def make_bb_plot(data, p0, save_dir, range=None,title='Plot of thing vs thing', xlabel='X axis', ylabel='Y axis',save_name='plot', overlay_reg_bins = True, edges=None,scale=None, bins=80):
normed=False
if scale=='normed':
normed=True
scale=None
if edges != None:
bb_edges=edges
else:
bb_edges = bayesian_blocks(data,p0=p0)
plt.figure()
#bin_content = np.histogram(data,bb_edges,density=True)[0]
#plt.yscale('log', nonposy='clip')
hist(data,bins=bins,range=range,histtype='stepfilled',alpha=0.2,label='{} bins'.format(bins),normed=normed,scale=scale)
#hist(data,bins=100,histtype='stepfilled',alpha=0.2,label='100 bins',normed=False)
bb_content, bb_edges,_ = hist(data,bins=bb_edges,range=range,histtype='step',linewidth=2.0,color='crimson',label='b blocks',normed=normed,scale=scale)
#fill_between_steps(plt.gca(), bb_edges, bin_content*len(data),bin_content*len(data)/2, alpha=0.5, step_where='pre',linewidth=2,label='norm attempt')
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_bb.pdf')
return bb_content,bb_edges
def generateToy():
np.random.seed(12345)
fig,ax = plt.subplots(4,sharex=True)
#fig,ax = plt.subplots(2)
powerlaw_arg = 2
triang_arg=0.7
n_samples = 500
#generate simple line with slope 1, from 0 to 1
frozen_powerlaw = powerlaw(powerlaw_arg) #powerlaw.pdf(x, a) = a * x**(a-1)
#generate triangle with peak at 0.7
frozen_triangle = triang(triang_arg) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_uniform = uniform(0.2,0.5)
frozen_uniform2 = uniform(0.3,0.2)
x = np.linspace(0,1)
signal = np.random.normal(0.5, 0.1, n_samples/2)
data_frame = pd.DataFrame({'powerlaw':powerlaw.rvs(powerlaw_arg,size=n_samples),
'triangle':triang.rvs(triang_arg,size=n_samples),
'uniform':np.concatenate((uniform.rvs(0.2,0.5,size=n_samples/2),uniform.rvs(0.3,0.2,size=n_samples/2))),
'powerlaw_signal':np.concatenate((powerlaw.rvs(powerlaw_arg,size=n_samples/2),signal))})
ax[0].plot(x, frozen_powerlaw.pdf(x), 'k-', lw=2, label='powerlaw pdf')
hist(data_frame['powerlaw'],bins=100,normed=True,histtype='stepfilled',alpha=0.2,label='100 bins',ax=ax[0])
#hist(data_frame['powerlaw'],bins='blocks',fitness='poly_events',normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[0])
ax[0].legend(loc = 'best')
ax[1].plot(x, frozen_triangle.pdf(x), 'k-', lw=2, label='triangle pdf')
hist(data_frame['triangle'],bins=100,normed=True,histtype='stepfilled',alpha=0.2,label='100 bins',ax=ax[1])
hist(data_frame['triangle'],bins='blocks',fitness='poly_events',normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[1])
ax[1].legend(loc = 'best')
#ax[0].plot(x, frozen_powerlaw.pdf(x), 'k-', lw=2, label='powerlaw pdf')
hist(data_frame['powerlaw_signal'],bins=100,normed=True,histtype='stepfilled',alpha=0.2,label='100 bins',ax=ax[2])
#hist(data_frame['powerlaw_signal'],bins='blocks',normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[2])
ax[2].legend(loc = 'best')
ax[3].plot(x, frozen_uniform.pdf(x)+frozen_uniform2.pdf(x), 'k-', lw=2, label='uniform pdf')
hist(data_frame['uniform'],bins=100,normed=True,histtype='stepfilled',alpha=0.2,label='100 bins',ax=ax[3])
#hist(data_frame['uniform'],bins='blocks',fitness = 'poly_events',p0=0.05,normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[3])
ax[3].legend(loc = 'best')
plt.show()
fig.savefig('plots/toy_plots.png')
def make_hist_ratio_blackhole2(bin_edges, data, mc, data_err, label, suffix = None, bg_est='data_driven', signal=None, mode='no_signal'):
bin_centres = (bin_edges[:-1] + bin_edges[1:])/2.
