PK!U<<susy_cross_section/__init__.py"""Module to handle CSV-like data of SUSY cross section.""" PK!}@@"susy_cross_section/axes_wrapper.py"""Functions for axes modifications before/after fitting. Each type of modifiers are provided as two functions: parameters-version and value-version, or one for 'x' and the other for 'y'. The former always returns a tuple of floats because there might be multiple parameters, while the latter returns single float value. """ import sys from typing import Any, Callable, Mapping, Sequence, Union, cast # noqa: F401 import numpy if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 VT = float FT = Callable[[VT], VT] # note: X-wrappers are not List[Value]->List[Value] but List[Value->Value]. XT = Sequence[VT] # X-point is always a sequence, even if one-parameter. YT = VT def _check_seq_length(obj, n): # type: (Any, int)->bool return hasattr(obj, '__len__') and len(obj) == n and ( obj[0].ndim == 0 if isinstance(obj[0], numpy.ndarray) else not hasattr(obj[0], '__len__') ) class AxesWrapper: """Toolkit to modify the x- and y-axes before interpolation.""" @staticmethod def identity(x): # type: (VT)->VT """Identity function.""" return x @staticmethod def log(x): # type: (VT)->VT """Log function.""" return cast(VT, numpy.log10(x)) @staticmethod def exp(x): # type: (VT)->VT """Exp function.""" return 10**x function_aliases = { 'id': 'identity', 'linear': 'identity', } # type: Mapping[str, str] function_inverses = { 'identity': 'identity', 'log': 'exp', 'exp': 'log', } # type: Mapping[str, str] @classmethod def get_function(cls, name=''): # type: (Union[FT, str])->FT """Return wrapper functions from its name. If "" is given as `name`, returns Identity function. """ if isinstance(name, str): # meaningless cast because of mypy false-positive name = cast(str, cls.function_aliases.get(name, name) or 'identity') return cast(FT, numpy.vectorize(getattr(cls, name))) else: return cast(FT, numpy.vectorize(name)) @classmethod def get_inverse_function(cls, name=''): # type: (str)->FT """Return the name of function to invert the wrapper.""" name = cls.function_aliases.get(name, name) or 'identity' return cls.get_function(cls.function_inverses[name]) def __init__(self, wx, wy, wy_inv=None): # type: (Sequence[Union[FT, str]], Union[FT, str], Union[FT, str])->None self.wx = [self.get_function(i) for i in wx] # Type: List[FT] self.wy = self.get_function(wy) # type: FT if wy_inv: self.wy_inv = self.get_function(wy_inv) # type: FT else: # guess wy_inv if not isinstance(wy, str): raise TypeError('y-wrapper guess only available if specified in name.') self.wy_inv = self.get_inverse_function(wy) def wx_point(self, x_list): # type: (XT)->XT """Apply the x wrappers to the points.""" return [w(x) for w, x in zip(self.wx, x_list)] def correct(self, f): # type: (Callable[[XT], YT])->Callable[[XT], YT] """Correct the fit result to follow the axis modification.""" def _corrected_fit(x, _f=f): # type: (XT, Callable[[XT], YT])->YT if not _check_seq_length(x, len(self.wx)): raise TypeError('Invalid arguments for %d-dim fit: %s', len(self.wx), x) return self.wy_inv(f(self.wx_point(x))) return _corrected_fit PK!ޭQ[[susy_cross_section/config.py"""Dictionary of the cross section tables.""" from __future__ import absolute_import, division, print_function # py2 import sys from typing import Mapping, Tuple, Union # noqa: F401 if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 table_names = { '13TeV.n2x1-.wino': 'data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_m.csv', '13TeV.n2x1+.wino': 'data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_p.csv', '13TeV.n2x1+-.wino': 'data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_pm.csv', '13TeV.x1x1.wino': 'data/lhc_susy_xs_wg/13TeVx1x1wino_envelope.csv', '13TeV.slepslep.ll': 'data/lhc_susy_xs_wg/13TeVslepslep_ll.csv', '13TeV.slepslep.rr': 'data/lhc_susy_xs_wg/13TeVslepslep_rr.csv', '13TeV.slepslep.maxmix': 'data/lhc_susy_xs_wg/13TeVslepslep_maxmix.csv', } # type: Mapping[str, Union[str, Tuple[str, str]]] PK!TeUm-m-)susy_cross_section/cross_section_table.py"""Classes for annotations to a table.""" from __future__ import absolute_import, division, print_function # py2 import logging import pathlib import sys from typing import (Any, List, Mapping, MutableMapping, Optional, # noqa: F401 Sequence, SupportsFloat, Tuple, Union, cast) import pandas from susy_cross_section.table_info import ParameterInfo, TableInfo, ValueInfo from susy_cross_section.utility import Unit if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) class CrossSectionAttributes(object): """Stores physical attributes of a cross section table.""" def __init__(self, processes=None, collider='', ecm='', order='', pdf_id=None, pdf_name=None): # type: (Union[str, List[str]], str, str, str, int, str)->None self.processes = [processes] if isinstance(processes, str) else processes or [] # type: List[str] self.collider = collider # type: str self.ecm = ecm # type: str # because it is always with units self.order = order # type: str self.pdf_id = pdf_id # type: Optional[int] self.pdf_name = pdf_name # type: Optional[str] # either of these is necessary def validate(self): # type: ()->None """Validate the content. A strict type-check is also performed in order to validate (human-written) info files. """ for attr, typ in [('processes', List), ('collider', str), ('ecm', str), ('order', str), ('pdf_id', int if self.pdf_id is not None else None), ('pdf_name', str if self.pdf_name is not None else None)]: if typ and not isinstance(getattr(self, attr), typ): raise TypeError('attributes: %s must be %s', attr, typ) if not all(isinstance(s, str) for s in self.processes): raise TypeError('attributes: processes must be a list of string.') if not (self.pdf_id or self.pdf_name): raise ValueError('attributes: pdf_id or pdf_name is required.') if self.pdf_name is not None and not self.pdf_name: raise ValueError('attributes: pdf_name is empty.') class CrossSectionInfo(TableInfo): """Stores annotations of a cross section table.""" def __init__(self, attributes=None, parameters=None, values=None, **kw): # type: (CrossSectionAttributes, List[ParameterInfo], List[ValueInfo], Any)->None self.attributes = attributes or CrossSectionAttributes() # type: CrossSectionAttributes self.parameters = parameters or [] # type: List[ParameterInfo] self.values = values or [] # type: List[ValueInfo] super(CrossSectionInfo, self).__init__(**kw) # py2 @classmethod def load(cls, source): # type: (Union[pathlib.Path, str])->CrossSectionInfo """Load and construct CrossSectionInfo from a json file.""" return cast(CrossSectionInfo, super(CrossSectionInfo, cls).load(source)) # py2 def _load(self, **kw): # noqa: C901 # type: (Any)->None try: attributes, data = kw['attributes'], kw['data'] except KeyError as e: logger.error('CrossSectionInfo lacks a key: %s', *e.args) exit(1) del kw['attributes'] del kw['data'] super(CrossSectionInfo, self)._load(**kw) # py2 if not isinstance(attributes, Mapping): logger.error('CrossSectionInfo.attributes must be a dict.') exit(1) self.attributes = CrossSectionAttributes(**attributes) # parse data try: parameters, values = data['parameters'], data['values'] except TypeError: logger.error('data must be required and dict.') exit(1) except KeyError: logger.error('data must contain "parameters" and "values".') exit(1) for unused_key in [k for k in data.keys() if k not in ['parameters', 'values']]: logger.warning('Unrecognized attribute "%s" in data.', unused_key) if not (isinstance(parameters, Sequence) and isinstance(values, Sequence)): logger.error('data["values"] and data["parameters"] must be list of dicts.') exit(1) try: self.parameters = [ParameterInfo.from_json(p_json) for p_json in parameters] self.values = [ValueInfo.from_json(value_json) for value_json in values] except ValueError as e: logger.error(*e.args) exit(1) except TypeError as e: logger.error(*e.args) exit(1) self.validate() class CrossSectionTable(object): """Data of a cross section with parameters, read from a table file.""" def _reader_options(self): # type: ()->Mapping[str, Any] default = { 'skiprows': [0], 'names': [c.name for c in self.info.columns], } default.update(self.info.reader_options) return default def __init__(self, table_path, info_path=None): # type: (Union[pathlib.Path, str], Union[pathlib.Path, str])->None self.table_path = pathlib.Path(table_path) # type: pathlib.Path self.info_path = pathlib.Path( info_path if info_path else self.table_path.with_suffix('.info')) # type: pathlib.Path self.info = CrossSectionInfo.load(self.info_path) # type: CrossSectionInfo try: self.raw_data = pandas.read_csv(self.table_path, **self._reader_options()) except ValueError as e: logger.error('Data parse failed: %s', *e.args) exit(1) self._parse_data() self.validate() @staticmethod def _validate_uncertainty_info(uncertainty_info): # type: (Mapping[str, str])->None for _name, typ in uncertainty_info.items(): if typ not in ['relative', 'absolute']: raise ValueError('Invalid type of uncertainty: %s', typ) def _uncertainty_factors(self, value_unit, uncertainty_info): # type: (Unit, Mapping[str, str])->Mapping[str, float] factors = {} for source_name, source_type in uncertainty_info.items(): unc_unit = Unit(self.info.get_column(source_name).unit) if source_type == 'relative': unc_unit *= value_unit # unc / unc_unit == "number in the table" # we want to get "unc / value_unit" = "number in the table" * unc_unit / value_unit factors[source_name] = float(unc_unit / value_unit) return factors @staticmethod def _combine_uncertainties(row, value_name, uncertainty_info, uncertainty_factors): # type: (Any, str, Mapping[str, str], Mapping[str, float])->float uncertainties = [] for source_name, source_type in uncertainty_info.items(): uncertainties.append(row[source_name] * uncertainty_factors[source_name] * ( row[value_name] if source_type == 'relative' else 1 )) return sum(x**2 for x in uncertainties) ** 0.5 def _parse_data(self): # type: ()->None self.data = {} # type: MutableMapping[str, pandas.core.frame.DataFrame] self.units = {} # type: MutableMapping[str, str] for value_info in self.info.values: name = value_info.column value_unit = self.info.get_column(name).unit parameters = self.info.parameters data = self.raw_data.copy() # set index with quantizing the values with granularity to avoid float precision problems for p in parameters: data[p.column] = (data[p.column] / p.granularity).apply(round) * p.granularity data.set_index([p.column for p in parameters], inplace=True) self._validate_uncertainty_info(value_info.unc_p) self._validate_uncertainty_info(value_info.unc_m) unc_p_factors = self._uncertainty_factors(Unit(value_unit), value_info.unc_p) unc_m_factors = self._uncertainty_factors(Unit(value_unit), value_info.unc_m) def unc_p(row): # type: (Any)->float return self._combine_uncertainties(row, name, value_info.unc_p, unc_p_factors) def unc_m(row): # type: (Any)->float return -self._combine_uncertainties(row, name, value_info.unc_m, unc_m_factors) self.data[name] = pandas.DataFrame() self.data[name]['value'] = data[name] self.data[name]['unc+'] = data.apply(unc_p, axis=1) self.data[name]['unc-'] = data.apply(unc_m, axis=1) self.units[name] = value_unit def validate(self): # type: ()->None """Validate the data grid.""" failed = False for key, data in self.data.items(): duplication = data.index[data.index.duplicated()] for d in duplication: failed = True logger.error('Found duplicated entries: %s, %s', key, d) if len(duplication) > 5: logger.error('Maybe parameter granularity is set too large?') if failed: exit(1) def __getitem__(self, key): # type: (str)->pandas.core.frame.DataFrame """Return the cross-section table.""" return self.data[key] def dump(self): # type: ()->None """Print out data table.""" for key, data_table in self.data.items(): print('# {name} [{unit}] with absolute uncertainties'.format(name=key, unit=self.units[key])) print(data_table) def str_information(self): # type: ()->str """Return the information in a formatted string display.""" rows = [] # type: List[str] # information rows.append('[Document]') for k, v in self.info.document.items(): rows.append(' {}: {}'.format(k, v)) attr = self.info.attributes rows.append('[Attributes]') rows.append(' collider : {}-collider with ECM={}'.format(attr.collider, attr.ecm)) rows.append(' order: {} with PDF={}'.format(attr.order, attr.pdf_name or attr.pdf_id)) rows.append('[Processes]') for i in attr.processes: rows.append(' {}'.format(i)) return '\n'.join(rows) def param_information(self): # type: ()->Sequence[Mapping[str,str]] """Return the information of parameters.""" result = [] # type: List[MutableMapping[str, Any]] for param in self.info.parameters: name = param.column column = self.info.get_column(name) result.append({'name': name, 'unit': column.unit, 'granularity': param.granularity}) return result def value_information(self): # type: ()->Sequence[Mapping[str,str]] """Return the information of parameters.""" result = [] # type: List[MutableMapping[str, str]] for value in self.info.values: name = value.column column = self.info.get_column(name) result.append({'name': name, 'unit': column.unit}) return result PK!rrCsusy_cross_section/data/fastlim/8TeV/NLO+NLL/gdcpl_8TeV_NLONLL.info{ "document": { "title": "NLO-NLL gg xsec in decoupling limit", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [{ "column": "mgl", "granularity": 1 }], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!=Csusy_cross_section/data/fastlim/8TeV/NLO+NLL/gdcpl_8TeV_NLONLL.xsecgg xsec in decoupling limit, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) mgl xsec[pb] delta xsec[pb] 400 18.8901495721 2.80106355854 435 11.0964329652 1.6608132505 472 6.56908837044 0.972410520902 510 3.96267716468 0.605742097036 547 2.47689575486 0.390739180686 585 1.5655834766 0.254522514771 622 1.01811399476 0.170951800557 660 0.665871147668 0.115060705476 697 0.44716278415 0.0791956156653 735 0.300770830718 0.054591656976 772 0.207015192875 0.0386687903797 835 0.11131439971 0.0224028007792 885 0.0689291249033 0.0146936146877 935 0.0433719207175 0.0098109517896 985 0.0275873948175 0.00666673255551 1035 0.0177323485616 0.00453015694184 1060 0.0142162196896 0.00371085357336 1110 0.00925462702327 0.00253825475199 1160 0.00606611473854 0.00175825263957 1210 0.0040020004969 0.00122928059286 1260 0.00265226835704 0.000859074925156 1285 0.0021665164136 0.000714418519255 1335 0.00144403292923 0.000505797262779 1385 0.000965552263627 0.00035210343675 1410 0.000790462912646 0.000295008443806 1485 0.000434855798245 0.000174246082307 1560 0.000240051342608 0.000103319565775 1635 0.000132590904537 6.11118990001e-05 1710 7.32389317539e-05 3.58438359627e-05 1735 6.00382398234e-05 2.99932034331e-05 1810 3.30319838551e-05 1.76129412862e-05 1885 1.80770554541e-05 1.02191042022e-05 1960 9.88140730538e-06 5.92461012007e-06 1985 8.04525116038e-06 4.90527497864e-06 PK!{)&@susy_cross_section/data/fastlim/8TeV/NLO+NLL/gg_8TeV_NLONLL.info{ "document": { "title": "gg xsec", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [ { "column": "msq", "granularity": 1 }, { "column": "mgl", "granularity": 1 } ], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!d!D!D!@susy_cross_section/data/fastlim/8TeV/NLO+NLL/gg_8TeV_NLONLL.xsecgg xsec, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq mgl xsec[pb] delta xsec[pb] 200 200 864.479961169 111.014961062 200 250 261.518159812 34.4186551235 200 300 95.7162510171 13.7170916123 200 350 39.9949698574 6.15061861576 200 400 18.2847452511 2.9570404519 200 450 8.94102747465 1.49417234494 200 500 4.62414786747 0.816702018365 200 550 2.48063292641 0.457371428396 200 600 1.38032239502 0.263433690538 200 650 0.790279195131 0.155788894966 200 700 0.46407201647 0.0941056811693 200 750 0.278554108628 0.0584301567847 200 800 0.169806131008 0.0378352032646 200 850 0.105121427675 0.0245697441853 200 900 0.0657268011465 0.0160039380183 200 950 0.0416521136481 0.0106304815994 200 1000 0.0267037749835 0.00716243866668 200 1050 0.0172934817433 0.0048857559328 200 1100 0.0112797669591 0.00334431870572 200 1150 0.00739633913543 0.00229324339129 200 1200 0.00487406282346 0.00157962688285 200 1250 0.00323482709018 0.0010944219524 200 1300 0.00215497861868 0.000763704098139 200 1350 0.00143539245193 0.000529463797055 200 1400 0.000960859976509 0.000373471540401 200 1450 0.000644692049502 0.00026244429045 200 1500 0.000432957434815 0.000183678808711 200 1550 0.000291356513521 0.000128809298106 200 1600 0.000196136953582 9.03947276765e-05 200 1650 0.000131985474534 6.35217982794e-05 200 1700 8.87779624403e-05 4.45795595552e-05 200 1750 5.9682882127e-05 3.12588886391e-05 200 1800 4.0062991767e-05 2.18064495303e-05 200 1850 2.68356596173e-05 1.51559213543e-05 200 1900 1.79139369836e-05 1.05110637177e-05 200 1950 1.19382120177e-05 7.2607185992e-06 200 2000 7.92440283499e-06 4.99098344338e-06 250 200 915.139609361 129.422892065 250 250 247.233025277 29.9246039491 250 300 87.3312972076 10.9039442814 250 350 36.4245382622 4.95021645754 250 400 16.8450912905 2.51617515512 250 450 8.36029484456 1.3685467863 250 500 4.30630661151 0.750630477093 250 550 2.3269497769 0.429164160723 250 600 1.29864838414 0.249514543837 250 650 0.744587137836 0.147947151525 250 700 0.438357758776 0.0898570739319 250 750 0.264342242933 0.0558095518241 250 800 0.161732237495 0.0353490388315 250 850 0.100117910617 0.0235538643174 250 900 0.0628836999758 0.015675161637 250 950 0.039930608312 0.0104531397549 250 1000 0.0256384234238 0.00703088565644 250 1050 0.0166735000919 0.00476553321435 250 1100 0.0108751982275 0.0032727085637 250 1150 0.00715074492827 0.00226063475744 250 1200 0.00471579588456 0.00155554476533 250 1250 0.00313178835708 0.00107517526812 250 1300 0.0020827312296 0.000750767388355 250 1350 0.00139386516092 0.000522498325393 250 1400 0.000936945675103 0.000367869291328 250 1450 0.000629136840038 0.000257616419107 250 1500 0.000423046770472 0.000181006676212 250 1550 