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scale factor class --- coffea/btag_tools/__init__.py | 10 ++ coffea/btag_tools/btagscalefactor.py | 153 +++++++++++++++++++++++++++ 2 files changed, 163 insertions(+) create mode 100644 coffea/btag_tools/__init__.py create mode 100644 coffea/btag_tools/btagscalefactor.py diff --git a/coffea/btag_tools/__init__.py b/coffea/btag_tools/__init__.py new file mode 100644 index 000000000..85eac8535 --- /dev/null +++ b/coffea/btag_tools/__init__.py @@ -0,0 +1,10 @@ +"""BTag tools: CMS analysis-level b-tagging corrections and uncertainties + +These classes provide computation of CMS b-tagging and mistagging +corrections and uncertainties on columnar data. +""" +from .btagscalefactor import BTagScaleFactor + +__all__ = [ + 'BTagScaleFactor', +] diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py new file mode 100644 index 000000000..4a5cb8e10 --- /dev/null +++ b/coffea/btag_tools/btagscalefactor.py @@ -0,0 +1,153 @@ +from threading import Lock +import pandas +import numpy +import numexpr +import numba + + +class BTagScaleFactor: + formulaLock = Lock() + formulaCache = {} + LOOSE, MEDIUM, TIGHT, RESHAPE = range(4) + wpString = {'loose': LOOSE, 'medium': MEDIUM, 'tight': TIGHT} + expectedColumns = [ + 'OperatingPoint', 'measurementType', 'sysType', 'jetFlavor', 'etaMin', + 'etaMax', 'ptMin', 'ptMax', 'discrMin', 'discrMax', 'formula' + ] + + @classmethod + def readcsv(cls, filename): + df = pandas.read_csv(filename, skipinitialspace=True) + discriminator = df.columns[0].split(';')[0] + + def cleanup(colname): + if ';' in colname: + _, colname = colname.split(';') + return colname.strip() + df.rename(columns=cleanup, inplace=True) + if not list(df.columns) == BTagScaleFactor.expectedColumns: + raise RuntimeError('Columns in BTag scale factor file %s as expected' % filename) + for var in ['eta', 'pt', 'discr']: + df[var + 'Bin'] = list(zip(df[var + 'Min'], df[var + 'Max'])) + del df[var + 'Min'] + del df[var + 'Max'] + return df, discriminator + + def __init__(self, filename, workingpoint, lightmethod='comb', keep_df=False): + try: + workingpoint = BTagScaleFactor.wpString[workingpoint] + except KeyError: + pass + if workingpoint not in [0, 1, 2, 3]: + raise ValueError('Unrecognized working point') + if lightmethod not in ['comb', 'mujets']: + raise ValueError('Unrecognized light jet correction method') + self.workingpoint = workingpoint + df, self.discriminator = BTagScaleFactor.readcsv(filename) + df = df[df['OperatingPoint'] == workingpoint] + df = df[(df['measurementType'] == lightmethod) | (df['jetFlavor'] == 2)] + df = df.set_index(['sysType', 'jetFlavor', 'etaBin', 'ptBin', 'discrBin']).sort_index() + if keep_df: + self.df = df + self._corrections = {} + for syst in list(df.index.levels[0]): + corr = df.loc[syst] + allbins = list(corr.index) + edges_flavor = numpy.array([0, 1, 2, 3]) # udsg, c, b + edges_eta = numpy.array(sorted(set(x for tup in corr.index.levels[1] for x in tup))) + edges_pt = numpy.array(sorted(set(x for tup in corr.index.levels[2] for x in tup))) + edges_discr = numpy.array(sorted(set(x for tup in corr.index.levels[3] for x in tup))) + alledges = numpy.meshgrid(edges_flavor[:-1], edges_eta[:-1], edges_pt[:-1], edges_discr[:-1], indexing='ij') + mapping = numpy.full(alledges[0].shape, -1) + + def findbin(flavor, eta, pt, discr): + for i, (fbin, ebin, pbin, dbin) in enumerate(allbins): + if flavor == fbin and ebin[0] <= eta < ebin[1] and pbin[0] <= pt < pbin[1] and dbin[0] <= discr < dbin[1]: + return i + return -1 + + for idx, _ in numpy.ndenumerate(mapping): + flavor, eta, pt, discr = (x[idx] for x in alledges) + mapidx = findbin(flavor, eta, pt, discr) + if mapidx < 0 and flavor == 2: + # it seems for b flavor it is abs(eta) + eta = eta + 1e-5 # add eps to avoid edge effects + mapidx = findbin(flavor, abs(eta), pt, discr) + mapping[idx] = mapidx + + self._corrections[syst] = ( + edges_flavor, + edges_eta, + edges_pt, + edges_discr, + mapping, + numpy.array(corr['formula']), + ) + self._compiled = {} + + def __getstate__(self): + state = dict(self.__dict__) + state.pop('_compiled') + return state + + def __setstate__(self, state): + self.__dict__ = state + self._compiled = {} + + @classmethod + def compile(cls, formula): + with BTagScaleFactor.formulaLock: + try: + return BTagScaleFactor.formulaCache[formula] + except KeyError: + if 'x' in formula: + feval = eval('lambda x: ' + formula, {'log': numpy.log, 'sqrt': numpy.sqrt}) + out = numba.vectorize([ + numba.float32(numba.float32), + numba.float64(numba.float64), + ])(feval) + else: + val = numexpr.evaluate(formula) + + def duck(_, out, where): + out[where] = val + out = duck + BTagScaleFactor.formulaCache[formula] = out + return out + + def lookup(self, axis, values): + return numpy.clip(numpy.searchsorted(axis, values, side='right') - 1, 0, len(axis) - 2) + + def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): + try: + functions = self._compiled[systematic] + except KeyError: + functions = [BTagScaleFactor.compile(f) for f in self._corrections[systematic][5]] + self._compiled[systematic] = functions + try: + flavor.counts + jin, flavor = flavor, flavor.flatten() + eta = eta.flatten() + pt = pt.flatten() + discr = discr.flatten() if discr is not None else None + except AttributeError: + jin = None + corr = self._corrections[systematic] + idx = ( + self.lookup(corr[0], flavor), + self.lookup(corr[1], eta), + self.lookup(corr[2], pt), + self.lookup(corr[3], discr) if discr is not None else 0, + ) + mapidx = corr[4][idx] + out = numpy.zeros(mapidx.shape, dtype=pt.dtype) + for ifunc in numpy.unique(mapidx): + if ifunc < 0 and not ignore_missing: + raise ValueError('No correction was available for some items') + func = functions[ifunc] + var = discr if self.workingpoint == BTagScaleFactor.RESHAPE else pt + func(var, out=out, where=(mapidx == ifunc)) + + if jin is not None: + out = jin.copy(content=out) + return out From 4fe4ee7a78aa10722f8d52991921d44ad4c7f3ed Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Thu, 13 Feb 2020 18:33:45 -0600 Subject: [PATCH 03/11] Docs and test --- coffea/btag_tools/btagscalefactor.py | 101 +++++++++++++++++++++------ docs/source/reference.rst | 1 + tests/test_btag.py | 22 ++++++ 3 files changed, 103 insertions(+), 21 deletions(-) create mode 100644 tests/test_btag.py diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py index 4a5cb8e10..25d973c6c 100644 --- a/coffea/btag_tools/btagscalefactor.py +++ b/coffea/btag_tools/btagscalefactor.py @@ -6,17 +6,49 @@ class BTagScaleFactor: - formulaLock = Lock() - formulaCache = {} + '''A class holding one complete BTag scale factor for a given working point + + Parameters + ---------- + filename : str + The BTag-formatted CSV file to read (accepts .csv, .csv.gz, etc.) + See pandas read_csv for all supported compressions. + workingpoint : str or int + The working point, one of LOOSE, MEDIUM, TIGHT, or RESHAPE (0-3, respectively) + lightmethod : str, optional + The light-jet scale factor derivation method to use + 'comb': combined ttbar- and qcd-based method + 'mujets': qcd-only, for precision analyses using ttbar phase space + keep_df : bool, optional + If set true, keep the parsed dataframe as an attribute (.df) for later inspection + ''' LOOSE, MEDIUM, TIGHT, RESHAPE = range(4) - wpString = {'loose': LOOSE, 'medium': MEDIUM, 'tight': TIGHT} - expectedColumns = [ + _formulaLock = Lock() + _formulaCache = {} + _wpString = {'loose': LOOSE, 'medium': MEDIUM, 'tight': TIGHT} + _expectedColumns = [ 'OperatingPoint', 'measurementType', 'sysType', 'jetFlavor', 'etaMin', 'etaMax', 'ptMin', 'ptMax', 'discrMin', 'discrMax', 'formula' ] @classmethod def readcsv(cls, filename): + '''Reads a BTag-formmated CSV file into a pandas dataframe + + This function also merges the bin min and max into a tuple representing the bin + + Parameters + ---------- + filename : str + The file to open + + Returns + ------- + df : pandas.DataFrame + A dataframe containing all info in the file + discriminator : str + The name of the discriminator the correction map is for + ''' df = pandas.read_csv(filename, skipinitialspace=True) discriminator = df.columns[0].split(';')[0] @@ -25,7 +57,7 @@ def cleanup(colname): _, colname = colname.split(';') return colname.strip() df.rename(columns=cleanup, inplace=True) - if not list(df.columns) == BTagScaleFactor.expectedColumns: + if not list(df.columns) == BTagScaleFactor._expectedColumns: raise RuntimeError('Columns in BTag scale factor file %s as expected' % filename) for var in ['eta', 'pt', 'discr']: df[var + 'Bin'] = list(zip(df[var + 'Min'], df[var + 'Max'])) @@ -34,12 +66,11 @@ def cleanup(colname): return df, discriminator def __init__(self, filename, workingpoint, lightmethod='comb', keep_df=False): - try: - workingpoint = BTagScaleFactor.wpString[workingpoint] - except KeyError: - pass if workingpoint not in [0, 1, 2, 3]: - raise ValueError('Unrecognized working point') + try: + workingpoint = BTagScaleFactor._wpString[workingpoint.lower()] + except (KeyError, AttributeError): + raise ValueError('Unrecognized working point') if lightmethod not in ['comb', 'mujets']: raise ValueError('Unrecognized light jet correction method') self.workingpoint = workingpoint @@ -95,10 +126,10 @@ def __setstate__(self, state): self._compiled = {} @classmethod - def compile(cls, formula): - with BTagScaleFactor.formulaLock: + def _compile(cls, formula): + with BTagScaleFactor._formulaLock: try: - return BTagScaleFactor.formulaCache[formula] + return BTagScaleFactor._formulaCache[formula] except KeyError: if 'x' in formula: feval = eval('lambda x: ' + formula, {'log': numpy.log, 'sqrt': numpy.sqrt}) @@ -112,17 +143,45 @@ def compile(cls, formula): def duck(_, out, where): out[where] = val out = duck - BTagScaleFactor.formulaCache[formula] = out + BTagScaleFactor._formulaCache[formula] = out return out - def lookup(self, axis, values): + def _lookup(self, axis, values): return numpy.clip(numpy.searchsorted(axis, values, side='right') - 1, 0, len(axis) - 2) def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): + '''Evaluate this scale factor as a function of jet properties + + Parameters + ---------- + systematic : str + Which systematic to