fig = plt.figure()
gs = gridspec.GridSpec(2,1,height_ratios=[3,1])
ax1=fig.add_subplot(gs[0])
ax2=fig.add_subplot(gs[1],sharex=ax1)
ax1.grid(True)
ax2.grid(True)
plt.setp(ax1.get_xticklabels(), visible=False)
fig.subplots_adjust(hspace=0.001)
#ax = plt.gca()
ax1.set_yscale("log", nonposy='clip')
if bg_est in ['data_driven','mc']:
#fill_between_steps(ax1, bin_edges, mc,1e-4, alpha=0.2, step_where='pre',linewidth=0,label='QCD MC')
hist(np.asarray([mc,signal]).T,bin_edges, ax=ax1, alpha=0.2)
else:
fill_between_steps(ax1, bin_edges, mc,1e-4, alpha=0.2, step_where='pre',linewidth=0,label='ST_mul2 excl. (normed)')
if mode in ['signal_search','signal_search_inj']:
fill_between_steps(ax1, bin_edges,mc+signal,mc,alpha=0.6,step_where='pre',linewidth=0,label='Signal', color='darkgreen')
ax1.errorbar(bin_centres, data, yerr=data_err, fmt='ok',label='data')
#plt.semilogy()
ax1.legend()
ax1.set_ylim(1e-4,ax1.get_ylim()[1])
if bg_est=='data_driven':
ax1.set_title('ST_mult '+label+' QCD MC and real data, binned from data')
elif bg_est=='mc':
ax1.set_title('ST_mult '+label+' QCD MC and real data, binned from MC')
elif bg_est=='low_ST':
ax1.set_title('ST_mult '+label+' data, bg est from ST mult_2 data')
if mode in ['signal_search','signal_search_inj']:
ratio = data/(mc+signal)
ratio_err = data_err/(mc+signal)
else:
ratio = data/mc
ratio_err = data_err/mc
fill_between_steps(ax2, bin_edges, ratio+ratio_err ,ratio-ratio_err, alpha=0.2, step_where='pre',linewidth=0,color='red')
ax2.errorbar(bin_centres, ratio, yerr=None, xerr=[np.abs(bin_edges[0:-1]-bin_centres),np.abs(bin_edges[1:]-bin_centres)], fmt='ok')
ax2.set_xlabel('ST (GeV)',fontsize=17)
ax2.set_ylabel('Data/BG',fontsize=17)
ax1.set_ylabel(r'N/$\Delta$x',fontsize=17)
ylims=[0.1,2]
#ylims = ax2.get_ylim()
#if ylims[0]>1: ylims[0] = 0.995
#if ylims[1]<1: ylims[1] = 1.005
ax2.set_ylim(ylims[0],ylims[1])
ax2.get_yaxis().get_major_formatter().set_useOffset(False)
ax2.axhline(1,linewidth=2,color='r')
tickbins = len(ax1.get_yticklabels()) # added
ax2.yaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))
if suffix: suffix = '_'.join([suffix,mode])
else: suffix = mode
if bg_est=='data_driven':
save_name = '../../plots/ST_mul'+label+'_mc_and_data_normed_databin'
elif bg_est=='mc':
save_name = '../../plots/ST_mul'+label+'_mc_and_data_normed_mcbin'
else:
save_name = '../../plots/ST_mul'+label+'_mc_and_data_normed_st2_bg'
if suffix: save_name+='_'+suffix
save_name+='.pdf'
plt.savefig(save_name)
def make_comp_plots(data, p0, save_dir,title='Plot of thing vs thing', xlabel='X axis', ylabel='Y axis',save_name='plot'):
bb_edges = bayesian_blocks(data,p0=p0)
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins=100,histtype='stepfilled',alpha=0.2,label='100 bins',normed=True)
hist(data,bins=bb_edges,histtype='step',linewidth=2.0,color='crimson',label='b blocks',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_binsVbb.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,'knuth',histtype='stepfilled',alpha=0.2,label='knuth',normed=True)
hist(data,bins=bb_edges,histtype='step',linewidth=2.0,color='crimson',label='b blocks',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_knuthVbb.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,'scott',histtype='stepfilled',alpha=0.2,label='scott',normed=True)
hist(data,bins=bb_edges,histtype='step',linewidth=2.0,color='crimson',label='b blocks',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_scottVbb.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,'freedman',histtype='stepfilled',alpha=0.2,label='freedman',normed=True)
