nt = NTuple()
nt.setLabels(['x'])
xmin = 1.
xmax = 100.
gamma = 2.1
xpmax = math.pow(xmax, 1. - gamma)
xpmin = math.pow(xmin, 1. - gamma)
nsamp = 10000
for i in range(nsamp):
x = shoot(0, 1)
xx = math.pow(x * (xpmax - xpmin) + xpmin, 1. / (1. - gamma))
nt.addRow((xx, ))
from hippo import Display
hist = Display('Histogram', nt, ('x', ))
canvas.addDisplay(hist)
# Set the x and y axis on log scale
hist.setLog('x', True)
hist.setLog('y', True)
# fit to power law function
from hippo import Function
datarep = hist.getDataRep()
powerlaw = Function("PowerLaw", datarep)
powerlaw.addTo(hist)
powerlaw.fit()
# this directory.
#
full_path = sys.path[0] + '/' + 'aptuple.tnt'
nt1 = ntc.createNTuple ( full_path )
canvas.setPlotMatrix ( 2, 3 )
from hippo import Display
hist = Display ( "Histogram", nt1, ("Cost", ) )
canvas.addDisplay( hist )
# Get the data representation so we can add function to it.
datarep1 = hist.getDataRep()
from hippo import Function
gauss = Function ( "Gaussian", datarep1 )
gauss.addTo ( hist )
# Get the function parameters and display them.
print "Before fitting"
parmnames = gauss.parmNames ( )
print parmnames
parms = gauss.parameters ( )
print parms
# Now do the fitting.
gauss.fit ( )
print "After fitting"
parms = gauss.parameters ( )
nt = NTuple ()
nt.setLabels ( [ 'x' ] )
xmin = 1.
xmax = 100.
gamma = 2.1
xpmax = math.pow ( xmax, 1. - gamma )
xpmin = math.pow ( xmin, 1. - gamma )
nsamp = 10000
for i in range(nsamp):
x = shoot(0, 1)
xx = math.pow ( x*( xpmax - xpmin ) + xpmin, 1./ (1. - gamma) )
nt.addRow ( (xx, ) )
from hippo import Display
hist = Display ( 'Histogram', nt, ( 'x', ) )
canvas.addDisplay ( hist )
# Set the x and y axis on log scale
hist.setLog ( 'x', True )
hist.setLog ( 'y', True )
# fit to power law function
from hippo import Function
datarep = hist.getDataRep ()
powerlaw = Function ( "PowerLaw", datarep )
powerlaw.addTo( hist )
powerlaw.fit()
from hippo import Cut
hits_cut = Cut ( ntuple, ('TkrTotalHits',) )
canvas.addDisplay ( hits_cut )
hits_cut.setLog ( 'y', True )
hits_cut.addTarget ( hist )
hits_cut.setCutRange ( 4, 110, 'x' )
# Change the range of the displayed data
hist.setRange ( 'x', 40, 700 )
# fit a function to the histogram
from hippo import Function
datarep = hist.getDataRep ()
exp1 = Function ( "Exponential", datarep )
exp1.addTo ( hist )
exp1.fit ()
# Print the results of the fit
pnames = exp1.parmNames ()
print pnames
parms = exp1.parameters ()
print parms
# add another function to the histogram and fit the linear sum
exp2 = Function ( "Exponential", datarep )
exp2.addTo ( hist )
