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 ( )
print parms
# Add another function.
gauss1 = Function ( "Gaussian", datarep1 )
gauss1.addTo ( hist )
# Do another fit, should fit to linear sum
gauss1.fit ()
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 ) exp1.fit() # always fit to linear sum # Build an array which is the sum of two columns and add it to the ntuple label = "Raw sum" ntuple [ label ] = ntuple [ 'TkrEnergy' ] + ntuple [ 'CalEnergyCorr' ] # Histogram the new column sum_hist = Display ( 'Histogram', ntuple, (label, ) ) canvas.addDisplay ( sum_hist )