0.000285105545753 0.000127448476602 250 1600 0.000192382008463 9.0039195532e-05 250 1650 0.00012954088953 6.30219275774e-05 250 1700 8.72394639278e-05 4.41189400224e-05 250 1750 5.86591669288e-05 3.08445613867e-05 250 1800 3.93834636152e-05 2.15281352656e-05 250 1850 2.63938994156e-05 1.49485248217e-05 250 1900 1.76499299271e-05 1.04071160259e-05 250 1950 1.17641681304e-05 7.18637423246e-06 250 2000 7.82435592984e-06 4.95089430563e-06 300 200 946.140282447 142.553556629 300 250 259.152044253 34.0216925574 300 300 87.9415530598 11.1965357277 300 350 34.977883992 4.54142067823 300 400 15.6313015697 2.12749551064 300 450 7.65940234019 1.15747751067 300 500 3.99059858315 0.664461113286 300 550 2.15992136251 0.387555609965 300 600 1.21577619031 0.231481856984 300 650 0.701548087013 0.139394271329 300 700 0.414807731419 0.0854667102829 300 750 0.25005874354 0.0533842306178 300 800 0.153089032042 0.0344379958733 300 850 0.0951354788841 0.0227482444908 300 900 0.0598923424769 0.015207991262 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148.782057026 350 250 275.564845086 40.635078107 350 300 91.4727765967 12.5565476824 350 350 34.8805019678 4.56307978086 350 400 15.0118806984 1.98783582051 350 450 7.19318069317 1.0412736666 350 500 3.72068455049 0.592254053419 350 550 2.01366341925 0.349023226454 350 600 1.14338692963 0.213603618701 350 650 0.660597357377 0.130428096767 350 700 0.392250643616 0.0809389922531 350 750 0.236358880828 0.05138033484 350 800 0.144429661554 0.0334160790412 350 850 0.0901792728375 0.0220890369757 350 900 0.0568853932144 0.014717095475 350 950 0.0362230397577 0.00986796135869 350 1000 0.0233414248363 0.00669274274172 350 1050 0.0152051205103 0.00458225484058 350 1100 0.0100020643862 0.00312744065859 350 1150 0.00659720850275 0.00215049103097 350 1200 0.00436942424837 0.00148548288881 350 1250 0.00291138868995 0.00103009046646 350 1300 0.00195558924941 0.000721757119163 350 1350 0.00131120219543 0.000506985194315 350 1400 0.000880714274397 0.000355072543019 350 1450 0.00059384862352 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1.99266271101e-05 1.37963399985e-05 1850 1850 1.31872296756e-05 9.36796145133e-06 1850 1900 8.78024633611e-06 6.40017822867e-06 1850 1950 5.86560114872e-06 4.37900307552e-06 1850 2000 3.92661853375e-06 2.9956588735e-06 1900 200 1003.68112673 150.446555659 1900 250 298.047693 44.3112616602 1900 300 105.154429659 15.5430520202 1900 350 42.0246967148 6.15523445007 1900 400 18.3582971898 2.68404583708 1900 450 8.62795708324 1.29786295863 1900 500 4.29772514402 0.684116619698 1900 550 2.23428649351 0.375802179827 1900 600 1.21221083983 0.21458155616 1900 650 0.674046600535 0.125667548931 1900 700 0.384988477037 0.0752988420565 1900 750 0.223964052449 0.0471095879561 1900 800 0.132554414926 0.0302841967533 1900 850 0.0795160582808 0.0196309372117 1900 900 0.0482986863789 0.0128752868232 1900 950 0.0297193032244 0.00854413804489 1900 1000 0.0184911888337 0.00570335805111 1900 1050 0.0116209012688 0.00384491191474 1900 1100 0.00735623289366 0.00259186556583 1900 1150 0.00469238842664 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1.2988537442 1950 500 4.30930015353 0.683771048043 1950 550 2.24503679537 0.375051877972 1950 600 1.21201870656 0.213558202749 1950 650 0.676080709518 0.125349815203 1950 700 0.387033974149 0.0753875588448 1950 750 0.224973633897 0.0471362647402 1950 800 0.133601811206 0.030402552142 1950 850 0.0799158417747 0.019597264095 1950 900 0.0486330002102 0.0128441830949 1950 950 0.0299903805813 0.00854667720962 1950 1000 0.0186818915153 0.00571660434565 1950 1050 0.0117256895342 0.00386128531003 1950 1100 0.00742249765855 0.00259789808874 1950 1150 0.00473252148631 0.00176863671922 1950 1200 0.00304171006362 0.00120588661202 1950 1250 0.00195923298181 0.000830589762561 1950 1300 0.00127312598198 0.000569349990349 1950 1350 0.000829197149064 0.000392088740336 1950 1400 0.000542530291281 0.000269587697117 1950 1450 0.000355699861807 0.000186127676277 1950 1500 0.000234438873143 0.00012902240265 1950 1550 0.000155027514361 8.90751524331e-05 1950 1600 0.000102766109353 6.16128081247e-05 1950 1650 6.81814234356e-05 4.2496434754e-05 1950 1700 4.53301425984e-05 2.93332708949e-05 1950 1750 3.0239403157e-05 2.02598927769e-05 1950 1800 2.02494913962e-05 1.40086468166e-05 1950 1850 1.34566370978e-05 9.55851875168e-06 1950 1900 8.97568667014e-06 6.54777068421e-06 1950 1950 5.9390336441e-06 4.44115735001e-06 1950 2000 3.93582366832e-06 3.00548402022e-06 2000 200 1003.68112673 150.446555659 2000 250 297.610789085 43.6589074124 2000 300 105.152133976 15.5453477036 2000 350 42.0180637843 6.26379776536 2000 400 18.4058817587 2.73885452875 2000 450 8.64141372964 1.29945249095 2000 500 4.30838496526 0.683517669655 2000 550 2.24524032153 0.376240820205 2000 600 1.21201870656 0.213558202749 2000 650 0.677417567531 0.125234538253 2000 700 0.388269250463 0.0753883097426 2000 750 0.22602968795 0.0473320638925 2000 800 0.133570063645 0.0302974522335 2000 850 0.0803195430148 0.0196162403778 2000 900 0.0489809657565 0.0128448370238 2000 950 0.0301797471906 0.00856688821783 2000 1000 0.018796008862 0.00573686764454 2000 1050 0.0118255333983 0.00386675978821 2000 1100 0.00747729057054 0.00259726972248 2000 1150 0.00477341902956 0.00177310482169 2000 1200 0.00306870721943 0.00121476721943 2000 1250 0.00197771414415 0.000830998116171 2000 1300 0.00128361133164 0.000569892989303 2000 1350 0.000837228527046 0.000392281093332 2000 1400 0.00054845108411 0.000271288494022 2000 1450 0.00035990082058 0.000186915766325 2000 1500 0.000236852320075 0.000129027185121 2000 1550 0.000156758246485 8.94675298238e-05 2000 1600 0.000103840032217 6.19499967731e-05 2000 1650 6.88695540649e-05 4.27800222139e-05 2000 1700 4.57242057046e-05 2.94867665132e-05 2000 1750 3.04160032783e-05 2.03619372629e-05 2000 1800 2.0258578756e-05 1.39711910923e-05 2000 1850 1.35644172694e-05 9.63758848253e-06 2000 1900 9.07142776607e-06 6.62118053564e-06 2000 1950 6.01608937036e-06 4.49365421261e-06 PK! T@susy_cross_section/data/fastlim/8TeV/NLO+NLL/sb_8TeV_NLONLL.info{ "document": { "title": "sb xsec", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [ { "column": "msq", "granularity": 1 }, { "column": "mgl", "granularity": 1 } ], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!-D!D!@susy_cross_section/data/fastlim/8TeV/NLO+NLL/sb_8TeV_NLONLL.xsecsb xsec, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq mgl xsec[pb] delta xsec[pb] 200 200 364.852606643 53.9976567805 200 250 329.983099997 45.3545499025 200 300 306.870143925 41.3051567669 200 350 291.195597877 39.1087185191 200 400 279.767204247 37.8593991561 200 450 269.902804085 37.2467393449 200 500 261.492714075 36.2335412563 200 550 253.46310407 35.6900158314 200 600 246.455520329 35.1808272031 200 650 240.956246398 34.2098788061 200 700 235.495083125 33.2983656716 200 750 230.932688703 33.3531295184 200 800 226.967466605 32.9528099351 200 850 223.391971685 32.1180382168 200 900 220.401760045 31.800553517 200 950 217.354236464 31.5838732586 200 1000 215.360864645 31.3716685377 200 1050 212.831626151 30.6904916294 200 1100 210.887575964 30.5269424061 200 1150 208.915957351 30.3358247643 200 1200 207.392062203 30.6588983572 200 1250 205.84409304 30.000838536 200 1300 204.847631516 29.8942863567 200 1350 203.42794328 30.2544168176 200 1400 202.431361192 30.1477440807 200 1450 201.401814451 30.0740365862 200 1500 200.895395338 29.4772020615 200 1550 199.898988364 29.3703560028 200 1600 198.8573747 29.3087172474 200 1650 198.808550762 29.2598933093 200 1700 197.860968695 29.201871453 200 1750 196.934973036 29.1654360063 200 1800 195.860520008 29.136391481 200 1850 194.819019735 29.0743949227 200 1900 194.770266684 29.0256418719 200 1950 194.218516703 28.4738918911 200 2000 194.263667116 28.4287414778 250 200 122.513988126 16.9175279836 250 250 121.529568694 17.4998366171 250 300 115.422781931 16.6164055102 250 350 106.587178975 14.3899118237 250 400 98.9205692372 13.1888374714 250 450 94.3348020572 12.2866843144 250 500 91.1828597848 12.0561447173 250 550 88.4303973007 11.8361703054 250 600 85.912705436 11.6731496001 250 650 83.4818244628 11.4339511182 250 700 81.1262943759 11.1828357535 250 750 79.0447692075 10.9684647711 250 800 77.1787125574 10.7586743368 250 850 75.5262385492 10.5728501925 250 900 74.0433193997 10.4446327247 250 950 72.8040975798 10.3690209713 250 1000 71.649730705 10.2267168372 250 1050 70.581328327 10.0611431892 250 1100 69.521360923 9.89749000376 250 1150 68.5580561028 9.77730837435 250 1200 67.8250154748 9.65372952428 250 1250 67.1105369495 9.58584478736 250 1300 66.5034274927 9.51059104154 250 1350 65.8650950027 9.51287803039 250 1400 65.3608570112 9.47474696191 250 1450 64.8681693623 9.42506756776 250 1500 64.4284776379 9.33978246737 250 1550 63.9716637904 9.25407011268 250 1600 63.5776299576 9.20557998118 250 1650 63.1796376001 9.16182853417 250 1700 62.7763147217 9.12418635702 250 1750 62.4701195446 9.11482590753 250 1800 62.158155604 9.08575951491 250 1850 61.8509406278 9.05277505852 250 1900 61.6024667131 9.06985397134 250 1950 61.3925104659 8.94841533476 250 2000 61.1917106359 8.82745585741 300 200 48.1979787725 6.48875417115 300 250 48.7928863537 7.074607043 300 300 47.0553729031 6.86625328618 300 350 44.1470590263 6.20235321676 300 400 41.043278196 5.49390625935 300 450 38.8978928093 5.09727204837 300 500 37.2315065198 4.8590029987 300 550 35.8805968149 4.69546874625 300 600 34.7434025651 4.5759948654 300 650 33.6412863743 4.502172388 300 700 32.6496857606 4.41982789409 300 750 31.6983521242 4.2972332451 300 800 30.8366896239 4.18556958479 300 850 30.0436691455 4.11305141971 300 900 29.3492967187 4.05958306087 300 950 28.7465306399 4.00004591249 300 1000 28.1538984008 3.94745887624 300 1050 27.5956060016 3.84328498687 300 1100 27.0878175108 3.78937729135 300 1150 26.6376166404 3.70437520189 300 1200 26.2367462201 3.66348894276 300 1250 25.8371078912 3.62857457554 300 1300 25.5340638716 3.590618569 300 1350 25.2303191568 3.56295826583 300 1400 24.9326911906 3.53877766368 300 1450 24.6283828983 3.51874304709 300 1500 24.4338477789 3.49228204928 300 1550 24.1869600223 3.42433922526 300 1600 23.9882056364 3.40203637689 300 1650 23.7894512579 3.37973357838 300 1700 23.5888797591 3.36634408084 300 1750 23.385948045 3.3481554078 300 1800 23.2818150459 3.33220089901 300 1850 23.1282920931 3.36563944807 300 1900 22.979240692 3.31021335282 300 1950 22.8245948241 3.24367351278 300 2000 22.7252453906 3.23243011656 350 200 21.3808935772 2.92417756274 350 250 21.1976135542 2.97572844598 350 300 20.8135445139 2.96181735324 350 350 20.0668949479 2.8466200315 350 400 19.2111307368 2.67489177913 350 450 18.1818143407 2.49806205952 350 500 17.2041386741 2.27971249683 350 550 16.489060782 2.18387264468 350 600 15.8324693838 2.07238450359 350 650 15.3334535759 2.02939926423 350 700 14.8388646681 1.99554890068 350 750 14.385399231 1.91722967857 350 800 13.9892678428 1.88459342609 350 850 13.5890996005 1.85286050992 350 900 13.2983831952 1.81604600418 350 950 12.944409881 1.74159156205 350 1000 12.6406288417 1.71405230156 350 1050 12.342650184 1.68424972183 350 1100 12.0930967572 1.71644979025 350 1150 11.8406129495 1.63743581327 350 1200 11.6392078836 1.61784237791 350 1250 11.4422656135 1.59725858957 350 1300 11.2367642877 1.5816996675 350 1350 11.0872342825 1.51361156943 350 1400 10.8879120599 1.50237193984 350 1450 10.7846086494 1.48455166469 350 1500 10.6361213147 1.52588137458 350 1550 10.4837280644 1.45821808141 350 1600 10.3840596306 1.44955201364 350 1650 10.2808039759 1.44217983525 350 1700 10.1790219378 1.43333404838 350 1750 10.0856517704 1.4194507995 350 1800 9.99517706085 1.39926343547 350 1850 9.89748053613 1.38679900734 350 1900 9.81378062074 1.36334541181 350 1950 9.72888114054 1.34719227127 350 2000 9.67114810664 1.35167594672 400 200 10.3216792078 1.45509611481 400 250 9.99939737658 1.34900987254 400 300 9.89872235585 1.35886924775 400 350 9.8449684133 1.3828066363 400 400 9.73536503838 1.42032895671 400 450 9.26427270575 1.31350167231 400 500 8.72915821099 1.18729129089 400 550 8.31523572112 1.11008837128 400 600 7.95854852121 1.0504843335 400 650 7.67550697759 1.01252988118 400 700 7.43729743765 0.98458108705 400 750 7.21651846362 0.960224926043 400 800 7.00799544542 0.933432206505 400 850 6.80506379589 0.911232424674 400 900 6.6188510049 0.889463274566 400 950 6.44154659607 0.869833192373 400 1000 6.27545279216 0.850975011578 400 1050 6.12080543138 0.832619207274 400 1100 5.97623120542 0.814240367408 400 1150 5.84663838554 0.799154716371 400 1200 5.72149051158 0.782432580101 400 1250 5.60536171253 0.765847654083 400 1300 5.49372506302 0.744885368418 400 1350 5.39338661024 0.736430442282 400 1400 5.30359669305 0.725713358155 400 1450 5.21866464328 0.712867594801 400 1500 5.13750594186 0.704557864602 400 1550 5.06122875933 0.692305012937 400 1600 4.99706238907 0.680760578455 400 1650 4.93099976183 0.670270224526 400 1700 4.87237473624 0.664220651508 400 1750 4.81160628493 0.658548961512 400 1800 4.76243670149 0.653165509523 400 1850 4.71160434017 0.64861271168 400 1900 4.66640812249 0.638423770324 400 1950 4.61646513142 0.634709425397 400 2000 4.57681084885 0.630040565956 450 200 5.19627919509 0.74647660749 450 250 5.1151005547 0.71936296263 450 300 5.06823913732 0.707237502698 450 350 5.02843312264 0.707375919443 450 400 4.96414237696 0.705100644925 450 450 4.83918367445 0.691418191657 450 500 4.68207627829 0.665241570953 450 550 4.46539853511 0.625150163821 450 600 4.25320238847 0.580436994742 450 650 4.07595827288 0.538703237503 450 700 3.92782688514 0.515318271841 450 750 3.80372357621 0.498995782115 450 800 3.6940924014 0.489928183462 450 850 3.58991087259 0.476545886679 450 900 3.48983770966 0.467985815415 450 950 3.39516685529 0.455113644324 450 1000 3.30984724477 0.441873906715 450 1050 3.22466708707 0.429548775432 450 1100 3.14408808614 0.421651932327 450 1150 3.0638224534 0.414469146245 450 1200 2.99366188713 0.406127105802 450 1250 2.92888191359 0.395535147649 450 1300 2.86952216282 0.389505527852 450 1350 2.81325706255 0.378417042405 450 1400 2.75765482634 0.36666611141 450 1450 2.70784252999 0.362106389825 450 1500 2.65755203178 0.357030096265 450 1550 2.61721399528 0.353112671311 450 1600 2.57616457215 0.349853977955 450 1650 2.53628597071 0.345424463389 450 1700 2.49620119832 0.342146058048 450 1750 2.46596322177 0.338217801204 450 1800 2.43701947721 0.334700303473 450 1850 2.40077703372 0.326768379747 450 1900 2.37086387666 0.32436474262 450 1950 2.34640725768 0.326644323034 450 2000 2.3217101398 0.319925164558 500 200 2.73093726001 0.400223671407 500 250 2.74883026738 0.398629129949 500 300 2.7259193516 0.388111299768 500 350 2.67842781599 0.370746046503 500 400 2.62848510663 0.363317262274 500 450 2.62225517844 0.375814270874 500 500 2.60121476631 0.381105614834 500 550 2.49891115895 0.362970113075 500 600 2.38458997518 0.338162000065 500 650 2.27644214156 0.312314415944 500 700 2.18326070848 0.286510395014 500 750 2.11277352874 0.278417918656 500 800 2.05223790582 0.272616334068 500 850 1.99253247813 0.267593965948 500 900 1.93780383586 0.257851367089 500 950 1.88826124185 0.253675778035 500 1000 1.84255380291 0.244069084868 500 1050 1.79763881521 0.235225282016 500 1100 1.74714965086 0.230383179594 500 1150 1.69690260256 0.225298961649 500 1200 1.65708390437 0.221432443341 500 1250 1.61774065877 0.218008345206 500 1300 1.58820805949 0.213969058622 500 1350 1.55243553771 0.204504571316 500 1400 1.5216353138 0.201145643313 500 1450 1.49177232454 0.198340098778 500 1500 1.46191643114 0.195516546346 500 1550 1.43185366262 0.193246961495 500 1600 1.41102221065 0.190987685083 500 1650 1.38093743257 0.188361715454 500 1700 1.36067334928 0.185531679366 500 1750 1.34087089781 0.183157100858 500 1800 1.32095568354 0.181428607732 500 1850 1.30053898887 0.178747909291 500 1900 1.28025471794 0.177377981658 500 1950 1.2605470737 0.175801340303 500 2000 1.25129136858 0.174485102134 550 200 1.48280624368 0.221716444623 550 250 1.50744397952 0.217812047645 550 300 1.505756927 0.213741807257 550 350 1.48968789996 0.213014155616 550 400 1.46370529353 0.202965040961 550 450 1.45448533153 0.204949403989 550 500 1.44532822266 0.207837853695 550 550 1.41655982315 0.206720556579 550 600 1.38101597831 0.197072956483 550 650 1.32455828157 0.180993942462 550 700 1.26724297657 0.174342045232 550 750 1.22135814941 0.16166876554 550 800 1.18034084064 0.155606135898 550 850 1.1404725793 0.152146760524 550 900 1.11559555271 0.144218276208 550 950 1.08533852044 0.141266009493 550 1000 1.06040856992 0.144299736671 550 1050 1.03511827087 0.136369974696 550 1100 1.00577326176 0.13356827701 550 1150 0.977768597289 0.128881042186 550 1200 0.954107352882 0.126849732495 550 1250 0.932213916273 0.124719429321 550 1300 0.911029473481 0.122448851801 550 1350 0.891485076205 0.118768027828 550 1400 0.871129519987 0.115585125153 550 1450 0.852911532417 0.112889289239 550 1500 0.834850803048 0.111138403257 550 1550 0.818719903675 0.109429730177 550 1600 0.803455455296 0.107963876296 550 1650 0.78779408469 0.105923274258 550 1700 0.773029461147 0.103769778109 550 1750 0.759601899875 0.101997020723 550 1800 0.747562068251 0.100625325475 550 1850 0.734914908651 0.0996602984426 550 1900 0.72331622492 0.0980651223511 550 1950 0.713303296731 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1900 7.8064115914e-05 3.60036577733e-05 1650 1950 7.72900565185e-05 3.56140688591e-05 1650 2000 7.65854994858e-05 3.52252923798e-05 1700 200 3.53679486055e-05 1.87480085333e-05 1700 250 3.62061238534e-05 1.90903962829e-05 1700 300 3.71577627555e-05 1.94759927178e-05 1700 350 3.82709037428e-05 1.99477477427e-05 1700 400 3.95237234548e-05 2.04643219149e-05 1700 450 4.07717441464e-05 2.10813455192e-05 1700 500 4.20882892976e-05 2.16785447922e-05 1700 550 4.34326615996e-05 2.22273608508e-05 1700 600 4.47540272149e-05 2.27464748478e-05 1700 650 4.5977424588e-05 2.32536728419e-05 1700 