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()
import hippo app = hippo.HDApp() canvas = app.canvas() plot = Display("Color Plot", darray, ('GLON', 'GLAT', 'nil', 'nil')) canvas.addDisplay(plot) # # Calculate Log(energy) and add it as new column # import numarray darray['LogE'] = numarray.log(darray['energy']) lplot = Display('Histogram', darray, ('LogE', )) lplot.setLog('y', True) canvas.addDisplay(lplot) # # Compare it with logrithmic binning # clplot = Display('Histogram', darray, ('energy', )) canvas.addDisplay(clplot) clplot.setLog('x', True) clplot.setLog('y', True) # # Apply a cut to the displays # cut = hippo.Cut(darray, ('time', )) canvas.addDisplay(cut)
print hdus events = ntc.createNTuple(full_path, hdus[1]) print "Names of the columns" labels = events.getLabels() print labels from hippo import Display # # Create the displays. # hist = Display("Histogram", events, ("energy",)) hist.setLog("x", True) hist.setLog("y", True) canvas.addDisplay(hist) color = Display("Color Plot", events, ("GLON", "GLAT")) color.setRange("z", 0.5, 150.0) color.setLog("z", True) canvas.addDisplay(color) contour = Display("Contour Plot", events, ("time", "GLON")) canvas.addDisplay(contour) profile = Display("Profile", events, ("time", "energy")) canvas.addDisplay(profile) print "Created the plots shown on the HippoDraw main page."
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()
# # If GemConditionsWord == 7., then fill element with `nbrTkrTriggered', # otherwise with -1 # daSvac [ label_TowerTkrTrigGemCond ] = \ numarray.choose ( t, ( nbrTkrTriggered, -1 ) ) daSvac [ label_TowerCalLeTrigGemCond ] = \ numarray.choose ( t, ( nbrCalLeTriggered, -1 ) ) tend = time.time() print "Took %f seconds create the 4 new columns with 500,000 rows each" % \ (tend -tstart) # # The rest is standard procedure # tkrtrighist = Display ("Histogram", daSvac, (label_TkrTriggered,) ) canvas.addDisplay ( tkrtrighist ) tkrtrighist.setLog ( 'y', True) calletrighist = Display ("Histogram", daSvac, (label_CalLeTriggered, ) ) canvas.addDisplay ( calletrighist ) calletrighist.setLog ( 'y', True) tkrtrighist_gemcond = Display ( "Histogram", daSvac, (label_TowerTkrTrigGemCond, ) ) canvas.addDisplay ( tkrtrighist_gemcond ) tkrtrighist_gemcond.setRange ( 'x', 0, 16) tkrtrighist_gemcond.setLog ( 'y', True) print tkrtrighist_gemcond.numberOfEntries()
print hdus events = ntc.createNTuple(full_path, hdus[1]) print "Names of the columns" labels = events.getLabels() print labels from hippo import Display # # Create the displays. # hist = Display("Histogram", events, ('energy', )) hist.setLog('x', True) hist.setLog('y', True) canvas.addDisplay(hist) color = Display("Color Plot", events, ('GLON', 'GLAT')) color.setRange('z', 0.5, 150.) color.setLog('z', True) canvas.addDisplay(color) contour = Display("Contour Plot", events, ('time', 'GLON')) canvas.addDisplay(contour) profile = Display("Profile", events, ('time', 'energy')) canvas.addDisplay(profile) print "Created the plots shown on the HippoDraw main page."
app = hippo.HDApp() canvas = app.canvas() plot = Display("Color Plot", darray, ("GLON", "GLAT", "nil", "nil")) canvas.addDisplay(plot) # # Calculate Log(energy) and add it as new column # import numarray darray["LogE"] = numarray.log(darray["energy"]) lplot = Display("Histogram", darray, ("LogE",)) lplot.setLog("y", True) canvas.addDisplay(lplot) # # Compare it with logrithmic binning # clplot = Display("Histogram", darray, ("energy",)) canvas.addDisplay(clplot) clplot.setLog("x", True) clplot.setLog("y", True) # # Apply a cut to the displays # cut = hippo.Cut(darray, ("time",)) canvas.addDisplay(cut)
print "Number of columns = ", ntuple.columns labels = ntuple.getLabels() print "First ten column labels are ... ", labels[:10] print "Number of rows = ", ntuple.rows # Create a histogram for one of the columns and add it to the canvas hist = Display ( "Histogram", ntuple, ('TkrEnergy', ) ) # Up to now, we didn't need the HippoDraw application running. # Now we do in order to view the data. app = HDApp() canvas = app.canvas() canvas.addDisplay ( hist ) # Set the Y axis on log scale of better viewing hist.setLog ( 'y', True ) # Add a cut from data in another column 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