def get_value(self):
fig = figure()
xname = "invariantMass"
xmin,xmax,xbins = -5.,15.,50
index = "run*"
x,counts = vis_bokeh.get_1d_hist(xname,xmin,xmax,xbins,es,index=index)
deltas = np.sqrt(counts)
fig=vis_bokeh.whiskered_histogram(xmin,xmax,xbins,counts,deltas,-deltas)
#fit params
model = mix
model.fit(sample_weight=counts)#,values_init={'sig_weightlog':np.log(0.4),'bck_weightlog':np.log(0.6)})
parameters = model.parameters
w_sig,w_bkg =np.exp(parameters['sig_weightlog']),np.exp(parameters['bck_weightlog'])
w_sum = w_sig+w_bkg
w_sig,w_bkg = w_sig/w_sum,w_bkg/w_sum
n_events = np.sum(counts)
norm = n_events*(xmax-xmin)/xbins
#plot lines
expo = lambda x_arr:st.expon(0,1./parameters['slope']).pdf(x_arr)*w_bkg*norm
gauss = lambda x_arr:st.norm(parameters['mean'],parameters['sigma']).pdf(x_arr)*w_sig*norm
pdf_x = np.arange(1000,dtype='float')/1000.*(xmax-xmin) + xmin
fig.line(pdf_x, expo(pdf_x), legend="Background", line_width=2,color = 'red')
fig.line(pdf_x, gauss(pdf_x), legend="Signal", line_width=2,color='blue')
fig.line(pdf_x, gauss(pdf_x)+expo(pdf_x), legend="Sum", line_width=2,color='green')
fig.xaxis.axis_label = time.strftime("%H:%M:%S")
return vis_bokeh.fig_to_html(fig)
def make_figure(self,hist,model,es,index="run*"):
xname,xmin,xmax,xbins = hist.name,hist.xmin,hist.xmax,hist.xbins
x,counts = vis_bokeh.get_1d_hist(xname,xmin,xmax,xbins,es,index=index)
deltas = np.sqrt(counts)
fig=vis_bokeh.whiskered_histogram(xmin,xmax,xbins,counts,deltas,-deltas)
#fit params
model.fit(sample_weight=counts,values_init={'sigma':1.})
parameters = model.parameters
w_sig,w_bkg =np.exp(parameters['sig_weightlog']),np.exp(parameters['bck_weightlog'])
w_sum = w_sig+w_bkg
w_sig,w_bkg = w_sig/w_sum,w_bkg/w_sum
n_events = np.sum(counts)
norm = n_events*(xmax-xmin)/xbins
#plot lines
#residual = np.exp(-parameters['slope']*(xmax-xmin))
#expo = lambda x_arr:st.expon(xmin,1./parameters['slope']).pdf(x_arr)*w_bkg*norm /(1.-residual)
uni = lambda x_arr: np.repeat(1./(xmax-xmin),len(x_arr))*w_bkg*norm
gauss = lambda x_arr:st.norm(parameters['mean'],parameters['sigma']).pdf(x_arr)*w_sig*norm
pdf_x = np.arange(1000,dtype='float')/1000.*(xmax-xmin) + xmin
sig = gauss(pdf_x)
bkg = uni(pdf_x)
upperLimit = np.max(counts)*1.1 # for exponential distribution peak
bkg[bkg > upperLimit] = upperLimit
fig.line(pdf_x, sig, legend="Signal", line_width=2,color='blue')
fig.line(pdf_x, bkg, legend="Background", line_width=2,color = 'red')
fig.line(pdf_x, sig+bkg, legend="Sum", line_width=2,color='green')
return fig