hist(data,bins=bb_edges,histtype='step',linewidth=2.0,color='crimson',label='b blocks',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_freedmanVbb.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins=bb_edges,histtype='stepfilled',alpha=0.4,label='b blocks',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_bb.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins=100,histtype='stepfilled',alpha=0.4,label='100 bins',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_bins.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins='knuth',histtype='stepfilled',alpha=0.4,label='knuth',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_knuth.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins='scott',histtype='stepfilled',alpha=0.4,label='scott',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_scott.pdf')
plt.figure()
plt.yscale('log', nonposy='clip')
hist(data,bins='freedman',histtype='stepfilled',alpha=0.4,label='freedman',normed=True)
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.savefig(save_dir+save_name+'_freedman.pdf')
def generateToy():
np.random.seed(12345)
fig,ax = plt.subplots()
triang_arg=0.5
#frozen_triangle = triang(c=triang_arg, loc=2) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_triangle = triang(c=0.5,loc=2) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_powerlaw = powerlaw(2) #powerlaw.pdf(x, a) = a * x**(a-1)
x = np.linspace(0,1,20)
x2 = np.linspace(0,1,20)
nx = x
nx2 = x2
#nd = frozen_powerlaw.ppf(nx)
#nd = np.array([0,0.3162,0.4472,0.5477,0.6324,0.7071,0.7746,0.8367,0.8944,0.9487])
nd = np.array([0,0.140175,0.264911,0.378405,0.48324,0.581139,0.67332,0.760682,0.843909,0.923538])
#nd = np.array([0.0723805,0.204159,0.322876,0.431782,0.532971,0.627882,0.717556,0.802776,0.884144,0.962142])
#pdf = frozen_powerlaw.pdf(x)
#nd = frozen_triangle.ppf(nx)
#print x
#print nd
#raw_input()
#pdf = frozen_triangle.pdf(x)
#print nd
#print pdf
#raw_input()
#for i in range(len(nd)-1):
# print (nd[i+1]-nd[i])*(nd[i+1]+nd[i])
#raw_input()
#nd2 = frozen_triangle2.ppf(nx2)
#pdf2 = frozen_triangle2.pdf(x2)
#print nd,nd2
#ndc = np.concatenate((nd,nd2),axis=0)
#print 'ndc', ndc
#nxc = np.concatenate((nx,nx2))
#print pdf, pdf2
#pdfc = np.concatenate((pdf,pdf2))
#xc = np.concatenate((x,x2))
#plt.plot(nd,len(nx)*[1],"x")
#plt.plot(x,pdf)
#hist(nd,'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#plt.plot(nd[0:11],len(nx[0:11])*[1],"x")
#plt.plot(x[0:11],pdf[0:11])
#hist(nd[0:11],'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#hist(ndc,bins=50,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#plt.plot(nd[11:],len(nx[11:])*[1],"x")
#plt.plot(x[11:],pdf[11:])
#hist(nd[11:],'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
print(nd)
plt.plot(nd,len(nd)*[1],"x")
#plt.plot(x,pdf)
hist(nd,'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax)
plt.show()
fig.savefig('plots/toy_plots2.png')
#weights: array([ 0.27436519, 0.04019762, 0.01657276])
df_mc1 = df_mc[np.isclose(df_mc.weightTree,0.27436519)]
df_mc2 = df_mc[np.isclose(df_mc.weightTree, 0.0401976)]
df_mc3 = df_mc[np.isclose(df_mc.weightTree, 0.01657276)]
#ratio: 42:5:1
for ST in [5]:
#my_ST = df_mc1[df_mc1['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*42,random_state=seed,replace=False).values
my_ST = []
my_ST = np.append(my_ST, df_mc2[df_mc2['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*5,random_state=seed).values)
my_ST = np.append(my_ST, df_mc3[df_mc3['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*1,random_state=seed).values)
fig = plt.figure()
ax = plt.gca()
hist(my_ST, bins=200, histtype='bar',alpha=0.2, label='standard histogram',normed=normed,log=log)
print('doing bb')
p0=0.01
hist(my_ST, bins = 'blocks', fitness = 'events', p0=p0, ax = ax, histtype='step', label='Bayesian Blocks', linewidth=2,normed=normed,log=log)
ax.legend()
