def choose_file():
"""Get graph & fit fcn from ROOT file via tkFileDialog, plot them on the canvas"""
ftypes = [('ROOT files', '*.root'), ('All files', '*')]
dlg = tkFileDialog.Open(filetypes=ftypes)
fl = dlg.show()
if fl != '':
root_file = ROOT.TFile(fl, "READ")
gr = root_file.Get(graph_entry.get())
fn = root_file.Get(fn_entry.get())
if gr and fn:
tkMessageBox.showinfo("Got graph & fit", "Got graph %s and function %s" % (graph_entry.get(), fn_entry.get()))
# store xy points so properly drawn when canvas updated
global graph_x, graph_errx, graph_y, graph_erry
graph_x, graph_y = cu.get_xy(gr)
graph_errx, graph_erry = cu.get_exey(gr)
new_params = [fn.GetParameter(i) for i in xrange(fn.GetNumberFreeParameters())]
for slider, box, param in zip(sliders, multiplier_boxes, new_params):
set_slider_box_values(slider, box, param)
else:
if not gr:
tkMessageBox.showwarning("No graph", "Graph with name %s does not exist" % graph_entry.get())
if not fn:
tkMessageBox.showwarning("No function", "Function with name %s does not exist" % graph_entry.get())
def setup_fit(graph, function, absetamin, absetamax, outputfile):
"""Setup for fitting (auto-calculate sensible range).
Returns a sub-graph of only sensible points (chop off turnover at low pT,
and any high pT tail), along with a corresponding fit function
whose range has been set to match the sub graph.
"""
print 'Setting up fit'
xarr, yarr = cu.get_xy(graph)
exarr, eyarr = cu.get_exey(graph)
# first test out graph isn't empty
if len(xarr) == 0:
raise RuntimeError("graph in setup_fit() is empty")
fit_max = max(xarr) # Maxmimum pt for upper bound of fit
# fit_min = 10 if absetamin > 2.9 else 10
fit_min = min(xarr) # Minimum pt for lower bound of fit
# For lower bound of fit, use either fit_min or the pt
# of the maximum correction value, whichever has the larger pT.
# Check to make sure it's not the last point on the graph
# (e.g. if no turnover), in which case just use the default fit_min
# Then find the index of the closest corresponding value in xarr (since
# fit_min could correspond to a pT that isn't in the list of x-points)
# Note that we want the maximum in the first half of the graph to avoid
# the 'flick' at high pT in HF
# JOE EDIT: (un)comment the next 4 lines to (not) have the flick
max_corr = max(yarr[:len(yarr) / 2])
max_corr_ind = yarr.index(max_corr)
max_corr_pt = xarr[max_corr_ind]
fit_min = max(fit_min, max_corr_pt) if (max_corr_pt != xarr[-1]) and (max_corr_pt != xarr[-1]) else fit_min
min_ind = next(i for i, x in enumerate(xarr) if x >= fit_min)
# To find upper limit of fit, we need to detect if & where there is a turnover
# (i.e. where the gradient goes from -ve to +ve)
# This is made difficult by the fact the graph may be 'noisy' and simply
# taking the gradient may not be enough (and give multiple points where
# the gradient changes). To counter this, we smooth the gradient by
# averaging over several points
def moving_average(arr, n):
"""Returns a np.array of moving-averages of array arr, where each
point in the rerurned array is the average of the consecutive n points
in the original array.
By definition, this will return an array of length len(arr) + 1 - n
"""
return np.array([np.mean(arr[i:i+n]) for i in range(0, len(arr) - n + 1)])
def calc_crossing(arr):
"""Calculate value at which value the array crosses 0.
Looks at points in groups of 4, and finds the smallest group
where the first 2 points < 0, and the next 2 points > 0.
This ignores values which peak above 0 for 1 point.
Returns the array (index, value) of the point closest to 0.