700 4.72315163644e-05 2.37714597965e-05 1700 750 4.83580752371e-05 2.42540899168e-05 1700 800 4.94775149111e-05 2.47500768071e-05 1700 850 5.04630854001e-05 2.51713320313e-05 1700 900 5.14206710472e-05 2.55675152364e-05 1700 950 5.21883136842e-05 2.58671424219e-05 1700 1000 5.28797419496e-05 2.6141984515e-05 1700 1050 5.3474607916e-05 2.63077403408e-05 1700 1100 5.40231369098e-05 2.65369976664e-05 1700 1150 5.44543368341e-05 2.67304150797e-05 1700 1200 5.47852610111e-05 2.68168882551e-05 1700 1250 5.498992812e-05 2.68409176581e-05 1700 1300 5.50986573796e-05 2.68680812984e-05 1700 1350 5.52123501236e-05 2.68935307716e-05 1700 1400 5.52156856806e-05 2.68901952147e-05 1700 1450 5.52190172553e-05 2.688686364e-05 1700 1500 5.51069540309e-05 2.68630211496e-05 1700 1550 5.49998573003e-05 2.68374833756e-05 1700 1600 5.49664761111e-05 2.68107799322e-05 1700 1650 5.52281211406e-05 2.69562206874e-05 1700 1700 5.54655038408e-05 2.71282767382e-05 1700 1750 5.51269731226e-05 2.70463235313e-05 1700 1800 5.46249664395e-05 2.68438994671e-05 1700 1850 5.38554695699e-05 2.64403185734e-05 1700 1900 5.31128680401e-05 2.59590376325e-05 1700 1950 5.24588199568e-05 2.56115033225e-05 1700 2000 5.19020094998e-05 2.52862540479e-05 1750 200 2.33294959335e-05 1.30068595621e-05 1750 250 2.38284813451e-05 1.31730366825e-05 1750 300 2.44061321308e-05 1.34344366141e-05 1750 350 2.51305407924e-05 1.37724668622e-05 1750 400 2.58674527441e-05 1.41123351721e-05 1750 450 2.67417699372e-05 1.44950196895e-05 1750 500 2.7629079631e-05 1.50037924673e-05 1750 550 2.85323226998e-05 1.53400345421e-05 1750 600 2.94309174598e-05 1.57768482262e-05 1750 650 3.02809911744e-05 1.6152385046e-05 1750 700 3.11159618323e-05 1.64922194625e-05 1750 750 3.19302127517e-05 1.68239256328e-05 1750 800 3.26597822624e-05 1.71707317759e-05 1750 850 3.33589833253e-05 1.75598043357e-05 1750 900 3.39954945158e-05 1.78872768384e-05 1750 950 3.45507148454e-05 1.81187584253e-05 1750 1000 3.5069880483e-05 1.84156053937e-05 1750 1050 3.55200447481e-05 1.85154462588e-05 1750 1100 3.58444502605e-05 1.87043863149e-05 1750 1150 3.61729094966e-05 1.87845170249e-05 1750 1200 3.64847872023e-05 1.88415799615e-05 1750 1250 3.66488115911e-05 1.89220600821e-05 1750 1300 3.6782462153e-05 1.89749812178e-05 1750 1350 3.68848371828e-05 1.89838513554e-05 1750 1400 3.69475778516e-05 1.9065859506e-05 1750 1450 3.70008226794e-05 1.90126146782e-05 1750 1500 3.69332413253e-05 1.89385562375e-05 1750 1550 3.68656591675e-05 1.89645096151e-05 1750 1600 3.6833853923e-05 1.89391688352e-05 1750 1650 3.69186372329e-05 1.90275735625e-05 1750 1700 3.70623121552e-05 1.90974706304e-05 1750 1750 3.70785108394e-05 1.91236261232e-05 1750 1800 3.70429957288e-05 1.91756662718e-05 1750 1850 3.66856797469e-05 1.89537833403e-05 1750 1900 3.62377570583e-05 1.86283370876e-05 1750 1950 3.57076896634e-05 1.84214012807e-05 1750 2000 3.52053919782e-05 1.81639149952e-05 1800 200 1.53507692926e-05 8.94961327618e-06 1800 250 1.56382918265e-05 9.07791336876e-06 1800 300 1.60132358132e-05 9.25717973803e-06 1800 350 1.64818519454e-05 9.49212379516e-06 1800 400 1.69557751993e-05 9.70766455063e-06 1800 450 1.75154510058e-05 9.98148716064e-06 1800 500 1.81384499721e-05 1.03096021498e-05 1800 550 1.87020276211e-05 1.05927586772e-05 1800 600 1.92806296021e-05 1.08546734677e-05 1800 650 1.9910092243e-05 1.11611027832e-05 1800 700 2.04840626452e-05 1.14155770396e-05 1800 750 2.1062540335e-05 1.16800713556e-05 1800 800 2.15248821176e-05 1.19162282079e-05 1800 850 2.1986315714e-05 1.21424674887e-05 1800 900 2.24150237658e-05 1.24244062213e-05 1800 950 2.28601721195e-05 1.26369479477e-05 1800 1000 2.32164226077e-05 1.28369395816e-05 1800 1050 2.3498438414e-05 1.29561690101e-05 1800 1100 2.3787393508e-05 1.30918261824e-05 1800 1150 2.3992767739e-05 1.314390268e-05 1800 1200 2.42174669813e-05 1.31951074582e-05 1800 1250 2.4395714896e-05 1.32834725836e-05 1800 1300 2.45108107002e-05 1.33186822467e-05 1800 1350 2.46244091877e-05 1.33523986461e-05 1800 1400 2.46836092995e-05 1.34179164955e-05 1800 1450 2.47169121052e-05 1.33650234484e-05 1800 1500 2.47200566387e-05 1.33618789149e-05 1800 1550 2.47200566387e-05 1.33618789149e-05 1800 1600 2.47328506192e-05 1.33746728954e-05 1800 1650 2.46289099546e-05 1.33568994129e-05 1800 1700 2.46353911864e-05 1.33594885658e-05 1800 1750 2.4839867706e-05 1.34864796996e-05 1800 1800 2.49644749343e-05 1.35438337247e-05 1800 1850 2.48540388662e-05 1.35070610803e-05 1800 1900 2.46027512762e-05 1.33230423615e-05 1800 1950 2.41807419775e-05 1.31521054693e-05 1800 2000 2.38674916811e-05 1.2986146056e-05 1850 200 1.01106053007e-05 6.17854883296e-06 1850 250 1.0298691117e-05 6.27326081247e-06 1850 300 1.05227808427e-05 6.37948244331e-06 1850 350 1.08055841715e-05 6.51490729879e-06 1850 400 1.11472753086e-05 6.69105141324e-06 1850 450 1.15221371518e-05 6.89338716724e-06 1850 500 1.18961440381e-05 7.08787894429e-06 1850 550 1.22747164706e-05 7.28247103622e-06 1850 600 1.26537907022e-05 7.47605314745e-06 1850 650 1.30778359146e-05 7.70695869377e-06 1850 700 1.34613558563e-05 7.89270997836e-06 1850 750 1.38369341159e-05 8.09017517069e-06 1850 800 1.41280998853e-05 8.22415406021e-06 1850 850 1.44861724255e-05 8.43474324348e-06 1850 900 1.48195205166e-05 8.63375340242e-06 1850 950 1.51003573789e-05 8.76674667088e-06 1850 1000 1.53726182189e-05 8.92149528422e-06 1850 1050 1.55731607797e-05 8.99927399016e-06 1850 1100 1.57668872339e-05 9.09477172191e-06 1850 1150 1.59026911022e-05 9.12933598582e-06 1850 1200 1.6063932963e-05 9.20633844072e-06 1850 1250 1.61728224362e-05 9.23841376795e-06 1850 1300 1.62681370107e-05 9.27419147829e-06 1850 1350 1.63619851384e-05 9.33013442659e-06 1850 1400 1.63882844986e-05 9.33853406851e-06 1850 1450 1.64572810454e-05 9.37350908884e-06 1850 1500 1.65353077586e-05 9.42602668038e-06 1850 1550 1.6544558614e-05 9.41677582497e-06 1850 1600 1.65554781863e-05 9.42104142509e-06 1850 1650 1.64685646308e-05 9.36115369875e-06 1850 1700 1.64615057635e-05 9.37112332036e-06 1850 1750 1.6510596297e-05 9.39566838594e-06 1850 1800 1.65862022693e-05 9.44186138494e-06 1850 1850 1.66189704703e-05 9.46997864014e-06 1850 1900 1.65924651406e-05 9.45909009619e-06 1850 1950 1.64252511956e-05 9.37624603343e-06 1850 2000 1.62251452263e-05 9.27139147346e-06 1900 200 6.61862664286e-06 4.21729621397e-06 1900 250 6.74538978272e-06 4.28898454867e-06 1900 300 6.91128105549e-06 4.37725879972e-06 1900 350 7.1049209676e-06 4.47844024699e-06 1900 400 7.31871708721e-06 4.590010789e-06 1900 450 7.54727589822e-06 4.71954861308e-06 1900 500 7.78991968198e-06 4.8535311023e-06 1900 550 8.04295730467e-06 4.99054318221e-06 1900 600 8.29744079726e-06 5.13182644168e-06 1900 650 8.54975944254e-06 5.27264327853e-06 1900 700 8.80197451428e-06 5.40840655411e-06 1900 750 9.038394129e-06 5.53816674001e-06 1900 800 9.27866892966e-06 5.6728014402e-06 1900 850 9.5427585094e-06 5.82711399991e-06 1900 900 9.74407764938e-06 5.92930262688e-06 1900 950 9.93142667936e-06 6.02529096466e-06 1900 1000 1.01176303482e-05 6.12513993785e-06 1900 1050 1.02870949727e-05 6.21586442198e-06 1900 1100 1.04149814132e-05 6.27499289265e-06 1900 1150 1.05076661432e-05 6.31611883137e-06 1900 1200 1.06065908964e-05 6.36289461823e-06 1900 1250 1.07018301195e-05 6.41318137307e-06 1900 1300 1.07990565071e-05 6.45850209012e-06 1900 1350 1.088583105e-05 6.51409072879e-06 1900 1400 1.08972292706e-05 6.5026925082e-06 1900 1450 1.09752264133e-05 6.56687459922e-06 1900 1500 1.10324298211e-05 6.60967119135e-06 1900 1550 1.10438273746e-05 6.59843866265e-06 1900 1600 1.10483241801e-05 6.59394185711e-06 1900 1650 1.09983241801e-05 6.54394185711e-06 1900 1700 1.09933241801e-05 6.54894185711e-06 1900 1750 1.099073817e-05 6.55416082317e-06 1900 1800 1.09966104443e-05 6.56122173236e-06 1900 1850 1.10896439996e-05 6.6349726125e-06 1900 1900 1.11026776591e-05 6.63963519029e-06 1900 1950 1.10990554445e-05 6.65043705483e-06 1900 2000 1.10080068826e-05 6.59302843417e-06 1950 200 4.34057286248e-06 2.87192891104e-06 1950 250 4.41867165717e-06 2.91061991626e-06 1950 300 4.52247380236e-06 2.97803671369e-06 1950 350 4.64374023267e-06 3.04562507368e-06 1950 400 4.78226273309e-06 3.13250843272e-06 1950 450 4.93533442176e-06 3.21518440012e-06 1950 500 5.08765632748e-06 3.29752374972e-06 1950 550 5.25319661069e-06 3.39531490286e-06 1950 600 5.42429494396e-06 3.49861425949e-06 1950 650 5.5851938748e-06 3.59407524946e-06 1950 700 5.74971155293e-06 3.69113851973e-06 1950 750 5.91519145518e-06 3.78932711817e-06 1950 800 6.07257596079e-06 3.87850569087e-06 1950 850 6.23470714031e-06 3.97217071642e-06 1950 900 6.37955974419e-06 4.0579945429e-06 1950 950 6.51811804747e-06 4.13341702367e-06 1950 1000 6.63789607464e-06 4.20177163466e-06 1950 1050 6.75670394384e-06 4.26439465656e-06 1950 1100 6.86576867286e-06 4.32909008046e-06 1950 1150 6.94808123731e-06 4.36528528152e-06 1950 1200 7.02325819836e-06 4.40320201962e-06 1950 1250 7.09950661169e-06 4.447854219e-06 1950 1300 7.15509806356e-06 4.48325961785e-06 1950 1350 7.20703357515e-06 4.51269933226e-06 1950 1400 7.24789900288e-06 4.52875868202e-06 1950 1450 7.28709352405e-06 4.56372077237e-06 1950 1500 7.32452846319e-06 4.5946146527e-06 1950 1550 7.34992888379e-06 4.59963457172e-06 1950 1600 7.35616813972e-06 4.58759851855e-06 1950 1650 7.35790238831e-06 4.5810591899e-06 1950 1700 7.34818936059e-06 4.57077221762e-06 1950 1750 7.33790782879e-06 4.56991752833e-06 1950 1800 7.33703603805e-06 4.56731698574e-06 1950 1850 7.35509746686e-06 4.58785769694e-06 1950 1900 7.37503890532e-06 4.61187320086e-06 1950 1950 7.40191857351e-06 4.63069995105e-06 1950 2000 7.39268985866e-06 4.6297834382e-06 2000 200 2.83840818639e-06 1.94577306449e-06 2000 250 2.88541702742e-06 1.97031985735e-06 2000 300 2.94886611832e-06 2.01140895894e-06 2000 350 3.02788328123e-06 2.06051348197e-06 2000 400 3.11775406247e-06 2.12139647287e-06 2000 450 3.21089195656e-06 2.17803999659e-06 2000 500 3.31393154869e-06 2.23403159279e-06 2000 550 3.4246335231e-06 2.29760661003e-06 2000 600 3.53247539591e-06 2.36992590088e-06 2000 650 3.64047204455e-06 2.4412152525e-06 2000 700 3.74776274826e-06 2.50541614708e-06 2000 750 3.86009394666e-06 2.57283264863e-06 2000 800 3.97111284061e-06 2.63826737424e-06 2000 850 4.07020096514e-06 2.70123089621e-06 2000 900 4.1682931536e-06 2.76319913245e-06 2000 950 4.25816229043e-06 2.81694490967e-06 2000 1000 4.34449601733e-06 2.86322871978e-06 2000 1050 4.42752237373e-06 2.91614550304e-06 2000 1100 4.50647797365e-06 2.96591772227e-06 2000 1150 4.57236408164e-06 3.00262045103e-06 2000 1200 4.633970058e-06 3.03261540889e-06 2000 1250 4.68668740074e-06 3.06154475279e-06 2000 1300 4.73136730638e-06 3.09217144955e-06 2000 1350 4.76813838438e-06 3.11433422323e-06 2000 1400 4.80474195311e-06 3.13632948768e-06 2000 1450 4.82977760166e-06 3.15433903107e-06 2000 1500 4.8492910924e-06 3.16682673361e-06 2000 1550 4.87223994549e-06 3.17049919658e-06 2000 1600 4.8842930205e-06 3.17496090816e-06 2000 1650 4.89623014218e-06 3.17875071592e-06 2000 1700 4.8960988301e-06 3.17861940383e-06 2000 1750 4.89596768225e-06 3.17848825599e-06 2000 1800 4.89062098907e-06 3.1731415628e-06 2000 1850 4.88738182811e-06 3.17804971577e-06 2000 1900 4.8841428213e-06 3.18366989979e-06 2000 1950 4.90649320925e-06 3.19953721076e-06 PK!,G"rrCsusy_cross_section/data/fastlim/8TeV/NLO+NLL/sdcpl_8TeV_NLONLL.info{ "document": { "title": "NLO-NLL ss xsec in decoupling limit", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [{ "column": "msq", "granularity": 1 }], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!]K/Csusy_cross_section/data/fastlim/8TeV/NLO+NLL/sdcpl_8TeV_NLONLL.xsecss xsec in decoupling limit, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq xsec[pb] delta xsec[pb] 400 3.54449486642 0.553589554667 435 2.08453142716 0.323014586015 472 1.23716263028 0.194566792458 510 0.742464361976 0.121719028721 547 0.464176081495 0.0785890173393 585 0.292132288609 0.051105510139 622 0.189317240122 0.0343123909575 660 0.12379627188 0.0231933952847 697 0.0825473595651 0.0159612586481 735 0.0552032148877 0.0110678948011 772 0.037673968413 0.00791521292169 835 0.0200040774982 0.00459484043333 885 0.012291895645 0.00301168800458 935 0.00766806659242 0.00202786955671 985 0.00484495693181 0.00137819903693 1035 0.00309150727194 0.000941612892573 1060 0.002474871935 0.000779251681788 1110 0.00160292885307 0.000533508552042 1160 0.00103747009106 0.000370066187554 1210 0.000674951933946 0.000255074216267 1260 0.000442624688379 0.000177685906349 1285 0.000359281811429 0.00014772600845 1335 0.000237716009821 0.000103309860869 1385 0.000157727466644 7.23714257527e-05 1410 0.000129069597314 6.08122883274e-05 1485 6.99904923982e-05 3.54816826271e-05 1560 3.83357791505e-05 2.07818761964e-05 1635 2.09774378422e-05 1.21237619744e-05 1710 1.14684175263e-05 7.0592282842e-06 1735 9.42534904082e-06 5.92765753222e-06 1810 5.19740840225e-06 3.46421833989e-06 1885 2.85105266706e-06 2.00048532979e-06 1960 1.56124876474e-06 1.14728878238e-06 1985 1.27032504533e-06 9.46226582967e-07 PK!KL@susy_cross_section/data/fastlim/8TeV/NLO+NLL/sg_8TeV_NLONLL.info{ "document": { "title": "sg xsec", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [ { "column": "msq", "granularity": 1 }, { "column": "mgl", "granularity": 1 } ], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!)D!D!@susy_cross_section/data/fastlim/8TeV/NLO+NLL/sg_8TeV_NLONLL.xsecsg xsec, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq mgl xsec[pb] delta xsec[pb] 200 200 1453.18630587 149.668119899 200 250 747.752295836 70.1495616888 200 300 424.762866423 38.7123992836 200 350 258.023308718 23.5092248805 200 400 164.078777095 15.942094014 200 450 107.683476715 11.2032080134 200 500 72.1492472172 7.66105003338 200 550 49.2630810812 5.28112023215 200 600 34.2019202545 3.68050746244 200 650 24.1633437164 2.6241849224 200 700 17.2620650078 1.88263435541 200 750 12.495198964 1.36884569676 200 800 9.16612154285 0.987683632942 200 850 6.7923496068 0.724697273784 200 900 5.07934077464 0.540034402807 200 950 3.84460636547 0.418434895541 200 1000 2.92691583861 0.326656146886 200 1050 2.2500880211 0.256834729938 200 1100 1.74204454142 0.203354451759 200 1150 1.35300132374 0.161363465679 200 1200 1.05322568478 0.129193498125 200 1250 0.827794048893 0.103531061594 200 1300 0.653611361452 0.0846596908657 200 1350 0.517020908992 0.0703558871916 200 1400 0.41050587284 0.0580755870924 200 1450 0.326660074613 0.0485840340676 200 1500 0.261486728182 0.0403703757374 200 1550 0.209958840772 0.0333092462492 200 1600 0.168829370925 0.0278287069127 200 1650 0.136209311456 0.0238369018246 200 1700 0.110244450098 0.0201003911387 200 1750 0.0892937654877 0.0166844403336 200 1800 0.0724357681279 0.0138330056034 200 1850 0.0589800234779 0.0116322792158 200 1900 0.0480412779697 0.0098453956663 200 1950 0.0392137916497 0.00823701127454 200 2000 0.0320595543373 0.00700471710413 250 200 947.208564751 109.62952575 250 250 493.805324282 54.9989512166 250 300 279.054712355 26.6496406719 250 350 169.814537377 14.7161188695 250 400 109.079124862 9.69222679988 250 450 73.0411924656 6.9858893141 250 500 50.2294449266 5.26034314324 250 550 34.8799918051 3.62976456028 250 600 24.4853959077 2.5162408189 250 650 17.3852304683 1.77454892556 250 700 12.5748636688 1.32203755 250 750 9.18315793092 0.958171559053 250 800 6.79528675852 0.704036532768 250 850 5.05385676008 0.534624322813 250 900 3.79463870511 0.409091414592 250 950 2.88740382869 0.317584126956 250 1000 2.21932483987 0.248826897579 250 1050 1.71014346973 0.19614747479 250 1100 1.33127386342 0.155551055515 250 1150 1.03172211757 0.124154304593 250 1200 0.809248818961 0.100273741508 250 1250 0.636660650542 0.0817537229919 250 1300 0.504870244902 0.0674098413307 250 1350 0.400402017837 0.0563991664695 250 1400 0.318845550148 0.0468088630886 250 1450 0.25447860735 0.0380450225387 250 1500 0.203755829108 0.0311803477726 250 1550 0.163176041135 0.0263564970077 250 1600 0.131096543922 0.0218852546665 250 1650 0.105866851929 0.0186329337842 250 1700 0.0859522413714 0.0158738505803 250 1750 0.0697607856484 0.0132027684917 250 1800 0.0566983964772 0.0110283297115 250 1850 0.0462187772033 0.00932663229892 250 1900 0.0376507778315 0.00782619428769 250 1950 0.0307382879003 0.00658763119393 250 2000 0.0251460662083 0.00551300382478 300 200 629.82237374 80.1518008912 300 250 339.718737673 40.1644150389 300 300 194.307475401 19.7283828366 300 350 118.04803582 10.4641549785 300 400 75.8209206865 6.61573145585 300 450 50.8146773169 4.55963340833 300 500 35.1559324409 3.28568298675 300 550 24.8126667504 2.35695524571 300 600 17.7580002092 1.72951678102 300 650 12.8158518141 1.25380245933 300 700 9.31021194846 0.952706843728 300 750 6.85591513455 0.708172404361 300 800 5.09962179387 0.52453276665 300 850 3.8333018447 0.402791261531 300 900 2.90694391078 0.314608354178 300 950 2.21857422063 0.245653715529 300 1000 1.71021542913 0.193706742888 300 1050 1.32022185069 0.153715993905 300 1100 1.03130541039 0.12222366054 300 1150 0.805617124324 0.0983251081104 300 1200 0.631810137029 0.080311374723 300 1250 0.498404861973 0.0660483255199 300 1300 0.395040770463 0.054141571901 300 1350 0.313139939638 0.0451734708828 300 1400 0.249901772836 0.0375600098958 300 1450 0.200106855982 0.0308412564796 300 1500 0.160612046823 0.025298472923 300 1550 0.129064052565 0.0214464296333 300 1600 0.103965560031 0.0180471962822 300 1650 0.0839955568591 0.015135169537 300 1700 0.0680931231651 0.0127257594238 300 1750 0.0553032462716 0.0107329204614 300 1800 0.0449893498827 0.00903366882642 300 1850 0.0366116814777 0.00758076405633 300 1900 0.0298898991328 0.00642806065825 300 1950 0.0244654255032 0.00542992330975 300 2000 0.020008531357 0.00460622548151 350 200 427.232134858 57.8429990056 350 250 239.004702293 28.9989307655 350 300 140.223059787 15.2018925542 350 350 85.7571726169 8.44836430434 350 400 54.8986921196 4.94105373001 350 450 36.5708843028 3.18367313047 350 500 25.2642768774 2.09642426909 350 550 17.988685184 