NTupleController.instance().registerNTuple ( ntuple )
# Create dynamic histogram and attach it to NTuple.
# It will automatically adjust its range, title, and labels
dyhist = Display ( "Histogram", ntuple, ('X value', ) )
canvas.addDisplay ( dyhist )
mean = 45
sigma = 10
gauss = random.gauss
# Generate some data, fill the static histograms and NTuple.
for i in range(10000):
x = gauss( mean, sigma )
sthist.addValues ( (x, ) )
ntuple.addRow ( (x, ) )
if i < 1000 :
sthists.addValues ( (x, ) ) # only fill with first 1000
# Print some statistics from static histogram
# Could do same for dynamic
datarep = sthist.getDataRep()
print "Histogram :"
print " Title : " + sthist.getTitle()
print " Entries : %i" % sthist.numberOfEntries()
print " Mean = %f" % datarep.getMean ( 'x' )
print " Rms = %f" % datarep.getRMS ( 'x' )
# Print the average X value on the display
# Create empty NTuple of 4 columns that we can fill by row.
from hippo import NTuple, NTupleController
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)
# Create empty NTuple of 4 columns that we can fill by row.
from hippo import NTuple, NTupleController
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 )
