def generate_initial_params(hgg_bg, hgg_signal, n_sigma):
'''Input bg and signal dataframes, and a sigma value for signal injection.
Output parameters for the pdfs that describe those distributions.'''
# grab a handful of bg events, and an ~X sigma number of signal events
hgg_bg_selection = hgg_bg[(hgg_bg.Mgg > 100) & (hgg_bg.Mgg < 180)][0:10000].Mgg
n_bg_under_sig = hgg_bg_selection[(118 < hgg_bg_selection) &
(hgg_bg_selection < 133)].size
n_sig = int(n_sigma*np.sqrt(n_bg_under_sig))
hgg_signal_selection = hgg_signal[(hgg_signal.Mgg >= 118) &
(hgg_signal.Mgg <= 133)][0:n_sig].Mgg
data_bg = hgg_bg_selection.values
data_sig = hgg_signal_selection.values
# fit to the data distributions
bg_model = ff.Model(bg_pdf, ['a1', 'a2', 'a3'])
bg_model.set_bounds([(-1., 1.), (-1., 1.), (-1., 1.)])
bg_fitter = ff.NLLFitter(bg_model, data_bg)
bg_result = bg_fitter.fit([0.0, 0.0, 0.0])
sig_model = ff.Model(sig_pdf, ['mu', 'sigma'])
sig_model.set_bounds([(110, 130), (1, 5)])
sig_fitter = ff.NLLFitter(sig_model, data_sig)
sig_result = sig_fitter.fit([120.0, 2])
n_bg = len(data_bg)
be_bg = bayesian_blocks(data_bg, p0=0.02)
be_sig = bayesian_blocks(data_sig, p0=0.02)
return bg_result, sig_result, n_bg, n_sig, be_bg, be_sig
def generate_initial_params(hgg_bg, hgg_signal, n_sigma):
'''Input bg and signal dataframes, and a sigma value for signal injection.
Output parameters for the pdfs that describe those distributions.'''
# grab a handful of bg events, and an ~X sigma number of signal events
hgg_bg_selection = hgg_bg[(hgg_bg.Mgg > 100)
& (hgg_bg.Mgg < 180)][0:10000].Mgg
n_bg_under_sig = hgg_bg_selection[(118 < hgg_bg_selection)
& (hgg_bg_selection < 133)].size
n_sig = int(n_sigma * np.sqrt(n_bg_under_sig))
hgg_signal_selection = hgg_signal[(hgg_signal.Mgg >= 118)
& (hgg_signal.Mgg <= 133)][0:n_sig].Mgg
data_bg = hgg_bg_selection.values
data_sig = hgg_signal_selection.values
# fit to the data distributions
bg_params = Parameters()
bg_params.add_many(('a1', 0., True, -1, 1, None, None),
('a2', 0., True, -1, 1, None, None),
('a3', 0., True, -1, 1, None, None))
bg_model = Model(bg_pdf, bg_params)
bg_fitter = NLLFitter(bg_model)
bg_result = bg_fitter.fit(data_bg, calculate_corr=False)