1.57230503741 350 600 13.025878053 1.23657999815 350 650 9.5144036901 0.91470952579 350 700 6.99139345373 0.690301754223 350 750 5.1867965714 0.522114747067 350 800 3.89125190529 0.400346102877 350 850 2.94542374514 0.311472163108 350 900 2.2482292591 0.244877384167 350 950 1.72905329907 0.192626831613 350 1000 1.33046218422 0.152908942465 350 1050 1.04121862643 0.121977843388 350 1100 0.808756332023 0.0977012852875 350 1150 0.633997710792 0.0791672083434 350 1200 0.49941994272 0.0651588092427 350 1250 0.394622023797 0.0537839442687 350 1300 0.312501264942 0.044153047846 350 1350 0.248235187322 0.0366020492484 350 1400 0.198031822209 0.0303940810227 350 1450 0.159048090011 0.0253775113117 350 1500 0.127978872086 0.0211583198736 350 1550 0.103111735598 0.0176691501059 350 1600 0.0832364881041 0.014927993269 350 1650 0.0673744508774 0.0124379969632 350 1700 0.0546053373525 0.0104586523314 350 1750 0.0443329655051 0.00885466642146 350 1800 0.0361031309156 0.00746514952263 350 1850 0.0294472927799 0.00631957612625 350 1900 0.0240287547628 0.00533944642844 350 1950 0.0196381899379 0.00459003886022 350 2000 0.0161009273918 0.00387887132411 400 200 295.074696652 41.1347796998 400 250 170.776999338 21.8009237162 400 300 102.782305385 12.0449475884 400 350 63.8731076754 6.87263386498 400 400 41.0443498319 3.99516501753 400 450 27.2577430427 2.40403748286 400 500 18.7511036884 1.54890243663 400 550 13.3320870637 1.09677179784 400 600 9.68374022617 0.855416436576 400 650 7.1261987676 0.649451788079 400 700 5.30684692906 0.506046047071 400 750 3.97785226042 0.395641013576 400 800 3.00341060007 0.310132279936 400 850 2.28682412397 0.243098280149 400 900 1.75866277772 0.192780515189 400 950 1.36029943881 0.152914047871 400 1000 1.05094665161 0.121632939394 400 1050 0.818363124707 0.0976965185896 400 1100 0.640083197769 0.0791937898425 400 1150 0.503237418747 0.0649809349432 400 1200 0.397438900552 0.0536626445851 400 1250 0.314707790946 0.0446167630388 400 1300 0.24957846553 0.0369797886453 400 1350 0.199004921378 0.0301896082468 400 1400 0.15895807345 0.0251993788163 400 1450 0.127940610073 0.0209786670221 400 1500 0.102936331571 0.0176986222314 400 1550 0.0830459916351 0.0147930354304 400 1600 0.0671623253194 0.0123712359832 400 1650 0.0544696784042 0.0103770749481 400 1700 0.0441874022736 0.00873516929047 400 1750 0.0359155361245 0.0074119286848 400 1800 0.0292581815075 0.00627173079199 400 1850 0.02381975804 0.00529333835199 400 1900 0.0194664179045 0.00448937024203 400 1950 0.0159375632288 0.00379473702778 400 2000 0.0130827556197 0.0032335866269 450 200 207.322374515 29.5405024326 450 250 123.34198389 15.6966422534 450 300 76.0888552372 9.5090084776 450 350 48.2544026195 5.6401936916 450 400 31.3814573767 3.29615638657 450 450 20.933388848 1.94983226228 450 500 14.330351274 1.210530531 450 550 10.1359385512 0.836330132647 450 600 7.30769159908 0.625075696135 450 650 5.41333105959 0.484208168888 450 700 4.07270625205 0.384776480742 450 750 3.08128522976 0.305092701944 450 800 2.34594573217 0.242711055913 450 850 1.79803257287 0.192165694208 450 900 1.37966848339 0.15263066434 450 950 1.07064941281 0.122167901934 450 1000 0.832427627758 0.0978790526312 450 1050 0.650591194315 0.0789726756272 450 1100 0.510087125674 0.064966048461 450 1150 0.401863573461 0.0531912887946 450 1200 0.318157779523 0.0441559435331 450 1250 0.252571988296 0.0371336686789 450 1300 0.200466011111 0.0308716609167 450 1350 0.160426072364 0.0257612128904 450 1400 0.128939452115 0.021005264604 450 1450 0.103667671478 0.0178858179476 450 1500 0.0834510247493 0.0148484602737 450 1550 0.0673024390689 0.0123955492008 450 1600 0.0544927285438 0.0103866064697 450 1650 0.0442802959044 0.00872326731202 450 1700 0.0360101451579 0.00739364352906 450 1750 0.0292965647663 0.00619354312623 450 1800 0.0238837450246 0.00532179188424 450 1850 0.0194753348482 0.00447306480991 450 1900 0.015946155391 0.00377527878718 450 1950 0.0130768014237 0.00321957631546 450 2000 0.0106683320313 0.00267651103382 500 200 148.007549804 21.0382346113 500 250 90.1446187328 12.3765149028 500 300 56.6807176496 7.39150098182 500 350 36.6526963024 4.45691383347 500 400 24.2515735283 2.7493691084 500 450 16.2857062279 1.69550782131 500 500 11.198914011 1.06332784034 500 550 7.85131260283 0.69292848565 500 600 5.65083420651 0.494260676709 500 650 4.18147910354 0.377387024147 500 700 3.14791924334 0.296671427624 500 750 2.39386513565 0.236733433295 500 800 1.83656812772 0.190194373872 500 850 1.40900746318 0.151863383904 500 900 1.08979988126 0.121966090995 500 950 0.847831101252 0.0980000440266 500 1000 0.663007132235 0.079353747383 500 1050 0.519716524263 0.0646594945823 500 1100 0.408880811218 0.0534457371515 500 1150 0.323165079157 0.0443879359513 500 1200 0.256024105823 0.036682079243 500 1250 0.203412771096 0.0309831642863 500 1300 0.162434870445 0.025911765298 500 1350 0.130385367399 0.0216506809644 500 1400 0.104835624025 0.0178123513187 500 1450 0.0843271409621 0.0148667945622 500 1500 0.0679443644968 0.0124539657472 500 1550 0.0548917984056 0.01039952631 500 1600 0.0444726921742 0.00872657414461 500 1650 0.0361436913257 0.00734315486659 500 1700 0.029450607118 0.00625558126268 500 1750 0.0240359031591 0.00527813217378 500 1800 0.0195810025551 0.00446594814634 500 1850 0.015989030517 0.00381534536964 500 1900 0.0130788884939 0.00321363356691 500 1950 0.0107337347044 0.00271810815817 500 2000 0.00878991188452 0.00226661868393 550 200 106.752317784 15.3631773898 550 250 66.4722675296 9.26346889222 550 300 42.565133875 5.68484091689 550 350 27.9131428905 3.51464448811 550 400 18.7147344551 2.24435806549 550 450 12.7467242525 1.38559020916 550 500 8.8375044177 0.904471773293 550 550 6.21394650538 0.586188519052 550 600 4.47161019427 0.413951844355 550 650 3.28751652707 0.307317070065 550 700 2.46138920341 0.234526083372 550 750 1.87601216047 0.186177877676 550 800 1.43800408146 0.149206991489 550 850 1.11922125355 0.12034931306 550 900 0.867496422562 0.0974738513994 550 950 0.677814613717 0.0790649523621 550 1000 0.53060188595 0.0645748356387 550 1050 0.41688212234 0.0533586667698 550 1100 0.329305562683 0.0445957841753 550 1150 0.260645364174 0.0363797838484 550 1200 0.207512273092 0.0300974139712 550 1250 0.1649229256 0.0254105239701 550 1300 0.13198625682 0.0212515703857 550 1350 0.105951964737 0.0177365013404 550 1400 0.0854523816465 0.0149609594059 550 1450 0.0688614970009 0.0125231500115 550 1500 0.0555405883595 0.0104776762823 550 1550 0.0449749391192 0.00885183038292 550 1600 0.0364943421473 0.00741746094876 550 1650 0.0296916366439 0.00621691534839 550 1700 0.0241659183252 0.00522893522525 550 1750 0.0196737559976 0.00448457827962 550 1800 0.0160909350974 0.00382913167902 550 1850 0.0131791634851 0.0032186460867 550 1900 0.0107607293167 0.00268805203752 550 1950 0.00883326931301 0.00228122979772 550 2000 0.00724890085901 0.00192385459905 600 200 78.3484369982 11.5558222184 600 250 49.4869477501 6.94719315454 600 300 32.1750980684 4.3241128723 600 350 21.3916795302 2.74661905694 600 400 14.5515142598 1.78837711297 600 450 10.0256977936 1.15544012619 600 500 7.02819070273 0.768210734047 600 550 4.97626428922 0.508316102833 600 600 3.58912684353 0.356556811277 600 650 2.62273592756 0.257303381345 600 700 1.94548843266 0.191878261823 600 750 1.47744935966 0.14986628785 600 800 1.13941151203 0.119686728512 600 850 0.885945921529 0.0967300361859 600 900 0.693669750716 0.0788326465436 600 950 0.543017756208 0.0650460816749 600 1000 0.427005704601 0.0535819156115 600 1050 0.336321397943 0.0446794421514 600 1100 0.266133857834 0.0369875175553 600 1150 0.211497618016 0.0302466251246 600 1200 0.168483796527 0.0251393669169 600 1250 0.134999346197 0.0214082639763 600 1300 0.108043870581 0.0178371555722 600 1350 0.0867910552292 0.0150006694715 600 1400 0.0698574833183 0.0125949303583 600 1450 0.0563617062396 0.0105605443887 600 1500 0.0456479910003 0.00886368336443 600 1550 0.0370163056176 0.00747143813859 600 1600 0.0300582732527 0.00631211677357 600 1650 0.024432691574 0.00531231485383 600 1700 0.0199254265998 0.00444506920429 600 1750 0.0162410274593 0.00379641483387 600 1800 0.0132815464752 0.00323376971488 600 1850 0.0108557935435 0.00270231492731 600 1900 0.00890740384398 0.00228209812184 600 1950 0.0073051429727 0.0019343158635 600 2000 0.00599639096733 0.00164078460471 650 200 58.0783621061 8.65408116501 650 250 37.198148572 5.29520455342 650 300 24.5202028043 3.34531976778 650 350 16.506298181 2.1501543331 650 400 11.319130438 1.44053021473 650 450 7.90885039966 0.947784078208 650 500 5.590331147 0.631812864865 650 550 4.00493823213 0.437200037404 650 600 2.90497421122 0.309292748536 650 650 2.12638455469 0.221792540351 650 700 1.57739574675 0.163107937008 650 750 1.18940653702 0.124821939585 650 800 0.912831257021 0.097856992999 650 850 0.70961795616 0.0785879989196 650 900 0.55592322939 0.0646566660961 650 950 0.436968482934 0.0535600462073 650 1000 0.344849814716 0.04422218837 650 1050 0.27218655625 0.0370690310159 650 1100 0.216003321662 0.030793762759 650 1150 0.171978789893 0.0257161612513 650 1200 0.137061802975 0.0215282650194 650 1250 0.110107529231 0.0179928501799 650 1300 0.0884797518407 0.0150746362863 650 1350 0.0711927128472 0.012704365035 650 1400 0.0574415813821 0.010697423284 650 1450 0.0463520575829 0.00891869774576 650 1500 0.0375670497219 0.00745812094259 650 1550 0.0305102066105 0.00629668849282 650 1600 0.0248328961571 0.00534152127856 650 1650 0.0202390421053 0.00448368296614 650 1700 0.0164960128158 0.00376061517073 650 1750 0.0134306804245 0.00319246978048 650 1800 0.0110169103453 0.00275204470919 650 1850 0.00900538580256 0.00230733367585 650 1900 0.00738530452663 0.00194054921128 650 1950 0.00605715165089 0.00164718069291 650 2000 0.00497595610479 0.00139491597532 700 200 43.4269855415 6.50427277579 700 250 28.2009920315 4.07836715223 700 300 18.810290328 2.60802747574 700 350 12.8611695896 1.708979397 700 400 8.88964896274 1.14400211959 700 450 6.25260638826 0.763919143381 700 500 4.46361042833 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0.0117181524587 0.00339305476763 1850 850 0.00934804856479 0.00276971226376 1850 900 0.00748463489909 0.00227495572151 1850 950 0.00600416206824 0.00187245562399 1850 1000 0.00482225208091 0.00153754715778 1850 1050 0.00388292798915 0.00126476297373 1850 1100 0.00312904350963 0.00104101952927 1850 1150 0.00251878576419 0.000859966156284 1850 1200 0.00203667074755 0.000710550753137 1850 1250 0.00165161764229 0.000589518043923 1850 1300 0.0013333996739 0.000489415064698 1850 1350 0.00107722102226 0.000403647537747 1850 1400 0.000872061113177 0.000333702424046 1850 1450 0.000705599226868 0.000275734708872 1850 1500 0.000572249510636 0.00022883041372 1850 1550 0.000463543439865 0.000189161767278 1850 1600 0.00037546015129 0.000156766218221 1850 1650 0.000303888791124 0.000129763860232 1850 1700 0.000246269797315 0.000107343851662 1850 1750 0.000199769977848 8.90923855502e-05 1850 1800 0.000162343641469 7.39169800017e-05 1850 1850 0.000131667317972 6.08960135243e-05 1850 1900 0.000106656644483 5.02036432522e-05 1850 1950 8.6681980256e-05 4.14831003037e-05 1850 2000 7.05825787287e-05 3.44460567777e-05 1900 200 0.206834779198 0.0452605103462 1900 250 0.152477151618 0.034661185531 1900 300 0.114623230711 0.0266090284034 1900 350 0.0870108992547 0.0206972347539 1900 400 0.0667762240075 0.016269316324 1900 450 0.0515953012168 0.0129048294179 1900 500 0.0401968147195 0.0103093605629 1900 550 0.0314461608606 0.00822184346358 1900 600 0.0247334928396 0.00662757257998 1900 650 0.0195228725904 0.00534408395866 1900 700 0.0154661594993 0.00436784717078 1900 750 0.0122978267586 0.00359371960018 1900 800 0.00985318283729 0.00294130638412 1900 850 0.00787508420348 0.00240903755649 1900 900 0.00630206058641 0.00197342486934 1900 950 0.00505668516774 0.00162524324847 1900 1000 0.00406481530467 0.00133220199511 1900 1050 0.00327350006627 0.00109486034201 1900 1100 0.0026355867194 0.000901614867385 1900 1150 0.00212538855609 0.000742288943593 1900 1200 0.00171825862621 0.000617180452371 1900 1250 0.00139209568196 0.00051211473708 1900 1300 0.00113106443923 0.000427746976003 1900 1350 0.00091342433237 0.000351890121263 1900 1400 0.00073778216799 0.000289240794711 1900 1450 0.000597230578024 0.000239141068286 1900 1500 0.000484474638291 0.000198049932845 1900 1550 0.000392521971474 0.000164158451645 1900 1600 0.000317919989212 0.000135711864764 1900 1650 0.000257683229069 0.000112250246063 1900 1700 0.000208598235378 9.30457878203e-05 1900 1750 0.000169414185024 7.68891292969e-05 1900 1800 0.00013743669214 6.37799413213e-05 1900 1850 0.000111223717799 5.25314537249e-05 1900 1900 8.99499022986e-05 4.32267763036e-05 1900 1950 7.29196367583e-05 3.57049169306e-05 1900 2000 5.92206367081e-05 2.94756352187e-05 1950 200 0.171345959115 0.0390540639461 1950 250 0.12674750206 0.0294016215501 1950 300 0.0952789623245 0.0226757522315 1950 350 0.0724694227071 0.0177586924146 1950 400 0.0557183788124 0.0139859393151 1950 450 0.0431651406854 0.0110799502923 1950 500 0.0336314353411 0.00890169774522 1950 550 0.0263390336634 0.00715875220719 1950 600 0.0207499378036 0.00575009012417 1950 650 0.0163778409066 0.00461222700811 1950 700 0.0129994930034 0.00378683613003 1950 750 0.0103550105109 0.00308943174796 1950 800 0.00827962513451 0.00253868353198 1950 850 0.00661941835172 0.00208354293366 1950 900 0.00530239353423 0.00170505401443 1950 950 0.00425616609957 0.00140074961567 1950 1000 0.00342791763334 0.00115086001283 1950 1050 0.00275809368344 0.000950617986499 1950 1100 0.00222799847303 0.000786401371568 1950 1150 0.00179736704343 0.000646546371921 1950 1200 0.00145172262214 0.000530732721186 1950 1250 0.0011730223181 0.000440881884877 1950 1300 0.000952729614894 0.000366831395293 1950 1350 0.000771357166783 0.000303907045094 1950 1400 0.000623217356842 0.000250722554028 1950 1450 0.000504904394269 0.000207289382894 1950 1500 0.000409538643482 0.000171588009871 1950 1550 0.000331167598515 0.000141877098113 1950 1600 0.000268695924736 0.000117560428907 1950 1650 0.000217851280907 9.74277285106e-05 1950 1700 0.000176922274806 8.03542816525e-05 1950 1750 0.000143221809308 6.66153209688e-05 1950 1800 0.000116034708371 5.49440254593e-05 1950 1850 9.37668722341e-05 4.52453648602e-05 1950 1900 7.59562484361e-05 3.73267345551e-05 1950 1950 6.14838668651e-05 3.06851469537e-05 1950 2000 4.97804322561e-05 2.52136178992e-05 2000 200 0.142433080895 0.0332671842454 2000 250 0.105575297037 0.0253004067853 2000 300 0.07927086848 0.0193923075855 2000 350 0.0603740791609 0.015187497722 2000 400 0.0465122376723 0.0119875891264 2000 450 0.0360340948954 0.00953766525868 2000 500 0.0281518132761 0.00761742498935 2000 550 0.0220944675186 0.00617361157567 2000 600 0.0174022686452 0.00497370140948 2000 650 0.0137499788464 0.00404716003886 2000 700 0.0109362511148 0.00327961247936 2000 750 0.00871525267834 0.00267626433879 2000 800 0.00695847739937 0.0021892027511 2000 850 0.00556685262584 0.00179497644884 2000 900 0.00446034101005 0.00147267527122 2000 950 0.00358232122733 0.00120741847088 2000 1000 0.00288483642167 0.000995474895532 2000 1050 0.00232733584596 0.000820136150375 2000 1100 0.00188156615826 0.000680879917959 2000 1150 0.00151599390532 0.000562741127862 2000 1200 0.0012223018781 0.000460186858047 2000 1250 0.00099099209029 0.000381745491903 2000 1300 0.000803260809471 0.000316435320235 2000 1350 0.000650143107386 0.000261992154726 2000 1400 0.000526498743231 0.000216819989314 2000 1450 0.000427108103781 0.00017933068959 2000 1500 0.000345946528859 0.000148500772651 2000 1550 0.000280076727898 0.000122520574586 2000 1600 0.000226500264535 0.000101368034792 2000 1650 0.000183971104055 8.44296306963e-05 2000 1700 0.000149561139785 6.97458391273e-05 2000 1750 0.000120825785478 5.72715428537e-05 2000 1800 9.76364372621e-05 4.71059108061e-05 2000 1850 7.91298208147e-05 3.89003177381e-05 2000 1900 6.4118834456e-05 3.21564886742e-05 2000 1950 5.18082553788e-05 2.64028972175e-05 PK!ONP/@susy_cross_section/data/fastlim/8TeV/NLO+NLL/ss_8TeV_NLONLL.info{ "document": { "title": "ss xsec", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0", "note": "scale uncertainty, pdf uncertainty and alphas uncertainty taken into account" }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [ { "column": "msq", "granularity": 1 }, { "column": "mgl", "granularity": 1 } ], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!s{wD!D!