def prepare_stageout(self):
outputs = mask_struct(self.task.sandbox_stageout_mask(), self.task.output())
if not outputs:
return None
# create a tmp dir
tmp_dir = LocalDirectoryTarget(is_tmp=True)
tmp_dir.touch()
# map output files to local local targets in tmp_dir
def map_target(target):
return make_staged_target(tmp_dir, target)
stage_outputs = map_struct(map_target, outputs)
return StageInfo(outputs, tmp_dir, stage_outputs)
def stagein(self):
inputs = mask_struct(self.task.sandbox_stagein_mask(), self.task.input())
if not inputs:
return None
# create a tmp dir
tmp_dir = LocalDirectoryTarget(is_tmp=True)
tmp_dir.touch()
# copy input files and map to local targets in tmp_dir
def map_target(target):
tmp_target = make_staged_target(tmp_dir, target)
target.copy(tmp_target)
return tmp_target
stage_inputs = map_struct(map_target, inputs)
return StageInfo(inputs, tmp_dir, stage_inputs)
def run(self):
# before_run hook
if callable(self.task.sandbox_before_run):
self.task.sandbox_before_run()
# create a temporary direction for file staging
tmp_dir = LocalDirectoryTarget(is_tmp=True)
tmp_dir.touch()
# stage-in input files
stagein_info = self.stagein(tmp_dir)
if stagein_info:
# tell the sandbox
self.sandbox_inst.stagein_info = stagein_info
logger.debug("configured sandbox stage-in data")
# prepare stage-out
stageout_info = self.prepare_stageout(tmp_dir)
if stageout_info:
# tell the sandbox
self.sandbox_inst.stageout_info = stageout_info
logger.debug("configured sandbox stage-out data")
# create the actual command to run
cmd = self.sandbox_inst.cmd(self.proxy_cmd())
# run with log section before and after actual run call
with self._run_log(cmd):
code, out, err = self.sandbox_inst.run(cmd)
if code != 0:
raise Exception("Sandbox '{}' failed with exit code {}".format(
self.sandbox_inst.key, code))
# actual stage_out
if stageout_info:
self.stageout(stageout_info)
# after_run hook
if callable(self.task.sandbox_after_run):
self.task.sandbox_after_run()
def env(self):
# strategy: unlike docker, singularity might not allow binding of paths that do not exist
# in the container, so create a tmp directory on the host system and bind it as /tmp, let
# python dump its full env into a file, and read the file again on the host system
if self.image not in self._envs:
tmp_dir = LocalDirectoryTarget(is_tmp=True)
tmp_dir.touch()
tmp = tmp_dir.child("env", type="f")
tmp.touch()
# determine whether volume binding is allowed
allow_binds_cb = getattr(self.task, "singularity_allow_binds",
None)
if callable(allow_binds_cb):
allow_binds = allow_binds_cb()
else:
cfg = Config.instance()
allow_binds = cfg.get_expanded(self.get_config_section(),
"allow_binds")
# arguments to configure the environment
args = ["-e"]
if allow_binds:
args.extend(["-B", "{}:/tmp".format(tmp_dir.path)])
env_file = "/tmp/{}".format(tmp.basename)
else:
env_file = tmp.path
# get the singularity exec command
singularity_exec_cmd = self._singularity_exec_cmd() + args
# build commands to setup the environment
setup_cmds = self._build_setup_cmds(self._get_env())
# build the python command that dumps the environment
py_cmd = "import os,pickle;" \
+ "pickle.dump(dict(os.environ),open('{}','wb'),protocol=2)".format(env_file)
# build the full command
cmd = quote_cmd(singularity_exec_cmd + [
self.image,
"bash",
"-l",
"-c",
"; ".join(
flatten(setup_cmds, quote_cmd(["python", "-c", py_cmd]))),
])
# run it
code, out, _ = interruptable_popen(cmd,
shell=True,
executable="/bin/bash",
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
if code != 0:
raise Exception(
"singularity sandbox env loading failed:\n{}".format(out))
# load the environment from the tmp file
env = tmp.load(formatter="pickle")
# cache
self._envs[self.image] = env
return self._envs[self.image]
def run(self):
ROOT.PyConfig.IgnoreCommandLineOptions = True
ROOT.gROOT.SetBatch()
ROOT.gSystem.Load('libCondFormatsBTauObjects')
