = np.zeros(bin_)
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_array[i] += predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))
gen_array[i] += gen_met[j, 0]
gen_array_number[i] += 1
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_array[i + 10] += predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))
gen_array[i + 10] += gen_met[j, 0]
gen_array_number[i + 10] += 1
predict_mean = predict_array / gen_array_number
gen_mean = gen_array / gen_array_number
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_RMS[i] += (predict_mean[i] - predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))) ** 2
gen_RMS[i] += (gen_mean[i] - gen_met[j, 0]) ** 2
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_RMS[i + 10] += (predict_mean[i + 10] - predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))) ** 2
gen_RMS[i + 10] += (gen_mean[i + 10] - gen_met[j, 0]) ** 2
predict_RMS = np.sqrt(predict_RMS / gen_array_number)
gen_RMS = np.sqrt(gen_RMS / gen_array_number)
response = predict_mean / gen_mean
response_error = response * np.sqrt((predict_RMS/predict_mean) ** 2 + (gen_RMS/gen_mean) ** 2)
plt.figure()
plt.errorbar(x=gen_mean, y=response, yerr=response_error, xerr=gen_RMS, color='r')
plt.xlabel("Gen MET [GeV]", fontsize=16)
plt.ylabel("response", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.grid(True)
plt.savefig(''+name+'.png')
plt.show(block=False)
# plt.pause(5)
plt.close("all")
feat = open(''+name+'.txt', 'w')
for i in range(bin_):
data = '%f, %f, %f, %f' % (gen_mean[i], gen_RMS[i], response[i], response_error[i])
feat.write(data)
feat.write('\n')
def response_parallel_opaque(predict_met, puppi_met, gen_met, bin_number, bin_minimum, bin_median, bin_maximum, path_, name='response.pdf'):
# phi calc
def phidiff(phi1, phi2):
phi_r = phi1 - phi2
if phi_r > math.pi:
phi_r = phi_r - 2 * math.pi
if phi_r < -math.pi:
phi_r = phi_r + 2 * math.pi
return phi_r
# Separate MET-bin
bin_ = bin_number
bin_mini = bin_minimum
bin_medi = bin_median
bin_maxi = bin_maximum
binning_le = (bin_medi - bin_mini)/(bin_/2.)
binning_gr = (bin_maxi - bin_medi)/(bin_/2.)
predict_array = np.zeros(bin_)
predict_RMS = np.zeros(bin_)
puppi_array = np.zeros(bin_)
puppi_RMS = np.zeros(bin_)
gen_array = np.zeros(bin_)
gen_RMS = np.zeros(bin_)
gen_array_number = np.zeros(bin_)
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_array[i] += predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))
puppi_array[i] += puppi_met[j, 0] * abs(math.cos(phidiff(puppi_met[j, 1], gen_met[j, 1])))
gen_array[i] += gen_met[j, 0]
gen_array_number[i] += 1
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_array[i + 10] += predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))
puppi_array[i + 10] += puppi_met[j, 0] * abs(math.cos(phidiff(puppi_met[j, 1], gen_met[j, 1])))
gen_array[i + 10] += gen_met[j, 0]
gen_array_number[i + 10] += 1
predict_mean = predict_array / gen_array_number
puppi_mean = puppi_array / gen_array_number
gen_mean = gen_array / gen_array_number
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_le) < gen_met[j, 0] <= ((i + 1) * binning_le)):
predict_RMS[i] += (predict_mean[i] - predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))) ** 2
puppi_RMS[i] += (puppi_mean[i] - puppi_met[j, 0] * abs(math.cos(phidiff(puppi_met[j, 1], gen_met[j, 1])))) ** 2
gen_RMS[i] += (gen_mean[i] - gen_met[j, 0]) ** 2
for i in range(int(bin_/2.)):
for j in range(gen_met.shape[0]):
if ((i * binning_gr) + 100 < gen_met[j, 0] <= ((i + 1) * binning_gr) + 100):
predict_RMS[i + 10] += (predict_mean[i + 10] - predict_met[j, 0] * abs(math.cos(phidiff(predict_met[j, 1], gen_met[j, 1])))) ** 2
