def __init__(self):
super(InputBase, self).__init__()
self.binnings = BinningsDict()
self.expressions = ExpressionsDict()
class InputBase(processor.Processor):
def __init__(self):
super(InputBase, self).__init__()
self.binnings = BinningsDict()
self.expressions = ExpressionsDict()
def modify_argument_parser(self, parser, args):
self.input_options = parser.add_argument_group("Input options")
self.input_options.add_argument(
"--nicks",
type=str,
nargs="+",
help=
"Defines nick names for inputs. Inputs with the same nick name will be merged. By default, every input gets a unique nick name."
)
self.input_options.add_argument("-x",
"--x-expressions",
type=str,
nargs="+",
help="x-value(s)")
self.input_options.add_argument("-y",
"--y-expressions",
type=str,
nargs="+",
help="y-value(s)")
self.input_options.add_argument("-z",
"--z-expressions",
type=str,
nargs="+",
help="z-value(s)")
self.input_options.add_argument(
"--x-bins",
type=str,
nargs='+',
default=[None],
help="Binings for x-axis. [Default: %(default)s]")
self.input_options.add_argument(
"--y-bins",
type=str,
nargs='+',
default=[None],
help=
"Binings for y-axis of 2D/3D histograms. [Default: %(default)s]")
self.input_options.add_argument(
"--z-bins",
type=str,
nargs='+',
default=[None],
help="Binings for z-axis of 3D histograms. [Default: %(default)s]")
self.input_options.add_argument("-s",
"--scale-factors",
nargs="+",
help="Scale factors.")
def prepare_args(self, parser, plotData):
super(InputBase, self).prepare_args(parser, plotData)
self.prepare_list_args(plotData, [
"nicks", "x_expressions", "y_expressions", "z_expressions",
"x_bins", "y_bins", "z_bins", "scale_factors"
],
help="Input options")
# replace binnings with values from dictionaries
for axis in ['x', 'y', 'z']:
plotData.plotdict[axis + "_bins"] = [
bins
if bins is None else self.binnings.get_binning(bins).split()
for bins in plotData.plotdict[axis + "_bins"]
]
# prepare scale factors
plotData.plotdict["scale_factors"] = [
float(scale) if scale != None else 1.0
for scale in plotData.plotdict["scale_factors"]
]
def run(self, plotData):
super(InputBase, self).run(plotData)
self.scale_histograms(plotData)
# merging objects with same nicks
for nick, root_objects in plotData.plotdict["root_objects"].iteritems(
):
if isinstance(root_objects,
collections.Iterable) and not isinstance(
root_objects, ROOT.TObject):
if len(root_objects) == 1:
plotData.plotdict["root_objects"][nick] = root_objects[0]
else:
merged_object = None
for root_object in root_objects:
if isinstance(root_object, ROOT.TH1):
if merged_object is None:
merged_object = root_object
else:
merged_object.Add(root_object)
elif isinstance(root_object, ROOT.TGraph):
if merged_object is None:
merged_object = root_object
else:
log.warning(
"Merging not yet implemented for TGraph objects!"
)
else:
log.warning(
"Merging not yet implemented for objects of type %s!"
% str(type(root_object)))
plotData.plotdict["root_objects"][nick] = merged_object
# remove all nick name copies
tmp_nicks = []
for nick in plotData.plotdict["nicks"]:
if nick not in tmp_nicks:
tmp_nicks.append(nick)
plotData.plotdict["nicks"] = tmp_nicks
def scale_histograms(self, plotData):
nick_occurences = {}
for index, (nick, scale_factor) in enumerate(
zip(*[
plotData.plotdict[key]
for key in ["nicks", "scale_factors"]
])):
root_object = plotData.plotdict["root_objects"][nick]
if isinstance(root_object,
collections.Iterable) and not isinstance(
root_object, ROOT.TObject):
root_object = root_object[nick_occurences.get(nick, 0)]
nick_occurences[nick] = nick_occurences.get(nick, 0) + 1
if isinstance(root_object, ROOT.TH1):
root_object.Scale(scale_factor)
log.debug("Scaling histogram {} by {}".format(
nick, scale_factor))
elif scale_factor != 1.0:
log.warning(
"Scaling currently only implemented for histograms!")
# this method must only to be called once, so this need to be done in the most specialised input module # TODO better solution?
