def array2data2D(self,
data,
weights=None,
normed=False,
binning=1,
reBin=None):
"""
Convert array of data to internal format
- Designed for arrays of raw, un-binned data.
- If you pass values here from an existing histogram ('weights' is not None
and the 'data' param is just bin centers), it is possible to re-bin
this histogram using the 'reBin' keyword
"""
try:
x = data['x']
y = data['y']
except TypeError:
x = data[0]
y = data[1]
data, bins_x, bins_y = np.histogram2d(x,
y,
bins=binning,
normed=normed,
weights=weights)
results = Hist()
results.content = data.flatten() # data is a ndarray (nxbins,nybins)
results.bins = {'x': bins_x, 'y': bins_y}
xcenter, ycenter = tools.dummy_bins2D(tools.midpoints(bins_x),
tools.midpoints(bins_y))
xwidth, ywidth = tools.dummy_bins2D(tools.widths(bins_x),
tools.widths(bins_y))
results.center = {'x': xcenter, 'y': ycenter}
results.width = {'x': xwidth, 'y': ywidth}
results.error = np.sqrt(data)
if weights is not None:
# scipy.stats to get sumw2 (x,y,weights should have the same shape)
results.error = results.sumw2_2D(xdata=x, ydata=y, values=weights)
if reBin is not None:
# re-binning after making data from array, likely that the user
# passed in binned data and wants to re-bin.
results.Rebin2D(reBin)
if normed: results.normalize() # normalize after re-binning
results.xbins = results.bins['x']
results.ybins = results.bins['y']
results.xcenter = results.center['x']
results.ycenter = results.center['y']
results.xwidth = results.width['x']
results.ywidth = results.width['y']
return results
## THE END ##
def array2data(self,
data,
weights=None,
normed=False,
binning=1,
reBin=None):
"""
Convert array of data to internal format
- Designed for arrays of raw, un-binned data.
- If you pass values here from an existing histogram ('weights' is not None
and the 'data' param is just bin centers), it is possible to re-bin
this histogram using the 'reBin' keyword
"""
data, bins = np.histogram(data,
bins=binning,
weights=weights,
normed=normed)
results = Hist()
results.content = data
results.bins = bins
results.center = tools.midpoints(bins)
results.width = tools.widths(bins)
results.error = np.sqrt(data)
if weights is not None:
# numpy digitize to get sumw2
results.error = results.sumw2_1D(xdata=data, values=weights)
if reBin is not None:
results.Rebin(reBin)
if normed: results.normalize() # normalize after re-binning
return results
def TEfficiency2data(self, histo):
"""Convert TEfficiency to internal format. No support for re-binning TEfficiencies."""
h_histo = histo.GetPassedHistogram()
bin_contents = []
bin_errors_up = []
bin_errors_dn = []
bin_centers = []
bin_widths = []
bin_edges = [h_histo.GetXaxis().GetBinLowEdge(1)]
for i in xrange(1, h_histo.GetNbinsX() + 1):
bin_contents.append(histo.GetEfficiency(i))
bin_errors_up.append(histo.GetEfficiencyErrorUp(i))
bin_errors_dn.append(histo.GetEfficiencyErrorLow(i))
bin_centers.append(h_histo.GetXaxis().GetBinCenter(i))
bin_edges.append(h_histo.GetXaxis().GetBinUpEdge(i))
bin_widths.append(h_histo.GetXaxis().GetBinWidth(1) / 2.)
results = Hist()
results.content = np.array(bin_contents)
results.error = [bin_errors_dn, bin_errors_up]
results.bins = np.array(bin_edges)
results.center = bin_centers
results.width = bin_widths
return results
def TEfficiency2data2D(self, histo):
"""Convert 2D TEfficiency to internal format. No support for re-binning TEfficiencies."""
