def run(self):
wrp = next(rebin_chhadiso(
gen.gen_sum(
[
gen.fs_filter_active_sort_load({
"analyzer" : sb_anzlrs,
"is_data" : True
})
]
)
))
# multiply with weight
if do_dist_reweighting:
wrp = gen.op.prod((
settings.post_proc_dict["TemplateFitToolChHadIsoSbBkgInputBkgWeight"],
wrp,
))
wrp.lumi = settings.data_lumi_sum()
self.result = [wrp]
gen.consume_n_count(
gen.save(
gen.canvas((self.result,)),
lambda c: self.plot_output_dir + c.name
)
)
def run(self):
top_sample = next(s for s in settings.active_samples if s[:2] == "TT")
wrp_fake = next(rebin_chhadiso(gen.fs_filter_sort_load({
"analyzer": "TemplateChHadIsoreal",
"sample": top_sample,#"TTMadG",
})))
wrp_sb = gen.op.merge(rebin_chhadiso(gen.fs_filter_sort_load({
"analyzer": "TemplateRandConereal",
"sample": top_sample,#"TTMadG",
})))
wrp = gen.op.div((
gen.op.norm_to_integral(wrp_fake),
gen.op.norm_to_integral(wrp_sb),
))
wrp.lumi = 1.
wrp.draw_option = "E1"
self.result = wrp
cnvs = list(gen.canvas(((wrp,),)),)
cnvs[0].canvas.SetGridy(1)
gen.consume_n_count(
gen.save(
cnvs,
lambda c: self.plot_output_dir + c.name
)
)
del wrp.draw_option
def store_results(self):
cnv = itertools.chain(
gen.canvas(
[self.plot_chi2] + self.plots_truth_vs_fitted,
[rnd.Legend(None, True, y_pos=0.25)]
),
gen.canvas(self.plots_diffs)
)
cnv = gen.save(cnv, lambda c: self.plot_output_dir + c.name)
gen.consume_n_count(cnv)
def test_gen_filter(self):
import re
aliases = list(gen.fs_content())
data = gen.filter(aliases, {"is_data": True})
tmplt = gen.filter(aliases, {"analyzer": "fakeTemplate"})
crtlplt = gen.filter(aliases, {"analyzer": re.compile("CrtlFilt*")})
crtlplt2 = gen.filter(aliases, {"analyzer": [re.compile("CrtlFilt*")]})
ttgam_cf = gen.filter(aliases, {"name": "cutflow", "sample": ["ttgamma", "tt"]})
self.assertEqual(gen.consume_n_count(data), 52)
self.assertEqual(gen.consume_n_count(tmplt), 9)
self.assertEqual(gen.consume_n_count(crtlplt), 39)
self.assertEqual(gen.consume_n_count(crtlplt2), 39)
self.assertEqual(gen.consume_n_count(ttgam_cf), 2)
def save_canvas(wrps, postfix):
canvas = gen.canvas(
wrps,
[rnd.BottomPlotRatio, rnd.Legend, com.SimpleTitleBox]
)
canvas = gen.save(
canvas,
lambda c: self.plot_output_dir + c.name + postfix
)
canvas = gen.switch_log_scale(canvas)
canvas = gen.save(
canvas,
lambda c: self.plot_output_dir + c.name + postfix + "_log"
)
gen.consume_n_count(canvas)
def test_gen_save(self):
wrps = gen.fs_filter_sort_load({"name": "cutflow", "sample": ["zjets", "ttgamma"]})
# create dir and go
if not os.path.exists("test"):
os.mkdir("test")
gen.consume_n_count(gen.save(wrps, lambda w: "test/" + w.name + "_" + w.sample))
# check the new files
self.assertTrue(os.path.exists("test/cutflow_ttgamma.root"))
self.assertTrue(os.path.exists("test/cutflow_ttgamma.info"))
self.assertTrue(os.path.exists("test/cutflow_zjets.root"))
self.assertTrue(os.path.exists("test/cutflow_zjets.info"))
self.tfile = TFile.Open("test/cutflow_ttgamma.root")
self.assertTrue(self.tfile.GetKey("histo"))
def run(self):
self.result = settings.post_proc_dict["XsecCalculatorChHadIsoSBID"]
for quantity in ["R", "R_fid", "xsec"]:
self.calc_variation(quantity)
histo = wrappers.HistoWrapper(
util.list2histogram(
self.values,
"PDF_uncert_distr_" + quantity,
";#Delta("+quantity+");CTEQ61 PDF eigenvector evaluation",
60
)
)
del self.values[:]
cnv = gen.canvas([[histo]])
cnv = gen.save(cnv, lambda c: self.plot_output_dir + c.name)
gen.consume_n_count(cnv)
def run(self):
"""
Load, stack, print and save histograms in a stream.