ROOT.gSystem.Load('libCondToolsBTau')
local_tmp = LocalDirectoryTarget(is_tmp=True)
local_tmp.touch()
jes_sources = self.config_inst.get_aux("jes_sources")
shifts = []
if self.shift == "ALL":
if self.flavor == "c":
shifts.extend(["cferr1", "cferr2"])
else:
shifts.extend([
"jes{}".format(jes_source) for jes_source in jes_sources
if jes_source != "Total"
])
shifts.extend([
"{}{}".format(region, type) for region, type in
itertools.product(["lf", "hf"], ["", "stats1", "stats2"])
])
elif self.shift == "NONE":
shifts = []
else:
shifts = [self.shift]
v_sys = getattr(ROOT, 'vector<string>')()
for shift in shifts:
v_sys.push_back("up_" + shift)
v_sys.push_back("down_" + shift)
flavor_ids = self.config_inst.get_aux("flavor_ids")
binning = self.config_inst.get_aux("binning")[self.flavor]
pt_binning = [
(start, end)
for start, end in zip(binning["pt"][:-1], binning["pt"][1:])
]
eta_binning = [(start, end) for start, end in zip(
binning["abs(eta)"][:-1], binning["abs(eta)"][1:])]
figures = {}
if self.compare_file is None:
csv_files, descriptions = [self.csv_file], ["csv"]
else:
csv_files, descriptions = [self.csv_file,
self.compare_file], ["new", "old"]
for input_file, id in zip(csv_files, descriptions):
# create calibration reader
calib = ROOT.BTagCalibration("csv_{}".format(id), input_file)
reader = ROOT.BTagCalibrationReader(
3, # 0 is for loose op, 1: medium, 2: tight, 3: discr. reshaping
"central", # central systematic type
v_sys, # vector of other sys. types
)
for jetFlavor in [0, 1, 2]:
reader.load(
calib,
jetFlavor, # 0 is for b flavour, 1: FLAV_C, 2: FLAV_UDSG
"iterativefit" # measurement type
)
for pt_idx, pt_range in enumerate(pt_binning):
for eta_idx, eta_range in enumerate(eta_binning):
key = pt_range + eta_range
if key not in figures:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("pt: %s to %s, eta: %.1f to %.1f" %
(pt_range + eta_range))
else:
fig, ax = figures[key]
if pt_range[1] == np.inf:
pt_val = pt_range[0] + 1
else:
pt_val = np.mean(pt_range)
eta_val = np.mean(eta_range)
def get_values(csv_reader, sys_type):
x_values = np.linspace(-0.1, 1., 10000)
y_values = []
for csv_value in x_values:
sf = csv_reader.eval_auto_bounds(
sys_type, # systematic (here also 'up'/'down' possible)
flavor_ids[self.flavor], # jet flavor
eta_val, # absolute value of eta
pt_val, # pt
csv_value)
y_values.append(sf)
return np.array(x_values), np.array(y_values)
x_values, nominal_values = get_values(reader, "central")
if not self.norm_to_nominal:
ax.plot(x_values,
nominal_values,
label="{}, {}".format(id, "nominal"))
if self.shift != "NONE":
total_errors_up = np.zeros(nominal_values.shape)
total_errors_down = np.zeros(nominal_values.shape)
for shift in shifts:
_, up_values = get_values(reader, "up_" + shift)
_, down_values = get_values(
reader, "down_" + shift)
if len(shifts) > 1: # build envelope
diff_up = up_values - nominal_values
diff_down = down_values - nominal_values
# shift with effect in up/down direction
errors_up = np.max([
diff_up, diff_down,
np.zeros(nominal_values.shape)
],
axis=0)
errors_down = np.min([
diff_up, diff_down,
np.zeros(nominal_values.shape)
],
axis=0)
# add in quadrature
total_errors_up += errors_up**2
total_errors_down += errors_down**2
total_errors_up = total_errors_up**0.5
total_errors_down = total_errors_down**0.5
if len(shifts) > 1:
up_values = nominal_values + total_errors_up
down_values = nominal_values - total_errors_down
if self.norm_to_nominal:
up_values /= nominal_values
down_values /= nominal_values
ax.plot(x_values,
up_values,
label="{}, {}".format(id, "up_" + self.shift))
ax.plot(x_values,
down_values,
label="{}, {}".format(id, "down" + self.shift))
if self.compare_file is None:
if self.flavor in ["c", "hf"]:
input_file = self.root_hf_file
elif self.flavor == "lf":
input_file = self.root_lf_file
else:
raise Exception("No .root file for c flavor SFs.")