# bg_model = ff.Model(bg_pdf, ['a1', 'a2', 'a3'])
# bg_model.set_bounds([(-1., 1.), (-1., 1.), (-1., 1.)])
# bg_fitter = ff.NLLFitter(bg_model, data_bg)
# bg_result = bg_fitter.fit([0.0, 0.0, 0.0])
# sig_model = ff.Model(sig_pdf, ['mu', 'sigma'])
# sig_model.set_bounds([(110, 130), (1, 5)])
# sig_fitter = ff.NLLFitter(sig_model, data_sig)
# sig_result = sig_fitter.fit([120.0, 2])
sig_params = Parameters()
sig_params.add_many(
('mu', 125, True, 110, 130, None, None),
('sigma', 1, True, 1, 5, None, None),
)
sig_model = Model(sig_pdf, sig_params)
sig_fitter = NLLFitter(sig_model)
sig_result = sig_fitter.fit(data_sig)
n_bg = len(data_bg)
be_bg = bayesian_blocks(data_bg, p0=0.02)
be_sig = bayesian_blocks(data_sig, p0=0.02)
return bg_result, sig_result, n_bg, n_sig, be_bg, be_sig
def generate_initial_params(data_bg_mul2, data_bg_mul8, seed=5):
# fit to the data distributions
bg_model = ff.Model(bg_pdf, ['alpha', 'beta', 'gamma'])
bg_model.set_bounds([(1e-20, 20), (-10, -1e-20), (1e-20, 10)])
bg_fitter = ff.NLLFitter(bg_model, data_bg_mul2)
bg_result = bg_fitter.fit([-1.80808e+01, -8.21174e-02, 8.06289e-01])
n_bg = len(data_bg_mul8)
gRandom.SetSeed(seed)
# Set up bg sampling
bg_pdf_ROOT = functools.partial(bg_pdf, doROOT=True)
tf1_bg_pdf = TF1("tf1_bg_pdf", bg_pdf_ROOT, 2800, 13000, 3)
tf1_bg_pdf.SetParameters(*bg_result.x)
mc_bg = [tf1_bg_pdf.GetRandom() for i in range(n_bg)]
be_bg = bayesian_blocks(mc_bg, p0=0.02)
be_bg[-1] += 0.1
be_bg = np.append(be_bg, [13000])
be_bg[0] = 2800
# print be_bg
# hist(data_bg_mul8, bins=be_bg, scale='binwidth')
# plt.show()
return bg_result, n_bg, be_bg
def generate_initial_params(data_bg_mul2, data_bg_mul8, seed=5):
# fit to the data distributions
bg_params = Parameters()
bg_params.add_many(
('alpha', -1.80808e+01, True, 1e-20, 20, None, None),
('beta', -8.21174e-02, True, -10, -1e-20, None, None),
('gamma', 8.06289e-01, True, 1e-20, 10, None, None)
)
bg_model = Model(bg_pdf, bg_params)
bg_fitter = NLLFitter(bg_model)
bg_result = bg_fitter.fit(data_bg_mul2, calculate_corr=False)
n_bg = len(data_bg_mul8)
gRandom.SetSeed(seed)
# Set up bg sampling
bg_pdf_ROOT = functools.partial(bg_pdf, doROOT=True)
tf1_bg_pdf = TF1("tf1_bg_pdf", bg_pdf_ROOT, 2800, 13000, 3)
tf1_bg_pdf.SetParameters(*bg_result.x)
mc_bg = [tf1_bg_pdf.GetRandom() for i in range(n_bg)]
be_bg = bayesian_blocks(mc_bg, p0=0.02)
be_bg[-1] += 0.1
be_bg = np.append(be_bg, [13000])
be_bg[0] = 2800
# print be_bg
# hist(data_bg_mul8, bins=be_bg, scale='binwidth')
# plt.show()
return bg_result, n_bg, be_bg
def generateToy():
plt.close('all')
def poly1(x):
return 2*x/100
nentries = 100
p0=0.01
x = np.arange(0.0, 10, 0.1)
np.random.seed(12345)
ROOT.gRandom.SetSeed(8675309)
poly1_gen = TF1("poly1","2*x",0,10)
my_rands = []
for i in range(nentries):
my_rands.append(poly1_gen.GetRandom())
fig = plt.figure()
hist(my_rands,bins=10,histtype='stepfilled',alpha=0.2,label='10 bins',normed=True)