"""
# combined operation for loading, filtering, stacking, etc..
# the output looks like: [(stack1, data1), (stack2, data2), ...]
stream_stack_n_data = gen.fs_mc_stack_n_data_sum({
"name":
"histo",
"analyzer": ["CrtlFiltEt", "CrtlFiltEta"]
})
# plot (stack, data) pairs into canvases, with legend
stream_canvas = gen.canvas(stream_stack_n_data,
[cmstoolsac3b.rendering.Legend])
# store into dir of this tool
stream_canvas = gen.save(
stream_canvas,
lambda wrp: self.plot_output_dir + wrp.
name, # this function returns a path without postfix
settings.rootfile_postfixes)
# pull everything through the stream
count = gen.consume_n_count(stream_canvas)
# make a nice statement
self.message("INFO: " + self.name + " produced " + count +
" canvases.")
def run(self):
"""
Load, stack, print and save histograms in a stream.
"""
# combined operation for loading, filtering, stacking, etc..
# the output looks like: [(stack1, data1), (stack2, data2), ...]
stream_stack_n_data = gen.fs_mc_stack_n_data_sum(
self.histo_filter_dict
)
# can be saved for later use.
if self.store_stack_and_data_in_pool:
stream_stack_n_data = self.store_to_pool(stream_stack_n_data)
# plot (stack, data) pairs into canvases, with decorators
stream_canvas = gen.canvas(
stream_stack_n_data,
self.canvas_decorators
)
# store into dir of this tool
stream_canvas = gen.save(
stream_canvas,
lambda wrp: self.plot_output_dir + wrp.analyzer
)
# pull everything through the stream
count = gen.consume_n_count(stream_canvas)
# make a nice statement
self.message("INFO: "+self.name+" produced "+count+" canvases.")
def run(self):
"""
Load, stack, print and save histograms in a stream.
"""
# combined operation for loading, filtering, stacking, etc..
# the output looks like: [(stack1, data1), (stack2, data2), ...]
stream_stack_n_data = gen.fs_mc_stack_n_data_sum(
self.histo_filter_dict)
# can be saved for later use.
if self.store_stack_and_data_in_pool:
stream_stack_n_data = self.store_to_pool(stream_stack_n_data)
# plot (stack, data) pairs into canvases, with decorators
stream_canvas = gen.canvas(stream_stack_n_data, self.canvas_decorators)
# store into dir of this tool
stream_canvas = gen.save(
stream_canvas, lambda wrp: self.plot_output_dir + wrp.analyzer)
# pull everything through the stream
count = gen.consume_n_count(stream_canvas)
# make a nice statement
self.message("INFO: " + self.name + " produced " + count +
" canvases.")
def test_gen_filter(self):
import re
aliases = list(gen.fs_content())
data = gen.filter(aliases, {"is_data": True})
tmplt = gen.filter(aliases, {"analyzer": "fakeTemplate"})
crtlplt = gen.filter(aliases, {"analyzer": re.compile("CrtlFilt*")})
crtlplt2 = gen.filter(aliases, {"analyzer": [re.compile("CrtlFilt*")]})
ttgam_cf = gen.filter(aliases, {
"name": "cutflow",
"sample": ["ttgamma", "tt"]
})
self.assertEqual(gen.consume_n_count(data), 52)
self.assertEqual(gen.consume_n_count(tmplt), 9)
self.assertEqual(gen.consume_n_count(crtlplt), 39)
self.assertEqual(gen.consume_n_count(crtlplt2), 39)
self.assertEqual(gen.consume_n_count(ttgam_cf), 2)
def run(self):
"""
Load, stack, print and save histograms in a stream.
"""
# combined operation for loading, filtering, stacking, etc..
# the output looks like: [(stack1, data1), (stack2, data2), ...]
stream_stack_n_data = gen.fs_mc_stack_n_data_sum(
{
"name" : "histo",
"analyzer" : ["CrtlFiltEt", "CrtlFiltEta"]
}
)
# plot (stack, data) pairs into canvases, with legend
stream_canvas = gen.canvas(
stream_stack_n_data,
[cmstoolsac3b.rendering.Legend]
)
# store into dir of this tool
stream_canvas = gen.save(
stream_canvas,
lambda wrp: self.plot_output_dir + wrp.name, # this function returns a path without postfix
settings.rootfile_postfixes
)
# pull everything through the stream
count = gen.consume_n_count(stream_canvas)
# make a nice statement
self.message("INFO: "+self.name+" produced "+count+" canvases.")