root_file = ROOT.TFile.Open(input_file)
func_name = "csv_ratio_Pt{}_Eta{}_final".format(
pt_idx, eta_idx)
if self.flavor == "c":
func_name = "c_" + func_name
func = root_file.Get(func_name)
y_values = []
x_values = np.linspace(-0.1, 1., 10000)
for csv_value in x_values:
y_val = func.Eval(csv_value)
y_values.append(y_val)
ax.plot(x_values,
y_values,
label="{}, {}".format(".root", "nominal"))
figures[key] = (fig, ax)
del reader
del calib
for key, (fig, ax) in figures.items():
ax.legend(loc="lower right")
ax.set_ylim(0., 2.)
fig.savefig(
os.path.join(
local_tmp.path, "SF_%s_%s_Pt%sTo%s_eta%.1fTo%.1f.pdf" %
((self.flavor, self.shift) + key)))
with self.output().localize("w") as tmp:
with tarfile.open(tmp.path, "w:gz") as tar:
for plot_file in os.listdir(local_tmp.path):
tar.add(os.path.join(local_tmp.path, plot_file),
arcname=plot_file)
def run(self):
ROOT.PyConfig.IgnoreCommandLineOptions = True
ROOT.gROOT.SetBatch()
local_tmp = LocalDirectoryTarget(is_tmp=True)
local_tmp.touch()
if self.flavor == "hf":
input_file = self.hf_file
else:
input_file = self.lf_file
root_file = ROOT.TFile.Open(input_file)
hist_tpl = "h_csv_ratio_Pt{}_Eta{}_final"
binning = self.config_inst.get_aux("binning")[self.flavor]
n_pt_categories = len(binning["pt"]) - 1
n_eta_categories = len(binning["abs(eta)"]) - 1
hist_names = [
hist_tpl.format(pt_idx, eta_idx)
for pt_idx in range(n_pt_categories)
for eta_idx in range(n_eta_categories)
]
##
## Plot histograms with total systematic envelope
##
for hist_name in hist_names:
nominal_hist = root_file.Get(hist_name)
errors_up = []
errors_down = []
# collect all shifts from file
if self.shift == "ALL":
shifts = [
key.GetName().split("_")[-1]
for key in root_file.GetListOfKeys()
if key.GetName().startswith(hist_name)
]
shifts = set(
[shift[:-2] for shift in shifts if shift[-2:] == "Up"])
else:
shifts = [self.shift]
for shift_idx, shift in enumerate(shifts):
hist_name_up = hist_name + "_" + shift + "Up"
hist_name_down = hist_name + "_" + shift + "Down"
hist_up = root_file.Get(hist_name_up)
hist_down = root_file.Get(hist_name_down)
for bin_idx in range(1, nominal_hist.GetNbinsX() + 1):
nominal_value = nominal_hist.GetBinContent(bin_idx)
# combine all shifts that have an effect in the same direction
# effect from <shift>_up/done systematics
diff_up = hist_up.GetBinContent(bin_idx) - nominal_value
diff_down = hist_down.GetBinContent(
bin_idx) - nominal_value
# detect systematics where up/down shift direction is the same
#if diff_up * diff_down > 0:
# print "One sided shift: {}, {}".format(shift, category)
# if multiple shifts, build envelope
if len(shifts) != 1:
# shift with effect in up/down direction
error_up = max([diff_up, diff_down, 0])
error_down = min([diff_up, diff_down, 0])
# add in quadrature
if shift_idx == 0:
errors_up.append(error_up**2)
errors_down.append(error_down**2)
else:
errors_up[bin_idx - 1] += error_up**2
errors_down[bin_idx - 1] += error_down**2
else:
errors_up.append(diff_up)
errors_down.append(-diff_down) # is subtracted later
# multiple shifts have been added quadratically, take square root
if len(shifts) != 1:
errors_up = np.sqrt(errors_up)
errors_down = np.sqrt(errors_down)
# build shifted histograms
combined_hist_up = nominal_hist.Clone()
combined_hist_down = nominal_hist.Clone()
for bin_idx in range(1, nominal_hist.GetNbinsX() + 1):
combined_hist_up.SetBinContent(
bin_idx,
combined_hist_up.GetBinContent(bin_idx) +
errors_up[bin_idx - 1])
combined_hist_down.SetBinContent(
bin_idx,
combined_hist_down.GetBinContent(bin_idx) -
errors_down[bin_idx - 1])
if self.norm_to_nominal:
combined_hist_up.Divide(nominal_hist)
combined_hist_down.Divide(nominal_hist)
plot = ROOTPlot(hist_name, hist_name)
plot.create_pads()
plot.cd(0, 0)
plot.draw({"nominal": nominal_hist}, line_color=1)
plot.draw({"up": combined_hist_up}, line_color=2)
plot.draw({"down": combined_hist_down}, line_color=4)
plot.save(os.path.join(local_tmp.path, "{}.pdf".format(hist_name)))
