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
ST_dict_data = pkl.load(open("files/BHTree.p", "rb"))
print "pickle loaded"
ST_dict_data[mult] = [i for i in ST_dict_data[mult] if i > 2300]
nentries = len(ST_dict_data[mult])
print nentries
my_pdf = TF1("my_pdf", "2.3949e6*3.74/(0.67472+x)**10.1809", 2300, 6000)
# my_pdf = TF1("my_pdf","2.3949e6/(0.67472+x)**10.1809",1900,5500)
my_rands = []
# for i in xrange(nentries):
for i in xrange(nentries):
my_rands.append(my_pdf.GetRandom())
normed_counts_mc, bb_edges = np.histogram(my_rands, bayesian_blocks(my_rands), density=True)
normed_counts_data, _ = np.histogram(ST_dict_data[mult], bb_edges, density=True)
counts_mc, _ = np.histogram(my_rands, bb_edges)
counts_data, _ = np.histogram(ST_dict_data[mult], bb_edges)
rescaled_counts_mc = normed_counts_mc * nentries
rescaled_counts_data = normed_counts_data * nentries
bin_centres = (bb_edges[:-1] + bb_edges[1:]) / 2.0
rescaled_err = np.sqrt(counts_data) / (bb_edges[1:] - bb_edges[:-1])
err = np.sqrt(counts_data)
fig = plt.figure()
ax = plt.gca()
ax.set_yscale("log", nonposy="clip")
fill_between_steps(ax, bb_edges, rescaled_counts_mc, 1e-3, alpha=0.2, step_where="pre", linewidth=0, label="fit MC")
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')
ST_dict_data = pkl.load(open( "files/BHTree.p", "rb" ))
print('pickle loaded')
ST_dict_data[mult]=[i for i in ST_dict_data[mult] if i>2300]
nentries = len(ST_dict_data[mult])
print(nentries)
my_pdf = TF1("my_pdf","2.3949e6*3.74/(0.67472+x)**10.1809",2300,6000)
#my_pdf = TF1("my_pdf","2.3949e6/(0.67472+x)**10.1809",1900,5500)
my_rands = []
#for i in xrange(nentries):
for i in range(nentries):
my_rands.append(my_pdf.GetRandom())
normed_counts_mc, bb_edges = np.histogram(my_rands,bayesian_blocks(my_rands), density=True)
normed_counts_data, _= np.histogram(ST_dict_data[mult],bb_edges, density=True)
counts_mc, _= np.histogram(my_rands,bb_edges)
counts_data, _= np.histogram(ST_dict_data[mult],bb_edges)
rescaled_counts_mc = normed_counts_mc*nentries
rescaled_counts_data = normed_counts_data*nentries
bin_centres = (bb_edges[:-1] + bb_edges[1:])/2.
rescaled_err = np.sqrt(counts_data)/(bb_edges[1:]-bb_edges[:-1])
err = np.sqrt(counts_data)
fig = plt.figure()
ax = plt.gca()
ax.set_yscale("log", nonposy='clip')
fill_between_steps(ax, bb_edges, rescaled_counts_mc,1e-3, alpha=0.2, step_where='pre',linewidth=0,label='fit MC')