@susy_cross_section/data/fastlim/8TeV/NLO+NLL/ss_8TeV_NLONLL.xsecss xsec, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq mgl xsec[pb] delta xsec[pb] 200 200 218.032095564 25.7959573535 200 250 169.949266368 14.3046218485 200 300 140.651247332 10.4265153084 200 350 120.587289382 8.48040131539 200 400 104.621304585 7.37452937038 200 450 91.6716658501 6.24822932892 200 500 80.8699908448 5.35229019744 200 550 71.8857862735 4.75116178055 200 600 64.2973330958 4.33872026978 200 650 57.8439484513 3.93364238987 200 700 52.3458003255 3.56318734633 200 750 47.6677197532 3.31860247642 200 800 43.7726986913 3.16324577071 200 850 40.4842426888 3.08241006145 200 900 37.737355159 2.99447874167 200 950 35.4055377583 3.00562961784 200 1000 33.414460487 3.01299007363 200 1050 31.8109435974 3.12183481622 200 1100 30.4552393915 3.31099978752 200 1150 29.3523786501 3.46187429878 200 1200 28.4873936283 3.58477839733 200 1250 27.8798594188 3.79937096875 200 1300 27.372184134 4.03463950916 200 1350 27.0620041792 4.2887142658 200 1400 26.8547577546 4.56319797782 200 1450 26.7662034034 4.85250727723 200 1500 26.8277387113 5.20030171272 200 1550 26.9843550306 5.55186227703 200 1600 27.2846755098 5.87008788205 200 1650 27.6904179947 6.21412037219 200 1700 28.0984715429 6.57585925585 200 1750 28.6130912623 6.95107764181 200 1800 29.1836250123 7.38042678317 200 1850 29.8938084204 7.7701696856 200 1900 30.6590078729 8.22492570732 200 1950 31.4320834214 8.70098702577 200 2000 32.3170654982 9.18598797416 250 200 98.773360903 11.7983536659 250 250 89.1095040323 10.2592448374 250 300 74.1436703398 7.25125385993 250 350 61.5102104968 5.13224615094 250 400 52.588078739 3.79532750861 250 450 46.4698183338 3.09719081719 250 500 41.9045039237 2.70196999142 250 550 37.8624302106 2.47154992874 250 600 34.2803446767 2.34731975378 250 650 31.0291539753 2.06978136905 250 700 28.1482098977 1.80202758021 250 750 25.6188222131 1.60636754582 250 800 23.3868892145 1.42833752696 250 850 21.5871698832 1.36736271389 250 900 20.1165443769 1.28701869776 250 950 18.6746101663 1.18419025872 250 1000 17.3164190197 1.09333424183 250 1050 16.1671178043 1.08368252251 250 1100 15.1930716436 1.03820007593 250 1150 14.2739373923 0.988336997995 250 1200 13.5594900173 0.958757999325 250 1250 12.9440291049 1.02236192147 250 1300 12.4263535461 1.09236384623 250 1350 11.8935786429 1.06531183671 250 1400 11.5164413648 1.10258259704 250 1450 11.1769499316 1.17340665415 250 1500 10.9057461642 1.21003562448 250 1550 10.6871760444 1.29578444214 250 1600 10.4931482693 1.41797122958 250 1650 10.3154199043 1.47355096179 250 1700 10.1542348716 1.49319921421 250 1750 10.0192877992 1.54754622135 250 1800 9.90998910486 1.62635310781 250 1850 9.83597311091 1.70989422899 250 1900 9.76071285174 1.77485301416 250 1950 9.6955689544 1.81917394119 250 2000 9.65535046045 1.85866760557 300 200 49.8854178337 6.10565998693 300 250 46.0660184294 5.5543212109 300 300 40.2118431144 4.42988036655 300 350 34.5663383377 3.38035590992 300 400 29.9843075604 2.53479156158 300 450 26.4981715902 2.00450859077 300 500 23.7450158115 1.64941052224 300 550 21.466007204 1.46904373047 300 600 19.5865818048 1.34302292036 300 650 17.9073766071 1.22820632498 300 700 16.3697270763 1.07859556715 300 750 14.9810177127 0.985577601421 300 800 13.7441959912 0.853615217977 300 850 12.668028122 0.779997825889 300 900 11.7870783627 0.719892152173 300 950 10.9344732344 0.629809965823 300 1000 10.1335756185 0.594319562888 300 1050 9.42932062454 0.545730705753 300 1100 8.80193044619 0.510943039965 300 1150 8.25665325664 0.48307872449 300 1200 7.78309134604 0.462944029639 300 1250 7.34101791269 0.449161338246 300 1300 6.94631601665 0.435087845995 300 1350 6.58997133928 0.41542357061 300 1400 6.27102745022 0.406260644723 300 1450 5.987798397 0.404045470191 300 1500 5.73347792398 0.401020739274 300 1550 5.4983620876 0.400698797704 300 1600 5.29097337821 0.407432741002 300 1650 5.10800850929 0.416186855729 300 1700 4.94567541079 0.427324836884 300 1750 4.79539415958 0.440542019505 300 1800 4.66566382341 0.454573073725 300 1850 4.55003815746 0.474828908775 300 1900 4.44854739127 0.491213011933 300 1950 4.35721239924 0.508842709909 300 2000 4.28181018277 0.523562993166 350 200 27.0532120777 3.43487794539 350 250 24.846646433 2.92609359988 350 300 22.6572501201 2.62301171631 350 350 20.4041628686 2.23644586319 350 400 18.2021055582 1.78871762383 350 450 16.1099470748 1.39177340249 350 500 14.3964104753 1.13674595661 350 550 12.9935955388 0.976960171055 350 600 11.8521452152 0.823189852397 350 650 10.9091805891 0.795795291496 350 700 10.063775362 0.679230461904 350 750 9.2696544474 0.610784090787 350 800 8.55679593016 0.547706624258 350 850 7.92730380762 0.500726165602 350 900 7.35555521434 0.450364776957 350 950 6.832823703 0.413130606785 350 1000 6.36177516497 0.373292192855 350 1050 5.93137425593 0.337826695439 350 1100 5.55336770538 0.309409065338 350 1150 5.21000693065 0.29073276575 350 1200 4.90059530665 0.279984705304 350 1250 4.60987926675 0.260889950407 350 1300 4.34819410615 0.23396678065 350 1350 4.11892406362 0.230566953832 350 1400 3.90659798028 0.217062758841 350 1450 3.70723262169 0.207485391285 350 1500 3.5279066739 0.19906577863 350 1550 3.36158410035 0.184857479868 350 1600 3.21362540714 0.180933188871 350 1650 3.07103499871 0.18153704621 350 1700 2.9392386396 0.185596659798 350 1750 2.8229524853 0.184522462479 350 1800 2.7210056596 0.179762343005 350 1850 2.62371173575 0.181428667241 350 1900 2.5468807436 0.182971602643 350 1950 2.46613726063 0.180532579505 350 2000 2.39724175973 0.189630339692 400 200 15.378730067 1.99537251393 400 250 14.2301499131 1.68696974853 400 300 13.3134068944 1.54839490132 400 350 12.3462336398 1.45108969696 400 400 11.2717744317 1.21528994539 400 450 10.1464547157 1.01053499248 400 500 9.11298639716 0.794462142579 400 550 8.25304513438 0.655642695536 400 600 7.53389893268 0.559365949398 400 650 6.94267272967 0.494859323355 400 700 6.42335162572 0.4471521404 400 750 5.95646472742 0.407888970638 400 800 5.53382402425 0.365795939691 400 850 5.14401722393 0.332124113294 400 900 4.78720315285 0.304291471396 400 950 4.46839955807 0.278485982318 400 1000 4.17409096163 0.251249536634 400 1050 3.90689276355 0.232110639645 400 1100 3.66503106627 0.21044097683 400 1150 3.44413166483 0.200848768288 400 1200 3.24194357009 0.183818090736 400 1250 3.05826683342 0.166424507258 400 1300 2.88604295692 0.151681853708 400 1350 2.7299356609 0.144644443735 400 1400 2.59344963517 0.138518953939 400 1450 2.45917504251 0.135526028434 400 1500 2.33569240252 0.124438974913 400 1550 2.22724310355 0.119402829561 400 1600 2.12350777783 0.110098845253 400 1650 2.02559010687 0.105690497726 400 1700 1.93179458737 0.105923101423 400 1750 1.84906418243 0.0987620019547 400 1800 1.77033515032 0.0960556290645 400 1850 1.70091736098 0.093204819317 400 1900 1.64183156493 0.0921657164855 400 1950 1.58368137454 0.0904999838758 400 2000 1.5252198428 0.0893542352946 450 200 9.03515878802 1.19750301997 450 250 8.58675462434 1.05704197254 450 300 8.12113443434 0.97375530743 450 350 7.6143068424 0.890058599806 450 400 7.06804490142 0.789044849491 450 450 6.50643386974 0.676845894166 450 500 5.94411929635 0.566050935627 450 550 5.41416338217 0.476644650853 450 600 4.94739476842 0.404307040439 450 650 4.56048714915 0.347821600265 450 700 4.22636499307 0.303301115504 450 750 3.93685955035 0.273173328032 450 800 3.67258822743 0.254998259294 450 850 3.42869654442 0.228442439667 450 900 3.19995865147 0.209094493412 450 950 3.00146619728 0.191097695244 450 1000 2.81854946096 0.17065554103 450 1050 2.64469769524 0.162272620269 450 1100 2.4860013289 0.150069863969 450 1150 2.33787007821 0.139535945042 450 1200 2.20350903712 0.124403180149 450 1250 2.08535880067 0.115876628719 450 1300 1.97226985511 0.103347520637 450 1350 1.86909026951 0.101838623471 450 1400 1.77044509811 0.096220323442 450 1450 1.68174211531 0.0911185922145 450 1500 1.60296709155 0.0864067841292 450 1550 1.52522542725 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Csusy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_m.csvm χ̃ [GeV], xsec [fb], uncertainty [fb] 100.0, 8766.08, 517.621 125.0, 3782.8, 235.43 150.0, 1907.04, 126.192 175.0, 1063.01, 73.7751 200.0, 637.14, 46.5066 225.0, 402.717, 30.8673 250.0, 265.255, 21.1263 275.0, 180.802, 14.9232 300.0, 126.627, 10.8212 325.0, 90.6096, 7.9809 350.0, 66.3319, 6.07824 375.0, 49.2359, 4.60991 400.0, 37.12, 3.57812 425.0, 28.3325, 2.80898 450.0, 21.8561, 2.20924 475.0, 17.0676, 1.77199 500.0, 13.4441, 1.43549 525.0, 10.6671, 1.1752 550.0, 8.52674, 0.962412 575.0, 6.86761, 0.797123 600.0, 5.56289, 0.665467 625.0, 4.52654, 0.544023 650.0, 3.71232, 0.458682 675.0, 3.05164, 0.387201 700.0, 2.5208, 0.327628 725.0, 2.09484, 0.283497 750.0, 1.73984, 0.238501 775.0, 1.45562, 0.202704 800.0, 1.2175, 0.175172 825.0, 1.02139, 0.142823 850.0, 0.862477, 0.128463 875.0, 0.727098, 0.111186 900.0, 0.623379, 0.0974831 925.0, 0.529463, 0.0826968 950.0, 0.450722, 0.0738302 975.0, 0.385788, 0.0622388 1000.0, 0.329002, 0.0521727 1025.0, 0.280284, 0.0474706 1050.0, 0.240707, 0.0430882 1075.0, 0.213287, 0.0377717 1100.0, 0.180922, 0.0360823 1125.0, 0.155356, 0.0280178 1150.0, 0.135615, 0.0262367 1175.0, 0.115356, 0.0232758 1200.0, 0.100687, 0.0168488 1225.0, 0.0862252, 0.0190064 1250.0, 0.0762914, 0.0178031 1275.0, 0.0662261, 0.0165332 1300.0, 0.0605883, 0.0110105 1325.0, 0.0504674, 0.00934855 1350.0, 0.0464939, 0.0137161 1375.0, 0.0406594, 0.00783768 1400.0, 0.0303342, 0.00620691 1425.0, 0.030426, 0.00632462 1450.0, 0.0303359, 0.00644575 1475.0, 0.0202839, 0.00440125 1500.0, 0.0200469, 0.00467065 1525.0, 0.0175652, 0.00419402 1550.0, 0.0154289, 0.00373529 1575.0, 0.0137032, 0.00346894 1600.0, 0.0120888, 0.0028772 1625.0, 0.010602, 0.002622 1650.0, 0.00942027, 0.00245189 1675.0, 0.00830173, 0.00217528 1700.0, 0.00718864, 0.00187223 1725.0, 0.00633142, 0.00162956 1750.0, 0.0055801, 0.00147077 1775.0, 0.00491951, 0.00133538 1800.0, 0.0043716, 0.00124894 1825.0, 0.00383388, 0.00106927 1850.0, 0.00337664, 0.00100039 1875.0, 0.00297123, 9.148310e-04 1900.0, 0.00263965, 8.395900e-04 1925.0, 0.00238853, 7.631700e-04 1950.0, 0.002042, 6.747160e-04 1975.0, 0.00176972, 5.753120e-04 2000.0, 0.00155291, 5.239350e-04 PK!njDsusy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_m.info{ "document": { "title": "NLO-NLL wino-like chargino-neutralino (N2C1) cross sections", "authors": "LHC SUSY Cross Section Working Group", "calculator": "resummino", "source": "https://twiki.cern.ch/twiki/bin/view/LHCPhysics/SUSYCrossSections13TeVn2x1wino", "version": "2017-06-15" }, "attributes": { "processes": "p p > wino0 wino-", "collider": "pp", "ecm": "13TeV", "order": "NLO+NLL", "pdf_name": "Envelope by LHC SUSY Cross Section Working Group" }, "columns": [ { "name": "m_wino", "unit": "GeV" }, { "name": "xsec", "unit": "fb" }, { "name": "uncertainty", "unit": "fb" } ], "reader_options": { "skipinitialspace": 1 }, "data": { "parameters": [{ "column": "m_wino", "granularity": 1 }], "values": [ { "column": "xsec", "unc": [{ "column": "uncertainty", "type": "absolute" }] } ] } } PK!\ Csusy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_p.csvm χ̃ [GeV], xsec [fb], uncertainty [fb] 100.0, 13895.1, 485.572 125.0, 6252.21, 222.508 150.0, 3273.84, 127.175 175.0, 1890.26, 80.6306 200.0, 1170.26, 54.8302 225.0, 762.405, 37.9761 250.0, 517.259, 27.6553 275.0, 362.248, 20.51 300.0, 260.319, 15.5522 325.0, 191.314, 12.0565 350.0, 143.126, 9.39716 375.0, 108.84, 7.50279 400.0, 83.9069, 6.05361 425.0, 65.4553, 4.94466 450.0, 51.5901, 4.04097 475.0, 41.0233, 3.28837 500.0, 32.9135, 2.73443 525.0, 26.6028, 2.29957 550.0, 21.644, 1.92376 575.0, 17.7159, 1.60859 600.0, 14.5767, 1.38165 625.0, 12.0454, 1.15947 650.0, 10.0188, 0.977468 675.0, 8.34633, 0.832887 700.0, 6.99008, 0.714013 725.0, 5.86536, 0.602628 750.0, 4.95438, 0.532335 775.0, 4.18041, 0.452388 800.0, 3.54111, 0.389213 825.0, 3.0052, 0.335727 850.0, 2.55793, 0.284029 875.0, 2.17847, 0.255221 900.0, 1.87338, 0.216666 925.0, 1.59963, 0.185445 950.0, 1.36565, 0.168945 975.0, 1.18309, 0.15545 1000.0, 1.01451, 0.123448 1025.0, 0.879192, 0.108224 1050.0, 0.757183, 0.0900054 1075.0, 0.651686, 0.0799626 1100.0, 0.55945, 0.0711016 1125.0, 0.491938, 0.0637444 1150.0, 0.419984, 0.0565834 1175.0, 0.371485, 0.0412257 1200.0, 0.31567, 0.0415881 1225.0, 0.27623, 0.0371856 1250.0, 0.240685, 0.0286937 1275.0, 0.210298, 0.0264908 1300.0, 0.18015, 0.0229469 1325.0, 0.159393, 0.0196224 1350.0, 0.139246, 0.0174043 1375.0, 0.120872, 0.0154592 1400.0, 0.10087, 0.0128138 1425.0, 0.0906208, 0.012095 1450.0, 0.0807007, 0.0110593 1475.0, 0.0704072, 0.0109494 1500.0, 0.0598541, 0.00896721 1525.0, 0.0520963, 0.00809093 1550.0, 0.0456082, 0.00758726 1575.0, 0.0394537, 0.00722754 1600.0, 0.0347174, 0.00671145 1625.0, 0.0307532, 0.00626805 1650.0, 0.0266118, 0.00561368 1675.0, 0.0231039, 0.00527505 1700.0, 0.0200153, 0.00498133 1725.0, 0.0176634, 0.00412917 1750.0, 0.0153969, 0.00373792 1775.0, 0.0134356, 0.00341071 1800.0, 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875.0, 2.90547, 0.366257 900.0, 2.49667, 0.314019 925.0, 2.12907, 0.26801 950.0, 1.8164, 0.242682 975.0, 1.56893, 0.217618 1000.0, 1.34352, 0.175604 1025.0, 1.15949, 0.155683 1050.0, 0.997903, 0.133077 1075.0, 0.86504, 0.117638 1100.0, 0.740372, 0.107178 1125.0, 0.647288, 0.0917503 1150.0, 0.555594, 0.0828113 1175.0, 0.486863, 0.0644736 1200.0, 0.415851, 0.0579252 1225.0, 0.362455, 0.0561888 1250.0, 0.316975, 0.046491 1275.0, 0.276522, 0.0430197 1300.0, 0.240739, 0.0339561 1325.0, 0.20999, 0.0288259 1350.0, 0.185601, 0.0309793 1375.0, 0.161343, 0.0231066 1400.0, 0.131074, 0.0188826 1425.0, 0.121045, 0.0184156 1450.0, 0.110889, 0.0173553 1475.0, 0.0906868, 0.0153453 1500.0, 0.0795585, 0.0130098 1525.0, 0.0694615, 0.0116491 1550.0, 0.0610387, 0.0106867 1575.0, 0.0531447, 0.0100985 1600.0, 0.0468796, 0.00943991 1625.0, 0.0413666, 0.00870228 1650.0, 0.0359383, 0.0078127 1675.0, 0.0313343, 0.00724488 1700.0, 0.0271773, 0.00682548 1725.0, 0.0239993, 0.00575157 1750.0, 0.0209773, 0.00520821 1775.0, 0.0183553, 0.00474575 1800.0, 0.0161098, 0.00436762 1825.0, 0.0139216, 0.00392561 1850.0, 0.0120539, 0.00376256 1875.0, 0.0104658, 0.00347714 1900.0, 0.00937288, 0.00298646 1925.0, 0.00814838, 0.0027116 1950.0, 0.00713734, 0.00244379 1975.0, 0.00621999, 0.00223617 2000.0, 0.00544778, 0.00207163 PK! ]Esusy_cross_section/data/lhc_susy_xs_wg/13TeVn2x1wino_envelope_pm.info{ "document": { "title": "NLO-NLL wino-like chargino-neutralino (N2C1) cross sections", "authors": "LHC SUSY Cross Section Working Group", "calculator": "resummino", "source": "https://twiki.cern.ch/twiki/bin/view/LHCPhysics/SUSYCrossSections13TeVn2x1wino", "version": "2017-06-15" }, "attributes": { "processes": ["p p > wino0 wino+", "p p > wino0 wino-"], "collider": "pp", "ecm": "13TeV", "order": "NLO+NLL", "pdf_name": "Envelope by LHC SUSY Cross Section Working Group" }, "columns": [ { "name": "m_wino", "unit": "GeV" }, { "name": "xsec", "unit": "fb" }, { "name": "uncertainty", "unit": "fb" } ], 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550.0, 8.91, -0.3, -6.4, 0.0, 6.5 575.0, 7.17, -0.5, -6.4, 0.0, 6.9 600.0, 5.81, -0.1, -6.4, 0.1, 7.2 625.0, 4.73, -0.4, -6.6, 0.0, 7.2 650.0, 3.88, -0.4, -6.7, 0.0, 7.5 675.0, 3.19, -0.6, -7.1, 0.1, 7.8 700.0, 2.63, -0.2, -6.8, 0.3, 8.3 725.0, 2.19, -0.7, -7.1, 0.0, 8.6 750.0, 1.82, -0.8, -7.6, 0.0, 8.7 775.0, 1.52, -0.5, -7.5, 0.1, 9.1 800.0, 1.28, -0.8, -8.0, 0.2, 8.8 825.0, 1.07, -0.5, -8.0, 0.3, 8.8 850.0, 0.9, -0.5, -8.2, 0.3, 10.1 875.0, 0.76, -1.0, -8.5, 0.1, 10.3 900.0, 0.65, -1.5, -9.0, 0.2, 10.9 925.0, 0.55, -1.3, -8.8, 0.2, 11.3 950.0, 0.47, -1.5, -9.4, 0.4, 11.6 975.0, 0.4, -1.7, -10.2, 0.4, 12.0 1000.0, 0.34, -1.6, -10.4, 0.5, 12.1 1025.0, 0.29, -1.3, -10.2, 0.7, 13.0 1050.0, 0.25, -1.5, -10.5, 0.7, 13.5 1075.0, 0.22, -1.1, -10.6, 0.7, 14.1 1100.0, 0.19, -1.1, -11.6, 0.6, 14.2 1125.0, 0.16, -1.1, -12.0, 0.5, 14.6 1150.0, 0.14, -1.2, -11.8, 0.5, 15.6 1175.0, 0.12, -0.9, -12.6, 0.9, 15.5 1200.0, 0.1, -1.3, -12.8, 0.9, 16.5 1225.0, 0.09, -1.3, -13.5, 0.9, 16.9 1250.0, 0.08, 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0.74, -1.0, -4.6, 0.8, 6.9 1000.0, 0.64, -1.3, -5.8, 0.5, 9.2 1025.0, 0.55, -1.0, -6.1, 0.5, 9.5 1050.0, 0.47, -1.0, -6.1, 0.5, 9.7 1075.0, 0.41, -1.1, -6.7, 0.5, 9.6 1100.0, 0.35, -1.1, -6.6, 0.5, 10.1 1125.0, 0.31, -1.0, -6.7, 0.5, 10.5 1150.0, 0.27, -1.0, -6.8, 0.7, 11.0 1175.0, 0.23, -1.5, -7.6, 0.5, 10.8 1200.0, 0.2, -1.4, -7.3, 0.8, 11.2 1225.0, 0.17, -1.4, -7.6, 0.8, 11.5 1250.0, 0.15, -1.4, -7.8, 0.9, 12.0 1275.0, 0.13, -1.4, -7.9, 0.9, 12.3 1300.0, 0.12, -1.9, -9.6, 0.1, 12.0 1325.0, 0.1, -1.6, -8.2, 0.4, 13.0 1350.0, 0.09, -1.7, -8.4, 0.4, 13.0 1375.0, 0.08, -1.1, -8.6, 1.0, 13.8 1400.0, 0.07, -1.1, -9.2, 1.2, 14.4 1425.0, 0.06, -1.5, -9.8, 1.1, 14.4 1450.0, 0.05, -1.7, -9.1, 1.8, 15.1 1475.0, 0.05, -1.9, -9.6, 1.7, 15.3 1500.0, 0.0396777, -1.9, -10.0, 1.5, 15.6 1525.0, 0.0348504, -2.0, -10.3, 1.6, 16.0 1550.0, 0.0306274, -2.0, -10.5, 1.6, 16.4 1575.0, 0.0269641, -2.1, -10.9, 1.7, 16.6 1600.0, 0.0236914, -1.6, -11.0, 1.7, 17.5 1625.0, 0.0208521, -2.2, -11.4, 1.7, 18.0 1650.0, 0.0183612, -2.2, -11.6, 2.7, 19.0 1675.0, 0.0161748, -2.3, -11.8, 1.8, 19.4 1700.0, 0.0143338, -2.2, -13.2, 1.8, 18.5 1725.0, 0.0125662, -2.4, -12.8, 1.9, 19.6 1750.0, 0.0110821, -2.4, -13.1, 1.9, 20.3 1775.0, 0.00977625, -2.5, -13.5, 2.0, 20.5 1800.0, 0.00862671, -2.5, -13.8, 2.0, 21.0 1825.0, 0.00762398, -2.7, -14.0, 1.9, 21.6 1850.0, 0.0068088, -3.0, -15.8, 1.6, 20.4 1875.0, 0.00603353, -4.0, -16.6, 2.4, 20.7 1900.0, 0.00531043, -2.8, -16.8, 2.2, 21.6 1925.0, 0.00464231, -1.8, -15.0, 2.2, 24.5 1950.0, 0.00410234, -2.9, -16.3, 2.0, 24.7 1975.0, 0.00363257, -2.1, -15.9, 1.9, 25.6 2000.0, 0.00320795, -3.1, -17.1, 2.1, 26.0 PK!`>susy_cross_section/data/lhc_susy_xs_wg/13TeVx1x1wino_mstw.info{ "document": { "title": "NLO-NLL wino-like chargino-chargino (C1C1) cross sections", "authors": "LHC SUSY Cross Section Working Group", "calculator": "resummino", "source": "https://twiki.cern.ch/twiki/bin/view/LHCPhysics/SUSYCrossSections13TeVx1x1wino", "version": "2017-06-15" }, "attributes": { "processes": "p p > wino+ wino-", "collider": "pp", "ecm": "13TeV", "order": "NLO+NLL", "pdf_name": "MSTW2008nlo90cl" }, "columns": [ { "name": "m_wino", "unit": "GeV" }, { "name": "xsec", "unit": "fb" }, { "name": "unc-_scale", "unit": "%" }, { "name": "unc-_pdf", "unit": "%" }, { "name": "unc+_scale", "unit": "%" }, { "name": "unc+_pdf", "unit": "%" } ], "reader_options": { "skipinitialspace": 1 }, "data": { "parameters": [{ "column": "m_wino", "granularity": 1 }], "values": [ { "column": "xsec", "unc-": [ { "column": "unc-_scale", "type": "relative" }, { "column": "unc-_pdf", "type": "relative" } ], "unc+": [ { "column": "unc+_scale", "type": "relative" }, { "column": "unc+_pdf", "type": "relative" } ] } ] } } PK!