##
## Check scale factors, uncertainties, and fits
##
for i, (pt_idx, eta_idx) in enumerate(
itertools.product(range(n_pt_categories),
range(n_eta_categories))):
data_hist = root_file.Get("h_csv_Data_Pt{}_Eta{}".format(
pt_idx, eta_idx))
if self.flavor == "hf":
signal_base = "h_csv_MC_bjets"
bg_base = "h_csv_MC_nonbjets"
else:
signal_base = "h_csv_MC_nonbjets" # actually lf
bg_base = "h_csv_MC_bjets" # actually b + c
signal_hist = root_file.Get("{}_Pt{}_Eta{}".format(
signal_base, pt_idx, eta_idx))
bg_hist = root_file.Get("{}_Pt{}_Eta{}".format(
bg_base, pt_idx, eta_idx))
#
with self.output().localize("w") as tmp:
with tarfile.open(tmp.path, "w:gz") as tar:
for plot_file in os.listdir(local_tmp.path):
tar.add(os.path.join(local_tmp.path, plot_file),
arcname=plot_file)
def merge_parquet_task(task,
inputs,
output,
local=False,
cwd=None,
force=True,
writer_opts=None):
"""
This method is intended to be used by tasks that are supposed to merge parquet files, e.g. when
inheriting from :py:class:`law.contrib.tasks.MergeCascade`. *inputs* should be a sequence of
targets that represent the files to merge into *output*. When *local* is *False* and files need
to be copied from remote first, *cwd* can be a set as the dowload directory. When empty, a
temporary directory is used. The *task* itself is used to print and publish messages via its
:py:meth:`law.Task.publish_message` and :py:meth:`law.Task.publish_step` methods. When *force*
is *True*, any existing output file is overwritten. *writer_opts* is forwarded to
:py:func:`merge_parquet_files` which is used internally for the actual merging.
"""
# ensure inputs are targets
inputs = [
LocalFileTarget(inp) if isinstance(inp, six.string_types) else inp
for inp in inputs
]
# ensure output is a target
if isinstance(output, six.string_types):
output = LocalFileTarget(output)
def merge(inputs, output):
with task.publish_step("merging {} parquet files ...".format(
len(inputs)),
runtime=True):
# clear the output if necessary
if output.exists() and force:
output.remove()
if len(inputs) == 1:
output.copy_from_local(inputs[0])
else:
# merge
merge_parquet_files([inp.path for inp in inputs],
output.path,
writer_opts=writer_opts)
# print the size
output_size = human_bytes(output.stat().st_size, fmt=True)
task.publish_message(f"merged file size: {output_size}")
if local:
# everything is local, just merge
merge(inputs, output)
else:
# when not local, we need to fetch files first into the cwd
if not cwd:
cwd = LocalDirectoryTarget(is_tmp=True)
elif isinstance(cwd, str):
cwd = LocalDirectoryTarget(cwd)
cwd.touch()
# fetch
with task.publish_step("fetching inputs ...", runtime=True):
def fetch(inp):
local_inp = cwd.child(inp.unique_basename, type="f")
inp.copy_to_local(local_inp, cache=False)
return local_inp
def callback(i):
task.publish_message("fetch file {} / {}".format(
i + 1, len(inputs)))
local_inputs = map_verbose(fetch,
inputs,
every=5,
callback=callback)
# merge into a localized output
with output.localize("w", cache=False) as local_output:
merge(local_inputs, local_output)
def hadd_task(task, inputs, output, cwd=None, local=False, force=True):
"""
This method is intended to be used by tasks that are supposed to merge root files, e.g. when
inheriting from :py:class:`law.contrib.tasks.MergeCascade`. *inputs* should be a sequence of
local targets that represent the files to merge into *output*. *cwd* is the working directory
in which hadd is invoked. When empty, a temporary directory is used. The *task* itself is
used to print and publish messages via its :py:meth:`law.Task.publish_message` and
:py:meth:`law.Task.publish_step` methods.
When *local* is *True*, the input and output targets are assumed to be local and the merging is
based on their local paths. Otherwise, the targets are fetched first and the output target is
localized.
When *force* is *True*, any existing output file is overwritten (by adding the ``-f`` flag to
``hadd``).