plt.title('ST_mul'+str(ST))
plt.xlabel('ST (GeV)')
if normed:
plt.ylabel(r'N/$\Sigma$N$\Delta$x')
else:
plt.ylabel('N')
plt.savefig('plots/ST_mul'+str(ST)+'_MC.pdf')
plt.show()
'''
for key in ST_dict_MC.iterkeys():
def generateToy():
np.random.seed(12345)
fig, ax = plt.subplots(4, sharex=True)
#fig,ax = plt.subplots(2)
powerlaw_arg = 2
triang_arg = 0.7
n_samples = 500
#generate simple line with slope 1, from 0 to 1
frozen_powerlaw = powerlaw(
powerlaw_arg) #powerlaw.pdf(x, a) = a * x**(a-1)
#generate triangle with peak at 0.7
frozen_triangle = triang(
triang_arg
) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_uniform = uniform(0.2, 0.5)
frozen_uniform2 = uniform(0.3, 0.2)
x = np.linspace(0, 1)
signal = np.random.normal(0.5, 0.1, n_samples / 2)
data_frame = pd.DataFrame({
'powerlaw':
powerlaw.rvs(powerlaw_arg, size=n_samples),
'triangle':
triang.rvs(triang_arg, size=n_samples),
'uniform':
np.concatenate((uniform.rvs(0.2, 0.5, size=n_samples / 2),
uniform.rvs(0.3, 0.2, size=n_samples / 2))),
'powerlaw_signal':
np.concatenate((powerlaw.rvs(powerlaw_arg,
size=n_samples / 2), signal))
})
ax[0].plot(x, frozen_powerlaw.pdf(x), 'k-', lw=2, label='powerlaw pdf')
hist(data_frame['powerlaw'],
bins=100,
normed=True,
histtype='stepfilled',
alpha=0.2,
label='100 bins',
ax=ax[0])
#hist(data_frame['powerlaw'],bins='blocks',fitness='poly_events',normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[0])
ax[0].legend(loc='best')
ax[1].plot(x, frozen_triangle.pdf(x), 'k-', lw=2, label='triangle pdf')
hist(data_frame['triangle'],
bins=100,
normed=True,
histtype='stepfilled',
alpha=0.2,
label='100 bins',
ax=ax[1])
hist(data_frame['triangle'],
bins='blocks',
fitness='poly_events',
normed=True,
histtype='stepfilled',
alpha=0.2,
label='b blocks',
ax=ax[1])
ax[1].legend(loc='best')
#ax[0].plot(x, frozen_powerlaw.pdf(x), 'k-', lw=2, label='powerlaw pdf')
hist(data_frame['powerlaw_signal'],
bins=100,
normed=True,
histtype='stepfilled',
alpha=0.2,
label='100 bins',
ax=ax[2])
#hist(data_frame['powerlaw_signal'],bins='blocks',normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[2])
ax[2].legend(loc='best')
ax[3].plot(x,
frozen_uniform.pdf(x) + frozen_uniform2.pdf(x),
'k-',
lw=2,
label='uniform pdf')
hist(data_frame['uniform'],
bins=100,
normed=True,
histtype='stepfilled',
alpha=0.2,
label='100 bins',
ax=ax[3])
#hist(data_frame['uniform'],bins='blocks',fitness = 'poly_events',p0=0.05,normed=True,histtype='stepfilled',alpha=0.2,label='b blocks',ax=ax[3])
ax[3].legend(loc='best')
plt.show()
fig.savefig('plots/toy_plots.png')
def generateToy():
np.random.seed(12345)
fig,ax = plt.subplots()
triang_arg=0.5
#frozen_triangle = triang(c=triang_arg, loc=2) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_triangle = triang(c=0.5,loc=2) #up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc+scale).
frozen_powerlaw = powerlaw(2) #powerlaw.pdf(x, a) = a * x**(a-1)
x = np.linspace(0,1,20)
x2 = np.linspace(0,1,20)
nx = x
nx2 = x2
#nd = frozen_powerlaw.ppf(nx)
#nd = np.array([0,0.3162,0.4472,0.5477,0.6324,0.7071,0.7746,0.8367,0.8944,0.9487])
nd = np.array([0,0.140175,0.264911,0.378405,0.48324,0.581139,0.67332,0.760682,0.843909,0.923538])
#nd = np.array([0.0723805,0.204159,0.322876,0.431782,0.532971,0.627882,0.717556,0.802776,0.884144,0.962142])
#pdf = frozen_powerlaw.pdf(x)
#nd = frozen_triangle.ppf(nx)
#print x
#print nd
#raw_input()
#pdf = frozen_triangle.pdf(x)
#print nd
#print pdf
#raw_input()
#for i in range(len(nd)-1):
# print (nd[i+1]-nd[i])*(nd[i+1]+nd[i])
#raw_input()
#nd2 = frozen_triangle2.ppf(nx2)
#pdf2 = frozen_triangle2.pdf(x2)
#print nd,nd2
#ndc = np.concatenate((nd,nd2),axis=0)
#print 'ndc', ndc
#nxc = np.concatenate((nx,nx2))
#print pdf, pdf2