evaluate. Nominal correction is 'central', the options depend + on the scale factor and method + flavor : numpy.ndarray or awkward.Array + The generated jet hadron flavor: 0: udsg, 1: c, 2: b. If you are using [0, 4, 5] as + the enumeration, a quick fix is to instead pass ``(flavor % 3)`` + eta : numpy.ndarray or awkward.Array + The jet pseudorapitiy + pt : numpy.ndarray or awkward.Array + The jet transverse momentum + discr : numpy.ndarray or awkward.Array, optional + The jet tagging discriminant value (default None), optional for all scale factors except + the reshaping scale factor + ignore_missing : bool, optional + If set true, any values that have no correction will return 1. instead of throwing + an exception. Out-of-bounds values are always clipped to the nearest bin. + + Returns + ------- + out : numpy.ndarray or awkward.Array + An array with shape matching ``pt``, with the per-jet scale factor + ''' + if self.workingpoint == BTagScaleFactor.RESHAPE and discr is None: + raise ValueError('RESHAPE scale factor requires a discriminant array') try: functions = self._compiled[systematic] except KeyError: - functions = [BTagScaleFactor.compile(f) for f in self._corrections[systematic][5]] + functions = [BTagScaleFactor._compile(f) for f in self._corrections[systematic][5]] self._compiled[systematic] = functions try: flavor.counts @@ -134,13 +193,13 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): jin = None corr = self._corrections[systematic] idx = ( - self.lookup(corr[0], flavor), - self.lookup(corr[1], eta), - self.lookup(corr[2], pt), - self.lookup(corr[3], discr) if discr is not None else 0, + self._lookup(corr[0], flavor), + self._lookup(corr[1], eta), + self._lookup(corr[2], pt), + self._lookup(corr[3], discr) if discr is not None else 0, ) mapidx = corr[4][idx] - out = numpy.zeros(mapidx.shape, dtype=pt.dtype) + out = numpy.ones(mapidx.shape, dtype=pt.dtype) for ifunc in numpy.unique(mapidx): if ifunc < 0 and not ignore_missing: raise ValueError('No correction was available for some items') diff --git a/docs/source/reference.rst b/docs/source/reference.rst index 9b117f05c..bdab12729 100644 --- a/docs/source/reference.rst +++ b/docs/source/reference.rst @@ -20,6 +20,7 @@ and/or heavy dependencies. Below lists the packages available in the ``coffea`` coffea.nanoaod.methods coffea.lookup_tools coffea.jetmet_tools + coffea.btag_tools coffea.lumi_tools coffea.hist coffea.util diff --git a/tests/test_btag.py b/tests/test_btag.py new file mode 100644 index 000000000..434422848 --- /dev/null +++ b/tests/test_btag.py @@ -0,0 +1,22 @@ +import pytest +from coffea.btag_tools import BTagScaleFactor +from dummy_distributions import dummy_jagged_eta_pt +import numpy + +def test_BTagScalefactor(): + sf1 = BTagScaleFactor('tests/samples/testBTagSF.btag.csv', 'medium') + sf2 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.RESHAPE) + sf3 = BTagScaleFactor('tests/samples/DeepJet_102XSF_WP_V1.btag.csv.gz', BTagScaleFactor.LOOSE) + + counts, test_eta, test_pt = dummy_jagged_eta_pt() + test_flavor = numpy.random.randint(3, size=len(test_eta)) + test_allb = numpy.ones_like(test_flavor) * 2 + test_discr = numpy.random.rand(len(test_eta)) + + sf1.eval('central', test_flavor, test_eta, test_pt, test_discr) + sf1.eval('up', test_flavor, test_eta, test_pt) + sf2.eval('central', test_allb, test_eta, test_pt, test_discr) + with pytest.raises(ValueError): + sf2.eval('up', test_allb, test_eta, test_pt) + sf3.eval('central', test_flavor, test_eta, test_pt, test_discr) + sf3.eval('up', test_flavor, test_eta, test_pt) From 48e73fe6fa4772956e7eb7097046cefd65bc294d Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Thu, 13 Feb 2020 18:37:52 -0600 Subject: [PATCH 04/11] Add pandas dependency --- setup.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 092bd40f0..05dcb3165 100644 --- a/setup.py +++ b/setup.py @@ -70,13 +70,13 @@ def get_description(): 'cloudpickle', 'mplhep>=0.0.16', 'packaging', - 'ipywidgets' + 'ipywidgets', + 'pandas', ] EXTRAS_REQUIRE = {} if six.PY3: - pandas = ['pandas'] templates = ['jinja2'] - EXTRAS_REQUIRE['spark'] = ['pyspark>=2.4.1', 'pyarrow>=0.10.0,!=0.14.0'] + templates + pandas + EXTRAS_REQUIRE['spark'] = ['pyspark>=2.4.1', 'pyarrow>=0.10.0,!=0.14.0'] + templates EXTRAS_REQUIRE['parsl'] = ['parsl>=0.7.2'] EXTRAS_REQUIRE['dask'] = ['dask>=2.6.0', 'distributed>=2.6.0', 'bokeh>=1.3.4', 'blosc'] if six.PY2: From 16bb382f77a3df541008b62158982363ed1c8f47 Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Thu, 13 Feb 2020 18:50:00 -0600 Subject: [PATCH 05/11] Remove numexpr, test legacy SF --- coffea/btag_tools/btagscalefactor.py | 3 +-- tests/test_btag.py | 7 ++++++- tests/test_lookup_tools.py | 1 - 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py index 25d973c6c..1cc32bc9f 100644 --- a/coffea/btag_tools/btagscalefactor.py +++ b/coffea/btag_tools/btagscalefactor.py @@ -1,7 +1,6 @@ from threading import Lock import pandas import numpy -import numexpr import numba @@ -138,7 +137,7 @@ def _compile(cls, formula): numba.float64(numba.float64), ])(feval) else: - val = numexpr.evaluate(formula) + val = eval(formula, {'log': numpy.log, 'sqrt': numpy.sqrt}) def duck(_, out, where): out[where] = val diff --git a/tests/test_btag.py b/tests/test_btag.py index 434422848..777898c36 100644 --- a/tests/test_btag.py +++ b/tests/test_btag.py @@ -6,7 +6,9 @@ def test_BTagScalefactor(): sf1 = BTagScaleFactor('tests/samples/testBTagSF.btag.csv', 'medium') sf2 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.RESHAPE) - sf3 = BTagScaleFactor('tests/samples/DeepJet_102XSF_WP_V1.btag.csv.gz', BTagScaleFactor.LOOSE) + sf3 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.TIGHT) + sf4 = BTagScaleFactor('tests/samples/DeepCSV_2016LegacySF_V1_TuneCP5.btag.csv.gz', BTagScaleFactor.RESHAPE, keep_df=True) + # import pdb; pdb.set_trace() counts, test_eta, test_pt = dummy_jagged_eta_pt() test_flavor = numpy.random.randint(3, size=len(test_eta)) @@ -20,3 +22,6 @@ def test_BTagScalefactor(): sf2.eval('up', test_allb, test_eta, test_pt) sf3.eval('central', test_flavor, test_eta, test_pt, test_discr) sf3.eval('up', test_flavor, test_eta, test_pt) + with pytest.raises(ValueError): + sf4.eval('central', test_flavor, test_eta, test_pt, test_discr) + sf4.eval('central', test_allb, test_eta, test_pt, test_discr) diff --git a/tests/test_lookup_tools.py b/tests/test_lookup_tools.py index fa48c40a1..176af2dd0 100644 --- a/tests/test_lookup_tools.py +++ b/tests/test_lookup_tools.py @@ -111,7 +111,6 @@ def test_btag_csv_scalefactors(): extractor.add_weight_sets(["testBTag * tests/samples/testBTagSF.btag.csv", "* * tests/samples/DeepCSV_102XSF_V1.btag.csv.gz", "* * tests/samples/DeepJet_102XSF_WP_V1.btag.csv.gz", - "* * tests/samples/DeepCSV_2016LegacySF_V1_TuneCP5.btag.csv.gz" ]) extractor.finalize() From f7a3830d66a92f43d288b73c3a084e0ca5a9b3de Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Thu, 13 Feb 2020 20:27:36 -0600 Subject: [PATCH 06/11] BTagEntry flavor enum is backwards! --- coffea/btag_tools/btagscalefactor.py | 64 ++++++++++++++++------------ tests/test_btag.py | 8 ++-- 2 files changed, 41 insertions(+), 31 deletions(-) diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py index 1cc32bc9f..bed614c56 100644 --- a/coffea/btag_tools/btagscalefactor.py +++ b/coffea/btag_tools/btagscalefactor.py @@ -14,16 +14,18 @@ class BTagScaleFactor: See pandas read_csv for all supported compressions. workingpoint : str or int The working point, one of LOOSE, MEDIUM, TIGHT, or RESHAPE (0-3, respectively) - lightmethod : str, optional - The light-jet scale factor derivation method to use - 'comb': combined ttbar- and qcd-based method - 'mujets': qcd-only, for precision analyses using ttbar phase space + methods : str, optional + The scale factor derivation method to use for each flavor, 'b,c,light' respectively. + Defaults to 'comb,comb,incl' keep_df : bool, optional If set true, keep the parsed dataframe as an attribute (.df) for later inspection ''' LOOSE, MEDIUM, TIGHT, RESHAPE = range(4) + FLAV_B, FLAV_C, FLAV_UDSG = range(3) _formulaLock = Lock() _formulaCache = {} + _btvflavor = numpy.array([0, 1, 2, 3]) + _flavor = numpy.array([0, 4, 5, 6]) _wpString = {'loose': LOOSE, 'medium': MEDIUM, 'tight': TIGHT} _expectedColumns = [ 'OperatingPoint', 'measurementType', 'sysType', 'jetFlavor', 'etaMin', @@ -64,18 +66,25 @@ def cleanup(colname): del df[var + 'Max'] return df, discriminator - def __init__(self, filename, workingpoint, lightmethod='comb', keep_df=False): + def __init__(self, filename, workingpoint, methods='comb,comb,incl', keep_df=False): if workingpoint not in [0, 1, 2, 3]: try: workingpoint = BTagScaleFactor._wpString[workingpoint.lower()] except (KeyError, AttributeError): raise ValueError('Unrecognized working point') - if lightmethod not in ['comb', 'mujets']: - raise ValueError('Unrecognized light jet correction method') + methods = methods.split(',') self.workingpoint = workingpoint df, self.discriminator = BTagScaleFactor.readcsv(filename) - df = df[df['OperatingPoint'] == workingpoint] - df = df[(df['measurementType'] == lightmethod) | (df['jetFlavor'] == 2)] + cut = (df['jetFlavor'] == self.FLAV_B) & (df['measurementType'] == methods[0]) + if len(methods) > 1: + cut |= (df['jetFlavor'] == self.FLAV_C) & (df['measurementType'] == methods[1]) + if len(methods) > 2: + cut |= (df['jetFlavor'] == self.FLAV_UDSG) & (df['measurementType'] == methods[2]) + cut &= df['OperatingPoint'] == workingpoint + df = df[cut] + mavailable = list(df['measurementType'].unique()) + if not all(m in mavailable for m in methods): + raise ValueError('Unrecognized jet correction method, available: %r' % mavailable) df = df.set_index(['sysType', 'jetFlavor', 'etaBin', 'ptBin', 'discrBin']).sort_index() if keep_df: self.df = df @@ -83,30 +92,28 @@ def __init__(self, filename, workingpoint, lightmethod='comb', keep_df=False): for syst in list(df.index.levels[0]): corr = df.loc[syst] allbins = list(corr.index) - edges_flavor = numpy.array([0, 1, 2, 3]) # udsg, c, b edges_eta = numpy.array(sorted(set(x for tup in corr.index.levels[1] for x in tup))) edges_pt = numpy.array(sorted(set(x for tup in corr.index.levels[2] for x in tup))) edges_discr = numpy.array(sorted(set(x for tup