"""
# ensure inputs are targets
inputs = [
LocalFileTarget(inp) if isinstance(inp, six.string_types) else inp
for inp in inputs
]
# ensure output is a target
if isinstance(output, six.string_types):
output = LocalFileTarget(output)
# default cwd
if not cwd:
cwd = LocalDirectoryTarget(is_tmp=True)
elif isinstance(cwd, six.string_types):
cwd = LocalDirectoryTarget(cwd)
cwd.touch()
# helper to create the hadd cmd
def hadd_cmd(input_paths, output_path):
cmd = ["hadd", "-n", "0"]
if force:
cmd.append("-f")
cmd.extend(["-d", cwd.path])
cmd.append(output_path)
cmd.extend(input_paths)
return quote_cmd(cmd)
if local:
# when local, there is no need to download inputs
input_paths = [inp.path for inp in inputs]
with task.publish_step("merging ...", runtime=True):
if len(inputs) == 1:
output.copy_from_local(inputs[0])
else:
# merge using hadd
cmd = hadd_cmd(input_paths, output.path)
code = interruptable_popen(cmd, shell=True, executable="/bin/bash")[0]
if code != 0:
raise Exception("hadd failed")
task.publish_message("merged file size: {}".format(human_bytes(
output.stat.st_size, fmt=True)))
else:
# when not local, we need to fetch files first into the cwd
with task.publish_step("fetching inputs ...", runtime=True):
def fetch(inp):
inp.copy_to_local(cwd.child(inp.unique_basename, type="f"), cache=False)
return inp.unique_basename
def callback(i):
task.publish_message("fetch file {} / {}".format(i + 1, len(inputs)))
bases = map_verbose(fetch, inputs, every=5, callback=callback)
# start merging into the localized output
with output.localize("w", cache=False) as tmp_out:
with task.publish_step("merging ...", runtime=True):
if len(bases) == 1:
tmp_out.path = cwd.child(bases[0]).path
else:
# merge using hadd
cmd = hadd_cmd(bases, tmp_out.path)
code = interruptable_popen(cmd, shell=True, executable="/bin/bash",
cwd=cwd.path)[0]
if code != 0:
raise Exception("hadd failed")
task.publish_message("merged file size: {}".format(human_bytes(
tmp_out.stat.st_size, fmt=True)))
def run(self):
import ROOT
ROOT.PyConfig.IgnoreCommandLineOptions = True
ROOT.gROOT.SetBatch()
inp = self.input()
outp = self.output()
local_tmp = LocalDirectoryTarget(is_tmp=True)
local_tmp.touch()
plots = {}
if self.norm_to_nominal and self.shifts[0] != "nominal":
raise KeyError("'norm_to_nominal' is set to true, but no nominal values found.")
for color_idx, (config, config_input) in enumerate(inp.items()):
b_tagger, iteration, version = config
config_ids = [b_tagger]
if len(self.iterations) > 1:
config_ids.append("iteration {}".format(iteration))
if len(self.versions) > 1:
config_ids.append("version {}".format(version))
config_id = ", ".join(config_ids)
nominal_hists = {}
nominal_fit_hists = {}
# combined errors for multiple shifts
up_shifted_hists = defaultdict(dict)
up_shifted_fit_hists = defaultdict(dict)
down_shifted_hists = defaultdict(dict)
down_shifted_fit_hists = defaultdict(dict)
if self.fix_normalization:
normalization_input = config_input.pop("norm")
for shift_idx, (shift, inp_target) in enumerate(config_input.items()):
# get scaling factors for normalization
if self.fix_normalization:
norm_factors = normalization_input.load()[shift]
with inp_target["fit"]["sf"].load("r") as fit_file, \
inp_target["hist"]["scale_factors"].load("r") as hist_file:
for category_key in fit_file.GetListOfKeys():
category_name = category_key.GetName()
if not self.config_inst.has_category(category_name):
raise KeyError("Unknown category {}".format(category_name))
category = self.config_inst.get_category(category_name)
pt_range = category.get_aux("pt")
eta_range = category.get_aux("eta")
region = category.get_aux("region")
# same category name for different b-taggers
if len(self.b_taggers) > 1:
plot_category = category.name.replace("__" + b_tagger, "")
else:
plot_category = category.name
fit_category_dir = fit_file.Get(category_name)
fit_hist = fit_category_dir.Get(self.hist_name)
hist_category_dir = hist_file.Get(category_name)
hist = hist_category_dir.Get(self.hist_name)
# truncate first bin
hist = self.rebin_hist(hist, region, b_tagger=b_tagger, truncate=True)
# normalize histogram if required
# fit histograms are already normalized in FitScaleFactors
if self.fix_normalization and not self.is_c_flavour:
hist.Scale(norm_factors[category_name])
if shift == "nominal":
# make sure histograms are not cleaned up when the file is closed
nominal_fit_hists[plot_category] = fit_hist.Clone()
nominal_fit_hists[plot_category].SetDirectory(0)
nominal_hists[plot_category] = hist.Clone()
nominal_hists[plot_category].SetDirectory(0)
# for c-jets, there is no nominal histogram
# Instead, all nominal values are set to 1
if shift_idx == 0 and self.is_c_flavour:
nominal_fit_hist = fit_hist.Clone()
for bin_idx in range(1, nominal_fit_hist.GetNbinsX() + 1):
nominal_fit_hist.SetBinContent(bin_idx, 1.0)
nominal_fit_hist.SetDirectory(0)
nominal_fit_hists[plot_category] = nominal_fit_hist
if shift != "nominal" and self.multiple_shifts:
# collect all shifted fit histograms to build envelope later
sys, direction = shift.rsplit("_", 1)
if direction == "up":
up_shifted_fit_hists[plot_category][sys] = fit_hist.Clone()
up_shifted_fit_hists[plot_category][sys].SetDirectory(0)
up_shifted_hists[plot_category][sys] = hist.Clone()
up_shifted_hists[plot_category][sys].SetDirectory(0)
elif direction == "down":
down_shifted_fit_hists[plot_category][sys] = fit_hist.Clone()
down_shifted_fit_hists[plot_category][sys].SetDirectory(0)
down_shifted_hists[plot_category][sys] = hist.Clone()
down_shifted_hists[plot_category][sys].SetDirectory(0)
else:
raise ValueError("Unknown direction {}".format(direction))
if self.norm_to_nominal:
fit_hist.Divide(nominal_fit_hists[plot_category])
# get same category key for all b-taggers
if plot_category in plots:
plot = plots[plot_category]
else:
plot = ROOTPlot(category.name, category.name)
plot.create_pads()
plots[plot_category] = plot
plot.cd(0, 0)
fit_hist.GetXaxis().SetRangeUser(-.1, 1.0)
y_min = 0.6 if self.norm_to_nominal else 0.
y_max = 1.4 if self.norm_to_nominal else 2.
fit_hist.GetYaxis().SetRangeUser(y_min, y_max)
if len(self.b_taggers) == 1:
title = self.config_inst.get_aux("btaggers")[b_tagger]["label"] + " discriminator"
else:
title = "B-Tag Discriminant"
fit_hist.GetXaxis().SetTitle(title)
fit_hist.GetYaxis().SetTitle("SF")
if shift_idx == 0:
if not self.multiple_shifts or shift == "nominal":
# only draw this fit histogram if it is not part of a shifted envelope
plot.draw({"sf": fit_hist}, line_color=1, add_to_legend=False)
line = ROOT.TLine(0., 0., 0., 2.)
line.SetLineStyle(9)
plot.draw({"line": line}, add_same_option=False, line_color=1, add_to_legend=False)
# add category information to plot
if not np.isinf(pt_range[1]):
text = r"#splitline{%d < p_{T} < %d}{%.1f < |#eta| < %.1f}" % \
(pt_range[0], pt_range[1], eta_range[0], eta_range[1])
else:
text = r"#splitline{p_{T} > %d}{%.1f < |#eta| < %.1f}" % \
(pt_range[0], eta_range[0], eta_range[1])
plot.draw_text(text)
elif not self.multiple_shifts:
plot.draw({shift: fit_hist}, line_color=None)
if shift == "nominal" and not self.norm_to_nominal:
plot.draw({config_id + ", nominal": hist}, line_color=1,
add_to_legend=(len(self.shifts) != 1))
if self.multiple_shifts:
for plot_category in plots:
plot = plots[plot_category]
plot.cd(0, 0)
# build shifted histograms
fit_hist_down, fit_hist_up = build_hist_envelope(nominal_fit_hists[plot_category],
up_shifted_fit_hists[plot_category], down_shifted_fit_hists[plot_category],
envelope_as_errors=False)
hist_down, hist_up = build_hist_envelope(nominal_hists[plot_category],
up_shifted_hists[plot_category], down_shifted_hists[plot_category],
envelope_as_errors=False)
if self.norm_to_nominal:
fit_hist_up.Divide(nominal_fit_hists[plot_category])
fit_hist_down.Divide(nominal_fit_hists[plot_category])
hist_up.Divide(nominal_hists[plot_category])
hist_down.Divide(nominal_hists[plot_category])
plot.draw({config_id + ", up": fit_hist_up}, line_color=None)
plot.draw({config_id + ", down": fit_hist_down}, line_color=None)
plot.draw({config_id + ", up": hist_up}, line_color=2, options=["hist"])
plot.draw({config_id + ", down": hist_down}, line_color=4, options=["hist"])
# save plots
for plot_category in plots:
plot = plots[plot_category]
plot_name = self.get_plot_name(plot_category, self.shifts_identifier, self.b_taggers[0],
self.iterations[0])
plot.save(os.path.join(local_tmp.path, plot_name), draw_legend=True,
lumi=self.config_inst.get_aux("lumi").values()[0] / 1000.)