"""
for i in range(2, len(arr)):
group = np.concatenate((-1 * arr[i-2: i], arr[i: i+2]))
if np.all(group > 0):
return i - 2 + list(group).index(np.min(group)), np.min(group)
return None, None
grad = np.gradient(yarr, 1)
n_sample = 5
intercept_ind, intercept = None, None
# keep incrementing the smooting value until we get a clean intercept
while not intercept_ind and not intercept:
x_ave = moving_average(xarr, n_sample)
grad_ave = moving_average(grad, n_sample)
intercept_ind, intercept = calc_crossing(grad_ave)
n_sample += 1
# quit if we've got stuck
if n_sample == 12:
break
if intercept and intercept_ind:
print 'Found minima'
print 'Smoothing param:', n_sample
# find closest x value to intercept
max_ind, fit_max = closest_element(xarr, x_ave[intercept_ind])
else:
print '! Could not find minima, falling back to just using min()'
# Here we assume a failure to find any minima, so fallback and use
# the smallest point.
max_ind = list(yarr).index(min(yarr))
fit_max = xarr[max_ind]
if fit_min > fit_max:
raise RuntimeError('fit_min > fit_max! (%f > %f)' % (fit_min, fit_max))
print "Correction fn fit range:", fit_min, fit_max
# Generate a correction function with suitable range
this_fit = function.Clone(function.GetName() + 'eta_%g_%g' % (absetamin, absetamax))
this_fit.SetRange(fit_min, fit_max)
# Make a sub-graph with only the points used for fitting
# Do not user graph.RemovePoint()! It doesn't work, and only removes every other point
# Instead make a graph with the bit of array we want
fit_graph = ROOT.TGraphErrors(max_ind + 1 - min_ind,
np.array(xarr[min_ind:max_ind + 1]),
np.array(yarr[min_ind:max_ind + 1]),
np.array(exarr[min_ind:max_ind + 1]),
np.array(eyarr[min_ind:max_ind + 1]))
fit_graph.SetName(graph.GetName() + "_fit")
return fit_graph, this_fit
def setup_fit(graph, function, absetamin, absetamax, outputfile):
"""Setup for fitting (auto-calculate sensible range).
Returns a sub-graph of only sensible points (chop off turnover at low pT,
and any high pT tail), along with a corresponding fit function
whose range has been set to match the sub graph.
"""
print 'Setting up fit'
xarr, yarr = cu.get_xy(graph)
exarr, eyarr = cu.get_exey(graph)
# first test out graph isn't empty
if len(xarr) == 0:
raise RuntimeError("graph in setup_fit() is empty")
fit_max = max(xarr) # Maxmimum pt for upper bound of fit
# fit_min = 10 if absetamin > 2.9 else 10
fit_min = min(xarr) # Minimum pt for lower bound of fit
# For lower bound of fit, use either fit_min or the pt
# of the maximum correction value, whichever has the larger pT.
# Check to make sure it's not the last point on the graph
# (e.g. if no turnover), in which case just use the default fit_min
# Then find the index of the closest corresponding value in xarr (since
# fit_min could correspond to a pT that isn't in the list of x-points)
# Note that we want the maximum in the first half of the graph to avoid
# the 'flick' at high pT in HF
# JOE EDIT: (un)comment the next 4 lines to (not) have the flick
max_corr = max(yarr[:len(yarr) / 2])
max_corr_ind = yarr.index(max_corr)
max_corr_pt = xarr[max_corr_ind]
fit_min = max(fit_min, max_corr_pt) if (max_corr_pt != xarr[-1]) and (
max_corr_pt != xarr[-1]) else fit_min
min_ind = next(i for i, x in enumerate(xarr) if x >= fit_min)
# To find upper limit of fit, we need to detect if & where there is a turnover
# (i.e. where the gradient goes from -ve to +ve)
# This is made difficult by the fact the graph may be 'noisy' and simply
# taking the gradient may not be enough (and give multiple points where
# the gradient changes). To counter this, we smooth the gradient by
# averaging over several points
def moving_average(arr, n):
"""Returns a np.array of moving-averages of array arr, where each
point in the rerurned array is the average of the consecutive n points
in the original array.