puppi_RMS[i + 10] += (puppi_mean[i + 10] - puppi_met[j, 0] * abs(math.cos(phidiff(puppi_met[j, 1], gen_met[j, 1])))) ** 2
gen_RMS[i + 10] += (gen_mean[i + 10] - gen_met[j, 0]) ** 2
predict_RMS = np.sqrt(predict_RMS / gen_array_number)
puppi_RMS = np.sqrt(puppi_RMS / gen_array_number)
gen_RMS = np.sqrt(gen_RMS / gen_array_number)
response = predict_mean / gen_mean
response_puppi = puppi_mean / gen_mean
response_error = response * np.sqrt((predict_RMS/predict_mean) ** 2 + (gen_RMS/gen_mean) ** 2)
response_puppi_error = response_puppi * np.sqrt((puppi_RMS/puppi_mean) ** 2 + (gen_RMS/gen_mean) ** 2)
plt.figure()
plt.errorbar(x=gen_mean, y=response, yerr=response_error, xerr=gen_RMS, color='g', label='Predicted MET')
plt.errorbar(x=gen_mean, y=response_puppi, yerr=response_puppi_error, xerr=gen_RMS, color='r', label='PUPPI MET')
plt.xlabel("Gen MET [GeV]", fontsize=16)
plt.ylabel("response", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.ylim(0, 4)
plt.grid(True)
plt.legend()
plt.savefig(''+name+'.png')
plt.show(block=False)
# plt.pause(5)
plt.close("all")
feat = open(''+name+'.txt', 'w')
for i in range(bin_):
data = '%f, %f, %f, %f' % (gen_mean[i], gen_RMS[i], response[i], response_error[i])
feat.write(data)
feat.write('\n')
def MET_rel_error_bad(predict_met, gen_met, name='Met_res.pdf'):
rel_err = (predict_met - gen_met)/gen_met
mask = (rel_err < 3)
rel_err = rel_err[~mask]
mean = np.mean(rel_err)
std = np.std(rel_err)
entry = rel_err.shape[0]
# for i in range(rel_err.shape[0]):
# std += (mean - rel_err[i]) **2
#std = std/rel_err.shape[0]
#std = math.sqrt(std)
mean = mean * 1000
mean = int(mean)
mean = float(mean) / 1000
std = std * 1000
std = int(std)
std = float(std) / 1000
plt.figure()
plt.hist(rel_err, bins=np.linspace(3., 50., 50+1), label='mean : '+str(mean)+'\nstandard deviation : '+str(std)+'\nentry : '+str(entry)+'')
plt.xlabel("relative error (predict - true)/true", fontsize=16)
plt.ylabel("Events", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_rel_error(predict_met, gen_met, name='Met_res.pdf'):
rel_err = (predict_met - gen_met)/gen_met
mask = (rel_err[:] > 3)
rel_err = rel_err[~mask]
mean = np.mean(rel_err)
std = np.std(rel_err)
entry = rel_err.shape[0]
mean = mean * 1000
mean = int(mean)
print(mean, "mean")
mean = float(mean) / 1000
std = std * 1000
std = int(std)
std = float(std) / 1000
plt.figure()
plt.hist(rel_err, bins=np.linspace(-3., 3., 50+1), label='mean : '+str(mean)+'\nstandard deviation : '+str(std)+'\nentry : '+str(entry)+'')
plt.xlabel("relative error (predict - true)/true", fontsize=16)
plt.ylabel("Events", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_rel_error_opaque(predict_met, predict_met2, gen_met, name='Met_res.pdf'):
rel_err = (predict_met - gen_met)/gen_met
mask = (rel_err[:] > 3)
rel_err = rel_err[~mask]
mean = np.mean(rel_err)
std = np.std(rel_err)
entry = rel_err.shape[0]
rel_err2 = (predict_met2 - gen_met)/gen_met
mask2 = (rel_err2[:] > 3)
rel_err2 = rel_err2[~mask2]
mean = np.mean(rel_err)
std = np.std(rel_err)
mean = mean * 1000
mean = int(mean)
print(mean, "mean")
mean = float(mean) / 1000
std = std * 1000
std = int(std)
std = float(std) / 1000
plt.figure()
plt.hist(rel_err, bins=np.linspace(-3., 3., 50+1), label='ML', alpha=0.5, color='red')
plt.hist(rel_err2, bins=np.linspace(-3., 3., 50+1), label='PUPPI', alpha=0.5, color='green')
plt.xlabel("relative error (predict - true)/true", fontsize=16)
plt.ylabel("Events", fontsize=16)
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.title('Relative Pt error', size=18, fontweight='bold', loc='right')