del plot
with outp.localize("w") as tmp:
with tarfile.open(tmp.path, "w:gz") as tar:
for plot_file in os.listdir(local_tmp.path):
tar.add(os.path.join(local_tmp.path, plot_file), arcname=plot_file)
def run(self):
def add_hist(hist, new_hist, sign=1.):
if hist is None:
hist = new_hist.Clone()
hist.Scale(sign)
else:
hist.Add(new_hist, sign)
return hist
import ROOT
ROOT.PyConfig.IgnoreCommandLineOptions = True
ROOT.gROOT.SetBatch()
inp = self.input()
outp = self.output()
if self.normalize:
scales = inp["scale"]["channel_scales"].load()
local_tmp = LocalDirectoryTarget(is_tmp=True)
local_tmp.touch()
categories = []
for category, _, _ in self.config_inst.walk_categories():
if category.has_tag((self.category_tag, self.b_tagger), mode=all):
categories.append(category)
# create plot objects
plot_dict = {}
for category in categories:
plot = ROOTPlot(category.name, category.name)
plot.create_pads(n_pads_y=2, limits_y=[0., 0.3, 1.0], legend_loc="upper")
plot_dict[category] = plot
with inp["hists"].load("r") as input_file:
for category in categories:
data_hist = None
mc_hists = defaultdict(lambda: defaultdict(lambda: None)) # shift -> key (process/flavor)
for leaf_cat, _, children in category.walk_categories():
# we are only interested in leaves
if children:
continue
flavor = leaf_cat.get_aux("flavor", None)
channel = leaf_cat.get_aux("channel")
region = leaf_cat.get_aux("region", None)
category_dir = input_file.GetDirectory(leaf_cat.name)
for process_key in category_dir.GetListOfKeys():
process = self.config_inst.get_process(process_key.GetName())
process_dir = category_dir.GetDirectory(process.name)
# avoid double counting of inclusive and flavor-dependent histograms
if flavor is not None: # Not needed in case region isn't flavor specific
if process.is_data and flavor != "inclusive":
continue
elif process.is_mc and flavor == "inclusive":
continue
for shift in self.shifts:
if process.is_data and shift != "nominal":
continue
for variable in self.variable:
# create variable name from template
aux = leaf_cat.aux.copy()
aux["b_tag_var"] = self.config_inst.get_aux("btaggers")[self.b_tagger]["variable"]
aux["b_tagger"] = self.b_tagger
aux["shift"] = shift
variable = variable.format(**aux)
hist = process_dir.Get(variable)
binning_type = "measurement" if self.rebin else None
hist = self.rebin_hist(hist, region, binning_type=binning_type, truncate=self.truncate)
add_to_data, sign = self.associate_hist(process=process, flavor=flavor, region=region)
if add_to_data:
if shift != "nominal":
raise Exception("Cannot add shifted samples to data.")