h_histo = histo.GetPassedHistogram()
bin_centers = {'x': [], 'y': []}
bin_contents = []
bin_error_ups = [] # eff uncertainty up
bin_error_dns = [] # eff uncertainty down
bin_widths = {'x': [], 'y': []}
binns = {'x':[h_histo.GetXaxis().GetBinLowEdge(1)],\
'y':[h_histo.GetYaxis().GetBinLowEdge(1)]}
bin_edges['x'] += [
h_histo.GetXaxis().GetBinUpEdge(i)
for i in xrange(1,
h_histo.GetNbinsX() + 1)
]
bin_edges['y'] += [
h_histo.GetYaxis().GetBinUpEdge(j)
for j in xrange(1,
h_histo.GetNbinsY() + 1)
]
for i in xrange(1, h_histo.GetNbinsX() + 1):
for j in xrange(1, h_histo.GetNbinsY() + 1):
bin_center['x'].append(h_histo.GetXaxis().GetBinCenter(i))
bin_center['y'].append(h_histo.GetYaxis().GetBinCenter(j))
this_bin = histo.GetGlobalBin(i, j)
bin_contents.append(histo.GetEfficiency(this_bin))
bin_error_ups.append(histo.GetEfficiencyErrorUp(this_bin))
bin_error_dns.append(histo.GetEfficiencyErrorLow(this_bin))
bin_widths['x'].append(h_histo.GetXaxis().GetBinWidth(1) / 2.)
bin_widths['y'].append(h_histo.GetYaxis().GetBinWidth(1) / 2.)
results = Hist()
results.content = np.array(bin_contents)
results.error = [np.array(bin_error_dns), np.array(bin_error_ups)]
results.bins = {
'x': np.array(bin_edges['x']),
'y': np.array(bin_edges['y'])
}
results.center = bin_centers
results.width = bin_widths
return results
## THE END ##
def hist2data(self, histo, reBin=None, normed=False):
"""Convert ROOT histogram for internal use."""
bin_contents, bin_edges = histo.numpy()
results = Hist()
results.content = bin_contents
results.bins = bin_edges
results.center = tools.midpoints(bin_edges)
results.width = tools.widths(bin_edges)
if len(histo.variances) > 0:
results.error = histo.variances
else:
results.error = np.sqrt(bin_contents)
if reBin is not None:
results.Rebin(reBin)
if normed: results.normalize()
return results
def hist2data(self, histogram, normed=False, reBin=None):
"""Convert ROOT histogram for internal use."""
results = Hist()
histo = histogram.Clone()
if not histo.GetSumw2N():
histo.Sumw2()
if reBin is not None:
# Use ROOT functionality to re-bin histogram
try:
histo.Rebin(reBin)
except TypeError:
newname = histo.GetName() + "_"
histo.Rebin(len(reBin) - 1, newname, reBin)
histo = ROOT.gROOT.FindObject(newname)
if normed:
integral = histo.Integral()
histo.Scale(1. / integral)
bin_centers = []
bin_contents = []
bin_errors = []
bin_widths = []
bin_edges = [histo.GetXaxis().GetBinLowEdge(1)]
for i in xrange(1, histo.GetNbinsX() + 1):
bin_edges.append(histo.GetXaxis().GetBinUpEdge(i))
bin_centers.append(histo.GetBinCenter(i))
bin_contents.append(histo.GetBinContent(i))
bin_errors.append(histo.GetBinError(i))
bin_widths.append(histo.GetXaxis().GetBinWidth(i) * 0.5)
results.content = np.array(bin_contents)
results.error = np.array(bin_errors)
results.bins = np.array(bin_edges)
results.center = bin_centers
results.width = bin_widths
return results
def hist2data2D(self, histo, reBin=None, normed=False):
"""Convert ROOT histogram for internal use."""