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_abs_error(predict_met, gen_met, name='Met_res.pdf'):
err = (predict_met - gen_met)
mask = gen_met[:] > 100
err = err[~mask]
plt.figure()
plt.hist(err, bins=np.linspace(-150., 150., 50+1))
plt.xlabel("abs error (predict - true)")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
target_array_xy1M
def Phi_abs_error(predict_met, gen_met, name='Met_res.pdf'):
err = (predict_met - gen_met)
for i in range(err.shape[0]):
if (err[i] > math.pi):
err[i] - 2*math.pi
if (err[i] < math.pi):
err[i] + 2*math.pi
plt.figure()
plt.hist(err, bins=np.linspace(-3.5, 3.5, 50+1))
plt.xlabel("abs error (predict - true)")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def Pt_abs_error_opaque(puppi_met, ml_met, gen_met, name='Met_res.pdf'):
puppi_err = (puppi_met - gen_met)
ml_err = (ml_met - gen_met)
#minErr = min(np.array([rel_err, rel_err2]).flatten())
#maxErr = max(np.array([rel_err, rel_err2]).flatten())
plt.figure()
plt.hist(puppi_err, bins=np.linspace(-250, 250, 50+1), alpha=0.5, label='puppi')
plt.hist(ml_err, bins=np.linspace(-250, 250, 50+1), alpha=0.5, label='ML')
plt.xlabel("abs error (predict - true)")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.legend()
plt.title('Abs Pt error', size=18, fontweight='bold', loc='right')
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def Phi_abs_error_opaque(puppi_phi, ml_phi, gen_phi, name='Met_res.pdf'):
puppi_err = (puppi_phi - gen_phi)
ml_err = (ml_phi - gen_phi)
plt.figure()
plt.hist(puppi_err, bins=np.linspace(-3.5, 3.5, 50+1), alpha=0.5, label='puppi')
plt.hist(ml_err, bins=np.linspace(-3.5, 3.5, 50+1), alpha=0.5, label='ML')
plt.xlabel("abs error (predict - true)")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.legend()
plt.title('Abs Phi error', size=18, fontweight='bold', loc='right')
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def dist(predict_met, min_, max_, bin_, name='dist.pdf'):
rel_err = predict_met
plt.figure()
plt.hist(rel_err, bins=np.linspace(min_, max_, bin_))
plt.xlabel("MET [GeV]")
plt.ylabel("Events")
plt.yscale("log")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def dist_opaque(predict_met1, predict_met2, name='dist.pdf'):
rel_err1 = predict_met1
rel_err2 = predict_met2
plt.figure()
plt.hist(rel_err1, bins=np.linspace(0, 500, 50+1), alpha=0.5)
plt.hist(rel_err2, bins=np.linspace(0, 500, 50+1), alpha=0.5)
plt.xlabel("MET [GeV]")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
plt.legend()
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def dist_xy(predict_met, name='dist.pdf'):
rel_err = predict_met
plt.figure()
plt.hist(rel_err, bins=np.linspace(-500, 500, 50+1))
plt.xlabel("MET [GeV]")
plt.ylabel("Events")
plt.figtext(0.25, 0.90, 'CMS', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14)
plt.figtext(0.35, 0.90, 'preliminary', style='italic', wrap=True, horizontalalignment='center', fontsize=14)
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_binned_predict_mean(predict_met, gen_met, binning, mini, maxi, genMET_cut, corr_check, name='predict_mean.pdf'):
bin_ = int((maxi - mini)/binning)
X_genMET = np.zeros(bin_)
X_error = np.zeros(bin_)
y_predict = np.zeros(bin_)
y_error = np.zeros(bin_)
for j in range(bin_):
mask = (gen_met > (j * binning)) & (gen_met < ((j + 1) * binning))
X_genMET[j] = np.mean(gen_met[mask])
y_predict[j] = np.mean(predict_met[mask])
X_error[j] = np.std(gen_met[mask])
y_error[j] = np.std(predict_met[mask])
plt.errorbar(X_genMET, y_predict, xerr=X_error, yerr=y_error,
label='cut = '+str(genMET_cut)+', '+str(corr_check)+'.')