in corr.index.levels[3] for x in tup))) - alledges = numpy.meshgrid(edges_flavor[:-1], edges_eta[:-1], edges_pt[:-1], edges_discr[:-1], indexing='ij') + alledges = numpy.meshgrid(self._btvflavor[:-1], edges_eta[:-1], edges_pt[:-1], edges_discr[:-1], indexing='ij') mapping = numpy.full(alledges[0].shape, -1) - def findbin(flavor, eta, pt, discr): + def findbin(btvflavor, eta, pt, discr): for i, (fbin, ebin, pbin, dbin) in enumerate(allbins): - if flavor == fbin and ebin[0] <= eta < ebin[1] and pbin[0] <= pt < pbin[1] and dbin[0] <= discr < dbin[1]: + if btvflavor == fbin and ebin[0] <= eta < ebin[1] and pbin[0] <= pt < pbin[1] and dbin[0] <= discr < dbin[1]: return i return -1 for idx, _ in numpy.ndenumerate(mapping): - flavor, eta, pt, discr = (x[idx] for x in alledges) - mapidx = findbin(flavor, eta, pt, discr) - if mapidx < 0 and flavor == 2: - # it seems for b flavor it is abs(eta) + btvflavor, eta, pt, discr = (x[idx] for x in alledges) + mapidx = findbin(btvflavor, eta, pt, discr) + if mapidx < 0 and btvflavor == self.FLAV_UDSG: + # it seems for light flavor it is abs(eta) eta = eta + 1e-5 # add eps to avoid edge effects - mapidx = findbin(flavor, abs(eta), pt, discr) + mapidx = findbin(btvflavor, abs(eta), pt, discr) mapping[idx] = mapidx self._corrections[syst] = ( - edges_flavor, edges_eta, edges_pt, edges_discr, @@ -157,8 +164,8 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): Which systematic to evaluate. Nominal correction is 'central', the options depend on the scale factor and method flavor : numpy.ndarray or awkward.Array - The generated jet hadron flavor: 0: udsg, 1: c, 2: b. If you are using [0, 4, 5] as - the enumeration, a quick fix is to instead pass ``(flavor % 3)`` + The generated jet hadron flavor, following the enumeration: + 0: uds quark or gluon, 4: charm quark, 5: bottom quark eta : numpy.ndarray or awkward.Array The jet pseudorapitiy pt : numpy.ndarray or awkward.Array @@ -180,7 +187,7 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): try: functions = self._compiled[systematic] except KeyError: - functions = [BTagScaleFactor._compile(f) for f in self._corrections[systematic][5]] + functions = [BTagScaleFactor._compile(f) for f in self._corrections[systematic][-1]] self._compiled[systematic] = functions try: flavor.counts @@ -192,12 +199,12 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): jin = None corr = self._corrections[systematic] idx = ( - self._lookup(corr[0], flavor), - self._lookup(corr[1], eta), - self._lookup(corr[2], pt), - self._lookup(corr[3], discr) if discr is not None else 0, + 2 - self._lookup(self._flavor, flavor), # transform to btv definiton + self._lookup(corr[0], eta), + self._lookup(corr[1], pt), + self._lookup(corr[2], discr) if discr is not None else 0, ) - mapidx = corr[4][idx] + mapidx = corr[3][idx] out = numpy.ones(mapidx.shape, dtype=pt.dtype) for ifunc in numpy.unique(mapidx): if ifunc < 0 and not ignore_missing: @@ -209,3 +216,6 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): if jin is not None: out = jin.copy(content=out) return out + + def __call__(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): + return self.eval(systematic, flavor, eta, pt, discr, ignore_missing) diff --git a/tests/test_btag.py b/tests/test_btag.py index 777898c36..bad95c48b 100644 --- a/tests/test_btag.py +++ b/tests/test_btag.py @@ -5,14 +5,14 @@ def test_BTagScalefactor(): sf1 = BTagScaleFactor('tests/samples/testBTagSF.btag.csv', 'medium') - sf2 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.RESHAPE) + sf2 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.RESHAPE, 'iterativefit') sf3 = BTagScaleFactor('tests/samples/DeepCSV_102XSF_V1.btag.csv.gz', BTagScaleFactor.TIGHT) - sf4 = BTagScaleFactor('tests/samples/DeepCSV_2016LegacySF_V1_TuneCP5.btag.csv.gz', BTagScaleFactor.RESHAPE, keep_df=True) + sf4 = BTagScaleFactor('tests/samples/DeepCSV_2016LegacySF_V1_TuneCP5.btag.csv.gz', BTagScaleFactor.RESHAPE, 'iterativefit', keep_df=True) # import pdb; pdb.set_trace() counts, test_eta, test_pt = dummy_jagged_eta_pt() - test_flavor = numpy.random.randint(3, size=len(test_eta)) - test_allb = numpy.ones_like(test_flavor) * 2 + test_flavor = numpy.random.choice([0, 4, 5], size=len(test_eta)) + test_allb = numpy.ones_like(test_flavor) * 5 test_discr = numpy.random.rand(len(test_eta)) sf1.eval('central', test_flavor, test_eta, test_pt, test_discr) From 4616ddea06fbbf8d98474d1f4b76424ac71101d5 Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Fri, 14 Feb 2020 00:06:22 -0600 Subject: [PATCH 07/11] Add validation Output of current version: 1.332735930503759e-07 1.3093963424637423e-07 1.326293705972148e-07 3.097314271727214e-08 3.699786566002672e-08 2.494842021860677e-08 --- coffea/btag_tools/btagscalefactor.py | 7 ++- validation/btag/setup.sh | 4 ++ validation/btag/validate.py | 69 ++++++++++++++++++++++++++++ 3 files changed, 78 insertions(+), 2 deletions(-) create mode 100644 validation/btag/setup.sh create mode 100644 validation/btag/validate.py diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py index bed614c56..3aded8805 100644 --- a/coffea/btag_tools/btagscalefactor.py +++ b/coffea/btag_tools/btagscalefactor.py @@ -180,7 +180,7 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): Returns ------- out : numpy.ndarray or awkward.Array - An array with shape matching ``pt``, with the per-jet scale factor + An array with shape matching ``pt``, containing the per-jet scale factor ''' if self.workingpoint == BTagScaleFactor.RESHAPE and discr is None: raise ValueError('RESHAPE scale factor requires a discriminant array') @@ -210,7 +210,10 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): if ifunc < 0 and not ignore_missing: raise ValueError('No correction was available for some items') func = functions[ifunc] - var = discr if self.workingpoint == BTagScaleFactor.RESHAPE else pt + if self.workingpoint == BTagScaleFactor.RESHAPE: + var = numpy.clip(discr, corr[2][0], corr[2][-1]) + else: + var = numpy.clip(pt, corr[1][0], corr[1][-1]) func(var, out=out, where=(mapidx == ifunc)) if jin is not None: diff --git a/validation/btag/setup.sh b/validation/btag/setup.sh new file mode 100644 index 000000000..e459ab610 --- /dev/null +++ b/validation/btag/setup.sh @@ -0,0 +1,4 @@ +wget https://github.com/cms-sw/cmssw/raw/CMSSW_10_0_X/CondTools/BTau/test/BTagCalibrationStandalone.cpp +wget https://github.com/cms-sw/cmssw/raw/CMSSW_10_0_X/CondTools/BTau/test/BTagCalibrationStandalone.h +cp ../../tests/samples/DeepCSV_102XSF_V1.btag.csv.gz . +gzip -d DeepCSV_102XSF_V1.btag.csv.gz diff --git a/validation/btag/validate.py b/validation/btag/validate.py new file mode 100644 index 000000000..53c36611c --- /dev/null +++ b/validation/btag/validate.py @@ -0,0 +1,69 @@ +import ROOT +ROOT.gROOT.SetBatch(True) +ROOT.PyConfig.IgnoreCommandLineOptions = True +ROOT.gInterpreter.LoadFile('BTagCalibrationStandalone.cpp') + +from functools import partial +import numpy +from coffea.btag_tools import BTagScaleFactor + +filename = 'DeepCSV_102XSF_V1.btag.csv' + +btagData = ROOT.BTagCalibration('DeepCSV', filename) + +def stdvec(l): + out = ROOT.std.vector('string')() + for item in l: + out.push_back(item) + return out + +def makesf(btagReader): + def btv_sf(syst, flavor, eta, pt, discr=None): + def wrap(flavor, eta, pt, discr): + if flavor == 5: + btvflavor = ROOT.BTagEntry.FLAV_B + elif flavor == 4: + btvflavor = ROOT.BTagEntry.FLAV_C + elif flavor == 0: + btvflavor = ROOT.BTagEntry.FLAV_UDSG + eta = abs(eta) + else: + raise ValueError + if discr is None: + return btagReader.eval_auto_bounds(syst, btvflavor, eta, pt) + return btagReader.eval_auto_bounds(syst, btvflavor, eta, pt, discr) + return numpy.vectorize(wrap, otypes='d')(flavor, eta, pt, discr) + return btv_sf + + +npts = 10000 +flavor = numpy.full(npts, 5) +eta = numpy.random.uniform(-2.5, 2.5, size=npts) +pt = numpy.random.exponential(50, size=npts) + numpy.random.exponential(20, size=npts) +pt = numpy.maximum(20.1, pt) +discr = numpy.random.rand(npts) + +coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.RESHAPE, 'iterativefit', keep_df=True) +btagData = ROOT.BTagCalibration('DeepCSV', filename) +btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_RESHAPING, 'central', stdvec(['up_jes', 'down_jes'])) +btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'iterativefit') +btv_sf = makesf(btagReader) + +for syst in ['central', 'up_jes', 'down_jes']: + csf = coffea_sf.eval(syst, flavor, eta, pt, discr) + bsf = btv_sf(syst, flavor, abs(eta), pt, discr) + print(abs(csf - bsf).max()) + +flavor = numpy.random.choice([0, 4, 5], size=npts) +coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.TIGHT, 'comb,mujets,incl', keep_df=True) +btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_TIGHT, 'central', stdvec(['up', 'down'])) +btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'comb') +btagReader.load(btagData, ROOT.BTagEntry.FLAV_C, 'mujets') +btagReader.load(btagData, ROOT.BTagEntry.FLAV_UDSG, 'incl') +btv_sf = makesf(btagReader) + +for syst in ['central', 'up', 'down']: + csf = coffea_sf.eval(syst, flavor, eta, pt, discr) + bsf = btv_sf(syst, flavor, eta, pt, discr) + print(abs(csf - bsf).max()) + From 0c3987bc4d9f16133b4eb344bba5a00750c67e8d Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Fri, 14 Feb 2020 10:46:40 -0600 Subject: [PATCH 08/11] More validation, still 1e-7 or better --- validation/btag/setup.sh | 1 + validation/btag/validate.py | 68 +++++++++++++++++++++---------------- 2 files changed, 40 insertions(+), 29 deletions(-) diff --git a/validation/btag/setup.sh b/validation/btag/setup.sh index e459ab610..6e003cc14 100644 --- a/validation/btag/setup.sh +++ b/validation/btag/setup.sh @@ -2,3 +2,4 @@ wget https://github.com/cms-sw/cmssw/raw/CMSSW_10_0_X/CondTools/BTau/test/BTagCa wget https://github.com/cms-sw/cmssw/raw/CMSSW_10_0_X/CondTools/BTau/test/BTagCalibrationStandalone.h cp ../../tests/samples/DeepCSV_102XSF_V1.btag.csv.gz . gzip -d DeepCSV_102XSF_V1.btag.csv.gz +cp ../../tests/samples/testBTagSF.btag.csv . diff --git a/validation/btag/validate.py b/validation/btag/validate.py index 53c36611c..eb4e27b3a 100644 --- a/validation/btag/validate.py +++ b/validation/btag/validate.py @@ -7,9 +7,6 @@ import numpy from