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
canvas.selectDisplay ( sthist)
canvas.addTextRep ( sthist, 'averagex' )
canvas.selectDisplay ( dyhist)
canvas.addTextRep ( dyhist, 'averagex' )
# Get the contents of the bins as a DataArray
high = sthist.createDataArray()
low = sthists.createDataArray ()
# Take difference with high statistics one scaled down, and a column
# to the low one.
low[ 'diff' ] = high[ 'Entries / bin' ] / 10 - low[ 'Entries / bin' ]
# Create an XY plot to see the difference
xyplot = Display ( "XY Plot", low, ( 'X', 'diff', 'nil', 'Error' ) )
canvas.addDisplay ( xyplot )
low.register ('difference ntuple' )
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 ()
# Add Chi-squared per d.f. display
canvas.addTextRep ( hist, 'Chi-squared' )
# Create an NTuple from the histogram.
# Calculate the residuals
result = hist.createNTuple ()
ntc.registerNTuple ( result )
coords = result.getColumn ( 'Cost' )
values = result.getColumn ( 'Density' )
res = []
for i in range ( result.rows ) :
x = coords[i]
diff = values[i] - gauss1.valueAt ( x )
res.append ( diff )
