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#data.variable(index = index, name = ""el_1_phi"", value = event.el_phi[0])
##data.variable(index = index, name = ""jet_1_pt"", value = event.jet_pt[0])
#data.variable(index = index, name = ""jet_1_eta"", value = event.jet_eta[0])
#data.variable(index = index, name = ""jet_1_phi"", value = event.jet_phi[0])
##data.variable(index = index, name = ""jet_1_e"", value = event.jet_e[0])
##data.variable(index = index, name = ""jet_2_pt"", value = event.jet_pt[1])
#data.variable(index = index, name = ""jet_2_eta"", value = event.jet_eta[1])
#data.variable(index = index, name = ""jet_2_phi"", value = event.jet_phi[1])
##data.variable(index = index, name = ""jet_2_e"", value = event.jet_e[1])
#data.variable(index = index, name = ""nJets"", value = event.nJets)
##data.variable(index = index, name = ""nBTags"", value = event.nBTags)
##data.variable(index = index, name = ""nLjets"", value = event.nLjets)
##data.variable(index = index, name = ""ljet_1_m"", value = event.ljet_m[0])
#data.variable(index = index, name = ""met"", value = event.met_met)
#data.variable(index = index, name = ""met_phi"", value = event.met_phi)
#data.variable(index = index, name = ""Centrality_all"", value = event.Centrality_all)
#data.variable(index = index, name = ""Mbb_MindR"", value = event.Mbb_MindR)
#data.variable(index = index, name = ""ljet_tau21"", value = event.ljet_tau21),
#data.variable(index = index, name = ""ljet_tau32"", value = event.ljet_tau32),
#data.variable(index = index, name = ""Aplan_bjets"", value = event.Aplan_bjets),
#data.variable(index = index, name = ""H4_all"", value = event.H4_all),
#data.variable(index = index, name = ""NBFricoNN_6jin4bin"", value = event.NBFricoNN_6jin4bin),
#data.variable(index = index, name = ""NBFricoNN_6jin3bex"", value = event.NBFricoNN_6jin3bex),
#data.variable(index = index, name = ""NBFricoNN_5jex4bin"", value = event.NBFricoNN_5jex4bin),
#data.variable(index = index, name = ""NBFricoNN_3jex3bex"", value = event.NBFricoNN_3jex3bex),
#data.variable(index = index, name = ""NBFricoNN_4jin3bex"", value = event.NBFricoNN_4jin3bex),
#data.variable(index = index, name = ""NBFricoNN_4jin4bin"", value = event.NBFricoNN_4jin4bin)
number_of_events_loaded += 1
log.info("""")
return data"
4547,"def select_event(
event = None,
selection = ""ejets""
):
""""""
Select a HEP event.
""""""
if selection == ""ejets"":
# Require single lepton.
# Require >= 4 jets.
if \
0 < len(event.el_pt) < 2 and \
len(event.jet_pt) >= 4 and \
len(event.ljet_m) >= 1:
return True
else:
return False"
4548,"def draw_neural_network(
axes = None,
left = None,
right = None,
bottom = None,
top = None,
layer_sizes = None
):
""""""
# abstract
This function draws a neural network representation diagram using
matplotilb.
# arguments
|*argument* |*description* |
|-----------|--------------------------------------------------------------|
|axes |matplotlib.axes.AxesSubplot: the axes on which to plot the |
| |diagram (returned by matplotlib.pyplot.gca()) |
|left |float: the position of the centers of the left nodes |
|right |float: the position of the centers of the right nodes |
|bottom |float: the position of the centers of the bottom nodes |
|top |float: the position of the centers of the top nodes |
|layer_sizes|list of integers: list of layer sizes, including input and |
| |output dimensionality |
# example
```Python
figure = matplotlib.pyplot.figure(figsize = (12, 12))
abstraction.draw_neural_network(
axes = figure.gca(),
left = .1,
right = .9,
bottom = .1,
top = .9,
layer_sizes = [4, 7, 2]
)
figure.savefig(""neural_network_diagram.png"")
```
""""""
spacing_vertical = (top - bottom) / float(max(layer_sizes))
spacing_horizontal = (right - left) / float(len(layer_sizes) - 1)
# nodes
for n, layer_size in enumerate(layer_sizes):
layer_top = spacing_vertical * (layer_size - 1)/2 + (top + bottom) / 2
for m in xrange(layer_size):
circle = matplotlib.pyplot.Circle(
(