By definition, this will return an array of length len(arr) + 1 - n
"""
return np.array(
[np.mean(arr[i:i + n]) for i in range(0,
len(arr) - n + 1)])
def calc_crossing(arr):
"""Calculate value at which value the array crosses 0.
Looks at points in groups of 4, and finds the smallest group
where the first 2 points < 0, and the next 2 points > 0.
This ignores values which peak above 0 for 1 point.
Returns the array (index, value) of the point closest to 0.
"""
for i in range(2, len(arr)):
group = np.concatenate((-1 * arr[i - 2:i], arr[i:i + 2]))
if np.all(group > 0):
return i - 2 + list(group).index(np.min(group)), np.min(group)
return None, None
grad = np.gradient(yarr, 1)
n_sample = 5
intercept_ind, intercept = None, None
# keep incrementing the smooting value until we get a clean intercept
while not intercept_ind and not intercept:
x_ave = moving_average(xarr, n_sample)
grad_ave = moving_average(grad, n_sample)
intercept_ind, intercept = calc_crossing(grad_ave)
n_sample += 1
# quit if we've got stuck
if n_sample == 12:
break
if intercept and intercept_ind:
print 'Found minima'
print 'Smoothing param:', n_sample
# find closest x value to intercept
max_ind, fit_max = closest_element(xarr, x_ave[intercept_ind])
else:
print '! Could not find minima, falling back to just using min()'
# Here we assume a failure to find any minima, so fallback and use
# the smallest point.
max_ind = list(yarr).index(min(yarr))
fit_max = xarr[max_ind]
#############
# JOE_HACKs #
# 1. to fix the maximum pt for all fits (high pt saturation issue)
# helps ease things into the right solution space
# if fit_max > 650.0:
# print "*** WARNING: about to apply a JOE_HACK ***"
# print "*** lowers the upper limit of the fits to 650GeV ***"
# fit_max = 650.0
# 2. to change the auto settings for a troublesome eta bin
#if absetamin == 2.5:
# print "*** WARNING: about to apply a JOE_HACK ***"
# print "*** messing with fit limits for 2.500<|eta|<2.964 ***"
# max_ind = 80
# fit_max = xarr[max_ind]
# print max_ind
# print fit_max
# fit_min = 40.0
# min_ind = 0
if absetamin == 2.964:
print "* WARNING: about to apply a JOE_HACK *"
print "* messing with fit limits for 2.964<|eta|<3.489 *"
max_ind = 17
fit_max = xarr[max_ind]
print max_ind
print fit_max
fit_min = 40.0
min_ind = 0
if absetamin == 3.489:
print "* WARNING: about to apply a JOE_HACK *"
print "* messing with fit limits for 3.489<|eta|<4.191 *"
max_ind = 17
fit_max = xarr[max_ind]
print max_ind
print fit_max
fit_min = 40.0
min_ind = 0
if absetamin == 4.191:
print "* WARNING: about to apply a JOE_HACK *"
print "* messing with fit limits for 4.191<|eta|<5.191 *"
max_ind = 17
fit_max = xarr[max_ind]
print max_ind
print fit_max
fit_min = 40.0
min_ind = 0
###############
###############
if fit_min > fit_max:
raise RuntimeError('fit_min > fit_max! (%f > %f)' % (fit_min, fit_max))
print "Correction fn fit range:", fit_min, fit_max
# Generate a correction function with suitable range
this_fit = function.Clone(function.GetName() + 'eta_%g_%g' %
(absetamin, absetamax))
this_fit.SetRange(fit_min, fit_max)
# Make a sub-graph with only the points used for fitting
# Do not user graph.RemovePoint()! It doesn't work, and only removes every other point
# Instead make a graph with the bit of array we want
fit_graph = ROOT.TGraphErrors(max_ind + 1 - min_ind,
np.array(xarr[min_ind:max_ind + 1]),
np.array(yarr[min_ind:max_ind + 1]),
np.array(exarr[min_ind:max_ind + 1]),
np.array(eyarr[min_ind:max_ind + 1]))
fit_graph.SetName(graph.GetName() + "_fit")
return fit_graph, this_fit