coffea.btag_tools import BTagScaleFactor -filename = 'DeepCSV_102XSF_V1.btag.csv' - -btagData = ROOT.BTagCalibration('DeepCSV', filename) def stdvec(l): out = ROOT.std.vector('string')() @@ -17,6 +14,7 @@ def stdvec(l): out.push_back(item) return out + def makesf(btagReader): def btv_sf(syst, flavor, eta, pt, discr=None): def wrap(flavor, eta, pt, discr): @@ -36,34 +34,46 @@ def wrap(flavor, eta, pt, discr): return btv_sf -npts = 10000 -flavor = numpy.full(npts, 5) -eta = numpy.random.uniform(-2.5, 2.5, size=npts) -pt = numpy.random.exponential(50, size=npts) + numpy.random.exponential(20, size=npts) -pt = numpy.maximum(20.1, pt) -discr = numpy.random.rand(npts) +def validate_btag(filename, btagtype, etamax): + btagData = ROOT.BTagCalibration(btagtype, filename) + npts = 10000 + flavor = numpy.full(npts, 5) + eta = numpy.random.uniform(-etamax, etamax, size=npts) + pt = numpy.random.exponential(50, size=npts) + numpy.random.exponential(20, size=npts) + pt = numpy.maximum(20.1, pt) + discr = numpy.random.rand(npts) + + coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.RESHAPE, 'iterativefit', keep_df=True) + btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_RESHAPING, 'central', stdvec(['up_jes', 'down_jes'])) + btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'iterativefit') + btv_sf = makesf(btagReader) -coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.RESHAPE, 'iterativefit', keep_df=True) -btagData = ROOT.BTagCalibration('DeepCSV', filename) -btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_RESHAPING, 'central', stdvec(['up_jes', 'down_jes'])) -btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'iterativefit') -btv_sf = makesf(btagReader) + for syst in ['central', 'up_jes', 'down_jes']: + csf = coffea_sf.eval(syst, flavor, eta, pt, discr) + bsf = btv_sf(syst, flavor, abs(eta), pt, discr) + print(abs(csf - bsf).max()) -for syst in ['central', 'up_jes', 'down_jes']: - csf = coffea_sf.eval(syst, flavor, eta, pt, discr) - bsf = btv_sf(syst, flavor, abs(eta), pt, discr) - print(abs(csf - bsf).max()) + flavor = numpy.random.choice([0, 4, 5], size=npts) + coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.TIGHT, 'comb,mujets,incl', keep_df=True) + btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_TIGHT, 'central', stdvec(['up', 'down'])) + btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'comb') + btagReader.load(btagData, ROOT.BTagEntry.FLAV_C, 'mujets') + btagReader.load(btagData, ROOT.BTagEntry.FLAV_UDSG, 'incl') + btv_sf = makesf(btagReader) -flavor = numpy.random.choice([0, 4, 5], size=npts) -coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.TIGHT, 'comb,mujets,incl', keep_df=True) -btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_TIGHT, 'central', stdvec(['up', 'down'])) -btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'comb') -btagReader.load(btagData, ROOT.BTagEntry.FLAV_C, 'mujets') -btagReader.load(btagData, ROOT.BTagEntry.FLAV_UDSG, 'incl') -btv_sf = makesf(btagReader) + for syst in ['central', 'up', 'down']: + csf = coffea_sf.eval(syst, flavor, eta, pt, discr) + bsf = btv_sf(syst, flavor, eta, pt, discr) + print(abs(csf - bsf).max()) -for syst in ['central', 'up', 'down']: - csf = coffea_sf.eval(syst, flavor, eta, pt, discr) - bsf = btv_sf(syst, flavor, eta, pt, discr) - print(abs(csf - bsf).max()) +validate_btag( + filename='DeepCSV_102XSF_V1.btag.csv', + btagtype='DeepCSV', + etamax=2.5, +) +validate_btag( + filename='testBTagSF.btag.csv', + btagtype='CSVv2', + etamax=2.4, +) From d7f9e0ba8fe5b1b164e4b0a206927c2da6a3663e Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Sun, 16 Feb 2020 11:59:23 -0600 Subject: [PATCH 09/11] Make btag scale factor assume abs(eta) always --- coffea/btag_tools/btagscalefactor.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/coffea/btag_tools/btagscalefactor.py b/coffea/btag_tools/btagscalefactor.py index 3aded8805..c93f0325f 100644 --- a/coffea/btag_tools/btagscalefactor.py +++ b/coffea/btag_tools/btagscalefactor.py @@ -92,7 +92,8 @@ def __init__(self, filename, workingpoint, methods='comb,comb,incl', keep_df=Fal for syst in list(df.index.levels[0]): corr = df.loc[syst] allbins = list(corr.index) - edges_eta = numpy.array(sorted(set(x for tup in corr.index.levels[1] for x in tup))) + # NOTE: here we force the class to assume abs(eta) based on lack of examples where signed eta is used + edges_eta = numpy.array(sorted(set(abs(x) for tup in corr.index.levels[1] for x in tup))) edges_pt = numpy.array(sorted(set(x for tup in corr.index.levels[2] for x in tup))) edges_discr = numpy.array(sorted(set(x for tup in corr.index.levels[3] for x in tup))) alledges = numpy.meshgrid(self._btvflavor[:-1], edges_eta[:-1], edges_pt[:-1], edges_discr[:-1], indexing='ij') @@ -106,12 +107,7 @@ def findbin(btvflavor, eta, pt, discr): for idx, _ in numpy.ndenumerate(mapping): btvflavor, eta, pt, discr = (x[idx] for x in alledges) - mapidx = findbin(btvflavor, eta, pt, discr) - if mapidx < 0 and btvflavor == self.FLAV_UDSG: - # it seems for light flavor it is abs(eta) - eta = eta + 1e-5 # add eps to avoid edge effects - mapidx = findbin(btvflavor, abs(eta), pt, discr) - mapping[idx] = mapidx + mapping[idx] = findbin(btvflavor, eta, pt, discr) self._corrections[syst] = ( edges_eta, @@ -153,9 +149,11 @@ def duck(_, out, where): return out def _lookup(self, axis, values): + if len(axis) == 2: + return numpy.zeros(shape=values.shape, dtype=numpy.uint) return numpy.clip(numpy.searchsorted(axis, values, side='right') - 1, 0, len(axis) - 2) - def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): + def eval(self, systematic, flavor, abseta, pt, discr=None, ignore_missing=False): '''Evaluate this scale factor as a function of jet properties Parameters @@ -166,8 +164,8 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): flavor : numpy.ndarray or awkward.Array The generated jet hadron flavor, following the enumeration: 0: uds quark or gluon, 4: charm quark, 5: bottom quark - eta : numpy.ndarray or awkward.Array - The jet pseudorapitiy + abseta : numpy.ndarray or awkward.Array + The absolute value of the jet pseudorapitiy pt : numpy.ndarray or awkward.Array The jet transverse momentum discr : numpy.ndarray or awkward.Array, optional @@ -192,7 +190,7 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): try: flavor.counts jin, flavor = flavor, flavor.flatten() - eta = eta.flatten() + abseta = abseta.flatten() pt = pt.flatten() discr = discr.flatten() if discr is not None else None except AttributeError: @@ -200,7 +198,7 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): corr = self._corrections[systematic] idx = ( 2 - self._lookup(self._flavor, flavor), # transform to btv definiton - self._lookup(corr[0], eta), + self._lookup(corr[0], abseta), self._lookup(corr[1], pt), self._lookup(corr[2], discr) if discr is not None else 0, ) @@ -220,5 +218,5 @@ def eval(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): out = jin.copy(content=out) return out - def __call__(self, systematic, flavor, eta, pt, discr=None, ignore_missing=False): - return self.eval(systematic, flavor, eta, pt, discr, ignore_missing) + def __call__(self, systematic, flavor, abseta, pt, discr=None, ignore_missing=False): + return self.eval(systematic, flavor, abseta, pt, discr, ignore_missing) From 2d708bb942a7936703b164992238a4e07026bfb0 Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Sun, 16 Feb 2020 12:01:32 -0600 Subject: [PATCH 10/11] Use abseta also in validation routine --- validation/btag/validate.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/validation/btag/validate.py b/validation/btag/validate.py index eb4e27b3a..8e513f369 100644 --- a/validation/btag/validate.py +++ b/validation/btag/validate.py @@ -16,21 +16,20 @@ def stdvec(l): def makesf(btagReader): - def btv_sf(syst, flavor, eta, pt, discr=None): - def wrap(flavor, eta, pt, discr): + def btv_sf(syst, flavor, abseta, pt, discr=None): + def wrap(flavor, abseta, pt, discr): if flavor == 5: btvflavor = ROOT.BTagEntry.FLAV_B elif flavor == 4: btvflavor = ROOT.BTagEntry.FLAV_C elif flavor == 0: btvflavor = ROOT.BTagEntry.FLAV_UDSG - eta = abs(eta) else: raise ValueError if discr is None: - return btagReader.eval_auto_bounds(syst, btvflavor, eta, pt) - return btagReader.eval_auto_bounds(syst, btvflavor, eta, pt, discr) - return numpy.vectorize(wrap, otypes='d')(flavor, eta, pt, discr) + return btagReader.eval_auto_bounds(syst, btvflavor, abseta, pt) + return btagReader.eval_auto_bounds(syst, btvflavor, abseta, pt, discr) + return numpy.vectorize(wrap, otypes='d')(flavor, abseta, pt, discr) return btv_sf @@ -38,7 +37,7 @@ def validate_btag(filename, btagtype, etamax): btagData = ROOT.BTagCalibration(btagtype, filename) npts = 10000 flavor = numpy.full(npts, 5) - eta = numpy.random.uniform(-etamax, etamax, size=npts) + abseta = numpy.random.uniform(0, etamax, size=npts) pt = numpy.random.exponential(50, size=npts) + numpy.random.exponential(20, size=npts) pt = numpy.maximum(20.1, pt) discr = numpy.random.rand(npts) @@ -49,8 +48,8 @@ def validate_btag(filename, btagtype, etamax): btv_sf = makesf(btagReader) for syst in ['central', 'up_jes', 'down_jes']: - csf = coffea_sf.eval(syst, flavor, eta, pt, discr) - bsf = btv_sf(syst, flavor, abs(eta), pt, discr) + csf = coffea_sf.eval(syst, flavor, abseta, pt, discr) + bsf = btv_sf(syst, flavor, abseta, pt, discr) print(abs(csf - bsf).max()) flavor = numpy.random.choice([0, 4, 5], size=npts) @@ -62,8 +61,8 @@ def validate_btag(filename, btagtype, etamax): btv_sf = makesf(btagReader) for syst in ['central', 'up', 'down']: - csf = coffea_sf.eval(syst, flavor, eta, pt, discr) - bsf = btv_sf(syst, flavor, eta, pt, discr) + csf = coffea_sf.eval(syst, flavor, abseta, pt, discr) + bsf = btv_sf(syst, flavor, abseta, pt, discr) print(abs(csf - bsf).max()) From 4c71d552abe60c2317cc6a004db78d3c33a75998 Mon Sep 17 00:00:00 2001 From: Nick Smith Date: Sun, 16 Feb 2020 12:26:26 -0600 Subject: [PATCH 11/11] Drop buggy mplhep version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 1c68cb877..f55798abe 100644 --- a/setup.py +++ b/setup.py @@ -68,7 +68,7 @@ def get_description(): 'lz4', 'six', 'cloudpickle', - 'mplhep>=0.0.16', + 'mplhep>=0.0.16,!=0.0.37', 'packaging', 'ipywidgets', 'pandas',