bb_edges = bayesian_blocks(my_rands,p0=p0)
hist(my_rands,bins=bb_edges,histtype='stepfilled',alpha=0.2,label='10 bins',normed=True)
plt.plot(x,poly1(x),'k')
plt.show()
def generateToy():
plt.close('all')
def poly1(x):
return 2*x/100
nentries = 100
p0=0.01
x = np.arange(0.0, 10, 0.1)
np.random.seed(12345)
ROOT.gRandom.SetSeed(8675309)
poly1_gen = TF1("poly1","2*x",0,10)
my_rands = []
for i in xrange(nentries):
my_rands.append(poly1_gen.GetRandom())
fig = plt.figure()
hist(my_rands,bins=10,histtype='stepfilled',alpha=0.2,label='10 bins',normed=True)
bb_edges = bayesian_blocks(my_rands,p0=p0)
hist(my_rands,bins=bb_edges,histtype='stepfilled',alpha=0.2,label='10 bins',normed=True)
plt.plot(x,poly1(x),'k')
plt.show()
def do_bh_analysis():
#set up variables
plt.close('all')
normed = True
log = True
STs = [2, 3, 4, 5, 6, 7, 8, 9, 10]
ST_low = [2300, 2300, 2300, 2600, 2600, 2600, 2800, 2800, 2900]
ST_low = [2500] * 9
ST_low_dict = dict(list(zip(STs, ST_low)))
samples = 5000
seed = 2
p0 = 0.005
bg_est = 'data_driven' #'data_driven','mc','low_ST'
mode = 'signal_search' #'no_signal','signal_search','signal_inj','signal_search_inj'
if mode not in [
'no_signal', 'signal_search', 'signal_inj', 'signal_search_inj'
]:
raise KeyError('mode is not allowed!')
if bg_est not in ['data_driven', 'mc', 'low_ST']:
raise KeyError('bg_est is not allowed!')
if mode in ['signal_search', 'signal_inj', 'signal_search_inj']:
signal_num = 10
else:
signal_num = 0
df_mc = pkl.load(open('../../files/BH/BHTree_mc.p', 'rb'))
df_signal = pkl.load(open('../../files/BH/BHTree_signal.p', 'rb'))
df_data = pkl.load(open('../../files/BH/BHTree_data.p', 'rb'))
weights = df_mc.weightTree.unique() #[0.27436519,0.0401976,0.01657276]
df_mc_list = []
for weight in weights:
df_mc_list.append(df_mc[np.isclose(df_mc.weightTree, weight)])
all_edges = {}
#for ST in range(2,11):
for ST in [8]:
my_ST_data = df_data[df_data['ST_mul' + str(ST)] > ST_low_dict[ST]][
'ST_mul' + str(ST)].values
nentries = len(my_ST_data)
my_ST_mc = []
if bg_est == 'low_ST':
my_ST_mc = df_data[df_data['ST_mul2'] > ST_low_dict[ST]][
df_data['n_multiplicity'] == 2]['ST_mul2'].values
else:
df_mc_st_list = [
df[df['ST_mul' + str(ST)] > ST_low_dict[ST]]['ST_mul' +
str(ST)]
for df in df_mc_list
]
if mode in ['signal_search', 'signal_inj', 'signal_search_inj']:
my_ST_signal = df_signal[
df_signal['ST_mul' + str(ST)] > ST_low_dict[ST]]['ST_mul' +
str(ST)]
samples, rel_weights = find_sample_number(df_mc_st_list, weights)
for i, mc in enumerate(df_mc_st_list):
if samples * rel_weights[i] == 0: continue
my_ST_mc = np.append(
my_ST_mc,
mc.sample(int(samples * rel_weights[i]),
random_state=seed).values)
print('ST_mult', ST)
print(' n_data', nentries)
print(' n_mc', len(my_ST_mc))
#get the edges from bb, and the normalized bin values (integral of all hists is 1)
#if signal and inject:
# my_ST_data = np.append(my_ST_data,my_ST_signal.
if mode in ['signal_inj', 'signal_search_inj']:
my_ST_data = np.append(
my_ST_data,
my_ST_signal.sample(signal_num, random_state=seed).values)
nentries += signal_num
elif mode in ['signal_search']:
my_ST_signal = my_ST_signal.values
return my_ST_data, my_ST_mc, my_ST_signal
print(len(my_ST_data))
normed_counts_data, bb_edges = np.histogram(my_ST_data,
bayesian_blocks(my_ST_data,
p0=p0),
density=True)
normed_counts_data_nobb, nobb_edges = np.histogram(my_ST_data,
20,
density=True)
normed_counts_mc, _ = np.histogram(my_ST_mc, bb_edges, density=True)
normed_counts_mc_nobb, _ = np.histogram(my_ST_mc,
nobb_edges,
density=True)
if mode in ['signal_search', 'signal_search_inj']:
normed_counts_signal, _ = np.histogram(my_ST_signal,
bb_edges,
density=True)
normed_counts_signal_nobb, _ = np.histogram(my_ST_signal,
nobb_edges,
density=True)
#rescale the values so that the integral of the data hist is = num of entries
rescaled_counts_data = normed_counts_data * nentries
rescaled_counts_data_nobb = normed_counts_data_nobb * nentries
if mode in ['signal_search', 'signal_search_inj']:
rescaled_counts_mc = normed_counts_mc * (nentries - signal_num)
rescaled_counts_mc_nobb = normed_counts_mc_nobb * (nentries -
signal_num)
rescaled_counts_signal = normed_counts_signal * signal_num
rescaled_counts_signal_nobb = normed_counts_signal_nobb * signal_num
else:
rescaled_counts_mc = normed_counts_mc * (nentries)
rescaled_counts_mc_nobb = normed_counts_mc_nobb * (nentries)
#properly calculate the error bars on the data
counts_data, _ = np.histogram(my_ST_data, bb_edges)
counts_data_nobb, _ = np.histogram(my_ST_data, nobb_edges)
rescaled_err = np.sqrt(counts_data) / (bb_edges[1:] - bb_edges[:-1])
rescaled_err_nobb = np.sqrt(counts_data_nobb) / (nobb_edges[1:] -
nobb_edges[:-1])
err = np.sqrt(counts_data)
#properly account for the BG error for ratio plot
counts_bg, _ = np.histogram(my_ST_mc, bb_edges)
counts_bg_nobb, _ = np.histogram(my_ST_mc, nobb_edges)
rescaled_err_bg = np.sqrt(counts_bg) / (bb_edges[1:] - bb_edges[:-1])
rescaled_err_bg_nobb = np.sqrt(counts_bg_nobb) / (nobb_edges[1:] -
nobb_edges[:-1])
if mode in ['signal_search', 'signal_search_inj']:
make_hist_ratio_blackhole(bb_edges,
rescaled_counts_data,
rescaled_counts_mc,
rescaled_err,
str(ST),
suffix=None,
bg_est=bg_est,
signal=rescaled_counts_signal,
mode=mode)
make_hist_ratio_blackhole2(nobb_edges,
rescaled_counts_data_nobb,
rescaled_counts_mc_nobb,
rescaled_err_nobb,
str(ST),
suffix='nobb',
bg_est=bg_est,
signal=rescaled_counts_signal_nobb,
mode=mode)
else:
make_hist_ratio_blackhole(bb_edges,
rescaled_counts_data,
rescaled_counts_mc,
rescaled_err,
str(ST),
suffix=None,
bg_est=bg_est,
mode=mode)
make_hist_ratio_blackhole(nobb_edges,
rescaled_counts_data_nobb,
rescaled_counts_mc_nobb,
rescaled_err_nobb,
str(ST),
suffix='nobb',
bg_est=bg_est,
mode=mode)
plt.show()
all_edges[ST] = bb_edges
for key in all_edges:
print('ST' + str(key), all_edges[key])
return all_edges
binned_A_100_mle = [[] for i in range(len(sig_params))]
binned_A_200_mle = [[] for i in range(len(sig_params))]
binned_A_400_mle = [[] for i in range(len(sig_params))]