data_hist = add_hist(data_hist, hist, sign=sign)
else:
if self.normalize and region is not None: # apply "trigger" sfs as part of the normalization
hist.Scale(scales[channel.name][region])
key = process if self.mc_split == "process" else flavor
mc_hists[shift][key] = add_hist(mc_hists[shift][key], hist, sign=sign)
if self.normalize: # normalize mc yield to data in this category
mc_yield = sum(hist.Integral() for hist in mc_hists["nominal"].values())
data_yield = data_hist.Integral()
norm_factor = data_yield / mc_yield
for shift in self.shifts:
for mc_hist in mc_hists[shift].values():
mc_hist.Scale(norm_factor)
# get maximum value of hists/ stacks drawn to set axis ranges
mc_hist_sum = mc_hists["nominal"].values()[0].Clone()
for mc_hist in mc_hists["nominal"].values()[1:]:
mc_hist_sum.Add(mc_hist)
hist_maximum = max([mc_hist_sum.GetMaximum(), data_hist.GetMaximum()])
# get plot names
mc_key = self.mc_key.format(**{"region": category.get_aux("region", None)})
data_key = self.data_key.format(**{"region": category.get_aux("region", None)})
plot = plot_dict[category]
# data and mc histograms
plot.cd(0, 1)
if self.draw_stacked:
plot.draw(mc_hists["nominal"], stacked=True, stack_maximum=1.5*hist_maximum, y_title="Entries")
else:
# fix axis range
invis_hist = mc_hist_sum.Clone() if mc_hist_sum.GetMaximum() > data_hist.GetMaximum() else data_hist.Clone()
invis_hist.Scale(1.5)
plot.draw({"invis": invis_hist}, invis=True)
plot.draw({mc_key: mc_hist_sum}, line_color=None)
plot.draw({data_key: data_hist})
if self.draw_systematics:
up_shifted_mc_hists = {}
down_shifted_mc_hists = {}
for shift in self.shifts:
# combine processes/ flavors
shifted_mc_hist_sum = mc_hists[shift].values()[0].Clone()
for mc_hist in mc_hists[shift].values()[1:]:
shifted_mc_hist_sum.Add(mc_hist)
if shift.endswith("_down"):
down_shifted_mc_hists[shift[:-5]] = shifted_mc_hist_sum.Clone()
elif shift.endswith("_up"):
up_shifted_mc_hists[shift[:-3]] = shifted_mc_hist_sum.Clone()
envelope = build_hist_envelope(mc_hist_sum, up_shifted_mc_hists,
down_shifted_mc_hists, envelope_as_errors=True)
plot.draw_as_graph(envelope, options="2", hatched=True)
# add category information to plot
pt_range = category.get_aux("pt", None)
eta_range = category.get_aux("eta", None)
if pt_range is not None and eta_range is not None:
if not np.isinf(pt_range[1]):
text = r"#splitline{%d < p_{T} < %d}{%.1f < |#eta| < %.1f}" % \
(pt_range[0], pt_range[1], eta_range[0], eta_range[1])
else:
text = r"#splitline{p_{T} > %d}{%.1f < |#eta| < %.1f}" % \
(pt_range[0], eta_range[0], eta_range[1])
plot.draw_text(text, size=0.05, xpos=0.505, ypos=0.5, align=11)
# ratio of data to mc below the main plot
plot.cd(0, 0)
# ratio histograms
# mc error band
ratio_mcerr_hist = mc_hist_sum.Clone()
# divide without error propagation
self.divide_hists(ratio_mcerr_hist, mc_hist_sum)
# ratio
ratio_hist = data_hist.Clone()
self.divide_hists(ratio_hist, mc_hist_sum)
y_axis = ratio_hist.GetYaxis()
y_axis.SetRangeUser(0.5, 1.5)
y_axis.SetTitle("data/MC")
y_axis.SetTitleSize(y_axis.GetTitleSize() * plot.open_pad.scale_factor)
y_axis.SetLabelSize(y_axis.GetLabelSize() * plot.open_pad.scale_factor)
y_axis.SetNdivisions(505)
y_axis.SetTitleOffset(0.65)
x_axis = ratio_hist.GetXaxis()
if self.x_title:
aux = category.aux.copy()
aux["b_tag_var"] = self.config_inst.get_aux("btaggers")[self.b_tagger]["label"]
x_axis.SetTitle(self.x_title.format(**aux))
x_axis.SetTitleSize(x_axis.GetTitleSize() * plot.open_pad.scale_factor)
x_axis.SetLabelSize(x_axis.GetLabelSize() * plot.open_pad.scale_factor)
plot.draw({"invis": ratio_hist}, invis=True)
plot.draw_as_graph(ratio_mcerr_hist, options="2")
plot.draw({"data/mc": ratio_hist})
if self.draw_systematics:
# build envelope of ratio to nominal hist
for hist in up_shifted_mc_hists.values():
hist.Divide(mc_hist_sum)
for hist in down_shifted_mc_hists.values():
hist.Divide(mc_hist_sum)
scaled_envelope = build_hist_envelope(ratio_mcerr_hist, up_shifted_mc_hists,
down_shifted_mc_hists, envelope_as_errors=True)
plot.draw_as_graph(scaled_envelope, options="2", hatched=True)
for category, plot in plot_dict.items():
plot_name = self.get_plot_name(category.name, self.variable, self.b_tagger, self.iteration)
plot.save(os.path.join(local_tmp.path, plot_name),
draw_legend=(False, True), log_y=self.logarithmic,
lumi=self.config_inst.get_aux("lumi").values()[0]/1000.)
del plot
with outp.localize("w") as tmp:
with tarfile.open(tmp.path, "w:gz") as tar:
for plot_file in os.listdir(local_tmp.path):
tar.add(os.path.join(local_tmp.path, plot_file), arcname=plot_file)