# x = y plot
X = np.arange(mini, maxi, binning)
plt.plot(X, X, 'r-')
###
plt.xlim(mini, maxi)
plt.ylim(mini, 700)
plt.xlabel('Gen MET mean [GeV]', fontsize=16)
#plt.ylabel('PUPPI MET mean [GeV]', fontsize = 16)
plt.ylabel('predicted MET mean [GeV]', fontsize=16)
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_binned_predict_mean_opaque(predict_met, predict_met2, gen_met, binning, mini, maxi, genMET_cut, corr_check, name='predict_mean.pdf'):
bin_ = int((maxi - mini)/binning)
X_genMET = np.zeros(bin_)
X_error = np.zeros(bin_)
y_predict = np.zeros(bin_)
y_error = np.zeros(bin_)
for j in range(bin_):
mask = (gen_met > (j * binning)) & (gen_met < ((j + 1) * binning))
X_genMET[j] = np.mean(gen_met[mask])
y_predict[j] = np.mean(predict_met[mask])
X_error[j] = np.std(gen_met[mask])
y_error[j] = np.std(predict_met[mask])
X_genMET2 = np.zeros(bin_)
X_error2 = np.zeros(bin_)
y_predict2 = np.zeros(bin_)
y_error2 = np.zeros(bin_)
for j in range(bin_):
mask2 = (gen_met > (j * binning)) & (gen_met < ((j + 1) * binning))
X_genMET2[j] = np.mean(gen_met[mask2])
y_predict2[j] = np.mean(predict_met2[mask2])
X_error2[j] = np.std(gen_met[mask2])
y_error2[j] = np.std(predict_met2[mask2])
plt.errorbar(X_genMET2, y_predict2, xerr=X_error2, yerr=y_error2,
label='PUPPI MET', color='green', uplims=y_error2, lolims=y_error2)
plt.errorbar(X_genMET, y_predict, xerr=X_error, yerr=y_error,
label='Predicted MET', color='red', uplims=y_error, lolims=y_error)
# x = y plot
X = np.arange(mini, maxi, binning)
plt.plot(X, X, 'r-')
##
plt.xlim(mini, maxi)
plt.ylim(mini, maxi)
plt.xlabel('Gen MET mean [GeV]', fontsize=16)
#plt.ylabel('PUPPI MET mean [GeV]', fontsize = 16)
plt.ylabel('predicted MET mean [GeV]', fontsize=16)
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def MET_binned_predict_ratio(predict_met, gen_met, binning, mini, maxi, genMET_cut, comment, name='predict_mean.pdf'):
bin_ = (maxi - mini)/binning
X_genMET = np.zeros(bin_)
X_error = np.zeros(bin_)
y_predict = np.zeros(bin_)
y_error = np.zeros(bin_)
for j in range(bin_):
mask = (gen_met > (j * binning)) & (gen_met < ((j + 1) * binning))
X_genMET[j] = np.mean(gen_met[mask])
y_predict[j] = np.mean(predict_met[mask]/gen_met[mask])
X_error[j] = np.std(gen_met[mask])
y_error[j] = np.std(predict_met[mask]/gen_met[mask])
plt.errorbar(X_genMET, y_predict, xerr=X_error, yerr=y_error,
label='cut = '+str(genMET_cut)+', '+str(comment)+'.')
# y = 1 plot
X = np.arange(mini, maxi, binning)
y = np.zeros(bin_)
y[:] = 1
plt.plot(X, y, 'r-')
##
plt.xlim(mini, maxi)
plt.ylim(mini, 3)
plt.xlabel('Gen MET mean [GeV]', fontsize=16)
plt.ylabel('(predicted MET/Gen MET) mean [GeV]', fontsize=16)
plt.legend()
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")
def extract_result(feat_array, targ_array, path, name, mode):
np.save(''+path+''+name+'_feature_array_'+mode+'MET', feat_array)
np.save(''+path+''+name+'_target_array_'+mode+'MET', targ_array)
def histo_2D(predict_pT, gen_pT, min_, max_, name='2D_histo.png'):
X_hist = np.arange(0, 500, 20)
Y_hist = X_hist # 1.25*X_hist
#Y_hist_1 = 0.75*X_hist
plt.plot(X_hist, Y_hist, '-r')
#plt.plot(X_hist, Y_hist_1, '-r')
x_bins = np.linspace(min_, max_, 50)
y_bins = np.linspace(min_, max_, 50)
plt.hist2d(gen_pT, predict_pT, bins=[x_bins, y_bins], cmap=plt.cm.jet)
plt.colorbar()
plt.xlabel('gen MET [GeV]')
plt.ylabel('predicted MET [GeV]')
plt.savefig(name)
plt.show(block=False)
# plt.pause(5)
plt.close("all")