binned_A_1000_mle = [[] for i in range(len(sig_params))]
binned_A_2000_mle = [[] for i in range(len(sig_params))]
cnc_A_mle = [[] for i in range(len(sig_params))]
sig_pdf_ROOT = functools.partial(sig_pdf, doROOT=True)
tf1_sig_pdf = TF1("tf1_sig_pdf", sig_pdf_ROOT, 2800, 13000, 2)
for i, sig_p in enumerate(tqdm_notebook(sig_params, desc='Signal Model')):
n_sig = n_bg
tf1_sig_pdf.SetParameters(*sig_p)
mc_sig = [tf1_sig_pdf.GetRandom() for ns in range(n_sig)]
be_sig = bayesian_blocks(mc_sig, p0=0.02)
true_sig_bc_bb = get_true_bin_content(be_bg, sig_pdf, sig_p)
true_sig_bc_50GeV = get_true_bin_content(be_50GeV, sig_pdf, sig_p)
true_sig_bc_100GeV = get_true_bin_content(be_100GeV, sig_pdf, sig_p)
true_sig_bc_200GeV = get_true_bin_content(be_200GeV, sig_pdf, sig_p)
true_sig_bc_400GeV = get_true_bin_content(be_400GeV, sig_pdf, sig_p)
true_sig_bc_1000GeV = get_true_bin_content(be_1000GeV, sig_pdf, sig_p)
true_sig_bc_2000GeV = get_true_bin_content(be_2000GeV, sig_pdf, sig_p)
be_hybrid = np.sort(np.unique(np.concatenate([be_bg, be_sig])))
true_bg_bc_bb_hybrid = get_true_bin_content(be_hybrid, bg_pdf,
bg_result.x)
true_sig_bc_bb_hybrid = get_true_bin_content(be_hybrid, sig_pdf, sig_p)
def do_bh_analysis():
#set up variables
plt.close('all')
normed = True
log = True
STs = [2,3,4,5,6,7,8,9,10]
ST_low = [2300,2300,2300,2600,2600,2600,2800,2800,2900]
ST_low = [2500]*9
ST_low_dict = dict(zip(STs,ST_low))
samples = 5000
seed = 2
p0=0.005
bg_est = 'data_driven' #'data_driven','mc','low_ST'
mode = 'signal_search' #'no_signal','signal_search','signal_inj','signal_search_inj'
if mode not in ['no_signal','signal_search','signal_inj','signal_search_inj']: raise KeyError('mode is not allowed!')
if bg_est not in ['data_driven','mc','low_ST']: raise KeyError('bg_est is not allowed!')
if mode in ['signal_search','signal_inj','signal_search_inj']:
signal_num = 10
else:
signal_num = 0
df_mc = pkl.load(open('../../files/BH/BHTree_mc.p','rb'))
df_signal = pkl.load(open('../../files/BH/BHTree_signal.p','rb'))
df_data = pkl.load(open('../../files/BH/BHTree_data.p','rb'))
weights = df_mc.weightTree.unique()#[0.27436519,0.0401976,0.01657276]
df_mc_list = []
for weight in weights:
df_mc_list.append(df_mc[np.isclose(df_mc.weightTree,weight)])
all_edges = {}
#for ST in range(2,11):
for ST in [8]:
my_ST_data = df_data[df_data['ST_mul'+str(ST)]>ST_low_dict[ST]]['ST_mul'+str(ST)].values
nentries = len(my_ST_data)
my_ST_mc = []
if bg_est == 'low_ST':
my_ST_mc = df_data[df_data['ST_mul2']>ST_low_dict[ST]][df_data['n_multiplicity']==2]['ST_mul2'].values
else:
df_mc_st_list = [df[df['ST_mul'+str(ST)]>ST_low_dict[ST]]['ST_mul'+str(ST)] for df in df_mc_list]
if mode in ['signal_search','signal_inj','signal_search_inj']:
my_ST_signal = df_signal[df_signal['ST_mul'+str(ST)]>ST_low_dict[ST]]['ST_mul'+str(ST)]
samples,rel_weights = find_sample_number(df_mc_st_list,weights)
for i,mc in enumerate(df_mc_st_list):
if samples*rel_weights[i]==0: continue
my_ST_mc = np.append(my_ST_mc, mc.sample(int(samples*rel_weights[i]),random_state=seed).values)
print 'ST_mult',ST
print ' n_data',nentries
print ' n_mc',len(my_ST_mc)
#get the edges from bb, and the normalized bin values (integral of all hists is 1)
#if signal and inject:
# my_ST_data = np.append(my_ST_data,my_ST_signal.
if mode in ['signal_inj','signal_search_inj']:
my_ST_data = np.append(my_ST_data, my_ST_signal.sample(signal_num,random_state=seed).values)
nentries+=signal_num
elif mode in ['signal_search']:
my_ST_signal = my_ST_signal.values
return my_ST_data, my_ST_mc, my_ST_signal
print len(my_ST_data)
normed_counts_data, bb_edges = np.histogram(my_ST_data,bayesian_blocks(my_ST_data,p0=p0), density=True)
normed_counts_data_nobb, nobb_edges = np.histogram(my_ST_data,20, density=True)
normed_counts_mc, _= np.histogram(my_ST_mc,bb_edges, density=True)
normed_counts_mc_nobb, _= np.histogram(my_ST_mc,nobb_edges, density=True)
if mode in ['signal_search','signal_search_inj']:
normed_counts_signal, _= np.histogram(my_ST_signal,bb_edges, density=True)
normed_counts_signal_nobb, _= np.histogram(my_ST_signal,nobb_edges, density=True)
#rescale the values so that the integral of the data hist is = num of entries
rescaled_counts_data = normed_counts_data*nentries
rescaled_counts_data_nobb = normed_counts_data_nobb*nentries
if mode in ['signal_search','signal_search_inj']:
rescaled_counts_mc = normed_counts_mc*(nentries-signal_num)
rescaled_counts_mc_nobb = normed_counts_mc_nobb*(nentries-signal_num)
rescaled_counts_signal = normed_counts_signal*signal_num
rescaled_counts_signal_nobb = normed_counts_signal_nobb*signal_num
else:
rescaled_counts_mc = normed_counts_mc*(nentries)
rescaled_counts_mc_nobb = normed_counts_mc_nobb*(nentries)
#properly calculate the error bars on the data
counts_data, _= np.histogram(my_ST_data,bb_edges)
counts_data_nobb, _= np.histogram(my_ST_data,nobb_edges)
rescaled_err = np.sqrt(counts_data)/(bb_edges[1:]-bb_edges[:-1])
rescaled_err_nobb = np.sqrt(counts_data_nobb)/(nobb_edges[1:]-nobb_edges[:-1])
err = np.sqrt(counts_data)
#properly account for the BG error for ratio plot
counts_bg, _= np.histogram(my_ST_mc,bb_edges)
counts_bg_nobb, _= np.histogram(my_ST_mc,nobb_edges)
rescaled_err_bg = np.sqrt(counts_bg)/(bb_edges[1:]-bb_edges[:-1])
rescaled_err_bg_nobb = np.sqrt(counts_bg_nobb)/(nobb_edges[1:]-nobb_edges[:-1])
if mode in ['signal_search','signal_search_inj']:
make_hist_ratio_blackhole(bb_edges, rescaled_counts_data, rescaled_counts_mc, rescaled_err, str(ST), suffix = None, bg_est=bg_est, signal = rescaled_counts_signal, mode = mode)
make_hist_ratio_blackhole2(nobb_edges, rescaled_counts_data_nobb, rescaled_counts_mc_nobb, rescaled_err_nobb, str(ST), suffix = 'nobb', bg_est=bg_est, signal = rescaled_counts_signal_nobb, mode=mode)
else:
make_hist_ratio_blackhole(bb_edges, rescaled_counts_data, rescaled_counts_mc, rescaled_err, str(ST), suffix = None, bg_est=bg_est, mode=mode)
make_hist_ratio_blackhole(nobb_edges, rescaled_counts_data_nobb, rescaled_counts_mc_nobb, rescaled_err_nobb, str(ST), suffix = 'nobb', bg_est=bg_est, mode=mode)
plt.show()
all_edges[ST]=bb_edges
for key in all_edges:
print 'ST'+str(key), all_edges[key]
return all_edges