bin_contents, (xbin_edges, ybin_edges) = histo.numpy()
bin_contents = bin_contents.T
if len(histo.allvariances) > 0:
bin_errors = histo.allvariances[
1:-1, 1:
-1] # variances() doesn't produce correct values in 2D right now
else:
bin_errors = np.sqrt(bin_contents)
xbin_centers, ybin_centers = tools.dummy_bins2D(
tools.midpoints(xbin_edges), tools.midpoints(ybin_edges))
xbin_widths, ybin_widths = tools.dummy_bins2D(tools.widths(xbin_edges),
tools.widths(ybin_edges))
results = Hist()
results.content = bin_contents.flatten()
results.error = bin_errors.flatten()
results.bins = {'x': xbin_edges, 'y': ybin_edges}
results.center = {'x': xbin_centers, 'y': ybin_centers}
results.width = {'x': xbin_widths, 'y': ybin_widths}
if reBin is not None:
results.Rebin2D(reBin)
if normed: results.normalize()
# Set extra attributes (placeholders for the moment)
results.xbins = results.bins['x']
results.ybins = results.bins['y']
results.xcenter = results.center['x']
results.ycenter = results.center['y']
results.xwidth = results.width['x']
results.ywidth = results.width['y']
return results
## THE END ##
def hist2data2D(self, histogram, normed=False, reBin=None):
"""Convert ROOT histogram for internal use."""
results = Hist()
histo = histogram.Clone()
if not histo.GetSumw2N():
histo.Sumw2()
if reBin is not None:
# Use ROOT functionality to re-bin histogram
if isinstance(reBin, (int, long)):
histo.Rebin2D(reBin, reBin)
else:
newname = histo.GetName() + "_"
old_histo = histo.Clone() # special rebinning, redefine histo
try:
new_x = reBin['x']
new_y = reBin['y']
except TypeError:
new_x = reBin[0]
new_y = reBin[1]
histo = ROOT.TH2F(old_histo.GetName() + newname,
old_histo.GetTitle() + newname,
len(new_x) - 1, new_x,
len(new_y) - 1, new_y)
xaxis = old_histo.GetXaxis()
yaxis = old_histo.GetYaxis()
for i in xrange(1, xaxis.GetNbins()):
for j in xrange(1, yaxis.GetNbins()):
histo.Fill(xaxis.GetBinCenter(i),
yaxis.GetBinCenter(j),
old_histo.GetBinContent(i, j))
if normed:
integral = histo.Integral()
histo.Scale(1. / integral)
bin_centers = {'x': [], 'y': []}
bin_contents = []
bin_errors = []
bin_widths = {'x': [], 'y': []}
bin_edges = {'x':[histo.GetXaxis().GetBinLowEdge(1)],\
'y':[histo.GetYaxis().GetBinLowEdge(1)]}
bin_edges['x'] += [
histo.GetXaxis().GetBinUpEdge(i)
for i in xrange(1,
histo.GetNbinsX() + 1)
]
bin_edges['y'] += [
histo.GetYaxis().GetBinUpEdge(j)
for j in xrange(1,
histo.GetNbinsY() + 1)
]
for i in xrange(1, histo.GetNbinsX() + 1):
for j in xrange(1, histo.GetNbinsY() + 1):
bin_contents.append(histo.GetBinContent(i, j))
bin_errors.append(histo.GetBinError(i, j))
bin_centers['x'].append(histo.GetXaxis().GetBinCenter(i))
bin_centers['y'].append(histo.GetYaxis().GetBinCenter(j))
bin_widths['x'].append(histo.GetXaxis().GetBinWidth(i) * 0.5)
bin_widths['y'].append(histo.GetYaxis().GetBinWidth(i) * 0.5)
results.content = np.array(bin_contents)
results.error = np.array(bin_errors)
results.xbins = np.array(bin_edges['x'])
results.ybins = np.array(bin_edges['y'])
results.xcenter = bin_centers['x']
results.ycenter = bin_centers['y']
results.xwidth = bin_widths['x']
results.ywidth = bin_widths['y']
results.bins = {'x': results.xbins, 'y': results.ybins}
results.center = bin_centers
results.width = bin_widths
return results