@ur$$"susy_cross_section/interpolator.py"""Interpolators of cross-section data.""" from __future__ import absolute_import, division, print_function # py2 import logging import sys from typing import (Any, Callable, List, Mapping, Sequence, # noqa: F401 Tuple, Union, cast) import numpy import pandas # noqa: F401 import scipy.interpolate from susy_cross_section.axes_wrapper import AxesWrapper from susy_cross_section.cross_section_table import CrossSectionTable if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) InterpolationType = Callable[[Sequence[float]], float] class InterpolationWithUncertainties: """An interpolation result of values accompanied by uncertainties.""" def __init__(self, central, central_plus_unc, central_minus_unc, param_names=None): # type: (InterpolationType, InterpolationType, InterpolationType, List[str])->None self._f0 = central self._fp = central_plus_unc self._fm = central_minus_unc self.param_index = {name: i for i, name in enumerate(param_names or [])} # type: Mapping[str, int] # py2 does not accept single kwarg after args. def __call__(self, *args, **kwargs): # py2; in py3, def __call__(self, *args, unc_level=0): # type: (Any, float)->float """Return the fitted value with requested uncertainty level.""" unc_level = kwargs.get('unc_level', 0) # py2 return self.f0(*args) + ( unc_level * self.unc_p_at(*args) if unc_level > 0 else unc_level * abs(self.unc_m_at(*args)) if unc_level < 0 else 0 ) def _interpret_args(self, *args, **kwargs): # type: (float, float)->Sequence[float] if not kwargs: return args tmp = list(args) # type: List[Union[float, None]] for key, value in kwargs.items(): index = self.param_index[key] if index >= len(tmp): tmp.extend([None for i in range(index + 1 - len(tmp))]) tmp[index] = value if any(v is None for v in tmp): raise ValueError('insufficient arguments: %s, %s.', args, kwargs) return cast(Sequence[float], tmp) def f0(self, *a, **kw): # type: (float, float)->float """Return the fitted value of central value.""" return self._f0(self._interpret_args(*a, **kw)) def fp(self, *a, **kw): # type: (float, float)->float """Return the fitted value of central value.""" return self._fp(self._interpret_args(*a, **kw)) def fm(self, *a, **kw): # type: (float, float)->float """Return the fitted value of central value.""" return self._fm(self._interpret_args(*a, **kw)) def tuple_at(self, *a, **kw): # type: (float, float)->Tuple[float, float, float] """Return the tuple (central, +unc, -unc) at the fit point.""" args = self._interpret_args(*a, **kw) return self.f0(*args), self.unc_p_at(*args), self.unc_m_at(*args) def unc_p_at(self, *a, **kw): # type: (float, float)->float """Return the fitted value of positive uncertainty.""" args = self._interpret_args(*a, **kw) return self.fp(*args) - self.f0(*args) def unc_m_at(self, *a, **kw): # type: (float, float)->float """Return the fitted (negative) value of negative uncertainty.""" args = self._interpret_args(*a, **kw) return -(self.f0(*args) - self.fm(*args)) class AbstractInterpolator: """Abstract class for interpolators of values with 1sigma uncertainties. Actual interpolators, inheriting this abstract class, will perform interpolation of pandas data frame. """ def interpolate(self, xs_table, name='xsec'): # type: (CrossSectionTable, str)->InterpolationWithUncertainties """Interpolate the values accompanied by uncertainties.""" df = xs_table.data[name] return InterpolationWithUncertainties( self._interpolate(df['value']), self._interpolate(df['value'] + df['unc+']), self._interpolate(df['value'] - abs(df['unc-'])), param_names=df.index.names) def _interpolate(self, df): # type: (pandas.DataFrame)->InterpolationType raise NotImplementedError class Scipy1dInterpolator(AbstractInterpolator): """Interpolator for one-dimensional data. `kind` should be either "linear" (for linear interpolation) or "cubic" (for cubic-spline interpolation). Scipy has several interpolators, among which linear and spline (with order=3) interpolators are sensible for cross-section interpolation. Polynomial interpolation is not recommended due to Runge's phenomenon, and also because it is approximately covered by linear fit with log-log axes. This class chooses natural boundary condition for cubic spline interpolation, while for quadratic spline the boundary condition is left default ('not-a-knot'). Users should notice that the accuracy of spline fits will be worse in the first- and last-segments. """ def __init__(self, kind=None, axes=None): # type: (str, str)->None self.kind = (kind or 'linear').lower() # type: str self.wrapper = { 'linear': AxesWrapper(['linear'], 'linear', 'linear'), 'log': AxesWrapper(['linear'], 'log', 'exp'), 'loglinear': AxesWrapper(['log'], 'linear', 'linear'), 'loglog': AxesWrapper(['log'], 'log', 'exp'), }[axes or 'linear'] # type: AxesWrapper def _interpolate(self, df): # type: (pandas.DataFrame)->InterpolationType if self.kind not in ['linear', 'cubic']: logging.info('Non-standard interpolation method is specified.') if df.index.nlevels != 1: raise ValueError('Scipy1dInterpolator not handle multiindex data.') x_list = [self.wrapper.wx[0](x) for x in df.index.to_numpy()] # array(n_points) y_list = [self.wrapper.wy(y) for y in df.to_numpy()] # array(n_points) if self.kind == 'cubic': # we should specify the "natural" boundary condition for cross-section fitting. fit = scipy.interpolate.CubicSpline(x_list, y_list, bc_type='natural', extrapolate=False) else: fit = scipy.interpolate.interp1d(x_list, y_list, self.kind) # now `fit` is float->float; we should convert it to Tuple[float]->float. def _fit(x, f=fit): # noqa: B008 # type: (Sequence[float], Callable[[float], float])->float return f(*x) return self.wrapper.correct(_fit) class ScipyGridInterpolator(AbstractInterpolator): """Interpolator for multi-dimensional structural data. Among the several implementations in scipy.interpolate for multi- dimensional structural data, "linear" (RegularGridInterpolator) and "spline" (spline-f2d; for 2d; RectBivariateSpline) are sensible for cross-section fitting. """ def __init__(self, param_axes, value_axis, kind='linear'): # type: (Sequence[str], str, str)->None self.wrapper = AxesWrapper(param_axes, value_axis) self.kind = kind or 'linear' def _interpolate(self, df): # type: (pandas.DataFrame)->InterpolationType np = df.index.nlevels if len(self.wrapper.wx) != np: raise ValueError('Interpolator accepts %d-parameters but %d-parameter data is given.', len(self.wrapper.wx), np) if np < 2: raise ValueError('ScipyGridInterpolator available for multi-index data.') # setup data and wrap by wrappers x0 = df.index.levels # arrays of grid "ticks" x = [w(axis) for w, axis in zip(self.wrapper.wx, x0)] y = df.apply(self.wrapper.wy).unstack().values # call scipy if self.kind == 'linear': # RGI works as: fit([700, 700]) -> [0.7] fit = scipy.interpolate.RegularGridInterpolator(x, y, method='linear', bounds_error=True) def _fit(x, f=fit): # type: (Sequence[float], Any)->float return cast(float, f(x)[0]) elif self.kind == 'spline' and np == 2: if numpy.isnan(x).any() or numpy.isnan(y).any(): raise ValueError('ScipyGridInterpolator does not allow missing grid points for spline fit.') # RBS works as: fit(700, 700) -> [[0.7]] kx, ky = 3, 3 # can be modified but cubic spline is default fit = scipy.interpolate.RectBivariateSpline(x[0], x[1], y, s=0, kx=kx, ky=ky) def _fit(x, f=fit): # type: (Sequence[float], Any)->float return cast(float, f(x[0], x[1])[0][0]) else: raise ValueError('ScipyGridInterpolator.kind is invalid.') return self.wrapper.correct(_fit) class ScipyMultiDimensionalInterpolator(AbstractInterpolator): """Interpolator for multi-dimensional non-structural data. Among the several implementations in scipy.interpolate for multi- dimensional non-structural data, "linear" (LinearNDInterpolator) and "spline" (LSQBivariateSpline) are sensible for cross-section fitting. """ NotImplemented PK!N<4,,susy_cross_section/scripts.py"""Scripts for user's ease of handling the data.""" from __future__ import absolute_import, division, print_function # py2 import logging import os import pathlib import sys from typing import Any # noqa: F401 import click import susy_cross_section.config import susy_cross_section.utility from susy_cross_section.cross_section_table import CrossSectionTable from susy_cross_section.interpolator import (AbstractInterpolator, Scipy1dInterpolator, ScipyGridInterpolator) __author__ = 'Sho Iwamoto' __copyright__ = 'Copyright (C) 2018-2019 Sho Iwamoto / Misho' __license__ = 'MIT' __scriptname__ = 'XS interpolator' __version__ = '0.0.3' if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) @click.command(help='Interpolate cross-section data using the standard scipy interpolator (with log-log axes).', context_settings={'help_option_names': ['-h', '--help']}) @click.argument('table', required=True, type=click.Path(exists=False)) @click.argument('args', type=float, nargs=-1) @click.option('--name', default='xsec', help='name of a table') @click.option('-0', 'simplest', is_flag=True, help='show in simplest format') @click.option('-1', 'simple', is_flag=True, help='show in simple format') @click.option('--unit/--no-unit', help='display unit', default=True, show_default=True) # @click.option('--config', type=click.Path(exists=True, dir_okay=False), # help='path of config json file for extra name dictionary') @click.option('--info', type=click.Path(exists=True, dir_okay=False), help='path of table-info file if non-standard file name') @click.version_option(__version__, '-V', '--version', prog_name=__scriptname__) @click.pass_context def command_get(context, **kw): # type: (Any, Any)->None """Script for cross-section interpolation.""" # handle arguments info_path = None if os.path.exists(kw['table']): table_path = pathlib.Path(kw['table']) elif kw['table'] in susy_cross_section.config.table_names: pwd = pathlib.Path(__file__).parent t = susy_cross_section.config.table_names[kw['table']] if isinstance(t, str): table_path = pwd / t else: table_path, info_path = pwd / t[0], pwd / t[1] else: click.echo('No table found: {}'.format(kw['table'])) exit(1) if kw['info']: info_path = kw['info'] # get table table = CrossSectionTable(table_path, info_path) params = table.param_information() values = table.value_information() # without arguments or with invalid number of arguments, show the table information. if not kw['args'] or len(kw['args']) != len(params): args = ' '.join([p['name'].upper() for p in params]) click.echo('Usage: {} [OPTIONS] {} {}\n'.format(context.info_name, kw['table'], args)) for p in params: click.echo(' {} in the unit of {}'.format(p['name'].upper(), p['unit'])) click.echo('') for v in values: name = v['name'] + ' (default)' if v['name'] == 'xsec' else v['name'] click.echo(' --name={} unit: [{}]'.format(name, v['unit'])) click.echo('') click.echo('-' * 80) click.echo(table.str_information()) exit(0) # data evaluation value_name = kw.get('name') or 'xsec' value_unit = table.units[value_name] if kw['unit'] else None if len(params) == 1: interp = Scipy1dInterpolator(axes='loglog', kind='linear') # type: AbstractInterpolator else: param_axes = ['log' for _ in params] interp = ScipyGridInterpolator(param_axes=param_axes, value_axis='log', kind='linear') central, unc_p, unc_m = interp.interpolate(table, name=value_name).tuple_at(*kw['args']) # display if kw['simplest']: click.echo(central) elif kw['simple']: click.echo('{} +{} -{}'.format(central, unc_p, abs(unc_m))) else: click.echo(susy_cross_section.utility.value_format(central, unc_p, unc_m, value_unit)) exit(0) @click.command(help='Show the interpreted cross-section table with positive and negative uncertainties.', context_settings={'help_option_names': ['-h', '--help']}) @click.argument('table', required=True, type=click.Path(exists=False)) # @click.option('--config', type=click.Path(exists=True, dir_okay=False), # help='path of config json file for extra name dictionary') @click.option('--info', type=click.Path(exists=True, dir_okay=False), help='path of table-info file if non-standard file name') @click.version_option(__version__, '-V', '--version', prog_name=__scriptname__) def command_show(**kw): # type: (Any)->None """Script for cross-section interpolation.""" # handle arguments info_path = None if os.path.exists(kw['table']): table_path = pathlib.Path(kw['table']) elif kw['table'] in susy_cross_section.config.table_names: pwd = pathlib.Path(__file__).parent t = susy_cross_section.config.table_names[kw['table']] if isinstance(t, str): table_path = pwd / t else: table_path, info_path = pwd / t[0], pwd / t[1] else: click.echo('No table found: {}'.format(kw['table'])) exit(1) if kw['info']: info_path = kw['info'] # data evaluation table = CrossSectionTable(table_path, info_path) click.echo('=' * 80) for key in table.data: unit_str = '[{}]'.format(table.units[key]) if table.units[key] else '' click.echo('{} {}'.format(key, unit_str)) click.echo('-' * 80) click.echo(table.data[key]) click.echo('=' * 80) exit(0) if __name__ == '__main__': command_get() PK! J]2]2 susy_cross_section/table_info.py"""Classes for annotations to a table.""" from __future__ import absolute_import, division, print_function # py2 import json import logging import pathlib # noqa: F401 import sys from typing import (Any, List, Mapping, MutableMapping, Optional, # noqa: F401 Sequence, Union) if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) JSONDecodeError = Exception if sys.version_info[0] < 3 else json.decoder.JSONDecodeError # py2 class ColumnInfo(object): """Stores information of a column. A column is defined by `index`, but is referreed to by `name` for flexibility and readability. Also in physics context a column is accompanied by a unit. note: In this version `unit` is just a `str` and thus just for annotation, but in future numeric units such as 1000 to describe "x1000" could be implemented. Attributes ---------- index : int The index (zero-based indexing) of column. Non-negative. name : str The name of column, used as an identifier and thus should be unique in one table. unit : str The unit of column. If without unit, it must be an empty string. """ def __init__(self, index, name, unit=''): # type: (int, str, str)->None self.index = index # type: int self.name = name # type: str self.unit = unit or '' # type: str def validate(self): # type: ()->None """Validate the content. Perform the validation only within ColumnInfo. This is intended to be called from outside of this class upon the user (of this class)'s request. """ if not isinstance(self.index, int): raise TypeError('ColumnInfo.index must be int: %s', self.index) if not self.index >= 0: raise ValueError('ColumnInfo.index must be non-negative: %s', self.index) if not isinstance(self.name, str): raise TypeError('Column %d: `name` must be string: %s', self.index, self.name) if not self.name: raise ValueError('Column %d: `name` missing', self.index) if not isinstance(self.unit, str): raise TypeError('Column %d: `unit` must be string: %s', self.index, self.unit) class ParameterInfo(object): """Stores information of parameter. Parameter is a number characterized by its `column` name and users will interpolate the data according to them. For grid-based interpolation, another property `granularity` should be provided, Attributes ---------- column: str Name of the column that stores this parameter. granularity : int or float granularity of the parameter when interpreted as a list of grid-points. For a parameter list `v`, the integers round(v[i] / granularity) should specify one grid-point. For example, for a parameter grid [10, 20, 30, 50, 70, 200], it will be 10 in principle, but 5, 2, 1, or 0.1 are possible. For [33.3, 50, 70], it should be 0.1 (or 0.05, etc.) to track the first decimal point of 33.3. """ def __init__(self, column='', granularity=None): # type: (str, float)->None self.column = column # type: str self.granularity = granularity or None # type: Optional[float] def validate(self): # type: ()->None """Validate the content. Perform the validation only within this class. This is intended to be called from outside of this class upon the user (of this class)'s request. """ if not isinstance(self.column, str): raise TypeError('ParameterInfo.column must be string: %s', self.column) if not self.column: raise ValueError('ParameterInfo.column is missing') if self.granularity is not None: try: if not float(self.granularity) > 0: raise ValueError('ParameterInfo.granularity is not positive: %s', self.granularity) except TypeError: raise TypeError('ParameterInfo.granularity is not a number: %s', self.granularity) @classmethod def from_json(cls, json_data): # type: (Any)->ParameterInfo """Construct an object from json-based data.""" if not isinstance(json_data, Mapping): raise TypeError('Entry of "values" must be a dict: %s', json_data) column = json_data.get('column') granularity = json_data.get('granularity', None) if not column: raise ValueError('Entry of "values" must have a key "column": %s', json_data) return cls(column=column, granularity=granularity) def to_json(self): # type: ()->MutableMapping[str, Union[str, float]] """Dump json-based data from an object.""" json_data = {'column': self.column} # type: MutableMapping[str, Union[str, float]] if self.granularity: json_data['granularity'] = self.granularity return json_data class ValueInfo(object): """Stores information of value accompanied by uncertainties. This includes `column` as the name of column the value is stored, and plus- and minus-directed uncertainty sources of the value. An uncertainty source is characterized by a column-name and a type, which currently includes "relative" or "absolute". Attributes ---------- column: str Name of the column that stores this value. unc_p : dict of (str, str) The sources of "plus" uncertainties, where each key describe `name` of another `ColumnInfo` and each value denotes the "type" of the source. unc_m : dict of (str, str) The sources of "minus" uncertainties. """ def __init__(self, column='', unc_p=None, unc_m=None, **kw): # type: (str, MutableMapping[str, str], MutableMapping[str, str], Any)->None self.column = column self.unc_p = unc_p or {} # type: MutableMapping[str, str] self.unc_m = unc_m or {} # type: MutableMapping[str, str] def validate(self): # type: ()->None """Validate the content. Perform the validation only within this class. This is intended to be called from outside of this class upon the user (of this class)'s request. """ if not isinstance(self.column, str): raise TypeError('ValueInfo.column must be string: %s', self.column) if not self.column: raise ValueError('ValueInfo.column is missing') for title, unc in [('unc+', self.unc_p), ('unc-', self.unc_m)]: if not isinstance(unc, MutableMapping): raise TypeError('Value %s: %s must be dict', self.column, title) for k in unc.keys(): if not isinstance(k, str): raise TypeError('Value %s: %s has invalid column name: %s', self.column, title, k) @classmethod def from_json(cls, json_data): # type: (Any)->ValueInfo """Construct an object from json-based data.""" if not isinstance(json_data, Mapping): raise TypeError('Entry of "values" must be a dict: %s', json_data) if 'column' not in json_data: raise KeyError('Entry of "values" must have a key "column": %s', json_data) obj = cls() obj.column = json_data['column'] if ('unc' in json_data) and ('unc+' in json_data or 'unc-' in json_data): raise ValueError('Invalid uncertainties (asymmetric and symmetric): %s', obj.column) for attr_name, key_name in [('unc_p', 'unc+'), ('unc_m', 'unc-')]: u = json_data.get(key_name) or json_data.get('unc') or None if u is None: logger.warning('The uncertainty (%s) is missing in value "%s".', key_name, obj.column) continue if not isinstance(u, Sequence) or not all(isinstance(source, Mapping) for source in u): raise TypeError('Entry of "%s" in "%s" must be a list of dicts.', key_name, obj.column) try: setattr(obj, attr_name, {source['column']: source['type'] for source in u}) except KeyError as e: raise ValueError('Entry of "%s" in "%s" has a missing key: %s', key_name, obj.column, *e.args) if not(obj.unc_p and obj.unc_m): logger.warning('Value %s lacks uncertainties.', obj.column) return obj def to_json(self): # type: ()->MutableMapping[str, Union[str, List[MutableMapping[str, str]]]] """Dump json-based data from an object.""" return { 'column': self.column, 'unc+': [{'column': key, 'type': value} for key, value in self.unc_p.items()], 'unc-': [{'column': key, 'type': value} for key, value in self.unc_m.items()], } class TableInfo(object): """Stores annotations of a table. Attributes ---------- document : dict of (Any, Any) Any information just for documentation, i.e., without physical meanings. columns : list of ColumnInfo The list of columns. """ def __init__(self, document=None, columns=None, reader_options=None): # type: (MutableMapping[Any, Any], List[ColumnInfo], MutableMapping[str, Any])->None self.document = document or {} # type: MutableMapping[Any, Any] self.columns = columns or [] # type: List[ColumnInfo] self.reader_options = reader_options or {} # type: MutableMapping[str, Any] def validate(self): # type: ()->None """Validate the content.""" if not isinstance(self.document, MutableMapping): raise TypeError('document must be a dict.') if not isinstance(self.columns, List): raise TypeError('columns must be list.') # validate columns (`index` matches actual index, names are unique) names_dict = {} # type: MutableMapping[str, bool] for i, column in enumerate(self.columns): column.validate() if column.index != i: raise ValueError('Mismatched column index: %d has %d', i, column.index) if names_dict.get(column.name): raise ValueError('Duplicated column name: %s', column.name) names_dict[column.name] = True if not isinstance(self.reader_options, MutableMapping): raise TypeError('reader_options must be a dict.') if not all(k and isinstance(k, str) for k, v in self.reader_options.items()): raise TypeError('keys of reader_options must be str.') @classmethod def load(cls, source): # type: (Union[pathlib.Path, str])->TableInfo """Load and construct TableInfo from a json file.""" obj = cls() with open(source.__str__()) as f: # py2 try: obj._load(**(json.load(f))) except JSONDecodeError: # type: ignore logger.error('Invalid JSON file: %s', source) exit(1) return obj def _load(self, **kw): # type: (Any)->None """Construct TableInfo from a json data. Since no type-check is performed here, developers must be sure on validity of the information, e.g., by calling `validate` in this method. """ self.document = kw.get('document') or {} self.columns = [ColumnInfo(index=i, name=c.get('name'), unit=c.get('unit')) for i, c in enumerate(kw['columns']) ] if 'columns' in kw else [] self.reader_options = kw.get('reader_options') or {} try: self.validate() except ValueError as e: logger.error(*e.args) exit(1) except TypeError as e: logger.error(*e.args) exit(1) if not self.document: logger.warning('No document is given.') for key in kw: if key not in ['document', 'columns', 'reader_options']: logger.warning('Unrecognized attribute "%s"', key) def get_column(self, name): # type: (str)->ColumnInfo """Return a column with specified name. Search for a column with name `name` and returns it, or raise an error if not found. Note that this method is slow. """ for c in self.columns: if c.name == name: return c raise KeyError(name) def get_column_safe(self, name): # type: (str)->Union[ColumnInfo, None] """Get a column with specified name if exists.""" try: return self.get_column(name) except KeyError: return None PK!- N$susy_cross_section/tests/__init__.py"""Test codes.""" PK! |:susy_cross_section/tests/data/sg_8TeV_NLONLL_modified.info{ "document": { "title": "sg xsec (modified)", "authors": "FastLim collaboration", "calculator": "NLL-fast,1206.2892", "source": "http://fastlim.web.cern.ch/fastlim/", "version": "FastLim-1.0-modified", "note": "As the original data lacks the value for (2000,2000) value, we eliminated msq=2000 or mgl=2000 grid points for testing." }, "attributes": { "processes": "??", "collider": "pp", "ecm": "8TeV", "order": "NLO+NLL" }, "columns": [ { "name": "msq", "unit": "GeV" }, { "name": "mgl", "unit": "GeV" }, { "name": "xsec", "unit": "pb" }, { "name": "delta_xsec", "unit": "pb" } ], "reader_options": { "skipinitialspace": 1, "delim_whitespace": 1, "skiprows": 4 }, "data": { "parameters": [ { "column": "msq", "granularity": 1 }, { "column": "mgl", "granularity": 1 } ], "values": [ { "column": "xsec", "unc": [{ "column": "delta_xsec", "type": "absolute" }] } ] } } PK!:susy_cross_section/tests/data/sg_8TeV_NLONLL_modified.xsecsg xsec, calculated as described in 1206.2892 (scale uncertainty, pdf uncertainty and alphas uncertainty taken into account) msq mgl xsec[pb] delta xsec[pb] 200 200 1453.18630587 149.668119899 200 250 747.752295836 70.1495616888 200 300 424.762866423 38.7123992836 200 350 258.023308718 23.5092248805 200 400 164.078777095 15.942094014 200 450 107.683476715 11.2032080134 200 500 72.1492472172 7.66105003338 200 550 49.2630810812 5.28112023215 200 600 34.2019202545 3.68050746244 200 650 24.1633437164 2.6241849224 200 700 17.2620650078 1.88263435541 200 750 12.495198964 1.36884569676 200 800 9.16612154285 0.987683632942 200 850 6.7923496068 0.724697273784 200 900 5.07934077464 0.540034402807 200 950 3.84460636547 0.418434895541 200 1000 2.92691583861 0.326656146886 200 1050 2.2500880211 0.256834729938 200 1100 1.74204454142 0.203354451759 200 1150 1.35300132374 0.161363465679 200 1200 1.05322568478 0.129193498125 200 1250 0.827794048893 0.103531061594 200 1300 0.653611361452 0.0846596908657 200 1350 0.517020908992 0.0703558871916 200 1400 0.41050587284 0.0580755870924 200 1450 0.326660074613 0.0485840340676 200 1500 0.261486728182 0.0403703757374 200 1550 0.209958840772 0.0333092462492 200 1600 0.168829370925 0.0278287069127 200 1650 0.136209311456 0.0238369018246 200 1700 0.110244450098 0.0201003911387 200 1750 0.0892937654877 0.0166844403336 200 1800 0.0724357681279 0.0138330056034 200 1850 0.0589800234779 0.0116322792158 200 1900 0.0480412779697 0.0098453956663 200 1950 0.0392137916497 0.00823701127454 250 200 947.208564751 109.62952575 250 250 493.805324282 54.9989512166 250 300 279.054712355 26.6496406719 250 350 169.814537377 14.7161188695 250 400 109.079124862 9.69222679988 250 450 73.0411924656 6.9858893141 250 500 50.2294449266 5.26034314324 250 550 34.8799918051 3.62976456028 250 600 24.4853959077 2.5162408189 250 650 17.3852304683 1.77454892556 250 700 12.5748636688 1.32203755 250 750 9.18315793092 0.958171559053 250 800 6.79528675852 0.704036532768 250 850 5.05385676008 0.534624322813 250 900 3.79463870511 0.409091414592 250 950 2.88740382869 0.317584126956 250 1000 2.21932483987 0.248826897579 250 1050 1.71014346973 0.19614747479 250 1100 1.33127386342 0.155551055515 250 1150 1.03172211757 0.124154304593 250 1200 0.809248818961 0.100273741508 250 1250 0.636660650542 0.0817537229919 250 1300 0.504870244902 0.0674098413307 250 1350 0.400402017837 0.0563991664695 250 1400 0.318845550148 0.0468088630886 250 1450 0.25447860735 0.0380450225387 250 1500 0.203755829108 0.0311803477726 250 1550 0.163176041135 0.0263564970077 250 1600 0.131096543922 0.0218852546665 250 1650 0.105866851929 0.0186329337842 250 1700 0.0859522413714 0.0158738505803 250 1750 0.0697607856484 0.0132027684917 250 1800 0.0566983964772 0.0110283297115 250 1850 0.0462187772033 0.00932663229892 250 1900 0.0376507778315 0.00782619428769 250 1950 0.0307382879003 0.00658763119393 300 200 629.82237374 80.1518008912 300 250 339.718737673 40.1644150389 300 300 194.307475401 19.7283828366 300 350 118.04803582 10.4641549785 300 400 75.8209206865 6.61573145585 300 450 50.8146773169 4.55963340833 300 500 35.1559324409 3.28568298675 300 550 24.8126667504 2.35695524571 300 600 17.7580002092 1.72951678102 300 650 12.8158518141 1.25380245933 300 700 9.31021194846 0.952706843728 300 750 6.85591513455 0.708172404361 300 800 5.09962179387 0.52453276665 300 850 3.8333018447 0.402791261531 300 900 2.90694391078 0.314608354178 300 950 2.21857422063 0.245653715529 300 1000 1.71021542913 0.193706742888 300 1050 1.32022185069 0.153715993905 300 1100 1.03130541039 0.12222366054 300 1150 0.805617124324 0.0983251081104 300 1200 0.631810137029 0.080311374723 300 1250 0.498404861973 0.0660483255199 300 1300 0.395040770463 0.054141571901 300 1350 0.313139939638 0.0451734708828 300 1400 0.249901772836 0.0375600098958 300 1450 0.200106855982 0.0308412564796 300 1500 0.160612046823 0.025298472923 300 1550 0.129064052565 0.0214464296333 300 1600 0.103965560031 0.0180471962822 300 1650 0.0839955568591 0.015135169537 300 1700 0.0680931231651 0.0127257594238 300 1750 0.0553032462716 0.0107329204614 300 1800 0.0449893498827 0.00903366882642 300 1850 0.0366116814777 0.00758076405633 300 1900 0.0298898991328 0.00642806065825 300 1950 0.0244654255032 0.00542992330975 350 200 427.232134858 57.8429990056 350 250 239.004702293 28.9989307655 350 300 140.223059787 15.2018925542 350 350 85.7571726169 8.44836430434 350 400 54.8986921196 4.94105373001 350 450 36.5708843028 3.18367313047 350 500 25.2642768774 2.09642426909 350 550 17.988685184 1.57230503741 350 600 13.025878053 1.23657999815 350 650 9.5144036901 0.91470952579 350 700 6.99139345373 0.690301754223 350 750 5.1867965714 0.522114747067 350 800 3.89125190529 0.400346102877 350 850 2.94542374514 0.311472163108 350 900 2.2482292591 0.244877384167 350 950 1.72905329907 0.192626831613 350 1000 1.33046218422 0.152908942465 350 1050 1.04121862643 0.121977843388 350 1100 0.808756332023 0.0977012852875 350 1150 0.633997710792 0.0791672083434 350 1200 0.49941994272 0.0651588092427 350 1250 0.394622023797 0.0537839442687 350 1300 0.312501264942 0.044153047846 350 1350 0.248235187322 0.0366020492484 350 1400 0.198031822209 0.0303940810227 350 1450 0.159048090011 0.0253775113117 350 1500 0.127978872086 0.0211583198736 350 1550 0.103111735598 0.0176691501059 350 1600 0.0832364881041 0.014927993269 350 1650 0.0673744508774 0.0124379969632 350 1700 0.0546053373525 0.0104586523314 350 1750 0.0443329655051 0.00885466642146 350 1800 0.0361031309156 0.00746514952263 350 1850 0.0294472927799 0.00631957612625 350 1900 0.0240287547628 0.00533944642844 350 1950 0.0196381899379 0.00459003886022 400 200 295.074696652 41.1347796998 400 250 170.776999338 21.8009237162 400 300 102.782305385 12.0449475884 400 350 63.8731076754 6.87263386498 400 400 41.0443498319 3.99516501753 400 450 27.2577430427 2.40403748286 400 500 18.7511036884 1.54890243663 400 550 13.3320870637 1.09677179784 400 600 9.68374022617 0.855416436576 400 650 7.1261987676 0.649451788079 400 700 5.30684692906 0.506046047071 400 750 3.97785226042 0.395641013576 400 800 3.00341060007 0.310132279936 400 850 2.28682412397 0.243098280149 400 900 1.75866277772 0.192780515189 400 950 1.36029943881 0.152914047871 400 1000 1.05094665161 0.121632939394 400 1050 0.818363124707 0.0976965185896 400 1100 0.640083197769 0.0791937898425 400 1150 0.503237418747 0.0649809349432 400 1200 0.397438900552 0.0536626445851 400 1250 0.314707790946 0.0446167630388 400 1300 0.24957846553 0.0369797886453 400 1350 0.199004921378 0.0301896082468 400 1400 0.15895807345 0.0251993788163 400 1450 0.127940610073 0.0209786670221 400 1500 0.102936331571 0.0176986222314 400 1550 0.0830459916351 0.0147930354304 400 1600 0.0671623253194 0.0123712359832 400 1650 0.0544696784042 0.0103770749481 400 1700 0.0441874022736 0.00873516929047 400 1750 0.0359155361245 0.0074119286848 400 1800 0.0292581815075 0.00627173079199 400 1850 0.02381975804 0.00529333835199 400 1900 0.0194664179045 0.00448937024203 400 1950 0.0159375632288 0.00379473702778 450 200 207.322374515 29.5405024326 450 250 123.34198389 15.6966422534 450 300 76.0888552372 9.5090084776 450 350 48.2544026195 5.6401936916 450 400 31.3814573767 3.29615638657 450 450 20.933388848 1.94983226228 450 500 14.330351274 1.210530531 450 550 10.1359385512 0.836330132647 450 600 7.30769159908 0.625075696135 450 650 5.41333105959 0.484208168888 450 700 4.07270625205 0.384776480742 450 750 3.08128522976 0.305092701944 450 800 2.34594573217 0.242711055913 450 850 1.79803257287 0.192165694208 450 900 1.37966848339 0.15263066434 450 950 1.07064941281 0.122167901934 450 1000 0.832427627758 0.0978790526312 450 1050 0.650591194315 0.0789726756272 450 1100 0.510087125674 0.064966048461 450 1150 0.401863573461 0.0531912887946 450 1200 0.318157779523 0.0441559435331 450 1250 0.252571988296 0.0371336686789 450 1300 0.200466011111 0.0308716609167 450 1350 0.160426072364 0.0257612128904 450 1400 0.128939452115 0.021005264604 450 1450 0.103667671478 0.0178858179476 450 1500 0.0834510247493 0.0148484602737 450 1550 0.0673024390689 0.0123955492008 450 1600 0.0544927285438 0.0103866064697 450 1650 0.0442802959044 0.00872326731202 450 1700 0.0360101451579 0.00739364352906 450 1750 0.0292965647663 0.00619354312623 450 1800 0.0238837450246 0.00532179188424 450 1850 0.0194753348482 0.00447306480991 450 1900 0.015946155391 0.00377527878718 450 1950 0.0130768014237 0.00321957631546 500 200 148.007549804 21.0382346113 500 250 90.1446187328 12.3765149028 500 300 56.6807176496 7.39150098182 500 350 36.6526963024 4.45691383347 500 400 24.2515735283 2.7493691084 500 450 16.2857062279 1.69550782131 500 500 11.198914011 1.06332784034 500 550 7.85131260283 0.69292848565 500 600 5.65083420651 0.494260676709 500 650 4.18147910354 0.377387024147 500 700 3.14791924334 0.296671427624 500 750 2.39386513565 0.236733433295 500 800 1.83656812772 0.190194373872 500 850 1.40900746318 0.151863383904 500 900 1.08979988126 0.121966090995 500 950 0.847831101252 0.0980000440266 500 1000 0.663007132235 0.079353747383 500 1050 0.519716524263 0.0646594945823 500 1100 0.408880811218 0.0534457371515 500 1150 0.323165079157 0.0443879359513 500 1200 0.256024105823 0.036682079243 500 1250 0.203412771096 0.0309831642863 500 1300 0.162434870445 0.025911765298 500 1350 0.130385367399 0.0216506809644 500 1400 0.104835624025 0.0178123513187 500 1450 0.0843271409621 0.0148667945622 500 1500 0.0679443644968 0.0124539657472 500 1550 0.0548917984056 0.01039952631 500 1600 0.0444726921742 0.00872657414461 500 1650 0.0361436913257 0.00734315486659 500 1700 0.029450607118 0.00625558126268 500 1750 0.0240359031591 0.00527813217378 500 1800 0.0195810025551 0.00446594814634 500 1850 0.015989030517 0.00381534536964 500 1900 0.0130788884939 0.00321363356691 500 1950 0.0107337347044 0.00271810815817 550 200 106.752317784 15.3631773898 550 250 66.4722675296 9.26346889222 550 300 42.565133875 5.68484091689 550 350 27.9131428905 3.51464448811 550 400 18.7147344551 2.24435806549 550 450 12.7467242525 1.38559020916 550 500 8.8375044177 0.904471773293 550 550 6.21394650538 0.586188519052 550 600 4.47161019427 0.413951844355 550 650 3.28751652707 0.307317070065 550 700 2.46138920341 0.234526083372 550 750 1.87601216047 0.186177877676 550 800 1.43800408146 0.149206991489 550 850 1.11922125355 0.12034931306 550 900 0.867496422562 0.0974738513994 550 950 0.677814613717 0.0790649523621 550 1000 0.53060188595 0.0645748356387 550 1050 0.41688212234 0.0533586667698 550 1100 0.329305562683 0.0445957841753 550 1150 0.260645364174 0.0363797838484 550 1200 0.207512273092 0.0300974139712 550 1250 0.1649229256 0.0254105239701 550 1300 0.13198625682 0.0212515703857 550 1350 0.105951964737 0.0177365013404 550 1400 0.0854523816465 0.0149609594059 550 1450 0.0688614970009 0.0125231500115 550 1500 0.0555405883595 0.0104776762823 550 1550 0.0449749391192 0.00885183038292 550 1600 0.0364943421473 0.00741746094876 550 1650 0.0296916366439 0.00621691534839 550 1700 0.0241659183252 0.00522893522525 550 1750 0.0196737559976 0.00448457827962 550 1800 0.0160909350974 0.00382913167902 550 1850 0.0131791634851 0.0032186460867 550 1900 0.0107607293167 0.00268805203752 550 1950 0.00883326931301 0.00228122979772 600 200 78.3484369982 11.5558222184 600 250 49.4869477501 6.94719315454 600 300 32.1750980684 4.3241128723 600 350 21.3916795302 2.74661905694 600 400 14.5515142598 1.78837711297 600 450 10.0256977936 1.15544012619 600 500 7.02819070273 0.768210734047 600 550 4.97626428922 0.508316102833 600 600 3.58912684353 0.356556811277 600 650 2.62273592756 0.257303381345 600 700 1.94548843266 0.191878261823 600 750 1.47744935966 0.14986628785 600 800 1.13941151203 0.119686728512 600 850 0.885945921529 0.0967300361859 600 900 0.693669750716 0.0788326465436 600 950 0.543017756208 0.0650460816749 600 1000 0.427005704601 0.0535819156115 600 1050 0.336321397943 0.0446794421514 600 1100 