def test_gen_save(self):
wrps = gen.fs_filter_sort_load({
"name": "cutflow",
"sample": ["zjets", "ttgamma"]
})
# create dir and go
if not os.path.exists("test"):
os.mkdir("test")
gen.consume_n_count(
gen.save(wrps, lambda w: "test/" + w.name + "_" + w.sample))
# check the new files
self.assertTrue(os.path.exists("test/cutflow_ttgamma.root"))
self.assertTrue(os.path.exists("test/cutflow_ttgamma.info"))
self.assertTrue(os.path.exists("test/cutflow_zjets.root"))
self.assertTrue(os.path.exists("test/cutflow_zjets.info"))
self.tfile = TFile.Open("test/cutflow_ttgamma.root")
self.assertTrue(self.tfile.GetKey("histo"))
def run(self):
wrp = tmpl_fit.get_merged_sbbkg_histo(sample)
# multiply with weight
if tmpl_fit.do_dist_reweighting:
wrp = gen.op.prod((
settings.post_proc_dict["TemplateFitToolChHadIsoSbBkgInputBkgWeight"],
wrp,
))
wrps = gen.gen_norm_to_data_lumi((wrp,))
wrps = list(wrps)
self.result = wrps
gen.consume_n_count(
gen.save(
gen.canvas((wrps,)),
lambda c: self.plot_output_dir + c.name
)
)
def run(self):
wrp = next(rebin_chhadiso(
gen.gen_sum(
[gen.fs_filter_active_sort_load({
"analyzer" : "TemplateRandConereal",
"is_data" : True
})]
)
))
# normalize to mc expectation
integral_real = next(
gen.gen_integral(
gen.gen_norm_to_data_lumi(
gen.filter(
settings.post_proc_dict["TemplateStacks"],
{"analyzer": "TemplateRandConereal"}
)
)
)
)
print integral_real
wrp = gen.op.prod((
gen.op.norm_to_integral(wrp),
integral_real
))
# multiply with weight
if do_dist_reweighting:
wrp = gen.op.prod((
settings.post_proc_dict["TemplateFitToolRandConeIsoInputSigWeight"],
wrp,
))
wrp.lumi = settings.data_lumi_sum()
self.result = [wrp]
gen.consume_n_count(
gen.save(
gen.canvas((self.result,)),
lambda c: self.plot_output_dir + c.name
)
)
def do_evaluation(self, wrp_list, maker_func=None):
if not maker_func:
maker_func = self.make_histo_1d
self.wrp_dict = dict((w.histo_key, w) for w in wrp_list)
min_wrp = min(wrp_list, key=lambda w: w.float)
histo_in_s = maker_func(min_wrp, "sieie")
histo_in_p = maker_func(min_wrp, "phoiso")
histo_in_n = maker_func(min_wrp, "neuiso")
histo_in_s.min_token = min_wrp.histo_key
histo_in_p.min_token = min_wrp.histo_key
histo_in_n.min_token = min_wrp.histo_key
self.result = [
histo_in_s,
histo_in_p,
histo_in_n,
]
gen.consume_n_count(
gen.save(
gen.canvas((h,) for h in self.result),
lambda c: self.plot_output_dir + c.name
)
)
def run(self):
wrps = tmpl_fit.rebin_chhadiso(gen.fs_filter_sort_load({
"analyzer": "PlotSBID",
"sample": sample,
}))
wrp = gen.op.merge(wrps)
# multiply with weight
if tmpl_fit.do_dist_reweighting:
wrp = gen.op.prod((
settings.post_proc_dict["TemplateFitToolChHadIsoSBIDInputBkgWeight"],
wrp,
))
wrps = gen.gen_norm_to_data_lumi((wrp,))
wrps = list(wrps)
self.result = wrps
gen.consume_n_count(
gen.save(
gen.canvas((wrps,)),
lambda c: self.plot_output_dir + c.name
)
)
def test_gen_special_treat(self):
sample = ["tt", "zjets"]
name = "cutflow"
class TreatCls(object):
def __init__(self, test):
self.test = test
self.n_times_called = 0
def __call__(self, alias):
self.n_times_called += 1
self.test.assertTrue(alias.sample in sample)
self.test.assertEqual(alias.name, name)
treat_func = TreatCls(self)
treated = gen.callback(gen.fs_content(), treat_func, {"sample": sample, "name": name})
self.assertEqual(gen.consume_n_count(treated), 150)
self.assertEqual(treat_func.n_times_called, 2)
def test_gen_special_treat(self):
sample = ["tt", "zjets"]
name = "cutflow"
class TreatCls(object):
def __init__(self, test):
self.test = test
self.n_times_called = 0
def __call__(self, alias):
self.n_times_called += 1
self.test.assertTrue(alias.sample in sample)
self.test.assertEqual(alias.name, name)
treat_func = TreatCls(self)
treated = gen.callback(gen.fs_content(), treat_func, {
"sample": sample,
"name": name
})
self.assertEqual(gen.consume_n_count(treated), 150)
self.assertEqual(treat_func.n_times_called, 2)