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()
#! /usr/bin/env python
from __future__ import division
import numpy as np
from scipy import stats
from bb.tools.bayesian_blocks_modified import bayesian_blocks
from matplotlib import pyplot as plt
import cPickle as pkl
import scipy.stats as st
import cPickle as pkl
from bb.tools.bb_plotter import make_hist_ratio_blackhole
from bb.tools.hist_tools_modified import hist
import os
plt.close('all')
p0=0.05
current_dir = os.path.dirname(__file__)
bb_dir=os.path.join(current_dir,'../..')
df_data = pkl.load(open(bb_dir+'/files/BH/BH_paper_data.p','rb'))
data_ST_mul8 = df_data[df_data.ST_mul8_BB>=2800].ST_mul8_BB.values
bc,be,_ = hist(data_ST_mul8, p0=p0, bins='blocks', scale='binwidth', log=True)
print be
plt.show()
def bh_ratio_plots(data,
mc,
be,
title='Black Hole Visual Example',
save_name='bh_vis_ex',
do_ratio=False,
signal_mc=None,
n_sig=10):
xlims = (be[0], be[-1])
ratlims = (0, 6)
bin_centers = (be[1:] + be[:-1]) / 2
if do_ratio:
fig = plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax1 = fig.add_subplot(gs[0])
ax1.set_yscale("log", nonposy='clip')
ax2 = fig.add_subplot(gs[1], sharex=ax1)
ax1.grid(True)
ax2.grid(True)
# ax2.set_yscale("log", nonposy='clip')
plt.setp(ax1.get_xticklabels(), visible=False)
fig.subplots_adjust(hspace=0.001)
ax1.set_xlim(xlims)
else:
fig, ax1 = plt.subplots()
ax1.set_yscale("log", nonposy='clip')
ax1.grid(True)
ax1.set_xlim(xlims)
# print 'lims'
bc_d, _ = np.histogram(data, bins=be)
bc_mc, _ = np.histogram(mc,
bins=be,
weights=[len(data) / (len(mc))] * (len(mc)))
hist(data,
ax=ax1,
bins=be,
scale='binwidth',
histtype='marker',
markersize=10,
color='k',
errorbars=True,
suppress_zero=True,
label='Sim Data')
hist(mc,
ax=ax1,
bins=be,
scale='binwidth',
weights=[len(data) / (len(mc))] * (len(mc)),
histtype='stepfilled',
alpha=0.2,
label='Sim Background')
if do_ratio:
ratio = bc_d / bc_mc
ratio_err = np.sqrt(bc_d) / bc_mc
fill_between_steps(ax2,
be,
ratio + ratio_err,
ratio - ratio_err,
alpha=0.2,
step_where='pre',
linewidth=0,
color='red')
ax2.errorbar(bin_centers,
ratio,
yerr=None,
xerr=[
np.abs(be[0:-1] - bin_centers),
np.abs(be[1:] - bin_centers)
],
fmt='ok')
ax2.set_xlabel(r'$S_T$ (GeV)', fontsize=17)
ax2.set_ylabel('Data/BG', fontsize=17)
ax2.get_yaxis().get_major_formatter().set_useOffset(False)
ax2.axhline(1, linewidth=2, color='r')
ax2.yaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))
ax2.set_ylim(ratlims)
ax1.set_ylabel(r'N/$\Delta$x', fontsize=17)
ax1.set_title(title)
if not isinstance(signal_mc, type(None)):
hist(signal_mc,
ax=ax1,
bins=be,
scale='binwidth',
weights=[n_sig / (len(signal_mc))] * (len(signal_mc)),
histtype='step',
linestyle='--',
linewidth=2,
label='Sim Signal')
ax1.legend()
plt.savefig('figures/{}.pdf'.format(save_name))
plt.savefig('figures/{}.png'.format(save_name))
plt.show()
#! /usr/bin/env python
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from scipy import stats
from bb.tools.bayesian_blocks_modified import bayesian_blocks
from matplotlib import pyplot as plt
import six.moves.cPickle as pkl
import scipy.stats as st
import six.moves.cPickle as pkl
from bb.tools.bb_plotter import make_hist_ratio_blackhole
from bb.tools.hist_tools_modified import hist
import os
plt.close('all')
p0=0.05
current_dir = os.path.dirname(__file__)
bb_dir=os.path.join(current_dir,'../..')
df_data = pkl.load(open(bb_dir+'/files/BH/BH_paper_data.p','rb'))
data_ST_mul8 = df_data[df_data.ST_mul8_BB>=2800].ST_mul8_BB.values
bc,be,_ = hist(data_ST_mul8, p0=p0, bins='blocks', scale='binwidth', log=True)
print(be)
plt.show()