0.266133857834 0.0369875175553 600 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0.0242646786681 1850 400 0.0801205844404 0.0189556497044 1850 450 0.0618594953546 0.0149947867349 1850 500 0.0480451447213 0.0119721853152 1850 550 0.0375974976082 0.00955370999305 1850 600 0.0295891164017 0.00768738916607 1850 650 0.0232948918927 0.00628145542049 1850 700 0.018413353629 0.00508229611396 1850 750 0.0146611165522 0.00414128158952 1850 800 0.0117181524587 0.00339305476763 1850 850 0.00934804856479 0.00276971226376 1850 900 0.00748463489909 0.00227495572151 1850 950 0.00600416206824 0.00187245562399 1850 1000 0.00482225208091 0.00153754715778 1850 1050 0.00388292798915 0.00126476297373 1850 1100 0.00312904350963 0.00104101952927 1850 1150 0.00251878576419 0.000859966156284 1850 1200 0.00203667074755 0.000710550753137 1850 1250 0.00165161764229 0.000589518043923 1850 1300 0.0013333996739 0.000489415064698 1850 1350 0.00107722102226 0.000403647537747 1850 1400 0.000872061113177 0.000333702424046 1850 1450 0.000705599226868 0.000275734708872 1850 1500 0.000572249510636 0.00022883041372 1850 1550 0.000463543439865 0.000189161767278 1850 1600 0.00037546015129 0.000156766218221 1850 1650 0.000303888791124 0.000129763860232 1850 1700 0.000246269797315 0.000107343851662 1850 1750 0.000199769977848 8.90923855502e-05 1850 1800 0.000162343641469 7.39169800017e-05 1850 1850 0.000131667317972 6.08960135243e-05 1850 1900 0.000106656644483 5.02036432522e-05 1850 1950 8.6681980256e-05 4.14831003037e-05 1900 200 0.206834779198 0.0452605103462 1900 250 0.152477151618 0.034661185531 1900 300 0.114623230711 0.0266090284034 1900 350 0.0870108992547 0.0206972347539 1900 400 0.0667762240075 0.016269316324 1900 450 0.0515953012168 0.0129048294179 1900 500 0.0401968147195 0.0103093605629 1900 550 0.0314461608606 0.00822184346358 1900 600 0.0247334928396 0.00662757257998 1900 650 0.0195228725904 0.00534408395866 1900 700 0.0154661594993 0.00436784717078 1900 750 0.0122978267586 0.00359371960018 1900 800 0.00985318283729 0.00294130638412 1900 850 0.00787508420348 0.00240903755649 1900 900 0.00630206058641 0.00197342486934 1900 950 0.00505668516774 0.00162524324847 1900 1000 0.00406481530467 0.00133220199511 1900 1050 0.00327350006627 0.00109486034201 1900 1100 0.0026355867194 0.000901614867385 1900 1150 0.00212538855609 0.000742288943593 1900 1200 0.00171825862621 0.000617180452371 1900 1250 0.00139209568196 0.00051211473708 1900 1300 0.00113106443923 0.000427746976003 1900 1350 0.00091342433237 0.000351890121263 1900 1400 0.00073778216799 0.000289240794711 1900 1450 0.000597230578024 0.000239141068286 1900 1500 0.000484474638291 0.000198049932845 1900 1550 0.000392521971474 0.000164158451645 1900 1600 0.000317919989212 0.000135711864764 1900 1650 0.000257683229069 0.000112250246063 1900 1700 0.000208598235378 9.30457878203e-05 1900 1750 0.000169414185024 7.68891292969e-05 1900 1800 0.00013743669214 6.37799413213e-05 1900 1850 0.000111223717799 5.25314537249e-05 1900 1900 8.99499022986e-05 4.32267763036e-05 1900 1950 7.29196367583e-05 3.57049169306e-05 1950 200 0.171345959115 0.0390540639461 1950 250 0.12674750206 0.0294016215501 1950 300 0.0952789623245 0.0226757522315 1950 350 0.0724694227071 0.0177586924146 1950 400 0.0557183788124 0.0139859393151 1950 450 0.0431651406854 0.0110799502923 1950 500 0.0336314353411 0.00890169774522 1950 550 0.0263390336634 0.00715875220719 1950 600 0.0207499378036 0.00575009012417 1950 650 0.0163778409066 0.00461222700811 1950 700 0.0129994930034 0.00378683613003 1950 750 0.0103550105109 0.00308943174796 1950 800 0.00827962513451 0.00253868353198 1950 850 0.00661941835172 0.00208354293366 1950 900 0.00530239353423 0.00170505401443 1950 950 0.00425616609957 0.00140074961567 1950 1000 0.00342791763334 0.00115086001283 1950 1050 0.00275809368344 0.000950617986499 1950 1100 0.00222799847303 0.000786401371568 1950 1150 0.00179736704343 0.000646546371921 1950 1200 0.00145172262214 0.000530732721186 1950 1250 0.0011730223181 0.000440881884877 1950 1300 0.000952729614894 0.000366831395293 1950 1350 0.000771357166783 0.000303907045094 1950 1400 0.000623217356842 0.000250722554028 1950 1450 0.000504904394269 0.000207289382894 1950 1500 0.000409538643482 0.000171588009871 1950 1550 0.000331167598515 0.000141877098113 1950 1600 0.000268695924736 0.000117560428907 1950 1650 0.000217851280907 9.74277285106e-05 1950 1700 0.000176922274806 8.03542816525e-05 1950 1750 0.000143221809308 6.66153209688e-05 1950 1800 0.000116034708371 5.49440254593e-05 1950 1850 9.37668722341e-05 4.52453648602e-05 1950 1900 7.59562484361e-05 3.73267345551e-05 1950 1950 6.14838668651e-05 3.06851469537e-05 PK!pص$$-susy_cross_section/tests/test_interpolator.py"""Test codes.""" from __future__ import absolute_import, division, print_function # py2 import itertools import logging import pathlib import unittest import numpy from nose.tools import (assert_almost_equals, assert_raises, eq_, # noqa: F401 ok_, raises) from susy_cross_section.cross_section_table import CrossSectionTable from susy_cross_section.interpolator import (Scipy1dInterpolator, ScipyGridInterpolator) logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) class TestInterpolator(unittest.TestCase): """Test codes for one-dimensional cross-section fit.""" @staticmethod def _is_scalar_number(obj): if isinstance(obj, numpy.ndarray): return obj.ndim == 0 return isinstance(obj, float) or isinstance(obj, int) @staticmethod def _assert_all_close(actual, expected, decimal=None): for a, e in zip(actual, expected): assert_almost_equals(a, e, decimal) def setUp(self): """Set up.""" self.dirs = { 'lhc_wg': pathlib.Path(__file__).parent / '..' / 'data' / 'lhc_susy_xs_wg', 'fastlim8': pathlib.Path(__file__).parent / '..' / 'data' / 'fastlim' / '8TeV' / 'NLO+NLL', 'fastlim8mod': pathlib.Path(__file__).parent / 'data', } def test_scipy_1d_interpolator(self): """Verify Scipy1dInterpolator.""" table = CrossSectionTable(self.dirs['lhc_wg'] / '13TeVn2x1wino_cteq_pm.csv') for kind in ['linear', 'slinear', 'quadratic', 'cubic']: for axes in ['linear', 'log', 'loglog', 'loglinear']: fit = Scipy1dInterpolator(kind=kind, axes=axes).interpolate(table, 'xsec') # on the grid points: # 300.0: 379.23, -0.47, -4.8, 0.4, 4.7 == 379.23 -18.29 +17.89 # 325.0: 276.17, -0.44, -5.1, 0.4, 4.8 == 276.17 -14.14 +13.30 self._assert_all_close(fit.tuple_at(300), (379.23, 17.89, -18.29), decimal=2) assert_almost_equals(fit(325), 276.17, 2) assert_almost_equals(fit.unc_p_at(325), +13.30, 2) assert_almost_equals(fit.unc_m_at(325), -14.14, 2) assert_almost_equals(fit(325, unc_level=1), 276.17 + 13.3, 2) assert_almost_equals(fit(325, unc_level=-1), 276.17 - 14.14, 2) assert_almost_equals(fit(325, unc_level=0.5), 276.17 + 13.3 * 0.5, 2) assert_almost_equals(fit(325, unc_level=-2), 276.17 - 14.14 * 2, 1) # interpolation: for uncertainty, returns sensible results ok_(13.30 < fit.unc_p_at(312.5) < 17.89) ok_(14.14 < -fit.unc_m_at(312.5) < 18.29) if kind == 'linear': if axes == 'linear': x, y = (300 + 325) / 2, (379.23 + 276.17) / 2 elif axes == 'loglinear': x, y = (300 * 325)**0.5, (379.23 + 276.17) / 2 elif axes == 'log': x, y = (300 + 325) / 2, (379.23 * 276.17) ** 0.5 else: x, y = (300 * 325) ** 0.5, (379.23 * 276.17) ** 0.5 assert_almost_equals(fit(x), y, 2) else: ok_(276.17 < fit(312.5) < 379.23) def test_scipy_1d_interpolator_nonstandard_args(self): """Verify Scipy1dInterpolator accepts/refuses argument correctly.""" table = CrossSectionTable(self.dirs['lhc_wg'] / '13TeVn2x1wino_cteq_pm.csv') fit = Scipy1dInterpolator().interpolate(table, 'xsec') for m in ['f0', 'fp', 'fm', 'unc_p_at', 'unc_m_at', 'tuple_at']: test_method = getattr(fit, m) value = test_method(333.3) if m == 'tuple_at': # the output should be (3,) array (or 3-element tuple) eq_(numpy.array(value).shape, (3,)) # method should accept 0-dim ndarray eq_(test_method(numpy.array(333.3)), value) # method should accept keyword arguments eq_(test_method(m_wino=333.3), value) else: # the output should be float or ndarray with 0-dim, not arrays. ok_(self._is_scalar_number(value)) # method should accept 0-dim ndarray eq_(test_method(numpy.array(333.3)), value) # method should accept keyword arguments eq_(test_method(m_wino=333.3), value) # method should not accept arrays or numpy.ndarray with >0 dim. for bad_input in ([333.3], [[333.3]], [333.3, 350]): with assert_raises(TypeError): test_method(bad_input) with assert_raises(TypeError): test_method(numpy.array(bad_input)) with assert_raises(TypeError): test_method(m_wino=bad_input) def test_scipy_grid_interpolator(self): """Verify ScipyGridInterpolator.""" table = CrossSectionTable(self.dirs['fastlim8mod'] / 'sg_8TeV_NLONLL_modified.xsec') midpoint = { 'linear': lambda x, y: (x + y) / 2, 'log': lambda x, y: (x * y) ** 0.5, } for x1a, x2a, ya in itertools.product(['linear', 'log'], repeat=3): for kind in ['linear', 'spline']: print(kind, x1a, x2a, ya) fit = ScipyGridInterpolator([x1a, x2a], ya, kind=kind).interpolate(table, 'xsec') # on the grid points: # 700 1400 0.0473379597888 0.00905940683923 # 700 1450 0.0382279746207 0.0075711349465 # 750 1400 0.0390134257995 0.00768847466247 # 750 1450 0.0316449395656 0.0065050745643 self._assert_all_close(fit.tuple_at(700, 1400), (0.04734, 0.00906, -0.00906), decimal=5) assert_almost_equals(fit(700, 1400), 0.04734, 5) assert_almost_equals(fit.unc_p_at(700, 1400), +0.00906, 5) assert_almost_equals(fit.unc_m_at(700, 1400), -0.00906, 5) assert_almost_equals(fit(700, 1400, unc_level=1), 0.04734 + 0.00906 * 1, 5) assert_almost_equals(fit(700, 1400, unc_level=-1), 0.04734 + 0.00906 * -1, 5) assert_almost_equals(fit(750, 1450, unc_level=0.5), 0.03164 + 0.00651 * 0.5, 5) assert_almost_equals(fit(750, 1450, unc_level=-1.5), 0.0316449 + 0.006505 * -1.5, 5) # interpolation: for uncertainty, returns sensible results for interp_axis in (1, 2): x1 = midpoint[x1a](700, 750) if interp_axis == 1 else 700 x2 = midpoint[x2a](1400, 1450) if interp_axis == 2 else 1400 y_upperend = 0.0390134 if interp_axis == 1 else 0.03822797 if kind == 'linear': assert_almost_equals(fit(x1, x2), midpoint[ya](0.0473379, y_upperend), 5) else: ok_(y_upperend < fit(x1, x2) < 0.047337959) ok_(0.0075711 < fit.unc_p_at(x1, x2) < 0.0090594) ok_(0.0075711 < -fit.unc_m_at(x1, x2) < 0.0090594) ok_(0.0316449 < fit(725, 1425) < 0.0473378) ok_(0.0065051 < fit.unc_p_at(725, 1425) < 0.0090594) ok_(0.0065051 < -fit.unc_m_at(725, 1425) < 0.0090594) def test_scipy_grid_interpolator_nonstandard_args(self): """Verify ScipyGridInterp accepts/refuses args correctly.""" table = CrossSectionTable(self.dirs['fastlim8mod'] / 'sg_8TeV_NLONLL_modified.xsec') for kind in ['linear', 'spline']: fit = ScipyGridInterpolator(['id', 'id'], 'id', kind=kind).interpolate(table, 'xsec') for m in ['f0', 'fp', 'fm', 'unc_p_at', 'unc_m_at', 'tuple_at']: test_method = getattr(fit, m) value = test_method(777, 888) if m == 'tuple_at': # the output should be (3,) array (or 3-element tuple) eq_(numpy.array(value).shape, (3,)) else: # it is a scalar ok_(self._is_scalar_number(value)) # method should accept keyword arguments eq_(test_method(msq=777, mgl=888), value) eq_(test_method(mgl=888, msq=777), value) eq_(test_method(777, mgl=888), value) # method should not accept arrays or numpy.ndarray with >0 dim. for bad_input in ([777, 888], [[777]], [[777, 888], [789, 890]], [777, 888, 999]): with assert_raises(TypeError): test_method(bad_input) with assert_raises(TypeError): test_method(numpy.array(bad_input)) with assert_raises(KeyError): test_method(777, 888, m_wino=100) with assert_raises(KeyError): test_method(777, m_wino=100) with assert_raises(KeyError): test_method(m_wino=100) with assert_raises(TypeError): test_method() with assert_raises(TypeError): test_method(777) PK!V_(susy_cross_section/tests/test_scripts.py"""Test codes.""" from __future__ import absolute_import, division, print_function # py2 import logging import pathlib import unittest from click.testing import CliRunner from nose.tools import assert_almost_equals, eq_, ok_, raises # noqa: F401 from susy_cross_section.scripts import command_get logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) class TestScripts(unittest.TestCase): """Test codes for command-line scripts.""" def setUp(self): """Set up.""" self.data_dir = pathlib.Path(__file__).parent / 'data' self.runner = CliRunner() def test_get(self): """Assert that command_get runs without error.""" result = {} for mass in [300, 350]: result[mass] = self.runner.invoke(command_get, ['13TeV.slepslep.ll', str(mass)]) if result[mass].exit_code: logger.debug('%s', result[mass].__dict__) eq_(result[mass].exit_code, 0) eq_(result[300].output.strip(), '(4.43 +0.19 -0.24) fb') eq_(result[350].output.strip(), '(2.33 +0.11 -0.14) fb') def test_get_simple(self): """Assert that command_get returns sensible interpolation results.""" result = {} output = {} for mass in [450, 458, 475]: result[mass] = self.runner.invoke(command_get, ['-1', '13TeV.n2x1+-.wino', str(mass)]) output[mass] = [float(x) for x in result[mass].output.strip().split(' ')] logger.debug('Exit code %s with output: %s', result[mass].exit_code, output[mass]) eq_(result[mass].exit_code, 0) eq_(len(output[mass]), 3) assert_almost_equals(output[450][0], 73.4361) assert_almost_equals(output[450][1], 6.2389) assert_almost_equals(output[450][2], -6.2389) assert_almost_equals(output[475][0], 58.0811) assert_almost_equals(output[475][1], 5.05005) assert_almost_equals(output[475][2], -5.05005) assert(output[450][0] > output[458][0] > output[475][0]) assert(output[450][1] > output[458][1] > output[475][1]) assert(output[450][2] < output[458][2] < output[475][2]) PK!+O+ + susy_cross_section/utility.py"""Utility functions and classes.""" from __future__ import absolute_import, division, print_function # py2 import sys from typing import List, Mapping, MutableMapping, Optional, Union # noqa: F401 import numpy if sys.version_info[0] < 3: # py2 str = basestring # noqa: A001, F821 class Unit: """The unit of a physical value. The constructor must be called with units, where units must be str. """ definitions = { '': [1], '%': [0.01], 'pb': [1000, 'fb'], } # type: Mapping[Union[float, str], List[Union[float, str]]] def __init__(self, *args): # type: (Union[float, str, Unit])->None self.factor = 1 # type: float self.units = {} # type: MutableMapping[str, int] for u in args: if isinstance(u, Unit): self.factor *= u.factor for k, v in u.units.items(): self.units[k] = self.units.get(k, 0) + v else: base_units = self.definitions.get(u, [u]) for b in base_units: if isinstance(b, str): self.units[b] = self.units.get(b, 0) + 1 else: try: self.factor *= float(b) except ValueError: raise TypeError('invalid unit: %s', u) def invert(self): # type: ()->Unit """Return an inverted unit.""" result = Unit() result.factor = 1 / self.factor result.units = {k: -v for k, v in self.units.items()} return result def __mul__(self, other): # type: (Union[float, str, Unit])->Unit """Return products of two units.""" return Unit(self, other) def __truediv__(self, other): # type: (Union[float, str, Unit])->Unit """Return division of two units.""" return self * Unit(other).invert() def __float__(self): # type: ()->float """Return factor if it is without units.""" if any(v != 0 for v in self.units.values()): raise ValueError('Unit conversion error: %s, %s', self.units, self.factor) return float(self.factor) def value_format(value, unc_p, unc_m, unit=None): # type: (float, float, float, Optional[str])->str """Format the value with uncertainty (and with unit).""" delta = min(abs(unc_p), abs(unc_m)) if delta == 0: value_str = '{:g} +0 -0'.format(value) return '({}) {}'.format(value_str, unit) if unit else value_str else: v_order = int(numpy.log10(value)) if abs(v_order) > 3: digits = max(int(-numpy.log10(delta / value) - 0.005) + 2, 3) v, p, m = value / 10**v_order, unc_p / 10**v_order, abs(unc_m) / 10**v_order f = '{{:.{}f}}'.format(digits) value_str = '({} +{} -{})'.format(f, f, f).format(v, p, m) + '*1e{}'.format(v_order) return '{} {}'.format(value_str, unit) if unit else value_str else: digits = max(int(-numpy.log10(delta) - 0.005) + (1 if delta > 1 else 2), 0) f = '{{:{}}}'.format('.{}f'.format(digits)) value_str = '{} +{} -{}'.format(f, f, f).format(value, unc_p, abs(unc_m)) return '({}) {}'.format(value_str, unit) if unit else value_str PK!HSJ{3susy_cross_section-0.0.3.dist-info/entry_points.txtN+I/N.,()*.-ԭ(MO-㓋SK2ꬒssRZ3ˉRPK!H|n-WY(susy_cross_section-0.0.3.dist-info/WHEEL A н#Z;/" bFF]xzwK;<*mTֻ0*Ri.4Vm0[H, JPK!HGx] +susy_cross_section-0.0.3.dist-info/METADATAXr6O删$jtq{6َk9M; $=ͽɽ} R8nĚOE)ncLJl*4s}RޅP*#_9g^}0lVCo5a"U#%aImٛ1;)2w<^rn؈4U٘l\R!_OGz N{~%rE’Hw"NԐEe[ ȎG xۆ*U{ubALϷdqDw{qg2VڏeKiCrzuVȬ/g9hVȻ"@&Dzy7W/JDlP7 \ˆ[ePqmYWl-5}YszκAH˕ C*AV5=dr3-tmd9ΐ!FK5 ΄lpf~,Xͭ>D (JMq@p"5woݺZEL1UCi2[0TYp48yux^!iTlT8&׉pKJW)<ern-\6اPrpcVy2cJ@؉4Ue=&=:޷,W9l\>Y, ݺw}ʧ*l6z{J&bNSjl@U4 WB.gCɷA?csN?*,Z?T:Ί%IY|ɕOR ,vHdŜ6jC ;#i˥Q)m1ٌYpX+)T7A\(]A'TBۏxq($K5sDl8jtnkcL$"ȓLɓF >S^9 CX&Ti&b!M#9 묖R*89BUUj17΃_TKby17nU:V+qp̮x_P HG6ς]ͷjN6oTZ%>B>] QMZu=y0DxL:3 5w}E %"Z^x [1EP!WIPCaeA "uQoVqsfDTt}v'(%е!ouYt^ Cjtvw+%tGcԭ"6Xs wى!,Y{\(zBuvyg96h9NN&чi Ѐ<ل_~Y$43 =48P>vG@㜹4lAɈTj?ZH06¦B:(*Ld3UIrfW~b:eӡӪT{v{5vQ4۫$!]>,$PK!H܂4r )susy_cross_section-0.0.3.dist-info/RECORDɒ~naQ H`<7տCVٮj/JBhQfm$ck;l`nJeF R `!)%̈ F:y |q&-,6srF-:"m攌R :B*V4#oh_N,gFƜqx(r/1Di~Aqb*_,M'#?OEᤓW@:We Hw6Ӓ+x欪`Zӯ!i+ e聻Ht@ ⏸ϙ`%qqERc=ȫa+,7sJ[F/n=)C 8,#Vִ^|#j,nGghUks-z\i{+я>X\v%N£GsDBPDl}Q(F , U <[O,~汵:lN+:&F)*&zoғ^\n^&R1[@J⹷Kю]7&/POg|Ku\T_JI𪁟xy^v/ƚ쨯/x~$Nh*!mlԚ0{e8j.M`~b?@m]boTD˭`mN\7PVƅ5d!w?{,n!&{| ,hʞxuN~g圯`KqJ]$r'U`6q;N/a~sT+6;ӳMFsI.YF̩}X }5- J{E!% W~[( 4]9XZdvAH5ۉgHNM$PdXaK/HT;&ıJyΉ~,];mvW<06`\ă5\[%gH 9nіvoKhET&$,:Vcm/-g KqI] K@2g9"~YkR2],P+WӸ'o_NXFpSZ7仓0s#LL`'^GL8jfH=->R>SMܹ7,2W xT7$VhԷs-=]J=ˆ2Iz]8BmmH87ֶ'm?W_؟JrN^$Q$ۊ*G&M wh͖S)JaqK>TMXL_o(mɏm"W|k[Fe݁dTY>G޾&e2F״* |j.~\od^y\ $8swzK#T] ST:*u]źPv.xenoix >7" 0)۳Ebtx5$6IC>VuP8y `3vjRKΈDcB#FZ5^?J-0(|WLE5S 9T Tz '\!R7aۘyb!Ɛ9ѷmNӞ/֘FZћ̫R~Hv. 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