#pdfc = np.concatenate((pdf,pdf2))
#xc = np.concatenate((x,x2))
#plt.plot(nd,len(nx)*[1],"x")
#plt.plot(x,pdf)
#hist(nd,'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#plt.plot(nd[0:11],len(nx[0:11])*[1],"x")
#plt.plot(x[0:11],pdf[0:11])
#hist(nd[0:11],'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#hist(ndc,bins=50,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
#plt.plot(nd[11:],len(nx[11:])*[1],"x")
#plt.plot(x[11:],pdf[11:])
#hist(nd[11:],'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax,normed=True)
print nd
plt.plot(nd,len(nd)*[1],"x")
#plt.plot(x,pdf)
hist(nd,'blocks',fitness='poly_events',p0=0.05,histtype='bar',alpha=0.2,label='b blocks',ax=ax)
plt.show()
fig.savefig('plots/toy_plots2.png')
fig, ax = plt.subplots()
plt.show()
plt.get_current_fig_manager().window.wm_geometry("-2800-600")
ax.set_xlim(0,0.55)
ax.set_ylim(0,5.2)
test_data=[ 0.03815414 , 0.09320462 , 0.11173259 , 0.3899594 , 0.48899476]
print test_data
my_hist = None
frame=0
for i in range(len(test_data)):
frame+=1
ax.plot(test_data[:i+1],len(test_data[:i+1])*[1],"o",zorder=40,color='k')
if my_hist != None and len(my_hist)>0:
for patch in my_hist:
patch.set_alpha(0.15)
plt.show()
plt.savefig('plots/frame{}.png'.format(frame))
#hist(test_data[:i+1],'blocks',fitness='events',p0=0.37,histtype='bar',alpha=0.2,label='b blocks',ax=ax,zorder=len(test_data)-i)
#my_hists.append(hist(test_data[:i+1],'blocks',fitness='events',p0=0.37,histtype='bar',label='b blocks',ax=ax,zorder=10-i)[-1])
my_hist = hist(test_data[:i+1],'blocks',fitness='events',p0=0.37,histtype='bar',label='b blocks',ax=ax,zorder=20-i)[-1]
#print my_hist
#plt.draw()
plt.show()
frame+=1
plt.savefig('plots/frame{}.png'.format(frame))
#raw_input()
#for patch in hist:
# patch.set_alpha(0.2)
#weights: array([ 0.27436519, 0.04019762, 0.01657276])
df_mc1 = df_mc[np.isclose(df_mc.weightTree,0.27436519)]
df_mc2 = df_mc[np.isclose(df_mc.weightTree, 0.0401976)]
df_mc3 = df_mc[np.isclose(df_mc.weightTree, 0.01657276)]
#ratio: 42:5:1
for ST in [5]:
#my_ST = df_mc1[df_mc1['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*42,random_state=seed,replace=False).values
my_ST = []
my_ST = np.append(my_ST, df_mc2[df_mc2['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*5,random_state=seed).values)
my_ST = np.append(my_ST, df_mc3[df_mc3['ST_mul'+str(ST)]>ST_low]['ST_mul'+str(ST)].sample(samples*1,random_state=seed).values)
fig = plt.figure()
ax = plt.gca()
hist(my_ST, bins=200, histtype='bar',alpha=0.2, label='standard histogram',normed=normed,log=log)
print 'doing bb'
p0=0.01
hist(my_ST, bins = 'blocks', fitness = 'events', p0=p0, ax = ax, histtype='step', label='Bayesian Blocks', linewidth=2,normed=normed,log=log)
ax.legend()
plt.title('ST_mul'+str(ST))
plt.xlabel('ST (GeV)')
if normed:
plt.ylabel(r'N/$\Sigma$N$\Delta$x')
else:
plt.ylabel('N')
plt.savefig('plots/ST_mul'+str(ST)+'_MC.pdf')
plt.show()
'''
for key in ST_dict_MC.iterkeys():
frame += 1
ax.plot(test_data[:i + 1],
len(test_data[:i + 1]) * [1],
"o",
zorder=40,
color='k')
if my_hist != None and len(my_hist) > 0:
for patch in my_hist:
patch.set_alpha(0.15)
plt.show()
plt.savefig('plots/frame{}.png'.format(frame))
#hist(test_data[:i+1],'blocks',fitness='events',p0=0.37,histtype='bar',alpha=0.2,label='b blocks',ax=ax,zorder=len(test_data)-i)
#my_hists.append(hist(test_data[:i+1],'blocks',fitness='events',p0=0.37,histtype='bar',label='b blocks',ax=ax,zorder=10-i)[-1])
my_hist = hist(test_data[:i + 1],
'blocks',
fitness='events',
p0=0.37,
histtype='bar',
label='b blocks',
ax=ax,
zorder=20 - i)[-1]
#print my_hist
#plt.draw()
plt.show()
frame += 1
plt.savefig('plots/frame{}.png'.format(frame))
#raw_input()
#for patch in hist:
# patch.set_alpha(0.2)