@staticmethod
def prepare_nicks(plotData):
plotData.plotdict["nicks"] = [
nick if nick != None else ("nick%d" % index)
for index, nick in enumerate(plotData.plotdict["nicks"])
]
class InputBase(processor.Processor):
def __init__(self):
super(InputBase, self).__init__()
self.binnings = BinningsDict()
def modify_argument_parser(self, parser, args):
self.input_options = parser.add_argument_group("Input options")
self.input_options.add_argument("--nicks", type=str, nargs="+",
help="Defines nick names for inputs. Inputs with the same nick name will be merged. By default, every input gets a unique nick name.")
self.input_options.add_argument("-x", "--x-expressions", type=str, nargs="+",
help="x-value(s)")
self.input_options.add_argument("-y", "--y-expressions", type=str, nargs="+",
help="y-value(s)")
self.input_options.add_argument("-z", "--z-expressions", type=str, nargs="+",
help="z-value(s)")
self.input_options.add_argument("--x-bins", type=str, nargs='+', default=[None],
help="Binings for x-axis. [Default: %(default)s]")
self.input_options.add_argument("--y-bins", type=str, nargs='+', default=[None],
help="Binings for y-axis of 2D/3D histograms. [Default: %(default)s]")
self.input_options.add_argument("--z-bins", type=str, nargs='+', default=[None],
help="Binings for z-axis of 3D histograms. [Default: %(default)s]")
self.input_options.add_argument("-s", "--scale-factors", nargs="+",
help="Scale factors.")
def prepare_args(self, parser, plotData):
super(InputBase, self).prepare_args(parser, plotData)
self.prepare_list_args(plotData, ["nicks", "x_expressions", "y_expressions", "z_expressions", "x_bins", "y_bins", "z_bins", "scale_factors"])
# replace binnings with values from dictionaries
for axis in ['x', 'y', 'z']:
plotData.plotdict[axis+"_bins"] = [bins if bins is None else self.binnings.get_binning(bins).split() for bins in plotData.plotdict[axis+"_bins"]]
# prepare scale factors
plotData.plotdict["scale_factors"] = [float(scale) if scale != None else 1.0 for scale in plotData.plotdict["scale_factors"]]
def run(self, plotData):
super(InputBase, self).run(plotData)
self.scale_histograms(plotData)
# merging objects with same nicks
for nick, root_objects in plotData.plotdict["root_objects"].iteritems():
if isinstance(root_objects, collections.Iterable) and not isinstance(root_objects, ROOT.TObject):
if len(root_objects) == 1:
plotData.plotdict["root_objects"][nick] = root_objects[0]
else:
merged_object = None
for root_object in root_objects:
if isinstance(root_object, ROOT.TH1):
if merged_object is None:
merged_object = root_object
else:
merged_object.Add(root_object)
elif isinstance(root_object, ROOT.TGraph):
if merged_object is None:
merged_object = root_object
else:
log.warning("Merging not yet implemented for TGraph objects!")
else:
log.warning("Merging not yet implemented for objects of type %s!" % str(type(root_object)))
plotData.plotdict["root_objects"][nick] = merged_object
# remove all nick name copies
tmp_nicks = []
for nick in plotData.plotdict["nicks"]:
if nick not in tmp_nicks:
tmp_nicks.append(nick)
plotData.plotdict["nicks"] = tmp_nicks
def scale_histograms(self, plotData):
nick_occurences = {}
for index, (nick, scale_factor) in enumerate(zip(*[plotData.plotdict[key] for key in ["nicks", "scale_factors"]])):
root_object = plotData.plotdict["root_objects"][nick]
if isinstance(root_object, collections.Iterable) and not isinstance(root_object, ROOT.TObject):
root_object = root_object[nick_occurences.get(nick, 0)]
nick_occurences[nick] = nick_occurences.get(nick, 0) + 1
if isinstance(root_object, ROOT.TH1):
root_object.Scale(scale_factor)
elif scale_factor != 1.0:
log.warning("Scaling currently only implemented for histograms!")
# this method must only to be called once, so this need to be done in the most specialised input module # TODO better solution?
@staticmethod
def prepare_nicks(plotData):
plotData.plotdict["nicks"] = [nick if nick != None else ("nick%d" % index) for index, nick in enumerate(plotData.plotdict["nicks"])]
def __init__(self): super(InputBase, self).__init__() self.binnings = BinningsDict()
def __init__(self): super(InputBase, self).__init__() self.binnings = BinningsDict() self.expressions = ExpressionsDict()
